{ "cells": [ { "cell_type": "markdown", "metadata": { "code_folding": [], "customInput": null, "hidden_ranges": [], "originalKey": "95e7a97a-bf78-48d4-a0c1-c0e8dfc4fed9", "showInput": true }, "source": [ "# Multi-Objective Optimization Ax API\n", "### Using the Service API\n", "For Multi-objective optimization (MOO) in the `AxClient`, objectives are specified through the `ObjectiveProperties` dataclass. An `ObjectiveProperties` requires a boolean `minimize`, and also accepts an optional floating point `threshold`. If a `threshold` is not specified, Ax will infer it through the use of heuristics. If the user knows the region of interest (because they have specs or prior knowledge), then specifying the thresholds is preferable to inferring it. But if the user would need to guess, inferring is preferable.\n", "\n", "\n", "To learn more about how to choose a threshold, see [Set Objective Thresholds to focus candidate generation in a region of interest](#Set-Objective-Thresholds-to-focus-candidate-generation-in-a-region-of-interest). See the [Service API Tutorial](/tutorials/gpei_hartmann_service.html) for more infomation on running experiments with the Service API." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "code_folding": [], "customInput": null, "hidden_ranges": [], "originalKey": "06bf2029-0ea4-40b4-aced-956f1411cb6e", "showInput": true }, "outputs": [ { "data": { "text/html": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:04] ax.utils.notebook.plotting: Injecting Plotly library into cell. Do not overwrite or delete cell.\n" ] }, { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from ax.service.ax_client import AxClient\n", "from ax.service.utils.instantiation import ObjectiveProperties\n", "\n", "import torch\n", "\n", "# Plotting imports and initialization\n", "from ax.utils.notebook.plotting import render, init_notebook_plotting\n", "from ax.plot.pareto_utils import compute_posterior_pareto_frontier\n", "from ax.plot.pareto_frontier import plot_pareto_frontier\n", "init_notebook_plotting()\n", "\n", "# Load our sample 2-objective problem\n", "from botorch.test_functions.multi_objective import BraninCurrin\n", "branin_currin = BraninCurrin(negate=True).to(\n", " dtype=torch.double, \n", " device= torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\"),\n", ")" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "code_folding": [], "customInput": null, "executionStartTime": 1628191188673, "executionStopTime": 1628191188746, "hidden_ranges": [], "originalKey": "c687973d-1b09-4a8f-9108-1f74adf64d4d", "requestMsgId": "ea523260-8896-48e4-a62f-3530d268b209", "showInput": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:04] ax.service.ax_client: Starting optimization with verbose logging. To disable logging, set the `verbose_logging` argument to `False`. Note that float values in the logs are rounded to 6 decimal points.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:04] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:04] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:04] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:04] ax.core.experiment: The is_test flag has been set to True. This flag is meant purely for development and integration testing purposes. If you are running a live experiment, please set this flag to False\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:04] ax.modelbridge.dispatch_utils: Using Bayesian optimization since there are more ordered parameters than there are categories for the unordered categorical parameters.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:04] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+MOO', steps=[Sobol for 5 trials, MOO for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.\n" ] } ], "source": [ "ax_client = AxClient()\n", "ax_client.create_experiment(\n", " name=\"moo_experiment\",\n", " parameters=[\n", " {\n", " \"name\": f\"x{i+1}\",\n", " \"type\": \"range\",\n", " \"bounds\": [0.0, 1.0],\n", " }\n", " for i in range(2)\n", " ],\n", " objectives={\n", " # `threshold` arguments are optional\n", " \"a\": ObjectiveProperties(minimize=False, threshold=branin_currin.ref_point[0]), \n", " \"b\": ObjectiveProperties(minimize=False, threshold=branin_currin.ref_point[1])\n", " },\n", " overwrite_existing_experiment=True,\n", " is_test=True,\n", ")" ] }, { "cell_type": "markdown", "metadata": { "code_folding": [], "customInput": null, "hidden_ranges": [], "originalKey": "70fd45e1-a2ce-4034-bb44-086507833472", "showInput": true }, "source": [ "### Create an Evaluation Function\n", "In the case of MOO experiments, evaluation functions can be any arbitrary function that takes in a `dict` of parameter names mapped to values and returns a `dict` of objective names mapped to a `tuple` of mean and SEM values." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "code_folding": [], "customInput": null, "executionStartTime": 1628191201840, "executionStopTime": 1628191201871, "hidden_ranges": [], "originalKey": "a0e4fa8d-ebc7-4dc6-b370-ed4a83e3208f", "requestMsgId": "9cfd336d-c317-4d1c-a028-42d45903bac6", "showInput": true }, "outputs": [], "source": [ "def evaluate(parameters):\n", " evaluation = branin_currin(torch.tensor([parameters.get(\"x1\"), parameters.get(\"x2\")]))\n", " # In our case, standard error is 0, since we are computing a synthetic function.\n", " # Set standard error to None if the noise level is unknown.\n", " return {\"a\": (evaluation[0].item(), 0.0), \"b\": (evaluation[1].item(), 0.0)}" ] }, { "cell_type": "markdown", "metadata": { "code_folding": [], "customInput": null, "hidden_ranges": [], "originalKey": "4200cd7c-8e13-4cbf-b0c1-72b52d900aaf", "showInput": true }, "source": [ "### Run Optimization" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "customInput": null, "executionStartTime": 1628191208271, "executionStopTime": 1628191238749, "originalKey": "f91b1a1e-c78a-4262-a211-a13115c007c1", "requestMsgId": "842a1cf8-97a3-43d6-83a3-f258ea96ae20", "showInput": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:04] ax.service.ax_client: Generated new trial 0 with parameters {'x1': 0.983525, 'x2': 0.880826}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:04] ax.service.ax_client: Completed trial 0 with data: {'a': (-110.04435, 0.0), 'b': (-4.415009, 0.0)}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:04] ax.service.ax_client: Generated new trial 1 with parameters {'x1': 0.284874, 'x2': 0.996169}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:04] ax.service.ax_client: Completed trial 1 with data: {'a': (-76.733429, 0.0), 'b': (-5.32105, 0.0)}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:04] ax.service.ax_client: Generated new trial 2 with parameters {'x1': 0.157631, 'x2': 0.170887}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:04] ax.service.ax_client: Completed trial 2 with data: {'a': (-74.338181, 0.0), 'b': (-12.619493, 0.0)}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:04] ax.service.ax_client: Generated new trial 3 with parameters {'x1': 0.919571, 'x2': 0.823327}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:04] ax.service.ax_client: Completed trial 3 with data: {'a': (-109.492561, 0.0), 'b': (-4.670225, 0.0)}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:04] ax.service.ax_client: Generated new trial 4 with parameters {'x1': 0.157077, 'x2': 0.747911}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:04] ax.service.ax_client: Completed trial 4 with data: {'a': (-1.574656, 0.0), 'b': (-6.496104, 0.0)}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:05] ax.service.ax_client: Generated new trial 5 with parameters {'x1': 0.0, 'x2': 0.873354}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:05] ax.service.ax_client: Completed trial 5 with data: {'a': (-29.427704, 0.0), 'b': (-1.307669, 0.0)}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:06] ax.service.ax_client: Generated new trial 6 with parameters {'x1': 0.0, 'x2': 0.733562}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:06] ax.service.ax_client: Completed trial 6 with data: {'a': (-50.964787, 0.0), 'b': (-1.482588, 0.0)}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:07] ax.service.ax_client: Generated new trial 7 with parameters {'x1': 0.088876, 'x2': 0.850553}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:07] ax.service.ax_client: Completed trial 7 with data: {'a': (-2.35604, 0.0), 'b': (-4.78763, 0.0)}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:08] ax.service.ax_client: Generated new trial 8 with parameters {'x1': 0.036278, 'x2': 1.0}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:08] ax.service.ax_client: Completed trial 8 with data: {'a': (-7.994541, 0.0), 'b': (-2.619044, 0.0)}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:09] ax.service.ax_client: Generated new trial 9 with parameters {'x1': 0.074384, 'x2': 0.997789}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:09] ax.service.ax_client: Completed trial 9 with data: {'a': (-3.624914, 0.0), 'b': (-3.873491, 0.0)}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:10] ax.service.ax_client: Generated new trial 10 with parameters {'x1': 0.015587, 'x2': 1.0}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:10] ax.service.ax_client: Completed trial 10 with data: {'a': (-12.827548, 0.0), 'b': (-1.814473, 0.0)}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:12] ax.service.ax_client: Generated new trial 11 with parameters {'x1': 0.101829, 'x2': 0.898378}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:12] ax.service.ax_client: Completed trial 11 with data: {'a': (-1.071999, 0.0), 'b': (-4.902384, 0.0)}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:12] ax.service.ax_client: Generated new trial 12 with parameters {'x1': 1.0, 'x2': 0.0}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:12] ax.service.ax_client: Completed trial 12 with data: {'a': (-10.960894, 0.0), 'b': (-10.179487, 0.0)}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:15] ax.service.ax_client: Generated new trial 13 with parameters {'x1': 0.052235, 'x2': 1.0}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:15] ax.service.ax_client: Completed trial 13 with data: {'a': (-5.431064, 0.0), 'b': (-3.185803, 0.0)}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:16] ax.service.ax_client: Generated new trial 14 with parameters {'x1': 0.665972, 'x2': 0.0}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:16] ax.service.ax_client: Completed trial 14 with data: {'a': (-14.253331, 0.0), 'b': (-10.860155, 0.0)}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:17] ax.service.ax_client: Generated new trial 15 with parameters {'x1': 0.081152, 'x2': 0.937224}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:17] ax.service.ax_client: Completed trial 15 with data: {'a': (-2.340555, 0.0), 'b': (-4.253287, 0.0)}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:18] ax.service.ax_client: Generated new trial 16 with parameters {'x1': 0.552481, 'x2': 0.549226}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:18] ax.service.ax_client: Completed trial 16 with data: {'a': (-37.395657, 0.0), 'b': (-6.807696, 0.0)}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:19] ax.service.ax_client: Generated new trial 17 with parameters {'x1': 0.02545, 'x2': 1.0}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:19] ax.service.ax_client: Completed trial 17 with data: {'a': (-10.319497, 0.0), 'b': (-2.205671, 0.0)}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:21] ax.service.ax_client: Generated new trial 18 with parameters {'x1': 0.113179, 'x2': 0.848205}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:21] ax.service.ax_client: Completed trial 18 with data: {'a': (-0.52506, 0.0), 'b': (-5.35144, 0.0)}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:22] ax.service.ax_client: Generated new trial 19 with parameters {'x1': 0.061773, 'x2': 0.99445}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:22] ax.service.ax_client: Completed trial 19 with data: {'a': (-4.357736, 0.0), 'b': (-3.510352, 0.0)}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:24] ax.service.ax_client: Generated new trial 20 with parameters {'x1': 0.020415, 'x2': 1.0}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:24] ax.service.ax_client: Completed trial 20 with data: {'a': (-11.554379, 0.0), 'b': (-2.007406, 0.0)}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:26] ax.service.ax_client: Generated new trial 21 with parameters {'x1': 0.006701, 'x2': 1.0}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:26] ax.service.ax_client: Completed trial 21 with data: {'a': (-15.391748, 0.0), 'b': (-1.454296, 0.0)}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:29] ax.service.ax_client: Generated new trial 22 with parameters {'x1': 0.089898, 'x2': 0.914038}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:29] ax.service.ax_client: Completed trial 22 with data: {'a': (-1.650684, 0.0), 'b': (-4.563845, 0.0)}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:30] ax.service.ax_client: Generated new trial 23 with parameters {'x1': 0.043787, 'x2': 1.0}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:30] ax.service.ax_client: Completed trial 23 with data: {'a': (-6.656959, 0.0), 'b': (-2.892798, 0.0)}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:33] ax.service.ax_client: Generated new trial 24 with parameters {'x1': 0.00252, 'x2': 1.0}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 03-10 17:10:33] ax.service.ax_client: Completed trial 24 with data: {'a': (-16.694204, 0.0), 'b': (-1.283513, 0.0)}.\n" ] } ], "source": [ "for i in range(25):\n", " parameters, trial_index = ax_client.get_next_trial()\n", " # Local evaluation here can be replaced with deployment to external system.\n", " ax_client.complete_trial(trial_index=trial_index, raw_data=evaluate(parameters))" ] }, { "cell_type": "markdown", "metadata": { "code_folding": [], "customInput": null, "hidden_ranges": [], "originalKey": "e0a6feb4-8c38-42e4-9d7c-62b79307e043", "showInput": false }, "source": [ "### Plot Pareto Frontier" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "code_folding": [], "customInput": null, "executionStartTime": 1628191262231, "executionStopTime": 1628191270720, "hidden_ranges": [], "originalKey": "c2c2b222-6b68-4f1a-839f-16b50019ada4", "requestMsgId": "563d345b-573c-4d93-a480-5db88a283250", "showInput": true }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": false }, "data": [ { "error_x": { "array": [ 0.03760334061559108, 0.032341147442670685, 0.037603340589101586, 0.03723684336331439, 0.03760334063325073, 0.05683751536596149, 0.03760334063325073, 0.019293378626778653, 0.02411399278552608, 0.03760334065091038, 0.01662910944613607, 0.024673048226595672, 0.025589869662089645, 0.019267430173303218, 0.03760334061559108, 0.05572788394776561, 0.0376033406244209, 0.05627746077064492, 0.04650861239945598, 0.022801954997779764 ], "color": "rgba(128,177,211,0.4)", "thickness": 2, "type": "data" }, "error_y": { "array": [ 0.003093647400804867, 0.0022115221990378236, 0.0030936473981771208, 0.0023576719272770594, 0.0030936473981771208, 0.0030005059731108958, 0.00309364739642529, 0.0016084212195985298, 0.0018387875602245169, 0.0030936473955493747, 0.0014543358653641043, 0.0019683151305503305, 0.0020061978480708413, 0.0015882982202203818, 0.0030936473981771208, 0.0029357444519660042, 0.0030936473999289517, 0.0029548305295951463, 0.002978164761262436, 0.0017814797952728804 ], "color": "rgba(128,177,211,0.4)", "thickness": 2, "type": "data" }, "hoverinfo": "text", "legendgroup": "mean", "marker": { "color": "rgba(128,177,211,1)" }, "mode": "markers", "name": "mean", "text": [ "Parameterization 0
a: -17.501 [-17.539, -17.463]
b: -1.18 [-1.183, -1.176]

Parameterization:
x1: 3.856046320780702e-17
x2: 0.9999999999999999", "Parameterization 1
a: -1.314 [-1.346, -1.282]
b: -4.729 [-4.731, -4.726]

Parameterization:
x1: 0.09471537015017564
x2: 0.9017173555656669", "Parameterization 2
a: -17.501 [-17.539, -17.463]
b: -1.18 [-1.183, -1.176]

Parameterization:
x1: 7.299360514809017e-17
x2: 0.9999999999999999", "Parameterization 3
a: -1.221 [-1.258, -1.184]
b: -4.778 [-4.781, -4.776]

Parameterization:
x1: 0.09613120144706586
x2: 0.8976025068144596", "Parameterization 4
a: -17.501 [-17.539, -17.463]
b: -1.18 [-1.183, -1.176]

Parameterization:
x1: 5.3072343398464015e-17
x2: 1.0", "Parameterization 5
a: -0.876 [-0.933, -0.819]
b: -4.99 [-4.993, -4.987]

Parameterization:
x1: 0.10237457242690604
x2: 0.8804314008273622", "Parameterization 6
a: -17.501 [-17.539, -17.463]
b: -1.18 [-1.183, -1.176]

Parameterization:
x1: 3.125131486735611e-16
x2: 0.9999999999999997", "Parameterization 7
a: -8.264 [-8.283, -8.244]
b: -2.568 [-2.569, -2.566]

Parameterization:
x1: 0.034902713811480954
x2: 1.0", "Parameterization 8
a: -5.017 [-5.041, -4.993]
b: -3.301 [-3.302, -3.299]

Parameterization:
x1: 0.0556894664830765
x2: 1.0", "Parameterization 9
a: -17.501 [-17.539, -17.463]
b: -1.18 [-1.183, -1.176]

Parameterization:
x1: 2.8837024029431483e-16
x2: 0.9999999999999999", "Parameterization 10
a: -12.005 [-12.022, -11.989]
b: -1.938 [-1.940, -1.937]

Parameterization:
x1: 0.018669713770116517
x2: 1.0", "Parameterization 11
a: -1.561 [-1.586, -1.536]
b: -4.604 [-4.606, -4.602]

Parameterization:
x1: 0.09121931464259231
x2: 0.9119834250797375", "Parameterization 12
a: -1.496 [-1.521, -1.470]
b: -4.636 [-4.638, -4.634]

Parameterization:
x1: 0.09213946215117888
x2: 0.9094850184374853", "Parameterization 13
a: -9.038 [-9.058, -9.019]
b: -2.425 [-2.427, -2.424]

Parameterization:
x1: 0.031145887007920505
x2: 1.0", "Parameterization 14
a: -17.501 [-17.539, -17.463]
b: -1.18 [-1.183, -1.176]

Parameterization:
x1: 4.3442529297228087e-16
x2: 0.9999999999999997", "Parameterization 15
a: -0.67 [-0.726, -0.615]
b: -5.16 [-5.163, -5.157]

Parameterization:
x1: 0.1075868757913903
x2: 0.8663522410283216", "Parameterization 16
a: -17.501 [-17.539, -17.463]
b: -1.18 [-1.183, -1.176]

Parameterization:
x1: 3.598638226444057e-15
x2: 0.9999999999999952", "Parameterization 17
a: -0.677 [-0.733, -0.620]
b: -5.154 [-5.157, -5.151]

Parameterization:
x1: 0.10739884859364608
x2: 0.8668974822843198", "Parameterization 18
a: -4.036 [-4.083, -3.990]
b: -3.618 [-3.621, -3.615]

Parameterization:
x1: 0.06449170880277177
x2: 0.9862087252369487", "Parameterization 19
a: -5.161 [-5.184, -5.139]
b: -3.259 [-3.261, -3.257]

Parameterization:
x1: 0.05443491713716612
x2: 1.0" ], "type": "scatter", "x": [ -17.501019553885794, -1.314109835537419, -17.501019553880862, -1.2212096233233964, -17.501019553880123, -0.8763119857202746, -17.50101955387878, -8.26362127752111, -5.0166263314992285, -17.501019553877835, -12.005314503532517, -1.5610254278969364, -1.4956084864655388, -9.038292218451272, -17.50101955388708, -0.6702342719376411, -17.501019553880656, -0.6765452803813652, -4.036413188650126, -5.161351841052106 ], "y": [ -1.1795693618618768, -4.728690969754292, -1.1795693618625598, -4.778396885511243, -1.1795693618619234, -4.989945115819639, -1.1795693618628538, -2.5675652577439045, -3.3005930642502133, -1.1795693618628538, -1.9380582607841896, -4.604408098623403, -4.636474015740585, -2.425474473637324, -1.1795693618623417, -5.160344352719329, -1.179569361862343, -5.154119575224339, -3.617943622996582, -3.2592460764183944 ] } ], "layout": { "height": 500, "hovermode": "closest", "legend": { "orientation": "h" }, "margin": { "b": 75, "l": 225, "pad": 4, "t": 75 }, "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": "#2a3f5f" }, "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "barpolar" } ], "carpet": [ { "aaxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "baxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "type": "carpet" } ], "choropleth": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "choropleth" } ], "contour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0.0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1.0, "#f0f921" ] ], "type": "contour" } ], "contourcarpet": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "contourcarpet" } ], "heatmap": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0.0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1.0, "#f0f921" ] ], "type": "heatmap" } ], "heatmapgl": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0.0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1.0, "#f0f921" ] ], "type": "heatmapgl" } ], "histogram": [ { "marker": { "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "histogram" } ], "histogram2d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0.0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1.0, "#f0f921" ] ], "type": "histogram2d" } ], "histogram2dcontour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0.0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1.0, "#f0f921" ] ], "type": "histogram2dcontour" } ], "mesh3d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "mesh3d" } ], "parcoords": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "parcoords" } ], "pie": [ { "automargin": true, "type": "pie" } ], "scatter": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter" } ], "scatter3d": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter3d" } ], "scattercarpet": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattercarpet" } ], "scattergeo": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergeo" } ], "scattergl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergl" } ], "scattermapbox": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattermapbox" } ], "scatterpolar": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolar" } ], "scatterpolargl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolargl" } ], "scatterternary": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterternary" } ], "surface": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0.0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1.0, "#f0f921" ] ], "type": "surface" } ], "table": [ { "cells": { "fill": { "color": "#EBF0F8" }, "line": { "color": "white" } }, "header": { "fill": { "color": "#C8D4E3" }, "line": { "color": "white" } }, "type": "table" } ] }, "layout": { "annotationdefaults": { "arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1 }, "autotypenumbers": "strict", "coloraxis": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "colorscale": { "diverging": [ [ 0, "#8e0152" ], [ 0.1, "#c51b7d" ], [ 0.2, "#de77ae" ], [ 0.3, "#f1b6da" ], [ 0.4, "#fde0ef" ], [ 0.5, "#f7f7f7" ], [ 0.6, "#e6f5d0" ], [ 0.7, "#b8e186" ], [ 0.8, "#7fbc41" ], [ 0.9, "#4d9221" ], [ 1, "#276419" ] ], "sequential": [ [ 0.0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1.0, "#f0f921" ] ], "sequentialminus": [ [ 0.0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1.0, "#f0f921" ] ] }, "colorway": [ "#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52" ], "font": { "color": "#2a3f5f" }, "geo": { "bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": { "angularaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "radialaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Pareto Frontier" }, "width": 750, "xaxis": { "ticksuffix": "", "title": { "text": "a" }, "zeroline": true }, "yaxis": { "ticksuffix": "", "title": { "text": "b" }, "zeroline": true } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "objectives = ax_client.experiment.optimization_config.objective.objectives\n", "frontier = compute_posterior_pareto_frontier(\n", " experiment=ax_client.experiment,\n", " data=ax_client.experiment.fetch_data(),\n", " primary_objective=objectives[1].metric,\n", " secondary_objective=objectives[0].metric,\n", " absolute_metrics=[\"a\", \"b\"],\n", " num_points=20,\n", ")\n", "render(plot_pareto_frontier(frontier, CI_level=0.90)) " ] }, { "cell_type": "markdown", "metadata": { "code_folding": [], "hidden_ranges": [], "originalKey": "f4f6ce29-4a0c-4ac5-84a7-f83a4de9112c", "showInput": true }, "source": [ "# Deep Dive\n", "\n", "In the rest of this tutorial, we will show two algorithms available in Ax for multi-objective optimization\n", "and visualize how they compare to eachother and to quasirandom search.\n", "\n", "MOO covers the case where we care about multiple\n", "outcomes in our experiment but we do not know before hand a specific weighting of those\n", "objectives (covered by `ScalarizedObjective`) or a specific constraint on one objective \n", "(covered by `OutcomeConstraint`s) that will produce the best result.\n", "\n", "The solution in this case is to find a whole Pareto frontier, a surface in outcome-space\n", "containing points that can't be improved on in every outcome. This shows us the\n", "tradeoffs between objectives that we can choose to make." ] }, { "cell_type": "markdown", "metadata": { "originalKey": "e04a24fa-dcfc-4430-960f-9c0e772fd754", "showInput": true }, "source": [ "### Problem Statement\n", "\n", "Optimize a list of M objective functions $ \\bigl(f^{(1)}( x),..., f^{(M)}( x) \\bigr)$ over a bounded search space $\\mathcal X \\subset \\mathbb R^d$.\n", "\n", "We assume $f^{(i)}$ are expensive-to-evaluate black-box functions with no known analytical expression, and no observed gradients. For instance, a machine learning model where we're interested in maximizing accuracy and minimizing inference time, with $\\mathcal X$ the set of possible configuration spaces" ] }, { "attachments": { "pareto_front%20%281%29.png": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAE5wAABCzCAIAAACj6tz9AAAABGdBTUEAALGPC/xhBQAADGVpQ0NQSUNDIFByb2ZpbGUAAEiJlZcHXJNHG8DvHZkkrEAEZIS9RJEZQEYIK4KATEFUQhJIGDEmBBUXoqUK1i2iOCpaFVCwDkDqQMQ6i+K2juJApVKLVVyofBcS0Npv/L7jd+/93+eee+55Hu7y3gGg08mXyXJRXQDypPnyuPBg1qSUVBbpESDAP20AAIUvUMg4sbFRkMFQ+/fy+jpAVO0VF5Wtf/b/16IvFCkEACBpkDOECkEe5BYA8GKBTJ4PADEEyq1n5stULIZsIIcOQp6r4iw1r1Rxhpp3DOokxHEhNwFApvH58iwAtNugnFUgyIJ2tB9BdpUKJVIAdAwgBwjEfCHkBMij8vKmq7gIsgPUl0HeDZmd8YXNrL/Zzxi2z+dnDbM6rsFCDpEoZLn82f9nav53yctVDs1hBytNLI+IU8UPc3gzZ3qkimmQe6QZ0TGqXEN+KxGq8w4AShUrIxLV+qipQMGF+QNMyK5CfkgkZFPIYdLc6CiNPCNTEsaDDFcLOkuSz0vQjF0iUoTGa2xukk+PixniTDmXoxlbx5cPzqvSb1PmJHI09m+KRbwh+68KxQnJkKkAYNQCSVI0ZLgGMQNFTnykWgezKhRzo4d05Mo4lf82kNkiaXiw2j6WlikPi9Poy/IUQ/FiJWIJL1rDFfnihAh1frAaAX/QfyPIDSIpJ3HIjkgxKWooFqEoJFQdO9YukiZq4sXuyfKD4zRje2W5sRp9nCzKDVfJrSCbKAriNWPxcflwcart41Gy/NgEtZ94ejZ/fKzaH7wARAEuCAEsoIQ1A0wH2UDS3tPYA9/UPWGAD+QgC4iAi0YyNCJ5sEcKn/GgEPwBSQQUw+OCB3tFoADKPw5L1U8XkDnYWzA4Igc8hpwHIkEufFcOjpIOz5YEHkGJ5B+zC6CvubCq+v4p40BJlEaiHLLL0hnSJIYSQ4gRxDCiI26CB+B+eBR8BsHqhrNxnyFvP+sTHhM6CA8I1widhFvTJMXyr3yZADqh/TBNxBlfRozbQZueeDDuD61DyzgTNwEuuAech4MHwpk9oZSr8VsVO+vfxDkcwRc51+hRXCkoZQQliOLw9UhtJ23PYSuqjH6ZH7WvGcNZ5Q73fD0/94s8C2Eb+bUmtgQ7gJ3GTmBnsSNYI2Bhx7Em7AJ2VMXDa+jR4Boami1u0J8caEfyj/n4mjlVmVS41rp2u37Q9IF80ax81QbjTpfNlkuyxPksDvwKiFg8qWD0KJabq5srAKpvivpn6iVz8FuBMM99lhXfBcA/ZWBg4MhnWRTcpwefwm3e81lmXwsA/RgAZ74RKOUFahmuehDgr4EO3FHGwBxYAwcYkRvwAn4gCISC8SAGJIAUMBXmWQzXsxzMBHPBQlACysBKsA5sBFvBdrAb7AX7QSM4Ak6An8F5cAlcA7fh+ukCz0AveA36EQQhIXSEgRgjFogt4oy4IWwkAAlFopA4JAVJR7IQKaJE5iKLkDJkNbIR2YZUIz8ih5ETyFmkA7mF3Ee6kb+Q9yiG0lAD1Ay1Q8egbJSDRqIJ6BQ0C52BFqKL0eVoBVqF7kEb0BPoefQa2ok+Q/swgGlhTMwSc8HYGBeLwVKxTEyOzcdKsXKsCqvDmuF/+grWifVg73AizsBZuAtcwxF4Ii7AZ+Dz8WX4Rnw33oC34Vfw+3gv/olAJ5gSnAm+BB5hEiGLMJNQQign7CQcIpyCu6mL8JpIJDKJ9kRvuBtTiNnEOcRlxM3EemILsYP4kNhHIpGMSc4kf1IMiU/KJ5WQNpD2kI6TLpO6SG/JWmQLshs5jJxKlpKLyeXkGvIx8mXyE3I/RZdiS/GlxFCElNmUFZQdlGbKRUoXpZ+qR7Wn+lMTqNnUhdQKah31FPUO9aWWlpaVlo/WRC2JVpFWhdY+rTNa97Xe0fRpTjQuLY2mpC2n7aK10G7RXtLpdDt6ED2Vnk9fTq+mn6Tfo7/VZmiP1uZpC7UXaFdqN2hf1n6uQ9Gx1eHoTNUp1CnXOaBzUadHl6Jrp8vV5evO163UPax7Q7dPj6E3Vi9GL09vmV6N3lm9p/okfTv9UH2h/mL97fon9R8yMIY1g8sQMBYxdjBOMboMiAb2BjyDbIMyg70G7Qa9hvqGHoZJhrMMKw2PGnYyMaYdk8fMZa5g7mdeZ74fYTaCM0I0YumIuhGXR7wxGmkUZCQyKjWqN7pm9N6YZRxqnGO8yrjR+K4JbuJkMtFkpskWk1MmPSMNRvqNFIwsHbl/5K+mqKmTaZzpHNPtphdM+8zMzcLNZGYbzE6a9ZgzzYPMs83Xmh8z77ZgWARYSCzWWhy3+J1lyOKwclkVrDZWr6WpZYSl0nKbZbtlv5W9VaJVsVW91V1rqjXbOtN6rXWrda+Nhc0Em7k2tTa/2lJs2bZi2/W2p23f2NnbJdt9a9do99TeyJ5nX2hfa3/Hge4Q6DDDocrhqiPRke2Y47jZ8ZIT6uTpJHaqdLrojDp7OUucNzt3jCKM8hklHVU16oYLzYXjUuBS63J/NHN01Oji0Y2jn4+xGZM6ZtWY02M+uXq65rrucL09Vn/s+LHFY5vH/uXm5CZwq3S76k53D3Nf4N7k/sLD2UPkscXjpifDc4Lnt56tnh+9vL3kXnVe3d423unem7xvsA3Ysexl7DM+BJ9gnwU+R3ze+Xr55vvu9/3Tz8Uvx6/G7+k4+3GicTvGPfS38uf7b/PvDGAFpAd8H9AZaBnID6wKfBBkHSQM2hn0hOPIyebs4TwPdg2WBx8KfsP15c7jtoRgIeEhpSHtofqhiaEbQ++FWYVlhdWG9YZ7hs8Jb4kgRERGrIq4wTPjCXjVvN7x3uPnjW+LpEXGR26MfBDlFCWPap6AThg/Yc2EO9G20dLoxhgQw4tZE3M31j52RuxPE4kTYydWTnwcNzZubtzpeEb8tPia+NcJwQkrEm4nOiQqE1uTdJLSkqqT3iSHJK9O7pw0ZtK8SedTTFIkKU2ppNSk1J2pfZNDJ6+b3JXmmVaSdn2K/ZRZU85ONZmaO/XoNJ1p/GkH0gnpyek16R/4Mfwqfl8GL2NTRq+AK1gveCYMEq4Vdov8RatFTzL9M1dnPs3yz1qT1S0OFJeLeyRcyUbJi+yI7K3Zb3JicnblDOQm59bnkfPS8w5L9aU50rbp5tNnTe+QOctKZJ0zfGesm9Erj5TvVCCKKYqmfAN4eL+gdFB+o7xfEFBQWfB2ZtLMA7P0ZklnXZjtNHvp7CeFYYU/zMHnCOa0zrWcu3Du/XmcedvmI/Mz5rcusF6weEFXUXjR7oXUhTkLfyl2LV5d/GpR8qLmxWaLixY//Cb8m9oS7RJ5yY1v/b7dugRfIlnSvtR96Yaln0qFpefKXMvKyz4sEyw7993Y7yq+G1ieubx9hdeKLSuJK6Urr68KXLV7td7qwtUP10xY07CWtbZ07at109adLfco37qeul65vrMiqqJpg82GlRs+bBRvvFYZXFm/yXTT0k1vNgs3X94StKVuq9nWsq3vv5d8f3Nb+LaGKruq8u3E7QXbH+9I2nH6B/YP1TtNdpbt/LhLuqtzd9zutmrv6uoa05oVtWitsrZ7T9qeS3tD9jbVudRtq2fWl+0D+5T7fv8x/cfr+yP3tx5gH6g7aHtw0yHGodIGpGF2Q2+juLGzKaWp4/D4w63Nfs2Hfhr9064jlkcqjxoeXXGMemzxsYHjhcf7WmQtPSeyTjxsndZ6++Skk1fbJra1n4o8debnsJ9PnuacPn7G/8yRs75nD59jn2s873W+4YLnhUO/eP5yqN2rveGi98WmSz6XmjvGdRy7HHj5xJWQKz9f5V09fy36Wsf1xOs3b6Td6LwpvPn0Vu6tF78W/Np/u+gO4U7pXd275fdM71X95vhbfadX59H7IfcvPIh/cPuh4OGzR4pHH7oWP6Y/Ln9i8aT6qdvTI91h3Zd+n/x71zPZs/6ekj/0/tj03OH5wT+D/rzQO6m364X8xcBfy14av9z1yuNVa19s373Xea/735S+NX67+x373en3ye+f9M/8QPpQ8dHxY/OnyE93BvIGBmR8OX/wKIDBimZmAvDXLnhOSAGAcQmeHyar73yDBVHfUwcJ/CdW3wsHixcAdbBRHde5LQDsg9WuCNqG76qjekIQQN3dh6umKDLd3dS2aPDGQ3g7MPDSDABSMwAf5QMD/ZsHBj7COyp2C4CWGeq7pqoQ4d3g+yAVXTMSFoGvivoe+kWMX7dA5YEH+Lr9F7miiVD3w44kAAAAIGNIUk0AAHomAACAhAAA+gAAAIDoAAB1MAAA6mAAADqYAAAXcJy6UTwAAAAJcEhZcwABM5UAATOVAWBIbPYAACAASURBVHic7N1BbttKEADRjOH7X3n+IkEQ2PiJSKk8HPq9pWFRLa16oUKPOecPAAAAAAAAAAAAAAAAAAAova0eAAAAAAAAAAAAAAAAAACA+xO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQe189AAAAfKkxxoe/zDmXTAIAAAAAAAAAAAAAAN/K8At+AABu7HPC+ghLMgAAAAAAAAAAAAAAvJyoFQCAGzrXsn5mWwYAAAAAAAAAAAAAgFcRtQIAcCuvyln/ZGcGAAAAAAAAAAAAAIDniVoBALiPomj9zeYMAAAAAAAAAAAAAADPELUCAHAHac76m+UZAAAAAAAAAAAAAABOE7UCALC9o0Xr5x348SfYnwEAAAAAAAAAAAAA4BxRKwAAe3t5j/rPB1qhAQAAAAAAAAAAAADgBFErAAC7OnSg9eje+/eH26IBAAAAAAAAAAAAAOCo99UDAABA61CAeiiUBQAAAAAAAAAAAAAAHve2egAAADjjwfo0Klq1rwAAAAAAAAAAAAAAcJRLrQAA7OflRatIFQAAAAAAAAAAAAAAai61AgCwmYsUrTpYAAAAAAAAAAAAAAA4RNQKAMBOpKQAAAAAAAAAAAAAALApUSsAAN+aShYAAAAAAAAAAAAAAL6GqBUAgG08HqDOOV/7QAAAAAAAAAAAAAAA4EmiVgAA9iBABQAAAAAAAAAAAACArYlaAQAAAAAAAAAAAAAAAADIiVoBANhAcabV6VcAAAAAAAAAAAAAAPhKolYAAO5mznmbdwEAAAAAAAAAAAAAgNsQtQIAcHVOqgIAAAAAAAAAAAAAwA2IWgEA4DBnWgEAAAAAAAAAAAAA4ChRKwAAAAAAAAAAAAAAAAAAOVErAACXNsaInnz62qozrQAAAAAAAAAAAAAAcIKoFQCAW1GcAgAAAAAAAAAAAADANYlaAQC4ru5M608nCljRLAAAAAAAAAAAAAAAnPO+egAAANiDnBUAAAAAAAAAAAAAAJ7hUisAAN/anPORWlXRCgAAAAAAAAAAAAAAT3KpFQCAixpjfNl7/WxWP7+jlhUAAAAAAAAAAAAAAF5F1AoAwH082aBKWAEAAAAAAAAAAAAAoPO2egAAAAAAAAAAAAAAAAAAAO5P1AoAwBWNMVaPAAAAAAAAAAAAAAAAvJKoFQAAAAAAAAAAAAAAAACAnKgVAAAAAAAAAAAAAAAAAICcqBUAgJuYc64eAQAAAAAAAAAAAAAA+F+iVgAAAAAAAAAAAAAAAAAAcqJWAAAuZ4yxegQAAAAAAAAAAAAAAODFRK0AAAAAAAAAAAAAAAAAAORErQAAAAAAAAAAAAAAAAAA5EStAADcwZxz9QgAAAAAAAAAAAAAAMDfiFoBAAAAAAAAAAAAAAAAAMiJWgEAuJYxxuoRAAAAAAAAAAAAAACA1xO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAsL055+oRAAAAAAAAAAAAAACAfxC1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAcCFjjNUjAAAAAAAAAAAAAAAACVErAAAAAAAAAAAAAAAAAAA5USsAAAAAAAAAAAAAAAAAADlRKwAAe5tzrh4BAAAAAAAAAAAAAAD4N1ErAAAAAAAAAAAAAAAAAAA5USsAAFcxxlg9AgAAAAAAAAAAAAAAUBG1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgCwsTnn6hEAAAAAAAAAAAAAAICHiFoBAAAAAAAAAAAAAAAAAMiJWgEAuIQxxuoRAAAAAAAAAAAAAACAkKgVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAAAAAgJyoFQCAXc05V48AAAAAAAAAAAAAAAA8aigBAAB43hhj9QhPsRUDAAAAAAAAAAAAAEBN1AoAwFN2z1n/ZDcGAAAAAAAAAAAAAIDO2+oBAADY2J2K1h+3+zgAAAAAAAAAAAAAAHApolYAAAAAAAAAAAAAAAAAAHKiVgAAAAAAAAAAAAAAAAAAcqJWAAD4Zc65egQAAAAAAAAAAAAAALgtUSsAAOfdqQK902cBAAAAAAAAAAAAAIALGn67DwDAWmOME6+yxwIAAAAAAAAAAAAAwF5cagUAYD+KVgAAAAAAAAAAAAAA2I6oFQAAAAAAAAAAAAAAAACAnKgVAAAAAAAAAAAAAAAAAICcqBUAgJXGGKtHAAAAAAAAAAAAAAAAvoKoFQAAAAAAAAAAAAAAAACAnKgVAAAAAAAAAAAAAAAAAICcqBUAgM3MOVePAAAAAAAAAAAAAAAAHCZqBQAAAAAAAAAAAAAAAAAg9756AAAA4KMxxoP/6W4tAAAAAAAAAAAAAAC7ELUCALDM4+nm7Z3+Kj68UOMKAAAAAAAAAAAAAP+xd/fIkSNHAEaJjrb2Irr/cXQRuViDK2pENtH4qURlZb1nTQzZjQTglMEvEkhL1AoAAH1ENL1/fqfAFQAAAAAAAAAAAACAVEStAACMpEaoec+K2q+r1HhoAAAAAAAAAAAAAACMTtQKAAD3uSdn/e2i6lYAAAAAAAAAAAAAADoStQIAQLguLetPn2NIWwEAAAAAAAAAAAAA6OLRewAAACaVpPO8QbY7zTYPAAAAAAAAAAAAAACTsKkVAACiHM1H9+9QvRimWtkKAAAAAAAAAAAAAMD9RK0AABBiZ3d6riz9+tSVulXaCgAAAAAAAAAAAADAnRZ/wg4AQBcnasyBzq577q7t7VypWwd6sAAAAAAAAAAAAAAAjOvRewAAAKjmbV+6rmvzjvTKF14JYgEAAAAAAAAAAAAAYCebWgEA6OBcRTnE2XX71m64hdOF6hCPFwAAAAAAAAAAAACAcdnUCgAAzWwEpRHbWdteyL5WAAAAAAAAAAAAAABCiVoBAKCN7aL1zklOX1HXCgAAAAAAAAAAAABAHFErAABjuL8LPSRV0XrlurpWAAAAAAAAAAAAAACCiFoBAOCqhEXrlavrWgEAAAAAAAAAAAAAiCBqBQCAS9IWrVdm0LUCAAAAAAAAAAAAANCcqBUAgLtNEkxmKFo/5ZkEAAAAAAAAAAAAAICZiVoBAOC83wLdAh3pJO0xAAAAAAAAAAAAAAC3EbUCAEBjCYvWhCMBAAAAAAAAAAAAADAbUSsAAAPI2WSOtcv0xDMc6wYBAAAAAAAAAAAAAEhO1AoAAC3l7G8BAAAAAAAAAAAAAKA7USsAALcqv/xz+a/eg7RR5kYAAAAAAAAAAAAAAOhO1AoAAGe8rT0T1q22yAIAAAAAAAAAAAAA0JGoFQAAYqXqWgEAAAAAAAAAAAAAoBdRKwAA2RXYL5qnaz3xMPMMDwAAAAAAAAAAAADA0EStAABwB2koAAAAAAAAAAAAAACTE7UCAMBhClUAAAAAAAAAAAAAADhK1AoAwH20oAAAAAAAAAAAAAAAMC1RKwAA3CRJ07uu69GPJJkcAAAAAAAAAAAAAIChiVoBAAAAAAAAAAAAAAAAAAgnagUAILUTa0UBAAAAAAAAAAAAAICERK0AAAAAAAAAAAAAAAAAAIQTtQIAcJNlWXqPAAAAAAAAAAAAAAAAdCNqBQCAw9Z1ve1TAAAAAAAAAAAAAABQg6gVAAAAAAAAAAAAAAAAAIBwolYAAPKy2hQAAAAAAAAAAAAAAMoQtQIAwBlHg1uBLgAAAAAAAAAAAAAAkxO1AgDASfs7VUUrAAAAAAAAAAAAAAA8ew8AAMAUlmXpPUKIz1p14+5q5Kw17gIAAAAAAAAAAAAAgL5ErQAAcNXLtDVtCFo1MAYAAAAAAAAAAAAAIDlRKwAAtJG2YgUAAAAAAAAAAAAAgAwevQcAAIDXNKIAAAAAAAAAAAAAAFCJqBUAAAAAAAAAAAAAAAAAgHCiVgAAwi3L0nsE/nHiXViZCwAAAAAAAAAAAABAE6JWAAAAAAAAAAAAAAAAAADCiVoBAGAWVuYCAAAAAAAAAAAAANCRqBUAgIzWde09Ah8fXgQAAAAAAAAAAAAAAO2IWgEAAAAAAAAAAAAAAAAACCdqBQAg1rIsvUfg4+PUi7CmFQAAAAAAAAAAAACAhkStAAAAAAAAAAAAAAAAAACEE7UCAEB91rQCAAAAAAAAAAAAANCdqBUAAAAAAAAAAAAAAAAAgHCiVgAA0rEjtC1rWgEAAAAAAAAAAAAAyEDUCgAAlSlaAQAAAAAAAAAAAABIQtQKAECgE0UlAAAAAAAAAAAAAABQkqgVAADKsqYVAAAAAAAAAAAAAIA8RK0AAFCTohUAAAAAAAAAAAAAgFRErQAA5KKr7MWTBwAAAAAAAAAAAAAglKgVAAAKOrqmVdEKAAAAAAAAAAAAAEA0USsAAFGOdpW0omgFAAAAAAAAAAAAACAhUSsAAJSiaAUAAAAAAAAAAAAAICdRKwAA1KFoBQAAAAAAAAAAAAAgLVErAABMStEKAAAAAAAAAAAAAMCdRK0AACQis7zi0JpWjxoAAAAAAAAAAAAAgJuJWgEAoAJFKwAAAAAAAAAAAAAAyYlaAQAIcaix5CJFKwAAAAAAAAAAAAAA+T17DwAAAFyyv2iVswIAAAAAAAAAAAAA0JFNrQAAMDBFKwAAAAAAAAAAAAAAoxC1AgCQheryKEUrAAAAAAAAAAAAAAADEbUCAMCQFK0AAAAAAAAAAAAAAIzl2XsAAAAK2t9bcs7OJyxnBQAAAAAAAAAAAAAgD5taAQBgMIpWAAAAAAAAAAAAAABGZFMrAAAMY/8KXEUrAAAAAAAAAAAAAADZiFoBAEhBhPmWBa0AAAAAAAAAAAAAAAzt0XsAAADgPUUrAAAAAAAAAAAAAACjE7UCANDYzvyS/RStAAAAAAAAAAAAAAAU8Ow9AAAAsGVP0SpnBQAAAAAAAAAAAAAgP5taAQAgL0UrAAAAAAAAAAAAAABliFoBAOhPlvmSohUAAAAAAAAAAAAAgEqevQcAAABeeFu0ylkBAAAAAAAAAAAAABiLTa0AAJCOohUAAAAAAAAAAAAAgHpErQAAtPS2xuQtRSsAAAAAAAAAAAAAACU9ew8AAMDsJJp/SlK0/hzDawIAAAAAAAAAAAAA4CJRKwAApJAkZwUAAAAAAAAAAAAAgCCP3gMAAAC5ita3wwAAAAAAAAAAAAAAwAmiVgAAerJ99CNZ0ZpzAAAAAAAAAAAAAAAAChC1AgDQjA2fJ+QvWgEAAAAAAAAAAAAAoIln7wEAAGBe20WrnBUAAAAAAAAAAAAAgEpsagUAoJvJo01FKwAAAAAAAAAAAAAAUxG1AgBAB4pWAAAAAAAAAAAAAABmI2oFAIC7ZS5at2cDAAAAAAAAAAAAAIDTnr0HAACgCDHkThsPKueC1pxTAQAAAAAAAAAAAAAwHJtaAQDoY85UcriiFQAAAAAAAAAAAAAAWhG1AgDATRStAAAAAAAAAAAAAADM7Nl7AAAAqG8jZ/3IVLRuzwkAAAAAAAAAAAAAAFfY1AoAQAd5Ms4bjFK0AgAAAAAAAAAAAABAKJtaAQBowIbP32w8mWw5q5cIAAAAAAAAAAAAAEAom1oBALhbtpgzzkBFKwAAAAAAAAAAAAAARBO1AgBAiLGK1t+mTTgqAAAAAAAAAAAAAACDevYeAAAACtooWt/+FAAAAAAAAAAAAAAAShK1AgBM52dReXEb56FEc4bNn5pVAAAAAAAAAAAAAAD4SdQKADCLjdLy80cz5KY3ULQCAAAAAAAAAAAAAMBLj94DAABwhz2l5Q01ZvluVtEKAAAAAAAAAAAAAAC/EbUCABS3LMv+0lKTeYWnBwAAAAAAAAAAAAAAG0StAACVncgsj35Eyfmp5HMov1kXAAAAAAAAAAAAAIA7PXsPAADALKoWkiVzVgAAAAAAAAAAAAAAaM6mVgCAsk7HlirN/TwrAAAAAAAAAAAAAADYSdQKAFBTttiy5JrWbA8ZAAAAAAAAAAAAAAAyE7UCAHDezFXnzPcOAAAAAAAAAAAAAAAniFoBAAhXck0rAAAAAAAAAAAAAABwiKgVAKCgVEtESxatqZ5wkJIvDgAAAAAAAAAAAACAjkStAAB8t7NmnCHsBAAAAAAAAAAAAAAAWhG1AgAQyLZPAAAAAAAAAAAAAADgk6gVAIAzrGkFAAAAAAAAAAAAAAAOEbUCAPB/Gu5WtaYVAAAAAAAAAAAAAAD48uw9AAAAiezMUPesaa1dtNa+OwAAAAAAAAAAAAAAiGBTKwAAAAAAAAAAAAAAAAAA4UStAAAFndsjak0rAAAAAAAAAAAAAAAQ59l7AAAA+mvboCpaAQAAAAAAAAAAAACAn2xqBQCY3aEG9e2aVkUrAAAAAAAAAAAAAADw0qI6AACoajtAPXEOVLQCAAAAAAAAAAAAAACn2dQKADCpt4Xq0d9XtAIAAAAAAAAAAAAAABtsagUAqGxPufr2QNjkSwAAAAAAAAAAAAAAgMmJWgEAitu/kfXnyXDnZx0pAQAAAAAAAAAAAACAt0StAAD17e9aT3CeBAAAAAAAAAAAAAAA9hC1AgBMIaJrdZIEAAAAAAAAbUHukQAAIABJREFUAAAAAAD2E7UCAEykYdrqGAkAAAAAAAAAAAAAABwiagUAmM7FtNUBEgAAAAAAAAAAAAAAOEHUCgAwr0N1q3MjAAAAAAAAAAAAAABwhagVAIB/fGtcHRQBAAAAAAAAAAAAAICGRK0AAAAAAAAAAAAAAAAAAIR79B4AAAAAAAAAAAAAAAAAAID6RK0AAAAAAAAAAAAAAAAAAIQTtQIAAAAAAAAAAAAAAAAAEE7UCgAAAAAAAAAAAAAAAABAOFErAAAAAAAAAAAAAAAAAADhRK0AAAAAAAAAAAAAAAAAAIQTtQIAAAAAAAAAAAAAAAAAEE7UCgAAAAAAAAAAAAAAAABAOFErAAAAAAAAAAAAAAAAAADhRK0AAAAAAAAAAAAAAAAAAIQTtQIAAAAAAAAAAAAAAAAAEE7UCgAAAAAAAAAAAAAAAABAOFErAAAAAAAAAAAAAAAAAADhRK0AAAAAAAAAAAAAAAAAAIQTtQIAAAAAAAAAAAAAAAAAEE7UCgAAAAAAAAAAAAAAAABAOFErAAAAAAAAAAAAAAAAAADhRK0AAAAAAAAAAAAAAAAAAIQTtQIAAAAAAAAAAAAAAAAAEE7UCgAAAAAAAAAAAAAAAABAOFErAAAAAAAAAAAAAAAAAADhRK0AAAAAAAAAAAAAAAAAAIQTtQIAAAAAAAAAAAAAAAAAEE7UCgAAAAAAAAAAAAAAAABAOFErAAAAAAAAAAAAAAAAAADhRK0AAAAAAAAAAAAAAAAAAIQTtQIAAAAAAAAAAAAAAAAAEE7UCgAAAAAAAAAAAAAAAABAOFErAAAAAAAAAAAAAAAAAADhRK0AAAAAAAAAAAAAAAAAAIQTtQIAAAAAAAAAAAAAAAAAEE7UCgAAAAAAAAAAAAAAAABAOFErAAAAAAAAAAAAAAAAAADhRK0AAAAAAAAAAAAAAAAAAIQTtQIAAAAAAAAAAAAAAAAAEE7UCgAAAAAAAAAAAAAAAABAOFErAAAAAAAAAAAAAAAAAADhRK0AAAAAAAAAAAAAAAAAAIQTtQIAAAAAAAAAAAAAAAAAEE7UCgAAAAAAAAAAAAAAAABAOFErAAAAAAAAAAAAAAAAAADhRK0AAAAAAAAAAAAAAAAAAIQTtQIAAAAAAAAAAAAAAAAAEE7UCgAAAAAAAAAAAAAAAABAOFErAAAAAAAAAAAAAAAAAADhRK0AAAAAAAAAAAAAAAAAAIQTtQIAAAAAAAAAAAAAAAAAEE7UCgAAAAAAAAAAAAAAAABAOFErAAAAAAAAAAAAAAAAAADhRK0AAAAAAAAAAAAAAAAAAIQTtQIAAAAAAAAAAAAAAAAAEE7UCgAAAAAAAAAAAAAAAABAOFErAAAAAAAAAAAAAAAAAADhRK0AAAAAAAAAAAAAAAAAAIQTtQIAAAAAAAAAAAAAAAAAEE7UCgAAAAAAAAAAAAAAAABAOFErAAAAAAAAAAAAAAAAAADhRK0AAAAAAAAAAAAAAAAAAIQTtQIAAAAAAAAAAAAAAAAAEE7UCgAAAAAAAAAAAAAAAABAOFErAAAAAAAAAAAAAAAAAADhRK0AAAAAAAAAAAAAAAAAAIQTtQIAAAAAAAAAAAAAAAAAEE7UCgAAAAAAAAAAAAAAAABAOFErAAAAAAAAAAAAAAAAAADhRK0AAAAAAAAAAAAAAAAAAIQTtQIAAAAAAAAAAAAAAAAAEE7UCgAAAAAAAAAAAAAAAABAOFErAAAAAAAAAAAAAAAAAADhRK0AAAAAAAAAAAAAAAAAAIQTtQIAAAAAAAAAAAAAAAAAEE7UCgAAAAAAAAAAAAAAAABAOFErAAAAAAAAAAAAAAAAAADhRK0AAAAAAAAAAAAAAAAAAIQTtQIAAAAAAAAAAAAAAAAAEE7UCgAAAAAAAAAAAAAAAABAOFErAAAAAAAAAAAAAAAAAADhRK0AAAAAAAAAAAAAAAAAAIQTtQIAAAAAAAAAAAAAAAAAEE7UCgAAAAAAAAAAAAAAAABAOFErAAAAAAAAAAAAAAAAAADhRK0AAAAAAAAAAAAAAAAAAIQTtQIAAAAAAAAAAAAAAAAAEE7UCgAAAAAAAAAAAAAAAABAOFErAAAAAAAAAAAAAAAAAADhRK0AAAAAAAAAAAAAAAAAAIQTtQIAAAAAAAAAAAAAAAAAEE7UCgAAAAAAAAAAAAAAAABAOFErAAAAAAAAAAAAAAAAAADhnr0HAACALJZl+fY/67p2mQQAAAAAAAAAAAAAAOpZ/Jk+AAD8zFn/5MwMAAAAAAAAAAAAAADXiVoBAJjads76JydnAAAAAAAAAAAAAAC44tF7AAAA6GZ/0Xr0lwEAAAAAAAAAAAAAgG9ErQAAAAAAAAAAAAAAAAAAhBO1AgAwqRObVy1rBQAAAAAAAAAAAACA00StAAAAAAAAAAAAAAAAAACEE7UCADCj0ztXLWsFAAAAAAAAAAAAAIBzRK0AAAAAAAAAAAAAAAAAAIQTtQIAMJ2L21YtawUAAAAAAAAAAAAAgBNErQAAAAAAAAAAAAAAAAAAhBO1AgAwlyZ7Vi1rBQAAAAAAAAAAAACAo0StAABMpGGMqmsFAAAAAAAAAAAAAIBDRK0AAAAAAAAAAAAAAAAAAIQTtQIAMIvmu1UtawUAAAAAAAAAAAAAgP1ErQAATCEoQNW1AgAAAAAAAAAAAADATqJWAAC4RNcKAAAAAAAAAAAAAAB7iFoBAKhPdwoAAAAAAAAAAAAAAN2JWgEAKO6GolU0CwAAAAAAAAAAAAAAb4laAQCo7LbcVNcKAAAAAAAAAAAAAADbRK0AANCGrhUAAAAAAAAAAAAAADaIWgEAKEtlCgAAAAAAAAAAAAAAeYhaAQCoqUvRKqMFAAAAAAAAAAAAAIDfiFoBACioY1yqawUAAAAAAAAAAAAAgJdErQAAVNM9K+0+AAAAAAAAAAAAAAAAJCRqBQCA9nStAAAAAAAAAAAAAADwjagVAIBS1KQAAAAAAAAAAAAAAJCTqBUAgDpSFa2phgEAAAAAAAAAAAAAgO5ErQAAFJEwIk04EgAAAAAAAAAAAAAA9CJqBQCggrT5aNrBAAAAAAAAAAAAAADgZqJWAACGlzwcTT4eAAAAAAAAAAAAAADcQ9QKAMDYhkhGhxgSAAAAAAAAAAAAAABCiVoBABjYQLHoQKMCAAAAAAAAAAAAAEAEUSsAAKMaLhMdbmAAAAAAAAAAAAAAAGhI1AoAwJAGDUQHHRsAAAAAAAAAAAAAAK4TtQIAMJ6h09ChhwcAAAAAAAAAAAAAgNNErQAADKZAFFrgFgAAAAAAAAAAAAAA4ChRKwAAIymTg5a5EQAAAAAAAAAAAAAA2EnUCgDAMIqFoMVuBwAAAAAAAAAAAAAAtolaAQAYQ8kEtORNAQAAAAAAAAAAAADAS8u6rr1nAACAN2rHn87kAAAAAAAADGH5138+/7H++6++k/CNVwMAAADAKEStAABkV7to/eRYDgAAAAAAQGZfzeQX8WQe3g4AAAAAA3n0HgAAALbMULR+THObAAAAAAAAjOhnM0lyXhkAAAAAadnUCgBAXrOlng7nAAAAAAAApLLRRtoFmspvb8prAgAAACAbm1oBAEgqomhtWI1GBKizRbwAAAAAAABkpmgdyG9vxMpWAAAAALKxqRUAgIziitZW39z2215+OQAAAAAAAPRi8+eIvDUAAAAA8hO1AgCQS3Qm2jZqbfiFv30/AAAAAAAA3MmC1qF5fQAAAAAk9+g9AAAA/E9QIBoqqD4d8VEAAAAAAAAwOknk6DZe08bLBQAAAIDbiFoBAMgiLuOM3nqqawUAAAAAAKAARWsNulYAAAAAMhO1AgDQ37Is4xatoVcJfTIAAAAAAADwRdFaia4VAAAAgLRErQAAdBYabd5TtEZfS9cKAAAAAABAKEVrPbpWAAAAAHIStQIA0E30GtI7i9boK1rZCgAAAAAAQBBFa1W6VgAAAAASErUCANBHdKJ5f9F6w3V1rQAAAAAA8Dd7d4/curEtYFRQKeJEFDo+Ho2HdkZzFDvkRJTiBXwl61ISiJ9G997da5WTa+uQ3WgQom/5qw2UpWjtm64VAAAAgGimVv+tPwAAw2qbs5Z69zrvsu/dAQAAAAAAYA1F6yAcNAAAAABxiFoBAKinwqDRh99v60StBd/oyBoAAAAAAADgJwOGjutnk/Z3BQY8bgAAAABiErUCAFBDhcLzaV3kWS1qLfhex1cCAAAAAAAAnw2SOK6vWBd0c0EGOXQAAAAAgntpvQAAAHpWp+q8Cdh2zvNc4Qp8vEXAKwAAAAAAAEBARVLPsIrv7uMFOy4/p9f3jncHAAAAQCgmtQIAUFjNkPXD+q+1NSe1ln3HTXzPBwAAAAAA4FvLzWfqsrFarJv3KnV8+gAAAABk8dx6AQAAdEXA+VWT5TU5CAAAAAAAAFJL2jROr++3v2q+Y7X3KivpEQMAAADQE5NaAQAoJkXRWn9Sa9n33coXfgAAAAAAAD4s1JhJc8e2fWl/Fy3pjgAAAABI5KX1AgAA6ESKorWheZ5NTwUAAAAAAKChvPNFv7VjOyuLzfWvfPvJnkLQ6fW9p+0AAAAAEJBJrQAAFJCoaG01qbX4AtbznR8AAAAAAIDlUDNXx7g1Z929u/VvlOsCPvV1PwAAAACQi6gVAIACKoeaR77ENo9aC65hJd/5AQAAAAAABtdTwVg/NF35jrku41NfdwUAAAAAiTy3XgAAAOlJNLeqvIUmc3QBAAAAAACguCajU+frZc2rbZ0fCwAAAABjMqkVAICjajaTx7++RpjUWnYla/jaDwAAAAAAMKw+BnI2yVl3rCHL9bzp494AAAAAIBeTWgEAyGGe587KzP52BAAAAAAAQDR9VIsRitaVL55rXuvyjnLtBQAAAIAsRK0AACTQcfzZ8dYAAAAAAADguCBF6/q30IICAAAAwAJRKwAAoY0wznSEPQIAAAAAAFBfB2NaQxWt698oUddqWCsAAAAAlYlaAQCIa6jUc6jNAgAAAAAAcLYOcsSARet6HVz/m242AgAAAEAQolYAACIac3jpmLsGAAAAAACgvoAV6J3IRWv8q7dJZ9sBAAAAIDhRKwAARxXvMAcPO4unrYNfTwAAAAAAgAEtF6EixuPWXMNEM06Xt5NoIwAAAADEJ2oFACAQo0o/uA4AAAAAAAAMK/KY1k3koAAAAABwZ/LfygMAUMQ0TUf+eLXvpQfX+aHmF+kja/aFHwAAAAAAYDTZx7QmKlrXLLX5ItfLfucMaOWHxdkBAAAAoby0XgAAAJ2Y53lHeym5XON2lVxeAAAAAAAAHso+GjT7+r+aXt/7SAq72Ug6xz8UC6/gTAEAAID6RK0AABSzqWvVW271ccVWXmRXGAAAAAAAgDs9BWwR9jJfLz1VuJ1tJ6+ap3D3XhE+VgAAAED3RK0AAJS0PFNUZlnEw7rVdQYAAAAAABhT9iIx+/p/0s2M0242ElCQm//zMpw1AAAAcBJRKwAA5Ykq63CdAQAAAAAAGJPcjg4ECVl/8rE8HzcAAACgrMl/Bw8AwFB+mm66lS/SAAAAAAAAhPIwkAtepm0K/ELtZeXKQ615QfYbKb7gLesCRw8AAAAUYVIrAAAAAAAAAAAAwInytqwfzG4FAAAAinhuvQAAAAAAAAAAAAAOMV0zvixN48NbJctG4phe3zu7aLcddbYpAAAAoBqTWgEAAAAAAAAAAGhJHUeXur+xbxvUzAMAAACbmNQKAAAAAAAAAACQWPfh3J1oBV209VQw2i231WiDTIfaLAAAAHCcqBUAAAAAAAAAAKBnA1aXMWUJ/9wwu42cd468dwAAAGATUSsAAAAAAAAAAADNCOHogzv5SdoKAAAArCBqBQAAAAAAAAAAyOphP2bqJjs8vG2Ei58pOe+4IAAAAMACUSsAAAAAAAAAAADUoPTrjHpzgSsDAAAAfEvUCgAAAAAAAAAAkJJmjIYGv/3krGu4SgAAAMBXolYAAAAAAAAAAIA+zddL6yU80EfwFv8679Dlpkrp476tRtoKAAAAfCZqBQAAAAAAAAAAAHhMn7mb6wYAAADciFoBAAAAAAAAAADyUYgl1dPB9bSXNUbbb3GSYAAAAOBJ1AoAAAAAAAAAAACwTI1ZiisJAAAAgxO1AgAAAAAAAAAAdGi+XlovgdzcQjfmixbnegIAAMDIRK0AAAAAAAAAAADJSMIIovtbsfsNtiIVBgAAgGGJWgEAAAAAAAAAAADuqS7P5goDAADAgEStAAAAAAAAAAAAUI+QLwXHVIfrDAAAAKMRtQIAAAAAAAAAAGSypgGbr5cKK6F7a26k/qLE6fW9v01F5moDAADAUEStAAAAAAAAAAAAAE9PAstGXHYAAAAYh6gVAAAAAAAAAAAAQFrZkosPAAAAgxC1AgAAAAAAAAAAAKMTVTbnCAAAAGAEolYAAAAAAAAAAIA01kRf8/VSYSVFJFrqsNacUQctYgdb6IODAAAAgO6JWgEAAAAAAAAAAIBxCSlDcRwAAADQN1ErAAAAAAAAAAAAMCgJZUAOBQAAADomagUAAAAAAAAAAABGJJ4My9EAAABAr0StAAAAAAAAAAAAOai8YtpxLv0dZcYdZVzzUBwQAAAAdEnUCgAAAAAAAAAAQDPz9dJ6CYxIMJmCYwIAAID+iFoBAAAAAAAAAAD60X0jKnKrr/ubish85AEAAKAzolYAAAAAAAAAAADYSXGXkVPLxXkBAABAT0StAAAAAAAAAAAAtGQQKDUpJAEAAAAaErUCAAAAAAAAAAAAQ1C0JuXgAAAAoBuiVgAAAAAAAAAAgAT6brqSDmvt+1C2in814q+QBY4PAAAA+iBqBQAAAAAAAAAAIBNtG4zJZx8AAAA6IGoFAAAAAAAAAACgvXTDWvV1uTgvAAAAgAhErQAAAAAAAAAAAEDPFK3dcJQAAACQnagVAAAAAAAAAACgE+mGnd5Zv/7mYVvzBVSW/daiJ6N9+gAAAKAzolYAAAAAAAAAAACgWxpIAAAAgDhErQAAAAAAAAAAAESRYljr17c2yDQsRWuXHCsAAADkJWoFAAAAAAAAAAAgkOBdq5oOIvBJBAAAgKRErQAAAAAAAAAAAMSSa/DpfL3o62JyLgAAAADRiFoBAAAAAAAAAAAIZ2XXWrla/Pp2ufrboShau+eIAQAAICNRKwAAAAAAAAAAQHRjtluhutbp9X3MU9jEJaIytxwAAACkI2oFAAAAAAAAAAAgqCBd60+vb0xrWFpHAAAAgJhErQAAAAAAAAAAAMTVvGs9o2hVw0IpAmYAAADIRdQKAAAAAAAAAABAaOu71uJ5mxmtGakcAQAAAMIStQIAAAAAAAAAABDd+oi0VNC4kMgqWiEUGTMAAAAk8tJ6AQAAAAAAAAAAAPDYfL2sTNduP7YvPX34ForW4PSNAAAAAJGJWgEAAAAAAAAAAMjhFpRuSls//8H1P7+8ACCa6fXdxxMAAABSELUCAAAAAAAAAACQyfqRrR+OT+/Uy6VgTCsAAABAcM+tFwAAAAAAAAAAAADbzNdLzcpU0QrxSZoBAAAgBZNaAQAAAAAAAAAASOnWmp5asp2Rs0pkT6JpBAAAAIhP1AoAAAAAAAAAAEBiH41o2aZRegoAAAAAxYlaAQAAAAAAAAAA6EGRulXLCnlNr+8+wgAAABCcqBUAAAAAAAAAAICu3FVty42rBK4PZef0AgAAAHASUSsAAAAAAAAAAAA9Oztb3ZRTamjhVIa1AgAAQHDPrRcAAAAAAAAAAAAAsJ8xrQAAAABZiFoBAAAAAAAAAACAAkzIBAAAAGCZqBUAAAAAAAAAACA6rSDASib3AgAAQGSiVgAAAAAAAAAAAKhBnHwGBSMAAABAIqJWAAAAAAAAAAAAAAAAAABOJ2oFAAAAAAAAAACAnYwJhYB8MAEAACAsUSsAAAAAAAAAAACcbr5eWi8BAAAAABoTtQIAAAAAAAAAAABLws69DLswAAAAAL4lagUAAAAAAAAAAOiEwC8sY1oBAAAA4EnUCgAAAAAAAAAAAPuoiCEsH08AAACISdQKAAAAAAAAAAAAJzKm9SSqRQAAAIB0RK0AAAAAAAAAAAAJCCMJzi0KAAAAwEOiVgAAAAAAAAAAANhs5ZhQqScAAAAAfHhpvQAAAAAAAAAAAAD4xtdqVCAKrDe9vntoAAAAQDQmtQIAAAAAAAAAABDOt3NQp9f3lfNRz2ZMKwAAAADsYFIrAAAAAAAAAABQ0l3sJ+pjq4e9qOmLlQUJie/EXBUAAAAAy0StAAAAAAAAAADAIctpmca1skGCz7bbNKYVAAAAAPZ5br0AAAAAAAAAAAAgq+n1feuwxB1/JJqGWxghklx/bYPfSCMc1mej7RcAAACAfUxqBQAAAAAAAAAANjvYE97+eK4K7m7LH/8z1y6CC96p3qRYJAAAAADEJGoFAAAAAAAAAAA2KFj0ZUlbl7ecZRfU5H6AIKbXd59HAAAACOW59QIAAAAAAAAAAIA0zphRGXzu5crlBd8Fpaw5aAUdAAAAAPzEpFao4e3trcjr/Pr1q8jrVGDLu9lyZKW2/DTkrm05sgG3XMTb21uiLQ94yh7aR9hyZLa824Bbfsqz6wG3/OTePsCWI7PlIwbctS1HZsu72XJkHtpH2HJktrybLUfmoX1EhS3vyPnWF6F/fv+7dT1BtrxGwVOer5eHq4owmXDvlv/a9NOVd7ryfjh43NEeX2t2/ef3vwfv8Wi7ZnCJfjWXYsu7DbjlpyF3bcuRDbhlAIDsTGoFAAAAAAAAAAAeWw7b5uvl9tfXv7/y9f/+Z1vNWIHhq/E1b3e/2pFns4OPJwAAAEBSolYAAAAAAAAAAOCBhX7s25b17gdWvkvArnUTlV3fVg4srbASAAAAAMhL1AoAAAAAAAAAACxZLlprrqQmhSqfKVoBAAAAoAhRKwAAAAAAAAAA8KOfWr6HA1rvfnjlT2Yf1lpHxy3xJtWuw5qitddDUfMCAAAAUJaoFQAAAAAAAAAA+N5C0Vp5JSmEuixJJ80GzCNHLloBAAAAoDhRKwAAAAAAAAAA8I1WRWuEYa1Ji9ChVOhIp9d3RSsAAAAAlCVqBQAAAAAAAAAA7pnRGtzgB1GnaA2ykuACDtcFAAAAIDJRKwAAAAAAAAAAAFEsRJLz9XL769QFrBzQ+jRA0WpkMQAAAADFiVoBAAAAAAAAAID/0XxM69///FXnjQoK2Df2VyRW2NH6nDXgiQMAAABAfKJWAAAAAAAAAADgP82L1ub6a0HTWRjWun6M6ia3lzWgFQAAAADOJmoFAAAAAAAAAAAeUPEF1PehLHStTxsb1GWbXseA1jvLxwQAAAAAX720XgAAAAAAAAAAABCFIaWdmV7f80aYf37/+/c/fy3/zN0du2azu2/yvFdyH08DAAAAAM4gagUAAAAAAAAAAJYcb/m6r+Na5Y7z9dL3tf3z+99fv36t3+NJV2O0nHWl+Xp5e2u9CAAAAACyeW69AAAAAAAAAAAAIIS+88iVXISAGjal8/WiaAUAAACAgkxqBQAAAAAAAAAAfqToy256fe/gEG9bqJYcd3DFDlJ3AwAAAHASk1oBAAAAAAAAAAANW1ZD5ZcV5qYazbqSqwQAAADAPia1AgAAAAAAAAAA3yvSrXWfy6r7Kvt8wYvcXU7wTvefWQAAAAAaErUCAAAAAAAAAMDoojVsf37/23oJmczXy/IJTq/vvXabX/e15mbu9WpU4wICAAAAsJuoFQAAAAAAAAAA+MaA3Vq0uJcdBrxvAQAAACCR59YLAAAAAAAAAAAAWjq15JSJ1vGw5HQQrPTwVpENAwAAAHCEqBUAAAAAAAAAALjXsFv78/vfVm+9g8APAAAAAGA9USsAAAAAAAAAAIzLAM9uyGs5LtGY1jgrAQAAAGATUSsAAAAAAAAAAHCKXMVsrtXu0P0GAQAAAID4pnmeW68BAADqmaapyOv4Ig0AAAAAAPTh29Cx1AjEHRVlw+mL+5rPaOMil3cRbbVEk+v+0WmzUrRbFwAAAAZnUisAAAAAAAAAAAxKEnZQulDKibPA7QEAAABABaJWAAAAAAAAAACgPIFcE+k6W7Jwa5GUWxcAAACiEbUCAAAAAAAAAAAhNEyPxklwx9kpm7gxAAAAAKhD1AoAAAAAAAAAAPynSFkqkGvIWEKKc1MBAAAAUIqoFQAAAAAAAAAAaC9dNZduwR8kx9xJekvk/QwCAAAAjEzUCgAAAAAAAAAA/D+RWB+cIwW5nQAAAAAoSNQKAAAAAAAAAACDOqlVSzf1Md2C11g+3C63zD7LN4OildTcwAAAABDQS+sFAAAAAAAAAAAAzQQJfoIsYyjT67vLjrwZAAAAgMpMagUAAAAAAAAAANgmRQ6aYpFE5hYCAAAAoDhRKwAAAAAAAAAAUIzBj6EsR4kOa3DLN0CKojXFIgEAAAD4TNQKAAAAAAAAAAC01DZLE3YyJnc+3dM8AwAAQEyiVgAAAAAAAAAAgG4Z1soOakAAAAAATiJqBQAAAAAAAAAAythRSGZs59KtWdfKneVDz3WH51otAAAAAKJWAAAAAAAAAAAAAAAAAABOJ2oFAAAAAAAAAAAGNc6QUsNa+dDTmFb4iTsZAAAAwnppvQBI7+3trcjr/Pr1q8jrAAAAQE/8ezcA5OJ3NwAkUuoX95Pf3XyyI4wUHdU0Xy8LZzS9vjuOEShaAQDq8H+YAwD8xKRWAAAAAAAAAACAtVJXf+a1Dq7XojXvygEAAAAGJGoFAAAAAAAAAAAYhfxvWL0WrfCV+xkAAAAiE7UCAAAAAAAAAABH7Rjy2Tw6Mpj0K9dkTM0/jAAAAACMQ9QKAAAAAAAAAAAwkOWCUdfape6PVZcLAAAAkIWoFQAAAAAAAAAAYJVuwjld61CWD7Sbuxpu3NIAAAAQnKgVAAAAAAAAAAA4ZEfXPN4XAAAgAElEQVQDKTpqTtc6iHGK1p72AgAAANAxUSsAAAAAAAAAADAc0eaTrnUA4xStcOOuBgAAgPhErQAAAAAAAAAAAIPStXZM0QoAAABAQKJWAAAAAAAAAABgvx3dY9KaLumyH9K1dmnMorXXfQEAAAD0RNQKAAAAAAAAAAAwNF1rZ8YsWsG9DQAAACmIWgEAAAAAAAAAgLGoNL/StXZj8KK1+w0CAAAAZCdqBQAAAAAAAAAAQNfag8GLVkbm9gYAAIAsRK0AAAAAAAAAAMBOO0LHpN1R0mVvNV8vCzvVtQa3cEDLJ9uZcXYKAAAAkJGoFQAAAAAAAAAAgP/oWjNaLlprrgTqc5MDAABAIqJWAAAAAAAAAABgILLMNXStuSha74y5awAAAIAURK0AAAAAAAAAAEAlSrNEdK1ZKFoZnPscAAAAchG1AgAAAAAAAAAAe4xTNg5bTM3Xy7B7z87ZDb59AAAAgLBErQAAAAAAAAAAAPxIHJiOI2Mc7nYAAABIR9QKAAAAAAAAAACMYpzpsmXdjf1UkYVydzRO54NL0T1HDAAAABm9tF4AAAAAAAAAAAAwBPVRdk4wLEfzk/l6kbIDAAAAhGJSKwAAAAAAAAAAwI/kggABeTgDAABAUqJWAAAAAAAAAABgM8MPgRSkjwAAAAChiFoBAAAAAAAAAIAhCHFhTLrW/jhTAAAAyGua57n1GgAAoJ5pmoq8ji/SAAAAAADA4HYEos0bpH1Ra/NlA8dp2nvisQwAAACpmdQKAAAAAAAAAADwPekU9MFnGQAAACAIUSsAAAAAAAAAAHA6RRnQlqdQH5wjAAAAZCdqBQAAAAAAAAAA+je9vrdeAgCHKFoBAACgA6JWAAAAAAAAAAAAoH+SyNQcHwAAAPRB1AoAAAAAAAAAAPANARX0x+caAAAAoC1RKwAAAAAAAAAAADAKXWtGTg0AAAC6IWoFAAAAAAAAAAA6N72+t14CEIhCMhfnBQAAAD0RtQIAAAAAAAAAAAAQkaIVAAAAOiNqBQAAAAAAAAAAerZvTKuMCvrmM56CYwIAAID+iFoBAAAAAAAAAACA4Qgmg3NAAAAA0CVRKwAAAAAAAAAA0K19Y1qBQcgmw3I0AAAA0CtRKwAAAAAAAAAAADAo8WRADgUAAAA6JmoFAAAAAAAAAAA2S1Ec7R7TmmJ3QCk+8qE4DgAAAOibqBUAAAAAAAAAAOjQ7qIVGJCQMggHAQAAAN0TtQIAAAAAAAAAAL1RtAJbySmbcwQAAAAwAlErAAAAAAAAAABwOpUpEJ+osiEXHwAAAAYhagUAAAAAAAAAAPYIGyAdDGjD7guowBOgCZcdAAAAxiFqBQAAAAAAAAAA+mEkLHCQwLIyFxwAAACGImoFAAAAAAAAAAB22lQiVchNFa1AEfP1orSsw3UGAACA0YhaAQAAAAAAAACAHihagbL0lmdzhQEAAGBA0zzPrdcAAAD1TNNU5HV8kQYAAAAAAPiwqSY9I2EqmLMqrIA7gvkzeNgCAADAsExqBQAAAAAAAAAADmnbJunNgFPJL4tzSQEAAGBkolYAAAAAAAAAAKCegg3q9PquaAUqmK8XHWYpriQAAAAMTtQKAAAAAAAAAAActSlSKlKiylmBytSYB2mDAQAAgKenp2me59ZrAACAeqZpKvI6vkgDAAAAAAB8tTU03Vc3rXmX+XrZUb2qrYA1RPU7eMACAAAAN6JWAADGImoFAAAAAAA41Xld68pX/nhBUStwHl3reh6tAAAAwGeiVgAAxiJqBQAAAAAAONvu1utr+LQ7kd23BuUVsIm09SHPVQAAAOCOqBUAgLGIWgEAAAAAAOqo3HrddVOiVqAOXetPPFEBAACAb4laAQAYi6gVAAAAAACgmmqt1/ERr9++CMBK0tbPPE4BAACABaJWAADGImoFAAAAAACo7NTW69t0yphWoAlpqwcpAAAA8NBL6wUAAAAAAAAAAAA9+2icCuZeuikgoNujacy01WMZAAAAWEnUCgAAAAAAAAAA1HC8bhVNAfGNlrZ6MgMAAACbTPM8t14DAADUM01TkdfxRRoAAAAAAKCsbwOwfa3UjpZMlAWcpOO61ZMTAAAA2EHUCgDAWEStAAAAAAAAfdvXj0mzgFP1lLZ6YAIAAABHvLReAAAAAAAAAAAAAEDPPkLQ1HWrnBUAAAA4TtQKAAAAAAAAAAAMTaYFVJOxbvWQBAAAAAoStQIAAAAAAAAAAJ1IFIkBgwtetwpZAQAAgJOIWgEAAAAAAAAAAADa+JyPtg1chawAAABABaJWAAAAAAAAAABgXCIuII67J9LZjasHIAAAAFCfqBUAAAAAAAAAAOhB2wmHAMX9FJ3ueNzpVwEAAIAgRK0AAAAAAAAAAAAAaShUAQAAgLyeWy8AAAAAAAAAAADgqH1jWoVhAAAAAAA1iVoBAAAAAAAAAAAAAAAAADjdS+sFAAAAAAAAAAAAaXwdiJp32GnelQMAAAAAJCVqBQAAAAAAAAAAHvjast79o7aB6MLyAAAAAACI47n1AgAAAAAAAAAAgNDWJKPT63uustSYVgAAAACA+kStAAAAAAAAAADAj+KnqjtWqGgFAAAAAGhC1AoAAAAAAAAAAHxvay8av4AFAAAAAKAhUSsAAAAAAAAAAPCNFIWqMa0AAAAAAImIWgEAAAAAAAAAgHu7i9aaKWyK7BYAAAAAgA+iVgAAAAAAAAAAYBTGtAIAAAAANCRqBQAAAAAAAAAA/keKCag7FqloBQAAAABoS9QKAAAAAAAAAAD0T9EKAAAAANCcqBUAAAAAAAAAAEgmxSxZAAAAAADuiFoBAAAAAAAAAID/HOxFKwxE3bFCY1oBAAAAACIQtQIAAAAAAAAAAGkoWgEAAAAA8nppvQBI7+3trcjr/Pr1q8jrAAAAQE/8ezcA5OJ3NwAkUuoX95Pf3YSnaAX64F+6ASAXv7sBAH5iUisAAAAAAAAAAFDG2QXp1jGtilYAAAAAgFBErQAAAAAAAAAAQAKKVgAAAACA7EStAAAAAAAAAABAAadGpIpWAAAAAIAOiFoBAAAAAAAAAICjFK0AAAAAADwkagUAAAAAAAAAAA5RtAIAAAAAsIaoFQAAAAAAAAAA+M+mKHS+XhStAAAAAACs9NJ6AQAAAAAAAAAAQDJ12lFFKwAAAABAZ0StAAAAAAAAAADAKtWq0a0565OiFQAAAAAgg+fWCwAAAAAAAAAAAGL5KRDd0ZrusGNAq6IVAAAAACAFUSsAAAAAAAAAALDW2V3rjqL1pJUAAAAAAFCcqBUAAAAAAAAAALi3EIue1LVOr++KVgAAAACAvr20XgAAAAAAAAAAABDRfL38VJne/n6ppnRHJStnBQAAAADIyKRWAAAAAAAAAABgj+MjW3dMZ31StAIAAAAApGVSKwAAAAAAAAAA8L2FYa03H/90a2i6L4iVswIAAAAApCZqBQAAAAAAAAAAfnTrSB82qJ9/4Kf09MhkVzkrAAAAAEAHRK0AAAAAAAAAAMADD0e2fnYkXv32rQu+GgAAAAAADT23XgAAAAAAAAAAAJBA/bh0vl4UrQAAAAAAPTGpFQAAAAAAAAAAWOWWmJYdxPrTuwAAAAAA0B9RKwAAAAAAAAAAsMFJaauWFQAAAACge6JWAAAAAAAAAABgs48G9UjdKmQFAAAAABiKqBUAAAAAAAAAANjv2zD1a+mqXwUAAAAAQNQKAAAAAAAAAAAUJmEFAAAAAOCr59YLAAAAAAAAAAAAAAAAAACgf9M8z63XAAAA9UzTVOR1fJEGAAAAAAAAAAAAAIBNTGoFAAAAAAAAAAAAAAAAAOB0olYAAAAAAAAAAAAAAAAAAE4nagUAAAAAAAAAAAAAAAAA4HSiVgAAAAAAAAAAAAAAAAAATidqBQAAAAAAAAAAAAAAAADgdKJWAAAAAAAAAAAAAAAAAABOJ2oFAAAAAAAAAAAAAAAAAOB0olYAAAAAAAAAAAAAAAAAAE4nagUAAAAAAAAAAAAAAAAA4HSiVgAAAAAAAAAAAAAAAAAATidqBQAAAAAAAAAAAAAAAADgdKJWAAAAAAAAAAAAAAAAAABOJ2oFAAAAAAAAAAAAAAAA+D/27iCpkWMJwLCY4AgcgRuwIDgd4nQTLLgBR+AOvAV+GAup1Wp1dVVmfl/MwnZgUV1VEuMY/5EANCdqBQAAAAAAAAAAAAAAAACgOVErAAAAAAAAAAAAAAAAAADNiVoBAAAAAAAAAAAAAAAAAGhO1AoAAAAAAAAAAAAAAAAAQHOiVgAAAAAAAAAAAAAAAAAAmhO1AgAAAAAAAAAAAAAAAADQnKgVAAAAAAAAAAAAAAAAAIDmRK0AAAAAAAAAAAAAAAAAADQnagUAAAAAAAAAAAAAAAAAoDlRKwAAAAAAAAAAAAAAAAAAzYlaAQAAAAAAAAAAAAAAAABoTtQKAAAAAAAAAAAAAAAAAEBzolYAAAAAAAAAAAAAAAAAAJoTtQIAAAAAAAAAAAAAAAAA0JyoFQAAAAAAAAAAAAAAAACA5kStAAAAAAAAAAAAAAAAAAA0J2oFAAAAAAAAAAAAAAAAAKA5USsAAAAAAAAAAAAAAAAAAM2JWgEAAAAAAAAAAAAAAAAAaE7UCgAAAAAAAAAAAAAAAABAc6JWAAAAAAAAAAAAAAAAAACaE7UCAAAAAAAAAAAAAAAAANCcqBUAAAAAAAAAAAAAAAAAgOZErQAAAAAAAAAAAAAAAAAANCdqBQAAAAAAAAAAAAAAAACgOVErAAAAAAAAAAAAAAAAAADNiVoBAAAAAAAAAAAAAAAAAGhO1AoAAAAAAAAAAAAAAAAAQHOiVgAAAAAAAAAAAAAAAAAAmhO1AgAAAAAAAAAAAAAAAADQnKgVAAAAAAAAAAAAAAAAAIDmRK0AAAAAAAAAAAAAAAAAADQnagUAAAAAAAAAAAAAAAAAoDlRKwAAAAAAAAAAAAAAAAAAzYlaAQAAAAAAAAAAAAAAAABoTtQKAAAAAAAAAAAAAAAAAEBzolYAAAAAAAAAAAAAAAAAAJoTtQIAAAAAAAAAAAAAAAAA0JyoFQAAAAAAAAAAAAAAAACA5kStAAAAAAAAAAAAAAAAAAA0J2oFAAAAAAAAAAAAAAAAAKA5USsAAAAAAAAAAAAAAAAAAM2JWgEAAAAAAAAAAAAAAAAAaO629wIgvL9//67yOk9PT6u8DgAAAGTiv7sBIBY/uwEgkLV+cO/87AaATfiPbgCIxc9uAIBTTGoFAAAAAAAAAAAAAAAAAKA5USsAAAAAAAAAAAAAAAAAAM2JWgEAAAAAAAAAAAAAAAAAaE7UCgAAAAAAAAAAAAAAAABAc6JWAAAAAAAAAAAAAAAAAACaE7UCAAAAAAAAAAAAAAAAANCcqBUAAAAAAAAAAAAAAAAAgOZErQAAAAAAAAAAAAAAAAAANCdqBQAAAAAAAAAAAAAAAACgOVErAAAAAAAAAAAAAAAAAADNiVoBAAAAAAAAAAAAAAAAAGhO1AoAAAAAAAAAAAAAAAAAQHOiVgAAAAAAAAAAAAAAAAAAmrvtvQAAAAAY3cfD/ddf3L29910JBxwNAL89Pu+//uL1Zd9zHRzjdAAAAAAAAAAAijOpFQAAAE76eLj/ziZ3PxJKRnBwNE4HgMfn/XczufvRTzKIg9NxQAAAAAAAAAAABYlaAQAA4DiRZDiODKAyhWRETg0AAAAAAAAAoBpRKwAAABwy9jOEu7f33//Q2QEUZOZnFK8v+9//0PEBAAAAAAAAAJQiagUAAID/mEgij1aUdHTqRHStAHVM9JBHE0r6OnUoulYAAAAAAAAAgCJErQAAAPCvUzHk3du7onVMulaAyk6VkK8ve0XrsHStAAAAAAAAAACViVoBAABgt9vtPh7uJ4rWjRfDRSa6VmkrQFaPz/uJonXTpXC5ia5V2goAAAAAAAAAkJuoFQAAAKameipaQ5g4Jl0rQD4T3aOiNYqJk9K1AgAAAAAAAAAkJmoFAACgOkVrDrpWgCIUrWnoWgEAAAAAAAAAChK1AgAAUJqiNRNdK0B6itZkdK0AAAAAAAAAANWIWgEAAKhL0ZqPrhUgMUVrSrpWAAAAAAAAAIBSbj4/P3uvAQAAtnNzc7PK6/iNNCSgaE3M4QLko2jNzfkCAAAAAAAAABRhUisAAAAViR5zM68VIBnFY3rmtQIAAAAAAAAAFCFqBQAAoBxFawW6VoA0FK1F6FoBAAAAAAAAACoQtQIAAFCLovWnj4f771+917I+XStAAhGL1sfnfZQIc7R16loBAAAAAAAAANK7+fz87L0GAADYzs3NzSqv4zfSEJSi9affu5FyExw6QFzhitajCx5tqeMvMty5AwAAAAAAAAAw323vBQAAAMBGDOf86ehufDzcl+o8qz0vQCxpJnN+P0jfIDPHfj4+73WtAAAAAAAAAAChmdQKAEAtJrVCWdNFa7WysdrwUqcPEM50gTls1jgzHN1y/QMuaY6gFwAAAAAAAAAAgLNMagUAAKC6Uk1jzXG1d2/vNR8cIKUEQeNBsbn6EyUYyvr6sk/wFAAAAAAAAAAA/GZSKwAAtZjUCjVVG0x61MyqM/GGuAYAUUzUjIMXrat0mJc+4/XfdNhdjXsTAAAAAAAAAAA4xaRWAAAAkjOi0w6c9fFwr2sFGIT5nBvvQNA69PF5H3TlAAAAAAAAAADF/em9AAAAAGhouudM3zF+PNwrWr9NH7eNAhjBdM8pYlzd4Fs6vTz9MwAAAAAAAABARKJWAAAA0ipbtH61rCrN33StACNTtG4sxJbqWgEAAAAAAAAAkrntvQAAAABgBYJMAGC+EEUrAAAAAAAAAAD53Hx+fvZeAwAAbOfm5maV1/EbaRhf+jGtjSrWBDtzVvq7ARBRjjGtUQaHRtnPbzmuBwAAAAAAAAAAO1ErAADViFqhiATVYq/JqyE253oJbghAJmmSxRBRa6D9/CnNJQEAAAAAAAAAKO5P7wUAAAAAAABbEH8CAAAAAAAAANCXqBUAAIBsDOE8UPCRz5rek15jcgFqMoFzG68v+9CbOb34EDNyAQAAAAAAAADYiVoBAABIRo747e7t/etX74WE5CIBbEOLuI3QOetM7hIAAAAAAAAAQAi3vRcAAAAA26lQeFZ4xlXcvb0rVwEGVyHFbC3THr6+7JWrAAAAAAAAAADRmdQKAABAHtONYuLa83soa+JnbGF6uySvAK1NB4qZaswuXl/2+fZw+okkrwAAAAAAAAAA4zOpFQAAAIJRrgIAE/K1rAAAAAAAAAAApHHz+fnZew0AALCdm5ubVV7Hb6RhQGXHtM4xf+howY1ycwK5dHyu44ORpRzT2n1SaNB9u1TKy5PVpW8KxwcAAAAAAAAA6ZnUCgAAQAaXpm4w08fDvTBye2u9o0+9jjOF7rrHn8noAH96fN7bkO2t9aY+9TrOFAAAAAAAAADSELUCAACQn4CNCXdv76Lo7jY+goNv5yMCRpOsXvt+nNVT3mQbNd/ry14X3d3GR3Dw7cpefgAAAAAAAABIQNQKAABAeIpEmjKstZFx3rk/V+KsYQNlc8RTGd78DRHyzWRYayPjvHl/rsRZAwAAAAAAAEAsolYAAAAANjJOyHrK9wrVrcBmJHmMbJyQ9ZTvFXorAQAAAAAAAEAIN5+fn73XAAAA27m5uVnldfxGGsZxtpFTpu0uKQnLbpeL1NT4LesERw/rOhvIhc7Sjj5d6CcaWe671N34LesERw8AAAAAAAAAIzOpFQAAAIBWQuesX8xuBaCO0C3rN7NbAQAAAAAAAGBkJrUCAFCLSa2QjOmaM5nUOofrtKIELesprgFcI/1oTZNaN5b+Rm0pR856lGsAAAAAAAAAAEMxqRUAAACA1STOWb8Y3ApAMolz1i8GtwIAAAAAAADAUP70XgAAAAAslL6dYzSu3LSPh/tSW1TteeF66cM5BuTWTXt83pfaomrPCwAAAAAAAABjErUCAACQljmKXMSFWaxy3ln52WF1hihyKXdmscp5Z+VnBwAAAAAAAIAR3PZeAAAAAABR6Tm/fO2DLhqA8ek5v3ztgy4aAAAAAAAAALZnUisAAAAhnU3p1GUscPbaaDi/mVD6mz2BCWc7OmkZy5y9ORrObyaU/mZDAAAAAAAAAGB7JrUCAAAAcAHd5rSPh3tRPQBDkW5OMLIVAAAAAAAAADZmUisAAADxaOroqPj1K/74MxnZCgcEdfRV/AYWf/yZjLEFAAAAAAAAgM2IWgEAAEjImEQWc3lOEWpeynbBfGYkcg335xSh5qVsFwAAAAAAAABsQNQKAAAAwBn6zGWUwAD0os9cRgkMAAAAAAAAAK2JWgEAAAhGIUZ3pS6hLPN6NpDi5GGMoNQ9lGVezwYCAAAAAAAAQDuiVgAAAACOU2OuRRsMwDbUmGuxkwAAAAAAAADQiKgVAACAbO7e3nsvgdhcoS8izNXZUjjq9WXfewmE5xZ90WGuy8xbAAAAAAAAAGhB1AoAAEAkkjAGkfsqGivajo2lGj0Y48h9G+WX7dhYAAAAAAAAAFiXqBUAAACAf6kuW7PDAKxLddmaHQYAAAAAAACAFYlaAQAAAPiH3nIbZuECsBa95TbsMwAAAAAAAACsRdQKAABAGHMasLu39w1WQnpzLlK+KDHfEw3OhpPenAbs9eX818Acc+5Svi4x3xON7PF5b8MBAAAAAAAA4HqiVgAAAAAEln3YdgAWE1h2YdsBAAAAAAAA4Eq3vRcAAAAAQGfSyo4+Hu6NmAZOmc7nzLktS1fZ1+Pz3rsPAAAAAAAAABYTtQIAAACUpmjtTtcKfLsoVjz4YpVdEYrWEehaAQAAAAAAAGCxm8/Pz95rAACA7dzc3KzyOn4jDdub092pwk6ZXy3aw58q3DpF6zii3yU4MKe7y5eEHX3qs4+5eqOYb2PnS3/xFK1DCX2XAAAAAAAAAKAXk1oBAAAAilK0DsW8VqimXZ34/cqKu2QUraMxrxUAAAAAAAAAFvjTewEAAAAAdKBoHZBDgSIen/fb1ImbfSMoy1sMAAAAAAAAAC4lagUAAAAoRzw5LEcDuXWpTKWtOTjEYTkaAAAAAAAAALjIbe8FAAAAwHkqL4b18XB/9/beexWX8YYaXMRLBQckXkddtC2vL+e/+KIXfHzez3nNCiJuhffU4CJeKgAAAAAAAADoRdQKAAAAUIiiNQRdKyQzs0i8NIr7/vqZr//1ZdK7cBStAAAAAAAAAEAmf3ovAAAAANYhAGN1+S6VojUQh0V6ddLKs0Xi68v+69fib3HRv567kMx3r3KfVyZOCgAAAAAAAABmErUCAAAAlCCSBBjNlS3r71eb+ZXquyicVCzOCwAAAAAAAADmELUCAAAAwIh0yJDYujnrgpdV343PGUXk1AAAAAAAAADgrNveC4Dw/v79u8rrPD09rfI6AAAA8Js8MqiPh/u7t/feqwBW1iJnPXj9OWXd43OTsBaK884CIJa1/rB758+7AWAT/kc1AIjFz24AgFNMagUAAGB0YjwGN/4VHX+FTHB8hGNQ4YRGA1qPfqM5X1b2sMZ/8PFXCAAAAAAAAACwjKgVAAAAIDNJJMAgxhzeKJ4ckEOJzgkCAAAAAAAAwARRKwAAAAAMTZkMCWxftM7/jgK8oTiOHJwjAAAAAAAAAJwiagUAAABISwyZhqOE0Mac0Qo0pWsFAAAAAAAAgKNErQAAAGRw9/beewnkFPpqySCTcaBkUiry7PiwNYe1hr5dmQ4CAAAAAAAAAOAoUSsAAAAAADQRKLCUU3bnCPJxpgAAAAAAAADwm6gVAAAAICFTPVNyrDC4g4R1hKL1ojUI8GB13lYAAAAAAAAAcOC29wIAAAAAWJn0EaCXEUJWIpI+AgAAAAAAAABFmNQKAAAAAGEoloFLGdY6Ptuem/MFAAAAAAAAgJ9ErQAAAACpiB7Tc8QAAAAAAAAAAAAEJWoFAABgaNotQhjnoo6zEqAmAwkTKHKI4zzmOCuhHacMAAAAAAAAAN9ErQAAAAAQjHoZuMjry773EqA6XSsAAAAAAAAAfBG1AgAAACQhdASAWISOAAAAAAAAAEA1olYAAAAAiEfDDLSjtIQWvLMAAAAAAAAAYCdqBQAAAMhB4ggAsUgcAQAAAAAAAICCRK0AAAAAEJKSGQBiUTIDAAAAAAAAgKgVAAAAIDxxIwDTXl/2F3299K41OwwAAAAAAAAA1CRqBQAAAICo9MwAEIueGQAAAAAAAIDiRK0AAAAAsckaASAWWSMAAAAAAAAAUJaoFQAAAAACUzUDAAAAAAAAAAAQhagVAAAAAABgI8a04g4AAAAAAAAAUJmoFQAAACAwUzrZuQZAG7o7AAAAAAAAAABWJ2oFAAAAAACA7YjGAQAAAAAAACjrtvcCILynp6feSwAAAKAo8zkBIBYpIwAwFH/YDQCx+NkNALH42Q0AcIqoFQAAAADC+3i4v3t7770KKOdUoPj6cvyfAwAAAAAAAABAcX96LwAAAAAgPDEhQDWPz/uJkZumcY5PeEx3PigAAAAAAAAAqEnUCgAAwNC0gnDKx8N97yUAHCoSCs5J0eRq/OZWAAAAAAAAAACIWgEAAAAgA50zbGN+l6hgBAAAAAAAAACAA6JWAAAAAABoQtcKTPARAQAAAAAAAEBBolYAAACAeMzkBOhCgcZiLg8AAAAAAAAAwE7UCgAAAABpqJ1hQFJGAAAAAAAAAAD4JmoFAAAAOEkiCEBZry/73ktYQkVMLG4sAAAAAAAAANWIWgEAAMhAeQgAgYi4qMadBwAAAAAAAAD4ImoFAAAACEbFDQAAAAAAAAAAAEQkagUAAACAPDTPMJrXl33vJex2JoUCAAAAAAAAADAGUSsAAACju3t7770EmOKKAvw0SMMJE9xShqI5BwAAAAAAAKAUUSsAAAAAAPAfss8VSRYBAAAAAAAAAMuEzHMAACAASURBVL6JWgEAAAAi+Xi4770EAOaShgIAAAAAAAAAwE+iVgAAAAAAWF/cojXuygEAAAAAAAAAGNxt7wUAAAAkcTA78e7tvddKgLWYiUpQHw/3fgzN8fi8//m3Kj7meH3ZH9ycia9su5RLzFxzAnWeFAAAAAAAAAAgKFErAADAQtO1m8Z1eyIuAKZN124a1409Pu8Tb3LiRwNayP2RCAAAAAAAAAA/iVoBAAAutmB449e/Ejq57PsId2/vZmYyptDva6hjwfDGr38ldGLU8RHmjzNNI/RVOZDpWY5K/4AAAAAAAAAAACMTtQIAAFzgyq4yYtp68MjffxvrKQAo68q0MmLaevDI338b6ylGdjTZHXnKYrXAeDT2HwAAAAAAAADgJ1ErAADALCuOCY2Stk4/cpSngGSMLIb5VgzJoqSt048c5Snoyw0BAAAAAAAAAKCpP70XAAAAEECLimzwMm3m8gZ/CgDKajEacfBxizOXN/hThDbm3l60KkUrAAAAAAAAAACtiVoBAADOmNNt3r29//y14it3MebC5mzsmCsnqJnv/Q1WAgsU/zycU/G9vux//lrxlbsYcGFzdnXAZc906uniPlEOM9/7zdcBAAAAAAAAAMBpolYAAIAp01HQqYo1dNc64JIAYL7pqu1UxRq6ax1wSRWEaCONaQUAAAAAAAAAYDSiVgAAgJMm8s6zE1lDd60Xib5+ADKZSPjOTmQN3bVeJPr6Bxd0exWt0F3QTw8AAAAAAAAAuJSoFQAA4LjponXLlWxJoQpAXNNF63br2JYIqqNT92qQQ5m/jMRvEAAAAAAAAAAARiNqBQAAOOJU23l2QOvBF1/57TiQOCdmNHPelS4kDOVUv3d2QOvBF1/57fipQio5bNfafQHbm/PIFe4kAAAAAAAAAMDgRK0AAACHJorWjVcSwmjbohD+7aI9sYFARBNF66brCGKobUncXnZ8tIu+9VD3AQAAAAAAAACA9EStAAAA/9GraB2hJBxhDQBwqV5F6wg15ghrYOKmdTkgRetQvEkBAAAAAAAAAA6IWgEAAP5lRuv4nMVFPh7uF7TKy/6talxFGIQZrYMrchDTXeuWWaOidUK15wUAAAAAAAAAGNNt7wUAAAAAK1slSf1+kYL1pqYXgEu9vkzFq4/P+6FGB+fLOw1EBQAAAAAAAACIQtQKAADwj+5jWj8e7sPVg2MuOOJOHjVIWrl4GTlOARhZ9zGtG5SKqxtwwRG38aizXeuu2f4XL1oBAAAAAAAAAAhE1AoAALDbDVC0djdIPwkAM3UvWrszmnJAX9fvbNq6W++iXnoN6rxBAAAAAAAAAAAYk6gVAADgpDpFayx3b+8SXPry4QDDEuwNaHp+aUpzHvnKunXBlhZ/dxR/fAAAAAAAAACAcYhaAQAADClN6OPhXnbIMj4QIIRqkWR6j8/7ZM3h/JT355dNb8I11z7Z9h7wgQAAAAAAAAAAEIioFQAA4Ljrk8j0aVzHatSwVjrSS8Owrs/20qdxvcrGgsNad//f7YsevMUu5c5ZZ7IJAAAAAAAAAADjELUCAADVaSN3NgGAUArmkb/ZhCgWpK3rfmsAAAAAAAAAABiKqBUAAOAIkxgT+Hi4r3COvZ4xawid9bmgAv1edI/P+8SHuGXamngbjxJ4AwAAAAAAAADEImoFAABKE7DFdff2nv74Ro5yR15bU2UfHMYhYAvq9WXv7L5z00ZbUS1nnanvtrj5AAAAAAAAAAAHRK0AAACHVonW9JYQTvq3LSS2SrSWPjxTPI5jxbq1+LGmf9sCAAAAAAAAAOQjagUAAOoaLWCTiV7q7LDWj4d7u8qKXCfobrSArXhPeKmzIysfn/fVtvT3805vUbX9uZ4dAwAAAAAAAAAYjagVAADgPwpGa6PFvQBwkYLR2mhxLysqeJ8BAAAAAAAAACjlT+8FAAAA9NG05JSJbuZshOwsmOnsVSlYvMNompacMtFtnC02HQTznb0tCmEAAAAAAAAAgAGJWgEAAP7VMVqL1cvFWi0AiXWM1mL1crFWC1CQD2oAAAAAAAAAihC1AgAAFZnemYnClusZ0wrjM8AzDdUWqzCmFQAAAAAAAAAgKFErAADAymIVs7FWu0yFZwQ4oMS+SKxiNtZqF0j/gAAAAAAAAAAAUJmoFQAA4B8d+x/p0ZVsIE25YDCsjpMYDYG8ht2jNXcMAAAAAAAAAGBYolYAAKAcczuvFDHwc+hMiHU9Ir4B4XpGd14pXODnxJnmhgAAAAAAAAAAxCVqBQAAWFOsOi4TpR+NuFqQnjqui3CdLYGMdrtGWw8AAAAAAAAAQF+iVgAAgM46JnOlEtxSD8t8LgawQMdErU6CW+dJuZS7AQAAAAAAAAAQmqgVAABgt1upLFXH9WWiJqtzqWBkq5Sl6riOjK+kBfeKoFxdAAAAAAAAAOoQtQIAAPQULpkLt+CfVMcccCXIKvRn9fjCdUfhFvxNcsxvbgUAAAAAAAAAQHSiVgAAAPFPHo6SFblOMLK4oSY/OUfW5UYBAAAAAAAAAIxP1AoAAJTTKFQLN/Ix3IJnmj7frE/NAtOXYeSideS1QSONQrVwIx/DLXiO6cNN+cgsNn0fFK0AAAAAAAAAACHc9l4AAABAB4MkYYMso5qPh3s7j7wZwhkkVxtkGaU8Pu9tO7vghfPryz70+gEAAAAAAAAAVmRSKwAAAHNFaUGjrJNhuUJAGiFy0BCLZHBuEaG5wAAAAAAAAACUImoFAABYgamPo5mOEp1XcdMXQNFKdO7wTKYmDmU66HJYTN8BQSAAAAAAAAAAQCC3vRcA4f39+3eV13l6elrldQAAiKJvdKTqpKwcl//u7T3Hg0AsfcM5YSdlufwAAO2s9YfdO3/eDQCb8D+qAUAsfnYDAJxiUisAAAA5GdbKAkZcAvRiWCvLRBnTGmWdAAAAAAAAAACtiVoBAACutSCPjBjO5VuzrrWg6UOPeMmBZRYUkhGDtHBr1rXy2/S5h7vk8JtrDAAAAAAAAEA1olYAAAAASEKbDQAAAAAAAAAAwMhErQAAAOWUmlBqWCvfko1pDbdg4Bp1hpQa1spPyca0hlswAAAAAAAAAEALolYAAICrLKgipWgb07WyS1e0AtdYEEZK0baka+VLsqIVjnKTAQAAAAAAAChI1AoAAMB50ZM/XWtxilaKcJn5KXQopWtF0QoAAAAAAAAAkJWoFQAAgBK0XmUlLlpDLx5gmmqxssRFa+jFAwAAAAAAAACsQtQKAACw3IIJn90jNFNJj7ItNXV/PwLbWzDks3uEZjDpb/akrO7vR1iR+wwAAAAAAABATaJWAAAAqpguGHWtKTlW6hBpk8907qVrzcrJAgAAAAAAAADkJmoFAADgjEyhlK61lOkDzXGxczwFsLo00/90rdVMn2mOi53jKQAAAAAAAAAAFhO1AgAALLQggJSfjUDXWkSFohW+udIzLWgg5Wfd6VrrqFC0wjdXGgAAAAAAAICyRK0AAACFKDa/6FrTK1W0Jnsc4IBoc6drrUHRCgAAAAAAAABQhKgVAACAinStiZUqWgGK0LXmVq1ozfdEXModAAAAAAAAAKAyUSsAAMASC6LHoCld0GXPoWtNSdFKQS72TAu6x6DRUdBln6Vrzapa0QoAAAAAAAAAUJyoFQAAgLp0rcmULVoTPxrAT7rWfMoWrYkfDQAAAAAAAABgmqgVAACgConmUbrWNMoWrRTnbuem0vxN15pJ2aKV4txtAAAAAAAAAIoTtQIAAFCdrjUBRWuFZwT4omvNQdFa4RkBAAAAAAAAAH4TtQIAAFxsQeUYtDcLuuwF7t7eJx5W1zq4iQOaPlmIzvWeb0HoGLQ3C7rsS72+7CeeVNc6vokzmj5ciM71BgAAAAAAAABRKwAAAPxD1xrRdNG65Uq6q/a8ALrWoKaL1u3WMYBqzwsAAAAAAAAAsBO1AgAAFKHJnEnXGouilcpc8vRkmXPoWsNRtFKZSw4AAAAAAAAAO1ErAADABnRHsehao1C0/lb2wWF1uqNAdK2BKFp/K/vgAAAAAAAAAEBZt70XAAAAEEydrLFyHff17HXOOpPK9/bL3du7q1uBq36ROmVj2Tru68HrHHQyZe8t1bjqAAAAAAAAAPDFpFYAAAA4TjMWjiMDqEwwFpFT29mEGpwyAAAAAAAAAHwzqRWu9fT01HsJAABwhrGNix2MbNVMDuXnSFJH85Nhrem58BUYOrrMwchWFdloXl/2TgcAYCj+sBsAYvGzGwBi8bMbAOAUUSsAAEBb0qMEHOKwHA0FufYb0NpF5wRH5nSO+pn7ko9rDwAAAAAAAAA//em9AAAAAEYkmoLQvIWhINEUhOYtDAAAAAAAAAAUIWoFAAC4wMfDfe8lAMyia03JsS5g+CEAHcmVAQAAAAAAAOCAqBUAACA5IS4AxCLEhZrUj/k4UwAAAAAAAAD4TdQKAAAAkJOpnsk4UID0NJAAAAAAAAAAQHqiVgAAAA7ppiANb+c0HCXThHAAo/HJDAAAAAAAAABHiVoBAAAa0iABcD0/TbakQQL68imUg3MEAAAAAAAAgFNErQAAAJl9PNz3XgLQmR4SYnl83vdeAtCZHjI6JwgAAAAAAAAAE0StAAAAAMnpWkNzfAAFqSIBAAAAAAAAgKxErQAAAPyHegpS8tYOysExh/gNYBw+kwEAAAAAAABgmqgVAAAAAEakaAWoTBsZkVMDAAAAAAAAgLNErQAAAGl9PNz3XgIwEIUkjO/xed97CcBAFJKxOC8AAAAAAAAAmEPUCgAAAFCFrjUQhwXATicZh5MCAAAAAAAAgJlErQAAADktG9OqoYL0vM1DcEw1LRvTKqOC9LzNAQAAAAAAAIBMRK0AAAAAtQgmB+eAADigax2cAwIAAAAAAACA+UStAAAACS0b0wrUIZsclqMpa9mYVqAO2eSwHA0AAAAAAAAAXETUCgAAAFCReHJADgUAYlG0AgAAAAAAAMClRK0AAAAXCJEbLR7TGuLpgBV51w/FcTQSIjdaPKY1xNMBa/GWH40TAQAAAAAAAIAFRK0AAACpLC5agZqElINwEJUtLlqBglSU43AWAAAAAAAAALCMqBUAACAPRSuwgJyyO0dQmaIVuNTry15O2Z0jAAAAAAAAAIDFRK0AAAANqUyBEESVHdn80ahMgRBElR3ZfAAAAAAAAAC4hqgVAADgMsPWR1cGtMM+F7ANHwJd2PZtDFsfXRnQDvtcwDZ8CHRh2wEAAAAAAADgSqJWAACADIyEBa4nsNyYDS/OSFjgegLLLb2+7G04AAAAAAAAAFxP1AoAAHCxizKkDXJTRSuwFpnlNu7e3m31xi7KkDbITRWtwFpkltuwzwAAAAAAAACwFlErAABAbIpWYF1iy9bsMIpWYF16y9bsMAAAAAAAAACs6Obz87P3GgAAYDs3NzervM7/2Lt75EaSMwHDKAattucIiqApQwaDfQHpGDrC3oDgDXQEHWP3AuqgMcaaHTFHkE231sCIiwGBQv3lz5f5PDHGzDRZyKwsgGzjjc8v0hwW1qQpEqYdc1aFFfCVZj4Fn7dlLapJUyRMO+asCivgK818Cj5vAQAAAAAAAGBfJrUCAACsVDZMEpsBqckvd+eWFlc2TBKbAanJL/f1/nZ0SwEAAAAAAABgd6JWAACAHHZsUP/9lz8pWoE8RJh7+eXX39zMcHZsUJ9fj4pWIA8R5l7cSQAAAAAAAABIRNQKAACw3qJCaZcSVc4KZKbG3M4NrMqiSGmXElXOCmRmvuh2biAAAAAAAAAApDOM41h6DQAAkM8wDLtcxy/SnFsamq6rm+a8yi+//raielVbAXOI6lfwAVutpaHpurppzqu8v60Z4qq2AuYQ1a/gAxYAAAAAAAAAUhO1AgDQF1EriaTrWmde+fOColYgKWnrfD5dK5eua5155c8LilqBpKSt8/l0BQAAAAAAAIAMRK0AAPRF1Eo6q0Ovr9XT6kR23RpkV8BS0tZpPlejWB16fa2eViey69YguwIW0bXe5XMVAAAAAAAAALIRtQIA0BdRK6llDr0uuilRK5CNrvUqn6gRZW69LropUSuQjbT1Kp+oAAAAAAAAAJCZqBUAgL6IWskgW+i1fcTr1YsAzCdtPecTNa5sodf2Ea9XLwIwn7T1k49TAAAAAAAAAChC1AoAQF9ErWSTNPS62k0Z0wqUIm31WdqGpKHX1XTKmFagFGmrz1IAAAAAAAAAKOWx9AIAAADa9Bk47dh6iaaAOp0+nfpMW30yt+QzcNqx9RJNAXU6fTr1mbb6ZAYAAAAAAACAskStAAAAaW2vWxVTQAi9pa0+nBu2vW5VTAEhdJW2+mQGAAAAAAAAgEoM4ziWXgMAAOQzDMMu1/GLNHu5Wn+tC6VWhGSKLCCRhtNWn5xcrb/WtVIrQjJRFpBIw2mrT04AAAAAAAAAqIqoFQCAvohaadW6fkyaBaTWUt3qM5N9revHpFlAai3VrT4zAQAAAAAAAKBCj6UXAAAAAECzPkPQuHWrlhWArpxC0NBpq5YVAAAAAAAAAGomagUAAOiUTAvIKVzd6kOSCsm0gGw+P3AC1a0+JAEAAAAAAAAgBFErAABAeFEKMYBD9XWrlpU8AkViQOfOS9E6P7u0rAAAAAAAAAAQi6gVAAAAgALO89GygauQFQDmqCdwFbICAAAAAAAAQFyiVgAAgB4puICqXHwopW5cfQYSkYILqMrFh1LSxtUHIAAAAAAAAAC0RNQKAAAQW9nxhgAp3IpOF33iKVepVtnxhgAp3OpOF33iiVcBAAAAAAAAoAeiVgAAAABi0KkCQCw6VQAAAAAAAADgwkPpBQAAALDeujGtqjAAKGXdmFZVGAAAAAAAAAAA0AZRKwAAAAAAAAAAAAAAAAAAyT2WXgAAAEAAXweixh12GnflADDf14GocYedxl05AAAAAAAAAADABVErAADATV9b1os/KhuITiwPAPr0tWW9+KOygejE8gAAAAAAAAAAAHrwUHoBAAAAlZqTjP77L3+KVZYa0wpAw+Yko8+vx1hlqTGtAAAAAAAAAABAS0StAAAAV9Sfqq5YoaIVgIbVn6quWKGiFQAAAAAAAAAAaIyoFQAA4NLSXrT+AhYA2ra0F62/gAUAAAAAAAAAAGiSqBUAAOAPQhSqxrQCwKcQhaoxrQAAAAAAAAAAAAdRKwAAwLnVRWvOFDZEdgsAeawuWnOmsCGyWwAAAAAAAAAAgAxErQAAAO0zphUAYjGmFQAAAAAAAAAAaNJj6QVAeD9+/NjlOi8vL7tcBwCA1UJMQF2xSEUrEJq/dzMhxATUFYtUtAKh+dkNAIHs9YP74Gc3AGThL90AEIuf3QAAt5jUCgAA0DJFKwDEomgFAAAAAAAAAAAaJmoFAAAII8QsWQDgU4hZsgAAAAAAAAAAANmIWgEAAA6Hzb1ohoGoK1ZoTCsADdvYi2YYiLpihca0AgAAAAAAAAAAbRO1AgAABKBoBYBYFK0AAAAAAAAAAABfiVoBAAAapGgFgFgUrQAAAAAAAAAAQA9ErQAAAFulLkiXjmlVtALAtNQF6dIxrYpWAAAAAAAAAACgE6JWAACAqilaASAWRSsAAAAAAAAAAMAtolYAAIBNkkakilYA2F3SiFTRCgAAAAAAAAAAMEHUCgAAsJ6iFQBiUbQCAAAAAAAAAAAUJGoFAABYSdEKALEoWgEAAAAAAAAAAMoStQIAABwOC6PQX379TdEKAGUtikLf346KVgAAAAAAAAAAgOIeSy8AAAAgjDztqKIVAPaSpx1VtAIAAAAAAAAAAMwkagUAALgjWzW6NGc9KFoB4Jps1ejSnPWgaAUAAAAAAAAAAPr2UHoBAAAAtbgViK5oTVdYMaBV0QpA524Foita0xVWDGhVtAIAAAAAAAAAAJ0TtQIAANyXumtdUbQmWgkAtCF117qiaE2yDgAAAAAAAAAAgFBErQAAAP9vIhZN1LX++y9/UrQCwGoTsWiirvX59ahoBQAAAAAAAAAAWOex9AIAAADq8suvv92qTE//f6+mdEUlK2cFgK/e325Wpqf/v1dTuqKSlbMCAAAAAAAAAACcM6kVAABgme0jW1dMZz0oWgFgre0jW1dMZz0oWgEAAAAAAAAAAL4wqRUAAODSxLDWk88/XRqargti5awAMG1iWOvJ558uDU3XBbFyVgAAAAAAAAAAgKtErQAAAFecOtK7Der5F9xKT7dMdpWzAsBMp470boN6/gW30tMtk13lrAAAAAAAAAAAABNErQAAADfdHdl6bku8evWld7waAHTi7sjWc1vi1asvvePVAAAAAAAAAAAAmvRQegEAAABVyx+X/vLrb4pWAFgtf1z6/nZUtAIAAAAAAAAAAMxhUisAAMAdp8R030Gst14FANjulJjuO4j11qsAAAAAAAAAAAAw3zCOY+k1AABAPsMw7HIdv0h3a/e0VcsKAEntnrZqWQEAAAAAAAAAAFYTtQIA0BdRK3vZUrcKWQEgvy11q5AVAAAAAAAAAABgF6JWAAD6Imolqa+lq34VAGr2tXTVrwIAAAAAAAAAAKQjagUAoC+iVgAAAAAAAAAAAAAAKOKh9AIAAAAAAAAAAAAAAAAAAGifqBUAAAAAAAAAAAAAAAAAgORErQAAAAAAAAAAAAAAAAAAJCdqBQAAAAAAAAAAAAAAAAAgOVErAAAAAAAAAAAAAAAAAADJiVoBAAAAAAAAAAAAAAAAAEhO1AoAAAAAAAAAAAAAAAAAQHKiVgAAAAAAAAAAAAAAAAAAkhO1AgAAAAAAAAAAAAAAAACQnKgVAAAAAAAAAAAAAAAAAIDkRK0AAAAAAAAAAAAAAAAAACQnagUAAAAAAAAAAAAAAAAAIDlRKwAAAAAAAAAAAAAAAAAAyYlaAQAAAAAAAAAAAAAAAABITtQKAAAAAAAAAAAAAAAAAEByolYAAAAAAAAAAAAAAAAAAJITtQIAAAAAAAAAAAAAAAAAkJyoFQAAAAAAAAAAAAAAAACA5EStAAAAAAAAAAAAAAAAAAAkJ2oFAAAAAAAAAAAAAAAAACA5USsAAAAAAAAAAAAAAAAAAMmJWgEAAAAAAAAAAAAAAAAASE7UCgAAAAAAAAAAAAAAAABAcqJWAAAAAAAAAAAAAAAAAACSE7UCAAAAAAAAAAAAAAAAAJCcqBUAAAAAAAAAAAAAAAAAgORErQAAAAAAAAAAAAAAAAAAJCdqBQAAAAAAAAAAAAAAAAAgOVErAAAAAAAAAAAAAAAAAADJiVoBAAAAAAAAAAAAAAAAAEhO1AoAAAAAAAAAAAAAAAAAQHKiVgAAAAAAAAAAAAAAAAAAkhO1AgAAAAAAAAAAAAAAAACQnKgVAAAAAAAAAAAAAAAAAIDkRK0AAAAAAAAAAAAAAAAAACQnagUAAAAAAAAAAAAAAAAAIDlRKwAAAAAAAAAAAAAAAAAAyYlaAQAAAAAAAAAAAAAAAABITtQKAAAAAAAAAAAAAAAAAEByolYAAAAAAAAAAAAAAAAAAJITtQIAAAAAAAAAAAAAAAAAkJyoFQAAAAAAAAAAAAAAAACA5EStAAAAAAAAAAAAAAAAAAAkJ2oFAAAAAAAAAAAAAAAAACA5USsAAAAAAAAAAAAAAAAAAMmJWgEAAAAAAAAAAAAAAAAASE7UCgAAAAAAAAAAAAAAAABAcqJWAAAAAAAAAAAAAAAAAACSE7UCAAAAAAAAAAAAAAAAAJCcqBUAAAAAAAAAAAAAAAAAgORErQAAAAAAAAAAAAAAAAAAJCdqBQAAAAAAAAAAAAAAAAAgOVErAAAAAAAAAAAAAAAAAADJPZZeAIT348ePXa7z8vKyy3UAAACgJf7eDQCx+NkNAIHs9YP74Gc3AGThL90AEIuf3QAAt5jUCgAAAAAAAAAAAAAAAABAcqJWAAAAAAAAAAAAAAAAAACSE7UCAAAAAAAAAAAAAAAAAJCcqBUAAAAAAAAAAAAAAAAAgORErQAAAAAAAAAAAAAAAAAAJCdqBQAAAAAAAAAAAAAAAAAgOVErAAAAAAAAAAAAAAAAAADJiVoBAAAAAAAAAAAAAAAAAEhO1AoAAAAAAAAAAAAAAAAAQHKiVgAAAAAAAAAAAAAAAAAAkhO1AgAAAAAAAAAAAAAAAACQnKgVAAAAAAAAAAAAAAAAAIDkRK0AAAAAAAAAAAAAAAAAACQnagUAAAAAAAAAAAAAAAAAIDlRKwAAAAAAAAAAAAAAAAAAyYlaAQAAAAAAAAAAAAAAAABITtQKAAAAAAAAAAAAAAAAAEByolYAAAAAAAAAAAAAAAAAAJJ7LL0AAAAAAAAAADr1/Ho8/cv727HkOvjC0QAAADRgePo4/cv481vZlXDB0QAA0DOTWgEAAAAAAADI7fn1+JlNHs4SSmpwcTROBwAAIJzh6eMzmzycJZTU4OJonA4AAL0RtQIAAAAAAACQlUgyHEcGAAAQiEgyHEcGAEBXRK0AAAAAAAAAZGLsZwjvb8ev/9PZAQAA1M/YzxDGn9++/k9nBwBAP0StAAAAAAAAAOQwkURerSgp6NaJ6FoBAACqNZFEXq0oKejWiehaAQDowTCOY+k1AABAPsMw7HIdv0gDAAAAwCK3Ykg5a82cGgAAQBS3Ykg5a82cGgAAfRK1AgDQF1ErAAAAAGRmQGtojg8AAKByBrSG5vgAAOjQQ+kFAAAAAAAAANAsSWR0E8c0cbgAAADkIYmMbuKYJg4XAABCE7UCAAAAAAAAkISitQ26VgAAgDopWtugawUAoDeiVgAAAAAAAAD2p2htia4VAACgNorWluhaAQDoiqgVAAAAAAAAgJ0pWtujawUAAKiHorU9ulYAAPohagUAAAAAAABgT4rWVulaAQAAaqBobZWuFQCATohaAQAAAAAAANiNorVtulYAAICyFK1t07UCANADUSsAAAAAAAAAayXeKwAAIABJREFU+1C09kDXCgAAUIqitQe6VgAAmjeM41h6DQAAkM8wDLtcxy/SAAAAAHBB0frV/Mgz3C1y3AAAAJn1VrQuCjjbuwO9HTcAAF0RtQIA0BdRKwAAAACkIHE82WtUaYib5tABAACy6SRx3GsSaRv3pJNDBwCgQ4+lFwAAAAAAAABAbHuVnHHtfgdOF4ybhj6/HuMuHgAAoDZ7pZ51SrG7z2u2Gn8OTx+tbg0AgB6Y1AoAQF9MagUAAACAfU33nG2Xjdlq3mpvY8+nDwAAkMd08xm6bMwZ6wa9UQ2fPgAAPXsovQAAAAAAAAAA2tRw0/j8esw5n7baWbgNHzEAAED9gjaNw9PH6Z/8L5rzFXcR9IgBAGCaSa0AAPTFpFYAAAAA2NFEbNlq7liwL632lnb4GAAAAOQx0WEGzR1rKEvD3br2HgMAADr3WHoBAAAAAAAAAIRU7fjQRNbtd2bVOefip6+JlYk+vx5jLRgAAKAeNfSfO1q0nUWt5tIbdfr6NnLQ4emjjY0AANAVk1oBAOiLSa0AAAAAsIvpCLOxjnFpzrpx+xHvbcQ1AwAA1Gw61IzVMc6MTnfZ1NLANcqdbOl5AAAAUSsAAH0RtQIAAADAdl0VjIuK1h33PvG6dd7hrp4KAACApFoqGO9Wpom2M79ujXI/W3oqAADo3GPpBQAAAAAAAABAjUrlrJ8XvLWA59ejRhQAAID6TXSYqSPM0/XnpK3D04ciFAAAcjKpFQCAvpjUCgAAAAAbdTKQc37RmnrLt1ZS4a3u5NkAAABIqpmBnAWL1vkr+RTixjbzbAAA0LmH0gsAAAAAAAAAIIxOqsV6itY8L7GX6aUumnwLAADQp2aqxVsbGX9+y7+LOa84J3wtbnojIbYAAACHw+Gx9AIAAAAAAAAAoCIz28s8renEYp5fj4F6VwAAAPoxUbRmXsn5S99tPoenj0DZMAAAxDWM41h6DQAAkM8wDLtcxy/SAAAAAHSohzGt9RStc1ZS5z3v4TkBAABIoY0xrRUWrefupq2VrHNCG88JAAA9eyi9AAAAAAAAAAACmFl7hharaA2q4a0BAABscTe2DKHyovUwYyXRDyL6+gEA6IGoFQAAAAAAAICtGhi/GbForTMQbeBhAAAAqE09UegKoRdfIfcTAIDoRK0AAAAAAAAA3DEdTzYQMUYsWms2faPa2CMAAMCOpqdrRokYo8wIbWBY6/QW6l8/AACdE7UCAAAAAAAA0LV6ilYAAAAI6lZIWWeRW+eqAACgE4+lFwAAAAAAAABA1doe06poTeT97Thxb59fj25pVVaMz3WCAACwlzbGtJLZ+PPbxJMzPH14cuqxbnauEwQAGiZqBQAAAAAAAOCmFalbIIrWgnStRez4jr51KccKAACLrKvdahNxF81HoQ1sIaId3wtXL+VMAYA2iFoBAAAAAAAAWCl0vVZh0dpYQjw9rJU88h/BxSuG/pQAAIDiogds0ddfuekulzwyH8HFy3mLAQBBiVoBAAAAAAAAuK7hIrHCovXQXwVqWGsiVT1F54tx3AAAcEGRSFKGtSZSzzv3fCXOGgAIRNQKAAAAAAAAABBeVS3rVZ8rVLcCAEBL6gn8lpqedKoI5Vz9z7nAFQAIZBjHsfQaAAAgn2EYdrmOX6QBAAAAaN7dQC5ullbnmNaTRV1iiCNo+EGqRP0t6wSnDwBAz+42clGytImN1L+F6VOof/2Hhh6kOtXfsk5z+gBAnUxqBQAAAAAAAKAjNRetp9cN3SiSTRvPidmtAAAA1Cl6znpy2oW0FQCozUPpBQAAAAAAAABQHdM16xflCO6us404M6fn1wbL5yY3BQAAEzqZrtlGFli5u4+KU1hkePo4/VN6IXtqclMAQGgmtQIAAAAAAADQi8rHtH6+urqPr3p4KgxuBQAAoJQemk+DWwGASpjUCgAAAAAAAMAf9NDOVW666Ht/OzaW/HnkpnU4xbTDLQMA0JUe2jmq4pGb1tsU0972CwBUaBjHsfQaAAAgn2EYdrmOX6QBAAAAaNjdlixoURliTOuFizVXtbZFWn2oUhN2HjwbAAC06G5OFmuOYujtTC++5pVfCH0KBWk7PRgAQBGPpRcAAAAAAAAAAMkFjQPlfN0K+sSm8Pza2mhiAADozfD0IZyjNnLWk9N98A4FADJ7KL0AAAAAAAAAACpioiYp3H1sNJzn3I0Lz69H9wQAgGaYqBlFrIO4u1oN56fh6cPduOCeAACZmdQKAAAAAAAAQOPm53CSXcqSbk443RxvUgAAiKjOYa0qvg459AmmtgIA2ZjUCgAAAAAAAMDvBHUU1PnjZxjpTG4UAACh9RzU9bz3SnR+BIaRzuQuAQAZiFoBADYZZiu9UgAAAACAHZiRyGoengkqzaXcMQAAWtX2jMSqYrnpxUQ8iIhrzkPOupQ7BgCk9lh6AQAAkWxpU79+7ziO25YDAAAAAMB98+M31SX5iTNXO906b1sAAIjlVMrJL8lGnLna8PThrQoAJGJSKwDAlKTTVo1yBQAAAACqIq6juN4ewt72m4J7CABAIOK6T8VvRfEFlNLVxo0b3c49BAASEbUCAFyXPzSVtgIAAAAA7E7wRrU8nHtxJwEAoKx1sxxrjuVMp2xAtU9XRG4mALA7USsAwB8Un5tafAEAAAAAALe8vx1LLyGhtndXCTf55Pn1qMPcl1sKAEAbOmwpi6StDRd6HT5CVzV8xKW4pQDAvkStAAC/qy0lVbcCAAAAADnpwahE849i8xssyL0FAKBmkrAJOdPWuy/UfBfa/KPY/AZLqXm6MgAQjqgVAGB9zjrOs315G68AAAAAANAnhRu18Uym5g4DAEARu4SgGZI5RWvbVJcZuMMAwC5ErQBA15bmrOtq1e2Bq5GtAAAAAAAQnd4yD/cZAABCS9clKlrbJrbMxq0GALYTtQIAnZqfie44c/Xzauu+V9oKAAAAACQypwF7f7v/NXDXnAepvSjx+fXY3qZq5oYDAFCbOQ1Y9KJy3/XvXs31k+HNOYj27kZ7O6qcGw4AbPRYegEAAAXMz1kTLeB05XWF6um70q0NAAAAAKANmZO2uy+nCu6WurKU59ej9x0AAMR1quZ2aWV7iIp7JrAsYnj68K4BAFYTtQIA3bmbkmbrRcdxXD15dRgGXSsAAAAAwF7WlW+LYsWLL9badULRWpauFQAAchp/fks0YXVLO6dobZuitSBdKwCwmqgVAOhIPTnrxSuuHtmqawUAAAAAyG+XTPH8IqK7Vilaa6BrBQCAnFJ0rYcNaauitW2K1uJ0rQDAOkIIAKAXFRat51aPbPXr3FKrb/UFdx4AAACAZszp7iImYYt6wpkbTNooRrzJq7X61J1TtFYl+uMEAEBovXWVqTvDmfeqt9t+oYftK1rrEf1ZAgDyeyi9AACAHKY7xnEciweKqxewV6IJAAAAAMCE59dj6kYxw0uQjaOsjRMBAIBsUudtw9PH3Zqxh6Szc4rWqjgOAGApUSsA0L67RWu2lSSiawUAAAAAuLBvwJYzh5O2NsAJ1sm5AABANhl60Vtp65zk9aBoDU5CWSGHAgAs8lh6ARDejx8/drnOy8vLLtcBYJGqitZxHFfnqcMwVLUXAIC9+Hs3AMTiZzfQnjkV3Pvb/a9ZWtM9vx7nXJYKKSdr5p0FQGj+0g3EMv78liFyO73EqVCd+XJy1ujEk9Uanj68vwCAmUStAEDjJjLRCitQXSsAAAAAkJkAb8LdmzO/jvv8yvk3/PSVPQd4EftDbygAAOBCzwFenq71sOQmK+7ORUwQe35DhRDxoQIAihC1AgAti1W0bqdrBQAAAADYy0SduKW0XFq3Rgw7oWbeUwAAkNOiGaoZVkJoNTxI3KVrBQDmeCi9AACAVIIWrTWvDQAAAADoU4cB2K3i9P1ttxxu/nWaHP7Z5EPV5Ek1yUkBAFCb5gOwshscf35r/g5/1d6WFa2BOCwA4C5RKwDQo2EYJpLX0FrdFwAAAABANhNF674vND+R1eDVzxnF4rwAACCzIo1lnzlrk0SS4TgyAGCaqBUAaNOctnP4jwzrWcSwVgAAAACALbbkale/d8cBrVcvPufLnl+PMrxqOZqInBoAAGQmMQUAAE5ErQAArQ1ubWkvAAAAAAA53SpaU7/u/JeQ4VXIocTl7AAAIL+caevw9GFcZAMcYlAODgCYIGoFAPidFhQAAAAA6I2g664MRWvmF4rFIwoAAISm6bpF2lqJ+u9M/StkguMDAG4RtQIADVqdpzbTtTazEQAAAACADE5B6dd+MnNoOvPldJ5VcRzROUEAACgoW9d6kLbG5Mga4BABgKtErQAAf1BJDjqOY+klAAAAAAB0pHjRuogMrxIOog3OEQAACsrZtR70dQAAUAdRKwAAAAAAAABUYX5JK8MrzhG0xGkCAEB+p9Gp+StTI1ujcEzNcJQAwFeiVgCAS5UMawUAAAAAqEHNA0v3UtWY1n661h4eLQAAgLsyjyqtQQ1ZaQ1rSC30o9X86fTGgQIAF0StAEBrJKkn7gMAAAAAwDpiS+6KXhTzlTMFAIAMaktJq1oMAAD0Q9QKAHCFIhQAAAAAgFL6GdYalNsOAACw1KKcdfz57fRP0iWd1NbZchAbN8qxAgDnRK0AAAAAAAAA8LtwY1oFlrAX7yYAAEhkac566z/TkdvVw1k0zOECAJ9ErQAAAAAAAAAAs+ge2+Z8AQBgX/PnoE70q3nSViNbAQAgG1ErAAAAAAAAABwONY1pXbQSGR4AAAAV2p6zXnzZ5hXdp2sty/1vniMGAE5ErQAAAAAAAAA9UkISQlUPalWLIRGnDADAXnput2ZOPV06gjXbyNbUL1GV3vYLAEANRK0AAAAAAAAAEJsMLwM3GQAAYI75A1rXXT9D2qrzLMJt74SDBgAOolYAoD3jOFZyEQAAAAAAAnl/O5ZeAlAFATMAAKyWumg9v0LStFV3B+l4fwEAj6UXAAAAAAAAAABQNZUjAADAXXNCtX1L1NPVBHINcIgAAF0xqRUAAAAAAAAAqrN0cqzqEnbkDQUAAEvlL1pTX1ZmmY1b3SGHDgCdE7UCAA0ax7HgtwMAAAAAAC3RNwIAAEwrWLR+XjzF9XV3AACQgqgVAOAPmilam9kIAAAAAMBSS2ecrvsWoG1iZgAAmKl40Xr+KnleCNhONA4APXssvQAI7+XlpfQSALhiHMdhGEqvYr3QiwcA2JG/dwNALH52A9AeZSMAUAl/6Qbiyhyanl5ur15uePoQyialbAQA6JBJrQBAs5aOKh3H0XRTAAAAAAAATiTNAABwV7VFoqmtAABQLVErANCy+Z2qnBUAAAAAgOgEeAAAAOQ0p2gtW5bukrZWG+42wL3tnAcAALolagUA2jf+x6I/Cq29HQEAAAAAQH46YTwDAACwRSWzUitZBgAAcCJqBQA6Mn5RekUAAAAAAAAAAADxxBqxuHFka6zNRuGucvAYAECvRK0AANUZhqH0EgAAAAAAoHdGdHLiSQAAgHUqnI9a4ZIAAKBDolYAgNaYQAsAAAAAAAAAAKRzd7hitfnoxpGtAADAdqJWAAAAAAAAAFrz/nYsvQQAAACgUiu61rsdL4u4n3zyMABAh0StAABNMaYVAAAAAICWlOqTn1/LvC518jwAAMC5Ngo081pP3AcAAPITtQIA1GUYhtJLAAAAAAC6YJYpAAAApNNzKxhl71HWCc1rI5UHAOYTtQIAtMOYVgAAAAAAAAAAgJl0rUUoGAEAOidqBQCoiDGtAAAAAABQ3PPrsfQSqI6nAgAAAAAAdiFqBQBohDGtAAAAAADn3t+OpZcAAAAArbk7YzPc7NNwCwYAgOhErQAAAAAAAAAAAAAAdErXmtPdLpo+eTAAoCuiVgCAWgzDsPp7jWkFAAAAANji+fVYegndqfaeV7swAAAAmE8iCABAtUStAADhKVoBAAAAgHQEfoG8vx1LLwFa5vMQAIAUlIcAAEBvRK0AAFXYMqYVAAAAAICrRJ4AAADAHOPPb6WX0AUVNxM8HgDQD1ErAEB5W4pWY1oBAAAAAAAAAAAAAIAQRK0AAIEpWgEAAACALQwypXL5H9Hn19yvCAAANMzwTyrnEQUAoAhRKwBAYavHtCpaAQAAAADu6ifc7WenUJDsGQAAAAAANhK1AgCUpGgFAAAAAKhEba1abesBAAAAAACA7UStAADxKFoBAAAAAAAAAID8xp/fpr9gePrIs5LM7m6cu1p9NtiRhwQAOvFYegEAAP1aN6ZV0QoAAABAhy5GVr6/Ha9/HVzz/nY09bQ2TgQAAOpxURAp96ABykAAAGomagUAKEPRCgAAAAATpoM3jWtmz6/HTm5y3J0GXXZtpLYAAHRiunbTuOY3PH24zwAAQD8eSi8AAKA7wzAoWgEAAADglufXxUM1V3xLbQpuoZMYspNtNsapUafoP3EAgM4NTx9L5zeu+JbalN2CWrUNTZ5jk5sCACAEk1oBALKSswIAAADALRszodO3x6rgLrb8+Z+xdkEKi94OHhgAAOCujVXn6dtjJXAXW/78z1i7qNP489vdeb/h7nP0eBsAAAIRtQIA5KNoBQAAAICrdpx6FyVtnd5ylF3E8v42axzu8+vRnQcAAFqyY6oXJW2921seIuyCenhaAABgX6JWyOHHjx+7XOfl5WWX62Rgy6vZcs322vKhy13bcs05a4envIsfP34E2nKHp+xDewtbrpktr9bhlg9xdt3hlg+e7Q1suWa2vEWHu7blmtnyara82o5F6/k1z7vE2j60Z255Y125466jaGbLi94U//jbX7dv3CcYFNfhs23Lq9lyzWx5iw53bcs1a2nLKYZPDk8f//rn/179o0BbntjFIjVseabMH9qVDGuduevvf//z9EUCHvTUjv74ldftvmWzcJmpkk8PACCph9ILAABo2fAfK77XgFYAAAAAejAn3nt/O57/s+OVi6hwYf/421/vfk2Fy15hzk5jCbqjmW/85OsAAIDWzUnIxp/fxp/f/vXP/z39M/PK0wVgQXVWc3PqrDpXPqHJTV21S/yc2Zx3aMR9AQDQDFErAMDOvn///v3799Ut6+FwGMdR0QoAAABAD6bDtlsVa+iutcIl8VXZY/KQAAAAu5juCU8t69cuMXTX2kZC2ZgQh1LhwwwAAG0TtQIALPZ90pYry1kBAAAA6MdEuXd3ImvornWR6Ouv0MzRpiHuvFmmAADALRMl4dWW9VzornWR6Osvbs6w1vpNPwbGmQIAQAqPpRcAAFCvjYXqIlpWAAAAALoyXbTmW0deITrJHvzjb3/9r//+n9KruG7+Q9LwO6UU71AAAJoxXbTmXElOISaCtmf8+e3unR+ePqp98BStAABQhEmtAADXZStaTWcFAAAAoDe3yrG7A1ovvnjjy3Fu5vzSZszZb/4np5Oidc42Q28QAADKulUY3h3Qeq6fYa3ZVFt15hExOY5btM55V8bdHQAAbRC1AgCUJGcFAAAAoDcTRWvWdQRR1W3pMA/OueVOilZoQ4efhwBAFBNFa+aVhFBb1xex/DzMfrpq2933v/9Zkg0AAP/H3t0bVY6sARhGU1gkgjkmxSSwG8aEsBnAZLAh3DB2ExhqzGuSCK6uce6eYvnRkVr9qf+ex5yhREvdR4Dx1leKqBUAoKTpH6UXAgAAAABHKFW0/vHX36HXX0MBVZuVw2mP2ThFKwAAsF+porWGMrC2YHI067vWSnbq4qGtLXgGAIDOiFoBAKowvVJ6LQAAAAAQwozWyq2MPHtSSdeqaH1jkNsEAIC8zGitX997sf7uinetitbubxAAgPqJWgEAPvbz589S31raCgAAAABwjOJd62hFq5HFAAAAvZqfbyof2frt+9flovXnf/7bevBZvBkGAIA1rksvAACgXsld67dv3/Z/91PXOs/z/ksBAAAAQHHFx7T+8dffzU0irTBivHt4rHBVO/35+29//PX3xS/Lfu+b8s7+HjsAAJBR8TGt375/bS4FrHPB0+1L6wNd5+eblV3l6cuOud+L01mvaj0SAADQJZNaAQDy+/nz5/xvyZea/pFxeQAAAABwsOJFa3GmU1Zu/bzWXFupaAUAAHIpXrQWZzRlbTadvdPU1qBNnG5fLk5nPVG0AgDAkUxqBQA4wrlrTc5TDW4FAAAAoDM6vQqtnFnapVPXunJk61XqAU5oYkf7pIx2vwAAEGScorUt66eYtu50Ajfd7PmL95/eTd93tJx1tPsFAKBOolYAgEPtrFunadK1AgAAANAWQ0o7c/fw2HF2uD7rfX2wlx9I8kegv+fsbQAAANkNUkgOZbp96aZJTkhb33z9mkeR/CnoL+/0QgAAoBWiVgCAMuZ5Tu5ar4xsBQAAAKBx+2u97uu4UkHjyMNaT/78/bf7+/tNByz7aewvZ11p2BsHAIC89ieR375/zbKSas3PN09Pxb71aOXhnluOe1b9Fa1rjHnXAABUSNQKAFDMKUw1shUAAACAjnWfnq7hIbTo14/HIhun6gQAANYbrY38kIfQhLSRrRFUnQAAUANRKwBAYUa2AgAAADAa2V7r7h4eR9jE0z0elrZ2/0jV3QAAcIz9Y1opbrp96XIfzzdVpG7tPmetoRkGAICVRK0AAOUld61XRrYCAAAAUDENW6P+/P23P/76u/QqanFuTYPOc/ct63oeBQAAbCJga9f8fDP49h1Zt3bfsq7nUQAAUA9RKwBAFXStAAAAAAwiS7fWfS6r7qtQ3rp1tC3u/jMLAACVyDLe89v3r/svUrMuh6A26s1e5GpcX1/26SnLJWs3eCYNAEBzRK0AALXQtQIAAADQk9oatj9//630ElpycVjr3cPjaFnm2fsbv3jah31W63lEtMWJBQCKqy1gMwFyq4vDWqfblwHj2w9v+eJpH/BBJfAhBQCgKqJWAICK6FoBAAAA6NuAFVBtcS8RBjzYAABAhQZM+2qLe4kw4MEGAIDufSm9AAAA/mVPmJocxAIAAABAXqElp0z0GBdn29oIVmplkm0lywAAgItCS85v37/GXZzXLraakl1WunhUjGkFAKA2olYAgK7oWgEAAACoVsFg7GKfWRVlHQAAAFsVnGbZVi9n7CcAAEBxolYAgOrsGdZ6pWsFAAAAoDQDPLvRVgxMnVoZ0woAAK0wvbMnClv2M6YVAIAWTTuTCQAAguxpU/2OtyBX9OshAwAAAHzms4YtS7qWVsyWqubaWu2HFIns1NYR0uSzUlXnFgAYzWcBW5Y8Mq2YLVVmtrXaz1y8i9oWTG0aOkKafNar59wCAEFMagUA6JBhrQAAAADUpmD/Iz3aw9MjlAMGAABZFIx/dEc7eYCEquqAVbUYauaoAMAIRK0AAJXaOQhU1woAAABAEeYc7tRc42fHWeB4AABAXuYc7tRiKGXTWeB4AADQKFErAEC3dK0AAAAAdEYgV0RznS2tcLQAAKAq6rhSWkxtaYKjBQBAtUStAAD12jmsFQAAAAAoWM2Nk+COc6ds4mDQKz02AMBZwWRuqAR3qJtlPQcDAIB2iVoBAHpmWCsAAAAANcjS/wjkClJwkV2dh6rOVQEAwLIsZak6riwTNcnOoQIAoGbXpRcAzXt6espynfv7+yzXAQAAgJ74uxsA2uJnN7Vprk9rbsFndw+P7S6eCCp0AIDs/NFNbZpL5ppb8GvT7UvT6yc7IToAAE0zqRUAoGrzPO+8gmGtAAAAAJSldeyDfSQjxwkAAHIROnbDVpJRtcep2oVRD4cEAAYhagUAAAAAAACyCWrVmpv62NyC11je3C5vmTTLh0HRCgAAyYJSn+ZGPja34JWW97fXuybB8mHQBAIAUL/r0gsAACDcNE37J74CAAAAwEqVFGuVLGModw+PHjvyZvrmLQcAFFdJrlbJMkYz3b548sibAQDogEmtAAAAAAAAAOU1EUo1sUhqVv8Rqn+FAABAglZa0FbWSbUcIQAAmiBqBQCoXZYhq9M07b8IAAAAABRh8GNVlpM/mzW45QOgFwUAgAqZ+lib5SjRfg1u+QA0UbQ2sUgAAKKJWgEAAAAAAIDelG3nhJ2MyckHAIBBlG3SVJ0My+Gne5pnABiHqBUAAAAAAACADQxrJYExrXTAMQYA4BiGtZJADQgAQENErQAAo5imqfQSAAAAAGCzhEKyxeiouTXrWnljedPbOuFtrRYAAPZIyCNbDOf6W7OudUDLm97iIQcAYGSiVgAAAAAAAAAAAAAAIJwElw85GAAwFFErAEAD5nkuvQQAAAAAYJVxhpQa1spZT2NaAQCAmg01odSwVs6MaQUAoDOiVgAAAAAAAKBSCWGkfO5IulauOi1aG102oZwKAKA/CVWkdu5gulauFK0AAPRI1AoAAAAAAABQUtOhlK51cF0WrQAAQE9aT/50rYPrtWhtd+UEcSQAYDSiVgCgW9M7pVdUnocAAAAAAGQnXByWohUAAOAAWq9h9Vq0AgCAqBUA6M1CwqpuBQAAAICGJAz5LB7RGUz6nmcypuIfxv06uAUych4AgP4kTPgsXtCZSvohj2VMxT+PAACwh6gVAOjH+mBV2goAAAAAkNFy7qVr7ZJtBQAAOMxywahr7VL326rL5cxhAIABiVoBgE4kRKq6VgAAAACguG6m/+lah7K8od2cajhxpAEA2tVTKKVrHcryhvZ0sAEAGJOoFQDoQXKeqmsFAAAAgDolNJCio+J0rYMYp2jt6V4AAOC1hABSRFcDXesgFK0AAHRP1AoANG9nmKprBQAAAACyEG1e6VoHME7RCgAAVEWxeaJr7d5QRWtnt0MaxwAAxiRqBQAAAAAAACAbXWvHBixau7wpNnEGAACoja61Y0MVrQAAjEzUCgC0LcucVcNaAQAAAKAqCd1jo9FRo8u+SNfapQGLVgAA6FJC9NhoStfostfQtXZpzKK11/tiJQcAAIYlagUAAAAAAAAgM11rZ0arcvdSAAAgAElEQVQuWvu+O5bZfQAAqqVr7cyYRSsAAMMStQIAAAAAAADspdJ8T9fajZGLVgAAoAYSzQ/pWrsxeNHa/Q3yGVsPACMTtQIADZumqcJLAQAAAABwomvtgKL1apjb5A37DgBA/XStHRi8aAUAYEyiVgCAgczzXHoJAAAAAHBBQujYaHfU6LK3+vXjceFOda2VW9ig5Z0FAADqlFA5NtrUNbrsBPPzzcLN6lort7BByzvbmXHulDObDgCDm4QNAEC78o5Xrfz3oiw3W/k9HiPXsfEwAQAAAII0GrU2uuwjLeeRx62D1WzZGxrsoYx5yAGAETQatTa67IMt55FHroSVbNlrAuzRDHjIAYDXTGoFAAAAAAAA2EXqtoZ5rW1RtAIAAJWQuq1kXmtbFK1vjHnXw7LdAICoFQCgAXln0gIAAABAT/R1DdG1tkLR+qGR73009hoA4Ex31BZdaysUrQAADO669AIAAKowz3PpJYQb4R4BAAAAaN04ZeOwxdTpxsfZ6J4Me2gBAKAb42SNI2eBp3sfZ697MvK5PZmfbxzdETjqAMCVSa0AAAAAAAAAHEwe2RxbduI5jMAuAwDQAc1Yc2wZAABDEbUCAAAAAAAApDN0NM2vH4+vyzEVWVXebI3dAQAASjG2Mdn8fPO6k9RMVuXN1tidM4+ie7YYADi5Lr0AAIB08zxP05TlOvsvUrkR7hEAAACAAWntWmcHq2VrPvPrx6OUvWNOPgDAa9KjDtjEatkaAABGZlIrAEDtsoS7AAAAAEA9RFPQNB/hXtlZAIA+aAWhaT7CHbO5AMCZqBUAGJ0RpgAAAABQCcMPAQAAIMh0+1J6CQCrSB+7ZFsBgNdErQDA0AYpWge5TQAAAAA4nhAXxmSkZ3/sKQBAhYS4AAAAXRK1AgBt25NrSj0BAAAAAIA0Gsie2E0AAICqmOrZGRsKALwhagUABtVK0TpN084rtHKnAAAAADAI6RQAAAAE0U1BN3ycu2ErAYD3RK0AwHDmedZ5AgAAAEAH1KFAWd5CfbCPAAAf0iABxXkRAQD0StQKADRvZaE6/yN6PRkZ0woAAAAANbt7eCy9BKAwPWTr7CAAQLWm25fSSwBgL2UyAPAhUSsA0LkWW9ZcxrxrAAAAAACANRStAAAAlZNENs32AQCfEbUCAD1YqDf3Dzstpd2VAwAAAACfEVBBf3yuAQCgEuop6JKPdqNsHACwQNQKAHSiy651D2NaAQAAAADgGLrWFtk1AACAVsgjm2PLAIBlolYAYAjNda07F6xoBQAAAIBodw+PpZcAVEQh2Rb7BQBQuen2pfQSAAAAiCJqBQD6oeQ88RwAAAAAAOB4OslW2CkAAIDmmPzZEJsFAFwkagUAurLQc07T1Mq81lbWCQAAAADDShvTKqOC7vmY188eAQDUL21Mq4YKuudj3gTbBACsIWoFAHqzPKe0/l505wqNaQUAAAAAAPiQohUAAKBpgsnK2SAAYCVRKwAwnJq7VkUrAAAAANQvbUwrMAjZZLVsDQBAE9LGtALjkE1Wy9YAAOuJWgGADl1sO2vuWpMpWgEAAAAAoAbiSQAAAIgjnqyQTQEANhG1AgB9mud5OfKcpqm2tHXPehStAAAAAHSgiQwseUxrE3cH5OIjXxs7AgDQRG6UPKa1ibsDMvKpr4rtAAC2ErUCAD1bM7K1hrR1zzIu5rsAAAAAQC7JRSswIBVlPewFAEATkotWYExCykrYCAAggagVAOjcmuCzbNpqQCsAAAAANEHRCmylpSzu149HuwAA0ARFK5BATlmcLQAA0ohaAYD+rSw/i6StilYAAAAASKYyBeonqizIkwcA2ENlCjRBVFmQhw8AJBO1AgBDmOd5U9p6QN2657usvx0AAAAAaEu1AdLOgLba+wKO4SVwPM8cAOC9auujnQFttfcFHMNLoAiPHQDYQ9QKAAxkUwgaV7fuvKycFQAAAAAOZiQssJ/G8kieNgBAQ4yEBfYTWB7MAwcAdhK1AgBjSZhxOr2y51vvv4gBrQAAAACMYFOJdEBuqmgFclFaHsNzBgBYsClDOiA3VbQCuczPN0rLY3jOAMB+olYAYETJdej0zvqvT1rp/8lZAQAAAKAIRSuQl94ymicMANAQRSuQnd4ylHIYAMhlUkcAAIPbmZtG89tadrl23NYAAAAAhNpUk0YkTBlzVoUV8IZgPoKXLQDAeptq0oh+KWPOKq8C3tPMR/C+BQAyMqkVABjd/I/SC/mXOlcFAAAAAIcp2ybpzYBQv348KjDz8jwBADYpGyaJzYBo8svsPFIAIC9RKwDA/1XSkdawBgAAAABoS8YG9e7hUdEKHEOHmYsnCQAQKmODOt2+KFqBY8zPNzrMLDxJACDCJJkAAPjMNE3HfCO/kh0p17baNQAAAIADbOpL90dNQTmr2gpYpqVP5gULALDHpr50f9EUlLNKrYBlWvo9vGMBgCCiVgCAtfI2rn4NK0XUCgAAANCWra1XWuC05rv8+pEyxFVwBayha03gBQsAsN/W1iutblrzXebnm4TwTG0FrCRt3coLFgAIJWoFANhrOZL061ZtRK0AAAAAzYnrWlde+XxBUSsQStq6klcrAEBGcV3ryiufLyhqBULpWtfzdgUAoolaAQAYi6gVAAAAoEXJodf78Ck5kU1bg/IK2ETXepH3KgBAdsmh1/vqKTmRTVuD7ArYStq6zHsVADiGqBUAgLGIWgEAAADadXDr9aabErUCh5G2fsgbFQAg1MGh15tuStQKHEna+p43KgBwJFErAABjEbUCAAAANO2w0Gv/iNcPLwKwnrT1zOsUAOAYh1Ve+0e8fngRgPV0ra95owIABxO1AgAwFlErAAAAQAdCQ68P0yljWoFSBk9bvUgBAI4XGnp92E0Z0wqUIm31LgUAirguvQAAAAAAAACAbc6NU8bWSzcF1On0dhowbfVaBgAo5Rw4ZWy9RFNAnU5vpzHTVm9mAKAgUSsAAAAAAADQqv11q2gKaMJQaas3MwBAJfbXrYopoAmjpa1ezgBAcdM8z6XXAAAAx5mmKct1/CINAAAAUL8P66+0ViohJBNlAUE6Tlu9OQEAGvJh/ZUWSiWEZIosIE6vdas3JwBQD1ErAABjEbUCAAAAsFVaPybNAqJ1U7d6YQIAjCwtHpNmAdF6Slu9MwGA2lyXXgAAAAAAAAAAAJudWtCm01Y5KwAAAHU6h6Dt1q1aVgCgWqJWAAAAAAAAgMxkWsBhzi+chupWL0kAAPaQaQFHaq5u9ZIEAOonagUAAAAAAAD4VEORGDC416Vohe8uISsAAO+1UogBXFVft2pZAYCGiFoBAAAAAAAAALpSSeAqZAUAAKA/r/PRsoGrkBUAaJSoFQAAAAAAACAnERdQlTcvpdDG1QsQAIBoCi6gKu9fSnGZqxcgANANUSsAAAAAAADAxwqONwQIstCdrnzpKVcBAMii7HhDgCAL6ema955yFQAYgagVAAAAAAAAAAC1KgAAAAQSrAIAnHwpvQAAAAAAAACAGqWNaZWEAQAAwEVpY1r1YAAAAB0QtQIAAAAAAAAAAAAAAAAAEO669AIAAAAAAACAob0fiNrusNN2Vw4AAECX3g9EbXfYabsrBwAA4DVRKwAAAAAAAFDA+5b1zX+VDUQXlgcAAACVe9+yvvmvsoHowvIAAADo3pfSCwAAAAAAAACGsyYZvXt4bKssNaYVAACAGqxJRqfbl7bKUmNaAQAAumFSK+z19PSU5Tr39/dZrgMAAAA98Xc3ALTFz25Wqj9VTVihohVoTq4f3Fd+dgPAIfzRzUr1p6oJK1S0Ai3ysxsA4DMmtQIAAAAAAADH2dqL1l/AAgAAQCW29qL1F7AAAAD0R9QKAAAAAAAAHKSJQtWYVgAAAFrURKFqTCsAAACiVgAAAAAAAOAIyUXrkSlsE9ktAAAAvJFctB6ZwjaR3QIAABBN1AoAAAAAAACQzphWAAAACGJMKwAAQH9ErQAAAAAAAEC4JiagJixS0QoAAEBxTUxATVikohUAAKBLolYAAAAAAACAFIpWAAAACKJoBQAA6JWoFQAAAAAAAKCNWbIAAADQoiZmyQIAAHAMUSsAAAAAAAAQa2cvesBA1IQVGtMKAABADXb2ogcMRE1YoTGtAAAAHRO1AgAAAAAAAENTtAIAAEAQRSsAAABviFoBAAAAAAAANlC0AgAAQBBFKwAAQPdErQAAAAAAAEC9ogvSrWNaFa0AAAB0I7og3TqmVdEKAAAwAlErAAAAAAAAMChFKwAAAARRtAIAAPAhUSsAAAAAAABQqdCIVNEKAADAyEIjUkUrAAAAnxG1AgAAAAAAADVStAIAAEAQRSsAAACliFoBAAAAAACA6ihaAQAAIIiiFQAAgIJErQAAAAAAAECsTVHorx+PilYAAABYaVMUOj/fKFoBAAAo67r0AgAAAAAAAAAOakcVrQAAAAzomHZU0QoAAMAaolYAAAAAAACgmMOq0a0565WiFQAAgMYdVo1uzVmvFK0AAAAD+1J6AQAAAAAAAED/PgtEE1rTBAkDWhWtAAAAtOKzQDShNU2QMKBV0QoAADAyUSsAAAAAAABQUnTXmlC0hqwDAAAADhfdtSYUrUErAQAAoBWiVgAAAAAAAOAIC7FoUNd69/CoaAUAAGAEC7FoUNc63b4oWgEAAEhwXXoBAAAAAAAAwCh+/fi0Mj39e66mNKGSlbMCAADQtPn55rPK9PTvuZrShEpWzgoAAMCZSa0AAAAAAABALfaPbE2YznqlaAUAAGAA+0e2JkxnvVK0AgAA8G/TPM+l1wAAAMeZpinLdfwiDQAAAJBsZXS6NTRNC2LlrAAAAPRkZXS6NTRNC2LlrAAAALwnagUAYCyiVgAAAIBKbGpQP0tP90x2lbMCAADQq00N6mfp6Z7JrnJWAAAAPiNqBQBgLKJWAAAAgHrsSVL3kLMCAADQvT1J6h5yVgAAAJaJWgEAGIuoFQAAAKAqB3etclYAAADGcXDXKmcFAABgDVErAABjEbUCAAAAVCg6bdWyAgAAMKzotFXLCgAAwCaiVgAAxiJqBQAAAKhW9rRVywoAAAAn2dNWLSsAAABpRK0AAIxF1AoAAABQvz11q5AVAAAAFuypW4WsAAAA7CdqBQBgLKJWAAAAgEa9L131qwAAAJDF+9JVvwoAAEAQUSsAAGMRtQIAAAAAAAAAAAAAQBFfSi8AAAAAAAAAAAAAAAAAAID+iVoBAAAAAAAAAAAAAAAAAAgnagUAAAAAAAAAAAAAAAAAIJyoFQAAAAAAAAAAAAAAAACAcKJWAAAAAAAAAAAAAAAAAADCiVoBAAAAAAAAAAAAAAAAAAgnagUAAAAAAAAAAAAAAAAAIJyoFQAAAAAAAAAAAAAAAACAcKJWAAAAAAAAAAAAAAAAAADCiVoBAAAAAAAAAAAAAAAAAAgnagUAAAAAAAAAAAAAAAAAIJyoFQAAAAAAAAAAAAAAAACAcKJWAAAAAAAAAAAAAAAAAADCiVoBAAAAAAAAAAAAAAAAAAgnagUAAAAAAAAAAAAAAAAAIJyoFQAAAAAAAAAAAAAAAACAcKJWAAAAAAAAAAAAAAAAAADCiVoBAAAAAAAAAAAAAAAAAAgnagUAAAAAAAAAAAAAAAAAIJyoFQAAAAAAAAAAAAAAAACAcKJWAAAAAAAAAAAAAAAAAADCiVoBAAAAAAAAAAAAAAAAAAgnagUAAAAAAAAAAAAAAAAAIJyoFQAAAAAAAAAAAAAAAACAcKJWAAAAAAAAAAAAAAAAAADCiVoBAAAAAAAAAAAAAAAAAAgnagUAAAAAAAAAAAAAAAAAIJyoFQAAAAAAAAAAAAAAAACAcKJWAAAAAAAAAAAAAAAAAADCiVoBAAAAAAAAAAAAAAAAAAgnagUAAAAAAAAAAAAAAAAAIJyoFQAAAAAAAAAAAAAAAACAcKJWAAAAAAAAAAAAAAAAAADCiVoBAAAAAAAAAAAAAAAAAAgnagUAAAAAAAAAAAAAAAAAIJyoFQAAAAAAAAAAAAAAAACAcKJWAAAAAAAAAAAAAAAAAADCiVoBAAAAAAAAAAAAAAAAAAgnagUAAAAAAAAAAAAAAAAAIJyoFQAAAAAAAAAAAAAAAACAcKJWAAAAAAAAAAAAAAAAAADCiVoBAAAAAAAAAAAAAAAAAAgnagUAAAAAAAAAAAAAAAAAIJyoFQAAAAAAAAAAAAAAAACAcKJWAAAAAAAAAAAAAAAAAADCiVoBAAAAAAAAAAAAAAAAAAgnagUAAAAAAAAAAAAAAAAAIJyoFQAAAAAAAAAAAAAAAACAcNelFwDNe3p6ynKd+/v7LNcBAACAnvi7GwDa4mc3ADQk1w/uKz+7AeAQ/ugGgLb42Q0A8BmTWgEAAAAAAAAAAAAAAAAACCdqBQAAAAAAAAAAAAAAAAAgnKgVAAAAAAAAAAAAAAAAAIBwolYAAAAAAAAAAAAAAAAAAMKJWgEAAAAAAAAAAAAAAAAACCdqBQAAAAAAAAAAAAAAAAAgnKgVAAAAAAAAAAAAAAAAAIBwolYAAAAAAAAAAAAAAAAAAMKJWgEAAAAAAAAAAAAAAAAACCdqBQAAAAAAAAAAAAAAAAAgnKgVAAAAAAAAAAAAAAAAAIBwolYAAAAAAAAAAAAAAAAAAMKJWgEAAAAAAAAAAAAAAAAACCdqBQAAAAAAAAAAAAAAAAAgnKgVAAAAAAAAAAAAAAAAAIBwolYAAAAAAAAAAAAAAAAAAMKJWgEAAAAAAAAAAAAAAAAACCdqBQAAAAAAAAAAAAAAAAAgnKgVAAAAAAAAAAAAAAAAAIBwolYAAAAAAAAAAAAAAAAAAMKJWgEAAAAAAAAAAAAAAAAACCdqBQAAAAAAAAAAAAAAAAAgnKgVAAAAAAAAAAAAAAAAAIBw0zzPpdcAAADHmaYpy3X8Ig0AAAAAAAAAAAAAAJuY1AoAAAAAAAAAAAAAAAAAQDhRKwAAAAAAAAAAAAAAAAAA4UStAAAAAAAAAAAAAAAAAACEE7UCAAAAAAAAAAAAAAAAABBO1AoAAAAAAAAAAAAAAAAAQDhRKwAAAAAAAAAAAAAAAAAA4UStAAAAAAAAAAAAAAAAAACEE7UCAAAAAAAAAAAAAAAAABBO1AoAAAAAAAAAAAAAAAAAQDhRKwAAAAAAAAAAAAAAAAAA4UStAAAAAAAAAAAAAAAAAACEE7UCAAAAAAAAAAAAAAAAABBO1AoAAAAAAAAAAAAAAAAAQDhRKwAAAAAAAAAAAAAAAAAA4UStAAAAAAAAAAAAAAAAAACEE7UCAAAAAAAAAAAAAAAAABBO1AoAAAAAAAAAAAAAAAAAQDhRKwAAAAAAAAAAAAAAAAAA4UStAAAAAAAAAAAAAAAAAACEE7UCAAAAAAAAAAAAAAAAABBO1AoAAAAAAAAAAAAAAAAAQDhRKwAAAAAAAAAAAAAAAAAA4UStAAAAAAAAAAAAAAAAAACEE7UCAAAAAAAAAAAAAAAAABBO1AoAAAAAAAAAAAAAAAAAQDhRKwAAAAAAAAAAAAAAAAAA4UStAAAAAAAAAAAAAAAAAACEE7UCAAAAAAAAAAAAAAAAABBO1AoAAAAAAAAAAAAAAAAAQDhRKwAAAAAAAAAAAAAAAAAA4UStAAAAAAAAAAAAAAAAAACEE7UCAAAAAAAAAAAAAAAAABBO1AoAAAAAAAAAAAAAAAAAQDhRKwAAAAAAAAAAAAAAAAAA4UStAAAAAAAAAAAAAAAAAACEE7UCAAAAAAAAAAAAAAAAABBO1AoAAAAAAAAAAAAAAAAAQDhRKwAAAAAAAAAAAAAAAAAA4UStAAAAAAAAAAAAAAAAAACEE7UCAAAAAAAAAAAAAAAAABBO1AoAAAAAAAAAAAAAAAAAQDhRKwAAAAAAAAAAAAAAAAAA4UStAAAAAAAAAAAAAAAAAACEE7UCAAAAAAAAAAAAAAAAABBO1AoAAAAAAAAAAAAAAAAAQDhRKwAAAAAAAAAAAAAAAAAA4UStAAAAAAAAAAAAAAAAAACEE7UCAAAAAAAAAAAAAAAAABDuf+zdsQ0DIRAAQSO5KfoPKOtdgQNL7L+wZgo4kR0EK0StAAAAAAAAAAAAAAAAAADkRK0AAAAAAAAAAAAAAAAAAORErQAAAAAAAAAAAAAAAAAA5EStAAAAAAAAAAAAAAAAAADkRK0AAAAAAAAAAAAAAAAAAORErQAAAAAAAAAAAAAAAAAA5EStAAAAAAAAAAAAAAAAAADkRK0AAAAAAAAAAAAAAAAAAOTeTx8AjrfW2jJnzrllDgAAAPwT724AOIvdDQAH2bW4X3Y3ANzCoxsAzmJ3AwB846dWAAAAAAAAAAAAAAAAAAByolYAAAAAAAAAAAAAAAAAAHKiVgAAAAAAAAAAAAAAAAAAcqJWAAAAAAAAAAAAAAAAAAByolYAAAAAAAAAAAAAAAAAAHKiVgAAAAAAAAAAAAAAAAAAcqJWAAAAAAAAAAAAAAAAAAByolYAAAAAAAAAAAAAAAAAAHKiVgAAAAAAAAAAAAAAAAAAcqJWAAAAAAAAAAAAAAAAAAByolYAAAAAAAAAAAAAAAAAAHKiVgAAAAAAAAAAAAAAAAAAcqJWAAAAAAAAAAAAAAAAAAByolYAAAAAAAAAAAAAAAAAAHKiVgAAAAAAAAAAAAAAAAAAcqJWAAAAAAAAAAAAAAAAAAByolYAAAAAAAAAAAAAAAAAAHKiVgAAAAAAAAAAAAAAAAAAcqJWAAAAAAAAAAAAAAAAAAByolYAAAAAAAAAAAAAAAAAAHKiVgAAAAAAAAAAAAAAAAAAcqJWAAAAAAAAAAAAAAAAAAByolYAAAAAAAAAAAAAAAAAAHLjuq6nzwAAAPcZY2yZ4yINAAAAAAAAAAAAAAA/8VMrAAAAAAAAAAAAAAAAAAA5USsAAAAAAAAAAAAAAAAAADlRKwAAAAAAAAAAAAAAAAAAOVErAAAAAAAAAAAAAAAAAAA5USsAAAAAAAAAAAAAAAAAADlRKwAAAAAAAAAAAAAAAAAAOVErAAAAAAAAAAAAAAAAAAA5USsAAAAAAAAAAAAAAAAAADlRKwAAAAAAAAAAAAAAAAAAOVErAAAAAAAAAAAAAAAAAAA5USsAAAAAAAAAAAAAAAAAADlRKwAAAAAAAAAAAAAAAAAAOVErAAAAAAAAAAAAAAAAAAA5USsAAAAAAAAAAAAAAAAAADlRKwAAAAAAAAAAAAAAAAAAOVErAAAAAAAAAAAAAAAAAAA5USsAAAAAAAAAAAAAAAAAADlRKwAAAAAAAAAAAAAAAAAAOVErAAAAAAAAAAAAAAAAAAA5USsAAAAAAAAAAAAAAAAAADlRKwAAAAAAAAAAAAAAAAAAOVErAAAAAAAAAAAAAAAAAAA5USsAAAAAAAAAAAAAAAAAADlRKwAAAAAAAAAAAAAAAAAAOVErAAAAAAAAAAAAAAAAAAA5USsAAAAAAAAAAAAAAAAAADlRKwAAAAAAAAAAAAAAAAAAOVErAAAAAAAAAAAAAAAAAAA5USsAAAAAAAAAAAAAAAAAADlRKwAAAAAAAAAAAAAAAAAAOVErAAAAAAAAAAAAAAAAAAA5USsAAAAAAAAAAAAAAAAAADlRKwAAAAAAAAAAAAAAAAAAOVErAAAAAAAAAAAAAAAAAAA5USsAAAAAAAAAAAAAAAAAADlRKwAAAAAAAAAAAAAAAAAAOVErAAAAAAAAAAAAAAAAAAA5USsAAAAAAAAAAAAAAAAAADlRKwAAAAAAAAAAAAAAAAAAOVErAAAAAAAAAAAAAAAAAAA5USsAAAAAAAAAAAAAAAAAADlRKwAAAAAAAAAAAAAAAAAAOVErAAAAAAAAAAAAAAAAAAA5USsAAAAAAAAAAAAAAAAAADlRKwAAAAAAAAAAAAAAAAAAOVErAAAAAAAAAAAAAAAAAAA5USsAAAAAAAAAAAAAAAAAADlRKwAAAAAAAAAAAAAAAAAAOVErAAAAAAAAAAAAAAAAAAA5USsAAAAAAAAAAAAAAAAAADlRKwAAAAAAAAAAAAAAAAAAOVErAAAAAAAAAAAAAAAAAAA5USsAAAAAAAAAAAAAAAAAADlRKwAAAAAAAAAAAAAAAAAAOVErAAAAAAAAAAAAAAAAAAA5USsAAAAAAAAAAAAAAAAAALn30weA4621tsyZc26ZAwAAAP/EuxsAzmJ3A8BBdi3ul90NALfw6AaAs9jdAADf+KkVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAAAAAgJyoFQAAAAAAAAAAAAAAAACAnKgVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAAAAAgJyoFQAAAAAAAAAAAAAAAACAnKgVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAAAAAgJyoFQAAAAAAAAAAAAAAAACAnKgVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAAAAAgJyoFQAAAAAAAAAAAAAAAACAnKgVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAAAAAgJyoFQAAAAAAAAAAAAAAAACAnKgVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAAAAAgJyoFQAAAAAAAAAAAAAAAACAnKgVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAAAAAgJyoFQAAAAAAAAAAAAAAAACAnKgVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAAAAAgJyoFQAAAAAAAAAAAAAAAACA3Liu6+kzAADAfcYYW+a4SAMAAAAAAAAAAAAAwE/81AoAAAAAAAAAAAAAAAAAQE7UCgAAAAAAAAAAAAAAAABATtQKAAAAAAAAAAAAAAAAAEBO1AoAAAAAAAAAAAAAAAAAQE7UCgAAAAAAAAAAAAAAAABATtQKAAAAAJ4/PxUAACAASURBVAAAAAAAAAAAAEBO1AoAAAAAAAAAAAAAAAAAQE7UCgAAAAAAAAAAAAAAAABATtQKAAAAAAAAAAAAAAAAAEBO1AoAAAAAAAAAAAAAAAAAQE7UCgAAAAAAAAAAAAAAAABATtQKAAAAAAAAAAAAAAAAAEBO1AoAAAAAAAAAAAAAAAAAQE7UCgAAAAAAAAAAAAAAAABATtQKAAAAAAAAAAAAAAAAAEBO1AoAAAAAAAAAAAAAAAAAQE7UCgAAAAAAAAAAAAAAAABATtQKAAAAAAAAAAAAAAAAAEBO1AoAAAAAAAAAAAAAAAAAQE7UCgAAAAAAAAAAAAAAAABATtQKAAAAAAAAAAAAAAAAAEBO1AoAAAAAAAAAAAAAAAAAQE7UCgAAAAAAAAAAAAAAAABATtQKAAAAAAAAAAAAAAAAAEBO1AoAAAAAAAAAAAAAAAAAQE7UCgAAAAAAAAAAAAAAAABATtQKAAAAAAAAAAAAAAAAAEBO1AoAAAAAAAAAAAAAAAAAQE7UCgAAAAAAAAAAAAAAAABATtQKAAAAAAAAAAAAAAAAAEBO1AoAAAAAAAAAAAAAAAAAQE7UCgAAAAAAAAAAAAAAAABATtQKAAAAAAAAAAAAAAAAAEBO1AoAAAAAAAAAAAAAAAAAQE7UCgAAAAAAAAAAAAAAAABATtQKAAAAAAAAAAAAAAAAAEBO1AoAAAAAAAAAAAAAAAAAQE7UCgAAAAAAAAAAAAAAAABATtQKAAAAAAAAAAAAAAAAAEBO1AoAAAAAAAAAAAAAAAAAQE7UCgAAAAAAAAAAAAAAAABATtQKAAAAAAAAAAAAAAAAAEBO1AoAAAAAAAAAAAAAAAAAQE7UCgAAAAAAAAAAAAAAAABATtQKAAAAAAAAAAAAAAAAAEBO1AoAAAAAAAAAAAAAAAAAQE7UCgAAAAAAAAAAAAAAAABATtQKAAAAAAAAAAAAAAAAAEBO1AoAAAAAAAAAAAAAAAAAQE7UCgAAAAAAAAAAAAAAAABATtQKAAAAAAAAAAAAAAAAAEBO1AoAAAAAAAAAAAAAAAAAQE7UCgAAAAAAAAAAAAAAAABATtQKAAAAAAAAAAAAAAAAAEBO1AoAAAAAAAAAAAAAAAAAQE7UCgAAAAAAAAAAAAAAAABA7v30AeB4a60tc+acW+YAAADAP/HuBoCz2N0AcJBdi/tldwPALTy6AeAsdjcAwDd+agUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgN67revoMAABwnzHGljku0gAAAAAAAAAAAAAA8BM/tQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQez99ADjeWmvLnDnnljkAAADwT7y7AeAsdjcAHGTX4n7Z3QBwC49uADiL3Q0A8I2fWgEAAAAAAAAAAAAAAAAAyIlaAQAAAAAAAAAAAAAAAADIiVoBAAAAAAAAAAAAAAAAAMiJWgEAAAAAAAAAAAAAAAAAyIlaAQAAAAAAAAAAAAAAAADIiVoBAAAAAAAAAAAAAAAAAMiJWgEAAAAAAAAAAAAAAAAAyIlaAQAAAAAAAAAAAAAAAADIiVoBAAAAAAAAAAAAAAAAAMiJWgEAAAAAAAAAAAAAAAAAyIlaAQAAAAAAAAAAAAAAAADIiVoBAAAAAAAAAAAAAAAAAMiJWgEAAAAAAAAAAAAAAAAAyIlaAQAAAAAAAAAAAAAAAADIiVoBAAAAAAAAAAAAAAAAAMiJWgEAAAAAAAAAAAAAAAAAyIlaAQAAAAAAAAAAAAAAAADIiVoBAAAAAAAAAAAAAAAAAMiJWgEAAAAAAAAAAAAAAAAAyIlaAQAAAAAAAAAAAAAAAADIiVoBAAAAAAAAAAAAAAAAAMiJWgEAAAAAAAAAAAAAAAAAyIlaAQAAAAAAAAAAAAAAAADIiVoBAAAAgA97d3fVuJI1YNhiEQIh0BFwRTAngxNOZ9DB+IoImhDIQd+F5/MwGAv9lFS1dz3PmouZc2i5SiUb6Fnv2gAAAAAAAAAAALC7YRzH2msAAIDjDMNQ5Dp+kAYAAAAAAAAAAAAAgEVMagUAAAAAAAAAAAAAAAAAYHeiVgAAAAAAAAAAAAAAAAAAdidqBQAAAAAAAAAAAAAAAABgd6JWAAAAAAAAAAAAAAAAAAB291h7AQAAAAAAUMzHy/Plvzy9vdddCbecDgAAAAAAAABA54ZxHGuvAQAAjjMMQ5Hr+EEaAABacw0mr5STTXFAAAAAAAAAAAA81F4AAAAAAABsdRtM0j6nBgAAAAAAAADQG5NaAQDoi0mtAACQzEQYaRBoa+4dlpMCAAAAAAAAAOiESa0AAAAAAESlaI3l3qEY2QoAAAAAAAAA0AmTWgEA6ItJrQAAkIaxn0E5OAAAAAAAAACAbolaAQDoi6gVAAASMKA1OicIAAAAAAAAANCnh9oLAAAAAACABfSQCUyc1MT5AgAAAAAAAAAQnagVAAAAAIAwFK1p6FoBAAAAAAAAADokagUAAAAAIAZFazK6VgAAAAAAAACA3ohaAQAAAAAIQNGakq4VAAAAAAAAAKArolYAAAAAAFqnaE1M1woAAAAAAAAA0A9RKwAAAAAATVO0pqdrBQAAAAAAAADohKgVAAAAAIB2KVo7oWsFAAAAAAAAAOjBMI5j7TUAAMBxhmEoch0/SAMAwAG6Klrnp5v59n7V1YkDAAAAAAAAAHRI1AoAQF9ErQAAEEX6vrHUANIcd+Mq/bkDAAAAAAAAAPTssfYCAAAAAADgq1LBZ2v22Nfna+bOPj9ennNvEAAAAAAAAAAgPZNaAQDoi0mtAADQvunyM2jWeHCmG/QuXaR8AAAAAAAAAAAAOJnUCgAAAABAIBGDxipTZ68vGvGOPb29Zx3VCwAAAAAAAADQOZNaAQDoi0mtAADQuImaMVaf2VSWGevWXaR5EgAAAAAAAAAAuHqovQAAAAAAAPiPpkLQLVrbSGvr2SjZdgAAAAAAAAAA+mFSKwAAfTGpFQAAmjVdKkYZzrm0t5y/ryIlZ5TbeJHjkQAAAAAAAAAA4ErUCgBAX0StAADQphz54szudON2ttetUe7nKcuDAQAAAAAAAADAxWPtBQAAAAAAQAZzWtMiEeb1Iqvr1o+XZzkoAAAAAAAAAADHM6kVAIC+mNQKAAANSjCN88e+dKddbJnaGuLGnlI8HgAAAAAAAAAAXIhaAQDoi6gVAABakyBZrL6F1WlriNt7auAOAwAAAAAAAABQxEPtBQAAAAAAQGATveXT2/sxveXqF9oy6BUAAAAAAAAAAJYStQIAAAAAUE30CZzTReuRK1n9iiG61umthdgCAAAAAAAAAAAnUSsAAAAAALVEbxGbKlq3vG70gzil2AIAAAAAAAAAQA9ErQAAAAAAtKjxMa0NFq1bXr39KLTx5wEAAAAAAAAAgDlErQAAAAAAVDBdUTZeMDZbtG5ZQ/Sutf31AwAAAAAAAAAgagUAAAAAgDJaKFov2lkJAAAAAAAAAABcDeM41l4DAAAcZxiGItfxgzQAAGyRckxra8teN7m0tV3cCv3wdGLj1FyHCAAAAAAAAACJPdZeAAAAAAAAfdkYvLWpwQzv6e095a2e9vHy3OBZJLbHM/btNR0rAAAAAAAAAORgUisAAH0xqRUAAKoLPWkzypjWqxXNYbN7uQr9CCXQQiztlAEAAAAAAAAgKJNaAQAAAAA4Tgs5XHH6uqYY1rqH1t65n9fjuAEAAAAAAAAgEFErAAAAAABscu3rcsR1olAuWgtZ70n2BgQAAAAAAACA3IZxHGuvAQAAjjMMQ5Hr+EEaAABW+LGRa7xJm9/4NbWRFWliU+v/VvRnqXFRctZ7nD4AAAAAAAAANMukVgAAAAAAKMywUyKK3rJeXTbiPQgAAAAAAAAADTKpFQCAvpjUCgAAtSQYrbk0+WtnR4a1MiFNy3qPJwEAAAAAAAAA2vFQewEAAAAAAJBT+laQ6D5ennt4SjvZJgAAAAAAAACEIGoFAAAAAGB3CYqyBFvojSOb0GHn2eGWAQAAAAAAAKBBolYAAAAAAOp7enuvvQSC8cys03nb2fn2AQAAAAAAAKA6USsAAAAAAOylkXxuRf/ZyMopSM955VYAAAAAAAAAQC2iVgAAAAAA9vVjPGbkJuv8+OQIF6/cilvSVgAAAAAAAAA4nqgVAAAAAAAgLenmNDcHAAAAAAAAAI4kagUAAAAAYEeCMerq+QmUs87kRgEAAAAAAADAYUStAABrDEvUXiwAAEDTnt7eay+BwDw/96g0l5K2AgAAAAAAAMABRK0AALNs6VSVrgAAAAmsiycllxxPnLmFWwcAAAAAAAAAuxK1AgDctWuDKnAFAAB6IA+jBV09h11tdifuIQAAAAAAAADs57H2AgAAmnN8ZXp9xXEcD35pAAAAIA01ZimXO2nSMgAAAAAAAAAUZ1IrAMB/VZ+bWn0BAAAAB4vVjC1dbazdxeU+n06nj5dnRWtxbikAAAAAAAAAFGdSK2x1Pp+LXOf19bXIdQBYp6mU1OBWAIArv3dDaCl7sKe395n7Ulo25ePlOfGJpHyvNSL3kwMApX7pPvm9GwAO4S/MASAW37sBAO4RtQIAvWsqZ/3isjZpKwAAQFMuhdtERpgjgcuxix4oWvd2ucPeEQAAAAAAAABQxEPtBQAA1NRy0Xo1DEOIdQIAAHTl6e39NnL79h+2QPeYlZM9jFsNAAAAAAAAAEWY1AoAdGpdJrpoaGrZEnUYBiNbAQCAQOYEYG32n4sk2EIOT2/vPz5yHy/Pmc5LY3m8ZI8QAAAAAAAAAFRhUisA0KNFuen4yaJXWf0H7zGyFQAAADgpWutx5wEAAAAAAABgI1ErANCX+V1o2R61bNpa5DoAAABARLrKutx/AAAAAAAAANhC1AoAdGRRzrrHAkpd2chWAAAA5lvR4D29ve+xErZTVLbAKQAAAAAAAADAaqJWAKAXcyrQ/XLWPV5F1woAADRrTvElm6SsOU9U9BYx+vozcRYAAAAAAAAAsI6oFQDowsyi9YCVfH657a+oawUAAGCa9C4NR9kaJwIAAAAAAAAAK4haAYD8fiw/jxnQeu+lN15B1woAAEBZhug2SD/ZJucCAAAAAAAAAEuJWgGA5OYUrcesZL8F6FoBAAAgMeVky5wOAAAAAAAAACwiagUAMpuuPSsOaP1C1woAAGQi8WrHirNIP6Y13PMZbsEdckYAAAAAAAAAMJ+oFQDoVCM565WuFQAAACAoXSsAAAAAAAAAzCRqBQDSmog8WytaL3StAABAP9KPA21Bh2Nao6//llQyEIcFAAAAAAAAAHOIWgGAnMIVrRe6VgAAAOBCJAkAAAAAAAAA5CNqBQASCh12ttzcAgAAEEWHY1qTUbRG5NQAAAAAAAAA4EeiVgCgLz0ko6GbXgAAALZTtEanjYzL2QEAAAAAAADANFErAJDNRNIZpWjdvk5dKwAAUIugi/Z5StmVBwwAAAAAAAAAJohaAYBeRClaL2KtFgAAgHYY0xqdJBIAAAAAAAAASEzUCgCkYkLplVsBAADQIUVrdIrWHJwjAAAAAAAAANwjagUAuhBx8GnENQMAABCLorUpSshMnCYAAAAAAAAAfEvUCgDkYTbpF24IAADQJiHlTpZGdCkPIuWmAAAAAAAAAADSELUCAPkZeQoAAEB6itboDPbMx5kCAAAAAAAAwC1RKwCQRMqppNtz3JS3BQAAgC8UrdAmXSsAAAAAAAAAfDEYXAYA5HCv3oz+0872KjX6HSiuVOjrxgIAwLfmFFyKyrIUrV9EfAilj4m19rABAAAAAAAAQF0mtQIAyQ2f1F7LGoa1AgAAUJC+rkGK1tycLwAAAAAAAAB8JmoFADKY2W1GD1wBAADgi0W9nKIVqtC1AgAAAAAAAMCVqBUA6JSuFQAAoDjh1sEUrau186y2sxIAAAAAAAAAgAOIWgGAfkUZ2TqOY+0lAAAA0BxFKwSiXgYAAAAAAACAi8faCwAAqGwYhvTVaA97BAAA6Mr8QE7O2jKhIwAAAAAAAADQG5NaAQAAAAAgEkUrRKRhBgAAAAAAAICTqBUASGAYhupXAAAAgGMoWtOQOAIAAAAAAAAAHRK1AgAAAABADIpWCE3JDAAAAAAAAACPtRcAANCEYRjGcay9irvGcTROFgAAoHMzczg5awjiRgAAAAAAAACgTya1AgB0QRMLAAAQmqIVctAzAwAAAAAAANA5k1oBAAAAAKBd8xM4RWsUskYAAAAAAAAAoFsmtQIAAAAAQKPmD2hVtEIUqmYAAAAAAAAAeiZqBQAAAACAFs0vWvdeCQUJGgEAAAAAAACAnolaAQAAAACgOYpWAAAAAAAAAADyEbUCAAAAAEBb5hStT2/vilYIysBeAAAAAAAAALolagUAwhvHsZGLAAAAwHYzi9YDVkJxUkYAAAAAAAAAoHOiVgAAAAAAaIWiFTqhcAYAAAAAAACgT4+1FwAAAAAAAJxOMyI3OWspVe6kiBEAAAAAAAAAwKRWACCDcRwr/nEAAAAuJJdbKFoBAAAAAAAAAEhP1AoAJLE6TFW0AgAAUJ2iFTpkeC8AAAAAAAAAHXqsvQAAgJoUrQAAAFTXSNF6uwwlbUHyRQAAAAAAAACAk6gVAMjkUqgOw7Do6zvR1WYBAACiaCRnBQAAAAAAAACAYzzUXgAAQGEz681Ykef8UhcAAIAomipazREFAAAAAAAAAOAAolYAIKHx/y36VwAAAHCYporWNhewjjqXWDyxAAAAAAAAAPRG1AoAZDbeqL0iAAAARFwBilbK8swDAAAAAAAAAFw81l4AAAAAAAB0ZLpvlLMCAAAAAAAAAJCYSa0AAPkZUQsAANAIRSsAAAAAAAAAAD0TtQIAtG4YhtpLAAAAmEuWOUHR2gi3mqZMfzIAAAAAAAAAQDKiVgAAAAAA2F3LRaumblduLwAAAAAAAADA1WPtBQAAsK9xHGsvAQAAoHcTWWObU0PbXBUAAAAAAAAAANGZ1AoA0LRhGGovAQAAgE3CFa0AAAAAAAAAALATk1oBAAAAgB596Qy1hexE0XqkibsNAAAAAAAAAEALRK0AAJmN41h7CQAA0Irp2k3jerCPl+f0N3n6kWtn+0JQqK6Hj0QAAAAAAAAAuHiovQAAAO4ahqH2EgAAIIOPl+el2d6KP9KailuQZp3iFK0dOvjmR/8kAQAAAAAAAAAoy6RWAIC0jGkFAICNOdnlj8fqD79s+fo/Y+0iuokHr7WDkFwCAAAAAAAAAHAkUSsAQKOMaQUAgC0KpnpR0tbpLUfZRQKBilYAAAAAAAAAADiYqBWOcD6fi1zn9fW1yHUOYMur2XLLSm351OWubfl488e0ptnywc7nc6Atd3jKPrS3sOWW2fJqHW75FGfXHW755NneYOaW9xg++fHyvKhIbHPLS3exyOct/1r49Z+FfrB//fvPvS/++/vP3/tPRZVd33tsph8Sn2CLzHkvQBXey1t0uGtbbpktr2bLLfOhvYUtt8yWV7PllvnQ3sKWW2bLq9lyy3xob2HLLetwywAA0YlaAQBaZEwrAACsNifv/NLstVCEbrFHxLvR399/JvLOi1///vP3959j1nOM6S1P/9uP0ovp0Ir3PgAAAAAAAAAAB3uovQAAAMqbP6YVAACSma7ant7eL/+5/edFrl9Fg0vq048RLwAAAAAAAAAAIGoFAGjOxjGtilYAALo1kXd+27J++YLtrxJC9PW3SdEKbOTDGQAAAAAAAIBOiFoBAAAAgAymi9YjV3IkEVQLFK0AAAAAAAAAADCTqBUAoC3GtAIAwAr32s4fB7R++eKNL8dnf3//qb2EIyhaGzHnXZm4bwcAAAAAAAAAiELUCgCQh6IVAIA+TRStB68khKZuS/QiNPr6v9XUEwIAAAAAAAAAQDKiVgCAhmwc0woAAB2qVbS2MKy1hTV06+Pl2f3nRymzZwAAAAAAAACALUStAACt2Fi0GtMKAECHzGht3N/ff2ovYRdy1oh8LAAAAAAAAAAAtEDUCgCQgaIVAADgGIrWBjkUAAAAAAAAAIAoRK0AAE3YMqZV0QoAQJ+qj2mN2NE1OKzy17//1F7CAhEPHQAAAAAAAAAA2iFqBQCoT9EKAABLVS9aq1NXAgAAAAAAAAAA4YhaAQAAAIAk+ilaA/n7+0/tJRTTQ0ic9U2UdV8AAAAAAAAAAOGIWgEAKjOmFQAAluqhLezKr3//qb0EAvOBAAAAAAAAAAAQiKgVAKAmRSsAAJSyfRJj+jSu1rDKTMNaiciYVgAAAAAAAACAdohaAQBCUrQCANCt9OnpHG4CAAAAAAAAAAAQkagVAKCa1WNaFa0AAPCFSYzR/fr3n9pLICSBNwAAAAAAAABALI+1FwAA0ClFKwAArCBgC+rv7z8JstXbdvp8Ppe6+Ovra6lL8Vnd4j3Hkw8AAAAAAAAAUJBJrQAAFShaAQCgoCLRWvpc1jBb8kn/tgUAAAAAAAAAyEfUCgBwNEUrAACs09rAQ5noIn9//5n+gtbOlwS8SQEAAAAAAAAAWvNYewEQ3vl8rr0EALqgaAUA+NHEL+mvr69HroQjdRitmU4JAAAUUfD/7PZ7NwAcoNT3bt+4AeAYvncDANxjUisAwKHWjWlVtAIAwK5jPGWixzCslYJ+fNt2WLwTmicWAAAAAAAAgE6IWgEAjqNoBQCAsiomQLHqo1irBQAAAAAAAAAAshK1AgAcRNEKAACrGeCZxo/DWmEOY1oBAAAAAAAAAIIaZBKw0fl8LnKd19fXItcBoE2K1nasO4tbTgcAjuH3bi7uBWxFurUf67j9XnqFWKv9lhyR7QI9Reves3SonYcWgK6U+qX75PduADiEvzAHgFh87wYAuMekVgCA3a2oKMdx1EwCAMC0iv2P9GgLd4+9ecYAAAAAAAAAAJolagUA2Ne6onWPlQAAQFDmHG4ULvBz4kzzhAAAAAAAAAAAxCVqBQDYkaIVAAAap46rIlxnSyCtPV2trQcAAAAAAAAAoC5RKwBAQxStAADQvoqJWj8Jbj87ZSnPBgAAAAAAAABAaKJWAIC9LB3TqmgFAID5ipSl6riKjK9kD54rgvLoAgAAAAAAANAPUSsAwC4WFa3jOCpaAQAghHDdUbgFX0mOueWpAAAAAAAAAACITtQKAFDe0qJ1v5UAAEBKcUNNPnOOlOWJAgAAAAAAAABon6gVAKAwRSsAAJS1U6gWbuRjlsxCrwAAIABJREFUuAXPMX24KbfMatPPQ8tFa8trAwAAAAAAAAA42GPtBQAApKJoBQCAPTSShDWyjK58vDy77ZwUzgAAAAAAAAAAWZjUCgBQjKIVAABoSogcNMQiaZyniNA8wAAAAAAAAAB0RdQKAFDG/KJ1HEdFKwAA1GXqY1Omgy6HxfQzIAgEAAAAAAAAAAhE1AoAUMCionXXlQAAADupG84JO+mWhx8AAAAAAAAAIBNRKwDAVopWAACAjQxrZZ0oY1qjrBMAAAAAAAAAYG+iVgCATRStAAAQzopCMmKQFm7NulZuTZ97uIccbnmMAQAAAAAAAOiNqBUAYD1FKwAAAAAAAAAAAAAAwEyiVgCAlWYWreM4KloBAIAt+hlSalgrnyUb0xpuwQAAAAAAAAAAexC1AgCsMb9o3XslAADAIivCSCnakXStXCQrWuFbnmQAAAAAAAAAOiRqBQBYTNEKAAA0LnQopWtF0QoAAAAAAAAAkJWoFQBgGUUrAADA3lSLPUtctIZePAAAAAAAAABAEYPcAgBgPkVrAjMP8UdOGQAgohVDPqtHaOsGk1Zf9naJy0amTRx9gnM3apirBM8zAAAAAAAAAKxgUisAwFyKVgAAgMNM517KwKycLAAAAAAAAABAbqJWAIBZYhWtw3dqLwoAADhImul/utbe9DCeN8cuAAAAAAAAAABWE7UCAPwsVtEKAAB8a0UDKT+rTtfajx6KVrjySAMAAAAAAADQLVErAMAP5hSt4zi2U7QaygoAAGmINk+61j50VbQm2w4AAAAAAAAAwCKiVgCAKTOL1gNWslGIRQIAAHxL15pbV0UrnDzVAAAAAAAAAPRN1AoAcFeaohUAAFjRPQaNjoIu+0e61qwUrQAAAAAAAAAAXRG1AgB8T9EKAADQFF1rPt0WrYm3xo+cPgAAAAAAAACdE7UCAHxD0QoAAFSn0ryla82k26IVAAAAAAAAAKBnolYAgK8UrQAAAM3SteagaO1hj9xy7gAAAAAAAAAgagUA+B/Ri9Y56wcAgN6sCB2DdkdBl73U09v7xE51re2bOKPpwwUAAAAAAAAAIDpRKwDAf0UvWu+JuGYAAIBputagpovWI1dSXW/7xYkDAAAAAAAAwEnUCgAw3ziO6lAAAOAYssw5dK3hKFoBAAAAAAAAADonagUA+I/pMa1yVgAA6Ie4LhBdayCK1lvdbrxDzhoAAAAAAAAALh5rLwAAoAk5itbpXQAAQJ/6KRu7LaYuG+/noJPp9rkFAAAAAAAAAOiTSa0AAEmKVgAAgJ5pIyNyaic3oQ9OGQAAAAAAAACuRK0AQO8UrQAAQGsMHV3n6e39czmmImvNl9NxQFduBQAAAAAAAADQj8faCwAAqEnRCgAAfKEui84Jtszp0CGPPQAAAAAAAAB8ZlIrANCvZEXrve2E2wgAALCCaApC8xbOyskCAAAAAAAAwBeiVgCgU8mKVgAA4FsfL8+1lwAwi/oRAAAAAAAAAOiBqBUA6JGiFQAAaJYQFyAHoTIAAAAAAAAA3BK1AgDdmShax3EMWrROZ7oAAABA+zSQmThNAAAAAAAAAPiWqBUA6Mt00XrkSgAAAEqRTkEa3s4AAAAAAAAAQG6iVgCgI1mLVmNaAQCgFDkZANv5bgIAAAAAAAAA94haAYBeZCpah/818ZXhtgYAAJ37eHmuvQSgMj1kdE4QAAAAAAAAACY81l4AAMARpstPk04BAACAdjy9vUvcAQAAAAAAAICUTGoFAPLTrAIAAIkZCQgpeWsH5eAAAAAAAAAAYJqoFQBITtEKAAAAwAEUrQAAAAAAAADwI1ErAJCZohUAAAjk4+W59hKAhigkY3FeAAAAAAAAADCHqBUASEvRCgAAAISmk4zCSQEAAAAAAADATKJWACAnRevpdBrHsfYSAACAudaNaZVRQXre5u1zRgAAAAAAAAAwn6gVAEhI0QoAAACkoZlsmdMBAAAAAAAAgEVErQBANopWAAAgnHVjWoF+KCcBAAAAAAAAgBxErQBAKopWAAAAICVda4McCgAAAAAAAAAsJWoFAPJQtAIAAF+EyI1Wj2kNsTugIO/6pjgOAAAAAAAAAFhB1AoAJKFoBQAAIlpdtAJ9ElI2wkEAAAAAAAAAwDqiVgCAnMZxrL0EAADgB4pWYAU5ZV1Pb++OAAAAAAAAAABWE7UCABkY0woAABShMgVCEFXW4s4DAAAAAAAAwEaiVgAgPEUrAAAwodkAaWNA2+y+gGP4EDieew4AAAAAAAAA24laAQAAAACOZiQssJ3G8kjuNgAAAAAAAAAUIWoFAAAAAJJbVCIdkJsqWoFSlJbHcJ8BAAAAAAAAoBRRKwAAAADAcRStQFlPb++Sy125vQAAAAAAAABQ0DCOY+01AABsMgxD7SU0x894E0o9MG4yAEA4i2rSPRKmgjmrwgq4pZkvzoctAAAAAAAAABRnUisAAAAA0IW6bZLYDNibArMs9xMAAAAAAAAA9vBYewEAAFsZmAkAABT38fJcKmeSswKHuXxw+djZTtEKAAAAAAAAADsxqRUAAAAA6MWiSKlIFSYtA44nyNzi6e3dDQQAAAAAAACA/QwmmwEA0JVhGIpcxw/SAABxLQ1N19VNc17l6e19RfWqtgJm0tUv5QMWAAAAAAAAAPYmagUAoC+iVgAATnt2rTOvfL2gqBXYla51Jh+tAAAAAAAAAHAMUSsAAH0RtQIAcLE69LoNn1YnsuvWoLwClpK2TvO5CgAAAAAAAACHEbUCANAXUSsAAJ8dHHp96aZErcCRpK23fKICAAAAAAAAwMEeai8AAAAAAKCaI3OmIq+lvwJW8wHy2dPbuxsCAAAAAAAAAMczqRUAgL6Y1AoAwLd2HWD4bTdlTCtQS+cjW32QAgAAAAAAAEBFj7UXAAAAAABQ37VxKth66aaANl0+nTpMW30sAwAAAAAAAEB1olYAAAAAgP/aXreKpoAQukpbfTIDAAAAAAAAQCOGcRxrrwEAAI4zDEOR6/hBGgCgT9/WX+taqRUhmSgL2E/WutUnJwAAAAAAAAA0RdQKAEBfRK0AALRgXTwmzQL2liZt9YEJAAAAAAAAAG16rL0AAAAAAAAAmnBtQYPWrVpWAAAAAAAAAGicqBW2Op/PRa7z+vpa5DoAAACQid+74UqpBRwpVt3qExIA1in1S/fJ790AcAh/YQ4AsfjeDQBwj6gVAAAAAOBQIQoxgIvPvWhrH19aVgAAAAAAAAAIR9QKAAAAAADAz6oHripWAAAAAAAAAIhO1AoAAAAA0DodF9Ca28+lPTJXn34AAAAAAAAAkIyoFQAAAADgOFVmGwIc4BKgns/nIld7fX0tch0AAAAAAAAAoCkPtRcAAAAAAAAAAAAAAAAAAEB+olYAAAAAgIOsG9N6GX4IAAAAAAAAAAAQnagVAAAAAAAAAAAAAAAAAIDdPdZeAAAAAABAYbcDUeMOO427cgAAAAAAAAAAgC9ErQAAAABAErct65d/VTcQnVgeAAAAAAAAAABADx5qLwAAAAAAoIA5yejHy3OsstSYVgAAAAAAAAAAIBNRKwAAAAAQXvup6ooVKloBAAAAAAAAAIBkRK0AAAAAQGxLe9H2C1gAAAAAAAAAAICURK0AAAAAQGAhClVjWgEAAAAAAAAAAE6iVgAAAAAgrtVF65EpbIjsFgAAAAAAAAAA4ACiVgAAAACAthjTCgAAAAAAAAAApCRqBQAAAABCCjEBdcUiFa0AAAAAAAAAAEBWolYAAAAAgFYoWgEAAAAAAAAAgMRErQAAAAAAuwgxSxYAAAAAAAAAAOAwolYAAAAAIJ6NvegBA1FXrNCYVgAAAAAAAAAAIDdRKwAAAABAYYpWAAAAAAAAAACAW6JWAAAAAIDKFK0AAAAAAAAAAEAPRK0AAAAAQF/2LkiXjmlVtAIAAAAAAAAAAJ0QtQIAAAAAFKNoBQAAAAAAAAAAuGcYx7H2GgAA4DjDMBS5jh+kAQDqWtqOXu0akSpaAQAAAAAAAAAAJpjUCgAAAAD0QtEKAAAAAAAAAABQkagVAAAAAOiCohUAAAAAAAAAAKCuYRzH2msAAIDjDMNQ5Dp+kAYAqG5+R7p3QapoBQAAAAAAAAAAmOOx9gIAAAAAAHZxTDuqaAUAAAAAAAAAAJhJ1AoAAAAApHJYNbo0Zz0pWgEAAAAAAAAAgL4N4zjWXgMAABxnGIYi1/GDNABAC+5lpQe0owa0AgAAAAAAAAAALPVQewEAAAAAAIWtmKG66/UVrQAAAAAAAAAAACeTWgEA6I1JrQAAyUz0pXukpCtyWUUrAAAAAAAAAADAhagVAIC+iFoBAPKZDk1LNaVyVgAAAAAAAAAAgI0eai8AAAAAAGBHK2LU2ysoWgEAAAAAAAAAALYzqRUAgL6Y1AoAkNLM6HRpaLouiJWzAgAAAAAAAAAAfEvUCgBAX0StAACJLWpQ76WnWya7ylkBAAAAAAAAAAAmiFqB/2PvDo4iObYFDMMEJmACsoAFMRZITsgDLeQB4MEs5IGckCyYYIEFwgR84C36RT8e0NXV1ZWV55z8vtDi6sbQZGVm00Mo/jgAMBZRKwBAbeckqeeQswIAAAAAAAAAABwlagUAYCyiVgCA8jbuWuWsAAAAAAAAAAAAM4laAQAYi6gVAGAQrdNWLSsAAAAAAAAAAMCpRK0AAIxF1AoAMJTV01YtKwAAAAAAAAAAwGKiVgAAxiJqBQAY0zl1q5AVAAAAAAAAAABgFaJWAADGImoFAGDvc+mqXwUAAAAAAAAAAGhH1AoAwFhErQAAAAAAAAAAAAAA0MW33gsAAAAAAAAAAAAAAAAAAKA+USsAAAAAAAAAAAAAAAAAAM2JWgEAAAAAAAAAAAAAAAAAaE7UCgAAAAAAAAAAAAAAAABAc6JWAAAAAAAAAAAAAAAAAACaE7UCAAAAAAAAAAAAAAAAANCcqBUAAAAAAAAAAAAAAAAAgOZErQAAAAAAAAAAAAAAAAAANCdqBQAAAAAAAAAAAAAAAACgOVErAAAAAAAAAAAAAAAAAADNiVoBAAAAAAAAAAAAAAAAAGhO1AoAAAAAAAAAAAAAAAAAQHOiVgAAAAAAAAAAAAAAAAAAmhO1AgAAAAAAAAAAAAAAAADQnKgVAAAAAAAAAAAAAAAAAIDmRK0AAAAAAAAAAAAAAAAAADQnagUAAAAAAAAAAAAAAAAAoDlRKwAAAAAAAAAAAAAAAAAAzYlaAQAAAAAAAAAAAAAAAABoTtQKAAAAAAAAAAAAAAAAAEBzolYAAAAAAAAAAAAAAAAAAJoTtQIAAAAAAAAAAAAAAAAA0JyoFQAAAAAAAAAAAAAAAACA5kStAAAAAAAAAAAAAAAAAAA0J2oFAAAAAAAAAAAAAAAAAKA5USsAAAAAAAAAAAAAAAAAAM2JWgEAAAAAAAAAAAAAAAAAaE7UCgAAAAAAAAAAAAAAAABAc6JWAAAAAAAAAAAAAAAAAACaE7UCAAAAAAAAAAAAAAAAANCcqBUAAAAAAAAAAAAAAAAAgOZErQAAAAAAAAAAAAAAAAAANCdqBQAAAAAAAAAAAAAAAACgOVErAAAAAAAAAAAAAAAAAADNiVoBAAAAAAAAAAAAAAAAAGhO1AoAAAAAAAAAAAAAAAAAQHOiVgAAAAAAAAAAAAAAAAAAmrvqvQBI7+fPn6u8zvfv31d5HQAAAKjE790AkIvPbgBIZK0P7guf3QCwCb90A0AuPrsBAA4xqRUAAAAAAAAAAAAAAAAAgOZErQAAAAAAAAAAAAAAAAAANCdqBQAAAAAAAAAAAAAAAACgOVErAAAAAAAAAAAAAAAAAADNiVoBAAAAAAAAAAAAAAAAAGhO1AoAAAAAAAAAAAAAAAAAQHOiVgAAAAAAAAAAAAAAAAAAmhO1AgAAAAAAAAAAAAAAAADQnKgVAAAAAAAAAAAAAAAAAIDmRK0AAAAAAAAAAAAAAAAAADQnagUAAAAAAAAAAAAAAAAAoDlRKwAAAAAAAAAAAAAAAAAAzYlaAQAAAAAAAAAAAAAAAABoTtQKAAAAAAAAAAAAAAAAAEBzolYAAAAAAAAAAAAAAAAAAJoTtQIAAAAAAAAAAAAAAAAA0JyoFQAAAAAAAAAAAAAAAACA5kStAAAAAAAAAAAAAAAAAAA0J2oFAAAAAAAAAAAAAAAAAKA5USsAAAAAAAAAAAAAAAAAAM2JWgEAAAAAAAAAAAAAAAAAaE7UCgAAAAAAAAAAAAAAAABAc6JWAAAAAAAAAAAAAAAAAACau3x7e+u9BgAA2M7l5eUqr+Mv0gAAAAAAAAAAAAAAcBKTWgEAAAAAAAAAAAAAAAAAaE7UCgAAAAAAAAAAAAAAAABAc6JWAAAAAAAAAAAAAAAAAACaE7UCAAAAAAAAAAAAAAAAANCcqBUAAAAAAAAAAAAAAAAAgOZErQAAAAAAAAAAAAAAAAAANCdqBQAAAAAAAAAAAAAAAACgOVErAAAAAAAAAAAAAAAAAADNiVoBAAAAAAAAAAAAAAAAAGhO1AoAAAAAAAAAAAAAAAAAQHNXvRcAAAAAAAAAR7ze3uz+x/XzS9+V8JnTAQAAyO7u/mH3P54eH3qug08cDQAAAPVcvr299V4DAABs5/LycpXX8RdpAAAA2MY+mNxTTobigAAAAFLbN5N74sk4nA4AAAAlfeu9AAAAAAAAAPja52CS+JwaAABAFp+bSYJzZAAAABRgUisAAGMxqRUAAABSmAgjDQKN5tBhOSkAAICwJtpIs0BDOXRSjgkAAIC8TGoFAAAAAAAgFkVrLocOxchWAACAmBStiRw6ESNbAQAAyMukVgAAxmJSKwAAAARn7GdSDg4AACAFkz8zcmoAAABUImoFAGAsolYAAAAIy4DW7JwgAABAZAa0pub4AAAAKONb7wUAAAAAAACAHrKCiZOaOF8AAAA2IInMbuKYJg4XAAAAAhK1AgAAAAAA0JmitQxdKwAAQECK1hp0rQAAANQgagUAAAAAAKAnRWsxulYAAIBQFK2V6FoBAAAoQNQKAAAAAABAN4rWknStAAAAQSha69G1AgAAkJ2oFQAAAAAAgD4UrYXpWgEAALpTtFalawUAACC1y7e3t95rAACA7VxeXq7yOv4iDQAAAGdStI7AKQMAAPQSuWj9vLbuS8oo8hEDAADABJNaAQAAAAAA2JrWcRDmtQIAAHSRLnc0X3QB81oBAABIyqRWAADGYlIrAAAAdDdU0bog3RxqE+o9LAAAQHfxi9YvVxhkbenEP24AAAD4QNQKAMBYRK0AAADQV/m+cfUBpOW3pcYDAgAABBE/cYy/wnRsKQAAALlc9V4AAAAAAAAAo1g9+Iyj3aPtX7lq/Pl6e1P10QAAADY2ETem8H79asxV3N0/2EkAAACiMakVAICxmNQKAAAAvUxnn0mzxi6Zbsm9SvpQAAAAcUwXrXHKxvPL2zjPEkeW0wcAAIALk1oBAAAAAADoLmPQ2HHq7O5bp9u06+eXwqN6AQAAIivWNGYcSNv6CJ4eHzJuCwAAAGP61nsBAAAAAAAA1DdRM6aLM19vbyLEmUGWcZKJs073LAAAAKFMBI3FilYOmThovSsAAAChmNQKAAAAAABAW2V6xVMf5NRed8FGJZ3a+qXX25saDwIAALAxySJH3d0/yJsBAAAI4vLt7a33GgAAYDuXl5ervI6/SAMAAMBM06FmoohxZnG6yhMty4CzbGaZKwEAABDBdNEarWMctr/d7CBy3QcAAADG9K33AgAAAAAAACirTL54tDK9fn7Z/bPKt1v2Ulkm4k4/WpanAAAAiEDByAfThz5sVAwAAEAoolYAAAAAAAA46PX25mia26jOLdy1AgAAwDjUxQAAAPCeqBUAAAAAAIAmCoxp7ZWzvv8Wp35Jiq7VsFYAAIDzGdPKlwxrBQAAIDhRKwAAAAAAAOsrULRO2+wRFqSzKaJQXSsAAMA5FK1ZdDkLXSsAAACRiVoBAAAAAADgC4e6yg0GtJ5PFAoAAADdqYsBAADgM1ErAAAAAAAAKyswpnWiaN14JYu/b/yu1bBWAACAZZKOaR1tQGjfgzCsFQAAgLBErQAAAAAAAKypQIsYrWhd/N2zn0X29QMAALQgR0whbFq85yIBAADQi6gVAAAAAACA7aQY03rI6+2NzHJdqe8DAABAQPFbSjbjMgAAABCTqBUAAAAAAIDVTDefKQrGo9lqx7S15LDW6YeKv34AAIAtTU/XFDEGEecgpldiWCsAAABdiFoBAAAAAADgZGJLAAAA4LM4RSsAAADEdPn29tZ7DQAAsJ3Ly8tVXsdfpAEAAOCzAmNaL06sVbd/qAUxbYqdr3F5Clul4naOAABwpuxjWkeYCxrzFLLfnBGs8u5wlAAAQBaiVgAAxiJqBQAAgEaONm9ZerZT4z1d6yrK3J8CNp5C7GQBAGCmo81b8J5N0dpR9stTzJbvBScLAADEdNV7AQAAAAAAANSXpVvbOOdj7/r5xeZ31HHz33/rLD8oAAAgoOzpWqL1HyoSIz/C0+PDCFFxWB03/8O3jnxLAQCAoYhaAQAAAAAAONfIOeLr7Y0SbwP2eXUB37YCVwAAOKRwkZirsstYtM5xd/+Q/RECCvi2fb8kJw4AAHQkagUAAAAAAAAYRcCW9Uv7dapbAQCAIAI2igSU5Z7s16luBQAAtnf59vbWew0AALCdy8vLVV7HX6QBAABg72gjl6hJW9b7bfyACxaZ5Qgq3aVosrSsE5w+AADDOtrIpWjSvnyKFCvfmTiFLE9R4yKFlaVlneACAAAAmzGpFQAAAAAAAKCmAi3rntmtAABALwWKVhop0LLu7Z7FlQYAADZgUisAAGMxqRUAAADWVWy0pkmt3RW7UR1Vylm/5CYAADCIGtM1Dz1FisVf5F//Xo3rFESlnPVLLgMAANCOSa0AAAAAAAAAdZTPWXd2jyltBQCAvLIkc2WKVlZRvmXdM7gVAABo51vvBQAAAAAAAJBVvXZuQSCnqdtYvVu3otfbm9H2Z8BHBgBgKOPkc2GNVrS6chPu7h8G3J8xnxoAAGhN1AoAAAAAAEArgk9O5c4sM3jbOfjjAwAwsqppZRD1Wj4XZhlhpx0AAADWJWoFAAAAAACA/3NSVKnApDs9556tAACAROLXlRMJX/zFsyIx5560FQAAWIuoFQAAAAAAgCWOxmN5g8+ZK+/ygCM0e0c3doRNmEPD+SXbAgBAGUfjMWllO4WL1qPrVy3uaTi/ZE8AAIDzXfVeAAAAAAAAAIRz/fwy3cUlSnYTLZX5dJvTXm9v3HwAAGCZwkUrM+k2p+32x9sBAABYTNQK5/r58+cqr/P9+/dVXgcAAAAq8Xs3AIQ1J6hb/FEe5LN7V8R9eFKZXBAjJ4ty1pl2GzXsPYE51vql+yLMZzcAVDKnqVv2ab79B3eZPnCchO/u/mGch/2szI1tTdoKR/mP3QAAh4haAQAAAAAAWN9/f/3dewnrUMR1cXRS7pjsyQLSVgAAqvrx26+9l1DToaCxUrn39Pig2/ySbVlg8AQaAABY5lvvBQAAAAAAAACzLGgatXxlKFrPYfcAACCUsAncCEUrhyhaF7u7l0kDAACnEbUCAAAAAABwGnkYEQx1D4d62EbsIQAAicjDurDt7w21G5rMVdhDAABgPlErAAAAAAAA1GRMawGvtzdqzLXYTAAA4JCJHs+Y1tqkmCuymQAAwExXvRcAAAAAAABANf/99XfvJRQ0Wox3/fwy2iN/ZgdaeL290XsDAJDdj99+7b2EE8SPQgcsWp8ezSa9uBBhNrDb0qpvHAAAYC0mtQIAAAAAAHAClV0WI2R7tW9j7afry94CABCZyi6OwcO82lfx7l7W25C9BQAApolaAQAAAAAAIDoN3lBeb2+ceGs2GQAA2DlU3w1etNYmudyATQYAACaIWgEAAAAAAKCaEca0VqW03JLdBgCAwSlaByS23IytBgAADhG1AgAAAAAAMNecAOy/v/7eYCVDObW7K1O0znmQYlFiscdJwZ4DABDKnAbsx2+/tl/IEAYv7uaEu/W2qN4TBWfDAQCAL4laAQAAAAAAIC7F3TicdS92HgAABjTR2hnTWpXAsou7+wc7DwAAfCBqBQAAAAAAgKAWtHZlxrSORlfZl/0HAAB2FK1V6Sr7sv8AAMB7olYAAAAAAAAoQtGalKIyAqcAAADjONTXKVqrUlRG4BQAAIA9USsAAAAAAACzzCm+/vvr7w1WMohTE7uSReuch8reImZffyXOAgCAvuYUXz9++7X9QopTtO7NeeQCIWKBRyjDWQAAADuiVgAAAAAAAIjl9fZG0ToIFWU0TgQAAGrT1I3GiUfjRAAAgAtRKwAAAAAAAISyIGdVtCaln4zJuQAAwIAGHNM6Av1kTM4FAAAQtQIAAAAAAEAUBrSOQzkZmdMBAICSDqV0itaSlJOROR0AABicqBUAAAAAAIDjJF5Nvd7e7P456asUrXvp7me6BQ/IGQEAsDGJV2t2eLGMW5dxzQAAAOMQtQIAAAAAAEA3C1rWi4uL6+cXRWteasksnBQAAIzAmNZ6FK0pOCYAABiZqBUAAAAAAIB1/PfX372XkMB+KKucdabRnpdQdK0AAITy47dfey8hq0P5nKK13g5IJRNxWAAAMKyr3gsAAAAAAACAIpr2b9rOGkSSAAAAGxPOjcNZp3N3/1CvrAYAAI4yqRUAAAAAAABW0C5WHHA6a1WK1oycGgAAVCWlgwikyAAAMCBRKwAAAAAAAJyradHa6JXZmDYyL2cHAAB56eXG4awBAACyELUCAAAAAABwhKCro9fbm90/vRcSXfAtCr48jnKCAAA0JcZrZGJjjWk9SfwrGn+FTHB8AAAwGlErAAAAAAAAJKBuBQAAAPhMElmAQwQAgKFc9V4ApPf9+/feSwAAAICy/N4NALmM/Nl9/fyyWW4b2wFPAAAgAElEQVS6+0bXzy/bfDvOJ0Wu4fX2xvuOYkb+4AaApHx8n8SYVsjl7v7Be5NifHADABwiagUAAAAAAIAVnFO7LYgepa1ZKFor0bUCAABEY8InAABAOt96LwAAAAAAAIAKhF7nuH5+2f9z0he+3t6M0Ey6XQAAAAYYnkToOF/qq+Wgi3GgAAAwCFErAAAAAAAABCJtrcS51ONMAQAgu9QNJ5SnawUAgBGIWgEAAAAAACCcZWlro8WwjBOpyskCAEBworhBOGgAAICkRK0AAAAAAAAQlJGtAAAAcBKh4yAcdGEOFwAAyhO1AgAAAAAAQFyndq0XxkjG4BRqc74AAJDU0+ND7yUAx+laAQCgNlErAAAAAAAAhHb9/LJgZGujxQA73mUAABCQEG4QDhoAACA1USsAAAAAAABThFtB6FqPivPIcVYCAADEJ88jBReVjblyAABQmKgVAAAAAAAAapJWdmHbx+GsAQAglKMJ3NPjkT9AClpHAACA7EStAAAAAAAAkMOpw1ovRHcAAABAIYrWoThuAACoStQKAAAAAAAAaehag7Pbo3HiAAAQhPgNAAAAshC1AgAAAAAAAAAAAAChSZcH5NABAKAkUSsAAAAAAABkYlhrWPZ5TM4dAABSeHp86L0EAAAA4OJC1AoAAAAAAAAj0N0BAABQlVmOUJg3OAAA1CNqBQAAAAAAgGQWDGulNdnwyJw+AABAa8pGAACAMkStAAAAAAAAMATdHQAAAPVoHaE8b3MAAChG1AoAAAAAAAAAZxGNAwBAZE+PD72XwFk0jQAAAJWIWgEAAAAAACCf6+eXBV+lu2vExgIAAAAAAADMIWoFAAAAAAAAAAAAIB8DPEfglLlwDQAAoBZRKwAAAAAAAAzETNHV2VJ23AQAAAAAAAA4StQKAAAAAAAAAAAAQDImN8JQvOUBAKAMUSsAAAAAAACkdP380nsJAAAAAA3pGAEAAOoRtQIAAAAAAAAVdKl8X29vtv+mhOU+AABAQE+PD72XUIFtBAAAYC2iVgAAAAAAAKYYB1qP7g4AACAOreAyBnjCgLzxAQCghqveCwAAAAAAAIBAPgefsl4AAACA7SkYAQAASjKpFQAAAAAAAC4uLi5eb2++HGF66P8HF4PP3AoAAAAAAACYIGoFAAAAAACA4x2atBUAAAAA+jK/FwAAChC1AgAAAAAAMLr5taquFQAAALpTtY3AKQMAAFQlagUAAAAAAGBoOtX4Yp5RzFURgbsBAAAkoh0FAABgY6JWAAAAAAAAVpA04lqw7KRPCgAAQGrKQwAAAKAGUSsAAAAAAAAAAAAAAAmI/AEAIDtRKwAAAAAAAJzGsFYAAADo5aSe7enxhD9MHKpFAACAwkStAAAAAAAAHHH9/NJ7Cayp5IFu/FDCZqa5IQAAnER4SXCuKAAAACsStQIAAAAAAAAAAAAAAAAA0JyoFQAAAAAAgEFlH6WYff0AAAAAsMDd/UPvJQAAAMuJWgEAAAAAABjU9fNL7yUAAAAA8P/oFQEAAGq76r0AAAAAAACA1XwYXClZpJHUVyvd4g2kBQCAOD6UZk+PD1//OWhG7rgu+wkAAMD2RK0AAAAAAEBi07WbxnVjr7c3Nhlgx49EAKCG6eBN47qxu/sHmwwAAABk9633AgAAAAAAAJZ4vb05dX7jgi+JpuMjSLOiyX6Z17Xx/bT5AACM4O7+4dQRjgu+JJqOj6BWJSyXEwAAgHWZ1AoAAAAAACRzZk62+/JcieaHR97/a66nKCP1tqdePAAAsI0zq87dl+eq4D488v5fcz0FAAAAQAqiVgAAAAAAII0VpyNmSVunHznLUwAAAJDCikNKs6St04+c5SkABnR3/+DnMwAAJPWt9wIAAAAAAABmWbFobfqaK5q5vOBPEdypSXCQhHjZoQdZPAAAENOKRWvT11zRzOUFfwoAAACAXExqBQAAAAAAEpiT8H0I9uYXoTFLv4Cp6vXzy9FVhd3P82V/rqTrX/DeBwAAFpjTbX4YiDe/CI05SS9gqvr0+HB0VWH3k4wWvPE3EPC9CQAAwLpErQAAAAAAQHTTVduhnm1Ogbl//WhRXMCitbDpqxLtbly4HgAAwNqmE7JDSducAnP/+tE6TNVcUg4OAAAACvjWewEAAAAAAABTpmvD6eBwfo6YvRLMvv6wAhaty5R5EAAAYHUTleDT45EYdX6qmr1FzL5+AAAAgDhErQAAAAAAQFy55meuRaG6vUPXKeBZLFhS4TcLBBfwZwgAwAfTRet269iWQhUAAACgI1ErAAAAAAAQ1KEW6OiA1g9/+Mxvx3sD5pGhLsZoReuc593+AUNdCQAAOMehtvPogNYPf/jMb8d7hVtiopnzlnQhAQAAaEHUCgAAAAAARDRRtG68khRCbUvS5G9iD4M8UZBlAAAANUwUrZuuI4lQ2yIPBgAAAFITtQIAAAAAAOH0KlojRIMR1jCs6a4149GEqp0BAIA4ehWtEWrMCGsAAAAAGJmoFQAAAAAAiMWM1uBqH8T00/XqWhcktdfPL7VPameEZwQAgNWZ0RqcgyAIVxEAAIBGRK0AAAAAAADwf452rRunrQu+XY3UM+NoXAAAAEjByGIAAAA6ErUCAAAAAACBdB/TmrGjC1gwZtzG945u6S5t3eAxhy1aAQCARrqPac2Y0gWcV5lxGwEAAAB2rnovAAAAAAAA4H91L1q7y96CVrK7dUdPZP8HVr+lyy7DOG8WAABgge5Fa3daUAAAAIDuTGoFAAAAAABCE+kFNM6hzH/StWa3Ln6d6+eXcc5lZ7TnBQCARsYpWhNxKHTnEgIAANCOSa0AAAAAAEAIhpQW83p7U6M5nDmyde/zn5yzD2fe/xpb/Z4fCAAAsDpDSou5u3+QHbKMnwYAAAD0JWqFc/38+XOV1/n+/fsqrwMAAACV+L0bgPNTvfJpXK+a8fr55ejeLv4oD/jZfWra+l7TS1gvZ51p2AcHWNFav3RfhPzsBmCm86vI8nVcr3D06fHh6N4u+zRP/cE9Z1s+/PlWSynNvgGswn/sBgA4RNQKAAAAAAD0Vz49ncMmxHdO2tpiJQAAADOVT0/nsAkAAAAAEXzrvQAAAAAAAICvyfay++WP33svoYnr55eOl7Pvd99GhGwYAABGYBhjdn/+82/vJXQw/9664V9SdwMAANCdSa0AAAAAAEBnArakrp9fRj6792XpBvtQPmQ9id0AAICTaNiSenp8cHZsTwwMAABAa6JWAAAAAAAgolWitfLJpbQviEaB65jnW/5tCwAAQazSrZVPLtV9Mc3JfZ3dl8q/ZwEAAEhB1AoAAAAAAPQULWAbMyNc7Oiw1l/++P2/v/7ebD0RfHmF5txzd2+m7hs1+IxiAADSidawSQ1PcrTe/POff3/89utWywlkd5G+3Bx37Bx2DwAAgA2IWgEAAAAAgHC6R2vb08jVNuCVBi689wGAwAbs1qLFvaxiwJsMAAAABXzrvQAAAAAAAGBcTUtOmeg2jiZbv/zx+zYroYCjb1uJIAAAnKRpySkT3cbRbvPPf/7dZCGkd/Q9KxIGAABgG6JWAAAAAAAglo7RWq5eLtdqAQAACKJjt5Yrmcu1WgAAAIAsRK0AAAAAAEAfJqmWIa9lFca0AgDAukxSLUNey/kSjWmNsxIAAAAaEbUCAAAAAAAF5Spmc612gV/++L33EgAAAFhTrmI212oX+POff3svAQAAAGAuUSsAAAAAABBIx0mMhkCew+7RmjsGAACr6DgC0fTFc9g9mnLByMi9BQCAvEStAAAAAABAB+Vnk7aWLvAzrJVpfiYAAMC6ys8mbS1dK2VYKxP8QAAAACAUUSsAAAAAAFCNOq6LdJ0tibhdAAAQikCuix+//dp7CdSULuEGAAAgO1ErAAAAAABAz2RunATXsFYOSfcuENkyh3sCAPBex2punATXsFa+NM5bAAAAgCxErQAAAAAAQBSr9D/p6rhK/vvr795LoCBlIAAArGWVslQg15FhrazOmFYAAAC2J2oFAAAAAABGly6ZS7fgPcNa+UyIDgAA5aWr5tIteM+wVj5IWqHnfQ8CAAAwh6gVAAAAAAAIIW+oyXuGtbIuPxkAAGAtIrEaDGtlRX4skJfbCwAAqYlaAQAAAACADhqFaulGPqZb8BzTXathrbw3/RZQtAIAwGKNap90Ux/TLXiO6a7VsFb2pu+/JhAAAIBernovAAAAAAAAGFSQXC3IMobyyx+/G+jKRfKo+/r5JfX6ac2HCwAQQZBiLcgyhvLnP/8a6ErJohsAAIAaTGoFAAAAAADIJEUopVnlfCmuOgAAUE+KClezyplS3HMAAACqErUCAAAAAABFmJoYynTX+ssfv2+2EmKafsMqWgEAICCDH0OZ7lr//OffzVZCQNPv1hRFa4pFAgAAsIyoFQAAAAAAGFffcE6Fy7BcfgAAGETfLE2Fy5jcfMrTPAMAQHaiVgAAAAAAAJowrJVlsoxpzbJOtuduAACwDcNaWUANCAAAQHeiVgAAAAAAoIIFgx8zRkfp1qxr5bPpd2u6Sw4AAINYMPsxYzuXbs26Vj6YfqvmuuG5VgsAAMB8V70XAOl9//699xIAAACgLL93A0AuX352v57+JQDABnwKA0AuBz+7J8tVn/hAMWpnEvEpDABwiEmtAAAAAAAAHSwYLZvU9ODNcfaBnWJjWtMtGAAAxrFgtGxS033XOPvARa0xrQAAABQmagUAAAAAANJbEEZK0baka2WnWNEKX3KTAYCSFoSR8rkt6Vq5ULQCAACQh6gVAAAAAAAgh9ShlK4VRSsAABBc6upP1zq4qkVr3pXTiCsBAAA1iFoBAAAAAADYgmpxZIWL1tSLBwAAKtF6Datq0QoAAEBVolYAAAAAACC3BUM+u0doBpN+Zk+G1f39CCtynwGAkhYM+ewe0RlM+pk9GVP3NyMAAAB8JmoFAAAAAABgI9O5l661KicLAACwjemCUddaUvlj1eWy5zIAAEAZolYAAAAAAIAEykz/07WOZvpMa1zsGk/B+dwEAIDUyrRSutahTB9omVsNAABAMaJWAAAAAAAgsQUNpOioO13rOEYoWgEAoLYFDaSOrjtd6yDGKVorPQsAAAAXolYAAAAAAICNiTYvdK1jULQCAABdiDYvdK0DGKdohR23GgAAKhG1AgAAAAAA0IGutbbRitZ6T8Sp3AEAAKLRtRamaAUAACA1USsAAAAAAJDVgu4xaXSUdNlH6VqrGq1oBQCAqhZ0j0lruqTLPkrXWtKYRWvV52ImFwAAAIoRtQIAAAAAANCNrrWeYYvWwo/GUU4fAICwdK3FjFm0AgAAUIyoFQAAAAAAYDsqzc90rZUMW7QCAABBqDQ/07WWMXjRWv4BOcTRAwBAPaJWAAAAAAAAOtO11qBoHeEZ+cy5AwAQn661gMGLVgAAACq5fHt7670GAADYzuXl5Sqv4y/SAAAA3S0IHSN0R0mXvZmJ/RlqHzJydjsa7AENdcMBgKEsCB0jZHVJl72Zif0Zah/ScXB7GuzRjHbDAQBgECa1AgAAAAAAEMVEG6YVjEzRujfa8+LEAQDIZSIP0wqGpWgFAACgGFErAAAAAADARmSZc+ha01G0AgAAQcgy59C15qJo/WDMpx6W4wYAgKpErQAAAAAAwBDEdYnoWhNRtH427IMPyFkDALwnPUpE15qFohUAAICSrnovAAAAAAAA4GTjlI3DFlO7Bx/noIsZ9t4CAEAZ45SNw5aBuwcf56ArGfbS7j09Pri6I3DVAQCgMJNaAQAAAAAACEobmZFTu7AJY3DKAAAUoBlLx5Ht2AcAAIDURK0AAAAAAABbMHR0mevnl/flmIosmg+n44D2bAUAAGzM5MZlnh4f3veBWsFQPhyN02EcbjsAANR21XsBAAAAAAAAzanLsnOCkTkdBuTaAwB8oD7KzgmG5WgOeXp8kLJX5doDAEB5JrUCAAAAAAAEJZqC1LyFq3KyAABl6KYAAAAAtidqBQAAAAAAknm9vem9BOB/2Lub47iNNADDAoshMARmwINL0VnOQRkoGJUPjEAMgTnMHrimZQ4GBBr99e/z1B5211VwoxuDGR7e+gAAgGEZfgh0QZc+JMcKAAAzELUCAAAAAACEE+LCnIz0HI8zBQBokxAX5iSABAAA6JGoFQAAAAAAACCKBnIkThMAAADiqJQBAGASolYAAAAAAIAWSadgGD7OAADQIOkUjMFneRiOEgAA5iFqBQAAAAAABicnA+A83yYAALfIkIC6vIUG4BABAGAqolYAAAAAAIBYr0+PtZcAVKaH7J0TBABo2R9/fqu9BKAySSQAAEBHRK0AAAAAAAAA4VSR/XJ2AAAAEEeTDAAAsxG1AgAAAAAANEdABUPy0QYAgEYIqGA8PtedcnAAADAhUSsAAAAAAAAArJMiAwAA9EIe2R1HBgAAcxK1AgAAAAAABHp9eqy9BKAhCsm+OC8AgPb98ee32ksAGiKS7IjDAgCAaYlaAQAAAAAAAMrRSfbCSQEAAPRIKgkAANA4USsAAAAAAECUtDGtMioYno95+5wRAEAX0sa0Ct5geD7m7XNGAAAwM1ErAAAAAAAAQGmayZY5HQAAAIijaAUAgMmJWgEAAAAAAEKkjWkF5qGcbJNzAQDoRdqYVmASsslmORoAAEDUCgAAAAAAAFCHfrI1TgQAAGAY4skGORQAAOCLqBUAAAAAAOhOF8VR8pjWLu4OyMinvh3OAgDgTRfFUfKY1i7uDsjFR74pjgMAAHgjagUAAAAAAMgsuWgF5qSlbIFTAADoSHLRCkxISNkIBwEAALwTtQIAAAAAAOSkaAUSKCrrsv8AAB1RtAJHySmrcwQAAMDvRK0AAAAAAMDgVKZAF3SVtdh5AICTVKZA+/7+65uushY7DwAAfCBqBQAAAAAA+tNsgHQyoG32voAyvATKs+cAAKuaDZBOBrTN3hdQhpdAefYcAAC4JmoFAAAAAADIw0hY4DyNZUl2GwCgL0bCAudpLIsxHRcAALhF1AoAAAAAAHTpUIlUIDdVtAK5KC3LsM8AANsOlUgFclNFK5CL0rIAmwwAAGwQtQIAAAAAAJylaAXyenh+kVzGsb0AAN1RtAJ5SS5D2V4AAGDbcrlcaq8BAADKWZYly3X8kAYAAGjEoZo0ImHKmLMqrIBrmvnsvGwBAA45VJNGVEwZc1aRFXBNM5+dly0AAPApk1oBAAAAAICO1W2TxGZANAVmXvYTAOCoum2S2AyIpsDM6O+/vtlPAABgD1ErAAAAAAAwi4wN6uvTo6IVKOPh+UWKeZ5tBAAoIGOD+sef3xStQBk6zCxsIwAAsJ+oFQAAAAAA6NuhSClLiSpnBcoTZJ5h9wAAzjjUKWUpUeWsQGFGjJ5k9wAAgEOWy+VSew0AAFDOsixZruOHNAAAQGuOhqZpgdOef8vD80tC9Sq4AnbS1R/i7QoAkMvR0DQtcNrzb/n7r5QhroIrYA9R/VHergAAQAJRKwAAcxG1AgAADCyua9155fcLilqBULrWnbxaAQDyiutad175/YKiViCUtHUP71UAACCZqBUAgLmIWgEAAMaWHHpdh0/JiWzaGpRXwFHS1g1eqgAAQZJDr+v2KTmRTVuD+Ao4RNe6zUsVAAA4Q9QKAMBcRK0AAAAzKBx6fUinRK1ASdLWD7xOAQAKKNx6fUinRK1AMdLWa16nAADAeaJWAADmImoFAACYRLHK6/yI19WLAOyna33ndQoAUEyx0Ov8iNfViwDsJ219410KAADkImoFAGAuolYAAICphIZeq+mUMa1ALZOnrV6kAABVhIZeq/WUMa1ALTOnrd6iAABAXve1FwDd+/nzZ5brfP36Nct1AAAAYCT+7gbgpPfGKWPrpZva4LsbKnp7O02YtnotQ7JcX9xffHcDTOw9c8rYekmnbvFHN9T19naaLW31ToYzfHcDANwiagUAAAAAAMZ3vm4VTQFdiIj5m+XNDADQjvN1q24K6MI8aavXMgAAEEfUCgAAAAAATGS1gFpNv4q1UqIsIMLAg1u9NgEAWrYaQa3WX8VyKV0WkF3EnOp2eG0CAADRRK0AAAAAAMDscvVRQ8ZjQNcGS1vlrAAAncrVRw0ZjwFdG2lwq5YVAAAoRtQKAAAAAAAAMLL3FrTTulXLCgAAQMu6HtyqZQUAAMoTtQIAAAAAAFSj1AJK6qtu9YYEAOA8sRZQUkd1q9cjAABQkagVAAAAAAAggy4KMYA3v/eiTb2+hKwAANzSfiEG8O73ZLSp15eWFQAAaIGoFQAAAAAAAGBe1QNXISsAAAADqxu4qlgBAIAGiVoBAAAAAADq0HEBrbl+L2XPXL36AAAoRsoFtOb6vZQ3c/XeAwAAuiBqBQAAAAAAOKvKbEOAAjSoAAC0oPxsQ4AyZKgAAMCE7movAAAAAAAAAAAAAAAAAACA8YlaAQAAAAAATkkb02r4IQAAAOyRNqbV8EMAAACANolaAQAAAAAAAAAAAAAAAAAId197AQAAAAAAALtcD0Ttd9hpvysHAABgVNcDUfsddtrvygEAAACGJ2oFAAAAAACadt2yfvhHdQPRjeUBAABA+65b1g//qG4gurE8AAAAAHp0V3sBAAAAAAAAN+1JRl+fHvsqS41pBQAAoBF7ktE//vzWV1lqTCsAAABAy0StAAAAAABAo9pPVRNWqGgFAACgEe2nqgkrVLQCAAAANE7UCgAAAAAAtOhoL9p+AQsAAADtONqLtl/AAgAAANAFUSsAAAAAANCcLgpVY1oBAADoVBeFqjGtAAAAAEMStQIAAAAAAG1JLlpLprBdZLcAAABwLbloLZnCdpHdAgAAAJBA1AoAAAAAAFCCMa0AAAAQx5hWAAAAgC6IWgEAAAAAgIZ0MQE1YZGKVgAAAFrQxQTUhEUqWgEAAAB6IWoFAAAAAACIpWgFAACAOIpWAAAAgI6IWgEAAAAAAA7oYpYsAAAAdKqLWbIAAAAAJFsul0vtNQAAQDnLsmS5jh/SAAAAEU72ogUGoias0JhWAAAAGnGyFy0wEDVhhca0AgAAAPTFpFYAAAAAAIBdFK0AAAAQR9EKAAAAMANRKwAAAAAAQAhFKwAAAMRRtAIAAAD0SNQKAAAAAACMILogPTqmVdEKAADASKIL0qNjWhWtAAAAAJ0StQIAAAAAAHxC0QoAAABxFK0AAAAA8xC1AgAAAAAA3QuNSBWtAAAATC40IlW0AgAAAExF1AoAAAAAAPRN0QoAAABxFK0AAAAAZCRqBQAAAAAAOqZoBQAAgDiKVgAAAADyWi6XS+01AABAOcuyZLmOH9IAAABB9nek0QWpohUAAIDx7O9IowtSRSsAAADAnO5rLwAAAAAAAOCAMu2oohUAAIA5lWlHFa0AAAAA0xK1AgAAAAAAHShWjR7NWb8oWgEAAOhfsWr0aM76RdEKAAAAMJblcrnUXgMAAJSzLEuW6/ghDQAAEOdWVlqgHTWgFQAAgOHdykoLtKMGtAIAAABwV3sBAAAAAAAAuyTMUA29vqIVAACAkSTMUA29vqIVAAAAYEgmtQIAMBeTWgEAALqw0ZdGpKQJuayiFQAAgH5t9KURKWlCLqtoBQAAABiVqBUAgLmIWgEAAHqxHZrmakrlrAAAAMxpOzTN1ZTKWQEAAAD44K72AgAAAAAAAA5LiFGvr6BoBQAAgFUJMer1FRStAAAAAFwzqRUAgLmY1AoAANCRndHp0dA0LYiVswIAADCYndHp0dA0LYiVswIAAABMQtQKAMBcRK0AAADdOdSg3kpPz0x2lbMCAAAwsEMN6q309MxkVzkrAAAAwFRErQAAzEXUCgAA0KMzSeoZclYAAABmcCZJPUPOCgAAADAhUSsAAHMRtQIAAHSqcNcqZwUAAGAqhbtWOSsAAADAtEStAADMRdQKAADQtei0VcsKAADAzKLTVi0rAAAAAKJWAADmImoFAAAYQPa0VcsKAAAA77KnrVpWAAAAAN6JWgEAmIuoFQAAYCRn6lYhKwAAAGw7U7cKWQEAAABYJWoFAGAuolYAAIDhXZeu+lUAAADI5bp01a8CAAAAsJ+oFQCAuYhaAQAAAAAAAAAAAACgirvaCwAAAAAAAAAAAAAAAAAAYHyiVgAAAAAAAAAAAAAAAAAAwolaAQAAAAAAAAAAAAAAAAAIJ2oFAAAAAAAAAAAAAAAAACCcqBUAAAAAAAAAAAAAAAAAgHCiVgAAAAAAAAAAAAAAAAAAwolaAQAAAAAAAAAAAAAAAAAIJ2oFAAAAAAAAAAAAAAAAACCcqBUAAAAAAAAAAAAAAAAAgHCiVgAAAAAAAAAAAAAAAAAAwolaAQAAAAAAAAAAAAAAAAAIJ2oFAAAAAAAAAAAAAAAAACCcqBUAAAAAAAAAAAAAAAAAgHCiVgAAAAAAAAAAAAAAAAAAwolaAQAAAAAAAAAAAAAAAAAIJ2oFAAAAAAAAAAAAAAAAACCcqBUAAAAAAAAAAAAAAAAAgHCiVgAAAAAAAAAAAAAAAAAAwolaAQAAAAAAAAAAAAAAAAAIJ2oFAAAAAAAAAAAAAAAAACCcqBUAAAAAAAAAAAAAAAAAgHCiVgAAAAAAAAAAAAAAAAAAwolaAQAAAAAAAAAAAAAAAAAIJ2oFAAAAAAAAAAAAAAAAACCcqBUAAAAAAAAAAAAAAAAAgHCiVgAAAAAAAAAAAAAAAAAAwolaAQAAAAAAAAAAAAAAAAAIJ2oFAAAAAAAAAAAAAAAAACCcqBUAAAAAAAAAAAAAAAAAgHCiVgAAAAAAAAAAAAAAAAAAwolaAQAAAAAAAAAAAAAAAAAIJ2oFAAAAAAAAAAAAAAAAACDcfe0FQPd+/vyZ5Tpfv37Nch0AAAAYib+7AaAvvrsBoCO5vri/+O4GgCL80Q0AffHdDQBwi0mtAAAAAAAAAAAAAAAAAACEE7UCAAAAAAAAAAAAAMdyVxgAACAASURBVAAAABBO1AoAAAAAAAAAAAAAAAAAQDhRKwAAAAAAAAAAAAAAAAAA4UStAAAAAAAAAAAAAAAAAACEE7UCAAAAAAAAAAAAAAAAABBO1AoAAAAAAAAAAAAAAAAAQDhRKwAAAAAAAAAAAAAAAAAA4UStAAAAAAAAAAAAAAAAAACEE7UCAAAAAAAAAAAAAAAAABBO1AoAAAAAAAAAAAAAAAAAQDhRKwAAAAAAAAAAAAAAAAAA4UStAAAAAAAAAAAAAAAAAACEE7UCAAAAAAAAAAAAAAAAABBO1AoAAAAAAAAAAAAAAAAAQDhRKwAAAAAAAAAAAAAAAAAA4UStAAAAAAAAAAAAAAAAAACEE7UCAAAAAAAAAAAAAAAAABBO1AoAAAAAAAAAAAAAAAAAQDhRKwAAAAAAAAAAAAAAAAAA4UStAAAAAAAAAAAAAAAAAACEE7UCAAAAAAAAAAAAAAAAABBO1AoAAAAAAAAAAAAAAAAAQLjlcrnUXgMAAJSzLEuW6/ghDQAAAAAAAAAAAAAAh5jUCgAAAAAAAAAAAAAAAABAOFErAAAAAAAAAAAAAAAAAADhRK0AAAAAAAAAAAAAAAAAAIQTtQIAAAAAAAAAAAAAAAAAEE7UCgAAAAAAAAAAAAAAAABAOFErAAAAAAAAAAAAAAAAAADhRK0AAAAAAAAAAAAAAAAAAIQTtQIAAAAAAAAAAAAAAAAAEE7UCgAAAAAAAAAAAAAAAABAOFErAAAAAAAAAAAAAAAAAADhRK0AAAAAAAAAAAAAAAAAAIQTtQIAAAAAAAAAAAAAAAAAEE7UCgAAAAAAAAAAAAAAAABAOFErAAAAAAAAAAAAAAAAAADh7msvAAAAAAAAAACAobw+Pb79l4fnl7or4QNHAwAAAABAXcvlcqm9BgAAKGdZlizX8UMaAAAAAACuvTeT78ST7XA6AAAAAABUd1d7AQAAAAAAAAAAjOC6maRxjgwAAAAAgMJMagUAYC4mtQIAAAAAQHYbbaRZoE25dVKOCQAAAACAMkxqBQAAAAAAAAAgnaK1I7dOxMhWAAAAAADKMKkVAIC5mNQKAAAAAAAZmfzZI6cGAAAAAEAtolYAAOYiagUAAAAAgCwMaO2a4wMAAAAAoIq72gsAAAAAAAAAAKAzksjebRzTxuECAAAAAMBJolYAAAAAAAAAAA5QtI5B1woAAAAAQHmiVgAAAAAAAAAA9lK0jkTXCgAAAABAYaJWAAAAAAAAAAB2UbSOR9cKAAAAAEBJolYAAAAAAAAAAD6naB2VrhUAAAAAgGKWy+VSew0AAFDOsixZruOHNAAAAAAAU1G0Jvt961reK0cMAAAAAEABolYAAOYiagUAAAAAgKPkjmlW963lHXPQAAAAAABEu6u9AAAAAAAAAAAA2iV0THNr3zb2s7qNA2152QAAAAAAdETUCgAAAAAAAADAOkVrmn4TUF0rAAAAAAChRK0AAAAAAAAAAKwQMaYZeN8GvjUAAAAAAMoQtQIAAAAAAAAA8NF2vmhM68C2D1fXCgAAAADAGaJWAAAAAAAAAAAOULRuGKP5dMQAAAAAAAQRtQIAAAAAAAAA8B8bZabcccMYReubjYMe6TYBAAAAAChM1AoAAAAAAAAAwL8ki2mm2repbhYAAAAAgIxErQAAAAAAAAAA/N92rGhM61S2j1vXCgAAAABAAlErAAAAAAAAAABfvihaTxi18NS1AgAAAACQl6gVAAAAAAAAAADSaTsBAAAAAGAnUSsAAAAAAAAAAMa0Jhq+aDWsFQAAAACAjEStAAAAAAAAAACzU7SyQdcKAAAAAEAuolYAAAAAAAAAAEih5wQAAAAAgENErQAAAAAAAAAAUzOmNc1URathrQAAAAAAZCFqBQAAAAAAAACYlxwxjX37wIYAAAAAALCHqBUAAAAAAAAAgHXGtPLOwwAAAAAAwHmiVgAAAAAAAACASW1P1xQx3jLtVNLtR2LabQEAAAAAYD9RKwAAAAAAAAAA7CXdBAAAAACAZKJWAAAAAAAAAIAZGdOa4NamzbNdhrUCAAAAAHDGfe0FAAAAAAAAAABQmvgwo3mK1j1enx5tSHVZPuDOEQAAAACIIGoFAAAAAAAAAOA/xGyrVkPBCffq4flFFN2OuLPwwAMAAAAAEUStAAAAAAAAAABzUSQmsGn7GdYareLT+Pu/2ikDAAAAAAlErQAAAAAAAAAAsOVWQyjqo5gGs2qBKwAAAACQQNQKAAAAAAAAADCRT9M4cdpOM2/Uw/PL9oNkWGsuDbasq97X6dwBAAAAgG2iVgAAAAAAAAAAuGm1KlTuEaqXlvWauhUAAAAA2HZXewEAAAAAAAAAABRiTOtR/baF0T59VGxdgtenx7f/1F5IBsPcCAAAAACQl0mtAAAAAAAAAACw4laSJ/0lu1H7T4NbAQAAAIAPTGoFAAAAAAAAAJjCqNVcYdq8/Txye0wy0XSS2wQAAAAAPmVSK5z18+fPLNf5+vVrlusAAADASPzdDQB98d0NAL379f3Hr+Nf6KN+d68GeIrW3z08v8gUz5hw995u2ecI0vijGwD64rsbAOAWUSsAAAAAAAAAAPzHhLUhJU3+gElbAQAAAGBmd7UXAAAAAAAAAABAuE8jul/ff5RZSb80eNc+3ZPJ681rr0+P9uSNrQAAAACAOZnUCgAAAAAAAAAA/1oN7RStnCTgXGVqKwAAAADMxqRWAAAAAAAAAIDBqen2U7Rm5/EzkvRT9gcAAAAA5iFqBQAAAAAAAACY3a/vP2ovoQnKugSK320eqp2kvwAAAAAwifvaCwAAGN+yLL//z8vlUmslAAAAAAAAHCXaJI1EM8Hr06NPHAAAAACMzaRWAIBYH4rW1f8HAAAAAAAgjrJup9WN0tdlMeFDOOEt52JkKwAAAACMTdQKABDoVr+qawUAAAAAAGiKopVcNJlZ2EMAAAAAGJWoFQAgxLIsylUAAAAAAKALv77/qL2EyuRzJ6l/33mWMpIHAwAAAMCQRK0AAPnJWQEAAAAAgEZIwpIJNfOa4VGc4R7Ls6sAAAAAMJj72gsAABiKnBUAAAAAAKAvq8mcopVDhJehXp8efSQBAAAAYBgmtQIAZKNoBQAAAAAA6IuilfMUrQXYZAAAAAAYhqgVACCDZVkUrQAAAAAAQGv2ZGC/vv8osJI2yeQy2lMCD7nhQ95Um16fHu02AAAAAAxA1AoAcIqcFQAAAAAAYCTGtLKfxrI8ew4AAAAAvRO1AgAkkrMCAAAAAAD0azWNU7Syn7qyFjsPAAAAAF0TtQIAHLP8o/ZCAAAAAAAASCSK4ySPUF32HwAAAAD6JWoFANhFywoAAAAAAPRlT/T16/uPAitpza2dMab1pD0bOEaLOMZd9M4pAAAAAECnRK0AAOuW/6q9HAAAAAAAAAIpWtlJS9kOZwEAAAAAPRK1AgB8+XKVsKpYAQAAAAAAhrRawSla2UlF2RonAgAAAADdEbUCAOO7DlYlrAAAAAAAABPSv3GG56dNzgUAAAAA+iJqBQAAAAAAAAAYjcrr2q09Maa1sE4fzk6XPQmnAwAAAAAdEbUCAKS7XC6Xy6X2KgAAAAAAAEikaGUPzWT7nBEAAAAA9ELUCgBw2OUftRcCAAAAAACQ7tf3H7WXUM5q8KZozW7ILVVL9sJJAQAAAEAXFjEGADC8ZVmyXOfWD6e06/sZVkv08wAAAAAAAC3YE3cN2R+uurUbeXdgf1A39s4P9uzpJPvS0aMFAAAAANMyqRUA4BPmsgIAAAAAAPSrTNEKtECEDAAAAADtE7UCAKy4/Kb2WgAAAAAAAMhM0coeCskeOTUAAAAAaJyoFQCY2uWG2usCAAAAAABIp+l6t7oVitbqunhEu1gkq5wdAAAAALTsvvYCAADCiVQBAAAAAAAmJGwjmYcHAAAAACCISa0AAAAAAAAAAIzmVpRoTCufUrQOwCECAAAAQLNErQAAAAAAAAAATEHRCvPQtQIAAABAm0StAAAAAAAAAADTGTvvXI3Zxr7ldvS+z0rIkThNAAAAAGiQqBUAAAAAAAAAgHHI2Ejm4QEAAAAAiCZqBQAAAAAAAABgELeixN7HhwJphMoAAAAA0BpRKwAAAAAAAAAAI1O0sof6cVROFgAAAACaImoFAAAAAAAAAGAEq+maopU9dI8AAAAAAGWIWgEAAAAAAAAA6J4oEbjF+wEAAAAA2iFqBQAAAAAAAAAYx5zt1q27Nqa1WU09qE0tBgAAAABgbKJWAAAAAAAAAAAGpGgF3kmXAQAAAKARolYAAAAAAAAAADq22qopWtlJ6wgAAAAAUJKoFQAAAAAAAACAXikSgZ28LgAAAACgBaJWAAAAAAAAAAC6dCtRM6aVnVSOs3HiAAAAAFCdqBUAAAAAAAAAgHEoWgEAAAAAoFmiVgAAAAAAAAAA+rM6cVHRyn6Gds7JuQMAAABAXaJWAAAAAAAAAAA6I0sDAAAAAIAeiVoBAAAAAAAAAOjJraLVmFb200UDAAAAAFQhagUAAAAAAAAAoHuKVmAnSTMAAAAAVCRqBQAAAAAAAACgG6s1mqKVQzSNAAAAAAC1iFoBAAAAAAAAAOiDFhHIwssEAAAAAGoRtQIAAAAAAAAA0IFbEZoxrQAAAAAA0AtRKwAAAAAAAAAAvVK0cpQRnbzxJAAAAABAFaJWAAAAAAAAAABat5qfKVoBAAAAAKAvolYAAAAAAAAAAJpmoOJ4agXJniUAAAAAgLruay8AAAAAAAAAAIBsHp5f5sn2Or3TM8s2nBYyen169JkCAAAAgMJMagUAAAAAAAAAAAAAAAAAIJyoFQAAAAAAAAAAGF+no30BAAAAAEYiagUAAAAAAAAAAGBGUmcAAAAAKEzUCgAAAAAAAAAAAAAAAABAOFErAAAAAAAAAACQR7NzL5tdGAAAAADAVEStAAAAAAAAAADTEfgBAAAAAADl3ddeAAAAAAAAAAAAbHl4fqm9hE8cioTbvx2YyuvTo08lAAAAABRjUisAAAAAAAAAAAAAAAAAAOFErQAAAAAAAAAAQzFykMaVf0QPjdIFAAAAACCOqBUAAAAAAAAAAAAAAAAAgHCiVgAAAAAAAAAAAOZlli8AAAAAFCNqBQAAAAAAAAAAAAAAAAAgnKgVAAAAAAAAAADIwLhLAAAAAAC23ddeAAAAAAAAAAAwqQ/928PzS62VzOn16dGeMwOpLQAAAABAO0StAAAAAAAAAEAh22mZxjWjh+cXIR9t8tEGAAAAAJjZXe0FwP/Yu7ujyJEsAKMlAhMwgbaApzKmPWhz8KCNqScsaEzAh5oHJpgefoSUypu/58S87CyIzJJKqtrYLy4AAAAAAAAA43t5uN/bWCb8SmsG2ALAJNyuAQAAAKAMk1oBAAAAAAAAgEAHM6HXX+9rtOO7Lb/9x752AQAAAAAAkJ2oFQAAAAAAAAAIkXHqXS9p6/qWe9kFAAAAAABAEFErlHC5XLIc53w+ZzlOAbaczJZblmvLpyl3PeGWOzLhWc7icrl0tOUJz7Kb9hG23DJbTjbhlk/97HrCLZ9c2wfYcsts+YgJd23LLbPlZLbcMjftI2y5ZY1vOWPR+vcx/zz+Pn6cult+ebjf3rUeOsuPv3/8+plxMWW4aR9Rd8tb3gJv19sYW4Z3Jryw3bSPsOWW2XIyW26Zm/YRttyyCbcMANA7USsAAAAAAAAAkNmutm37r5xOpx+/fmbpWrOLiHgBAAAAAAAGc1N7AQAAAAAAAADAUNbzzrun59d/Pv77jcf/dvpoeYpWAAAAAACALUStAAAAAAAAAEA2K3nnpy3rux/Y+Fca7Fp3EcFCMd5uAAAAAABNEbUCAAAAAAAAAHmsF60lV1JSy8ncn8fftZfALLa8EQa+DwAAAAAAsJGoFQAAAAAAAADI4Kuk7dsBre9+eONP9j6stR0tR7m98BoCAAAAAMBGolYAAAAAAAAA4KiVorXwSrrgZZmcCBYAAAAAgGmJWgEAAAAAAACAQ2oVrS0Ma22/Tvzz+Lv2EgaXdg20f+Vkp+WmfRO+MQEAAACgvNvaCwAAAAAAAAAAOmZGK3M6Hr+9HWGAN4sUEAAAAACAjUStcNTlcqm9BAAAAOB0Wv2Sfj6fS64EADgd+9/PPbsBoLCXh/sjA12HeXbXKjNfHu5/bP5ho3cBppXr/6g2zIMbABrn2Q0A8JWb2gsAAAAAAAAAAHpVfUzrj18/y/yhjAYYywkAAAAAAJBG1AoAAAAAAAAApKhetFZXa7DnXmZ7Ut08twUAAAAAANaJWgEAAAAAAACAbKRrnepx5i2N6KXuBgAAAACgBaJWAAAAAAAAAGA3GVtfxMZU5PIDAAAAAOCNqBUAAAAAAAAAyON4ujZ8K6vuAwAAAAAAZnZbewEAAAAAAAAAQGeGT0+3GO9F+PHr55/H37VX0ZMsiXLvF1Lv6wcAAAAAoDBRKwAAAAAAAACQgRmkjbt7etYfrqtyDb/+0cvlUv5Pl+HOAAAAAADA325qLwAAAAAAAAAA6IkwEnjlbsBgNNgAAAAAUICoFQAAAAAAAAA4KksINHwgVz2X+nYBP379LLMSJlH9mgcAAAAAoDWiVgAAAAAAAABgq9a60z+Pv2svAQAAAAAAgK1ErQAAAAAAAADAIRMOY2wt7t3OsFZy+fZdMOGdAQAAAACAb4laAQAAAAAAAIBNQkvOfjNRAAAAAAAANhK1AgAAAAAAAADpKg5j/PP4u9afTtDO1Mp2VkK/OhrT2s5KAAAAAAA4iVoBAAAAAAAAgC1MUp3Hj18/ay8BAAAAAAAY03K9XmuvAfp2uVyyHOd8Pmc5DgDlLcuS8Fs+htWSdr4+cgYBoAzfuwGgL57dAGP7KmrNMgUxrZitNYCxr9V+ZX0Xu6bgenZPaP366etqhzetXbrwji/dANAXz24AgK+Y1AoAAAAAAAAAJKrY/0iPQhnWygqNKAAAAAAAyUStAAAAAAAAAMA3NGwHNZjg7prFCts1eLUDAAAAANAOUSsAAAAAAAAAUJNitk2GtfIpb1hGpccGAAAAgDJErQAAAAAAAABAZyqmRyMVfYa1kp0sEAAAAACAdaJWAAAAAAAAACBFlnptpEZ0PIa18k6nb1ipLQAAAABAO0StAAAAAAAAAEBPuuvTWl6wYa1k1PKlDgAAAABAI0StAAAAAAAAAMBu6rVhrHethrXyZn1Mq3sCAAAAAABbiFoBAAAAAAAAgG8E5WrrjVyDultwFrpWTrNe/MxDlQ0AAAAAxSzX67X2GgAA+rYsS8Jv+RhWS9r5+sgZBAAAAAA4LiGTq9sdpXV9XbRShnCyrvcrRJTLuvavYQAAAAAYhkmtAAAAAAAAAABRegml1tcpCJxc70XrqZNFAgAAAADMQNQKAAAAAAAAAFSgk4QueKsCAAAAAJCRqBUAAAAAAAAA6EPdWYvDp32GtZLABFQG4DIGAAAAgJJErQAAAAAAAAAAnE66Vj5YP+lSQAAAAAAA9hK1AgAAAAAAAAClJeSRPeZzPa4ZRuX9CAAAAADQAlErAAAAAAAAAAD/MqyVN8a0AgAAAACQnagVAAAAAAAAAOAbU8WculZOilam4WIGAAAAgMJErQAAAAAAAABAUQlVpOioMF3r5EYtWvtdOQAAAADAMEStAAAAAAAAAAD59Z7P9b5+ko1atAIAAAAA0AJRKwAAAAAAAAAA+xjWOidFK4NxSQMAAABAeaJWAAAAAAAAAKCchBiyenQ0bcC5/spP+7KMbfjTWv1+AgAAAAAwOVErAAAAAAAAAACf07VOZf2EykEBAAAAADhO1AoAAAAAAAAAkNlI+Z+udRKKVmbjqgYAAACAKkStAAAAAAAAAEAhCQGk6KgFutbhTVW0DrYdAAAAAIC+iFoBAAAAAAAAAL6k2Hylax3YVEUrvHJhAwAAAEAtolYAAAAAAAAAAL6nax3SnEXrqPsCAAAAAGifqBUAAAAAAAAAKCEheuw0POt02VvoWgczZ9EKAAAAAEBFolYAAAAAAAAAALbStQ5j8qJ1+A2ywtkHAAAAgIpErQAAAAAAAAAAn5NofkrXOoDJi1YAAAAAAGoRtQIAAAAAAAAAsM/d0/NK96hrbdzKCVo/s4OZZ6f8zXkHAAAAgLpErQAAAAAAAABAuITKsdPuqNNlp9G19mi9aC25EgAAAAAAJiRqBQAAAAAAAAAgka61L4rWd+bc9cyccQAAAACoTtQKAAAAAAAAAPAJTeZGutZeKFoBAAAAAKhO1AoAAAAAAAAANEdi15e7p2enrFPO3eTbn4pzDQAAAAAtELUCAAAAAAAAALHmGdc5eTE1+fZ75JQxD1c7AAAAADRC1AoAAAAAAAAAQB7vxn6qyJry7tQ4O2+8FAAAAAAAxdzWXgAAAAAAAAAAQHPmmS4bQSLYLKeGObnyAQAAAKAdJrUCAAAAAAAAAG1RHwGFue0AAAAAAJQhagUAAAAAAAAAyEAUB13zFh6VMwsAAAAATRG1AgAAAAAAAACBXh7uay8BgEkpWgEAAACgNaJWAAAAAAAAAID/EeLCnASQAAAAAADRRK0AAAAAAAAAAACnk651LM4mAAAAADRI1AoAAAAAAAAAcJR0CqApbssAAAAA0CZRKwAAAAAAAADQEBkSUJe7EAAAAABAHFErAAAAAAAAAMB/Xh7uay8BqEzX2jtnEAAAAACaJWoFAAAAAAAAAAD4H1Vkv5w7AAAAAGiZqBUAAAAAAAAA4BABFUAj3JABAAAAoHGiVgAAAAAAAAAAgPfkkd1xygAAAACgfaJWAAAAAAAAAIB/vTzc114C0BCRJAAAAABAXqJWAAAAAAAAAACAz+lae+FMAQAAAEAXRK0AAAAAAAAAAKdT6phWGRUMz9u8fc4RAAAAAPRC1AoAcMiyLIV/EQAAAAAAAChMM9kyZwcAAAAAOiJqBQBIdzBM1bUCAAAAAEA70sa0AvNQTrbJeQEAAACAvohaAQASZUlSda0AAAAAAADQC/1ka5wRAAAAAOiOqBUAIEXGGFXXCgAAAADA2LoojpLHtHaxOyAj7/p2OBcAAAAA0CNRKwDAVstfejkyAAAAAADwreSiFZiTlrIFzgIAAAAAdOq29gIAAEroKBbNstTr9Xr8IAAAAAAAMANFK5Dg7unZ3aMiRSsAAAAA9MukVgAAAAAAAACgIToxoAu6ylq88gAAAADQNVErAAAAAAAAABCr2QDpYEDb7L6AMtwEyvOaAwAAAEDvRK0AAAAAAAAAwIyMhAWO01iW5NUGAAAAgAGIWgEAAAAAAACAcLtKpAK5qaIVyEVpWcDd07PXGQAAAADGIGoFAAAAAAAAAOaiaAXyklyG8toCAAAAwEiW6/Vaew0AAOGWZam9hKJ8xluR62LwIgMAAAAAJNhVk0ZUTBlzVpEV8JFmPjs3WwAAAAAYjEmtAAD/c21Y7dcGAAAAAACOqtsmic2AaArMvLyeAAAAADCe29oLAAAoYYwidIxdAAAAAADARi8P97lyJjkrUMzrjctt5yA5KwAAAACMyqRWAAAAAAAAAKCcXZ1SlipMWgaUp8k8wqsHAAAAAANbzPuCgy6XS5bjnM/nLMcBANYty5LlOD5IA0AZvncDQF88uwHYbm9omhY4bfkrd0/PCdXrAMFVrgf3ybMbVunqdxng7gpxfOkGgL54dgMAfMWkVgAAAAAAAACgtL3Z0q4q7OXh/vWfb9egngKiuc9s57UCAAAAgBnc1l4AAAAAAAAAADCjvSNS//7hj+FT8uhXQxSBaK83HHebFXJWAAAAAJiHqBUAAAAAAAAAqCM59DrShkmngCr2lvyTcE8GAAAAgNnc1F4AAAAAAAAAADC1kkVTlr8lwQLS3D09u4G88WoAAAAAwJxMagUAAAAAAAAAKkse2br3T7xjaiJQXoE7XuO0rAAAAAAwM1ErAAAAAAAAANCEt8wpY+slnQLaNGfa6p4MAAAAAIhaAQAAAAAAAIC2HK9bdVNAF+ZJW92WAQAAAIBXolYAAAAAAAAAoFGfRlCf1l/FcildFpBdxJzqdrhtAgAAAAB/E7UCAAAAAAAAAD3J1UcNGY8BXRtpcKuWFQAAAAD4lKgVAAAAAAAAAACgFV0PbtWyAgAAAADrRK0AAAAAAAAAAJuItYCSOqpb3R4BAAAAgI1ErQAAAAAAAADAdNovxADe/J2MNnX70rICAAAAAHuJWgEAAAAAAAAAAPpQN3BVsQIAAAAAB4laAQAAAAAAAAC+J+UCWvPxvpQ3c3XfAwAAAACyE7UCAAAAAAAAAHMpP9sQoAwZKgAAAADQuJvaCwAAAAAAAAAAAAAAAAAAYHyiVgAAAAAAAABgImljWg0/BAAAAAAAOE7UCgAAAAAAAAAAAAAAAABAuNvaCwAAAAAAAAAABvRxIGq/w077XTkAAAAAAEBTRK0AAAAAAAAAQDYfW9Z3/1XdQHRleQAAAAAAAES7qb0AAAAAAAAAAGAQW5LRl4f7vspSY1oBAAAAAAByEbUCAAAAAAAAABm0n6omrFDRCgAAAAAAkJGoFQAAAAAAAAA4am8v2n4BCwAAAAAAQHaiVgAAAAAAAADgkC4KVWNaAQAAAAAAqluu12vtNQAAQDnLsmQ5jg/SAAAAAACvjhStxarRtEWKWgEAAAAAAPIyqRUAAAAAAAAA4D1FKwAAAAAAQHaiVgAAAAAAAAAg0ZExrcUkLFLRCgAAAAAAEEHUCgAAAAAAAADwH0UrAAAAAABAEFErAAAAAAAAADCsLmbJAgAAAAAATELUCgAAAAAAAACkONiLFhiImrBCY1oBAAAAAADiiFoBAAAAAAAAgAEpWgEAAAAAAFojagUAAAAAAAAAULQCAAAAAACEE7UCAAAAAAAAAKVFF6R7x7QqWgEAAAAAAAoQtQIAAAAAAAAAQ1G0AgAAAAAAtEnUCgAAAAAAAAAUFRqRWP2IVAAAIABJREFUKloBAAAAAACaJWoFAAAAAAAAAMpRtAIAAAAAAExL1AoAAAAAAAAAFKJoBQAAAAAAmNlyvV5rrwEAAMpZliXLcXyQBgAAAAA47elIowtSRSsAAAAAAED7bmsvAAAAAAAAAAAYVpl2VNEKAAAAAADQBVErAAAAAAAAAJBZsWp0b856UrQCAAAAAADUs1yv19prAACAcpZlyXIcH6QBAAAAAF59lZUWaEcNaAUAAAAAAOjLTe0FAAAAAAAAAAADSpihGnp8RSsAAAAAAEB1JrUCADAXk1oBAAAAALJb6UsjUtKEXFbRCgAAAAAA0AJRKwAAcxG1AgAAAABEWA9NczWlclYAAAAAAICu3dReAAAAAAAAAAAwuIQY9eMRFK0AAAAAAAC9M6kVAIC5mNQKAAAAABBkY3S6NzRNC2LlrAAAAAAAAA0StQIAMBdRKwAAAABAqF0N6lfp6ZHJrnJWAAAAAACAZolaAQCYi6gVAAAAACDakST1CDkrAAAAAABA40StAADMRdQKAAAAAFBA4a5VzgoAAAAAANAFUSsAAHMRtQIAAAAAFBOdtmpZAQAAAAAA+iJqBQBgLqJWAAAAAIDCsqetWlYAAAAAAIBOiVoBAJiLqBUAAAAAoJYjdauQFQAAAAAAYACiVgAA5iJqBQAAAABoysfSVb8KAAAAAAAwKlErAABzEbUCAAAAAAAAAAAAAEAVN7UXAAAAAAAAAAAAAAAAAADA+EStAAAAAAAAAAAAAAAAAACEE7UCAAAAAAAAAAAAAAAAABBO1AoAAAAAAAAAAAAAAAAAQDhRKwAAAAAAAAAAAAAAAAAA4UStAAAAAAAAAAAAAAAAAACEE7UCAAAAAAAAAAAAAAAAABBO1AoAAAAAAAAAAAAAAAAAQDhRKwAAAAAAAAAAAAAAAAAA4UStAAAAAAAAAAAAAAAAAACEE7UCAAAAAAAAAAAAAAAAABBO1AoAAAAAAAAAAAAAAAAAQDhRKwAAAAAAAAAAAAAAAAAA4UStAAAAAAAAAAAAAAAAAACEE7UCAAAAAAAAAAAAAAAAABBO1AoAAAAAAAAAAAAAAAAAQDhRKwAAAAAAAAAAAAAAAAAA4UStAAAAAAAAAAAAAAAAAACEE7UCAAAAAAAAAAAAAAAAABBO1AoAAAAAAAAAAAAAAAAAQDhRKwAAAAAAAAAAAAAAAAAA4UStAAAAAAAAAAAAAAAAAACEE7UCAAAAAAAAAAAAAAAAABBO1AoAAAAAAAAAAAAAAAAAQDhRKwAAAAAAAAAAAAAAAAAA4UStAAAAAAAAAAAAAAAAAACEE7UCAAAAAAAAAAAAAAAAABDutvYCoHuXyyXLcc7nc5bjAAAAwEh87waAvnh2A0BHcj24T57dAFCEL90A0BfPbgCAr5jUCgAAAAAAAAAAAAAAAABAOFErAAAAAAAAAAAAAAAAAADhRK0AAAAAAAAAAAAAAAAAAIQTtQIAAAAAAAAAAAAAAAAAEE7UCgAAAAAAAAAAAAAAAABAOFErAAAAAAAAAAAAAAAAAADhRK0AAAAAAAAAAAAAAAAAAIQTtQIAAAAAAAAAAAAAAAAAEE7UCgAAAAAAAAAAAAAAAABAOFErAAAAAAAAAAAAAAAAAADhRK0AAAAAAAAAAAAAAAAAAIQTtQIAAAAAAAAAAAAAAAAAEE7UCgAAAAAAAAAAAAAAAABAOFErAAAAAAAAAAAAAAAAAADhRK0AAAAAAAAAAAAAAAAAAIQTtQIAAAAAAAAAAAAAAAAAEE7UCgAAAAAAAAAAAAAAAABAOFErAAAAAAAAAAAAAAAAAADhRK0AAAAAAAAAAAAAAAAAAIQTtQIAAAAAAAAAAAAAAAAAEE7UCgAAAAAAAAAAAAAAAABAOFErAAAAAAAAAAAAAAAAAADhRK0AAAAAAAAAAAAAAAAAAIRbrtdr7TUAAEA5y7JkOY4P0gAAAAAAAAAAAAAAsItJrQAAAAAAAAAAAAAAAAAAhBO1AgAAAAAAAAAAAAAAAAAQTtQKAAAAAAAAAAAAAAAAAEA4USsAAAAAAAAAAAAAAAAAAOFErQAAAAAAAAAAAAAAAAAAhBO1AgAAAAAAAAAAAAAAAAAQTtQKAAAAAAAAAAAAAAAAAEA4USsAAAAAAAAAAAAAAAAAAOFErQAAAAAAAAAAAAAAAAAAhBO1AgAAAAAAAAAAAAAAAAAQTtQKAAAAAAAAAAAAAAAAAEA4USsAAAAAAAAAAAAAAAAAAOFErQAAAAAAAAAAAAAAAAAAhBO1AgAAAAAAAAAAAAAAAAAQTtQKAAAAAAAAAAAAAAAAAEA4USsAAAAAAAAAAAAAAAAAAOFErQAAAAAAAAAAAAAAAAAAhBO1AgAAAAAAAAAAAAAAAAAQTtQKAAAAAAAAAAAAAAAAAEA4USsAAAAAAAAAAAAAAAAAAOFErQAAAAAAAAAAAAAAAAAAhBO1AgAAAAAAAAAAAAAAAAAQTtQKAAAAAAAAAAAAAAAAAEA4USsAAAAAAAAAAAAAAAAAAOFErQAAAAAAAAAAAAAAAAAAhBO1AgAAAAAAAAAAAAAAAAAQTtQKAAAAAAAAAAAAAAAAAEA4USsAAAAAAAAAAAAAAAAAAOFErQAAAAAAAAAAAAAAAAAAhBO1AgAAAAAAAAAAAAAAAAAQTtQKAAAAAAAAAAAAAAAAAEA4USsAAAAAAAAAAAAAAAAAAOFErQAAAAAAAAAAAAAAAAAAhBO1AgAAAAAAAAAAAAAAAAAQTtQKAAAAAAAAAAAAAAAAAEA4USsAAAAAAAAAAAAAAAAAAOFErQAAAAAAAAAAAAAAAAAAhBO1AgAAAAAAAAAAAAAAAAAQTtQKAAAAAAAAAAAAAAAAAEA4USsAAAAAAAAAAAAAAAAAAOFErQAAAAAAAAAAAAAAAAAAhBO1AgAAAAAAAAAAAAAAAAAQTtQKAAAAAAAAAAAAAAAAAEA4USsAAAAAAAAAAAAAAAAAAOFErQAAAAAAAAAAAAAAAAAAhBO1AgAAAAAAAAAAAAAAAAAQTtQKAAAAAAAAAAAAAAAAAEA4USsAAAAAAAAAAAAAAAAAAOFErQAAAAAAAAAAAAAAAAAAhBO1AgAAAAAAAAAAAAAAAAAQTtQKAAAAAAAAAAAAAAAAAEA4USsAAAAAAAAAAAAAAAAAAOFErQAAAAAAAAAAAAAAAAAAhBO1AgAAAAAAAAAAAAAAAAAQTtQKAAAAAAAAAAAAAAAAAEA4USsAAAAAAAAAAAAAAAAAAOFuay8Aune5XLIc53w+ZzkOAAAAjMT3bgDoi2c3AHQk14P75NkNAEX40g0AffHsBgD4ikmtAAAAAAAAAAAAAAAAAACEE7UCAAAAAAAAAAAAAAAAABBO1AoAAAAAAAAAAAAAAAAAQDhRKwAAAAAAAAAAAAAAAAAA4UStAAAAAAAAAAAAAAAAAACEE7UCAAAAAAAAAAAAAAAAABBO1AoAAAAAAAAAAAAAAAAAQDhRKwAAAAAAAAAAAAAAAAAA4UStAAAAAAAAAAAAAAAAAACEE7UCAAAAAAAA8A97d2zDQAgEQNBIbor+gy8LV+DAEvsvrJkCTmQHwQoAAAAAAACAnKgVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAAAAAgJyoFQAAAAAAAAAAAAAAAACAnKgVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAAAAAgJyoFQAAAAAAAAAAAAAAAACAnKgVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAAAAAgJyoFQAAAAAAAAAAAAAAAACAnKgVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAAAAAgJyoFQAAAAAAAAAAAAAAAACAnKgVAAAAAAAAAAAAAAAAAIDcWGs9fQYAALjPGGPLHBdpAAAAAAAAAAAAAAD4iZ9aAQAAAAAAAAAAAAAAAADIiVoBAAAAAAAAAAAAAAAAAMiJWgEAAAAAAAAAAAAAAAAAyIlaAQAAAAAAAAAAAAAAAADIiVoBAAAAAAAAAAAAAAAAAMiJWgEAAAAAAAAAAAAAAAAAyIlaAQAAAAAAAAAAAAAAAADIiVoBAAAAAAAAAAAAAAAAAMiJWgEAAAAAAAAAAAAAAAAAyIlaAQAAAAAAAAAAAAAAAADIiVoBAAAAAAAAAAAAAAAAAMiJWgEAAAAAAAAAAAAAAAAAyIlaAQAAAAAAAAAAAAAAAADIiVoBAAAAAAAAAAAAAAAAAMiJWgEAAAAAAAAAAAAAAAAAyIlaAQAAAAAAAAAAAAAAAADIiVoBAAAAAAAAAAAAAAAAAMiJWgEAAAAAAAAAAAAAAAAAyIlaAQAAAAAAAAAAAAAAAADIiVoBAAAAAAAAAAAAAAAAAMiJWgEAAAAAAAAAAAAAAAAAyIlaAQAAAAAAAAAAAAAAAADIiVoBAAAAAAAAAAAAAAAAAMiJWgEAAAAAAAAAAAAAAAAAyIlaAQAAAAAAAAAAAAAAAADIiVoBAAAAAAAAAAAAAAAAAMiJWgEAAAAAAAAAAAAAAAAAyIlaAQAAAAAAAAAAAAAAAADIiVoBAAAAAAAAAAAAAAAAAMiJWgEAAAAAAAAAAAAAAAAAyIlaAQAAAAAAAAAAAAAAAADIiVoBAAAAAAAAAAAAAAAAAMiJWgEAAAAAAAAAAAAAAAAAyIlaAQAAAAAAAAAAAAAAAADIiVoBAAAAAAAAAAAAAAAAAMiJWgEAAAAAAAAAAAAAAAAAyIlaAQAAAAAAAAAAAAAAAADIiVoBAAAAAAAAAAAAAAAAAMiJWgEAAAAAAAAAAAAAAAAAyIlaAQAAAAAAAAAAAAAAAADIiVoBAAAAAAAAAAAAAAAAAMiJWgEAAAAAAAAAAAAAAAAAyIlaAQAAAAAAAAAAAAAAAADIiVoBAAAAAAAAAAAAAAAAAMiJWgEAAAAAAAAAAAAAAAAAyIlaAQAAAAAAAAAAAAAAAADIiVoBAAAAAAAAAAAAAAAAAMiJWgEAAAAAAAAAAAAAAAAAyIlaAQAAAAAAAAAAAAAAAADIiVoBAAAAAAAAAAAAAAAAAMiJWgEAAAAAAAAAAAAAAAAAyIlaAQAAAAAAAAAAAAAAAADIiVoBAAAAAAAAAAAAAAAAAMiJWgEAAAAAAAAAAAAAAAAAyIlaAQAAAAAAAAAAAAAAAADIiVoBAAAAAAAAAAAAAAAAAMi9nz4AHO+6ri1z5pxb5gAAAMA/8e4GgLPY3QBwkF2L+2V3A8AtPLoB4Cx2NwDAN35qBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACA31lpPnwEAAO4zxtgyx0UaAAAAAAAAAAAAAAB+4qdWAAAAAAAAAAAAAAAAAAByolYAAAAAAAAAAAAAAAAAAHKiVgAAAAAAAAAAAAAAAAAAcqJWAAAAAAAAAAAAAAAAAAByolYAAAAAAAAAAAAAAAAAAHKiVgAAAAAAAAAAAAAAAAAAcqJWAAAAAAAAAAAAAAAAAAByolYAAAAAAAAAAAAAAAAAAHKiVgAAAAAAAAAAAAAAAAAAcqJWAAAAAAAAAAAAAAAAAAByolYAAAAAAAAAAAAAAAAAAHKiVgAAAAAAAAAAAAAAAAAAcqJWAAAAAAAAAAAAAAAAAAByolYAAAAAAAAAAAAAAAAAAHKiVgAAAAAAAAAAAAAAAAAAcqJWAAAAAAAAAAAAAAAAAAByolYAAAAAAAAAAAAAAAAAAHKiVgAAAAAAAAAAAAAAAAAAcqJWAAAAAAAAAAAAAAAAAAByolYAAAAAAAAAAAAAAAAAAHKiVgAAAAAAAAAAAAAAAAAAcqJWAAAAAAAAAAAAAAAAAAByolYAAAAAAAAAAAAAAAAAAHKiVgAAAAAAAAAAAAAAAAAAcqJWAAAAAAAAAAAAAAAAAAByolYAAAAAAAAAAAAAAAAAAHKiVgAAAAAAAAAAAAAAAAAAcqJWAAAAAAAAAAAAAAAAAAByolYAAAAAAAAAAAAAAAAAAHKiVgAAAAAAAAAAAAAAAAAAcqJWAAAAAAAAAAAAAAAAAAByolYAAAAAAAAAAAAAAAAAAHKiVgAAAAAAAAAAAAAAAAAAcqJWAAAAAAAAAAAAAAAAAAByolYAAAAAAAAAAAAAAAAAAHKiVgAAAAAAAAAAAAAAAAAAcqJWAAAAAAAAAAAAAAAAAAByolYAAAAAAAAAAAAAAAAAAHKiVgAAAAAAAAAAAAAAAAAAcqJWAAAAAAAAAAAAAAAAAAByolYAAAAAAAAAAAAAAAAAAHKiVgAAAAAAAAAAAAAAAAAAcqJWAAAAAAAAAAAAAAAAAAByolYAAAAAAAAAAAAAAAAAAHKiVgAAAAAAAAAAAAAAAAAAcqJWAAAAAAAAAAAAAAAAAAByolYAAAAAAAAAAAAAAAAAAHKiVgAAAAAAAAAAAAAAAAAAcqJWAAAAAAAAAAAAAAAAAAByolYAAAAAAAAAAAAAAAAAAHKiVgAAAAAAAAAAAAAAAAAAcqJWAAAAAAAAAAAAAAAAAAByolYAAAAAAAAAAAAAAAAAAHKiVgAAAAAAAAAAAAAAAAAAcqJWAAAAAAAAAAAAAAAAAAByolYAAAAAAAAAAAAAAAAAAHLvpw8Ax7uua8ucOeeWOQAAAPBPvLsB4Cx2NwAcZNfiftndAHALj24AOIvdDQDwjZ9aAQAAAAAAAAAAAAAAAADIiVoBAAAAAAAAAAAAAAAAAMiJWgEAAAAAAAAAAAAAAAAAyIlaAQAAAAAAAAAAAAAAAADIiVoBAAAAAAAAAAAAAAAAAMiJWgEAAAAAAAAAAAAAAAAAyIlaAQAAAAAAAAAAAAAAAADIiVoBAAAAAAAAAAAAAAAAAMiJWgEAAAAAAAAAAAAAAAAAyIlaAQAAAAAAAAAAAAAAAADIiVoBAAAAAAAAAAAAAAAAAMiJWgEAAAAAAAAAAAAAAAAAyIlaAQAAAAAAAAAAAAAAAADIiVoBAAAAAAAAAAAAAAAAAMiJWgEAAAAAAAAAAAAAAAAAyIlaAQAAAAAAAAAAAAAAAADIiVoBAAAAAAAAAAAAAAAAAMiJWgEAAAAAAAAAAAAAAAAAyIlaAQAAAAAAAAAAAAAAAADIiVoBAAAAAAAAAAAAAAAAAMiJWgEAAAAAAAAAAAAAAAAAyIlaAQAAAAAAAAAAAAAAAADIiVoBAAAAAAAAAAAAAAAAAMiNtdbTZwAAgPuMMbbMcZEGAAAAAAAAAAAAAICf+KkVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAAAAAgJyoFQAAAAAAAAAAAAAAAACAnKgVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAAAAAgJyoFQAAAAAAAAAAAAAAAACAnKgVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAAAAAgJyoFQAAAAAAAAAAAAAAAACAnKgVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAAAAAgJyoFQAAAAAAAAAAAAAAAACAnKgVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAAAAAgJyoFQAAAAAAAAAAAAAAAACAnKgVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAAAAAgJyoFQAAAAAAAAAAAAAAAACAnKgVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAAAAAgJyoFQAAAAAAAAAAAAAAAACAnKgVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAAAAAgJyoFQAAAAAAAAAAAAAAAACAnKgVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAAAAAgJyoFQAAAAAAAAAAAAAAAACAnKgVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAAAAAgJyoFQAAAAAAAAAAAAAAAACAnKgVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAAAAAgJyoFQAAAAAAAAAAAAAAAACAnKgVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAAAAAgJyoFQAAAAAAAAAAAAAAAACAnKgVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAAAAAgJyoFQAAAAAAAAAAAAAAAACAnKgVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAAAAAgJyoFQAAAAAAAAAAAAAAAACAnKgVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAAAAAgJyoFQAAAAAAAAAAAAAAAACAnKgVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAAAAAgJyoFQAAAAAAAAAAAAAAAACAnKgVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAAAAAgJyoFQAAAAAAAAAAAAAAAACAnKgVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAAAAAgJyoFQAAAAAAAAAAAAAAAACAnKgVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAAAAAgNz76QPA8a7r2jJnzrllDgAAAPwT724AOIvdDQAH2bW4X3Y3ANzCoxsAzmJ3AwB846dWAAAAAAAAAAAAAAAAAAByolYAAAAAAAAAAAAAAAAAAHKiVgAAAAAAAAAAAAAAAAAAcqJWAAAAAAAAAAAAAAAAAAByolYAAAAAAAAAAAAAAAAAAHKiVgAAAAAAAAAAAAAAAAAAcqJWAAAAAAAAAAAAAAAAAAByolYAAAAAAAAAAAAAAAAAAHKiVgAAAAAAAAAAAAAAAAAAcqJWAAAAAAAAAAAAAAAAAAByolYAAAAAAAAAAAAAAAAAAHKiVgAAAAAAAAAAAAAAAAAAcqJWAAAAAAAAAAAAAAAAAAByolYAAAAAAAAAAAAAAAAAAHKiVgAAAAAAAAAAAAAAAAAAcqJWAAAAAAAAAAAAAAAAAAByolYAAAAAAAAAAAAAAAAAAHKiVgAAAAAAAAAAAAAAAAAAcqJWAAAAAAAAAAAAAAAAAAByolYAAAAAAAAAAAAAAAAAAHKiVgAAAAAAAAAAAAAAAAAAcqJWAAAAAAAAAAAAAAAAAAByolYAAAAAAAAAAAAAAAAAAHJjrfX0GQAA4D5jjC1zXKQBAAAAAAAAAAAAAOAnfmoFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAA4MPevSU1knMLGEUEk4L5RzGsPA+O5vg3YPKiy97SWm/dVWVkEJZR5ocAmhO1AgAAAAAAAAAAAAAAAADQnKgVAAAAAAAAAAAAAAAAAIDmRK0AAAAAAAAAAAAAAAAAADQnagUAAAAAAAAAAAAAAAAAoDlRKwAAAAAAAAAAAAAAAAAAzYlaAQAAAAAAAAAAAAAAAABoTtQKAAAAAAAAAAAAAAAAAEBzolYAAAAAAAAAAAAAAAAAAJoTtQIAAAAAAAAAAAAAAAAA0JyoFQAAAAAAAAAAAAAAAACA5kStAAAAAAAAAAAAAAAAAAA0J2oFAAAAAAAAAAAAAAAAAKA5USsAAAAAAAAAAAAAAAAAAM2JWgEAAAAAAAAAAAAAAAAAaE7UCgAAAAAAAAAAAAAAAABAc6JWAAAAAAAAAAAAAAAAAACaE7UCAAAAAAAAAAAAAAAAANCcqBUAAAAAAAAAAAAAAAAAgOZErQAAAAAAAAAAAAAAAAAANCdqBQAAAAAAAAAAAAAAAACgOVErAAAAAAAAAAAAAAAAAADNiVoBAAAAAAAAAAAAAAAAAGhO1AoAAAAAAAAAAAAAAAAAQHOiVgAAAAAAAAAAAAAAAAAAmhO1AgAAAAAAAAAAAAAAAADQnKgVAAAAAAAAAAAAAAAAAIDmRK0AAAAAAAAAAAAAAAAAADQnagUAAAAAAAAAAAAAAAAAoDlRKwAAAAAAAAAAAAAAAAAAzb2NHgAAAAAAAAAAAAAAAAAA8KtSysP/2bZtyEjgIlErAAAAAAAAAAAAAAAAAETxPWHd83dkrqQgagUAAAAAAAAAAAAAAACAwfa0rHv+ubqVyEStAAAAAAAAAAAAAAAAADDMxZz1x0eTthLT6+gBAAAAAAAAAAAAAAAAAMB8okWhAAAgAElEQVSi6hat9w/b6JHhClErAAAAAAAAAAAAAAAAAPTWoTvVtRLN2+gBAAAAAAAAAAAAAAAAAMBajuam27ade4RSyvd/C6OIWgEAAAAAAAAAAAAAAACgn/1F65Me9f6Pnj+grpU4RK1w1efnZ5XHeX9/r/I4AAAAMBM/dwNALtZuAEik1sL9Yu0GgC780A0AuVi7AXji0AGt+0vU29988uC6VoIQtQIAAAAAAAAAAAAAAABAIIcC1EOhLIz1OnoAAAAAAAAAAAAAAAAAADC/nfVpo6JV+0oETmoFAAAAAAAAAAAAAAAAgLaqF60iVTJyUisAAAAAAAAAAAAAAAAANBSkaNXBMpyoFQAAAAAAAAAAAAAAAABakZLCF1ErAAAAAAAAAAAAAAAAAKShkiUvUSsAAAAAAAAAAAAAAAAANLE/QN22re4DQkCiVgAAAAAAAAAAAAAAAACoT4AKD0StAAAAAAAAAAAAAAAAAAA0J2oFAAAAAAAAAAAAAAAAgMpaHNPq6FeyE7UCAAAAAAAAAAAAAAAAwEjbtk3zUeAJUSsAAAAAAAAAAAAAAAAA1ORIVfiRqBUAAAAAAAAAAAAAAAAAJueYViIQtQIAAAAAAAAAAAAAAAAA0JyoFQAAAAAAAAAAAAAAAACqKaU0euTTp606ppUgRK0AAAAAAAAAAAAAAAAAMIzilHWIWgEAAAAAAAAAAAAAAACgjnbHtN6cKGBFs8TxNnoAAAAAAAAAAAAAAAAAAEB9claicVIrAAAAAAAAAAAAAAAAAKSxbdueWlXRSkBOagUAAAAAAAAAAAAAAACACkop3T7WrVn9/hG1rEQmagUAAAAAAAAAAAAAAACAMS42qBJWcnkdPQAAAAAAAAAAAAAAAAAAAOYnagUAAAAAAAAAAAAAAACAq0opo4cA0YlaAQAAAAAAAAAAAAAAAABormzbNnoMAADQT63ffuSNNAAAAAAAAAAAAMBFv93Y6UZNkjp3r7IJz1LeRg8AAAAAAAAAAAAAAAAAWMvz9u/2p0o/VmCes5rX0QMAAAAAAAAAAAAAAAAAFrLzNMtzh14CEJmoFQAAAAAAAAAAAAAAAOjkUKqqayUR0xX2ELUCAAAAAAAAAAAAAAAAQQkFAWYiagUAAAAAAAAAAAAAAAB6UKgCLE7UCgAAAAAAAAAAAAAAAMQlhWVW27aNHgL0JmoFAAAAAAAAAAAAAAAAmtOmAiBqBQAAAAAAAAAAAAAAAIDzNNuwk6gVAAAAAAAAAAAAAAAAAIDmRK0AAAAAAAAAAAAAAAAAADQnagUAAAAAAAAAAAAAAABCK6WMHgJUtm3b6CHAAKJWAAAAAAAAAAAAAAAAoDkJHwCiVgAAAAAAAAAAAAAAACA6h7UCTEDUCgAAAAAAAAAAAAAAAAAnKa5hP1ErAAAAAAAAAAAAAAAAkIB0ECA7USsAAAAAAAAAAAAAAADQw7Zto4cAwEiiVgAAAAAAAAAAAAAAACAHh7UyB4E3yxK1AgAAAAAAAAAAAAAAAGnoWgHyErUCAAAAAAAAAAAAAAAAnTigksmorOEQUSsAAAAAAAAAAAAAAACQiYwQIClRKwAAAAAAAAAAAAAAANBPlcNada0AGYlaAQAAAAAAAAAAAAAAAABoTtQKAAAAAAAAAAAAAAAAdOWwVlZWZf5DUqJWAAAAAAAAAAAAAAAAICVdK0AuolYAAAAAAAAAAAAAAACgt1qHVepaGcj0g6NErQAAAAAAAAAAAAAAAAAANCdqBQAAAAAAAAAAAAAAAAZwWCvAakStAAAAAAAAAAAAAAAAQG66VrKo1XJDUsX3AAAAS6m1YeGNNAAAAAAAAAAAAEAVFXtUd3hySPYW2oQnIye1AgAAAAAAAAAAAAAAAMNUDPOyN4p0U0qZYLbM8SxYjagVAAAAAAAAAAAAAAAAmITGjz9NNkkmezpMT9QKAAAAAAAAAAAAAAAAjFTxsFYAIhO1AgAAAAAAAAAAAAAAAINV7FodXAkQlqgVAAAAAAAAAAAAAAAAmIqulXU46JhcRK0AAAAAAAAAAAAAAADAeHXbPF0rv5mpAp3pubCIYtYCALCUWtsT3kgDAAAAAAAAAAAAtFA3RnXPJ42cm6gmJDipFQAAAAAAAAAAAAAAAIjCea3MStEKL6JWAAAAAAAAAAAAAAAAYGK6VoA4RK0AAAAAAAAAAAAAAABAINUPtNS1AgQhagUAAAAAAAAAAAAAAABi0bUSmekEp4laAQAAAAAAAAAAAAAAgHB0rQDzEbUCAAAAAAAAAAAAAAAAEelaASYjagUAAAAAAAAAAAAAAACCatG1Slvpr/pMhqTeRg8AAAAAAAAAAAAAAAAAoKtSisgQ+rtPyn0PrslJrQAAAAAAAAAAAAAAAEBcjco357VCZw/fdL4H1yRqBQAAAAAAAAAAAAAAAELTtRKHaXPOj583n8wFiVoBAAAAAAAAAAAAAACA6HStkJdvNL6IWgEAAAAAAAAAAAAAAIAEdK0k1WjqplBK8S3GPVErAAAAAAAAAAAAAAAAkEO7rlV3B3X5tuJHolYAAAAAAAAAAAAAAAAgjXaHXgrwoAo5K0+IWgEAAAAAAAAAAAAAAIBMdK2MYoY8J2flT6JWAAAAAAAAAAAAAAAAIBldK8RR/jN6ICTwNnoAAAAAAAAAAAAAAAAAAIdt29Yoors9bLtuFiYgYeUcUSsAAAAAAAAAAAAAAACQUruu9eXlpZSia+W6aWaRipUqRK0AAAAAAAAAAAAAAABAVq271peJokR4QrNKH6+jBwAAAAAAAAAAAAAAAABwXuvoVOzHjZkA1zmpFa76/Pys8jjv7+9VHgcAAABm4uduAMjF2g0AidRauF+s3QDQhR+6ASAXazcM0fS81hdHtsIRrb8fSU3UCj18fHyMHgIAUJmfsgAAAAAAAAAAAABWU0rRtcJvfHewh6gVAAAAAAAAAAAAAAAAYBdHtnLIClNlhedIRaJWAAAAAAAAAAAAAAAAgAOkrSzO5Oc0USsAAAAAAAAAAAAAAADAYdJWFmGSU5GoFQAAAAAAAAAAAAAAAOCkUorkbwW3hnlipjF9vI4eAAAAAAAAAAAAAAAAAEBi0+eOALWIWgEAAAAAAAAAAAAAAAAu0bUC7CFqBQAAAAAAAAAAAAAAALhK18qDbdtGDwHCEbUCAAAAAAAAAAAAAAAAANCcqBUAAAAAAAAAAAAAAAAAfuUYXqhF1AoAAAAAAAAAAAAAAAAAQHOiVgAAAAAAAAAAAAAAAAAAmhO1AgAAAAAAAAAAAAAAAEBN27aNHgJEJGoFAAAAAAAAAAAAAAAAAKA5USsAAAAAAAAAAAAAAAAAAM2JWgEAAAAAAAAAAAAAAADgZ6WU0UOAeYhaAQAAAAAAAAAAAAAAAK7atm30EACiE7UCAAAAAAAAAAAAAAAAANCcqBUAAAAAAAAAAAAAAADgEse0cs98gN+IWgEAAAAAAAAAAAAAAADOUzAC7CRqBQAAAAAAAAAAAAAAADhJ0Tq3UsroIcBU3kYPAAAAAAAAAAAAAAAAACAZLSvACaJWAAAAAAAAAAAAAAAAgL3krACnFa+h0IFzxgEAAAAAAAAAAAAAACagxlrNiSzIJHk58nnz6VqNk1qhB6+tABBHrV82YX0HAAAAAAAAAAAACKX1mWRuHwW47nX0AAAAAAAAAAAAAAAAAAAuUbQCpOCkVgAAAAAAAAAAAAAAACCxpkWrnHVlrWNpWJCTWgEAAAAAAAAAAAAAAICsFK0AiYhaAQAAAAAAAAAAAAAAgJQUrQC5vI0eAAAAAAAAAAAAAAAAAMBh7YpWOSunmTzwnJNaAQAAAAAAAAAAAAAAgGQUrQAZiVoBAAAAAAAAAAAAAACATBStdNBumsHK3kYPAAAAAAAAAAAAAAAAAGCvRqmhnBWgAye1AgAAAAAAAAAAAAAAADkoWgFSE7UCAAAAAAAAAAAAAAAACShaCc5cgj+JWgEAAAAAAAAAAAAAAIDoFK0AExC1AgAAAAAAAAAAAAAAAKEpWums0ZQD3kYPAAAAAAAAAAAAAAAAAOBXLfJCOSvAEE5qBQAAAAAAAAAAAAAAAIJStALMRNQKAAAAAAAAAAAAAAAArELRCjCQqBUAAAAAAAAAAAAAAACIqPoxrYpW2jG7YA9RKwAAAAAAAAAAAAAAABCOohVgPqJWAAAAAAAAAAAAAAAAIBZFKwNVn37AF1ErAAAAAAAAAAAAAAAAEIiiFWBWolYAAAAAAAAAAAAAAAAgCkUrwMRErQAAAAAAAAAAAAAAAMCcFK30YabBTqJWAAAAAAAAAAAAAAAAIIS6x7TqDAGiEbUCAAAAAAAAAAAAAAAA4ylaiaDuPAQeiFoBAAAAAAAAAAAAAACAwRStACsQtQIAAAAAAAAAAAAAAADzULQChCVqBQAAAAAAAAAAAAAAAEaqeEyrohUgMlErAAAAAAAAAAAAAAAAMEzFohWGkFLDfqJWAAAAAAAAAAAAAAAAYAbaQoDgRK0AAAAAAAAAAAAAAADAGBWPaVW0cp1zg6E1USsAAAAAAAAAAAAAAACQm6IVIAVRKwAAAAAAAAAAAAAAADBArVMxFa0AWYhaAQAAAAAAAAAAAAAAAABoTtQKAAAAAAAAAAAAAAAA9OaYVuZgBsIholYAAAAAAAAAAAAAAAAAAJoTtQIAAAAAAAAAAAAAAABdOaaVgGpNS+AJUSsAAAAAAAAAAAAAAACQj6IVIB1RKwAAAAAAAAAAAAAAAJCMohUgI1ErAAAAAAAAAAAAAAAA0E8pZfQQoA5xNRwlagUAAAAAAAAAAAAAAAAyURICJCVqBQAAAAAAAAAAAAAAADq5fkyropUWHCAMfYhaAQAAAAAAAAAAAAAAAABoTtQKAAAAAAAAAAAAAAAA5OCYVoDURK0AAAAAAAAAAAAAAABAD6WU0UOAaiTWcIKoFQAAAAAAAAAAAAAAAEhAQwiQnagVAAAAAAAAAAAAAAAAAIDmRK0AAAAAAAAAAAAAAABAdI5ppZ1SyughwCpErQAAAAAAAAAAAAAAAEBzukFmorKGc0StAAAAAAAAAAAAAAAAQGgCQoA5iFoBAAAAAAAAAAAAAAAAAGhO1AoAAAAAAAAAAAAAAAAAezk6GE4TtQIAAAAAAAAAAAAAAADN6QCJqZQyegiwEFErAAAAAAAAAAAAAAAAEJcaFmAaolYAAAAAAAAAAAAAAAAA2EVlDVeIWgEAAAAAAAAAAAAAAIAe1IAAixO1AgAAAAAAAAAAAAAAAEHpYAFmImoFAAAAAAAAAAAAAAAAOjkUqSpaaa2UMnoIsJa30QOA9D4/P6s8zvv7e5XHAQAAgJn4uRsAcrF2A0AitRbuF2s3AHThh24AyMXaDX+6pap/xoSKVgIyLeEiUSsAAAAAAAAAAAAAAADQ25O0VTcIMCtRKwAAAAAAAAAAAAAAADCGfhVgKa+jBwAAAAAAAAAAAAAAAAAA0Wmw4TpRKwAAAAAAAAAAAAAAAADLKaWMHgIsR9QKAAAAAAAAAAAAAAAAAM84phWqELUCAAAAAAAAAAAAAAAAANCcqBUAAAAAAAAAAAAAAAAAgObeRg8AAAAAAAAAAAAAAAAAAJ4ppTz8n23b6j7gExc/FvBF1AoAAAAAAAAAAAAAAABARE/S09sfyU0hl9fRAwAAAAAAAAAAAAAAAACAR3sOUz104Oo5ulmoSNQKAAAAAAAAAAAAAAAAQCCllP21aoeulUMOfUV8+VYjagUAAAAAAAAAAAAAAAAgihOV49F/IqSEUd5GDwAAAAAAAAAAAAAAAAAAItq2bfQQMjlXC9/+lU/1IkStAAAAAAAAAAAAAAAAAIRw+gzVUooqcpQqJ99+PYiv49xErQAAAAAAAAAAAAAAAACMV6WNrGi1ujLI5//0MFb7eiUlagUAAABgHh8fH/f/+e/fv1EjAYBlPSzHz1msASCO/Yu4FRwAAAAAyC5IuglrErUCAAAAMInvd99+fHy40RYAWrtyuffHeMbyDQAd1F3B/eZ7AAAAAGBKNj+hhdfRAwAAAACACn47T+bQYXEAwE7lTvUH//j4aPfgALCs8r8SPTgAAAAAsI5QG4yKVmjESa0AAAAA5CZbBYBu+l9C/vqILhgDwGmjVnDLNwAAAADQ2c5tyVDpLCxI1AoAAABAYopWAOhj+GVdbQwAHBVk+X6xggMAAAAACdnYhHZErUA4Lm0CcXhFAgCITM4KAH0M72Hu2a4BgD1CLd8vVnAAAAAAIJJoO6gZjdrs9bWbhqgVCORhdSmluKgJDHT/ouQVCQAgGkUrAHQQ+Yqgg1sB4Eenl+9///7t+WsXfx53wQUAAAAAaKfi9uPKO5mRn3vksXGIqBWI4sfLqy5qAqN8f1FyoyQAQBByVgDoI3LR+sWODQB8Obp2b9v2+fl59KPct6/nfkK3fAMAAAAALezcddyzlWoDE1pTiwEhPHlb4GUKGMLr0sRq3ZJrJgBAf6fXcQs3ABxybs09tOBWL2Yt9wCsbP/C2mLFvLKsW8EBAAAAgAetbxAStUIEolZgsOdvCLxGAQN5gZqVqBUAMrq4glu4AWC/Q8tulUW2YuBq0QdgQTtX0tarpLQVAAAAAKjlxH6johVyEbUCIwnGgOC8TE1J1AoAiVi4AaCnvIe83bPuA7CUPxfQniujrhUAAAAAqKLd7+ENtacKKxO1AsNIxYAUvFjNxz2yABBfxePabizcAPCnCQ55u2f1B2B6YW+9Or2aW74BAAAAgJtGv4037LYqLEjUCowhEgMS8ZI1GXfHAkBA1SvWBxZuAHhuz1qc5Zy3L94AADCx4Lde6VoBAAAAgIuq378dfFsVViNqBQaQhwG5+BlmMqJWABiudcL6nYUbAJ6IVrR+uf6ewXsAAKaU4mKrrhUAAAAAuKLu/dvuBodoRK1AbykusgI88JPMTEStANBU/2B1Dws3APwm+KaHrhUAHmS52HplEY/zLAAAAACAgar8ct6wv+EXFvc6egDAWrJcZAV4UOUHHgAAAIBQghetVQZg0waAdQxfuO9dGYzlGwAAAAB42bfNWP7z2x9V+ShAdU5qBfpRtALZxb/Rkz2c1AoATbW78fS2+J57fAs3AHyXaMPWea0AcPNkTYy52DmvFQAAAAC4qOlvwbMPCaM4qRXoJNENUgC/cV4rAEB/239GDwQAFhJt5XVeKwC8JCxaL7J8AwAAAAAvzfY/3Y8EY4lagR4UrcA0dK0AAH1oWQGgqXRVjK4VgMWlW7tvIo8NAAAAAMii+k1Eti5hOFEr0JyiFZiMrhUAoB0tKwB0sGwVY9MGgCmVUmZd42Z9XgAAAADACVXuKXJjEgTxNnoAwOQUrcCUtm17/vpWSvESBwCwkzdOANBT6jjkzz0ZAJjSnuXv6+9E+ynb8g0AAAAAVPS1BXpo4zHaxikgagUacnkSmJiuFQDgKO+OACC4FRZrOzYATO928WKa9c7aDQAAAAD86GHn8OG+bvuKEJyoFWjlz6LVuwQgO10rAMB33v8AQGRPtjKyLOLXT3uzYwPACqx3AAAAAMBS7IhCLq+jBwAsyjsGYA5/vpo5sxoAAACIL9eGba7RAsBFpy80THOFYponAgAAAAAA3IhagSaeX1l0yxGwFDdbAAAAABHYo/jiUwHAIoIsea4OAwAAAAAA995GDwCYkKL1ud8+Pz4zxGf2/mjbtiA3hQAAAACckHFvx4YMAAAAAAAAACQlagUqcyPRvUOfDbkgoZi9h/x5G2UpZZ3PBgAAABCQndsHtmsAWIQlDwAAAAAAiEbUCtT0531RK1wxrX5z2NcDrvDZYyyz9wpdKwAAAJCR/QoAiMyvpbhxkQUAAAAAAGYiagWqWbxo7XBFeak+kJ7M3m7ccgEAAAAMMWUP8+evGPuTvRoAFmHJAwAAAAAAQhG1AlzV/4YwfSC1mL117bmT0o0jAAAAQBy2KQAAAAAAAACAzkStQB1rHtM6/HyD2wCm/NzSmtnbyPUTQgAAAAC6ud/HyLhR47BWAAAAAAAAAEhH1ApUsGDRevFOqR8/Iacfc9Y4kEbM3uHcKwkAAAD0tHPrJnvgCgAAAAAAAACkIGoFrlrtTMJzz3fPTWAPf+foB1ozDuQQs7ePPSeE6FoBAACAyOxdAAAAAAAAAACNiFqB5ma6+eloqnfluX/920Mf1N1m/Mbs7UnXCgAAAGSX5deQ7dmHAQAAAAAAAADieB09ACC3P+8Win/P036Hbo3atq3Wcz/6UKUUd3HxwOwFAAAA4IQV9mpWeI4A5FXlks1MV2wBAAAAAIAJiFqB89a51+dQaFcxCLzysOt8dXjO7B1lz1Oe45kCAAAAAAAAAAAAAADsJGoFGprjl/4ePeKy3UiOPr5eDrN3LF0rAAAAMNz1zQfbFwAAAAAAAABARaJW4KRF7mQafsTljx9o/19e5MvEj8xeAAAAAACA7C5exJnj1xADAAAAAAAzEbUCZ+yJzSa4PnqoCWw6ku8fbv9HVAauyewNwmGtAAAAwASCb19MsBcNAI1Ms0pO80QAAAAAAIAXUSvQyASXFcM2gSc+bvB7zqjO7E1nkacJAAAAEJPNGQCCy37t1VILAAAAAADcE7UCh61w0TF+E3j0o6/wVePG7I0m+40mAAAAAADAcEcvN2zb5goFAAAAAAAQk6gVqC/79dEsTeDRMWQvA9nD7M1rhecIAAAAAACctr9TjXAZCAAAAAAA4DeiVuAY2VVqvnzklXr2uncEAAAAAACoYvvPoT9Kbb5nBAAAAAAAixO1AgfsicqyX1PMddDlzaGRpC4Dec7sjWzPM039BAEAAAAAgJ62b0aPCAAAAAAAYBdRK8D/y9gE3kQbD/2ZvXPQtQIAAAAAADNx7QMAAAAAAHggagX2mv6Y1nWup67zTNexztc09TNN/QoJAAAAJFVlR8K2BgAwivchAAAAAAAwH1ErwGExL50eGlXqMpArzN74pn+CAAAAAAAAAAAAAADAskStwC6OaYWwzN5EUr9OAgAAAAD/x94dZTVsawEUjVlMCua/yLD0PtLyUpI4si1ZutLeny1Q21EtsHIiAM5kYQUAAAAAAIYkagXY1gT2vHRqu8sJGb1DGv4EAQAAgDMdfCjU8zMlAKBzljwAAAAAAIBHolbgPWuNAAAAAABx7Q5TFa0AQCt+DwEAAAAAgFGJWoEy4q4pDrPR5Y3tLqdi9EaUeZpxTxAAAAAYRv8PlACAnlnsAAAAAAAAnhK1Am9YawQAAAAAiC6ltClSnaponepkASAEszMAAAAAAAxM1AoUEHdNUbLrCsTltYt7BeLeMwEAAIDoMp9LxHp8EfcxEQAAAAAAAABMSNQKrPFmoHtR3sgV5Tg5U5RREeU4z+EODAAAANSQ/rXpXwEAbHJkmcNvIwAAAAAAMLbP1gcAhBd3TVEwdrMsS9wXcVpG703c0ZtS8iICAAAAbQV9rgIAjM2vKAAAAAAAMDw7tQIvCa4A2nIfBgAAAAAAwrHAAQAAAAAArBC1ApOyknrP1YjF63XP1QAAAADgCNvBAUBZR9ZuzMsAAAAAADADUSvwXOZa4zzLirHONNbRUlus8RDraI/IPFPVLgAAAMAKD08AYBjzLBIBAAAAAMDkRK0AAAAAAAAAABy1+/MmFK0AAAAAADAPUSswIx/e/8g1icIr9cg1AQAAAGAf/QwAFKRoBQAAAAAAcohagScylxstLgIckXkXVe0CAAAAPOWxCQAMwKIzAAAAAADMRtQKAAAAAAAAAMB++z5sQtEKAAAAAAATErUCfw3/2fbDn+Burkz/vEavxL0yNmsFAAAAaEVFAwClKFoBAAAAAIB8olZgp6mWGCOebMRjpoaIIyHiMQMAAABwMp8CBgA9WJZF0QoAAAAAAGzy2foAAAAAAAAA4FRCGgA4Ts4KAAAAAADsIGqFo67Xa5Gf8/X1VeTnHDT8Z9sPf4IHLctiFblbRu+64Ufv8CcIAPBKkb+7r9drJ393A8Dwznlm7lkZABRxZOL+/v7e8V0WOwDgiMHeqAYAwzN3AwC8ImoF9rDWCFBKSsm7MAEAAADO5BE3ABwhZwUAAAAAAI4QtQIAAAAAABCGDwgDgCb2taw3ilYAAAAAAOCXqBX4P+8EAujZsize8wEAAABwkAcsAJDvSMh6Y+YFAAAAAAD+ELUCm8Vdd1Tt5lDN9cnozRF39KaUvMQAAAAAOQ4+RQn6+AgA6jmerb5i2gUAAAAAAJ4StQIAAAAAAAAAjOzMj9eUswIAAAAAACs+Wh8A0AubBAL0z70aAAAAmJltWgFgn9PWF1JKJlwAAAAAAGCdqBUAAAAAAIDBCWwAoDazLQAAAAAAkEPUCmwz4UrkhKdsK8hhGL2BTPhiAQAAAGwS98kPAExi+VfrAwEAAAAAALomagUulzneDDTDOf6hkRuG0cu9CccDAAAAwMFHIh43AcCZljutjwUAAAAAAOiOqBUAAAAAAIBhKVoBoOFsKG0FAAAAAAD++Gx9AAAAAAAAAPDSkRJG0QoAN0/nxOv1+vYbv7+/j//Xb7O5eRkAAAAAALiIWoFNrDIC1JNS8lHlAAAAAH8oWgGgrZ+fnz//ZHfm+jutm6MBAAAAAGBmolbg0FuCADjfsize7QEAAAAAADTxm7kerFstdgAAAAAAwJxErQAAAAAAAPTINq0A0LODdasP8QQAAAAAgDl9tD4AgDPYjXYrV6wfXoutXDEAAACAMShaASCK37p1q2VZrOwAAAAAAMBsRK0AAAAAAACMQ9EKAOdLKe2egnWtAAAAAAAwFVErzC5/gdDbgABqy7/TensHAAAAMLbdTz88ygaAho50rdY+AAAAAABgEqJWAAAAAAAAOvL9/b3vGxWtANDckelY1woAAAAAADMQtQIAAAAAANALRSsARKdrBQAAAAAAVohaAQAAAAAA6CZXjScAACAASURBVIKiFQDGoGsFAAAAAABeEbXC1CwHAsTlHg4AAABwo2gFgA7pWgEAAAAAgKdErQAAHfEWTAAAAGBa+7Zp9TgFALqlawUAAAAAAB6JWoEs3hUEAAAAAEA9ilYA4A9dKwAAAAAADGmx2A8zy18FjH6vOLLeGfrcpz3xkUz7Ik574peZbs6tlHoTjOsPAE3sm8pN3ADQOVM8AAzs4GN5Mz4AAAAAAAzGTq0AAAAAAAA0o2gFgLGZtQEAAAAAgHuiVmB8pXbkm43r1gOvwj6uGwAAAEAUO57kpJS0MQAwD+s+AAAAAAAwGFErzMviH0B07uQAAABAaPuK1hpHAgBUdXAGtyACAAAAAAAjEbUCAAAAAABwNkUrAJBP1woAAAAAAMMQtQIAAAAAANA7RSsAhGYqBwAAAAAAbkStwHvWFwHO5K4LAAAADG/rZmsemAAANmsFAAAAAIAxfLY+AAAAAAAAACayqUiRswIAAAAAAADASOzUCgAAAAAAwEkUrQAwreMzu81aAQAAAABgAKJWmJTVPgAAAAAATqZoBQAAAAAAAIDJiVoBAALzIQUAAABAFIpWAOA4KyMAAAAAABCdqBUAAAAAAIC6FK0AAAAAAAAAwEXUCgAAAAAAQFX5RWtKSdEKAAMrMtHbrBUAAAAAAEITtQIAAAAAAFDLpqK16pEAAAAAAAAAAM2JWgEAAAAAAKhC0QoAAAAAAAAA3BO1AgAAAAAAUJ6iFQCoJP/XDAAAAAAAoDeiVgAAAAAAAApTtAIAAAAAAAAAj0StwBveTgRwPvdeAAAAILTMojWl5DEIAMzG7A8AAAAAAJMTtQIAAAAAAFBMftFa+0gAAAAAAAAAgN6IWgEAAAAAAChD0QoAAAAAAAAArPhsfQBAA5lvKgIghGVZvA0UAAAA6IGiFQAqeZxkzafWRwAAAAAAIChRKwAAAAAAAEcpWgGguJXp9fdfmVsBAAAAAIBYRK0AAAAAAAAcomgFgLIy59bfrzTJAgAAAAAAUYhaAQAAAAAA2C9W0fr0aDs5NgC4yS9a77/FdAYAAAAAAITw0foAAAAAAAAAiCpW0QoA/dtRtB78RgAAAAAAgDOJWgEAAAAAANgjJ55JKfVTtKp9AOjcwanKTAcAAAAAAPRP1AoAAAAAAMBmmUXrCUdyUIiDBAAAAAAAAIAxiFoBAAAAAADYZpiiFQD6UWSfVZu1AgAAAAAAnRO1AgAAAAAAsIGiFQAAAAAAAADYR9QKjM97p/Zx3XrgVdjHdQMAAACoR9EKAAAAAAAAAOwmagUAAAAAACCLohUAKsmZZM//UQAAAAAAAMWJWgEAAAAAAHgvetGq8AGAkfT8WwcAAAAAALBC1AoAAAAAAMAb0YvWVyIeMwAAAAAAAADE9dn6AAAAAAAAAIhNGgoAAAAAAAAA5LBTKwAAAAAAAGvWt2lVtAIA+XK2fwcAAAAAAAYmagUAAAAAAOClMYpW/QwA84gyOx8xwzkCAAAAAMCoRK0AAAAAAAA8N0bRCgAAAAAAAAB0QtQKAAAAAADAE4pWAAAAAAAAAKAsUSsAAAAAAAB/KVoB4Eyl5tYZ5ugZzhEAAAAAAAYmagUAAAAAAOA/BitaX51OuBMBgOjWf8cAAAAAAABmIGoFpuCdSVu5Yv3wWmzligEAAAAcNFjRCgDzME0DAAAAAAD9E7UCDMvnHBOX0QsAAADQiqIVAIKaZJqe5DQBAAAAAGBgolaAN8R1xGX0AgAAALDJygOllFLQhsRTMgCiODLVBp2mAQAAAACACYlaAQAAAAAAeFO0nnkkAMAmUWbq4580EeVMAQAAAACAFaJWmJGlPoCRuKsDAAAAx41atNqmFYCxxd1KHQAAAAAAmNZn6wMAAAAAAACgpZGK1vyKNdypATCDlFLOXBZxFrNNKwAAAAAAcCNqBWaRuQAMHTJ6AQAAAKhn/dGTB1MA0I+Zq86Zzx0AAAAAAAbz0foAAOiOJWHiGmb0ercoAAAAcA5PIQCgNyuLHXEn7rhHDgAAAAAAFCdqBd6wvggAAAAAMCSPfwGgT0N2rUcM86mmAAAAAADARdQKAAAAAAAwoTmTGAAYQLhJ/OABK1oBAAAAAGAwolYAAAAAAIC5hIthAGA2Ss4b1wEAAAAAAMYjagUmYskzh6vUJ69LDlcJAAAAIIei9eJREgARrMxWy7JEmdCjHCcAAAAAAHAaUSsAAAAAMex+I6x30ALAL9MiAASy/ikM/U/rB4/Qh1AAAAAAAMCQRK0AY+p/DRteMXoBAHjq4C+Kfs8EgIsJEQCG0/PkrmgFAAAAAACeErXCpCwBbtLzYjCsM3oBABhDkd9s/XoMwORMhQAQ0duF3SGneMvZAAAAAAAwMFErMBfLn+tcn555ddZNe32mPXEAYCoF35475Dt9ASCHSRAA4koprS8HLMvS21x/5HisfQAAAAAAwNg+Wx8AAAAAAPxV78249z/Z22QBmERvlQsAsENKaX1Ov/3b5n/qylkBAAAAAIB1olY46nq9Fvk5X19fRX4OAAAAtBUomylyqN5xCwDhmL4BCOpt13rZmLaWWuy+/LverWgFgKq8UQ0AYjF3AwC88tH6AIAAAr0dOYfV0Fdcmf55jV4Z7MoMdtcFAAAA2vKoAQBGkrkmsizL+b8DKFoBAAAAAIAcdmoFGJC3qRGX0QsAAABQkIctADCeW/+ZM8v/fk3tZPT7+3v398pZAQAAAABgNqJWgCzLslhPJSijFwAAAAAAYDAppfxPr6hXtx7JWS+KVgAAAAAAmJKoFea1aZlzMDOf+ysWjKMweh/NPHpnPncAAAAAAJhc/patv+6/+Mgqw8GW9eB/HQAAAAAACE3UCgAAAAAAAAAQ0o609ebxW35+fla+/njIeiNnBQAAAACAyYlaAQAAAAAAmIWQBoAh7U5b75XKVl8xCwMAAAAAABdRKzCtlNLWNd1lWUKss+5Yq+7tvO5Pobdj64HRey/EeQEA8ErOr3PX67XIf+vr62vT1x98HzAAAACc7/cP7a7+qrWaAwAAAAAA3BO1AtCLp4vrAldm09W7TAAA9hnjV/cxzgIAzp/RWn0gBQBwr5O61R/XAAAAAADAI1ErkCXKNo+b7Njuckg9vLKZL8Tty3o44OaM3huDAQAAAAAAYMX5davlGwAAAAAAYN1H6wMAWrKguFX/GWH/R/ho6zFHPMce9H/d+j/C3riHAwAAAAAAmdJ/lf3hPz8/lX4yAAAAAAAwHju1AlOz3WXzdeV913/IrYO3MnqNAQAAAAAAgH1u6yzX6/XPP//+/l75rp+fn4rHBAAAAAAAzEHUCkAzR5pMXSsAAAAAAACUJVsFAAAAAABq+2h9AACNbQ0je94bc+uxRY9Ce34tzmH0AgAAAAAAAAAAAAAAEIioFWaXH4b1nMMRkREFj/L/v5D1AgAAAAAAAAAAAAAA4YhaAQbZ7nLOjS77fC3OZPQCAAAAAAAAAAAAAAAQhagVYEaaQOIyegEAAAAAAAAAAAAAAIIStQJcLvG3u+zteN4qeMDhzr04oxcAAAAAAAAAAAAAAIAQRK2AbQ//EbcM3HokXvHxGL2zcR0AAAAAAAAAAAAAAICIRK3ABv2EcJXM0InNcI5zmuGVHf4ch7/HAgAAAAAAAAAAAAAAkxO1AuzXQ4G26RhGbQJ7eCHC6eGiGb0AAAAAAAAAAAAAAABTEbUC/MfWcK5tGagJ5J7RCwAAAAAAAAAAAAAAQM9ErcDlIhj7ryhloCaQR0bvDFwQAAAAAAAAAAAAAAAgKFErsE3brR1P038ZGL0J7PCQhmH0BjXJ3RUAAAAAAAAAAAAAAJjZMk8rAqzL76nmuW9sbcxOuzJjNIFlE75uT7MVozccN+Ezlbr/eC0AAAAAAAAAAAAAAGATO7UC/1DmPOpzx0tNIDmM3lG5LAAAAAAAAAAAAAAAQFyiVoA1KaVNCdmyLPXiwK0/vPP4rezhndNkxmL0AgAAAAAAAAAAAAAA0BVRK7DZhPXgjk0vi1+lrT9QE8iN0RvChPdVAAAAAAAAAAAAAABgQqJWgCw7KrtSldqOyDBKExjlOKMzeofh4gAAAAAAAAAAAAAAAKF9tj4AoCMpJbsFrrjlZJsu0e8Xn1YVhmvejLpzGL0AAAAAAAAAAAAAAAA0J2oF2GZHHPj49U/jvYNtZ9wgcEfXKoXdx+jtk8EMAAAAAAAAAAAAAABMYpk8IwEeZeZV7h79dGjDvBY5l/T3ZB+/eJjrcAKjtyvuuucr9b+AFwUAAAAAAAAAAAAAADaxUyvATvs2vaxxDMNYv6SDnWxbRi8AAAAAAAAAAAAAAADnE7UCHLKyceg5/93xDHxqvTF6e9DPrrkAAAAAAAAAAAAAAAC1iVqBv1JKOZHVsizKtHvn9IGuOTUYvf1z9QAAAAAAAAAAAAAAgAGIWgEKu2/PiiSCYjZOY/QCAAAAAAAAAAAAAABQj6gVoKKnRd9KK6gApB9G7wmqbo0LAAAAAAAAAAAAAADQG1Er8ERKKSe1WpZFxraDi0ZcRm8TLjsAAAAAAAAAAAAAADCGj9YHAAAAAAAAAAAAAAAAAADA+EStwHOZGwPmbOgKxT0OPFtZEk7m/dPYBgAAAAAAAAAAAAAAhiFqBQAAAAAAAAAAAAAAAACgOlEr8JLtAQHach8GAAAAAAAAAAAAAABGImoFjlqWpfUhAATjzgkAAAAAAAAAAAAAAExI1AqssUkgQCvuwAAAAAAAAAAAAAAAwGBErUABthzkTMYb0RnDAAAAAAAAAAAAAADAnEStAIRnQ0vGY1QDAAAAAAAAAAAAAADjEbUCbwirAAqyTSsAAAAAAAAAAAAAADAtUSvwXk7XKtMCKMWnCQAAAAAAAAAAAAAAAEMStQIQiXwaAAAAAAAAAAAAAAAAghK1Alls1kq37GlJIDn3SUMaAAAAAAAAAAAAAAAYlagVAAAAAAAAAAAAAAAAAIDqRK1ALpu10pwBRmi2aQUAAAAAAAAAAAAAACYnagUgMAUgAAAAAAAAAAAAAAAARCFqBTawWSvAPrZpBQAAAAAAAAAAAAAAELUCEINeGgAAAAAAAAAAAAAAAEITtQLb2KyVftjWkihs0woAAAAAAAAAAAAAAHARtQI7KK84n1KasbmvAgAAAAAAAAAAAAAAMxC1AlVIEKlNBEgU7ocAAAAAAAAAAAAAAAA3olZgDz0hZ9IEElfO6HVHBQAAAAAAAAAAAAAAJiFqBWoRIgK8pWgFAAAAAAAAAAAAAADmIWoFdsoJsXStHPd0FOkACcE9EAAAAAAAAAAAAAAA4J6oFdhPWEhtmkDG5i4KAAAAAAAAAAAAAABMRdQK1CVKpDgdICG8vfsZyQAAAAAAAAAAAAAAwGxErcAhOVGWrpV9no4cHSAhuO8BAAAAAAAAAAAAAAA8ErUCR4kMqUETyNjcOQEAAAAAAAAAAAAAgAmJWoEC3tZZAkU2eTVgdICE8PaOZyQDAAAAAAAAAAAAAABzErUCJ9G1AjNQtAIAAAAAAAAAAAAAALwiagXKkGlRim1aAQAAAAAAAAAAAAAAYEiiVqCYt82hzVp5S9FKaLZpBQAAAAAAAAAAAAAAWCFqBUrStXKEopXQFK0AAAAAAAAAAAAAAADrRK3A2XStPGVgMDZFKwAAAAAAAAAAAAAAgKgVKEy4xQ4rRasRRQiqbAAAAAAAAAAAAAAAgLdErUB5bytE9Rf3FK1E9/aeZiQDAAAAAAAAAAAAAABcRK1AJbpWMilaiU7RCgAAAAAAAAAAAAAAkEnUCtSia+UIHSAhKFoBAAAAAAAAAAAAAADyiVqBinSt7KMDZAxGMgAAAAAAAAAAAAAAwD1RK1CXpot1jyPEmCGK9SzfSAYAAAAAAAAAAAAAAPhD1ApUt1522ayV+xGiAyQKRSsAAAAAAAAAAAAAAMBWi+gCOIcADBiGG1p0pT5PwWsNAAAAAAAAAAAAAACb2KkVOIn9WoExKFoBAAAAAAAAAAAAAAD2EbUC59G1AtEpWgEAAAAAAAAAAAAAAHb7bH0AEN71ei3yc76+vor8nM6llFaSsGVZJGFAtxStAABN+LsbAGIxdwNAIKUm7ou5GwBO4Y9uAIjF3A0A8IqdWoGz2a8ViEjRCgAAAAAAAAAAAAAAcJCoFWhA1wrEomgFAAAAAAAAAAAAAAA4TtQKtKFrBaJQtAIAAAAAAAAAAAAAABQhagWa0bUC/VO0AgAAAAAAAAAAAAAAlCJqBVrStQI9U7QCAAAAAAAAAAAAAAAUJGoFGksprYRhulaglZX7z/qNCwAAAAAAAAAAAAAAgKdErUAX5GFAFO5XAAAAAAAAAAAAAAAA+4hagV487cTEY0ArbkoAAAAAAAAAAAAAAABliVqBjqSU7oMx8RjQ1p87kpsSAAAAAAAAAAAAAADAEZ+tDwDgL9kY0A93JAAAAAAAAAAAAAAAgFLs1AoAAAAAAAAAAAAAAAAAQHWiVgAAAAAAAAAAAAAAAAAAqhO1AgAAAAAAAAAAAAAAAABQnagVAAAAAAAAAAAAAAAAAIDqRK0AAAAAAAAAAAAAAAAAAFQnagUAAAAAAAAAAAAAAAAAoDpRKwAAAAAAAAAAAAAAAAAA1YlaAQAAAAAAAAAAAAAAAACoTtQKAAAAAAAAAAAAAAAAAEB1olYAAAAAAAAAAAAAAAAAAKoTtQIAAAAAAAAAAAAAAAAAUJ2oFQAAAAAAAAAAAAAAAACA6kStAAAAAAAAAAAAAAAAAABUt6SUWh8DAACcZ1mWIj/HL9IAAAAAAAAAAAAAALCJnVoBAAAAAAAAAAAAAAAAAKhO1AoAAAAAAAAAAAAAAAAAQHWiVgAAAAAAAAAAAAAAAAAAqhO1AgAAAAAAAAAAAAAAAABQnagVAAAAAAAAAAAAAAAAAIDqRK0AAAAAAAAAAAAAAAAAAFQnagUAAAAAAAAAAAAAAAAAoDpRKwAAAAAAAAAAAAAAAAAA1YlaAQAAAAAAAAAAAAAAAACoTtQKAAAAAAAAAAAAAAAAAEB1olYAAAAAAAAAAAAAAAAAAKoTtQIAAAAAAAAAAAAAAAAAUJ2oFQAAAAAAAAAAAAAAAACA6kStAAAAAAAAAAAAAAAAAABUJ2oFAAAAAAAAAAAAAAAAAKA6USsAAAAAAAAAAAAAAAAAANWJWgEAAAAAAAAAAAAAAAAAqE7UCgAAAAAAAAAAAAAAAABAdaJWAAAAAAAAAAAAAAAAAACqE7UCAAAAAAAAAAAAAAAAAFCdqBUAAAAAAAAAAAAAAAAAgOpErQAAAAAAAAAAAAAAAAAAVCdqBQAAAAAAAAAAAAAAAACgOlErAAAAAAAAAAAAAAAAAADViVoBAAAAAAAAAAAAAAAAAKhO1AoAAAAAAAAAAAAAAAAAQHWiVgAAAAAAAAAAAAAAAAAAqhO1AgAAAAAAAAAAAAAAAABQnagVAAAAAAAAAAAAAAAAAIDqRK0AAAAAAAAAAAAAAAAAAFQnagUAAAAAAAAAAAAAAAAAoDpRKwAAAAAAAAAAAAAAAAAA1YlaAQAAAAAAAAAAAAAAAACoTtQKAAAAAAAAAAAAAAAAAEB1olYAAAAAAAAAAAAAAAAAAKoTtQIAAAAAAAAAAAAAAAAAUJ2oFQAAAAAAAAAAAAAAAACA6kStAAAAAAAAAAAAAAAAAABUJ2oFAAAAAAAAAAAAAAAAAKA6USsAAAAAAAAAAAAAAAAAANWJWgEAAAAAAAAAAAAAAAAAqE7UCgAAAAAAAAAAAAAAAABAdaJWAAAAAAAAAAAAAAAAAACqE7UCAAAAAAAAAAAAAAAAAFCdqBUAAAAAAAAAAAAAAAAAgOpErQAAAAAAAAAAAAAAAAAAVCdqBQAAAAAAAAAAAAAAAACgOlErAAAAAAAAAAAAAAAAAADViVoBAAAAAAAAAAAAAAAAAKhO1AoAAAAAAAAAAAAAAAAAQHWiVgAAAAAAAAAAAAAAAAAAqhO1AgAAAAAAAAAAAAAAAABQnagVAAAAAAAAAAAAAAAAAIDqRK0AAAAAAAAAAAAAAAAAAFQnagUAAAAAAAAAAAAAAAAAoDpRKwAAAAAAAAAAAAAAAAAA1X22PgAI73q9Fvk5X19fRX4OAAAAjMTf3QAQi7kbAAIpNXFfzN0AcAp/dANALOZuAIBX7NQKAAAAAAAAAAAAAAAAAEB1olYAAAAAAAAAAAAAAAAAAKoTtQIAAAAAAAAA/I+9O8ZtFYgCKGokb4r9F7MsfvNbK4nNBcacU1ujeUrkZ4orAAAAAAAAyIlaAQAAAAAAAAAAAAAAAADIiVoBAAAAAAAAAAAAAAAAAMiJWgEAAAAAAAAAAAAAAAAAyIlaAQAAAAAAAAAAAAAAAADIiVoBAAAAAAAAAAAAAAAAAMiJWgEAAAAAAAAAAAAAAAAAyIlaAQAAAAAAAAAAAAAAAADIiVoBAAAAAAAAAAAAAAAAAMiJWgEAAAAAAAAAAAAAAAAAyIlaAQAAAAAAAAAAAAAAAADIiVoBAAAAAAAAAAAAAAAAAMiJWgEAAAAAAAAAAAAAAAAAyIlaAQAAAAAAAAAAAAAAAADIiVoBAAAAAAAAAAAAAAAAAMiJWgEAAAAAAAAAAAAAAAAAyIlaAQAAAAAAAAAAAAAAAADIiVoBAAAAAAAAAAAAAAAAAMiJWgEAAAAAAAAAAAAAAAAAyIlaAQAAAAAAAAAAAAAAAADIiVoBAAAAAAAAAAAAAAAAAMiJWgEAAAAAAAAAAAAAAAAAyC3btp19BwAAOM6yLLuc44c0AAAAAAAAAAAAAAD8iTe1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJB7nn0BmN4YY5dz1nXd5RwAAAD4Jp67AWAudjcATGSvxf2wuwHgEB66AWAudjcAwCve1AoAAAAAAAAAAAAAAAAAQE7UCgAAAAAAAAAAAAAAAABATtQKAAAAAAAAAAAAAAAAAEBO1AoAAAAAAAAAAAAAAAAAQE7UCgAAAAAAAAAAAAAAAABATtQKAAAAAAAAAAAAAAAAAEBO1AoAAAAAAAAAAAAAAAAAQE7UCgAAAAAAAAAAAAAAAABATtQKAAAAAAAAAAAAAAAAAEBO1AoAAAAAAAAAAAAAAAAAQE7UCgAAAAAAAAAAAAAAAABATtQKAAAAAAAAAAAAAAAAAEBO1AoAAAAAAAAAAAAAAAAAQE7UCgAAAAAAAAAAAAAAAABATtQKAAAAAAAAAAAAAAAAAEBO1AoAAAAAAAAAAAAAAAAAQE7UCgAAAAAAAAAAAAAAAABATtQKAAAAAAAAAAAAAAAAAEBO1AoAAAAAAAAAAAAAAAAAQE7UCgAAAAAAAAAAAAAAAABATtQKAAAAAAAAAAAAAAAAAEBO1AoAAAAAAAAAAAAAAAAAQE7UCgAAAAAAAAAAAAAAAABAbtm27ew7AADAcZZl2eUcP6QBAAAAAAAAAAAAAOBPvKkVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAAAAAgJyoFQAAAAAAAAAAAAAAAACAnKgVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAAAAAgJyoFQAAAAAAAAAAAAAAAACAnKgVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAAAAAgJyoFQAAAAAAAAAAAAAAAACAnKgVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAJ98IBgAAH+JJREFUAAAAgJyoFQAAAAAAAAAAAAAAAACAnKgVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAAAAAgJyoFQAAAAAAAAAAAAAAAACAnKgVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAAAAAgJyoFQAAAAAAAAAAAAAAAACAnKgVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAAAAAgJyoFQAAAAAAAAAAAAAAAACAnKgVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAAAAAgJyoFQAAAAAAAAAAAAAAAACAnKgVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAAAAAgJyoFQAAAAAAAAAAAAAAAACAnKgVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAAAAAgJyoFQAAAAAAAAAAAAAAAACAnKgVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAAAAAgJyoFQAAAAAAAAAAAAAAAACAnKgVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAAAAAgJyoFQAAAAAAAAAAAAAAAACAnKgVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAAAAAgJyoFQAAAAAAAAAAAAAAAACAnKgVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAAAAAgJyoFQAAAAAAAAAAAAAAAACAnKgVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAAAAAgJyoFQAAAAAAAAAAAAAAAACAnKgVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAAAAAgJyoFQAAAAAAAAAAAAAAAACAnKgVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAAAAAgJyoFQAAAAAAAAAAAAAAAACAnKgVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAAAAAgJyoFQAAAAAAAAAAAAAAAACAnKgVAAAAAAAAAAAAAAAAAICcqBUAAAAAAAAAAAAAAAAAgNzz7AvA9MYYu5yzrusu5wAAAMA38dwNAHOxuwFgInst7ofdDQCH8NANAHOxuwEAXvGmVgAAAAAAAAAAAAAAAAAAcqJWAAAAAAAAAAAAAAAAAAByolYAAAAAAAAAAAAAAAAAAHKiVgAAAAAAAAAAAAAAAAAAcqJWAAAAAAAAAAAAAAAAAAByolYAAAAAAAAAAAAAAAAAAHKiVgAAAAAAAAAAAAAAAAAAcqJWAAAAAAAAAAAAAAAAAAByolYAAAAAAAAAAAAAAAAAAHKiVgAAAAAAAAAAAAAAAAAAcqJWAAAAAAAAAAAAAAAAAAByolYAAAAAAAAAAAAAAAAAAHKiVgAAAAAAAAAAAAAAAAAAcqJWAAAAAAAAAAAAAAAAAAByolYAAAAAAAAAAAAAAAAAAHKiVgAAAAAAAAAAAAAAAAAAcqJWAAAAAAAAAAAAAAAAAAByolYAAAAAAAAAAAAAAAAAAHKiVgAAAAAAAAAAAAAAAAAAcqJWAAAAAAAAAAAAAAAAAAByolYAAAAAAAAAAAAAAAAAAHKiVgAAAAAAAAAAAAAAAAAAcqJWAAAAAAAAAAAAAAAAAAByy7ZtZ98BAACOsyzLLuf4IQ0AAAAAAAAAAAAAAH/iTa0AAAAAAAAAAAAAAAAAAORErQAAAAAAAAAAAAAAAAAA5EStAAAAAAAAAAAAAAAAAADkRK0AAAAAAAAAAAAAAAAAAORErQAAAAAAAAAAAAAAAAAA5EStAAAAAAAAAAAAAAAAAADkRK0AAAAAAAAAAAAAAAAAAORErQAAAAAAAAAAAAAAAAAA5EStAAAAAAAAAAAAAAAAAADkRK0AAAAAAAAAAAAAAAAAAORErQAAAAAAAAAAAAAAAAAA5EStAAAAAAAAAAAAAAAAAADkRK0AAAAAAAAAAAAAAAAAAORErQAAAAAAAAAAAAAAAAAA5EStAAAAAAAAAAAAAAAAAADkRK0AAAAAAAAAAAAAAAAAAORErQAAAAAAAAAAAAAAAAAA5EStAAAAAAAAAAAAAAAAAADkRK0AAAAAAAAAAAAAAAAAAORErQAAAAAAAAAAAAAAAAAA5EStAAAAAAAAAAAAAAAAAADkRK0AAAAAAAAAAAAAAAAAAORErQAAAAAAAAAAAAAAAAAA5EStAAAAAAAAAAAAAAAAAADkRK0AAAAAAAAAAAAAAAAAAORErQAAAAAAAAAAAAAAAAAA5EStAAAAAAAAAAAAAAAAAADkRK0AAAAAAAAAAAAAAAAAAORErQAAAAAAAAAAAAAAAAAA5EStAAAAAAAAAAAAAAAAAADkRK0AAAAAAAAAAAAAAAAAAORErQAAAAAAAAAAAAAAAAAA5EStAAAAAAAAAAAAAAAAAADkRK0AAAAAAAAAAAAAAAAAAORErQAAAAAAAAAAAAAAAAAA5EStAAAAAAAAAAAAAAAAAADkRK0AAAAAAAAAAAAAAAAAAORErQAAAAAAAAAAAAAAAAAA5EStAAAAAAAAAAAAAAAAAADkRK0AAAAAAAAAAAAAAAAAAORErQAAAAAAAAAAAAAAAAAA5EStAAAAAAAAAAAAAAAAAADkRK0AAAAAAAAAAAAAAAAAAORErQAAAAAAAAAAAAAAAAAA5EStAAAAAAAAAAAAAAAAAADkRK0AAAAAAAAAAAAAAAAAAORErQAAAAAAAAAAAAAAAAAA5EStAAAAAAAAAAAAAAAAAADkRK0AAAAAAAAAAAAAAAAAAORErQAAAAAAAAAAAAAAAAAA5EStAAAAAAAAAAAAAAAAAADkRK0AAAAAAAAAAAAAAAAAAORErQAAAAAAAAAAAAAAAAAA5EStAAAAAAAAAAAAAAAAAADkRK0AAAAAAAAAAAAAAAAAAORErQAAAAAAAAAAAAAAAAAA5J5nXwCmN8bY5Zx1XXc5BwAAAL6J524AmIvdDQAT2WtxP+xuADiEh24AmIvdDQDwije1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJB7nn0B4L8xxo+fWdf1gJvs4jfj/IaRr8zIn7jh1Ea+shuOvIsxxkQj3/Cv7Ev7E0a+MiO/7YYjP+aZeseRJ+J/+21GvjIjf+KGUxv5ym64nf2V33bDkR+3nNrIV2bktxn5ynxpf8LIV2bktxn5ynxpf8LIV2bktxn5ynxpf8LIV3bDkQEAZudNrQAAAAAAAAAAAAAAAAAA5EStAAAAAAAAAAAAAAAAAADkRK0AAAAAAAAAAAAAAAAAAORErQAAAAAAAAAAAAAAAAAA5EStAAAAAAAAAAAAAAAAAADkRK0AAAAAAAAAAAAAAAAAAORErQAAAAAAAAAAAAAAAAAA5EStAAAAAAAAAAAAAAAAAADkRK0AAAAAAAAAAAAAAAAAAORErQAAAAAAAAAAAAAAAAAA5EStAAAAAAAAAAAAAAAAAADknmdfAKa3ruuPnxljHHATAOA3tm2zmgHgW/3mIR0AOIzVDAAXZ1kDwFzsbgCYi90NAPCKN7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAAAAAAAAAAAAAAAkBO1AgAAAAAAAAAAAAAAAACQE7UCAAAAAAAAAAAAAAAAAJATtQIAAAD/2LsDEgBgAIZh3L/oXUU5nETBDJQBAAAAAAAAAAAAQO5se70BAAAAAAAAAAAAAAAAAIDPeWoFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAAAgJ2oFAAAAAAAAAAAAAAAAACAnagUAAAAAAAAAAAAAAAAAICdqBQAAAAAAAAAAAAAAAG77diwAAAAAMMjfehT7yiMA2EmtAAAAAAAAAAAAAAAAAADspFYAAAAAAAAAAAAAAAAAAHZSKwAAAAAAAAAAAAAAAAAAO6kVAAAAAAAAAAAAAAAAAICd1AoAAAAAAAAAAAAAAAAAwE5qBQAAAAAAAAAAAAAAAABgJ7UCAAAAAAAAAAAAAAAAALCTWgEAAAAAAAAAAAAAAAAA2EmtAAAAAAAAAAAAAAAAAADspFYAAAAAAAAAAAAAAAAAAHZSKwAAAAAAAAAAAAAAAAAAO6kVAAAAAAAAAAAAAAAAAICd1AoAAAAAAAAAAAAAAAAAwE5qBQAAAAAAAAAAAAAAAABgJ7UCAAAAAAAAAAAAAAAAALCTWgEAAAAAAAAAAAAAAAAA2EmtAAAAAAAAAAAAAAAAAADspFYAAAAAAAAAAAAAAAAAAHZSKwAAAAAAAAAAAAAAAAAAO6kVAAAAAAAAAAAAAAAAAICd1AoAAAAAAAAAAAAAAAAAwE5qBQAAAAAAAAAAAAAAAABgJ7UCAAAAAAAAAAAAAAAAALCTWgEAAAAAAAAAAAAAAAAA2EmtAAAAAAAAAAAAAAAAAADsAmIeM7La1XzAAAAAAElFTkSuQmCC" } }, "cell_type": "markdown", "metadata": { "code_folding": [], "hidden_ranges": [], "originalKey": "1842c5bf-4113-406b-b2c7-bc2535e9dd6c", "showInput": false }, "source": [ "### Pareto Optimality\n", "\n", "In a multi-objective optimization problem, there typically is no single best solution. Rather, the *goal* is to identify the set of Pareto optimal solutions such that any improvement in one objective means deteriorating another. Provided with the Pareto set, decision-makers can select an objective trade-off according to their preferences. In the plot below, the red dots are the Pareto optimal solutions (assuming both objectives are to be minimized).\n", "![pareto front](attachment:pareto_front%20%281%29.png)" ] }, { "attachments": { "hv_figure%20%281%29.png": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAEo0AABAwCAIAAAAyLI+dAAAABGdBTUEAALGPC/xhBQAADGVpQ0NQSUNDIFByb2ZpbGUAAEiJlZcHXJNHG8DvHZkkrEAEZIS9RJEZQEYIK4KATEFUQhJIGDEmBBUXoqUK1i2iOCpaFVCwDkDqQMQ6i+K2juJApVKLVVyofBcS0Npv/L7jd+/93+eee+55Hu7y3gGg08mXyXJRXQDypPnyuPBg1qSUVBbpESDAP20AAIUvUMg4sbFRkMFQ+/fy+jpAVO0VF5Wtf/b/16IvFCkEACBpkDOECkEe5BYA8GKBTJ4PADEEyq1n5stULIZsIIcOQp6r4iw1r1Rxhpp3DOokxHEhNwFApvH58iwAtNugnFUgyIJ2tB9BdpUKJVIAdAwgBwjEfCHkBMij8vKmq7gIsgPUl0HeDZmd8YXNrL/Zzxi2z+dnDbM6rsFCDpEoZLn82f9nav53yctVDs1hBytNLI+IU8UPc3gzZ3qkimmQe6QZ0TGqXEN+KxGq8w4AShUrIxLV+qipQMGF+QNMyK5CfkgkZFPIYdLc6CiNPCNTEsaDDFcLOkuSz0vQjF0iUoTGa2xukk+PixniTDmXoxlbx5cPzqvSb1PmJHI09m+KRbwh+68KxQnJkKkAYNQCSVI0ZLgGMQNFTnykWgezKhRzo4d05Mo4lf82kNkiaXiw2j6WlikPi9Poy/IUQ/FiJWIJL1rDFfnihAh1frAaAX/QfyPIDSIpJ3HIjkgxKWooFqEoJFQdO9YukiZq4sXuyfKD4zRje2W5sRp9nCzKDVfJrSCbKAriNWPxcflwcart41Gy/NgEtZ94ejZ/fKzaH7wARAEuCAEsoIQ1A0wH2UDS3tPYA9/UPWGAD+QgC4iAi0YyNCJ5sEcKn/GgEPwBSQQUw+OCB3tFoADKPw5L1U8XkDnYWzA4Igc8hpwHIkEufFcOjpIOz5YEHkGJ5B+zC6CvubCq+v4p40BJlEaiHLLL0hnSJIYSQ4gRxDCiI26CB+B+eBR8BsHqhrNxnyFvP+sTHhM6CA8I1widhFvTJMXyr3yZADqh/TBNxBlfRozbQZueeDDuD61DyzgTNwEuuAech4MHwpk9oZSr8VsVO+vfxDkcwRc51+hRXCkoZQQliOLw9UhtJ23PYSuqjH6ZH7WvGcNZ5Q73fD0/94s8C2Eb+bUmtgQ7gJ3GTmBnsSNYI2Bhx7Em7AJ2VMXDa+jR4Boami1u0J8caEfyj/n4mjlVmVS41rp2u37Q9IF80ax81QbjTpfNlkuyxPksDvwKiFg8qWD0KJabq5srAKpvivpn6iVz8FuBMM99lhXfBcA/ZWBg4MhnWRTcpwefwm3e81lmXwsA/RgAZ74RKOUFahmuehDgr4EO3FHGwBxYAwcYkRvwAn4gCISC8SAGJIAUMBXmWQzXsxzMBHPBQlACysBKsA5sBFvBdrAb7AX7QSM4Ak6An8F5cAlcA7fh+ukCz0AveA36EQQhIXSEgRgjFogt4oy4IWwkAAlFopA4JAVJR7IQKaJE5iKLkDJkNbIR2YZUIz8ih5ETyFmkA7mF3Ee6kb+Q9yiG0lAD1Ay1Q8egbJSDRqIJ6BQ0C52BFqKL0eVoBVqF7kEb0BPoefQa2ok+Q/swgGlhTMwSc8HYGBeLwVKxTEyOzcdKsXKsCqvDmuF/+grWifVg73AizsBZuAtcwxF4Ii7AZ+Dz8WX4Rnw33oC34Vfw+3gv/olAJ5gSnAm+BB5hEiGLMJNQQign7CQcIpyCu6mL8JpIJDKJ9kRvuBtTiNnEOcRlxM3EemILsYP4kNhHIpGMSc4kf1IMiU/KJ5WQNpD2kI6TLpO6SG/JWmQLshs5jJxKlpKLyeXkGvIx8mXyE3I/RZdiS/GlxFCElNmUFZQdlGbKRUoXpZ+qR7Wn+lMTqNnUhdQKah31FPUO9aWWlpaVlo/WRC2JVpFWhdY+rTNa97Xe0fRpTjQuLY2mpC2n7aK10G7RXtLpdDt6ED2Vnk9fTq+mn6Tfo7/VZmiP1uZpC7UXaFdqN2hf1n6uQ9Gx1eHoTNUp1CnXOaBzUadHl6Jrp8vV5evO163UPax7Q7dPj6E3Vi9GL09vmV6N3lm9p/okfTv9UH2h/mL97fon9R8yMIY1g8sQMBYxdjBOMboMiAb2BjyDbIMyg70G7Qa9hvqGHoZJhrMMKw2PGnYyMaYdk8fMZa5g7mdeZ74fYTaCM0I0YumIuhGXR7wxGmkUZCQyKjWqN7pm9N6YZRxqnGO8yrjR+K4JbuJkMtFkpskWk1MmPSMNRvqNFIwsHbl/5K+mqKmTaZzpHNPtphdM+8zMzcLNZGYbzE6a9ZgzzYPMs83Xmh8z77ZgWARYSCzWWhy3+J1lyOKwclkVrDZWr6WpZYSl0nKbZbtlv5W9VaJVsVW91V1rqjXbOtN6rXWrda+Nhc0Em7k2tTa/2lJs2bZi2/W2p23f2NnbJdt9a9do99TeyJ5nX2hfa3/Hge4Q6DDDocrhqiPRke2Y47jZ8ZIT6uTpJHaqdLrojDp7OUucNzt3jCKM8hklHVU16oYLzYXjUuBS63J/NHN01Oji0Y2jn4+xGZM6ZtWY02M+uXq65rrucL09Vn/s+LHFY5vH/uXm5CZwq3S76k53D3Nf4N7k/sLD2UPkscXjpifDc4Lnt56tnh+9vL3kXnVe3d423unem7xvsA3Ysexl7DM+BJ9gnwU+R3ze+Xr55vvu9/3Tz8Uvx6/G7+k4+3GicTvGPfS38uf7b/PvDGAFpAd8H9AZaBnID6wKfBBkHSQM2hn0hOPIyebs4TwPdg2WBx8KfsP15c7jtoRgIeEhpSHtofqhiaEbQ++FWYVlhdWG9YZ7hs8Jb4kgRERGrIq4wTPjCXjVvN7x3uPnjW+LpEXGR26MfBDlFCWPap6AThg/Yc2EO9G20dLoxhgQw4tZE3M31j52RuxPE4kTYydWTnwcNzZubtzpeEb8tPia+NcJwQkrEm4nOiQqE1uTdJLSkqqT3iSHJK9O7pw0ZtK8SedTTFIkKU2ppNSk1J2pfZNDJ6+b3JXmmVaSdn2K/ZRZU85ONZmaO/XoNJ1p/GkH0gnpyek16R/4Mfwqfl8GL2NTRq+AK1gveCYMEq4Vdov8RatFTzL9M1dnPs3yz1qT1S0OFJeLeyRcyUbJi+yI7K3Zb3JicnblDOQm59bnkfPS8w5L9aU50rbp5tNnTe+QOctKZJ0zfGesm9Erj5TvVCCKKYqmfAN4eL+gdFB+o7xfEFBQWfB2ZtLMA7P0ZklnXZjtNHvp7CeFYYU/zMHnCOa0zrWcu3Du/XmcedvmI/Mz5rcusF6weEFXUXjR7oXUhTkLfyl2LV5d/GpR8qLmxWaLixY//Cb8m9oS7RJ5yY1v/b7dugRfIlnSvtR96Yaln0qFpefKXMvKyz4sEyw7993Y7yq+G1ieubx9hdeKLSuJK6Urr68KXLV7td7qwtUP10xY07CWtbZ07at109adLfco37qeul65vrMiqqJpg82GlRs+bBRvvFYZXFm/yXTT0k1vNgs3X94StKVuq9nWsq3vv5d8f3Nb+LaGKruq8u3E7QXbH+9I2nH6B/YP1TtNdpbt/LhLuqtzd9zutmrv6uoa05oVtWitsrZ7T9qeS3tD9jbVudRtq2fWl+0D+5T7fv8x/cfr+yP3tx5gH6g7aHtw0yHGodIGpGF2Q2+juLGzKaWp4/D4w63Nfs2Hfhr9064jlkcqjxoeXXGMemzxsYHjhcf7WmQtPSeyTjxsndZ6++Skk1fbJra1n4o8debnsJ9PnuacPn7G/8yRs75nD59jn2s873W+4YLnhUO/eP5yqN2rveGi98WmSz6XmjvGdRy7HHj5xJWQKz9f5V09fy36Wsf1xOs3b6Td6LwpvPn0Vu6tF78W/Np/u+gO4U7pXd275fdM71X95vhbfadX59H7IfcvPIh/cPuh4OGzR4pHH7oWP6Y/Ln9i8aT6qdvTI91h3Zd+n/x71zPZs/6ekj/0/tj03OH5wT+D/rzQO6m364X8xcBfy14av9z1yuNVa19s373Xea/735S+NX67+x373en3ye+f9M/8QPpQ8dHxY/OnyE93BvIGBmR8OX/wKIDBimZmAvDXLnhOSAGAcQmeHyar73yDBVHfUwcJ/CdW3wsHixcAdbBRHde5LQDsg9WuCNqG76qjekIQQN3dh6umKDLd3dS2aPDGQ3g7MPDSDABSMwAf5QMD/ZsHBj7COyp2C4CWGeq7pqoQ4d3g+yAVXTMSFoGvivoe+kWMX7dA5YEH+Lr9F7miiVD3w44kAAAAIGNIUk0AAHomAACAhAAA+gAAAIDoAAB1MAAA6mAAADqYAAAXcJy6UTwAAAAJcEhZcwABM5UAATOVAWBIbPYAACAASURBVHic7N1BbttAEADBrOD/f3lzSGAkduyQLUpL2lVXCas5zkGNGXPOHwAAAAAAAAAAAAAAAABAcls9AAAAAAAAAAAAAAAAAABcmE4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAALqX1QMAAMCRxhgffTTnfOYkAAAAAAAAAAAAAMA3MfxZGQCAS/skzPuENRgAAAAAAAAAAAAAOIpODwCAq2qF3hv2YQAAAAAAAAAAAADgTjo9AACu55BC70+2YgAAAAAAAAAAAAAg0+kBAHAlhxd6ryzGAAAAAAAAAAAAAEBzWz0AAABs9bhI79GPAwAAAAAAAAAAAABfmHt6AABcwK6I7qMVd+MjNmQAAAAAAAAAAAAAYJeX1QMAAMB/bI/0Pk/sXj91Og8AAAAAAAAAAAAAOJB7egAAnNpRkd6uly3JAAAAAAAAAAAAAMB2Oj0AAM5rY6S3a6d9xJsAAAAAAAAAAAAAwHf2snoAAAC4y/agbvtpPgAAAAAAAAAAAACA7W6rBwAAgH87NqsT6QEAAAAAAAAAAAAAD6LTAwDgjDZmdRuP6YVIT9cHAAAAAAAAAAAAAGyk0wMA4HSWR3oAAAAAAAAAAAAAANvp9AAAAAAAAAAAAAAAAACg0+kBAHAux56/c0wPAAAAAAAAAAAAAHg0nR4AAAAAAAAAAAAAAAAAdDo9AABOZPv5uznnga8BAAAAAAAAAAAAAGQ6PQAAzuJUWd2WDhAAAAAAAAAAAAAA4IdODwAAAAAAAAAAAAAAAADuodMDAAAAAAAAAAAAAAAAgE6nBwDAKYwxtn95zvm4SZ7wPgAAAAAAAAAAAADwlej0AAD4suR2AAAAAAAAAAAAAMAT6PQAAFhv1zG9R1P3AQAAAAAAAAAAAAC76PQAAPjK9kZ3Ij0AAAAAAAAAAAAAYC+dHgAA/CbSAwAAAAAAAAAAAACC4b/IAACsNcbY9f22wX7+K7ZiAAAAAAAAAAAAACB7WT0AAAA8w68S732tp9ADAAAAAAAAAAAAAO6k0wMA4BtR5QEAAAAAAAAAAAAAh7utHgAAgG/t/YE7AAAAAAAAAAAAAIBr0ekBAAAAAAAAAAAAAAAAQKfTAwDgSuacq0cAAAAAAAAAAAAAAPiLTg8AAAAAAAAAAAAAAAAAOp0eAADLjDFWjwAAAAAAAAAAAAAAcC+dHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAuIw55+oRAAAAAAAAAAAAAADe0ukBAAAAAAAAAAAAAAAAQKfTAwBgjTHG6hEAAAAAAAAAAPjJ3h3sNqsDARj9HeX9X9l30auqahMCg43H5pxl1cJAF40QXwcAAGhApwcAAAAAAAAAAAAAAAAAcTo9AAAAAAAAAAAAAAAAAIjT6QEAMIda6+gRAAAAAAAAAAAAAABe0OkBAAAAAAAAAAAAAAAAQJxODwCAAUopo0cAAAAAAAAAAAAAAGhDpwcAAAAAAAAAAAAAAAAAcTo9AAAAAAAAAAAAAAAAAIjT6QEAMIFa6+gRAAAAAAAAAAAAAABe0+kBAAAAAAAAAAAAAAAAQJxODwCAq5VSRo8AAAAAAAAAAAAAANCMTg8AAAAAAAAAAAAAAAAA4nR6AAAAAAAAAAAAAAAAABCn0wMAAAAAAAAAAAAAAACAOJ0eAADZ1VpHjwAAAAAAAAAAAAAA8JZODwAAAAAAAAAAAAAAAADidHoAAFyqlDJ6BAAAAAAAAAAAAACAlnR6AAAAAAAAAAAAAAAAABCn0wMAAAAAAAAAAAAAAACAOJ0eAAAAAAAAAAAAAAAAAMSVWuvoGQAAmEYpZfQIcT76AgAAAAAAAAAAAAA92KcHAMBeU0d6/+afHwAAAAAAAAAAAADISacHAMAuIjcAAAAAAAAAAAAAgJd0egAA3IjaEAAAAAAAAAAAAABoTqcHAMCN1FpHjwAAAAAAAAAAAAAArEanBwAAAAAAAAAAAAAAAABxxUYRAAD2K6WMHiHOR18AAAAAAAAAAAAAoAedHgAAFwk0fj6sAgAAAAAAAAAAAAD5PUYPAAAAAAAAAAAAAAAAAAAT0+kBAJCUZXoAAAAAAAAAAAAAwBR0egAAAAAAAAAAAAAAAAAQp9MDAAAAAAAAAAAAAAAAgDidHgAAVyiljB4BAAAAAAAAAAAAAKALnR4AAAAAAAAAAAAAAAAAxD1HDwAAALfzcrtgrfX6SQAAAAAAAAAAAACA83R6AABktFK09rLK2/9tK90KAAAAAAAAAAAAAFiSTg8AANrb2eYFDiXbAwAAAAAAAAAAAIBsdHoAAHTXMFrLr/fFfh1frQcAAAAAAAAAAAAAeej0AACggYtbxO/TCfYAAAAAAAAAAAAAYDidHgAAnDJ2W6D1egAAAAAAAAAAAAAwnE4PAIB0JqrODkV6G9d1MvZT6wEAAAAAAAAAAADAQDo9AACI2FnW7Wznfn5buNkrpUj1AAAAAAAAAAAAAOB6XuQFAKCvQHWW/zPqx4tqcgnhYC//DQQAAAAAAAAAAACAlTxGDwAAAJPZzudqra0yufChwoEfAAAAAAAAAAAAABCg0wMAgAM2EriGhd75w0r1AAAAAAAAAAAAAOAyOj0AANhrO9LremqpHgAAAAAAAAAAAACkpdMDACCX3sFb2LvsrdMavZcnOvojUj0AAAAAAAAAAAAAuIBODwAAPtuI9K4cQ6oHAAAAAAAAAAAAAAnp9AAA6GiNSCxJpBc+6Rq/BQAAAAAAAAAAAABIS6cHAAARQyK98KmlegAAAAAAAAAAAADQj04PAAC2vCzcBkZ6SQYAAAAAAAAAAAAAAL7p9AAASCRbfpZ5Dd3Re5X5WgAAAAAAAAAAAABgajo9AAA4JltMuJ9UDwAAAAAAAAAAAAB60OkBANDL7FXYy/lTRXqphgEAAAAAAAAAAACA29LpAQDAjcweTwIAAAAAAAAAAABAQjo9AAB44V3PVkpJlbpZqQcAAAAAAAAAAAAAwz1HDwAAAPP5TvVkcgAAAAAAAAAAAACAfXoAAGQxY/OWYbfe0fuWYWYAAAAAAAAAAAAAWIlODwAATpG9AQAAAAAAAAAAAMDN6fQAAOhi6nrt6PBTXywAAAAAAAAAAAAAcJJODwAAAAAAAAAAAAAAAADidHoAANDA2JV6tdaBZwcAAAAAAAAAAACAm9PpAQCQgtLsSmOrQgAAAAAAAAAAAABYjE4PAAAAAAAAAAAAAAAAAOJ0egAAtGddGwAAAAAAAAAAAABwHzo9AAAAAAAAAAAAAAAAAIjT6QEAAAAAAAAAAAAAAABAnE4PAAB+q7Ve8CMAAAAAAAAAAAAAwBp0egAAjCdyAwAAAAAAAAAAAADmpdMDAKCxUsroERo4lA7qDAEAAAAAAAAAAADgznR6AADw2s76TqQHAAAAAAAAAAAAADen0wMAgLc+NngiPQAAAAAAAAAAAADgOXoAAABI7avEK6X8/WIev8b7KNv8AAAAAAAAAAAAADA1nR4AAINN0YxNMSQAAAAAAAAAAAAAMMRj9AAAAAAAAAAAAAAAAAAAMDGdHgAALZVSRo8AAAAAAAAAAAAAAHApnR4AAMztaBtZa+00CQAAAAAAAAAAAADck04PAAAAAAAAAAAAAAAAAOJ0egAAAAAAAAAAAAAAAAAQp9MDAGCkWuvoEeZWSjn0/W44AAAAAAAAAAAAADSn0wMAoJmjzRgAAAAAAAAAAAAAwAJ0egAAMCvL9AAAAAAAAAAAAAAgA50eAAAAAAAAAAAAAAAAAMTp9AAAYEqW6QEAAAAAAAAAAABAEjo9AACGUY4BAAAAAAAAAAAAAAvQ6QEAwHws0wMAAAAAAAAAAACAPHR6AAC0cbQcI0ykBwAAAAAAAAAAAACp6PQAAGBlIj0AAAAAAAAAAAAA6E2nBwAAM7G3EAAAAAAAAAAAAACy0ekBAMA0jkZ6lukBAAAAAAAAAAAAwAV0egAAjCEh680dBgAAAAAAAAAAAIBr6PQAAGjg6J43Ag7dZJEeAAAAAAAAAAAAAFxGpwcAABMQ6QEAAAAAAAAAAABAWjo9AADITqQHAAAAAAAAAAAAAJnp9AAAIDWRHgAAAAAAAAAAAAAkp9MDAGAAOdlOIj0AAAAAAAAAAAAAyE+nBwDAWYdaMvYT6QEAAAAAAAAAAADAFJ6jBwAAAE5R6AEAAAAAAAAAAADAWPbpAQBARjuX6Yn0AAAAAAAAAAAAAGA4nR4AAKQj0gMAAAAAAAAAAACAiTxHDwAAwO2oyzYo9AAAAAAAAAAAAABgOvbpAQBAFiI9AAAAAAAAAAAAAJiRTg8AgFN2pmV8JNIDAAAAAAAAAAAAgEnp9AAAYDyRHgAAAAAAAAAAAADM6zl6AAAAuLs9kZ5CDwAAAAAAAAAAAADSsk8PAIBL6c1+EekBAAAAAAAAAAAAwOx0egAAMIxIDwAAAAAAAAAAAAAW8Bw9AAAAE9uTmfHOx7un0AMAAAAAAAAAAACAKdinBwAAA4j0AAAAAAAAAAAAAGAZOj0AALiaSA8AAAAAAAAAAAAAVqLTAwDgOvKzfyI9AAAAAAAAAAAAAFjOc/QAAABwI9uRnkIPAAAAAAAAAAAAAGZknx4AAFxEpAcAAAAAAAAAAAAAS7JPDwCAoO3qjJ8+3qvekd7fAWSBAAAAAAAAAAAAANCKfXoAAFzktmHY8EgPAAAAAAAAAAAAAOjKPj0AAOhoO9JT6AEAAAAAAAAAAADAAuzTAwCAXtJGevpAAAAAAAAAAAAAAGhIpwcAwBVuGIblifS2JwEAAAAAAAAAAAAATtLpAQAQIf3alifSAwAAAAAAAAAAAAB6e44eAAAAVrMR6Sn0AAAAAAAAAAAAAGA99ukBAEBLIj0AAAAAAAAAAAAAuBudHgAA3d2nTxPpAQAAAAAAAAAAAMAN6fQAAKCNnJHe36kUgwAAAAAAAAAAAADQlk4PAIDDNoK028oZ6QEAAAAAAAAAAAAAF3iOHgAAgMXdoVJ7F+nd4doBAAAAAAAAAAAAAJ0eAADEJV+jZ/MhAAAAAAAAAAAAAFzgMXoAAACYVfJIDwAAAAAAAAAAAAC4hk4PAICOFs7V8kd6lukBAAAAAAAAAAAAwDV0egAAHKP++ifSAwAAAAAAAAAAAAB+eI4eAACAZSUp1prbTuCSB3Kr/lIAAAAAAAAAAAAAYCD79AAA4IDkGR4AAAAAAAAAAAAAcD379AAAFve3K7NRLUykBwAAAAAAAAAAAAD8pdMDAFjWu6js6+uxWm9/qLZeDSjSAwAAAAAAAAAAAABe0ukBACxoT1F2pta7IZEeAAAAAAAAAAAAAPDOY/QAAAA0pihrzi0FAAAAAAAAAAAAADbo9AAAlnK0KOtUoK20pm+lSG+l3wsAAAAAAAAAAAAA5KHTAwBYR++ibKVibacbXjIAAAAAAAAAAAAAcJRODwDg7pqnaMssbRPpAQAAAAAAAAAAAAB76PQAABYhKmvL/QQAAAAAAAAAAAAAdtLpAQCwy85ubY1leiI9AAAAAAAAAAAAAGA/nR4AAAAAAAAAAAAAAAAAxOn0AABWkGT/2xrL9FbltwMAAAAAAAAAA1nkOQAAIABJREFUAAAAnej0AADubk++tacDlIEBAAAAAAAAAAAAAPek0wMAAAAAAAAAAAAAAACAuOfoAQAAGKnVErzFluktdjkAAAAAAAAAAAAAQFf26QEA8EEpZfQIAAAAAAAAAAAAAAB56fQAAO7LMj0AAAAAAAAAAAAAgPN0egAAN7Uzrvu4TE+kBwAAAAAAAAAAAADcnE4PAGAFR2M5kR4AAAAAAAAAAAAAQCvP0QMAAHApZR0AAAAAAAAAAAAAQFvFi9oAAMvYXn939IOfZXoAAAAAAAAAAAAAAHs8Rg8AAEBGIj0AAAAAAAAAAAAAgJ3s0wMAWMr5vu7jEfYcBAAAAAAAAAAAAADgPnR6AACr2RPa/XvV2oV/EAAAAAAAAAAAAADgznR6AAAL2lncBfj0CAAAAAAAAAAAAADwi04PAGBNzVM9nxsBAAAAAAAAAAAAAF56jB4AAIAu2mZ1Ij0AAAAAAAAAAAAAgHfs0wMAWNzJxXo+LgIAAAAAAAAAAAAAbNPpAQDcxaFgz6dEAAAAAAAAAAAAAICddHoAAHf0rtnz4RAAAAAAAAAAAAAA4CidHgAAAAAAAAAAAAAAAADEPUYPAAAAAAAAAAAAAAAAAAAT0+kBAAAAAAAAAAAAAAAAQJxODwAAAAAAAAAAAAAAAADidHoAAAAAAAAAAAAAAAAAEKfTAwAAAAAAAAAAAAAAAIA4nR4AAAAAAAAAAAAAAAAAxOn0AAAAAAAAAAAAAAAAACBOpwcAAAAAAAAAAAAAAAAAcTo9AAAAAAAAAAAAAAAAAIjT6QEAAAAAAAAAAAAAAABAnE4PAAAAAAAAAAAAAAAAAOJ0egAAAAAAAAAAAAAAAAAQp9MDAAAAAAAAAAAAAAAAgDidHgAAAAAAAAAAAAAAAADE6fQAAAAAAAAAAAAAAAAAIE6nBwAAAAAAAAAAAAAAAABxOj0AAAAAAAAAAAAAAAAAiNPpAQAAAAAAAAAAAAAAAECcTg8AAAAAAAAAAAAAAAAA4nR6AAAAAAAAAAAAAAAAABCn0wMAAAAAAAAAAAAAAACAOJ0eAAAAAAAAAAAAAAAAAMTp9AAAAAAAAAAAAAAAAAAgTqcHAAAAAAAAAAAAAAAAAHE6PQAAAAAAAAAAAAAAAACI0+kBAAAAAAAAAAAAAAAAQJxODwAAAAAAAAAAAAAAAADidHoAAAAAAAAAAAAAAAAAEKfTAwAAAAAAAAAAAAAAAIA4nR4AAAAAAAAAAAAAAAAAxOn0AAAAAAAAAAAAAAAAACBOpwcAAAAAAAAAAAAAAAAAcTo9AAAAAAAAAAAAAAAAAIjT6QEAAAAAAAAAAAAAAABAnE4PAAAAAAAAAAAAAAAAAOJ0egAAAAAAAAAAAAAAAAAQp9MDAAAAAAAAAAAAAAAAgDidHgAAAAAAAAAAAAAAAADE6fQAAAAAAAAAAAAAAAAAIE6nBwAAAAAAAAAAAAAAAABxOj0AAAAAAAAAAAAAAAAAiNPpAQAAAAAAAAAAAAAAAECcTg8AAAAAAAAAAAAAAAAA4nR6AAAAAAAAAAAAAAAAABCn0wMAAAAAAAAAAAAAAACAOJ0eAAAAAAAAAAAAAAAAAMTp9AAAAAAAAAAAAAAAAAAgTqcHAAAAAAAAAAAAAAAAAHE6PQAAAAAAAAAAAAAAAACI0+kBAAAAAAAAAAAAAAAAQJxODwAAAAAAAAAAAAAAAADidHoAAAAAAAAAAAAAAAAAEKfTAwAAAAAAAAAAAAAAAIA4nR4AAAAAAAAAAAAAAAAAxOn0AAAAAAAAAAAAAAAAACBOpwcAAAAAAAAAAAAAAAAAcTo9AAAAAAAAAAAAAAAAAIjT6QEAAAAAAAAAAAAAAABAnE4PAAAAAAAAAAAAAAAAAOJ0egAAAAAAAAAAAAAAAAAQp9MDAAAAAAAAAAAAAAAAgLjn6AEAAAAAAAAAAAAAAOBqpZSXX6+1XjzJL2kHAwA2FH+qAQAAAAAAAAAAAAC4iXcV3EvXvG9/aKQvQgAAyEanBwAAAAAAAAAAAADA+gI53JdOb92H5/kmBwCAPHR6AAAAAAAAAAAAAAAsLlUUd36Yn0QBAJCBTg8AAAAAAAAAAAAAgGWliuLaDvOTNAAAxtLpAQAAAAAAAAAAAACwph5dXOwl/H6F3jd1AAAMpNMDAAAAAAAAAAAAAGBB22nc33fp96d0h97DP1TobRx5z3EEAgAwik4PAAAAAAAAAAAAAIAFvQvbTrZwHw9y9IANqz+BAACMotMDAAAAAAAAAAAAAGA1gUhv+wd7HCr8Pv/GkTUCADCETg8AAAAAAAAAAAAAgKWEI73tH99/wH6FXu+DAwAxz9EDAAAAAAAAAAAAAABAd5fVa9sdXddCDwAY5TF6AAAAAAAAAAAAAAAAaOZlzHaojtv/zX/P1SnSK6WI9AAgM50eAAAAAAAAAAAAAAA00C/S6/r9AMB5Oj0AAAAAAAAAAAAAABZxfple+IwbdVyt9bJIDwAYQqcHAAAAAAAAAAAAAACnbEd6PQ4LAKSi0wMAAAAAAAAAAAAAYAXXL9P7aOzZAYDL6PQAAAAAAAAAAAAAACDu3da7k5GeZXoAMBGdHgAAAAAAAAAAAAAA08u2TM8mPQC4FZ0eAAAAAAAAAAAAAADkYpkeAMxFpwcAAAAAAAAAAAAAwIJuu0zPKj8AuJ5ODwAAAAAAAAAAAACAuaXaPieTA4Ab0ukBAAAAAAAAAAAAAEAbIj0AuKfn6AEAAOA6P/9plsdhAAAAAAAAAACwhpfL9G77gtBtLxwAxtLpAQCwvpeP4b6/6LEUAAAAAAAAAADw08s3jvbwMhIA3NZj9AAAANDXx0dm4WdqAAAAAAAAAAAA39pGerGjCQUBYBSdHgAAK9vZ4En1AAAAAAAAAABgUgu//HM0uhPpAcBAOj0AAJZ16AHcwk/rAAAAAAAAAADgbq4v1oY3csMHAICbe44eAAAAutDdAQAAAAAAAAAAAalePfqq77ZHUugBQAb26QEAwP9SPV8DAAAAAAAAAABm0buUq7W+PMW7rwMA17NPDwAAAAAAAAAAAACAKb3839yrpmurXhcArME+PQAAFhTejGelHgAAAAAAAAAAcIh8DgD4p9MDAAAAAAAAAAAAAIAv/tM3ABCj0wMAYDUnn5R50AYAAAAAAAAAAAAAHKLTAwBgKU0qO6keAAAAAAAAAADk9/I9n1rrlTNcfDoAIC2dHgAAAAAAAAAAAAAA89HIAQB56PQAAFhHwz14VuoBAAAAAAAAAEB+v1K9k+Wet4YAgLDn6AEAAKCN5s/ISin+4RYAAAAAAAAAACTnJR8AIAP79AAAAAAAAAAAAAAA4DCJIADwTacHAMAKmi/T63pYAAAAAAAAAAAAAGAlOj0AAKbXtaaT6gEAAAAAAAAAwB14UwgAOEOnBwDA3C54OuYBHAAAAAAAAAAAAACwQacHAAAAAAAAAAAAAAAAAHE6PQAAJnbZpjsr9QAAAAAAAAAAgJ9qraNHAAAS0ekBADCri9s5qR4AAAAAAAAAAKzK20EAwEk6PQAApjTkuZiHcQAAAAAAAAAAAADAXzo9AADmM7CXk+oBAAAAAAAAAAAAAL/o9AAAmIxSDgAAAAAAAAAAGKvWOnoEACAXnR4AABwjFAQAAAAAAAAAgJV4IwgAOE+nBwDATJI8EUsyBgAAAAAAAAAAAACQgU4PAIBppKrjUg0DAAAAAAAAAAAAAAyk0wMAYA4Ju7iEIwEAAAAAAAAAAL3VWkePAACko9MDAGACaYu4tIMBAAAAAAAAAAB7eAUIAGhCpwcAQHbJH4QlHw8AAAAAAAAAAAAA6E2nBwBAalNUcFMMCQAAAAAAAAAAAAB0otMDACCvifq3iUYFAAAAAAAAAADCaq2jRwAAMtLpAQCQ1HTl23QDAwAAAAAAAADAzXnnBwBoRacHAEBGkz7/mnRsAAAAAAAAAAAAAOAMnR4AAOlMXbtNPTwAAAAAAAAAAAAAEKDTAwAglwU6twUuAQAAAAAAAAAA+KvWOnoEACApnR4AAIksU7gtcyEAAAAAAAAAALAqL/kAAA3p9AAAyGKxx16LXQ4AAAAAAAAAAAAA8I5ODwCAFJas2pa8KAAAAAAAAAAAAADgF50eAADjLdyzLXxpAAAAAAAAAABwK7XW0SMAAHnp9AAAGGz5km35CwTgP/buJilyJAnAaEYZZwGuwP0XeQWUV8D6DJpFztBMVUFKrvhVvLfqRZsIVy6SkunDAQAAAAAAABiOt3oAgLx0egAAtNTn067sf/iqzzEBAAAAAAAAAAAAgCx0egAANJO9XsvY10n1AAAAAAAAAAAAAICNdHoAADSQUuo50it0wRJTAwAAAAAAAAAAFWR/mwgAOBmdHgAAtZVo1Qo9BStxWakeAAAAAAAAAAC05R0eACA7nR4AAFUNFOmVu7jHfAAAAAAAAAAAAABwJjo9AAAqSSkNF+mV+xGF7gYAAAAAAAAAAAAAUJ9ODwCAGgo1aRUivaI/SKoHAAAAAAAAAAD9q/aeEgAwLp0eAABllVscV/nhV7lUT60HAAAAAAAAAADVeF0HAChBpwcAQEHlHmk1+QtV5X6oZ38AAAAAAAAAAAAAMC6dHgAARRRdE9ck0iv9oy3WAwAAAAAAAAAAAIBB6fQAAMisdG/WMNKrcAC1HgAAAAAAAAAAdKX5C0sAwBCeWh8AAICTqFOXdfLMa13XovN+XryTeQEAAAAAAAAA4Bz8EW0AoBCdHgAAcZUfWnUVrZVO9e6+/oiuxgcAAAAAAAAAAAAAPv1qfQAAAIaUUpo50rurfKT69xwAAAAAAAAAAAAA2EKnBwDAbvVrsQ4jvbv6B5PqAQAAAAAAAABANd2+uQQA9EanBwDAPvXX6HX+qKv+CaV6AAAAAAAAAAAQ4MUbAKAcnR4AAP3qvND7aqCjAgAAAAAAAAAAAAB56fQAANih5h+UGq58q3lgf9kLAAAAAAAAAAAAAPqh0wMAoEfDRXp3gx4bAAAAAAAAAAD4k9eBAIDtnlofAAAA/s/oz7bu57fvDgAAAAAAAAAAuuKVHgCgKPv0AADoyOiR3qfTDAIAAAAAAAAAAAAAPKTTAwCgC+u6nqxtO9k4AAAAAAAAAAAAAMB3dHoAALR31qTtfPEhAAAAAAAAAABMwps/AMAuOj0AAFqaoWQ7/YAAAAAAAAAAANC5lFLrIwAAJ6fTAwCgmXkCthlyRAAAAAAAAAAAAACY1lPrAwAAMKM5o7X71Fn+NNecNxAAAAAAAAAAAAAA+mSfHgAAOxzPw2yWcwcAAAAAAAAAAKBz3vABAPayTw8AgH3WdY1thPPo6qsju/XcSQAAAAAAAAAA2C72lk7RS3kFCADOR6cHAMBue1M9D5W+E6j13EwAAAAAAAAAAKgjY+D38MreCwKA0SVf5wAAxDx8CNXkV80sj8Za/ZLc5y0FAAAAAAAAAIDRlSvuSvCaEACMyD49AACCvj4Muj/G8njoIDcQAAAAAAAAAAD4WhV6pwgARqHTAwAgAw+DAAAAAAAAAAAAsvts9ryjBQCd+9X6AAAAAAAAAAAAAAAAUNAJIrf0P60PAgD8nU4PAAAAAAAAAAAAAADGoNYDgD49tT4AAAAAAAAAAAAAAACUcsqq7T7UCfYEAsBp6PQAAAAAAAAAAAAAADihUxZ6X30OKNgDgOZ0egAAAAAAAAAAAAAAnMrpC73fWK8HAM39an0AAAAAAAAAAAAAAADII6U0W6T3adrBAaAH9ukBAAAAAAAAAAAAADA8ldrFYj0AaMc+PQAAAAAAAAAAAAAAxibS+2rmpYIA0IpODwAAAAAAAAAAAACAUWnSvuO2AEBNOj0AAAAAAAAAAAAAAIYkRfuZiBEAqtHpAQAAAAAAAAAAAAAwGAXadm4UAFSg0wMAAAAAAAAAAAAAYCTCs71kjQBQmk4PAAAAAAAAAAAAAIAx6M2OcOsAoBydHgAAAAAAAAAAAAAAA5CZHSd0BIBCdHoAAAAAAAAAAAAAAPROXZaRmwkA2en0AAAAAAAAAAAAAADomq4sO7cUAPLS6QEAAAAAAAAAAAAA0KmUkqKsEDcWADLS6QEAAAAAAAAAAAAA0CMhWWnuMADkotMDAAAAAAAAAAAAAKA7ErI63GcAyEKnBwAAAAAAAAAAAABAX8RjNbnbAHCcTg8AAAAAAAAAAAAAgI7IxupzzwHgIJ0eAAAAAAAAAAAAAAC9EIy14s4DwBE6PQAAAAAAAAAAAAAAuiAVa8v9B4AwnR4AAAAAAAAAAAAAAO2JxHrgUwCAGJ0eAAAAAAAAAAAAAACNycP64bMAgACdHgAAAAAAAAAAAAAA8C+pHgDspdMDAAAAAAAAAAAAAKAlVViHfCgAsItODwAAAAAAAAAAAACAZvRg3fLRAMB2Oj0AAAAAAAAAAAAAANpQggEA56DTAwAAAAAAAAAAAACgAZFe/3xGALCRTg8AAAAAAAAAAAAAAPg7qR4AbKHTAwAAAAAAAAAAAACgNvXXQHxYAPCQTg8AAAAAAAAAAAAAgKp0XwDAyej0AAAAAAAAAAAAAACoR6Q3Ip8aAPxMpwcAAAAAAAAAAAAAADwg1QOAH+j0AAAAAAAAAAAAAACoROsFAJySTg8AAAAAAAAAAAAAgBpEeqPzCQLAd3R6AAAAAAAAAAAAAADAJlI9APgrnR4AAAAAAAAAAAAAAMXpuwCAE9PpAQAAAAAAAAAAAAAAW0kuAeBPOj0AAAAAAAAAAAAAAMpSdgEA56bTAwAAAAAAAAAAAAAAdhBeAsBvdHoAAAAAAAAAAAAAABSk6QIATk+nBwAAAAAAAAAAAAAA7CO/BICvdHoAAAAAAAAAAAAAAJSi5gIAZqDTAwAAAAAAAAAAAAAAdhNhAsAnnR4AAAAAAAAAAAAAAEXouACASej0AAAAAAAAAAAAAACACCkmANzp9AAAAAAAAAAAAAAAyE/BBQDMQ6cHAAAAAAAAAAAAAAAECTIB4KLTAwAAAAAAAAAAAAAgO+0WADAVnR4AAAAAAAAAAAAAAAAAxOn0AAAAAAAAAAAAAACAOOsTAUCnBwAAAAAAAAAAAABATqotAGA2Oj0AAAAAAAAAAAAAAOAQcSYAk9PpAQAAAAAAAAAAAAAAAECcTg8AAAAAAAAAAAAAgGzsVQMAJqTTAwAAAAAAAAAAAAAAAIA4nR4AAAAAAAAAAAAAAHCUVYoAzEynBwAAAAAAAAAAAABAHkotAGBOOj0AAAAAAAAAAAAAAAAAiNPpAQAAAAAAAAAAAAAAGVioCMC0dHoAAAAAAAAAAAAAAAAAEKfTAwAAAAAAAAAAAAAgA7vUAIBp6fQAAAAAAAAAAAAAAAAAIE6nBwAAAAAAAAAAAAAAAABxOj0AAAAAAAAAAAAAACCPlFLrIwBAAzo9AAAAAAAAAAAAAAAAAIjT6QEAAAAAAAAAAAAAAABAnE4PAAAAAAAAAAAAAICjUkqtjwAA0IxODwAAAAAAAAAAAAAAAADidHoAAAAAAAAAAAAAAAAAEPfU+gDAVtfr9fhF3t7ejl+kKGNuZ8xOGHM7Y3ZikjEPul6v/c84yUdpzO2M2QljbmfMTsww5gwzXoy5hzE7YcztjNmJGcacYcaLMfcwZieMuZ0xOzHDmDPMeDHmHsbshDG3M2YnZhhzhhkvxtzDmJ0w5nbGhCNSSuu6tj4FAFRlnx4AAAAAAAAAAAAAAAAAxOn0AAAAAAAAAAAAAAAAACBOpwcAAAAAAAAAAAAAAAAAcTo9AAAAAAAAAAAAAAAAAIjT6QEAAAAAAAAAAAAAAABAnE4PAAAAAAAAAAAAAIBDUkqtjwAA0JJODwAAAAAAAAAAAAAAAADidHoAAAAAAAAAAAAAAAAAEKfTAwAAAAAAAAAAAAAAAIA4nR4AAAAAAAAAAAAAAAAAxD21PgAAAAAA4/n4+Lj/x+vr6+12a3uYn30e9YjOZ7wYcw9jdsKY2xmzEzOMOcOMF2PuYcxOGHM7Y3ZihjFnmPFizD2M2QljbmfMTsww5gwzXoy5hzE7YcztjAkAwC726QEAAAAAAAAAAAAAAABAnE4PAAAAAAAAAAAAAAAAAOJ0egAAAAAAAAAAAAAAAAAQp9MDAAAAAAAAAAAAAAAAgDidHgAAAAAAAAAAAAAAhyzL0voIAAAt6fQAAAAAAAAAAAAAAAAAIE6nBwAAAAAAAAAAAAAAAABxOj0AAAAAAAAAAAAAAAAAiNPpAQAAAAAAAAAAAAAAAECcTg8AAAAAAAAAAAAAAAAA4nR6AAAAAAAAAAAAAABANuu6tj4CANSWfP8BAHAmKaXjF/FLMgA8dLvdWh8BAAAAAAAA6MvLy0vrI9ALr2ABMCH79AAAAAAAAAAAAAAAAAAgTqcHAAAAAAAAAAAAAAAAAHE6PQAAAAAAAAAAAAAAjlqWpfURAACa0ekBAAAAAAAAAAAAAAAAQJxODwAAAAAAAAAAAAAAAADidHoAAAAAAAAAAAAAAEAe67q2PgIANKDTAwAAAAAAAAAAAAAAAIA4nR4AAAAAAAAAAAAAABksy9L6CAAAbej0AAAAAAAAAAAAAAAAACBOpwcAAAAAAAAAAAAAAGSwrmvrIwBAGzo9AAAAAAAAAAAAAAAAAIjT6QEAAAAAAAAAAAAAkMeyLK2PAADQgE4PAAAAAAAAAAAAAAAAAOJ0egAAAAAAAAAAAAAAwFHrurY+AgA0k3wRQieu1+vxi7y9vR2/CAAMLaV0/CJ+SQaAh/9K/fj4eHiR19fXTMcBgFG9v78fv4ivVADwlQoAWfhKBaCyl5eX1kegAW9eATAz+/QAAAAAAAAAAAAAAAAAIE6nBwAAAAAAAAAAAAAAHGKZHgCT0+kBAAAAAAAAAAAAAJDTsiytjwAAUJVODwAAAAAAAAAAAAAAAADidHoAAAAAAAAAAAAAAEDcuq6tjwAAjen0AAAAAAAAAAAAAADIbFmW1kcAAKhHpwcAAAAAAAAAAAAAAARZpgcAF50eAAAAAAAAAAAAAAAlWKkHAMxDpwcAAAAAAAAAAAAAAAAAcTo9AAAAAAAAAAAAAAAgYl3X1kcAgC7o9AAAAAAAAAAAAAAAKGJZltZHAACoQacHAAAAAAAAAAAAAADsZpkeAHzS6QEAAAAAAAAAAAAAUIqVegDADHR6AAAAAAAAAAAAAADAPpbpAcBXOj0AAAAAAAAAAAAAAAqyUg8AOD2dHgAAAAAAAAAAAAAAZUn1TsYyPQD4jU4PAAAAAAAAAAAAAADYSqQHAH/S6QEAAAAAAAAAAAAAUJyVegDAien0AAAAAAAAAAAAAACATSzTA4C/0ukBAAAAAAAAAAAAAFCDlXoAwFnp9AAAAAAAAAAAAAAAqESqNzTL9ADgOzo9AAAAAAAAAAAAAADgAZEeAPwg+aYEAOBMUkrHL+KXZAB46Ha7tT4CAAAAAAAAMLCXl5fWR2A3L1YBwA/s0wMAAAAAAAAAAAAAoKplWVofgX1EegDwM50eAAAAAAAAAAAAAADwLZEeADyk0wMAAAAAAAAAAAAAoDYr9UYh0gOALXR6AAAAAAAAAAAAAAA0INUDAE5DpwcAAAAAAAAAAAAAQBtSvc5ZpgcAG+n0AAAAAAAAAAAAAACA34n0AGA7nR4AAAAAAAAAAAAAAM1YqdcnkR4A7KLTAwAAAAAAAAAAAACgJaleb0R6ALCXTg8AAAAAAAAAAAAAgMakegDA0HR6AAAAAAAAAAAAAAC0J9XrhGV6ABCg0wMAAAAAAAAAAAAAoAtSveZEegAQo9MDAAAAAAAAAAAAAKAXUr2GRHoAEKbTAwAAAAAAAAAAAACgI1K9JkR6AHCETg8AAAAAAAAAAAAAgL5I9SoT6QHAQTo9AAAAAAAAAAAAAAC6I9WrRqQHAMfp9AAAAAAAAAAAAAAA6JFUr7R1XUV6AJCFTg8AAAAAAAAAAAAAgE5J9cpR6AFARjo9AAAAAAAAAAAAAAD6JdUrQaQHAHnp9AAAAAAAAAAAAAAA6NqyLGq9jER6AJCdTg8AAAAAAAAAAAAAgAFI9bIQ6QFACTo9AAAAAAAAAAAAAADGINU7Yl1XkR4AFKLTAwAAAAAAAAAAAABgGMuyqPUCFHoAUJRODwAAAAAAAAAAAACAwUj1trNGDwAq0OkBAAAAAAAAAAAAADAei/W2UOgBQB06PQAAAAAAAAAAAAAARiXV+441egBQk04PAAAAAAAAAAAAAICBWaz3J4UeAFT21PoAAAAAAAAAAAAAAABw1D3Ve3l5aX2QxhR6ANCEfXoAAAAAAAAAAAAAAJzEzIv11nUV6QFAK/bpAQAAAAAAAAAAAABwHhMu1pPnAUBzOj0AAAAAAAAAAAAAAM7mc7HeuYM9hR4AdEKnBwAAAAAAAAAAAADAaZ11vZ5CDwC6otMDAAAAAAAAAAAAAODkTlPryfMAoE86PQAAAAAAAAAAAAAApnCv9S5jBnsKPQDomU4PAAAAAAAAAAAAAIC5DBTsyfMAYAg6PQAAAAAAAAAAAAAAJtVnsKfNA4Dh6PQAAAAAAAAAAAAAAJjdZ7B3adTsafMAYGg6PQAAAAAAAAAAAAAA+NfXZu+uRLknzAOAM9HpAQAAAAAAAAAAAADAT/4s9/70teXT4AHAbHR6AAAAAAAAAAAAAABw1L3le35+bn0QAKCBX60PAAAAAAAAAAAAAAAAAAAD0+kBAAAAAAAAAAAAAAAAQJxODwAAAAAAAAAAAAAAAADidHoAAAAAAAAAAAAAAAAAEKfTAwAAAAAAAAAAAAAAAIA4nR4AAAAAAAAAAAAAAAAAxOn0AAAAAAAAAAAAAAAAACBOpwcAAAAAAAAAAAAAAAAAcTo9AAAAAAAAAAAAAAAAAIjT6QEAAAAAAAAAAAAAAABAnE4PAAAAAAAAAAAAAAAAAOJ0egAAAAAAAAAAAAAAAAB3zlr+AAAgAElEQVQQp9MDAAAAAAAAAAAAAAAAgDidHgAAAAAAAAAAAAAAAADEPbU+APBf1+v1+EXe3t6OXwQAAAAe/iv14+Pj4UVeX18zHQcARvX+/n78Ir5SAcBXKgBk4SsVALLY8pX6zz///Pw/eOMXAE7JPj0AAAAAAAAAAAAAAAAAiNPpAQAAAAAAAAAAAAAAAECcTg8AAAAAAAAAAAAAAAAA4nR6AAAAAAAAAAAAAAAAABCn0wMAAAAAAAAAAAAAAACAOJ0eAAAAAAAAAAAAAAAAAMTp9AAAAAAAAAAAAAAAAAAgTqcHAAAAAAAAAAAAAAAAAHE6PQAAAAAAAAAAAAAAAACI0+kBAAAAAAAAAAAAAAAAQJxODwAAAAAAAAAAAAAAAADidHoAAAAAAAAAAAAAAAAAEKfTAwAAAAAAAAAAAAAAAIA4nR4AAAAAAAAAAAAAAAAAxOn0AAAAAAAAAAAAAAAAACBOpwcAAAAAAAAAAAAAAAAAcTo9AAAAAAAAAAAAAAAAAIjT6QEAAAAAAAAAAAAAAABAnE4PAAAAAAAAAAAAAAAAAOJ0egAAAAAAAAAAAAAAAAAQp9MDAAAAAAAAAAAAAAAAgDidHgAAAAAAAAAAAAAAAADEpXVdW58BAACySSkdv4hfkgHgodvt1voIAAAAAAAAAD16fn5ufQQAoAH79AAAAAAAAAAAAAAAAAAgTqcHAAAAAAAAAAAAAAAAAHE6PQAAAAAAAAAAAAAAAACI0+kBAAAAAAAAAAAAAAAAQJxODwAAAAAAAAAAAAAAAADidHoAAAAAAAAAAAAAAAAAEKfTAwAAAAAAAAAAAAAAAIA4nR4AAAAAAAAAAAAAAAAAxOn0AAAAAAAAAAAAAAAAACBOpwcAAAAAAAAAAAAAAAAAcTo9AAAAAAAAAAAAAAAAAIjT6QEAAAAAAAAAAAAAAABAnE4PAAAAAAAAAAAAAAAAAOJ0egAAAAAAAAAAAAAAAAAQp9MDAAAAAAAAAAAAAAAAgDidHgAAAAAAAAAAAAAAAADE6fQAAAAAAAAAAAAAAAAAIE6nBwAAAAAAAAAAAAAAAABxOj0AAAAAAAAAAAAAAAAAiNPpAQAAAAAAAAAAAAAAAECcTg8AAAAAAAAAAAAAAAAA4nR6AAAAAAAAAAAAAAAAABCn0wMAAAAAAAAAAAAAAACAOJ0eAAAAAAAAAAAAAAAAAMTp9AAAAAAAAAAAAAAAAAAgTqcHAAAAAAAAAAAAAAAAAHE6PQAAAAAAAAAAAAAAAACI0+kBAAAAAAAAAAAAAAAAQJxODwAAAAAAAAAAAAAAAADidHoAAAAAAAAAAAAAAAAAEKfTAwAAAAAAAAAAAAAAAIA4nR4AAAAAAAAAAAAAAAAAxOn0AAAAAAAAAAAAAAAAACBOpwcAAAAAAAAAAAAAAAAAcTo9AAAAAAAAAAAAAAAAAIjT6QEAAAAAAAAAAAAAAABAnE4PAAAAAAAAAAAAAAAAAOJ0egAAAAAAAAAAAAAAAAAQp9MDAAAAAAAAAAAAAAAAgDidHgAAAAAAAAAAAAAAAADE6fQAAAAA/sPe3eQ0kmwBGMWIFZS8BAsx94gt1A5qhb2D3gIjzxHysGdlagu8ASWeBTidGRmZEXHjHL0RD5KbP7aru+vTBQAAAAAAAAAAgHQ6PQAAAAAAAAAAAAAAAABIp9MDAAAAAAAAAAAAAAAAgHQ6PQAAAAAAAAAAAAAAAABIp9MDAAAAAAAAAAAAAAAAgHQ6PQAAAAAAAAAAAAAAAABIp9MDAAAAAAAAAAAAAAAAgHQ6PQAAAAAAAAAAAAAAAABIp9MDAAAAAAAAAAAAAAAAgHQ6PQAAAAAAAAAAAAAAAABIp9MDAAAAAAAAAAAAAAAAgHQ6PQAAAAAAAAAAAAAAAABIp9MDAAAAAAAAAAAAAAAAgHR3pQcA/np6epp/kMfHx/kHAQAAgKv/lPr79++rB3l4eMg0DgC06vn5ef5BfKQCgI9UAMjCRyoAZDHmI/X19XX4G/yNXwAIyT49AAAAAAAAAAAAAAAAAEin0wMAAAAAAAAAAAAAAACAdDo9AAAAAAAAAAAAAAAAAEin0wMAAAAAAAAAAAAAAACAdDo9AAAAAAAAAAAAAAAAAEin0wMAAAAAAAAAAAAAAACAdDo9AAAAAAAAAAAAAAAAAEin0wMAAAAAAAAAAAAAAACAdDo9AAAAAAAAAAAAAAAAAEin0wMAAAAAAAAAAAAAAACAdDo9AAAAAAAAAAAAAAAAAEin0wMAAAAAAAAAAAAAAACAdDo9AAAA4jjtd6f9rvQUXXMLAAAAAAAAAAAA6JBODwAAgAjO8zCdWCnnt8BdAAAAAAAAAAAAoB93pQcAAACAWfRg1Xq/NdvDsfQgAAAAAAAAAAAAsCz79AAAAGiYSK9+7hEAAAAAAAAAAADh2acHAABAkwbqLwvcSnm/8l9vjcV6AAAAAAAAAAAAxGafHgAAAO0R6dXs0i2wWA8AAAAAAAAAAICodHoAAAA0RqRXP6keAAAAAAAAAAAAXdHpAQAA0JJLodf2cBTpVUWqBwAAAAAAAAAAQD90egAAADRjINJbeRLGkOoBAAAAAAAAAADQCZ0eAAAAbRDptUiqBwAAAAAAAAAAQA82b29vpWcAAIBsNpvN/IP4QzJUSKTXNLcvpJeXl9IjAAAAAAAAANTo/v6+9AgAQAH26QEAAFA7lVfrbNUDAAAAAAAAAAAgNp0eAAAAVRPpxSDVAwAAAAAAAAAAIDCdHgAAAPXqPOI67Xcf/ys9SwZSPQAAAAAAAAAAAKLS6QEAAFCpgXyrh2V6n04/dswW++wAAAAAAAAAAAAIT6cHAABAjUR6I7/YloF7F+DsAAAAAAAAAAAA6JZODwAAgOqI9EqPsCCpHgAAAAAAAAAAAPHo9AAAAKhLz5Heab/roVWT6gEAAAAAAAAAABCMTg8AAICKdJtpdVLofZDqAQAAAAAAAAAAEIlODwAAgDYEXqanTAMAAAAAAAAAAICm6fQAAACoxUCuFjLSe9+h122kZ6UeAAAAAAAAAAAAYej0AAAAqEJXkV7Ped45qR4AAAAAAAAAAAAx3JUeAAAAALqIsno4x7xO+128RBMAAAAAAAAAAICQdHoAAABUrdFSS5U30vZwdK0AAAAAAAAAAABonU4PAACAwgYyrZojPXVZLgOpnpV6AAAAAAAAAAAANOG29AAAAAB0Te3GME8IAAAAAAAAAAAA9dPpAQAAUKkeFqltD8ceTvMqFwEAAAAAAAAAAICm3ZUeAAAAgH51uypNljbJab9zxQAAAAAAAAAAAKiZTg8AAIAyhiO9eF1WvDPKa3s4DjwSUj0AAAAAAAAAAABqptMDAACA/ERlAAAAAAAAAAAA0I/N29tb6RkAACCbzWYz/yD+kAwr6G2Z3rDhq3Eu/JXxYFRr/FP6559/F50EAAAAAAAAoHL39/elRwAACrBPDwAAAIC/xvd4l/z49fPbr+v3AAAAAAAAAACAwOzTAwAgFPv0oAl2pn1in945j8fK5od5aWR7AAAAAAAAQFT26QFAn+zTAwAAAOhLqTbv3PnaPc0eAAAAAAAAAADQOvv0AAAIxT49qJ9taV/Zp/eJh2QJNbR5Vwn2AAAAAAAAgADs0wOAPtmnBwAAABBWE3neh48le4I9AAAAAAAAAACgLfbpAQAQin16UDl70r5ln95XHpX52ir0LhHsAQAAAAAAAM2xTw8A+mSfHgAAAEAcMfK8D+8b9tR6AAAAAAAAAABA5XR6AAAArCRYPkRBp/3OSr2vAr/E3mu9G8EeAAAAAAAAAABQq9vSAwAAAMDNzc2N7IpznodJTvtd4Ejv3I9fPz+aPQAAAAAAAAAAgHro9AAAAFhDJxERq/FEveun0Dun1gMAAAAAAAAAAGqj0wMAAABqZKXesD4LvXNSPQAAAAAAAAAAoB46PQAAAMpTZMEknRd6HyzWAwAAAAAAAAAAKqHTAwAAYHGaIpbQ53Nljd5Xaj0AAAAAAAAAAKA4nR4AAACFWabHJZ6Ncwq9YVI9AAAAAAAAAACgIJ0eAAAAy1IWsZx+nq5+znQOi/UAAAAAAAAAAIBSdHoAAAAAVRPpTSLVAwAAAAAAAAAA1ndXegAAAAAAvqfQS/Oe6v3559/SgwAAAAAAAAAAAL2wTw8AAIAFXa2MtofjOpPQqKtPSOCSLfCprcNiPQAAAAAAAAAAYDU6PQAAAIDqiPSykOoBAAAAAAAAAADr0OkBAAAAVOS034n0MpLqAQAAAAAAAAAAK9DpAQAAsBStEVlsD8fhb4j0pEU6l3r8+PVTrQcAAAAAAAAAACxKpwcAAEAxV/sr6IpIb1FSPQAAAAAAAAAAYDk6PQAAAIDyRHorkOoBAAAAAAAAAAAL0ekBAAAAFCbSW41UDwAAAAAAAAAAWIJODwAAgEXojlhT089b08O3SKoHAAAAAAAAAABkp9MDAACgjO3hWHoEmhH4aRHpFSHVAwAAAAAAAAAA8tLpAQAAAJQh0itIqgcAAAAAAAAAAGSk0wMAAAAoQKRXnFQPAAAAAAAAAADIRacHAABAfgIk1tfWU9fWtIFJ9QAAAAAAAAAAgCx0egAAAACrEulVRaoHAAAAAAAAAADMp9MDAACggO3hWHoEGhPmmRHpVUiqBwAAAAAAAAAAzKTTAwAAAFiJSA8AAAAAAAAAACAknR4AAAAAvbNSDwAAAAAAAAAAmOOu9ADAX09PT/MP8vj4OP8gAAAwk41hlHLa77aHY+kpLvLSqNyPXz///PNv6SkAiOb5+Xn+QR4eHuYfBACa5iMVALLwkQoAWYz5SH19fR3+Bn/jFwBCsk8PAAAAYHEivSbYqgcAAAAAAAAAAKTR6QEAAAAsS6QHAAAAAAAAAAAQm04PAAAAAP6yUg8AAAAAAAAAAEig0wMAAABYkGV6zZHqAQAAAAAAAAAAU+n0AAAAWNv2cCw9AqxEpNcoqR4AAAAAAAAAADCJTg8AAABog8ITAAAAAAAAAACAOun0AAAAABZhmV7TrNQDAAAAAAAAAADG0+kBAACQkzCJsup5AuuZBAAAAAAAAAAAgKXp9AAAAADgG1bqAQAAAAAAAAAAI+n0AAAAADKzTC8MqR4AAAAAAAAAADCGTg8AAAAAAAAAAAAAAAAA0un0AAAAAHKyTC8YK/UAAAAAAAAAAICrdHoAAAAA2Yj0AAAAAAAAAAAAOqTTAwAAAIAhVuoBAAAAAAAAAADDdHoAAAAAeVimBwAAAAAAAAAA0CedHgAAAABcYaUeAAAAAAAAAAAwQKcHAAAAAAAAAAAAAAAAAOl0egAAAAAZnPa70iOwLCv1AAAAAAAAAACAS3R6AAAAAAAAAAAAAAAAAJBOpwcAAAAwl2V6nbBSDwAAAAAAAAAA+JZODwAAAAAAAAAAAAAAAADS6fQAAAAAZrFMDwAAAAAAAAAAoHObt7e30jMAAEA2m81m/kH8IRnmGBMsbQ/HFSZpyPjKy6Wr8wHT6fXmzz//lh4BAAAAAAAAqNf9/X3pEQCAAuzTAwAAAAAAAAAAAAAAAIB0Oj0AAACAdJbpAQAAAAAAAAAAoNMDAAAAgAl+/PpZegQAAAAAAAAAAKAuOj0AAAAAAAAAAAAAAAAASKfTAwAAIKft4Vh6BLq28hN42u/W/HUAAAAAAAAAAADUSacHAAAAANP8+PWz9AgAAAAAAAAAAEBFdHoAAAAAAAAAAAAAAAAAkE6nBwAAwNpO+13pEWhSbU9ObfMAAAAAAAAAAABQik4PAAAAACb78etn6REAAAAAAAAAAIBa6PQAAAAAAAAAAAAAAAAAIJ1ODwAAAAAAAAAAAAAAAADS6fQAAAAAAAAAAAAAAAAAIJ1ODwAAgMy2h2PpEejUms/eab9b7XcBAAAAAAAAAABQOZ0eAAAAAKT48etn6REAAAAAAAAAAIAq3JUeAAAAoArfrsayFw7qYX8dAAAAAAAAAAAA1dLpAQAA/bqa/Zx/g2Yvr9N+55ICAAAAAAAAAAAAMej0AACAHiUs5nr/kbbSMp0hAAAAAAAAAAAAwAp0egAAQF8SCr2vP15/8/b1ND++ss7w28Nx5qWGqdZ8YXq8AQAAAAAAAAAAOHdbegAAAICVnPa7XGlNzYnO1dOseXgAAAAAAAAAAACAFun0AACALmSP0zJWfxmNHKnCyQEa9ePXz9IjAAAAAAAAAAAA5d2VHgAAAGBxV7O07eE49UeANXlJAgAAAAAAAAAAUDP79AAAgOCG857t4fg10hv4+qSDr6yqYcZobmBqNuYFCwAAAAAAAAAAAAvR6QEAAJENlGAjS7w5v2JNlYxxTjcFAAAAAAAAAAAAdEKnBwAAhDUc6Y05wshvq7CRAwAAAAAAAAAAAGA1Oj0AACCmS+1crjV6ZCFxZAzPCQAAAAAAAAAAAJXT6QEAAAENRHor/8Z1VFsxSSJZhycNAAAAAAAAAACAsnR6AABAL5Q81GlSZlltkwkAAAAAAAAAAAA9uys9AAAAQGZCJlqR9qy+/5TuFAAAAAAAAAAAAOqh0wMAAEK5FD6tEDWd9rtW0qk159wejsM1WkPXbb6MEemnQwW+hlcvWuBzBwAAAAAAAAAAoBU6PQAAII6CkV5B9gcWVM/FnzpJ7BcFAAAAAAAAAAAArEynBwAABKdHAgAAqvXff/+VHgEAAAAAgMzu7+9LjwAAFKDTAwAAgqhnsxmfbA9Hd4eFCHGpgf/GBgAAAAAAAAAA3JYeAAAAYEEzG56QdVmFXVPI60wWng0AAAAAAAAAAACaoNMDAAAi6Dbm6fbEAQAAAAAAAAAAAOpxV3oAAACApVS4OK5b28Oxh6Sw5keuxet/deaaLzgAAAAAAAAAAABd0ekBAADN+zbmEfC05bTftXjLGpq5oVEBAAAAAAAAAACgObelBwAAAKhUi/vHrioYa+nEyMsTBQAAAAAAAAAAQD10egAAQNvqqenWr4bqOfcsgp0OM3keAAAAAAAAAAAAaIhODwAACMiirTq5L+TiWQIAAAAAAAAAAKAqOj0AAKBhy23csssLAAAAAAAAAAAAgJF0egAAQDRFFm01sd2rhiGHZ5BH8m74SajhSQYAAAAAAAAAAIBzOj0AAIAmSdoAAAAAAAAAAAAAKqHTAwAAWrVcqCaBW5SVegyzTA8AAAAAAAAAAIDm6PQAAIBQijQ8wiGAbvkIAAAAAAAAAAAAbnR6AAAAn0Td51ZVSWKlHpe0skyvnkkAAAAAAAAAAACogU4PAABoUucpV+enDwAAAAAAAAAAAFAVnR4AABBHkQ1X1mqlsVKPr1pZpgcAAAAAAAAAAACf6PQAAAD+Tx4GAAAAAAAAAAAAwFQ6PQAAgHStLPiqc04r9ThnmR4AAAAAAAAAAADt0ukBAABN+hrt9JPxRArY+rlrzOE5oVoeTgAAAAAAAAAA4N1d6QEAAAASZa8jIvVvMZz2Ow1MJ1p89W0PxxbHBgAAAAAAAAAAYAn26QEAACSSkGXhMjLMEwIAAAAAAAAAAED9dHoAAAA3N22u8xqp/sxpYMLA94UPA3e5/qcXAAAAAAAAAAAAbm5u7koPAPz19PQ0/yCPj4/zDwIAQM067NZO+51YK7AOH2nC8NYEwBhX/8Xv79+/rx7k4eEh0zgA0Krn5+f5B/GRCgA+UgEgizEfqa+vr8Pf4G/8AkBI9ukBAACkkGfk5XryVf1PRf0TAgAAAAAAAAAAsA6dHgAAgI1eVRhIntygqAburAQOAAAAAAAAAACAhuj0AAAAImsrdpLqdUWkBwAAAAAAAAAAQBg6PQAAgMlKRURaNaiNpLBbbj0AAAAAAAAAAHBOpwcAAPRO/FYVK/U6YZkeAAAAAAAAAAAAkej0AAAAqItULzyRHgAAAAAAAAAAAMHo9AAAAKZpqCNqaNRPpHqBxYv0Gh2bOdx0AAAAAAAAAADgE50eAADQtYair4ZGzUKqF1K8SA8AAAAAAAAAAABudHoAAABUS7jVD/caAAAAAAAAAACApun0AAAAJlATrezSBbdSr0WX7lqAl1WAU2A8txsAAAAAAAAAAPhKpwcAAPQrdusVviSJffviCRzpAQAAAAAAAAAAgE4PAACgAT03aQMdV8+XpS0iPcLw0AIAAAAAAAAAAN/S6QEAAIwlzyhlezheuvhSvfp1EukFOx0AAAAAAAAAAAAm0ekBAACd0nc1R6rXok4iPTrhuQUAAAAAAAAAAC7R6QEAAAQUNSaR6rWlt0gv6nkBAAAAAAAAAABwlU4PAABgFAVOJaR6regt0iM8jy4AAAAAAAAAADBApwcAAFA7BdonUr36dRvphT9BAAAAAAAAAAAAvqXTAwAAeiToat1AqufmFtdtpEdgnl4AAAAAAAAAAGCYTg8AAOC6tgqNtqZNtj0cLdar0LcXf+BmxdPPmQIAAAAAAAAAAPBBpwcAAEDDNFFVuRTprT8JZOQZBgAAAAAAAAAArtLpAQAAVM1quKu62tXWlm5vTZ9nDQAAAAAAAAAA0DOdHgAA0B3lW0jnZZRKqhR34UPnpx+JWwkAAAAAAAAAAIyxeXt7Kz0DAABks9ls5h/EH5LDS+j0CnYaU6eVlEAlJMExeFMFYGkvLy+lRwAAAAAAILP7+/vSIwAABdinBwAAcIVIA0jgrSMANxEAAAAAAAAAABhJpwcAAFAv+7gAShHpAQAAAAAAAAAA4+n0AAAAABYh9AIAAAAAAAAAAOiETg8AACAOURDUxquyUW4cAAAAAAAAAAAwiU4PAADoy2m/Kz0CAFUT6QEAAAAAAAAAAFPp9AAAAIYUrDUkhRCD6AsAAAAAAAAAACA8nR4AAADAsqR6DXGzAAAAAAAAAACABDo9AACAILQlUDOv0Ca4TQAAAAAAAAAAQBqdHgAAAACI9AAAAAAAAAAAgHQ6PQAAgBqd9rvSIwCZycAAAAAAAAAAAACi0ukBAAAArESqVy23BgAAAAAAAAAAmEOnBwAAEIHCBFrh1VohNwUAAAAAAAAAAJhJpwcAAFCd035XegRgQaqwqrgdAAAAAAAAAADAfDo9AACgL3oMoAbeiyrhRgAAAAAAAAAAAFno9AAAAOpimR50QiFWnFsAAAAAAAAAAADkotMDAACoSFqkJzUBmMo7JwAAAAAAAAAAkJFODwAAAKAMqVgprjwAAAAAAAAAAJCXTg8AAKAWacv0gKZtD0fN2MpccAAAAAAAAAAAIDudHgAA0J06Cw2RHvSszvelkFxqAAAAAAAAAABgCTo9AACAIevkc3N+i+YEYvBaXoGLDAAAAAAAAAAALESnBwAAUJhNesA7FdmiXF4AAAAAAAAAAGA5Oj0AAKBH9dQaIj3gXD3vTsG4sAAAAAAAAAAAwKJ0egAAAFcsl9KJ9ICvFGXZuaQAAAAAAAAAAMDSdHoAAECnimcbWSK94mcBLMFLO5ft4ehiAgAAAAAAAAAAK9DpAQAAXJd98Z1NesAwgdl8LiAAAAAAAAAAALAanR4AANCvSQlHrrLutN+J9ICRlGbJXDoAAAAAAAAAAGBNOj0AAKBrK4ccCj1gKov1pnLFAAAAAAAAAACA9en0AACA3o3POeaswrv6swlViRAF+uH1PpILBQAAAAAAAAAAFHFXegAAAIDytofj+ADvtN9NSvtGDjDygEC33t8orOW8xBspAAAAAAAAAABQkE4PAADg5mZ6qvfxU8PfMOb3Tv0RoGeT3qz6IdIDAAAAAAAAAADK0ukBAAD8lVC/zKxlhCVAAov1znkjBQAAAAAAAAAAaqDTAwAA+L/V6pf5YYk0BTqn1vM2CAAAAAAAAAAA1EOnBwAA8FnCYr1JB//6xZ5LG2COPms9hR4AAAAAAAAAAFAbnR4AAMA3PiKQjPWLsARYSD+1njdSAAAAAAAAAACgTjo9AACAIfODPVUJsI7YtZ73UgAAAAAAAAAAoGY6PQAAgFE+JSLDJczSPYleBbgkXq3nHQ8AAAAAAAAAAKifTg8AACBFxm4kUk4DVGL+LtDi5HkAAAAAAAAAAEBDdHoAAAAAYTW3Xk+eBwAAAAAAAAAAtEinBwAAABDcefxWZ7MnzwMAAAAAAAAAAJqm0wMAACgpIZhRswBzfLyHFA/2vJsBAAAAAAAAAABh6PQAAAAAevQpk1sn29PmAQAAAAAAAAAAIen0AAAAWiJxARby7dvLnHjP+xUAAAAAAAAAANAPnR4AAEAx62yvAkimtQMAAAAAAAAAABjjtvQAAAAAAAAAAAAAAAAAANAwnR4AAEAZCcv0LLYCAAAAAAAAAAAAqNBd6QEAAADW8DWK07wBAAAAAAAAAAAAkIVODwAACO7S2rr3r5eq9SzTAwAAAAAAAAAAAAhDpwcAAIQ1poUrW+uNV/+EAAAAAAAAAAAAAN26LT0AAADAIiYtrEvYbjfHyr8OAAAAAAAAAAAAgEXp9AAAgICChXCW6QEAAAAAAAAAAADUTKcHAABEkxbprZb2BWsIAQAAAAAAAAAAANDpAQAAVM0yPQAAAAAAAAAAAIDK6fQAAIBQKt9WN3U8kR4AAAAAAAAAAABA/e5KDwD89fT0NP8gj4+P8w8CAMBCKm8IAc75p1QAyOLqR+rv37+vHuTh4SHTOADQqufn5/kH8ZEKAD5SASCLMR+pr6+vw9/gv6UCQEj26QEAAFTKMj0AAAAAAAAAAACAJuj0AACAOGpeWDd1NpEeAAAAAAAAAAAAQCt0egAAADc3C3dxIj0AAAAAAAAAAACAwHR6AAAAyxLpAQAAAAAAAAAAAMSm0wMAAJDGAQAAAAAAAAAAAJBOpwcAALAgy/QAAAAAAAAAAAAAwtPpAQAAvVsujRPpAQAAAAAAAAAAAPRApwcAAHRNpAcAAAAAAAAAAADATDo9AACgXyI9AAAAAAAAAAAAAObT6QEAAHGMr922h6NIDwAAAAAAAH+6iMQAACAASURBVAAAAIAs7koPAAAAsJ4VijiRHgAAAAAAAAAAAEBvdHoAAEAX1snhJkV6Cj0AAAAAAAAAAACAGG5LDwAAAJDTpfht6pq7qU77nUgPAAAAAAAAAAAAoE86PQAAoBfLpXpTjyzSAwAAAAAAAAAAAIhEpwcAAEQzUMEtkepNXaMn0gMAAAAAAAAAAAAIRqcHAAD0JWOqd9rvpkZ6uX41AAAAAAAAAAAAAPXQ6QEAAAENF3HzU72phd6NSA8AAAAAAAAAAAAgrrvSAwAAACxiezgOpHQf/9fUfC6h8VPoAQAAAAAAAAAAAMSm0wMAAMIaTvXejQn2kvfvKfQAAAAAAAAAAAAAeqDTAwAAIhuT6r1LjvEu/d6MRwMAAAAAAAAAAACgZjo9AAAguPdkLm+Gd/XXAQAAAAAAAAAAANCP29IDAAAArGGFfG57OIr0AAAAAAAAAAAAADq0eXt7Kz0DAABks9ls5h/EH5Jjy75YT5sHAAAke3l5KT0CAAAAAACZ3d/flx4BACjgrvQAAAAAq/rI6mYGe/I8AAAAAAAAAAAAAN7p9AAAgE59Cu2Gsz1VHgAAAAAAAAAAAACX6PQAAABubpR4AAAAAAAAAAAAAKS6LT0AAAAAAAAAAAAAAAAAADRMpwcAAAAAAAAAAAAAAAAA6XR6AAAAAAAAAAAAAAAAAJBOpwcAAAAAAAAAAAAAAAAA6XR6AAAAAAAAAAAAAAAAAJBOpwcAAAAAAAAAAAAAAAAA6XR6AAAAAP9j7+6O48a1BYw2XYpFVAo6SShWJ3GcgqkUXI6B96FtHV+pm02CJLABrPU0NSOxwX+7pr/aAAAAAAAAAAAAkE6nBwAAAAAAAAAAAAAAAADpdHoAAAAAAAAAAAAAAAAAkE6nBwAAAAAAAAAAAAAAAADpdHoAAAAAAAAAAAAAAAAAkE6nBwAAAAAAAAAAAAAAAADpdHoAAAAAAAAAAAAAAAAAkE6nBwAAAAAAAAAAAAAAAADpdHoAAAAAAAAAAAAAAAAAkE6nBwAAAAAAAAAAAAAAAADpdHoAAAAAAAAAAAAAAAAAkE6nBwAAAAAAAAAAAAAAAADpdHoAAAAAAAAAAAAAAAAAkE6nBwAAAAAAAAAAAAAAAADpdHoAAAAAAAAAAAAAAAAAkE6nBwAAAAAAAAAAAAAAAADpdHoAAAAAAAAAAAAAAAAAkE6nBwAAAAAAAAAAAAAAAADpdHoAAAAAAAAAAAAAAAAAkE6nBwAAAAAAAAAAAAAAAADpdHoAAAAAAAAAAAAAAAAAkE6nBwAAAAAAAAAAAAAAAADpdHoAAAAAAAAAAAAAAAAAkE6nBwAAAAAAAAAAAAAAAADpdHoAAAAAAAAAAAAAAAAAkE6nBwAAAAAAAAAAAAAAAADpdHoAAAAAAAAAAAAAAAAAkE6nBwAAAAAAAAAAAAAAAADpdHoAAAAAAAAAAAAAAAAAkE6nBwAAAAAAAAAAAAAAAADpdHoAAAAAAAAAAAAAAAAAkE6nBwAAAAAAAAAAAAAAAADpdHoAAAAAAAAAAAAAAAAAkE6nBwAAAAAAAAAAAAAAAADpdHoAAAAAAAAAAAAAAAAAkE6nBwAAAAAAAAAAAAAAAADpdHoAAAAAAAAAAAAAAAAAkE6nBwAAAAAAAAAAAAAAAADpdHoAAAAAAAAAAAAAAAAAkE6nBwAAAAAAAAAAAAAAAADpdHoAAAAAAAAAAAAAAAAAkE6nBwAAAAAAAAAAAAAAAADpdHoAAAAAAAAAAAAAAAAAkE6nBwAAAAAAAAAAAAAAAADpnkovAPjjx48f+zfy+vq6fyMAAADgb6kAcIiHr9Rfv3493MjLy8tBywGAWv38+XP/RrxSAcArFQAOseaV+vv37+Uf8P9SAaBJ5ukBAAAAAAAAAAAAAAAAQDqdHgAAAAAAAAAAAAAAAACk0+kBAAAAAAAAAAAAAAAAQLqn0gsAAAAAAAAAAIDqjeP49V9O05R/JQAAAABAfjo9AAAAAAAAAADY5maVt/7H9HsAAAAA0BidHgAAAAAAAAAAPLayzUvYlGwPAAAAAGqn0wMAAAAAAAAAgCUHFnoL21frAQAAAEC9dHoAAAAAAAAAAHDD2XnevY8T7AEAAABAdXR6AAAAAAAAAADw/2Qu9G5+uloPAAAAACqi0wMAAAAAAAAAgP/ZFOkt1HQ7Yz+1HgAAAABURKcHAAAAAAAAAACXy+qybmU79++PJTd74zhK9QAAAAAgPp0eAAAAAAAAAAA8Tun29HIfv5sQ7BmsBwAAAADxfSu9AAAAAAAAAAAAKGw5n5um6ahMLnlTyRP5AAAAAIAMdHoAAAAAAAAAAHRtIYE7sNDbv1mpHgAAAACEpdMDAAAAAAAAAKBfy5HeqR8t1QMAAACAZuj0AAAAAAAAAADo1L3s7aQxejc/aOuvSPUAAAAAICCdHgAAAAAAAAAAPVqI9HIuQ6oHAAAAAA3Q6QEAAAAAAAAA0J0gkV7yh0r1AAAAACAUnR4AAAAAAAAAAFwuhSK95I+W6gEAAABAHDo9AAAAAAAAAAD6crNwKxjpBVkAAAAAAJBMpwcAAAAAAAAAQEcij6HbmupF3hcAAAAA6IpODwAAAAAAAACA3tU7y06qBwAAAAARDPM8l14DAAAcZhiG/Rvxh2QAAADyeH9/L70EAIDu3KzaokV6W9O7aOsHAIDOPT8/l14CAFCAeXoAAAAAAAAAAFAxI/UAAAAAoDidHgAAAAAAAAAAXbjXs43jGCp1Mx8PAAAAAKrzVHoBAAAAAAAAAABQ3keqJ5MDAAAAALYyTw8AAAAAAAAAAP4nwmy9ra1ghDUDAAAAQM90egAAAAAAAAAA8P/I3gAAAACATXR6AAAAAAAAAAC0b2t6J9UDAAAAANbT6QEAAAAAAAAAAAAAAABAOp0eAAAAAAAAAADcUHak3jRNBT8dAAAAANhEpwcAAAAAAAAAANUrWxUCAAAAQOd0egAAAAAAAAAAAAAAAACQTqcHAAAAAAAAAAAAAAAAAOl0egAAAAAAAAAAAAAAAACQTqcHAAAAAAAAAAAAAAAAAOl0egAAAAAAAAAAtG+apgy/AgAAAAD0SacHAAAAAAAAAAAAAAAAAOl0egAAAAAAAAAAdGHTfDzD9AAAAACA9Z5KLwAAAAAAAAD4YxzH5R84MBh4+Fl5llHQ8hE4+1C3cQyhDXueh1Xr9kE0TdOak97t8QEAAAAA0gzzPJdeAwAAHGYYhv0b8YdkAAAA8nh/fy+9BGKpLhSpN2AIcqjrPYDQjCBPg1D6eTRlq7V32nqVxlk5AAD07Pn5ufQSAIACzNMDAAAAAACA8rQiHRrHUU0BRPPpfdTwY+q6a8H3V6QHAAAAABXR6QEAAAAAAAAAcMO/nViTDViTOwUAAAAAFKHTAwAAAAAAAADggY9mT9sGAAAAAPCVTg8AAAAAAADYTKSxn2MIVEqwBwAAAADw1bfSCwAAAAAAAAAu0zRVVDtUtNSbIqw/whoAd+JO4zh+NHscbuuxdT0DAAAAQFnm6QEAAAAAAEAUD79hny2HaP67/l938Lxj2/zBhKpd71Cx2R7Xo+dZBwAAAAB0TqcHAAAAAAAA1fi3gjijKum5sji21en5SEKN9mfSB971ax5Emz4uT4Wo1gMAAAAAOjfM81x6DQAAcJhhGPZvxB+SAQAAyOP9/b30EqjesemFuOKy+5A6htC8hafEIU+A5afQ/o/I0+x5GO639Uw55gAAEMrz83PpJQAABXwrvQAAAAAAAAAg0YFfyvf9/qvk4zBNk2MIPTj1Tj870rtsfFglf+I4jnmCQAAAAACAOJ5KLwAAAAAAAABIN02TFqIseR6QwbGPmuvW1rw+/v3cra+bcRw9IdMYpgcAAAAANdLpAQAAAAAAAKTQRQB5nPS0eVjrfQrtPv55fUV2/UlPSwAAAACgB99KLwAAAAAAAADYRf9QhMMO3Trp9i81HHV5d26uapqmTQfB3NdNDNMDAAAAgErp9AAAAAAAAAARxR8rj4MoAvjqpAdphgdO2kdsqvW8ZQAAAACA5un0AAAAAAAAoHqysWy2TpECqMLCk205sVv/VJTqrWGYHgAAAADUS6cHAAAAAAAAXC4KihVHQA4BNGzPI25lredFs0ykBwAAAABV0+kBAAAAAAAAiPQA7lrZj62p9aR6R/FWAgAAAIBonkovAAAAAAAAADjANE3ih/PIIUi4v1w2VOeQV8nDjYzj6O74ykscAAAAAGqn0wMAAAAAAAD+6LadWK4j+jwmnTskmPm6EdcS9dr0gpDqbbX1mePoAQAAAEBAOj0AAAAAAABohJF6aRYOmhCiKxlun08f4QIjoKNeJVK98zhuAAAAABCTTg8AAAAAAAD4n97CCZFe58qmrR+f7mKjSerxlTYdJY8LAAAAAAjrW+kFAADUZ9ii9GIBAAAA6Iuv728i0uvW+FfphfwRbT107t4DMOESXX6WuuYvIj0AAAAAaIh5egAAD+xs7T79+jzP+5YDAAAAAKfrZKSeSK9D8aMgE/Zoj6l6C0R6AAAAANASnR4AwA3nzcGT7QEAAABABCK93hyYCV2vkJsbXPhPWwn2aMlCqtdJGX6TSA8AAAAAGqPTAwD4n/PyvOVPVOsBAAAAcCCTi/YQQjRm572Qdj3c/K3klVx/0ZVJZoe/SqR6n4j0AAAAAKA9Oj0AgMulRKF389MFewAAAAAE0XY1ca+OaHiXO5SWGJ13DXza8tblqfUIou23QzYiPQAAAABokk4PAOhd2ULvE+P1AAAAADiEkXoLRHrNS7j485/9j0/ctFq1HlXzbtrKzQ4AAAAAddHpAQD9Sij0NhV0yQWgWg8AAACACJocmiTSa151U6oSgr0m700619VVvfJm7+eAAAAAAEAzdHoAQI/WF3R7YrmP300L9tR6AAAAAHAskV7b1nduMc/4pmDPYD0qZaSeSA8AAAAAGqbTAwC6s6aaO7aOu25NrQcAAABAToe0EC0NOBLpNaz2Qu+T6yLVetAYhR4AAAAANO9b6QUAAOQzDMPDWG6e55OiuD1bTmv8AAAAAICrzsc3tW19+lJX/bJ+wS5vTnLSLXNvs21fySI9AAAAAOiBeXoAQC/WFHoZlpE8W28YBlP1AAAAANjkkJF6DVg4CIqIqvXQvaycrWewHkTWw8MKAAAAALiYpwcAdGK5iztvht7CJyb8lql6AAAAAORXe+kn0mvVmiuzuhl696zckdrvVmrhSttEpAcAAAAA/dDpAQDtexjpZVvJ/s8dhkGtBwAAAMB6nX/vX6TXqofdSzOF3r/W7JSACkLpqigGAAAAAHR6AEDjFqq2/GP0vi4g7RelegAAAADkVGn5I9Jr0jiOayK9PIsp4mHSs+YQQVlt36QfVkZ6GVYCAAAAAOSh0wMAWrYc6eVcCQAAAACQk0ivSX2O0bvJYD2a1NJ1K9IDAAAAgA49lV4A8MePHz/2b+T19XX/RgB6ECfSm+c5bTjeMAxx9gIAaI+/pQLAIR6+Un/9+vVwIy8vLwcth35N09RS+bCGSK9JnY/R++q6vwuHZRzHZo7Jz58/92/EK5VsPK+AsLxSAeAQa16pv3//Xv4B/y8VAJpknh4A0Kx7/Vu0vC15PWmBHwAAAAAkaKP000XUa/kK7GeM3lfLO97GnQt1EekBAAAAQLd0egBAm2qJ9HaS6gEAAACwRldJwL1AoquD0JiHkV62lcQk1YM4RHoAAAAA0DOdHgDQl2EYtG0AAAAAkKCK2kek1x6R3hpSPYhApAcAAAAAndPpAQANeljiDX/lWc9De6b8xdkLAAAAACLroQ0Q6bVnIXqZpsmZ/dfyAZHqwdkeRsUeWQAAAADQPJ0eANA1kRsAAAAArBc59RHptWc50su5kopI9aAIkz8BAAAAgMvl8lR6AQAAB9ua3l1/fs9Eu+KGYah6/QAAAACwk0ivPSK9ZNM03Tt64zg6enCshwXs2Tfd1wW4zQEAAACgFPP0AAAulwCD9YR2AAAAAJyt1S/umxLWHpHeTqbqQR7FIz0AAAAAIBSdHgAAAAAAALBWtMhH0NUe5/QQUj042/KtNE2TRxYAAAAA9EanBwDwR/GRentUvXgAAAAAsmmsGRB0tcc5PZBUj+pUdJs/jPSyrSTORwMAAAAAOj0AoClyNQAAAAA4W5DCR9DVFec0jeNGTEHeI8niRHq1H0kAAAAAaIxODwDgf2R+AAAAAFAFkV6T7p1W53SPe0dP3gNp4kR6AAAAAEA0Oj0AAAAAAADoyCEJQdnCR6TXJJHeeaR6cJTlF5DnFQAAAAB0TqcHAAAAAAAAVEOk1yTBWCmOPAm6fdh6AQEAAAAAy3R6AAAAAAAA0Jcmc4Imdwqn9SiOJHFUWoeK9AAAAACAh3R6AAAAAAAAwGZFQot7H6qRqJrTmse941lpNEV7It/yMSO9r6uKfAwBAAAAoAc6PQAAAAAAAOhOjV/lV3M1yWnNSarHJi6Mq5iRHgAAAAAQkE4PAAAAAAAASJEz4VBzATSguvZv4e3jBQQAAAAAfKLTAwCaMs9zwV8HAAAAAM4g0muVM5ufkXrEFPCuH8cx8jPKPQsAAAAAAen0AAAAAAAAoEcRMoM1pAitihzAtE2qRykVXWMLS/WMAgAAAADu0ekBAK1JnolnmB4AAAAAbHV2dKGUAAgl+dlb0fM8/lIrKh4BAAAAoCs6PQAAAAAAAOhUkN7gnvilBMkM0yvLSD0e6vZiiP/q6fbUAAAAAEB8T6UXAABwvHmeh2HY+isnLSaP2tcPAAAAQL3GcTwjXYhfSnA4ZzanaZrUPmRTyyN9+aYIfsuEOpIAAAAA0CedHkTx+vpaegkATdmU6oncAAA+8bdUADjEw1fq+/t7npXAgpitTi1FB2kCXnJ8OKm83enl5aX0EkhXyy1fyzoB9vBKBYBDrHmlPj8/Z1gJABDNt9ILAAA4yzzPDwO8NT+Tx9YBgAAAAAAQx7Ftg0ivT05ufo4599x7DidcM8sviDgXoUgPAAAAANjPPD0AoHHXDO9rBRckzwMAAAAA/iXSa54YJr6YI/WozsObPc5l5rkEAAAAABzCPD0AoAvzF6VXdKTGdgcAAACAzKooJeIskjM4v6U48nx1SLQm0gMAAAAAOqTTAwAAAAAAAPY6tXOIk3Owkx6mFs5Ut/b30uM4ivQAAAAAgD7p9AAAyhuGofQSAAAAAOhahGTiXiwRYW2cyikuy/HnQGuytziXXEuRXpyjCgAAAAA9eyq9AAAAdpnnufQSAAAAAOByuVzGcUzuBER6AMUlD9NbH7zFeaq3FOkBAAAAAEGYpwcAAAAAAACUJNLrxM0T7SxHcPMsqJh6k3bGx3EU6QEAAAAAXJmnBwBQ2DAMyb9rmB4AAAAAR5mmaX+3kDBST6QHUNbDh//XB3LC+yLOU12kBwAAAACcRKcHAAAAAAAAlCGW6IdzXaOE8pa6rLkxP66BPXdxnAvJswgAAAAAOI9ODwCgVobpAQAAAHCsQ0bqrbfwWXGKDk7lRMeR+fanuPWne+eF4TYHAAAAAPqh0wMAKGkYhrRfFOkBAAAAENPKAVwiPYDM8qeYnud5OM4AAAAAEIRODwAAAAAAAPgjz0wtkV5vDGqr18rylmiK33QuGwAAAACgQzo9AIBiDNMDAAAAoEnLYY9IjyunO5o8mS4JKjov7msAAAAAoGc6PQCAyoj0AAAAAKiXSA+gSbU8w2tZJwAAAABQI50eAEAZycP0AAAAAOBU583UEukBNMbTGwAAAADgw7fSCwAA6FFypGeYHgAAAABV+JrkifS6dfPUO+kx3TwvJ4W7VG36q/RCAAAAAAACMU8PAKAaIj0AAAAA8jh8pJ5ID6B2HtcAAAAAAMt0egAAuaUN0xPpAQAAAFCXcRwfRh2qD4DIPKUBAAAAANb7VnoBAAB9EekBAAAAUIUD24x7w/TkHwAAAAAAADTDPD0AgHxEegAAAAB05V6hdxHpdePmNeDsRzZN09eztmY8JgGtP2v3HtdOPQAAAADAejo9AIBMRHoAAAAAcKX6AFjv3jNTCw0AAAAAEMq30gsAAOiCSA8AAACA6sg8ACJbeEovJHxnbwoAAAAAoFs6PQCA0yVEevM8i/QAAAAAaJL8DyAgqR4AAAAAwE46PQCAEw3DkBbpnbEYAAAAANjq8KZOpAdwIHEdAAAAAEAcT6UXAKz148eP/Rt5fX3dv5FT2c317GYQdnO93nYzIc+7ZCn0OjmbO/348SP+PnZyKu3menYzCLu5nt0Moofd7GEfL3ZzC7sZhN1cz24G0cNu/vz5c/9GXl5e9m/kVHZzvTN289hIL+xuHqu93bx5GbS3mzfVspvTNO1JvGrZzT0W9nH97h+1jwvnaxzHTQ/e5U19//49YXnBT+Wljyv2Yje3sJtB2M31gu9mD/t4sZtb2M0g7OZ6djOITnYTAGiAeXoAAAdLm6F3MUYPAAAAgJCOiutM0gOo2tvbW+klAAAAAACEZp4eAMAu//nPf/ZvRKEHAAAAQNtEen3aM5ONaLZOZiOnPCP1AAAAAABYptMDALjrkAZvmUIPAAAAgOZpewDOlifVe3t7+/79e8r6AAAAAAA6oNMDAPgsQ553UegBAAAAUI8945VEegAAAAAAAPTgW+kFAADEcnakN/916qcAAAAAAABdWeiit7bWC5t6e3vbtCkAAAAAgH6YpwcAkNUwDBfD9AAAAADog2F6fJU8m7EKbe8d8S2MPx3HcdMzeWFTb29v379/T1kfAAAAAEDTdHoAAAVca70rzR4AAAAAwUmPAAAAAAAAYNm30gsAAIjlv//9b+ZPHP7K/LkAAAAAAEBjFobmbY2uF4bmvb29bdoUAAAAAEAPdHoAAJ/lT/Wu1HoAAAAAAEAcUj0AAAAAgPWeSi8AACCi5FTv9fX1+g/Jxd31F+d5Tvt1AAAAAAhiYaYTAOeZpune6LxxHD2cAQAAAABOYp4eAMAp5r/Sft1sPQAAAACCuBd7ABDWQoy39alupB4AAAAAwEo6PQCAc+0J9tR6AAAAAABAWVI9AAAAAIA1nkovAACgF/M8p0V3wzAkz+UDAAAAAAjl5pivnz9/7t/yy8vL/o2sd3Mo2cIQs0tVu2mQZgOmabp3HsdxXL5WAQAAAABIYJ4eAEA+BusBAAAA0AkFCEBxC4/irSmmkXoAAAAAAA/p9AAActtT6x2+GAAAAABYYKYWAA9J9QAAAAAALjo9AIBSpHoAAAAAAMCp8ozUAwAAAADgotMDAChIqgcAAAAAQBUWci+Cy5PqGakHAAAAAKDTAwAoSaoHAAAAQHv0PFy5EqArUj0AAAAAoHM6PQCAwqR6AAAAAMS0dc4SrOG6is85ak+ekXoAAAAAAJ17Kr0AAAASXVO9tMwPAAAAAAAgwffv3++Nznt7exPy0Zvl2NVoWQAAAICu6PQAAMqb59l8PAAAAAAA4CTTNN2ricZxPDAlkurRqrRZo/d+S78HAAAA0CSdHgBA3YZhMFIPAAAAgDh87xwgpgNTvYWRetCStDZv65b92QkAAACgGb7VDQAQxZ6Rev5Q9+GQyYSOJwAAAHm8v7+XXgLclfzFdN815183L6Q2LhK71rkzZmRlmLu18GxP+JRjtwZxnJfnPeTeAQBoxvPzc+klAAAFfCu9AAAA/tjThh0SpwEAAAAAHOtmb1Cwf+AhkV7bFk5lwo157NaguPGv4msouAAAAAAA9tDpAQAAAAAAAAB0IVtcpzWiItHquGjrAQAAAGAlnR4AQCOM1AMAAACgOEO3ALrisU/tIhdxkdcGAAAAwE06PQCAQOZ53vPrUj0AAAAADuFL4QANO3akXrYBfXC4Ki5RtR4AAABARZ5KLwD448ePH/s38vr6un8jAAAA4G+pAHCIh6/UX79+PdzIy8vLQcsBCGQcR2O4Agqbgvz8+XP/RrxS/zVN073Tfezt6WYnpmMfdxlqVbcSR/FKBYBDrHml/v79e/kH/L9UAGiSTg8AIJZ5nveMxRuGYedQPgAAAACAAy3kQFRBGcJDbnPqsv9yXf9g/Pcnd36uVA8AAAAgvm+lFwAAAAAAAAC0wHfHASpy7ASwDPPEcqpxzay08+RO05T8B57pr+RPH8fRxQkAAAAQmU4PAKA1e8bxAQAAAIDvfwN0IltcV8ubZfzr0z/TjD0ndGdid+CmXJYAAAAAYen0AAAAAAAAADjRzRpBZhDNzTNiTibrLV8t8W/5+Ctkp+RTfGCh92mzyb/rcgUAAACISacHABDOPM87t2CkHgAAAAAA8NCxI/WaDDsFUW3YE+kdu5JsGwcAAAAgP50eAAAAAAAAsJcvmgNwOTr8g0PEjPR2foQbCgAAACAgnR4AAAAAAADwh+98c5KbHYLrLY6b50J/24mcZZ27nvwiR3o7P8gNBQAAABCNTg8AoE3DMJReAgAAAAAAUIFjU73l6EhZRE7xI72dH+eGAgAAAAhFpwcAENE8z6WXAAAAAABwOoFBBM4Ch2tsGKN7hAykegAAAAAN0OkBAAAAAAAAuzTWY3AS10ldnK/eHDtS74wNwla1DNMDAAAAoBk6PQAAAAAAAOByEU4A9C1nWeeNw9lqvMaM1AMAAAConU4PAAAAAAAAgGLUBWU5/pxnOTpy7RGTYXoAAAAAJNPpAQANGm4pvagC+txrAAAAACAs8UM090IpZ6pbh4/Ua+ZaUhXWpcPz1eEuAwAAAASk0wMA2rGc5HVb6wEAAADAqZppMChIXQBx5Ez1Qt37XmdE4DoEAAAAqJpODwBoxMoGT60HAAAAADeFiiVomAIhDsP0iMDbh8PtuaiqfgC6mwAAAACK0+kBAC3Ymt5J9QAAAAAAQlEXQBw5R+olbxMAAAAAIBqdHgBQvbToTqoHAAAAAFDEvWJHq5OTYXosO/xKaCDVq2KRdM5VCgAAD3uZlAAAIABJREFUAFCWTg8AqJvcDgAAAAAKkvQA9CY5BIqf6nmpNSDChQQAAABAt3R6AEC/NH4AAAAAcOVL7WRmpF5ZhumxxsL10HCqtyz+CmmARzEAAABAvXR6AEDFhHYAAAAAALCJ0IjIXJ/s4fq5OAgAAAAARen0AICuKf0AAAAAAIowUi8aE5z4Kv9IveIeLs8zCgAAAAC4R6cHAAAAAAAAQAFSvfzuHdvg6RQF5U/1PAEAAAAAgEo9lV4A8Mfr62vpJQDQmnmeSy8BAKiVv6UCwCEevlLf39/zrASWtToTCfik3vzp5eWl9BJW2flUnKap3nO01fLOXv9T2LfMOI5h18YeTis9qOWVCgDBrXmlPj8/Z1gJABCNeXoAAAAAAAAAlGGkXgTSlK0yX5/Fb4czRuotb3b/xvdwR1Sq+J0Sh0MBAAAAUIpODwCo1TAMpZcAAAAAANXzTW6Kk+rlce94SpJYo7dU76GwCwMAAAAACtLpAQC1mue59BJOpEIEAAAAAFDCHMWRJKyYqd6aftVtBQAAAAB8otMDAGhT2x0jAAAAAIfQGBDESaO6uFo4hobpJShyTUa4Ec67T9ekehGOwFcxVwUAAAAAlKLTAwAAAAAAgO4c0jzoEziQYCw/x3yT8a97P3DI8Vxu4cLmapfzU71L9lrPDULVwj4rAAAAANr2VHoBAAAlGToHAAAAQD/O+Mb2xzb1DJxnHEcXWDKpxkoVpcsPP+i8+2WapoVPz3MErp+S55mwvL8f6/GAisCzDgAAAIAIdHoAAA3SHwIAAAD0I8gX09cvQ8/APQtJjBImzdnD39oQ5Cl6oFNLtjXpWgY5a72HPKAAAAAAgKtvpRcAAFBM2JhtGIbSSwAAAACgAuM4Rogltqp02eSx0Lq4bLYS6a3R8HXV8K59yLCPK2+WHo42OXlKAwAAAFRKpwcAAAAAAABAFFK9Q4j0OE9vl5BUDwAAAABYSacHAFRsz0C8sMP09mt41wAAAACAHkj1dhLpcTYX0k0eUAAAAADQOZ0eANCdeZ4jl2zDMJReAgAAAABAYVK9ZCI9ONz6e8cDCgAAAAB6ptMDAPoSudA7RPM7CAAAAEADxEKsIdVLINLjw9lnPMIVlXMNm1I9zygAAAAA6NNT6QUAAOwyz/OaAXSd1Gud7CYAAAAAV9M0rSkBsmUMK7OECGkHDRjH0bX0L11QmutV1N7Ry3N3rHwNnffp+T9x/f56RgEAAABAhwZf5gYAarfQ6VX3R501zeGC6vb3DDuP4ZUjCQAAQB7v7++llwBQgeUwplQJc3NVoRbzL70QHGVrmujuy+OQZDTUyWpvjwAAevP8/Fx6CQBAAd9KLwAAYC9J1ZXjAAAAAAA0aTkzaG8S2lYPO0adBhxo6w3lGZWBgwwAAABAEDo9AKBlh4xWy2bPakV6AAAAAEDDpHo3jeMYc9ggtC0h1ev2MQUAAAAAXdHpAQAtWKjU6kr1AAAAAAC46WGq11sG83B/RXpwnoT7q8PHFAAAAAD0RqcHADSi9lTPMD0AAAAAgGUPw5h+GhiRHhSXdpep9QAAAACgYTo9AKAd9aZ6Ij0AAAAAgDXWpHptNzBrdlCkB3kk32vNP6kAAAAAoE86PQCgF2FTPZEeAAAAAMB6a8KYJgOYlWGPSA9ymqZJrQcAAAAAXD2VXgAAwJHmeV7I3oZhaClsa2lfAAAAAADWu1Yxy33L9b82E60p9CCyaZqSi7uPX3QLAwAAAEDtzNMDAFqzXK8NwxBqsF7yYkR6AAAAAEDnVg7Wq31clTF6UIU9g/WuGnheAQAAAEDndHoAQIMeNmxBaj2RHgAAAADAHiurmErrl/XLFulBEPtvxvGvQ9YDAAAAAOT0VHoBAACnmOf5YQU3DEOp4G1PJSjSAwAAAAD4cK1i1jQtHz8TvGrb1OcE3xfo0PqH0rJ/t+BOBwAAAIAq6PQAgGZde7blIu7jv+aM34zRAwAAAAA41qYw5vpjAbsXhR4046ha70qzBwAAAABV0OkBAI1bM1jv8redOzuEM0YPAAAAAOA80zStr2LijNdLKHmKrxlYY9NDaSXNHgAAAACEpdMDANq3ZrDe1Xnj9RR6AAAAAAAZJMywKhXspdU7shyoy7GD9T75tFnPBwAAAAAoS6cHAPRifa336cd2ZnJ7Cr39nw4AAAAA0KG0MObs4mVnqKPAgXp93L8nBXv3Nu65AQAAAAA56fQAgL5sqvWuPv3wmnBuZ5u3/oMAAAAAALhn5xirncXLgTWO0gaacep4va9ufpBHCgAAAACcRKcHAPRonufklO6QBm+ZQg8AAAAA4CgHVjEPN3J4eyOngSblGa93z5oP9fABAAAAgAQ6PQCgUx8tXIbubj2FHgAAAADAGcpWMVspZKATmcfrrWciKAAAAAAk0OkBAL2LEOzJ8wAAAAAA8ghbxVwJWqBD/974YZ9Oya575OEGAAAAQA90egAAf+QP9uR5AAAAAABFRBuvp2ABrqI9nY4yjqMHHQAAAADN0+kBAHz2KZ87NtvT5gEAAAAAxFFwjJVkBVjQ9pA9AAAAAGiSTg8A4IGvZd3Kck+SBwAAAABQkU/h3BlhjDYPSKDZWzZNk8PylTcOAAAAQH46PQCAzQR4AAAAAADNu1k4rExB1BHA4aRoAAAAABCcTg8AAAAAAAAAVhHgATlp8/rkvAMAAABUSqcHAAAAAAAAAAAhNNloiZwBAAAA6IFODwAAAAAAAAAASmoyzwMAAACAruj0AAAAAAAAAACggLbzPGP0AAAAAOiKTg8AAAAAAAAAALIKWOjJ6gAAAABgD50eAAAAAAAAAADkUDbPU+IBAAAAwHl0egAAAADwf+zdXW4iORiG0VQrW+gtELbA/i/YAmYLiDVUXySa6elJ8/PalOvnnGvifIAlg9AjAwAAALzWlIXe1nq8UsoM7ycEAAAAYGt0egAAAAAAAAAA8CoTJGRbC/O4zX4AAAAA6EKnBwAAAAAAAAAA7b2u0BNiAQAAAMDc6PQAAAAAAAAAAKCl5oWeMO/VPj4+vMgAAAAA1NDpAQAAAAAAAABAGw0LPdkYAAAAACyITg8AAAAAAAAAABpoEunJ8wKllOZ3GC6U/QMAAADQi04PAAAAAAAAAACq1Edi8ioAAAAAWDSdHgAAAAAAAAAA5GoiPXkeAAAAAKyDTg8AAAAAAAAAABIKvfkopdTfatjdCp4CAAAAwGbp9AAAAAAAAAAA4GlxUqXQ40VsLQAAAICOdHoAAAAAAAAAAPCcLNKTUQEAAADAWg3jOPaeAXh7e3s7Ho/1ixwOh/pFAGDRhmGoX8SHZADwLRUAmrh7pF4ul7uL7Pf7RuMAwFKdTqf6RRyptBVEegq9acSXHH7q/jbVzN99eObPkQoATTxypP78+fP2A/yWCgCr5D49AAAAAAAAAAB4iEIPAAAAAPiWTg8AAAAAAAAAAO57NtJT6PGUyssAAQAAAOjrR+8BAAAAAAAAAABg7kR6i7DZl32zTxwAAABgPnR6AAAAAAAAAADQTClFNNVRzYvvRjsAAAAAYjo9AAAAAAAAAAC45fF8S6FHpiYRtOsAAAAA5kCnBwAAAAAAAAAAfyXSWxxX6gEAAAAwPZ0eAAAAAAAAAAB8T6THBFymBwAAALACOj0AAAAAAAAAAPiGSG+5NnKlno0HAAAAMB86PQAAAAAAAAAAyGml5mkpqV78v2w8AAAAgFnR6QEAAAAAAAAAwJ8erKe0Ums1Taq3oLv7AAAAALhNpwcAAAAAAAAAAP8h0luHyjfo1RFdzfr2HgAAAMDc6PQAAAAAAAAAAOBpQqlFmG2qJ9IDAAAAWBmdHgAAAAAAAAAA/OuRgEootSBzS/U+Pj5EegAAAADro9MDAAAAAAAAAIAnCKUWpz7Va1XrVa5j7wEAAADM1nvvAQAAAAAAAAAAYC6aX57GTHwWbjXv7+ffZqVck30l0gMAAACYM50eAAAAAAAAAAA8Siu1aKWUymTujz+/sR8aNp92HQAAAMD86fQAAAAAAAAAAODt7YGwSi61AvWp3u8muIDRrgMAAABYBJ0eAAAAAAAAAACwIZ/l2wSJXSWFHgAAAMCC/Og9AAAAAAAAAAAALIBoamVKKXN+T+c8GwAAAAD/5z49AAAAAAAAAABYwO1qvMLc7taT5wEAAAAslE4PAAAAAAAAAADuUE+t2xxqPXsMAAAAYNF0egAAAAAAAAAAbN18rlOjo99Lucm2hDwPAAAAYB2GcRx7zwAAAM0Mw1C/iA/JAAAATON8PvceAQCAL3ejLDHVljVv9mwnAIB12+12vUcAADpwnx4AAAAAAAAAANyiqtq4v22A2/2ebQMAAACwKTo9AAAAAAAAAACApynxAAAAAPjHj94DAAAAAAAAAABAT7dvRQMAAAAAuEunBwAAAAAAAAAAf+XONAAAAADgLp0eAAAAAAAAAAAAAAAAAOR0egAAAAAAAAAAAAAAAACQ0+kBAAAAAAAAAAAAAAAAQE6nBwAAAAAAAAAAAAAAAAA5nR4AAAAAAAAAAHyvlNJ7BAAAAABgAXR6AAAAAAAAAAAAAAAAAJDT6QEAAAAAAAAAAAAAAABATqcHAAAAAAAAAADfKKX0HgEAAAAAWAadHgAAAAAAAAAAm/ZtjyfSAwAAAAAep9MDAAAAAAAAAGDrfq/ySikiPQAAAADgKe+9BwAAAAAAAAAAgP60eQAAAABAzH16AAAAAAAAAAAAAAAAAJDT6QEAAAAAAAAAAAAAAABATqcHAAAAAAAAAAAAAAAAADmdHgAAAAAAAAAAAAAAAADkdHoAAAAAAAAAAAAAAAAAkNPpAQAAAAAAAAAAAAAAAEBOpwcAAAAAAAAAAAAAAAAAOZ0eAAAAAAAAAAAAAAAAAOR0egAAAAAAAAAAAAAAAACQ0+kBAAAAAAAAAAAAAAAAQE6nBwAAAAAAAAAAAAAAAAA5nR4AAAAAAAAAAAAAAAAA5HR6AAAAAAAAAAAAAAAAAJDT6QEAAAAAAAAAAAAAAABATqcHAAAAAAAAAAAAAAAAADmdHgAAAAAAAAAAAAAAAADkdHoAAAAAAAAAAAAAAAAAkNPpAQAAAAAAAAAAAAAAAEBOpwcAAAAAAAAAAAAAAAAAOZ0eAAAAAAAAAAAAAAAAAOR0egAAAAAAAAAAAAAAAACQ0+kBAAAAAAAAAAAAAAAAQE6nBwAAAAAAAAAAAAAAAAA5nR4AAAAAAAAAAAAAAAAA5HR6AAAAAAAAAAAAAAAAAJDT6QEAAAAAAAAAAAAAAABATqcHAAAAAAAAAAAAAAAAADmdHgAAAAAAAAAAAAAAAADkdHoAAAAAAAAAAAAAAAAAkNPpAQAAAAAAAAAAAAAAAEBOpwcAAAAAAAAAAAAAAAAAOZ0eAAAAAAAAAAAAAAAAAOR0egAAAAAAAAAAAAAAAACQ0+kBAAAAAAAAAAAAAAAAQE6nBwAAAAAAAAAAAAAAAAA5nR4AAAAAAAAAAAAAAAAA5HR6AAAAAAAAAAAAAAAAAJDT6QEAAAAAAAAAAAAAAABA7r33AMCX4/FYv8jhcKhfBAAAAHxLBYAm7h6pl8vl7iL7/b7ROACwVKfTqX4RRyoAOFIBoIlHjtTr9Xr7AX5LBYBVcp8eAAAAAAAAAAAAAAAAAOR0egAAAAAAAAAAAAAAAACQ0+kBAAAAAAAAAAAAAAAAQE6nBwAAAAAAAAAAAAAAAAA5nR4AAAAAAAAAAAAAAAAA5HR6AAAAAAAAAAAAAAAAAJDT6QEAAAAAAAAAAAAAAABATqcHAAAAAAAAAAAAAAAAADmdHgAAAAAAAAAAAAAAAADkdHoAAAAAAAAAAAAAAAAAkNPpAQAAAAAAAAAAAAAAAEBOpwcAAAAAAAAAAAAAAAAAOZ0eAAAAAAAAAAAAAAAAAOR0egAAAAAAAAAAAAAAAACQ0+kBAAAAAAAAAAAAAAAAQE6nBwAAAAAAAAAAAAAAAAA5nR4AAAAAAAAAAAAAAAAA5HR6AAAAAAAAAAAAAAAAAJDT6QEAAAAAAAAAAAAAAABATqcHAAAAAAAAAAAAAAAAADmdHgAAAAAAAAAAAAAAAADkdHoAAAAAAAAAAAAAAAAAkBvGcew9AwAANDMMQ/0iPiQDAAAwjfP53HsEAAAAAAAa2+12vUcAADpwnx4AAAAAAAAAAAAAAAAA5HR6AAAAAAAAAAAAAAAAAJDT6QEAAAAAAAAAAAAAAABATqcHAAAAAAAAAAAAAAAAADmdHgAAAAAAAAAAAAAAAADkdHoAAAAAAAAAAAAAAAAAkNPpAQAAAAAAAAAAAAAAAEBOpwcAAAAAAAAAAAAAAAAAOZ0eAAAAAAAAAAAAAAAAAOR0egAAAAAAAAAAAAAAAACQ0+kBAAAAAAAAAAAAAAAAQE6nBwAAAAAAAAAAAAAAAAA5nR4AAAAAAAAAAAAAAAAA5HR6AAAAAAAAAAAAAAAAAJDT6QEAAAAAAAAAAAAAAABATqcHAAAAAAAAAAAAAAAAADmdHgAAAAAAAAAAAAAAAADkdHoAAAAAAAAAAAAAAAAAkNPpAQAAAAAAAAAAAAAAAEBOpwcAAAAAAAAAAAAAAAAAOZ0eAAAAAAAAAAAAAAAAAOR0egAAAAAAAAAAAAAAAACQ0+kBAAAAAAAAAAAAAAAAQE6nBwAAAAAAAAAAAAAAAAA5nR4AAAAAAAAAAAAAAAAA5HR6AAAAAAAAAAAAAAAAAJDT6QEAAAAAAAAAAAAAAABATqcHAAAAAAAAAAAAAAAAADmdHgAAAAAAAAAAAAAAAADkdHoAAAAAAAAAAAAAAAAAkNPpAQAAAAAAAAAAAAAAAEBOpwcAAAAAAAAAAAAAAAAAOZ0eAAAAAAAAAAAAAAAAAOR0egAAAAAAAAAAAAAAAACQ0+kBAAAAAAAAAAAAAAAAQE6nBwAAAAAAAAAAAAAAAAA5nR4AAAAAAAAAAAAAAAAA5HR6AAAAAAAAAAAAAAAAAJDT6QEAAAAAAAAAAAAAAABATqcHAAAAAAAAAAAAAAAAADmdHgAAAAAAAAAAAAAAAADkdHoAAAAAAAAAAAAAAAAAkNPpAQAAAAAAAAAAAAAAAEBOpwcAAAAAAAAAAAAAAAAAOZ0eAAAAAAAAAAAAAAAAAOR0egAAAAAAAAAAAAAAAACQ0+kBAAAAAAAAAAAAAAAAQE6nBwAAAAAAAAAAAAAAAAA5nR4AAAAAAAAAAAAAAAAA5HR6AAAAAAAAAAAAAAAAAJDT6QEAAAAAAAAAAAAAAABATqcHAAAAAAAAAAAAAAAAADmdHgAAAAAAAAAAAAAAAADk3nsPAHw5Ho/1ixwOh/pFAAAAwLdUAGji7pF6uVzuLrLf7xuNAwBLdTqd6hdxpAKAIxUAmnjkSL1er7cf4LdUAFgl9+kBAAAAAAAAAAAAAAAAQE6nBwAAAAAAAAAAAAAAAAA5nR4AAAAAAAAAAAAAAAAA5HR6AAAAAAAAAAAAAAAAAJDT6QEAAAAAAAAAAAAAAABATqcHAAAAAAAAAAAAAAAAADmdHgAAAAAAAAAAAAAAAADkdHoAAAAAAAAAAAAAAAAAkNPpAQAAAAAAAAAAAAAAAEBOpwcAAAAAAAAAAAAAAAAAOZ0eAAAAAAAAAAAAAAAAAOR0egAAAAAAAAAAAAAAAACQ0+kBAAAAAAAAAAAAAAAAQE6nBwAAAAAAAAAAAAAAAAA5nR4AAAAAAAAAAAAAAAAA5HR6AAAAAAAAAAAAAAAAAJDT6QEAAAAAAAAAAAAAAABATqcHAAAAAAAAAAAAAAAAADmdHgAAAAAAAAAAAAAAAADkdHoAAAAAAAAAAAAAAAAAkNPpAQAAAAAAAAAAAAAAAEBOpwcAAAAAAAAAAAAAAAAAuWEcx94zAABAM8Mw1C/iQzIAAADTOJ/PvUcAAAAAAKCx3W7XewQAoAP36QEAAAAAAAAAAAAAAABATqcHAAAAAAAAAAAAAAAAADmdHgAAAAAAAAAAAAAAAADkdHoAAAAAAAAAAAAAAAAAkNPpAQAAAAAAAAAAAAAAAEBOpwcAAAAAAAAAAAAAAAAAOZ0eAAAAAAAAAAAAAAAAAOR0egAAAAAAAAAAAAAAAACQ0+kBAAAAAAAAAAAAAAAAQE6nBwAAAAAAAAAAAAAAAAA5nR4AAAAAAAAAAAAAAAAA5HR6AAAAAAAAAAAAAAAAAJDT6QEAAAAAAAAAAAAAAABATqcHAAAAAAAAAAAAAAAAADmdHgAAAAAAAAAAAAAAAADkdHoAAAAAAAAAAAAAAAAAkNPpAQAAAAAAAAAAAAAAAEBOpwcAAAAAAAAAAAAAAAAAOZ0eAAAAAAAAAAAAAAAAAOR0egAAAAAAAAAAAAAAAACQ0+kBAAAAAAAAAAAAAAAAQE6nBwAAAAAAAAAAAAAAAAA5nR4AAAAAAAAAAAAAAAAA5HR6AAAAAAAAAAAAAAAAAJDT6QEAAAAAAAAAAAAAAABATqcHAAAAAAAAAAAAAAAAADmdHgAAAAAAAAAAAAAAAADkdHoAAAAAAAAAAAAAAAAAkNPpAQAAAAAAAAAAAAAAAEBOpwcAAAAAAAAAAAAAAAAAOZ0eAAAAAAAAAAAAAAAAAOR0egAAAAAAAAAAAAAAAACQ0+kBAAAAAAAAAAAAAAAAQE6nBwAAAAAAAAAAAAAAAAA5nR4AAAAAAAAAAAAAAAAA5HR6AAAAAAAAAAAAAAAAAJDT6QEAAAAAAAAAAAAAAABATqcHAAAAAAAAAAAAAAAAADmdHgAAAAAAAAAAAAAAAADkdHoAAAAAAAAAAAAAAAAAkNPpAQAAAAAAAAAAAAAAAEBOpwcAAAAAAAAAAAAAAAAAOZ0eAAAAAAAAAAAAAAAAAOR0egAAAAAAAAAAAAAAAACQ0+kBAAAAAAAAAAAAAAAAQE6nBwAAAAAAAAAAAAAAAAA5nR4AAAAAAAAAAAAAAAAA5HR6AAAAAAAAAAAAAAAAAJDT6QEAAAAAAAAAAAAAAABATqcHAAAAAAAAAAAAAAAAADmdHgAAAAAAAAAAAAAAAADkdHoAAAAAAAAAAAAAAAAAkNPpAQAAAAAAAAAAAAAAAEDuvfcAwJfj8Vi/yOFwqF8EAAAAfEsFgCbuHqmXy+XuIvv9vtE4ALBUp9OpfhFHKgA4UgGgiUeO1Ov1evsBfksFgFVynx4AAAAAAAAAAAAAAAAA5HR6AAAAAAAAAAAAAAAAAJDT6QEAAAAAAAAAAAAAAABATqcHAAAAAAAAAAAAAAAAADmdHgAAAAAAAAAAAAAAAADkdHoAAAAAAAAAAAAAAAAAkNPpAQAAAAAAAAAAAAAAAEBOpwcAAAAAAAAAAAAAAAAAOZ0eAAAAAAAAAAAAAAAAAOR0egAAAAAAAAAAAAAAAACQ0+kBAAAAAAAAAAAAAAAAQE6nBwAAAAAAAAAAAAAAAAA5nR4AAAAAAAAAAAAAAAAA5HR6AAAAAAAAAAAAAAAAAJDT6QEAAAAAAAAAAAAAAABATqcHAAAAAAAAAAAAAAAAADmdHgAAAAAAAAAAAAAAAADkdHoAAAAAAAAAAAAAAAAAkNPpAQAAAAAAAAAAAAAAAEBOpwcAAAAAAAAAAAAAAAAAOZ0eAAAAAAAAAAAAAAAAAOR0egAAAAAAAAAAAAAAAACQG8Zx7D0DAAA0MwxD/SI+JAMAADCN8/ncewQAAAAAABrb7Xa9RwAAOnCfHgAAAAAAAAAAAAAAAADkdHoAAAAAAAAAAAAAAAAAkNPpAQAAAAAAAAAAAAAAAEBOpwcAAAAAAAAAAAAAAAAAOZ0eAAAAAAAAAAAAAAAAAOR0egAAAAAAAAAAAAAAAACQ0+kBAAAAAAAAAAAAAAAAQE6nBwAAAAAAAAAAAAAAAAA5nR4AAAAAAAAAAAAAAAAA5HR6AAAAAAAAAAAAAAAAAJDT6QEAAAAAAAAAAAAAAABATqcHAAAAAAAAAAAAAAAAADmdHgAAAAAAAAAAAAAAAADkdHoAAAAAAAAAAAAAAAAAkNPpAQAAAAAAAAAAAAAAAEBOpwcAAAAAAAAAAAAAAAAAOZ0eAAAAAAAAAAAAAAAAAOR0egAAAAAAAAAAAAAAAACQ0+kBAAAAAAAAAAAAAAAAQE6nBwAAAAAAAAAAAAAAAAA5nR4AAAAAAAAAAAAAAAAA5HR6AAAAwC/27t62kbNrwLBGUK7IUAeEsLESZu7BmXrYAlSBCnAPztTDZkwUGwI7MBYKVMF8Af3qk1fUcHjm5/m7ruiFwR2eZzgyd7G+3wMAAAAAAAAAAADE6fQAAAAAAAAAAAAAAAAAIE6nBwAAAAAAAAAAAAAAAABxOj0AAAAAAAAAAAAAAAAAiNPpAQAAAAAAAAAAAAAAAECcTg8AAAAAAAAAAAAAAAAA4nR6AAAAAAAAAAAAAAAAABCn0wMAAAAAAAAAAAAAAACAOJ0eAAAAAAAAAAAAAAAAAMTp9AAAAAAAAAAAAAAAAAAgTqcHAAAAAAAAAAAAAAAAAHE6PQAAAAAAAAAAAAAAAACI0+kBAAAAAAAAAAAAAAAAQJxODwAAAAAAAAAAAAAAAADidHoAAAAAAAAAAAAAAAAAEKfTAwAAAAAAAAAAAAAAAIA4nR4AAAAAAAAAAAAAAAAAxOn0AAAAAAAAAAAAAAAAACBOpwcAAAAAAAAAAAAAAAAAcTo9AAAAAAAAAAAAAAAAAIjT6QEAAAAAAAAAAAAAAABAnE4PAAAAAAAAAAAAAAAAAOJ0egAAAAAAAAAAAAAAAAAQp9MDAAAAAAAAAAAAAAAAgDidHgAAAAAAAAAAAAAAAADE6fQAAAAAAAAAAAAAAAAAIE6nBwAAAAAAAAAAAAAAAABxOj0AAAAAAAAAAAAAAAAAiNPpAQAAAAAAAAAAAAAAAECcTg8AAAAAAAAAAAAAAAAA4nR6AAAAAAAAAAAAAAAAABCn0wMAAAAAAABamvBCAAAgAElEQVQAAAAAAACAuKvUAwD/2u120y+y3W6nXwQAAAD8KRUAZnHyK/Xnz58nL3J7ezvTOABQqpeXl+kX8ZUKAL5SAWAWY75SX19fh1/g71IBoEr26QEAAAAAAAAAAAAAAABAnE4PAAAAAAAAAAAAAAAAAOJ0egAAAAAAAAAAAAAAAAAQp9MDAAAAAAAAAAAAAAAAgDidHgAAAAAAAAAAAAAAAADE6fQAAAAAAAAAAAAAAAAAIE6nBwAAAAAAAAAAAAAAAABxOj0AAAAAAAAAAAAAAAAAiNPpAQAAAAAAAAAAAAAAAECcTg8AAAAAAAAAAAAAAAAA4nR6AAAAAAAAAAAAAAAAABCn0wMAAAAAAAAAAAAAAACAOJ0eAAAAAAAAAAAAAAAAAMTp9AAAAAAAAAAAAAAAAAAgTqcHAAAAAAAAAAAAAAAAAHE6PQAAAAAAAAAAAAAAAACI0+kBAAAAAAAAAAAAAAAAQJxODwAAAAAAAAAAAAAAAADidHoAAAAAAAAAAAAAAAAAEKfTAwAAAAAAAAAAAAAAAIA4nR4AAAAAAAAAAAAAAAAAxOn0AAAAAAAAAAAAAAAAACCu6/s+9QwAADCbruumX8RvkgEAAFjHfr9PPQIAAAAAADPbbDapRwAAErBPDwAAAAAAAAAAAAAAAADidHoAAAAAAAAAAAAAAAAAEKfTAwAAAAAAAAAAAAAAAIA4nR4AAAAAAAAAAAAAAAAAxOn0AAAAAAAAAAAAAAAAACBOpwcAAAAAAAAAAAAAAAAAcTo9AAAAAAAAAAAAAAAAAIjT6QEAAAAAAAAAAAAAAABAnE4PAAAAAAAAAAAAAAAAAOJ0egAAAAAAAAAAAAAAAAAQp9MDAAAAAAAAAAAAAAAAgDidHgAAAAAAAAAAAAAAAADE6fQAAAAAAAAAAAAAAAAAIE6nBwAAAAAAAAAAAAAAAABxOj0AAAAAAAAAAAAAAAAAiNPpAQAAAAAAAAAAAAAAAECcTg8AAAAAAAAAAAAAAAAA4nR6AAAAAAAAAAAAAAAAABCn0wMAAAAAAAAAAAAAAACAOJ0eAAAAAAAAAAAAAAAAAMTp9AAAAAAAAAAAAAAAAAAgTqcHAAAAAAAAAAAAAAAAAHE6PQAAAAAAAAAAAAAAAACI0+kBAAAAAAAAAAAAAAAAQJxODwAAAAAAAAAAAAAAAADidHoAAAAAAAAAAAAAAAAAEKfTAwAAAAAAAAAAAAAAAIA4nR4AAAAAAAAAAAAAAAAAxOn0AAAAAAAAAAAAAAAAACBOpwcAAAAAAAAAAAAAAAAAcTo9AAAAAAAAAAAAAAAAAIjT6QEAAAAAAAAAAAAAAABAnE4PAAAAAAAAAAAAAAAAAOJ0egAAAAAAAAAAAAAAAAAQp9MDAAAAAAAAAAAAAAAAgDidHgAAAAAAAAAAAAAAAADE6fQAAAAAAAAAAAAAAAAAIE6nBwAAAAAAAAAAAAAAAABxOj0AAAAAAAAAAAAAAAAAiNPpAQAAAAAAAAAAAAAAAECcTg8AAAAAAAAAAAAAAAAA4nR6AAAAAAAAAAAAAAAAABCn0wMAAAAAAAAAAAAAAACAOJ0eAAAAAAAAAAAAAAAAAMTp9AAAAAAAAAAAAAAAAAAgTqcHAAAAAAAAAAAAAAAAAHE6PQAAAAAAAAAAAAAAAACI0+kBAAAAAAAAAAAAAAAAQJxODwAAAAAAAAAAAAAAAADidHoAAAAAAAAAAAAAAAAAEKfTAwAAAAAAAAAAAAAAAIA4nR4AAAAAAAAAAAAAAAAAxOn0AAAAAAAAAAAAAAAAACBOpwcAAAAAAAAAAAAAAAAAcVepBwD+tdvtpl9ku91OvwgAAAD4UyoAzOLkV+rPnz9PXuT29namcQCgVC8vL9Mv4isVAHylAsAsxnylvr6+Dr/A36UCQJXs0wMAAAAAAAAAAAAAAACAOJ0eAAAAAAAAAAAAAAAAAMTp9AAAAAAAAAAAAAAAAAAgTqcHAAAAAAAAAAAAAAAAAHE6PQAAAAAAAAAAAAAAAACI0+kBAAAAAAAAAAAAAAAAQJxODwAAAAAAAAAAAAAAAADidHoAAAAAAAAAAAAAAAAAEKfTAwAAAAAAAAAAAAAAAIA4nR4AAAAAAAAAAAAAAAAAxOn0AAAAAAAAAAAAAAAAACBOpwcAAAAAAAAAAAAAAAAAcTo9AAAAAAAAAAAAAAAAAIjT6QEAAAAAAAAAAAAAAABAnE4PAAAAAAAAAAAAAAAAAOJ0egAAAAAAAAAAAAAAAAAQp9MDAAAAAAAAAAAAAAAAgDidHgAAAAAAAAAAAAAAAADE6fQAAAAAAAAAAAAAAAAAIE6nBwAAAAAAAAAAAAAAAABxOj0AAAAAAAAAAAAAAAAAiNPpAQAAAAAAAAAAAAAAAEBc1/d96hkAAGA2XddNv4jfJAMAALCO/X6fegQAAAAAAGa22WxSjwAAJGCfHgAAAAAAAAAAAAAAAADE6fQAAAAAAAAAAAAAAAAAIE6nBwAAAAAAAAAAAAAAAABxOj0AAAAAAP7f2+Pd2+Nd6ila51MAAAAAAAAAgLJ0fd+nngEAAGbTdd30i/hNMgAAbfolDLt+eE41SeN8EE3Z7/epRwAAAAAAYGabzSb1CABAAlepBwAAAAAAIDGr23J2+HTUegAAAAAAAACQs8vUAwAAAAAAkJJIrwg+JgAAAAAAAADIWdf3feoZAABgNl3XTb+I3yQDANCIgfTL9rbkvvp0fDSV2e/3qUcAAAAAAGBmm80m9QgAQAL26QEAAAAAtEikl7mvPgWL9QAAAAAAAAAgQzo9AAAAAIDmiPSKINUDAAAAAAAAgFLo9AAAAAAA2vJV5XX98CzSy41UDwAAAAAAAACKoNMDAAAAAGjIQKS38iSMJNUDAAAAAAAAgPzp9AAAAAAAWiHSK5RUDwAAAAAAAAAyp9MDAAAAAGiCSK9oUj0AAAAAAAAAyJlODwAAAACgfiK9Ckj1AAAAAAAAACBbOj0AAAAAgMqJ9Koh1QMAAAAAAACAPF2lHgAAAAAAgAU1UnAdPWaVIeL1w/PRw7493lV5XgAAAAAAAAAogk4PAAAAAKBaA5Fe0U3XyPiwtUWCUj0AAAAAAAAASKXr+z71DAAAMJuu66ZfxG+SAQCoQ2WR3nKLASu7GyUep2X7/T71CAAAAAAAzGyz2aQeAQBIQKcHAEBVdHoAAHBQU8e1XKH3UU23pbiztEynBwAAAABQH50eALRJpwcAQFV0egAAcFFLwbVOnvdZHbeooFM0TqcHAAAAAFAfnR4AtOky9QAAAAAAAMwpVd42o7fHu4SnSPvuZxmI8Uo5AgAAAAAAAADU4Sr1AAAAAAAArKSIHWtnBWbLhWqHX17EHQMAAAAAAAAAkuv6vk89AwAAzKbruukX8ZtkAADKNRCn5Z+cjSzrAgeZ0uwVfd/yH579fp96BAAAAAAAZrbZbFKPAAAkoNMDAKAqOj0AAFpWdK91MqWb5QjhYC/zG1j0R984nR4AAAAAQH10egDQpsvUAwAAAAAAMIMpK+OSGx7++uF5rtgsfKlyb2+5kwMAAAAAAABAQXR6AAAAAACVy3yj2vAuuCWGj1025+At848YAAAAAAAAAKqn0wMAAAAAKN5w6rbmJOdKOHk7qV7OYwMAAAAAAABAHXR6AAAAAABlK7fC+mryhdboHX2jc39JoXe70LEBAAAAAAAAoBQ6PQAAAACAauW8TG8g0ltzjJpSvZw/bgAAAAAAAACom04PAAAAAKBg2TZjwzKJ9MJvWuJtL3FmAAAAAAAAACiFTg8AAAAAoFTD5VVx29USDlxNqjd8kDxnBgAAAAAAAIAK6PQAAAAAAFjV0VoseVWYfAAAAAAAAAAAoFw6PQAAAACAIhW6TC/nlW7n3rQ8z2KlHgAAAAAAAACs7yr1AAAAAAAAtC7bqvCkt8e7codnBSfDyJsRF/nnj79mGQYAAAAAAACA5XR936eeAQAAZtN13fSL+E0yAAD5q2mZXm7TnrtxLrf5Dwp9Qkq35rpC8R4AAAAAQLY2m03qEQCABOzTAwAAAACAOCv1Grdmm/fRzdP9+//W7AEAAAAAAAAkZ58eAABVsU8PAIAWFLoqraCxrdRjWKo2bwzNHgAAAABAcvbpAUCb7NMDAAAAACC99/BJP0a2cs7z3r3v2RPsAQAAAAAAAKzJPj0AAKpinx4AANUrd0na+MYph1NYqce7IvK8AYI9AAAAAICV2acHAG26TD0AAAAAAAD8R+lZFNV4e7yr4Gm8ebp/X7IHAAAAAAAAwEJ0egAAAAAAxSg3GTp38nJPWhA3+SuHPK+y+6PWAwAAAAAAAFiUTg8AAAAAoBLXD8+pRyAvHolz1Zfn/UKtBwAAAAAAALAQnR4AAAAAQBnqzoc+S3veRgq31h6qAdUXeh+p9QAAAAAAAABmp9MDAAAAAICpsk28GgkOp2iq0PtIrQcAAAAAAAAwI50eAAAAAEAN5FgQ0Gah95FaDwAAAAAAAGAWOj0AAAAAgAKoiVhIs49Ws2v0jlLrAQAAAAAAAEyk0wMAAAAAKJ5legzwePxCofcVqR4AAAAAAABAmE4PAOBs3QipZwQAAKqiKWJRTT1gTR02wGI9AAAAAAAAgBidHgDACbEGT7kHAADwUWClmy1wzMsavfGkegAAAAAAAADnuko9AABApmYv6z5esO/7eS8OAAAADFDoneuQ6v3zx1+pBwEAAAAAAAAog316AAD/sc76O0v2AACA8U72RaXsnTtrzlIOVYqT97PujK3u0y3KYj0AAAAAAACAkezTAwD4V5Jq7v1NbdgDAABacP3wPKaYEukxF4XedBbrAQAAAAAAAIyh04Nc7Ha76RfZbrfTLwLQoBz22h1mUOsBAJnwp1RgOSdTPZEecxHpzejm6V6qB0BCLy8v0y9ye3s7/SIAUDRfqQAwizFfqa+vr8Mv8HepAFCly9QDAACk1HVdDpHeu9zmAQAAkqsyNLp+eP4c4x3+YT6R3rl3Pp/Jv3JywsoetsqOk4PDYj0AAAAAAAAAjrJPDwBoVCyHG953N1diZ7ceAAAwXv552FfKnZz8ifQWYqseAAAAAAAAwFd0egBAi84K6sb3ch9fOb3Z67pOqgcAAABnUegtTaoHAAAAAAAAcNRl6gEAANY2sqDr/yf2LhN/+UHXdXPt6AMAAIDqifTWcfN0n3oEAAAAAAAAgOzo9ACAhozJ3mbp6z5fcMoVpHoAANAs0VEq597564fnhSZZWdGPXNHDF0eqBwAAAAAAAPALnR4A0IqRhd5C7z7x4hbrAQAAR1WTh7GOih8Ykd76pHoAAAAAAAAAH+n0AIAmjIn0VhjDYj0AAACYnUgvFakeAAAAAAAAwDudHgBQv+G8bdE1erO/nVQPAABgaedGXxUvqSuCSC8tqR4AAAAAAADAgU4PAKjcyUhvtUnmel+pHgAANEJ9RBJlPXhlTVsrqR4AAAAAAADAhU4PAKhbnpHe9HeX6gEAACzEMj0IkOoBAAAAAAAA6PQAgGrlHOlNn0GqBwAAKMQIqOmxsUwvK1I9AAAAAAAAoHE6PQCgRTlEegf5TAIAAIBlegUR6WVIqgcAAAAAAAC0TKcHANRpYN1cNWmclXoAAAC0SaSXLakeAAAAAAAA0CydHgBQobIivSkjSfUAAKBWMqT1Wab3LvPHL/PxkOoBAAAAAAAAbdLpAQANyTDSO8h2MAAAgEaI9Eoh0gMAAAAAAAAgTzo9AKA2ra2Ya+28AAAAyYn0YJiVegAAAAAAAECDdHoAQCsy31k3ZTypHgAAwBRWtJXCJ1UQqR4AAAAAAADQGp0eAFCVr4q1zCO9gyKGBAAAqMy56ZdleqmI9Ioj1QMAAAAAAACaotMDAKiBlXoAANAatVgSbjsAAAAAAAAAcJRODwCoR9HL9AAAAFjfWSvaqon0SjyIZXqFslIPAAAAAAAAaIdODwAgI1OSQiv1AAAAztJmpFcikV7RpHoAAAAAAABAI3R6AEAlLNMDAACqoUpagUhvmIcQAAAAAAAAAM7S+S/XAYA61NTphTfjlXjY2c2yV9CdBAAgrTGJVIPl2IxEegU9Y4rBOvzzx1+pRwAAAAAAWM9ms0k9AgCQwFXqAQAAlvWx2qo+vuo6/y8MAAAAJ4j0CiLSAwAAAAAAAKAUl6kHAACYwcgVat3/LD0PAAAAeRLpQRI3T/epRwAAAAAAAABYln16AECLDqme1XMAAAAcpdDLgWV6AAAAAAAAABTEPj0AoF3ZLtabEhBmeygAAIDkRnZfIj1YgpV6AAAAAAAAQN10egBA01RtAAAAjRDplcUyvSpJ9QAAAAAAAICKXaUeAAAAAAAAFqTQAwAAAAAAAACWZp8eAFC8iTvxrNQDAAComEivRJbpVcxKPQAAAAAAAKBWOj0AgBz1fZ96BAAAgOKJ9AAAAAAAAACAdej0AABqW6lX2XEAAABiRHqFskwPAAAAAAAAgBLp9AAAAAAAqM2Y1uv64VmkB+u7ebpPPQIAAAAAAADA/HR6AAAAAABUZWSkt8IknMsyPQAAAAAAAAAKpdMDAAAAAKAeIj3In5V6AAAAAAAAQH2uUg8AAAAAAADzOBnpKfQAAAAAAAAAgCXYpwcAAAAAQA1EeqUbswuRalipBwAAAAAAAFRGpwcAAAAAQPFEegAAAAAAAABAQjo9AKB4fd8nvwIAAAAJifQqYJkeAAAAAAAAAEXT6QEAAAAAULDhvuv64VmkB3m6ebpPPQIAAAAAAADAbHR6AEANpizEs0wPAACgXCcjvdUmAQAAAAAAAABadpV6AAAAAAAA/uP64Xk4P+PiVKF3sXyk93mAmrLAlc/igQcAAAAAAACgdPbpAQCViK3Fs0wPAACgRMkjPWAWN0/3qUcAAAAAAAAAmId9egBAPfq+77rurNcvN0xCtZ4LAADgYDjSU+gBAAAAAAAAAOuzTw8AqErf92MqtZEvS+is4BAAAGjQyYVytco20iuoD2z24QEAAAAAAACA5dinBwBU6NDgHU3dMs/zAAAAGJBPpCd1m5GbCQAAAAAAAEAF7NMDAKrVH5N6KAAAAILyifSAGd083aceAQAAAAAAAGAG9ukBAAAAAJC7gUhPoQcAAAAAAAAAJGefHgBAVewMBAAA6iPSAwAAAAAAAAAyp9MDAMhO13WpRwAAABKTn70T6a1vzRs78PkCAAAAAAAAQEF0egAAAAAAZCrPSO/zVIpBmOLm6T71CAAAAAAAAABT6fQAAAAAAMhRnpFe6eyvAwAAAAAAAIAlXKUeAACA/+i6Lvxr+76fcRIAAAg4mgBJqhby9nhX8b39Kier+MgAAAAAAAAAQLl0egAAAADAJCfXc318gciKkzJfo2cfHQAAAAAAAADwmU4PAKASlukBALC+QLB0+CU51FbjpeoMrx+eG0zCMo/0WuA+AwAAAAAAAECATg8AICNd16UeAQAARpnYj5VS630+5vs/yX/4EuUf6TVYTi7K/QQAAAAAAACgGpepBwAAYAaW6QEAsJq3x7u50pqcE52Tx8x5+EKJ9KBlN0/3qUcAAAAAAAAAmMQ+PSjGbrebfpHtdjv9IotyzPEcMxOOOZ5jnrTOMr3kxyzCbrfL/4yNfJSOOZ5jZsIxx3PMTLRwzBbOeLHiMWfvlM5arJfbMd8e75boxz4e89s5L/6ouIf224/vAy/OPJAbeAxy+1fQQncyfMyTTzgs4eXlZfpFbm9vp19kUY45nmNmwjHHc8wctHDGC8c8h2NmwjHHc8xMtHDMFs544ZjncMxMOOZ4jpmJRo4JAFRApwcAUDzL9AAAWMfJwudzrZR5XlWBbz++//37n6mnmGo40mNNmawuBAAAAAAAAIDiXKYeAACAi4u1lukBAEDYcHF3/fB8NO/56p+fdfGVZTVMBQ3eSSI9AAAAAAAAAKACOj0AgLJZpgcAwAoG0rWRJd6Ut1hTJmO0Q6QHAAAAAAAAANRBpwcAkJ5legAA5Gw40htzhZEv08jFlJu6lTt5ofyIAQAAAAAAAMBydHoAAAWzTA8AgKV9FfbMtUaPk/7+/c/UIyxCpJchP9QAAAAAAAAAEKbTAwBILLxMT6QHAMDSBiK9ld9xHbaNraamW61tAwAAAAAAAAAudHoAAIUS6QEAkIoqiYlqivQAAAAAAAAAAA50egAAKYWX6QEAwNLEVPn4+/c/h1/w7cf3dSaZznOVysk7L8EFAAAAAAAAgCl0egAAyYQjPcv0AABY2ldJzwolT0Edl67pXAV9uAAAAAAAAAAAZ9HpAQAURqQHAMDSEkZ6CUnIluYOAwAAAAAAAAAV0+kBAKQRW6Yn0gMAIJW6I738/f37n6lHoGZ+wAEAAAAAAABgoqvUAwAAtEikBwBAtiw9K9S3H9+1fOsrJW/zc00RNpvNOm/0+vo6/SKrTRvmmOONOeZ+v5/+RgAAAAAAANTNPj0AAAAA4ISJMVKVjVApgRYAAAAAAAAAACvQ6QEArM0yPQAAslVlUDdGEQe3Lo+Yk4+36BQAAAAAAAAAprtKPQAAQFtEegAAFEfDU4pvP77n3PL98iDtdrvp19xut9MvAgAAAAAAAAAwnX16AADrEekBAJCzo0u3RHpZyTnDo1B+xgEAAAAAAABgFjo9AICsifQAACjd0fyvdNmmTd9+fE89Anmp8gcQAAAAAAAAADKk0wMAWElgmZ5IDwCA1eTTd62fwJUVMlmpx4yyLU4BAAAAAAAAoDhXqQcA/rXb7VKPAMCCRHoAQMW++iPtdrtdeRJmp+EBAABy8PLyMv0it7e30y8CAEXzlQoAsxjzlfr6+jr8An+XCgBVsk8PAGBxIj0AADK33DK9slbVlWJ4pV4+qxFJbvgHUIgLAAAAAAAAADPS6QEALEukBwBAoZI0PEWEQ0UMCQAAAAAAAADAmnR6AAALEukBAEDmCt34Z6UeJ1mmBwAAAAAAAABr6vyH4JCJ3W43/SLb7Xb6RQCYi0gvicBt/8wHAQD+lNqUr2Ke6RlPIIFbvx0qYsijVFgMK+IJKTSUZQmZPJPwlf1+n3oEaN3Ly8v0i9ze3k6/CAAUzVcqAMxizFfqb7/9NvwCf5cKAFWyTw8AIBfaMAAA8pGklxBpnGX4dsmfGldEpAcAAAAAAAAANdHpAQAs4qytbn3fi/QAAKhMrZ2YwAlm5AcKAAAAAAAAgGro9AAA5ndupLfcJAAAMKzWmm6k0o9vpR5HWaYHAAAAAAAAAOvT6QEAzEykBwBA6ZJkPNohAAAAAAAAAADKpdMDAJiTSA8AAC5scluRlXr8wjI9AAAAAAAAAEhCpwcAMBuRHgAAxJTSDuU5Z55TkSGPCtnycAIAAAAAAAAV0OkBAMxjfKTX971IDwCATHxOI9qJJRrZNdfIMTko8eNu5985AAD/x97947aRbAscZgvKFd0tEMKLDRjKXjazBhtawluAIsuR0wHeEgxpHS8TDCi+MLgHB15Bv4ADXY1ENtnVf6pO1ffBkcfTOl1NUfQMfjgAAAAAANTtMvcAAAA1GBXpLToJAACMNXskE7EUiu7q7tmxM0wOBwAAAAAAAACLsk8PAGAqkR4AAEwhH5rFwDFK+Box8KB9lwEAAAAAAADA0nR6AACTiPQAAOC1ipOw0KVTxc+FPY8YAAAAAAAAAPLS6QEApBPpAQBAUFVGTaFLQpbjhUHhvEQBAAAAAACAOuj0AACW1fe9SA8AAI6RZ8xr4DyrTBPZG3i4Ib7FQgwJAAAAAAAAAMN0egAAic5ZpqfQAwCgKUqwEkj1WhM90gMAAAAAAACAOuj0AABSiPQAAKApeieAJXh3BQAAAAAAAKqh0wMAGE2kBwAAs8iVZ1S/Vs5KvXZUs0wv1rQAAAAAAAAA8J5ODwBgHJEeAAAcJAArilSvBdVEegAAAAAAAABQAZ0eAMAIIj0AACAKqV7dRHpUwGsVAAAAAAAAqIlODwDgXCI9AACYUaA8I9Cob0j1alVlpBd3cgAAAAAAAADYbDaXuQcA/nZzc5N7BACGnIz0FHoAQE38LZWxAkVfgUadxdXdc2u33DKpGwBQq+vr69wjAEAN/EgFgFmc8yN1u92uMAkAUBr79AAAThPpAQAAcR1rt/R7QR17cCI9YvGKBQAAAAAAACqj0wMAOKHwSK87JOM8AABwDnlGIaR64dQd6dVxFwAAAAAAAAC0SacHADCk8EgPAABKUHfrVUc4NHAXdT++ytQd6QEAAAAAAABAaDo9AICjRHoAAFCZlpu0q7vnYzVXy8cSSCORXmW3wzEeNAAAAAAAAFAfnR4AwGFxI71iBwMAgD15RkZSvaAaifQAAAAAAAAAIC6dHgDAAXEjPQAAWJm+KxypXjitRXq13hcvPGIAAAAAAACgSjo9AIC3RHoAAMBerTGJVC+Q1iI9AAAAAAAAAAhKpwcA8A+xIr2T0wIAQGnERYWQ6oXQbKRX/Q22zMMFAAAAAAAAaqXTAwD4j1iRHgAAMIoC7Y2BVM9ZlaDZSA8AAAAAAAAAItLpAQD8bTjS6/tepAcAAG+ouaK7unu2WK9MB89/4HnVp507bYrHCgAAAAAAAFRMpwcAsNmcEemtNslEgUYFAKBBsQqNWNNO0c6dRnEs0lt/EgAAAAAAAADgTDo9AIB6Ij0AAIA0TS1qC6fZp9PmXVfMAwUAAAAAAADqptMDAFoXN9IbnhwAAHjt4IIy3nhd0ShqMvIgAAAAAAAAACCcy9wDAADkFDfSAwCA7JRvVVKFFcKD2Lu6e/ZWUwcvaQAAAAAAAKB69ukBAO0S6QEAAMdISgDm4h0VAAAAAAAAaIFODwBolEgPAABWptMAEnjrAAAAAAAAACAEnR4A0KIKIr3hWwAAAF77/e1D7hGAdFK90Dw+AAAAAAAAoBE6PQCgORVEeseEHh4AAACojEgPAAAAAAAAaIdODwBoy5KenokAACAASURBVECk1/e9zg0AANgIS6A8visBAAAAAAAAKJxODwBoyHCkt+YkEw2vBAQAgBX8/vYh9whAW6R64XhkAAAAAAAAQFN0egBAK0R6AACQUcZaQ1IIsD6RHgAAAAAAANAanR4A0ITokV73Su5ZAAAAIA/pFwAAAAAAAADF0ukBAK3rIsh9SAAA0BAhEJTMd2gIHhMAAAAAAADQIJ0eAFC/Rjq3EIsBAQAAYCINWOE8IAAAAAAAAKBNOj0AoHKNRHoAAMBBv799yD0CQENEegAAAAAAAECzdHoAQM1EegAAAFAfMViZPBcAAAAAAACgZTo9AKBaIj0AAGAskQlE4bsVAAAAAAAAgKLo9ACAOon0AACA398+5B4BWJBUrygeBwAAAAAAANA4nR4AUCGRHgAALE2PAZTAe1EhPAgAAAAAAAAAnR4AUJs2I72+73OPAAAAZbFMDxqhEMvOIwAAAAAAAADY6PQAgMq0GekBAABvpEV6UhMIyjdvRg4fAAAAAAAAYE+nBwDUQ6QHAAAAbVKLZeHYAQAAAAAAAF7o9ACASoj0AACAvbRlekB0mrGVOXAAAAAAAACA13R6AEANRHoAALC+MgsNkR60rMz3pSo5agAAAAAAAIA3dHoAQHgiPQAAKN86+dyUr6I5gTr4Xl6BQwYAAAAAAAB4T6cHAAAAANTAJj1gT0W2nKu7Z8cLAAAAAAAAcJBODwAgvL7vc48AAECjyqk1RHrAa+W8O9XEqQIAAAAAAAAM0OkBAAAAAGtYLqUT6QHv2fw2L4cJAAAAAAAAMEynBwAAAACky15uzBLpZb8LYCG+u2fhGAEAAAAAAABO0ukBAAAAACuZffGdTXrASRqzKawlBAAAAAAAADhT1/d97hkAAGA2XddNv4gPyQAAY43q5WZJPuYt9FQo0AJl71jeG+HFbrfLPQIAAAAAkWy329wjAAAZ2KcHAAAAAEy1csshtgESqM5GcVwAAAAAAAAAo9inBwBAVezTAwDIaIWteie/xNXd89iKT4sCrdH6DvOuCO/ZpwcAAADAKPbpAUCb7NMDAAAAAOYxKu0Y1cn8/vZh/2vGAYBmea8Y4HAAAAAAAAAA0tinBwBAVezTAwDILmFR1bEs5PxLvVxhxq8OVM9ivde8GcIA+/QAAAAAGMU+PQBo02XuAQAAAACAqlzdPY9NXyamMtoSIM3+3UOt510UAAAAAAAAYDqdHgAAAAAws9XSl+ltiToFSKiLq+E9EAAAAAAAAGAuOj0AAAAAYBGLpi8H25JmSxtgogYX6yn0AAAAAAAAAOal0wMAAAAAlvLSgcxYv2hLgIUs8ZZVIO+iAAAAAAAAAEvQ6QEAAAAAi5tevwhLgNXUul7PGykAAAAAAADAcnR6AAAAAMB63lQiwxnM0kmJZAUYUFOt5+0OAAAAAAAAYGk6PQAAAAAgmxnTkTpaGqA009eBZiTPAwAAAAAAAFiNTg9K8fT0NP0iNzc30y8CAAAA/pYKAG8ECvbkeQDU5+fPn9Mvcn19Pf0iABCaH6kAMItzfqT++vVr+A/4f6kAUCWdHgAAAAAAwLmKDfbkeQAAAAAAAAAZ6fQAAAAAgPASahlBCzDR67eRLM2e9zEAAAAAAACAcuj0AAAAAAAAJnmfzC1R7gnzAAAAAAAAAIql0wMAAAAAmqN1AZa2f595eno6+E//6//+5+Dv//u///f9b97c3Mw4GAAAAAAAAABL0OkBAAAAALEtsbQKYFEHezwAAAAAAAAA4rrIPQAAAAAAAAAAAAAAAAAABKbTAwAAAAACS1imd3X3vMQkAAAAAAAAAAA06zL3AAAAAABAGO+jOM0bAAAAAAAAAADo9AAAAACA046trdv/fq5azzI9AAAAAAAAAABKoNMDAAAAAIac08LlrfXOV/6EAAAAAAAAAABEdJF7AAAAAACgXKMW1iVst5ti5S8HAAAAAAAAAADH6PQAAAAAgMMqC+Es0wMAAAAAAAAAYCE6PQAAAADggLRIb7W0r7KGEAAAAAAAAACA0HR6AAAAAED9LNMDAAAAAAAAAGA5Oj0AAAAA4K3Ct9WNHU+kBwAAAAAAAADAonR6AAAAAEAkhTeEAAAAAAAAAAA0SKcHAAAAANTMMj0AAAAAAAAAAJam0wMAAAAA/qHkhXVjZxPpAQAAAAAAAACwAp0eAAAAADCbRbs4kR4AAAAAAAAAAGXS6QEAAAAAAYj0AAAAAAAAAAAolk4PAAAAAJiHNA4AAAAAAAAAgDbp9AAAAACA0lmmBwAAAAAAAABAybq+73PPAAAAs+m6bvpFfEgGABo3NorbWy6NE+kBAHntdrvcIwAAAAAQyXa7zT0CAJCBfXoAAAAAwFQiPQAAAAAAAAAAWqbTAwAAAAAmEekBAAAAAAAAANA4nR4AAAAA8A/n125Xd88iPQAAAAAAAAAAuMw9AAAAAAAQzApFnEgPAAAAAAAAAIBAdHoAAAAAwLnWyeFGRXoKPQAAAAAAAAAAsrvIPQAAAAAAUJxj8dvYNXdj/f72QaQHAAAAAAAAAEA4Oj0AAAAAYITlUr2xVxbpAQAAAAAAAABQCJ0eAAAAAHDAQAW3RKo3do2eSA8AAAAAAAAAgHLo9AAAAACA0WZM9X5/+zA20pvrSwMAAAAAAAAAwCx0egAAAADAYcNF3PRUb2yhtxHpAQAAAAAAAABQpK7v+9wzAADAbLqum34RH5IBAF47J6Ubm88lNH4KPQAgl91ul3sEAAAAACLZbre5RwAAMtDpAQBQFZ0eAMASzs/qBmq65P17Cj0AIC+dHgAAAACj6PQAoE06PQAAqqLTAwBYSHJlN4VCDwAogU4PAAAAgFF0egDQpsvcAwAAAAAAAeyTudVqPYUeAAAAAAAAAACBXOQeAAAAAAAIY4V87uruWaQHAAAAAAAAAEAs9ukBAAAAACMstFhPmwcAAAAAAAAAQFw6PQAAAABgtJesbmKwJ88DAAAAAAAAAKACOj0AAAAAIN2b0G4421PlAQAAAAAAAABQJZ0eAAAAADAbJR4AAAAAAAAAAA26yD0AAAAAAAAAAAAAAAAAAASm0wMAAAAAAAAAAAAAAACAdDo9AAAAAAAAAAAAAAAAAEin0wMAAAAAAAAAAAAAAACAdDo9AAAAAAAAAAAAAAAAAEin0wMAAAAAAAAAAAAAAACAdDo9AAAAAAAAAAAAAAAAAEin0wMAAAAAAAAAAAAAAACAdDo9AAAAAAAAAAAAAAAAAEin0wMAAAAAAAAAAAAAAACAdDo9AAAAAAAAAAAAAAAAAEin0wMAAAAAAAAAAAAAAACAdDo9AAAAAAAAAAAAAAAAAEin0wMAAAAAAAAAAAAAAACAdDo9AAAAAAAAAAAAAAAAAEin0wMAAAAAAAAAAAAAAACAdDo9AAAAAAAAAAAAAAAAAEin0wMAAAAAAAAAAAAAAACAdDo9AAAAAAAAAAAAAAAAAEin0wMAAAAAAAAAAAAAAACAdDo9AAAAAAAAAAAAAAAAAEin0wMAAAAAAAAAAAAAAACAdDo9AAAAAAAAAAAAAAAAAEin0wMAAAAAAAAAAAAAAACAdDo9AAAAAAAAAAAAAAAAAEin0wMAAAAAAAAAAAAAAACAdDo9AAAAAAAAAAAAAAAAAEin0wMAAAAAAAAAAAAAAACAdDo9AAAAAAAAAAAAAAAAAEin0wMAAAAAAAAAAAAAAACAdDo9AAAAAAAAAAAAAAAAAEin0wMAAAAAAAAAAAAAAACAdDo9AAAAAAAAAAAAAAAAAEin0wMAAAAAAAAAAAAAAACAdJe5BwD+9vT0NP0iNzc30y8CAAAA/pYKALPwIxUAZvHz58/pF7m+vp5+EQAIzY9UAJjFOT9Sf/36NfwH/IdfAKiSfXoAAAAAAAAAAAAAAAAAkE6nBwAAAAAAAAAAAAAAAADpdHoAAAAAAAAAAAAAAAAAkE6nBwAAAAAAAAAAAAAAAADpdHoAAAAAAAAAAAAAAAAAkE6nBwAAAAAAAAAAAAAAAADpdHoAAAAAAAAAAAAAAAAAkE6nBwAAAAAAAAAAAAAAAADpdHoAAAAAAAAAAAAAAAAAkE6nBwAAAAAAAAAAAAAAAADpdHoAAAAAAAAAAAAAAAAAkE6nBwAAAAAAAAAAAAAAAADpdHoAAAAAAAAAAAAAAAAAkE6nBwAAAAAAAAAAAAAAAADpdHoAAAAAAAAAAAAAAAAAkE6nBwAAAAAAAAAAAAAAAADpdHoAAAAAAAAAAAAAAAAAkE6nBwAAAAAAAAAAAAAAAADpdHoAAAAAAAAAAAAAAAAAkE6nBwAAAAAAAAAAAAAAAADpdHoAAAAAAAAAAAAAAAAAkE6nBwAAAAAAAAAAAAAAAADpur7vc88AAACz6bpu+kV8SAYAAADgxW63yz0CAAAAAJFst9vcIwAAGdinBwAAAAAAAAAAAAAAAADpdHoAAAAAAAAAAAAAAAAAkE6nBwAAAAAAAAAAAAAAAADpdHoAAAAAAAAAAAAAAAAAkE6nBwAAAAAAAAAAAAAAAADpdHoAAAAAAAAAAAAAAAAAkE6nBwAAAAAAAAAAAAAAAADpdHoAAAAAAAAAAAAAAAAAkE6nBwAAAAAAAAAAAAAAAADpdHoAAAAAAADBfPxy//HLfe4pmuYRAAAAAAAAAK91fd/nngEAAGbTdd30i/iQDAAAQLHetGE/vt4f/nMsyVNozW63yz0CAAAAAJFst9vcIwAAGVzmHgAAAAAAAIDTbG8r1v7RqPUAAAAAAACgZRe5BwAAAAAAAOAEkV75PCMAAAAAAABoWdf3fe4ZAABgNl3XTb+ID8kAAACUY6D+ssAtr2OPxnOpz263yz0CAAAAAJFst9vcIwAAGdinBwAAAAAAUCiRXsmOPQKL9QAAAAAAAKBBOj0AAAAAAIASifTKJ9UDAAAAAAAA9rq+73PPAAAAs+m6bvpFfEgGAAAgu2Ohl0KvQB5W9Xa7Xe4RAAAAAIhku93mHgEAyMA+PQAAAAAAgLLovmKxVQ8AAAAAAADQ6QEAAAAAABREpBeRVA8AAAAAAAAap9MDAAAAAAAohUgvLqkeAAAAAAAAtEynBwAAAAAAUASRXnRSPQAAAAAAAGiWTg8AAAAAACA/kV4dpHoAAAAAAADQpq7v+9wzAADAbLqum34RH5IBAABY2UDEVU2nNxyqVXObe6rLyux2u9wjAAAAABDJdrvNPQIAkIFODwCAquj0AAAACKfKSG+W9XFV3n7cm2qZTg8AAACAUXR6ANAmnR4AAFXR6QEAABBLTUHXLG3eMTWdRrh7QacHAAAAwCg6PQBok04PAICq6PQAAAAIpI6Ua9E87706TibQXbDR6QEAAAAwkk4PANqk0wMAoCo6PQAAAKKoIOJaudB7rYIjinILbHR6AAAAAIyk0wOANun0AACoik4PAACAEIYLt/ILroRCb+Cmknu/8g9qI9Wrgk4PAAAAgFF0egDQpsvcAwAAAAAAAPAfhbdb5zd159/I6z85qtnb/+HCTwwAAAAAAABogX16AABUxT49AAAAyhd3wdo5Ed0stzB2w17ccyt8cvbs0wMAAABgFPv0AKBNOj0AAKqi0wMAAKBwcZOt4XZuieFrqvXiPnc2Oj0AAAAARtLpAUCbLnIPAAAAAAAA0Iqx4Vk5hjOzhUqzsZcNerxBxwYAAAAAAABe0+kBAAAAAADkV/JStYy74KpJ9Up+vgAAAAAAAMB0Xd/3uWcAAIDZdF03/SI+JAMAALCEjLXbFMfGXnPmsfVdsecZ9DXAbrfLPQIAAAAAkWy329wjAAAZ2KcHAAAAAACwuGL3vA0rIdJL+HIRTzvizAAAAAAAAMALnR4AAAAAAEBO4RapZRk43CkdVMddAAAAAAAAAO/p9AAAAAAAAJYVdFXawbGjlGYRzzzizAAAAAAAAMCeTg8AAAAAAGBBw/FVlOztxccv97lysrFnVWb2NnwXZc4MAAAAAAAAnKTTAwAAAAAA4K3hYCxjrQcAAAAAAABQoK7v+9wzAADAbLqum34RH5IBAACYS9xleudneGveRUIcWOYhx31htOCcl9n3z5+WHwQAAACAqLbbbe4RAIAMLnMPAAAAAAAAAGf58dUeP2Yz5bV0+/B47B9J+AAAAAAAANpknx4AAFWxTw8AAIByxN2ZNrZfKnmlXrHnHPflEVSuwlO2BwAAANAg+/QAoE326QEAAAAAAAAVKmH74uvNe5o9AAAAAACAitmnBwBAVezTAwAAoBCht6Ul1E2r3VHJs40V+kVSshLyvJM0ewAAAAAVs08PANpknx4AAAAAAAAQXog878XLnj3BHgAAAAAAQB0ucg8AAAAAAABQG3vSOMfwKyFWdZbRxy/3+1+5B0l0+/D40uwBAAAAAAAQl316AAAAAAAAQDxx27z3rNcDAAAAAACIzj49AAAAAACAOdXUDpGX19IxoRfoDbNeDwAAAAAAICidHgAAAAAAwHp+fL3PPcJpY4cMcVNlcnRjVVzovabWAwAAAAAACEenBwAAAAAAMJsWCqJYoodwXlEvGin0XlPrAQAAAAAABKLTAwAAAAAA4K3z87boIVx2DvCkBgu919R6AAAAAAAAIVzmHgAAAAAAAKAV9RVZ9d0RRWk5z3vj9uHx++dPuacAAAAAAADgKPv0AAAAAAAA5lFZU/Tj6/1whifSW0dlr6vzNXvjx1isBwAAAAAAULKu7/vcMwCbzWbz9PQ0/SI3NzfTLwIAoXVdN/0iPiQDgL+lAkCa4azorz//SLhmIT9S39xarkJvbLgVqCQcvrVANzILhd5JduvRmp8/f06/yPX19fSLAEBofqQCwCzO+ZH6r3/9a/gPFPIffgGAeV3mHgAAAAAAAKAGdZdFrXVipfn45cRuw5rU/a00l9uHR6keAAAAAABAUXR6AAAAAAAAQH4KvVFuHx43FusBAAAAAAAU4yL3AAAAAAAAAHDa2IirnQV0dRDppdnXegAAAAAAAGSn0wMAAAAAAJjqZGL0159/rDIIUZ2sCivO2D5+ua/47lYg1QMAAAAAACiBTg8AAAAAAIDSWaZXK4XeLG4fHtV6AAAAAAAAeen0AAAAAAAAgAxEevOS6gEAAAAAAGSk0wMAAAAAAJhEa7S0RpbpnRy7sldaZbdTCKkeAAAAAABALjo9AAAAAACAZf315x+5R2hI0EivNSK95Uj1AAAAAAAAsrjMPQAAAAAAAAAcJeiqjAe6gtuHx++fP+WeAgAAAAAAoC326QEAAAAAAFCosU2XZXqFE+mtxlY9AAAAAACAlen0AAAAAAAA0umOliPSey/06y308BFJ9QAAAAAAANak0wMAAAAAAFjQX3/+kXuEJtQR6dVxFweJ9LKQ6gEAAAAAAKzmMvcAAAAAAAAA8A826VVGpJfR7cPj98+fck8BAAAAAABQP50eAAAAAAAABRnVdCn0yifSy06qBwAAAAAAsIKL3AMAAAAAAABEJUCanUjvpFivuljTVuz24TH3CAAAAAAAAJWzTw8AAAAAAID8xgZdbUZ6kMxWPQAAAAAAgEXp9AAAAAAAAJby159/5B6hdAn71uou9H58va9mB101N1INqR4AAAAAAMBydHoAAAAAAACsZ0q7VXehVxmRXpmkegAAAAAAAAvR6QEAAAAAADCP5dIshV4sIr2SSfUAAAAAAACWoNMDAAAAAABIoUR6Y4kDkecd9PHLfckn41sDAAAAAACABl3kHgAAAAAAAAAO+/jlXvQFs7t9eMw9AgAAAAAAQG3s0wMAAAAAAKBoL6leyUvk2NNVRnH78Pj986fcUwAAAAAAANSj6/s+9wwAADCbruumX8SHZAAAAM5xTo/UWle2TqNV/anGfWmJ9MKR6gEAAAAsYbvd5h4BAMjAPj0AAAAAAADmkRyPjeq7rNcrk0gPAAAAAACAlun0AAAAAAAAFqEiO9/LWSUEe84Zkt0+PFqpBwAAAAAAMIuL3AMAAAAAAADA3358vd//Ov9fqW+NW8TysL6n0I7bh8fcIwAAAAAAANRApwcAAAAAAEBxxqZ6OrGMHD4AAAAAAADo9AAAAAAAAEYTJq3AYr0BTd0sS7NSDwAAAAAAYDqdHgAAAAAAAOWyWK9wDhwAAAAAAAA2Oj0AAAAAAAAKNyrV2yjHYDwr9QAAAAAAACbS6QEAAAAAAFA6qV6ZnHNNpHoAAAAAAABT6PQAAAAAAAAIQKoHAAAAAAAAFEunBwAAAAAAQAxSvaI43vpYqQcAAAAAAJBMpwcAAAAAAACMI9IDAAAAAACA13R6AAAAAAAAhGGlHizKSj0AAAAAAIA0Oj0AAAAAAABqJtWbnSMFAAAAAACAN3R6AAAAAAAARDJ2pR4wipV6AAAAAAAACXR6AAAAAAAAVM7+NwAAAAAAAGBROj0AAAAAAADgXKLHFlipBwAAAAAAMJZODwAAAAAAgGB+fL0f+6+oywAAAAAAAIDl6PQAAAAAAACAs8gdAQAAAAAA4CCdHgAAAAAAAE3QmMH5bh8ec48AAAAAAAAQiU4PAAAAAAAAAAAAAAAAANLp9AAAAAAAAIjnx9f73CM0x0LC1lipBwAAAAAAcD6dHgAAAAAAAAAAAAAAAACk0+kBAAAAAADQChvhkjk6AAAAAAAAGHCZewAAAAAAAACK8z7K+vH17e8A1bt9ePz++VPuKQAAAAAAAAKwTw8AAAAAAGC0iqO1j1/uD25OO/b7ZFHxKxAAAAAAAAAi0ukBAAAAAADwt5MlnlQPAAAAAAAA4D2dHgAAAAAAAJvN2Q2eVK9BHjoAAAAAAAAM0+kBAAAAAAAsIlbaNGraWLcWjuOlKLcPj7lHAAAAAAAACECnBwAAAAAAAAAAAAAAAADpdHoAAAAAAACtS1jgZudbOzxrAAAAAAAAOEmnBwAAAAAAAAAAAAAAAADpdHoAAAAAAAC04sfX+9wjQDy3D4+5RwAAAAAAACjd/7N3/8htJFcAhzkqRjqDY4g3UPEEduIL2OU77A1E3sBHUJXLvsAmuxcQi6EyirFSpkrhADKNJYDBoKdnuvv194USNXzzBwBZpV89nR4AAAAAAEAKxRelePYAAAAAAACgNjo9AAAAAAAA2vPx013pEQAAAAAAAAB+ui49AAAAAAAAsKqjdZP1XFCJ2vrD2uYBAAAAAACAOun0AAAAAAAgvrOlzf4XaPYy+vjpzvWsh3sBAAAAAAAALESnBwAAAAAAkSXswtr9k7aKJp3h+lxn6Mo//v2ff/39b6WnAAAAAAAAqJdODwAAAAAAYkoo9A7/ef0t1uFpvv7JCsM/3t/NvM6kSbjs9T/ME4U5EQAAAAAAAIjkXekBAAAAAACAzD5+yhaP1RyhnT3NmoevzaXpl1QMAAAAAAAAYJ9ODwAAAAAAQskep2Ws/jKaOFKFkwfQYqTX4swAAAAAAABAQ65LDwAAAAAAAGRzNks7rJWUbFykhuAt8EMb+NQAAAAAAAAgNvv0AAAAAAAgiPHC5/H+7mhhderPLzr4yqoa5qxWph1/DHbPSQ2RXoJGxz5q5XNp5ekFAAAAAACA4nR6AAAAAAAQwUhOkyuvqqTYqWSMV5EasFOqOsdLH4CqhgcAAAAAAACi0ukBAAAAAEDzxiO9KUeY+GW1NXLkdeoxqOe+1zMJAAAAAAAAwD6dHgAAAAAAtO1UuZRrjR7zBajLGj2Ftl4CjV5k+vGPf/+n9AgAAAAAAAD10ukBAAAAAEDDRiK9lb/jOuoMmdqKwcaNnEvxi3/pAJHuy068MwIAAAAAAIAwdHoAAAAAABCNmIc56kz1RHoAAAAAAABAzXR6AAAAAADQquL7zYhqPNVb/8ET6QEAAAAAAACV0+kBAAAAAECTTpVLKxRKDfWBq/VaZ79RQxdtZ/yM1qz1LvpGj/d3jUZ6Z0+z0fMCAAAAAACATuj0AAAAAACgPQUjvYKaS91aNyU+XPSmXHr82M8/AAAAAAAAULPr0gMAAAAAAAB5iJTIbvdQjcdyr3+b8Qm8NP/z8AMA1O/79++lRwAAgJVsNpvSIwAABej0AAAAAACgMdbK1enxftnlcgVNPLUswV7CNewh0uvhHGmC/2EGAAAAAABwik4PAAAAAAAimJnxhAzMakubPn66q22k6aYs1nt1+GVnTzztCWz3er4R8gUIAAAAAAAAXdHpQS0eHh7mH+T29nb+QQAAAMBvqQA167bniXHiCR+yVX2kXlTr7ct++8IUegBM4bdUAMji6elp/kFubm7mHwQAmjblI/Xl5WX8C/yWCgAh6fQAAAAAAKB5mqVKPN7fxegJx70+b0VONt7TfvYyxjtlAAAAAAAAiEenBwAAAAAAzTja82h4GvLLb7//8y9/Lj1FNmsGe55zAAAAAAAAoGY6PQAAAAAA6F3IFXClsq5OVuq9sX+1M56+Nu/KRQAAAAAAAIBG6PQAAAAAAKAN9dRf64dD9Zz7fMFW6h06+nhMuYN9BmmRnm3C6/NFCgAAAAAAMJFODwAAAAAAGqaaqFCfK/XGeVDTuG4AAAAAAADQinelBwAAAAAAAM5brvtSlAEAAAAAAADATDo9AAAAAABoVZFdW00s+Co+5PgAv/z2+1qDULXxSrb4YwwAAAAAAABMp9MDAAAAAACqZuMflKIVBAAAAAAAgIl0egAAAAAAULvlQjUJ3HKs1GOcZXoAAAAAAAAQiU4PAAAAAACaVCTj0Q4BAAAAAAAAwCGdHgAAAAAAdCrqMr16YsJ//uXPI39rpV7PLNMDAAAAAACAYHR6AAAAAABQtag13USdnz4AAAAAAAAATdDpAQAAAABAe4qs27LjK4GVehyyTI8WeTIBAAAAAADG6fQAAAAAAKBH9tQBU6izAAAAAAAAYAqdHgAAAAAAcF4rrU6Fc1qpxz7L9AAAAAAAACAknR4AAAAAAFTtsNvpp+QJs/RvPNWDnX5e2gAAXTj+/wAAIABJREFUAAAAABDPdekBAAAAAACAM7LXO2H6txh++e13IV8nvPQAAAAAAAAgKvv0AAAAAACAM6z5mk+JxzivMmrm+QQAAAAAADhLpwcAAAAAAH0JvNGr8pJkJNX75bff15yEIkZeepU/upWPBwAAAAAAADXQ6QEAAAAAADUK3BMeJdWLrbfnGQAAAAAAAHqj0wMAAAAAAMZYpZXRyEo9uuUlBgAAAAAAAAHo9AAAAAAAoCOWehU3kupZqRfVyOtOpEf9PKUAAAAAAABTDNvttvQMAACQzTAM8w/ih2QAACCwhE6vVKFx6ahtlSTCrX6Eudcq32619aACULPn5+fSIwAAwEo2m03pEQCAAuzTAwAAAAAATmol0gMAAAAAAACAgnR6AAAAAADQC/FbPUYCSLcpkjDL9OiWBxUAAAAAAGAinR4AAAAAAEABUr3wgkV6Lc4MAAAAAAAAq9HpAQAAAAAAxzWU5TQ06j6pXmDBIj0AAAAAAABgnE4PAAAAAAC60FD31dCo80n1QhLpEYPHFQAAAAAAYDqdHgAAAAAAQElKmH60fq9bnx8AAAAAAACWo9MDAAAAAACOEOSs6dTVtlKvRafumtcUAAAAAAAABKbTAwAAAACA+GLnXrHzp9j3Lp7wkV6YE+Es9xoAAAAAAOAiOj0AAAAAAKAi3WZpI0lMt9ekOeEjPQAAAAAAAOAUnR4AAAAAAPCWrKiIx/u7U1deqlc/kR6ReG4BAAAAAAAupdMDAAAAAIDgJF5tkeq1qKtIL+RJAQAAAAAAwEw6PQAAAAAAoGEhkyGpXlu6ivTogUcXAAAAAAAggU4PAAAAAAD4A4VGDaR6regz0ot9dgAAAAAAAJBApwcAAAAAANRChLZPqle/PiM9YvP0AgAAAAAApNHpAQAAAABAZJqupo2keu5scZ1Hep2cJgAAAAAAAEyk0wMAAAAAAP6vrfamrWnTPN7fWaxXoaMXf+RmQRM8wAAAAAAAAMl0egAAAAAAALUTz1TlVKS39hyldXjKAAAAAAAAcMp16QEAAAAAAACurmyHO2fXRLlKFZKrEYMnGQAAAAAAYA779AAAAAAAICxNVzz7IY2ophR34VXnpw8AAAAAAACv7NMDAAAAAACa1G0g1O2JV8VdePV4fycJDsAjDQAAAAAAMJN9egAAAAAAwE86DQAAAAAAAABIoNMDAAAAAADKs48L2iXxbZ07CAAAAAAAMJ9ODwAAAAAAAKBTIj0AAAAAAIAsdHoAAAAAAEB7hCVQFS9JAAAAAAAAOqfTAwAAAACAmD5+uis9AtARqV6L3DUAAAAAAIBcdHoAAAAAAMDVVdFaQ1IIsD6RHgAAAAAAQEY6PQAAAAAAACAD3VdD3CwAAAAAAIC8dHoAAAAAAEBj5CVQLS9PAAAAAAAA+qTTAwAAAAAAALKR6tXPPQIAAAAAAMhOpwcAAAAAAJT08dNd6RGAzGRgNXN3AAAAAAAAlqDTAwAAAAAAAOiCSA8AAAAAAGAhOj0AAAAAAKAlIhNogpdqhdwUAAAAAACA5ej0AAAAAACAYj5+uis9ArAUVRgAAAAAAAD90OkBAAAAAEBMChmgOG9E9XAvAAAAAAAAFqXTAwAAAAAAyrBMD3ogD6uBuwAAAAAAALA0nR4AAAAAAFBAWqQnNYEWeeWW5foDAAAAAACsQKcHAAAAAAAALEsqVoorDwAAAAAAsA6dHgAAAAAAsLa0ZXpA0wRj63PNAQAAAAAAVqPTAwAAAACAsOosNER60K0635SicrUBAAAAAADWpNMDAAAAAACurtbK5+Z8F80JBOCFvA7XGQAAAAAAYGU6PQAAAAAAYCU26QFXErLlucIAAAAAAADr0+kBAAAAAEBk9dQaIj3gVT1vTcE83t+5tgAAAAAAAEXo9AAAAAAAgJ+WS+lEesAbirLsXE8AAAAAAICCdHoAAAAAABBc8XIjS6RX/CyAJXhp5+JKAgAAAAAAlKXTAwAAAAAA/i/74jub9IBxArP5XEMAAAAAAIDihu12W3oGAADIZhiG+QfxQzIAABDSRb1cluQjb6GnQoHYNL1pvDcC0JDn5+fSIwAAwEo2m03pEQCAAuzTAwAAAACALqzcckhugIs83t9Jzi7ligEAAAAAANTDPj0AAEKxTw8AAGDcClv1zn6Lx/u7Sys+LQr0Q+U7hXdFAFpknx4AAP2wTw8A+qTTAwAgFJ0eAADAWQulehMPuzugTg8Yp9Yb4S0RgEbp9AAA6IdODwD6pNMDACAUnR4AAMAUCQHMqSxk+qFej5DxuwOBSfUOeTMEoGk6PQAA+qHTA4A+6fQAAAhFpwcAADDRygHMflui0wOmU+u98k4IQOt0egAA9EOnBwB90ukBABCKTg8AAOAiKwQwh2HJpd9UmgJ0Xut5GwQgBp0eAAD90OkBQJ90egAAhKLTAwAAuNRy9cvRsMQyPSBZh7WeN0AAItHpAQDQD50eAPTpuvQAwE8PDw/zD3J7ezv/IAAAAOC3VICuvEYgGQMYYcmOj1TIa/fe0kmt540U9vlIBYAsnp6e5h/k5uZm/kEAoGlTPlJfXl7Gv8BvqQAQkk4PAAAAAAC4usoR7KlKgBWEr/W8lwIAAAAAALRIpwcAAAAAAPzBm0RkPIZZuifRqwBHxav1vN0BAAAAAAA0TacHAAAAAACMyZiORCpqgBrMXwRaA4UeAAAAAABAADo9AAAAAAAAoG0tBnvyPAAAAAAAgEh0egAAAAAAAEAQ9Qd78jwAAAAAAICQdHoAAAAAAMAaEpoZNQuQrKpgz7sZAAAAAABAeDo9AAAAAAAAIKw3jdxq2Z42DwAAAAAAoCs6PQAAAAAAoEYSF2AJh+8t88s971cAAAAAAADo9AAAAAAAgMWttsAK4FIqOwAAAAAAAOZ7V3oAAAAAAAAAAAAAAAAAAGiYTg8AAAAAAFhWwjI9660AAAAAAAAAaMh16QEAAAAAAICcDqM4zRsAAAAAAAAALEqnBwAAAAAAQZxaW7f781K1nmV6AAAAAAAAAISn0wMAAAAAgOZNaeHK1nrT1T8hAAAAAAAAALzxrvQAAAAAAADALBctrEvYbjfHyt8OAAAAAAAAAIrQ6QEAAAAAQMOChXCW6QEAAAAAAADQIp0eAAAAAAC0Ki3SWy3tC9YQAgAAAAAAAMApOj0AAAAAAKAKlukBAAAAAAAA0CidHgAAAAAANKnybXWXjifSAwAAAAAAAKBdOj0AAAAAACCzyhtCAAAAAAAAAMhLpwcAAAAAABRmmR4AAAAAAAAATdPpAQAAAABAe2peWHfpbCI9AAAAAAAAAFo3bLfb0jMAAEA2wzDMP4gfkgEAgPrN6fQW7eJEegAAwFHPz8+lRwAAgJVsNpvSIwAABdinBwAAAAAA5CHSAwAAAAAAAKBPOj0AAAAAAOiINA4AAAAAAAAAstPpAQAAAAAAGVimBwAAAAAAAEC3dHoAAAAAANCL5dI4kR4AAAAAAAAAPdPpAQAAAABAF0R6AAAAAAAAALAQnR4AAAAAAMQn0gMAAAAAAACA5Qzb7bb0DAAAkM0wDPMP4odkAACgCRMDuUW7OJEeAAAw0fPzc+kRAABgJZvNpvQIAEAB16UHAAAAAAAA8luhiBPpAQAAAAAAAMCOTg8AAAAAAEJZJ4e7KNJT6AEAAAAAAAAQ27DdbkvPAAAA2QzDMP8gfkgGAABacSqWW7SLs0YPAABI8Pz8XHoEAABYyWazKT0CAFDAu9IDAAAAAAAAmV2a0i13ZJEeAAAAAAAAAD2wTw8AgFDs0wMAAHozEs5lb+QuivQUegAAwD779AAA6Id9egDQJ/v0AAAAAAAgpoxb9T5+uhPpAQAAAAAAAMAp9ukBABCKfXoAAECHxgu6mclcQuwn0gMAAA7ZpwcAQD/s0wOAPun0AAAIRacHAAD0aUpNd2k+p9ADAAAy0ukBANAPnR4A9EmnBwBAKDo9AACgW9OzupGaLqHNO3tMAACAK50eAAA90ekBQJ90egAAhKLTAwAAepZc2c2h0AMAAKbQ6QEA0A+dHgD0SacHAEAoOj0AAIDVaj2FHgAAMJ1ODwCAfuj0AKBPOj0AAELR6QEAAFwtn+op9AAAgEvp9AAA6IdODwD6pNMDACAUnR4AAMCr7LWePA8AAEim0wMAoB86PQDok04PAIBQdHoAAACHZgZ78jwAAGA+nR4AAP3Q6QFAn3R6AACEotMDAAA4azzbU+UBAABL0OkBANAPnR4A9EmnBwBAKDo9AAAAAACACun0AADoh04PAPr0rvQAAAAAAAAAAAAAAAAAANAwnR4AAAAAAAAAAAAAAAAApNPpAQAAAAAAAAAAAAAAAEA6nR4AAAAAAAAAAAAAAAAApNPpAQAAAAAAAAAAAAAAAEA6nR4AAAAAAAAAAAAAAAAApNPpAQAAAAAAAAAAAAAAAEA6nR4AAAAAAAAAAAAAAAAApNPpAQAAAAAAAAAAAAAAAEA6nR4AAAAAAAAAAAAAAAAApNPpAQAAAAAAAAAAAAAAAEA6nR4AAAAAAAAAAAAAAAAApNPpAQAAAAAAAAAAAAAAAEA6nR4AAAAAAAAAAAAAAAAApNPpAQAAAAAAAAAAAAAAAEA6nR4AAAAAAAAAAAAAAAAApNPpAQAAAAAAAAAAAAAAAEA6nR4AAAAAAAAAAAAAAAAApNPpAQAAAAAAAAAAAAAAAEA6nR4AAAAAAAAAAAAAAAAApNPpAQAAAAAAAAAAAAAAAEA6nR4AAAAAAAAAAAAAAAAApNPpAQAAAAAAAAAAAAAAAEA6nR4AAAAAAAAAAAAAAAAApNPpAQAAAAAAAAAAAAAAAEA6nR4AAAAAAAAAAAAAAAAApNPpAQAAAAAAAAAAAAAAAEA6nR4AAAAAAAAAAAAAAAAApNPpAQAAAAAAAAAAAAAAAEA6nR4AAAAAAAAAAAAAAAAApLsuPQDw08PDw/yD3N7ezj8IAAAA+C0VALLwkQoAWfhIBYAsnp6e5h/k5uZm/kEAoGlTPlJfXl7Gv8BvqQAQkn16AAAAAAAAAAAAAAAAAJBOpwcAAAAAAAAAAAAAAAAA6XR6AAAAAAAAAAAAAAAAAJBOpwcAAAAAAAAAAAAAAAAA6XR6AAAAAAAAAAAAAAAAAJBOpwcAAAAAAAAAAAAAAAAA6XR6AAAAAAAAAAAAAAAAAJBOpwcAAAAAAAAAAAAAAAAA6XR6AAAAAAAAAAAAAAAAAJBOpwcAAAAAAAAAAAAAAAAA6XR6AAAAAAAAAAAAAAAAAJBOpwcAAAAAAAAAAAAAAAAA6XR6AAAAAAAAAAAAAAAAAJBOpwcAAAAAAAAAAAAAAAAA6XR6AAAAAAAAAAAAAAAAAJBOpwcAAAAAAAAAAAAAAAAA6XR6AAAAAAAAAAAAAAAAAJBOpwcAAAAAAAAAAAAAAAAA6XR6AAAAAAAAAAAAAAAAAJBOpwcAAAAAAAAAAAAAAAAA6XR6AAAAAAAAAAAAAAAAAJBOpwcAAAAAAAAAAAAAAAAA6Ybtdlt6BgAAyGYYhvkH8UMyAAAAAABAXs/Pz6VHAACAlWw2m9IjAAAF2KcHAAAAAAAAAAAAAAAAAOl0egAAAAAAAAAAAAAAAACQTqcHAAAAAAAAAAAAAAAAAOl0egAAAAAAAAAAAAAAAACQTqcHAAAAAAAAAAAAAAAAAOl0egAAAAAAAAAAAAAAAACQTqcHAAAAAAAAAAAAAAAAAOl0egAAAAAAAAAAAAAAAACQTqcHAAAAAAAAAAAAAAAAAOl0egAAAAAAAAAAAAAAAACQTqcHAAAAAAAAAAAAAAAAAOl0egAAAAAAAAAAAAAAAACQTqcHAAAAAAAAAAAAAAAAAOl0egAAAAAAAABAj4YPP4YPP0pP0TW3AAAAAAAIQ6cHAAAAAAAAAPRlPw/TiZWyfwvcBQAAAACgddelBwAAAAAAAAAAWIkerFq7W7P99r70IAAAAAAAKezTAwAAAAAAAAC6INKrn3sEAAAAADTKPj0AAAAAAAAAILiR+ssCt1J2V/7w1lisBwAAAAC0yD49AAAAAAAAACAykV7NTt0Ci/UAAAAAgLbo9AAAAAAAAACAsER69ZPqAQAAAAAB6PQAAAAAAAAAgJhOhV7bb+9FelWR6gEAAAAArdPpAQAAAAAAAAABjUR6K0/CFFI9AAAAAKBpOj0AAAAAAAAAIBqRXoukegAAAABAu3R6AAAAAAAAAEAoIr12SfUAAAAAgEZdlx4AAAAAAAAAACAbkd5Zby5RbVdm++390Zs4fPhR26gAAAAAAK/s0wMAAAAAAAAAghDpnXV4iSpcVWerHgAAAADQHJ0eAAAAAAAAABCBiOusU5eowksn1QMAAAAA2qLTAwAAAAAAAACaN5JvWaa3E6ZwC3MiAAAAAEAkOj0AAAAAAAAAoG0ivbNabNtG7l2LpwMAAAAAxKbTAwAAAAAAAAAaJtILTKoHAAAAALRCpwcAAAAAAAAAtEqkN0XTSZtUDwAAAABogk4PAAAAAAAAAGiSTGuKAFdJqgcAAAAA1E+nBwAAAAAAAABEY5nejowNAAAAAGAdOj0AAAAAAAAAoD0jBZpILx4r9QAAAACAyun0AAAAAAAAAIDGiPSmCBawSfUAAAAAgJrp9AAAAAAAAACAloiypujtKvV2vgAAAABAbXR6AAAAAAAAAEAQluntRI3W3F8AAAAAoFo6PQAAAAAAAACgGSMFmoirByN3OWqdCAAAAAA0QacHAAAAAAAAALRBiDVFz1ep53MHAAAAAMrS6QEAAAAAAAAAzbNMb6eHUM29BgAAAAAqpNMDAAAAAAAAABrQQ4E2k0t05SIAAAAAAIXo9AAAAAAAAACA2o3HVxas9Wb8jkv1AAAAAID16fQAAAAAAAAAAJonTgMAAAAAKEinBwAAAAAAAABUzTK9s45eothXxko9AAAAAKAq16UHAAAAAAAAAAAgXYeRHnU6l0f+afyff/v1e8ZhAAAAAGBlOj0AAAAAAAAAoF6W6XHK9tv7kcdj+PDD47GcJTYWfvjrkZBPvAcAAABAK3R6AAAAAAAAAACtskyP1SzR5p21H+9p9gAAAACo2bDdbkvPAAAA2QzDMP8gfkgGAAAAAKiEZXrjzkZ608Oqdi+mh2RRRdq8KTR7AADUbLPZlB4BACjAPj0AAAAAAAAAgPbYpMdyqs3zXr3u2RPsAQAAAFAJnR4AAAAAAAAAUCN70phi++39yKMyfPjhUZmu/jzvkGAPAAAAgEro9AAAAAAAAAAAGmOZHhm1mOcd2gV7aj0AAAAAStHpAQAAAAAAAADViREOLUSkdxEr9UbEe6FZrwcAAABAKe9KDwAAAAAAAAAAcJmesyuR3qHOTz/N8OFHvEhv34e//um12QMAAACAFdinB7V4eHiYf5Db29v5BwEAAAC/pQJAFj5SASDZ2Xzo0s9ZH6mds1JvX+w8741dqme3Huw8PT3NP8jNzc38gwBA06Z8pL68vIx/gd9SASAk+/QAAAAAAAAAANpgmd4pLsIU4XfonWK3HgAAAAAr0OkBAAAAAAAAAC358vlr6RHKEOmRrNtCb59UDwAAAIBF6fQAAAAAAAAAgIqoiY4S6c3U83PV87m/YbEeAAAAAMvR6QEAAAAAAAAAzeh2mR5nqRYPWaN3lFoPAAAAgCXo9AAAFjH8UelxAAAAAACgDZqioyzTy6Krp0uhd5ZUDwAAAIC8dHoAAPkdhnlSPQAAAAAAII1Ij0sp9CayWA8AAACAjHR6AACZnUrypHoAAAAAAMClRHpcxBq9BFI9AAAAALLQ6QEA5CTGAwAAAACAZGf7oi+fv64zCY06WzDGbthin92iLNYDAAAAYD6dHgBANiI9AAAAAAAgI8v0mE6kN59UDwAAAIA5dHoAABkMwyDSAwAAAAAAMhLpMZ1ILxepHgAAAADJdHoAAHMp9AAAAAAAYD6h0VkivSnOXqVgT9rw4UewMypOqgcAAABAmuvSAwAANEyhBwAAAAAAq/ny+WvpEdajvGIKz8lCdqnet1+/lx4EAAAAgJbYpwcAkGIYBpEeAAAAAACwhKPxlWV6vCHSW5rFegAAAABcRKcHAHAZhR4AAAAAALAckR5TiPTWIdUDAAAAYDqdHgDAJMP/lB4EAAAAAAACEh2xptaft9bnb4tUDwAAAICJdHoAAGPkeQAAAAAAUIMvn7+WHmENlullEfuKifTWJ9UDAAAAYAqdHgDAW8Oe0rMAAAAAAAC9EOlxlkivFKkeAAAAAGfp9ACArg3HlB4KAAAAAADojkiPs0R6ZUn1AAAAABin0wMAgjta4knyAAAAAACgHuoj1tfcU9fcwCFJ9QAAAAAYodMDAAAAAAAAACjJMj3GifTqIdUDAAAA4BSdHgDAZbb/U3oQAAAAAADoxZfPX0uPsCCR3hIiXUCRHgAAAAA0QacHADCJPA8AAAAAAMhOpMc4kV6FrNQDAAAA4KjB/zUHAGIbhmHOPx/5YSnhyH70WsHMO77jTgEAAAAArOlsiRS4W1u605teecW7yDGeK51etb79+r30CAAA1Guz2ZQeAQAowD49AOC/7N07UhvpGoDhboqI3BmxROYY78F78Comm2iyWYX3cPZgxc6Q4skop6R9AlwMg6S+/H35b89TE/lA8/VFEpyqtz4+6t6JPQsAAAAAABSu5gzJMr240n/20p+wZrbqAQAAAPCBTg8AqFp3SeyhAAAAAACA8on06CfSS59UDwAAAID3bmMPAACwLt0dAAAAAACQGpEe/UR6AAAAAJAd+/QAAAAAAAAAACAVIr2MWKkHAAAAwBudHgAAAAAAAADAdizTg5JI9QAAAAB4pdMDAAAAAAAAANJVWMAm0qOfZXo5kuoBAAAA0Oj0AAAAAAAAAAC2IdLbUo4XVqQHAAAAAPnS6QEAAAAAAAAAAISzUg8AAAAAnR4AAAAAAAAAEEdV28Ms00tQUk9gUsMQQKoHAAAAUDmdHgAAAAAAAADAukR6AAAAAABl0+kBAAAAAAAAAKxIpMcgy/TKYKUeAAAAQM10egAAAAAAAAAAEI1IDwAAAAAKoNMDAAAAAAAAAFiLZXpQFSv1AAAAAKql0wMAAAAAAAAAWIVIj0GW6QEAAABAGXR6AAAAAAAAAADLE+lBnazUAwAAAKiTTg8AAAAAAAAAACKwTA8AAAAAiqHTAwAAAAAAAABYmGV6UDMr9QAAAAAqpNMDAAAAAAAAAFiSSI8xLNMDAAAAgJLo9AAAAAAAAAAAFiPSAxor9QAAAADqo9MDAAAAAAAAAIBNWaYHAAAAAIXR6QEAAAAAAAAALMMyPeCNlXoAAAAAVdHpAQAAAAAAAAAsQKTHSJbpAQAAAEB5dHoAAAAAAAAAAHOJ9IBzVuoBAAAA1EOnBwAAAAAAAAAAAAAAAADhdHoAAAAAAAAAALNYpsd4F58WAAAAACB3Oj0AAAAAAAAAgHAiPaDH/ut97BEAAAAA2MJt7AEAAAAAAAAAAEqT3c604IEViQAAAAAAjX16AAAAAAAAAACwjewCTgAAAABgJJ0eAAAAAAAAABCHVWzE5QlkG/uv97FHAAAAAGB1Oj0AAAAAAAAAAAAAAAAACKfTAwAAAAAAAACA1bX7l9gjAAAAAABr0ekBAAAAAAAAAOmSNhHGk0NS9l/vY48AAAAAwLpuYw8AAAAAAAAAAJCx7ngXe4TLJoVqyZ4FAAAAAEAW7NMDAAAAAAAAAAAAAAAAgHA6PQAAAAAAAAAAWNek9YYAAAAAQHZ0egAAAAAAAAAAAOvaf72PPQIAAAAAK9LpAQAAAAAAAADRdMe72CNQKc8eAAAAALAgnR4AAAAAAAAAAAAAAAAAhNPpAQAAAAAAAABQlHb/EnuE/0htHgAAAABgcbexBwAAAAAAAACA/Fysbrrj3faT1KDdv7i2AAAAAACkTKcHAAAAAAAAAKMMbsR6/wW6MgA+2H+9P/7vn9hTAAAAALAKnR4AAAAAAAAADBgs9K59S161XqzOsDveBVxhmCOv1yYAAAAAkD6dHgAAAAAAAABcNbMfy6XWOz/Nt39Jf3gAAAAAAIjuJvYAAAAAAAAAAJCidv+y1JK3lJfFDZ5mysMDAAAAAEAi7NODbBwOh/kHeXx8nH+QVTnN8ZxmIpzmeJWcZvoquZszHQ6H9M+xklvpNMdzmolwmuM5zUTUcJo1nGPjNKdwmolwmuM5zUTUcJo1nGPjNKdwmolwmuMtfpqLx2mvB/zx/eecg8Q6zXb/MnKr3oy7+Tn0G7eW5hO7uKxPc/zrN+vTpE5PT0/zD/Lw8DD/IKtymuMlfpo1nGPjNKdwmolwmuM5zURUcpoAQAF0egAAAAAAAADwH4ORz3m0Zunc2sa3gjBo42fJ+wMAAAAA1OAm9gAAAAAAAAAAkJD+oqY73l0sfK79+wdfviW0NS6pdmjmpkEAAAAAAIhLpwcAAAAAAAAAv/WkayNLvEGJpHpJRXoAAAAAAJA7nR4AAAAAAAAANM1QpDfmCCO/LJFULzvaQsbwnJC4/df72CMAAAAAsAqdHgAAAAAAAABcbXuWWqPHoB/ff8YegSp4RQMAAAAAa9DpAQAAAAAAAFC7nkhvpZ8Yd6WehWM1mHqXPRUAAAAAAHPo9AAAAAAAAADgAku3yFdYdCfVAwAAAAAIptMDAAAAAAAAoGrapHT8+P6z/wvcrB7t/uXtv/kHibvyMdjguetvAQAAAICV3MYeAPjtcDjEHgEAAABCXPuT9vHxceNJACBxYf8/sI9UgLVdq3o2iHm+fPs8mKUlIqm0qcKP1Fh14phUL5dnGICmaZ6enuYf5OHhYf5BACBrYz5Sf/361f8FWf+VCgBcY58eAAAAAAAAAJWKGOlFZCUdAAAAAAAsTqcHAAAAAAAAAP8qO9JLn/1srMerGwAAAABYz23sAQAAAAAAAAAgAmvlMvU6UjP/AAAgAElEQVTl22ctHxd5UZOL3W4X/L2/fv2KO8A2nOZ4k05z8Cc+Pz/PGwcAAACqZp8eAAAAAAAAAPw2c91WkZmQFWQAAAAAADDIPj0AAAAAAAAAqlNkUDdGFifeHe+ymDOutfvJw+Fw/o9fvn1e9YfOMfjMKE4BAAAAgFXp9AAAAAAAAACgaWQ8+fjy7fOP7z9jT7GddJ7Mqi47AAAAAMAkN7EHAAAAAAAAAIBNXdy7lU4KReN2sDRPFAAAAACwNp0eAAAAAAAAACzgYv6Xu2Trpi/fPscegYQU+eoDAAAAAPKi0wMAAAAAAACgIun0PD++/9z4J6Zz7mMkmwiSHc8SSfFAAgAAAJRKpwcAAAAAAABA7VQTAAAAAADAHDo9AAAAAAAAAGqx3kK5vFbV5aK/n/zy7fNmk5Cy/lefChcAAAAA2IZODwAAAAAAAICqRcl4fnz/uf0PnUrgBIvwUgIAAACAGuj0AAAAAAAAAKB8mW78s1KPfpbpAQAAAACJaLuuiz0D0DRNczgc5h/k8fFx/kEAGKlt26nf4levDQTcl3PuFAD4KxUAFuEjFSA113qe+SVPQAK3fT6UxZAX9U8+cjOhj9RS5dLpZRrKsoZ0HksqNPhX6vPz8+BBHh4eFhoHAHL19PQ0+DWfPn3q/wJ/pQJAkezTAwAAAAAAAKBeUXoJkcYk/SWelXo1yyXSAwAAAABqoNMDAAAAAAAAgHClrsnSOAEAAAAAwHg6PQAAAAAAAADKV2pNN1Lup2+lHucs0yNHnkwAAACAgun0AAAAAAAAAKhUlF5CpAEAAAAAAOXR6QEAAAAAAABAoNz31GXESj3ey26ZXoIjAQAAAADL0ukBAAAAAAAAwEZyaXXSnLM/1YNXaT69AAAAAEDxdHoAAAAAAAAAlO883akn5qlk6Z+VevWo5JEGAAAAAPKi0wMAAAAAAACgCt3x7v1/8w8oFtqelXr0q6e/JUeeTwAAAICytV3XxZ4BACBLbdtO/Ra/em0g4L6cc6cAAAAAgDGmdnqxCo2AnjDxmKTnjBKfnPnyvfvKXhJ/RKFpmtPpFHsEACjEbreLPQIAEIF9egAAAAAAAAAwWcHJTdYlScH3hSbz+5v1KwsAAAAAGKTTAwAAAAAAAIBiZd01XaN34pynAgAAAACIS6cHAAAAAAAAAKsTES2r53oWmSbS9N5Zry/S5ykFAAAAKJ5ODwAAAAAAAACmUYKlQKpXlTIivYxGBQAAAACm0ukBAAAAAAAAAL/piAAAAAAAIIBODwAAAAAAAADWFSt+K36tnJV6lShjmR4AAAAAUDadHgAAAAAAAABMIABLilSveCI9CuBZBQAAAKiBTg8AAAAAAAAAyJhUr2DlRXqZjg0AAAAADNLpAQAAAAAAAMCKMspyMhr1A6lekcqL9AAAAACAgun0AAAAAAAAAGCsjKKvjEZdhHCrHrnf69znZyp3HAAAAKASOj0AAAAAAAAAoATXYpjaksUyXLtrkicAAAAAIE06PQAAAAAAAABYi6YoEVK9vIj0KIaHFgAAAKAeOj0AAAAAAAAAGKXs1quMmKTnLMq+fSWpIdIr6VwAAAAAgFc6PQAAAAAAAAAoTc1NWne8uxZB1XxZclFDpAcAAAAAFEmnBwAAAAAAAACrUBZFJNXLUVWRXpEnxQfuMgAAAEBVdHoAAAAAAAAAMEzflR2pXl6qivQAAAAAgPLo9AAAAAAAAACgdqWmUFK9XIj0KI+nFwAAAKA2Oj0AAAAAAAAAWJ5CIxFSvfRVG+kVf4IAAAAAUBWdHgAAAAAAAAAURYH2QU+q51pFV22kR9k8wAAAAAAV0ukBAAAAAAAAwAA1V+66453Fegm6ePF7blZ56jlTAAAAACieTg8AAAAAAAAAFpZXe5PXtHPUc6ZZuBbpbT9JXBWecvHcUwAAAIA63cYeAAAAAAAAAABgI6/9jB16CZI2AQAAAABZs08PAAAAAAAAAMqhQBvjfRImD4vFXXhT+ekXxt0EAAAAqJZ9egAAAAAAAADQR/lWJC1NCtwFCuORBgAAAKiZfXoAAAAAAAAAUC9VCaTAKxEAAAAAcqfTAwAAAAAAAIAl6W0AKuTNHwAAAKByOj0AAAAAAAAAKES7f4k9AhBI5QUAAAAAWdPpAQAAAAAAAABAfFK9fLl3AAAAAOj0AAAAAAAAAKBSwhKA+byXAgAAANDo9AAAAAAAAACgR7t/iT0CUBHFFwAAAABkSqcHAAAAAAAAAIuJ2NhICqEMUr28uF8AAAAAvNLpAQAAAAAAAABAQqRfuXCnAAAAAHij0wMAAAAAAACAGslLAObwLgoAAADAezo9AAAAAAAAAABIiwYMAAAAAPKi0wMAAAAAAACA7LX7l9gjAAuT6qXM3QEAAADgA50eAAAAAAAAAACkSAyWJvcFAAAAgHM6PQAAAAAAAACojsgEcuHVmhp3BAAAAICLdHoAAAAAAAAAkLd2/xJ7BGBFwrB0uBcAAAAAXKPTAwAI0bbtZt8FAAAAAEBEkgwAXvlEAAAAAKCHTg8AYLI5uZ1UDwAAAACAZVmmBzVQiEXnFgAAAADQT6cHADDN/NBOqgcAAAAAwFLCIj21CeTIKzciFx8AAACAQTo9AICx2rZdKrFb8FAAAAAAAABUQi0WhcsOAAAAwBg6PQCAq9r/yu74AAAAAACULWyZHpA1zdjGXHAAAAAARrqNPQAAwOpySeCC5+y6btlJAAAAAAB4rzveJVjEJTgSsI0035SKJNIDAAAAYDz79AAAAAAAAABgMdvEM3N+iuwECtAd77yW1+YKAwAAADCJTg8AAAAAAAAAcmKPFvBKSLYe1xYAAACAqXR6AAAAAAAAADAgnWBDpAe8l867U0lcVQAAAAAC6PQAAAAAAAAAYEnrpXQiPeCcqGxZricAAAAAYXR6AAAAAAAAADAsermxSKQX/SyANXhpL6I73rmSAAAAAAS7jT0AAEAcXdfFHiFE27axRwAAAAAAYFi7f1k29rBJD+j3+p7jvSKYQg8AAACAmezTAwDK110Se6hAJZ0LAAAAAEB2JlUcS9Uy7f5FeAOMJDYL47oBAAAAMJ9ODwAAAAAAAADG2rjlUOgBU3XHO9XZeC4XAAAAAEvR6QEAAAAAAADABOOLjjmr8Aa/NyAs0aJAPbzex3CVAAAAAFhQ23Vd7BmApmmaw+Ew/yCPj4/zDwIAWWvbdv5B/JIMAP5KBYBF+EgFKNukAG9S2jf+gFMjwEyLFB+pMIe1nBdl+n4IMw1+pD4/Pw8e5OHhYaFxACBXT09Pg1/z6dOn/i/wVyoAFOk29gAAAAAAAAAAkJ/ueDe+fnn7ymtlyPhDvR1BewOMMenNqgYKPQAAAABWotMDAAAAAAAAgBAB9cvMWkZeAgQI28BZJO+iAAAAAKxHpwcAAAAAAAAAgTarX+a3JeoUqFzltZ73QAAAAADWptMDAAAAAAAAgFkCFutNOvj5P1Zb2gAzVVjrKfQAAAAA2IZODwAAAAAAAADmeutAFqxftCXASiqp9byLAgAAALAlnR4AAAAAAAAALGZ+sCcsAbZRcK3njRQAAACA7en0AAAAAAAAAGB5HyqR/hJm7aREsgJcs8Y60Fi81wEAAAAQkU4PAAAAAAAAAFa3YD1SQEsDJCjr9XoKPQAAAACi0+kBAAAAAAAAAABNk9t6PXkeAAAAAOnQ6QEAAAAAAAAAAP+RbLCnzQMAAAAgTTo9AAAAAAAAAMhGQDCjaQHmeP8eEqvZ8z4GAAAAQPp0egAAAAAAAAAAwLDzXm6lck+YBwAAAEB2dHoAAAAAAAAAUCytC7CqnjeZwYTPGxQAAAAAJdHpAQAAAAAAAEAeVtpbBbAGGR4AAAAAVbmJPQAAAAAAAAAAAAAAAAAAZEynBwAAAAAAAAAZCFimZ5kVAAAAAABs4zb2AAAAAAAAAACQnPMoTvMGAAAAAABco9MDAAAAAAAAgH9dW1v3+u+xaj3L9AAAAAAAIGU6PQAAAAAAAABomnEtXNxab7z0JwQAAAAAgJLcxB4AAAAAAAAAAOKbtLAuYLvdHBv/OAAAAAAAYCqdHgAAAAAAAAC1KyyEs0wPAAAAAAA2ptMDAAAAAAAAoGphkd5maV9hDSEAAAAAABSp7bou9gwAALCYtm3nH8QvyQAAAABQleAQbpu1dVPHs0wPAAhzOp1ijwAAhdjtdrFHAAAisE8PAAAAAAAAgHolvq1OpAcAAAAAAFnQ6QEAAAAAAABAihJvCAEAAAAAgDc6PQAAAAAAAAAogWV6AAAAAAAQi04PAAAAAAAAgEqlvLBu6mwiPQAAAAAAiEinBwAAAAAAAACTrdrFifQAAAAAACAvOj0AAAAAAAAASIhIDwAAAAAAsqPTAwAAAAAAAIBppHEAAAAAAMB7Oj0AAAAAAAAASIVlegAAAAAAkCOdHgAAAAAAAABMsF4aJ9IDAAAAAIBM6fQAAAAAAAAAYCyRHgAAAAAAcE6nBwAAAAAAAACjiPQAAAAAAICLdHoAAAAAAAAAVGp87dYd70R6AAAAAADANbexBwAAAAAAAACARG1QxIn0AAAAAACgADo9AAAAAAAAAPhomxxuUqSn0AMAAAAAgGTdxB4AAAAAAAAAAKK5Fr9NXXM3Vbt/EekBAAAAAEAxdHoAAAAAAAAAcMF6qd7UI4v0AAAAAAAgcTo9AAAAAAAAAKrWU8GtkepNXaMn0gMAAAAAgPTp9AAAAAAAAADgqgVTvXb/MjXSW+pHAwAAAAAAq9LpAQAAAAAAAFC7/iJufqo3tdBrRHoAAAAAAJCV29gDAAAAAAAAAEB83fGuJ6V7+5+m5nMBjZ9CDwAAAAAAsqPTAwAAAAAAAICmGUr1Xo0J9oL37yn0AAAAAAAgUzo9AAAAAAAAAPhtTKr3KjjGu/ZzFzwaAAAAAACwMZ0eAAAAAAAAAPzrNZlbNsMb/HEAAAAAAEDWbmIPAAAAAAAAAADJ2SCf6453Ij0AAAAAACiDfXoAAAAAAAAAcMFKi/W0eQAAAAAAUB6dHgAAAAAAAABc9ZbVzQz25HkAAAAAAFAwnR4AAAAAAAAADPsQ2vVne6o8AAAAAACoik4PAAAAAAAAACZT4gEAAAAAAG9uYg8AAAAAAAAAAAAAAAAAABnT6QEAAAAAAAAAAAAAAABAOJ0eAAAAAAAAAAAAAAAAAITT6QEAAAAAAAAAAAAAAABAOJ0eAAAAAAAAAAAAAAAAAITT6QEAAAAAAAAAAAAAAABAOJ0eAAAAAAAAAAAAAAAAAITT6QEAAAAAAAAAAAAAAABAOJ0eAAAAAAAAAAAAAAAAAITT6QEAAAAAAAAAAAAAAABAOJ0eAAAAAAAAAAAAAAAAAITT6QEAAAAAAAAAAAAAAABAOJ0eAAAAAAAAAAAAAAAAAITT6QEAAAAAAAAAAAAAAABAOJ0eAAAAAAAAAAAAAAAAAITT6QEAAAAAAAAAAAAAAABAOJ0eAAAAAAAAAAAAAAAAAITT6QEAAAAAAAAAAAAAAABAOJ0eAAAAAAAAAAAAAAAAAITT6QEAAAAAAAAAAAAAAABAOJ0eAAAAAAAAAAAAAAAAAITT6QEAAAAAAAAAAAAAAABAOJ0eAAAAAAAAAAAAAAAAAITT6QEAAAAAAAAAAAAAAABAuNvYAwC/HQ6H+Qd5fHycfxAAAADwVyoALMJHKgAswkcqACxi8CP1+fl58CAPDw8LjQMAuXp6ehr8ml+/fvV/gb9SAaBI9ukBAAAAAAAAAAAAAAAAQDidHgAAAAAAAAAAAAAAAACE0+kBAAAAAAAAAAAAAAAAQDidHgAAAAAAAAAAAAAAAACE0+kBAAAAAAAAAAAAAAAAQDidHgAAAAAAAAAAAAAAAACE0+kBAAAAAAAAAAAAAAAAQDidHgAAAAAAAAAAAAAAAACE0+kBAAAAAAAAAAAAAAAAQDidHgAAAAAAAAAAAAAAAACE0+kBAAAAAAAAAAAAAAAAQDidHgAAAAAAAAAAAAAAAACE0+kBAAAAAAAAAAAAAAAAQDidHgAAAAAAAAAAAAAAAACE0+kBAAAAAAAAAAAAAAAAQDidHgAAAAAAAAAAAAAAAACE0+kBAAAAAAAAAAAAAAAAQDidHgAAAAAAAAAAAAAAAACE0+kBAAAAAAAAAAAAAAAAQDidHgAAAAAAAAAAAAAAAACE0+kBAAAAAAAAAAAAAAAAQDidHgAAAAAAAAAAAAAAAACEa7uuiz0DAAAspm3b+QfxSzIAAAAAAABQm9PpFHsEACjEbreLPQIAEIF9egAAAAAAAAAAAAAAAAAQTqcHAAAAAAAAAAAAAAAAAOF0egAAAAAAAAAAAAAAAAAQTqcHAAAAAAAAAAAAAAAAAOF0egAAAAAAAAAAAAAAAAAQTqcHAAAAAAAAAAAAAAAAAOF0egAAAAAAAAAAAAAAAAAQTqcHAAAAAAAAAAAAAAAAAOF0egAAAAAAAAAAAAAAAAAQTqcHAAAAAAAAAAAAAAAAAOF0egAAAAAAAAAAAAAAAAAQTqcHAAAAAAAAAAAAAAAAAOF0egAAAAAAAAAAAAAAAAAQTqcHAAAAAAAAAAAAAAAAAOF0egAAAAAAAAAAAAAAAAAQTqcHAAAAAAAAAAAAAAAAAOF0egAAAAAAAAAAAAAAAAAQTqcHAAAAAAAAAAAAAAAAAOF0egAAAAAAAAAAAAAAAAAQTqcHAAAAAAAAAACk6I/7P/+4/zP2FLVzFwAAAADGaLuuiz0DAAAspm3b+QfxSzIAAAAAAEBcH8Kwv//5K9YklXMjqnI6nWKPAACF2O12sUcAACK4jT0AAAAAAAAAAADAb1a3pez17qj1AAAAAM7dxB4AAAAAAAAAAACgaUR6mXCbAAAAAM61XdfFngEAABbTtu38g/glGQAAAAAAYGM96ZftbdFduztuTWFOp1PsEQCgELvdLvYIAEAE9ukBAAAAAAAAAAAxifQSd+0uWKwHAAAA8EanBwAAAAAAAAAARCPSy4JUDwAAAKBf23Vd7BkAAGAxbdvOP4hfkgEAAAAAALZxrfJS6KXJ/Srb6XSKPQIAFGK328UeAQCIwD49AAAAAAAAAAAgAtFXdmzVAwAAALhGpwcAAAAAAAAAAGxNpJcpqR4AAADARTo9AAAAAAAAAABgUyK9rEn1AAAAAM7p9AAAAAAAAAAAgO2I9Aog1QMAAAD4QKcHAAAAAAAAAABsRKRXDKkeAAAAwHs6PQAAAAAAAAAAYAu5FFx/3P+Z+KiJTCjVAwAAAHjTdl0XewYAAFhM27bzD+KXZAAAAAAAgMX1tFtJLdM7nzOd8RKcLZfbyhin0yn2CABQiN1uF3sEACACnR4AAEXR6QEAAAAAACQoo5orwVH719NFv4AJXjHC6PQAYCk6PQCok04PAICi6PQAAAAAAABSk1fH1R/Fvdpm7HQmGZTXLeYanR4ALEWnBwB10ukBAFAUnR4AAAAAAEBSsiu4xtRx7y17FnF/+hzZ3WjO6fQAYCk6PQCok04PAICi6PQAAAAAAADS0V+dpZlvTS3lzk06rzk/LrULKNXLnU4PAJai0wOAOt3GHgAAAAAAAAAAAKhOweHW/NIPAAAAgOzcxB4AAAAAAAAAAAAokO1qq0rwGvaMpF0EAAAAiqfTAwAAAAAAAAAAFibSW1Wy11CqBwAAAFRLpwcAAAAAAAAAACxJkbWqZCO9QR4MAAAAoGA6PQAAAAAAAAAAYCP5NmaJSP8Cpj8hAAAAwBp0egAAAAAAAAAAwGJ6dqYpuOb4+5+/crmAPXNaqQcAAACUSqcHAAAAAAAAAAAsQ4W1klwKvTE8JAAAAECRdHoA/J+9u0VuZOkSMGx1eAsfE+64WzA1HDZgYLNeg5iQmNZgZjhg2MCm3kIvQLTX4AHq8KexpVJVVlZl5snniYv62uVTP5Js8MYBAAAAAAAAgMVFKs1W1uKla3FmAAAAgDl0egAAAAAAAAAAQAb2pGV3PB1CBm8eFQAAACAenR4AAAAAAAAAADDXcHkVMjZbVIBCb3h+qR4AAAAQzGPpAQAAAAAAAAAAAPir9TwPAAAAoE+b9/f30jMAAEA2m81m/kH8kgwAAAAAADBJpGV6Bfe8tXWhRor0bIQ0fIN+/vqx2iQAEMn3799LjwAAFGCfHgAAAAAAAAAAQElyNZYzJz19eX699b8kfAAAAPCJfXoAAIRinx4AAAAAAMDKgi1M+3o6H6eQd9Vec1cmWbAnpH5FdkLK9gDgkn16ANAn+/QAAAAAAAAAAADu+1qUTQqiBGksp0ibd+nT5j3ZHgAAAB2yTw9q8fb2Nv8gT09P8w8CAE2zTw8AsvBXKgBk4SMVALLwkQqVi7cqbWCfHsniPSeVKJ7n3SXYAyCe379/3/2af/3rX8Nf4K9UAAjJPj0AAAAAAAAAAABoRv153oePPXuCPQAAAMLT6QEAAAAAAAAAACksSWOk4+kw8LTstntPyxgN5XlfCfYAAAAIT6cHAAAAAAAAAAAA9Wq60PvkHOyp9QAAAIhHpwcAAAAAAAAAAEwWKRyiOCv1rgr8KlPrAQAAEM+30gMAAAAAAAAAAADRaK74xCMxyW67DxzpfXh5fj0HewAAABCATg8AAAAAAAAAAJimh4KIlXmozjop9C6p9QAAAIhBpwcAAAAAAAAAAMDirNS7q7dC75JaDwAAgNY9lh4AAAAAAAAAAAAIRY4FU/Vc6F06p3o/f/0oPQgAAABMZp8eAAAAAAAAAAAwgaCIhfT5aO22+z5PfIDFegAAALRIpwcAAAAAAAAAAGRjmR4DPB6fKPRueXl+VesBAADQFp0eAAAAAAAAAAAwlqyIRfXzgFmjN4ZaDwAAgIbo9AAAAAAAAAAAAGA9Cr1JpHoAAAA04bH0AAAAAAAAAAAAANAFhV6al+fXn79+lJ4CAAAAhtinBwAAAAAAAAAAjHI3MTqeDutMQrvuPiSBS7bAp7aCl+dXi/UAAAComU4PAAAAAAAAAAAAliXSy0KqBwAAQLUeSw8AAAAAAAAAAAAAYSn08np5fv3560fpKQAAAOAz+/QAAAAAAAAAAID7tEbkcjwdhr8g0sMW6VzqYaseAAAAFdLpAQAAAAAAAAAAGdyNr6A3Ir3lvDy/qvUAAACoik4PAAAAAAAAAAAAMhPprUCqBwAAQD0eSw8AAAAAAAAAAAAQzZhGywbCwER6q3l5fv3560fpKQAAAECnBwAAAAAAAAAA3CM6uivhEl1+i2bvk9123+418XpZmVQPAACAGuj0AAAAAAAAAACAudpNqubImGN9HKqTK3k8HaLGbFHPq3JSPQAAAIrT6UEtnp6eSo8AAAAAf/krFQCy8JEKAFn4SAXqtFyL1VuwF4xIryCpHgDr+Oeff+5+zffv31eYBACozbfSAwAAAAAAAAAAADRjt92v02IpvprjlhX38vxaegQAAAD6ZZ8eAAAAAAAAAAAwRH10Nuk6DG/DG3mo85d1u1hvt903dO5eJpWwVQ8AAIBSdHoAAAAAAAAAAAB33K2wJhVll19898id13pNEOkBAAAA30oPAAAAAAAAAAAAtC12Qrbb7ocrrOPpMOcKjPzeeCVYmMcm3q1p3cvza+kRAAAA6JF9egAAAAAAAAAAAJNlzMzOhxqzWC9M2xaGSK9OL8+vP3/9KD0FAAAAfbFPDwAAAAAAAAAAYJolerkxx1SFwUi26gEAALAynR4AAAAAAAAAAHCTMOyT4+mw3FK7kalePzel8jOtfDykegAAAKxJpwcAAAAAAAAAADDKcoXemj+CLER6TZDqAQAAsBqdHgAAAAAAAAAAwH2rFXQjt+qtMAm3uP4AAADAJzo9AAAAAAAAAACAOypccycVgzGs1AMAAGAdOj0AAAAAAAAAAIAh60d6I3+iVK8Il705Uj0AAABWoNMDAAAAAAAAAADSVbhoLq/KT1AztjIXHAAAALhKpwcAAAAAAAAAAHBdwUiv8j4wix7OkUpYqQcAAMDSdHoAAAAAAAAAAAB/XZZjrVRkNrytxqVumlQPAACARen0AAAAAAAAAACA6/qsko6nw/m/0oM0Ewouqp6HsJ5JAAAAgArp9AAAAAAAAAAAANomIYMxrNQDAABgOTo9AAAAAAAAAAAAGKKEBAAAAIbp9AAAAAAAAAAAAJonJIMxrNQDAABgITo9AAAAAAAAAACASh1Ph9IjoIGMRqoHAADAEnR6AAAAAAAAAAAAEcjJluCqAgAAAGPo9AAAAAAAAAAAAICOWKkHAABAdjo9AAAAAAAAAAAAuMIyPQAAAGAknR4AAAAAAAAAAEAQujIYyUo9AAAA8tLpAQAAAAAAAAAA1Ot4OpQeoVOiRwAAAGA8nR4AAAAAAAAAAADQHSv1AAAAyEinBwAAAAAAAAAAAAAAAADpdHoAAAAAAAAAAADw/+y2+9IjAAAAAC3R6QEAAAAAAAAAAMQhMIPxXp5fS48AAABAEDo9AAAAAAAAAAAA+DetIwAAADCVTg8AAAAAAAAAAADolJV6AAAAZPFYegAAAAAAAAAAAIBirm5OO54O608CAAAAQLvs0wMAAAAAAAAAADp1NdIb+Hd64O4DAAAACXR6AAAAAAAAAABAj4ZzrN12r9eCTrw8v5YeAQAAgObp9AAAAAAAAAAAgO5o8AAAAADISKcHAAAAAAAAAABcdzwdSo+wiPGRnpyvuJUfQnccAAAASKPTAwAAAAAAAAAAALr28vxaegQAAADaptMDAAAAAAAAAAAAAAAAgHQ6PQAAAAAAAAAAIN1uuy89wjRTB27uBI+nQ+kRxqrt2tY2DwAAANAQnR4AAAAAAAAAAAAAAAAApNPpAQAAAAAAAAAAAL17eX4tPQIAAAAN0+kBAAAAAAAAAADcdDwdyg6w2+7LDgAAAADAXTo9AAAAAAAAAAAAeqeHBAAAAObQ6QEAAAAAAAAAADcV3wPTlyMAACAASURBVCaXXbwzuhTs7IKdDgAAABCYTg8AAAAAAAAAAOA6nRh05eX5tfQIAAAAtOqx9AAAAAAAAAAAAJDfbrv/+o+aKyZp7oFpa+CrL1IAAACARun0AAAAAAAAAAAI4m72c/kFbRVNldtt9/GuZyVnJGYDAAAAaIJODwAAAAAAAACA5iW0TOdvqaTFGklnmMvxdBh4ZlxbAAAAAKbS6QEAAAAAAAAA0LCZ28ZaqfW+nubHv6ww/HDVFkn9T8KApoe/as0z6uQJBwAAAJbzrfQAAAAAAAAAAACQYrfd50prak507p5mzcMz0/ibGy/SAwAAAGiLTg8AAAAAAAAAgPZkj9MyVn8ZjRypwsnrdzVscyUBAAAASPNYegAAAAAAAAAAAJjmbkz1NcHSX9Gc2Mv0vCSp1svz689fP0pPAQAAQHvs0wMAAAAAAAAAoCXDec/xdLjaLN3690kHX1lVw9zV1rRnVurVrMX4EAAAAOiZTg8AAAAAAAAAgGYMNFQjS7w5P2JNlYzxoatoqoaLH3uZHgAAAEA8j6UHAP56e3ubf5Cnp6f5BwEAAAB/pQJAFj5SASALH6lcGo70xhzheDqMyZ922732qRMjH4mVifQAAKr1+/fvu1/z58+f4S/wVyoAhGSfHgAAAAAAAAAADbhVLuVao8d8FQZvY1x9fpo4l3af/CYuLwAAAMAkOj0AAAAAAAAAAGo3EOmt/BPXUWfF1G4VlqbUXRj5c2PfjthnBwAAAISk0wMAAAAAAAAAoElKHnK59SzttvuVaz2RHgAAAECjdHoAAAAAAAAAAFStzuVyBDNQvq3zBI5vAkV62XmTAQAAAObT6QEAAAAAAAAAUK9b/cwKqVJD6c5q4dbdH9TQRfuqYKo3/vgBIr27JxvgHAEAAIAO6fQAAAAAAAAAAKhUwUivoKZTt9YNp3oL3Zrxa/RiP/kAAAAATXssPQAAAAAAAAAAAEwgVWJR5wfsVjt3/vdcD2FXa/QAAAAAYtPpAQAAAAAAAABQI2vl6nQ8HXq4NcOn+fG/0vK5SRewt0Kvt/OlTt+/fy89AgD1+vPnT+kRAIBK6fQAAAAAAAAAAGjGzIYnZGBWW9e02+5rGynN8GK9s8v/O3zWac9ejCt5KeRrEAAAAOBBpwcAAAAAAAAAQIW6jXm6PfFqjan1zjLeu3h5HgAAAEB430oPAAAAAAAAAAAAo4iXKtHhjTieDuuc9Wo/qIi7KWOpcw98zQEAAIDV6PQAAAAAAAAAAKjL1ZhHSNOQqFsBF43oYhd6AAAAAOE9lh4AAAAAAAAAAADWEDIeK7h/LOT1HOPyms+8CMK8S64GAAAA0DSdHgAAAAAAAAAAFamn/lq/Gqrn3Ofbbfc9ZFefznH4DvZwQQZEerwBAAAAvtLpAQAAAAAAAABQu84Dpzr1vFLvFg9qMpcOAAAAaN230gMAAAAAAAAAAMBfy3VfijIAAAAAYDk6PQAAAAAAAAAAqlZk0VYT272KDzk8gDaSD8MPQ/EnGQAAAGA+nR4AAAAAAAAAAJSnagMAAACAdun0AAAAAAAAAACownKhmgRuOVbqcZdlerTFMwkAAEAanR4AAAAAAAAAAPUq0kuINAAAAACASXR6AAAAAAAAAABEFnWlWz0xoZV6DLBMDwAAAOiETg8AAAAAAAAAgPI6r7k6P30oTjEIAAAAzPRYegDgr6enp9IjAAAAwF/+SgWALHykAkAWPlI7V6SckeskOJ4OA6nhbrt3VftkmR4AEI+/UgGAW+zTAwAAAAAAAAAgLHvqAAAAAIAV6PQAAAAAAAAAAOCvVhZ8VTjn8EiCyQ5ZpkeLPJkAAAAk0+kBAAAAAAAAAFDe1zSin1giTMPWzy1jJo8KAAAAEM9j6QEAAAAAAAAAAODhYYF0J0z/FsNuu1dn9aPFV9/xdGhxbAAAAKAS9ukBAAAAAAAAAMDDgx1fObiG3OUhAQAAAELS6QEAAAAAAAAAEFDgvViVZ04D4wW+KVwauNGVP70AAAAAyXR6AAAAAAAAAABQTG/pWm/n2yG3mHbpSAEAAJhDpwcAAAAAAAAAAPKMnFxMrqr/wah/QgAAAKBaOj0AAAAAAAAAAKKx0au4gd7J3Qls4OZK4AAAAIDYdHoAAAAAAAAAANCMhmInqV5vRHo0zVMKAADATDo9AAAAAAAAAAB6VyrPkKsBAAAAQAw6PQAAAAAAAAAAQhG/1cNKvX6EWabX1rQAAABAPXR6AAAAAAAAAADAUqR6PQgT6QEAAAAk0+kBAAAAAAAAANC1hjqihka9JNWLTaRHAJ5VAAAA5tPpAQAAAAAAAAAQR0PdV0OjzifViypkpNfu5AAAAEBBOj0AAAAAAAAAAGBx2qeuuN00xOMKAABAFjo9AAAAAAAAAAD6Jc9Y062rbaVeo27duAAvqwCnAAAAAKxMpwcAAAAAAAAAQBCxc6/Y4VDsexdS4EgPAAAAIIFODwAAAAAAAAAA1tZtljYQcXV7TVok0iMMDy0AAAC56PQAAAAAAAAAAOiUPKOI4+lw68pL9ZrQSaQX7HQAAACApen0AAAAAAAAAACIQOLVFqleozqJ9OiE5xYAAICMdHoAAAAAAAAAAFC7kDGJVK85vUV6Uc8LAAAAWIJODwAAAAAAAACAHilwaiDVa0hvkR7heXQBAADIS6cHAAAAAAAAAACrEqFdkuo1odtIL/wJAgAAALno9AAAAAAAAAAAaJ6mq2kDqZ47W4NuIz0C8/QCAACQnU4PAAAAAAAAAIDutFVotDVtmuPpYLFena5e/4H7FU8/ZwoAAADModMDAAAAAAAAAACqIIiqza1Ib/1JICPPMAAAAEvQ6QEAAAAAAAAAwHpshxvW1aK25nR7d/o8awAAAGASnR4AAAAAAAAAAG1TvsVzmUVJpApyI4jHkwwAAMBCHksPAAAAAAAAAAAA3NRtUtLtidfGjTg7ng6SYAAAAGCAfXoAAAAAAAAAAPRFdwQk8NYRgJsIAADAcnR6AAAAAAAAAACwEvu4AEoR6QEAALAonR4AAAAAAAAAAADcJ/QCAAAAbtHpAQAAAAAAAABApURBUBuvyka5cQAAACxNpwcAAAAAAAAAQMN2233pEQComkgPAACAFej0AAAAAAAAAADoSMFaQ1IIMYi+AAAAgK90egAAAAAAAAAAADCBVK8hbhYAAADr0OkBAAAAAAAAAECNtCUAM3kjBQAAYDU6PQAAAAAAAAAAAJhGAFY/9wgAAIA16fQAAAAAAAAAAGBxu+2+9AhAZjIwAAAA4INODwAAAAAAAAAAAFJI9arl1gAAALAynR4AAAAAAAAAAFRHYQKt8GqtkJsCAADA+nR6AAAAAAAAAACwrN12X3oEgF6I9AAAAChCpwcAAAAAAAAAQMP0GEBx3ojq4V4AAABQik4PAAAAAAAAAAAWZJke9EAeVgN3AQAAgIJ0egAAAAAAAAAAsJS0SE9qAi3yyi3L9QcAAKAsnR4AAAAAAAAAAABkIBUrxZUHAACgOJ0eAAAAAAAAAAAsIm2ZHtA0wdj6XHMAAABqoNMDAAAAAAAAAKBtdRYaIj3oVp1vSlG52gAAAFRCpwcAAAAAAAAAQEfWyefm/BTNCQTghbwO1xkAAIB66PQAAAAAAAAAACAnm/SAh4eH4+mgIluUywsAAEBVdHoAAAAAAAAAADSvnlpDpAdcqufdKRgXFgAAgNro9AAAAAAAAAAA6MtyKZ1ID/hKUZadSwoAAECFNu/v76VnAB4eHh7e3t7mH+Tp6Wn+QQCgaZvNZv5B/JIMAP5KBYAsfKQCQBY+UplkfCa3ROORJdITn0BUOt4svEkCUJy/UgGAW+zTAwAAAAAAAACgO9mDGQUOMExgNp9rCAAAQM10egAAAAAAAAAABDEp4chV1u22e5EeMMbxdFCaJXPpAAAAqJxODwAAAAAAAACAOFYOORR6wFR6s6n0jQAAADRBpwcAAAAAAAAAQCjjc445q/Dufm9CVSJEgU4Iz8ZzoQAAAGiFTg8AAAAAAAAAgGgmdR2TUr1znnf3W4QlwF3eKIapGQEAAGjLY+kBAAAAAAAAAAAgv+PpMD7A+/jKW03I+EN9HCF5Ux/Qj/M7hreLrxR6AAAANEenBwAAAAAAAABATJNSvbOZtYywBEig1rvkjRQAAIBG6fQAAAAAAAAAAAhrtfplflgiTYHOJaTFwXgbBAAAoGk6PQAAAAAAAAAAglu0frkalnQe2wBpul2sp9ADAAAgAJ0eAAAAAAAAAADxfUQgGQMYYQmwhK5qPW+kAAAAhKHTAwAAAAAAAACgI/ODPVUJsILwtZ73UgAAAILR6QEAAAAAAAAA0KNPichwDLN0T6JXAa5aYhdocd7xAAAACEmnBwAAAAAAAAAAObuRSDkNUIkA6/XkeQAAAMSm0wMAAAAAAAAAAIAGNLpeT6EHAABAD3R6AAAAAAAAAAAA0JL6gz1tHgAAAL3R6QEAAAAAAAAAQDYJzYyaBUh2+QZSQ7PnDQ0AAIBu6fQAAAAAAAAAAACgeUWaPWEeAAAAnOn0AAAAAAAAAACgGIkLsISv7y1vb2//81//m/2wAAAAwJlODwAAAAAAAAAA8lhtgRVAgv/87/8oPQIAAACE9a30AAAAAAAAAAAAAAAAAADQMJ0eAAAAAAAAAABkkLBM73g6LDEJAAAAALCyx9IDAAAAAAAAAADAZF+jOM0bAAAAAFDK5v39vfQMAACQzWazmX8QvyQDAAAAAEDNhtfWlar1LNMDAAAAgJ7ZpwcAAAAAAAAAQBvGtHDnr6k/gat/QgAAAABgvG+lBwAAAAAAAAAAgPsmLaxL2G43x8o/DgAAAACojU4PAAAAAAAAAIDaBQvhLNMDAAAAgGB0egAAAAAAAAAAVC0t0lst7QvWEAIAAAAACXR6AAAAAAAAAACwHsv0AAAAACAenR4AAAAAAAAAAPWqfFvd1PFEegAAAAAQkk4PAAAAAAAAAABSVN4QAgAAAACr0ekBAAAAAAAAAMAaLNMDAAAAgKh0egAAAAAAAAAAVKrmhXVTZxPpAQAAAEBgOj0AAAAAAAAAAAJatIsT6QEAAAAAl3R6AAAAAAAAAAAwgUgPAAAAAPhEpwcAAAAAAAAAQDTSOAAAAABgTTo9AAAAAAAAAAAYyzI9AAAAAOArnR4AAAAAAAAAAKEsl8aJ9AAAAACAq3R6AAAAAAAAAADEIdIDAAAAANan0wMAAAAAAAAAIAiRHgAAAABQxOb9/b30DAAAkM1ms5l/EL8kAwAAAABAPUYGcot2cSI9AAAAAGDYY+kBAAAAAAAAAAAg0QpFnEgPAAAAALhLpwcAAAAAAAAAQHvWyeEmRXoKPQAAAADo1ub9/b30DAAAkM1ms5l/EL8kAwAAAABAVW7Fcot2cdboAQAAAADjfSs9AAAAAAAAAAAApJia0i13ZJEeAAAAAHTOPj0AAEKxTw8AAAAAAEIaCOeyN3KTIj2FHgAAAADwYJ8eAAAAAAAAAABNy7hVb7fdi/QAAAAAgAT26QEAEIp9egAAAAAAENVwQTczmUuI/UR6AAAAAMAHnR4AAKHo9AAAAAAAILAxNd3UfE6hBwAAAADMp9MDACAUnR4AAAAAAMQ2PqsbqOkS2ry7xwQAAAAAeqbTAwAgFJ0eAAAAAACEl1zZzaHQAwAAAAAG6PQAAAhFpwcAAAAAAJ1YrdZT6AEAAAAAd+n0AAAIRacHAAAAAAD9WDrVU+gBAAAAACPp9AAACEWnBwAAAAAAvcle68nzAAAAAICpdHoAAISi0wMAAAAAgG7NDPbkeQAAAABAMp0eAACh6PQAAAAAAICz4WxPlQcAAAAAZKTTAwAgFJ0eAAAAAAAAAAAAALCyb6UHAAAAAAAAAAAAAAAAAICG6fQAAAAAAAAAAAAAAAAAIJ1ODwAAAAAAAAAAAAAAAADS6fQAAAAAAAAAAAAAAAAAIJ1ODwAAAAAAAAAAAAAAAADS6fQAAAAAAAAAAAAAAAAAIJ1ODwAAAAAAAAAAAAAAAADS6fQAAAAAAAAAAAAAAAAAIJ1ODwAAAAAAAAAAAAAAAADS6fQAAAAAAAAAAAAAAAAAIJ1ODwAAAAAAAAAAAAAAAADS6fQAAAAAAAAAAAAAAAAAIJ1ODwAAAAAAAAAAAAAAAADS6fQAAAAAAAAAAAAAAAAAIJ1ODwAAAAAAAAAAAAAAAADS6fQAAAAAAAAAAAAAAAAAIJ1ODwAAAAAAAAAAAAAAAADS6fQAAAAAAAAAAAAAAAAAIJ1ODwAAAAAAAAAAAAAAAADS6fQAAAAAAAAAAAAAAAAAIN1j6QGAv97e3uYf5Onpaf5BAAAAwF+pAJCFj1QAyMJHKgBk4SMVALLwkQoA3GKfHgAAAAAAAAAAAAAAAACk0+kBAAAAAAAAAAAAAAAAQDqdHgAAAAAAAAAAAAAAAACk0+kBAAAAAAAAAAAAAAAAQDqdHgAAAAAAAAAAAAAAAACk0+kBAAAAAAAAAAAAAAAAQDqdHgAAAAAAAAAAAAAAAACk0+kBAAAAAAAAAAAAAAAAQDqdHgAAAAAAAAAAAAAAAACk0+kBAAAAAAAAAAAAAAAAQDqdHgAAAAAAAAAAAAAAAACk0+kBAAAAAAAAAAAAAAAAQDqdHgAAAAAAAAAAAAAAAACk0+kBAAAAAAAAAAAAAAAAQDqdHgAAAAAAAAAAAAAAAACk0+kBAAAAAAAAAAAAAAAAQDqdHgAAAAAAAAAAAAAAAACk0+kBAAAAAAAAAAAAAAAAQDqdHgAAAAAAAAAAAAAAAACk0+kBAAAAAAAAAAAAAAAAQDqdHgAAAAAAAAAAAAAAAACk0+kBAAAAAAAAAAAAAAAAQLrN+/t76RkAACCbzWYz/yB+SQYAAAAAAAAAAAAAxrNPDwAAAAAAAAAAAAAAAADS6fQAAAAAAAAAAAAAAAAAIJ1ODwAAAAAAAAAAAAAAAADS6fQAAAAAAAAAAAAAAAAAIJ1ODwAAAAAAAAAAAAAAAADS6fQAAAAAAAAAAAAAAAAAIJ1ODwAAAAAAAAAAAAAAAADS6fQAAAAAAAAAAAAAAAAAIJ1ODwAAAAAAAAAAAAAAAADS6fQAAAAAAAAAAAAAAAAAIJ1ODwAAAAAAAAAAAAAAAADS6fQAAAAAAAAAAAAAAAAAIJ1ODwAAAAAAAAAAAAAAAADS6fQAAAAA/o+9O8puHEUDMBpyvKlk/6ezLOYhPRlPYssSAvED977NdMpBtsqUkT4DAAAAAAAAAAAA5XR6AAAAAAAAAAAAAAAAAFBOpwcAAAAAAAAAAAAAAAAA5XR6AAAAAAAAAAAAAAAAAFBOpwcAAAAAAAAAAAAAAAAA5XR6AAAAAAAAAAAAAAAAAFBOpwcAAAAAAAAAAAAAAAAA5XR6AAAAAAAAAAAAAAAAAFBOpwcAAAAAAAAAAAAAAAAA5XR6AAAAAAAAAAAAAAAAAFBOpwcAAAAAAAAAAAAAAAAA5XR6AAAAAAAAAAAAAAAAAFBOpwcAAAAAAAAAAAAAAAAA5XR6AAAAAAAAAAAAAAAAAFBOpwcAAAAAAAAAAAAAAAAA5XR6AAAAAAAAAAAAAAAAAFBOpwcAAAAAAAAAAAAAAAAA5XR6AAAAAAAAAAAAAAAAAFBOpwcAAAAAAAAAAAAAAAAA5XR6AAAAAAAAAAAAAAAAAFBOpwcAAAAAAAAAAAAAAAAA5XR6AAAAAAAAAAAAAAAAAFBOpwcAAAAAAAAAAAAAAAAA5XR6AAAAAAAAAAAAAAAAAFBOpwcAAAAAAAAAAAAAAAAA5XR6AAAAAAAAAAAAAAAAAFBOpwcAAAAAAAAAAAAAAAAA5XR6AAAAAAAAAAAAAAAAAFBOpwcAAAAAAAAAAAAAAAAA5XR6AAAAAAAAAAAAAAAAAFBOpwcAAAAAAAAAAAAAAAAA5W69BwAAAAAAAAAAAAAAAAAQWkrp2X/KOV85EmLS6QEAAAAAAAAAAAAAAAD8n40wb+MnNXvL0ukBAAAAAAAAAAAAAAAA/Gt/obfxZwV7q9HpAQAAAAAAAAAAAAAAAJwq9B4+lFpvHe+9BwAAAAAAAAAAAAAAAADQU0qpYqR3/7DVH5OYdHoAAAAAAAAAAAAAAADAuprWdFK9Rdx6DwAAAAAAAAAAAAAAAACgg0MRXc657EG+f+DZH2cOOj0AAAAAAAAAAAAAAABgOfsjve3E7ue/2jpvZe+9BwAAAAAAAAAAAAAAAABwqVqR3q+f3PhhFd/c7KcHUXx9fZ1/kI+Pj/MPAgAAAD6lAkAVplQAqMKUCgBVmFIBoApTKsA0diZz+wu9nY+ZUjr0mAxEpwcAAAAAAAAAAAAAAADwf/YHdTbK4+3t7b33AAAAAAAAAAAAAAAAAAAuUjerE+nxTacHAAAAAAAAAAAAAAAALGFnVrdzM72CSE/XNyudHgAAAAAAAAAAAAAAADC/7pEeE9PpAQAAAAAAAAAAAAAAAEA5nR4AAAAAAAAAAAAAAAAwubrb39lMj190egAAAAAAAAAAAAAAAABQTqcHAAAAAAAAAAAAAAAAzGz/9nc554qPxjp0egAAAAAAAAAAAAAAAMC0QmV1ezpARqTTAwAAAAAAAAAAAAAAAIByOj0AAAAAAAAAAAAAAAAAKKfTAwAAAAAAAAAAAAAAAOaUUtr/wznndiO54PHpSKcHAAAAAAAAAAAAAAAAsJfcjr90egAAAAAAAAAAAAAAAMCEDm2m15q6b246PQAAAAAAAAAAAAAAAIADjkZ3Ir3p6fQAAAAAAAAAAAAAAAAAWhHpreDWewAAAAAAAAAAAAAAAAAAlaWUDv182RZ5279FobcOnR4AAAAAAAAAAAAAAABAiWe1nkJvNTo9AAAAAAAAAAAAAAAAgHKqPN57DwAAAAAAAAAAAAAAAACgpr8b3EFTOj0AAAAAAAAAAAAAAAAAKKfTAwAAAAAAAAAAAAAAAJaWc+49BMaWnEMAAMykyh7l/pEMAAAAAAAAAAAAfz27Sc99dwR09J5SpzEn2U8PAAAAAAAAAAAAAACALSmljeqpypfsQ0XOSa6n0wMAAAAAAAAAAAAAAOCpPcnTdsgHMD2dHgAAAAAAAAAAAAAAAI+p7wD20OkBAAAAAAAAAAAAAABQgagPWJZODwAAAAAAAAAAAAAAgAcKujupHiPKOfceAsPT6QEAAAAAAAAAAAAAAABAOZ0eAAAAAAAAAAAAAAAAMAmbOtKFTg8AAAAAAAAAAAAAAAAAyun0AAAAAAAAAAAAAAAAqMZuZsCCdHoAAAAAAAAAAAAAAADAonLOvYfADHR6AAAAAAAAAAAAAAAAAFBOpwcAAAAAAAAAAAAAAEBNKaXeQ2BRzj160ekBAAAAAAAAAAAAAAAAQDmdHgAAAAAAAAAAAAAAAA/knIv/rG3NgKXo9AAAAAAAAAAAAAAAAIAVnYlR4Z5ODwAAAAAAAAAAAAAAgPpsqQesQ6cHAAAAAAAAAAAAAABAE1I9ruR8oyOdHgAAAAAAAAAAAAAAAI/lnHsPAWAAOj0AAAAAAAAAAAAAAABascUZsAKdHgAAAAAAAAAAAAAAAE+d31JPqgdMT6cHAAAAAAAAAAAAAAAALOd8gwo/dHoAAAAAAAAAAAAAAABssaUewDadHgAAAAAAAAAAAAAAAM1J9WjKCUZfOj0AAAAAAAAAAAAAAABeOL+lHsDEdHoAAAAAAAAAAAAAAABcwY5nwKx0egAAAAAAAAAAAAAAALxWZUs9qR4wpVvvAQAAAAAAAAAAAAAAALCQlFKV5I+ZdAk4a/1S5zNv9tMDAAAAAAAAAAAAAABgp1o9kl31uDf6+TD6+KlCpwcAAAAAAAAAAAAAAMBeUj3qciYwB50eAAAAAAAAAAAAAAAAHQi0mIaTGZ0eAAAAAAAAAAAAAAAAB9TaUu9N3cQsKv6lYFA6PQAAAAAAAAAAAAAAAI6R6gHc0+kBAAAAAAAAAAAAAABwmA3EqCLnPPq5NPr4qeLWewAAAAAAAAAAAAAAAAAsLaWkdFrcmROgYEtG5xvV2U8PAAAAAAAAAAAAAACAEhVjp4LUCiAOnR4AAAAAAAAAAAAAAACFpHoMx2Z6tKDTAwAAAAAAAAAAAAAAoJxUD0CnBwAAAAAAAAAAAAAAQBQpJbUeMBydHgAAAAAAAAAAAAAAAKdU3FLvm1SPnZwqBKHTAwAAAAAAAAAAAAAA4CypHrAynR4AAAAAAAAAAAAAAAAVSPWAZen0AAAAAAAAAAAAAAAAqEOqR3DVT9FRpDu9xzInnR4AAAAAAAAAAAAAAADVSPUgml9/ifydakGnBwAAAAAAAAAAAAAAQE1SPa7hxNjj4bPkqatOpwcAAAAAAAAAAAAAAEBlUj2IwF+cy+j0AAAAAAAAAAAAAAAAqK9Fqic6gp38fbmYTg8AAAAAAAAAAAAAAIAmqqd6b/YH44QWJ2RACr0udHoAAAAAAAAAAAAAAAC0ItWDK/nb0YtODwAAAAAAAAAAAAAAgIakerTgHLiX/qv3QNal0wMAAAAAAAAAAAAAAKCtRqmeKgn8RQji1nsAAAAAAAAAAAAAAAAAzC/n3KImSim1iAAhMmFeQDo9AAAAAAAAAAAAAAAAriDVgwKqvCHo9AAAAAAAAAAAAAAAALhIu1Tv+8GrPzLTiHx6KPEmoNODKL6+vs4/yMfHx/kHAQAAAJ9SAaAKUyoAVGFKBYAqTKkAUIUpFWpplOq92VgP6EenB8P4/PzsPQQAWIWvJAEAAAAAAAAAAIBB2VhvEe72/Ov7tPfM9KLTAwAAAAAAAAAAAAAAYCpqPdbhPA9CpwcAAAAAAAAAAAAAAMCE1HrMylkdkE4PAAAAAAAAAAAAAACAaan1eBv8BBh68OvQ6QEAAAAAAAAAAAAAADC5lJLYibCcnBN47z0AAAAAAAAAAAAAAAAAaO57Yz0m4KUkIJ0ehdH+uAAAIABJREFUAAAAAAAAAAAAAAAAS9B3AY3o9AAAAAAAAAAAAAAAAACgnE4PAAAAAAAAAAAAAAAAAMrp9AAAAAAAAAAAAAAAAFhFSqn3ELhazrn3EJifTg8AAAAAAAAAAAAAAAAAyun0AAAAAAAAAAAAAAAAgDHYEZGYdHoAAAAAAAAAAAAAAAAAUE6nBwAAAAAAAAAAAAAAAADldHoAAAAAAAAAAAAAAACsIufcewhcyivONXR6AAAAAAAAAAAAAAAAAFBOpwcAAAAAAAAAAAAAAMASbK02upRS7yHAYzo9AAAAAAAAAAAAAAAAACin0wMAAAAAAAAAAAAAAGB+NtMD2rn1HgAAAAAAAAAAAAAAAAA0pNADWtPpAQAAAAAAAAAAAAAAMCeF3uKcAFxGpwcAAAAAAAAAAAAAAMBsBFrzSSn1HgI8lbzpwChMJwAAAAAAAAAAAAAA8JJYZlYFYcWCJ8P+Z2nBJ6cp++nBMLz9AcAeVcp20y4AAAAAAAAAAAC003QbGzcBAl3o9AAAAAAAAAAAAAAAALhIu0hPoQd09N57AAAAAAAAAAAAAAAAACxBpMeVnBVcSacHAAAAAAAAAAAAAABAcyI9YGK33gMAAAAAAAAAAAAAAABgco0iPYXeOtp1nlCF/fQAAAAAAAAAAAAAAABoSKQHTE+nBwAAAAAAAAAAAAAAQCsiPWAFt94DAAAAAAAAAAAAAAAAYE4tIj2FHhCQ/fQAAAAAAAAAAAAAAACoT6RHR04VLqbTAwAAAAAAAAAAAAAAoDKRHhW1OJ2gLp0eAAAAAAAAAAAAAAAANYn0gNXo9AAAAAAAAAAAAAAAAKhGpAcs6NZ7AAAAAAAAAAAAAAAAAPCYQg8Ygv30AAAAAAAAAAAAAAAAqKPuZnoiPco4c7ieTg8AAAAAAAAAAAAAAIAKRHrAsnR6AAAAAAAAAAAAAAAAnCXSo5G6pxY0otMDAAAAAAAAAAAAAADgFJEesDidHgAAAAAAAAAAAAAAAOVEegA6PQAAAAAAAAAAAAAAAEIQ6QGD0ukBAAAAAAAAAAAAAABQqOJmeiI9qnAi0YVODwAAAAAAAAAAAAAAgBIiPVqreI5BUzo9AAAAAAAAAAAAAAAADhPpAfzQ6QEAAAAAAAAAAAAAANCNSA+YgE4PAAAAAAAAAAAAAACAY2ptpifSA+ag0wMAAAAAAAAAAAAAAOAAkR5hOanoRacHAAAAAAAAAAAAAADAXiI9LlPrZIML6PQAAAAAAAAAAAAAAAAAoJxODwAAAAAAAAAAAAAAgF1spgfwkE4PAAAAAAAAAAAAAACA64j0gPno9AAAAAAAAAAAAAAAAHitymZ6Ij3acXbRkU4PAAAAAAAAAAAAAACAF0R6ABt0egAAAAAAAAAAAAAAADQn0uOQKmkoXEanBwAAAAAAAAAAAAAAwBbFFMA2nR4AAAAAAAAAAAAAAABt2UwPmJtODwAAAAAAAAAAAAAAgIZEelzAaUZfOj0AAAAAAAAAAAAAAACeSin1HgJAdDo9AAAAAAAAAAAAAAAAWrHLGQXUoQxHpwcAAAAAAAAAAAAAAMBjJ3MpkR6wCJ0eAAAAAAAAAAAAAAAAAJTT6QEAAAAAAAAAAAAAAFCfzfS4jJON7nR6AAAAAAAAAAAAAAAAPJBS6j0EgDHo9AAAAAAAAAAAAAAAAKjM/mbAUnR6AAAAAAAAAAAAAAAAQBQ2cmREOj0AAAAAAAAAAAAAAABqspkeV3K+EYFODwAAAAAAAAAAAAAAAADK6fQAAAAAAAAAAAAAAAAAoJxODwAAAAAAAAAAAAAAgGpyzr2HwEKcbwSh0wMAAAAAAAAAAAAAAABCSCn1HgKU0OkBAAAAAAAAAAAAAAAAQDmdHgAAAAAAAAAAAAAAAHXknHsPAaADnR4AAAAAAAAAAAAAAAAPiO4IzilKHDo9AAAAAAAAAAAAAAAAHjvUQYmmgGXp9AAAAAAAAAAAAAAAAHhqZ30n0uO8lFLvIUChW+8BAP/6+vo6/yAfHx/nHwQAAAB8SgWAKkypAFCFKRUAqjClAkAVplRWlnPeDqhEelzPWUcoOj0AAAAAAAAAAAAAAABe+G6iftV6QimAbzo9AAAAAAAAAAAAAAAAdhHmATz03nsAAAAAAAAAAAAAAAAAAAdIRolGpwcAAAAAAAAAAAAAAAB0llLqPQQop9MDAAAAAAAAAAAAAAAAhmEzPQLS6QEAAAAAAAAAAAAAAABAuVvvAQAAAAAAAAAAAAAAAACDSSn9+n9sc8fKdHoAAAAAAAAAAAAAAADAXn8Lvfv/v6zWe/aYf6kBiUmnBwAAAAAAAAAAAAAAALy2p6Y7U+vBuN57DwAAAAAAAAAAAAAAAACIbv+Wd7AgnR4AAAAAAAAAAAAAAACw5Wik1yjqs00fYen0AAAAAAAAAAAAAAAAgKda76Rnp75aDj2Tnva6dHoAAAAAAAAAAAAAAABAZdUzMJvpbUgpFTzhZX+Kh269BwAAAAAAAAAAAAAAAAAEpeOKqeLr8uuh9JBldHoAAAAAnPL5+Xn/P//5559eIwGAsH5Nlw+ZQwHgpT1T6g9zKwAAAACMYmdvNn08FqeHPDqS6V+anXR6AAAAAJT7e4vk5+enWyEBWFzZ9bO/s6opFYDFnbwl5dfc6jYRAAAAAACa0ukBAAAAUOjZPgZSPQDWVP3rLe+nWmkBAIto93XRvx7Z3AoAAAAAOwXZ5M2aHvHp9AAAAAAo8SzSA4DVXHNh8ue3uAAJwJSuv9Hn+zeaWAEAAACgqT1LcHuWBy3lMQSdHgAAAACHifQA4K3TV4cK9gCYTN+v4jaxAgAAAABQi04PmIeraEAt3k8AADYo9ADgrXdRcD8GyxcAjCvCfPrDxAoAAAAA1dVacFt24S7ygYda4I1DpwfM4NdbfEop8oQEBHf/luKqPADALyI9AIh2wcnyBQAjKphP//nnn/0/XPzp1cQKAAAAABeLdvWtr4EWJwca6pV0esDwHk7MUj2gIm8pAABvCj0AKL1GuB0V1JphRQUAjGL/fHo/r319fR36LT/zb9lUa2IFAAAAgPNspseC3HQODGzjMp43N6CY95bRVfliFa81ANwrm17NpwBM5tCEWDYP+kgLwPT2THYt5rIzk6y5FQAAAAAKVth2Lqy9fGQLdIxFpweMSkgDtOMdZmhuagSAitzICADfds6Jtaa/859tTcQARNOr0Ds6hodMrAAAAADQ4kstRXrM59Z7AAAlJDRAUznnZ+8zKfmaAwBgflW6dwCYQ5eo4PsBz8zIVjAACCXI3TbFM6yJFQAAAAB2spLG4qwmA+MR6QHX8G4zKPvpAUCxunme+RSACUSICk5O0GZkALrbnsu6TFVl06tZFQAAAIDF1V3ri3AlDqp77z0AgGNkM8BlNt5V7DADAEwj3ek9FgCIJcilwZO/xRQPQF8BI73i3+uzMwAAAADUEuRKHFRnPz1gJCI94HreeYZjPz0AeObiuwnNpwAMLWBUcGYqNy8D0EXwBfbiuTXC4AEAAACgi/N93Z51OUtwjEunB4wh4G0xwDqC30nALzo9AFYW6nv9zacAjCvsaqRUD4CBxF9a1+kBAAAAQIGdC2t/l9GK/yAMRKcHDCDsbTHAOuLfT8APnR4AK9PpAcB5wVcjpXoAjOLZnBVqPpLqAQAAAECBdjeoWHljdO+9BwDwQvDbYoBFbLzbpJRC3RAPAFBR/q/eAwGAi8RfjTwzBisYAFxmiEjv7cR4zKoAAAAArKzFQp8bVJiDTg8ILf5tMQAAAPOR5wHAX3FmxjgjAYCHRon0TpLqAQAAALCyust9ky0esjKdHjAqkzFwse23HdfjAYAJyPMAWNzGp/tp5kcrGAB0lFIyEwEAAADANKrcZOJOFSaTnNBAWCvcFgMMxz6f8VW5z8NLCcCgCubBnbNeu0cGgCCGW4088/k35hEBMIf9M1Sc+cisCgAAAADnHVpns7DGrG69BwDwmG/TBGLKOW+8QaXkSxAAgLj8QwUACoSdQLfXKAAgPivqAAAAADCT++W+Z5exLAkyPZ0eEJHtqoDIpHoAQFj+HQIAZVYL3ixfANDI0Sn1++eHnpXMqgAAAADwl0UzlvXeewAAv4n0gPi234tWu7cPAAAAZhV8NfLM8CxfABBH91kp+IwPAAAAAMAodHpALCI9YA7d7yoAAAAAdnr2KX6I1cghBgkAAAAAAACwglvvAQD8j6zl3stnwy04dOHM/JFz3n42UkrrPBsAAADAcKxdABDH0LPS0IMHAAAAAKAinR4wjLmvbxU0in//yNxPEV04M7dJ9QAAAGB0Q2+mBwCh+EZOAAAAAAAWp9MDoti+dDflbTHVr1b+POCUTxeXcWYe8jLVAwAAAGjqzOqE7xgCIA6zEgAAAAAAo9PpASGsFum1rnrmzqJox5nZghsLAAAAICyb6QEAAAAAAABQy3vvAQA0T4NCSSldebwX/zrG5cw84+WtezMdLAAAABCQsBAAAAAAAACgO/vpAZ29zFemucWkY6jz/auneSapy5lZRc55+5m0qx4AAAAM5P5j/vSf6K1aAAAAAAAAAFSh0wNCm+MGkeIOauPwCx5zpiaKKpyZAAAAwMp2rmP8/JjlCwAAAAAAAAA26PSAnjpu5HWNggPcebvP/Y8d+i2aKN6cmW3YUg8AAADmNsHyBQAAAAAAAADtvPceALCulw3P6Le8HIqU8n8V/KKCPzt9IckGZ2Y7Lw929AMEAAAAwn66P7OaGvagAAAAAAAAAAai0wNoYv+tLcUR1MmHcvPNmpyZ3U1/gAAAADA9n+4B4KGTlxVG/wZPAAAAAADQ6QF9TLyZXkrpUApVfQCHgig3Fa3DmXmNcd+7AAAAAAAAAAAAAAAoptMDOpg70tv5kxU3Kzv54OMGUeznzAxl4kMDAACAUZz8eO7TPQA8VHyJYdyLgwAAAAAA8EOnB1BN383KzvwW9xXNzZl5sT0HOOihAQAAAJEpHAAAAAAAAAA60ukBV5t4M72drjzA6YMoKnJmVjT9+xgAAAAw6KrFM5MdDgAdFayQj76oPvr4AQAAAACoRacHUMfOG1muv1A3fRDFNmdmWLMeFwAAAAAAizt00UHkBgAAAADANHR6wKVm3UwvbAp19PcKhybjzOxo0HczAAAAAAA4L+f8cp18z89cY8TLEAAAAAAABKTTA64z6yWu4ClUhN9OF87M+GZ9VwQAAAAAgLfnJV6cQg8AAAAAACq69R4AwP+MeEFuvswmpTTiC8EvzswIcs4vX4gRjwsAAAAAAPabexl87qMDAAAAAOAQ++kBF5mvGno7clARLtHtH8OUL9ZSnJkAAAAAAAAAAAAAAHAlnR4QRYReqJE4hxZnJEQQ53yIM5Lq9hya/hAAAAAAAHqxSg8AAAAAQC06PeAKU17fmvKgfsx9dHOb+7Wb++gAAACAy5z/2qCJv3gIANjJvwcAAAAAALin0wNCGO4i1v5YKNqh7R+PIGpEzsyAbKkHAAAAAAAAAAAAADA9nR7QnP4EAAAAAIjmzNcYRfsKJACgzJnrmP49AAAAAADALzo9oL/hLmKNu2XZt4k3LlucMzOsmE84AAAAAAAAAAAAAAC16PSAtobLaQC68G4JAAAA1yv7bh3fyAMA+PcAAAAAAAB/6fQAjlmtpVnteMe12iu12vECAAAAjRy9yX7Wm/JnPS4A2FB8rcG8CQAAAADAQzo9oKE9F7cmvo4V+dAij43WIr/6kcd20p5DEx8CAABAFznnPZ/cd/5YR9YWAAAAAAAAADrS6QEcsOadLmse9VjWfI3WPGoAAACgkY0ML36hBwAcZTM9AAAAAACqu/UeADCtxTfTA/gl5/zyjTGl5I0RAAAAOvLBHADY4J8KAAAAAABssJ8ewF4rb9618rHHt/Krs/KxAwAAAAAAZVxfAAAAAACgBZ0e0ISLW/G/TTP+CGkh/usef4Stef8EAAAALmZBBoClFK/DmzEBAAAAANim0wO6GetSlnKGmJyZYxnrfQ8AAAAYiGUiAGjKCj8AAAAAAC/p9ADYxY0+xOTMBAAAAAAAdiq7rCDSAwAAAABgD50eUJ9sBuAM76IAAAAAAFCdSA8AAAAAgKZ0ekAfY13Q0sx88zxE4xX5NtbzMNa7HwAAADCEM8sjFisAWIRIDwAAAACA1nR6AOtyZZGYnJlvo8WHAAAAAAAQmUgPAAAAAIAL6PSAyrQlby7aEZUzEwAAAIB71osAWIFIDwAAAACAa+j0gA7GuqylPLzn2YjDa3FvrGdjrPdAAAAAILixFkYA4GIFE2XO2Uo+AAAAAAAFbr0HAPzr6+vr/IN8fHycf5Az3BECUEtKyX0AAEBH5z+lfn19df+UCgDdRV74tfIAwEAKptTPz8+CX2R+BGBukT+lAsBATKkAwDM6PQAAAAAAgCH56jQA+EuhBwAAAABAFzo94GoucQHknN1FBwAAAHRknRaAKZUVem9mRgAAAAAAatDpAdVM2ZxMeVAnpZRcquzOmfnXlGfmlAcFAAAA1GKNCIDFFVd596zDAwAAAABQi04PAAAAAABgIYIEAOK7IEc3IQIAAAAAUJdODwAAAAAAYDA20wNgStdMcAo9AAAAAABa0OkBl3LRC+BbztntdAAA/Ie9O0pqXOcWMIq7mBSZf5Fh+X/IuVwKiGNvy9KWtNZTVzWdlo3KIrE/BAD1+ZAWgLSu/tjcIggAAAAAwKX+tR4AMAi1yZcZ7vD5dvfIzOzUkAcFAAAAnBT+xGCGz4gA4JllWXzqDgAAAADAdXR6AE/NcKPOczk9MjMBAAAACPCRCwC8/V+tp9kDAAAAAKA4nR4AAAAAAEA3RAUAjKp+Ui7YAwAAAACgIJ0eUMDOe1d+WzPAdzuvip4PAAAAAL6EPyjw8SwAXWi1YKn1AAAAAAA47731AAAAAAAAALiQSA+Ajvy5bN3v9/2vcLvdYv/1I9WzbgIAAAAAEKPTAwAAAAAA6EBsnx+xAQCz+fz8fPwhFuyp9QAAAAAAiNHpAQAAAAAAZCfSA4CjzgR7aj0AAAAAAI7613oAQPdiT4fkN+pxleL8tOLMb+vu/Oy8wd/dcQEAAAAAQB5fwd5RPp8HAAAAAGA/nR5QiV82CQAAAAAQYzM9ADjp8/MztjIuy6LWAwAAAABgD50eAAAAAABAXiI9AChlXddwrVd8MAAAAAAADEanBwAAAAAAkNTtdgv8K5EeAGyQ6gEAAAAAcAWdHnCK21EAdbjeAgAAADuJ9ADgJakeAAAAAADF6fQAAAAAAAAyCmymJ9IDgJ2kegAAAAAAlKXTA2rwaAjAM66QAAAAwJ9EegBwNakeAAAAAAAF6fQAAAAAAAByEekBQGbLsqj1AAAAAAD4YXHXFjhj5/2nHi814Vtr3R3sPEc6hnm+X/Mc6dvQ19Imijwb4WwDwA+BFdZ6CgBhVl4AqGmqWxIAAAAAAFzHfnoAAAAAAABZiPQAoBe21AMAAAAA4DudHhDnzhNATa66AAAAMDyRHgDUd2Yx9dE9AAAAAABfdHoAf3BHbQ9nqT7nfA9nCQAAAJiHSA8AipDqAQAAAABw3nvrAQAAAAAAAHDsKX+FHgAAAAAAAEAq9tMDLud5EYBtrpMAAACASA8A+mVLPQAAAAAA3nR6AAAAAAAAbYn0AKC5kyusVA8AAAAAAJ0eAAAAAABAMyI9AAAAAAAAgAHo9IAgvxISoD7XXgAAABiMSA8A8rClHgAAAAAAZ7y3HgAAAAAAAMCM9j/Nr9ADAAAAAAAASM5+egAAAAAAALWJ9ABgPLbUAwAAAACYmU4PAAAAAACgKpEeAAAAAAAAwGB0egAAAAAAAPWI9AAgrfOLry31AAAAAACmpdMDAAAAAACoRKQHAAAAAAAAMKT31gMAAAAAAADg/yn0AAAAAAAAALpjPz0AAAAAAIAa9mymJ9IDgN7t3z4XAAAAAICR6PSAa3mmBGAPV0sAAAAYnkgPALpgOQYAAAAAIEanBwAAAAAAcC2RHgAAAAAAAMDYdHoAAAAAAAAXEukBAAAAAAAADE+nB0TseawEgCu4AgMAAEBfRHoAAAAAAAAAM3hvPQAAAAAAAIAxifQAmM2fa9+Ei92yLBMeNQAAAADA5HR6AAAAAAAA5b2M9Dy+D8Awtle9x99a+AAAAAAAGNu/1gMAAAAAAAAYjUgPgHns2T/28WU7vxIAAAAAAHpkPz0AAAAAAICSkkd6fw5PNwhAzNH0blkWiw4AAAAAAEOynx4AAAAAAEAxySM9ACgotj+eXfUAAAAAABiSTg8AAAAAAKAMkR4A85DbAQAAAADAdzo9AAAAAACAAvqN9NIODIBRafwAAAAAABiPTg8AAAAAAOCsfiM9AAgQ2gEAAAAAwA86PQAAAAAAgFNEegBwlNIPAAAAAIDB6PQA/uChmT2cpfqc8z2cJQAAAKCyviI9UQQAAAAAAADAFXR6AAAAAAAAQX1FegBAHX4AAAAAAACY0HvrAQAAAAAAAHRpO9LzgD4AAAAAAADAPOynBwAAAAAAcNgwkV5HQwUgiZfbyQIAAAAAwIR0egAAAAAAAMcME+kBQMDYK50KEQAAAACAGJ0eAAAAAADAAf1GesIDAKgg8w8DAAAAAABcR6cHAAAAAACwV7+RHgAAAAAAAADX0ekBAAAAAADsItIDgFKsmwAAAAAADEanBwAAAAAA8JpIDwB4yY8EAAAAAADT0ukBAAAAAAC8MECkt30IAFBT2qXTcgkAAAAAQJhOD+Bvae8OMjkzEwAAAKC+ASK9Z7oePAAAAAAAAEAeOj0AIjy+Q05mJgAAAFDcRqS3rquPIwCY05kVcODVc+BDAwAAAADgJZ0eQGHbv1o7m75Gyxl9fa/7Gi0AAAAwsO1Ir+ZITvJ5CwAZJE/cLZcAAAAAAJyh0wMiMt8/AxibKzAAAABUI9IDgIL6Wj0Dhj9AAAAAAAC2vbceAAAAAAAAQDq9R3raPACutq7rnuWmi3XzvEkOEwAAAACADTo9AAAAAACAAyRwAPBSd92a9R0AAAAAgJP+tR4AQF7d3T5kEmbmkNz+BwAAgFQmeavugyYATrKUPDgPAAAAAAC86fSAq03yOMts3GskJzMTAAAAKMKnmgBwXl/r6ZnRuj0BAAAAAMCDTg8AAAAAAOA/fUUFANDcRqVmVQUAAAAAYCo6PQAAAAAAgLc3OQEAhPSe6tlMDwAAAACAInR6AAAAAAAAfYQEAJBTv6meSA8AAAAAgFJ0egBb3F37zTnJwHfhN+cEAOA6sWcWkz+ICQA/WLkA4Dpp11mRHgAAAAAABen0AAAAAHjqzDOLaR/EBIAf5lyz1AUAlLW9sgy22lpGAQAAAAD4TacHMK/BbogyDDMTACCP8z+b+ekOgPysVgBQystUL9WyGx6MSA8AAAAAgD/p9ICgee4/BY401S1GRmVmzmmeay8A0FzBpyezPYgJAN9ZpACgrJefYyd5kyjSAwAAAACguPfWAwD+c7/fz7/Ix8fH+ReBDW49kpOZCQBw3tVPSf54fT/CAZBBhkgAAMazruvLRXZZlkNvDAveSz3zA4A3swD0zuNJAFCEJRUAeEanBwAAADCOXnoDGxcA0FwviyYA9Ojx3m17tf3625pv9LwbBQAAAADgOv9aDwAYn+ddALa5TgIAAEBl3owDQAU727ZlWSoszbfbTaQHAAAAAMCl7KcH8Nq6rh7ceXMPMh8z88HMBAAAAACAnPZsrPdw3fZ6t9st/G/dgwAAAAAAYD+dHsAllmVJft9O3zUnMxMAAACAN9UBAHXtr/V+fNnJBetMoXf+fwcAAAAAYDY6PSBuqr28pjpYOmJmTsUDAQAAAAAA9OtQrffw44s/Pz9f/pOTbd6DD+QBAAAAAAjQ6QGwi/uR5GRmAgAAAABAR878CsIiDd429x0AAAAAAAjT6QEAAAAMLvCU4f1+P///fnx8nPnn9o4GAAAY0te71FTv+xR6AAAAAACcpNMD2Ovob/dcliXt/byjdz0zHMizMWcYW1tmJgAA3430M9JIxwJANtVWmQzpOwCklSHY894TAAAAAIBSdHpADZm7IPLbvjX7+FsTjH6l+m3BAAAAAABQX/1gz60lAAAAAACK0+kBHHB047IxtL1PufOET17rmZkAAAAAAMAAfnz4X/b2hzsLAAAAAABcSqcHnDJnHbRfzo0Ee/mWBcaZ84QnlPNE9TIzW0n4LQMAAAAAgEt9fTZ+v98ff7jdbnv+4efn51VjAgAAAACAJ3R6AMfMliZ2lwblLNAqMDMBAAAAAIDhCfAAAAAAAEjrX+sBAMAfziRnU+VqAAAAAAAAAAAAAABAczo9gMMObeSVrRk7NJ5WW5ZlO2m9MDN7lO0bAQAAAAAAAAAAAABAgE4POGtnMKNF6U7XKZT5NrCuZ2bYnEcNAAAAAAAAAAAAANALnR5ARKcbl+UZCRcxMwEAAAAAAAAAAAAAoD6dHkBQd0HUoTE03Lyr1LnKcM6bMDMBAAAAAAAAAAAAAKAynR4AP0mhyMnMBAAAAAAAAAAAAAAgJ50eUMC08UxHG5dl2DaNaszMXuw8/GmvsQAAAAAAAAAAAAAAvdDpAfUMGeR0EUQd+n9HKoKGnHI7mZkAAAAAAAAAAAAAAFCNTg/grC6CqJ2kUCMxMwEAAAAAAAAAAAAAoA6dHkABmSOi/f1VkqNIMowxZD6Z3c1MAAAAAAAAAAAAAAB4ZvHsO1DKnupm7GvOoR3J6pyKTlOognu7pTquVszMnHaehIHPwHWKXEOceQAAAAAAAAAAAABgP/vpAVUV7K8SOhR1XH0qlmWRQvFgZgIAAAAAAAAAAAAAwKV0egAlJQmiEu6fdkjBIY2dhu5nZgIAAAAAAAAAAAAAwHV0ekAxopqHo0FU2Sbq6Av6rs3DzOyR8wAAAAAAAAAAAAAA0IXF89+xtnr6AAAgAElEQVRAQTs7nEmuPEczp/OnZbAOqlQnlv9IKzMzM3C1vFSRq4eTDwAAAAAAAAAAAADs9956AADDWtf1UCvy+OJAGRIoUrroT46eQHYyMwEAAAAAAAAAAAAAoCz76QGF7SlzprrynCnNNk5U+GW7O/lHt2L7/fXdHXIdZmZbLpWXsp8eAAAAAAAAAAAAAFCZ/fQArvUoPWLRSNnd5DptTnaewE6PriEzsyEbRQIAAAAAAAAAAAAADEanBzSwLNNt5nmmiSo4gH59jf/7Oez9oDIwMwEAAAAAAAAAAAAA4DydHlDYuq62inrmz9iszv84jPGOKAMzMyGnCAAAAAAAAAAAAACgIzo9gAYqbGIm8iHAzKxAyQwAAAAAAAAAAAAAMB6dHlDeni31lmWR63w/A0XSHaeUIszM5pwxAAAAAAAAAAAAAIC+6PQAUvid5bzso5Q8VGBmAgAAAAAAAAAAAADASzo9gKTETuRkZp5RZH9CAAAAAAAAAAAAAACy+dd6AMCY9pQ8ehWA35SQAAAAAAAAAAAAAADd0ekBMI7f8afkCQAAAAAAAAAAAAAAuJpOD7iKLfUAvttzxVOWAgAAAAAAAAAAAAD0SKcHAAAAAAAAAAAAAAAAAHE6PeBCNoYC2M81EwAAAAAAAAAAAACgUzo9oLFlWVoPAeByrnUAAAAAAAAAAAAAAAPT6QHXsj0U1fzuoEw/OmK6AgAAAAAAAAAAAAD0S6cHtGebKWBsrnIAAAAAAAAAAAAAAGPT6QGXs0kUwDbXSQAAAAAAAAAAAACArun0gBRsNgWMyvUNAAAAAAAAAAAAAGB4Oj2gBltFcbXfKZRZRy/MVQAAAAAAAAAAAACA3un0gCxsOQWMx5UNAAAAAAAAAAAAAGAGOj2gEhtGAfzm2ggAAAAAAAAAAAAAMACdHgDds2UZOZmZAAAAAAAAAAAAAACT0OkB9bzcNkrTQin2KKMLJioAAAAAAAAAAAAAwBh0ekAuUj2OMmfIycwEAAAAAAAAAAAAAJiHTg+oyuZRVGCa0QUTFQAAAAAAAAAAAABgGDo9IB2bULGf2UJOL2emSA8AAAAAAAAAAAAAYCQ6PaA2dQoAAAAAAAAAAAAAAAAj0ekBDbxM9WySxh5/zhMhKM3ZTA8AAAAAAAAAAAAAYDY6PQCAekR6AAAAAAAAAAAAAADj0ekBbdhSj5NspkdOrl0AAAAAAAAAAAAAABPS6QHNSPWAwby8aklJAQAAAAAAAAAAAACGpNMDoD8206NHpigAAAAAAAAAAAAAwKh0ekBLttQjQKRHTq5XAAAAAAAAAAAAAADT0ukBjUn1OMR8IKeXM1NKCgAAAAAAAAAAAAAwMJ0e0J58hZNMIZIzRQEAAAAAAAAAAAAAxqbTAzpgCzUezARy2p6ZIj0AAAAAAAAAAAAAgOHp9IAUXnYsAi2ezQERFG25OgEAAAAAAAAAAAAAoNMDslBbsUGkR6dMUQAAAAAAAAAAAACAGej0gES2gxabVk1LpEda29clUxQAAAAAAAAAAAAAYBI6PSAXqR4/+KaTlkgPAAAAAAAAAAAAAIAHnR7QGdXWVDa+3SIo2hLpAQAAAAAAAAAAAADwRacHpPOyb5HqTUKkR6fMTwAAAAAAAAAAAACA2ej0gIxULmwwPWhOLQwAAAAAAAAAAAAAwHc6PSCp7RZLJDMtkR7NbV9/TFEAAAAAAAAAAAAAgAnp9IC8pHqT+z0BFFA0J9IDAAAAAAAAAAAAAOA3nR6QmlRvcl8TYF1XBRTNifQAAAAAAAAAAAAAAPiTTg/ITqo3OYUeSYj0AAAAAAAAAAAAAAB4RqcHdECqB7Ql0gMAAAAAAAAAAAAAYMN76wEA/7nf7+df5OPj4/yL5LSu60YnsyyLTga4iEgPAJiTd6kAUIQlFQCKsKQCQBGWVAAowpIKADxjPz2gG3bVA+oT6QEAAAAAAAAAAAAA8JJOD+iJVA+oSaQHAAAAAAAAAAAAAMAeOj2gM1I9oA6RHgAAAAAAAAAAAAAAO+n0gP5I9YCrifQAAAAAAAAAAAAAANhPpwd0aV3XjU5GqgecsXEN2b74AAAAAAAAAAAAAAAwJ50e0DG1DFCTaw4AAAAAAAAAAAAAAH/S6QF9e5bN2FIPKEukBwAAAAAAAAAAAADAMzo9oHt/xjOKGqAglxQAAAAAAAAAAAAAADbo9IARrOv6vaJR1ABn/LieuKQAAAAAAAAAAAAAALDtvfUAAIrR0gCluJ4AAAAAAAAAAAAAALCf/fQAAAAAAAAAAAAAAAAAIE6nBwAAAAAAAAAAAAAAAABxOj0AAAAAAAAAAAAAAAAAiNPpAQAAAAAAAAAAAAAAAECcTg8AAAAAAAAAAAAAAAAA4nR6AAAAAAAAAAAAAAAAABCn0wMAAAAAAAAAAAAAAACAOJ0eAAAAAAAAAAAAAAAAAMTp9AAAAAAAAAAAAAAAAAAgTqcHAAAAAAAAAAAAAAAAAHE6PQAAAAAAAAAAAAAAAACI0+kBAAAAAAAAAAAAAAAAQNyyrmvrMQAAQDHLspx/ET8kAwAAAAAAAAAAAAD72U8PAAAAAAAAAAAAAAAAAOJ0egAAAAAAAAAAAAAAAAAQp9MDAAAAAAAAAAAAAAAAgDidHgAAAAAAAAAAAAAAAADE6fQAAAAAAAAAAAAAAAAAIE6nBwAAAAAAAAAAAAAAAABxOj0AAAAAAAAAAAAAAAAAiNPpAQAAAAAAAAAAAAAAAECcTg8AAAAAAAAAAAAAAAAA4nR6AAAAAAAAAAAAAAAAABCn0wMAAAAAAAAAAAAAAACAOJ0eAAAAAAAAAAAAAAAAAMTp9AAAAAAAAAAAAAAAAAAgTqcHAAAAAAAAAAAAAAAAAHE6PQAAAAAAAAAAAAAAAACI0+kBAAAAAAAAAAAAAAAAQJxODwAAAAAAAAAAAAAAAADidHoAAAAAAAAAAAAAAAAAEKfTAwAAAAAAAAAAAAAAAIA4nR4AAAAAAAAAAAAAAAAAxOn0AAAAAAAAAAAAAAAAACBOpwcAAAAAAAAAAAAAAAAAcTo9AAAAAAAAAAAAAAAAAIjT6QEAAAAAAAAAAAAAAABAnE4PAAAAAAAAAAAAAAAAAOJ0egAAAAAAAAAAAAAAAAAQp9MDAAAAAAAAAAAAAAAAgDidHgAAAAAAAAAAAAAAAADE6fQAAAAAAAAAAAAAAAAAIE6nBwAAAAAAAAAAAAAAAABxOj0AAAAAAAAAAAAAAAAAiNPpAQAAAAAAAAAAAAAAAECcTg8AAAAAAAAAAAAAAAAA4nR6AAAAAAAAAAAAAAAAABCn0wMAAAAAAAAAAAAAAACAOJ0eAAAAAAAAAAAAAAAAAMTp9AAAAAAAAAAAAAAAAAAgTqcHAAAAAAAAAAAAAAAAAHE6PQAAAAAAAAAAAAAAAACI0+kBAAAAAAAAAAAAAAAAQJxODwAAAAAAAAAAAAAAAADidHoAAAAAAAAAAAAAAAAAEKfTAwAAAAAAAAAAAAAAAIA4nR4AAAAAAAAAAAAAAAAAxOn0AAAAAAAAAAAAAAAAACBOpwcAAAAAAAAAAAAAAAAAcTo9AAAAAAAAAAAAAAAAAIjT6QEAAAAAAAAAAAAAAABAnE4PAAAAAAAAAAAAAAAAAOJ0egAAAAAAAAAAAAAAAAAQp9MDAAAAAAAAAAAAAAAAgDidHgAAAAAAAAAAAAAAAADE6fQAAAAAAAAAAAAAAAAAIE6nBwAAAAAAAAAAAAAAAABx760HAPznfr+ff5GPj4/zLwIAAADepQJAEZZUACjCkgoARVhSAaAISyoA8Iz99AAAAAAAAAAAAAAAAAAgTqcHAAAAAAAAAAAAAAAAAHE6PQAAAAAAAAAAAAAAAACI0+kBAAAAAAAAAAAAAAAAQJxODwAAAAAAAAAAAAAAAADidHoAAAAAAAAAAAAAAAAAEKfTAwAAAAAAAAAAAAAAAIA4nR4AAAAAAAAAAAAAAAAAxOn0AAAAAAAAAAAAAAAAACBOpwcAAAAAAAAAAAAAAAAAcTo9AAAAAAAAAAAAAAAAAIjT6QEAAAAAAAAAAAAAAABAnE4PAAAAAAAAAAAAAAAAAOJ0egAAAAAAAAAAAAAAAAAQp9MDAAAAAAAAAAAAAAAAgDidHgAAAAAAAAAAAAAAAADE6fQAAAAAAAAAAAAAAAAAIE6nBwAAAAAAAAAAAAAAAABxOj0AAAAAAAAAAAAAAAAAiNPpAQAAAAAAAAAAAAAAAECcTg8AAAAAAAAAAAAAAAAA4nR6AAAAAAAAAAAAAAAAABC3rOvaegwAAFDMsiznX8QPyQAAAAAAAAAAAADAfvbTAwAAAAAAAAAAAAAAAIA4nR4AAAAAAAAAAAAAAAAAxOn0AAAAAAAAAAAAAAAAACBOpwcAAAAAAAAAAAAAAAAAcTo9AAAAAAAAAAAAAAAAAIjT6QEAAAAAAAAAAAAAAABAnE4PAAAAAAAAAAAAAAAAAOJ0egAAAAAAAAAAAAAAAAAQp9MDAAAAAAAAAAAAAAAAgDidHgAAAAAAAAAAAAAAAADE6fQAAAAAAAAAAAAAAAAAIE6nBwAAAAAAAAAAAAAAAABxOj0AAAAAAAAAAAAAAAAAiNPpAQAAAAAAAAAAAAAAAECcTg8AAAAAAAAAAAAAAAAA4nR6AAAAAAAAAAAAAAAAABCn0wMAAAAAAAAAAAAAAACAOJ0eAAAAAAAAAAAAAAAAAMTp9AAAAAAAAAAAAAAAAAAgTqcHAAAAAAAAAAAAAAAAAHE6PQAAAAAAAAAAAAAAAACI0+kBAAAAAAAAAAAAAAAAQJxODwAAAAAAAAAAAAAAAADidHoAAAAAAAAAAAAAAAAAEKfTAwAAAAAAAAAAAAAAAIA4nR4AAAAAAAAAAAAAAAAAxOn0AAAAAAAAAAAAAAAAACBOpwcAAAAAAAAAAAAAAAAAcTo9AAAAAAAAAAAAAAAAAIjT6QEAAAAAAAAAAAAAAABAnE4PAAAAAAAAAAAAAAAAAOJ0egAAAAAAAAAAAAAAAAAQp9MDAAAAAAAAAAAAAAAAgDidHvyPvTvGbR2GAiAYAb6U7l/wWPrFd+tEWRERE8/UBsEHF88uFgQAAAAAAAAAAAAAAADodHoAAAAAAAAAAAAAAAAA0On0AAAAAAAAAAAAAAAAAKDT6QEAAAAAAAAAAAAAAABAp9MDAAAAAAAAAAAAAAAAgE6nBwAAAAAAAAAAAAAAAACdTg8AAAAAAAAAAAAAAAAAOp0eAAAAAAAAAAAAAAAAAHQ6PQAAAAAAAAAAAAAAAADodHoAAAAAAAAAAAAAAAAA0On0AAAAAAAAAAAAAAAAAKDT6QEAAAAAAAAAAAAAAABAp9MDAAAAAAAAAAAAAAAAgE6nBwAAAAAAAAAAAAAAAACdTg8AAAAAAAAAAAAAAAAAOp0eAAAAAAAAAAAAAAAAAHQ6PQAAAAAAAAAAAAAAAADodHoAAAAAAAAAAAAAAAAA0On0AAAAAAAAAAAAAAAAAKDT6QEAAAAAAAAAAAAAAABA97j7AsDTGOP6Ifu+Xz8EAAAA/EsFgCmsVACYwkoFgCmsVACYwkoFAF7xnh4AAAAAAAAAAAAAAAAAdDo9AAAAAAAAAAAAAAAAAOh0egAAAAAAAAAAAAAAAADQ6fQAAAAAAAAAAAAAAAAAoNPpAQAAAAAAAAAAAAAAAECn0wMAAAAAAAAAAAAAAACATqcHAAAAAAAAAAAAAAAAAJ1ODwAAAAAAAAAAAAAAAAA6nR4AAAAAAAAAAAAAAAAAdDo9AAAAAAAAAAAAAAAAAOh0egAAAAAAAAAAAAAAAADQ6fQAAAAAAAAAAAAAAAAAoNPpAQAAAAAAAAAAAAAAAECn0wMAAAAAAAAAAAAAAACATqcHAAAAAAAAAAAAAAAAAJ1ODwAAAAAAAAAAAAAAAAA6nR4AAAAAAAAAAAAAAAAAdDo9AAAAAAAAAAAAAAAAAOh0egAAAAAAAAAAAAAAAADQ6fQAAAAAAAAAAAAAAAAAoNPpAQAAAAAAAAAAAAAAAECn0wMAAAAAAAAAAAAAAACAbjuO4+47AADANNu2XT/Ej2QAAAAAAAAAAAAA4Dzv6QEAAAAAAAAAAAAAAABAp9MDAAAAAAAAAAAAAAAAgE6nBwAAAAAAAAAAAAAAAACdTg8AAAAAAAAAAAAAAAAAOp0eAAAAAAAAAAAAAAAAAHQ6PQAAAAAAAAAAAAAAAADodHoAAAAAAAAAAAAAAAAA0On0AAAAAAAAAAAAAAAAAKDT6QEAAAAAAAAAAAAAAABAp9MDAAAAAAAAAAAAAAAAgE6nBwAAAAAAAAAAAAAAAACdTg8AAAAAAAAAAAAAAAAAOp0eAAAAAAAAAAAAAAAAAHQ6PQAAAAAAAAAAAAAAAADodHoAAAAAAAAAAAAAAAAA0On0AAAAAAAAAAAAAAAAAKDT6QEAAAAAAAAAAAAAAABAp9MDAAAAAAAAAAAAAAAAgE6nBwAAAAAAAAAAAAAAAACdTg8AAAAAAAAAAAAAAAAAOp0eAAAAAAAAAAAAAAAAAHQ6PQAAAAAAAAAAAAAAAADodHoAAAAAAAAAAAAAAAAA0On0AAAAAAAAAAAAAAAAAKDT6QEAAAAAAAAAAAAAAABAp9MDAAAAAAAAAAAAAAAAgE6nBwAAAAAAAAAAAAAAAACdTg8AAAAAAAAAAAAAAAAAOp0eAAAAAAAAAAAAAAAAAHQ6PQAAAAAAAAAAAAAAAADodHoAAAAAAAAAAAAAAAAA0On0AAAAAAAAAAAAAAAAAKDT6QEAAAAAAAAAAAAAAABAp9MDAAAAAAAAAAAAAAAAgE6nBwAAAAAAAAAAAAAAAACdTg8AAAAAAAAAAAAAAAAAOp0eAAAAAAAAAAAAAAAAAHQ6PQAAAAAAAAAAAAAAAADodHoAAAAAAAAAAAAAAAAA0On0AAAAAAAAAAAAAAAAAKDT6QEAAAAAAAAAAAAAAABAp9MDAAAAAAAAAAAAAAAAgE6nBwAAAAAAAAAAAAAAAACdTg8AAAAAAAAAAAAAAAAAOp0eAAAAAAAAAAAAAAAAAHQ6PQAAAAAAAAAAAAAAAADodHoAAAAAAAAAAAAAAAAA0On0AAAAAAAAAAAAAAAAAKDT6QEAAAAAAAAAAAAAAABAp9MDAAAAAAAAAAAAAAAAgE6nBwAAAAAAAAAAAAAAAACdTg8AAAAAAAAAAAAAAAAAOp0eAAAAAAAAAAAAAAAAAHSPuy8API0xrh+y7/v1QwAAAMC/VACYwkoFgCmsVACYwkoFgCmsVADgFe/pAQAAAAAAAAAAAAAAAECn0wMAAAAAAAAAAAAAAACATqcHAAAAAAAAAAAAAAAAAJ1ODwAAAAAAAAAAAAAAAAA6nR4AAAAAAAAAAAAAAAAAdDo9AAAAAAAAAAAAAAAAAOh0egAAAAAAAAAAAAAAAADQ6fQAAAAAAAAAAAAAAAAAoNPpAQAAAAAAAAAAAAAAAECn0wMAAAAAAAAAAAAAAACATqcHAAAAAAAAAAAAAAAAAJ1ODwAAAAAAAAAAAAAAAAA6nR4AAAAAAAAAAAAAAAAAdDo9AAAAAAAAAAAAAAAAAOh0egAAAAAAAAAAAAAAAADQ6fQAAAAAAAAAAAAAAAAAoNPpAQAAAAAAAAAAAAAAAECn0wMAAAAAAAAAAAAAAACATqcHAAAAAAAAAAAAAAAAAJ1ODwAAAAAAAAAAAAAAAAA6nR4AAAAAAAAAAAAAAAAAdDo9AAAAAAAAAAAAAAAAAOi24zjuvgMAAEyzbdv1Q/xIBgAAAAAAAAAAAADO854eAAAAAAAAAAAAAAAAAHQ6PQAAAAAAAAAAAAAAAADodHoAAAAAAAAAAAAAAAAA0On0AAAAAAAAAAAAAAAAAKDT6QEAAAAAAAAAAAAAAABAp9MDAAAAAAAAAAAAAAAAgE6nBwAAAAAAAAAAAAAAAACdTg8AAAAAAAAAAAAAAAAAOp0eAAAAAAAAAAAAAAAAAHQ6PQAAAAAAAAAAAAAAAADodHoAAAAAAAAAAAAAAAAA0On0AAAAAAAAAAAAAAAAAKDT6QEAAAAAAAAAAAAAAABAp9MDAAAAAAAAAAAAAAAAgE6nBwAAAAAAAAAAAAAAAACdTg8AAAAAAAAAAAAAAAAAOp0eAAAAAAAAAAAAAAAAAHQ6PQAAAAAAAAAAAAAAAADodHoAAAAAAAAAAAAAAAAA0On0AAAAAAAAAAAAAAAAAKDT6QEAAAAAAAAAAAAAAABAp9MDAAAAAAAAAAAAAAAAgE6nBwAAAAAAAAAAAAAAAACdTg8AAAAAAAAAAAAAAAAAOp0eAAAAAAAAAAAAAAAAAHQ6PQAAAAAAAAAAAAAAAADodHoAAAAAAAAAAAAAAAAA0On0AAAAAAAAAAAAAAAAAKDT6QEAAAAAAAAAAAAAAABAp9MDAAAAAAAAAAAAAAAAgE6nBwAAAAAAAAAAAAAAAACdTg8AAAAAAAAAAAAAAAAAOp0eAAAAAAAAAAAAAAAAAHQ6PQAAAAAAAAAAAAAAAADodHoAAAAAAAAAAAAAAAAA0On0AAAAAAAAAAAAAAAAAKDT6QEAAAAAAAAAAAAAAABAp9MDAAAAAAAAAAAAAAAAgE6nBwAAAAAAAAAAAAAAAACdTg8AAAAAAAAAAAAAAAAAOp0eAAAAAAAAAAAAAAAAAHQ6PQAAAAAAAAAAAAAAAADodHoAAAAAAAAAAAAAAAAA0On0AAAAAAAAAAAAAAAAAKDT6QEAAAAAAAAAAAAAAABAp9MDAAAAAAAAAAAAAAAAgE6nBwAAAAAAAAAAAAAAAACdTg8AAAAAAAAAAAAAAAAAOp0eAAAAAAAAAAAAAAAAAHQ6PQAAAAAAAAAAAAAAAADodHoAAAAAAAAAAAAAAAAA0On0AAAAAAAAAAAAAAAAAKDT6QEAAAAAAAAAAAAAAABA97j7AsDTGOP6Ifu+Xz8EAAAA/EsFgCmsVACYwkoFgCmsVACYwkoFAF7xnh4AAAAAAAAAAAAAAAAAdDo9AAAAAAAAAAAAAAAAAOh0egAAAAAAAAAAAAAAAADQ6fQAAAAAAAAAAAAAAAAAoNPpAQAAAAAAAAAAAAAAAECn0wMAAAAAAAAAAAAAAACATqcHAAAAAAAAAAAAAAAAAJ1ODwAAAAAAAAAAAAAAAAA6nR4AAAAAAAAAAAAAAAAAdDo9AAAAAAAAAAAAAAAAAOged18AmGmM8fkH9n3/mZtkX45whjEXYczzjLkIY563/pgXjTHWn/FNvkpjnmfMRRjzPGMuYsqYi/NVnmfMRRjzPGMuwph/hq/yPGMuwpjnGXMRxjxv8THfYcYPY36HMRdhzPOMuYh3GPMdZvww5ncYcxHGPM+Yi3iTMQGAP8B7egAAAAAAAAAAAAAAAADQ6fQAAAAAAAAAAAAAAAAAoNPpAQAAAAAAAAAAAAAAAECn0wMAAAAAAAAAAAAAAACATqcHAAAAAAAAAAAAAAAAAJ1ODwAAAAAAAAAAAAAAAAA6nR4AAAAAAAAAAAAAAAAAdDo9AAAAAAAAAAAAAAAAAOh0egAAAAAAAAAAAAAAAADQ6fQAAAAAAAAAAAAAAAAAoNPpAQAAAAAAAAAAAAAAAED3uPsCwNO+759/YIzxMzcBgF/tOI4PexMAftaXf2kBgP8sTQBo7FAAmMJKBYAprFQA4BXv6QEAAAAAAAAAAAAAAABAp9MDAAAAAAAAAAAAAAAAgE6nBwAAAAAAAAAAAAAAAACdTg8AAAAAAAAAAAAAAAAAOp0eAAAAAAAAAAAAAAAAAHQ6PQAAAAAAAAAAAAAAAADodHoAAAAAAAAAAAAAAAAA0On0AAAAAAAAAAAAAAAAAKDT6QEAAAAAAAAAAAAAAABAp9MDAAAAAAAAAAAAAAAAgG47juPuOwAAAAAAAAAAAAAAAADAb+U9PQAAAAAAAAAAAAAAAADodHoAAAAAAAAAAAAAAAAA0On0AAAAAAAAAAAAAAAAAKDT6QEAAAAAAAAAAAAAAABAp9MDAAAAAAAAAAAAAAAAgE6nBwAAAAAAAAAAAAAAAACdTg8AAAAAAAAAAAAAAAAAOp0eAAAAAAAAAAAAAAAAAHQ6PQAAAAAAAAAAAAAAAADodHoAAAAAAAAAAAAAAAAA0On0AAAAAAAAAAAAAAAAAKDT6QEAAAAAAAAAAAAAAABAp9MDAAAAAAAAAAAAAAAAgE6nBwAAAAAAAAAAAAAAAACdTg8AAAAAAAAAAAAAAAAAOp0eAAAAAAAAAAAAAAAAAHQ6PQAAAAAAAAAAAAAAAADodHoAAAAAAAAAAAAAAAAA0On0AAAAAAAAAAAAAAAAAKDT6QEAAAAAAAAAAAAAAABAp9MDAAAAAAAAAAAAAAAAgE6nBwAAAAAAAAAAAAAAAACdTg8AAAAAAAAAAAAAAAAAOp0eAAAAAAAAAAAAAAAAAHQ6PQAAAAAAAAAAAAAAAADodHoAAAAAAAAAAAAAAAAA0On0AAAAAAAAAAAAAAAAAKDT6QEAAAAAAAAAAAAAAABAp9MDAAAAAAAAAAAAAAAAgE6nBwAAAAAAAAAAAAAAAACdTg8AAAAAAAAAAAAAAAAAOp0eAAAAAAAAAAAAAAAAAHQ6PQAAAAAAAAAAAAAAAADodHoAAAAAAAAAAAAAAAAA0On0AAAAAAAAAAAAAAAAAKDT6QEAAAAAAAAAAAAAAABAp9MDAAAAAAAAAAAAAAAAgE6nBwAAAAAAAAAAAAAAAACdTg8AAAAAAAAAAAAAAAAAOp0eAAAAAAAAAAAAAAAAAHQ6PQAAAAAAAAAAAAAAAADodHoAAAAAAAAAAAAAAAAA0On0AAAAAAAAAAAAAAAAAKDT6QEAAAAAAAAAAAAAAABAp9MDAAAAAAAAAAAAAAAAgE6nBwAAAAAAAAAAAAAAAACdTg8AAAAAAAAAAAAAAKBD41sAABGaSURBVAAAOp0eAAAAAAAAAAAAAAAAAHQ6PQAAAAAAAAAAAAAAAADodHoAAAAAAAAAAAAAAAAA0On0AAAAAAAAAAAAAAAAAKDT6QEAAAAAAAAAAAAAAABAp9MDAAAAAAAAAAAAAAAAgE6nBwAAAAAAAAAAAAAAAACdTg8AAAAAAAAAAAAAAAAAOp0eAAAAAAAAAAAAAAAAAHQ6PQAAAAAAAAAAAAAAAADodHoAAAAAAAAAAAAAAAAA0On0AAAAAAAAAAAAAAAAAKDT6QEAAAAAAAAAAAAAAABAp9MDAAAAAAAAAAAAAAAAgE6nBwAAAAAAAAAAAAAAAACdTg8AAAAAAAAAAAAAAAAAOp0eAAAAAAAAAAAAAAAAAHQ6PQAAAAAAAAAAAAAAAADodHoAAAAAAAAAAAAAAAAA0On0AAAAAAAAAAAAAAAAAKDT6QEAAAAAAAAAAAAAAABAp9MDAAAAAAAAAAAAAAAAgE6nBwAAAAAAAAAAAAAAAACdTg8AAAAAAAAAAAAAAAAAOp0eAAAAAAAAAAAAAAAAAHQ6PQAAAAAAAAAAAAAAAADodHoAAAAAAAAAAAAAAAAA0On0AAAAAAAAAAAAAAAAAKDT6QEAAAAAAAAAAAAAAABAp9MDAAAAAAAAAAAAAAAAgE6nBwAAAAAAAAAAAAAAAACdTg8AAAAAAAAAAAAAAAAAOp0eAAAAAAAAAAAAAAAAAHQ6PQAAAAAAAAAAAAAAAADodHoAAAAAAAAA/9q7g9sKYSiKgkRKU/S/oCzSgjlfMU48U4DFXbzlEQAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIDu++0PYEXXdX3+yHmenz/yq8wcZ+YizBxn5iJ2mLnDxsPMJ8xchJnjzFyEmeMWn7nDxsPMJ8xchJnjzFyEmeN2mLnDxsPMZZg5zsxFmDnOzEXsMHOHjYeZT5i5CDPHmbkIM8ctPnOHjYeZT5i5CDPHmbkIM8eZuYIdNh5mPmHmIubM9D89AAAAAAAAAAAAAAAAAOh0egAAAAAAAAAAAAAAAADQ6fQAAAAAAAAAAAAAAAAAoNPpAQAAAAAAAAAAAAAAAECn0wMAAAAAAAAAAAAAAACATqcHAAAAAAAAAAAAAAAAAJ1ODwAAAAAAAAAAAAAAAAA6nR4AAAAAAAAAAAAAAAAAdDo9AAAAAAAAAAAAAAAAAOh0egAAAAAAAAAAAAAAAADQ6fQAAAAAAAAAAAAAAAAAoNPpAQAAAAAAAAAAAAAAAECn0wMAAAAAAAAAAAAAAACATqcHAAAAAAAAAAAAAAAAAJ1ODwAAAAAAAAAAAAAAAAA6nR4AAAAAAAAAAAAAAAAAdDo9AAAAAAAAAAAAAAAAAOh0egAAAAAAAAAAAAAAAADQ6fQAAAAAAAAAAAAAAAAAoNPpAQAAAAAAAAAAAAAAAECn0wMAAAAAAAAAAAAAAACATqcHAAAAAAAAAAAAAAAAAN3Xfd9vfwMAAAAAAAAAAAAAAAAA/FX+pwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6HR6AAAAAAAAAAAAAAAAANDp9AAAAAAAAAAAAAAAAACg0+kBAAAAAAAAAAAAAAAAQKfTAwAAAAAAAAAAAAAAAIBOpwcAAAAAAAAAAAAAAAAAnU4PAAAAAAAAAAAAAAAAADqdHgAAAAAAAAAAAAAAAAB0Oj0AAAAAAAAAAAAAAAAA6L7f/gBecF3X54+c5/n5I/DvOTeYxrnBNM4NpnFuMI1zg2mcG0zj3GAa5wbTODeYxrnBNM4NpnFuMI1zg2mcG0zj3HjE//QAAAAAAAAAAAAAAAAAoNPpAQAAAAAAAAAAAAAAAECn0wMAAAAAAAAAAAAAAACATqcHAAAAAAAAAAAAAAAAAJ1ODwAAAAAAAAAAAAAAAAA6nR4AAAAAAAAAAAAAAAAAdDo9AAAAAAAAAAAAAAAAAOh0egAAAAAAAAAAAAAAAADQ6fQAAAAAAAAAAAAAAAAAoNPpAQAAAAAAAAAAAAAAAECn0wMAAAAAAAAAAAAAAACATqcHAAAAAAAAAAAAAAAAAJ1ODwAAAAAAAAAAAAAAAAA6nR4AAAAAAAAAAAAAAAAAdDo9AAAAAAAAAAAAAAAAAOh0egAAAAAAAAAAAAAAAADQ6fQAAAAAAAAAAAAAAAAAoNPpAQAAAAAAAAAAAAAAAECn0wMAAAAAAAAAAAAAAACATqcHAAAAAAAAAAAAAAAAAJ1ODwAAAAAAAAAAAAAAAAA6nR4AAAAAAAAAAAAAAAAAdF/3fb/9DQAAAAAAAAAAAAAAAADwV/0Ae/yY8fK/eN0AAAAASUVORK5CYII=" } }, "cell_type": "markdown", "metadata": { "code_folding": [], "hidden_ranges": [], "originalKey": "cefa89be-ef41-40d9-9458-d6faed3c6c91", "showInput": false }, "source": [ "### Evaluating the Quality of a Pareto Front (Hypervolume)\n", "\n", "Given a reference point $ r \\in \\mathbb R^M$, which we represent as a list of M `ObjectiveThreshold`s, one for each coordinate, the hypervolume (HV) of a Pareto set $\\mathcal P = \\{ f(x_i)\\}_{i=1}^{|\\mathcal P|}$ is the volume of the space dominated (superior in every one of our M objectives) by $\\mathcal P$ and bounded from above by a point $ r$. The reference point should be set to be slightly worse (10% is reasonable) than the worst value of each objective that a decision maker would tolerate. In the figure below, the grey area is the hypervolume in this 2-objective problem.\n", "![hv_figure](attachment:hv_figure%20%281%29.png)" ] }, { "attachments": { "objective_thresholds_comparison.png": { "image/png": "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" } }, "cell_type": "markdown", "metadata": { "code_folding": [], "hidden_ranges": [], "originalKey": "1819970e-9b48-4b57-b280-35bf2c4919d2", "showInput": false }, "source": [ "### Set Objective Thresholds to focus candidate generation in a region of interest\n", "\n", "The below plots show three different sets of points generated by the qNEHVI [1] algorithm with different objective thresholds (aka reference points). Note that here we use absolute thresholds, but thresholds can also be relative to a status_quo arm.\n", "\n", "The first plot shows the points without the `ObjectiveThreshold`s visible (they're set far below the origin of the graph).\n", "\n", "The second shows the points generated with (-18, -6) as thresholds. The regions violating the thresholds are greyed out. Only the white region in the upper right exceeds both threshold, points in this region dominate the intersection of these thresholds (this intersection is the reference point). Only points in this region contribute to the hypervolume objective. A few exploration points are not in the valid region, but almost all the rest of the points are.\n", "\n", "The third shows points generated with a very strict pair of thresholds, (-18, -2). Only the white region in the upper right exceeds both thresholds. Many points do not lie in the dominating region, but there are still more focused there than in the second examples.\n", "![objective_thresholds_comparison.png](attachment:objective_thresholds_comparison.png)" ] }, { "cell_type": "markdown", "metadata": { "originalKey": "f2f39a8f-279f-49a1-b645-d51caed24d9c" }, "source": [ "### Further Information\n", "A deeper explanation of our the qNEHVI [1] and qNParEGO [2] algorithms this notebook explores can be found at \n", "\n", "[1] [S. Daulton, M. Balandat, and E. Bakshy. Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement. Advances in Neural Information Processing Systems 34, 2021.](https://arxiv.org/abs/2105.08195)\n", "\n", "[2] [S. Daulton, M. Balandat, and E. Bakshy. Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization. Advances in Neural Information Processing Systems 33, 2020.](https://arxiv.org/abs/2006.05078)\n", "\n", "In addition, the underlying BoTorch implementation has a researcher-oriented tutorial at https://botorch.org/tutorials/multi_objective_bo." ] }, { "cell_type": "markdown", "metadata": { "code_folding": [], "hidden_ranges": [], "originalKey": "0ac396dd-8040-4f87-8abe-472127734aef" }, "source": [ "## Setup" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "code_folding": [], "executionStartTime": 1628191302514, "executionStopTime": 1628191302546, "hidden_ranges": [], "originalKey": "500597fc-a996-48f4-a8fe-defd429162b8", "requestMsgId": "07dd11c9-cd20-4bfa-b2d9-9a7bf70b2e44" }, "outputs": [], "source": [ "import pandas as pd\n", "from ax import *\n", "\n", "import numpy as np\n", "\n", "from ax.metrics.noisy_function import NoisyFunctionMetric\n", "from ax.service.utils.report_utils import exp_to_df\n", "from ax.runners.synthetic import SyntheticRunner\n", "\n", "# Factory methods for creating multi-objective optimization modesl.\n", "from ax.modelbridge.factory import get_MOO_EHVI, get_MOO_PAREGO\n", "\n", "# Analysis utilities, including a method to evaluate hypervolumes\n", "from ax.modelbridge.modelbridge_utils import observed_hypervolume" ] }, { "cell_type": "markdown", "metadata": { "originalKey": "0b43c263-41da-4aa8-99f3-4a2a7fc49e4b" }, "source": [ "## Define experiment configurations" ] }, { "cell_type": "markdown", "metadata": { "code_folding": [], "hidden_ranges": [], "originalKey": "963a036d-a250-4e3c-9570-afe6f2192f9a" }, "source": [ "### Search Space" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "code_folding": [], "executionStartTime": 1628191313915, "executionStopTime": 1628191313944, "hidden_ranges": [], "originalKey": "90637eb4-730f-4f3d-8712-875bf88d6c2d", "requestMsgId": "fbb9db8e-5414-4add-ad10-0bd00583ebf5" }, "outputs": [], "source": [ "x1 = RangeParameter(name=\"x1\", lower=0, upper=1, parameter_type=ParameterType.FLOAT)\n", "x2 = RangeParameter(name=\"x2\", lower=0, upper=1, parameter_type=ParameterType.FLOAT)\n", "\n", "search_space = SearchSpace(\n", " parameters=[x1, x2],\n", ")" ] }, { "cell_type": "markdown", "metadata": { "code_folding": [], "hidden_ranges": [], "originalKey": "ac3cf1fe-d39d-48bb-a31d-e3ee0d70418b", "showInput": false }, "source": [ "### MultiObjectiveOptimizationConfig\n", "\n", "To optimize multiple objective we must create a `MultiObjective` containing the metrics we'll optimize and `MultiObjectiveOptimizationConfig` (which contains `ObjectiveThreshold`s) instead of our more typical `Objective` and `OptimizationConfig`\n", "\n", "We define `NoisyFunctionMetric`s to wrap our synthetic Branin-Currin problem's outputs. Add noise to see how robust our different optimization algorithms are." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "code_folding": [], "executionStartTime": 1628191319191, "executionStopTime": 1628191319220, "hidden_ranges": [], "originalKey": "9fdb11b6-7845-4f06-90fd-527fee088d76", "requestMsgId": "febe0d60-fe60-4d55-ba6f-724c8ce7601d" }, "outputs": [], "source": [ "class MetricA(NoisyFunctionMetric):\n", " def f(self, x: np.ndarray) -> float:\n", " return float(branin_currin(torch.tensor(x))[0])\n", " \n", "class MetricB(NoisyFunctionMetric):\n", " def f(self, x: np.ndarray) -> float:\n", " return float(branin_currin(torch.tensor(x))[1])\n", "\n", "metric_a = MetricA(\"a\", [\"x1\", \"x2\"], noise_sd=0.0, lower_is_better=False)\n", "metric_b = MetricB(\"b\", [\"x1\", \"x2\"], noise_sd=0.0, lower_is_better=False)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "code_folding": [], "executionStartTime": 1628191321755, "executionStopTime": 1628191321791, "hidden_ranges": [], "originalKey": "27065b03-7234-49c1-b3ae-f6442ec4e3d6", "requestMsgId": "d4010fca-5cbd-4a41-a779-cfa97ec15cc3" }, "outputs": [], "source": [ "mo = MultiObjective(\n", " objectives=[Objective(metric=metric_a), Objective(metric=metric_b)],\n", ")" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "executionStartTime": 1628191323464, "executionStopTime": 1628191323491, "originalKey": "c58b70de-06b5-4e03-8958-c3c55d4c295a", "requestMsgId": "27e7efe5-d29e-4211-944e-41e6de065299" }, "outputs": [], "source": [ "objective_thresholds = [\n", " ObjectiveThreshold(metric=metric, bound=val, relative=False)\n", " for metric, val in zip(mo.metrics, branin_currin.ref_point)\n", "]" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "code_folding": [], "executionStartTime": 1628191325491, "executionStopTime": 1628191325519, "hidden_ranges": [], "originalKey": "4b1ce9ba-e2e5-4a8a-9c15-5d01a2940a55", "requestMsgId": "314ea591-0d2e-4fb5-b091-2aa2ea27f0eb" }, "outputs": [], "source": [ "optimization_config = MultiObjectiveOptimizationConfig(\n", " objective=mo,\n", " objective_thresholds=objective_thresholds,\n", ")" ] }, { "cell_type": "markdown", "metadata": { "code_folding": [], "hidden_ranges": [], "originalKey": "3b7b797c-2478-48d6-84ea-c62a886db31f", "showInput": false }, "source": [ "## Define experiment creation utilities\n", "\n", "These construct our experiment, then initialize with Sobol points before we fit a Gaussian Process model to those initial points." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "executionStartTime": 1628191328765, "executionStopTime": 1628191328792, "originalKey": "a52ace6c-8144-446b-97d5-2f27879ca187", "requestMsgId": "6a222fb5-231e-4476-86a6-c29ca5113332" }, "outputs": [], "source": [ "# Reasonable defaults for number of quasi-random initialization points and for subsequent model-generated trials.\n", "N_INIT = 6\n", "N_BATCH = 25" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "code_folding": [], "executionStartTime": 1628191330913, "executionStopTime": 1628191330991, "hidden_ranges": [], "originalKey": "9fd6ec68-4c53-4276-a98a-61431cdc05d5", "requestMsgId": "8f659995-6b8f-4544-8392-03daaf8220b8" }, "outputs": [], "source": [ "def build_experiment():\n", " experiment = Experiment(\n", " name=\"pareto_experiment\",\n", " search_space=search_space,\n", " optimization_config=optimization_config,\n", " runner=SyntheticRunner(),\n", " )\n", " return experiment" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "code_folding": [], "executionStartTime": 1628191334273, "executionStopTime": 1628191334299, "hidden_ranges": [], "originalKey": "a8eef6a6-1d53-494a-907f-10ca35492a8c", "requestMsgId": "b207dbd4-0a53-4efd-bbb9-9dee8835d60b" }, "outputs": [], "source": [ "## Initialize with Sobol samples\n", "\n", "def initialize_experiment(experiment):\n", " sobol = Models.SOBOL(search_space=experiment.search_space, seed=1234)\n", "\n", " for _ in range(N_INIT):\n", " experiment.new_trial(sobol.gen(1)).run()\n", "\n", " return experiment.fetch_data()" ] }, { "cell_type": "markdown", "metadata": { "code_folding": [], "hidden_ranges": [], "originalKey": "0c918735-9fda-4c36-90b5-163443e66c72", "showInput": false }, "source": [ "# Sobol\n", "We use quasirandom points as a fast baseline for evaluating the quality of our multi-objective optimization algorithms." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "code_folding": [], "executionStartTime": 1628191356513, "executionStopTime": 1628191356896, "hidden_ranges": [], "originalKey": "5ee13832-804a-413f-a6bc-1f8f96a817d8", "requestMsgId": "5b40f1e6-45b9-40e4-8569-9d459e98ca57" }, "outputs": [], "source": [ "sobol_experiment = build_experiment()\n", "sobol_data = initialize_experiment(sobol_experiment)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "code_folding": [], "executionStartTime": 1628191362562, "executionStopTime": 1628191408255, "hidden_ranges": [], "originalKey": "0c6a6d44-29db-43dd-982d-dc664d00b009", "requestMsgId": "8aca7b5b-aab8-4a39-9a49-d7b1e0c714c5" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 0, HV: 0.0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 1, HV: 0.0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 2, HV: 10.64117026485585\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 3, HV: 10.64117026485585\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 4, HV: 10.64117026485585\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 5, HV: 10.64117026485585\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 6, HV: 10.64117026485585\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 7, HV: 10.64117026485585\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 8, HV: 10.64117026485585\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 9, HV: 10.64117026485585\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 10, HV: 10.64117026485585\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 11, HV: 10.64117026485585\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 12, HV: 10.64117026485585\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 13, HV: 10.64117026485585\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 14, HV: 10.64117026485585\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 15, HV: 10.64117026485585\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 16, HV: 10.64117026485585\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 17, HV: 10.64117026485585\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 18, HV: 10.64117026485585\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 19, HV: 10.64117026485585\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 20, HV: 10.64117026485585\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 21, HV: 10.64117026485585\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 22, HV: 10.64117026485585\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 23, HV: 10.64117026485585\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 24, HV: 10.64117026485585\n" ] } ], "source": [ "sobol_model = Models.SOBOL(\n", " experiment=sobol_experiment, \n", " data=sobol_data,\n", ")\n", "sobol_hv_list = []\n", "for i in range(N_BATCH):\n", " generator_run = sobol_model.gen(1)\n", " trial = sobol_experiment.new_trial(generator_run=generator_run)\n", " trial.run()\n", " exp_df = exp_to_df(sobol_experiment)\n", " outcomes = np.array(exp_df[['a', 'b']], dtype=np.double)\n", " # Fit a GP-based model in order to calculate hypervolume.\n", " # We will not use this model to generate new points.\n", " dummy_model = get_MOO_EHVI(\n", " experiment=sobol_experiment, \n", " data=sobol_experiment.fetch_data(),\n", " )\n", " try:\n", " hv = observed_hypervolume(modelbridge=dummy_model)\n", " except:\n", " hv = 0\n", " print(\"Failed to compute hv\")\n", " sobol_hv_list.append(hv)\n", " print(f\"Iteration: {i}, HV: {hv}\")\n", "\n", "sobol_outcomes = np.array(exp_to_df(sobol_experiment)[['a', 'b']], dtype=np.double)" ] }, { "cell_type": "markdown", "metadata": { "code_folding": [], "hidden_ranges": [], "originalKey": "767a7e9b-8902-424e-bfc4-f7afdba47302" }, "source": [ "## qNEHVI\n", "Noisy Expected Hypervolume Improvement. This is our current recommended algorithm for multi-objective optimization." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "code_folding": [], "executionStartTime": 1628191422463, "executionStopTime": 1628191422803, "hidden_ranges": [], "originalKey": "8fc6bfb4-3012-4ce2-99ed-288378098c50", "requestMsgId": "0fd945a2-ac45-4a74-82cc-7173e15ced85" }, "outputs": [], "source": [ "ehvi_experiment = build_experiment()\n", "ehvi_data = initialize_experiment(ehvi_experiment)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "code_folding": [], "executionStartTime": 1628191425090, "executionStopTime": 1628191500240, "hidden_ranges": [], "originalKey": "27dd9425-b77e-4027-8412-30dd40c5abf1", "requestMsgId": "65430b82-de1e-4946-9d8d-4a75762354c1" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 0, HV: 0.0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 1, HV: 0.0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 2, HV: 0.0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 3, HV: 0.0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 4, HV: 2.369795709893773\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 5, HV: 24.236066149735638\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 6, HV: 30.77590702593481\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 7, HV: 41.218700737712446\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 8, HV: 45.771357911402\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 9, HV: 49.18292612802855\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 10, HV: 50.5780226425872\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 11, HV: 52.116287465257\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 12, HV: 53.60549817534729\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 13, HV: 54.392787480186044\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 14, HV: 54.82610251036763\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 15, HV: 55.04980800955486\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 16, HV: 55.43513070793488\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 17, HV: 55.8064797324934\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 18, HV: 56.1605633911855\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 19, HV: 56.53559907373636\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 20, HV: 56.88861590136981\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 21, HV: 56.90096873066695\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 22, HV: 57.00444738517544\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 23, HV: 57.10933174161882\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 24, HV: 57.200870530641495\n" ] } ], "source": [ "ehvi_hv_list = []\n", "ehvi_model = None\n", "for i in range(N_BATCH): \n", " ehvi_model = get_MOO_EHVI(\n", " experiment=ehvi_experiment, \n", " data=ehvi_data,\n", " )\n", " generator_run = ehvi_model.gen(1)\n", " trial = ehvi_experiment.new_trial(generator_run=generator_run)\n", " trial.run()\n", " ehvi_data = Data.from_multiple_data([ehvi_data, trial.fetch_data()])\n", " \n", " exp_df = exp_to_df(ehvi_experiment)\n", " outcomes = np.array(exp_df[['a', 'b']], dtype=np.double)\n", " try:\n", " hv = observed_hypervolume(modelbridge=ehvi_model)\n", " except:\n", " hv = 0\n", " print(\"Failed to compute hv\")\n", " ehvi_hv_list.append(hv)\n", " print(f\"Iteration: {i}, HV: {hv}\")\n", "\n", "ehvi_outcomes = np.array(exp_to_df(ehvi_experiment)[['a', 'b']], dtype=np.double)" ] }, { "cell_type": "markdown", "metadata": { "code_folding": [], "hidden_ranges": [], "originalKey": "e93178b6-5ba4-4c01-b8a2-e05971b7326f", "showInput": false }, "source": [ "## Plot qNEHVI Pareto Frontier based on model posterior \n", "\n", "The plotted points are samples from the fitted model's posterior, not observed samples." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "code_folding": [], "executionStartTime": 1628191505148, "executionStopTime": 1628191521900, "hidden_ranges": [], "originalKey": "71e013c5-638f-4ba4-bb9a-3e4a7d3eb9fa", "requestMsgId": "681433c5-fc21-4699-9fe1-8e444c671153" }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": false }, "data": [ { "error_x": { "array": [ 0.0398353916875787, 0.039835391786792115, 0.028652398886095068, 0.03983539153875858, 0.032525458844617106, 0.03983539148915187, 0.0345879042194335, 0.0398353916875787, 0.03983539163797199, 0.032980199875022015, 0.032619266828377985, 0.0398353916875787, 0.03983539156356193, 0.0388458139921148, 0.0398353916875787, 0.039835391786792115, 0.03983539161316864, 0.03983539143954517, 0.021157611867226887, 0.03983539148915187 ], "color": "rgba(128,177,211,0.4)", "thickness": 2, "type": "data" }, "error_y": { "array": [ 0.0019200402679696814, 0.0019200402646582888, 0.0012946933866470286, 0.001920040272384871, 0.0014586521592386084, 0.001920040266865884, 0.0015486311549121176, 0.0019200402657620864, 0.001920040266865884, 0.0014789752735749544, 0.0014962933504414672, 0.0019200402635544916, 0.0019200402646582888, 0.001705278442662137, 0.0019200402657620864, 0.0019200402602430992, 0.001920040269073479, 0.001920040266865884, 0.001025346361444487, 0.001920040272384871 ], "color": "rgba(128,177,211,0.4)", "thickness": 2, "type": "data" }, "hoverinfo": "text", "legendgroup": "mean", "marker": { "color": "rgba(128,177,211,1)" }, "mode": "markers", "name": "mean", "text": [ "Parameterization 0
a: -17.505 [-17.545, -17.465]
b: -1.18 [-1.182, -1.178]

Parameterization:
x1: 2.4618177748017678e-15
x2: 0.9999999999999993", "Parameterization 1
a: -17.505 [-17.545, -17.465]
b: -1.18 [-1.182, -1.178]

Parameterization:
x1: 3.1389761689177155e-16
x2: 1.0", "Parameterization 2
a: -2.845 [-2.873, -2.816]
b: -4.049 [-4.050, -4.048]

Parameterization:
x1: 0.07592208852739116
x2: 0.9551755101917688", "Parameterization 3
a: -17.505 [-17.545, -17.465]
b: -1.18 [-1.182, -1.178]

Parameterization:
x1: 8.178512432993884e-17
x2: 1.0", "Parameterization 4
a: -2.203 [-2.236, -2.171]
b: -4.308 [-4.309, -4.307]

Parameterization:
x1: 0.08306488222667019
x2: 0.936162697639015", "Parameterization 5
a: -17.505 [-17.545, -17.465]
b: -1.18 [-1.182, -1.178]

Parameterization:
x1: 0.0
x2: 0.9999999999999983", "Parameterization 6
a: -1.979 [-2.013, -1.944]
b: -4.406 [-4.408, -4.405]

Parameterization:
x1: 0.08591759769811143
x2: 0.9295024013843196", "Parameterization 7
a: -17.505 [-17.545, -17.465]
b: -1.18 [-1.182, -1.178]

Parameterization:
x1: 2.4637912913822597e-15
x2: 0.9999999999999983", "Parameterization 8
a: -17.505 [-17.545, -17.465]
b: -1.18 [-1.182, -1.178]

Parameterization:
x1: 0.0
x2: 0.9999999999999987", "Parameterization 9
a: -2.167 [-2.200, -2.134]
b: -4.324 [-4.325, -4.322]

Parameterization:
x1: 0.08350486213499163
x2: 0.9349937098218561", "Parameterization 10
a: -5.428 [-5.461, -5.396]
b: -3.187 [-3.188, -3.185]

Parameterization:
x1: 0.052269247887437364
x2: 1.0", "Parameterization 11
a: -17.505 [-17.545, -17.465]
b: -1.18 [-1.182, -1.178]

Parameterization:
x1: 0.0
x2: 1.0", "Parameterization 12
a: -17.505 [-17.545, -17.465]
b: -1.18 [-1.182, -1.178]

Parameterization:
x1: 0.0
x2: 0.9999999999999999", "Parameterization 13
a: -1.878 [-1.917, -1.840]
b: -4.451 [-4.453, -4.450]

Parameterization:
x1: 0.08710661010181873
x2: 0.925357294716753", "Parameterization 14
a: -17.505 [-17.545, -17.465]
b: -1.18 [-1.182, -1.178]

Parameterization:
x1: 0.0
x2: 0.9999999999999984", "Parameterization 15
a: -17.505 [-17.545, -17.465]
b: -1.18 [-1.182, -1.178]

Parameterization:
x1: 1.7026603250928062e-15
x2: 1.0", "Parameterization 16
a: -17.505 [-17.545, -17.465]
b: -1.18 [-1.182, -1.178]

Parameterization:
x1: 0.0
x2: 0.9999999999999979", "Parameterization 17
a: -17.505 [-17.545, -17.465]
b: -1.18 [-1.182, -1.178]

Parameterization:
x1: 2.919996137483579e-16
x2: 0.9999999999999998", "Parameterization 18
a: -13.351 [-13.372, -13.329]
b: -1.738 [-1.739, -1.737]

Parameterization:
x1: 0.013696903191616525
x2: 1.0", "Parameterization 19
a: -17.505 [-17.545, -17.465]
b: -1.18 [-1.182, -1.178]

Parameterization:
x1: 1.4449365149523451e-15
x2: 0.9999999999999983" ], "type": "scatter", "x": [ -17.50523033597689, -17.50523033598993, -2.8446091260101625, -17.505230336006232, -2.203379412560164, -17.50523033598341, -1.9788378200929486, -17.50523033597689, -17.50523033598341, -2.1666829280494326, -5.428122263657432, -17.50523033600297, -17.50523033598993, -1.8784183135496662, -17.505230335987207, -17.505230335983946, -17.50523033598993, -17.50523033598015, -13.350509912323224, -17.50523033596711 ], "y": [ -1.1800767990913528, -1.1800767990913528, -4.048895664672052, -1.1800767990919567, -4.308003515623604, -1.1800767990907488, -4.406095104178524, -1.1800767990919567, -1.1800767990904468, -4.32371133766996, -3.1869254048574613, -1.1800767990919567, -1.1800767990901448, -4.451446352562722, -1.1800767990901448, -1.1800767990895409, -1.1800767990922587, -1.1800767990916547, -1.7384085535708778, -1.1800767990907488 ] } ], "layout": { "height": 500, "hovermode": "closest", "legend": { "orientation": "h" }, "margin": { "b": 75, "l": 225, "pad": 4, "t": 75 }, "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": "#2a3f5f" }, "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "barpolar" } ], "carpet": [ { "aaxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "baxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "type": "carpet" } ], "choropleth": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "choropleth" } ], "contour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0.0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1.0, "#f0f921" ] ], "type": "contour" } ], "contourcarpet": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "contourcarpet" } ], "heatmap": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0.0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1.0, "#f0f921" ] ], "type": "heatmap" } ], "heatmapgl": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0.0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1.0, "#f0f921" ] ], "type": "heatmapgl" } ], "histogram": [ { "marker": { "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "histogram" } ], "histogram2d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0.0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1.0, "#f0f921" ] ], "type": "histogram2d" } ], "histogram2dcontour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0.0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1.0, "#f0f921" ] ], "type": "histogram2dcontour" } ], "mesh3d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "mesh3d" } ], "parcoords": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "parcoords" } ], "pie": [ { "automargin": true, "type": "pie" } ], "scatter": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter" } ], "scatter3d": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter3d" } ], "scattercarpet": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattercarpet" } ], "scattergeo": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergeo" } ], "scattergl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergl" } ], "scattermapbox": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattermapbox" } ], "scatterpolar": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolar" } ], "scatterpolargl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolargl" } ], "scatterternary": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterternary" } ], "surface": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0.0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1.0, "#f0f921" ] ], "type": "surface" } ], "table": [ { "cells": { "fill": { "color": "#EBF0F8" }, "line": { "color": "white" } }, "header": { "fill": { "color": "#C8D4E3" }, "line": { "color": "white" } }, "type": "table" } ] }, "layout": { "annotationdefaults": { "arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1 }, "autotypenumbers": "strict", "coloraxis": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "colorscale": { "diverging": [ [ 0, "#8e0152" ], [ 0.1, "#c51b7d" ], [ 0.2, "#de77ae" ], [ 0.3, "#f1b6da" ], [ 0.4, "#fde0ef" ], [ 0.5, "#f7f7f7" ], [ 0.6, "#e6f5d0" ], [ 0.7, "#b8e186" ], [ 0.8, "#7fbc41" ], [ 0.9, "#4d9221" ], [ 1, "#276419" ] ], "sequential": [ [ 0.0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1.0, "#f0f921" ] ], "sequentialminus": [ [ 0.0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1.0, "#f0f921" ] ] }, "colorway": [ "#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52" ], "font": { "color": "#2a3f5f" }, "geo": { "bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": { "angularaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "radialaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Pareto Frontier" }, "width": 750, "xaxis": { "ticksuffix": "", "title": { "text": "a" }, "zeroline": true }, "yaxis": { "ticksuffix": "", "title": { "text": "b" }, "zeroline": true } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "frontier = compute_posterior_pareto_frontier(\n", " experiment=ehvi_experiment,\n", " data=ehvi_experiment.fetch_data(),\n", " primary_objective=metric_b,\n", " secondary_objective=metric_a,\n", " absolute_metrics=[\"a\", \"b\"],\n", " num_points=20,\n", ")\n", "\n", "render(plot_pareto_frontier(frontier, CI_level=0.90)) " ] }, { "cell_type": "markdown", "metadata": { "code_folding": [], "hidden_ranges": [], "originalKey": "77b2dbce-f1e4-443a-8f81-2e1cbe207301" }, "source": [ "## qNParEGO\n", "This is a good alternative algorithm for multi-objective optimization when qNEHVI runs too slowly." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "code_folding": [], "hidden_ranges": [], "originalKey": "2f796182-558b-47aa-8072-4dbf40123133" }, "outputs": [], "source": [ "parego_experiment = build_experiment()\n", "parego_data = initialize_experiment(parego_experiment)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "code_folding": [], "hidden_ranges": [], "originalKey": "72999188-90f5-43e0-b1d9-d468e7d51191" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 0, HV: 0.0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 1, HV: 0.0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 2, HV: 0.0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 3, HV: 2.265499006355323\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 4, HV: 34.88507336576994\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 5, HV: 34.88507336576994\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 6, HV: 34.88507336576994\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 7, HV: 35.055957736737064\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 8, HV: 35.055957736737064\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 9, HV: 38.477159861238256\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 10, HV: 38.84355736118608\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 11, HV: 38.84355736118608\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 12, HV: 38.84355736118608\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 13, HV: 38.84355736118608\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 14, HV: 38.84355736118608\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 15, HV: 44.41015452007566\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 16, HV: 44.41015452007566\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 17, HV: 44.41015452007566\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 18, HV: 44.41015452007566\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 19, HV: 44.41015452007566\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 20, HV: 44.41015452007566\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 21, HV: 44.41015452007566\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 22, HV: 44.41015452007566\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 23, HV: 48.73086251492163\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 24, HV: 48.73086251492163\n" ] } ], "source": [ "parego_hv_list = []\n", "parego_model = None\n", "for i in range(N_BATCH): \n", " parego_model = get_MOO_PAREGO(\n", " experiment=parego_experiment, \n", " data=parego_data,\n", " )\n", " generator_run = parego_model.gen(1)\n", " trial = parego_experiment.new_trial(generator_run=generator_run)\n", " trial.run()\n", " parego_data = Data.from_multiple_data([parego_data, trial.fetch_data()])\n", " \n", " exp_df = exp_to_df(parego_experiment)\n", " outcomes = np.array(exp_df[['a', 'b']], dtype=np.double)\n", " try:\n", " hv = observed_hypervolume(modelbridge=parego_model)\n", " except:\n", " hv = 0\n", " print(\"Failed to compute hv\")\n", " parego_hv_list.append(hv)\n", " print(f\"Iteration: {i}, HV: {hv}\")\n", "\n", "parego_outcomes = np.array(exp_to_df(parego_experiment)[['a', 'b']], dtype=np.double)" ] }, { "cell_type": "markdown", "metadata": { "code_folding": [], "hidden_ranges": [], "originalKey": "67ded85f-7c58-4c31-8df5-b0d8d07e4299", "showInput": false }, "source": [ "## Plot qNParEGO Pareto Frontier based on model posterior \n", "\n", "The plotted points are samples from the fitted model's posterior, not observed samples." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "code_folding": [], "hidden_ranges": [], "originalKey": "3b1f39fd-ef75-4ea4-865b-f7b54b90da07" }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": false }, "data": [ { "error_x": { "array": [ 0.05765848370442958, 0.05765848388245875, 0.05907292020820084, 0.05284668559129787, 0.05765848390789149, 0.05765848385702601, 0.08296628895828606, 0.3280962481698496, 0.05765848439111349, 0.05765848418765159, 0.05003935664545007, 0.05050901550875057, 0.08501969380553948, 0.05765848408592065, 0.05765848390789149, 0.05765848385702601, 0.07552411302895064, 0.05765848385702601, 0.05765848385702601, 0.0576584837807278 ], "color": "rgba(128,177,211,0.4)", "thickness": 2, "type": "data" }, "error_y": { "array": [ 0.0024265611248644823, 0.0024265611152361165, 0.002544223960511182, 0.002200556470540867, 0.0024265611163059348, 0.0024265611163059348, 0.004337971729640502, 0.017016569359512856, 0.0024265611312833933, 0.0024265611163059348, 0.0021701806158726912, 0.0021956424474958327, 0.004484365143069466, 0.0024265611323532115, 0.002426561121655027, 0.0024265611163059348, 0.0035620908037684385, 0.0024265611163059348, 0.0024265611163059348, 0.002426561114166298 ], "color": "rgba(128,177,211,0.4)", "thickness": 2, "type": "data" }, "hoverinfo": "text", "legendgroup": "mean", "marker": { "color": "rgba(128,177,211,1)" }, "mode": "markers", "name": "mean", "text": [ "Parameterization 0
a: -17.51 [-17.568, -17.452]
b: -1.18 [-1.183, -1.178]

Parameterization:
x1: 0.0
x2: 0.9999999999999977", "Parameterization 1
a: -17.51 [-17.568, -17.452]
b: -1.18 [-1.183, -1.178]

Parameterization:
x1: 4.5901470322099357e-17
x2: 0.9999999999999994", "Parameterization 2
a: -2.865 [-2.924, -2.806]
b: -4.042 [-4.045, -4.040]

Parameterization:
x1: 0.07573439109693714
x2: 0.9554686776284921", "Parameterization 3
a: -2.683 [-2.736, -2.630]
b: -4.114 [-4.116, -4.112]

Parameterization:
x1: 0.0777043736961482
x2: 0.9505049381372721", "Parameterization 4
a: -17.51 [-17.568, -17.452]
b: -1.18 [-1.183, -1.178]

Parameterization:
x1: 4.45260833625291e-18
x2: 1.0", "Parameterization 5
a: -17.51 [-17.568, -17.452]
b: -1.18 [-1.183, -1.178]

Parameterization:
x1: 0.0
x2: 1.0", "Parameterization 6
a: -10.006 [-10.089, -9.923]
b: -2.259 [-2.264, -2.255]

Parameterization:
x1: 0.026790535616001598
x2: 1.0", "Parameterization 7
a: -0.275 [-0.603, 0.053]
b: -5.661 [-5.678, -5.644]

Parameterization:
x1: 0.12149170287082159
x2: 0.8182380100022272", "Parameterization 8
a: -17.51 [-17.568, -17.452]
b: -1.18 [-1.183, -1.178]

Parameterization:
x1: 0.0
x2: 0.9999999999999909", "Parameterization 9
a: -17.51 [-17.568, -17.452]
b: -1.18 [-1.183, -1.178]

Parameterization:
x1: 2.278071581736349e-16
x2: 1.0", "Parameterization 10
a: -7.991 [-8.041, -7.941]
b: -2.62 [-2.622, -2.618]

Parameterization:
x1: 0.03629416479732887
x2: 1.0", "Parameterization 11
a: -8.031 [-8.081, -7.980]
b: -2.612 [-2.614, -2.610]

Parameterization:
x1: 0.036090158471115866
x2: 1.0", "Parameterization 12
a: -10.13 [-10.215, -10.045]
b: -2.238 [-2.243, -2.234]

Parameterization:
x1: 0.026254671542604475
x2: 1.0", "Parameterization 13
a: -17.51 [-17.568, -17.452]
b: -1.18 [-1.183, -1.178]

Parameterization:
x1: 1.2902912244266845e-11
x2: 0.9999999999397234", "Parameterization 14
a: -17.51 [-17.568, -17.452]
b: -1.18 [-1.183, -1.178]

Parameterization:
x1: 7.458158759983168e-16
x2: 1.0", "Parameterization 15
a: -17.51 [-17.568, -17.452]
b: -1.18 [-1.183, -1.178]

Parameterization:
x1: 0.0
x2: 1.0", "Parameterization 16
a: -3.61 [-3.686, -3.535]
b: -3.767 [-3.770, -3.763]

Parameterization:
x1: 0.06838654147154609
x2: 0.9754205882450694", "Parameterization 17
a: -17.51 [-17.568, -17.452]
b: -1.18 [-1.183, -1.178]

Parameterization:
x1: 0.0
x2: 1.0", "Parameterization 18
a: -17.51 [-17.568, -17.452]
b: -1.18 [-1.183, -1.178]

Parameterization:
x1: 0.0
x2: 1.0", "Parameterization 19
a: -17.51 [-17.568, -17.452]
b: -1.18 [-1.183, -1.178]

Parameterization:
x1: 3.358009085719674e-17
x2: 0.9999999999999999" ], "type": "scatter", "x": [ -17.510021612131496, -17.510021612115203, -2.86498335253512, -2.6829745703266, -17.51002161213249, -17.510021612131496, -10.005976108095624, -0.2752144412848736, -17.51002161213745, -17.51002161213286, -7.991029561948416, -8.03054379141853, -10.130048658539181, -17.510021611751114, -17.51002161213087, -17.510021612131496, -3.6102645988007964, -17.510021612131496, -17.510021612131496, -17.510021612127524 ], "y": [ -1.1804375063053172, -1.1804375063054007, -4.04238473467537, -4.113910905168235, -1.1804375063050658, -1.1804375063050658, -2.2592261059980356, -5.660829704158961, -1.1804375063050658, -1.1804375063060686, -2.619828014252257, -2.612250308909407, -2.2384654445526104, -1.1804375068910806, -1.1804375063055996, -1.1804375063050658, -3.76662611302589, -1.1804375063050658, -1.1804375063050658, -1.1804375063055677 ] } ], "layout": { "height": 500, "hovermode": "closest", "legend": { "orientation": "h" }, "margin": { "b": 75, "l": 225, "pad": 4, "t": 75 }, "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": "#2a3f5f" }, "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "barpolar" } ], "carpet": [ { "aaxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "baxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "type": "carpet" } ], "choropleth": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "choropleth" } ], "contour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0.0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1.0, "#f0f921" ] ], "type": "contour" } ], "contourcarpet": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "contourcarpet" } ], "heatmap": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0.0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1.0, "#f0f921" ] ], "type": "heatmap" } ], "heatmapgl": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0.0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1.0, "#f0f921" ] ], "type": "heatmapgl" } ], "histogram": [ { "marker": { "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "histogram" } ], "histogram2d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0.0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1.0, "#f0f921" ] ], "type": "histogram2d" } ], "histogram2dcontour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0.0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1.0, "#f0f921" ] ], "type": "histogram2dcontour" } ], "mesh3d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "mesh3d" } ], "parcoords": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "parcoords" } ], "pie": [ { "automargin": true, "type": "pie" } ], "scatter": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter" } ], "scatter3d": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter3d" } ], "scattercarpet": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattercarpet" } ], "scattergeo": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergeo" } ], "scattergl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergl" } ], "scattermapbox": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattermapbox" } ], "scatterpolar": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolar" } ], "scatterpolargl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolargl" } ], "scatterternary": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterternary" } ], "surface": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0.0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1.0, "#f0f921" ] ], "type": "surface" } ], "table": [ { "cells": { "fill": { "color": "#EBF0F8" }, "line": { "color": "white" } }, "header": { "fill": { "color": "#C8D4E3" }, "line": { "color": "white" } }, "type": "table" } ] }, "layout": { "annotationdefaults": { "arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1 }, "autotypenumbers": "strict", "coloraxis": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "colorscale": { "diverging": [ [ 0, "#8e0152" ], [ 0.1, "#c51b7d" ], [ 0.2, "#de77ae" ], [ 0.3, "#f1b6da" ], [ 0.4, "#fde0ef" ], [ 0.5, "#f7f7f7" ], [ 0.6, "#e6f5d0" ], [ 0.7, "#b8e186" ], [ 0.8, "#7fbc41" ], [ 0.9, "#4d9221" ], [ 1, "#276419" ] ], "sequential": [ [ 0.0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1.0, "#f0f921" ] ], "sequentialminus": [ [ 0.0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1.0, "#f0f921" ] ] }, "colorway": [ "#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52" ], "font": { "color": "#2a3f5f" }, "geo": { "bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": { "angularaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "radialaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Pareto Frontier" }, "width": 750, "xaxis": { "ticksuffix": "", "title": { "text": "a" }, "zeroline": true }, "yaxis": { "ticksuffix": "", "title": { "text": "b" }, "zeroline": true } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "frontier = compute_posterior_pareto_frontier(\n", " experiment=parego_experiment,\n", " data=parego_experiment.fetch_data(),\n", " primary_objective=metric_b,\n", " secondary_objective=metric_a,\n", " absolute_metrics=[\"a\", \"b\"],\n", " num_points=20,\n", ")\n", "\n", "render(plot_pareto_frontier(frontier, CI_level=0.90)) " ] }, { "cell_type": "markdown", "metadata": { "code_folding": [], "collapsed": true, "hidden_ranges": [], "originalKey": "a67f7345-1777-4372-8704-bb80c4c4e783" }, "source": [ "## Plot empirical data" ] }, { "cell_type": "markdown", "metadata": { "code_folding": [], "collapsed": true, "hidden_ranges": [], "originalKey": "de878adc-0eb2-4599-8c1b-e0adbc0c0765", "showInput": false }, "source": [ "#### Plot observed hypervolume, with color representing the iteration that a point was generated on.\n", "\n", "To examine optimization process from another perspective, we plot the collected observations under each algorithm where the color corresponds to the BO iteration at which the point was collected. The plot on the right for $q$NEHVI shows that the $q$NEHVI quickly identifies the Pareto frontier and most of its evaluations are very close to the Pareto frontier. $q$NParEGO also identifies has many observations close to the Pareto frontier, but relies on optimizing random scalarizations, which is a less principled way of optimizing the Pareto front compared to $q$NEHVI, which explicitly attempts focuses on improving the Pareto front. Sobol generates random points and has few points close to the Pareto front." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "code_folding": [], "hidden_ranges": [], "originalKey": "c6296697-ef07-422d-b965-35e4e5104a12" }, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Iteration')" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "from matplotlib import pyplot as plt\n", "%matplotlib inline\n", "\n", "from matplotlib.cm import ScalarMappable\n", "fig, axes = plt.subplots(1, 3, figsize=(20,6))\n", "algos = [\"Sobol\", \"qNParEGO\", \"qNEHVI\"]\n", "outcomes_list = [sobol_outcomes, parego_outcomes, ehvi_outcomes]\n", "cm = plt.cm.get_cmap('viridis')\n", "BATCH_SIZE = 1\n", "\n", "n_results = N_BATCH*BATCH_SIZE + N_INIT\n", "batch_number = torch.cat([torch.zeros(N_INIT), torch.arange(1, N_BATCH+1).repeat(BATCH_SIZE, 1).t().reshape(-1)]).numpy()\n", "for i, train_obj in enumerate(outcomes_list):\n", " x = i\n", " sc = axes[x].scatter(train_obj[:n_results, 0], train_obj[:n_results,1], c=batch_number[:n_results], alpha=0.8)\n", " axes[x].set_title(algos[i])\n", " axes[x].set_xlabel(\"Objective 1\")\n", " axes[x].set_xlim(-150, 5)\n", " axes[x].set_ylim(-15, 0)\n", "axes[0].set_ylabel(\"Objective 2\")\n", "norm = plt.Normalize(batch_number.min(), batch_number.max())\n", "sm = ScalarMappable(norm=norm, cmap=cm)\n", "sm.set_array([])\n", "fig.subplots_adjust(right=0.9)\n", "cbar_ax = fig.add_axes([0.93, 0.15, 0.01, 0.7])\n", "cbar = fig.colorbar(sm, cax=cbar_ax)\n", "cbar.ax.set_title(\"Iteration\")" ] }, { "cell_type": "markdown", "metadata": { "code_folding": [], "hidden_ranges": [], "originalKey": "ca12287f-c7b8-4ef8-8eb9-57760eda5fed", "showInput": true }, "source": [ "# Hypervolume statistics\n", "The hypervolume of the space dominated by points that dominate the reference point." ] }, { "cell_type": "markdown", "metadata": { "code_folding": [], "hidden_ranges": [], "originalKey": "ec8b764b-c27d-4722-9e3d-d81cebb3624a" }, "source": [ "#### Plot the results\n", "The plot below shows a common metric of multi-objective optimization performance when the true Pareto frontier is known: the log difference between the hypervolume of the true Pareto front and the hypervolume of the approximate Pareto front identified by each algorithm. The log hypervolume difference is plotted at each step of the optimization for each of the algorithms.\n", "\n", "The plot show that $q$NEHVI vastly outperforms $q$NParEGO which outperforms the Sobol baseline." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "code_folding": [], "hidden_ranges": [], "originalKey": "d50b98bc-5ab1-4826-a5b2-474a13f4bae0" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfEAAAFzCAYAAAAuSjCuAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAABODElEQVR4nO3dd3hUZfrG8e+TSUISOiT03qX3JirYEZUmAqIrrg1XXVl1V9cGYq8r9gVFLKzIT6oo6yqKjSIdBBSQIlV6DSXl/f0xE4yRJEOYyckk9+e6zpWZM2fec8/JwJPT3tecc4iIiEjkifI6gIiIiOSNiriIiEiEUhEXERGJUCriIiIiEUpFXEREJEKpiIuIiESoaK8DnKrExERXq1Ytr2OIiIjkm4ULF+5yziVlnR9xRbxWrVosWLDA6xgiIiL5xsw2nmy+DqeLiIhEKBVxERGRCKUiLiIiEqFUxEVERCJU2Iq4mY0xsx1m9kM2r5c2s4/MbKmZrTCz68KVRUREpDAK5574WODiHF6/FVjpnGsBdAWeM7PYMOYREREpVMJWxJ1zXwN7cloEKGlmBpQILJsarjwiIiKFjZfnxF8GzgC2AsuBO5xz6Sdb0MxuMrMFZrZg586d+ZlRRESkwPKyiF8ELAGqAC2Bl82s1MkWdM6Ncs61dc61TUr6Q4c1IiIiRZKXRfw6YJLzWwusBxp5mEdERCSieFnEfwHOAzCzikBDYJ2HeURERCJK2PpON7P38V91nmhmm4FhQAyAc+514BFgrJktBwy4xzm3K1x5Tmbj1tV8teTD024nLsZH5dLxp9eILxbK1oLY4nluonhMcRqVa0RCTMLpZRERkYgQtiLunBuYy+tbgQvDtf5gzPnhY57Z+X5oGtsammZOV5RFUb9MfZolNaN5YnOaJTajTpk6RJn69RERKWwibhSzUDq7VR+Kx5U+7XZm/7ybxb/s4/IWVehSPzFvjRw/BDtXw67VsOsnOJxxFX4UlK0JiQ0hqSEkNYBydcEX84cm9h7dy/Jdy1m+azmfrv+UD1f7jzKUiClBk8QmJ4p6s6RmJMbnMaeIiBQY5pzzOsMpadu2rStoQ5GmpqVz638W8emKX3myTzMGtK9x+o0e/BW2Loati2DLIv/P5N3+16JioFJTqNIKqrSGqq39Rd73299k6S6dDQc2sHynv6gv27mMNXvXkOr8t+JXLVHVX9ATm9E8qTlnlD+DYr5ip59bRERCzswWOufa/mG+inhoHEtN46Z3FvL1mp280L8lPVtWDe0KnIP9mwIFPVDcty6BYwf8r8ckQMPucMWYbJs4knqEVbtXnSjqy3ctZ9vhbQBER0XTsGxD+tTvw5UNrwxtdhEROS0q4vngaEoag9/6nvkb9vLKVa25uGml8K4wPR32/Owv6j9MhNX/hb+thNLB/wGxM3kny3YtY/nO5Xyz5Rt+3vczn13xGUkJuh9fRKSgyK6I62qnEIqL8fHGte1oXq00t7+/iFk/7QjvCqOiILE+NL8SzvmHf97m70+piaSEJM6rcR5D2wzl+a7Pk+bSmPrz1DCEFRGRUFMRD7ESxaIZe1176lcoyc3vLmTOz7vzZ8WVmkN0PGyan+cmapaqSbtK7Zi0ZhKRdoRGRKQoUhEPg9LxMbx7fXtqlEvg+rfns+iXveFfqS/Gf6HbKe6JZ9Wnfh82HdzEgl8L5ikLERH5jYp4mJQvUYxxN3QgqWQxrh3zPT9s2R/+lVZvB9uWQuqxPDdxfo3zKRlbkolrJoYwmIiIhIOKeBhVKBXHuBs6ULJYNH8a8z1rfj0Y3hVWaw9px/2FPI/iouPoUbsHn234jP3H8uEPDxERyTMV8TCrVjaB/9zYEV+UMeiNeWzYdTh8K6ve3v9z0+kdUu/boC/H04/z8bqPQxBKRETCRUU8H9RKLM64GzqQkpbOoDfmsWXfkfCsqEQFKFMTNs07rWYalWtE4/KNmbhmoi5wExEpwFTE80mDiiV59/oOHDiawqDRc9lx4Gh4VlS9PWye7+8c5jT0rd+X1XtXs3L3yhAFExGRUFMRz0dNq5Zm7HXt2XHwGIPemMeew8dDv5Jq7eHgNti/+bSa6V67O3G+OF3gJiJSgKmI57M2NcvyxrVt+WVPMte8OY/9R1JCu4Lq7fw/T/NWs5KxJbmw1oV8sv4TklOSQxBMRERCTUXcA53rJvL6NW1Y/etBrnvrew4fSw1d4xWbnnanLxn61u/L4ZTD/G/j/0IQTEREQk1F3CPdGlbgpYGtWLp5Pze8vYCjKWmhadgXA1XbnPaeOECrCq2oVaoWk9ZMCkEwEREJtSI9nrjXLm5amef6pfO3CUvoP2oujSqWDE27h2ty1u4PGDZhPqlRpze8aFxKZxYf+A+3TphBiagQj8wmIlJIDb+8CfGxvrCvR0XcY71aVeV4WjovzlzDV/tDc8V6enp1urlUdq6ex7KoM06rLRd1BlSKYvaOGRQ70DMk+URECrvU9HRARbxIuLJtda5sWz10DR5qDs8+yahu6XDmeafd3J2zvmbB9gXMvOU5YnwxIQgoIiKhoHPihVGJJChb+7Q7fcnQp34f9h7by5ebvgxJeyIiEhoq4oVViDp9AehUuROVilfSBW4iIgWMinhhVa0dHPoV9v1y2k35onz0rteb2Vtns/XQ1hCEExGRUFARL6wyBkPZfPr3iwP0qtcLgClrp4SkPREROX0q4oVVhSYQU/y0RzTLUKVEFTpX6czktZNJSw/RPe0iInJaVMQLK180VG0dkk5fMvSp34fth7czZ9uckLUpIiJ5pyJemFVvD9uXQ0pohj7tVr0bZYuV1QVuIiIFhIp4YVatPaSnwtbFIWkuxhfD5XUv58tfvmT3kd0haVNERPJORbwwqxYY0SxE58XBf0g91aXy0c8fhaxNERHJGxXxwqx4eShXN6RFvE6ZOrSq0IqJaybiQnAPuoiI5J2KeGFXvb3/4rYQFtw+9fuw4cAGFu8IzWF6ERHJGxXxwq5aOzi8E/ZuCFmTF9a8kOIxxZm4ZmLI2hQRkVOnIl7YhbjTF4CEmAS61+7O/zb8j4PHD4asXREROTVhK+JmNsbMdpjZDzks09XMlpjZCjP7KlxZirQKjSG2REjPiwP0rd+Xo2lHmbF+RkjbFRGR4IVzT3wscHF2L5pZGeBV4HLnXBOgXxizFF1RvpB3+gLQpHwTGpRtoEPqIiIeClsRd859DezJYZGrgEnOuV8Cy+8IV5Yir3oH2P4DHD8csibNjD71+7By90pW7V4VsnZFRCR4Xp4TbwCUNbNZZrbQzP6U3YJmdpOZLTCzBTt37szHiIVEtfbg0kLW6UuGS+tcSmxUrHpwExHxiJdFPBpoA/QALgIeNLMGJ1vQOTfKOdfWOdc2KSkpPzMWDtXa+n+G+Lx46WKlOb/m+Xy87mOOph4NadsiIpI7L4v4ZuBT59xh59wu4GughYd5Cq+EclC+fsiLOPgvcDuYcpDPNn4W8rZFRCRnXhbxqUAXM4s2swSgA6CTq+EShk5fANpWakv1ktV1SF1ExAPhvMXsfWAO0NDMNpvZ9WY2xMyGADjnVgH/BZYB3wNvOOeyvR1NTlO1dpC8G/asC2mzURZFn/p9WPDrAjYe2BjStkVEJGfhvDp9oHOusnMuxjlXzTn3pnPudefc65mWecY519g519Q590K4sghh6fQlw+V1L8dnPiavmRzytkVEJHvqsa2oSGoEsSXDcl68QkIFzqp2FlN/nkpKekrI2xcRkZNTES8qonxQrU3IO33J0Ld+X3Yd2cU3m78JS/siIvJHKuJFSbX28OsKOHYo5E13qdqFpPgkXeAmIpKPVMSLkuodwKXD1kUhbzo6Kppe9XrxzZZv+PXwryFvX0RE/khFvCip1sb/MwznxQF61+tNuktn6s9Tw9K+iIj8nop4URJfFhIbhq2IVy9VnU6VOzHmhzHqT11EJB+oiBc11dv5bzMLcacvGUacOYJSsaUY8vkQfjnwS1jWISIifiriRU219nBkD+z+OSzNVypeiX9f8G+cc9z02U3sTNaANSIi4aIiXtSc6PQlPIfUAWqXrs2r57/KnqN7uPnzm9l/bH/Y1iUiUpSpiBc1iQ2hWOmwnRfP0DSxKSO7jWTD/g3c/sXtHEk9Etb1iYgURSriRU1UVKDTl9B3v5pVpyqdePKsJ1myYwl3f3W3enMTEQkxFfGiqFp72LESjh0M+6ourHUhD3R8gK83f82w74aR7tLDvk4RkaIi2usA4oHq7f2dvmxZCHW6hn11Vza8kr1H9/LykpcpE1eGv7f9O2YW9vWKiBR2KuJFUbW2gMGm+flSxAFuan4Te47u4d2V71Iurhw3NLshX9YrIlKYqYgXRXGl/aOabZqXb6s0M+5pfw/7ju1j5KKRlC1Wlr4N+ubb+kVECiMV8aKqejtYOQ3S0/0Xu+WDKIvi0TMfZf/x/YyYO4LSxUpzfs3z82XdIiKFkS5sK6qqtYej+2D32nxdbYwvhufPeZ5mic34x9f/4Ptt4b3VTUSkMFMRL6ryodOX7CTEJPDKea9Qo2QN/vrlX1m5e2W+ZxARKQxUxIuq8vX958bD3OlLdkoXK83rF7xOqdhS3PL5LWw8sNGTHCIikUxFvKiKioJq7fKl05fsVCpeiVEXjMI5x82f3cyO5B2eZRERiUQq4kVZtfawYxUc9a5v81qla/HaBa+x9+hebv5M/ayLiJwKFfGirHo7wPk7ffFQk/JNePHcF9l4YCO3zbxN/ayLiARJRbwoq5qp0xePdajcgSfPepKlO5dy56w71c+6iEgQVMSLsrhSUKFxvnb6kpMLa13Ig50e5Nst3/L8gue9jiMiUuCpiBd11dvB5gX+Tl8KgH4N+tGrXi8+XP2hzo+LiORCRbyoq9Yeju2HXau9TnLCoDMGcTTtKFPXTvU6iohIgaYiXtR52OlLdhqVa0TLpJZMWD1BQ5eKiORARbyoK18P4st61ulLdgY0GsDGAxuZu3Wu11FERAosFfGizszzTl9O5oKaF1AurhzjfxrvdRQRkQJLRVz858V3/ghH9nmd5IRYXyx96/flq81fse3QNq/jiIgUSCriEuj0BdiywNscWVzR4AoA/m/1/3mcRESkYFIRF6jaBiyqQHT6klmVElU4u9rZTFwzkeNpx72OIyJS4IStiJvZGDPbYWY/5LJcOzNLNbMrwpVFclGsJFRoUmA6fclsYMOB7Dm6h882fuZ1FBGRAiece+JjgYtzWsDMfMBTwP/CmEOCUb2dvw/1AtLpS4aOVTpSo2QNPvjpA6+jiIgUOGEr4s65r4E9uSx2OzAR0BiUXqvWHo4d8F/gVoBEWRT9G/Zn8Y7F/LTnJ6/jiIgUKJ6dEzezqkBv4LUglr3JzBaY2YKdO3eGP1xRVAA7fcnQs15P4nxxut1MRCQLLy9sewG4x7ncu+Ryzo1yzrV1zrVNSkoKf7KiqFwdSCjv7/QlPc37ybkT0UoXK0332t35eN3HHDx+0MONJCJSsEQHs5CZxQM1nHOhPJ7ZFhhvZgCJwCVmluqcmxLCdUiwzPyH1JeM80+eM4iJh+g4iElgQLFYJpdIZdp7FzHIl+h/LSYeouMhxr+Mf9n436aooL7eOUSIgnoXQMmKoflIIiIhluv/cmZ2GfAsEAvUNrOWwAjn3OWns2LnXO1M6xgLTFcB99j5w6Fqa69T+KWlQEoypB6FlCM0TjlC8yMrGO87ylWpR7Eje0+8dmJKPQKh7mu9REW48h2o0TG07YqIhEAwuyrDgfbALADn3BIzq53TGwDM7H2gK5BoZpuBYUBMoI3X8xZXwqpCI/9UQPX/eRr3f3s/8y4ZQcfKJymqzkHa8d+Kuks7vRUe3A6TboSxl8IlT0PbP59eeyIiIRZMEU9xzu0PHPbO4LJb+MQCzg0MNoRzbnCwy0rRdVGti3hm/jN88OMHJy/iZhBdzD/Flzn9FZauBjd+ARNvhOl/g21LofvT/vZFRAqAYC5sW2FmVwE+M6tvZi8Bs8OcS+QPivmK0bt+b77c9CXbD2/Pn5XGl4WrPoAud8LCsf698oP5tG4RkVwEU8RvB5oAx4D/APuBoWHMJJKtKxtcSbpL58PVH+bfSqN8cP4w6DcWfv0B/n1OgeuiVkSKplyLuHMu2Tl3v3OuXWB6wDl3ND/CiWRVrWQ1zqp2FhPXTCQlLSV/V96kN9zwuf9q+LGXwKJ38nf9IiJZ5FrEzewzMyuT6XlZM/s0rKlEctC/YX92HdnFzF9m5v/KKzaBG7+EWl1g2u3w8V2QqsFZRMQbwRxOT3TO7ct44pzbC1QIWyKRXHSp2oWqJap614NbQjkY9CGceQfMfwPeuRwOqedgEcl/wRTxdDOrkfHEzGoSxNXpIuGS0Z/6wl8XsmbvGo9C+OCCEdD3Tdi6xH+efMtCb7KISJEVTBG/H/jWzN41s/eAr4F/hjeWSM561+tNbFSs96ObNbsCrv8f+KJhTHdYXBB6uxORoiKYC9v+C7QGPgDGA22cczonLp4qE1eGi2tfzEc/f8Sh44e8DVO5Odw4C2p0gKl/gRn3+HucExEJs2AHQCmGf1jRA0BjMzs7fJFEgjOg4QCSU5P5aN1HXkeB4uXh6snQ6TaY9zq80wsO7/I6lYgUcsFcnf4U8B3+w+p/D0x3hzmXSK6aJTWjSfkmfPDjBzhXAC7T8EXDRY9Bn9GwZQGM6uo/Xy4iEibBdLvaC2jonDsW5iwip6x/w/48NPshFvy6gHaV2nkdx6/5lZDYAD64GsZc5B+rvUTFwFQhy8+KEF8OorwcFVhEIlUwRXwd/oFLVMSlwOleuzvPLniW8T+OLzhFHKBKS7hpFnw+DHatgc3z4eCv/pHWsjJfoKifpMCXqADFkyAqJr8/QcFVtiaUrOR1CpECIZgingwsMbOZZCrkzrm/hi2VSJDiouPoXa8341aNY0fyDiokFKAuDIonQs9XfnvuHBw/5L+n/NCvgekkj7f/AId3QHqqd9kLstgS8Jc5UKZG7suKFHLBFPFpgUmkQLqy4ZW8vfJtJq6eyC0tb/E6TvbMoFhJ/1S+bs7LpqfDkb3+on54B6Sf5rCqhUXKEf/wsJ/eB/3f8zqNiOdyLeLOubfNLB6o4Zz7KR8yiZySGqVqcGbVM/lw9Yfc0PwGYgrDoeeoKP8V78XLA429TlOwnHUXfPEIrJ0J9c7zOo2Ip4K5Ov0yYAnw38DzlmamPXMpUAY0HMCOIzv48pcvvY4i4db5dihXB2b8Q/3WS5EXzCWxw4H2wD4A59wSoE7YEonkwVlVz6JK8Sre9+Am4RddDLo/DbvXwtxXcl9epBALpoinOOf2Z5mXHo4wInnli/LRr2E/vt/+Pev2rfM6joRb/QugYQ/46hnYv8XrNCKeCaaIrzCzqwCfmdU3s5eA2WHOJXLK+tTvQ0xUjHejm0n+uvgJcGnwv/u9TiLimWCK+O1AE/y3l/0H2A8MDWMmkTwpF1eOi2pdxLSfp5Gckux1HAm3sjWhy52wYjKsm+V1GhFP5FjEzcwHfOycu9851y4wPeCcO5pP+UROSf+G/Tmccpjp66Z7HUXyw5l3QNla8IkucpOiKcci7pxLwz+eeOl8yiNyWloktaBRuUaM/2l8wehPXcIrJg4ufhJ2/eQfeEakiAnmcPohYLmZvWlmL2ZM4Q4mkhdmxoCGA1izdw2Ldyz2Oo7kh4bdof5F8NVTcGCb12lE8lUwRXwS8CDwNbAw0yRSIHWv3Z2SsSV5belr2hsvKro/6R/D/bMHvU4ikq9yLeLOubeBCcBc59zbGVP4o4nkTUJMAne0uoO52+YyZe0Ur+NIfihXx39+fPn/wYZvvU4jkm/UY5sUSv0a9qNNxTY8M/8ZdiTv8DqO5Icuf4PSNeCTv/v3ykWKAPXYJoVSlEUxovMIUtJTeGTuIzqsXhTEJvjvHd+xEr4f7XUakXyhHtuk0KpRqga3tbqNWZtm8d8N//U6juSHRj2g7nkw6wn/+O0ihZx6bJNC7eozrqZZYjOemPcEe47u8TqOhJuZv1/1lCPw2UNepxEJO/XYJoWaL8rHiM4jOJhykCfnPel1HMkPifX8I50tGw8b53idRiSssi3iZvZu4OGN6rFNIlm9svW4ufnNzNgwgy9++cLrOJIfzr4bSlWDT+6GtFSv04iETU574m3MrArwZzMra2blMk/5FVAkFK5vdj0Nyzbk0bmPcuD4Aa/jSLjFFoeLHoNff4AFY7xOIxI2ORXx14GZQCN+38nLQmBBbg2b2Rgz22FmP2Tz+iAzW2Zmy81stpm1OPX4IsGJiYphxJkj2HN0D8/Of9brOJIfGveEOl3hi0fh0E6v04iERU5F/CPn3BnAGOdcHedc7UxTMLeYjQUuzuH19cA5zrlmwCPAqKBTi+RB4/KNua7pdUxeO5nZW3RtZqFnBt2fgZTD8Plwr9OIhEVORfzDwM8GeWnYOfc1kO3lwM652c65vYGnc4FqeVmPyKkY0mIItUvX5uE5D2u40qIgqQF0/AsseQ82fe91GpGQy6mIR5nZfUADM7sz6xTiHNcDM0LcpsgfFPMVY0TnEWw7vI0XFr3gdRzJD+f8A0pW9l/klp7mdRqRkMqpiA8A0oBooORJppAws274i/g9OSxzk5ktMLMFO3fq3JacnpYVWjLojEG8/+P7LPxVY/kUesVKwoWPwralsPAtr9OIhJTl1h2lmXV3zuVpL9nMagHTnXNNs3m9OTAZ6O6cWx1Mm23btnULFuR6XZ1IjpJTkukzrQ/RUdF8eNmHxEXHeR1Jwsk5ePsy2L4cbl8Exct7nUjklJjZQudc26zzc7pP/OrAw8bhOJxuZjXwD3N6TbAFXCRUEmISGN55OBsPbOTVJa96HUfCzQwueQaOH4KZD3udRiRkcjqcXjzwswR5OJxuZu8Dc4CGZrbZzK43syFmNiSwyENAeeBVM1tiZtq9lnzVsXJH+tbvy9sr3+aHXSe9E1IKkwpnQIchsOgd2KLTKFI45Ho4vaDR4XQJpYPHD9Jrai9KxZZiwqUTiPHFeB1JwunoAXi5LRw7CLElvE4joZZQDq58B5Iaep0k5LI7nB6dy5u6Abfh7/AFYBXwsnNuVsgTinigZGxJhnUaxq0zb2X08tH8peVfvI4k4RRXCga877/lLMJ2YCQIP0yCT++Hqz/MfdlCItsibmY9gJeBEYHJgNbAGDO7zTn3Sf5EFAmvs6udTY86PRi9bDTn1TiPhuUK31/xkkm1Nv5JCp/ydeF/D8DPX0Ddc71Oky9yOif+d6CXc+4t59xS59wS59wYoBc53A4mEonubXcvpYqV4qHZD5GargEzRCJS+5ugTA3434NFpk+AnIp4Jefc0qwznXPLgIrhiySS/8rEleG+DvexcvdK3ln5jtdxRCQvoovB+cP9A98sHe91mnyRUxE/nMfXRCLShTUv5Pwa5/PK4ldYv3+913FEJC+a9IGqbeCLR+B44e9aOaciXtfMpp1k+ggIZgAUkYhiZtzf8X7iouMYNnsY6S7d60gicqrM/D30HdwGc1/xOk3Y5XR1es8cXtNYjlIoJcYnck/7e7j/2/t5/8f3GXTGIK8jicipqtkZGl0K374Ara+FEhW8ThQ22e6JO+e+ymnKz5Ai+emyOpdxZtUzGbloJJsPbvY6jojkxfkPQ+pRmPWE10nCKqfD6SJFkpkxrOMwoiyKh+c8TKR1iCQiQGI9aHs9LHwbdvzodZqwUREXOYnKJSpzZ5s7mbttLpPXTvY6jojkxTn3QGxx+HyY10nCJugibmYJ4QwiUtBc0eAK2lZsy7Pzn2VH8g6v44jIqSpeHs66E1b/F9Z/7XWasMi1iJtZZzNbCfwYeN7CzDTskxR6URbFw50fJiU9hUfmPKLD6iKRqMMQKF3d35NbeuG74ySYPfF/ARcBuwECHcCcHc5QIgVFjVI1uK3VbczaPIsZ62d4HUdETlVMPJz3EGxbCsv/z+s0IRfU4XTn3KYss4pGf3YiwNVnXE3zxOY8+f2T7Dm6x+s4InKqml4BlVvCzBGQcsTrNCEVTBHfZGadAWdmMWZ2N/7RzESKBF+Uj4c7P8yhlEM8Oe9Jr+OIyKmKivJ3AHNgM8x9zes0IRVMER8C3ApUBbYALQPPRYqMemXrcXPzm5mxYQZf/PKF13FE5FTVPgsadIdvnofDu7xOEzK5FnHn3C7n3CDnXEXnXAXn3NXOud35EU6kIPlzsz/TsGxDHp37KAeOH/A6joicqgsehpRkmFV4jqgFc3V6bTN73swmZe5DPT/CiRQkMVExjDhzBHuO7uHZ+ep5WCTiJDWENoNh4Vuwa43XaUIimMPpU4ANwEvAc5kmkSKncfnGXNf0OiavnczsLbO9jiMip6rrPyE6Hj4f7nWSkAimiB91zr3onPtSfaeLwJAWQ6hdujYPz3mYwykalVckopRIgi53wI/TYcN3Xqc5bcEU8ZFmNszMOplZ64wp7MlECqhivmKM6DyCbYe38cLCF7yOIyKnquOtULJKoegAJpgi3gy4EXiS3w6l64SgFGktK7Rk0BmDGP/TeBb+utDrOCJyKmIT4LwHYesiWDHJ6zSnxXLrStLM1gKNnXPH8ydSztq2besWLFjgdQwRklOS6TOtD9FR0Xx42YfERcd5HUlEgpWeBqPOgSP74bb5EFOw//2a2ULnXNus84PZE/8BKBPyRCIRLiEmgeGdh7PxwEZeXaLhBEQiSpTP3wHM/l/g+1Fep8mzYIp4GeBHM/tUt5iJ/F7Hyh3pW78vb698mx92/eB1HBE5FXW6Qr0L4OtnITkyu1QO5nD6OSeb79UV6jqcLgXNweMH6TW1F6ViSzHh0gnE+GK8jiQiwfp1Jbx+JrS/GboX3E5g8nw4PfNtZbrFTOSPSsaWZFinYazdt5bRy0d7HUdETkXFxtDqGpg/Gnb/7HWaUxZMj20HzexAYDpqZmlmpj4nRTI5u9rZ9KjTg9HLRrN672qv44jIqeh2H/iKRWQHMMHsiZd0zpVyzpUC4oG+gK7iEcni3nb3UqpYKR767iFS01O9jiMiwSpZCc68A1ZNg1/mep3mlOR6TvykbzJb7JxrFYY8udI5cSnIPt3wKXd/dTd/a/M3/tz0z17HEZFgHT8ML7aGkhWh3Q2n317zARAde/rtBGR3Tjw6iDf2yfQ0CmgLHA1ZMpFC5MKaF3JejfN4dcmrnFv9XGqVruV1JBEJRmxxuPARmHwzTLv99Ntr3DOkRTw7wVyd/lamp6n4B0MZ7ZzbEcZc2dKeuBR0O5N30mtqL+qVqcdbF79FlAVzJ6eIFAiHd0FqCPZTS1aBqND928/znrhz7ro8rnAMcCmwwznX9CSvGzASuARIBgY75xblZV0iBUlSQhL/aPcPHvjuAcb/OJ6rzrjK60giEqziiV4nOCXZFnEzewnIdjfdOffXXNoeC7wMvJPN692B+oGpA/Ba4KdIxLu87uXM2DCDFxa9wJlVz6RmqZpeRxKRQiinff0FwMIcphw5574GcuoCpyfwjvObC5Qxs8rBBhcpyMyMYR2HERMVw6BPBvHtlm+9jiQihVC2e+LOubczPzezEoH5h0K07qrApkzPNwfmbQtR+yKeqlyiMuN7jOdvs/7GXz7/C7e0vIWbm9+sc+QiEjLBdPbS1MwWAyuAlWa20MyahD/a7zLcZGYLzGzBzp0783PVIqeleqnqvHvJu1xa51JeXfIqt828jf3H9nsdS0QKiWB2CUYBdzrnajrnagB3AaHoW3ILUD3T82qBeX/gnBvlnGvrnGublJQUglWL5J/46Hge6/IYD3R4gDnb5tB/en9W7V7ldSwRKQSCKeLFnXNfZjxxzs0Ciodg3dOAP5lfR2C/c06H0qVQMjP6N+rP2IvHkpqeyjUzrmHq2qlexxKRCBdMEV9nZg+aWa3A9ACwLrc3mdn7wBygoZltNrPrzWyImQ0JLPJJoJ21+Pfs/5LHzyASMVokteCDSz+gZVJLHvjuAUbMGcHxtONexxKRCBVMZy9lgYeBLoFZXwMPO+f2hjnbSamzFykMUtNTeXnxy7z5w5s0Ld+U57s+T+USujlDRE4uz0ORArWdc391zrUOTEO9KuAihUV0VDRD2wzlha4vsP7AevpP78+crXO8jiUiESaYIv6cma0ys0fM7A89r4lI3p1X8zzG9xhP+fjyDPl8CG8sf4N0l+51LBGJEMEMRdoN6AbsBP5tZssD58VFJARqla7FuEvGcVHNixi5aCR3fHkHB44f8DqWiESAoHqdcM5td869CAwBlgAPhTOUSFGTEJPAU2c/xb3t7+Xbzd8ycPpAVu9d7XUsESngguns5QwzG25my4GXgNn47+kWkRAyMwadMYgxF4/hSOoRBn08iOnrpnsdS0QKsGD2xMcAe4GLnHNdnXOveTUMqUhR0KpCKyZcNoEmiU345zf/5PF5j5OSnuJ1LBEpgHIs4mbmA9Y750Y657bmUyaRIi8xPpHRF47mT43/xPs/vs89X9+jQi4if5DjeOLOuTQzq25msc459Ughko9iomL4e7u/U7l4ZZ6a/xS+b3w8cdYTREfl+M9WRIqQYP43WA98Z2bTgMMZM51zz4ctlYiccHXjq0lNT+W5hc8RZVE83uVxfFE+r2OJSAEQTBH/OTBFASXDG0dETmZw08GkulRGLhpJdFQ0j5z5iIY0FZHci7hz7mEAM0twziWHP5KInMwNzW4gJT2FV5e8SnRUNMM6DVMhFynici3iZtYJeBMoAdQwsxbAzc45DVgiks9uaXELaelp/HvZv/GZjwc7PoiZeR1LRDwSzOH0F4CL8A8dinNuqZmdHc5QIpK9W1veSmp6Km/+8CY+83Ffh/tUyEWKqKAuc3XObcryn0RaeOKISG7MjDta30Fqeipvr3yb6Kho/tHuHyrkIkVQMEV8k5l1BpyZxQB3AKvCG0tEcmJm3NX2LtJcGu+teo+YqBj+1uZvKuQiRUwwRXwIMBKoCmwFPgVuDWcoEcmdmfGPdv8gJT2Ft1a8hS/Kx19b/VWFXKQICebq9F3AoHzIIiKnyMy4r8N9pLk03lj+BtFR0dzaUn9jixQVwVydXgf/nnhHwAFzgL8559aFOZuIBCHKoniw44Okpafx+tLXibZobm5xs9exRCQfBHM4/T/AK0DvwPMBwPtAh3CFEpFTE2VRDOs0jNT0VF5e8jLRUdFc3+x6r2OJSJgFU8QTnHPvZnr+npn9PVyBRCRvfFE+HjnzEVJdKi8seoHoqGiubXKt17FEJIyCKeIzzOxeYDz+w+n9gU/MrByAc25PGPOJyCnwRfl4vMvjpKWn8eyCZ4mOimbQGbqkRaSwCqaIXxn4mfUk2wD8Rb1OSBOJyGmJjormybOfJP2rdJ78/kl85mNAowFexxKRMAjm6vTa+RFEREInJiqGp89+mjtn3clj8x7DF+WjX4N+XscSkRDLdfQEM1toZn8xszL5kEdEQiTGF8NzXZ/jrKpnMWLOCD7b+JnXkUQkxIIZAqk//o5eFpjZeDO7yNSbhEhEiPXF8q9u/6JFUgvu//Z+ftrzk9eRRCSEci3izrm1zrn7gQb4bzcbA2w0s4czLm4TkYKrmK8YL3R7gVKxpbj9i9vZfWS315FEJESCGozYzJoDzwHPABOBfsAB4IvwRRORUEmMT2TkuSPZc3QPd866k5S0FK8jiUgIBHVOHPgXMB9o7pz7q3NunnPuOUC9tolEiCblm/DImY+waMciHpv3GM45ryOJyGkK5hazftl1seqc6xPiPCISRt1rd2fN3jWMXj6aBmUbcNUZV3kdSUROQ7ZF3MzuzPT4D687554PUyYRCaPbWt3G2n1reXr+09QpU4eOlTt6HUlE8iinw+klM013Z3leMvzRRCQcoiyKJ856gtqla3PXrLvYdGCT15FEJI8smPNiZrbYOdcqH/Lkqm3btm7BggVexxCJeJsObmLgxwNJjEvkvUveo0RsCa8jiUg2zGyhc65t1vlBXZ2Ov3tVESlEqpeszvPnPM+GAxu495t7SUtP8zqSiJyiYIt4npjZxWb2k5mtDQyikvX1Gmb2pZktNrNlZnZJOPOIyO+1r9yee9vfy1ebv+KlxS95HUdETlFOF7Yt57c98HpmtizjJcA555rn1LCZ+fCPQ34BsBmYb2bTnHMrMy32ADDBOfeamTUGPgFq5emTiEieDGg0gDV71/DmD29Sv2x9etTp4XUkEQlSTreYXXqabbcH1mbcnmZm44GeQOYi7oBSgcelga2nuU4RyYN729/Luv3rGDZ7GDVL1aRpYlOvI4lIELI9nO6c25jTFETbVYHMl71uDszLbDhwtZltxr8XfvvJGjKzm8xsgZkt2LlzZxCrFpFTkTFYSmJ8Ind8cQc7knd4HUlEghDWc+JBGAiMdc5VAy4B3jWzP2Ryzo1yzrV1zrVNSkrK95AiRUG5uHKM7DaSgykHGfrlUI6lHfM6kojkIpxFfAtQPdPzaoF5mV0PTABwzs0B4oDEMGYSkRw0LNeQJ7o8wfJdy3l49sPqmlWkgAtnEZ8P1Dez2mYWCwwApmVZ5hfgPAAzOwN/EdfxchEPnVfzPG5reRsfrfuIt1e87XUcEclBrn2nZ7lKPcN+YAHwqHPupOMaOudSzew24FPAB4xxzq0wsxHAAufcNOAuYLSZ/S2wjsFOf/qLeO6m5jexeu9qnl/4PHXK1OHsamd7HUlETiLXHtvM7GkgDf9Y4uDfo04AtgNdnHOXhTVhFuqxTSR/JKckM/i/g9l0cBPjLhlHnTJ1vI4kUmSdTo9t5zvn/umcWx6Y7gfOcc49he7pFim0EmISGNltJLG+WG7/4nb2H9vvdSQRySKYIu4zs/YZT8ysHf7D4wCpYUklIgVC5RKVeaHbC2w9vJW7v7pbV6yLFDDBFPEbgDfNbL2ZbQDeBG4ws+LAE+EMJyLea1WhFQ91fIi52+bypxl/YsuhrDeZiIhXci3izrn5zrlmQEughXOueWDeYefchLAnFBHP9a7fmxe7vcimA5voP70/32751utIIkIQRdzMSpvZ88BMYKaZPWdmpcMfTUQKkm41ujH+0vFUSKjAXz7/C68tfY10l+51LJEiLZjD6WOAg8CVgekA8FY4Q4lIwVSjVA3GXTKOHnV68OqSV7lt5m264E3EQ8EU8brOuWHOuXWB6WFA95qIFFHx0fE83uVx7u9wP3O2zaH/9P6s2r3K61giRVIwRfyImXXJeGJmZwJHwhdJRAo6M2NAowGMvXgsqempXP3J1UxeM9nrWCJFTjBFfAjwipltCFyd/jJwc1hTiUhEaJHUggmXTfBfwT77IYbPHq7b0ETyUTBXpy91zrUAmgPNnXOtgHPDnkxEIkK5uHK8fsHrXN/0eiaumcifZvyJrYe2eh1LpEgIegAU59wB59yBwNM7w5RHRCJQdFQ0Q9sMZWS3kfxy4BeunH4ls7fM9jqWSKGX11HMLKQpRKRQOLfGuYy/dDxJ8UkM+XwIry99XbehiYRRXou4RhoTkZOqWaom4y4ZxyV1LuGVJa+o33WRMMq2iJvZQTM7cJLpIFAlHzOKSIRJiEngiS5PcF+H+5i9dTYDpg/gxz0/eh1LpNDJtog750o650qdZCrpnMt1HHIRKdrMjIGNBvLWRW9xPP04V39yNZPWTNLhdZEQyuvhdBGRoLSs0JIJl06gRVILhs0eRt9pfflk3Sekpad5HU0k4qmIi0jYlY8vz6gLRvHkWU/inOOeb+6h59SeTF4zmZT0FK/jiUQscy6yrlFr27atW7BggdcxRCSP0l06X/zyBaOWjWLVnlVULl6ZPzf9M73r96aYr5jX8UQKJDNb6Jxr+4f5KuIi4gXnHN9s+YZRy0axdOdSEuMTGdxkMP0a9CMhJsHreCIFioq4iBRIzjnmb5/PqGWjmLd9HmWKleGaxtcwoNEASsWW8jqeSIGgIi4iBd6SHUsYvXw0X2/+mhIxJRjYaCDXNL6GsnFlvY4m4ikVcRGJGKt2r2L08tF8vvFz4qLj6NegH4ObDCYpIcnraCKeUBEXkYizbt863lj+Bp+s/wSf+ehdvzfXNrmW6iWrex1NJF+piItIxNp0YBNv/vAmU3+eSmp6KrVL16ZT5U50rNyRdpXaUSK2hNcRRcJKRVxEIt72w9v5dMOnzNk2h4XbF3I07Sg+89EssRmdqviLerOkZsRExXgdVSSkVMRFpFA5nnacpTuXMmfrHOZum8uK3StId+kkRCfQrlI7OlXpRKfKnahdujZmGnhRIpuKuIgUavuP7Wf+9vknivovB38BoEJCBTpW7kjHyh3pVKUTifGJHicVOXUq4iJSpGw5tIW5W+cyZ9sc5m2bx75j+wCoV6Ye59Y4l0FnDKJcXDlvQ4oESUVcRIqsdJfOj3t+ZM7WOczZNofvt31PXHQcAxoO4Nom11I+vrzXEUVypCIuIhKwbt86Ri0fxYz1MyjmK8aVDa5kcNPBOtQuBZaKuIhIFuv3r2f0stF8vP5jYqNi6dewH9c1uU6dykiBoyIuIpKNjQc2MmrZKD5e9zHRUdFc0eAK/tz0z1RIqOB1NBEg+yIe1vHEzexiM/vJzNaa2b3ZLHOlma00sxVm9p9w5hEROZmapWryWJfH+KjXR1xS+xLG/zie7hO78/i8x9l+eLvX8USyFbY9cTPzAauBC4DNwHxgoHNuZaZl6gMTgHOdc3vNrIJzbkdO7WpPXETCbfPBzbyx/A2mrp2KmdGnfh9uaHYDlYpX8jqaFFFe7Im3B9Y659Y5544D44GeWZa5EXjFObcXILcCLiKSH6qVrMbwzsOZ3mc6Pev1ZOKaiXSf1J0Rc0aw9dBWr+OJnBDOIl4V2JTp+ebAvMwaAA3M7Dszm2tmF5+sITO7ycwWmNmCnTt3himuiMjvVS1RlWGdhvFJ70/oU68Pk9dOpsfkHgyfPZwth7Z4HU8kvOfEgxAN1Ae6AgOB0WZWJutCzrlRzrm2zrm2SUm6alRE8lflEpV5sNODzOgzgyvqX8G0n6dx6eRLeXze4+w6ssvreFKEhbOIbwEyjxdYLTAvs83ANOdcinNuPf5z6PXDmElEJM8qFa/E/R3v55M+n9C7Xm8m/DSBSyZdwshFI9l/bL/X8aQICmcRnw/UN7PaZhYLDACmZVlmCv69cMwsEf/h9XVhzCQictoqFa/EQ50eYlqvaXSt3pU3lr9B90ndeWP5GySnJHsdT4qQsBVx51wqcBvwKbAKmOCcW2FmI8zs8sBinwK7zWwl8CXwd+fc7nBlEhEJpRqlavD02U/z4WUf0rpCa0YuGsklky7h/R/fJyUtxet4UgSosxcRkRBZvGMxIxeNZOGvC6laoip/afkXetTugS/K53U0iXCedPYiIlKUtKrQircueovXz3+dUrGluP/b++k7rS8zN84k0naYJDKoiIuIhJCZcWbVMxl/6XieO+c50lwaQ2cN5aqPr2LO1jlex5NCRkVcRCQMoiyKC2tdyOSekxnReQS7ju7ips9u4oZPb2DZzmVex5NCQkVcRCSMoqOi6V2/Nx/3/ph72t3Dmn1rGPTJIO744g7W7l3rdTyJcLqwTUQkHx1OOcx7K99j7IqxJKcm06d+H25teavGMpcc6cI2EZECoHhMcW5ucTMz+szgqkZXMWXNFHpM6sG/l/6bI6lHvI4nEUZFXETEA2XiynBP+3uY0msKnat05uUlL3PppEuZsnYKaelpXseTCKEiLiLioZqlavKvbv/i7YvfpmLxijz43YP0n95fV7JLUFTERUQKgNYVWzPuknE8c/YzHEo5xE2f3cQtn9/Cmr1rvI4mBZiKuIhIAWFmXFz7Yqb1msZdbe5i6Y6lXPHRFQyfPVyjpclJqYiLiBQwsb5YBjcdzCd9PuGqRlcxde1ULpl0Ca8tfU0DrMjv6BYzEZECbuOBjYxcNJLPNn5GhfgK3NbqNi6ve7lnfbKnpKSwefNmjh496sn6C7O4uDiqVatGTEzM7+Znd4uZiriISIRYvGMxz85/lmW7ltGgbAPuansXnat0zvcc69evp2TJkpQvXx4zy/f1F1bOOXbv3s3BgwepXbv2717TfeIiIhGuVYVWvHfJezxz9jMcTjnMzZ/dzB1f3MHO5J35muPo0aMq4GFgZpQvX/6UjnCoiIuIRJDMF78NbT2U77Z+R6+pvZj287R8HSlNBTw8TnW7qoiLiESgWF8s1ze7ng8v+5C6Zepy/7f3c+vMW9l+eLvX0fLNY489RpMmTWjevDktW7Zk3rx52S5bq1Ytdu0K/gr/4cOH8+yzz4YiZlipiIuIRLBapWvx1kVvcU+7e5i/fT69p/Zm0ppJhX788jlz5jB9+nQWLVrEsmXL+Pzzz6levbrXsfKdiriISITzRfm4uvHVTLp8Eo3KNWLY7GHc/NnNbD201etoYbNt2zYSExMpVqwYAImJiVSpUoWZM2fSqlUrmjVrxp///GeOHTt24j1PP/00zZo1o3379qxd6x9BbsOGDZx77rk0b96c8847j19++cWTz5NX0V4HEBGR0KheqjpvXvQmE36awPMLn6f31N7c2eZO+jXsR5SFZ5/t4Y9WsHLrgZC22bhKKYZd1iTHZS688EJGjBhBgwYNOP/88+nfvz8dOnRg8ODBzJw5kwYNGvCnP/2J1157jaFDhwJQunRpli9fzjvvvMPQoUOZPn06t99+O9deey3XXnstY8aM4a9//StTpkwJ6ecJJ+2Ji4gUIlEWxYBGA5jcczLNk5rz6LxHueF/N7Dp4Cavo4VUiRIlWLhwIaNGjSIpKYn+/fvz73//m9q1a9OgQQMArr32Wr7++usT7xk4cOCJn3Pm+PumnzNnDldddRUA11xzDd9++20+f5LToz1xEZFCqGqJqoy6YBST1kzi2QXP0ndaX+5ofQcDGw0M6V55bnvM4eTz+ejatStdu3alWbNmvPLKKzkun/nK78Jydb32xEVECikzo2+DvkzuOZk2Fdvw5PdPMvi/g9mwf4PX0U7bTz/9xJo1vw0Os2TJEurWrcuGDRtOnO9+9913Oeecc04s88EHH5z42alTJwA6d+7M+PHjARg3bhxnnXVWfn2EkNCeuIhIIVepeCVePe9Vpv08jafmP8UVH13BbS1v45rG13jWdevpOnToELfffjv79u0jOjqaevXqMWrUKAYOHEi/fv1ITU2lXbt2DBky5MR79u7dS/PmzSlWrBjvv/8+AC+99BLXXXcdzzzzDElJSbz11ltefaQ8UberIiJFyI7kHTwy9xFmbZpF88TmjDhzBHXL1D2lNlatWsUZZ5wRnoBy0u2rbldFRIQKCRV4sduLPHnWk2w8uJF+H/XjrR/eIt2lex1N8kBFXESkiDEzetTpwZSeUzi72tk8v/B5bvzfjUWqt7fCQkVcRKSISoxP5F9d/8WIziNYvms5fab14b/r/+t1LDkFKuIiIkWYmdG7fm8+vOxDapeqzd+//jv3fXMfh44f8jqaBEFFXEREqFGqBmO7j2VIiyF8vP5jrvjoChbvWOx1LMmFiriIiAAQExXDrS1v5e2L3wZg8H8H89Lil0hJT/E4mWRHRVxERH6nZYWWfHjZh1xa51JGLRvFtTOuZeOBjV7HCsrgwYOpWrXqiYFPdu3aRa1atQD/YCfx8fG0bNmSxo0bM2TIENLTg78qf+zYsSQlJdGyZcsT08qVKwFYs2YNl156KXXr1qVNmzZ069btd12+TpkyhebNm3PGGWfQrFmzkPXPriIuIiJ/UCK2BI91eYxnz3mWjQf8t6JNXD0xIoY49fl8jBkz5qSv1a1blyVLlrBs2TJWrlwZdDFNTU0FoH///ixZsuTE1LhxY44ePUqPHj246aab+Pnnn1m4cCEvvfQS69atA2Dp0qXcfffdTJ06lVWrVjFt2jTuvvtuli1bdtqfNaxF3MwuNrOfzGytmd2bw3J9zcyZ2R9uZBcREe9cVOsiJl4+keaJzRk+ZzhDvxxaYO4pf+yxx2jQoAFdunRh4MCBPPvsswAMHTqUf/3rXycK78lER0fTuXNn1q5dy+jRo2nXrh0tWrSgb9++JCcnA/69+iFDhtChQwf+8Y9/ZNvWuHHj6NSpE5dffvmJeU2bNmXw4MEAPPvss9x3333Url0bgNq1a/PPf/6TZ5555nQ3Qfi6XTUzH/AKcAGwGZhvZtOccyuzLFcSuAOYF64sIiKSd5WKV2LUhaN4d+W7jFw0kt6lenPo+CFKxJaAGffC9uUhXmEz6P5kjossXLiQ8ePHs2TJElJTU2ndujVt2rQBoEaNGnTp0oV3332Xyy677KTvT05OZubMmYwYMYL27dtz4403AvDAAw/w5ptvcvvttwOwefNmZs+ejc/nY+zYsXzwwQe/G+lszpw5rFixgtatW2ebdcWKFdx9992/m9e2bdtcB2wJRjj7Tm8PrHXOrQMws/FAT2BlluUeAZ4C/h7GLCIichqiLIprm1xLx8od2b5+OxsPbKRcfDkq4fBiPLBvvvmG3r17k5CQAPC7vWCAf/7zn/Ts2ZMePXr8bv7PP/9My5YtMTN69uxJ9+7d+eqrr3jggQfYt28fhw4d4qKLLjqxfL9+/fD5futfvn///rz88ss5Zuvduzdr1qyhQYMGTJo06XQ/ao7CWcSrApkHsN0MdMi8gJm1Bqo75z42s2yLuJndBNwE/r+wRETEGw3LNSRtexpl48qy58geDne6kWolqhEXHed1tN+pX78+LVu2ZMKECb+bn3FOPLPBgwczZcoUWrRowdixY5k1a9aJ14oXL57rupo0afK7i9gmT57MggULTux9N27cmIULF9KiRYsTyyxcuJAmTU5/GFfPLmwzsyjgeeCu3JZ1zo1yzrV1zrVNSkoKfzgREcmWmVG5RGVqlKpBWnoa6/avY83eNazdt5Z1+9axfv96NuzfwMYDG/nlwC9sOriJLQe3sPXQVrYd2sb2w9v59fCv7Ejewc7knew6sos9R/eQnJIc9Pn2s88+mylTpnDkyBEOHjzIRx999Idl7r///hPnyXNy8OBBKleuTEpKCuPGjTvl7XHVVVfx3XffMW3atBPzMs6rA9x999088cQTbNiwAfBfJf/4449z1125lr9chXNPfAtQPdPzaoF5GUoCTYFZgcHZKwHTzOxy55yGKRMRKeBKxpakbpm67Dyyk9T0VJxzOBzOOdJJx6X/9vzEa1meZ2VmxEXHkRCdQEJ0AvEx8cRExfxhudatW9O/f39atGhBhQoVaNeu3R+WadKkCa1bt2bRokU5fo5HHnmEDh06kJSURIcOHTh48GC2y2Y9J/7qq6/SuXNnpk+fzp133snQoUOpWLEiJUuW5IEHHgCgZcuWPPXUU1x22WWkpKQQExPD008/TcuWLXPMFYywDUVqZtHAauA8/MV7PnCVc25FNsvPAu7OrYBrKFIREW+FaijS3xV9l86R1CMkpyaTnJrM0dSjJ25ni/XFEh8dT0KMv7AX8xUjsPN3wvDhwylRosQfLiCLRKcyFGnY9sSdc6lmdhvwKeADxjjnVpjZCGCBc25azi2IiEhhZmYYBgY+fMT4YihVrBQA6S6do6lH/UU9JZlDKYfYf2w/4L/ILiEmwV/Yo/0/i6pwHk7HOfcJ8EmWeQ9ls2zXcGYREZHIkVGoE2ISIN6/156SnkJySvKJvfVDyb8N0nLN0GuIj45n/7H9JEQnEOP74yH4wiisRVxERCQUzIxYXyyxvljKUAaAtPS03w7BpySz/9h+9h7dC0CML4bi0cVP/CEQGxX7h0PwhYGKuIiIRCRflI8SsSX8nc7g31s/mnaU5JRkDqcc5mDKQfYd2wdAdFT0iXPqxWOKn/S8eiRSERcRkULBzIiPjic+Op7y8eVxznEs7diJPfXDKYc5cOwAAD7zndhLT4hOIC46jiiLvOFEVMRFRKRQyrhdLS46jnJx5X53Xv1w6mGSU5I5eNx/O1mURf3uCvj46Hh8Ub5c1uC9yPuzQ0REJBs5DUW6ceNGSpcoTdeOXenRpQf9uvVj3vR5VCtZjQtbX8iOXf7OZzYe2Mg7H71Dt4u6MXfFXKpUrULy8eTfjeDWsmVL5s2bx/Dhw4PqUCZctCcuIiKFSsZQpLfccssfXjtZt6vgP7xep3QdypYry5HUI6wutpooi6JM5TJUqFqBCTMm0KFLB+Kj49m8bjP7D+ynTbs2zJgxIx8+Ufa0Jy4iIhHpdIYizU7GxXJl48oSHx1Pw7INufaqa/n6468pXaw0aS6N/7z/Hy7oeQE/7fmJ3Ud2s+/YPvYc2cOR1CP5Pt669sRFRCTPnvr+KX7c82NI22xUrhH3tL8nx2XyOhRpxihmGV566SXOOussALp163ZixLJDhw7RqFEjzIyrBl5Fy5YtGfXqKKKjo/nyoy95+z9vUyGhAtFR0RxPO862w9sA/7n1uOg4qpWsdtLuYkNNRVxERCJOXocize5wOsCXX35JYmIiALNmzTqxZ1+xYkWaNm3KzJkzqVixItHR0bRv1R6A0sVKUzy+OPXL1j9xz/rR1KNEW/6UVxVxERHJs9z2mL2S3VCkeTVw4EDGjx9PxYoVGThw4O9ey9wRTelipUOyvmDpnLiIiEScUA5FGow+ffrwySef8MEHHzBgwICQtBkKKuIiIhJxMg9F2r179xyHIs0s45x4xvTiiy8Gtb4yZcrQqVMnKlasSJ06dULyGUIhbEORhouGIhUR8VaohiINpaI6FKn2xEVERCKULmwTEZGIN3z4cK8jeEJ74iIiIhFKRVxERE5ZpF1PFSlOdbuqiIuIyCmJi4tj9+7dKuQh5pxj9+7dxMXFBf0enRMXEZFTUq1aNTZv3szOnTu9jlLoxMXFUa1ataCXVxEXEZFTEhMTQ+3atb2OIehwuoiISMRSERcREYlQKuIiIiIRKuK6XTWzncDGTLMSgV0exSmMtD1DT9s0tLQ9Q0/bNLTCsT1rOueSss6MuCKelZktOFl/spI32p6hp20aWtqeoadtGlr5uT11OF1ERCRCqYiLiIhEqMJQxEd5HaCQ0fYMPW3T0NL2DD1t09DKt+0Z8efERUREiqrCsCcuIiJSJEVsETezi83sJzNba2b3ep2nMDCzDWa23MyWmNkCr/NEGjMbY2Y7zOyHTPPKmdlnZrYm8LOslxkjTTbbdLiZbQl8T5eY2SVeZowkZlbdzL40s5VmtsLM7gjM1/c0j3LYpvnyPY3Iw+lm5gNWAxcAm4H5wEDn3EpPg0U4M9sAtHXO6X7RPDCzs4FDwDvOuaaBeU8De5xzTwb+2CzrnLvHy5yRJJttOhw45Jx71stskcjMKgOVnXOLzKwksBDoBQxG39M8yWGbXkk+fE8jdU+8PbDWObfOOXccGA/09DiTFHHOua+BPVlm9wTeDjx+G/8/bglSNttU8sg5t805tyjw+CCwCqiKvqd5lsM2zReRWsSrApsyPd9MPm60QswB/zOzhWZ2k9dhComKzrltgcfbgYpehilEbjOzZYHD7Tr0mwdmVgtoBcxD39OQyLJNIR++p5FaxCU8ujjnWgPdgVsDhzIlRJz/3FXknb8qeF4D6gItgW3Ac56miUBmVgKYCAx1zh3I/Jq+p3lzkm2aL9/TSC3iW4DqmZ5XC8yT0+Cc2xL4uQOYjP+0hZyeXwPnzDLOne3wOE/Ec8796pxLc86lA6PR9/SUmFkM/mIzzjk3KTBb39PTcLJtml/f00gt4vOB+mZW28xigQHANI8zRTQzKx64KAMzKw5cCPyQ87skCNOAawOPrwWmepilUMgoNgG90fc0aGZmwJvAKufc85le0vc0j7Lbpvn1PY3Iq9MBApfrvwD4gDHOuce8TRTZzKwO/r1vgGjgP9qmp8bM3ge64h/B6FdgGDAFmADUwD/63pXOOV2oFaRstmlX/IcoHbABuDnT+VzJgZl1Ab4BlgPpgdn34T+Hq+9pHuSwTQeSD9/TiC3iIiIiRV2kHk4XEREp8lTERUREIpSKuIiISIRSERcREYlQKuIiIiIRSkVcIpaZzTKztvmwnr+a2SozGxfk8oPN7OVw5woiRy8za5zp+QgzOz+M64s3s6/MzGdmXc1serjWdSrM7FA282cH8d43Mrahmd2Xh/efdN2nKnOOHJbpFcQyl5rZiFBkkoJBRVyKJDOLPoXF/wJc4JwbFK48uTnFvBl6ASf+U3fOPeSc+zxkof7oz8Ak51xaGNcRMs65zkEsc0Om0RHvy/Jaru8PlSw5stOLTL/vbHwMXGZmCSEJJp5TEZewMrNagb3Y0YGxdv9nZvGB107sSZtZYmAo1Iw92SmBcY03mNltZnanmS02s7lmVi7TKq4JjNX7g5m1D7y/eGDAge8D7+mZqd1pZvYFMPMkWe8MtPODmQ0NzHsdqAPMMLO/ZVk+zszeMv8Y7IvNrFuml6sHPt8aMxuWKdfHZrY0sI7+gfltAnuwC83s00zdX84ysxfMP7b7/Wa20cyiMrW1ycxizOxGM5sfaHeimSWYWWfgcuCZwPapa2ZjzeyKwPvPC2ReHthWxQLzN5jZw2a2KPBao8D8c+y3cZEXW6B3vywG8fuevkoFPu9PZvZ6puwXmtmcwDr+z8xKmNm5ZjYl07a9wMwmBx4PDGT5wcyeyrTMITN7LPC555pZxcD82oH2l5vZoyfJeeL9gZ9dA9v6QzP70czGmZll+h20NbMngfjA5x+X5f0lzGxmpm2W44iK5v83kbGeVYH1JuTye8n8b+UPnzub3/dfzT/G9TIzGw8n+kWfBVyaU0aJIM45TZrCNgG1gFSgZeD5BODqwONZ+McvB3+PXBsCjwcDa4GSQBKwHxgSeO1f+AcYyHj/6MDjs4EfAo8fz7SOMvjHni8eaHczUO4kOdvg73GpOFACWAG0Cry2AUg8yXvuwt9bIEAj4BcgLrCebUB5IB5/d4ttgb4ZeQPvKQ3EALOBpMC8/pnanAW8mmn5qUC3TMu9EXhcPtMyjwK3Bx6PBa7I9NpY4IpAxk1Ag8D8dzJt0w2Z3v+XTOv4CDgz8LgEEJ1lW8QC2zM97wocxf8HkA/4LLDuROBroHhguXuAhwADfsy0Hf4DXAZUCWzXJPw9CX4B9Aos44DLAo+fBh4IPJ4G/Cnw+Fb8Yzqf7Lt5KFPW/fjHYIgC5uAfDCjjd9A28/IneX80UCrT93gtv3Wk9Yd14/834TJtzzHA3bn8XjLnyO5zZ/19bwWKZfw7yDR/EPCS1/83aArNpD1xyQ/rnXNLAo8X4v9PLDdfOucOOud24v8P9qPA/OVZ3v8+nBh3upSZlcHf7/u9ZrYE/39+cfi7kwT4zJ28O8kuwGTn3GHn3CFgEnBWLhm7AO8F1v8j/u4qG2Raz27n3JFAW10C2S8ws6fM7Czn3H6gIdAU+CyQ9wH8xSTDB1ke9w88HpDptaZm9o2ZLcf/H3STXHI3xP87WR14/jb+P4IyZAyKkfl39R3wvJn9FX9BSM3SZiKwL8u8751z65z/8Pr7+LdBR/yHfL8LfN5rgZrOOQe8C1wd+B12AmYA7YBZzrmdgXWOy5T1OJBx3j1z1jMD6yPQZjC+d85tdv7BKpYQ3Hc0gwGPm9ky4HP8wyLnNpTnJufcd4HH7+HfNrn9XjJk97mzWgaMM7Or8f8hnWEH/j+OpBDIy3k2kVN1LNPjNPx7p+D/jyXjD8m4HN6Tnul5Or//3mbtN9jh/0+1r3Pup8wvmFkH4PApJc+7P+Ryzq02s9bAJcCjZjYTf3/1K5xznbJpJ3PeafiLRTn8Rw6+CMwfi3/vdKmZDca/Z3k6MrZ1GoFt7Zx70sw+DmT/zswuCvzhkuEIf/wdZve7+cw5N/Ak630L/x9rR4H/c86lBo5qZyclUPx/lzWbdecm63f0VP5vHIT/SEEb51yK+U8LZd0WWZ1s2wQrp8+dWQ/8fwRchv90TLPAH0Jx+H9fUghoT1y8tAF/MQL/oda8yDiv3AXYH9i7/RS4PdN5zVZBtPMN0CtwPrk4/lGHvgniPYMC62iAf28/4w+HC8ysnPnP//fCX/iqAMnOufeAZ4DWgeWTzKxToJ0YMzvpnnTgCMF8YCQw3f12AVlJYJv5h0PMfPHdwcBrWf0E1DKzeoHn1wBf5fRBzayuc265c+6pQIZGWbLtBXxmlrl4tQ+cn47C/3v6FpgLnJmxbvOf228QaGMr/kPAD+Av6ADfA+eY/5oJH/5BJXLMiv+owYDA41BejJgS2MZZlQZ2BAp4N6BmEG3VyPidA1fh3zan/HvJ4sTvO7DNqzvnvsR/yqI0/tMg4D9apJHfCgkVcfHSs8AtZrYY/+HYvDgaeP/rwPWBeY/gP9e8zMxWBJ7nyDm3CP8e7ff4R3R6wzm3OJe3vQpEBQ5jfwAMds5l7NF9j3984WXAROfcAqAZ8H3gMPIw4FHn3HH8f8A8ZWZL8R/Kzemq5w+Aq/n9YfYHA5m/w39eOcN44O+BC6XqZvqsR4HrgP8LZE/Hv/1yMjRwYdkyIAX/oe6s/of/sHCG+cDLwCpgPf7TFTvxXzPwfqCtOfz+D4Jx+A81rwpk3QbcC3wJLAUWOudyGybzDuDWwGermsuyp2IU/u9U1lsNxwFtA+v7E7//HWTnp0DGVUBZ4LU8/l4yO/H7BuoD7wXaWQy86JzbF1iuG/6r1KUQ0ChmIhISgVMFf3POXXMabbwMLHbOvRm6ZAWLmdXCfySlqQfrroh/mOHz8nvdEh46Jy4iIeGcW2RmX5qZz+XhXnEzW4j/GoC7Qp9OAmqg7VuoaE9cREQkQumcuIiISIRSERcREYlQKuIiIiIRSkVcREQkQqmIi4iIRCgVcRERkQj1/0PEQCrSXg/VAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "from matplotlib import pyplot as plt\n", "%matplotlib inline\n", "\n", "iters = np.arange(1, N_BATCH + 1)\n", "log_hv_difference_sobol = np.log10(branin_currin.max_hv - np.asarray(sobol_hv_list))[:N_BATCH + 1]\n", "log_hv_difference_parego = np.log10(branin_currin.max_hv - np.asarray(parego_hv_list))[:N_BATCH + 1]\n", "log_hv_difference_ehvi = np.log10(branin_currin.max_hv - np.asarray(ehvi_hv_list))[:N_BATCH + 1]\n", "\n", "fig, ax = plt.subplots(1, 1, figsize=(8, 6))\n", "ax.plot(iters, log_hv_difference_sobol, label=\"Sobol\", linewidth=1.5)\n", "ax.plot(iters, log_hv_difference_parego, label=\"qNParEGO\", linewidth=1.5)\n", "ax.plot(iters, log_hv_difference_ehvi, label=\"qNEHVI\", linewidth=1.5)\n", "ax.set(xlabel='number of observations (beyond initial points)', ylabel='Log Hypervolume Difference')\n", "ax.legend(loc=\"lower right\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "python3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.12" } }, "nbformat": 4, "nbformat_minor": 2 }