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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Linking a dataset of real historical persons \n", | |
| "\n", | |
| "In this example, we deduplicate a more realistic dataset. The data is based on historical persons scraped from wikidata. Duplicate records are introduced with a variety of errors introduced." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "from splink.duckdb.duckdb_linker import DuckDBLinker\n", | |
| "import altair as alt\n", | |
| "alt.renderers.enable('mimetype')\n", | |
| "\n", | |
| "import pandas as pd \n", | |
| "pd.options.display.max_rows = 1000\n", | |
| "df = pd.read_parquet(\"./data/historical_figures_with_errors_50k.parquet\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
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| }, | |
| { | |
| "field": "percentile_inc_nulls", | |
| "type": "quantitative" | |
| }, | |
| { | |
| "field": "total_non_null_rows", | |
| "type": "quantitative" | |
| }, | |
| { | |
| "field": "total_rows_inc_nulls", | |
| "type": "quantitative" | |
| } | |
| ], | |
| "x": { | |
| "field": "percentile_ex_nulls", | |
| "sort": "descending", | |
| "title": "Percentile", | |
| "type": "quantitative" | |
| }, | |
| "y": { | |
| "field": "value_count", | |
| "title": "Count of values", | |
| "type": "quantitative" | |
| } | |
| }, | |
| "mark": { | |
| "interpolate": "step-after", | |
| "type": "line" | |
| }, | |
| "title": { | |
| "subtitle": "In this col, 67 values (0.1%) are null and there are 4413 distinct values", | |
| "text": "Distribution of counts of values in column first_name" | |
| } | |
| }, | |
| { | |
| "data": { | |
| "values": [ | |
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| "total_rows_inc_nulls": 50578, | |
| "value": "william", | |
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| "group_name": "first_name", | |
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| "total_rows_inc_nulls": 50578, | |
| "value": "john", | |
| "value_count": 2736 | |
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| "distinct_value_count": 4413, | |
| "group_name": "first_name", | |
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| "total_rows_inc_nulls": 50578, | |
| "value": "thomas", | |
| "value_count": 1448 | |
| }, | |
| { | |
| "distinct_value_count": 4413, | |
| "group_name": "first_name", | |
| "total_non_null_rows": 50511, | |
| "total_rows_inc_nulls": 50578, | |
| "value": "george", | |
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| }, | |
| { | |
| "distinct_value_count": 4413, | |
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| "total_rows_inc_nulls": 50578, | |
| "value": "henry", | |
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| { | |
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| "group_name": "first_name", | |
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| "total_rows_inc_nulls": 50578, | |
| "value": "james", | |
| "value_count": 1265 | |
| }, | |
| { | |
| "distinct_value_count": 4413, | |
| "group_name": "first_name", | |
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| "total_rows_inc_nulls": 50578, | |
| "value": "sir", | |
| "value_count": 1262 | |
| }, | |
| { | |
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| "value": "charles", | |
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| }, | |
| { | |
| "distinct_value_count": 4413, | |
| "group_name": "first_name", | |
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| "total_rows_inc_nulls": 50578, | |
| "value": "edward", | |
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| }, | |
| { | |
| "distinct_value_count": 4413, | |
| "group_name": "first_name", | |
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| "value": "robert", | |
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| }, | |
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| "tooltip": [ | |
| { | |
| "field": "value", | |
| "type": "nominal" | |
| }, | |
| { | |
| "field": "value_count", | |
| "type": "quantitative" | |
| }, | |
| { | |
| "field": "total_non_null_rows", | |
| "type": "quantitative" | |
| }, | |
| { | |
| "field": "total_rows_inc_nulls", | |
| "type": "quantitative" | |
| } | |
| ], | |
| "x": { | |
| "field": "value", | |
| "sort": "-y", | |
| "title": null, | |
| "type": "nominal" | |
| }, | |
| "y": { | |
| "field": "value_count", | |
| "title": "Value count", | |
| "type": "quantitative" | |
| } | |
| }, | |
| "mark": "bar", | |
| "title": "Top 10 values by value count" | |
| }, | |
| { | |
| "data": { | |
| "values": [ | |
| { | |
| "distinct_value_count": 4413, | |
| "group_name": "first_name", | |
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| "value": "clifford,", | |
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| "value": "jerald", | |
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| "value": "watts", | |
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| "value": "pzul", | |
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| } | |
| ] | |
| }, | |
| "encoding": { | |
| "tooltip": [ | |
| { | |
| "field": "value", | |
| "type": "nominal" | |
| }, | |
| { | |
| "field": "value_count", | |
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| }, | |
| { | |
| "field": "total_non_null_rows", | |
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| }, | |
| { | |
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| "scale": { | |
| "domain": [ | |
| 0, | |
| 2780 | |
| ] | |
| }, | |
| "title": "Value count", | |
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| } | |
| }, | |
| "mark": "bar", | |
| "title": "Bottom 5 values by value count" | |
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| { | |
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| "value_count": 34 | |
| } | |
| ] | |
| }, | |
| "encoding": { | |
| "tooltip": [ | |
| { | |
| "field": "value_count", | |
| "type": "quantitative" | |
| }, | |
| { | |
| "field": "percentile_ex_nulls", | |
| "type": "quantitative" | |
| }, | |
| { | |
| "field": "percentile_inc_nulls", | |
| "type": "quantitative" | |
| }, | |
| { | |
| "field": "total_non_null_rows", | |
| "type": "quantitative" | |
| }, | |
| { | |
| "field": "total_rows_inc_nulls", | |
| "type": "quantitative" | |
| } | |
| ], | |
| "x": { | |
| "field": "percentile_ex_nulls", | |
| "sort": "descending", | |
| "title": "Percentile", | |
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| }, | |
| "y": { | |
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| "title": "Count of values", | |
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| } | |
| }, | |
| "mark": { | |
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| "type": "line" | |
| }, | |
| "title": { | |
| "subtitle": "In this col, 11,426 values (22.6%) are null and there are 12363 distinct values", | |
| "text": "Distribution of counts of values in column postcode_fake" | |
| } | |
| }, | |
| { | |
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| "field": "percentile_ex_nulls", | |
| "type": "quantitative" | |
| }, | |
| { | |
| "field": "percentile_inc_nulls", | |
| "type": "quantitative" | |
| }, | |
| { | |
| "field": "total_non_null_rows", | |
| "type": "quantitative" | |
| }, | |
| { | |
| "field": "total_rows_inc_nulls", | |
| "type": "quantitative" | |
| } | |
| ], | |
| "x": { | |
| "field": "percentile_ex_nulls", | |
| "sort": "descending", | |
| "title": "Percentile", | |
| "type": "quantitative" | |
| }, | |
| "y": { | |
| "field": "value_count", | |
| "title": "Count of values", | |
| "type": "quantitative" | |
| } | |
| }, | |
| "mark": { | |
| "interpolate": "step-after", | |
| "type": "line" | |
| }, | |
| "title": { | |
| "subtitle": "In this col, 11,403 values (22.5%) are null and there are 537 distinct values", | |
| "text": "Distribution of counts of values in column substr(dob, 1,4)" | |
| } | |
| }, | |
| { | |
| "data": { | |
| "values": [ | |
| { | |
| "distinct_value_count": 537, | |
| "group_name": "substr_dob_1_4_", | |
| "total_non_null_rows": 39175, | |
| "total_rows_inc_nulls": 50578, | |
| "value": "1862", | |
| "value_count": 2231 | |
| }, | |
| { | |
| "distinct_value_count": 537, | |
| "group_name": "substr_dob_1_4_", | |
| "total_non_null_rows": 39175, | |
| "total_rows_inc_nulls": 50578, | |
| "value": "1860", | |
| "value_count": 1753 | |
| }, | |
| { | |
| "distinct_value_count": 537, | |
| "group_name": "substr_dob_1_4_", | |
| "total_non_null_rows": 39175, | |
| "total_rows_inc_nulls": 50578, | |
| "value": "1861", | |
| "value_count": 1751 | |
| }, | |
| { | |
| "distinct_value_count": 537, | |
| "group_name": "substr_dob_1_4_", | |
| "total_non_null_rows": 39175, | |
| "total_rows_inc_nulls": 50578, | |
| "value": "1859", | |
| "value_count": 1616 | |
| }, | |
| { | |
| "distinct_value_count": 537, | |
| "group_name": "substr_dob_1_4_", | |
| "total_non_null_rows": 39175, | |
| "total_rows_inc_nulls": 50578, | |
| "value": "1857", | |
| "value_count": 1599 | |
| }, | |
| { | |
| "distinct_value_count": 537, | |
| "group_name": "substr_dob_1_4_", | |
| "total_non_null_rows": 39175, | |
| "total_rows_inc_nulls": 50578, | |
| "value": "1858", | |
| "value_count": 1594 | |
| }, | |
| { | |
| "distinct_value_count": 537, | |
| "group_name": "substr_dob_1_4_", | |
| "total_non_null_rows": 39175, | |
| "total_rows_inc_nulls": 50578, | |
| "value": "1856", | |
| "value_count": 1362 | |
| }, | |
| { | |
| "distinct_value_count": 537, | |
| "group_name": "substr_dob_1_4_", | |
| "total_non_null_rows": 39175, | |
| "total_rows_inc_nulls": 50578, | |
| "value": "1855", | |
| "value_count": 1299 | |
| }, | |
| { | |
| "distinct_value_count": 537, | |
| "group_name": "substr_dob_1_4_", | |
| "total_non_null_rows": 39175, | |
| "total_rows_inc_nulls": 50578, | |
| "value": "1851", | |
| "value_count": 1204 | |
| }, | |
| { | |
| "distinct_value_count": 537, | |
| "group_name": "substr_dob_1_4_", | |
| "total_non_null_rows": 39175, | |
| "total_rows_inc_nulls": 50578, | |
| "value": "1850", | |
| "value_count": 1164 | |
| } | |
| ] | |
| }, | |
| "encoding": { | |
| "tooltip": [ | |
| { | |
| "field": "value", | |
| "type": "nominal" | |
| }, | |
| { | |
| "field": "value_count", | |
| "type": "quantitative" | |
| }, | |
| { | |
| "field": "total_non_null_rows", | |
| "type": "quantitative" | |
| }, | |
| { | |
| "field": "total_rows_inc_nulls", | |
| "type": "quantitative" | |
| } | |
| ], | |
| "x": { | |
| "field": "value", | |
| "sort": "-y", | |
| "title": null, | |
| "type": "nominal" | |
| }, | |
| "y": { | |
| "field": "value_count", | |
| "title": "Value count", | |
| "type": "quantitative" | |
| } | |
| }, | |
| "mark": "bar", | |
| "title": "Top 10 values by value count" | |
| }, | |
| { | |
| "data": { | |
| "values": [ | |
| { | |
| "distinct_value_count": 537, | |
| "group_name": "substr_dob_1_4_", | |
| "total_non_null_rows": 39175, | |
| "total_rows_inc_nulls": 50578, | |
| "value": "1092", | |
| "value_count": 1 | |
| }, | |
| { | |
| "distinct_value_count": 537, | |
| "group_name": "substr_dob_1_4_", | |
| "total_non_null_rows": 39175, | |
| "total_rows_inc_nulls": 50578, | |
| "value": "1085", | |
| "value_count": 1 | |
| }, | |
| { | |
| "distinct_value_count": 537, | |
| "group_name": "substr_dob_1_4_", | |
| "total_non_null_rows": 39175, | |
| "total_rows_inc_nulls": 50578, | |
| "value": "1338", | |
| "value_count": 1 | |
| }, | |
| { | |
| "distinct_value_count": 537, | |
| "group_name": "substr_dob_1_4_", | |
| "total_non_null_rows": 39175, | |
| "total_rows_inc_nulls": 50578, | |
| "value": "1262", | |
| "value_count": 1 | |
| }, | |
| { | |
| "distinct_value_count": 537, | |
| "group_name": "substr_dob_1_4_", | |
| "total_non_null_rows": 39175, | |
| "total_rows_inc_nulls": 50578, | |
| "value": "1295", | |
| "value_count": 1 | |
| } | |
| ] | |
| }, | |
| "encoding": { | |
| "tooltip": [ | |
| { | |
| "field": "value", | |
| "type": "nominal" | |
| }, | |
| { | |
| "field": "value_count", | |
| "type": "quantitative" | |
| }, | |
| { | |
| "field": "total_non_null_rows", | |
| "type": "quantitative" | |
| }, | |
| { | |
| "field": "total_rows_inc_nulls", | |
| "type": "quantitative" | |
| } | |
| ], | |
| "x": { | |
| "field": "value", | |
| "sort": "-y", | |
| "title": null, | |
| "type": "nominal" | |
| }, | |
| "y": { | |
| "field": "value_count", | |
| "scale": { | |
| "domain": [ | |
| 0, | |
| 2231 | |
| ] | |
| }, | |
| "title": "Value count", | |
| "type": "quantitative" | |
| } | |
| }, | |
| "mark": "bar", | |
| "title": "Bottom 5 values by value count" | |
| } | |
| ] | |
| } | |
| ] | |
| }, | |
| "image/png": 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VQ5XIALJdFnJM5hYiQYatGrENmcdwjy1kmEkphd/xnSSn3ELe+OY2kDi+0+V9gdhWvhPixu+CvpwUy3d/yS2LZAjRj1JJbJnUQpCqbUWGqEPY+V4xlHCgFe8lYwVxCd8HV5qRLBCkNRBWfg+eZNQoZK0hvBA4ShJb/s9iAu2iQN74jpZFAIgvpILDdkLbqAOSFLYiQ+wg4JA2JvJs5axny0rdG+nWiNiSEYccY4eQzeQdZE+xfbAvmXTwCRn7ynrrYUBbWWhJ4huyv9iOhZlwyBDvxlZ8Qx6+sQxtDsQ2+GgghHyvCYZsZ2WhpNpWZOoI332H+jhgisOw2GLfyB+baS/EFn8NW5EDGQxbkVloYgGm8hvbtO0I39iS/Qfb5De2+F1l3+Zb13oYkx3FvhQWG8jg1iqhLbX8mYPCyIzj52TD2dFAJh17840scSXEnbCoE7YiYwP6AAQcYsvCDYtF4eA62oqfIK8iBISAEBACQkAICAELAiK2FvRykGV7J1uR2bbHoTgcuBIKk9vp06f7rEnYKptWJSbQfKdHBrCycNIpWeWw9Tf5+0bvhFQxEad+vpOtdkVLWh15ju+An3nmGbfEEkvMc3JymnrY6ggZRZfwPXESP+omY8q1JpWnspLJDdtrkyc6p3kvOPBNa7hGB5l6tqysE5zr6ZZGh2rPoBf1rrzyyj6L26g0woCsKf7CYkOlD9J+SDRZVYhbOCit0Tub/T1+zHvw5crtrM36Y6P2Nqtbu5+vhzFtARsWWvi7WqF/1vpeOvk8dRCH6EfN9oXK9wab0Lepjz6iIgSEgBAQAkJACAgBKwIitlYEJS8EhIAQiBgBdgBw6nC1wgIHWX0VISAEhIAQEAJCQAh0OgIitp1uQekvBISAEBACQkAICAEhIASEgBD4lCMgYvspdwA1XwgIASEgBISAEBACQkAICAEh0OkIiNh2ugWlvxAQAkJACAiBSBEI17tVU8/6vXZsTQ5tLVu70uD8aW57Gnxa+Uxln/o0+lsaPD/NPknbP61+IWKbpnc08QzOxKmwOhClCdBa/Cg2eOmll9wyyyzT4prrV8chStUO48pViYhexgFYjfoBd96mOcQqomZFqQpYczhXuw7oirLRUip6BMKd2rUU5WC/VsdMrqTipgGuWeO08lA4oZuT3jmlnnuTOa2fU71bWRqdWN/Kd4W6uD2AE8o5DZ7Ty4so3JjAfea1bikoQqdWvJNT+znZfvDgwf40+BgKN2ZwD32ycLo82HPCfZrDAP/3v//50/K5Bm7rrbf2VXEDAQd/chsGh3W2s9xzzz3+7vlk4cDSSy65pGWvLconw+0j3ELB4YhFFK5V5FpHrh4MV+oVoUer35nWR0VsK5Dnrk2uimmmg3GvarhH9pZbbvHXyEydOnUem+Lw++23n7/DsR2FyS0k4d///rcfuOuV66+/3o0dO9af3Lv//vv7eze5//T888+fR4zBf9dddzWpzD20XMUC4Wx3wXZcJ0KQp31cJ8RVI1w5w329lSfnJvVBhtN9uSooFCZE3H+7/fbb+5OpKWDN/aDczxpWxbA7g0K9q1Xa3fZG9SftwBVEXJuUvJankXza3z/++ON+IlC5svzuu++6yy+/fK4/cVI1JxpzKnWnF+4JxrfwsWThihv6/I033jj3bmB+z1Vd/Jzro8KJ0lypwxVYDz30kOOKMPwp3ElcD59hw4a5TTbZxPf73XffvW4/4+oqJmtcEUa8avR8vfcmY5/Vfu30R6tuks+GANdkhYPLmEjj39x7zKFlFOInV821onDa+2233eYJHnePc00Yd6hTGAd4J+/n+ikICwWSy/9bVYogtuFatosuuqhtc4tG+IR4XzZiG66TY17A1YYxlEBsuQ6PMZbFYeZuXO/HdY1cq9eoPPDAA/46PcYXrnukMG+54YYb/Niz1lprNarC9PtwxRx9L4x9kOk///nPpnqTwkX5ZCC2Tz/9tFt11VVb1p5mKmLO+/3vf98vgBx55JHNiEb9bFofFbGtMCPEk8l3M5N9JrJcrcE9tPWILXc8kkVs9Qp1aEIgtpAV7jGtVdBx55139is5Sy+9tBsxYoRfvUOvcL8tsuDAialgsccee5gcHmK4ww47+GuA2lnCBAYMlltuOU+mmbywqs3dmwQ7Anfl1T78jLaDSfLuVyZl3FkLAWES9ve//92vwkGemSQdeOCBc5vDxGrDDTf01+n07Nmznc3MXHfSDqzmTZw40RPNVpdaxPa1117zq8HPP/+8v1KqLMSW+57pU2So8JNQkncak9Gn/3PNDZMJfOjmm2/2V3b17t3bi3BfL0SYRRP8iIEJzBpN/gOx5e5a5OuRYSYv9Hnqp780er6ebyRjn9WH2umPVt0kb0eAu5hZCIFsQjop+B8xlwkt16F96Utf8lkGyC8xGT/dbLPNHNe2sVpP7GUBZN11151Hod///vfukEMO8WMxsTlJbFmwZQE33Mk+ZswY31fJ2nJ/cyhci7Xjjjv6heqrrrrK91cWA9ER3ejb3BHNWM/ElcUnyBz9IFztxrshG4ybkE3ewyISbSBGkFEj5s6ePduRWUE3MmXcXU1/5752CuPVZZdd5ucUEA0Wn8nCVJZAbGkbfZqFWBa5aBfXeYWFVhazibmMYT//+c/9WJXMmkGORo4c6dtM2ym0kwVw7szebbfd/PPELmISWUziDm2rJBG0u17bwYgsM3dkU7D3b37zm6qLzsxraAt3ppONJ8PIXdjoT2HBENzI0C211FKerHEHPYQJ7Bn/zzvvPL/ox8IH7+UZYiwkEfvShquvvtovNnKnNvjcfvvt3v8gjJ/73OfcqFGjuix423tEthoCsU2S7ZCdJHMb5li1cCFby2Imiz88z4I898uDIXbhXnvwxh8h9ixOMU/AL7m7nn7EYv6hhx7q++iQIUN8n0AWTOkHyGEr5lL4TmWhn9KX6G/1rqpspU828oV6PklfhSzir/jTV7/6VY9DtR0fgdjig4y1xDXmofgd7YFocj0iuFJGjx7t+9QPfvCDLgmk3/3ud77/M28ONxdgK/r4aaed5ufrRxxxhLvzzjs95vQHYgB2SxJb/HaLLbbwsSAsepxyyim+LcyLia/USV133HGH7//EGvys2o47rnj82c9+5usinnBnPXGJ7H+9eE5MbKQHySOuNiV+sUCJfzL3hpedfPLJ8/goMahaEbGtQCVJbPk3AwoZFTo2k06cMEmKmNDutddevhYcFqciaJDxY2CDODJ4MRBDgjAcjoocHfvBBx/0TsRAy2pvsuAkODsBitU1nJugzkQYsoVjEsjJFNHJ0hJbnIZATkenEOxxIN6RLDj95MmT/QCVLHRYdKBzUAiqdDgcnewbEw8IVNCZQStJqHBUBsQwuWH7FH8IEtOmTfOTEwZfJg2/+tWv5q4e8u8LL7zQEwMCLHhXfkPAQAwZJVCgCxMh2sBqPR2SjBq6EHySBfz5Oe+lLWHAB2f0wJ50fMhyCGhMOCrv6aXjMgHAdyoLiwcMGBAJ7Mg7GFDA99lnn/Vkj0CCvxD0wuDAokK1IMNzyCAL9vgewYCBgmCFf6AvAwyTL/yIbH5YYKhHJMgaElTxKZ7/9a9/7Sdr+A7YY3t2NoBJaCv9BDn6B+/DXpUZW/QCY1ZqCfrYhYDPRIr6aGfY0UBWkQkjeBE8wQNSnBUv7FGtXfycSTcrnEyuwI3+UMsPK+2KDSCSrJ6zQpsktkyI2ZLEpCkQW7ZfMgGgfyeJbei/rKZjS/DgLmlwYrKdLPQ/+i9buhjQmBgzwSDQ0//wf+oHWyb8+D3+xnZB+hl9g8GW2BGer+VPxJhqsYhJSTL2Ja8NQl92eYQFMXyJiToT+lr9IOmP9WJELb+o1t6ddtop2+xRUi1HoJLYEhvYMkzcZZxcbbXV/GSbwuSReE0coaywwgp+gsjkk74J2aj1qQl1EN+TxBbfZGKEHzI+Q7DRh9hWmSXCd4mVLDxBoMIk/KyzzvL6sGiMDhAg4i5kKGwDTmZs6VeQTuYM1EG/J95Dkohp1MecgLrQl/5BgTCz4EfsZgJMP4U40MdDbEgaJxBbfkZcZTwAJ8Z44mqIufybn/FO3s1YylgXCgsCffv29bJMdHk3ZBCsw5ZcYj0/h/yExX/iG7LEv5CxZY5Qr+3MpYiJYLH44ot7e/Bv5lmVi87EMOYi+AjjBbuxKMRTnmfMpfBufpdsO+MV/sU8C2LGO3l3WOBg7kU8ovB7iBqFsQy9GH/4GXMZFkGsO9da0akCsWVexbyScQNCgo3Cojxzulq4MI4yhuPjYMq4h93BGV8mZoI3CyH8jML4G5Ie+A4y1M97KPgEY0ooyf9X25LLPBh/B1fsSD9jXlW5c6uVPtnIF+r5JItxLBCBOfNB5otgVy2JEYgtWNC+4K/MTWlnmOfTn4kHzFHBrnKnJXJ8TkHs4w55djtCZokXxEYWDIhn9EV8lXkCOvFcktjiw2GeGuZjIQYQE7AFMQ19IOy8C32wMbZOFuTBgBjLDkgWUZCj7xCb6sVzYkk9PYhNyfl8sj8yx6KfVvporZ0FIrYVkSZJbIODEsBZbWUyyioexg+FTsmqHtlNJsWQKLaDsCKI4xFEWO1BjvogSAQOJos4FZkYVr6YIBI4kwXyxyAFGSIIsHLKIAMJxIkgG0wuydIQdAjsbEVulLGlY+EgBCUcDZ0CyQzvD52q2jdQITsVVtuOO+44P0Fn5QjnDatZTKhDlpRV0UComLSDZZj08n8m2GFizioQq0wQVCbjIatEYMG5wZgt4xBLOn6yYD+CBYEZeeyQzBLTdohHre3gBDeCciC2TFzQlXr5Ge0gc4DdqmWxmcCwDT1s7wm6kbljpZ6VYMgHhGfgwIHeP5jQswLG4MHABKEh2KDnF77wBT/5IOgjW21CQ1sJMgQr/qADGIYtvpAg9CU4EIgbEVtW/CE/rBgiR8AhUKIfEy4mIKwW478XXHCBn9QQTNmmzUIA9mTln0GyktiCJ/WADxMu6qc+CCd9hAGD9qMzE1fIG37BpBQ74mNZ8arVLvoj/YYBg0GANrDwUM0P+VmykNmh74SsBe0NxBYCT9+izRDeQGyDPPowEU1mbMN3yZA3+jsLCAxiDFzJwqBD/2WSw3uwA9gltyITj+gj4IXP4mNkWrAlk236Jv8PW5HDBLnSn/C7arGIyRSxK8S+5LYr+gwLXmGFGFvzh35erx+EHQS1YgSTk1p+Ua29+FKPHj1aMZ9UHUYEKolt2A5JvyNWMBYR9xnXiHcsEDKmEY/pKxTGFsYm/L1aJohnqhFb+iFEDsISdvGQgWXiSV9LFp6hP/OHPoi/oR+ZLmIoYy2xG+JJ/4Mg0HcYU5shtoE0EgvRBTLIgib1sABF3KPtjBXMF+hnxEom1tXGAeYQyKMnk2UIHmM0cZoxhHkCE+HQnmo7QcI30Uxomcew2MAEE2LIxJm5BZNX4hQkj0ktMQZilJbYEucZoynUR/8Mi22M2WExI7QxYEpsYDwEH8gmuLB4gF5h8YB5GlhRmCdAJNIQW8Z2xl4WTfEr5FjYiHkrcrXuGL6pDFs2a+GCj9Tbisy4FLJ1YVGABSDGSogWc5xAbFkI5ufUh13ClviwoEJ/Cd/xBp0DmWMuwByXeS2+zphRmSxolU/WI7Ys0NfzSdrInAd9w0I+Yz2kkjiQLIE3BBzoH/SlsOhDX6SfMr8CI/oYC+N8LldZkgtL2AHZsHgBUSZpEeIZ8YJC4oeFtrAVuRGxRYZ5L4QVOeYbIcnFnIv4HAqLJ8QffIAFOBahmD8wzvIeYkOteA5+aYktMZwFe7gO/CsswmkrcsZBuJLY0iEDoYBEQagqs3GVW5FxYhwOEsDkkUkhDhKILQGBlUcMDcFigIEcJL+L5Xm2JzJ5xfkZSJiY8jzBPRkscCy2vuLojYgtegVHZesJQYVJQgheATaIFZNLSGtloePQtrCljEEeUs7kATKLLCsvkFAGCNrGZKQRsSWwsQKJTpAkAgdFKETXAAAgAElEQVSDMIMsiwN0PgY+AhSDHANY5bZubESWG5wILAxYrI6HQpsYuJJbiJPtqyS2vJd2MnkBZwI+K/rYhUGZwRNiEwYBbAAZgUAmCzaGgLAVjUGFSQxb0yHKDEaQIYgPh//QeVl5C98706nJfibbQd1MppgkQPYoDPR8PwleFmJLu1hMgIAzcWGCh+0gq9g9+CQTJyYdPE9QZhAl00IJW/0qiW21rcgsijCxISOD3tQHEWNBh9VDfIm2s6oIRvhrFrxqtYvMP/0mDET0hVp+yCCSLNgaXemPLHAFYgt+6HvNNdf4gQs/TkNsQ91MFJlMM3DSj5KHeYR+wfY6fIXYAPFlcSBJbPFxMiEMVvRRBgsmqfShsBU5+Y1tLX/iAJ5qsYgJBxO/8BlGEhcyyeiOvVnUof2sSNNn6/WDRsQWYlHLL4hV1dpbb6tbxmFCYhkQqCS2jBWQVzKfYUsqcZb4yiSLBRYmg4FkhBiHn/O7yoW+oFI1Ysv4hK+yqEO2Kkw2majSf5MlmSUirqF3yH7SrxkHQnY1yKELOtUjtvguE0LIKbGoMjMZ6qK99DnGuZBB5Hf0J2Jf5fkZYUEqSfYD1owv9P3wKQP9g0k04xykvbKEg5IY04j5ZDZDho7YjhwkJFmI3/TvesQ22XbiEfpVK5XfCYddVjxb7XTtQJCS34UyxvN/xkbskiS2tBlCXpmxxc7ggl2ZvwR8Yia2xEHaweIq+ocMLfMn/s3Cdi1cmC/WI7bE6XAIFXMgxmB8PyywssiCjzAXCHPHkP0OpCRkQJOfHgSbk8WFDLGAE8Yv5hPVvu9tlU9WEtukL9Cn6/kkmUG4AGNysp8STyrHl0BsQwY2zJeRY9cb4x8cIdnHQlyq7BPYGH7Bwj42JqkEuaSvsUjFwnJSJ+RZzGJRph6xZY5MjGHRC39J7rZK6lD5nTBZfOJ1MmaH5xvFc+JEJbFN6pHM2OLTzIVZcGAOH/ARsc0w8CJSSWyThz1tu+22fmWqkhRVEtvkYSwMLAzSBOVAbFl1CQ7LOxlACKBMgEMJ30wwICRXhMLPWZkMk10GNkgSK7+NiG3I3iVPS4N4E8TIeFCYXDIYhq0S1aCkDTgpEwYG6pBVYhBkgKezhe0ojYgt7wpbXgiGlYUOyAo+xDnsu+f9THAqgwoBOJB+OhoLEcmMLZMCyAiYVyuVxLbyGQgpQQ6SwkSathPksTMlkPnKb4l5hoUEvo0ggDPBYXEjENvkt65hlS757uS3M+Hn2B3iF7aKQ375hoP/J4ltWMVOm7FlNRayx0pZWAQhgAZimwx24P3II4943+GdLNZQ0IOJQhpiG+oLp4lTH6QeclxZyNyAcRa8arUrENuQEQjb1ar5YfIbNxYvyFIyoWQLFbalsMCA/tSHj0DuWE2lXlY0Q0ahWsY2+U4Gf4goWSwWtUJJkvywHTN84pAktvRfJnFMNOiLZFGZCCW/sa0kttX8iTYwAamMRehT6xtbJkP4N9k3+j+2ZAKUth9UZmxDjGARp5Zf4H/V2ptxKJBYixGoJLbYkv4QMkC8jhhGVp/sImMvxJZFvvCZRpjYsKhT7XMP6qhGbFl0w/+ZKJHlI2PCGML4wBb8yhKyRMQwyCDZKsYM/J2xm4VT4jn9nLGrGrFlTKfvMmaEbzmZC4StyGHxirrpX4wLLO7xcybAxCsm/8QBtvAz6U+S/OQ4wDvYaUIMShKFkHFhTsI3qpXtqWbiMP6ErYAsxPIpA5kaJtXEePQAD7CuRmzp97XaDtEhbjK2EBP4po4xirby7uThjowffLtHLINY0ccZ3yAN2ICJMUQhnAMSPt8I8xjGLcgqz+Br2J54X0lsGa9YFO8kYpv8xjYsHISxF6JSDxfITz1iyw6wQELCzq+wWBnmIiFj24jYgilz51CwI7pjZ+xHYf6JjWsd5tYKnwzbn6v5Agu99XySxRzGcQpJGogWc9zK7fz8PhBbFrVJZISFchammO/RP8kOIx+2Koc+Vtkfw+cL4cC7sHsiuTOBBRzsRdygVBJb4mlIvIQdTJB0+i3ElvGZ3R7EWtpDwZ/oe/SJ5K0V4fO+oAfjPPNQFq8h6/ypFc8h2/X0SBLbMG8UsW3RIFxJbJnIhi2naYlt8lTkasSWwZnOTWcmWOAY4UCJ0IyQWQ1BAweCNDKYMElNBgDIGBNBgngjYkv9kBEGOVbLKWxdZQAMBIkBCXJR73Q9VrnpDLyTgR9SHSb6kM+wsstgWI3YMnFgYMKBWZmhg5EVYgWd72wDoYIcMEFm1YstS/xhMs7AxMQCwpssrJiHbxkC9mHLVVg5q3cMez1iSyDCFyDObB1jxYyVMybdkBaCC4GBSVfltpKwfZvtd8gymcIG1YgtE0AG4ZCRwFfABBsnC35DAAqZ3UpiGwgjix4EorTElgkgNmVhAOzBhDYGYps8xTgQWzIuvCdkXpiIgVUaYhvqSxJbtgOCM4MQhYkeB5QwEDBpShLbtHjValcgtmELf9guXc0PAylFpzDRDDYJJ6GTfWZlO/gAA1nYasX2o7CyXkls8Uv6N30+HD7GohPf2YaDJnhXyJQnd1kQPxjAksQWvCDcLDqxGMDEF9x4T62MbTV/YkAKk1D8KMQiyDoLGtUytugZdhAwWSd20mfT9gN8vVqMYGJdyy/AuFp72eGiUjwClcSWRQ5iJv2f7AU7HCCDFMgPE+uwLRWiSwwgzlLCN6DVWlWN2IYJGX7MwisTU/ylctId6gsLc/w/fNPG2EofI84yL2ACS1aE/sjCEwtAyYwtYyQL4cgz1tL3KYHYBvLMhJdJHeM7OtFXmSjyM4goC+lMptmlUo2IJ7+xZUwFG7KUyW+MA2EL7WFiDKGsVujTxHpKyFTz75AJRU/mJ8wTKPRHFt+TGduwbbVa24lBTNT5G33BI1xJUm3xLGxPpz1MtLFfiIOME8y3eA8kifEU+zNOMIZhL8YL5kxsbWTMpqQltmHnEfMS3lvrsJo8e1f4xhbcSIYwvjD2Mj8Ji0C0sx4uzB+J5eACPhAw/hCfGRsYK5l/MJejj5KNZRyAfIatsGmJbbWtyIH8YXc+Y2JxGF1YsKh2/V8rfDL4UTVfYOGrnk+SRMEXWNChTzPHgxgm59HBB0Lb8EmSMfgouIFzuDEhEDZkkidTV/OjQMj5Xdg9EXYy8A5sjd3CHIG+yUJP8lTksCgF8QxZY+pj3k1fCd82M6dm3kNb8Q/iTuUVgqEu4hdzamxHnCZm14vn9N96eqQhtpU+yqJXtaJvbCtQyUJsCZ6k89naUHkqcjVii3H5VgjySCdjYGKSWnmIBQSYiTQZRhyXgMZ7mFCy2sXKMYMqzsSqDfUGYoujkumkDt6RLHROtqxCXCBNOCWdInxnC4lhkKi1XZe66BxkjHl/+G4pbBlhcOJ3OD4BgBUk2he2IkOUCMaQQFa1wDwcckF7CQBMwgkcbEtjgg6uDM4cogEBBr/kAVihfaxEMYFiEAo6EqQJKKysEwRYBYNw0Xkr8alHbMlOE+DZ0sJEhQkJ28TBjywaq8nYiAGmMguADQkADCgQbbBmYgH5qTzEiTrAg0BB4GIyA7mh7clSi9iCKRgxaGADFi54d1piC+b4D6uqkDewZrDDzytPMQ7EFqzBgUkZExwmotivktiGHQOBdCXrSxJbBjsmE3zHGcgsCxa0gYllktimxatWuxjAkwtCYTW0mh/SN2oVfIwJafLwKJ4N268bbUUO2Qm+K2HywASVBQ7iSuU38Pgt5BkiAEYMjpXf2OKrDNbgQz+lT2JP/J/t+GDJwkHyG9tqxJZFq1qxiAl9iH2VuIQFFX4eJqxp+0GtGBEWv6r5BXGxWnvbfXVFnpPaTn5XILZJMsniDnZLbm9lXCPWMqYRU5KHlND+Rqf0h8xSktghF+52DBiyKEsfrzyAkN/TFyHa6JU8JZ9FIeJSOCiHOMfkNmRjksSWmMinC2HbMuMuugdiywSUbdHIhxKyv8RC+mjydgZwYNEtfAuYlGG8I06HhUAIDvE3XGWUHK+J5WEBoZo/JbN/IVPNcyxaEpvCjiPiPG1DZ2yYvMeWxa56bWe8BI9wsA7jHG1li2NlYa5C/AlXNPEscw7GenCi7clPpsIhUWHHGJP5YC/iJHOpSmLL3IVxhsUI/CZsRWaHHD7ImF55gnZRfbHWPbYQKgggftIIl+Rp/fgI8xLmF/gzhXGc3QUkL9jhE0rYJs+4HbJ+jTK21YgtC0f0v3BYHLZinKn8fjy8txU+iQ/U84V6PsknZywUhP6FXvg3Y3PlJwWB2CYP3KJv0r7wSUBY1KYefo5f1irJHWTJnZosyLMDgUL9jOvEKz41IqmRvMc2fOLBs8QGfIS2UDd9l98Tb8K2Zn4fkjWVerH9nLkFcYBCPGOuymcB9eI5zzbSI8TiMG9kDsuiQtiKXOmjta7WFLGtsBokgqBMkMVB02Rsg7EIlgTmehnbsLU5fH+Ak4XtNeH4+qASnZ+OGAYSVpXoTOGOvuBYrAoxaOMM4R5btiXQiap938B2Q1adwkCBzhApiFZYCQqBvl7wDivO4RAp3k+94EFhAGXQJoPF6hErT2SGQ0aMZ1iRZuLPSlE4rCN58iAdl9UtiA7fPbLyxcBGMGa/f+WhOpBE2hW+HwqTI96FHHhgV2zMNuZKfMiQEWTpTMlCYKM9ISPJgEdg5m8G1XAYVfgIP5w4Heog6DBQsOWEwqIB3w5gN4JV8j5ZyB92DicOMuAykFeuTlUjtkwUeDacSsm7woSKwYHgHuxQ695Q7BMOImChgechTeH7rMqMLYMikxraDqmm4FMMjtW+i0peYM72n8qMbdiNAL7h5G36CUGNSUal3mnxqtUuBmayEclTCcP3N8F+wQ/r9YdmiW24IoOMZjgpMWyRDP7KxIJJaOWx+yyYYRdiANjgs2TIkxnb4Pth9wP2IbuPL0L2iDssFtQjtsGfasWiZOwL29CTGKEPiyThO8a0/aBejKjlF7XaW89m+l3xCBAjiOuMDUyOwhbIYE9iK4ugLKBA6iyHgbEVjwUjYmnyUJRmUEBf6mA3S7XsUmVdLB5Tal3ZxZjLohd9uPLTGmSJjyxksyujMntS+S4ILH2s2rNhYaHyPI1m2g4hYv6AnaotCDTT9mB3sj7UV+1qkVAfz7IjhrkGWcrKZ6kDm7B4V3mYD3WAL/GOBeJmC4vwjDHYptO+16+HC5jiX+GaKnBhXgTGLCAG38Y/mTvQZyoPT2wWy8rn8VXeyZkqtb45b/SOZn2yni808kniFHM4fCxs/a2nHzENPCvPggnniSQPWmrUzmq/x37gV83nK59nsYN5NPOFan0NP6cP0UeIH/X6N3GGuEQsJg4mn60Vz4M+jfRohEM1H62UEbFthGLK3+PAYatsShGfNaVTJr+trZTF2cJAlXSeMEDzjV3yYJmkPJlKiHq1rXhsJ2SiQKBu9b26DEAQTlb10JM/lQGRbWgMFtWCA4EgTGKSkwFk6Ez8rNZVD2DKSjLyARcwRg4SVW/wrGc3gjsdNvn9T7VBnE4OQaoWaMKEiCCOHgQ3FhOqDbY8S1vCSZhpJhGV+lA/JdyRmtYveY734nfoyrsJ5ti00eDDAgAT01r2CToQkBvdzcqzvJdgjO0qT0tMtictXs20q5YfNoNjlmchq/QhVkLr+SuTljDJrDXBJi5hEyaO9MdQiCsMEM1M7mvFomZjX9p+UC9G1PKLWu3NYgfJFItAkthWXjlXrGad9fbwiQPZnGqnQHdWa6StEOhsBMiEkgQjO8ouuMokSme3Lg7tRWzjsEPLtWDSy1Zkts9kIUUtVyjHCtm+wDeK4budvF5NgGIyHg7Cyuu9eo8QEAJCoGwIsPOGre7s0mC3jEo2BCC2ZL3ZHcWZFJWnKmerVVJCQAhkQYBdbXwrG3YytjoLnkWnssmI2JbNomqP/6CdLZJsbc6z8M0ye/47bbtSnhjpXUJACAgBISAEhIAQEAJCoB0IiNi2A1XVKQSEgBAQAkJACAgBISAEhIAQEAK5ISBimxvUepEQEAJCQAgIASEgBISAEBACQkAItAMBEdt2oKo6hYAQEAJCQAgIASEgBISAEBACQiA3BEpLbLl/jbuYVISAEBACQkAINItA5f3fzcqX+XmNr2W2rtrWCgQUP+ZFUXGjFZ7V2jrK6KelJbZ7HPV//i6mdpSTvru5uVqun4nZoaSfzcTCT/jZELBJy//KjZ+tdXbpX/3qV+7www+3V6QahEAJEYg9/hYFueJGUchXf29Z/bS0xHanI8a2zYNuOGmYue7YHUr62Uws/ISfDQGbtPyv3PjZWmeXZoL6n/nX+KSiOdnrPPew7bMLS1IIRIhA7PG3KMhEbItCXsQ2LuQzavPTE05zu+22W0bp6mI/Pe92/wsR25bCmqmy2AcO6ZfJrHOFhJ/wsyFgk47d/2yts0szQR0/vZ+5om7dnLv+RPtCsVkRVSAEWoiA4kd1MEVsW+hkLaiqrH5a2oxtOzrQ1396lZs9Z4770wm7uO7du5ncKnaHkn4m8zrhJ/xsCNik5X/lxs/WOru0iK0dQ9VQXgRij79FId+OeXlRbSnDe8vqpyK2TXhn2N58wv6bORutde7FF190yy677Ny3916kh1upb+8mtGnvo7E7vPSz2V/4CT8bAjZp+Z8Nv6KlRWyLtoDeHzMCsce3orATsS0K+ervLaufitg24Wft/G53w4EruCP22KgJbdr7aOwOL/1s9hd+ws+GgE1a/mfDr2jpdhDbF199syXNWnaJni2pR5UIgawIxB7fsrbLKidia0WwtfJl9VMR2yb85Mjz/+5mz57dhETtR2fNmuUWXHBBN/Pt99x/X5rhRGybgzX2Din9mrNn5dPCT/jZELBJx+5/ttbZpdtBbFuxcKxvdu22VQ12BBQ/qmMoYmv3rVbWUFY/FbFtpZc0UVdwqHv+9Zw76bK73ZcHruB+qoxtagRj75DSL7Upqz4o/ISfDQGbdOz+Z2udXVrE1o6haigvAoofIrad4N1l9VMR24K8T8TWBnzsHVL6yb42BGzS8r9y42drnV1axNaOoWooLwKxx9+ikFfGtijkq7+3rH4qYluQn4nY2oCPvUNKP9nXhoBNWv5XbvxsrbNLi9jaMVQN5UUg9vhbFPIitkUhL2IbF/IZtYm9A4nYZjTsx2KxDxzST/a1IWCTlv+VGz9b6+zSIrZ2DFVDeRGIPf4WhXzs8/KicCnqvWX1U2VsC/IoEVsb8LF3SOkn+9oQsEnL/8qNn611dmkRWzuGqqG8CMQef4tCXsS2KOSVsY0L+YzaxN6BRGwzGlYZWxtwwk/4tQQBWyWxT/xi18+Gvl1axNaOoWooLwKKH9VtG/u8vLweKWJbCtvG3oFEbG1uFvvAIf1kXxsCNmn5X7nxs7XOLi1ia8dQNZQXgdjjb1HIxz4vLwqXot5bVj/VVuSCPErE1gZ87B1S+sm+NgRs0vK/cuNna51dWsTWjqFqKC8CscffopAXsS0KeWVs40I+ozaxdyAR24yG/Vgs9oFD+sm+NgRs0vK/cuNna51dWsTWjqFqKC8CscffopCPfV5eFC5FvbesfqqMbUEeJWJrAz72Din9ZF8bAjZp+V+58bO1zi4tYmvHUDWUF4HY429RyIvYFoW8MrZxIZ9Rm9g7kIhtRsMqY2sDTvgJv5YgYKsk9olf7PrZ0LdLi9jaMVQN5UVA8aO6bWOfl5fXI0VsS2Hb2DuQiK3NzWIfOKSf7GtDwCYt/ys3frbW2aVFbO0YqobyIhB7/C0K+djn5UXhUtR7y+qn2opckEeJ2NqAj71DSj/Z14aATVr+V278bK2zS4vY2jFUDeVFIPb4WxTyIrZFIa+MbVzIZ9Qm9g4kYpvRsB+LxT5wSD/Z14aATVr+V278bK2zS4vY2jFUDeVFIPb4WxTysc/Li8KlqPeW1U+VsS3Io0RsbcDH3iGln+xrQ8AmLf8rN3621tmlRWztGKqG8iIQe/wtCnkR26KQV8Y2LuQzahN7BxKxzWhYZWxtwAk/4dcSBGyVxD7xi10/G/p2aRFbO4aqobwIKH5Ut23s8/LyeqSIbSlsG3sHErG1uVnsA4f0k31tCNik5X/lxs/WOru0iK0dQ9VQXgRij79FIR/7vLwoXIp6b1n9VFuRC/IoEVsb8LF3SOkn+9oQsEnL/8qNn611dmkRWzuGqqG8CMQef4tCXsS2KOSVsY0L+YzaxN6BRGwzGvZjsdgHDukn+9oQsEnL/8qNn611dmkRWzuGqqG8CMQefz/44AP35ptvusUWW6wpI8yePdu98847bpFFFuki99Zbb7mFFlrIde/evW59sc/LmwKjBA/H7qdZIVbGNityRjkRWxuAsXdI6Sf72hCwScv/yo2frXV2aRFbO4aqobwIxBx/TznlFHfuuee6wYMHu5kzZzr68oABA+Ya484773T777+/GzRokP/Zzjvv7HbddVd30UUXudGjR7vll1/eQYwvv/xy161bNzd8+HA3//zzuylTpriRI0e6vffeu6ZhRWzj8vmY/dSCVNuJrVaGqptHxNbits7F3iGln+xrQ8AmLf8rN36hdUWOr+On97OB7Jzr1s25608c5uvZ6YixLa3PXJkqEAIZEYg1/r733nuuR48ePltL1vX44493L730kjvjjDPmtvT8889377//vie3EFYKcWaBBRZw06dPd71793YHH3ywW3bZZT2xfeONN9wJJ5zgpk2b5n9G9nbhhReuipyIbUaHapNYrH5qbW5bia1WhmqbR8TW5rqxd0jpJ/vaELBJy//KjR+tK3p8FbG1+Ziky4tAzPH39ddfd3369HFvv/2222KLLdwhhxziM7KhjBo1yp1zzjmesA4dOtRndClbbrmle+aZZ/y/IcKPPPKIJ7b8HPk5c+b4rcg8079/fxHbDnDvmP3UAl/biK1WhuqbRcTW4rbK2NrQE37Cz4qATT72ATV2/WIYX0VsbX1A0uVFIPb48dBDD7l99tnHffGLX3QXXHCBW3DBBeca48wzz3TLLbec22qrrRwkd7755vPZW0juk08+6Z+79NJL3R133OG3Mu+yyy5uyJAh/ud9+/Z19913n+vXr58bP368u/vuu+cxMvWoxINArUWIeDRsXpO2EVtU0cpQbYOI2DbvrEmJ2AcO6Sf72hCwScv/yo1fDOOriK3NxyRdXgRijr+33nqr/y729NNPd8OGffQZQLJwOBQHQVEeeOAB/8zjjz/utxdzeBRZ2lNPPdX/nqxur1693IgRI9yHH37oM8FsV651iJS2Isfl8zH7qQWpthJbFNPKUH3zPDr5dXfhzZPcmv36uO9starFlpIVAkJACAiBFiHQCSvZRY6vLSG2zrnR+6/nLXbIefebLdctUZ+5MlUgBAwIxBg/2C7MN7K33HKLW3/99ee2jm9un3jiCbfeeuu5jTfe2P3whz/0hPbkk092kydPdmeddZY/TIq/11hjDbfNNtu4Y445xs2aNcuNGTPGjRs3zl199dX+84gJEybURE3E1uBQbRAVsc0AqlaGaoOmjG0Gh0qIxN4hpZ/sa0PAJi3/Kzd+tK7o8bUlxFaHR9kcVdJRIhBr/J00aZJbbbXVumC21157uYMOOshtvvnmfmsxpJdtymRoeRZyu/rqq7vrr7/e7bHHHl52hx12cFdccYUntttvv72bOHGivwbo5ptv9qct1yoitnG5a6x+akWpbRlbrQzVN42Irc11Y++Q0k/2tSFgk5b/lRu/GMZXEVubj0m6vAjEHn8rkecgKU465kRkCtuKX3vtNbfUUkt1eZTnZsyY4U8/TpapU6e6ZZZZxp+cXK+I2Mbl853mp2nRaxux1cqQiG1aJ8zyXOwdUvplseonMsJP+NkQsEnH7n8xjK8itjYfk3R5EYg9flQiz1U9ENbkfbbtsI6IbTtQzV5np/lp2pa2jdjWUkArQx8ho4xtWhet/lzsHVL6yb42BGzS8r9y4xfD+Cpia/MxSZcXgdjjb1HIi9gWhXxnzqOzopU7sdXKkIhtVmdNysU+cEg/m5WFn/CzIWCTjt3/arUuz/FVxNbmY5IuLwKdGj/abRER23Yj3Fz9ZfXT3Iltc7Bnfzr2DqSMbXbbJjPetlraJx17wJB+NtsLP+FnQ6CzpRlfRWw724bSvn0IxD4+tK/l9WuOfV5eFC5FvbesfipiW5BHidjagI+9Q0o/2deGgE1a/ldu/Gyts0uL2NoxVA3lRSD2+FsU8iK2RSFf/b1l9VMR24L8TMTWBnzsHVL6yb42BGzS8r9y42drnV1axNaOoWooLwKxx9+ikBexLQp5Edu4kM+oTewdSMQ2o2E/Fot94JB+sq8NAZu0/K/c+NlaZ5cWsbVjqBrKi0Ds8bco5GOflxeFS1HvLaufKmNbkEeJ2NqAj71DSj/Z14aATVr+V278bK2zS4vY2jFUDeVFIPb4WxTyIrZFIa+MbVzIZ9Qm9g4kYpvRsMrY2oATfsKvJQjYKol94he7fjb07dIitnYMVUN5EVD8qG7b2Ofl5fVIEdtS2Db2DiRia3Oz2AcO6Sf72hCwScv/yo2frXV2aRFbO4aqobwIxB5/i0I+9nl5UbgU9d6y+qm2IhfkUSK2NuBj75DST/a1IWCTlv+VGz9b6+zSIrZ2DFVDeRGIPf4WhbyIbVHIK2MbF/IZtYm9A4nYZjTsx2KxDxzST/a1IWCTlv+VGz9b6+zSIrZ2DFVDeRGIPf4WhXzs8/KicCnqvWX1U2VsC/IoEVsb8LF3SOkn+9oQsEnL/8qNn611dmkRWzuGqqG8CMQef4tCXsS2KOSVsY0L+YzaxN6BRGwzGlYZWxtwwk/4tQQBWyWxT/xi18+Gvl1axNaOoWooLwKKH9VtG/u8vLweKWJbCtvG3oFEbG1uFvvAIf1kXxsCNmn5X7nxs7XOLi1ia8dQNZQXgdjjb5+Uf98AACAASURBVFHIxz4vLwqXot5bVj/VVuSCPErE1gZ87B1S+sm+NgRs0vK/cuNna51dWsTWjqFqKC8CscffopAXsS0KeWVs40I+ozaxdyAR24yG/Vgs9oFD+sm+NgRs0vK/cuNna51dWsTWjqFqKC8CscffopCPfV5eFC5FvbesfqqMbUEeJWJrAz72Din9ZF8bAjZp+V+58bO1zi4tYmvHUDWUF4HY429RyIvYFoW8MrZxIZ9Rm9g7kIhtRsMqY2sDTvgJv5YgYKsk9olf7PrZ0LdLi9jaMVQN5UVA8aO6bWOfl5fXI0VsS2Hb2DuQiK3NzWIfOKSf7GtDwCYt/ys3frbW2aVFbO0YqobyIhB7/C0K+djn5UXhUtR7y+qn2opckEeJ2NqAj71DSj/Z14aATVr+V278bK2zS4vY2jFUDeVFIPb4WxTyIrZFIa+MbVzIZ9Qm9g4kYpvRsB+LxT5wSD/Z14aATVr+V278bK2zS4vY2jFUDeVFIPb4WxTysc/Li8KlqPeW1U+VsS3Io0RsbcDH3iGln+xrQ8AmLf8rN3621tmlRWztGKqG8iIQe/wtCnkR26KQV8Y2LuQzahN7BxKxzWhYZWxtwAk/4dcSBGyVxD7xi10/G/p2aRFbO4aqobwIKH5Ut23s8/LyeqSIbSlsG3sHErG1uVnsA4f0k31tCNik5X/lxs/WOru0iK0dQ9VQXgRij79FIR/7vLwoXIp6b1n9VFuRC/IoEVsb8LF3SOkn+9oQsEnL/8qNn611dmkRWzuGqqG8CMQef4tCXsS2KOSVsY0L+YzaxN6BRGwzGvZjsdgHDukn+9oQsEnL/8qNn611dmkRWzuGqqG8CMQef4tCPvZ5eVG4FPXesvqpMrYFeZSIrQ342Duk9JN9bQjYpOV/5cbP1jq7tIitHUPVUF4EYo+/RSEvYlsU8srYxoV8Rm1i70AithkNq4ytDTjhJ/xagoCtktgnfrHrZ0PfLi1ia8dQNZQXAcWP6raNfV5eXo8UsS2FbWPvQCK2NjeLfeCQfrKvDQGbtPyv3PjZWmeXFrG1Y6gayotA7PG3KORjn5cXhUtR7y2rn2orckEeJWJrAz72Din9ZF8bAjZp+V+58bO1zi4tYmvHUDWUF4HY429RyIvYFoW8MrZxIZ9Rm9g7kIhtRsN+LBb7wCH9ZF8bAjZp+V+58bO1zi4tYmvHUDWUF4HY429RyMc+Ly8Kl6LeW1Y/Vca2II8SsbUBH3uHlH6yrw0Bm7T8r9z42VpnlxaxtWOoGsqLQOzxtyjkRWyLQl4Z27iQz6hN7B1IxDajYZWxtQEn/IRfSxCwVRL7xC92/Wzo26VFbO0YqobyIqD4Ud22sc/Ly+uRIralsG3sHUjE1uZmsQ8c0k/2tSFgk5b/lRs/W+vs0iK2dgxVQ3kRiD3+FoV87PPyonAp6r1l9VNtRS7Io0RsbcDH3iGln+xrQ8AmLf8rN3621tmlRWztGKqG8iIQe/wtCnkR26KQV8Y2LuQzahN7BxKxzWjYj8ViHzikn+xrQ8AmLf8rN3621tmlRWztGKqG8iIQe/wtCvnY5+VF4VLUe8vqp8rYFuRRIrY24GPvkNJP9rUhYJOW/5UbP1vr7NIitnYMVUN5EYg9/haFvIhtUcgrYxsX8hm1ib0DidhmNKwytjbghJ/wawkCtkpin/jFrp8Nfbu0iK0dQ9VQXgQUP6rbNvZ5eXk9UsS2FLaNvQOJ2NrcLPaBQ/rJvjYEbNLyv3LjZ2udXVrE1o6haigvArHH36KQj31eXhQuRb23rH6qrcgFeZSIrQ342Duk9JN9bQjYpOV/5cbP1jq7tIitHUPVUF4EYo+/RSEvYlsU8srYxoV8Rm1i70AithkN+7FY7AOH9JN9bQjYpOV/5cbP1jq7tIitHUPVUF4EYo+/RSEf+7y8KFyKem9Z/VQZ24I8SsTWBnzsHVL6yb42BGzS8r9y42drnV1axNaOoWooLwKxx9+ikBexLQp5ZWzjQj6jNrF3IBHbjIZVxtYGnPATfi1BwFZJ7BO/2PWzoW+XFrG1Y6gayouA4kd128Y+Ly+vR4rYlsK2sXcgEVubm8U+cEg/2deGgE1a/ldu/Gyts0uL2NoxVA3lRSD2+FsU8rHPy4vCpaj3ltVPtRW5II8SsbUBH3uHlH6yrw0Bm7T8r9z42VpnlxaxtWOoGsqLQOzx94MPPnBvvvmmW2yxxWoa4Y033nCLLrpol9/Pnj3bvfPOO26RRRbp8vO33nrLLbTQQq579+51jSpiG5fPx+6nWdFqO7FVB6q/BeCefz3nTrrsbvflgSu4n+6xUVY7tlwudoeXfjaTCz/hZ0PAJi3/s+EXpIscX8dP72duRLduzl1/4jBfz05HjG1pfebKVIEQyIhAzPHtlFNOceeee64bPHiwmzlzpoNsDhgwYG5LH3zwQbfvvvu6lVde2U2ZMsVdcMEFbt1113UXXXSRGz16tFt++eUdcefyyy933bp1c8OHD3fzzz+/f3bkyJFu7733romaiG1Gh2qTWMx+amlyW4mtOlBt0yhja3Fb52LvkNJP9rUhYJOW/5UbP1pX9PgqYmvzMUmXF4FY4+97773nevTo4bO1ZF2PP/5499JLL7kzzjhjrjG23nprd9hhhzn+vuaaazwJ/stf/uIWWGABN336dNe7d2938MEHu2WXXdYTWzK7J5xwgps2bZr/GdnbhRdeuKpxRWzj8vlY/dSKUtuIrTpQfdOI2NpcN/YOKf1kXxsCNmn5X7nxi2F8FbG1+Ziky4tAzPH39ddfd3369HFvv/2222KLLdwhhxzidt1117nGWHHFFd0999zj+Pvhhx922267rbv33nvdlltu6Z555hn/HET4kUce8cSWnyM/Z84cvxWZZ/r37y9i2wHuHbOfWuBrG7FFKXUgZWwtzllPNvYOKf1slhd+ws+GgE06dv+LYXwVsbX5mKTLi0Ds8eOhhx5y++yzj/viF7/otxovuOCCc43Rq1cv9+STT/rsK+3YdNNN3Y033uiGDh3qf0659NJL3R133OG3Mu+yyy5uyJAh/ud9+/Z19913n+vXr58bP368u/vuu+cxMvWoxINArUWIeDRsXpO2ElvUyaMDVWt27FselLFt3lmTErEPHNJP9rUhYJOW/5Ubv9C6IsdXEVubj0m6vAjEHH9vvfVW/13s6aef7oYN++j79mTZZJNN3KmnnurWWWcd98ADD7hjjz3WjR071m8v5vAosrT8nsI2ZIjwiBEj3IcffugzwWxXrnWIVOzz8vJ6ZPWWxeynFlu0ldjm1YE6eWXo0cmvuwtvnuTW7NfHfWerVS22lKwQEAJCQAi0CIHYV7KLHl9bQmydc6P3X89b7JDz7jdbrluiPnNlqkAIGBCIMX6wXZhvZG+55Ra3/vrrz20d39w+8cQTbr311nOHHnqoW3LJJd2oUaP8t7Y9e/Z0xx13nBs0aJA766yz3BprrOG22WYbd8wxx7hZs2a5MWPGuHHjxrmrr77af/c/YcKEmqiJ2Bocqg2iIrZNgqoOVB8wZWybdKiKx2PvkNJP9rUhYJOW/5UbvxjG15YQW52KbHNUSUeJQKzxd9KkSW611Vbrgtlee+3lDjroILf55pv7rcV8I7vhhhv6Z7gOCKK6+OKLu+uvv97tscce/uc77LCDu+KKKzyx3X777d3EiRP9NUA333yzP225VhGxjctdY/VTK0pty9iqA4nYWp2znnzsHVL62awv/ISfDQGbdOz+F8P4KmJr8zFJlxeB2ONHJfIcJMVJx+eff77/Fdf5vPDCC/4AKbYeh8JzM2bM8N/fJsvUqVPdMsss409OrldEbOPy+U7z07TotY3Y1lJAHegjZJSxTeui1Z+LvUNKP9nXhoBNWv5XbvxiGF9FbG0+JunyIhB7/K1Enqt6IKzJ+2zbYR0R23agmr3OTvPTtC3NndiqA4nYpnXOes/F3iGln83Kwk/42RCwScfuf7Val+f4KmJr8zFJlxeBTo0f7baIiG27EW6u/rL6ae7EtjnYsz8dewdSxja7bZGMvUNKP9nXhoBNWv5XbvxsrbNLM76K2NpxVA3lRCD2+FsU6rHPy4vCpaj3ltVPRWwL8igRWxvwsXdI6Sf72hCwScv/yo2frXV2aRFbO4aqobwIxB5/i0JexLYo5Ku/t6x+KmJbkJ+J2NqAj71DSj/Z14aATVr+V278bK2zS4vY2jFUDeVFIPb4WxTyIrZFIS9iGxfyGbWJvQOJ2GY07MdisQ8c0k/2tSFgk5b/lRs/W+vs0iK2dgxVQ3kRiD3+FoV87PPyonAp6r1l9VNlbAvyKBFbG/Cxd0jpJ/vaELBJy//KjZ+tdXZpEVs7hqqhvAjEHn+LQl7EtijklbGNC/mM2sTegURsMxpWGVsbcMJP+LUEAVslsU/8YtfPhr5dWsTWjqFqKC8Cih/VbRv7vLy8HiliWwrbxt6BRGxtbhb7wCH9ZF8bAjZp+V+58bO1zi4tYmvHUDWUF4HY429RyMc+Ly8Kl6LeW1Y/1VbkgjxKxNYGfOwdUvrJvjYEbNLyv3LjZ2udXVrE1o6haigvArHH36KQF7EtCnllbONCPqM2sXcgEduMhv1YLPaBQ/rJvjYEbNLyv3LjZ2udXVrE1o6haigvArHH36KQj31eXhQuRb23rH6qjG1BHiViawM+9g4p/WRfGwI2aflfufGztc4uLWJrx1A1lBeB2ONvUciL2BaFvDK2cSGfUZvYO5CIbUbDKmNrA074Cb+WIGCrJPaJX+z62dC3S4vY2jFUDeVFQPGjum1jn5eX1yNFbEth29g7kIitzc1iHzikn+xrQ8AmLf8rN3621tmlRWztGKqG8iIQe/wtCvnY5+VF4VLUe8vqp9qKXJBHidjagI+9Q0o/2deGgE1a/ldu/Gyts0uL2NoxVA3lRSD2+FsU8iK2RSGvjG1cyGfUJvYOJGKb0bAfi8U+cEg/2deGgE1a/ldu/Gyts0uL2NoxVA3lRSD2+FsU8rHPy4vCpaj3ltVPlbEtyKNEbG3Ax94hpZ/sa0PAJi3/Kzd+ttbZpUVs7RiqhvIiEHv8LQp5EduikFfGNi7kM2oTewcSsc1oWGVsbcAJP+HXEgRslcQ+8YtdPxv6dmkRWzuGqqG8CCh+VLdt7PPy8nqkiG0pbBt7BxKxtblZ7AOH9JN9bQjYpOV/5cbP1jq7tIitHUPVUF4EYo+/RSEf+7y8KFyKem9Z/VRbkQvyKBFbG/Cxd0jpJ/vaELBJy//KjZ+tdXZpEVs7hqqhvAjEHn+LQl7EtijklbGtifztt9/uNttsMzdu3Dj39NNPu913390ttthicVnqY21i70Aitja3iX3gkH6yrw0Bm7T8r9z42VpnlxaxtWOoGsqLQOzxtyjkY5+XF4VLUe8tq5+mztiOGjXK/frXv3b33XefGzx4sLfD2muv7R588MGibFL3vbF3IBFbm9vE3iGln+xrQ8AmLf8rN3621tmlRWztGKqG8iIQe/wtCvnY5+VF4VLUe8vqp6mI7Zw5c9wyyyzjdtllF/fOO++4Cy64wF1xxRVu+PDh7sUXX/S/i63E3oFEbG0eE3uHlH6yrw0Bm7T8r9z42VpnlxaxtWOoGsqLQOzxtyjkY5+XF4VLUe8tq5+mIrbvvvuuW3DBBd21117rDjjgALfaaqu5s846yw0aNMj9+9//dgMGDCjKLjXfG3sHErG1uUzsHVL6yb42BGzS8r9y42drnV1axNaOoWooLwKxx9+ikI99Xl4ULkW9t6x+morYAvqwYcPcVVdd5fE/77zz3EknneTee+89N3Xq1KJsUve9sXcgEVub28TeIaWf7GtDwCYt/ys3frbW2aVFbO0YqobyIhB7/C0K+djn5UXhUtR7y+qnqYntyy+/7M4++2zXrVs3d9hhh7l99tnHHXjggW6TTTYpyiYitm1EPnaHl3424ws/4WdDwCYt/7PhV7S0iG3RFtD7Y0Yg9vhWFHYitkUhX/29ZfXT1MQWWCZPnuwmTJjg+vfv75ZYYgm36qqrxmWlhDaxdyBlbG2uE3uHlH6yrw0Bm7T8r9z42VpnlxaxtWOoGsqLQOzxtyjkY5+XF4VLUe8tq5+mJrY33HCD+9rXvubxP+KII9z48ePdWmut5U477bSibFL3vbF3IBFbm9vE3iGln+xrQ8AmLf8rN3621tmlRWztGKqG8iIQe/wtCvnY5+VF4VLUe8vqp6mJ7YorruiWXnppf28t1/3MP//87rjjjnPPP/+8W2655YqyS833xt6BRGxtLhN7h5R+sq8NAZu0/K/c+NlaZ5cWsbVjqBrKi0Ds8bco5GOflxeFS1HvLaufpiK24VTkM888002ZMsXNN998bujQof4e28cff9wNHDiwKLuI2LYJ+dgdXvrZDC/8hJ8NAZu0/M+GX9HSIrZFW0DvjxmB2ONbUdiJ2BaFfPX3ltVPUxFbIIG8vvLKK/7OWrK1HCa18MILuyeffDIuS32sTewdSBlbm9vE3iGln+xrQ8AmLf/rLPzuv/9+169fP7fUUkt5xWfNmuX+8Y9/+N1RPXr0sDWmDdIitm0AVVWWBoG84m8nxo3DDz+8NHbu9Ibk5ad545Sa2D788MPu6KOPdnxrG8qf/vSnud/d5q14o/eJ2DZCqP7vY3d46Sf72hCwScv/hJ8NgY+kJ02a5BeH9913X/9n44039j9/+umn3Y9+9CN/YOPKK6/cile1tA4R25bCqcpKhkC7x4dOjhsitvE4e7v9tKiWpia2QcHXX3/dPffcc26VVVbxGdtYi4itzTKxO7z0k31tCNik5X/Cz4bAR9Inn3yyqzXRW3TRRR3jLZ/+xFZEbGOziPSJCYF2jw+dHDdEbOPx1Hb7aVEtTU1st9hiC78VubLcc889bpFFFilK/5rvFbG1mSR2h5d+sq8NAZu0/E/42RD4SLpW5oXf8fkP25NjLCK2MVpFOsWCQLvHh06OGyK2sXipc+3206JamprYbrbZZv67WsrMmTN91pZTkp999tkoM7citjaXit3hpZ/sa0PAJi3/E342BLpKv/DCC26hhRbyGdpkgdh27969la9qSV0iti2BUZWUFIG8xodOjBsitvE4fV5+mneLUxPbSsWOPfZYF05JXnDBBfPWu+H7RGwbQlT3gdgdXvrJvjYEbNLyP+FnQ6Cr9Nlnn+23JL/xxhtdfjFjxgzXq1evVr6qJXWJ2LYERlVSUgTyGh86MW6I2Mbj9Hn5ad4tTk1sp02b5mbPnu31+/DDD90555zjTjzxRH/Ixaqrrpq33g3fJ2LbECIRWxtEwk/4tREBW9WxD1jS7xP7zpkzx982QNlnn33cZz7zmbm/PPLII3UqchNdoVs3564/cVgTEnpUCLQegTziW6fGDRHb1vtb1hrz8NOsulnkUhPbvn37zt2KHF7I4RavvvqqW2CBBSw6tEVWxNYGa+wOL/1kXxsCNmn5n/CzITAvsT3ooIPcUUcd1apq21qPMrZthVeVdzgCeYwPgdh2WtwQsY3HufPw0yJam5rYsvX4zTff9Dryzc9KK63ktttuO/fZz362CL0bvlPEtiFEdR+I3eGln+xrQ8AmLf8TfjYEukrvvvvu/io9xq0+ffrM/eWQIUP8vfGxFRHb2CwifWJCIK/xoRPjhohtPJ6al5/m3eKGxJaGszJUq0BsYz3cIuYOFBzqnn8950667G735YEruJ/usVHe9q/5vtgdXvrZXEX4CT8bAjZp+V9X/KrtiOIJfWPbnJ9pK3JzeOnp9iCQV3zrxLgR87y8Pd4Qb615+WneCDQktt0YKeqUmAfemDuQiK3N1WPvkNJP9rUhYJOW/3UWfvfee697991351F6o402Usa2CVOK2DYBlh5tGwJ5xd9OjBsxz8vb5hCRVpyXn+bd/IbE9je/+Y0/LKpW+dGPftTlsIu8G1DrfdqKbLNE7A4v/WRfGwI2afmf8LMh0FV63LhxVYktn/toK3J6pEVs02OlJ9uHQF7jQyfGDRHb9vldszXn5afN6mV9viGxTb6AFWUytBS2J3NJ9Prrr6/DozJYQRnbDKAlRGLvkNJP9rUhYJOW/3UWfp24pXD89H42kJ1zSSK60xFjW1qfuTJVIAQyIpBX/O3EuCFim9Gp2iCWl5+2QfW6VaYmtmPHjnUHH3zwPCcjaytyNpOJ2GbDLUjF3iGln+xrQ8AmLf/rLPxuuummuRnbt956y5188sk+U3vfffcpY9uEKZWxbQIsPdo2BPKKv50YN0Rs2+Z2TVecl582rZhRIDWxXWWVVdxiiy3m763dYIMN3BNPPOGWXnppxx5/XffTvBVEbJvHLCkRe4eUfrKvDQGbtPyvs/FjIXnXXXd1zz//vFtuueVsjWmDtE5FbgOoqrI0CBQVfzshbojYxuPmRflpuxFIRWzff/99/x3tZZdd5u6880635JJLugMOOMBf+fP66697whtb0Te2NovE7vDST/a1IWCTlv8JPxsCXaWPOOIIN3PmTP/D2bNnO76dw8deeuklv4AcWxGxjc0i0icmBPIaHyxxg+s7F1lkEdfogNgkrsSmd955x8slC7tMFlpooYY3pMQ+L4/Jh/LQJS8/zaMtyXekIrYIrLjiip7cfv/733fcafutb33LXXzxxe6pp55yq622Wl291YHmhUcZW5urx94hpZ/sa0PAJi3/6yz8Kr+VW3TRRd0PfvAD98tf/rJhQ4oaX/WNbUPT6IFPKQJ5xd8scePll192jz32mNt55539OTmVC2ckr/bff383aNAgbz2eY/fIRRdd5EaPHu2WX35598EHH7jLL7/ck+Lhw4f7zyWmTJniRo4c6fbee++aVhexjatD5OWnebc6NbG97bbbHCcgX3rppW6HHXZwzz33nNtpp53c9ddfX1NndaDa5hSxtbl67B1S+sm+NgRs0vK/zsJv+vTpc++LZ7KYZhdU0eOriK3NxyRdXgTyir9Z4sY111zj7r77bnfqqadW3RFy/vnnO3ZpQm7DiewQWT455H29e/f25+0su+yynti+8cYb7oQTTnDTpk3zPyN7u/DCC1c1rohtXD6fl5/m3erUxPa8885z22yzjd9+jJMzqDb69kcdSMS2XQ4de4eUfjbLCz/hZ0PAJp23/zE5ZNH4iiuu8BNFMiR77bVX3TG26PFVxNbmY5IuLwJ5xY8scSOgDimt9qnDqFGj3DnnnOPj0NChQx1klLLlllu6Z555xv/7jDPOcI888ogntvyceMVNKd27d/fP9O/fX8S2A9w7Lz/NG4rUxDZsedh8883dnnvu6b7xjW+kWlWmQepA85pVGVubq8feIaWf7GtDwCYt/+ss/PhWjm3HbEEmI8KOqNVXX909+uijDU9FLmp8FbG1+Ziky4tAXvG3HXHjzDPP9AtqW221lYPkzjfffD57C8l98sknvdFYhLvjjjv8uQC77LKLGzJkiP85PIGT3Pv16+fGjx/vM8OVhXpU4kGg1iJEPBo2r0lqYnv77be7G2+80V133XX+UAsKqzR8Z9ujR4+6b6418KoDOffo5NfdhTdPcmv26+O+s9WqzVtQEkJACAgBIdByBPIa8Nm617NnTz95JFNC1oNv2fj0h9sHPv/5z0c5vraE2DrnRu+/nm/fIefdb7Zht0R95spUgRAwINDu+NGuuMHhUBwERXnggQfcsGHD3OOPP+63F3N4FPN5tjFTyOr26tXLjRgxwn344YeuT58+frsyMaxa0VZkg0O1QTSvBZg2qF5/TJzD/oEmCis2Y8aM8X8oae6xrUVsP80dSBnbJpyuyqOxd0jpJ/vaELBJy/86B7+3337bnzL6i1/8wh199NFe8auvvtpnQphYrrPOOpmIbbvH15YQ227OXX/iMN++nY4YazOa3x32SX3mylSBEMiIQB7xt5VxgwPoWERbb7313MYbb+x++MMfekLLfdqTJ092Z511lj9Mir/XWGMN/1niMccc42bNmuW5AKe4E7NOOeUUN2HChJqoidhmdKg2ieXhp21SvTXEli0PXPfDFinK2muv7fbZZx9/7U/4wLzWm5LEVh3oI5REbG3uHnuHlH6yrw0Bm7T8r7PwYzLJtr3tttvOZ0b4fpZJJtv6Gl3HUdT4KmJr8zFJlxeBvOKvNW5wVs5SSy3lF9D4zJCtxbfccouf2xOHuPEEcstnERwUu8cee3ijcYAs5wFAbLfffns3ceJEfw3QzTff7AYPHixi2yGunZef5g1H6q3I7J3nup/99tvPryR/4QtfSK0rA686UFe4RGxTu0/VB2PvkNJP9rUhYJOW/3UWflyVceKJJ7orr7zSb+9jjD3yyCPdmmuu2bAhRY2vIrYNTaMHPqUI5BV/LXEjaRqyv5x0zInIFLYVv/baa570Vj7HLk1OP06WqVOnumWWWcafnFyvKGMbV4fIy0/zbnVqYvvwww/7rQi19s6nVVwd6COkgkNN+Ndz7sTL7nYD+y3ldt/qi2lhdANWXMJ9ZoH5Uj/f7IOxO7z0a9ai1RdWbLW0T1r2tWEr/DoPP7IebEnmzAq+U2v0bW21FuY5vorY2nxM0uVFIM/424q4wVU9ENYBAwa01Sgitm2Ft+nK8/TTppUzCKQmtoZ3dBFVB6pObJvF93eH7eCWXaJns2Kpn4/d4aVfalNWfVD4CT8bAjZp+V9X/PhOje/abrrpJn+l3sCBA/2JpOGqjbRo5zm+xk5sX3z1zbSw1X2uneNsSxRUJdEhkFd8a1XcyAtAEdu8kE73nrz8NJ02rXsqd2LbOtXr1xR7BwoO9a/J/3OXjftnalieeu419977HzoR2//UvCstNZhtfDD2gCH9bMYXfsLPhkBXaTIlbPv7wx/+4LO2Bx54oL9S4/nnn294X3wr9UhbF+Nr7MRWh1GltaaeazUCeY0PnRg3Dj/88FbDrfoyIpCXn2ZUL7NYXWLL+hEIQwAAIABJREFUPnuu+OEQi4ceeshvRV5hhRUyvyxPwU4hts1i8t3f3OhYiT730O3dcksu2qx46udjd3jpl9qUVR8UfsLPhoBNWv73CX4ffPCB/zaNgxjPPvts/4vLL7/cH9TCCaMbbLCBDew2SIvYtgFUVVkaBPKIb50aN0Rs43HzPPy0iNbWJbbvvvuuW3DBBd13v/td98c//tEPtJWD7De/+c2GpyIX0bCyEtvv/fYv7oVX3nA7bbia67nQZ1JBu+EXV3T9lumd6tnwUOwOL/2aMuc8Dws/4WdDwCYt/+uK3yabbOLuuusu9/Wvf90tuuii7k9/+pPr3bu3mzRpUsN74m2WyCYtYpsNN0l9OhDIK751YtwQsY2nD+Tlp3m3uOFWZO7QI1tbq6S5xzbvRvG+shPbZjA9bNgGbtMvrdyMyNzDrZoSyvHh2Duk9LM5g/ATfjYEbNJ5+9+zzz7rTj31VDd27Fh/gwDXaYwcOdJtuummtoa0SfrTSGzHXHt/S9A8aOf1WlKPKokXgbziRyfGDRHbePw2Lz/Nu8UNiS33zkJshw4d6r/7+cpXvtJFR/7f6B7bvBtVZmL753uedjPffjcVpPc8/pyb8tIMJ2KbCq6WPhR7wJB+NnMLP+FnQ6C69OzZs/19kHxnG3P5NBJbfbMbs0fGpVve40MnxQ0R23h8NW8/zavlDYltUOSFF15wPXv2dPfff79766233JZbbukvcI61lDVj2wzev/79BHfno/8VsW0GtBY9G3vAkH42Qws/4WdDoLOlRWyz2a9bN+euP3FYNmFJdQwCsY8PRQEZ+7y8KFyKem9Z/TQ1sb3jjjvcTjvt5C+PD2X06NHukEMOKcomdd8bewfKw6FEbItzzTzsa2md9LOg98k91LZa2ict+9qwjR0/W+vs0iK22TAUsc2GW6dJKX5Ut1js8/JO8zOrvmX101TElm0OnIzMtz8/+9nPfKb2jDPO8NnbV155xS2xxBJWfFsuH3sHysOhRGxb7lapK8zDvqmVqfKg9LOgJ2JrQ0/4VcNvzpw5burUqf7AxsUXXzzKT3yC3iK22XqAiG023DpNKs/xtdPihrYix+PNefppnq1ORWxfffVVt+SSS7rTTz/dXyJPueWWW9xWW23l7r33Xjd48OA8dU71LhFb50RsU7lKWx6KPWBIP5vZhZ/wsyHQVfrpp5922267rT+074gjjnCPP/64Gz58uNt1111b+ZqW1SVimw1KEdtsuHWaVF7jQyfGDRHbeLw5Lz/Nu8WpiC0rQlw9MHDgQH9yIxnbE044wV111VVu2rRprm/fvnnr3fB9IrYitg2dpI0PxB4wpJ/N+MJP+NkQ6CrN7QNMUpdaaik3bNgw99xzz7lLL73UTZ8+3Y+9sRUR22wWEbHNhlunSeU1PnRi3BCxjceb8/LTvFucitii1GmnneZGjBjRRT+2JR933HF565zqfSK2IrapHKVND8UeMKSfzfDCT/jZEPhEOtwXP2bMGPff//7XzTfffP4WgrXXXtvfSLDWWmu16lUtq0fENhuUIrbZcOs0qTzGh06NGyK28XhzHn5aRGtTE1uUY9C97rrr3MyZM93OO+/sM7ixFhFbEdsifTP2gCH9bN4h/ISfDYGu0r169XJrrrmmW2yxxdxnPvMZ1717dzdu3DjHZ0ALLLBAK1/VkrpEbLPBKGKbDbdOk8prfOjEuCFiG4835+Wnebe4KWKbt3KW94nYitha/McqG3vAkH42Cws/4WdDoKv0lVde6b73ve91uXWA3VDsioqxiNhms4qIbTbcOk0qr/GhE+OGiG083pyXn+bdYhHbvBH/+H15OJQOjyrIuE6nvlqRz6N/WHSUfhb01D+qocfWwttvv909++yzbtCgQW7DDTe0gdxGaRHbbOCK2GbDrdOk8hwfOi1uiNjG4815+mmerRaxzRPtxLvycKhAbNM2cf0vLOeO+vZX/ON56JdWr2rPST8LerKvDT3hJ/ysCHSVP/bYY/0nPpXl+OOP99f/xFZEbLNZRMQ2G26dJpXX/KQT44aIbTzenJef5t3i1MT229/+tttvv/3cJpts4nXk1MY999zT/f73v9epyBmslodDidhmMEyLRPKwr0VV6WdBT8TWhp7wq8SPmwW4J76yzJgxw/EdXWxFxDabRZLEdvhxf/y4kjnZKvtY6oqjvmmSl3DrEchrfO3EuCFi23p/y1pjXn6aVb+scg2J7cUXX+zOOOMMfzrjCius4JZeemn/LgZhyC2HW3CZfGxF39imt8h9Tzzvjr9kvFPGNj1mjZ6MPWBIv0YWrP974Sf8bAh0lWYcnT17tv/h22+/7UaOHOkmT57sJkyY4E9Jjq2I2GazSJLY7nTE2GyVJKSUATZD2JYK8hofOjFuiNi2xeUyVZqXn2ZSziDUkNhecskl/qqfSmLLO7fcckvHABdjEbFNbxUR2/RYpX0y9oAh/dJasvpzwk/42RCoL80dtuySmjRpkltllVXa+apMdYvYZoLNidhmw63TpIoaHzohbojYxuPNRflpuxFoSGyDAj//+c/d17/+dceF0J1QRGzTW+kfT7zgjrvkLrf8kou6TQat5AVff/1116dPn6qV9Fq4h1t5md4NX7Dowj1cvxTPNayoygOxd0jpl8Wqn8gIP+FnQ8Amnbf/bbzxxu7FF1+cqzTvp7zxxhuuZ8+etsa0QVrENhuoIrbZcOs0qbziRyfGDRHbeLw5Lz/Nu8Wpie1dd93lfvvb3/qtUcnyzDPPRDvwxtyBYnKoQGxb7XzrfX459/O9PjqMqtUlJvyqtU362Swu/ISfDQGbdN7+t/XWW7uXXnrJKz3//PP7LC0Z2x133NHWkDZJi9hmA7adxPbivz6WQqnG3/Puve0gX89Tz72Wor7Gj3xuhfg+VWuste2JvOJHJ8aNmOflNqt3nnRefpo3MqmJ7cCBA93EiRPd2muv7S+QD+XWW291Cy+8cN56N3yfMrYNIZr7wPP/e8Pd8eiULgLVMrZvvvOee/bF6Q0r5rnJ02Y4Edv+DbEq6oHYA5r0s3mG8Cs3frbW2aVFbLNh2E5i2+pvdltdXzbEOlMq9vhbFKqxz8uLwqWo95bVT1MR2w8//NCvIh999NHuF7/4RVE2aOq9sXeg2B3Kot/9/37BHft/d7l1Byzrjt77o1O0W10s+rVal2r1ST8bysJP+NkQsEnn5X8rrriie++992oqG/OOqPHT+9lAdq6t35y2mpipvubN/Wk93Krd8aOT44Yyts33o3ZJtNtP26V3o3pTEVsq2Weffdw//vEPf71P8tvL5Zdf3nUjekVWRGxtBrE4vIitrjOxeZ/wE35WBGzylvjXzJvZavzBBx/UFLn22muj3RElYtuMpT96Vhnb5jHrRIl2x49OjhsitvF4dLv9tKiWpia2ui+rtSaK3aEs+j345IvuFxff6QastITba5s1GwK3Ut/ervciPRo+l3zAol9TL8r4sPTLCNzHYsJP+NkQsEnH4H8zZ87051d0797d1pg2SGsrcjZQRWyz4dZpUkXGj9jjhohtPN5cpJ+2E4XUxHb06NH+hMbKMmrUKNejR3OkpJ0NCnUrY2tD2eLwgdim1eDw4Ru6jddYMe3j/jmLfk29KOPD0i8jcCK2NuCEX0fi969//csdc8wxjr8pZHE5JZm74nv16tWSNrWyEhHbbGh+montg099cup3avSqnHW1zoBlU4sX9WBe438nxg0R26K8ct735uWnebc4NbHlMKE5c+aNMosvHueJdyK2NleyOPyTU191F9/0aEMFpr480814610nYtsQqpY/YLFvy5WpUqH0s6Es/DoLv/XXX9/df//9Xumll17avfzyy65///7u8ccfdwsttJCtMW2QFrHNBuqnmdi2+hvlbBbIRyqv+NuJcUPENh8fTPOWvPw0jS6tfCY1sdVW5FbCrowjaP7qinvc+H9OdYfvtqHbeE1lbFvrYfVriz2gST+bNwi/zsHv/fff9zcNXHnlle66665z6667rttwww3ddttt51555ZUutxDYWtU6aRHbbFiK2GbDLUh1ymFUecTfTo0bIra2PtBK6Tz8tJX6pq0rNbHlROSwFfnNN9/0h0hxMttDDz2krchp0U48F7tD5aGfiG0Gx2mRSB72tagq/SzoaeHMhl7++LHdGDLLn7Fjx7rDDjvM7bvvvj5jy1V7sRUR22wWEbHNhpuIbXXcOjFuiNja+kArpWOfZ2Vta2piW/mCiy66yA+8r732WpdTkrMq0mo5bUW2IZqHw4vY2mxkkc7DvtLPgoBNVvbtDPy4Sm+++eZzp556qvvxj3/sbrvtNrf55pt75dmS/Pzzz/ur9mIrIrbZLCJimw03EduuuHVy3BCxtfWBVkrHPk/I2tbUxPaGG25ws2bN8u+hU1122WXuxhtvdM8++6zr189+n13WBtSSE7G1IZqHw4vY2mxkkc7DvtLPgoBNVvbtDPz4xGfYsGFu6NChbrXVVnNLLrmke+qpp/whUltvvbXr3bu3rSFtkhaxzQasiG023ERsu+LWyXFDxNbWB1opHfs8IWtbUxPbat/YbrTRRu6uu+7SPbYZ0I/dofLQT8Q2g+O0SCQP+1pUlX4W9PLfStustrLvR4jxOQ8nH1PI0O65555uyJAhjkNhYrzmJ9hZxLZZj//oeRHbbLiJ2HbFrZPjhoitrQ+0Ujr2cThrW1MT21tuuWVuxrZbt25+QP785z8f5cEWgKGMbVaX+EguD4cXsbXZyCKdh32lnwUBm6zs2xn4cdPAP//5T/e3v/3NsSuKhWLKCius4EkuVwAtsMACtsa0QVrENhuoIrbZcBOx7YpbJ8cNEVtbH2ildOzzhKxtTU1secFjjz3mrr32WscF0N/61rfcBhts4L8PirGI2NqskofDi9jabGSRzsO+0s+CgE1W9u1M/O6++2538MEH+0MZKTNmzNA9tk2YUsSxCbCqPNpJ+Nla2l7pvONvJ8UNEdv2+l4zteftp83oZnk2NbE9//zz3f7779/lXXwXdNVVV1ne3zZZEVsbtHk4vIitzUYW6TzsK/0sCNhkZd/OwI8bBu644w6fseWqn7AtefXVV3e77767GzVqlA6PasKUnUTMWn2v66etvibcIvdH2x1/OzluiNjm7o41X9huPy2qpamI7Xvvvee3HjPYjhkzxl8Yf9xxx7mLL77YTZkyxa200kpF6V/zvSK2NpPk4fAitjYbWaTzsK/0syBgk5V9OwM/PusJ5XOf+5wns+yGivGKnySi2oqczb9EvLPhFqR0j+1HSHRy3BCxtfWBVkrHPk/I2tZUxHb69On+Sh+uJBgxYoR/17hx49w222zj2ALB3XuxFRFbm0XycHgRW5uNLNJ52Ff6WRCwycq+nYHfgAED3G677eZ23nlnt+aaa9qUzlFaxDYb2CK22XATse2KWyfHDRFbWx9opXTs84SsbU1FbKmcjC2ZWy6OX3jhhd3vfvc7N23aNDd58mSfwY2tiNjaLJKHw4vY2mxkkc7DvtLPgoBNVvYtN3621tmlRWyzYShimw03EVsbbrFIxz4vjwWnvPSIfZ6QFYfUxPbvf/+7+853vuNPy6Usuuii7sILL/RXE8RYYu9AsTtUHvqJ2BbXc/Kwr6V10s+CXj6nmls0lH0t6BUvK2KbzQYittlwE7G14RaLdOzz8lhwykuP2MfhrDikJra8gCPGH374YceH62w/nn/++bO+t+1ysXeg2B0qD/1EbNveDWq+IA/7Wlon/Szoidja0IsfP2v7rPIittkQFLHNhpuIrQ23WKRjn5fHglNeesQ+z8qKQypie8YZZ7iJEye6s88+27/nJz/5idt8883d1ltvnfW9bZeLvQPF7lB56Cdi2/ZuIGLbJojz6B8W1aWfBT0R20boidg2Qqj670Vss+EmYmvDLRbp2OflseCUlx6xzxOy4tCQ2F555ZVu+PDhnsjeeuut/j3bb7+9u+mmm9wBBxwwl+xmVaBdcrF3oNgdKg/9RGzb5f2N683Dvo21qP2E9LOgFz8xk31t9i1aWsQ2mwVEbLPhJmJrwy0W6djn5bHglJcesY/DWXGoS2zff/99t8QSS/iDoyZMmDD3ovi33nrL7bjjjo7vbp9//nm33HLLZX1/2+Ri70CxO1Qe+onYts39G1ach30bKlHnAelnQU/E1oZe/PhZ22eVF7HNhqCIbTbcRGxtuMUiHfu8PBac8tIj9nlWVhzqEltOPV522WXdUUcd5Y499tgu77jooovcvvvu6+666y638cYbZ31/2+Ri70CxO1Qe+onYts39G1ach30bKiFia4Gorqzsa4M2dvxsrbNLi9hmw1DENhtuIrY23GKRjn1eHgtOeelR1nGuLrH98MMP/QFRK6ywgnvyySf9NT+UDz74wG9Hvvnmm92UKVPcSiutVNcOHDa1yCKLdLlUOinwxhtv+FOWk2X27NnunXfe8XLJQraY64W6d+9e952xd6DYHSoP/URs8wpf874nD/taWif9LOjFn3GUfW32TUoXNb6On97P3AgRPRuEn2b8bMi1Vzr2+Ebri4obuse2vb7XTO2d4KfNtGfuAtgcjjquU7i39re//a1/YrvttvMElO9rIaPbbLON++tf/1pT+uWXX3aPPfaYv3x+0qRJbumll+7y7IMPPuizviuvvLInyBdccIFbd911Hdng0aNHu+WXX96T6Msvv9yTYr71hWjz7MiRI93ee+9d890itlnc4ROZPBxexNZmI4t0HvaVfhYEbLKyb7nxo3VFj68its372KeZiO50xNjmAauQSOJnrqyNFcQcf4uOGyK2bXS8JquO2U+bbEqXxxseHjVr1ix30kknzbMVec8993SnnHKKW3LJJWu+/5prrnF33323O/XUU91LL700D7HlVGWIM3/z7Lnnnuv+8pe/uAUWWMBNnz7d9e7d2x188MF+OzTEFjJ9wgknuLBFmuxtyCJXKiFia3GLfDI+IrY2G1mkYw9o0s9i3Xz6r0VD2deC3keyRY+vIrbN21DEtnnMkhIitjb8YogbIrZ2G7aqhtjH4aztbEhsQ8Xvvvuumzx5suPvfv36zT1IKs2LIaXViC2HUt1zzz3+cCrux912223dvffe67bcckv3zDPP+Kq5auiRRx7xxJaf77rrrv4+XbYi80z//v2rqiBim8YytZ/Jw+FFbG02skjnYV/pZ0HAJiv7lhu/rpP9YsZXEdvmfUzEtnnMRGxtmNWS1ry8Pbh2Uq2xzxOyYpma2GZ9AXK1OlCvXr38t7tkZAF40003dTfeeKMbOnSo/znl0ksvdXfccYebOXOm22WXXdyQIUP8z/v27evuu+8+T7KrFRFbi8XyyfiI2NpsZJGOPaBJP4t18+m/Fg1lXwt6XWWLGl9FbJu3oYht85iJ2Nowa5bYtmpePn78eL9js7Iwv1eJB4FaycF4NGxek0KJ7SabbOK3Ka+zzjrugQce8Nudx44d67cXc3gUAza/p7ANmQ43YsQIx6FWffr08duVydyqAzVv+BgkLrr1GffIf15ze2+xilur/+IxqCQdhIAQEAIegU4Z8GsR23aPry0hts650fuv5/E+5Lz7zZ7XTfWZMOwk/EwNzUE49vjR7rhRDeLYE045uEVUr4h9gTkrWLkTW05ie+KJJ9x6663nDj30UP+N7qhRo/y3tj179nTHHXecGzRokDvrrLPcGmus4Q+oOuaYYxzf+o4ZM8aNGzfOXX311f77Xu7WrVVi70CxO1Qe+iljm7Xb2uXysK9FS+lnQU8ZWxt68eOXbF9ygprn+NoSYtvNuetPHOab0+rDhVRf872gkzLKzbcuP4nYxy+QKCpu6Bvb/Pyw0Zs6wU8btaHa73MjtpzEttRSS/nM7Oabb+63FvON7IYbbuj1WmyxxTxRXXzxxd3111/v9thjD//zHXbYwV1xxRWe2HLF0MSJE/01QFw1NHjwYBHbLFZPIZOHw4vYpjBEmx7Jw74W1aWfBb34iZnsa7NvJbEtYnwVsW3ehp1EHGNfGGge/fwkYo9vgdgWETdEbPPzw0Zv6gQ/bdSGwoht8sVvv/22P+n4/PPP9z/mOp8XXnjBHyDFClIoPDdjxgz//W2yTJ061S2zzDL+5OR6RRnbLO7wiUweDi9ia7ORRToP+0o/CwI2Wdm33PjVal2e46uIbfM+JmLbPGZdF3E+yfDbamqvdOzxt7L1ecYNEdv2+l4ztXean6ZtWy4Z26QyXNUDYR0wYEBaHTM9J2KbCba5Qnk4vIitzUYW6TzsK/0sCNhkZd9y41erdXmOryK2zfuYiG3zmInY2jBLI51n3BCxTWORfJ6JfZ6QFYXciW1WRZuVE7FtFrGuz+fh8CK2NhtZpPOwr/SzIGCTlX3LjZ+tdXZpxlcR2+ZxFLFtHjMRWxtmMUnHPi+PCas8dIl9npAVAxHbrMgZ5WJ3qDz0E7E1OpFBPA/7GtTz13/FfKqk9LNYV98A29ArXlrENpsNRGyz4RakkvjZamqvdOzjQ3tbX7t2EduikK/+3rL6qYhtQX4Wu0PloZ+IbUHO50QsrMjn0T8sOko/C3rx9w9b6+zSIrbZMBSxzYabiK0Nt1ikRWxjscRHesQ+T8iKlohtVuSMcrE7VB76BWK70f+zdybwn03lHz+WwcwYY8Zg7GPsW1kjoYTIUsieilCpRAnVn4SUVJYsqSjZkkKJFmRLIdSIbNn3ZcwMsxjG8H+9j57pzp3vcu85936/5/v9fc7rNa+Z+f3uufc5n/Occ57P8zznnDWXccsuvlBDRFdbbpRbZ6XRc/yuE/LFdLHki0Ev/QlX/av+jUOgt2uL2Ib1n4htGG4itnG4pVJbxDaVnhCxTasnCkqT+gCSYeycEdtWXbrTpqu4T267tohtQb0v+pj0ryhSjZ8TfsIvDoHeri1iG9Z/IrZhuDUitqdfdnvcy/5b+/M7b1DJe7IvSX19qLzBBV+Yul1esBl981i/6qkitl1S0dQVqhPy3Xz3k+6J519u2AP3P/GS++d/nnMitvUoaCf6N0ZyyReDniLeceilj19s+2Lri9iGIShiG4ZbI2Jb9T27cZLNXjv19avKtpZ5l4htGbTqf7Zf9VTEtn7dafiF1BWq2/L99uYH3NlXjXOjhg9xo0cOnQPD6dOnu63etbLbdqMVu9SDrT/bbfzagSL52iGk/o1DSPjViV+33y1iG9YDIrZhuInYxuGWSm0R21R64m05UrcDQ9ESsQ1FLrJe6grVbfmM2LaCuVk0N7JrKqnebfzaNULytUNIxCwOIeFXJ37dfreIbVgPiNiG4dYJYnvMuTfFCfff2kfvs1nfEoZYgERsYxGstn7qdmBoa0VsQ5GLrJe6QnVbvvEvT3PPvjSlIcqkMP/+1oeapilHdk0l1buNX7tGSL52CImYxSEk/OrEr9vvFrEN6wER2zDcOkFsq0xtTn19jeuF8NoituHY1VGzX/VUxLYObSnwztQVKmX5Lv/LA+6nvx/ndtxkFbffdnMeLFUA/tofSRk/Gi/54lRA+Am/OAR6u7aIbVj/idiG4SZiG4dbKrVFbFPpibflSN2OCUVLxDYUuch6qStUyvKJ2EYqXw9MaCnrXy8sCMIvboykjl9c6+Jri9iGYShiG4abiG0cbqnUFrFNpSdEbNPqiYLSpD6AUjecUpZPxLbgIGjxWMr9K+Ko/o1HIO4NqY+PuNbF1xaxDcNQxDYMNxHbONxSqZ26XZ4KTp2So1/XOUVsO6VBue+krlApyydiG6+0KfeviK36Nx6BuDekPj7iWhdfW8Q2DEMR2zDcRGzjcEultohtKj3xthz9us6J2HZJz1JXqJTlE7GNV9qU+7cXJlzhF6eDwi8Ov27XFrEN6wER2zDcRGzjcEultohtKj0hYptWTxSUJvUBJMOuYEc2eMyuAtrkHcu4bTec8x7beeeZ26223KjwD1RQU/0bB6LwE35xCMTVTl3/4loXX1vENgxDEdsw3ERs43BLpXbqdnkqOHVKjn5d5xSx7ZQG5b6TukKlLF+7O26HDZnPXXTUTl3q2d7whKXcvyAo+eLUV/j1N35xrYuvLWIbhqGIbRhuIrZxuKVSW8Q2lZ7oDTs1FC0R21DkIuvJ8AwHkHtsL73+brfAAgvM9pKZb77l7nt8vBOxbY+t9K89Rq2eEH7CLw6B3q4tYhvWfyK2YbiJ2MbhlkptEdtUekLENq2eKChN6gNIhnHBjmzyWCP8prz6utvz2Mvd0AUGuYuP3jnuA5G11b9xAAo/4ReHQFzt1PUvrnXxtUVswzAUsQ3DTcQ2DrdUaqdul6eCU6fk6Nd1ThHbTmlQ7jupK1QvyidiW1yZe7F/i7eu/ieFXxzGwi8Ov27XFrEN6wER2zDcRGzjcEultohtKj3xthypr8OhaInYhiIXWS91hepF+URsiytlL/Zv8dbV/6Twi8NY+MXh1+3aIrZhPSBiG4abiG0cbqnUFrFNpSdEbNPqiYLSpD6AZNgV7MgmjykVuXr84t5YbW2Njzg8hV9/4xfXuvjaIrZhGIrYhuEmYhuHWyq1U7fLU8GpU3KkbieE4qCIbShykfVSV6helE8R2+JK2Yv9W7x19T8p/OIwFn5x+HW7tohtWA+I2IbhJmIbh1sqtUVsU+mJt+VIfR0ORUvENhS5yHqpK1QvyidiW1wpe7F/i7eu/ieFXxzGwi8Ov27XFrEN6wER2zDcRGzjcEultohtKj0hYptWTxSUJvUBJMOuYEc2eUypyNXjF/fGamtrfMThKfz6G7+41sXXFrENw1DENgw3Eds43FKpnbpdngpOnZIjdTshFAdFbEORi6yXukL1onyK2BZXyl7s3+Ktq/9J4ReHsfCLw6/btUVsw3pAxDYMNxHbONxSqS1im0pPvC1H6utwKFoitqHIRdZLXaF6UT4R2+JK2Yv9W7x19T8Jh1SFAAAgAElEQVQp/OIwFn5x+HW7tohtWA+I2IbhJmIbh1sqtUVsU+kJEdu0eqKgNKkPIBl2BTuyyWNKRa4ev7g3Vltb4yMOT+HX3/jFtS6+tohtGIYitmG4idjG4ZZK7dTt8lRw6pQcqdsJoTgoYhuKXGS91BWqF+VTxLa4UvZi/xZvXf1PCr84jIVfHH7dri1iG9YDIrZhuInYxuGWSm0R21R64m05Ul+HQ9ESsQ1FLrJe6grVi/KJ2BZXyl7s3+Ktq/9J4ReHsfCLw6/btUVsw3pAxDYMNxHbONxSqS1im0pPiNim1RMFpUl9AMmwK9iRTR5TKnL1+MW9sdraGh9xeAq//sYvrnXxtUVswzAUsQ3DTcQ2DrdUaqdul6eCU6fkSN1OCMVBEdtQ5CLrpa5QvSifIrbFlbIX+7d46+p/UvjFYSz84vDrdm0R27AeELENw03ENg63VGqL2KbSE2/Lkfo6HIqWiG0ocpH1UleoXpTPiG2zrvnZV3Zwo4YPiey5YtV7Eb9iLevMU8IvDmfh19/4xbUuvraIbRiGIrZhuInYxuGWSm0R21R6QsQ2rZ4oKE3qA0iGZ8GObPJYq1RkEdv22Er/2mPU6gnhJ/ziEOjt2iK2Yf0nYhuGm4htHG6p1E7dLk8Fp07JkbodE4qDIrahyEXWS12h+km+fU/4nRv/8jSniO3/lLaf+jdyKAZVF35BsM2qJPzi8Ot2bRHbsB4QsQ3DTcQ2DrdUaovYptITb8uR+jocipaIbShykfVSV6h+ks+IbaMuu+iondywIfNF9uac1fsJv8rBKfBC4VcApBaPCL/+xi+udfG1RWzDMBSxDcNNxDYOt1Rqi9im0hMitmn1REFpUh9AMjwLdmSTx8rgJ2Ir4h2nbcJP+FWNQG+/T8Q2rP9EbMNwE7GNwy2V2qnb5ang1Ck5ytjRnZKpiu8oYlsFigHvSF2h+l2+PY+93HHYlCK2AcrbgSr9rn91Qyj84hBOHb+41sXXFrENw1DENgw3Eds43FKpLWKbSk+8LUe/rnMitl3Ss9QVqt/lE7F9xI0dO7ZL2t/+s/2uf+0RiHtC+PU3fnGti68tYhuGoYhtGG4itnG4pVJbxDaVnhCxTasnCkqT+gCS4VmwI5s8FoufiK2IbYwGxupfzLeL1JV8RVBq/kzq+MW1Lr62iG0YhiK2YbiJ2Mbhlkrt1O3yVHDqlBz9us4pYtspDcp9J3WF6nf5RGxFbGOGfr+PjxhsitQVfkVQSvcZEduwvhGxDcNNxDYOt1Rqi9im0hOK2KbVEwWlSX0AybAr2JGK2MYBVRN+tQiVeanGRxzCwq+/8YtrXXxtEdswDEVsw3ATsY3DLZXaqdvlqeDUKTlStxNCcVDENhS5yHqpK1S/y6eIrSK2MUO438dHDDZF6gq/Iiil+4yIbVjfiNiG4SZiG4dbKrVFbFPpibflSH0dDkVLxDYUuch6qStUv8snYitiGzOE+318xGBTpK7wK4JSus+I2Ib1jYhtGG4itnG4pVJbxDaVnhCxTasnCkqT+gCSYVewI5s8FoufiK2IbYwGxupfzLeL1JV8RVBq/kzq+MW1Lr62iG0YhiK2YbiJ2Mbhlkrt1O3yVHDqlBz9us4lG7F988033auvvuqGDh06Wx9PnTrVDR482M0999wt+z71AZS6QvW7fCK2IrYxi0e/j48YbIrUFX5FUKrvmSrW15snjYkWUEQvDkLhlyZ+qc9voahVMW8cccQRoZ9XvYoR6Fc97Sqxvemmm9wBBxzg3vnOd/ru2nnnnd0ee+zhfvazn7lTTjnFLbXUUu6NN95wF154oZtrrrncXnvt5eadd173+OOPu8MOO8zts88+TbtZxDZuBKSu8LHyidiK2MaMkFj9i/l2kbqSrwhK/R2xrXt9FbEtr2MiouUxy9boFfxSn39b9ULd84aIbdwYqLJ2L+tpKxy6SmzPPvtsN2PGDE9uIawUiOygQYPcpEmT3PDhw90XvvAFt8QSS3hiO3nyZHf88ce75557zv+M6O2QIUMatk/ENk79U1f4WPn2Ou5yN3na62615Ua5eeaeqylYKy29iFtgvnlm/X6bd63gRi40uC24sfK1/UDkA5IvDkDhJ/ziEKi/dt3rq4ht+T7sFWJGy3b46i/LNzBXY6C2N/X1oVXH1j1viNhGD6vKXtDLepossT388MPdWWed5Qnrrrvu6iCjlC233NI9/PDD/t+nnXaaGzdunCe2/JyI7ltvveVTkXlm7NixIraVqfn/XpS6wsfKZ8S2LHSf23F9t9Siw2ZVW2WZRdx8g/5HfO0XsfKVlavs85KvLGKzPy/8hF8cAvXXrnt9FbEt34cDlegNNKKc+vrQSnPrnjdEbMvPG3XV6GU9TZbYnnHGGW7JJZd0W221lWMwzTPPPD56C8l94IEHvNznn3++u/HGG90rr7zidtttN7fLLrv4ny+++OLutttuc2PGjHE333yz++tf/zpHO3mPihBohMAjz09xb775VlNwHn9xqnt9xsxZv//b/S+6V6bNmOP5o3Z/hxu10PwCWQgIgT5DoJnTtFeaWff6Wgmxdc6dcsAGHtKDf3J7NLTk3uh94TAKv3DsqJnFr1fnj7rnDdnlcTpWde1e1dNkiS2HQ3EQFOWOO+5wu+++u7vnnnt8ejGb1InSnnzyyf73RHUXWmghd8ghh7iZM2e6ESNG+HTlZodIKRU5Tv1T9+R0Wr4zLr/DPfXiK7NAffCpCZ74/vjL27klFllwDrA7LV/Z3pZ8ZRGb/XnhJ/ziEKi/dt3rayXEdi7nrvjW7h4Mpb6W1wlFgMtjlq1RF36prw+tUKt73lDENk5nq6zdy3qaLLHdZJNN3EEHHeQJ7Yknnugee+wxd+aZZ/rDpPh7rbXWcltvvbU75phj3PTp093pp5/urr76averX/3KnXTSSe6WW25p2jYR2zj1T13huy3fp753lXv2pSnuR4du65Yc9b/UZEO92/K1633J1w6h1r8XfsIvDoH6a9e9vorYlu/DuoiUHAPl+4IadfVH6utDK7TqnjdEbMN0tY5avaynyRLba6+91u27774+QrvSSit5crv66qu7K664wu29995e7u22285ddNFFnthuu+227t577/XXAF1zzTVuww03FLGtQ9udc6krfLfl+8z3f++eHj/ZrbzMSDffvHPusUVft3/Pam6L9ZavqYfiXttt/NpJL/naISTiHYdQb+NXpO11r68itkV6YfZn6iJSIrbl+0LEtjFmdc8bIrZhulpHrdTtrNA2d/VUZIQmrXjChAlu0UUXna0N06ZNcy+//LI//ThbnnzySTd69Gh/cnKroohtqEq8XS91he+2fEZsW6G85xZruL22XDOuI2qq3W382jVL8rVDqLeJmfo3rn+L1q5zfRWxLdoL/3tOxLY8ZtkavYJf6vNbu16oc94QsW2Hfud+3+t62gyprhPburpQxDYO2dQVvtvyPfjkBPfajDcagvznOx91f/7HY07ENlwHu92/7SSXfO0QEvGOQyjt2qyvIrbl+6hXiBkt057n8P5NfX0o37JqaqRul1fTyt55S7/qqYhtl3QwdYWSfOGKcdG197hf/PnfIrbhECpjIAI7qmr8xgGYOn5xrYuvLWIbhqGIbRhuVqtX8NP80bifRWzj9L/q2v2qpyK2VWtKwfelrlCSr2BHNnhMxDYcO6sp/YvDUPj1N35xrYuvLWIbhmGvEDNFbOP6N/X5N6x18bVEbOMxrPIN/aqnIrZVakmJd6WuUJKvRGfmHjVim/3xmNHD3WkHbxP+0oprqn/jABV+wi8Ogd6uLWIb1n8itmG4Wa1ewS/19SGuF8Jri9iGY1dHzX7VUxHbOrSlwDtTVyjJV6ATmzwiYhuOndWU/sVhKPz6G7+41sXXFrENw7BXiBmt0x7b8n1s/Zv6/Fu+ZdXUELGtBseq3tKveipiW5WGlHxP6gol+Up2aO5xw++x5ya5g079k1PEthye0r9yeOWfFn79jV9c6+Jri9iGYShiG4ab1eoV/FKff+N6Iby2iG04dnXU7Fc9FbGtQ1sKvDN1hZJ8BTqxxSMittXgF/eW+mprfMRhK/zi8Ot2bRHbsB7oFWJG6xSxLd/Hiti2xkzEtrxO1Vkj9XU4tO0itqHIRdZLXaEkX1wHi9hWg1/cW+qrrfERh63wi8Ov27VFbMN6QMQ2DDer1Sv4pT6/xfVCeG0R23Ds6qjZr3oqYluHthR4Z+oKJfkKdGKLR/LEdviC87ttN1zR11h85IJui3XHxH0gsrb6Nw5A4Sf84hDo7doitmH91yvEjNYpYlu+jxWxbY2ZiG15naqzRup2TGjbRWxDkYusl7pCSb64Ds4T2+zb3rHCYu74/TeP+0BkbfVvHIDCT/jFIdDbtUVsw/pPxDYMN6vVK/ilvj7E9UJ4bRHbcOzqqNmveipiW4e2FHhn6gol+Qp0YotHDL9JU6a739/6kH/y+YlT3XX/eMwtPmKoe/9/I7ZrjV3M8afTRf0bh7jwE35xCPR2bRHbsP7rFWJG6xSxLd/Hiti2xkzEtrxO1VkjdTsmtO0itqHIRdZLXaEkX1wHN8LvXw8/7/7v7Btme/GeW6zh9tpyzbiPBdRW/waAlqki/IRfHAK9XVvENqz/RGzDcLNavYJf6utDXC+E1xaxDceujpr9qqcitnVoS4F3pq5Qkq9AJ7Z4pBF+RGz/fOejvtY9j77o7n7kBSdi2xhE6V/1+hf3xmprq3+rxbPTbxOxDUO8V4gZrVPEtnwfK2LbGjMR2/I6VWeN1Nfh0LaL2IYiF1kvdYWSfHEd3A6/i6/7t7vwmnvcYgsPdYuNGOI/tst7V3PzDZrHjVxosFtq1LA4AdrUbidfrR8v8HLJVwCkko6VuDdWW1v9Wy2enX6biG0Y4iK2YbhZrV7BL/X5La4XwmuL2IZjV0fNftVTEds6tKXAO1NXKMlXoBMjiMUvr7vXXXDN3Q3fsOJSI9wGqy7pf/eR967m5h80T5wwDWqrf+MgFX7CLw6B3q4tYhvWf71CzGidIrbl+1gR29aYidiW16k6a6Rux4S2XcQ2FLnIeqkrlOSL6+B2+L0waap7fsJU/5FLb7rfvfb6G27i5Onu6fGTZ/vwhUfu6BYaOn+cMCK2wq9yBOJe2G58xL09vnbq8sW3MO4NIrZh+InYhuFmtXoFP80fjftZxDZO/6uu3a96KmJbtaYUfF/qCiX5CnZkk8dC8Hv4mYnutnufnkV2X58xc9bb99tubbfjJqvECZWpHSJfZR8v8CLJVwCkFo8Iv/7GL6518bVFbMMw7BViRusUsS3fx4rYtsZMxLa8TtVZI3U7IbTtIrahyEXWS12hJF9cB8fit9dxl7vJ016fJcQSiyzoFllosFt5mUXcvh98Z5xwzrlY+aIFaPMCyReHsPDrb/ziWhdfW8Q2DEMR2zDcrFav4Jf6/BvXC+G1RWzDsaujZr/qqYhtHdpS4J2pK5TkK9CJHYiYnXPVOPebmx+Y9aUFB8/nxowe7vj7/z62SbCQ6t9g6HxF4Sf84hDo7doitmH91yvEjNYpYlu+jxWxbY2ZiG15naqzRup2TGjbRWxDkYusl7pCSb64Dq4Kv2dfmuLGvzzNPfjUBHfuH+5qKNQl3/iIGzz/vKUErkq+Uh8t8bDkKwFWg0eFX3/jF9e6+NoitmEYitiG4Wa1egW/1OffuF4Iry1iG45dHTX7VU9FbOvQlgLvTF2hJF+BTmzxSNX4kZb82HOT3LTpM9w3z795ti8fvc9m/uTktcYuVljoquUr/OGCD0q+gkA1eUz49Td+ca2Lry1iG4ZhrxAzWqeIbfk+VsS2NWYituV1qs4aqdsJoW0XsQ1FLrJe6gol+eI6uBP47f6Ny9y012bMEvTio3d2QxcYVEjwTshXSBARsxiYmtZV/8bBmjp+ca2Lry1iG4ahiG0YblarV/DT/NG4n0Vs4/S/6tr9qqcitlVrSsH3pa5Qkq9gR3aRmB1z7k1u+utvuHsefdFLsct7V/Mpybttvnpb4dW/bSFq+YDwE35xCPR2bRHbsP7rFWJG6xSxLd/Hiti2xkzEtrxO1VkjdTsmtO0itqHIRdZLXaEkX1wHdxK/PY+93E159X8nKCP52CVHuFMP+kDTRnRSvhAkJV8Iav+rI/z6G7+41sXXFrENw1DENgw3q9Ur+KU+/8b1QnhtEdtw7Oqo2a96KmJbh7YUeGfqCiX5CnRii0c6id+vb7zPvfraG+6S6++dJZGIbVz/tavdyf5tJ0uj30u+ENR6xzEQ17r42iK2YRj2CjGjdYrYlu9jRWxbYyZiW16n6qyRup0Q2nYR21DkIuulrlCSL66Du4Xfw89MdIecdrUitnHd17Z2t/q3rWD/fUDyFUWq8XOp4xfXuvjaIrZhGIrYhuFmtXoFP80fjftZxDZO/6uu3a96KmJbtaYUfF/qCiX5CnZkk8e6hd8jz0x0B592tVtgvnndikuNcDtuuorbcLWl5pCyW/IVRVXyFUWqN4mZ+jeuf7tdW8Q2rAd6hZjROkVsy/exIratMROxLa9TddZIfR0ObbuIbShykfVSVyjJF9fB3cLPiK1J/9kd13cf3HAFEdu47hR+wq9iBHr7dSK2Yf0nYhuGm9XqFfy6tf7HoVt/bRHb+jEu84V+1VMR2zJaUOGzqSuU5Ivr7G7hxynJ/3lqgvvNzQ+4v9/3jNtg1SV95Jay+Tpj3BKLLOj/3S35iqIq+Yoi1fg54dff+MW1Lr62iG0Yhr1CzGidIrbl+1gR29aYidiW16k6a6RuJ4S2XcQ2FLnIeqkrlOSL6+Bu43fGb+5wf7zt4dkaMWb0cLf0ogu5ZRZbyE2cONFt/e413IpLj4xraE21u41fu2ZJvnYItf698IvDr9u1RWzDekDENgw3q9Ur+KU+v8X1QnhtEdtw7Oqo2a96KmJbh7YUeGfqCiX5CnRii0e6jd/t9z/jI7eUG8Y97p59aUrLBq223Ci36/tW8xHeFEq38WuHgeRrh5CIbRxCadcWsQ3rn14hZrROEdvyfayIbWvMRGzL61SdNVK3Y0LbLmIbilxkvdQVSvLFdXBK+D323CT31IuT3RPPv+wbBel96OmJDRs4fMH53QpLjnCrLLPIbL9fdOEhbqv1x8aBUqJ2Svg1ElvylejMBo8Kvzj8ul1bxDasB0Rsw3CzWr2CX+rzW1wvhNcWsQ3Hro6a/aqnIrZ1aEuBd6auUJKvQCe2eKRX8Lvv8fH+/ts7Hni2UIP33GINf9LykPkHFXo+9KFewS+0fXXXE35xCKeOX1zr4muL2IZh2CvEjNYpYlu+jxWxbY2ZiG15naqzRr+ucyK2dWpNHxCfLsHT9rOpD8hek+/lKdPdw89Mcvc/MX427F965VV39e2PzNEfW79rBbfuyqPdsMHz+X27I4Yt0LbPyjzQa/iVaVsnnhV+cSinjl9c6+Jri9iGYShiG4ab1eoV/DR/NO5nEds4/a+6dr/qqYht1ZpS8H2pK5TkK9iRTR7rN/wuuvYed8VfH3RTp89o2GI7fXmpUcPcWiss5kYOGxwFYL/hFwVGQGXhFwBapkrq+MW1Lr62iG0Yhr1CzGidIrbl+1gR29aYidiW16k6a/TrOidiW6fWtHh36gol+eIUo1/xI3p75wPPulemveb37U6aMn0OoCC3RHDfv+7ybvTIoW6tsYuVBrNf8SsNRGAF4RcI3H+rpY5fXOvia4vYhmEoYhuGm9XqFfw0fzTuZxHbOP2vuna/6qmIbdWaUvB9qSuU5CvYkU0eGyj42enLz7w0xf3r4efdxMlzEl0g2mK95d0hu7yrMKgDBb/CgJR8UPiVBCz3eOr4xbUuvraIbRiGvULMaJ0ituX7WBHb1piJ2JbXqTpr9Os6J2Jbp9a0eHfqCiX54hRjIOL39IuT3YTJr/rrhZ4ZP9nd8+iLc4A4euSCbtTwwW7++eZ1G622lFtq0WFulWUXcfPNO89szw5E/OI0bvbawi8OzdTxi2tdfG0R2zAMRWzDcLNavYKf5o/G/SxiG6f/VdfuVz0Vsa1aUwq+L3WFknwFO7LJY8LvbWCu+8dj7uRf3dYWzDWXX3S2Z6ZPn+5Gj1rYLbPYQm6N5Rd1pDePGj6k7Xs69YD6Nw5p4ReHX7dri9iG9UCvEDNap4ht+T5WxLY1ZiK25XWqzhqpr8OhbRexDUUusl7qCiX54jpY+M2O33MTprgXJ01zr8+Y6W6972n31AuvuAefnOBef2NmYaCJ7A6aZ25PdAfNM49bfcwoN3SBQW7skiMKv6OqB9W/cUgKvzj8ul1bxDasB0Rsw3CzWr2CX+rzW1wvhNcWsQ3Hro6a/aqnIrZ1aEuBd6auUJKvQCe2eET4FcPv7kdemOPBl6e+5u66/zH37CszPQHmyqF2xSK+kNzhQ+d3qy03ylcJObiq3bf4vfq3CErNnxF+cfh1u7aIbVgP9Aoxo3WK2JbvY0VsW2MmYltep+qskfo6HNp2EdtQ5CLrpa5Qki+ug4Vfdfjd/8RLbsYbM929j413M2bOdP9+9EU37bU33CPPTGz7kUUWGuyWWIR9vUP832uOXczN9d9ayy+xsFtw8Hxt39HoAfVvEGyzKgm/OPy6XVvENqwHRGzDcLNavYJf6vNbXC+E1xaxDceujpr9qqcitnVoS4F3pq5Qkq9AJ7Z4RPh1Bj+L+D7y7CT3ytTX3L2PvX1gVaODqxpJtNLSI91CQ+d3Ky890v96zOiF3bAh8/l7eDnYqllR/3amf+O+El479f4Nb1k1NUVsw3DsFWJG6xSxLd/Hiti2xkzEtrxO1VmjX9c5Eds6tUbEpzZ0Ux+Qki+u62PxI32Zk5nt738/9qJ78823vFCPPjvJTZ0+o5CApDTPM/fbMd4VlhzhhiwwyC0yfIib6/XJbokllvAkGDKcWonFr+72SL66Ea73/SK2YfiK2IbhZrV6Bb/U57e4XgivLWIbjl0dNftVT0Vs69CWAu9MXaEkX4FOlOMiDqQu4vefpyb4CO8DT77kpXj8+Zf9/ydOme64tqhsWWzhoW6xEUPc8KELuGUXX8hXt/29Cy+4gD/duZNF4zcO7dTxi2tdfG0R2zAMe4WY0TpFbMv3sSK2rTETsS2vU3XW6Nd1rueI7dSpU93gwYPd3HPP3bK/Ux9AqSuU5IubToRfb+N33+Pj3Rsz3/SNIM156quvuwmTIb2v+J9NeHmKe2ZC+0OtGqGw5KhhbuSwBdwC883rSIWmjBg22C3dIPXZ9geXRVP6Vxax2Z9PHb+41jWvXWZ9vXnSmGgxRPTiIBR+aeI30OaPMvPGEUccEddpql0ZAv2qpz1DbMePH+/22msvN++887rHH3/cHXbYYW6fffZp2sEitnG6n7rCSz71bxwCcbVN/16YONU9P3Gqe2Xaa+7x5172L2V/71tvveU43fnJF94mwlWW+QbNM2tPcPa92YOxJk6c6EaMaH4NEhFkIsn5Mv+ged3Ky7xNtussGr91olv+3SHrq4hteZxFRMtjlq3RK/ilPr/F9cL/aofMGyK2VaEf/55+1dOeIbYnnHCCmzx5sjv++OPdc8895/e34SUaMmRIw94VsY1T+tQVXvKpf+MQiKsdon9Pj5/sJrzyqnttxkz34H9ToCdNgfy+TYiz5bkJU934l6fFCVlTbYs4l3k9B3Qtt/jwWVWaEe+lF13IjRg2J+Eu+q2qrncK6d+iMqb4XMj6KmJbvid7hZjRMqUih/fvQJk/QuYNEdvyelVXjX7V054htvvvv7/bcsst3R577OGjIaQiP/zww27s2LEitjVofeoKL/niOl349R5+r8+YOWtPcFZ6ixBnf9aMOD714mQ3cfKcKdSQbfYdq7yNwKkHbNB0belHjELWVxHb8pogYlses2yNXsEv9fU1rhf+Vztk3vj8wV+q5PNDFxjk31P0IMh2Hx2I7+tXPe0ZYrvbbrs5/uyyyy5ePxdffHF32223uTFjxribb77Z/fWvf51NbwcNGuRmzCh28mk7hdfvhYAQEAIDHYFX3xzkXn9znlIwzHhrbjdtZvu7gqfNHORmvFXu3VlBXn4jPMrbqEE7rzzN7bvvvqXa2ssPl11fyZSaNi3NjIJe7gfJ3h8IrLHGGm777bfvj8a0aIXmjd7u4n7V054htscee6xbaKGF3CGHHOJmzpzp949NmjSp6SFSqaciS764CUH4Cb84BOJqS/+EXxwCadUuu74Wlb7qcaL3FUW+8XPCLy384qTpfm3NG9X0QerjsppWdu4tPUNsr7jiCnf66ae7q6++2v3qV79yJ510krvllluaIlW1olTdJZIvDlHhJ/ziEIirLf0TfnEIpFW77PpaVPqqx4neVxR5EVsQSF1f4nqz+7U1b1TTB9LTanC0t/QMsX311Vfdtttu6+69917Hv6+55hq34YYbithWqw+z3lb1QKtaTMkXh6jwE35xCMTVlv7F4Vd17bLra9HvV93Pel9R5EVsRWzjdKVIbc0bRVBq/0zq81r7FqT1RM8QW4PtySefdKNHj3bsoW1VqlaUqrtN8sUhKvyEXxwCcbWlf8IvDoE0axddX4tKX/U40fuKIi9iK2IbpytlamveKIPWnM+mPq/Fta7ztXuO2BaFiAOlNtlkk6KPd/w5yRcHeer4cZjZe97znrhG1lg7dfwkX1znC7/+xi+udZ2rXbUe6n1xfSf80sIvTpr+rS09jevbqvGLk6bztfuW2HYeSn1RCAgBISAEhIAQEAJCQAgIASEgBLqBgIhtN1DXN4WAEBACQkAI9DECr7/+unvjjTdmtZArgmLKm2++6fhjZd555415nZeNd/znP/9xK620UtS7qPzEE0842sxWqQUXXDD4fbznqaeechtttFHTWx9CXl5Ve++88053//33u49+9KMhYjStEyvfW2+95W/MaFZC9QlrWAsAACAASURBVOWZZ55xzz77rFtvvfVmvZpDTDfddFM3ePDgSjHQy6pDgH7j9pRsH/35z3/2/TbffO2voDNJ6tIre3+s3leHWP+8aUAQ2ylTprihQ4e6ubjdu0GZPHmyGzZsWG29ymLMJntkaFaQgcWwmYy1CeecNxZ6Xb468Wn37iL4TZ061eNcp541k7OIfO3aWOfvi8o3YcIEP4bmn3/+OsWZ491F5Js4caJfRLtRisrHdWnzzBN+V2xo24rIx+IOKYglPzFjgDGKETT33HOHNlX1/ruebLPNNv6Ax2yxNS4EpJ133tldfvnllbwPXXvf+97njjzySPfLX/7S/f73v3frrLOO++Mf/xgimidTe+21l/vDH/7g37vwwgu75Zdf3h1zzDFB77vvvvvcLrvs4l555RW3//77uz322MOtssoqQe+iUtXthXizzebWW291Sy21VLBcWcO+iv74+9//7rbcckv/WnQtW1h3n3vuudLzy9lnn+1v4njsscfc5ptv7l8J0TnrrLPcgw8+6J0YKvUg8NJLL7mPfexjbvXVV3df/epX3SKLLFLqQ6TjnnDCCe7SSy/1d24fdthh7pJLLnHPP/98KYdEHXpV5bi866673Ac+8IGm2DzyyCMtuUcpUHvk4b4mti+88IL717/+5VgUH3roIbfYYovN1i14Hj/5yU+65ZZbzj3++OPunHPOceuvv36lXfezn/3MnXLKKX4BYIG58MIL3aKLLjrrGw888IDjLjCMKWT41Kc+5fbee+9KZWj1snbyYbAfcMABnpC9+OKLfsHdZ599kpHvu9/9rmM/KwUD+ne/+537xz/+4Q2VTpR2+E2fPt3tt99+7uWXX/Z9jFyhBk9Ie9rJ99prr3l9xBClYEAdd9xxIZ8KqtNOPnspY2OttdbyxufGG28c9K2QSu3ks8jFCius4BdPxi6GaKdKO/kwQnfffXffx0Qs1l13XW/Qd6q0kw9ScM899/i5F9J98sknVy5aOxnGjx/viQn4oGcYQJ2c4ypvcJdfCBGgPy+44IK2hzwWEfXGG2903/jGN9xVV11Vmpg0ej8EmWtK0LVll13Wr2u77rqrO/PMM93SSy9dRKTZnvnLX/7iPvOZzziiQazfkOUxY8Y47IuQ99nLIbjI+vOf/9yNHDnSffazn/VyLrDAAqVkrLq9ENp3v/vdXoasTRVqQFctHyR0u+22c7/5zW/c2LFjvT3AvPzPf/6ztP7grMBBwzyPHWllxRVX9E4MlfoQYD29+OKLPWmbNGmSW3PNNUt/7Ctf+Yr729/+5rCz6b9vfetbwQ7oKvWKhlSl9/CKvCMnC1S3HO6lO6vCCn1NbPHUQHpYwPDS5IktA+bLX/6yHzg8+6Mf/ch756oqKBynNzMohw8f7r7whS+4JZZYwnufrLAQrrzyyl4OUqL4N2SjTKpEqLxF5Dv33HO9RxtPFxPEvvvu6yeJTpQi8mXlOOKII7wH9Ytf/GInxPOOinb9C354/DCa8PQymX34wx/uSOSsiHws2F//+tfdRRdd5A37TpYi8iEPkbzddtvNPfroo+6HP/xhx4htEfm4T5sxveeee7prr73WHXrooQ4PaidKEfm++c1vuhkzZnhnCk4WIpJPP/20W3LJJWsXsYh8ZNOgf3fccYdP9aua2BaRAa8+hsHxxx/vozr0J9HbuqLHtQPf5Q9wJR+OAe6bx2kcW26//Xb3ve99zxPGKsr111/vLrvsMvfOd77Tk9Ff/OIX/t+s/YsvvnjpT7Au/vjHP/Yy4iiHNL/rXe/ycyoEKKQw591www1+vcAZ/v73v9+NGjXKgQUErUxWQdXtbWZIhxrQVct35ZVX+j4FNyvMLfx/1VVXLd0d2D84Vc4444zSdVWhPQI4/VutmWQHlMk0Qj/JdqBgc2EPQkoZ84wbnEQhpWq9qlrvCeKx3ucLjrGBljLf18TWOpj03kbEdplllvFkjb9ZLIha8VxVBUOc1JiHH37Yv/K0005z48aN895sK0RrIUeQXfOEdsrwLCIfe0vWXnttv7CS2nHIIYd4470TpYh8JgeDeqeddnJ4uTvhFOC7ReQzo92890w8H/zgBzsBXyH5mKyJVmHYb7DBBv5Ce0u5qlvIIvghw5e+9CW3xRZb+PEDnp2K2BaVDxlxXOAYI3UKJ1UnShH52GLA/EeU57e//a3HkuyVTmx5KCKf4YTRiFxVE9siMpDuyTxNpB1DCOOHOZtoj0p5BMjygUCQMZUtoanIOFKZmyAnZEYYqfvBD35QOnqJPDh62B96yy23eGP3pz/9qXdknHfeeeUb+98aRDCJ/rK+kxWBsQ4hCilEGIkGrrHGGt5B8JGPfMSTWgoONDJqyhDmqtpLRJY5DvKOMyhfcDqWjSbX0R/YLGQeMa6xXQhu/Pvf//b2S0ghok+08PTTT/fvs7mT+aGMgyHk2wOhDuObjAwy7gigEIBirNNfZBrRd2X0ymzBZtjdfffdQU7LqvWqqnFp7WSrFnMHhXWf+e1Pf/qTD6yUcQz0g84NaGLLnjMGFR56Ju33vve9jvu4qioMMFKHLMJ5/vnnO9Kq2LdhBe82h0SQJoEism8j1AAoK3cR+fBiY6wTWb7ttts8aYQMdaIUkc/kYBHDuCDtt1OliHxEuG+66SZv5OA8Ic2R9NBOEIsi8hGxYDL8/Oc/7yMi3/72t33aVSrykUkBIcPoxPHUSWJbBD/TNQgZshLlqzLro5UuF5WP6A/9+v3vf9+n5+Gk6kQpKh+y1EVsi8hANgB/2GZBIWrHXEc6qUp5BBivOG8Zs9n98KFRAwhyI1LyoQ99qJIsE4g4/U1kKPQMBNImydhgrscxiKMk9CwA5g/S83FCVVnscCtIQkiKtOHEdi3amS+x/RErX1Ye7Di2JREsoD+QreweTXsfxIDtWPmCvaasjuo0FOc/TuHrrrvO2x9kGOHcIUshNMqKdFUeOlelXuUPJWN80dYqDyXDJmbND8lEqa5nO/+mAU1sN9tsMx8hwDtEKhzRU9KIqip4TZj4GFgMVItG5FNl8e5CFvFGQ4SrJNet2lJEPogZ3nfSfCHcOAMaRb+rwiz7niLy8TyHDODRZmLg4I5OlSLyYZxg4EAszGjGg1zG4x7aniLyQXrw5vGH/Y6kI6N/IYZPWTmLyMfEjGGLUcKkT6o+KWVV74VvJHsR+Vg0SDsktZctB6TjdSrjooh8GAeQNhxSRLxx4nWqFJHPZKmL2BaRgXmfeY1sFMYAfUhfKhoTpilEyXGGEn2pKuWdfayQT/TZyuGHHx6UncNWH+pmHbRE5Rqdw1EEAQxU9hNnC+s9bd9+++39NqQyperDmao+3Iq2QL5ZI1hzieKyh7XVATat2l+1fDgYcCSzTUSldxDg/AzOTMHZTsE2wSbGXiIbIqRUcegccwOp6M0Kul92G1cdh5IRUcYpYAW54RrYxzGOgRDcu11nwBFbPKukNpDaREothIhFDk8RpxJXfXAOe3dIU+Tgm6233trvddtkk01myYBBR7SYaApebiK6MSlRZRWqnXzsG0I+5ORglQ033NAb7mUHclm57Pl28vEckTL2EVTplCgqbzv5iNL/5Cc/8d58rnCAqGEIdSo1pJ18Rx99tD88BR0lLR+D1FLni2IQ81w7+SDZZswSjecAFbzvnfKUt5OPwylwXIAj2RekTKfUv+z9w4DvxthAL9rhVzexbSYDRjhEiXmZsUmaIcY6+0IxiInGqIQhwDxC9JNCWqFtDSFzKWTc4tBiXGGkZW8WwBERsu0E+U488USfeo4hiJOMlOTQU5FZD1dbbTV/YBGpquydg9yzVrJ3kEhUmfWy6sOZqj7cin5kDuZgJbLcjjrqKHfqqaf6jKSQ/qhavjpShyHvbCOiT0lBxoYDA5XqEGA7ANF1/sZZzDhFv1jDQkpVh85hg+AUblbgDGWzM+o4lIxMO7ZnWCFDZocddhiQh5wNGGJL1Id8fVLTmJTYXI4Bb/v1iPRhzFTt2cCgtFOOWfg4UIIcf5MBsrjtttv6iAHEB2OrKi93kcmgnXxEZxkceIMopII2Sssp8q2QZ9rJxzuJipJqQVS506WdfEQHODQMDzJGHY6NTp6a204++pW0OQwv/iAfUYZOlXbyZeVArq997Wsd22PLt9vJB5klTZ+D3zDqOHUR50CnSjv5yLjgALNsYbtDFfd2FmljO/nsHTjOmI/riLI0koGIGnOupRwzB9OXRHg5BRUDViUMASItRPLyBZIbssUB0onzAedbFYUsC9Z6iDJ7bYkQ7bjjjt65TISobCFNGsPXDrdinzYEl2+QVojTtcyJrlUfzlT14VYczGSnzDLXQd5J42ftIHW0bKlavqpTh0kLx07EQYijn3Mo2AMNBp3MECuLa689z7zBvlPWAZxB2Mgx61TVh86BJ9kFOK6tsD+fE9FDUn3ZAoZuffzjH5/1Phxu2BMheoVcjMXsGS7cCMAcF+Jw6jX9yco7IIhtqw5iEUEhOEAqZNEt0vlEiUk3bpUGaOmfdcnQSs4i8oERjgEOuup0KSJfp2XKfq+IfBh63bpHtIh8nAbbrTv5isiXev/iIGBx60b6aj/gV3f/FsGIOZgx0I05ru72d/r9OGmz+2KJiJOuW2bfMk5B5iUicFxZRcSMSI5FP0lPDFkvud4JxwWRIA5t5IwLSCmpyCF7bDHEP/GJT/iDWogos1ZCaDEyt9pqK5+SjX1RpkD2SMEEAyuhqdfUr/JwK/qWCDr2AA4hSB8El5NnQzORqpSvEc5E40P3UBNdwzlGZM5OvSaySJT6He94R5lu1bMtEKCPcBqQ8VBFqfrQObanoEO2VZCteWyNgoST7VmmHHTQQT7Ywdxm5B0uQuAtdEsd2SeMS85LwUHMOoajCedtWfnKtCXFZwc8sU2xUySTEBACQkAICIFeRADDj/ReTqYlrZBsKc4UIBJeJiWX1NZW2S0h95Ianmxv4BRR/uauTA5Ug+CGFiItpKtSiDyRuUF0h6it/bzou9kTt/rqq7sDDzxwNqJN5k+o06XKw61oBwY4xJ1TmokKQfBishyqlK/qPdREEYnAE5nDAUKEDVJL5l03HJlF9ajXnrOUfpwkzB/mtArZw0rbqz50DuLNdhUioOyRZVsU26NwQpWZ15CNORLHH2Moe2f6UkstFeRc450QW8Yicy6BNLbV4GATse21kSB5hYAQEAJCQAgIgWQQIHUYox9SSpQVYkfqJpGY7B7ZEIFJ8w2J0ua/hdHL1XCQFivIWNZANSOVKDD7yTFYLZLMoZEhxIdUafYHVnn1VZXtDem3RnXqOJSH71S9h5p3QkA4+4SUeFI9ISMhd+JWhV0/voftPJxHki8he1jtHVVkjti76HsOrjzllFO8I4xILfuscWqF6kKV8kFsufcapwvXJyErY4wMEkVs+3HEqE1CQAgIASEgBIRA7Qhg8LF/lUgoKZtcDcNp/xyOGLpnjndBkHk3Z1UQLSEaEVKIDhJNJo0wewUREY6QK4m4UYEoTlXXpHGuBYYz98Jm061DU6+rbm8I5o3q1HEoD9+peg817wRDHDOQL3SaCHUVDpaqsOyX95DlQCSTvafgTOZCaKkqc8S+jw4QrWc/Ps47DqHlZxwUF+oQqyKzxeRjzy4HsR188MH+R8yZZFR06vrQ0H6qo55SketAVe8UAkJACAgBITAAEcA4Peuss9xyyy3nI7cc+LbTTjt5YzCksM8WEsqeU1L1iGZyjQfpdpxbULZwzy4phRxYVkWB0LL3lzM0SMe1KC0YcGds2cKpyOzZzZfQ1Ouq21u2Pe2er/p6nqr3UHOgHHvDOaGXyByRMQ6CJDU1JCLfDo+B+nsyHDilnn2hOHFwiHHmAY6KEJyrzhxhiwH7YDkBmcwRxikp6qHR0KrlQ2/Q1RtuuME7ANFVIsoD7eAocBCxHaiziNotBISAEBACQqAiBNjbSLSRQ4WyJ4fyc4xBrkkJiXJhABIty165wYmpXG2x7rrrlpYeoszVexzGxDU9VjBSQwxoiBRGbr5ss802QZEcew/32WJMY9yHGs+8q+r2lga8TYU6ruepcg81kXx0kNOgKZAbIv4Q29AMhKox7If3Mca5x5ZDlSjMG+zN5+f8XbZUnTlS9eFWVcuHzqOP3HLBYXWcaYADxu4FLotfLz8vYtvLvSfZhUAXEMArSAlJ2+uCuPqkEBACHUCA6Oxhhx3mjj/+eLfXXnvN8cXQiOOUKVM8AeUUY1ITuc6FK944xbjs/ZEIxem9pJJiBGbnMKK4Iffs8k5SVEn9Yy8mdyQTKQk9ZZ6IN/hxGi/7frn6Y/nll/fX6YSUOtobIkezOlVfz8MeW67l4ZpCTqVeeumlo8QlxfNzn/ucJwjoB6fWctAYP4/dMx4lWJ9VBk/uhWccZh0IEDScYmVL1ZkjVR9uVbV8OF/IZmGPLYUIOPMm80gIfmXxTul5EduUekOyCAHnfHod+yKssEBzhDsGUzcKKX9c14GRxXH3HJKCd5Dj9EnBw7OKQaciBISAEKgDAdLruI8ZI40rZjihNvQUXlJzSW/kBNEqCvvsILFcVQKJIsLENzicKiQNkH1ytA8ixZ2WnMRLKiwRnhCSVnV7q8Cs3Tsg47Q1ZO8iaxP7NEkZ5gomIqzsizzqqKNKvY+Tn9nLTSENnrLBBhv4vdQ4RJBRzt12PVnu9ziEIJDrrbeex5xroDiBuIqCXoRsDbBvV324FZkUZLjYfu1Y+bDH2F/LAVLoJXbbZptt5sdCyDVmVWDerXeI2HYLeX1XCDRBwPaNcd0D96Oef/75/snQPWWxQJMSx365b3/7296jSkogxgIHxLAAMSFzkIKKEBACAxcBi1Y2Q+CRRx4pFeEiDZc01UUWWcQbgI0Kac9lo6ykSXMwDSc2M3/Z3auhhzNBHH/3u9/NZoDzXog4ewbLFiKORKeJOtq9qTgQL7rooqCUzKrbW7Y97Z5nvyLGOJE5K1deeaU3yFl3QoqdNksEi0gW0Xn28pYhNmQmsfZlS/ZUbt4Zkroe0p5+rsP9sK+88oobMWKE/xvcuSYLUhuSgpzFigPnGOc4wYhasoUB8hxakBXnGnMO+/3XXHPNUjqV/S6OL8Y38yJnBnDaNsGDkHMDeC/zL9spILFkeOCY4d+WLs8cteSSS4Y2vafqidj2VHdJ2IGAABMbE5NFQffdd19vJJGqw0R15pln+qPc2efD0fPsMyNawMRFNINoBOldnKpJpBXiyc+5W5EJmb1CHGzy+OOPO+6I4zh97j4jbYsF4N5773UYBnwXIkukGKMDDzr72jAUMDqJJGSJLSlaeFf5gwFBWiLvVxECQqD/EcDZlc00ybcYw7VMIbX5kEMO8ddpNEpt5ntE9MqeRlz14UxEmDjFmPfSRv7PnMw8CikPKRj1EO1x48b5d+HUtL2HZd9XdXvLfr/d85yAy6nSrD8PPvigd36wlrCnMaQQ3cdxwYFjkBrWr+HDh4e8aladKk/ljhKkDytzqvinP/1px550oo7oQraQsRYSuUevNt54Y4eTBCcRNhLEkW+Q3l+2MLfZKcbU5d84oUL11L7P2MZpdcQRR/j5k60XISnuZI5wEnSzArEPvQe7LFbdfl7Etts9oO8LgRwCEFsiEZdccomPWLC/B68eUYvdd9/dG0xES7nzjd8T1YWAQmIpkEnuMcPbCRnFQwkZ5mAGUqmY3LmHj3QVyO8JJ5zg9t9/fzdq1Chfn/QrjAO+A/mFyPI9TjaFBHPkPZMvC0SW2PK7r3/96/7bpBFBhvmbxUVFCAiB/kaAOerYY49t2sjQU4Jboca+U9J2F1988dLgMochM1GX2MgbhrPtbUOQo48+2s+poQUjlQgjJ8NC0jgQJmQ/cbPvY4yzrSSFFEUOZ2KtYU0ilRLHLGT0ggsucIsttlhpCCEHtA/sSEfmHeAHcQghSFWfyl26QX1egSg44xiCCzFj/3K2MK7KRNqtLpHVK664wtsslvnAWOJ9pP+WLVdddZW3lwgy2PvYlkWgIORaIrIByIJjTzFEG2KPgwwZY+ejsm3rt+dFbPutR9Wenkcgv8cW4+PII4/0hJX0FyZ+vNuQT/ZjMYEz6UJsjUiefPLJ/oAVPOCkovAMXkCMCJ5jkScFjyguZJffQ2wPOOAAnwbHZE2UhIWByTubigyZbURsiSCTqoP3lf1HpFB/9atf9d9QEQJCoL8RIMpmexEbtTT2lOAq0TvooIM8gRo5cqQ/NRjHIM67mMKcx52WRHJiCSPz+m233eazbaxwinPInl3OQKAukSsrOEQ5fCuEOMZg1KgucnBKNf2xyy67+PWCK6LYqxyaOkmfEk0jnZkMIq4+IS05ZE9s1adyV41fr78PQkvq8TrrrOPtGWyGKsqMGTN8ai6OL7LLcMhDaskECSGO6ClnnZx33nneaYVeYPOgZyGONVKFCRbgbMG24zopbLEQHa0Cr356h4htP/Wm2tIXCFjEFhKKp5LJExKK0cgeETztePWsEMVlLwmElT0qGFWQWsgt+6u4X5H9HKShEI0lkgqxtbQUCC0TK39j4OFF/PWvf+123XXXwsSWlDmMLgwlDjyxwkQds6elLzpUjRACAwABrr0hSsb8xKE9+YKjLCRiVjV0nAfAnjb+Rh4yXiBQ/B1yknHVpxjffvvtbosttvBEO5uSSFp2CLHF8Cade4899vARTIg3RnlsCmWV/YKRT2o5WUlEvznsCedDSIHAkr7KH/YtEq0N6Vf7dtWncoe0qZ/r4BDDwWTOIK6qyRbGQ9l99Faf7VwQUbIf0AWyAnByhBay57CN2AcMAcWJ3ypLpch3sNEYjzhzYlKRi3xroDwjYjtQelrt7BkE8ntsTfAJEyb4PVvsHfrpT3/qr5fAq8/hJXi5Iba2P4MTNVnQIacYcXvuuaffr8Z9iOw7wtCBMPNvjAgm6HbElkUBo4M0sUYRW/aFsZ+L6C+pZcjHpE+kRkUICIH+RoBtCz//+c99pkf2zllrNXNOlem0oWhC6IjgZE9F5nAmInshKYpVn2KMkYshzvaRKgqRUCJiEGX2L7I2MOd///vfdyussEIVn4h+B/sM2StNdNkKDhI72KvIB3gHRIZ34LDI1y37vuw3qzyVu0hbBtoz9B3RdU7+xs7IFohumfuvLQJMWi9XjFV9zscLL7zgzzDB3gndQ0/7SLtmexhOQLYeYKdhK5G9EBJRHmg606q9IrbSBiGQGALNiC1ickAUe1g5SIqCcUJ0Fu92lthyuBMTJSnKFAw2CCde0UMPPdTvz7Wf804iAxBbSx2+9NJLfVoYqch4OklHhqzyDUi1nYScve6H35PyZt/cbbfdfDpySJQhsS6ROEJACBREgOwQIoM4wOxEzoJVO/IYEVacgzj9OLWYiCbzKeQvpFR1ijGEjBRa0oTJwsHpyPxqUe7QU5uJpHMoIFtMmN/ZygKxJ7UyNmU6BK98HdK3WV9Yo3C8WrFrS4p+gzMfWBu5v5PzKcgeYtsMhIkzK4jQh+zVxMnAwV0nnXRSUVH0XAkEqs70qDoCjH5yPgDpxxDRRoVxWnZPPfMkukUAgvtmVapDQMS2Oiz1JiHQMQQ4eAAi2i5FB0MJgylvFOEhJALMnt0i3lCMBdKc+V47oophhrec9CIVISAEBhYCpPRyyAqR0ZdeeslHB3GOEUFJJRJB1AWyQrR100039dslYuarKk4xJrpEunCzwu/bzffN6uKIZL7nbzJp6Iv8IT3d0lK23NAXVd0rTNTvy1/+sneusLbR5jXWWMOR0hrSx6xnHDCGU5c9m7ZecspsKvrcrb6r4rt1ZHpUGQGGKJOJxrYqtlrkC3t5SSMmUlz25Pcq8NM75kRAxFZaIQSEgBAQAkJACFSKAFeGsU2CKGFqe8eqPJwJ0Oo+xTikY3Aq2J7n7B2x2Xex35CoZjcLkeOPfexjPoqMAza24FAhskbKNYXsJVKuOaMi5P2knXKoYr4Q4Q91NMS2sZ/q43hgrmhWesGBADnnLJN2Tv9+6reU2yJim3LvSDYhIASEgBAQAj2EgJ32icik+xId5MqvbJppN5tT9eFMnDfAnZn5gqHLafLdKhjbbBuBNDba80ykiW0onJzfjZNYiawSzacYsSF92MgBqcUhxJE72Tlckb9JESVVnBNnScVWSQ8BUsQb3VNtksqBkF6fpS6RiG3qPST5hIAQEAJCQAj0CAIcisLd2By4A1HhhF+IBYf3pJC6WfXhTEQc2UvMflUIPEQKogiR587wc845J9meg9huttlmbvjw4R2XEWJNerQViChnPfBzTuxHd4psk2kkOHejcq0L5JmbBFLc691xwHvgg2x34tos9lvjLCIFXEUIlEVAxLYsYnpeCAgBISAEhIAQaIkAV7dwNUYqqch1Hc4EgTrwwAN92i/EnT157F9lz92KK67oT64fM2aMtKUJAqSictAh95gef/zxPjWa1F/uHw0tXH/H4VscmkUqK+RW186FotmZejg5OESJGx72228/t/322/s90VzXoyIEyiAgYlsGLT0rBISAEBACQkAINEWAVGRSkCdNmuTvEt122219ami3D1ap63AmDpbhznD2iFK4j5XDijhpmet1IGv8X6UxApwMS5owh1plHQOhh2UR6SNyfuWVV/rr6b7zne/4yDmpzQsvvLC6IVEEyKRg7ztjyQpODm5zIHqvIgSKIiBiWxQpPScEhIAQEAJCQAi0RIBTkTmNnStcUkg9zgv7zDPPOGTMHpp09dVX+9ORQ/eaQt5fffVVx2FMRG656oi9xUceeaQbN25ccErtQFA1HANcp3Luuef65pKKDI4Q3uWXX740BBAhrqk77rjj3Cc/+Un/bxwrp556atA9xaUFUIUgBB555BEfqeXeZVLj2dKw/vrr+5RkrmtSEQJFERCxLYqUnhMCQkAICAEhIAR6FoGzzz7bQWLZxwfZIMje+QAAIABJREFUobAfE2LFIUqjR48ObhsRQdKP2UtM6jHkmQhhKFkOFqQHK+IY4D5TItvsUSbCSjpxSIEYcy0PqcxE0T/+8Y97UkvKeIqOlpA29msdoutcq8RJ1hT26u+777792ly1qyYERGxrAlavFQJCQAgIASEgBNJBgGgeV9/cf//9fl+nFfbCQqZUuocAh3BxAi77k2MPe7rrrrv83kyivtyhvM8++/gosEr6COBoevTRR71zSI6I9PsrRQlFbFPsFckkBISAEBACQkAIVI4AhxXxR/stK4dWLxQCQkAIdB0BEduud4EEEAJCQAgIASEgBISAEBACQkAICIEYBERsY9BTXSEgBISAEBACQkAICAEhIASEgBDoOgIitl3vAgkgBISAEBACQkAICAEhIASEgBAQAjEIiNjGoKe6QkAICAEhIASEgBAQAkJACAgBIdB1BERsu94FEkAICAEhIASEgBAQAkJACAgBISAEYhAQsY1BT3WFgBAQAkJACAgBISAEhIAQEAJCoOsIiNh2vQskgBAQAkJACAgBISAEhIAQEAJCQAjEICBiG4Oe6goBISAEhIAQEAJCQAgIASEgBIRA1xEQse16F0gAISAEhIAQEAJCQAgIASEgBISAEIhBQMQ2Bj3VFQJCQAgIASEgBISAEBACQkAICIGuIyBi2/UukABCQAgIASEgBISAEBACQkAICAEhEIOAiG0MeqorBISAEBACQkAICAEhIASEgBAQAl1HQMS2610gAYSAEBACQkAICAEhIASEgBAQAkIgBgER2xj0VFcICAEhIASEgBAQAkJACAgBISAEuo6AiG3Xu0ACCAEhIASEgBAQAkJACAgBISAEhEAMAiK2MeiprhAQAkJACAgBISAEhIAQEAJCQAh0HQER2653gQQQAkJACAgBISAEhIAQEAJCQAgIgRgERGxj0FNdISAEhIAQEAJCQAgIASEgBISAEOg6AiK2Xe8CCSAEhIAQEAJCQAgIASEgBISAEBACMQiI2Magp7pCQAgIASEgBISAEBACQkAICAEh0HUERGy73gUSQAgIASEgBISAEBACQkAICAEhIARiEBCxDUTvrbfemq3mXHPNNdv/33zzTTd58mQ377zzuqFDhwZ+ZfZqr7zyiuO7w4cPr+R92ZfUIW9ZIWnbxIkT3ZAhQ9wCCyxQtnrPPl9Xu3lvXi/rBqlOHW0nezfa206mgfz7uvR6IGOqtgsBISAEhIAQEALNERCxDdCO3/3ud+5DH/rQbDWHDRvmPvrRj7rvfOc7bqGFFnL/+te/3Dvf+U636aabuptuuqntV372s595ErLPPvs0fdZIymuvveb+8pe/uC233NJ98pOfdOecc07b9+cfePHFF91vf/tbt+yyy7oPfOADpeUt/cE2Fe688063+eabe2cAWLTCoepvt3vfH//4R/fUU0+5nXbayS2yyCLtHi/1+7ra/aMf/ch95jOfcccff7z72te+VkqmmIezOjrffPPFvKpU3W61t5SQFTzMvPL3v//dbbjhhm6ttdaq4I2zv4K54JprrnH/+Mc/3DrrrBP8/jJ6fdJJJ7lDDz00uXEf3HhVFAJCQAgIASEgBLqCgIhtAOxGbBdbbDG3/fbbu+nTp7tbb73VPfLII27s2LHu7rvvdi+99JI77rjj3EorreQOO+ywtl8xQpCPBGcrfuELX/DfOvPMM90NN9zgttpqK08AIYJlyx133OE22GADT9Yuu+wy9+STT5aSt+z32j3/pS99yZ188snuYx/7mDviiCPcGmus0a5Kx36PE4M+jzX2GwlcV7vPOussd+CBB7pvfvOb7v/+7/86hlVWR8lW6FTpVns71T77DmMEnTnttNPc5z//+co/v8UWW7jrrrsuWtfL6DXOwK985SvuJz/5idt///0rb5NeKASEgBAQAkJACAwMBERsA/rZiK2RQl7x+uuv+wjHvffe68444wwfBSWauvbaa7sf/OAHjlRfjO/f/OY37q677nLve9/73AEHHOCjrkR6L7roIi/JDjvs4C688EK33XbbuaWWWsptsskmnsgSXcWQnTZtmjc8jdjy3KhRo9yf//xnt/LKK7tvf/vb7l3vepcnu/w56KCD3K677urGjx/vdt55Z7fMMsu4b33rW/6bf/3rXx2R5n333dc/l5UXWa666ipPjP7973+7RRdd1JPgY4891qcK//rXv/bt3Hbbbd3tt9/uiNBgFH/1q191yy+//ByokmJM5PDKK690L7/8sseFaCIR7RNPPNF9//vfdy+88ILbaKONPBHDYZAt99xzj69/2223+Ug4pB6ZSVlu9e4ZM2Z4uXju6quv9q8kQkQ/HHPMMT5KTBuILm622Wbu5z//uccEhwFGNrKBAZHk97znPV5OnBc//OEP3aWXXuodDe9973s9GV9hhRUqbfePf/xjd8EFF3hd4P0U+urhhx92p556qltiiSU83mQEQCI33nhjryPrrbee1zUjtocffnhbDHgn77rxxhvd6NGj3Y477uiOOuoo/16i+0Xbu80228zS0UcffdR96lOf8tHFqVOnut///ve+7yC/6H++vPHGGx5fxsKUKVP8GMLRQbva6WOZ9tJf6ASOKeRhvPFv+hmdp19J92fcoo9ldN30bbnllnNrrrmmbwsYfvGLX3R77723b0crfeX3OMYgeZdffrnvY3QUXb3++uvdIYcc4h588EE/1unX/fbbz88N3/3ud91//vMfjyt9t+eee/pv3X///e7LX/6y15HBgwd7Xf3e977nMzUozAFHHnmke+ihh9zWW2/t/vnPf3pSyx+wIRJ+9tln+4wF9AqM1l133ZazZqPxzHg94YQTvBNtwoQJXid23313PzdliS36jbzMObSFMc98h5OQuRVH4ec+97lZ7QuYvlVFCAgBISAEhIAQ6FMERGwDOrYRseU1GK+nnHKKJ0X8O5uKbHUgcvz89NNP98Y0hALjjQgMBXKJQb3gggvOJhlGK0YdhVRkDFWMRcrSSy/tySYGL6QMwxHyevTRR3t5Dj74YPfMM894ooxBjKH46U9/2hvxyAABgvRm5eUZjH/K+9//fk9eIXcYopdccomXF4JCgehBfvg9ZBP5s4UoNAY16dN8j3ZgUFMg+bfccos3mDGeIXFEe/imFd4NaeT94Ddu3Dj/b8geRL7Vu1dZZZVZ+3UtGo5hjLOAdtCe7D5UjHaMesrTTz/tScM3vvENT7pxOkAwIB0QPUgXRObiiy92H/7whz1ZrrLdYI6Tgv594okn3PPPP++JDn1MRgC6AvGFcI8cOdJHlcGX57JED6Jge5YbYQCxX3XVVT3+kAm+RfYBegFOn/3sZwu1l7ZnU5EhJ0aCkBnnCO9FBx944IE5Rh44gy9tgBjSfsqzzz7rSU0rfSzTXnTdosnIBeGjf63wff5vuDNWi+o6Y9OwzraZd//pT3/yY7aVvq6++uo+W4GxTHoumSCMFcbxO97xDk/60E+yLRgnYIpzjELf4fCi4ECCPDI2GSvUxVkA/rQPpwPOLnC2OQSHE89S+AbjkrFCO3Cw/eEPf/C/Yy5accUV5+g/+wFkOD+e+R6OIuRBX88//3z/OIQaxwERWxw5kHrmFhwKOKKQFycYhbmBeQ8ZGXMQYxUhIASEgBAQAkJACBgCIrYButCM2BJFI6IC4cEAzRJFDGMMNqIilm4HycDIW3LJJWcRAiMeRhAw9CGmRJCypMGILYYi0UwijhjMGMH8juhOM2ILqcinIuf3BFv6rRFjjGCMaMpjjz3mrrjiCm/sQ/YwTB9//HFPWIxYZWG1b2EgQxgw/Ik2EWUiao1Ba1FrIoZETrOFyDCRyL322stH14hCUX/MmDHu4x//uDfym70bzFuRuiyxBTuig7wTw/unP/2pj5DmU5Eh2RjcyET7adP888/vI2WDBg2aJXpsu3kRxBySQ/8QFUceCD3RL3CBrHzkIx/xDoJddtnFf5uoPpFni9i2I7bU2W233bzTgHqQZjClcBgUxKJIe3m+EbFFJyC5Cy+8sHeugBdtMkeNAcbedEgLJHa11VbzDgUIFo4fHDWMu2b6COkq2t4sscVpMs8883jHAAXSxni0A9/AFz0oqutZYoue0kYIKnIzL/CeVvrKmIVQUxgfEFJkZOwRwc6nItPnRJgZQ4wlxuJ5553nHS0QVb5tTpeZM2d6RwmY4hDBgQG26A9EkT6xLQA8s8cee/ifEWVFB8jWOPfcc32/IGerkh/P9N19993n5ygi87wbR8q1117r5yKILXMZega5v/nmm92IESO8owzHAjqPMw7nAP3MvMkzKkJACAgBISAEhIAQELGN0IFmxNYiW0T0MCCzxBbigWGajQxh6BJpItqa32Nr/3/11VdnEbNGxJZ3YMhSSB0lUgIJIJKYJbZEHzGYLVrWjtgaecvuKyUVkf9DmomsYKSTRgjJhABBviGY/DtbMJpJjcxGNYkWk25sh2u1IrYY56QPG6kp824OwskTW+unfMQWw3/uuef2KbkQR/oGYzpPbCG9kF8rtBmCgHFeRjYcEK3azbtIzYWYkt5JBI30VEguBwdBTsAmq1PUIe0XnWhF9LIYEE2zVOf8sIDo8d0i7W1GbHHmcAAXhXFBJBZnTHYfNWSalHpKo33m7fQRElm0vVlim3ck2f+NZEMOIftFdd2IbdbBQ9s/+MEPel0H93ZjwfanWl8wbtFFIpZ5YsscY06PfIoweFCPDAoIIoV+RpdwmEFsOXjOSDG/p09wLDA/rL/++g1nyeyc02wazes18x/OE4hytjA+cdhkxw7thQSTtUJqO2Q2Xxo50CKmdFUVAkJACAgBISAE+gABRWwDOrERsSUKSjonBaOMPbdZYvu3v/3NpwhTMOwxOCEkFhVsRmyzRn4jYptN6zQCxn5bjEUIJ1EWCC/RDQzrosSWfX2kNULi2BvIXlJSNikYvkRaMPbZrwe5akVsIXBEajBYOaSKYvvqLArbiuAZkbRIJZEeIrG8D4xbvRuCZ2mntIHIqp38mie2hnUrYkskkcgukUWIMBgRUaJAroiwWoltN++xFHIidxAYyAt9m42g42ggyrX44ov7T+eJLaShFQY4Tz7xiU/49G/0kYIjBDz4LqfwFmkv9RpFbCFkpMZSmhFbvsW+T/qWPahEd9kjDqak9kMIW+kjkX4jtu3aW4bYotdEKYvqejZiyzzAnGBkFF3HUdJKX3FkEN0GD4gnuOHMgMihC5DU7OFR7LGlz9i/DHlG53AakfXAflnGPs+wT5ZiRBEyyzYIxiHjl3FMJJXINX2NA4tnmaOQgf3C/Byij561SkXmO/nxbASccQsxRicYR1liSyo1Y4otCzbWyVAAf2sHDjtS7XFW4WhTEQJCQAgIASEgBISAISBiG6ALRmyJ1GFATpo0adaeUQw2fk9EM0tsLaUOo470VaK6GHUcLkMExwgBJIlU5XyUMU8asnts2beHsUkEjwKB4918Bxl5P4YsxYgtRi8kifQ/Uh4xVLPyYkxiVFIfUgExxhAljZJ9f6TBljH2IV6kGWJ8m7GPPGaQtyK22WgeB07RNkgO/8Yob/duM6rBCcPY9iEWJbYQIcgFaeakhdPHkHucBrSFlGQIAEQBPK1ActrJ1i5iy7vYA2v7G9nzCoEzTOgf+h356DMK6bM4A7KnIrfCgD61A7+I+D/33HOerKMfdlhXkfbGEFvqWno6OgIBJqpIgVCRYttKH9Hvou0tQ2zt+qkyum5jl32iRD3N8fGLX/zCH8DWSieIxHPAG0SWjAscEpBh9IpoJ3u7mUuIgoMPacqMc8Y1+DCvMM4gh6Qvm7MNcosDww6pgySzv9XS/jnkC8cJY5sCsYUwIztpv6Tso3vIwL7z/HVn+Wk0r9eW7cE70E/6mkI0lrnITkUGM7vGCHlwGPJt8CArgnFAHcg9TgAVISAEhIAQEAJCQAiI2EboQKN7bDFWSa2F7BAVtD2rRCGIqLEvFcPTSBWfZ2/bL3/5S7/Hz4gvPyca1ygt09Ijs/fYQj6Iclk6qhFljFjeb4QIQg3ZMWJLRBlCg5xE5fgd5MfkJXKC4cxppFb41q9+9StvZNvhURiXGJkQAORrlIpMfYg++wGzqYhWl9+bIQxhJ7KcLxb1sp/TNlJE2QvZ7t2W9kxdDGT6in6gLciUj5ZjQHMolaUiQ0iItlEw6iEu9Fe2LTgHSBnOl3aytWs370NO0jhNN+wuXU6PJu2cQr9BVpCJiD0pxNl7bNthwO85jMr0CIz4Ln9ziE/R9mZ11A6PKhKxpQ3s04YwoZMUdAlCS4S9nT7m77Ft1V7IpUWw26UiZ/fYFtH1bMQWPYZgUiDG6DCp7u10gj7NXtEEDjguODwLTIms00+MO5w7kFYjrHyL+mQdUNh/y7xjh0Jl38XvLXOCfzOu0S0ySiC27A+GVGffzf7ar3/967MduNZoKs3rNQerkc6OHGRaQF6Zm3gfmSBEaIkq0xY7j8D20ebxoP8g3UT1VYSAEBACQkAICAEhYAgoYtthXYCEEmXhgBpLHTURSHskHZBDU7In9bYT0dIWOTGXQ6SyhbROCu/MF+rxe4x8CEmjwkFERCIxeJE5pvA92k872TNpka2i7yTFlmgU7TTib3XbvRtiRAojxDbkflUcBXyffcR2QBSppvQXJ8s2ww/52slWtP2NnqP/kK1I37TDgLbQ1xANooZ5HSza3pj2gBXp6mANscr3VRl9bNfeGDmb1c3vscXZgPMFvcmWdjpByi+pyIxnDknDWWaFfiJLhL35/KHgDONbjCs7+Cr7PP2KA43IPH9nC99iToLYQrzzhewATqbm93yPq8t4X7PCAWEmV/YZnGm0CRnLzG+8A31gLzhj3xw7dfSf3ikEhIAQEAJCQAj0LgIitr3bd5JcCAiBxBBodHhUYiJGi4NzoVF2gr2YK4KyB4NFf1AvEAJCQAgIASEgBIRAAQREbAuApEeEgBAQAkUQIJpKejsZEhx+pSIEhIAQEAJCQAgIASHQGQREbDuDs74iBISAEBACQkAICAEhIASEgBAQAjUhIGJbE7B6rRAQAkJACAiBfkSg0V3T1s6y+6dTxyd/wFzq8lYp30Bue5U46l1CQAh0DgER2/9izQTOYTMhBwt1rrv0pVAEOGyHg3gaHWoT+s6YehzGw0E4Km8jQApvu7HHMxxu1OiAI+FYDQJF+qGaL+ktvYpA9gT/Rm3gELOq5zZOJ+f+Z64B48orK5wwzrVYd911l79DnJPCt9xyy0qhtZPeuSqu3RxV1Ye5kYATwzn5n9PEu1E4gZ37qzmFnZP2+6Vw6j53s2+44Yazrhbrl7apHUJACDjXN8SWexi5v/W8884r3K9cL8IExz2J1157rb8WgxNZ84UFhut67J7Qwh8o+CDGJCft3n///X5xble46mOfffbxV6FkiRpX2HC9B1fxtCvcG8nJwnYIDKeicsUNBgJ3W+6xxx6zZOGeSa7XuOeee/wVQUceeWTLU4Dbfdt+z/UlXKPDFR91Fk4N5j5RrhdZdtll/TU2YIcjgzt+uSeWk4Dph5NOOslfBcRJtPxuk002aXiC6xVXXOGvauJU4gMOOMDtuOOO/jkIK1cCcTUSRpgVriz5xCc+4TgxloKucW1Ju/tA68Sl3buzeol+cMUSJ0vXUTAeuRqHK6eyhWtuuI6Kk3TpC64w2nvvvesQoWPvbDROW40/rlvCwOVgqm222cZf/WSncDcbm9z/esopp/iThLkKivGMTrcr1g/Iw5U9rfo7P3+2e77Vt7P93E7Gdr9nrHGCN1eYqVSPAOugER3mOq5w4morToen/OAHP2h4Cn+IJJy+fd1113n95x517rjm7nMKcy/f5PvMEXZNGGsY/6+qdIPYQta5OpB9+qz13Sis91zL1W/E1q4OxE7iDnoVISAE+guBviG2+++/vzf8yhgz3Ed7yCGH+KsrWhFbjEPeveqqq9bS+0YgMMZafYPTSC+77DJP3q+55hp/5QfXiHCFDkSLeza52/HOO+9sKSdG5M477+zvJsUIoay33nqe2B122GH+Dksmf675oN2QbUje1ltv7e+2heBwl2VsgdjyXvquzoKh+9xzz/m22v2mePYhn7QDo5/7QCH7GBLcY4tnnkXvT3/6k79HNVvQFfDjOa4PQocw8NZee23ff1/84hfdlVde6e8A5f5g7pTlXtJLLrlk1mvQuY033tjr3oILLlhn84PfndVLiA730XJVUx2lGbGF6OJIwfnU68S21ThtNv4gj+gpd7vSfpxsu+++u9dbnCiNxiZ32HIlDgYpZJN7mLkOiDui2xXrBxxAkOONNtqoaZXs/AnJaPd8q29n+7mdjO1+z1i+99573YUXXtjuUf0+EoF3v/vd7tZbb/VkE9JJQReYG5kDuUqKeZE+gfyytjBfbr755l4n//jHP3qnFXM0zsd8ufjii93BBx/srzSDwGaJLfce41RknoaknH766X5NY25nLbTCVVHcMY8jkzl49OjR7uabb/YyIhvrAmPkjDPO8HM1usjYwelrV+HxbSK22BesEXyHOYk2sBZwPRr6xlVY3KmNbIx3xvU3v/lNP4YpOFcvuOACb2+ss8463iGKsy5fjNjSNtYIItM4nGkX1+SZQ5R1nzUfZxlzAmvKCSecMOt1t912m1/TabOtP7QTpyz3lrP+8jx2xYQJE3wUk/mFtuWJLe1u1XYwYn7izmoK/f29731vjqv5+B1rC23hjmyi8ayxrJXIT2GdBjeixosuuqh3AkP0ceSDPc6Ln/zkJ37+w/HBd3mGtfeoo47y/UsbcGITCPjwhz/s8bn++uu9/nHf+8orr+wOP/zw2h3rkUNM1YWAECiJQF8SWxaWp59+2kcriHawuODxZYK0wu9IWeJeVIgaiwtGINFDFi8IC+9hsT3nnHM8MYKcQApZCCGPLEospvk7TJm0mZRJmSIqeuCBB/rFl4WVqCjv5U5HvseCT7SWP+2ILSleTNwYDllii/f8xz/+sY/4shC3IrbggrGKJ3bFFVf0bWHi505dfsd9qMjJqa78jnsnMUZssUJGFv9spJEFnMWdRc+u+cC4YLFHXtrMYg4ukKPvfve7/i5NI7Ysrhg6fIPvs9BhWLC40WdECCDa/JxoHQsX7WRxwyDCWKCPzzrrrDki3hgaLPw33nijNySQB+xZ3Cn8zYJJZArZWSBZECmcbgvpzEfqMTB4hgWWwsI6bNgwjxVtQH+QiX7m//QzurP66qvPNjxbEXswp21EMYmSH3HEEd5QAGN0l74CA77RDJ/sx8hMwJCgjRgE6DdtRxeICGDMmGFK6ht/iPSZXrYitpB63g2xwUDBUCIyjcMCzImOQzSINBJFpO8xmjBY0R0cC/TBuHHjZovY0s+Mo6WXXtq3m77B6ORd/CHaj+HE+xifvA8Dj2e+853v+G8T0WSMYbChg+gMWGC0Ii/OGnSr0d2ozdrFdTY4MOhbovDIj9GEEYWuYUjys3xpNk5bjT/GOsYYBiyFsYSxTzvArNHYZAyhM5Y6SaQLUoyjauTIkbOJ1awfqIv+YfAiA9jzXeYz+pcUxez8iVPInm81/2KgMx8SXWMuOO644/zcnO3nbCophAQsLUpPHzK2IBHN+seILXNiq3mlmR40au8OO+xQcmkdGI/niS1bekgZZt5ijuEuavSPgs6ig8ztFMY1xBdCxPwJ2YCANSqmw1liy5x44okn+nHNHAvBRh70ClKdLegR6wlOYQgUcwXr25lnnunlYTwjAwQIm4ExaWnA2YgtOg7pZB7jHazJzHWQJOYT3sf8wLsYc+gqhTWT+6UZm9wHjc0BmWb+u+OOO/z6lS1GbPkZ0WfmfHBi3YGgotfMdfybn/FNvk1mFdlAVnAIsLZTF4LMtyGDYG0puczT/Pw973nPrMDAQw895J0J2Ygt802rtjO/MweCBfMM/cG/mavzd2czn+AkR0eIut9+++1eZJx1PM/aSKFv+F227ay/6Bd2Dg4Dvsm3zcGBXcbcQOH3zJEU1iHkYu7nZ8yR2CmN5uqBMXrVSiHQnwj0JbG1RYGJEzLBpM+fbJSR6Oepp57qFygWH7yVW221lff64cnE2IUUYVTyPhYW0lQhWiwmEL9DDz3UG7ZMjtmCYYdxy8KFkcRCCoHGS0sdjFMWDBZjSAXvL0Js7RsYhyx2FrG1n0Ok8RQ3I7YQVgwD88ZihLC4QxhZ5MxbCkFebbXVvCECCWXxgdhApjAwIUH5vaoQJn5HtAgPOYQQ7CAMGKoYmWDGtyGg4GzEjp9BCMBo+eWX9+3iWb6HDKQKIycysWjjpacvMWIwongX/cjilzdoSCHnnciEzCyaGCoYVRT6hqg0fcpCjL7we8gvkQTa89nPfna2/uX3LMwQWrznOEPA1VLjkI2UcNLjIVkYHPRLvmAYkfoOecgXjA+IJW2FUKCjkBAMKyKYEDIMO9Lvm+GTfSe6Z/pNe5CfvapE8TCqIPxGHvg/RAVc2xFbcMKBQZshj0RB0AXGD4YdBilEn8gG30AnaBfYYvSCHeOQ5/LEFkORtjHGkBmDhecYXzhJGNM4LCD8fBsdwgMPgYR80R8W6cDAYkxCUDCecUrguMAwJgqdj2a2ahd6S9/TvxhJOL4wriDTzCvIa+1stGzkx2mr8QeBZJwy/hij6CuZABiZ/LvR2CStnrGOXmPAQR4ZG7fccssc4jTrh2wqMs4D9AfCSIQN/WO+ZBxn509LRW42/9I3GLGQDOY9m1vol2w/Q56toDNEbSAbFAx6/qCfzfTOiC3jrtm8gqHdTA8atZdxNv/88/enFRDRqjyxhaRBGCB2kEPmR/qdvmYuw4kJWYFIMTYpzAeQF8YRa2+j0ojYMndA5CAsEEXWCiKwfB+nRbbwDOsMf4hW0vfIZ+samTnMWxBPHGqQYPSYebIMsTXSyJyOLIxTHI8dRHurAAAgAElEQVS8h7HMnEPbmSexJRijzE35LRg2hphLqI+cjFXGMWsZ6xVrIc49iL21h3HJ2pkttiea8cAawLwL4cNOIOsCZzXrKHMqJA+yzXiHEBcltsx3ts2G9zFWzPHFfGzODJPLMGU9Zl0HH+YqcGGNQy5zHrB2WFCC9Zx5rwixZQ1gjcQpil5RD8dGqqnI+QPZ+u0QtohpZraqA/lAM9ouvWivSX1LbJlozVhlsiSlL5+alk9FZtLHyMSzCpGAwGH0GrElqoBhRtoThhmLCF7N/L5Y9lZivBmRxgCEMGEEQ0gwNCmQHcg3pLkTxBZijmHKIk46jhHbPAHCQMU4gCTSFowOvKGQVN6B/JCybGERwbhnsSSShfGNNxvPMAQD4sGCiWGBRx1MixBbIukY70YMMWQgEJAeFnq8tyyEkFZ+lt3XinwYxCxo+b2C9CsOCYwpjCYj9dTBW23OCBbpbJQL/cBoo6AP9B3vMO+x7XnE+40RQyQbfWGhx8MOASZ9nELEFDzxpOcLZBZjn0kM7zPyoGtERiH5eLYhpkTOm+GT71f0G8MIpwN6gJcbHGKILWOAdtFOjC8MOTAgFQxiSwTXxgF9AxnEiMKY43lIDM9jaOWJLfLnU5HRGcYMBbmJ/hGZJbOCvmAfKXqNoYchBzboCBETMhQwlsCQ6DrYojNEa8AzGy1q1S5kIFUXgwnnFyQJ8m6OAaLdECpL88/3bSsHFAZldvxZXRxDjCkKDjMM0HZj03ClDuMXgy57+A2YNOuHLLHFgYDThwgWxj/jhf7CYM1u5cgS20bzL/ig0+Zkor9oF46+ZqnIf/vb33wkif7A2UAf4XjD0G2md0WILcSgmR4wvzVqbyoHz7Vf1jv3RJ7YMm9BXol8WpaPbf/AAcXYheQYyUBS1md0gN812+LSiNgyf6PTrK+MDXPescYyv2VLNnKJgwe5LfrJ+oTuWnTV6iELMrUitjhfcZIyjlgr8pFJexftZdwzF1kEkd+h26wfeRvCiG2W7BvWzK2MQyOw6CqZN8znkPZ8sYOSWHtYe5kLmBuZO3EGUI91OlvICGOstSK22bYzNyBfo5LfJ0zU3pxXjU7XZl3HyQ1OzO021/N/5hX6JUtsaTPzYT5iSz+DC/3KvGP4pEhsLTspix92BmMF+wEnXrvCnI2twNpk26cYB9hdOKkbZSW1e2eZ3zNXZ9Pgqcs6WObcm3bf69aBZuakxzZEP7tRcOhjD7O+MWf2S6lDR/uW2GK4MqFSIELsNclHxvLENnv4CYsHCzETrxFbBijGkB24xCLBJIkHNFtYCFmQLKXVfsfPMYBZ9CkYhng1Sfurm9hCnjDskYsIJlEyCgMEAxlvLWSVBQ+iSBQFIxjywORkh4VgRLBAsyBmCwMeHIhA8k4830xy/J80XNpthJCodStiax5aFk6iM/lFl/dAyJAJjzWed4g476RPsgXyyKKb9eBj8HMIDwYJzo7svma843jCMcIbHYJi0eTs5IIDhIWHSE+2YHjjHIDoMDGySBHpw6BnkTHC2mjfKv2AUUXb8PCz0Buxze4ftAhBfkHMv9MIkxF8Iwzod57YWkp2kYgtEQcIPvoEaQZTnD9GbElzs8NPwJyIKVEcIqWQSoqdRl6E2GbfB8GEUENeMVryBT2lLdm9wc1OdCV6RETFSqt28V0MKSJQloqY/3YzI5PnGhHbZuMv+16iwegd4xNyyPzUbmzyXvY/MucQ3cawtsJc0KwfssTW0sUhF+giUVSMyGbzJ+Ow0fwL4cCRY/2ebVszYovjAH0i4of+s/UDJ1Kr/mlGbLPzCkSKd+ULegCm9G++vf1iSFTZjjyxxZmM44p5wA5iZP0lws5czfhlHmTM2nYKnHPoElskmp230IjY4qRCF8mqYUzY3I3zDCMwX2zsQ3Ygg8zNzA2s56zrzFPMZThRiSA2Iras90SgceDZXk7GoaUiE+VkzubdzCfMiaRb83PWeTAh+s96ydoDWcuSfJPZiC1765lbGcc4WHkfcztrIPYKe1Tz7WnUv7ZOWGquORUZd2SCQJ6QAzzAuhGxZQw2aztzDPYFcjE+uYGA8UZb+XY2C4M1B/LFvIJTgfGGrYbDij4gO4e105yFvIMsFAprH/YDZJVn0DX6HhsjT2yx+bBteonYYsfgTEBXkB/bB4e0Zay0GruWLcG6bbauja2sk6DK8Z99FzqEvpJNaE5A7Jx8Fl3M97t1oJkRW3OQx7QhtC62N84o5ij0vV9KHTrat8SWRdOifEWJbfZU5EbElkWNiRgSAzFg8bFDI7JKhvFIapRFNZlkmMghi0TxiICYgQu5YeDXTWxtATU5zeiAWCMfiySTBpGMrPcY4kB0xAwFnmVwsacxnxLBIsLiyORmEymGB0YEnnwMVAx+yEYjYmuThkWa6ENwZPEyTz74822MBhY5FmbkhsASIWCxBEsrRIQwQIzQ4fHDGONnePyzHnYMKxZNPMC237TR5EG7MSiITFDoZ4wNc6TwMxZvsKXd6B9ePnDjmxBpjC+iz3wznyJqTggIuXnTaWcjYmvp2Hl88nu28oej5YktRh9GBXITCUYPihBbO4iMRZXUO/QEbIzYZk8xNmJL6hnk0PaAkk1BpLUIsc2+z4gtDhVIE3phzhN0A33DWMoSW/qdjAWMIgoy2GnfGGNWWrUruzfa0u7ZA2v7whhb6BWGXqPSiNg2G3/oCyl+6CXFPNY4R/hdo7GJIcw4tcNi6FOyBHDUZE9YxcnSrB+yxBZ8cIYRaWG+YnyDL8Z5s4hto/kXHYPUWL/jFCBln5+3OjzKMm4gCOgQBmyr/skT20bzCn3QTA8wJhu1F71VmR2BPLHF4UB2Eo445lDmOtZNCuQHp5alpUJ00QUyfSi2B7QRxo2ILd9gXmXM4wTE8KPvIDLoSb5AFOwwNOqgz6wVkG7maOYWDFjmaQgkKfdE77MRW6ILOOeoz/zP+kIxYmvkmUgsjm0clMjEuCHjgJ8hAzYAmVCMyUZEPLvHljELNkQps3uMjbDxfeSBSGbnsGz7cbpif1AsUs2/WSdYq5GTbCSIK4WxgXM6G7HFTmnWduYDCA1/Iy94WFTJzu7IymPp6bQHu8GycMCdtHFsMdoEyWUtof8hfdgS9BckGycbmVakYlOKEls7ZMwOwySbrNvFIrbZk5ptrjdHPjI2O1TL5mtw4nnwY92ABIEp+ojzhv5mPOIEoq9wdJDtgJMWu4Z1Bn3ATiUziLrYcAQ57JwT0rsbbRmwfeuWkdMM0yoPNGt3kFirA81wmjJnYDcSqLGtX42uCzNii0MLJxxrEbYpDjDaA9FEH+08FpxurMHob3YPN/MF+opNAo4U+orxzbrNNWk46nAQgjn6jV7bGTJGbBk/rN3Mp+b0wGlMWxhLBIR4J+/CoU32BVu57HyafN8wFzLf8S4cTYwN5kN4S6vDAJm/28lBtgTzErwImxb9ZI7AiYljPa+jVYzHAU1smSQYoEy8GLrtiC1GHMqPAY4Ss/jgncx7pJgAUF6MNgY5JIzFjYUdLy1kB482AwjiiOIascWgwujCi8E3GpUye2z5HsqJVzlb8FKz0LKwGrFAXjv8iWfxqjM4mdgYHMjMnh+MUCIe+WILhi3y/J6UbNrEhM2ECX7gyARhBIG9UhjeLIRMVEyQRJPoF7DAQAc7BgRGAN5bJhCMeiZfJgk7qRhyk00ZNAJnky31ITsQSitGQnBSQDCyh2/wTRZ4yC5/gyPkHczoRwY9hhryZMkwhgz9xGTBZIZRgpFE9NZIPZMM3v18dMFSx2g/Hk8wQV7agG5lI7a8oxE+4JItrYgt5JmIAEQbHWDCKUpsmUzpKxYG0kXBgEkMsp4/xdiILYsoHnw87Yw/dAHDshGxZUKnz1h88+8zYkvEHF3A2IF0EW1A7yBkyJQltkzcTNroM4YSdXCKWPTIMGvVrvyhXxj3jDH6EbLG++kX5G5U8sS21fhjbmHxZBxDGHgv+gG+4N5obIIvBj99iCGKoYjOWpTGZGL8N+uHLLFlQcRo5dsYCugkY5m2ZufPbCpyI2Jrh0/R18wjGFE4axg72X7OY5ZNqTYjuVX/GLFlDm42r6AbzfQAo65Rey0tsttGcErfN2KbJZPMe2CYzbTBqMYRQyop8yW6jDMQRyQlm8rfqH02j2eJHc9Zep7VQacY0432oeHggWgjF0alpU3ioEEXIMoUCDfOOcgApCpLbDHkMGgtbZlxhey25kEOmauob8Wiv2SmMF6yNzeAA3OS7U/N1sFAZb00JxzrMXObXWWELIxFxiRrqjkQGuFnacP8ziLV/BtDmDXJsm0gsrQNmenD7D22jNVWbScTAzzsICjWzmyGWlYu5jxsHLuiiWeZp0ihBSfablu2qGeHRFn2ElFM6y8csDi68sSWeZI5HmcEemNZNBBGdJC1N3+CdrfGlhFb7Au21bAes0aBj+mqOSKRMX+oFn2P3mHToicQILIE6EfayToApjhg+RkFnceBQbHUdDs/gp8Z1oZJ9v+NUnIhMOgOmLO1B11gLcL+y5YqDzRrd5BYqwPNsCnRQTDHpsV+BbtGN1UYsaUdtM90HNuBdtoBssxnjGVLp89f4Uk9HDnY0mQ1EXCBzJqjjfmBdYu5hwwNSDQy8Vw2Yov9DqmlWDq/HSDHuk9fMNchD3MI32K8oBf0dbZQHwxwimTnZcYcAYtWhwESBGslB0607FycPcyNOQk7Oa+jVayzfUNsMSIZMEykEIe8YYVCMXFmC4YZIOKhQEFbEVsmYgwwGygom6XQZPdn8n4mEiZSjD8UFqLGJA3pYjKxydz2saLAdo8tkxF7Z7LXJ+QnW0vHwGuUPZEZxYecZNNuMRhZBPJ7H7LEFjLDIpYvYALJwQjFKKWwgLMQ252FeTwZ1HZoA7/jWXs35IMBxwQCiWFRsXts7cRF6pixQB9CUllMmdRtUqEfIRKWikZf0Pd4f/JXJ/Bz3mGpps1SdzF2mGDyxbzbTGz8oW1MEiwU1o+keTKZmLecRclOdab/6ScMLp5BPpwCkGl0iQnFTle2bzPR8H4WKQpGC4YSjhAcD9k7OkmNboZPti2tiK2RPZ4nmgAZxItmEVsmZ4gOfZdPcWYM0QZ+T2FyBSMMTlL3G0VsecbSlqjDJA6WGJH5+ydt/x3Y4dFsFLHlfbbPytoMacZbyTvzctMXECMKuoMTIH+4Sat28a3s/ct2wq8Z6eDGM9nMgWxf5Mdpq/Fn12zZQgpW9A2LFcS02dhkPjTnDQYJ0ZpG14o06wfmMCOqRkYsGo7ekg2Tnz9bEVubf5HJCD/zH3Mqi2m2n/NbOMCOcYuBZHsnW/UPkSsbI83mFZxUzfSgWXvnmBz0g6YIMIcxHpgvMPLM+DFs8dizVpF5wJoRczAXqaoYo6wJpqNluwZ5eQfrWrNxm30nDk1K/qAme4YICMY9Yy+/N5u6GJk4lXCmkiHTqkBg0fdGz5pjwc54KNtunmceYU2jn4ocTNOq7dbvbJvgfdk9/XnZeJbsFhy2ZN3kn+Ud9AnknfGaL+CLDWZpymXazhrN2knfpLB3vtEeW2uPbXuylM1mh2qxBjOnNktFZm01jE1fLApvWweM2OLwwAbhfdixtk/aTuNudA2ikTlsXMY1NhX6jyPBxr+1qaoDzVoRW9b/Vgea0UbWP+S1LRDoJGtsXt+M2BoOtp/f7niGO7CW2Un86DO2VKMDG7OnktMP1DXnBTY3zjE7DM9sZ8YC0U6L2LYjtuBs9iX1mI/oS4ptZbC+wH6B2KMDZDdhm7KWM6/yHYhts8MAwa8osbWzbHCkwdPsBHelIpeZuQo+ywQH+SmzIEJyWQzye2vzn2Rhx3DOpgfhjWTQ8zMWqkYLCVEYjL8qUt/wZGLo4TmLKSwiDC6My3YLcf47pKexMJNuSnuZgCHz+YM2IIAUO1zJ3sNkA+Z2gmQWM0gYOGMcN1tESQdhccx6gGOwoC5pLPQji2KeECMnpLxZxJ36doIypLHRos0zLPrgxGFmTDL8YSFvZCA0w6doO5GZhR7HStliRiH9Sx/Qj+h3O4MDveRZcGplUNHH6ESzg1lMXgwkM5SbGZz2LDqIF5R04fyim9U7DKsi7WIewYvNwVx24nZZHJs9j65heIJpMwOv0diEnLKINUuJtu8V6Qfew4mkGC/oo5WQ+RNZ6ff8uCnaz2X7p9m8wnua6UGz9lbVpwP1PVlim926MVDxCG03cw2ZKJCHRqdAh75X9bqDgBFbMhvsakhIkW1vYV3j360O1WLtaUVsCSzYIVSsKay52CF2OCY2AAEZgghGfO3qJCMlFthpFHhBJ7FRiPJn94Q32t9b1YFmeWKbPUgMctbqQDOCWmQIsE5aIaiC4znv7DBiaxFY1jBbB3E8kVVBACl7QJsdapfXKDunh0AMfYyNDrkkW4JoLQ7XrEzUx7YhUNCK2GLnWoYj+mKpzvnv5/cJE8UnOJM98M/qtDsMkAySPLHNypGN2MJ94A44HAh+GD4itt2Zczr6VQYMykk0s4j3tJ1wpJ+QntnO0G/3nl7+PaSc9A88h/k7h7vVLqK2LCSWFtQtOfRdISAEhECnEMCAY3sGkQ4yUVTCEIBEEPUmKoTTOn+qcthbVatbCDTaY5tNH2cfJ0Sl1aFakJ9WxBbnv5EQu1rRUvxtH69FbNsR2/w+dggy0TuyEOx6REg0QQe2KeUzscC5igPNLP250UFizC+tDjTDKYQzlUJGEUQLQpm/C5rfG7Elu4rtg+BDyrhtV4DIEx22g0x5X37rj+mWHTgJJsyHdvVW9lorHID0lwUa8sSWfeYWyLGr6CDpEE2y2wjWkc3FPEt7KOgTQQhs4WxWip1VYHLg9CDrleAdZJ0/zQ4DhGy3kiNLbC1lWsS2W7OMvtt3CLAZn4k2v9+jWw3FE4anKoU0qG5hoO8KASEgBISAEBjoCBixZasXmYAQJc4WgCjZCeJE81odqgWBhJyQAfX/7J0J3FVV1f8XCIqgICoCioaoYWJSFmo5hZGzaSpDGE5pWfqm9eZApSaOf3MWp8z0zSk0xwYLp0yQUNM0MhxIERFUUhRRROD5f37HDj483Pvcc9ba+9x17v2dz4ePve9z1r5rf9faw+/sffbBe+YQYPiHFUGIPOwExCGT2JoK0YTVWAgTiM90K2xWYVtpK3Iq/vBaCXZD4QwI+IJVzkrb+0McaJYeQlbpIDFs2W7vQLP0ZHys0mLbNV6TgTBsfTBompdp3fAAAGfC4HU4cAPn9CspqWCDTevt4JVyOxXk+Fv6fnP6GSz8BmKNuKWHUWF7Mrbvtj4VOT3RHMIzXTVGeRC2eMCRznXx6hJeg8QrgMgPiO62uy7TsrBFHItriB1EMVbm2zsMECvU7fmRRdi2zVG8UmK9GuYdWysI2pMACZAACZAACZAACZBAkQSqfccWggoCEOKi1qFaWGGDoMEqIAQdhC7Oj8B5ELhwUA/Ow8BKHg4lSq/0G8vYWpuu+tVasa0kbLGqjG202LaMC2ITh49C+FS6QhxohtXG9g4Sa+9AM7xagwcF6eFs8BHvtmI7c9vXnlJh2/rALZxzgfqluyHTQ11RDv7/ELfVLpwEn36iECu16TeGcboyzgfBhfKxuouVZXzaEluIW3/HNv0+OO7Fww/kCOqCsvFubno4brqtGX/H3/Dfthe2n+NBBw6Rw4UHLBC1ePWovcMAcW8tP9Kdp+mKbXqeRroVuW2OYsHHelHYWgnSngRIgARIgARIgARIgAQiE2jvUC2IB2wLxvbQ9LUrbEvFVlacU5GunGJ1EMISq2OVzu2wVAFiDL+JsylqnYtR7XfyHmjW3kFitQ40w8ortg3j/IosZ4zg/AXwbHtGBM5yAMvWBy1pOCJ+4Fft7JXWZeJhB1bHIWwrnTGDlX+cE4LzTqqd6ZOWh1VfPCjAQX44RK/1q5DVDgNMbWv5UYtDpRytZdPe3ylsLfRoSwIkQAIkQAIkQAIkQAIk0JQEsBKKQ7+wOor3Xyt9DrMpwdSp0hS2dQLPnyUBEiABEiABEiABEiABEigvARwqhXdl8UlGvAcbehW8vGTq47lbYYutCFhG5+E69UkM/ioJkAAJkEB5CHDMLE+s6CkJkAAJkEAcAi6FLZby8TIzXnLGtzVvvPHGZP84vsO1++67JyRwVHjI75LGwctSSYAESIAESCAuAY6ZcfmydBIgARIggXIQcCds8VL20KFDBR9wxoXTyHCkN07pwlHlOIq70kvS5cBNL0mABEiABEggHAGOmeFYsiQSIAESIIFyE3AnbFOc06ZNk2uvvTb5nhO+g4UjxHFsOY6jxpHm/+///b9EAPMiARIgARIggWYnwDGz2TOA9ScBEiABEnArbPEtriuuuCLZhnznnXcKjpt+4oknkpPH8J0pnDoGwYsjqSdNmrT821lpSLFtGd+B4kUCJEACJEACeQngO4xlujhmlila9LWMBMrWJxTBGN/EHTRoUBE/xd/ISKDZ89SdsMW3tR5//HHZd999kxDiw9X4gPHFF1+cfBML//DNJGxHnjVrlvTr169iqLGie+KJJ2ZMg+Jvw/eiPCcf/bPlBPmRn42AzZr519j8WteuWcZMW0RpTQI2At77VFvt9Nbe59r6mpXTknkq4k7Y4uPEm2++uTz55JPSp08fGTNmjOy8887Jx6TxEWUcqf3II48k/398eLja5b2xeU8++mfr1MiP/GwEbNbMv8bm17p2zTJm2iJKaxKwEfDep9pqp7f2PtfW16yclsxTh8IWqTRu3Dg577zzpEePHjJ48GC5/vrrZdGiRck3orBSi3+nnXaa7L333hS2kdqe98ZB/2yBJz/ysxGwWTP/bPzaWjfDmBmWGEsjgXwEvPdZ+WoT7m4K23AsQ5TEPHUqbBHc999/PxGzPXv2XCHWc+fOTVZya13eG5v35KN/tTKs/b+TH/nZCNismX+Nza9S7Rp9zLRFlNYkYCPgvU+11U5v7X2ura9ZOS2Zp46FrTWlvDc278lH/2wZSH7kZyNgs2b+NTY/W+0qW3sfM2PUmWWSQFYC3vvUrPUIfR/7jdBEbeUxTylsbRlksPaefPTPEFwRIT/ysxGwWTP/GpufrXYUtjH4sczGJuC9T60XfQrbepGv/LvM0wYWtkefdpWccNQo+UTvHr6y7r/eeE8++mdLG/IjPxsBmzXzr7H52WpHYRuDH8tsbALe+9R60aewrRd5Cttq5N2dihwiRSZPmyXn3PiIHD/qC7LT4I1CFBm8DO+dJP2zhZz8yM9GwGbN/GtsfrbaVRe2g3baP0jRe39hsyDlsBAS8ELAe59aL04UtvUiT2HbVML23JunyMNPv0xha2hv3jtx+mcILrdK2+CRH/mZCfgrABPUSfP7mx3r0EHk7rNGmsthASTgiYD3OUe9WFHY1os8hS2Fra/c4zuYxnh4H2Tony3A5Ed+NgI2a+/5Z6td9RVbCtsYZFlmIxBoxj4hS9wobLNQKu4e5mmDvmPLFVt7I/LeOOifLcbkR342AjZr5p+NXwxrrtjGoMoyG4WA9z6rXpwpbOtFniu2XLH1lXtcsTXGw/sgQ/9sASY/8rMRsFl7zz9b7bhiG4Mfy2xsAs3YJ2SJKIVtFkrF3cM85YptcdnW5pe8Jx/9s6UG+ZGfjYDNmvnX2PxstaOwjcGPZTY2Ae99ar3oU9jWizxXbLli6yv3uGJrjIf3QYb+2QJMfuRnI2Cz9p5/ttpR2MbgxzIbm0Az9glZIkphm4VScfcwT7liW1y2ccU2KGvvjZf+2cJNfuRnI2Cz9p5/ttpR2MbgxzIbm0Az9glZIkphm4VScfcwTylsi8s2CtugrL03XvpnCzf5kZ+NgM3ae/7ZakdhG4Mfy2xsAs3YJ2SJKIVtFkrF3cM8pbAtLtsobIOy9t546Z8t3ORHfjYCNmvv+WerHYVtDH4ss7EJNGOfkCWiFLZZKBV3D/OUwra4bKOwDcrae+Olf7Zwkx/52QjYrL3nn612FLYx+LHMxibQjH1ClohS2GahVNw9zFMK2+KyjcI2KGvvjZf+2cJNfuRnI2Cz9p5/ttpR2MbgxzIbm0Az9glZIkphm4VScfcwTylsi8s2CtugrL03XvpnCzf5kZ+NgM3ae/7ZakdhG4Mfy2xsAs3YJ2SJKIVtFkrF3cM8pbAtLtsobIOy9t546Z8t3ORHfjYCNmvv+WerHYVtDH4ss7EJNGOfkCWiFLZZKBV3D/OUwra4bKOwDcrae+Olf7Zwkx/52QjYrL3nn612FLYx+LHMxibQjH1ClohS2GahVNw9zFMK2+KyjcI2KGvvjZf+2cJNfuRnI2Cz9p5/ttpR2MbgxzIbm0Az9glZIkphm4VScfcwTylsi8s2CtugrL03XvpnCzf5kZ+NgM3ae/7ZakdhG4Mfy2xsAs3YJ2SJKIVtFkrF3cM8pbAtLtsobIOy9t546Z8t3ORHfjYCNmvv+WerHYVtDH4ss7EJNGOfkCWiFLZZKBV3D/OUwra4bKOwDcrae+Olf7Zwkx/52QjYrL3nn612FLYx+LHMxibQjH1ClohS2GahVNw9zFMK2+KyjcI2KGvvjZf+2cJNfuRnI2Cz9p5/ttpR2MbgxzIbm0Az9glZIkphm4VScfcwTylsi8s2CtugrL03XvpnCzf5kZ+NgM3ae/7ZakdhG4Mfy2xsAs3YJ2SJKIVtFkrF3cM8pbAtLtsobIOy9t546Z8t3ORHfjYCNmvv+WerHYVtDH4ss7EJNGOfkCWiFLZZKBV3D/OUwra4bKOwDcrae+Olf7Zwkx/52QjYrL3nn612FLYx+LHMxibQjH1ClohS2GahVNw9zFMK2+KyjcI2KGvvjZf+2cJNfuRnI2Cz9p5/ttpR2MbgxzIbm0Cj9gmLFy+WJUuWSNeuXVcI4MKFC2X11VeXjh07thtYCltfed+oeZqHcoeWlpaWPEGEe/4AACAASURBVAZluPfcm6fIw0+/LMeP+oLsNHgjly57Tz76Z0sb8iM/GwGbNfOvsfnZakdhG4Mfy2xsAt77VA39k046SR544AHZYostZP78+XLjjTfK+++/L6NHj5ZOnTrJzJkz5fjjj5dDDz20avEUthry8WwaMU/z0qKwzUss0P3ek4/+2QJNfuRnI2CzZv41Nj9b7ShsY/BjmY1NwHufmpc+VmSHDh0qjz76aGK6ww47yAknnCDPPPOMLFiwQM4880yZO3eu9O3bV3Bv2xXd9PcobPOSj3t/o+WphhaFrYZaABvvyUf/bEEmP/KzEbBZM/8am5+tdhS2MfixzMYm4L1P1dKfNm2aXHvttXLDDTfI9OnTkxXaYcOGyahRowQbOrEVecaMGTJgwICKP0FhqyUfx65R8zQPLQrbPLQC3us9+eifLdjkR342AjZr5l9j87PVjsI2Bj+W2dgEvPepWvpPP/20XHHFFck25DvvvFOuvPJKGTFihBx44IFJkb1795apU6dK//79ZdKkSTJ58uSVfmr48OHan6ddBALVHkJE+CmXRVLY1iks3jtJ+mdLDPIjPxsBmzXzr7H52WpHYRuDH8tsbALe+9S89GfPni2PP/647LvvvonpySefLPPmzUu2Hnfv3l2OO+44Wbp0qfTs2TN5/7baIVJcsc1LPu79jZanGloUthpqAWy8Jx/9swWZ/MjPRsBmzfxrbH622lHYxuDHMhubgPc+NS/9t956SzbffHN58sknpU+fPjJmzBjZeeedk/89fvx4mThxotx6661ywQUXyJQpU6oWT2Gbl3zc+xstTzW03ApbyxHkPBVZkwor2nhvHPTPFmPyIz8bAZs188/Gr5K1ZcxEeZigTprf3+xYhw4id5810lwOCyABTwS891kaVuPGjZPzzjtPevToIYMHD5brr79eunTpInvuuWdyiBROSL733ntl2223pbDVAK6DTSPmaV6MLoWt9QhyCtu8abDy/d4bB/2zxZj8yM9GwGbN/LPxa2ttHTMpbMPGg6U1HgHvfZaWOMTrokWLki3Hra9Zs2Ylq7edO3dut2iu2GrJx7Fr1DzNQ8udsA1xBDmFbZ4UqHyv98ZB/2wxJj/ysxGwWTP/bPxaW4cYMylsw8WDJTUmAe99Vr2oU9jWi3w55+5F0HInbNNKW44gp7C1p473Tpz+2WJMfuRnI2CzZv7Z+FWytoyZFLbh48ESG4uA9z6rXrQpbOtFnsK2Gnm3wjbPEeRtK0dha29o3jtx+meLMfmRn42AzZr5Z+NXydoyZlLYho8HS2wsAt77rHrRprCtF3kK29IIW80R5G2/rTV9YS+Z92E3OWSXTWTrTdb2lXX0hgRIgARIwD2BsnwLMMSYmQYjyOFRInLRkUPcx5cOkkBeAmXpE/LWy3I/ha2FXnhbPoARcbdiG+IIcq7Y2huL98ZB/2wxJj/ysxGwWTP/bPxaW4cYM7liGy4eLKkxCXjvs+pFncK2XuS5YluaFVs4aj2CnMLW3tC8d+L0zxZj8iM/GwGbNfPPxq+ttXXMpLANGw+W1ngEvPdZ9SJOYVsv8hS2pRK2cNZyBDmFrb2hee/E6Z8txuRHfjYCNmvmn41fJWvLmElhGz4eLLGxCHjvs+pFm8K2XuQpbEsnbC2pQmFrofeRrfdOnP7ZYkx+5GcjYLNm/tn4xbDGBDXIO7YdRO4+a2QMF1kmCdSNgPc+q15gKGzrRZ7ClsLWV+5ROBrj4X2QoX+2AJMf+dkI2Ky955+tdpWtKWxjUGWZjUKgGfuELLGjsM1Cqbh7mKcOD48KEX6u2Nopem8c9M8WY/IjPxsBmzXzz8YvhjWFbQyqLLNRCHjvs+rFmcK2XuS5YssVW1+5xxVbYzy8DzL0zxZg8iM/GwGbtff8s9WOK7Yx+LHMxibQjH1ClohS2GahVNw9zFOu2BaXbW1+yXvy0T9bapAf+dkI2KyZf43Nz1Y7CtsY/FhmYxPw3qfWiz6Fbb3Ic8WWK7a+co8rtsZ4eB9k6J8twORHfjYCNmvv+WerHYVtDH4ss7EJNGOfkCWiFLZZKBV3D/OUK7bFZRtXbIOy9t546Z8t3ORHfjYCNmvv+WerHYVtDH4ss7EJNGOfkCWiFLZZKBV3D/OUwra4bKOwDcrae+Olf7Zwkx/52QjYrL3nn612FLYx+LHMxibQjH1ClohS2GahVNw9zFMK2+KyjcI2KGvvjZf+2cJNfuRnI2Cz9p5/ttpR2MbgxzIbm0Az9glZIkphm4VScfcwTylsi8s2CtugrL03XvpnCzf5kZ+NgM3ae/7ZakdhG4Mfy2xsAs3YJ2SJKIVtFkrF3cM8pbAtLtsobIOy9t546Z8t3ORHfjYCNmvv+WerHYVtDH4ss7EJNGOfkCWiFLZZKBV3D/OUwra4bKOwDcrae+Olf7Zwkx/52QjYrL3nn612FLYx+LHMxibQjH1ClohS2GahVNw9zNMGF7ZIpYEbrSPnfWdYcVmV8Ze8Jx/9yxjIKreRH/nZCNismX+Nzc9WOwrbGPxYZmMT8N6n1os+hW29yFf+XeYphW3dMtJ78tE/W2qQH/nZCNismX+Nzc9Wu+KE7T5jJwRx9bdnj0zKYXk6nCk/nTWtQMB7n1qvKFHY1os8hW018h1aWlpafIUljDc/OfMieerdvlyxVeL03onTP2Vg/2tGfuRnI2CzZv7Z+MWwxgR10vz+5qI7dBC5+ywKUQvIWELe4lOz23rvs+oVHwrbepGnsKWw9ZV77p/+ee/E6Z8tocmP/GwEbNbMPxu/GNYUtnqqsYRo6BVqfQ1p6b3PqleEKGzrRZ7ClsLWV+5R2Brj4X2QoX+2AJMf+dkI2Ky955+tdpWtKWz1VCls9ezKYtmMfUKW2FDYZqFU3D3M0wZ9xxYpxK3ItobkvXHQP8bXRsBmzfwjPxsBf9YUtvqYUNjq2ZXF0nufXy+OFLb1Is8VW67Y+so9rtga4+F9kKF/tgCTH/nZCNisveefrXZcsS2LEOVW5BiZriuzGfuELKQobLNQKu4e5ilXbIvLtja/5D356J8tNciP/GwEbNbMv8bmZ6sdhS2FbYwMauwyvfep9aJPYVsv8lyxbb4V27MukqcW9JV1e3SVcYfvLBuu191V9nnvJOmfLV3Ij/xsBGzWzL/G5merHYUthW2MDGrsMr33qfWiT2FbL/IUtk0rbFHxkw76omy/5Yauss97J0n/bOlCfuRnI2CzZv41Nj9b7Shsm1XY/m7K80FSZ+8vbJaUE7q8IM5FKsR7nxqp2jWLpbCtiajQG5inDbwVedzZ58vcLlvIrNffobBVNCvvjYP+KYLayoT8yM9GwGbN/LPxi2HNw6P0VJtJKIf+TnHr8vQRiG/pvc+KT6D6A7ETTzyxXj/P321DgHnawMIWg/SyfjvII9NeobBVNH3vjYP+KYJKYWuDRn7kF4yAv4IobPUxobDVs6Ow1bPzYMkVWw9R+NgH73PjImh1aGlpaSnih4r+DQpbG3HvjYP+Mb42AjZr5h/52Qj4s6aw1ceEwlbPjsJWz86DJYWthyhQ2LaOAoVtnXKSE2MbePIjPxsBmzXzj/xsBPxZU9jqY0Jhq2dHYatn58GSwtZDFChsKWwd5CEnxrYgkB/52QjYrJl/5Gcj4M+awlYfEwpbPTsKWz07D5YUth6iQGFLYesgDzkxtgWB/MjPRsBmzfwjPxsBf9YUtvqYUNjq2VHY6tl5sKSw9RAFClsKWwd5yImxLQjkR342AjZr5h/52Qj4s6aw1ceEwlbPjsJWzy6E5ZIlS+Tdd9+VtdZaS1Ucha0KWzQj73OTaBVvVTDfsS2CcoXf8J589M+WGORHfjYCNmvmX2Pzs9WusjWFrZ4qha2eHYWtnp3V8oILLpCrrrpKtt12W3nnnXcEfUD//v2lV69esvvuuyfFDxw4UE4//fSqP0Vha41CWHvvY3/Y2lYujcK2CMoUtsEpe2+89M8WcvIjPxsBm7X3/LPVjsK2mYQooh2yvvyObYzWV3yZixcvltVWWy1Zre3WrZucccYZ8tprr8nRRx8tp5xyitx0003SqVOnmo5R2NZEVOgNzTh2tQVMYVtoyn38Y96Tj/7ZEoP8yM9GwGbN/GtsfrbaUdiGFHqhhaP38ihsY7S++pT51ltvSc+ePeW9996TL3/5y3LsscfKGmusIaNHj5YFCxbIkCFDklXcoUOHVnWQwrY+sav2q97H/iJoUdgWQbnCb3hPPvpnSwzyIz8bAZs186+x+dlqR2FLYavPIApbPTuPlk888YQcdthhsuWWW8o111wjkydPFvz/jjnmGJkwYYKcffbZMn36dOnQoYNMmjQp+Xvba/jw4R6r1rQ+DRgwoGnrjopT2NYp/Jx42sCTH/nZCNismX/kZyPgz5rv2Opj0kxCmcJWnyfeLO+///5kdfaSSy6RkSNHJu5hi/Iqq6yS/Fu6dGmyHXnWrFnSr1+/iu5zxdZXVL3PTYqg5VbYhjipbVm/HeSRaa/ISQd9UbbfcsMieGb+De/JR/8yh7LijeRHfjYCNmvmX2Pzq1S7EGPmpPn9beDwtLyDyN1nfTRJ3mfsBHN5KKCZhKP3+oaOLw+PCtJEchfS0tIiPXr0kPvuu0+22Wab5fannnqqvPHGG3L55ZfLI488ImPGjJEZM2ZULZ/CNjf6qAbex/6olf9v4S6FbaiT2ihs9SnkvXHQP31sYUl+5GcjYLNm/tn4tbUONWZS2Ori0kzCm8JWlyPerF544QXZbLPNVnDrkEMOSbYeDxs2LFmpxb/TTjtN9t57bwpbbwGs4o/3sbUIjO6EbciT2ihs9SnkvXHQP31sKWxt7MiP/OwEwpUQcsyksNXFhcJWxw1WXLHVs4tpOXfuXOnTp0/Nn+CKbU1Ehd7gfW5cBAx3whaVDnVSWyps/2f/IbLrEF8vU3tPPvpna37kR342AjZr5l9j82tbu1BjJoWtLm8obHXcKGz13LxYUth6icRHfngf+4ug5VLYouJ5TmqrBAqNLRW2e31hMznqq1sXwTPzb3hPPvqXOZQVbyQ/8rMRsFkz/xqbX6XahRgzKWx1eUNhq+PWVtj+7bk5+QtqWdnkcwP75i+nhoX3PjV4hTMWSGGbEVRBtzFPnZ6KnPektmpHkC/tM0SmTH9DdtxiPTlw+08UlFb8GRIgARIggbITKNMnE0KNmUGErYhcdOSQJPzHXv1YkDS4mOWZOIbk1yFwfGOWZ4JWwbhMfULoulcrj8K2KNLZfofC1qGwDXlS25Y7HSBX3v032XO7TeU7+34uW1YUdJf35KN/tkQgP/KzEbBZM/8am1/r2oUcM4MIW56KbEs+56dAxzw8KsQp2rHe2fXep5qTTlkAha0SXCQz5qlDYRvypDYKW33L8d446J8+trAkP/KzEbBZM/9s/FpbhxwzKWx1ceFWZB03WMUUynqvVrb03meFrGuesihs89CKfy/z1KGwrRX2PCe1UdjWoln9794bB/3Tx5bC1saO/MjPTqC4EvKMmRS2urhQ2Oq4UdjquXmxpLD1EomP/PA+Ny6CltvDo6yVR2OjsNVT9N446J8+tmXo/BhfxtdGwGbtPf9statsjTGTwlZHlsJWx43CVs/NiyWFrZdIUNimkaCwrVNOep840T9bYpAf+dkI2KyZf43Nz1Y7CttmEqKIdsj6xtw6zHdsY7TsuGVS2Mblm7d072N/3vpo7qew1VALYOM9+eifLcjkR342AjZr5l9j87PVjsI2pNALLRy9l0dhG6P1lbdMCltfsfM+9hdBq6GF7ad3PkCuuOtv8qlPrCvnHvXlInhm/g3vyUf/Moey4o3kR342AjZr5l9j87PVjsKWwlafQRS2enaNaElh6yuq3sf+Img1hbDdqHcPuey43Yvgmfk3vCcf/cscSgpbGyryI78IBGxFeu//bLWjsKWw1WdQmYTtadf9RV/RVpanHroTD+WpQpLCNkiKBSukGceutvAaWth+ddQ35YQr7xcK2/xtxnvjoH/5Y9ragvzIz0bAZs38s/GLYc3Do/RUm0kol0nYhnxn13ufpc9emyWFrY1faGvmaQk/95M1CdDYRh16lBxz0R8pbLNCa3Wf98ZB/xRBZXxt0MiP/IIR8FcQha0+JhS2enZlEcre5xz6CNgsKWxt/EJbM08pbEPnVObyvCcf/cscyoo3kh/52QjYrJl/jc3PVrvK1hS2eqoUtnp2FLZ6dh4sKWw9ROFjH7yP/UXQauityFyx1aeQ98ZB//SxhSX5kZ+NgM2a+WfjF8OawlZPlcJWz47CVs/OgyWFrYcoUNi2jgKFbZ1ykhM7G3jyIz8bAZs184/8bAT8WVPY6mNCYatnR2GrZ+fBksLWQxQobClsHeQhJ8a2IJAf+dkI2KyZf+RnI+DPmsJWHxMKWz07Cls9Ow+WFLYeokBhS2HrIA85MbYFgfzIz0bAZs38Iz8bAX/WFLb6mFDY6tlR2OrZebCksPUQBQpbClsHeciJsS0I5Ed+NgI2a+Yf+dkI+LOmsNXHhMJWz47CVs/OgyWFrYcoUNhS2DrIQ06MbUEgP/KzEbBZM//Iz0bAnzWFrT4mFLZ6dhS2enYeLClsPUSBwpbC1kEecmJsCwL5kZ+NgM2a+Ud+NgL+rCls9TGhsNWzo7DVs/NgSWHrIQoUthS2DvKQE2NbEMiP/GwEbNbMP/KzEfBnTWGrjwmFrZ4dha2enQdLClsPUaCwpbB1kIecGNuCQH7kZyNgs2b+kZ+NgD9rClt9TChs9ewobPXsPFhS2HqIAoUtha2DPOTE2BYE8iM/GwGbNfOP/GwE/FlT2OpjQmGrZ0dhq2fnwZLC1kMUKGwpbB3kISfGtiCQH/nZCNismX/kZyPgz5rCVh8TCls9OwpbPTsPlhS2HqJAYVu4sH3wwQdl6NChMnHiRHn++efloIMOkrXWWitqNqCxjTr0KDnmoj/KRr17yGXH7R719/IWzolxXmIr3k9+5GcjYLNm/pGfjYA/awpbfUwobPXsKGz17DxYUth6iAKFbaHC9oQTTpCf/exnMnXqVNl2222T3956663lb3/7W9RsaC1se3RbTW74yX5Rfy9v4ZwY5yVGYWsjRn7kF5KArSz2fzZ+MawpbPVUKWz17Chs9ew8WFLYeogChW1hwralpUX69OkjI0aMkPfff1+uueYauemmm2T06NEyZ86c5G+xrtbCFr8x4af7S9fVOsf6udzlcmKXG9kKBuRHfjYCNmvmH/nZCPizprDVx4TCVs+OwlbPzoMlha2HKFDYFiZsP/jgA+nSpYvcfvvtctRRR8lmm20ml19+uQwePFimT58uAwcOjJYRaGxHHX2sfOPMu5LfoLDNh5oT93y82t5NfuRnI2CzZv41Nj9b7SpbU9jqqVLY6tlR2OrZebCksPUQBQrbwoQtfmjkyJFyyy23JL959dVXy9lnny2LFy+WWbNmRc2GtLGN+Olt8v4HSyhsc9LmxDgnsDa3kx/52QjYrJl/jc3PVjsK22YSooh2yPqWRYii3vuMnWBuKml9vfep5ooqC6CwVYKLZMY8FenQgv3CEa/XX39drrjiCunQoYP88Ic/lMMOO0yOPvpo2WmnnSL+qgiFrQ2v98ZB/xhfGwGbNfOP/GwE/FlzxVYfk5DCMbQQDV0eha0+TxrRksLWV1S9z02KoBVd2KISL730kkyZMkUGDBgg66yzjmy66abR60Zha0PsvXHQP8bXRsBmzfwjPxsBf9YUtvqYUNjq2ZVFKHvv8/URsFlS2Nr4hbZmnhawYvvb3/5WvvrVryaxGzt2rEyaNEk++9nPysUXXxw6niuUR2Frw+u9cdA/xtdGwGbN/CM/GwF/1hS2+phQ2OrZUdjq2XmwpLD1EIWPffA+NymCVvQV2w033FDWW2+95Lu1+NxPp06d5PTTT5fZs2fL+uuvH62OFLY2tN4bB/1jfG0EbNbMP/KzEfBnTWGrjwmFrZ4dha2enQdLClsPUaCwbR2FqMI2PRX5sssuk5kzZ8oqq6wiw4cPT75jO23aNBk0aFC0jKCwtaHlxJ38bARs1sw/8rMRsFl7zz9b7SpbU9jqqVLY6tlR2OrZebCksPUQBQrbwoQtfgjidd68eck3a7Fai8OkunbtKs8++2zUbGgrbMfs+mkZMXSLqL+Zp3DvEyf6lyeaK99LfuRnI2CzZv6Vl99jjz0m/fv3l169eiWVWLRokTz66KPJjqfVVlvNVrF2rCls9WgpbPXsKGz17FpbavuNJUuWyLvvvpvsqmx9LVy4UFZffXXp2LFjuw5S2IaJX6hSvI/9oerZXjlRV2zxw08++aSceuqpgndt0+uuu+5a/t5trEpS2NrIem8c9I/xtRGwWTP/yM9GYGXrF154IXnge/jhhyf/dthhh+Sm559/Xr7//e8nhzB+4hOfCP2zy8ujsNWjpbDVs6Ow1bODpaXfuOCCC+Sqq65KHpq98847yddEcMDr6NGjk4Uo7LQ8/vjj5dBDD63qJIWtLX6hrb3PTULXt1J50YVt+qNvvfWWvPLKK7LJJpskK7axr7SxXT/xH3LLg88IV2zzEffeOOhfvni2vZv8yM9GwGbN/FuZ37nnnisnnnhiRbBrrrmmYAzF6zyxLgpbPVkKWz07Cls9O1hq+43FixcnO0CwWtutWzc544wz5LXXXpMNNthAFixYIGeeeabMnTtX+vbtK1i9rTZvp7C1xS+0tfexNXR96yJsv/zlLydbkdtejzzySNKYYl0Utjay3hsH/WN8bQRs1sw/8rMRWNm62soL7sQrPdieHPOisNXTpbDVs6Ow1bODpaXfwMOynj17ynvvvSeYqx977LFy3333ybBhw2TUqFHS0tKSbEWeMWNG8rnOSheFrS1+oa29z01C17cuwnbo0KHJe7W4sNUBq7Y4JfnFF1+MunJLYWtLH++Ng/4xvjYCNmvmH/nZCFS3fvXVV5N32zDpbH1B2NZ6383iE4Wtnh6FrZ4dha2eXWtLbb/xxBNPyGGHHSZbbrmlXHPNNXLwwQfLiBEj5MADD0yK7927t0ydOrXqgzUK2zDxC1WK97lJqHq2V05hW5FTJ8aNGyfpKcldunSp6luoF9q5FVmXRt4bB/3TxTW1Ij/ysxGwWTP/qvO74oorki3J2A7Y+nr77bele/fuUcfMSfPtq8KhhQoq3EzC0Xt9Q8e3LOV577M0/cb999+fvE97ySWXyMiRI5O+BXN09DPHHXecLF26NFnRnT9/fvJQbdKkSTJ58uSV+iB87YSXHwLVVtf9eBjXk+jCFnv0ly1bltQCjeTKK6+Us846KzkQY9NNN61Yu5AvtFPY6hLIeydO/3RxpbC1cSM/8gtDoHIp2PqHLwjgwirKqquuuvzGH//4x1VPRQ41ZlLY6qLbTMK7LEIUkdxn7ARdQFtZpfX1POfQ9Buw6dGjR7L1eJtttlle47vvvlvGjx8vEydOlFtvvVXQt0yZMqUqR67YmlMsaAGe8zRoRdspLLqwxTaGdCty6gcOwvjPf/4jnTt3Xsm10C+0U9jqUsl746B/urhSmNm4kR/5hSHQvrA95phj5OSTT870UyHHTArbTMhXuonCVscNVmURyp7nHKmwzdNv4N3czTbbbIXAHXLIIYKV3z333FOeeeYZef/99+Xee+9NTk2udlHY6nM/hqXnPI1R30plRhe22NaAU9dwYSvDRhttJHvssYdsvPHGVesY8oX2VNhut8UG8uMxH30+wcPlPfnony1LyI/8bARs1sy/8vI76KCDks/jYcKIbYDphXfe8AmOSleoMZPCVpc3FLY6bhS2em5tLTX9Rnu/PmvWrGT3SKUFqNZ2FLbhYhiiJO9jf4g61iojmrAFXDxFqnZB2LZ3EEaoF9pTYbtO99XlurFfrcWjsL97Tz76Z0sF8iM/GwGbNfOvvPwq7XJCbWq9YxtizKSw1eUNha2OG4WtnltbS22/YfWAwtZKMKy997E/bG0rlxZN2HbA/pJ2rvYG6ZAvtP/jpbfkF/e+ID26rSrjRg8ugil/gwRIgARIoOQE6nUAx1//+lf54IMPVqK3/fbbV12xDTVmBhG2InLRkUMS/4+9+rEgWXAxyzNxDMkPM7uQ8S1TefXqE7IEX9NvZCm31j0UtrUIFft3CluRaML2vPPOSw6LqnZ9//vfX+FgjPS+0C+0z3v7PTnsnN8KV2zzNS7vjYP+5Ytn27vJj/xsBGzWzL/q/HBoSyVhi1d4Km1FDjlmBhG2HUTuPuujE1ZDHN6DcpppRdR7fcvyTmyo/CvD4VGoa95+w9aDf2xNYRuKZJhyvI+tYWrZfinRhG3rn8UgjRVaXBiE8dI6TmGrtHc/9AvtFLa6NPLeOOifLq6pFfmRn42AzZr5V51f3i2FIcdMCltdXjeT8Kaw1eVIbKu8/UYofyhsQ5EMU473sTVMLessbCdMmCDf+973VjoZudb7QtXczvtCO4WtLo28Nw76p4srha2NG/mRXxgC1Uu55557lq/YLly4UM4999xkpXbq1KlVtyK351OeMZPCVhddClsdN1iVRSh7n3OE7jeyRpTCNiupYu7znqdFUIi+YrvJJpvIWmutlXy3drvttpN//etfst566wneB6h12poFQNrYKGx1FL03DvqniyuFmY0b+ZFfGALZS8HD4VGjRsns2bNl/fXXz26Y806MmRS2OaH993YKWx03Cls9t1qWRfYbJ554Yi13+PeCCHifGxeBIaqw/fDDD5P3aG+44Qb5y1/+Iuuuu64cddRRySd/8HkCCN5YF4Wtjaz3xkH/GF8bAZs184/8bASqW48dO1beeeed5IZly5Yl784h31577bXkoXCsi8JWT5bCVs+OK7Z6dq0t69lvUNiGiWGIUrzPTULUsVYZUYUtfnzDDTdMxO13vvMdwTdtDzjgALnuuuvkueeeW+nj0LWc6uLY9wAAIABJREFUzfN3Cts8tFa+13vjoH+Mr42AzZr5R342AtWt274rt+aaa8p3v/tdOeecc2L9ZFIuha0eL4Wtnh2FrZ5da8t69hsUtmFiGKIU73OTEHWsVUZ0YfvAAw8ITkC+/vrrZa+99pJXXnlF9tlnH7n77rtr+Wb6e1thi8IuPPorsmm/tU3lhjL2nnz0zxZp8iM/GwGbNfOvvPzmz5+//Bvw+GxezJ1NrSlR2OpzhsJWz47CVs+utWU9+w0K2zAxDFGK97E/RB1rlRFd2F599dWy2267JduPlyxZkhwiFfM9obTClYTt6YfvLJ/ZrE8tJoX83Xvy0T9bGpAf+dkI2KyZf+Xlt2DBguRB8E033ST433i/9pBDDok+blLY6nOGwlbPjsJWz661ZT37DQrbMDEMUYr3sT9EHWuVEV3YptsjdtllFxkzZozst99+hTyBbn1S2w8uu1eef+VNobCtlQ4f/91746B/2WNZ6U7yIz8bAZs18686P7wrh23H2ILco0ePZJfTFltsIU899ZTqVOSskaKwzUpq5fsobPXsKGz17Fpb1rPfoLANE8MQpXgfW0PUsVYZ0YXtgw8+KL///e/ljjvuSA7AwIUn0HjPdrXVVqvln/rvrYXtKb98SJ58fq6cdtjOsvUnuWKbBar3xkH/skSx+j3kR342AjZr5l9lfvi8zxprrCFHHnmkXHnlldKxY0e56KKLktd58EWBzTff3Aa+HWsKWz1aCls9OwpbPbvUst79BoWtPYahSvA+toaqZ3vlRBe26Y8/++yzMn78+OQfLu13bLNCobDNSqryfd4bB/1jfG0EbNbMP/KzEahs/d5770m3bt3kpz/9qZx66qnJTbfeequMGDFCHn/8cfnc5z4X42eTMils9WgpbPXsKGz17FLLevcbFLb2GIYqwfvcJFQ96ypssT0Cn/vBdipcW2+9tRx22GHJZ3/w0flYF4Wtjaz3xkH/GF8bAZs184/8bASqW++www4yefJk2WOPPaRr165y2223yZAhQ2Tq1KmCw6RiXRS2erIUtnp2FLZ6dq0t69lvUNiGiWGIUrzPTULUsVYZ0Vds8Y4tPvdzxBFHJE+dP/WpT9XyKcjfKWxtGL03DvrH+NoI2KyZf+RnI1DdeubMmXLWWWfJzTffnBwehXHzxz/+sWy11VaxfjIpl8JWj5fCVs+OwlbPrrVlPfsNCtswMQxRive5SYg61iojurB98sknZfDgwcm7QkVeFLY22t4bB/1jfG0EbNbMP/KzEWjf+plnnkm2JOMcCnzGI+a7taknFLb6iFLY6tlR2OrZtbWsV79BYRsuhtaSvM9NrPXLYh9d2GZxIsY9rYXtqdc+JE88N1cO33OwDN60jwzou1aMn8xVpvfko3+5wrnSzeRHfjYCNmvmX3n54RyK//mf/5F77rkn+UzeoEGD5IQTTkhWVGNeFLZ6uhS2enYUtnp2rS3r2W9Q2IaJYYhSvI/9IepYq4ymEraAseXGveTsb+1Si0v0v3tPPvpnSwHyIz8bAZs186+8/AYOHCi9evWS3/zmN8mq7dFHH51813b27NlRv2VLYavPGQpbPTsKWz271pb17DcobMPEMEQp3sf+EHWsVUYUYbt06dLkEz848OKJJ55ItiL369evli9B/956xfbae55KPvfz4pz5FLYZKXtvHPQvYyCr3EZ+5GcjYLNm/lXmt2TJEuncuXNyuOIVV1yR3HTjjTfKN77xDZkyZYpst912NvDtWFPY6tFS2OrZUdjq2aWW9e43KGztMQxVgvexNVQ92ysnirD94IMPpEuXLvKtb31L7rzzzmRQbjsgf+1rXyvsVGQA+Me/X5cfXf0ghW3GrPLeOOhfxkBS2NpAkR/5RSFQvdCddtpJHn74Ydl3331lzTXXlLvuukt69OghL7zwQvRvv0+a399c29BCBQ41k3D0Xt/Q8S1Led7nHPXsNyhszd1msAK852mwirZTUBRhi9/D9/awWlvtKvI7thS2+VPJe+Ogf/lj2tqC/MjPRsBmzfyrzu/FF1+UCy+8UCZMmCCvv/667LXXXnL88cfLzjvvbINew5ortnq8zSS8yyJEEc19xk7QB/W/lml9vfdZ9ew3KGzNaRasAO95Gqyi9RC27777biJshw8fnrwjtOOOO67gBv7vor5jS2GbP5W8Nw76lz+mFLY2ZuRHfuEI1C5p2bJl8v777yfv2RZxUdjqKVPY6tmVRSh7n3OkEahHv0Fhq8//0JZlydPQ9W5dXrQV2/RHXn31VVljjTXksccek4ULF8qwYcOSj87Hvlq/Y0thm5+298ZB//LHlMLMxoz8yC8cAX8lUdjqY0Jhq2dHYatn58Gy7Vzbg0/N7IP3uXERsYkubB966CHZZ599kg/Np9dFF10kxx57bNT6Udja8HpvHPSP8bURsFkz/8jPRsCfNYWtPiYUtnp2FLZ6dh4sKWw9ROFjH7zPTYqgFVXYYksETkbGe0I/+clPkpXaSy+9NFm9nTdvnqyzzjrR6khha0PrvXHQP8bXRsBmzfwjPxuB9q1bWlpk1qxZySGMa6+9dtTXdlJPKGz1EaWw1bOjsNWza2tZr36DW5HDxdBakve5ibV+WeyjCtv//Oc/su6668oll1ySfHAe13333Sdf+cpX5K9//atsu+22WXxU3VNN2Hbr0ll2GvwJ+e5+n1OVG8rIe/LRP1ukyY/8bARs1sy/8vJ7/vnnZffddxfEcOzYsTJt2jQZPXq0jBo1ylapGtYUtnq8FLZ6dhS2enatLevZb1DYholhiFK8j/0h6lirjKjCFk+P8JmCQYMGJac8YsX2zDPPlFtuuUXmzp0rvXv3ruWf+u/VhC0K/OSGa8v53/2KuuwQht6Tj/7Zokx+5GcjYLNm/pWXH74ogElqr169ZOTIkfLKK6/I9ddfL/Pnz0/G01gXha2eLIWtnh2FrZ5da8t69hsUtmFiGKIU72N/iDrWKiOqsMWPX3zxxXLcccet4Ae2JZ9++um1fDP9va2wXbjoQ3n4qZflsjsfp7DNQNZ746B/GYLYzi3kR342AjZr5l9lfuk34MePHy8vv/yyrLLKKsmXBbbeeuvkKwOf/exnbeDbsaaw1aOlsNWzo7DVs0st691vUNjaYxiqBO9ja6h6tldOdGGLH8cAfccdd8g777wj+++/f7KCG/uq9EL786+8KT+47F7ZrN/acsHRXLFtLwbeGwf9s7Ug8iM/GwGbNfOvOr/u3bvLVlttJWuttZasuuqq0rFjR5k4caLg1Z7OnTvbwFPYJgSaSYiGrm9ZhCjq3Uzfsa1nv0FhG61bzl2w97E1d4UUBoUIW4VfZhMKWxtC742D/jG+NgI2a+Yf+dkIVLe++eab5dvf/vYKXxLADifsdIp5ccVWT7eZhDKFrT5PYlrWs9+gsI0Z2Xxle5+b5KuN7m4KWx03s5X35KN/thCTH/nZCNismX/l5oethQ8++KC8+OKLMnjwYPniF79oq1AGawrbDJCq3EJhq2dXFqHsvU9FBOrVb1DY6vM/tGUZ8jR0nduWR2Ebm3CV8r0nH/2zJQb5kZ+NgM2a+VdefuPGjUte22l7nXHGGcnnf2JdFLZ6shS2enYUtnp2rS3r2W9Q2IaJYYhSvI/9IepYq4zowvbggw+WI444QnbaaafEF5zwOGbMGPn1r39d6KnI+G2+Y1srHT7+u/fGQf+yx7LSneRHfjYCNmvmX3V++FoAvv3e9nr77bcF79HFuihs9WQpbPXsKGz17Fpb1rPfoLANE8MQpXgfW0PUsVYZ0YTtddddJ5deemlykmO/fv1kvfXWS3zBgA1xi4Mw8OH5WFeld2xfeOVN+f5l90q/Xt2Xf8f20wM+8qvoy3vy0T9bRpAf+dkI2KyZf+Xlh7Fx2bJlSQXee+89Of744+Wll16SKVOmJKckx7oobPVkKWz17Chs9exaW9az36CwDRPDEKV4H/tD1LFWGdGE7a9+9avkUz9thS0cGjZsmGAQjXm1J2xb/+6NP9lPundbLaYrFcv2nnz0z5YS5Ed+NgI2a+Zf4/DDN2yx8+mFF16QTTbZxFaxdqwpbPVoKWz17Chs9ezasyyy36CwjRNDTanex35NnfLaRBO2qSOnnHKK7LvvvoKPRxd5VRK2s99YIOPveCxxY9qLbyT/pbCtHBXvjYP+2VoT+ZGfjYDNmvlXnd8OO+wgc+bMWX4DWOFasGCBrLHGGjbwFLYJgWYSoqHrWxYhino30+d+LP3Gu+++K926dZMOCG7Oq9JcO2cRvD0gAe9ja8CqVi0qurB9+OGH5fzzz0+2UbW+ZsyYEX2Qbu8p0kFn3CnvLPxAbvjJftKDK7YrJYj3xkH/bN0D+ZGfjYDNmvlXnd+uu+4qr732WnJDp06dklVarNjuvffeNug1rLliq8fbTEKZwlafJzEtNf0GXg18+umnZf/99092hOCVQZys3KtXL9l9990TdwcOHCj43Fi1i8I2ZlTzl+19bM1fo/wW0YXtoEGD5JlnnpGtt946+dh8et1///3StWvX/B5ntKjV2Chs2wfpvXHQv4wNocpt5Ed+NgI2a+afjV8MawpbPVUKWz27sghl732WJgK33XabTJ48WS688MLkYRqE7fTp0wU7LW+66abkwVqtq9Zcu5Y9/x6WQCPmaV5CUYXt0qVLk4Zx6qmnyk9/+tO8vpnur9XYKGwpbE0JVsPYe+dC/2zRJz/ysxFY2XrDDTeUxYsXVy22iF1Ok+b3N1crtFCBQ80kHL3XN3R8y1Ke1z4/RL+BLcipsP3d734no0ePTl59GDJkSHIeztChQ6v2C7Xm2uYOhQXkIuA1T3NVwnhzVGEL3w477DB59NFHk8/79OzZc7m7G2ywQc39/DH3/VPYUtga20675t47F/pniz75kZ+NwMrW2Gq8ZMmSqsXefvvtNXc5WcdMCltdVJtJeJdFiCKSzfCObYh+o7WwxW5KHPp6zDHHyIQJE+Tss89OVnGrvX9LYavrM2JZeZ+bxKp363KjC1vNt7WK2PdPYUthG7OBee9c6J8t+uRHfjYC+azfeeed5EyKjh07VjQMNWZS2OaLS3o3ha2OG6zKIpS99/mVIlCr30htWgtb7BrBZ8XwL911OWvWrOSznZMmTUq2Lre9hg8frk8AWgYnMGDAgOBllqnA6ML2oosuSrY0tL1OOOEEWW21yp/ZKWLfP4UthW3Mhup9EKR/tuiTH/nZCFS3/uc//ymnnXaa4L+4sIqLU5Lx/ffu3btXNAw1ZlLY6qJKYavjRmGr59bWUtNvVBK2eHXwjTfekMsvv1weeeQRGTNmjOA1iGoXV2zDxTBESd7nJiHqWKuM6ML2rbfekpaWlpX8WHvttWv5lmx9iLXv/xtn3ClvL/xA9t9pc1mt84ofvf/SZz4h66+7Zk3/LDd4Tz76Z4muCPmRn42AzZr5V15+22yzjTz22EefpcNhLliNxRP4adOmyeqrr95uxaxjJoWtLm8obHXcKGz13NpaWvsN9DM4DRkP0YYNG5acj4N/eMjW3onsFLbhYhiiJO9jf4g61iojurDVbEWu9BSpvX3/mu0RP77+SXl3UeX3mb69+ydliw171GLHv5MACZAACTQogXps5/rwww+TrwfcfPPNcscdd8jnP/95+eIXvyh77LGHzJs3b4UvC1TCnvVduWpjZhBhKyIXHTkkce/Yqz8S6NbrYpZnQhiSH750GjK+ZSqvHn1ClsBb+41KvzF37lzp06dPzZ+nsK2JqNAbKGxFogtbbGtItyLjYAscIoVT3PByerWtyJWEbXv7/itlTa3GdvtfpsuixSsK24eeellenbdATj10J/n8wL5Rk9F78tE/W/jJj/xsBGzWzL/y8sN2Y4hZ/MPhLT/84Q/l8MMPT1Zs8fm89q6s78pVGzODCNsOInefNTL5iRCH96CcZloR9V7fsrwTGyr/0vp671Mt/Yalt6w117aUTdv8BLznaf4a5beILmzbunTttdcmg/Sbb765winJlVxvPUgXse//tOv+Io8/O0dOOWRHGbL5+vlp5rDwnnz0L0cwK9xKfuRnI2CzZv6Vjx8OasGBLfim5A9+8AN54IEHZJdddkkqgi3Js2fPrvldSeuYSWGry5tmEt4UtrociWUVot+w+EZha6EX3tb72B++xiuXGF3Y/va3v5VFixYlv4wGeMMNN8jvf/97efHFF6V///a/mYdBush9/xS2HyeI98ZB/2zdA/mRn42AzZr5tzI/vLYzcuRIwQmjm222may77rry3HPPJYdI7brrrtKjR+3XY6xjJoWtLq8pbHXcYFUWoey1zwrRb+ijJ8l3bk888URLEbQNSMBrngasYs2iogvbSu/Ybr/99vLwww/X/I5tJe9j7vunsKWwrdliMt7gvXOhfxkDWeU28iM/G4GVrfGKDk4+xoUVWpxGeuCBBwoOhan2mZ8sPuQZMylssxBd+R4KWx03Cls9t9QyVr+R1TMK26ykirnP+9ykCArRhe199923fMUWT5PRCDfffPOah2BYK69pbBS2FLbWvEvtvXcu9M8WafIjPxuBla3x9YB//OMf8qc//Umw0wkPf3Hh+5EQuTidtHPnzqF/dnl5GDMpbHV4KWx13Chs9dxSSw/9Blds7XEMVYL3uUmoerZXTnRhix9/+umn5fbbbxd8LPqAAw6Q7bbbLnmXKOZFYWuj671x0D/G10bAZs38Iz8bgdrWkydPlu9973vJQYu43n777arfsa1dWu07KGxrM6p2B4Wtnh23IuvZVbKsR79BYRs2hpbSvM9NLHXLahtd2P7iF7+QI488cgV/8A7RLbfcktVH1X0Utipsy428Nw76x/jaCNismX/kZyOwsjW+GvDQQw8lK7b41E+6LXmLLbaQgw46SE444YSah0dZfKKw1dOjsNWzo7DVs4Olh36DwtYWw5DW3ucmIetarayowhaf6MHWYwzM48ePTz4uf/rpp8t1110nM2fOlI022ihaHS3CNotT39zrM7LfDgOz3FrxHu/JR//UoU0MyY/8bARs1sy/8vHDqzrp9clPfjIRs9jhVOsTP7aafmxNYasnSWGrZ0dhq2cHSw/9BoWtLYYhrb2P/SHrWhdhO3/+/OSTPvh8wXHHHZf4MHHiRNltt90E2yXwnb5YF4Wtjaz3xkH/GF8bAZs184/8bARWth44cKB8/etfl/3331+22mqr0MXXLI/CtiaiqjdQ2OrZUdjq2cHSQ79BYWuLYUhr73OTkHWti7DFj2LFFiu3+Mh8165d5ec//7nglMaXXnopWcGNdWmEbRZffvH7v8tdk54VrthmoRXvHu+Nl/7ZYk9+5GcjYLP2nn+22lW2prDVU6Ww1bOjsNWz82AZa67toW5l9KEZx662cYq6FRk/9uc//1m++c1vJlszca255pryy1/+MvmMQcwrVmOjsI0Ztexle2+89C97LCvdSX7kZyNgs/aef7baUdg2kxBFtEPWtyxCFPXeZ+wEc1NJ69uMfUIWeLHm2ll+m/esTIB5KhJd2AI7jiN/8sknk5fcsf24U6dO0fMxVmOjsI0eukw/4L3x0r9MYax6E/mRn42Azdp7/tlqR2EbUuiFFo7ey6OwjdH6yltmrLl2eYnU1/NmHLvaEo8qbC+99FJ55pln5Iorrkh+96STTpJddtlFdt111+iRj9XYKGyjhy7TD3hvvPQvUxgpbG2YyK9J+cWoNrci66k2k1CmsNXnSSNaxpprNyKrIurkfe5ZBINowvbmm2+W0aNHJ0L2/vvvT+qy5557yj333CNHHXXUcrEbq5KxGhuFbayI5SvXe+Olf/ni2fZu8iM/GwGbtff8s9WusjWFrZ4qha2eXVmEcjP2CVmiGmuuneW3ec/KBJinkbYif/jhh7LOOuskB0dNmTJl+UflFy5cKHvvvXfy3u3s2bNl/fXXj5aXsRobhW20kOUq2HvjpX+5wrnSzeRHfjYCNmvv+WerHYVtMwlRRDtkfcsiRFFvvmMbo6dYscxYc+34njfmLzTj2NU2klFWbHHqcd++feXkk0+WcePGrfCb1157rRx++OHy8MMPyw477BAts2I1tlTYfnazPrL5Ruvk9n/Qxr1k8Ca9+Z3T3ORWNPDeeOmfLcDkR342AjZr7/lnqx2FbUihF1o4ei+PwjZG6ytvmbHm2uUlUl/Pm3HsKkTYLl26NDkgql+/fvLss88mn/nBtWTJkmQ78r333iszZ86UjTbaKFoGxGpsqbDVOj78S5+Sg3fbisJWC/C/dt4bL/2zBZj8yM9GwGbtPf9staOwpbDVZxCFrZ5dI1rGmms3Iqsi6tSMY1chwhY/gu/Wnn/++cnv7bHHHslnfvB+7YIFC2S33XaTP/7xj1FjHKuxPfHcXJn+8rzcvv/zpXny9IzXhMI2N7qKBt4bL/2zxZn8yM9GwGbtPf9staOwpbDVZxCFrZ5dI1rGmms3Iqsi6tSMY1dhwnbRokVy9tlnr7QVecyYMXLBBRfIuuuuGzXG3hrbLQ8+I9dP/AeFbaCoe2+89M8WaPIjPxsBm7X3/LPVjsKWwlafQRS2enaNaOltrt2IjPPUqRnHrsKEbfpDH3zwgbz00kuC//bv33/5QVJ5AqW511tjo7DVRLG6jffGS/9s8SY/8rMRsFl7zz9b7ShsKWz1GURhq2fXiJbe5tqNyDhPnZpx7Cpc2OYJSMh7vTU2CtuQ0RW+o2zE6b3zo3+2AJNfY/Oz1Y7ClsJWn0EUtnp2jWjpba7diIzz1Mn72J+nLtp7o5yKrHUmpJ23xkZhGzK6FLZWmt47P/pnizD5NTY/W+0obCls9RlEYatn14iW3ubajcg4T528j/156qK9l8JWSy6nHYVtTmA1bvfeeOmfLd7kR342AjZr7/lnqx2FLYWtPoMobPXsGtGSwtZXVJtx7GobAQrbgnKSwjYsaO+Nl/7Z4k1+5GcjYLP2nn+22lHYUtjqM4jCVs+uES0pbH1FtRnHLgrbOuXgrX/+l/zqT09Lr7W6Su+e3QSnRnfp0sXszYE7f0o+N7CvuZy2BXhvHPTPFnLyIz8bAZs188/GL4Y1JqiT5vc3Fx1a+MAhClFbWELyCx3fspTnvc+yZYjemsJWzy6GJfNUhCu2MTKrQpmpsA39c8ceuI0M+9zGoYvl4UxGot47F/pnCzD5kZ+NgD9rClt9TEIKR+9CvixCFBz3GTtBH9T/Wqb19d7nmyuqLIDCVgkukhnzlMI2UmqtXOwb89+TuW++u/wPc+bMkb599Sutt/1luvzt2TlCYVtYCHP9kPfOhf7lCudKN5Mf+dkI+LOmsNXHhMJWz64sQtl7n6+PgM2SwtbGL7Q185TCNnROZS7PmnwX/+ZRue9vL8r3DthGvvJ5rthmBl/Qjdb4xnaT/tkIkx/52Qj4s6aw1ceEwlbPjsJWz86DJYWthyh87IP3uUkRtLgVuQjKFX7DmnwUtv+WAQMG1Cl6tX/WGt/av2C7g/6Rn42AzZr5Z+MXw5rCVk+VwlbPjsJWz86DJYWthyhQ2LaOAoVtnXLSOrGjsKWwtaSuNf8sv53Flv5loVT9HvJrbH622lW2prDVU6Ww1bOjsNWz82BJYeshChS2FLYO8tA68aSwpbC1pLE1/yy/ncWW/mWhRGFro1RefjHqTWGrp0phq2dHYatn58GSwtZDFChsKWwd5KF14k5hS2FrSWNr/ll+O4st/ctCqbzCjPG1xTeGNYWtniqFrZ4dha2enQdLClsPUaCwpbB1kIfWid0ltz0m9z7+bzlgp82jfMe20qnNA9bvKd26dHZAT/g5ImMUrPln/Pma5vSvJqJ2byC/xuZnq11lawpbPVUKWz07Cls9Ow+WFLYeokBhS2HrIA+tE89U2BZZlXO+vYsM6t+ryJ+s+ltWfrErQf9shMmP/GwEbNbe889WOwrbZhKiiHbI+pZFiKLe/I5tjJ5ixTIpbOMzzvMLzTh2teXDw6PyZEzAe63Jh+/YPj791YAerVjUokWLpEuXLsn/88U582Xhog+FwjY7bmt8s/+S7k76p+OWWpEf+dkI+LPmiq0+JiGFY2ghGro8Clt9njSiJYWtr6h6n5sUQYvCtgjKFX7De/K19u+kqx6Qf770hpz9rV1ky425YpslZcoU3yz1Kfoe8rMRJ7/G5merXWVrCls9VQpbPbuyCGXvfao+AiLvvvuudOvWTTogGP+9Fi5cKKuvvrp07Nix3aIpbC3kw9s2cp5mpUVhm5VU4Pu8Jx+FrS3gZYqvraZxrMnPxpX8GpufrXYUts0kRBHtkPUtixBFvbkVuf2e4vXXX5enn35a9t9/f3nhhRdkvfXWk3nz5sno0aOlU6dOMnPmTDn++OPl0EMPrVoQhW2M3lhfpvexX1+z7JYUttlZBb3Te/JR2NrCXab42moax5r8bFzJr7H52WpHYRtS6IUWjt7Lo7CN0frqU+Ztt90mkydPlgsvvFBee+21RNiec845smDBAjnzzDNl7ty50rdvX8HqbdeuXSs6SWFbn9hV+1XvY38RtFwL20beHuE9+Shsbc2vTPG11TSONfnZuJJfY/OrVjvrmDlpfn8bOBEJLXy8C71m8y90fMtSnvc+1dJwsQU5FbZHHHGEDBs2TEaNGiUtLS3JVuQZM2bIgAEDKGwtkAuybeQ8zYrQpbBthu0R3pOPwjZrE6p8X5nia6tpHGvys3Elv8bm17Z2ocZMCltd3jTTCnBZhCgiya3I2fK5tbAdMWKE4N+BBx6YGPfu3VumTp0q/ftXfujFFdtsjIu6y/vYXwQHl8K2GbZHeE8+Cltb8ytTfG01jWNNfjau5NfY/NrWLtSYSWGryxsKWx03WJVFKHvvU/URQAw+XrEdN26cdO/eXY477jhZunSp9OzZU+bPn5+s3E6aNCnZutz2Gj58uOXnaRuYQLXV9cA/47Y4l8JMDvtTAAAgAElEQVQ2pdXI2yO8d5Kt/Rv78wdk2otvyIC+a0nXLp3rksyH7D5YNt9oneW/XSZ+dQFW40fJzxYV8iM/G4E41tYxk8JWFxcKWx03Cls9t5CWrfuNu+++W8aPHy8TJ06UW2+9VS644AKZMmVK1Z/jim3ISNjL8j43sdewdgmlEbbtbY/gU6TagbbcccnvpsuMOQssRZhtv7XbZjJoo7XM5bAAEiABEshCoIxPvbNuKaw2ZgYRtiJy0ZFDEsTHXv1YFtQ177mY5dVk1N4NIfnhgzAh41um8srYJ2RJHPQbeJ2hV69e8v7778uee+4pzzzzTPK/7733Xtl2220pbLOAdHAPha1IaYRte9sjKuWS96dI3pOvtX//njNfFr6/uC5N9vqJ/5B/zZwnpxyyowzZfP3lPpSJX13A1fhR8rNFhfzIz0YgjnXWLYXVxswgwraDyN1njUx+IsQ7jiinmVZEvde3LFuHQ+VfWl/vfX7oHmXWrFnSp08f6dy5/V163ufaobl4L6/Z8rRSPEojbBtte4T35PPi37j/e1gem/6qnHzwjrLNpyhsQ3WqXuJbrT70zxZp8mtsftVqZ91SSGGry5tmEt4UtrocaVQrCltfkfU+9hdBy72wbdTtEd6Tz4t/FLZxugEv8aWwZXzjELCV6r19tCdsLWMmha0ubyhsddxgVRahXNY+QR+ZbJYUttk4FXUX89T5VuRKidAo2yO8J58X/yhs43SHXuJLYcv4xiFgK9V7+8hTuzxjJoVtHrIf30thq+NGYavn5sWSwtZLJD7yo5HGLi1Z1yu22krBzntj8558XvyjsLW0guq2XuJLYcv4xiFgK9V7+7DVrrI1xkwKWx1ZClsdNwpbPTcvlt7n2l44FeVHM45dbdlS2BaVbW1+x3vyefGPwjZOgnqJL4Ut4xuHgK1U7+3DVjsK22YSooh2yPqWZesw6h3i8LJmPTwqax9DYZuVVDH3NePYRWFbTG7V/BXvyefFvzN+NUmm/mu2HLzbVit8x3bOnDnSt2/fmpytN3x6wHqqIrzwo3BUha+mEeNbE1G7N5CfjV8Ma67Y6qmGFI6hhWjo8ihs9XnSiJYUtr6i6n1sLYIWV2yLoFzhN7wnnxf/UmFbpzDJr360r/Rcs0vun/fCj8I2d+gyGTC+mTBVvYn8bPxiWFPY6qlS2OrZlUUoe++z9BGwWVLY2viFtmaelvDwqKxJ4L2xeU8+L/7hO7bPvPTGSmFftGiRdOmSX3BmzZ9pL370mxS2WYmFvc9L/vHBQNi4pqUxvnG4WkqlsNXTo7DVs6Ow1bPzYOl9ru2BUZE+eB9bi2DBFdsiKFf4De/J1+z+jTnzLpn/7iIKW7aPigSavX1Y04L8rATD21PY6plS2OrZUdjq2XmwpLD1EIWPffA+thZBi8K2CMoUtsEpx268FLbBQ5arwNjxzeUM268V10r2jG9wpOYCKWz1CCls9ewobPXsPFhS2HqIAoVt6yhQ2NYpJzmxs4GPzY/C1hYfq3Xs+NI/KwGbPeNr4xfDmsJWT5XCVs+OwlbPzoMlha2HKFDYUtg6yENO7GxBiM2PwtYWH6t17PjSPysBmz3ja+MXw5rCVk+VwlbPjsJWz86DJYWthyhQ2FLYOshDTuxsQYjNj8LWFh+rdez40j8rAZs942vjF8OawlZPlcJWz47CVs/OgyWFrYcoUNhS2DrIQ07sbEGIzY/C1hYfq3Xs+NI/KwGbPeNr4xfDmsJWT5XCVs+OwlbPzoMlha2HKFDYUtg6yENO7GxBiM0vFbZf23GgdFm1U25n33rrLenZs2duu369ustOgzfKbZfXIDa/vP60vZ/+2QiSX2Pzs9WusjWFrZ4qha2eHYWtnp0HSwpbD1GgsKWwdZCHnHjaghCbXypsbV7mt97mU+vLyQfvmN8wp0VsfjndWel2+mcjSH6Nzc9WOwrbZhKiiHbI+pZFiKLe+4ydYG4qaX2996nmiioLoLBVgotkxjwV4anIkZKrVrHek6/Z/bvj4eny/gdLaoWx6t/zrtjOnrdA/vLUy0Jh+xHSZs8/deL915D8bAS987PVjsI2pNALLRy9l0dhG6P1lbdMCltfsWvGsattBChs65ST3pOP/tkSIy+/R//1qpz+q4dlyObryymHcMU2Lz9btPJb07/8zFpbkJ+NXwxrbkXWU20moUxhq8+TRrSksPUVVe9jaxG0KGyLoFzhN7wnH/2zJUZefhS2K/LOy88WrfzW9C8/MwpbG7PY1hS2esIUtnp2ZRHK3vt8fQRslhS2Nn6hrZmn3IocOqcyl+c9+ehf5lBWvDEvPwpbCltbxpFfM/ELWde0LApbPVUKWz07Cls9Ow+WFLYeovCxD3nnnr68D+MNV2zDcMxdivfko3+5Q7qCQV5+FLYUZraMI79m4heyrhS2dpoUtnqGFLZ6dh4sKWw9RIHCtnUUKGzrlJN5hU/RbtI/G/G8/ChsKcxsGUd+zcQvZF0pbO00KWz1DCls9ew8WFLYeogChS2FrYM8zCt8inaZ/tmI5+VHYUthZss48msmfiHrSmFrp0lhq2dIYatn58GSwtZDFChsKWwd5GFe4VO0y/TPRjwvv1TY2n41n/UdZwyXTqt0zGdU0N15+RXk1vKfoX824uRn4xfDmu/Y6qlS2OrZUdjq2XmwpLD1EAUKWwpbB3nIiZ0tCI3Gj8KWK462FkF+zcQvZF3Tsihs9VQpbPXsKGz17DxYUth6iAKFLYWtgzxsNGFWNFLy0xPf78e3yNJlLcIVWz1D5p+eHSzJz8YvhjWFrZ4qha2eHYWtnp0HSwpbD1GgsKWwdZCHnNjZgkB+en4Utnp2qSXzz8aQ/Gz8YlhT2OqpUtjq2VHY6tl5sKSw9RAFClsKWwd5yImdLQjkp+dHYatnR2FrZ8cV2zAMQ5dCYasnSmGrZ0dhq2fnwZLC1kMUKGwpbB3kIYWZLQjkp+dHYatnR2FrZ0dhG4Zh6FIobPVEKWz17Chs9ew8WFLYeogChS2FrYM8pDCzBYH89PwobPXsKGzt7ChswzAMXQqFrZ4oha2eHYWtnp0HSwpbD1GgsKWwdZCHFGa2IJCfnh+FrZ4dha2dHYVtGIahS6Gw1ROlsNWzo7DVs/NgSWHrIQoUthS2DvKQwswWBPLT89tn7ITEeNQugwSTitjX6GFb5v4Jxjc3shUMyK+x+dlqV9mawlZPlcJWz47CVs/OgyWFrYcoUNhS2DrIQ048bUEgPz2/VNjqS8hnmU768lgxvnlorXwv+TU2P1vtKGybSYgi2iHrWxYhinqHGOvS+nrvU2P0CVnKpLDNQqm4e5inIh1aWlpaikNe3C95b2zek4/+2XLVM79fP/BP+c9/3pSePXvaKlnD+ub7/5ncQWEbFXPFwj3nHxymf8XnRK1f5IptLULV/x5SOIYWoqHLo7DV50kjWnqfazci8/bq5H1sLSIeFLZFUK7wG96Tj/7ZEoP8RL76owmCx2Z3nzVCOuTc80x+zD8bAZu19/yz1a6yNYWtniqFrZ5dWYRyM/YJWaJKYZuFUnH3ME+5YltctrX5Je/JR/9sqUF+FLa2DLJZM/8am5+tdhS2zSREuWJray3citw+PwpbW36FtvY+9oeub6XyuGJbBGWu2Aan7L3x0j8K2+BJn6NA5l8OWCXsn221o7ClsNVnUFlWWFFDvmObP84ffPCB9OrVS3bffffEeODAgXL66adXLYjCNj/jmBbex/6YdU/LprAtgnIJJ07eGwf9syVuEfy4FdkWI4t1EfGlfxYC/my5FVkfk2YSyhS2+jwpg+X06dPllFNOkZtuukk6depU02UK25qICr3B+9hfBIzSCNtGe4rkPfnon635kR9XbG0ZZLNm/jU2vyy104yZk+b3z1J0u/eEFj74sWYSjt7rGzq+ZSnPe59qbrj/LeB3v/udjB49WhYsWCBDhgwRCNehQ4dyxTYU4MjlNEuetoexNMK20Z4ieU8++mfrfciPwtaWQTZr5l9j88tSO82YSWGbhezK9zST8C6LEEWUuBU5fz7ff//98sQTT8gxxxwjEyZMkLPPPlvQl1Q7AJIrtvkZx7TwPvbHrHtadmmEbaM9RfKefPTP1vzI7+NJxZYb98oNc9GiRdKlS5dMdptusLZ8c6/PZLo31E2Mr40k+dn4ZbHWjJkUtlnIUtjefdbIBEJI4ei9PO99li5zV7ZavHixrLLKKsm/pUuXJtuRZ82aJf369ZNJkybJ5MmTVzIaPnx4qJ9nOQEIDBgwIEAp5S2iNMK2vadIbGzlTUB63rgEjr36sUIq98kNusvRew4s5Lf4I81DoOyTA82YGUTYishFRw5JEiVUH3AxyzM1vJD8OgSOb5nKK3ufkCWJTj31VHnjjTfk8ssvl0ceeUTGjBkjM2bMqGrKFdssVIu7p1kewLRHtDTCtr2nSJUq6L2xeU8++mfriMhPZNqLb0gLPmSruObMmSN9+/Zt1/KF2W/JL//wdxm8aW8545tfUvyK3oTx1bODJfnZ+GWx1oyZQYRtB3y7OtyKHuraTFt9vdeXW5GztL7y3oOxd9iwYclKLf6ddtppsvfee1PYliSk3sfWIjCWRtg22lMk78lH/2zNj/zi8/v7C6/Jydf8mcK2AmrmX/z8s/1CfGvNmElhq4tLMwlvCltdjpTNau7cudKnT5+abntfRKpZgQa7wfvYXwTu0gjbRnuK5D356J+t+ZFffH4UttUZM//i55/tF+Jba8ZMCltdXChsddxgVRah7L1P1UfAZklha+MX2pp5KlIaYZsGv1GeInlPPvpn627ILz4/ClsKW1uWlZdfnnrnGTMpbPOQ/fheClsdNwpbPTcvlhS2XiLxkR/e555F0CqdsM0KxXtj85589C9rplW+j/zi86OwLa8wY/uwtY8Y1hgzKWx1ZClsddwobPXcvFh6n2t74VSUH97H1iI4UNgWQbnCb3hPPvpnSwzyi8+PwpbC1pZl5eUXo94UtnqqFLZ6dtyKrGfnwZLC1kMUPvbB+9yzCFoUtkVQprANTtl746V/tpBn4ZcK203W7xn1O7YDN1xHVu28ygoVyuKfjYDNmv41Nj9b7SpbU9jqqVLY6tlR2OrZebCksPUQBQrb1lGgsK1TTnLiaQNPfuSXClsbidrWV/3vnrL+umtS2NZGlfkOtt/MqAq7kcJWj5rCVs+OwlbPzoMlha2HKFDYUtg6yENO7GxBID/yw3dsr/n9kzYQ7Vg/98qbsvjDpUJhGx4x2294ptYSKWz1BCls9ewobPXsPFhS2HqIAoUtha2DPOTEzhYE8iM/G4Ha1t8+/w/y6rwFcuX/7ikbcMW2NrAcd7D95oBV0K0UtnrQFLZ6dhS2enYeLClsPUSBwpbC1kEecmJnCwL5kZ+NQG1rCtvajLR3sP1qycWzo7DVs6Ww1bOjsNWz82BJYeshChS2FLYO8pATO1sQyI/8bARqW1PY1makvYPtV0sunh2FrZ4tha2eHYWtnp0HSwpbD1GgsKWwdZCHnNjZgkB+5GcjUNuawrY2I+0dbL9acvHsKGz1bCls9ewobPXsPFhS2HqIAoUtha2DPOTEzhYE8iM/G4Ha1hS2tRlp72D71ZKLZ0dhq2dLYatnR2GrZ+fBksLWQxQobClsHeQhJ3a2IJAf+dkI1LamsK3NSHsH26+WXDw7Cls9WwpbPTsKWz07D5YUth6iQGFLYesgDzmxswWB/MjPRqC29VHn/0Fmz1sgn9xwbVm10yorGCxatEi6dOlSuxDFHWd/axeF1YombB82hN752WpX2ZrCVk+VwlbPjsJWz86DJYWthyhQ2FLYOshD7xMn+mdLEvIrP79U2Npqkt86nSTntyzP4Mb2YYluHFsKWz1XCls9OwpbPTsPlhS2HqJQnrG/CFodWlpaWor4oaJ/w3tj48TOlhHkR342ArWtn5v1pnzw4ZKKN86ZM0f69u1bu5Acd/zo6geTuylsc0CLdKv3/iVGtSls9VQpbPXsKGz17DxYep9re2BUpA/NOHa15UthW2TGtfot78lH/2yJQX7kl5fAvj+6RZa1tMhdZ46Qjh075DVf4X7mnwmfeOdnq11lawpbPVUKWz07Cls9Ow+WFLYeovCxD804dlHYOslB78lH/2yJQn7kl5cAhW1eYvHu995+Y9ScwlZPlcJWz47CVs/OgyWFrYcoUNi2jgJXbOuUk94nTvTPlhjkR355CVDY5iUW737v7TdGzSls9VQpbPXsKGz17DxYUth6iAKFLYWtgzz0PnGif7YkIT/yy0uAwjYvsXj3e2+/MWpOYaunSmGrZ0dhq2fnwZLC1kMUKGwpbB3kofeJE/2zJQn5kV9eAhS2eYnFu997+41RcwpbPVUKWz07Cls9Ow+WFLYeokBhS2HrIA+9T5zony1JyI/88hLYZ+yExOTMI4eK7egoEcupzR06dJAtN+6V1/1c97N95MJVyM0UtnrMFLZ6dhS2enYeLClsPUSBwpbC1kEecmJnCwL5kZ+NgM06Rv6lwtbmWRjrEJ8cas+TGPzC1PyjUrz7F7KuaVkUtnqqFLZ6dhS2enYeLClsPUSBwpbC1kEeep840T9bkpAf+eUl8ONf/FmWLVuW16zi/YsWLZIuXbrkLmvai28kNhS2/5YBAwbk5ldmAwpbffQobPXsKGz17DxYUth6iAKFLYWtgzyk8LEFgfzIz0bAZt2I+bdsWYvs++NbpGOHDnLXWSNsgGpYNyK/qMAKKJzCVg+ZwlbPjsJWz86DJYWthyhQ2FLYOshDTuxsQSA/8rMRsFk3Yv5R2Db35IDCVt8nUNjq2VHY6tl5sKSw9RCF5h672kaA37GtU0424sS4SJTkZ6NNfuTXlgCFbXNPDihs9X0Cha2eHYWtnp0HSwpbD1Fo7rGLwtZJDlJY2AJBfuRnI2CzbsT8o7Bt7skBha2+T6Cw1bOjsNWz82BJYeshCs09dlHYOsnBRpwYF4mW/Gy0yY/8uGJbPQe8tw9b9la2prDVU6Ww1bOjsNWz82BJYeshChS2raPArch1yknvEyf6Z0sM8iM/GwGbtSb/uGLb3JMDClt9m6Ow1bOjsNWz82BJYeshCs09dnHF1kkOaiaeRbpO/2y0yY/8bARs1tr8S7+l+/UvD7I5UMP6rbfekp49e0b9DUvh2w3oys/9KAGGFipwo5mEo/f6ho5vWcrT9qnKZlQaMwpbX6FinopwxbZOOek9+eifLTHIj/xsBGzW2vxLha3t18tvffGRQyhslWEMLVS8C71m8y90fMtSnrZPVTaj0phB2L7S5TNB/L302N2Scv7n4j+xPAUB8GOeUtgqUieMiffko3+2OJMf+dkI2Ky1+XfTfdNsP5zRmiu2GUEVeBu3IuthN9OKclmEKKIZ4kFdWl9tn6rPqnJYeu03mKflyJ8YXnLFNgbVDGV67yTpX4YgtnML+ZGfjYDNmvnX2Pxstats7XWCCm+bSTh6ry8FQ4zWV94yvfYbzNPy5pTVcwpbK0GlPSeeSnD/NSM/8rMRsFkz/8jPRsCftdcJqneh12z+UTD4a7v19Mhrv8E8rWdW1Pe3KWzrxJ8TYxt48iM/GwGbNfOP/GwE/Fl7naA2m3D0Xl8KBn9tt54eee03mKf1zIr6/nbphO3ChQtl9dVXl44dO7ZLzvtJbZwY2xKf/MjPRsBmzfwjPxuB4qzzjJmT5vc3OxZ6Quld6DWbf6HjW5byvPf55obbpoCy9xtlyStg57vgYbO3NMJ23rx5Mnr0aOnUqZPMnDlTjj/+eDn00EOr0qCwtSWK906c/jG+NgI2a+Yf+dkIxLfWjJkUtrq4NNM7wBQMuhwpi1Wj9BvM07JkXHg/SyNszznnHFmwYIGceeaZMnfuXOnbt6/giVLXrl0rUqGwtSULJ+7kZyNgs2b+kZ+NgM3ae/5lqZ1mzKSwzUJ25XsobHXcYFUWAdIIfUKWKDVKv1GWvOKKbZaszHdPaYTtEUccIcOGDZNRo0ZJS0tLshV5xowZVb81SGGbLxHa3u29E6d/jK+NgM2a+Ud+NgLxrTVjJoWtLi4UtjpuFLZ6brEsG6XfoLCNlSH+yy2NsB0xYoTg34EHHphQ7d27t0ydOlX69+8vkyZNksmTJ69Au3PnzvLhhx/6jwA9JAESIAEScEWgV69ecvjhh7vyKa8zecdM7H5677338v4M7yeBpiAwaNAg2XvvvRu+ruw3yh3iZsnT9qJUGmE7btw46d69uxx33HGydOlS6dmzp8yfP7/qIVLeV2zpn63zID/ysxGwWTP/yM9GIL513jEzq0ehc5/lZSVf+T7y88XP5k39rdlvhImB93YZppY+SymNsL377rtl/PjxMnHiRLn11lvlggsukClTplSlGjqpQoeP/tmIkh/52QjYrJl/5GcjEN8675iZ1aPQuc/yspKnsAUB7/lii2b9rdlvhIkB8zQMR00ppRG277//vuy5557yzDPPCP73vffeK9tuuy2FrSbqGWxCN8oMP5nrFvqXC9dKN5Mf+dkI2KyZfzZ+WazzjplZyiyDsAidWywva2ZQeNtI+bBmvxEmDt77jTC19FlKaYRtim/WrFnSp08fwTu07V2hkyp0+OifjSj5kZ+NgM2a+Ud+NgLFWWcdM7N6FDr3WV5W8hSOZXiwYoumH2v2G7ZYeO/XbLXzbV06YZsVJw6U2mGHHbLeXvh99M+G3Ds/HGa2/fbb2yoZ0do7P/pnCz75NTY/W+1s1qFzi+UxHnkIeM+XPHVppnu9x63Z/Gvk3GtYYdvIQWPdSIAESIAESIAESIAESIAESIAEPiZAYctsIAESIAESIAESqCuBP/7xj8kulzXXXFPtx7JlywT/0qtTp06qsvCe4bvvviv47FN6Pf3004JPaayyyiqqMmEUyj+1A1UMW1pakq9NVLu0HEP7GYrfyy+/LK+88opst912Vb+sEdr3ZirvhRdeEDAeOHCgvPTSS8F2r82dO1dee+012WqrraQDPlQb6HrzzTflxRdflMGDB4sl19944w1ZffXVZY011ljuGc4F2myzzWq+PhmoKixGRJpC2GKA6tatW9WGsGDBAtNgWiuT0BljoIQP1S74gMYQsrHW8iv9eyP4l7WuMe7Lwm/hwoXJpMYyadP6nsU/bdkh7LL6h8EHbWi11VYL8bOZy8ji31tvvZV8gqweV1b/8Lk0y6RcW7cs/i1ZskQWL14s+JZqjCuLD2ijmJR07NgxhgulLvPJJ5+UrbfeeqU69OvXT84880w5+OCDc9Xvgw8+kBNOOEF+97vfLbfDpBAT4vXWWy9XWbgZn/474IAD5IEHHljBNh1X8xaIcr785S/LLbfcIsOHD0/MIWqnTp26wqQ1a7mh/Hvqqadk1113rfqz//73v9udZ1QzxDbMHXfcsWq5ECef+MQnalYX4v/b3/62XHLJJbL//vtXvB9cr7vuuppltb4hFL+0zH/9619y4IEHyjvvvCNHHHGEjBo1KhFhvMIQQB5+4QtfkM997nOJEN1pp51WKPicc85RjeOIG74vjvKRX3vssYd86UtfSj4FqrnwQAf9xn333Ze0b5R77LHHyo9+9CNNcfLII4/ImDFj5MYbb5QhQ4bIpZdeKqeccopAkOcZ22K1I1WlSmjU0ML29ddfFyQIGkClAfNvf/tb0kjQYc+cOVOuueYa+fznPx80jNdee61cdNFFssEGGwgmb0j41k+Bn332WcF3wzCZgg/f+ta35Bvf+EZQH9orrJZ/mLAfeeSRiSDDxAODwaGHHurGv5/97GeC91lxYfL629/+Vp544gn57Gc/W4iPtfgtWrRIvvnNb8rbb7+dxBh+nXbaaYX4hh+p5R8mmMjH3XffPfEJg/vpp5/uxr/UEbSNT3/604JVnS9+8Ytu/Js+fbocdNBBsskmm8h7772XtF1Mkoq6asUXT81HjhyZxBhPoiFOfvKTnxTlXs38wyrRtGnTkr4XovvCCy8M7lstRvPmzZPRo0cnfJBnxx9/fKF9XPAKRyhw9uzZsssuu8gdd9yR9BEQePim/K9+9Sv51Kc+lYwN6667buZfxgTw3HPPTdoKRCTG3dtvvz1p35rrtttuk6uuukruvPPOXBPIar8Fn376058K6g3RfvLJJyf9j1bYhvIPcwiI9dbXhx9+uHw1SPtwDf0EJuKIy8Ybbyz/+Mc/krkRBC8e9mS94B9EaI8ePQQPInGhjacP1PD3r3/96zJhwgTp27dv1mIlFL+2PwihhJz+v//7P1l77bXlu9/9bvIgo0uXLpl9442VCUAs/uUvf5G///3vScxbX2BsWRnFfOqmm26SE088MWkP6eJV3ljAR7Tzhx56KMlRzHehB5AXmLNrrnvuuScZP9Afoj1ibMv70CRtRzgkd86cOSs8NIdIRrmYa+RtR5r6lNGmoYUtOkOIHkyW8NSo7ZNgPPn84Q9/mDwBTTtOfCc31IXkRGKmHf33vve9pDMfO3bs8p+AkP3kJz+Z+PH8888n/xtiY9VVVw3lRtVysviHJ6t/+MMfkifXGPQOO+wwgRgv4sriX2s/0MnhxOzvf//7RbiXPKioFV/we/TRR+Xyyy8XPB3EILrvvvsWsnKWxT8IMzxRxCBhGWg0wLP4h3KxkjdixIhkq9AVV1xRmLDN4h++p402jYEbg+T//u//ClZViriy+HfGGWcIJr54mIKHLJikYrK+/vrrR3cxi3+YkCD/Hn/88eTpfmhhm8UHrB5gcoSVR0waEE+s3uZ5wh4dZp1/ALkN4Yl+LL2wbQ8TVzz4POmkkyqu6FZzGwIU36FHX40HQ/fff7/st99+cv755ycPifJef/3rXxOhDB9DXBC26KtPPfXUROjgwRD8xYS39TbDrL8V2j/87pVXXilXX3118iAX8wY85P3qV7+a1aUV7kNd8Q8PKtILn1fEyuumm26qKnP8+PHJg6Ub22YAACAASURBVC3497WvfU2OPvroZBVcc8Xgh3Hlz3/+c1JvLDjgwQ3EyGOPPSbYocCdG5pIfWQD4YlxEGMP/rXtS/HKgWb3EB7AnH322YJ5+lprrZU8kEfchg4dqooXHqQhRyEQcWGOhs+I4lu+mEvmvWCPOR9WffGACA94IGyxFTnvhYdo+KwpFucOOeSQ5f6hbAhx7JbhVZlAQwvbtMrY3ltJ2G644YaJWMN/0ZGhkeC+UBcm4sOGDZMZM2YkRWJbAp5eIdHTC6u1EEcQu+i8sX2jqIlnFv/wtOgzn/lM0nng6S2e0mPyXsSVxb/UDzR+DJ6YeBTxUAC/m8W/dNKO3QHoiCA0sH2miCuLf9gKiNUqTOzxxB5H1GOQKOLK4h/8+MEPfpBMiNJtPUWt2Gb1Dz5iwo8VI2xDwkOqIq4s/uEVCPR/WIG46667EpbYvVLEKw9Z/Es5XXbZZYlfoYVtFh+wFRH9NFYPMTHBhBZ99oABA4oIYyl+Aw8IsFUPqwSYpOGBMR7EYnfH3nvvnbxPl+cVAaySYwL585//PBn78K4jJpfIgTyva+B+CFrs1sG4iphha2I6aYYw06y+pcIWfQ4EECaTEJJ5tzbH8g8rqlgVwgNnPIjBAwY8/Mu75TFNvldffTXZTYSHDNjVgfJRJsS85sJ4vNdeeyUPxfGgAnOH3XbbLVkRgyDJesXiB7GNPEFOgyO2o6Y7DvCQEnmtFfRZ69bI92HxAyuhaJfIUSwq4cEl8gAPif75z3+q2mU6z8NDePRFO++8czJvybOrANyxM+eXv/xlsjhx1llnJbmAPghzcOQc+n/NPBIPSrAV/+abb07mzXgQj/5N0y7xwPVPf/qToG223km6+eabJ7uuihjDy5qjTS1ssS8fDRADA/bWo5Hg212hLjRCPO1NVzivv/765EnLL37xi+U/gRfL0aCwXRqDyHPPPZd78NT6m8U/PBnDZB0ry3iChMbe+r0o7W9nscviX1oOJqd4KIBtv0VdWfzDCjcmCBjg8fAE2xwxCSyiU8riH1ZKMMgfc8wxycQST0OxiuvFP+ykgCDDSgIePOFBQVHCNgu/NNcgyOArnkyH3PXRXi5n9Q8Tc8QVq2FYKcNDqiKurP7Bl1jCNosPEAT4h9cscPXu3Tvp6/r3718EptL8Bh76/v73v08mf3hnDsIF714i5/NutUOlsYMA21Xx31//+tdJXmIszHP95z//ScbUahdWMDU7UfAaEwRx63f3fvOb3yQiPo9QjuUf4vDwww8LJr/phZ1neMCmFWR4iI1tlHjYj/iirtieq7nACmMLJvbphTKxOpZnRT4WPwgN7BbRstIwaUYbPNDHg148KMKcAm0dDxOwKq7NLXCE2MPuDLwLq9mKjPijDVW7sEKq6TeQr+gP8ZAvPcQOc3rt4VFok+iLsDsG2gT9EVaBebVPoKmFLTpaTEjxJAlb4bB6ii0IoS6sliDJkeBo1OlqRNutsti2AbGIDh9COKS4bq8uWfyDMMMTovRdBjSsSqvfoZi1LieLf7gfnQmetuZ9Gmz1OYt/WCFDJwdhkU6asdpRxICaxT+IHqxu4B/ehUJnjvwrYptLFv/wsAId+zrrrJMMhthyh21jod+Fr5QLWfyDUNxmm22Srb145QDvvhS14yKLf5hIQLThgRRWn/K811ZE+0h/I5awzcII/T76NexGQRtADBFLbkVcMQPwwAb9AvpZ7E7Aikl7BxnVyh+cSosVnPTC1tUbbrgh1wMF9P14cIitjdi2V+nC+IUxPs8V6hCY1r8Zor5peehjsLIK0YCHCtj2CX5YhcKFXTdZttKjr8f7pdUuHPCVZ+UKuQExgzkN5g6wT+dX2KKJSb5mCyr8wxwNW6+xw26jjTZKHnDmHUdjHb6VJ7ea6V5s9cUWeTzkwIX5Bua5mAMhhnkv5CvKw0omzrTAghAeeGM3QJ7+Ola/EfKQMwhvCHcIWeQ6dtNhNwtWa9GueFUn0HTCFo0B2yCwfQFbaiGIkCQYIPDuTOiDc/CkBU9RcfAEGh/eddthhx2W+4AJHVaLsZqCrVR4+tz6PZfYyVvLv/POOy/xD35i+wa2j2FQ1TzN0tSlln8oEytlGJxDPpTI6mst/7BKj8EY76hhYgOhhqeN2sE9q1/pfbX8wztkOPgFOZpO5tKt83l/S3N/Lf8wkEGc4cJqPA73wCpMlkmbxp+2NrX8w7uFeHABjth9gS3TnuKLrZ54aFaPtgGWtfilvGMJ22o+QJBhVRb9MtomRAGE26233ip4b1q7BTNEznksA7uO0O6wooedTThM6eKLL052oeQRPmnd0K7xcAqiDO8z4+EVRArEaZ6xJd1SiF1FeHDT9sL7fThQEIIq73bFEIfAhK5vWh7igddaql3I4daHVFa7DytC4INzPXAGCDjiUyp4WIBxAEIwTzxq+YWxMM+Kd+o3+tR0CyYehmBFDfO2vK8eVTp8qzUb7eFbHtusB5/QtvGQBf/FA2DMMdB/YFzSXIg3+mu8OoJD67RXrH4j5CFnGDuhC9AGoU+w0w8aBf0m5hp5Xv3QciqrXdMIWwyc6OixNQ3CEse8o+NOtzXivQ9MZizbIyolASaU6SnH2LqFQ3rw/krqAxoYDmnAigGEDyZbRRzskvpayz+szu6zzz7JyWy4sBUUh4UUddXyD35gVRTbB7GqXPRVy790woCtyBBjeLBR5Km5tfxDXDFIYPKCf/APW9CKumr519oP+IUnmEVtRcZv1/IPAwy26eN9Q0zw8b4OJodFXbX8w6pJ209rpFujivCxln+pDxC26I8xIQ99VfIBO2jQ56ZbjtEHI5ZY4YW4wgM8Xh8TwDtjEC1YIUF+Y7KFrdvoL7C1MO+FbYQYB9EXQqBhBRjvuT344IO5P1cDsYKY4aE1xqpUaONBMU40xqoRdmfl2cIX8hAYsAlZX5SHcRmfPsMqFfIb7zCCX55Vq9YxA3e8IoXdMOmFiTVeA8m7Jb9WPHCmSN4LfqCeWLXCac3434gpHq7k/QIC8hj9DE69xQJH+nASc5six768DMp6P9oS2jpW8jHv1RyklNYdr021d8YL5tdZdyXh4Rx2M+HhZqULD3zy9huhDjlrfegh5mV47zt9OI0dEHh1w8KxrLmU1e+mELbtwUAC4WkgtrfEeq8QAy625rTX4NLtn7F8aI9BFv/ACA8GNINS1mSsdl8W/6y/YbHP4h+2aNXrO6JZ/MM7J5pTAC3cUtss/oX4HW0ZWfzDAwI8XNFOLLW+wS6Lf5byrbYe/MviA/pgtIF69HFWxrHt8dAVuxEwBuAhAB4uQeDiHVvN7hNMcvFKDiZoWNHBCiFew9GcIPqd73wnWQnCQ2k8sMYDaohbjOnaU4xDHgKD2ISsL96rxQM0vFsIMYv3lLFFEQ/EcVCP5kpXQcEOIhkP3LHChvMW8q5ixogHxk88gMUBXthVBwbYAQeRmnf3DvIY73Pj4Qx2aEA041UXCGbs5suzQq1h3Sw22GmFE6fRp+JQN+yoQN+K81DyvGfdmhd2C+J1COzQw2tJaFd4SJF+1QEiNesOEszHMCfH+SI48CmrXaX4xTjkDA9Z4BMOx8NuFrR37KxDXYv68kJZc7XphW1ZA0e/SYAESIAESKAoAtjthAkVVg9wCBC2rGpXtvEuM0QKvhmJ1RzsxMCp9nk/14MVdjxQSk8dxaQZk2iUZxG26SEw2L6MVzXwwAonOaNszYOPUPXF6hcmufiEHHYYYEUJ7y3CJ2zr1u46Q7k4vRUT53TLNsRz3sMYY8UDOY7XeiBoUe/0e9zImTwXFjLwAAQLDRBF+MZ3+uoNHrDglQTNDoQ8PjTLvThhGw8kwBcPfrHTD6/+4WAxsNdcWEXFwxvkQnpBAOL8mrwrmOnhscgpvK6DnX/Ihzzf4059iHHIGYQstjYj19HXgmO6CwXb8nlVJ0Bhy+wgARIgARIgARKoSADbByEY8VmUWBdWdzTvXuJgRrxGhMNpsK0UE2lM+vAeHlaWNSu2mDzjXX58h7L1oS14zw3bVUNc2vqmwgwPGLCVFhN6vIKD/z+ELU5KtrzKhHLwiSysqGl2n8SIB0QDVlTTCzHOu4rcOmZ4V3yLLbaQadOmJSfj4sAt5NBRRx1V6PkIIfLIaxlpniJ2OPQRgjE9FBUPEPBQS3OSOh6CYeUery5gqzwecuBd67zvgoMbhC18wisK6CfSc24gIrGlOO87+RDH6HPQbrDjA6vKWEHGg8Cs26O9xrNsflHYli1i9JcESIAESIAECiKACSBOHsXKIE4k/f/snQv4VkW1/wcvoWKCFy5GJsdzjDxKaImk5g0xFI5pBIiIF/KSV7wCqV1EA/NyAJVDWSiVYplRSXZR1MJQtFCJEjShI+BR8a+JCool+n8+cxrOZvNe9t6z9/vOu3/feR4f9f3t2XvmO2tm1netNWvSekbizUTZJbMnXkdCnDkjybkxrkJLc8epey8hjpdffrkh0SHn3d0dpXg80t47yzvx2HFNDSSRduJtItQXzwmZedPcs8v7qM85YpRl2obX96CDDrLvTpvVl/dxJpQQWs7Dcj0Sx4ToNyXLVXyEUdJfFHzGgSRSKOKQ+CxZr/MeD+SPs914U0me41vAiyRW9BejDdnQeS9efjzgKvkgQCg7Z/A5JoD8E9bOWHK/K8aTtCHkrlUk+SN7OufCIbmEEae9JixObN27MThBxEnymTYknb4R3UGyVY4IML/pJ/OrWckb8xnJ1nuLiG3rjZlaLASEgBAQAkKgIQg4zwZe0fPPP9+GH5OUCVKQJdki5AFihzcUMooHByUSDweekyyeQkJLIbHumjLChiGSnMNMEzqMpwlPKJ4cSGzU04SiDplMS+xRkvEmk8mXK044701CK4g87cxytg9lmeRREFASY0KYOTea5V2cOWUswR7CTKgnHlEIA9/Jkvcjr/FwBGTChAlmypQpNvs8Ccey9LMhk0Uf2YAA57YxwrBGMA85y4pRCLIbv/IyCWz1kj0leUf0GcJ88d6nWR9qfcMRWzy/9PO8886zj7NucC2gTyRF2r619edFbNu6BKj/QkAICAEhIASqIBAN2cNrC/HBY8Jdkmk9oi5EkYRHkFm8LyRDIXM3XmGUwmafcTznnHNsaCMJlCBQkHCIHpntSVaUpri7welvPHwWhZdMxIQ+NqtEx4PEW4R3upBRyC5n+rKEjObZHyd/YIgXjDBzEpfhFTvqqKMyJS/Ls316VzIEWCswWmGQyVLyTPaU5fv16tA+DF8YxOgrSa1c0j2y76eNRiki+Va9PpTl7yK2ZRlJ9UMINAgBEoRQ0p5BaVDz9BkhIARyRCBKbKOvxfPIGpDWo0dSGe6gJMwVjy2EFk8rHg4IZZaztjl213BOlPvk6TfJrQiBJWySrLlZEstw7g4PDv0m9BgCiTeLq2oIz8yq6OfVZ4gr56fJZkwSKUJ08XbjrSaUMm1IZl7tcu+Jyx9JdEhAhFGE89BZQ1rzbqfeVywCeSZ7KqKlGKk488t1eswbMmxjvCK5VZaz+UUk3yqi3yG+U8Q2xFFRm9o0AizgWPxc4W5GwniynHfKA0jCylDusJAfeOCB1qqPNRHvBQs3d/Uq/XweSOsdQiA8BLhigzC6tAS2Wk9Q+ggrhdx9/vOft2cc8dTuu+++1gOXV+EqIghzs4nZ2rVr7RlDzuxy3zWJiwhPhihnOWMbx+cf//iHVzgl5Jr7YUeOHGmvdCL7NcnCyJKcNutwrbHLOh6c+8XQkMf5WtonT1heM6yx78k72VMjWp810VlRybca0ecQviFiG8IoqA1CIIIACzgFCzqJR1xqewim+1sjAVuxYoX1XFx99dX2zAzJQVh4OSeD0oGigHVSRQgIASHQDATwNHJn5pw5czZ8nkRKkGfWrjIWMsuSBInztdzpSWIvzgyGUEIeD3nCGichnNt+8MEHrVE+7dn0eCsrRY74JHvCWMI5d/QqDE/co00hIziG+rwMKVnRLir5Vtb2tFI9EdtWGi21tU0gwALOVRPOCzpq1Ch7Bg0FhpA27hrkLAchZHg+yNaJxZ2wLMLeSFRAxlEyXeJpZfHnd+4lJGzrBz/4gU23zxkyrPRceI9X4YgjjrCJYQin4WwI34XIsimhMOL9uPHGG+1dk5y1u/POOzcitlgnCcfhH8IJ2TR4v4oQEAJCoEgEuLoFwsIaRigg4b2sRyRpKmPh2hPChznvTAKphx56yAwbNmzDfb7N7nOo4yFPWGMlA8M8egjzkGzoxx57rI3KINFZ2iRxeSZ7wvCy1157GRJSEY3C2kHmYjK0+9x/nSe6eSffyrNtob9LxDb0EVL72hwCENvOnTtbpYX08SQzIYX8qlWr7LkniCfeUqz1/J3NAwIKiaVAJq+44gob5gYZxYoPGcaiT3ZOQopRijgfx6bDBeWEnrnzY9zhyD1sfAfyC5Hle4SlQYLJiLpmzRobihz12PI3zpLw7YcfftiSYf5N9lMVISAEhEBRCJDNl/WKdY3MzRj3MMiR5KpLly5FfbZp7+X+VUKbWbtdob+s83mEN/t2LOTxkCfMd3TT18dQfvfdd9vwdo5ZoT8082w5eUIw8mPQ59+/+tWvzJVXXmkjPHr06JHp/us4KmQUR//KmjOANpHAzV0v5pt8K/2otW4NEdvWHTu1vKQIxM/YsrCRDRLCylUU3NmGZwLyiXLDAoiiA7F1RHLy5Mnmoosust4LQoB4ho0EhYPnxo0bZ7NJ4sWF7PJ3iO3pp59uvv3tb1uv7ogRI6wVkzO10VBkyGwlYosHefXq1faaCM5TEULNRsY3VISAEBACUQRYu8gWSkSJK2PHjs10lQtKJFeKQGiHDBli1xzuJeWuy7TXbJCcqFrhDtq0Z43xEpJRmXWd634wMHbr1s2escU7lKVw7plInUsuucRG7hDdw7p+66232tdxhVIzkyrlOR5Z8KlVR56wvBGt/j7Oa7tz2oT64qnF0N3sMF9azDl3MpITWYYxCL2FSDQ8w9x1nLaNvI/ICc68kxBv4cKFFhh0sVNPPTU16KwXrA/oUcx1leQIiNgmx0pPCoGGIOA8tpBQrH0oLpBQSCsWPBI4obi4ghf35ptvtooNGTchwpBaFlQWaELVOOvCfW14Y/GkQmzd/W0QWkgz/8bzy1lask6yESUltiziXI2Bd+SMM87Y0DZIcyjnvhoyePqIECgRAlxXQzRHtULERxaPxB/+8AebqIhIkKjnhkRSWe8oRYmGRBLZQrZlwh7dXZJJh4T+4rHBw8RaFm8LUSppCSNhjqzDkD3O8+FV5uofjIkcGclSaAfnAqsVrhoh6idJyZvIu2/mMR68q6j28W7kBe8dCc2aee1SknFqxWe4OoqoAozwzCfm/CGHHGJ1mLShyHn3nzlJvhCSuXF9FLLAnOKs/iOPPJLao+zuscW4ht40Y8YMwxVV9JccJGmT2PE+Mn8TsYdsslZKRpNJgYhtMpz0lBBoGALxM7buwyySO+64o00UgmX+hz/8ofV4EOKDhwJi60J8HnjgAdO/f39LTgcPHmyOP/54m30UKySL97XXXmsJM/+NAoi1sh6xRSFDYSTkrZLHFosl10SgsBEWSPsI78F6qSIEhEBrIoDR7Mwzz7RKYPwuxuHDh6dW2ECB4w3cR0robGgFotO1a1cb7YJn1aegLBNlg4eVIyPc0Qu5xetLSCzGy2YmtyqCyPvgFa9bVPsYF47kEAlFrgg86pyvLOuZ7DzHJMu7MDZhLA8lFDlLH+rVccQWHQlnAhEZFPKbkJsk7REBlyyLI2Pf//73rcMB/Y3IlAkTJnhlQq/Xl1b/u4htq4+g2l86BKoRWzpKgijOsJJIisI1GXhn8UxEiS0hNRBaQpQpWPognHhzL774Yns+1/3OO/GaQGxd6DDhOIT0YXkk2QPhyJBVF+7mMiFHr/vh74QSum+SzIQwmqwemNINrDokBFoUAeY5a4Ev0eNqsJdeesnmBiDSBMMa73beDEJ104b6FgEpiiTrZ9pwxEptYT0HO6JtCFUkHJPzcii6eHHdGboi+pHknXkS+STfS/tMEe3DK8eYENWEzCGPGIExtHAuUiUfBPDagytHlLi2EJJHtBkRC2kKyZ7c7RCV6nFsKq1HNM33kzxL2DCEloLxiqgKDPvf+9737DxPu67Fs0BjjCHCg+MVHEFo3759kma1yWdEbNvksKvTrY4AV/BAROuFxaFEokzGFca33nrLhsngTUiy4L733ns2zJnv1SOqKAmETu+www6tDrPaLwSEgDH27Fm9eZ8EqCeffNLg5a1W+Hu9NS1a13lEq72PpHs+SWpYZ+k7hD4rycVTRTghuQuOOeYYe5cteQoILRw/fnwS2DY8U1R/8yLyobfPAQk5IGoAgy3RTeSU4L8J/cRbr5IPAkQpoIOArU/oMfoHUWck1GT9iM9pDO5pjkQUJafoPoS1E4LNHd14b2lvFmMJDggMf3hsVdIhIGKbDi89LQSEgBAQAkKgzSGwYMECG+mBZwJDGUlg0obXFQEaV8v06tXLHoOIZ0DmvGwSw128XYTA4gUiyoWQVUKwuYItLRGNk3C8ShgVMTam9Vq5dxXR3zzHpcj25WFooK8k+OHs8/z58y3B5WgPGXIh+CrhIsAds+Qa8Y0coYd5yykRGeQj4MhWngUvLTJKRBxhyCTjSpsQL8/2tMK7RGxbYZTURiEgBISAEBACTUKARCsf+9jH7JlEssqefPLJ9tgBSeny8OT6doujGJDOvKJEyNjMuWK8eSTD4xwmJPnxxx9P5X0hWzE5CaqVrB7lvPobevscbkUYGqJjwp3HWQ0NvrJbxvpFJZ1DXvfYY4/c1py85hFjSOgw+USI8CDzObdR+Ba83eRUueaaa+z75s6da7773e/a0GaFIldHV8TWV/JUXwgIASEgBIRAiREgQR1nElGwSIrCfx988MHmhhtusEl3kpaiFF7O/OMNzeucHVlRCR0mlND1l7PAd9xxRyovNSGPnKetVrKSqbz6W6l9eDNdxvxmt8/hlpehIamc6jl/BIpIOuffqvpv4KpCQofTriXuTCzGMO7S7tu3rznppJPs2fqsBjdC5DmXT/Z5V1iHiCzA0KhSGQERW0mGEBACQkAICAEhUBUBPFpkWf/Wt75lrrrqKnuFB2FxWa6/KULhhYjiLSHDOwlqaCs5CHwKWd4JueYqM+6RJCEM2XNDKEX0l7El1JzEhHiJ8Dplvaot7/blZWgIYezaUhvySjpXlEGMpFQkEuMKRFfuueceG5WSNlt5NNkTXlvOA99+++3m3nvvtcatNGf0OVOM8Wr58uV2TWNe7r333oYcBCQL5TaMtMS7LcmdiG1bGm31VQgIASEgBIRABgTISgqh5awXRI+wZM57ZSl5KbzRb6M8cv6OOzO5Q5LQPa4USpNUJvo+zsJSH48JmVwhyyGF/+XZ3z/96U/2bCDKOJld6TNZ7QmFTJPMq8jxCNnQkGUOtIU6eSWdA6siDGKcs+Ws9RFHHGGv9yIpFUa8LNc+xbMYu/FlHSEBVJqz/u6e72oy4jMv24Lcidi2hVFWH4WAEBACQkAIeCLgQldfeeUVrzNkeSq8dAmSxzVjnEEjsy33feO5vfzyyzPf98h7CElE0UWpHjlyZM3zsp7Qpqqed3/BjnBfDBeucDaY62+yJAjLu320KXRDQ6oBbCMPc4ac8P1q5dxzz93kbuxa0ORtEOMeaQx1GHUIH/7Zz35m5zie1ngiunpDxl3VJHWCwLJO4lElA3oeZ23rfVt/3xgBEVtJhBAQAkJACAgBIVAVARQ1sgPjpSWREiG5nK3N4tngI4SWcn6Vuy3xlmS5DiPaWAgt7SOE9uqrr7ZhyVk9jbyXEGvCcMmKfMghh5ivfOUr9jwxoYAhJMvKu78o5XjhuR+zZ8+ehiQ97s5y8MBjnQbPvNtHG0I2NGjpqIwAxJZMxpxT50woIbQYUL70pS9ZAjhq1CjTsWPHxPCtWbPGXhuURhZrvZwkTBypgNAOGTLETJw40Vx22WX2rtgsmYfzXicTA6MHN0JAxFYCIQSEgBAQAkJACFRFgHNoJIyaPHmyPXfKfY1Dhw61Hr0spJTzY5xje/DBB+0ZNBRCzpJBILOeHSNzM6HIhNESUrvXXntZL26WUGSStkBuBw8ebE488URL9FB8yby85557BiEpefaXvnJnZrUyadIk07lz51T9zrt9IRsaUgHThh5mfl977bUbGcAwkpD8iGvD0hYMaSSwg3jmVRYtWmTXn1WrVlljG+sQ2ZKzlLzXySxtUB1jRGwlBUJACAgBISAEhEBVBFAkuUuxd+/e9gociB//jReta9eumZB7/vnnLbnFu8e7fc7E4smBIENqORdLgQhdeeWVmUKRadvhhx9uyRwhzdzZC8ElY+rmm2+eqb95Vsq7v3m2jXfl3b5WMDTkjWEZ3gexvfDCC21yJkJ7OcLAvGKepvHUOiyILOC6H+bivvvuu+HcKscEshrECHFnfSNsGNL98Y9/PNV52Og4FbFOlkEOGt0HEdtGI67vCQEhIASEgBBoIQS4AoYkK/Pnz7ck9NZbbzVr1661npcshQRPKJGEDuMp5FxbFkXXfZvQZjxDgwYNsqHN3DnrW/Dk4Kk9/vjjbTIqFF6u8AihFNHfPPuVd/tCNzTkiV2Z3kXW4a997Wv2jDoh/PxDGPLpp5+eqZuQTzIExwuZ2rMkdnv77bftWsFxA0KlIeKEOeMZJuQ5bcl7nUz7fT3/vwiI2EoShIAQEAJCQAgIgcQIkFAp6x2nfITkQiiPeG5QJvHmkHV43LhxmT0vbe0MZuj9zbt9IRsaEk+cNvYg4egvvviiPY/P1TWQSLy3Bx10kM0UnLYsWbLErhcYwvJIykTyKAx1RARQCEnmjDlrUx7v910n0+Kj50VsJQNCQAgIASEgBIRAExDgygo89JvA0gAAIABJREFUe4QjT58+3XpMCEvOovCGnuwpb3hD72/o7ct7PPS+TRFgTmPcIHyf6AwKHlzuS+ZqnW7duqWGDZL8zW9+0xLPV1991Z6HPeqoo0y/fv0yeVi5s/mcc86xxyvw1EJEP/WpT9m7nLn6R6U1EZDHtjXHTa0WAkJACAgBIdCSCEBgyYjMPyimeGuzKLqu823tDGbo/Q29fS05aVqs0WQUxzv79NNP2yRsrnB9FBnMfQqh6Xfffbe59NJLbfQHZ7rTEFG8/2effbZtAgnnKH369LFtxbAGGc9iYPPpk+rmh4CIbX5Y6k1CQAgIASEgBIRAHQQ424Z3hERM/PPzn//cEtys5LatncEMvb+ht08TtDEIkP2cfzp16pTLByGkn/vc5+y7yMqOp5bEbttuu22q97P+rFixomodwpCznLFN1Qg9XBgCIraFQasXCwEhIASEgBAQAnEEILUkY+J6DK7W2G233cyCBQusx4T7LbOUtnYGM/T+ht6+LDKmOs1FYOXKleYb3/iGIfkc5/LJsEzipwMPPFBEtLlDE9TXRWyDGg41RggIASEgBIRAuRG4/fbbDcRn9OjRZv/997fek+HDh9t7YjlrqyIEhIAQqIYAd87++Mc/zhyKLGTLjYCIbbnHV70TAkJACAgBIRAUApBarv0gS+qWW25pLrvsMnsvJUmHSOKiIgSEgBCII8C6QQjy6tWr7fl87pgmMZVPhnahXD4ERGzLN6bqkRAQAkJACAiBoBGYNm2aefzxxw13UF5zzTVmjz32MGeeeWbQbVbjhIAQaB4CZEUmm3rv3r0Vety8YQj+yyK2wQ+RGigEhIAQEAJCQAgIASEgBISAEBACtRAQsZV8CAEhIASEgBAQAkJACAgBISAEhEBLIyBi29LDp8YLASEgBISAEBACQkAICAEhIASEgIitZEAICAEhIASEgBAQAkJACAgBISAEWhoBEduWHj41XggIASEgBISAEBACQkAICAEhIAREbCUDQkAICAEhIASEgBAQAkJACAgBIdDSCIjYtvTwqfFCQAgIASEgBISAEBACQkAICAEhIGIrGRACQkAICAEhIASEgBAQAkJACAiBlkZAxLalh0+NFwJCQAgIASEgBISAEBACQkAICAERW8mAEBACQkAICAEhIASEgBAQAkJACLQ0AiK2LT18arwQEAJCQAgIASEgBISAEBACQkAIiNhKBoSAEBACQkAICAEhIASEgBAQAkKgpREQsW3p4VPjhYAQEAJCQAgIASEgBISAEBACQkDEVjIgBISAEBACQkAICAEhIASEgBAQAi2NgIhtSw+fGi8EhIAQEAJCQAgIASEgBISAEBACIraSASEgBISAEBACQkAICAEhIASEgBBoaQREbFt6+NR4ISAEhIAQEAJCQAgIASEgBISAEBCxlQwIASEgBISAEBACQkAICAEhIASEQEsjIGLb0sOnxgsBISAEhIAQEAJCQAgIASEgBISAiK1kQAgIASEgBISAEBACQkAICAEhIARaGgER25YePjVeCAgBISAEhIAQEAJCQAgIASEgBERsJQNCQAgIASEgBISAEBACQkAICAEh0NIIiNimGL73339/o6fbtWu30f///e9/N2+//bbZaqutTPv27VO8ufKj7733nnnzzTfNFltsYTp06OD9vvgLin5/kga/++675rXXXjM77LCD2XzzzZNUKcUzRfTbyWdcLosErJky1Iz+Foll0e9etWqVXU/+7d/+rehPFf7+l156yaxZs6YUfSkcLH1ACAgBISAEhEAbQUDENuFAX3LJJeY///M/N3p6t912M+edd54ZPXq02Wyzzcx//dd/mXPPPdeMHz/efPWrX6355v/3//6fufvuu81HPvIR85nPfKbis4sWLTK9e/c2Bx10kHnooYfse6+44grzve99z5x00kkJW/5/j/G+3//+96Zv376mV69eJv7+1C/0rHDLLbeY0047zb7l2WefDUpJnTFjhoEgnnLKKZ693LR6Uf0+7rjjzI9+9CPzu9/9znz605/Ovd2VXthMGWpGf/MGFUPYNttsY7p06WIgnmlLfE5Xq//666+bj370o+aQQw6xMhIvrEFz5swxTzzxhNlnn31qNuOBBx4w/fv3N1/4whcMspy1sF5OmzbN/OAHPzAf//jHa75m8uTJZtasWWb33Xc3zM3bb7/dnHjiiebBBx80hx12WNYmqJ4QEAJCQAgIASFQIgREbBMOpiO2ffr0saQQRfG+++6zHpCRI0ea2267zSpZP/zhD83RRx9t/6lVFixYYHjX5z73OfOTn/yk4qMrV640V111lVXmxowZY772ta+ZK6+80ip2WQgXyuFFF11kbrrpJkvA4+9PCEVuj+25555m8eLFlqyff/75plOnTrm92/dFzusZ99L7vpf6RfXbET2MIBhDGlGaKUPN6G/emL711ls2GiMrsY3P6WrtwwA3derUqkaPww8/3K5fSYjt/fffb4444gi7BrEWpS0Q1Mcff9xcffXVtur8+fPNpz71qaqvccYTHoAA//GPfzTvvPOO6dy5s/3nz3/+s9l6663TNkPPCwEhIASEgBAQAiVDQMQ24YA6YutIIdX+53/+x3z4wx+2b0DZWrp0qZkyZYr1ZKD04ZX95je/aT0N69ats96ScePG2dDiE044wTz88MPmgx/8oBk1apQ5/fTTzdlnn229ty+//LJ59NFHzR133GHftffee5sbb7xxA7GFlC5btsz86U9/MgcffLD1JHfr1s16j2nHd77zHdOzZ0+rqEIaIc8f+9jHzAUXXGD+8pe/WM/N2LFjbXui7yc8FkX5u9/9riWcn/jEJ8wXv/hF2zaI3le+8hUzb948M3ToUHPXXXfZdh5zzDHWO034dbw8/fTTBtwgWiiefO/666+3Xmr6T/8oGAFuuOEG8y//8i8bvQKP9nXXXWe9uYceeqg59thjzfHHH2+fqfVulHP6euCBB25QnvkeJOynP/2p+dvf/mbOOOMM67leu3at+eUvf2k943je+U68bTNnzjTPPfecxZX6O++8s/US4UHPu99nnXWWeeqpp8yXv/xlKwsYUMBnyy23tO1EiWccIAa77rqr9cx+6UtfssQoSvQgS7Uw2HHHHa18YDhhrDGenHPOORvwRbaS9BeZj8rQj3/8Yxu5MHDgQPOHP/zBthPSdOmll24yvozjq6++avuKkQjDxoABA8yZZ55pZaSePKbpL3MV4wkEim/idcQzSf8xFiHXn/zkJ60H8UMf+lBqWf/Vr35lvYiQPt6LrNKPf/zjH7b/yAl9pEyaNMn87Gc/s/KDDDpiC/7IWseOHa3hyUVlVBuL3/zmN5vMab5z8803W2MbbenatavtC/9m3jNvmMusPeDO+IH5k08+aUmtI7a15pcjtoMGDTI77bSTxZJ3Q1T322+/uisqc41QYtaPesQWGQAjjikgT47YUu+yyy6z38SoSH9VhIAQEAJCQAgIgbaNgIhtwvGvRGyp6rxvhAfjvY2GIkNUIbaEzKHg4s2FCKL4Qxh/8YtfWEICmYHEQMRcgTDz92gosvPY8gwK3n//93/bb0I0CS+E5BKGCpmAlEI+hw0bZts0ePBgSzJRXPEU47n993//94qhzrwfZffee++1zUHZp40o65BNCu/nXRTIzOc///mNkHzllVcModq0j7ZyHu6vf/2r7S/tRqmGRFNOPfVUS9p32WWXDe9w4Y78ANn87W9/a/92zz33WEW31rsfe+wx069fP4s1BILyr//6r/b7EBxCPmk/BcMCXh/+hnL+zDPPWIKLAYMCaYOIoLBjFLj44out0QFigBEDshQtvv3mW3zDhXnizQdbxpiQTUgXhABjxfPPP2+Vfec5ixI9CEEtDDC6YDChQFAwPjBWyCjfQ66T9Dceigxu4EdhjPgO78XognEmWvCG41kGS2TxjTfesH1ibJBhF3pfTR7T9JfxdV5s5hbfcQWZdCQLWZw+fXoqWX/xxRftuNBfsGOceD9RGXvttdcG44fz/kNgmVPMWbB35+eRRQwMbl5hpAKXamPBvIrP6eXLl1tDhyv0B1n5j//4D0sC+duKFSusUYQCFhhPGCMK32Ye1ppfjzzyiPXYuvqEUiMrtB+jEYa7JMWFP9fy2GK0o48coWAORomtkz0MCKyzKkJACAgBISAEhEDbRkDENuH4VyO2juzdeuuthrDCKLF1ZIrf8LqhPJNUijoordFQZJRFR2xReFFG8aRVIrYnn3yy9apCGvgGSimexyOPPLIqsYVwxMMWo6QE749TSB0xhkTSbpRfvJ2ur9/4xjes55kwRAgYxP373//+Rkg6gubI5fr1661iiuKMZwuv6HbbbWfbDumPK8NDhgyxnu5vf/vb1mMMeeYbvA9FHPJX7d2QjCTEFkKDdxRPYffu3e34oKBDLqKhyFHPPB5klGuwhxDHz0f79vuFF16wbYEkkFSLM8iMNWOBt5EzjXjJkBVw/PrXv27/G49jGqIHySE0lXHEyIIRA+MF77rzzjs3RCLU6281YovcMGYQLQwGlUJtXV3kC688XjnkgugGxnr77be3MlVNHtP01xFbSDMk6de//rUlexA4vOAYQ/DCgzFEK42sI6fIK+/Ci4hcECpLv4mUcF79esTWyR4REHiSGVuMFi4qpNJYxOf0NddcY8krhBiP+wEHHGAjKngfRgswY+xpJySc3/gu5NkRW9aCWvML7y/E1mH3gQ98wEZjYKBIEwZfj9jiTWYust4wD5D7KLFlHWDuOoNUwqVcjwkBISAEhIAQEAIlRUDENuHAViO2jrwuWbLEhuRFiS0hctEkT5AVFEoUz/gZW0dsXaIomhUnDc5jC7mBUFL2339/60GE2BDuGfXYQlCGDx9u21SP2BIW7DymZLqF2LlsxXwHRR3vLx5b52Eh/JKQU37nW9ECSfrWt75lvXR46yiQqGuvvXZDcq1axBZCT/8dqUnzbjBMQmzxSkNwKJBuvJ+QHJT8+BlbPNzOw8zzkA36h8ctTdsgGbX6zbsgXHjrGUvwpRA+C/HjrDXkOVqyEFvkx3nko+9yBDRpf6sRWwgW4e54YQmtRfb572iZPXu2NU5EPevu73g868kjBhWMQJCpeh5qR2zdtzCwEHrsZMD1A2MTxNcR2ySyjlGJaAnnaaUPjAnztEePHpsQWxfJEfXYRok/Icu0C/LIf9cai2rENprA7rOf/az5+c9/bscbMglJpG3OaER7XeQJfeD3WnMXskzbogYtwp0h4hMnTrTrUJJSj9i6IwEYYMg4Dw7gxP8TwYChgKR9FIwheWSiT9JuPSMEhIAQEAJCQAiEiYCIbcJxqURs8XZw5hOlHe8B4YWO2KLU48HAI4m3klBaFDIK59f4vZLHNqrkVyO2hHpyJhVlnmtyeBekAW8MZJNv4UFBybz88ssTEVsyojrPEiSA866ObDtS4pR9vFsQwVrEFiUXZdeFdtJvPMoo106hrkXwqIcXnHOlRx11lCUvkD0IBOcBa72b5F4QfkKY8T65zLO0IRqKDCnFE0qpRWw5D4hnF0WaME7qcNYWJRsPa/SaIt9+0xY8nSjueA8xWmAQwGsFOYGk4LUi3JVvQzAqEVvO5NbCALnAE+xwxGtOiDYygMc4aX+rEVvOUuP1q0VskTMMQy70GIMKxhtIDAYgl0ysmjxGPbb1+puV2CaRdYxaRDRArvAyck4WggjxI6rBRSM48uUIXTwUGTnDG0qI+4UXXmjPx+NNrzUWGI6iCeGcxzYaJu/mkjOIYVjjOcaHcYquI7SbeVZrfrE2IHdRT6kjzxj3MColKfWIrTPwxN+Fp5gcAy66gb8XkeQtSR/0jBAQAkJACAgBIRAOAiK2CcfCEVuUOcLjOCdKqDCFEEH+Hr3uhzOkECyeITkNIYmQXsJdUcogwij0KGnUJ/kTBCUJseWbhCjyHrx6ED8IIGSad/FOvCqEqlKcxxZijfcUbxAeHRI6RUOdeSdnhQljhMQSDornzBGrNMSWs6r0mYJiDbl0yaJQSEnAVIvYujBo8Ca5FmGVkBP6S6hnrXdDziD8FMgFBgbqZiG2YAa+EDCILMQLogLhAGfCOKPE1rfftBGsIDju3CPkEVlyhgQIL+QEsgvxdeGzUaLH2c5aGGC0gDzTJ0gk78boAEmCVOEtTdJfH2JLX51nHrkklB/yBTGCINWTxzT9LZLYRg0RnJ/F8w/hJEEZiZxcHyGDGBDcefE4sUXWmZsYHBh7xhYjQ62x4GxpdE7zbmQjSmxZE1gbHJFlDmEgotBGxpBvUSC2nJmtNb8g2u6MLf9GVt1aA3knuRSk1I1jtSW2ErF1Bib6geEPvCicXef+XdYm2oqxjWei4eMJl3I9JgSEgBAQAkJACJQUARHbhANb6R5biCihxiiHFEdsIbJ4PAgjROmE/LjiSDBeKRQ3lEo8cCjAeNgqEVvnecSDAyGNJlOCfOBt5R0o7yiajsRxThfPoiO2KKQom5BrCAxndVG63fsJPeY3PIOuQAxRnklw44gtoZq0nTBeSF+lUGTqc/YQUuoIGsoo7SFLLKUWsUVBhhA7Mszz0TDHeu+OJtpinDjrCUmHVOMZw6hQy2PrrkdxSjXjg5fTlXhfomJUr231QpF5l/u+82byG541xtcRI4gfJIi2kLWZ8O/oPba1MMCw4Dz6ru3IC15yPKXxv1XrryO2ToZc8iiS/iBjjD39rRSKzHfJzIxcORmB3GGkwZBQTx7j99jW6i9zggzSbn7h9QdbF4pM5mHmUPyMbRJZZ1wI7ebogSsQMOY/xBQPKESPwnzlb4whyd2QQYgk+BAO7AgmGa3BkFDbWmMRn9OQTAxRUWLrjC3R0Hvn2aVNGGjIkg0hd1mRa8mwS+wGfswpl3iLuUoEi+uvk4lqS2wlYkt4OG1wUSeuLqH48TO2hEsj85WSuCVc1vWYEBACQkAICAEhUCIERGwbMJiEKqL8kokUJd8VwudQ3vEARn9P0iQ8GZA0vDnunJmrR5ZWyEmlux1px+rVq60yzT+VCkokIbtcueJ7tyzfw7OMV5MQxqh3M0k/8dTQT4iOyx7r6tV7N55PPOMo++7MbJJvumcIo+UbJDGiPu8iFJlkOZydrHWmr17b0rQj/ixkAnlJIjP1MOB8KNEHEF3ITbSk6a9Pf5BliCd4MkfiY5VGHuv116ed9eoyl+kHBCw+LzmOQKg3xLZW1mCMLszL+NjWGoskc9plTIeEEvFA4Z0kQYPYxtcQ/p5Ehl14PvLDvHCF4wacT057zy2ywPl1oizw0NYq7u5dFwFSb3z0dyEgBISAEBACQqDcCIjYlnt81TshIASEgPXGEuZL1Aee7SILXmfC3AltxzudpnBWl6MeeKtrFTzpXPsVTZKV5jt6VggIASEgBISAECgfAiK25RtT9UgICAEhsAkCHCkgyRWhzUUW7qzGK+2S0aX5FhEaeLzrFbKwk9iLZG3VIk/qvUN/FwJCQAgIASEgBMqFgIhtucZTvRECQkAICAEhIASEgBAQAkJACLQ5BERs29yQq8NCQAgIASEgBBqDQK2rmLLkPmhMq7N9xfW1bP1KgkZb7nsSfPSMEBACjUFAxDYBziR+oaRNfJTg1XokAARI6kNCnUoJdBrdPJcIKEk4ZqPb1qzvgUmthEu0i6RD3GWrUh8B7gvmn3qY1n+TnhACtRGIZpev9GQRib8Iheced86UE37vCpnPOV/+xz/+0fTs2dNmbe/fv3+uQ5gk432uHzTGnjPn7DxX9XFLQzMK2dm5Yq7eFV/NaJvPN8n6787zc+WeihAQAuEj0CaILRlKWfy5kiNp4Voarv8gGy+bIVmCp0+fvlF17t0kU++zzz5bN4Nn0u/Gn5s3b575/Oc/bzOqJilct8KZM67KiZZqv8ffSVZZEr+w6bNJUbiXcubMmYYrioYMGWK4FobCFS1XX321vZ6Da4P4netSfAtEBpLy9NNPWwWkyMJVKCNGjLCbFxbnSZMm2atGyHrM1SVcEYP1neyx9BWliAyzw4cPr9i2n/3sZ4Y7eKOFO3ghEr/5zW/s9Shcb+OyTXNND9e+jBkzxlZx10BxZQrZYUMtUbkkOy0YxmUuj7b/+c9/tnf4xr0+77zzjpVJxoHCPCQjMZm3W7FUkxtkj2uDUFrp85FHHmmv10KBvvTSS61cxsvkyZPt9UHVCtfk8E7unq23flXCOet6x5VbzDPWF9+Sdl30/Z7qZ0OA68i4Hopy99132z3joIMOstnPKdz3TNb5PArZ97k+jLlCVm13vzvvJmM53+T77OsQFgrrOf+fV2kGsXXXnJGBnCvgmlHcOl02Ystaii6E/sS1iSpCQAiEj0CbIbZYNNMo3hC1L3/5y1YJq0ZsISsoWPvuu29hCUx+97vf2bsuuXqmVoFckoX0sssus/dYfuMb37CPV/u92rsuvPBCey/kj3/8Y0uoXTZVFnVIK/dGsoHyNxRsiCF1UCpI5PK3v/3NW1FxxJZrkj72sY8VOosgZWeffbbtz5e+9CXbNzDEm0WfwZT7Nrlfk3ZBQMGUDY9raHbYYYeN2oeM4eE/4IADNvzOPcfI38KFC+3dqShe4MY3eJ7v7LPPPhueh3CAP2Qn1BKVSzDECHDaaafl3txqxBY543oiDE4YCVqd2FaTG3ff7ujRo62RBTni/t6vfvWr9g5jruxx5Ve/+pW9ugllvVbiJkdskbF661cU527dutV9vpYAcD829/OyVviWpOui73dUPz8EuKedjNncUw3ppEA4WW8xBiLLe++9t5kwYYIlv6yzyMthhx1mSCrGvekYmkl+xp4bLz/84Q/N+eefbzDOQmCjxBaj9Omnn77hTvepU6fau8IhLdEM3Fx/xp3TrOHcCe5knjbSNoj4D37wA3tnPQYe9ATIHPfcu2v7+DYRJNxrzX7Cd9Aj6MPgwYPtVWAY5dAfuB+dtmHwZI9hD2WPoDCf2QswLLM/YBA988wzN+m3I7bsI8wtPNMYX+kX6wCZximzZ8+2ayWGW9YP9iinJ/B3krGxv9Fn+k6hnyRqwynAGs/zP/nJT+w+T1Zy1iL6Fie29LtW38GItcztcYz39ddfXzFxHPsufeGebLzx7Mcnnnjihj0WIzC44TUm8goD+5VXXml1MrBnPcS4jJEcwwff5Rn0lq985St2TaMPGJi5Do27zsEHQzTy95e//MVwv/rYsWNT6ZD5zRy9SQgIgTQItClie8IJJ9gF/4tf/KL1zK1bt856PeIKOYsdCyUeMxZnlKjHH3/cKs9sCmxAbFrbbrutOeKII+yCy7PXXHONufXWWw2eXL5x+eWXb3InJ5sO5BCLMYT1qquust5eSBwEi+9A5iBOBx54oP12EmLLRs2ztG/UqFEbNqxqv1cSEjZSyDz33F577bWW7KFwE4LNvZSUiy++2BI0CB9e1QULFlg8wJJ7c1FeUQxcQWHhXWwobPxsUmDG5kLoEgoDmxtXfGBtZiyixBZrP8oP7aLQH5QPyDveTTYgNiQ2bDZl58HD444SAp4oBNSL3w3KhseGyiZN2/bcc0/bLudRwjvNGNN+7sKFRH3oQx+ySg9eBt4ft5BDPpCdQw89dCOI2VRRLPg3IXIoNOCHzCBL0eKwXLx4sdljjz02GSo8xyg84IRsXHfddXaMzjnnHCs74HnyySdbLKrhE30pmKM4QXT4JgYLjBu0A6WS99FvlAqUOBQJvOlOLqsRW4h/pfHlvbXmIe2gj/SJ74FR3GNLu5B1vC3IHN4Yxg6FjPczh93Y4CVE8cSYMGDAADu3IMVZ8arWL+QC5Z35jZIGbnhOUbZpK0owfYkaMdw4VJMb2oqChexQwAUFHtmNFu6/5f1z587dxBiEAs06wzoFTt27d7ckAgW93voF/g5nCAnKLe8hhBRlkzmD0sj9vCjBGL+QS+YAYaCE1IMHskLYJ1511hXG2BWUUNYc1mQKCiprGF5lnofIsLb16dPHznvmbHRd5LvV1ghwqYZ/kvU6zWaqZ2sjECe2zGlChhlL5Ic9AE8rBflmnrmoIfZXZBZCxJyCbEDAKhXewXyKElvkhnWcdRM9AIJNe5DLeIQN+xdr3Pe//31LoJAf1vpp06bZ9rDG0gYIEDIKGXJhwFGPLXIL6WQt5R3MGeYeJIm1iPexBvEu2sv+S4Ewo2uw5nLHNPOFuQr5dftttN+O2PIb6yHGLXBiH2M9dGsl/81vfJNvo6swz1zBIMA+R10IMt9mvwJrF5LLXsHv6CZu31q6dKk1JhBZ4zy2zOlafWePYd8GC4zDjAf/zf4TP/LFuk7EHTLC+oWxnfLiiy/a59l/KHybv0X77u7vRg/AYMA3+TZGALyw6AZEElD4O8YUCusT7WLf4DfWHowgTr9o9lyP74dt8Ux3kjFoy+e+6XtblYs2RWxRytgwWEBZ1Fjob7nlFrsoRz0cbDoDBw605ACyybMs1CjpKKVY+iCuLHS8j4UfcoRllEWS8FK8dFhaITGuoHCzMLNxonRDSJxCCLllY+AbkMYbbrjBsGHQliTE1n2DRZuNJ2qJ5W/Vfnf12Jz5Pm2G0LEps6Gy4TrCybN4e9iwCI12Zx8hDlh1UUzjXkw2HzZorKJshijekD480BB/Ng3aCmYoEZBHMIH0QfbxSmHpdmHgeFXZuFBm+W82KhRUjAmMFyQAwozllvGFgELG+RvjFS0oOfSHtlHYXLl3E+UE0ox3AMMH55bY2J0XFlIH4awUxkZdnkO5YJyRA5QD+opRgw0SIwH9RR7BmbGPF+rgpUMJiRbeAUHgHfQNRYU+YIFmg8eAwDfZkJHtSvjESTdjjDKIogLWyDc4IusYDFyIL+SDbzJGKD71iC3trzS+zsNaaR4iV4SBUxeDCcYKZDO+kaNQQlLxHqC40S7eB/Gj7RgymG+0GQUYyz1yjXKL7D300EOZ8ao7CzipAAAgAElEQVTWLye3KKrgz3N4F+gv1n6MNMg6JMyForuxrSY3kFL6TsgxMsgcYe1hPYoW1h4UbvoXLxBhZALlELlm3iAvLhS51voFfnGceZ6wYkgx72GNRPnkjDq4YrRh3FDG6StthqyyNqLko8A7rxRtxQiFJwuDFV4WSPicOXPsu5gHyCveNvpBXbxDzFknf/Sn0hqBDOBVqoQ/62q99TqJ4qRnkiMQJ7aQNNZD5gtznD2YecK+yHizPkJW2M9Y9yjIAuSFta3a0aJKxBYDLUTORSKxduOB5fvsX9HCM6yr/MO+xvpB+5iDrH3szxh02NcwoEGC2ROZA2mIrSONrGG0hTnNfueMvqxX9B2dgP2MYwGscRiwosURW/Y46tNO9i72SeYU+yAEluMizHnXn0rRVe5MNMSX+Y2xgbkKMcSAyJ6MMYK9H5IH2UZngBAnJbaszxB8Cu9r3779BqMXa6QzZkTXRvriDL3gw74CLuyftMsZD1gHwIridArnmKhFbFmXMX7gyECuWLMwbIQYiuwcL1EZYA6BG7oqEQH1CjLCevyRj3zE7lEUIiIYT9Z0DL9FlkceeWQTHZV1GszzKs069+30KfR35mEzCnsw0R1EvzBPylKSymibJLYo/yjDLJZsRJVCXuOhyCy4jgChSHEfJJ4KR2zxYkIeeQ4rIYswC+zOO++8QaZQyiDMToGD/EBQ2CiwLqNwu7NxtAvhxFJdNLFFccb6jOIBkWPDdcSWDQsF0Xkx2cQhbFGiwTNs/BBXFsv4osjCyabC5gG5ZFGFRDEOLKwsxODFMw6/esSWBRzLLu8ZOXKkxRgCyuIIqcCAwDOQcjZwjBfx87qQTRQVlIFoYUFiY4PQIRvRcGM2cSz+KERxS78jfiwobDIoXyhBbBYQL9oKIYSYgAHhaBgwwAHPcPR8Moo+uEaTnzhFALKAws57GRfkA+UGYguebM4Qomr44H2IFhZixgjvLgVjDESId/sQ22rjSz+ZN5Xm4fz5862S4jySLmQwTmwrhSJDhiD9eHZoN2PHQohnccWKFdZ6CaGBAGJwYRyz4FVPbt1ZN5RBQiaZ55B1+oBSidKI0ciVWnLjFEDWCeSGAmGMeg6c9wmZqpR0jHnBWuRCLpEtSpTYVlu/UDrjId9RYssaiuyCM+sfaxjywxrh5AnDCwYjxqVSKLLLVeBCVCENrBOQEcgsayZjx9rCvGQuQ27qEVsU1Gr4Mz/qrddlUQZC6Uec2DK2kFf2HheS6vYXvPIYYlhHHcmgHyhpGK/4m5OveP8qEVvkCZKCLDKPWMchbhhtkN1oiXouWY9ot/N+svaybjjvqqtHW2hTLWLLXsN+DjllbaqWjJL+su6jRzgPIt9hf2VPie9jjthGyb7DGp2F+eTOMbNWsP4i+5D2eHGJktBHmF94NlmviLpgTaYe8z9aWHdZ12oR22jf8W7Tvkolfk4YQ7lb0ypl14Y4sE+Dk4uEwSDK/0OgGZcosaXPrJ1xjy3jDC5OR3P4hExs0UHAnLB3wsgZF2Qao2+94oxKkFgMKxQMfRgJoljWe0/Wv6ObYfglwsDdQ84+E9epsr6fes069+2IbdZcFD59dnVZ55izRE6h55alJJXRNklsowKHwoRSF7eCxoktHgpHgCB9LAxRxRDLKooYizbkFAspG7CbtAgWRBWvJsp1tPA7ZAMLkytMDiy2LNJFE1sIFpMASzWEEkWYvrCBk6wGosjfKXgAIago7dECGYSYYnF3YcPu7+CGFwbcIfr8PxsnIY6EaOGJYsOHqNUjtigoWK6ZrE7pj7bDbUgo8bSbAn5sUFifowV8URKwakXbythBlqOJTbBQ01Y2ecYfj1Q8qywbryNcvA8lCFJcKakHyjpeaLzJWJaph5KHNZpCP/FeMjbRgkGFvkOSnRGEdzhiy6aNt8OFvcUXtEoKDbJGiJkL3WXMjz76aItBlNg6a3hSj2218XXEttI8JISXb7LxUdiweT4JsXXvIySYsWFegzHkOF7wADGeWfCqJ7fOs+DC3uLfRmmNnpVLKjcQQGQV+XPGMd7NPAUzohgqFZRtvFXIOwWMsZgnWb/welcjtnzXJbXjfSje9IXvoWTHk0TVOmPLnMNjh9yj+LvID+YbhAHDGWssSmw9YuvWCLx7yHsl/Jkj9dbrimDqx8wIxIktHnmMuqz9rGsUom8wHONdZE2C2BIJwZrDnHaKDUblamf6KxFbjGVEomC4Zf6wByFzGPDYf+PFeS5ZeyCDKNzsI+wp7C2slSiNrC+s2ZWILXsO+yGeWHeWE8LuQpGdQZh3E2WEkQjjD79DVsCE+cfeyj4A4YiSfNdmR2whChhK2avYd3ifM5YzVzlCEO9PpcF0nmQXmusMZuhE7NWszbQDPMC6ErHF616t7xiwiFJi/0KP+MAHPmAjLugr344a51hPMD6iG7hIF/ZToqYYA6JFWI+dgdsd46FfrMPsjZBVnkHWGHv2zzixZZ/BINdKxDaa0Mp5J8HU5WOpdvaY8UQeGDunq6JLIc/IDOs4Mo7uiQwzV5zjAaMQayd6M9gik+wrGFupyxxjnlKPo0t4wStFVrjw/ug+VkkW8zz3Xe+8da1z3xhCIYsY4DDwuCiiSlnVHbFl3rPnMacxZLNO0B/kj73M8QnWJowLyHLUYI0xmz2aeQaOFNYsnCPMc3Rp1jPWUTAnUhC5ZtyixJZ5iNOD/dUZPeAh9IW9FScf7+RdRFRipCJEnzWr0s0J7M3o+LwLvQPjIPoM87RWzgTWtHrtQC9mPcCo57iUixglGi0uozh4KpU2SWyj2VOTEttoVuRKxJbBwLvBP1iDETAGgUXAFSyIbNQMMAID+Zg1a5Yl1Xg4EAqsuCzmHTt2tJZklPSiiS0TiIniCosYygQKIsKL1wOFg0LfIRBMTtrFxoTHhkIiCc7Zxj2gjuCh7OKxYROjn3ixmEAsAEwQ+sxkQ6ijHluUZzYzCs+zCCPkfJezh+6MDYoA78U6zTfxBKNws/G7hB3RSQCRxPBAuygoSyzWWHVdchP3PBsBFkDCuqplanaWY0cg3FlcFieXwIP3Ma5sKigiKDTIFu3F+oz3jpBOjAt4nTEIRAsbB4oW3g7+juKORd4RW5fEiQ2jGj7x0GcWYhYm5/mOE1tHGF0ioyTElr5XG19HbCvNQ8af7zgPDgod7UpCbN37osQWYw1ecxQbCnOPccTazRinxatWv6Jyi1fYhUtzztMZIZhbjJsLl6NNteSGdiPvyCXFKTDOC+yMDchTNGFZVGYYX0gmygQFZZ56UWJbbf1iQ65GbNlkHSGJEltwoJ77Hps2mx5e8mrJo5gThFLST+SbOmy2yCobnPMUsVZWIraV1gg8gWy6lfDHSFBvva64Y+rHzAjEiS0GSjz5yD9z0eWW4AOQHxQxF5YK0WXuYiyiuDOglRpTidjyDdZX5iHKHIofHi6IDOtdvDiDGr9TB2WOPQnSzV7Iesm8wuCLQoyix1od9dgi0xgIqc864/JUOGLryDNEgvmJEYc2EZnAkQF+Y63kOAoRChhaKxHx6BlblECwwUsZPWPsCJvrD/OfOV+pYPhyWfqdp5rnnCeUdkJaUJgprK+EZUc9tpD1an1nDcRTx79pL3i4cEmXxyLaLheeTn8gn4wfBdxZ31mHwBhigIea8Wd9Zx1hvCDZEAnWR9Y8SlJi6yKGWEf4bjVFOvOkyFDRhSJjJEE3w5ABIcHo4BKHElVU7ewxpBOCCvFFz0OGMaZAvDAgMm70Fd3KGdrpP+NMcR58d/SJ35zR0XUn+v+VQnIhMMgk48S+gKEEXTl+q0Ge577rnbeude6b9QgdDszZg5ALsGOuOf3X9d0RW/6f/rkz4Rie6afL9YKxhr3dRR3EbwFxiVsx/BFxxh4HmXXrEQYD5Jn1A0MWJJo28VyU2GKMc0ctnR7lztmjzzIWrL20B8LOt1gvkQvGOlqoDwbMMdZujCjUwwiGs69WzgT05lrtYK2JngmOnnlnXWBdjMtopXwltFfEtorHlkWcTYQFIJ4VuRKxRfCZqFiuIGgICAPF5uUKmxXWSDZGp8QxcSFUKI8swHgRsIBCEvHiESLjiC3CxmLDAl5tU0p6xrbWu5i4WKBRTpmMWHLYhFzoK3jQTpRVCBiLKRMcoa5ECuk/4Y+cpQMPNmgsq0xCSCXf4WwFGxR9BztHbFmgsR5iLID8MS70n3ahKNEGLO4YCVh8WfRZ7HkHHjsWWNoLAY+HueB55jcXYg75hcBGE2qwiKBQQdIwQkAiXaH9TGz6RZsYLxYeSCchPigy9DdqlcTyx4aA4s67WdiYsBAEjAuuLfQN4otCFC0YQFj8sdZiLaWfkAWMA/EkTtXwQa6jpRqxpU/IMkoH/YCoMF5JiG2t8UXBiGcxdgYmsEaZBRMUJRRaZCJObF34LiQVPKLvixJbFnnkgo2fTYY5huzQB36PZnNOgldSuWWjcKSTbzKW9IPxo81ROYJ4VZMbZI7FHILPRoJ8Ey7IukBx4bnOIFJJT0KRYQNkbkImwJfNMEpsq61fbMQYiqI4u1DkasTWfY+2I/sYb6jDP2zGzvsWbSvjzhrolFXWBBcWyfP8DRlkreSdzGe3LrL5VlojUM4wIFTCn/Wk3nqdQedUlRoIOGIbJZNETmAIjIa3MpYYVZ0hOKpA8fro8ZNKn3NGliix4zl37szVwYiCbFRKsMJ6wxymXdFbBlDSWTfYPymsT3hCmU+s3VFii0wjxy5smblE2x2xhRyiX1DfFef9ZQ1jrkQTC4IDxrJ4pJJLrsRe4Ax47E2sN+4qo+j8Yr9gjlYrLmyYvztPNf+NsZH9njmKss36TN/4PmMYvccWvaZW34laAw+n9LMX0leMUfFCtBykwl3RxLMQWo7PgBPzPGoAdmdpHbkiPNeNF/sy+3Wc2LLWsD9gjEBuXGQThkTWSwhfPIN2syZ7pTO2ri3uTKWLbKh29hivLf2tFoqM3DhvnUu85YwVLsLCEVvkAjLM+9BJXISaS1rmbpaI4uXIHHMJIy3zjPEC73hG/7zOfdcitui4tc5900d0LtrrIkVYI5j3GHXi+hTEz+Hgjj24xGrsmzh/0A/BiEhCDFhuT4++K5q8jXGgrluP0GNYQ5hneJGdQwWjLcZgF4pcj9jyPeQdwko9jHiMJSV6PJL/Zw7CD5ABHB44ktCZ0Yv4DhygWs4E8EtKbJ2hnvWGdcElulMockQ6ICwsvi55VNxTVCkJkDvLg0UDpS7usWUCQ8bcPZAMJoQK8sd/szBAQuPXwaCYOWKB8CDgbFgIKYstddk43HkZNkssKgiYO3+EEgvhqFQgxCwQhEFHS/z3Wu+KEls2RAi6yxzIf0PeEWgXzsV3aDdknQ2zUviCC8uMepZoIzhTF7KDp5L+QhDdPbZ4gFk8wJXJBIlhQwUvl1mav1FYpFHeqeusb2zweINZZOIeLZdZlVARZw2LY0p/wc4tGtG/0wYWHsJR3Jhg7YZkMoYUlKkokaR9kFgXJo2MYB1HAacuCxzklzZDxuIWKRQb1xbqIBtYDyHxbLzR+2Rr4RNfiCt5bLHqueyWPO8UM+YCc8jJZbV7bKuNL0SuErFlHqIcMXaQIgqKDrJX6XxVNEkDnr24x9bNaxRYl3EXeUB+2WTi7U6KVxK5dV59d6bL4Y1RjE00XqrJDcYcNlOnADIXolm36RtKYq2zSVjEkVGMUxQIIRtM0vUrjjMbGuHz1YgtSiDKIWukywqN4onxySUkix/HoF1OgXGGIMYchYm1ioJCDgmgL2zaTv6wMFdbI6rhz3xPsl5XXGT1Y64IMM6s4exxKLxO+XHEFq8eig0KMMonnvasBQUMIywG0Vp3Pdd6P+3lHUResNfUK8xhSrX7elEkmaPs29GjS9ShLusaBnH0GPbIWoX9mnlW6VlnWHBEpV67K/0d4zzzjXFKknG1Vt/duKOI875KeoNrA8+yb7I2QATiz/IOxgQDWJxo8A7wZU9Fn0hb8IiyvzM28fFJ+648nnfEFgMQOiPkHlLkPLTME/671tljvIy1iC26hUtChT7CWLvIO/qAgZQ1HseDkyeXYdrpeE4Hi17v5fqPgYi5yH4fDZ2vdL43r3PfcWIbPW8NOat17pt9C0MKe5sr6EN4RuMy4fZL54F1OSSox/zE+ISxJXqO3Z39j8uHO1JHhCJjjFEGPQUdGMM0+3+0TdRnjrC31yK2OC04EgW/QV5cqHP8+/Fzwuir6GTRvAiuTr2cCRja4sQ22o6oxxaZZq1DV8IB5fARsc1hBYFgQSCrJXmIf4LJzmLCBlbtKgLqIOiQqWhiKX5n8WSDr7TB8Xc2FQYWK2mSTaUWBGnfBZkBh3i/IHBsOFiha21M1doCxkx2QmHYvJik/He0sLC67MrxfrMoskiyUEN8owVLL/2Mn611zzB5MCpgSXKZAXMQG9sf5ADFJ259ZBFh3GvdM4qFjwWLkNxKigxyhvKCFxk8sL5iQKkkp7XwSdpXd1dqNWNKrfckGd9K9SFOYFRrHlGPzbaawhh9LxhBZiDAtbBPileafrHROKW8VluryQ3yjzJJlEYlpS3JONIvFD/qV1Poa61fSXGOtoW1DENCNLqEduDNSUMqWF+Qb4whKEP8E88qXWuNqIZ/0vU6Cb56Jn8EosTWHUXJ/yvlfyP7I8YsvGKVskCXH4Fy9dAR2+gZ26iXnTB6iEqts8esibWILXqR2yedwdhFQrhzvM5jW4/YxsP92UtoO8YaiA0FEs1+UcnJxN/zOPftwp8rnbfGaFrr3Le7OYC2YGSGaKGrxq/M4u+O2GKcJ1LRJZF0UR3sgRjoqO9ClaslfnS5UnCUQWhdhvJo9m/WScbL6c1xYks4vtPN2TsxDKLvQjQhtuzHOKUw9tMfCvKEPk7of9R45450uHaw7xIRiZ4NWeefajkTINu12hElts6RIWJbrrWrYm+YUAh0/Pxnlq7n+a4s3w+lDqFHhKORtCqUwqLDxuQssKG0S+0QAkJACDQKARQ5zq6icKF4qmRDAGKL15toIM4xVssRke3tqtVoBByxxZmA0R6ihBEcouQSreHNq3X2GAIJOcGJgv4DAXPHxZhr5PLgOBqhqZAmvLEQE8inC4VNSmwrhSI78kfEHgZnIhRpC17OSlEQeZz7dme1K523JmS71rlvorSIjMNLSxQRkZcQQyKI4sfFXN8wAHBkj7B5cANnF6nnCBuyEw0HryRLjpDzN3e+2WUL5xuMNZ5cl9uG8GTCd6NZkV3iN4in8xrzPogt5NqdbSYajIg6+op8wBPizhX3LiKscNIxdqzReOZr5UyAt9RqRxJiG5dRIm8qlTZxxrbRC4++11oIEJrKhPEJccurx1jPOM8RXyzzer/eIwSEgBAQAkJACLQmAtXusYVQQQAhF/XOHuNhg9BgPILQQXTRgzjKROH4D2cu8eSRlMgVdxUV0TPO61fPY1uJ2OJVJiqNsGUKZJO8I/HbSdx38zj3jbex1nnrWue+3dEbd4addnG2lXDmeKScI7bRhFucf6d/LmKL3ALu/Du/Q26rlejtCji23HWaJHDlaBOF9+PdxbOMk4YQ4ug9tu7oIc9i/EBG6Avv5mgbf+dMvwtr5u/8jX/HC+HnGDrccTsMLJBajhPUypnAe+q1w0VkOo+tOxLqQpHjMhpNyhptp4hta65tarUQEAJCQAgIASEgBISAEKiIQK2zx5AHwoIJD3WZejGsE8rKsSPnOcU7CLHEOxY/AuILO2SMb3KsKumRv/g30577rnXeut65bzyvhA1zpCd+ZK4SFuQNAM/4sUOOd4FlNNFSFiwZP/BLckQJYwfecYhtpWOD7rgS59AJDa913NEdm8IZxJG76LPVcia4/tVrRz0cKslovI6IbT0U9XchIASEgBAQAkJACAgBISAEhIAHAnhCSfqFd5Tzr/FErx6vVtV/IiBiK1EQAkJACAgBISAEhIAQEAJCQAgUiABJpTgr67L75+0FL7DpLfNqEduWGSo1VAgIASEgBISAEBACQkAICAEhIAQqIVA4sSVumyxd9awSxNpH48RJI00sNYfUo4V4dWLA693npuEWAkJACAgBIVBmBLS/lnl01TchIASEgBBIi0ChxHbSpEnm5ptvNn379rWXr19zzTUVU83fc889NoW2uwiaO+tIv81dT2zcM2fOtIeTydjGoWfuJx0zZoxNYa8iBISAEBACQqCtIaD9ta2NuPorBISAEBAC9RAojNiSqYyMWXhr8bpyPxIZubiYOVpIo03aau6DgtiSMYtsbKtXr7aZ2UaPHm0zikFsSS89YcIEe88Sv+G93Wabber1UX8XAkJACAgBIVAaBLS/lmYo1REhIASEgBDIEYHCiC1tJBU19zaRcpzLnM8//3wzfPjwDc1/5513DJcPcwk06aUhthBdDlUvW7bMPgcRXrhwoSW2/E590kkTiswz3H+lIgSEgBAQAkKgLSGg/bUtjbb6KgSEgBAQAkkQKJTY0oAnnnjCjBo1yuy1117mlltuMVtttdWGdkF09957b/t3iCvElgt+hw4dargsmXLbbbeZuXPn2lDmYcOG2cuEKdwhxSXP3H81b968DRc9u5d37tzZXlqsIgSEgBAQAkIgLQKtYDTV/pp2VPW8EGgMAiGvH0nO5nN/7Y477rgRWL65b37+85+bPffcszEDoK8kQiBkOU3UgQoPFUpsH3jgAXsu9sYbbzTHHXfcRp8njHjbbbc1ffr0sb//4Q9/sP89Z84cm2jKnbedPHmy/TthyFwifcEFF9hwZTzBhCtXSyLFed5x48ZlxaXwen/961+D9jarfX4iIPyEnx8CfrUlf+XGj95pf/UbY9UWAkUhEPL6W+9s/oMPPmjOOuss84lPfMIe9zvttNPMZz/7WZNH7pvQ9fKi5CHU94Yspz6YFUZsCRfmjOz9999v9ttvvw1t5MztkiVLzL777muWLl264fePfvSj5umnnzb8Gy8u9zz16tXLDBgwwIwfP96sW7fOTJ061dx3332GC46ZnPPnz6/a99AnUOgCpfb5TCtjhJ/w80PAr7bkr9z4aX/1G1/VFgJFIhDq+pvkbP5hhx1m895w9O+hhx4yp59+unnqqadyyX0Tul5epEyE+O5Q5dQXq8KILaR1991336h9J598sjn33HNNv379bGhxtLhQZP49e/ZsM3LkSPvnQYMG2cRSEFvO4y5evNheA4Rnl2zL1UroEyh0gVL7/KaW8BN+fgj41Zb8lRs/7a9+46vaQqBIBEJef+udzSc6kqSsm2++ubnooousvj127Nhcct+ErpcXKRMhvjtkOfXBqzBiW61RJJIi0/H06dNrtpvnXn/9dZv9OFpWrlxpunXrZq1HtUroEyh0gVL7fKaVPLZ+6Ak/4eeLgF/90Nc/7a9+46vaQqBIBEJfP2qdzQcXbjA555xzzLPPPmsdTejieeS+4d28RyUcBHTGNoex4KoeJknPnj1zeFv1V4jY+sEb+sKs9ml8/RDwqy35E35+CBRTW/trMbjqrUIgDQIh7w+1zubTxxUrVhjCkU866SSbp4aEr3ht8eKWPfdNmjEuw7Mhy6kPvg332Po0Nk1dEds0aG36bOgCr/ZpfP0Q8Kst+RN+fgi0du3Q99fWRletb3UEQt0f6p3NJ4Hr8ccfbxO5EoYcLb179y597ptWl7u07Q9VTtP2I/68iK0vghnrhy5Qal/Ggf1nNeEn/PwQ8Kst+Ss3fn69868tYuuPod5QXgRCXX+TnM3nKs2XX355w+B06dLFhia3hdw35ZXIyj0LVU59x0HE1hfBjPVDFyi1L+PAitj6ASf8hF8uCPi9JPT1z693/rVFbP0x1BvKi0CrrR/KfVNeWazVs1aT06SjJGKbFKmcnwtdoNQ+vwEXfsLPDwG/2pK/cuPn1zv/2iK2/hjqDeVFIPT1N468zuaXVxZFbEs0tqFvvKEvfGqf32QQfsLPDwG/2pK/cuPn1zv/2qHvr/491BuEQHYEQl9/s/fMr6bWDT/88q5dVjmVxzZvSUn4vtAFSu1LOJBVHhN+ws8PAb/akr9y4+fXO//aUlD9MdQbyotA6Otvs5DXutEs5Ct/t6xyKmLbJDkLXaDUPj/BEH7Czw8Bv9qSv3Lj59c7/9pSUP0x1BvKi0Do62+zkNe60SzkRWzDQj5ja0KfQKEvfGpfRsH7ZzXhJ/z8EPCrLfkrN35+vfOvHfr+6t9DvUEIZEcg9PU3e8/8amrd8MMv79pllVN5bPOWlITvC12g1L6EA1nlMeEn/PwQ8Kst+Ss3fn69868tBdUfQ72hvAiEvv42C3mtG81CvvJ3yyqnIrZNkrPQBUrt8xMM4Sf8/BDwqy35Kzd+fr3zry0F1R9DvaG8CIS+/jYLea0bzUJexDYs5DO2JvQJFPrCp/ZlFLx/VhN+ws8PAb/akr9y4+fXO//aoe+v/j3UG4RAdgRCX3+z98yvptYNP/zyrl1WOZXHNm9JSfi+0AVK7Us4kFUeE37Czw8Bv9qSv3Lj59c7/9pSUP0x1BvKi0Do62+zkNe60SzkK3+3rHIqYtskOQtdoNQ+P8EQfsLPDwG/2pK/cuPn1zv/2lJQ/THUG8qLQOjrb7OQ17rRLORFbMNCPmNrQp9AoS98al9GwftnNeEn/PwQ8Kst+Ss3fn69868d+v7q30O9QQhkRyD09Td7z/xqat3wwy/v2mWV09J6bEd+5Xtml112yVsO7Pt27dbJnPnZT3i9O3SBUvu8htcIP+Hnh4BfbclfufHz651/bSmo/hjqDeVFIPT1t1nIa91oFvKVv1tWOS2c2L777rtmzZo1plOnTlVH9NVXXzU77rjjRn9/7733zNtvv206dOiw0e9r1641W2+9tdlss81qSsjRl95ZmATtsetO5tozD/d6f+gCpfZ5Da+IrR98wk/4eSLgVz309c/1rln7Kx5bHcgAACAASURBVArqX9rt6QfyP2vfMvY/cnmPXiIEQkGgVdaPRuMlYttoxGt/r6xyWiixnTRpkrn55ptN3759zRtvvGEQ6p49e25A+sEHHzRnnXWW+cQnPmEgrKeddpr57Gc/a2bMmGGmTJliunfvbti4Z86cadq1a2dGjBhhtthiC7N8+XIzZswYc8opp1Qdtcsm3GCOP/74XKVo5ctvmG/e/bgRsc0V1kwvC31Cqn2ZhnVDJeEn/PwQ8KsduvzRu2bur+zl81b38APZGNOunTGzJx7n/R69QAiEhEArrB/NwEvEthmoV/9mWeW0MGL797//3bRv3956a/G6fv3rXzerVq0yN9100waUDzvsMHP55Zeb/v37m4ceesicfvrp5qmnnjJbbrmlWb16tenYsaMZPXq02XnnnS2xffPNN82ECRPMSy+9ZH+DDG+zzTYVR62ICfT0ilfNmG/eb3p+ZEdz/Vn9vSQ0dIFS+7yGVx5HP/iEn/DzRMCveujrXwj7q4itn4ypdnkRCH39aBbyRejlzepLGb5bVjktjNgy6K+99prZfvvtzVtvvWUOP/xwc/7555vhw4dvkAeIKsR08803NxdddJENPR47dqwlusuWLbPPQYQXLlxoiS2/U//999+3ocg8s9tuu4nYFjDDQhd4tc9v0IWf8PNDwK+25M8PvxD2VxFb/zHUG8qJQOjrW7NQF7FtFvKVv1tWOS2U2ALlE088YUaNGmX22msvc8stt5itttpqI4Tx4p5zzjnm2WefNbNnzzavv/66GTp0qHnmmWfsc7fddpuZO3euDWUeNmyYGTJkiP29a9eu5rHHHjM9evQw8+bNMw8//PAmI8d78izPrVpjJs9eYnp02dZceMweeb5a7xICQkAICIGAEKhmNA2oiU3dX3MhtsaYKaf3CQlStUUI5IJAK6wfuXQ0xUtEbFOA1YBHRWwzgPzAAw/Yc7E33nijOe64Tc/RrFixwhCOfNJJJ5lx48ZZ0ovXFi8uyaPw0k6ePNl+Ge/udtttZy644AKzfv166wkmXLlaEqkiJpBCkTMIQUFVQp+Qap/fwAs/4eeHgF/t0OWP3jV7f82F2OqMrZ+gqnaQCLTC+tEM4IrQy5vRj7J8s6xyWpjHlnBhzsjef//9Zr/99tsgB5y5XbJkienTp49N7sS/CUOOlt69e5tp06aZXr16mQEDBpjx48ebdevWmalTp5r77rvP3HXXXTZxxvz586vKVxETSMQ2nOkc+oRU+/xkRfgJPz8E/GqHLn8h7K8itn4yptrlRSD09SNJNnWcSR/84Ac3GiTf20qK0MvLK0XF9yx0Oc2KQGHEdunSpWb33XffqF0nn3yyOffcc02/fv1saDHhxC+//PKGZ7p06WITTBGSPHLkSPv7oEGDzB133GGJ7cCBA83ixYutV3fOnDk223K1UsQEErHNKmb51wt9Qqp9fmMu/ISfHwJ+tUOXvxD2VxFbPxlT7fIiEPL6US+b+uOPP26+8IUvmF133dXeQMIRwn333TeX20qK0MvLK0XF9yxkOfXpfWHEtlqjSCRFpuPp06fXbDfPcd6W7MfRsnLlStOtWzebOblWKWICidj6iFq+dUOfkGqf33gLP+Hnh4Bf7dDlL4T9VcTWT8ZUu7wIhLp+JMmm/pnPfMZccsklhn/PmjXLXtn5y1/+MtjbSsorRcX3LFQ59e15w4ktV/VAWKP32fp2olJ9EVs/VEMXeLVP4+uHgF9tyZ/w80OgmNqN3F9FbIsZQ7219REIeX+od1vJLrvsYh555BHDv5988klz5JFHmkcffTTY20paX1qa14OQ5dQHlYYTW5/GpqkrYpsGrU2fDV3g1T6Nrx8CfrUlf8LPD4HWrs3+KmLb2mOo1heHQOj7Q63bSkjSyq0kREvSj0MOOcT84he/CPa2kuJGsW28uYzZu0VsU8iuQpFTgFXwo6FvHGqfnwAIP+Hnh4Bf7dDlz693/rVFbP0x1BvKi0DI60e9bOoHH3ywvY3kk5/8pFmwYIG58sorzZ133hnsbSXllaLiexaynPr0XsQ2BXoitinAKvjR0Cek2ucnAMJP+Pkh4Fc7dPnz651/bRFbfwz1hvIiEOr6kSSb+sUXX2x22mknM3bsWHvWdttttzVXXXWVCfW2kvJKUfE9C1VOfXsuYpsCQRHbFGAV/GjoE1Lt8xMA4Sf8/BDwqx26/Pn1zr+2iK0/hnpDeREIdf1Ikk192bJl5oADDrCD06lTJ3ut5g477BDsbSXllaLiexaqnPr2XMQ2BYIitinAKvjR0Cek2ucnAMJP+Pkh4Fc7dPnz651/bRFbfwz1hvIi0GrrR/y2Eu65feGFF2wCqXbt2m0YqBBvKymvFBXfs1aT06SIiNgmRcoYI2KbAqyCHw19Qqp9fgIg/ISfHwJ+tUOXP7/e+dcWsfXHUG8oLwKttn40Mpv6uHHjyjvwLdazVpPTpPCK2CZFSsQ2BVLFPxr6hFT7/GRA+Ak/PwT8aocuf369868tYuuPod5QXgS0flQe2yJuKymvFBXfs7LKqYhtCtmRxzYFWAU/GvqEVPv8BED4CT8/BPxqhy5/fr3zry1i64+h3lBeBLR+iNi2gnSXVU5FbFNIn4htCrAKfjT0Can2+QmA8BN+fgj41Q5d/vx6519bxNYfQ72hvAho/RCxbQXpLqucitimkD4R2xRgFfxo6BNS7fMTAOEn/PwQ8Ksduvz59c6/toitP4Z6Q3kR0PohYtsK0l1WORWxTSF9IrYpwCr40dAnpNrnJwDCT/j5IeBXO3T58+udf20RW38M9YbyIqD1Q8S2FaS7rHIqYptC+kRsU4BV8KOhT0i1z08AhJ/w80PAr3bo8ufXO//aIrb+GOoN5UVA64eIbStId1nlVMQ2hfSJ2KYAq+BHQ5+Qap+fAAg/4eeHgF/t0OXPr3f+tUVs/THUG8qLgNYPEdtWkO6yyqmIbQrpE7FNAVbBj4Y+IdU+PwEQfsLPDwG/2qHLn1/v/GuL2PpjqDeUFwGtHyK2rSDdZZVTEdsU0idimwKsgh8NfUKqfX4CIPyEnx8CfrVDlz+/3vnXFrH1x1BvKC8CWj9EbFtBussqp4UT23fffdesWbPGdOrUqeo4v/nmm+aDH/zgRn9/7733zNtvv206dOiw0e9r1641W2+9tdlss81qyk0RF0GL2IYzVUOfkGqfn6wIP+Hnh4Bf7dDlz/WumfvrvNU9/EA2xrRrZ8zsicd5v0cvEAIhIdAq60ejMStCL290H8r0vbLKaaHEdtKkSebmm282ffv2NW+88YZBqHv27LlBLh5//HHzhS98wey6665m+fLl5pZbbjH77ruvmTFjhpkyZYrp3r27YeOeOXOmadeunRkxYoTZYost7LNjxowxp5xySlUZK2ICidiGM6VDn5Bqn5+sCD/h54eAX+3Q5Y/eNXt/FbH1kzHVLi8CrbB+NAP9IvTyZvSjLN8sq5wWRmz//ve/m/bt21tvLV7Xr3/962bVqlXmpptu2iATn/nMZ8wll1xi+PesWbMsCf7lL39pttxyS7N69WrTsWNHM3r0aLPzzjtbYotnd8KECeall16yv+G93WabbSrKWBETSMQ2nOkc+oRU+/xkRfgJPz8E/GqHLn8h7K8itn4yptrlRSD09aNZyBehlzerL2X4blnltDBiy6C/9tprZvvttzdvvfWWOfzww835559vhg8fvkEedtllF/PII48Y/v3kk0+aI4880jz66KOmf//+ZtmyZfY5iPDChQstseV36r///vs2FJlndtttNxHbAmZY6AKv9vkNuvATfn4I+NWW/PnhF8L+KmLrP4Z6QzkRCH19axbqIrbNQr7yd8sqp4USW6B84oknzKhRo8xee+1lQ4232mqrDQhvt9125plnnrHeVwA+5JBDzC9+8QszdOhQ+zvltttuM3PnzrWhzMOGDTNDhgyxv3ft2tU89thjpkePHmbevHnm4Ycf3mTkeE+e5blVa8zk2UtMjy7bmguP2SPPV+tdQkAICAEhEBAC1YymATWxqftrLsTWGDPl9D4hQaq2CIFcEGiF9SOXjqZ4iYhtCrAa8KiIbQaQH3jgAXsu9sYbbzTHHbdpgoiDDz7YTJ482Xzyk580CxYsMFdeeaW58847bXgxyaPw0vJ3CmHIEOELLrjArF+/3nqCCVeulkSqiAmkUOQMQlBQldAnpNrnN/DCT/j5IeBXO3T5o3fN3l9zIbZKHuUnqKodJAKtsH40A7gi9PJm9KMs3yyrnBbmsSVcmDOy999/v9lvv/02yAFnbpcsWWL69OljLr74YrPTTjuZsWPH2rO22267rbnqqqtM7969zbRp00yvXr3MgAEDzPjx4826devM1KlTzX333Wfuuusumzhj/vz5VeWriAkkYhvOdA59Qqp9frIi/ISfHwJ+tUOXvxD2VxFbPxlT7fIiEPr60Szki9DLm9WXMny3rHJaGLFdunSp2X333Tca+5NPPtmce+65pl+/fja0mDOyBxxwgH2G64AgqjvssIOZPXu2GTlypP190KBB5o477rDEduDAgWbx4sX2GqA5c+bYbMvVShETSMQ2nKkc+oRU+/xkRfgJPz8E/GqHLn8h7K8itn4yptrlRSD09QPkXWJXIiMrlddff906p6IlxGs4yytFxfesFeQ0CwqFEdtqjSGRFJmOp0+fbh/hOp8XXnjBJpCKTjCeY2Jx/jZaVq5cabp162YzJ9cqIrZZxOH/6oQu8GqfxtcPAb/akj/h54dAMbUbub+K2BYzhnpr6yMQ8v7w8ssvm0WLFpnBgwcbDGRdunTZCPDf//73ZuLEidbZxLNETBJhGeo1nK0vLc3rQchy6oNKw4ktV/VAWKP32fp0oFpdEVs/VEMXeLVP4+uHgF9tyZ/w80OgmNqN3F9FbIsZQ7219REIeX/gak2SrZK/his448SWZK8cHzzrrLMMevTzzz9vnw31Gs7Wl5bm9SBkOfVBpeHE1qexaeqK2KZBa9NnQxd4tU/j64eAX23Jn/DzQ6C1a7O/iti29hiq9cUhEPr+QM+JkKxEbB988EFz7LHHmmOOOcbcfffdNq8Nt5CEeg1ncaNY/je3gpxmGQUR2xSo6YxtCrAKfjT0Can2+QmA8BN+fgj41Q5d/vx6519bxNYfQ72hvAi0wvpRjdheccUVNkErN5nMnDnTnHHGGeaII45IfQ1npdEtwuFUXikqvmetIKdZUBCxTYGaiG0KsAp+NPQJqfb5CYDwE35+CPjVDl3+/HrnX1vE1h9DvaG8CLTC+lGN2OKd/elPf2oTu+K9Pe+88+x1nGmv4Zw3b54NeY6XoUOHlnfgW7BnZbxvWcQ2hSCK2KYAq+BHQ9841D4/ARB+ws8PAb/aocufX+/8a4vY+mOoN5QXgVZYP6LENnoNJ7ePnHTSSWb48OHWY0s48o9+9KNgr+EsrxQV37NWkNMsKIjYpkBNxDYFWAU/GvqEVPv8BED4CT8/BPxqhy5/fr3zry1i64+h3lBeBFph/YDYkvW4c+fO1iPrruH87W9/a0488UTrod1iiy3Md77zHeu9DfUazvJKUfE9awU5zYKCiG0K1ERsU4BV8KOhT0i1z08AhJ/w80PAr3bo8ufXO//aIrb+GOoN5UWg1daP+DVh77//vs2GzDWc0RLiNZzllaLie9ZqcpoUERHbpEgZY0RsU4BV8KOhT0i1z08AhJ/w80PAr3bo8ufXO//aIrb+GOoN5UWg1daPRl4TNm7cuPIOfIv1rNXkNCm8IrZJkRKxTYFU8Y+GPiHVPj8ZEH7Czw8Bv9qhy59f7/xri9j6Y6g3lBcBrR+Vx1ZZkcOS+bLKqYhtCjmTxzYFWAU/GvqEVPv8BED4CT8/BPxqhy5/fr3zry1i64+h3lBeBLR+iNi2gnSXVU5FbFNIn4htCrAKfjT0Can2+QmA8BN+fgj41Q5d/vx6519bxNYfQ72hvAho/RCxbQXpLqucitimkD4R2xRgFfxo6BNS7fMTAOEn/PwQ8Ksduvz59c6/toitP4Z6Q3kR0PohYtsK0l1WORWxTSF9IrYpwCr40dAnpNrnJwDCT/j5IeBXO3T58+udf20RW38M9YbyIqD1Q8S2FaS7rHIqYptC+kRsU4BV8KOhT0i1z08AhJ/w80PAr3bo8ufXO//aIrb+GOoN5UVA64eIbStId1nlVMQ2hfSJ2KYAq+BHQ5+Qap+fAAg/4eeHgF/t0OXPr3f+tUVs/THUG8qLgNYPEdtWkO6yyqmIbQrpE7FNAVbBj4Y+IdU+PwEQfsLPDwG/2qHLn1/v/GuL2PpjqDeUFwGtHyK2rSDdZZXThhDbNWvWmA4dOph27dpVHOvXX3/ddOzYcaO/vffee+btt9+29aJl7dq1ZuuttzabbbZZTbkp4r4sEdtwpmroE1Lt85MV4Sf8/BDwqx26/EV716z9dd7qHn4gG2NQCWZPPM77PXqBEAgJgVZaPxqJWxF6eSPbX7ZvlVVOCyW2L7/8slm0aJEZPHiwWbp0qenSpctGcvH73//eTJw40XTq1Mnw7Pjx402fPn3MjBkzzJQpU0z37t3Nu+++a2bOnGlJ8YgRI8wWW2xhli9fbsaMGWNOOeWUqnJWxAQSsQ1nWoc+IdU+P1kRfsLPDwG/2qHLH71r9v4qYusnY6pdXgRaYf1oBvpF6OXN6EdZvllWOS2U2M6aNcs8/PDDZvLkyWbVqlWbENtRo0aZ/fbbz5x11lkGgX/++efts1tuuaVZvXq19eKOHj3a7LzzzpbYvvnmm2bChAnmpZdesr/hvd1mm20qylgRE0jENpzpHPqEVPv8ZEX4CT8/BPxqhy5/9K7Z+6uIrZ+MqXZ5EWiF9aMZ6BehlzejH2X5ZlnltFBi6wYfUlqJ2D744IPm2GOPNcccc4y5++67zX333We6du1q+vfvb5YtW2ar33TTTWbhwoWW2PL78OHDzfvvv29DkXlmt912E7EtYJaFLvBqn9+gCz/h54eAX23Jnx9+0drN2l9FbPMbQ72pXAiEvr41C20R22YhX/m7ZZXTphLbK664wtx1113muOOOs+HGZ5xxhjniiCPM0KFDzTPPPGNH4rbbbjNz5841b7zxhhk2bJgZMmSI/R0C/Nhjj5kePXqYefPmWc9wvPCePMtzq9aYybOXmB5dtjUXHrNHnq/Wu4SAEBACQiAgBKoZTQNqom1KNWJb9P6aC7E1xkw5vU9okKo9QsAbgVZZP7w7muIFIrYpwGrAoyK2HiBX23ghpz/96U/NAQccYPDennfeeWbBggU2vJjkUdQjNJlCGPJ2221nLrjgArN+/Xqz/fbb23DlakmkiphACkX2EIKcq4Y+IdU+vwEXfsLPDwG/2qHLX7R3zdpfcyG2Sh7lJ6iqHSQCrbR+NBLAIvTyRra/bN8qq5w23GNLBsclS5bYJFEDBw40J510kg0vxmNLOPKPfvQj07t3bzNt2jTTq1cvM2DAAJtUat26dWbq1Kk2XBkv76RJk8z8+fOrylkRE0jENpxpHfqEVPv8ZEX4CT8/BPxqhy5/1YhtI/dXEVs/GVPt8iLQSutHI0ehCL28ke0v27fKKqcNI7ZkcOzcubP1yPbr18+GFv/2t781J554ovXQku34O9/5jvXezp4924wcOdLK0KBBg8wdd9xhiS1EePHixfYaoDlz5pi+ffuK2BY000IXeLXPb+CFn/DzQ8CvtuTPD784sW3G/ipim98Y6k3lQiD09a1ZaIvYNgv5yt8tq5w2hNhGIX3rrbdspuPp06fbn0kERTbkXXbZZSPkeY77bcl+HC0rV6403bp1s5mTa5UiJpA8tuFMytAnpNrnJyvCT/j5IeBXO3T5q9a7Ru6vIrZ+Mqba5UWgVdePokekCL286DaX+f1lldOGE1uu6oGw9uzZs1B5KWICPbPiVXPJN+/Prd3bdWhvPtJlO+/3HfTxj5iBn/o37/dEXxC6wKt9fsMt/ISfHwJ+tSV/fvhVq93I/TVvYvvya2tzAaXL9h1yeY9eIgSyIhD6+ka/OLbQoUMHm8umUnn11VdtXpuoE4ncN0RMUi9auHpz6623rprzxj1bhF6edYxUz5hWkNMs49RwYpulkVnqFDGB8ia2WfpVqc6xn+5pTh20d16vs+8JXeDVPr/hFn7Czw8Bv9qSPz/8ml2b/TVvYnv0pXd6dwsdffbE4+x7Tr32Hu/38YJbxv5HLu/RS9oOAiGvbxxbWLRokRk8eLBZunSp6dKly0YDs3z5cnPCCSfYKEqeJakr13LOmDHDTJkyxXTv3t28++67Ni8OpHjEiBH2KCH1xowZY0455ZSqA12EXt52pCr/noYspz69FbH1QS9j3TfWvmMeXfj0JmHWaV83708rzS8fXWpEbNMiV/zzoS8Yap+fDAg/4eeHQGvXbgVimzdRbu0RU+sbiUDI+8OsWbPs9ZjcOLJq1apNiC3E9KijjrLXcOK1hQQfdNBB1nPLTSQdO3a0xwk5Jgix5caSCRMmGKJF+A3vLXlzKhUR20ZKYf1vhSyn9Vtf/QkRWx/0POrmIVA/m/eMueUXC80xn+5pTpPH1mM08q+ax/jm36r/e6Pa54eu8BN+fgi0dm0R29YeP7W+WARC3x/ofbVrwvbff3/rrf31r39tDj30UHPttdea9u3bm/79+5tly5ZZ4G666SazcOFC+w5+52YT8uVw/SbPVLvDV8S2WLlL+/ZWkNO0fbKy/T7SWMIS+gTKQ6BEbHcLVnLzGN8iO6f2+aEr/ISfHwKtXVvEtrXHT60vFoHQ94daxLZr167mkEMOMdddd50lsHhp8dAOHTrUPPPMMxa42267zcydO9febjJs2DAzZMgQ+zt1H3vsMdOjR4+KAIeulxcrFeG9vRXkNAtqIrZZUMuhTh4CJWIrYptVFPOQv6zfTlJP7UuCUvVnhF+58fPrnX9tEVt/DPWG8iIQ+vpbi9juueee5vrrr7fhyHhfuYLzueees+HFJI/CS0sYM4UwZBJMXXDBBWb9+vVm++23t0QYz+28efNsyHO8QJBVwkGgmnc9nBamb4mIbXrMcqmRx8InYitim1UY85C/rN9OUk/tS4KSiK0fSq2LX1H9TvpeEdukSOm5tohA6PtXnNiSIXnJkiWmT58+5tRTT7We14kTJ5rbb7/d3HvvvdZD27t3bzNt2jTTq1cvM2DAADN+/Hizbt06M3XqVHPfffeZu+66y0yaNMnMnz+/6pDLYxvWbGgFOc2CmIhtFtRyqJOHQInYithmFcU85C/rt5PUU/uSoNS6xEzj6ze+za4tYtvsEdD3Q0Yg9PXNEVuyHnfu3NksWLDA9OvXz4YW0/azzz7bPPXUU+bDH/6wJbP77LOPmT17thk5cqSFfdCgQeaOO+6wxHbgwIFm8eLF9hqgOXPmmL59+4rYhiyckba1gpxmgVLENgtqOdTJQ6BEbEVss4piHvKX9dtJ6ql9SVASsfVDqXXxK6rfSd8rYpsUKT3XFhEIff+Kj8lbb71lz9FOnz59w59eeeUVs9NOO230KM+9/vrrm9zosXLlStOtW7eN7rytNO7y2IY1G1pNTpOiJ2KbFKmcn8tDoERsRWyzimUe8pf120nqqX1JUGpdYqbx9RvfZtcWsW32COj7ISMQ+voWx46reiCsPXv2LBRWEdtC4U398laT06QdFLFNilTOz+UhUCK2IrZZxTIP+cv67ST11L4kKInY+qHUuvgV1e+k7xWxTYqUnmuLCIS+fzVrTERsm4V85e+WVU5FbJskZ3kIlIitiG1W8c1D/rJ+O0k9tS8JSq1LzDS+fuPb7Noits0eAX0/ZARCX9+ahZ2IbbOQF7ENC/mMrQl9AuWx8InYithmnB42QUTIad7Vvqwj+7/1hF+58fPrnX9tEVt/DPWG8iIQ+vrbLORD18ubhUuzvltWOZXHtkkSlYdAidiK2GYV3zzkL+u3k9RT+5KgJI+tH0qti19R/U76XhHbpEjpubaIQOj7V7PGRMS2WcjLYxsW8hlbE/oEymPhE7EVsc04PeTRywrcP+vlMX89m1Czutrnh27o+Pn1zr+2iK0/hnpDeRHQ+lF5bEPXy8srkSK2pRjb0CdQHgufiK2IbdbJmof8Zf12knpqXxKUWtfjqPH1G99m1xaxbfYI6PshIxD6+tYs7ELXy5uFS7O+W1Y5bUgo8po1a0yHDh1Mu3btKo7fq6++arbbbruN7sB677337IXP1IuWtWvXmq233tpsttlmNWUh9AmUh0CJ2IrYZl0Q85C/rN9OUk/tS4KSiK0fSq2LX7Tlzdpf563u4Q0/KsHsicfZ9xx96Z1Bv8+7cXpBm0Eg9P2rWQMRul7eLFya9d2yymmhxPbll182ixYtMoMHDzZLly41Xbp02Wj8li9fbk444QSzyy67GJ4977zzzLHHHmtmzJhhpkyZYrp3727effddM3PmTEuKR4wYYbbYYgtDvTFjxphTTjmlqjyEPoHyEChHbPOYFHdeMdhs037LDa/Ko315tKvaO9Q+P3SFn/DzQ8CvtuTPDz9qN3t/FbH1H0O9oZwIhL6+NQv10PXyZuHSrO+WVU4LJbazZs0yDz/8sJk8ebJZtWrVJsQWYnrUUUeZ4447zuC1hQQfdNBB1nO7evVq07FjRzN69Giz8847W2L75ptvmgkTJhguk+Y3vLfbbLNNRZkIfQLlIVAitvLYZl0Q85C/rN9OUk/tS4JS9WeEX7nxo3fN3l/bGrHNw6PMuP386v/1UKuUF4HQ199mIR+6Xt4sXJr13bLKaaHE1g0WpLQSsd1///2tt/bXv/61OfTQQ821115r2rdvb/r372+WLVtmq990001m4cKFltjy+/Dhw837779vQ5F5ptqVJaFPoFAE6rgrfmLeeucf5t977GQ2i4SKr1u3zmy11VaJ59un/r272e1D2yd+Pv5gp223Mrt0jy5WegAAIABJREFU2S5x/VDwq9ZgtS/xUFZ8UPgJPz8E/GqHLn/R3jVrfxWxzSZjIrbZcGulWq20fjQS19D18kZiEcK3yiqnTSW2Xbt2NYcccoi57rrrLIHFS4uHdujQoeaZZ56x437bbbeZuXPnmjfeeMMMGzbMDBkyxP5O3ccee8z06NHDzJs3z3qG44X3qNRGYNz3njDr/r6+6TDts9sO5pTD/7Xp7VADhIAQEAIgEPI9z0mIbdH7ay7E1hgz5fQ+tjvnf+cP3oJHFo+Q30cHb/hnf8f/cJF3f3nB14Z/3L5n6Ytv5vK+f9v5g7m8p62/pFXWj0aOk4htI9Gu/y0R2/oYVX2imkV5zz33NNdff70NR8b7esABB5jnnnvOhheTPIp6hDFTCEMmwdQFF1xg1q9fb7bffntLhKslkQp9AoUiUIufe8Wsf++9TcbuxRdftOHe9cpji//HLHvhtXqPVf37G2vfMStefsMc9PGPmLHH75/4PaHgV63Bal/ioaz4oPATfn4I+NUOXf6SENui99dciG0LJY/KOxQ5j/cVmXzLbwa17dqttH40cqRC18sbiUUI3yqrnDbcY0sGxyVLlpg+ffqYU0891XpeJ06caG6//XZz7733Wg9t7969zbRp00yvXr3MgAEDzPjx4w2hsVOnTjX33Xefueuuu8ykSZPM/Pnzq8pG6BModIFqVPt+t2iFufYH882nP76LGXf8AYnneqPal7hBsQfVvqzI/W894Sf8/BDwqx26/FUjto3cX0Vss8mYC0UWsc2GXyvUaqX1o5F4hq6XNxKLEL5VVjltGLElg2Pnzp3NggULTL9+/WxoMaCeffbZ5qmnnjIf/vCHLZndZ599zOzZs83IkSPtuA8aNMjccccdltgOHDjQLF682F4DNGfOHNO3b18R24JmR6MEXsS2oAGs89pGjW/W3ql9WZGTYcAPudbAL05sm7G/ithmkzQR22y4tVKt0PevZmEpYtss5Ct/t6xy2hBiG4X0rbfesudop0+fvuHnV155xey0004bIc9zr7/++ibhsCtXrjTdunXb6M7bSkMW+gQKXaAa1T4R2+YsdI0a36y9U/uyItcaxEzj6ze+1Wo3cn8Vsc02hiK22XBrpVqhr2/NwjJ0vbxZuDTru2WV04YTW67qgbD27Nmz0LEMfQKFLlCNap+IbaHToOrLGzW+WXun9mVFTsTWD7nWwK9aHxu5v4rYZpM0EdtsuLVSrdD3r2ZhGbpe3ixcmvXdssppw4ltowYw9AkUukA1qn0ito2aERt/p1Hjm7V3al9W5FqDmGl8/ca32bXZX0Vss42CiG023FqpVujrW7OwDF0vbxYuzfpuWeVUxLZJEhW6QDWqfSK2zRHARo1v1t6pfVmRE7H1Q6418Mujjz7vELHNjp6IbXbsWqVm6PsXOJJorkOHDvb2kWrlhRdeMB/60Ic2/JnbSshxQ71oWbt2rdl6662r3lLinhWxDUuCW0FOsyAmYpsFtRzqhC5QjWqfiG0OwpThFY0a3wxNs1XUvqzItQYx0/j6jW+za4vYZh8BEdvs2LVKzZDXNxLNLVq0yAwePNgsXbrUdOnSpSKs99xzjzn66KM3XL05Y8YMM2XKFNO9e3fz7rvvmpkzZ1pSPGLECLPFFluY5cuXmzFjxphTTjml6jCJ2IYlwSHLqQ9SqYjtb37zG3PYYYfZK3eeffZZc8IJJ5hOnTr5fL+wuqFPoNAFqlHtE7EtbArUfHGjxjdr79S+rMiJ2Poh1xr45dFHn3eI2GZHT8Q2O3atUjPk/WvWrFnm4YcfNpMnTzarVq2qSGyfe+45c/nll9sbSfDSrl+/3iZsXb16tenYsaNNALvzzjtbYvvmm2+aCRMm/P/2rgTcyqpqLwSUeR4FFFHBVMSJMGcNQ0HUjElEwzHNVLLQyL8UDSwrwCCs1NAITElNzCxwQkHEIREVRUVBUAFBJrnM8D/vpgP3wj337G/t73zfOue++3l8Lt671/7Wftfaw7uHtQX3+/E77N7WqlWrXFNZn5cXin/FpadlPw2pozexvfHGG+XXv/61zJo1a+czO0cffbS8/vrrId/Pm6z1BmTdoZLSj8Q2b02AxDaP0CbVPrRVoH5a5EhsfZAjsfVBqfw8JLZ67ApF0nr/CxxBSssjths3bnRPaz7wwAPSpk0bR2xBdLt27Srz5893Jhg9erTMnj3blYHf9+vXT7Zv3+6OIiNPu3btSGwLwFkLwU81MHoRWzgsntjp06ePO19/3333uZUcHEH4/PPP3d+sJRLbMIsk5fAktmF20konZV/qp0UgTI72LW78wmoXLk1iq8eQxFaPXaFIWu9/KyK2119/vRx55JFyySWXOOIKYvvWW29J7969Zd68ec4E48ePl2nTpsmaNWscL+jVq5f7ffPmzd3mV9u2bUlsC8BZC8FPNTB6EVus4NSoUUMeffRRueqqq+Tggw+WsWPHSqdOneS9997L+9M9moqR2GpQ2yWTlMOT2IbZSSudlH2pnxaBMDnat7jxC6tduDSJrR5DEls9doUiab3/zUZscYy4Tp060rlzZwf1q6++6v49depUd+0QJBdkF8eYkXAMuV69ejJo0CB3XLlhw4buuDJ2bqdPn+6OPO+eQJCZ7CCQbXfdjobRNfEitii2b9++8vDDD7sv3HPPPXLHHXfIpk2bZNGiRdG/moAEiW0YyEl1zCS2YXbSSidlX+qnRSBMjvYtbvzCahcuTWKrx5DEVo9doUha7393J7aIkPzuu+/Kscce6wJKZVL79u3d5hV+YhcXG1odO3aUbt26ydChQ2XDhg0yZswYF3dn0qRJMmLECJk5c2ZWM1mflxeKf8WlZyH4qaau3sQWkdTuvvtut1rz4x//2B1TuOaaa+Tkk0/WfDfvMtYbkHWHSko/Etu8N4VyP5CUfbW1o35a5HbIEb/ixi+sduHSJLZ6DEls9dgViqT1/jdDbDGvb9q0qbz22mty+umnu6PFpVPmKDJ+Tp48WQYMGOD+3KNHD3cdEcQW93Hnzp3rriliZ7dLly4ktgXiqIXgpxoovYktCscFcqzGYOu6cePGctBBB2m+mYgMiW0YzEk5PIltmJ200knZl/ppEQiTo32LG7+w2oVLk9jqMSSx1WNXKJLW+9/dcSwpKXGRju+9994KIUa+1atXu+jHpRNObiLWDiInV5Ssz8sLxb/i0rPQ/NS33t7E9oknnpBzzjnHlTtkyBB3fv6oo46Su+66y/dbieaz3oCsO1RS+pHYJtosdn4sKftqa0f9tMjtkCN+xY1fWO3CpUls9RiS2OqxKxRJ6/3v7jjiqR4Q1g4dOuQVYuvz8rxW3mDhheanvhB6E1uE/cZDzrhAjqMGeJD59ttvl08//VT23Xdf3+8lls96A7LuUEnpR2KbWJMo86Gk7KutHfXTIkdiG4ZcYeAXRx1DyiCx1aNHYqvHrlAkrY9faeFofV6eFi5pfbdY/dSL2GaiIv/+97+XhQsXStWqVV3ob7xj+/bbb8thhx2Wll2yftd6A7LuUEnpR2KbTtNJyr7a2lE/LXKFQcxo3zD7pi1NYqu3AImtHrtCkbTev6WFo/V5eVq4pPXdYvVTL2IL0EFely9f7s7RY7cWl85r1aq1812rtAyT7bvWG5B1h0pKPxLbdFpOUvbV1o76aZEjsQ1DLh388KwG3n5EIBckBGV55ZVX3OmoffbZJ44qxVoGia0eThJbPXaFIpnU+FWI/cZNN91UKGYsej2T8tOkgfQmtm+88Ybccsstgru2mfT444/vvHebtOK5vkdimwuhiv+elMOT2IbZSSudlH2pnxaBMDnatzDww9Ma8+bNk0svvdT9d+KJJzrFP/jgA/nhD3/oAjbuv//+YZXJgzSJrR5UEls9doUime/+t5D7DRJbO16cbz9Nq6bexDaj4MqVK2Xx4sVy4IEHuh1bn4Q3smrXru2eCsqWPvvsszJ3dfEQNMKHQ650wgPSNWvWdA9AV5RIbH0skz1PUg5PYhtmJ610UvalfloEwuRo38LA784775RsE726desKxltc/akopTW+Tl/VNgxkEcGUYPLwvq6cnkMeKvryUEES22Azmy8g3/1vHP1GGiBan5engUma38y3n6ZVN29i+81vftMdRd49vfTSS3uQz0weHFeeM2eOnH/++e7RZwSfKi/985//lJ49ewrILMjvuHHjZNSoUdKqVSvZsmWLTJgwwf2+f//+7hg07vkOHjxYBg4cmBU36w3IukMlpR+JbTpNPyn7amtH/bTI7ZAjfoWBX7adF2iP6z84npwtpT2+ktjqfIzEVodbIUnlu/8N6TfSxNH6vDxNbNL4dr79NI064ZvexPa0005z92qR8Igzdm1BVD/++OOsO7ePPPKIzJgxQ0aOHClLly4tl9jiqNXNN9/sHnsGsd26dat7C2vVqlVSv35997YW3swCsV27dq0MGzZMEJocv8PubbZdY+sNyLpDJaUfiW06TT8p+2prR/20yJHYhiGXDn44sYSTSNihLZ1AbLOdTkp7fCWx1Xkaia0Ot0KSSmr80vQbaeJofV6eJjZpfDspP026bt7EdnfFbrvtNslESa5Ro0aFeoOUlkdsEW25e/fu8sADDwieEwKxBdHt2rWrzJ8/35U5evRomT17tiO2+H2/fv1k+/btbrBHnnbt2pX7besNyLpDJaUfiW3STT6diXvUWiblf1H1yuSnflrk6H/lIXf33Xe7I8lYvC2d8LZkvXr1TI6vJLa6NkBiq8OtkKSSGh9C+o008LQ+L08DkzS/mZSfJl1Hb2KLXVIQTyTsqv7hD3+Q4cOHuyAXBx10kGrgvf766+XII4+USy65xBFXlP/WW2+5p4QQUANp/PjxMm3aNLdL3KdPH+nVq5f7ffPmzWXWrFnuqNb06dPdzvDuCeUw2UbgjY++lPufmS9HtWskA795oG1lqR0RIAKVBoFsi6ZxA4CFWrw2gISxcO+99975CZxmyhUVOdvCcb7H11iIrYiMuqKzq+/197waDC2ieFguDxW8q0DqG2yMSl5AvvuP0H4jDfOQ2KaBevZvVnpiCyKZOYqcgQnBLVasWOGODleUyht4cYy4Tp060rnzjkENYcvx76lTp0qDBg123rfFMWYkrGRj5XrQoEGOWDds2NAdV852TMt6A7LuUEnpxx3bdDq6pOyrrR310yK3Q474FQ5+mQnqD37wA/nZz34WWfG0xtdYiC2DR0W2d2mBfAbfClKskgsn0f+G9htpmMj6vDwNTNL8ZhJ+mkb9vHdscfQY0ReRQCb3228/Oeuss+SAAw7IqXfpgRdlvPvuu3Lssce6gFKZ1L59e3nvvfcEP7GLO3bsWOnYsaN069ZNhg4d6t71GzNmjEyZMkUmTZokI0aMkJkzZ2b9tvUGZN2hktKPxDZn88lLhqTsq1We+mmRI7ENQy4d/C688EL3lB7GLSzaZhJOKCFgYkUprfGVxFbnaTyKrMOtkKSSGr9C+o008LQ+L08DkzS/mZSfJl3HnMQWFcfKULYEYpvr6R0MvNjtxePzr732mpx++unuaHHplDmKjJ+TJ0+WAQMGuD/36NHDBZYCscV93Llz57pngLCzi8frsyXrDci6QyWl3/S3FsmvJr4kRxzYTPqdfpi3/3/++ecugFiT+rWkZeM63nJJZUwKP219qJ8WuXSIT1Rtad+oiJXNnzR+5Z2Igka+d2zTGF9JbHU+RmKrw62QpJLqP0L6jTTwtD4vTwOTNL+ZlJ8mXcecxLait2d9B97SlSopKXGRju+9994K64p8GNRBXkqnRYsWuftIuY4/W29A1h0qKf0yxFbr+Oec0F6uOPsorXje5JLCT1sB6qdFjsQ2DDniVx5+L7/8siCY4u7phBNOyLljm9b4SmKrawmFQmzf+mjHKxihqWO78p95DC3XsnxS42tc/UZSWFqflyeFg5XvJOWnSdc3J7H9zW9+4+60Zks//OEPywS7yFUBBKECYe3QoUOurEF/t96ArDtUUvph8Jz49NuRbb10xRr5Ys1GIbGNDJ0TSMq+Ou2onxa3jBztG4Zg0vjhik15xBbXfXIdRS5d0yTHVxJbnY8VCrHtOeQhXQVLSZW+AxxcWAEVkFT/EVe/kRS01uflSeFg5TtJ+WnS9c1JbEsrhIEXpBQJx5NxR/brX/96zt3TpCuF71lvQNYdyrp+4ybPlEdnfiI9j28vV/bkjm3UNmbdvtQvqkXL5id+hYVfIR4pJLHV+RiJrQ63QpJKqv8txH4Dz5ox2UAgKT9NurbexPahhx5yR4h3j4zscwco6UqR2IYjbt3hSWzDbGzdvtSP9g1DIEw6af976qmndu7Y4sWAO++80+3U4km7KDu2YbX2l8bCMYmtP16lc5LY6nArJKmk+o9C7DdIbO14clJ+mnSNvYntgQce6J7hwbu1xx13nIts3KxZM8EZ/1z3XZOuFIltOOLWHZ7ENszG1u1L/WjfMATCpNP2Pywk9+vXTz799FPZd999wyqTB2kSWz2oJLZ67ApFMq3+oxD6DRJbO16clp/mGwEvYrt582Z3j/avf/2rvPDCC9KkSRO56qqr3JM/K1eudITXWuJR5DCLWHd4Etvitq91/6N+9L8wBMpKDxkyZOdLAdu2bXPP2sHHli5d6haQrSUSW71FSGz12BWKZFLjQ0i/gac3a9euLdkCxK5YsUIaN25cBnL0TXiVBHKlE06Z1KxZM+cLKdbn5YXiX3HpmZSfxqWvbzlexBaFtWnTxpHbq6++WvCm7Xe+8x25//775f3335eDDz7Y93uJ5bPegKw7lHX9SGzDmpJ1+1I/2jcMgTDppP1v97tydevWle9///vyy1/+MqwieZImsdUDS2Krx65QJJPqPzT9Bq4TzpkzR84//3wXJ2f3hbNnn33WzfOPPvpoAWG9/PLL5ZxzzpFx48bJqFGjpFWrVrJlyxaZMGGCI8X9+/d31yUWLlwogwcPloEDB2Y1k/V5eaH4V1x6JuWncenrW443sYWzIwLy+PHj3duyixcvlp49e7o3Zy0m6w3IukNZ1+/+J2bKIy99Iscf3lrO/ka0hZUae1eTg1s3yqvbWseP+oWZn/gRvzAEykqvWrVq53vxmCxaPAVVWmMSW731SWz12BWKZFLjg6bfeOSRR2TGjBkycuTIck+EnHbaaXLzzTdL165d3QnNK664Qt555x135RDfq1+/vou3g6c40VetXbtWhg0bJojIjt+BDNeqVatcU1mflxeKf8WlZ1J+Gpe+vuV4E9t77rlHunXr5o4fY7UGqz4W7/5kKm69AVl3KOv6ZYitr6OXzrdf8/ry+0FnakS9ZazjR/28TVluRuJH/MIQKCuNySEWjSdOnOgmirhf+93vftfsGEtiq7d+ZSW2z7z+sR60UpLfPOaAWMrJZyFJjQ8h/QZIaXlXHVAmiGnVqlXlhhtucEePb7zxRkd058+f72AbPXq0zJ492xFb/B79FV5K2WuvvVyedu3akdjm08FiKjspP41JXe9ivIlt5sjD6aefLhdddJGcd955pleVSWy9faAgJ+6PPfu6vPLhjqenfNPGzVvlg8VfCokt34n19Zls+awPCNQvzMJJ44e7cjh2jCPI2BHBiahDDz1U3nzzTUZFjmDK0u+mxv0OaxzloSqVldjGgV+hvIubVP8R0m9kI7bwURDea665xgWLxalMvH7Su3dvmTdvnmuNWISbNm2aiwvQp08f6dWrl/s9eAIiubdt25bENkK/lVbWpPw06fp5E9vnnntOnnzySXnsscdcUAskrNLgnu0+++yTtN45v0dimxOiCjNYd3iNfguXrpYfjPo3ia2Q2Ia1DuJH/EIR2CWPo3t16tRxR/7+8Ic/uF0P3GXD1R+8PnDIIYfE97GYSuKOrR5IEls9diS28fUb2YjtJ598IjiOfPHFFwsiGNeoUcPt2mIXF8GjIIdjzEjY3a1Xr54MGjRItm7dKg0bNnTHldGHTZ8+3R153j2BIDPZQSDb7rodDaNr4k1sM0VjxWbMmDHuPyS+YxsddEhoiJnuSzqpYtSPxHaXLxSjfXWerpMifjrcMlLEbxd+JSUlLsrorbfeKrfccov7w6RJk9xOyGuvvSbHHHNMGNh5kCax1YNKYqvHjsQ2vn6jNLFFhGQsonXu3FkuuOAC9xPHkEunTp06ydixY6Vjx47uWuLQoUNlw4YNjgsgijv6rBEjRsjMmTOzGtj6hpPeMwtT0vo4rEXVm9jiyAOe+8ERKSRETLvkkkvcsz9WH5C3/F6WdYcqRv1IbElstR3l7nLF2D7iwsanHOJXFqUTTzzR7W6cddZZbmcEAV4wucSxvmzPcfjgnK88JLZ6ZEls9diR2MbXb6BfQaycpk2bugU0XDPE0eLdIy0jajKOJuNI8oABA5wCCCCLeAAgtt27d5e5c+e6Xd2pU6dKly5dSGz1Lp6opPVxWAuGN7GFs+O5H4T+xkry1772Ne03E5GzvjJk3aGKUT8SWxLbuDqfYmwfcWHjUw7xK4sSnsoYPny4PPjgg+54H8ZYRCY94ogjfOBMPA+JrR5yEls9diS2+ek3cGoEkY7vvffeCo2DfDiliejHpdOiRYukRYsWLnJyRcn6vFzvmYUpaX0c1qLqTWzfeOMNwVEEnJ0vhGS9AVl3qGLUj8SWxDauvqsY20dc2PiUQ/z2RAm7HjiSjJgVuKdm8W5tRmsSWx8vLz8Pia0eOxLb/PQbeKoHhLVDhw5643hIWp+Xe1ShqLJYH4e1YHsTW+0H0pKz3oCsO1Qx6kdiS2IbV39UjO0jLmx8yiF+ZVHCPbVrr71WnnrqKfek3mGHHeae2MA4ZjGR2OqtQmKrx47EtvD7DctXBPWeWZiS1sdhLaoktlrkAuWsO1Qx6kdiS2Ib2Gx3ihdj+4gLG59yiF9ZlLBTgrtuf//7392uLZ7awJMan376qcm3bElsfbycO7b5fH5Jb4H8SybVvxViv0Fim3//8/1CUn7qq09c+SoktgjfjSd+EMTiv//9rzuK3Lp168jfRsQ1DNbZgmCsWLFCGjduXKZchBXHZXTIlU54GqFmzZo5j0RzxzaymcoIWHd4jX4ktiS2Ya2C+BG/uBDYVc6WLVvc3TQEYrz77rvdHyZMmOACtSDC6HHHHVfhR9MaX6evKv+tyigI5ZP4xP1uahzlARvu2EbxkLJ5uWMbX7+ht4Je0vq8XF+zwpTUzKMLoaYVEtuNGze6N6yuvPJK+cc//uEG2t0H2W9/+9tZoyIj4tqcOXPk/PPPlw8//FAQXa10evbZZ+Xqq692EZZBWBGY6pxzzpFx48a5d/xatWolGPQxyIMU9+/f330LgTYGDx4sAwcOzIqx9QZk3aGKUT8SWxKzuDrlYmwfcWHjUw7xK4vSySefLC+++KKce+65UrduXXn88celfv36btzM9k582uMria2Pp++Zh8RWhxukSGzD+w09+uGS1ufl4TUsrBKsj8NaNHMeRcYbetitzZYqescWTxbgCQM85oxw4bsTWzwCjciPXbt2lRdeeME9UP/OO++41WsEz8DAjkhtiMAGYotokcOGDRNcdMfvQIbxNEJ5yXoDsu5QxahfhtjWr72PdD/uoEht5twTO0jtGhVH/CtdYDHiFwmwwMzELwxA4ldY+H388cdunHzooYfcExx4TgOLt6ecckrWiqQ9vpLY6nyMxFaHG4ntnrhp+g09+uGS1ufl4TUsrBKszxO0aOYktjjmBGLbu3dvd+/npJNOKvMt/H+ud2xLPwRdWhhEFcS0atWq7jFoHD1GwAwQ3fnz57uso0ePltmzZztii9/369dPtm/f7o4iI0+7du1IbLXWr0DOusNr9MsQWw1cf76ppzRtUP4iSnnlafTT6KWVoX5a5HbIET/iF4ZA+dLZruBU9K20xlcSW50HkNjqcNud2D74zDs5C8JcMVfq3/XwXFki/z3p8UHTb0SuVAwCJLYxgBhjEUn7aYyqV1hUTmKbkf7ss8+kTp068uqrr7qdUpDMbLulu38x28CLfNjJBWH+4IMP3APQ2AEGiZ43b54rBgE0pk2b5h6Oxtt+vXr1cr/Hu7p4vL5t27Yyffp0tzO8e0I5TEQgg8BX6zfLi3OXRQJk2ttLZf2mrXLrBZ2kYZ29I8kyMxEgAoWLQLZFU2s1Smt8jYXYisioKzo7SK+/59VgaKsYLw8VvKsS1de6PUrrF+x8uxVQKP1H3PWuqDwS2yTRzv2tSk9sQS579uzpjgNnEu7BXn/99TnRyzbwfvLJJ4LjyBdffLEgUhru82LXFoQZK1CQw/EsJHy3Xr16MmjQIEFQq4YNG7rjytne1bXegKw7FPXb4daX/uoJ+WJVidx309nSrEHZQGYVOT7xy9ktVJiB+BG/MATCpK37X+napTW+xkJsq4hMHt7XVSeO4EzWg1Ghntyx1bfNfNpXr9WekoXUf8RZ71xlWZ+X59K/2P5erH7qtWMLkonIyLj783//93+OeOKIMHZvly9fvkdE492NX3rgxdHmd99915V3wQUXuJ84hlw6Ifry2LFjpWPHjtKtWzcZOnSobNiwQfDW35QpU2TSpEkyYsQIFzEyW7LegKw7FPUjsU2zE6f/haFP/AoPPxybXLRokVvgbdSoUc4rPpkapjW+ktjqfIzEVocbpEhs98RO22/oraCXtD4v19esMCWtzxO0qHoRWzzH06RJE/nd737nHpFHevrpp+WMM86Ql19+Wbp06VLh9zHwghTjnb7XXntNTj/9dHe0GMeJ8ftMQnApHE3GkWREYEZCEI2JEyc6Ytu9e3eZO3eu29WdOnVqhd+13oCsOxT12+GVl/3qn7Js1To594T2UitC8Kjj2tXKev9b21jjlKN9w9AkfsQvDIGy0riKc+aZZ7q720OGDJG3337bvQKAmBK5UlrjK4ltLsuU/3cSWx0oMTTEAAAgAElEQVRuJLZ74hbSb+itoJe0Pi/X16wwJa3PY7SoehFbrAghQvFhhx3mjgZjxxbRiR9++GEXoRgE1TeVlJS4SMf33ntvhSLIh/u2iH5cOmFFu0WLFi5yckXJegOy7lDUryyx9fXvTD7co7J8x4b2jWrRsvmJH/ELQ6CsNF4fwCQVi799+/aVxYsXu/gSmdcBfL+V5PhKYutrlbL5SGx1uOWb2MYZjCqp8SGufkNvkWiS1ufl0WpT+LmT8tOkkfIitlDqrrvucvdbSyccS7799tsj6QwiDMLaoUOHSHJRM1tvQNYdivrt8LjHZ7wv69Zv8na/zOBIYusNWbkZ6X/ELwyBMOkk/S/zXjyu2iDuBF4JQOBDvO+OFwmOOuoo78okOb6S2HqbpUxGElsdbvkmtnHe8U6i/4iz39BbJJqk9Xl5tNoUfu4k/DQNlLyJLZTDoPvYY4+5Y8Tnn3++28G1mqw3IOsORf10nn3OTx8SjxcGyhTe+ZB95effLfuMlu7r/lK0rz9W5eUkfsQvDIGy0giMeMQRR0iDBg1k7733dkEREU8C14BynU6KUw/fsjC+ktj6olU2H4mtDjcS2z1xK8R+A4FimWwgYH0eo0UpErHVfiQNORLbMNStO7xV/Uhsw/wuI23VvtSP9o0HgbKlPPjgg/K9732vzKsDOA2FU1EWE4mt3ioktnrs8hk8qtB2bIFiIfYbJLZ6/49b0vo8S1tfElstcoFy1h2K+oUZ2Ae/V979TG7/y4vCHds9sfbBL8xCYdLUj/iFIbCnNI4WPvfcc/Lxxx8LXgY4/vjj4/5EbOWR2OqhJLHVY0diW/j9Bomt3v/jlrQ+j9HWl8RWi1ygnHWHon5hBvbBj8Q2O8Y++IVZKEya+hG/MATKSt92223uis/u6Re/+IV7/sdaIrHVW4TEVo8diW3h9xsktnr/j1vS+jxGW19vYnvxxRfL5ZdfLieffLL7FqI2XnTRRfK3v/0tUlRkraJR5XgUOSpiZfNbd/hi0I/ElsQ2rJUSv2LBb/en7zL1QqBF3KOzlkhs9RYhsdVjR2JbFrtC7DdIbPX+H7ek9Xm0tr45ie39998vo0ePdtEZW7duLXhrFgnvz4LcIrgFHpO3lkhswyxi3eGLQT8SWxKzsFZK/IoFP4yj27Ztc9XBkz2DBw+WBQsWyMyZM12UZGuJxFZvERJbPXYktmWxC+k3vvrqK6ldu7bgHezy0tq1a6Vu3bpl/oQ+av369U6udFq3bp3UrFnTBb2rKFmfl+s9szAlrc+jtajmJLZ/+ctf3FM/uxNbfLBr164CR7WYrDcg6w5F/cK82gc/ElsSszAvI37Fih/esMUpqQ8//FAOPPDAfFVTXS6JrRo6IbHVY0diWzF2Pv0GNqXmzJnjXjZB/5LZrMqU/Prrr8ull14q+++/vyxcuFDuu+8+OfbYY2XcuHEyatQoadWqlWzZskUmTJjgSHH//v2lWrVqLi8W5AYOHJhVSevzcr1nFqakzzy1EGuWk9hmKvXzn/9czj33XMGD0IWQrDcg6w5F/cK83Ac/ElsSszAvI37Fgt+JJ54on3/++c7qoP9Awq5JnTp18lVNdbkktmroSGz10AmJbVnwNP3GI488IjNmzJCRI0fK0qVL9yC23/rWt+THP/6x4Cfy/vGPf5R//etf7tmxVatWSf369eW6666Tli1bOmKLPmrYsGGCN7TxO+ze1qpVq1wrW5+XB7hmQYr6zFMLsWLexPbFF1+U3/72t+5oVOk0f/58swOv5bP81h2K+oU1Zx/8SGxJzMK8jPgVC36YRGKSiYTdD+zSYsf27LPPzlcVg8olsdXDxx1bPXYktmWxC+k3QErLI7Zt2rSRl156SfDzjTfekDPPPFNefvlld0IT830kXE+cPXu2I7b4fb9+/WT79u3uKDLytGvXjsRW7+aJSfrMUxNTJsYPeRPbww47TObOnStHH320e0A+k5555pmsqzMx6hm5KOsrQ9YdivpFdrkyAj74kdiSmIV5GfGrrPjlq96+5ZLY+iK1Zz4SWz12JLZ67HaXzEZsEaxu3rx5bvcV85hTTjlFnnzySendu7f7PRKOPE+bNs1Fcu/Tp4/06tXL/R7BrGbNmiVt27YlsY3PVHkryWeemreP57FgL2K7detWt4p8yy23yK233ppHdeIrmsQ2DEvrDl8M+r363mdy2wMvytf2byIXfaujl8GaN6wtzRqWDdzgJbhbpmLAT1PvuGSIXxiSxG8HftgV2bRpU1YwLZ+Imr6q/MlrFM8oFKKCOvUc8lCUqmXNS2Krh7FQ/CXf/Vsc/UY2YouXT3BMGdcOX3vtNcFTZA899JDbwELwKMjh70g4hgwiPGjQIAFPaNiwoTuujJ3b6dOnuyPPuycQZCY7CGTbXbejYXRNvIgtir3kkkvklVdecc/7wHkzCRfJs0VVi65OfBIktmFY5rtjDtNO3Eqi5Qbpo1+G2EbB4oJvHib9ux4eRaTcvD76BX8koADqFwCeFEf7CEMgTDop/8NRYwRiyZYeffRRsyeiSGx1PkZiq8MNUiS2O7CLo98oTWwRIfndd9+Vzp07y49+9CNp0qSJ3Hjjje6uLe7433777dKpUycZO3asdOzYUbp16yZDhw6VDRs2yJgxY2TKlCkyadIkGTFixB7XFUtb2/q8XO+ZhSmZ1DiXNDrexJbvZcVrGusORf3C7O2D33ufrJAH/v2m14eWrSyRZavWCYmtF1x5z+Rj37wrUcEHqF8Y+hbwwzE/TCpzPaERVlOdNI8i63CDFImtHjsS29zY+fYbILaIkNy0aVO3M3v66ae7o8U4JXL88ce7DzVo0MARVTzpOXnyZBkwYID7fY8ePWTixImO2Hbv3t1dU8QzQFOnTpUuXbpkVZLENrf9ksxhYZzLR329iS3CfOPYwe4Jqzr77LNPPnQLKtN6A7LuUNQvyP1i31Ge+PTb8uAz75DYhpklNmm2jzAoiV9Z/N555x23A4KfSNjFRZRkvBWPo37WEomt3iIktnrsSGzz02/g7WxEOr733nt39j+fffaZuypR+kQm8q1evdrdvy2dFi1aJC1atHCRkytK1ufles8sTEnr47AWVW9iu3LlShf1bPeElRyLyXoDsu5Q1C/Mq+PGj8Q2zB5xS8dtX+oXNwJh5SVt369//evy6quvOqXxriR2UnDV4u2335aaNWuGVSYP0iS2elBJbPXYkdiWxS6ufgNP9YCwdujQQW8cD0nr83KPKhRVlqTHuaTA8ya2IUeRcX6/du3aWe/iYie4bt26ZeqMS+o42gC50glvZGGgz3U8y3oDsu5Q1C+sCcaNX4bYfvPotvLNYw7IqVz1alXlkP0aZ80Xt345FYqYgfpFBGy37MSvcPDbvHmze2ngwQcflMcee0yOPfZYdxTwrLPOkuXLl5d5haC8WqU1vvKOrc7HSGx1uEGKxHYXdqH9ht4Keknr83J9zQpT0vo8QYuqN7FFROTMUWQMpAgihWMK//3vf7MeRcaq85w5c+T888+XDz/8cI+HoF9//XW59NJLZf/995eFCxfKfffd5wb1cePGCY4+IzAVjmRNmDDBkeL+/fu76MzIO3jwYBk4cGDWeltvQNYdivppm9QOubjxyxBbX60a16sp9w85h8TWF7CI+eK2b8TP58xO/XJCVGGGpPHDcWOQWfyHCKQI2oKxETu2eGqvvJT2+Epiq/MxElsdbiS2e+Km6Tf06IdLWp+Xh9ewsEpIepxLCh1vYru7QiCfGHi//PLLMlGSS+d75JFHXLhvhAYv7yFoPC6NARw/kfePf/yj/Otf/3Ln9BEyvH79+u7cP87zg9iCWA8bNkxwbAK/w+4tQpCXl6w3IOsORf3CmmDc+D3z+sfy9Osf51Rqy9ZtgqBUJLY5oQrKELd9g5QpR5j6hSGaFH54IqNq1apujLzhhhvk2WefdUFckHAk+dNPP3WLueWltMdXEludj5HY6nAjsd2FW0i/oUc/XNL6vDy8hoVVQlLjXNKoeBPbJ554wkVAQ0Kj+utf/+oebf7444+zPsacqUy297Kw4/vSSy+5nd833nhDzjzzTHn55Zela9euLjIb0ujRo2X27NmO2OL3/fr1c3d9cRQZebI9+WK9AVl3KOoX1hTTwm/FmvUy8I7J0qheTXmAO7ZhRqxAOi37+laI+vkiVX6+pPDDFZ++ffsK3nY8+OCD3TMb77//vgsihQVfLO7mSmmNryS2uSxT/t9JbHW4kdjuwi2OfkNvBb2k9Xm5vmaFKZnUOJc0Ot7Etrw7tieccIK8+OKLOd+xzTbw4hjFvHnz3O4rAD7llFMcWcYgj98jjR8/XqZNm+bCkPfp00d69erlfg99Zs2a5Ug1H4JO2m34PasIrF63SX4+8U2pX6u63HbhkVbVpF5EwDwCSbyTjUVdRD5Gwg7tRRdd5MY4BIXJFUciA2Ba42ssxFZERl3R2VXl+nt2BM8KSVWMl4e63VWJ6mvdHvnUL5/9Rxz9Rkg708qS2GqRy49cpSe2Tz/99M4dWwykaFiHHHJIzsAWMEe2gffkk092R7COOeYY947Wbbfd5u4X4XgxgkdBDn9HwjFkEOFBgwa5HeOGDRu648rZBn/rDci6Q1G/sI4kLfy4YxtmN1/ptOxL/XwRCMuXlH1x+uitt96S//znP4JTUVgoRmrdurUjuXgCKNcTGmmNr7EQ2yoik4f3dXXuOeShMKMVQHAhVJA7tnozM3jUDuzi6Df0VtBLWp+X62tWmJJJjXNJo+O9YwvFEAjq0Ucfdbun3/nOd+S4445z94NypdIDLwJPvfvuu9K5c2f50Y9+5I5e4S1c3LXFY/S33367dOrUScaOHSsdO3aUbt26ucEdx6DHjBkjU6ZMkUmTJsmIESPcw9HZkvUGZN2hqF8ur67472nhR2IbZjdf6bTsS/18EQjLl5Z9EZMCcSUQlBEJT3Dkesc2rfGVxFbnYyS2OtwgRWJbPnaafkNvBb2k9Xm5vmaFKZnWOJdvtLyJLR5uvuKKK8rogyPDDz/8cE4dMfAigmPTpk3dziyCY4Ac444sokAiNWjQwBFVvIs7efJkGTBggPt9jx49ZOLEiY7Ydu/eXebOneueAZo6dap06dKFxDYn+roM1h2e+pVvVxJbnb9HlaL/RUWsbH7itwMPLPTiqg12bPHUT+ZY8qGHHioXXnihW/TNFjwqg2ha4yuJra4NkNjqcCOx3YVbHP2G3gp6SRJbPXb5kLQ+Dmvr7EVsN23a5I4eY7DFrinekcXO6v333++e3tlvv/28v19SUuJWpEGUkfCcz2effebKxwCdSciH1Wrcvy2dFi1aJC1atMh5PMt6A7LuUNTP26XLzZgWfiS2YXbzlU7LvtTPF4GwfEnZt/SY1759e0dmcRoq2xM/uWqV5PhKYpvLGuX/ncRWhxuJ7S7c4u439BaJJml9Xh6tNoWfO6lxLmmkvIgt7rLiTivuu+KOKxKOBOOYMI5AZHZdfZTHUz0grB06dPDJrs5jvQFZdyjqp3Y9J5gWfiS2YXbzlU7LvtTPF4GwfEnZF+PgBRdc4N56P+KII8KUFnFP4SU1vpLY6sxFYqvDjcR2F25x9xt6i0STtD4vj1abws+d1DiXNFJexBZKYUcVO7e4C4vgTn/605/cILpgwQK3g2stWW9A1h2K+oV5dFr4kdiG2c1XOi37Uj9fBMLyWbdvWO3CpTG+ktjqcCSx1eFGYqvHzYqk9Xm5FZyS0qNYxzlvYvv888/LZZdd5naikOrWrSt//vOfdz6/k5QhfL9jvQFZdyjq5+tp5edLCz8S2zC7+UqnZV/q54tAWD7r9g2rXbg0ia0eQxJbPXYMHqXHzoKk9Xm5BYyS1KFYxzlvYguwEWL8jTfecAEvcPw4V1CLJA20+7esNyDrDkX9wrw3LfxIbMPs5iudln2pny8CYfms2zesduHSJLZ6DEls9diR2OqxsyBpfV5uAaMkdSjWcc6L2I4ePdpFI7777rsd5j/5yU9cZONvfetbSdog0resNyDrDkX9IrnbHpnTwo/ENsxuvtJp2Zf6+SIQls+6fcNqFy5NYqvHkMRWjx2JrR47C5LW5+UWMEpSh2Id53IS2wcffFD69+/viOwzzzzjMMezO0899ZRcddVVO8luksbw+Zb1BmTdoaifj5dlz5MWfiS2YXbzlU7LvtTPF4GwfNbtG1a7cGkSWz2GJLZ67Ehs9dhZkLQ+L7eAUZI6FOs4VyGx3bx5szRu3NgFjsIbs5mH4tetWydnn3224N7tp59+Kvvuu2+StvD6lvUGZN2hqJ+Xm2XNlBZ+JLZhdvOVTsu+1M8XgbB81u0bVrtwaRJbPYYktnrsSGz12FmQtD4vt4BRkjoU6zhXIbFF1GO8I/uzn/1MbrvttjJ4jxs3Ti699FJ58cUX5cQTT0zSFl7fst6ArDsU9fNyMxLbMJjM4edbHbYPX6TKz0f8wvBLW5rEVm8BEls9diS2euwsSFqfl1vAKEkdrI/DWiwqJLZbt251AaJat24t8+bNc8/8IG3ZssUdR546daosXLhQ9ttvP+338yZnvQFZdyjqF+aaaeHHHdswu/lKp2Vf6ueLQFg+6/YNq124NImtHkMSWz12JLZ67KJK4j3s+vXrlxHbtm2brF+/XmrXrl3m9zjFiWc/99prrwo/Y31eHhWjQs9frONczju2eLf2t7/9rbPfWWed5Z75wf3atWvXSrdu3eTf//63Sdtab0DWHYr6hbl1WviR2IbZzVc6LftSP18EwvJZt29Y7cKlSWz1GJLY6rEjsdVj5yv5yiuvyPDhw6VBgwaybNkyGTp0qHTu3FlwSnPUqFHSqlUrt7k1YcIEqVKliovBgw0wbHINHjxYBg4cmPVT1uflvhgVS75iHedyEtsNGzbIHXfcscdR5IsuukhGjBghTZo0MWlj6w3IukNRvzC3Tgs/Etswu/lKp2Vf6ueLQFg+6/YNq124NImtHkMSWz12JLZ67HwlL7nkEvn6178uV199taCdL168WEaOHCnVq1eXVatWuV3c6667zl1TBLHFJtewYcMkc3URu7eZ0527f9P6vNwXo2LJV6zjXE5imzHgxo0bZcGCBYKfbdu23RlIyqqBrTcg6w5F/cI8Oy38SGzD7OYrnZZ9qZ8vAmH5rNs3rHbh0iS2egxJbPXYkdjqsfOVfPbZZ+W8886Tc889Vx5//HGZMmWKNG/eXLp27Srz5893xeAJ0NmzZztii9/369dPtm/f7o4iI0+7du3K/Zz1ebkvRsWSr1jHOW9iW2iGtN6ArDsU9Qvz+LTwI7ENs5uvdFr2pX6+CITls27fsNqFS5PY6jEksdVjR2Krx85X8tZbb5VJkyZJ37593XHjK6+8Us444wzp3bu3i7WDNH78eJk2bZqsWbNG+vTpI7169XK/BwGeNWuW2/yaPn26zJgxY4/PohwmOwhkW4Swo2F0TUhso2MWi4T1iRP1CzNzWviR2IbZzVc6LftSP18EwvJZt29Y7cKlSWz1GJLY6rEjsdVj5ysJcvrYY4/J8ccfL9i9vfbaa+W1115zx4sRPAq7tDiajIRjyHgGdNCgQYJgsw0bNnTHlbMFkbK+4eSLUbHkK9ZxjsQ2JQ+17lDUL8wx0sKPxDbMbr7SadmX+vkiEJbPun3DahcuTWKrx5DEVo8dia0eO19JvHhy8cUXu+PF2LHFceSHH35YOnXqJGPHjpWOHTu6wLEIKoUYPGPGjHHHlbHLi7g7M2fOzPopEltfKySTr1jHORLbZPxnj69YdyjqF+YYaeFHYhtmN1/ptOxL/XwRCMtn3b5htQuXJrHVY0hiq8eOxFaPna/k888/LwgOix1aRDu+55573O7t5MmTZcCAAa6YHj16yMSJEx2xBRGeO3euewYIT4B26dKFxNYX7JTzFes4Z4LYVsb3sqw7FPUL63HSwo/ENsxuvtJp2Zf6+SIQls+6faPULl/j6/RVbaOoUW7eQiEqUL7nkIeC64sCSGz1MBaKvxR6/4FAUIiG3KZNmzLGKikpEfQniIhcOi1atEhatGjhIidXlLhjq/f9fEgWup9mwyRVYluZ38uy7lDUL6wbSQs/Etswu/lKp2Vf6ueLQFg+6/b1qV2+x1cSWx8r7JmHxFaHG6RIbPXYWZAksbVghV06FMM4Vx6iqRLbyvxelnWHon5hHVBa+JHYhtnNVzot+1I/XwTC8lm3r0/t8j2+ktj6WIHEdvLwvg6EOHa8SWx1PmdFisTWiiV26FEM45w5YluZ38uy7lDUL6wDSgs/Etswu/lKp2Vf6ueLQFg+6/b1qV2+x1cSWx8rkNiS2Or8pBilSGxtWbUYxjlzxJbvZdlycmpT+AisXrdJfj7xTalfq7rcduGRhV8h1oAIpIRAob/vl+/xNRZiKyKjrujsLHz9Pa8GW7qK8fJQwbsqUX2t2yOf+hV6/xHcGMspgMQ2H6jqyySx1WOXVbIyv5dl3aGoX5jDp4Ufd2zD7OYrnZZ9qZ8vAmH5rNvXp3b5Hl9jIbZVRCrTjh7sxju2Pt5bfh4eRdZjZ0GSxNaCFXbpUAzjnLkd28r8XpZ1h6J+YR1QWviR2IbZzVc6LftSP18EwvJZt69P7fI9vpLY+lhhzzwktjrcIEViq8fOgiSJrQUrkNjm1QqV+b0s6xMn6hfm+mnhR2IbZjdf6bTsS/18EQjLZ92+PrXL9/hKYutjBRLbyrQjnyHexdB/6Ly7YikS23ygqi+zWP001ajIMEdlfS/LukNRP31nAcm08Pty7Xr57vDJTvnDD2iatRJtm+wj3zv/hLBK5lE6Lfx8q0T9fJEqPx/xC8PPVzqf4yuJra8Vyubjjq0ON0hxx1aPnQVJElsLVtilg/VxWItW6sRWq3guOesNyLpDUb9cHlbx39PCrzSxrUjDzgc3lp9f2jWsknmUTgs/3ypRP1+kSGzDkLIpjfGVxFZnGxJbHW4ktnrcrEhan5dbwSkpPazPY7Q4kNhqkQuUs+5Q1C/MwGni99ZHy7IqP/vDpfLwc3OFxLZw7eujeZr+R/18ECjsPCS2evuR2Oqx446tHjsLkiS2FqywSwfr8wQtWiS2WuQC5aw7FPULM7BV/J57Y4GMeHiWHHtQY7nlMu7Yaq1s1b6Z+lA/rWV3yFnHL6x24dIktnoMSWz12JHY6rGzIElia8EKJLa2rBBBG+sNyPrEifpFcLZyslrFj8Q2zK4kjsQvHgQKuxQSW739SGz12JHY6rGzIGl9Xm4BoyR1sDpPDcWAO7ahCCrlrTsU9VMa9n9iVvEjsQ2zK4kt8YsHgcIuhcRWbz8SWz12JLZ67CxIkthasAJ3bG1ZIYI21huQVeLDiXsEJ6sgq1X7ktgWt33ZfiuHfeOppb4UEls9diS2euxIbPXYWZC0Pi+3gFGSOlidp4ZiwB3bUASV8tYdivopDcsd2zDgjONH4hiLec3fYbXe/8VjBX0pJLZ67Ehs9diR2OqxsyBJYmvBCrt0KNZxjsQ2JT+z7lDUL8wxrOLHHdswu5LYEr94ECjsUkhs9fYjsdVjR2Krx86CJImtBSuQ2NqyQgRtrDcgq8SHE/cITlZBVqv2JbEtbvuy/VYO+8ZTS30pJLZ67Ehs9diR2OqxsyBpfV5uAaMkdbA6Tw3FgDu2oQgq5a07FPVTGvZ/YlbxI7ENsyuJI/GLB4HCLoXEVm8/Els9diS2euwsSJLYWrACd2xtWSGCNtYbkFXiw4l7BCfjjm08YJVTCttHGLTEr7jxC6tduDSJrR5DEls9diS2euyiSq5YsULq1asn1atX3ym6bds2Wb9+vdSuXbtMcevWrZOaNWvKXnvtVeFnrM/Lo2JU6PmtzxO0+HLHVotcoJx1h6J+YQa2ih93bMPsyoUf4hcPAoVdComt3n4ktnrsSGz12PlKLly4UC688EJp06aNLFu2TK699lo577zzZNy4cTJq1Chp1aqVbNmyRSZMmCBVqlSR/v37S7Vq1QRygwcPloEDB2b9FImtrxWSyWd1nhpaexLbUASV8tYdivopDfs/Mav4kdiG2ZXElvjFg0Bhl0Jiq7cfia0eOxJbPXa+kiCmZ511lvTt21ewaztnzhw56aST3M7tqlWrpH79+nLddddJy5YtHbFdu3atDBs2TJYsWeJ+h93bWrVqlfs5EltfKySTz+o8NbT2JLahCCrlrTsU9VMalsQ2DDjj+JHYxmJePvcTD4yplUJiq4eexFaPHYmtHjtfyW984xtut/bf//63nHrqqXLnnXfKPvvsI127dpX58+e7YkaPHi2zZ892xBa/79evn2zfvt0dRUaedu3akdj6Ap5iPuvzfC00JLZa5ALlrDsU9QszsFX8nntjoYx4+GXZt1EtOeWoA8qt5LkndpDaNXbdqwlDQidtFT8SW509d5eifePBMa1SSGz1yJPY6rEjsdVj5yvZvHlzOeWUU+TXv/61I7DYpcUObe/evWXevHmumPHjx8u0adNkzZo10qdPH+nVq5f7PWRnzZolbdu2lenTp8uMGTP2+CzKYbKDQLZFCDsaRteExDY6ZrFIcGIXBiPx0+GXIbYVSd9349nSrGHZ4BC6r+mlaF89dpAkfsWNX1jtwqVJbPUYktjqsSOx1WPnK3nYYYfJb37zG3ccGbuvxx9/vCxYsMAdL0bwKOzSjhw50hWHY8gIMDVo0CDZunWrNGzY0BHhbEGkeBTZ1wrJ5LM+T9CiYILYVsboa9Ydivppm9QOOav4LViySl56e7GsXLnSDUKl0+QZ78u6DZuFxDa37a3aN6M59cttw4pyWMcvSu3yNb5OX9U2ihrl5i0UogLlew55KLi+KIDEVg9jofhLIfcfl112mdt5HT58uPz1r3+V//znP26HtlOnTjJ27Fjp2LGjdOvWTYYOHSobNmyQMWPGyJQpU2TSpEkyYsQImTlzZpaa47oAACAASURBVFYDk9jqfT8fkoXspxXhkSqxrczR16w7FPUL60YKEb/L7/ynLF25Tu698Wxpzh3bCh2gEO0b5tHxShO/ePEsr7R8j68ktjobktjqcIMUia0eO19J9M3f//735Z133pHWrVs7MnvUUUfJ5MmTZcCAAa6YHj16yMSJEx2x7d69u8ydO9c9AzR16lTp0qULia0v2Cnnsz4Oa+FJldhW5uhr1h2K+mmb1A65QsSPxNbf5oVoX//a5T8n8cs/xvkeX0lsdTYksdXhRmKrx00juXz5cmnSpEkZ0ZKSElm9erWLflw6LVq0SFq0aFHmzdvyvskdW40l8idjfRzW1jxVYluZo69Zdyjqp21ShU9ss9V82OWnyhEHNg8DxlOa/ucJVJZsxK+48fOpXb7HVxJbHyvsmYfEVocbia0eNyuSJLZWLFEY81QtWqkSW0Zf05qNckQgfgSG/m2OfLl2Y9aCr+nRQdrvWy/+D7NEImAQgUKPFpnv8TUWYisio67o7Kx//T2vBntBFePloYJ3VaL6WrdHPvUr9P4juDGWUwCJbT5Q1ZdpfQFcW7NUiW1ljr5m3aGon7ZJFcZKWBT73nzvczJn/jLhju0un4iCX5gn6aSpnw63jJR1/Hxql+/xNRZiW0Vk8vC+rjpxBGeyfgcT9eSOrY/3lp/Hun0z+hVD/6G3UnZJEtt8oKovs1j9NFViW5mjr1l3KOqn7ywgWUz4kdju6QvFZN8wT9dJEz8dblGk8j2+kthGscauvCS2OtwgRWKrx86CJImtBSsUzgK9Fq1UiW1ljr7GiZ3WZXfIEb/k8COxJbEN8zbiFzd+PuXle3wlsfWxwp55SGx1uJHY6nGzIklia8UShTGP1qKVKrHNKF0Zo6+RmGldtjAaZDHZl8SWxCystRK/uPGLUl6+xlcS2yhW2JWXxFaHG4mtHjcrkiS2VixRGPNoLVomiK1W+YrkrDegYiI++bBfrjKJXy6EKv57FPxIbEnMwryN+MWNX9rlYXwlsdVZgcRWhxuJrR43K5LW5+VWcEpKjyjzwKR0iuM7JLZxoKgow7pDUT+FUUuJFBN+JLYkZmGtgfjFjV/a5ZHY6i1AYqvHjnds9dhZkCSxtWCFXTpYn6dq0SKx1SIXKGfdoahfmIGLCb8MsS0PkSMPai63X3ZqGFjlSBcTfrGD41Eg8fMAqYIs1vELq124NImtHkMSWz12JLZ67CxIkthasAKJrS0rRNDGegOyPnGifhGcrciJGYktdxzDWgPxixu/tMsjsdVbgMRWjx2JrR47C5LW5+UWMEpSB+vzfC0W3LHVIhcoZ92hqF+YgYsdv9kfLJGf/XmacMc2zE/yJV3s/pcv3DLlWscv3/XPVT6JbS6Esv+dxFaPHYmtHjsLkiS2FqzAHVtbVoigjfUGZH3iRP0iOFuR79iWhwSJ7UfSrl27MCfJozTbbxi41vELq124NImtHkMSWz12JLZ67CxIWp+XW8AoSR2KdZzjjm2SXlTqW9YdivqFOUax40diS2Ib0kKKvX2EYFMIsiS2eiuR2OqxI7HVY2dBksTWghW4Y2vLChG0sd6AOLGLYMxKuCMahk5u6VD/I7Elsc3tZdlzhPpfyLd9ZK3r51OHfOYhsdWjS2Krx47EVo+dBUnr83ILGCWpQ7GOc9yxTdKLuGMbG9rWG2Sx60diS2Ib0piLvX2EYFMIsiS2eiuR2OqxI7HVY2dBksTWghW4Y2vLChG0sd6AOLGLYEzu2IaBlQf8SGxJbEOckv1fCHrpy5LY6m1AYqvHjsRWj50FSevzcgsYJamD9XFYiwV3bLXIBcpZdyjqF2bgYsePxJbENqSFFHv7CMGmEGRJbPVWIrHVY0diq8fOgiSJrQUrcMfWlhUiaGO9AXFiF8GYedhxDPt6bulity+JLYlt7laQPUext48QbApBlsRWbyUSWz12JLZ67CxIWp+XW8AoSR2sj8NaLLhjq0UuUM66Q1G/MAMXO36zP1wqP7vveWnRqI6cdtT+ZcA6/vA20rZF/SAAix2/IHA8hImfB0gVZLGOX1jtwqVJbPUYktjqsSOx1WOnkfzss89k33333Sm6bds2Wb9+vdSuXbtMcevWrZOaNWvKXnvtVeFnSGw1VsifTLGOcyS2+fOZCku27lDUL8wxih2/DLEtD6W+px8qnQ5svvNPe1erKh32axwJ0GLHLxIYiszETwFaKRHr+IXVLlyaxFaPIYmtHjsSWz12USX/+c9/Ss+ePQVktkqVKjJu3DgZNWqUtGrVSrZs2SITJkxwv+/fv79Uq1ZNFi5cKIMHD5aBAwdm/RSJbVQr5Dd/sY5zJLb59ZuspVt3KOoX5hjFjt+SL7+SZ/+7oAxIM99ZLAuWrN4DuGYNast9N50dCdBixy8SGIrMxE8BGomtN2gktt5Q7ZGRxFaPHYmtHrsokgsWLJCbb75ZJk6c6Ijt1q1bpXr16rJq1SqpX7++XHfdddKyZUtHbNeuXSvDhg2TJUuWuN9h97ZWrVrlfo7ENooV8p/X+jxBiwCJrRa5QDnrDkX9wgxcGfF7+Lm58sYHS3YCt3nrNpn3yQohsQ3zJY10ZfQ/DU7ZZKzjF2ddNWWR2GpQ2yFDYqvHjsRWj52v5MaNG6V79+7ywAMPSJs2bRyxBdHt2rWrzJ8/3xUzevRomT17tiO2+H2/fv1k+/bt7igy8rRr147E1hfwFPMV6zhnhthWtrP81h2K+oX1NsRPZNmqdXLZr/4pTRvUkj/f1DMSoMQvElx7ZCZ+xY1f1NrlY3ydvqptVDX2yF8oRAWK9xzyUHB9SWzDICwUf7He/1Zkheuvv16OPPJIueSSSxxxBbF96623pHfv3jJv3jwnOn78eJk2bZqsWbNG+vTpI7169XK/b968ucyaNUvatm0r06dPlxkzZuzxKZTDZAeBbIsQdjSMrokJYlsZz/Jb7/ioX/TGVFqC+JHYhnlQmDT9r7jxi1K7fI2vJLZRrLArL3dsdbhBisRWj52PJI4R16lTRzp37uyyv/rqq+7fU6dOlQYNGuy8bzty5Ej3dxxDrlevngwaNMgdV27YsKE7rpwtiBSPIvtYIbk81ucJWiRSJ7aV9Sy/dYeiftomtUOO+JHYhnlQmDT9r7jx861dPsdXEltfK5TNR2Krw43EVo+brySOE3/44Yc7s7dv317ee+89wU/s4o4dO1Y6duwo3bp1k6FDh8qGDRtkzJgxMmXKFJk0aZKMGDFCZs6cmfVzJLa+lkgmn/V5ghaFVIltZT7Lb92hqJ+2SZHYZpD7YlWJXPqrJ6R2jepyzgntdwK6z97V5DsnH1IhwPQ/+l8YAmHS1v3Pp3b5Hl9JbH2ssGceElsdbiS2ety0kpmjyPg5efJkGTBggCuqR48eLrAUiC3u486dO9c9A4Sd3S5dupDYagFPWK4YxrnyIEuV2PIsf8JezM8RgQQRWPnVJrn1wTf3+GLtGtVk+EVHJagJP0UEoiNQ6HeP8j2+xkJsRWTUFTuOPV5/z6vRjbSbRBXj5UHduypRfa3bI5/6FXr/UV5jLCkpkdWrV7vox6XTokWLpEWLFi5yckWJO7bBXVysBZDYxgqnuJDglfksv3WHon5hDk/8RNZt2CyPT98RbAJp4+at8ugL78ne1auW2bFtWLemnNXlwDKAEz/6XxgCYdLW/S9X7ZIYX2MhtlVEJg/v66oTR3Am63cwUU/u2Oby3ux/t27fjH6F3n/oLVSxJIltvpDVlVusfprajm1lP8tv3aGon66jyEgRvz3xW7Nuo1z4i3/s8YcDWjaQ313XjcQ2zOWIXyXCL1dVkxhfSWxzWaH8v5PY6nCDFImtHjsLkiS2FqywSwfr81QtWqkR290Vrmxn+a07FPXTNqkdcsRvT/ywY/vItHd3/mHlVxvk37PmC4ltmK+VJ03/C8PUOn5Ra5eP8ZXENqoVduQnsdXhRmKrx82KJImtFUsUxjxVi5YZYrt7BYr9LL/1iRP10zapwugwLNj3o89XyfW/+w+JbZirlSttwb4VVYv65cHoEYqMY3wlsY0AeKmsJLY63Ehs9bhZkSSxtWKJwpinatEyS2y1FcrIWW9AnNiFWZj4FT5+GWK7e02uPvcYOaTZXmI5+Ab9r/D9r5CJdxj64dIYX0lsdTiS2OpwI7HV42ZF0vq83ApOSelhfR6jxYHEVotcoJx1h6J+YQYmfrnxy0ZsS0viqaC/3XJ+7sISzkH7hgFO/MLwS1uaxFZvARJbPXa8Y6vHzoIkia0FK+zSwfo4rEWLxFaLXKCcdYeifmEGJn7R8bv78dflXy/vehweJZDYRscREvQ/HW4ZKev4hdUuXJrEVo8hia0eOxJbPXYWJElsLViBxNaWFSJoY70BWZ84Ub8IzlZOVuIXht87730gP3ngvyS2Shjpf0rg/idmHb+w2oVLk9jqMSSx1WNHYqvHzoKk9Xm5BYyS1KFYxznu2CbpRaW+Zd2hqF+YYxC/MPxIbMPwo/8VN35htQuXJrHVY0hiq8eOxFaPnQVJElsLVuCOrS0rRNDGegPixDOCMbkjGgZWAeKXIbZQ/fADmu6swU8HnCh1a+0dOx5RC2T7jYpY2fzELwy/tKVJbPUWILHVY0diq8fOgqT1ebkFjJLUwfo4rMWCO7Za5ALlrDsU9QszMPELw680sS1d0gM/PUca1a0ZVngM0rRvGIjELwy/tKVJbPUWILHVY0diq8fOgiSJrQUr7NLB+jisRYvEVotcoJx1h6J+YQYmfuH4rZM6Owu5Y8IMWVuySUhs/XCl//nhlC2XdfzCahcuTWKrx5DEVo8dia0eOwuSJLYWrEBia8sKEbSx3oCsT5yoXwRnKycr8YsXv+8Of1y+XLtBHhhyjjSqxx3bXOjS/3IhVPHfreMXVrtwaRJbPYYktnrsSGz12FmQtD4vt4BRkjoU6zjHHdskvajUt6w7FPULcwziFy9+GWKbKXXv6lXlkdt6hX0kQJr2DQCPzxGFgWdAmsRWbwQSWz12JLZ67CxIkthasAJ3bG1ZIYI21hsQJ8YRjMkd0TCwigA/EttoLsD+JRpeu+e2jl9Y7cKlSWz1GJLY6rEjsdVjZ0HS+rzcAkZJ6lCs4xx3bJP0olLfsu5Q1C/MMYhffvDbuHmr9Pr534U7thXjS//Lj/+FlVo80iS2eluS2OqxI7HVY2dBksTWghW4Y2vLChG0sd6AOPGMYMwi2HEMq2380oXqfyS2fr5QqPb1q13+c1nHL/8IVPwFElu9BUhs9diR2OqxsyBpfV5uAaMkdSjWcY47tkl6EXdsY0PbeoOkfmGmzoYfia0frvQ/P5yy5bKOX1jtwqVJbPUYktjqsSOx1WNnQZLE1oIVuGNrywoRtLHegKxPnKhfBGfjjnIYWBHwI7H1g5rt1w8nElsdTiS2OtwgRWKrx47EVo9dVMkVK1ZI48aNy4ht27ZN1q9fL7Vr1y7z+3Xr1knNmjVlr732qvAz1uflUTEq9PzW5wlafLljq0UuUM66Q1G/MAMTv/zgt2nzVvnOz//uCj/8gKbu54BvdZTD2u74d1KJ9g1DmviF4Ze2NImt3gIktnrsSGz12PlKPvvss3L11VfL0UcfLSCsl19+uZxzzjkybtw4GTVqlLRq1Uq2bNkiEyZMkCpVqkj//v2lWrVqsnDhQhk8eLAMHDgw66dIbH2tkEw+6+OwFgUTxLYyrgxZdyjqp21SO+SIX37wK01sM1846Yj9pHXTutJhv8ZyTPuWYR/2lKZ9PYHKko34heEXRTpf4+v0VW2jqFFu3kIhKlC+55CHguuLAkhs9TAWir9Y798qssBpp50mN998s3Tt2lVeeOEFueKKK+Sdd96R6tWry6pVq6R+/fpy3XXXScuWLR2xXbt2rQwbNkyWLFnifgcyXKtWrXI/QWKr9/18SBayn1aER6rEtjKvDFl3KOoX1o0Qv/zh99ZHy1zhE59+W97++IudHzr+8NZy9jcOlrq19pa2LRqEKZBDmvYNg5f4heHnI53v8ZXE1scKe+YhsdXhBikSWz12vpIgqiCmVatWlRtuuMEdPb7xxhsd0Z0/f74rZvTo0TJ79mxHbPH7fv36yfbt291RZORp164dia0v4Cnmsz4Oa6FJldhW5pUh6w5F/bRNaocc8cs/fi/O+UQWLVsj7y/+Ul6f9/nODx7dvoUMveSUMAVIbIlflslZXoGJsfB8j68ktjpjkdjqcCOx1eMWVXLp0qVyzTXXyAcffCCTJ0+W1atXS+/evWXevHmuqPHjx8u0adNkzZo10qdPH+nVq5f7ffPmzWXWrFnStm1bmT59usyYMWOPT6McJjsIZFuEsKNhdE1SJbaVeWWIxCe6s5aWIH7EL4PAS+8slidmvC9frd8sC5askpaN68ipR+4vLRvXldOO2j8MqCzS9L8wWIlfGH4+0vkeX0lsfaywZx4SWx1uJLZ63KJIfvLJJ4JFsYsvvlhuuukmqVGjhtu1xS4ugkdhl3bkyJGuSPQx9erVk0GDBsnWrVulYcOG7rhytiBSPIocxRL5z2t9HNYikCqxhdJcGdKajnJEgAiURuC9xavl7qfe3wOUI9o2lMvOOIhgEYFICBTDSnY+x9dYiK2IjLqis7PL9fe8Gsk+5WWuYrw86HxXJaqvdXvkU79C7T8uuOAC6dy5szuGXDp16tRJxo4dKx07dpRu3brJ0KFDZcOGDTJmzBiZMmWKTJo0SUaMGCEzZ87M2o5JbIO7uFgLILGNFc4dhVXmlSHrDkX9whye+CWP3+crvpLn3lggS75c535mEiImX3jG4dK8YW1p1rDsMwVaLWlfLXI75IhfGH4+0vkeX2MhtlVEJg/v66oTR3Am63cwUU/u2Pp4b/l5rNs3o5/1/q0iC+A48bJlO+JYIDVr1sxtQOFI8oABA9zvevToIRMnTnTEtnv37jJ37ly3qzt16lTp0qULia3exROVLGQ/rQioVHdsK/PKkHWHon5h/QvxSx+/l+d+KsPGT99Dkf2b13cBpr62fxOpVnXHu3uN6taUVk3ryn7N6kn9OjVyKk/75oSowgzELww/H+l8j68ktj5W2DMPia0ON0iR2Oqxi0OypKTE3bdF9OPSadGiRdKiRQsXObmixB3bOKwQXxnWx2FtTVMltpV5Zci6Q1E/bZPaIUf80sdv7oLlMn7KHFm2skSWrVrnpRAiK4P4ItWrtY/s36J+uWSX9vWCM2sm4heGn490vsdXElsfK5DYVqYd+WLYsdV5tZ8Uia0fTknlsj4Oa3FIldhWpHSxrwxZdyjqp21SJLZhyOUPv4VLV8uadRvl3YXLZcvWbe5DK9dukMVfrJFFX6yR1V9tLFf1Ns3qScO6NQRHmhvXryX7Nq4j1beskUPa2723y/Yb5oXW8QurnUgc4yuJrc4K3LHV4QYp7tjqsbMgSWJrwQq7dCjWcc4ssQ01v/UGZN2hqF+YBxK/wsIPkZUXfL7KKb22ZJOLrlwR2T3p0GbSumVTl//g1o3c3d3MTm9YzeORpv+F4Wgdv7DahUtjfCWx1eFIYqvDjcRWj5sVSevzcis4JaVHsY5zJLZJedBu37HuUNQvzDGIX+HjhzdyV361Qd7+aJl8uXaDfPrFGvlg8ZeycfPWrJXrftxBgnd0u3ytVRgAgdL0vzAAreMXVrtwaRJbPYYktnrsuGOrx86CJImtBSvs0qFYxzkS25T8zLpDUb8wxyB+xYnfkzM/lNXrNsjKlStlRYnIspXrBEecs6XOh+wrnQ5qLu1aNnBZWjWpK43q1QwDx0Oa/ucBUgVZrOMXVrtwaRJbPYYktnrsSGz12FmQJLG1YAUSW1tWiKCN9QZkfeJE/SI4WzlZiV/lwu+pWfPlv+9/LojEnCvVqbm3tG1RX+648vRcWdV/p/+poXOC1vELq124NImtHkMSWz12JLZ67CxIWp+XW8AoSR2KdZzjjm2SXlTqW9YdivqFOQbxq9z4vfreZ/Lmh0tl/mcrHRCfrfhKvlyzvgwohx/QVL7V+UDpdFAz99xQnIn+F4amdfzCahcuTWKrx5DEVo8dia0eOwuSJLYWrLBLh2Id50hsU/Iz6w5F/cIcg/gRv90R+Gr9Jvn481Xy03ueK/Onb5/UQc4+/mBp1qB2GGhcOKs0+MVWUWVBJLZK4ESExFaPHYmtHjsLkiS2FqxAYmvLChG0sd6ASHwiGLOcrMSP+IUhECYd4n9vf/yFTHn1I3nzwyUuKFUmNalfS1o0qi0N6taQNk3r7fx97RrVpWO7ZtJu34beSofo5/2RgIzULwA8A6IktnojkNjqsSOx1WNnQdL6vNwCRknqYH0c1mLBHVstcoFy1h2K+oUZmPgRv1wI/GP6PJk84335YlVJrqzu7yC4B/wvCBXIb6cDm8tRB7eQ5g333Oml/3lBmjWTdfzCahcuTWKrx5DEVo8dia0eOwuSJLYWrLBLh2Id50hsU/Iz6w5F/cIcg/gRP18Elq8ukc9XfCWrvtogeGIok0o2bJa3PlomH/3vfd1s5bVsXEca16vp3tNtWLeGfPukQ8wHP2L78PUOm/lIbPV2IbHVY0diq8fOgiSJrQUrkNjaskIEbaw3IE7sIhiznKzEj/iFIRAmnaT/rduwWT76XxCq1V9tlNnzl8rsD5fI0i/XlVsJBKVq3bC6DOx5nNvltZiSxE9Tf+v6aeoUpwyJrR5NEls9diS2euwsSFqfl1vAKEkdinWc445tkl5U6lvWHYr6hTkG8SN+YQjklv5s+VpZsWa9fPjpSlm5dr089uK8PYRAcpE6tGkse1ev6u7pguy2bdFA6tbaO/dH8pSD7SNPwCZULImtHmgSWz12JLZ67CxIkthasMIuHayPw1q0SGy1yAXKWXco6hdmYOJH/MIQiC6NY8vvfPyF4O4udnkrSvs3r++IbY29q7kjzIe2bSq19qku7ds0iv5hhQTbhwI0QyIktnpjkNjqsSOx1WNnQZLE1oIVSGxtWSGCNtYbECd2EYxZTlbiR/zCEAiTtu5/U2a8KS1btpT3F38pGzdtcc8M4bmhhUtXy9qSTRVWHru89WvXkP2a15O2LRtI3Zp7y37N6kn9OjXCQCslbR0/6/rFZghlQSS2SuD43I8eOBEhsQ2CL3Vh6/Py1AFKWIFiHee4Y5uwI2U+Z92hqF+YYxA/4heGQJh0Nv9bsATEdqNs3LxV3l+0Qt5duNzt7n6w+EuvD4Lg7tukrovOjOeJELgKO7842hwlsX1EQcteXhJbvU24Y6vHjsRWj50FSRJbC1bYpYP1cViLFomtFrlAOesORf3CDEz8iF8YAmHSGv/D+7qr122QhUtWu//WlGyURV+sEQSs8kmtmtaVxvVqyWFtm7jsB7VqJDX3qeb+3bppPRexmQt7Pkjaz0Niq7cRia0eOxJbPXYWJElsLViBxNaWFSJoY70BaSaeEaofnJX6hUFI/IhfGAJh0nH73ydLV7snieZ/ttIFrELgqq/Wb5YFS1ZFVhQkt0a17VK/Xh1p33rXnV7c8626VxUX3ApBrtJMceOXZl3y8W0SWz2qJLZ67Ehs9dhZkLQ+L7eAUZI6FOs4V3A7tuvWrZOaNWvKXnvtVaH9rTcg6w5F/cK6F+JH/MIQCJNO0v8+/QLRmUsEO75IIL94gxfp0+VrZeXaDerKNGtQW5o1rOXkMxGd8e+a+1SXg1rtIr+N6tYU7BjHlZLELy6d4ygnyvg6fVXb4E8WClFBRXsOeSi4viiAxFYPY6H4S2XrP6L0GzfddJPeASgZKwLF6qcFQ2yXL18u/fv3l2rVqsnChQtl8ODBMnDgwKxGJrEN83/rDk/9aN8wBMKk6X/R8Vu0bI2s+mqDbNq8VV6e86E0bLiDmOKe75at26Rk45ad7/VGL32XRL3a+7hgV5nUtEFtadGo9s7//9r+TaRa1b2kUb2a0qpJ+WTYun1D8ClPVjO+ktjqrEBiq8MNUiS2euzyIanpN0hs82EJXZnFOs4VDLH95S9/KWvXrpVhw4bJkiVLXMRPrBLVqrVjNX/3RGKrc/SMlHWHp360bxgCYdL0v/zit2zVOln65Tr3kUxEZ/x7w6YtZQJdYTcYu8IhqWmDWtK8Ye2du8BtmtWTBtU2SMdD24cUW1CymvGVxFZnYhJbHW4ktnrc8iWp6TdIbPNljejlWp/HRK/RDomCIbaXX365dO3aVfr16yfbt293R5Hnz58v7dq1I7HVWr8COesOT/3CjE78iF8YAmHS+fC/Nes2uueMMmn56hJ3LziT3vtkhWzeslW+XLvB3RGuKF3Z7WDpeerRYZUsIGnN+EpiqzMwia0ONxJbPW75ktT0G1dfOygWderV2seVgyCHcaTKWF4+xuE4bBFaRsEQ2z59+gj+69Wrl6tz8+bNZdasWdK2bVuZPn26zJgxowwW1atXl82bd9zzYiICRIAIEAEiUBqBjduqyYZt1WTr9iry1dZ9pGRrdVm1paZs2b6XfPvgdXLppZdWGsCijq84KVVSUlJp8GFFiUAUBA477DA5++yzo4gUZF72GwVptp1KF6ufFgyxve2226RevXoyaNAg2bp1q7uftWrVqqxBpKwfRaZ+YR0C8SN+YQiESdP/iF8YArako46vvtrH3U5Yni/y5ecjfrbwC9MmfWn2G/HYwHq7jKeWyZVSMMR28uTJMmbMGJkyZYpMmjRJRowYITNnzsyKVNyOErdJqF8YosSP+IUhECZN/yN+YQjYko46vvpqH3c7YXm+yJPYAgHr/hJmzfSl2W/EYwP6aTw4ZkopGGK7fv166d69u8ydO1fw76lTp0qXLl1IbOP1h52lxd3Q4laT+oUhSvyIXxgCYdL0vzD84paOOr76fj9uO7M8X+RJbElsw3zFR5r9hg9KufNY79dy18BWjoIhthnYFi1aJC1atBDcoa0oxe0ocZuN+oUhSvyIXxgCYdL0WL5aswAAF3VJREFUP+IXhoBNad/x1Vf7uNsJy/NFnsSWxDbMV6JIs9+Igtaeea33a2G1S1664IitL0QIKHXiiSf6Zk88H/ULg9w6fghmdsIJJ4RVMo/S1vGjfmHGJ37FjV9Y7ZKTjtsPWV6Y7YifLfzCtCleafppmG3jxi9Mm+Sli5bYJg8lv0gEiAARIAJEgAgQASJABIgAESACaSBAYpsG6vwmESACRIAIEIEiRODLL7+UDRs2yL777ruzdnPmzBE8LVG1alVVjVevXi3vv/++tGzZUlq3bq0qI59CK1eulBUrVrjnB6tVq6b+1FNPPeVOmtWtW1ddRkWCX3zxhXtJonHjxuryYdsaNWo4ecQ8gT3wYkUc6cMPP3TlZcqPo8w4yrDuf3HUMe0y8tFvZOoUt1/F0Y4Kqb5p+0bU71cKYvvVV19J7dq1pUqVKuXis3bt2rwNJPjgtm3bXMAr6JAtQYc6depk1TGqYaPkLwb9otQ37rw++K1bt875Qb4mLBXVyUe/uDGJUp6vfhgI0Ib22WfHw+xJJR/9MLHFE2RpJF/9MPnUEouQevnot2XLFtm0aZPgfdR8JB8d0EZr1qyZ9Qm5fOhVbGXefffd8v3vf9/1cwMHDpTf/e53ropt2rSRd999141xURKudFx88cXy0UcfyaGHHipr1qxxfoLvnH/++VGKykvel19+WS688EKnHxLqfcghh8gf//hHOeqooyJ/E20UWI0fP16OPvroyPK7CzzxxBPOHgcccIAcf/zxLkowdPzxj38sP//5zyOXP2rUKBk3bpzALqeccoosW7ZMQPrw7AueYoyavvGNb8gLL7wgmzdvljPPPFNmz57tihg5cqRcdtllUYuLPb91/4u9wikVGHe/Ebdfxd2OrNc3JTeI7bNFTWzR6WKlGAMgVmyaNWtWBrjXX39dLr30Utl///1l4cKFct9998mxxx4bG7goCIMABoNWrVoJJm8TJkyQpk2b7vzGvHnz3KCAVVTocOWVV8qAAQNi1aGiwnLphwn7FVdc4QZDrFL16tXLTViSSrn0+/Wvf+0GWSRMXtEB/fe//1VNKjR1yqUfVrcxQGPwh40x2Rk6dKjmUyqZXPpt3LjR+SMmFUgdOnSQ22+/XfUtjVAu/TJlom107NhR/v3vf7sJWlIpl37vvfeem9geeOCBUlJS4tpuv379klIvZ//yySefSN++fZ2NsZOEyfL//d//mdEPb5K//fbbru8F6caENu6Uy4bLly+X/v37O3zgZ4MHD060j4u7vmmVB3KC3TaQvSZNmsi3vvUt+dGPfuTGDC2xbd68uRs/0aYyC9PwF/QFeMe+fv363tWFr11++eVZ82OyGXWnEP0l9DvuuOPcOL7ffvtJ586d3QsOS5cudQslURKILZ40vOaaa+SII46QH/7wh+6nNmGXHPV68cUXXbv//PPP3ViOfuC1116LtNCKsQxzqA8++ECefvpp+de//uXmM+j3MK698cYbkRemUF/MK/7xj38Ino5BW8UCJkjzO++8E2n3Ox/2jdP/tDYsdrl89Btx+hXwj7MdFUJ9C93niprYPvLII470YLKEQWZ3YouBFyuX+Im8WGXFoBJXApFF9ObMAHzddde5o1RDhgzZ+QkQ2fbt2zs9MGDg3yAbe++9d1xqZC3HR7/777/fDWAPP/ywvPTSS3LJJZcIyHgSyUe/0nrcdNNNLmI2JgNJJB/9gN8rr7wiY8eOle3bt8tjjz0m5557biI7Zz76gZhh5X7ixImRJhFx4OujH76DHZo+ffrIxx9/7CZpSRFbH/3wnjba9AUXXOAme5jIv/nmm3HAk7MMH/1+8YtfuN0QLKZgYoqJ9qefflrmmGjODykz+OiH0zTwP0yyjznmmNiJrY8Ov/zlLwUnZoYNGyZLlixx9sTubb52j5VwmheDn+GIK3ZmsZCLvuWMM85wCxeHH3545B1bEBWcgnjrrbfc4nPp9PWvf13+/Oc/u3KjJIzxV111lYwePVoaNGhQRhTkOcoxYvgW6rlgwQLXrp588kn5+9//7sgZyDzaHCbEURIm5Igwi/L+8pe/yB133OF2ubt16+b8M9drEKW/ldEP5aF9YYEL/0YCfiClBx98sLd6sAeOl2Me8Mwzz7iysAiKcQ12wO8w/kZJGQLym9/8xrU7bDQgYWEAu/0HHXRQlOLcHC4u++bD/yJVppJkjrvfAGxx+lXc7ch6fYvB7Yqa2GYMhJXe8ogtVpHRSeMnVhuxa4V8cSVMxLt27Srz5893RWIwxVEb7E5kElZ5MViB7GKlG0cokpp4+uiHFd4jjzxSTj/9dEGkNRw3wuQ9ieSjX0YP7Mx/+9vfdpOnJBYF8F0f/TKTdpwOwG4GiMZZZ52VBHxe+v3zn/90u1WY2GOnAUfVTjvtNDP6QZEbbrhBvvnNb7r2AzyTIrY+9s0AhYULTKouuugit0iVRPLRD1cg0P9hJ+rxxx93WOL0SrZrGXHq7aNf5nu///3vnV5x79j66IBdPPTTIDaYpONkBfrsdu3axQlHpSgLC5/YaRs+fLjDFAt7OFWDu5iZ6zZRgMBiJXbzzj77bHeaBIvEzz///M637KMQ0cx3QeqwOxiVhJWnN/RDfdFngmj/9re/dUQeC3BYqIYvRUkZYpvZicakGgujzz33nOtXol7DwM4vFtpwEgHj4kknneQWC7BYrVmgHjNmjBsjcA/4b3/7mzuhsnjxYjeHAhGPmiAHQouEU02ZE2wPPPCAup+K275x+19UjCpD/rj7jbj9Ku52ZL2+he5zlZrYYhBBR4qOFXdkcPwls6IZh2FBtnr37r1zAMG9mWnTpsm99967s3gM+DjGhOPSM2fOdAEyNBMAjb4++mEHG5N17CzPmjXLDY4gQ0kkH/0yemByikWBJO/l+OiHDgx3iDCRwOIJjjnieGgSxMJHP6yy4+j2D37wA3nooYfcDgF2Wqzoh5MUIGSYNGHhKUli64Nfxv9AyKArdvniPPVRUTvz1Q873rArJt2YpGGRKonkqx90yRex9dEBpwHwH3bZkHD8EH0dAgExRUMAuxE4uYDjrpnn9vD/aBNYxI161BdfR0AlkDssUmAHD9eFTj75ZPWOOtpDXIufWDjK6AfyjSfecB0Gpw+wmxs1YdEYi59RjzBn+w6u52DOgbkOdr1BTNFHYWcUx8U1CUQZi9yYq6As7Eqj/WgxxVFk2BZzr+985zuC3VssMmmDhMVp33z4nwbzYpfJR78Rp1/F3Y6s17fQ/a1SE1sMjpiQYhDCUR0MvFjJjSth0EPHj0YBopDZjdj9qCxWKkEWcU8PRDhOcl1RXXz0AzFDMAysTINwY4Asb/c7LsxKl+OjH/IjGiUGadwH3v14WT70ypTpox92yLDKDmKRmTTjeHzUI1aaevjoh0kA7jbiPxy9wg4I/E87qYiip49+WKzAXXkccXz11VfdUX0coYv7Lnx5evvoB6KIHQIc0cNuEnZDkjpx4aMfjh9nJp3Y8c7sjkSxkzavj36ZsvNFbH10QL+Pfg2nUTLHD2HLqLttWpyKSQ59B8ZQ7HCA8GFBCuMH4jLsfpzYp97Wy8O4jWOz8BXsJGOnEfd/cTVB09ZYXnHj5+PzlTEP5si47obFf4z36DNwbxtXGTSL7Bj3cO0LpzJw8gZjH05GYgME8+yoCScnsDmBcQL36HEKBWVjgQi7w1FT3P1a3PhFrY+1/JWO2CLQAY4O4dglVkdBiG688UZ3zAd3WeIOnNOpUyd3vxKDHe7J4N4NVrIzOmBCh91i7KbgiDJWVzVHerSOlUs/rJ5CP+iJ40xdunRxE3fNETCNjrn0Q5nYKcOEIs5FCV9dc+mHXfp77rnH7WLgyBaI2meffZbIHVvUIZd+t9xyiwveAR/FsXzszmeOzvtiEJIvl34YADBIIWE3HhE+zznnHPVuTVRdc+n3k5/8xC1cAEecvsCRaUv2/dOf/uQWzdJoGz7+l29im00HxFXAriz6ZbRN7GRhV3HSpEmCe9M4PcMUDQEc48b4gBgCPXv2dJNSBFZCG4YP4uRKlGS9PNQlc5cPY+Spp57qAjRh9xHH6jVtjuUVN35R/L8y5f3e977nTgCg/8AddfQhuLsO8qiZDyOgIzY6MJfBdToEQMVcH3fgcS0sakJ5GNfRrrERhYU6LGJnTmtEKS/ufg3fjhu/KPWxmLfSEFusAiEyKI6mgVji2QA4fea+Hnb6MJlp1KhRrHbC4JaJctyjRw8XpAfBMDI6gCxixRcDGogPJlul3/+LVZlyCsulH3ZnMUlB54CEo6DoJJJKufSDHtgVxfFB7ConnXLph0BgCBqG1T7s3mNhI8moubn0g11xFw4LFfgP+uFIXVIpl36l9YBeP/3pTxO7Y4tv59IPZBbH9HGfDkfxcLcQiwNJpVz64cQF7jmWTphARAkaE1KXXPplysbCGfpjkMq4U3k6YBcAfW7myDH6YNgSO7xTp051EyymaAhg0RgTUUS1ReAfYHnzzTe7QrCg9+CDD0Y63m29PNQrQ0SxMI3j19dee62rL05x4DRH1LGc5RU3ftFaVOXIjd1QzLszUcSxCIbj/Linjl1b7LxGiXeQKQ8n+XDKq3TQNNyF/8Mf/uDu6/umTPAobOhgvoSj9yC3GEPQzrEYGuU0Stz9Wtz4+eJiOV+lILYVGQBOgZUYHCfQHHnwMS4cGQ2hoqNJmeOf+dKhIj199ANGWBiIEpXRBxufPD76+ZSTrzw++mH1MK13RH30QzTYOIKpaDD20U9TblwyPvphwMPiShrHV330iwsLTTkW9PPRAX0w2kAafZwGV4syILA4mYSjhDgFgtMqaBvYUcDCQdR3lK2Xhz4dVyNwlQlXdbAwgwVqnNzAoknUqzEsr7jxs9hmLeiEa4G4EoLgZoj+jXkwTmidd955blcUV5GiJBBOLNLjFBrurCNuCNoWAqHiREXUiPeQxQIpNqRw8gRxKtDecaUMu7hY1IqS4u7X4sYvSl0s5q30xNaiUagTESACRIAIEIFCQwC7Lti1xHvTmPgh4b4+rtng+HfUZL08BIIEYccpCEyYccUIk2qcLsHppqiJ5RU3flH9obLkxxU8nHxCOwJpxGIRTrrhGSlNO0JwM1wTwk4wFtoQHBPkE2RX8xwkjjAjbgCu9uCYNE5oITYPykKZUVPc/Vrc+EWtj7X8JLbWLEJ9iAARIAJEgAgUMAK4R4brP4jui92O0GS9vNL1w+kc3L+LK7G8MCSt4xdWu+KSRtC+zCkHBESK6wQUFtlQVu3atYMAwwlPXNnCCSBcMwtt53H3a/nCLwi0FIRJbFMAnZ8kAkSACBABIlBsCOD5PBzHxVM3CNCYCa6GXRfN3X2WtyM4HfHzaynW/cWvFpUvF64r4Mjwyy+/7J4fxJ1aHEtGMDbNCxJxl4fjxrjzi/elsVOL3WRcLUS71JxEwQ4rnn7EFUXUG6+hICFiM57TQiDbKCnu8qJ822JeEluLVqFORIAIEAEiQAQKDAHcLcVRQkzOEFQFb1DjfhyexcAx3ajR9Fke8YvSBKz7S5S6VKa8uHOKhbDvfve77jkd7IjiOgPI3/LlyyO/kRx3efAr3K999913Xb+Gp4mwW4vYASC9UWLjYJcWR6x/9rOfueByiLiM+7sIlIVYP/hGFGIbd3nF4HcktsVgRdaBCBABIkAEiECKCGSic2IXAk9qIBpp5ukwRCPFk0qIKOqbWB7xKyZ/8fX7ypYPb4djwQvR1Hc/2ov76rh3fsQRR3jDEnd5mX4I+uE1FTwBiECDSCC7iB8QJcpy5m31devWuSBWCI6FwFm4F9y2bdvIxDbu8ryBNpyRxNawcagaEbCIADpSJNyfYyICRIAIZBDAG9OHHnqovP322/Lkk0/Kn//8Z3dn7qqrrlK978zyiF+U1mXdX6LUpTLlxXNrCO6E3UscPQaBBNG76667XETjqHdj4y4PxBVv11599dWCHVIcScaxaSzeIWhc1JMo2FEGWceONOqLnd9NmzbJI4884vrJKDu28JO4yyt03yOxLXQLUv+iQwDBVjIRRVE5vCOMqH6auxxxgIMdGOy2nHrqqXLCCSe4FcsNGzYI7jNhRRVBFHD0kIkIEIHKjQAmZXjiBxM/7GYMGjTITdLw/jSepIiaWB7xi+Iz1v0lSl0qU17sXuKu6YsvvugiDmNxDGQNVxg0d2zjLg/k+le/+pUMGDDAPeU1Z84cufzyy2XIkCHy7W9/O7Kp4KejR4929cS799gVRv3xZi+eKIpK5OMuL3KFjAmQ2BozCNUhApkoolgdRBCE8ePHO1BAMOOIMBoV4U8++cQ9QH7HHXe4YDDokNERI9Q9AimA5OL+HBMRIAJEgAgQASJABIgAEUgLARLbtJDnd4lAFgRAXg844ICdu6CXXHKJ3H///S5aII7rjB071r3zhuMxo0aNkqOPPtqtJj7xxBOCIzj/+Mc/5JVXXnFR/LDTCuKJ3w8fPtzd6XjwwQflD3/4gyxcuNCtQN5+++2CFc4zzjhDunTp4t5lXLx4seC7ILLYKZ46dap7j/J3v/udPProo+7YzEMPPVSG2OJZBdyHwX81atRwx2xQPhMRIAJEgAgQASJABIgAEcg3AiS2+UaY5ROBiAiA2DZt2tRF3vviiy/cw+AfffSR4FFv3OkA8cRuKY784e/Y1QUBBYlFApm89dZb3REekFHcOwIZ/vWvf+0iD+JIMe6L4I4syO8vf/lLd6ymSZMmTv773/++PP/88+47IL8gsvgejtyABF988cXy1VdfuaPIpXds8TeEv8e3Z8yY4cgwfh5//PEREWB2IkAECg0BnOLAMz/ZEvqwKEfsWF5ZJIlfxS3Cur8UWntOSl/rdqts+iVl93x+h8Q2n+iybCKgQGD3O7bYpcV7biCsmDged9xxbncV5BN3UhBVD4FaQGwzRHLkyJFyww03yPvvvy8HH3ywy4NJJULVI99NN90kVatWdbu4ILv4O4jtFVdcIX/605/crm7//v1dGHrcqS19FBlktjxiix1kPBCOQAgLFixwR6hxBwXfYCICRKD4EUDQqI4dO7rAKs2aNStTYUT8jPIsBoRZ3i4IiV/u9mPdX3LXoHLmsG63yqZfoXshiW2hW5D6Fx0CmR1bkFAc6QVhBAkFaUVAFgRwwvMZmYRd3D/+8Y+OsCKaIIgwSC3ILYIK4CFxvI1WvXp1txuLnVQQW/w/EggtSDN+YucXd2n//ve/u0fDfYnt7Nmz3VtzmMxeeeWVO3UDacaOMRMRIAKVA4Frr71Whg4dKo0aNYqlwiwvDEbiZwu/MG2KV5p+GmbbuPEL0yZdaRLbdPHn14nAHgjsfsc2kwHvqDVu3Fjat2/vntH429/+JrNmzZLHH3/c7YqC2GInFTuzzzzzjHTt2tWR0/PPP18uuOCCnRFKEX3vzjvvdIQZ/z7vvPPcLmsuYovjy7/5zW/cndvydmwRxRA7Ndj9xTFm6If32c4880xamQgQASJABIgAESACRIAI5BUBEtu8wsvCiUB0BLIRW5SEAFG4w4pAUki//e1v3e4sVutKE1sEdwKhxRFlJLyZBsKJ3dwf/ehH7n5u5vcoE2QYxDZzdBjvqfXq1cvt2J511lnuODLIKr4BUp2JhFz6uR/8/cYbb9z5zT59+rjjyNjJZSICRIAIEAEiQASIABEgAvlEgMQ2n+iybCKQJwTwBA+IKKIcV5SWLFni3pndb7/9ytxvKykpEewA486uz723bdu2uWPO+F4uooqAVjg6HddRxDxByGKJABEgAkSACBABIkAEiggBEtsiMiarQgSIABEgAkSACBABIkAEiAARqIwIkNhWRquzzkSACBABIkAEiAARIAJEgAgQgSJCgMS2iIzJqhABIkAEiAARIAJEgAgQASJABCojAiS2ldHqrDMRIAJEgAgQASJABIgAESACRKCIECCxLSJjsipEgAgQASJABIgAESACRIAIEIHKiACJbWW0OutMBIgAESACRIAIEAEiQASIABEoIgT+H6MtBQreD4scAAAAAElFTkSuQmCC", | |
| "text/plain": [ | |
| "<VegaLite 4 object>\n", | |
| "\n", | |
| "If you see this message, it means the renderer has not been properly enabled\n", | |
| "for the frontend that you are using. For more information, see\n", | |
| "https://altair-viz.github.io/user_guide/troubleshooting.html\n" | |
| ] | |
| }, | |
| "execution_count": 2, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# Simple settings dictionary will be used for exploratory analysis\n", | |
| "settings = {\n", | |
| " \"link_type\": \"dedupe_only\",\n", | |
| " \"blocking_rules_to_generate_predictions\": [\n", | |
| " \"l.first_name = r.first_name and l.surname = r.surname\",\n", | |
| " \"l.surname = r.surname and l.dob = r.dob\",\n", | |
| " \"l.first_name = r.first_name and l.dob = r.dob\",\n", | |
| " \"l.postcode_fake = r.postcode_fake and l.first_name = r.first_name\",\n", | |
| " ],\n", | |
| "}\n", | |
| "linker = DuckDBLinker(df, settings)\n", | |
| "\n", | |
| "linker.profile_columns(\n", | |
| " [\"first_name\", \"postcode_fake\", \"substr(dob, 1,4)\"], top_n=10, bottom_n=5\n", | |
| ")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "application/vnd.vegalite.v4+json": { | |
| "$schema": "https://vega.github.io/schema/vega-lite/v5.json", | |
| "data": { | |
| "values": [ | |
| { | |
| "cartesian": 1279041753, | |
| "cumulative_rows": 243656, | |
| "reduction_ratio": "The rolling reduction ratio with your given blocking rule(s) is 0.99981. This represents the reduction in the total number of comparisons due to your rule(s).", | |
| "row_count": 243656, | |
| "rule": "l.first_name = r.first_name and l.surname = r.surname", | |
| "start": 0 | |
| }, | |
| { | |
| "cartesian": 1279041753, | |
| "cumulative_rows": 268697, | |
| "reduction_ratio": "The rolling reduction ratio with your given blocking rule(s) is 0.99979. This represents the reduction in the total number of comparisons due to your rule(s).", | |
| "row_count": 25041, | |
| "rule": "l.surname = r.surname and l.dob = r.dob", | |
| "start": 243656 | |
| }, | |
| { | |
| "cartesian": 1279041753, | |
| "cumulative_rows": 298602, | |
| "reduction_ratio": "The rolling reduction ratio with your given blocking rule(s) is 0.999767. This represents the reduction in the total number of comparisons due to your rule(s).", | |
| "row_count": 29905, | |
| "rule": "l.first_name = r.first_name and l.dob = r.dob", | |
| "start": 268697 | |
| }, | |
| { | |
| "cartesian": 1279041753, | |
| "cumulative_rows": 307023, | |
| "reduction_ratio": "The rolling reduction ratio with your given blocking rule(s) is 0.99976. This represents the reduction in the total number of comparisons due to your rule(s).", | |
| "row_count": 8421, | |
| "rule": "l.postcode_fake = r.postcode_fake and l.first_name = r.first_name", | |
| "start": 298602 | |
| } | |
| ] | |
| }, | |
| "encoding": { | |
| "color": { | |
| "field": "rule", | |
| "legend": null, | |
| "scale": { | |
| "scheme": "category20c" | |
| } | |
| }, | |
| "order": { | |
| "field": "cumulative_rows" | |
| }, | |
| "tooltip": [ | |
| { | |
| "field": "rule", | |
| "title": "SQL Condition", | |
| "type": "nominal" | |
| }, | |
| { | |
| "field": "row_count", | |
| "format": ",", | |
| "title": "Comparisons Generated", | |
| "type": "quantitative" | |
| }, | |
| { | |
| "field": "cumulative_rows", | |
| "format": ",", | |
| "title": "Cumulative Comparisons", | |
| "type": "quantitative" | |
| }, | |
| { | |
| "field": "cartesian", | |
| "format": ",", | |
| "title": "Cartesian Product of Input Data", | |
| "type": "quantitative" | |
| }, | |
| { | |
| "field": "reduction_ratio", | |
| "title": "Reduction Ratio (cumulative rows/cartesian product)", | |
| "type": "nominal" | |
| } | |
| ], | |
| "x": { | |
| "field": "start", | |
| "title": "Comparisons Generated by Rule(s)", | |
| "type": "quantitative" | |
| }, | |
| "x2": { | |
| "field": "cumulative_rows" | |
| }, | |
| "y": { | |
| "field": "rule", | |
| "sort": [ | |
| "-x2" | |
| ], | |
| "title": "SQL Blocking Rule" | |
| } | |
| }, | |
| "height": { | |
| "step": 20 | |
| }, | |
| "mark": "bar", | |
| "title": { | |
| "subtitle": "(Counts exclude comparisons already generated by previous rules)", | |
| "text": "Count of Additional Comparisons Generated by Each Blocking Rule" | |
| }, | |
| "width": 450 | |
| }, | |
| "image/png": 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", | |
| "text/plain": [ | |
| "<VegaLite 4 object>\n", | |
| "\n", | |
| "If you see this message, it means the renderer has not been properly enabled\n", | |
| "for the frontend that you are using. For more information, see\n", | |
| "https://altair-viz.github.io/user_guide/troubleshooting.html\n" | |
| ] | |
| }, | |
| "execution_count": 3, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "linker.cumulative_num_comparisons_from_blocking_rules_chart()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "import splink.duckdb.duckdb_comparison_template_library as ctl\n", | |
| "import splink.duckdb.duckdb_comparison_library as cl\n", | |
| "\n", | |
| "settings = {\n", | |
| " \"link_type\": \"dedupe_only\",\n", | |
| " \"blocking_rules_to_generate_predictions\": [\n", | |
| " \"l.first_name = r.first_name and l.surname = r.surname\",\n", | |
| " \"l.surname = r.surname and l.dob = r.dob\",\n", | |
| " \"l.first_name = r.first_name and l.dob = r.dob\",\n", | |
| " \"l.postcode_fake = r.postcode_fake and l.first_name = r.first_name\",\n", | |
| " ],\n", | |
| " \"comparisons\": [\n", | |
| " ctl.name_comparison(\"first_name\", term_frequency_adjustments_name=True),\n", | |
| " ctl.name_comparison(\"surname\", term_frequency_adjustments_name=True),\n", | |
| " cl.levenshtein_at_thresholds(\"dob\", [1, 2], term_frequency_adjustments=True),\n", | |
| " cl.levenshtein_at_thresholds(\"postcode_fake\", 2, term_frequency_adjustments=True),\n", | |
| " cl.exact_match(\"birth_place\", term_frequency_adjustments=True),\n", | |
| " cl.exact_match(\"occupation\", term_frequency_adjustments=True),\n", | |
| " ],\n", | |
| " \"retain_matching_columns\": True,\n", | |
| " \"retain_intermediate_calculation_columns\": True,\n", | |
| " \"max_iterations\": 10,\n", | |
| " \"em_convergence\": 0.01\n", | |
| "}\n", | |
| "\n", | |
| "linker = DuckDBLinker(df, settings)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "Probability two random records match is estimated to be 0.000136.\n", | |
| "This means that amongst all possible pairwise record comparisons, one in 7,362.31 are expected to match. With 1,279,041,753 total possible comparisons, we expect a total of around 173,728.33 matching pairs\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "linker.estimate_probability_two_random_records_match(\n", | |
| " [\n", | |
| " \"l.first_name = r.first_name and l.surname = r.surname and l.dob = r.dob\",\n", | |
| " \"substr(l.first_name,1,2) = substr(r.first_name,1,2) and l.surname = r.surname and substr(l.postcode_fake,1,2) = substr(r.postcode_fake,1,2)\",\n", | |
| " \"l.dob = r.dob and l.postcode_fake = r.postcode_fake\",\n", | |
| " ],\n", | |
| " recall=0.6,\n", | |
| ")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "----- Estimating u probabilities using random sampling -----\n", | |
| "/Users/rosskennedy/splink_demos/venv/lib/python3.9/site-packages/ipykernel/comm/comm.py:79: DeprecationWarning: The `ipykernel.comm.Comm` class has been deprecated. Please use the `comm` module instead.For creating comms, use the function `from comm import create_comm`.\n", | |
| " warn(\n", | |
| "/Users/rosskennedy/splink_demos/venv/lib/python3.9/site-packages/ipykernel/comm/comm.py:79: DeprecationWarning: The `ipykernel.comm.Comm` class has been deprecated. Please use the `comm` module instead.For creating comms, use the function `from comm import create_comm`.\n", | |
| " warn(\n", | |
| "/Users/rosskennedy/splink_demos/venv/lib/python3.9/site-packages/ipykernel/comm/comm.py:79: DeprecationWarning: The `ipykernel.comm.Comm` class has been deprecated. Please use the `comm` module instead.For creating comms, use the function `from comm import create_comm`.\n", | |
| " warn(\n" | |
| ] | |
| }, | |
| { | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "de22f4a528c642588cd738d5e00c1ca7", | |
| "version_major": 2, | |
| "version_minor": 0 | |
| }, | |
| "text/plain": [ | |
| "FloatProgress(value=0.0, layout=Layout(width='100%'), style=ProgressStyle(bar_color='black'))" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| "Estimated u probabilities using random sampling\n", | |
| "\n", | |
| "Your model is not yet fully trained. Missing estimates for:\n", | |
| " - first_name (no m values are trained).\n", | |
| " - surname (no m values are trained).\n", | |
| " - dob (no m values are trained).\n", | |
| " - postcode_fake (no m values are trained).\n", | |
| " - birth_place (no m values are trained).\n", | |
| " - occupation (no m values are trained).\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "linker.estimate_u_using_random_sampling(max_pairs=5e6)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 7, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| "----- Starting EM training session -----\n", | |
| "\n", | |
| "Estimating the m probabilities of the model by blocking on:\n", | |
| "l.first_name = r.first_name and l.surname = r.surname\n", | |
| "\n", | |
| "Parameter estimates will be made for the following comparison(s):\n", | |
| " - dob\n", | |
| " - postcode_fake\n", | |
| " - birth_place\n", | |
| " - occupation\n", | |
| "\n", | |
| "Parameter estimates cannot be made for the following comparison(s) since they are used in the blocking rules: \n", | |
| " - first_name\n", | |
| " - surname\n", | |
| "\n", | |
| "Iteration 1: Largest change in params was -0.527 in probability_two_random_records_match\n", | |
| "Iteration 2: Largest change in params was -0.0356 in probability_two_random_records_match\n", | |
| "Iteration 3: Largest change in params was 0.0127 in the m_probability of birth_place, level `Exact match`\n", | |
| "Iteration 4: Largest change in params was -0.00402 in the m_probability of birth_place, level `All other comparisons`\n", | |
| "\n", | |
| "EM converged after 4 iterations\n", | |
| "\n", | |
| "Your model is not yet fully trained. Missing estimates for:\n", | |
| " - first_name (no m values are trained).\n", | |
| " - surname (no m values are trained).\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "training_blocking_rule = \"l.first_name = r.first_name and l.surname = r.surname\"\n", | |
| "training_session_names = linker.estimate_parameters_using_expectation_maximisation(training_blocking_rule)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 8, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| "----- Starting EM training session -----\n", | |
| "\n", | |
| "Estimating the m probabilities of the model by blocking on:\n", | |
| "l.dob = r.dob\n", | |
| "\n", | |
| "Parameter estimates will be made for the following comparison(s):\n", | |
| " - first_name\n", | |
| " - surname\n", | |
| " - postcode_fake\n", | |
| " - birth_place\n", | |
| " - occupation\n", | |
| "\n", | |
| "Parameter estimates cannot be made for the following comparison(s) since they are used in the blocking rules: \n", | |
| " - dob\n", | |
| "/Users/rosskennedy/splink_demos/venv/lib/python3.9/site-packages/ipykernel/comm/comm.py:79: DeprecationWarning: The `ipykernel.comm.Comm` class has been deprecated. Please use the `comm` module instead.For creating comms, use the function `from comm import create_comm`.\n", | |
| " warn(\n", | |
| "/Users/rosskennedy/splink_demos/venv/lib/python3.9/site-packages/ipykernel/comm/comm.py:79: DeprecationWarning: The `ipykernel.comm.Comm` class has been deprecated. Please use the `comm` module instead.For creating comms, use the function `from comm import create_comm`.\n", | |
| " warn(\n", | |
| "/Users/rosskennedy/splink_demos/venv/lib/python3.9/site-packages/ipykernel/comm/comm.py:79: DeprecationWarning: The `ipykernel.comm.Comm` class has been deprecated. Please use the `comm` module instead.For creating comms, use the function `from comm import create_comm`.\n", | |
| " warn(\n" | |
| ] | |
| }, | |
| { | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "39522893703a4ec496572228a5c2168e", | |
| "version_major": 2, | |
| "version_minor": 0 | |
| }, | |
| "text/plain": [ | |
| "FloatProgress(value=0.0, layout=Layout(width='100%'), style=ProgressStyle(bar_color='black'))" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| "Iteration 1: Largest change in params was -0.355 in the m_probability of first_name, level `Exact match first_name`\n", | |
| "Iteration 2: Largest change in params was 0.0409 in the m_probability of first_name, level `All other comparisons`\n", | |
| "Iteration 3: Largest change in params was 0.00542 in the m_probability of first_name, level `All other comparisons`\n", | |
| "\n", | |
| "EM converged after 3 iterations\n", | |
| "\n", | |
| "Your model is fully trained. All comparisons have at least one estimate for their m and u values\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "training_blocking_rule = \"l.dob = r.dob\"\n", | |
| "training_session_dob = linker.estimate_parameters_using_expectation_maximisation(training_blocking_rule)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "The final match weights can be viewed in the match weights chart:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 9, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "application/vnd.vegalite.v4+json": { | |
| "$schema": "https://vega.github.io/schema/vega-lite/v5.2.json", | |
| "config": { | |
| "header": { | |
| "title": null | |
| }, | |
| "mark": { | |
| "tooltip": null | |
| }, | |
| "title": { | |
| "anchor": "middle" | |
| }, | |
| "view": { | |
| "height": 60, | |
| "width": 400 | |
| } | |
| }, | |
| "data": { | |
| "values": [ | |
| { | |
| "bayes_factor": 0.00013584539607096294, | |
| "bayes_factor_description": "The probability that two random records drawn at random match is 0.000 or one in 7,362.3 records.This is equivalent to a starting match weight of -12.846.", | |
| "comparison_name": "probability_two_random_records_match", | |
| "comparison_sort_order": -1, | |
| "comparison_vector_value": 0, | |
| "has_tf_adjustments": false, | |
| "is_null_level": false, | |
| "label_for_charts": "", | |
| "log2_bayes_factor": -12.845746707461347, | |
| "m_probability": null, | |
| "m_probability_description": null, | |
| "max_comparison_vector_value": 0, | |
| "probability_two_random_records_match": 0.00013582694460587586, | |
| "sql_condition": null, | |
| "tf_adjustment_column": null, | |
| "tf_adjustment_weight": null, | |
| "u_probability": null, | |
| "u_probability_description": null | |
| }, | |
| { | |
| "bayes_factor": 43.81352458633812, | |
| "bayes_factor_description": "If comparison level is `exact match first_name` then comparison is 43.81 times more likely to be a match", | |
| "comparison_name": "first_name", | |
| "comparison_sort_order": 0, | |
| "comparison_vector_value": 3, | |
| "has_tf_adjustments": true, | |
| "is_null_level": false, | |
| "label_for_charts": "Exact match first_name", | |
| "log2_bayes_factor": 5.453304372061299, | |
| "m_probability": 0.5532690621971292, | |
| "m_probability_description": "Amongst matching record comparisons, 55.33% of records are in the exact match first_name comparison level", | |
| "max_comparison_vector_value": 3, | |
| "probability_two_random_records_match": 0.00013582694460587586, | |
| "sql_condition": "\"first_name_l\" = \"first_name_r\"", | |
| "tf_adjustment_column": "first_name", | |
| "tf_adjustment_weight": 1, | |
| "u_probability": 0.012627814525783414, | |
| "u_probability_description": "Amongst non-matching record comparisons, 1.26% of records are in the exact match first_name comparison level" | |
| }, | |
| { | |
| "bayes_factor": 33.70962319479371, | |
| "bayes_factor_description": "If comparison level is `jaro_winkler_similarity >= 0.95` then comparison is 33.71 times more likely to be a match", | |
| "comparison_name": "first_name", | |
| "comparison_sort_order": 0, | |
| "comparison_vector_value": 2, | |
| "has_tf_adjustments": false, | |
| "is_null_level": false, | |
| "label_for_charts": "Jaro_winkler_similarity >= 0.95", | |
| "log2_bayes_factor": 5.07508859589561, | |
| "m_probability": 0.02361588563927614, | |
| "m_probability_description": "Amongst matching record comparisons, 2.36% of records are in the jaro_winkler_similarity >= 0.95 comparison level", | |
| "max_comparison_vector_value": 3, | |
| "probability_two_random_records_match": 0.00013582694460587586, | |
| "sql_condition": "jaro_winkler_similarity(\"first_name_l\", \"first_name_r\") >= 0.95", | |
| "tf_adjustment_column": null, | |
| "tf_adjustment_weight": 1, | |
| "u_probability": 0.0007005680693257794, | |
| "u_probability_description": "Amongst non-matching record comparisons, 0.07% of records are in the jaro_winkler_similarity >= 0.95 comparison level" | |
| }, | |
| { | |
| "bayes_factor": 21.755123704097016, | |
| "bayes_factor_description": "If comparison level is `jaro_winkler_similarity >= 0.88` then comparison is 21.76 times more likely to be a match", | |
| "comparison_name": "first_name", | |
| "comparison_sort_order": 0, | |
| "comparison_vector_value": 1, | |
| "has_tf_adjustments": false, | |
| "is_null_level": false, | |
| "label_for_charts": "Jaro_winkler_similarity >= 0.88", | |
| "log2_bayes_factor": 4.443283315249137, | |
| "m_probability": 0.11351377446430672, | |
| "m_probability_description": "Amongst matching record comparisons, 11.35% of records are in the jaro_winkler_similarity >= 0.88 comparison level", | |
| "max_comparison_vector_value": 3, | |
| "probability_two_random_records_match": 0.00013582694460587586, | |
| "sql_condition": "jaro_winkler_similarity(\"first_name_l\", \"first_name_r\") >= 0.88", | |
| "tf_adjustment_column": null, | |
| "tf_adjustment_weight": 1, | |
| "u_probability": 0.005217794943769009, | |
| "u_probability_description": "Amongst non-matching record comparisons, 0.52% of records are in the jaro_winkler_similarity >= 0.88 comparison level" | |
| }, | |
| { | |
| "bayes_factor": 0.31545170095004876, | |
| "bayes_factor_description": "If comparison level is `all other comparisons` then comparison is 3.17 times less likely to be a match", | |
| "comparison_name": "first_name", | |
| "comparison_sort_order": 0, | |
| "comparison_vector_value": 0, | |
| "has_tf_adjustments": false, | |
| "is_null_level": false, | |
| "label_for_charts": "All other comparisons", | |
| "log2_bayes_factor": -1.664508964906201, | |
| "m_probability": 0.309601277699288, | |
| "m_probability_description": "Amongst matching record comparisons, 30.96% of records are in the all other comparisons comparison level", | |
| "max_comparison_vector_value": 3, | |
| "probability_two_random_records_match": 0.00013582694460587586, | |
| "sql_condition": "ELSE", | |
| "tf_adjustment_column": null, | |
| "tf_adjustment_weight": 1, | |
| "u_probability": 0.9814538224611218, | |
| "u_probability_description": "Amongst non-matching record comparisons, 98.15% of records are in the all other comparisons comparison level" | |
| }, | |
| { | |
| "bayes_factor": 1108.5019818791598, | |
| "bayes_factor_description": "If comparison level is `exact match surname` then comparison is 1,108.50 times more likely to be a match", | |
| "comparison_name": "surname", | |
| "comparison_sort_order": 1, | |
| "comparison_vector_value": 3, | |
| "has_tf_adjustments": true, | |
| "is_null_level": false, | |
| "label_for_charts": "Exact match surname", | |
| "log2_bayes_factor": 10.114395634249066, | |
| "m_probability": 0.7838061038780122, | |
| "m_probability_description": "Amongst matching record comparisons, 78.38% of records are in the exact match surname comparison level", | |
| "max_comparison_vector_value": 3, | |
| "probability_two_random_records_match": 0.00013582694460587586, | |
| "sql_condition": "\"surname_l\" = \"surname_r\"", | |
| "tf_adjustment_column": "surname", | |
| "tf_adjustment_weight": 1, | |
| "u_probability": 0.000707085884094934, | |
| "u_probability_description": "Amongst non-matching record comparisons, 0.07% of records are in the exact match surname comparison level" | |
| }, | |
| { | |
| "bayes_factor": 334.8112492638975, | |
| "bayes_factor_description": "If comparison level is `jaro_winkler_similarity >= 0.95` then comparison is 334.81 times more likely to be a match", | |
| "comparison_name": "surname", | |
| "comparison_sort_order": 1, | |
| "comparison_vector_value": 2, | |
| "has_tf_adjustments": false, | |
| "is_null_level": false, | |
| "label_for_charts": "Jaro_winkler_similarity >= 0.95", | |
| "log2_bayes_factor": 8.387204191332616, | |
| "m_probability": 0.05589582285650198, | |
| "m_probability_description": "Amongst matching record comparisons, 5.59% of records are in the jaro_winkler_similarity >= 0.95 comparison level", | |
| "max_comparison_vector_value": 3, | |
| "probability_two_random_records_match": 0.00013582694460587586, | |
| "sql_condition": "jaro_winkler_similarity(\"surname_l\", \"surname_r\") >= 0.95", | |
| "tf_adjustment_column": null, | |
| "tf_adjustment_weight": 1, | |
| "u_probability": 0.00016694726649535307, | |
| "u_probability_description": "Amongst non-matching record comparisons, 0.02% of records are in the jaro_winkler_similarity >= 0.95 comparison level" | |
| }, | |
| { | |
| "bayes_factor": 110.02455275800244, | |
| "bayes_factor_description": "If comparison level is `jaro_winkler_similarity >= 0.88` then comparison is 110.02 times more likely to be a match", | |
| "comparison_name": "surname", | |
| "comparison_sort_order": 1, | |
| "comparison_vector_value": 1, | |
| "has_tf_adjustments": false, | |
| "is_null_level": false, | |
| "label_for_charts": "Jaro_winkler_similarity >= 0.88", | |
| "log2_bayes_factor": 6.781681697066163, | |
| "m_probability": 0.07830546872818925, | |
| "m_probability_description": "Amongst matching record comparisons, 7.83% of records are in the jaro_winkler_similarity >= 0.88 comparison level", | |
| "max_comparison_vector_value": 3, | |
| "probability_two_random_records_match": 0.00013582694460587586, | |
| "sql_condition": "jaro_winkler_similarity(\"surname_l\", \"surname_r\") >= 0.88", | |
| "tf_adjustment_column": null, | |
| "tf_adjustment_weight": 1, | |
| "u_probability": 0.000711709039167113, | |
| "u_probability_description": "Amongst non-matching record comparisons, 0.07% of records are in the jaro_winkler_similarity >= 0.88 comparison level" | |
| }, | |
| { | |
| "bayes_factor": 0.08212283017384549, | |
| "bayes_factor_description": "If comparison level is `all other comparisons` then comparison is 12.18 times less likely to be a match", | |
| "comparison_name": "surname", | |
| "comparison_sort_order": 1, | |
| "comparison_vector_value": 0, | |
| "has_tf_adjustments": false, | |
| "is_null_level": false, | |
| "label_for_charts": "All other comparisons", | |
| "log2_bayes_factor": -3.606072842315862, | |
| "m_probability": 0.08199260453729654, | |
| "m_probability_description": "Amongst matching record comparisons, 8.20% of records are in the all other comparisons comparison level", | |
| "max_comparison_vector_value": 3, | |
| "probability_two_random_records_match": 0.00013582694460587586, | |
| "sql_condition": "ELSE", | |
| "tf_adjustment_column": null, | |
| "tf_adjustment_weight": 1, | |
| "u_probability": 0.9984142578102426, | |
| "u_probability_description": "Amongst non-matching record comparisons, 99.84% of records are in the all other comparisons comparison level" | |
| }, | |
| { | |
| "bayes_factor": 347.22157895951074, | |
| "bayes_factor_description": "If comparison level is `exact match` then comparison is 347.22 times more likely to be a match", | |
| "comparison_name": "dob", | |
| "comparison_sort_order": 2, | |
| "comparison_vector_value": 3, | |
| "has_tf_adjustments": true, | |
| "is_null_level": false, | |
| "label_for_charts": "Exact match", | |
| "log2_bayes_factor": 8.439712800260082, | |
| "m_probability": 0.6178418282395027, | |
| "m_probability_description": "Amongst matching record comparisons, 61.78% of records are in the exact match comparison level", | |
| "max_comparison_vector_value": 3, | |
| "probability_two_random_records_match": 0.00013582694460587586, | |
| "sql_condition": "\"dob_l\" = \"dob_r\"", | |
| "tf_adjustment_column": "dob", | |
| "tf_adjustment_weight": 1, | |
| "u_probability": 0.0017793877618175018, | |
| "u_probability_description": "Amongst non-matching record comparisons, 0.18% of records are in the exact match comparison level" | |
| }, | |
| { | |
| "bayes_factor": 17.946390194317825, | |
| "bayes_factor_description": "If comparison level is `levenshtein <= 1` then comparison is 17.95 times more likely to be a match", | |
| "comparison_name": "dob", | |
| "comparison_sort_order": 2, | |
| "comparison_vector_value": 2, | |
| "has_tf_adjustments": false, | |
| "is_null_level": false, | |
| "label_for_charts": "Levenshtein <= 1", | |
| "log2_bayes_factor": 4.1656217789087, | |
| "m_probability": 0.3419541720944316, | |
| "m_probability_description": "Amongst matching record comparisons, 34.20% of records are in the levenshtein <= 1 comparison level", | |
| "max_comparison_vector_value": 3, | |
| "probability_two_random_records_match": 0.00013582694460587586, | |
| "sql_condition": "levenshtein(\"dob_l\", \"dob_r\") <= 1", | |
| "tf_adjustment_column": null, | |
| "tf_adjustment_weight": 1, | |
| "u_probability": 0.019054203569177988, | |
| "u_probability_description": "Amongst non-matching record comparisons, 1.91% of records are in the levenshtein <= 1 comparison level" | |
| }, | |
| { | |
| "bayes_factor": 0.5017243864603689, | |
| "bayes_factor_description": "If comparison level is `levenshtein <= 2` then comparison is 1.99 times less likely to be a match", | |
| "comparison_name": "dob", | |
| "comparison_sort_order": 2, | |
| "comparison_vector_value": 1, | |
| "has_tf_adjustments": false, | |
| "is_null_level": false, | |
| "label_for_charts": "Levenshtein <= 2", | |
| "log2_bayes_factor": -0.9950330324670263, | |
| "m_probability": 0.03730340153555845, | |
| "m_probability_description": "Amongst matching record comparisons, 3.73% of records are in the levenshtein <= 2 comparison level", | |
| "max_comparison_vector_value": 3, | |
| "probability_two_random_records_match": 0.00013582694460587586, | |
| "sql_condition": "levenshtein(\"dob_l\", \"dob_r\") <= 2", | |
| "tf_adjustment_column": null, | |
| "tf_adjustment_weight": 1, | |
| "u_probability": 0.07435038547504416, | |
| "u_probability_description": "Amongst non-matching record comparisons, 7.44% of records are in the levenshtein <= 2 comparison level" | |
| }, | |
| { | |
| "bayes_factor": 0.00320573249826891, | |
| "bayes_factor_description": "If comparison level is `all other comparisons` then comparison is 311.94 times less likely to be a match", | |
| "comparison_name": "dob", | |
| "comparison_sort_order": 2, | |
| "comparison_vector_value": 0, | |
| "has_tf_adjustments": false, | |
| "is_null_level": false, | |
| "label_for_charts": "All other comparisons", | |
| "log2_bayes_factor": -8.285130239557049, | |
| "m_probability": 0.0029005981305073145, | |
| "m_probability_description": "Amongst matching record comparisons, 0.29% of records are in the all other comparisons comparison level", | |
| "max_comparison_vector_value": 3, | |
| "probability_two_random_records_match": 0.00013582694460587586, | |
| "sql_condition": "ELSE", | |
| "tf_adjustment_column": null, | |
| "tf_adjustment_weight": 1, | |
| "u_probability": 0.9048160231939604, | |
| "u_probability_description": "Amongst non-matching record comparisons, 90.48% of records are in the all other comparisons comparison level" | |
| }, | |
| { | |
| "bayes_factor": 4418.178674737492, | |
| "bayes_factor_description": "If comparison level is `exact match` then comparison is 4,418.18 times more likely to be a match", | |
| "comparison_name": "postcode_fake", | |
| "comparison_sort_order": 3, | |
| "comparison_vector_value": 2, | |
| "has_tf_adjustments": true, | |
| "is_null_level": false, | |
| "label_for_charts": "Exact match", | |
| "log2_bayes_factor": 12.109236048295376, | |
| "m_probability": 0.6884268238802628, | |
| "m_probability_description": "Amongst matching record comparisons, 68.84% of records are in the exact match comparison level", | |
| "max_comparison_vector_value": 2, | |
| "probability_two_random_records_match": 0.00013582694460587586, | |
| "sql_condition": "\"postcode_fake_l\" = \"postcode_fake_r\"", | |
| "tf_adjustment_column": "postcode_fake", | |
| "tf_adjustment_weight": 1, | |
| "u_probability": 0.00015581688169756194, | |
| "u_probability_description": "Amongst non-matching record comparisons, 0.02% of records are in the exact match comparison level" | |
| }, | |
| { | |
| "bayes_factor": 253.5488457667427, | |
| "bayes_factor_description": "If comparison level is `levenshtein <= 2` then comparison is 253.55 times more likely to be a match", | |
| "comparison_name": "postcode_fake", | |
| "comparison_sort_order": 3, | |
| "comparison_vector_value": 1, | |
| "has_tf_adjustments": false, | |
| "is_null_level": false, | |
| "label_for_charts": "Levenshtein <= 2", | |
| "log2_bayes_factor": 7.986119896597389, | |
| "m_probability": 0.14279157769269107, | |
| "m_probability_description": "Amongst matching record comparisons, 14.28% of records are in the levenshtein <= 2 comparison level", | |
| "max_comparison_vector_value": 2, | |
| "probability_two_random_records_match": 0.00013582694460587586, | |
| "sql_condition": "levenshtein(\"postcode_fake_l\", \"postcode_fake_r\") <= 2", | |
| "tf_adjustment_column": null, | |
| "tf_adjustment_weight": 1, | |
| "u_probability": 0.0005631718703387632, | |
| "u_probability_description": "Amongst non-matching record comparisons, 0.06% of records are in the levenshtein <= 2 comparison level" | |
| }, | |
| { | |
| "bayes_factor": 0.16890303781141733, | |
| "bayes_factor_description": "If comparison level is `all other comparisons` then comparison is 5.92 times less likely to be a match", | |
| "comparison_name": "postcode_fake", | |
| "comparison_sort_order": 3, | |
| "comparison_vector_value": 0, | |
| "has_tf_adjustments": false, | |
| "is_null_level": false, | |
| "label_for_charts": "All other comparisons", | |
| "log2_bayes_factor": -2.5657328188732578, | |
| "m_probability": 0.16878159842704615, | |
| "m_probability_description": "Amongst matching record comparisons, 16.88% of records are in the all other comparisons comparison level", | |
| "max_comparison_vector_value": 2, | |
| "probability_two_random_records_match": 0.00013582694460587586, | |
| "sql_condition": "ELSE", | |
| "tf_adjustment_column": null, | |
| "tf_adjustment_weight": 1, | |
| "u_probability": 0.9992810112479636, | |
| "u_probability_description": "Amongst non-matching record comparisons, 99.93% of records are in the all other comparisons comparison level" | |
| }, | |
| { | |
| "bayes_factor": 169.33047487057027, | |
| "bayes_factor_description": "If comparison level is `exact match` then comparison is 169.33 times more likely to be a match", | |
| "comparison_name": "birth_place", | |
| "comparison_sort_order": 4, | |
| "comparison_vector_value": 1, | |
| "has_tf_adjustments": true, | |
| "is_null_level": false, | |
| "label_for_charts": "Exact match", | |
| "log2_bayes_factor": 7.403697832164039, | |
| "m_probability": 0.8461489363543415, | |
| "m_probability_description": "Amongst matching record comparisons, 84.61% of records are in the exact match comparison level", | |
| "max_comparison_vector_value": 1, | |
| "probability_two_random_records_match": 0.00013582694460587586, | |
| "sql_condition": "\"birth_place_l\" = \"birth_place_r\"", | |
| "tf_adjustment_column": "birth_place", | |
| "tf_adjustment_weight": 1, | |
| "u_probability": 0.00499702689076556, | |
| "u_probability_description": "Amongst non-matching record comparisons, 0.50% of records are in the exact match comparison level" | |
| }, | |
| { | |
| "bayes_factor": 0.15462372254516688, | |
| "bayes_factor_description": "If comparison level is `all other comparisons` then comparison is 6.47 times less likely to be a match", | |
| "comparison_name": "birth_place", | |
| "comparison_sort_order": 4, | |
| "comparison_vector_value": 0, | |
| "has_tf_adjustments": false, | |
| "is_null_level": false, | |
| "label_for_charts": "All other comparisons", | |
| "log2_bayes_factor": -2.69316641875033, | |
| "m_probability": 0.1538510636456584, | |
| "m_probability_description": "Amongst matching record comparisons, 15.39% of records are in the all other comparisons comparison level", | |
| "max_comparison_vector_value": 1, | |
| "probability_two_random_records_match": 0.00013582694460587586, | |
| "sql_condition": "ELSE", | |
| "tf_adjustment_column": null, | |
| "tf_adjustment_weight": 1, | |
| "u_probability": 0.9950029731092345, | |
| "u_probability_description": "Amongst non-matching record comparisons, 99.50% of records are in the all other comparisons comparison level" | |
| }, | |
| { | |
| "bayes_factor": 22.411479557463675, | |
| "bayes_factor_description": "If comparison level is `exact match` then comparison is 22.41 times more likely to be a match", | |
| "comparison_name": "occupation", | |
| "comparison_sort_order": 5, | |
| "comparison_vector_value": 1, | |
| "has_tf_adjustments": true, | |
| "is_null_level": false, | |
| "label_for_charts": "Exact match", | |
| "log2_bayes_factor": 4.486165990489565, | |
| "m_probability": 0.8989352822927716, | |
| "m_probability_description": "Amongst matching record comparisons, 89.89% of records are in the exact match comparison level", | |
| "max_comparison_vector_value": 1, | |
| "probability_two_random_records_match": 0.00013582694460587586, | |
| "sql_condition": "\"occupation_l\" = \"occupation_r\"", | |
| "tf_adjustment_column": "occupation", | |
| "tf_adjustment_weight": 1, | |
| "u_probability": 0.040110483557673014, | |
| "u_probability_description": "Amongst non-matching record comparisons, 4.01% of records are in the exact match comparison level" | |
| }, | |
| { | |
| "bayes_factor": 0.10528786488033355, | |
| "bayes_factor_description": "If comparison level is `all other comparisons` then comparison is 9.50 times less likely to be a match", | |
| "comparison_name": "occupation", | |
| "comparison_sort_order": 5, | |
| "comparison_vector_value": 0, | |
| "has_tf_adjustments": false, | |
| "is_null_level": false, | |
| "label_for_charts": "All other comparisons", | |
| "log2_bayes_factor": -3.2475889290445217, | |
| "m_probability": 0.10106471770722844, | |
| "m_probability_description": "Amongst matching record comparisons, 10.11% of records are in the all other comparisons comparison level", | |
| "max_comparison_vector_value": 1, | |
| "probability_two_random_records_match": 0.00013582694460587586, | |
| "sql_condition": "ELSE", | |
| "tf_adjustment_column": null, | |
| "tf_adjustment_weight": 1, | |
| "u_probability": 0.959889516442327, | |
| "u_probability_description": "Amongst non-matching record comparisons, 95.99% of records are in the all other comparisons comparison level" | |
| } | |
| ] | |
| }, | |
| "resolve": { | |
| "axis": { | |
| "y": "independent" | |
| }, | |
| "scale": { | |
| "y": "independent" | |
| } | |
| }, | |
| "selection": { | |
| "zoom_selector": { | |
| "bind": "scales", | |
| "encodings": [ | |
| "x" | |
| ], | |
| "type": "interval" | |
| } | |
| }, | |
| "title": { | |
| "subtitle": "Use mousewheel to zoom", | |
| "text": "Model parameters (components of final match weight)" | |
| }, | |
| "vconcat": [ | |
| { | |
| "encoding": { | |
| "color": { | |
| "field": "log2_bayes_factor", | |
| "scale": { | |
| "domain": [ | |
| -10, | |
| 0, | |
| 10 | |
| ], | |
| "range": [ | |
| "red", | |
| "orange", | |
| "green" | |
| ] | |
| }, | |
| "title": "Match weight", | |
| "type": "quantitative" | |
| }, | |
| "tooltip": [ | |
| { | |
| "field": "comparison_name", | |
| "title": "Comparison name", | |
| "type": "nominal" | |
| }, | |
| { | |
| "field": "probability_two_random_records_match", | |
| "format": ".4f", | |
| "title": "Probability two random records match", | |
| "type": "nominal" | |
| }, | |
| { | |
| "field": "log2_bayes_factor", | |
| "format": ",.4f", | |
| "title": "Equivalent match weight", | |
| "type": "quantitative" | |
| }, | |
| { | |
| "field": "bayes_factor_description", | |
| "title": "Match weight description", | |
| "type": "nominal" | |
| } | |
| ], | |
| "x": { | |
| "axis": { | |
| "domain": false, | |
| "labels": false, | |
| "ticks": false, | |
| "title": "" | |
| }, | |
| "field": "log2_bayes_factor", | |
| "scale": { | |
| "domain": [ | |
| -10, | |
| 10 | |
| ] | |
| }, | |
| "type": "quantitative" | |
| }, | |
| "y": { | |
| "axis": { | |
| "title": "Prior (starting) match weight", | |
| "titleAlign": "right", | |
| "titleAngle": 0, | |
| "titleFontWeight": "normal" | |
| }, | |
| "field": "label_for_charts", | |
| "sort": { | |
| "field": "comparison_vector_value", | |
| "order": "descending" | |
| }, | |
| "type": "nominal" | |
| } | |
| }, | |
| "height": 20, | |
| "mark": { | |
| "clip": true, | |
| "height": 15, | |
| "type": "bar" | |
| }, | |
| "selection": { | |
| "zoom_selector": { | |
| "bind": "scales", | |
| "encodings": [ | |
| "x" | |
| ], | |
| "type": "interval" | |
| } | |
| }, | |
| "transform": [ | |
| { | |
| "filter": "(datum.comparison_name == 'probability_two_random_records_match')" | |
| } | |
| ] | |
| }, | |
| { | |
| "encoding": { | |
| "color": { | |
| "field": "log2_bayes_factor", | |
| "scale": { | |
| "domain": [ | |
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| 0, | |
| 10 | |
| ], | |
| "range": [ | |
| "red", | |
| "orange", | |
| "green" | |
| ] | |
| }, | |
| "title": "Match weight", | |
| "type": "quantitative" | |
| }, | |
| "row": { | |
| "field": "comparison_name", | |
| "header": { | |
| "labelAlign": "left", | |
| "labelAnchor": "middle", | |
| "labelAngle": 0 | |
| }, | |
| "sort": { | |
| "field": "comparison_sort_order" | |
| }, | |
| "type": "nominal" | |
| }, | |
| "tooltip": [ | |
| { | |
| "field": "comparison_name", | |
| "title": "Comparison name", | |
| "type": "nominal" | |
| }, | |
| { | |
| "field": "label_for_charts", | |
| "title": "Label", | |
| "type": "ordinal" | |
| }, | |
| { | |
| "field": "sql_condition", | |
| "title": "SQL condition", | |
| "type": "nominal" | |
| }, | |
| { | |
| "field": "m_probability", | |
| "format": ".4f", | |
| "title": "M probability", | |
| "type": "quantitative" | |
| }, | |
| { | |
| "field": "u_probability", | |
| "format": ".4f", | |
| "title": "U probability", | |
| "type": "quantitative" | |
| }, | |
| { | |
| "field": "bayes_factor", | |
| "format": ",.4f", | |
| "title": "Bayes factor = m/u", | |
| "type": "quantitative" | |
| }, | |
| { | |
| "field": "log2_bayes_factor", | |
| "format": ",.4f", | |
| "title": "Match weight = log2(m/u)", | |
| "type": "quantitative" | |
| }, | |
| { | |
| "field": "bayes_factor_description", | |
| "title": "Match weight description", | |
| "type": "nominal" | |
| } | |
| ], | |
| "x": { | |
| "axis": { | |
| "title": "Comparison level match weight = log2(m/u)" | |
| }, | |
| "field": "log2_bayes_factor", | |
| "scale": { | |
| "domain": [ | |
| -10, | |
| 10 | |
| ] | |
| }, | |
| "type": "quantitative" | |
| }, | |
| "y": { | |
| "axis": { | |
| "title": null | |
| }, | |
| "field": "label_for_charts", | |
| "sort": { | |
| "field": "comparison_vector_value", | |
| "order": "descending" | |
| }, | |
| "type": "nominal" | |
| } | |
| }, | |
| "height": { | |
| "step": 12 | |
| }, | |
| "mark": { | |
| "clip": true, | |
| "type": "bar" | |
| }, | |
| "resolve": { | |
| "axis": { | |
| "y": "independent" | |
| }, | |
| "scale": { | |
| "y": "independent" | |
| } | |
| }, | |
| "selection": { | |
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| "bind": "scales", | |
| "encodings": [ | |
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| } | |
| }, | |
| "transform": [ | |
| { | |
| "filter": "(datum.comparison_name != 'probability_two_random_records_match')" | |
| } | |
| ] | |
| } | |
| ] | |
| }, | |
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| |
| "text/plain": [ | |
| "<VegaLite 4 object>\n", | |
| "\n", | |
| "If you see this message, it means the renderer has not been properly enabled\n", | |
| "for the frontend that you are using. For more information, see\n", | |
| "https://altair-viz.github.io/user_guide/troubleshooting.html\n" | |
| ] | |
| }, | |
| "execution_count": 9, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "linker.match_weights_chart()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 10, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "application/vnd.vegalite.v4+json": { | |
| "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json", | |
| "config": { | |
| "view": { | |
| "continuousHeight": 300, | |
| "continuousWidth": 400 | |
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| } | |
| ], | |
| "x": { | |
| "field": "match_weight", | |
| "type": "quantitative" | |
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| "type": "quantitative" | |
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| "selection": "selector112", | |
| "value": 1 | |
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| "x": { | |
| "axis": { | |
| "title": "Threshold match weight" | |
| }, | |
| "field": "match_weight", | |
| "type": "quantitative" | |
| }, | |
| "y": { | |
| "axis": { | |
| "format": "%", | |
| "title": "Percentage of unlinkable records" | |
| }, | |
| "field": "cum_prop", | |
| "type": "quantitative" | |
| } | |
| }, | |
| "mark": "point" | |
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| "title": { | |
| "subtitle": "Records with insufficient information to exceed a given match threshold", | |
| "text": "Unlinkable records" | |
| }, | |
| "width": 400 | |
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", 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| "text/plain": [ | |
| "<VegaLite 4 object>\n", | |
| "\n", | |
| "If you see this message, it means the renderer has not been properly enabled\n", | |
| "for the frontend that you are using. For more information, see\n", | |
| "https://altair-viz.github.io/user_guide/troubleshooting.html\n" | |
| ] | |
| }, | |
| "execution_count": 10, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "linker.unlinkables_chart()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 11, | |
| "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>match_weight</th>\n", | |
| " <th>match_probability</th>\n", | |
| " <th>unique_id_l</th>\n", | |
| " <th>unique_id_r</th>\n", | |
| " <th>first_name_l</th>\n", | |
| " <th>first_name_r</th>\n", | |
| " <th>gamma_first_name</th>\n", | |
| " <th>tf_first_name_l</th>\n", | |
| " <th>tf_first_name_r</th>\n", | |
| " <th>bf_first_name</th>\n", | |
| " <th>...</th>\n", | |
| " <th>bf_birth_place</th>\n", | |
| " <th>bf_tf_adj_birth_place</th>\n", | |
| " <th>occupation_l</th>\n", | |
| " <th>occupation_r</th>\n", | |
| " <th>gamma_occupation</th>\n", | |
| " <th>tf_occupation_l</th>\n", | |
| " <th>tf_occupation_r</th>\n", | |
| " <th>bf_occupation</th>\n", | |
| " <th>bf_tf_adj_occupation</th>\n", | |
| " <th>match_key</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>18.246490</td>\n", | |
| " <td>0.999997</td>\n", | |
| " <td>Q2296770-1</td>\n", | |
| " <td>Q2296770-14</td>\n", | |
| " <td>thomas</td>\n", | |
| " <td>thomas</td>\n", | |
| " <td>3</td>\n", | |
| " <td>0.028667</td>\n", | |
| " <td>0.028667</td>\n", | |
| " <td>43.813525</td>\n", | |
| " <td>...</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>politician</td>\n", | |
| " <td>politician</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0.088932</td>\n", | |
| " <td>0.088932</td>\n", | |
| " <td>22.41148</td>\n", | |
| " <td>0.451024</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>41.233210</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>Q90404618-1</td>\n", | |
| " <td>Q90404618-3</td>\n", | |
| " <td>emlie</td>\n", | |
| " <td>emlie</td>\n", | |
| " <td>3</td>\n", | |
| " <td>0.000099</td>\n", | |
| " <td>0.000099</td>\n", | |
| " <td>43.813525</td>\n", | |
| " <td>...</td>\n", | |
| " <td>169.330475</td>\n", | |
| " <td>4.957391</td>\n", | |
| " <td>playwright</td>\n", | |
| " <td>playwright</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0.002728</td>\n", | |
| " <td>0.002728</td>\n", | |
| " <td>22.41148</td>\n", | |
| " <td>14.700783</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>41.233210</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>Q90404618-2</td>\n", | |
| " <td>Q90404618-3</td>\n", | |
| " <td>emlie</td>\n", | |
| " <td>emlie</td>\n", | |
| " <td>3</td>\n", | |
| " <td>0.000099</td>\n", | |
| " <td>0.000099</td>\n", | |
| " <td>43.813525</td>\n", | |
| " <td>...</td>\n", | |
| " <td>169.330475</td>\n", | |
| " <td>4.957391</td>\n", | |
| " <td>playwright</td>\n", | |
| " <td>playwright</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0.002728</td>\n", | |
| " <td>0.002728</td>\n", | |
| " <td>22.41148</td>\n", | |
| " <td>14.700783</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>62.523251</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>Q631006-1</td>\n", | |
| " <td>Q631006-2</td>\n", | |
| " <td>moses</td>\n", | |
| " <td>moses</td>\n", | |
| " <td>3</td>\n", | |
| " <td>0.001168</td>\n", | |
| " <td>0.001168</td>\n", | |
| " <td>43.813525</td>\n", | |
| " <td>...</td>\n", | |
| " <td>169.330475</td>\n", | |
| " <td>109.062610</td>\n", | |
| " <td>rabbi</td>\n", | |
| " <td>rabbi</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0.000316</td>\n", | |
| " <td>0.000316</td>\n", | |
| " <td>22.41148</td>\n", | |
| " <td>126.794252</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>16.829677</td>\n", | |
| " <td>0.999991</td>\n", | |
| " <td>Q7795446-1</td>\n", | |
| " <td>Q7795446-6</td>\n", | |
| " <td>thomas</td>\n", | |
| " <td>thomas</td>\n", | |
| " <td>3</td>\n", | |
| " <td>0.028667</td>\n", | |
| " <td>0.028667</td>\n", | |
| " <td>43.813525</td>\n", | |
| " <td>...</td>\n", | |
| " <td>169.330475</td>\n", | |
| " <td>2.117721</td>\n", | |
| " <td>judge</td>\n", | |
| " <td>judge</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0.010479</td>\n", | |
| " <td>0.010479</td>\n", | |
| " <td>22.41148</td>\n", | |
| " <td>3.827751</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "<p>5 rows × 47 columns</p>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " match_weight match_probability unique_id_l unique_id_r first_name_l \n", | |
| "0 18.246490 0.999997 Q2296770-1 Q2296770-14 thomas \\\n", | |
| "1 41.233210 1.000000 Q90404618-1 Q90404618-3 emlie \n", | |
| "2 41.233210 1.000000 Q90404618-2 Q90404618-3 emlie \n", | |
| "3 62.523251 1.000000 Q631006-1 Q631006-2 moses \n", | |
| "4 16.829677 0.999991 Q7795446-1 Q7795446-6 thomas \n", | |
| "\n", | |
| " first_name_r gamma_first_name tf_first_name_l tf_first_name_r \n", | |
| "0 thomas 3 0.028667 0.028667 \\\n", | |
| "1 emlie 3 0.000099 0.000099 \n", | |
| "2 emlie 3 0.000099 0.000099 \n", | |
| "3 moses 3 0.001168 0.001168 \n", | |
| "4 thomas 3 0.028667 0.028667 \n", | |
| "\n", | |
| " bf_first_name ... bf_birth_place bf_tf_adj_birth_place occupation_l \n", | |
| "0 43.813525 ... 1.000000 1.000000 politician \\\n", | |
| "1 43.813525 ... 169.330475 4.957391 playwright \n", | |
| "2 43.813525 ... 169.330475 4.957391 playwright \n", | |
| "3 43.813525 ... 169.330475 109.062610 rabbi \n", | |
| "4 43.813525 ... 169.330475 2.117721 judge \n", | |
| "\n", | |
| " occupation_r gamma_occupation tf_occupation_l tf_occupation_r \n", | |
| "0 politician 1 0.088932 0.088932 \\\n", | |
| "1 playwright 1 0.002728 0.002728 \n", | |
| "2 playwright 1 0.002728 0.002728 \n", | |
| "3 rabbi 1 0.000316 0.000316 \n", | |
| "4 judge 1 0.010479 0.010479 \n", | |
| "\n", | |
| " bf_occupation bf_tf_adj_occupation match_key \n", | |
| "0 22.41148 0.451024 0 \n", | |
| "1 22.41148 14.700783 0 \n", | |
| "2 22.41148 14.700783 0 \n", | |
| "3 22.41148 126.794252 0 \n", | |
| "4 22.41148 3.827751 0 \n", | |
| "\n", | |
| "[5 rows x 47 columns]" | |
| ] | |
| }, | |
| "execution_count": 11, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df_predict = linker.predict()\n", | |
| "df_e = df_predict.as_pandas_dataframe(limit=5)\n", | |
| "df_e" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "You can also view rows in this dataset as a waterfall chart as follows:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 12, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "application/vnd.vegalite.v4+json": { | |
| "$schema": "https://vega.github.io/schema/vega-lite/v5.2.0.json", | |
| "config": { | |
| "view": { | |
| "continuousHeight": 300, | |
| "continuousWidth": 400 | |
| } | |
| }, | |
| "data": { | |
| "values": [ | |
| { | |
| "bar_sort_order": 0, | |
| "bayes_factor": 0.00013584539607096294, | |
| "bayes_factor_description": null, | |
| "column_name": "Prior", | |
| "comparison_vector_value": null, | |
| "label_for_charts": "Starting match weight (prior)", | |
| "log2_bayes_factor": -12.845746707461347, | |
| "m_probability": null, | |
| "record_number": 0, | |
| "sql_condition": null, | |
| "term_frequency_adjustment": null, | |
| "u_probability": null, | |
| "value_l": "", | |
| "value_r": "" | |
| }, | |
| { | |
| "bar_sort_order": 1, | |
| "bayes_factor": 43.81352458633812, | |
| "bayes_factor_description": "If comparison level is `exact match first_name` then comparison is 43.81 times more likely to be a match", | |
| "column_name": "first_name", | |
| "comparison_vector_value": 3, | |
| "label_for_charts": "Exact match first_name", | |
| "log2_bayes_factor": 5.453304372061299, | |
| "m_probability": 0.5532690621971292, | |
| "record_number": 0, | |
| "sql_condition": "\"first_name_l\" = \"first_name_r\"", | |
| "term_frequency_adjustment": false, | |
| "u_probability": 0.012627814525783414, | |
| "value_l": "thomas", | |
| "value_r": "thomas" | |
| }, | |
| { | |
| "bar_sort_order": 2, | |
| "bayes_factor": 0.44049968198331907, | |
| "bayes_factor_description": "Term frequency adjustment on first_name makes comparison 2.27 times less likely to be a match", | |
| "column_name": "tf_first_name", | |
| "comparison_vector_value": 3, | |
| "label_for_charts": "Term freq adjustment on first_name with weight {cl.tf_adjustment_weight}", | |
| "log2_bayes_factor": -1.1827871172882207, | |
| "m_probability": null, | |
| "record_number": 0, | |
| "sql_condition": "\"first_name_l\" = \"first_name_r\"", | |
| "term_frequency_adjustment": true, | |
| "u_probability": null, | |
| "value_l": "thomas", | |
| "value_r": "thomas" | |
| }, | |
| { | |
| "bar_sort_order": 3, | |
| "bayes_factor": 1108.5019818791598, | |
| "bayes_factor_description": "If comparison level is `exact match surname` then comparison is 1,108.50 times more likely to be a match", | |
| "column_name": "surname", | |
| "comparison_vector_value": 3, | |
| "label_for_charts": "Exact match surname", | |
| "log2_bayes_factor": 10.114395634249066, | |
| "m_probability": 0.7838061038780122, | |
| "record_number": 0, | |
| "sql_condition": "\"surname_l\" = \"surname_r\"", | |
| "term_frequency_adjustment": false, | |
| "u_probability": 0.000707085884094934, | |
| "value_l": "chudleigh", | |
| "value_r": "chudleigh" | |
| }, | |
| { | |
| "bar_sort_order": 4, | |
| "bayes_factor": 2.3264640770760674, | |
| "bayes_factor_description": "Term frequency adjustment on surname makes comparison 2.33 times more likely to be a match", | |
| "column_name": "tf_surname", | |
| "comparison_vector_value": 3, | |
| "label_for_charts": "Term freq adjustment on surname with weight {cl.tf_adjustment_weight}", | |
| "log2_bayes_factor": 1.2181389106179061, | |
| "m_probability": null, | |
| "record_number": 0, | |
| "sql_condition": "\"surname_l\" = \"surname_r\"", | |
| "term_frequency_adjustment": true, | |
| "u_probability": null, | |
| "value_l": "chudleigh", | |
| "value_r": "chudleigh" | |
| }, | |
| { | |
| "bar_sort_order": 5, | |
| "bayes_factor": 17.946390194317825, | |
| "bayes_factor_description": "If comparison level is `levenshtein <= 1` then comparison is 17.95 times more likely to be a match", | |
| "column_name": "dob", | |
| "comparison_vector_value": 2, | |
| "label_for_charts": "Levenshtein <= 1", | |
| "log2_bayes_factor": 4.1656217789087, | |
| "m_probability": 0.3419541720944316, | |
| "record_number": 0, | |
| "sql_condition": "levenshtein(\"dob_l\", \"dob_r\") <= 1", | |
| "term_frequency_adjustment": false, | |
| "u_probability": 0.019054203569177988, | |
| "value_l": "1630-08-01", | |
| "value_r": "1638-08-01" | |
| }, | |
| { | |
| "bar_sort_order": 6, | |
| "bayes_factor": 1, | |
| "bayes_factor_description": "If comparison level is `levenshtein <= 1` then comparison is 17.95 times more likely to be a match", | |
| "column_name": "tf_dob", | |
| "comparison_vector_value": 2, | |
| "label_for_charts": "Levenshtein <= 1", | |
| "log2_bayes_factor": 0, | |
| "m_probability": 0.3419541720944316, | |
| "record_number": 0, | |
| "sql_condition": "levenshtein(\"dob_l\", \"dob_r\") <= 1", | |
| "term_frequency_adjustment": true, | |
| "u_probability": 0.019054203569177988, | |
| "value_l": "", | |
| "value_r": "" | |
| }, | |
| { | |
| "bar_sort_order": 7, | |
| "bayes_factor": 253.5488457667427, | |
| "bayes_factor_description": "If comparison level is `levenshtein <= 2` then comparison is 253.55 times more likely to be a match", | |
| "column_name": "postcode_fake", | |
| "comparison_vector_value": 1, | |
| "label_for_charts": "Levenshtein <= 2", | |
| "log2_bayes_factor": 7.986119896597389, | |
| "m_probability": 0.14279157769269107, | |
| "record_number": 0, | |
| "sql_condition": "levenshtein(\"postcode_fake_l\", \"postcode_fake_r\") <= 2", | |
| "term_frequency_adjustment": false, | |
| "u_probability": 0.0005631718703387632, | |
| "value_l": "tq13 8df", | |
| "value_r": "tq1w 8df" | |
| }, | |
| { | |
| "bar_sort_order": 8, | |
| "bayes_factor": 1, | |
| "bayes_factor_description": "If comparison level is `levenshtein <= 2` then comparison is 253.55 times more likely to be a match", | |
| "column_name": "tf_postcode_fake", | |
| "comparison_vector_value": 1, | |
| "label_for_charts": "Levenshtein <= 2", | |
| "log2_bayes_factor": 0, | |
| "m_probability": 0.14279157769269107, | |
| "record_number": 0, | |
| "sql_condition": "levenshtein(\"postcode_fake_l\", \"postcode_fake_r\") <= 2", | |
| "term_frequency_adjustment": true, | |
| "u_probability": 0.0005631718703387632, | |
| "value_l": "", | |
| "value_r": "" | |
| }, | |
| { | |
| "bar_sort_order": 9, | |
| "bayes_factor": 1, | |
| "bayes_factor_description": "If comparison level is `null` then comparison is 1.00 times more likely to be a match", | |
| "column_name": "birth_place", | |
| "comparison_vector_value": -1, | |
| "label_for_charts": "Null", | |
| "log2_bayes_factor": 0, | |
| "m_probability": null, | |
| "record_number": 0, | |
| "sql_condition": "\"birth_place_l\" IS NULL OR \"birth_place_r\" IS NULL", | |
| "term_frequency_adjustment": false, | |
| "u_probability": null, | |
| "value_l": "devon", | |
| "value_r": "nan" | |
| }, | |
| { | |
| "bar_sort_order": 10, | |
| "bayes_factor": 1, | |
| "bayes_factor_description": "If comparison level is `null` then comparison is 1.00 times more likely to be a match", | |
| "column_name": "tf_birth_place", | |
| "comparison_vector_value": -1, | |
| "label_for_charts": "Null", | |
| "log2_bayes_factor": 0, | |
| "m_probability": null, | |
| "record_number": 0, | |
| "sql_condition": "\"birth_place_l\" IS NULL OR \"birth_place_r\" IS NULL", | |
| "term_frequency_adjustment": true, | |
| "u_probability": null, | |
| "value_l": "", | |
| "value_r": "" | |
| }, | |
| { | |
| "bar_sort_order": 11, | |
| "bayes_factor": 22.411479557463675, | |
| "bayes_factor_description": "If comparison level is `exact match` then comparison is 22.41 times more likely to be a match", | |
| "column_name": "occupation", | |
| "comparison_vector_value": 1, | |
| "label_for_charts": "Exact match", | |
| "log2_bayes_factor": 4.486165990489565, | |
| "m_probability": 0.8989352822927716, | |
| "record_number": 0, | |
| "sql_condition": "\"occupation_l\" = \"occupation_r\"", | |
| "term_frequency_adjustment": false, | |
| "u_probability": 0.040110483557673014, | |
| "value_l": "politician", | |
| "value_r": "politician" | |
| }, | |
| { | |
| "bar_sort_order": 12, | |
| "bayes_factor": 0.45102446362382964, | |
| "bayes_factor_description": "Term frequency adjustment on occupation makes comparison 2.22 times less likely to be a match", | |
| "column_name": "tf_occupation", | |
| "comparison_vector_value": 1, | |
| "label_for_charts": "Term freq adjustment on occupation with weight {cl.tf_adjustment_weight}", | |
| "log2_bayes_factor": -1.1487224073233344, | |
| "m_probability": null, | |
| "record_number": 0, | |
| "sql_condition": "\"occupation_l\" = \"occupation_r\"", | |
| "term_frequency_adjustment": true, | |
| "u_probability": null, | |
| "value_l": "politician", | |
| "value_r": "politician" | |
| }, | |
| { | |
| "bar_sort_order": 13, | |
| "bayes_factor": 310986.05216345465, | |
| "bayes_factor_description": null, | |
| "column_name": "Final score", | |
| "comparison_vector_value": null, | |
| "label_for_charts": "Final score", | |
| "log2_bayes_factor": 18.246490350851023, | |
| "m_probability": null, | |
| "record_number": 0, | |
| "sql_condition": null, | |
| "term_frequency_adjustment": null, | |
| "u_probability": null, | |
| "value_l": "", | |
| "value_r": "" | |
| }, | |
| { | |
| "bar_sort_order": 0, | |
| "bayes_factor": 0.00013584539607096294, | |
| "bayes_factor_description": null, | |
| "column_name": "Prior", | |
| "comparison_vector_value": null, | |
| "label_for_charts": "Starting match weight (prior)", | |
| "log2_bayes_factor": -12.845746707461347, | |
| "m_probability": null, | |
| "record_number": 1, | |
| "sql_condition": null, | |
| "term_frequency_adjustment": null, | |
| "u_probability": null, | |
| "value_l": "", | |
| "value_r": "" | |
| }, | |
| { | |
| "bar_sort_order": 1, | |
| "bayes_factor": 43.81352458633812, | |
| "bayes_factor_description": "If comparison level is `exact match first_name` then comparison is 43.81 times more likely to be a match", | |
| "column_name": "first_name", | |
| "comparison_vector_value": 3, | |
| "label_for_charts": "Exact match first_name", | |
| "log2_bayes_factor": 5.453304372061299, | |
| "m_probability": 0.5532690621971292, | |
| "record_number": 1, | |
| "sql_condition": "\"first_name_l\" = \"first_name_r\"", | |
| "term_frequency_adjustment": false, | |
| "u_probability": 0.012627814525783414, | |
| "value_l": "emlie", | |
| "value_r": "emlie" | |
| }, | |
| { | |
| "bar_sort_order": 2, | |
| "bayes_factor": 127.56870790236921, | |
| "bayes_factor_description": "Term frequency adjustment on first_name makes comparison 127.57 times more likely to be a match", | |
| "column_name": "tf_first_name", | |
| "comparison_vector_value": 3, | |
| "label_for_charts": "Term freq adjustment on first_name with weight {cl.tf_adjustment_weight}", | |
| "log2_bayes_factor": 6.995130674907623, | |
| "m_probability": null, | |
| "record_number": 1, | |
| "sql_condition": "\"first_name_l\" = \"first_name_r\"", | |
| "term_frequency_adjustment": true, | |
| "u_probability": null, | |
| "value_l": "emlie", | |
| "value_r": "emlie" | |
| }, | |
| { | |
| "bar_sort_order": 3, | |
| "bayes_factor": 1108.5019818791598, | |
| "bayes_factor_description": "If comparison level is `exact match surname` then comparison is 1,108.50 times more likely to be a match", | |
| "column_name": "surname", | |
| "comparison_vector_value": 3, | |
| "label_for_charts": "Exact match surname", | |
| "log2_bayes_factor": 10.114395634249066, | |
| "m_probability": 0.7838061038780122, | |
| "record_number": 1, | |
| "sql_condition": "\"surname_l\" = \"surname_r\"", | |
| "term_frequency_adjustment": false, | |
| "u_probability": 0.000707085884094934, | |
| "value_l": "clifford", | |
| "value_r": "clifford" | |
| }, | |
| { | |
| "bar_sort_order": 4, | |
| "bayes_factor": 0.9047360299740261, | |
| "bayes_factor_description": "Term frequency adjustment on surname makes comparison 1.11 times less likely to be a match", | |
| "column_name": "tf_surname", | |
| "comparison_vector_value": 3, | |
| "label_for_charts": "Term freq adjustment on surname with weight {cl.tf_adjustment_weight}", | |
| "log2_bayes_factor": -0.1444311687668022, | |
| "m_probability": null, | |
| "record_number": 1, | |
| "sql_condition": "\"surname_l\" = \"surname_r\"", | |
| "term_frequency_adjustment": true, | |
| "u_probability": null, | |
| "value_l": "clifford", | |
| "value_r": "clifford" | |
| }, | |
| { | |
| "bar_sort_order": 5, | |
| "bayes_factor": 347.22157895951074, | |
| "bayes_factor_description": "If comparison level is `exact match` then comparison is 347.22 times more likely to be a match", | |
| "column_name": "dob", | |
| "comparison_vector_value": 3, | |
| "label_for_charts": "Exact match", | |
| "log2_bayes_factor": 8.439712800260082, | |
| "m_probability": 0.6178418282395027, | |
| "record_number": 1, | |
| "sql_condition": "\"dob_l\" = \"dob_r\"", | |
| "term_frequency_adjustment": false, | |
| "u_probability": 0.0017793877618175018, | |
| "value_l": "1861-01-01", | |
| "value_r": "1861-01-01" | |
| }, | |
| { | |
| "bar_sort_order": 6, | |
| "bayes_factor": 0.13941503113840126, | |
| "bayes_factor_description": "Term frequency adjustment on dob makes comparison 7.17 times less likely to be a match", | |
| "column_name": "tf_dob", | |
| "comparison_vector_value": 3, | |
| "label_for_charts": "Term freq adjustment on dob with weight {cl.tf_adjustment_weight}", | |
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| "sql_condition": "\"dob_l\" = \"dob_r\"", | |
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| "bayes_factor": 253.5488457667427, | |
| "bayes_factor_description": "If comparison level is `levenshtein <= 2` then comparison is 253.55 times more likely to be a match", | |
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| "comparison_vector_value": 1, | |
| "label_for_charts": "Levenshtein <= 2", | |
| "log2_bayes_factor": 7.986119896597389, | |
| "m_probability": 0.14279157769269107, | |
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| "sql_condition": "levenshtein(\"postcode_fake_l\", \"postcode_fake_r\") <= 2", | |
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| "u_probability": 0.0005631718703387632, | |
| "value_l": "wr11 7qp", | |
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| "bayes_factor": 1, | |
| "bayes_factor_description": "If comparison level is `levenshtein <= 2` then comparison is 253.55 times more likely to be a match", | |
| "column_name": "tf_postcode_fake", | |
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| "bayes_factor": 169.33047487057027, | |
| "bayes_factor_description": "If comparison level is `exact match` then comparison is 169.33 times more likely to be a match", | |
| "column_name": "birth_place", | |
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| "label_for_charts": "Exact match", | |
| "log2_bayes_factor": 7.403697832164039, | |
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| "sql_condition": "\"birth_place_l\" = \"birth_place_r\"", | |
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| "bayes_factor": 4.9573913820183515, | |
| "bayes_factor_description": "Term frequency adjustment on birth_place makes comparison 4.96 times more likely to be a match", | |
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| "comparison_vector_value": 1, | |
| "label_for_charts": "Term freq adjustment on birth_place with weight {cl.tf_adjustment_weight}", | |
| "log2_bayes_factor": 2.309581162904922, | |
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| "label_for_charts": "Term freq adjustment on occupation with weight {cl.tf_adjustment_weight}", | |
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| "label_for_charts": "Final score", | |
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| "label_for_charts": "Starting match weight (prior)", | |
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| "bayes_factor": 43.81352458633812, | |
| "bayes_factor_description": "If comparison level is `exact match first_name` then comparison is 43.81 times more likely to be a match", | |
| "column_name": "first_name", | |
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| "label_for_charts": "Exact match first_name", | |
| "log2_bayes_factor": 5.453304372061299, | |
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| "value_l": "emlie", | |
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| "bayes_factor": 127.56870790236921, | |
| "bayes_factor_description": "Term frequency adjustment on first_name makes comparison 127.57 times more likely to be a match", | |
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| "label_for_charts": "Term freq adjustment on first_name with weight {cl.tf_adjustment_weight}", | |
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| "bayes_factor": 1108.5019818791598, | |
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| "log2_bayes_factor": 10.114395634249066, | |
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| "column_name": "tf_dob", | |
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| "label_for_charts": "Term freq adjustment on dob with weight {cl.tf_adjustment_weight}", | |
| "log2_bayes_factor": -2.8425419800240883, | |
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| "bayes_factor": 253.5488457667427, | |
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| "label_for_charts": "Levenshtein <= 2", | |
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| "value_l": "wr11 7qp", | |
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| "bayes_factor": 1, | |
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| "label_for_charts": "Levenshtein <= 2", | |
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| "bayes_factor": 43.81352458633812, | |
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| "label_for_charts": "Exact match first_name", | |
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| "bayes_factor": 347.22157895951074, | |
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| "sql_condition": "\"dob_l\" = \"dob_r\"", | |
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| "bayes_factor": 4418.178674737492, | |
| "bayes_factor_description": "If comparison level is `exact match` then comparison is 4,418.18 times more likely to be a match", | |
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| "label_for_charts": "Exact match", | |
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| "sql_condition": "\"postcode_fake_l\" = \"postcode_fake_r\"", | |
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| "value_l": "ex20 3pz", | |
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| "bayes_factor": 3.0502712761114723, | |
| "bayes_factor_description": "Term frequency adjustment on postcode_fake makes comparison 3.05 times more likely to be a match", | |
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| }, | |
| "description": "Filter by the interation number", | |
| "name": "record_number", | |
| "value": 0 | |
| } | |
| ], | |
| "resolve": { | |
| "axis": { | |
| "y": "independent" | |
| } | |
| }, | |
| "title": { | |
| "subtitle": "How each comparison contributes to the final match score", | |
| "text": "Match weights waterfall chart" | |
| }, | |
| "transform": [ | |
| { | |
| "filter": "(datum.record_number == record_number)" | |
| }, | |
| { | |
| "frame": [ | |
| null, | |
| 0 | |
| ], | |
| "window": [ | |
| { | |
| "as": "sum", | |
| "field": "log2_bayes_factor", | |
| "op": "sum" | |
| }, | |
| { | |
| "as": "lead", | |
| "field": "column_name", | |
| "op": "lead" | |
| } | |
| ] | |
| }, | |
| { | |
| "as": "sum", | |
| "calculate": "datum.column_name === \"Final score\" ? datum.sum - datum.log2_bayes_factor : datum.sum" | |
| }, | |
| { | |
| "as": "lead", | |
| "calculate": "datum.lead === null ? datum.column_name : datum.lead" | |
| }, | |
| { | |
| "as": "previous_sum", | |
| "calculate": "datum.column_name === \"Final score\" || datum.column_name === \"Prior match weight\" ? 0 : datum.sum - datum.log2_bayes_factor" | |
| }, | |
| { | |
| "as": "top_label", | |
| "calculate": "datum.sum > datum.previous_sum ? datum.column_name : \"\"" | |
| }, | |
| { | |
| "as": "bottom_label", | |
| "calculate": "datum.sum < datum.previous_sum ? datum.column_name : \"\"" | |
| }, | |
| { | |
| "as": "sum_top", | |
| "calculate": "datum.sum > datum.previous_sum ? datum.sum : datum.previous_sum" | |
| }, | |
| { | |
| "as": "sum_bottom", | |
| "calculate": "datum.sum < datum.previous_sum ? datum.sum : datum.previous_sum" | |
| }, | |
| { | |
| "as": "center", | |
| "calculate": "(datum.sum + datum.previous_sum) / 2" | |
| }, | |
| { | |
| "as": "text_log2_bayes_factor", | |
| "calculate": "(datum.log2_bayes_factor > 0 ? \"+\" : \"\") + datum.log2_bayes_factor" | |
| }, | |
| { | |
| "as": "dy", | |
| "calculate": "datum.sum < datum.previous_sum ? 4 : -4" | |
| }, | |
| { | |
| "as": "baseline", | |
| "calculate": "datum.sum < datum.previous_sum ? \"top\" : \"bottom\"" | |
| }, | |
| { | |
| "as": "prob", | |
| "calculate": "1. / (1 + pow(2, -1.*datum.sum))" | |
| }, | |
| { | |
| "as": "zero", | |
| "calculate": "0*datum.sum" | |
| } | |
| ], | |
| "width": { | |
| "step": 75 | |
| } | |
| }, | |
| "image/png": 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