A recommender system using deep learning in Torch.ipynb

{"cells":[{"metadata":{},"cell_type":"markdown","source":"## Prepare packages"},{"metadata":{},"cell_type":"markdown","source":"### Basic data science libraries"},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport pickle\nimport glob","execution_count":12,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### Learn-to-rank libraries"},{"metadata":{"trusted":true},"cell_type":"code","source":"# make sure to run this only once\n# !conda install -y -c maciejkula -c pytorch spotlight=0.1.5","execution_count":13,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from spotlight.cross_validation import random_train_test_split\nfrom spotlight.evaluation import mrr_score\nfrom spotlight.factorization.implicit import ImplicitFactorizationModel\nfrom spotlight.evaluation import rmse_score\nfrom spotlight.factorization.explicit import ExplicitFactorizationModel\nfrom spotlight.interactions import Interactions","execution_count":14,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from spotlight.evaluation import sequence_mrr_score\nfrom spotlight.evaluation import precision_recall_score","execution_count":15,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### Deep learning libraries"},{"metadata":{"trusted":true},"cell_type":"code","source":"import torch","execution_count":16,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### Other"},{"metadata":{"trusted":true},"cell_type":"code","source":"import pandas_profiling","execution_count":17,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from scipy import stats","execution_count":18,"outputs":[]},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","collapsed":true,"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":false},"cell_type":"markdown","source":"## Prepare data"},{"metadata":{"trusted":true},"cell_type":"code","source":"import os\nprint(os.listdir(\"../input\"))","execution_count":19,"outputs":[{"output_type":"stream","text":"['articles_embeddings.pickle', 'clicks', 'articles_metadata.csv', 'clicks_sample.csv']\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"### Read articles metadata"},{"metadata":{"trusted":true},"cell_type":"code","source":"articles_meta = pd.read_csv('../input/articles_metadata.csv')\nf'There are {len(articles_meta)} rows in the {articles_meta.ndim}-dimensional data frame'","execution_count":20,"outputs":[{"output_type":"execute_result","execution_count":20,"data":{"text/plain":"'There are 364047 rows in the 2-dimensional data frame'"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### Read articles embeddings"},{"metadata":{"trusted":true},"cell_type":"code","source":"articles_emb = pickle.load(open('../input/articles_embeddings.pickle', 'rb'))\nf'There are {len(articles_emb)} rows in the {articles_emb.ndim}-dimensional data frame'","execution_count":21,"outputs":[{"output_type":"execute_result","execution_count":21,"data":{"text/plain":"'There are 364047 rows in the 2-dimensional data frame'"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"articles_emb_df = pd.DataFrame(articles_emb, columns=[f'V{i}' for i in range(0,len(articles_emb[0]))])  # assign colnames basing on the embedding index\n# articles_emb_df","execution_count":22,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### Read clicks data"},{"metadata":{"trusted":true},"cell_type":"code","source":"all_files = glob.glob(\"../input/clicks/clicks/*\")\nf\"We're going to read {len(all_files)} files\"","execution_count":23,"outputs":[{"output_type":"execute_result","execution_count":23,"data":{"text/plain":"\"We're going to read 385 files\""},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"There are many seperate CSV file holding information on all click events made by registered users. Data is basicly a set of pairs (user_id, article_id) together with metadata like: timestamp of the click, country, etc. The 'click' dataset holds only \"positive\" labels, meaning that the data don't hold information on the opposite event of 'no click'. "},{"metadata":{"trusted":true},"cell_type":"code","source":"clicks = pd.concat([pd.read_csv(_) for _ in all_files], ignore_index=True)\nf'Successfully read {len(clicks)} rows of the click-data'","execution_count":24,"outputs":[{"output_type":"execute_result","execution_count":24,"data":{"text/plain":"'Successfully read 2988181 rows of the click-data'"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"f'There are {len(set(clicks.user_id.values))} distinct users'","execution_count":25,"outputs":[{"output_type":"execute_result","execution_count":25,"data":{"text/plain":"'There are 322897 distinct users'"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"Join articles (metadata) together with their embeddings to make just one pandas dataframe holding information on all articles. "},{"metadata":{"trusted":true},"cell_type":"code","source":"articles = pd.concat([articles_meta, articles_emb_df], axis=1)","execution_count":26,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"articles.columns","execution_count":27,"outputs":[{"output_type":"execute_result","execution_count":27,"data":{"text/plain":"Index(['article_id', 'category_id', 'created_at_ts', 'publisher_id',\n       'words_count', 'V0', 'V1', 'V2', 'V3', 'V4',\n       ...\n       'V240', 'V241', 'V242', 'V243', 'V244', 'V245', 'V246', 'V247', 'V248',\n       'V249'],\n      dtype='object', length=255)"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"## Exploratory analysis"},{"metadata":{},"cell_type":"markdown","source":"### First glance at the data"},{"metadata":{},"cell_type":"markdown","source":"#### Article data"},{"metadata":{"trusted":true},"cell_type":"code","source":"articles.head(10)","execution_count":28,"outputs":[{"output_type":"execute_result","execution_count":28,"data":{"text/plain":"   article_id  category_id    ...         V248      V249\n0           0            0    ...     0.597416  0.409623\n1           1            1    ...     0.397090 -0.834364\n2           2            1    ...     0.805932 -0.285284\n3           3            1    ...     0.243541 -0.885329\n4           4            1    ...     0.185484 -0.580292\n5           5            1    ...     0.426587 -0.065801\n6           6            1    ...     0.166825 -0.728330\n7           7            1    ...     0.243181 -0.596663\n8           8            1    ...     0.163397 -0.499539\n9           9            1    ...     0.304353 -0.721567\n\n[10 rows x 255 columns]","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>article_id</th>\n      <th>category_id</th>\n      <th>created_at_ts</th>\n      <th>publisher_id</th>\n      <th>words_count</th>\n      <th>V0</th>\n      <th>V1</th>\n      <th>V2</th>\n      <th>V3</th>\n      <th>V4</th>\n      <th>V5</th>\n      <th>V6</th>\n      <th>V7</th>\n      <th>V8</th>\n      <th>V9</th>\n      <th>V10</th>\n      <th>V11</th>\n      <th>V12</th>\n      <th>V13</th>\n      <th>V14</th>\n      <th>V15</th>\n      <th>V16</th>\n      <th>V17</th>\n      <th>V18</th>\n      <th>V19</th>\n      <th>V20</th>\n      <th>V21</th>\n      <th>V22</th>\n      <th>V23</th>\n      <th>V24</th>\n      <th>V25</th>\n      <th>V26</th>\n      <th>V27</th>\n      <th>V28</th>\n      <th>V29</th>\n      <th>V30</th>\n      <th>V31</th>\n      <th>V32</th>\n      <th>V33</th>\n      <th>V34</th>\n      <th>...</th>\n      <th>V210</th>\n      <th>V211</th>\n      <th>V212</th>\n      <th>V213</th>\n      <th>V214</th>\n      <th>V215</th>\n      <th>V216</th>\n      <th>V217</th>\n      <th>V218</th>\n      <th>V219</th>\n      <th>V220</th>\n      <th>V221</th>\n      <th>V222</th>\n      <th>V223</th>\n      <th>V224</th>\n      <th>V225</th>\n      <th>V226</th>\n      <th>V227</th>\n      <th>V228</th>\n      <th>V229</th>\n      <th>V230</th>\n      <th>V231</th>\n      <th>V232</th>\n      <th>V233</th>\n      <th>V234</th>\n      <th>V235</th>\n      <th>V236</th>\n      <th>V237</th>\n      <th>V238</th>\n      <th>V239</th>\n      <th>V240</th>\n      <th>V241</th>\n      <th>V242</th>\n      <th>V243</th>\n      <th>V244</th>\n      <th>V245</th>\n      <th>V246</th>\n      <th>V247</th>\n      <th>V248</th>\n      <th>V249</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1513144419000</td>\n      <td>0</td>\n      <td>168</td>\n      <td>-0.161183</td>\n      <td>-0.957233</td>\n      <td>-0.137944</td>\n      <td>0.050855</td>\n      <td>0.830055</td>\n      <td>0.901365</td>\n      <td>-0.335148</td>\n      <td>-0.559561</td>\n      <td>-0.500603</td>\n      <td>0.165183</td>\n      <td>0.428434</td>\n      <td>0.355056</td>\n      <td>0.874437</td>\n      <td>-0.528883</td>\n      <td>0.625487</td>\n      <td>0.268920</td>\n      <td>-0.822835</td>\n      <td>-0.703853</td>\n      <td>-0.625845</td>\n      <td>-0.152855</td>\n      <td>-0.666241</td>\n      <td>0.043295</td>\n      <td>0.178638</td>\n      <td>0.046890</td>\n      <td>0.594531</td>\n      <td>-0.183348</td>\n      <td>0.195107</td>\n      <td>-0.467640</td>\n      <td>-0.304807</td>\n      <td>0.353175</td>\n      <td>0.278188</td>\n      <td>0.538623</td>\n      <td>-0.371209</td>\n      <td>0.489898</td>\n      <td>-0.103833</td>\n      <td>...</td>\n      <td>0.764381</td>\n      <td>0.275644</td>\n      <td>-0.692138</td>\n      <td>-0.393240</td>\n      <td>-0.325693</td>\n      <td>0.163377</td>\n      <td>-0.154952</td>\n      <td>-0.701639</td>\n      <td>0.711825</td>\n      <td>-0.839063</td>\n      <td>-0.459145</td>\n      <td>0.919913</td>\n      <td>0.621834</td>\n      <td>-0.640471</td>\n      <td>0.389934</td>\n      <td>0.764178</td>\n      <td>0.164851</td>\n      <td>-0.747023</td>\n      <td>0.411034</td>\n      <td>0.750901</td>\n      <td>0.860643</td>\n      <td>0.614755</td>\n      <td>0.549660</td>\n      <td>0.334437</td>\n      <td>-0.388059</td>\n      <td>-0.703730</td>\n      <td>-0.567420</td>\n      <td>0.006478</td>\n      <td>-0.206590</td>\n      <td>-0.385272</td>\n      <td>0.321248</td>\n      <td>0.313999</td>\n      <td>0.636412</td>\n      <td>0.169179</td>\n      <td>0.540524</td>\n      <td>-0.813182</td>\n      <td>0.286870</td>\n      <td>-0.231686</td>\n      <td>0.597416</td>\n      <td>0.409623</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1405341936000</td>\n      <td>0</td>\n      <td>189</td>\n      <td>-0.523216</td>\n      <td>-0.974058</td>\n      <td>0.738608</td>\n      <td>0.155234</td>\n      <td>0.626294</td>\n      <td>0.485297</td>\n      <td>-0.715657</td>\n      <td>-0.897996</td>\n      <td>-0.359747</td>\n      <td>0.398246</td>\n      <td>0.672840</td>\n      <td>-0.011180</td>\n      <td>-0.347506</td>\n      <td>-0.541346</td>\n      <td>0.458492</td>\n      <td>0.482316</td>\n      <td>0.713665</td>\n      <td>-0.474050</td>\n      <td>0.405787</td>\n      <td>0.671431</td>\n      <td>-0.480856</td>\n      <td>0.434779</td>\n      <td>0.468499</td>\n      <td>0.376424</td>\n      <td>0.368048</td>\n      <td>0.192545</td>\n      <td>0.095572</td>\n      <td>-0.542234</td>\n      <td>0.065547</td>\n      <td>0.363267</td>\n      <td>-0.162804</td>\n      <td>0.319617</td>\n      <td>0.469473</td>\n      <td>0.578466</td>\n      <td>-0.803637</td>\n      <td>...</td>\n      <td>0.344321</td>\n      <td>-0.534050</td>\n      <td>0.728764</td>\n      <td>0.860110</td>\n      <td>-0.028166</td>\n      <td>0.491114</td>\n      <td>0.531556</td>\n      <td>0.545350</td>\n      <td>-0.241544</td>\n      <td>-0.091119</td>\n      <td>0.001625</td>\n      <td>0.950923</td>\n      <td>-0.367805</td>\n      <td>-0.215411</td>\n      <td>-0.131891</td>\n      <td>0.709081</td>\n      <td>0.612876</td>\n      <td>0.814885</td>\n      <td>0.278680</td>\n      <td>0.003690</td>\n      <td>-0.286773</td>\n      <td>-0.100936</td>\n      <td>-0.382605</td>\n      <td>-0.191886</td>\n      <td>-0.416420</td>\n      <td>-0.856593</td>\n      <td>0.537442</td>\n      <td>0.281693</td>\n      <td>-0.681829</td>\n      <td>0.666095</td>\n      <td>-0.487843</td>\n      <td>0.823124</td>\n      <td>0.412688</td>\n      <td>-0.338654</td>\n      <td>0.320787</td>\n      <td>0.588643</td>\n      <td>-0.594137</td>\n      <td>0.182828</td>\n      <td>0.397090</td>\n      <td>-0.834364</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>1</td>\n      <td>1408667706000</td>\n      <td>0</td>\n      <td>250</td>\n      <td>-0.619619</td>\n      <td>-0.972960</td>\n      <td>-0.207360</td>\n      <td>-0.128861</td>\n      <td>0.044748</td>\n      <td>-0.387535</td>\n      <td>-0.730477</td>\n      <td>-0.066126</td>\n      <td>-0.754899</td>\n      <td>-0.242004</td>\n      <td>0.670484</td>\n      <td>-0.280388</td>\n      <td>-0.557285</td>\n      <td>-0.084145</td>\n      <td>0.027782</td>\n      <td>0.294074</td>\n      <td>0.362697</td>\n      <td>-0.368549</td>\n      <td>0.147960</td>\n      <td>-0.011751</td>\n      <td>0.030209</td>\n      <td>0.106317</td>\n      <td>0.628013</td>\n      <td>0.388849</td>\n      <td>0.615911</td>\n      <td>-0.445113</td>\n      <td>0.106028</td>\n      <td>0.137109</td>\n      <td>-0.095536</td>\n      <td>0.342532</td>\n      <td>0.592646</td>\n      <td>-0.261791</td>\n      <td>0.342123</td>\n      <td>0.704539</td>\n      <td>-0.433067</td>\n      <td>...</td>\n      <td>-0.600840</td>\n      <td>-0.539323</td>\n      <td>0.418196</td>\n      <td>0.028715</td>\n      <td>0.135008</td>\n      <td>-0.313483</td>\n      <td>0.744361</td>\n      <td>-0.145113</td>\n      <td>-0.485484</td>\n      <td>-0.591555</td>\n      <td>0.030112</td>\n      <td>0.952736</td>\n      <td>-0.569376</td>\n      <td>-0.359680</td>\n      <td>0.561676</td>\n      <td>0.381671</td>\n      <td>-0.189300</td>\n      <td>0.420950</td>\n      <td>0.022337</td>\n      <td>-0.329559</td>\n      <td>0.232765</td>\n      <td>0.424368</td>\n      <td>-0.484598</td>\n      <td>0.394343</td>\n      <td>-0.329527</td>\n      <td>-0.852293</td>\n      <td>0.634992</td>\n      <td>-0.532673</td>\n      <td>-0.469396</td>\n      <td>0.714991</td>\n      <td>0.454756</td>\n      <td>0.473184</td>\n      <td>0.377866</td>\n      <td>-0.863887</td>\n      <td>-0.383365</td>\n      <td>0.137721</td>\n      <td>-0.810877</td>\n      <td>-0.447580</td>\n      <td>0.805932</td>\n      <td>-0.285284</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>1</td>\n      <td>1408468313000</td>\n      <td>0</td>\n      <td>230</td>\n      <td>-0.740843</td>\n      <td>-0.975749</td>\n      <td>0.391698</td>\n      <td>0.641738</td>\n      <td>-0.268645</td>\n      <td>0.191745</td>\n      <td>-0.825593</td>\n      <td>-0.710591</td>\n      <td>-0.040099</td>\n      <td>-0.110514</td>\n      <td>0.591640</td>\n      <td>-0.245205</td>\n      <td>-0.788418</td>\n      <td>-0.345293</td>\n      <td>-0.756407</td>\n      <td>0.141115</td>\n      <td>0.283783</td>\n      <td>-0.369959</td>\n      <td>0.374733</td>\n      <td>0.922633</td>\n      <td>0.172219</td>\n      <td>0.253582</td>\n      <td>0.552108</td>\n      <td>-0.454131</td>\n      <td>0.385160</td>\n      <td>-0.151101</td>\n      <td>0.598559</td>\n      <td>-0.614190</td>\n      <td>-0.258765</td>\n      <td>0.591907</td>\n      <td>0.618749</td>\n      <td>0.414598</td>\n      <td>0.698184</td>\n      <td>-0.114862</td>\n      <td>-0.069056</td>\n      <td>...</td>\n      <td>-0.137121</td>\n      <td>-0.245490</td>\n      <td>0.170559</td>\n      <td>0.568089</td>\n      <td>-0.252295</td>\n      <td>-0.455726</td>\n      <td>0.756765</td>\n      <td>0.626615</td>\n      <td>-0.005399</td>\n      <td>-0.472355</td>\n      <td>0.324625</td>\n      <td>0.954345</td>\n      <td>-0.840553</td>\n      <td>-0.800332</td>\n      <td>0.111637</td>\n      <td>-0.501806</td>\n      <td>0.115986</td>\n      <td>0.805840</td>\n      <td>-0.041481</td>\n      <td>0.320630</td>\n      <td>-0.450113</td>\n      <td>-0.378700</td>\n      <td>0.509616</td>\n      <td>0.142787</td>\n      <td>0.149670</td>\n      <td>-0.896181</td>\n      <td>0.234389</td>\n      <td>-0.189831</td>\n      <td>-0.597612</td>\n      <td>0.263807</td>\n      <td>0.271535</td>\n      <td>0.036040</td>\n      <td>0.480029</td>\n      <td>-0.763173</td>\n      <td>0.022627</td>\n      <td>0.565165</td>\n      <td>-0.910286</td>\n      <td>-0.537838</td>\n      <td>0.243541</td>\n      <td>-0.885329</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>1</td>\n      <td>1407071171000</td>\n      <td>0</td>\n      <td>162</td>\n      <td>-0.279052</td>\n      <td>-0.972315</td>\n      <td>0.685374</td>\n      <td>0.113056</td>\n      <td>0.238315</td>\n      <td>0.271913</td>\n      <td>-0.568816</td>\n      <td>0.341194</td>\n      <td>-0.600554</td>\n      <td>-0.125644</td>\n      <td>0.698668</td>\n      <td>0.467503</td>\n      <td>-0.740664</td>\n      <td>-0.258062</td>\n      <td>-0.094110</td>\n      <td>0.256941</td>\n      <td>0.582071</td>\n      <td>-0.025041</td>\n      <td>0.339164</td>\n      <td>-0.348473</td>\n      <td>0.413076</td>\n      <td>0.159945</td>\n      <td>0.511137</td>\n      <td>0.608007</td>\n      <td>0.626225</td>\n      <td>-0.540690</td>\n      <td>0.623640</td>\n      <td>-0.019697</td>\n      <td>0.027607</td>\n      <td>-0.236370</td>\n      <td>0.311671</td>\n      <td>-0.559831</td>\n      <td>0.766381</td>\n      <td>0.412553</td>\n      <td>-0.663338</td>\n      <td>...</td>\n      <td>0.234426</td>\n      <td>-0.689234</td>\n      <td>0.751153</td>\n      <td>0.647424</td>\n      <td>-0.033631</td>\n      <td>-0.578617</td>\n      <td>0.820915</td>\n      <td>0.370377</td>\n      <td>-0.528718</td>\n      <td>-0.536308</td>\n      <td>-0.088349</td>\n      <td>0.944439</td>\n      <td>-0.887679</td>\n      <td>-0.617357</td>\n      <td>0.254590</td>\n      <td>0.084341</td>\n      <td>0.286366</td>\n      <td>0.360714</td>\n      <td>0.369629</td>\n      <td>-0.452543</td>\n      <td>-0.292962</td>\n      <td>-0.530023</td>\n      <td>-0.237123</td>\n      <td>0.233008</td>\n      <td>0.002609</td>\n      <td>-0.835145</td>\n      <td>0.494137</td>\n      <td>0.029535</td>\n      <td>-0.619638</td>\n      <td>0.813445</td>\n      <td>0.238286</td>\n      <td>0.809268</td>\n      <td>0.427521</td>\n      <td>-0.615932</td>\n      <td>-0.503697</td>\n      <td>0.614450</td>\n      <td>-0.917760</td>\n      <td>-0.424061</td>\n      <td>0.185484</td>\n      <td>-0.580292</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>5</td>\n      <td>1</td>\n      <td>1407413929000</td>\n      <td>0</td>\n      <td>196</td>\n      <td>-0.724885</td>\n      <td>-0.974610</td>\n      <td>0.114743</td>\n      <td>0.135984</td>\n      <td>-0.059124</td>\n      <td>0.139286</td>\n      <td>-0.720786</td>\n      <td>-0.311302</td>\n      <td>-0.543599</td>\n      <td>0.613687</td>\n      <td>0.762005</td>\n      <td>0.709754</td>\n      <td>-0.736228</td>\n      <td>-0.403499</td>\n      <td>0.171004</td>\n      <td>-0.297515</td>\n      <td>0.874423</td>\n      <td>-0.305240</td>\n      <td>0.400254</td>\n      <td>-0.439359</td>\n      <td>0.066276</td>\n      <td>-0.190274</td>\n      <td>0.553277</td>\n      <td>0.585319</td>\n      <td>0.835951</td>\n      <td>-0.420721</td>\n      <td>0.657003</td>\n      <td>0.147884</td>\n      <td>-0.681257</td>\n      <td>0.414024</td>\n      <td>0.354221</td>\n      <td>-0.535813</td>\n      <td>0.709872</td>\n      <td>0.040972</td>\n      <td>-0.653258</td>\n      <td>...</td>\n      <td>0.176661</td>\n      <td>-0.518598</td>\n      <td>0.231858</td>\n      <td>0.640447</td>\n      <td>-0.333733</td>\n      <td>-0.704026</td>\n      <td>0.851587</td>\n      <td>0.679020</td>\n      <td>-0.746762</td>\n      <td>-0.405075</td>\n      <td>0.412475</td>\n      <td>0.952301</td>\n      <td>-0.801871</td>\n      <td>-0.077079</td>\n      <td>0.200865</td>\n      <td>0.581785</td>\n      <td>0.639167</td>\n      <td>0.256305</td>\n      <td>0.550526</td>\n      <td>-0.735179</td>\n      <td>-0.202093</td>\n      <td>-0.526555</td>\n      <td>-0.046890</td>\n      <td>-0.150501</td>\n      <td>-0.431276</td>\n      <td>-0.884736</td>\n      <td>0.702977</td>\n      <td>0.261991</td>\n      <td>-0.374430</td>\n      <td>0.656620</td>\n      <td>0.430414</td>\n      <td>0.492010</td>\n      <td>0.650828</td>\n      <td>-0.627942</td>\n      <td>0.562439</td>\n      <td>0.792561</td>\n      <td>-0.877570</td>\n      <td>-0.002212</td>\n      <td>0.426587</td>\n      <td>-0.065801</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>6</td>\n      <td>1</td>\n      <td>1409896802000</td>\n      <td>0</td>\n      <td>203</td>\n      <td>-0.161707</td>\n      <td>-0.967413</td>\n      <td>0.637266</td>\n      <td>0.249817</td>\n      <td>0.471958</td>\n      <td>0.391854</td>\n      <td>-0.815684</td>\n      <td>-0.296389</td>\n      <td>-0.804284</td>\n      <td>-0.513054</td>\n      <td>0.785833</td>\n      <td>0.026876</td>\n      <td>-0.671800</td>\n      <td>-0.220470</td>\n      <td>-0.352501</td>\n      <td>0.053875</td>\n      <td>-0.236351</td>\n      <td>-0.292241</td>\n      <td>0.004806</td>\n      <td>0.818565</td>\n      <td>-0.002179</td>\n      <td>0.361274</td>\n      <td>0.459593</td>\n      <td>-0.423408</td>\n      <td>0.440065</td>\n      <td>-0.073019</td>\n      <td>0.313988</td>\n      <td>-0.592909</td>\n      <td>-0.427994</td>\n      <td>0.354607</td>\n      <td>0.314822</td>\n      <td>-0.075500</td>\n      <td>0.415698</td>\n      <td>0.494282</td>\n      <td>-0.371775</td>\n      <td>...</td>\n      <td>-0.618818</td>\n      <td>-0.472592</td>\n      <td>0.268117</td>\n      <td>0.331901</td>\n      <td>0.654300</td>\n      <td>-0.292657</td>\n      <td>0.602945</td>\n      <td>0.718730</td>\n      <td>-0.137349</td>\n      <td>-0.757190</td>\n      <td>0.008487</td>\n      <td>0.934082</td>\n      <td>-0.514351</td>\n      <td>-0.800751</td>\n      <td>-0.031917</td>\n      <td>-0.061942</td>\n      <td>-0.106038</td>\n      <td>0.502453</td>\n      <td>0.010406</td>\n      <td>0.622789</td>\n      <td>-0.449857</td>\n      <td>-0.066503</td>\n      <td>-0.040897</td>\n      <td>0.599051</td>\n      <td>0.731874</td>\n      <td>-0.811536</td>\n      <td>-0.144244</td>\n      <td>-0.277359</td>\n      <td>-0.710827</td>\n      <td>0.097672</td>\n      <td>0.020420</td>\n      <td>0.509749</td>\n      <td>0.559321</td>\n      <td>-0.794207</td>\n      <td>-0.366995</td>\n      <td>0.616669</td>\n      <td>-0.626267</td>\n      <td>-0.452933</td>\n      <td>0.166825</td>\n      <td>-0.728330</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>7</td>\n      <td>1</td>\n      <td>1412559620000</td>\n      <td>0</td>\n      <td>154</td>\n      <td>-0.104534</td>\n      <td>-0.965899</td>\n      <td>0.800449</td>\n      <td>0.351384</td>\n      <td>-0.389743</td>\n      <td>0.421198</td>\n      <td>-0.600862</td>\n      <td>-0.021568</td>\n      <td>-0.265164</td>\n      <td>-0.061443</td>\n      <td>0.529295</td>\n      <td>0.554996</td>\n      <td>-0.736896</td>\n      <td>-0.629256</td>\n      <td>0.031549</td>\n      <td>0.264446</td>\n      <td>0.738236</td>\n      <td>-0.063248</td>\n      <td>0.446135</td>\n      <td>0.528500</td>\n      <td>-0.071187</td>\n      <td>0.568788</td>\n      <td>0.498562</td>\n      <td>0.408146</td>\n      <td>0.751686</td>\n      <td>-0.416993</td>\n      <td>0.675601</td>\n      <td>-0.035497</td>\n      <td>-0.437063</td>\n      <td>0.485897</td>\n      <td>-0.200215</td>\n      <td>-0.278555</td>\n      <td>0.639770</td>\n      <td>0.596259</td>\n      <td>-0.792836</td>\n      <td>...</td>\n      <td>0.189375</td>\n      <td>-0.577892</td>\n      <td>0.690582</td>\n      <td>0.736201</td>\n      <td>0.099963</td>\n      <td>-0.592421</td>\n      <td>0.604038</td>\n      <td>0.770763</td>\n      <td>-0.456483</td>\n      <td>-0.336677</td>\n      <td>0.005851</td>\n      <td>0.936196</td>\n      <td>-0.838248</td>\n      <td>-0.438001</td>\n      <td>0.003680</td>\n      <td>0.075793</td>\n      <td>0.397864</td>\n      <td>0.683909</td>\n      <td>0.035378</td>\n      <td>-0.355743</td>\n      <td>-0.586051</td>\n      <td>-0.512331</td>\n      <td>-0.404841</td>\n      <td>0.361335</td>\n      <td>0.232215</td>\n      <td>-0.787002</td>\n      <td>0.648803</td>\n      <td>0.145114</td>\n      <td>-0.554226</td>\n      <td>0.875462</td>\n      <td>0.124667</td>\n      <td>0.545793</td>\n      <td>0.372644</td>\n      <td>-0.510156</td>\n      <td>-0.217100</td>\n      <td>0.676449</td>\n      <td>-0.852480</td>\n      <td>0.198584</td>\n      <td>0.243181</td>\n      <td>-0.596663</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>8</td>\n      <td>1</td>\n      <td>1414351550000</td>\n      <td>0</td>\n      <td>209</td>\n      <td>-0.417274</td>\n      <td>-0.977311</td>\n      <td>0.054967</td>\n      <td>0.351192</td>\n      <td>0.049049</td>\n      <td>-0.460898</td>\n      <td>-0.443555</td>\n      <td>0.154505</td>\n      <td>0.018687</td>\n      <td>0.378063</td>\n      <td>-0.186111</td>\n      <td>0.050940</td>\n      <td>-0.676748</td>\n      <td>-0.287247</td>\n      <td>-0.641594</td>\n      <td>0.192993</td>\n      <td>0.426948</td>\n      <td>-0.640258</td>\n      <td>0.306624</td>\n      <td>0.000994</td>\n      <td>0.670012</td>\n      <td>0.262158</td>\n      <td>0.422815</td>\n      <td>0.481606</td>\n      <td>0.341325</td>\n      <td>-0.434050</td>\n      <td>-0.394699</td>\n      <td>-0.255908</td>\n      <td>-0.378905</td>\n      <td>0.154670</td>\n      <td>0.335810</td>\n      <td>-0.389351</td>\n      <td>0.686418</td>\n      <td>0.031918</td>\n      <td>-0.858204</td>\n      <td>...</td>\n      <td>-0.521964</td>\n      <td>-0.670205</td>\n      <td>0.174340</td>\n      <td>0.668877</td>\n      <td>0.041758</td>\n      <td>0.115617</td>\n      <td>0.699685</td>\n      <td>0.698377</td>\n      <td>-0.677529</td>\n      <td>-0.491539</td>\n      <td>-0.313109</td>\n      <td>0.955452</td>\n      <td>-0.812667</td>\n      <td>-0.127691</td>\n      <td>0.250130</td>\n      <td>-0.515665</td>\n      <td>0.683172</td>\n      <td>0.861159</td>\n      <td>-0.052384</td>\n      <td>-0.030429</td>\n      <td>-0.628825</td>\n      <td>-0.399839</td>\n      <td>0.191211</td>\n      <td>-0.468560</td>\n      <td>-0.485720</td>\n      <td>-0.851513</td>\n      <td>0.421991</td>\n      <td>-0.236139</td>\n      <td>-0.735500</td>\n      <td>0.554666</td>\n      <td>0.601953</td>\n      <td>0.756593</td>\n      <td>-0.257438</td>\n      <td>-0.130827</td>\n      <td>-0.061200</td>\n      <td>0.438138</td>\n      <td>-0.719562</td>\n      <td>-0.744506</td>\n      <td>0.163397</td>\n      <td>-0.499539</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>9</td>\n      <td>1</td>\n      <td>1412526792000</td>\n      <td>0</td>\n      <td>181</td>\n      <td>-0.348442</td>\n      <td>-0.966953</td>\n      <td>0.682574</td>\n      <td>-0.400164</td>\n      <td>0.646688</td>\n      <td>0.085186</td>\n      <td>-0.636333</td>\n      <td>-0.351318</td>\n      <td>-0.542529</td>\n      <td>0.198339</td>\n      <td>0.639160</td>\n      <td>0.116398</td>\n      <td>-0.565867</td>\n      <td>-0.466943</td>\n      <td>-0.387493</td>\n      <td>0.134520</td>\n      <td>-0.105635</td>\n      <td>-0.274094</td>\n      <td>0.390694</td>\n      <td>0.692602</td>\n      <td>-0.530828</td>\n      <td>0.642726</td>\n      <td>0.566932</td>\n      <td>-0.244221</td>\n      <td>0.261095</td>\n      <td>0.066121</td>\n      <td>0.552338</td>\n      <td>-0.474602</td>\n      <td>-0.259136</td>\n      <td>-0.007225</td>\n      <td>0.364449</td>\n      <td>0.205759</td>\n      <td>0.553315</td>\n      <td>0.760930</td>\n      <td>-0.586314</td>\n      <td>...</td>\n      <td>-0.443223</td>\n      <td>-0.466292</td>\n      <td>0.335697</td>\n      <td>0.460732</td>\n      <td>0.155530</td>\n      <td>-0.452083</td>\n      <td>0.774534</td>\n      <td>0.686354</td>\n      <td>-0.088361</td>\n      <td>-0.689979</td>\n      <td>0.587864</td>\n      <td>0.937130</td>\n      <td>-0.322540</td>\n      <td>-0.807357</td>\n      <td>0.133773</td>\n      <td>-0.040301</td>\n      <td>0.060819</td>\n      <td>0.490660</td>\n      <td>-0.193324</td>\n      <td>0.430582</td>\n      <td>-0.490337</td>\n      <td>0.438179</td>\n      <td>0.085634</td>\n      <td>0.292708</td>\n      <td>0.159592</td>\n      <td>-0.800538</td>\n      <td>0.346945</td>\n      <td>-0.288825</td>\n      <td>-0.289413</td>\n      <td>0.562637</td>\n      <td>0.220249</td>\n      <td>0.025085</td>\n      <td>0.510007</td>\n      <td>-0.832078</td>\n      <td>-0.555277</td>\n      <td>0.317832</td>\n      <td>-0.576926</td>\n      <td>-0.671720</td>\n      <td>0.304353</td>\n      <td>-0.721567</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"articles_meta.describe()","execution_count":29,"outputs":[{"output_type":"execute_result","execution_count":29,"data":{"text/plain":"          article_id    category_id      ...        publisher_id    words_count\ncount  364047.000000  364047.000000      ...            364047.0  364047.000000\nmean   182023.000000     283.108239      ...                 0.0     190.897727\nstd    105091.461061     136.723470      ...                 0.0      59.502766\nmin         0.000000       0.000000      ...                 0.0       0.000000\n25%     91011.500000     199.000000      ...                 0.0     159.000000\n50%    182023.000000     301.000000      ...                 0.0     186.000000\n75%    273034.500000     399.000000      ...                 0.0     218.000000\nmax    364046.000000     460.000000      ...                 0.0    6690.000000\n\n[8 rows x 5 columns]","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>article_id</th>\n      <th>category_id</th>\n      <th>created_at_ts</th>\n      <th>publisher_id</th>\n      <th>words_count</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>364047.000000</td>\n      <td>364047.000000</td>\n      <td>3.640470e+05</td>\n      <td>364047.0</td>\n      <td>364047.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>182023.000000</td>\n      <td>283.108239</td>\n      <td>1.474070e+12</td>\n      <td>0.0</td>\n      <td>190.897727</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>105091.461061</td>\n      <td>136.723470</td>\n      <td>4.293038e+10</td>\n      <td>0.0</td>\n      <td>59.502766</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.159356e+12</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>91011.500000</td>\n      <td>199.000000</td>\n      <td>1.444925e+12</td>\n      <td>0.0</td>\n      <td>159.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>182023.000000</td>\n      <td>301.000000</td>\n      <td>1.489422e+12</td>\n      <td>0.0</td>\n      <td>186.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>273034.500000</td>\n      <td>399.000000</td>\n      <td>1.509891e+12</td>\n      <td>0.0</td>\n      <td>218.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>364046.000000</td>\n      <td>460.000000</td>\n      <td>1.520943e+12</td>\n      <td>0.0</td>\n      <td>6690.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"articles_emp_profile = pandas_profiling.ProfileReport(articles_emb_df.assign(category_id=articles.category_id).sample(n=3*1000, \n                                                                                                                      weights='category_id', \n                                                                                                                      random_state=42))  # stratified sample per category","execution_count":30,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"articles_emp_profile.get_rejected_variables(threshold=0.9)","execution_count":31,"outputs":[{"output_type":"execute_result","execution_count":31,"data":{"text/plain":"['V127', 'V134', 'V150', 'V198', 'V221', 'V235', 'V43', 'category_id']"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"#### Clean the article data"},{"metadata":{"trusted":true},"cell_type":"code","source":"articles.drop(['publisher_id', 'V127', 'V134', 'V150', 'V198', 'V221', 'V235', 'V43'], axis=1, inplace=True)","execution_count":32,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"articles.columns","execution_count":33,"outputs":[{"output_type":"execute_result","execution_count":33,"data":{"text/plain":"Index(['article_id', 'category_id', 'created_at_ts', 'words_count', 'V0', 'V1',\n       'V2', 'V3', 'V4', 'V5',\n       ...\n       'V240', 'V241', 'V242', 'V243', 'V244', 'V245', 'V246', 'V247', 'V248',\n       'V249'],\n      dtype='object', length=247)"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"#### Click data"},{"metadata":{"trusted":true},"cell_type":"code","source":"clicks.head(10)","execution_count":34,"outputs":[{"output_type":"execute_result","execution_count":34,"data":{"text/plain":"  user_id        session_id         ...         click_region click_referrer_type\n0  137066  1508106983217640         ...                   25                   2\n1  137066  1508106983217640         ...                   25                   2\n2  312478  1508106983626641         ...                   25                   4\n3  312478  1508106983626641         ...                   25                   4\n4  266161  1508106984212642         ...                   20                   2\n5  266161  1508106984212642         ...                   20                   2\n6  266161  1508106984212642         ...                   20                   2\n7   33723  1508106984307643         ...                   21                   2\n8   33723  1508106984307643         ...                   21                   2\n9  115570  1508106985263644         ...                   26                   2\n\n[10 rows x 12 columns]","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>user_id</th>\n      <th>session_id</th>\n      <th>session_start</th>\n      <th>session_size</th>\n      <th>click_article_id</th>\n      <th>click_timestamp</th>\n      <th>click_environment</th>\n      <th>click_deviceGroup</th>\n      <th>click_os</th>\n      <th>click_country</th>\n      <th>click_region</th>\n      <th>click_referrer_type</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>137066</td>\n      <td>1508106983217640</td>\n      <td>1508106983000</td>\n      <td>2</td>\n      <td>218028</td>\n      <td>1508107123103</td>\n      <td>4</td>\n      <td>1</td>\n      <td>17</td>\n      <td>1</td>\n      <td>25</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>137066</td>\n      <td>1508106983217640</td>\n      <td>1508106983000</td>\n      <td>2</td>\n      <td>214753</td>\n      <td>1508107153103</td>\n      <td>4</td>\n      <td>1</td>\n      <td>17</td>\n      <td>1</td>\n      <td>25</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>312478</td>\n      <td>1508106983626641</td>\n      <td>1508106983000</td>\n      <td>2</td>\n      <td>74719</td>\n      <td>1508107495538</td>\n      <td>4</td>\n      <td>1</td>\n      <td>17</td>\n      <td>1</td>\n      <td>25</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>312478</td>\n      <td>1508106983626641</td>\n      <td>1508106983000</td>\n      <td>2</td>\n      <td>74722</td>\n      <td>1508107525538</td>\n      <td>4</td>\n      <td>1</td>\n      <td>17</td>\n      <td>1</td>\n      <td>25</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>266161</td>\n      <td>1508106984212642</td>\n      <td>1508106984000</td>\n      <td>3</td>\n      <td>331116</td>\n      <td>1508107023602</td>\n      <td>4</td>\n      <td>3</td>\n      <td>2</td>\n      <td>1</td>\n      <td>20</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>266161</td>\n      <td>1508106984212642</td>\n      <td>1508106984000</td>\n      <td>3</td>\n      <td>199474</td>\n      <td>1508107335633</td>\n      <td>4</td>\n      <td>3</td>\n      <td>2</td>\n      <td>1</td>\n      <td>20</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>266161</td>\n      <td>1508106984212642</td>\n      <td>1508106984000</td>\n      <td>3</td>\n      <td>277492</td>\n      <td>1508107365633</td>\n      <td>4</td>\n      <td>3</td>\n      <td>2</td>\n      <td>1</td>\n      <td>20</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>33723</td>\n      <td>1508106984307643</td>\n      <td>1508106984000</td>\n      <td>2</td>\n      <td>284802</td>\n      <td>1508107248277</td>\n      <td>4</td>\n      <td>1</td>\n      <td>17</td>\n      <td>1</td>\n      <td>21</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>33723</td>\n      <td>1508106984307643</td>\n      <td>1508106984000</td>\n      <td>2</td>\n      <td>215613</td>\n      <td>1508107278277</td>\n      <td>4</td>\n      <td>1</td>\n      <td>17</td>\n      <td>1</td>\n      <td>21</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>115570</td>\n      <td>1508106985263644</td>\n      <td>1508106985000</td>\n      <td>2</td>\n      <td>202355</td>\n      <td>1508107182256</td>\n      <td>4</td>\n      <td>4</td>\n      <td>20</td>\n      <td>1</td>\n      <td>26</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"clicks = clicks.infer_objects()","execution_count":35,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"clicks.dtypes","execution_count":36,"outputs":[{"output_type":"execute_result","execution_count":36,"data":{"text/plain":"user_id                int64\nsession_id             int64\nsession_start          int64\nsession_size           int64\nclick_article_id       int64\nclick_timestamp        int64\nclick_environment      int64\nclick_deviceGroup      int64\nclick_os               int64\nclick_country          int64\nclick_region           int64\nclick_referrer_type    int64\ndtype: object"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"pandas_profiling.ProfileReport(clicks)","execution_count":37,"outputs":[{"output_type":"execute_result","execution_count":37,"data":{"text/plain":"<pandas_profiling.ProfileReport at 0x7f4b30eafe10>","text/html":"<meta charset=\"UTF-8\">\n\n<style>\n\n        .variablerow {\n            border: 1px solid #e1e1e8;\n            border-top: hidden;\n            padding-top: 2em;\n            padding-bottom: 2em;\n            padding-left: 1em;\n            padding-right: 1em;\n        }\n\n        .headerrow {\n            border: 1px solid #e1e1e8;\n            background-color: #f5f5f5;\n            padding: 2em;\n        }\n        .namecol {\n            margin-top: -1em;\n            overflow-x: auto;\n        }\n\n        .dl-horizontal dt {\n            text-align: left;\n            padding-right: 1em;\n            white-space: normal;\n        }\n\n        .dl-horizontal dd {\n            margin-left: 0;\n        }\n\n        .ignore {\n            opacity: 0.4;\n        }\n\n        .container.pandas-profiling {\n            max-width:975px;\n        }\n\n        .col-md-12 {\n            padding-left: 2em;\n        }\n\n        .indent {\n            margin-left: 1em;\n        }\n\n        .center-img {\n            margin-left: auto !important;\n            margin-right: auto !important;\n            display: block;\n        }\n\n        /* Table example_values */\n            table.example_values {\n                border: 0;\n            }\n\n            .example_values th {\n                border: 0;\n                padding: 0 ;\n                color: #555;\n                font-weight: 600;\n            }\n\n            .example_values tr, .example_values td{\n                border: 0;\n                padding: 0;\n                color: #555;\n            }\n\n        /* STATS */\n            table.stats {\n                border: 0;\n            }\n\n            .stats th {\n                border: 0;\n                padding: 0 2em 0 0;\n                color: #555;\n                font-weight: 600;\n            }\n\n            .stats tr {\n                border: 0;\n            }\n\n            .stats td{\n                color: #555;\n                padding: 1px;\n                border: 0;\n            }\n\n\n        /* Sample table */\n            table.sample {\n                border: 0;\n                margin-bottom: 2em;\n                margin-left:1em;\n            }\n            .sample tr {\n                border:0;\n            }\n            .sample td, .sample th{\n                padding: 0.5em;\n                white-space: nowrap;\n                border: none;\n\n            }\n\n            .sample thead {\n                border-top: 0;\n                border-bottom: 2px solid #ddd;\n            }\n\n            .sample td {\n                width:100%;\n            }\n\n\n        /* There is no good solution available to make the divs equal height and then center ... */\n            .histogram {\n                margin-top: 3em;\n            }\n        /* Freq table */\n\n            table.freq {\n                margin-bottom: 2em;\n                border: 0;\n            }\n            table.freq th, table.freq tr, table.freq td {\n                border: 0;\n                padding: 0;\n            }\n\n            .freq thead {\n                font-weight: 600;\n                white-space: nowrap;\n                overflow: hidden;\n                text-overflow: ellipsis;\n\n            }\n\n            td.fillremaining{\n                width:auto;\n                max-width: none;\n            }\n\n            td.number, th.number {\n                text-align:right ;\n            }\n\n        /* Freq mini */\n            .freq.mini td{\n                width: 50%;\n                padding: 1px;\n                font-size: 12px;\n\n            }\n            table.freq.mini {\n                 width:100%;\n            }\n            .freq.mini th {\n                overflow: hidden;\n                text-overflow: ellipsis;\n                white-space: nowrap;\n                max-width: 5em;\n                font-weight: 400;\n                text-align:right;\n                padding-right: 0.5em;\n            }\n\n            .missing {\n                color: #a94442;\n            }\n            .alert, .alert > th, .alert > td {\n                color: #a94442;\n            }\n\n\n        /* Bars in tables */\n            .freq .bar{\n                float: left;\n                width: 0;\n                height: 100%;\n                line-height: 20px;\n                color: #fff;\n                text-align: center;\n                background-color: #337ab7;\n                border-radius: 3px;\n                margin-right: 4px;\n            }\n            .other .bar {\n                background-color: #999;\n            }\n            .missing .bar{\n                background-color: #a94442;\n            }\n            .tooltip-inner {\n                width: 100%;\n                white-space: nowrap;\n                text-align:left;\n            }\n\n            .extrapadding{\n                padding: 2em;\n            }\n\n            .pp-anchor{\n\n            }\n\n</style>\n\n<div class=\"container pandas-profiling\">\n    <div class=\"row headerrow highlight\">\n        <h1>Overview</h1>\n    </div>\n    <div class=\"row variablerow\">\n    <div class=\"col-md-6 namecol\">\n        <p class=\"h4\">Dataset info</p>\n        <table class=\"stats\" style=\"margin-left: 1em;\">\n            <tbody>\n            <tr>\n                <th>Number of variables</th>\n                <td>12 </td>\n            </tr>\n            <tr>\n                <th>Number of observations</th>\n                <td>2988181 </td>\n            </tr>\n            <tr>\n                <th>Total Missing (%)</th>\n                <td>0.0% </td>\n            </tr>\n            <tr>\n                <th>Total size in memory</th>\n                <td>273.6 MiB </td>\n            </tr>\n            <tr>\n                <th>Average record size in memory</th>\n                <td>96.0 B </td>\n            </tr>\n            </tbody>\n        </table>\n    </div>\n    <div class=\"col-md-6 namecol\">\n        <p class=\"h4\">Variables types</p>\n        <table class=\"stats\" style=\"margin-left: 1em;\">\n            <tbody>\n            <tr>\n                <th>Numeric</th>\n                <td>10 </td>\n            </tr>\n            <tr>\n                <th>Categorical</th>\n                <td>0 </td>\n            </tr>\n            <tr>\n                <th>Boolean</th>\n                <td>0 </td>\n            </tr>\n            <tr>\n                <th>Date</th>\n                <td>0 </td>\n            </tr>\n            <tr>\n                <th>Text (Unique)</th>\n                <td>0 </td>\n            </tr>\n            <tr>\n                <th>Rejected</th>\n                <td>2 </td>\n            </tr>\n            <tr>\n                <th>Unsupported</th>\n                <td>0 </td>\n            </tr>\n            </tbody>\n        </table>\n    </div>\n    <div class=\"col-md-12\" style=\"padding-left: 1em;\">\n        \n        <p class=\"h4\">Warnings</p>\n        <ul class=\"list-unstyled\"><li><a href=\"#pp_var_click_timestamp\"><code>click_timestamp</code></a> is highly correlated with <a href=\"#pp_var_session_start\"><code>session_start</code></a> (ρ = 0.99901) <span class=\"label label-primary\">Rejected</span></li><li><a href=\"#pp_var_session_start\"><code>session_start</code></a> is highly correlated with <a href=\"#pp_var_session_id\"><code>session_id</code></a> (ρ = 1) <span class=\"label label-primary\">Rejected</span></li> </ul>\n    </div>\n</div>\n    <div class=\"row headerrow highlight\">\n        <h1>Variables</h1>\n    </div>\n    <div class=\"row variablerow\">\n    <div class=\"col-md-3 namecol\">\n        <p class=\"h4 pp-anchor\" id=\"pp_var_click_article_id\">click_article_id<br/>\n            <small>Numeric</small>\n        </p>\n    </div><div class=\"col-md-6\">\n    <div class=\"row\">\n        <div class=\"col-sm-6\">\n            <table class=\"stats \">\n                <tr>\n                    <th>Distinct count</th>\n                    <td>46033</td>\n                </tr>\n                <tr>\n                    <th>Unique (%)</th>\n                    <td>1.5%</td>\n                </tr>\n                <tr class=\"ignore\">\n                    <th>Missing (%)</th>\n                    <td>0.0%</td>\n                </tr>\n                <tr class=\"ignore\">\n                    <th>Missing (n)</th>\n                    <td>0</td>\n                </tr>\n                <tr class=\"ignore\">\n                    <th>Infinite (%)</th>\n                    <td>0.0%</td>\n                </tr>\n                <tr class=\"ignore\">\n                    <th>Infinite (n)</th>\n                    <td>0</td>\n                </tr>\n            </table>\n\n        </div>\n        <div class=\"col-sm-6\">\n            <table class=\"stats \">\n\n                <tr>\n                    <th>Mean</th>\n                    <td>194920</td>\n                </tr>\n                <tr>\n                    <th>Minimum</th>\n                    <td>3</td>\n                </tr>\n                <tr>\n                    <th>Maximum</th>\n                    <td>364046</td>\n                </tr>\n                <tr class=\"ignore\">\n                    <th>Zeros (%)</th>\n                    <td>0.0%</td>\n                </tr>\n            </table>\n        </div>\n    </div>\n</div>\n<div class=\"col-md-3 collapse in\" id=\"minihistogram-4915986537900427630\">\n    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAATBJREFUeJzt2lGKwjAUQNFxcEkuYvY03%2B7JRbinuAG52II0pOf8F0Ll8pLYyxhj/ABv/R69AJjZ9egFrOz2/9j8zPP%2B94WVsJcJAkEgEAQCwRnkhJyNPmeCQBAIBFusydj%2BzMUEgSAQCAKBIBAIAoHgFmsBe26%2B%2BIwJAkEgEAQCwRlkA3v98zFBIAgEgi0WX7PCh5dLBLLCD8GcbLEgCASCQCAIBIJAICxxi7WHf8W3Oev7MkEgCASCQCAIBIJAIEx3i3XW2xLmZIJAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAuIwxxtGLgFmZIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBBeWBUeooEoFJkAAAAASUVORK5CYII%3D\">\n\n</div>\n<div class=\"col-md-12 text-right\">\n    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives-4915986537900427630,#minihistogram-4915986537900427630\"\n       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n        Toggle details\n    </a>\n</div>\n<div class=\"row collapse col-md-12\" id=\"descriptives-4915986537900427630\">\n    <ul class=\"nav nav-tabs\" role=\"tablist\">\n        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles-4915986537900427630\"\n                                                  aria-controls=\"quantiles-4915986537900427630\" role=\"tab\"\n                                                  data-toggle=\"tab\">Statistics</a></li>\n        <li role=\"presentation\"><a href=\"#histogram-4915986537900427630\" aria-controls=\"histogram-4915986537900427630\"\n                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n        <li role=\"presentation\"><a href=\"#common-4915986537900427630\" aria-controls=\"common-4915986537900427630\"\n                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n        <li role=\"presentation\"><a href=\"#extreme-4915986537900427630\" aria-controls=\"extreme-4915986537900427630\"\n                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n\n    </ul>\n\n    <div class=\"tab-content\">\n        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles-4915986537900427630\">\n            <div class=\"col-md-4 col-md-offset-1\">\n                <p class=\"h4\">Quantile statistics</p>\n                <table class=\"stats indent\">\n                    <tr>\n                        <th>Minimum</th>\n                        <td>3</td>\n                    </tr>\n                    <tr>\n                        <th>5-th percentile</th>\n                        <td>42223</td>\n                    </tr>\n                    <tr>\n                        <th>Q1</th>\n                        <td>124230</td>\n                    </tr>\n                    <tr>\n                        <th>Median</th>\n                        <td>202380</td>\n                    </tr>\n                    <tr>\n                        <th>Q3</th>\n                        <td>277070</td>\n                    </tr>\n                    <tr>\n                        <th>95-th percentile</th>\n                        <td>336250</td>\n                    </tr>\n                    <tr>\n                        <th>Maximum</th>\n                        <td>364046</td>\n                    </tr>\n                    <tr>\n                        <th>Range</th>\n                        <td>364043</td>\n                    </tr>\n                    <tr>\n                        <th>Interquartile range</th>\n                        <td>152840</td>\n                    </tr>\n                </table>\n            </div>\n            <div class=\"col-md-4 col-md-offset-2\">\n                <p class=\"h4\">Descriptive statistics</p>\n                <table class=\"stats indent\">\n                    <tr>\n                        <th>Standard deviation</th>\n                        <td>90768</td>\n                    </tr>\n                    <tr>\n                        <th>Coef of variation</th>\n                        <td>0.46566</td>\n                    </tr>\n                    <tr>\n                        <th>Kurtosis</th>\n                        <td>-0.94305</td>\n                    </tr>\n                    <tr>\n                        <th>Mean</th>\n                        <td>194920</td>\n                    </tr>\n                    <tr>\n                        <th>MAD</th>\n                        <td>76806</td>\n                    </tr>\n                    <tr class=\"\">\n                        <th>Skewness</th>\n                        <td>-0.12344</td>\n                    </tr>\n                    <tr>\n                        <th>Sum</th>\n                        <td>582464155343</td>\n                    </tr>\n                    <tr>\n                        <th>Variance</th>\n                        <td>8238900000</td>\n                    </tr>\n                    <tr>\n                        <th>Memory size</th>\n                        <td>22.8 MiB</td>\n                    </tr>\n                </table>\n            </div>\n        </div>\n        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram-4915986537900427630\">\n            <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3Xt0jXe%2Bx/FPLkWJJEKjPcY0jnuuLiUaKkJRVarucazW1K2lrGh1KKPVdqbMoLQ4yvQwtJ2RolOXUo66jLbaOTO92IlbBYdmoknJlosESX7nD8s%2B3aWSzS/ddrxfa1mW328/z%2B/7fXb29vE8z978jDFGAAAAsMbf2wUAAABUNQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGBZoLcLuFXk5ORb36e/v5/CwmrpzJlClZUZ6/u/2dBv1Ua/Vdet1KtEvzebO%2B6o7ZV1OYPlw/z9/eTn5yd/fz9vl/KzoN%2BqjX6rrlupV4l%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%2B1apV69uypNm3aKDk5WWlpaa658%2BfP6/nnn1fnzp0VHx%2BviRMnKjc31zWfmZmpMWPGKD4%2BXklJSZozZ47KysoqvDYAAEB5vB6wDhw4oPXr17v%2BnJ2drXHjxmno0KHau3evpk%2BfrhkzZsjhcEiSduzYoYULF%2BoPf/iDPv30UyUlJemJJ57QuXPnJEnz589Xenq6UlNTtXXrVhlj9Nxzz7n2P2HCBNWvX1/bt2/XihUrtH37dq1cubJCawMAAFSEVwNWWVmZXnjhBY0YMcI1tnHjRkVERGjgwIGqXr26EhIS1LVrV61Zs0aSlJqaqv79%2BysuLk41atTQqFGjJEk7d%2B5USUmJ1q5dq3Hjxumuu%2B5SaGioUlJStGvXLn333XdyOBw6ePCgJk%2BerNq1aysiIkIjRoxQampqhdYGAACoiEBvLr569WpVr15dffr00YIFCyRJ6enpioyMdHtcZGSktmzZ4pp/8MEHXXP%2B/v5q2bKlHA6HWrZsqfz8fEVFRbnmGzdurBo1aig9PV3Z2dlq0KCBQkJCXPNRUVE6duyYCgoKyl27orKzs5WTk%2BM2FhhYU%2BHh4R7tpzwBAf5uv1d19AtvCQy0/xzcSs9vVeq1Ij8LVanfirjV%2Bq0orwWs77//XgsXLtRbb73lNu50OlW/fn23sdDQUNd9VE6n0y0gSVJISIhyc3PldDolScHBwW7zwcHBrvkfz13e1%2BX5a61dUampqVq0aJHb2Pjx4zVx4kSP9lNRwcG3V8p%2Bb1b0i59bnTq1Km3ft9LzWxV69eRnoSr064lbrd/yeC1gzZo1S/3791eTJk307bfferStMea658vb1oYhQ4aoa9eubmOBgTWVm1todZ2AAH8FB9%2BuvLwilZaWlb%2BBj6NfeMs90z/0dgke%2Be/J93m7BDdV6We5Iu/jVanfirjZ%2B63MfyBdi1cC1t69e/Xll19q06ZNV8zVqVPHdSbqstzcXIWFhf3kvNPpVNOmTV2PcTqdqlXr/w/o2bNnVbduXZWWll51Wz8/P4WFhZW7dkWFh4dfcTkwJydfJSWV84NXWlpWafu%2BGdEvcG03689LVfhZ9qT%2BqtCvJ261fsvjlQumGzZs0OnTp5WUlKT4%2BHj1799fkhQfH69mzZq5fe2CJKWlpSkuLk6SFB0drfT0dNdcaWmp9u/fr7i4ODVs2FAhISFu84cPH9aFCxcUHR2t6OhoZWVl6cyZM655h8OhJk2aqFatWoqJibnm2gAAABXhlYA1depUbd26VevXr9f69eu1bNkySdL69evVp08fZWZmas2aNTp//rx2796t3bt3a/DgwZKk5ORkvf/%2B%2B/rqq69UVFSkJUuWqFq1aurSpYsCAgI0ePBgvfHGG8rKylJubq5effVVde/eXfXq1VNkZKRiYmI0b948FRQUKCMjQytWrFBycrIklbs2AABARXjlEmFISIjbjeolJSWSpDvvvFOStHTpUv32t7/Viy%2B%2BqAYNGmjOnDlq0aKFJKlz5856%2BumnlZKSotOnTysmJkbLli1TjRo1JEkTJ05UYWGhHn74YZWUlCgpKUkzZ850rfX6669rxowZ6tixo4KCgjR06FANGzZMklS3bt1rrg0AAFARfubnuOsbysnJt77PwEB/1alTS7m5hbfEdW/6rTp6LfjE2yVUaVtSOnq7BDfX%2Bln2tZ%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%2BPl4TJ05Ubm6uaz4zM1NjxoxRfHy8kpKSNGfOHJWVlbnmy1sbAADgWrwWsC5cuKDHH39c7du31969e7Vp0yadPn1aM2fOVHZ2tsaNG6ehQ4dq7969mj59umbMmCGHwyFJ2rFjhxYuXKg//OEP%2BvTTT5WUlKQnnnhC586dkyTNnz9f6enpSk1N1datW2WM0XPPPedae8KECapfv762b9%2BuFStWaPv27Vq5cqUklbs2AABAebwWsIqKijRp0iSNHTtW1apVU1hYmLp3765vvvlGGzduVEREhAYOHKjq1asrISFBXbt21Zo1ayRJqamp6t%2B/v%2BLi4lSjRg2NGjVKkrRz506VlJRo7dq1GjdunO666y6FhoYqJSVFu3bt0nfffSeHw6GDBw9q8uTJql27tiIiIjRixAilpqZKUrlrAwAAlMdrASskJESDBg1SYGCgJOno0aP661//ql69eik9PV2RkZFuj4%2BMjHRdBvzxvL%2B/v1q2bCmHw6ETJ04oPz9fUVFRrvnGjRurRo0aSk9PV3p6uho0aKCQkBDXfFRUlI4dO6aCgoJy1wYAAChPoLcLyMzMVM%2BePVVSUqLBgwdr4sSJGj16tOrXr%2B/2uNDQUNd9VE6n0y0gSZcCW25urpxOpyQpODjYbT44ONg1/%2BO5y/u6PH%2BttSsiOzvbdS/ZZYGBNRUeHl7hfVREQIC/2%2B9VHf0C8LbAwPJfj7faa/dW67eivB6wGjRoIIfDof/93//V888/r1//%2BtcV2s4Yc93z5W17o1JTU7Vo0SK3sfHjx2vixImVsl5w8O2Vst%2BbFf0C8JY6dWpV%2BLG32mv3Vuu3PF4PWJLk5%2BeniIgITZo0SUOHDlViYqLrTNRlubm5CgsLkyTVqVPninmn06mmTZu6HuN0OlWr1v%2B/EM6ePau6deuqtLT0qtv6%2BfkpLCzsqvv%2B4doVMWTIEHXt2tVtLDCwpnJzCyu8j4oICPBXcPDtyssrUmlpWfkb%2BDj6BeBtFXkfv9Veuzd7v56EYpu8FrD27t2rmTNnasuWLfL3v3Ra8fLvsbGx2rp1q9vj09LSFBcXJ0mKjo5Wenq6HnnkEUlSaWmp9u/fr4EDB6phw4YKCQlx3WslSYcPH9aFCxcUHR2t7OxsZWVl6cyZM67Q5HA41KRJE9WqVUsxMTFat27dT65dEeHh4VdcDszJyVdJSeX84JWWllXavm9G9Ht1vRZ88jNUA9zaPHnv4b3q1ubxBdOuXbtq0aJFysrKuqGFo6OjVVBQoDlz5qioqEhnzpzRwoULdc899yg5OVmZmZlas2aNzp8/r927d2v37t0aPHiwJCk5OVnvv/%2B%2BvvrqKxUVFWnJkiWqVq2aunTpooCAAA0ePFhvvPGGsrKylJubq1dffVXdu3dXvXr1FBkZqZiYGM2bN08FBQXKyMjQihUrlJycLEnq06fPNdcGAAAoj8cBa8CAAdq8ebPuv/9%2BjRo1Stu2bVNJSYnHC9euXVvLly9XWlqaOnTooN69e6t27dp69dVXVbduXS1dulRvv/222rZtq1deeUVz5sxRixYtJEmdO3fW008/rZSUFLVv316ffvqpli1bpho1akiSJk6cqLi4OD388MPq1q2batWqpd/97neutV9//XVlZ2erY8eOevTRR9WvXz8NGzZMkspdGwAAoDx%2B5jrv%2BE5PT9emTZu0ZcsWXbx4Uf369dPAgQPVqFEj2zVWCTk5%2Bdb3GRjorzp1aik3t/CWOC1Lv9fGJUKg8m1J6VjuY3ivurnccUdtr6x73Z%2BpjIqK0pQpU7Rz505NmzZN7777rh588EGNHDlS%2B/bts1kjAACAT7nugHXx4kVt3rxZo0eP1pQpU1S/fn0999xzatmypUaMGKGNGzfarBMAAMBnePwpwoyMDK1du1bvv/%2B%2BCgsL1bNnT61cuVJt27Z1PaZdu3aaOXOm%2BvTpY7VYAAAAX%2BBxwOrdu7caNWqksWPHql%2B/fgoNDb3iMYmJiTpz5oyVAgEAAHyNxwFr1apVat%2B%2BfbmP%2B/rrr6%2BrIAAAAF/n8T1YzZs31xNPPKHt27e7xv70pz9p9OjRV3wDOgAAwK3I44A1a9Ys5efnq0mTJq6xLl26qKysTLNnz7ZaHAAAgC/y%2BBLhxx9/rI0bN6pOnTqusYiICM2dO1cPPfSQ1eIAAAB8kcdnsIqLi1W9evUrd%2BTvr6KiIitFAQAA%2BDKPA1a7du00e/ZsnT171jX23Xff6cUXX3T7qgYAAIBblceXCKdNm6bHH39c9957r4KCglRWVqbCwkI1bNhQb731VmXUCAAA4FM8DlgNGzbUBx98oL/97W86ceKE/P391ahRI3Xq1EkBAQGVUSMAAIBP8ThgSVK1atV0//33264FAACgSvA4YJ08eVLz5s3TN998o%2BLi4ivmP/roIyuFAQCAG9NrwSfeLqHCtqR09HYJVl3XPVjZ2dnq1KmTatasWRk1AQAA%2BDSPA1ZaWpo%2B%2BugjhYWFVUY9AAAAPs/jr2moW7cuZ64AAACuweOANXbsWC1atEjGmMqoBwAAwOd5fInwb3/7m7744gu99957%2BsUvfiF/f/eMtnr1amvFAQAA%2BCKPA1ZQUJA6d%2B5cGbUAAABUCR4HrFmzZlVGHQAAAFWGx/dgSdLRo0e1cOFCPffcc66xL7/80lpRAAAAvszjM1h79%2B7V6NGj1ahRIx0/flyzZs3SyZMn9eijj2rBggXq1q1bZdSJKsCXvvBOqnpfegcA%2BPl4fAZr/vz5evbZZ7Vx40b5%2BflJuvT/E86ePVuLFy%2B2XiAAAICv8ThgHT58WMnJyZLkCliS9MADDygjI8NeZQAAAD7K44BVu3btq/4fhNnZ2apWrZqVogAAAHyZxwGrTZs2euWVV1RQUOAaO3bsmKZMmaJ7773XanEAAAC%2ByOOb3J977jk99thjio%2BPV2lpqdq0aaOioiI1bdpUs2fProwaAQAAfIrHAevOO%2B/Upk2btHv3bh07dkw1atRQo0aN1LFjR7d7sgAAAG5VHgcsSbrtttt0//33264FAACgSvA4YHXt2vWaZ6o%2B%2BuijGyoIAADA13kcsB588EG3gFVaWqpjx47J4XDoscces1ocAACAL/I4YE2ePPmq41u3btXnn39%2BwwUBAAD4uuv6vwiv5v7779cHH3xga3cAAAA%2By1rA2r9/v4wxtnYHAADgszy%2BRDh06NArxoqKipSRkaEePXpYKQoAAMCXeRywIiIirvgUYfXq1TVw4EANGjTIWmEAAAC%2ByuOAxbe1AwAAXJvHAev999%2Bv8GP79evn6e4BAAB8nscBa/r06SorK7vihnY/Pz%2B3MT8/PwIWAAC4JXkcsN58800tX75cTzzxhJo3by5jjA4dOqQ//vGPGj58uOLj4yujTgAAAJ9xXfdgLVu2TPXr13eN3XPPPWrYsKFGjhypTZs2WS0QAADA13j8PVjHjx9XSEjIFePBwcHKzMy0UhQAAIAv8zhgNWjQQLNnz1Zubq5rLC8vT/PmzdMvf/lLq8UBAAD4Io8vEU6bNk3PPPOMUlNTVatWLfn7%2B6ugoEA1atTQ4sWLK6NGAAAAn%2BJxwOrUqZN27dql3bt369SpUzLGqH79%2BrrvvvtUu3btyqgRAADAp3gcsCTp9ttvV7du3XTq1Ck1bNjQdk0AAAA%2BzeN7sIqLizVlyhS1bt1avXr1knTpHqxRo0YpLy/PeoEAAAC%2BxuOANWfOHB04cEBz586Vv///b15aWqq5c%2BdaLQ4AAMAXeRywtm7dqtdff10PPPCA6z99Dg4O1qxZs7Rt2zbrBQIAAPgajwNWYWGhIiIirhgPCwvTuXPnbNQEAADg0zwOWL/85S/1%2BeefS5Lb/z344Ycf6t/%2B7d/sVQYAAOCjPP4U4bBhwzRhwgQNGDBAZWVlWrFihdLS0rR161ZNnz69MmoEAADwKR4HrCFDhigwMFBvv/22AgIC9MYbb6hRo0aaO3euHnjggcqoEQAAwKd4HLDOnDmjAQMGaMCAAZVRDwAAgM/z%2BB6sbt26ud17dSMyMzM1fvx4xcfHKyEhQVOnTnV9l9aBAwc0fPhwtW3bVj169NDy5cvdtt28ebP69Omj1q1bq3///vr4449dc2VlZZo/f766deumdu3aaeTIkTp58qRr3ul0KiUlRQkJCerUqZOmT5%2Bu4uJi13x5awMAAFyLxwErPj5eW7ZssbL4E088oeDgYO3YsUPvvfeevvnmG/3%2B979XcXGxxo4dqw4dOmjPnj2aP3%2B%2Bli5d6voaiAMHDmjKlCmaPHmyPvvsM40YMUJPPfWUTp06JUl65513tHHjRi1btkw7d%2B5URESExo8f7wqGM2bMUFFRkTZt2qR169YpIyPD9R1e5a0NAABQHo8D1l133aXf/e536tevnyZOnKhnnnnG7VdF5eXlKTo6Ws8884xq1aqlO%2B%2B8U4888oj%2B8Y9/aNeuXbp48aKefPJJ1axZU1FRURo0aJBSU1MlSWvWrFFiYqISExNVvXp19e3bV82aNdOGDRskSampqRoxYoQaN26soKAgTZo0SRkZGfr666/1/fffa/v27Zo0aZLCwsJUv359jRs3TuvWrdPFixfLXRsAAKA8Ht%2BDdeTIEf37v/%2B7JCk3N/e6F7785aQ/lJWVpfDwcKWnp6t58%2BYKCAhwzUVGRmrNmjWSpPT0dCUmJrptGxkZKYfDoeLiYh05ckSRkZGuuaCgIN19991yOBzKz89XQECAmjdv7pqPiorSuXPndPTo0XLXrojs7Gzl5OS4jQUG1lR4eHiF91ERAQH%2Bbr/DrsBA7x5Xnl/g5lOR9wVeu9fH2%2B%2B5tlU4YE2aNEnz58/XW2%2B95RpbvHixxo8fb6UQh8Oht99%2BW0uWLNGWLVsUHBzsNh8aGiqn06mysjI5nU6FhIS4zYeEhOjIkSM6e/asjDFXnc/NzVVoaKiCgoJc30J/eU66FBidTuc11/7hfw/0U1JTU7Vo0SK3sfHjx2vixInlH4jrEBx8e6Xs91ZXp04tb5cgiecXuJl48r7Aa9czN8t7ri0VDlg7duy4YmzZsmVWAtY///lPPfnkk3rmmWeUkJDwk/d4/TAUlXej/bXmr%2Bcm/R%2BuXZ4hQ4aoa9eubmOBgTWVm1vo8brXEhDgr%2BDg25WXV6TS0jKr%2B4asP1%2Be4vkFbj4VeV/gtXt9Kus911vBrcIB62qhxManCXfs2KFnn31WM2bMUL9%2B/SRd%2Bm93jh8/7vY4p9Op0NBQ%2Bfv7q06dOnI6nVfMh4WFuR5ztfm6desqLCxMBQUFKi0tdV0GvPzYy/PXWrsiwsPDr7gcmJOTr5KSynmhlZaWVdq%2Bb2U3yzHl%2BQVuHp68FnnteqaqHasKX/C82hkcT87qXM0XX3yhKVOm6LXXXnOFK0mKjo7WoUOHVFJS4hpzOByKi4tzzaelpbnt6/J89erV1bRpU6Wnp7vm8vLydOLECcXGxqply5YyxujgwYNu2wYHB6tRo0blrg0AAFAej29yt6WkpES/%2Bc1vNHnyZHXq1MltLjExUUFBQVqyZIlGjRqlw4cPa%2B3atZozZ44kafDgwRo4cKB27dqle%2B%2B9Vxs3btTx48fVt29fSVJycrKWLVumzp07q379%2Bpo7d65atmypmJgYSVLPnj21YMEC/f73v9eFCxe0ePFiDRw4UIGBgeWujVtHrwWfeLsEADcZ3hdQUX6mgtf54uLi9PXXX5c7VlH/%2BMc/9B//8R%2BqVq3aFXMffvihCgsL9cILLygtLU316tXT6NGjNWzYMNdjtm3bpnnz5ikzM1NNmjTR9OnT1a5dO0mXLl0uXLhQq1evVmFhoeLj4/XSSy/pzjvvlCTl5%2BfrhRde0M6dO3XbbbfpoYce0tSpU121HD58%2BJprX4%2BcnPwb2v5qAgP9VadOLeXmFvrEqVXemAAAP2VLSsdK2e8dd9SulP2Wp8IBKzIyUr169XIb27JlyxVj8%2BbNs1ddFULAImABAH5aVQtYFb5E2LZtW2VnZ5c7BgAAcKurcMD64fdfAQAA4KdVra9NBQAAuAkQsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsC/R2Abgx90z/0NslAACAH%2BEMFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZV4NWHv27FFCQoImTZp0xdzmzZvVp08ftW7dWv3799fHH3/smisrK9P8%2BfPVrVs3tWvXTiNHjtTJkydd806nUykpKUpISFCnTp00ffp0FRcXu%2BYPHDig4cOHq23bturRo4eWL19e4bUBAADK47WA9cc//lG//e1vdffdd18xd%2BDAAU2ZMkWTJ0/WZ599phEjRuipp57SqVOnJEnvvPOONm7cqGXLlmnnzp2KiIjQ%2BPHjZYyRJM2YMUNFRUXatGmT1q1bp4yMDM2dO1eSVFxcrLFjx6pDhw7as2eP5s%2Bfr6VLl2rbtm0VWhsAAKA8XgtY1atX19q1a68asNasWaPExEQlJiaqevXq6tu3r5o1a6YNGzZIklJTUzVixAg1btxYQUFBmjRpkjIyMvT111/r%2B%2B%2B/1/bt2zVp0iSFhYWpfv36GjdunNatW6eLFy9q165dunjxop588knVrFlTUVFRGjRokFJTUyu0NgAAQHm8FrAeffRR1a5d%2B6pz6enpioyMdBuLjIyUw%2BFQcXGxjhw54jYfFBSku%2B%2B%2BWw6HQwcOHFBAQICaN2/umo%2BKitK5c%2Bd09OhRpaenq3nz5goICHDbd1paWrlrAwAAVESgtwu4GqfTqZCQELexkJAQHTlyRGfPnpUx5qrzubm5Cg0NVVBQkPz8/NzmJCk3N1dOp1PBwcFu24aGhsrpdKqsrOyaa1dUdna2cnJy3MYCA2sqPDy8wvuoiIAAPqMAAKgaAgOr1t9pN2XAkuS6n%2Bp65svb9mp%2BGMiuZ/sfSk1N1aJFi9zGxo8fr4kTJ97QfgEAqKrq1Knl7RKsuikDVp06deR0Ot3GnE6nwsLCFBoaKn9//6vO161bV2FhYSooKFBpaanrMuDlx16eP378%2BBXbXt7vtdauqCFDhqhr165uY4GBNZWbW1jhfVQEZ7AAAFWF7b8jL/NWcLspA1Z0dLTrnqjLHA6HevfurerVq6tp06ZKT09X%2B/btJUl5eXk6ceKEYmNj1aBBAxljdPDgQUVFRbm2DQ4OVqNGjRQdHa2//OUvKikpUWBgoGs%2BLi6u3LUrKjw8/IrLgTk5%2BSopKfPsQAAAcIuoan9H3pSnQAYPHqxPP/1Uu3bt0vnz57UBgWnMAAAQgElEQVR27VodP35cffv2lSQlJydr1apVysjIUEFBgebOnauWLVsqJiZGYWFh6tmzpxYsWKAzZ87o1KlTWrx4sQYOHKjAwEAlJiYqKChIS5YsUVFRkb7%2B%2BmutXbtWycnJFVobAACgPH7mRm84uk4xMTGSpJKSEklyO5skSdu2bdO8efOUmZmpJk2aaPr06WrXrp2kS/dILVy4UKtXr1ZhYaHi4%2BP10ksv6c4775Qk5efn64UXXtDOnTt122236aGHHtLUqVNVrVo1SdLhw4f1wgsvKC0tTfXq1dPo0aM1bNgwV23XWvt65eTk39D2VxMY6K/uc/dY3y8AAD%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%2B/btOn36tMaOHat69erpV7/6lbdLAwAAPoAzWD/icDh08OBBTZ48WbVr11ZERIRGjBih1NRUb5cGAAB8BGewfiQ9PV0NGjRQSEiIaywqKkrHjh1TQUGBgoKCyt1Hdna2cnJy3MYCA2sqPDzcaq0BAeRjAEDVEBhYtf5OI2D9iNPpVHBwsNvY5bCVm5tboYCVmpqqRYsWuY099dRTmjBhgr1CdSnIPXbnNxoyZIj18HYzys7OVmpqKv1WUfRbdd1KvUr0i0uqVly0xBhzQ9sPGTJE7733ntuvIUOGWKru/%2BXk5GjRokVXnC2rqui3aqPfqutW6lWiX1zCGawfCQsLk9PpdBtzOp3y8/NTWFhYhfYRHh5OigcA4BbGGawfiY6OVlZWls6cOeMaczgcatKkiWrVquXFygAAgK8gYP1IZGSkYmJiNG/ePBUUFCgjI0MrVqxQcnKyt0sDAAA%2BImDmzJkzvV3Ezea%2B%2B%2B7Tpk2b9PLLL%2BuDDz7QwIEDNXLkSPn5%2BXm7tCvUqlVL7du3v2XOrtFv1Ua/Vdet1KtEv5D8zI3e0Q0AAAA3XCIEAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyA5YMyMzM1ZswYxcfHKykpSXPmzFFZWZm3y7qm5s2bKzo6WjExMa5fL7/8siRp7969GjhwoNq0aaPevXtrw4YNbtuuWrVKPXv2VJs2bZScnKy0tDTX3Pnz5/X888%2Brc%2BfOio%2BP18SJE5Wbm%2Bua/7mO1Z49e5SQkKBJkyZdMbd582b16dNHrVu3Vv/%2B/fXxxx%2B75srKyjR//nx169ZN7dq108iRI3Xy5EnXvNPpVEpKihISEtSpUydNnz5dxcXFrvkDBw5o%2BPDhatu2rXr06KHly5dXeO3K6Pe9995TixYt3J7nmJgY7du3z6f7zczM1Pjx4xUfH6%2BEhARNnTpVeXl5N1xTZR8P2/1%2B%2B%2B23at68%2BRXP73/913/5dL8HDx7UY489prZt2yohIUEpKSnKycmR5N33p/LWtt3v559/ftXnd8uWLT7dr9cY%2BJxHHnnE/OY3vzF5eXnm2LFjpkePHmb58uXeLuuamjVrZk6ePHnF%2BHfffWdatWpl1qxZY4qLi80nn3xiYmNjzb59%2B4wxxnz00UfmnnvuMV999ZUpKioyS5cuNR07djSFhYXGGGNmzZpl%2Bvfvb/71r3%2BZ3Nxc89RTT5mxY8e69v9zHKtly5aZHj16mKFDh5qUlBS3uf3795vo6Giza9cuU1xcbNavX2/i4uJMVlaWMcaYVatWmaSkJHPkyBGTn59vXnrpJdOnTx9TVlZmjDHmqaeeMmPGjDGnT582p06dMkOGDDEvv/yyMcaYoqIic99995mFCxeawsJCk5aWZtq3b2%2B2bt1aobUro99169aZ4cOH/%2BS2vtivMcY89NBDZurUqaagoMBkZWWZ/v37m2nTpt1wTZV5PCqj35MnT5pmzZr95Ha%2B2O/58%2BfNvffeaxYtWmTOnz9vTp8%2BbYYPH27GjRvn1fen8taujH4/%2B%2Bwzk5SU9JPb%2BmK/3kTA8jH79u0zLVu2NE6n0zX25z//2fTs2dOLVZXvpwLWm2%2B%2Bafr16%2Bc2lpKSYmbMmGGMMWbMmDHmlVdecc2Vlpaajh07mk2bNpmLFy%2Batm3bmu3bt7vmjxw5Ypo3b25OnTr1sx2rlStXmry8PDNlypQrAseLL75oxo8f7zY2aNAgs3TpUmOMMb179zYrV650zeXn55vIyEjz5ZdfmpycHNOiRQtz4MAB1/zu3btNq1atzIULF8yWLVtMhw4dTElJiWt%2Bzpw55vHHH6/Q2pXRb3kByxf7PXv2rJk6darJyclxjb311lumR48eN1xTZR6Pyui3vIDli/06nU7z7rvvmosXL7rGVq5cabp37%2B7V96fy1q6MfssLWL7YrzdxidDHpKenq0GDBgoJCXGNRUVF6dixYyooKPBiZeWbN2%2BeunTponvuuUczZsxQYWGh0tPTFRkZ6fa4yMhI12nnH8/7%2B/urZcuWcjgcOnHihPLz8xUVFeWab9y4sWrUqKH09PSf7Vg9%2Buijql279lXnfqo/h8Oh4uJiHTlyxG0%2BKChId999txwOhw4cOKCAgAA1b97crf5z587p6NGjSk9PV/PmzRUQEOC27586dj9cu7L6laSsrCz96le/Urt27dStWzetX79ekny23%2BDgYM2aNUv16tVz6zE8PPyGaqrs41EZ/V7261//Wp06dVKHDh00b948Xbx40Wf7DQkJ0aBBgxQYGChJOnr0qP7617%2BqV69eXn1/Km/tyuhXkgoLC12Xh%2B%2B77z6tWLFCxhif7debCFg%2Bxul0Kjg42G3s8g/sD69132xatWqlhIQEbdu2Tampqfrqq6/04osvXrWf0NBQVy9Op9PtBSld6jc3N1dOp1OSrtg%2BODjYNe/tY3Wt%2Bs%2BePStjzDX7CwoKkp%2Bfn9ucpJ/sLzQ0VE6nU2VlZddcu7KEhYUpIiJCzz77rD755BM9/fTTmjZtmvbu3Vtl%2BnU4HHr77bf15JNP3lBNlX08KqPfatWqqXXr1urevbt27typZcuWacOGDfrP//xPSd79eb9RmZmZio6O1oMPPqiYmBhNnDjRq%2B9P5a19o67Wb1BQkJo1a6bHHntMe/bs0axZs7Ro0SKtW7fO5/v1BgKWD7r8rwlfkpqaqkGDBqlatWpq3LixJk%2BerE2bNrn%2B5Xst5fV7rfmb4Vj93PX/8C%2Bon7v/Ll266M0331RkZKSqVaum3r17q3v37nrvvfcqVNPN3u8///lPjRw5Us8884wSEhKs1FSZx%2BNG/bjf8PBwrV69Wt27d9dtt92m2NhYjR07tsLPb3nz3uy3QYMGcjgc%2BvDDD3X8%2BHH9%2Bte/rtB2vvr%2BdLV%2Bo6Ki9NZbb6l9%2B/aqVq2aOnXqpKFDh3r1%2BfVlBCwfExYW5vqXwmVOp1N%2Bfn4KCwvzUlWe%2B8UvfqHS0lL5%2B/tf0U9ubq6rlzp16ly137CwMNdjfjx/9uxZ1a1b96Y4VteqPzQ09Kr9O51OV/0FBQUqLS11m5Pkmv/xv%2B6cTqdrv9da%2B%2BfUoEEDZWdn%2B3y/O3bs0JgxYzRt2jQ9%2BuijknRDNVX28aiMfq%2BmQYMG%2Bv7772WM8el%2BpUthLSIiQpMmTdKmTZsUGBjotfenq%2B37h2vb8ON%2Bz5w5c8VjLr9%2BJd/v9%2BdGwPIx0dHRysrKcnshOBwONWnSRLVq1fJiZT9t//79mj17tttYRkaGqlWrpsTExCuusaelpSkuLk7SpX7T09Ndc6Wlpdq/f7/i4uLUsGFDhYSEuM0fPnxYFy5cUHR09E1xrKKjo6/oz%2BFwKC4uTtWrV1fTpk3d6s/Ly9OJEycUGxurli1byhijgwcPum0bHBysRo0aKTo6WocOHVJJSckV%2By5v7cryl7/8RZs3b3Yby8jIUMOGDX263y%2B%2B%2BEJTpkzRa6%2B9pn79%2BrnGb6Smyj4eldHv3r17tWTJErfHHj16VA0aNJCfn59P9rt371717NnT7TLj5cAWGxvrtfenmJiYa65dGf3u3r1bf/7zn90ef/ToUTVs2NBn%2B/WqSr%2BNHtYNGjTITJs2zeTn55sjR46Yrl27mrffftvbZf2kU6dOmVatWpmlS5ea8%2BfPm6NHj5oHH3zQvPzyy%2Bb77783rVu3Nu%2B%2B%2B64pLi42u3btMrGxsa5PEu3evdu0bdvWfPnll%2BbcuXNm4cKFJjEx0RQVFRljLn2K6JFHHjH/%2Bte/zJkzZ8zYsWPNhAkTXGv/nMfqap%2BqO3TokImJiTE7d%2B40xcXFZs2aNaZ169YmOzvbGHPpUzRdunRxfWx9xowZZsCAAa7tU1JSzKhRo8zp06dNVlaWGTBggJk9e7Yx5tLHrZOSkszrr79uzp07Z7766itzzz33mJ07d1Zo7cro909/%2BpPp0KGD2bdvn7lw4YLZuHGjadmypXE4HD7b78WLF02vXr3M6tWrr5i70Zoq83hURr8Oh8NERUWZ999/31y4cMHs27fPdOzY0fVRe1/sNy8vzyQkJJjZs2ebc%2BfOmdOnT5uRI0eaYcOGefX9qby1K6Pf//7v/zaxsbFmz5495sKFC%2Bbjjz82rVq1cn0Vhi/2600ELB%2BUlZVlRo0aZWJjY01CQoJ5/fXXXd8jc7P6%2B9//boYMGWJatWpl2rdvb2bNmmWKi4tdc3379jVRUVGmR48eV3yvzTvvvGMSExNNdHS0SU5ONocOHXLNnT9/3sycOdO0a9fOtG7d2jz99NMmLy/PNf9zHKvo6GgTHR1tWrRoYVq0aOH682Vbt241PXr0MFFRUebhhx82f//7311zZWVl5rXXXjP33nuviY2NNaNHj3b73qa8vDwzadIk06pVK9OuXTvz4osvmvPnz7vmDx06ZIYOHWqio6NNly5dzDvvvONW27XWrox%2By8rKzOLFi01SUpKJjo42DzzwgNmxY4dP9/s///M/plmzZq4%2Bf/jr22%2B/vaGaKvt4VEa/27ZtM3379jWxsbGmY8eO5o033jClpaU%2B268xxhw8eNAMHz7cxMbGmg4dOpiUlBRz6tQpY4x335/KW7sy%2Bl29erXp0aOHiYmJMUlJSebdd9/1%2BX69xc%2BYW%2ByuMwAAgErGPVgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYNn/AUSQ9caos2hDAAAAAElFTkSuQmCC\"/>\n        </div>\n        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common-4915986537900427630\">\n            \n<table class=\"freq table table-hover\">\n    <thead>\n    <tr>\n        <td class=\"fillremaining\">Value</td>\n        <td class=\"number\">Count</td>\n        <td class=\"number\">Frequency (%)</td>\n        <td style=\"min-width:200px\">&nbsp;</td>\n    </tr>\n    </thead>\n    <tr class=\"\">\n        <td class=\"fillremaining\">160974</td>\n        <td class=\"number\">37213</td>\n        <td class=\"number\">1.2%</td>\n        <td>\n            <div class=\"bar\" style=\"width:2%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">272143</td>\n        <td class=\"number\">28943</td>\n        <td class=\"number\">1.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:2%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">336221</td>\n        <td class=\"number\">23851</td>\n        <td class=\"number\">0.8%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">234698</td>\n        <td class=\"number\">23499</td>\n        <td class=\"number\">0.8%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">123909</td>\n        <td class=\"number\">23122</td>\n        <td class=\"number\">0.8%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">336223</td>\n        <td class=\"number\">21855</td>\n        <td class=\"number\">0.7%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">96210</td>\n        <td class=\"number\">21577</td>\n        <td class=\"number\">0.7%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">162655</td>\n        <td class=\"number\">21062</td>\n        <td class=\"number\">0.7%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">183176</td>\n        <td class=\"number\">20303</td>\n        <td class=\"number\">0.7%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">168623</td>\n        <td class=\"number\">19526</td>\n        <td class=\"number\">0.7%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"other\">\n        <td class=\"fillremaining\">Other values (46023)</td>\n        <td class=\"number\">2747230</td>\n        <td class=\"number\">91.9%</td>\n        <td>\n            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n        </td>\n</tr>\n</table>\n        </div>\n        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme-4915986537900427630\">\n            <p class=\"h4\">Minimum 5 values</p>\n            \n<table class=\"freq table table-hover\">\n    <thead>\n    <tr>\n        <td class=\"fillremaining\">Value</td>\n        <td class=\"number\">Count</td>\n        <td class=\"number\">Frequency (%)</td>\n        <td style=\"min-width:200px\">&nbsp;</td>\n    </tr>\n    </thead>\n    <tr class=\"\">\n        <td class=\"fillremaining\">3</td>\n        <td class=\"number\">1</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">27</td>\n        <td class=\"number\">1</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">69</td>\n        <td class=\"number\">1</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">81</td>\n        <td class=\"number\">2</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">84</td>\n        <td class=\"number\">1</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n        </td>\n</tr>\n</table>\n            <p class=\"h4\">Maximum 5 values</p>\n            \n<table class=\"freq table table-hover\">\n    <thead>\n    <tr>\n        <td class=\"fillremaining\">Value</td>\n        <td class=\"number\">Count</td>\n        <td class=\"number\">Frequency (%)</td>\n        <td style=\"min-width:200px\">&nbsp;</td>\n    </tr>\n    </thead>\n    <tr class=\"\">\n        <td class=\"fillremaining\">364017</td>\n        <td class=\"number\">22</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">364022</td>\n        <td class=\"number\">1</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">364028</td>\n        <td class=\"number\">1</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">364043</td>\n        <td class=\"number\">8</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:37%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">364046</td>\n        <td class=\"number\">2</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:10%\">&nbsp;</div>\n        </td>\n</tr>\n</table>\n        </div>\n    </div>\n</div>\n</div><div class=\"row variablerow\">\n    <div class=\"col-md-3 namecol\">\n        <p class=\"h4 pp-anchor\" id=\"pp_var_click_country\">click_country<br/>\n            <small>Numeric</small>\n        </p>\n    </div><div class=\"col-md-6\">\n    <div class=\"row\">\n        <div class=\"col-sm-6\">\n            <table class=\"stats \">\n                <tr>\n                    <th>Distinct count</th>\n                    <td>11</td>\n                </tr>\n                <tr>\n                    <th>Unique (%)</th>\n                    <td>0.0%</td>\n                </tr>\n                <tr class=\"ignore\">\n                    <th>Missing (%)</th>\n                    <td>0.0%</td>\n                </tr>\n                <tr class=\"ignore\">\n                    <th>Missing (n)</th>\n                    <td>0</td>\n                </tr>\n                <tr class=\"ignore\">\n                    <th>Infinite (%)</th>\n                    <td>0.0%</td>\n                </tr>\n                <tr class=\"ignore\">\n                    <th>Infinite (n)</th>\n                    <td>0</td>\n                </tr>\n            </table>\n\n        </div>\n        <div class=\"col-sm-6\">\n            <table class=\"stats \">\n\n                <tr>\n                    <th>Mean</th>\n                    <td>1.3577</td>\n                </tr>\n                <tr>\n                    <th>Minimum</th>\n                    <td>1</td>\n                </tr>\n                <tr>\n                    <th>Maximum</th>\n                    <td>11</td>\n                </tr>\n                <tr class=\"ignore\">\n                    <th>Zeros (%)</th>\n                    <td>0.0%</td>\n                </tr>\n            </table>\n        </div>\n    </div>\n</div>\n<div class=\"col-md-3 collapse in\" id=\"minihistogram7236223021592160917\">\n    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAAQlJREFUeJzt1cEJAlEQBUEVQzIIc/JsTgaxOY13kQaF5YtU3QfepZnjzMwBeOu0egD8svPqAa8ut8fHN9v9usMS8EEgCQSCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCATCefUA/tfl9vj4Zrtfd1jyPR8EwnFmZvUI%2BFU%2BCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCIQn/z8Okdc9YtkAAAAASUVORK5CYII%3D\">\n\n</div>\n<div class=\"col-md-12 text-right\">\n    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives7236223021592160917,#minihistogram7236223021592160917\"\n       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n        Toggle details\n    </a>\n</div>\n<div class=\"row collapse col-md-12\" id=\"descriptives7236223021592160917\">\n    <ul class=\"nav nav-tabs\" role=\"tablist\">\n        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles7236223021592160917\"\n                                                  aria-controls=\"quantiles7236223021592160917\" role=\"tab\"\n                                                  data-toggle=\"tab\">Statistics</a></li>\n        <li role=\"presentation\"><a href=\"#histogram7236223021592160917\" aria-controls=\"histogram7236223021592160917\"\n                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n        <li role=\"presentation\"><a href=\"#common7236223021592160917\" aria-controls=\"common7236223021592160917\"\n                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n        <li role=\"presentation\"><a href=\"#extreme7236223021592160917\" aria-controls=\"extreme7236223021592160917\"\n                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n\n    </ul>\n\n    <div class=\"tab-content\">\n        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles7236223021592160917\">\n            <div class=\"col-md-4 col-md-offset-1\">\n                <p class=\"h4\">Quantile statistics</p>\n                <table class=\"stats indent\">\n                    <tr>\n                        <th>Minimum</th>\n                        <td>1</td>\n                    </tr>\n                    <tr>\n                        <th>5-th percentile</th>\n                        <td>1</td>\n                    </tr>\n                    <tr>\n                        <th>Q1</th>\n                        <td>1</td>\n                    </tr>\n                    <tr>\n                        <th>Median</th>\n                        <td>1</td>\n                    </tr>\n                    <tr>\n                        <th>Q3</th>\n                        <td>1</td>\n                    </tr>\n                    <tr>\n                        <th>95-th percentile</th>\n                        <td>1</td>\n                    </tr>\n                    <tr>\n                        <th>Maximum</th>\n                        <td>11</td>\n                    </tr>\n                    <tr>\n                        <th>Range</th>\n                        <td>10</td>\n                    </tr>\n                    <tr>\n                        <th>Interquartile range</th>\n                        <td>0</td>\n                    </tr>\n                </table>\n            </div>\n            <div class=\"col-md-4 col-md-offset-2\">\n                <p class=\"h4\">Descriptive statistics</p>\n                <table class=\"stats indent\">\n                    <tr>\n                        <th>Standard deviation</th>\n                        <td>1.7259</td>\n                    </tr>\n                    <tr>\n                        <th>Coef of variation</th>\n                        <td>1.2712</td>\n                    </tr>\n                    <tr>\n                        <th>Kurtosis</th>\n                        <td>21.553</td>\n                    </tr>\n                    <tr>\n                        <th>Mean</th>\n                        <td>1.3577</td>\n                    </tr>\n                    <tr>\n                        <th>MAD</th>\n                        <td>0.68281</td>\n                    </tr>\n                    <tr class=\"\">\n                        <th>Skewness</th>\n                        <td>4.8023</td>\n                    </tr>\n                    <tr>\n                        <th>Sum</th>\n                        <td>4056922</td>\n                    </tr>\n                    <tr>\n                        <th>Variance</th>\n                        <td>2.9786</td>\n                    </tr>\n                    <tr>\n                        <th>Memory size</th>\n                        <td>22.8 MiB</td>\n                    </tr>\n                </table>\n            </div>\n        </div>\n        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram7236223021592160917\">\n            <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3X1U1WW%2B//8XsIUS2MBWwTnmqKVy5EYzMxqGGUxL7UYrM41uPHSsvEFNRhos89h0Z61wtKMulWZsxqlTjOjJ24xMLR1rzliNsyGtRD0aSwJ1bxEC5e73h1/2r51bYfL6sA/4fKzFanVdn891vX0vluvl53OxCWhsbGwUAAAAjAn0dwEAAADtDQELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhm83cBl4vy8tP%2BLsGvAgMD5HCE6uTJKjU0NPq7nHaH/lqH3lqH3lqL/p7TpUu4X/blCRZaRWBggAICAhQYGODvUtol%2Bmsdemsdemst%2ButfBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMMzm7wJwaW5d9Bd/l9Bie14Y6e8SAABoFTzBAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhmacAqKSlRRkaGkpKSlJycrNmzZ6uiokLffPONYmNjlZiY6PX1%2B9//3nPv5s2bNWrUKA0cOFBjxozRrl27PHMNDQ1auHChhg0bpsGDB2vixIk6evSoZ97tdmvmzJlKTk5WSkqK5syZo5qaGs/8vn379OCDD2rQoEEaPny4Vq5c6VX3xfYGAABojqUBa/LkybLb7dq2bZvWrl2rr7/%2BWi%2B//LJn3ul0en1NnDhR0rkAlJ2draysLH3yySdKT0/XtGnTVFpaKkl68803tWHDBuXm5mr79u3q2bOnMjIy1NjYKEmaO3euqqurtXHjRq1Zs0bFxcXKycmRJNXU1GjSpEm68cYbtXPnTi1cuFArVqxQQUFBi/YGAABojmUBq6KiQgkJCZo1a5ZCQ0PVtWtX3X333dqzZ0%2Bz965evVqpqalKTU1VSEiIRo8erb59%2B2r9%2BvWSpLy8PKWnp%2Buaa65RWFiYMjMzVVxcrL179%2Br48ePaunWrMjMz5XA4FBMTo6lTp2rNmjWqra3Vjh07VFtbqylTpqhjx46Kj4/Xvffeq7y8vBbtDQAA0BzLApbdbtf8%2BfPVuXNnz9ixY8cUHR3t%2Bf9f//rXSklJ0Y033qgFCxaotrZWklRUVKS4uDiv9eLi4uR0OlVTU6MDBw54zYeFhalHjx5yOp3at2%2BfgoKCFBsb65mPj4/Xd999p4MHD6qoqEixsbEKCgryWruwsLDZvQEAAFrC1lobOZ1OvfHGG1q2bJmCg4M1cOBA3XLLLXrhhRe0b98%2BTZ8%2BXTabTY8//rjcbrciIiK87o%2BIiNCBAwd06tQpNTY2%2Bpx3uVyKjIxUWFiYAgICvOYkyeVyye12y263e90bGRkpt9uthoaGi%2B7dUmVlZSovL/cas9k6eoXLy1VQED9XYYWmvtJf8%2Bitdeitteivf7VKwPr00081ZcoUzZo1S8nJyZKkt99%2B2zPfv39/TZo0SStWrNDjjz8uSZ7zVBdysfnm7vXl%2B4Hsx9z/fXl5eVqyZInXWEZGhmbMmHFJ67YHdvuV/i6hXaO/1qG31qG31qK//mF5wNq2bZueeOIJzZ07V3fdddcFr%2BvWrZuOHz%2BuxsZGRUVFye12e8273W45HA5FRkYqMDDQ53ynTp3kcDhUWVmp%2Bvp6z2vApmub5g8fPnzevU3rXmzvlho/fryGDh3qNWazdZTLVdXiNdqriopq1dc3%2BLuMdicoKFB2%2B5X01wL01jr01lr095yoqFC/7GtpwPrss8%2BUnZ2tV199VSkpKZ7xjz/%2BWH//%2B981ZcoUz9jBgwfVrVs3BQQEKCEhwXMmqonT6dTtt9%2BukJAQ9enTR0VFRbrhhhsknTtQf%2BTIEfXv31/dunVTY2Oj9u/fr/j4eM%2B9drtdvXr1UkJCgt566y3V1dXJZrN55gcMGCBJF927paKjo897HVheflp1dZfvN3iT%2BvoG%2BmAh%2Bmsdemsdemst%2Busflr2Yraur09NPP62srCyvcCVJ4eHhWrp0qdatW6fa2lo5nU79/ve/V1pamiRp3Lhx2r17t3bs2KEzZ84oPz9fhw8f1ujRoyVJaWlpWrVqlYqLi1VZWamcnBz169dPiYmJcjgcGjFihBYtWqSTJ0%2BqtLRUS5cu1dixY2Wz2ZSamqqwsDAtW7ZM1dXV2rt3r/Lz81u8NwAAQHMCGi/1wNEF7NmzRw888ICCg4PPm9uyZYu%2B%2BOILLVmyRIcPH1Z4eLgeeughPfroowoMPJf5CgoKtGDBApWUlKh3796aM2eOBg8eLOncGanFixfr7bffVlVVlZKSkvTss8%2Bqa9eukqTTp09r3rx52r59uzp06KA77rhDs2fP9tTy1Vdfad68eSosLFTnzp316KOP6v777/fUd7G9f6zy8tOXdP%2BF3LroL5asa4U9L4yUy1XFv6QsYLMFKioqlP5agN5ah95ai/6e06VLuF/2tSxgwRsBi4BlJf4itQ69tQ69tRb9PcdfAYuf3QQAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDBLA1ZJSYkyMjKUlJSk5ORkzZ49WxUVFZKkffv26cEHH9SgQYM0fPhwrVy50uvezZs3a9SoURo4cKDGjBmjXbt2eeYaGhq0cOFCDRs2TIMHD9bEiRN19OhRz7zb7dbMmTOVnJyslJQUzZkzRzU1NZ75S9kbAACgOZYGrMmTJ8tut2vbtm1au3atvv76a7388suqqanRpEmTdOONN2rnzp1auHChVqxYoYKCAknnAlB2draysrL0ySefKD09XdOmTVNpaakk6c0339SGDRuUm5ur7du3q2fPnsrIyFBjY6Mkae7cuaqurtbGjRu1Zs0aFRcXKycnR5IueW8AAIDmWBawKioqlJCQoFmzZik0NFRdu3bV3XffrT179mjHjh2qra3VlClT1LFjR8XHx%2Bvee%2B9VXl6eJGn16tVKTU1VamqqQkJCNHr0aPXt21fr16%2BXJOXl5Sk9PV3XXHONwsLClJmZqeLiYu3du1fHjx/X1q1blZmZKYfDoZiYGE2dOlVr1qxRbW3tJe8NAADQHJtVC9vtds2fP99r7NixY4qOjlZRUZFiY2MVFBTkmYuLi9Pq1aslSUVFRUpNTfW6Ny4uTk6nUzU1NTpw4IDi4uI8c2FhYerRo4ecTqdOnz6toKAgxcbGeubj4%2BP13Xff6eDBg5e0d0uVlZWpvLzca8xm66jo6OgWr9FeBQVx7M8KTX2lv%2BbRW%2BvQW2vRX/%2ByLGD9kNPp1BtvvKFly5bp3Xffld1u95qPjIyU2%2B1WQ0OD3G63IiIivOYjIiJ04MABnTp1So2NjT7nXS6XIiMjFRYWpoCAAK85SXK5XHK73T9675bKy8vTkiVLvMYyMjI0Y8aMFq/RXtntV/q7hHaN/lqH3lqH3lqL/vpHqwSsTz/9VFOmTNGsWbOUnJysd9991%2Bd13w9FTeepLuRi883de6l7N2f8%2BPEaOnSo15jN1lEuV9UlrdseVFRUq76%2Bwd9ltDtBQYGy26%2Bkvxagt9aht9aiv%2BdERYX6ZV/LA9a2bdv0xBNPaO7cubrrrrskSQ6HQ4cPH/a6zu12KzIyUoGBgYqKipLb7T5v3uFweK7xNd%2BpUyc5HA5VVlaqvr7e8xqw6dqm%2BR%2B7d0tFR0ef9zqwvPy06uou32/wJvX1DfTBQvTXOvTWOvTWWvTXPyx9MfvZZ58pOztbr776qidcSVJCQoK%2B/PJL1dXVecacTqcGDBjgmS8sLPRaq2k%2BJCREffr0UVFRkWeuoqJCR44cUf/%2B/dWvXz81NjZq//79Xvfa7Xb16tXrkvYGAABoCcsCVl1dnZ5%2B%2BmllZWUpJSXFay41NVVhYWFatmyZqqurtXfvXuXn5ystLU2SNG7cOO3evVs7duzQmTNnlJ%2Bfr8OHD2v06NGSpLS0NK1atUrFxcWqrKxUTk6O%2BvXrp8TERDkcDo0YMUKLFi3SyZMnVVpaqqVLl2rs2LGy2WyXvDcAAEBzAhov9cDRBezZs0cPPPCAgoODz5vbsmWLqqqqNG/ePBUWFqpz58569NFHdf/993uuKSgo0IIFC1RSUqLevXtrzpw5Gjx4sKRzZ6QWL16st99%2BW1VVVUpKStKzzz6rrl27SpJOnz6tefPmafv27erQoYPuuOMOzZ4921PLV1999aP3/rHKy09f0v0Xcuuiv1iyrhX2vDBSLlcVj6otYLMFKioqlP5agN5ah95ai/6e06VLuF/2tSxgwRsBi4BlJf4itQ69tQ69tRb9PcdfAYsPxwAAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhvkMWEOHDtWSJUt07Nix1q4HAACgzfMZsO655x5t3rxZN998sx555BEVFBSorq6utWsDAABok3wGrIyMDG3evFl//vOf1adPH7344otKTU3VK6%2B8okOHDrV2jQAAAG3KRc9gxcfHKzs7W9u3b9dTTz2lP//5z7rttts0ceJE/eMf/2itGgEAANqUiwas2tpabd68WY8%2B%2Bqiys7MVExOjJ598Uv369VN6ero2bNjQWnUCAAC0GTZfg8XFxcrPz9c777yjqqoqjRgxQn/84x81aNAgzzWDBw/WM888o1GjRrVasQAAAG2Bz4B1%2B%2B23q1evXpo0aZLuuusuRUZGnndNamqqTp48aXmBAAAAbY3PgLVq1SrdcMMNzd68d%2B9e4wUBAAC0dT7PYMXGxmry5MnaunWrZ%2BwPf/iDHn30Ubnd7lYrDgAAoC3yGbDmz5%2Bv06dPq3fv3p6xIUOGqKGhQS%2B99FKrFQcAANAW%2BXxFuGvXLm3YsEFRUVGesZ49eyonJ0d33HFHqxUHAADQFvl8glVTU6OQkJDzLw4MVHV1teVFAQAAtGU%2BA9bgwYP10ksv6dSpU56xb7/9Vr/5zW%2B8PqoBAAAA5/P5ivCpp57Sv//7v%2BtnP/uZwsLC1NDQoKqqKnXv3l1/%2BtOfWrtGAACANsVnwOrevbs2bdqkjz76SEeOHFFgYKB69eqllJQUBQUFtXaNAAAAbYrPgCVJwcHBuvnmm1uzFgAAgHbBZ8A6evSoFixYoK%2B//lo1NTXnzX/wwQeWFwYAANBWXfAMVllZmVJSUtSxY8fWrgkAAKBN8xmwCgsL9cEHH8jhcLR2PQAAAG2ez49p6NSpE0%2BuAAAAfiSfAWvSpElasmSJGhsbW7seAACANs/nK8KPPvpIn332mdauXaurrrpKgYHeOeztt99uleIAAADaIp8BKywsTL/85S9buxYAAIB2wWfAmj9/fmvXAQAA0G74PIMlSQcPHtTixYv15JNPesY%2B//zzVikKAACgLfMZsD7%2B%2BGONHj1aBQUF2rhxo6RzHz46YcIEPmQUAACgGT4D1sKFC/XEE09ow4YNCggIkHTu9xO%2B9NJLWrp0aasWCAAA0Nb4DFhfffWV0tLSJMkTsCRp5MiRKi4ubp3KAAAA2iifASs8PNzn7yAsKytTcHBwixffuXOnkpOTlZmZ6TW%2Bdu1a/eu//qsSExO9vv7xj39IkhoaGrRw4UINGzZMgwcP1sSJE3X06FHP/W63WzNnzlRycrJSUlI0Z84cr3r37dunBx98UIMGDdLw4cO1cuVKr/03b96sUaNGaeDAgRozZox27drlmWtubwAAgOb4DFjXXXedXnzxRVVWVnrGDh06pOzsbP3sZz9r0cKvvfaann/%2BefXo0cPn/ODBg%2BV0Or2%2B%2BvfvL0l68803tWHDBuXm5mr79u3q2bOnMjIyPB98OnfuXFVXV2vjxo1as2aNiouLlZOTI0mqqanRpEmTdOONN2rnzp1auHChVqxYoYKCAknnwld2draysrL0ySefKD09XdOmTVNpaWmL9gYAAGiOz4D15JNP6vPPP1dSUpLOnDmj6667Trfddpvcbrdmz57dooVDQkKUn59/wYB1MXl5eUpPT9c111yjsLAwZWZmqri4WHv37tXx48e1detWZWZmyuFwKCYmRlOnTtWaNWtUW1urHTt2qLa2VlOmTFHHjh0VHx%2Bve%2B%2B9V3l5eZKk1atXKzU1VampqQoJCdHo0aPVt29frV%2B/vtm9AQAAWsLn52B17dpVGzdu1IcffqhDhw7piiuuUK9evfTzn//c60zWxUyYMOGi88eOHdPDDz%2BswsJC2e12zZgxQ3feeadqamp04MABxcXFea4NCwtTjx495HQ6dfr0aQUFBSk2NtYzHx8fr%2B%2B%2B%2B04HDx5UUVGRYmNjFRQU5JmPi4vT6tWrJUlFRUVKTU31qiUuLk5Op7PZva%2B99toW/dkBAMDlzWfAkqQOHTro5ptvtmRTh8Ohnj176le/%2BpV69%2B6t999/X7/%2B9a8VHR2tq6%2B%2BWo2NjYqIiPC6JyIiQi6XS5GRkQoLC/MKek3Xulwuud1u2e12r3sjIyPldrvV0NAgt9vtc%2B0DBw7o1KlTF927pcrKylReXu41ZrN1VHR0dIvXaK%2BCgi740Wu4BE19pb/m0Vvr0Ftr0V//8hmwhg4detEnVZf6WVhDhgzRkCFDPP9/%2B%2B236/3339fatWuVlZUlSRc98/RjzkN9/8/T3P2Xet4qLy9PS5Ys8RrLyMjQjBkzLmnd9sBuv9LfJbRr9Nc69NY69NZa9Nc/fAas2267zSuQ1NfX69ChQ3I6nfq3f/s3Swrp1q2bCgsLFRkZqcDAQLndbq95t9utTp06yeFwqLKyUvX19Z7XgE3XNs0fPnz4vHub1o2KivK5tsPhaHbvlho/fryGDh3qNWazdZTLVdXiNdqriopq1dc3%2BLuMdicoKFB2%2B5X01wL01jr01lr095yoqFC/7OszYDU9Rfqh9957T3/9618vedO33npLERERuu222zxjxcXF6t69u0JCQtSnTx8VFRXphhtukCRVVFToyJEj6t%2B/v7p166bGxkbt379f8fHxkiSn0ym73a5evXopISFBb731lurq6mSz2TzzAwYMkCQlJCSosLDQqx6n06nbb7%2B92b1bKjo6%2BrzXgeXlp1VXd/l%2Bgzepr2%2BgDxaiv9aht9aht9aiv/7xT72Yvfnmm7Vp06ZL3vTs2bN67rnn5HQ6VVtbq40bN%2Bqjjz7SfffdJ0lKS0vTqlWrVFxcrMrKSuXk5Khfv35KTEyUw%2BHQiBEjtGjRIp08eVKlpaVaunSpxo4dK5vNptTUVIWFhWnZsmWqrq7W3r17lZ%2Bf7/ng1HHjxmn37t3asWOHzpw5o/z8fB0%2BfFijR49udm8AAICWuOAhd1%2B%2B%2BOKLFp9PagokdXV1kqStW7dKOve0aMKECaqqqtLjjz%2Bu8vJyXXXVVVq6dKkSEhIkSffdd5/Ky8v10EMPqaqqSklJSV5nmp599lnNmzdPw4YNU4cOHXTHHXd4Psw0ODhYy5cv17x585Sbm6vOnTsrMzPTc%2Barb9%2B%2BysnJ0fz581VSUqLevXtrxYoV6tKlS4v2BgAAaE5Ao4/E1PQk6fuqq6tVXFys4cOH67e//W2rFNeelJeftmTdWxf9xZJ1rbDnhZFyuap4VG0Bmy1QUVGh9NcC9NY69NZa9PecLl3C/bKvzydYPXv2PO%2BnCENCQjR27Fjde%2B%2B9rVIYAABAW%2BUzYL300kutXQcAAEC74TNgvfPOOy1e4K677jJWDAAAQHvgM2DNmTNHDQ0N5x1oDwgI8BoLCAggYAEAAPyAz4D1u9/9TitXrtTkyZMVGxurxsZGffnll3rttdf04IMPKikpqbXrBAAAaDMueAYrNzdXMTExnrHrr79e3bt318SJE7Vx48ZWKxAAAKCt8flBo4cPHz7vFx5Lkt1uV0lJieVFAQAAtGU%2BA1a3bt300ksvyeVyecYqKiq0YMEC/fSnP2214gAAANoin68In3rqKc2aNUt5eXkKDQ1VYGCgKisrdcUVV2jp0qWtXSMAAECb4jNgpaSkaMeOHfrwww9VWlqqxsZGxcTE6Be/%2BIXCw/3ziagAAABtxQV/F%2BGVV16pYcOGqbS0VN27d2/NmgAAANo0n2ewampqlJ2drYEDB%2BrWW2%2BVdO4M1iOPPKKKiopWLRAAAKCt8RmwXnnlFe3bt085OTkKDPz/L6mvr1dOTk6rFQcAANAW%2BQxY7733nv7zP/9TI0eO9PzSZ7vdrvnz56ugoKBVCwQAAGhrfAasqqoq9ezZ87xxh8Oh7777zuqaAAAA2jSfAeunP/2p/vrXv0qS1%2B8e3LJli/7lX/6ldSoDAABoo3z%2BFOH999%2Bv6dOn65577lFDQ4Nef/11FRYW6r333tOcOXNau0YAAIA2xWfAGj9%2BvGw2m9544w0FBQVp%2BfLl6tWrl3JycjRy5MjWrhEAAKBN8RmwTp48qXvuuUf33HNPa9cDAADQ5vk8gzVs2DCvs1cAAABoOZ8BKykpSe%2B%2B%2B25r1wIAANAu%2BHxF%2BJOf/EQvvPCCcnNz9dOf/lQdOnTwml%2BwYEGrFAcAANAW%2BQxYBw4c0NVXXy1JcrlcrVoQAABAW%2BcVsDIzM7Vw4UL96U9/8owtXbpUGRkZrV4YAABAW%2BV1Bmvbtm3nXZCbm9tqxQAAALQHXgHL108O8tOEAAAA/xyvgNX0i52bGwMAAMCF%2BfyYBgAAAPx4BCwAAADDvH6KsLa2VrNmzVJzY3wOFgAAwIV5BaxBgwaprKxMzY0BAADgwrwC1vc//woAAAA/DmewAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADDM0oC1c%2BdOJScnKzMz87y5zZs3a9SoURo4cKDGjBmjXbt2eeYaGhq0cOFCDRs2TIMHD9bEiRN19OhRz7zb7dbMmTOVnJyslJQUzZkzRzU1NZ75ffv26cEHH9SgQYM0fPhwrVy50tjeAAAAzbEsYL322mt6/vnn1aNHj/Pm9u3bp%2BzsbGVlZemTTz5Renq6pk2bptLSUknSm2%2B%2BqQ0bNig3N1fbt29Xz549lZGRocbGRknS3LlzVV1drY0bN2rNmjUqLi5WTk6OJKmmpkaTJk3SjTfeqJ07d2rhwoVasWKFCgoKjOwNAADQHMsCVkhIiPLz830GrNWrVys1NVWpqakKCQnR6NGj1bdvX61fv16SlJeXp/T0dF1zzTUKCwtTZmamiouLtXfvXh0/flxbt25VZmamHA6HYmJiNHXqVK1Zs0a1tbXasWOHamtrNWXKFHXs2FHx8fG69957lZeXd8l7AwAAtITNqoUnTJhwwbmioiKlpqZ6jcXFxcnpdKqmpkYHDhxQXFycZy4sLEw9evSQ0%2BnU6dOnFRQUpNjYWM98fHy8vvvuOx08eFBFRUWKjY1VUFCQ19qrV6%2B%2B5L2vvfbaFv3Zy8rKVF5e7jVms3VUdHR0i%2B5vz4KCOPZnhaa%2B0l/z6K116K216K9/WRawLsbtdisiIsJrLCIiQgcOHNCpU6fU2Njoc97lcikyMlJhYWEKCAjwmpMkl8slt9stu93udW9kZKTcbrcaGhouae%2BWysvL05IlS7zGMjIyNGPGjBav0V7Z7Vf6u4R2jf5ah95ah95ai/76h18ClqRmzzRdbP7HnIf6fiC7lL1bYvz48Ro6dKjXmM3WUS5X1SWt2x5UVFSrvr7B32W0O0FBgbLbr6S/FqC31qG31qK/50RFhfplX78ErKioKLndbq8xt9sth8OhyMhIBQYG%2Bpzv1KmTHA6HKisrVV9f73kN2HRt0/zhw4fPu7dp3UvZu6Wio6PPex1YXn5adXWX7zd4k/r6BvpgIfprHXprHXprLfrrH355MZuQkKDCwkKvMafTqQEDBigkJER9%2BvRRUVGRZ66iokJHjhxR//791a9fPzU2Nmr//v1e99rtdvXq1UsJCQn68ssvVVdXd97al7o3AABAS/glYI0bN067d%2B/Wjh07dObMGeXn5%2Bvw4cMaPXq0JCktLU2rVq1ScXGxKisrlZOTo379%2BikxMVEOh0MjRozQokWLdPLkSZWWlmrp0qUaO3asbDabUlNTFRYWpmXLlqm6ulp79%2B5Vfn6%2B0tLSLnlvAACAlrDsFWFTIGl6krR161ZJ554W9e3bVzk5OZo/f75KSkrUu3dvrVixQl26dJEv8K%2BOAAAPQ0lEQVQk3XfffSovL9dDDz2kqqoqJSUleR0af/bZZzVv3jwNGzZMHTp00B133OH5MNPg4GAtX75c8%2BbNU25urjp37qzMzEwNGTJEki55bwAAgOYENPIJmq2ivPy0JeveuugvlqxrhT0vjJTLVcVZAAvYbIGKigqlvxagt9aht9aiv%2Bd06RLul335cAwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIb5LWDFxsYqISFBiYmJnq/nnntOkvTxxx9r7Nixuu6663T77bdr/fr1XveuWrVKI0aM0HXXXae0tDQVFhZ65s6cOaP/%2BI//0C9/%2BUslJSVpxowZcrlcnvmSkhI99thjSkpK0k033aRXXnlFDQ0Nnvnm9gYAAGiOzZ%2Bbb9myRVdddZXXWFlZmaZOnao5c%2BZo1KhR%2BvTTTzVlyhT16tVLiYmJ2rZtmxYvXqzf/e53io2N1apVqzR58mQVFBSoY8eOWrhwoYqKipSXl6crr7xSc%2BfO1ZNPPqnly5dLkqZPn674%2BHht3bpVJ06c0KRJk9S5c2c9/PDDze4NAADQEv/nXhFu2LBBPXv21NixYxUSEqLk5GQNHTpUq1evliTl5eVpzJgxGjBggK644go98sgjkqTt27errq5O%2Bfn5mjp1qn7yk58oMjJSM2fO1I4dO/Ttt9/K6XRq//79ysrKUnh4uHr27Kn09HTl5eW1aG8AAICW8GvAWrBggYYMGaLrr79ec%2BfOVVVVlYqKihQXF%2Bd1XVxcnOc14A/nAwMD1a9fPzmdTh05ckSnT59WfHy8Z/6aa67RFVdcoaKiIhUVFalbt26KiIjwzMfHx%2BvQoUOqrKxsdm8AAICW8NsrwmuvvVbJycl6%2BeWXdfToUc2cOVO/%2Bc1v5Ha7FRMT43VtZGSk5xyV2%2B32CkiSFBERIZfLJbfbLUmy2%2B1e83a73TP/w7mmtZrmL7Z3S5WVlam8vNxrzGbrqOjo6H9qnfYoKOj/3EPTdqGpr/TXPHprHXprLfrrX34LWE2v5aRzT5mysrI0ZcoUDRo0qNl7Gxsbf/R8c/eakJeXpyVLlniNZWRkaMaMGZbv/X%2Bd3X6lv0to1%2Bivdeitdeitteivf/j1kPv3XXXVVaqvr1dgYKDnSVQTl8slh8MhSYqKijpv3u12q0%2BfPp5r3G63QkNDPfOnTp1Sp06dVF9f7/PegIAAORwOn2t/f%2B%2BWGj9%2BvIYOHeo1ZrN1lMtV9U%2Bt0x5VVFSrvr6h%2BQvxTwkKCpTdfiX9tQC9tQ69tRb9PScqKrT5iyzgl4D1xRdfaP369Zo9e7ZnrLi4WMHBwUpNTdV///d/e11fWFioAQMGSJISEhJUVFSku%2B%2B%2BW5JUX1%2BvL774QmPHjlX37t0VERHhOWslSV999ZXOnj2rhIQElZWV6dixYzp58qQnNDmdTvXu3VuhoaFKTEzUmjVrLrh3S0VHR5/3OrC8/LTq6i7fb/Am9fUN9MFC9Nc69NY69NZa9Nc//PJitlOnTsrLy1Nubq7Onj2rQ4cO6dVXX9X48eN15513qqSkRKtXr9aZM2f04Ycf6sMPP9S4ceMkSWlpaXrnnXf097//XdXV1Vq2bJmCg4M1ZMgQBQUFady4cVq%2BfLmOHTsml8ul3/72t7rlllvUuXNnxcXFKTExUQsWLFBlZaWKi4v1%2BuuvKy0tTZI0atSoi%2B4NAADQEgGNrXEoyYe//e1vWrBggb788ksFBwfr7rvvVmZmpkJCQvS3v/1Nzz//vIqLi9WtWzfNmjVLw4cP99z7X//1X8rNzdWJEyeUmJioZ555Rn379pUknT17VvPnz9emTZtUV1enm266Sc8884zCw8MlSaWlpZo7d67%2B53/%2BR2FhYbrvvvs0bdo0BQQEeOq62N4/Vnn56Utew5dbF/3FknWtsOeFkXK5qviXlAVstkBFRYXSXwvQW%2BvQW2vR33O6dAn3y75%2BC1iXGwIWActK/EVqHXprHXprLfp7jr8CFj%2B7CQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACG2fxdAAAAsMb1c7b4u4QWe3fmz/1dglE8wQIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFg%2BlJSU6LHHHlNSUpJuuukmvfLKK2poaPB3WQAAoI3gYxp8mD59uuLj47V161adOHFCkyZNUufOnfXwww/7uzQAANAG8ATrB5xOp/bv36%2BsrCyFh4erZ8%2BeSk9PV15enr9LAwAAbQRPsH6gqKhI3bp1U0REhGcsPj5ehw4dUmVlpcLCwppdo6ysTOXl5V5jNltHRUdHG6%2B3LWlLH3gnSe9n/cLfJbRYUFCg139hTlBQIN%2B7FmmLvYV1bLb29fcXAesH3G637Ha711hT2HK5XC0KWHl5eVqyZInX2LRp0zR9%2BnRzhf4/e14YaXxNK5SVlSkvL0/jx4%2B/7IOmFcrKyvTHP/6O/lqgrKxM/9b1a3prAXprLf7e9a/2FRcNaWxsvKT7x48fr7Vr13p9jR8/3lB1bVN5ebmWLFly3pM9mEF/rUNvrUNvrUV//YsnWD/gcDjkdru9xtxutwICAuRwOFq0RnR0NP9aAADgMsYTrB9ISEjQsWPHdPLkSc%2BY0%2BlU7969FRoa6sfKAABAW0HA%2BoG4uDglJiZqwYIFqqysVHFxsV5//XWlpaX5uzQAANBGBD3zzDPP%2BLuI/2t%2B8YtfaOPGjXruuee0adMmjR07VhMnTlRAQIC/S2vTQkNDdcMNN/Ak0CL01zr01jr01lr0138CGi/1RDcAAAC88IoQAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFixXUlKijIwMJSUlKTk5WbNnz1ZFRYW/y2p3XnzxRcXGxvq7jHZn2bJlSklJ0bXXXqv09HR98803/i6pXfjiiy80YcIEXX/99fr5z3%2BurKwsnTx50t9ltVk7d%2B5UcnKyMjMzz5vbvHmzRo0apYEDB2rMmDHatWuXHyq8/BCwYLnJkyfLbrdr27ZtWrt2rb7%2B%2Bmu9/PLL/i6rXdm3b5/WrVvn7zLanTfffFPr16/XqlWrtGvXLvXu3Vt/%2BMMf/F1Wm1dXV6fHHntM1157rXbv3q2NGzfq5MmT4lfj/jivvfaann/%2BefXo0eO8uX379ik7O1tZWVn65JNPlJ6ermnTpqm0tNQPlV5eCFiwVEVFhRISEjRr1iyFhoaqa9euuvvuu7Vnzx5/l9ZuNDQ0aN68eUpPT/d3Ke3OypUrlZmZqauvvlphYWF6%2Bumn9fTTT/u7rDavvLxc5eXluvPOOxUcHKyoqCjdcsst2rdvn79La5NCQkKUn5/vM2CtXr1aqampSk1NVUhIiEaPHq2%2Bfftq/fr1fqj08kLAgqXsdrvmz5%2Bvzp07e8aOHTum6OhoP1bVvrz99tsKCQnRqFGj/F1Ku/Ltt9/qm2%2B%2B0alTp3TbbbcpKSlJM2bM4DWWATExMerXr5/y8vJUVVWlEydOqKCgQEOGDPF3aW3ShAkTFB4e7nOuqKhIcXFxXmNxcXFyOp2tUdpljYCFVuV0OvXGG29oypQp/i6lXTh%2B/LgWL16sefPm%2BbuUdqfpFcqWLVv0%2Buuva926dSotLeUJlgGBgYFavHixPvjgA1133XVKTk5WXV2dZs2a5e/S2h23262IiAivsYiICLlcLj9VdPkgYKHVfPrpp5o4caJmzZql5ORkf5fTLsyfP19jxoxR7969/V1Ku9PY2ChJeuSRRxQTE6OuXbtq%2BvTp2rZtm86cOePn6tq2s2fPavLkyRo5cqT27Nmjjz76SOHh4crKyvJ3ae1S0/cyWhcBC61i27Zteuyxx/TUU09pwoQJ/i6nXfj444/1%2BeefKyMjw9%2BltEtNr7XtdrtnrFu3bmpsbNSJEyf8VVa78PHHH%2Bubb77Rr371K4WHhysmJkYzZszQ%2B%2B%2B/L7fb7e/y2pWoqKjzeup2u%2BVwOPxU0eWDgAXLffbZZ8rOztarr76qu%2B66y9/ltBvr16/XiRMndNNNNykpKUljxoyRJCUlJWnTpk1%2Brq7t69q1q8LCwrwOXpeUlKhDhw6cIbxE9fX1amho8HqycvbsWT9W1H4lJCSosLDQa8zpdGrAgAF%2BqujyQcCCperq6vT0008rKytLKSkp/i6nXZk9e7bee%2B89rVu3TuvWrVNubq4kad26dRo6dKifq2v7bDabxo4dq%2BXLl%2Bt///d/deLECS1dulSjRo2SzWbzd3lt2sCBA9WxY0ctXrxY1dXVcrlcWrZsmQYPHqzIyEh/l9eujBs3Trt379aOHTt05swZ5efn6/Dhwxo9erS/S2v3Ahp5OQsL7dmzRw888ICCg4PPm9uyZYu6devmh6rap2%2B%2B%2BUbDhg3Tl19%2B6e9S2o2zZ89q/vz52rRpk2prazVixAjNnTtXoaGh/i6tzSssLNTLL7%2Bs/fv3Kzg4WDfccINmz56tmJgYf5fW5iQmJko69w9aSZ5/ADT9pGBBQYEWLFigkpIS9e7dW3PmzNHgwYP9U%2BxlhIAFAABgGK8IAQAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMCw/w%2BhtrGQJxjaeQAAAABJRU5ErkJggg%3D%3D\"/>\n        </div>\n        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common7236223021592160917\">\n            \n<table class=\"freq table table-hover\">\n    <thead>\n    <tr>\n        <td class=\"fillremaining\">Value</td>\n        <td class=\"number\">Count</td>\n        <td class=\"number\">Frequency (%)</td>\n        <td style=\"min-width:200px\">&nbsp;</td>\n    </tr>\n    </thead>\n    <tr class=\"\">\n        <td class=\"fillremaining\">1</td>\n        <td class=\"number\">2852406</td>\n        <td class=\"number\">95.5%</td>\n        <td>\n            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">10</td>\n        <td class=\"number\">61377</td>\n        <td class=\"number\">2.1%</td>\n        <td>\n            <div class=\"bar\" style=\"width:3%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">11</td>\n        <td class=\"number\">29999</td>\n        <td class=\"number\">1.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:2%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">8</td>\n        <td class=\"number\">9556</td>\n        <td class=\"number\">0.3%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">6</td>\n        <td class=\"number\">7256</td>\n        <td class=\"number\">0.2%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">9</td>\n        <td class=\"number\">6746</td>\n        <td class=\"number\">0.2%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">2</td>\n        <td class=\"number\">6101</td>\n        <td class=\"number\">0.2%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">3</td>\n        <td class=\"number\">4540</td>\n        <td class=\"number\">0.2%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">5</td>\n        <td class=\"number\">3498</td>\n        <td class=\"number\">0.1%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">4</td>\n        <td class=\"number\">3389</td>\n        <td class=\"number\">0.1%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr>\n</table>\n        </div>\n        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme7236223021592160917\">\n            <p class=\"h4\">Minimum 5 values</p>\n            \n<table class=\"freq table table-hover\">\n    <thead>\n    <tr>\n        <td class=\"fillremaining\">Value</td>\n        <td class=\"number\">Count</td>\n        <td class=\"number\">Frequency (%)</td>\n        <td style=\"min-width:200px\">&nbsp;</td>\n    </tr>\n    </thead>\n    <tr class=\"\">\n        <td class=\"fillremaining\">1</td>\n        <td class=\"number\">2852406</td>\n        <td class=\"number\">95.5%</td>\n        <td>\n            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">2</td>\n        <td class=\"number\">6101</td>\n        <td class=\"number\">0.2%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">3</td>\n        <td class=\"number\">4540</td>\n        <td class=\"number\">0.2%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">4</td>\n        <td class=\"number\">3389</td>\n        <td class=\"number\">0.1%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">5</td>\n        <td class=\"number\">3498</td>\n        <td class=\"number\">0.1%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr>\n</table>\n            <p class=\"h4\">Maximum 5 values</p>\n            \n<table class=\"freq table table-hover\">\n    <thead>\n    <tr>\n        <td class=\"fillremaining\">Value</td>\n        <td class=\"number\">Count</td>\n        <td class=\"number\">Frequency (%)</td>\n        <td style=\"min-width:200px\">&nbsp;</td>\n    </tr>\n    </thead>\n    <tr class=\"\">\n        <td class=\"fillremaining\">7</td>\n        <td class=\"number\">3313</td>\n        <td class=\"number\">0.1%</td>\n        <td>\n            <div class=\"bar\" style=\"width:6%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">8</td>\n        <td class=\"number\">9556</td>\n        <td class=\"number\">0.3%</td>\n        <td>\n            <div class=\"bar\" style=\"width:16%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">9</td>\n        <td class=\"number\">6746</td>\n        <td class=\"number\">0.2%</td>\n        <td>\n            <div class=\"bar\" style=\"width:11%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">10</td>\n        <td class=\"number\">61377</td>\n        <td class=\"number\">2.1%</td>\n        <td>\n            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">11</td>\n        <td class=\"number\">29999</td>\n        <td class=\"number\">1.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:49%\">&nbsp;</div>\n        </td>\n</tr>\n</table>\n        </div>\n    </div>\n</div>\n</div><div class=\"row variablerow\">\n    <div class=\"col-md-3 namecol\">\n        <p class=\"h4 pp-anchor\" id=\"pp_var_click_deviceGroup\">click_deviceGroup<br/>\n            <small>Numeric</small>\n        </p>\n    </div><div class=\"col-md-6\">\n    <div class=\"row\">\n        <div class=\"col-sm-6\">\n            <table class=\"stats \">\n                <tr>\n                    <th>Distinct count</th>\n                    <td>5</td>\n                </tr>\n                <tr>\n                    <th>Unique (%)</th>\n                    <td>0.0%</td>\n                </tr>\n                <tr class=\"ignore\">\n                    <th>Missing (%)</th>\n                    <td>0.0%</td>\n                </tr>\n                <tr class=\"ignore\">\n                    <th>Missing (n)</th>\n                    <td>0</td>\n                </tr>\n                <tr class=\"ignore\">\n                    <th>Infinite (%)</th>\n                    <td>0.0%</td>\n                </tr>\n                <tr class=\"ignore\">\n                    <th>Infinite (n)</th>\n                    <td>0</td>\n                </tr>\n            </table>\n\n        </div>\n        <div class=\"col-sm-6\">\n            <table class=\"stats \">\n\n                <tr>\n                    <th>Mean</th>\n                    <td>1.8193</td>\n                </tr>\n                <tr>\n                    <th>Minimum</th>\n                    <td>1</td>\n                </tr>\n                <tr>\n                    <th>Maximum</th>\n                    <td>5</td>\n                </tr>\n                <tr class=\"ignore\">\n                    <th>Zeros (%)</th>\n                    <td>0.0%</td>\n                </tr>\n            </table>\n        </div>\n    </div>\n</div>\n<div class=\"col-md-3 collapse in\" id=\"minihistogram-7742476033087729653\">\n    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAAQlJREFUeJzt3LEJQkEQQEG/WJJF2JOxPVmEPa25yEMD%2BQfO5AebPDba22ZmDsBbx70HgJWd9h7g1fl6//rN43b5wSRgg0ASCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUBY7ib937nJX4sNAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBALBv1h/yN9bn7NBIAgEwjYzs/cQsCobBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBMITf64Rk/pMTgkAAAAASUVORK5CYII%3D\">\n\n</div>\n<div class=\"col-md-12 text-right\">\n    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives-7742476033087729653,#minihistogram-7742476033087729653\"\n       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n        Toggle details\n    </a>\n</div>\n<div class=\"row collapse col-md-12\" id=\"descriptives-7742476033087729653\">\n    <ul class=\"nav nav-tabs\" role=\"tablist\">\n        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles-7742476033087729653\"\n                                                  aria-controls=\"quantiles-7742476033087729653\" role=\"tab\"\n                                                  data-toggle=\"tab\">Statistics</a></li>\n        <li role=\"presentation\"><a href=\"#histogram-7742476033087729653\" aria-controls=\"histogram-7742476033087729653\"\n                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n        <li role=\"presentation\"><a href=\"#common-7742476033087729653\" aria-controls=\"common-7742476033087729653\"\n                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n        <li role=\"presentation\"><a href=\"#extreme-7742476033087729653\" aria-controls=\"extreme-7742476033087729653\"\n                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n\n    </ul>\n\n    <div class=\"tab-content\">\n        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles-7742476033087729653\">\n            <div class=\"col-md-4 col-md-offset-1\">\n                <p class=\"h4\">Quantile statistics</p>\n                <table class=\"stats indent\">\n                    <tr>\n                        <th>Minimum</th>\n                        <td>1</td>\n                    </tr>\n                    <tr>\n                        <th>5-th percentile</th>\n                        <td>1</td>\n                    </tr>\n                    <tr>\n                        <th>Q1</th>\n                        <td>1</td>\n                    </tr>\n                    <tr>\n                        <th>Median</th>\n                        <td>1</td>\n                    </tr>\n                    <tr>\n                        <th>Q3</th>\n                        <td>3</td>\n                    </tr>\n                    <tr>\n                        <th>95-th percentile</th>\n                        <td>3</td>\n                    </tr>\n                    <tr>\n                        <th>Maximum</th>\n                        <td>5</td>\n                    </tr>\n                    <tr>\n                        <th>Range</th>\n                        <td>4</td>\n                    </tr>\n                    <tr>\n                        <th>Interquartile range</th>\n                        <td>2</td>\n                    </tr>\n                </table>\n            </div>\n            <div class=\"col-md-4 col-md-offset-2\">\n                <p class=\"h4\">Descriptive statistics</p>\n                <table class=\"stats indent\">\n                    <tr>\n                        <th>Standard deviation</th>\n                        <td>1.0422</td>\n                    </tr>\n                    <tr>\n                        <th>Coef of variation</th>\n                        <td>0.57286</td>\n                    </tr>\n                    <tr>\n                        <th>Kurtosis</th>\n                        <td>-1.427</td>\n                    </tr>\n                    <tr>\n                        <th>Mean</th>\n                        <td>1.8193</td>\n                    </tr>\n                    <tr>\n                        <th>MAD</th>\n                        <td>0.99976</td>\n                    </tr>\n                    <tr class=\"\">\n                        <th>Skewness</th>\n                        <td>0.57639</td>\n                    </tr>\n                    <tr>\n                        <th>Sum</th>\n                        <td>5436415</td>\n                    </tr>\n                    <tr>\n                        <th>Variance</th>\n                        <td>1.0862</td>\n                    </tr>\n                    <tr>\n                        <th>Memory size</th>\n                        <td>22.8 MiB</td>\n                    </tr>\n                </table>\n            </div>\n        </div>\n        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram-7742476033087729653\">\n            <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzs3Xt0FPXh/vEnyUK4hNyAoI0poVxibiAgxgZKuAioCCpySTyKsVgRojFRFIEioiD6JRQvUCBarVRrtwRrAVEpChQr9qtWcQmgEuALzQ9MhF1CQgIkmd8fnGxds5KNTBJ2%2Bn6dkz%2Bcz1w%2Bz84Zz8PMZBNgGIYhAAAAmCawpScAAABgNRQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATGZr6Qn8tygtPWn6PgMDAxQZ2V7Hj1eottYwff8txaq5JOtms2ouiWz%2ByKq5JOtma8pcnTt3MHV/vuIOlh8LDAxQQECAAgMDWnoqprJqLsm62ayaSyKbP7JqLsm62ayYi4IFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMltLTwAX5so577T0FHz2ds7Alp4CAADNgjtYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGCyJi1Y27dvV2pqqnJzcz2W//rXv1ZycrLHT0JCgmbNmiVJeuSRR5SQkOAxfuWVV7q3d7lcysnJUWpqqgYNGqQ5c%2BaoqqrKPb5nzx7ddttt6t%2B/v0aOHKmXXnrJ4/gbN27UmDFj1LdvX40bN04ffPCBe6y2tlZLly7V8OHDNWDAAE2ZMkWHDx9uio8HAABYVJMVrBdeeEELFixQ165d640tWLBADofD/fPZZ5/pZz/7ma699lr3OtOmTfNY55NPPnGPzZ07V5WVldqwYYPWrl2roqIi5eXlSZKqqqo0depUXX311dq%2BfbuWLl2qVatWadOmTZLOla%2BZM2dqxowZ%2Buijj5SZmal7771XR48elSS99tprWr9%2BvfLz87VlyxbFxsYqKytLhmE01UcFAAAspskKVnBwsAoKCrwWrO975ZVX9JOf/ERpaWkNrvvtt99q8%2BbNys3NVWRkpLp06aLp06dr7dq1Onv2rLZu3aqzZ89q2rRpateunRITEzVhwgTZ7XZJ0po1a5SWlqa0tDQFBwdr7Nix6tWrl9atWydJstvtyszMVPfu3RUSEqLc3FwVFRVp586dF/aBAACA/xpN9qdyJk%2Be7NN6ZWVlWrlypf74xz96LP/oo4/03nvv6f/%2B7//UvXt3PfbYY0pKStKePXsUFBSkuLg497qJiYk6deqU9u/fr8LCQsXFxSkoKMg9npCQoDVr1kiSCgsL6xW5hIQEORwOVVVVad%2B%2BfUpISHCPhYSEqGvXrnI4HLriiit8ylRSUqLS0lKPZTZbO0VFRfm0va%2BCgvzrFTqbzbf51uXyt3y%2BsGo2q%2BaSyOaPrJpLsm42K%2BZq8b9F%2BOqrr2rAgAHq2bOne1lMTIwCAwN1//33q3379lq2bJl%2B%2Bctf6t1335XL5VJISIgCAgLc64eFhUmSnE6nXC6XQkNDPY4RHh4ul8ul2tpauVwu9/rf3X7fvn06ceKEDMPwOu50On3OZLfbtWzZMo9lWVlZys7O9nkfVhQR0b5R64eGtm2imbQ8q2azai6JbP7Iqrkk62azUq4WLVg1NTV67bXXtGTJEo/lWVlZHv/90EMPacOGDdq8ebPatGnzo96H%2Bm4ha2j7C33fatKkSRo2bJjHMputnZzOigva7/f5W9P3NX9QUKBCQ9uqrKxSNTW1TTyr5mXVbFbNJZHNH1k1l2TdbE2Zq7H/uDdLixasjz/%2BWGfOnPH4DUFvgoKCdOmll6qkpERXXHGFysvLVVNT434M6HK5JEkdO3ZUZGSkDh486LG9y%2BVSeHi4AgMDFRER4V7/u%2BORkZHudbyNd%2BzY0edcUVFR9R4HlpaeVHW1dS6GH6Ox%2BWtqai37mVk1m1VzSWTzR1bNJVk3m5VytegtkPfee09XX321bLb/9DzDMLRo0SLt3bvXvezMmTM6dOiQYmJiFB8fL8MwPMYdDodCQ0PVrVs3JSUl6csvv1R1dbXHeJ8%2BfSRJSUlJ2rVrl8c86saDg4PVs2dPFRYWusfKysp06NAh9e7d2/T8AADAmlq0YO3Zs0eXXXaZx7KAgAD9%2B9//1vz58/XNN9%2BooqJCeXl5atWqla655hpFRkZq1KhReuaZZ3T8%2BHEdPXpUy5cv1/jx42Wz2ZSWlqaQkBCtWLFClZWV2rlzpwoKCpSRkSFJmjhxoj788ENt3bpVp0%2BfVkFBgQ4ePKixY8dKkjIyMrR69WoVFRWpvLxceXl5io%2BPV3JycrN/PgAAwD812SPCukJSdydp8%2BbNks7dLapTWlqqTp061dt24cKFevrppzVu3DiVl5erd%2B/eeuWVV9SuXTtJ0uOPP6558%2BZp%2BPDhatWqlW644Qb3l5m2bt1aK1eu1Lx585Sfn69OnTopNzdXQ4YMkST16tVLeXl5WrRokYqLi9WjRw%2BtWrVKnTt3liSlp6ertLRUt99%2BuyoqKpSSklLvhXUAAIDzCTD4Bs1mUVp60vR92myBGpG33fT9NpW3cwb6tJ7NFqiIiPZyOiss8yy%2BjlWzWTWXRDZ/ZNVcknWzNWWuzp07mLo/X/nXr6EBAAD4AQoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACZr0oK1fft2paamKjc312P5G2%2B8ocsvv1zJyckeP1988YUkqba2VkuXLtXw4cM1YMAATZkyRYcPH3Zv73K5lJOTo9TUVA0aNEhz5sxRVVWVe3zPnj267bbb1L9/f40cOVIvvfSSx/E3btyoMWPGqG/fvho3bpw%2B%2BOAD91hDxwYAAGhIkxWsF154QQsWLFDXrl29jg8YMEAOh8Pjp3fv3pKk1157TevXr1d%2Bfr62bNmi2NhYZWVlyTAMSdLcuXNVWVmpDRs2aO3atSoqKlJeXp4kqaqqSlOnTtXVV1%2Bt7du3a%2BnSpVq1apU2bdok6Vz5mjlzpmbMmKGPPvpImZmZuvfee3X06FGfjg0AANCQJitYwcHBKigo%2BMGCdT52u12ZmZnq3r27QkJClJubq6KiIu3cuVPffvutNm/erNzcXEVGRqpLly6aPn261q5dq7Nnz2rr1q06e/aspk2bpnbt2ikxMVETJkyQ3W6XJK1Zs0ZpaWlKS0tTcHCwxo4dq169emndunUNHhsAAMAXtqba8eTJk887fuTIEd15553atWuXQkNDlZ2drRtvvFFVVVXat2%2BfEhIS3OuGhISoa9eucjgcOnnypIKCghQXF%2BceT0xM1KlTp7R//34VFhYqLi5OQUFB7vGEhAStWbNGklRYWKi0tDSPuSQkJMjhcDR47CuuuMKn7CUlJSotLfVYZrO1U1RUlE/b%2ByooyL9eobPZfJtvXS5/y%2BcLq2azai6JbP7Iqrkk62azYq4mK1jnExkZqdjYWD3wwAPq0aOH/va3v%2Bnhhx9WVFSUfvazn8kwDIWFhXlsExYWJqfTqfDwcIWEhCggIMBjTJKcTqdcLpdCQ0M9tg0PD5fL5VJtba1cLpfXfe/bt08nTpw477F9ZbfbtWzZMo9lWVlZys7O9nkfVhQR0b5R64eGtm2imbQ8q2azai6JbP7Iqrkk62azUq4WKVhDhgzRkCFD3P89evRo/e1vf9Mbb7yhGTNmSNJ533n6Me9DfbeQNbT9hb5vNWnSJA0bNsxjmc3WTk5nxQXt9/v8ren7mj8oKFChoW1VVlapmpraJp5V87JqNqvmksjmj6yaS7JutqbM1dh/3JulRQqWN9HR0dq1a5fCw8MVGBgol8vlMe5yudSxY0dFRkaqvLxcNTU17seAdevWjR88eLDetnX7jYiI8LrvyMjIBo/tq6ioqHqPA0tLT6q62joXw4/R2Pw1NbWW/cysms2quSSy%2BSOr5pKsm81KuVrkFsjrr7%2BujRs3eiwrKipSTEyMgoOD1bNnTxUWFrrHysrKdOjQIfXu3Vvx8fEyDEN79%2B51jzscDoWGhqpbt25KSkrSl19%2Bqerqao/xPn36SJKSkpK0a9cuj2PXjTd0bAAAAF%2B0SME6c%2BaMnnjiCTkcDp09e1YbNmzQ3//%2Bd6Wnp0uSMjIytHr1ahUVFam8vFx5eXmKj49XcnKyIiMjNWrUKD3zzDM6fvy4jh49quXLl2v8%2BPGy2WxKS0tTSEiIVqxYocrKSu3cuVMFBQXKyMiQJE2cOFEffvihtm7dqtOnT6ugoEAHDx7U2LFjGzw2AACAL5rsEWFdIam7k7R582ZJ5%2B4WTZ48WRUVFbr//vtVWlqqyy67TMuXL1dSUpIkKT09XaWlpbr99ttVUVGhlJQUj5fGH3/8cc2bN0/Dhw9Xq1atdMMNN7i/zLR169ZauXKl5s2bp/z8fHXq1Em5ubnud7569eqlvLw8LVq0SMXFxerRo4dWrVqlzp07%2B3RsAACAhgQYfINmsygtPWn6Pm22QI3I2276fpvK2zkDfVrPZgtURER7OZ0VlnkWX8eq2ayaSyKbP7JqLsm62ZoyV%2BfOHUzdn6/869fQAAAA/AAFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTNWnB2r59u1JTU5Wbm1tvbNOmTRo7dqz69u2rUaNG6c9//rN77Pnnn1d8fLySk5M9fr799ltJ0unTp/Xoo49q8ODBSklJUXZ2tpxOp3v74uJi3X333UpJSdHQoUO1ePFi1dbWusd37Nih8ePHq1%2B/fho9erTWrVvnMbfVq1dr1KhR6tevnzIyMrRr1y6zPxoAAGBhTVawXnjhBS1YsEBdu3atN/bFF19oxowZys7O1scff6zZs2fr8ccf1yeffOJe58Ybb5TD4fD46dSpkyRp6dKlKiwslN1u17vvvivDMDRr1iz3tvfdd5%2B6dOmizZs36%2BWXX9bmzZv1yiuvSJJKSko0ffp0paena8eOHZozZ47mzp0rh8MhSXr//ff1/PPP63/%2B53/04YcfaujQobrnnnt06tSppvqoAACAxTRZwQoODlZBQYHXguVyuTR16lRdc801stlsSktLU69evTwK1g%2Bprq5WQUGBpk%2BfrksvvVTh4eHKycnR1q1b9c0338jhcGjv3r2aMWOGOnTooNjYWGVmZsput0uS1q9fr9jYWI0fP17BwcFKTU3VsGHDtGbNGkmS3W7XuHHj1KdPH7Vp00Z33XWXJGnLli0mfjoAAMDKbE2148mTJ//g2ODBgzV48GD3f1dXV6u0tFRdunRxL/vyyy%2BVnp6ur776SpdeeqlmzZqlQYMG6dChQzp58qQSExPd63bv3l1t2rRRYWGhSkpKFB0drbCwMPd4YmKiDhw4oPLychUWFiohIcFjPgkJCXr77bclSYWFhbr%2B%2BuvdY4GBgYqPj5fD4dDo0aN9yl5SUqLS0lKPZTZbO0VFRfm0va%2BCgvzrFTqbzbf51uXyt3y%2BsGo2q%2BaSyOaPrJpLsm42K%2BZqsoLVGHl5eWrXrp272FxyySWKiYnRgw8%2BqKioKNntdt1zzz1at26dXC6XJCk0NNRjH6GhoXI6nXK5XPXG6spW3fh3i5wkhYeHu9/hcrlcHuWsbvvvvuPVELvdrmXLlnksy8rKUnZ2ts/7sKKIiPaNWj80tG0TzaTlWTWbVXNJZPNHVs0lWTeblXK1aMEyDEN5eXnasGGDVq9ereDgYEnShAkTNGHCBPd6mZmZeuutt7Ru3Tr3nS/DMM673wud14WYNGmShg0b5rHMZmsnp7Pigvb7ff7W9H3NHxQUqNDQtiorq1RNTW3DG/gRq2azai6JbP7Iqrkk62ZrylyN/ce9WVqsYNXW1mrWrFn64osv9PrrrysmJua860dHR6ukpESRkZGSzt1pat/%2BPx/aiRMn1LFjR9XU1LjvctVxuVwKCAhQZGSkIiIi6o07nU73fr2Nu1wu9ezZ0%2BdsUVFR9R4HlpaeVHW1dS6GH6Ox%2BWtqai37mVk1m1VzSWTzR1bNJVk3m5VytdgtkCeffFJff/2113L129/%2BVjt27PBYVlRUpJiYGMXExCgsLEyFhYXusa%2B%2B%2BkpnzpxRUlKSkpKSdOTIER0/ftw97nA41KNHD7Vv317Jycn1vnZh165d6tOnjyQpKSnJY981NTXavXu3exwAAKAhLVKwPv30U61bt075%2BfkKDw%2BvN%2B5yuTR//nzt379fp0%2Bf1ksvvaRDhw7p5ptvVlBQkCZOnKiVK1fqyJEjcjqd%2Bs1vfqMRI0aoU6dOSkhIUHJyspYsWaLy8nIVFRXp5ZdfVkZGhiRpzJgxKi4u1po1a3T69Glt27ZN27Zt08SJEyVJGRkZevPNN/X555%2BrsrJSK1asUOvWrTVkyJDm/IgAAIAfa7JHhMnJyZLO/YagJG3evFnSubtJa9eu1cmTJzV06FCPbQYMGKCXXnpJDz74oKRz7165XC716NFDv//973XJJZdIkrKzs1VRUaEbb7xR1dXVGjp0qB577DH3fp577jnNnTtXAwcOVEhIiNLT03XrrbdKkjp27KhVq1ZpwYIFmj9/vqKjo7V48WJdfvnlks79huMDDzygnJwcHTt2TMnJycrPz1ebNm2a6qMCAAAWE2Bc6Bvd8Elp6UnT92mzBWpE3nbT99tU3s4Z6NN6NlugIiLay%2BmssMyz%2BDpWzWbVXBLZ/JFVc0nWzdaUuTp37mDq/nzlX7%2BGBgAA4AcoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAybwWrGHDhmnZsmU6cuRIc88HAADA73ktWLfccos2btyoa665RnfddZc2bdrk/puCAAAAOD%2BvBSsrK0sbN27Un//8Z/Xs2VNPPvmk0tLStHjxYh04cKC55wgAAOBXzvsOVmJiombOnKktW7Zo9uzZ%2BvOf/6zrr79eU6ZM0RdffNFccwQAAPAr5y1YZ8%2Be1caNG/WrX/1KM2fOVJcuXTRr1izFx8crMzNT69evb655AgAA%2BA2bt4VFRUUqKCjQm2%2B%2BqYqKCo0aNUqvvPKK%2Bvfv715nwIABeuyxxzRmzJhmmywAAIA/8FqwRo8erW7dumnq1Km66aabFB4eXm%2BdtLQ0HT9%2BvMknCAAA4G%2B8FqzVq1frqquuanDjnTt3mj4hAAAAf%2Bf1Hay4uDjdc8892rx5s3vZ73//e/3qV7%2BSy%2BVqtskBAAD4I68Fa9GiRTp58qR69OjhXjZkyBDV1tbqqaeearbJAQAA%2BCOvjwg/%2BOADrV%2B/XhEREe5lsbGxysvL0w033NBskwMAAPBHXu9gVVVVKTg4uP7KgYGqrKxs8kkBAAD4M68Fa8CAAXrqqad04sQJ97JvvvlG8%2BfP9/iqBgAAANTn9RHh7Nmz9ctf/lI///nPFRISotraWlVUVCgmJkZ/%2BMMfmnuOAAAAfsVrwYqJidFbb72lv//97zp06JACAwPVrVs3DRo0SEFBQc09RwAAAL/itWBJUuvWrXXNNdc051wAAAAswWvBOnz4sJYsWaKvv/5aVVVV9cbfe%2B%2B9Jp8YAACAv/rBd7BKSko0aNAgtWvXrrnnBAAA4Ne8Fqxdu3bpvffeU2RkZHPPBwAuOlfOeaelp9Aob%2BcMbOkpAP/1vH5NQ8eOHblzBQAA8CN5LVhTp07VsmXLZBhGc88HAADA73ktWH//%2B9/1l7/8RQMHDtTEiROVnp7u8eOr7du3KzU1Vbm5ufXGNm7cqDFjxqhv374aN26cPvjgA/dYbW2tli5dquHDh2vAgAGaMmWKDh8%2B7B53uVzKyclRamqqBg0apDlz5ni8jL9nzx7ddttt6t%2B/v0aOHKmXXnrJtGMDAAA0xGvBCgkJ0eDBg5WWlqbu3burW7duHj%2B%2BeOGFF7RgwQJ17dq13tiePXs0c%2BZMzZgxQx999JEyMzN177336ujRo5Kk1157TevXr1d%2Bfr62bNmi2NhYZWVlue%2BozZ07V5WVldqwYYPWrl2roqIi5eXlSTr3Z36mTp2qq6%2B%2BWtu3b9fSpUu1atUqbdq0yZRjAwAANMTrS%2B6LFi264B0HBweroKBACxcu1OnTpz3G1qxZo7S0NKWlpUmSxo4dq1dffVXr1q3T3XffLbvdrszMTHXv3l2SlJubq5SUFO3cuVOXXXaZNm/erL/85S/ul/CnT5%2Bu%2B%2B%2B/XzNnztTWrVt19uxZTZs2TUFBQUpMTNSECRNkt9s1cuTICzr2FVdcccGfCwAAsD6vd7Akaf/%2B/Xr%2B%2Bec1a9Ys97LPPvvM5x1PnjxZHTp08DpWWFiohIQEj2UJCQlyOByqqqrSvn37PMZDQkLUtWtXORwO7dmzR0FBQYqLi3OPJyYm6tSpU9q/f78KCwsVFxfn8Y3zCQkJ2rVr1wUfGwAAwBde72Dt2LFDv/rVr9StWzcdPHhQixYt0uHDhzV58mQ988wzGj58%2BAUd1OVyKSwszGNZWFiY9u3bpxMnTsgwDK/jTqdT4eHhCgkJUUBAgMeYJDmdTrlcLoWGhnpsGx4eLpfLpdra2gs6tq9KSkpUWlrqscxma6eoqCif9%2BGLoKAf7McXJZvNt/nW5fK3fL6wajar5pL8M9N/%2B7Vm1VySdbNZMZfXgrV06VI99NBDuuOOO9S7d29J5/4%2B4VNPPaXly5dfcMGS1OA7Tecb/zHvQ323kF3IsX1ht9u1bNkyj2VZWVnKzs6%2BoP36u4iI9o1aPzS0bRPNpOVZNZtVc/kbrrVzrJpLsm42K%2BXyWrC%2B%2Buorvfrqq5I8i8m1116r2bNnX/BBIyIi5HK5PJa5XC5FRkYqPDxcgYGBXsc7duyoyMhIlZeXq6amxv0YsG7duvGDBw/W27ZuvxdybF9NmjRJw4YN81hms7WT01nh8z584W9N39f8QUGBCg1tq7KyStXU1DbxrJqXVbNZNZfkf9eZxLVm1VySdbM1Za7G/oPDLF4LVocOHVRVVaXWrVt7LC8pKam37MdISkpyvxNVx%2BFwaPTo0QoODlbPnj1VWFioq666SpJUVlamQ4cOqXfv3oqOjpZhGNq7d68SExPd24aGhqpbt25KSkrS66%2B/rurqatlsNvd4nz59LvjYvoqKiqr3OLC09KSqq61zMfwYjc1fU1Nr2c/MqtmsmsvfcK2dY9VcknWzWSmX13%2Ba9evXT08%2B%2BaTKy8vdyw4cOKCZM2fq5z//%2BQUfdOLEifrwww%2B1detWnT59WgUFBTp48KDGjh0rScrIyNDq1atVVFSk8vJy5eXlKT4%2BXsnJyYqMjNSoUaP0zDPP6Pjx4zp69KiWL1%2Bu8ePHy2azKS0tTSEhIVqxYoUqKyu1c%2BdOFRQUKCMj44KPDQAA4Auvd7BmzZqlO%2B64QykpKaqpqVG/fv1UWVmpnj176qmnnvJpx3WFpLq6WpK0efNmSefuFvXq1Ut5eXlatGiRiouL1aNHD61atUqdO3eWJKWnp6u0tFS33367KioqlJKS4vFO0%2BOPP6558%2BZp%2BPDhatWqlW644Qb3l5m2bt1aK1eu1Lx585Sfn69OnTopNzdXQ4YMkaQLPjYAAEBDAowfeKP77Nmz2rZtmw4cOKA2bdqoW7duGjhwoMc7WfBdaelJ0/dpswVqRN520/fbVHz9A7Q2W6AiItrL6aywzK3iOlbNZtVckv9dZxLXmlVzSdbN1pS5Onf2/pVRTc3rHSxJatWqla655prmnAsAAIAleC1Yw4YNO%2B%2Bdqvfee6/JJgQAAODvvBas66%2B/3qNg1dTU6MCBA3I4HLrjjjuabXIAAAD%2ByGvBmjFjhteV3333Xf3zn/9s0gkBAAD4u0Z9g94111yjt956q6nmAgAAYAmNKli7d%2B%2B%2B4D8jAwAAYHVeHxGmp6fXW1ZZWamioiKNHDmyyScFAADgz7wWrNjY2Hq/RRgcHKzx48drwoQJzTIxAAAAf%2BW1YPn6be0AAACoz2vBevPNN33ewU033WTaZAAAAKzAa8GaM2eOamtr673QHhAQ4LEsICCAggUAAPA9XgvWiy%2B%2BqJdeekn33HOP4uLiZBiGvvzyS73wwgu67bbblJKS0tzzBAAA8Bs/%2BA5Wfn6%2BunTp4l525ZVXKiYmRlOmTNGGDRuabYIAAAD%2Bxuv3YB08eFBhYWH1loeGhqq4uLjJJwUAAODPvBas6OhoPfXUU3I6ne5lZWVlWrJkiX7605822%2BQAAAD8kddHhLNnz9aDDz4ou92u9u3bKzAwUOXl5WrTpo2WL1/e3HMEAADwK14L1qBBg7R161Zt27ZNR48elWEY6tKli37xi1%2BoQ4cOzT1HAAAAv%2BK1YElS27ZtNXz4cB09elQxMTHNOScAAAC/5vUdrKoXGqoKAAAgAElEQVSqKs2cOVN9%2B/bVddddJ%2BncO1h33XWXysrKmnWCAAAA/sZrwVq8eLH27NmjvLw8BQb%2BZ5Wamhrl5eU12%2BQAAAD8kdeC9e677%2Bq5557Ttdde6/6jz6GhoVq0aJE2bdrUrBMEAADwN14LVkVFhWJjY%2Bstj4yM1KlTp5p6TgAAAH7Na8H66U9/qn/%2B85%2BS5PG3B9955x395Cc/aZ6ZAQAA%2BCmvv0V466236r777tMtt9yi2tpavfzyy9q1a5feffddzZkzp7nnCAAA4Fe8FqxJkybJZrPp1VdfVVBQkFauXKlu3bopLy9P1157bXPPEQAAwK94LVjHjx/XLbfcoltuuaW55wMAAOD3vL6DNXz4cI93rwAAAOA7rwUrJSVFb7/9dnPPBQAAwBK8PiK89NJLtXDhQuXn5%2BunP/2pWrVq5TG%2BZMmSZpkcAACAP/JasPbt26ef/exnkiSn09msEwIAAPB3HgUrNzdXS5cu1R/%2B8Af3suXLlysrK6vZJwYAAOCvPN7Bev/99%2ButkJ%2Bf3yQH/vjjj5WcnOzxk5SUpLi4OP3zn/9UXFxcvfHvvhe2evVqjRo1Sv369VNGRoZ27drlHjt9%2BrQeffRRDR48WCkpKcrOzva4E1dcXKy7775bKSkpGjp0qBYvXqza2lr3%2BI4dOzR%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%2B3b0lKSEjweAQJAABwPl5/i7C5/fvf/9amTZu0adMmSVJISIh69eqlO%2B64Q0uXLtX//u//6v7771eHDh00fvx4uVwuj4IkSWFhYXI6nXK5XJKk0NBQj/HQ0FD3%2BPfH6vZVN96lSxeP8fDw8Eb9NmVJSYlKS0s9ltls7RQVFeXzPnwRFHTR9GOf2Gy%2Bzbcul7/l84VVs1k1l%2BSfmf7brzWr5pKsm82KuTwK1tmzZ/Xggw%2BqoWVmfw/Wa6%2B9ppEjR6pz586Szt1R%2Bu5vMg4aNEjp6el64403NH78eEkNvxt2vvGm/pZ6u92uZcuWeSzLyspSdnZ2kx73YhcR0b5R64eGtm2imbQ8q2azai5/w7V2jlVzSdbNZqVcHgWrf//%2BKikpUUPLzPbuu%2B9q5syZ510nOjpa7777riQpIiLCfaeqjsvlUs%2BePRUZGen%2B7/bt//M/mRMnTqhjx46qqanxum1AQIAiIyO97tvpdLr364tJkyZp2LBhHststnZyOit83ocv/K3p%2B5o/KChQoaFtVVZWqZqa2oY38CNWzWbVXJL/XWcS15pVc0nWzdaUuRr7Dw6zeBSs7941ai579uxRcXGxBg4c6F729ttvy%2Bl06tZbb3Uv279/v2JiYiRJSUlJKiws1M033yxJqqmp0e7duzV%2B/HjFxMQoLCzM/a6VJH311Vc6c%2BaMkpKSVFJSoiNHjuj48ePu0uRwONSjRw%2B1b99eycnJWrt2rcccd%2B3apT59%2BvicKSoqqt7jwNLSk6quts7F8GM0Nn9NTa1lPzOrZrNqLn/DtXaOVXNJ1s1mpVwt/k%2Bz3bt3Kzw8XCEhIe5lrVq10tNPP60PPvhAZ8%2Be1T/%2B8Q%2BtXbtWGRkZkqSMjAy9%2Beab%2Bvzzz1VZWakVK1aodevWGjJkiIKCgjRx4kStXLlSR44ckdPp1G9%2B8xuNGDFCnTp1UkJCgpKTk7VkyRKVl5erqKhIL7/8snvfY8aMUXFxsdasWaPTp09r27Zt2rZtmyZOnNginw8AAPA/Lf6S%2B7fffut%2B96rONddco9mzZ%2BuJJ57QkSNH1KlTJ82ePVsjR46UJA0ePFgPPPCAcnJydOzYMSUnJys/P19t2rSRJGVnZ6uiokI33nijqqurNXToUD322GPu/T/33HOaO3euBg4cqJCQEKWnp7vvlnXs2FGrVq3SggULNH/%2BfEVHR2vx4sW6/PLLm%2BcDAQAAfi/AaOo3viHp3CNCs9lsgRqRt930/TaVt3MGNrySzuWKiGgvp7PCMreK61g1m1VzSf53nUlca1bNJVk3W1Pm6ty5g6n781WLPyIEAACwGgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACZrsYIVFxenpKQkJScnu3%2BeeOIJSdKOHTs0fvx49evXT6NHj9a6des8tl29erVGjRqlfv36KSMjQ7t27XKPnT59Wo8%2B%2BqgGDx6slJQUZWdny%2Bl0useLi4t19913KyUlRUOHDtXixYtVW1vrHm/o2AAAAA2xteTB33nnHV122WUey0pKSjR9%2BnTNmTNHY8aM0aeffqpp06apW7duSk5O1vvvv6/nn39eL774ouLi4rR69Wrdc8892rRpk9q1a6elS5eqsLBQdrtdbdu21dy5czVr1iytXLlSknTfffcpMTFRmzdv1rFjxzR16lR16tRJd955Z4PHBgAA8MVF94hw/fr1io2N1fjx4xUcHKzU1FQNGzZMa9askSTZ7XaNGzdOffr0UZs2bXTXXXdJkrZs2aLq6moVFBRo%2BvTpuvTSSxUeHq6cnBxt3bpV33zzjRwOh/bu3asZM2aoQ4cOio2NVWZmpux2u0/HBgAA8EWL3sFasmSJPvvsM5WXl%2Bu6667TI488osLCQiUkJHisl5CQoLfffluSVFhYqOuvv949FhgYqPj4eDkcDsXHx%2BvkyZNKTEx0j3fv3l1t2rRRYWGhSkpKFB0drbCwMPd4YmKiDhw4oPLy8gaP7auSkhKVlpZ6LLPZ2ikqKqpR%2B2lIUNBF14/Py2bzbb51ufwtny%2Bsms2quST/zPTffq1ZNZdk3WxWzNViBeuKK65Qamqqnn76aR0%2BfFg5OTmaP3%2B%2BXC6XunTp4rFueHi4%2Bz0ql8vlUZAkKSwsTE6nUy6XS5IUGhrqMR4aGuoe//5Y3b7qxs93bF/Z7XYtW7bMY1lWVpays7MbtR%2BriYho36j1Q0PbNtFMWp5Vs1k1l7/hWjvHqrkk62azUq4WK1h1j%2BWkc3eZZsyYoWnTpql///4NbmsYxo8eb2hbM0yaNEnDhg3zWGaztZPTWWHqcfyt6fuaPygoUKGhbVVWVqmamtqGN/AjVs1m1VyS/11nEteaVXNJ1s3WlLka%2Bw8Os7ToI8Lvuuyyy1RTU6PAwED3nag6TqdTkZGRkqSIiIh64y6XSz179nSv43K51L79fz7QEydOqGPHjqqpqfG6bUBAgCIjI73u%2B7vH9lVUVFS9x4GlpSdVXW2di%2BHHaGz%2Bmppay35mVs1m1Vz%2BhmvtHKvmkqybzUq5WuSfZrt379ZTTz3lsayoqEitW7dWWlqax9cuSNKuXbvUp08fSVJSUpIKCwvdYzU1Ndq9e7f69OmjmJgYhYWFeYx/9dVXOnPmjJKSkpSUlKQjR47o%2BPHj7nGHw6EePXqoffv2Sk5OPu%2BxAQAAfNEiBatjx46y2%2B3Kz8/XmTNndODAAT377LOaNGmSbrzxRhUXF2vNmjU6ffq0tm3bpm3btmnixImSpIyMDL355pv6/PPPVVlZqRUrVqh169YaMmSIgoKCNHHiRK1cuVJHjhyR0%2BnUb37zG40YMUKdOnVSQkKCkpOTtWTJEpWXl6uoqEgvv/yyMjIyJEljxow577EBAAB80SKPCLt06aL8/HwtWbLEXZBuvvlm5ebmKjg4WKtWrdKCBQs0f/58RUdHa/Hixbr88sslSYMHD9YDDzygnJwcHTt2TMnJycrPz1ebNm0kSdnZ2aqoqNCNN96o6upqDR06VI899pj72M8995zmzp2rgQMHKiQkROnp6br11lslnSt%2B5zs2AACALwKM5njrGyotPWn6Pm22QI3I2276fpvK2zkDfVrPZgtURER7OZ0VlnkWX8eq2ayaS/K/60ziWrNqLsm62ZoyV%2BfOHUzdn6/879djAAAALnIULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABM1qIFq7i4WFlZWUpJSVFqaqoeeeQRlZWV6d///rfi4uKUnJzs8fO73/3Ove3GjRs1ZswY9e3bV%2BPGjdMHH3zgHqutrdXSpUs1fPhwDRgwQFOmTNHhw4fd4y6XSzk5OUpNTdWgQYM0Z84cVVVVucf37Nmj2267Tf3799fIkSP10ksvNc8HAgAALKFFC9Y999yj0NBQvf/%2B%2B3rjjTf09ddf6%2Bmnn3aPOxwOj58pU6ZIOleAZs6cqRkzZuijjz5SZmam7r33Xh09elSS9Nprr2n9%2BvXKz8/Xli1bFBsbq6ysLBmGIUmaO3euKisrtWHDBq1du1ZFRUXKy8uTJFVVVWnq1Km6%2BuqrtX37di1dulSrVq3Spk2bmvnTAQAA/qrFClZZWZmSkpL04IMPqn379rrkkkt0880365NPPmlw2zVr1igtLU1paWkKDg7W2LFj1atXL61bt06SZLfblZmZqe7duyskJES5ubkqKirSzp079e2332rz5s3Kzc1VZGSkunTpounTp2vt2rU6e/astm7dqrNnz2ratGlq166dEhMTNWHCBNnt9qb%2BSAAAgEXYWurAoaGhWrRokceyI0eOKCoqyv3fDz/8sD788ENVV1drwoQJys7OVqtWrVRYWKi0tDSPbRMSEuRwOFRVVaV9%2B/YpISHBPRYSEqKuXbvK4XDo5MmTCgoKUlxcnHs8MTFRp06d0v79%2B1VYWKi4uDgFBQV57HvNmjU%2BZyspKVFpaanHMputnUc2MwQF%2BdcrdDabb/Oty%2BVv%2BXxh1WxWzSX5Z6b/9mvNqrkk62azYq4WK1jf53A49Oqrr2rFihVq3bq1%2BvbtqxEjRmjhwoXas2eP7rvvPtlsNt1///1yuVwKCwvz2D4sLEz79u3TiRMnZBiG13Gn06nw8HCFhIQoICDAY0ySnE6nXC6XQkNDPbYNDw%2BXy%2BVSbW2tAgMbPvl2u13Lli3zWJaVlaXs7OxGfSZWExHRvlHrh4a2baKZtDyrZrNqLn/DtXaOVXNJ1s1mpVwXRcH69NNPNW3aND344INKTU2VJP3pT39yj/fu3VtTp07VqlWrdP/990uS%2B32qH3K%2B8Ya29ea7hawhkyZN0rBhwzyW2Wzt5HRWNPq45%2BNvTd/X/EFBgQoNbauyskrV1NQ28ayal1WzWTWX5H/XmcS1ZtVcknWzNWWuxv6DwywtXrDef/99PfTQQ5o7d65uuummH1wvOjpa3377rQzDUEREhFwul8e4y%2BVSZGSkwsPDFRgY6HW8Y8eOioyMVHl5uWpqatyPAevWrRs/ePBgvW3r9uuLqKioeo8DS0tPqrraOhfDj9HY/DU1tZb9zKyazaq5/A3X2jlWzSVZN5uVcrXoP83%2B9a9/aebMmXr22Wc9ytWOHTu0YsUKj3X379%2Bv6OhoBQQEKCkpSbt27fIYdzgc6tOnj4KDg9WzZ08VFha6x8rKynTo0CH17t1b8fHxMgxDe/fu9dg2NDRU3bp1U1JSkr788ktVV1fX2zcAAIAvWqxgVVdX69e//rVmzJihQYMGeYx16NBBy5cv11//%2BledPXtWDodDv/vd75SRkSFJmjhxoj788ENt3bpVp0%2BfVkFBgQ4ePKixY8dKkjIyMrR69WoVFRWpvLxceXl5io%2BPV3JysiIjIzVq1Cg988wzOn78uI4eParly5dr/PjxstlsSktLU0hIiFasWKHKykrt3LlTBQUF7mMDAAA0pMUeEX7%2B%2BecqKirSggULtGDBAo%2Bxd955R0uXLtWyZcv06KOPqkOHDrr99tt1xx13SJJ69eqlvLw8LVq0SMXFxerRo4dWrVqlzp07S5LS09NVWlqq22%2B/XRUVFUpJSfF46fzxxx/XvHnzNHz4cLVq1Uo33HCDcnNzJUmtW7fWypUrNW/ePOXn56tTp07Kzc3VkCFDmueDAQAAfi/A%2BDFvfKPRSktPmr5Pmy1QI/K2m77fpvJ2zkCf1rPZAhUR0V5OZ4VlnsXXsWo2q%2BaS/O86k7jWrJpLsm62pszVuXMHU/fnK//79RgAAICLHAULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk9laegIAgP9e1z3zj5aeQqO8nTOwpacAP8EdLAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBcuL4uJi3X333UpJSdHQoUO1ePFi1dbWtvS0AACAn7C19AQuRvfdd58SExO1efNmHTt2TFOnTlWnTp105513tvTUAACAH%2BAO1vc4HA7t3btXM2bMUIcOHRQbG6vMzEzZ7faWnhoAAPAT3MH6nsLCQkVHRyssLMy9LDExUQcOHFB5eblCQkIa3EdJSYlKS0s9ltls7RQVFWXqXIOC/Ksf22y%2Bzbcul7/l84VVs1k1l%2BSfmbjWmo6vn21Tseo5s2IuCtb3uFwuhYaGeiyrK1tOp9OngmW327Vs2TKPZffee6/uu%2B8%2B8yaqc0Xujku%2B1qRJk0wvby2ppKREr7zyouVySdbNZtVcknWvM%2BniOG%2BfLLzW9H2WlJTIbrdzzvyIFXNZpyqayDCMC9p%2B0qRJeuONNzx%2BJk2aZNLs/qO0tFTLli2rd7fM31k1l2TdbFbNJZHNH1k1l2TdbFbMxR2s74mMjJTL5fJY5nK5FBAQoMjISJ/2ERUVZZkGDgAAGo87WN%2BTlJSkI0eO6Pjx4%2B5lDodDPXr0UPv27VtwZgAAwF9QsL4nISFBycnJWrJkicrLy1VUVKSXX35ZGRkZLT01AADgJ4Iee%2Byxx1p6EhebX/ziF9qwYYOeeOIJvfXWWxo/frymTJmigICAlp5aPe3bt9dVV11lubtrVs0lWTebVXNJZPNHVs0lWTeb1XIFGBf6RjcAAAA88IgQAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFKyL3Pbt25Wamqrc3NzzrldbW6ulS5dq%2BPDhGjBggKZMmaLDhw830yx/HF%2BzPfLII%2B6/EVn3c%2BWVVzbTLBuvuLhYWVlZSklJUWpqqh555BGVlZV5XXfjxo0aM2aM%2Bvbtq3HjxumDDz5o5tk2jq/Z3njjDV1%2B%2BeUe5yw5OVlffPFFC8y6YXv37tUdd9yh/v37KzU1VTk5OSotLfW67urVqzVq1Cj169dPGRkZ2rVrVzPPtnF8zfb8888rPj6%2B3jn79ttvW2DWjfPkk08qLi7uB8f97ZzVOV8ufzxfcXFxSkpK8pjvE0884XVdfz1nHgxctPLz842RI0ca6enpRk5OznnXXb16tTF06FBj3759xsmTJ43HH3/cGDNmjFFbW9tMs22cxmSbOXOm8dxzzzXTzC7cDTfcYDzyyCNGeXm5ceTIEWPcuHHG7Nmz6623e/duIykpydi6datRVVVl/PWvfzX69OljHDlypAVm7Rtfs61du9a47bbbWmCGjXf69Gnj5z//ubFs2TLj9OnTxrFjx4zbbrvNmD59er1133vvPePKK680Pv/8c6OystJYtWqVMXDgQKOioqIFZt6wxmR77rnnjJkzZ7bALC/M7t27jauuusro1auX13F/O2d1Gsrlj%2BerV69exuHDhxtcz1/P2fdxB%2BsiFhwcrIKCAnXt2rXBde12uzIzM9W9e3eFhIQoNzdXRUVF2rlzZzPMtPEak82flJWVKSkpSQ8%2B%2BKDat2%2BvSy65RDfffLM%2B%2BeSTeuuuWbNGaWlpSktLU3BwsMaOHatevXpp3bp1LTDzhjUmmz%2BprKxUbm6upk6dqtatWysyMlIjRozQ119/XW9du92ucePGqU%2BfPmrTpo3uuusuSdKWLVuae9o%2BaUw2f1RbW6t58%2BYpMzPzB9fxt3Mm%2BZbLyvzxnHlDwbqITZ48WR06dGhwvaqqKu3bt08JCQnuZSEhIeratascDkdTTvFH8zVbnY8%2B%2Bkg33XST%2Bvbtq/Hjx1%2B0t4tDQ0O1aNEiderUyb3syJEjioqKqrduYWGhxzmTpISEhIv2nDUmW93YnXfeqQEDBmj48OH661//2lxTbZSwsDBNmDBBNptNkrR//3795S9/0XXXXVdv3e%2Bfs8DAQMXHx1%2B056wx2STpyy%2B/VHp6uvr166fRo0df9I%2Bs//SnPyk4OFhjxoz5wXX87ZxJvuWS/O98SdKSJUs0ZMgQXXnllZo7d64qKirqreOP58wbCpYFnDhxQoZhKCwszGN5WFiYnE5nC83KPDExMeratatWrVql7du368orr9Qvf/lLv8jmcDj06quvatq0afXGXC6XX5%2Bz82WLjIxUbGysHnroIf3jH//QAw88oNmzZ2vHjh0tMFPfFBcXKykpSddff72Sk5OVnZ1dbx1/PWe%2BZLvkkksUExOjp59%2BWv/4xz80YcIE3XPPPdq/f38LzLhh3377rZ5//nnNmzfvvOv52znzNZe/nS9JuuKKK5SamqpNmzbJbrfr888/1/z58%2But52/n7IdQsCzEMIyWnkKTyMrK0pNPPqkuXbooJCREDz30kFq3bq3Nmze39NTO69NPP9WUKVP04IMPKjU11es6/nrOGso2ZMgQvfjii0pISFDr1q01evRojRgxQm%2B88UYLzNY30dHRcjgceuedd3Tw4EE9/PDDXtfzx3PmS7YJEyboueeeU9euXdW2bVtlZmYqPj7%2Bon1kvWjRIo0bN049evRocF1/Ome%2B5vK38yWde/Q3YcIEtW7dWt27d9eMGTO0YcMGnTlzpt66/nTOfggFywLCw8MVGBgol8vlsdzlcqljx44tNKumExQUpEsvvVQlJSUtPZUf9P777%2Bvuu%2B/W7NmzNXnyZK/rREREeD1nkZGRzTHFH82XbN5ER0df1OdMkgICAhQbG6vc3Fxt2LBBx48f9xj313MmNZzNm4v1nO3YsUOfffaZsrKyGlzXn85ZY3J5c7Gerx9y2WWXqaamRseOHfNY7k/n7HwoWBYQHBysnj17qrCw0L2srKxMhw4dUu/evVtwZhfOMAwtWrRIe/fudS87c%2BaMDh06pJiYmBac2Q/717/%2BpZkzZ%2BrZZ5/VTTfd9IPrJSUl1XuXzOFwqE%2BfPk09xR/N12yvv/66Nm7c6LGsqKjoojxnO3bs0KhRo1RbW%2BteFhh47n%2BNrVq18lg3KSnJ4zqrqanR7t27L9pz1phsv/3tb%2Bs9wr1Yz9m6det07NgxDR06VCkpKRo3bpwkKSUlRW%2B99ZbHuv50zhqTy5/OlyTt3r1bTz31lMeyoqIitW7dut57nP50zs6rBX%2BDET6aOXNmva8yOHr0qDFq1Cjj0KFDhmEYxh//%2BEdjyJAh7q9pmDt3rnHLLbe0xHQbxZds06dPN9LT042jR48a5eXlxsKFCy/aX9k9e/ascd111xl/%2BtOfvI5PnjzZeOuttwzDMIwvv/zSSE5ONrZs2WJUVVUZa9asMfr27WuUlJQ055R91phsv//9742rr77a%2BOKLL4wzZ84Y69evN%2BLj4w2Hw9GcU/ZJWVmZkZqaajz11FPGqVOnjGPHjhlTpkwxbr31VsMwDGPUqFHGxx9/bBiGYWzbts3o37%2B/8dlnnxmnTp0ynn/%2BeSMtLc2orKxsyQg/qDHZFi5caIwaNcooKioyqqqqjN/97ndG7969L8qvDXG5XMaRI0fcP5999pnRq1cv48iRI8apU6f89pw1Jpc/nS/DOPf/9SuuuMJYtWqVcfr0aWP//v3G9ddfbzzxxBOGYfj3dfZDbC1d8PDDkpOTJUnV1dWS5H7nyOFw6OzZszpw4ID72XV6erpKS0t1%2B%2B23q6KiQikpKVq2bFnLTNwHjcm2cOFCPf300xo3bpzKy8vVu3dvvfLKK2rXrl3LTP48Pv/8cxUVFWnBggVasGCBx9g777yjw4cP68SJE5KkXr16KS8vT4sWLVJxcbF69OihVatWqXPnzi0x9QY1JtvkyZNVUVGh%2B%2B%2B/X6Wlpbrsssu0fPlyJSUltcTUz6tDhw566bqblAoAAAD8SURBVKWXtGDBAl199dVq166drr76ai1cuFCSdODAAZ06dUqSNHjwYD3wwAPKycnRsf/fzh2jOAgGYRj%2BtpF0lnoA2xzGG3gHz2EtWHnBgDdIqixskYXAQNbleU7wDwPyFoO3W67Xa7Zty%2BVy%2BeQIL70z2zzPSZJpmnIcR4ZhyL7v6fv%2BY%2B9/pW3bH0fQz%2B/I861n3dk7c51pX0nSdV22bcuyLFnXNU3TZBzH7x9Nn3Vnv/m63//BJRkAwB/iBgsAoJjAAgAoJrAAAIoJLACAYgILAKCYwAIAKCawAACKCSwAgGICCwCgmMACACgmsAAAigksAIBiAgsAoNgDxLCaAjRU3ZAAAAAASUVORK5CYII%3D\"/>\n        </div>\n        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common-7742476033087729653\">\n            \n<table class=\"freq table table-hover\">\n    <thead>\n    <tr>\n        <td class=\"fillremaining\">Value</td>\n        <td class=\"number\">Count</td>\n        <td class=\"number\">Frequency (%)</td>\n        <td style=\"min-width:200px\">&nbsp;</td>\n    </tr>\n    </thead>\n    <tr class=\"\">\n        <td class=\"fillremaining\">1</td>\n        <td class=\"number\">1823162</td>\n        <td class=\"number\">61.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">3</td>\n        <td class=\"number\">1047086</td>\n        <td class=\"number\">35.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:57%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">4</td>\n        <td class=\"number\">117640</td>\n        <td class=\"number\">3.9%</td>\n        <td>\n            <div class=\"bar\" style=\"width:7%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">5</td>\n        <td class=\"number\">283</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">2</td>\n        <td class=\"number\">10</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr>\n</table>\n        </div>\n        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme-7742476033087729653\">\n            <p class=\"h4\">Minimum 5 values</p>\n            \n<table class=\"freq table table-hover\">\n    <thead>\n    <tr>\n        <td class=\"fillremaining\">Value</td>\n        <td class=\"number\">Count</td>\n        <td class=\"number\">Frequency (%)</td>\n        <td style=\"min-width:200px\">&nbsp;</td>\n    </tr>\n    </thead>\n    <tr class=\"\">\n        <td class=\"fillremaining\">1</td>\n        <td class=\"number\">1823162</td>\n        <td class=\"number\">61.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">2</td>\n        <td class=\"number\">10</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">3</td>\n        <td class=\"number\">1047086</td>\n        <td class=\"number\">35.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:57%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">4</td>\n        <td class=\"number\">117640</td>\n        <td class=\"number\">3.9%</td>\n        <td>\n            <div class=\"bar\" style=\"width:7%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">5</td>\n        <td class=\"number\">283</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr>\n</table>\n            <p class=\"h4\">Maximum 5 values</p>\n            \n<table class=\"freq table table-hover\">\n    <thead>\n    <tr>\n        <td class=\"fillremaining\">Value</td>\n        <td class=\"number\">Count</td>\n        <td class=\"number\">Frequency (%)</td>\n        <td style=\"min-width:200px\">&nbsp;</td>\n    </tr>\n    </thead>\n    <tr class=\"\">\n        <td class=\"fillremaining\">1</td>\n        <td class=\"number\">1823162</td>\n        <td class=\"number\">61.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">2</td>\n        <td class=\"number\">10</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">3</td>\n        <td class=\"number\">1047086</td>\n        <td class=\"number\">35.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:57%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">4</td>\n        <td class=\"number\">117640</td>\n        <td class=\"number\">3.9%</td>\n        <td>\n            <div class=\"bar\" style=\"width:7%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">5</td>\n        <td class=\"number\">283</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr>\n</table>\n        </div>\n    </div>\n</div>\n</div><div class=\"row variablerow\">\n    <div class=\"col-md-3 namecol\">\n        <p class=\"h4 pp-anchor\" id=\"pp_var_click_environment\">click_environment<br/>\n            <small>Numeric</small>\n        </p>\n    </div><div class=\"col-md-6\">\n    <div class=\"row\">\n        <div class=\"col-sm-6\">\n            <table class=\"stats \">\n                <tr>\n                    <th>Distinct count</th>\n                    <td>3</td>\n                </tr>\n                <tr>\n                    <th>Unique (%)</th>\n                    <td>0.0%</td>\n                </tr>\n                <tr class=\"ignore\">\n                    <th>Missing (%)</th>\n                    <td>0.0%</td>\n                </tr>\n                <tr class=\"ignore\">\n                    <th>Missing (n)</th>\n                    <td>0</td>\n                </tr>\n                <tr class=\"ignore\">\n                    <th>Infinite (%)</th>\n                    <td>0.0%</td>\n                </tr>\n                <tr class=\"ignore\">\n                    <th>Infinite (n)</th>\n                    <td>0</td>\n                </tr>\n            </table>\n\n        </div>\n        <div class=\"col-sm-6\">\n            <table class=\"stats \">\n\n                <tr>\n                    <th>Mean</th>\n                    <td>3.9427</td>\n                </tr>\n                <tr>\n                    <th>Minimum</th>\n                    <td>1</td>\n                </tr>\n                <tr>\n                    <th>Maximum</th>\n                    <td>4</td>\n                </tr>\n                <tr class=\"ignore\">\n                    <th>Zeros (%)</th>\n                    <td>0.0%</td>\n                </tr>\n            </table>\n        </div>\n    </div>\n</div>\n<div class=\"col-md-3 collapse in\" id=\"minihistogram-5594948405442907746\">\n    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAAQ1JREFUeJzt1cEJwkAURVEjlmQR6Snr9GQR6WlsQC5E0EQ5Zz0Db3P50xhjXICXrkcPgDO7HT2A/3VfHrv/bOv8gSXvc0EgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCATC7egBv%2BS%2BPHa939b5Q0v4FhcEwjTGGEePgLNyQSAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCA8ATGUDpEOzhC5AAAAAElFTkSuQmCC\">\n\n</div>\n<div class=\"col-md-12 text-right\">\n    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives-5594948405442907746,#minihistogram-5594948405442907746\"\n       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n        Toggle details\n    </a>\n</div>\n<div class=\"row collapse col-md-12\" id=\"descriptives-5594948405442907746\">\n    <ul class=\"nav nav-tabs\" role=\"tablist\">\n        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles-5594948405442907746\"\n                                                  aria-controls=\"quantiles-5594948405442907746\" role=\"tab\"\n                                                  data-toggle=\"tab\">Statistics</a></li>\n        <li role=\"presentation\"><a href=\"#histogram-5594948405442907746\" aria-controls=\"histogram-5594948405442907746\"\n                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n        <li role=\"presentation\"><a href=\"#common-5594948405442907746\" aria-controls=\"common-5594948405442907746\"\n                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n        <li role=\"presentation\"><a href=\"#extreme-5594948405442907746\" aria-controls=\"extreme-5594948405442907746\"\n                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n\n    </ul>\n\n    <div class=\"tab-content\">\n        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles-5594948405442907746\">\n            <div class=\"col-md-4 col-md-offset-1\">\n                <p class=\"h4\">Quantile statistics</p>\n                <table class=\"stats indent\">\n                    <tr>\n                        <th>Minimum</th>\n                        <td>1</td>\n                    </tr>\n                    <tr>\n                        <th>5-th percentile</th>\n                        <td>4</td>\n                    </tr>\n                    <tr>\n                        <th>Q1</th>\n                        <td>4</td>\n                    </tr>\n                    <tr>\n                        <th>Median</th>\n                        <td>4</td>\n                    </tr>\n                    <tr>\n                        <th>Q3</th>\n                        <td>4</td>\n                    </tr>\n                    <tr>\n                        <th>95-th percentile</th>\n                        <td>4</td>\n                    </tr>\n                    <tr>\n                        <th>Maximum</th>\n                        <td>4</td>\n                    </tr>\n                    <tr>\n                        <th>Range</th>\n                        <td>3</td>\n                    </tr>\n                    <tr>\n                        <th>Interquartile range</th>\n                        <td>0</td>\n                    </tr>\n                </table>\n            </div>\n            <div class=\"col-md-4 col-md-offset-2\">\n                <p class=\"h4\">Descriptive statistics</p>\n                <table class=\"stats indent\">\n                    <tr>\n                        <th>Standard deviation</th>\n                        <td>0.33968</td>\n                    </tr>\n                    <tr>\n                        <th>Coef of variation</th>\n                        <td>0.086155</td>\n                    </tr>\n                    <tr>\n                        <th>Kurtosis</th>\n                        <td>33.013</td>\n                    </tr>\n                    <tr>\n                        <th>Mean</th>\n                        <td>3.9427</td>\n                    </tr>\n                    <tr>\n                        <th>MAD</th>\n                        <td>0.11148</td>\n                    </tr>\n                    <tr class=\"\">\n                        <th>Skewness</th>\n                        <td>-5.8487</td>\n                    </tr>\n                    <tr>\n                        <th>Sum</th>\n                        <td>11781358</td>\n                    </tr>\n                    <tr>\n                        <th>Variance</th>\n                        <td>0.11538</td>\n                    </tr>\n                    <tr>\n                        <th>Memory size</th>\n                        <td>22.8 MiB</td>\n                    </tr>\n                </table>\n            </div>\n        </div>\n        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram-5594948405442907746\">\n            <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3Xt0VNXB/vEnFxJIwiQZIGAjJZRLSi5QQAzNjxouCihCFbmlVYyicgkiKSgoIqgoWkLRAi8SW6xUqymBykVESgFFwbZoxUkElQAvNAtMgBlCQgK5/f5gMa9jhpmBnJmY4ftZK4vF2efsvXk60Mc5J5OAurq6OgEAAMAwgY29AQAAAH9DwQIAADAYBQsAAMBgFCwAAACDUbAAAAAMRsECAAAwGAULAADAYBQsAAAAg1GwAAAADEbBAgAAMBgFCwAAwGAULAAAAINRsAAAAAxGwQIAADAYBQsAAMBgFCwAAACDUbAAAAAMRsECAAAwGAULAADAYBQsAAAAg1GwAAAADEbBAgAAMBgFCwAAwGAULAAAAINRsAAAAAxGwQIAADAYBQsAAMBgFCwAAACDUbAAAAAMRsECAAAwGAULAADAYBQsAAAAg1GwAAAADEbBAgAAMBgFCwAAwGAULAAAAINRsAAAAAxGwQIAADAYBQsAAMBgwY29gWtFSclZw%2BcMDAyQ2Ryu06fLVVtbZ/j8/oCMXCMf98jIPTJyjXzc82ZGbdq0NHQ%2BT3n1HawDBw7o3nvvVe/evZWamqrp06erpKREkrRnzx6NGjVKvXr10rBhw7RhwwaHa1evXq0hQ4aoV69eSk9PV35%2Bvn3s/Pnzeuqpp3TTTTcpJSVF06ZNk9VqtY8XFRXpoYceUkpKigYMGKBFixaptrbWPt6QtX9IAgMDFBAQoMDAgMbeyg8WGblGPu6RkXtk5Br5uOePGXmtYF24cEH333%2B/brzxRu3Zs0ebNm3SqVOnNH/%2BfBUXF2vKlCkaN26c9uzZozlz5mju3LmyWCySpO3bt2vp0qX67W9/q927d2vAgAGaNGmSzp07J0lasmSJCgoKlJubq/fff191dXV6/PHH7Ws//PDDatu2rbZt26bXXntN27Zt0%2Buvvy5JDV4bAADAHa8VrIqKCmVlZWnixIkKCQmR2WzWLbfcom%2B%2B%2BUYbN25UXFycRo0apdDQUKWmpmrgwIFas2aNJCk3N1cjR45Ujx491Lx5cz3wwAOSpB07dqi6ulp5eXmaMmWKrrvuOkVFRWn69OnauXOnvv32W1ksFh04cEAzZ85Uy5YtFRcXp4yMDOXm5kpSg9YGAADwhNeewYqMjNTo0aPtvz906JD%2B9re/6dZbb1VBQYESEhIczk9ISNB7770nSSooKNBtt91mHwsMDFS3bt1ksVjUrVs3nT17VomJifbxTp06qXnz5iooKFBxcbFiY2MVGRlpH09MTNThw4dVVlbWoLWHDRvm0Z%2B9uLjYfiv0kuDgMMXExHh0vaeCggIdfkV9ZOQa%2BbhHRu6RkWvk454/ZuT1h9yLioo0ZMgQVVdXa8yYMZo2bZoefPBBtW3b1uG8qKgo%2B3NUNpvNoSBJFwub1WqVzWaTJJlMJodxk8lkH//%2B2KW5Lo1f7dqeys3N1bJlyxyOZWZmatq0aR7PcSVMphZemdefkJFr5OMeGblHRq6Rj3v%2BlJHXC1ZsbKwsFov%2B93//V0899ZQee%2Bwxj66rq3P9XQSuxt1d29C13Rk7dqwGDhzocCw4OExWa3mD5v2%2BoKBAmUwtVFpaoZqaWvcXXIPIyDXycY%2BM3CMj18jHPW9mFB0dbuh8nvLJxzQEBAQoLi5OWVlZGjdunNLS0uzvRF1itVplNpslSdHR0fXGbTabunTpYj/HZrMpPPz/Qjtz5oxatWqlmpoap9cGBATIbDY7ndvTtT0VExNT73ZgSclZVVd75y9WTU2t1%2Bb2F2TkGvm4R0bukZFr5OOeP2XktZude/bs0ZAhQxw%2BHiEw8OJy3bt3r/fRB/n5%2BerRo4ckKSkpSQUFBfaxmpoaffnll%2BrRo4fat2%2BvyMhIh/Gvv/5aFy5cUFJSkpKSknT8%2BHGdPn3aPm6xWNS5c2eFh4crOTn5qtcGAADwhNcKVlJSksrKyrRo0SJVVFTo9OnTWrp0qW644Qalp6erqKhIa9as0fnz5/XBBx/ogw8%2B0JgxYyRJ6enpeuedd/T555%2BroqJCK1asUEhIiPr376%2BgoCCNGTNGr7zyio4fPy6r1arf/e53uuWWW9S6dWslJCQoOTlZixcvVllZmQoLC/Xaa68pPT1dkjR8%2BPCrXhsAAMATAXUNfeDIha%2B%2B%2BkoLFizQF198obCwMPXt21ezZ89W27Zt9e9//1sLFixQYWGhYmNjNWPGDA0ePNh%2B7V/%2B8hfl5OTo1KlTSk5O1vz589W1a1dJFz9ja%2BHChXr33XdVXV2tAQMGaP78%2BWrZ8uKntZ44cUJz587Vv/71L0VERGjcuHGaOnWqAgIufoBZQ9a%2BWt74JPfg4EBFR4fLai33m7dUjUZGrpGPe2TkHhm5Rj7ueTOjxvokd68WLPwfClbjICPXyMc9MnKPjFwjH/f8sWD5zwdOAAAA/EBQsAAAAAxGwQIAADCYTz4HCwAA%2BN6tL33c2Fvw2N7nhjb2FgzFO1gAAAAGo2ABAAAYjIIFAABgMAoWAACAwShYAAAABqNgAQAAGIyCBQAAYDAKFgAAgMEoWAAAAAajYAEAABiMggUAAGAwChYAAIDBKFgAAAAGo2ABAAAYjIIFAABgMAoWAACAwShYAAAABqNgAQAAGIyCBQAAYDAKFgAAgMEoWAAAAAajYAEAABiMggUAAGAwChYAAIDBKFgAAAAGo2ABAAAYjIIFAABgMAoWAACAwShYAAAABqNgAQAAGIyCBQAAYDAKFgAAgMEoWAAAAAajYAEAABiMggUAAGAwChYAAIDBKFgAAAAG82rBKioqUmZmplJSUpSamqrZs2ertLRU//3vfxUfH6/k5GSHrz/%2B8Y/2azdv3qzhw4erZ8%2BeGjlypD766CP7WG1trZYsWaJBgwapT58%2BmjBhgo4dO2Yft9lsmj59ulJTU9WvXz/NmTNHlZWV9vH9%2B/fr7rvvVu/evTV48GCtWrXKYd%2Bu1gYAAHDHqwVr0qRJMplM2r59u9atW6dvvvlGL774on3cYrE4fE2YMEHSxQI0a9YszZw5U5988okyMjI0depUnThxQpL05ptvauPGjcrJydGOHTsUFxenzMxM1dXVSZLmzp2riooKbdq0SWvXrlVhYaGys7MlSZWVlZo4caL69u2rXbt2acmSJVq5cqW2bt3q0doAAADueK1glZaWKikpSTNmzFB4eLjatWunO%2B%2B8U3v37nV77Zo1a5SWlqa0tDSFhoZqxIgR6tq1qzZs2CBJys3NVUZGhjp16qSIiAhlZWWpsLBQ%2B/bt08mTJ7Vt2zZlZWXJbDarbdu2mjJlitauXauqqirt3LlTVVVVmjx5ssLCwpSYmKjRo0crNzfXo7UBAADc8VrBMplMWrhwoVq3bm0/dvz4ccXExNh//9hjj6lfv37q27evFi9erKqqKklSQUGBEhISHOZLSEiQxWJRZWWlDh486DAeERGhDh06yGKxaP/%2B/QoKClJ8fLx9PDExUefOndOhQ4dUUFCg%2BPh4BQUFOcydn5/vdm0AAABPBPtqIYvFojfeeEMrVqxQSEiIevbsqVtuuUXPPfec9u/fr4cffljBwcF65JFHZLPZFBkZ6XB9ZGSkDh48qDNnzqiurs7puNVqVVRUlCIiIhQQEOAwJklWq1U2m00mk8nh2qioKNlsNtXW1rpc21PFxcUqKSlxOBYcHOZQLo0QFBTo8CvqIyPXyMc9MnKPjFwjH8/5U0Y%2BKViffvqpJk%2BerBkzZig1NVWS9Pbbb9vHu3fvrokTJ2rlypV65JFHJMn%2BPNXluBp3d60z3y1kV3P9d%2BXm5mrZsmUOxzIzMzVt2rQGzXs5JlMLr8zrT8jINfJxj4zcIyPXyMc9f8rI6wVr%2B/btevTRRzV37lzdcccdlz0vNjZWJ0%2BeVF1dnaKjo2Wz2RzGbTabzGazoqKiFBgY6HS8VatWMpvNKisrU01Njf024KVzL40fOXKk3rWX5nW1tqfGjh2rgQMHOhwLDg6T1Vru8RyeCAoKlMnUQqWlFaqpqTV0bn9BRq6Rj3tk5B4ZuUY%2BnvNGRtHR4YbO5ymvFqzPPvtMs2bN0ssvv6x%2B/frZj%2B/Zs0eff/65Jk%2BebD926NAhxcbGKiAgQElJSfZnoi6xWCwaNmyYQkND1aVLFxUUFOjGG2%2BUdPGB%2BqNHj6p79%2B6KjY1VXV2dDhw4oMTERPu1JpNJHTt2VFJSkt566y1VV1crODjYPt6jRw9Jcrm2p2JiYurdDiwpOavqau/8xaqpqfXa3P6CjFwjH/fIyD0yco183POnjLx2s7O6ulpPPvmkZs6c6VCuJKlly5Zavny51q9fr6qqKlksFv3xj39Uenq6JGnMmDHavXu3du7cqfPnzysvL09HjhzRiBEjJEnp6elavXq1CgsLVVZWpuzsbHXr1k3Jyckym80aMmSIXnrpJZ0%2BfVonTpzQ8uXLNWrUKAUHBystLU0RERFasWKFKioqtG/fPuXl5Xm8NgAAgDsBdQ194Ogy9u7dq1//%2BtcKCQmpN7ZlyxZ9%2BeWXWrZsmY4cOaKWLVvqnnvu0YMPPqjAwIudb%2BvWrVq8eLGKiorUuXNnzZkzR3369JF08RmppUuX6u2331Z5eblSUlL0zDPPqF27dpKks2fPat68edqxY4eaNWum22%2B/XbNnz7bv5euvv9a8efOUn5%2Bv1q1b68EHH9SvfvUr%2B/5crX21SkrONuh6Z4KDAxUdHS6rtdxvGr/RyMg18nGPjNwjI9caM59bX/rYp%2Bs1xN7nhnolozZtWho6n6e8VrDgiILVOMjINfJxj4zcIyPXKFie8beC5T/fDwkAAPADQcECAAAwGAULAADAYBQsAAAAg1GwAAAADEbBAgAAMBgFCwAAwGAULAAAAINRsAAAAAxGwQIAADAYBQsAAMBgFCwAAACDUbAAAAAMRsECAAAwGAULAADAYBQsAAAAg1GwAAAADEbBAgAAMBgFCwAAwGAULAAAAINRsAAAAAxGwQIAADAYBQsAAMBgFCwAAACDUbAAAAAMRsECAAAwGAULAADAYBQsAAAAg1GwAAAADEbBAgAAMBgFCwAAwGAULAAAAINRsAAAAAxGwQIAADAYBQsAAMBgFCwAAACDUbAAAAAMRsECAAAwGAULAADAYBQsAAAAg1GwAAAADEbBAgAAMJhXC1ZRUZEyMzOVkpKi1NRUzZ49W6WlpZKk/fv36%2B6771bv3r01ePBgrVq1yuHazZs3a/jw4erZs6dGjhypjz76yD5WW1urJUuWaNCgQerTp48mTJigY8eO2cdtNpumT5%2Bu1NRU9evXT3PmzFFlZaV9vCFrAwAAuOPVgjVp0iSZTCZt375d69at0zfffKMXX3xRlZWVmjhxovr27atdu3ZpyZIlWrlypbZu3SrpYgGaNWuWZs6cqU8%2B%2BUQZGRmaOnWqTpw4IUl68803tXHjRuXk5GjHjh2Ki4tTZmam6urqJElz585VRUWFNm3apLVr16qwsFDZ2dmS1OC1AQAA3PFawSotLVVSUpJmzJih8PBwtWvXTnfeeaf27t2rnTt3qqqqSpMnT1ZYWJgSExM1evRo5ebmSpLWrFmjtLQ0paWlKTQ0VCNGjFDXrl21YcMGSVJubq4yMjLUqVMnRUREKCsrS4WFhdq3b59Onjypbdu2KSsrS2azWW3bttWUKVO0du1aVVVVNXhtAAAAd4K9NbHJZNLChQsdjh0/flwxMTEqKChQfHy8goKC7GMJCQlas2aNJKmgoEBpaWkO1yYkJMhisaiyslIHDx5UQkKCfSwiIkIdOnSQxWLR2bNnFRQUpPj4ePt4YmKizp07p0OHDjVobU8VFxerpKTE4VhwcJhiYmI8nsMTQUGBDr%2BiPjJyjXzcIyP3yMg18vGcP2XktYL1fRaLRW%2B88YZWrFih9957TyaTyWE8KipKNptNtbW1stlsioyMdBiPjIzUwYMHdebMGdXV1Tkdt1qtioqKUkREhAICAhzGJMlqtcpms1312p7Kzc3VsmXLHI5lZmZq2rRpHs9xJUymFl6Z15%2BQkWvk4x4ZuUdGrpGPe/6UkU8K1qeffqrJkydrxowZSk1N1Xvvvef0vO%2BWokvPU12Oq3F31zZ0bXfGjh2rgQMHOhwLDg6T1VreoHm/LygoUCZTC5WWVqimptbQuf0FGblGPu6RkXtk5Br5eM4bGUVHhxs6n6e8XrC2b9%2BuRx99VHPnztUdd9whSTKbzTpy5IjDeTabTVFRUQoMDFR0dLRsNlu9cbPZbD/H2XirVq1kNptVVlammpoa%2B23AS%2BdeGr/atT0VExNT73ZgSclZVVd75y9WTU2t1%2Bb2F2TkGvm4R0bukZFr5OOeP2Xk1Zudn332mWbNmqWXX37ZXq4kKSkpSV999ZWqq6vtxywWi3r06GEfz8/Pd5jr0nhoaKi6dOmigoIC%2B1hpaamOHj2q7t27q1u3bqqrq9OBAwccrjWZTOrYsWOD1gYAAPCE1wpWdXW1nnzySc2cOVP9%2BvVzGEtLS1NERIRWrFihiooK7du3T3l5eUpPT5ckjRkzRrt379bOnTt1/vx55eXl6ciRIxoxYoQkKT09XatXr1ZhYaHKysqUnZ2tbt26KTk5WWazWUOGDNFLL72k06dP68SJE1q%2BfLlGjRql4ODgBq8NAADgTkBdQx84uoy9e/fq17/%2BtUJCQuqNbdmyReXl5Zo3b57y8/PVunVrPfjgg/rVr35lP2fr1q1avHixioqK1LlzZ82ZM0d9%2BvSRdPEZqaVLl%2Brtt99WeXm5UlJS9Mwzz6hdu3aSpLNnz2revHnasWOHmjVrpttvv12zZ8%2B27%2BXrr7%2B%2B6rWvVknJ2QZd70xwcKCio8NltZb7zVuqRiMj18jHPTJyj4xca8x8bn3pY5%2Bu1xB7nxvqlYzatGlp6Hye8lrBgiMKVuMgI9fIxz0yco%2BMXKNgecbfCpb/fOAEAADADwQFCwAAwGAULAAAAINRsAAAAAxGwQIAADAYBQsAAMBgFCwAAACDUbAAAAAMRsECAAAwGAULAADAYE4L1sCBA7Vs2TIdP37c1/sBAABo8pwWrLvuukubN2/WzTffrAceeEBbt25VdXW1r/cGAADQJDktWJmZmdq8ebP%2B%2Bte/qkuXLnr%2B%2BeeVlpamRYsW6fDhw77eIwAAQJPi8hmsxMREzZo1Szt27NATTzyhv/71r7rttts0YcIEffHFF77aIwAAQJPismBVVVVp8%2BbNevDBBzVr1iy1bdtWjz/%2BuLp166aMjAxt3LjRV/sEAABoMoKdHSwsLFReXp7eeecdlZeXa8iQIXr99dfVu3dv%2Bzl9%2BvTR/PnzNXz4cJ9tFgAAoClwWrCGDRumjh07auLEibrjjjsUFRVV75y0tDSdPn3a6xsEAABoapwWrNWrV%2BvGG290e/G%2BffsM3xAAAEBT5/QZrPj4eE2aNEnbtm2zH/vTn/6kBx98UDabzWebAwAAaIqcFqyFCxfq7Nmz6ty5s/1Y//79VVtbqxdeeMFnmwMAAGiKnN4i/Oijj7Rx40ZFR0fbj8XFxSk7O1u33367zzYHAADQFDl9B6uyslKhoaH1Tw4MVEVFhdc3BQAA0JQ5LVh9%2BvTRCy%2B8oDNnztiPffvtt3r66acdPqoBAAAA9Tm9RfjEE0/o/vvv189//nNFRESotrZW5eXlat%2B%2Bvf785z/7eo8AAABNitOC1b59e7377rv68MMPdfToUQUGBqpjx47q16%2BfgoKCfL1HAACAJsVpwZKkkJAQ3Xzzzb7cCwAAgF9wWrCOHTumxYsX65tvvlFlZWW98X/84x9e3xgAAEBTddlnsIqLi9WvXz%2BFhYX5ek8AAABNmtOClZ%2Bfr3/84x8ym82%2B3g8AAECT5/RjGlq1asU7VwAAAFfJacGaOHGili1bprq6Ol/vBwAAoMlzeovwww8/1GeffaZ169bp%2BuuvV2CgYw97%2B%2B23fbI5AACApshpwYqIiNBNN93k670AAAD4BacFa%2BHChb7eBwAAgN9w%2BgyWJB06dEhLly7V448/bj/2n//8xyebAgAAaMqcFqw9e/ZoxIgR2rp1qzZt2iTp4oePjh8/ng8ZBQAAcMNpwVqyZIkeffRRbdy4UQEBAZIu/nzCF154QcuXL/fpBgEAAJoapwXr66%2B/Vnp6uiTZC5YkDR06VIWFhb7ZGQAAQBPltGC1bNnS6c8gLC4uVkhIiNc3BQAA0JQ5LVi9evXS888/r7KyMvuxw4cPa9asWfr5z3/us80BAAA0RU4/puHxxx/Xvffeq5SUFNXU1KhXr16qqKhQly5d9MILL/h6jwAAAE2K03ew2rVrp02bNunll1/WjBkzlJWVpVdffVXr16/Xj370I48n37Vrl1JTU5WVleVwfN26dfrpT3%2Bq5ORkh68vvvhCklRbW6slS5Zo0KBB6tOnjyZMmKBjx47Zr7fZbJo%2BfbpSU1PVr18/zZkzx%2BGW5v79%2B3X33Xerd%2B/eGjx4sFatWuWw/ubNmzV8%2BHD17NlTI0eO1EcffWQfc7c2AACAO07fwZKkZs2a6eabb77qiV999VXl5eWpQ4cOTsf79OmjP//5z07H3nzzTW3cuFGvvvqq2rZtqyVLligzM1Pr169XQECA5s6dqwsXLmjTpk2qqqrSI488ouzsbD355JOqrKzUxIkTNWbMGOXk5Ojw4cO6//77df3112vw4MHav3%2B/Zs2apWXLlqlv3756//33NXXqVG3ZskXt2rVzuzYAAIA7Tt/BGjhwoAYNGnTZL0%2BEhoa6LFiu5ObmKiMjQ506dVJERISysrJUWFioffv26eTJk9q2bZuysrJkNpvVtm1bTZkyRWvXrlVVVZV27typqqoqTZ48WWFhYUpMTNTo0aOVm5srSVqzZo3S0tKUlpam0NBQjRgxQl27dtWGDRvcrg0AAOAJp%2B9g3XbbbQ7v1tTU1Ojw4cOyWCy69957PZp4/PjxLsePHz%2Bu%2B%2B67T/n5%2BTKZTJo2bZp%2B%2BctfqrKyUgcPHlRCQoL93IiICHXo0EEWi0Vnz55VUFCQ4uPj7eOJiYk6d%2B6cDh06pIKCAsXHxysoKMg%2BnpCQoDVr1kiSCgoKlJaW5rCXhIQEWSwWt2v/7Gc/8%2BjPDgAArm1OC9bMmTOdnvz%2B%2B%2B/rn//8Z4MXNZvNiouL029%2B8xt17txZf//73/XYY48pJiZGP/nJT1RXV6fIyEiHayIjI2W1WhUVFaWIiAiHAnjpXKvVKpvNJpPJ5HBtVFSUbDabamtrZbPZnM598OBBnTlzxuXaniouLlZJSYnDseDgMMXExHg8hyeCggIdfkV9ZOQa%2BbhHRu6RkWvk4zl/yuiyz2A5c/PNN%2Bupp57SU0891aBF%2B/fvr/79%2B9t/P2zYMP3973/XunXr7OWurq7uste7Gruc7xYyd9dfzfzflZubq2XLljkcy8zM1LRp0xo07%2BWYTC28Mq8/ISPXyMc9MnKPjFwjH/f8KaMrKlhffvllg8vH5cTGxio/P19RUVEKDAyUzWZzGLfZbGrVqpXMZrPKyspUU1Njvw146dxL40eOHKl37aV5o6Ojnc5tNpvdru2psWPHauDAgQ7HgoPDZLWWezyHJ4KCAmUytVBpaYVqamoNndtfkJFr5OMeGblHRq6Rj%2Be8kVF0dLih83nKacEaN25cvWMVFRUqLCzU4MGDG7zoW2%2B9pcjISN122232Y4WFhWrfvr1CQ0PVpUsXFRQU6MYbb5QklZaW6ujRo%2BrevbtiY2NVV1enAwcOKDExUZJksVhkMpnUsWNHJSUl6a233lJ1dbWCg4Pt4z169JAkJSUlKT8/32E/FotFw4YNc7u2p2JiYurdDiwpOavqau/8xaqpqfXa3P6CjFwjH/fIyD0yco183POnjJze7IyLi1PHjh0dvnr27KlZs2bp%2Beefb/CiFy5c0LPPPiuLxaKqqipt2rRJH374ob3Ypaena/Xq1SosLFRZWZmys7PVrVs3JScny2w2a8iQIXrppZd0%2BvRpnThxQsuXL9eoUaMUHBystLQ0RUREaMWKFaqoqNC%2BffuUl5dn/9mKY8aM0e7du7Vz506dP39eeXl5OnLkiEaMGOF2bQAAAE84fQfLiE9rv1RIqqurJUnbtm2TdPHdovHjx6u8vFyPPPKISkpKdP3112v58uVKSkqSdPEdtJKSEt1zzz0qLy9XSkqKwzNNzzzzjObNm6dBgwapWbNmuv322%2B0fZhoSEqJXXnlF8%2BbNU05Ojlq3bq2srCz7M19du3ZVdna2Fi5cqKKiInXu3FkrV65UmzZtPFobAADAnYA6Jw9VvfPOOx5PcMcddxi6IX9VUnLW8DmDgwMVHR0uq7Xcb95SNRoZuUY%2B7pGRe2TkWmPmc%2BtLH/t0vYbY%2B9xQr2TUpk1LQ%2BfzlNN3sObMmaPa2tp6D7QHBAQ4HAsICKBgAQAAfI/TgvWHP/xBq1at0qRJkxQfH6%2B6ujp99dVXevXVV3X33XcrJSXF1/sEAABoMi77DFZOTo7atm1rP3bDDTeoffv2mjBhgjZt2uSzDQIAADQ1Tr%2BL8MiRI/U%2BzVySTCaTioqKvL4pAACApsxpwYqNjdULL7zg8ONhSktLtXjxYv34xz/22eYAAACaIqe3CJ944gnNmDFDubm5Cg8PV2BgoMrKytS8eXMtX77c13sEAABoUpwWrH79%2Bmnnzp364IMPdOLECdXV1alt27b6xS9%2BoZYtG%2BfbHQEAAJqKy/4swhYtWmjQoEE6ceKE2rdv78s9AQAANGlOn8GqrKzUrFmz1LNnT916662SLj6D9cADD6i0tNSnGwQAAGhqnBasRYsWaf/%2B/crOzlZg4P%2BdUlNTo%2BzsbJ9tDgAAoClyWrDef/99/f73v9fQoUMVEBAg6eJHNCxcuFBbt2716QYBAACaGqcFq7y8XHFxcfWOm81mnTt3ztt7AgAAaNKcFqwf//jH%2Buc//ylJDj97cMuWLfrRj37km50BAAA0UU4kcX8mAAAYMElEQVS/i/BXv/qVHn74Yd11112qra3Va6%2B9pvz8fL3//vuaM2eOr/cIAADQpDgtWGPHjlVwcLDeeOMNBQUF6ZVXXlHHjh2VnZ2toUOH%2BnqPAAAATYrTgnX69Gnddddduuuuu3y9HwAAgCbP6TNYgwYNcnj2CgAAAJ5zWrBSUlL03nvv%2BXovAAAAfsHpLcLrrrtOzz33nHJycvTjH/9YzZo1cxhfvHixTzYHAADQFDktWAcPHtRPfvITSZLVavXphgAAAJo6h4KVlZWlJUuW6M9//rP92PLly5WZmenzjQEAADRVDs9gbd%2B%2Bvd4JOTk5PtsMAACAP3AoWM6%2Bc5DvJgQAALgyDgXr0g92dncMAAAAl%2Bf0YxoAAABw9ShYAAAABnP4LsKqqirNmDFD7o7xOVgAAACX51CwevfureLiYrk7BgAAgMtzKFjf/fwrAAAAXB2ewQIAADAYBQsAAMBgFCwAAACDUbAAAAAMRsECAAAwGAULAADAYBQsAAAAg1GwAAAADEbBAgAAMBgFCwAAwGAULAAAAINRsAAAAAxGwQIAADCYVwvWrl27lJqaqqysrHpjmzdv1vDhw9WzZ0%2BNHDlSH330kX2strZWS5Ys0aBBg9SnTx9NmDBBx44ds4/bbDZNnz5dqamp6tevn%2BbMmaPKykr7%2BP79%2B3X33Xerd%2B/eGjx4sFatWmXY2gAAAO54rWC9%2BuqrWrBggTp06FBvbP/%2B/Zo1a5ZmzpypTz75RBkZGZo6dapOnDghSXrzzTe1ceNG5eTkaMeOHYqLi1NmZqbq6uokSXPnzlVFRYU2bdqktWvXqrCwUNnZ2ZKkyspKTZw4UX379tWuXbu0ZMkSrVy5Ulu3bjVkbQAAAHe8VrBCQ0OVl5fntGCtWbNGaWlpSktLU2hoqEaMGKGuXbtqw4YNkqTc3FxlZGSoU6dOioiIUFZWlgoLC7Vv3z6dPHlS27ZtU1ZWlsxms9q2baspU6Zo7dq1qqqq0s6dO1VVVaXJkycrLCxMiYmJGj16tHJzcxu8NgAAgCeCvTXx%2BPHjLztWUFCgtLQ0h2MJCQmyWCyqrKzUwYMHlZCQYB%2BLiIhQhw4dZLFYdPbsWQUFBSk%2BPt4%2BnpiYqHPnzunQoUMqKChQfHy8goKCHOZes2ZNg9f%2B2c9%2B5tGfvbi4WCUlJQ7HgoPDFBMT49H1ngoKCnT4FfWRkWvk4x4ZuUdGrpGP5/wpI68VLFdsNpsiIyMdjkVGRurgwYM6c%2BaM6urqnI5brVZFRUUpIiJCAQEBDmOSZLVaZbPZZDKZHK6NioqSzWZTbW1tg9b2VG5urpYtW%2BZwLDMzU9OmTfN4jithMrXwyrz%2BhIxcIx/3yMg9MnKNfNzzp4wapWBJcvtMk6vxq3ke6ruFrCFre2Ls2LEaOHCgw7Hg4DBZreUNmvf7goICZTK1UGlphWpqag2d21%2BQkWvk4x4ZuUdGrpGP57yRUXR0uKHzeapRClZ0dLRsNpvDMZvNJrPZrKioKAUGBjodb9Wqlcxms8rKylRTU2O/DXjp3EvjR44cqXftpXkbsranYmJi6t0OLCk5q%2Bpq7/zFqqmp9drc/oKMXCMf98jIPTJyjXzc86eMGuVmZ1JSkvLz8x2OWSwW9ejRQ6GhoerSpYsKCgrsY6WlpTp69Ki6d%2B%2Bubt26qa6uTgcOHHC41mQyqWPHjkpKStJXX32l6urqenM3dG0AAABPNErBGjNmjHbv3q2dO3fq/PnzysvL05EjRzRixAhJUnp6ulavXq3CwkKVlZUpOztb3bp1U3Jyssxms4YMGaKXXnpJp0%2Bf1okTJ7R8%2BXKNGjVKwcHBSktLU0REhFasWKGKigrt27dPeXl5Sk9Pb/DaAAAAnvDaLcJLheTSO0nbtm2TdPHdoq5duyo7O1sLFy5UUVGROnfurJUrV6pNmzaSpHHjxqmkpET33HOPysvLlZKS4vDQ%2BDPPPKN58%2BZp0KBBatasmW6//Xb7h5mGhITolVde0bx585STk6PWrVsrKytL/fv3l6QGrw0AAOBOQB2foOkTJSVnDZ8zODhQ0dHhslrL/eaetdHIyDXycY%2BM3CMj1xozn1tf%2Btin6zXE3ueGeiWjNm1aGjqfp/znAycAAAB%2BIChYAAAABqNgAQAAGIyCBQAAYDAKFgAAgMEoWAAAAAajYAEAABiMggUAAGAwChYAAIDBKFgAAAAGo2ABAAAYjIIFAABgMAoWAACAwShYAAAABqNgAQAAGIyCBQAAYDAKFgAAgMEoWAAAAAajYAEAABiMggUAAGAwChYAAIDBKFgAAAAGo2ABAAAYjIIFAABgMAoWAACAwShYAAAABqNgAQAAGIyCBQAAYDAKFgAAgMEoWAAAAAajYAEAABiMggUAAGAwChYAAIDBKFgAAAAGo2ABAAAYjIIFAABgMAoWAACAwShYAAAABqNgAQAAGIyCBQAAYDAKFgAAgMEarWDFx8crKSlJycnJ9q9nn31WkrRnzx6NGjVKvXr10rBhw7RhwwaHa1evXq0hQ4aoV69eSk9PV35%2Bvn3s/Pnzeuqpp3TTTTcpJSVF06ZNk9VqtY8XFRXpoYceUkpKigYMGKBFixaptrbWPu5ubQAAAHeCG3PxLVu26Prrr3c4VlxcrClTpmjOnDkaPny4Pv30U02ePFkdO3ZUcnKytm/frqVLl%2BoPf/iD4uPjtXr1ak2aNElbt25VWFiYlixZooKCAuXm5qpFixaaO3euHn/8cb3yyiuSpIcffliJiYnatm2bTp06pYkTJ6p169a677773K4NAADgiR/cLcKNGzcqLi5Oo0aNUmhoqFJTUzVw4ECtWbNGkpSbm6uRI0eqR48eat68uR544AFJ0o4dO1RdXa28vDxNmTJF1113naKiojR9%2BnTt3LlT3377rSwWiw4cOKCZM2eqZcuWiouLU0ZGhnJzcz1aGwAAwBONWrAWL16s/v3764YbbtDcuXNVXl6ugoICJSQkOJyXkJBgvw34/fHAwEB169ZNFotFR48e1dmzZ5WYmGgf79Spk5o3b66CggIVFBQoNjZWkZGR9vHExEQdPnxYZWVlbtcGAADwRKPdIvzZz36m1NRUvfjiizp27JimT5%2Bup59%2BWjabTW3btnU4Nyoqyv4clc1mcyhIkhQZGSmr1SqbzSZJMplMDuMmk8k%2B/v2xS3NdGne1tqeKi4tVUlLicCw4OEwxMTFXNI87QUGBDr%2BiPjJyjXzcIyP3yMg18vGcP2XUaAXr0m056eK7TDNnztTkyZPVu3dvt9fW1dVd9bi7a42Qm5urZcuWORzLzMzUtGnTvLKeydTCK/P6EzJyjXzcIyP3yMg18nHPnzJq1Ifcv%2Bv6669XTU2NAgMD7e9EXWK1WmU2myVJ0dHR9cZtNpu6dOliP8dmsyk8PNw%2BfubMGbVq1Uo1NTVOrw0ICJDZbHY693fX9tTYsWM1cOBAh2PBwWGyWsuvaB53goICZTK1UGlphWpqat1fcA0iI9fIxz0yco%2BMXCMfz3kjo%2BjocPcneUGjFKwvv/xSGzZs0OzZs%2B3HCgsLFRISorS0NP3tb39zOD8/P189evSQJCUlJamgoEB33nmnJKmmpkZffvmlRo0apfbt2ysyMtL%2BrJUkff3117pw4YKSkpJUXFys48eP6/Tp0/bSZLFY1LlzZ4WHhys5OVlr16697NqeiomJqXc7sKTkrKqrvfMXq6am1mtz%2Bwsyco183CMj98jINfJxz58yapSbna1atVJubq5ycnJ04cIFHT58WC%2B//LLGjh2rX/7ylyoqKtKaNWt0/vx5ffDBB/rggw80ZswYSVJ6erreeecdff7556qoqNCKFSsUEhKi/v37KygoSGPGjNErr7yi48ePy2q16ne/%2B51uueUWtW7dWgkJCUpOTtbixYtVVlamwsJCvfbaa0pPT5ckDR8%2B3OXaAAAAngio88VDSU78%2B9//1uLFi/XVV18pJCREd955p7KyshQaGqp///vfWrBggQoLCxUbG6sZM2Zo8ODB9mv/8pe/KCcnR6dOnVJycrLmz5%2Bvrl27SpIuXLighQsX6t1331V1dbUGDBig%2BfPnq2XLlpKkEydOaO7cufrXv/6liIgIjRs3TlOnTlVAQIB9X67WvlolJWcbPMf3BQcHKjo6XFZrud80fqORkWvk4x4ZuUdGrjVmPre%2B9LFP12uIvc8N9UpGbdq0NHQ%2BTzVawbrWULAaBxm5Rj7ukZF7ZOQaBcsz/law/Of7IQEAAH4gKFgAAAAGo2ABAAAYjIIFAABgMAoWAACAwShYAAAABqNgAQAAGIyCBQAAYDAKFgAAgMEoWAAAAAajYAEAABiMggUAAGAwChYAAIDBKFgAAAAGo2ABAAAYjIIFAABgMAoWAACAwShYAAAABqNgAQAAGIyCBQAAYDAKFgAAgMEoWAAAAAajYAEAABiMggUAAGAwChYAAIDBKFgAAAAGo2ABAAAYjIIFAABgMAoWAACAwShYAAAABqNgAQAAGIyCBQAAYDAKFgAAgMEoWAAAAAajYAEAABiMggUAAGAwChYAAIDBKFgAAAAGC27sDQAwxq0vfdzYW/DYe9P/X2NvAQC8inewAAAADEbBAgAAMBgFCwAAwGAULCeKior00EMPKSUlRQMGDNCiRYtUW1vb2NsCAABNBA%2B5O/Hwww8rMTFR27Zt06lTpzRx4kS1bt1a9913X2NvDQAANAG8g/U9FotFBw4c0MyZM9WyZUvFxcUpIyNDubm5jb01AADQRPAO1vcUFBQoNjZWkZGR9mOJiYk6fPiwysrKFBER4XaO4uJilZSUOBwLDg5TTEyMoXsNCgp0%2BBX1kdEPU3Bw0/nfg9eQe2TkGvl4zp8yomB9j81mk8lkcjh2qWxZrVaPClZubq6WLVvmcGzq1Kl6%2BOGHjduoLha511//g8aOHWt4efMX11JGe58besXXFBcXKzc395rI52pdS6%2Bhq0VGrjVmPlfz70JjKC4u1tKlS/3qNeQ/VdFAdXV1Dbp%2B7NixWrduncPX2LFjDdrd/ykpKdGyZcvqvVuG/0NGrpGPe2TkHhm5Rj7u%2BWNGvIP1PWazWTabzeGYzWZTQECAzGazR3PExMT4TQMHAABXjnewvicpKUnHjx/X6dOn7ccsFos6d%2B6s8PDwRtwZAABoKihY35OQkKDk5GQtXrxYZWVlKiws1Guvvab09PTG3hoAAGgigubPnz%2B/sTfxQ/OLX/xCmzZt0rPPPqt3331Xo0aN0oQJExQQENDYW6snPDxcN954I%2B%2BuuUBGrpGPe2TkHhm5Rj7u%2BVtGAXUNfaIbAAAADrhFCAAAYDAKFgAAgMEoWAAAAAajYAEAABiMggUAAGAwChYAAIDBKFgAAAAGo2ABAAAYjIIFAABgMArWD9yuXbuUmpqqrKwsl%2BfV1tZqyZIlGjRokPr06aMJEybo2LFjPtpl4/I0o9mzZ9t/1uSlrxtuuMFHu2w8RUVFyszMVEpKilJTUzV79myVlpY6PXfz5s0aPny4evbsqZEjR%2Bqjjz7y8W4bh6cZrVu3Tj/96U8dXkPJycn64osvGmHXvnXgwAHde%2B%2B96t27t1JTUzV9%2BnSVlJQ4PXf16tUaMmSIevXqpfT0dOXn5/t4t77naT5Lly5Vt27d6r2GTp482Qi7bjzPP/%2B84uPjLzvuD68hCtYP2KuvvqoFCxaoQ4cObs998803tXHjRuXk5GjHjh2Ki4tTZmam/P0nIV1JRpI0efJkWSwW%2B9fevXu9vMPGN2nSJJlMJm3fvl3r1q3TN998oxdffLHeefv379esWbM0c%2BZMffLJJ8rIyNDUqVN14sSJRti1b3makST16dPH4TVksVjUvXt3H%2B/Yty5cuKD7779fN954o/bs2aNNmzbp1KlTcvajbLdv366lS5fqt7/9rXbv3q0BAwZo0qRJOnfunO837iNXko8k/fKXv6z3GmrdurVvN92I9u/fr/Xr11923F9eQxSsH7DQ0FDl5eV5VB5yc3OVkZGhTp06KSIiQllZWSosLNS%2Bfft8sNPGcyUZXYtKS0uVlJSkGTNmKDw8XO3atdOdd97ptFiuWbNGaWlpSktLU2hoqEaMGKGuXbtqw4YNjbBz37mSjK5VFRUVysrK0sSJExUSEiKz2axbbrlF33zzTb1zc3NzNXLkSPXo0UPNmzfXAw88IEnasWOHr7ftM1eSz7WutrZW8%2BbNU0ZGxmXP8ZfXEAXrB2z8%2BPFq2bKl2/MqKyt18OBBJSQk2I9FRESoQ4cOslgs3txio/M0o0s%2B%2BeQT3XHHHerZs6dGjRrVJN92vhImk0kLFy50%2BK/j48ePKyYmpt65BQUFDq8hSUpISPD719CVZHRp7L777lOfPn00aNAgl/8l7i8iIyM1evRoBQcHS5IOHTqkv/3tb7r11lvrnfv911FgYKC6devm16%2BjK8lHkr766iuNGzdOvXr10rBhw66ZW/GS9Pbbbys0NFTDhw%2B/7Dn%2B8hqiYPmBM2fOqK6uTpGRkQ7HIyMjZbVaG2lXPzzt27dXhw4dtHLlSu3atUs33HCD7r///msqI4vFojfeeEOTJ0%2BuN2az2XgNyXVGZrNZcXFxevTRR/Xxxx/rN7/5jZ544gnt2bOnEXbqe0VFRUpKStJtt92m5ORkTZs2rd451/LryJN82rVrp/bt2%2BvFF1/Uxx9/rNGjR2vSpEk6dOhQI%2BzYt06ePKmlS5dq3rx5Ls/zl9cQBcuP%2BPvzVg2VmZmp559/Xm3btlVERIQeffRRhYSEaNu2bY29NZ/49NNPNWHCBM2YMUOpqalOz7nWX0PuMurfv7/%2B8Ic/KCEhQSEhIRo2bJhuueUWrVu3rhF263uxsbGyWCzasmWLjhw5oscee8zpedfq68iTfEaPHq3f//736tChg1q0aKGMjAx169bN72/FS9LChQs1cuRIde7c2e25/vAaomD5gaioKAUGBspmszkct9lsatWqVSPt6ocvKChI1113nYqLixt7K163fft2PfTQQ3riiSc0fvx4p%2BdER0c7fQ2ZzWZfbLHReZKRM7GxsdfEa%2BiSgIAAxcXFKSsrS5s2bdLp06cdxq/115G7fJy5Fl5De/bs0X/%2B8x9lZma6PddfXkMULD8QGhqqLl26qKCgwH6stLRUR48e9fvvbvJUXV2dFi5cqAMHDtiPXbhwQUePHlX79u0bcWfe99lnn2nWrFl6%2BeWXdccdd1z2vKSkpHrPpFksFvXo0cPbW2x0nmb01ltvafPmzQ7HCgsL/f41tGfPHg0ZMkS1tbX2Y4GBF//vo1mzZg7nJiUlOfxbVFNToy%2B//NKvX0dXks///M//1LulfC28hjZs2KBTp05pwIABSklJ0ciRIyVJKSkpevfddx3O9ZfXEAWrifr22281dOhQ%2B2ddpaena/Xq1SosLFRZWZmys7Ptn7VyrfpuRgEBAfrvf/%2Brp59%2BWt9%2B%2B63Ky8uVnZ2tZs2a6eabb27srXpNdXW1nnzySc2cOVP9%2BvWrN37vvffaC8OYMWO0e/du7dy5U%2BfPn1deXp6OHDmiESNG%2BHrbPnUlGV24cEHPPvusLBaLqqqqtGnTJn344YcaN26cr7ftU0lJSSorK9OiRYtUUVGh06dPa%2BnSpbrhhhvUsmVLDR061P5dl%2Bnp6XrnnXf0%2Beefq6KiQitWrFBISIj69%2B/fuH8IL7qSfGw2m55%2B%2BmkdOnRI58%2Bf16pVq3T06FHdeeedjfyn8K7Zs2fr/fff1/r167V%2B/Xrl5ORIktavX6%2BBAwf65WsouLE3gMu7VI6qq6slyf6s0KV/3A8fPqwLFy5IksaNG6eSkhLdc889Ki8vV0pKipYtW9Y4G/ehK8noueee04svvqiRI0eqrKxM3bt31%2Buvv66wsLDG2bwPfP755yosLNSCBQu0YMECh7EtW7bo2LFjOnPmjCSpa9euys7O1sKFC1VUVKTOnTtr5cqVatOmTWNs3WeuJKPx48ervLxcjzzyiEpKSnT99ddr%2BfLlSkpKaoyt%2B0zLli21atUqLViwQH379lVYWJj69u2r5557TpJ0%2BPBh%2B2cU3XTTTfrNb36j6dOn69SpU0pOTlZOTo6aN2/emH8Er7qSfGbMmCFJysjIkM1mU%2BfOnfWnP/1J7dq1a7T9%2B0JkZKTDg%2BuX/s2%2B9Of2x9dQQJ0/PEkGAADwA8ItQgAAAINRsAAAAAxGwQIAADAYBQsAAMBgFCwAAACDUbAAAAAMRsECAAAwGAULAADAYBQsAAAAg1GwAAAADEbBAgAAMBgFCwAAwGAULAAAAIP9f2ZGBQpg3hiXAAAAAElFTkSuQmCC\"/>\n        </div>\n        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common-5594948405442907746\">\n            \n<table class=\"freq table table-hover\">\n    <thead>\n    <tr>\n        <td class=\"fillremaining\">Value</td>\n        <td class=\"number\">Count</td>\n        <td class=\"number\">Frequency (%)</td>\n        <td style=\"min-width:200px\">&nbsp;</td>\n    </tr>\n    </thead>\n    <tr class=\"\">\n        <td class=\"fillremaining\">4</td>\n        <td class=\"number\">2904478</td>\n        <td class=\"number\">97.2%</td>\n        <td>\n            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">2</td>\n        <td class=\"number\">79743</td>\n        <td class=\"number\">2.7%</td>\n        <td>\n            <div class=\"bar\" style=\"width:3%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">1</td>\n        <td class=\"number\">3960</td>\n        <td class=\"number\">0.1%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr>\n</table>\n        </div>\n        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme-5594948405442907746\">\n            <p class=\"h4\">Minimum 5 values</p>\n            \n<table class=\"freq table table-hover\">\n    <thead>\n    <tr>\n        <td class=\"fillremaining\">Value</td>\n        <td class=\"number\">Count</td>\n        <td class=\"number\">Frequency (%)</td>\n        <td style=\"min-width:200px\">&nbsp;</td>\n    </tr>\n    </thead>\n    <tr class=\"\">\n        <td class=\"fillremaining\">1</td>\n        <td class=\"number\">3960</td>\n        <td class=\"number\">0.1%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">2</td>\n        <td class=\"number\">79743</td>\n        <td class=\"number\">2.7%</td>\n        <td>\n            <div class=\"bar\" style=\"width:3%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">4</td>\n        <td class=\"number\">2904478</td>\n        <td class=\"number\">97.2%</td>\n        <td>\n            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n        </td>\n</tr>\n</table>\n            <p class=\"h4\">Maximum 5 values</p>\n            \n<table class=\"freq table table-hover\">\n    <thead>\n    <tr>\n        <td class=\"fillremaining\">Value</td>\n        <td class=\"number\">Count</td>\n        <td class=\"number\">Frequency (%)</td>\n        <td style=\"min-width:200px\">&nbsp;</td>\n    </tr>\n    </thead>\n    <tr class=\"\">\n        <td class=\"fillremaining\">1</td>\n        <td class=\"number\">3960</td>\n        <td class=\"number\">0.1%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">2</td>\n        <td class=\"number\">79743</td>\n        <td class=\"number\">2.7%</td>\n        <td>\n            <div class=\"bar\" style=\"width:3%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">4</td>\n        <td class=\"number\">2904478</td>\n        <td class=\"number\">97.2%</td>\n        <td>\n            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n        </td>\n</tr>\n</table>\n        </div>\n    </div>\n</div>\n</div><div class=\"row variablerow\">\n    <div class=\"col-md-3 namecol\">\n        <p class=\"h4 pp-anchor\" id=\"pp_var_click_os\">click_os<br/>\n            <small>Numeric</small>\n        </p>\n    </div><div class=\"col-md-6\">\n    <div class=\"row\">\n        <div class=\"col-sm-6\">\n            <table class=\"stats \">\n                <tr>\n                    <th>Distinct count</th>\n                    <td>8</td>\n                </tr>\n                <tr>\n                    <th>Unique (%)</th>\n                    <td>0.0%</td>\n                </tr>\n                <tr class=\"ignore\">\n                    <th>Missing (%)</th>\n                    <td>0.0%</td>\n                </tr>\n                <tr class=\"ignore\">\n                    <th>Missing (n)</th>\n                    <td>0</td>\n                </tr>\n                <tr class=\"ignore\">\n                    <th>Infinite (%)</th>\n                    <td>0.0%</td>\n                </tr>\n                <tr class=\"ignore\">\n                    <th>Infinite (n)</th>\n                    <td>0</td>\n                </tr>\n            </table>\n\n        </div>\n        <div class=\"col-sm-6\">\n            <table class=\"stats \">\n\n                <tr>\n                    <th>Mean</th>\n                    <td>13.278</td>\n                </tr>\n                <tr>\n                    <th>Minimum</th>\n                    <td>2</td>\n                </tr>\n                <tr>\n                    <th>Maximum</th>\n                    <td>20</td>\n                </tr>\n                <tr class=\"ignore\">\n                    <th>Zeros (%)</th>\n                    <td>0.0%</td>\n                </tr>\n            </table>\n        </div>\n    </div>\n</div>\n<div class=\"col-md-3 collapse in\" id=\"minihistogram-6332040200241203170\">\n    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAARpJREFUeJzt3MEJAjEQQFEVS7IIe/JsTxZhT7EB%2BehBN8b37gtz%2BQxLQvZjjLEDnjpsPQDM7Lj1APyG0%2BX29jf36/kDk3yXDQJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJhupcV//UFP%2BZkg0AQCASBQBAIBIFAEAgEgUAQCASBQBAIhOmumrCOFa4N2SAQBAJBIBAEAkEgEAQCQSAQnINMZoWzg5UIZAHvRiWo1%2B3HGGPrIWBW/kEgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgPAD8aReXcc1tAwAAAABJRU5ErkJggg%3D%3D\">\n\n</div>\n<div class=\"col-md-12 text-right\">\n    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives-6332040200241203170,#minihistogram-6332040200241203170\"\n       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n        Toggle details\n    </a>\n</div>\n<div class=\"row collapse col-md-12\" id=\"descriptives-6332040200241203170\">\n    <ul class=\"nav nav-tabs\" role=\"tablist\">\n        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles-6332040200241203170\"\n                                                  aria-controls=\"quantiles-6332040200241203170\" role=\"tab\"\n                                                  data-toggle=\"tab\">Statistics</a></li>\n        <li role=\"presentation\"><a href=\"#histogram-6332040200241203170\" aria-controls=\"histogram-6332040200241203170\"\n                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n        <li role=\"presentation\"><a href=\"#common-6332040200241203170\" aria-controls=\"common-6332040200241203170\"\n                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n        <li role=\"presentation\"><a href=\"#extreme-6332040200241203170\" aria-controls=\"extreme-6332040200241203170\"\n                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n\n    </ul>\n\n    <div class=\"tab-content\">\n        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles-6332040200241203170\">\n            <div class=\"col-md-4 col-md-offset-1\">\n                <p class=\"h4\">Quantile statistics</p>\n                <table class=\"stats indent\">\n                    <tr>\n                        <th>Minimum</th>\n                        <td>2</td>\n                    </tr>\n                    <tr>\n                        <th>5-th percentile</th>\n                        <td>2</td>\n                    </tr>\n                    <tr>\n                        <th>Q1</th>\n                        <td>2</td>\n                    </tr>\n                    <tr>\n                        <th>Median</th>\n                        <td>17</td>\n                    </tr>\n                    <tr>\n                        <th>Q3</th>\n                        <td>17</td>\n                    </tr>\n                    <tr>\n                        <th>95-th percentile</th>\n                        <td>20</td>\n                    </tr>\n                    <tr>\n                        <th>Maximum</th>\n                        <td>20</td>\n                    </tr>\n                    <tr>\n                        <th>Range</th>\n                        <td>18</td>\n                    </tr>\n                    <tr>\n                        <th>Interquartile range</th>\n                        <td>15</td>\n                    </tr>\n                </table>\n            </div>\n            <div class=\"col-md-4 col-md-offset-2\">\n                <p class=\"h4\">Descriptive statistics</p>\n                <table class=\"stats indent\">\n                    <tr>\n                        <th>Standard deviation</th>\n                        <td>6.8817</td>\n                    </tr>\n                    <tr>\n                        <th>Coef of variation</th>\n                        <td>0.5183</td>\n                    </tr>\n                    <tr>\n                        <th>Kurtosis</th>\n                        <td>-0.93175</td>\n                    </tr>\n                    <tr>\n                        <th>Mean</th>\n                        <td>13.278</td>\n                    </tr>\n                    <tr>\n                        <th>MAD</th>\n                        <td>6.0178</td>\n                    </tr>\n                    <tr class=\"\">\n                        <th>Skewness</th>\n                        <td>-0.95412</td>\n                    </tr>\n                    <tr>\n                        <th>Sum</th>\n                        <td>39675882</td>\n                    </tr>\n                    <tr>\n                        <th>Variance</th>\n                        <td>47.358</td>\n                    </tr>\n                    <tr>\n                        <th>Memory size</th>\n                        <td>22.8 MiB</td>\n                    </tr>\n                </table>\n            </div>\n        </div>\n        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram-6332040200241203170\">\n            <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzs3X1cVHX%2B//8nMIICcjEqVuSqeUFcaWpmi254lZamlXlFNzPKNi8ok7RMXVcty1pxqdRVqdXNT66fSexTara6prhatp9qNxtRK1G/Gh8N1BkRQuVifn/0c7aJUTEPjHAe99uNmzfe73Pxes2JmWfnHA5%2BLpfLJQAAABjG39cFAAAA1DcELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxm8XUBZlFYeMbXJcjf309Wa4hOnSpRZaXL1%2BXUCrP1bLZ%2BJfP1bLZ%2BJXo2Q8812W%2BzZo0N3V51cQbLRPz9/eTn5yd/fz9fl1JrzNaz2fqVzNez2fqV6NkM6mO/BCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMFiNBqwdO3YoKSlJ6enpHuO/%2B93vlJiY6PEVFxenadOmSZKee%2B45xcXFeczfeuut7vWdTqcmTZqkpKQk9ejRQzNmzNDZs2fd8/v27dOoUaPUpUsX9evXT8uXL/fY/8aNGzVo0CB16tRJQ4YM0c6dO91zlZWVyszMVJ8%2BfdS1a1eNGTNGR48erYmXBwAA1FM1FrDeeOMNzZ07Vy1btqwyN3fuXNntdvfXv//9b910002666673MuMHz/eY5nPP//cPTdz5kyVlpZqw4YNWrt2rfLy8pSRkSFJOnv2rMaOHavbb79dO3bsUGZmppYtW6bNmzdL%2BjF8TZ06VVOmTNGnn36q1NRUPfHEEzp%2B/LgkadWqVVq/fr2ysrK0bds2tWrVSmlpaXK56v%2BTdAEAgDFqLGAFBQUpOzvba8D6ubfeeks33HCDkpOTL7vsiRMntGXLFqWnp8tqtap58%2BaaMGGC1q5dq7KyMuXk5KisrEzjx49XcHCw4uPjNWzYMNlsNknSmjVrlJycrOTkZAUFBWnw4MFq37691q1bJ0my2WxKTU1VmzZtFBoaqvT0dOXl5Wn37t1X94IAAADTqLG/RTh69OhqLVdUVKSlS5fqr3/9q8f4p59%2Bqo8%2B%2Bkj/7//9P7Vp00azZ89WQkKC9u3bp4CAAMXExLiXjY%2BP1w8//KCDBw8qNzdXMTExCggIcM/HxcVpzZo1kqTc3NwqQS4uLk52u11nz57VgQMHFBcX554LDQ1Vy5YtZbfbdcstt1Srp4KCAhUWFnqMWSzBioqKqtb6NSUgwN/jXzMwW89m61cyX89m61eiZzOoj/36/I89v/322%2BratavatWvnHmvRooX8/f311FNPKSQkRIsWLdKjjz6qTZs2yel0KjQ0VH5%2B//l7ReHh4ZIkh8Mhp9OpsLAwj31ERETI6XSqsrJSTqfTvfxP1z9w4IBOnz4tl8vldd7hcFS7J5vNpkWLFnmMpaWlaeLEidXeRk0KC2vk6xJqndl6Nlu/kvl6Nlu/Ej2bQX3q16cBq6KiQqtWrdKCBQs8xtPS0jy%2Bf%2BaZZ7RhwwZt2bJFDRs2/EX3Q/00kF1u/au932rEiBHq3bu3x5jFEiyHo%2BSqtnu1AgL8FRbWSEVFpaqoqPRpLbXFbD2brV/JfD2brV%2BJns3Qc032GxkZYuj2qsunAeuzzz7T%2BfPnPX5D0JuAgABdf/31Kigo0C233KLi4mJVVFS4LwM6nU5JUpMmTWS1WnX48GGP9Z1OpyIiIuTv76/IyEj38j%2Bdt1qt7mW8zTdp0qTafUVFRVW5HFhYeEbl5dfGD0lFReU1U0ttMVvPZutXMl/PZutXujZ6vvvVj326/yv14aTuvi7hilwLx9goPr3Y%2BdFHH%2Bn222%2BXxfKfnOdyuTRv3jzt37/fPXb%2B/HkdOXJELVq0UGxsrFwul8e83W5XWFiYWrdurYSEBH399dcqLy/3mO/YsaMkKSEhQXv27PGo48J8UFCQ2rVrp9zcXPdcUVGRjhw5og4dOhjePwAAqJ98GrD27dunG2%2B80WPMz89P3333nebMmaPvv/9eJSUlysjIUIMGDdS3b19ZrVb1799fr776qk6dOqXjx49r8eLFGjp0qCwWi5KTkxUaGqolS5aotLRUu3fvVnZ2tlJSUiRJw4cP1yeffKKcnBydO3dO2dnZOnz4sAYPHixJSklJ0cqVK5WXl6fi4mJlZGQoNjZWiYmJtf76AACAuqnGLhFeCCQXziRt2bJF0o9niy4oLCxU06ZNq6z74osv6pVXXtGQIUNUXFysDh066K233lJwcLAk6fnnn9esWbPUp08fNWjQQPfcc4/7YaaBgYFaunSpZs2apaysLDVt2lTp6enq2bOnJKl9%2B/bKyMjQvHnzlJ%2Bfr7Zt22rZsmVq1qyZJGnkyJEqLCzUQw89pJKSEnXr1q3KDesAAACX4ufiCZq1orDwjK9LkMXir8jIEDkcJfXmGvflmK1ns/Urma9ns/UrXVs9cw9WzajJY9ysWWNDt1dd9eeBEwAAANcIAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGKxGA9aOHTuUlJSk9PR0j/F3331XN998sxITEz2%2BvvrqK0lSZWWlMjMz1adPH3Xt2lVjxozR0aNH3es7nU5NmjRJSUlJ6tGjh2bMmKGzZ8%2B65/ft26dRo0apS5cu6tevn5YvX%2B6x/40bN2rQoEHq1KmThgwZop07d7rnLrdvAACAy6mxgPXGG29o7ty5atmypdf5rl27ym63e3x16NBBkrRq1SqtX79eWVlZ2rZtm1q1aqW0tDS5XC5J0syZM1VaWqoNGzZo7dq1ysvLU0ZGhiTp7NmzGjt2rG6//Xbt2LFDmZmZWrZsmTZv3izpx/A1depUTZkyRZ9%2B%2BqlSU1P1xBNP6Pjx49XaNwAAwOXUWMAKCgpSdnb2RQPWpdhsNqWmpqpNmzYKDQ1Venq68vLytHv3bp04cUJbtmxRenq6rFarmjdvrgkTJmjt2rUqKytTTk6OysrKNH78eAUHBys%2BPl7Dhg2TzWaTJK1Zs0bJyclKTk5WUFCQBg8erPbt22vdunWX3TcAAEB11FjAGj16tBo3bnzR%2BWPHjumRRx5R165d1adPH73//vuSfjwDdeDAAcXFxbmXDQ0NVcuWLWW327Vv3z4FBAQoJibGPR8fH68ffvhBBw8eVG5urmJiYhQQEOCej4uL0549eyRJubm5Htu%2BMG%2B32y%2B7bwAAgOqw%2BGKnVqtVrVq10tNPP622bdvq73//u5599llFRUXppptuksvlUnh4uMc64eHhcjgcioiIUGhoqPz8/DzmJMnhcMjpdCosLMxj3YiICDmdTlVWVsrpdHrd9oEDB3T69OlL7ru6CgoKVFhY6DFmsQQrKiqq2tuoCQEB/h7/moHZejZbv5L5ejZbv5I5ezaKxVI3XrP6eIx9ErB69uypnj17ur8fOHCg/v73v%2Bvdd9/VlClTJOmS9zz9kvuhfhrILrf%2B1d5vZbPZtGjRIo%2BxtLQ0TZw48aq2a5SwsEa%2BLqHWma1ns/Urma9ns/UrmbPnqxUZGeLrEq5IfTrGPglY3kRHR2vPnj2KiIiQv7%2B/nE6nx7zT6VSTJk1ktVpVXFysiooK92XAC8temD98%2BHCVdS9sNzIy0uu2rVbrZfddXSNGjFDv3r09xiyWYDkcJdXeRk0ICPBXWFgjFRWVqqKi0qe11Baz9Wy2fiXz9Wy2fiVz9mwUX3/uVFdNHmNfhUyfBKzVq1crPDxcAwYMcI/l5eWpRYsWCgoKUrt27ZSbm6vbbrtNklRUVKQjR46oQ4cOio6Olsvl0v79%2BxUfHy9JstvtCgsLU%2BvWrZWQkKDVq1ervLxcFovFPd%2BxY0dJUkJCgvt%2BrAvsdrsGDhx42X1XV1RUVJXLgYWFZ1Refm28MVRUVF4ztdQWs/Vstn4l8/Vstn4lc/Z8tera61WfjrFPLnaeP39eL7zwgux2u8rKyrRhwwb94x//0MiRIyVJKSkpWrlypfLy8lRcXKyMjAzFxsYqMTFRVqtV/fv316uvvqpTp07p%2BPHjWrx4sYYOHSqLxaLk5GSFhoZqyZIlKi0t1e7du5Wdna2UlBRJ0vDhw/XJJ58oJydH586dU3Z2tg4fPqzBgwdfdt8AAADVUWNnsC4EkvLycknSli1bJP14tmj06NEqKSnRU089pcLCQt14441avHixEhISJEkjR45UYWGhHnroIZWUlKhbt24e9zQ9//zzmjVrlvr06aMGDRronnvucT/MNDAwUEuXLtWsWbOUlZWlpk2bKj093X3PV/v27ZWRkaF58%2BYpPz9fbdu21bJly9SsWbNq7RsAAOBy/Fw8QbNWFBae8XUJslj8FRkZIoejpN6cgr0cs/Vstn4l8/Vstn6la6vnu1/92Kf7v1IfTuru6xKqpSaPcbNmF39kVE2qP78PCQAAcI0gYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABqvRgLVjxw4lJSUpPT29ytzmzZs1ePBgderUSf3799c777zjnlu4cKFiY2OVmJjo8XXixAlJ0rlz5/T73/9ed9xxh7p166aJEyfK4XC418/Pz9fjjz%2Bubt26qVevXpo/f74qKyvd87t27dLQoUPVuXNnDRw4UOvWrfOobeXKlerfv786d%2B6slJQU7dmzx%2BiXBgAA1GM1FrDeeOMNzZ07Vy1btqwy99VXX2nKlCmaOHGiPvvsM02fPl3PP/%2B8Pv/8c/cy9957r%2Bx2u8dX06ZNJUmZmZnKzc2VzWbTpk2b5HK5NG3aNPe6Tz75pJo3b64tW7ZoxYoV2rJli9566y1JUkFBgSZMmKCRI0dq165dmjFjhmbOnCm73S5J2rp1qxYuXKg//OEP%2BuSTT9SrVy%2BNGzdOP/zwQ029VAAAoJ6psYAVFBSk7OxsrwHL6XRq7Nix6tu3rywWi5KTk9W%2BfXuPgHUx5eXlys7O1oQJE3T99dcrIiJCkyZNUk5Ojr7//nvZ7Xbt379fU6ZMUePGjdWqVSulpqbKZrNJktavX69WrVpp6NChCgoKUlJSknr37q01a9ZIkmw2m4YMGaKOHTuqYcOGeuyxxyRJ27ZtM/DVAQAA9VmNBazRo0ercePGXufuuOMOpaWlub8vLy9XYWGhmjdv7h77%2BuuvNXLkSPdlvJ07d0qSjhw5ojNnzig%2BPt69bJs2bdSwYUPl5uYqNzdX0dHRCg8Pd8/Hx8fr0KFDKi4uVm5uruLi4jzqiYuLc18G/Pm8v7%2B/YmNj3We4AAAALsfi6wIkKSMjQ8HBwRowYIAk6brrrlOLFi00efJkRUVFyWazady4cVq3bp2cTqckKSwszGMbYWFhcjgccjqdVeYuhK0L8z8NcpIUERHhvofL6XR6hLML6//0Hq/LKSgoUGFhoceYxRKsqKioam%2BjJgQE%2BHv8awZm69ls/Urm69ls/Urm7NkoFkvdeM3q4zH2acByuVzKyMjQhg0btHLlSgUFBUmShg0bpmHDhrmXS01N1QcffKB169bpjjvucK97qe1ebV1Xw2azadGiRR5jaWlpmjhx4lVt1yhhYY18XUKtM1vPZutXMl/PZutXMmfPVysyMsTXJVyR%2BnSMfRawKisrNW3aNH311VdavXq1WrRoccnlo6OjVVBQIKvVKunHM00hIf/5D%2Bf06dNq0qSJKioq3Ge5LnA6nfLz85PValVkZGSVeYfD4d6ut3mn06l27dpVu7cRI0aod%2B/eHmMWS7AcjpJqb6MmBAT4KyyskYqKSlVRUXn5FeoBs/Vstn4l8/Vstn4lc/ZsFF9/7lRXTR5jX4VMnwWsl156Sd9%2B%2B61Wr16tiIgIj7k//elP6tSpk37961%2B7x/Ly8jRgwAC1aNFC4eHh7nutJOmbb77R%2BfPnlZCQoIKCAh07dkynTp1yhya73a62bdsqJCREiYmJWrt2rcf%2B9uzZo44dO0qSEhISlJubq/vvv1%2BSVFFRob1792ro0KHV7i0qKqrK5cDCwjMqL7823hgqKiqvmVpqi9l6Nlu/kvl6Nlu/kjl7vlp17fWqT8fYJxc7v/jiC61bt05ZWVlVwpX04xmjOXPm6ODBgzp37pyWL1%2BuI0eO6P7771dAQICGDx%2BupUuX6tixY3I4HPrjH/%2BoO%2B%2B8U02bNlVcXJwSExO1YMECFRcXKy8vTytWrFBKSookadCgQcrPz9eaNWt07tw5bd%2B%2BXdu3b9fw4cMlSSkpKXrvvff05ZdfqrS0VEuWLFFgYKB69uxZmy8RAACow2rsDFZiYqKkH39DUJK2bNki6cezSWvXrtWZM2fUq1cvj3W6du2q5cuXa/LkyZJ%2BvPfK6XSqbdu2%2Bstf/qLrrrtOkjRx4kSVlJTo3nvvVXl5uXr16qXZs2e7t/P6669r5syZ6t69u0JDQzVy5Eg9%2BOCDkqQmTZpo2bJlmjt3rubMmaPo6GjNnz9fN998s6Qff8Px6aef1qRJk3Ty5EklJiYqKytLDRs2rKmXCgAA1DN%2Brqu9oxvVUlh4xtclyGLxV2RkiByOknpzCvZyzNaz2fqVzNez2fqVrq2e7371Y5/u/0p9OKm7r0uolpo8xs2aeX9kVE2rP78PCQAAcI0gYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwbwGrN69e2vRokU6duxYbdcDAABQ53kNWA888IA2btyovn376rHHHtPmzZtVXl5e27UBAADUSV4DVlpamjZu3Kh33nlH7dq100svvaTk5GTNnz9fhw4dqu0aAQAA6pRL3oMVHx%2BvqVOnatu2bZo%2BfbreeecdDRgwQGPGjNFXX31VWzUCAADUKZcMWGVlZdq4caN%2B%2B9vfaurUqWrevLmmTZum2NhYpaamav369bVVJwAAQJ1h8TaYl5en7OxsvffeeyopKVH//v311ltvqUuXLu5lunbtqtmzZ2vQoEG1ViwAAEBd4DVgDRw4UK1bt9bYsWN13333KSIiosoyycnJOnXqVI0XCAAAUNd4DVgrV67UbbfddtmVd%2B/ebXhBAAAAdZ3Xe7BiYmI0btw4bdmyxT32l7/8Rb/97W/ldDprrTgAAIC6yGvAmjdvns6cOaO2bdu6x3r27KnKykq9/PLLtVYcAABAXeT1EuHOnTu1fv16RUZGusdatWqljIwM3XPPPbVWHAAAQF3k9QzW2bNnFRQUVHVhf3%2BVlpbWeFEAAAB1mdeA1bVrV7388ss6ffq0e%2Bz777/XnDlzPB7VAAAAgKq8Bqzp06dr165d%2BvWvf63bbrtNt956q3r27Kk9e/Zo7ty51d74jh07lJSUpPT09CpzGzdu1KBBg9SpUycNGTJEO3fudM9VVlYqMzNTffr0UdeuXTVmzBgdPXrUPe90OjVp0iQlJSWpR48emjFjhs6ePeue37dvn0aNGqUuXbqoX79%2BWr58uWH7BgAAuByvAatFixb64IMP9Prrr2vcuHF64okntHTpUn344Ydq3rx5tTb8xhtvaO7cuWrZsmWVuX379mnq1KmaMmWKPv30U6WmpuqJJ57Q8ePHJUmrVq3S%2BvXrlZWVpW3btqlVq1ZKS0uTy%2BWSJM2cOVOlpaXasGGD1q5dq7y8PGVkZEj68fLm2LFjdfvtt2vHjh3KzMzUsmXLtHnzZkP2DQAAcDkX/VM5gYGB6tu3rx599FGlpqYqOTlZAQEB1d5wUFCQsrOzvQasNWvWKDk5WcnJyQoKCtLgwYPVvn17rVu3TpJks9mUmpqqNm3aKDQ0VOnp6crLy9Pu3bt14sQJbdmyRenp6bJarWrevLkmTJigtWvXqqysTDk5OSorK9P48eMVHBys%2BPh4DRs2TDab7ar3DQAAUB1ef4vw6NGjWrBggb799luPS28XfPTRR5fd8OjRoy86l5ubq%2BTkZI%2BxuLg42e12nT17VgcOHFBcXJx7LjQ0VC1btpTdbteZM2cUEBCgmJgY93x8fLx%2B%2BOEHHTx4ULm5uYqJifEIg3FxcVqzZs1V7/uWW265bN8AAABeA9b06dNVUFCgHj16KDg42PCdOp1OhYeHe4yFh4frwIEDOn36tFwul9d5h8OhiIgIhYaGys/Pz2NOkhwOh5xOp8LCwjzWjYiIkNPpVGVl5VXtu7oKCgpUWFjoMWaxBCsqKqra26gJAQH%2BHv%2Bagdl6Nlu/kvl6Nlu/kjl7NorFUjdes/p4jL0GrD179uijjz6S1WqtsR1f7p6mS83/kvuhfhrIrmbf1WGz2bRo0SKPsbS0NE2cOPGqtmuUsLBGvi6h1pmtZ7P1K5mvZ7P1K5mz56sVGRni6xKuSH06xl4DVpMmTWrkzNUFkZGRVf7kjtPplNVqVUREhPz9/b3ON2nSRFarVcXFxaqoqHBfBryw7IX5w4cPV1n3wnavZt/VNWLECPXu3dtjzGIJlsNRUu1t1ISAAH%2BFhTVSUVGpKioqfVpLbTFbz2brVzJfz2brVzJnz0bx9edOddXkMfZVyPQasMaOHatFixZp8uTJHmd%2BjJKQkKA9e/Z4jNntdg0cOFBBQUFq166dcnNz3X9wuqioSEeOHFGHDh0UHR0tl8ul/fv3Kz4%2B3r1uWFiYWrdurYSEBK1evVrl5eWyWCzu%2BY4dO171vqsrKiqqyuXAwsIzKi%2B/Nt4YKioqr5laaovZejZbv5L5ejZbv5I5e75ade31qk/H2OvFzn/84x/6n//5H3Xv3l3Dhw/XyJEjPb6u1vDhw/XJJ58oJydH586dU3Z2tg4fPqzBgwdLklJSUrRy5Url5eWpuLhYGRkZio2NVWJioqxWq/r3769XX31Vp06d0vHjx7V48WINHTpUFotFycnJCg0N1ZIlS1RaWqrdu3crOztbKSkpV71vAACA6vB6Bis0NFR33HHHVW34QiApLy%2BXJG3ZskXSj2eL2rdvr4yMDM2bN0/5%2Bflq27atli1bpmbNmkmSRo4cqcLCQj300EMqKSlRt27dPO5pev755zVr1iz16dNHDRo00D333ON%2BmGlgYKCWLl2qWbNmKSsrS02bNlV6erp69uwpSVe9bwAAgMvxc/EEzVpRWHjG1yXIYvFXZGSIHI6SenMK9nLM1rPZ%2BpXM17PZ%2BpWurZ7vfvVjn%2B7/Sn04qbuvS6iWmjzGzZo1NnR71XXR34c8ePCgFi5cqGnTprnH/v3vf9dKUQAAAHWZ14C1a9cuDR48WJs3b9aGDRsk/fjw0dGjR1frIaMAAABm5jVgZWZm6plnntH69evdv0XYokULvfzyy1q8eHGtFggAAFDXeA1Y33zzjfu37n76mIa77rpLeXl5tVMZAABAHeU1YDVu3Njr3yAsKChQYGBgjRcFAABQl3kNWJ07d9ZLL72k4uJi99ihQ4c0depU/frXv6614gAAAOoir8/BmjZtmh5%2B%2BGF169ZNFRUV6ty5s0pLS9WuXTu9/PLLtV0jAABAneI1YF133XXasGGDtm/frkOHDqlhw4Zq3bq1unfvXiN/OgcAAKA%2B8RqwJKlBgwbq27dvbdYCAABQL3gNWL17977kmSqehQUAAHBxXgPWgAEDPAJWRUWFDh06JLvdrocffrjWigMAAKiLvAasKVOmeF1406ZN%2Buc//1mjBQEAANR1F/1bhN707dtXH3zwQU3VAgAAUC9cUcDau3evXC5XTdUCAABQL3i9RDhy5MgqY6WlpcrLy1O/fv1qvCgAAIC6zGvAatWqVZXfIgwKCtLQoUM1bNiwWikMAACgrvIasHhaOwAAwC/nNWC999571d7AfffdZ1gxAAAA9YHXgDVjxgxVVlZWuaHdz8/PY8zPz4%2BABQAA8DNeA9abb76p5cuXa9y4cYqJiZHL5dLXX3%2BtN954Q6NGjVK3bt1qu04AAIA646L3YGVlZal58%2BbusVtvvVUtWrTQmDFjtGHDhlorEAAAoK7x%2Bhysw4cPKzw8vMp4WFiY8vPza7woAACAusxrwIqOjtbLL78sh8PhHisqKtKCBQv0q1/9qtaKAwAAqIu8XiKcPn26Jk%2BeLJvNppCQEPn7%2B6u4uFgNGzbU4sWLa7tGAACAOsVrwOrRo4dycnK0fft2HT9%2BXC6XS82bN9dvfvMbNW7cuLZrBAAAqFO8BixJatSokfr06aPjx4%2BrRYsWtVkTAABAneb1HqyzZ89q6tSp6tSpk%2B6%2B%2B25JP96D9dhjj6moqKhWCwQAAKhrvAas%2BfPna9%2B%2BfcrIyJC//38Wqag3DH/%2BAAAgAElEQVSoUEZGRq0VBwAAUBd5DVibNm3S66%2B/rrvuusv9R5/DwsI0b948bd68uVYLBAAAqGu8BqySkhK1atWqyrjVatUPP/xQ0zUBAADUaV4D1q9%2B9Sv985//lCSPvz34t7/9TTfccEPtVAYAAFBHef0twgcffFBPPvmkHnjgAVVWVmrFihXas2ePNm3apBkzZtR2jbiEu1/92NclVNuHk7r7ugQAAGqF14A1YsQIWSwWvf322woICNDSpUvVunVrZWRk6K677qrtGgEAAOoUrwHr1KlTeuCBB/TAAw/U2I4/%2B%2BwzPfroox5jLpdLZWVlWrlypUaPHq3AwECP%2BT/84Q/ux0asXLlSq1atUmFhoWJiYjRjxgwlJCRIks6dO6cXX3xROTk5OnfunLp166Y5c%2BYoMjJSkpSfn685c%2BZo9%2B7dCg4O1oABAzR58mT3b0zu2rVLCxYs0MGDB3X99ddr7NixGjx4cI29FgAAoH7xGrD69Omjf/3rX%2B7fIKwJXbt2ld1u9xhbunSp9u/fL%2BnHv4e4detWr%2Btu3bpVCxcu1JtvvqmYmBitXLlS48aN0%2BbNmxUcHKzMzEzl5ubKZrOpUaNGmjlzpqZNm6alS5dKkp588knFx8dry5YtOnnypMaOHaumTZvqkUceUUFBgSZMmKAZM2Zo0KBB%2BuKLLzR%2B/Hi1bt1aiYmJNfZ6AACA%2BsPrTe7dunXThx9%2BWKuF/N///Z9WrFihZ5999rLL2mw2DRkyRB07dlTDhg312GOPSZK2bdum8vJyZWdna8KECbr%2B%2BusVERGhSZMmKScnR99//73sdrv279%2BvKVOmqHHjxmrVqpVSU1Nls9kkSevXr1erVq00dOhQBQUFKSkpSb1799aaNWtqtH8AAFB/eD2Ddf311%2BvFF19UVlaWfvWrX6lBgwYe8wsWLDC8kNdee00PPPCAbrjhBh09elQlJSVKS0vT559/rsDAQD366KNKTU2Vn5%2BfcnNzNWDAAPe6/v7%2Bio2Nld1uV2xsrM6cOaP4%2BHj3fJs2bdSwYUPl5uaqoKBA0dHRCg8Pd8/Hx8fr0KFDKi4uVm5uruLi4jxqi4uLu6LAWVBQoMLCQo8xiyVYUVFRV/qy1CsWi9c8X6MCAvw9/q3vzNavZL6ezdavZM6ejeKL991foj4eY68B68CBA7rpppskSQ6Ho8aL%2BO6777R582b3Q0xDQ0PVvn17Pfzww8rMzNT//u//6qmnnlLjxo01dOhQOZ1Oj4AkSeHh4XI4HHI6nZJ%2BfDDqT4WFhbnnfz53YVsX5ps3b%2B4xHxERcUWvg81m06JFizzG0tLSNHHixGpvoz6KjAzx2b7Dwhr5bN%2B%2BYLZ%2BJfP1bLZ%2BJXP2fLV8%2Bb77S9SnY%2BwRsNLT05WZman/%2Bq//co8tXrxYaWlpNVrEqlWr1K9fPzVr1kzSj2eUflpDjx49NHLkSL377rsaOnSoJM/nc3lzqfnLrXu1RowYod69e3uMWSzBcjhKanS/1zpf9B8Q4K%2BwsEYqKipVRUVlre%2B/tpmtX8l8PZutX8mcPRulrnzu1OQx9lXI9AhY3m4qz8rKqvGAtWnTJk2dOvWSy0RHR2vTpk2SpMjISPeZqgucTqfatWsnq9Xq/j4k5D8v6unTp9WkSRNVVFR4XdfPz09Wq9Xrth0Oh3u71REVFVXlcmBh4RmVl5v7jcGX/VdUVJrq9Tdbv5L5ejZbv5I5e75ade31qk/H2ONip7czOzV9tmffvn3Kz89X9%2B7/eQjlhx9%2BqL/%2B9a8eyx08eFAtWrSQJCUkJCg3N9c9V1FRob1796pjx45q0aKFwsPDPea/%2BeYbnT9/XgkJCUpISNCxY8d06tQp97zdblfbtm0VEhKixMRE7dmzx2Pfe/bsUceOHQ3tGwAA1F8eAcvbYxlq8lENkrR3715FREQoNDTUPdagQQO98sor2rlzp8rKyvTxxx9r7dq1SklJkSSlpKTovffe05dffqnS0lItWbJEgYGB6tmzpwICAjR8%2BHAtXbpUx44dk8Ph0B//%2BEfdeeedatq0qeLi4pSYmKgFCxaouLhYeXl5WrFihXvbgwYNUn5%2BvtasWaNz585p%2B/bt2r59u4YPH16jrwMAAKg/vN7kXptOnDjhvvfqgr59%2B2r69Ol64YUXdOzYMTVt2lTTp09Xv379JEl33HGHnn76aU2aNEknT55UYmKisrKy1LBhQ0nSxIkTVVJSonvvvVfl5eXq1auXZs%2Be7d7%2B66%2B/rpkzZ6p79%2B4KDQ3VyJEj9eCDD0qSmjRpomXLlmnu3LmaM2eOoqOjNX/%2BfN18882184IAAIA6z8/1k2uAHTt21O7duz0W8DaGK1dYeKZGtsvfIrw0i8VfkZEhcjhK6s11/UsxW7%2BS%2BXo2W7/StdVzXXrPlerO34CtyWPcrFljQ7dXXR5nsMrKyjR58mRdbqwmnoMFAABQX3gErC5duqigoECXGwMAAMDFeQSsnz57CgAAAL9M/XkmPQAAwDWCgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMJ8FrJiYGCUkJCgxMdH99cILL0iSdu3apaFDh6pz584aOHCg1q1b57HuypUr1b9/f3Xu3FkpKSnas2ePe%2B7cuXP6/e9/rzvuuEPdunXTxIkT5XA43PP5%2Bfl6/PHH1a1bN/Xq1Uvz589XZWWle/5y%2BwYAALgciy93/re//U033nijx1hBQYEmTJigGTNmaNCgQfriiy80fvx4tW7dWomJidq6dasWLlyoN998UzExMVq5cqXGjRunzZs3Kzg4WJmZmcrNzZXNZlOjRo00c%2BZMTZs2TUuXLpUkPfnkk4qPj9eWLVt08uRJjR07Vk2bNtUjjzxy2X0DAABUxzV3iXD9%2BvVq1aqVhg4dqqCgICUlJal3795as2aNJMlms2nIkCHq2LGjGjZsqMcee0yStG3bNpWXlys7O1sTJkzQ9ddfr4iICE2aNEk5OTn6/vvvZbfbtX//fk2ZMkWNGzdWq1atlJqaKpvNVq19AwAAVIdPz2AtWLBA//73v1VcXKy7775bzz33nHJzcxUXF%2BexXFxcnD788ENJUm5urgYMGOCe8/f3V2xsrOx2u2JjY3XmzBnFx8e759u0aaOGDRsqNzdXBQUFio6OVnh4uHs%2BPj5ehw4dUnFx8WX3XV0FBQUqLCz0GLNYghUVFXVF26lvLJbaz/MBAf4e/9Z3ZutXMl/PZutXMmfPRvHF%2B%2B4vUR%2BPsc8C1i233KKkpCS98sorOnr0qCZNmqQ5c%2BbI6XSqefPmHstGRES476NyOp0eAUmSwsPD5XA45HQ6JUlhYWEe82FhYe75n89d2NaF%2BUvtu7psNpsWLVrkMZaWlqaJEyde0Xbqm8jIEJ/tOyyskc/27Qtm61cyX89m61cyZ89Xy5fvu79EfTrGPgtYFy7LST%2BeZZoyZYrGjx%2BvLl26XHZdl8v1i%2Bcvt64RRowYod69e3uMWSzBcjhKanzf1zJf9B8Q4K%2BwsEYqKipVRUXl5Veo48zWr2S%2Bns3Wr2TOno1SVz53avIY%2Bypk%2BvQS4U/deOONqqiokL%2B/v/tM1AUOh0NWq1WSFBkZWWXe6XSqXbt27mWcTqdCQv7zgp4%2BfVpNmjRRRUWF13X9/PxktVq9bvun%2B66uqKioKpcDCwvPqLzc3G8Mvuy/oqLSVK%2B/2fqVzNez2fqVzNnz1aprr1d9OsY%2Budi5d%2B9evfzyyx5jeXl5CgwMVHJyssdjFyRpz5496tixoyQpISFBubm57rmKigrt3btXHTt2VIsWLRQeHu4x/8033%2Bj8%2BfNKSEhQQkKCjh07plOnTrnn7Xa72rZtq5CQECUmJl5y3wAAANXhk4DVpEkT2Ww2ZWVl6fz58zp06JBee%2B01jRgxQvfee6/y8/O1Zs0anTt3Ttu3b9f27ds1fPhwSVJKSoree%2B89ffnllyotLdWSJUsUGBionj17KiAgQMOHD9fSpUt17NgxORwO/fGPf9Sdd96ppk2bKi4uTomJiVqwYIGKi4uVl5enFStWKCUlRZI0aNCgS%2B4bAACgOnxyibB58%2BbKysrSggUL3AHp/vvvV3p6uoKCgrRs2TLNnTtXc%2BbMUXR0tObPn6%2Bbb75ZknTHHXfo6aef1qRJk3Ty5EklJiYqKytLDRs2lCRNnDhRJSUluvfee1VeXq5evXpp9uzZ7n2//vrrmjlzprp3767Q0FCNHDlSDz74oKQfg9%2Bl9g0AAFAdfq7auOsbKiw8UyPbvfvVj2tkuzXhw0nda32fFou/IiND5HCU1Jvr%2Bpditn4l8/Vstn6la6vnuvSeK/nmffeXqMlj3KxZY0O3V13154ETAAAA1wgCFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAaz%2BLoAAABQM%2B5%2B9WNfl1Btn794l69LMBRnsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAM5tOAlZ%2Bfr7S0NHXr1k1JSUl67rnnVFRUpO%2B%2B%2B04xMTFKTEz0%2BPrzn//sXnfjxo0aNGiQOnXqpCFDhmjnzp3uucrKSmVmZqpPnz7q2rWrxowZo6NHj7rnnU6nJk2apKSkJPXo0UMzZszQ2bNn3fP79u3TqFGj1KVLF/Xr10/Lly%2BvnRcEAADUCz4NWOPGjVNYWJi2bt2qd999V99%2B%2B61eeeUV97zdbvf4GjNmjKQfA9DUqVM1ZcoUffrpp0pNTdUTTzyh48ePS5JWrVql9evXKysrS9u2bVOrVq2UlpYml8slSZo5c6ZKS0u1YcMGrV27Vnl5ecrIyJAknT17VmPHjtXtt9%2BuHTt2KDMzU8uWLdPmzZtr%2BdUBAAB1lc8CVlFRkRISEjR58mSFhITouuuu0/3336/PP//8suuuWbNGycnJSk5OVlBQkAYPHqz27dtr3bp1kiSbzabU1FS1adNGoaGhSk9PV15ennbv3q0TJ05oy5YtSk9Pl9VqVfPmzTVhwgStXbtWZWVlysnJUVlZmcaPH6/g4GDFx8dr2LBhstlsNf2SAACAesLiqx2HhYVp3rx5HmPHjh1TVFSU%2B/tnn31Wn3zyicrLyzVs2DBNnDhRDRo0UG5urpKTkz3WjYuLk91u19mzZ3XgwAHFxcW550JDQ9WyZUvZ7XadOXNGAQEBiomJcc/Hx8frhx9%2B0MGDB5Wbm6uYmBgFBAR4bHvNmjXV7q2goECFhYUeYxZLsEdvZmSx1H6eDwjw9/i3vjNbv5L5ejZbv5I5ezar%2BnSMfRawfs5ut%2Bvtt9/WkiVLFBgYqE6dOunOO%2B/Uiy%2B%2BqH379unJJ5%2BUxWLRU089JafTqfDwcI/1w8PDdeDAAZ0%2BfVoul8vrvMPhUEREhEJDQ%2BXn5%2BcxJ0kOh0NOp1NhYWEe60ZERMjpdKqyslL%2B/pc/%2BDabTYsWLfIYS0tL08SJE6/oNalvIiNDfLbvsLBGPtu3L5itX8l8PZutX8mcPZtNfTrG10TA%2BuKLLzR%2B/HhNnjxZSUlJkqT//u//ds936NBBY8eO1bJly/TUU09Jkvt%2Bqou51Pzl1vXmp4HsckaMGKHevXt7jFkswXI4Sq54v/WJL/oPCPBXWFgjFRWVqqKistb3X9vM1q9kvp7N1q9kzp7NqiaOsa/%2B597nAWvr1q165plnNHPmTN13330XXS46OlonTpyQy%2BVSZGSknE6nx7zT6ZTValVERIT8/f29zjdp0kRWq1XFxcWqqKhwXwa8sOyF%2BcOHD1dZ98J2qyMqKqrK5cDCwjMqLzf3G4Mv%2B6%2BoqDTV62%2B2fiXz9Wy2fiVz9mw29ekY%2B/Ri57/%2B9S9NnTpVr732mke42rVrl5YsWeKx7MGDBxUdHS0/Pz8lJCRoz549HvN2u10dO3ZUUFCQ2rVrp9zcXPdcUVGRjhw5og4dOig2NlYul0v79%2B/3WDcsLEytW7dWQkKCvv76a5WXl1fZNgAAQHX4LGCVl5frd7/7naZMmaIePXp4zDVu3FiLFy/W%2B%2B%2B/r7KyMtntdv35z39WSkqKJGn48OH65JNPlJOTo3Pnzik7O1uHDx/W4MGDJUkpKSlauXKl8vLyVFxcrIyMDMXGxioxMVFWq1X9%2B/fXq6%2B%2BqlOnTun48eNavHixhg4dKovFouTkZIWGhmrJkiUqLS3V7t27lZ2d7d43AADA5fjsEuGXX36pvLw8zZ07V3PnzvWY%2B9vf/qbMzEwtWrRIv//979W4cWM99NBDevjhhyVJ7du3V0ZGhubNm6f8/Hy1bdtWy5YtU7NmzSRJI0eOVGFhoR566CGVlJSoW7duHjedP//885o1a5b69OmjBg0a6J577lF6erokKTAwUEuXLtWsWbOUlZWlpk2bKj09XT179qydFwYAANR5fq5fcsc3rlhh4Zka2e7dr35cI9utCR9O6l7r%2B7RY/BUZGSKHo6TeXNe/FLP1K5mvZ7P1K11bPdel99y65vMX76qRY9ysWWNDt1dd9eeBEwAAANcIAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBLL4uAACudXe/%2BrGvS7giH07q7usSANPjDBYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABuMxDV7k5%2Bdrzpw52r17t4KDgzVgwABNnjxZ/v7kUQDXvrr0WAkeKYH6ioDlxZNPPqn4%2BHht2bJFJ0%2Be1NixY9W0aVM98sgjvi4NAADUAZyS%2BRm73a79%2B/drypQpaty4sVq1aqXU1FTZbDZflwYAAOoIzmD9TG5urqKjoxUeHu4ei4%2BP16FDh1RcXKzQ0NDLbqOgoECFhYUeYxZLsKKiogyvty6xWGo/zwcE%2BHv8W9%2BZrV/JnD3XJ9V5X%2BAYm0d9OsYErJ9xOp0KCwvzGLsQthwOR7UCls1m06JFizzGnnjiCT355JPGFfr/%2B/zFu6q9bEFBgWw2m0aMGGGasFdQUKC33nrTND2brV%2Bpdnq%2Bkp%2BzmsbPsW97rq3/Fsx2nAsKCrRw4cJ61W/9iYoGcrlcV7X%2BiBEj9O6773p8jRgxwqDqfrnCwkItWrSoytm1%2BsxsPZutX8l8PZutX4mezaA%2B9ssZrJ%2BxWq1yOp0eY06nU35%2BfrJardXaRlRUVL1J4AAA4MpxButnEhISdOzYMZ06dco9Zrfb1bZtW4WEhPiwMgAAUFcQsH4mLi5OiYmJWrBggYqLi5WXl6cVK1YoJSXF16UBAIA6ImD27NmzfV3EteY3v/mNNmzYoBdeeEEffPCBhg4dqjFjxsjPz8/XpV21kJAQ3XbbbaY6G2e2ns3Wr2S%2Bns3Wr0TPZlDf%2BvVzXe0d3QAAAPDAJUIAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsOqZ/Px8paWlqVu3bkpKStJzzz2noqKiKsu9%2B%2B67uvnmm5WYmOjx9dVXX/mg6l8uJiZGCQkJHj288MILXpdduXKl%2Bvfvr86dOyslJUV79uyp5Wqv3meffVblmCUkJCgmJqbKsgsXLlRsbGyV5U%2BcOOGDyq/Mjh07lJSUpPT09CpzGzdu1KBBg9SpUycNGTJEO3fuvOh2nE6nJk2apKSkJPXo0UMzZszQ2bNna7L0X%2BxSPW/evFmDBw9Wp06d1L9/f73zzjsX3c5DDz2k%2BPh4j2M%2BePDgmiz9F7tYz1f6/lRXjvPF%2Bv3d735Xpde4uDhNmzbN63Z69%2B5d5X1v3LhxtdHCFbnU59G%2Bffs0atQodenSRf369dPy5csvup3KykplZmaqT58%2B6tq1q8aMGaOjR4/WVhu/nAv1yj333ON67rnnXMXFxa5jx465hgwZ4po%2BfXqV5dauXesaNWqUDyo0Vvv27V1Hjx697HIfffSR69Zbb3V9%2BeWXrtLSUteyZctc3bt3d5WUlNRClTVryZIlrqeeeqrK%2BOuvv%2B6aOnWqDyq6OllZWa5%2B/fq5Ro4c6Zo0aZLH3N69e10JCQmunJwc19mzZ13vv/%2B%2Bq2PHjq5jx4553dYTTzzhevzxx10nT550HT9%2B3DVixAjXCy%2B8UBttXJFL9bx7925XYmKi6%2B9//7urrKzMlZOT44qPj3d99tlnXrc1atQo19q1a2uj7KtyqZ6v9P2pLhznS/X7c2VlZa6BAwe6cnJyvM736tXL9emnn9ZEmYa62OdRaWmp6ze/%2BY1r4cKFrpKSEteePXtct912m2vTpk1et7Ny5UpXr169XAcOHHCdOXPG9fzzz7sGDRrkqqysrOWOrgxnsOqRoqIiJSQkaPLkyQoJCdF1112n%2B%2B%2B/X59//rmvS/M5m82mIUOGqGPHjmrYsKEee%2BwxSdK2bdt8XNnV%2Bb//%2Bz%2BtWLFCzz77rK9LMUxQUJCys7PVsmXLKnNr1qxRcnKykpOTFRQUpMGDB6t9%2B/Zat25dlWVPnDihLVu2KD09XVarVc2bN9eECRO0du1alZWV1UYr1Xapnp1Op8aOHau%2BffvKYrEoOTlZ7du3r/M/15fq%2BUrUleN8Jf2%2B9dZbuuGGG5ScnFwLldWMS30e5eTkqKysTOPHj1dwcLDi4%2BM1bNgw2Ww2r9uy2WxKTU1VmzZtFBoaqvT0dOXl5Wn37t213NWVIWDVI2FhYZo3b56aNm3qHjt27JiioqK8Ln/s2DE98sgj6tq1q/r06aP333%2B/tko11IIFC9SzZ0/deuutmjlzpkpKSqosk5ubq7i4OPf3/v7%2Bio2Nld1ur81SDffaa6/pgQce0A033OB1/uuvv9bIkSPVuXNnDRw48JKX064Vo0ePVuPGjb3O/fw4SlJcXJzX47hv3z4FBAR4XD6Nj4/XDz/8oIMHDxpb9FW6VM933HGH0tLS3N%2BXl5ersLBQzZs3v%2Bj2Nm7cqAEDBqhTp05KTU3VkSNHDK/5al2qZ6n670915Thfrt8LioqKtHTpUj3zzDOXXG7lypXq27evOnXqpIkTJ%2BrkyZNGlWqIS30e5ebmKiYmRgEBAe65uLg4r7dtnD17VgcOHPD4uQ8NDVXLli2v%2BfdvAlY9Zrfb9fbbb2v8%2BPFV5qxWq1q1aqVnnnlGH3/8sZ5%2B%2BmlNnz5du3bt8kGlv9wtt9yipKQkbd68WTabTV9%2B%2BaXmzJlTZTmn06nw8HCPsfDwcDkcjtoq1XDfffedNm/erEceecTr/HXXXacWLVrolVde0ccff6xhw4Zp3Lhx19SHzpW6kuPodDoVGhoqPz8/j2Ul1enjnpGRoeDgYA0YMMDrfJs2bdSuXTv99a9/1UcffSSr1arHHntM58%2Bfr%2BVKf7kreX%2Bqb8f57bffVteuXdWuXbuLLhMbG6sOHTro/fff18aNG%2BV0OvXUU0/VYpVX7qefR06nU2FhYR7zERERcjqdqqys9Bg/ffq0XC5XnXz/JmDVU1988YXGjBmjyZMnKykpqcp8z5499eabbyouLk6BgYEaOHCg7rzzTr377rs%2BqPaXs9lsGjZsmAIDA9WmTRtNmTJFGzZs8Pph4nK5fFBhzVm1apX69eunZs2aeZ0fNmyYXn/9dbVs2VKNGjVSamqqYmNjvV5Oq0uu5DjWp2Pucrk0f/58bdiwQUuWLFFQUJDX5WbPnq2pU6cqIiJCVqtVzz//vPLz8/XFF1/UcsW/3JW%2BP9WX41xRUaFVq1Zp9OjRl1xu8eLFGjt2rEJCQnT99ddr1qxZ%2Buyzz67JM5XS5T%2BPLvhpSP65uniMCVj10NatW/X4449r%2BvTpl/1B/ano6GgVFBTUYGU178Ybb1RFRUWV0%2BWRkZFyOp0eY06nU1artTbLM9SmTZvUu3fvK1qnrh/jKzmOVqtVxcXFqqio8FhWkpo0aVKzhRqssrJSzz33nLZu3arVq1frpptuqva6oaGhCg8P1/fff1%2BDFda8i/23W5%2BO82effabz58/r1ltvvaL1oqOjJema/Nn29nlktVqrnH1yOp2KiIiQv79nLLkw5u3n/lo/vgSseuZf//qXpk6dqtdee0333XffRZdbvXq1Nm7c6DGWl5enFi1a1HSJhtm7d69efvllj7G8vDwFBgZWue8sISFBubm57u8rKiq0d%2B9edezYsVZqNdq%2BffuUn5%2Bv7t27X3SZP/3pT1UuqdS1Y/xzCQkJVe7TsNvtXo9jbGysXC6X9u/f77FsWFiYWrduXeO1Gumll17St99%2Bq9WrV1/y%2BBUXF2v27NkeYerUqVM6depUnTruV/L%2BVJ%2BO80cffaTbb79dFovlosvk5%2Bdr1qxZHmfp8/LyJOmaO8YX%2BzxKSEjQ119/rfLycvfYxX6Og4KC1K5dO4/376KiIh05ckQdOnSo2QauEgGrHikvL9fvfvc7TZkyRT169Kgy//DDD7vftM6fP68XXnhBdrtdZWVl2rBhg/7xj39o5MiRtV32L9akSRPZbDZlZWXp/PnzOnTokF577TWNGDFCAQEBuuuuu9y/aWSMIW4AAAM1SURBVJWSkqL33ntPX375pUpLS7VkyRIFBgaqZ8%2Bevm3iF9q7d68iIiIUGhrqMf7Tnp1Op%2BbMmaODBw/q3LlzWr58uf6/du6epZUmDOP4/xRCihSCghYpgzZaWIjYKEEkKWwCIkYQ/Aa%2BfYCgLCZFBAsXdRER69hYCYpY2QnGPqwvCIZVSYhJMBbnKYKL4nkiRxajh%2BtXZkmYOxtmrsnOzNXVFdFotBFN9sTY2BgnJyccHx/z9PREOp3m4uLCPefp4OCAiYkJoDZLDofDrKys8PDwwO3tLaZpMjo6WncA%2B25OT0/Z29vDsiyam5vfXT8/PycSiVCtVvH7/WQyGQzDIJ/PUygUWFhYoLOzk56enga0/nM%2B6p/%2BxfsMtYlTIBB49/rreltaWjg6OiKZTFIul8nlciQSCUKhUN2ND1%2Bt3ng0ODiI3%2B9nbW2NSqVCJpMhnU4Ti8UAyOVyRCIR96yrWCzGzs4O2WyWx8dHUqmUe8bfd/azfn1S19nZGdlsFsMwMAzjzbX9/X2ur68pFApAbUdLqVRienoax3EIBAKYpklXV1cjmv4pbW1tWJbF8vKyG5ii0ah7iJ9t25TLZaC2E2tubo6ZmRnu7%2B/p7u7Gsix8Pl8jS/i0u7u7P669el3z/Pw8AFNTU%2BTzeYLBINvb27S3t39pW//WS6f5Mrs9PDwEajPcjo4OUqkUiUSCm5sbgsEgGxsb7ndRLBa5vLx0P2txcZF4PM7Q0BBNTU2MjIz88SDPRqtX8%2B7uLsVikVAo9OY9vb29bG1tUalUsG3bXaNimiZLS0uEw2Gq1Sr9/f1YlvXu0Uuj1av5o/7pJ97nevW%2BcBznza67F6/r9fl8bG5ukkwmGRgYAGB4ePh/DyVtlI/Go/X1deLxOJZl0drayuzsrDvhfX5%2BxrZt91%2B68fFxHMdhcnKSUqlEX18fq6urX13SX/v1%2ByeuHBMRERH5xr7XlEZERETkH6CAJSIiIuIxBSwRERERjylgiYiIiHhMAUtERETEYwpYIiIiIh5TwBIRERHxmAKWiIiIiMcUsEREREQ8poAlIiIi4jEFLBERERGPKWCJiIiIeEwBS0RERMRj/wFdXm5KvklGZQAAAABJRU5ErkJggg%3D%3D\"/>\n        </div>\n        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common-6332040200241203170\">\n            \n<table class=\"freq table table-hover\">\n    <thead>\n    <tr>\n        <td class=\"fillremaining\">Value</td>\n        <td class=\"number\">Count</td>\n        <td class=\"number\">Frequency (%)</td>\n        <td style=\"min-width:200px\">&nbsp;</td>\n    </tr>\n    </thead>\n    <tr class=\"\">\n        <td class=\"fillremaining\">17</td>\n        <td class=\"number\">1738138</td>\n        <td class=\"number\">58.2%</td>\n        <td>\n            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">2</td>\n        <td class=\"number\">788699</td>\n        <td class=\"number\">26.4%</td>\n        <td>\n            <div class=\"bar\" style=\"width:45%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">20</td>\n        <td class=\"number\">369586</td>\n        <td class=\"number\">12.4%</td>\n        <td>\n            <div class=\"bar\" style=\"width:22%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">12</td>\n        <td class=\"number\">60096</td>\n        <td class=\"number\">2.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:4%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">13</td>\n        <td class=\"number\">23711</td>\n        <td class=\"number\">0.8%</td>\n        <td>\n            <div class=\"bar\" style=\"width:2%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">19</td>\n        <td class=\"number\">6384</td>\n        <td class=\"number\">0.2%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">5</td>\n        <td class=\"number\">1513</td>\n        <td class=\"number\">0.1%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">3</td>\n        <td class=\"number\">54</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr>\n</table>\n        </div>\n        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme-6332040200241203170\">\n            <p class=\"h4\">Minimum 5 values</p>\n            \n<table class=\"freq table table-hover\">\n    <thead>\n    <tr>\n        <td class=\"fillremaining\">Value</td>\n        <td class=\"number\">Count</td>\n        <td class=\"number\">Frequency (%)</td>\n        <td style=\"min-width:200px\">&nbsp;</td>\n    </tr>\n    </thead>\n    <tr class=\"\">\n        <td class=\"fillremaining\">2</td>\n        <td class=\"number\">788699</td>\n        <td class=\"number\">26.4%</td>\n        <td>\n            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">3</td>\n        <td class=\"number\">54</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">5</td>\n        <td class=\"number\">1513</td>\n        <td class=\"number\">0.1%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">12</td>\n        <td class=\"number\">60096</td>\n        <td class=\"number\">2.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:8%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">13</td>\n        <td class=\"number\">23711</td>\n        <td class=\"number\">0.8%</td>\n        <td>\n            <div class=\"bar\" style=\"width:3%\">&nbsp;</div>\n        </td>\n</tr>\n</table>\n            <p class=\"h4\">Maximum 5 values</p>\n            \n<table class=\"freq table table-hover\">\n    <thead>\n    <tr>\n        <td class=\"fillremaining\">Value</td>\n        <td class=\"number\">Count</td>\n        <td class=\"number\">Frequency (%)</td>\n        <td style=\"min-width:200px\">&nbsp;</td>\n    </tr>\n    </thead>\n    <tr class=\"\">\n        <td class=\"fillremaining\">12</td>\n        <td class=\"number\">60096</td>\n        <td class=\"number\">2.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:4%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">13</td>\n        <td class=\"number\">23711</td>\n        <td class=\"number\">0.8%</td>\n        <td>\n            <div class=\"bar\" style=\"width:2%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">17</td>\n        <td class=\"number\">1738138</td>\n        <td class=\"number\">58.2%</td>\n        <td>\n            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">19</td>\n        <td class=\"number\">6384</td>\n        <td class=\"number\">0.2%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">20</td>\n        <td class=\"number\">369586</td>\n        <td class=\"number\">12.4%</td>\n        <td>\n            <div class=\"bar\" style=\"width:22%\">&nbsp;</div>\n        </td>\n</tr>\n</table>\n        </div>\n    </div>\n</div>\n</div><div class=\"row variablerow\">\n    <div class=\"col-md-3 namecol\">\n        <p class=\"h4 pp-anchor\" id=\"pp_var_click_referrer_type\">click_referrer_type<br/>\n            <small>Numeric</small>\n        </p>\n    </div><div class=\"col-md-6\">\n    <div class=\"row\">\n        <div class=\"col-sm-6\">\n            <table class=\"stats \">\n                <tr>\n                    <th>Distinct count</th>\n                    <td>7</td>\n                </tr>\n                <tr>\n                    <th>Unique (%)</th>\n                    <td>0.0%</td>\n                </tr>\n                <tr class=\"ignore\">\n                    <th>Missing (%)</th>\n                    <td>0.0%</td>\n                </tr>\n                <tr class=\"ignore\">\n                    <th>Missing (n)</th>\n                    <td>0</td>\n                </tr>\n                <tr class=\"ignore\">\n                    <th>Infinite (%)</th>\n                    <td>0.0%</td>\n                </tr>\n                <tr class=\"ignore\">\n                    <th>Infinite (n)</th>\n                    <td>0</td>\n                </tr>\n            </table>\n\n        </div>\n        <div class=\"col-sm-6\">\n            <table class=\"stats \">\n\n                <tr>\n                    <th>Mean</th>\n                    <td>1.839</td>\n                </tr>\n                <tr>\n                    <th>Minimum</th>\n                    <td>1</td>\n                </tr>\n                <tr>\n                    <th>Maximum</th>\n                    <td>7</td>\n                </tr>\n                <tr class=\"ignore\">\n                    <th>Zeros (%)</th>\n                    <td>0.0%</td>\n                </tr>\n            </table>\n        </div>\n    </div>\n</div>\n<div class=\"col-md-3 collapse in\" id=\"minihistogram1498225605498042138\">\n    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAASFJREFUeJzt3cEJAjEQQFEVS7IIe/JsTxZhT7EB%2BbCLYYO8dw/M5RMCgTmPMcYJ%2BOpy9ACwsuvRA/zC7fHafOb9vE%2BYhH/jBoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBsNxfrD3/qmAWNwgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAiE5faDsN3WnSrv533SJP9HIEyzZxnSavEKZDGrbthada7ZzmOMcfQQsCqPdAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgfkrAVzPO%2BHyQAAAAASUVORK5CYII%3D\">\n\n</div>\n<div class=\"col-md-12 text-right\">\n    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives1498225605498042138,#minihistogram1498225605498042138\"\n       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n        Toggle details\n    </a>\n</div>\n<div class=\"row collapse col-md-12\" id=\"descriptives1498225605498042138\">\n    <ul class=\"nav nav-tabs\" role=\"tablist\">\n        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles1498225605498042138\"\n                                                  aria-controls=\"quantiles1498225605498042138\" role=\"tab\"\n                                                  data-toggle=\"tab\">Statistics</a></li>\n        <li role=\"presentation\"><a href=\"#histogram1498225605498042138\" aria-controls=\"histogram1498225605498042138\"\n                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n        <li role=\"presentation\"><a href=\"#common1498225605498042138\" aria-controls=\"common1498225605498042138\"\n                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n        <li role=\"presentation\"><a href=\"#extreme1498225605498042138\" aria-controls=\"extreme1498225605498042138\"\n                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n\n    </ul>\n\n    <div class=\"tab-content\">\n        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles1498225605498042138\">\n            <div class=\"col-md-4 col-md-offset-1\">\n                <p class=\"h4\">Quantile statistics</p>\n                <table class=\"stats indent\">\n                    <tr>\n                        <th>Minimum</th>\n                        <td>1</td>\n                    </tr>\n                    <tr>\n                        <th>5-th percentile</th>\n                        <td>1</td>\n                    </tr>\n                    <tr>\n                        <th>Q1</th>\n                        <td>1</td>\n                    </tr>\n                    <tr>\n                        <th>Median</th>\n                        <td>2</td>\n                    </tr>\n                    <tr>\n                        <th>Q3</th>\n                        <td>2</td>\n                    </tr>\n                    <tr>\n                        <th>95-th percentile</th>\n                        <td>5</td>\n                    </tr>\n                    <tr>\n                        <th>Maximum</th>\n                        <td>7</td>\n                    </tr>\n                    <tr>\n                        <th>Range</th>\n                        <td>6</td>\n                    </tr>\n                    <tr>\n                        <th>Interquartile range</th>\n                        <td>1</td>\n                    </tr>\n                </table>\n            </div>\n            <div class=\"col-md-4 col-md-offset-2\">\n                <p class=\"h4\">Descriptive statistics</p>\n                <table class=\"stats indent\">\n                    <tr>\n                        <th>Standard deviation</th>\n                        <td>1.1564</td>\n                    </tr>\n                    <tr>\n                        <th>Coef of variation</th>\n                        <td>0.6288</td>\n                    </tr>\n                    <tr>\n                        <th>Kurtosis</th>\n                        <td>9.1175</td>\n                    </tr>\n                    <tr>\n                        <th>Mean</th>\n                        <td>1.839</td>\n                    </tr>\n                    <tr>\n                        <th>MAD</th>\n                        <td>0.67065</td>\n                    </tr>\n                    <tr class=\"\">\n                        <th>Skewness</th>\n                        <td>2.84</td>\n                    </tr>\n                    <tr>\n                        <th>Sum</th>\n                        <td>5495209</td>\n                    </tr>\n                    <tr>\n                        <th>Variance</th>\n                        <td>1.3372</td>\n                    </tr>\n                    <tr>\n                        <th>Memory size</th>\n                        <td>22.8 MiB</td>\n                    </tr>\n                </table>\n            </div>\n        </div>\n        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram1498225605498042138\">\n            <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzs3X1Y1HW%2B//EXMIIJDDAq2HJccb1LbrI0pUVXFDNbTUvDG9rWZTc3TZQg3bUyV23drMSs1Mub9mhZXTVHbFMp08N6s7pap5vNHUetJD0mPxWSGRUEFZjfHx5mdxIZ0i8O4PNxXV5dfj7z/XzevBnyxff7nRk/l8vlEgAAAAzj7%2BsCAAAAmhsCFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYz%2BbqAG0Vx8VnD1/T395PFEqySkjJVV7sMX785oEd1oz/e0SPv6FHd6I93Ddmjtm1DDV2vvjiD1YT5%2B/vJz89P/v5%2Bvi6l0aJHdaM/3tEj7%2BhR3eiPd82xRwQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDNWjA2rlzp5KSkpSdnX3Z3MmTJ/Xoo4/qtttuU1JSkhYuXKjq6mpJUnV1tRYtWqRBgwapd%2B/eevjhh/Xtt9%2B6j3U6ncrKylJSUpL69eunmTNnqqKiwj1/4MABPfTQQ%2BrVq5fuvvturVq1ymPvDz74QMOHD9ftt9%2BuUaNGadeuXe45b3sDAAB402AB69VXX9W8efPUoUOHy%2BZcLpemTJmi6Oho7dq1S2%2B88Yb27Nmjjz/%2BWJL01ltvaePGjVq5cqW2bdummJgYZWRkyOW69PlEs2bNUnl5ufLy8rRu3ToVFBQoJydHklRRUaGJEyfqzjvv1M6dO7Vo0SKtWLFCW7ZskXQpfM2YMUPTp0/XRx99pPT0dE2ZMkUnTpyo194AAADeNFjACgoKUm5ubq0B65NPPtG3336r3//%2B9woJCVGnTp2Um5urn/70p5Ikq9Wq9PR0derUSSEhIcrOzlZBQYH27t2r7777Tvn5%2BcrOzpbFYlFUVJQmT56sdevW6eLFi9q%2BfbsuXryoRx99VK1atVJcXJxGjx4tq9UqSVq7dq2Sk5OVnJysoKAgjRgxQl27dtWGDRu87g0AAFAfDRawxo8fr9DQ2j/B%2BrPPPlPXrl21aNEiJSYmatCgQe7LeBUVFTp06JBiY2Pdjw8JCVGHDh1ks9l04MABBQQEqFu3bu75uLg4nTt3Tt98843sdru6deumgIAA93xsbKz27dsnSbLb7R5r18zbbDavewMAANSHyRebnjhxQl988YX69%2B%2Bv7du363/%2B5380ZcoU/fjHP1ZCQoJcLpfCwsI8jgkLC5PD4VB4eLhCQkLk5%2BfnMSdJDodDTqdTZrPZ49jw8HA5nU5VV1fL6XTWuvahQ4d0%2BvTpOveur6KiIhUXF3uMmUytFBkZWe816iMgwN/jv43d4Jydvi7hB/nv6T/zdQkNrqk9h3yBHnlHj%2BpGf7xrjj3yScByuVyyWCyaMGGCJCk5OVmDBw/Wpk2blJCQ4H5MXcf/UP8eyLwdf633W1mtVi1ZssRjLCMjQ5mZmde07pWYzTc1yLo3uoiIYF%2BXcN3wHPKOHnlHj%2BpGf7xrTj3yScBq27btZZcPo6OjtXfvXoWHh8vf319Op9Nj3ul0qnXr1rJYLCotLVVVVZX7MmDNY2vmjxw5ctmxNetGRETUurbFYvG6d32NHTtWKSkpHmMmUys5HGX1XqM%2BAgL8ZTbfpDNnylVVVW3o2pDh36/GiOeQd/TIO3pUN/rjXUP2yFe/LPskYHXq1EnffvutysrKFBx86QsvLCxUdHS0goKC1KVLF9ntdvXp00eSdObMGR09elS33nqroqOj5XK5dPDgQcXFxUmSbDabzGazOnbsqPj4eL399tuqrKyUyWRyz/fo0UOSFB8f774fq4bNZtOwYcO87l1fkZGRl10OLC4%2Bq8rKhvnBqqqqbrC1b2Q3Uk95DnlHj7yjR3WjP941px755GJnSkqKzGazXnjhBZ07d0579uxRfn6%2BRo0aJUlKS0vTmjVrVFBQoNLSUuXk5Kh79%2B5KSEiQxWLRkCFD9NJLL6mkpEQnTpzQ0qVLlZqaKpPJpOTkZIWEhGjZsmUqLy/X3r17lZubq7S0NEnSmDFjtHv3bm3fvl3nz59Xbm6ujhw5ohEjRnjdGwAAoD4a7AxWTSCprKyUJOXn50u6dLaoZcuW%2BvOf/6zZs2frzjvvlMVi0dy5c9W7d29J0rhx41RcXKxf/vKXKisrU2Jiosc9Tc8884xmz56tQYMGqUWLFrr33nvdb2YaGBio5cuXa/bs2Vq5cqXatGmj7OxsDRgwQJLUtWtX5eTkaP78%2BSosLFTnzp21YsUKtW3btl57AwAAeOPn4h00r4vi4rOGr2ky%2BSsiIlgOR1mTOKX685f%2B7usSfpBNWX19XUKDa2rPIV%2BgR97Ro7rRH%2B8askdt29b%2BllENrfm8HhIAAKCRIGABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgsAYNWDt37lRSUpKys7Ov%2BJiysjINGDBATzzxhHusurpaixYt0qBBg9S7d289/PDD%2Bvbbb93zTqdTWVlZSkpKUr9%2B/TRz5kxVVFS45w8cOKCHHnpIvXr10t13361Vq1Z57PnBBx9o%2BPDhuv322zVq1Cjt2rWr3nsDAAB402AB69VXX9W8efPUoUOHOh%2B3ePFilZaWeoy99dZb2rhxo1auXKlt27YpJiZGGRkZcrlckqRZs2apvLxceXl5WrdunQoKCpSTkyNJqqio0MSJE3XnnXdq586dWrRokVasWKEtW7ZIuhS%2BZsyYoenTp%2Bujjz5Senq6pkyZohMnTtRrbwAAAG8aLGAFBQUpNze3zoB18OBB5eXlaeTIkR7jVqtV6enp6tSpk0JCQpSdna2CggLt3btX3333nfLz85WdnS2LxaKoqChNnjxZ69at08WLF7V9%2B3ZdvHhRjz76qFq1aqW4uDiNHj1aVqtVkrR27VolJycrOTlZQUFBGjFihLp27aoNGzZ43RsAAKA%2BGixgjR8/XqGhoVecd7lcmjNnjrKzs2U2m93jFRUVOnTokGJjY91jISEh6tChg2w2mw4cOKCAgAB169bNPR8XF6dz587pm2%2B%2Bkd1uV7du3RQQEOCej42N1b59%2ByRJdrvdY%2B2aeZvN5nVvAACA%2BjD5amOr1So/Pz%2BNGjVKS5YscY%2BfPn1aLpdLYWFhHo8PCwuTw%2BFQeHi4QkJC5Ofn5zEnSQ6HQ06n0yOwSVJ4eLicTqeqq6vldDprXfvQoUNe966voqIiFRcXe4yZTK0UGRlZ7zXqIyDA3%2BO/MJbJ1Pz7ynPIO3rkHT2qG/3xrjn2yCcB69SpU3r55Zf12muveQSlf1fXPU9Xcz/Uv%2B/j7fhrvd/KarV6hEZJysjIUGZm5jWteyVm800Nsu6NLiIi2NclXDc8h7yjR97Ro7rRH%2B%2BaU498ErCee%2B453X///R6X%2BWqEh4fL399fTqfTY9zpdKp169ayWCwqLS1VVVWV%2BzJgzWNr5o8cOXLZsTXrRkRE1Lq2xWLxund9jR07VikpKR5jJlMrORxl9V6jPgIC/GU236QzZ8pVVVVt6NqQ4d%2BvxojnkHf0yDt6VDf6411D9shXvyz7JGBt2LBBZrNZ7777rqRL911VV1dr27Zt%2Bvjjj9WlSxfZ7Xb16dNHknTmzBkdPXpUt956q6Kjo%2BVyuXTw4EHFxcVJkmw2m8xmszp27Kj4%2BHi9/fbbqqyslMlkcs/36NFDkhQfH%2B%2B%2BH6uGzWbTsGHDFBQUVOfe9RUZGXnZ5cDi4rOqrGyYH6yqquoGW/tGdiP1lOeQd/TIO3pUN/rjXXPqkU8udu7YsUMbN27U%2BvXrtX79eo0bN04pKSlav369JCktLU1r1qxRQUGBSktLlZOTo%2B7duyshIUEWi0VDhgzRSy%2B9pJKSEp04cUJLly5VamqqTCaTkpOTFRISomXLlqm8vFx79%2B5Vbm6u0tLSJEljxozR7t27tX37dp0/f165ubk6cuSIRowY4XVvAACA%2BmiwM1g1gaSyslKSlJ%2BfL%2BnS2aJ27dp5PDYkJEQ33XSTe3zcuHEqLi7WL3/5S5WVlSkxMdHjnqZnnnlGs2fP1qBBg9SiRQvde%2B%2B97jczDQwM1PLlyzV79mytXLlSbdq0UXZ2tgYMGCBJ6tq1q3JycjR//nwVFhaqc%2BfOWrFihdq2bVuvvQEAALzxc/EOmtdFcfFZw9c0mfwVEREsh6OsSZxS/flLf/d1CT/Ipqy%2Bvi6hwTW155Av0CPv6FHd6I93Ddmjtm2v/JZRDan5vB4SAACgkSBgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYLAGDVg7d%2B5UUlKSsrOzL5vbsmWLRowYodtvv11DhgzRf/3Xf3nMr1mzRkOGDFHPnj2Vlpamffv2uefOnz%2BvP/zhD%2Brfv78SExOVmZkph8Phni8sLNQjjzyixMREDRw4UAsWLFB1dbV7fs%2BePUpNTVXPnj01bNgwbdiwod57AwAAeNNgAevVV1/VvHnz1KFDh8vm/vnPf2r69OnKzMzUJ598oqeeekrPPPOMPv30U0nS1q1btXjxYr3wwgvavXu3Bg4cqEmTJuncuXOSpEWLFslut8tqtWrz5s1yuVx68skn3etPnTpVUVFRys/P1%2BrVq5Wfn6/XX39dklRUVKTJkydr3Lhx2rNnj2bOnKlZs2bJZrPVa28AAABvGixgBQUFKTc3t9aA5XQ6NXHiRN11110ymUxKTk5W165d3QHLarVq1KhR6tGjh1q2bKkJEyZIkrZt26bKykrl5uZq8uTJuvnmmxUeHq6srCxt375dJ0%2BelM1m08GDBzV9%2BnSFhoYqJiZG6enpslqtkqSNGzcqJiZGqampCgoKUlJSklJSUrR27VqvewMAANRHgwWs8ePHKzQ0tNa5/v37KyMjw/33yspKFRcXKyoqSpJkt9sVGxv7ryL9/dW9e3fZbDYdPXpUZ8%2BeVVxcnHu%2BU6dOatmypex2u%2Bx2u6KjoxUWFuaej4uL0%2BHDh1VaWnrZ2pIUGxvrvgxY194AAAD1YfJ1AZKUk5OjVq1aaejQoZIuneH694AkSWFhYXI4HHI6nZIks9nsMW82m93z35%2BrWatmvibI1QgPD3ffw1XX3vVVVFSk4uJijzGTqZUiIyPrvUZ9BAT4e/wXxjKZmn9feQ55R4%2B8o0d1oz/eNcce%2BTRguVwu5eTkKC8vT2vWrFFQUJDHnLdjr2auvnVdC6vVqiVLlniMZWRkKDMz85rWvRKz%2BaYGWfdGFxER7OsSrhueQ97RI%2B/oUd3oj3fNqUc%2BC1jV1dV68skn9c9//lNvv/222rdv756LiIhwn6mq4XQ61aVLF1ksFvffg4P/9Q/g6dOn1bp1a1VVVdV6rJ%2BfnywWS61rOxwO97p17V1fY8eOVUpKiseYydRKDkdZvdeoj4AAf6U8v8PQNfEvRn%2B/GqOAAH%2BZzTfpzJlyVVVVez/gBkSPvKNHdaM/3jVkj3z1y7LPAtazzz6rr7/%2BWm%2B//bbCw8M95uLj42W32zVy5EhJUlVVlfbv36/U1FS1b99eYWFh7nutJOmrr77ShQsXFB8fr6KiIh0/flwlJSXu0GSz2dS5c2cFBwcrISFB69at89hv37596tGjh9e96ysyMvKyy4HFxWdVWckPVlNyI32/qqqqb6iv92rQI%2B/oUd3oj3fNqUc%2Budj52WefacOGDVq5cuVl4UqS0tLS9N577%2BmLL75QeXm5li1bpsDAQA0YMEABAQEaM2aMli9fruPHj8vhcOjFF1/U4MGD1aZNG8XGxiohIUELFy5UaWmpCgoKtHr1aqWlpUmShg8frsLCQq1du1bnz5/Xjh07tGPHDo0ZM8br3gAAAPXRYGewEhISJF16haAk5efnS7p0NmndunU6e/asBg4c6HFM7969tWrVKvXv31%2BPP/64srKydOrUKSUkJGjlypVq2bKlJCkzM1NlZWW67777VFlZqYEDB2rOnDnudV555RXNmjVLffv2VUhIiMaNG6cHH3xQktS6dWutWLFC8%2BbN09y5cxUdHa0FCxbolltukSSvewMAAHjj57rWO7pRL8XFZw1f02Ty1%2BCcnYavi0s2ZfX1dQkNzmTyV0REsByOsmZzWt5o9Mg7elQ3%2BuNdQ/aobdva3zKqoTWf10MCAAA0EgQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADNagAWvnzp1KSkpSdnb2ZXMffPCBhg8frttvv12jRo3Srl273HPV1dVatGiRBg0apN69e%2Bvhhx/Wt99%2B6553Op3KyspSUlKS%2BvXrp5kzZ6qiosI9f%2BDAAT300EPq1auX7r77bq1atcqwvQEAALxpsID16quvat68eerQocNlcwcOHNCMGTM0ffp0ffTRR0pPT9eUKVN04sQJSdJbb72ljRs3auXKldq2bZtiYmKUkZEhl8slSZo1a5bKy8uVl5endevWqaCgQDk5OZKkiooKTZw4UXfeead27typRYsWacWKFdqyZYshewMAAHhTa8BKSUnRkiVLdPz48ateOCgoSLm5ubUGrLVr1yo5OVnJyckKCgrSiBEj1LVrV23YsEGSZLValZ6erk6dOikkJETZ2dkqKCjQ3r179d133yk/P1/Z2dmyWCyKiorS5MmTtW7dOl28eFHbt2/XxYsX9eijj6pVq1aKi4vT6NGjZbVar3lvAACA%2Bqg1YD3wwAP64IMPdNddd2nChAnasmWLKisrf9DC48ePV2hoaK1zdrtdsbGxHmOxsbGy2WyqqKjQoUOHPOZDQkLUoUMH2Ww2HThwQAEBAerWrZt7Pi4uTufOndM333wju92ubt26KSAgwGPtffv2XfPeAAAA9WGqbTAjI0MZGRmy2%2B3Ky8vTs88%2Bq7lz5%2Br%2B%2B%2B9XamqqOnbseE2bOp1OhYWFeYyFhYXp0KFDOn36tFwuV63zDodD4eHhCgkJkZ%2Bfn8ecJDkcDjmdTpnNZo9jw8PD5XQ6VV1dfU1711dRUZGKi4s9xkymVoqMjKz3GvUREMBrFBqSydT8%2B1vzHOK5dGX0yDt6VDf6411z7FGtAatGXFyc4uLi9Pvf/14ffPCB5syZo1WrVikpKUmPPfaYbr311qve2Ns9TXXNX839UP8eyK5l7/qwWq1asmSJx1hGRoYyMzOvaV1cXxERwb4u4boxm2/ydQmNHj3yjh7Vjf5415x6VGfAunjxov77v/9b7777rj766CPFxMRo6tSpKioqUnp6uubOnavhw4f/4E0jIiLkdDo9xpxOpywWi8LDw%2BXv71/rfOvWrWWxWFRaWqqqqir3ZcCax9bMHzly5LJja9a9lr3ra%2BzYsUpJSfEYM5layeEoq/ca9dGckn5jZPT3qzEKCPCX2XyTzpwpV1VVta/LaZTokXf0qG70x7uG7JGvflmuNWAVFBQoNzdX7733nsrKyjRkyBC9/vrr6tWrl/sxvXv31pw5c64qYMXHx7vviaphs9k0bNgwBQUFqUuXLrLb7erTp48k6cyZMzp69KhuvfVWRUdHy%2BVy6eDBg4qLi3Mfazab1bFjR8XHx%2Bvtt99WZWWlTCaTe75Hjx7XvHd9RUZGXnY5sLj4rCor%2BcFqSm6k71dVVfUN9fVeDXrkHT2qG/3xrjn1qNZTIMOGDdP27ds1ceJE/e1vf9OCBQs8wpUkJScnq6Sk5Ko2HTNmjHbv3q3t27fr/Pnzys3N1ZEjRzRixAhJUlpamtasWaOCggKVlpYqJydH3bt3V0JCgiwWi4YMGaKXXnpJJSUlOnHihJYuXarU1FSZTCYlJycrJCREy5YtU3l5ufbu3avc3FylpaVd894AAAD1UesZrDVr1rjP4NSlrrcuqAkkNa8%2BzM/Pl3TpbFHXrl2Vk5Oj%2BfPnq7CwUJ07d9aKFSvUtm1bSdK4ceNUXFysX/7ylyorK1NiYqLHPU3PPPOMZs%2BerUGDBqlFixa699573W9mGhgYqOXLl2v27NlauXKl2rRpo%2BzsbA0YMECSrnlvAAAAb/xctdzRffr0ac2YMUOpqam66667JEmvvfaa/v73v2vBggUKDw%2B/7oU2dcXFZw1f02Ty1%2BCcnYavi0s2ZfX1dQkNzmTyV0REsByOsmZzWt5o9Mg7elQ3%2BuNdQ/aobdva3zKqodV6iXD%2B/Pk6e/asOnfu7B4bMGCAqqur9dxzz1234gAAAJqiWi8R7tq1Sxs3blRERIR7LCYmRjk5Obr33nuvW3EAAABNUa1nsCoqKhQUFHT5g/39VV5e3uBFAQAANGW1BqzevXvrueee0%2BnTp91jJ0%2Be1Ny5cy97NSEAAAA81XqJ8KmnntJvfvMb/fSnP1VISIiqq6tVVlam9u3b64033rjeNQIAADQptQas9u3b6/3339ff/vY3HT16VP7%2B/urYsaP69evn8SHKAAAAuNwVPyonMDDQ/RYNAAAAqL9aA9a3336rhQsX6uuvv1ZFRcVl83/9618bvDAAAICm6or3YBUVFalfv35q1arV9a4JAACgSas1YO3bt09//etfZbFYrnc9AAAATV6tb9PQunVrzlwBAABcpVoD1sSJE7VkyRLV8jGFAAAA8KLWS4R/%2B9vf9Pnnn%2Bvdd9/Vf/zHf8jf3zOHvfPOO9elOAAAgKao1oAVEhKi/v37X%2B9aAAAAmoVaA9b8%2BfOvdx0AAADNRq33YEnSN998o8WLF%2BvJJ590j/3jH/%2B4LkUBAAA0ZbUGrD179mjEiBHasmWL8vLyJF1689Hx48fzJqMAAABe1BqwFi1apN/97nfauHGj/Pz8JF36fMLnnntOS5cuva4FAgAANDW1BqyvvvpKaWlpkuQOWJJ0zz33qKCg4PpUBgAA0ETVGrBCQ0Nr/QzCoqIiBQYGNnhRAAAATVmtAatnz5569tlnVVpa6h47fPiwZsyYoZ/%2B9KfXrTgAAICmqNa3aXjyySf1q1/9SomJiaqqqlLPnj1VXl6uLl266LnnnrveNQIAADQptQasdu3aKS8vTzt27NDhw4fVsmVLdezYUX379vW4JwsAAACXqzVgSVKLFi101113Xc9aAAAAmoVaA1ZKSkqdZ6p4LywAAIArqzVgDR061CNgVVVV6fDhw7LZbPrVr3513YoDAABoimoNWNOnT6/1wZs3b9bHH3/coAUBAAA0dVf8LMLa3HXXXXr//fcN23z//v0aP3687rjjDvXt21fTp09XSUmJpEsf15OamqqePXtq2LBh2rBhg8exa9as0ZAhQ9SzZ0%2BlpaVp37597rnz58/rD3/4g/r376/ExERlZmbK4XC45wsLC/XII48oMTFRAwcO1IIFC1RdXe2e97Y3AABAXX5QwNq/f79cLpchG1dWVuqRRx7Rbbfdpt27dysvL08lJSWaM2eOioqKNHnyZI0bN0579uzRzJkzNWvWLNlsNknS1q1btXjxYr3wwgvavXu3Bg4cqEmTJuncuXOSLn3Uj91ul9Vq1ebNm%2BVyuTw%2BtHrq1KmKiopSfn6%2BVq9erfz8fL3%2B%2BuuS5HVvAAAAb2q9RDhu3LjLxsrLy1VQUKC7777bkI2Li4tVXFys%2B%2B67T4GBgQoMDNTgwYO1atUqbdy4UTExMUpNTZUkJSUlKSUlRWvXrlVCQoKsVqtGjRqlHj16SJImTJigNWvWaNu2bRoyZIhyc3P1/PPP6%2Babb5YkZWVladiwYTp58qSKiop08OBBrV69WqGhoQoNDVV6erpef/11/frXv/a6NwAAgDe1BqyYmJjLXkUYFBSk1NRUjR492pCNo6Ki1L17d1mtVj322GOqqKjQli1bNGDAANntdsXGxno8PjY2Vps2bZIk2e12DR061D3n7%2B%2Bv7t27y2azqXv37jp79qzi4uLc8506dVLLli1lt9tVVFSk6OhohYWFuefj4uJ0%2BPBhlZaWet0bAADAm1oD1vV4t3Z/f38tXrzYffZIkvr06aNp06Zp8uTJioqK8nh8eHi4%2Bz4qp9PpEZAkKSwsTA6HQ06nU5JkNps95s1ms3v%2B%2B3M1a9XM17V3fRQVFam4uNhjzGRqpcjIyHqvUR8BAT/oCi9%2BIJOp%2Bfe35jnEc%2BnK6JF39Khu9Me75tijWgPWe%2B%2B9V%2B8F7r///qva%2BMKFC5o0aZLuuece9/1Tc%2BfOveIrGL/P271gdc0bdR/ZlVitVi1ZssRjLCMjQ5mZmQ26L4wVERHs6xKuG7P5Jl%2BX0OjRI%2B/oUd3oj3fNqUe1BqzNiRAqAAAfN0lEQVSZM2equrr6siDi5%2BfnMebn53fVAWvPnj06duyYHn/8cQUEBCg0NFSZmZm677779LOf/cx9JqqGw%2BGQxWKRJEVERFw273Q61aVLF/djnE6ngoP/9Q/k6dOn1bp1a1VVVdV6rJ%2BfnywWS61r//ve9TF27FilpKR4jJlMreRwlNV7jfpoTkm/MTL6%2B9UYBQT4y2y%2BSWfOlKuqqtr7ATcgeuQdPaob/fGuIXvkq1%2BWaw1Yf/7zn7Vq1SpNmjRJ3bp1k8vl0pdffqlXX31VDz30kBITE69546qqqstC3IULFyRdurH8L3/5i8fj9%2B3b576pPT4%2BXna7XSNHjnSvtX//fqWmpqp9%2B/YKCwuT3W5XdHS0JOmrr77ShQsXFB8fr6KiIh0/flwlJSXu0GSz2dS5c2cFBwcrISFB69atu%2BLe9REZGXnZ5cDi4rOqrOQHqym5kb5fVVXVN9TXezXokXf0qG70x7vm1KNaT4E899xzmjdvnnr16qWQkBCFhobqjjvu0DPPPKPnn3/e/aq/wMDAq9749ttvV6tWrbR48WKVl5fL4XBo2bJl6t27t%2B677z4VFhZq7dq1On/%2BvHbs2KEdO3ZozJgxkqS0tDS99957%2BuKLL1ReXq5ly5YpMDBQAwYMUEBAgMaMGaPly5fr%2BPHjcjgcevHFFzV48GC1adNGsbGxSkhI0MKFC1VaWqqCggKtXr1aaWlpkqThw4fXuTcAAIA3tQasI0eOXHYTuXTpRvHCwkJDNo6IiNB//ud/6vPPP1f//v117733qmXLllq4cKFat26tFStW6M0331SvXr307LPPasGCBbrlllskSf3799fjjz%2BurKws9enTR7t379bKlSvVsmVLSVJmZqZ69Oih%2B%2B67T4MGDVJwcLD%2B9Kc/ufd%2B5ZVXVFRUpL59%2B2r8%2BPG6//779eCDD0qS170BAAC88XPVcsf30KFD1adPHz322GOKiIiQJJ05c0avvPKKPvnkE61fv/66F9rUFRefNXxNk8lfg3N2Gr4uLtmU1dfXJTQ4k8lfERHBcjjKms1peaPRI%2B/oUd3oj3cN2aO2bUMNXa%2B%2Bar0H66mnntK0adNktVoVHBwsf39/lZaWqmXLllq6dOn1rhEAAKBJqTVg9evXT9u3b9eOHTt04sQJuVwuRUVF6Wc/%2B5lCQ32TBAEAAJqKWgOWJN10000aNGiQTpw4ofbt21/PmgAAAJq0Wm9yr6io0IwZM3T77bfr5z//uaRL92BNmDBBZ86cua4FAgAANDW1BqwFCxbowIEDysnJkb//vx5SVVWlnJyc61YcAABAU1RrwNq8ebNeeeUV3XPPPe4PfTabzZo/f762bNlyXQsEAABoamoNWGVlZYqJibls3GKx6Ny5cw1dEwAAQJNWa8D68Y9/rI8//liS5wcjf/jhh/rRj350fSoDAABoomp9FeGDDz6oqVOn6oEHHlB1dbVWr16tffv2afPmzZo5c%2Bb1rhEAAKBJqTVgjR07ViaTSW%2B%2B%2BaYCAgK0fPlydezYUTk5Obrnnnuud40AAABNSq0Bq6SkRA888IAeeOCB610PAABAk1frPViDBg1SLR9RCAAAgHqoNWAlJiZq06ZN17sWAACAZqHWS4Q333yz/vSnP2nlypX68Y9/rBYtWnjML1y48LoUBwAA0BTVGrAOHTqkn/zkJ5Ikh8NxXQsCAABo6jwCVnZ2thYtWqQ33njDPbZ06VJlZGRc98IAAACaKo97sLZu3XrZA1auXHndigEAAGgOPAJWba8c5NWEAAAAP4xHwKr5YGdvYwAAALiyWt%2BmAQAAAFePgAUAAGAwj1cRXrx4UdOmTZO3Md4HCwAA4Mo8AlavXr1UVFQkb2MAAAC4Mo%2BA9e/vfwUAAICrwz1YAAAABiNgAQAAGIyABQAAYDCfB6xly5apX79%2Buu2225Senq5jx45Jkvbs2aPU1FT17NlTw4YN04YNGzyOW7NmjYYMGaKePXsqLS1N%2B/btc8%2BdP39ef/jDH9S/f38lJiYqMzPT40OrCwsL9cgjjygxMVEDBw7UggULVF1d7Z73tjcAAEBdfBqw3nrrLW3YsEFr1qzRrl271LlzZ7322msqKirS5MmTNW7cOO3Zs0czZ87UrFmzZLPZJF36zMTFixfrhRde0O7duzVw4EBNmjRJ586dkyQtWrRIdrtdVqtVmzdvlsvl0pNPPuned%2BrUqYqKilJ%2Bfr5Wr16t/Px8vf7665LkdW8AAABvfBqwVq1apezsbP3kJz9RSEiInn76aT399NPauHGjYmJilJqaqqCgICUlJSklJUVr166VJFmtVo0aNUo9evRQy5YtNWHCBEnStm3bVFlZqdzcXE2ePFk333yzwsPDlZWVpe3bt%2BvkyZOy2Ww6ePCgpk%2BfrtDQUMXExCg9PV1Wq1WSvO4NAADgjc8C1smTJ3Xs2DGdPn1aQ4cOdV/KKykpkd1uV2xsrMfjY2Nj3ZcBvz/v7%2B%2Bv7t27y2az6ejRozp79qzi4uLc8506dVLLli1lt9tlt9sVHR2tsLAw93xcXJwOHz6s0tJSr3sDAAB4Y/L%2BkIZx4sQJSdKHH36o1atXy%2BVyKTMzU08//bQqKioUFRXl8fjw8HD3fVROp9MjIElSWFiYHA6HnE6nJMlsNnvMm81m9/z352rWqpmva%2B/6KCoqUnFxsceYydRKkZGR9V6jPgICfH4LXbNmMjX//tY8h3guXRk98o4e1Y3%2BeNcce%2BSzgOVyuSRJEyZMcAeaqVOn6re//a2SkpLqffzVzHs79lpZrVYtWbLEYywjI0OZmZkNui%2BMFRER7OsSrhuz%2BSZfl9Do0SPv6FHd6I93zalHPgtYbdq0keR5pik6Oloul0sXL150n4mq4XA4ZLFYJEkRERGXzTudTnXp0sX9GKfTqeDgf/0Defr0abVu3VpVVVW1Huvn5yeLxVLr2v%2B%2Bd32MHTtWKSkpHmMmUys5HGX1XqM%2BmlPSb4yM/n41RgEB/jKbb9KZM%2BWqqqr2fsANiB55R4/qRn%2B8a8ge%2BeqXZZ8FrHbt2ikkJEQHDhxw3y9VWFioFi1aKDk5WevXr/d4/L59%2B9SjRw9JUnx8vOx2u0aOHClJqqqq0v79%2B5Wamqr27dsrLCzMfa%2BVJH311Ve6cOGC4uPjVVRUpOPHj6ukpMQdmmw2mzp37qzg4GAlJCRo3bp1V9y7PiIjIy%2B7HFhcfFaVlfxgNSU30verqqr6hvp6rwY98o4e1Y3%2BeNeceuSzUyAmk0mpqalavny5/vd//1enTp3S0qVLNXz4cI0cOVKFhYVau3atzp8/rx07dmjHjh0aM2aMJCktLU3vvfeevvjiC5WXl2vZsmUKDAzUgAEDFBAQoDFjxmj58uU6fvy4HA6HXnzxRQ0ePFht2rRRbGysEhIStHDhQpWWlqqgoECrV69WWlqaJGn48OF17g0AAOCNz85gSdK0adN04cIFjR49WhcvXtSQIUP09NNPKzg4WCtWrNC8efM0d%2B5cRUdHa8GCBbrlllskSf3799fjjz%2BurKwsnTp1SgkJCVq5cqVatmwpScrMzFRZWZnuu%2B8%2BVVZWauDAgZozZ45731deeUWzZs1S3759FRISonHjxunBBx%2BUJLVu3brOvQEAALzxczX0Hd%2BQdOkSodFMJn8Nztlp%2BLq4ZFNWX1%2BX0OBMJn9FRATL4ShrNqfljUaPvKNHdaM/3jVkj9q2DTV0vfriLmkAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwWKMIWM8%2B%2B6y6devm/vuePXuUmpqqnj17atiwYdqwYYPH49esWaMhQ4aoZ8%2BeSktL0759%2B9xz58%2Bf1x/%2B8Af1799fiYmJyszMlMPhcM8XFhbqkUceUWJiogYOHKgFCxaourq63nsDAAB44/OAdeDAAa1fv97996KiIk2ePFnjxo3Tnj17NHPmTM2aNUs2m02StHXrVi1evFgvvPCCdu/erYEDB2rSpEk6d%2B6cJGnRokWy2%2B2yWq3avHmzXC6XnnzySff6U6dOVVRUlPLz87V69Wrl5%2Bfr9ddfr9feAAAA9eHTgFVdXa3Zs2crPT3dPbZx40bFxMQoNTVVQUFBSkpKUkpKitauXStJslqtGjVqlHr06KGWLVtqwoQJkqRt27apsrJSubm5mjx5sm6%2B%2BWaFh4crKytL27dv18mTJ2Wz2XTw4EFNnz5doaGhiomJUXp6uqxWa732BgAAqA%2BfBqx33nlHQUFBGj58uHvMbrcrNjbW43GxsbHuy4Dfn/f391f37t1ls9l09OhRnT17VnFxce75Tp06qWXLlrLb7bLb7YqOjlZYWJh7Pi4uTocPH1ZpaanXvQEAAOrD5KuNv/vuOy1evFhvvPGGx7jT6VRUVJTHWHh4uPs%2BKqfT6RGQJCksLEwOh0NOp1OSZDabPebNZrN7/vtzNWvVzNe1d30VFRWpuLjYY8xkaqXIyMgftI43AQE%2Bv8LbrJlMzb%2B/Nc8hnktXRo%2B8o0d1oz/eNcce%2BSxgzZ8/X6NGjVLnzp117NixH3Ssy%2BW66nlvxxrBarVqyZIlHmMZGRnKzMxs8L1hnIiIYF%2BXcN2YzTf5uoRGjx55R4/qRn%2B8a0498knA2rNnj/7xj38oLy/vsrmIiAj3magaDodDFovlivNOp1NdunRxP8bpdCo4%2BF//OJ4%2BfVqtW7dWVVVVrcf6%2BfnJYrF43bu%2Bxo4dq5SUFI8xk6mVHI6yH7SON80p6TdGRn%2B/GqOAAH%2BZzTfpzJlyVVVVez/gBkSPvKNHdaM/3jVkj3z1y7JPAtaGDRt06tQpDRw4UNK/ziolJibqN7/5zWXBa9%2B%2BferRo4ckKT4%2BXna7XSNHjpQkVVVVaf/%2B/UpNTVX79u0VFhbmvtdKkr766itduHBB8fHxKioq0vHjx1VSUuIOTTabTZ07d1ZwcLASEhK0bt26K%2B5dX5GRkZddDiwuPqvKSn6wmpIb6ftVVVV9Q329V4MeeUeP6kZ/vGtOPfLJKZAnnnhCmzdv1vr167V%2B/XqtXLlSkrR%2B/XoNHz5chYWFWrt2rc6fP68dO3Zox44dGjNmjCQpLS1N7733nr744guVl5dr2bJlCgwM1IABAxQQEKAxY8Zo%2BfLlOn78uBwOh1588UUNHjxYbdq0UWxsrBISErRw4UKVlpaqoKBAq1evVlpamiR53RsAAKA%2BfHIGKywszONG9crKSklSu3btJEkrVqzQvHnzNHfuXEVHR2vBggW65ZZbJEn9%2B/fX448/rqysLJ06dUoJCQlauXKlWrZsKUnKzMxUWVmZ7rvvPlVWVmrgwIGaM2eOe69XXnlFs2bNUt%2B%2BfRUSEqJx48bpwQcflCS1bt26zr0BAADqw891Pe76hoqLzxq%2Bpsnkr8E5Ow1fF5dsyurr6xIanMnkr4iIYDkcZc3mtLzR6JF39Khu9Me7huxR27ahhq5XX9wlDQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAbzacAqLCxURkaGEhMTlZSUpCeeeEJnzpyRJB04cEAPPfSQevXqpbvvvlurVq3yOPaDDz7Q8OHDdfvtt2vUqFHatWuXe666ulqLFi3SoEGD1Lt3bz388MP69ttv3fNOp1NZWVlKSkpSv379NHPmTFVUVLjnve0NAABQF58GrEmTJslsNmvr1q1699139fXXX%2Bv5559XRUWFJk6cqDvvvFM7d%2B7UokWLtGLFCm3ZskXSpQA0Y8YMTZ8%2BXR999JHS09M1ZcoUnThxQpL01ltvaePGjVq5cqW2bdummJgYZWRkyOVySZJmzZql8vJy5eXlad26dSooKFBOTo4ked0bAADAG58FrDNnzig%2BPl7Tpk1TcHCw2rVrp5EjR%2BrTTz/V9u3bdfHiRT366KNq1aqV4uLiNHr0aFmtVknS2rVrlZycrOTkZAUFBWnEiBHq2rWrNmzYIEmyWq1KT09Xp06dFBISouzsbBUUFGjv3r367rvvlJ%2Bfr%2BzsbFksFkVFRWny5Mlat26dLl686HVvAAAAb3wWsMxms%2BbPn682bdq4x44fP67IyEjZ7XZ169ZNAQEB7rnY2Fjt27dPkmS32xUbG%2BuxXmxsrGw2myoqKnTo0CGP%2BZCQEHXo0EE2m00HDhxQQECAunXr5p6Pi4vTuXPn9M0333jdGwAAwBuTrwuoYbPZ9Oabb2rZsmXatGmTzGazx3x4eLicTqeqq6vldDoVFhbmMR8WFqZDhw7p9OnTcrlctc47HA6Fh4crJCREfn5%2BHnOS5HA45HQ669zb3997Ji0qKlJxcbHHmMnUSpGRkd4b8QMEBPAahYZkMjX//tY8h3guXRk98o4e1Y3%2BeNcce9QoAtZnn32mRx99VNOmTVNSUpI2bdpU6%2BP%2BPRTV3E91JXXNezvW297eWK1WLVmyxGMsIyNDmZmZP3hf%2BE5ERLCvS7huzOabfF1Co0ePvKNHdaM/3jWnHvk8YG3dulW/%2B93vNGvWLN1///2SJIvFoiNHjng8zul0Kjw8XP7%2B/oqIiJDT6bxs3mKxuB9T23zr1q1lsVhUWlqqqqoq92XAmsfWzNe1d32MHTtWKSkpHmMmUys5HGX1Or6%2BmlPSb4yM/n41RgEB/jKbb9KZM%2BWqqqr2dTmNEj3yjh7Vjf5415A98tUvyz4NWJ9//rlmzJihl19%2BWf369XOPx8fH6%2B2331ZlZaVMpksl2mw29ejRwz3//XuibDabhg0bpqCgIHXp0kV2u119%2BvSRdOmG%2BqNHj%2BrWW29VdHS0XC6XDh48qLi4OPexZrNZHTt29Lp3fURGRl52ObC4%2BKwqK/nBakpupO9XVVX1DfX1Xg165B09qhv98a459chnp0AqKyv19NNPa/r06R7hSpKSk5MVEhKiZcuWqby8XHv37lVubq7S0tIkSWPGjNHu3bu1fft2nT9/Xrm5uTpy5IhGjBghSUpLS9OaNWtUUFCg0tJS5eTkqHv37kpISJDFYtGQIUP00ksvqaSkRCdOnNDSpUuVmpoqk8nkdW8AAABv/FxXc0OSAT799FP94he/UGBg4GVzH374ocrKyjR79mzt27dPbdq00W9/%2B1s9%2BOCD7sds2bJFCxcuVGFhoTp37qyZM2eqd%2B/eki7dY7V48WK98847KisrU2Jiop555hm1a9dOknT27FnNnj1b27ZtU4sWLXTvvffqiSeecNfy1Vdf1bn31SguPntNx9fGZPLX4Jydhq%2BLSzZl9fV1CQ3OZPJXRESwHI6yZvNbo9HokXf0qG70x7uG7FHbtqGGrldfPgtYNxoCVtNDwIJEj%2BqDHtWN/njXHAMWd0kDAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGMzk6wIAAMb6%2BUt/93UJ9bYpq6%2BvSwAaBGewAAAADMYZLAAAmqmmdDbz0z/d4%2BsSDMUZLAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYLzRaC0KCws1d%2B5c7d27V61atdLQoUM1bdo0%2BfuTR4Eb0R0zP/R1CWgkmtIbd8K3CFi1mDp1quLi4pSfn69Tp05p4sSJatOmjX7961/7ujQAANAEcErme2w2mw4ePKjp06crNDRUMTExSk9Pl9Vq9XVpAACgieAM1vfY7XZFR0crLCzMPRYXF6fDhw%2BrtLRUISEhXtcoKipScXGxx5jJ1EqRkZGG1hoQQD5uSCZT8%2B9vzXOI59KV0ZuGdSP8nKH%2BmtPPGwHre5xOp8xms8dYTdhyOBz1ClhWq1VLlizxGJsyZYqmTp1qXKG6FOR%2B1e5rjR071vDw1lwUFRXJarXSoysoKirS66//mf7UgZ8z726kn7Or%2BUDiG6k/V6uoqEiLFy9uVj1qPlHRQC6X65qOHzt2rN59912PP2PHjjWoun8pLi7WkiVLLjtbhn%2BhR3WjP97RI%2B/oUd3oj3fNsUecwfoei8Uip9PpMeZ0OuXn5yeLxVKvNSIjI5tNAgcAAD8cZ7C%2BJz4%2BXsePH1dJSYl7zGazqXPnzgoODvZhZQAAoKkgYH1PbGysEhIStHDhQpWWlqqgoECrV69WWlqar0sDAABNRMCcOXPm%2BLqIxuZnP/uZ8vLy9Mc//lHvv/%2B%2BUlNT9fDDD8vPz8/XpV0mODhYffr04exaHehR3eiPd/TIO3pUN/rjXXPrkZ/rWu/oBgAAgAcuEQIAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyA1UTt3LlTSUlJys7O9nUpjVJhYaEyMjKUmJiopKQkPfHEEzpz5oyvy2pUDh48qF/96lfq1auXkpKSlJWVpeLiYl%2BX1Wg9%2B%2Byz6tatm6/LaFS6deum%2BPh4JSQkuP/88Y9/9HVZjc6yZcvUr18/3XbbbUpPT9exY8d8XVKj8cknn3g8fxISEhQfH98sftYIWE3Qq6%2B%2Bqnnz5qlDhw6%2BLqXRmjRpksxms7Zu3ap3331XX3/9tZ5//nlfl9VoXLhwQb/5zW/Up08f7dmzR3l5eTp16pT4aNLaHThwQOvXr/d1GY3Shx9%2BKJvN5v4za9YsX5fUqLz11lvasGGD1qxZo127dqlz58567bXXfF1Wo9G7d2%2BP54/NZtOUKVP085//3NelXTMCVhMUFBSk3NxcAtYVnDlzRvHx8Zo2bZqCg4PVrl07jRw5Up9%2B%2BqmvS2s0ysvLlZ2drYkTJyowMFAWi0WDBw/W119/7evSGp3q6mrNnj1b6enpvi4FTdCqVauUnZ2tn/zkJwoJCdHTTz%2Btp59%2B2tdlNVr/7//9P61evVq///3vfV3KNSNgNUHjx49XaGior8totMxms%2BbPn682bdq4x44fP67IyEgfVtW4hIWFafTo0TKZTJKkb775Rn/5y1%2BaxW%2BNRnvnnXcUFBSk4cOH%2B7qURmnhwoUaMGCA7rjjDs2aNUtlZWW%2BLqnROHnypI4dO6bTp09r6NChSkxMVGZmpkpKSnxdWqP18ssv64EHHtCPfvQjX5dyzQhYaPZsNpvefPNNPfroo74updEpLCxUfHy8hg4dqoSEBGVmZvq6pEblu%2B%2B%2B0%2BLFizV79mxfl9Io3XbbbUpKStKWLVtktVr1xRdfaO7cub4uq9E4ceKEpEuXUVevXq3169frxIkTnMG6gmPHjmnLli369a9/7etSDEHAQrP22Wef6eGHH9a0adOUlJTk63IanejoaNlsNn344Yc6cuRIszgtb6T58%2Bdr1KhR6ty5s69LaZSsVqtGjx6twMBAderUSdOnT1deXp4uXLjg69IaBZfLJUmaMGGCoqKi1K5dO02dOlVbt27V%2BfPnfVxd4/PWW2/p7rvvVtu2bX1diiEIWGi2tm7dqkceeURPPfWUxo8f7%2BtyGi0/Pz/FxMQoOztbeXl5XL74P3v27NE//vEPZWRk%2BLqUJuM//uM/VFVVpVOnTvm6lEah5jYFs9nsHouOjpbL5aJHtdi8ebNSUlJ8XYZhCFholj7//HPNmDFDL7/8su6//35fl9Po7NmzR0OGDFF1dbV7zN//0v8OWrRo4auyGpUNGzbo1KlTGjhwoBITEzVq1ChJUmJiot5//30fV%2Bd7%2B/fv13PPPecxVlBQoMDAQO53/D/t2rVTSEiIDhw44B4rLCxUixYt6NH3HDhwQIWFherbt6%2BvSzGMydcFAEarrKzU008/renTp6tfv36%2BLqdRio%2BPV2lpqRYsWKDMzEyVl5dr8eLFuuOOO3gBxf954okn9Nhjj7n/fuLECY0dO1br169XWFiYDytrHFq3bi2r1SqLxaL09HQVFhbq5Zdf1tixYxUQEODr8hoFk8mk1NRULV%2B%2BXL1791ZISIiWLl2q4cOHu19ggkv279%2Bv8PBwhYSE%2BLoUw/i5ai4So8lISEiQdClISHL/oNpsNp/V1Jh8%2Bumn%2BsUvfqHAwMDL5j788ENFR0f7oKrG58svv9S8efP0z3/%2BU61atdKdd96pJ554QlFRUb4urVE6duyYBg0apC%2B//NLXpTQan3zyiRYuXKgvv/xSgYGBGjlypLKzsxUUFOTr0hqNCxcuaP78%2BXr//fd18eJFDRkyRLNmzVJwcLCvS2tUVqxYoY0bNyovL8/XpRiGgAUAAGAw7sHC/2%2B3jgUAAAAABvlbT2JnUQQAzAQLAGAmWAAAM8ECAJgJFgDATLAAAGaCBQAwEywAgJlgAQDMBAsAYCZYAAAzwQIAmAkWAMAskNXBc2/7FlUAAAAASUVORK5CYII%3D\"/>\n        </div>\n        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common1498225605498042138\">\n            \n<table class=\"freq table table-hover\">\n    <thead>\n    <tr>\n        <td class=\"fillremaining\">Value</td>\n        <td class=\"number\">Count</td>\n        <td class=\"number\">Frequency (%)</td>\n        <td style=\"min-width:200px\">&nbsp;</td>\n    </tr>\n    </thead>\n    <tr class=\"\">\n        <td class=\"fillremaining\">2</td>\n        <td class=\"number\">1602601</td>\n        <td class=\"number\">53.6%</td>\n        <td>\n            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">1</td>\n        <td class=\"number\">1194321</td>\n        <td class=\"number\">40.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:74%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">5</td>\n        <td class=\"number\">80766</td>\n        <td class=\"number\">2.7%</td>\n        <td>\n            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">7</td>\n        <td class=\"number\">69798</td>\n        <td class=\"number\">2.3%</td>\n        <td>\n            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">6</td>\n        <td class=\"number\">20455</td>\n        <td class=\"number\">0.7%</td>\n        <td>\n            <div class=\"bar\" style=\"width:2%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">4</td>\n        <td class=\"number\">19820</td>\n        <td class=\"number\">0.7%</td>\n        <td>\n            <div class=\"bar\" style=\"width:2%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">3</td>\n        <td class=\"number\">420</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr>\n</table>\n        </div>\n        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme1498225605498042138\">\n            <p class=\"h4\">Minimum 5 values</p>\n            \n<table class=\"freq table table-hover\">\n    <thead>\n    <tr>\n        <td class=\"fillremaining\">Value</td>\n        <td class=\"number\">Count</td>\n        <td class=\"number\">Frequency (%)</td>\n        <td style=\"min-width:200px\">&nbsp;</td>\n    </tr>\n    </thead>\n    <tr class=\"\">\n        <td class=\"fillremaining\">1</td>\n        <td class=\"number\">1194321</td>\n        <td class=\"number\">40.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:74%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">2</td>\n        <td class=\"number\">1602601</td>\n        <td class=\"number\">53.6%</td>\n        <td>\n            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">3</td>\n        <td class=\"number\">420</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">4</td>\n        <td class=\"number\">19820</td>\n        <td class=\"number\">0.7%</td>\n        <td>\n            <div class=\"bar\" style=\"width:2%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">5</td>\n        <td class=\"number\">80766</td>\n        <td class=\"number\">2.7%</td>\n        <td>\n            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n        </td>\n</tr>\n</table>\n            <p class=\"h4\">Maximum 5 values</p>\n            \n<table class=\"freq table table-hover\">\n    <thead>\n    <tr>\n        <td class=\"fillremaining\">Value</td>\n        <td class=\"number\">Count</td>\n        <td class=\"number\">Frequency (%)</td>\n        <td style=\"min-width:200px\">&nbsp;</td>\n    </tr>\n    </thead>\n    <tr class=\"\">\n        <td class=\"fillremaining\">3</td>\n        <td class=\"number\">420</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">4</td>\n        <td class=\"number\">19820</td>\n        <td class=\"number\">0.7%</td>\n        <td>\n            <div class=\"bar\" style=\"width:25%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">5</td>\n        <td class=\"number\">80766</td>\n        <td class=\"number\">2.7%</td>\n        <td>\n            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">6</td>\n        <td class=\"number\">20455</td>\n        <td class=\"number\">0.7%</td>\n        <td>\n            <div class=\"bar\" style=\"width:26%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">7</td>\n        <td class=\"number\">69798</td>\n        <td class=\"number\">2.3%</td>\n        <td>\n            <div class=\"bar\" style=\"width:86%\">&nbsp;</div>\n        </td>\n</tr>\n</table>\n        </div>\n    </div>\n</div>\n</div><div class=\"row variablerow\">\n    <div class=\"col-md-3 namecol\">\n        <p class=\"h4 pp-anchor\" id=\"pp_var_click_region\">click_region<br/>\n            <small>Numeric</small>\n        </p>\n    </div><div class=\"col-md-6\">\n    <div class=\"row\">\n        <div class=\"col-sm-6\">\n            <table class=\"stats \">\n                <tr>\n                    <th>Distinct count</th>\n                    <td>28</td>\n                </tr>\n                <tr>\n                    <th>Unique (%)</th>\n                    <td>0.0%</td>\n                </tr>\n                <tr class=\"ignore\">\n                    <th>Missing (%)</th>\n                    <td>0.0%</td>\n                </tr>\n                <tr class=\"ignore\">\n                    <th>Missing (n)</th>\n                    <td>0</td>\n                </tr>\n                <tr class=\"ignore\">\n                    <th>Infinite (%)</th>\n                    <td>0.0%</td>\n                </tr>\n                <tr class=\"ignore\">\n                    <th>Infinite (n)</th>\n                    <td>0</td>\n                </tr>\n            </table>\n\n        </div>\n        <div class=\"col-sm-6\">\n            <table class=\"stats \">\n\n                <tr>\n                    <th>Mean</th>\n                    <td>18.313</td>\n                </tr>\n                <tr>\n                    <th>Minimum</th>\n                    <td>1</td>\n                </tr>\n                <tr>\n                    <th>Maximum</th>\n                    <td>28</td>\n                </tr>\n                <tr class=\"ignore\">\n                    <th>Zeros (%)</th>\n                    <td>0.0%</td>\n                </tr>\n            </table>\n        </div>\n    </div>\n</div>\n<div class=\"col-md-3 collapse in\" id=\"minihistogram5650834470530900475\">\n    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAATJJREFUeJzt3NFpAkEUQNEYUlKKSE9%2B21OKsKdJA%2BGigu44c86/8ECub2dx9zTGGB/Avz6PHgBm9nX0ALyH7/Pv3Z%2B5Xn6eMMlr2SAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAsEDUxt65OGnXdkgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBALBX02eaNf32a7EBoGw7Qbx684tbBAIAoEgEAjbnkFm5Ww0FxsEgkAgCATCEmcQLyGY0wrnKRsEgkAgCASCQCAIBMISd7FeZda7ZbPOtYLpAvFlMxOXWBBOY4xx9BAwKxsEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEwh%2Bstx6gjgXeOQAAAABJRU5ErkJggg%3D%3D\">\n\n</div>\n<div class=\"col-md-12 text-right\">\n    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives5650834470530900475,#minihistogram5650834470530900475\"\n       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n        Toggle details\n    </a>\n</div>\n<div class=\"row collapse col-md-12\" id=\"descriptives5650834470530900475\">\n    <ul class=\"nav nav-tabs\" role=\"tablist\">\n        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles5650834470530900475\"\n                                                  aria-controls=\"quantiles5650834470530900475\" role=\"tab\"\n                                                  data-toggle=\"tab\">Statistics</a></li>\n        <li role=\"presentation\"><a href=\"#histogram5650834470530900475\" aria-controls=\"histogram5650834470530900475\"\n                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n        <li role=\"presentation\"><a href=\"#common5650834470530900475\" aria-controls=\"common5650834470530900475\"\n                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n        <li role=\"presentation\"><a href=\"#extreme5650834470530900475\" aria-controls=\"extreme5650834470530900475\"\n                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n\n    </ul>\n\n    <div class=\"tab-content\">\n        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles5650834470530900475\">\n            <div class=\"col-md-4 col-md-offset-1\">\n                <p class=\"h4\">Quantile statistics</p>\n                <table class=\"stats indent\">\n                    <tr>\n                        <th>Minimum</th>\n                        <td>1</td>\n                    </tr>\n                    <tr>\n                        <th>5-th percentile</th>\n                        <td>5</td>\n                    </tr>\n                    <tr>\n                        <th>Q1</th>\n                        <td>13</td>\n                    </tr>\n                    <tr>\n                        <th>Median</th>\n                        <td>21</td>\n                    </tr>\n                    <tr>\n                        <th>Q3</th>\n                        <td>25</td>\n                    </tr>\n                    <tr>\n                        <th>95-th percentile</th>\n                        <td>27</td>\n                    </tr>\n                    <tr>\n                        <th>Maximum</th>\n                        <td>28</td>\n                    </tr>\n                    <tr>\n                        <th>Range</th>\n                        <td>27</td>\n                    </tr>\n                    <tr>\n                        <th>Interquartile range</th>\n                        <td>12</td>\n                    </tr>\n                </table>\n            </div>\n            <div class=\"col-md-4 col-md-offset-2\">\n                <p class=\"h4\">Descriptive statistics</p>\n                <table class=\"stats indent\">\n                    <tr>\n                        <th>Standard deviation</th>\n                        <td>7.064</td>\n                    </tr>\n                    <tr>\n                        <th>Coef of variation</th>\n                        <td>0.38573</td>\n                    </tr>\n                    <tr>\n                        <th>Kurtosis</th>\n                        <td>-0.97551</td>\n                    </tr>\n                    <tr>\n                        <th>Mean</th>\n                        <td>18.313</td>\n                    </tr>\n                    <tr>\n                        <th>MAD</th>\n                        <td>6.2052</td>\n                    </tr>\n                    <tr class=\"\">\n                        <th>Skewness</th>\n                        <td>-0.54588</td>\n                    </tr>\n                    <tr>\n                        <th>Sum</th>\n                        <td>54723498</td>\n                    </tr>\n                    <tr>\n                        <th>Variance</th>\n                        <td>49.9</td>\n                    </tr>\n                    <tr>\n                        <th>Memory size</th>\n                        <td>22.8 MiB</td>\n                    </tr>\n                </table>\n            </div>\n        </div>\n        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram5650834470530900475\">\n            <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3Xt0VOW9//FPLgYk94AJnogNNULJRQTF2IBCQECloGAE4rE2Kje5FSQWEaN4pAWFiBY4wbQ1BW3rLJDF/bZSLlbJqVqrZzKA1hiPNSuYSGaAYLgkmd8f/ph2CJAM8%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%2Beab1bdvX%2BXl5am2tlaSVFpaquzsbPXu3VvDhg3Thg0bvPZdtWqVhg4dqt69eysnJ0dlZWWeuRMnTuiZZ57R7bffroyMDE2fPl1Op9MzX1lZqQkTJigjI0NZWVlatGiRmpqaPPMtrQ0AAHA%2BlgWshoYGTZgwQTfeeKP27t2rTZs2qba2VvPmzVN1dbUmT56ssWPHqrS0VHPnzlV%2Bfr7sdrskaefOnVq6dKlefPFF7d27V1lZWZo0aZK%2B/fZbSdKSJUvkcDhks9m0fft2ud1uzZkzx7P2tGnTlJCQoJKSEhUXF6ukpEQrV66UpBbXBgAAaIllAaumpkY1NTW65557FBYWptjYWA0ePFj79%2B/Xxo0blZSUpOzsbLVr106ZmZkaOHCgVq9eLUmy2WwaNWqUevbsqfbt22vcuHGSpF27dqmhoUFr1qzR5MmTdfXVVysmJkYzZszQ7t279fXXX8tut%2BvAgQPKy8tTZGSkkpKSlJubK5vNJkktrg0AANCSUKsWTkhIUI8ePWSz2fTzn/9cx48f144dOzRgwAA5HA6lpKR4bZ%2BSkqKtW7dKkhwOh%2B6%2B%2B27PXHBwsHr06CG73a4ePXro6NGjSk1N9cxfd911at%2B%2BvRwOh6qrq5WYmKjo6GjPfGpqqioqKlRXV9fi2q1RXV2tmpoar7HQ0A6Kj4/3GgsJCfb6Ct/QP//RQ//RQ//Rw4snNJSeWsWygBUcHKylS5cqNzfXc3vulltu0axZszR58mQlJCR4bR8TE%2BN5jsrlcnkFJEmKjo6W0%2BmUy%2BWSJEVFRXnNR0VFeebPnDt9rNPz51u7NWw2m5YtW%2BY1NmXKFE2fPv2s20dFXdnqY6M5%2Buc/eug/eug/emhebGy41SVctiwLWCdPntSkSZN05513ep6feu6555SXl9eq/d1u9wXPt7Svv8aMGaOBAwd6jYWGdpDTecxrLCQkWFFRV%2BrIkXo1NjYJvqF//qOH/qOH/qOHF8%2BZP3cuR1aFTMsCVmlpqb766is9/vjjCgkJUWRkpKZPn6577rlHt912m%2BdK1GlOp1NxcXGSpNjY2GbzLpdL119/vWcbl8ul8PB/NfXw4cPq2LGjGhsbz7pvUFCQ4uLiznrsf1%2B7NeLj45vdDqypOaqGhrN/42hsbDrnHFpG//xHD/1HD/1HD82jn9ax7OZsY2OjmpqavK4mnTx5UpKUmZnp9doFSSorK1PPnj0lSWlpaXI4HF7H2rdvn3r27KkuXbooOjraa/7TTz/VyZMnlZaWprS0NFVVVXleByFJdrtdycnJCg8PV3p6%2BnnXBgAAaIllAatXr17q0KGDli5dqvr6ejmdThUWFqpPnz665557VFlZqdWrV%2BvEiRPas2eP9uzZo9GjR0uScnJytG7dOn300Ueqr69XYWGhwsLCNGDAAIWEhGj06NFasWKFqqqq5HQ69dJLL2nw4MHq1KmTUlJSlJ6eroKCAtXV1am8vFzFxcXKycmRJA0fPvy8awMAALQkyH2xH0g6j7KyMr3wwgs6cOCAwsLCdMstt%2BjJJ59UQkKC3n//fc2fP1/l5eVKTEzUrFmzNGTIEM%2B%2Bf/zjH1VUVKRDhw4pPT1d8%2BbNU7du3SR9dyVswYIF2rx5sxoaGpSVlaV58%2BYpMjJSknTw4EHl5%2BfrvffeU0REhMaOHaupU6cqKChIklpc%2B0LU1BxtNhYaGqzY2HA5nce4jHsB6J//6KH/6KH/AqmHd738rtUl%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%2B/XTjjTcqNzdXX331lSSptLRU2dnZ6t27t4YNG6YNGzZ47bdq1SoNHTpUvXv3Vk5OjsrKyjxzJ06c0DPPPKPbb79dGRkZmj59upxOp2e%2BsrJSEyZMUEZGhrKysrRo0SI1NTV55ltaGwAA4HwsDVh/%2BMMftGHDBq1atUrvvPOOkpOT9fvf/17V1dWaPHmyxo4dq9LSUs2dO1f5%2Bfmy2%2B2SpJ07d2rp0qV68cUXtXfvXmVlZWnSpEn69ttvJUlLliyRw%2BGQzWbT9u3b5Xa7NWfOHM%2B606ZNU0JCgkpKSlRcXKySkhKtXLlSklpcGwAAoCWWBqzXXntNM2fO1A9/%2BENFRETo6aef1tNPP62NGzcqKSlJ2dnZateunTIzMzVw4ECtXr1akmSz2TRq1Cj17NlT7du317hx4yRJu3btUkNDg9asWaPJkyfr6quvVkxMjGbMmKHdu3fr66%2B/lt1u14EDB5SXl6fIyEglJSUpNzdXNptNklpcGwAAoCWWBayvv/5aX331lQ4fPqy7777bcyuvtrZWDodDKSkpXtunpKR4bgOeOR8cHKwePXrIbrfryy%2B/1NGjR5WamuqZv%2B6669S%2BfXs5HA45HA4lJiYqOjraM5%2BamqqKigrV1dW1uDYAAEBLQq1a%2BODBg5Kkbdu2qbi4WG63W9OnT9fTTz%2Bt48ePKyEhwWv7mJgYz3NULpfLKyBJUnR0tJxOp1wulyQpKirKaz4qKsozf%2Bbc6WOdnj/f2q1RXV2tmpoar7HQ0A6Kj4/3GgsJCfb6Ct/QP//RQ//RQ//Rw4snNJSeWsWygOV2uyVJ48aN8wSaadOmafz48crMzGz1/hcy39K%2B/rLZbFq2bJnX2JQpUzR9%2BvSzbh8VdeVFredSR//8Rw/9Rw8vzM1zt1ldwiUtNjbc6hIuW5YFrE6dOknyvtKUmJgot9utU6dOea5EneZ0OhUXFydJio2NbTbvcrl0/fXXe7ZxuVwKD//XiXX48GF17NhRjY2NZ903KChIcXFxZz32v6/dGmPGjNHAgQO9xkJDO8jpPOY1FhISrKioK3XkSL0aG5sE39A//9FD/9FDtGVn/ty5HFkVMn0OWAMHDtSoUaN033336eqrr77ghTt37qyIiAjt37/f87xUZWWlrrjiCvXv31/r16/32r6srEw9e/aUJKWlpcnhcGjkyJGSpMbGRu3bt0/Z2dnq0qWLoqOjPc9aSdKnn36qkydPKi0tTdXV1aqqqlJtba0nNNntdiUnJys8PFzp6el66623zrl2a8THxze7HVhTc1QNDWf/5tvY2HTOObSM/vmPHvqPHqIt4py0js83Z%2B%2B77z5t2bJFd9xxh8aNG6cdO3aooaHB54VDQ0OVnZ2tFStW6P/%2B7/906NAhLV%2B%2BXMOHD9fIkSNVWVmp1atX68SJE9qzZ4/27Nmj0aNHS5JycnK0bt06ffTRR6qvr1dhYaHCwsI0YMAAhYSEaPTo0VqxYoWqqqrkdDr10ksvafDgwerUqZNSUlKUnp6ugoIC1dXVqby8XMXFxcrJyZEkDR8%2B/LxrAwAAtCTIfYEPJDkcDm3atElbt27VqVOndO%2B99yo7O1tdu3Zt9TFOnjypBQsWaPPmzTp16pSGDh2q/Px8hYeH6/3339f8%2BfNVXl6uxMREzZo1S0OGDPHs%2B8c//lFFRUU6dOiQ0tPTNW/ePHXr1q3ZcRsaGpSVlaV58%2BYpMjJS0ncP2Ofn5%2Bu9995TRESExo4dq6lTpyooKEiSWlz7QtTUHG02FhoarNjYcDmdx/gt4wLQP//RQ//RQ//c9fK7VpdwSds6o6/VJVjuqqsiLVn3ggPWaW63W1u2bNG8efNUV1enzMxM/fznP9cNN9xgqsZLAgHLPPrnP3roP3roHwLWxUXAsi5gXfDnN0%2BdOqUtW7Zo/Pjxmj17thISEjRnzhz16NFDubm52rhxo8k6AQAAAobPD7mXl5drzZo1WrdunY4dO6ahQ4dq5cqVuummmzzb9OnTR/PmzdPw4cONFgsAABAIfA5Yw4YNU9euXTVx4kTde%2B%2B9iomJabZN//79VVtba6RAAACAQONzwFq1apVuueWWFrf7%2BOOPL6ggAACAQOfzM1jdu3fXpEmTVFJS4hn7/e9/r/Hjxzd7QScAAMDlyOeAtWDBAh09elTJycmesQEDBqipqUkLFy40WhwAAEAg8vkW4TvvvKONGzcqNjbWM5aUlKTFixfrJz/5idHiAAAAApHPV7COHz%2Budu3aNT9QcLDq6%2BuNFAUAABDIfA5Yffr00cKFC3X48GHP2Ndff63nnnvO61UNAAAAlyufbxE%2B9dRTeuSRR/TjH/9YERERampq0rFjx9SlSxe9/vrrF6NGAACAgOJzwOrSpYs2b96st99%2BW19%2B%2BaWCg4PVtWtX9evXTyEhIRejRgAAgIDic8CSpLCwMN1xxx2mawEAALgk%2BByw/vnPf6qgoED/%2BMc/dPz48Wbzf/7zn40UBgAAEKgu6Bms6upq9evXTx06dLgYNQEAAAQ0nwNWWVmZ/vznPysuLu5i1AMAABDwfH5NQ8eOHblyBQAAcB4%2BB6yJEydq2bJlcrvdF6MeAACAgOfzLcK3335bH374odauXatrrrlGwcHeGe3NN980VhwAAEAg8jlgRURE6Pbbb78YtQAAAFwSfA5YCxYsuBh1AAAAXDJ8fgZLkj7//HMtXbpUc%2BbM8Yz9/e9/N1YUAABAIPM5YJWWlmrEiBHasWOHNm3aJOm7l48%2B9NBDvGQUAABAFxCwlixZoieeeEIbN25UUFCQpO/%2BPuHChQu1fPly4wUCAAAEGp8D1qeffqqcnBxJ8gQsSbrzzjtVXl5urjIAAIAA5XPAioyMPOvfIKyurlZYWJiRogAAAAKZzwGrd%2B/e%2BtWvfqW6ujrPWEVFhWbPnq0f//jHRosDAAAIRD6/pmHOnDn62c9%2BpoyMDDU2Nqp3796qr6/X9ddfr4ULF16MGgEAAAKKzwGrc%2BfO2rRpk/bs2aOKigq1b99eXbt2Vd%2B%2Bfb2eyQIAALhc%2BRywJOmKK67QHXfcYboWAACAS4LPAWvgwIHnvVLFu7AAAMDlzueAdffdd3sFrMbGRlVUVMhut%2BtnP/uZ0eIAAAACkc8BKy8v76zj27dv11//%2Ble/CwIAAAh0F/S3CM/mjjvu0ObNm00dDgAAIGAZC1j79u2T2%2B02dTgAAICA5fMtwrFjxzYbq6%2BvV3l5uYYMGWKkKAAAgEDmc8BKSkpq9inCdu3aKTs7W/fff7%2BxwgAAAAKVzwGLt7UDAACcn88Ba926da3e9t577/X18AAAAAHP54A1d%2B5cNTU1NXugPSgoyGssKCiIgAUAAC5LPges3/72t3rttdc0adIkde/eXW63W5988ol%2B85vf6MEHH1RGRsbFqBMAACBgXNAzWEVFRUpISPCM3XzzzerSpYseffRRbdq0yWiBAAAAgcbn92B98cUXio6ObjYeFRWlyspKI0UBAAAEMp8DVmJiohYuXCin0%2BkZO3LkiAoKCnTttdcaLQ4AACAQ%2BXyL8KmnntKsWbNks9kUHh6u4OBg1dXVqX379lq%2BfPnFqBEAACCg%2BByw%2BvXrp927d2vPnj06ePCg3G63EhISdNtttykyMvJi1AgAABBQfA5YknTllVdq0KBBOnjwoLp06WK6JgAAgIDm8zNYx48f1%2BzZs9WrVy/dddddkr57BmvcuHE6cuSI8QIBAAACjc8Ba9GiRdq/f78WL16s4OB/7d7Y2KjFixcbLQ4AACAQ%2BRywtm/frl//%2Bte68847PX/0OSoqSgsWLNCOHTuMFwgAABBofA5Yx44dU1JSUrPxuLg4ffvttyZqAgAACGg%2BB6xrr71Wf/3rXyXJ628Pbtu2Tf/xH/9hrjIAAIAA5fOnCB944AFNmzZN9913n5qamlRcXKyysjJt375dc%2BfOvRg1AgAABBSfA9aYMWMUGhqqN954QyEhIVqxYoW6du2qxYsX684777wYNQIAAAQUnwNWbW2t7rvvPt13330Xox4AAICA5/MzWIMGDfJ69sqEX/3qV%2Brevbvnv0tLS5Wdna3evXtr2LBh2rBhg9f2q1at0tChQ9W7d2/l5OSorKzMM3fixAk988wzuv3225WRkaHp06d7/d3EyspKTZgwQRkZGcrKytKiRYvU1NTU6rUBAABa4nPAysjI0NatW40VsH//fq1fv97z39XV1Zo8ebLGjh2r0tJSzZ07V/n5%2BbLb7ZKknTt3aunSpXrxxRe1d%2B9eZWVladKkSZ5PMC5ZskQOh0M2m03bt2%2BX2%2B3WnDlzPMefNm2aEhISVFJSouLiYpWUlGjlypWtWhsAAKA1fL5FePXVV%2BuXv/ylioqKdO211%2BqKK67wmi8oKGj1sZqamvTss88qNzdXL7/8siRp48aNSkpKUnZ2tiQpMzNTAwcO1OrVq5Weni6bzaZRo0apZ8%2BekqRx48Zp1apV2rVrl4YOHao1a9bohRde0NVXXy1JmjFjhoYNG6avv/5a1dXVOnDggIqLixUZGanIyEjl5uZq5cqVevjhh1tcGwAAoDV8voL12Wef6Yc//KEiIyPldDpVXV3t9c8Xb775ptq1a6fhw4d7xhwOh1JSUry2S0lJ8dwGPHM%2BODhYPXr0kN1u15dffqmjR48qNTXVM3/dddepffv2cjgccjgcSkxMVHR0tGc%2BNTVVFRUVqqura3FtAACA1mj1FayZM2dqyZIlev311z1jy5cv15QpUy5o4W%2B%2B%2BUZLly71Op4kuVwuJSQkeI3FxMR4nqNyuVxeAUmSoqOj5XQ65XK5JH33Zvl/FxUV5Zk/c%2B70sU7Pn2/t1qqurlZNTY3XWGhoB8XHx3uNhYQEe32Fb%2Bif/%2Bih/%2Bgh2rLQUM5Lq7Q6YO3cubPZWFFR0QUHrAULFmjUqFFKTk7WV1995dO%2BLT1kf7550w/on43NZtOyZcu8xqZMmaLp06efdfuoqCsvek2XMvrnP3roP3qItig2NtzqEi5brQ5YZwsmFxpWSktL9fe//12bNm1qNhcbG%2Bu5EnWa0%2BlUXFzcOeddLpeuv/56zzYul0vh4f86qQ4fPqyOHTuqsbHxrPsGBQUpLi6uxbVba8yYMRo4cKDXWGhoBzmdx7zGQkKCFRV1pY4cqVdjY5PgG/rnP3roP3qItuzMnzuXI6tCZqsD1uk/7NzSWGts2LBBhw4dUlZWlqR/BbWMjAw98sgjzYJXWVmZ56H2tLQ0ORwOjRw5UpLU2Nioffv2KTs7W126dFF0dLTnWStJ%2BvTTT3Xy5EmlpaWpurpaVVVVqq2t9YQmu92u5ORkhYeHKz09XW%2B99dY5126t%2BPj4ZrcDa2qOqqHh7N98GxubzjmHltE//9FD/9FDtEWck9ax5Obsk08%2Bqe3bt2v9%2BvVav369ioqKJEnr16/X8OHDVVlZqdWrV%2BvEiRPas2eP9uzZo9GjR0uScnJytG7dOn300Ueqr69XYWGhwsLCNGDAAIWEhGj06NFasWKFqqqq5HQ69dJLL2nw4MHq1KmTUlJSlJ6eroKCAtXV1am8vFzFxcXKycmRpBbXBgAAaA2fX9NgQnR0tNeD6g0NDZKkzp07S5JeffVVzZ8/X88995wSExO1aNEi/ehHP5Ik3X777Xr88cc1Y8YMHTp0SOnp6SoqKlL79u0lSdOnT9exY8d0zz33qKGhQVlZWZo3b55nrV//%2BtfKz89X3759FRERobFjx%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%2B/a3UJPtk6o6/VJQCXPa5gAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwSwNWZWWlpkyZooyMDGVmZurJJ5/UkSNHJEn79%2B/Xgw8%2BqJtuuklDhgzRa6%2B95rXvli1bNHz4cPXq1UujRo3SO%2B%2B845lramrSkiVLNGjQIPXp00ePPvqo/vnPf3rmXS6XZsyYoczMTPXr109z587V8ePHPfMtrQ0AAHA%2BlgasSZMmKSoqSjt37tTatWv1j3/8Qy%2B88IKOHz%2BuiRMn6tZbb9Vf/vIXLVmyRK%2B%2B%2Bqp27Ngh6bsANHv2bOXl5el//ud/lJubq6lTp%2BrgwYOSpD/84Q/auHGjioqKtGvXLiUlJWnKlClyu92SpPz8fNXX12vTpk166623VF5ersWLF0tSi2sDAAC0xLKAdeTIEaWlpWnWrFkKDw9X586dNXLkSH3wwQfavXu3Tp06pccee0wdOnRQamqq7r//ftlsNknS6tWr1b9/f/Xv31/t2rXTiBEj1K1bN23YsEGSZLPZlJubq%2Buuu04RERGaOXOmysvL9fHHH%2Bubb75RSUmJZs6cqbi4OCUkJGjy5Ml66623dOrUqRbXBgAAaIllASsqKkoLFixQp06dPGNVVVWKj4%2BXw%2BFQ9%2B7dFRIS4plLSUlRWVmZJMnhcCglJcXreCkpKbLb7Tp%2B/Lg%2B%2B%2Bwzr/mIiAj94Ac/kN1u1/79%2BxUSEqLu3bt75lNTU/Xtt9/q888/b3FtAACAloRaXcBpdrtdb7zxhgoLC7V161ZFRUV5zcfExMjlcqmpqUkul0vR0dFe89HR0frss890%2BPBhud3us847nU7FxMQoIiJCQUFBXnOS5HQ65XK5zrt2cHDLmbS6ulo1NTVeY6GhHRQfH%2B81FhIS7PUVvqF/3kJDfe8DPbw0Xci5gEsT54J12kTA%2Btvf/qbHHntMs2bNUmZmprZu3XrW7f49FJ1%2Bnupczjff0r4trd0Sm82mZcuWeY1NmTJF06dPP%2Bv2UVFX%2BlwP/oX%2BfSc2NvyC96WHlxZ/zgVcWjgXrGN5wNq5c6eeeOIJ5efn695775UkxcXF6YsvvvDazuVyKSYmRsHBwYqNjZXL5Wo2HxcX59nmbPMdO3ZUXFyc6urq1NjY6LkNeHrb0/PnW7s1xowZo4EDB3qNhYZ2kNN5zGssJCRYUVFX6siRejU2NrXq2PgX%2BuftzPOrNejhpelCzgVcmjgXrAuZlgasDz/8ULNnz9Yrr7yifv36ecbT0tL0pz/9SQ0NDQoN/a5Eu92unj17eubPfCbKbrdr2LBhateuna6//no5HA7dcsstkr57oP7LL79LGVZ8AAALp0lEQVTUDTfcoMTERLndbh04cECpqamefaOiotS1a9cW126N%2BPj4ZrcDa2qOqqHh7D/AGhubzjmHltG/7/jTA3p4aeH/JU7jXLCOZTdnGxoa9PTTTysvL88rXElS//79FRERocLCQtXX1%2Bvjjz/WmjVrlJOTI0kaPXq09u7dq927d%2BvEiRNas2aNvvjiC40YMUKSlJOTo1WrVqm8vFx1dXVavHixevToofT0dMXFxWno0KF6%2BeWXVVtbq4MHD2r58uXKzs5WaGhoi2sDAAC0xLIrWB999JHKy8s1f/58zZ8/32tu27ZtWrFihZ599lkVFRWpU6dOmjlzpgYMGCBJ6tatmxYvXqwFCxaosrJSycnJevXVV3XVVVdJksaOHauamhr99Kc/1bFjx5SRkeH1TNR//dd/6dlnn9WgQYN0xRVX6Cc/%2BYlmzpwpSQoLCzvv2gAAAC0Jcl/IE9/wWU3N0WZjoaHBio0Nl9N57LK4jHvXy%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%2BdW/yx099B899B899B89bHsurbhoiNvt9mv/MWPGaO3atV7/xowZ02y7mpoaLVu2rNnVLrQO/fMfPfQfPfQfPfQfPWx7uIJ1hri4OLlcLq8xl8uloKAgxcXFteoY8fHx/AYBAMBljCtYZ0hLS1NVVZVqa2s9Y3a7XcnJyQoPD7ewMgAAECgIWGdISUlRenq6CgoKVFdXp/LychUXFysnJ8fq0gAAQIAImTdv3jyri2hrbrvtNm3atEnPP/%2B8Nm/erOzsbD366KMKCgoyvlZ4eLhuueUWro5dIPrnP3roP3roP3roP3rYtgS5/X2iGwAAAF64RQgAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAHLApWVlZowYYIyMjKUlZWlRYsWqampyeqyAkr37t2Vlpam9PR0z7/nn3/e6rLavL/85S/KzMzUzJkzm81t2bJFw4cPV69evTRq1Ci98847FlTYtp2rf2vXrtWPfvQjr/MxPT1d//u//2tRpW1XZWWlpkyZooyMDGVmZurJJ5/UkSNHJEn79%2B/Xgw8%2BqJtuuklDhgzRa6%2B9ZnG1bdO5evjVV1%2Bpe/fuzc7D3/3ud1aXfFkKtbqAy9G0adOUmpqqkpISHTp0SBMnTlSnTp308MMPW11aQNm2bZuuueYaq8sIGL/5zW%2B0Zs0a/eAHP2g2t3//fs2ePVvLli3Trbfequ3bt2vq1Knatm2bOnfubEG1bc/5%2BidJffr00euvv/49VxV4Jk2apLS0NO3cuVNHjx7VlClT9MILLyg/P18TJ07U6NGjVVRUpIqKCj3yyCO65pprNGTIEKvLblPO1cPHHntMkmS32y2uEBJXsL53drtdBw4cUF5eniIjI5WUlKTc3FzZbDarS8Mlrl27ducMCKtXr1b//v3Vv39/tWvXTiNGjFC3bt20YcMGCyptm87XP7TOkSNHlJaWplmzZik8PFydO3fWyJEj9cEHH2j37t06deqUHnvsMXXo0EGpqam6//77%2Bd54hvP1EG0LAet75nA4lJiYqOjoaM9YamqqKioqVFdXZ2FlgaegoEADBgzQzTffrPz8fB07dszqktq0hx56SJGRkWedczgcSklJ8RpLSUnhN%2BF/c77%2BSVJVVZUefvhh9enTR4MGDdL69eu/x%2BoCQ1RUlBYsWKBOnTp5xqqqqhQfHy%2BHw6Hu3bsrJCTEM5eSkqKysjIrSm2zztfD037xi1%2BoX79%2BuvXWW1VQUKBTp05ZUeplj4D1PXO5XIqKivIaOx22nE6nFSUFpBtvvFGZmZnasWOHbDabPvroIz333HNWlxWwXC6XV%2BiXvjsvOSdbJy4uTklJSXriiSf07rvv6vHHH9dTTz2l0tJSq0tr0%2Bx2u9544w099thjZ/3eGBMTI5fLxTOq5/HvPQwLC1OvXr00ePBg7dq1S0VFRdqwYYP%2B%2B7//2%2BoyL0sELAu43W6rSwh4NptN999/v8LCwnTdddcpLy9PmzZt0smTJ60uLWBxXl64AQMG6Le//a1SUlIUFhamYcOGafDgwVq7dq3VpbVZf/vb3/Too49q1qxZyszMPOd2QUFB32NVgeXMHsbHx%2BvNN9/U4MGDdcUVV%2BiGG27QxIkTOQ8tQsD6nsXFxcnlcnmNuVwuBQUFKS4uzqKqAt8111yjxsZGHTp0yOpSAlJsbOxZz0vOyQuXmJio6upqq8tok3bu3KkJEyboqaee0kMPPSTpu%2B%2BNZ14xdblciomJUXAwP6rOdLYenk1iYqK%2B%2BeYbfoGyAGft9ywtLU1VVVWqra31jNntdiUnJys8PNzCygLHvn37tHDhQq%2Bx8vJyhYWFeT2HgNZLS0tr9qyL3W5Xz549LaoosPzpT3/Sli1bvMbKy8vVpUsXiypquz788EPNnj1br7zyiu69917PeFpamj755BM1NDR4xjgHz%2B5cPSwtLVVhYaHXtp9//rkSExO5EmgBAtb3LCUlRenp6SooKFBdXZ3Ky8tVXFysnJwcq0sLGB07dpTNZlNRUZFOnjypiooKvfLKKxozZozXA7JovdGjR2vv3r3avXu3Tpw4oTVr1uiLL77QiBEjrC4tIJw8eVLPP/%2B87Ha7Tp06pU2bNuntt9/W2LFjrS6tTWloaNDTTz%2BtvLw89evXz2uuf//%2BioiIUGFhoerr6/Xxxx9rzZo1fG88w/l6GBkZqeXLl2v9%2BvU6deqU7Ha7fve739FDiwS5uW74vTt48KDy8/P13nvvKSIiQmPHjtXUqVP5DcMH77//vgoKCvTJJ58oLCxMI0eO1MyZM9WuXTurS2uz0tPTJclzhSA09LvX4J3%2BpOCOHTtUUFCgyspKJScna%2B7cuerTp481xbZB5%2Buf2%2B1WYWGh1qxZo5qaGl1zzTX6xS9%2BoaysLMvqbYs%2B%2BOAD/ed//qfCwsKazW3btk3Hjh3Ts88%2Bq7KyMnXq1Enjx4/XAw88YEGlbVdLPdy3b5%2BWLVumL774QpGRkfrpT3%2Bq8ePHc5vVAgQsAAAAw4i0AAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGDY/wMTdh6Zw83jIgAAAABJRU5ErkJggg%3D%3D\"/>\n        </div>\n        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common5650834470530900475\">\n            \n<table class=\"freq table table-hover\">\n    <thead>\n    <tr>\n        <td class=\"fillremaining\">Value</td>\n        <td class=\"number\">Count</td>\n        <td class=\"number\">Frequency (%)</td>\n        <td style=\"min-width:200px\">&nbsp;</td>\n    </tr>\n    </thead>\n    <tr class=\"\">\n        <td class=\"fillremaining\">25</td>\n        <td class=\"number\">804985</td>\n        <td class=\"number\">26.9%</td>\n        <td>\n            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">21</td>\n        <td class=\"number\">464230</td>\n        <td class=\"number\">15.5%</td>\n        <td>\n            <div class=\"bar\" style=\"width:58%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">13</td>\n        <td class=\"number\">320957</td>\n        <td class=\"number\">10.7%</td>\n        <td>\n            <div class=\"bar\" style=\"width:40%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">8</td>\n        <td class=\"number\">179339</td>\n        <td class=\"number\">6.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:23%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">16</td>\n        <td class=\"number\">164884</td>\n        <td class=\"number\">5.5%</td>\n        <td>\n            <div class=\"bar\" style=\"width:21%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">28</td>\n        <td class=\"number\">135793</td>\n        <td class=\"number\">4.5%</td>\n        <td>\n            <div class=\"bar\" style=\"width:17%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">24</td>\n        <td class=\"number\">130537</td>\n        <td class=\"number\">4.4%</td>\n        <td>\n            <div class=\"bar\" style=\"width:17%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">20</td>\n        <td class=\"number\">120884</td>\n        <td class=\"number\">4.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:15%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">5</td>\n        <td class=\"number\">96979</td>\n        <td class=\"number\">3.2%</td>\n        <td>\n            <div class=\"bar\" style=\"width:12%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">9</td>\n        <td class=\"number\">84693</td>\n        <td class=\"number\">2.8%</td>\n        <td>\n            <div class=\"bar\" style=\"width:11%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"other\">\n        <td class=\"fillremaining\">Other values (18)</td>\n        <td class=\"number\">484900</td>\n        <td class=\"number\">16.2%</td>\n        <td>\n            <div class=\"bar\" style=\"width:60%\">&nbsp;</div>\n        </td>\n</tr>\n</table>\n        </div>\n        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme5650834470530900475\">\n            <p class=\"h4\">Minimum 5 values</p>\n            \n<table class=\"freq table table-hover\">\n    <thead>\n    <tr>\n        <td class=\"fillremaining\">Value</td>\n        <td class=\"number\">Count</td>\n        <td class=\"number\">Frequency (%)</td>\n        <td style=\"min-width:200px\">&nbsp;</td>\n    </tr>\n    </thead>\n    <tr class=\"\">\n        <td class=\"fillremaining\">1</td>\n        <td class=\"number\">7110</td>\n        <td class=\"number\">0.2%</td>\n        <td>\n            <div class=\"bar\" style=\"width:8%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">2</td>\n        <td class=\"number\">16728</td>\n        <td class=\"number\">0.6%</td>\n        <td>\n            <div class=\"bar\" style=\"width:18%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">3</td>\n        <td class=\"number\">3997</td>\n        <td class=\"number\">0.1%</td>\n        <td>\n            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">4</td>\n        <td class=\"number\">30265</td>\n        <td class=\"number\">1.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:31%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">5</td>\n        <td class=\"number\">96979</td>\n        <td class=\"number\">3.2%</td>\n        <td>\n            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n        </td>\n</tr>\n</table>\n            <p class=\"h4\">Maximum 5 values</p>\n            \n<table class=\"freq table table-hover\">\n    <thead>\n    <tr>\n        <td class=\"fillremaining\">Value</td>\n        <td class=\"number\">Count</td>\n        <td class=\"number\">Frequency (%)</td>\n        <td style=\"min-width:200px\">&nbsp;</td>\n    </tr>\n    </thead>\n    <tr class=\"\">\n        <td class=\"fillremaining\">24</td>\n        <td class=\"number\">130537</td>\n        <td class=\"number\">4.4%</td>\n        <td>\n            <div class=\"bar\" style=\"width:17%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">25</td>\n        <td class=\"number\">804985</td>\n        <td class=\"number\">26.9%</td>\n        <td>\n            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">26</td>\n        <td class=\"number\">18893</td>\n        <td class=\"number\">0.6%</td>\n        <td>\n            <div class=\"bar\" style=\"width:3%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">27</td>\n        <td class=\"number\">18711</td>\n        <td class=\"number\">0.6%</td>\n        <td>\n            <div class=\"bar\" style=\"width:3%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">28</td>\n        <td class=\"number\">135793</td>\n        <td class=\"number\">4.5%</td>\n        <td>\n            <div class=\"bar\" style=\"width:17%\">&nbsp;</div>\n        </td>\n</tr>\n</table>\n        </div>\n    </div>\n</div>\n</div><div class=\"row variablerow ignore\">\n    <div class=\"col-md-3 namecol\">\n        <p class=\"h4 pp-anchor\" id=\"pp_var_click_timestamp\"><s>click_timestamp</s><br/>\n            <small>Highly correlated</small>\n        </p>\n    </div><div class=\"col-md-3\">\n    <p><em>This variable is highly correlated with <a href=\"#pp_var_session_start\"><code>session_start</code></a> and should be ignored for analysis</em></p>\n</div>\n<div class=\"col-md-6\">\n    <table class=\"stats \">\n        <tr>\n            <th>Correlation</th>\n            <td>0.99901</td>\n        </tr>\n    </table>\n</div>\n</div><div class=\"row variablerow\">\n    <div class=\"col-md-3 namecol\">\n        <p class=\"h4 pp-anchor\" id=\"pp_var_session_id\">session_id<br/>\n            <small>Numeric</small>\n        </p>\n    </div><div class=\"col-md-6\">\n    <div class=\"row\">\n        <div class=\"col-sm-6\">\n            <table class=\"stats \">\n                <tr>\n                    <th>Distinct count</th>\n                    <td>1048594</td>\n                </tr>\n                <tr>\n                    <th>Unique (%)</th>\n                    <td>35.1%</td>\n                </tr>\n                <tr class=\"ignore\">\n                    <th>Missing (%)</th>\n                    <td>0.0%</td>\n                </tr>\n                <tr class=\"ignore\">\n                    <th>Missing (n)</th>\n                    <td>0</td>\n                </tr>\n                <tr class=\"ignore\">\n                    <th>Infinite (%)</th>\n                    <td>0.0%</td>\n                </tr>\n                <tr class=\"ignore\">\n                    <th>Infinite (n)</th>\n                    <td>0</td>\n                </tr>\n            </table>\n\n        </div>\n        <div class=\"col-sm-6\">\n            <table class=\"stats \">\n\n                <tr>\n                    <th>Mean</th>\n                    <td>1507500000000000</td>\n                </tr>\n                <tr>\n                    <th>Minimum</th>\n                    <td>1506825423271737</td>\n                </tr>\n                <tr>\n                    <th>Maximum</th>\n                    <td>1508211379189330</td>\n                </tr>\n                <tr class=\"ignore\">\n                    <th>Zeros (%)</th>\n                    <td>0.0%</td>\n                </tr>\n            </table>\n        </div>\n    </div>\n</div>\n<div class=\"col-md-3 collapse in\" id=\"minihistogram-3152880652672153600\">\n    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAA4dJREFUeJzt271LK2kYhvH7bCQRLIWAELQQSWMgqE1CCsFmU6ngB4KIICKWAQtBLSQu2ERQu%2BNfEFAhjV%2BthYiLEQJKChEEwUKtbIyG2WJZd4XjczKe8WP1%2BoHNxHd4A14%2Bk0nyzXEcRwB%2B6Lf33gDwkVW99wbwVNvUlus1f/7x%2ByvsBBITBDB9ignCf128FiYIYCAQwEAggIFAAAOBAIZPcRfrJbjzhUowQQDDl50gn4nbacgkrBwTBDAQCGAgEMBAIICBQAADd7Fc4G7R18MEAQwfboK85B1ufEyf4dMKHy4QvL7P8If7VrjEAgwEAhgIBDAQCGAgEMDAXaxXxC3r/z8mCGAgEMBAIICBQAADgQAGAgEMBAIYeB8EFfmq7%2BkwQQADgQAGAgEMBAIYCAQwEAhgIBDAQCCAgUAAA4EABgIBDAQCGAgEMHxzHMd5700AHxUTBDAQCGAgEMBAIICBQODa7u6u4vG4UqmU67VXV1caGRlROBzW3d3dk8fC4bCam5sViUQef9LptFfbfhG%2Bkw5XVlZWtLq6qoaGBtdri8WixsbG1Nra%2BuzvbG1tKRQK/coWPcUEgSuBQMAMZGNjQ52dnYpGo%2Bro6FA2m3187ObmRgsLC%2Brr63ur7f4yJghcGRoaevaxQqGgqakpLS8vKxaLKZ/Pa3R0VE1NTWppaVEsFpMk7e/vP3uOTCajfD6v29tbJZNJTU5OqqamxvPnUSkmCDyzvr6u9vZ2JRIJ%2BXw%2BtbW1KZlMKpfLVbQ%2BGo0qHo9rZ2dH2WxWR0dHmp2dfeVd25gg8Mz5%2Bbn29vYUiUQejzmOo0QiUdH6/16ONTY2amJiQuPj45qbm5Pf7/d8v5UgEHimurpaAwMDmpmZ8eR8oVBI5XJZ19fXqqur8%2BScbnGJBc/U19erWCw%2BOXZ5ealyufzTtcfHx5qfn39y7PT0VH6/X8Fg0NN9ukEg8ExPT48ODw%2B1tramUqmkk5MT9fb2ant7%2B6dra2trlc1m9f37d5VKJZ2dnWlxcVH9/f3y%2BXxvsPsf49O8cOWf1xcPDw%2BSpKqqv6/SC4WCJGlzc1NLS0u6uLhQMBjU4OCghoeHJUnT09PK5XJyHEf39/ePryvS6bS6urp0cHCgTCajYrEov9%2Bv7u5upVIpBQKBN36W/yIQwMAlFmAgEMBAIICBQAADgQAGAgEMBAIYCAQwEAhgIBDA8Bc04%2BJY81RFXAAAAABJRU5ErkJggg%3D%3D\">\n\n</div>\n<div class=\"col-md-12 text-right\">\n    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives-3152880652672153600,#minihistogram-3152880652672153600\"\n       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n        Toggle details\n    </a>\n</div>\n<div class=\"row collapse col-md-12\" id=\"descriptives-3152880652672153600\">\n    <ul class=\"nav nav-tabs\" role=\"tablist\">\n        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles-3152880652672153600\"\n                                                  aria-controls=\"quantiles-3152880652672153600\" role=\"tab\"\n                                                  data-toggle=\"tab\">Statistics</a></li>\n        <li role=\"presentation\"><a href=\"#histogram-3152880652672153600\" aria-controls=\"histogram-3152880652672153600\"\n                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n        <li role=\"presentation\"><a href=\"#common-3152880652672153600\" aria-controls=\"common-3152880652672153600\"\n                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n        <li role=\"presentation\"><a href=\"#extreme-3152880652672153600\" aria-controls=\"extreme-3152880652672153600\"\n                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n\n    </ul>\n\n    <div class=\"tab-content\">\n        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles-3152880652672153600\">\n            <div class=\"col-md-4 col-md-offset-1\">\n                <p class=\"h4\">Quantile statistics</p>\n                <table class=\"stats indent\">\n                    <tr>\n                        <th>Minimum</th>\n                        <td>1506825423271737</td>\n                    </tr>\n                    <tr>\n                        <th>5-th percentile</th>\n                        <td>1506900000000000</td>\n                    </tr>\n                    <tr>\n                        <th>Q1</th>\n                        <td>1507100000000000</td>\n                    </tr>\n                    <tr>\n                        <th>Median</th>\n                        <td>1507500000000000</td>\n                    </tr>\n                    <tr>\n                        <th>Q3</th>\n                        <td>1507700000000000</td>\n                    </tr>\n                    <tr>\n                        <th>95-th percentile</th>\n                        <td>1508200000000000</td>\n                    </tr>\n                    <tr>\n                        <th>Maximum</th>\n                        <td>1508211379189330</td>\n                    </tr>\n                    <tr>\n                        <th>Range</th>\n                        <td>1385955917593</td>\n                    </tr>\n                    <tr>\n                        <th>Interquartile range</th>\n                        <td>625260000000</td>\n                    </tr>\n                </table>\n            </div>\n            <div class=\"col-md-4 col-md-offset-2\">\n                <p class=\"h4\">Descriptive statistics</p>\n                <table class=\"stats indent\">\n                    <tr>\n                        <th>Standard deviation</th>\n                        <td>385520000000</td>\n                    </tr>\n                    <tr>\n                        <th>Coef of variation</th>\n                        <td>0.00025574</td>\n                    </tr>\n                    <tr>\n                        <th>Kurtosis</th>\n                        <td>-1.1114</td>\n                    </tr>\n                    <tr>\n                        <th>Mean</th>\n                        <td>1507500000000000</td>\n                    </tr>\n                    <tr>\n                        <th>MAD</th>\n                        <td>335290000000</td>\n                    </tr>\n                    <tr class=\"\">\n                        <th>Skewness</th>\n                        <td>0.18076</td>\n                    </tr>\n                    <tr>\n                        <th>Sum</th>\n                        <td>3594316781523237933</td>\n                    </tr>\n                    <tr>\n                        <th>Variance</th>\n                        <td>1.4863e+23</td>\n                    </tr>\n                    <tr>\n                        <th>Memory size</th>\n                        <td>22.8 MiB</td>\n                    </tr>\n                </table>\n            </div>\n        </div>\n        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram-3152880652672153600\">\n            <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3Xt0TXf%2B//FXLhWVSOLQRL9GxbhVLlK3plIVQhlVqiZF%2Bu1SVXVtTdJqUdXSC6aotlhKu6rVdtqM6LdIKV9DjJa20wuTBG0FX5qFhJyDRFyS7N8f/TnjoJITnzi5PB9rWZbPZ3/25/POObaXvffZx8uyLEsAAAAwxtvTCwAAAKhpCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDBfTy%2BgtsjLO%2BX2GG9vL9ls/srPL1RpqVUJq6q6qJ3aqb32oHZqr8zab7qpfqXt%2B2o4g1WFeXt7ycvLS97eXp5eynVH7dRe21A7tdc2Nb12AhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGObr6QUAQFXX9/WvPL0Et6xLutPTSwBqPc5gAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMr8rBdcPXjQAAagvOYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGVYmANXPmTLVp08b55%2B3btyshIUEdOnRQv379tHr1apftly9frj59%2BqhDhw5KTExUZmams%2B/s2bN6/vnn1a1bN8XExGjChAmy2%2B3O/pycHI0aNUoxMTHq0aOH5syZo9LS0nLPDQAAUBaPB6zdu3dr1apVzj/n5uZq3LhxGjp0qLZv366pU6dq2rRpysjIkCRt2rRJCxYs0Kuvvqpt27apR48eGjNmjE6fPi1Jmj9/vrKyspSSkqL169fLsixNmTLFuf8nnnhCoaGh2rhxo5YtW6aNGzfq/fffL9fcAAAA5eHRgFVaWqoXXnhBw4cPd7atWbNGYWFhSkhIkJ%2Bfn2JjYxUfH68VK1ZIklJSUjRo0CBFR0erbt26GjlypCRp8%2BbNKi4uVmpqqsaNG6ebb75ZwcHBSkpKUnp6uo4ePaqMjAzt2bNHEydOVP369RUWFqbhw4crJSWlXHMDAACUh0cD1ieffCI/Pz/179/f2ZaVlaXw8HCX7cLDw52XAS/t9/b2Vtu2bZWRkaGDBw/q1KlTioiIcPa3aNFCdevWVVZWlrKystSkSRMFBQU5%2ByMiIrR//34VFBSUOTcAAEB5eOy7CI8dO6YFCxbogw8%2BcGl3OBwKDQ11aQsODnbeR%2BVwOFwCkiQFBQXJbrfL4XBIkgIDA136AwMDnf2X9l3Y14X%2Bq81dXrm5ucrLy3Np8/Wtp5CQELf24%2BPj7fI7ri9fX8/83Gvz616bazfJU%2B/diqrNrzu119zaPRawZs2apUGDBqlly5b69ddf3RprWVaF%2B8saa0JKSooWLlzo0jZ%2B/HhNmDChQvsLDLzRxLLgpgYN/D06f21%2B3Wtz7SZ4%2Br1bUbX5daf2mscjAWv79u368ccflZaWdllfgwYNnGeiLrDb7bLZbL/b73A41KpVK%2Bc2DodD/v7/OcCcOHFCDRs2VElJyRXHenl5yWazlTl3eQ0ZMkTx8fEubb6%2B9WS3F7q1Hx8fbwUG3qiTJ4tUUlJa9gAY5e7rZUptft1rc%2B0meeq9W1G1%2BXWn9sqv3VP/4fBIwFq9erWOHz%2BuHj16SPrPWaWYmBiNGDHisuCVmZmp6OhoSVJkZKSysrJ0//33S5JKSkq0a9cuJSQkqGnTpgoKCnLeayVJP//8s86dO6fIyEjl5ubq8OHDys/Pd4amjIwMtWzZUv7%2B/oqKitLKlSt/d%2B7yCgkJuexyYF7eKRUXV%2BwNVFJSWuGxqDhP/8xr8%2Btem2s3obr%2B7Grz607tNa92j1z4nDx5stavX69Vq1Zp1apVWrp0qSRp1apV6t%2B/v3JycrRixQqdPXtWW7Zs0ZYtWzR48GBJUmJioj777DPt2LFDRUVFWrx4serUqaPu3bvLx8dHgwcP1ltvvaXDhw/Lbrfrtdde0913361GjRopPDxcUVFRmjdvngoKCpSdna1ly5YpMTFRksqcGwAAoDw8cgYrKCjI5Ub14uJiSVLjxo0lSUuWLNHLL7%2BsGTNmqEmTJpozZ45uvfVWSVK3bt305JNPKikpScePH1dUVJSWLl2qunXrSpImTJigwsJC3XfffSouLlaPHj00ffp051xvvvmmpk2bpjvvvFMBAQEaOnSoHnzwQUlSw4YNrzo3AABAeXhZ1%2BOubygv75TbY3x9vdWggb/s9sIacfq07%2BtfeXoJblmXdKdH5q1pr7s7qmrtvHcrV1V93a8Haq/82m%2B6qX6l7ftqauZnIwEAADyIgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYJivpxcAVFV9X//K00twy7qkOz29BADA/8cZLAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYxpc9A0ANU52%2BqJwvKUdNxRksAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGObRgLVnzx49/PDD6tixo2JjY5WUlKS8vDxJ0vbt25WQkKAOHTqoX79%2BWr16tcvY5cuXq0%2BfPurQoYMSExOVmZnp7Dt79qyef/55devWTTExMZowYYLsdruzPycnR6NGjVJMTIx69OihOXPmqLS01Nlf1twAAABX47GAde7cOY0YMUK33367tm/frrS0NB0/flzTp09Xbm6uxo0bp6FDh2r79u2aOnWqpk2bpoyMDEnSpk2btGDBAr366qvatm2bevTooTFjxuj06dOSpPnz5ysrK0spKSlav369LMvSlClTnHM/8cQTCg0N1caNG7Vs2TJt3LhR77//viSVOTcAAEBZPBawioqKlJycrNGjR6tOnTqy2Wy6%2B%2B679csvv2jNmjUKCwtTQkKC/Pz8FBsbq/j4eK1YsUKSlJKSokGDBik6Olp169bVyJEjJUmbN29WcXGxUlNTNW7cON18880KDg5WUlKS0tPTdfToUWVkZGjPnj2aOHGi6tevr7CwMA0fPlwpKSmSVObcAAAAZfH11MRBQUF64IEHnH/et2%2Bf/ud//kd9%2B/ZVVlaWwsPDXbYPDw/XunXrJElZWVm65557nH3e3t5q27atMjIy1LZtW506dUoRERHO/hYtWqhu3brKyspSbm6umjRpoqCgIGd/RESE9u/fr4KCgjLnLo/c3Fznpc4LfH3rKSQkpNz7kCQfH2%2BX34Gr8fWt/u8T3vO1j6%2Bvd61%2B3am95tbusYB1QU5Ojvr06aPi4mINHjxYEyZM0GOPPabQ0FCX7YKDg533UTkcDpeAJP0W2Ox2uxwOhyQpMDDQpT8wMNDZf2nfhX1d6L/a3OWRkpKihQsXurSNHz9eEyZMKPc%2BXNd%2BY4XGoXZp0MDf00swhvd87XHx%2B7Y2v%2B7UXvN4PGA1adJEGRkZ%2Br//%2Bz89//zzeuaZZ8o1zrKsCveXNfZaDRkyRPHx8S5tvr71ZLcXurUfHx9vBQbeqJMni1RSUlr2ANRq7r6/qiLe87WP3V5Yq193aq/82j31n0%2BPByxJ8vLyUlhYmJKTkzV06FDFxcU5z0RdYLfbZbPZJEkNGjS4rN/hcKhVq1bObRwOh/z9//NDPXHihBo2bKiSkpIrjvXy8pLNZrvivi%2BeuzxCQkIuuxyYl3dKxcUVewOVlJRWeCxqj5r0HuE9X3tc/DrX5ted2mte7R678Ll9%2B3b16dPH5fEI3t6/Laddu3Yuj12QpMzMTEVHR0uSIiMjlZWV5ewrKSnRrl27FB0draZNmyooKMil/%2Beff9a5c%2BcUGRmpyMhIHT58WPn5%2Bc7%2BjIwMtWzZUv7%2B/oqKirrq3AAAAGXxWMCKjIxUQUGB5syZo6KiIuXn52vBggXq1KmTEhMTlZOToxUrVujs2bPasmWLtmzZosGDB0uSEhMT9dlnn2nHjh0qKirS4sWLVadOHXXv3l0%2BPj4aPHiw3nrrLR0%2BfFh2u12vvfaa7r77bjVq1Ejh4eGKiorSvHnzVFBQoOzsbC1btkyJiYmSpP79%2B191bgAAgLJ4LGDVr19f7777rjIzM3XHHXeoX79%2Bql%2B/vl577TU1bNhQS5Ys0YcffqiOHTtq5syZmjNnjm699VZJUrdu3fTkk08qKSlJt99%2Bu7Zt26alS5eqbt26kqQJEyYoOjpa9913n3r27Cl/f3%2B98sorzrnffPNN5ebm6s4779SwYcM0cOBAPfjgg5JU5twAAABl8bIq%2B45vSPrtHix3%2Bfp6q0EDf9nthTXi%2BnTf17/y9BJqtHVJd3p6Cdesqr7nee9WnnVJd1bZ1/16oPbKr/2mm%2BpX2r6vpmY%2BfAIAAMCDqsSnCAFcu%2Bp0lqUmnG0DgKvhDBYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYW4HrPj4eC1cuFCHDx%2BujPUAAABUe24HrD//%2Bc9au3atevXqpZEjR2rDhg0qLi6ujLUBAABUS24HrPHjx2vt2rX6%2B9//rlatWmnmzJmKi4vTnDlztH///spYIwAAQLVS4S97joiIUEREhJ555hmtXbtW06dP17vvvqvY2Fj95S9/Ubt27UyuEwAAuIkvgfecCt/kfv78ea1du1aPPfaYJk2apNDQUE2ZMkVt27bV8OHDtWbNGpPrBAAAqDbcPoOVnZ2t1NRUffbZZyosLFSfPn30/vvvq2PHjs5tOnfurOnTp6t///5GF4vLVaf/nQAAUFu4HbD69eun5s2ba/To0Ro4cKCCg4Mv2yYuLk75%2BflGFggAAFDduB2wli9frttvv73M7Xbu3FmhBQEAAFR3bgesNm3aaMyYMUpISFCvXr0kSe%2B9956%2B%2BuorzZkz54pntAAAuJLqdptDTbsRG5XH7ZvcZ82apVOnTqlly5bOtu7du6u0tFSzZ882ujgAAIDqyO0zWF9%2B%2BaXWrFmjBg0aONvCwsI0d%2B5c3XvvvUYXBwAAUB25fQbrzJkz8vPzu3xH3t4qKioysigAAIDqzO2A1blzZ82ePVsnTpxwth09elQzZsxweVQDAABAbeX2JcJnn31WI0aMUJcuXRQQEKDS0lIVFhaqadOm%2BuCDDypjjQAAANWK2wGradOm%2Bvzzz/XPf/5TBw8elLe3t5o3b66uXbvKx8enMtYIAABQrVTouwjr1KnjfEQDAAAAXLkdsA4dOqR58%2Bbpl19%2B0ZkzZy7r/8c//mFkYQAAANVVhe7Bys3NVdeuXVWvXr3KWBMAAEC15nbAyszM1D/%2B8Q/ZbLbKWA8AAEC15/ZjGho2bMiZKwAAgKtwO2CNHj1aCxculGVZlbEeAACAas/tS4T//Oc/9cMPP%2BjTTz/VH/7wB3l7u2a0Tz75xNjiAAAAqiO3A1ZAQIC6detWGWsBAACoEdwOWLNmzaqMdQAAANQYbt%2BDJUn79u3TggULNGXKFGfbjz/%2BaGxRAAAA1ZnbAWv79u0aMGCANmzYoLS0NEm/PXx02LBhPGQUAABAFQhY8%2BfP19NPP601a9bIy8tL0m/fTzh79mwtWrTI%2BAIBAACqG7cD1s8//6zExERJcgYsSfrTn/6k7OxscysDAACoptwOWPXr17/idxDm5uaqTp06RhYFAABQnbkdsDp06KCZM2eqoKDA2bZ//35NmjRJXbp0Mbo4AACA6sjtxzRMmTJFDz/8sGJiYlRSUqIOHTqoqKhIrVq10uzZsytjjQAAANWK2wGrcePGSktL05YtW7R//37VrVtXzZs315133ulyTxYAAEBt5XbAkqQbbrhBvXr1Mr0WAACAGsHtgBUfH3/VM1U8CwsAANR2bgese%2B65xyVglZSUaP/%2B/crIyNDDDz9sdHEAAADVkdsBa%2BLEiVdsX79%2Bvb755ptrXhAAAEB1V6HvIrySXr166fPPPze1OwAAgGrLWMDatWuXLMsytTsAAIBqy%2B1LhEOHDr2sraioSNnZ2erdu7eRRQEAAFRnbgessLCwyz5F6Ofnp4SEBD3wwAPGFgYAAFBduR2weFo7AADA1bkdsD777LNybztw4EB3dw8AAFDtuR2wpk6dqtLS0stuaPfy8nJp8/LyImABAIBaye2A9c477%2Bjdd9/VmDFj1KZNG1mWpZ9%2B%2Bklvv/22HnroIcXExFTGOgEAAKqNCt2DtXTpUoWGhjrbOnXqpKZNm%2BrRRx9VWlqa0QUCAABUN24/B%2BvAgQMKCgq6rD0wMFA5OTlGFgUAAFCduR2wmjRpotmzZ8tutzvbTp48qXnz5umWW24xujgAAIDqyO1LhM8%2B%2B6yeeuoppaSkyN/fX97e3iooKFDdunW1aNGiylgjAABAteJ2wOratavS09O1ZcsWHTlyRJZlKTQ0VHfddZfq16/v1r5ycnI0c%2BZMfffdd/Lx8VG3bt307LPPKjAwULt379Yrr7yi3bt3q2HDhho6dKhGjBjhHLt27VotXrxYv/76q5o3b64nn3xSXbt2lSSVlpbqjTfeUFpamk6ePKl27dpp%2BvTpatq0qSTJ4XBo%2BvTp%2Bvbbb%2BXt7a24uDhNmzZNdevWlaQy5wYAALiaCn0X4Y033qiePXuqZ8%2BeeuSRR3TPPfe4Ha4kacyYMQoMDNSmTZv06aef6pdfftFf//pXnTlzRqNHj9Ydd9yhrVu3av78%2BVqyZIk2bNgg6bcANGnSJE2cOFFff/21hg8frscff1xHjhyRJH300Udas2aNli5dqs2bNyssLEzjx493PkZi2rRpKioqUlpamlauXKns7GzNnTtXksqcGwAAoCxuB6wzZ85o0qRJat%2B%2Bvfr27Svpt3uwRo4cqZMnT5Z7PydPnlRkZKSeeuop%2Bfv7q3Hjxrr//vv13XffKT09XefPn9fYsWNVr149RURE6IEHHlBKSookacWKFYqLi1NcXJz8/Pw0YMAAtW7dWqtXr5YkpaSkaPjw4WrRooUCAgKUnJys7Oxs7dy5U8eOHdPGjRuVnJwsm82m0NBQjRs3TitXrtT58%2BfLnBsAAKAsbl8inDNnjnbv3q25c%2BfqmWeecbaXlJRo7ty5evHFF8u1n8DAQM2aNcul7fDhwwoJCVFWVpbatGkjHx8fZ194eLhWrFghScrKylJcXJzL2PDwcGVkZOjMmTPau3evwsPDnX0BAQFq1qyZMjIydOrUKfn4%2BKhNmzbO/oiICJ0%2BfVr79u0rc%2B7yyM3NVV5enkubr289hYSElHsfkuTj4%2B3yO1BT9H39K08vAagQX1%2Bzx2OO8/9h%2BmfraW4HrPXr1%2BvDDz9UWFiYJk2aJOk/YWngwIHlDliXysjI0IcffqjFixdr3bp1CgwMdOkPDg6Ww%2BFQaWmpHA7HZY%2BKCAoK0t69e3XixAlZlnXFfrvdruDgYAUEBLh8YfWFbe12uxwOx1Xn9vYu%2Bw2QkpKihQsXurSNHz9eEyZMKPsHcQWBgTdWaBwAwKwGDfwrZb8c5yvvZ%2BspbgeswsJChYWFXdZus9l0%2BvTpCi3i%2B%2B%2B/19ixY/XUU08pNjZW69atu%2BJ2F4eiS7%2Bq51JX6y9rbFlzl2XIkCGKj493afP1rSe7vdCtOX18vBUYeKNOnixSSUmpW2MBAOa5exwvC8f5/zD9s73AU8HN7YB1yy236JtvvlFMTIxLUPniiy/0X//1X24vYNOmTXr66ac1bdo053cX2mw2HThwwGU7h8Oh4OBgeXt7q0GDBnI4HJf122w25zZX6m/YsKFsNpsKCgpUUlLivAx4YdsL/VebuzxCQkIuuxyYl3dKxcUV%2B8tTUlJa4bEAAHMq61jMcb7yfrae4vYFzwcffFBPPPGE/vrXv6q0tFTLli3TU089pWeffVYPP/ywW/v64YcfNGnSJL3xxhsuXwwdGRmpn376ScXFxc62jIwMRUdHO/szMzNd9nWh38/PT61atVJWVpaz7%2BTJkzp48KDatWuntm3byrIs7dmzx2VsYGCgmjdvXubcAAAAZXE7YA0ZMkSTJk3S119/LR8fH7311lvKycnR3LlzlZiYWO79FBcX67nnntPEiROdz6%2B6IC4uTgEBAVq8eLGKioq0c%2BdOpaamOvc/ePBgbdu2Tenp6Tp79qxSU1N14MABDRgwQJKUmJio5cuXKzs7WwUFBZo7d67atm2rqKgo2Ww29enTR6%2B//rry8/N15MgRLVq0SAkJCfL19S1zbgAAgLJ4WW7ekJSfny%2BbzXbNE3/33Xf67//%2Bb9WpU%2Beyvi%2B%2B%2BEKFhYV64YUXlJmZqUaNGumxxx7Tgw8%2B6Nxmw4YNmjdvnnJyctSyZUtNnTpVnTt3lvTbPVYLFizQJ598osLCQsXExOjFF19U48aNJUmnTp3SCy%2B8oM2bN%2BuGG27Qvffeq8mTJzvX8vPPP1917orIyzvl9hhfX281aOAvu73wd0%2Bd8mksALh%2B1iXdaXR/5TnOX4vq9G%2BE6Z/tBTfd5P5zOk1wO2C1b99eP/zwg1s3fYOABQA1AQGr8tS0gOX2JcKYmJjf/ZQfAAAAKvApwptvvlmvvPKKli5dqltuuUU33HCDS/%2B8efOMLQ4AAKA6cjtg7d27V3/84x8l/fZgTgAAALgqd8BKTk7W/Pnz9cEHHzjbFi1apPHjx1fKwgAAAKqrct%2BDtWnTpsvali5danQxAAAANUG5A9aVPmxYka%2BcAQAAqOnKHbCu9FgGHtUAAABwObcf0wAAAICrI2ABAAAYVu5PEZ4/f15PPfVUmW08BwsAANR25Q5YHTt2VG5ubpltAAAAtV25A9bFz78CAADA7%2BMeLAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADDM19MLAACguuj7%2BleeXgKqCc5gAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAY5tGAtXXrVsXGxio5OfmyvrVr16p///5q3769Bg0apC%2B//NLZV1paqvnz56tnz57q3LmzHn30UR06dMjZ73A4lJSUpNjYWHXt2lVTp07VmTNnnP27d%2B/WQw89pI4dO6p379569913yz03AABAWTwWsN5%2B%2B229/PLLatas2WV9u3fv1qRJkzRx4kR9/fXXGj58uB5//HEdOXJEkvTRRx9pzZo1Wrp0qTZv3qywsDCNHz9elmVJkqZNm6aioiKlpaVp5cqVys7O1ty5cyVJZ86c0ejRo3XHHXdo69atmj9/vpYsWaINGzaUa24AAICyeCxg%2Bfn5KTU19YoBa8WKFYqLi1NcXJz8/Pw0YMAAtW7dWqtXr5YkpaSkaPjw4WrRooUCAgKUnJys7Oxs7dy5U8eOHdPGjRuVnJwsm82m0NBQjRs3TitXrtT58%2BeVnp6u8%2BfPa%2BzYsapXr54iIiL0wAMPKCUlpVxzAwAAlMVjAWvYsGGqX7/%2BFfuysrIUHh7u0hYeHq6MjAydOXNGe/fudekPCAhQs2bNlJGRod27d8vHx0dt2rRx9kdEROj06dPat2%2BfsrKy1KZNG/n4%2BLjsOzMzs8y5AQAAysPX0wu4EofDoaCgIJe2oKAg7d27VydOnJBlWVfst9vtCg4OVkBAgLy8vFz6JMlut8vhcCgwMNBlbHBwsBwOh0pLS686d3nl5uYqLy/Ppc3Xt55CQkLKvQ9J8vHxdvkdAICayte3Zv1bVyUDliTn/VQV6S9r7JVcHMgqMv5iKSkpWrhwoUvb%2BPHjNWHChArtLzDwxmtaDwAAVV2DBv6eXoJRVTJgNWjQQA6Hw6XN4XDIZrMpODhY3t7eV%2Bxv2LChbDabCgoKVFJS4rwMeGHbC/0HDhy4bOyF/V5t7vIaMmSI4uPjXdp8fevJbi8s9z6k385cBQbeqJMni1RSUurWWAAAqhN3/40sL08FtyoZsCIjI533RF2QkZGhfv36yc/PT61atVJWVpZuv/12SdLJkyd18OBBtWvXTk2aNJFlWdqzZ48iIiKcYwMDA9W8eXNFRkbq448/VnFxsXx9fZ390dHRZc5dXiEhIZddDszLO6Xi4oqFpJKS0gqPBQCgOqhp/85VyQuegwcP1rZt25Senq6zZ88qNTVVBw4c0IABAyRJiYmJWr58ubKzs1VQUKC5c%2BeY1hT4AAAQOElEQVSqbdu2ioqKks1mU58%2BffT6668rPz9fR44c0aJFi5SQkCBfX1/FxcUpICBAixcvVlFRkXbu3KnU1FQlJiaWa24AAICyeFnXesNRBUVFRUmSiouLJcnlbJIkbdiwQfPmzVNOTo5atmypqVOnqnPnzpJ%2Bu0dqwYIF%2BuSTT1RYWKiYmBi9%2BOKLaty4sSTp1KlTeuGFF7R582bdcMMNuvfeezV58mTVqVNHkvTzzz/rhRdeUGZmpho1aqTHHntMDz74oHNtV5u7ovLyTrk9xtfXWw0a%2BMtuL/zdZN/39a%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%2BHp6AVXRE088oYiICG3cuFHHjx/X6NGj1ahRIz3yyCOeXhoAAKgGOIN1iYyMDO3Zs0cTJ05U/fr1FRYWpuHDhyslJcXTSwMAANUEZ7AukZWVpSZNmigoKMjZFhERof3796ugoEABAQFl7iM3N1d5eXkubb6%2B9RQSEuLWWnx8vF1%2BBwCgpvL1rVn/1hGwLuFwOBQYGOjSdiFs2e32cgWslJQULVy40KXt8ccf1xNPPOHWWnJzc/X%2B%2B%2B9oyJAhvxvOvnvlT27ts7rIzc1VSkrKVWuvqaid2qm99qD2mlt7zYqLhliWdU3jhwwZok8//dTl15AhQ9zeT15enhYuXHjZ2bDagNqpvbahdmqvbWp67ZzBuoTNZpPD4XBpczgc8vLyks1mK9c%2BQkJCamQaBwAA5cMZrEtERkbq8OHDys/Pd7ZlZGSoZcuW8vf39%2BDKAABAdUHAukR4eLiioqI0b948FRQUKDs7W8uWLVNiYqKnlwYAAKoJn%2BnTp0/39CKqmrvuuktpaWl66aWX9PnnnyshIUGPPvqovLy8rvta/P39dfvtt9fKs2fUTu21DbVTe21Tk2v3sq71jm4AAAC44BIhAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEErEqwdetWxcbGKjk5%2BarbTZ482fndhxd%2BderUydnvcDiUlJSk2NhYde3aVVOnTtWZM2ec/efPn9fMmTMVExOjDh06aMKECXI4HM7%2Bjz76SH369FH79u3Vp08fffDBB%2BaLvcT1qP2zzz5zGRcVFaXIyEjFx8c7x69du1b9%2B/dX%2B/btNWjQIH355ZeVU/BFqkrtGzZs0IABA5yv%2B9///vfKKfgiVaX2CwoLC9W9e3dNnjzZbKFXUFVqP3r0qMaOHavbbrtNsbGxmjdvnkpLSyunaFWdumvqcU6Stm/frgceeEAdOnRQt27dNGPGDBUVFTn7a%2BpxTiq7dk8c59xmwailS5davXv3toYOHWolJSVdddtJkyZZb7755u/2P/7449aoUaOs48ePW0eOHLGGDBlivfTSS87%2B2bNnW4mJidaRI0es48ePW0lJSdaSJUssy7Ks9PR0Kzo62tqxY4dVUlJi7dixw4qOjrY2b95spM4ruZ61X%2Bq5556zXn31VcuyLGvXrl1WZGSklZ6ebp05c8ZatWqVFR0dbR0%2BfLhihZVDVal9586dVlRUlPW///u/1vnz56309HQrIiLC%2Bte//lWxwsqhqtR%2BsVmzZlkdO3a0Jk2aVP5CKqCq1F5aWmolJCRYL730knXq1Clr79691p///Gdr27ZtFSusDFWl7pp8nDt%2B/Lh12223WR999JF1/vx56/Dhw1b//v2t2bNnW5ZVs49zZdXuieNcRXAGyzA/Pz%2BlpqaqWbNm17SfY8eOaePGjUpOTpbNZlNoaKjGjRunlStX6vz58zpz5ow%2B/vhjTZ06VaGhobLZbJo/f75GjRolScrMzFSrVq0UHR0tb29vRUdHq3Xr1tq1a5eJMq/oetV%2BqX//%2B99KT0/X2LFjJUkrVqxQXFyc4uLi5OfnpwEDBqh169ZavXr1Na3raqpK7Q6HQ6NHj1avXr3k6%2BuruLg4tW7dWt999901retqqkrtF%2BzZs0dpaWm6//77r2k95VFVav/Xv/6lQ4cO6ZlnnlFAQIBatGih1NRUdenS5ZrW9XuqSt01%2BTi3b98%2BnT59WoMGDZKvr68aN26sbt26affu3ZJq9nGurNo9cZyrCAKWYcOGDVP9%2BvXLvf3XX3%2BtgQMHqn379kpISFBmZqYkaffu3fLx8VGbNm2c20ZEROj06dPat2%2BfsrKyVFxcrF9%2B%2BUU9e/ZUly5d9Nxzz%2Bn06dOSpLvuukt79%2B7VN998o3PnzunHH39Udna2unbtarbgi1yv2i/16quvasyYMQoICJAkZWVlKTw83GWb8PBwZWRkVKSscqkqtXfr1k3jx4939hcXFysvL0%2BhoaEVLa1MVaV2SbIsS9OnT1dycrICAwOvoaryqSq1f//992rdurXmz5%2BvmJgY9ezZU%2B%2B%2B%2B%2B41Vvf7qkrdNfk417ZtW4WEhOhvf/ubzp49q19//VVbtmxR9%2B7dJdXs41xZtXviOFcRBCwPatq0qZo1a6YlS5Zo69at6tSpk0aMGCG73S6Hw6GAgAB5eXk5tw8KCpIk2e12HT16VNJv18NXrlypDz/8UN9%2B%2B63mz58vSWrXrp2mTJmiESNGKCoqSg899JCSkpLUrl2761/oFVxL7Rf7/vvvdeDAASUkJDjbHA6Hc/uLx1861lMqs/ZLzZ07V/Xq1dM999xTOcW4qbJrT0lJkZeXlwYNGlT5xbipMms/cuSIduzYoYYNGyo9PV3PP/%2B85s%2Bfr40bN16f4q6iMuuuycc5f39/LVq0SEuXLlW7du3Us2dPtWrVSg8//LCkmn2cK6v2S1W149wFvp5eQG12cQKXpKefflppaWnauHGj6tatK8uyfnesZVk6f/68kpKSFBwcrODgYI0YMUILFy7U1KlT9fXXX2vevHl655131KFDB2VkZOgvf/mLbr75ZvXq1auySyvTtdR%2Bsffff1%2BDBw%2BWn5%2BfS3t5x3tCZdcu/Vb/3LlzlZaWpuXLl19xG0%2BozNqPHz%2BuN954Q%2B%2B9957LgbuqqMzaLcuSzWbTyJEjJUlxcXG6%2B%2B67tW7dOo//fa/MumvycS4/P1/jxo3TuHHjlJCQoGPHjumZZ57R7NmzNWXKFEk19zhXntqlqnucu4AzWFWIj4%2BPbr75ZuXm5spms6mgoEAlJSXO/gufEGzYsKEaNWokSS6na5s0aaL8/HxZlqWPP/5YvXv3VpcuXeTn56dOnTqpX79%2BSk1Nvb5FlZM7tV9QVFSkLVu2XPYpsgYNGrh8mvLCeJvNVokVVJzJ2iWptLRUkydP1qZNm/Txxx/rj3/8Y%2BUXUUEma589e7YGDhzoctmhKjNZ%2B0033XTZpZsmTZooLy%2BvEiuoGJN11%2BTj3Lp16%2BTv769hw4apXr16uuWWWzRy5EitWLFCUs0%2BzpVVu1Q9jnMELA%2BxLEuzZs3Snj17nG3nzp3TwYMH1bRpU7Vt21aWZbn0Z2RkKDAwUM2bN1eLFi3k5eXlvOlPknJyctS4cWN5eXmptLTU5c17Yf9VwbXWfsFXX32lunXrKiIiwmX/kZGRzmv9F4%2BPjo6upIrKr7Jrl6SZM2fql19%2B0ccff6ymTZtWbkFuqOzaV69erdTUVMXExCgmJkbvvPOOPv/8c8XExFR%2BcWWo7NpbtGihQ4cOqbCw0NmWk5OjJk2aVGJVZavsumvyca60tPSyx2ycO3fOeXa2Jh/nyqpdqrrHuYsRsK6jo0eP6k9/%2BpMOHTokLy8v/frrr5oxY4aOHj2qwsJCzZ07VzfccIN69eolm82mPn366PXXX1d%2Bfr6OHDmiRYsWKSEhQb6%2BvmrUqJF69eql1157TXl5eTp06JCWLVvmvPckPj5e69ev13fffafi4mL9%2B9//1rp163T33XdX%2B9ov2LVrl5o0aXLZ5aDBgwdr27ZtSk9P19mzZ5WamqoDBw5owIAB17tsSde39u%2B//16rV6/W0qVLFRwcfL1Lvcz1rH3Lli1as2aNVq1apVWrVmno0KGKj4/XqlWrrnfZkq5v7fHx8QoMDNSrr76q06dPa/v27dq4caNH7kW73nXX1ONc165ddeTIEf3tb3/TuXPndOTIEb3//vvOS581%2BThXVu1V7Tj3uyr/SRC1S2RkpBUZGWndeuut1q233ur8s2VZ1qFDh6zWrVtbe/futSzLsux2uzV58mQrNjbWateunfXQQw85%2ByzLsk6ePGklJydbt912m9W5c2drxowZ1tmzZ539J06csJKTk6327dtbnTp1smbPnm2dO3fO2f/ee%2B9ZvXv3tqKjo63evXtb77zzjlVaWlojarcsy5o2bZo1atSoK65l/fr1Vu/eva2IiAjrvvvus7799ttKqvo3VaX2KVOmuMx/4dcjjzxS42u/1Jtvvlnpz8GqSrX/9NNP1tChQ62oqCgrLi7O%2BvTTTyup6qpVd00%2Bzn355ZdWQkKC1b59e%2Buuu%2B6ypk2bZp08edLZX5OPc1er3RPHuYrwsqwqfJccAABANcQlQgAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAALgutm7dqtjYWCUnJ7s99tixY3r00UfVpk0bnT171qWvTZs2ioyMVFRUlPPXSy%2B9ZGrZFeJb9iYAAADX5u2331ZqaqqaNWvm9tiffvpJo0ePVseOHX93my%2B%2B%2BEJ/%2BMMfrmWJRnEGCwAAVDo/P7%2BrBqy1a9fqvvvu02233aaePXsqJSXF2Zefn6/XXntNgwcPvl7LvWacwQIAAJVu2LBhv9uXkZGhqVOnasGCBerSpYt%2B/PFHPfbYY2rVqpU6dOigLl26SJK%2B%2Beab393HvHnz9OOPP6qgoEB9%2B/bV5MmT5e/vb7yO8uIMFgAA8KhPP/1U3bt3V9euXeXj46NOnTqpb9%2B%2BWrVqVbnG33bbbYqNjdWGDRuUkpKiHTt2aMaMGZW86qvjDBYAAPCogwcPavv27YqKinK2WZalrl27lmv8xZcTW7RooYkTJ2rs2LF6%2BeWXVadOHePrLY//B4fi2uSwq3MnAAAAAElFTkSuQmCC\"/>\n        </div>\n        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common-3152880652672153600\">\n            \n<table class=\"freq table table-hover\">\n    <thead>\n    <tr>\n        <td class=\"fillremaining\">Value</td>\n        <td class=\"number\">Count</td>\n        <td class=\"number\">Frequency (%)</td>\n        <td style=\"min-width:200px\">&nbsp;</td>\n    </tr>\n    </thead>\n    <tr class=\"\">\n        <td class=\"fillremaining\">1507563657895091</td>\n        <td class=\"number\">124</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">1507896573228093</td>\n        <td class=\"number\">107</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">1507133567968022</td>\n        <td class=\"number\">106</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">1507309773225261</td>\n        <td class=\"number\">98</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">1508112331270612</td>\n        <td class=\"number\">94</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">1507647366292530</td>\n        <td class=\"number\">92</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">1507475403662486</td>\n        <td class=\"number\">86</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">1506959499272114</td>\n        <td class=\"number\">82</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">1508154737228813</td>\n        <td class=\"number\">79</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">1506999909218419</td>\n        <td class=\"number\">75</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"other\">\n        <td class=\"fillremaining\">Other values (1048584)</td>\n        <td class=\"number\">2987238</td>\n        <td class=\"number\">100.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n        </td>\n</tr>\n</table>\n        </div>\n        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme-3152880652672153600\">\n            <p class=\"h4\">Minimum 5 values</p>\n            \n<table class=\"freq table table-hover\">\n    <thead>\n    <tr>\n        <td class=\"fillremaining\">Value</td>\n        <td class=\"number\">Count</td>\n        <td class=\"number\">Frequency (%)</td>\n        <td style=\"min-width:200px\">&nbsp;</td>\n    </tr>\n    </thead>\n    <tr class=\"\">\n        <td class=\"fillremaining\">1506825423271737</td>\n        <td class=\"number\">2</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">1506825426267738</td>\n        <td class=\"number\">2</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">1506825435299739</td>\n        <td class=\"number\">2</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">1506825442704740</td>\n        <td class=\"number\">2</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">1506825528135741</td>\n        <td class=\"number\">2</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n        </td>\n</tr>\n</table>\n            <p class=\"h4\">Maximum 5 values</p>\n            \n<table class=\"freq table table-hover\">\n    <thead>\n    <tr>\n        <td class=\"fillremaining\">Value</td>\n        <td class=\"number\">Count</td>\n        <td class=\"number\">Frequency (%)</td>\n        <td style=\"min-width:200px\">&nbsp;</td>\n    </tr>\n    </thead>\n    <tr class=\"\">\n        <td class=\"fillremaining\">1508211367250326</td>\n        <td class=\"number\">2</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:29%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">1508211369104327</td>\n        <td class=\"number\">7</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">1508211372158328</td>\n        <td class=\"number\">2</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:29%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">1508211376302329</td>\n        <td class=\"number\">2</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:29%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">1508211379189330</td>\n        <td class=\"number\">2</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:29%\">&nbsp;</div>\n        </td>\n</tr>\n</table>\n        </div>\n    </div>\n</div>\n</div><div class=\"row variablerow\">\n    <div class=\"col-md-3 namecol\">\n        <p class=\"h4 pp-anchor\" id=\"pp_var_session_size\">session_size<br/>\n            <small>Numeric</small>\n        </p>\n    </div><div class=\"col-md-6\">\n    <div class=\"row\">\n        <div class=\"col-sm-6\">\n            <table class=\"stats \">\n                <tr>\n                    <th>Distinct count</th>\n                    <td>72</td>\n                </tr>\n                <tr>\n                    <th>Unique (%)</th>\n                    <td>0.0%</td>\n                </tr>\n                <tr class=\"ignore\">\n                    <th>Missing (%)</th>\n                    <td>0.0%</td>\n                </tr>\n                <tr class=\"ignore\">\n                    <th>Missing (n)</th>\n                    <td>0</td>\n                </tr>\n                <tr class=\"ignore\">\n                    <th>Infinite (%)</th>\n                    <td>0.0%</td>\n                </tr>\n                <tr class=\"ignore\">\n                    <th>Infinite (n)</th>\n                    <td>0</td>\n                </tr>\n            </table>\n\n        </div>\n        <div class=\"col-sm-6\">\n            <table class=\"stats \">\n\n                <tr>\n                    <th>Mean</th>\n                    <td>3.9019</td>\n                </tr>\n                <tr>\n                    <th>Minimum</th>\n                    <td>2</td>\n                </tr>\n                <tr>\n                    <th>Maximum</th>\n                    <td>124</td>\n                </tr>\n                <tr class=\"ignore\">\n                    <th>Zeros (%)</th>\n                    <td>0.0%</td>\n                </tr>\n            </table>\n        </div>\n    </div>\n</div>\n<div class=\"col-md-3 collapse in\" id=\"minihistogram-8861839800859874581\">\n    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAAQpJREFUeJzt1sEJAkEQRUEVQzIIc/JsTgaxObV3kQeCy4hU3Rv%2B5TFznJk5AG%2BdVg%2BAX3ZePeDV5fb4%2BGa7X3dYAl4QSAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAI59UDvuFye3x8s92vOyzh3xxnZlaPgF/liwVBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBALhCV3BDpFBjPb7AAAAAElFTkSuQmCC\">\n\n</div>\n<div class=\"col-md-12 text-right\">\n    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives-8861839800859874581,#minihistogram-8861839800859874581\"\n       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n        Toggle details\n    </a>\n</div>\n<div class=\"row collapse col-md-12\" id=\"descriptives-8861839800859874581\">\n    <ul class=\"nav nav-tabs\" role=\"tablist\">\n        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles-8861839800859874581\"\n                                                  aria-controls=\"quantiles-8861839800859874581\" role=\"tab\"\n                                                  data-toggle=\"tab\">Statistics</a></li>\n        <li role=\"presentation\"><a href=\"#histogram-8861839800859874581\" aria-controls=\"histogram-8861839800859874581\"\n                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n        <li role=\"presentation\"><a href=\"#common-8861839800859874581\" aria-controls=\"common-8861839800859874581\"\n                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n        <li role=\"presentation\"><a href=\"#extreme-8861839800859874581\" aria-controls=\"extreme-8861839800859874581\"\n                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n\n    </ul>\n\n    <div class=\"tab-content\">\n        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles-8861839800859874581\">\n            <div class=\"col-md-4 col-md-offset-1\">\n                <p class=\"h4\">Quantile statistics</p>\n                <table class=\"stats indent\">\n                    <tr>\n                        <th>Minimum</th>\n                        <td>2</td>\n                    </tr>\n                    <tr>\n                        <th>5-th percentile</th>\n                        <td>2</td>\n                    </tr>\n                    <tr>\n                        <th>Q1</th>\n                        <td>2</td>\n                    </tr>\n                    <tr>\n                        <th>Median</th>\n                        <td>3</td>\n                    </tr>\n                    <tr>\n                        <th>Q3</th>\n                        <td>4</td>\n                    </tr>\n                    <tr>\n                        <th>95-th percentile</th>\n                        <td>9</td>\n                    </tr>\n                    <tr>\n                        <th>Maximum</th>\n                        <td>124</td>\n                    </tr>\n                    <tr>\n                        <th>Range</th>\n                        <td>122</td>\n                    </tr>\n                    <tr>\n                        <th>Interquartile range</th>\n                        <td>2</td>\n                    </tr>\n                </table>\n            </div>\n            <div class=\"col-md-4 col-md-offset-2\">\n                <p class=\"h4\">Descriptive statistics</p>\n                <table class=\"stats indent\">\n                    <tr>\n                        <th>Standard deviation</th>\n                        <td>3.9299</td>\n                    </tr>\n                    <tr>\n                        <th>Coef of variation</th>\n                        <td>1.0072</td>\n                    </tr>\n                    <tr>\n                        <th>Kurtosis</th>\n                        <td>158.46</td>\n                    </tr>\n                    <tr>\n                        <th>Mean</th>\n                        <td>3.9019</td>\n                    </tr>\n                    <tr>\n                        <th>MAD</th>\n                        <td>2.0089</td>\n                    </tr>\n                    <tr class=\"\">\n                        <th>Skewness</th>\n                        <td>9.0901</td>\n                    </tr>\n                    <tr>\n                        <th>Sum</th>\n                        <td>11659539</td>\n                    </tr>\n                    <tr>\n                        <th>Variance</th>\n                        <td>15.444</td>\n                    </tr>\n                    <tr>\n                        <th>Memory size</th>\n                        <td>22.8 MiB</td>\n                    </tr>\n                </table>\n            </div>\n        </div>\n        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram-8861839800859874581\">\n            <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3XtU1HXi//EXMIJxGWA0cH/kinnhKxdNTWnJDS%2BlZWlpeKFtXUrLFDVZ2dVS0%2B1mfcW1vuqx6Lv5Xbe2CGzzkrdMsdrcLWtzB7yUqFtxNEhnRAiUy/z%2B8Dg1OTOQfLjp83GOx%2BP7/fl83u95HbIX8/k4%2BDgcDocAAABgGN%2BW3gAAAMDlhoIFAABgMAoWAACAwShYAAAABqNgAQAAGIyCBQAAYDAKFgAAgMEoWAAAAAajYAEAABiMggUAAGAwChYAAIDBKFgAAAAGo2ABAAAYjIIFAABgMAoWAACAwShYAAAABqNgAQAAGIyCBQAAYDAKFgAAgMEoWAAAAAajYAEAABiMggUAAGAwChYAAIDBKFgAAAAGo2ABAAAYjIIFAABgMAoWAACAwShYAAAABqNgAQAAGIyCBQAAYDAKFgAAgMEoWAAAAAajYAEAABiMggUAAGAwChYAAIDBKFgAAAAGo2ABAAAYjIIFAABgMAoWAACAwUwtvYErRWnpmUs6z9fXRxZLkE6dqlBdncPgXbVtZOMd%2BXhGNt6Rj2dk411rzOfqq0NaZF3ewWrlfH195OPjI19fn5beSqtDNt6Rj2dk4x35eEY23pHP9yhYAAAABqNgAQAAGKxJC9bBgwf1m9/8Rv3791dSUpJmz56t0tJSSdKePXuUkpKifv366fbbb9eGDRtczl27dq1GjBihfv36KTU1VQUFBc65s2fP6rHHHtNNN92kxMREzZo1SzabzTlfXFysBx98UImJiRoyZIiWLl2quro653xj1gYAAKhPkxWsc%2BfO6f7779fAgQO1Z88ebdq0SSdPntTixYtVUlKi6dOna%2BLEidqzZ4/mz5%2BvhQsXymq1SpJ27typFStW6L//%2B7/14YcfasiQIXrooYf03XffSZKWL1%2BuwsJC5eTkaNu2bXI4HHrkkUeca8%2BcOVORkZHasWOH1qxZox07dujPf/6zJDV6bQAAgPo0WcGqrKxURkaGpk6dKn9/f1ksFt1yyy364osvtHHjRkVHRyslJUUBAQFKSkrS0KFDlZubK0nKycnR2LFj1adPH7Vv315TpkyRJO3atUs1NTXKy8vT9OnT9bOf/UxhYWGaPXu28vPz9c0338hqtergwYPKzMxUSEiIoqOjlZaWppycHElq1NoAAAAN0WQFKzQ0VOPGjZPJdP6TII4cOaK//e1vuu2221RYWKjY2FiX42NjY5234n487%2Bvrq169eslqterLL7/UmTNnFBcX55zv1q2b2rdvr8LCQhUWFioqKkqhoaHO%2Bbi4OB09elTl5eWNWhsAAKAhmvxzsIqLizVixAjV1NRo/PjxmjVrlh544AFFRka6HBcWFuZ8jsput7sUJOl8YbPZbLLb7ZIks9nsMm82m53zP567cK0L85e6dkOVlJQ4nzW7wGQKVERERIOvcYGfn6/L7/ge2XhHPp6RjXfk4xnZeEc%2B32vyghUVFSWr1ar//Oc/euyxx/T73/%2B%2BQec5HN4/oMzbfH3nNnbt%2BuTk5GjlypUuY%2Bnp6Zo1a9YlX9NsvqpRe7qckY135OMZ2XhHPp6RjXfk00yf5O7j46Po6GhlZGRo4sSJSk5Odr4TdYHNZpPFYpEkhYeHXzRvt9vVo0cP5zF2u11BQUHO%2BdOnT6tDhw6qra11e66Pj48sFovbazd07YaaMGGChg4d6jJmMgXKZqto8DUu8PPzldl8lcrKKlVbW1f/CVcQsvGOfDwjG%2B/IxzOy8a415hMeHlT/QU2gyQrWnj17tHjxYm3ZskW%2BvuffKrzwe%2B/evbVt2zaX4wsKCtSnTx9JUnx8vAoLCzVmzBhJUm1trfbv36%2BUlBR17txZoaGhzmetJOnzzz/XuXPnFB8fr5KSEh0/flynTp1yliar1aru3bsrKChICQkJWrdu3SWt3VAREREX3Q4sLT2jmppL/2Krra1r1PmXM7Lxjnw8IxvvyMczsvGOfJrwIff4%2BHiVl5dr6dKlqqys1KlTp7RixQpdf/31Sk1NVXFxsXJzc3X27Fnt3r1bu3fv1vjx4yVJqampeuutt/TZZ5%2BpsrJSq1evlr%2B/vwYPHiw/Pz%2BNHz9eL7zwgo4fPy6bzaY//vGPuuWWW9SxY0fFxsYqISFBy5YtU3l5uYqKirRmzRqlpqZKkkaNGnXJawMAADSEj6OxDxx5cejQIT355JP697//rcDAQN1www2aN2%2BeIiMj9fHHH%2BvJJ59UUVGRoqKiNGfOHA0fPtx57l//%2BldlZ2fr5MmTSkhI0OLFi9WzZ09J5z9ja8mSJXr77bdVU1OjIUOGaPHixQoJOf8DHU%2BcOKGFCxfqo48%2BUnBwsCZOnKgZM2bIx%2Bf8z0ZqzNqX6lJ/2LPJ5Kvw8CDZbBVX/HcDP0Y23pGPZ2TjHfl4RjbetcZ8WuqHPTdpwcL3KFjGIxvvyMczsvGOfDwjG%2B9aYz4tVbCa5SF3NJ3bnvt7S2%2BhwbbMvrGltwAAQLPggyoAAAAMRsECAAAwGAULAADAYBQsAAAAg1GwAAAADEbBAgAAMBgFCwAAwGAULAAAAINRsAAAAAxGwQIAADAYBQsAAMBgFCwAAACDUbAAAAAMRsECAAAwGAULAADAYBQsAAAAg1GwAAAADEbBAgAAMBgFCwAAwGAULAAAAINRsAAAAAxGwQIAADAYBQsAAMBgFCwAAACDUbAAAAAMRsECAAAwGAULAADAYBQsAAAAg1GwAAAADEbBAgAAMBgFCwAAwGAULAAAAINRsAAAAAxGwQIAADAYBQsAAMBgFCwAAACDUbAAAAAMRsECAAAwGAULAADAYBQsAAAAgzVpwSouLlZ6eroSExOVlJSkefPmqaysTF9//bViYmKUkJDg8utPf/qT89zNmzdr1KhR6tu3r8aOHasPPvjAOVdXV6fly5dr2LBhGjBggCZPnqyvvvrKOW%2B32zV79mwlJSVp0KBBmj9/vqqqqpzzBw4c0L333qv%2B/ftr%2BPDhevnll1327W1tAACA%2BjRpwXrooYdkNpu1c%2BdOvfnmm/riiy/07LPPOuetVqvLr8mTJ0s6X4Dmzp2rzMxM/eMf/1BaWppmzJihEydOSJJeffVVbdy4UdnZ2dq1a5eio6OVnp4uh8MhSVq4cKEqKyu1adMmrVu3TkVFRcrKypIkVVVVaerUqbrhhhv0/vvva/ny5XrxxRe1ffv2Bq0NAABQnyYrWGVlZYqPj9ecOXMUFBSkTp06acyYMdq7d2%2B95%2Bbm5io5OVnJyckKCAjQ6NGj1bNnT23YsEGSlJOTo7S0NHXr1k3BwcHKyMhQUVGR9u3bp2%2B//VY7duxQRkaGLBaLIiMjNX36dK1bt07V1dXKz89XdXW1pk2bpsDAQMXFxWncuHHKyclp0NoAAAD1abKCZTabtWTJEnXs2NE5dvz4cUVERDj//Pvf/16DBg3SDTfcoGXLlqm6ulqSVFhYqNjYWJfrxcbGymq1qqqqSocPH3aZDw4OVpcuXWS1WnXgwAH5%2BfkpJibGOR8XF6fvvvtOR44cUWFhoWJiYuTn5%2Bdy7YKCgnrXBgAAaAhTcy1ktVr1yiuvaPXq1fL391ffvn11yy236KmnntKBAwc0c%2BZMmUwmPfzww7Lb7QoNDXU5PzQ0VIcPH9bp06flcDjczttsNoWFhSk4OFg%2BPj4uc5Jks9lkt9tlNptdzg0LC5PdblddXZ3XtRuqpKREpaWlLmMmU6BLuWwoPz9fl9/bMpPJ2NdwOWXTFMjHM7Lxjnw8IxvvyOd7zVKwPvnkE02bNk1z5sxRUlKSJOn11193zvfu3VtTp07Viy%2B%2BqIcffliSnM9TeeJtvr5z3flhIbuU838oJydHK1eudBlLT0/XrFmzLvmaZvNVjdpTaxAeHtQk170csmlK5OMZ2XhHPp6RjXfk0wwFa%2BfOnfrd736nhQsX6q677vJ4XFRUlL799ls5HA6Fh4fLbre7zNvtdlksFoWFhcnX19ftfIcOHWSxWFReXq7a2lrnbcALx16YP3bs2EXnXriut7UbasKECRo6dKjLmMkUKJutosHXuMDPz1dm81UqK6tUbW3dTz6/NbmU1%2B/N5ZRNUyAfz8jGO/LxjGy8a435NNU39/Vp0oL16aefau7cuXr%2B%2Bec1aNAg5/iePXv02Wefadq0ac6xI0eOKCoqSj4%2BPoqPj3c%2BE3WB1WrV7bffroCAAPXo0UOFhYUaOHCgpPMP1H/55Zfq3bu3oqKi5HA4dPDgQcXFxTnPNZvN6tq1q%2BLj4/Xaa6%2BppqZGJpPJOd%2BnTx9J8rp2Q0VERFx0O7C09Ixqai79i622tq5R57cGTbX/yyGbpkQ%2BnpGNd%2BTjGdl4Rz5N%2BJB7TU2NFixYoMzMTJdyJUkhISFatWqV1q9fr%2BrqalmtVv3pT39SamqqJGn8%2BPH68MMPlZ%2Bfr7NnzyovL0/Hjh3T6NGjJUmpqalau3atioqKVF5erqysLPXq1UsJCQmyWCwaMWKEnnvuOZ06dUonTpzQqlWrlJKSIpPJpOTkZAUHB2v16tWqrKzUvn37lJeX1%2BC1AQAA6uPjaOwDRx7s3btXv/rVr%2BTv73/R3NatW7V//36tXLlSx44dU0hIiH7961/rgQcekK/v%2Bc63fft2LVu2TMXFxerevbvmz5%2BvAQMGSDr/jNSKFSv0%2Buuvq6KiQomJiXr88cfVqVMnSdKZM2e0aNEi7dq1S%2B3atdMdd9yhefPmOffy%2Beefa9GiRSooKFDHjh31wAMP6J577nHuz9val6q09MwlnWcy%2BSo8PEg2W4Xb7wZue%2B7vjdpXc9oy%2B0ZDr1dfNlc68vGMbLwjH8/IxrvWmM/VV4e0yLpNVrDgioJFwWpu5OMZ2XhHPp6RjXetMZ%2BWKlj8O0oAAACDUbAAAAAMRsECAAAwGAULAADAYBQsAAAAg1GwAAAADEbBAgAAMBgFCwAAwGAULAAAAINRsAAAAAxGwQIAADAYBQsAAMBgFCwAAACDUbAAAAAMRsECAAAwGAULAADAYBQsAAAAg1GwAAAADEbBAgAAMBgFCwAAwGAULAAAAINRsAAAAAxGwQIAADAYBQsAAMBgFCwAAACDUbAAAAAMRsECAAAwGAULAADAYBQsAAAAg1GwAAAADEbBAgAAMBgFCwAAwGAULAAAAINRsAAAAAxGwQIAADAYBQsAAMBgFCwAAACDUbAAAAAMRsECAAAwGAULAADAYBQsAAAAgzVpwSouLlZ6eroSExOVlJSkefPmqaysTJJ04MAB3Xvvverfv7%2BGDx%2Bul19%2B2eXczZs3a9SoUerbt6/Gjh2rDz74wDlXV1en5cuXa9iwYRowYIAmT56sr776yjlvt9s1e/ZsJSUladCgQZo/f76qqqqc841ZGwAAoD5NWrAeeughmc1m7dy5U2%2B%2B%2Baa%2B%2BOILPfvss6qqqtLUqVN1ww036P3339fy5cv14osvavv27ZLOF6C5c%2BcqMzNT//jHP5SWlqYZM2boxIkTkqRXX31VGzduVHZ2tnbt2qXo6Gilp6fL4XBIkhYuXKjKykpt2rRJ69atU1FRkbKysiSp0WsDAADUp8kKVllZmeLj4zVnzhwFBQWpU6dOGjNmjPbu3av8/HxVV1dr2rRpCgwMVFxcnMaNG6ecnBxJUm5urpKTk5WcnKyAgACNHj1aPXv21IYNGyRJOTk5SktLU7du3RQcHKyMjAwVFRVp3759%2Bvbbb7Vjxw5lZGTIYrEoMjJS06dP17p161RdXd3otQEAAOpjaqoLm81mLVmyxGXs%2BPHjioiIUGFhoWJiYuTn5%2Beci42NVW5uriSpsLBQycnJLufGxsbKarWqqqpKhw8fVmxsrHMuODhYXbp0kdVq1ZkzZ%2BTn56eYmBjnfFxcnL777jsdOXKkUWs3VElJiUpLS13GTKZARURENPgaF/j5%2Bbr83paZTMa%2Bhsspm6ZAPp6RjXfk4xnZeEc%2B32uygvVjVqtVr7zyilavXq0tW7bIbDa7zIeFhclut6uurk52u12hoaEu86GhoTp8%2BLBOnz4th8Phdt5msyksLEzBwcHy8fFxmZMkm80mu91%2ByWs3VE5OjlauXOkylp6erlmzZjX4Gj9mNl91yee2FuHhQU1y3cshm6ZEPp6RjXfk4xnZeEc%2BzVSwPvnkE02bNk1z5sxRUlKStmzZ4va4H5aiC89TeeJtvr5zG7t2fSZMmKChQ4e6jJlMgbLZKn7ytfz8fGU2X6WyskrV1tY1al8t7VJevzeXUzZNgXw8IxvvyMczsvGuNebTVN/c16fJC9bOnTv1u9/9TgsXLtRdd90lSbJYLDp27JjLcXa7XWFhYfL19VV4eLjsdvtF8xaLxXmMu/kOHTrIYrGovLxctbW1ztuAF469MH%2BpazdURETERbcDS0vPqKbm0r/YamvrGnV%2Ba9BU%2B78csmlK5OMZ2XhHPp6RjXfk08T/ivDTTz/V3Llz9fzzzzvLlSTFx8fr0KFDqqmpcY5ZrVb16dPHOV9QUOByrQvzAQEB6tGjhwoLC51zZWVl%2BvLLL9W7d2/16tVLDodDBw8edDnXbDara9eujVobAACgIZqsYNXU1GjBggXKzMzUoEGDXOaSk5MVHBys1atXq7KyUvv27VNeXp5SU1MlSePHj9eHH36o/Px8nT17Vnl5eTp27JhGjx4tSUpNTdXatWtVVFSk8vJyZWVlqVevXkpISJDFYtGIESP03HPP6dSpUzpx4oRWrVqllJQUmUymRq8NAABQHx9HYx848mDv3r361a9%2BJX9//4vmtm7dqoqKCi1atEgFBQXq2LGjHnjgAd1zzz3OY7Zv365ly5apuLhY3bt31/z58zVgwABJ55%2BRWrFihV5//XVVVFQoMTFRjz/%2BuDp16iRJOnPmjBYtWqRdu3apXbt2uuOOOzRv3jznXj7//PNLXvtSlZaeuaTzTCZfhYcHyWarcPt2623P/b1R%2B2pOW2bfaOj16svmSkc%2BnpGNd%2BTjGdl41xrzufrqkBZZt8kKFlxRsChYzY18PCMb78jHM7LxrjXm01IFiw%2BqAAAAMBgFCwAAwGAULAAAAINRsAAAAAxGwQIAADAYBQsAAMBgFCwAAACDUbAAAAAM5rZgDR06VCtXrtTx48ebez8AAABtntuCdffdd2vz5s26%2BeabNWXKFG3fvt3lhyMDAADAM7cFKz09XZs3b9Ybb7yhHj166Omnn1ZycrKWLl2qo0ePNvceAQAA2hSvz2DFxcVp7ty52rVrlx599FG98cYbGjlypCZPnqx///vfzbVHAACANsVrwaqurtbmzZv1wAMPaO7cuYqMjNQjjzyiXr16KS0tTRs3bmyufQIAALQZJneDRUVFysvL01tvvaWKigqNGDFCf/7zn9W/f3/nMQMGDNDixYs1atSoZtssAABAW%2BC2YN1%2B%2B%2B3q2rWrpk6dqrvuukthYWEXHZOcnKxTp041%2BQYBAADaGrcFa%2B3atRo4cGC9J%2B/bt8/wDQEAALR1bp/BiomJ0UMPPaQdO3Y4x/7v//5PDzzwgOx2e7NtDgAAoC1yW7CWLFmiM2fOqHv37s6xwYMHq66uTs8880yzbQ4AAKAtcnuL8IMPPtDGjRsVHh7uHIuOjlZWVpbuuOOOZtscAABAW%2BT2HayqqioFBARcfLCvryorK5t8UwAAAG2Z24I1YMAAPfPMMzp9%2BrRz7JtvvtEf/vAHl49qAAAAwMXc3iJ89NFHdf/99%2BsXv/iFgoODVVdXp4qKCnXu3Fl/%2BctfmnuPAAAAbYrbgtW5c2e9/fbbeu%2B99/Tll1/K19dXXbt21aBBg%2BTn59fcewQAAGhT3BYsSfL399fNN9/cnHsBAAC4LLgtWF999ZWWLVumL774QlVVVRfNv/vuu02%2BMQAAgLbK4zNYJSUlGjRokAIDA5t7TwAAAG2a24JVUFCgd999VxaLpbn3AwAA0Oa5/ZiGDh068M4VAADAJXJbsKZOnaqVK1fK4XA0934AAADaPLe3CN977z19%2BumnevPNN3XNNdfI19e1h73%2B%2BuvNsjkAAIC2yG3BCg4O1k033dTcewEAALgsuC1YS5Ysae59AAAAXDbcPoMlSUeOHNGKFSv0yCOPOMf%2B9a9/NcumAAAA2jK3BWvPnj0aPXq0tm/frk2bNkk6/%2BGjkyZN4kNGAQAA6uG2YC1fvly/%2B93vtHHjRvn4%2BEg6//MJn3nmGa1atapZNwgAANDWuC1Yn3/%2BuVJTUyXJWbAk6dZbb1VRUVHz7AwAAKCNcluwQkJC3P4MwpKSEvn7%2Bzf5pgAAANoytwWrX79%2Bevrpp1VeXu4cO3r0qObOnatf/OIXzbY5AACAtsjtxzQ88sgj%2Bs1vfqPExETV1taqX79%2BqqysVI8ePfTMM8809x4BAADaFLcFq1OnTtq0aZN2796to0ePqn379uratatuvPFGl2eyAAAAcDG3BUuS2rVrp5tvvrk59wIAAHBZcFuwhg4d6vWdqoZ%2BFtb777%2BvuXPnKjExUcuXL3eOv/nmm3r00UfVrl07l%2BNfffVV9e7dW3V1dXr%2B%2Bee1adMmlZWVqXfv3lq8eLE6d%2B4sSbLb7Vq8eLE%2B%2Bugj%2Bfr6Kjk5WQsXLlT79u0lSQcOHNBTTz2lAwcOqEOHDpo4caLuv/9%2B5zqbN2/W6tWr9fXXX6tr16767W9/q0GDBklSvWsDAADUx23BGjlypEvBqq2t1dGjR2W1WvWb3/ymQRd%2B6aWXlJeXpy5duridHzBggP7yl7%2B4nXv11Ve1ceNGvfTSS4qMjNTy5cuVnp6u9evXy8fHRwsXLtS5c%2Be0adMmVVdX6%2BGHH1ZWVpYWLFigqqoqTZ06VePHj1d2draOHj2q%2B%2B%2B/X9dcc42GDx%2BuAwcOaO7cuVq5cqVuuOEGbdu2TTNmzNDWrVvVqVOnetcGAACoj9uClZmZ6fbgbdu26Z///GeDLhwQEKC8vDw99dRTOnv27E/aVE5OjtLS0tStWzdJUkZGhhITE7Vv3z5dc8012rFjh/72t7/JYrFIkqZPn66HH35Yc%2BfOVX5%2BvqqrqzVt2jT5%2BfkpLi5O48aNU05OjoYPH67c3FwlJycrOTlZkjR69Gi98sor2rBhgx588EGva1933XU/6XUAAIArk8dnsNy5%2Beab9dhjj%2Bmxxx6r99hJkyZ5nT9%2B/Ljuu%2B8%2BFRQUyGw2a9asWbrzzjtVVVWlw4cPKzY21nlscHCwunTpIqvVqjNnzsjPz08xMTHO%2Bbi4OH333Xc6cuSICgsLFRMTIz8/P%2Bd8bGyscnNzJUmFhYXOcvXDeavVWu/aDS1YJSUlKi0tdRkzmQIVERHRoPN/yM/P1%2BX3tsxkMvY1XE7ZNAXy8YxsvCMfz8jGO/L53k8qWPv375fD4Wj0ohaLRdHR0frtb3%2Br7t2765133tHvf/97RURE6Nprr5XD4VBoaKjLOaGhobLZbAoLC1NwcLDL7boLx9psNtntdpnNZpdzw8LCZLfbVVdXJ7vd7vbahw8f1unTp72u3VA5OTlauXKly1h6erpmzZrV4Gv8mNl81SWf21qEhwc1yXUvh2yaEvl4RjbekY9nZOMd%2BXgoWBMnTrxorLKyUkVFRRo%2BfHijFx08eLAGDx7s/PPtt9%2Bud955R2%2B%2B%2Babz9qS3IncpJe%2BHhay%2B8xtbIidMmKChQ4e6jJlMgbLZKn7ytfz8fGU2X6WyskrV1tY1al8t7VJevzeXUzZNgXw8IxvvyMczsvGuNebTVN/c18dtwYqOjr7oge6AgAClpKRo3LhxTbKRqKgoFRQUKCwsTL6%2BvrLb7S7zdrtdHTp0kMViUXl5uWpra523AS8ce2H%2B2LFjF5174brh4eFur22xWOpdu6EiIiIuuh1YWnpGNTWX/sVWW1vXqPNbg6ba/%2BWQTVMiH8/Ixjvy8YxsvCMfDwWrqT%2Bt/bXXXlNoaKhGjhzpHCsqKlLnzp0VEBCgHj16qLCwUAMHDpQklZWV6csvv1Tv3r0VFRUlh8OhgwcPKi4uTpJktVplNpvVtWtXxcfH67XXXlNNTY1MJpNzvk%2BfPpKk%2BPh4FRQUuOzHarXq9ttvr3dtAACAhnBbsN56660GX%2BCuu%2B76yYueO3dOTzzxhDp37qz/%2Bq//0rZt2/Tee%2B/pjTfekCSlpqYqOztbN910kyIjI5WVlaVevXopISFBkjRixAg999xzevbZZ3Xu3DmtWrVKKSkpMplMSk5OVnBwsFavXq0pU6bo888/V15enpYuXSpJGj9%2BvFJSUpSfn69f/OIX2rhxo44dO6bRo0c3aG0AAID6uC1Y8%2BfPV11d3UXPIvn4%2BLiM%2Bfj4eCxYFwpJTU2NJGnHjh2Szr9bNGnSJFVUVOjhhx9WaWmprrnmGq1atUrx8fGSzj8DVlpaql//%2BteqqKhQYmKiy0Pjjz/%2BuBYtWqRhw4apXbt2uuOOO5SRkSFJ8vf31wsvvKBFixYpOztbHTt2VEZGhvOZr549eyorK0tLlixRcXGxunfvrhdffFFXX311g9YGAACoj4/DzRPde/bs0csvv6yHHnpIMTExcjgcOnTokF566SXde%2B%2B9SkxMdB7r7%2B/frBtuq0pLz1zSeSaTr8LDg2SzVbi9n33bc39v7NaazZbZNxp6vfqyudKRj2dk4x35eEY23rXGfK6%2BOqRF1vX4DFZ2drYiIyOdY9dff706d%2B6syZMna9OmTc22QQAAgLbG7SeBHTt27KLPgpIks9ms4uLiJt8UAABAW%2Ba2YEVFRemZZ55x%2BXDNsrIyLVu2TD//%2Bc%2BbbXMAAABtkdtbhI8%2B%2BqjmzJmjnJwcBQUFydfXV%2BXl5Wrfvr1WrVrV3HsEAABoU9wWrEGDBik/P1%2B7d%2B/WiRMn5HA4FBkZqV/%2B8pcKCWmZh8UAAADaCo8/i/Cqq67SsGHDdOLECXXu3Lk59wQAANCmuX0Gq6qqSnPnzlXfvn112223STr/DNaUKVNUVlbWrBsEAABoa9wWrKVLl%2BrAgQPKysqSr%2B/3h9TW1iqoMivNAAAaCklEQVQrK6vZNgcAANAWuS1Y27Zt0//8z//o1ltvdf7QZ7PZrCVLlmj79u3NukEAAIC2xm3BqqioUHR09EXjFotF3333XVPvCQAAoE1zW7B%2B/vOf65///Kckufzswa1bt%2Br//b//1zw7AwAAaKPc/ivCe%2B65RzNnztTdd9%2Bturo6rVmzRgUFBdq2bZvmz5/f3HsEAABoU9wWrAkTJshkMumVV16Rn5%2BfXnjhBXXt2lVZWVm69dZbm3uPAAAAbYrbgnXq1Cndfffduvvuu5t7PwAAAG2e22ewhg0b5vLsFQAAABrObcFKTEzUli1bmnsvAAAAlwW3twh/9rOf6amnnlJ2drZ%2B/vOfq127di7zy5Yta5bNAQAAtEVuC9bhw4d17bXXSpJsNluzbggAAKCtcylYGRkZWr58uf7yl784x1atWqX09PRm3xgAAEBb5fIM1s6dOy86IDs7u9k2AwAAcDlwKVju/uUg/5oQAADgp3EpWBd%2BsHN9YwAAAPDM7cc0AAAA4NJRsAAAAAzm8q8Iq6urNWfOHNU3xudgAQAAeOZSsPr376%2BSkhLVNwYAAADPXArWDz//CgAAAJeGZ7AAAAAMRsECAAAwGAULAADAYBQsAAAAg1GwAAAADEbBAgAAMBgFCwAAwGAULAAAAINRsAAAAAxGwQIAADAYBQsAAMBgFCwAAACDUbAAAAAMRsECAAAwWJMWrPfff19JSUnKyMi4aG7z5s0aNWqU%2Bvbtq7Fjx%2BqDDz5wztXV1Wn58uUaNmyYBgwYoMmTJ%2Burr75yztvtds2ePVtJSUkaNGiQ5s%2Bfr6qqKuf8gQMHdO%2B996p///4aPny4Xn75ZcPWBgAAqE%2BTFayXXnpJTz75pLp06XLR3IEDBzR37lxlZmbqH//4h9LS0jRjxgydOHFCkvTqq69q48aNys7O1q5duxQdHa309HQ5HA5J0sKFC1VZWalNmzZp3bp1KioqUlZWliSpqqpKU6dO1Q033KD3339fy5cv14svvqjt27cbsjYAAEB9mqxgBQQEKC8vz23Bys3NVXJyspKTkxUQEKDRo0erZ8%2Be2rBhgyQpJydHaWlp6tatm4KDg5WRkaGioiLt27dP3377rXbs2KGMjAxZLBZFRkZq%2BvTpWrdunaqrq5Wfn6/q6mpNmzZNgYGBiouL07hx45STk9PotQEAABrC1FQXnjRpkse5wsJCJScnu4zFxsbKarWqqqpKhw8fVmxsrHMuODhYXbp0kdVq1ZkzZ%2BTn56eYmBjnfFxcnL777jsdOXJEhYWFiomJkZ%2Bfn8u1c3NzG732dddd16DXXlJSotLSUpcxkylQERERDTr/h/z8fF1%2Bb8tMJmNfw%2BWUTVMgH8/Ixjvy8YxsvCOf7zVZwfLGbrcrNDTUZSw0NFSHDx/W6dOn5XA43M7bbDaFhYUpODhYPj4%2BLnOSZLPZZLfbZTabXc4NCwuT3W5XXV1do9ZuqJycHK1cudJlLD09XbNmzWrwNX7MbL7qks9tLcLDg5rkupdDNk2JfDwjG%2B/IxzOy8Y58WqhgSar3mSZv85fyPNQPC1lj1m6ICRMmaOjQoS5jJlOgbLaKn3wtPz9fmc1XqaysUrW1dY3aV0u7lNfvzeWUTVMgH8/Ixjvy8YxsvGuN%2BTTVN/f1aZGCFR4eLrvd7jJmt9tlsVgUFhYmX19ft/MdOnSQxWJReXm5amtrnbcBLxx7Yf7YsWMXnXvhuo1Zu6EiIiIuuh1YWnpGNTWX/sVWW1vXqPNbg6ba/%2BWQTVMiH8/Ixjvy8YxsvCOfFvocrPj4eBUUFLiMWa1W9enTRwEBAerRo4cKCwudc2VlZfryyy/Vu3dv9erVSw6HQwcPHnQ512w2q2vXroqPj9ehQ4dUU1Nz0bUbuzYAAEBDtEjBGj9%2BvD788EPl5%2Bfr7NmzysvL07FjxzR69GhJUmpqqtauXauioiKVl5crKytLvXr1UkJCgiwWi0aMGKHnnntOp06d0okTJ7Rq1SqlpKTIZDIpOTlZwcHBWr16tSorK7Vv3z7l5eUpNTW10WsDAAA0RJPdIrxQSC68k7Rjxw5J598t6tmzp7KysrRkyRIVFxere/fuevHFF3X11VdLkiZOnKjS0lL9%2Bte/VkVFhRITE10eGn/88ce1aNEiDRs2TO3atdMdd9zh/DBTf39/vfDCC1q0aJGys7PVsWNHZWRkaPDgwZLU6LUBAADq4%2BPgEzSbRWnpmUs6z2TyVXh4kGy2Crf3s2977u%2BN3Vqz2TL7RkOvV182Vzry8YxsvCMfz8jGu9aYz9VXh7TIunxQBQAAgMEoWAAAAAajYAEAABiMggUAAGAwChYAAIDBKFgAAAAGo2ABAAAYjIIFAABgMAoWAACAwShYAAAABqNgAQAAGIyCBQAAYDAKFgAAgMEoWAAAAAajYAEAABiMggUAAGAwChYAAIDBKFgAAAAGo2ABAAAYjIIFAABgMAoWAACAwShYAAAABqNgAQAAGIyCBQAAYDAKFgAAgMEoWAAAAAajYAEAABiMggUAAGAwChYAAIDBKFgAAAAGo2ABAAAYjIIFAABgMAoWAACAwShYAAAABqNgAQAAGIyCBQAAYDAKFgAAgMEoWAAAAAajYAEAABiMggUAAGAwChYAAIDBWqxgxcTEKD4%2BXgkJCc5fTzzxhCRpz549SklJUb9%2B/XT77bdrw4YNLueuXbtWI0aMUL9%2B/ZSamqqCggLn3NmzZ/XYY4/ppptuUmJiombNmiWbzeacLy4u1oMPPqjExEQNGTJES5cuVV1dnXO%2BvrUBAADqY2rJxbdu3aprrrnGZaykpETTp0/X/PnzNWrUKH3yySeaNm2aunbtqoSEBO3cuVMrVqzQ//7v/yomJkZr167VQw89pO3btyswMFDLly9XYWGhcnJydNVVV2nhwoV65JFH9MILL0iSZs6cqbi4OO3YsUMnT57U1KlT1bFjR9133331rg0AANAQre4W4caNGxUdHa2UlBQFBAQoKSlJQ4cOVW5uriQpJydHY8eOVZ8%2BfdS%2BfXtNmTJFkrRr1y7V1NQoLy9P06dP189%2B9jOFhYVp9uzZys/P1zfffCOr1aqDBw8qMzNTISEhio6OVlpamnJychq0NgAAQEO06DtYy5Yt07/%2B9S%2BVl5frtttu07x581RYWKjY2FiX42JjY7VlyxZJUmFhoUaOHOmc8/X1Va9evWS1WtWrVy%2BdOXNGcXFxzvlu3bqpffv2KiwsVElJiaKiohQaGuqcj4uL09GjR1VeXl7v2g1VUlKi0tJSlzGTKVARERE/6TqS5Ofn6/J7W2YyGfsaLqdsmgL5eEY23pGPZ2TjHfl8r8UK1nXXXaekpCQ9%2B%2Byz%2BuqrrzR79mz94Q9/kN1uV2RkpMuxYWFhzueo7Ha7S0GSpNDQUNlsNtntdkmS2Wx2mTebzc75H89duNaFeW9rN1ROTo5WrlzpMpaenq5Zs2b9pOv8kNl81SWf21qEhwc1yXUvh2yaEvl4RjbekY9nZOMd%2BbRgwbpwW046/y5TZmampk2bpv79%2B9d7rsPhuOT5%2Bs41woQJEzR06FCXMZMpUDZbxU%2B%2Blp%2Bfr8zmq1RWVqna2rr6T2jFLuX1e3M5ZdMUyMczsvGOfDwjG%2B9aYz5N9c19fVr0FuEPXXPNNaqtrZWvr6/znagLbDabLBaLJCk8PPyiebvdrh49ejiPsdvtCgr6PtDTp0%2BrQ4cOqq2tdXuuj4%2BPLBaL22v/cO2GioiIuOh2YGnpGdXUXPoXW21tXaPObw2aav%2BXQzZNiXw8IxvvyMczsvGOfFroIff9%2B/frmWeecRkrKiqSv7%2B/kpOTXT52QZIKCgrUp08fSVJ8fLwKCwudc7W1tdq/f7/69Omjzp07KzQ01GX%2B888/17lz5xQfH6/4%2BHgdP35cp06dcs5brVZ1795dQUFBSkhI8Lo2AABAQ7RIwerQoYNycnKUnZ2tc%2BfO6ejRo3r%2B%2Bec1YcIE3XnnnSouLlZubq7Onj2r3bt3a/fu3Ro/frwkKTU1VW%2B99ZY%2B%2B%2BwzVVZWavXq1fL399fgwYPl5%2Ben8ePH64UXXtDx48dls9n0xz/%2BUbfccos6duyo2NhYJSQkaNmyZSovL1dRUZHWrFmj1NRUSdKoUaO8rg0AANAQPo7meCjJjY8//ljLli3ToUOH5O/vrzFjxigjI0MBAQH6%2BOOP9eSTT6qoqEhRUVGaM2eOhg8f7jz3r3/9q7Kzs3Xy5EklJCRo8eLF6tmzpyTp3LlzWrJkid5%2B%2B23V1NRoyJAhWrx4sUJCQiRJJ06c0MKFC/XRRx8pODhYEydO1IwZM%2BTj4%2BPcl7e1L1Vp6ZlLOs9k8lV4eJBstgq3b7fe9tzfG7u1ZrNl9o2GXq%2B%2BbK505OMZ2XhHPp6RjXetMZ%2Brrw5pkXVbrGBdaShYFKzmRj6ekY135OMZ2XjXGvNpqYLFB1UAAAAYjIIFAABgMAoWAACAwShYAAAABqNgAQAAGIyCBQAAYDAKFgAAgMEoWAAAAAajYAEAABiMggUAAGAwChYAAIDBKFgAAAAGo2ABAAAYjIIFAABgMAoWAACAwShYAAAABqNgAQAAGIyCBQAAYDAKFgAAgMEoWAAAAAajYAEAABiMggUAAGAwChYAAIDBKFgAAAAGo2ABAAAYjIIFAABgMAoWAACAwShYAAAABqNgAQAAGIyCBQAAYDAKFgAAgMEoWAAAAAajYAEAABiMggUAAGAwChYAAIDBKFgAAAAGo2ABAAAYjIIFAABgMAoWAACAwShYAAAABqNgAQAAGMzU0htojYqLi/WHP/xB%2B/btU2BgoEaOHKk5c%2BbI15c%2B2hi3Pff3lt7CT7Jl9o0tvQUAQBtFwXJj5syZiouL044dO3Ty5ElNnTpVHTt21H333dfSWwMAAG0Ab8n8iNVq1cGDB5WZmamQkBBFR0crLS1NOTk5Lb01AADQRvAO1o8UFhYqKipKoaGhzrG4uDgdPXpU5eXlCg4OrvcaJSUlKi0tdRkzmQIVERHxk/fj5%2Bfr8juaT1u7pflO5i9d/szXjmdk4x35eEY23pHP9yhYP2K322U2m13GLpQtm83WoIKVk5OjlStXuozNmDFDM2fO/Mn7KSkp0Z///L%2BaMGGC24K296lbf/I1LxclJSXKycnxmM2Vrr6vnSsZ2XhHPp6RjXfk8z0qphsOh6NR50%2BYMEFvvvmmy68JEyZc0rVKS0u1cuXKi94RA9nUh3w8IxvvyMczsvGOfL7HO1g/YrFYZLfbXcbsdrt8fHxksVgadI2IiIgrvrkDAHAl4x2sH4mPj9fx48d16tQp55jValX37t0VFBTUgjsDAABtBQXrR2JjY5WQkKBly5apvLxcRUVFWrNmjVJTU1t6awAAoI3wW7x48eKW3kRr88tf/lKbNm3SE088obffflspKSmaPHmyfHx8WmQ/QUFBGjhwIO%2BguUE23pGPZ2TjHfl4Rjbekc95Po7GPtENAAAAF9wiBAAAMBgFCwAAwGAULAAAAINRsAAAAAxGwQIAADAYBQsAAMBgFCwAAACDUbAAAAAMRsECAAAwGAWrlSouLtaDDz6oxMREDRkyREuXLlVdXV1Lb6vFFBcXKz09XYmJiUpKStK8efNUVlYmSTpw4IDuvfde9e/fX8OHD9fLL7/cwrttOU8//bRiYmKcf96zZ49SUlLUr18/3X777dqwYUML7q7lrF69WoMGDdJ1112ntLQ0ff3115LIR5L279%2BvSZMm6frrr9eNN96ozMxM5w%2B7vxLzef/995WUlKSMjIyL5jZv3qxRo0apb9%2B%2BGjt2rD744APnXF1dnZYvX65hw4ZpwIABmjx5sr766qvm3HqT85bN9u3bNXr0aPXt21cjRozQG2%2B84TK/du1ajRgxQv369VNqaqoKCgqaa9stx4FWacyYMY4FCxY4ysrKHEePHnUMHz7c8fLLL7f0tlrMHXfc4Zg3b56jvLzccfz4ccfYsWMdjz76qKOystLxy1/%2B0rFixQpHRUWFo6CgwDFw4EDHtm3bWnrLzW7//v2OgQMHOnr27OlwOByOb775xnHdddc5cnNzHVVVVY6///3vjt69ezv%2B/e9/t/BOm9crr7ziuPXWWx1FRUWOM2fOOJ544gnHE088QT4Oh6O6utpx4403OpYtW%2BY4e/as49SpU4777rvPMXPmzCsyn%2BzsbMfw4cMdEydOdMyePdtlbv/%2B/Y74%2BHhHfn6%2Bo6qqyrF%2B/XpHnz59HMePH3c4HA7H2rVrHUOGDHEcPnzYcebMGcfjjz/uGDVqlKOurq4lXorhvGWzb98%2BR0JCguOdd95xVFdXO/Lz8x1xcXGOjz/%2B2OFwOBzvvvuu4/rrr3d89tlnjsrKSseLL77ouPHGGx0VFRUt8VKaDe9gtUJWq1UHDx5UZmamQkJCFB0drbS0NOXk5LT01lpEWVmZ4uPjNWfOHAUFBalTp04aM2aM9u7dq/z8fFVXV2vatGkKDAxUXFycxo0bd8VlVVdXp0WLFiktLc05tnHjRkVHRyslJUUBAQFKSkrS0KFDlZub23IbbQEvv/yyMjIydO211yo4OFgLFizQggULyEdSaWmpSktLdeedd8rf31/h4eG65ZZbdODAgSsyn4CAAOXl5alLly4XzeXm5io5OVnJyckKCAjQ6NGj1bNnT%2Be7ejk5OUpLS1O3bt0UHBysjIwMFRUVad%2B%2Bfc39MpqEt2zsdrumTp2qm2%2B%2BWSaTScnJyerZs6f27t0r6Xw2Y8eOVZ8%2BfdS%2BfXtNmTJFkrRr165mfQ3NjYLVChUWFioqKkqhoaHOsbi4OB09elTl5eUtuLOWYTabtWTJEnXs2NE5dvz4cUVERKiwsFAxMTHy8/NzzsXGxl4Zbz//wOuvv66AgACNGjXKOVZYWKjY2FiX4660bL755ht9/fXXOn36tEaOHKnExETNmjVLp06dIh9JkZGR6tWrl3JyclRRUaGTJ09q%2B/btGjx48BWZz6RJkxQSEuJ2zlMeVqtVVVVVOnz4sMt8cHCwunTpIqvV2qR7bi7esrnpppuUnp7u/HNNTY1KS0sVGRkp6eLsfH191atXr8smG08oWK2Q3W6X2Wx2GbtQtmw2W0tsqVWxWq165ZVXNG3aNLdZhYWFyW63XzHPrH377bdasWKFFi1a5DLuKZsr6WvoxIkTkqStW7dqzZo1Wr9%2BvU6cOKEFCxaQj87/j27FihV699131a9fPyUlJammpkZz5swhnx%2Bx2%2B0u3/RK5/9ettlsOn36tBwOh8f5K01WVpYCAwM1cuRISd6zu5xRsFoph8PR0ltolT755BNNnjxZc%2BbMUVJSksfjfHx8mnFXLWvJkiUaO3asunfv3tJbaXUu/Hc0ZcoURUZGqlOnTpo5c6Z27tzZwjtrHc6dO6eHHnpIt956q/bu3av33ntPISEhyszMbOmttUr1/b18pf%2B97XA4tHTpUm3atEmrV69WQECAy9yVhoLVClksFtntdpcxu90uHx8fWSyWFtpVy9u5c6cefPBBPfroo5o0aZKk81n9%2BLsgu92usLAw%2Bfpe/l/ee/bs0b/%2B9S%2BXt%2BcvCA8Pv%2BjryGazXVFfQxduK//wnZioqCg5HA5VV1df8fns2bNHX3/9tX77298qJCREkZGRmjVrlt555x35%2Bvpe8fn8kLv/nux2uywWi/PvG3fzHTp0aM5ttpi6ujrNmzdPO3fu1GuvvaZrr73WOectu8vZ5f9/oDYoPj5ex48fd/5Taen8bbHu3bsrKCioBXfWcj799FPNnTtXzz//vO666y7neHx8vA4dOqSamhrnmNVqVZ8%2BfVpim81uw4YNOnnypIYMGaLExESNHTtWkpSYmKiePXte9LxMQUHBFZONJHXq1EnBwcE6cOCAc6y4uFjt2rVTcnLyFZ9PbW2t6urqXN5dOHfunCQpKSnpis/nh%2BLj4y/K48LfNQEBAerRo4cKCwudc2VlZfryyy/Vu3fv5t5qi3j66af1xRdf6LXXXlPnzp1d5uLj412yqa2t1f79%2By/7ryUKVisUGxurhIQELVu2TOXl5SoqKtKaNWuUmpra0ltrETU1NVqwYIEyMzM1aNAgl7nk5GQFBwdr9erVqqys1L59%2B5SXl3fFZDVv3jxt27ZN69ev1/r165WdnS1JWr9%2BvUaNGqXi4mLl5ubq7Nmz2r17t3bv3q3x48e38K6bj8lkUkpKil544QX95z//0cmTJ7Vq1SqNGjVKY8aMueLz6du3rwIDA7VixQpVVlbKZrNp9erVGjBggO68884rPp8fGj9%2BvD788EPl5%2Bfr7NmzysvL07FjxzR69GhJUmpqqtauXauioiKVl5crKytLvXr1UkJCQgvvvOl98skn2rBhg7KzsxUWFnbRfGpqqt566y199tlnqqys1OrVq%2BXv76/Bgwc3/2abkY/jSrwx2gacOHFCCxcu1EcffaTg4GBNnDhRM2bMuKKeLbpg7969%2BtWvfiV/f/%2BL5rZu3aqKigotWrRIBQUF6tixox544AHdc889LbDTlvf1119r2LBhOnTokCTp448/1pNPPqmioiJFRUVpzpw5Gj58eAvvsnmdO3dOS5Ys0dtvv63q6mqNGDFCCxcuVFBQEPno/LtSzz77rA4ePCh/f38NHDhQ8%2BbNU2Rk5BWXz4UydOEdcZPJJEnOf%2B22fft2LVu2TMXFxerevbvmz5%2BvAQMGSDr/jNGKFSv0%2Buuvq6KiQomJiXr88cfVqVOnFnglxvOWzaOPPqq//e1vzrELBgwY4Pzg57/%2B9a/Kzs7WyZMnlZCQoMWLF6tnz57N%2BAqaHwULAADAYNwiBAAAMBgFCwAAwGAULAAAAINRsAAAAAxGwQIAADAYBQsAAMBgFCwAAACDUbAAAAAMRsECAAAwGAULAADAYBQsAAAAg1GwAAAADEbBAgAAMNj/B7%2BugFaRz9pnAAAAAElFTkSuQmCC\"/>\n        </div>\n        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common-8861839800859874581\">\n            \n<table class=\"freq table table-hover\">\n    <thead>\n    <tr>\n        <td class=\"fillremaining\">Value</td>\n        <td class=\"number\">Count</td>\n        <td class=\"number\">Frequency (%)</td>\n        <td style=\"min-width:200px\">&nbsp;</td>\n    </tr>\n    </thead>\n    <tr class=\"\">\n        <td class=\"fillremaining\">2</td>\n        <td class=\"number\">1260372</td>\n        <td class=\"number\">42.2%</td>\n        <td>\n            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">3</td>\n        <td class=\"number\">670185</td>\n        <td class=\"number\">22.4%</td>\n        <td>\n            <div class=\"bar\" style=\"width:53%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">4</td>\n        <td class=\"number\">374240</td>\n        <td class=\"number\">12.5%</td>\n        <td>\n            <div class=\"bar\" style=\"width:30%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">5</td>\n        <td class=\"number\">220105</td>\n        <td class=\"number\">7.4%</td>\n        <td>\n            <div class=\"bar\" style=\"width:18%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">6</td>\n        <td class=\"number\">135762</td>\n        <td class=\"number\">4.5%</td>\n        <td>\n            <div class=\"bar\" style=\"width:11%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">7</td>\n        <td class=\"number\">88354</td>\n        <td class=\"number\">3.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:7%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">8</td>\n        <td class=\"number\">58544</td>\n        <td class=\"number\">2.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">9</td>\n        <td class=\"number\">40878</td>\n        <td class=\"number\">1.4%</td>\n        <td>\n            <div class=\"bar\" style=\"width:4%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">10</td>\n        <td class=\"number\">29530</td>\n        <td class=\"number\">1.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:3%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">11</td>\n        <td class=\"number\">21714</td>\n        <td class=\"number\">0.7%</td>\n        <td>\n            <div class=\"bar\" style=\"width:2%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"other\">\n        <td class=\"fillremaining\">Other values (62)</td>\n        <td class=\"number\">88497</td>\n        <td class=\"number\">3.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:7%\">&nbsp;</div>\n        </td>\n</tr>\n</table>\n        </div>\n        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme-8861839800859874581\">\n            <p class=\"h4\">Minimum 5 values</p>\n            \n<table class=\"freq table table-hover\">\n    <thead>\n    <tr>\n        <td class=\"fillremaining\">Value</td>\n        <td class=\"number\">Count</td>\n        <td class=\"number\">Frequency (%)</td>\n        <td style=\"min-width:200px\">&nbsp;</td>\n    </tr>\n    </thead>\n    <tr class=\"\">\n        <td class=\"fillremaining\">2</td>\n        <td class=\"number\">1260372</td>\n        <td class=\"number\">42.2%</td>\n        <td>\n            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">3</td>\n        <td class=\"number\">670185</td>\n        <td class=\"number\">22.4%</td>\n        <td>\n            <div class=\"bar\" style=\"width:53%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">4</td>\n        <td class=\"number\">374240</td>\n        <td class=\"number\">12.5%</td>\n        <td>\n            <div class=\"bar\" style=\"width:30%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">5</td>\n        <td class=\"number\">220105</td>\n        <td class=\"number\">7.4%</td>\n        <td>\n            <div class=\"bar\" style=\"width:18%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">6</td>\n        <td class=\"number\">135762</td>\n        <td class=\"number\">4.5%</td>\n        <td>\n            <div class=\"bar\" style=\"width:11%\">&nbsp;</div>\n        </td>\n</tr>\n</table>\n            <p class=\"h4\">Maximum 5 values</p>\n            \n<table class=\"freq table table-hover\">\n    <thead>\n    <tr>\n        <td class=\"fillremaining\">Value</td>\n        <td class=\"number\">Count</td>\n        <td class=\"number\">Frequency (%)</td>\n        <td style=\"min-width:200px\">&nbsp;</td>\n    </tr>\n    </thead>\n    <tr class=\"\">\n        <td class=\"fillremaining\">94</td>\n        <td class=\"number\">94</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:76%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">98</td>\n        <td class=\"number\">98</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:79%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">106</td>\n        <td class=\"number\">106</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:85%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">107</td>\n        <td class=\"number\">107</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:86%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">124</td>\n        <td class=\"number\">124</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n        </td>\n</tr>\n</table>\n        </div>\n    </div>\n</div>\n</div><div class=\"row variablerow ignore\">\n    <div class=\"col-md-3 namecol\">\n        <p class=\"h4 pp-anchor\" id=\"pp_var_session_start\"><s>session_start</s><br/>\n            <small>Highly correlated</small>\n        </p>\n    </div><div class=\"col-md-3\">\n    <p><em>This variable is highly correlated with <a href=\"#pp_var_session_id\"><code>session_id</code></a> and should be ignored for analysis</em></p>\n</div>\n<div class=\"col-md-6\">\n    <table class=\"stats \">\n        <tr>\n            <th>Correlation</th>\n            <td>1</td>\n        </tr>\n    </table>\n</div>\n</div><div class=\"row variablerow\">\n    <div class=\"col-md-3 namecol\">\n        <p class=\"h4 pp-anchor\" id=\"pp_var_user_id\">user_id<br/>\n            <small>Numeric</small>\n        </p>\n    </div><div class=\"col-md-6\">\n    <div class=\"row\">\n        <div class=\"col-sm-6\">\n            <table class=\"stats \">\n                <tr>\n                    <th>Distinct count</th>\n                    <td>322897</td>\n                </tr>\n                <tr>\n                    <th>Unique (%)</th>\n                    <td>10.8%</td>\n                </tr>\n                <tr class=\"ignore\">\n                    <th>Missing (%)</th>\n                    <td>0.0%</td>\n                </tr>\n                <tr class=\"ignore\">\n                    <th>Missing (n)</th>\n                    <td>0</td>\n                </tr>\n                <tr class=\"ignore\">\n                    <th>Infinite (%)</th>\n                    <td>0.0%</td>\n                </tr>\n                <tr class=\"ignore\">\n                    <th>Infinite (n)</th>\n                    <td>0</td>\n                </tr>\n            </table>\n\n        </div>\n        <div class=\"col-sm-6\">\n            <table class=\"stats \">\n\n                <tr>\n                    <th>Mean</th>\n                    <td>107950</td>\n                </tr>\n                <tr>\n                    <th>Minimum</th>\n                    <td>0</td>\n                </tr>\n                <tr>\n                    <th>Maximum</th>\n                    <td>322896</td>\n                </tr>\n                <tr class=\"ignore\">\n                    <th>Zeros (%)</th>\n                    <td>0.0%</td>\n                </tr>\n            </table>\n        </div>\n    </div>\n</div>\n<div class=\"col-md-3 collapse in\" id=\"minihistogram2695586589509889855\">\n    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAATNJREFUeJzt2sFpw0AARcE4uKQUkZ5yTk8pwj1t7iE8ZMGitTRzN%2Bzl8SWtb2OM8Qb86/3oA8DK7kcf4K%2BPr5%2Bnf/P4/pxwErAgkAQCQSAQBAJBIBCW%2B4q1hy9fzGJBIAgEgkAgCASCQCAIBIJAIAgEwikuCvdwucgWFgSCQCAIBIJAIAgEgkAgCATCZe9B9nj27sS9yeuzIBAEAsEj1kT%2BzvL6LAgEgUAQCASBQPCSvhgv9muxIBAsyAm44Z/HgkAQCASPWBfkQ8B2FgSCBWGTq66OBYFgQZjmDKtjQSAIBIJAIAgEgkAgCATCbYwxjj4ErMqCQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQPgFnpQmoS%2BXDNcAAAAASUVORK5CYII%3D\">\n\n</div>\n<div class=\"col-md-12 text-right\">\n    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives2695586589509889855,#minihistogram2695586589509889855\"\n       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n        Toggle details\n    </a>\n</div>\n<div class=\"row collapse col-md-12\" id=\"descriptives2695586589509889855\">\n    <ul class=\"nav nav-tabs\" role=\"tablist\">\n        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles2695586589509889855\"\n                                                  aria-controls=\"quantiles2695586589509889855\" role=\"tab\"\n                                                  data-toggle=\"tab\">Statistics</a></li>\n        <li role=\"presentation\"><a href=\"#histogram2695586589509889855\" aria-controls=\"histogram2695586589509889855\"\n                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n        <li role=\"presentation\"><a href=\"#common2695586589509889855\" aria-controls=\"common2695586589509889855\"\n                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n        <li role=\"presentation\"><a href=\"#extreme2695586589509889855\" aria-controls=\"extreme2695586589509889855\"\n                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n\n    </ul>\n\n    <div class=\"tab-content\">\n        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles2695586589509889855\">\n            <div class=\"col-md-4 col-md-offset-1\">\n                <p class=\"h4\">Quantile statistics</p>\n                <table class=\"stats indent\">\n                    <tr>\n                        <th>Minimum</th>\n                        <td>0</td>\n                    </tr>\n                    <tr>\n                        <th>5-th percentile</th>\n                        <td>6370</td>\n                    </tr>\n                    <tr>\n                        <th>Q1</th>\n                        <td>40341</td>\n                    </tr>\n                    <tr>\n                        <th>Median</th>\n                        <td>86229</td>\n                    </tr>\n                    <tr>\n                        <th>Q3</th>\n                        <td>163260</td>\n                    </tr>\n                    <tr>\n                        <th>95-th percentile</th>\n                        <td>274160</td>\n                    </tr>\n                    <tr>\n                        <th>Maximum</th>\n                        <td>322896</td>\n                    </tr>\n                    <tr>\n                        <th>Range</th>\n                        <td>322896</td>\n                    </tr>\n                    <tr>\n                        <th>Interquartile range</th>\n                        <td>122920</td>\n                    </tr>\n                </table>\n            </div>\n            <div class=\"col-md-4 col-md-offset-2\">\n                <p class=\"h4\">Descriptive statistics</p>\n                <table class=\"stats indent\">\n                    <tr>\n                        <th>Standard deviation</th>\n                        <td>83648</td>\n                    </tr>\n                    <tr>\n                        <th>Coef of variation</th>\n                        <td>0.7749</td>\n                    </tr>\n                    <tr>\n                        <th>Kurtosis</th>\n                        <td>-0.46867</td>\n                    </tr>\n                    <tr>\n                        <th>Mean</th>\n                        <td>107950</td>\n                    </tr>\n                    <tr>\n                        <th>MAD</th>\n                        <td>69871</td>\n                    </tr>\n                    <tr class=\"\">\n                        <th>Skewness</th>\n                        <td>0.72312</td>\n                    </tr>\n                    <tr>\n                        <th>Sum</th>\n                        <td>322567642028</td>\n                    </tr>\n                    <tr>\n                        <th>Variance</th>\n                        <td>6997000000</td>\n                    </tr>\n                    <tr>\n                        <th>Memory size</th>\n                        <td>22.8 MiB</td>\n                    </tr>\n                </table>\n            </div>\n        </div>\n        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram2695586589509889855\">\n            <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3Xl0VPX9//FXkjEg2QMGPZEayp5MCIIQDRQIFlAQRAhL/HqUVgQFyQlKC4IoqBUsICpYlvqFQrV1CloFFOFQlrqk324ukwFEAlZNg4kwQxaSQJLP7w9%2BTBiDWeCGIeH5OIdz6ud97/185t3J5DX33swEGGOMAAAAYJlAfy8AAACguSFgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDGbvxdwpSgoKLL8mIGBAYqODtHx4yWqqjKWH78poRe%2B6Icv%2BlGNXviiH9Waay%2BuuSbML/NyBqsJCwwMUEBAgAIDA/y9FL%2BjF77ohy/6UY1e%2BKIf1eiFtQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFrP5ewG4ODfNfc/fS6i3bZl9/b0EAAAuCc5gAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMb8HrJUrV6pfv37q0aOHJk6cqG%2B%2B%2BUaSlJWVpbS0NPXs2VPDhw/X5s2bffbbsGGDhg4dqp49eyo9PV3Z2dneWnl5uZ544gn1799fycnJysjIkNvt9tZzc3M1efJkJScnKzU1VYsXL1ZVVZW3XtfcAAAAtfFrwHrttde0efNmbdiwQR988IE6duyo3/3ud8rPz9fUqVM1YcIEZWVlae7cuZo3b56cTqckadeuXVq%2BfLl%2B/etf66OPPlJqaqoefPBBnTx5UpK0bNkyuVwuORwObd%2B%2BXcYYPfbYY955p0%2BfrrZt22rnzp1at26ddu7cqfXr10tSnXMDAADUxa8Ba%2B3atZoxY4Z%2B/OMfKzQ0VI8//rgef/xxbdmyRXFxcUpLS1OLFi2UkpKiQYMGaePGjZIkh8Oh0aNHKykpSS1bttSkSZMkSbt371ZFRYU2bdqkqVOn6rrrrlNkZKQyMzO1Z88effvtt3I6nTpw4IBmzpypsLAwxcXFaeLEiXI4HJJU59wAAAB18VvA%2Bvbbb/XNN9/oxIkTGjZsmPdS3vHjx%2BVyuRQfH%2B%2BzfXx8vPcy4PfrgYGB6tatm5xOp7766isVFRUpISHBW%2B/QoYNatmwpl8sll8ul2NhYRUREeOsJCQk6cuSIiouL65wbAACgLjZ/TXz06FFJ0nvvvad169bJGKOMjAw9/vjjKisrU9u2bX22j4yM9N5H5fF4fAKSJEVERMjtdsvj8UiSwsPDferh4eHe%2BvdrZ491tl7b3PWRn5%2BvgoICnzGbrZViYmLqfYz6CAry%2By10DWKzNd56z/aiqfWksdAPX/SjGr3wRT%2Bq0Qtr%2BS1gGWMkSZMmTfIGmunTp%2BuBBx5QSkpKvfe/kHpd%2B14sh8OhFStW%2BIxNmzZNGRkZjTrv5S4qKqTR5wgPv7rR52hK6Icv%2BlGNXviiH9XohTX8FrDatGkjyfdMU2xsrIwxOn36tPdM1Flut1vR0dGSpKioqBp1j8ejTp06ebfxeDwKCan%2BhX7ixAm1bt1alZWV5903ICBA0dHR5z32uXPXx/jx4zVo0CCfMZutldzuknofoz6a2rsMqx//uYKCAhUefrUKC0tVWVlV9w7NHP3wRT%2Bq0Qtf9KNac%2B3FpXhzfz5%2BC1jXXnutQkNDtX//fu/9Urm5ubrqqqs0YMAAvf322z7bZ2dnKykpSZJkt9vlcrl01113SZIqKyu1b98%2BpaWlqV27doqIiPDeayVJBw8e1KlTp2S325Wfn6%2B8vDwdP37cG5qcTqc6duyokJAQJSYm6o033vjBuesjJiamxuXAgoIiVVQ0nyfshbgUj7%2BysuqK7/O56Icv%2BlGNXviiH9XohTX8dgrEZrMpLS1Nq1at0n/%2B8x8dO3ZML7/8skaMGKG77rpLubm52rhxo8rLy7V3717t3btX48aNkySlp6frrbfe0ieffKLS0lKtXLlSwcHBGjhwoIKCgjRu3DitWrVKeXl5crvdev755zV48GC1adNG8fHxSkxM1NKlS1VcXKycnBytW7dO6enpkqQRI0bUOjcAAEBd/HYGS5IeffRRnTp1SmPHjtXp06c1dOhQPf744woJCdHq1av1zDPPaMGCBYqNjdXixYvVtWtXSVL//v31yCOPKDMzU8eOHVNiYqLWrFmjli1bSpIyMjJUUlKiO%2B%2B8UxUVFUpNTdX8%2BfO987700kuaN2%2Be%2Bvbtq9DQUE2YMEF33323JKl169a1zg0AAFCXANPYd3xD0plLhFaz2QI1eMn7lh%2B3sWzL7Ntox7bZAhUVFSK3u4RT26If30c/qtELX/SjWnPtxTXXhPll3qZ1lzQAAEATQMACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwmM3fC8CV4/YXPvT3EhpkW2Zffy8BANBEcQYLAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAs5reA1aVLF9ntdiUmJnr/Pf3005KkrKwspaWlqWfPnho%2BfLg2b97ss%2B%2BGDRs0dOhQ9ezZU%2Bnp6crOzvbWysvL9cQTT6h///5KTk5WRkaG3G63t56bm6vJkycrOTlZqampWrx4saqqqrz1uuYGAACoi82fk7/33nu6/vrrfcby8/M1depUzZ07VyNGjNC//vUvPfTQQ2rfvr0SExO1a9cuLV%2B%2BXK%2B88oq6dOmiDRs26MEHH9SOHTvUqlUrLVu2TC6XSw6HQ1dffbXmzZunxx57TKtWrZIkTZ8%2BXQkJCdq5c6eOHTumKVOmqE2bNvrZz35W59wAAAD1cdldItyyZYvi4uKUlpamFi1aKCUlRYMGDdLGjRslSQ6HQ6NHj1ZSUpJatmypSZMmSZJ2796tiooKbdq0SVOnTtV1112nyMhIZWZmas%2BePfr222/ldDp14MABzZw5U2FhYYqLi9PEiRPlcDjqNTcAAEB9%2BPUM1tKlS/Xxxx%2BruLhYt99%2Bu2bPni2Xy6X4%2BHif7eLj47Vt2zZJksvl0rBhw7y1wMBAdevWTU6nU926dVNRUZESEhK89Q4dOqhly5ZyuVzKz89XbGysIiIivPWEhAQdOXJExcXFdc5dX/n5%2BSooKPAZs9laKSYmpkHHqUtQ0GWXj5sVm63p9vfsc4PnyBn0oxq98EU/qtELa/ktYPXo0UMpKSl67rnn9PXXXyszM1MLFiyQx%2BNR27ZtfbaNjIz03kfl8Xh8ApIkRUREyO12y%2BPxSJLCw8N96uHh4d7692tnj3W2Xtvc9eVwOLRixQqfsWnTpikjI6NBx4F/RUWF%2BHsJFy08/Gp/L%2BGyQj%2Bq0Qtf9KMavbCG3wLW2cty0pmzTDNnztRDDz2kXr161bmvMeaC63Xta4Xx48dr0KBBPmM2Wyu53SWWzsO7jMZl9f9fl1JQUKDCw69WYWGpKiur6t6hmaMf1eiFL/pRrbn2wl9vlv16ifBc119/vSorKxUYGOg9E3WW2%2B1WdHS0JCkqKqpG3ePxqFOnTt5tPB6PQkKqG3rixAm1bt1alZWV5903ICBA0dHR5z32uXPXV0xMTI3LgQUFRaqoaD5P2CtBc/j/q7Kyqlk8DqvQj2r0whf9qEYvrOGXUyD79u3TokWLfMZycnIUHBysAQMG%2BHzsgiRlZ2crKSlJkmS32%2BVyuby1yspK7du3T0lJSWrXrp0iIiJ86gcPHtSpU6dkt9tlt9uVl5en48ePe%2BtOp1MdO3ZUSEiIEhMTa50bAACgPvwSsFq3bi2Hw6E1a9bo1KlTOnLkiF588UWNHz9ed955p3Jzc7Vx40aVl5dr79692rt3r8aNGydJSk9P11tvvaVPPvlEpaWlWrlypYKDgzVw4EAFBQVp3LhxWrVqlfLy8uR2u/X8889r8ODBatOmjeLj45WYmKilS5equLhYOTk5WrdundLT0yVJI0aMqHVuAACA%2Bggwl%2BKmpPP4xz/%2BoaVLl%2Brzzz9XcHCw7rrrLs2YMUMtWrTQP/7xDz3zzDPKyclRbGysHn30UQ0ZMsS77x/%2B8AetWbNGx44dU2JioubPn6/OnTtLkk6dOqWFCxfqnXfeUUVFhVJTUzV//nyFhYVJko4ePap58%2Bbp73//u0JDQzVhwgQ9/PDDCggI8K6rtrkvVEFB0UUf4/tstkANXvK%2B5cfFGdsy%2B/p7CRfMZgtUVFSI3O4STvWLfpyLXviiH9Waay%2BuuSbML/P6LWBdaQhYTQ8Bq/mgH9XohS/6Ua259sJfAYs/QwMAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiNn8vALhc3f7Ch/5eQoNsy%2Bzr7yUAAP4/zmABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGCxyyJgPfvss%2BrSpYv3v7OyspSWlqaePXtq%2BPDh2rx5s8/2GzZs0NChQ9WzZ0%2Blp6crOzvbWysvL9cTTzyh/v37Kzk5WRkZGXK73d56bm6uJk%2BerOTkZKWmpmrx4sWqqqqq99wAAAB18XvA2r9/v95%2B%2B23vf%2Bfn52vq1KmaMGGCsrKyNHfuXM2bN09Op1OStGvXLi1fvly//vWv9dFHHyk1NVUPPvigTp48KUlatmyZXC6XHA6Htm/fLmOMHnvsMe/xp0%2BfrrZt22rnzp1at26ddu7cqfXr19drbgAAgPrwa8CqqqrSk08%2BqYkTJ3rHtmzZori4OKWlpalFixZKSUnRoEGDtHHjRkmSw%2BHQ6NGjlZSUpJYtW2rSpEmSpN27d6uiokKbNm3S1KlTdd111ykyMlKZmZnas2ePvv32WzmdTh04cEAzZ85UWFiY4uLiNHHiRDkcjnrNDQAAUB82f07%2B%2Buuvq0WLFhoxYoReeOEFSZLL5VJ8fLzPdvHx8dq2bZu3PmzYMG8tMDBQ3bp1k9PpVLdu3VRUVKSEhARvvUOHDmrZsqVcLpfy8/MVGxuriIgIbz0hIUFHjhxRcXFxnXPXV35%2BvgoKCnzGbLZWiomJadBx6hIU5PcTkLiM2GzVz4ezzw2eI2fQj2r0whf9qEYvrNXggDVo0CCNHj1aY8aM0XXXXXfBE3/33Xdavny5fv/73/uMezwetW3b1mcsMjLSex%2BVx%2BPxCUiSFBERIbfbLY/HI0kKDw/3qYeHh3vr36%2BdPdbZem1z15fD4dCKFSt8xqZNm6aMjIwGHQdoiKiokBpj4eFX%2B2Elly/6UY1e%2BKIf1eiFNRocsMaMGaN33nlHK1eu1C233KJx48Zp0KBBstkadqiFCxdq9OjR6tixo7755psG7WuMueB6XftaYfz48Ro0aJDPmM3WSm53iaXz8C4D5zr3%2BRUUFKjw8KtVWFiqysqqWva6MtCPavTCF/2o1lx7cb43n5dCgwPWtGnTNG3aNLlcLm3dulXPPvusFixYoFGjRiktLU3t27ev8xhZWVn6%2BOOPtXXr1hq1qKgo75mos9xut6Kjo3%2Bw7vF41KlTJ%2B82Ho9HISHVDT1x4oRat26tysrK8%2B4bEBCg6OjoOueur5iYmBqXAwsKilRR0XyesLj8nO/5VVlZxfPuHPSjGr3wRT%2Bq0QtrXPApkISEBM2aNUu7d%2B/WnDlz9Kc//UnDhg3T/fffr88%2B%2B6zWfTdv3qxjx44pNTVVycnJGj16tCQpOTlZnTt39vnYBUnKzs5WUlKSJMlut8vlcnlrlZWV2rdvn5KSktSuXTtFRET41A8ePKhTp07JbrfLbrcrLy9Px48f99adTqc6duyokJAQJSYm1jo3AABAfVxwwDp9%2BrTeffddPfDAA5o1a5batm2rxx57TN26ddPEiRO1ZcuWH9x39uzZ2r59u95%2B%2B229/fbbWrNmjSTp7bff1ogRI5Sbm6uNGzeqvLxce/fu1d69ezVu3DhJUnp6ut566y198sknKi0t1cqVKxUcHKyBAwcqKChI48aN06pVq5SXlye3263nn39egwcPVps2bRQfH6/ExEQtXbpUxcXFysnJ0bp165Seni5Jdc4NAABQHw2%2BRJiTk6NNmzbprbfeUklJiYYOHar169erV69e3m169%2B6t%2BfPna8SIEec9RkREhM%2BN6hUVFZKka6%2B9VpK0evVqPfPMM1qwYIFiY2O1ePFide3aVZLUv39/PfLII8rMzNSxY8eUmJioNWvWqGXLlpKkjIwMlZSU6M4771RFRYVSU1M1f/5871wvvfSS5s2bp759%2Byo0NFQTJkzQ3XffLUlq3bp1rXMDAADUR4Bp4F3fXbt2Vfv27TV%2B/HiNGjVKkZGR590uKSlJn376qSWLbA4KCoosP6bNFqjBS963/LhomrZl9vX%2Bb5stUFFRIXK7S7iXQvTjXPTCF/2o1lx7cc01YX6Zt8FnsDZs2KA%2BffrUuR3hCgAAXKkafA9Wly5d9OCDD2rnzp3esd/97nd64IEHavwFHgAAwJWowQFr4cKFKioqUseOHb1jAwcOVFVVlRYtWmTp4gAAAJqiBl8i/OCDD7RlyxZFRUV5x%2BLi4rRkyRLdcccdli4OAACgKWrwGayysjK1aNGi5oECA1VaWmrJogAAAJqyBges3r17a9GiRTpx4oR37Ntvv9WCBQt8PqoBAADgStXgS4Rz5szRz3/%2Bc91yyy0KDQ1VVVWVSkpK1K5duxpf3AwAAHAlanDAateund555x399a9/1VdffaXAwEC1b99e/fr1U1BQUGOsEQAAoElpcMCSpODgYP30pz%2B1ei0AAADNQoMD1tdff62lS5fqiy%2B%2BUFlZWY36X/7yF0sWBgAA0FRd0D1Y%2Bfn56tevn1q1atUYawIAAGjSGhywsrOz9Ze//EXR0dGNsR4AAIAmr8Ef09C6dWvOXAEAANSiwQFrypQpWrFihYwxjbEeAACAJq/Blwj/%2Bte/6t///rfefPNNXX/99QoM9M1or7/%2BumWLAwAAaIoaHLBCQ0PVv3//xlgLAABAs9DggLVw4cLGWAcAAECz0eB7sCTp8OHDWr58uR577DHv2Mcff2zZogAAAJqyBgesrKwsjRw5Ujt27NDWrVslnfnw0XvvvZcPGQUAANAFBKxly5bpF7/4hbZs2aKAgABJZ76fcNGiRXr55ZctXyAAAEBT0%2BCAdfDgQaWnp0uSN2BJ0m233aacnBzrVgYAANBENThghYWFnfc7CPPz8xUcHGzJogAAAJqyBgesnj176tlnn1VxcbF37MiRI5o1a5ZuueUWSxcHAADQFDX4Yxoee%2Bwx3XfffUpOTlZlZaV69uyp0tJSderUSYsWLWqMNQIAADQpDQ5Y1157rbZu3aq9e/fqyJEjatmypdq3b6%2B%2Bffv63JMFAABwpWpwwJKkq666Sj/96U%2BtXgsAAECz0OCANWjQoFrPVPFZWAAA4ErX4IA1bNgwn4BVWVmpI0eOyOl06r777rN0cQAAAE1RgwPWzJkzzzu%2Bfft2/d///d9FLwgAAKCpu6DvIjyfn/70p3rnnXesOhwAAECTdUE3uZ/Pvn37ZIyx6nAAGuj2Fz709xLqbVtmX38vAQAaVYMD1oQJE2qMlZaWKicnR0OGDLFkUQAAAE1ZgwNWXFxcjb8ibNGihdLS0jR27FjLFgYAANBUNThg8WntAAAAtWtwwHrrrbfqve2oUaMaengAAIAmr8EBa%2B7cuaqqqqpxQ3tAQIDPWEBAAAELAABckRocsF555RWtXbtWDz74oLp06SJjjD7//HP99re/1T333KPk5OTGWCcAAECT0eDPwVq0aJGeeeYZ9erVS6GhoQoLC9NNN92kp556Ss8995yCg4O9/%2Bpy4MAB3XffferVq5dSUlKUmZmpgoICSVJWVpbS0tLUs2dPDR8%2BXJs3b/bZd8OGDRo6dKh69uyp9PR0ZWdne2vl5eV64okn1L9/fyUnJysjI0Nut9tbz83N1eTJk5WcnKzU1FQtXrxYVVVV3npdcwMAANSmwQHryy%2B/VERERI3x8PBw5ebm1vs4p06d0s9//nP16dNHWVlZ2rp1q44dO6b58%2BcrPz9fU6dO1YQJE5SVlaW5c%2Bdq3rx5cjqdkqRdu3Zp%2BfLl%2BvWvf62PPvpIqampevDBB3Xy5ElJ0rJly%2BRyueRwOLR9%2B3YZY/TYY495554%2Bfbratm2rnTt3at26ddq5c6fWr18vSXXODQAAUJcGB6zY2FgtWrTI54xQYWGhli5dqh/96Ef1Pk5paalmzJihKVOmKDg4WNHR0Ro8eLC%2B%2BOILbdmyRXFxcUpLS1OLFi2UkpKiQYMGaePGjZIkh8Oh0aNHKykpSS1bttSkSZMkSbt371ZFRYU2bdqkqVOn6rrrrlNkZKQyMzO1Z88effvtt3I6nTpw4IBmzpypsLAwxcXFaeLEiXI4HJJU59wAAAB1afA9WHPmzNGjjz4qh8OhkJAQBQYGqri4WC1bttTLL79c7%2BNERET4fG7W4cOH9ec//1m33367XC6X4uPjfbaPj4/Xtm3bJEkul0vDhg3z1gIDA9WtWzc5nU5169ZNRUVFSkhI8NY7dOigli1byuVyKT8/X7GxsT5n4RISEnTkyBEVFxfXOXd95Ofney91nmWztVJMTEy9j1EfQUGWfdMRcEnZbJf2uXv2Z4WfGXrxffSjGr2wVoMDVr9%2B/bRnzx7t3btXR48elTFGbdu21U9%2B8hOFhYU1eAG5ubkaOnSoKioqNG7cOGVkZOiBBx5Q27ZtfbaLjIz0njXzeDw1LlNGRETI7XbL4/FIOnPJ8lzh4eHe%2BvdrZ491tl7b3PXhcDi0YsUKn7Fp06YpIyOj3scAmrOoqBC/zBsefrVf5r0c0Qtf9KMavbDGBX0X4dVXX61bb71VR48eVbt27S5qAbGxsXI6nfrPf/6jJ554Qr/85S/rtV9d33tYW72xvzNx/PjxGjRokM%2BYzdZKbneJpfPwLgNNldU/C3UJCgpUePjVKiwsVWVlVd07NGP0whf9qNZce%2BGvN3QNDlhlZWV68skn9c4770iSsrOzVVhYqEceeUTPP/98jbND9REQEKC4uDjNmDFDEyZM0IABA7xnos5yu92Kjo6WJEVFRdWoezwederUybuNx%2BNRSEh1U0%2BcOKHWrVursrLyvPsGBAQoOjr6vMc%2Bd%2B76iImJqXE5sKCgSBUVzecJC1wMf/0sVFZW8XP4/9ELX/SjGr2wRoNPgSxevFj79%2B/XkiVLFBhYvXtlZaWWLFlS7%2BNkZWVp6NChPh%2BPcPZ43bt39/nYBelMkEtKSpIk2e12uVwun7n37dunpKQktWvXThERET71gwcP6tSpU7Lb7bLb7crLy9Px48e9dafTqY4dOyokJESJiYm1zg0AAFCXBges7du366WXXtJtt93m/dLn8PBwLVy4UDt27Kj3cex2u4qLi7V48WKVlpbq%2BPHjWr58uW666Salp6crNzdXGzduVHl5ufbu3au9e/dq3LhxkqT09HS99dZb%2BuSTT1RaWqqVK1cqODhYAwcOVFBQkMaNG6dVq1YpLy9Pbrdbzz//vAYPHqw2bdooPj5eiYmJWrp0qYqLi5WTk6N169YpPT1dkjRixIha5wYAAKhLgwNWSUmJ4uLiaoxHR0d7P4eqPsLCwrR27VplZ2fr5ptv1vDhwxUWFqbnn39erVu31urVq/Xqq6%2BqV69eevbZZ7V48WJ17dpVktS/f3898sgjyszMVJ8%2BffTRRx9pzZo1atmypSQpIyNDSUlJuvPOO3XrrbcqJCREv/rVr7xzv/TSS8rPz1ffvn117733atSoUbr77rslqc65AQAA6hJgGnjH95133qk5c%2BYoOTlZ3bt312effSZJ2rx5s37zm9/ovffea5SFNnUFBUWWH9NmC9TgJe9bflygsW3L7HtJ57PZAhUVFSK3u%2BSKv7eEXviiH9Waay%2Buuabhn3BghQbf5H733Xdr%2BvTpGjNmjKqqqrRu3TplZ2dr%2B/btmjt3bmOsEQAAoElpcMAaP368bDabXn31VQUFBWnVqlVq3769lixZottuu60x1ggAANCkNDhgHT9%2BXGPGjNGYMWMaYz0ArgC3v/Chv5fQIJf6kiaApq/BN7nfeuutjf5BnQAAAE1ZgwNWcnJyg76XDwAA4ErT4EuE1113nX71q19pzZo1%2BtGPfqSrrrrKp7506VLLFgcAANAUNThgHTp0SD/%2B8Y8lqUFfgAwAAHClqHfAmjFjhpYtW6bf//733rGXX35Z06ZNa5SFAQAANFX1vgdr165dNcbWrFlj6WIAAACag3oHrPP95SB/TQgAAFBTvQPW2S92rmsMAADxaOptAAAaD0lEQVTgStfgj2kAAABA7QhYAAAAFqv3XxGePn1ajz76aJ1jfA4WAAC40tU7YPXq1Uv5%2Bfl1jgEAAFzp6h2wzv38KwAAAPww7sECAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGI2fy8AAC53t7/wob%2BX0CDbMvv6ewnAFY8zWAAAABYjYAEAAFjMrwErNzdX06ZNU3JyslJSUjR79mwVFhZKkvbv36977rlHvXr10pAhQ7R27Vqffd99912NGDFCN954o0aPHq0PPvjAW6uqqtKyZct06623qnfv3rr//vv19ddfe%2Bsej0eZmZlKSUlRv379NHfuXJWVlXnrdc0NAABQG78GrAcffFDh4eHatWuX3nzzTX3xxRd67rnnVFZWpilTpujmm2/W%2B%2B%2B/r2XLlmn16tXasWOHpDMBaNasWZo5c6b%2B9re/aeLEiXr44Yd19OhRSdJrr72mLVu2aM2aNdq9e7fi4uI0bdo0GWMkSfPmzVNpaam2bt2qN954Qzk5OVqyZIkk1Tk3AABAXfwWsAoLC2W32/Xoo48qJCRE1157re666y7985//1J49e3T69Gk99NBDatWqlRISEjR27Fg5HA5J0saNGzVgwAANGDBALVq00MiRI9W5c2dt3rxZkuRwODRx4kR16NBBoaGhmjFjhnJycvTpp5/qu%2B%2B%2B086dOzVjxgxFR0erbdu2mjp1qt544w2dPn26zrkBAADq4reAFR4eroULF6pNmzbesby8PMXExMjlcqlLly4KCgry1uLj45WdnS1Jcrlcio%2BP9zlefHy8nE6nysrKdOjQIZ96aGiobrjhBjmdTu3fv19BQUHq0qWLt56QkKCTJ0/q8OHDdc4NAABQl8vmYxqcTqdeffVVrVy5Utu2bVN4eLhPPTIyUh6PR1VVVfJ4PIqIiPCpR0RE6NChQzpx4oSMMeetu91uRUZGKjQ0VAEBAT41SXK73fJ4PLXOHRhYdybNz89XQUGBz5jN1koxMTF1N6IBgoL4GwUANdlsP/zacPZ1g9ePM%2BhHNXphrcsiYP3rX//SQw89pEcffVQpKSnatm3bebc7NxSdvZ/qh9RWr2vfuuaui8Ph0IoVK3zGpk2bpoyMjAbPCwANFRUVUuc24eFXX4KVNB30oxq9sIbfA9auXbv0i1/8QvPmzdOoUaMkSdHR0fryyy99tvN4PIqMjFRgYKCioqLk8Xhq1KOjo73bnK/eunVrRUdHq7i4WJWVld7LgGe3PVuvbe76GD9%2BvAYNGuQzZrO1kttdUq/964t3GQDOp7bXmqCgQIWHX63CwlJVVlZdwlVdnuhHtebai/q84WgMfg1Y//73vzVr1iy9%2BOKL6tevn3fcbrfrj3/8oyoqKmSznVmi0%2BlUUlKSt/79e6KcTqeGDx%2BuFi1aqFOnTnK5XOrTp4%2BkMzfUf/XVV%2BrevbtiY2NljNGBAweUkJDg3Tc8PFzt27evc%2B76iImJqXE5sKCgSBUVzecJC%2BDyVZ/XmsrKKl6TzkE/qtELa/jtFEhFRYUef/xxzZw50ydcSdKAAQMUGhqqlStXqrS0VJ9%2B%2Bqk2bdqk9PR0SdK4ceP00Ucfac%2BePSovL9emTZv05ZdfauTIkZKk9PR0bdiwQTk5OSouLtaSJUvUrVs3JSYmKjo6WkOHDtULL7yg48eP6%2BjRo3r55ZeVlpYmm81W59wAAAB1CTAXckOSBf75z3/qf/7nfxQcHFyj9t5776mkpERPPvmksrOz1aZNGz3wwAO6%2B%2B67vdvs2LFDS5cuVW5urjp27Ki5c%2Beqd%2B/eks7cY7V8%2BXK9/vrrKikpUXJysp566ilde%2B21kqSioiI9%2BeST2r17t6666irdcccdmj17tnctBw8erHXuC1FQUHRR%2B5%2BPzRaowUvet/y4AJq22r6L0GYLVFRUiNzuEs5SiH6cq7n24pprwvwyr98C1pWGgAXgUiFg1R/9qNZce%2BGvgMVd0gAAABYjYAEAAFjM7x/TAACw1u0vfOjvJdRbbZczgaaMM1gAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYzObvBQAArly3v/Chv5fQINsy%2B/p7CWgiOIMFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFrP5ewEAADQVt7/wob%2BX0CDbMvv6ewlXLM5gAQAAWIyABQAAYDECFgAAgMX8GrDef/99paSkaMaMGTVq7777rkaMGKEbb7xRo0eP1gcffOCtVVVVadmyZbr11lvVu3dv3X///fr666%2B9dY/Ho8zMTKWkpKhfv36aO3euysrKvPX9%2B/frnnvuUa9evTRkyBCtXbu23nMDAADUxW8B67e//a2eeeYZ3XDDDTVq%2B/fv16xZszRz5kz97W9/08SJE/Xwww/r6NGjkqTXXntNW7Zs0Zo1a7R7927FxcVp2rRpMsZIkubNm6fS0lJt3bpVb7zxhnJycrRkyRJJUllZmaZMmaKbb75Z77//vpYtW6bVq1drx44d9ZobAACgLn4LWC1atNCmTZvOG7A2btyoAQMGaMCAAWrRooVGjhypzp07a/PmzZIkh8OhiRMnqkOHDgoNDdWMGTOUk5OjTz/9VN9995127typGTNmKDo6Wm3bttXUqVP1xhtv6PTp09qzZ49Onz6thx56SK1atVJCQoLGjh0rh8NRr7kBAADq4reAde%2B99yosLOy8NZfLpfj4eJ%2Bx%2BPh4OZ1OlZWV6dChQz710NBQ3XDDDXI6ndq/f7%2BCgoLUpUsXbz0hIUEnT57U4cOH5XK51KVLFwUFBfkcOzs7u865AQAA6uOy/Bwsj8ejiIgIn7GIiAgdOnRIJ06ckDHmvHW3263IyEiFhoYqICDApyZJbrdbHo9H4eHhPvtGRkbK4/Goqqqq1rnrKz8/XwUFBT5jNlsrxcTE1PsY9REUxN8oAAB%2BmM1W/98TZ3%2Bn8LvFGpdlwJLkvZ/qQup17Xs%2B5wayC9n/XA6HQytWrPAZmzZtmjIyMi7quAAANERUVEiD9wkPv7oRVnLluSwDVlRUlDwej8%2BYx%2BNRdHS0IiMjFRgYeN5669atFR0dreLiYlVWVnovA57d9mz9yy%2B/rLHv2ePWNnd9jR8/XoMGDfIZs9laye0uqfcx6oN3GQCA2jTk905QUKDCw69WYWGpKiurGnFVl9aFhEwrXJYBy263e%2B%2BJOsvpdGr48OFq0aKFOnXqJJfLpT59%2BkiSCgsL9dVXX6l79%2B6KjY2VMUYHDhxQQkKCd9/w8HC1b99edrtdf/zjH1VRUSGbzeatJyUl1Tl3fcXExNS4HFhQUKSKiubzhAUAXP4u5PdOZWUVv68scFkGrHHjxiktLU179uzRLbfcoi1btujLL7/UyJEjJUnp6elas2aN%2Bvfvr7Zt22rJkiXq1q2bEhMTJUlDhw7VCy%2B8oOeee06nTp3Syy%2B/rLS0NNlsNg0YMEChoaFauXKlJk2apIMHD2rTpk1avHhxveYGAKCpaErfndjcvjcxwFzsDUcX6GwYqqiokCSfs0mStGPHDi1dulS5ubnq2LGj5s6dq969e0s6c4/U8uXL9frrr6ukpETJycl66qmndO2110qSioqK9OSTT2r37t266qqrdMcdd2j27NkKDg6WJB08eFBPPvmksrOz1aZNGz3wwAO6%2B%2B67vWurbe4LVVBQdFH7n4/NFqjBS963/LgAAFxqjRWwrrnm/J9Y0Nj8FrCuNAQsAAB%2BWHMLWNwlDQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWOeRm5uryZMnKzk5WampqVq8eLGqqqr8vSwAANBE2Py9gMvR9OnTlZCQoJ07d%2BrYsWOaMmWK2rRpo5/97Gf%2BXhoAAGgCOIP1PU6nUwcOHNDMmTMVFhamuLg4TZw4UQ6Hw99LAwAATQRnsL7H5XIpNjZWERER3rGEhAQdOXJExcXFCg0NrfMY%2Bfn5Kigo8Bmz2VopJibG0rUGBZGPAQDNg83WvH6nEbC%2Bx%2BPxKDw83GfsbNhyu931ClgOh0MrVqzwGXv44Yc1ffp06xaqM0Huvmu/0Pjx4y0Pb01Nfn6%2BHA4Hvfj/6Icv%2BlGNXviiH9XohbWaV1y0iDHmovYfP3683nzzTZ9/48ePt2h11QoKCrRixYoaZ8uuRPTCF/3wRT%2Bq0Qtf9KMavbAWZ7C%2BJzo6Wh6Px2fM4/EoICBA0dHR9TpGTEwM6R8AgCsYZ7C%2Bx263Ky8vT8ePH/eOOZ1OdezYUSEhIX5cGQAAaCoIWN8THx%2BvxMRELV26VMXFxcrJydG6deuUnp7u76UBAIAmImj%2B/Pnz/b2Iy81PfvITbd26VU8//bTeeecdpaWl6f7771dAQIC/l1ZDSEiI%2BvTpw9k10Yvvox%2B%2B6Ec1euGLflSjF9YJMBd7RzcAAAB8cIkQAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBKwmKDc3V5MnT1ZycrJSU1O1ePFiVVVV%2BXtZF6VLly6y2%2B1KTEz0/nv66aclSVlZWUpLS1PPnj01fPhwbd682WffDRs2aOjQoerZs6fS09OVnZ3trZWXl%2BuJJ55Q//79lZycrIyMDLndbm/9cunl%2B%2B%2B/r5SUFM2YMaNG7d1339WIESN04403avTo0frggw%2B8taqqKi1btky33nqrevfurfvvv19ff/21t%2B7xeJSZmamUlBT169dPc%2BfOVVlZmbe%2Bf/9%2B3XPPPerVq5eGDBmitWvX1nvuxvJDvXjzzTfVtWtXn%2BdIYmKiPvvsM0nNsxfSmefotGnTlJycrJSUFM2ePVuFhYUXvebG7ldj%2BKFefPPNN%2BrSpUuN58b//u//NtteSNKBAwd03333qVevXkpJSVFmZqYKCgok%2Bfd1s665rxgGTc5dd91lHn/8cVNYWGiOHDlihgwZYtauXevvZV2Uzp07m6%2B//rrG%2BLfffmt69OhhNm7caMrKysyHH35ounfvbj777DNjjDF/%2BctfzE033WQ%2B%2BeQTU1paalavXm369u1rSkpKjDHGLFy40IwePdr897//NW632zz88MNmypQp3uNfDr1cs2aNGTJkiJkwYYLJzMz0qe3bt8/Y7XazZ88eU1ZWZt5%2B%2B22TlJRk8vLyjDHGbNiwwaSmpppDhw6ZoqIi89RTT5kRI0aYqqoqY4wxDz/8sJk8ebI5duyYOXr0qBk/frx5%2BumnjTHGlJaWmp/85Cdm%2BfLlpqSkxGRnZ5s%2BffqY7du312vuS92LN954w9xzzz0/uG9z68VZd9xxh5k9e7YpLi42eXl5ZvTo0WbOnDkXvebG7Nel7sXXX39tOnfu/IP7NcdelJeXm1tuucWsWLHClJeXm2PHjpl77rnHTJ061a%2Bvm3XNfSUhYDUxn332menWrZvxeDzesT/84Q9m6NChflzVxfuhgPXKK6%2BYUaNG%2BYxlZmaaefPmGWOMmTx5snn22We9tcrKStO3b1%2BzdetWc/r0adOrVy%2Bzc%2BdOb/3QoUOmS5cu5ujRo5dNL9evX28KCwvNrFmzaoSKBQsWmGnTpvmMjR071qxevdoYY8zw4cPN%2BvXrvbWioiITHx9vPv74Y1NQUGC6du1q9u/f763v3bvX9OjRw5w6dcps27bN3HzzzaaiosJbX7x4sfn5z39er7kbQ229qCtgNbdeGGPMiRMnzOzZs01BQYF37Pe//70ZMmTIRa%2B5MfvVGGrrRV0Bq7n1whhjPB6P%2BdOf/mROnz7tHVu/fr0ZPHiwX18365r7SsIlwibG5XIpNjZWERER3rGEhAQdOXJExcXFflzZxVu6dKkGDhyom266SfPmzVNJSYlcLpfi4%2BN9touPj/eezv5%2BPTAwUN26dZPT6dRXX32loqIiJSQkeOsdOnRQy5Yt5XK5Lpte3nvvvQoLCztv7Ycev9PpVFlZmQ4dOuRTDw0N1Q033CCn06n9%2B/crKChIXbp08dYTEhJ08uRJHT58WC6XS126dFFQUJDPsX%2Bot%2BfO3Vhq64Uk5eXl6Wc/%2B5l69%2B6tW2%2B9VW%2B//bYkNcteSFJ4eLgWLlyoNm3aeMfy8vIUExNzUWtu7H41htp6cdYvf/lL9evXTzfffLOWLl2q06dPS2p%2BvZCkiIgIjR07VjabTZJ0%2BPBh/fnPf9btt9/u19fNuua%2BkhCwmhiPx6Pw8HCfsbNP9HOvkTc1PXr0UEpKinbs2CGHw6FPPvlECxYsOO/jjYyM9D5Wj8fj84MunemH2%2B2Wx%2BORpBr7h4eHe%2BuXey9re3wnTpyQMabWxx8aGqqAgACfmqQffPyRkZHyeDyqqqqqdW5/iI6OVlxcnH7xi1/oww8/1COPPKI5c%2BYoKyvriumF0%2BnUq6%2B%2Bqoceeuii1tzY/boUzu1FcHCwbrzxRg0ePFi7d%2B/WmjVrtHnzZv3mN7%2BR5N%2Bfo8aWm5sru92uYcOGKTExURkZGX593axr7isJAasJMsb4ewmWczgcGjt2rIKDg9WhQwfNnDlTW7du9b4DrU1d/ait3hR6eakf37m/SC6n/gwcOFCvvPKK4uPjFRwcrOHDh2vw4MF68803vds0517861//0v33369HH31UKSkpP7hdQ9bcmP1qTN/vRUxMjF5//XUNHjxYV111lbp3764pU6bU%2B7lRV/1y7kVsbKycTqfee%2B89ffnll/rlL39Zr/2a%2B%2Bvm5YCA1cRER0d732Gc5fF4FBAQoOjoaD%2BtynrXX3%2B9KisrFRgYWOPxut1u72ONioo6bz%2Bio6O923y/fuLECbVu3bpJ9LK2xxcZGXne/ng8Hu/jKy4uVmVlpU9Nkrf%2B/XeVHo/He9za5r5cxMbGKj8/v9n3YteuXZo8ebLmzJmje%2B%2B9V5Iuas2N3a/GdL5enE9sbKy%2B%2B%2B47GWOabS/OCggIUFxcnGbMmKGtW7fKZrP57XXzfMc%2Bd%2B4rCQGribHb7crLy9Px48e9Y06nUx07dlRISIgfV3bh9u3bp0WLFvmM5eTkKDg4WAMGDKhx7T47O1tJSUmSzvTD5XJ5a5WVldq3b5%2BSkpLUrl07RURE%2BNQPHjyoU6dOyW63N4le2u32Go/f6XQqKSlJLVq0UKdOnXweX2Fhob766it1795d3bp1kzFGBw4c8Nk3PDxc7du3l91u1%2Beff66Kiooax65rbn/44x//qHfffddnLCcnR%2B3atWvWvfj3v/%2BtWbNm6cUXX9SoUaO84xez5sbuV2P5oV5kZWVp5cqVPtsePnxYsbGxCggIaJa9yMrK0tChQ30uQ54NdN27d/fb62ZiYmKtc19RLsmt9LDU2LFjzZw5c0xRUZE5dOiQGTRokHn11Vf9vawLdvToUdOjRw%2BzevVqU15ebg4fPmyGDRtmnn76afPdd9%2BZG2%2B80fzpT38yZWVlZs%2BePaZ79%2B7ev%2BjZu3ev6dWrl/n444/NyZMnzfLly82AAQNMaWmpMebMX/Pcdddd5r///a85fvy4mTJlipk%2Bfbp37supl%2Bf7y7nPP//cJCYmmt27d5uysjKzceNGc%2BONN5r8/HxjzJm/3hk4cKD3z8vnzZtnxowZ490/MzPTTJo0yRw7dszk5eWZMWPGmEWLFhljzvyZd2pqqnnppZfMyZMnzSeffGJuuukms3v37nrNfal78bvf/c7cfPPN5rPPPjOnTp0yW7ZsMd26dTNOp7PZ9uL06dPm9ttvN6%2B//nqN2sWuuTH7dal74XQ6TUJCgnnrrbfMqVOnzGeffWb69u3r/eiA5tYLY4wpLCw0KSkpZtGiRebkyZPm2LFj5v777zd33323X18365r7SkLAaoLy8vLMpEmTTPfu3U1KSop56aWXvJ/X0lT9/e9/N%2BPHjzc9evQwffr0MQsXLjRlZWXe2siRI01CQoIZMmRIjc%2BXee2118yAAQOM3W436enp5vPPP/fWysvLzfz5803v3r3NjTfeaB555BFTWFjorV8OvbTb7cZut5uuXbuarl27ev/7rO3bt5shQ4aYhIQEc%2Bedd5q///3v3lpVVZV58cUXzS233GK6d%2B9uHnjgAZ/PZiosLDQzZswwPXr0ML179zYLFiww5eXl3vrnn39uJkyYYOx2uxk4cKB57bXXfNZW29yNobZeVFVVmZdfftmkpqYau91ubrvtNrNr165m2wtjjPnHP/5hOnfu7O3Duf%2B%2B%2Beabi1pzY/frUvdix44dZuTIkaZ79%2B6mb9%2B%2BZtWqVaaysrJZ9uKsAwcOmHvuucd0797d3HzzzSYzM9McPXrUGOPf18265r5SBBjD3WoAAABW4h4sAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAi/0/jbFs8bnacjMAAAAASUVORK5CYII%3D\"/>\n        </div>\n        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common2695586589509889855\">\n            \n<table class=\"freq table table-hover\">\n    <thead>\n    <tr>\n        <td class=\"fillremaining\">Value</td>\n        <td class=\"number\">Count</td>\n        <td class=\"number\">Frequency (%)</td>\n        <td style=\"min-width:200px\">&nbsp;</td>\n    </tr>\n    </thead>\n    <tr class=\"\">\n        <td class=\"fillremaining\">5890</td>\n        <td class=\"number\">1232</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">73574</td>\n        <td class=\"number\">939</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">15867</td>\n        <td class=\"number\">900</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">80350</td>\n        <td class=\"number\">783</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">15275</td>\n        <td class=\"number\">746</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">2151</td>\n        <td class=\"number\">722</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">4568</td>\n        <td class=\"number\">529</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">12897</td>\n        <td class=\"number\">513</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">11521</td>\n        <td class=\"number\">502</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">34541</td>\n        <td class=\"number\">501</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"other\">\n        <td class=\"fillremaining\">Other values (322887)</td>\n        <td class=\"number\">2980814</td>\n        <td class=\"number\">99.8%</td>\n        <td>\n            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n        </td>\n</tr>\n</table>\n        </div>\n        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme2695586589509889855\">\n            <p class=\"h4\">Minimum 5 values</p>\n            \n<table class=\"freq table table-hover\">\n    <thead>\n    <tr>\n        <td class=\"fillremaining\">Value</td>\n        <td class=\"number\">Count</td>\n        <td class=\"number\">Frequency (%)</td>\n        <td style=\"min-width:200px\">&nbsp;</td>\n    </tr>\n    </thead>\n    <tr class=\"\">\n        <td class=\"fillremaining\">0</td>\n        <td class=\"number\">8</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:47%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">1</td>\n        <td class=\"number\">12</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:70%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">2</td>\n        <td class=\"number\">4</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:24%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">3</td>\n        <td class=\"number\">17</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">4</td>\n        <td class=\"number\">7</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:41%\">&nbsp;</div>\n        </td>\n</tr>\n</table>\n            <p class=\"h4\">Maximum 5 values</p>\n            \n<table class=\"freq table table-hover\">\n    <thead>\n    <tr>\n        <td class=\"fillremaining\">Value</td>\n        <td class=\"number\">Count</td>\n        <td class=\"number\">Frequency (%)</td>\n        <td style=\"min-width:200px\">&nbsp;</td>\n    </tr>\n    </thead>\n    <tr class=\"\">\n        <td class=\"fillremaining\">322892</td>\n        <td class=\"number\">2</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">322893</td>\n        <td class=\"number\">2</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">322894</td>\n        <td class=\"number\">2</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">322895</td>\n        <td class=\"number\">2</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n        </td>\n</tr><tr class=\"\">\n        <td class=\"fillremaining\">322896</td>\n        <td class=\"number\">2</td>\n        <td class=\"number\">0.0%</td>\n        <td>\n            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n        </td>\n</tr>\n</table>\n        </div>\n    </div>\n</div>\n</div>\n    <div class=\"row headerrow highlight\">\n        <h1>Correlations</h1>\n    </div>\n    <div class=\"row variablerow\">\n    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqMAAAJYCAYAAABWyu%2ByAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzs3Xl8TXf%2Bx/HXzd4KSZGQErRKFImJ2req6E8tbYyZliqdWqpERP1KtUXRGKpkqmrXRpkqOqVUa35qmQ7Tmk7V0kgpQoktCRKRENnu74/IHbdJKrnnpLl4Px%2BPPMK553zu537v9snnnPM9FqvVakVEREREpAK4VHQCIiIiInLnUjEqIiIiIhVGxaiIiIiIVBgVoyIiIiJSYVSMioiIiEiFUTEqIiIiIhVGxaiIiIiIVBgVoyIiIiJSYVSMioiIiEiFUTEqIiIiIhXGraITEBExQ1BQUIm3ubu74%2BvrS9OmTenduzfdunXDYrH8htmJiEhJLLo2vYjcDgqL0ZCQEPz8/Oxuu3btGseOHePMmTMAPPLII8ydOxcPD4/fPE8REbGnYlREbguFxej8%2BfPp2rVrsets3bqVcePGceXKFQYPHsz48eN/yxRFRKQYOmZURO4YXbt25aWXXgJg1apVXLt2rYIzEhERHTMqIneU//mf/yE6OpqrV6/y448/Ehoaanf7rl27WLlyJfv27SMtLQ1vb28aNWrEH/7wBx5//PFiY6alpbFs2TL%2B%2Bc9/cuLECbKzs/Hx8SEkJIRBgwbRunXrItt06dKF06dPs3TpUq5du8bbb79NYmIiH3zwAQ899BAAhw8fJjY2lt27d5OUlISbmxv%2B/v60atWKAQMGFHuc7OXLl1m%2BfDnbtm3jxIkT5OTkUL16dZo3b85zzz1HcHCw3fqnTp0iLCwMgB9%2B%2BIGEhAQWLlzI3r17SUtLw9/fn7CwMMaMGcPdd9/t0JiLiPwadUZF5I7i6%2Btr%2B3d6errdbW%2B//TbPPfccW7Zswdvbm9atW1O1alV27drF2LFjefHFF8nLy7PbJiUlhT59%2BrBo0SKOHTtG48aNadOmDR4eHvzjH//gT3/6E2vXri0xn2PHjjFmzBjc3d1p06YNXl5eAPznP//hD3/4A59%2B%2BinXrl3joYceIjQ0lIyMDD7%2B%2BGOeeuopdu3aZRfr9OnT/P73v%2Bfdd9/l6NGjNGrUiDZt2mC1Wvn888956qmn%2BOSTT0rMZc%2BePfTv35/4%2BHgaNWrEfffdx%2BnTp1mxYgWjRo0q9RiLiJSJVUTkNtCwYUNrw4YNrVu2bPnV9RISEmzr/vDDD7blX375pbVhw4bWli1bWr/99lu7bf7zn/9Y27VrZ23YsKF12bJldrdNmzbN2rBhQ2u3bt2s58%2Bfty3Pzc21/vnPf7Y2bNjQ%2BtBDD1kzMjLstnvkkUesDRs2tHbp0sW6cOHCInk%2B%2BeST1oYNG1rffPNNa15enl3ct99%2B29qwYUNrr1697Lbp37%2B/tWHDhtY%2BffpYk5KSbMvz8vKs7777rrVhw4bWpk2bWo8dO2a7LTEx0TYenTt3ti5ZssSan59vu/3TTz%2B13X7w4MFfG1oREYeoMyoid5Qvv/wSAB8fHx588EHb8oULFwIwfvx4WrVqZbdNy5YtefnllwFYvny53W3%2B/v707NmTyMhIqlWrZlvu6urKmDFjcHFx4fLly%2Bzbt6/YfPLz8xk2bFiR5QcPHgSgT58%2BuLj896Pa1dWVqKgooqKiGDhwINnZ2QDs37%2Bf3bt3A/DWW2/h7%2B9v28bFxYXIyEiaNGlCdnY2q1evLjaXBg0a8Pzzz9tNexUeHk7VqlVt9yEiYjYdMyoid4x//vOfLF68GIChQ4fi5lbwEZiUlER8fDwAjz76aLHbdu3aFYvFwpkzZzh%2B/Dj33XcfAM8//3yJ93fXXXdRrVo1UlJSSElJKXadNm3a2BWbhapUqcL58%2BfZtGkTo0ePtrvNxcWFkSNH2i3buXMnUDCrQP369Ut8DPHx8UV27xcq7phYi8VCYGAgFy9eJDU1tdjtRESMUDEqIreVxYsXs27dOrtl2dnZ/PzzzyQmJgIF3cahQ4fabj98%2BLDt36%2B88kqJsd3c3MjJyeHnn3%2B2FaOF8f/5z39y4MABkpOTSU9Px3p91rzLly8DBR3Q4tSoUaPY5X/605%2BIiYlhwYIF7Ny5k169etG2bdsSJ/c/evQoUNDdLMn9998PFBynWpw6deoUu9zT0xOAnJycEmOLiDhKxaiI3FZ%2B%2BOGHIstcXV255557CAsLo2/fvjz88MN2t1%2B6dMn2723btt30PgoLTIBDhw4xcuRITp065VC%2BPj4%2BxS4v3HW/ePFi4uLiiIuLA8DPz4/HHnuMP/3pTwQGBhbJqUqVKiXeV%2BXKlYGCojIrK8t2slQhXQRARCqCilERua382qT3JSk8RtLd3Z24uLhSXyo0KyuLESNGcObMGerVq0dERARt27blnnvuwd3dHfjvFE43u%2B/iDBs2jH79%2BrF161b%2B%2Bc9/8s0335CSksJf//pX1qxZw1tvvUX37t1L/TitN1zjpLhDA0REKoKKURG54xVO95STk0NqaqrthJ2b2bFjB2fOnMFisbBkyRLq1q1bZJ2srCxDuVWpUoU%2BffrQp08f8vLy%2BOabb5g/fz579%2B5lwoQJtGnThnvuucfWYf3ldFU3KrzNy8tLXVARcRr601hE7ngNGza0/fvIkSOl3u7nn38GCo61LK4QPXnyJBcuXDCcXyFXV1c6duzIihUrqFGjBpmZmbbDEgqPFb3x%2BNdfKrzt144rFRH5rakYFZE7np%2BfH02aNAHg448/Lnad48ePEx4ebpsCCv57DGZJlxWdP3%2B%2B7d%2B/nCz/1%2Bzfv5/XXnuN2NjYYm/38PCgevXqwH87r4XHwR4%2BfLjYgjQ/P982rVXHjh1LnYuISHlTMSoiAgwfPhyAzz//nA8%2B%2BMDu%2BMoTJ04QFRXFoUOHOHv2rG15o0aNADh37hxbt261Lb969SrTpk0jLi7OdrnRks5gL46rqytr165l7ty5fPXVV0Vu37JlCwcPHsTd3Z3mzZsD0LhxYzp06ADAa6%2B9xsWLF23r5%2BbmMmvWLBISEqhcuTL9%2BvUrdS4iIuVNx4yKiFBwzfphw4axZMkSZsyYwYoVK7j//vtJTU3l4MGD5OXl0bhxY8aOHWvbJjQ0lA4dOvCvf/2LUaNG0axZMzw9PTlw4ABubm4sW7aML774gr1797JixQp%2B%2BuknRowYQcuWLX81l6ZNmzJ48GBiY2N54YUXqFWrFnXr1sXFxYVTp07ZDg%2BYMGECfn5%2Btu2mT5/On/70J%2BLi4ujSpQsNGzbE09OTI0eOkJqaipeXFzExMSVOJyUiUhFUjIqIXPfSSy/Rrl07PvzwQ/bt28euXbvw9PSkadOm9OjRg/79%2Bxc58WfOnDnMnj2bbdu2ceDAAfz9/enWrRvDhw%2BnTp061KxZkx9//JE9e/Zw5MiRUp%2BpP378eFq2bMn69es5cOAAe/fuJTc3Fz8/P3r16sWAAQNsXddCNWrU4JNPPuGDDz5gy5YtHDlyhNzcXGrUqMH//M//MGTIkGKPbRURqUgW6437okREREREfkM6ZlREREREKoyKURERERGpMCpGRURERKTCqBgVERERuc3s3LmTdu3aMWbMmF9dLz8/n7fffpuwsDBatmzJkCFDSExMtN2elpbGiy%2B%2BSLt27ejQoQMTJkwwfGW5X1IxKiIiInIbWbp0KdOmTSvV7BkrV65k48aNLFmyhH/84x/Uq1ePkSNH2uZanjRpElevXuXzzz9n7dq1JCQkMHv2bFPzVTEqIiIichvx9PTkk08%2BKVUxumbNGp577jnq16%2BPt7c3Y8aMISEhgf3793P%2B/Hm2bt3KmDFjqFq1KjVq1CAiIoK1a9eSk5NjWr6aZ1RERETEySQnJ5OSkmK3zM/PD39//5tu%2B%2Byzz5bqPrKysjh69CiNGze2LfP29qZu3brExcVx%2BfJlXF1dCQoKst3epEkTrly5wrFjx%2ByWG6FiVMqmlBN2l%2Bi%2B%2B%2BDIEWjQAI4fNxbrs8%2BMbQ9w993QpQts3w5XrhiP5yS5nAh53ND2bm5w771w5gzk5hoKRd06TjaV8eefG9vezNdMjx7GtgdwcYH8fMNhrC6uxnMxkdFD0iwW8PSEa9fA6GzaLibsQ/TwgOxs43HMyMXNzfj7upD7lUvGAlgsULkyXL5s/Iny8TG2vaOMfi%2BWYM3cucybN89uWWRkJKNGjTLtPi5duoTVasXnF2Pn4%2BNDamoqvr6%2BeHt7212so3Dd1NRU0/JQMSq/LV9fcHUt%2BO0M3N0LPkjc3Ss6E6fKxcWlIBUzvvhMY7EY/7IygxM9T0C5fRE6ylmeJnCeobFY/vvjDGPjLOMCON/gOJG%2BffvSpUsXu2U3Xv7XTL92/aPf4tpIKkZFREREHFVOf7X7%2B/uXape8Eb6%2Bvri4uJCWlma3PC0tjWrVqlG1alUyMjLIy8vD1dXVdhtAtWrVTMvDmfoeIiIiIrcWF5fy%2BfkNeHp60qBBA%2BLj423L0tPTOXnyJCEhITz44INYrVYOHTpkuz0uLo4qVapw3333mZaHilERERGRO0RSUhKPPfaYbS7Rp59%2BmhUrVpCQkEBGRgazZ8/mwQcfJDg4mKpVq9KtWzfmzJnDxYsXOXfuHPPnz%2BePf/wjbm7m7VzXbnoRERERRznVwfUFgoODAci9fqba1q1bgYKuZk5ODsePHyf7%2Bhl1/fr1IyUlhYEDB5KZmUnr1q3tTpx64403mDx5MmFhYbi7u9OrV6%2BbTqRfVipGRURERG4jcXFxJd5Wu3ZtfvrpJ9v/LRYLUVFRREVFFbt%2B5cqV%2Bctf/mJ6jjdSMSoiIiLiKCfsjN5qVIyKiIiIOErFqGEaQRERERGpMOqMioiIiDhKnVHDNIIiIiIiUmHUGRURERFxlDqjhmkEbxPfffcdwcHBtnnDfmnVqlVFrnErIiIiBt3CV2ByFuqM3iZatmz5q/OKiYiIiDgjFaMiIiIijrrDupjlQcWoQadOnSIsLIxNmzZRv359AGbPns3%2B/ftZsmQJU6ZMYceOHWRlZREUFMTEiRNp2rQpAJs2bWLx4sWcOHGCatWqMWzYMPr27QvAK6%2B8gqurKydPniQ1NZXPP//8V/P49ttvefbZZ/nhhx/w9PRk//79TJo0iZMnTxIaGspDDz1U5seWnJxMSkqK3TK/%2B%2B7D39e3zLFsGjWy/22Ej4/xGN7e9r8rkom5eHgY277wksMmXnrYeRh93TjTa%2BY2ZrGYs73ROM6Wi9MxWogVbm80Tn6%2Bse2lQt2OXzVOY/ny5Zw/f54tW7bg4eHB0qVLmTRpEp9%2B%2BilxcXFMmDCBd999l7Zt27J3716ef/55GjRoQPPmzQHYtm0bM2bMoHPnzmW637y8PKKioujZsyejR4/m0KFDjBo1CrcyVhZr1qyxuz4tQOTo0YwaPbpMcYr10UfGY5jp%2Bpg7BRNyCTAhDQA/PzOimPgNbMa3eadOxmOAc71mXF0NhzCzTjLjafLyMh4DwNPTnDhmcHev6Az%2By7Rc3CubE6dSJWPbX7pkTh6OUGfUMBWj5Sg9PR13d3e8vLxwc3MjIiKCiIgIANatW0fnzp3p0KEDAC1atKB79%2B5s2LDBVozWqlWLRx55pMz3e%2BDAAZKTkxkxYgSenp40a9aMRx99lH/84x9litO3b98iJz35Pf44LF9e5pxsGjUqKET794dDhxyPAzBnjrHtoaC71bw57NkDGRnG4zlJLmcbGCu43NwKCtGUFMjNNRSKgJpWYwEKWSxgNSHWzp3GtjfzNdO%2BvbHtoaAQzcszHMbqYrygBfOepmvXjOfh6VkQx2g%2BRmsNi6Wg%2BMvJMZ6LGYV%2BYS5mcM%2B6bCyAi0tBIZqZeet2N1WMGqZitBz179%2BfIUOG8PDDD9OxY0e6du1KWFgYACdPnmTXrl0EBwfb1rdarbbiFAqKUUecO3eOKlWqULnyf/9irVevXpnj%2BPv74%2B/vb7/w%2BHGHciri0CHYu9dYDDP/Es7IqNi/rG9kQi4lTKpQZrm55sVyGmY9z870mrkNmVHQFsYxGsuZcnG6Xf1mFZD5%2BbduMSqGqRgtB3nXuxS1a9dm06ZNfPvtt2zfvp3XX3%2Bdzz77jLlz5%2BLl5cXTTz/NpEmTSozj6uCut%2BzsbFsOhfL1JhcRETGfOqOGqRg1yPP6AUlZWVm2ZYmJiQBkZmbi7u5Ou3btaNeuHYMGDaJLly6kpqZSp06dIlMxnTt3Dj8/P4eL0EL%2B/v5kZGRw%2BfJlW3c0ISHBUEwRERGR8qBy3qCqVatSuXJlvvzyS/Ly8vjXv/7Fvn37AIiKimLmzJlkZGSQn5/P3r178fX1xcfHhz/%2B8Y/s2bOHtWvXkp2dzcGDB3nyySfZvHmz4ZyaNWuGj48P7733HtnZ2ezevbvMx4uKiIhIKWjSe8PurEdbDlxdXZk8eTKffvopLVq0YP369TzzzDMAREdHc%2BLECTp16kTLli358MMPmT9/Pi4uLtSvX5%2BYmBjee%2B89WrRowahRoxgyZAg9evQwnJOXlxfz589n27ZttGzZknnz5jF48GDDcUVEROQXVIwaZrFazTo0W%2B4IRo%2BeDw0tOAu5eXPjJzB99pmx7aFgzslOnWDHjoo/GcXEXE6EPG5oew8PCAiAs2eNn8BUt46TnU1/kzl7b8rM14wJf3zermfT33Dkk8N5eHkVxHGGs%2Bk9PAreSxWdC5h8Nv0Vg%2B8BFxeoXBkuXzZ%2BApMZc087IjCwfOJeP%2BTvTqBjRkVEREQcdYd1McuDitFbwPnz528636iuSy8iIiK3IhWjt4Dq1aur2BQREXFG6owapmJURERExFEqRg3TCIqIiIhIhVFnVERERMRR6owaphEUERERkQqjzqiIiIiIo9QZNUzFqIiIiIijVIwapiswSdls3GhsezOvXvPEE8a2B3OvCOVMueTmGs/HpCv7cOWK8RguLlCpEmRmGr9KS5UqxrY38XnKyzX%2B8WvW0%2BSae814EBMvNXQNT2dJBQ8PY9sX5uMs37Zm5mL07QgmvobNuYhY2T34YPnEPXiwfOI6IXVGRURERBylzqhhGkERERERqTDqjIqIiIg4Sp1Rw1SMioiIiDhKxahhGkERERERqTDqjIqIiIg4Sp1RwzSCIiIiIlJh1BkVERERcZQ6o4apGBURERFxlIpRwzSCIiIiIlJh1BkVERERcZQ6o4apGBURERFxlIpRwzSCIiIiIlJh1BkVERERcZQ6o4ZpBMvZ4MGDmTNnTrnfz/r16%2BnSpUuJt8%2BePZuBAweWex4iIiJS8U6fPs2wYcNo3bo1jzzyCLNmzSI/P7/IeoMHDyY4ONju58EHH2TevHkADBw4kCZNmtjd/sQTT5iaqzqj5Sw2NvY3uZ/evXvTu3fv3%2BS%2BRERE5Don7YyOGjWKJk2asHXrVi5cuMALL7xA9erVGTRokN16v6xT0tPT6dGjB48%2B%2BqhtWXR0NH369Cm3XJ1zBEVERERuBS4u5fNjQFxcHIcOHWLs2LFUrlyZevXq8dxzz7FmzZqbbjtnzhweffRRgoKCDOVQFndkZ3TJkiWsWrWKixcvUrNmTSIiIggPD%2BfQoUPMmDGD%2BPh43Nzc6NWrF%2BPHj8fd3Z3z588zefJkdu/eTU5ODiEhIURHRxMYGMjx48eZOnUqBw4cwGKx0Lp1a6Kjo7nnnnsYOHAgzZo1Y%2BzYsQCsXr2a5cuXc%2BbMGQICAoiKiqJHjx5AQSu8ffv2JCQksG3bNipVqsTYsWMJDw%2B/6WNat24dMTExfP311wBs376dmTNnkpyczMMPP0z16tXLPE7JycmkpKTYLfPLycHfgVg23t72v40IDTUeo1Ej%2B98VyZlyMZMZXYPCGGbEMvq6uV2fJ4vFvBgmxDIawcRURCpEsd/Bfn74%2B/vfdNv4%2BHhq1aqFj4%2BPbVmTJk04fvw4GRkZeJfwHXzixAnWr1/P1q1b7ZZv2rSJ9957j7Nnz9KsWTPeeOMN6tSp48CjKt4dV4zu2bOHFStW8PHHHxMQEMDXX3/NqFGjaNmyJUOHDmXgwIEsXbqUpKQkIiIieP/99xk%2BfDjvvPMOPj4%2B7Nixg7y8PN58801mzpzJvHnziI6Opnnz5rz33ntkZmYyfvx4Fi5cyGuvvWZ339u3b2fWrFksXryYZs2asWXLFsaNG0f9%2BvVtf4GsXLmS6dOnM336dBYtWsQbb7xBjx49cHd3L/VjTE9PZ8yYMYwdO5a%2Bffuya9cuXnrpJR588MEyjdWaNWtsx4wUihw5klFmtOqbNzceY88e4zEKffSRebGMcqZcXF2Nx6hUyXiMQnfdZTyGWa8bE54nE0a3II4ZgVw9TAhyXRk%2Br0piVjYmpGIaZyqMzcrFlNeeCXHy8szJwyHltJu%2B2O/gyEhGjRp1023T0tKoUqWK3bLCwjQ1NbXEYnTJkiX84Q9/oGrVqrZl9evX56677mL27Nnk5%2Bczbdo0hg4dyueff46Hhznv1DuuGL18%2BTIuLi54eXlhsVjo0KED33//PZs3b8ZqtfLCCy8AEBgYyJAhQ1i8eDHDhw8nPT0dX19fPDw8sFgsTJkyBZfrL8D09HS8vLxwc3PDx8eHBQsW2G670SeffEKvXr1o0aIFAD169CA2NpbNmzfbitHQ0FA6duwIQPfu3Zk3bx7JycnUqlWr1I/xX//6F3fffTfPPPMMLi4uPPzww7Ro0YLMzMwyjVXfvn2LnBTl99NPsGNHmeLY8fYuKET37IGMDMfjALz4orHtoaC79dFH0L8/HDpkPJ6z5PLdd8bzcXU15xM%2BK8t4DBeXgkL06lUo5gD8Mrn%2B/nKYic9T3nfGC2OznibXvGzjQSyWguovJwesVkOhsg2WoyamYkpBa7EYz8MsZuZi9O0I5r2GbzfFfgf7%2BZV6e2sZn%2BS0tDQ2bNjA3//%2Bd7vlU6ZMsfv/G2%2B8QevWrfn%2B%2B%2B9p27Ztme6jJHdcMdq2bVsaN25Mly5daNu2LZ06dSI8PJzExEQuXLhAcHCwbV2r1Wqr%2BocOHcqIESPYuXMnHTp0oHv37rYnITIyknHjxrF%2B/Xo6dOhAr169CAkJKXLfp06dok2bNnbL6taty%2BnTp23/r127tu3fXl5eAGSV8cv83LlzBAQE2BXE9erVIz4%2Bvkxx/P39i%2B4OOHYMLl0qU5xiZWQYj7N3r/E8Ch06ZG48I5wpFzOY8W11Yyyj8cwa29vteTKzUrJaDcczKxsTUhH5deXUGS32O7iUqlatSlpamt2ytLQ0LBaLXdfzRtu2beO%2B%2B%2B4jMDDwV2N7e3vj4%2BNDUlKSQ7kV5447gcnDw4NFixaxevVqmjZtysqVKwkPD8fT05MGDRoQFxdn%2Bzlw4AB7ru/SCw4OZvv27UyYMAGr1UpkZCQzZ84EoHPnznz11VdERkZy4cIFBgwYwIcffljkvrOzi%2B88WG7YX1JcR7WssrOzyfvFn5nFTecgIiIiBjnhCUxNmzbl7NmzXLx40bYsLi6OBx54gEolHDq1bds22rdvb7csIyODKVOm2BWeFy9e5OLFizctWsvijitGc3JyyMjIoFGjRowcOZL169djsVioUqUKiYmJdruyU1NTybi%2BKzktLQ13d3fCwsKIjo5m4cKFrF692rZepUqV6NGjBzExMUydOrXYM9bq1KnDsWPH7JYdO3bM1CcUCv6aSkpKsmvRJyQkmHofIiIi4pwaN25McHAwMTExZGRkkJCQwLJly3j66acBeOyxx9i9e7fdNgcPHrTbOwsFXdD9%2B/czbdo00tLSuHTpElOnTiUoKIhQM04ivu6OK0ZjY2N5/vnnOXfuHFBQpF26dIn69etTtWpVZs6cSUZGBikpKYwePZrZs2cD0K9fP5YuXcq1a9fIyclh//791K1bl6ysLLp168aGDRvIzc0lKyuL%2BPj4Ys8yCw8PZ%2BPGjezbt4%2BcnBzWrVvHkSNH6Nmzp6mPsV27dmRkZLB69Wqys7PZunUr%2B/fvN/U%2BREREBKfsjALMnTuX5ORk2rdvz7PPPkvv3r3p378/AMePH%2BfKlSt266ekpBQ78878%2BfOxWq1069aNzp07k5OTw5IlS0zZk1vojjtmdNCgQZw5c4bevXuTlZVFQEAAY8eOJSQkhAULFjBt2jTat2%2BPt7c3YWFhjB8/HiiYd2vq1KksXLgQNzc3goODmT17Nl5eXrzzzju89dZbTJ48GS8vL1q0aMHrr79e5L579uzJ6dOnefnllzl//jz3338/sbGx1KtXz9THWLNmTWJiYpg9ezYzZ86kU6dO9O/fn7230/FtIiIiUqKaNWuydOnSYm/76aefiiw7cOBAsevee%2B%2B9Rc7qN5vFWtbTreTOtnGjse19fKBTp4Iz8o2ewGTG5chCQwvO7G/evOJPRjEzl9xc4/mYdYrrL/76doiLS8EUUZmZxk9g%2BsV0J2Vm4vOUl2v849e0s%2BlzrxkPYrGAhwdkZxs%2Ba%2Bgans6SCmbMXqOz6Utm2mvYrLnSyqp79/KJ%2B4uz2m9nd1xnVERERMQ0Tno50FuJitFbwA8//MAzzzxT4u333nsvmzdv/g0zEhERETGHitFbQEhICHFxcRWdhoiIiPySOqOGaQRFREREpMKoMyoiIiLiKHVGDVMxKiIiIuIoFaOGaQRFREREpMKoMyoiIiLiKHVGDdMIioiIiEiFUWdURERExFHqjBqmYlRERETEUSpGDdMIioiIiEiFUWdURERExFHqjBqmERQRERGRCqPOqIiIiIij1Bk1TMWoiIiIiKNUjBqmERQRERGRCqPOqIiIiIij1Bk1TCMoIiIiIhVGnVERERERR6kzapiKURERERFHqRg1TCMoIiIiIhVGnVERERERR6kzaphG8CYGDx7MnDlzKjoNERERkduSOqM3ERsbW9EpkJeXx4oVKxg0aJDDMb788kuCgoKoW7euiZmJiIjc4dQZNUwjeAv48ccfee%2B99wzFmDt3LidOnDApIxEREQEKitHy%2BLmD3JKd0SVLlrBq1SouXrxIzZo1iYiIIDw8nEOHDjFjxgzi4%2BNxc3OjV69ejB8/Hnd3d86fP8/kyZPZvXs3OTk5hISEEB0dTWBgIMePH2fq1KkcOHAAi8VC69bDkwH9AAAgAElEQVStiY6O5p577mHgwIE0a9aMsWPHArB69WqWL1/OmTNnCAgIICoqih49egAwcOBA2rdvT0JCAtu2baNSpUqMHTuW8PDwmz6mq1evMmXKFHbs2EFWVhZBQUFMnDiR/Px8nn76aXJzcwkODmbp0qW0bt2amJgYNm7cSHp6OvXq1eO1116jZcuWtjxCQkLYsWMHAQEBnDt3jiNHjhAREcHjjz/OjBkzSjXOycnJpKSk2C3zy8nBv3r1sjxd9ry97X8bERpqPEajRva/K5Iz5WImMz5UC2OYEcvo6%2BZ2fZ4sFvNimBDLaAQTUxGRcmaxWq3Wik6iLPbs2UNUVBQff/wxAQEBfP3114waNYovvviCp556ioEDBzJo0CCSkpKIiIigZ8%2BeDB8%2BnEmTJpGXl8fkyZPJy8vjzTff5OLFi8ybN4/Bgwfzu9/9joiICDIzMxk/fjx16tThtddesytGt2/fzrhx41i8eDHNmjVjy5YtjBs3jnXr1hEUFMTAgQP5%2BeefmT59Om3atGHRokV88MEH/Pvf/8bd3f1XH9eiRYv47rvveOedd/Dw8GDp0qVs3bqVTz/9lHXr1hETE8PXX38NwPr165k9ezaffPIJfn5%2BLFy4kJUrV/Kvf/0LV1dXBg4cSGJiInPnziU4OBiLxUJQUBBLly6lU6dOpR7rd999l3nz5tktixw5klFRUWV/4kRERMpJXh64ulbQnY8cWT5x588vn7hO6JbrjF6%2BfBkXFxe8vLywWCx06NCB77//ns2bN2O1WnnhhRcACAwMZMiQISxevJjhw4eTnp6Or68vHh4eWCwWpkyZgsv1Lkt6ejpeXl64ubnh4%2BPDggULbLfd6JNPPqFXr160aNECgB49ehAbG8vmzZsJCgoCIDQ0lI4dOwLQvXt35s2bR3JyMrVq1frVx5Weno67u7stj4iICCIiIopd9/HHHycsLIzKlSsD0LNnT959913OnDlDYGAgACEhIYSEhJR1eO307duXLl262C3z%2B%2Bkn2LHD8aDe3tC8OezZAxkZhvLjxReNbQ8F3a2PPoL%2B/eHQIePxnCWX774zno%2Bra8EnvFFZWcZjuLjAXXfB1auQn28s1vX3p8NMfJ7yvttjLBfMe5pc87KNB7FYwN0dcnLAYJ8jGw9nSYWb9BJKnY%2BztH7MzMXo2xHMew1XmDtsl3p5uOWK0bZt29K4cWO6dOlC27Zt6dSpE%2BHh4SQmJnLhwgWCg4Nt61qtVjw8Cj7Qhg4dyogRI9i5cycdOnSge/futG3bFoDIyEjGjRvH%2BvXr6dChA7169Sq2kDt16hRt2rSxW1a3bl1Onz5t%2B3/t2rVt//by8gIgqxRfxv3792fIkCE8/PDDdOzYka5duxIWFlbsulevXmX69Ons2LGDS5cu2ZZnZ//3y%2BRmxW9p%2BPv74%2B/vb7/w2DG44T4dlpFhPM7evcbzKHTokLnxjHCmXMxgxrfVjbGMxjNrbG%2B358nMSslqNRzPrGxMSEVEytktV857eHiwaNEiVq9eTdOmTVm5ciXh4eF4enrSoEED4uLibD8HDhxgz56CzkNwcDDbt29nwoQJWK1WIiMjmTlzJgCdO3fmq6%2B%2BIjIykgsXLjBgwAA%2B/PDDIvd9Y7F3I8sNByUV11Etjdq1a7Np0yZmzZqFt7c3r7/%2BOqNHjy523alTpxIfH8/KlSuJi4tj06ZNRdZxrbD9FSIiIncQncBk2C33aHNycsjIyKBRo0aMHDmS9evXY7FYqFKlComJiWRmZtrWTU1NJeP6ruC0tDTc3d0JCwsjOjqahQsXsnr1att6lSpVokePHsTExDB16lTWrFlT5L7r1KnDsWPH7JYdO3bMtmvciMzMTPLy8mjXrh0TJ07kb3/7G5s3byY1NbXIuj/88ANPPPEE9erVw2KxEB8fb/j%2BRURERCrCLVeMxsbG8vzzz3Pu3DkAEhISuHTpEvXr16dq1arMnDmTjIwMUlJSGD16NLNnzwagX79%2BLF26lGvXrpGTk8P%2B/fupW7cuWVlZdOvWjQ0bNpCbm0tWVhbx8fHUqVOnyH2Hh4ezceNG9u3bR05ODuvWrePIkSP07NnT8OOKioqy5Z6fn8/evXvx9fXFx8cHLy8vLl%2B%2BTFJSEllZWdSuXZu4uDiys7PZt28fX3zxBVBw9ntJPD09OXHihK04FxEREROoM2rYLXfM6KBBgzhz5gy9e/cmKyuLgIAAxo4dS0hICAsWLGDatGm0b98eb29vwsLCGD9%2BPABz5sxh6tSpLFy4EDc3N4KDg5k9ezZeXl688847vPXWW0yePBkvLy9atGjB66%2B/XuS%2Be/bsyenTp3n55Zc5f/48999/P7GxsdSrV8/w44qOjub111%2BnU6dOWCwWGjRowPz583FxcaFNmzbUrl2brl27MnPmTF566SVefvllWrVqRbNmzXjrrbcAiIiIKPbwAigoxt966y2%2B%2BeYbFi5caDhfERER4Y4rHMvDLTe1k1SwjRuNbe/jA506FZyRb/QEpieeMLY9FMw5uWdPwRn%2BFX0yipm55OYaz8esU1yvXDEew8UFKlWCzEzjJzBVqWJsexOfp7xc4x%2B/pp1Nn3vNeBCLBTw8IDvb8FlD1/B0llTwMHZivy0fZ/m2vV3Ppq%2BwUyVeeql84sbElE9cJ3TLdUZFREREnIY6o4apGP0N/PDDDzzzzDMl3n7vvfeyefPm3zAjEREREeegYvQ3EBISQlxcXEWnISIiImZTZ9QwFaMiIiIijlIxaphGUEREREQqjDqjIiIiIo5SZ9QwjaCIiIjIbeb06dMMGzaM1q1b88gjjzBr1izyi5mL69133%2BXBBx8kODjY7uf8%2BfMAXLt2zTYPeuvWrYmKiir26pBGqBgVERERcZSTXoFp1KhR1KhRg61bt7Js2TK2bt3K8uXLi103PDycuLg4u5/q1asD8PbbbxMfH8%2BaNWvYvHkzVquVV1991XB%2BN1IxKiIiIuIoJyxG4%2BLiOHToEGPHjqVy5crUq1eP5557jjVr1pQpTm5uLp988gkREREEBATg6%2BvLiy%2B%2ByFdffUVSUpKhHG%2BkY0alTE6EPG5oew8PCADONuhEdraxXOqacZWhQt99Z14so8zIxc3gW7vwKkMtWxq%2BylBOtjmXenEHcjwqGY9j9IVX6NtvDYcwfNUjiwVcPXDNM36ZoYRTxq54BAXv78BASEzyMPz%2Brn/3WWMB3NzAzw%2BPSymGr0hmrRlgLBcTmXXFIzPiAFzfk%2BswNzeoVg3S0oxfOK5GDWPbO5vk5GRSUlLslvn5%2BeHv73/TbePj46lVqxY%2BPj62ZU2aNOH48eNkZGTg7e1tt/5PP/1Ev379OHz4MAEBAbz66qt06NCBkydPcvnyZZo0aWJbt379%2Bnh5eREfH08NkwZdxaiIiIiIo8rpBKY1a9Ywb948u2WRkZGMGjXqptumpaVR5ReXPi4sTFNTU%2B2K0Zo1axIYGMhLL72Ev78/a9asYfjw4Xz22WekpaUBFIlVpUoVU48bVTEqIiIi4mT69u1Lly5d7Jb5%2BfmVentrKfeWPPnkkzz55JO2/z/33HN88cUXfPbZZ3Tq1KlMsRylYlRERETEUeXUGfX39y/VLvniVK1a1dbVLJSWlobFYqFq1ao33b5WrVokJyfb1k1LS6NSpf8eJnXp0iWqVavmUG7F0QlMIiIiIo5ywhOYmjZtytmzZ7l48aJtWVxcHA888IBdUQmwYMECdu3aZbcsISGBwMBAAgMD8fHxIT4%2B3nbb4cOHyc7OpmnTpoZyvJGKUREREZHbSOPGjQkODiYmJoaMjAwSEhJYtmwZTz/9NACPPfYYu3fvBgq6nlOnTuXYsWNcu3aN2NhYTp48ye9//3tcXV156qmnWLRoEWfPniU1NZW//OUvPProo7apn8yg3fQiIiIijnLSKzDNnTuXSZMm0b59e7y9venXrx/9%2B/cH4Pjx41y5cgWAl156CSg4VjQtLY0HHniADz74gJo1awIQFRVFZmYm4eHh5Obm8sgjjzBlyhRTc7VYy/uoVLmtnDhhbHsPDwgIgLNnMT61U%2B08YwEKubpCnkmxjDIrF7Omdmre3HmmdnKHnBwT4mBGEJOSMTq/jsVS8KbKdrKpnRKNv7/NmtqJFOeY2sliMfwUAeZN7WTWR55ZUztduHALT%2B00Y0b5xDV5Ynlnps6oiIiIiKOctDN6K1ExKiIiIuIoFaOGaQRFREREpMKoMyoiIiLiKHVGDdMIioiIiEiFUWdURERExFHqjBqmESylwYMHM2fOnArNYcGCBQwYMKBCcxAREZEbOOEVmG416oyWUmxsbEWnQEREBBERERWdhoiIiIhpVIyKiIiIOOoO62KWh1u6GF2yZAmrVq3i4sWL1KxZk4iICMLDwzl06BAzZswgPj4eNzc3evXqxfjx43F3d%2Bf8%2BfNMnjyZ3bt3k5OTQ0hICNHR0QQGBnL8%2BHGmTp3KgQMHsFgstG7dmujoaO655x4GDhxIs2bNGDt2LACrV69m%2BfLlnDlzhoCAAKKioujRowcAAwcOpH379iQkJLBt2zYqVarE2LFjCQ8Pv%2Bljunr1KlOmTGHHjh1kZWURFBTExIkTadq0Ke%2B%2B%2By47d%2B7k448/ZuLEiWzYsMG2XX5%2BPrm5ufz0008A7Nq1izlz5nD48GHbZcBGjhxZpvFNTk4mJSXFbllenh9%2Bfv5linOjwgsDGb1AkNxEaKix7Rs1sv8t5cNiMWd7o3EouHqSUe7u9r8NMfohoQ%2Bb34TR4XV1tf/tKKNXb5KKdcu%2BS/fs2cOKFSv4%2BOOPCQgI4Ouvv2bUqFG0bNmSoUOHMnDgQJYuXUpSUhIRERG8//77DB8%2BnHfeeQcfHx927NhBXl4eb775JjNnzmTevHlER0fTvHlz3nvvPTIzMxk/fjwLFy7ktddes7vv7du3M2vWLBYvXkyzZs3YsmUL48aNo379%2BgQFBQGwcuVKpk%2BfzvTp01m0aBFvvPEGPXr0wP0mn9LLly/n/PnzbNmyBQ8PD5YuXcqkSZP49NNP7dabNm0a06ZNAyAvL48BAwbQsGFDAM6dO0dERASTJ0/m8ccf5%2BjRowwdOpQ6derw%2BOOPl3qM16xZw7x58%2ByWjRwZSVTUqFLHKImfn%2BEQgMFPL7tQJsYyyoxc9uwxHgPgo48MhzCjLrHFMiWYSRmZk4w5TMglMNCEPK67fklrg0z5kIB77jEcwnipfz2OCYHM%2BqgyK061aubE8fU1tn1Skjl5OESdUcNu2WL08uXLuLi44OXlhcVioUOHDnz//fds3rwZq9XKCy%2B8AEBgYCBDhgxh8eLFDB8%2BnPT0dHx9ffHw8MBisTBlyhRcrr%2BQ0tPT8fLyws3NDR8fHxYsWGC77UaffPIJvXr1okWLFgD06NGD2NhYNm/ebCtGQ0ND6dixIwDdu3dn3rx5JCcnU6tWrV99XOnp6bi7u9vyKM1xonPnzuXKlSu2ovnzzz%2BnQYMG9O7dG4CgoCD69evHhg0bylSM9u3bly5dutgty8vz46yBS0abeLloAvx1bfoStWxpbPtGjQoK0f794dAhQ6FyvjWnML4tr01v9GLlFst/czEYKzHJeGvU3b2gED13zvjwBHql3HylX%2BPmVlCIpqYavzZ9deOF8e16bfq0NOO5%2BPoWxHGWj%2BEyUzFq2C1bjLZt25bGjRvTpUsX2rZtS6dOnQgPDycxMZELFy4QHBxsW9dqteJxfR/U0KFDGTFiBDt37qRDhw50796dtm3bAhAZGcm4ceNYv349HTp0oFevXoSEhBS571OnTtGmTRu7ZXXr1uX06dO2/9euXdv2by8vLwCysrJu%2Brj69%2B/PkCFDePjhh%2BnYsSNdu3YlLCysxPW/%2BeYbPvzwQ/72t7/h6ekJwMmTJ4mLiysyBvfdd99N7/9G/v7%2B%2BPvb75I/cQKys8sUpli5uebEkRLs3WtOnEOHzIslRZlRnRTGMRjLzPdjTo4J8dxM2u%2Bam6t9uOXIrKHNy9PTdCe7Zct5Dw8PFi1axOrVq2natCkrV64kPDwcT09PGjRoQFxcnO3nwIED7Lm%2B2zI4OJjt27czYcIErFYrkZGRzJw5E4DOnTvz1VdfERkZyYULFxgwYAAffvhhkfvOLuFT1nLDPpjiOqqlUbt2bTZt2sSsWbPw9vbm9ddfZ/To0cWue/78ecaNG8ekSZO4//77bcu9vLx4%2BOGHi4zBxo0bHcpJRERESqCpnQy7ZR9tTk4OGRkZNGrUiJEjR7J%2B/XosFgtVqlQhMTGRzMxM27qpqalkZGQAkJaWhru7O2FhYURHR7Nw4UJWr15tW69SpUr06NGDmJgYpk6dypo1a4rcd506dTh27JjdsmPHjhFowkFXmZmZ5OXl0a5dOyZOnMjf/vY3Nm/eTGpqqt16VquVl19%2BmU6dOtl2x9%2BY3%2BHDh7He0ClJSUkpsYgWERERqSi3bDEaGxvL888/z7lz5wBISEjg0qVL1K9fn6pVqzJz5kwyMjJISUlh9OjRzJ49G4B%2B/fqxdOlSrl27Rk5ODvv376du3bpkZWXRrVs3NmzYQG5uLllZWcTHx1OnTp0i9x0eHs7GjRvZt28fOTk5rFu3jiNHjtCzZ0/DjysqKsqWe35%2BPnv37sXX1xcfHx%2B79ZYsWUJSUhKvv/56kRg9e/YkLS2NBQsWkJWVRWJiIoMHD2b58uWG8xMREZEbqDNq2C17zOigQYM4c%2BYMvXv3Jisri4CAAMaOHUtISAgLFixg2rRptG/fHm9vb8LCwhg/fjwAc%2BbMYerUqSxcuBA3NzeCg4OZPXs2Xl5evPPOO7z11ltMnjwZLy8vWrRoUWKxd/r0aV5%2B%2BWXOnz/P/fffT2xsLPXq1TP8uKKjo3n99dfp1KkTFouFBg0aMH/%2B/CK7/desWUNSUhKtWrWyWx4bG0vLli1ZsGABb731FosWLaJq1aqEh4czePBgw/mJiIjIDe6wwrE8WKxWs46glzvBiRPGtvfwgIAAOHvW%2BAkOdWvrbPoSGZ38LzS0YHqo5s0Nn8CUk23OR8xteTa90VOjLZaCN1V2tuETmBJOeRrLhYJUAgMhMdH4%2B7v%2B3Qam7QBTp%2B6w1gwwlgu379n0588b297NrWB6qAsXjJ/AVKOGse0d9v775RN3yJDyieuEbtnOqIiIiEiFU2fUMBWjv6EffviBZ555psTb7733XjZv3vwbZiQiIiJSsVSM/oZCQkKIi4ur6DRERETELOqMGqZiVERERMRRKkYN0wiKiIiISIVRZ1RERETEUeqMGqZiVERERMRRKkYN0wiKiIiISIVRZ1RERETEUeqMGqYrMEnZmPFyMetSJBkZxmO4uEClSpCZac6lTZwklxyvyobTMe2KRx4W40FMvCIU6enGtjfzNePtbWx7cK5L%2B4Bpl/exurgajmHW0JjBrFwspxKNBXB3h5o14dw5c97gtWsbj2Ha4JjwWeOINWvKJ27fvuUT1wmpMyoiIiLiKHVGDVMxKiIiIuIoFaOGaQRFREREpMKoMyoiIiLiKHVGDdMIioiIiEiFUWdURERExFHqjBqmYlRERETEUSpGDdMIioiIiEiFUWdURERExFHqjBqmERQRERGRCqPOqIiIiIij1Bk1TMWoiIiIiKNUjBqmERQRERGRCqPOqIiIiIij1Bk17JYYwVOnThEUFERCQgIAwcHBfP311zfdrkuXLqxataq80yvW4MGDmTNnzk3Xe%2BWVVxgzZowp9/lr45KQkEBQUBCnTp0y5b5ERETEeZ0%2BfZphw4bRunVrHnnkEWbNmkV%2Bfn6x665atYpu3boRGhpKeHg4W7dutd32yiuv0LhxY4KDg20/LVq0MDXXW7IzGhcXV9EpFJGWlsaWLVt48sknAYiNjf3Nc3DGcREREbmtOWlndNSoUTRp0oStW7dy4cIFXnjhBapXr86gQYPs1tu8eTMxMTEsXryYkJAQ1q9fz4svvsjf//53AgMDARgxYgSjRo0qt1xvyWLUGf373//mb3/7m60YvR0kJyeTkpJit8yvenX8/f0rKKNfMOMDoDCGM3yYOFMuZgoNNR6jUSP730YYHd/b9XmSW4O7u7Ht3dzsf4tx5fRZUOx3sJ9fqb6D4%2BLiOHToEMuWLaNy5cpUrlyZ5557juXLlxcpRrOysvjf//1fHnroIQCefPJJZs%2Bezb59%2B2zFaHlzuldjYmIikydPZu/evfj6%2BjJo0CC6dOlit05QUBBLly6lU6dOXL16lenTp/Pll19isVh49NFHmTRpEh4eHnbbZGdn89xzz1GnTh3efPPNm%2BYRFxfHjBkzOHz4MB4eHjz66KNMnDgRd3d3vv32W4YPH87o0aOZO3cuQ4YMYf78%2BeTn5xMcHMymTZt47bXXaNasGWPHjgUKOqV//etfuXTpEqGhoUydOpXatWsXud9NmzaxePFiTpw4QbVq1Rg2bBh9%2B/Yt1djdOC4XLlxg/PjxfP/999x7770MHTq0VDFutGbNGubNm2e3LHLkSEZFRZU5VhEWi/EYlSoZj1HorrvMi2WUCbkY/Lr6bxwzAu3ZY0KQ6z76yLxYRjnTa8aM95Orq/EYJsYy4REVxDErkAlMyaVmTROCANWrmxPHLEYHx2o1Jw8nUux3cGRkqTqU8fHx1KpVCx8fH9uyJk2acPz4cTIyMvD29rYtDw8Pt9s2PT2dzMxMatSoYVv273//m23btnHixAnq16/PlClTaNq0qaMPrQinK0YjIyNp1aoV8%2BfP5%2Beff%2BaZZ57By8urxPX/8pe/cPToUf7%2B978DMHToUObPn1/kOMzJkyfj4eFBdHR0qfIYM2YMTzzxBH/9619JSkqiX79%2BPPDAAwwcOBCAnJwcTpw4wTfffIOnpyf5%2Bfns3LmTjz/%2BuEisrVu3snTpUpYtW8b999/PG2%2B8wdixY1m9erXdenFxcUyYMIF3332Xtm3bsnfvXp5//nkaNGhA8%2BbNS5V3oenTp3Pt2jW%2B%2BuorsrKybEVxWfTt27fIHwJ%2B1asbf9NbLOZ8cFy5YjyGi0tBUXH1KpRwLM1vxsRccjyMF%2Bru7pCTYzgM7q3L9totVqNGBYVo//5w6JCxWDt3GtvezNfM3Xcb2x7Mez%2BZ9fp3dYW8PMNhrC4mFLQmDY0ZzMrFknTOWAA3t4JC9Px5yM01ntANBYvDnOmJckQ5dUaL/Q728yvVtmlpaVSpUsVuWWFhmpqaaleM3shqtTJx4kSaNWtGq1atAAgMDMTFxYXRo0dTqVIl5s2bx%2BDBg9m8eTP33HNPWR9WsZyqGP3xxx/56aefWL58OXfddRcPPvgg8%2BbNs6vOb2S1Wlm/fj3Tp0%2BnatWqQEERlp6ebrfe%2B%2B%2B/T1xcHKtWrcK9lK2e9evX4%2BHhgaurK/feey8tW7bkwIEDtttzcnLo37//rxbKhdauXUvPnj1pdH0X45gxY/jPf/5T5EDidevW0blzZzp06ABAixYt6N69Oxs2bChzMbp161befvttfHx88PHxYcCAAfznP/8pUwx/f/%2BiuwOc6QPDzOIxP7/ii9FCzpSLGfbuNS/WoUPG45k1trfb8yS3BjP%2BQoSCQtSsWFIuiv0OLgNrGb%2Bvc3JyeOWVVzh69CgrVqywLR85cqTdeuPGjePzzz9n69atph2a6FTF6MmTJ/H29sbX19e2rF27diWeAZ6amkp6errd7u5GvzimbMeOHXz11Ve8//77VK5cudS5/Pvf/7Z1Z3Nzc8nNzeWxxx6zW%2Bfee%2B8tVazExERat25t%2B3%2B1atXo3r17kfVOnjzJrl27CA4Oti2zWq224rS0UlNTycrKshuXevXqlSmGiIiIlIITHj9etWpV0tLS7JalpaVhsVhszbsbZWVlERERwdWrV1m5cuWvdjxdXV0JCAggOTnZtHydagRdXFxKnHagpPWBX91m7969PPzww7z99tvklXLXUUJCAqNHj%2Bb3v/89u3btIi4ujl69ehVZz62UB4BbLJZS/YXi5eXF008/TVxcnO3nwIEDLFq0qFT3Uyg7OxvA7vGW9S8kERERKQUXl/L5MaBp06acPXuWixcv2pbFxcXxwAMPUOkX51tYrVbGjBmDm5sbH3zwgV0harVamTFjBoduODwqOzubkydPmnpyk1MVo4GBgWRmZtpV21u3buXMmTPFru/r60uVKlU4fvy4bVl8fDwbNmyw/X/UqFHExMRw8eLFUhd1Bw8exMPDg2effRYvLy%2BsVisHDx508FEVPK4bc7x48SKxsbHk/GIXSZ06dfjpp5/slp07d67URXShqlWr4u7uztmzZ23Ljh496kDmIiIicqspnBc0JiaGjIwMEhISWLZsGU8//TQAjz32GLt37wZg48aNHD16lHfeeQdPT0%2B7OBaLhVOnTjF16lSSkpLIzMxk9uzZuLu707VrV9Pydapi9MEHH6Rx48bMmTOHzMxMDh8%2BzIQJE2yT3RenT58%2BvPfeeyQlJZGamkp0dDRHjhyx3e7i4kKlSpWYMWMGixYt4scff7xpHrVq1SIrK4uDBw9y6dIlZs2ahYeHB8nJySV2GD09PUlJSSEtLc3WmSz0hz/8gS%2B%2B%2BIL9%2B/eTnZ3N/Pnz%2Bb//%2B78ix6/%2B8Y9/ZM%2BePaxdu5bs7GwOHjzIk08%2ByebNm2%2Ba843c3d1p06YNK1as4PLly5w%2BfZqVK1eWKYaIiIiUghN2RgHmzp1LcnIy7du359lnn6V37970798fgOPHj3Pl%2BknAa9eu5fTp07Rq1cpuYvuJEycC8Oc//5l69erRp08f2rVrx8GDB1m%2BfDl3m3EC5nVOdcwowKJFi3j55Zdp164d1apVIyIigo4dO5a4/ksvvcS0adPo0aMHHh4edO3alcjIyCLrtWrViqeffpqXX36ZdevWFZn66UahoaE888wzDBgwgLvuuosRI0bw2muvMWLECMaMGWP7y%2BJGXbt25aOPPqJz585FJrwPCwtjzJgxjBw5kitXrhAaGkpMTEyRGPXr15haTlgAACAASURBVCcmJoa5c%2BcydepU/P39GTJkCD169Pi1ISvWn//8Z8aPH0%2BnTp0ICAggKiqKPWZOsSMiIiJOq2bNmixdurTY227cC7t8%2BfJfjePr68uMGTNMze2XLFYdTChlYcrcJCZN45GRYTyGi0vBfKWZmRV/ZrSJueR4lf5kvZKYNrWThwmTK4aGFsxX2ry58bPpfzHbRpmZ%2BZopYXqVMtHUTiVyphmDTJva6VSisQDu7gVzlZ47Z84bvJj5ssvMtMGpoEllv/22fOLecOLz7c7pOqMiIiIitwwnPJv%2BVnNHFqMtWrTg2rVrJd7%2Bf//3f9SqVes3zOjmnnjiCbuToH4pNjaWli1b/oYZiYiIiBh3RxajhWeQ3Uo%2B%2B%2Byzik5BREREfkmdUcM0giIiIiJSYe7IzqiIiIiIKdQZNUzFqIiIiIijVIwaphEUERERkQqjzqiIiIiIo9QZNUwjKCIiIiIVRldgkrLZuNHY9j4%2B0KkT7NgBly4Zi/XEE8a2B3Ov7ONMuWRnG8/HrEswZWUZj2HmVY%2BqVDG2vYnPkzXf%2BMevaRevyS557uUyJePhUfD6M5jUNTydJRV%2B5erRZcrHWb5tzczFjAt3mXTRLlyNX7TLMfHx5RO3SZPyieuEtJteRERExFHaTW%2BYRlBEREREKow6oyIiIiKOUmfUMBWjIiIiIo5SMWqYRlBEREREKow6oyIiIiKOUmfUMI2giIiIiFQYdUZFREREHKXOqGEqRkVEREQcpWLUMI2giIiIiFQYdUZFREREHKXOqGEaQRERERGpME5RjJ46dYqgoCASEhIACA4O5uuvv77pdl26dGHVqlWG7/%2B7774jODiY7Oxsw7FERETkDuLiUj4/dxCn3E0fFxdX7vfx5ZdfEhQURN26dWnZsuVvcp%2B/FB8fz6VLl2jXrt1vft8iIiJigjuscCwPd%2BwIzp07lxMnTlRoDmvXruWbb76p0BxEREREKtJvXowmJiYyePBgQkNDeeSRR1ixYkWRdYKCgtixYwcAV69eZdKkSbRu3Zo2bdowadKkYnenZ2dn079/f1555ZWb5vDEE09w5MgRIiIiePXVV/n2228JCgri2rVrtvv/4osv6NOnDyEhIQwbNoxz584xZMgQQkND6dOnD6dOnbLF27RpE%2BHh4fzud78jLCyMNWvW2G7bv38/Tz31FKGhobRu3ZoJEyaQlZVFdHQ0H330EbGxsTz66KMAnDx5kiFDhtC6dWtat27N//7v/5Keng7891CGr776iu7du9OsWTNeffVVTp48Sb9%2B/fjd737HwIEDuXTpEgCvvPIKr776Km%2B88QbNmzenTZs2fPTRR6V9mkRERKQ0tJvesN98N31kZCStWrVi/vz5/PzzzzzzzDN4eXmVuP5f/vIXjh49yt///ncAhg4dyvz5/8/evcdFVef/A3/NcJdrIHiFMi%2BICgqpJCoIaipmlJW3ajM0KwTNtSVbJUgKqxWxAkUTV92S3NJFM9PEdPmJXb4m2xKIFzQBjZuCyHVgOL8/iFlGQIf5HBrU1/Px8IGcOec9b%2BZy5j3vcz7nk4Bly5ZprRcZGQlTU1NER0ffNod9%2B/bB1dUVGzZsgK%2BvL3744YdW63z22WdITExEdXU1ZsyYgRdffBHvvfceXFxc8Mwzz%2BDvf/87IiIikJmZiZUrV%2BKjjz7CmDFjkJGRgRdffBEDBw6El5cXwsPDsXDhQjz55JMoLS1FSEgIdu3ahYiICJw9exbDhw/Ha6%2B9BgBYtWoV%2BvTpg//3//4fKisrsWDBAmzYsEGrwE5JScE///lP5OTk4Nlnn8WlS5fwt7/9Debm5njiiSewe/duBAcHAwAOHjyIN954A99//z3S0tIQGhoKLy8vDB48%2BPZPFIDi4mKUlJRoLXOsr4dT9%2B46bd8mKyvtnyI8PcVjND8WOj4mnaor5SInOXaqzTHkiCX6urlbnyeFQr4YMsQSjSBjKkTUyf7QYjQ7OxtnzpzB9u3bYWFhATc3N8THx6NHjx5tri9JElJSUhATEwN7e3sAQExMjKZb2CwpKQmZmZlITk6GiYmJLLlOnz4dTk5OAIAHH3wQQ4cOxZAhQwAAo0ePxoULFwAAe/bswYQJEzBu3DgAwMiRIzFt2jTs3bsXXl5eqKioQLdu3aBUKuHk5IR//vOfULbzgbp582YoFAqYmprC3t4e48ePx6lTp7TWefLJJ2FtbY1Ro0bB2toaY8eOhbOzMwDAw8MDv/76q2bd3r17Y9asWQCASZMmwc3NDUePHtW5GN21axfi4%2BO1loUuXoywmTN12v6WvLzEY9z02AjpSl3jrpSLHO8nmd6TAAALC/EYcr1uZHie5KqTZCm4TE1lCPI7GZ5zubKR8%2BUnqisVxnLlYmTUNeKo1fLkoZd7rIvZGf7QYjQvLw9WVlaws7PTLPPx8dE65N1SWVkZKioq0LdvX82ymwuptLQ0HDt2DElJSbC2tpYt1169emn%2Bb2ZmplUwm5mZaU4VyMvLw3fffQd3d3fN7ZIkaYrTP//5z/jrX/%2BKpKQkjBs3DkFBQejfv3%2Bb9/nLL78gNjYWZ86cQX19PdRqNYYNG6ZXXgDQr18/rW179%2B6N4uJinR%2BD2bNnIyAgQGuZ45kzwO%2BnUOjFyqqpED11Cqis1D8OALz6qtj2QFN3a%2BdOYN48ICdHPF5XyaWNbn%2BHmZgA9fXiceS4SoVS2VSI1tQAjY1iscaPF9texudJ%2Bkm8MFYoAEkSDgNFvQzPk0Lxv9eNYFIqwXJUxlRkKWjlep7kIGcuom9HoKkQNWgxKYrFqLA/tBhVKpVo7MArt7mDeKttMjIy4Ofnh7i4OHh7e8NIpq9pipu%2BNrbXzTQ3N8fcuXMRERHR5u1PP/00Jk2ahG%2B//RZHjhzB448/jri4OEyaNElrvevXr2PRokWYO3cuPv74Y1hZWWH9%2BvWtBjjpmhcAqG96d0uS1Gr7W3FyctJ0hzUuXAB%2BPy9VSGWleJyMDPE8muXkyBtPRFfKRQ5yfFq1jCUaT67H9m57nuSslCRJOJ5c2ciQChF1sj%2B0nHd2dkZVVZVWdy41NRVXrlxpc307OzvY2Njg4sWLmmVZWVnYu3ev5vewsDDExsbi2rVrSExM7Lzk2%2BHi4oIzZ85oLSssLNQUgmVlZbjvvvvw5JNPYsOGDXjppZfwxRdftIpz4cIFVFVVYcGCBbD6/XzK7Oxsodzy8/O1fr9y5Qp69uwpFJOIiIha4AAmYX/oX%2Bvm5oYhQ4Zg/fr1qKqqwtmzZ7Fy5UrNxe7bMnPmTGzZsgVFRUUoKytDdHQ0zp07p7ldqVTC0tISa9asQWJios4FnJmZGS5duoRKwUPFTz31FE6dOoXdu3dDpVLh9OnTePrpp3Ho0CEUFhYiICAAx48fR2NjI27cuIGzZ8/CxcVFk0NBQQGuX7%2BO3r17Q6lUIiMjA9XV1di2bRtKS0tRWlqKhoYGvXK7fPkyUlJSUF9fj8OHDyMnJwcTJkwQ%2BnuJiIiI5PSHl96JiYm4fPkyfHx88PLLLyMkJATjb3EO1/Lly%2BHh4YHAwEAEBgZi4MCBCA0NbbXe6NGjMXfuXISHh%2Bs0k9KcOXPw/vvv4y9/%2BYvQ39O/f3/ExsZiy5YtGDlyJMLCwrBgwQIEBgaiZ8%2BeeOedd/DOO%2B/A09MTU6dOhaWlJZYsWQKgqdBOS0vDI488gu7du2vOL/X398f169exdu1azSWr9OHr64uMjAzNJbGioqIwaNAgob%2BXiIiIWmBnVJhCkng2zd1oxYoVqKurQ1xcnLyBv/xSbHtbW8DXt2kQlOg5o489JrY90HSZn1OnmgZVGfr8PzlzkWPQkFwDmGprxWMolYClJVBVJX7OqI2N2PYyPk9So/juV7YBTKo6GYIomkblq1TCSdXBrKukIsuFBjiAqX1yDWCSa2R/h9240TlxZRyU3dXdW6U3EREREXUpXXJuelEjR47UzKbUloMHD6JPnz5/YEZERER0V7rHDql3hruyGD158qShUzC4d99919ApEBEREd3WXVmMEhEREf0h2BkVxmKUiIiISF8sRoXxESQiIiIig2FnlIiIiEhf7IwK4yNIREREdJe5fPkyFi1aBG9vb/j7%2B%2BNvf/sbGtu5MOyOHTswZcoUeHl5Ye7cufjll180t9XV1eHNN9%2BEr68vvL29sWTJEpSVlcmaK4tRIiIiIn110RmYwsLC0KNHD6SmpuLvf/87UlNTsX379lbrffvtt/joo4/w/vvv48SJE/D398fLL7%2BM6upqAEBcXByysrKwa9cuHDp0CJIk4Y033hDOryUWo0RERET66qRitLi4GFlZWVr/iouLdUopMzMTOTk5eO2112BtbY0HHngA8%2BfPx65du1qtu2vXLsycORPDhw%2BHubk5Fi5cCAA4evQoGhoa8MUXXyAkJAS9evWCnZ0dXn31VRw7dgxFRUWyPYQ8Z5Q6JjBQnjhjxwqHUDfIM5%2BdEQD1/52SJZYouXIxahCc2lGhaPopSeLzBlpZiW3fUrduwiFkmYITgPST%2BPOkUCrEAvw%2BNaniIfGpSQ8dFH9crK0BHx/gxElT4RkSp4y8KhbAyAgwtYNpdbnwXJOVZg5C2yuVTS/dmhrx6TNNTMS2b54mtb5enilBRWcMbn5s6urEHxs5dzVdwa5duxAfH6%2B1LDQ0FGFhYbfdNisrC3369IGtra1m2dChQ3Hx4kVUVlbCqsWDlZWVhcAWn%2B1KpRJubm7IzMyEm5sbbty4gaFDh2pu79%2B/P8zNzZGVlYUePXqI/IkaLEaJiIiI9CRB8EtlO2bPno2AgACtZY6OjjptW15eDhsbG61lzYVpWVmZVjFaXl6uVbQ2r1tWVoby8nIAaBXLxsZG1vNGWYwSERERdTFOTk5wcnLSe3upA63v263bkVj64DmjRERERHpqbOycfyLs7e01Xc1m5eXlUCgUsLe311p%2B3333tbmuvb29Zt2bb79%2B/TocHMROX2mJxSgRERGRnrpiMTps2DD89ttvuHbtmmZZZmYmBgwYAEtLy1brZmVlaX5Xq9XIzs7G8OHD4ezsDFtbW63bz549C5VKhWHDhokl2QKLUSIiIqK7yJAhQ%2BDu7o7Y2FhUVlYiNzcXf//73zF37lwAwNSpU3Hy5EkAwNy5c5GSkoL//Oc/qKmpwcaNG2FqaooJEybAyMgIs2bNQmJiIn777TeUlZVh3bp1mDx5Mrp37y5bvjxnlIiIiEhPol3MzvLhhx8iIiICY8eOhZWVFebMmYN58%2BYBAC5evKi5jqivry/%2B/Oc/49VXX8XVq1fh7u6OzZs3w9zcHACwZMkSVFVVISgoCA0NDfD390dUVJSsuSqkzj4rle4ugpdIAdB0yRUZ4qhhJJ4LZEtHFnLlIsulnUxNAZVK/PovpqZi27fMSYbdlRwjX2VKRbZLO8Gri13a6QS6xqWd7OyA8q5zaafq6q5zaSc53tqAfJd2kuOxMdSlneoEd7ftMTPrnLhdETujRERERHrqqp3ROwmLUSIiIiI9sRgVxwFMRERERGQw7IwSERER6YmdUXEsRomIiIj0xGJUHA/TExEREZHByF6MFhQUwNXVFbm5uQAAd3d3pKen33a7gIAAJCcny52OkFWrViE8PNzQaRAREVEX1RVnYLrTdPph%2BszMzM6%2Bi07z9ttvGzoF2XzzzTdwdXXF/fffb%2BhUiIiIiDR4mP4e8eGHH%2BLSpUuGToOIiOiuws6oOKHOaH5%2BPiIjI5GRkQE7Ozu88MILCAgI0FrH1dUVH3/8MXx9fVFTU4OYmBh88803UCgUmDx5MiIiImB60wwtKpUK8%2BfPh4uLC959993b5nH58mVER0cjIyMDjY2N8Pf3x5tvvgkrKyv88MMPCAkJQVxcHGJiYlBYWIiHHnoI69atw3/%2B8x8sXboU33//vWbaq2vXrmHcuHHYuXMnPvvsM9TV1SEuLg579uzB1q1bMW7cOHz22Wc4dOgQHB0dsXHjRqSkpKCoqAj9%2B/dHeHg4xowZA6Dp1INXXnkFhw8fxv/93//BwcEBUVFRGDduHAoKCjBx4kRs2rQJ7733Hq5cuYLAwEC88sorCA8PR05ODtzd3REfHw9bW1sAwCeffIJPP/0UV65cQd%2B%2BfbFs2TJMmjQJAPDcc89h7NixyM3NxZEjR2BpaYnXXnsNQUFBeOyxx3Du3DmEhIRgxowZWLNmjU7Pb3FxMUpKSrSWOTo4wMnRUaftyYAUgjP7NG8vGoduzdNTbPvBg7V/CrC2Fg4BS0vtn0KMBGdYa95eNA6aZgkS0fLtJFcsOXKRQ1d5bAxZvN1rhWOnkAQ89thj0ttvvy1VV1dL2dnZkqenp7Rr1y5p0KBB0vnz5yVJkqRBgwZJ//73vyVJkqS3335bmjNnjnT16lXp6tWr0hNPPCGtW7dOkiRJ8vf3l3bu3ClJkiStWLFCev755yWVSnXbHBobG6XHHntMioqKkqqrq6XS0lLpT3/6k7Rq1SpJkiTp%2B%2B%2B/l9zc3KTw8HCpvLxc%2Bu2336SxY8dKmzdvllQqlTRq1Cjp8OHDmniff/655O/vL0mSJL3%2B%2BuvSq6%2B%2BKkmSJO3evVsaNWqUtG7dOk1eO3bskPz8/KScnByprq5OSkpKkkaMGCGVlpZq/qYpU6ZIP//8s1RXVye98cYb0rRp0yRJkqT8/Hxp0KBB0tKlS6WKigrpxx9/lAYNGiTNnTtXysvLk4qLi6WxY8dKSUlJkiRJ0qFDhyRvb28pMzNTqq%2Bvlw4dOiQNHTpUunz5siRJkvTss89K48aNk9LS0iSVSiV9%2BOGHkpeXlybXls%2BDrj788ENp0KBBWv8%2B/OCDDsUgIiLqbDduGO6%2Bi4s759%2B9RO/OaHZ2Ns6cOYPt27fDwsICbm5uiI%2BPR48ePdorepGSkoKYmBjY29sDAGJiYlBRUaG1XlJSEjIzM5GcnAwTHSbhzczMxLlz55CcnAwLCwtYWFggLCwMCxYswOrVqwEAarUaCxcuhK2tLWxtbfHQQw/hwoULMDExwcSJE3HkyBFNhzE1NRXTpk1r875u3LiBF198UZPXF198gXnz5sHV1RUAEBwcjC1btuDYsWN48sknAQD%2B/v7w8PAAAEyZMgUpKSlobPE16sknn4S1tTVGjRoFa2trjB07Fs7OzgAADw8P/Prrr5r7euqppzBs2DAAwCOPPIKHHnoI%2B/fvx6JFiwAAnp6eGD9%2BPABg2rRpiI%2BPR3FxMfr06XPbx7Ets2fPbtXpdnRwEJ88nXPTt0u2uenVKrEACkXTJNj19eITWItOpt0yp7ttbvqHvMQCDB4M7NwJzJsH5OQIhToRf0osFzR1RIcPB37%2BGaiqEovlM6RcLICRUVO798YN4TdVtamd0PYKBWBhAdTUiL9ujAVHesj51gaAhgbxfOR6bAyFnVFxer%2Bs8/LyYGVlBTu7/71JfXx8UFBQ0Ob6ZWVlqKioQN%2B%2BfTXLBt90aCktLQ3Hjh1DUlISrHU8ZpSfnw%2B1Wg1vb2%2Bt5Wq1GmVlZZrfW96vhYUFamtrAQBTp07F66%2B/DrVajdraWpw4cQJLlixp875sbGxgZWWl%2Bb2goAD9%2B/fXWsfFxQWXL19u837Nzc2hVqtRX1%2BvWdarVy/N/83MzLSKeTMzM6hUTUVFXl4e0tPTsX37ds3tkiRhwIAB7d4XAM3fqQ8nJyc4OTlpL%2BwqVRvdmlx7dUm6cz8h7gQZGfLEyckRjnXjhjypAE2FqHA8ufY1arVwLNFio/nwsySJx%2Bpqb%2B2u9NjQnUvvYlSpVGp1%2BHRZH8Att8nIyICfnx/i4uLg7e0NIx3O9TEzM0O3bt2QcZsdsbKdk1F8fHzQ2NiIn376CaWlpejVqxeGDBnS5rrGN30lbS4Ub6ZocTJOe/fb1rq3Wt/c3BzLly9HcHBwu7Fud19EREQkLxbR4vSuXpydnVFVVYXi4mLNstTUVFy5cqXN9e3s7GBjY4OLFy9qlmVlZWHv3r2a38PCwhAbG4tr164hMTFRpzxcXFxQXV2N/Px8zbLKykqtruitNB%2BqP3r0KA4fPozAwECdtmu%2B7wsXLmh%2Bb2howKVLlzSH2eXk4uKCM2fOaC27cuUKJHatiIiIDIaj6cXpXYy6ublhyJAhWL9%2BPaqqqnD27FmsXLlSc7H7tsycORNbtmxBUVERysrKEB0djXPnzv0vGaUSlpaWWLNmDRITE5GdnX3bPAYNGgRPT0%2B88847uHbtGioqKhAZGdmhi9VPmzYNx48fx/HjxztUjAYFBWHnzp3Izc2FSqVCYmIi1Gp1q/Ms5TB79mwcOHAAx44dQ0NDA77//ns8%2Buij%2BPnnn3Xa3szMDJcuXUJlZaXsuRERERHpS%2Bi4bmJiIi5fvgwfHx%2B8/PLLCAkJ0Qygacvy5cvh4eGBwMBABAYGYuDAgQgNDW213ujRozF37lyEh4e3eyi8pdjYWEiShIkTJ2Ly5MlQq9U6XRKq2ZgxY1BcXIyePXti4MCBOm8XHByMqVOn4sUXX4SPjw9%2B%2BOEH7NixAzY2NjrH0NXYsWPx%2BuuvY/Xq1fDy8sLq1asRFRWFESNG6LT9nDlz8P777%2BMvf/mL7LkRERHdq9gZFaeQeJyXOkKWod4cTd8e2UbTN9SJBVAoAFNTQKUSH%2BVw03WEhXK620bTKwVz8fQETp0CvLyEBzAdOij%2BB1lbAz4%2BwIkT4gOYpoy8KhbAyAiwswPKy4XfVJVmDkLbK5VAt25AdbV4kSF6cQo539pA06h8EXI%2BNi3GF/%2BhWpwlKKtOOOOvy%2Br06UCJiIiI7lb3WhezM3T5YnTkyJGoq2u/y3Pw4EG9r6NJREREJILFqLguX4yePHnS0CkQERERUSfp8sUoERERUVfFzqg4XiWdiIiIiAyGnVEiIiIiPbEzKo7FKBEREZGeWIyK42F6IiIiIjIYdkaJiIiI9MTOqDgWo9QhklJ81iOFTHGMVIKzDAFN05EYmcJILdN0JF0kl9wCM6HtTU2bZv/ILzKFDjPy3lL/B2Sa3srISJa9vqKhQTBA0xQ2inrx50l01iNra8AHwIn4U%2BIzHk0Vn5mqeUYon1DxGaGCXxB7bFxcgKgoIGq9HfLyhEJh65oisQDGxkA3B3SruQqIvv5Ep5tWKACYw7SxVpZ9npnorvz3fLopawGFaD4WgtuTobAYJSIiItITO6PiWIwSERER6YnFqDgOYCIiIiIig2FnlIiIiEhP7IyKY2eUiIiIiAyGnVEiIiIiPbEzKo7FKBEREZGeWIyK42F6IiIiIjIYdkaJiIiI9MTOqDh2RomIiIjIYNgZJSIiItITO6PiWIwSERER6YnFqDgepiciIiIig5GtGC0oKICrqytyc3MBAO7u7khPT7/tdgEBAUhOTpYlh%2BTkZAQEBAjHWbVqFcLDw2XIiIiIiO5mjY2d8%2B9e0mmH6TMzMzsrdKd7%2B%2B23ZYulUqmwbds2fPnllygoKICxsTFcXFwwa9YszJ49W7b7ISIiItJFeXk5oqKi8OOPP0KpVMLPzw8REREwNzdvc/1vvvkG8fHxyM/Ph5OTExYsWIBZs2YBAD766CNs2LABxsbaJeXRo0fRvXt3nfLhOaOdqKGhAQsXLkRtbS3eeecdDBs2DDU1Nfj2228RHR2NixcvYsWKFYZOk4iIiPR0J3YxIyIioFKpsH//ftTX12Pp0qVYu3YtVq1a1Wrd//73v3jttdewbt06TJgwAenp6Vi8eDEefPBBjBw5EgAQFBSEd999V%2B989CpG8/PzERkZiYyMDNjZ2eGFF15odXjc1dUVH3/8MXx9fVFTU4OYmBh88803UCgUmDx5MiIiImBqaqq1jUqlwvz58%2BHi4qLTH/Xzzz8jIiICeXl58PT0xEMPPaR1%2B3fffYf169fj7NmzsLKywpw5c7B48WL8%2B9//xtKlS/H9999rvgVcu3YN48aNw86dO/HZZ5%2Bhrq4OcXFxAIC9e/diw4YNKC4uhqurKyIjI%2BHm5gYAOHDgADZt2oRLly7BwcEBixYt0nQ8//WvfyEzMxNHjhyBvb09AMDS0hIzZsxA9%2B7dUVhYqMn1ueeeg4eHB9LS0tCrVy9s3rwZhYWFeOutt3Dq1Ck0NDTA19cXkZGRsLOzw549exAbG6t1KsSsWbMwfvx4hIWFYcWKFVAoFLCwsEBKSgpMTU2xZMkSzJs3T6fnGACKi4tRUlKitax7d0c4OTnpHKNTKRTyxZAjligZc7nprdVhJibaP%2B8qoo%2BvjM%2BTtbXY9paW2j%2BFeHqKxxg8WPunABcXse179dL%2BKcRYsG9jZKT9U0QXev3KQq58JEk8Fz11VjHa1mewo6P4Z3BpaSlSU1Pxr3/9S1ObhISEYOnSpXj99ddhctOOv7y8HC%2B99BImTZoEAPDz88OgQYNw8uRJTTEqSq93WGhoKEaPHo2EhAT8%2BuuveOaZZ9pt7QLAunXrcP78eXz99dcAgIULFyIhIQHLli3TWi8yMhKmpqaIjo6%2BbQ5qtRpLlizB9OnTsXTpUuTk5CAsLEzTJi4sLERISAgiIyMxY8YMnD9/HgsXLoSLiwumTp0KU1NTHD9%2BXPPgfvvtt%2BjZsydGjBiBzz77THM/v/zyC6KiorBx40Y89NBD2LRpE0JCQpCamors7GysXLkSH330EcaMGYOMjAy8%2BOKLGDhwILy8vPDNN99g%2BvTpmie7pTFjxrRa9tVXX%2BHDDz%2BEu7s7gKYXx4ABA3DkyBHU1tZi6dKliIyMxAcffHDbxwcADh48iDfeeAPff/890tLSEBoaCi8vLwzW8YNi165diI%2BP11q2eHEoliwJ02n7W5FlPyhacbXUlaouGXJxdpYhDwA9e8oRRYYPYE0oGWLJEQOQ5Xny8ZEhDwDDh8sQ5NQpGYL8budO4RBR4lkAAF56SY4oDnIEAezs5IkjBzMzQ2egTTSfmhp58uhC2voMDg0NRViY2Gfw6dOnYWRkBFdXV82yoUOHorq6GhcuGKI9eAAAIABJREFUXNBaDgC%2Bvr7w9fXV/N7Q0ICSkhL06NFDs%2BzMmTOYM2cOzp49i169euGNN97AuHHjdM6pw8VodnY2zpw5g%2B3bt8PCwgJubm6Ij4/XSqolSZKQkpKCmJgYTVEWExODiooKrfWSkpKQmZmJ5OTkVlV5W3755RcUFxfjlVdegZmZGYYPH47Jkyfj6NGjAID9%2B/dj4MCBePzxxwE0dWrnzJmDvXv3YsaMGZg4cSKOHDmiKUZTU1Mxbdq0VveTkpKChx9%2BGA8//DAAYMGCBejXrx/q6uqwZ88eTJgwQfOAjxw5EtOmTcPevXvh5eWF/Px8jB49WpeHFQDg4eEBDw8PAE0vlqysLGzatAlWVlawsrLCokWLsHjxYqhUKp3i9e7dW3NOx6RJk%2BDm5oajR4/qXIzOnj27Vce7e3dH4S%2BgCoU8X2IV9bo9DrdNxsQEqK836DdruXPJLxIr1E1MmgrRwsKmdEQ491aLBWhmZASoZYglGkPG5%2BnESbHnydKyqRD9%2BWegqkooFHxCvcQCAE0d0Z07gXnzgJwcoVBRj4kVx716NRWimzYBv/0mFApRYVfFAhgZNRWi5eXirz/RNrhC0VT41dUZfp/XFfPRQ2d1Rtv6DHZ0dBSOW15eDisrKyhadIVsbW0BAGVlZbfdfu3atejWrRsCAwMBAD179oSzszOWL18OJycn7Nq1Cy%2B//DL27duHBx98UKecOlyM5uXlwcrKCnYtvuH5%2BPigoKCgzfXLyspQUVGBvn37apbdXAylpaXh2LFjSEpKgrWOx60KCwthY2Ojtf4DDzyglWdmZqamywg0Fcb9%2BvUDAEydOhWvv/461Go1amtrceLECSxZsqTV/eTn58OlxfEiCwsLTJ8%2BXXMf3333Xav7aC5OFQoF1DfteKZMmYIrV64AaOrubtu2TVOw9unTR7NeQUEBbG1ttV54Li4uqK%2BvR1FRkU6PUfPf2qx3794oLi7WaVsAcHJyanU4oEvtK%2BRMRpK6zh8nQy46fl%2B5rfp6%2BWJ1GXI9zzI8TzduyJNKVZUMsTIyZMkFQFMhKhgvb4Q8qfz2G5CXJxikoUGWXKBWi8fqQq9fWXW1fLqAtj6DdbV37952rwq0bNkySHo81pIkYe3atdi/fz927NgBs9%2B72U8//TSefvppzXrz58/HV199hX379uHVV1/VKXaHi1GlUonGDnwNUCqbrh51q20yMjLg5%2BeHuLg4eHt7w0iHw2gqlapVodfyPszNzeHn54fExMQ2t/fx8UFjYyN%2B%2BuknlJaWolevXhgyZEir9RQKRbtPmrm5OebOnYuIiIg2b%2B/Xr5/mUlfNDh06pPm/q6urVuyWf/etup%2BKdo5x3/x43Py7JEntbktEREQd1xUHMAUFBSEoKKjN29LT01FZWQm1Wq2pO8rLywEADg5tn5LS2NiIN954A//973%2BRnJwM59ucC9anT58ONb86fJ1RZ2dnVFVVad1Jamqqptt3Mzs7O9jY2ODixYuaZVlZWdi7d6/m97CwMMTGxuLatWvtFo83c3JyQmVlJW60aAe0LPxcXFxw9uxZrWKvpKREU%2BSZmJhg4sSJOHr0KA4fPqxpN7f197bMXaVSISkpCWVlZXBxccGZM2e01i8sLNQUgYGBgTh48GCbj83tCnpnZ2dcv34dpaWlmmUXLlyAmZkZevToATMzM9S0OEdGrVbj8uXLWjHy8/O1fr9y5Qp6ynMSIBEREeHOu86om5sbJElCTotTaTIzM2FjY9PqiGqzmJgYnDt3rs1CdMOGDfjuu%2B%2B0luXm5t62YG2pw8Wom5sbhgwZgvXr16Oqqgpnz57FypUrW3UAW5o5cya2bNmCoqIilJWVITo6GufOnftfEkolLC0tsWbNGiQmJiI7O/u2eQwfPhy2trbYsmULVCoVTp48qTlfFACmT5%2BO8vJybNiwAbW1tcjPz0dwcDC2b9%2BuWWfatGk4fvw4jh8/3m4xOnPmTPzwww84evQo6uvrsW3bNuzYsQNWVlZ46qmncOrUKezevRsqlQqnT5/G008/rel%2BTp8%2BHWPHjsWzzz6L9PR0NDQ0aHJ94YUX4ODgoHVoviV3d3f0798fsbGxqK6uRlFRETZu3Ijp06fDxMQE999/P6qqqnD8%2BHGoVCps2rSpVQf38uXLSElJQX19PQ4fPoycnBxMmDDhto8tERER3Z3s7e0xZcoUrF%2B/HteuXUNhYSESEhLw1FNPaQaBP//88zhw4AAA4KeffsK%2BffuwefNmrVM0m5WXl%2BOtt97ChQsXUFdXh61btyIvLw9PPPGEzjnpNZo%2BMTER4eHh8PHxgYODA0JCQjB%2B/Ph211%2B%2BfDnefvttBAYGwtTUFJMmTUJoaGir9UaPHo25c%2BciPDwce/bsaXXpp5bMzc2RkJCAqKgobNu2DZ6enggODsY//vEPAMB9992HDRs24P3330diYiLs7e0RFBSE4OBgTYwxY8aguLgYPXv2xMCBA9u8Hzc3N6xduxbR0dG4du0aBg8ejI0bN8LExERTLH744Yd46623NBeCbS5sFQoF4uPjsWPHDrz33nuaTqWzszMCAgIQFxfX5kj75m03bNiA6OhoTJgwARYWFpg0aRJee%2B01AMCwYcMwf/58LFu2DEZGRggODobnTZdm8fX1RUZGBqKjo2FiYoKoqCgMGjSo3ceUiIiIOqYrHqa/ndWrVyMyMhITJ06EiYkJHn30Ua0rHOXn5%2BP69esAgN27d%2BPGjRvw9/fXijFq1Chs3boVy5cvB9B0rmh5eTkGDBiAbdu2dehIrELS5yxW6vJWrFihda1UucgyCl6u0fSqOnmSMTVtGqVj6LeCjLnkFohdJsXUtOnyUPn54gOY%2Bj/QxUbTiw4gkfF5OnRM7Hmytm66PNSJE%2BIDmKZMleF8ck/PpktEeXkJD2AKfkHssXVxAaKimv6JDmDauka3QaPtMjYGHByAq1fFX382NmLbKxSAuTlQW2v4fZ7c%2BVhYyJNTB/1%2B1UrZtXGBn7uWbHPTExERERF1VJedDnTkyJGoq2u/83Xw4MF2z7ckIiIi%2BiPciYfpu5ouW4yePHnS0Cnc0UTmiCUiIiL6o3TZYpSIiIioq2NnVByLUSIiIiI9sRgVxwFMRERERGQw7IwSERER6YmdUXHsjBIRERGRwbAzSkRERKQndkbFsRglIiIi0hOLUXEsRqlDamvFtm%2Be%2Ba2uTnzmN6VSbCpFAFAAMAWggikMPTGenLn07/abWABjYwCOcDYvAYzFpi%2BUlL3EcvmdAoCkNBKOo4JYDDmfpykjr4oFMDICYAefIeXCU6WKTr8J/D4FJ4Cox04hb4RYrK1/F5ye1NMTiDqFqH3iU5MioVpse8Xvf4ulpfiOz1imj20j8fcSANTDRDiGCYB6I3NZ4tCdicUoERERkZ7YGRXHAUxEREREZDDsjBIRERHpiZ1RcSxGiYiIiPTEYlQcD9MTERERkcGwM0pERESkJ3ZGxbEzSkREREQGw84oERERkZ7YGRXHYpSIiIhITyxGxfEwPREREREZDDujRERERHpiZ1QcO6NEREREZDDsjBIRERHpiZ1RceyMyqSgoACurq7Izc0FALi7uyM9Pf222wUEBCA5Obmz0yMiIqJO0NjYOf/uJeyMdpLMzExDp0BERETU5bEYJSIiItLTvdbF7AwsRvWQn5%2BPyMhIZGRkwM7ODi%2B88AICAgK01nF1dcXHH38MX19f1NTUICYmBt988w0UCgUmT56MiIgImJqaam2jUqkwf/58uLi44N13371tHoWFhXjrrbdw6tQpNDQ0wNfXF5GRkbCzs0NNTQ2ioqKQlpaG2tpauLq6YtWqVRg2bJjOf2dxcTFKSkq0ltnYOMLR0UnnGDdTKLR/ipAzhhyxRMmai7HgW7t5e9E4XZDo4yvr82RkJM/2onEAuLgIh0CvXto/hXh6im0/eLD2TxFd6kVDdPe5%2Bz5p/gChoaEYPXo0EhIS8Ouvv%2BKZZ56Bubl5u%2BuvW7cO58%2Bfx9dffw0AWLhwIRISErBs2TKt9SIjI2Fqaoro6Gid8ggJCcGAAQNw5MgR1NbWYunSpYiMjMQHH3yA7du3o7S0FIcPH4apqSk%2B/vhjRERE4F//%2BpfOf%2BeuXbsQHx%2BvtWzx4lAsWRKmc4z2mJkJh5CViYmhM/gfWXJxdJQhCID77hMOIefHrxyf5Td9B9SbLM%2BTqZ0MQQBYWwuHiIoST6PZSy/JECTqlAxBAOzcKU8cOXSlHZ9MOz25dp2i6dTXy5OHPtgZFcditIOys7Nx5swZbN%2B%2BHRYWFnBzc0N8fDx69OjR5vqSJCElJQUxMTGwt7cHAMTExKCiokJrvaSkJGRmZiI5ORkmOrwrT58%2BjaysLGzatAlWVlawsrLCokWLsHjxYqhUKlRUVMDExATm5uYwNjZGSEgIQkJCOvS3zp49u1XH18bGEbW1HQqjRaFo2h/X1QGSpH8cAFDKMPxOoWjaCdbXi%2BfTlXIxvV5y%2B5Vuxdi4qRAtKwMaGoRCSd3lKYwVCnmeI9EPLVmfp%2BpysQBGRk2F6I0bgFotFCpqvXhh3KtXUyG6aRPw229isaL2eYkFGDy4qRCdNw/IyRGLdeKE2PZy7vhk6IJrXsAyqJehHJUxHYNgMSqOxWgH5eXlwcrKCnZ2/9tx%2B/j4oKCgoM31y8rKUFFRgb59%2B2qWDb7psFFaWhqOHTuGpKQkWOvY4SgoKICtrS0cW3TAXFxcUF9fj6KiIsybNw8LFiyAn58fxo8fj0mTJmHixIkd%2BVPh5OQEJyftQ/I1NfIUBJIkHkfO4lGOfOQiSy6CBaRWHLlidRFyPc%2ByPE%2BCBaRWHMFYeXnypAI0FaLC8TIyZMkFOTnisbrUi4bo7sNLO3WQUqlEYwe%2BBil/b9/dapuMjAz4%2BfkhLi4Oah0/UFQqVbu3KRQK9O3bFwcOHMDf/vY3WFlZ4c0338TSpUt1zpuIiIhuj5d2EsditIOcnZ1RVVWF4uJizbLU1FRcuXKlzfXt7OxgY2ODixcvapZlZWVh7969mt/DwsIQGxuLa9euITExUec8rl%2B/jtLSUs2yCxcuwMzMDD169EBVVRXUajV8fHywatUqfP755zh06BDKyso6%2BicTERERdRoWox3k5uaGIUOGYP369aiqqsLZs2excuVKzcXu2zJz5kxs2bIFRUVFKCsrQ3R0NM6dO6e5XalUwtLSEmvWrEFiYiKys7Nvm4e7uzv69%2B%2BP2NhYVFdXo6ioCBs3bsT06dNhYmKCJUuW4L333kNlZSUaGxs1I/9tbW1leRyIiIiInVE5sBjVQ2JiIi5fvgwfHx%2B8/PLLCAkJwfjx49tdf/ny5fDw8EBgYCACAwMxcOBAhIaGtlpv9OjRmDt3LsLDw295GB5oOhS/YcMGFBcXY8KECZg1axaGDx%2BON998EwAQHR2NS5cuwdfXF6NGjcInn3yChIQEzWkDREREJI7FqDiFJPFsatJdTY3Y9goFYG4O1NZ2ndH0pqaASmX4cQVy5mJ2TXAos7Fx0%2BWhSkrER9P3lOOik/KNpr/N9zyd8pDteaq8KhbAyAiwswPKy4UHMAX/xUEsFzRdqzQqqumf6ACmrX8XvI6Xpydw6hTg5SU%2BgKm6Wmx7OXd8clz79y4dTW%2BoS/TFxnZO3OXLOyduV8TR9ERERER6ute6mJ2BxWgXNXLkSNTV1bV7%2B8GDB9GnT58/MCMiIiIi%2BbEY7aJOnjxp6BSIiIjoNtgZFcdilIiIiEhPLEbFcWg1ERERERkMi1EiIiIiPd2Jl3YqLy/Hq6%2B%2BCh8fH4wbNw4rV65EbW1tm%2Bvu2bMHgwcPhru7u9a///73v7///Y2Ii4vDxIkTMWrUKCxYsAD5%2BfkdyofFKBEREZGe7sRiNCIiAjU1Ndi/fz92796N3NxcrF27tt31R40ahczMTK1/Hh4eAIBPP/0UX375JTZv3oyjR4/igQcewOLFi9GRK4eyGCUiIiK6R5SWliI1NRXLli2Dvb09evTogZCQEOzevRv1elzwddeuXZg/fz769%2B8PKysrLFu2DLm5ufj55591jsEBTERERER66qwuZnFxMUpKSrSWOTo6wsnJSSju6dOnYWRkBFdXV82yoUOHorq6GhcuXNBa3uy3337DCy%2B8gF9%2B%2BQU2NjZYsmQJgoKCUFtbi/Pnz2PIkCGada2srHD//fcjMzMTI0aM0CknFqPUIaKzHikU/4sjOhGJqanY9i0ZauaOtsiRixyzHikASN0dxZPpYuR63cjxPFWaic16pFQC3QBUm9oJfyBuXVMkFgD4fXYgB0SFXRWeuQsJMsx6BAAnTojvbLp1E9u%2BeTYoHx/x2aAOHBDb3sYGGDsW%2BPFHoKJCLBYAk6lThWMACpgYyzEFnuCsXV3Mrl27EB8fr7UsNDQUYWFhQnHLy8thZWUFheJ/j5etrS0AoKysrNX69vb2eOCBB/DnP/8ZAwYMwOHDhxEeHg4nJyc8%2BOCDkCRJs33LeG3Fag%2BLUSIiIiI9dVZndPbs2QgICNBa5uioW4Ng7969CA8Pb/O2ZcuWdeh8zgkTJmDChAma36dPn47Dhw9jz549eO211wCgQ/HawmKUiIiISE%2BdVYw6OTnpfUg%2BKCgIQUFBbd6Wnp6OyspKqNVqGBkZAWjqlgKAg4NuR2v69OmDX375BXZ2dlAqlZrtm5WXl%2BscC%2BAAJiIiIqJ7hpubGyRJQk5OjmZZZmYmbGxs0K9fv1brJycn48BNp4fk5ubC2dkZZmZmGDhwILKysjS3VVRUIC8vTzPaXhcsRomIiIj0dKdd2sne3h5TpkzB%2BvXrce3aNRQWFiIhIQFPPfUUjI2bDpg///zzmgJUpVIhOjoamZmZqK%2Bvx/79%2B5GWloY5c%2BYAAObOnYsdO3YgNzcXlZWVWLt2Ldzc3ODu7q5zTjxMT0RERHQPWb16NSIjIzFx4kSYmJjg0UcfxbJlyzS35%2Bfn4/r16wCAP/3pT6iqqsLSpUtRUlKCvn37IiEhAcOGDQMAzJkzByUlJXjuuedQVVUFb2/vVgOvbkchiZ51SveUujqx7RWKptHMKlXXGU2vUIjnIhfm0r6ulI9cuVRViW2vVDYN9K6uFu%2BkWFXJNJrewQG4KsNoehsbse0VCsDcHKit7Tqj6b28us5o%2BvR0WUbTQ47R9HK9oRSGGU3/xhudE3fNms6J2xWxM0pERESkp86eLelewHNGiYiIiMhg2BklIiIi0hM7o%2BLYGSUiIiIig2FnlIiIiEhP7IyKYzFKREREpCcWo%2BLuqsP0BQUFcHV1RW5uLgDA3d0d6enpt90uICAAycnJnZ0eEREREd3kru6MZmZmGjqFTpOVlYXr16/Dx8fH0KkQERHds9gZFXdXdUbvJbt378aJEycMnQYRERGRkDu2GM3Pz0dwcDA8PT3h7%2B%2BPHTt2tFrH1dUVaWlpAICamhpERETA29sbDz/8MCIiIqBSqVpto1KpMG/ePKxYsUI4j8LCQrzyyivw9vbGQw89hGXLlqG8vBwAsGfPHowdO1Yr1qxZs/DRRx8BAD766CO88sor%2BPjjjzF27FiMGjUKb7/9NgAgOjoaO3fuxNatWzF58mQATacabNy4ERMnTkRkZCQmT56Mf/zjH1rx//rXv2L58uU6/V1ERER0e3fa3PRd0R17mD40NBSjR49GQkICfv31VzzzzDMwNzdvd/1169bh/Pnz%2BPrrrwEACxcuREJCgtZcrAAQGRkJU1NTREdH651H//79MXbsWISEhGDAgAE4cuQIamtrsXTpUkRGRuKDDz7QKfapU6fg4eGBo0eP4qeffsL8%2BfPx2GOPISIiAmfPnsXw4cPx2muvadb/6quvsHXrVri4uCAhIQFffvklnnvuOQCAWq3GkSNH8P777%2Bt03wBQXFyMkpISrWW2to5wcnLSOcbNmmdrM9CsbURdilKwHdDy/SQaC8YyfBwYGWn/FCG6k5BzZ%2BPpKbb94MHaP0WITpNqaan9k4Tda4VjZ7gji9Hs7GycOXMG27dvh4WFBdzc3BAfH48ePXq0ub4kSUhJSUFMTAzs7e0BADExMai4aV7epKQkZGZmIjk5GSYmJkJ5nD59GllZWdi0aROsrKxgZWWFRYsWYfHixW12ZNtiZGSEl156CUqlEmPGjIG9vT1yc3Ph4eHR5vrjx4/H/fffDwAICgpCfHw88vLy4OLigh9//BFKpbJVN/ZWdu3ahfj4eK1lixeHYsmSMJ1jtEeHh/cP1ZWKY%2BbSvq6Ujxy5iE553szCQoYg3RxkCPI7Ozv5YokyMxOPceqUeAwA2LlTnjhyGDHC0BloE31DyTG3PRnMHVmM5uXlwcrKCnYtdng%2BPj4oKChoc/2ysjJUVFSgb9%2B%2BmmWDb/qGmpaWhmPHjiEpKQnW1tZCeQDA4cOHYWtrC0dHR81tLi4uqK%2BvR1FRkU7xe/fuDWWLdoeFhQVqa2vbXb9Pnz6a/zs7O8PLywv79u1DaGgoDh8%2BjGnTpsG4A92P2bNnIyAgQGuZra0jdKyl26RQNBWi9fXi%2Bw65ClqFouvsx5hL%2B7pSPnLlUlMjnoeFRVMc0Xy61VwVCwA0dUTt7IDyckCtFosl2rlTKJoK0bo68QdHdKDo4MFNhei8eUBOjlis30/l0pulZVMh%2Bp//AFVVYrEA8ccG6Fpvbj2wMyrujixGlUolGjvw7DcXdLfaJiMjA35%2BfoiLi4O3tzeMdDjMdKs8btX9VLTzDVB9085b2cHjbjfn/Pjjj2Pr1q1YvHgxUlNTdT49oJmTk1OrQ/Jy7NeBphh38L6HSBaiH2LNuwhJkuEDsaFBMEALarV4PLl2EHLsbDIy5MklJ0c81k1H9PRWVSVfLCJBd%2BQAJmdnZ1RVVaG4uFizLDU1FVeuXGlzfTs7O9jY2ODixYuaZVlZWdi7d6/m97CwMMTGxuLatWtITEwUyuPHH3%2BEs7Mzrl%2B/jtLSUs1tFy5cgJmZGXr06AEzMzPUtGiLqNVqXL58Waf71dW0adNw5coVJCcnw8zMDJ6i5z0RERGRFg5gEndHFqNubm4YMmQI1q9fj6qqKpw9exYrV67UXOy%2BLTNnzsSWLVtQVFSEsrIyREdH49y5c5rblUolLC0tsWbNGiQmJiI7O1vvPGpra%2BHu7o7%2B/fsjNjYW1dXVKCoqwsaNGzF9%2BnSYmJjg/vvvR1VVFY4fPw6VSoVNmzZB6sC3dzMzMxQUFOD69evtrmNtbY2AgADExsbi0Ucf1Tk2ERER6YbFqLg7shgFgMTERFy%2BfBk%2BPj54%2BeWXERISgvHjx7e7/vLly%2BHh4YHAwEAEBgZi4MCBCA0NbbXe6NGjMXfuXISHh%2Bs00KitPHx9faFQKLBhwwYUFxdjwoQJmDVrFoYPH44333wTADBs2DDMnz8fy5Ytg6%2BvL4yNjTvUuZw5cybS0tLwyCOPtDq839Ljjz%2BOyspKzJgxQ%2BfYRERERH8UhdSRdhzdcXbv3o3du3djp0yjOOvqxLZXKABTU0ClEj%2BNy9RUbPuWOXWVdwFzaV9XykeuXETHjyiVTSPyq6vFOylWVboNrLwlY2PAwQG4elX8nFHRSxgpFIC5OVBbK8PoLsHLHnh6No3I9/ISP2f0wAGx7W1sgLFjgfR0ec4ZnTpVPIZcbygDXW5jwYLOiZuU1Dlxu6I7tjNKt3fx4kV88MEHeOmllwydChEREVGb7sjR9H%2BUkSNHou4WrcCDBw9qXU6pK3nzzTdx6NAhzJ8/H35%2BfoZOh4iI6K50r53f2RlYjN7CyZMnDZ2C3lavXo3Vq1cbOg0iIqK7GotRcTxMT0REREQGw84oERERkZ7YGRXHzigRERERGQw7o0RERER6YmdUHItRIiIiIj2xGBXHw/REREREZDDsjBIRERHpiZ1RcSxGqUOUMvXSFQqDzdx2T5Bj52hkJFOcK/niQUxMgJ49oSgqBOrrhUJJfZ3F85GJiYnY9s3vIWNjGWZTFJ1%2BE/hfQpaW4gkZy/TxZGQkHkOOKTgB4KOPxKfgDAwU2755atKwMPGpSYGmuZ1FmZiITx/bHIfuSCxGiYiIiPTEzqg4FqNEREREemIxKo4DmIiIiIjIYNgZJSIiItITO6PiWIwSERER6YnFqDgepiciIiIig2FnlIiIiEhP7IyKY2eUiIiIiAyGnVEiIiIiPbEzKo7FKBEREZGeWIyK42F6IiIiIjIYdkaJiIiI9MTOqLh7pjNaUFAAV1dX5ObmAgDc3d2Rnp5%2B2%2B0CAgKQnJzc2enpZMqUKfj8888NnQYRERGRbO7ZzmhmZqahU%2BiwQ4cOGToFIiIiauFO7IyWl5cjKioKP/74I5RKJfz8/BAREQFzc/NW665atQp79%2B7VWqZWqxEUFIQ1a9ZgxYoV2LdvH4yMjDS3m5mZ4eTJkzrnc890RomIiIjk1tjYOf86U0REBGpqarB//37s3r0bubm5WLt2bZvrvv3228jMzNT8y8jIwIMPPoipU6dq1nnllVe01ulIIQrcpcVofn4%2BgoOD4enpCX9/f%2BzYsaPVOq6urkhLSwMA1NTUICIiAt7e3nj44YcREREBlUrVahuVSoV58%2BZhxYoVOuXh6uqKbdu2Ydy4cdi8eTMA4LvvvsPs2bPh6emJ8ePHIyEhQbO%2BWq3G6tWr4enpiQkTJuCrr77CI488gj179gDQPmWgsbERCQkJmDx5Mjw8PPDEE0/gu%2B%2B%2B08QKCAjA559/jkWLFsHT0xOTJk3C8ePHdXwEiYiI6G5UWlqK1NRULFu2DPb29ujRowdCQkKwe/du1NfX33b77du3o3fv3vDz85Mtp7vyMH1oaChGjx6NhIQE/Prrr3jmmWfabD03W7duHc6fP4%2Bvv/4aALBw4UIkJCRg2bJlWutFRkbC1NQU0dHROueSmpqKlJQUODg4oLCwECEhIYiMjMSMGTNw/vx5LFy4EC4uLpgxYwb%2B8Y9/4Ouvv8Y///lP9OjRA3/9619RXFwFPJRNAAAgAElEQVTcZtxPP/0Un3/%2BOTZt2oR%2B/frhk08%2BQUhICFJTU%2BHg4AAASEpKwvvvv4/BgwcjKioKMTExOHDggM65FxcXo6SkRGvZffc5wtHRSecYRDAxEY9hbKz98y6hUMizvWgc2YLImlAXYmMjtr2lpfZPEZ6eYtsPHqz9k4R1Vhezrc9gR0dHODmJfQafPn0aRkZGcHV11SwbOnQoqqurceHCBa3lN6uoqEBiYiJ27typtfz777/HkSNHcOnSJfTv3x9RUVEYNmyYzjndXXt2ANnZ2Thz5gy2b98OCwsLuLm5IT4%2BHj169GhzfUmSkJKSgpiYGNjb2wMAYmJiUFFRobVeUlISMjMzkZycDJMOfLhOmzYN3bt3BwDs378fAwcOxOOPPw6gqXM6Z84c7N27FzNmzMC///1vPProoxg4cCAAYPny5Th8%2BHCbcb/44gvMmzdP86IJDg7Gli1bcOzYMTz55JMAAH9/f3h4eABoGvyUkpKCxsZGKJW6NcR37dqF%2BPh4rWWLF4diyZIwnf/%2B9shRn8ipK312ypFLi1N3DB%2BnZ08Zgvzu9/eSCLmeajmeJ1NT8RiAXO%2Bn9r%2Bwd5iZmXyxRMnx4IwdKx4DAEaMEI9x6pR4DAC4qZgwONHnSYeO3p2mrc/g0NBQhIWJfQaXl5fDysoKihY7MVtbWwBAWVnZLbf95JNPMGrUKE2dAgDOzs5QKpVYunQpLC0tER8fj%2BDgYBw6dAj33XefTjnddcVoXl4erKysYGdnp1nm4%2BODgoKCNtcvKytDRUUF%2Bvbtq1k2%2BKZvjGlpaTh27BiSkpJgbW3doXx69%2B6tlVtmZibc3d01yyRJQr9%2B/QAAJSUlWm3vfv36wcrKqs24BQUF6N%2B/v9YyFxcXXL58WfN7y7/J3NwcarUa9fX1MNPxg2L27NkICAjQWnbffY7C73kTE3n2G3I1yRQKQJLkiSVKrlzk%2BKZuZASo1TLEKSkUD2Js3FSIlpYCDQ1CoaQe4sWxXM%2BT6PtAofjf%2B0k0H9PGWrEAzQmZmQF1deIJyfFNSK6dzY8/im1vadlUiP7nP0BVlVgswUIEgwc3FaLz5gE5OWKxAOCHH8RjyPU8GUhndUbb%2Bgx2dHTUadu9e/ciPDy8zduWLVsGSY/3p1qtxqefforY2Fit5YsXL9b6/S9/%2BQv279%2BP1NRUPP300zrFvuuKUaVSicYOvDKau4S32iYjIwN%2Bfn6Ii4uDt7e31oix22m5rrm5Ofz8/JCYmNjmuo2Nja26ru11Mds6pxWA1jcdXTug7XFycmp1OOAO3l%2BQocj5omlouKtehHJ9CZIkGWLJ%2BY1MloS6kJuOlOmtqko8VkaGPLnk5MgX6x7XWcVoW5/BugoKCkJQUFCbt6Wnp6OyshJqtVpTo5SXlwOA5jS/tvzf//0fVCoVRo4cecv7NjIyQq9evdo9zbAtd90AJmdnZ1RVVWk9CKmpqbhy5Uqb69vZ2cHGxgYXL17ULMvKytK6jEFYWBhiY2Nx7dq1dgtJXbi4uODs2bNa30hKSko0haWDg4NWZ/PSpUutThdoGevChQua3xsaGnDp0iU4OzvrnR8RERHd3dzc3CBJEnJadMYzMzNhY2OjOVLbliNHjuDhhx%2BGcYvDkpIkYc2aNVqxVCoV8vLyOlSP3HXFqJubG4YMGYL169ejqqoKZ8%2BexcqVKzUXu2/LzJkzsWXLFhQVFaGsrAzR0dE4d%2B6c5nalUglLS0usWbMGiYmJyM7O1iu36dOno7y8HBs2bEBtba1m1P/27dsBAN7e3vjyyy9x8eJF3LhxA3FxcejWrVubsYKCgrBz507k5uZCpVIhMTERarW6VUufiIiIOs%2Bddmkne3t7TJkyBevXr8e1a9dQWFiIhIQEPPXUU5pC8/nnn2814Pn06dNap/8BTUdjCwoK8NZbb6GoqAhVVVVYu3YtTExMMGnSJJ1zuuuKUQBITEzE5cuX4ePjg5dffhkhISEYP358u%2BsvX74cHh4eCAwMRGBgIAYOHIjQ0NBW640ePRpz585FeHh4u4fJb%2BW%2B%2B%2B7Dhg0bcOTIEYwaNQrPPvss/P39ERwcDKBpFP/IkSMRFBSEp556Co8//jgsLCzaPNweHByMqVOn4sUXX4SPjw9%2B%2BOEH7NixAzaioz6JiIjorrZ69WpYW1tj4sSJeOyxx%2BDh4aF1BaH8/Hxcv35da5uSkhLNgOyW3nnnHTzwwAOYOXMmfHx8cPr0aWzfvr3dZlpbFJI%2BZ7FSp1GpVDD9fYhtfX09RowYgS1btmDMmDEGzqyJHKfrcQBT%2B%2B7KAUxX8sWDmJg0jcovLBR%2B8Uh9xU9lket50uM7bas8TE2b4ojmY9ZYIxagOSFzc6C2VjwhOd7gcu1sUlPFtrexaRqRn54ufs5oYKDY9p6eTSPyvbzkOWdU9EUMyPc8GegyLZ318dzi0uF3vbuyM3qnSklJgb%2B/Py5evIj6%2Bnps2rQJ1tbWWqPviYiIqOu40w7Td0V33Wj6P8rIkSNRV1fX7u0HDx5Enz59OhTzscceQ25uLv70pz%2BhsrISAwYMQEJCQruXdyIiIiK607EY1VNH513VhVKpxPLly7F8%2BXLZYxMREZH87rUuZmfgYXoiIiIiMhh2RomIiIj0xM6oOBajRERERHpiMSqOh%2BmJiIiIyGDYGSUiIiLSEzuj4tgZJSIiIiKDYWeUiIiISE/sjIpjMUpERESkJxaj4jg3PXXM9eti2yuVgLU1cOOG8DtYbWUrlsvv5JqDXQ5y5VJaKra9sTHg4ABcvQo0NIjF6uEk0y5Gpgnh1Y0K4RhyPU81gtPBK5VAt25AdbX4B6KVUdeam77e2EI4HdmmPDeW4TUs0%2BtX%2BA0JyPfAAICpqdj2np7AqVOAlxeQkSEWy0DlzNChnRM3K6tz4nZF7IwSERER6YmdUXEcwEREREREBsPOKBEREZGe2BkVx2KUiIiISE8sRsXxMD0RERERGQw7o0RERER6YmdUHDujRERERGQw7IwSERER6YmdUXEsRomIiIj0xGJUHA/TExEREZHBsDNKREREpCd2RsWxM0pEREREBiNLMVpQUABXV1fk5uYCANzd3ZGenn7b7QICApCcnCxHCrclSRKWLFmCESNGYP/%2B/X/IfRIREdHdrbGxc/7dSzrlMH1mZmZnhBVy%2BvRpHDp0CPv27YOrq6uh02nXF198gYCAANjb2xs6FSIiIrqNe61w7Az3zGH6yspKAMADDzxg2ERuQa1W491330VZWZmhUyEiIiL6Q3S4GM3Pz0dwcDA8PT3h7%2B%2BPHTt2tFrH1dUVaWlpAICamhpERETA29sbDz/8MCIiIqBSqVpto1KpMG/ePKxYsUKnPFxdXbFt2zaMGzcOmzdvBgB89913mD17Njw9PTF%2B/HgkJCQAANLT0xEcHAwAGDlyJFJSUtDY2IgPP/wQkyZNwvDhw/Hkk0/ip59%2B0sQPCAjAxo0bMXHiRERGRmpORdi5cydGjx6tOdR/4MABBAX9f/buPp7K%2B/8D%2BOsQTiXdbKMRat1vkpvom26U7tymbbV0t5abWiGrpluFUK2iO6FFyyybWoaiO6SytDIJLbXujVCIEI7j/P7wc32dziGN87muvj7Px6NHXNdxrrfjcN7nc/N%2B20JPTw%2BTJ09GVFQUcx/r1q3Dxo0bsXDhQlhbW7/xezI2NsbLly9ha2uLwMBAGBgYIDk5Wew2ixYtQkBAAKKjozF16lQcP34c48ePh56eHjZv3oz6%2Bnrmtj/99BMsLCwwcuRIWFlZITExsU2PLUVRFEVRbUOn6dvvrafpXVxcYGxsjAMHDuDRo0eYP38%2B%2BHx%2Bi7cPCAjAvXv3cPr0aQCAo6MjDhw4gJUrV4rdztPTE4qKivDx8WlzLImJiYiJicF7772HwsJCLF%2B%2BHJ6enrCxscG9e/fg6OgILS0t2NjYICwsDF9%2B%2BSXS09OhpKSEH374AfHx8QgNDYW6ujqioqKwbNkypKSkoFu3bgCA%2BPh4HD58GFpaWsjPzwcAXLt2DcnJyejevTuys7OxceNG7N%2B/H2PGjMGNGzfg5OSEwYMHw8DAAACQlJSEbdu2YeLEiW/8fmJjYzF58mTExsZi4MCByM/Px8mTJ2FmZgYAKCsrw/Xr17Fp0yZkZWWhqKgI2dnZOHfuHAoKCrBo0SIMHDgQixYtwrlz5xAYGIjQ0FAMGzYMycnJ%2BOabb3Du3Dmoq6u36fEtLi7Gs2fPxI590LUrVD/4oK0/IklycuL/UzLRpZ0LcOTlxf%2BnZKO9vwY83n//b/evVNOddcR9dMR9Ue8Gff32ff2wYeL//1s3brTv69uhsyWOsvBWL1l//fUX7ty5g/DwcHTt2hXDhw9HYGAg1NTUpN5eJBIhJiYGW7duZdZAbt26FRUVFWK3CwsLQ3Z2Nn7%2B%2BWcoKCi0OR4LCwu8//77AIBTp05h8ODBmDlzJoDGkVM7OzvExsbCxsZG4mt//fVXfPXVV8y0/cKFCxEeHo6UlBRYWloCAMaPHw9tbW2xr5s5cyaUlZUBANHR0Zg4cSLGjRsHoHHU1cLCArGxsUwyqqGhgUmTJrX5e2rO1tYWS5cuRWVlJZSVlZGUlIQhQ4Zg0KBByMrKQm1tLb755ht07doVAwcOhJWVFVJSUrBo0SL8%2BuuvmDVrFnR0dAAA06ZNg6GhIU6dOoUlS5a06fpRUVEIDAwUO%2Bbi7AzXFSv%2B1fcjpnv3dt9FR%2BZJXEq6OiKW995r/30AQK9eHXEvHZiYdECS01E/6464n/9/39tuXbt2xL20PKjw1pSU2n0XbX8leMP9dMgdddBzuCOS9I75hjrufjIyOuZ%2BIiPb9/X0DdA77a2S0SdPnkBZWRm9mr1CmZiY4J9//pF6%2B7KyMlRUVKBfv37MsWGvvfu5dOkSUlJSEBYWhh49erxNOGIjfE%2BePEF2djZGjBjBHBOJRBgwYECL34ufnx%2B2bt3KHGtoaMDTp0%2BZzzU0NN54zbS0NIlrNiWnLd1HW40ePRp9%2BvRBYmIiZs6cifPnz4sl1j179hTb6KSuro7U1FQmtt9//x3h4eFisQ0aNKjN158zZw4zKtvkg65dgZcv/%2B231Dh80707UFXV7reTwm5v93xpibw8IBR2yF21W0fF8uJF%2B%2BPo1avxftobz3t9RO27gyY8HiBq/30JGzomoe2In1Ntbfu%2BnsdrTERfvWr/Q9NNrqZ9d9AUkJJS4zfWzoAE8u1PjhUUAIGg3XcDhS4d8BzuoOcvmi3F%2Btc66oEBgNGj2/f1w4Y1JqLz5gG5uR0TE2F0ZLT93ioZlZOTQ8NbPOpy/z9v1NrX3LhxA6ampti9ezdGjx4N%2BbcYbmh%2BWz6fD1NTU4SEhLTpa/l8Pnx9fTF9%2BvQ23X9L15w7dy42bdr0VvfRVjweDzNmzMDJkycxZcoU/PHHH9iyZQtzXvjaq6FIJALv/98d8vl8rF69mlkr%2B2%2BoqqpCVVVV/GB5ecf85nXGRTEEdcTrFdCYcHXUfVGS2vsr0DQ1LxJ1wK8Tr4PeNACNAXVE4kVxX0dNj%2BfmsjrVTrHrrVYZaWpqoqqqCsXFxcyxxMREFBQUSL19r169oKKigocPHzLHbt26hdjYWOZzV1dX%2BPv7o7S0tM2JpDRaWlq4e/cuRM3%2BAD579kzqZqmm7%2BXOnTtix1oa4W3tmq/fR2FhoUSS2B62trb4448/EB0djZEjR4otiaisrERpaSnzeUFBAXNeWmwFBQVijw9FURRFUe1DNzC131slo8OHD8fHH3%2BMPXv2oKqqCnfv3sXGjRuZYvfSfPbZZwgNDUVRURHKysrg4%2BODv//%2B%2B78ByMmhe/fu2LZtG0JCQvDXX3/9q2/EysoKL168QFBQEGpqaphd/82nqZuzs7PD0aNHkZmZCaFQiISEBFhbW7eYWEsza9YsZGRk4MSJE6irq8Pt27cxe/ZsnD179l99D00bwR49esSUovroo48wfPhw7N27V2Ltq6KiIg4cOICamhrcu3cP8fHxzLT6nDlzkJCQgJSUFNTX1%2BPq1auwtrbGzZs3/1VsFEVRFEVJoslo%2B731/suQkBDk5%2BfDxMQEX3/9NZYvX47x48e3ePvVq1dDV1cXlpaWsLS0xODBg%2BHi4iJxO2NjY8ydOxdr1qxpcTSzNb1790ZQUBCSkpJgZGSEBQsWYNKkSS1OU8%2BaNQvz5s2Di4sLDA0NERoaisDAwDbvNAeAgQMHwt/fH6GhoRg1ahRcXV3h4ODAbIB6W%2B%2B//z6mT58ONzc37Nmzhzk%2Bc%2BZM1NXVSSwpUFFRwZAhQzB16lTMmjULkydPhp2dHQBg7NixWLt2LbZs2QIDAwNs2bIFXl5e0NPT%2B1exURRFURRFyQJPROdtOW/fvn3Iy8vDzp07mWPR0dHw9/dvU9vVDlVe3r6vl5MDevRo3ATV3g1Myj3bF8v/%2B1/cwPT8efu%2BvkuXxh35JSXtXzOqpko3MLXk1av2fb2cXOOO/Orq9o%2BkKMu3Mxig8WfE5wM1Ne3fwNSl/SUC6AamFnTkBiZFxfZ9vb5%2B4458A4P2rxllKZ15y73XbdaevcLvGpm0A6U6TmZmJiIiIhAREcF2KBRFURRFUR2Ok8noqFGjUNtKzZMzZ860q2QSG54/f/7GeqPZ2dlinzs4OODOnTtYu3atREksiqIoiqLY19nWd8oCnaan3g6dppcpOk3fCjpNL4FO07eOTtO3gE7Td6iOaTohqb1/H94ltCcjRVEURVEUxRpOTtNTFEVRFEW9C%2Bg0ffvRkVGKoiiKoiiKNTQZpSiKoiiK%2Bpfe1aL32dnZmDp1Kr744os33vbHH3/E9OnTYWBggLlz5yInJ4c5V1tbi82bN2PChAkYPXo0VqxYgbKysreKhSajFEVRFEVR/9K7mIzGxcXB1dUV2trab7xtcnIy9u/fjx07duDKlSuYNGkSvv76a1RXVwMAdu/ejVu3biEqKgpnz56FSCTC%2BvXr3yoemoxSFEVRFEV1IrW1tYiKisLIkSPfeNuoqCh89tlnGDlyJPh8PhwdHQEAFy5cQH19PX799VcsX74cH374IXr16oVvvvkGKSkpKCoqanM8dAMTRVEURVHUvySrUczi4mI8e/ZM7NgHH3wAVVXVdt/37Nmz23zbW7duibU5l5OTw/Dhw5GdnY3hw4fj5cuX%2BOSTT5jzAwcOBJ/Px61bt6Cmptama9BklHo7PdtX27O4uBhRR45gzpw57f6Fkm/XVzeLJyqqQ%2BLhUixt/P1vNZb9%2BxtjUVNr7%2BPS/rqeHfnYyLfzidORsSgrtz%2BWsLCOev62v1hicXExog4d6pB4FDoilg773W7fc7hDY1Fo3yPT4X/z2lnbs7i4GFH792POmTOs/w3%2Bt2RV3nT//igEBgaKHXNxcYGrq6tsLtiCFy9eoOdrr/09e/ZEWVkZXrx4AQBQUVERO6%2BiovJW60bpND1F1LNnzxAYGCjxbo8tXIqHxtIyLsVDY2kZl%2BKhsXA/FoB78XDJnDlzEB0dLfZvzpw5bfra2NhYDB06VOq/6Ojot47lTf2R2ts/iY6MUhRFURRFcYyqquq/Hi22tbWFra1th8TRu3dvZgS0yYsXLzB48GD06dOH%2Bbx79%2B7M%2BfLycrz33nttvgYdGaUoiqIoiqKk0tHRwa1bt5jPhUIh/vrrL4wcORKampro2bOn2Pm7d%2B%2Birq4OOjo6bb4GTUYpiqIoiqIohrm5OdLT0wEAc%2BfORUxMDDIzM/Hq1SsEBwdDUVEREydOhLy8PL744guEhITg6dOnKCsrQ0BAAKZOnYr333%2B/zdeT9/Ly8pLR90JRUnXv3h3GxsZiQ/ps4lI8NJaWcSkeGkvLuBQPjYX7sQDci6czmD59Onbs2IFr166hsLAQBw8eRHBwMGxtbaGiogIfHx9YWFhAW1sb2traUFZWhp%2BfH/bt24e6ujr4%2B/szO%2BVHjRqFv//%2BG1u2bMGRI0cwZMgQ%2BPj4QElJqc3x8ETtXXVKURRFURRFUf8SnaanKIqiKIqiWEOTUYqiKIqiKIo1NBmlKIqiKIqiWEOTUYqiKIqiKIo1NBmlKIqiKIqiWEOTUYqiKIqiKIo1NBmlKIqiKIqiWEOTUYqiKIqiKIo1NBmlKIqiKIqiWEOTUYqiKIqiKIo1NBmlKBZt3LhR6vHKyko4OzsTjoZ9BQUFbf7XmdHnTduVl5ezct1vv/0Wly9fRkNDAyvXb0ldXZ3EP4piWxe2A6D%2Bd5mZmYHH47XptklJSTKNZeHChW2O5ccff5RpLACQl5eHR48eIS4uDpaWlhCJRGLnHz9%2BjNTUVJnHIU18fDxiYmLw7NkzxMTEoK6uDhEREbC3t2/zY/hvvc1z5vbt2zKN5XV37txBSkoKiouLwePxoKamhkmTJmHQoEHEYuDi84YLj0uTW7duYdOmTYiOjgYAuLm54ezZs%2BjduzeCgoKgr69PLBZFRUW4u7tDXl4eFhYWsLGxwciRI4ldv7krV65g69atePToEYRCocR50r9LhYWFOHXqFAoLC%2BHh4QEAyMrKgq6uLtE4KO6gySglM0uWLGE%2BLikpwbFjxzB16lT0798fDQ0NuHfvHlJSUmBvby/zWPT09JiPX716hdjYWBgaGmLAgAFMLFlZWZg3b57MYwGA3Nxc7Nu3DwKBAA4ODhLnlZSUYGdnRySW5oKCghAVFYU5c%2BYgJCQEAFBRUYGYmBi8fPkS33zzjUyvn5CQwHyclZWFEydOYOHChWLPmcjISHz11VcyjeN1P//8M7Zs2YLhw4dDXV0dIpEIf/75J3bv3g1vb2/Mnj2bSBxce95w5XFp4uvriylTpgAAEhMT8eeffyI5ORkZGRnYuXMnIiMjicWydetWNDQ04I8//kBiYiJWrFgBPp8PGxsbWFtbo3///sRi8fT0hLGxMdzd3dG1a1di15UmKSkJK1euhIGBAf788094eHjg6dOnWLx4MbZs2QIrKytW46NYIqIoAuzt7UU3b96UOH79%2BnXR4sWLicaycuVK0cWLFyWOnz9/XuTm5kY0lhkzZhC93ptMmDBBdPfuXZFIJBLp6uoyx588eSKaOHEi0Visra1FRUVFEsf/%2BecfkZWVFdFYxowZIzp//rzE8XPnzonGjBlDNBaRiDvPG649LgYGBiKBQCASiUSidevWifz8/EQikUjU0NAgGjVqFPF4XhcZGSkyNDQUDRs2TDR//nzRpUuXiFxXT0%2BPeVzYZm1tzTxnRowYwRxPS0sj/ntNcQddM0oRkZGRgWHDhkkc19XVxY0bN4jGkpKSAhMTE4njpqamuHjxItFYPvzwQ6LXe5OXL19i8ODBEsdVVVVRWlpKNJb8/Hx069ZN4njPnj2Rn59PNJba2lpMnDhR4vikSZNYWXMXGxsLoVCIp0%2Bf4uHDhxL/SOHa46KkpASBQAChUIjLly9j0qRJAIDq6mrW1m7m5eUhKCgIlpaW%2BO677zB%2B/HiEhITg008/hYeHB77//nuZx2BsbIzc3FyZX6ct8vLyYGZmBgBiS3KMjIzwzz//sBUWxTI6TU8RoaWlhf3792Pp0qVQVlYG0LjZIjQ0FP369SMai6qqKqKiojB//nyx49HR0fjggw%2BIxlJQUICcnBzo6OgQvW5LhgwZgri4OMyYMUPs%2BOHDhzFw4ECisRgYGGD58uVwcHCAhoYG6uvrUVhYiB9//JHo2j8AmDlzJmJiYjBr1iyx4wkJCbCxsSEaCwCcPHkSW7ZsQWVlpcS6UR6PR2wNINcelwkTJmDFihXo0qULlJWV8Z///AcCgQC7d%2B%2BGgYEB0ViOHj2KkydPIisrC/r6%2Bli8eDEsLCyYv39A43N87ty5YkuaZGHKlClYs2YNJk2ahH79%2BkFOTnwcas6cOTK9fnPq6uq4c%2BcOhg8fLnY8NTUV7733HrE4KG7hiV7/S0ZRMnDz5k24ubnh2bNnUFFRgVAoRGVlJVRUVHDgwAEYGhoSiyU5ORmrVq1C9%2B7d8eGHH0IoFKKoqAgvX75EQEAApk6dSiyWXbt2ISEhAbq6ulBXV0eXLuLvD1etWkUsFgBIS0uDs7MzhgwZgqysLJiamuLu3bsoLy9HUFAQjI2NicVSWloKPz8/JCUloaamBgDQpUsXjBkzBn5%2BflBVVSUWy4YNG3D27Fmoq6tjwIABEAqFePLkCfLz82Fqair24u7v7y/zeCZMmIAvvvgCFhYW4PP5Euc1NDRkHgPAvcelpqYGR44cwcuXLzFv3jxoaGiguroarq6u8PPzQ9%2B%2BfWUeQ5Np06ZhxowZsLW1haamZou38/T0hLe3t0xjaRqJlIbH48l8A2lzP/30E4KCgjBr1iwcPnwY3377Le7cuYOEhASsWbNGYpCA6hxoMkoR09DQgOzsbBQVFaGurg6qqqoYOXIklJSUiMdSUVGB1NRUsVhMTEygpqZGNI6FCxe2eI7H4xHZ2f%2B6wsJCxMfH48mTJ%2BDz%2BdDS0oKVlRV69epFPJYmL168QF1dHfr06SORsJOwfv36Nt9227ZtMoykkaGhIa5duwZ5eXmZX6s1XHtcmggEArHd/Ww8Tl5eXvDy8iJ%2B3XfBuXPncOLECeZvjKamJuzs7KQun6I6B5qMUjIjEAigoKAAAG9cP6aoqEgipHfK7du3Jaay2JSeno5Ro0bJ9BppaWkYM2YMALyxRNG4ceNkGguXrVmzBjNnzqQv3q8pLy%2BHl5cXEhMTUV9fD6BxHam1tTU2bdpE9I3vlClT8MMPP7Q6KkpSaWkpLl68yKy31tbWxqRJk8SWDVAUW2gySsnMyJEjcfPmTQDAsGHDpNaPFIlERNa4TZ48mZmKelMSQ7pOo0gkQkFBgVjCXlRUhOXLlyMjI4NoLMOHD8fcuXOxbt06iTcIzX%2BesqKrq4usrCwAkLrhrQnJdZFA48auEydO4NGjR6itrZWIZevWrcRiAYCDBw8iMjIS%2Bvr6UtcAklreERgY2Op5FxcXInE0WbVqFYqLi5x9G/kAACAASURBVOHk5ARtbW0AwP379xESEgI9Pb0WmwXIQkhICOLj4zFhwgSpS3BIrtNsWn7TtWtXJjl%2B8uQJhEIhIiIiMGTIEGKxNDQ04OjRo0xtWgDo27cvzMzMYGdnJ/NaxhQ30Q1MlMyEhYUxH7Mx3dzcihUrmI9Xr17NYiTi0tPTsWLFCpSVlQH4b3IOgKmXSJK8vDxyc3MxZ84c7Nmzh3lBb4pN1poSUQBt3v1LYsTW2dkZDx8%2BhK6uLivLSl6XmpoKLS0tlJSUoKSkROwcyRfzy5cvi30uFAqRn58PkUhEfMNQUzxnz55Fnz59mGP9%2B/eHjo4O7OzsiCajx44dAwCcPn1a4hyPxyOajO7cuROurq5YvHgxc0woFCI4OBh%2Bfn4IDw8nFouXlxeSk5NhbW0NU1NT5s34gQMHcPfuXXh6ehKLheIQFspJUVSLmte2ZJu5ubnMr/Hpp5%2BKAgMDRQ8ePBDp6OiInjx5IkpISBC5uLhIrbEpa7q6uiKhUCgKCAgQjRo1ShQXFyd2jotIxKWnpyd6/vy5zK/zv0AoFIoOHDggOnToEPFrjx49WlRdXS1xvLq6WmRsbEw8npYIhUKi1xs5cqTUOqO1tbUiIyMjorHo6emJ7t%2B/L3H877//Funp6RGNheIOWmeU4hQRh1aNkKhl%2BfDhQyxfvhwDBgwAj8eDpqYmLCws4ODggLVr18r8%2BtLIyclh5cqV2Lt3L7777jts3LhRYmqaS0g8Z4YOHcq5x6CwsBChoaHw8/NjjjUfWWaLnJwcnJyccPjwYeLXNjAwwJYtW8Rq4paWlsLHxwcjRowgGsv06dOlHq%2BoqMDYsWOJxqKqqopHjx5JHM/LyyO%2BZrRHjx5Sy/lpamqiR48eRGOhuINO01OcwqX1QiRi6dmzJ549ewZVVVWoqKggLy8Pmpqa%2BOSTT5CZmSnz67%2BueWJnYmKCmJgYrF27FrNmzZLa05oLSPycdu7ciTVr1mDs2LFQU1OTWKM5c%2BZMmcfQ3OstFTdu3MiplorXr19nNhCR5OnpiWXLlmHs2LFQUVEB0Jj8DRw4EEFBQURiSEtLw5UrV5Cfn4%2BAgACJ8//88w/xhgC2trZYsmQJ5s%2Bfj48%2B%2BggA8ODBAxw9ehTm5uZEY1mxYgX8/PywcuVKpkLHixcvEBgYCFdXV6KxUNxBk1GKYpG1tTU%2B//xznD59GuPHj4erqytmzJiB7Oxs4s0AgMYXrebef/99hIWF4eDBg8wauM4oODgY6enpuHPnjtQ1o6ST0T179iAgIABTpkyBrq4ugMZuXgcOHICvry%2BxZFTaZsCamhpUVVXhq6%2B%2BIhJDc2pqaoiOjkZubi6T9GlqakqMij59%2BlRm3c969uyJ6upqCIVCqd3l%2BHw%2BfH19ZXLtlixfvhwqKio4ceIE87hoaWnBzs4ODg4ORGP5/vvvUVRUhGPHjkFFRQUNDQ2orKyEgoICVFRUsHfvXua2pDeTUuyhu%2BkpTiGxY7utSMUSExMDW1tbVFVVwdvbG9nZ2dDQ0IC7u3urO8qpRiR%2BTnp6eggNDZX5Rqm20tPTQ0ZGBuTk5MS%2Bf6FQCENDQ2Kj6r/99pvEMSUlJWhra%2BOTTz4hEsO/QeI54%2BvrCw8PD5le410k7TnTkk8//VSGkVBcQkdGKYpF165dY0bVlJWVsXPnTgCNPb%2BTk5OJJKMLFy5EREQEAMDOzq7V2/7yyy8yj4eL1NXVOfXGgCstFZuSBS4UmX8bJMZgPDw8kJ2djfv370tdbyzr3fS//vor06Y1Kiqq1duS3Nn/6NEjzJgxg3h7YYrbaDJKUSxycnKSOkJTXl6OdevWwcLCQuYxNC%2Bc3pkLybdm7dq12Lx5M2bNmiV1zeiAAQOIxjNv3jw4ODgwa3mPHDki1lKRlIqKCnh6enKiyPzbILHOeOvWrfjxxx/Rp08fiZatJEo7hYaGMsnowYMHW7wd6TJTN2/eRFhYGIYMGQIbGxtYWVkRbe1LcROdpqc4pXnRc7bJMpYjR44gNDQUJSUlUkeyKisroaGhgfj4eJlc/22Ul5ejZ8%2BebIfRIltbW8TGxsr0GtJGRXk8HrGmDdJwoaUil4rMvw0S0/TGxsbYu3cv01GM%2Bq8XL14gKSkJ58%2Bfxx9//AE9PT3Y2Nhg2rRptCNUJ0WTUYoIGxsbnDx58o23ayk562hCoRDFxcWoqamRONc0ypWVlcVsDuloDQ0NuHXrFubOnQsfHx%2BJ80pKShgzZgx69%2B4tk%2Bu35NatW9i0aROio6MBAG5ubjh79ix69%2B6NoKAg6OvrE40nMzMTsbGxKCoqQlBQEBoaGnDu3DniO4DfVOZLQ0ODUCTcYmRkJFFkHmjsIGZnZ4cLFy6wFFnrSCSj48aNw4ULF5iWyGyKiYlp8VzT0ooRI0age/fuBKNqVFVVhejoaOzduxcCgQDm5uZwdHTE4MGDicdCsYdO01NE9OrVCxcvXoSpqWmrtyORiMbFxcHHxweVlZUA/tv16PVRLlklokBjLcYRI0YgMjJS4jpsjkT6%2BvoynZ8SExPx559/Ijk5GRkZGdi5cyciIyOJxXLs2DFs374dFhYWTKefZ8%2BeYevWrSguLsaXX35JLJaWks2GhgbMmzeP%2BFra1tqTAsC2bduIxCEvL4%2BuXbtKHFdRUUF1dTWRGLjqq6%2B%2BwuHDh7F06VK2Q0FwcDCKi4vx6tUrKCsrQ05ODhUVFejWrRu6du2K8vJyqKioICgoCHp6ekRiqq6uxvnz53Hq1CmkpaXh448/hq2tLUpLS7FgwQKsW7eObmDqRGgyShGhra2N9evXQ0NDQ2qfZn9/f2Kx%2BPv7Y9GiRbCwsJBYy0WavLw8PvvsM86MRN69e5fZzJSUlARLS0uoq6vjww8/hLe3N9FYDh06hEOHDsHQ0JAZVVdTU8PBgwfh5uZGNBmtrKzEgQMHkJOTA4FAwBx//vw58ZqRAHfakzYVmXd3d2dGR0tLS7Fr1y7iRea5JiMjAxkZGYiIiIC6urrEOmOSb2Dc3Nxw%2BvRprF69Gv379wcAPH78GHv27MHMmTMxduxYhISEYNu2bW/c7NReKSkpOHnyJJKTk9G7d2/Y2Nhgw4YNYuuum8rc0WS086DJKEWEUCh846goKZWVlVi2bBkndvxyaSQSaFweIBAIwOPxcPnyZWZ3f3V1NRoaGojG8vz5c6a/efMNJ4MGDUJxcTHRWDw9PfHw4UOMHz8eYWFhcHJywl9//QWBQED0jVST7OxsJCYmEt05Lw0Xisy3lUAgYKbMFRUVZX69jz/%2BGB9//LHMr9MWu3btQlxcnNh6TG1tbWzZsgWzZ8/GmTNnsHTpUvzwww8yj2X16tWYNm0aQkJCMHr0aKm30dPTw9ChQ2UeC8UdNBmliCA1bdgWkydPxh9//EF0o0dLuDQSCQATJkzAihUr0KVLFygrK%2BM///kPBAIBdu/ezSSGpGhra%2BPq1asSG0BOnToFdXV1orH8/vvvOHPmDHr16oUffvgBbm5uAIDw8HCcPHmSeOcYrrQnbWuReVLWr18PX19fiTeaubm5cHd3Z0bYr1%2B/LvNYXFxcZH6NtiorK0NRUZHE5qCSkhI8ffoUQOO6aBJrRhcvXiz1samqqsKePXuYTW%2BhoaEyj4XiDpqMUsRwZTPKwIEDsX79eujr66Nfv34S02erVq0iFguXRiIBwMvLC%2BHh4aioqICHhwd4PB4EAgEePnwo1gOdhCVLlmD58uUwMzNDfX09fH19cefOHdy4cYP4aKRIJGL6ZisoKKC6uhrdunXDF198ATMzM%2BLJKFfak3799dcICQnBsGHDOFGH9fHjx3BycsK%2BffugrKwMkUiE4OBghISEYN68eURjCQwMbPU8yWT1008/xZdffglra2toaGigS5cuKCgowMmTJzFlyhTU1dVhwYIF%2BPzzz2UWw4sXL1BaWorvv/8e1tbWErVeHz16hKioKM5WYKBkiyajFBFc2oySmpoKLS0tlJSUoKSkROwcifqDzXFpJBJobFX4%2BoaLbt26ISwsTOyYhYUFTp8%2BLdNYLC0toampid9%2B%2Bw1jxoxBYWEhdHR04O3tzfTXJmXEiBHw9PTE5s2bMXToUISEhGDx4sXIzMxk5U0DV9qTFhQUICcnBzo6OkSu9ybh4eHYvHkz5s6diw0bNmD37t0oLS1FWFgYjIyMiMbS9HeuiVAoRH5%2BPkQiEfHfbQ8PD2hpaeHSpUu4cuUKGhoa0KdPH3zxxRdwdHSEoqIi3N3dMWPGDJnFkJSUhO3btzM75ps0bR4FgGnTpsns%2BhS30dJOFBFTp07F9u3bYWhoKFa/8/bt23Bzc8O5c%2BdYjpAdNTU1zEjkvHnzoKGhgerqari6usLPzw99%2B/ZlO0SpuFQPloQnT55g06ZNOHToELKysrB06VJUV1dDTk4OK1euhKOjI9F4uNKedNeuXUhISICurq7UjYkkZxmaCwsLQ0BAACZMmAB/f39069aNlThe19DQgJCQECgqKhJ/znCBUCjE6NGjpdYF5vP5rK%2BBpthDk1GKCH19fWRkZIDH44nV%2BBMIBDAyMiLWS7vJ33//jbNnzzL1I7W1tWFlZQVNTU2icbQViZHItyGrOo2rV69u823Z2DjUpKKiAg8ePMCHH34IVVVV4iPqlpaWOHbsGOsFwhcuXNjiOR6Phx9//FHmMbS0%2B/vixYvIzMyEq6srs4yBZKehlggEApiamuLKlStErxsfH4/Y2FgUFxcjJiYGdXV1iIiIgL29PfHnb1uQqAVLcQedpqeI4NJmlKaWicOHD4eWlhaAxm42QUFBOHz4MOujTdK8qej6/woSu5z/jenTp%2BPs2bPM5yoqKtDT00NFRQVMTEyQlpZGNB6utCdt2nzHptZaXfL5fBw6dAgA%2BbaXLbl%2B/TrTOpWUoKAgREVFYc6cOQgJCQHQ%2BIYqJiYGL1%2B%2BxDfffEM0nrag42SdC01GKSK4tBklMDAQO3bsgKWlpdjx6Oho7NixA8eOHSMaT1twceRCFqRVXWhqRgAA9fX1ElPBspSWloYrV64gPz8fAQEBEuebdpCT1rSuNyEhgTnGdnvS9PR0JoFoqidMQnJyMpHrvK1x48ZJHKupqUFVVRW%2B%2BuororFERUUhNDQUgwcPZpL3999/H0FBQfjyyy85mYx2lr95VCOajFJEcGkzSkFBAaZPny5xfMaMGdi6dSvRWKiW5efnw93dHYsWLWJ%2BXhERETh79iwCAgKIJDs9e/ZEdXU1hEIhbty4IXGez%2BfD19dX5nG8Likpifg1X/fdd99h6NChzGapRYsWQSgUAmhMdE6dOoVevXoRjUkgEODAgQMYN24cM8MRFxeHe/fuwcXFhejIu7QlJ0pKStDW1sYnn3xCLA6gsWOXtPaaqqqqKC0tJRoLRUlDk1GKmBEjRojVH2Sr7aW6ujpu3rwpsaP11q1bdAE9h3h6emLgwIFiu6BtbW3xzz//wNPTk5l%2BlaWmwuU8Hg8eHh4yv15bNW9PysbI7LFjxxAdHY19%2B/Yxx%2BTl5XHr1i00NDTA3t4eP/74I1asWEE0Lh8fH%2BTk5IjNegwaNAiRkZHw8/MjWru3qXuQQCBAcXEx0wOejWYbQ4YMQVxcnMRu%2BcOHD2PgwIHE46Go19ENTBQRt27dwqZNmzjR9vKXX37Brl27YGNjw/whfvDgAU6ePImlS5dycpcr1xbzk9hNP2rUKKSlpTFdc5rU1dXBxMQE6enpMr1%2Bc7W1tTh27BizYScpKQm//vor%2BvfvDxcXFyLFwpu7cuUKtm7dikePHjGjkc3Jepp%2B9uzZWLRoEaytrZljzZ%2Bj6enp8PX1RUxMjEzjeJ2JiQni4%2BPRu3dvseNlZWWwtrbG77//TiyWiooKeHp6IjExkVkjqqSkBGtra2zatIloG9e0tDQ4OztjyJAhyMrKgqmpKe7evYvy8nIEBQXB2NiYWCxtxbW/eZRs0ZFRiggutb20s7ODqqoqTpw4gYyMDNTV1UFLSwve3t4S60g7I6FQiOLiYtTU1Eica9oY89NPP8k8DhUVFTx8%2BBBDhgwRO56bm4uuXbvK/PrNNa1xXrhwIR48eIBVq1bByckJd%2B/ehZ%2BfH/HlHZ6enjA2Noa7uzvxxwJoLHU1efJksWMffvgh8/HIkSORl5dHOiwIhUKpaw0FAgHxjlVeXl549uwZAgMDoa2tDQC4f/8%2BQkJCsGvXLqLF3ceMGYPTp0/j1KlTGDp0KPh8PsaNGwcrKyviSykoShqajFJEcK3tpZmZGczMzIhf998iNYERFxcHHx8fVFZWMteVtjFGV1dX5rEsWLAAixcvhpWVFfr164eGhgY8fPgQCQkJxDdcJCYmMq0kY2NjMW7cOLi4uODly5ewsLAgGgsAPH/%2BHN7e3kQ3czVXV1cnkQSfOXOG%2BVhOTo6VZgDTpk2Ds7Mz7O3toaGhAZFIhIcPHyI0NBRWVlZEY7l8%2BTLOnj2LPn36MMf69%2B8PHR0d2NnZEU1GDx06BCcnJzg4OBC7ZnvRSdvOhSajFBFst73ct28fs35N2q7o5kgX6ubKSCTQWLtz0aJFsLCwAJ/PJ3LNltjb20NLSwvR0dFIS0sDj8eDpqYmvvvuO%2BJvJGpra/H%2B%2B%2B8DaOxTP3/%2BfACAsrIyqqqqiMYCAMbGxsjNzWWt85GmpiZu3brV4kacP/74gymbRtLGjRvh7%2B%2BP9evXo6KiAkDjCPtnn332VjVsO4K8vLzUUWsVFRVUV1cTjSU8PByff/65WGLMFhsbG%2BaNXWsuXLhAIBqKK2gyShExfvx4VtteNl97JG1XdBPS5US4NBIJAJWVlVi2bBkrmyykmTJlCrO8g02DBw9GdHQ0%2BHw%2B7t27xyTDV65cEZueJmXKlClYs2YNJk2ahH79%2BknUGZV1PU1ra2v4%2BPjg4MGDEpsQCwsLsXnzZixYsECmMUjD5/OxceNGbNy4EWVlZZCTk2NlkyQAGBgYYMuWLXB3d2eSwNLSUuzatUtsIycJjo6OcHNzY2akXv/9llaGSlZ69eqFixcvwtTUtNXb0c2knQvdwEQR0dT28uXLl5g3bx7U1dXfibaXsmZqaorZs2e3OBLZfNc0CWvWrMHMmTNhYmJC9LpNuDqCnZWVBXd3d7x8%2BRIrV67E7Nmz8eLFC0yaNAlbt24lPlXf2sgwj8eTeemnuro6ODo64tatW7CysoK2tjaEQiHu37%2BPM2fOYOzYsQgMDJRIkmUhLS2NaaaRmpra6m1JJl1FRUVYtmwZbt%2B%2BDRUVFQCNm5oGDhyIoKAgoiPHw4YNa/Ec6bq0Hh4eSE5OZmrRvr7UhM3OahR7aDJKEfHll1%2B2ep5E28Am5eXlCA4Oxrp16wAAR48eRVRUFPr37w8PDw%2BoqqoSi8XQ0BDXrl3jzEjkwYMHERkZCX19fakjbrJOAB0cHBAWFgaAG60m36SoqAhqampsh8EKoVCI6OhonD9/Hnl5eeDxeBgwYAAsLS2Jrs9sXtmBS0lXk9zcXKY5gqamJvFR0bfx9OlTmY/0r1%2B/vtXz0hpfUP/7aDJKEfH6u12hUIi8vDxkZmZiwYIFTEcZElxdXSEUChEUFITs7GwsXLgQXl5eyMnJQXFxsVjtRFljeyTydVxKAP/55x/069dP4nhdXR3%2B%2Busv6OnpEYsFADIzMxEbG4uioiIEBQWhoaEB586dg7m5OdE4mpSWluLixYtMq1htbW1MmjSJ9X711H%2BlpaVBTU2NaeyRnp6O2tpajB07luXIpKPllCi20DWjFBEtbR5ITU1FXFwc0ViuXbuGxMREAMCpU6cwZcoUzJw5E%2Bbm5sQ3xgwcOBDr169nbSTydVzoNd7EyspK6gvjq1evsHjx4lbX/na0Y8eOYfv27bCwsMDly5cBAM%2BePcPWrVtRXFz8xpH/jtZUN7Jr167Q1NQEAERGRsLX1xcRERES5bBkie0k/eHDh226HY/HQ//%2B/WUbTDMRERHYu3cv9u/fzySj5eXl2LBhA1xdXVlZU/smpMam2H7OUNxDk1GKVSYmJsS7tDQ0NDCjR7///jucnZ0BAAoKCnj16hXRWFJTU6GlpYWSkhKUlJSInWOrN/Pff/%2BNs2fPio24WVlZMUmPrB0/fhy//vor6urqYGdnJ3G%2BuLiYeG3EQ4cO4dChQzA0NGR2AqupqeHgwYNwc3Mjnozu3LkTrq6uWLx4MXNMKBQiODgYfn5%2BCA8PJxIHF5J0CwsLZtMfALGPm39Oepr%2Bhx9%2BwE8//SS2dGDy5MmIiIjAsmXLOJmMkvibw4XnDMU9NBmliJA2elFTU4Nz584xi/tJ0dHRwYEDB6CkpITi4mJMnDgRAJCQkMCUUiKFSyORQONjsGbNGgwfPpzZYHHu3DkEBQXh8OHDTL9vWZo2bRp69OiB1atXS91woqSkRHyH/fPnz5mqD81fsAcNGoTi4mKisQCNHcNeX1IhLy%2BPJUuWEF1KwYUk/fXNWleuXIGBgQGzIbCgoAD379/H%2BPHjZR5Lc2VlZcyIaHP9%2BvXr1P3gufCcobiHJqMUES2NXvTo0QNeXl5EY/H09ISPjw8qKiqwc%2BdOdO3aFS9evICvry/R9aJN2B6JbC4wMBA7duyQ6EQVHR2NHTt24NixYzKPoWfPnjA3N8fDhw%2BxbNkymV%2BvLbS1tXH16lVm13aTU6dOQV1dnXg8qqqqePToEQYNGiR2PC8vj%2BiaUS4k6c0rTjSfGm/6WeXm5mL37t2or68nOhppYGCAgIAAODs7o0ePHgAaH689e/Zg5MiRxOLgGi48ZyjuockoRYS0UjNKSkro06cPkfIvzfXv35/Zsd2kV69euHTpEtF%2B0QA3RiKbKygowPTp0yWOz5gxg3jLy7CwMDg4OEBRUZHodaVZsmQJli9fDjMzM9TX1zPtQW/cuMFKKRpbW1ssWbIE8%2BfPZ0bfHjx4gKNHjxJdd8e1JJ1LU%2BNeXl5wdXXFjz/%2BCGVlZTQ0NKCqqgrDhw9HSEgIsTi4hmvPGYobaDJKEUG6XmZr8vLysHPnTmYUdMeOHYiKioK2tjZ27tyJgQMHEouFCyORzamrq%2BPmzZsSjQhu3bpFvAi1q6srvvvuO6Yu7evlr0gmqZaWltDU1MRvv/2GMWPGoLCwEDo6OvD29pY6FStry5cvh4qKCk6cOMGUDdLS0oKdnR3Rlo9cS9Jbmxp/fU22rGlqaiImJga3b9/GkydPICcnB01NTYnyUyTKKXEJ154zFDfQ0k5Up2Nvbw9NTU14e3vj6tWrcHV1RVBQEDIzM3H16lWJUVNZ0tPTw59//imRaNXX1%2BM///kP0tPTicUCAL/88gt27doFGxsbJil/8OABTp48iaVLl8LR0ZFYLPr6%2Bqivr0d9fb3U82zUjOSKZ8%2Be4YMPPmA7DABAdnY2fvvtNzx58gR8Ph%2BampqYPXs2K0m6g4MDBg8eLHVq/J9//sGRI0eIx/QmbJdTEggEUFBQAAAYGRnh%2BvXrMr8ml54zFDfQZJTqdEaNGoXU1FTw%2BXx4enpCJBJhy5YtEAgEGDduHP744w9isVhaWsLX11diJPLmzZtYs2YNzp49SyyWJsnJyRIjbra2thKjt7J27dq1Vs8bGxsTiqRx%2BcLhw4fx%2BPFj1NbWSpwnXYBfX18ff/75J/ElLtKIRCLU1NQwfdjLy8uhrKzMSiOHvLw8uLq64u7du1Knxkk2tGir5kX7ZWX9%2BvXw9fWV%2BJnk5ubC3d29Tb3iO0pCQgLxvyUU99FpeqrTkZeXZ/4op6amwsPDA0Dji6pAICAay5dffoklS5a0OBLJBjMzM%2BL1VqVpLdlctWoV0WTUxcUFDQ0NMDY2Jr6uWBo7Ozvs378fjo6O6N69O2tx5ObmYvny5fj222%2BZBOPEiROIiIhAcHBwqx2RZKGtU%2BNcQqKc0uPHj%2BHk5IR9%2B/ZBWVkZIpEIwcHBCAkJwbx582R%2B/ea8vb0xceJEdOvWjeh1KW6jI6NUp%2BPi4oJevXpBQUEBiYmJSE5OhoKCAr7//nukpKQgMjKSaDxsj0RytR%2B8UCjEL7/8gpycHNTV1THHi4uLcffuXaIj2Pr6%2BkhNTWU18WvOxsYGxcXFqKyshIqKisSI15t6tHeUuXPnYty4cXBwcGBKKdXV1eHIkSOs/C69i0hM0wsEAmzevBk5OTnYsGEDdu/ejdLSUmzbtg1GRkYyvfbrfv75Z1y/fh0zZ86UuhacdHk9ihvoyCjV6Xh7e2Pv3r0oKytDcHAwFBQUUF5ejt9%2B%2Bw179uwhHg/bI5HNXwhb62pEugi/j48PLly4gFGjRuHMmTOwsrLC7du3oaioiODgYKKxGBoaIi8vjzMjbPb29myHAAC4c%2BcOfvrpJ7GEQlFREfb29sR/RlTLFBQUsG3bNoSFhcHR0RETJkzAkSNHWBmd9Pb2BtA4Xd%2BErcYEFHfQkVGKIoyrI5FcM27cOPz666/o27cvs65OJBJh165d6NevH%2BbOnUsslqKiIjg5OUFXVxdqamoSibmLiwuxWLjE3NwcPj4%2BEqNrFy9exJYtW6SWdKPEyWpkNCoqSurxixcvIjMzE66ursya4zlz5nT49VvSVE%2B5JVyqvEKRQ0dGqU5HKBQiLCwMsbGxKC4uxvXr11FVVQV/f3%2BsXbtW5msCuToSCTRuPgkODsa6desAAEePHkVUVBT69%2B8PDw8PohtAamtr0bdvXwCN63zr6uqgqKjIrLElmYx6enri0aNHkJOTw99//y12jsfjEUlG161bh%2B3btwMAVq9e3eptSZXIWbZsGZYsWQITExP069cPDQ0NePjwIa5du4Zdu3YRiYGS7uDBgy2e4/P5OHToEIDG5y/JZDQ0NBSenp7Erke9G2gySnU627dvx7Vr17BkyRJs2rQJQOOaqvv372Pbtm0y7wjVvHQU19qBenh4QCgUAmgsv7Jz5054eXkhJyeHeIeqIUOGIDAwEEuXLsWAAQNw/PhxzJ8/H0%2BfPkV1dTWxOAAgLS0Np06drwaLXAAAFt1JREFUYhoTsKGp/A5AtsZqa2xtbTFw4EDExsbi8ePH4PF4%2BOijj/Dtt99yZkkDFzUvpySrn2VycrJM7re9Ll%2B%2BjLy8PFY6zFHcRafpqU7HxMQEx48fh4aGhtgU2bNnz/Dpp58S2/wBcGskEgBGjx6NxMRE9OjRA9u2bUNJSQl27dqFmpoamJmZ4cqVK8Riyc7OxqpVqxAbG4srV67gm2%2B%2BgaKiImprazF//nxs2LCBWCyff/45vv/%2Be%2BKF/1uSlZUFXV1dtsOgWsGlckoCgQAHDhzAuHHjmK5ucXFxuHfvHlxcXIi%2BuQkJCUF8fDwmTJgAdXV1dOkiPiZGcpSW4g46Mkp1OgKBgJn%2Bba5r166oqqoiGguXRiIBoKGhgelt/vvvv8PZ2RlA46jcq1eviMYyYsQInD9/HgAwZcoUxMXF4fbt29DQ0ICenh5zOxIdbBwcHLBy5UrY2NhATU1Nor7nuHHjZHr9182dOxcffvghLC0tYWlpSXQUkovLBbiIS%2BWUfHx8kJOTI1ahY9CgQYiMjISfnx%2BzqYiEpq5yp0%2BfljhHeskAxR00GaU6nU8%2B%2BQSHDx%2BGk5MTc%2BzVq1fYtWsXdHR0iMZy7do1JCYmAmjszTxlyhTMnDkT5ubmrOyw19HRwYEDB6CkpITi4mJMnDgRQOPOV7ZLrnz00UdSO7SYm5vLvDRO00YyaYX42dgBnJqaiqSkJCQmJiI8PBzq6upMYirrdrZcXC7AReHh4di8eTPmzp0rVk4pLCyMeDmlxMRExMfHo3fv3syxjz/%2BGMHBwbC2tiaajHJ1%2BQDFLjpNT3U6ubm5TFvLkpISDB48GHl5eejTpw%2BCgoIwdOhQYrEYGRnh2rVr4PF4sLa2hrOzMywsLCAUCjFq1KhWNzjJwqNHj%2BDj44OKigq4uLjA1NQUL168wPTp07Fv3z6MHj2aaDxtQaKDDZdVVVXh0qVLOH/%2BPH7//Xf07dsXsbGxRK5Nlwu8WVhYGAICAjBhwgT4%2B/uzUk5p9OjROHv2LHr16iV2vLi4GJaWlsTbDtfU1ODChQsoKirCV199BQAoLCyUOmNFdQ50ZJTqdIYNG4bjx4/j5s2bTG9kFRUVDBo0iGgiCnBvJLJ///5iG6wAoFevXrh06RInOg9JI6uqA803mTQvui8NmyOE3bp1wwcffIC%2BfftCTU0NhYWFxK7N5nIBLpJWTklZWRmmpqbIzMxEbGwsK%2BWUpk2bBmdnZ9jb20NDQwMikQgPHz5EaGgorKysiMUBABkZGVi2bBlUVFTw9OlTfPXVV8jPz4e1tTWCgoIwZswYovFQ3EBHRqlOJy4uDl5eXsjIyMCrV69ga2sLHo%2BH8vJyuLu74/PPPycWC9dGIvPy8rBz505mreqOHTsQFRUFbW1t7Ny5U%2BZTwP%2BGrOo0Nr/fYcOGSU162SrUXVdXh9TUVCQmJiIlJQVCoRBTpkyBubk5xowZI7EpRFbKysqY5QJpaWlElwtwUVuX1vB4PKI1WGtqauDv74/Y2FhUVFQAAFRUVPDZZ59h9erVYksvZG327NmYOXMm5s%2BfLzarkZCQgLCwMJw4cYJYLBR30GSU6nQsLCzg4eGBsWPH4pdffsHRo0cRExODe/fuYdWqVYiPj2c7RNTW1rIyEmlvbw9NTU14e3vj6tWrcHV1RVBQEDIzM3H16lWJUVMukFUymp6ezuw8lrZWtDljY%2BMOv35r9PX1oaioiMmTJ8Pc3BwmJibEEtCWsLlcgGq7srIyyMnJoWfPnqxcX09PD%2Bnp6ejSpYvY725DQwMMDQ2JL02iuIFO01OdTmFhIcaOHQsAuHTpEiwtLSEvL4%2BhQ4eioKCAaCxcG4nMyspCUFAQgMbdrhYWFjAyMoKenh5CQ0OJxsK2pkQUAGJjY%2BHn5ydxm8rKSqxdu5Z4Mrp3715OJKDNsblcgIvYLqeUlpbGTHm/qVwdyWoQH3zwAZ4%2BfSpRZzQ7O5up5EF1Ptz5S0ZRhPTu3RtFRUVQVFREWloa3NzcADS2fOTz%2BURj8fT0ZP4oX716FcePH0dISAgyMzOxdetW4iOR8vLyTF3E1NRUeHh4AGicjhYIBERj4YK8vDw8evQIcXFxsLS0xOsTSY8fPyZal7aJvr4%2BwsPDcf/%2BfdTW1kqcJ1VSqaXlAu7u7p1%2B7R/b5ZSWLl3KTIE3bdiUhvQyExsbGzg5OWHx4sVoaGhAYmIicnNzcfToUeIlryjuoMko1enY2dlh1qxZkJeXx%2BjRozF06FBUVlZi5cqVMDc3JxoL10YijYyM4O3tDQUFBdTV1TEjJkeOHOHU5hQSHWyAxsoL%2B/btg0AggIODg8R5JSUl2NnZyez6LVm1ahVyc3NhaGiIrl27Er9%2Bk9GjRzPLBbZv38650Vo2sV1OqXmFidzcXJle6224urqiR48eiIiIAI/Hw4YNG6CpqYlVq1Zh1qxZbIdHsYT%2B1aA6nSVLlsDIyAgVFRXM6A2fz8fEiROxePFiorFwbSTS29sbe/fuRVlZGYKDg6GgoIDy8nL89ttv2LNnD9FY2trB5vr16zKLYerUqZg6dSpsbW3btP6RRAF%2BoHE965kzZ6Cmpibza7WGi8sFuEIoFErd9CYQCKSOZne0hw8ftul2PB4P/fv3l2ksCQkJzAjxyZMnsXjxYuJ/ayluoxuYKIpFLi4u6NWrFxQUFJCYmIjk5GQoKCjg%2B%2B%2B/R0pKCiIjI9kOkTXz5s0Dn89vsYNNUwtVLpHVZqrXWVhY4Pjx45xYYxcfH4/Y2FgUFxcjJiYGdXV1iIiIgL29vczKbr0LNm3ahAcPHkgtpzRixAiZj4w2VYBoeolv/nHzz0lM0%2Bvr6%2BP48ePQ0tKCkZER0tPTJZa8NKGNFDonmoxSFItKSkqwd%2B9eVFRUwNHRETo6OigvL4ednR327NlDvO6pUChEWFgYk1xcv34dVVVV8Pf3x9q1a4nu8BcIBNi8eTNycnLEOths27aNeAebtiJVgP/y5ctISEiAo6Mj%2BvXrJ5H0kXpBDwoKQlRUFObMmYOQkBBkZWXh%2BfPnWLx4MSZPnoxvvvmGSBxcxHY5pfz8fLHPr1y5AgMDA2ZdfEFBAe7fv4/x48dDQ0NDprGsWLEC586da9ObE9Jl0ihuoMkoRVEMPz8/XLt2Dfb29ti0aROysrLw4sULuLm5YcCAAfDy8iIeExc62LQVqZHRUaNG4dWrV2hoaJB6ntQLuqmpKUJDQzF48GCx7z0vLw9ffvklLly4QCQOrmO7nFJERAT27t2L/fv3M0uTkpKSsGHDBri6umLBggUyj%2BH27dt4%2BfIl7O3tcfjw4RZvR7oyBcUNNBmlKBZxaSQSAExMTHD8%2BHFoaGiIJRfPnj3Dp59%2BKvOd49I62ADAxYsXkZmZCVdXV1Y62LQVqWSUK3VPDQwMkJGRAUD8e6%2BtrYWxsTGRx4JLuFpOyczMDEFBQRKbEO/evYtly5YRLcB/5coVmJiYELse9W6gq84pikXbt2/HtWvXsGTJEmzatAlA4/T0/fv3sW3bNuIjkQKBQGp/6K5du6Kqqkrm1z948GCL5/h8Pg4dOgSgcb0bF5NRUponm2VlZWI7tkkaMmQI4uLiMGPGDLHjhw8f7pQdmLhaTqmsrAwfffSRxPF%2B/fqhpKSEWBxA4xteus6Yeh1NRimKRfHx8cxI5ObNmwE09oLftWsXPv30U%2BLJ6CeffILDhw/DycmJOfbq1Svs2rULOjo6Mr9%2BcnKyzK/xv6Cqqgrfffcd4uLiUF9fj5ycHLx48QJr167Ftm3b0KdPHyJxuLm5wdnZGZGRkRAIBFi2bBnu3r2L8vJypmRZZ8LVckoGBgYICAiAs7MzevToAQB4/vw59uzZAz09PaKxvL7OGAAqKioQExODly9fdup1xp0ZnaanKBYZGRnh6tWrkJeXF5vmrKysxPjx44m3xsvNzWVGdEpKSjB48GDk5eWhT58%2BCAoKIrqhiu0ONv8GqWn6tWvXori4GM7OzrC3t0dWVhaqqqqwefNmiEQiBAQEyDyGJoWFhTh16hTy8vLA5/OhpaUFKysr9OrVi1gMXMGlckrN5eXlwdXVFXfv3oWysjIaGhpQVVWF4cOHIyQkBKqqqsRioeuMKWnoyChFsYjtkcjXDRs2DMePH8fNmzfx5MkT8Pl8qKioYNCgQcR39rPdwaatSBXgby4lJQWnT59Gnz59mGnN7t27w9PTE9OnTycSQ5O%2Bffu2OiXdmVhYWHCmnFJzmpqaiImJwe3bt/HkyRPIyclBU1OTlUYWL1%2B%2BxODBgyWOq6qqorS0lHg8FDfQZJSiWLRu3To4OjoiPDwcdXV1mDFjhthIJGlxcXHw8vJCRkYGXr16BVtbW/B4PJSXl8Pd3R2ff/45sVjY7mDTHBcK8DfH4/Gk1hgVCoUyL6huZmbW5nV9JDfGcMHr329r5ZTYMHz4cAwfPpyVazeh64wpaWgySlEs4tJIJAAEBwdj//79AIDY2FgoKSkhJiYG9%2B7dw6pVq4gmo2x3sGnu8ePHcHJyarEAP2n6%2BvrYsWMHvv32W%2BZYfn4%2B/Pz8ZL6TfsmSJczHJSUlOHbsGKZOnYr%2B/fujoaEB9%2B7dQ0pKCuzt7WUaBxc1r9cprZxSbm4udu/ejfr6eiLllLiIrjOmpKFrRimKRVwaiQQak5ymdarLly/HiBEjsGzZMolzJLDdwaY5rhXgLygowPLly3Hv3j3U19eje/fuqK6uhr6%2BPvz9/Ym0JAUABwcHuLm5QVdXV%2Bx4eno6goKCWq0n%2Bb%2BOS%2BWUuKaoqAinTp1i3oB35nXGVCM6MkpRLOLSSCQA9O7dG0VFRVBUVERaWhrc3NwANL54NE01krJx40b4%2B/tj/fr1UjvYkKSgoIBt27YhLCwMjo6OmDBhAo4cOcJaAX51dXXExMQgOzsbeXl5UFJSgpaWlsRavKdPn8o0Mc3IyJC67lBXV5f45juu4VI5JS45dOgQnJyc4ODgwHYoFIfQZJSiWFRYWIixY8cCAC5dugRLS0vIy8tj6NChKCgoIB6PnZ0dZs2aBXl5eYwePRpDhw5FZWUlVq5cCXNzc6Kx8Pl8bNy4ERs3bmSlg420AvzKysowNTVFZmYmYmNjWS/AP2LECIwYMaLF8%2Bbm5jLd3a%2BlpYX9%2B/dj6dKlzBrWyspKhIaGol%2B/fjK77ruAS%2BWUuCQ8PByff/45sfJj1LuBTtNTFIvMzMzw888/Q1FREWZmZvjll18wdOhQFBUVYebMmUhLSyMe040bN1BRUYExY8ZAUVER9fX1OHz4MBYvXizzftpc6mBjZmbWptvxeDzOTrnq6uqK1b7saDdv3oSbmxuePXsGFRUVCIVCVFZWQkVFBQcOHIChoaHMrs11XCqnxCVHjhxBUlISLC0toa6uLrEpkGRnKoo7aDJKUSz6/vvvERERAXl5eQwbNgwhISGorKzEkiVLMHToUHh6erIdIlHNk6fWys6QLo3zriJR97ShoQE5OTkoLCxEXV0dVFVVMXLkSLFWtrJeLsBlXCinxCX095qShiajFMUyNkciqbZ5FwvwA%2BSK8L8rcVDvjs78BqYzomtGKYpl%2Bvr6Yp936dJFrHxOZ8LVDjbvSgF%2BrqJjHtTbkvV6Z4pbaDJKURRncLWDDZcK8L%2BL2lokn6Ka0DcwnQtNRimK4gyudrDhUgF%2BiuoM6BuYzkWO7QAoiqKaaGhoMP%2BSk5Px3Xffobi4mDlWUVGB3bt348KFC0TjmjZtGpydnZGUlITc3Fzcvn0bCQkJ%2BPrrr2FlZUU0ljcRCATMx1xdy0pRFNUc3cBEURQncamDTU1NDfz9/REbGyu1AD/pjWbr16%2BHr6%2BvRFmc3NxcuLu74%2BTJk0TjeRO6gYl6W/Q507nQaXqKojiJSx1s2C7A/7rHjx/DyckJ%2B/btg7KyMkQiEYKDgxESEoJ58%2BaxFhdFUdS/QZNRiqI4ie0ONlwqwP%2B68PBwbN68GXPnzsWGDRuwe/dulJaWIiwsDEZGRkRjaYlAIGBGjOlyAYqiWkOn6SmK4iS2O9i8CwX4w8LCEBAQgAkTJsDf3x/dunUjev13bbkAxW3N38AYGRnh%2BvXrLEdEkUKTUYqiOI12sGkUFRUl9fjFixeRmZkJV1dXyMk17kmdM2cOkZjmzZsHPp/f4nKBdevWEYmDenfQNzCUNDQZpSiKkoJrBfjNzMzadDsej0dsc5dAIMDmzZuRk5Mjtlxg27ZtnFkuQHELfQNDSUOTUYqiKCmGDRvGyQL8XMT2cgHq3UHfwFDS0GSUoihKivz8fLHPWyvAr6GhQTQ2gUCAAwcOYNy4cRg1ahQAIC4uDvfu3YOLi4tMNwxxcbkA9e6hb2Co5uhueoqiKCmaJ5gRERHYu3cv9u/fz%2Bywz83Nxe7du1FfX48FCxYQjc3Hxwc5OTmwtLRkjg0aNAiRkZHw8/OTaXvSgwcPtniOz%2Bfj0KFDABpHjmkySgHS38AoKyvD1NQUmZmZiI2NpW9gOjk6MkpRFPUGXCrADwAmJiaIj49H7969xY6XlZXB2toav//%2BO9F4KKo1XFzvTHELHRmlKIp6Ay4V4AcAoVAotXe3QCBAbW0tsTjYXC5AvTuSk5PZDoHiONqbnqIo6g2aCvC/fPmSOfb8%2BXNs3bqVSAH%2B102bNg3Ozs5ISkpCbm4ubt%2B%2BjYSEBHz99dewsrIiFoePjw8uXboEFRUV5tigQYNw7do1%2BPn5EYuDencIBALs2bMH6enpzLG4uDgEBASgrq6OxcgoNtFpeoqiqDdguwD/62pqauDv74/Y2FhUVFQA/9fe/bo0F8VxHP/sBjH4K1mmIIK6E5S5IogWm0azSZPI0ppgMZn8gSAGg%2BBfYbCJYhODykAHbhpEWdIk%2BoSHjU0vm88j955z8P1K22VwTxjj8z33c%2B8kdXR0aHZ2Vrlcrvrg8KhRF8C/qtxJv7a2psHBQUnS5eWlVldXNTQ0FGnfGe4ijALAN7n4AP5yuawgCNTZ2Rn7ucfGxnR4eKiurq6644%2BPj5qZmanb/QIkBhiEozMKAN9kjJExxsq5T09Pq3fyHx8fN/zsxMREHEuq1gXm5%2BeVTCb18fGhQqGgvb29WOsC8IcrfWe4hZ1RAPDAyMiILi4uJKnhjmycD%2BF3pS4Af6ysrOj29jZ0gBkeHuYy/S9FGAUA/JjNugD8wQCDMIRRAPBAoVD41ucSiYT6%2BvoiW4eLdQH4iQEGFYRRAPBAKpVSIpFQ5Se79nXt%2B6gv07tYF4DbGGDQDGEUADxwf39f9/7k5ESZTEatra2SpIeHB93c3GhycrLur0wB2xhg0Ax30wOAB2oD5sHBgba2trS9vV3dcbq%2BvtbGxobe3t40NzcX2TpcqQvAH5UgKv39ngKfsTMKAJ6ZmprSzs7Ol12mfD6vxcXFSP/f25W6APzBAINm2BkFAM%2BUy2X19/d/Od7T06Pn5%2BdIz/056DaqCwCSND09zQCDhgijAOCZTCaj9fV1LS0tqb29XZL09PSkzc1NpdPpSM/tSl0A/mCAQTNcpgcAzxSLRWWzWeXzebW1ten9/V0vLy8yxmh3d1fd3d2xrMNmXQB%2BChtgjo6OtLy8rGw2ywDzSxFGAcBTV1dXuru7UxAE6u3tbXinchRGR0d1dnamlpaWuuOvr68aHx/X%2Bfl5rOuB%2BxhgEIbL9ADgKWOMjDHWzm%2BzLgA/2ew7w13sjAIA/osrdQH4Y2FhQQMDA6EDTKlU0v7%2Bvt0FwgrCKADgR2zXBeAPBhiEIYwCAIBYMcCgFmEUAAAA1gS2FwAAAIDfizAKAAAAawijAAAAsIYwCgAAAGsIowAAALCGMAoAAABrCKMAAACw5g/oqNra1nlSKQAAAABJRU5ErkJggg%3D%3D\" class=\"center-img\">\n    <img 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z8bI83bdqUy5cvc/ToUbvlRqgYleIxeqHuhx%2BGw4ehYUMweqvB7duNtQdwc4M2beC778Dkc2DKMpeDNboYau/snLerEhPB6JdfX19j7c1m%2BfILYwHMfM106GCsPYCTE5hwD/n0q86GY1gs8OCDcPkyGD0BzNl4Ori4QFaW8Ti5ucbaWyzg6gpXrxrvFwcTjmea1S8ArpdTjAVwcIBKlSAtzXhHV65srH1JldINLGLmz2fhwoV2y8LCwggPDzftOS5evIjVasXDw8NuuYeHBykpKXh6euLu7m53k478dVNSDO77G6gYlT%2BWpyc4Oub9Lg%2BcnPLeSJzKwZ9COcrFwSEvFTM%2B%2BMxisRj/IDdFOdpPQKl9EJaExfKfn7LeV%2BUpl/x8yoPy1i92CYmdfv36ERQUZLfM29u7VJ7rdtOH/oipReXk3VRERETkLlRK39p9fHyKdEjeCE9PTxwcHEhNTbVbnpqaSpUqVfDy8iI9PZ2cnBwcHR1tjwFUqVLFtDzK0biHiIiIyF3GwaF0fv4Arq6uNGzYkPj4eNuytLQ0jh8/TvPmzWncuDFWq5WEhATb43FxcVSqVImHH37YtDxUjIqIiIjcJ86ePcuTTz5pu5Zo//79Wb16NUeOHCE9PZ3Zs2fTuHFj/P398fLyonv37sybN48LFy5w5swZFi1axNNPP42Tiacq6TC9iIiISEmVp5Prr/P39wfg2vXJjVu3bgXyRjWzs7NJTEwk6/ostmeeeYbk5GQGDRpERkYGrVu3tps49cYbbzB58mS6du2Ks7MzvXr1uuOF9ItLxaiIiIjIPSQuLu6Wj9WqVYuDBw/a/m%2BxWIiIiCAiIqLQ9StWrMg//vEP03O8kYpRERERkZIqhyOjdxsVoyIiIiIlpWLUMPWgiIiIiJQZjYyKiIiIlJRGRg1TD4qIiIhImdHIqIiIiEhJaWTUMPXgPeLHH3/E39/fdt2wm61bt67APW5FRETEoLv4DkzlhUZG7xGBgYG3va6YiIiISHmkYlRERESkpO6zUczSoGLUoJMnT9K1a1c2b95MgwYNAJg9ezb79u1j%2BfLlTJkyhR07dpCZmYmfnx8TJ06kWbNmAGzevJlly5Zx7NgxqlSpwgsvvEC/fv0AeOWVV3B0dOT48eOkpKTw6aef3jaP77//nueee46ff/4ZV1dX9u3bx%2Buvv87x48cJCAjg0UcfLfa2JSUlkZycbLfM%2B%2BGH8fH0LHYsm0aN7H8b4e5uPMaDD9r/Lksm5uLqaqy9i4v973uK0ddNeXrNmMiMz1OL5T%2B/jcbLj2VGLkYpl9twdDTWPv%2BFYvQFk5NjrL2UKRWjpWjVqlWcO3eOLVu24OLiQlRUFK%2B//joff/wxcXFxvPbaayxYsIC2bduyZ88enn/%2BeRo2bEjLli0B2LZtGzNmzKBz587Fet6cnBwiIiLo2bMno0ePJiEhgfDwcJycire7Y2Ji7O5PCxA2ejTho0cXK06h3n3XeAwzNW1a1hn8hwm51DOeBQA1apgUyCSmfIAGBpoQhPL1mnF2NhziQeMhbB54wLxYRpnQNaYx%2BiXRTKb1i0slc%2BIY/ZKYkmJOHiWhkVHDVIyWorS0NJydnXFzc8PJyYnQ0FBCQ0MB%2BOijj%2BjcuTMdOnQAoFWrVvTo0YONGzfaitGaNWvSpUuXYj/v/v37SUpKYuTIkbi6utKiRQueeOIJvvjii2LF6devX4FJT969e8OqVcXOyaZRo7xCdMAASEgoeRyAZcuMtYe80a2mTSE%2BHi5fNh6vnOTym7exgsvFJa8QPX0abjEnrsjq1jXWPp/FAlarCXF%2B%2BtFYADNfM3/6k7H2kFdVZGcbDnM523h1YrHkFaJXrhjfV8X87lxoLvldYzSX3Fzjubi6wtWrxnMxY8TZrH4BcMlMMxbAwSGvEE1PN97RZUXFqGEqRkvRgAEDGDZsGJ06deLxxx%2BnW7dudO3aFYDjx4%2Bzc%2BdO/P39betbrVZbcQp5xWhJnDlzhkqVKlGxYkXbsnr16hU7jo%2BPDz4%2BPvYLExNLlFMBCQmwZ4%2BxGOnp5uQCeUWFmfGMMCGXqyYNVmRl5X2A3lPM2s/l6TVjAjPqgPzPZKvVeDwzCqX8OEZjKZfbMOvweG6uDrXfx1SMloKc639QtWrVYvPmzXz//fds376dSZMm8cknnzB//nzc3Nzo378/r7/%2B%2Bi3jOJbwXJysrCxbDvly79ZvnCIiIuWZRkYNUzFqkOv1k4AyMzNty06cOAFARkYGzs7OtGvXjnbt2jFkyBCCgoJISUmhTp06BS7FdObMGby9vUtchObz8fEhPT2dS5cu2UZHjxw5YiimiIiISGlQOW%2BQl5cXFStW5PPPPycnJ4evv/6avXv3AhAREcHMmTNJT08nNzeXPXv24OnpiYeHB08//TS7d%2B/mww8/JCsriwMHDtC3b19iY2MN59SiRQs8PDxYsWIFWVlZ/PTTT8U%2BX1RERESKQBe9N%2Bz%2B2tpS4OjoyOTJk/n4449p1aoVGzZs4NlnnwUgMjKSY8eO0bFjRwIDA1mzZg2LFi3CwcGBBg0aMGfOHFasWEGrVq0IDw9n2LBhBAcHG87Jzc2NRYsWsW3bNgIDA1m4cCFDhw41HFdERERuomLUMIvVatbp0HJfMHptnYAA2L0bWrY0PoFp%2B3Zj7SFvFmdgIPz4Y9lPRjExl4M1in8Vhhu5ukK9evDbb8YnMPn6Gmufz7TZ9F8aPEpg5mvmhgmLJWbSbPr0q8Zn0zs45F1s4PJl4xOYjF56yGLJuypEVlb5mE3v5gaZmeVjNr1Z/QLgetngJZUcHaFSJUhLMz6BqXJlY%2B1Lqnbt0ol7/ZS/%2B4HOGRUREREpqftsFLM0qBi9C5w7d%2B6O1xvVfelFRETkbqRi9C5QtWpVFZsiIiLlkUZGDVMxKiIiIlJSKkYNUw%2BKiIiISJnRyKiIiIhISWlk1DD1oIiIiIiUGY2MioiIiJSURkYNUzEqIiIiUlIqRg3THZikeIze497Mu9cEBRlrD%2BbeEao85WL0ljFg3i2P0tKMx3BwgIoV4dIl49vm6WmsvYn7yZprvH9NuzNVlsFbbeUnY9LtfbIdXA2nY9LNqXAyYdjGtP2EGUFMSgbIvmbwrnyYt5%2BM3rWrxBo3Lp24Bw6UTtxySCOjIiIiIiWlkVHD1IMiIiIiUmY0MioiIiJSUhoZNUzFqIiIiEhJqRg1TD0oIiIiImVGI6MiIiIiJaWRUcPUgyIiIiJSZjQyKiIiIlJSGhk1TMWoiIiISEmpGDVMPSgiIiIiZUYjoyIiIiIlpZFRw1SMioiIiJSUilHD1IMiIiIiUmY0MioiIiJSUhoZNUw9WMqGDh3KvHnzSv15NmzYQFBQ0C0fnz17NoMGDSr1PERERKTsnTp1ihdeeIHWrVvTpUsXZs2aRW5uboH1hg4dir%2B/v91P48aNWbhwIQCDBg2iadOmdo8/9dRTpuaqkdFSFh0d/Yc8T58%2BfejTp88f8lwiIiJyXTkdGQ0PD6dp06Zs3bqV8%2BfP8%2BKLL1K1alWGDBlit97NdUpaWhrBwcE88cQTtmWRkZH85S9/KbVcy2cPioiIiNwNHBxK58eAuLg4EhISGDt2LBUrVqRevXoMHjyYmJiYO7adN28eTzzxBH5%2BfoZyKI77cmR0%2BfLlrFu3jgsXLlCtWjVCQ0MJCQkhISGBGTNmEB8fj5OTE7169WLChAk4Oztz7tw5Jk%2BezE8//UR2djbNmzcnMjKS2rVrk5iYyNSpU9m/fz8Wi4XWrVsTGRlJ5cqVGTRoEC1atGDs2LEArF%2B/nlWrVnH69GmqV69OREQEwcHBQN5QePv27Tly5Ajbtm2jQoUKjB07lpCQkDtu00cffcScOXP45ptvANi%2BfTszZ84kKSmJTp06UbVq1WL3U1JSEsnJyXbLvDMz8SlBLJsHH7T/bURAgPEYjRrZ/y5L5SkXM5kxapAfw4xYRl839%2Bp%2BsljMi2FGLJH7XKGfwd7e%2BPj43LFtfHw8NWvWxMPDw7asadOmJCYmkp6ejru7e6Htjh07xoYNG9i6davd8s2bN7NixQp%2B//13WrRowRtvvEGdOnVKsFWFu%2B%2BK0d27d7N69Wree%2B89qlevzjfffEN4eDiBgYEMHz6cQYMGERUVxdmzZwkNDeWdd95hxIgRvP3223h4eLBjxw5ycnJ48803mTlzJgsXLiQyMpKWLVuyYsUKMjIymDBhAkuWLOHVV1%2B1e%2B7t27cza9Ysli1bRosWLdiyZQvjxo2jQYMGtm8ga9euZfr06UyfPp2lS5fyxhtvEBwcjLOzc5G3MS0tjTFjxjB27Fj69evHzp07efnll2ncuHGx%2BiomJsZ2zki%2BsFGjCO/Ro1hxCtW0qfEYu3cbj5Hv3XfNi2VUecrFjKKiYkXjMfJVqGA8hlmvGxP2k1klmym1n4uLCUGuK8b71S1DmJAGmJKKacyp0U161Zj0hcGs/jUaJzvbnDxKpJQO0xf6GRwWRnh4%2BB3bpqamUqlSJbtl%2BYVpSkrKLYvR5cuX89e//hUvLy/bsgYNGvDAAw8we/ZscnNzmTZtGsOHD%2BfTTz/FxaT3jfuuGL106RIODg64ublhsVjo0KEDu3btIjY2FqvVyosvvghA7dq1GTZsGMuWLWPEiBGkpaXh6emJi4sLFouFKVOm4HD9BZiWloabmxtOTk54eHiwePFi22M3%2BuCDD%2BjVqxetWrUCIDg4mOjoaGJjY23FaEBAAI8//jgAPXr0YOHChSQlJVGzZs0ib%2BPXX3/Ngw8%2ByLPPPouDgwOdOnWiVatWZGRkFKuv%2BvXrV2BSlPfx4/Djj8WKY%2BfBB/MK0fh4uHy55HEAru8rQxo1yisqBgyAhATj8cpLLrt2Gc/HYgGr1Xic9HTjMRwc8grRjAwo5AT8YunUyVh7E/eTdZfxwtis3WTJzjIhiCWvqsjONpxUtsX4h1x%2BKkY5mfBJadp%2BwowgJiUDZF8zXtSatZ/uNYV%2BBnt7F7m9tZj7ODU1lY0bN/Lvf//bbvmUKVPs/v/GG2/QunVrdu3aRdu2bYv1HLdy3xWjbdu2pUmTJgQFBdG2bVs6duxISEgIJ06c4Pz58/j7%2B9vWtVqttqp/%2BPDhjBw5kq%2B%2B%2BooOHTrQo0cP204ICwtj3LhxbNiwgQ4dOtCrVy%2BaN29e4LlPnjxJmzZt7JbVrVuXU6dO2f5fq1Yt27/d3NwAyMzMLNY2njlzhurVq9sVxPXq1SM%2BPr5YcXx8fAoeDkhKMqe4uHzZeJw9e4znkS8hwdx4RpSnXMxgtHi8OZbReGb17b22n0wqTmyxjMbTkX65W5TSyGihn8FF5OXlRWpqqt2y1NRULBaL3ajnjbZt28bDDz9M7dq1bxvb3d0dDw8Pzp49W6LcCnPfTWBycXFh6dKlrF%2B/nmbNmrF27VpCQkJwdXWlYcOGxMXF2X7279/P7uuH9Pz9/dm%2BfTuvvfYaVquVsLAwZs6cCUDnzp358ssvCQsL4/z58wwcOJA1a9YUeO6srMJHHiw3HC4pbES1uLKyssjJybFbVtjlHERERMSgcjiBqVmzZvz%2B%2B%2B9cuHDBtiwuLo5HHnmECrc43Wnbtm20b9/ebll6ejpTpkyxKzwvXLjAhQsX7li0Fsd9V4xmZ2eTnp5Oo0aNGDVqFBs2bMBisVCpUiVOnDhhdyg7JSWF9Oujd6mpqTg7O9O1a1ciIyNZsmQJ69evt61XoUIFgoODmTNnDlOnTi10xlqdOnU4evSo3bKjR4%2BaukMh79vU2bNn7Ybojxw5YupziIiISPnUpEkT/P39mTNnDunp6Rw5coSVK1fSv39/AJ588kl%2B%2BuknuzYHDhywOzoLeaOg%2B/btY9q0aaSmpnLx4kWmTp2Kn58fAWZMIr7uvitGo6Ojef755zlz5gyQV6RdvHiRBg0a4OXlxcyZM0lPTyc5OZnRo0cze/ZsAJ555hmioqK4evUq2dnZ7Nu3j7p165KZmUn37t3ZuHEj165dIzMzk/j4%2BEJnmYWEhLBp0yb27t1LdnY2H330EYcPH6Znz56mbmO7du1IT09n/fr1ZGVlsXXrVvbt22fqc4iIiAjlcmQUYP78%2BSQlJdG%2BfXuee%2B45%2BvTpw4ABAwBITEzk8k3zNpKTkwu98s6iRYuwWq10796dzp07k52dzfLly005kpvvvjtndMiQIZw%2BfZo%2BffqQmZlJ9erVGTt2LM2bN2fx4sVMmzaN9u3b4%2B7uTteuXZkwYQKQd92tqVOnsmTJEpycnPD392f27Nm4ubnx9ttv89ZbbzF58mTc3Nxo1aoVkyZNKvDcPXv25NSpU4wfP55z585Rv359oqOjqVevnqnbWK1aNebMmcP/6ix8AAAgAElEQVTs2bOZOXMmHTt2ZMCAAey5l85vExERkVuqVq0aUVFRhT528ODBAsv2799f6Lo1atQoMKvfbBZrcadbyf3tiy%2BMtXd3h8DAvBn5Ricw3eb2p0UWEJB3qZ%2BWLct%2BMoqZuZhxjrBZM27T0ozHcHDIu0TUpUvGt83T01h7E/eTNdd4/5o2SzvrqglBLHmXiMrKMj6b3sHVcDqaTV/KyVC%2BZtOX2WW8zLjcYWFumtV%2BL7vvRkZFRERETFNObwd6N1Exehf4%2BeefefbZZ2/5eI0aNYiNjf0DMxIRERExh4rRu0Dz5s2Ji4sr6zRERETkZhoZNUw9KCIiIiJlRiOjIiIiIiWlkVHDVIyKiIiIlJSKUcPUgyIiIiJSZjQyKiIiIlJSGhk1TD0oIiIiImVGI6MiIiIiJaWRUcNUjIqIiIiUlIpRw9SDIiIiIlJmNDIqIiIiUlIaGTVMPSgiIiIiZUYjoyIiIiIlpZFRw1SMioiIiJSUilHD1IMiIiIiUmY0MioiIiJSUhoZNUw9KCIiIiJlRiOjIiIiIiWlkVHDVIyKiIiIlJSKUcPUgyIiIiJSZjQyKiIiIlJSGhk1TD14B0OHDmXevHllnYaIiIjIPUkjo3cQHR1d1imQk5PD6tWrGTJkSIljfP755/j5%2BVG3bl0TMxMREbnPaWTUMPXgXeCXX35hxYoVhmLMnz%2BfY8eOmZSRiIiIAHnFaGn83EfuypHR5cuXs27dOi5cuEC1atUIDQ0lJCSEhIQEZsyYQXx8PE5OTvTq1YsJEybg7OzMuXPnmDx5Mj/99BPZ2dk0b96cyMhIateuTWJiIlOnTmX//v1YLBZat25NZGQklStXZtCgQbRo0YKxY8cCsH79elatWsXp06epXr06ERERBAcHAzBo0CDat2/PkSNH2LZtGxUqVGDs2LGEhITccZuuXLnClClT2LFjB5mZmfj5%2BTFx4kRyc3Pp378/165dw9/fn6ioKFq3bs2cOXPYtGkTaWlp1KtXj1dffZXAwEBbHs2bN2fHjh1Ur16dM2fOcPjwYUJDQ%2BnduzczZswoUj8nJSWRnJxst8w7MxOfqlWLs7vsPfig/W8jAgKMx2jUyP53WSpPuZjJjDfV/BhmxDL6urlX95PFYl4MM2KJyH3jritGd%2B/ezerVq3nvvfeoXr0633zzDeHh4QQGBjJ8%2BHAGDRpEVFQUZ8%2BeJTQ0lHfeeYcRI0bw9ttv4%2BHhwY4dO8jJyeHNN99k5syZLFy4kMjISFq2bMmKFSvIyMhgwoQJLFmyhFdffdXuubdv386sWbNYtmwZLVq0YMuWLYwbN44GDRrg5%2BcHwNq1a5k%2BfTrTp09n6dKlvPHGGwQHB%2BPs7Hzb7Vq1ahXnzp1jy5YtuLi4EBUVxeuvv87HH39MZGQkc%2BbM4ZtvvgFgw4YNbNiwgQ8%2B%2BABvb2%2BWLFlCREQEX3/9NY6OjgB89tlnzJ8/H39/fywWC35%2BfixevJiOHTsWua9jYmJYuHCh3bKwUaMI79GjyDFuqWlT4zF27zYeI9%2B775oXy6jylIsZRUXFisZj5KtQwXgMs143Juwns0o2U2o/FxcTglx3h/e7IoUwIQ0wJRXTmFOjm/SqMekLg1n9azROdrY5eZTIfTaKWRruumL00qVLODg44ObmhsVioUOHDuzatYvY2FisVisvvvgiALVr12bYsGEsW7aMESNGkJaWhqenJy4uLlgsFqZMmYLD9RdQWloabm5uODk54eHhweLFi22P3eiDDz6gV69etGrVCoDg4GCio6OJjY21FaMBAQE8/vjjAPTo0YOFCxeSlJREzZo1b7tdaWlpODs72/IIDQ0lNDS00HV79%2B5N165dqXj9Q75nz54sWLCA06dPU7t2bQCaN29O8%2BbNi9u9dvr160dQUJDdMu/jx%2BHHH0se9MEH8wrR%2BHi4fNlQflzf14Y0apRXVAwYAAkJxuOVl1x27TKej8UCVqvxOOnpxmM4OOQVohkZkJtrLFanTsbam7ifrLuMF8Zm7SZLdpYJQSx5VUV2tuGksi3Gi%2BP8VIxyMuGT0rT9hBlBTEoGyL5mvKg1az%2BVGRWjht11xWjbtm1p0qQJQUFBtG3blo4dOxISEsKJEyc4f/48/v7%2BtnWtVisu17/tDx8%2BnJEjR/LVV1/RoUMHevToQdu2bQEICwtj3LhxbNiwgQ4dOtCrV69CC7mTJ0/Spk0bu2V169bl1KlTtv/XqlXL9m83NzcAMjMz77hdAwYMYNiwYXTq1InHH3%2Bcbt260bVr10LXvXLlCtOnT2fHjh1cvHjRtjwr6z8fJncqfovCx8cHHx8f%2B4VJSeYUF5cvG4%2BzZ4/xPPIlJJgbz4jylIsZjBaPN8cyGs%2Bsvr3X9pNJxYktltF4OtIvct%2B468p5FxcXli5dyvr162nWrBlr164lJCQEV1dXGjZsSFxcnO1n//797L5%2BSM7f35/t27fz2muvYbVaCQsLY%2BbMmQB07tyZL7/8krCwMM6fP8/AgQNZs2ZNgee%2Bsdi7keWGwx2FjagWRa1atdi8eTOzZs3C3d2dSZMmMXr06ELXnTp1KvHx8axdu5a4uDg2b95cYJ38w/UiIiJSijSBybC7bmuzs7NJT0%2BnUaNGjBo1ig0bNmCxWKhUqRInTpwgIyPDtm5KSgrp10ffUlNTcXZ2pmvXrkRGRrJkyRLWr19vW69ChQoEBwczZ84cpk6dSkxMTIHnrlOnDkePHrVbdvToUduhcSMyMjLIycmhXbt2TJw4kffff5/Y2FhSUlIKrPvzzz/z1FNPUa9ePSwWC/Hx8YafX0RERKQs3HXFaHR0NM8//zxnzpwB4MiRI1y8eJEGDRrg5eXFzJkzSU9PJzk5mdGjRzN79mwAnnnmGaKiorh69SrZ2dns27ePunXrkpmZSffu3dm4cSPXrl0jMzOT%2BPh46tSpU%2BC5Q0JC2LRpE3v37iU7O5uPPvqIw4cP07NnT8PbFRERYcs9NzeXPXv24OnpiYeHB25ubly6dImzZ8%2BSmZlJrVq1iIuLIysri7179/LZZ58BebPfb8XV1ZVjx47ZinMRERExgUZGDbvrzhkdMmQIp0%2Bfpk%2BfPmRmZlK9enXGjh1L8%2BbNWbx4MdOmTaN9%2B/a4u7vTtWtXJkyYAMC8efOYOnUqS5YswcnJCX9/f2bPno2bmxtvv/02b731FpMnT8bNzY1WrVoxadKkAs/ds2dPTp06xfjx4zl37hz169cnOjqaevXqGd6uyMhIJk2aRMeOHbFYLDRs2JBFixbh4OBAmzZtqFWrFt26dWPmzJm8/PLLjB8/nscee4wWLVrw1ltvARAaGlro6QWQV4y/9dZbfPvttyxZssRwviIiIsJ9VziWBovVauZZ63LP%2B%2BILY%2B3d3SEwMG9GvtFR2ptm%2BpdIQEDepX5atiz7yShm5mLGpCGzZtympRmP4eCQd4moS5eMb5unp7H2Ju4na67x/jVtlnbWVROCWPIuEZWVZXw2vYOr4XQ0m76Uk6F8zaYvs8t4vfxy6cSdM6d04pZDd93IqIiIiEi5oZFRw1SM/gF%2B/vlnnn322Vs%2BXqNGDWJjY//AjERERETKBxWjf4DmzZsTFxdX1mmIiIiI2TQyapiKUREREZGSUjFqmHpQRERERMqMRkZFRERESkojo4apB0VERETuMadOneKFF16gdevWdOnShVmzZpFbyKXxFixYQOPGjfH397f7OXfuHABXr161XQe9devWREREFHp3SCNUjIqIiIiUVDm9A1N4eDgPPfQQW7duZeXKlWzdupVVq1YVum5ISAhxcXF2P1WrVgVg7ty5xMfHExMTQ2xsLFarlf/5n/8xnN%2BNVIyKiIiIlFQ5LEbj4uJISEhg7NixVKxYkXr16jF48GBiYmKKFefatWt88MEHhIaGUr16dTw9PXnppZf48ssvOXv2rKEcb6RzRqVYDtboYqi9qyvUA37zDuRqJWO5%2BJlxl6F8u3aZF8soM3Ix%2Bq06/y5Djz5q%2BC5D2Vnm3OnFGch2q2g8jlmvGxP2kyXzisEAFnBzw3I10/AddQ789oCxXAA3N3j4YUg85UJmprFYjasZPAzo6AjOlXC%2BkgY5OYZC5VSqbCyX6%2BmY8dLLzTXpjkcm3DkJ4NNPjbX38Mi7md5XX8HFi8Zi/fnPxtqXN0lJSSQnJ9st8/b2xsfH545t4%2BPjqVmzJh4eHrZlTZs2JTExkfT0dNzd3e3WP3jwIM888wyHDh2ievXq/M///A8dOnTg%2BPHjXLp0iaZNm9rWbdCgAW5ubsTHx/PQQw8Z3Mo8KkZFRERESqqUJjDFxMSwcOFCu2VhYWGEh4ffsW1qaiqVKtmP%2BOQXpikpKXbFaLVq1ahduzYvv/wyPj4%2BxMTEMGLECD755BNSU1MBCsSqVKmSqeeNqhgVERERKWf69etHUFCQ3TJvb%2B8it7cW8WhJ37596du3r%2B3/gwcP5rPPPuOTTz6hY8eOxYpVUipGRUREREqqlEZGfXx8inRIvjBeXl62Uc18qampWCwWvLy87ti%2BZs2aJCUl2dZNTU2lQoUKtscvXrxIlSpVSpRbYTSBSURERKSkyuEEpmbNmvH7779z4cIF27K4uDgeeeQRu6ISYPHixezcudNu2ZEjR6hduza1a9fGw8OD%2BPh422OHDh0iKyuLZs2aGcrxRipGRURERO4hTZo0wd/fnzlz5pCens6RI0dYuXIl/fv3B%2BDJJ5/kp59%2BAvJGPadOncrRo0e5evUq0dHRHD9%2BnD//%2Bc84Ojry3//93yxdupTff/%2BdlJQU/vGPf/DEE0/YLv1kBh2mFxERESmpcnoHpvnz5/P666/Tvn173N3deeaZZxgwYAAAiYmJXL58GYCXX34ZyDtXNDU1lUceeYR//vOfVKtWDYCIiAgyMjIICQnh2rVrdOnShSlTppiaq4pRERERkXtMtWrViIqKKvSxgwcP2v7t6urKq6%2B%2Byquvvlroui4uLkyePJnJkyeXSp6gYlRERESk5MrpyOjdRMWoiIiISEmpGDVMPSgiIiIiZUYjoyIiIiIlpZFRw9SDIiIiIlJmNDIqIiIiUlIaGTVMPVhEQ4cOZd68eWWaw%2BLFixk4cGCZ5iAiIiI3KId3YLrbaGS0iKKjo8s6BUJDQwkNDS3rNERERERMo2JUREREpKTus1HM0nBXF6PLly9n3bp1XLhwgWrVqhEaGkpISAgJCQnMmDGD%2BPh4nJyc6NWrFxMmTMDZ2Zlz584xefJkfvrpJ7Kzs2nevDmRkZHUrl2bxMREpk6dyv79%2B7FYLLRu3ZrIyEgqV67MoEGDaNGiBWPHjgVg/fr1rFq1itOnT1O9enUiIiIIDg4GYNCgQbRv354jR46wbds2KlSowNixYwkJCbnjNl25coUpU6awY8cOMjMz8fPzY%2BLEiTRr1owFCxbw1Vdf8d577zFx4kQ2btxoa5ebm8u1a9dsd1XYuXMn8%2BbN49ChQ7bbgI0aNapY/ZuUlERycvJN%2BXlTtapPseLcyMXF/reUkoAAY%2B0bNbL/LaXDYjGnvdE4gJub4RDm/n07Ohprn18gqFAoVR4extq7u9v/LqmLF421l7J11xaju3fvZvXq1bz33ntUr16db775hvDwcAIDAxk%2BfDiDBg0iKiqKs2fPEhoayjvvvMOIESN4%2B%2B238fDwYMeOHeTk5PDmm28yc%2BZMFi5cSGRkJC1btmTFihVkZGQwYcIElixZUuAWWdu3b2fWrFksW7aMFi1asGXLFsaNG0eDBg3w8/MDYO3atUyfPp3p06ezdOlS3njjDYKDg3F2dr7tdq1atYpz586xZcsWXFxciIqK4vXXX%2Bfjjz%2B2W2/atGlMmzYNgJycHAYOHIivry8AZ86cITQ0lMmTJ9O7d29%2B/fVXhg8fTp06dejdu3eR%2BzgmJoaFCxfaLRs1KoyIiPAix7iVGjUMhwCMfwD/J5SJsYwyI5fdu43HAHj3XcMhbv%2BKL2YsU4KZtK/N2E9mVIAArq6GQzz8sAl5XFezphlRKpkRxHiVAxgsi/8Tx4RAZsQAs/6WICjInDiPPWas/U0fkX8sfeEx7K4tRi9duoSDgwNubm5YLBY6dOjArl27iI2NxWq18uKLLwJQu3Zthg0bxrJlyxgxYgRpaWl4enri4uKCxWJhypQpOFx/IaWlpeHm5oaTkxMeHh4sXrzY9tiNPvjgA3r16kWrVq0ACA4OJjo6mtjYWFsxGhAQwOOPPw5Ajx49WLhwIUlJSdS8w7t0Wloazs7OtjyKcp7o/PnzuXz5sq1o/vTTT2nYsCF9%2BvQBwM/Pj2eeeYaNGzcWqxjt168fQTe901y54s1vvxU5RAEuLnmF6OnTkJVV8jgA9epajQXIZ7GA1aRYRpmVy6OPGmvfqFFeITpgACQkGAqV/b05hbGzM2RnmxDHyYT%2BNWs/Xb1qPA9X17w4BvNJ/N14YezikleInjpl/O/74SppxgI4OOQVounpkJtrKFROBeOFsaMj5OQYDmN0UwDz/pYAvvrKWHt397xC9Icf8nbVXUnFqGF3bTHatm1bmjRpQlBQEG3btqVjx46EhIRw4sQJzp8/j7%2B/v21dq9WKy/XjRsOHD2fkyJF89dVXdOjQgR49etC2bVsAwsLCGDduHBs2bKBDhw706tWL5s2bF3jukydP0qZNG7tldevW5dSpU7b/16pVy/Zvt%2BujH5mZmXfcrgEDBjBs2DA6derE448/Trdu3ejatest1//2229Zs2YN77//Pq7XR0eOHz9OXFxcgT54uJhDHz4%2BPvj42B%2BSP3jQ%2BOcn5H1QmRFHbmHPHnPiJCSYF0sKMutLkNVqOFYR3p6KLCvLhHhmVG6QV72ZFUsKMOvweHq6DrXfz%2B7act7FxYWlS5eyfv16mjVrxtq1awkJCcHV1ZWGDRsSFxdn%2B9m/fz%2B7rx%2B29Pf3Z/v27bz22mtYrVbCwsKYOXMmAJ07d%2BbLL78kLCyM8%2BfPM3DgQNasWVPgubNu8ZXfcsNhu8JGVIuiVq1abN68mVmzZuHu7s6kSZMYPXp0oeueO3eOcePG8frrr1O/fn3bcjc3Nzp16lSgDzZt2lSinEREROQWdGknw%2B7arc3OziY9PZ1GjRoxatQoNmzYgMVioVKlSpw4cYKMjAzbuikpKaRfH/9PTU3F2dmZrl27EhkZyZIlS1i/fr1tvQoVKhAcHMycOXOYOnUqMTExBZ67Tp06HD161G7Z0aNHqV27tuHtysjIICcnh3bt2jFx4kTef/99YmNjSUlJsVvParUyfvx4OnbsaDscf2N%2Bhw4dwnrDSElycvIti2gRERGRsnLXFqPR0dE8//zznDlzBoAjR45w8eJFGjRogJeXFzNnziQ9PZ3k5GRGjx7N7NmzAXjmmWeIiori6tWrZGdns2/fPurWrUtmZibdu3dn48aNXLt2jczMTOLj46lTp06B5w4JCWHTpk3s3buX7OxsPvroIw4fPkzPnj0Nb1dERIQt99zcXPbs2YOnpyceN01ZXL58OWfPnmXSpEkFYvTs2ZPU1FQWL15MZmYmJ06cYOjQoaxatcpwfiIiInIDjYwadteeMzpkyBBOnz5Nnz59yMzMpHr16owdO5bmzZuzePFipk2bRvv27XF3d6dr165MmDABgHnz5jF16lSWLFmCk5MT/v7%2BzJ49Gzc3N95%2B%2B23eeustJk%2BejJubG61atbplsXfq1CnGjx/PuXPnqF%2B/PtHR0dSrV8/wdkVGRjJp0iQ6duyIxWKhYcOGLFq0qMBh/5iYGM6ePctjN01BjI6OJjAwkMWLF/PWW2%2BxdOlSvLy8CAkJYejQoYbzExERkRvcZ4VjabBYreVlGrHcDa5fxrTEXF2hXj347TfjE5j8fDWb/paMvjkGBORdHqplS8MTmLKzzOnbe3I2vdFZPhZL3uWhMjMN53PgtweM5UJeKg8/DImJxjetcbWUO690O46OUKkSpKUZnsCUU6mysVy4d2fTf/qpsfYeHnmXh9q%2B3fgEpj//2Vj7EnvnndKJO2xY6cQth%2B7akVERERGRMqeRUcNUjP6Bfv75Z5599tlbPl6jRg1iY2P/wIxEREREypaK0T9Q8%2BbNiYuLK%2Bs0RERExCwaGTVMxaiIiIhISakYNUw9KCIiIiJlRiOjIiIiIiWlkVHDVIyKiIiIlJSKUcPUgyIiIiJSZjQyKiIiIlJSGhk1THdgkmIx49Vi1s1rLGkGb9cBeW8iFSvCpUvm3NqknOSS/aCH4XRMu%2BORi8V4EBPvCEVqqrH2Zr5mKlUy1h7M%2B4My6/Vv0q2GrA6OhmOUp64x6w5MjmdOGQvg7Aw%2BPpCUZM4fuKensfYODvDAA3DlivGOrlDBWPuSiokpnbj9%2BpVO3HJII6MiIiIiJaWRUcNUjIqIiIiUlIpRw9SDIiIiIlJmNDIqIiIiUlIaGTVMPSgiIiIiZUYjoyIiIiIlpZFRw1SMioiIiJSUilHD1IMiIiIiUmY0MioiIiJSUhoZNUw9KCIiIiJlRiOjIiIiIiWlkVHDVIyKiIiIlJSKUcPUgyIiIiJSZjQyKiIiIlJSGhk17K7owZMnT%2BLn58eRI0cA8Pf355tvvrlju6CgINatW1fa6RVq6NChzJs3747rvfLKK4wZM8aU57xdvxw5cgQ/Pz9OnjxpynOJiIhI%2BXXq1CleeOEFWrduTZcuXZg1axa5ubmFrrtu3Tq6d%2B9OQEAAISEhbN261fbYK6%2B8QpMmTfD397f9tGrVytRc78qR0bi4uLJOoYDU1FS2bNlC3759AYiOjv7DcyiP/SIiInJPK6cjo%2BHh4TRt2pStW7dy/vx5XnzxRapWrcqQIUPs1ouNjWXOnDksW7aM5s2bs2HDBl566SX%2B/e9/U7t2bQBGjhxJeHh4qeV6Vxaj5dF3333H%2B%2B%2B/bytG7wVJSUkkJyfbLata1RsfH58yyugmZrwB5McoD28m5SkXMwUEGI/RqJH9byOM9u%2B9up/k7uDsbKy9k5P9b6OM/h1YLP/5bSTWLUb8/hCl9F5Q2Gewt3fRPoPj4uJISEhg5cqVVKxYkYoVKzJ48GBWrVpVoBjNzMzk//2//8ejjz4KQN%2B%2BfZk9ezZ79%2B61FaOlrdwVoydOnGDy5Mns2bMHT09PhgwZQlBQkN06fn5%2BREVF0bFjR65cucL06dP5/PPPsVgsPPHEE7z%2B%2Buu4uLjYtcnKymLw4MHUqVOHN9988455xMXFMWPGDA4dOoSLiwtPPPEEEydOxNnZme%2B//54RI0YwevRo5s%2Bfz7Bhw1i0aBG5ubn4%2B/uzefNmXn31VVq0aMHYsWOBvJHSf/3rX1y8eJGAgACmTp1KrVq1Cjzv5s2bWbZsGceOHaNKlSq88MIL9OvXr0h9d2O/nD9/ngkTJrBr1y5q1KjB8OHDixTjRjExMSxcuNBu2ahRYUREGP92lP/%2BY0jFiiYEua5CBfNiGWVCLgY/rv4Tx4xAu3ebEOS6d981L5ZR5ek1Y8YflKOj8RgmxjLjLQLKV9eYEseswQAvL3PimMXNzVj7jAxz8ihHCvsMDgsLK9IIZXx8PDVr1sTDw8O2rGnTpiQmJpKeno67u7tteUhIiF3btLQ0MjIyeOihh2zLvvvuO7Zt28axY8do0KABU6ZMoVmzZiXdtALKXTEaFhbGY489xqJFi/jtt9949tlncbvNi/Qf//gHv/76K//%2B978BGD58OIsWLSpwHubkyZNxcXEhMjKySHmMGTOGp556in/961%2BcPXuWZ555hkceeYRBgwYBkJ2dzbFjx/j2229xdXUlNzeXr776ivfee69ArK1btxIVFcXKlSupX78%2Bb7zxBmPHjmX9%2BvV268XFxfHaa6%2BxYMEC2rZty549e3j%2B%2Bedp2LAhLVu2LFLe%2BaZPn87Vq1f58ssvyczMtBXFxdGvX78CXwSqVvXGai12KDsWC4ZjAFjSLxkP4uCQV1RkZJTtN2uTc8l2M16oOztDdrbhMDi3Lt5rt1CNGuUVogMGQEKCsVj/93/G2pv5mrnhA6HEzPqDMuv17%2BgIOTmGw1gdTChoy1HXmNQtOJ5PMhbAySmvEL1wAa5dM56Q0UEBiyWvEM3MNGdnlYVSGhkt7DPY29u7SG1TU1OpVKmS3bL8wjQlJcWuGL2R1Wpl4sSJtGjRgsceewyA2rVr4%2BDgwOjRo6lQoQILFy5k6NChxMbGUrly5eJuVqHKVTH6yy%2B/cPDgQVatWsUDDzxA48aNWbhwoV11fiOr1cqGDRuYPn06Xte/5U2fPp20tDS79d555x3i4uJYt24dzkUc6tmwYQMuLi44OjpSo0YNAgMD2b9/v%2B3x7OxsBgwYcNtCOd%2BHH35Iz549aXT9EOOYMWP44YcfCpxI/NFHH9G5c2c6dOgAQKtWrejRowcbN24sdjG6detW5s6di4eHBx4eHgwcOJAffvihWDF8fHwKHA4oV%2B8VZhaPubllX4zmK0%2B5mGHPHvNiJSQYj2dW395r%2B0nuDmZ8Q4S8QtSMWEb/BvILOatVf083KewzuDisxfzAzs7O5pVXXuHXX39l9erVtuWjRo2yW2/cuHF8%2BumnbN261bRTE8tVMXr8%2BHHc3d3x9PS0LWvXrt0tZ4CnpKSQlpZmd7i70U3nlO3YsYMvv/ySd955h4rF%2BAb33Xff2UZnr127xrVr13jyySft1qlRo0aRYp04cYLWrVvb/l%2BlShV69OhRYL3jx4%2Bzc%2BdO/P39bcusVqutOC2qlJQUMjMz7fqlXr16xYohIiIiRVAOzx/38vIiNTXVbllqaioWi8U2eHejzMxMQkNDuXLlCmvXrr3tiKejoyPVq1cnKcngKP0NylUPOjg43PKyA7daH7htmz179tCpUyfmzp1LThGPkRw5coTRo0fz5z//mZ07dxIXF0evXr0KrOdUxBPALRZLkb6huLm50b9/f%2BLi4mw/%2B/fvZ%2BnSpUV6nnxZWVkAdttb3G9IIiIiUgQODqXzY0CzZs34/Y9Wdp4AACAASURBVPffuXDhgm1ZXFwcjzzyCBVuOt/darUyZswYnJyc%2BOc//2lXiFqtVmbMmEHCDadHZWVlcfz4cVMnN5WrYrR27dpkZGTYVdtbt27l9OnTha7v6elJpUqVSExMtC2Lj49n48aNtv%2BHh4czZ84cLly4UOSi7sCBA7i4uPDcc8/h5uaG1WrlwIEDJdyqvO26MccLFy4QHR1N9k2HSOrUqcPBgwftlp05c6bIRXQ%2BLy8vnJ2d%2Bf33323Lfv311xJkLiIiIneb/OuCzpkzh/T0dI4cOcLKlSvp378/AE8%2B%2BSQ//fQTAJs2beLXX3/l7bffxtXV1S6OxWLh5MmTTJ06lbNnz5KRkcHs2bNxdnamW7dupuVbrorRxo0b06RJE%2BbNm0dGRgaHDh3itddes13svjB/%2BctfWLFiBWfPniUlJYXIyEgOHz5se9zBwYEKFSowY8YMli5dyi%2B//HLHPGrWrElmZiYHDhzg4sWLzJo1CxcXF5KSkm45wujq6kpycjKpqam2kcl8f/3rX/nss8/Yt28fWVlZLFq0iP/93/8tcP7q008/ze7du/nwww/JysriwIED9O3bl9jY2DvmfCNnZ2fatGnD6tWruXTpEqdOnWLt2rXFiiEiIiJFUA5HRgHmz59PUlIS7du357nnnqNPnz4MGDAAgMTERC5fvgzkzWs5deoUjz32mN2F7SdOnAjA3//%2Bd%2BrVq8df/vIX2rVrx4EDB1i1ahUPPvig4RzzlatzRgGWLl3K%2BPHjadeuHVWqVCE0NJTHH3/8luu//PLLTJs2jeDgYFxcXOjWrRthYWEF1nvsscfo378/48eP56OPPipw6acbBQQE8OyzzzJw4EAeeOABRo4cyauvvsrIkSMZM2aM7ZvFjbp168a7775L586dC1zwvmvXrowZM4ZRo0Zx%2BfJlAgICmDNnToEYDRo0YM6cOcyfP5%2BpU6fi4%2BPDsGHDCA4Ovl2XFervf/87EyZMoGPHjlSvXp2IiAh2m3mJHRERESm3qlWrRlRUVKGP3XgUdtWqVbeN4%2BnpyYwZM0zN7WYWq04mlGIw5ZJMZl3aKe2i8SAODnmXJrl0qexncpqYS/aDHnde6Q5Mu7STiwkXegwIyLteacuWxmfT33RSf7GZ%2BZq56dIrJVKerl8EurTTLZh2aaczp4wFcHbOu1ZpUpI5f%2BA3TDguEQcHeOABuHLFeEeX1bV/v/%2B%2BdOLeMPH5XlfuRkZFRERE7hrlcDb93ea%2BLEZbtWrF1atXb/n4//7v/1KzZs0/MKM7e%2Bqpp%2BwmQd0sOjqawMDAPzAjEREREePuy2I0fwbZ3eSTTz4p6xRERETkZhoZNUw9KCIiIiJl5r4cGRURERExhUZGDVMxKiIiIlJSKkYNUw%2BKiIiISJnRyKiIiIhISWlk1DD1oIiIiIiUGd2BSYrniy%2BMtXd3h8BA%2BPFHSE///%2Bzde1wU9foH8M8uctPlIibmBcq8ICoopKKoIKCpmFFq3roZmhWCRnZIjxImhdURMQPFkk56SuKUHiwrL5AejpZ1TH5FKF7Q5GKABohcF5b5/YHsYQV02e/Qon7er5cvZHbm2YeZ3Z1nn5nvjFgsX1%2Bx5QF57%2BzTkXKR45Yxct2%2BpqxMPIacdz0SvWOMjNtJqhdfv7Ld0Uzd%2BrWX25SMmRmgVgsnVas0F05HrruIdZLhGKJs2wkd6DZ4AGrrxO%2BwJtvd3kzFYxgkM7N94g4Z0j5xOyAepiciIiIyFA/TC%2BMaJCIiIiKjYWeUiIiIyFDsjApjMUpERERkKBajwrgGiYiIiMho2BklIiIiMhQ7o8K4BomIiIjIaNgZJSIiIjIUO6PCWIwSERERGYrFqDCuQSIiIiIyGnZGiYiIiAzFzqgwrkEiIiIiMpoOUYzm5eXByckJ2dnZAAAXFxccPXr0lsv5%2BvoiMTFR%2BPn/%2B9//wsXFBWq1WjgWERER3UWUyvb5dxfpkIfpMzIy2v05Dhw4ACcnJ9x3330YOXLkn/KcN8rMzMTVq1fh6en5pz83ERERyeAuKxzbw127Bjdt2oSLFy8aNYddu3bhu%2B%2B%2BM2oORERERMb0pxejubm5CAwMhJubG3x8fLBjx45m8zg5OSEtLQ0AUFVVhfDwcHh4eGD06NEIDw9v8XC6Wq3G/PnzsWLFilvm8Mgjj%2BDs2bMICgrCypUr8cMPP8DJyQk1NTXa5//qq68wY8YMuLq6YvHixSgoKMDChQvh5uaGGTNmIC8vTxvv66%2B/RkBAAIYPHw4/Pz8kJSVpH/v5558xe/ZsuLm5wcPDA6tWrUJ1dTUiIyOxc%2BdOfPjhh5g0aRIAICcnBwsXLoSHhwc8PDzw8ssvo6ysDMD/TmU4fPgwpk6dimHDhmHlypXIycnB3LlzMXz4cDz11FO4evUqAGDFihVYuXIl1q5dC3d3d4wePRo7d%2B7UdzMRERGRPniYXtiffpg%2BODgYo0aNQlxcHH777Tc88cQTsLCwaHX%2BDRs24Ny5c/jmm28AAIsWLUJcXBxCQ0N15ouIiICZmRkiIyNvmcMXX3wBJycnbN68GV5eXvjhhx%2BazfPpp58iPj4elZWVmD59Op577jm8/fbbcHR0xBNPPIG///3vCA8PR0ZGBlatWoX33nsPY8aMQXp6Op577jkMGDAA7u7uCAsLw6JFizBz5kxcuXIFQUFBSEpKQnh4OM6cOYNhw4bhlVdeAQCsXr0avXv3xn/%2B8x%2BUl5dj4cKF2Lx5s06BnZycjH/%2B85/IysrCk08%2BiYsXL%2BJvf/sbLCws8Nhjj2HXrl0IDAwEAOzbtw8rV67EsWPHkJaWhuDgYLi7u2PQoEG33lAAioqKcPnyZZ1p3aurYX/PPXot36LOnXV/inBzE4/RuC70XCftqiPlIic5PlQbY8gRS/R1c6duJ4VCvhhyxCKiu8afWoyePHkSp0%2Bfxvbt22FpaQlnZ2fExsaiR48eLc4vSRKSk5MRFRUFOzs7AEBUVJS2W9goISEBGRkZSExMhKmpqSy5Tps2Dfb29gCABx54AEOGDMHgwYMBAKNGjcL58%2BcBALt378aECRMwbtw4AMCIESMwdepU7NmzB%2B7u7igrK0Pnzp2hVCphb2%2BPf/7zn1C2skN9//33oVAoYGZmBjs7O4wfPx4nTpzQmWfmzJmwsrLCyJEjYWVlhbFjx8LBwQEA4Orqit9%2B%2B007b69evTB79mwAwMSJE%2BHs7IxDhw7pXYwmJSUhNjZWZ1rwkiUImTpVr%2BVvasgQ8Rg3rBshHalr3JFykaOosLISj9GoSxfxGHK9bmTYTnKVbLLUfmZmMgS5TobPYXk%2ByWVJRTby1OgyvWpk%2BsIg1/oVjVNbK08eBrnLupjt4U8tRnNycqBSqWBra6ud5unpqXPIu6mSkhKUlZWhT58%2B2mk3FlJpaWk4fPgwEhISYCXjTq9nz57a/5ubm%2BsUzObm5tpTBXJycvD999/DxcVF%2B7gkSdri9OWXX8Zf//pXJCQkYNy4cQgICEC/fv1afM5ff/0V0dHROH36NGpra6HRaDB06FCD8gKAvn376izbq1cvFBUV6b0O5syZA19fX51p3XNygP/%2BV%2B8YzXTu3FCIZmYClZWGxwGA558XWx5o6G7t3AnMnw9kZYnH6yi5/PSTeD4KBSBJ4nHKy8VjKJUNhWhFBVBfLxbL21tseRm3k/STeGEs12ZS1MpwNRGFoqGqqK0VTqpWIV4cN6YiqpMMe0rZthPkCCJTMgBq68SLWrm2k9GwGBX2pxajSqUS9W3YkTR2EG%2B2THp6Ory9vRETEwMPDw%2BYmJgI5wkAihu%2BNbbWzbSwsMC8efMQHh7e4uOPP/44Jk6ciG%2B//Rapqal49NFHERMTg4kTJ%2BrMd/XqVSxevBjz5s3DBx98AJVKhY0bNzYb4KRvXgCg0Wh0fpckqdnyN2Nvb6/tDmsVFclTXFRWisdJTxfPo1FWlrzxRHSkXOQgWjzeGEs0nlzr9k7bTjIVJ9pYovF4pJ/orvGnlvMODg6oqKjQ6c6lpKTg0qVLLc5va2sLa2trXLhwQTstMzMTe/bs0f4eEhKC6OhoFBcXIz4%2Bvv2Sb4WjoyNOnz6tM62goEBbCJaUlKBr166YOXMmNm/ejOeffx6ff/55szjnz59HRUUFFi5cCJVKBaDhtAYRubm5Or9funQJ9957r1BMIiIiaoIDmIT9qX%2Bts7MzBg8ejI0bN6KiogJnzpzBqlWrtBe7b8mMGTOwbds2FBYWoqSkBJGRkTh79qz2caVSiS5dumDdunWIj4/Xu4AzNzfHxYsXUS7YnZs1axZOnDiBXbt2Qa1W49SpU3j88cexf/9%2BFBQUwNfXF0eOHEF9fT2uXbuGM2fOwNHRUZtDXl4erl69il69ekGpVCI9PR2VlZX46KOPcOXKFVy5cgV1dXUG5Zafn4/k5GTU1tbi4MGDyMrKwoQJE4T%2BXiIiIiI5/emld3x8PPLz8%2BHp6YkXXngBQUFBGD9%2BfKvzL1%2B%2BHK6urvD394e/vz8GDBiA4ODgZvONGjUK8%2BbNQ1hYmF53Upo7dy7eeecd/OUvfxH6e/r164fo6Ghs27YNI0aMQEhICBYuXAh/f3/ce%2B%2B9ePPNN/Hmm2/Czc0NU6ZMQZcuXbB06VIADYV2WloaHnroIdxzzz3a80t9fHxw9epVrF%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%2B233%2BK9997DO%2B%2B8g%2B%2B%2B%2Bw4%2BPj544YUXUFlZCQCIiYlBZmYmkpKSsH//fkiShJUrVwrn1xSLUSIiIiJDtVMxWlRUhMzMTJ1/RUVFeqWUkZGBrKwsvPLKK7CyssL999%2BPBQsWICkpqdm8SUlJmDFjBoYNGwYLCwssWrQIAHDo0CHU1dXh888/R1BQEHr27AlbW1u89NJLOHz4MAoLC2VbhTxnlNpm3Dh54gwfLhxCjlspAoACgPTTCVliiZIrF0V1lWAABWBhAdTUiN820NpabPmmVCrhELLcghMybSel4K0Ur9%2BaVPGg%2BK1JDx4QXy9WVsDo0cCxE2bCd0icOFE4HQDy3MqzokJseaUS6NwZqKoSv5utiYnYa6bxrV1do5DljqAajdjySuX/bgcqum6MdjvQdpKUlITY2FidacHBwQgJCbnlspmZmejduzdsbGy004YMGYILFy6gvLwcqiafpZmZmfD399f%2BrlQq4ezsjIyMDDg7O%2BPatWsYMmSI9vF%2B/frBwsICmZmZ6NGjh8ifqMVilIiIiMhAEgS/VLZizpw58PX11ZnWvXt3vZYtLS2F9Q2NgMbCtKSkRKcYLS0t1SlaG%2BctKSlBaWkpADSLZW1tLet5oyxGiYiIiDoYe3t72NvbG7y81IbW963mbUssQ/CcUSIiIiID1de3zz8RdnZ22q5mo9LSUigUCtjZ2elM79q1a4vz2tnZaee98fGrV6%2BiW7duYkk2wWKUiIiIyEAdsRgdOnQofv/9dxQXF2unZWRkoH///ujSpUuzeTMzM7W/azQanDx5EsOGDYODgwNsbGx0Hj9z5gzUajWGDh0qlmQTLEaJiIiI7iCDBw%2BGi4sLoqOjUV5ejuzsbPz973/HvHnzAABTpkzB8ePHAQDz5s1DcnIy/u///g9VVVXYsmULzMzMMGHCBJiYmGD27NmIj4/H77//jpKSEmzYsAGTJk3CPffcI1u%2BPGeUiIiIyECiXcz2smnTJoSHh2Ps2LFQqVSYO3cu5s%2BfDwC4cOGC9jqiXl5eePnll/HSSy/hjz/%2BgIuLC95//31YWFgAAJYuXYqKigoEBASgrq4OPj4%2BWLNmjay5KqT2PiuV7iy1teIxGq/jIUjqJM91PBQK8asXyUWuXGS7tFN1tXhC1z/QhMm0cuQY%2BSrbdpLp0k5w72CXdjqGDnFpJ7m2k1yXdqqslOPSTmLLy/nWBuS5tJNc60aGK78ZpKamfeKam7dP3I6InVEiIiIiA3XUzujthMUoERERkYFYjIrjACYiIiIiMhp2RomIiIgMxM6oOBajRERERAZiMSqOh%2BmJiIiIyGhkL0bz8vLg5OSE7OxsAICLiwuOHj16y%2BV8fX2RmJgodzpCVq9ejbCwMGOnQURERB1UR7wD0%2B2m3Q/TZ2RktPdTtJs33njD2CnI5sCBA3BycsJ9991n7FSIiIiItHiY/i6xadMmXLx40dhpEBER3VHYGRUn1BnNzc1FREQE0tPTYWtri2effRa%2Bvr468zg5OeGDDz6Al5cXqqqqEBUVhQMHDkChUGDSpEkIDw%2BHmZmZzjJqtRoLFiyAo6Mj3nrrrVvmkZ%2Bfj8jISKSnp6O%2Bvh4%2BPj547bXXoFKp8MMPPyAoKAgxMTGIiopCQUEBHnzwQWzYsAH/93//h2XLluHYsWPa214VFxdj3Lhx2LlzJz799FPU1NQgJiYGu3fvxocffohx48bh008/xf79%2B9G9e3ds2bIFycnJKCwsRL9%2B/RAWFoYxY8YAaDj14MUXX8TBgwfx3//%2BF926dcOaNWswbtw45OXlwc/PD1u3bsXbb7%2BNS5cuwd/fHy%2B%2B%2BCLCwsKQlZUFFxcXxMbGwsbGBgDw8ccf45NPPsGlS5fQp08fhIaGYuL125Q89dRTGDt2LLKzs5GamoouXbrglVdeQUBAAB555BGcPXsWQUFBmD59OtatW6fX9i0qKsLly5d1pnXv2hX23bvrtTwZkULwzj6Ny4vGoZtzcxNbftAg3Z8CrKyEQ6BzZ92fdwqlYNum6dtJrlhy5CKHjrJujFm83W2FY7uQBDzyyCPSG2%2B8IVVWVkonT56U3NzcpKSkJGngwIHSuXPnJEmSpIEDB0r//ve/JUmSpDfeeEOaO3eu9Mcff0h//PGH9Nhjj0kbNmyQJEmSfHx8pJ07d0qSJEkrVqyQnnnmGUmtVt8yh/r6eumRRx6R1qxZI1VWVkpXrlyRnn76aWn16tWSJEnSsWPHJGdnZyksLEwqLS2Vfv/9d2ns2LHS%2B%2B%2B/L6nVamnkyJHSwYMHtfE%2B%2B%2BwzycfHR5IkSXr11Vell156SZIkSdq1a5c0cuRIacOGDdq8duzYIXl7e0tZWVlSTU2NlJCQIA0fPly6cuWK9m%2BaPHmy9PPPP0s1NTXSypUrpalTp0qSJEm5ubnSwIEDpWXLlkllZWXSjz/%2BKA0cOFCaN2%2BelJOTIxUVFUljx46VEhISJEmSpP3790seHh5SRkaGVFtbK%2B3fv18aMmSIlJ%2BfL0mSJD355JPSuHHjpLS0NEmtVkubNm2S3N3dtbk23Q762rRpkzRw4ECdf5vefbdNMYiIiNrbtWvGe%2B6iovb5dzcxuDN68uRJnD59Gtu3b4elpSWcnZ0RGxuLHj16tFb0Ijk5GVFRUbCzswMAREVFoaysTGe%2BhIQEZGRkIDExEaamt773eEZGBs6ePYvExERYWlrC0tISISEhWLhwIdauXQsA0Gg0WLRoEWxsbGBjY4MHH3wQ58%2Bfh6mpKfz8/JCamqrtMKakpGDq1KktPte1a9fw3HPPafP6/PPPMX/%2BfDg5OQEAAgMDsW3bNhw%2BfBgzZ84EAPj4%2BMDV1RUAMHnyZCQnJ6O%2BydeomTNnwsrKCiNHjoSVlRXGjh0LBwcHAICrqyt%2B%2B%2B037XPNmjULQ4cOBQA89NBDePDBB7F3714sXrwYAODm5obx48cDAKZOnYrY2FgUFRWhd%2B/et1yPLZkzZ06zTnf3rl3F7yvPe9O3SrZ7ntdUiydibt5w02XRhOS6wfKdeG/6B93FAgwaBOzcCcyfD2RlCYU6tvmEWC5o6Ii6ugK//NJwr3ERHh7C6ci2naqqxPOwtGyII5qPHJ1Iud7agHhXUM51YyzsjIozuBjNycmBSqWCra2tdpqnpyfy8vJanL%2BkpARlZWXo06ePdtqgGw4tpaWl4fDhw0hISICVnseMcnNzodFo4HHDJ5dGo0FJSYn296bPa2lpierqhp31lClT8Oqrr0Kj0aC6uhrfffcdli5d2uJzWVtbQ6VSaX/Py8tDv379dOZxdHREfn5%2Bi89rYWEBjUaD2iaFWM%2BePbX/Nzc31ynmzc3NoVarATSs76NHj2L79u3axyVJQv/%2B/Vt9LgDav9MQ9vb2sLe3150oQxFJfwK5PtUl6fbdQ9wO0tPliZOVJRzr2jV5UgEaClE54xmbaLHRWEBKkjzFmxzkemt3pHVDty%2BDi1GlUqnT4dNnfgA3XSY9PR3e3t6IiYmBh4cHTExMbhnX3NwcnTt3RvotPoiVrXyd9PT0RH19PX766SdcuXIFPXv2xODBg1uct1Mn3dXVWCjeSNHk06K1521p3pvNb2FhgeXLlyMwMLDVWLd6LiIiIpIXi2hxBlcvDg4OqKioQFFRkXZaSkoKLl261OL8tra2sLa2xoULF7TTMjMzsWfPHu3vISEhiI6ORnFxMeLj4/XKw9HREZWVlcjNzdVOKy8v1%2BmK3kzjofpDhw7h4MGD8Pf312u5xuc%2Bf/689ve6ujpcvHhRe5hdTo6Ojjh9%2BrTOtEuXLkFi14qIiMhoOJpenMHFqLOzMwYPHoyNGzeioqICZ86cwapVq7QXu2/JjBkzsG3bNhQWFqKkpASRkZE4e/bs/5JRKtGlSxesW7cO8fHxOHny5C3zGDhwINzc3PDmm2%2BiuLgYZWVliIiIaNPF6qdOnYojR47gyJEjbSpGAwICsHPnTmRnZ0OtViM%2BPh4ajabZeZZymDNnDr7%2B%2BmscPnwYdXV1OHbsGB5%2B%2BGH8/PPPei1vbm6Oixcvory8XPbciIiIiAwldFw3Pj4e%2Bfn58PT0xAsvvICgoCDtAJqWLF%2B%2BHK6urvD394e/vz8GDBiA4ODgZvONGjUK8%2BbNQ1hYWKuHwpuKjo6GJEnw8/PDpEmToNFo9LokVKMxY8agqKgI9957LwYMGKD3coGBgZgyZQqee%2B45eHp64ocffsCOHTtgbW2tdwx9jR07Fq%2B%2B%2BirWrl0Ld3d3rF27FmvWrMHw4cP1Wn7u3Ll455138Je//EX23IiIiO5W7IyKU0g8zkttIccAJo6mb5Vso7SrZRj%2Ba2EBVFeLJ3R9MJ2wO3E0vVIwFzc34MQJwN1deADTwQPif5CVFTB6NHDsmPgApusXOBEi13aqqBBbXqlsuNJAZaV4kaHHUIqbkvOtDQAajdjycq6bJuOL/1RNzhKUVTuc8ddhtfvtQImIiIjuVHdbF7M9dPhidMSIEaipqWn18X379hl8HU0iIiIiESxGxXX4YvT48ePGToGIiIiI2kmHL0aJiIiIOip2RsXxKulEREREZDTsjBIREREZiJ1RcSxGiYiIiAzEYlQcD9MTERERkdGwM0pERERkIHZGxbEYpTYprxG765FSCXQ2BSprTcXvtlHf%2BvVn9aZQAGZmUNSqjX8bJhlzOfWbpdDyFhZA377Ahd8tUF0tFArOAwVv0dLIxESWT31FXZ1gAPm2k%2Bhdj6ysgNEAjm0%2BIXzHo0kPid%2BZqvGOUKODxO8ItetzsXVjawv4%2BQHffguUlgqFwsxxhWIBOnUCOndD56o/ANHXn%2BhthpRKAJawkKrkqaIkwRiSEkAXdJYqxGPBSnB5MhYWo0REREQGYmdUHItRIiIiIgOxGBXHAUxEREREZDTsjBIREREZiJ1RceyMEhEREZHRsDNKREREZCB2RsWxGCUiIiIyEItRcTxMT0RERERGw84oERERkYHYGRXHzigRERERGQ07o0REREQGYmdUHItRIiIiIgOxGBXHw/REREREZDSyFaN5eXlwcnJCdnY2AMDFxQVHjx695XK%2Bvr5ITEyUJYfExET4%2BvoKx1m9ejXCwsJkyIiIiIjuZPX17fPvbtJuh%2BkzMjLaK3S7e%2BONN2SLpVar8dFHH%2BHLL79EXl4eOnXqBEdHR8yePRtz5syR7XmIiIiI9FFaWoo1a9bgxx9/hFKphLe3N8LDw2FhYdHi/AcOHEBsbCxyc3Nhb2%2BPhQsXYvbs2QCA9957D5s3b0anTrol5aFDh3DPPffolQ/PGW1HdXV1WLRoEaqrq/Hmm29i6NChqKqqwrfffovIyEhcuHABK1asMHaaREREZKDbsYsZHh4OtVqNvXv3ora2FsuWLcP69euxevXqZvP%2B8ssveOWVV7BhwwZMmDABR48exZIlS/DAAw9gxIgRAICAgAC89dZbBudjUDGam5uLiIgIpKenw9bWFs8%2B%2B2yzw%2BNOTk744IMP4OXlhaqqKkRFReHAgQNQKBSYNGkSwsPDYWZmprOMWq3GggUL4OjoqNcf9fPPPyM8PBw5OTlwc3PDgw8%2BqPP4999/j40bN%2BLMmTNQqVSYO3culixZgn//%2B99YtmwZjh07pv0WUFxcjHHjxmHnzp349NNPUVNTg5iYGADAnj17sHnzZhQVFcHJyQkRERFwdnYGAHz99dfYunUrLl68iG7dumHx4sXajue//vUvZGRkIDU1FXZ2dgCALl26YPr06bjnnntQUFCgzfWpp56Cq6sr0tLS0LNnT7z//vsoKCjA66%2B/jhMnTqCuv2TtnwAAIABJREFUrg5eXl6IiIiAra0tdu/ejejoaJ1TIWbPno3x48cjJCQEK1asgEKhgKWlJZKTk2FmZoalS5di/vz5em1jACgqKsLly5d1pnXp0h329vZ6x7iRQvG/n0rRk0Qag8kRQ45YomTMpZUvt3prfGve8Ba9M4iuXxm3k5WV2PKdO%2Bv%2BFOLmJh5j0CDdnwJsbcWWb1y3ousYANBJsG9jYqL7U4ToB6esH8IyaMxBNBcjVoTt9dQt7YO7dxfbBwPAlStXkJKSgn/961/a2iQoKAjLli3Dq6%2B%2BClNTU535S0tL8fzzz2PixIkAAG9vbwwcOBDHjx/XFqOiDHqHBQcHY9SoUYiLi8Nvv/2GJ554otXWLgBs2LAB586dwzfffAMAWLRoEeLi4hAaGqozX0REBMzMzBAZGXnLHDQaDZYuXYpp06Zh2bJlyMrKQkhIiLZNXFBQgKCgIERERGD69Ok4d%2B4cFi1aBEdHR0yZMgVmZmY4cuSIduV%2B%2B%2B23uPfeezF8%2BHB8%2Bumn2uf59ddfsWbNGmzZsgUPPvggtm7diqCgIKSkpODkyZNYtWoV3nvvPYwZMwbp6el47rnnMGDAALi7u%2BPAgQOYNm2admM3NWbMmGbTvvrqK2zatAkuLi4AGl4c/fv3R2pqKqqrq7Fs2TJERETg3XffveX6AYB9%2B/Zh5cqVOHbsGNLS0hAcHAx3d3cM0nNHkZSUhNjYWJ1pS5YEY%2BnSEL2WvxlLS%2BEQAGSslG548xmVDLn07StDHgB695Yjigw7YG0oGWLJEQOQZTuNHi1DHgBcXWUIcuKEDEGu27lTOISfDGkAwKhRckTpJkcQ8QpbTqLfWOUmulO4dk2ePDqQlvbBwcHBCAkR2wefOnUKJiYmcHJy0k4bMmQIKisrcf78eZ3pAODl5QUvLy/t73V1dbh8%2BTJ69OihnXb69GnMnTsXZ86cQc%2BePbFy5UqMGzdO75zaXIyePHkSp0%2Bfxvbt22FpaQlnZ2fExsbqJNWUJElITk5GVFSUtiiLiopCWVmZznwJCQnIyMhAYmJis6q8Jb/%2B%2BiuKiorw4osvwtzcHMOGDcOkSZNw6NAhAMDevXsxYMAAPProowAaOrVz587Fnj17MH36dPj5%2BSE1NVVbjKakpGDq1KnNnic5ORmjR4/G6Ot7jYULF6Jv376oqanB7t27MWHCBO0KHzFiBKZOnYo9e/bA3d0dubm5GNWGT0JXV1e4Xt%2BrnDp1CpmZmdi6dStUKhVUKhUWL16MJUuWQK1W6xWvV69e2nM6Jk6cCGdnZxw6dEjvYnTOnDnNOt5dunRHZaXef1IzCkXDZ05VFSBJhscBgM6d9FsPt0zI1BSorRVPqAPlciFfrFA3M2soRPPzAT1fbq3q66gRC9DIxATQyBBLNIaM2%2BnYCbHt1LlzQyH6yy8Qel8CwOggd7EAQENHdOdOYP58ICtLKFTq38SKYyurhkL0xx/F6xS/4X%2BIBTAxaShES0vFX3%2BibXCFoqEQra6W5zNPtC2oVP5vp3A7Hu9G%2B6Xd0j64e/fuwnFLS0uhUqmgaHJ0x8bGBgBQUlJyy%2BXXr1%2BPzp07w9/fHwBw7733wsHBAcuXL4e9vT2SkpLwwgsv4IsvvsADDzygV05tLkZzcnKgUqlg2%2BQbnqenJ/Ly8lqcv6SkBGVlZejTp4922o3FUFpaGg4fPoyEhARY6XlMpaCgANbW1jrz33///Tp5ZmRkaLuMQENh3Pd6y2jKlCl49dVXodFoUF1dje%2B%2B%2Bw5Lly5t9jy5ublwdHTU/m5paYlp06Zpn%2BP7779v9hyNxalCoYDmhg%2BeyZMn49KlSwAaursfffSRtmDt3aQNlZeXBxsbG50XnqOjI2pra1FYWKjXOup7Q3usV69eKCoq0mtZALC3t292OKC8XOyN13gkRpJkeAPLWTxKkvGL0UYy5FJdLU8qarV8sToMubazDNtJrmZOZaUMsdLTZckFQEMhKhivtFSeVK5dkyFWXZ0suUCjEY8lR/EHyPQhLEM%2BTePcpsVoe2lpH6yvPXv2tHpVoNDQUEgGfHZJkoT169dj79692LFjB8zNzQEAjz/%2BOB5//HHtfAsWLMBXX32FL774Ai%2B99JJesdtcjCqVStS34QWjvP7Cv9ky6enp8Pb2RkxMDDw8PGCix2E0tVrdrNBr%2BhwWFhbw9vZGfHx8i8t7enqivr4eP/30E65cuYKePXti8ODBzeZTKBStbjQLCwvMmzcP4eHhLT7et29f7aWuGu3fv1/7fycnJ53YTf/um3U/Fa2cq3bj%2Brjxd0mSWl2WiIiI2q4j1tABAQEICAho8bGjR4%2BivLwcGo1GW3eUXv/G1q1by6ek1NfXY%2BXKlfjll1%2BQmJgIBweHmz5/796929T8avMZww4ODqioqNB5kpSUFG2370a2trawtrbGhQsXtNMyMzOxZ88e7e8hISGIjo5GcXFxq8Xjjezt7VFeXo5rTdoBTQs/R0dHnDlzRqfYu3z5srbIMzU1hZ%2BfHw4dOoSDBw9q280t/b1Nc1er1UhISEBJSQkcHR1x%2BvRpnfkLCgq0RaC/vz/27dvX4rq5VUHv4OCAq1ev4sqVK9pp58%2Bfh7m5OXr06AFzc3NUVVVpH9NoNMjPz9eJkZubq/P7pUuXcO%2B99970eYmIiEh/t9t1Rp2dnSFJErKanEqTkZEBa2vrZkdUG0VFReHs2bMtFqKbN2/G999/rzMtOzv7lgVrU20uRp2dnTF48GBs3LgRFRUVOHPmDFatWtWsA9jUjBkzsG3bNhQWFqKkpASRkZE4e/bs/5JQKtGlSxesW7cO8fHxOHny5C3zGDZsGGxsbLBt2zao1WocP35ce74oAEybNg2lpaXYvHkzqqurkZubi8DAQGzfvl07z9SpU3HkyBEcOXKk1WJ0xowZ%2BOGHH3Do0CHU1tbio48%2Bwo4dO6BSqTBr1iycOHECu3btglqtxqlTp/D4449ru5/Tpk3D2LFj8eSTT%2BLo0aOoq6vT5vrss8%2BiW7duOofmm3JxcUG/fv0QHR2NyspKFBYWYsuWLZg2bRpMTU1x3333oaKiAkeOHIFarcbWrVubdXDz8/ORnJyM2tpaHDx4EFlZWZgwYcIt1y0RERHdmezs7DB58mRs3LgRxcXFKCgoQFxcHGbNmqUdBP7MM8/g66%2B/BgD89NNP%2BOKLL/D%2B%2B%2B/rnKLZqLS0FK%2B//jrOnz%2BPmpoafPjhh8jJycFjjz2md04GjaaPj49HWFgYPD090a1bNwQFBWH8%2BPGtzr98%2BXK88cYb8Pf3h5mZGSZOnIjg4OBm840aNQrz5s1DWFgYdu/e3ezST01ZWFggLi4Oa9aswUcffQQ3NzcEBgbiH//4BwCga9eu2Lx5M9555x3Ex8fDzs4OAQEBCAwM1MYYM2YMioqKcO%2B992LAgAEtPo%2BzszPWr1%2BPyMhIFBcXY9CgQdiyZQtMTU21xeKmTZvw%2Buuvay8E21jYKhQKxMbGYseOHXj77be1nUoHBwf4%2BvoiJiamxZH2jctu3rwZkZGRmDBhAiwtLTFx4kS88sorAIChQ4diwYIFCA0NhYmJCQIDA%2BF2w6VZvLy8kJ6ejsjISJiammLNmjUYOHBgq%2BuUiIiI2qYjHqa/lbVr1yIiIgJ%2Bfn4wNTXFww8/rHOFo9zcXFy9ehUAsGvXLly7dg0%2BPj46MUaOHIkPP/wQy5cvB9BwrmhpaSn69%2B%2BPjz76qE1HYhWSIWexUoe3YsUKnWulyqW8XGx5pbJhMGhlpfgbWGVaIxYAaBhZambWMFLH2G8FGXM5dd5caHkLi4bLQ124ID6AyXlgBxtNLzqARMbtdDBNbDtZWTVcHurYMfEBTJMekuF8cje3hktEubsLD2Da9bnYurW1Bfz8gNRU8QFMM8fpN2i0VZ06Ad26AX/8If76U6nElpd79LocA6q6dAEqKsRjyXJR2ba7ftVK2bVwgZ87Vge44i0RERER3a067O1AR4wYgZqa1jtf%2B/bta/V8SyIiIqI/w%2B14mL6j6bDF6PHjx42dwm1N5B6xRERERH%2BWDluMEhEREXV07IyKYzFKREREZCAWo%2BI4gImIiIiIjIadUSIiIiIDsTMqjp1RIiIiIjIadkaJiIiIDMTOqDgWo0REREQGYjEqjrcDpTa5yX0I9CLn3TeVMp1kYmoK1NbKE0uUXLmYlpeIBTAxAaytgbIy4VtwSrZdxXK5TqGQ546tondjBOTbTp1kaAfItV527xaPIestOGcJ3p5UxluToqhIbPlOnYCuXYGSEuPfDlShaLjfb3W1LC8cycJSOIZcr2GFDHe0NcRnn7VP3Mcfb5%2B4HRE7o0REREQGYmdUHAcwEREREZHRsDNKREREZCB2RsWxGCUiIiIyEItRcTxMT0RERERGw84oERERkYHYGRXHzigRERERGQ07o0REREQGYmdUHItRIiIiIgOxGBXHw/REREREZDTsjBIREREZiJ1RceyMEhEREZHRsDNKREREZCB2RsWxMyqTvLw8ODk5ITs7GwDg4uKCo0eP3nI5X19fJCYmtnd6RERE1A7q69vn392EndF2kpGRYewUiIiIiDo8FqNEREREBrrbupjtgcWoAXJzcxEREYH09HTY2tri2Wefha%2Bvr848Tk5O%2BOCDD%2BDl5YWqqipERUXhwIEDUCgUmDRpEsLDw2FmZqazjFqtxoIFC%2BDo6Ii33nrrlnkUFBTg9ddfx4kTJ1BXVwcvLy9ERETA1tYWVVVVWLNmDdLS0lBdXQ0nJyesXr0aQ4cO1fvvLCoqwuXLl3Wm2dh0h729vd4xbqRQ6P6kdmJiIra8Uqn7k%2B54trbiMaysdH8KcXMTW37QIN2fIjoJ7iob34%2Bi70tA/MOTH8LUAbEYNUBwcDBGjRqFuLg4/Pbbb3jiiSdgYWHR6vwbNmzAuXPn8M033wAAFi1ahLi4OISGhurMFxERATMzM0RGRuqVR1BQEPr374/U1FRUV1dj2bJliIiIwLvvvovt27fjypUrOHjwIMzMzPDBBx8gPDwc//rXv/T%2BO5OSkhAbG6szbcmSYCxdGqJ3jNaYmgqHkFVHykeWXEytZQgCQKUSDiHnLk%2BO/adc27ojvWbkWC9%2BfuIxGo0aJUOQEydkCAJg50554sjBWqb3pRzMzWUJI9f7W/Q1LEny5GEIdkbFsRhto5MnT%2BL06dPYvn07LC0t4ezsjNjYWPTo0aPF%2BSVJQnJyMqKiomBnZwcAiIqKQllZmc58CQkJyMjIQGJiIkz12MudOnUKmZmZ2Lp1K1QqFVQqFRYvXowlS5ZArVajrKwMpqamsLCwQKdOnRAUFISgoKA2/a1z5sxp1vG1sekOtbpNYXQoFA078dpa8Q8Pub7YN%2BbTEciVi2lV2a1nuhmlsqEQLS8X/qSVrOTZASsU8uxw6urEY8i1nUQbboB86%2BXbb8VjWFk1FKI//ghcuyYWy%2B8v7mIBBg1qKETnzweyssRipaaKLW9i0lCIlpUBGo1YLEtLseUVioZCtKZGlheOZN56I6YtKRmzmBTFYlQci9E2ysnJgUqlgm2TY1qenp7Iy8trcf6SkhKUlZWhT58%2B2mmDbjhslJaWhsOHDyMhIQFWeh7fysvLg42NDbp3766d5ujoiNraWhQWFmL%2B/PlYuHAhvL29MX78eEycOBF%2BbWx92NvbNzskL9PnFySp4xSjdyTRHV6j%2Bnr5YlGHVloqX6xr12SIl54uSy7IyhKPJcc3GKDhvSQaS66qTY4PYSKZ8ISwNlIqlahvw9cg5fVz7m62THp6Ory9vRETEwONnjt%2B9U3akwqFAn369MHXX3%2BNv/3tb1CpVHjttdewbNkyvfMmIiKiW%2BOlncSxGG0jBwcHVFRUoKioSDstJSUFly5danF%2BW1tbWFtb48KFC9ppmZmZ2LNnj/b3kJAQREdHo7i4GPHx8XrncfXqVVy5ckU77fz58zA3N0ePHj1QUVEBjUYDT09PrF69Gp999hn279%2BPkpKStv7JRERERO2GxWgbOTs7Y/Dgwdi4cSMqKipw5swZrFq1Snux%2B5bMmDED27ZtQ2FhIUpKShAZGYmzZ89qH1cqlejSpQvWrVuH%2BPh4nDx58pZ5uLi4oF%2B/foiOjkZlZSUKCwuxZcsWTJs2Daampli6dCnefvttlJeXo76%2BXjvy38bGRpb1QEREROyMyoHFqAHi4%2BORn58PT09PvPDCCwgKCsL48eNbnX/58uVwdXWFv78//P39MWDAAAQHBzebb9SoUZg3bx7CwsJuehgeaDgUv3nzZhQVFWHChAmYPXs2hg0bhtdeew0AEBkZiYsXL8LLywsjR47Exx9/jLi4OO1pA0RERCSOxag4hSTxDGbSX02N2PIKBWBmBqjV4ufOy1VX35Gj6csFT8eQcfSvZNtVLJfrOJq%2BZXKtl927xWPY2jZcIio1VXwA08xZgiMU3dwaLg/l7i4%2BgKnJaVkG6dQJ6NoVKCkRfwGKXm5NoQAsLIDqanlG01sIju6HfK9hYw1qjY5un7jLl7dP3I6Io%2BmJiIiIDHS3dTHbA4vRDmrEiBGouUkbct%2B%2Bfejdu/efmBERERGR/FiMdlDHjx83dgpERER0C%2ByMimMxSkRERGQgFqPiOLSaiIiIiIyGxSgRERGRgW7HSzuVlpbipZdegqenJ8aNG4dVq1ahurq6xXl3796NQYMGwcXFReffL7/8cv3vr0dMTAz8/PwwcuRILFy4ELm5uW3Kh8UoERERkYFux2I0PDwcVVVV2Lt3L3bt2oXs7GysX7%2B%2B1flHjhyJjIwMnX%2Burq4AgE8%2B%2BQRffvkl3n//fRw6dAj3338/lixZgrZcOZTFKBEREdFd4sqVK0hJSUFoaCjs7OzQo0cPBAUFYdeuXag14ALKSUlJWLBgAfr16weVSoXQ0FBkZ2fj559/1jsGBzARERERGai9uphFRUW4fPmyzrTu3bvD3t5eKO6pU6dgYmICJycn7bQhQ4agsrIS58%2Bf15ne6Pfff8ezzz6LX3/9FdbW1li6dCkCAgJQXV2Nc%2BfOYfDgwdp5VSoV7rvvPmRkZGD48OF65cRilNpE9E3XeIeM%2BnrxO26YmYkt35Qcd8KRixy5aKzF73pkAkDTxVo8GZk%2BqE1M5PnQl2tbyxGnokJseaUS6NwZqKoSXzczxxWKBQCur5Ru8Bv%2Bh/idhuS46xHQcDso0VwEd/7au0H5%2BYnfDWrHDrHl7eyAadMa1ktxsVgsAIq5c4VjwNQUijo5bj1nKh6jA0lKSkJsbKzOtODgYISEhAjFLS0thUqlgqLJLatsbGwAACUlze/eZ2dnh/vvvx8vv/wy%2Bvfvj4MHDyIsLAz29vZ44IEHIEmSdvmm8VqK1ZoOtAsmIiIiur20V2d0zpw58PX11ZnWvXt3vZbds2cPwsLCWnwsNDS0TedzTpgwARMmTND%2BPm3aNBw8eBC7d%2B/GK6%2B8AgBtitcSFqNEREREBmqvYtTe3t7gQ/IBAQEICAho8bGjR4%2BivLwcGo0GJiYmABq6pQDQrVs3veL37t0bv/76K2xtbaFUKrXLNyotLdU7FsABTERERER3DWdnZ0iShKysLO20jIwMWFtbo2/fvs3mT0xMxNdff60zLTs7Gw4ODjA3N8eAAQOQmZmpfaysrAw5OTna0fb6YDFKREREZKDb7dJOdnZ2mDx5MjZu3Iji4mIUFBQgLi4Os2bNQqfr51o/88wz2gJUrVYjMjISGRkZqK2txd69e5GWloa5188XnjdvHnbs2IHs7GyUl5dj/fr1cHZ2houLi9458TA9ERER0V1k7dq1iIiIgJ%2BfH0xNTfHwww8jNDRU%2B3hubi6uXr0KAHj66adRUVGBZcuW4fLly%2BjTpw/i4uIwdOhQAMDcuXNx%2BfJlPPXUU6ioqICHh0ezgVe3wmKUiIiIyEC3473prayssGHDhlYf//bbb7X/VygUCAoKQlBQUIvzKhQKLF26FEuXLjU4HxajRERERAa6HYvRjobnjBIRERGR0bAzSkRERGQgdkbFsTNKREREREbDzigRERGRgdgZFcdilIiIiMhALEbF3VGH6fPy8uDk5ITs7GwAgIuLC44ePXrL5Xx9fZGYmNje6RERERHRDe7ozmhGRoaxU2g3mZmZuHr1Kjw9PY2dChER0V2LnVFxd1Rn9G6ya9cufPfdd8ZOg4iIiEjIbVuM5ubmIjAwEG5ubvDx8cGOHTuazePk5IS0tDQAQFVVFcLDw%2BHh4YHRo0cjPDwcarW62TJqtRrz58/HihUrhPMoKCjAiy%2B%2BCA8PDzz44IMIDQ1FaWkpAGD37t0YO3asTqzZs2fjvffeAwC89957ePHFF/HBBx9g7NixGDlyJN544w0AQGRkJHbu3IkPP/wQkyZNAtBwqsGWLVvg5%2BeHiIgITJo0Cf/4xz904v/1r3/F8uXL9fq7iIiI6NZut3vTd0S37WH64OBgjBo1CnFxcfjtt9/wxBNPwMLCotX5N2zYgHPnzuGbb74BACxatAhxcXE692IFgIiICJiZmSEyMtLgPPr164exY8ciKCgI/fv3R2pqKqqrq7Fs2TJERETg3Xff1Sv2iRMn4OrqikOHDuGnn37CggUL8MgjjyA8PBxnzpzBsGHD8Morr2jn/%2Bqrr/Dhhx/C0dERcXFx%2BPLLL/HUU08BADQaDVJTU/HOO%2B/o9dwAUFRUhMuXL%2BtMs7buju7d7fWOcSOFQvcn0d1MKdgOaPp%2BEo2FTjLsDkxMdH%2BKEP2D5MzFzU1s%2BUGDdH%2BKsLMTW97aWvcnCbvbCsf2cFsWoydPnsTp06exfft2WFpawtnZGbGxsejRo0eL80uShOTkZERFRcHu%2Bhs5KioKZWVlOvMlJCQgIyMDiYmJMDU1Fcrj1KlTyMzMxNatW6FSqaBSqbB48WIsWbKkxY5sS0xMTPD8889DqVRizJgxsLOzQ3Z2NlxdXVucf/z48bjvvvsAAAEBAYiNjUVOTg4cHR3x448/QqlUNuvG3kxSUhJiY2N1pi1ZEoylS0P0jtEac3PhELLqSMWxHLnIsf%2BVM45cOlI%2Bcmynzp3FYwCApaUMQTp3kyHIdba28sUSJUfRdeKEeAwA2LlTnjhyGD/e2Bno0mOfe1O1tfLkQUZxWxajOTk5UKlUsG3ygefp6Ym8vLwW5y8pKUFZWRn69OmjnTbohm%2BoaWlpOHz4MBISEmBlZSWUBwAcPHgQNjY26N69u/YxR0dH1NbWorCwUK/4vXr1grJJd8DS0hLV1dWtzt%2B7d2/t/x0cHODu7o4vvvgCwcHBOHjwIKZOnYpObeh%2BzJkzB76%2BvjrTrK274yYp3JJC0VCI1tQAkmR4HEC%2BglahEM9FLnLlIsc3dRMTQKMRjyMXufIR7iBCvu1UVSWeh6VlQxzRfDpX/SEWAGjYSLa2QGmp%2BMaSozNqbQ2UlYnn4ucntvygQQ2F6Pz5QFaWWCw9j9q1ytq6oRD9z38a1o2ohx4Sj2FqelsXk%2ByMirsti1GlUon6Nmz9xoLuZsukp6fD29sbMTEx8PDwgIkeLZib5XGz7qeilZaK5oYPTGUbP4xvzPnRRx/Fhx9%2BiCVLliAlJUXv0wMa2dvbw95e95C8HDs9oCFGRykAiYxFdCfW%2BBEhSTLsEOvqBAM0odGIx5PjW4NcuaSny5NLVpZ4rOJieXIpK5MvFpGg23IAk4ODAyoqKlBUVKSdlpKSgkuXLrU4v62tLaytrXHhwgXttMzMTOzZs0f7e0hICKKjo1FcXIz4%2BHihPH788Uc4ODjg6tWruHLlivax8%2BfPw9zcHD169IC5uTmqmrRFNBoN8vPz9XpefU2dOhWXLl1CYmIizM3N4SZ63hMRERHp4AAmcbdlMers7IzBgwdj48aNqKiowJkzZ7Bq1Srtxe5bMmPGDGzbtg2FhYUoKSlBZGQkzp49q31cqVSiS5cuWLduHeLj43Hy5EmD86iuroaLiwv69euH6OhoVFZWorCwEFu2bMG0adNgamqK%2B%2B67DxUVFThy5AjUajW2bt0KqQ2tQnNzc%2BTl5eHq1autzmNlZQVfX19ER0fj4Ycf1js2ERER6YfFqLjbshgFgPj4eOTn58PT0xMvvPACgoKCMP4mJ2QvX74crq6u8Pf3h7%2B/PwYMGIDg4OBm840aNQrz5s1DWFiYXgONWsrDy8sLCoUCmzdvRlFRESZMmIDZs2dj2LBheO211wAAQ4cOxYIFCxAaGgovLy906tSpTZ3LGTNmIC0tDQ899FCzw/tNPfrooygvL8f06dP1jk1ERET0Z1FIbWnH0W1n165d2LVrF3bKNIpTjgEXFhZAdbX4OaM3uZJXm3PqKO8CDmBq3Z04gKmiQmx5pbJhRH5lpfg2V1XoN7Dypjp1Arp1A/74w/jnjHbqBHTtCpSUiOdib/jl7AA0XBrqxAnA3V38nNEWrqndJnZ2wLRpwFdfyXPO6Ny54jHkGsAkOiLfQAsXtk/chIT2idsR3badUbq1Cxcu4N1338Xzzz9v7FSIiIiIWnRbjqb/s4wYMQI1NTWtPr5v3z6dyyl1JK%2B99hr279%2BPBQsWwNvb29jpEBER3ZHutvM72wOL0Zs4fvy4sVMw2Nq1a7F27Vpjp0FERHRHYzEqjofpiYiIiMho2BklIiIiMhA7o%2BLYGSUiIiIio2FnlIiIiMhA7IyKYzFKREREZCAWo%2BJ4mJ6IiIiIjIadUSIiIiIDsTMqjsUotYnoHfoUiv/FEb2dogJy3cNTIWMsUfLkUl%2BvEI5hYiLPh6xpUb4MQUwBe3uY/FEkftvAXr3E85FpO5mYiG2npu8nhegmV6kEA%2BB/HxCdO4u/eOT6sLG0FP%2BwkeMWnAAQGSl%2BC86nnxZb3s2t4Xag4eHityYFgNmzxZZv3E6S1HHuy0x/OhajRERERAZiZ1Qci1EiIiIiA7EYFccBTERERERkNOyMEhERERmInVFxLEaJiIiIDMRiVBwP0xMRERGR0bAzSkRERGQgdkbZOBu1AAAgAElEQVTFsTNKREREREbDzigRERGRgdgZFcdilIiIiMhALEbF8TA9ERERERkNO6NEREREBmJnVNxd0xnNy8uDk5MTsrOzAQAuLi44evToLZfz9fVFYmJie6enl8mTJ%2BOzzz4zdhpEREREsrlrO6MZGRnGTqHN9u/fb%2BwUiIiIqInbsTNaWlqKNWvW4Mcff4RSqYS3tzfCw8NhYWHRbN7Vq1djz549OtM0Gg0CAgKwbt06rFixAl988QVMTEy0j5ubm%2BP48eN653PXdEaJiIiI5FZf3z7/2lN4eDiqqqqwd%2B9e7Nq1C9nZ2Vi/fn2L877xxhvIyMjQ/ktPT8cDDzyAKVOmaOd58cUXdeZpSyEK3KHFaG5uLgIDA%2BHm5gYfHx/s2LGj2TxOTk5IS0sDAFRVVSE8PBweHh4YPXo0wsPDoVarmy2jVqsxf/58rFixQq88nJyc8NFHH2HcuHF4//33AQDff/895syZAzc3N4wfPx5xcXHa%2BTUaDdauXQs3NzdMmDABX331FR566CHs3r0bgO4pA/X19YiLi8OkSZPg6uqKxx57DN9//702lq%2BvLz777DMsXrwYbm5umDhxIo4cOaLnGiQiIqI70ZUrV5CSkoLQ0FDY2dmhR48eCAoKwq5du1BbW3vL5bdv345evXrB29tbtpzuyMP0wcHBGDVqFOLi4vDbb7/hiSeeaLH13GjDhg04d%2B4cvvnmGwDAokWLEBcXh9DQUJ35IiIiYGZmhsjISL1zSUlJQXJyMrp164aCggIEBQUhIiIC06dPx7lz57Bo0SI4Ojpi%2BvTp%2BMc//oFvvvkG//znP9GjRw/89a9/RVFRUYtxP/nkE3z22WfYunUr%2Bvbti48//hhBQUFISUlBt27dAAAJCQl45513MGjQIKxZswZRUVH4%2Buuv9c69qKgIly9f1plmY9Md9vb2ese4kUKh%2B5PuAqam4jE6ddL9eYcQfR/I%2Bn5SytCbaJqQaLyOtHLs7MSWt7bW/SnCzU1s%2BUGDdH%2BK6ijbSZLElhfQXl3MlvbB3buL7YMB4NSpUzAxMYGTk5N22pAhQ1BZWYnz58/rTL9RWVkZ4uPjsXPnTp3px44dQ2pqKi5evIh%2B/fphzZo1GDp0qN453Vmf7ABOnjyJ06dPY/v27bC0tISzszNiY2PRo0ePFueXJAnJycmIioqC3fUPnKioKJSVlenMl5CQgIyMDCQmJsK0DTvXqVOn4p577gEA7N27FwMGDMCjjz4KoKFzOnfuXOzZswfTp0/Hv//9bzz88MMYMGAAAGD58uU4ePBgi3E///xzzJ8/X/uiCQwMxLZt23D48GHMnDkTAODj4wNXV1cADYOfkpOTUV9fD6WeO4mkpCTExsbqTFuyJBhLl4bo/fe3Ro76BJCxou1I1bEMucizfmWKI/jBqUO0KJCTDNvpJt%2BR28TcXI4olnIEaSDXHyYHOVbOtGniMQBg/HjxGHLlckMxYXSiHzY1NfLk0YG0tA8ODg5GSIjYPri0tBQqlQqKJp9hNjY2AICSkpKbLvvxxx9j5MiR2joFABwcHKBUKrFs2TJ06dIFsbGxCAwMxP79%2B9G1a1e9crrjitGcnByoVCrY2tpqp3l6eiIvL6/F%2BUtKSlBWVoY%2Bffpopw264RtjWloaDh8%2BjISEBFhZWbUpn169eunklpGRARcXF%2B00SZLQt29fAMDly5d12t59%2B/aFSqVqMW5eXh769eunM83R0RH5%2Bfna35v%2BTRYWFtBoNKitrYW5nh/Oc%2BbMga%2Bvr840G5vuaOEMBr0pFA2fObW14l9kzUxl%2BiasUBj1W7UOmXKprZOnoNXjiM2t45S03N1vk06dGgrR4mKgrk4sVvfu4vnItJ2qa8S2k0LRUGvV1IinYyFViQVoTMjCAqiuFk9Ijo6bXCsnNVVseWvrhkL0P/8Bbmh0tFl4uNjygwY1FKLz5wNZWWKxAODYMbHl5dwpGEl7dUZb2gd31/Pza8%2BePQgLC2vxsdDQUEgGrGuNRoNPPvkE0dHROtOXLFmi8/tf/vIX7N27FykpKXj88cf1in3HFaNKpRL1bXhlNHYJb7ZMeno6vL29ERMTAw8PD50RY7fSdF4LCwt4e3sjPj6%2BxXnr6%2BubdV1b62K2dE4rAJ1vOvp2QFtjb2/f7HCAHJ/rQEOM2/Rzh9pKjoq2UV2dvPGMTK73gCzvJzn2qI2fOZIkHk%2BO0wYacxFdOcXF8uRSViYeKz1dnlyysuSJ1aFexMbRXsVoS/tgfQUEBCAgIKDFx44ePYry8nJoNBptjVJaWgoA2tP8WvLf//4XarUaI0aMuOlzm5iYoGfPnq2eZtiSO24Ak4ODAyoqKnRWQkpKCi5dutTi/La2trC2tsaFCxe00zIzM3UuYxASEoLo6GgUFxe3Wkjqw9HREWfOnNH5RnL58mVtYdmtWzedzubFixebnS7QNNb58%2Be1v9fV1eHixYtwcHAwOD8iIiK6szk7O0OSJGQ16YxnZGTA2tpae6S2JampqRg9ejQ6NTl3X5IkrFu3TieWWq1GTk5Om%2BqRO64YdXZ2xuDBg7Fx40ZUVFTgzJkzWLVqlfZi9y2ZMWMGtm3bhsLCQpSUlCAyMhJnz57VPq5UKtGlSxesW7cO8fHxOHnypEG5TZs2DaWlpdi8eTOqq6u1o/63b98OAPDw8MCXX36JCxcu4Nq1a4iJiUHnzp1bjBUQEICdO3ciOzsbarUa8fHx0Gg0zVr6RERE1H5ut0s72dnZYfLkydi4cSOKi4tRUFCAuLg4zJo1S1toPvPMM80GPJ86dUrn9D%2Bg4WhsXl4eXn/9dRQWFqKiogLr16%2BHqakpJk6cqHdOd1wxCgDx8fHIz8%2BHp6cnXnjhBQQFBWH8TU4cX758OVxdXeHv7w9/f38MGDAAwcHBzeYbNWoU5s2bh7CwsFYPk99M165dsXnzZqSmpmLkyJF48skn4ePjg8DAQAANo/hHjBiBgIAAzJo1C48%2B%2BigsLS1bPNweGBiIKVOm4LnnnoOnpyd%2B%2BOEH7NixA9ZyjNYkIiKiO9batWthZWUFPz8/PPLII3B1ddW5glBubi6uXr2qs8zly5e1A7KbevPNN3H//fdjxowZ8PT0xKlTp7B9%2B/ZWm2ktUUiGnMVK7UatVsPMzAwAUFtbi%2BHDh2Pbtm0YM2aMkTNrIDpgUaEAzMwAtVr89CBzMw5gak2HGsBUlH/rmfRJxt4eKCoST6rJoEKDybSdqqrFBzDJNV7Isr5CLADQcJ6npSVQVWX8c0blXDmffy62vJ1dwyj4r74SP2f06afFlndzA06cANzd5TlntLpabHlZdwqyXFaizdpr99zk0uF3vDuyM3q7Sk5Oho%2BPDy5cuIDa2lps3boVVlZWOqPviYiIqOO43Q7Td0R33Gj6P8uIESNQc5M24b59%2B9C7d%2B82xXzkkUeQnZ2Np59%2BGuXl5ejfvz/i4uJavbwTERER0e2OxaiB2nrfVX0olUosX74cy5cvlz02ERERye9u62K2Bx6mJyIiIiKjYWeUiIiIyEDsjIpjMUpERERkIBaj4niYnoiIiIiMhp1RIiIiIgOxMyqOnVEiIiIiMhp2RomIiIgMxM6oOBajRERERAZiMSqO96b/f/buPS7G9P0D%2BGdKBySH725sqViH2CUdyMo5p45iscJaqwNflBbrfCgSFjmmQlm07ObQVsgpOcvSJsWKdY5UVEpFTTPz%2B6Nfz7cxE1FzP89u1/v18lLPM819NTPVNfdz39dFPkxeXs2%2BXl0d0NUFCgoAiaRGdyXWaVqzWP5fbfVgrw21FcvhwzX7%2BsaNARsbID4eyM%2Bv2X0NHyysnudizYY1Dqe2nqd3NHGrFjU1oEEDoLi45n8QdWSvanYHFQE1bAgUFdU4IJlOoxqHIxLVvN05AIjKauHJrq0XTU2f6NrsBQ8A2to1%2B3pzcyApCbCwAK5dq9l98ZTOfPmlau735k3V3K8Q0cwoIYQQQshHopnRmqMNTIQQQgghhDc0M0oIIYQQ8pFoZrTmKBklhBBCCPlIlIzWHF2mJ4QQQgghvKGZUUIIIYSQj0QzozVHM6OEEEIIIYQ3NDNKCCGEEPKRaGa05igZJYQQQgj5SJSM1hxdpieEEEIIIbyhmVFCCCGEkI9EM6M1RzOjhBBCCCGEN7WSjD558gQmJia4d%2B8eAKBz5864ePHie7/OxsYGv/76a22E8F4ymQzTp0%2BHmZkZDh8%2BzGRMQgghhPy7SaWq%2BVeXqOQyfWpqqirutkZu3bqF48ePIyYmBiYmJnyHU6UDBw7AxsYGzZo14zsUQgghhLxHXUscVaHOXKYvLCwEALRq1YrfQN5BIpFg1apVyMvL4zsUQgghhBAmPjgZTU9Ph6urK8zNzdG/f3/s3r1b4TYmJiY4d%2B4cAOD169dYvHgxunfvjq%2B%2B%2BgqLFy9GaWmpwteUlpZi7NixmDdvXrXiMDExwc6dO9GrVy9s27YNAJCQkIDRo0fD3NwcvXv3xpYtWwAAFy9ehKurKwCga9euiIqKglQqxaZNmzBw4EB06dIFI0aMwJ9//sndv42NDYKDgzFgwAD4%2BPhwSxH27t0LKysr7lJ/bGwsnJ2dYWZmhgEDBiAiIoK7j3nz5mHhwoUYP348HB0d3/s9WVlZ4dWrV3B2dkZgYCAsLCwQHx8vd5sJEyZg3bp1iIyMxKBBg7B//3707t0bZmZmWLJkCcrKyrjb/vLLL7Czs0OXLl3g4OCAuLi4aj22hBBCCKkeukxfcx98md7T0xNWVlbYsmULHj58iHHjxkFbW7vK269btw53797F0aNHAQDu7u7YsmULZsyYIXc7Hx8faGpqws/Pr9qxxMXFISoqCv/5z3%2BQmZmJqVOnwsfHB05OTrh79y7c3d1hZGQEJycnhIWF4bvvvkNiYiK0tLTw888/48iRIwgNDYW%2Bvj4iIiIwZcoUnDlzBg0aNAAAHDlyBDt27ICRkRGePn0KALhy5Qri4%2BPRsGFDpKamYuHChdi8eTN69OiBa9euwcPDA%2B3atYOFhQUA4NSpU1i5ciX69ev33u8nOjoaAwYMQHR0NNq0aYOnT5/i0KFDsLGxAQDk5eXh6tWrWLx4MVJSUpCVlYXU1FScOHECGRkZmDBhAtq0aYMJEybgxIkTCAwMRGhoKDp06ID4%2BHj88MMPOHHiBPT19av1%2BGZnZ%2BP58%2Bdyxz7V1obep59W9ylSpKYm/z9RicaNa/b1Ojry/9dIbTzXItH//v8XvXZq%2Bq3U6sMiq4XHlX6%2BVa/iSa/p19f0fiqYm9fs6zt0kP//Y127VrOvr4G6ljiqwgclo3/99Rdu376NXbt2oX79%2BujYsSMCAwPRvHlzpbeXyWSIiorCihUruDWQK1asQEFBgdztwsLCkJqail9//RUaGhrVjsfOzg6ffPIJAODw4cNo164dhg0bBqB85tTFxQXR0dFwcnJS%2BNoDBw7g%2B%2B%2B/5y7bjx8/Hrt27cKZM2dgb28PAOjduzeMjY3lvm7YsGHQ%2Bf%2B/0JGRkejXrx969eoFoHzW1c7ODtHR0VwyamBggP79%2B1f7e6rM2dkZkydPRmFhIXR0dHDq1Cm0b98ebdu2RUpKCkpKSvDDDz%2Bgfv36aNOmDRwcHHDmzBlMmDABBw4cwMiRI9GpUycAwODBg2FpaYnDhw9j0qRJ1Ro/IiICgYGBcsc8p02D1/TpH/X9yKmFLKf6r5Rq3Fdt3lkN1UYs///%2BpcasrGrjXurXxp2Ue8cb3%2Bqqrae6Np6n2nrd1a%2BVh7hhbdxJuVoIqJZSpdrJuWrrifq3/aIBgKSk2rmfvXtr9vW1lVwTXnxQMvr48WPo6OigSZMm3DFra2s8efJE6e3z8vJQUFCAli1bcsc6vPXu59y5czhz5gzCwsLQqFGjDwlHbobv8ePHSE1NRefOnbljMpkMrVu3rvJ78ff3x4oVK7hjUqkUz5494z43MDB475gJCQkKY1Ykp1XdR3V1794dzZo1Q1xcHIYNG4aTJ0/KJdaNGzeW2%2Bikr6%2BPCxcucLFdvHgRu3btkoutbdu21R5/9OjR3KxshU%2B1tYG33kx8EDW18kS0sLDGbyfF9XVr9PUVNDQAsbhW7qrGaiuW8%2Bdr9vU6OuWJ6JUr5U9VTdj0eF2zOwDK/9BoawNv3gAyWY3uSlyv5olSbT1PNb0Pkag873v9usYPCxrIimp2B0D5z3dFQDX8%2BZY1qHlyLBLV/HEBAFFZLTzZtfWiqek3JBL9L5baeHC%2B%2BqpmX9%2BhQ3kiOnYskJZW83h4QDOjNfdByaiamhqkH/Coq/3/pZp3fc21a9fQt29frF%2B/Ht27d4e6unq177/ybbW1tdG3b1%2BEhIRU62u1tbWxfPlyDBkypFr3X9WYY8aMweLFiz/oPqpLJBJh6NChOHToEAYOHIg//vgDy5Yt485LJBK528tkMoj%2B/92htrY2Zs2axa2V/Rh6enrQ09OTP5iXB7w17keRSmvnfohS%2Bfm1cz%2BFhbVwX7Xxm7risq9M9q/6zV/Tb6VWHxZZLT6udXHRGyu1kUBW3E9t3FdtXR5PS%2BP1Ujvh1wct7DE0NERRURGys7O5Y3FxccjIyFB6%2ByZNmkBXVxcPHjzgjt28eRPR0dHc515eXggICEBubm61E0lljIyMcOfOHcgq/XA9f/5c6Wapiu/l9u3bcseqmuF915hv30dmZqZCklgTzs7O%2BOOPPxAZGYkuXbrILYkoLCxEbm4u93lGRgZ3XllsGRkZco8PIYQQQmqGNjDV3Aclox07dsQXX3yBDRs2oKioCHfu3MHChQu5YvfKfP311wgNDUVWVhby8vLg5%2BeHv//%2B%2B38BqKmhYcOGWLlyJUJCQvDXX3991Dfi4OCAly9fIigoCG/evOF2/Ve%2BTF2Zi4sL9uzZg%2BTkZEgkEsTGxsLR0bHKxFqZkSNHIikpCQcPHkRpaSlu3bqFUaNG4fjx4x/1PVRsBHv48CFXiurzzz9Hx44dsXHjRoW1r5qamtiyZQvevHmDu3fv4siRI9xl9dGjRyM2NhZnzpxBWVkZLl%2B%2BDEdHR1y/fv2jYiOEEEKIIkpGa%2B6DtzyGhITg6dOnsLa2xn//%2B19MnToVvXv3rvL2s2bNgqmpKezt7WFvb4927drB09NT4XZWVlYYM2YM5syZU%2BVs5rs0bdoUQUFBOHXqFLp164Zvv/0W/fv3r/Iy9ciRIzF27Fh4enrC0tISoaGhCAwMrPZOcwBo06YNAgICEBoaiq5du8LLywtubm7cBqgP9cknn2DIkCHw9vbGhg0buOPDhg1DaWmpwpICXV1dtG/fHoMGDcLIkSMxYMAAuLi4AAB69uyJuXPnYtmyZbCwsMCyZcvg6%2BsLMzOzj4qNEEIIIUQVRDK6bit4mzZtQnp6OtasWcMdi4yMREBAQLXartaqmhbkV1cHdHXLN0HVcDmDWKdpzWL5f//GDUw17XjbuHH5jvz4%2BJqvGR0%2BWFgbY8SaNd8YU1vPU0lJzb5eTQ1o0AAoLq75TIqO7FXN7qAioIYNgaKimm9g0vmwDa3K/Cs3MNX0iRaJAE1NoLS0dh6cmla4MDcv35FvYVHzNaM8pTMfuPe62l7Vwo/kP4VK2oGS2pOcnIzw8HCEh4fzHQohhBBCSK0TZDLatWtXlLxjyuDYsWM1KpnEhxcvXry33mhqaqrc525ubrh9%2Bzbmzp2rUBKLEEIIIfyra%2Bs7VUGQyWhiYiLfIdS6Tz75RCHZfJ%2BwsLAqz3399df4%2BuuvaxoWIYQQQmqAktGao55thBBCCCGEN4KcGSWEEEII%2BSegmdGao5lRQgghhBDCG0pGCSGEEEI%2B0j%2B16H1qaioGDRqEb7755r233b17N4YMGQILCwuMGTMGN27c4M6VlJRgyZIl6NOnD7p3747p06cj7wPLQFIySgghhBDykf6JyWhMTAy8vLxgbGz83tvGx8dj8%2BbNWL16NS5duoT%2B/fvjv//9L4qLiwEA69evx82bNxEREYHjx49DJpNh/vz5HxQPJaOEEEIIIXVISUkJIiIi0KVLl/feNiIiAl9//TW6dOkCbW1tuLu7AwBOnz6NsrIyHDhwAFOnTsVnn32GJk2a4IcffsCZM2eQlZVV7XhoAxMhhBBCyEdS1SxmdnY2nj9/Lnfs008/hZ6eXo3ve9SoUdW%2B7c2bN%2BXanKupqaFjx45ITU1Fx44d8erVK3z55Zfc%2BTZt2kBbWxs3b95E8%2BbNqzUGJaPkwzStWQvO7OxsROzahdGjR9f4B0qjRl9dKZ6IiFqJR0ixDB9e81g2b66tx6Xm7Tezs7MRERoqiNdNbT5PGjUMJjs7G2FhtfU81bynYXZ2NiJ27KiVeES1EUtt/WzX8In6t/6eAVDjFpzZ2dmI2LwZo48d4/2x%2BViq6kK6eXMEAgMD5Y55enrCy8tLNQNW4eXLl2jcuLHcscaNGyMvLw8vX74EAOjq6sqd19XV/aB1o3SZnjD1/PlzBAYGKrzb44uQ4qFYqiakeCiWqgkpHopF%2BLEAwotHSEaPHo3IyEi5f6NHj67W10ZHR8PExETpv8jIyA%2BORfaejPt959%2BHZkYJIYQQQgRGT0/vo2eLnZ2d4ezsXCtxNG3alJsBrfDy5Uu0a9cOzZo14z5v2PB/V8Hy8/Pxn//8p9pj0MwoIYQQQghRqlOnTrh58yb3uUQiwV9//YUuXbrA0NAQjRs3ljt/584dlJaWolOnTtUeg5JRQgghhBDCsbW1RWJiIgBgzJgxiIqKQnJyMl6/fo3g4GBoamqiX79%2BUFdXxzfffIOQkBA8e/YMeXl5WLduHQYNGoRPPvmk2uOp%2B/r6%2BqroeyFEqYYNG8LKykpuSp9PQoqHYqmakOKhWKompHgoFuHHAggvnrpgyJAhWL16Na5cuYLMzExs3boVwcHBcHZ2hq6uLvz8/GBnZwdjY2MYGxtDR0cH/v7%2B2LRpE0pLSxEQEMDtlO/atSv%2B/vtvLFu2DDt37kT79u3h5%2BcHLS2tascjktV01SkhhBBCCCEfiS7TE0IIIYQQ3lAySgghhBBCeEPJKCGEEEII4Q0lo4QQQgghhDeUjBJCCCGEEN5QMkoIIYQQQnhDySghhBBCCOENJaOEEEIIIYQ3lIwSQgghhBDeUDJKCCGEEEJ4Q8koITxauHCh0uOFhYWYNm0a42j4l5GRUe1/dRm9bqovPz%2Bfl3F//PFHnD9/HlKplJfxq1JaWqrwjxC%2B1eM7APLvZWNjA5FIVK3bnjp1SqWxjB8/vtqx7N69W6WxAEB6ejoePnyImJgY2NvbQyaTyZ1/9OgRLly4oPI4lDly5AiioqLw/PlzREVFobS0FOHh4XB1da32Y/ixPuQ1c%2BvWLZXG8rbbt2/jzJkzyM7OhkgkQvPmzdG/f3%2B0bduWWQxCfN0I4XGpcPPmTSxevBiRkZEAAG9vbxw/fhxNmzZFUFAQzM3NmcWiqamJ2bNnQ11dHXZ2dnByckKXLl2YjV/ZpUuXsGLFCjx8%2BBASiUThPOufpczMTBw%2BfBiZmZlYtGgRACAlJQWmpqZM4yDCQckoUZlJkyZxH%2Bfk5GDfvn0YNGgQWrVqBalUirt37%2BLMmTNwdXVVeSxmZmbcx69fv0Z0dDQsLS3RunVrLpaUlBSMHTtW5bEAQFpaGjZt2gSxWAw3NzeF81paWnBxcWESS2VBQUGIiIjA6NGjERISAgAoKChAVFQUXr16hR9%2B%2BEGl48fGxnIfp6Sk4ODBgxg/frzca2bv3r34/vvvVRrH23799VcsW7YMHTt2hL6%2BPmQyGf7880%2BsX78eS5cuxahRo5jEIbTXjVAelwrLly/HwIEDAQBxcXH4888/ER8fj6SkJKxZswZ79%2B5lFsuKFSsglUrxxx9/IC4uDtOnT4e2tjacnJzg6OiIVq1aMYvFx8cHVlZWmD17NurXr89sXGVOnTqFGTNmwMLCAn/%2B%2BScWLVqEZ8%2BeYeLEiVi2bBkcHBx4jY/wREYIA66urrLr168rHL969aps4sSJTGOZMWOG7OzZswrHT548KfP29mYay9ChQ5mO9z59%2BvSR3blzRyaTyWSmpqbc8cePH8v69evHNBZHR0dZVlaWwvEnT57IHBwcmMbSo0cP2cmTJxWOnzhxQtajRw%2BmschkwnndCO1xsbCwkInFYplMJpPNmzdP5u/vL5PJZDKpVCrr2rUr83jetnfvXpmlpaWsQ4cOsnHjxsnOnTvHZFwzMzPuceGbo6Mj95rp3LkzdzwhIYH5zzURDlozSphISkpChw4dFI6bmpri2rVrTGM5c%2BYMrK2tFY737dsXZ8%2BeZRrLZ599xnS893n16hXatWuncFxPTw%2B5ublMY3n69CkaNGigcLxx48Z4%2BvQp01hKSkrQr18/heP9%2B/fnZc1ddHQ0JBIJnj17hgcPHij8Y0Voj4uWlhbEYjEkEgnOnz%2BP/v37AwCKi4t5W7uZnp6OoKAg2Nvb46effkLv3r0REhKC4cOHY9GiRdi2bZvKY7CyskJaWprKx6mO9PR02NjYAIDckpxu3brhyZMnfIVFeEaX6QkTRkZG2Lx5MyZPngwdHR0A5ZstQkND0bJlS6ax6OnpISIiAuPGjZM7HhkZiU8//ZRpLBkZGbhx4wY6derEdNyqtG/fHjExMRg6dKjc8R07dqBNmzZMY7GwsMDUqVPh5uYGAwMDlJWVITMzE7t372a69g8Ahg0bhqioKIwcOVLueGxsLJycnJjGAgCHDh3CsmXLUFhYqLBuVCQSMVsDKLTHpU%2BfPpg%2BfTrq1asHHR0dfPXVVxCLxVi/fj0sLCyYxrJnzx4cOnQIKSkpMDc3x8SJE2FnZ8f9/gPKX%2BNjxoyRW9KkCgMHDsScOXPQv39/tGzZEmpq8vNQo0ePVun4lenr6%2BP27dvo2LGj3PELFy7gP//5D7M4iLCIZG//JiNEBa5fvw5vb288f/4curq6kEgkKCwshK6uLrZs2QJLS0tmscTHx2PmzJlo2LAhPiQ3/IEAACAASURBVPvsM0gkEmRlZeHVq1dYt24dBg0axCyWtWvXIjY2FqamptDX10e9evLvD2fOnMksFgBISEjAtGnT0L59e6SkpKBv3764c%2BcO8vPzERQUBCsrK2ax5Obmwt/fH6dOncKbN28AAPXq1UOPHj3g7%2B8PPT09ZrEsWLAAx48fh76%2BPlq3bg2JRILHjx/j6dOn6Nu3r9wf94CAAJXH06dPH3zzzTews7ODtra2wnkDAwOVxwAI73F58%2BYNdu7ciVevXmHs2LEwMDBAcXExvLy84O/vjxYtWqg8hgqDBw/G0KFD4ezsDENDwypv5%2BPjg6VLl6o0loqZSGVEIpHKN5BW9ssvvyAoKAgjR47Ejh078OOPP%2BL27duIjY3FnDlzFCYJSN1AyShhRiqVIjU1FVlZWSgtLYWenh66dOkCLS0t5rEUFBTgwoULcrFYW1ujefPmTOMYP358ledEIhGTnf1vy8zMxJEjR/D48WNoa2vDyMgIDg4OaNKkCfNYKrx8%2BRKlpaVo1qyZQsLOwvz586t925UrV6owknKWlpa4cuUK1NXVVT7WuwjtcakgFovldvfz8Tj5%2BvrC19eX%2Bbj/BCdOnMDBgwe53zGGhoZwcXFRunyK1A2UjBKVEYvF0NDQAID3rh/T1NRkEdI/yq1btxQuZfEpMTERXbt2VekYCQkJ6NGjBwC8t0RRr169VBqLkM2ZMwfDhg2jP95vyc/Ph6%2BvL%2BLi4lBWVgagfB2po6MjFi9ezPSN78CBA/Hzzz%2B/c1aUpdzcXJw9e5Zbb21sbIz%2B/fvLLRsghC%2BUjBKV6dKlC65fvw4A6NChg9L6kTKZjMkatwEDBnCXot6XxLCu0yiTyZCRkSGXsGdlZWHq1KlISkpiGkvHjh0xZswYzJs3T%2BENQuXnU1VMTU2RkpICAEo3vFVguS4SKN/YdfDgQTx8%2BBAlJSUKsaxYsYJZLACwdetW7N27F%2Bbm5krXALJa3hEYGPjO856enkziqDBz5kxkZ2fDw8MDxsbGAIB79%2B4hJCQEZmZmVTYLUIWQkBAcOXIEffr0UboEh%2BU6zYrlN/Xr1%2BeS48ePH0MikSA8PBzt27dnFotUKsWePXu42rQA0KJFC9jY2MDFxUXltYyJMNEGJqIyYWFh3Md8XG6ubPr06dzHs2bN4jESeYmJiZg%2BfTry8vIA/C85B8DVS2RJXV0daWlpGD16NDZs2MD9Qa%2BITdUqElEA1d79y2LGdtq0aXjw4AFMTU15WVbytgsXLsDIyAg5OTnIycmRO8fyj/n58%2BflPpdIJHj69ClkMhnzDUMV8Rw/fhzNmjXjjrVq1QqdOnWCi4sL02R03759AICjR48qnBOJREyT0TVr1sDLywsTJ07kjkkkEgQHB8Pf3x%2B7du1iFouvry/i4%2BPh6OiIvn37cm/Gt2zZgjt37sDHx4dZLERAeCgnRUiVKte25Jutra3Kxxg%2BfLgsMDBQdv/%2BfVmnTp1kjx8/lsXGxso8PT2V1thUNVNTU5lEIpGtW7dO1rVrV1lMTIzcOSFiEZeZmZnsxYsXKh/n30Aikci2bNki2759O/Oxu3fvLisuLlY4XlxcLLOysmIeT1UkEgnT8bp06aK0zmhJSYmsW7duTGMxMzOT3bt3T%2BH433//LTMzM2MaCxEOqjNKBEUmoFUjLGpZPnjwAFOnTkXr1q0hEolgaGgIOzs7uLm5Ye7cuSofXxk1NTXMmDEDGzduxE8//YSFCxcqXJoWEhavGRMTE8E9BpmZmQgNDYW/vz93rPLMMl/U1NTg4eGBHTt2MB/bwsICy5Ytk6uJm5ubCz8/P3Tu3JlpLEOGDFF6vKCgAD179mQai56eHh4%2BfKhwPD09nfma0UaNGikt52doaIhGjRoxjYUIB12mJ4IipPVCLGJp3Lgxnj9/Dj09Pejq6iI9PR2Ghob48ssvkZycrPLx31Y5sbO2tkZUVBTmzp2LkSNHKu1pLQQsnqc1a9Zgzpw56NmzJ5o3b66wRnPYsGEqj6Gyt1sqLly4UFAtFa9evcptIGLJx8cHU6ZMQc%2BePaGrqwugPPlr06YNgoKCmMSQkJCAS5cu4enTp1i3bp3C%2BSdPnjBvCODs7IxJkyZh3Lhx%2BPzzzwEA9%2B/fx549e2Bra8s0lunTp8Pf3x8zZszgKnS8fPkSgYGB8PLyYhoLEQ5KRgnhkaOjI0aMGIGjR4%2Bid%2B/e8PLywtChQ5Gamsq8GQBQ/kersk8%2B%2BQRhYWHYunUrtwauLgoODkZiYiJu376tdM0o62R0w4YNWLduHQYOHAhTU1MA5d28tmzZguXLlzNLRpVtBnzz5g2Kiorw/fffM4mhsubNmyMyMhJpaWlc0mdoaKgwK/rs2TOVdT9r3LgxiouLIZFIlHaX09bWxvLly1UydlWmTp0KXV1dHDx4kHtcjIyM4OLiAjc3N6axbNu2DVlZWdi3bx90dXUhlUpRWFgIDQ0N6OrqYuPGjdxtWW8mJfyh3fREUFjs2K4uVrFERUXB2dkZRUVFWLp0KVJTU2FgYIDZs2e/c0c5KcfieTIzM0NoaKjKN0pVl5mZGZKSkqCmpib3/UskElhaWjKbVf/9998VjmlpacHY2Bhffvklkxg%2BBovXzPLly7Fo0SKVjvFPpOw1U5Xhw4erMBIiJDQzSgiPrly5ws2q6ejoYM2aNQDKe37Hx8czSUbHjx%2BP8PBwAICLi8s7b/vbb7%2BpPB4h0tfXF9QbA6G0VKxIFoRQZP5DsJiDWbRoEVJTU3Hv3j2l641VvZv%2BwIEDXJvWiIiId96W5c7%2Bhw8fYujQoczbCxNho2SUEB55eHgonaHJz8/HvHnzYGdnp/IYKhdOr8uF5N9l7ty5WLJkCUaOHKl0zWjr1q2ZxjN27Fi4ublxa3l37twp11KRlYKCAvj4%2BAiiyPyHYLHOeMWKFdi9ezeaNWum0LKVRWmn0NBQLhndunVrlbdjXWbq%2BvXrCAsLQ/v27eHk5AQHBwemrX2JMNFleiIolYue802VsezcuROhoaHIyclROpNVWFgIAwMDHDlyRCXjf4j8/Hw0btyY7zCq5OzsjOjoaJWOoWxWVCQSMWvaoIwQWioKqcj8h2Bxmd7KygobN27kOoqR/3n58iVOnTqFkydP4o8//oCZmRmcnJwwePBg6ghVR1EySphwcnLCoUOH3nu7qpKz2iaRSJCdnY03b94onKuY5UpJSeE2h9Q2qVSKmzdvYsyYMfDz81M4r6WlhR49eqBp06YqGb8qN2/exOLFixEZGQkA8Pb2xvHjx9G0aVMEBQXB3NycaTzJycmIjo5GVlYWgoKCIJVKceLECeY7gN9X5svAwIBRJMLSrVs3hSLzQHkHMRcXF5w%2BfZqnyN6NRTLaq1cvnD59mmuJzKeoqKgqz1UsrejcuTMaNmzIMKpyRUVFiIyMxMaNGyEWi2Frawt3d3e0a9eOeSyEP3SZnjDRpEkTnD17Fn379n3n7VgkojExMfDz80NhYSGA/3U9enuWS1WJKFBei7Fz587Yu3evwjh8zkQuX76c6/wUFxeHP//8E/Hx8UhKSsKaNWuwd%2B9eZrHs27cPq1atgp2dHdfp5/nz51ixYgWys7Px3XffMYulqmRTKpVi7NixzNfSvqs9KQCsXLmSSRzq6uqoX7%2B%2BwnFdXV0UFxcziUGovv/%2Be%2BzYsQOTJ0/mOxQEBwcjOzsbr1%2B/ho6ODtTU1FBQUIAGDRqgfv36yM/Ph66uLoKCgmBmZsYkpuLiYpw8eRKHDx9GQkICvvjiCzg7OyM3Nxfffvst5s2bRxuY6hBKRgkTxsbGmD9/PgwMDJT2aQ4ICGAWS0BAACZMmAA7OzuFtVysqaur4%2BuvvxbMTOSdO3e4zUynTp2Cvb099PX18dlnn2Hp0qVMY9m%2BfTu2b98OS0tLbla9efPm2Lp1K7y9vZkmo4WFhdiyZQtu3LgBsVjMHX/x4gXzmpGAcNqTVhSZnz17Njc7mpubi7Vr1zIvMi80SUlJSEpKQnh4OPT19RXWGbN8A%2BPt7Y2jR49i1qxZaNWqFQDg0aNH2LBhA4YNG4aePXsiJCQEK1eufO9mp5o6c%2BYMDh06hPj4eDRt2hROTk5YsGCB3LrrijJ3lIzWHZSMEiYkEsl7Z0VZKSwsxJQpUwSx41dIM5FA%2BfIAsVgMkUiE8%2BfPc7v7i4uLIZVKmcby4sULrr955Q0nbdu2RXZ2NtNYfHx88ODBA/Tu3RthYWHw8PDAX3/9BbFYzPSNVIXU1FTExcUx3TmvjBCKzFeXWCzmLplramqqfLwvvvgCX3zxhcrHqY61a9ciJiZGbj2msbExli1bhlGjRuHYsWOYPHkyfv75Z5XHMmvWLAwePBghISHo3r270tuYmZnBxMRE5bEQ4aBklDDB6rJhdQwYMAB//PEH040eVRHSTCQA9OnTB9OnT0e9evWgo6ODr776CmKxGOvXr%2BcSQ1aMjY1x%2BfJlhQ0ghw8fhr6%2BPtNYLl68iGPHjqFJkyb4%2Beef4e3tDQDYtWsXDh06xLxzjFDak1a3yDwr8%2BfPx/LlyxXeaKalpWH27NncDPvVq1dVHounp6fKx6iuvLw8ZGVlKWwOysnJwbNnzwCUr4tmsWZ04sSJSh%2BboqIibNiwgdv0FhoaqvJYiHBQMkqYEcpmlDZt2mD%2B/PkwNzdHy5YtFS6fzZw5k1ksQpqJBABfX1/s2rULBQUFWLRoEUQiEcRiMR48eCDXA52FSZMmYerUqbCxsUFZWRmWL1%2BO27dv49q1a8xnI2UyGdc3W0NDA8XFxWjQoAG%2B%2BeYb2NjYME9GhdKe9L///S9CQkLQoUMHQdRhffToETw8PLBp0ybo6OhAJpMhODgYISEhGDt2LNNYAgMD33meZbI6fPhwfPfdd3B0dISBgQHq1auHjIwMHDp0CAMHDkRpaSm%2B/fZbjBgxQmUxvHz5Erm5udi2bRscHR0Var0%2BfPgQERERgq3AQFSLklHChJA2o1y4cAFGRkbIyclBTk6O3DkW9QcrE9JMJFDeqvDtDRcNGjRAWFiY3DE7OzscPXpUpbHY29vD0NAQv//%2BO3r06IHMzEx06tQJS5cu5fprs9K5c2f4%2BPhgyZIlMDExQUhICCZOnIjk5GRe3jQIpT1pRkYGbty4gU6dOjEZ73127dqFJUuWYMyYMViwYAHWr1%2BP3NxchIWFoVu3bkxjqfg9V0EikeDp06eQyWTMf7YXLVoEIyMjnDt3DpcuXYJUKkWzZs3wzTffwN3dHZqampg9ezaGDh2qshhOnTqFVatWcTvmK1RsHgWAwYMHq2x8ImxU2okwMWjQIKxatQqWlpZy9Ttv3boFb29vnDhxgucI%2BfHmzRtuJnLs2LEwMDBAcXExvLy84O/vjxYtWvAdolJCqgfLwuPHj7F48WJs374dKSkpmDx5MoqLi6GmpoYZM2bA3d2daTxCaU%2B6du1axMbGwtTUVOnGRJZXGSoLCwvDunXr0KdPHwQEBKBBgwa8xPE2qVSKkJAQaGpqMn/NCIFEIkH37t2V1gXW1tbmfQ004Q8lo4QJc3NzJCUlQSQSydX4E4vF6NatG7Ne2hX%2B/vtvHD9%2BnKsfaWxsDAcHBxgaGjKNo7pYzER%2BCFXVaZw1a1a1b8vHxqEKBQUFuH//Pj777DPo6ekxn1G3t7fHvn37eC8QPn78%2BCrPiUQi7N69W%2BUxVLX7%2B%2BzZs0hOToaXlxe3jIFlp6GqiMVi9O3bF5cuXWI67pEjRxAdHY3s7GxERUWhtLQU4eHhcHV1Zf76rQ4WtWCJcNBlesKEkDajVLRM7NixI4yMjACUd7MJCgrCjh07eJ9tUuZ9Rdf/LVjscv4YQ4YMwfHjx7nPdXV1YWZmhoKCAlhbWyMhIYFpPEJpT1qx%2BY5P72p1qa2tje3btwNg3/ayKlevXuVap7ISFBSEiIgIjB49GiEhIQDK31BFRUXh1atX%2BOGHH5jGUx00T1a3UDJKmBDSZpTAwECsXr0a9vb2cscjIyOxevVq7Nu3j2k81SHEmQtVUFZ1oaIZAQCUlZUpXApWpYSEBFy6dAlPnz7FunXrFM5X7CBnrWJdb2xsLHeM7/akiYmJXAJRUU%2BYhfj4eCbjfKhevXopHHvz5g2Kiorw/fffM40lIiICoaGhaNeuHZe8f/LJJwgKCsJ3330nyGS0rvzOI%2BUoGSVMCGkzSkZGBoYMGaJwfOjQoVixYgXTWEjVnj59itmzZ2PChAnc8xUeHo7jx49j3bp1TJKdxo0bo7i4GBKJBNeuXVM4r62tjeXLl6s8jredOnWK%2BZhv%2B%2Bmnn2BiYsJtlpowYQIkEgmA8kTn8OHDaNKkCdOYxGIxtmzZgl69enFXOGJiYnD37l14enoynXlXtuRES0sLxsbG%2BPLLL5nFAZR37FLWXlNPTw%2B5ublMYyFEGUpGCTOdO3eWqz/IV9tLfX19XL9%2BXWFH682bN2kBvYD4%2BPigTZs2crugnZ2d8eTJE/j4%2BHCXX1WponC5SCTCokWLVD5edVVuT8rHzOy%2BffsQGRmJTZs2ccfU1dVx8%2BZNSKVSuLq6Yvfu3Zg%2BfTrTuPz8/HDjxg25qx5t27bF3r174e/vz7R2b0X3ILFYjOzsbK4HPB/NNtq3b4%2BYmBiF3fI7duxAmzZtmMdDyNtoAxNh4ubNm1i8eLEg2l7%2B9ttvWLt2LZycnLhfxPfv38ehQ4cwefJkQe5yFdpifha76bt27YqEhASua06F0tJSWFtbIzExUaXjV1ZSUoJ9%2B/ZxG3ZOnTqFAwcOoFWrVvD09GRSLLyyS5cuYcWKFXj48CE3G1mZqi/Tjxo1ChMmTICjoyN3rPJrNDExEcuXL0dUVJRK43ibtbU1jhw5gqZNm8odz8vLg6OjIy5evMgsloKCAvj4%2BCAuLo5bI6qlpQVHR0csXryYaRvXhIQETJs2De3bt0dKSgr69u2LO3fuID8/H0FBQbCysmIWS3UJ7XceUS2aGSVMCKntpYuLC/T09HDw4EEkJSWhtLQURkZGWLp0qcI60rpIIpEgOzsbb968UThXsTHml19%2BUXkcurq6ePDgAdq3by93PC0tDfXr11f5%2BJVVrHEeP3487t%2B/j5kzZ8LDwwN37tyBv78/8%2BUdPj4%2BsLKywuzZs5k/FkB5qasBAwbIHfvss8%2B4j7t06YL09HTWYUEikShdaygWi5l3rPL19cXz588RGBgIY2NjAMC9e/cQEhKCtWvXMi3u3qNHDxw9ehSHDx%2BGiYkJtLW10atXLzg4ODBfSkGIMpSMEiaE1vbSxsYGNjY2zMf9WKwuYMTExMDPzw%2BFhYXcuMo2xpiamqo8lm%2B//RYTJ06Eg4MDWrZsCalUigcPHiA2Npb5hou4uDiulWR0dDR69eoFT09PvHr1CnZ2dkxjAYAXL15g6dKlTDdzVVZaWqqQBB87doz7WE1NjZdmAIMHD8a0adPg6uoKAwMDyGQyPHjwAKGhoXBwcGAay/nz53H8%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%2B7u7vD29uauSL39862sDJWqNGnSBGfPnkXfvn3feTvaTFq30AYmwkRF28tXr15h7Nix0NfX/0e0vVS1vn37YtSoUVXORFbeNc3CnDlzMGzYMFhbWzMdt4JQZ7BTUlIwe/ZsvHr1CjNmzMCoUaPw8uVL9O/fHytWrGB%2Bqf5dM8MikUjlpZ9KS0vh7u6OmzdvwsHBAcbGxpBIJLh37x6OHTuGnj17IjAwUCFJVoWEhASumcaFCxfeeVuWSVdWVhamTJmCW7duQVdXF0D5pqY2bdogKCiI6cxxhw4dqjzHui7tokWLEB8fz9WifXupCZ%2Bd1Qh/KBklTHz33XfvPM%2BibWCF/Px8BAcHY968eQCAPXv2ICIiAq1atcKiRYugp6fHLBZLS0tcuXJFMDORW7duxd69e2Fubq50xk3VCaCbmxvCwsIACKPV5PtkZWWhefPmfIfBC4lEgsjISJw8eRLp6ekQiURo3bo17O3tma7PrFzZQUhJV4W0tDSuOYKhoSHzWdEP8ezZM5XP9M%2BfP/%2Bd55U1viD/fpSMEibefrcrkUiQnp6O5ORkfPvtt1xHGRa8vLwgkUgQFBSE1NRUjB8/Hr6%2Bvrhx4ways7PlaieqGt8zkW8TUgL45MkTtGzZUuF4aWkp/vrrL5iZmTGLBQCSk5MRHR2NrKwsBAUFQSqV4sSJE7C1tWUaR4Xc3FycPXuWaxVrbGyM/v37896vnvxPQkICmjdvzjX2SExMRElJCXr27MlzZMpROSXCF1ozSpioavPAhQsXEBMTwzSWK1euIC4uDgBw%2BPBhDBw4EMOGDYOtrS3zjTFt2rTB/PnzeZuJfJsQeo1XcHBwUPqH8fXr15g4ceI71/7Wtn379mHVqlWws7PD%2BfPnAQDPnz/HihUrkJ2d/d6Z/9pWUTeyfv36MDQ0BADs3bsXy5cvR3h4uEI5LFXiO0l/8OBBtW4nEonQqlUr1QZTSXh4ODZu3IjNmzdzyWh%2Bfj4WLFgALy8vXtbUvg%2BruSm%2BXzNEeCgZJbyytrZm3qVFKpVys0cXL17EtGnTAAAaGhp4/fo101guXLgAIyMj5OTkICcnR%2B4cX72Z//77bxw/flxuxs3BwYFLelRt//79OHDgAEpLS%2BHi4qJwPjs7m3ltxO3bt2P79u2wtLTkdgI3b94cW7duhbe3N/NkdM2aNfDy8sLEiRO5YxKJBMHBwfD398euXbuYxCGEJN3Ozo7b9AdA7uPKn7O%2BTP/zzz/jl19%2BkVs6MGDAAISHh2PKlCmCTEZZ/M4RwmuGCA8lo4QJZbMXb968wYkTJ7jF/ax06tQJW7ZsgZaWFrKzs9GvXz8AQGxsLFdKiRUhzUQC5Y/BnDlz0LFjR26DxYkTJxAUFIQdO3Zw/b5VafDgwWjUqBFmzZqldMOJlpYW8x32L1684Ko%2BVP6D3bZtW2RnZzONBSjvGPb2kgp1dXVMmjSJ6VIKISTpb2/WunTpEiwsLLgNgRkZGbh37x569%2B6t8lgqy8vL42ZEK2vZsmWd7gcvhNcMER5KRgkTVc1eNGrUCL6%2Bvkxj8fHxgZ%2BfHwoKCrBmzRrUr18fL1%2B%2BxPLly5muF63A90xkZYGBgVi9erVCJ6rIyEisXr0a%2B/btU3kMjRs3hq2tLR48eIApU6aofLzqMDY2xuXLl7ld2xUOHz4MfX195vHo6enh4cOHaNu2rdzx9PR0pmtGhZCkV644UfnSeMVzlZaWhvXr16OsrIzpbKSFhQXWrVuHadOmoVGjRgDKH68NGzagS5cuzOIQGiG8ZojwUDJKmFBWakZLSwvNmjVjUv6lslatWnE7tis0adIE586dY9ovGhDGTGRlGRkZGDJkiMLxoUOHMm95GRYWBjc3N2hqajIdV5lJkyZh6tSpsLGxQVlZGdce9Nq1a7yUonF2dsakSZMwbtw4bvbt/v372LNnD9N1d0JL0oV0adzX1xdeXl7YvXs3dHR0IJVKUVRUhI4dOyIkJIRZHEIjtNcMEQZKRgkTrOtlvkt6ejrWrFnDzYKuXr0aERERMDY2xpo1a9CmTRtmsQhhJrIyfX19XL9%2BXaERwc2bN5kXofby8sJPP/3E1aV9u/wVyyTV3t4ehoaG%2BP3339GjRw9kZmaiU6dOWLp0qdJLsao2depU6Orq4uDBg1zZICMjI7i4uDBt%2BSi0JP1dl8bfXpOtaoaGhoiKisKtW7fw%2BPFjqKmpwdDQUKH8FItySkIitNcMEQYq7UTqHFdXVxgaGmLp0qW4fPkyvLy8EBQUhOTkZFy%2BfFlh1lSVzMzM8OeffyokWmVlZfjqq6%2BQmJjILBYA%2BO2337B27Vo4OTlxSfn9%2B/dx6NAhTJ48Ge7u7sxiMTc3R1lZGcrKypSe56NmpFA8f/4cn376Kd9hAABSU1Px%2B%2B%2B/4/Hjx9DW1oahoSFGjRrFS5Lu5uaGdu3aKb00/uTJE%2BzcuZN5TO/DdzklsVgMDQ0NAEC3bt1w9epVlY8ppNcMEQZKRkmd07VrV1y4cAHa2trw8fGBTCbDsmXLIBaL0atXL/zxxx/MYrG3t8fy5csVZiKvX7%2BOOXPm4Pjx48xiqRAfH68w4%2Bbs7Kwwe6tqV65ceed5KysrRpGUL1/YsWMHHj16hJKSEoXzrAvwm5ub488//2S%2BxEUZmUyGN2/ecH3Y8/PzoaOjw0sjh/T0dHh5eeHOnTtKL42zbGhRXZWL9qvK/PnzsXz5coXnJC0tDbNnz65Wr/jaEhsby/x3CRE%2BukxP6hx1dXXul/KFCxewaNEiAOV/VMViMdNYvvvuO0yaNKnKmUg%2B2NjYMK%2B3qsy7ks2ZM2cyTUY9PT0hlUphZWXFfF2xMi4uLti8eTPc3d3RsGFD3uJIS0vD1KlT8eOPP3IJxsGDBxEeHo7g4OB3dkRShepeGhcSFuWUHj16BA8PD2zatAk6OjqQyWQIDg5GSEgIxo4dq/LxK1u6dCn69euHBg0arGb7TQAAGdxJREFUMB2XCBvNjJI6x9PTE02aNIGGhgbi4uIQHx8PDQ0NbNu2DWfOnMHevXuZxsP3TKRQ%2B8FLJBL89ttvuHHjBkpLS7nj2dnZuHPnDtMZbHNzc1y4cIHXxK8yJycnZGdno7CwELq6ugozXu/r0V5bxowZg169esHNzY0rpVRaWoqdO3fy8rP0T8TiMr1YLMaSJUtw48YNLFiwAOvXr0dubi5WrlyJbt26qXTst/3666%2B4evUqhg0bpnQtOOvyekQYaGaU1DlLly7Fxo0bkZeXh%2BDgYGhoaCA/Px%2B///47NmzYwDwevmciK/8hfFdXI9ZF%2BP38/HD69Gl07doVx44dg4ODA27dugVNTU0EBwczjcXS0hLp6emCmWFzdXXlOwQAwO3bt/HLL7/IJRSamppwdXVl/hyRqmloaGDlypUICwuDu7s7%2BvTpg507d/IyO7l06VIA5ZfrK/DVmIAIB82MEsKYUGcihaZXr144cOAAWrRowa2rk8lkWLt2LVq2bIkxY8YwiyUrKwseHh4wNTVF8%2BbNFRJzT09PZrEIia2tLfz8/BRm186ePYtly5YpLelG5KlqZjQiIkLp8bNnzyI5ORleXl7cmuPRo0fX%2BvhVqainXBUhVV4h7NDMKKlzJBIJwsLCEB0djezsbFy9ehVFRUUICAjA3LlzVb4mUKgzkUD55pPg4GDMmzcPALBnzx5ERESgVatWWLRoEdMNICUlJWjRogWA8nW%2BpaWl0NTU5NbYskxGfXx88PDhQ6ipqeHvv/%2BWOycSiZgko/PmzcOqVasAALNmzXrnbVmVyJkyZQomTZoEa2trtGzZElKpFA8ePMCVK1ewdu1aJjEQ5bZu3VrlOW1tbWzfvh1A%2BeuXZTIaGhoKHx8fZuORfwZKRkmds2rVKly5cgWTJk3C4sWLAZSvqbp37x5Wrlyp8o5QlUtHCa0d6KJFiyCRSACUl19Zs2YNfH19cePGDeYdqtq3b4/AwEBMnjwZrVu3xv79%2BzFu3Dg8e/YMxcXFzOIAgISEBBw%2BfJhrTMCHivI7ANsaq%2B/i7OyMNm3aIDo6Go8ePYJIJMLnn3%2BOH3/8UTBLGoSocjklVT2X8fHxKrnfmjp//jzS09N56TBHhIsu05M6x9raGvv374eBgYHcJbLnz59j%2BPDhzDZ/AMKaiQSA7t27Iy4uDo0aNcLKlSuRk5ODtWvX4s2bN7CxscGlS5eYxZKamoqZM2ciOjoaly5dwg8//ABNTU2UlJRg3LhxWLBgAbNYRowYgW3btjEv/F%2BVlJQUmJqa8h0GeQchlVMSi8XYsmULevXqxXV1i4mJwd27d%2BHp6cn0zU1ISAiOHDmCPn36QF9fH/Xqyc%2BJsZylJcJBM6OkzhGLxdzl38rq16%2BPoqIiprEIaSYSAKRSKdfb/OLFi5g2bRqA8lm5169fM42lc%2BfOOHnyJABg4MCBiImJwa1bt2BgYAAzMzPudiw62Li5uWHGjBlwcnJC8%2BbNFep79urVS6Xjv23MmDH47LPPYG9vD3t7e6azkEJcLiBEQiqn5Ofnhxs3bshV6Gjbti327t0Lf39/blMRCxVd5Y4ePapwjvWSASIclIySOufLL7/Ejh074OHhwR17/fo11q5di06dOjGN5cqVK4iLiwNQ3pt54MCBGDZsGGxtbXnZYd%2BpUyds2bIFWlpayM7ORr9%2B/QCU73zlu%2BTK559/rrRDi62trcpL41RsJFNWiJ%2BPHcAXLlzAqVOnEBcXh127dkFfX59LTFXdzlaIywWEaNeuXViyZAnGjBkjV04pLCyMeTmluLg4HDlyBE2bNuWOffHFFwgODoajoyPTZFSoywcIv%2BgyPalz0tLSuLaWOTk5aNeuHdLT09GsWTMEBQXBxMSEWSzdunXDlStXIBKJ4OjoiGnTpsHOzg4SiQRdu3Z95wYnVXj48CH8/PxQUFAAT09P9O3bFy9fvsSQIUOwadMmdO/enWk81cGig42QFRUV4dy5czh58iQuXryIFi1aIDo6msnYtFzg/cLCwrBu3Tr06dMHAQEBvJRT6t69O44fP44mTZrIHc/Ozoa9vT3ztsNv3rzB6dOnkZWVhe%2B//x4AkJmZqfSKFakbaGaU1DkdOnTA/v37cf36da43sq6uLtq2bcs0EQWENxPZqlUruQ1WANCkSROcO3dOEJ2HlFFV1YHKm0wqF91Xhs8ZwgYNGuDTTz9FixYt0Lx5c2RmZjIbm8/lAkKkrJySjo4O%2Bvbti%2BTkZERHR/NSTmnw4MGYNm0aXF1dYWBgAJlMhgcPHiA0NBQODg7M4gCApKQkTJkyBbq6unj27Bm%2B//57PH36FI6OjggKCkKPHj2YxkOEgWZGSZ0TExMDX19fJCUl4fXr13B2doZIJEJ%2Bfj5mz56NESNGMItFaDOR6enpWLNmDbdWdfXq1YiIiICxsTHWrFmj8kvAH0NVdRor32%2BHDh2UJr18FeouLS3FhQsXEBcXhzNnzkAikWDgwIGwtbVFjx49FDaFqEpeXh63XCAhIYHpcgEhqu7SGpFIxLQG65s3bxAQEIDo6GgUFBQAAHR1dfH1119j1qxZcksvVG3UqFEYNmwYxo0bJ3dVIzY2FmFhYTh48CCzWIhwUDJK6hw7OzssWrQIPXv2xG%2B//YY9e/YgKioKd%2B/excyZM3HkyBG%2BQ0RJSQkvM5Gurq4wNDTE0qVLcfnyZXh5eSEoKAjJycm4fPmywqypEKgqGU1MTOR2HitbK1qZlZVVrY//Lubm5tDU1MSAAQNga2sLa2trZgloVfhcLkCqLy8vD2pqamjcuDEv45uZmSExMRH16tWT%2B9mVSqWwtLRkvjSJCANdpid1TmZmJnr27AkAOHfuHOzt7aGurg4TExNkZGQwjUVoM5EpKSkICgoCUL7b1c7ODt26dYOZmRlCQ0OZxsK3ikQUAKKjo%2BHv769wm8LCQsydO5d5Mrpx40ZBJKCV8blcQIj4LqeUkJDAXfJ%2BX7k6ltUgPv30Uzx79kyhzmhqaipXyYPUPcL5TUYII02bNkVWVhY0NTWRkJAAb29vAOUtH7W1tZnG4uPjw/1Svnz5Mvbv34%2BQkBAkJydjxYoVzGci1dXVubqIFy5cwKJFiwCUX44Wi8VMYxGC9PR0PHz4EDExMbC3t8fbF5IePXrEtC5tBXNzc%2BzatQv37t1DSUmJwnlWJZWqWi4we/bsOr/2j%2B9ySpMnT%2BYugVds2FSG9TITJycneHh4YOLEiZBKpYiLi0NaWhr27NnDvOQVEQ5KRkmd4%2BLigpEjR0JdXR3du3eHiYkJCgsLMWPGDNja2jKNRWgzkd26dcPSpUuhoaGB0tJSbsZk586dgtqcwqKDDVBeeWHTpk0Qi8Vwc3NTOK%2BlpQUXFxeVjV%2BVmTNnIi0tDZaWlqhfvz7z8St0796dWy6watUqwc3W8onvckqVK0ykpaWpdKwP4eXlhUaNGiE8PBwikQgLFiyAoaEhZs6ciZEjR/IdHuEJ/dYgdc6kSZPQrVs3FBQUcLM32tra6NevHyZOnMg0FqHNRC5duhQbN25EXl4egoODoaGhgfz8fPz%2B%2B%2B/YsGED01iq28Hm6tWrKoth0KBBGDRoEJydnau1/pFFAX6gfD3rsWPH0Lx5c5WP9S5CXC4gFBKJROmmN7FYrHQ2u7Y9ePCgWrcTiURo1aqVSmOJjY3lZogPHTqEiRMnMv9dS4SNNjARwiNPT080adIEGhoaiIuLQ3x8PDQ0NLBt2zacOXMGe/fu5TtE3owdOxba2tpVdrCpaKEqJKraTPU2Ozs77N%2B/XxBr7I4cOYLo6GhkZ2cjKioKpaWlCA8Ph6urq8rKbv0TLF68GPfv31daTqlz584qnxmtqABR8Se%2B8seVP2dxmd7c3Bz79%2B%2BHkZERunXrhsTERIUlLxWokULdRMkoITzKycnBxo0bUVBQAHd3d3Tq1An5%2BflwcXHBhg0bmNc9lUgkCAsL45KLq1evoqioCAEBAZg7dy7THf5isRhLlizBjRs35DrYrFy5knkHm%2BpiVYD//PnziI2Nhbu7O1q2bKmQ9LH6gx4UFISIiAiMHj0aISEhSElJwYsXLzBx4kQMGDAAP/zwA5M4hIjvckpPnz6V%2B/zSpUuwsLDg1sVnZGTg3r176N27NwwMDFQay/Tp03HixIlqvTlhXSaNCAMlo4QQjr%2B/P65cuQJXV1csXrwYKSkpePnyJby9vdG6dWv4%2Bvoyj0kIHWyqi9XMaNeuXfH69WtIpVKl51n9Qe/bty9CQ0PRrl07ue89PT0d3333HU6fPs0kDqHju5xSeHg4Nm7ciM2bN3NLk06dOoUFCxbAy8sL3377rcpjuHXrFl69egVXV1fs2LGjytuxrkxBhIGSUUJ4JKSZSACwtrbG/v37YWBgIJdcPH/%2BHMOHD1f5znFlHWwA4OzZs0hOToaXlxcvHWyqi1UyKpS6pxYWFkhKSgIg/72XlJTAysqKyWMhJEItp2RjY4OgoCCFTYh37tzBlClTmBbgv3TpEqytrZmNR/4ZaNU5ITxatWoVrly5gkmTJmHx4sUAyi9P37t3DytXrmQ%2BEykWi5X2h65fvz6KiopUPv7WrVurPKetrY3t27cDKF/vJsRklJXKyWZeXp7cjm2W2rdvj5iYGAwdOlTu%2BI4dO%2BpkByahllPKy8vD559/rnC8ZcuWyMnJYRYHUP6Gl9YZk7dRMkoIj44cOcLNRC5ZsgRAeS/4tWvXYvjw4cyT0S%2B//BI7duyAh4cHd%2Bz169dYu3YtOnXqpPLx4%2BPjVT7Gv0FRURF%2B%2BuknxMTEoKysDDdu3MDLly8xd%2B5crFy5Es2aNWMSh7e3N6ZNm4a9e/dCLBZjypQpuHPnDvLz87mSZXWJUMspWVhYYN26dZg2bRoaNWoEAHjx4gU2bNgAMzMzprG8vc4YAAoKChAVFYVXr17V6XXGdRldpieER926dcPly5ehrq4ud5mzsLAQvXv3Zt4aLy0tjZvRycnJQbt27ZCeno5mzZohKCiI6YYqvjvYfAxWl%2Bnnzp2L7OxsTJs2Da6urkhJSUFRURGWLFkCmUyGdevWqTyGCpmZmTh8%2BDDS09Ohra0NIyMjODg4oEmTJsxiEAohlVOqLD09HV5eXrhz5w50dHQglUpRVFSEjh07IiQkBHp6esxioXXGRBmaGSWER3zPRL6tQ4cO2L9/P65fv47Hjx9DW1sburq6aNu2LfOd/Xx3sKkuVgX4Kztz5gyOHj2KZs2acZc1GzZsCB8fHwwZMoRJDBVatGjxzkvSdYmdnZ1gyilVZmhoiKioKNy6dQuPHz%2BGmpoaDA0NeWlk8erVK7Rr107huJ6eHnJzc5nHQ4SBklFCeDRv3jy4u7tj165dKC0txdChQ%2BVmIlmLiYmBr68vkpKS8Pr1azg7O0MkEiE/Px%2BzZ8/GiBEjmMXCdwebyoRQgL8ykUiktMaoRCJReUF1Gxubaq/rY7kxRgje/n7fVU6JDx07dkTHjh15GbsCrTMmylAySgiPhDQTCQDBwcHYvHkzACA6OhpaWlqIiorC3bt3MXPmTKbJKN8dbCp79OgRPDw8qizAz5q5uTlWr16NH3/8kTv29OlT%2BPv7q3wn/aRJk7iPc3JysG/fPgwaNAitWrWCVCrF3bt3cebMGbi6uqo0DiGqXK9TWTmltLQ0rF%2B/HmVlZUzKKQkRrTMmytCaUUJ4JKSZSKA8yalYpzp16lR07twZU6ZMUTjHAt8dbCoTWgH%2BjIwMTJ06FXfv3kVZWRkaNmyI4uJimJubIyAggElLUgBwc3ODt7c3TE1N5Y4nJiYiKCjonfUk/%2B2EVE5JaLKysnD48GHuDXhdXmdMytHMKCE8EtJMJAA0bdoUWVlZ0NTUREJCAry9vQGU//GouNTIysKFCxEQEID58%2Bcr7WDDkoaGBlauXImwsDC4u7ujT58%2B2LlzJ28F%2BPX19REVFYXU1FSkp6dDS0sLRkZGCmvxnj17ptLENCkpSem6Q1NTU%2Bab74RGSOWUhGT79u3w8PCAm5sb36EQAaFklBAeZWZmomfPngCAc%2BfOwd7eHurq6jAxMUFGRgbzeFxcXDBy5Eioq6uje/fuMDExQWFhIWbMmAFbW1umsWhra2PhwoVYuHAhLx1slBXg19HRQd%2B%2BfZGcnIzo6GjeC/B37twZnTt3rvK8ra2tSnf3GxkZYfPmzZg8eTK3hrWwsBChoaFo2bKlysb9JxBSOSUh2bVrF0aMGMGs/Bj5Z6DL9ITwyMbGBr/%2B%2Bis0NTVhY2OD3377DSYmJsjKysKwYcOQkJDAPKZr166hoKAAPXr0gKamJsrKyrBjxw5MnDhR5f20hdTBxsbGplq3E4lEgr3kampqKlf7srZdv34d3t7eeP78OXR1dSGRSFBYWAhdXV1s2bIFlpaWKhtb6IRUTklIdu7ciVOnTsHe3h76%2BvoKmwJZdqYiwkHJKCE82rZtG8LDw6Guro4OHTogJCQEhYWFmDRpEkxMTODj48N3iExVTp7eVXaGdWmcfyoWdU%2BlUilu3LiBzMxMlJaWQk9PD126dJFrZavq5QJCJoRySkJCP9dEGUpGCeEZnzORpHr%2BiQX4AXZF%2BP8pcZB/jrr8BqYuojWjhPDM3Nxc7vN69erJlc%2BpS4TaweafUoBfqGjOg3woVa93JsJCySghRDCE2sFGSAX4/4mqWySfkAr0BqZuoWSUECIYQu1gI6QC/ITUBfQGpm5R4zsAQgipYGBgwP2Lj4/HTz/9hOzsbO5YQUEB1q9fj9OnTzONa/DgwZg2bRpOnTqFtLQ03Lp1C7Gxsfjvf/8LBwcHprG8j1j8f%2B3dTUhUXxjH8d/MIoaYrDZtLIiw8i6KkiQQK3ARZK3aROKqCAqbVQQl5CKz2syMJpVRQiEELccoaGFQ9EIviyEkB6mkF1uENVAU1rX8L8Rhxm4z1p%2B551z8flbOceA%2BC5HnOfc5z3FzP9vaywoA%2BTjABMBKNt1gMz4%2Brng8rlQq5TmA3%2B%2BDZkePHtWJEyd%2BG4uTyWR0%2BPBhXb9%2B3dd4SuEAE/4WfzNzC6/pAVjJphtsTA/gn%2Bn169fat2%2Bfzpw5o2g0qsnJSZ0/f149PT1qamoyFhcA/AuSUQBWMn2DjU0D%2BGe6cuWK2tratHv3brW2tiqZTOrTp0/q7e1VbW2tr7H8ieu6uR1j2gUAFMNregBWMn2DTRAG8Pf29iqRSGjz5s2Kx%2BOaP3%2B%2Br88PWrsA7JZfwNTW1urJkyeGI4JfSEYBWI0bbKZcu3bNc/3OnTtKp9OKxWIKh6fOpO7atcuXmJqamhSJRP7YLnDkyBFf4kBwUMDAC8koAHiwbQB/Q0PDrL4XCoV8O9zluq7a2to0ODhY0C5w6tQpa9oFYBcKGHghGQUAD9XV1VYO4LeR6XYBBAcFDLyQjAKAh9HR0YLPxQbwV1ZW%2Bhqb67o6e/as6uvrtWHDBklSf3%2B/Xrx4oYMHD5b1wJCN7QIIHgoY5OM0PQB4yE8w%2B/r61NXVpe7u7twJ%2B0wmo2QyqYmJCTU3N/saW3t7uwYHB9XY2Jhbq6qq0tWrV9XR0VHW60kvXLjwx99FIhFdvHhR0tTOMckoJO8CJhqNasuWLUqn00qlUhQwcxw7owBQgk0D%2BCWprq5ON27c0OLFiwvWs9msduzYofv37/saD1CMjf3OsAs7owBQgk0D%2BCXp58%2Bfnnd3u66r79%2B/%2BxaHyXYBBMft27dNhwDLcTc9AJQwPYD/y5cvubWxsTGdPHnSlwH8M23dulUtLS0aGBhQJpPR0NCQbt68qf3792v79u2%2BxdHe3q67d%2B%2BqoqIit1ZVVaXHjx%2Bro6PDtzgQHK7rqrOzU0%2BfPs2t9ff3K5FI6MePHwYjg0m8pgeAEkwP4J9pfHxc8XhcqVRKnz9/liRVVFRo586dOnToUG5weLnRLoC/NX2S/vTp01q1apUk6fnz5zp%2B/LhWr15d1n5n2ItkFABmycYB/NlsVuFwWAsXLvT92Rs3btStW7e0aNGigvUPHz6osbGxYPcLkChg4I2eUQCYJcdx5DiOkWc/fPgwd5L/3r17Rb9bX1/vR0i5doE9e/aosrJSk5OTGhkZ0aVLl3xtF0Bw2NLvDLuwMwoAAbB27Vo9e/ZMkoruyPo5hN%2BWdgEEx7Fjx/Tq1SvPAmbNmjW8pp%2BjSEYBAP%2BbyXYBBAcFDLyQjAJAAIyMjMzqe6FQSMuXLy9bHDa2CyCYKGAwjWQUAAKgurpaoVBI0/%2By83/O/1zu1/Q2tgvAbhQwKIVkFAACYHR0tODzgwcPVFNTo0gkIkl6//69Xr58qU2bNhVcZQqYRgGDUjhNDwABkJ9g9vX1qaurS93d3bkdp0wmo2QyqYmJCTU3N5ctDlvaBRAc04moNPV3CszEzigABExDQ4POnTv32y7T8PCwDhw4UNb7vW1pF0BwUMCgFHZGASBgstmsVqxY8dv60qVL9fHjx7I%2Be2aiW6xdAJCkbdu2UcCgKJJRAAiYmpoaJRIJtbS0aMGCBZKksbExdXZ2at26dWV9ti3tAggOChiUwmt6AAiYt2/fKhaLaXh4WNFoVL9%2B/dLXr1/lOI56enq0ZMkSX%2BIw2S6AYPIqYAYGBtTa2qpYLEYBM0eRjAJAQA0NDenNmzcKh8NatmxZ0ZPK5bB%2B/Xo9evRI8%2BbNK1j/9u2b6urqlE6nfY0H9qOAgRde0wNAQDmOI8dxjD3fZLsAgslkvzPsxc4oAOCf2NIugODYu3evVq5c6VnAvHv3TpcvXzYbIIwgGQUA/C%2Bm2wUQHBQw8EIyCgAAfEUBg3wkowAAADAmbDoAAAAAzF0kowAAADCGZBQAAADGkIwCAADAGJJRAAAAGEMyCgAAAGNIRgEAAGDMf7Thdfiuy59eAAAAAElFTkSuQmCC\" class=\"center-img\">\n</div>\n    <div class=\"row headerrow highlight\">\n        <h1>Sample</h1>\n    </div>\n    <div class=\"row variablerow\">\n    <div class=\"col-md-12\" style=\"overflow:scroll; width: 100%%; overflow-y: hidden;\">\n        <table border=\"1\" class=\"dataframe sample\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user_id</th>\n      <th>session_id</th>\n      <th>session_start</th>\n      <th>session_size</th>\n      <th>click_article_id</th>\n      <th>click_timestamp</th>\n      <th>click_environment</th>\n      <th>click_deviceGroup</th>\n      <th>click_os</th>\n      <th>click_country</th>\n      <th>click_region</th>\n      <th>click_referrer_type</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>137066</td>\n      <td>1508106983217640</td>\n      <td>1508106983000</td>\n      <td>2</td>\n      <td>218028</td>\n      <td>1508107123103</td>\n      <td>4</td>\n      <td>1</td>\n      <td>17</td>\n      <td>1</td>\n      <td>25</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>137066</td>\n      <td>1508106983217640</td>\n      <td>1508106983000</td>\n      <td>2</td>\n      <td>214753</td>\n      <td>1508107153103</td>\n      <td>4</td>\n      <td>1</td>\n      <td>17</td>\n      <td>1</td>\n      <td>25</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>312478</td>\n      <td>1508106983626641</td>\n      <td>1508106983000</td>\n      <td>2</td>\n      <td>74719</td>\n      <td>1508107495538</td>\n      <td>4</td>\n      <td>1</td>\n      <td>17</td>\n      <td>1</td>\n      <td>25</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>312478</td>\n      <td>1508106983626641</td>\n      <td>1508106983000</td>\n      <td>2</td>\n      <td>74722</td>\n      <td>1508107525538</td>\n      <td>4</td>\n      <td>1</td>\n      <td>17</td>\n      <td>1</td>\n      <td>25</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>266161</td>\n      <td>1508106984212642</td>\n      <td>1508106984000</td>\n      <td>3</td>\n      <td>331116</td>\n      <td>1508107023602</td>\n      <td>4</td>\n      <td>3</td>\n      <td>2</td>\n      <td>1</td>\n      <td>20</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n    </div>\n</div>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"## Recommender model"},{"metadata":{"trusted":true},"cell_type":"code","source":"small_data = clicks.sample(n=200*1000, weights=\"click_region\", random_state=42)","execution_count":38,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### Prepare train-test data"},{"metadata":{"trusted":true},"cell_type":"code","source":"dataset = Interactions(user_ids=small_data.user_id.values, \n                       item_ids=small_data.click_article_id.values, \n                       timestamps=small_data.click_timestamp.values)","execution_count":39,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train, test = random_train_test_split(dataset, test_percentage=0.1)","execution_count":40,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train, test","execution_count":41,"outputs":[{"output_type":"execute_result","execution_count":41,"data":{"text/plain":"(<Interactions dataset (322895 users x 364044 items x 180000 interactions)>,\n <Interactions dataset (322895 users x 364044 items x 20000 interactions)>)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"model = ImplicitFactorizationModel(n_iter=4, \n                                   loss='bpr', \n                                   batch_size=32,\n                                   use_cuda=torch.cuda.is_available(), \n                                   random_state=np.random.RandomState(42))","execution_count":42,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.fit(train, verbose=True)","execution_count":43,"outputs":[{"output_type":"stream","text":"/opt/conda/lib/python3.6/site-packages/torch/nn/functional.py:1332: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n  return torch._C._nn.nll_loss(input, target, weight, size_average, ignore_index, reduce)\n","name":"stderr"},{"output_type":"stream","text":"Epoch 0: loss 0.144397094260984\nEpoch 1: loss 0.013176932133899795\nEpoch 2: loss 0.0005321995268265406\nEpoch 3: loss 0.00023682927323712244\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"mrr = mrr_score(model, test)","execution_count":44,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"f'Mean reciprocal rank score is {np.mean(mrr)}'","execution_count":45,"outputs":[{"output_type":"execute_result","execution_count":45,"data":{"text/plain":"'Mean reciprocal rank score is 0.029870174154422592'"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"stats.describe(mrr)","execution_count":46,"outputs":[{"output_type":"execute_result","execution_count":46,"data":{"text/plain":"DescribeResult(nobs=18031, minmax=(2.747418800037365e-06, 1.0), mean=0.029870174154422592, variance=0.011798552605954624, skewness=7.174885597789388, kurtosis=57.29601146956058)"},"metadata":{}}]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.4","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}