diff --git a/visualization.ipynb b/visualization.ipynb
index 302eaed..cb51207 100644
--- a/visualization.ipynb
+++ b/visualization.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 194,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -15,7 +15,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 248,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -63,7 +63,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 249,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [
     {
@@ -96,420 +96,140 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>10.909697</td>\n",
-       "      <td>ASSIN 1 (PT-PT)</td>\n",
-       "      <td>1</td>\n",
-       "      <td>RoBERTa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>5.876683</td>\n",
-       "      <td>ASSIN 1 (PT-PT)</td>\n",
-       "      <td>2</td>\n",
-       "      <td>RoBERTa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2.033864</td>\n",
-       "      <td>ASSIN 1 (PT-PT)</td>\n",
-       "      <td>3</td>\n",
-       "      <td>RoBERTa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.308391</td>\n",
-       "      <td>ASSIN 1 (PT-PT)</td>\n",
-       "      <td>4</td>\n",
-       "      <td>RoBERTa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.174404</td>\n",
-       "      <td>ASSIN 1 (PT-PT)</td>\n",
-       "      <td>5</td>\n",
-       "      <td>RoBERTa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>12.632929</td>\n",
-       "      <td>ASSIN 1 (PT-BR)</td>\n",
-       "      <td>1</td>\n",
-       "      <td>RoBERTa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>6.194206</td>\n",
-       "      <td>ASSIN 1 (PT-BR)</td>\n",
-       "      <td>2</td>\n",
-       "      <td>RoBERTa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2.937050</td>\n",
-       "      <td>ASSIN 1 (PT-BR)</td>\n",
-       "      <td>3</td>\n",
-       "      <td>RoBERTa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.724028</td>\n",
-       "      <td>ASSIN 1 (PT-BR)</td>\n",
-       "      <td>4</td>\n",
-       "      <td>RoBERTa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.064349</td>\n",
-       "      <td>ASSIN 1 (PT-BR)</td>\n",
-       "      <td>5</td>\n",
-       "      <td>RoBERTa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>9.952024</td>\n",
+       "      <td>1.413047</td>\n",
        "      <td>ASSIN 2</td>\n",
        "      <td>1</td>\n",
        "      <td>RoBERTa</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>4.991293</td>\n",
+       "      <td>2.391685</td>\n",
        "      <td>ASSIN 2</td>\n",
        "      <td>2</td>\n",
        "      <td>RoBERTa</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>2.177242</td>\n",
+       "      <td>1.058928</td>\n",
        "      <td>ASSIN 2</td>\n",
        "      <td>3</td>\n",
        "      <td>RoBERTa</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.672338</td>\n",
+       "      <td>0.426405</td>\n",
        "      <td>ASSIN 2</td>\n",
        "      <td>4</td>\n",
        "      <td>RoBERTa</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0.025624</td>\n",
+       "      <td>0.089732</td>\n",
        "      <td>ASSIN 2</td>\n",
        "      <td>5</td>\n",
        "      <td>RoBERTa</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>6.907404</td>\n",
-       "      <td>ASSIN 1 (PT-PT)</td>\n",
-       "      <td>1</td>\n",
-       "      <td>BERT-multilingual</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>3.459370</td>\n",
-       "      <td>ASSIN 1 (PT-PT)</td>\n",
-       "      <td>2</td>\n",
-       "      <td>BERT-multilingual</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1.129345</td>\n",
-       "      <td>ASSIN 1 (PT-PT)</td>\n",
-       "      <td>3</td>\n",
-       "      <td>BERT-multilingual</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.167825</td>\n",
-       "      <td>ASSIN 1 (PT-PT)</td>\n",
-       "      <td>4</td>\n",
-       "      <td>BERT-multilingual</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.283227</td>\n",
-       "      <td>ASSIN 1 (PT-PT)</td>\n",
-       "      <td>5</td>\n",
-       "      <td>BERT-multilingual</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>8.330867</td>\n",
-       "      <td>ASSIN 1 (PT-BR)</td>\n",
-       "      <td>1</td>\n",
-       "      <td>BERT-multilingual</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>4.018704</td>\n",
-       "      <td>ASSIN 1 (PT-BR)</td>\n",
-       "      <td>2</td>\n",
-       "      <td>BERT-multilingual</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1.632923</td>\n",
-       "      <td>ASSIN 1 (PT-BR)</td>\n",
-       "      <td>3</td>\n",
-       "      <td>BERT-multilingual</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.330470</td>\n",
-       "      <td>ASSIN 1 (PT-BR)</td>\n",
-       "      <td>4</td>\n",
-       "      <td>BERT-multilingual</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.146850</td>\n",
-       "      <td>ASSIN 1 (PT-BR)</td>\n",
-       "      <td>5</td>\n",
-       "      <td>BERT-multilingual</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>8.773182</td>\n",
+       "      <td>6.265721</td>\n",
        "      <td>ASSIN 2</td>\n",
        "      <td>1</td>\n",
        "      <td>BERT-multilingual</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>4.314068</td>\n",
+       "      <td>3.696093</td>\n",
        "      <td>ASSIN 2</td>\n",
        "      <td>2</td>\n",
        "      <td>BERT-multilingual</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>1.598185</td>\n",
+       "      <td>1.358086</td>\n",
        "      <td>ASSIN 2</td>\n",
        "      <td>3</td>\n",
        "      <td>BERT-multilingual</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.416740</td>\n",
+       "      <td>0.383777</td>\n",
        "      <td>ASSIN 2</td>\n",
        "      <td>4</td>\n",
        "      <td>BERT-multilingual</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0.030240</td>\n",
+       "      <td>0.032836</td>\n",
        "      <td>ASSIN 2</td>\n",
        "      <td>5</td>\n",
        "      <td>BERT-multilingual</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>4.548327</td>\n",
-       "      <td>ASSIN 1 (PT-PT)</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Ensemble ( stacking, 5-fold )</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2.156652</td>\n",
-       "      <td>ASSIN 1 (PT-PT)</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Ensemble ( stacking, 5-fold )</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.861034</td>\n",
-       "      <td>ASSIN 1 (PT-PT)</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Ensemble ( stacking, 5-fold )</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.248775</td>\n",
-       "      <td>ASSIN 1 (PT-PT)</td>\n",
-       "      <td>4</td>\n",
-       "      <td>Ensemble ( stacking, 5-fold )</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.369529</td>\n",
-       "      <td>ASSIN 1 (PT-PT)</td>\n",
-       "      <td>5</td>\n",
-       "      <td>Ensemble ( stacking, 5-fold )</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>4.981972</td>\n",
-       "      <td>ASSIN 1 (PT-BR)</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Ensemble ( stacking, 5-fold )</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2.657034</td>\n",
-       "      <td>ASSIN 1 (PT-BR)</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Ensemble ( stacking, 5-fold )</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.999332</td>\n",
-       "      <td>ASSIN 1 (PT-BR)</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Ensemble ( stacking, 5-fold )</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.248256</td>\n",
-       "      <td>ASSIN 1 (PT-BR)</td>\n",
-       "      <td>4</td>\n",
-       "      <td>Ensemble ( stacking, 5-fold )</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.375841</td>\n",
-       "      <td>ASSIN 1 (PT-BR)</td>\n",
-       "      <td>5</td>\n",
-       "      <td>Ensemble ( stacking, 5-fold )</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>9.079303</td>\n",
+       "      <td>3.585998</td>\n",
        "      <td>ASSIN 2</td>\n",
        "      <td>1</td>\n",
        "      <td>Ensemble ( stacking, 5-fold )</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>4.411754</td>\n",
+       "      <td>1.765864</td>\n",
        "      <td>ASSIN 2</td>\n",
        "      <td>2</td>\n",
        "      <td>Ensemble ( stacking, 5-fold )</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>1.512965</td>\n",
+       "      <td>0.536087</td>\n",
        "      <td>ASSIN 2</td>\n",
        "      <td>3</td>\n",
        "      <td>Ensemble ( stacking, 5-fold )</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.347414</td>\n",
+       "      <td>0.159993</td>\n",
        "      <td>ASSIN 2</td>\n",
        "      <td>4</td>\n",
        "      <td>Ensemble ( stacking, 5-fold )</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0.054075</td>\n",
+       "      <td>0.128111</td>\n",
        "      <td>ASSIN 2</td>\n",
        "      <td>5</td>\n",
        "      <td>Ensemble ( stacking, 5-fold )</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>8.780042</td>\n",
-       "      <td>ASSIN 1 (PT-PT)</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Ensemble ( averaging )</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>4.569723</td>\n",
-       "      <td>ASSIN 1 (PT-PT)</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Ensemble ( averaging )</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1.532580</td>\n",
-       "      <td>ASSIN 1 (PT-PT)</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Ensemble ( averaging )</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.213651</td>\n",
-       "      <td>ASSIN 1 (PT-PT)</td>\n",
-       "      <td>4</td>\n",
-       "      <td>Ensemble ( averaging )</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.214348</td>\n",
-       "      <td>ASSIN 1 (PT-PT)</td>\n",
-       "      <td>5</td>\n",
-       "      <td>Ensemble ( averaging )</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>10.357174</td>\n",
-       "      <td>ASSIN 1 (PT-BR)</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Ensemble ( averaging )</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>5.035915</td>\n",
-       "      <td>ASSIN 1 (PT-BR)</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Ensemble ( averaging )</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2.225965</td>\n",
-       "      <td>ASSIN 1 (PT-BR)</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Ensemble ( averaging )</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.491161</td>\n",
-       "      <td>ASSIN 1 (PT-BR)</td>\n",
-       "      <td>4</td>\n",
-       "      <td>Ensemble ( averaging )</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.084493</td>\n",
-       "      <td>ASSIN 1 (PT-BR)</td>\n",
-       "      <td>5</td>\n",
-       "      <td>Ensemble ( averaging )</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>9.345122</td>\n",
+       "      <td>3.111972</td>\n",
        "      <td>ASSIN 2</td>\n",
        "      <td>1</td>\n",
        "      <td>Ensemble ( averaging )</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>4.630961</td>\n",
+       "      <td>2.928381</td>\n",
        "      <td>ASSIN 2</td>\n",
        "      <td>2</td>\n",
        "      <td>Ensemble ( averaging )</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>1.858004</td>\n",
+       "      <td>1.164618</td>\n",
        "      <td>ASSIN 2</td>\n",
        "      <td>3</td>\n",
        "      <td>Ensemble ( averaging )</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.524902</td>\n",
+       "      <td>0.382613</td>\n",
        "      <td>ASSIN 2</td>\n",
        "      <td>4</td>\n",
        "      <td>Ensemble ( averaging )</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0.022092</td>\n",
+       "      <td>0.039629</td>\n",
        "      <td>ASSIN 2</td>\n",
        "      <td>5</td>\n",
        "      <td>Ensemble ( averaging )</td>\n",
@@ -519,132 +239,30 @@
        "</div>"
       ],
       "text/plain": [
-       "   Average absolute error          dataset  score  \\\n",
-       "0               10.909697  ASSIN 1 (PT-PT)      1   \n",
-       "1                5.876683  ASSIN 1 (PT-PT)      2   \n",
-       "2                2.033864  ASSIN 1 (PT-PT)      3   \n",
-       "3                0.308391  ASSIN 1 (PT-PT)      4   \n",
-       "4                0.174404  ASSIN 1 (PT-PT)      5   \n",
-       "0               12.632929  ASSIN 1 (PT-BR)      1   \n",
-       "1                6.194206  ASSIN 1 (PT-BR)      2   \n",
-       "2                2.937050  ASSIN 1 (PT-BR)      3   \n",
-       "3                0.724028  ASSIN 1 (PT-BR)      4   \n",
-       "4                0.064349  ASSIN 1 (PT-BR)      5   \n",
-       "0                9.952024          ASSIN 2      1   \n",
-       "1                4.991293          ASSIN 2      2   \n",
-       "2                2.177242          ASSIN 2      3   \n",
-       "3                0.672338          ASSIN 2      4   \n",
-       "4                0.025624          ASSIN 2      5   \n",
-       "0                6.907404  ASSIN 1 (PT-PT)      1   \n",
-       "1                3.459370  ASSIN 1 (PT-PT)      2   \n",
-       "2                1.129345  ASSIN 1 (PT-PT)      3   \n",
-       "3                0.167825  ASSIN 1 (PT-PT)      4   \n",
-       "4                0.283227  ASSIN 1 (PT-PT)      5   \n",
-       "0                8.330867  ASSIN 1 (PT-BR)      1   \n",
-       "1                4.018704  ASSIN 1 (PT-BR)      2   \n",
-       "2                1.632923  ASSIN 1 (PT-BR)      3   \n",
-       "3                0.330470  ASSIN 1 (PT-BR)      4   \n",
-       "4                0.146850  ASSIN 1 (PT-BR)      5   \n",
-       "0                8.773182          ASSIN 2      1   \n",
-       "1                4.314068          ASSIN 2      2   \n",
-       "2                1.598185          ASSIN 2      3   \n",
-       "3                0.416740          ASSIN 2      4   \n",
-       "4                0.030240          ASSIN 2      5   \n",
-       "0                4.548327  ASSIN 1 (PT-PT)      1   \n",
-       "1                2.156652  ASSIN 1 (PT-PT)      2   \n",
-       "2                0.861034  ASSIN 1 (PT-PT)      3   \n",
-       "3                0.248775  ASSIN 1 (PT-PT)      4   \n",
-       "4                0.369529  ASSIN 1 (PT-PT)      5   \n",
-       "0                4.981972  ASSIN 1 (PT-BR)      1   \n",
-       "1                2.657034  ASSIN 1 (PT-BR)      2   \n",
-       "2                0.999332  ASSIN 1 (PT-BR)      3   \n",
-       "3                0.248256  ASSIN 1 (PT-BR)      4   \n",
-       "4                0.375841  ASSIN 1 (PT-BR)      5   \n",
-       "0                9.079303          ASSIN 2      1   \n",
-       "1                4.411754          ASSIN 2      2   \n",
-       "2                1.512965          ASSIN 2      3   \n",
-       "3                0.347414          ASSIN 2      4   \n",
-       "4                0.054075          ASSIN 2      5   \n",
-       "0                8.780042  ASSIN 1 (PT-PT)      1   \n",
-       "1                4.569723  ASSIN 1 (PT-PT)      2   \n",
-       "2                1.532580  ASSIN 1 (PT-PT)      3   \n",
-       "3                0.213651  ASSIN 1 (PT-PT)      4   \n",
-       "4                0.214348  ASSIN 1 (PT-PT)      5   \n",
-       "0               10.357174  ASSIN 1 (PT-BR)      1   \n",
-       "1                5.035915  ASSIN 1 (PT-BR)      2   \n",
-       "2                2.225965  ASSIN 1 (PT-BR)      3   \n",
-       "3                0.491161  ASSIN 1 (PT-BR)      4   \n",
-       "4                0.084493  ASSIN 1 (PT-BR)      5   \n",
-       "0                9.345122          ASSIN 2      1   \n",
-       "1                4.630961          ASSIN 2      2   \n",
-       "2                1.858004          ASSIN 2      3   \n",
-       "3                0.524902          ASSIN 2      4   \n",
-       "4                0.022092          ASSIN 2      5   \n",
-       "\n",
-       "                           model  \n",
-       "0                        RoBERTa  \n",
-       "1                        RoBERTa  \n",
-       "2                        RoBERTa  \n",
-       "3                        RoBERTa  \n",
-       "4                        RoBERTa  \n",
-       "0                        RoBERTa  \n",
-       "1                        RoBERTa  \n",
-       "2                        RoBERTa  \n",
-       "3                        RoBERTa  \n",
-       "4                        RoBERTa  \n",
-       "0                        RoBERTa  \n",
-       "1                        RoBERTa  \n",
-       "2                        RoBERTa  \n",
-       "3                        RoBERTa  \n",
-       "4                        RoBERTa  \n",
-       "0              BERT-multilingual  \n",
-       "1              BERT-multilingual  \n",
-       "2              BERT-multilingual  \n",
-       "3              BERT-multilingual  \n",
-       "4              BERT-multilingual  \n",
-       "0              BERT-multilingual  \n",
-       "1              BERT-multilingual  \n",
-       "2              BERT-multilingual  \n",
-       "3              BERT-multilingual  \n",
-       "4              BERT-multilingual  \n",
-       "0              BERT-multilingual  \n",
-       "1              BERT-multilingual  \n",
-       "2              BERT-multilingual  \n",
-       "3              BERT-multilingual  \n",
-       "4              BERT-multilingual  \n",
-       "0  Ensemble ( stacking, 5-fold )  \n",
-       "1  Ensemble ( stacking, 5-fold )  \n",
-       "2  Ensemble ( stacking, 5-fold )  \n",
-       "3  Ensemble ( stacking, 5-fold )  \n",
-       "4  Ensemble ( stacking, 5-fold )  \n",
-       "0  Ensemble ( stacking, 5-fold )  \n",
-       "1  Ensemble ( stacking, 5-fold )  \n",
-       "2  Ensemble ( stacking, 5-fold )  \n",
-       "3  Ensemble ( stacking, 5-fold )  \n",
-       "4  Ensemble ( stacking, 5-fold )  \n",
-       "0  Ensemble ( stacking, 5-fold )  \n",
-       "1  Ensemble ( stacking, 5-fold )  \n",
-       "2  Ensemble ( stacking, 5-fold )  \n",
-       "3  Ensemble ( stacking, 5-fold )  \n",
-       "4  Ensemble ( stacking, 5-fold )  \n",
-       "0         Ensemble ( averaging )  \n",
-       "1         Ensemble ( averaging )  \n",
-       "2         Ensemble ( averaging )  \n",
-       "3         Ensemble ( averaging )  \n",
-       "4         Ensemble ( averaging )  \n",
-       "0         Ensemble ( averaging )  \n",
-       "1         Ensemble ( averaging )  \n",
-       "2         Ensemble ( averaging )  \n",
-       "3         Ensemble ( averaging )  \n",
-       "4         Ensemble ( averaging )  \n",
-       "0         Ensemble ( averaging )  \n",
-       "1         Ensemble ( averaging )  \n",
-       "2         Ensemble ( averaging )  \n",
-       "3         Ensemble ( averaging )  \n",
-       "4         Ensemble ( averaging )  "
+       "   Average absolute error  dataset  score                          model\n",
+       "0                1.413047  ASSIN 2      1                        RoBERTa\n",
+       "1                2.391685  ASSIN 2      2                        RoBERTa\n",
+       "2                1.058928  ASSIN 2      3                        RoBERTa\n",
+       "3                0.426405  ASSIN 2      4                        RoBERTa\n",
+       "4                0.089732  ASSIN 2      5                        RoBERTa\n",
+       "0                6.265721  ASSIN 2      1              BERT-multilingual\n",
+       "1                3.696093  ASSIN 2      2              BERT-multilingual\n",
+       "2                1.358086  ASSIN 2      3              BERT-multilingual\n",
+       "3                0.383777  ASSIN 2      4              BERT-multilingual\n",
+       "4                0.032836  ASSIN 2      5              BERT-multilingual\n",
+       "0                3.585998  ASSIN 2      1  Ensemble ( stacking, 5-fold )\n",
+       "1                1.765864  ASSIN 2      2  Ensemble ( stacking, 5-fold )\n",
+       "2                0.536087  ASSIN 2      3  Ensemble ( stacking, 5-fold )\n",
+       "3                0.159993  ASSIN 2      4  Ensemble ( stacking, 5-fold )\n",
+       "4                0.128111  ASSIN 2      5  Ensemble ( stacking, 5-fold )\n",
+       "0                3.111972  ASSIN 2      1         Ensemble ( averaging )\n",
+       "1                2.928381  ASSIN 2      2         Ensemble ( averaging )\n",
+       "2                1.164618  ASSIN 2      3         Ensemble ( averaging )\n",
+       "3                0.382613  ASSIN 2      4         Ensemble ( averaging )\n",
+       "4                0.039629  ASSIN 2      5         Ensemble ( averaging )"
       ]
      },
-     "execution_count": 249,
+     "execution_count": 80,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -669,26 +287,26 @@
     "        dataplot['model'] = plot_title\n",
     "        return dataplot\n",
     "\n",
-    "    test_file = './sources/assin-ptpt-test.xml'\n",
-    "    submission_file = './generated_submissions/assin1ptpt/{0}.xml'.format(model_file)\n",
+    "    test_file = './assin2-test.xml'\n",
+    "    submission_file = './samples/{0}.xml'.format(model_file)\n",
     "    task_name = 'similarity'\n",
-    "    dataset_name = \"ASSIN 1 (PT-PT)\"\n",
+    "    dataset_name = \"ASSIN 2\"\n",
     "    dataplot = generate_section(test_file, submission_file, dataset_name, plot_title)\n",
     "\n",
     "\n",
-    "    test_file = './sources/assin-ptbr-test.xml'\n",
-    "    submission_file = './generated_submissions/assin1ptbr/{0}.xml'.format(model_file)\n",
-    "    task_name = 'similarity'\n",
-    "    dataset_name = \"ASSIN 1 (PT-BR)\"\n",
-    "    df = generate_section(test_file, submission_file, dataset_name, plot_title)\n",
-    "    dataplot = dataplot.append(df)\n",
+    "#     test_file = './sources/assin-ptbr-test.xml'\n",
+    "#     submission_file = './generated_submissions/assin1ptbr/{0}.xml'.format(model_file)\n",
+    "#     task_name = 'similarity'\n",
+    "#     dataset_name = \"ASSIN 1 (PT-BR)\"\n",
+    "#     df = generate_section(test_file, submission_file, dataset_name, plot_title)\n",
+    "#     dataplot = dataplot.append(df)\n",
     "\n",
-    "    test_file = './sources/assin2-test.xml'\n",
-    "    submission_file = './generated_submissions/assin2-trainonly/{0}.xml'.format(model_file)\n",
-    "    task_name = 'similarity'\n",
-    "    dataset_name = \"ASSIN 2\"\n",
-    "    df = generate_section(test_file, submission_file, dataset_name, plot_title)\n",
-    "    dataplot = dataplot.append(df)\n",
+    "#     test_file = './sources/assin2-test.xml'\n",
+    "#     submission_file = './generated_submissions/assin2-trainonly/{0}.xml'.format(model_file)\n",
+    "#     task_name = 'similarity'\n",
+    "#     dataset_name = \"ASSIN 2\"\n",
+    "#     df = generate_section(test_file, submission_file, dataset_name, plot_title)\n",
+    "#     dataplot = dataplot.append(df)\n",
     "\n",
     "\n",
     "    return dataplot\n",
@@ -699,24 +317,24 @@
     "\n",
     "\n",
     "\n",
-    "model_file = \"submission-roberta\"\n",
+    "model_file = \"roberta\"\n",
     "task_name = 'similarity'\n",
     "plot_title = \"RoBERTa\"\n",
     "dataplot = generate_grid(model_file, task_name, plot_title)\n",
     "\n",
-    "model_file = \"submission-bert\"\n",
+    "model_file = \"bert\"\n",
     "task_name = 'similarity'\n",
     "plot_title = \"BERT-multilingual\"\n",
     "df = generate_grid(model_file, task_name, plot_title)\n",
     "dataplot = dataplot.append(df)\n",
     "\n",
-    "model_file = \"final_submission_5folds\"\n",
+    "model_file = \"5fold_stacking\"\n",
     "task_name = 'similarity'\n",
     "plot_title = \"Ensemble ( stacking, 5-fold )\"\n",
     "df = generate_grid(model_file, task_name, plot_title)\n",
     "dataplot = dataplot.append(df)\n",
     "\n",
-    "model_file = \"submission-average\"\n",
+    "model_file = \"average\"\n",
     "task_name = 'similarity'\n",
     "plot_title = \"Ensemble ( averaging )\"\n",
     "df = generate_grid(model_file, task_name, plot_title)\n",
@@ -727,33 +345,83 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 251,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Text(0.5, 0.98, 'Semantic similarity: Mean squared error on each score range')"
+       "Text(0, 0.5, 'Mean squared error')"
       ]
      },
-     "execution_count": 251,
+     "execution_count": 81,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
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\n",
       "text/plain": [
-       "<Figure size 1440x2160 with 0 Axes>"
+       "<Figure size 1440x720 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "plt.rcParams[\"figure.figsize\"] = (20,10)\n",
+    "\n",
+    "# sns.set(font_scale=0.95)\n",
+    "# g = sns.FacetGrid(dataplot, row=\"dataset\", col=\"model\", margin_titles=True, palette=\"gray\")\n",
+    "# g.map(sns.lineplot, x=\"score\", y=\"Average absolute error\", hue=\"model\");\n",
+    "dataplot = dataplot.rename(columns={\n",
+    "    \"Average absolute error\" : \"Mean squared error\",\n",
+    "    \"score\": \"score range\"\n",
+    "})\n",
+    "# g = sns.FacetGrid(dataplot, col=\"dataset\", hue=\"model\", margin_titles=True, legend_out=True)\n",
+    "# g.map(sns.lineplot, \"score\", \"Mean squared error\")\n",
+    "# g.add_legend()\n",
+    "# g.fig.subplots_adjust(top=0.8)\n",
+    "# g.fig.suptitle('Semantic similarity: Mean squared error on each score range', fontsize=14)\n",
+    "\n",
+    "sns.set_style(\"white\")\n",
+    "g = sns.barplot( x=\"score range\", y=\"Mean squared error\", hue=\"model\", data=dataplot, palette='gray')\n",
+    "\n",
+    "g.set(xticklabels=['[1.0, 1.4]', '[1.5, 2.4]', '[2.5, 3.4]', '[3.5, 4.4]', '[4.5, 5.0]'])\n",
+    "\n",
+    "ax = g\n",
+    "plt.setp(ax.get_legend().get_texts(), fontsize='14') # for legend text\n",
+    "plt.setp(ax.get_legend().get_title(), fontsize='14') # for legend title\n",
+    "\n",
+    "ax.tick_params(axis='both', which='major', labelsize=15)\n",
+    "ax.tick_params(axis='both', which='minor', labelsize=15)\n",
+    "\n",
+    "plt.xlabel('score intervals', fontsize=18)\n",
+    "plt.ylabel('Mean squared error', fontsize=16)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Pearson correlation')"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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zc9ja2kJdXR0AEB0dXeZ7tsTLly9RVFQkctxLjt2nxwoADA0NRT4/evQIUVFR2LFjB7es5Ls2IyNDpB5+qvS5B4C0tDRs2rQJd+7c4b6zP3z4gDdv3lR4XGRkZNC0aVPuuJTUY1lZWS6NkZGRSP7Hjx/j9u3b5Q4FTkhIgL6+frkxE0LKRw0fQj4hKysLCwsLWFhYwN3dHZ6envD394ebmxsYY+DxeDh69CikpET/dEpPzP90fckF46cTa3k8nsgcDA8PD6Snp2PRokXQ0NCAjIwMJkyYgMLCwgrLLa+c+uDi4gJLS0tcuXIFN27cwMiRIzFt2jTMnDmzwjzl7X95y6ob+7p16xAWFgYPDw+0adMG8vLy8PDwKHN8qtOIKo+ysjKsra0RGBgILS0tXLp0SeTiqKaUlJTQt29fLFmyBFlZWdzcok8JhUKoqqqW+8S9xo0bAwB2796NgIAALF68GDo6OlBQUICPjw9ycnJE0te0XlhZWeHy5cu4evUqbty4gSlTpsDBwQGrV6+u1v6VNF4YY9yyoqIikTQlc+ZmzpwJCwsLNGnSBBcuXCgzN6T0OSuJe+fOnSKNLwC1mpxek3IVFBTK5JeTk6t24690uvLKq65PyyrvPAMV/x3VZR2rSunvN0C0fpTm7e0NNzc3hIWFcfVi+/btZRr1NVH6uJf3fTBz5kz079+/zHJFRcUKyy3v3P/yyy/Izs7G4sWL0bJlS8jIyMDV1bXMd1LpulXT72yhUAgrKytuzuKnVFRUql0OIeQjavgQUon27dujqKgIAoEAenp6YIwhNTW1yrufNRUeHo4lS5agT58+AD7eTSw92b4qJT+wlf2oNmvWDGpqarh58yYsLCyqXba6ujpGjBiBESNGYNeuXQgICKi04VPXIiIiMHjwYAwYMADA/+5Yt23btkblVHaMhg8fjlmzZqF169ZQVVWt1QUY8PEBCm5ubhg3bhxkZGTKNAw6deqEtLQ0SEpKolWrVuWWER4ejr59+8LJyQnAxwvJFy9e1MkFj7KyMgYPHozBgwfjhx9+gIeHB5YvXw5tbW0kJSXh7du3XAMhMjJS5CK2pCfs0zpa+j0uERERaNmyJWbMmMEtq2oiPvCxZ1BaWhpv3ryBmZlZuWnatWuHkydPIj8/n7vpEBkZWetya0JJSQnNmjVDRESESHnh4eFo3759jcrS1tZGUVERIiMjuZ6M7OxsxMTEYNSoUZ8dY13UsQ4dOkBaWhq3bt1C69atPzuW8ujp6UFPTw9Tp07FxIkTERQUhJ49e0JPTw83b97ErFmzyuRp06YNpKSkEBERAQ0NDQDgjp2zs3OV23vx4kWZHr7PER4ejlWrVqF3794AgJSUlBo/cKBdu3Y4deoU9wRKACIPSAA+nsN///0XGhoaZRq+hJCao78iQgBkZmZi9uzZGDp0KHR1ddGoUSM8evQIe/bsgbm5Ofh8Pvh8PhwdHbFo0SJ4eHhAX18fWVlZuHPnDlq3bl3uXcTq0tLSwokTJ9C5c2fk5eXB29u7xne3mzVrBjk5OYSFhUFDQwOysrLcXd1PTZ8+HWvXroWKigp69+6N/Px83Lx5s8wQoBKrV69Gr169oKWlhdzcXISFhdX4wq622rZtiwsXLqBv376QkpLC1q1bUVBQUONyKjtGFhYWUFJSgr+/P6ZOnSoyJKu4uBj29vYYP358tS9Ee/bsiZs3b5Y7BAkALC0tYWRkhJ9++gnz58+HlpYWUlNTcfXqVVhaWsLExARt27bFxYsXERERAUVFRfz1119ISkqqdcPHz88PhoaGXMP+4sWLaNOmDaSlpfHDDz9AU1MTCxYswKJFi/D+/XusW7dO5Hjw+XwYGBhg165d0NDQQFZWFnx9fUW2oaWlhaSkJJw8eRJGRka4evUqzpw5U2VsjRs3xoQJE7B27VoIhUKYmpoiNzcX9+/fh4yMDFxcXODk5ITNmzfD09MTM2bMQFJSEnbu3Fnrcmtq0qRJ2LZtGzQ1NaGnp4egoCBERkZi5cqVNSpHW1sbffr0wdKlS7Fy5Urw+Xz4+vpCSUkJdnZ2NY6rRF3UscaNG2Ps2LHw9vaGlJQUunbtiszMTERHR2PkyJGfFdfLly9x9OhRWFtbQ01NDS9fvkRMTAw3BHfy5MkYOXIkli9fjlGjRkFGRgZ3795F7969uZsw69evh6KiIjQ0NPD7778jKyuryr9Nd3d3/Pjjj2jRogVsbW0hISGBZ8+e4fHjx+X2qFSmbdu2OHHiBAwNDfH+/XusX7++xo+sL6nHS5cuxdSpU5GcnIzdu3cD+F/vlaurK44ePYq5c+di8uTJaNq0KRISEnD69GksXbq0zl8DQEhDRw0fQvDxXTfGxsYICAhAQkICBAIB1NTU4ODgIHLHeu3atdixYwe8vb3x9u1bKCoqwtDQsMwY+pr67bffsHTpUjg7O6N58+Zwd3cXedx1dUhJSWHJkiXYunUrtm7dClNT03KHuIwePRrS0tLYu3cvfHx8oKioiF69elVYLmMMq1evRlJSEho1agRzc3MsXLiwxvtYGwsXLoSnpyfGjBmDJk2aYPz48Z/V8KnsGPF4PDg7O8Pf37/MneOSu+A1OSc8Hq/COQPAx+Fie/bsgZ+fHxYvXozMzEw0a9YMXbt25bbv7u6OpKQkTJo0CXJychg6dCjs7OxEHgX+OaSlpeHr64vXr19DVlYWXbp0wbZt2wB8nM+0detWLF26FMOGDYOGhgYWLVpUpofPy8sLS5cuxdChQ6GpqYmlS5dyj1AHABsbG4wfPx6rV69GQUEBLC0tMWvWLKxZs6bK+ObNmwcVFRXs3r0by5YtA5/PR6dOnTBlyhQAHxteO3bswIoVKzB48GBoa2vjl19+wY8//lircmtq4sSJyMvLg5eXFzIyMqClpQV/f3+R+WzVtW7dOqxZswbTp09HYWEhTExMsGfPHpH5HzVVV3VswYIFUFJSwpYtW5CSkgIVFZUqe1cqIy8vj+fPn+P48ePIysqCqqoqhgwZwt18MTAwwN69e+Hr64vhw4dDRkYGhoaG3JDRBQsWgMfjwcPDAzk5OdDX18eePXu4+YQV6d27N7Zv345t27Zhz549kJSUhJaW1mfti5eXF5YtW4bBgwdDXV0ds2bNqnGPD5/Px/bt27Fy5Uo4OTmhQ4cOmDlzJmbPns2dd3V1dRw8eBC+vr6YNGkSCgoK0KJFC1haWlIPECGfgccqG4RLCCHfkeXLlyMhIQF79+4VdyhfHUNDQ6xevZobEkUIqXvnzp3DnDlzcOvWrUrnHRFCPg/dLiCEfPdycnIQGxuL4OBgbNy4UdzhEEK+E4GBgWjTpg3U1dXx7NkzeHl5oV+/ftToIaSeUMOHEPLd+/HHH/HgwQMMGzaMe8AEIYTUt7S0NPj7+yM1NRXNmzeHlZUV5s2bJ+6wCGmwaKgbIYQQQgghpMGTqDoJIYQQQgghhHzbqOFDCCGEEEIIafCo4UMIIYQQQghp8KjhQwghhBBCCGnwqOFDCCGEEEIIafCo4UMIIYQQQghp8KjhQwghhBBCCGnwqOFDCCGEEEIIafCo4UMIIYQQQghp8KjhQwghhBBCCGnwqOFDCCGE1CFra2s8fPiwynR//vkn3r59W29xXLx4EREREfVS9vnz56Grq4uQkBCR5cnJyfjpp5/g6OjI/Tt58iQAoLCwEL/99hvs7e0xaNAg2NraYt26dVxeXV1dZGRkAAAWLlyITp06IS4ujlv/+++/Y+HCheXGs3PnTtjb28PR0RGDBg1CcHBwXe8yIaQBkBJ3AN8Ca2trbNq0CYaGhpWm+/PPPzFw4ECoqanVSxwXL16EsrIyTExM6rzs8+fPY+bMmfDx8YGjoyO3PDk5Gb/++isSEhK4ZdOmTYODgwMKCwvh7e2N69evQ1JSEgKBAFZWVvDw8ADw8Ufs5s2bUFZWxsKFC3HixAmEhIRAW1sbwMcfsZiYGHh5eZWJ5969e/D29saTJ0/Qo0cP7Ny5s9L4Dx8+jMTERMydOxeJiYmwsbGBjo4OGGMAAHd3d+Tn5+P3338HAKSlpaG4uJg7V0OHDsW4ceNEyrxx4wamT58OLS0tMMYgKysLDw8PRERE4NSpUwCApKQkyMrKQllZmdtOfHw8JCUl4ebmVv0T8BWgek71HAAEAgGcnZ0xZcoUAICfnx8OHToENTU1FBUVQU1NDStWrICmpiYAYMWKFTA3N8eAAQOqeRZIiYCAAHTt2rVe/5Y6dOhQL39Lhw8fhoWFBQ4fPizyt7RixQp07doVW7duBQDk5uYiLS0NALBv3z68evUKQUFBkJaWRlFREWJiYirchrq6Ory9vbFjx44q4+nUqRPGjBkDPp+P169fw9nZGUZGRly9JoQQgBo+dYp+xOruR0xdXR2enp6IiorC5cuXK00rEAiwY8cOBAUFccvk5OS4O34PHjyAq6srQkNDMWjQIADAli1bkJmZiWXLllVatpaWFlfOqVOnMGfOHFy7dg1Tp04FAMyfPx8GBgaYMGEClyc/Px92dnYYMWIEGjVqVOW+fmuonjfsep6ZmQkbGxv07dsX7dq1AwA4OjrC09MTALB69WqsXbsW27dvBwDMmDED48ePh42NDSQkvs9BBJcvX4a3tzckJSVhZmbGNUQB4K+//kJISAiKioogISEBDw8PdO/eHZs2bUJKSgrmz58POTk5LFq0CCoqKli+fDny8vJQUFAAGxsb/PzzzwCAS5cuwc/PDxISEigqKsLUqVPh5OSE9+/fw9vbG48ePUJhYSHatWuH5cuXIzw8HJcuXcL169dx4sQJODo6YvLkyXWyv2/evEFkZCTOnz8PW1tbxMfHo23btgA+3gz69LuBz+eDz+dz61RUVCAtLQ0AkJKSgp6eXoXbGT58OI4cOYI7d+7AzMys0pgsLS25/2toaEBVVRVJSUnU8CGEiKCGTznoR0z8P2KtWrVCq1atEBsbW2X858+fh4GBAZo0aVLueiMjIygoKCAxMRFNmzatsryK9O7dG3PnzkV2dnaF2wI+Xoz27NkTp06dwvDhwz97e/WN6jnV8/K8f/8ePB6vwka7ubk5Nm3axH1WU1NDixYtcPPmTVhYWHz2dr9VGRkZ8PDwQEBAADp27IiQkBDs37+fW+/o6Ijx48cDAJ4+fYrJkycjLCwMs2fPRnBwMHx8fLhe1tzcXPzxxx+QlZWFQCDAuHHjEBYWBktLS2zcuBErV66EiYkJGGPIzs4GAKxbtw6dOnXCihUrAAD+/v7w9fXFqlWrYG1tjQ4dOmDSpEnlxn7v3j38+uuv5a5r06YNNm/eXO66o0ePYuDAgWjatCkcHR1x5MgR/PLLLwA+9pQuWbIE+/fvh5GREXr16oVevXoBAEaOHInJkydjwIABMDExgZmZGezt7SEjI1PudqSlpfHzzz9j/fr1OHLkSGWnQcT169eRlZWFzp07VzsPIeT7QA2fUuhH7Ov7EavKzZs3K+0dCAsLQ2FhIXeR+7mCg4OhqalZaaOnhLGxMcLCwr7ahg/Vc6rnn3rx4gWcnJxQXFyM+Ph4/PTTT+X26BUVFeHChQtcj1IJY2NjXL9+/bts+Ny/fx/a2tro2LEjgI9/OytXruTWR0VFYefOncjMzISkpCTS0tKQlpYGFRWVMmUVFBRg1apViI6OBo/HQ0pKCqKjo2FpaQkLCwusWbMGAwYMQI8ePWBkZATgf/N4Dh48CODjPBp1dfVqxW5qalrjuTDFxcUIDAyEv78/AMDFxQVubm6YM2cOpKWlYWdnh169euH27duIjIzE4sWLYWtriyVLlkBbWxvnz5/H/fv38d9//yEgIAD79u3DoUOHuBsLpdnb22Pv3r04ffp0teKLjo7GwoULsXHjxgbZ404IqR1q+JRCP2Jf149YdSQnJ8Pc3FxkWX5+PpycnAAASkpK2L59Oxo3blzjsksuCAGgRYsW2LZtW7XylQyz+FpRPad6/qlPh7plZGRg/Pjx6NSpE3r37g0ACAkJwZ07d/D69Wuoqalh8eLFIvlVVFRw7969z9mtbx6Px6twnUAggLu7O/bu3YsuXbpAIBDA2NgY+fn55ab39fVF48aNcezYMUhLS2PBggUoKCgAAHh4eLxDkeYAACAASURBVCAuLg63bt3CihUr0KNHDyxYsABCoRAbN25E+/btaxz759xECAsLQ3p6OmbPns0te/fuHS5dusTN8+Lz+ejbty/69u0LKysrTJo0CUuWLAHw8caAmZkZzMzMMGHCBJibm+PZs2fQ19cvNw4ej4cFCxbA09MTw4YNq3R/oqOjMX36dKxbtw6mpqbVOgaEkO8LNXxKoR+xr+dHrLrk5OTKnINP5z5U18iRI/HhwwcA4O7Uf3pBWBMFBQWQk5Orcb4vheo51XMA5fZIKSsro2fPnggLC+MaPiVzfHJzczFjxgysWrUKPj4+XB6BQPBV1/f6ZGxsjLi4ODx58gQdO3bEqVOnkJOTA+DjcREIBGjZsiUA4MCBAyguLuby8vl85Obmcp+zs7PRpk0bSEtL49WrVwgNDYWrqysAIC4uDtra2tDW1gafz8fhw4cBAP3798fOnTuxZs0ayMjIIC8vD69evYKuri74fD4XS3k+5ybC4cOHMW/ePEycOJFbdvDgQRw+fBgDBgzAv//+C3NzcygoKAAAHj16xD0I4+7du9DU1OR6E2NjY1FcXIwWLVpUus3u3bujffv2OHLkCLp161ZumqioKMyYMQNeXl5lbhAQQkgJaviUQj9iX8ePWE3o6uri+fPntS7nn3/+qXUZJeLi4tCpU6c6K6+uUT2nel6R/Px8hIeHw9nZucw6Pp+P3377DQ4ODoiOjubmOn3t9b0+KSsrY926dZg7dy6kpKRgZmbG/e3w+XzMnTsXI0eORNOmTWFtbc3VMQAYN24cVqxYwc2XmzFjBhYsWICQkBC0bNlS5ALez88PL168gLS0NKSlpbmHTSxcuBB+fn5wdnbmbmhMmjQJurq6GDx4MBYuXIjQ0FA4ODjUer5cSkoKrl27hlWrVokst7Ozg5eXF16/fo3w8HD4+PhAUlISPB4P6urq2LBhA4CP8+x+++035OfnQ1ZWFlJSUvD19eWeilmZ+fPnlxli+amlS5fiw4cPIk9PnDVrFvr27fuZe0sIaYio4VMK/Yh9HT9iT548wdSpU5Gfn4/8/Hz06tULY8aMwbRp08qktbW1xbx587BgwYIaHoH6c/XqVe5xx18jqudUzz/16ZBOgUAAS0tLjBgxoty0rVu3xvjx4+Hn54ddu3ZBKBTi1q1bcHd3r/O4vhVWVlawsrLiPpf0FALA5MmTRerwp8dp2LBhZXoES955U1rJMM3SFBQUuL+r0gwMDCos73M0b94cDx48KLNcUVERkZGRAIAFCxZUWEednJy4elaep0+fcv8v/fj39u3bIyoqqsK8gYGBlcZOCCEAwGOfPsqJkG/Ujz/+CFdX169iiENUVBS8vLwQEBAg7lBIA/M11fMSFy5cwKVLl7B27Vpxh0IIIYRU6vt86QJpcDw9PfHu3TtxhwEAePv2Lfe0MkLq0tdUz0vk5eVh3rx54g6DEEIIqRL1+BBCCCGEEEIaPOrxIYQQQgghhDR4DfrhBunpuRAKy+/QatpUAZmZeV84ouqj+GrnW49PVbXm72Kh+l5/KL7aqev6TnW9/lB8tVMf3+2EkLrz3fb4SElJijuESlF8tUPxiXd7NUXx1Q7FJ55tfQ6Kr3YoPkJIbXy3DR9CCCGEEELI94MaPoQQQgghhJAGr17n+CxfvhyXLl1CSkoK92KyEydOYM+ePWCMQUpKCvPnz4eFhUWZvLdv38b06dO5t6e3adMGmzdvrs9wCSGEEEIIIQ1UvTZ8HBwcMHPmTJGGTatWrfDXX3+hadOmePr0KcaNG4cbN25AUrLsuFgDAwPs27evPkMkpMELvvYC/c3bQl6SJ+5QCCGEEELEpl6HunXr1g0qKioiy0xMTNC0aVMAgI6ODoqLi/H+/fv6DIOQ79r1h0nwCriLwqJicYdCCCGEECI2X+QFprq6utxQt0+dPHkSBw4cwIEDB8qsu337Nn766Se0atUKCgoKmDFjBiwtLes7VEIanIinKVi+6yaG9GkPN0d9cYdDCCGEECIWYnuPT1RUFDZs2IC9e/eWu15fXx+hoaHg8/l48uQJpkyZggMHDqB169bV3kZl73pQVW2M1NScz4r9S6D4audbj68u3+PTWlkeA83bIig0FroaTaDTWqnGZde3b/18idu3Hl9dvsfnWz8W4kbx1U59fLcTQupOpUPdiouLsXTp0jrf6IsXLzBz5kz4+vqibdu25abh8/ng8/kAgI4dO6JLly6Iioqq81gI+R5MdNRHM0U5/HEqGgUCGvJGCCGEkO9PpQ0fSUlJPHnypE43mJycjOnTp2PFihXo0qVLhelSUlJQMgovOTkZkZGRaN++fZ3GQsj3Ql5WCpPs9ZCa9QFHQmPFHQ4hhBBCyBdX5VC33r17Y8uWLRg2bBjk5eW55UpKVQ+X8fT0RFhYGACgV69esLS0BI/HQ1paGnx8fODj4wMA2Lp1K1q1aoVNmzahefPmGDVqFM6fP4+DBw9CSkoKjDHMnTsX2tran7ufhHz3dDWbwqZba5y/+wpddFSh31ZZ3CERQgghhHwxVT7coGPHjmUz8XiIjo6ut6DqCo0Drz8UX+18yTk+n25PUFiMFXvvQlBUjFVu3aEgJ7ZpfiK+9fMlbt96fDTH5+tB8dUOzfEh5OtW5VVPXQ91I4SIj4y0JCY56OG3feH451IM3Oz0xB0SIYQQQsgXUa33+Lx//x53797F3bt3kZeXV98xEULqkXZLRdj1aINrD5LwX2yauMMhhBBCCPkiquzxiYiIwMyZM7kXkaanp8Pf3x/Gxsb1HhwhpH4MstBCZGwa/jrzBO0ndwdfXlrcIRFCCCGE1Ksqe3zWrVsHPz8/BAcHIzg4GBs3bsTatWu/RGyEkHoiLSWByQ6dkPuhEH9feCbucAghhBBC6l2VDZ/8/HyYmZlxn01NTZGfn1+vQRFC6p+mWmMMsmiL21FvcfdJirjDIYQQQgipV1U2fKSkpEReHBodHQ0pqa/jSVCEkNqxM2+DtuqNse/cU7x7LxB3OIQQQggh9abKFsy8efMwceJE7uWhcXFx8PPzq/fACCH1T1JCApMcOmHl3rsIOPsE7s6G4PF44g6LEEIIIaTOVdnw6dmzJ86cOYPIyEgAQOfOnaGsTC8+JKSh0FBpBOde7XD4cixuPk5GT4MW4g6JEEIIIaTOVTrUrbi4GM7OzlBWVoaVlRWsrKwaRKPnv5g0rNxzC3n5ReIOhZCvQv9urdG+lSL+vhCDjGyaw0cIIYSQhqfSho+kpCRkZWVRWFj4peL5IvgK0rj/NAXbgx6iqFgo7nAIETsJCR4m2euhWCjEn2eegDEm7pAIIYQQQupUlQ83MDAwwPTp0xEUFITz589z/75l7TUU4e7SGY/jM7Hv3FO6yCMEgFpTBbj0aY9HLzJwJfKNuMMhhBBCCKlTVc7xefLkCQAgMDCQW8bj8dC/f//6i+oL6GfWBs9fZSHkRjxUlOTh2LOtuEMiROysTDQQ8SwVh/6NhX5bZagqyYs7JEIIIYSQOlFpw0coFGLp0qXQ0dH5UvF8UYMttZD27gOOX30OFUU5mOurizskQsRKgseDm50elv5+G3+cisYvo7tAgp7yRgghhJAGoNKhbhISEpg3b96XiuWL4/F4mDBQD7qtlbD3dDSeJmSKOyRC6hRjDG92bENmeES18zRTlMOovh3w9FUW/r2XWI/REUIIId+X27dvY9asWZWmWbhwIS5fvvyFIvq+VDnHp2XLlkhPT69xwcuXL4elpSV0dXVFlv/111+wsbGBjY0NAgICKsx/+vRpDBgwADY2NtiwYUONt19d0lIScB9qCFUlefgfe4ik9Pf1ti1CvjQej4eirEw8Wb8BBYmvqp3vB6MWMNJuhqNX4uhvghBCCCENQpUNHxkZGTg5OWHRokVYvXo1968qDg4OOH78uMiy+Ph4/P333zh+/DiOHz+O/fv34+XLl2Xy5uTkwMvLCwEBAThz5gzu3r2Lmzdv1mC3aqaRnDTmuHSGpAQPfocjkU1vsCcNSMvpP0JKQQGv/TehOCenWnk+9oZ2hIyUBP44FY1iIT39kBBCyPcrMTERgwYNwrx589C/f38sX74cFy9ehIuLCxwcHBAfH49Xr17B1dUVjo6OmD59OrKysgAADx48gIODA5ycnHD27FmuzIyMDLi7u8PZ2RkjRoxAVFSUuHbvu1Flw0dXVxcjR46EhoYGlJSUuH9V6datG1RUVESWnT9/Hra2tuDz+eDz+RgwYEC5T4gLCwtDt27doKamBikpKQwePBjnzp2rwW7VnKqSPGYN64zs9wJsDnyAgsLiet0eIV+KlFJTdFzsgeKsLLzZ7g9WVL33VynxZTGmvw7i3mTj7O2Eeo6SkNoTMkavKCCE1Jvnz5/jp59+wpkzZ3Dnzh2Eh4fjyJEjGDt2LPbv34/Vq1dj1KhRCAkJgYmJCfz9/QEAnp6eWLt2LYKDg5GZ+b9pFWvWrMHUqVNx7NgxrFu3DsuXLxfXrn03qnyqm7u7e51t7O3bt9DW1uY+t2jRAnFxcWXSJScnQ11dXSRdaGhojbfXrBm/0vWqqo3LfJ4vIYG1f91BwPln8BjXDZIS4pvYXTq+rw3FVzt1HV+l9V21MdrP/AkxfpuQE3wE2tOnVqtMh158PI7PQvC1ePTp1gZtWzSpo2jLCfE7O1917XuKr6K6HhL2HEcv3cDan35AS5XKv//F6Xs6V/WB4iPioqWlhXbt2gEAtLW10bNnTwCAjo4Orly5gocPH2LHjh0AACcnJ0ybNg3Z2dkQCAQwNDQEADg6OiI4OBgAcPPmTcTGxnLlZ2dnf8nd+S5V2fBJSkrC0qVLkZycjJMnTyIqKgq3bt2Cm5tbjTdW3ffl1NV7ddLTcyEUll+WqmpjpKaWHfbTXp2PkdYdcPDfGGw7fB8j+3aok1hqqqL4vhYUX+1UFd/n/HBWVd95+l3Q1NYOyWdOQ6isBiUr62qV69KnHR7GpsJ7310sGWcKKckqO4pr7Fs/X+L2rcdX0/peUV3XUmuEwiKGpTtuYLFrVzRRkKlxrPXtWz9X4vatx0eNom+btLQ0938JCQnIyMhw/xcKheB9xlNQAwMDISVV5eU4qSNVXsEsX74cDg4OkJD4mLRDhw44duzYZ21MXV0dycnJ3OekpCSoqamVSdeiRYtqpasvNt1ao1/XVjh/9xUu3qv+hHBCvnYqzsPQyKgzUv75G3lPoquVp4mCDMbZdkTC21ycvBFfvwESUgtqTRWwbFJ3ZOYUYMtRGrJMCPmyDAwMuKkZISEhMDU1RZMmTSAjI4PHjx8DAE6dOsWl7969Ow4ePMh9Lnl3Jqk/VTZ80tLSMHjwYK7hIy0tDUlJyc/amI2NDc6ePYvc3Fzk5ubi7NmzsLGxKZPO0tISd+7cwdu3b1FUVISgoKAv/sLUkX07wLi9Cg7+G4P7MalfdNuE1BeehATUp0yHTHM1vNmxFYLUlGrlM9FRhbm+Ok7eeIn4ZOqKJ1+vjm2VMdVRH8/fZGPXiccV9oISQkhdW7JkCfbv3w9HR0fcu3ePmy6yevVqeHh4YPDgwSLz5JcuXYo7d+5g0KBBGDhwIEJCQsQV+nejyr610t1v79+/h7AaT3jy9PREWFgYAKBXr16wtLTEmjVrMHr0aAwePBgAMHbsWGhpaQEADh48iJSUFMyePRuNGzfGwoUL4erqCqFQiP79+8PCwqLGO1cbEhI8TBukj3UHIrDzxGN4jDaBVj3ObyDkS5GUl0dL99lIWLMKb/w3Q3ORJyTk5KvMN9qmA6JfZmDPyWgsn2AKaanPuwFCSH3rqquKUf064MDFGBy8GIPRNh0+awgKIYSUaNWqlciIp82bN3P/NzY2xs6dOwEA+/fvL5O3c+fOOHnyZJnlysrK2LJlS5nlXl5edREyKQePVTGhZtOmTRAKhTh37hwWL16MvXv3olu3bvjxxx+/VIyf7XPm+JT27r0AawLuQVAkxJJxXaGiWPUFYl341scxi9u3Hl99zPEpvb286Cgk+vmgkVFntPxxJngSVc/defg8HX6HI2HbXRPDrdrXOMaKfOvnS9y+9fjqao5PwevXkMlIBgxMwOPxcOhSDM7deYXhVu1h212zxnHXh2/9XInbtx4fzfEhRLyqvNJxd3dHo0aN0KRJE2zevBnm5uaYPn36l4jtq6DYSAZzXDqjqEiIjUceIC+/UNwhEVInFPQ6QXXEKLz/7z7STxyvOgMAw3bN0Nu4Jc7dTkBs4rt6jpCQmvkQG4OYTVuQcerjcBEXq/bo1rE5Dl+Oxe2ot2KOjhBCiLhV2fCRlJTE1KlTcfjwYRw9ehRTp07l5vt8L1qqNMJPzoZ4m5EH/2MP6T0RpMFQsu6HJpa9kHEyBDl3blcrz3Cr9mimKIc9p6JQIKDJ4+Tr0fiHH9CstyXSg44h6/IlSPB4mOygB51Wivj9VBSeJmRWXQghhJAG6/tqwdSCXpummGjXEU8SsvDnmSd19shtQsSJx+NBbcw4yHfQQfKfvyP/ZXyVeeRlpeBmp4eUzA84Glr2PVyEiMvVN7fg2/Y1JA30kHJgH7Jv34K0lCTchxpBVUkeWwIf4nXae3GHSQghREyo4VMDPQ1aYPAPWrjxKBknrseLOxxC6gRPSgotZrhDks/HG//NKHqXVWWejm2aop9pK/wbkYjo+IwvECUhVTNU0QMkJfCncQGktLWQ/MduvH/4AHx5afzs0hnSUhLYePg/ZOYUiDtUQgghYvBdNnyKc3OR8/TZZ+V1tGgLCwN1BF97gesPk+o4MkLEQ6pJE7R0n43i97l4s80fwsKq57IN7a0Ntaby+ON0ND4UFH2BKAmpXDNJeXgYj0S+RDEOmEtBqmULvNnujw8xMVBRksccl87I/VCETUciqc4SQsh36Lts+OTcuYUHCxYh8+L5Gufl8XgYP7Aj9No0xZ9nntDdbtJgyGm2gbrbZOTHxSJl319VDueUlZbEJIdOyMgpwD//xnyhKAmpWGF0KHgH1mJqcwukIw/HejeBhJISXm/xQ8GrV2ij3hg/DjFAYup7bAt6RPM1CSHkO1Phe3wcHR0rzfgtv2RJsVcfFL+IQeo/B8AEAijbOdQov5SkBH4aYoC1+yPgf/wRFrt2hYZKo3qKlpAvp7GpGQocXyMjJBiyrVujqc2AStO311DEwO5tcPrWS3TVVYWRtsoXipSQsqT1rIDE/6B+7RAmW7hgV/IVnLFRw8BTAiT6eaO1hycM26lhnK0u/jzzBAFnn2KiXUd6xw8hX5Curi46duwIoVAIKSkpeHp6wtTUtNI8iYmJsLW1hba2NhhjYIzB3d0dAwZ8/I2ytraGnJwcpKWlAQBNmjTBvn37cPv2bUyfPh2ampooLCxEy5YtsWbNGqipqcHFxQUCgQB5eXlIS0uDpubHR96PGzcOQ4cOrd+DQMSmwobP0qVLAQBXrlxBbGwsXFxcAABHjx5F+/Z19/4OceBJSUH3l3l4uM4XaceOQlhYiGaDBtfox09BThqzXYywJiAcGw9HYsm4rlDky9Zj1IR8Gc0cnSBITETq4X8g01IDjfQNKk3v9IMWIuPSsPfME/w6qTv48tJfKFJCRPFk5NFi5BIkBCyH5vWjGG8xBHuTwyA/sC16n4hFop83NBd6olfnlsjIzseJ6/FopigHpx+0xB06Id+V4OBgAMA///yDJUuW4OzZs1Xmad68OZcvNjYWY8aM4Ro+ALBr1y60atWqTD4DAwPs27cPjDHMnz8fW7duxapVq3DkyBEAwO3bt+Hv7499+/bVxa6Rr1yFDR8zMzMAgLe3Nw4dOsQ9wrpPnz4YNWrUl4muHvEkJaE+aSp40tLICAkGEwigMmx4jRo/KorymO1iBK+/I7Dx6AMsHG0CWRl6mz35ugizU8CU5aqdnichAfVJU5DgtQZJO7dBc/EyyKirV5heWkoCk+07YXXAPRy48AxTB+nXRdiEfBYJuUZQsJuPvJPr0eHGcYwyH4S/k69BwUEXpkGPkOjrg9YLFsHpBy2kZ+cj+NoLKDeWhWXnluIOnZAv5tK9BFy4k1CnZdqYacLatGYvCu7WrRvWrl3LfU5OToanpydSUlIgLS0NT09PdO3atUy+nJwcKCkp1WhbPB4PXbt2xeXLl6tMe+TIERw6dAhFRUVo3LgxfvvtN7Ru3bpG2yNfpyrn+Lx79w5C4f/GQQuFQmRnZ9drUF8KT0ICauMmQtHKGpnnziD14H4wYc3GfLdVb4LpgwyQ8DYHO088Lvdt4oSIC2NCvD+2AkkHV4MVVv9JVhJyctBwnwWehCRe+29EcV5epenbqDeGQ8+2uBX1FveepNQ2bEJqhSfbCAr2v0BCsQUMb57E0ObdcU3yFR47GkGQ8havN/uBCQQYb9sR+m2b4q+zT/Hoebq4wybku/Pvv//C1taW+/zrr7/ihx9+QEhICFasWIG5c+dCIBAAAFJSUuDk5AQ7OztMmDAB8+fPFylr6tSpcHJygpOTExYtWlRmW4WFhQgLCxPpJapIv379cPToUQQFBWHUqFHw8/Or5Z6Sr0WFPT4lLC0tMWHCBAwePBjAx+5JS0vLeg/sS+FJSKD5aFdISMsg8/xZCAsLoeY6AbwavKTVuIMKRvfTwd8XnuHgxRiMtulAY8bJV4HHk4CcxVjkh+6B5FlfyNv+DJ509Xp/pFVU0eJHdyRuWI+kXduhMevnSv8u7M3b4L+YNAScewqd1kpo0kimrnaDkBrjyfEhb/8LPpxch253zuGDqTVOp0VA3qErtILv4M22LWjpPhs/DjGE198R2Br0CAtHm6CNemNxh05IvbM2rXnvTF1ycnJCVlYWcnJyuCFnAHDnzh14eXkBAIyMjKCkpITnz5+Dz+eLDHWLi4uDq6srjIyMoKamBqDioW6PHj2Ck5MT3rx5g5YtW8Le3r7K+OLi4rBp0yZkZWWhuLiYG/VEvn1VnsnFixfDzs4OV65cwdWrV2Fvb19uS/pbxuPxoOIyAsoOjsgOu4rk33eDFdfsjfR9u7ZC/26t8W9EIi7cS6ynSAmpOekOPdHcaRaKk5/hwxlfMMGHaudV0NFF89GuyHv0EGmBhytNKyUpgckOesgXFCHg3FN6yS8ROwn5JpC3XwAJfjNYhofCqpkhTii8QLKdGfIeP0Ly77sgJy2BOS6d0UhOChuPRCLtXfX/Pgghnyc4OBiXL1/G8OHDsWDBAm556ZvGFd1E1tbWhoaGBiIiIqrcloGBAYKDgxEaGgo+n4/NmzdXmWfevHmYO3cuQkJC4OPjg4ICevdXQ1Flw0dSUhKjR4/G+vXrsXnzZowcORKSkg1vHguPx4PK4KFoNmQocm7fRNKu7WBFNXvPw3Dr9uiqq4pD/8Yg/GlqPUVKSM3x9S0hZz0DxW9jkXdmQ40aP0q9+0DRqi8yz51F9o3rlabVUOVjiGU7RDxLxa2ot7UNm5Bak1BQhLyDByQUFNE/4gZ6KHXEIcUXyLLphtx7d5GyPwBKfBn87NIZgiIh/A5H4n1+1e+xIoTUjoSEBObOnYusrCyEhoYC+Di/PDAwEMDHnpqsrCy0a9euTN63b98iPj4ebdu2rfb2GjVqhFWrVuHQoUPIyKj8VSS5ublcT9KhQ4eqvQ3y9auy4fP06VMMHDiQGxP58OFDrFu3rt4DE5dm9o5QHT4KueH38GbbFggLBdXOK8HjYYpDJ7Rr2QS7Qx4j7s27eoyUkJqR1jaDXN8ZEKa8QN5pHzBB5fN2PtV8xCjId9TD24C9+BAXW2naAWaa0NZogr/PP0NmDt0lI+InoaAEBXsPSMjxMejBPRgrauMv1ZfIszTBu6uhSDt2FBqqfMx0NkRq1gdsCXyIwiJ6xw8h9U1GRgazZs3Cjh07AABLlixBWFgYHB0dsWzZMmzYsAEyMh+HTZfM8Rk0aBAmTJgAd3d36OnpcWV9OsfHyckJxeWM3NHW1sbAgQPx559/VhrXggULMHbsWDg7O6Np06Z1t8NE7HisivEorq6umDVrFtasWYOgoCAwxuDg4IBTp0599kYTEhIwc+ZM7nNqaiq6dOmCrVu3iqQr/Vz2DRs21OhR2unpuRU+bEBVtTFSU3MqzJt1+RJS/g6AQid9tPxpFiRkq/+o6uz3AqzZdw/5gmJ4jjNFcyX5auetbnziRvHVTlXxqarWfJ5Bdet74Ytw5F/cBgmVNlCwmweebPXeQVWcm4uE1SshLBRAc8kKSFfyY/A2Iw/L/7gDHU0l/OzSuco5b9/6+RK3bz2+mtb3z/1uF+akIS9kLQoL87FfryOe5rzCjDh1SN958HG484CBuBWVjF0nomCm1xxTB+lDoo7na37r50rcvvX4Pue7nRBSd6rs8cnLy0O3bt24zzwej2uIfC5NTU0EBwdz/0pa4OXZtWsXl+5Lvj9IycoaahMnIS86Cq83+UKYX/2hQU0ayWCOS2cIhQwbD0ci9wMNmyBfD2mtrpC3cYcw/eXHnp+C99XKJ8nno+XMORDmF+DN1s0QCiruDVVTVsCwPtp49DwDYQ+S6ip0QmpForEKFBwWQlpKFmOePkNbfgvs0k6B0EgPaUcO4d21q+jRSR0ufbRxJzoFRy/HiTtkQgghdahac3w+fZx1VeMia+r169eIjo5Gv3796rTcuqBoYQn1KdPwITYGib4+KM6r3gUiALRo1gjuzoZIe/cB/sdo2AT5uki17QJ5m5kQpr9C3qn1YPm51conq6GBFlOmoeBlPN7+9UelDzCw7toKHTWVcPDfGKRl0YRx8nWQaKIKBQcPyPAkMT7mBdQUlLFb/x14Otp4+9de5ITfg213TViZaODsnQT8G04PqyGEkIaiyqFuAQEBePz4Me7du4fJkyfj4MGDGDVqVJ29xHTHjh1ISEjAb7/9VmadtbU1GjduDMYYrKys4O7uXuveps+RfvM2nvr4QkGzNfRXLoN0kybVGoXVYAAAIABJREFUznslIhE+f4ejd5dWmDfGhB5zTb4qebHheHvUG9IqrdBi9HJIKlRvGEbi0WN4ue9vtHEdg1bDnCtM9zYjDzN9LqNDayX8Oq0nJCSo/pOvgyD9NZL2LUOOBLBLSx15ee8x9bYkil4motMyTzQ2NMTaP+/gTlQyFo3vBnNDesEpIYR866ps+ADA6dOnceHCBQiFQvTv379az0CvroEDB2LFihXo3r17mXXJyclQV1fH+/fvsWDBAhgZGWHatGnVLrs2c3xKe//wAd5s2wLp5mpoNfcXSCkqVjvvqZvxCLzyHA4928K5V9mnk9RFfF8axVc74pzjU1rRq4f4cH4zJBTVIW//CyTkq27YM8aQvHsHcu7eQcufZoFv3KXCtFcj3+DPM08wul8H9DMt/83X3/r5ErdvPb4vNcentOLM1/gQ4oVMaSnsaKUMyYIijLtSAJaRiVbzPCDRug28D97Hq5Rc/DKqC9prVP97vyLf+rkSt289PprjQ4h4VTrUrbi4GP7+/rCzs4Ofnx82bdpUp42ehw8foqCgAGZmZuWuV1dXB/DxEYTDhg3D/fv362zbNdXI0Agas35GYWoKXq1fi8IaDPmz69EGvTq3wMkb8QiLfFOPURJSc1KtDSE/YA6E75Lx4eR6CD9kV5mHx+NBbbwbZDXbIGn3ThS8fl1hWkujFjBs1wxHQ+OQnFH9J8kRUt8km2pA3mEBmgoKMelNFgpkeDjUSwE8Ph+vN20AUpMxa5gRmvJlsfnoA7yl+ksIId+0Shs+kpKSuHz5cr1tPDg4GI6OjuUO/8rLy0Nu7sd5B0VFRTh//jx0dXXrLZbqUNDrhFY/z0fxuywkrl+LwrTqvauHx+NhbH9d6GspI+DcUzx+UbfzpAipLalW+pC3/RnC7BR8OOkFYV7Vj2KXkJX9/yceyuCN/0YU55Y/T4jH42HCwI6QkpTA76eiKrxTT4g4SCq3hrz9L1DNz4fb21xkyAhwoq8yICmJ134+kP+QjZ9HdP4/9s48LKrqjeOfOysDw77vyI4IIiruG+4L4pKlmbZopWmlqaVlamkplS2i/VpsMXMpTBM1FXHfFRXFBRRXQNlUZIcZmN8fJEnsiAI5n+fheWDuOed+77mHOfe95z3vC8AXv58mM6fmKQ60aNGiRUvjotrgBl27dmXDhg1lAhzUB2q1mr/++oshQ4aUfhYTE8PLL78MwO3btxk9ejRBQUEEBwcjEomYMGFCvZxboy5EnXW3TnUVbu7YvvU2Rbk5JHyykMKU5BrVk4hFvDakBdamuizbEENias02k2vR8riQ2DZH0X8qxVnpfxs/GdXWkZqYYDPpDdR373Lzm2WVJv011pczuo87l5My2X7sRn1L16LloRCbOaI7YAY2OTm8kF7IDVkOEb2tKSrIJ/HzTzEVqXjzKV8ysgv4at0ZCgrL5wfRokVLzfDw8CjNtTNgwABWrVpVemzMmDH07t27TD6e/Px8EhMTadGiBcHBwQwaNIhRo0YRH1+SU27SpEmlbd0vExwczP/+979Hon/69Ols3LgRgIiICGJjY0uP7dixg6VLlwIQFhbGe++9V+7zx8mDWrWUUO0en1atWpGXl4dEIkEmk6HRaBAEgZMnTz4ujXWmMj/wwnM7KTi8Gnn7kUi9e9Up4ED+jeskff4ZiEXYTXsbuY1tjerdycxnwS9RCILA7LFtMNavOD9QU/djbmiaur7Hucfn36hvxZG39XMEPWN0B72DSK/65G33Dh4g5aflGAX2wuLZ5yoso9Fo+HrDWU5fTmfOC22xM1fWSV9DoNX3cDTWPT7/pij1MrlbPuWioSErjAVa5RjTeUs8Mitr7GbM5ExiDks3xNDSxYxJw1ogFlX77rBe9T0OtPoeDu0en+rx8PAgLi4OKIkU3LNnT/bv349SqWTMmDFMnjy53L7vxMRExo4dy65du4CSvI7nz5/nhx9+qLTMo2L69Ol06dKF4ODgMr//m7CwMKKjo/noo48eqZ6qqErfk4qkugKbN29+HDoeK1K3johTY8k9tIqi1CvodH0BQVLzBKUAOg6O2L09k8TFn5D4ySJs35qOjoNjtfVMDHSYMqIlC1ed5Kuw07wz2h+FvNrboEXLY0Ni7YFiwHTyti4md9OiEuNHaVJlHcNOnSlMTODuju3I7Oww6tq9XBlBEBjT14O45Rn8sPkC741tjURc+wdHLVoeFWILF3T7T8P9r88YKTJmtcFtFP3c8d8Sy83QL2k5dTrP9nJn1Y6LrN5xief6uGsjdWppkmSd2UPW6fo1EPRbBqLv271WdXJyclAoFEgktXsOatOmDTt37qxVHYBRo0bh5+dHVFQU6enpvPfee1y4cIGdO3dSXFzM0qVLcXBw4IsvvkAul/Paa68BFRsQhw4dYt++fZw4cYIff/yR999/n6tXr1Zo7DxoBIWFhbFr1y40Gg3Xrl3D2dmZL7/8EplMRkJCAjNmzCA7Oxt/f392795NWFgYZmZm+Pr6cv78eQCuX7/O+PHj2bFjBzk5OUyaNInMzEwKCgoYOnQo48ePr3XfPClU+9Rha2tb4U9TRpApsBzxNrI2w1DHHyF34wKKM1Nr3Y7cxhb7t2chyKQkfhZC/tUrNarnYKnPxOAWJKbl8G34OYrq2Y1Qi5aHRWLlhu6A6Wjy7pG7aSHF2berrWP21NPoercgddVKci/GVVjGQE/G8/08uJ6SxZbD1+tbthYtD43Yyg1F/7fwuXOHYdli9uunEdvXi7z4S9z6ZhmBLa3o186B3aeS2HpU67apRUtdCA4OJigoiP79+/Paa6+ho6NTemz27Nml7mrjxo0rV1ej0bBnzx769etXp3Pn5uYSFhbGF198wfTp03F2dubPP/9k0KBB/PjjjzVup2PHjnTt2pUpU6awceNG2rRpU+O6Z8+eZeHChWzdupW8vDwiIyMBmD9/PsOGDWPz5s10796d1NTqn03lcjmhoaGsX7+eDRs2sGXLFi5f1iZfroxqTezk5GQ+/fRTYmNjyc/PL/28LpZ2Y0IQRMj9ByM2cyJv1zfkbPgAReCrSOx9a9WOzNIK+7dnkfjZJyQu/gTbN99C4eZebT1fF1Oe6+vOL9viWLXjEmO0bw61NDLElq7oDpxB7pbP/ln50TertLwgFmP96kRufDSfW/9bisPsuUhNy5dv7WFBe29LNh+6hp+rGY5WWtcPLY0LibUHir5TaLvtC/JFZmwxTkWnZwucIk+T/NMPDH9pPHcy81m35zIm+nLae1s1tGQtWmqFvm/3Wq/O1Cf3952kpqYycuRIWrZsiY+PDwALFiyoMMVJamoqwcHBpKWlIZFI2LBhQ53Ofd9gat68Ofn5+fTp0wcALy8vjh49Wqc2a0v79u0xNi5xI2/RogWJiSWJkqOiovjqq6+AklyWenp61bal0WhYsmRJqfbk5GQuXbqEi4vLI1LftKl2xee9997D39+f4uJiPvroI1q0aMHQoUMfh7bHgsTBF71h8xDpmZC39QsKToaj0dRuBUZqZo7d27MQGxqR+MVn5F44X6N63f1s6d/egT2nkth+LKEu8rVoeaSILVzQHTgDTUFOycpPZtWRDMW6eti+/iYatZqk0K8ofuBlyYOM7u2OUlfK8s3nUam1K55aGh8S2+Yo+r5Jl5Q0AvMlbLRIIaWrN1lHD3P7tzW8NMALD3sjfthygQvX6xYsR4uWJx0LCwt8fX1rZHBYWFiwceNG9uzZg7+/Px988EG1de6vHM2aNav0M6lUCoBIJEIkEpX+LRaLKSoqCVwikUjKBPUqLKzfaI4ymaz0d5FIhPrvwECVbbsXiUQIglB6vKCgoPTYn3/+SWJiIuvWrSM8PJzWrVuXOa6lLNUaPunp6YwePRqJREL79u1ZvHgx+/btexzaHhsiAwt0h8xG4tqOwqj15EeEoimsXb4GqYkJ9m/PQmpuQdJXn5MTc6ZG9YZ3c6GtpwW/747neGzt3e20aHnUiC2c0R34NhpVPrmbF1XrFiqzssb61dcoTEok+aflaCpw5dTTkfJif0+S0nP480DNXES1aHncSOxaoOj9Br1vptKxQMpa21Qy2nmRsSuSrK2bmDzcB0sTXZaujyExTRupU4uW2pKdnc25c+dwcnKqcR2ZTMbcuXM5dOgQFy9erLLsxo0b2bhxIwsXLqyVLnt7e86ePQtAWloax48fr7CcUqksTb1SH7Rt27Z0b/2ePXvIyckBSgwfKysrzp07B8Bff/1VWic7OxtjY2NkMhnXr1/nyJEj9abnv0i1hs99q1Qmk3Hnzh0EQeDu3f/e2y1BIkenx6vIO45GfeM0ORs+pOhu5UkZK0JiaIj99HeQ2diStPQrsk6eqLaOSBAYP8gLV1tDvt90nvjE6vOnaNHyuBGbO/1j/GxaRPG9lCrL67XwwXzEM2SfiOLO5vAKy/i6mNHF15ptR29w5OytRyFbi5aHRuLgi27vSQxKTMZfLWOFczp5Ld25Hf4nhQf3MGWELzKpiC9+P83dLO1bVi1aasL9lZjhw4fTr18/evXqVXrswT0+wcHB3LpVfn4wNjbmpZde4ttvv30k+vr160dxcTH9+/dnzpw5pW54/2bw4MGsWrWKIUOGEBUV9dDnnT17NuvWrWPQoEHs3r0bIyMjlMqSCKizZs1iypQpDB8+vEydIUOGcPXqVQYOHMjChQtp27btQ+v4L1NtOOuZM2fyzjvvEB4ezooVK9DT08PNzY3PP//8cWmsM3UNeaq+FUd+5DI0qgJ0uo9H6ly7QVSUm0PSl5+Tf+0qVuNfwSCgfbV1snIL+XjlCXLy1bw3tjUt3C2bdMjOhqap62vIcNZVUXT7BnmbPwGxBN1BMxEZVb63QaPRkPLTcjIPHcR64iT0W5f/P8orUBOy+iQ3UrLp186BYV2dG2Wkt6Y+nhqaphLOuipUV6PIifyaVY5WXBCpmHhaH+mFK1iNf4WMZj4sXHUSc0MFs56rOlJnU79XDU1T16cNZ62lKvLy8pDL5YhEIo4dO8bcuXPZunVrQ8v6TyGeN2/evKoK9OrVC4VCgZ+fH97e3rRu3Zrx48cjqkP+gsdNXl4hlZl1enpycnMr9tkU6ZshcW1P0a1YVDHbQV2I2MYTQajZNYukMvTbBpB36RIZOyKQmJpWG+paLhXj42LK/tO3OBGXRnd/O4rUjTdJXlX91xho6vr09GoXXh3qPt5rg0jXELGDD+q4A6guHULs4ItIp+KJXBAEdFv4kHvhPPf27kbp2xKJoWGZMlKJiE4+VhRpBLYduc75a3do7mSMro70obXWJ019PDU09T3eH8dY/zdiYxvERta4n97NdUNDdlsW0DrPlJzde7Bo7o67nxuRUYlcuZVJgJclIlHFwWqa+r1qaJq6vrp8t2t5coiJiWHixImsXr2aw4cPs2DBAqystMFT6pNqDZ8HsbGxoVmzZk3C6IGHmxwFmQKpW0c0+dmozu6gKOUyYgffGuf7ESRS9NsEkH/1Chk7tiM2MEDHqVmVdZQKKW52RkSeSCTmcjp+rmbIpOIane9x09Qnn4amqRo+ACKFIWL7lqgvHkB98UCJ8aMwqLCsIBaj59OSrCOHyDp2DP12HRDJy16bWCSie1sHDBUS9p+5xb7TN7E20cXatPpoNo+Lpj6eGpr/guEDIDaxRWJgiWf0Pi4aG3DAWk3rDCXZe/fiGOCHpZMtEccTuJ2ZTys3swojdTb1e9XQNHV9WsNHS1VYW1szatQonn32WZ555hmt0fMIqNaC8fT0xMvLq9zPk4AglqLT5Xl0ur5EUXIcuevnUZR2rcb1RXI5Nq+/iZ5vS1J//YW7O7ZXW8fVzpAJwd5cTrzHwl9PkH4v7yGuQIuWR4PYxBbFoJmAQN7mEIruJFZaVmJkhM2kNyjKyuTW/5ai+Tt6zb8J8LJk7ottMTNUELo+hjWRl1AXaSO+aWlcSF07YNjtJV64loyBSODnDqAxNuRm6Je0MSokuHMzDp1N5s/9VxtaqhYtWrRo+RfVrviMHj2acePGMW7cOJ577jmMjIzo2LEjrVu3fkwS6059vRUUmzkisfdBfeUYqnM7EOkZIzar2nXtPoJYjH7rthTeuknGjggQi9F196iyjrWpHm19rNlx9AYHY5LxcjTGSNm43hI19bduDU1TXvG5j0ihj8TRD9WlQ6jj9iO290GkMKywrMTIGKmFBRk7tqO+l4FeS78yb8Pv61MqpHTysSavQE3kiUTOXr1NcycT9BrY9a2pj6eG5r+y4nMfsakDMl0jPGIOctJEyTk7KT43isg+dAj/wT24VywlMioRY315uTxVTf1eNTRNXZ92xUeLloalWsNHoVCU/iiVStq0aUNISAgjRox4TBLrTn1OjiI9YySuHShOu4oqJgJNXiZiW2+EGrj9CSIRSv/WqNLSyIiMQFNchMLDq8qEpc3sjHGzMeDYhRR2nUzC0UofS2PdGut91DT1yaeh+S8YPgCCjj4Sh7+Nn9j9iO1bINKt2PiR29qhUavJ2LkDsb4+imbOFeoTiwR8XUyxM9dj/5lk9kTfxMpEgY1Zw7m+NfXx1ND81wwfALGZEzo6+ridO8xRU10u2+vidTmX7OPH6Ph0X67fVREZlYiTtQGWJv98dzf1e9XQNHV9WsNHi5aGpdabde7cucPt27cfhZZGj0hhgKL/NGQtB6A6v6skp0lOzUJ7C2IxVuNexqBzV+5s3kR62G+VJqq6j62ZHu+OaYOlsYKvws6w7/TN+rgMLVrqFZGRFbpBs0AiI3dzCEXp1yotazpkGHot/Uhbu7raRL+tPSyY92JbrEwULNtwllURF7XJTrU0KmTNA7EJGMVLCWnc0VHxV6A5RTnZJH/1Oa/0csTOQo///XmWa8mZDS1VixYtWrRQA8MnKCio9GfgwIH06tWLkSNHPg5tjRJBJEbe7ml0er1G8e0EctfPRX0rroZ1RViOfQHDHj25G7GN1NUrK0zu+CDG+nLeGe1Pcydjft4ay5/7r1RrMGnR8rgRGVqiGzQTQapD7uZPKt0LJ4hEWI1/FZmVNTf/t4zC1KqToZobKZj1XGv6tLVn58lEPl55gpS7tUsurEXLo0TWojeO/iN4MTGd68o8dgXaUJiWyu3/LeGNIA+UCglfhp0hLUO7X1OLFgAPD48yeXqmTZvWYFqOHj3KmDFjKjwWGhpKaGhordtcunRpmQSj1REZGcmFCxdqfZ4H8fCoeAvFyy+/TEpK1Xn36kpoaCidOnUqvY/fffddpWXfeecdgoKCCAkJqbLNyq6jqvv08ssvc/369RrrrjzZwN+8//77pb+LxWLs7e2xsLCo8QkqIzAwEB0dHaTSEt/9xYsX4+rqWqbMxYsXeeedd8jJycHZ2ZnPPvusNJFTQyN1DkBkZEvejlDyNn+CvMNIpN69qnRfg5IHP4tnn0Mkk3J3+zY0KhWWY1+s0mVOIZfwxlO+/LI9jvCD17idmc/z/TwbZb4TLU8uIgMLdINmkrtpEblbQtAdMAOxhXO5cmKFApvJb3Ljow+4ufRL7Ge9D1Se20IiFjGypxseDkb8uOUCH/x0nBf6exLgZfkIr0aLlpoj8+2HS3ERY2P+5CdbUPR0pP2Oq4hXfMuU0a+wcM0Zvvj9NO+OaY15Q4vVoqURsHHjxoaW8EjIzs5m27ZthIdXnLi7IiIjIwkICHgkgcO+//77em/zQUaOHMnrr79eZZm0tDQOHjzIgQMHHomGsWPH8s0337Bw4cIala/W8AkICHhoUZXx3XffYWdnV+nxuXPnMmXKFLp168Ynn3zC8uXLmTJlyiPTU1vEJrboDZ1D/u7vKTi0iqLUK+h0faHakNeCIGD21DMIMjl3Nm1Eo1Jh9dLLCOLKQ1dLxCJe7O+JqYEOGw9cJSOrgNeG+lSZKE+LlseNSN8c3aBZ5G4OIXfLp+gOmIbY0rVcOZmFBTYTJpH4xWckf/8NlvPeq7btVm7mzH1Rybcbz/HNxnPEXr/LyJ5ujTbku5YnC7nfQLyKi3j2/GZ+tdGg6N6MlrvOo1y/kteHjmJxWAyhf5xh0eQuDS1Vi5ZGyfr164mIiEAQBK5du4ajoyNLlixBJpOxZs0aVq1ahUgkQiwWs3btWuRyORERESxfvhyVSoWJiQkff/wxlpaWzJw5E7lczsWLF7l16xbjxo1DLpcTFhZGZmYmixYtolWrVgDk5+czefJkrly5gr29PZ9++ikGBmVTNBQUFBASEsKZM2coKCigV69evPnmm+WuYdu2bXTs2LHCtC/p6em89dZb3Lt3D7VazbPPPouLiwu7du3iyJEjrFixglmzZqFUKlmwYAEFBQUUFxczbdo0unbtCkBsbCwLFiwgMzMTQRBYsGABPj4+ZXROmTKFli1bMmHCBAIDA/nll1+ws7MjMDCQ4OBg9u/fT0ZGBu+++y6BgYFAiYEUFhaGkZERHh4eqFQqFi1a9ND3tLCwkOeff56MjAyCg4N59dVX6dKlC++//z6XL18G4M0336RXr17l6oaHh7N06VKUSiX+/v6VnqNjx468++675Ofno6OjU62map+aJ0yYUOXxb775ptqT1IX09HQSExPp1q0bAE899RSTJ09uVIYPgCDTRafP6xSe2kxh1AZy7yai6P06IoOqV8UEQcAseCgiqZT09evQqFRYvzIRQVL5LREEgeDOzTAxkPPLtjgWrTrJlBEtMdbXbpbUUjmRN/bioXLETuJY7YpkfSDSNytZ+dkcQu5fn6HbfxpiK7dy5XS9mmMx8llSV//K+Q8/wviZ55CaV/0+3MxQwTuj/dmw7wpbj94gPimTiUO8G1XOHy1PLnL/wfgVqym4uJ3frEC3iwtu+09gqKvH+IED+Cb8PIt+Oc6Y3u4oFY0rSa+WJ4u9V4+w++qhem2zR7OOdGvWvkZlg4ODS3/v3r07U6dOBeDs2bOEh4djbGzM+PHj2b59O0FBQXzzzTds374dHR0dsrKykEqlXLt2jVWrVrFy5Urkcjnh4eGEhITw+eefA5CYmMjKlSu5c+cOffv2ZcKECYSFhbFt2zZCQ0P58ccfATh37hzz58/H09OThQsXsmzZMmbNmlVG73fffYezszNz5syhqKiIV199lf3799OlS9kXGSdOnKBDhw4VXvOmTZvo1KkTr776KgD37t3D0NCQwMBAAgICGDZsGFCyarRy5UqkUikpKSmMGjWKnTt3olarmTx5MvPnz6dDhw6oVCoKCgpK28/KymLSpEkMHDiQZ555pkINEomEdevWcerUKWbOnElgYCDnz59n3bp1rF+/HrlczvPPP4+Dg0ON7uMff/xBREQE9vb2zJgxg2bNyuarlMlkfPfdd4wdO7Z0le/jjz/GzMyML7/8ksTERJ555hn8/PwwMzMrrZeWlkZISAjr16/HwsKCN954o1INYrEYFxcXYmJiaNu2bbWaqzV8bGxsiI6OZvDgwQiCQHh4OH5+frRo0aLaxqtj0qRJaDQaevToweTJk0vd3gCSk5PLJG6ysbHh1q1btWrf1LRqtzhz88rda2pN39HkuniRuvFL8v78EIshU9B1aVVtNfPnR6Fvos/V5T+Rvvx/eL4zHZFMVqW+YT09cLIzZtGKYyxcdZJ5L7fH0ariBJKPknrtv0fAk6avovFeXFxM9KkzbIjfgpe5K6N8huBp7lKv560Qc33Uzy/g1qq55G1bjPXI2ejYl1/GN3t6CEp9BddWrCRz3mwcRo/CZtCAKlc/AV57uhUBPjZ8vvok81dEMemplnRvbf+orgZ48sZTfVOf+h7rd3st0fQbSw8dMfnntrHRHp7u6AYH9tHczIgJQzrzffg5Lt3I4JUhPnT2s3ksLyNqy5M0lh4FjV1fY6AyV7eAgABMTEwA8PHxISEhAQAvLy+mTZtG9+7dCQwMRF9fn4MHDxIfH8/TTz8NlMx3enr/vATr3bs3EokECwsLDAwM6N27NwDNmzcvbRdK9pV4enoCMHTo0HJGD8CePXvIy8sjLCwMgNzcXK5evVrO8ElNTcXU1LTCa/P19eXdd98lJyeHzp07V+pRlZOTw3vvvceVK1cQiUSkpqaSnp7O3bt30dXVLTWspFJpmefmMWPG8PLLLzNw4MAK2wXo379/qZb7fXD8+HG6d+9eupWkX79+nD9fdfAhKHFzmzhxIhKJhPDwcCZMmMD27dXnqzx27FjpapKdnR0tW7bk9OnT9OzZs7TM6dOnadWqFZaWJS7twcHBrFixotI2TU1Na7yXqVrD5/z586xatQqFQgHAiBEjGDduXJm9P3Vh9erVWFlZkZOTw9tvv82PP/5YagUD9bKB//btbIqLK27H3FyftLSshz5HGQzdUAyZS15EKMlrP0LWZiiyVoMQhKr34kjbd8OioJjUlSs4PWcBNpPfwNLOrEp99iYK3nnWny/CTjNjyX4mD/PBy9G4fq+nCh5J/9UjTV1fXSbOysb71JavcSbrDL/HbGbOrs/wMfMiyLkftkrrWp+jdsiQ9X8b9eYQbq6ej6L/W0isy29clLTthH/b1pz/8muu/fgzybv3Yfn8i8jtqjZkHM10mftCG74NP8fi1Sc5dvYWz/Z2R/4IXN+a+nhqaOp7vD/27/ZaovEeTNfsPPIS9vG7o4Znc5vBn+G4DpPxxdQBfL7qBJ/8GkXEETOe6+OOiUH17hmPi8bQf1XR1PU1FqOoW7P2NV6deZzI/n7xCyVv8ouKigD4+uuviY6O5tChQwwbNowVK1ZQXFxM3759mTNnTo3auv+3SCQqbbemaDQaQkJCyriVVYRcLi+zCvMgrVu3Zs2aNezbt49vv/2WzZs38+GHH5Yr9+WXX+Lp6cmXX36JIAi0a9eu0jYfpG3btuzZs4f+/ftX6GoH//TJg31bV8wf8NAYPHgwH330EXfv3iUyMpJff/0VKNmy8u+4AP9+2VMfL38KCgoWUaK/AAAgAElEQVRq5OYGNYjqlpGRUWr0QElen7t3axbCuSrur+bo6enx1FNPcerUqXLHk5OTS/++efNmmRWgxorIwALdIbORuLajMGo9+RGhaAqrj0Jl1K0Hli+OJzf2PElfLkadW30dB0t9Zo9pg7G+nM9/i+bIueRq62h58hCLxPRx7cq8Du8w2Lkf8RlXWXjsS345/xu38+480nOL9IzRDZqJSGlC3tbFqG9WHLlGbm6O7ZtTsXr5VVRpaVyfP4/0P9dTrFJV2b6JgQ5vP9uKgR0c2X/mFgt+ieJmes6juBQtWmqMIAjI2z1DH5uOdMnIY7VXNpleDqSvX4fOqUO8O8afp3u4cv7aHWYvP8ruU0kUa6N1atFSIWq1mqSkJPz9/Zk8eTKurq7Ex8fTsWNHIiMjSUxMBEr2k8TGxta6/bi4OOLiSqLzbty4kXbt2pUr061bN37++WfUajUAKSkppKWllSvn5uZWaYSxxMRE9PX1GTJkCJMmTeLMmTMAKJVKsrOzS8tlZWVhYWGBIAhs27aNjIwMAJo1a0ZeXh6HDx8GQKVSlan33nvvoa+vz8yZMymuJmLwg7Rp04Y9e/aQnZ2NSqUiIiKi9FhKSgr9+vWrsN6DKyyHDh1CLpdjZGTEiBEj2LhxIxs3bqxwb05AQAB//PEHUPJsf/r0aVq2bFmmTMuWLYmOjiY1NRWNRsOmTZuqvIYrV65UGhHu31Rr+Hh7ezNt2jQOHz7M4cOHmTFjBt7e3jVqvDJyc3NLb5ZarSYiIqKcYHNzc2xtbdm7dy8A69ato0+fPg913vskZCWxNiacewWPJreCIJGj0+NV5B1Ho75xmpwNH1J0N6naeoadOmP98gTyLsdzdvZcClOqN2RMDXWY9Zw/rraGfLfpPFsOX9OGu9ZSIXKxjL5OgXzQYSY9HbpyMvU0Hx75lHUXw8kqzK6+gToi0jVCMegdRPpm5G39AnVSxUvogiBg0K4DTvM/Rr9tAHc2h3PjgznkXbpUZftikYjh3Vx46+mWZOYU8uGK4xyMqZ1brBYt9Y0gCOh0HE2wRVvaZOazwjePPFc7rny3nJSvQ+nppseH4wJoZm3Ayu1xfLL6FMl3tKHatTwZPBjOety4cVWWLS4uLg2HHBQUhLm5OV27dsXFxYU5c+bwxhtvMHjwYIYOHcqJEydqrcXb25tly5YxYMAALl++zGuvvVauzIQJEzA1NWXo0KEEBQXxxhtvkJlZ/hmyZ8+eHDpU8d6po0ePMnToUIYMGcKCBQuYMWMGUJI2Zu3atQQHB3PkyBFeeeUVli9fzuDBgzl+/Dg2NjZAiWtbaGgoS5cuJSgoiBEjRnDt2rUy55gzZw66urrMmjWrxsaPt7c3w4cPZ9iwYYwePRpbW9tSt7eUlBQklew9X7x4MYMGDWLw4MEsXbqUpUuX1mj1ZvLkyaSlpREUFMSECROYN29emf09UGIDvP322zz33HMMHz68ymjS9zXa29fM3V3QVPOUnJuby9dff116Izt16sTEiRPR1dWtqlqVJCQkMHnyZIqLiykuLsbPz4/Zs2cTHx/PkiVLSsPvxcbGMnPmTHJzc2nWrBmfffYZ+vo1XyauzB0iJv0838f8glgQ09OhG70cuqIjeTSuBupbceRHLkOjKkCn+3ikztVvvMo+HU3qT8spKizE/JlRGHbtXu1gUqmL+WHLeY5dSKVHK1ue7e2GuIoQ2Q9LU3c3aGgep6tbZee7m5/BX1cjOXzrODKxlJ72XQl06IriEf0vFOdlkrf5E4ozU1D0fROJ3T/7BCvSlxNzhpSVK1DfvYNh90DMhz+FSEfx72bLXlNWAd9vOkfsjQw6tbDiuT4eyGUP7/rW1MdTQ/Okubo9iEZTTN6+n/gl4zRn9eS8muOJzvajIIgwGzYcw+6BHDybwm+74ilUFxPc2Ym+AQ4Nlq6gsfXfv2nq+hqLq5uWR8eYMWMICQkpNViaAjk5Oejp6aFSqZg0aVKpQbNixQqMjIzKBKNobCxbtgwLCwtGjBhRo/LVGj5NmaomxyKdPH6O+oOTqWfQlykZ2Kw3Ha0DEIvqf39Acc5d8nYspTj1MrKWA5C1HY5QzXkMRIWc/+wrcs+fQ8/HF8sXXkJiaFT1eTQa/thzma1Hb+Dnasarg73r5aGvIpr65NPQNAbD5z7JOalsvrKdU2kxKKV69HPqSWfb9khF9R8qvTgvk7wtn1J87xaKPm8gsfetUl9xfh7pG9aTsSsSiZExFmPGovT1q/ocxRrCD15l08FrWJnqMnFIC+zMHy7/V1MfTw3Nk2z4QInxk73nB37KPkucnpwOMjc6H71N4YVYdJydsRz7IvlGFqzacZGouDTsLZS8OMATpwYKWtPY+u9Bmro+reHz3yc2Npbs7GzatGnT0FJqzKxZs7hw4QL5+fl06NCB2bNnI64myFBjYc2aNTz99NM11iueN2/evKoKLF++HFdXV+RyOVOnTuWTTz7ByckJJyenepD7aMnLK6Qys87S2AQPpSfNTTxIyEpif9JhTqaewUhuiKWueb1G2hFkCqRuHdHkZ6M6u4OilMuIHXyrzPdjYGaE2McfsVLJvX17ubd/H1JzC+RVvEEQBAHvZiYoFVIiTyRw7todWrmZPxLjR09PTm5uYb23W180dX16erUPUV7VeK/qfEqZHv6WLWlh6snNnGT2Jx3m6K0T6EoV2Cit6vd/QSpH4tyWooQYVGcjEZs5IjK0qlSfIJGi5+OLrncLcs/FkBG5g8LkZBTu7ojkFfeRIAh4OhrjZmfIkfMp7DyRiKGeDAdLZZ2vpamPp4amvsd7Xcd6QyEIAjLHVrS4mYjo9g0OyLI5ZVtMc89OiKNjubtzB3KhmC5BnbC3MuR4XCo7jidQoCrC1c7wsa7+NMb+e5Cmrq8u3+1amhZmZmZNarUHoFevXowcOZLnnnuO7t27VxocoTHi4+NTK73VGj5z587lxRdf5Pjx4+zfv5/Zs2fz6aefVhojvDFRk8nRWMeQ9tZtsNe3JfZuPPuSDhF3Nx4rPQuMdapeYakNgkiMxNEPkZ4Jqgu7UMcfQWzlgUiv4nPo6cnJzVOhcHZB6d+GvNgLZERGoEpPR+HphUhaeQ4IZxsDHCyU7D6VxLHYFHycTes9Z0RTn3wamsZk+NzHSG5IO+vWuBg6cTXzOvuTjhCdFoOR3BCLenwZIEjkSJ3bok782/gxdUDfxrFKfVITEww6d0UQi8nYu5t7+/chMTREZmdfqS5zIwUdvC25eiuLyKhEUjPyaO5kglRS+y/0pj6eGpon3fCBEuNH6uRPK3N7mp2N4rJYxW5ZCupWzXGXWpG1ezdZUcdw8nWnZ6Av2Xkqdp5I4viFVGzNlZgbVe3mWV801v67T1PXpzV8tGhpWKo1fMLCwhg5ciQbNmygdevW9OzZk7Vr1zJy5MjHJLHu1HRyFAQBSz0LOtu0w0huyKm0GPYkHuRWdjJ2+jboSesvOaLYzBGJvQ/qK8dQnduBSM8YsZljlfok+voYduoMQMauSLKOHkbu6ITU1KxcvftYm+rh5WTMgTPJ7D9zCzc7o3oNmdrUJ5+GpjEaPvcxU5jSyaYd1korYu9eYl/SYS7cuYS5wgxTRf2ETBckMqTOAaiTzqE6uwNBIqXIwK5KF1BBLEbXwxOlfxvy4y+RsSuS/CuXUbi7I65kz6GOTEIHbytEIoGdJxKJikvD3c4QQ2Xt+repj6eGRmv4lCAIAsZObsht29E6/Q7i1KsclGRx0iwXD/9e6Fy8TkZkBGRm0KF/RzxdLDgdn86OqATuZuXjbm+EVPJo3U8ac/9B09enNXy0aGlYqjV8fv/9d5RKJWvXruW1115DX1+flStXMnr06Mckse7UdnIUCSIcDOzobFOyv+FI8gn2JB4kW5WNg74dcrGs4sZqiUjPGIlrB4rTrqKKiUCTl4nY1hvhgaW6f+sTRCJ0Pb3Q9W5BzqlTZERGUFxYiMLNvdJkjyb6Ovh7mHMiLo2dJxOxMdXDxqx+jLimPvk0NI3Z8IGSBzRrPUu62LTHWG7EmfRz7E06yLXMG9joWWEgf3g/9RLjpy1FdxLIjY5AdfEggo4SkYldlatLEgMDDDp1QaxUknnwABm7dyGS66Dj1KzCeoIg4OFgjKeD0d+ub0no60pxtNKv8SpWUx9PDY3W8PkHPT05uQUaZI5+uJp54HUphsuaXPYISeS1dKW5qSfZe/eReWA/Vs1s6NW/LcXFGnafvMmBmFuYG+nU2/d4pfoae/81YX1aw0eLloalWsPH09OTNWvW0K9fPzp37syVK1fIyMiga9euj0li3anr5CgRSXAzdqGDdVvyiwo4ePMo+5MOowEc9G3rJQCCIJUjcW0PRWpUZyNQ3zyPxN4HQaaoUp/UxATDzl0oys4mY+cOsk9Ho3B1Q2JgWOF5lAopAc0tibt+l4jjCejqSHCxqbhsbWjqk09D09gNn/vcfxnQxbY9CokOUSmn2Z14gNTcNOyUNuhJ6x7dEf42flw7YOrpR87186jO70J9/SQiAwtEBpWHrxQEAYWzCwbtO1CQmMi9XZHknjuLjrMrEoOKN4SbGSro6G3F9ZQS17dbt3Np0axmrm9NfTw1NFrD5x8e1CdSmmDk0Y02uRrESbEcFmVyXHkH984DUCbd4d7OSFTXr9GmTzv8fRy4cP0ukScSSUzLxsPeCB1Z/QcgaUr91xjRGj5atDRuqjV8rKysGDhwYGlyIWNj4yZh9MDDT446Ejk+Zl74W/hyO+8O+5IOc+RWFAqJAlul9UPveRAEERI7b0TGNqgu7EF9cT8iC2dE+mZV6hMkEpQt/ZA7OpF19AgZuyIRpFJ0nF0q1CSXimnX3JKk9Bx2RCWSV6CmeTOTh9Lf1CefhqapGD73EYvEuBg50dmmHYIgcOTWCfYkHiCrMAt7fTt0qgjUURMMbe0pdGiPyMgadcIZVOciKUq+hMjEDpFu5XvtxLq66LfrgMzSksyjR8jYuQNNcTE6Lq4VroTKZWLae1silYjYdbJk/4SbnRFG1bi+NfXx1NBoDZ9/KLeaL4iQWrnjah+A17VLXFHdZa/6Bpme1vg0a0fukSNk7NqJsbE+vYI7IZeJ2Rt9i73RN1HqSh8qaEdN9DU2mro+reEDHh4eREZGsnbtWtauXUtUVBR9+/ZtEC1Hjx5l1qxZDBs2rNyx0NBQjh07VmFC06pYunQp6enpuLm51ZfMR0ZMTAxLliyhZ8+e9dJeVFQUoaGh9OrVq17aexRUa/g0ZeprclTK9Ght6YeHsSvXMxPYl3SY02lnMdExwlxh9tCTjtjYFomjP+rrp1DF7ECQ66Lv5FmtPpmVFQadOlGYfIt7OyPJuxiHrqdXhfsdJGIRbT0tyMlTE3kikZu3c/FzNa1zrp+mPvk0NE3N8LmPVCzF08SN9tZtKCgq5ODNY+xLPISqSIWDgS1SUd2CaJToUyE2sUPavAeCXInqylFUZ3dQnJmK2MwRQV7x6pIgCMjt7DHo1Bn1nbvc2xVJ9qkTyB0ckZqYVFje3d4IT0djjsWmEnkiAV0dKc2sK3d9a+rjqaHRGj7/UGkEQ7kuRi4dCZCYIr4Rw1EyOSa9iWu3ARhnFXNv907yzsXg06UVHdq7c+VWJjtPJHIp8R5udobo1VMAm6baf40FreFTPUuXLuXgwYOMGjWKUaNGNZjRA5CUlMSxY8cqNHyOHTsGUCvDJzs7m0WLFvH+++/X6wuJ6lCr1XWKxGZpaVlvRg+AjY0Ny5cvp127dhgaPrx30aPgic3jU9dcABqNhlNpMYRf3kpa3m3cjV0Z6jIABwO7h5WLpjCX/N3fo75+Cj2vjtBqOCKlaY00ZR48QOqaVQgiAYtnx6DfvkOF/3QajYbtxxL4fXc8bnaGvD7ct04R35p6LoWGpjHl8XkYUnPT2HwlghOpp9GT6NLHqQfdbDsiFdduTFWkT1OQQ2H0FgrPRgAg9e6FvFUQgrzq/Q3ZZ06T+usK1HfvYtSjJ2bDhlea+DQrt5AftlzgzOXbtPYw58X+nujqlNfe1MdTQ/Ok5/F5kJro0xTmkXBsNWsyz3JDR4q3jiVDNf7krttAUU42xn36YTxoMAcu3CZsTzxFRRqGdHGmd1u7h05c/V/ov4ZEm8enejw8PIiLiyv3+fr164mIiEAQBK5du4ajoyNLlixBJpOxZs0aVq1ahUgkQiwWs3btWuRyORERESxfvhyVSoWJiQkff/wxlpaWzJw5E7lczsWLF7l16xbjxo1DLpcTFhZGZmYmixYtolWrVhw9epTPPvsMS0tLrly5gr29PZ9++ikGBgaEhoYC8Prrr1NQUEBISAhnzpyhoKCAXr168eabb5a7hnXr1nHx4kXefffdcsfy8/OZOHEi9+7do6CggODgYF555RUSEhIYN24cERElc11xcTE9e/ZkzZo1WFpasnTpUvbu3UthYSF+fn7MmTMHiURCYGAg/fv358iRI/Tu3RsvLy+WLVtGYWEhEomEuXPn4uPjA8DGjRtZtmwZSqUSf39/4uLiWLlyJUePHmXp0qWlvy9evBhHR0cuXLiAoaEhoaGhmJiYkJGRwYwZM0hKSsLFxYXk5GSmT59eoVH4448/kpuby+TJkx9qnDwq6t9B+D+OIAj4W/jia9acAzePsvVqJCFRS2hj6cdg536YKsq/Ya5x2zJddPq8TmH0FnJPhqO5eKzkYc9vEIJO5QkYBUHAsHMXdD08Sf7xe5J/+I7s6JNYjnkBsVJZrmy/dg6YGMhZvvk8C389wdQRLTF7TKFStfy3sNA156UWo+mV1Y3wy9vYEL+F3QkHGNisN+2sWj/UfjhBroe83dNIvXtSELUe1ZntqOL2I281CGnzngiSioONKH1bovvhR6SvX0fG7p1kR5/Ccszz6Pn4liurryvjjad8iTiWwB97LzMv+TgTh7SgmfXjTxypRct9BJkCh87jeCvtCpHHfmBbcTKLhW08NX4ATseTuLvtL7JPHKftmBdoOb49K7fH8fvueI5dSOGF/p44WGofrrVUTequPaTs3FWvbVr2DMQisHuNygYHB5f+3r17d6ZOnQrA2bNnCQ8Px9jYmPHjx7N9+3aCgoL45ptv2L59Ozo6OmRlZSGVSrl27RqrVq1i5cqVyOVywsPDCQkJ4fPPPwcgMTGRlStXcufOHfr27cuECRMICwtj27ZthIaG8uOPPwJw7tw55s+fj6enJwsXLmTZsmXMmjWrjN7vvvsOZ2dn5syZQ1FREa+++ir79++nS5cuZcqdOHGCDh06VHjNUqmUr776CgMDAwoLCxk1ahQ9evTAzc0NExMToqOj8fPz49ixY9jb22NlZcX69evJzc0lLCwMQRB4//33WbduXWlkZbFYzB9//AHAvXv3WLt2LSKRiHPnzjFnzhz++OMP0tLS+PTTT1m/fj3m5ua89dZbld6X2NhYQkJCaNasGXPmzOH3339nwoQJLF26FHd3d77//nvi4uIYOnRopW34+vqyZMmSSo83NFrDp45IRBK623WinVVrIq/vYWfCfqJTY+hq15F+Tj3rvOlbEETIWwVh1a43NyNWoYrZjurCXmQt+yPz6YsgrXyZXGpujt2MmdzdvpX0P9eTFx+P1Ysvodei/ANfgJclhnoyQv+IYcHKE0wZ4dsgWcK1/Ddw0Ldjst94Lt6NZ+PlbayKXUfkjX0Mdu5LS/MWD7XkL1Kaouj+MkU+fSk4+jsFR36j8Gwk8rbDkbi2RxDKv+EW6ShKVj4D2pOy4ieSvvoc/XYdMB85Col+2XEu+vtlgJudId9sPMvHK08woocrvdtUHV1Oi5ZHjdTcmX795+NzJpzVN/exMmUXzV1MeKrVRHJ/X0/S559i0KETr414hpPelqzecZH5K6Lo186BwZ2cHnnoay1a6srGjRsr/DwgIACTv12UfXx8SEhIAMDLy4tp06bRvXt3AgMD0dfX5+DBg8THx/P0008DJSslenr/eAT07t0biUSChYUFBgYG9O7dG4DmzZuXtgslK1Cenp4ADB06tJzRA7Bnzx7y8vIICwsDIDc3l6tXr5YzfFJTUzE1rdhTR6PR8PXXX3Po0CE0Gg0pKSlcvHgRNzc3goKCCA8Px8/Pj/DwcAYPHlx63gsXLnDo0CGgZNVI+cAL7aCgoNLf09PTmTFjBjdv3kQsFnPlyhUATp8+jZ+fHxYWJQGDBg0axM8//1yhRi8vL5o1a1ba/9HR0UDJ3p1FixaV9peHh0eF9aEkgWtKSkqlxxuaag2fgwcP8tFHH3Hjxg2KiorQaDQIgsCFCxceh75Gj0KiQ5BLPzrbtmfL1R3sTjjA4VtR9HMKrJPLz30khuYouo+jyLcfhVF/UBi1HtW5SGT+wUg9uyGIK751gkiESf+B6Hq3IHn5dyR9+TmGPQIxf+qZcpnuPRyMmTWmNV/+Hk3IqlNMHOKNr0vluYG0aKkOd2NXpreexOn0c4Rf3sb3Z1fiaGBPsHN/PExcH6ptsakDugOmo048S8HR38nf/R2iM9uRt38GiW3zCusoXN1wmPMBd/7azJ2/NpN77izmI0eh3668K6iLrSFzXwzgxy0XWLvzErHX7/LSQK96T/6rRUttEEQi7PyG8JZrF3Yd+Za/Cm7zSdofDBvZEfdLau5u30pOzBk8nxnF/PHt+H1XPFsOX+dEXBov9PfE3b7+EnFr+e9gEdi9xqszjxOZ7J+VfLFYTFFREQBff/010dHRHDp0iGHDhrFixQqKi4vp27cvc+bMqVFb9/8WiUSl7dYUjUZDSEhIqetYZcjlcgoKCio8tmnTJq5cuUJYWBhyubzUhQ5gwIABDB48mGnTprFv375SVzmNRsO0adPo169fhW0qFP9468ybN4/hw4czZMgQsrOzad26da2uESrv/9qQn59fRldjo1pn4Pnz5zN16lT27t3LoUOHOHz4cKnlqeUfjHWMeM5rBLMCptDM0IEN8Vv44MinHEs+SbGmuM7tik1sUfR5A93g2YiMrCk4uJKc32ehij+Cpop2dRwccXh/Lsa9+3Jv9y6ufziXvL+t/wexNdPjvbFtsDRRsGRdDHujk+qsVYsWKHGn9DNvwXsBUxntOYJ7BZksif6OpdHLuZGZ+NDtS+xaoDtsHjo9XkFTkE3elk/I3bqYotsJFZYXSaWYBQ/Fcc4HSC3MSV7+HTeXfIHq9u1yZZUKKa8P92FkTzdirtzmg5+OEZ9076E1a9HysEiUpvTp9S7vOA3GWq1hddoh1hqdw2jqpJJx/cN33Pt2CWPamfHWMy1RFxWzaNVJVm6PI69A3dDytWipM2q1mqSkJPz9/Zk8eTKurq7Ex8fTsWNHIiMjSUwsmVcKCwuJjY2tdftxcXGle442btxY4b6Vbt268fPPP6NWl/wvpaSkkJaWVq6cm5sb169fr/A8WVlZGBsbI5fLSUhI4ODBg6XHjI2N8fb2ZsGCBbRu3bp0Vadbt26sWrWKvLw8ADIyMsqsVj1IdnY2VlZWAPz222+ln/v6+hIdHV2q96+//qq6QyqgTZs2bNmyBYBLly5x8eLFSsteuXIFd3f3Wp/jcVHtio9CoShdHtRSPbZKaya1HEfsnUv8efkvVpxfy64b+xjiOhBPk7qHNhRbuqIYNJOihBgKjoeRv+sbRKf/Qh7wFGI7nwpdckRSGebPjELPtyXJPy0nYdECTIOCMRkwqEyoXyOlnHee9ed/f55lxbY4bmcWMLRLxckgtWipKWKRmI42bWlr6ce+pMNsv7aLkKgl+Fv4Msi5L5a65nVuWxBESN06ImnWBtX5nRSc3ETuH3OQuHdE3mZYhUFB5LZ22M+cTcauSNLXr+PanPcwG/4URt0DyyQPFgSBPm3tcbMz5H9/niVk1UkGXLtLj5bW1Ya91qLlUWPt2oWpDm3YfWw5m/NvsChxDUP7+uGd0YE7G/7g+tzZ2AQP5YMXAvnz4A0ioxKIjk9nTF8P/Fy1K/paGgcP7vExMzPjhx9+qLRscXEx77zzDllZJUEjvL296dq1KzKZjDlz5vDGG2+gVqspKiri2WefLXVbqyne3t4sW7aM+Ph47Ozs+Oyzz8qVmTBhAosXLy7d26Krq8vHH3+MuXnZeaxnz56Ehoby4osvVnjN27dvZ8CAAdjZ2REQEFDm+ODBg5k6dSpff/116WfDhw8nNTW11J1PKpXy7rvvYm9vX679KVOmMHv2bJRKZZlIeRYWFkyfPp3Ro0ejVCrx8fEhNze3Fj0EkyZNYvr06QwYMABXV1dcXV3R1694L+Hhw4frNVJcfVNtVLePP/6Yfv364e/v/7g01RsNHfmnWFPMiZTThF/Zxp38uzQ38WCI6wBsldbV1q1Kn0ZTjPryUQqOr0eTlYbY2gN5wAjElpW7EhXl5pC6+leyjhxGp5kzVuNeQfb3m4H7qIuK+WV7HAfO3KJjCyte6O+JRFzxomBTj6zT0PxXorrVhjx1HpE39rHrxj7UmiI6Wrelf7NeGMkNH1qfJj+bgujNqM5GggCyFn2Q+Q2sNAKcKj2NlJUrSpKeurhi+fyLyG1sy5XLzVfx++54DsQkIxYJ9GhlS//2jhjqVRxYoaFoDPe3KrRR3f6hPvUl3zzLqrOruCIpwr1Iyiinoai2HyQn+hRyB0csx75IksyYn7fGkpSWQ4CXBc/2csegivH7JPXfo0Ab1U3LmDFjCAkJwcbGpqGllJKTk4Oenh4ajYa5c+dibW3NxIkTa1y/sLAQQRBKg0qMGTOG7du3o/uv9CnZ2dmMHTuW3377Dam0cbqJV2v4DBw4kGvXrmFnZ1fG92/Tpk11PumtW7eYOXMmqampiEQiunXrxowZM8qtMAQGBqKjo1PaeYsXL8bVteb7BBrL5KgqUrEv6TDbru0kT51PO6vWDHLug7FO5b7XNQp5WqRGFbuHwpPhaPIykTj5I2v7FGLjyiPgnMEAACAASURBVP/Zso4fI2XlCjRqFeZPj8SwW48y/a7RaAg/eI2NB67S3MmY14b4oKtTfmGwqU8+Dc2TaPjcJ7Mwi23XdnIg6SgiQUR3u0485dcPdfbDheEFKM5KpyBqPepLh0Gui7zVYKTegQgV7LXTaDRkHTlE6trVaAoKMBkYhEn/gQiS8uNdLYhYseksh84lI5WICPS3o187Bwx0G4cB1Jjub0VoDZ9/qG99RcVq9p74lU33ziEAgxXO+Ev8SPt9LUWZmRj36oNh0BC2nUxm06Fr6MjEjOrlRgdvqwpX9Z+0/qtvtIaPltjYWLKzs2nTpk1DSynliy++YO/evRQUFODl5cX8+fPLBIKojqSkJCZPnly613/KlCkVruo0xmv/N9UaPvcTOP2bfy/R1YbU1FRSUlLw8fGhsLCQF154geeff75cEqvAwEB++eUX7OzqliOnsU2Ouapctl3fxd6EgwiCQA/7LvRx7I5CUn4TWG30aVT5FMZsp/D0VlAXIHHrjLzNkEpzAKnu3iXl5x/IPXcW3Ra+WL3wEhKjskbY/jM3+WVbHNamekx9uiXG+mVdfJr65NPQPMmGz33S826z+UoEUSnRCIJAcxMPOli3oYWZFxLRwwWcLEq/TsHR3ylKOoegb4a87VNIXAIqjACnzswkbe1qso4dQWZji+XzL6JwKfuC5X7/Jd/JZdPBqxw5n4JMIqZXGzv6Bjg0eACExnh/H0Rr+PzDI8updecav0b/yGXycSuEkU5BCMcvc2/vHiRmZlg+9zwZVs78vPUCl5MyadHMhLH9PDAzLDv/PKn9V19oDR8tWho34nnz5s2rqoCtrW2FPw+Dnp4elpaWJQLEYi5cuIBYLMbPz69MuRUrVjB06FAMDOoWZrmxZfeWiqV4mbgTYOVPZmE2+5MOc/DmMSQiCfb6NogeeCirjT5BLEFi7YnUqxsUF6GO24fqXCSawlzEZk7l8p2IFQr023dAoq/PvX17uHdgH1Jz8zKuPo6W+jjbGLAn+iaHzyXj3cykjHtEU8+e3dA8iuzejW28V4euVBc/Cx9aW/phpK/kdPI5Dt06zoGkI9wryMRApo+BvG4PCSJdI6TunRBbulKUHIfq/C7UN04jMrREpF/WJ1skl6Pfug1yRyeyT54gIzKCotwcFK7upas/9/tPqZDS2sOCAC8L7uUUsudUErtPJVGgKsbBUomsgcIHN8b7+yD1Pd6b2lh/kEelT09hRDunbihzsziam8TBzFiMDcEzcBR5F2LJiIxAJ+sOvYb9v707j46zPg89/n3f2Rfty0gysixL1uJNMnjHK0lJGggxwc1tloYbShp6SnM4gUDaZmkP0NOFhMNNuGkamnvae2LamARIQi8x4GCMsfEub1osW9jWrpFG2+zzvr/7xxjZwvumGcnP55w5GmlG8z56f4/9zjO/bTVZORm8e7CL3+/twGGzUF6cOdb7c7Oev+vlRvzfLoS4fi7Z4zM6OspPf/pTGhsbiUQiYz//j//4j+sSwODgIOvWrePf/u3fqKioGPfYh2u1K6VYu3YtDz/8cNqOGbwaxwdO8vMDv+JgTzM+Tz6fn7+OZaW3XvOiAomhPgbe+S9GD25BszvJXvoZshbfjW53nvPcUHs7R5/9X4y2HqNg7RpmfvUBrGd1fx7vGOLvXthONGbw119ZzPzKq5+QLsTFGKZBQ3cjb7dtZ1dnA4ZpUJ5Tytry5ayYvgjvBebrXIoyDUYPbWVgy4sYw35cFbeSd8eXsBeWnfPcRCjEif/7c7r/+3UchQVU/PnXyLl1wQVf+0T3MC9uamZbQydup5V1qyq4Z1UFHlkCW6RQ92An//v3P6QpNkhlxOBPZ30S6wcxOn75MhaXkxlfuR8WLOHHvzzAnqZeqsty+MvP1VMme7kJIaa4SxY+jzzyCCUlJWzatIk///M/56WXXmLBggU8/vjj13zwWCzGgw8+yJo1a3jggQfOeby7u5uioiKCwSCPP/448+fP52tf+9plv/5kGA6hlOLIQAuvtL5GZ7CbssxS7q24i+VVddccnzHQQWzXSyRO7ENzZV5wDyCVSND/218z8NpvsObmUvTAV3FXn1kVpX8owrMbG+gZCPHAXbUsm1OUNufvQiZ7fDfDULezfTS+0XiQ3d372dG1i1OjnVg1C/MK5rCseCG1uVXjekcvl0rEiB9+k+i+30AsgrVqBY6F96J7c895bvjoUXr+/WfEurvIWLacqq/8CSP6hfclONU7yq/fbWNPSx9uh5VPLC7l4wtLcTkmZo/oyda+53v8SkylXL9RlFJsbd3EKyffQimTu2Juls1YR/+v/x+R1qO4amop/JP72dsHL751lHA0wd3LZ3D/p+cyGAje8Piu1mRvXxnqJkRqXXKo2/PPP8/zzz/Pxo0befLJJ7nrrrv4yU9+wvr166/pwIZh8Oijj1JeXs7Xv/718z7nw3XM7XY7brebN998k7vvvvuyjzEZhkNomkahO58V05aQ58rloP8Ib7dvo9l/nHA8Sqbdi8t6bk/N5dBdmdgql2KdNgez/yTxxt8Tb92O5spEzykZ61nSdB13TS3uOXMJ7t/P4JubMKNRXFXVaBYLbqeVJbN9tLYPsWnXKawWjbqqwrQ4fxeSLu17ITLUbbyPxme32JmRNZ0V05YyP38OuqZz0H+E9zp38l7nTkbjQXKc2Xhtl98LpOkWLEWzsNesQZ01JJREDEvBjHELINjy8shcuQo0jaEtv6frN78lfKwVzW7HXlg4bvlrgCyPncW1Puor8/EPRXh7fydb9neglGK6z3vB1RGvl8nWvud7/EpMpVy/UTRNoyyvkoXFt3HK38y72jDH+vYxv7aGnNrljOzYztDmtygtyuRj624nMBrnrT3t/H7PKYaCMTLddjLctrTb1mCyt68MdRMitS5Z+PzqV7/ic5/7HBs3buTTn/40brebF154gS996UvXdODvfOc72Gw2vve97533P9ZQKEQ0GsVut5NIJHjhhRcoKytj2bJll32MyXRx1DSN0owSVk5bitPioCnQwvtde9l8aisH+g4zGB3CrtvJcmRc8YVI9+ZhrVqBpXAmRndLcr7DiX3oGXlomYVjr2fLzSXr9pUYwVEG33qT0f37cFXOwpqVhd1qYclsH32DYd7Y3U6XP0hepuOiy6KmUrq170dJ4TPexeLLcmQwJ6+GNaUruMVbwkhshB1de9jSvo2mgaOAosCVd9kLImhWO9bSedhmLUOFhogf2Uy8cQtYbeh5ZWNFjWax4K6pJXP5CjLyshja38Dw1i0MvfM2xugotvwCLB9ZFSfb62DJbB/zK/LoHQzz9r5OtuzvBA2mF2bcsAJoMrfvh49fiama6zeC2+ZicelysixOdo608V6sC/fIcebc+TnMiMng5reIHz7A8j9YRNXsMgKjMd5t6GLzvg52N/cxGo6T7XWkfAGPD0329pXCR4jUumThs2nTJlatWsXg4CDPP/88b7zxBh6PZ9zmU1dqz549PPnkkxiGwX/913/xn//5n8TjcXRd59vf/jb33HMP3d3dPPjgg2zYsIEXX3yR4uJivvGNb1zRHJ/JeHG06BYqsstZX/9Jar015DpzGIgG2N2zn22d77O1YztdwR4MZZLtyMJ2uW/2NA09qwhb7Rr0LB+JUweJH34Lo6sJPbt4bLiPZrXiravHMWMGIzt3MLj5TTSbDefMCiwWnVurCjCVYvOedt7a08Ghtn40DYpy3Df8U+0rka7t+yEpfMa7nPgsmk6xx8eiogXcXrKEDLuX40MfsL1rN2+fepfekB+X1UWOM/uyPhzQHB5s5QuxTq/HHDiVLIBat6O5s8f1iFrcbkqW3Ip96Sqc5TMxRoYZ3raVwbfeIHz0KJrNht3nG9cLlJPhYOmcIubOzKVnIMTb+zrZ2tCZ/ICj8Pr3AE329pXC58bSNI3p2WUsLl7IqUAb2/QRWv0Hme3LJn/ppwg2HGDwzU3kO0w++z8/ydK5xRRkOekbjPDewW7e3NPO/qN+IjGDnAzHebc4mCiTvX2l8BEitS45x0cphaZpKKX4zW9+w/DwMOvWrRsbhpbOptI48GA8RGN/Mwf7GznS30woEcaiWajMLmdufi1z82opdF/+rtzKSBBvfJvYvgvvAZQYGab3P/6d0X17cFVVU/SnX8WWlzyG3WXn12+38k5DJ90DIVwOC0tmF7G6roSyotSPYZ5s7Xu+x6/UVMr3y6WUom34JDu6drGnp4GIESXfmcvS4oUsKb6NXGfOZb+O0X6Q6Pu/wBxoRy8ox7Hkf2AtqTlvfPFAgOFtWxl6ZwuJgX4sGRlkLl9B1srV52wMDNDaPsQr7x7nyAcBsjx2PrWsjDX1Jdiu0ypwk719ZY7PxFFKsb1jB79s+TWGmeCTg1FWlX+SkUY/Q1t+j8Xtxj13Ht66BbjnzmMoobO7qZf3G3to60rGXXlLFktqfSysKZzwzXxTff4uReb4CJHeLln4QHKRgePHj7N8+XJisRimaeJ0Xt28k4k0VS+OhmnQNnySQ/5GDvY30h3sAcDnLmBuXi1z82upyJqBRb/0myoVjxA78DtiB86/B5BSiuH33qXvxZ+DplH4+S+RsWw5hYWZ9PWNoJTiaPsQW/Z3sru5l3jCpMyXwar6EpbO9k3Y5O6Pmszt++HjV2qq5vvlihkx9vcdYnvXbloCrWhoVOdUsqx4IfML5mI/zyamH6VMk8TRbUR3v4wKDmCZXodj8ecoqq45b3zKNAkdOcTQli2MNuwD08RVXUPWqjV4b70V3Tb+TWHLqUFe2XqcppODZHvt3LVsBqvqSrBZr60HaLK3rxQ+Ey8QGWTDoRc5MtzGjHCMzyWyyC/9A+LNx+nfuQtjZAQsFtzVNXjqF+Ctqyegu9nZ2MvOxh7a+4JoGtSW5bC41sdt1QV4nDd+OFy6nL8LkcJHiPR2ycLn9ddf5x/+4R/QdZ3NmzfT1NTEM888wwsvvDBRMV61m+Xi6A/3c8jfxKH+Ro4GjpFQBi6rk9m51czNr2V2bjVe+8UngZvhYWL7fkv8yGbQwDbn4zjq70ZzJnv24n19dP/sp4SPtuC9bSGzH/kLBqPjhxMFI3G2H+rmnYZO2vuC2G06i2t9rK4rYWZJ5oROkp3s7SuFz7Xxhwd4v2s3O7r3MBAJ4LI6uc1Xz7LihZRllF4yF1UiRuzQG8T2/RYSEZzTZ6OK52KdXo+eXXze30kMDjL83rsMbd1CvK8P3eMZ6wVylJSMe27jiQCvbj1OS/sQuZkO7l42gxXzi696CNxkb18pfFJDKcWOrj281PwyhhHjEwNBPjF9KYnC2SSiToKHjxDcv49YdxcAjtJSPHUL8NYvwO/KY2ezn52NPfQGwlh0jbnluSye7WPBrHyc9hvzoVc6nb/zkcJHiPR2ycLns5/9LC+88AIPPPAAr7zyCgCf+tSn+O///u8JCfBa3IwXx0giQlOglcOne4NGYqNoaJRnlTHvdG9Qscd3wTd+5oif6J6XSbS8BzYn9ro/xD7vE2g2B8o0Cfzudfyv/BLdZsNVOxtv3QI88+uwnrXJrFKK413DvLO/k52NvUTjBtMKPKyqK2HZnKIJmSQ72dtXCp/rw1QmRwPH2d61m/19B4mbcYo8PpYVL2Rx0a1k2i9+ns3ICPFDb0B7A7HeEwBoWT6s0+uxltVjKZqF9pF5dso0CTU1MvTO24zu2wuGgWtWFVmrVuO9bRG6PdkLpJTiyIkAr2w9zrGOYfIynXz69hksn1t0xQXQZG9fKXxSazA6xIYjv+Bw4Ci+mEFNMEJlOEFFRinOW+ZiOooItw8QPNBA+GgLKIUlOxtvXT2eugX05dzCrqMB3m/sITASxW7VmV+Zz5LaQubNzMNuu34b+6bj+TubFD5CpLdLFj7r16/npZdeYt26dWOFz9n309nNfnE0lcmpkQ4O+Rs51N/IyZEOAHKdOczNq2Fufi1V2RXYzjMEyBhoJ7brl+P3AKpdjaZbibafIvr+u/h37CIRGABNw1k+E2/9Ajx19dhLpo0VVuFogvcbe3hnfycfdI9gtegsrClgdV0JVaWXNwn9akz29pXC5/oLJ8Ls7TnA9q5dtA2fRNd05uTVsKx4IXPzai86NLSgIIOe420kTjaQOLkfo6MRzATYXFhL52Etq8daOn+sh/RDieHhZC/QO1uI9/agu91kLl1O1uo1OKbdAiQLoMNtA7y8tY22rmHys5zcc3s5y+b6sOiXVwBN9vaVwif1lFLs6tnHzt5dNPuPY6KwKpgRjlEZijErrlFaUIWeO4voEIRajhM8dAgVjaA5HHjmzMVdV09vfjk7TwTZ3dTLcCiO025hwawClsz2MXtGzjUv7JGu5+9DUvgIkd4uWfh88Ytf5Mc//jH3338/L7/8MocOHeLpp5/mxRdfnKgYr5pcHMcbjA5xuL+JQ/4mmgZaiJlx7LqN6txZzMurZU5+DdmOrHG/Y3QfJbpzI0Z3C1pmIY6Fn8VasZjCwix6e4eJnjpJsGE/ow37iX7QBoA1Pz/ZE1RXj7uqGs2a/ET8RPcI7zR0suNIN+GoQVGum1V1JSyfV0Sm+/pOkJ3s7SuFz43VHexhR9ce3u/ew3BsBK/Nw+KiW1lavJBp3nOHsn00PhWPkOg4gnFiP4mTDajwEGgalsJKLGX1ySFxZ60Mp5Qi3NzE0DtbGN27G5VI4KyoJGvVajIWLkZ3OFBKceBYP6+828aJ7hEKc1zcc/sMls4uQtcv/gHBZG9fKXzSR0FBBqe6/LQOHqc50EpjfzNdoV4A3KaiIhilMhyjSvNSUFhLLJFFpHOI4KFDJAIB0DRclbNwza+jp6CC93sVe5v7CEUTeJxWFtYUsrjWR3Vp9iXz+kLxpfv5k8JHiPR1ycJn+/btPPPMM3R2drJ06VJ27NjBc889x+LFiycqxqsmF8cLixtxWgaPj/UGDUQCAJRmTGNuXi3z8mspzZiGrunJFa9ONRDd+VJyxau86eSv/iOCnjJ015khbonBAKMNDQQb9hFqPIKKx9FdLtxz5iWHRMybj8XrJRoz2NXUyzsNnbR2DGHRNRZUJXuBamfkoF+HXqDJ3r5S+EwMwzRoHGhhe9duDvqPYCiD6Rm3sKx4IQt99bht7kvGp5SJ6T9B4sR+Eif3Y/pPD4nLKMA6vS45JK64emyDVGNkhOHt2xh6Zwux7i50l4uMpcvIWrka5/QylFLsb/Xz6tY2TvaOUpTr5p4VM1hc47vgG8XJ3r5S+KSP88U3FB2mOdBK08BRmvubGYyPApAbN6kMRZkVilHhLsLlmE5kwCR87BTRUycBsBUV4Z5XT09hBTuGnexrHSAaN8jy2llUU8iSWt8VzQGdjOfvo48LIVLnslZ1a29vZ+vWrZimyapVqygtLZ2I2K6ZXBwvj1KKrmAPh/obOeRv5PjQCRSKDLs3uUpcXg01ubNw6HYSx3YQ3f0r1IgfSL65s/gqsBRWYPFVoueWolmsmNEoocYjjDbsI9iwH2N4OPlJ4KwqPHX1eOvqsRcV09E3yjsNXbx3qItgJEF+lpOVdSWsmFdMTsbV73cw2dtXCp+JNxoLsqtnH9u7dtEx2oVVt1KXPye5NHblPEYCl7d3iBkMJIfEndiP0XEEjBjYnFinzUkWQaXz0d1ZyV6goy3JuUC7d6ESCRwzysletYaMxUvA4WBfi59X3z1Oe1+Q4jw3n1lRzsKawnM+HEiH83cxUvicMdnjU0rRE+qjaeAoTQMttARaiZpxNAUl0TizwjEqo4oyRylm2Eu4c4jwsTYwDCzeDJxz59FbWMGOSDb7ToyQMEzys5wsqk0WQaWF3osWQZP9/EnhI0RqXVbhM1nJxfHqjMaDHOlv5pC/kSMDzYQTESyahVnZM5N7BuVUMdMao7/5IEZPK0bvMVRoMPnLFhuW/BnoZxVDmiuLyAcfEDyQLIKip04BYPP5xobEWWfMZG/rAO80dNJ0chBd06irzGNVXQnzZuZd8ZCIyd6+Uvik1qmRDrZ37WZ39z6CiRAaGnmuXEo8RZR4fBR7iyjxFFHozsd6kU2EVSKG0XmExImG5JC44ACgoReWJxdImF6HnjcdMxhkeMd2ht55m1hnB5rDSeaSpWStWoO9rIw9zX28+m4bnf4g0wo8fOb2cm6tLhgrgNLt/H2UFD5nTLX4DNPgxMipZCHU30zb8Kmx+UHlY/OD7OTFC4kGTMJtnZihEJrViqO6lj5fBe8n8tnbFcdUiuI8N4trfSyuLaQ479zVSCf7+ZPCR4jUumDh89BDD130F//lX/7lhgR0PcnF8doZpsHxoQ842N/IIX8TPafHehd48sh35FHgyiPflUe+5iB3dITsQA96Xxtm3wfJyd+A5sk93StUicVXgYGH0OHDjDbsJ9zchEok0N0ePPPm462rJzitkq0tAbYd7GI4FCcnw8HK+cWsnF9CXtbl7R+VLufvQqTwGS9d44ubCZoGWhgw+2ntPUFnsIfeUB+mMgHQNR2fu4ASTxHFniJKvD6KPUXku3LRtfGTuJVSmP0nxxZIMHvbAIXmyT09JK4OvbiW6MlTDG15m5HdO1GxGI7pZckV4RYtZc+JEV59t43ugRClhV7WrSinflb+2L5a6UoKnzOmenyRRJTWweM0BY7S5G+iK5wcHeA2FJWhKJXBKBWhTByjHsIdgyQCyQ/NbNPL6C+qZLcqZNeAFaVpTC/0smS2j0W1heRnua5LfDeaFD5CpLcLFj41NTXMnz+fu+++m4yMc/+h3nvvvTc8uGslF8frrzfk51B/I13RLjoCPfSG/YQT4bHHNTSyHVkUuHLJ1+zkxhLkjQyRM9BN7lA/dgXoVvT86VgKK9CySokGEgSbjxE82IA5OprcNK+qGte8Oj7InM6WE1EOtw0AMGdmLqvrSqirzL/o6kDpev4+JIXPeJMpvriZoDfUR9doN53BHjqD3XSNduOPDIw936bbKPYUni6GThdFHh/ZjqyxYTxmaAjj1IHk3KCOwxCPgMWOZdpsrGX1aPlVBA81MrjlbWLtp9DsdjIWLyVzxSoawh5+/d4H9ATCTPd5WTynmEynlaJcN0V57glZMv5KSOFzxs0W35n5QS00+ZsZSgQByIsbVAajVPfHKRnJxBwwifYMgFLoObkMlsxir17EzpAXU7NQMS2TxbU+1i4qQzeMq1oYYSJI4SNEertg4dPW1sbGjRt5/fXXWbRoEX/0R3/EwoULJzq+ayIXxxvn7PiC8RB9YT99of7k13D/2P3ReHDc72VanOQpK3nRGLkjQ+TFYuTFDfItHjz5M4mrHCK9IcJHT4xtmmcvmYZeM5fD9hLe7NIIBBNkeuysmFfMqrpiCnPcF40vHUnhM95UiC+SiNIT6qVztDtZDAV76BztZig2PPYcl9U5VgR9OFyuxFOEx2LH6GoeWyDhwzl0en4ZltI6DEshIwdbGNn5PioaxX5LKZkrV9GUVcHvDvjp9Acxzmp7r8tGUa4bX64rWQydvhXmuLFZr2054ashhc8ZN3N8yflBvTQNtNLU30zL4LGx+UHTonFqBqNUdisyhxzEekZQCQOcTkaKKzhgLWZnIo+oxYHVolOY48KX4zqd5+6xr5lu24Rulv1RUvgIkd4uOccnkUjw1ltvsXHjRrq6uvj2t7/NsmXLJiq+ayIXxxvncuMLJyL4w/2ni6HTRdHpIunsN4QAHhPyYnHyYgZ5CQNfwkveoBN7b5R4ey+YJnpGBrGyag5ai/n9iJeYZqO2LIdVdSXcWlUw9qZusp8/KXzSy7XEF4yHxoqgrmCyKOoc7SZ0Vk9pht07VgQVe3z4TAsF/g5sJw9h9LaCUmjubPSi2YSHHIwePkb05Ek0ux3vbQvJrZlFb0RnEAd9CRudUQudIwZdgTBDo2cWZdA0yMt0UpTnHlcQFeW6yclwpGxfLSl80sdExnf2/KBGfyMfjHScnh+kqBiJMa89zDS/Bb3fwAzHQNcxCqcxYvMwaHHTp5x0xW0EdA/DVjdBiwuX04ov50wh9GHx78tx43JceD7e9SKFjxDp7ZL/C1itVqqrq6mpqaGlpYVAIDARcYkpwmV1UpoxjdKMaec8FjViY0WR/3Rh1Bvs4USwj/2JIAoFhWGogsxwNrWdccq7DPJb9rEgtpt6i4WRolIOtExjQ2sRP8/MZvncIlbVlUzIxUUpRcJMkFAJEqZBwkwQNxPn/OzD29mP1Vmr8ZJ9w2MUqeexuanMLqcyu3zsZ0ophmMjY8PkOk8XRts63ydmxseel1eQQ/H05fgS4BsKkN/dQEE4SO40K0ZlJaE+jeC+vYxsfw8AB3DL6ZtmtWLJzETPyCTu8hK2uhnRHQzE7PSesHKs2cJuHAQtTuK6DbtNpygnOVTOd/pr8g2jC7tdG5fD8bPyOqESxI3TX8/zuEXTuStnzYSeczE5WHQLM7NmMDNrBp8q/wMiiQitg200DrTQ7G/kpczk8FF3wuDWzjCz2mNkBfvICfaQHUowwzDHvZ7SdWJOL6MdXgZ0N37l5ITVw4jVw7DVg8rKJjcvC1+e56zCyE1htislPaFCiIl3wcInEonw+uuvs3HjRsLhMPfddx+//e1vyczMvNCvXJGWlhaeeOIJgsEgM2fO5JlnnsHrHb/reU9PD9/4xjfw+/3k5+fzgx/8AJ/Pd12OL1LPYbEzzVt83g0j42aC/vAAvcE+egeO0zd4kj6XnzdmBBnCjc+fYGZHlJntp1gx+gErgIEsJy3Defz4UBHR/BJ0K1gsCt2i0C0mmp68r+nJ+5pugnb6phugKZRmoEjezA9vKnH6q4Fx+pZQBgbmuX/UZTrUXsLXFj9yDWdPTGaappHlyCTLkUltbtXYz01lMhAJnB4u15PsIRrtpjHUh6EbUOJBx0u+ZscXHsZnG6Uw38QSt2MaOiphQYtraHHQYqDHolii3ViGOLgnogAADNVJREFUTGwRg/yoQYGC6o/EE7dqhJ0WQn0WRh06QadOs0tjn0sn6NIIuXRCzuTNtFxZr5BVs3DbjNl4pNAXl+C0OpMrh+bXQtVnGIoOj60Wd9DZwrvTQ2e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33nlnWrX6/54KHDVqVEaOHJlx48YJqAAAAADYKgXtQfXyyy/nrLPOahBOJUmrVq1y5pln5qWXXmqW4gAAAADY/hUUUJWWlmbZsmUbPbZ8+fKUlpY2aVEAAAAA7DgKCqj69u2b2267LfPnz2/Q/tprr2XcuHHp169fsxQHAAAAwPavoD2oLr300nz+85/Ppz71qfTs2TMdO3bMW2+9ldmzZ6e8vDyXXnppc9cJAAAAwHaqoBVUhxxySB599NGcffbZqaury4svvpjVq1fnnHPOySOPPJKDDz64mcsEAAAAYHtV0AqqJNl7771z2WWXNWctAAAAAOyAClpBNXfu3MycOXOjx/7rv/4rf/vb35qyJgAAAAB2IAUFVDfeeGN+9atfbfTYr371q4wZM6ZJiwIAAABgx1FQQPXCCy+kd+/eGz3Wp0+f/OlPf2rSogAAAADYcRQUUC1fvjxt27bd6LE2bdpk6dKlTVoUAAAAADuOggKqAw88MM8+++xGjz333HPp1KlTkxbFh8e7775b7BIabVusGQAAAHZkBX2L36mnnprbb789+++/f4YOHZrS0tLU1dXlpz/9aX70ox+lqqqqueukSHbaaaeMHDmy2GU0yp133lnsEgAAAIBGKCigOu+88/LCCy/kuuuuyw033JAOHTpkyZIlWbt2bU466aRUVlY2d50AAAAAbKcKCqhat26d22+/Pc8++2xmzJiRd955J7vvvnv69++ffv36NXeNAAAAAGzHCgqo1jv22GNz7LHHNlctAAAAAOyACtokHQAAAACai4AKAAAAgKISUAEAAABQVAIqAAAAAIpKQAUAAABAUQmoAAAAACiqNoV0qqury1133ZXHH388CxcuTF1dXYPjJSUlefHFF5ulQAAAAAC2bwUFVN/5zndy//33Z+DAgTnppJNSWlra3HUBAAAAsIMoKKB66qmnUlVVla985SvNXQ8AAAAAO5iC9qBasWJFevXq1dy1AAAAALADKiigOv744zNr1qzmrgUAAACAHVBBj/idffbZ+cY3vpGSkpIMGjQou+222wZ9DjzwwCYvDgAAAIDtX0EB1ec+97kkSXV1dcaPH7/RPi+99FLTVQUAAADADqOggOrGG29MSUlJk5xwzpw5ue666zJ79uy0b98+Q4cOzYUXXpjWrVtvcswf//jHPPDAA5k1a1befPPN7Lvvvjn55JNTWVmZtm3bNuj7u9/9LmPHjs3LL7+cjh07ZsSIETnnnHOapHYAAAAAml5BAdVpp53WJCdbsmRJRowYka5du2bChAmZN29ebrrppqxduzYXX3zxJsc98cQTmTdvXiorK3PQQQfllVdeyW233ZZXXnkl48aNq+/397//PV/60pfy8Y9/PJdcckn+9Kc/ZezYsWnXrl2GDh3aJNcAAAAAQNMqKKBab926dZkzZ06WLFmSDh06pGvXro1aWTVlypSsXr061dXVKSsrS//+/bNs2bJUV1ensrIyZWVlGx1XWVmZPfbYo/51v3790rZt23z729/OggUL0qlTpyTJD3/4w+y99965+eab06ZNmxx77LF57bXXUl1dnTPOOKPJVoEBAAAA0HQK+ha/JPnpT3+aAQMG5JRTTsnZZ5+dU045JR/72Mfy05/+tOCTTZs2LQMGDGgQRA0ZMiSrVq3KzJkzNznu/eHUeocddliS5M0332ww/+DBg9Omzf+Xuw0ZMiSvv/56/vznPxdcJwAAAAAtp6AVVI8++mi+9a1v5dhjj80pp5ySvfbaK2+99VYee+yxfPvb3067du3yb//2b1ucp6amJsccc0yDtv333z/t2rVLTU1NTjjhhIILnz17dlq1apXOnTsnSVasWJGFCxemoqKiQb8uXbrUn7tbt24Fzw8AAABAyygooPrBD36Qk08+OTfffHOD9s9+9rP5+te/nrvvvruggKq2tjbt27ffoL28vDy1tbUFlpwsWrQoEydOzKmnnpo999wzSbJ06dL6uf557vXnBgAAAODDp6BH/ObOnZtTTjllo8dOOeWUzJ07t0mL2py6urqMHj06u+yyS6644ooWOy8AAAAAzaOgFVS77rprXn/99Y0ee/3117PrrrsWdLLy8vIsW7Zsg/ba2toNVj5tzLp163LZZZdlzpw5uf/++9OhQ4f6Y+tXZq1fSfX+udefGwAAAIAPn4JWUA0cODC33nprZs2a1aD997//fW677bYMHDiwoJNVVFSkpqamQdvChQuzcuXKDfaO2pgbbrghv/zlLzN+/Pj6vaXW22WXXbLffvttMP/614XMDwAAAEDLK2gF1de//vX84Q9/yNlnn5199tknHTt2zFtvvZXXX389nTt3zte//vWCTjZw4MD88Ic/zLJly+q/yW/q1KnZeeed07dv382OvfPOO3Pffffl+9//fnr37r3J+X/+859n9OjRad26df38++23X/7lX/6loBoBAAAAaFkFraDq2LFjHnnkkXzzm99Mr169UlZWlp49e+aqq67KI488kr322qugkw0bNiylpaWpqqrKjBkz8uCDD6a6ujojRoyoD6ySZPDgwbnyyivrXz/22GP53ve+l8985jPZZ599Mnv27Po/ixcvru933nnn5Y033sg3vvGNPPfcc7n77rvz4IMPZtSoUSkpKSn0vwkAAAAALaigFVRJ0q5du3zhC1/IF77wha0+WYcOHTJp0qRce+21+fKXv5zy8vIMHz48VVVVDfqtWbMma9eurX/929/+Nkny8MMP5+GHH27Qd8yYMTnttNOSJAcddFDuvvvujB07NpWVlenYsWMuu+yyDB06dKtrBgAAAKB5FRRQLV68OKtWrcr+++9f3zZlypT85S9/yYABA3L88ccXfMKuXbvm3nvv3Wyfp59+usHrsWPHZuzYsQXN37t37zz00EMF1wMAAABAcRX0iN+VV16Zu+66q/71+PHjc/XVV+fxxx/PBRdckKlTpzZbgQAAAABs3woKqF544YUce+yx9a+nTJmSkSNH5vnnn89ZZ52Ve+65p9kKBAAAAGD7VlBAtWTJkuy5555Jkj//+c9566238tnPfjZJ8olPfCJz585tvgoBAAAA2K4VFFDttttueeONN5Ikzz33XPbee+8cfPDBSZL33nuvwYbmAAAAANAYBW2Sftxxx2XcuHF5++23c8899+TEE0+sP1ZTU5NOnTo1W4EAAAAAbN8KWkH19a9/Pfvtt1+++93v5sADD8yoUaPqjz322GM56qijmq1AAAAAALZvBa2gatu2be644460bdt2g2OTJk1KaWlpkxcGAAAAwI5hiyuo3nvvvfTr1y+//e1vN3q8rKxMQAUAAADAVttiQNWmTZvsueeead26dUvUAwAAAMAOpqA9qE455ZT89Kc/be5aAAAAANgBFbQHVadOnfL444/n9NNPzyc+8Yl07NgxJSUlDfqcccYZzVIgAAAAANu3ggKqa6+9Nknyxhtv5H/+5382OF5SUiKgAgAAAGCrFBRQ/fKXv2zuOgAAAADYQRX8iB8AAAAANIeCNkkHAAAAgOZS0AqqJJk+fXoeeOCBzJ07N6tXr97guMcAAQAAANgaBa2geuaZZ1JZWZlVq1alpqYmFRUV2X///fP666+nVatW6du3b3PXCQAAAMB2qqCAasKECTnrrLNy1113JUlGjx6dH//4x3n88cezZs2afOxjH2vWIgEAAADYfhUUUNXU1OT4449Pq1atUlJSkjVr1iRJDjnkkFRVVWXixInNWiQAAAAA26+CAqpWrVqldevWKSkpyR577JHXXnut/tjee++defPmNVuBAAAAAGzfCgqoDjnkkCxYsCBJcsQRR+RHP/pR3nzzzSxevDj/8R//kU6dOjVrkQAAAABsvwr6Fr+TTz45f/3rX5MkVVVV+eIXv5hBgwYlSVq3bp1bbrml+SoEAAAAYLtWUEB11lln1f/9iCOOyGOPPZZp06Zl1apVOe6449K1a9dmKxAAAAC2BatXr07btm2LXQZskwoKqP7Zvvvum3//939v6loAAABgm9W2bdv07t272GU0yqxZs4pdAiRpREC1bt26PP3005k1a1beeeedXHjhhenUqVNmzpyZgw46KPvss09z1gkAAADAdqqggGrJkiU5//zz84c//CG77rprVqxYkS984Qvp1KlTfvKTn2S33XbLVVdd1dy1AgAAALAdKuhb/L7zne9k4cKFeeCBB/L8889n3bp19ceOO+64PPvss81WIAAAAADbt4ICql/+8pe5+OKLc+SRR6akpKTBsf322y8LFy5sluIAAAAA2P4VFFCtWLFik3tM1dXVNVhRBQAAAACNUVBAdcghh2T69OkbPTZz5sx069atSYsCAAAAYMdRUEB15pln5t57783EiRPz2muvJUlqa2vzf//v/819992XM888s1mLBAAAAGD7VdC3+H3uc5/L/PnzM27cuNx+++1JknPPPTetWrXKl770pZxyyinNWiQAAAAA26+CAqokufTSSzNs2LA8++yz+cc//pHddtst/fv3z4EHHtic9QEAAACwnSsooFq8eHF23XXXHHDAARk6dGhz1wQAAADADmSTe1CtWbMm48aNS58+fdK/f/8cffTRqaqqSm1tbUvWBwAAAMB2bpMrqKZMmZLx48enb9++6d69e+bPn59f/OIXKSsry5gxY1qyRgAAAAC2Y5sMqH7yk5/k3//933PttdfWt02ZMiXXXXddrrnmmpSWlrZIgQAAAABs3zb5iN/8+fPzr//6rw3aPv3pT2fNmjV57bXXmr0wAAAAAHYMmwyoVqxYkbKysgZtu+66a5Jk+fLlzVsVAAAAADuMzX6L3xtvvJH58+fXv16zZk19e3l5eYO+Bx54YDOUBwAAAMD2brMB1UUXXbTR9lGjRm3Q9tJLLzVNRQAAAADsUDYZUPmmPgAAAABawiYDqs9+9rMtWQcAAAAAO6hNbpIOAAAAAC1BQAUAAABAUQmoAAAAACgqARUAAAAARSWgAgAAAKCoNvktfs1lzpw5ue666zJ79uy0b98+Q4cOzYUXXpjWrVtvckxdXV1uvfXW/OEPf8gLL7yQ1atX55VXXtmg3+WXX57//M//3KB96tSp6dKlS5NeBwAAAABNo0UDqiVLlmTEiBHp2rVrJkyYkHnz5uWmm27K2rVrc/HFF29y3KpVq/LQQw+lR48eOfLII/Pcc89tsm9FRUXGjBnToO2AAw5osmsAAAAAoGm1aEA1ZcqUrF69OtXV1SkrK0v//v2zbNmyVFdXp7KyMmVlZRsdV15enpkzZ6akpCSTJ0/ebEDVrl279OrVq7kuAQAAAIAm1qJ7UE2bNi0DBgxoEEQNGTIkq1atysyZMzc7tqSkpLnLAwAAAKAIWjSgqqmpSUVFRYO2/fffP+3atUtNTU2TnOOvf/1rjjrqqBxxxBH5/Oc/v8XgCwAAAIDiatFH/Gpra9O+ffsN2svLy1NbW/uB5z/ssMPSo0ePdO3aNYsXL84999yTc889N/fff3969OjxgecHAAAAoOm1+Lf4Nafhw4c3eD1o0KAMGTIkd9xxRyZMmFCkqgAAAADYnBZ9xK+8vDzLli3boL22tjbl5eVNfr527dpl0KBBefHFF5t8bgAAAACaRosGVBUVFRvsNbVw4cKsXLlyg72pmkpJSYkN1gEAAAA+xFo0oBo4cGCmT5/eYBXV1KlTs/POO6dv375Nfr5Vq1bl17/+dQ4//PAmnxsAAACAptGie1ANGzYsP/7xj1NVVZXKysrMnz8/1dXVGTFiRMrKyur7DR48OH369MmNN95Y3/bMM89k5cqVeemll5IkTz75ZJKke/fu6dSpU5YuXZqRI0fmlFNOyUEHHZS33347kyZNyptvvpnbbrutJS8TAAAAgEZo0YCqQ4cOmTRpUq699tp8+ctfTnl5eYYPH56qqqoG/dasWZO1a9c2aLvmmmuyYMGC+tdf/epXkyRjxozJaaedltLS0uyxxx6ZOHFi/vGPf6Rt27bp1atXJk+enO7duzf/xQEAAACwVVr8W/y6du2ae++9d7N9nn766YLa3q9t27aprq7+QLUBAAAA0PJadA8qAAAAAPhnAioAAAAAikpABQAAAEBRCagAAAAAKCoBFQAAAABFJaACAAAAoKgEVAAAAAAUlYAKAAAAgKISUAEAAABQVAIqAAAAAIpKQAUAAABAUQmoAAAAACgqARUAAAAARSWgAgAAAKCoBFQAAAAAFJWACgAAAICiElABAAAAUFQCKgAAAACKSkAFAAAAQFEJqAAAAAAoKgEVAAAAAEUloAIAAACgqARUAAAAABSVgAoAAACAohJQAQAAAFBUAioAAAAAikpABQAAAEBRCagAAAAAKCoBFQAAAABFJaACAAAAoKgEVAAAAAAUlYAKAAAAgKISUAEAzWb16tXFLqFRtrV6AQC2F22KXQAAsP1q27ZtevfuXewyCjZr1qxilwAAsEOyggoAAACAohJQAQAAAFBUAioAAAAAikpABQAAAEBRCagAAAAAKCoBFQAAAABFJaACAAAAoKgEVAAAAAAUlYAKAAAAgKISUAEAAABQVAIqAAAAAIpKQAUAQItau3ZtsUtolG2tXgDYFrUpdgEAAOxYWrVqld/97nfFLqNgRx99dLFLAIDtXouvoJozZ06GDx+enj17ZsCAAbntttuyZs2azY6pq6vLTTfdlDPPPDM9evRIt27dNtn3F7/4RU4++eR07949n/70pzN16tSmvgQAAAAAmlCLBlRLlizJiBEjUlJSkgkTJmTUqFG55557cvvtt2923KpVq/LQQw+lXbt2OfLIIzfZb9asWbnooovSr1+/3H333Rk0aFAuueSSTJ8+vakvBQAAAIAm0qKP+E2ZMiWrV69OdXV1ysrK0r9//yxbtizV1dWprKxMWVnZRseVl5dn5syZKSkpyeTJk/Pcc89ttN/EiRPTu3fvXHXVVUmSY445JnPmzMn48eMzYMCAZrsuAAAAALZei66gmjZtWgYMGNAgiBoyZEhWrVqVmTNnbnZsSUnJZo/X1dXl+eefz6c+9akG7UOGDMns2bOzdOnSrS8cAAAAgGbTogFVTU1NKioqGrTtv//+adeuXWpqaj7Q3PPmzcu77767wfwVFRVZu3Zt5s6d+4HmBwAAAKB5tGhAVVtbm/bt22/QXl5entra2g8095IlS+rner8OHTrUnxsAAACAD58W/xY/AACaznvvvVfsEgAAPrAW3SS9vLw8y5Yt26C9trZ2g5VPjbV+pdQ/7zW1qZVVAADbgzZt2uSuu+4qdhmNcv755xe7BADgQ6ZFV1BVVFRssNfUwoULs3Llyg32jmqszp07Z6eddtpg/pqamrRq1SqHHHLIB5ofAAAAgObRogHVwIEDM3369AarqKZOnZqdd945ffv2/UBzl5aWpl+/fnnyyScbtD/xxBPp1avXRve+AgAAAKD4WjSgGjZsWEpLS1NVVZUZM2bkwQcfTHV1dUaMGJGysrL6foMHD86VV17ZYOwzzzyTJ598Mi+99FKS5Mknn8yTTz6ZBQsW1Pf5yle+kpkzZ+aGG27I888/n+985zt55plnMmrUqJa5QAAAAAAarUX3oOrQoUMmTZqUa6+9Nl/+8pdTXl6e4cOHp6qqqkG/NWvWZO3atQ3arrnmmgZh1Fe/+tUkyZgxY3LaaaclSXr37p3bb7893//+9/PAAw/kgAMOyHe/+90MGDCgma8MAAAAgK3VogFVknTt2jX33nvvZvs8/fTTBbVtzIknnpgTTzxxq2oDAAAAoOW16CN+AAAAAPDPBFQAAAAAFJWACgAAAICiElABAAAAUFQCKgAAAACKSkAFAAAAQFEJqAAAAAAoKgEVAAAAAEUloAIAAACgqARUAAAAABSVgAoAAACAohJQAQAAAFBUAiq2O++9916xSwAAAAAaoU2xC4Cm1qZNm9x1113FLqNg559/frFLAAAAgKKyggoAAACAohJQAQAAAFBUAioAAAAAikrKug9vAAAgAElEQVRABQAAAEBRCagAAAAAKCoBFQAAAABFJaACAAAAoKgEVAAAAAAUlYAKAAAAgKISUAEAAABQVAIqAAAAAIpKQAUAAABAUQmoAAAAACgqARUAAAAARSWgAgAAAKCoBFQAAAAAFJWACgAAAICiElABAAAAUFQCKgAAAACKSkAFAAAAQFEJqAAAAAAoKgEVAAAAAEUloAIA+P979913i10CAMAOqU2xCwAA+LDYaaedMnLkyGKX0Sh33nlnsUsAAPjArKACAAAAoKgEVAAAAAAUlYAKAAAAgKISUAEAAABQVAIqAAAAAIpKQAUAAABAUQmoAAAAACgqARUAAAAARSWgAgAAAKCoBFQAAAAAFFWLB1Rz5szJ8OHD07NnzwwYMCC33XZb1qxZs8VxS5cuzRVXXJE+ffrk6KOPzte+9rW8/fbbDfpcfvnl6dat2wZ//vrXvzbX5QAAAADwAbVpyZMtWbIkI0aMSNeuXTNhwoTMmzcvN910U9auXZuLL754s2NHjx6duXPn5vrrr0+rVq1yyy23ZNSoUbn//vsb9KuoqMiYMWMatB1wwAFNfi0AAAAANI0WDaimTJmS1atXp7q6OmVlZenfv3+WLVuW6urqVFZWpqysbKPjfv/732f69OmZPHly+vTpkyTZZ599MnTo0MyYMSPHHXdcfd927dqlV69eLXI9AAAAAHxwLfqI37Rp0zJgwIAGQdSQIUOyatWqzJw5c7Pj9tprr/pwKkl69OiRAw44INOmTWvWmgEAAABoXi0aUNXU1KSioqJB2/7775927dqlpqamUeOSpEuXLhuM++tf/5qjjjoqRxxxRD7/+c9vNvgCAAAAoPha9BG/2tratG/ffoP28vLy1NbWbtW4V199tf71YYcdlh49eqRr165ZvHhx7rnnnpx77rm5//7706NHj6a5CAAAAACaVIsGVM1t+PDhDV4PGjQoQ4YMyR133JEJEyYUqSoAAAAANqdFH/ErLy/PsmXLNmivra1NeXn5ZsctXbq00ePatWuXQYMG5cUXX9y6ggEAAABodi0aUFVUVGywZ9TChQuzcuXKje4x9f5xc+fO3aB9U3tTvV9JSUlKSkq2rmAAAAAAml2LBlQDBw7M9OnTG6yimjp1anbeeef07dt3s+MWLVqUWbNm1bf96U9/yvz58zNw4MBNjlu1alV+/etf5/DDD2+aCwAAAACgybVoQDVs2LCUlpamqqoqM2bMyIMPPpjq6uqMGDEiZWVl9f0GDx6cK6+8sv71kUcemQEDBuSyyy7Lz372s/ziF7/IpZdemqOPPjrHHXdckmTp0qU588wzM2XKlDz77LOZOnVqzjnnnLz55psZOXJkS14mAAAAAI3Qopukd+jQIZMmTcq1116bL3/5yykvL8/w4cNTVVXVoN+aNWuydu3aBm233nprxowZkyuvvDJr167N8ccfn29+85v1x0tLS7PHHntk4sSJ+cc//pG2bdumV69emTx5crp3794i1wcAAABA47X4t/h17do1995772b7PP300xu0lZeXZ8yYMRkzZsxGx7Rt2zbV1dVNUiMAAAAALadFH/EDAAAAgH8moAIAAACgqARUAAAAABSVgAoAAACAohJQAQAAAFBUAioAAAAAikpABQAAAEBRCagAAAAAKCoBFQAAAABFJaACAAAAoKgEVAAAAAAUlYAKAAAAgKISUAEAAABQVAIqAAAAAIpKQAUAAABAUQmoAAAAACgqARUAAAAARSWgAgAAAKCoBFQAAAAAFJWACgAAAICiElABAAAAUFQCKgAAAACKSkAFAAAAQFEJqAAAAAAoKgEVAAAAAEUloAIAAACgqARUAAAAABSVgAoAAACAohJQAQAAAFBUAioAAAAAikpABQAAAEBRCagAAAAAKCoBFQAAAABFJaACAAAAoKgEVAAAAAAUlYAKAAAAgKISUAEAAABQVAIqAAAAAIpKQAUAAABAUQmoAAAAACgqARUAAAAARSWgAgAAAKCoBFQAAAAAFJWACgAAAICiElABAAAAUFQCKgAAAACKSkAFAAAAQFEJqAAAAAAoqhYPqObMmZPhw4enZ8+eGTBgQG677basWbNmi+OWLl2aK664In369MnRRx+dr33ta3n77bc36PeLX/wiJ598crp3755Pf/rTmTp1anNcBgAAAABNpEUDqiVLlmTEiBEpKSnJhAkTMmrUqNxzzz25/fbbtzh29OjRef7553P99ddn7NixeeGFFzJq1KgGfWbNmpWLLroo/fr1y913351BgwblkksuyfTp05vrkgAAAAD4gNq05MmmTJmS1atXp7q6OmVlZenfv3+WLVuW6urqVFZWpqysbKPjfv/732f69OmZPHly+vTpkyTZZ599MnTo0MyYMSPHHXdckmTixInp3bt3rrrqqiTJMccckzlz5mT8+PEZMGBAy1wkAAAAAI3Soiuopk2blgEDBjQIooYMGZJVq1Zl5syZmx2311571YdTSdKjR48ccMABmTZtWpKkrq4uzz//fD71qU81GDtkyJDMnj07S5cubeKrAQAAAKAptOgKqpqamhxzzDEN2vbff/+0a9cuNTU1OeGEEzY5rqKiYoP2Ll26pKamJkkyb968vPvuuxv0q6ioyNq1azN37tz06NFjizWu3w/r9ddfL+iaGmvt2rXNMm9zefXVV7Ny5cpil9Eor776apYsWVLsMgr26quvZtGiRcUuo1FeffXVYpewVbal+8+91zK2tfvPvdf83Hstw73XMraley/Z9u4/917L2BbvP/de83LvtYzmuPfW5yyb2oe8RQOq2tratG/ffoP28vLy1NbWbtW49f/R1r9By8vLG/Tp0KFD/RyFWP+mOeusswrqv737xCc+UewSGm1bq/kHP/hBsUvgQ2hbex8n22bN7j/+2bb4Pt4Wa3bvsTHb2nt5W6s3ce+xcdvae3lbqzdx7/2zRYsW5aCDDtqgvUUDqm3BEUcckfvuuy8dO3ZM69ati10OAAAAwDZvzZo1WbRoUY444oiNHm/RgKq8vDzLli3boL22tnaDlU//PG7x4sWbHbd+pdQ/7zW1qZVVm7Lzzjund+/eBfUFAAAAoDAbWzm1Xotukl5RUVG/Z9R6CxcuzMqVKze6x9T7x82dO3eD9vfvTdW5c+fstNNOG8xfU1OTVq1a5ZBDDmmCKwAAAACgqbVoQDVw4MBMnz69wSqqqVOnZuedd07fvn03O27RokWZNWtWfduf/vSnzJ8/PwMHDkySlJaWpl+/fnnyyScbjH3iiSfSq1evje5hBQAAAEDxtb766quvbqmTfeQjH8mDDz6Y559/PnvvvXdmzJiR733vexk+fHgGDRpU32/w4MF5+eWX6zc/22+//TJ79uw89NBD2W+//TJ37txcffXV6dKlS0aPHl0/rlOnTqmurs4777yTtm3b5v77788jjzyS66+/Pp07d26pywQAAACgEUrWrVu3riVPOGfOnFx77bWZPXt2ysvLc8YZZ6SqqqrBhuQnnHBC+vbtm7Fjx9a31dbWZsyYMfn5z3+etWvX5vjjj883v/nN7LHHHg3m/8UvfpHvf//7+dvf/pYDDjggVVVVGTJkSItdHwAAAACN0+IBFQAAAAC8X4vuQQUAAAAA/0xABQAAAEBRCagAAAAAKCoBFU3m7LPPTrdu3dKtW7dMmjSpvv1Pf/pTLr/88nzyk5/MoYcemssvv7zgOd94442MGjUqRx55ZPr165drr702K1eubHRtixcvzvXXX58zzjgjRxxxRE444YRGz5Eko0aNSrdu3TJ58uQG7Zdffnn9td90001bNTdsraa+955//vn6+d7/55Zbbml0bTNmzMjo0aNz/PHHp2fPnvm3f/u3TJ48OWvWrGnUPDfeeONG769x48bV13fRRRc1uj74oDZ1/02ZMiVf/OIXc9xxx+Xoo4/OsGHDMn369C3O9+qrr270/rv44osbXdtf/vKXnHfeeRkwYECOOOKIfPzjH883v/nNvPnmm42ax/3Hh9Gm7r377rsvp512Wvr06ZOePXvm5JNPzn333ZctbbvblD/73m/FihUZNGhQunXrlj//+c+NGutzJx9Gm7r33u/FF1/MYYcdln79+m1xvqa89z7IXHV1dRk7dmyOPfbY9OrVK+eff35effXVBn3ce82vTbELYPvSr1+/XHLJJenUqVN923//93/nd7/7XXr27Jnly5cXPNe7776b8847LzvttFNuvfXW1NbWZuzYsamtrW30P1hvvPFGpk6dmh49euTQQw/N4sWLGzU+SaZPn57Zs2dv9NgFF1yQYcOG5cILL2z0vNAUmvLeW++WW27JgQceWP96n332afQcDz74YFatWpWvfvWr2W+//fK73/0uY8eOzauvvlpwYDZnzpw89NBDKSsr2+DY0KFD87GPfSzXXHNNo2uDprKx+++OO+7Ixz72sZx11llp165dHn300XzpS1/K+PHj84lPfGKLc1522WU56qij6l/vvvvuja5r6dKlOeCAA/KZz3wme++9d1599dWMHz8+//M//5OHHnoobdps+WOg+48Ps43de7W1tRk8eHC6deuWdu3a5dlnn811112XVatW5bzzztvinE3xs+/97rjjjrz33nuNHudzJx9mG7v31lu3bl2uvfba7LHHHo167zflvbc1c11//fV56qmncsUVV2T33XdPdXV1zj333Dz22GNp27ZtEvdeSxBQ0aR222239OrVq0Hb2WefneHDhydJTjvttILneuqpp/LXv/41P/vZz+r/gWnTpk0uueSSXHjhhTn44IMLnqtbt26ZMWNGkuSmm27KU089VfDY5H/DshtuuCGjR4/OVVddtcHxzp07p3PnziktLW3UvNBUmvLeW69bt275l3/5lw9U1//5P/8ne+yxR/3rfv36ZdWqVZk0aVIuueSSgu6Z6667Luecc04effTRDY7tu+++2XfffTf6yzO0lI3dfw8//HCD937//v3z97//PZMmTSoooDrkkEM2mLOxjjrqqAYhV79+/bLvvvvm3HPPzSuvvJLDDz98i3O4//gw29i995WvfKXB62OPPTYLFizII488UlBA1RQ/+9b7+9//nh//+Mf5xje+kauvvrrgcT538mG3sXtvvf/3//5f/vGPf+T000/Pgw8+WPCcTXnvNXau119/PQ899FBuvPHGfOYzn0mSHHroofnEJz6RRx99NEOHDk3i3msJHvGj2bVqtXVvs2nTpqV79+4N0u8TTzwxO+20U37zm9+0SA3r3Xvvvdl5551z+umnf6B5oCV90Pd9U3j/L+jrHXbYYVm9enXeeeedLY5/8sknU1NTk/PPP785yoNms6n3fmMfr2tqu+22W5L//QV4S9x/bC923333gt7zTe3GG2/MGWeckYqKikaN87mTbdWyZctyyy235LLLLstOO+1U7HIKtv4R/MGDB9e37bPPPjnqqKMybdq0YpW1Qyr+by+wCTU1NRv8QC8tLU3nzp1TU1PTYnUsWrQoEyZMyJVXXvmh+IUfWsrw4cNz2GGH5YQTTsiECRMavW/UpsyePTvl5eXZc889N9tv1apVuemmm/K1r30tu+yyS5OcG4pp9uzZOeSQQwrqe8UVV+Swww7LgAEDMmbMmKxatWqrz7t27drU1dWlpqYm3/3ud9O9e/f06NFjs2Pcf2zr3nvvvSxfvjzPPPNMHnnkkZx11lkFjWuqn32//vWvM3v27EY/CuRzJ9uy8ePHp0uXLjnxxBMbPbYpP3c2dq6amprsu+++2XXXXRu0d+nSpUV/78QjfnyI1dbWpn379hu0l5eXp7a2tsXquPnmmzNgwID06dOnxc4JxdS+ffucf/756d27d3baaaf86le/yrhx47J48eKNPmrQGHPmzMkDDzyQysrKtG7derN977zzznTs2DGnnnrqBzonfBg89NBDefHFF3PZZZdttl9paWnOOuus9O/fP2VlZZk5c2buvvvuzJs3LxMnTtyqc1dWVtb/3+HDDz88d9999xZ/8XX/sS1btGhRBgwYUP/6K1/5Ss4+++zNjmnKn311dXW58cYbc9FFF6VDhw6NGutzJ9uqmpqa3H///fnJT37SqHFNee9t7Vyb+71zyZIljaqBD0ZABZvx+9//Pk899VSmTp1a7FKgxXz0ox/NRz/60frXxx13XEpLSzNp0qRccMEFG310qRBLlixJVVVVunXrlpEjR2627/z58/Mf//Efuffee1NSUrJV54MPixdeeCHXX399zjnnnBxzzDGb7bv33nvn29/+dv3rfv36Zc8998w111yTl19+OYceemijz/+tb30rS5Ysyd/+9rdMnDgxlZWVeeCBB+o3ff1n7j+2dbvvvnseeuihrFixIjNnzsxdd92VXXfdNZWVlZsc05Q/+yZNmpS2bdtm2LBhjarb5062ZTfccEM++9nPplu3bo0a15T3XnN9hqXlWDfKh1Z5eXmWLVu2QXttbW3Ky8tbpIYbb7wxn/vc59K+ffvU1tbWr9xatWpVli5d2iI1wIfBJz/5ybz33nt55ZVXtmr86tWrc8EFF6Suri4TJ07c4uaS3/3udzNw4MAccsgh9ffe+seUamtrt/h14fBhMX/+/IwcOTLHHHNMwd9c+c/+9V//Ncn/Bl1b4+CDD07Pnj1z6qmn5oc//GFefPHFPPbYY5vs7/5jW9emTZt07949/fr1S1VVVUaOHJlx48Zl5cqVjZpna372LV68OBMnTsyFF16Y5cuXp7a2NitWrEiSLF++vP7vG+NzJ9uqZ555Jv/93/+dL37xi/Xv3dWrVyf539/d6urqGjXfB/3c2di5ysvLN3qP1dbWNnoVJB+MFVR8aFVUVGzwzG9dXV3mz5/f6P8jtbXmzp2bP/7xj/nRj37UoP3mm2/O9773vbz44ostUgcU2/pVFFuzmmLNmjX52te+Vv9431577bXFMXPnzs3LL7+cn/3sZw3aJ0+enMmTJ+eZZ57Jvvvu2+haoCX94x//yHnnnZf9998/t9566xYfa92SpljN1KlTp3To0CHz58/fZB/3H9ubww8/PKtXr86bb76Zgw46qOBxW/Oz74033siKFSty0UUXbXBs2LBhOfbYYzNp0qSNjvW5k23V3Llzs2LFipx00kkbHOvTp0+++tWv5oILLih4vg/yuXNr5qqoqMjrr7+eFStWNNh3cWN7ItO8BFR8aA0cODDf+MY3smDBgv9fe/cdFMX5xgH8C0qTk8AlKkgRhIAgiA1OsOAhEcFCiRFQEBMz1lgmMWoYM1gI6MSSBE1QYTAiaLAiseWAWEJUiIxmTEhULAiOiCKKaGh3vz8c9udJ8UTxQL+fGWbcvXfffd8dH3Z57n3fhampKQAgKysL1dXVGDp06CtpQ1xcXINF9SZPnoywsLBGfwETva6OHDmCjh07PvewbQBYtmwZTpw4gcTERJVv8lFRUQ2+Zf7000/h4uKCkJAQDtGmNq+yslKYTrRx40bo6em1uK4jR44AePxH9ou6fPkyysvLYWZm1mQZxh+9bvLy8qCtrY2uXbs+13EtufdZWFhg69atSvvy8/MRExOD6OjoZuOYz53UXo0aNQr29vZK+/bu3QuZTIbvv/++2XtOY17kubMlddWvWSeTyYS1F0tKSnDmzBlERka+cBtIdUxQUasrKytDTk4OgMfDJIuLi3H48GEA/5+2ADyeMzxr1izhbSfe3t6Ii4vDnDlzMG/ePFRUVCAmJgZjxoyBpaWlcNzixYuRk5ODrKysZttRf84rV67g0aNHwrarq6vwsB0eHg4AwjdXAwcObLQuS0tLuLq6Ptd1IHrVWhp7kZGREIvFcHJygpaWFo4dO4bk5GSEh4fDyMhIOE6V2IuLi8NPP/2E6dOnQ1NTE2fPnhU+s7GxgUgkAgBEREQgNzcXMpkMAODk5NSgLh0dHZiYmEAikbT0khC9MnPmzMGFCxcQExODwsJCFBYWCp/17dtX+Pd7770HFxcXREdHAwBiY2NRWVmJ/v37QyQSITc3FwkJCRg5cqTS+lOxsbFYv359s1MWVq1ahQ4dOsDZ2RmdO3dGQUEBEhISYGFhgdGjRwvlGH/0Onn//fcREBAAKysr1NbWIjs7G8nJyfjwww+VEsWtde/T19dvMk6cnJxga2srbPO5k14XxsbGDUbW5uTkoGPHjg3ioTWfO1Wt6+nYMzY2xvjx4xEdHQ2FQgGxWIz169eje/fuGDdu3ItdHHouTFBRq7t48SLmzZsnbF+/fl34o/nJB+u6ujqldS20tLQQHx+P5cuXY/78+dDW1oavry8WLlyoVP+jR49U+jb3yTY8ub1161bhF6dcLn/O3hG1XS2NPWtra+zcuRNbtmxBTU0NevTogUWLFgk383qqxF52djaAxyNINm7cqPTZ07H3Iq8TJmpr6v/vL1iwoMFnT8ffk/eenj17IiEhATt37kRVVRVMTEwwdepUzJw5U6kOVeLP0dERSUlJSE1NFeoaOXIkpk2bpjSFgfFHrxN7e3skJSXh5s2b0NXVhaWlJaKjoxv8kdma9z5V8bmT3kStGXuq1tVY7C1ZsgR6enpYuXIl/vvvP7i4uGDNmjVNvlCEWoeGgitd0ksSFhYGQ0NDYZ2NV/XmH6lUirlz5yIgIOCVnK8xcrkccrkcI0eOhLe39zNfI070MjH25JgyZQrEYjG+++47tbWF3kzqir/Q0FAMGjRI+AZaHRh/pE689/G5k9SDscfYa018ix+9VL/88gt69+7dYHHH1lJSUoKamhqlqQrqEBERgd69e6O4uFit7aA315saexs2bEDv3r2Rm5ur1nbQm+1Vx19tbS0uXryIiRMnvpLzNYXxR+r2pt77+NxJ6sbYY+y1Fo6gopfm8uXLqKysBAB0794db7/9tppb9OoUFRXh7t27AIAuXbrw7Ub0Sr3JsVdSUoJbt24BAAwNDWFubq7mFtGbhvHH+CP1eJNjj8+dpE6MPcZea2KCioiIiIiIiIiI1IpT/IiIiIiIiIiISK2YoCIiIiIiIiIiIrVigoqIiIioDVq8eDHs7OzU3Qy1CQsLg6enp7qbQURERK8IE1REREREr6GioiLExsYiPz9f3U0hIiIieiYmqIiIiIjaoBUrVuDPP/9s8fHFxcVYv349E1RERETULjBBRURERPSSPHjw4KXVpaWlBR0dnZdW38v2MvtKRERExAQVERERtVtVVVWIjY2Ft7c3nJ2dMXDgQIwdOxarVq1qUPbUqVOYNm0aJBIJnJycMGLECERERKCsrEwoU1tbi02bNsHX1xdOTk6QSCSYPXs2/v33X6W6ioqKYGdnh9jYWBw8eBCBgYHo06cPoqKihDK3bt1CZGQkhg8fDkdHRwwZMgRffvkl7ty5o1LfGluDqn5fRUUFIiMj4ebmBicnJwQHB+PcuXNCuT179mDy5MkAgC+++AJ2dnaws7NDWFiYUEahUCAlJQWBgYFwdnZGv379EBYWhlOnTqnc16+//hp2dnb4559/GrS/oqICffr0waxZs4R9Bw8exIwZM4RrIpFIMGvWrEaPb8zFixcxd+5cDB06FI6Ojhg8eDDCwsJw9OhRlY4nIiKitqujuhtARERE1FLLli3D7t274e/vj379+qGurg5Xr17F6dOnlcrt2LEDS5cuRbdu3RAcHAxTU1PcuHEDv/76K0pKSiAWiwEACxYswKFDhzB48GCEhITg9u3bSE5ORnBwMJKTk+Hg4KBUb0ZGBpKSkhASEoLg4GCIRCIAwI0bNxAUFISamhqMHz8eFhYWuHbtGrZv347Tp09j9+7d6Ny5c4v7PXXqVIjFYsyePRvl5eVITEzEtGnTkJmZCZFIBBcXF8yYMQNxcXEICgrCgAEDAADvvPOOUMfnn3+OAwcOwNvbG4GBgaiurkZ6ejo++ugjxMbGYsSIEc/sq62tLeLj45GWloZevXoplT906BCqqqoQEBAg7Nu2bRsMDQ0xYcIEdOnSBYWFhUhNTUVISAj27t0LS0vLJvt89+5dhIeHAwCCg4PRvXt33L17F+fPn8e5c+cwfPjwFl9PIiIiUj8mqIiIiKjdysjIwLBhwxodMVXv5s2biIqKQs+ePbFjxw4YGBgIn82fPx9yuRwAkJ2djUOHDsHHxwfr1q2DhoYGAMDHxweBgYGIiopCSkqKUt2XLl3C/v37YW1trbR/xYoVqK2txb59+2BsbCzsHzVqFIKCgrBlyxbMmTOnxf12cHDA0qVLhW1ra2vMnz8fP//8M4KDg2Fubg53d3fExcWhb9++8PPzUzpeJpMhPT0dy5cvR1BQkLB/8uTJmDBhAr766it4enoK16C5vjo6OiI9PR0LFixAhw4dhP379u2DoaEhPDw8hH3x8fHo1KmT0vH+/v7w8/PDli1blPr0tLy8PNy5cwfr1q2Dr6+vSteJiIiI2g9O8SMiIqJ2SyQS4dKlS7hw4UKTZQ4fPoyamhp88sknSsmpepqajx+HZDIZAGDGjBlKiZlevXpBKpXizJkzStMBAcDDw6NBwqaiogJHjx6Fp6cntLW1UVZWJvyYmprCwsIC2dnZLe4zAEyZMkVpe9CgQQCAa9euqXT8/v37oa+vDy8vL6X23b9/H56eniguLsbVq1eVjmmsrwAQEBCA0tJSpT5dv34deXl5GDNmDLS1tYX99ckphUKBBw8eoKysDEZGRrCysnrmgvD1I85OnDjB9a+IiIheQxxBRURERO1WREQEFi5ciLFjx8Lc3BwSiQRSqRSenp5C4qk+0WJvb99sXUVFRdDU1Gw0CWNjY4OMjAwUFRUJ0wEBNDol7cqVK5DL5di1axd27drV6LnMzc1V7GHjnj7eyMgIAFBeXq7S8QUFBaisrIS7u3uTZe7cuQMrKythu6npd6NHj8bKlSuRlpaGYcOGAQDS0tKgUCgajNz6+++/8e2334ccz0gAAAUhSURBVCInJwcPHz5U+szMzKzZNru6usLf3x979uxBeno6HB0d4e7uDl9fX9jY2DR7LBEREbV9TFARERFRu+Xl5YWsrCwcO3YMubm5+P3337Fr1y4MHDgQiYmJSqN3WoOenl6DfQqFAgAwbtw4pfWXnvSib+d7cipdY+d+FoVCAbFYjDVr1jRZ5t1331XabqyvwOPkmIeHBzIyMvDgwQOIRCKkpaXB2toaffr0EcrduHEDkyZNgkgkwsyZM9GzZ0/o6elBQ0MD0dHRDRJWjVm1ahWmTp2K48eP448//kBiYiLi4uIQERGB0NBQlfpOREREbRMTVERERNSuGRoaws/PD35+flAoFFi9ejXi4+ORmZkJHx8fYeRPfn6+0oigp5mbm0Mul6OgoKDBgt8FBQUAnj3KBwAsLCygoaGBmpqaZkcotbYnpyk+rUePHrh69SqcnZ2hr6//wucKCAhARkYGDh8+DCsrKxQWFuKzzz5TKiOTyfDw4UP88MMPwpTEeuXl5SonE21tbWFra4uPP/4Y9+/fxwcffIA1a9Zg0qRJzfaZiIiI2jauQUVERETtUl1dHe7fv6+0T0NDQ3jT3r179wA8XphcS0sLGzZsaHTtovpRR15eXgCATZs2KY1EunDhArKysjBgwACl6X1NqR9RJJPJcPbs2UbP9/RaVq2hfr2n+uvwJH9/f8jlcqxdu7bRY2/fvv1c5/Lw8ICRkRHS0tKQlpYGTU3NBtP76kd9PT3KKzU1FaWlpc88R3l5ubCgfT0DAwOYmZnh0aNHqKqqeq42ExERUdvCEVRERETULlVWVmLIkCHw9PSEg4MDxGIxioqKsH37drz11luQSqUAAGNjY0RERGD58uUYO3Ys/Pz8YGpqipKSEmRmZiI6Ohr29vYYPHgwfHx8cODAAdy7dw9SqRSlpaVISUmBjo4OlixZonLbli5diokTJyI0NBR+fn5wcHCAXC7H9evXkZmZCX9//xd6i58qbGxsoK+vj5SUFOjq6sLAwABisRhubm4YNWoUAgMDsW3bNvz111+QSqUwMjLCzZs3cfbsWVy7dg2ZmZkqn0tLSwtjxozBtm3bcP78ebi7u6Nbt25KZYYNGwY9PT0sXLgQoaGhMDAwQF5eHo4fPw4LCwvU1dU1e459+/bhxx9/hJeXF3r06IGOHTsiNzcXv/32G3x8fKCrq9ui60RERERtAxNURERE1C7p6uoiPDwcJ0+exMmTJ1FZWYmuXbvC09MT06dPV0qQTJw4ERYWFkhISEBSUhKqq6vRtWtXuLm5wdjYWCi3evVqODg4YO/evVi5ciU6deoEFxcXzJs3D3Z2diq3zcTEBLt378bmzZuRlZWF/fv3Q0dHByYmJpBKpfDx8Xmp16Ixurq6WLduHb755htER0ejuroarq6ucHNzAwDExMRAIpEgNTUVGzduRE1NDbp06QIHB4cG0/NU4e/vj6SkJDx8+LDB6Cng8dTHzZs3Y+3atYiLi0OHDh3Qv39/JCUlYcWKFSguLm62folEgvz8fBw9ehSlpaXQ1NSEmZkZFi1axPWniIiIXgMaClVX0yQiIiIiIiIiImoFXIOKiIiIiIiIiIjUigkqIiIiIiIiIiJSKyaoiIiIiIiIiIhIrZigIiIiIiIiIiIitWKCioiIiIiIiIiI1IoJKiIiIiIiIiIiUismqIiIiIiIiIiISK2YoCIiIiIiIiIiIrVigoqIiIiIiIiIiNSKCSoiIiIiIiIiIlKr/wF2ZkMkoUYEMwAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<Figure size 837.87x216 with 3 Axes>"
+       "<Figure size 1440x720 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -761,21 +429,99 @@
     }
    ],
    "source": [
+    "from collections import defaultdict\n",
+    "from scipy.stats import pearsonr\n",
+    "def generate_scores_df(f1=None, f2=None, f1_label='f1_label', f2_label='f2_label', task=None):\n",
+    "    f1_list = np.array(extract_scores(f1, task))\n",
+    "    f2_list = np.array(extract_scores(f2, task))\n",
+    "    df_dct = defaultdict(list)\n",
+    "    for idx, item in enumerate(f1_list):\n",
+    "        df_dct[round(item)].append({'gold': item, 'pred': f2_list[idx]})\n",
+    "    for key, value in df_dct.items():\n",
+    "        new_df = pd.DataFrame(df_dct[key])\n",
+    "        gold = new_df['gold'].values.tolist()\n",
+    "        pred = new_df['pred'].values.tolist()\n",
+    "        goal = pearsonr(gold, pred)[0]\n",
+    "        df_dct[key] = goal\n",
+    "    keys = []\n",
+    "    values = []\n",
+    "    for key, value in dict(df_dct).items():\n",
+    "        keys.append(key)\n",
+    "        values.append(value)\n",
+    "    return pd.DataFrame({'score': keys, 'pearson': values}).sort_values('score')\n",
+    "\n",
+    "def generate_grid(model_file, task_name, plot_title):\n",
+    "    def generate_section(test_file, submission_file, dataset_name, plot_title):\n",
+    "        dataplot = generate_scores_df(test_file, \n",
+    "                           submission_file, \n",
+    "                           task=task_name,\n",
+    "                           f1_label=\"Gold scores\",\n",
+    "                           f2_label=\"Average absolute error\")\n",
     "\n",
-    "# figure size in inches\n",
-    "plt.figure(figsize=(20, 30))\n",
+    "#         value_rename = \"Gold scores vs. absolute error\"\n",
+    "#         idx_rename = \"sentence pair\"\n",
+    "#         variable_rename = \"model\"\n",
     "\n",
-    "sns.set(font_scale=0.95)\n",
-    "# g = sns.FacetGrid(dataplot, row=\"dataset\", col=\"model\", margin_titles=True, palette=\"gray\")\n",
-    "# g.map(sns.lineplot, x=\"score\", y=\"Average absolute error\", hue=\"model\");\n",
-    "dataplot = dataplot.rename(columns={\n",
-    "    \"Average absolute error\" : \"Mean squared error\"\n",
-    "})\n",
-    "g = sns.FacetGrid(dataplot, col=\"dataset\", hue=\"model\", margin_titles=True, legend_out=True)\n",
-    "g.map(sns.lineplot, \"score\", \"Mean squared error\")\n",
-    "g.add_legend()\n",
-    "g.fig.subplots_adjust(top=0.8)\n",
-    "g.fig.suptitle('Semantic similarity: Mean squared error on each score range', fontsize=14)"
+    "#         dataplot = get_dataplot(dataplot, value_rename, idx_rename, variable_rename)\n",
+    "\n",
+    "#         dataplot['dataset'] = dataset_name\n",
+    "#         dataplot['score'] = dataplot.index + 1\n",
+    "        dataplot['model'] = plot_title\n",
+    "        return dataplot\n",
+    "\n",
+    "    test_file = './assin2-test.xml'\n",
+    "    submission_file = './samples/{0}.xml'.format(model_file)\n",
+    "    task_name = 'similarity'\n",
+    "    dataset_name = \"ASSIN 2\"\n",
+    "    dataplot = generate_section(test_file, submission_file, dataset_name, plot_title)\n",
+    "    return dataplot\n",
+    "\n",
+    "model_file = \"roberta\"\n",
+    "task_name = 'similarity'\n",
+    "plot_title = \"RoBERTa\"\n",
+    "dataplot = generate_grid(model_file, task_name, plot_title)\n",
+    "\n",
+    "model_file = \"bert\"\n",
+    "task_name = 'similarity'\n",
+    "plot_title = \"BERT-multilingual\"\n",
+    "df = generate_grid(model_file, task_name, plot_title)\n",
+    "dataplot = dataplot.append(df)\n",
+    "\n",
+    "model_file = \"5fold_stacking\"\n",
+    "task_name = 'similarity'\n",
+    "plot_title = \"Ensemble ( stacking, 5-fold )\"\n",
+    "df = generate_grid(model_file, task_name, plot_title)\n",
+    "dataplot = dataplot.append(df)\n",
+    "\n",
+    "model_file = \"average\"\n",
+    "task_name = 'similarity'\n",
+    "plot_title = \"Ensemble ( averaging )\"\n",
+    "df = generate_grid(model_file, task_name, plot_title)\n",
+    "dataplot = dataplot.append(df)\n",
+    "\n",
+    "dataplot\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.rcParams[\"figure.figsize\"] = (20,10)\n",
+    "dataplot = dataplot.rename(\n",
+    "    columns={\n",
+    "        \"score\": \"score range\"\n",
+    "    }\n",
+    ")\n",
+    "sns.set_style(\"white\")\n",
+    "g = sns.barplot( x=\"score range\", y=\"pearson\", hue=\"model\", data=dataplot, palette='gray')\n",
+    "\n",
+    "g.set(xticklabels=['[1.0, 1.4]', '[1.5, 2.4]', '[2.5, 3.4]', '[3.5, 4.4]', '[4.5, 5.0]'])\n",
+    "\n",
+    "ax = g\n",
+    "plt.setp(ax.get_legend().get_texts(), fontsize='14') # for legend text\n",
+    "plt.setp(ax.get_legend().get_title(), fontsize='14') # for legend title\n",
+    "\n",
+    "ax.tick_params(axis='both', which='major', labelsize=15)\n",
+    "ax.tick_params(axis='both', which='minor', labelsize=15)\n",
+    "\n",
+    "plt.xlabel('score intervals', fontsize=18)\n",
+    "plt.ylabel('Pearson correlation', fontsize=16)"
    ]
   },
   {
@@ -805,6 +551,8 @@
     }
    ],
    "source": [
+    "\n",
+    "\n",
     "sns.distplot(extract_scores('./sources/assin2-test.xml', task=\"similarity\"))"
    ]
   },
@@ -916,88 +664,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 153,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Gold</th>\n",
-       "      <th>Prediction</th>\n",
-       "      <th>dataset</th>\n",
-       "      <th>model</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Entailment</td>\n",
-       "      <td>None</td>\n",
-       "      <td>ASSIN 1 (PT-PT)</td>\n",
-       "      <td>RoBERTa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Paraphrase</td>\n",
-       "      <td>Entailment</td>\n",
-       "      <td>ASSIN 1 (PT-PT)</td>\n",
-       "      <td>RoBERTa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>ASSIN 1 (PT-PT)</td>\n",
-       "      <td>RoBERTa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>ASSIN 1 (PT-PT)</td>\n",
-       "      <td>RoBERTa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>ASSIN 1 (PT-PT)</td>\n",
-       "      <td>RoBERTa</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Gold  Prediction          dataset    model\n",
-       "0  Entailment        None  ASSIN 1 (PT-PT)  RoBERTa\n",
-       "1  Paraphrase  Entailment  ASSIN 1 (PT-PT)  RoBERTa\n",
-       "2        None        None  ASSIN 1 (PT-PT)  RoBERTa\n",
-       "3        None        None  ASSIN 1 (PT-PT)  RoBERTa\n",
-       "4        None        None  ASSIN 1 (PT-PT)  RoBERTa"
-      ]
-     },
-     "execution_count": 153,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "NameError",
+     "evalue": "name 'np' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-1-58f7e6d2fb80>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     73\u001b[0m \u001b[0mtask_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'similarity'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m \u001b[0mplot_title\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"RoBERTa\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 75\u001b[0;31m \u001b[0mdataplot\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgenerate_grid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtask_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplot_title\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     76\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     77\u001b[0m \u001b[0mmodel_file\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"submission-bert\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-1-58f7e6d2fb80>\u001b[0m in \u001b[0;36mgenerate_grid\u001b[0;34m(model_file, task_name, plot_title)\u001b[0m\n\u001b[1;32m     44\u001b[0m     \u001b[0mtask_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'similarity'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m     \u001b[0mdataset_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"ASSIN 1 (PT-PT)\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m     \u001b[0mdataplot\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgenerate_section\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubmission_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataset_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplot_title\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     47\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-1-58f7e6d2fb80>\u001b[0m in \u001b[0;36mgenerate_section\u001b[0;34m(test_file, submission_file, dataset_name, plot_title)\u001b[0m\n\u001b[1;32m     28\u001b[0m                            \u001b[0msubmission_file\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m                            \u001b[0mf1_label\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Gold\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 30\u001b[0;31m                            f2_label=\"Prediction\")\n\u001b[0m\u001b[1;32m     31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m         \u001b[0mvalue_rename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"Gold scores vs. absolute error\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-1-58f7e6d2fb80>\u001b[0m in \u001b[0;36mgenerate_scores_df\u001b[0;34m(f1, f2, f1_label, f2_label)\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mgenerate_scores_df\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf1_label\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'f1_label'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf2_label\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'f2_label'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m     \u001b[0mf1_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mextract_scores\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m     \u001b[0mf2_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mextract_scores\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mf1_label\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf1_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf2_label\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf2_list\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined"
+     ]
     }
    ],
    "source": [
@@ -1100,20 +782,81 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 175,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.6176862005394158"
+       "array([[1150,   74],\n",
+       "       [ 212, 1012]])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "\n",
+    "test_file = './sources/assin2-test.xml'\n",
+    "submission_file = './samples/5fold_stacking.xml'\n",
+    "df = generate_scores_df(test_file, submission_file)\n",
+    "y_true = df['f1_label']\n",
+    "y_pred = df['f2_label']\n",
+    "\n",
+    "cm = confusion_matrix(y_true, y_pred)\n",
+    "cm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1146,   78],\n",
+       "       [ 206, 1018]])"
       ]
      },
-     "execution_count": 175,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "test_file = './sources/assin2-test.xml'\n",
+    "submission_file = './samples/roberta.xml'\n",
+    "df = generate_scores_df(test_file, submission_file)\n",
+    "y_true = df['f1_label']\n",
+    "y_pred = df['f2_label']\n",
+    "\n",
+    "cm = confusion_matrix(y_true, y_pred)\n",
+    "cm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'dataplot' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-2-83e4c017de11>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgenerate_confusion_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"ASSIN 2\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"RoBERTa\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0mmatthews_corrcoef\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-2-83e4c017de11>\u001b[0m in \u001b[0;36mgenerate_confusion_matrix\u001b[0;34m(selected_dataset_name, model_name, top_param)\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mgenerate_confusion_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mselected_dataset_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtop_param\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0mdataplot_section\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataplot\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdataplot\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'dataset'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0mselected_dataset_name\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m     \u001b[0mdataplot_section\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataplot_section\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdataplot_section\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'model'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0mmodel_name\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0my_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataplot_section\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Gold'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'dataplot' is not defined"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.metrics import confusion_matrix\n",
     "from sklearn.metrics import matthews_corrcoef\n",