{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Trigger Word Detection\n", "\n", "Welcome to the second and last programming assignment of Week 3! \n", "\n", "In this week's videos, you learned about applying deep learning to speech recognition. In this assignment, you will construct a speech dataset and implement an algorithm for trigger word detection (sometimes also called keyword detection, or wake word detection). \n", "\n", "* Trigger word detection is the technology that allows devices like Amazon Alexa, Google Home, Apple Siri, and Baidu DuerOS to wake up upon hearing a certain word. \n", "* For this exercise, our trigger word will be \"activate\". Every time it hears you say \"activate\", it will make a \"chiming\" sound. \n", "* By the end of this assignment, you will be able to record a clip of yourself talking, and have the algorithm trigger a chime when it detects you saying \"activate\". \n", "* After completing this assignment, perhaps you can also extend it to run on your laptop so that every time you say \"activate\" it starts up your favorite app, or turns on a network connected lamp in your house, or triggers some other event? \n", "\n", "\n", "\n", "In this assignment you will learn to: \n", "- Structure a speech recognition project\n", "- Synthesize and process audio recordings to create train/dev datasets\n", "- Train a trigger word detection model and make predictions\n", "\n", "Let's get started!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Table of Contents\n", "\n", "- [Packages](#0)\n", "- [1 - Data synthesis: Creating a Speech Dataset](#1)\n", " - [1.1 - Listening to the Data](#1-1)\n", " - [1.2 - From Audio Recordings to Spectrograms](#1-2)\n", " - [1.3 - Generating a Single Training Example](#1-3)\n", " - [Exercise 1 - is_overlapping](#ex-1)\n", " - [Exercise 2 - insert_audio_clip](#ex-2)\n", " - [Exercise 3 - insert_ones](#ex-3)\n", " - [Exercise 4 - create_training_example](#ex-4)\n", " - [1.4 - Full Training Set](#1-4)\n", " - [1.5 - Development Set](#1-5)\n", "- [2 - The Model](#2)\n", " - [2.1 - Build the Model](#2-1)\n", " - [Exercise 5 - modelf](#ex-5)\n", " - [2.2 - Fit the Model](#2-2)\n", " - [2.3 - Test the Model](#2-3)\n", "- [3 - Making Predictions](#3)\n", " - [3.1 - Test on Dev Examples](#3-1)\n", "- [4 - Try Your Own Example! (OPTIONAL/UNGRADED)](#4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## Packages" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from pydub import AudioSegment\n", "import random\n", "import sys\n", "import io\n", "import os\n", "import glob\n", "import IPython\n", "from td_utils import *\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## 1 - Data synthesis: Creating a Speech Dataset \n", "\n", "Let's start by building a dataset for your trigger word detection algorithm. \n", "* A speech dataset should ideally be as close as possible to the application you will want to run it on. \n", "* In this case, you'd like to detect the word \"activate\" in working environments (library, home, offices, open-spaces ...). \n", "* Therefore, you need to create recordings with a mix of positive words (\"activate\") and negative words (random words other than activate) on different background sounds. Let's see how you can create such a dataset. \n", "\n", "\n", "### 1.1 - Listening to the Data \n", "\n", "* One of your friends is helping you out on this project, and they've gone to libraries, cafes, restaurants, homes and offices all around the region to record background noises, as well as snippets of audio of people saying positive/negative words. This dataset includes people speaking in a variety of accents. \n", "* In the raw_data directory, you can find a subset of the raw audio files of the positive words, negative words, and background noise. You will use these audio files to synthesize a dataset to train the model. \n", " * The \"activate\" directory contains positive examples of people saying the word \"activate\". \n", " * The \"negatives\" directory contains negative examples of people saying random words other than \"activate\". \n", " * There is one word per audio recording. \n", " * The \"backgrounds\" directory contains 10 second clips of background noise in different environments.\n", "\n", "Run the cells below to listen to some examples." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "IPython.display.Audio(\"./raw_data/activates/1.wav\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "IPython.display.Audio(\"./raw_data/negatives/4.wav\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "IPython.display.Audio(\"./raw_data/backgrounds/1.wav\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You will use these three types of recordings (positives/negatives/backgrounds) to create a labeled dataset." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "### 1.2 - From Audio Recordings to Spectrograms\n", "\n", "What really is an audio recording? \n", "* A microphone records little variations in air pressure over time, and it is these little variations in air pressure that your ear also perceives as sound. \n", "* You can think of an audio recording as a long list of numbers measuring the little air pressure changes detected by the microphone. \n", "* We will use audio sampled at 44100 Hz (or 44100 Hertz). \n", " * This means the microphone gives us 44,100 numbers per second. \n", " * Thus, a 10 second audio clip is represented by 441,000 numbers (= $10 \\times 44,100$). \n", "\n", "#### Spectrogram\n", "* It is quite difficult to figure out from this \"raw\" representation of audio whether the word \"activate\" was said. \n", "* In order to help your sequence model more easily learn to detect trigger words, we will compute a *spectrogram* of the audio. \n", "* The spectrogram tells us how much different frequencies are present in an audio clip at any moment in time. \n", "* If you've ever taken an advanced class on signal processing or on Fourier transforms:\n", " * A spectrogram is computed by sliding a window over the raw audio signal, and calculating the most active frequencies in each window using a Fourier transform. \n", " * If you don't understand the previous sentence, don't worry about it.\n", "\n", "Let's look at an example. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "IPython.display.Audio(\"audio_examples/example_train.wav\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "x = graph_spectrogram(\"audio_examples/example_train.wav\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The graph above represents how active each frequency is (y axis) over a number of time-steps (x axis). \n", "\n", "\n", "
**Figure 1**: Spectrogram of an audio recording
\n", "\n", "\n", "* The color in the spectrogram shows the degree to which different frequencies are present (loud) in the audio at different points in time. \n", "* Green means a certain frequency is more active or more present in the audio clip (louder).\n", "* Blue squares denote less active frequencies.\n", "* The dimension of the output spectrogram depends upon the hyperparameters of the spectrogram software and the length of the input. \n", "* In this notebook, we will be working with 10 second audio clips as the \"standard length\" for our training examples. \n", " * The number of timesteps of the spectrogram will be 5511. \n", " * You'll see later that the spectrogram will be the input $x$ into the network, and so $T_x = 5511$." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Time steps in audio recording before spectrogram (441000,)\n", "Time steps in input after spectrogram (101, 5511)\n" ] } ], "source": [ "_, data = wavfile.read(\"audio_examples/example_train.wav\")\n", "print(\"Time steps in audio recording before spectrogram\", data[:,0].shape)\n", "print(\"Time steps in input after spectrogram\", x.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, you can define:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "Tx = 5511 # The number of time steps input to the model from the spectrogram\n", "n_freq = 101 # Number of frequencies input to the model at each time step of the spectrogram" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Dividing into time-intervals\n", "Note that we may divide a 10 second interval of time with different units (steps).\n", "* Raw audio divides 10 seconds into 441,000 units.\n", "* A spectrogram divides 10 seconds into 5,511 units.\n", " * $T_x = 5511$\n", "* You will use a Python module `pydub` to synthesize audio, and it divides 10 seconds into 10,000 units.\n", "* The output of our model will divide 10 seconds into 1,375 units.\n", " * $T_y = 1375$\n", " * For each of the 1375 time steps, the model predicts whether someone recently finished saying the trigger word \"activate\". \n", "* All of these are hyperparameters and can be changed (except the 441000, which is a function of the microphone). \n", "* We have chosen values that are within the standard range used for speech systems." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "Ty = 1375 # The number of time steps in the output of our model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "### 1.3 - Generating a Single Training Example\n", "\n", "#### Benefits of synthesizing data\n", "Because speech data is hard to acquire and label, you will synthesize your training data using the audio clips of activates, negatives, and backgrounds. \n", "* It is quite slow to record lots of 10 second audio clips with random \"activates\" in it. \n", "* Instead, it is easier to record lots of positives and negative words, and record background noise separately (or download background noise from free online sources). \n", "\n", "#### Process for Synthesizing an audio clip\n", "* To synthesize a single training example, you will:\n", " - Pick a random 10 second background audio clip\n", " - Randomly insert 0-4 audio clips of \"activate\" into this 10 sec. clip\n", " - Randomly insert 0-2 audio clips of negative words into this 10 sec. clip\n", "* Because you had synthesized the word \"activate\" into the background clip, you know exactly when in the 10 second clip the \"activate\" makes its appearance. \n", " * You'll see later that this makes it easier to generate the labels $y^{\\langle t \\rangle}$ as well. \n", "\n", "#### Pydub\n", "* You will use the pydub package to manipulate audio. \n", "* Pydub converts raw audio files into lists of Pydub data structures.\n", " * Don't worry about the details of the data structures.\n", "* Pydub uses 1ms as the discretization interval (1 ms is 1 millisecond = 1/1000 seconds).\n", " * This is why a 10 second clip is always represented using 10,000 steps. " ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "background len should be 10,000, since it is a 10 sec clip\n", "10000 \n", "\n", "activate[0] len may be around 1000, since an `activate` audio clip is usually around 1 second (but varies a lot) \n", "916 \n", "\n", "activate[1] len: different `activate` clips can have different lengths\n", "1579 \n", "\n" ] } ], "source": [ "# Load audio segments using pydub \n", "activates, negatives, backgrounds = load_raw_audio('./raw_data/')\n", "\n", "print(\"background len should be 10,000, since it is a 10 sec clip\\n\" + str(len(backgrounds[0])),\"\\n\")\n", "print(\"activate[0] len may be around 1000, since an `activate` audio clip is usually around 1 second (but varies a lot) \\n\" + str(len(activates[0])),\"\\n\")\n", "print(\"activate[1] len: different `activate` clips can have different lengths\\n\" + str(len(activates[1])),\"\\n\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Overlaying positive/negative 'word' audio clips on top of the background audio\n", "\n", "* Given a 10 second background clip and a short audio clip containing a positive or negative word, you need to be able to \"add\" the word audio clip on top of the background audio.\n", "* You will be inserting multiple clips of positive/negative words into the background, and you don't want to insert an \"activate\" or a random word somewhere that overlaps with another clip you had previously added. \n", " * To ensure that the 'word' audio segments do not overlap when inserted, you will keep track of the times of previously inserted audio clips. \n", "* To be clear, when you insert a 1 second \"activate\" onto a 10 second clip of cafe noise, **you do not end up with an 11 sec clip.** \n", " * The resulting audio clip is still 10 seconds long.\n", " * You'll see later how pydub allows you to do this. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Label the positive/negative words\n", "* Recall that the labels $y^{\\langle t \\rangle}$ represent whether or not someone has just finished saying \"activate\". \n", " * $y^{\\langle t \\rangle} = 1$ when that clip has finished saying \"activate\".\n", " * Given a background clip, we can initialize $y^{\\langle t \\rangle}=0$ for all $t$, since the clip doesn't contain any \"activate\". \n", "* When you insert or overlay an \"activate\" clip, you will also update labels for $y^{\\langle t \\rangle}$.\n", " * Rather than updating the label of a single time step, we will update 50 steps of the output to have target label 1. \n", " * Recall from the lecture on trigger word detection that updating several consecutive time steps can make the training data more balanced.\n", "* You will train a GRU (Gated Recurrent Unit) to detect when someone has **finished** saying \"activate\". \n", "\n", "##### Example\n", "* Suppose the synthesized \"activate\" clip ends at the 5 second mark in the 10 second audio - exactly halfway into the clip. \n", "* Recall that $T_y = 1375$, so timestep $687 = $ `int(1375*0.5)` corresponds to the moment 5 seconds into the audio clip. \n", "* Set $y^{\\langle 688 \\rangle} = 1$. \n", "* We will allow the GRU to detect \"activate\" anywhere within a short time-internal **after** this moment, so we actually **set 50 consecutive values** of the label $y^{\\langle t \\rangle}$ to 1. \n", " * Specifically, we have $y^{\\langle 688 \\rangle} = y^{\\langle 689 \\rangle} = \\cdots = y^{\\langle 737 \\rangle} = 1$. \n", "\n", "##### Synthesized data is easier to label\n", "* This is another reason for synthesizing the training data: It's relatively straightforward to generate these labels $y^{\\langle t \\rangle}$ as described above. \n", "* In contrast, if you have 10sec of audio recorded on a microphone, it's quite time consuming for a person to listen to it and mark manually exactly when \"activate\" finished. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Visualizing the labels\n", "* Here's a figure illustrating the labels $y^{\\langle t \\rangle}$ in a clip.\n", " * We have inserted \"activate\", \"innocent\", \"activate\", \"baby.\" \n", " * Note that the positive labels \"1\" are associated only with the positive words. \n", "\n", "\n", "
**Figure 2**
\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Helper functions\n", "\n", "To implement the training set synthesis process, you will use the following helper functions. \n", "* All of these functions will use a 1ms discretization interval\n", "* The 10 seconds of audio is always discretized into 10,000 steps. \n", "\n", "\n", "1. `get_random_time_segment(segment_ms)`\n", " * Retrieves a random time segment from the background audio.\n", "2. `is_overlapping(segment_time, existing_segments)`\n", " * Checks if a time segment overlaps with existing segments\n", "3. `insert_audio_clip(background, audio_clip, existing_times)`\n", " * Inserts an audio segment at a random time in the background audio\n", " * Uses the functions `get_random_time_segment` and `is_overlapping`\n", "4. `insert_ones(y, segment_end_ms)`\n", " * Inserts additional 1's into the label vector y after the word \"activate\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Get a random time segment\n", "\n", "* The function `get_random_time_segment(segment_ms)` returns a random time segment onto which we can insert an audio clip of duration `segment_ms`. \n", "* Please read through the code to make sure you understand what it is doing. " ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "def get_random_time_segment(segment_ms):\n", " \"\"\"\n", " Gets a random time segment of duration segment_ms in a 10,000 ms audio clip.\n", " \n", " Arguments:\n", " segment_ms -- the duration of the audio clip in ms (\"ms\" stands for \"milliseconds\")\n", " \n", " Returns:\n", " segment_time -- a tuple of (segment_start, segment_end) in ms\n", " \"\"\"\n", " \n", " segment_start = np.random.randint(low=0, high=10000-segment_ms) # Make sure segment doesn't run past the 10sec background \n", " segment_end = segment_start + segment_ms - 1\n", " \n", " return (segment_start, segment_end)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Check if audio clips are overlapping\n", "\n", "* Suppose you have inserted audio clips at segments (1000,1800) and (3400,4500).\n", " * The first segment starts at step 1000 and ends at step 1800. \n", " * The second segment starts at 3400 and ends at 4500.\n", "* If we are considering whether to insert a new audio clip at (3000,3600) does this overlap with one of the previously inserted segments? \n", " * In this case, (3000,3600) and (3400,4500) overlap, so we should decide against inserting a clip here.\n", "* For the purpose of this function, define (100,200) and (200,250) to be overlapping, since they overlap at timestep 200. \n", "* (100,199) and (200,250) are non-overlapping. \n", "\n", "\n", "### Exercise 1 - is_overlapping\n", "\n", "* Implement `is_overlapping(segment_time, existing_segments)` to check if a new time segment overlaps with any of the previous segments. \n", "* You will need to carry out 2 steps:\n", "\n", "1. Create a \"False\" flag, that you will later set to \"True\" if you find that there is an overlap.\n", "2. Loop over the previous_segments' start and end times. Compare these times to the segment's start and end times. If there is an overlap, set the flag defined in (1) as True. \n", "\n", "You can use:\n", "```python\n", "for ....:\n", " if ... <= ... and ... >= ...:\n", " ...\n", "```\n", "Hint: There is overlap if:\n", "* The new segment starts before the previous segment ends **and**\n", "* The new segment ends after the previous segment starts." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "# UNQ_C1\n", "# GRADED FUNCTION: is_overlapping\n", "\n", "def is_overlapping(segment_time, previous_segments):\n", " \"\"\"\n", " Checks if the time of a segment overlaps with the times of existing segments.\n", " \n", " Arguments:\n", " segment_time -- a tuple of (segment_start, segment_end) for the new segment\n", " previous_segments -- a list of tuples of (segment_start, segment_end) for the existing segments\n", " \n", " Returns:\n", " True if the time segment overlaps with any of the existing segments, False otherwise\n", " \"\"\"\n", " \n", " segment_start, segment_end = segment_time\n", " \n", " ### START CODE HERE ### (≈ 4 lines)\n", " # Step 1: Initialize overlap as a \"False\" flag. (≈ 1 line)\n", " overlap = False\n", " \n", " # Step 2: loop over the previous_segments start and end times.\n", " # Compare start/end times and set the flag to True if there is an overlap (≈ 3 lines)\n", " for previous_start, previous_end in previous_segments: # @KEEP\n", " if segment_start <= previous_end and segment_end >= previous_start:\n", " overlap = True\n", " break\n", " ### END CODE HERE ###\n", "\n", " return overlap" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[92m All tests passed!\n" ] } ], "source": [ "# UNIT TEST\n", "def is_overlapping_test(target):\n", " assert target((670, 1430), []) == False, \"Overlap with an empty list must be False\"\n", " assert target((500, 1000), [(100, 499), (1001, 1100)]) == False, \"Almost overlap, but still False\"\n", " assert target((750, 1250), [(100, 750), (1001, 1100)]) == True, \"Must overlap with the end of first segment\"\n", " assert target((750, 1250), [(300, 600), (1250, 1500)]) == True, \"Must overlap with the begining of second segment\"\n", " assert target((750, 1250), [(300, 600), (600, 1500), (1600, 1800)]) == True, \"Is contained in second segment\"\n", " print(\"\\033[92m All tests passed!\")\n", " \n", "is_overlapping_test(is_overlapping)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overlap 1 = False\n", "Overlap 2 = True\n" ] } ], "source": [ "overlap1 = is_overlapping((950, 1430), [(2000, 2550), (260, 949)])\n", "overlap2 = is_overlapping((2305, 2950), [(824, 1532), (1900, 2305), (3424, 3656)])\n", "print(\"Overlap 1 = \", overlap1)\n", "print(\"Overlap 2 = \", overlap2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Expected Output**:\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
\n", " **Overlap 1**\n", " \n", " False\n", "
\n", " **Overlap 2**\n", " \n", " True\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Insert audio clip\n", "\n", "* Let's use the previous helper functions to insert a new audio clip onto the 10 second background at a random time.\n", "* We will ensure that any newly inserted segment doesn't overlap with previously inserted segments. \n", "\n", "\n", "### Exercise 2 - insert_audio_clip\n", "* Implement `insert_audio_clip()` to overlay an audio clip onto the background 10sec clip. \n", "* You implement 4 steps:\n", "\n", "1. Get the length of the audio clip that is to be inserted.\n", " * Get a random time segment whose duration equals the duration of the audio clip that is to be inserted.\n", "2. Make sure that the time segment does not overlap with any of the previous time segments. \n", " * If it is overlapping, then go back to step 1 and pick a new time segment.\n", "3. Append the new time segment to the list of existing time segments\n", " * This keeps track of all the segments you've inserted. \n", "4. Overlay the audio clip over the background using pydub. We have implemented this for you." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "# UNQ_C2\n", "# GRADED FUNCTION: insert_audio_clip\n", "\n", "def insert_audio_clip(background, audio_clip, previous_segments):\n", " \"\"\"\n", " Insert a new audio segment over the background noise at a random time step, ensuring that the \n", " audio segment does not overlap with existing segments.\n", " \n", " Arguments:\n", " background -- a 10 second background audio recording. \n", " audio_clip -- the audio clip to be inserted/overlaid. \n", " previous_segments -- times where audio segments have already been placed\n", " \n", " Returns:\n", " new_background -- the updated background audio\n", " \"\"\"\n", " \n", " # Get the duration of the audio clip in ms\n", " segment_ms = len(audio_clip)\n", " \n", " ### START CODE HERE ### \n", " # Step 1: Use one of the helper functions to pick a random time segment onto which to insert \n", " # the new audio clip. (≈ 1 line)\n", " segment_time = get_random_time_segment(segment_ms)\n", " \n", " # Step 2: Check if the new segment_time overlaps with one of the previous_segments. If so, keep \n", " # picking new segment_time at random until it doesn't overlap. To avoid an endless loop\n", " # we retry 5 times(≈ 2 lines)\n", " retry = 5 # @KEEP \n", " while is_overlapping(segment_time, previous_segments) and retry >= 0:\n", " segment_time = get_random_time_segment(segment_ms)\n", " retry = retry - 1\n", " ### END CODE HERE ###\n", "\n", " # if last try is not overlaping, insert it to the background\n", " if not is_overlapping(segment_time, previous_segments):\n", " ### START CODE HERE ### \n", " # Step 3: Append the new segment_time to the list of previous_segments (≈ 1 line)\n", " previous_segments.append(segment_time)\n", " ### END CODE HERE ###\n", " # Step 4: Superpose audio segment and background\n", " new_background = background.overlay(audio_clip, position = segment_time[0])\n", " print(segment_time)\n", " else:\n", " print(\"Timeouted\")\n", " new_background = background\n", " segment_time = (10000, 10000)\n", " \n", " return new_background, segment_time" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(7286, 8201)\n", "Timeouted\n", "\u001b[92m All tests passed!\n" ] } ], "source": [ "# UNIT TEST\n", "def insert_audio_clip_test(target):\n", " np.random.seed(5)\n", " audio_clip, segment_time = target(backgrounds[0], activates[0], [(0, 4400)])\n", " duration = segment_time[1] - segment_time[0]\n", " assert segment_time[0] > 4400, \"Error: The audio clip is overlaping with the first segment\"\n", " assert duration + 1 == len(activates[0]) , \"The segment length must match the audio clip length\"\n", " assert audio_clip != backgrounds[0] , \"The audio clip must be different than the pure background\"\n", " \n", " # Not possible to insert clip into background\n", " audio_clip, segment_time = target(backgrounds[0], activates[0], [(0, 9999)])\n", " assert segment_time[0] == 10000 and segment_time[1] == 10000, \"Segment must match the out by max-retry mark\"\n", " assert audio_clip == backgrounds[0], \"output audio clip must be exactly the same input background\"\n", "\n", " print(\"\\033[92m All tests passed!\")\n", "\n", "insert_audio_clip_test(insert_audio_clip)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.random.seed(5)\n", "audio_clip, segment_time = insert_audio_clip(backgrounds[0], activates[0], [(3790, 4400)])\n", "audio_clip.export(\"insert_test.wav\", format=\"wav\")\n", "print(\"Segment Time: \", segment_time)\n", "IPython.display.Audio(\"insert_test.wav\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Expected Output**\n", "\n", "\n", " \n", " \n", " \n", " \n", "
\n", " **Segment Time**\n", " \n", " (2254, 3169)\n", "
" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Expected audio\n", "IPython.display.Audio(\"audio_examples/insert_reference.wav\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Insert ones for the labels of the positive target\n", "\n", "* Implement code to update the labels $y^{\\langle t \\rangle}$, assuming you just inserted an \"activate\" audio clip.\n", "* In the code below, `y` is a `(1,1375)` dimensional vector, since $T_y = 1375$. \n", "* If the \"activate\" audio clip ends at time step $t$, then set $y^{\\langle t+1 \\rangle} = 1$ and also set the next 49 additional consecutive values to 1.\n", " * Notice that if the target word appears near the end of the entire audio clip, there may not be 50 additional time steps to set to 1.\n", " * Make sure you don't run off the end of the array and try to update `y[0][1375]`, since the valid indices are `y[0][0]` through `y[0][1374]` because $T_y = 1375$. \n", " * So if \"activate\" ends at step 1370, you would get only set `y[0][1371] = y[0][1372] = y[0][1373] = y[0][1374] = 1`\n", "\n", "\n", "### Exercise 3 - insert_ones \n", "Implement `insert_ones()`. \n", "* You can use a for loop. \n", "* If you want to use Python's array slicing operations, you can do so as well.\n", "* If a segment ends at `segment_end_ms` (using a 10000 step discretization),\n", " * To convert it to the indexing for the outputs $y$ (using a $1375$ step discretization), we will use this formula: \n", "```\n", " segment_end_y = int(segment_end_ms * Ty / 10000.0)\n", "```" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "# UNQ_C3\n", "# GRADED FUNCTION: insert_ones\n", "\n", "def insert_ones(y, segment_end_ms):\n", " \"\"\"\n", " Update the label vector y. The labels of the 50 output steps strictly after the end of the segment \n", " should be set to 1. By strictly we mean that the label of segment_end_y should be 0 while, the\n", " 50 following labels should be ones.\n", " \n", " \n", " Arguments:\n", " y -- numpy array of shape (1, Ty), the labels of the training example\n", " segment_end_ms -- the end time of the segment in ms\n", " \n", " Returns:\n", " y -- updated labels\n", " \"\"\"\n", " _, Ty = y.shape\n", " \n", " # duration of the background (in terms of spectrogram time-steps)\n", " segment_end_y = int(segment_end_ms * Ty / 10000.0)\n", " \n", " if segment_end_y < Ty:\n", " # Add 1 to the correct index in the background label (y)\n", " ### START CODE HERE ### (≈ 3 lines)\n", " for i in range(segment_end_y+1, segment_end_y+51):\n", " if i < Ty:\n", " y[0, i] = 1\n", " ### END CODE HERE ###\n", " \n", " return y" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[92m All tests passed!\n" ] } ], "source": [ "# UNIT TEST\n", "import random\n", "def insert_ones_test(target):\n", " segment_end_y = random.randrange(0, Ty - 50) \n", " segment_end_ms = int(segment_end_y * 10000.4) / Ty; \n", " arr1 = target(np.zeros((1, Ty)), segment_end_ms)\n", "\n", " assert type(arr1) == np.ndarray, \"Wrong type. Output must be a numpy array\"\n", " assert arr1.shape == (1, Ty), \"Wrong shape. It must match the input shape\"\n", " assert np.sum(arr1) == 50, \"It must insert exactly 50 ones\"\n", " assert arr1[0][segment_end_y - 1] == 0, f\"Array at {segment_end_y - 1} must be 0\"\n", " assert arr1[0][segment_end_y] == 0, f\"Array at {segment_end_y} must be 1\"\n", " assert arr1[0][segment_end_y + 1] == 1, f\"Array at {segment_end_y + 1} must be 1\"\n", " assert arr1[0][segment_end_y + 50] == 1, f\"Array at {segment_end_y + 50} must be 1\"\n", " assert arr1[0][segment_end_y + 51] == 0, f\"Array at {segment_end_y + 51} must be 0\"\n", "\n", " print(\"\\033[92m All tests passed!\")\n", " \n", "insert_ones_test(insert_ones)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sanity checks: 0.0 1.0 0.0\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "arr1 = insert_ones(np.zeros((1, Ty)), 9700)\n", "plt.plot(insert_ones(arr1, 4251)[0,:])\n", "print(\"sanity checks:\", arr1[0][1333], arr1[0][634], arr1[0][635])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Expected Output**\n", "\n", " \n", " \n", " \n", " \n", "
\n", " **sanity checks**:\n", " \n", " 0.0 1.0 0.0\n", "
\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Creating a training example\n", "Finally, you can use `insert_audio_clip` and `insert_ones` to create a new training example.\n", "\n", "\n", "### Exercise 4 - create_training_example\n", "\n", "Implement `create_training_example()`. You will need to carry out the following steps:\n", "\n", "1. Initialize the label vector $y$ as a numpy array of zeros and shape $(1, T_y)$.\n", "2. Initialize the set of existing segments to an empty list.\n", "3. Randomly select 0 to 4 \"activate\" audio clips, and insert them onto the 10 second clip. Also insert labels at the correct position in the label vector $y$.\n", "4. Randomly select 0 to 2 negative audio clips, and insert them into the 10 second clip. \n" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "# UNQ_C4\n", "# GRADED FUNCTION: create_training_example\n", "\n", "def create_training_example(background, activates, negatives, Ty):\n", " \"\"\"\n", " Creates a training example with a given background, activates, and negatives.\n", " \n", " Arguments:\n", " background -- a 10 second background audio recording\n", " activates -- a list of audio segments of the word \"activate\"\n", " negatives -- a list of audio segments of random words that are not \"activate\"\n", " Ty -- The number of time steps in the output\n", "\n", " Returns:\n", " x -- the spectrogram of the training example\n", " y -- the label at each time step of the spectrogram\n", " \"\"\"\n", " \n", " # Make background quieter\n", " background = background - 20\n", "\n", " ### START CODE HERE ###\n", " # Step 1: Initialize y (label vector) of zeros (≈ 1 line)\n", " y = np.zeros((1, Ty))\n", "\n", " # Step 2: Initialize segment times as empty list (≈ 1 line)\n", " previous_segments = []\n", " ### END CODE HERE ###\n", " \n", " # Select 0-4 random \"activate\" audio clips from the entire list of \"activates\" recordings\n", " number_of_activates = np.random.randint(0, 5)\n", " random_indices = np.random.randint(len(activates), size=number_of_activates)\n", " random_activates = [activates[i] for i in random_indices]\n", " \n", " ### START CODE HERE ### (≈ 3 lines)\n", " # Step 3: Loop over randomly selected \"activate\" clips and insert in background\n", " for random_activate in random_activates: # @KEEP\n", " # Insert the audio clip on the background\n", " background, segment_time = insert_audio_clip(background, random_activate, previous_segments)\n", " # Retrieve segment_start and segment_end from segment_time\n", " segment_start, segment_end = segment_time\n", " # Insert labels in \"y\" at segment_end\n", " y = insert_ones(y, segment_end)\n", " ### END CODE HERE ###\n", "\n", " # Select 0-2 random negatives audio recordings from the entire list of \"negatives\" recordings\n", " number_of_negatives = np.random.randint(0, 3)\n", " random_indices = np.random.randint(len(negatives), size=number_of_negatives)\n", " random_negatives = [negatives[i] for i in random_indices]\n", "\n", " ### START CODE HERE ### (≈ 2 lines)\n", " # Step 4: Loop over randomly selected negative clips and insert in background\n", " for random_negative in random_negatives: # @KEEP\n", " # Insert the audio clip on the background \n", " background, _ = insert_audio_clip(background, random_negative, previous_segments)\n", " ### END CODE HERE ###\n", " \n", " # Standardize the volume of the audio clip \n", " background = match_target_amplitude(background, -20.0)\n", "\n", " # Export new training example \n", " # file_handle = background.export(\"train\" + \".wav\", format=\"wav\")\n", " \n", " # Get and plot spectrogram of the new recording (background with superposition of positive and negatives)\n", " x = graph_spectrogram(\"train.wav\")\n", " \n", " return x, y" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(2885, 3793)\n", "(1726, 2450)\n", "(6882, 7460)\n", "\u001b[92m All tests passed!\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# UNIT TEST\n", "def create_training_example_test(target):\n", " np.random.seed(18)\n", " x, y = target(backgrounds[0], activates, negatives, 1375)\n", " \n", " assert type(x) == np.ndarray, \"Wrong type for x\"\n", " assert type(y) == np.ndarray, \"Wrong type for y\"\n", " assert tuple(x.shape) == (101, 5511), \"Wrong shape for x\"\n", " assert tuple(y.shape) == (1, 1375), \"Wrong shape for y\"\n", " assert np.all(x > 0), \"All x values must be higher than 0\"\n", " assert np.all(y >= 0), \"All y values must be higher or equal than 0\"\n", " assert np.all(y < 51), \"All y values must be smaller than 51\"\n", " assert np.sum(y) % 50 == 0, \"Sum of activate marks must be a multiple of 50\"\n", " assert np.isclose(np.linalg.norm(x), 39745552.52075), \"Spectrogram is wrong. Check the parameters passed to the insert_audio_clip function\"\n", "\n", " print(\"\\033[92m All tests passed!\")\n", "\n", "create_training_example_test(create_training_example)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(2885, 3793)\n", "(1726, 2450)\n", "(6882, 7460)\n" ] }, { "data": { "image/png": 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LKVea1xwCC/ICRL7ZuPY6WNHzukqeq8Yb21YaDCLxjPkZwjrcBvUxeBAS63JQe5BeG6U/CUlGMDzAOJjgPAEWKAtpwDLCXIVFvGo2Kqt2E+12fUIjOT/TkpUCyGhgvhcqvWD5vRgpyFWSXBczohwU4vpdqBNcWRkL+3mpb/23ioyJYOOzGGurL4QvVjxfEj9/nxys3I9FnRkdr2jQJIM5jJJDSmwUveoMiKwtifLHKH/pZZHhvZpu4WW+l5UX5Tn0+rgurm9Rh1+7ZBgzPVTPymvRzEMqexFYpLyT/sqJ9MLk9XKZDASujNXro/SB5YDn7XdL5ZsnBQmyx+O7MvCwQR9Ae/gluMFWzYEi8N+GokhIVLAMFYVJRvHTll8HCqVBd+vvgq0RNboSl9BOUFZUl/b95nmXjIiGeD6Kjq2VnXAp2rfj9WtGkxmDirb/Xum7T0QZeQp8MXcVxvAQCLn9RrMtQPv+7MzbPt4eM91Hvc73nDKveVIRaHBa+++6LepLIAJA2BkT9oGTbHOe/JsneZDN2uP6QobO5gktOkp/ApSfYwgIVppc8OLlBGLKoGvIsfYPGWUI5Qd5Rqvf+sLbpnPizQW8H90Xp23uULiW99yHMTcekKL33XpAHC8d+ZfzlvrB+X0uVYQ5HjZd6KCV+60se+RNhd1YGHXaHv5/bZR+VrBBMTOiWeXnohkZ3NJe+h1i4wltLNHJQVqihUXbS7d/auxyW5c8oOBi4phuocI6MehCu1lZLMaA1wMm5bJAu6HdjG44/wVeZjQbsl3hxZT3CHFyOqrrSCbp+k1IG2TQr+VPNN8auz8cB6uT82uSk9X4Z95dS0npLumwrHnh/agtNuI38pnLTZ76UVrK/fX8pmc4vv9SdKb0Wih9RgSMhoJykMgI3nZog1/OcUIsY4L5+kp5mEU+YR33NqtrMXqOL5dZSBjZy472UAgYDUnco22IWDQaAFJezLPQ5x7LdVRcYr6wnbT3u1bBdkXJF0PuioHoAfIIhgIYD4zRfT5YjcbVUecZMa7dUXV4cpERsp0NRBMkrwvxAn5AdEJ8oe/D80r3eDwLxoM11A+lesKDNxrraDwZ++s9LXYOjQKkEOwSybgvJrInwWiReWJKJS2gFt6hpoO33BYj6uBhsrJNBpafLg1hpgOj6+sbNF+YLleKe98CafXk3UfUXtiyC6onG4hskHXUAZC8EbrPtAcZ7esVjuy3mc6ww8rki71Lo4XpSms/Ob0WSj8kudChCyj26P7V5i5Y5JWLtUQzVJ7jo0dI/3YXYZSdvQNZ9/cIAefqL5GwQngrRHeEOF0RHBBxMI5HY5BReUC6B2h0FQdf0nklHdWRw4n+9RtBaEcyf8mjOUoH/Z6u8XBqLDe1meWZ08E1VoC3pIt8O5r3lzweiffyOspLtX9LuiIDkmjJv8P3K7ToFb6+Fko/ogc40nAh5DhdjRN6WrQz604WGgI/xpcfqnFkRHUYWoc09OPhpYTsgfGwltER4u2kqByddFR/JLCyC+Xr5bc4i3Nc19s4rZW5ih1Y1y/wTgnKu5WKjRdrFX0XTzuPp5Qa4/elVe6oXiWgftlqe6DLyvCEK2h5eRtlGf12PlssWcf1gMAW/eAJ5QhP6HpGyMRH42/eaSJpnFlerE3rm8VdOTQYxjuBFJetjmrNC7T6TaaMDyyL5RzR52oth+ma5J76595JgZ8QyQiXy4W4vuha8Sd0z/PBnxPJHjfTTvPbPCxGzDyOIU4viMdfKPWXjJp7HXYmEaH3lVfED79ZvimEZvO2Dhp4DCfDw/0t1Fdux07lTOt7r//unRUK0cX1S2VympAxfU3I8WIdyXhcbOPouk/89YNZ0VsZivMqjQTNjpCn/T6KaU8/+ve8J3/14suLaxNdkC/xLkx6QjiXxseR0DX5uOQFrVJGgUk5H9G19JquoOpLY3GRpzl/pnGl8I94dMkbuMVLCnVdYOyirqV3narIa3OHXsYK7ZtxS+Av843DSKE+rt9Sna8dgg2WzQvjeWSgL/FlWSalV17pc4wsxEgX+5w5Vqa8eyMhQ6/XrLOMExqZI8sJxsaBaAjXUpwcSWjcunf01GJ70suPI4g9lq8H9VmbdpIlCbkk4cyIKpwaaCjG6Nnn9Q8AYwdP9zicpqIo+aUojFy0E871VYHwfn8AFpKyI5fz3vVpz7tNSB7TBX8MCVrfjCeHE85kLisN8wpsbSbRc7SrItDG45OUuSiWa0ROv/GAjQzTp2PsnCZTXNusLBiNGm15VxR/SlJs/MzEFMdOKJXLrQwQP4Vq15VldYs8dP4p0Z8MrK/3UJ85eQw9eUpBNrLRzus7IIXOMXoqZ/M9GAFaS+KdRn4Uw8oosLzwziqhvtI4HqVXXukDCBNlsmQ0kVh48+IYECcMkBhTklVHzBtiaQfWlQdx2Q2y9lwuhyMmZJ4m6whFSKA9IwJXtAe0cIxWmIdp21ksaIWpH3mPfoBRB+0bfRifgQcq8wNLK57kcTkaM8ueJt+1tOxClhM5+MzfU30TItd4LYSuTLYu1AdEpc3JeJG9hkwDg6yLayXAUkYz2BiVjhu5HiyyTd5OnlPHouVtsw4I4mhtpDAcG9Mj0XWZYr4xmDvgzwqM+Hgaf2viPRsinXni+Vg2DnQUp9dD6Sfhs8mdLWqeNKFMqgsYZ4gHIexKbxJAQ0R01kZW4AEtKVlzqt+vJUSU477L/vvJlTSBdpnieV5mYRhZmIKQaqrDBKkjpb2WtmUzxPWl5dkZwhB9mfcW1++GQy1ubwh/wavZkGEYd7t2wC9O9pYx3+nTkdKEUBMqnSYVEHbn8Diz5zjxlcnTxSfJ86Wyy/UCRXg6PBwPboY8tcWGJLcZlDxGH/0+ew1WR79fOV7On15RNGABrNBe/bAThpUfRr28g8j5dUHZrQy1RwHsGvEv8MPGOMlbPh6b+3YEMEL95uHooGVp/DovyjkZE9q1xLJ8KaL2yiv9rh+CpRRi0hLNgxgukYHBLdsisyzxxFghxfZlvpcR+eQaWjkyNEsEsFJiZmhSW6utc55FMBQt18X9UkQlhcgnW2itnWkcu3e+VoFy23nC8zgoINqVfY2Lu5HwUS4o2GwEMBtgKxuuUSjCx5zGcHowht3v1B9+/wIb2il0tFDel1Db0qVfjSlfl2S8y3goK4AHo3MxTpe8SvaiXZHlEBMi30KdojDPbQIkND9CaG41/+ie9zVv08xAhkjIbeXtq95lMgJB+aY8XE+OEATjfQBAjEfDaycaF2PkRvAcO8eA1fVA9pBTeuWV/oRWMTM5KFdBNAbU+Yx6LGkSQCDW4YKQlQCXzxN71RU1RYqAyPzQNK/AlJLGXSQ+AUhapnvp8yhUxIiKEUniZawc8ZwdFjifrDS5F/ufG/863bY2QAwSOxFUNO6E4GyE/paoWIlFHKoyljFdbPQ5Jm91s7LJQML6Q7KSDQErzKPQSh6fIGs5BIG5zVD3kRxQWW5r8jRXhof5kWU/hUyn47pzm2mO8bqSjVdoJxn+SSHymOa+MI25T4qx8wUL/nEzOVxGsr9C5UHmdFH+oJ1wfaG32o+Ylw0MeyQrY2Pp1Vf6lniCc6eI4WzRp8mJkS+4tnQ9CAfVgYUBmIwOt1GoPDDT2u/bO2kH3aTE+zHJjoSpDfZ0NCthJst+L87F4VBF3rqY6yilHcOgKiN+b8i/tkIiGmgVRQs9EQ/yQiA4LMQTu2/59MX4jGaU8jG6CX22jibeWOhNhhK3LZfTg1YsW6TMefttUFygscnGOAMLNhhJKYXtvwfKKIQVrkzyoxBnQNepjhU6NtpWxs69Ij7ywOS5h3WWBoMVJfeTjN4UG0/98PI6X4cibI9kfpQdUSYzkLBb/BIZzsN/rCc05uUHsKytKdxG9YfPdM3frUw6wNrUGxQ+8Dop/aRYV5ZvibBXSGY1CWhyBdfvAgLIE9iu+aSn+zn2OLnBqb4lclMJ16I7LOOPk4qfp79UQKv+cX96fZXrDTQ1QviIhmBUK31yyid1svBfEVor4p+ZnywrZvyTy8tjFBQvty8x7xQCo3is5Z9kMBmPTP+qX5MiMFoW/Fkp9FsUyipmnOsOCi3VEeRbEJUg05y/d0MQvJyspFeGHjRmifalgTzgwTIUmPt1KfEYL9ZXVovDK94EeVry6UAOcvhxIZv/ykpfRJ6KyGdE5O+LyOdF5G/0639dRP6piPx6//sBKvPjIvIFEfltEfl+uv4BEflcv/eJ/oL060niYGV0tRqoiQmEGFjQPOSSX0IsqR1SEpdihiHeyYs/ddTvcdEk8B6b3PtDTb1dC/MwGlIBUG1LZ1fqCbUbbfa4t9OUXgSdFT8fASAVEAF2FVQVf0euPxTU69020uocruroX/yNWJ2HRaPipzzMj1UoLseqpaM5G4cQbjPZWcQ5eUtipe1uYz0j8sXpp/KrHUYMMHxrbv8dPoEJySKNc95Jk78f1tX7MoXASAlNhouNtY6+he3RVhchzEoLk7JCtUm+tCj0pF6PkhyIYmx5NvpNruvgOc9hluVsxPw4jjr6Gx7WMhpJXjgck3VLiMfnNUXTI9Y+Ke5pflL9AagkIJbDk7W/F/lo0V808X+RbnlH7nMAf1pVvyoidwD+rojYC83/lqr+N5xZRL4bwEcBfA+AfxvA/yYif7K/HP0nAXwMwN8D8AsAPoIbX44e0mICuPIWUhYXLPoy5Xs2CCtklLOm6wZemeZLKDAMeDZYpMUuke/5cxuJrpz/IgK92iDmLZsrOpDa6c8WjP5nBt5el/02VgWk6xlGXbe4wKu08vpEMRaxk8KfvJujtOrLBb5bmy77R31RLA2EdkUcZJTlLyshXfCsKxfp9brXRJ+BLHeXjvsFuUncvDrrS65zuc7T6z/kq/X1Zdu+cJ/bDd9vaSvrAQy50DQOU7tXmHgV6WtLX+0/7/rfpWp/EMDPqupzVf0dAF8A8CEReTeAd6nqr6qqAvgZAD90rX1LbukJnbhlTVZyGcPTUQ/v1OCjlXmBj+Pm3n5GmRi/mSavwyy7DiRidEltj7PnRZ68GKSb9u8yhSFGnHkMh23rDEg1oQf+rOThMHLKiQ9cEx2zUzdgu9tH2wC5oJ2JtX3nrWr3T84oT3ZYaCqHxMYxze16PSW6CtYTP6FmYIyl8DiUxPsUz3dPhtGtxN8GLCw2nNGny1NG84yOyVBMqJw8tAxi+CGeutE4EuhxD5STIhwUmMNJ2cPVrW8TpKMjJu+L1rB0A+pJgZNOsmn0ejskD24sToMWBnKT0bbf5k2nbYru4docNptD8x2Lvhvf3StQrOdcfh5CqE5+9zEivRySMbqDXqF5abotrLmZ7JqMmtfS+7Y0DCndFNMXkU1Efh3AlwH8kqr+Wr/1l0XkN0Tk74jIt/Vr7wHwu1T8i/3ae/r3fP3l0woFLtDSyqJetPQrlCsX7meyKM+htb+lPkZFQtcwJocP9K3Q6IjWxEtWQJcrGPmvBukWaw3bVt1DyLHy8eNKvbfmWdX9svVckiMey2voL8vHAs1dpSfLzoJnC5aHNi61E7yVGxBpXCvSrohfXjCzxxnoOOpvJmcFBC608w2lg66tQpHtRvxcsSbQezQfMg235Fukm5S+qu6q+n4Ab6Kh9u9FC9X8CQDvB/AlAH+zZ181v8Icdn1KIvIxEfmsiHx2f+trozQpGmvJ4nj7PYY1T3EzK5cflvCHdTAOp2LUwmjB8ntZRggppmlIgA9h4vUEJZSZt7yNeJ3GyemKXgeC4PCIdXkRJnEDaP3g45sJDfn+7p5/9ZCHvSzFt5x2xW3v0fX1C9p9VB7smFv1/Hfb3tYBCtwTsP65R0RHEBu/yg7fix5e+MIIzvp9Gtf4+GU7CM/QsrJsLBRiuJ6VdZYzQuthS26apMb3cu70EEjhbYu+VsF9QONBsSMXSE7DlsfER5b37K2YfNlxxDlGHmL5VL9umA7ps4ceB/MwtvR6w+177WtX3E9PaW3AvICwY6WPp0qjw47gdhmhtTPuQ35Og/ke4vJMQkfxvHYUdEof+0qv7rQ/1i3SjzjJYSIrl70Ujuv7GkFaS/J2brC1L7V7R1X/BYBfAfARVf39bgwqgL8N4EM92xcBvJeKvQng9wMf+aIAACAASURBVPr1NxfXV+38lKp+UFU/uD17o10jpen5Foq93TjIs2rrGrK89RpdvxrTu5ZuQVhHRTV+mfpPimmKfWdkYj9Fx9wg2o5RiMy/8wQSHVv6Mn0yPIDlOsiimUP0lOu+lq5MmkM0t6jnEtqejId9Pxgv/87tHskbKaWpjSM6cl4CWSF7Qt1TuIfLZIS+SlnWbnEQFob2sLzQOGS+UJmbkLIu+pruc5vTmUOrxLxa5bsmP3nOrsBISrfs3vl2EfnW/v0ZgD8L4B/2GL2lPwfgH/TvnwbwURF5IiLfBeB9AD6jql8C8BUR+XDftfPDAH7uWvsA/DAhRilu1cj6mQDm/bd8+Fp+vNvr6YOUH/JwRN53d/huDEJIYHSFXu9pxKGDBTaBECQPQVDO4uX9Ou11b4IrccKf21OtujXFHOL/HVnlffiHh1DpoM0vSVPQG1sI/q5A3Ysr8Cbs/b4ds1AGerdyey3uHfgxzNYvldZv64/M/AtImNZ7/MUZC6HPseF6GvKA1L28pZYRl++AWk24lSJeGrbYn0wjP7wX4r1K5U2mk0fkzx/o+M315Lg2o2avV0fe8DChDCXJHpCvu5ziHDIjrn1cc/3mVeQ07Z/PHjd9Z/Qd+Gh9JvmZxoxS2K1HdHH8nY1H0DHULy1AvQfO74B79vs98byMOjM65732vK4Sohz0x7ulWA9eSrfs3nk3gE+KiDltn1LVnxeR/1FE3t+78Y8B/CUAUNXPi8inAPwmgDOAH+s7dwDgRwH8NIBnaLt2ru/cIeFmxh5aRaU/YpQglakAyI0LqChNKmu7y6+7yV3nzW6pTdoyJk2ohweP6V516ZJXYQpKR73+8FHmCfd9wTueFAG9Wb0hM31WaQ9tcSWyoDstVCmMbh0NBOk/6Hemn/tMfLwU0+X7U8juQv6StvK54cGQDQYfgeSjyXhEYzJumUaTR1ZGPiwWxllsJ7X7rZKFrGQyTJaZnws0DQB2QuxqG6uXu6KQJs8n07IQFe+DzPmWBtdIWfQjy/7Ub9JFlzwcLUC967Jtyn2x/XZKC11gbWXa+P7LpKtKX1V/A8D3La7/hQtlPg7g44vrnwXwvS9HIpzZfrwqv1qvC1nYW7vyX0wQUoxPCyBniqcl5GfXbNW8bh3IypgMRlN+OYN5D3pqbwO0e7o1MsoDhuGhNMVbOzqwi/y0riMZ2wXQ224egklKVOYcdmDDlA+Ty658K0DWj4mlsVDIaKsrn2FUBaiCfS/QWkaZjva5rhAL1UFfRomMzGN9lJcQvI9bJePMOzEI/UIQD1Kz2LmNM0Ze41d4zkFjWT/viemRUWdGpLJ3j4RYU2mHSx6ao2cSovdI/JurQd2AbQf2O0QUnefERnw2PiQFJH3BXndpE4cVJ+98IwUrSruRaAxsj7o982Jjbx4b8w0YsX4es2BTyOjbPV5fmZStULtZXrht+929oxCrT8dehzqSQne5TgaOaWfwGaIWF9JLxfT/qBI/WMEDBZBiIpQ1eQdeEU16ct04/OE6bMXorvhdiRJ6LaQYOHkoYINvlbOwz+Gj4144tu/5iMbsRfCpfVAJwup1Jp4YnbzQtUJUITGPlc7a1zQB6X24TEO18I4pPO6rrUewdFK9HmGi8Q7ILqMiq5s9AvpjxZv7bPJxtGYwxYfzVj4e34UitjFb1cWeCCuUYJRT/ksP5rB3c0k5hHAOL9ySYhp19PEtVJ8A9pIdKdr+kkHIhjrP23CqZZJ57q/TuxoLek+2lRX6C/1XypvAgofcjD4GFKv6SDGrtONW6l3aHo2Yx3iySuF60hcOiNKW4Uue8muh9IExICHemlerM9KgMn4faVIKKX4ebK4mcWnlTnHs0L/b04B2tos0l29/pvElL1lgFeNJVhf6rsA7yp/4k554XaaFsvefJMA5T63tadwiCqW94zDDUnS8LpGVp8XApTdQx5vBSqnYTuNQk6ikxff88/WVknM6ju7R94nH1Hc2JOyZTCCAlJskfuVQSIhLY2Gg7Y9365hSsXzp5Sd5d9nEh14mGLXkTYadOTZexBtbv2JDyrwLSpY8SQQ600Qy40D0OmpfpBVqHnXRdeJD9gR591Reo+G6ea+8A8JkoILxTfUP75r0VO2ePID9qeL8jvZX7xANL+jTdMUBL5bKX2N5v3ekA/AaKf2BXg8GEBhCzgNWFwzOVtwGjVGBZRFEZNUZypZ9Ql12fae/Xm+9A/Zn2lw+Xt3ng9MQBZjA7zKmZ4rKMySlEJCKFck7C9jopYmmKth7Ye3hGe+qNpR/t9XJ6OaHx8bvtk//7i4/Dz/aZqWVX0U3jftqPLMyU8wnEKb+LmUgk2dKYYU8mW67J6QgZZEHYwupK3QCM/YegNB+RoslGiAGRXlxsjWYDAPLtpKy5IVMoom3jg4vnK1f6qtfM0M+8uajN7hPphxVkvEz2U5j4Iv+eeG6DpqnsF8yIMHzc2IwZCkbR8pj4E761tHtRQM5+7OK/Y0d5zfUQz3sJfhn2iTC+iZENChl0JGvrdJrofRDSCcJFO/DZUYGt58MhdcBTLG5sOfWJivtXV+5xEslnNpxZbLHeyEkYZ9dQwgwo/qsaBIaspAOC1GmJbu7k5Llflm9oiN8k8v1z63UqGhFQzgsJAFOpb9MfRvETwg5pyNlv+I9K0raNy65zyBjZPmzEk28OWxTU15G/WxMk2E8DMkwijtAdO6l8C6dJBdBUa3oTnMjhFRym5wMvVNoL9yza4tG3VvIr8zM+ZI8BC+FmuE6rR/B2F4ATbz+wsbN+BBCpAuFGu73PDbfs94Ku76miiItjbiZ7ml8qT/Leym9Fkrft1LWOLBa4rZIgATfFpnYK1igXX+cHQiHopnCr3ez4Jg1t/am+ymZq8dttffaEm1oE4DDQbIPHWrtRiSeGuRQFsevOfFEpvrC1rb+ZwthpeiwS0UhOt6AJbVduysp8G2I6aTxyFkFUBRP7s6423bIHa2kgSZ56ms2oDwG/m5QDDngIy7qiRY/jWYCAjY5XRFtNAYK+PtwV8qTlQv1OyB3Ni5UxmgJR4lk5MZDXFM9ZNjtbHgOE4XFfctP61D2l4/ikL7tlUFKmCcEqPidzlLFMMeYdwcv2HHeXHqREWgsOs3TXOf5wIcmZhBjO5xoE4gbW/YIkhfHY7kMqSHe51QeEB9c2xT1XgMQsjpd/tJDaoX6nhE8z9XQ50XeQNfxrVcnscUO7jvomn1PgxM8gVuSLD4X1yYX+wiFgpSUdmX0ECsYSkySlh9VTf0+oj1NMEaCIesKzWQlgwPhOUKtmbbsHRiNnO+KwQz5E/pZ5V2tSSzL6nzt4vrQog5Gm8sJiaQoMrmr9iceRvnP96z8UchzpaiWdVEfgudzRYEcolYMecifUx86jcu68vy7MM9uojHPy2wUU33LtZ0jGnI7prgfBHJuDS3Dc7l/uU/X5HL1+3VW+pOAOsIwy6iO6v2QogJH7XYYlZUJiVGH1c2WHjThKF+YYIsBt4nmaO48EMbpa4LT1yUiyQrI3l4ELkyHVVuA1YtQhkTOPLI6fE2DDn9rZXvW2pFeXsi2vlVMu3NGXmm8tzK8kCuIB68xnRbbZfp1vDGLUz2Nvq3CMNxn3+LZ27ZDzfKOmhwO8d1blJRkgVG515EX9lhJ2mK38SHJk/dnT+VJ8YXFUqMlKwdTmOwtHCgAVzaE/gMPjEZbgFR6yJH6N4XHeNHZzlIC0gtykqYt7dO8Kn/3MyKvnA8875ifRJMDm97XEE5bHT1B9TPPbMtmXoR38n0NI92X0Y6DrV7H9nXB9rUyDq5La0sj1EX6h3TIyujyURE2llzPa630AWJQHgCHwNdTiLthzcglMsz3FuUOF3IXFltqP+fHUEYJt9sfKXjlG7ndLJhHrLgBDS1R5mHmxSUZL1IJyDIriUW5iMRuJEIPvt96PxBB+YR4kWRmKnYDX6+WoUmdkb+n5FlMxu7K+K88kSmxYufw0cumyeXpLDQibh1fpms1L1lpZmRrRnqR1/Pj4PfRGNxCZ2rfUjlL39AhMQ/S2BzppAPv42VZ6fR8Y8X+8NMylleB8gJhQD3+l3oW4r/AoVJ3BGToIb8qjZCRI8QsmKREPHZM8ePtLfjDJuenLb+eFHqnYx3B9rc7apC48Ey7kiahJyVhRmOJTA48H6PZvCaRgfTVjnw4DQVvr1OUQrRIM15y7kcquKS2j72SKyM6UA0/OGTxTY5N53GlSWNlWYmqdMRK3gcv0IvGh52YJ47qCHQEBauYDASv8bBH4U4Nxd4N9eXnAFZeSSG+2Gs2M5I1WpfPChAtgW6M684/o4fyedyZPY9srOyeNatA2RRSKsQW7Kt1KvKN+cAeFo8P0+4I3eLgvB5XUj+NHqPvNH5PoZ1s+Hq/3HsnOnMcPi+k2/XTW8DdVwSnr4nT4M/q0Lh6nfmkAJK9vH4Yjupg43bBILweSj9NuOwteh5gQsWuFI6s6LWmSYEexb1zmoQJkXYOUyiFL1SYYK5w/i75+lF+68MBurmGFlQQULw3fqmcWZsbeCVJiC8i0luRzQFSziQu681trPi04OWRtzf9XoGNFb15zEnOa36OfsGXq+sIVrWk+ZHoOZST3J9sFO2e6IjlH/FnQesyhs5z+YIseDjuiuz9gaUrcunP69CprlOoCljqrovtkEF4GZpeC6WvQi+doMkVhDULH3kF0+P7hmQlfQeV1SQ8ZO3LPtc1bZcDgkHKutws+v5UsT/R1o65fwvjFiZTIdkwIyJwRJwXJJdbAiWiI28j/y6ICh/wp2w9hqqChz2+lUPOAnkYjA1xbhXcbQ3iFDvQRsfDLMb/EPYg4+keCKGroBBWCoPHJwEE3rXhBpvk4vA5j6z8Kfbsx/r6TQzvSUjm0tqAKPVNqb866j2/Ub1tj+cSUmTZdflMYCPw0mhLO3vYi7FdbFyP8cJj831c2o6t5vkVUV8TkqJNdlSCwvK9+mQ0At/ryOtj0MfU8tf70U/zACxl78Z+mwc4rekoKWUa1/ydZdTkhhW6y7M0HbQ9b596Al78G4rzU0xbOLMu4TUV9yDptYsBYILKXtDsr4XSBzCjhIVi5HyT2wb6zdlXaCAr7oyC6kH+a0iUjYANWj+eYSxkLsrcglgWyGmq7xaknITI5mdIvKjcv9rDWwGV5RADBr8FzZhIX/y1ENYhnVlZUTtHi13Z6C7rXiFHHs+MhFdpZegXuzS4zlB3pmeVn5KHd1bVp34GGTjqq107kA8PpzCNScbcuSOlFwjqiJ938Cw9I/oMIaaUfMxN6fODUkmGl2NHAGFahljNQ6ZzVTdfJ+DAfDIjowWo9z1EmmUn6S8PM2vMFzxLHl/E8qv06iv9zkwVeCyO44/Z0gXUnhY68xGsjvCSRQ+r8ykdPXDEdHiMNO935gHusV0fKH4il1EKCQ2vIazc38nzycZKEZ8i5PWJfV0Pr2mMF6NLqDu+FF1Ce46yOM7cid+S8QhrEyT8HLcM3s9q7PjJS4qn+w4OpidPElbwkugxWkhR64KvF0M9EuUzTFpJtCPzrPflBOA09sZzm8tTNUm5GRpnzyCvk2jpqL57JfXUvYt+hAAro2WYwnZw2U4eaWs+Wkv0GBdATGksOX4dvDmm3VCxgQY+8hxUJ9eBzqcyylmbtj7AfeL1GADhmYHwHMTKcFCc3aIV5UWrTO/b07lT/J7pAe08lEFLpedSjCZeh7kGEl99pQ/ESQHEQV3tZEhWFxhMY2QYHqzK6GCFwpDycTn+01j/EoWSIBtCMkL93JmUL4eyVsqHr/t9jN+sKLORYKHLdUxP5BI/CinvYKSYpjoe6LL6ypLBo177nBCNkcHKaqFIJwVONIZQx6L+HOrKKRgJxPqXdXFfDuqdgEZS4jZ+am+bsvYzEEg0BMV21Fcqo/2IEA/5FEDviD7ma0nV5QVFgPbpRxmajGOmhfL6Z1pgnYxHVnpC91I72aO1sVmGj+2a6YzUzpE3EpIMo6Gbxs0pnZ+TR5rktpVN1WZjf0CPpddC6Q8EoK782w4XTNY0DDLth/UJw2h+pZAVUWhNUfLklljPlK5MLENI5Qyc3pbxijoBIP3oZC7AfbSveUcITX7dqOk6BGriJ/e/pH4Zqu73ay/UzuyX9kf391pQd1kvVgFjD3eVtofe69PAbzcMeXIbS8ygmuI+SEtvLclAniiV4tt+nxRABhjWziHIyBOYdlvxGEyx/wPFo6mejOyCUjceSmyDn00IcfPUPoc+HF0aPUY7gzBvq6OYHsu3ZzhEtJ+qaheGTPAajo3r5Inl3Wcg3pMMB54rK9n4O4RIlHZ4ZV5Lur7w8i0/7+rhp/s52ZP58tDmwf6kGdkJuKVtswFAKc1P5g8bjtda6QuCQrIJUe91HFnMk4jfrpUs9xSWWVnsxcJaFjglWqytdgNBAaxCRGw0ygOwvd2e2Gv06RBWpfmbbMAKZUyhCox6kPtg/ab+52NfJwQW/PqkdNCVeJXlXmT77jxlzyDk6XVXBK8gKEbmdUaKbNysDCkvgMZtZfAtv7n+i7Wbo9DNJRBwhBwnL2FRL4/hUGQyP0yXyrunuTDiSuOf110OvdtN52vGR1bQBJAAtHcmdIJy1T6meY3I6ODw7IKvRgM/lOX57SsvbJpC5nnPxoH7n8bVebeNfgdPSga9Hka2/tBYlv5+ajk3j35/qr7oz/M4GycOxTp9SemzEbsEiF59pa/pD4juTRLU6WhSTd/ToIY8Mv/2UNDCMEga0Pz04qErn5XmSmGvUlIKGcEbTXniTmGgXC0JaN45Y/m3UscOjEXZ0hs+2vsf6NB24FrNnV0pd6T+JIUVwlBCii6V4ckQFDeTcDBR+EnJaXzSGOaw2OR254NFj+SQjbLV15VTebssH8zLMfaM7q95sXyGvaPtRegA6PmIP6M/Guan9N1ZLa7f14QAjKe01T+4T4zi2UiF+/adUO8loBUW19PcZLnj3XlhNxSnNG5hB9AKkIyuQipweku64kd/4h4zMAPVk4DmCngEgHAhvfpKHxjW3wSGHq7ISC7E3LBQ2NkIYEYKrBh8UuRwCqMkmiRsZR0VMH2aypHAaBdM7lsOC4XvE3TCWCDO/AMC8m3EICqWHdNWSFbqW6ko/FIUE26lUzb5kXpLPEm1Eb+Vij0dxuXx6pWhT4vbjPAygg2oiRUIaKzpkXnle4lnjO78PimIozDTcs0A3bVP4CDLrF9LITbRHhL8uoTdXwApBkK/YfGavBQGMhwWMG9PmMaMMLlPhKJ9fE8KnMZOHVvvKfb2rCpNRlI4EYj1ZY906cnabz5wj0NOScGvPAH7zXPftlo67xZlwtqYhXMWZdxYE/+kAnf/UtqBhj22X1PUIuga2NzAZBB53K4pe0uvh9I3IU1Px9q9o33oId9BneFSMggrV3fp/tr1FWozWrLlXyjs0HZWQKIzQlyhj3zNbmVUnNoM1xJtqy6zIuWF3BAFkoM+i7adQA5bUiNUiCdN4HGqm+8FI8DIKCHovK5ziMDp2pE3dyQX3j9WQGToA/25udQHBxd5HiyUCxvZZThqcc3r4F1d9kfPkASlTPm4f5r6Uwp5icmg+7X0PYyZ8XABfKa97HR/AkpZJlPK6y0hMZ8yzamNSRdwf42nZ0F4n8Cij6s5nXkdQmuJ1lW6qvRF5KmIfEZE/r6IfF5E/ka//sdE5JdE5B/1z2+jMj8uIl8Qkd8Wke+n6x8Qkc/1e58QWZ67lwgYHdheILigdt8XafhamoxBiVodebElbY9bITXf2si7b2h7FeczRVTTarvXY8kmc033aUKHsAsLb+rz0X5qiwG7686CSO2HM8CpfFXBbtvutqhdTqViEx2LsiAlsQ1U530rir2/LrG9lAWzos9KJfEHwPD42DuSaOCmhTcd8uNHMdAinpdLh3RZXWF7XL828Zz6kcOC9lKUcNhaKuepwhf5fMHx3OcB9cPqDluZ6TH/8AYpQfBUnEc6+FDOmGSiPId7H+1PxxjxvDFPr49/Q/yK+9O5HcVAC/pH6wk81rYt037nBWXpMfKyCpux0TDv3caro/MpVFsQnpJ38sgQ8GL4yoAr6STPh/HdYvTbc/gR4MZ3Q/RTuEqoLvJWs3yazrhk2G5B+s8B/GlV/fcAvB/AR0TkwwD+GoBfVtX3Afjl/hsi8t0APgrgewB8BMBPiIiJ3U8C+BiA9/W/j9zQ/jK1Ca5RiCmxW8yLd172DyhNyMpvGKFHBa/cz/ksafiYDfqq3sPM63S0iCtZi1m1i2tLHq+QDNO14sUBGpxi8rls+n0Y4jvIfzVlVtgEP6rnhvoPEeQKOR7Ixbri1EamPfFwNXZll8noBgRulxhoSNvBY8d4CLB8V67REMAc4vfJa8ykZP5QmjwR/qTvL9VGTgsAdlUWOto/9BiYzoVs3Lz+l9JVpa8tfbX/vOt/CuAHAXyyX/8kgB/q338QwM+q6nNV/R0AXwDwIRF5N4B3qeqvqqoC+Bkqc5mGbjnZnVN7MYa0Ywy0H8rkLvt5IJZKD5mwB8BW1NEEDV54uUXaDpbj8nn13c5HKYToeZvltEe3P9SkqT1l+gxVrOKMjN47XR4WM+tPi5GMRkIMslJeo1Pb+3E30RHK4TgO4Aeu+T3RGBdWLOVQRMdhdAvhZ48rLKqtvBr71MGv4Fr3PrtHQ/zKnpd5TSxzUhFp7bxdvdfU6cxojybzUUhwWtDsqM7XFmhNSUl2dcXH3g+WbfYIvBzJdN4oAe2HBFJ7MbaGIZPnzrTSL1dBKYqt2MFrlR5E7HVUWtBkw8ILyXlRGvA1KAjCy47CMSnJm2K5NhJ8LrJBsnlD88h4y3PYx4Z0yUouQ0y//5UdOH19oH32JsIRzCR/YadU6ptdu7RzB7gN6UNENhH5dQBfBvBLqvprAL5TVb8EAP3zO3r29wD4XSr+xX7tPf17vr5q72Mi8lkR+ez+ta/NVhrxd7bQk/JYoUtKenD/aOH0MObH94/aWyCMVcMvCzwzDRdTRhFHyNuqvIKigbHv/giZH9V9mFhZ4wDVXEFmQDTEN6c8Rt9IHRfyX0OUh/Ke7y9v3pjvIM8k85aPw52X2tVUae6oGewL3uGKbp5Th/P7cD5hlqNLZVY8WSDtm2X5KL/O4ONimW9YKcR0k9JX1V1V3w/gTTTU/r0Xsh+x8Qb2ens/paofVNUPbu94Y164InSqZVRc9mHl+XFqjlW3BRREJGao19ATxdUCErDeZcHJKIAmgO+fTXVwnzg2lxdlBAPZcX2CTlvnge2PnjwWbRmnh3OonfDKSfMCCEHWWlypn89b24/vno7g3O/Xs4w2tL8Uxq4RykJtnkMptYWOGAXtMj47fWHXgiElU0LWT4v9Gq95ooLKC71sp8brMJ72uuyFGnbdx1RGPpZsi9Ua73zHBUYelxkaL5ZV3kIZdiylQ7ZyaGW1mSEsOvMM1PFyGo5Ra4qXZ9rDepDx1HaeWJzfjuGQ9jBW7V6iakP8pWhD+vRUsd6NgeJYuI+tjLaMfjbEK/Dlc5Hi7xMAIN6FHUiIfXR5Q5q3NpYki2FNbNGO97kkXlrWvHbVafaTVbMNzWOiiJ7aIr3U7h1V/RcAfgUtFv/7PWSD/vnlnu2LAN5Lxd4E8Hv9+puL6y+dgtuO+dMVDE8QYFas9D0riiBIB+jnCH0u819ABUfxyKxoJmRCdeuVNrzuVD+HMVa0igKqjORl1GX19GtaJcW5ZdkegPU5LCphbN3oZroOkNEhQj5AjiE//fm9IwSW6r/F27B8zMbc/rSWkuViRcuqz4mHQrx0eo5kd1HnShl66JHo53p4i0YRRbVy0nfxkOYJ73q+xMtL9zJv8lxfyNUt9WbwEBQy9XvpfRzRx/xa6KKZCMTwcv6k77d4d7fs3vl2EfnW/v0ZgD8L4B8C+DSAH+nZfgTAz/XvnwbwURF5IiLfhbZg+5keAvqKiHy479r5YSpzNck+FDkAlLO0d09afFLmuD0fb3tpW5e3QRMrxGw5xpripkwToy1W0oxQPZ8hul7//qQjnzuFPttR7zQikfv29J4pjtqPZa73bU2j3vd7fa1DpcU59yfwnRaiGGsfJkRCnxbf1XFNpSn1U6lRbisaqgOw7wXnWlo8t18zt7WNjzTvwCbcLv4SFdV2n9crLHaew2QBDSPuMpmUosVbbXxsXM0LYmPHKE/6Xvoz5QflNSRqCDBNNj8axIkefOW91oFmiqsHrzMpCm47vLhjJbMst8QHix3nOeDeneXtddmJnuHgPVtbIPL8x6Zt77kCpdQGhFWwGcoXRTkp5H4HSt+GvLCaqxBpOBSwRppXRnoy6jo+ww41jpUnAx2e3alrvrvnnsCJj7u1TXXbmATPThCMirUTnkMg/ZPBq5e9EtPPr2NYpXcD+GTfgVMAfEpVf15EfhXAp0TkLwL4JwD+PACo6udF5FMAfhPAGcCPqaqJzI8C+GkAzwD8Yv+7LdGA+UCZ8JN7yg8z8OAGK8mDs0AzlgzlOgmGYNI1q2tpZEnhh8FmhKBoD7X0R7PlvkLfLlCRRp5gHMP6QpqS2LSdtghpEw0CO2ffwwv+IJV1kpQ996krwoCAknEs0Bi314Hqa+1KnNxMQeedClDTMwYmwMlr8LJ1MR42bgnRCt/nrkqLItSsPFeDlFCXu9g86SwL1T8tzvbvk0IkmVs+U5ISK12vhxUX4jhNNIBop5DCFDtWzHu7eZ51WjxslpRa7qIzvcN8KeoKqMh4EU8pFTiVScldTSwDizEVYD2+/abUXoXxgXnCeoHK8OKy1AaolovySU4meq3dJCshXy7b9UYlw2D1XFsTOmIDcIPSV9XfAPB9i+v/HMCfOSjzcQAfX1z/LIBL6wFzEozDi3JPXMO1G3lvLDOmZgvNTOzxW5uszrSUx41LFwabVGFypdiy12F9sPp554wAKgrZWyVyqqj3CryFYbU7etJNZ6n0UQAAIABJREFUoSLNIzh1y9fXNcIugh3+SkPdZDZ2vX9lpWB7Hu+vAmct2BxCDj60mL/gtO2NxqIej5fazkgKyoTaz0cr+0eJ+dxDwDCYvivKvIK0q4bRlk1ufhEPK5ugfJKyCx5Cp6meCFz0dnj/eAinsKFStP3z57ZjIzzVaTTXWMYNiFK/DYXSugTnVeov824yEAZIeAyYfwxokrFdfl/ET/nYjm2r2PeCWtFlGd3LY61NbTPqNVm0ZtP5+YEeHXPB/nitJ79e0Z9lWGlKk4fO60lx25oMXZvWZcy49rEwQxKOq2DDqoGFgfYgpwYw+liX/jT+tXDjS8X0/6iSK/DEiIHeWy89bEJKJqPaVjhe822QBbMAEQ08IdzWSKIl1c3uNienFaOcDWixhS7u86kCpb9D117AsKk/KOVHtd7171tX+r2d5XqGhVPyJM6THPAFOQE6miMtrYK7UofylaFkfIEv1W/HOkSm9M8slUR7eNhKh2ysXGUPVxgfLcTBxh+j7qjARjusyI8QoW/vBcbCHoguu9UXMDnkkxUc0589UfdANNbrdHAZUkYsn0debfie5Zj+prACte9fBT3EM47Qbg/w0RPcQtt6k9IM892+Zzmg79N6SIn8DX2ghVv3itM8zvHzEMaVBV8x8mua18AwwIzgZWHUWA+w5xv4QG35tTL6wHSv0muh9JEZbAO9RcYw4rZrIVZ6YcEytLVCRBm1dTpCjJSUUU7h5QxAVDKM1Eh62bBYI2q7HwId3ejZSYgd8kkVYJehgHny0ETjsJPxLRjLOpgqHTbyW65Kf4lKeMy+35uMrrbOV5XlNk/pvycllP+sTn4qkZQSy8rg4bg2jQUljnGHHRckQ6B7WWGHOKzx3bLet3WW1VParnjS7o0wVrSTyTxOpm+SK97x0ml29J/XoLhNpkf72hAZHvceeI4ofGeObLUdvZCY67+JniyHAREDwfuZlCwGv/K8ZuNlxt8BHmLewAfuJ8+FVO8q5BV2QCUDspInRvg55HsU4lyuHdo9MmpH6ZVX+ookzMbcMlATgIZYd1J8vXA4epmZkZS4D0whpmfrXlK5nE/SQNEk9gOVFhx33WeHjVnHe0zUlDYUgCF4K8jKjA7Yci8jLTAtBZmUflCw/b49nBUIpjr8zVm+MEd15wmDYRz2/N4AUP7VNUJIg2exTAipUfs5Bh4QeKaZZCXICxC9mXSgGSuLKfzQZXd/qtifxZMoAVLCWelTv8yNZ5om+jCU0tL7IcMe1m8yL+xaV5LnZwQ00lx0r7pKP2oB2E7Vd/DwaaqG8n3HF8/XFKILmzCcEaM/q3CIK3qBhzqU+B9AABsbEkUn12Sc6pmMYzJY7vEl4MT6pV0YdWT5WYHBCWBSX73tXkfNmwlSeuWVfkArIMvW/Uq33gsF44kE+igMk8NHy3qwyINjq75CelM9rky0n5eiqH23i/W1CZ74HvYcRrDr/sCL3+8TK6G90C9hnqY+pf4GxZ8kp2qnrUqU3YXgOwvYH838WI1FHiM2ZjZ5iO4QCrI6F2MRZCKhQy+64g39vrog6bSm46d54nK+C20t685yZ3Wv+pzHhPubeWYphdVCVaR0hEJ/dvxCEfXdWtJDPO4V5vHpn67QyfgGFM6GGpgVtgGEg7EN7a3mPuchMZ1QeFLAXG9eF1omNrxc50omJH1f9TP3bZFefaWP8WCCW9GOstqkVl+I4e11AIYwUpx/ddBZ2NZHFnOKLVK5LHzuNqZ7eQtW9gac1pOiPmlx8fp8aw81FRKcvZ2/3VzdPsodKcuDtLAQCYu3XeC7eIKbychFBh05BCTWHjBc84rolRiLtL8FjBa8mtDPWmTrD2b5q/RCPbMS5QW5lUsue3+cnVEgjysw0O2BgjUCJm+NFoMnr8CIprUFRpRhMnalWk8at1CyR8nKjRUVj0uinZEfI3sO4+Sth85jxQiRWV5enzC5KIuQFMlzMxiaXpAz5GbvAMQeyhN7C56971cRwVuvu57gh8cFgNLzs1Ln+VV2hAellPhrtHOfhWSW8+e+8iFtgR5gHKbn847q5XFLYzf4F69nTyT0w8acxlKyrByk10LpQ+YXQS8RjPSbKzSIpBAIndxIwrK9jJKPkFhGJoeGpD+4oiX22RTL5GMmdNvq0UPvItRJ15d052zaT8XMhusSE5Ue2NJxbUXbuvz1+5N3tVCInmRxLdfNPJeDPBeuLYGC1XtkDBAV2MV0w1it0sp7W/Y1tRVk96hfnn/8sEXbrcf2q7b1JZ3iJCmlUFM2wlNTl8UvXbgxL9e9ULiuQxb6JXhdnFY6Z0Gf5j4uDNEt8/UovfpKX+APvDiaNzTTlbwhGdtKxxY/xNrSoPBj9i7caZLnrX1ZaNj15J0IOTTgRyUDMRRh7Z5L+35fsT09A/e1P+jSy5/pQTRT/kUhZ0Hxow6k3dvgi72+c8YWjE6YhHHyUpIhs7BOVUG1x+y9c+3hrOruhY1L5J9PFAC6i+/bViUeW3EaJzfUHbU5r3nsrHyXC394KHtZoHxEkysX9nZ6mTy2juRobYQ3FdjvQu8KZuQqe/fMaIeGH35mfVwpMd7dwTRL5JcdMRyQLdHtnq6JBR1LzrrY0LLPlYf20g9frzAxMBnr9NhRHOiI/v50xpPTGXdbe1Pavhfse2lbNu1BChBNinBYoc9Jom8FXHhu2hiEbZlmbPN8xsLYWjt0UKPfN0+I5MV3/9grF5PH715KiVtugwEri++6vr4KJ/LY8fbyVXr1lX5Pk1vT5SyulqT8C2s9pWuo6tL9A2R05GlMxTmfC4r6ZAp1KLzzQsyQKr7YxnkuIZCL6Qj1guL2lNduh3h/rkvn/NOxDkftJxqmW7mNPtaHSCiPT27jEpK8Jiuk3A/pXZ3NYnUvlI/Ve1Nsn+ngz1vSKi+h/CMlcsQj6UZh61s2eRePHo0BSMkvANacOZU7ypaB2gJdH66VpHbs91F7F9u6RX4u1LXMlw3XBd5aevWVvoLi2zo+7bbFRhk1E+JqcUHFft/K8IszAmogK8mIb3+CuOuGOFZPqV2Qlcf49Pg2ITTLa/nL26W9+3RrL5AW2tVgh2NB+v782ngiu6C83eLo8iAozwlOk6Hgtx6xQPj2PT7+OSGrcRF+cBZEHdQrMCa0oNMGfzbAvS5+DSLgL1HZd2M2xqsEqVkfI5ZUgR8nYb9th5R9n54/wFC4htB9PEjJePy7pmt81K31FQktUn8rx6Fp++bpqwWnr0lAfzxxeU3AZHOSKWrLHiq0/jltWdnIkCMOcwKD59kL4HbLi8irgKxJvrbTcFW2Uv2vSFP+T+7OHRajycjRsdS8NlIH6mflZl6MeWJhwRdDdsKDdCRLVq/VF9Y7mN/EM1+3wchv9To96cFLbxvDiwlel7XDNADjBSvssdTUV+MTrx1cArl4HZQ+EsMXVjeHG+YKMDF/lVaoxc6tWYaheYvZrSkjZZo4PnmCz464L98mGys2NeXdCc1Ij/g3ockbkmT+HqAwX7hbKKgcNstHOjiBiT/+NTsESamtYuQTfazoD+4vr13h1XL8D3bB2EQGDmQxIzfgkN7lWOa8+fcN477yKq55FjaM9iJ0P3KhK3vp309bsur5K42PJpmZaMxzAge/r9CeZXPKstI5SDy5xFfuz8o7WQEH0PdL9B+0e8uSyaubXCmiKd++86He9bFNcVhWSjl2HhS/oSjLA7i1DhaazvPJMT+un8vYEQ1MV0aE7oXQXm/ZgfpQsL8oy1GTvSGk8rxge7ugPBdH1SoNjVks2fqrd3Xq66RQCqYHOni3hE1gf4LWtoIaL3Rs02Rka30qLwYSFB3lzx3tTw+DyRgjN/gWNz9HRBaerFRCYrLg+UI5uidi6J77YONWKc9iu+WkYC4o9SWyldEX5kGog/tC8jvVI7GelfHwflT4oXXhaHHqf15TWfXBl3JsrakCT+7POG3twLW9Fjw5nfGOJy9wv+043e3hvco8lgDG2gHtwsuyOfHF0LEdlMcInXd+AeMsHaV2SJak7/xxHaKjDfOMfa2Q5M4PhWQ+J8W+MiCTcettKh+Sw2sRSWtX2m3lT2G/1krfkk1OZl5CYRMiYRScJ/3KukscTABj0fgWEg8mau6HcF/sz8D8Lu0p2oxyqb++Ha2/WNkVJHsLlpKSCgqQAfYlYbSqbnFpiIfeJseDGckZss9jJpjbN8We3W+M/P59dT1NKEZQywW91Hdd0OQylWXrlnQNvQOTIcz0rMbtcMJr7F8ALVQuo89VqOdwgZyIOG17OGajiOJ+23G37dOTunnhM+xIsyqzAs1811Q2gby80JvIDX2e0DXJTqibrxvvJNY/8TOlLE/c5hI0JJ3ndWd9+ForfWlP3garfwL45R/1NLgVUIANgu1xlyGwfgQzCOWcRx05dhjcTyTlSbQys1lR5IdLrB3Ph6bIy1dOkK9vkK+fWv1+tALCLpvyQPHIrLRCvHiUt79VLD8jkUPl4bxt6F4UqHvBi31DPSdxMv4TMjf+ne1oZd69IQp7YbZ7PhynByICN2OZEJZXx+joNPjCO6msfo7Vrl6YHeKlqX/WFitGViRhd9IlhWkTlz0fQXhS1RSY79Lhe3k2J9nMCN2O4Lb+BVmuiSdkNPyFHgpfX7N6LGx3f9rxZNuhGGN96kbg/nRuldmLVDgUZnOGD0ik9jOfnQ5CxcF742xbzOPPJCRe+fzgOUPKPCN/G2N/nsg8qWwYMOp0r4JAzoqOfJCfA1njy8JAXnsg7LVQ+uGMclKg9mCHDxwzkATZFEhQ3sRce7SdwzwhmUJhRU9KYIV2JqVJv8OCUkKj29uC8rygvC0uHMGFNeHZZVbaTp9CRcee/0yjKcuMaLgeFj7SBlPcvrbs5700LyWjHEdv4zx9AHiwBdwamR22slYM+vPYLJTvIULDAgxYHWmBOIQEqW77HpQ5TeQpbJbG1fMlxQEkZYu5HuW+Wb2Ejlc0rXgQPC5TisaXrDiYR+netJZliroMI76J4m5rArprf4MW2kYAj+uXWD548jznDvrDtDo4IKN7cR3I8rBeYdk6kmOaZ9mDXYXn+H4YezYKLCcM4IgvwcvA+JyiG/jXQelnBQRTmtoNgo7fSArCyrBwWVZFiFuypQYwYmhKOzF6HTwg/LRjEIaVwrf7GVWZoG/A+R2K+qw2pV4lDLLVy09MDgTcT9k8mVKWlqe/oETZwBDS4Pa9Dyxkq8SuuQJqISV71SFPCIWfi+RKU4Aiw4AMo9pobmiu7XDJRxiH5oV4QmMbxscMXI6JLhSye2R8TpIbraTcaQw5JpwfLOLk6wPpmk/SmnhnY1sQFZQpDWDaAjo9M4LBAx5z3iUyoc3ehtPC8ks8bms34vkBQM8FqE3RWzrvGx72DS/qhnMt/nRuqwzBm+X2J6Ns7SfjBQyv18eb51kyhsErZB4BQbbYI/axY8XNxkFiPWGe5fWExbzK4cMcIvVUUtvZ+Fj/stGJVbz6aY6VDwXvi5B2b+Hm2zEELvQ20Obu94GwtwRBhxC1BZV2Lk4O3YRtU0Sab+MiRQIgosE8wJ0efeMMfccetx36RG1f6knD5HXjVzQaLkE8ryehmdWCZ0Cvxiddn7vjMmmT2N6da4KoyQh439XPZfHxMV4bzR3l88N2GcmbcsvKOfPdQkBgGcEYI5445jlOvAn9SnSUaDQOJ9wiTOheV6WHoSr1jdEejZO7/enF2uzNTLHyNAeyYciKT5iXGRhQv/2aAvrQGtj5vco9/LfX9oa1876FHWHjAMHYNs+tHO+e0K1tYOAxXwACo9v4sUqa+dRlJ/SVFoC9zmwcK0aUwuQsyRoyb9NYG92Sxjf0KXnBU5gvpddC6QsQhFfszBmKcY+MmBnZf+QQQVAibKEXCtmrWnkNiPlDOOUIZdA1c/d0U8hJUe527E/G07iBlo5EG+0S6Gtxa4U/mQu0MM9BX0K/LvQ5TFDq3JgE/c1ZuVw2IvQOXTciRWO7nV7XXdmIk1Lz+xjXDJlFQmI/Q5iG+nGoDMho+n2JtC0nMDBPyIXyDLQlY56VxFS/KZkkj36vjPtZLr3PCzrYGw3hjZw0Fe9hRT5DH2ixfkP/inY8A5LBXsbqebzzOHLWo/WMRX3u+QJBPi3PNF/z2HHdq7AhLvw+4KWDBgIiXsyus4wxqGH+IcnAIr0WSh+AM8smtbvaHNssiUEYZXgrZniDEoUdpjAIqM5C95IgWTvh026zkpfxQJdNJHMbdVPUJ4rtfsfdkzPqO8/Qe4t9ao8Bqr8m0UMmNGmbodDeLwmTnelcxvI5JcHLZ6LD2xiX+PjcGJLq132smkZvRzDIdAZ/eDVhaYYsoD9DX90Lml5Rh75tb4WeNH2CkJKhMgGUDFFA0baIbHKQ14hym4hAI4QaUl6TPfYsmtczZNQX6FkB8PtTjTdkxCZlqKTQZXGfaPdkIR7uKytLzt9f7GNn7Tw5nf3AtYe9MbDW0l6ZWAV2/n7mFYeWIiggOnp/nDckd96V/F1SfuMX94O+K6/35TUItOiAzfE81stYe+Ip6xQPDx7oFH4pjgNAHsMVAF6kq0pfRN4rIv+HiPyWiHxeRP5Kv/7XReSfisiv978foDI/LiJfEJHfFpHvp+sfEJHP9Xuf6C9Iv5765FZgGnCpiyou1XqkpEmYspKflGRWCAsFGib4CtHl/Ib0S41vzjpAN/VeUe/an9OUDNbSo8EQyCUi0CScKdnRuJwf0pEdvYB96nBWGrnexYS9mFjR5Vsp3LYqF+5N4ymTFxgMZ04HaJRd9UteFhAV+WpWHvaD+MBeZ1hTSfdXdR+iw2ww6XqP3vnvgJB7h+2l6HatatuO7Nt1j5R46mcwLgvjOoVIUsr3A2pnXl4waMGby0b+UrJ6Mq3X5Clfy/Mny2du8yDdgvTPAP4LVf13AHwYwI+JyHf3e39LVd/f/34BAPq9jwL4HgAfAfAT/aXqAPCTAD4G4H397yM3tA8oUJ90NBAeXW4PJ3l/00C2+GyLdUM0KLuVq+vou/SDlu40LCaGkEIXzkIx1aAsCwICCO4kDZJP1HsFnlRsm+LutGO7r02JUrsQQPb29qyHdyrO71Sc3xjGod5XQgV9B0/nWWiX0CSIJx635pfLe3cacmsvySBDo4AUxf1p91CNL0J32tnTEh2dthM73QMAhoeSFjP7cAfXPN+zz3o/FJ8h5rD1k+55P6TVZw8G2fg2GRp9dWW3CIuslBhvCZRM7wJIINPKskVeLctwoM+qsYeUjOcpzJPR6BRCIkPgHg4dNGZzKij7LgvQtjtLe9iv7dlvaH+v7aTW2kO04VWa1GfeTWfe2MpzCeOzmv92rax5bt4U8zTzkD11X+vpYVZ+RzHz0ceRdIZ7kunBN6eZ+ZDaXIEOn9dAAKgXAQpuUPqq+iVV/b/6968A+C0A77lQ5AcB/KyqPlfV3wHwBQAfEpF3A3iXqv6qqiqAnwHwQ9fadzpWCMiUh2dKjKSyRzHXFQKb4vEatxuOwlwIawt9QE++r5tCulu8lbT/T2XE5k2xbNoXmOMzDC27RlryBMhtr1BEylsD5En1SH9ad9GvKa2um8K/hDhxYKQX5a6h6oPmB315/F+m/OreSuYO0N6Un400I9Ib6HrZdDR3XjppU+znvphrcf3lEdxHxi/TdA0dH9B6qFitriNU/RJtrGV6XXYpa7eMJcnitTH5A43pi8gfB/B9AH6tX/rLIvIbIvJ3ROTb+rX3APhdKvbFfu09/Xu+vmrnYyLyWRH5bP3q15r1OmnbtXIazHOruxEKYZRiebqVrSdtMThCdvldphkB5geLVosqvs3P0BQh/DAAGuu17xCgfMsDyv0+9jGrtJin9YVHalPoXYXeV+iTiv2NOuoCxqF0goa4zWDSFrTgtVh/lHgJEjJ3yzs6o5CaVMG2tUO1+Iyg/BBNVFhNEaiihYvMk8oxSes775gxZMueU548FGs3JGjPagS0npCheWRhbAidC7ePUX82lO41gVAaxu+pnNI4WFV0BK9uDTXKTp4lt29lsqeZUOJqt4k9v+L3CGVmA8TxYzXZYiRb0GRjF7z94g5vPZzwYt98v357ixbRSO90DiFVasuPm+YXkjCPmA+Cgc6FZN14TIrTULLzj+a9x86TNzc9GEZz3I8/4bFYjA2HDa0+X8/pYxxAGMvfYoMC7ww0eb0UngVeQumLyLcA+F8A/FVV/ZdooZo/AeD9AL4E4G8OUqZ0ZFOXNklVf0pVP6iqHyzf8kYUwKVl1vV1q48nJQ8EK6IVVUmhLC2oo+vU3iWEke+jvWfWdjxMxx3wRDXCBb4I5vucw4ygdlnBXUHTmvJysngsd9bCNQLMsf5bUPLkehH93Eai5xtDoKnOKyjvUhz81nama0cyfKm6C57BpTLX4vS38HDKk8d4dU+BWgW1I32W57ApIMsr0ebfD0DTUTrs87Ux4csvgf6PumP1h9DXJT3D176R/uH2OXGT0heROzSF/z+r6v8KAKr6+6q6q2oF8LcBfKhn/yKA91LxNwH8Xr/+5uL6DQTET7OOhszqXdcxG8aefLP8u0TkhlGHT4yFkFn9oa1Eh6OohNisTkdR2/hu5ZgGCNohVFsdqDn3/a5if6pjT3Pf0SOn2l64AowOkRH0h9c4deTEj9P7nmJCD4dmmuKrFpO17XhyHvUFBbrF364IaBtneOqaFJPH641mQkahLUNndZTjEBDvmQ4vvOhtG/IzmQIQELCvwxCamsIHhkY7IvRzktJktT35IaZOv8NOngMAMdZIBk+cfyWOaaCbQUyWbTPW5ElJbUjWjnG2h+eYHn8RTGlHadT+Yp29Cp6fm6BtRfH0dMZmT+RuOryFAfq9XZcBRrMYfXM+8C4y8tyZR1ke3RPIskf33bNJ3lkeS3+mQA7y9e/lIf7O7x0WGpMpykBtBc+cPV7gD+bAtb7D5n8A8Fuq+t/S9XdTtj8H4B/0758G8FEReSIi34W2YPsZVf0SgK+IyId7nT8M4Oeutc9pmgTazyW3sILM8e08mfxeVvahoV7GXlByI0rI6JFDDHwvozAV9EOoMG+PtPpKV/i8iCrtnaSykd+bB5x31Bwhs94fXiTLhqJkgpN3pd0LWHpPSj8XZdp1DfdXDznlLbNHHskUUqOxmsocIScKey1l51JZTmkXTaAr/060Tildm1DfSo5X125IkxHIYY0DMZ3GTOkViUB4oYpkJL+iM41ZkMvVWFqdWQaO8q94lMHdEQ8PjPGUj9viSwwkLsgUG4Kr8nFB2Vs6Xc+CPwXgLwD4nIj8er/2XwH4T0Tk/Z2MfwzgLwGAqn5eRD4F4DfRdv78mKqaLf5RAD8N4BmAX+x/l5MgvrjZOgeMB3uqdEQ7ttqxghArlydgZyQfahSUTgWE4oyrpzG9DQwUZHvxOV7OKMBRjNLj4wqUUptbzFah07TdV+zP9hYHPRfA0JJGOkaZNENYcHgSLXYgOC/6ZymK0iu3yasboB5/7DQHKU71EX+gZETsNZGG+HzCtR8BobEhT0iKeSW8pixo8W3eOWNo/2DC+2s5mWZCV5Ph0dF1QTzqdnQew9tIAMDy8NPF2cAFNA5MNIRbRC8fRqelx56751G3MS5TfiQZ5rq530l+yqbY7yu2U23dFMXbDyfUWvD0/qGt4aAbA/PyThpBSZLpVXw8eOpo88jO0PKyiSf2xi5+Zgc0ftnAhLUZAUol/ZNlRwe/9ATIGWM9sSSdYDTkXXW9bgeqaa0FQPSYWYaE6sXldFXpq+rfnVkIAPiFC2U+DuDji+ufBfC919qcyvk/BKVliy2T66bwhaxy7opKdbmnfwrP2ASySWhKLruUPOGYtmRYWBkExdHzcJ2lKGrf0uZ19AEvpaLeixs+EUA57wqtpElkdsAQlmhbK/aJb3kzjQHl94oSqvB91/Z7paCIP/ZwFvbYVw+PEd3eFitPqt54bK9qBU/ozJYECGzCBfEpOraOMh2cVte4v6v+03gG748Vew4lmHJbGOZsPLw+fg8rjf3EP5v9ee7wT5ZdU7b8QJwoIOJtiShwqu3hq57soSzbxdPewIbhPWyx0TC+Rm+mMctpRXs3tAweBsWso64gtzT2R4u3ziO9rFAly52FQKXTZvdJ6Wfj4eEzbUZqFRVYfR9EYBrDnF5q984fVRIg9FAFLXZPg7vaFeGWt4dEliEOSoxEg5AAfqKnEWSD6XFR2hFjA+9P60nbO+4v7F4MoinvbatpsavVu50qCk8OUcip9n3RVOlK2ckQXiFeOU+sTzyZrd89r73yTvLicWlPWJ73Ms7esclj9Fh9rvibsRLR8RAaEWTeQ/aS8u4edo09rppjsNSX6TF/q4PbwIJ2IBhnZV4Zv81Y5bAUtbH0KkwJSBwXbiuADVqvYMTHqDRc7+PhCDPvaS+jjVUsmOPobGDs1aXt3mCqiOL0pJ+Z36+dzxvO5w27tqdydxXs560/kYs4IYQMf6fXDQrNx2B8Umx8MnQ9j8m+PwvA46uIcx/0med9iXWzDGlpr1g9P23ofr9HROe9nJdPY2lP/vpaIO/wWcgPfwY2XtjB8+or/YWitgW9oMyZAYnBF3uZLCMrR1+QQ5wsHMVghcLoH9Jp7C5nvUdAIXmRx9Ldti9375xOO72LtinKsjUlrAkt5P5r0RAemJQLCSw34WhTFKfSjZEA5VT9uAcVoO5tT3bY9iaxbu+KALD8AHCqno/LZV46jxcL6k4vIyvrJ8sPT+osL0wjb0tkRMkGJ/FQE/2DsFGn81Mjf3O7k+I1WTR55LbYoFn/6L4biY14ZO2SIlkZjBBlZMVaWBlLkj3F/ZMHf00iAJwfNpzP7aC1XfuunocGEsLDWcaHbf7uStnEpWo8FgNxHk11cn8Z8dNcmdb/dC7Lc1/z2PV7+1PF/lQb0LvXadtuRvbjB7VjxiUt5rocrPqqB99TuiWm/0fXJZkiAAAgAElEQVSfbJKkQeGFVunvZdCjiZFQuKMeasPzI33PyCqllbDtT4DzGzq80AKUO/Q4nrRF2zrIMuRr+5jDy0VYwVDoRysgVkA6ISywyaAxP5xm4lVGB462iClSdBylbO3Wvi3vQKEc8ajkCZ8MoCmdjOgnQ5KVJSnXgKoyH9JvAaKcYEy8CUwg/naPj7uTlSrvyOn3a8HY6prGzkJxzFNGdRltejX8nZRRNpYOcNLnxBcdO73KA+gQOUZL2gFCe4EK0Lb41n289ebFeUNVYN/XFjOE9ox/nI2K1ZMMvUB9msYIcRzCvYVchPuJZxwpYNkMNBRgf6Z0NlY/ofettumk0K6xDLQYgPg0Ty95CXqIAUzWTwe6CngdlL4NbGcUC0I7jKqhhfJAislio+Y6Kjyeb3E7fqlKUEgcD2RlYgNEh72Z+8wut03E8xuKh29tRxPYi0LkQVCet4zlAf6SbFfEAE7b3vbDW7AdCGiIFa5W8fCOlLaQ7bSrUJCbksTJbQtgomix4F6ewwq1ii/kFlHsIkHIdJfxFLGgexakeYymlLZS3fg5nxEnVyBd+7ilQ9ZsHFnBWWzWlHbdhnIVTYv3OdFE4snsyMuUMv1WK9fBhPeBvI0QGsj0k6cYwjAY/eIHgPKCKivyehpy6ie1lsgnR5BdpuuJjJs3MthR7wYNgVYySloAEcU7nrzA15/fA0A/fVVRa8GLh1Pbxrk30NN2e8mIXRcEGcwL9ryoWe8pn90zfiAqeqFxYA+y7EnMVoqyYAJGrqztuO69097pqO88t7m3N+bUF00ZyRnAW+JbYJVkzA0/j1Ufy6LUd4x28gNm7PldSq++0pch9A5h1BQAwSYT5t5pR/2wSapQCJZHvLFQSRSM4NrzBDuypDwAp9qFTXszBRXAfu5n+qwWlvsItoXOg/rrsDC+IyHQGPli9GTEM6EGmsCu8ICZZ4xEFUDRdqiW7zaQKW9GkRzCCu6vUsPWfhJ274+5vVb+wFiMig6uL9rTQrvBmE8S8031J36HeyRPvDEgG7mAyqlNR/D5fmpjUgS9jQlRaswz8YfzpxBFbt8MH8svuvdqyTcpsHxWOChaeTre78Sz0Od86QbFF0BLGqvAJ0srmTRjlXgNQTijSgHsTwSlNEtXHsYrQXNIZhVRCJ4s84hpy0DiSNbxGil93dqOCkfFe0PKFjc3VOMIvhLq6bFDXgQKTZjwA75Vk9G9oRtPaVLZRASGAtNNIfcV5dROrRFR7HuB7oLz6QR50Uby9PVWsNoLUmzS9Jh5U5jtgCohn161xfS1ytiCY0fVdgTnXoYpg7ztD0OgGEUbL82Nb08LV5xKjUcoS3+n7aY4bTtka7H+8kLm0AaaoGt/uMr5bWi097ecZToAK7wVaXMWTMbZkU9GRVSXh2FMieQjKXqqdxiP9Bs/k9HkGCwbN2+aZCIgYml93mpy9207JyE9Nspa4E6cv++3xvtA61O9GzQxrUyTly2IgCAZtlqaERSJ729wJdTnjGgL3VRS9kW070pr90pp8wD9wDY5F/de3QjakeFsIM8Ix2wv4+rGl33wOitE9sb9QS4aF4D6cj/KWvTAPXsKu/A23DZmitJ3MJ1OFecnBee7O5zf3iAvBNvbBaKC7fngu4GY/5+7twm57tnqxH6rau9znvf/vx9qtBtzr0EJDqIODDYiZCJk0BICmkEHM4gOhBvEkA5kkDaTzkToSTrQAQVDN62QtBES0EFLCJLQBEzLJTQxKk0Em+4bL95oX+/9f7zvc87etTKotVb9qnbt87xem3jfFDzPOWd/1MeqqrV+a9WqVa6ZuTUhLBYMOolW/h6ba2d7XDh9/S/kAr2Em90DdRgP9HhGT1t6JhFjULmJ5GRSdIOlYwjth0hdcE1JkdYCLBY7x49hlIYIxrZFPSYVbeFptb3MSEEeNXBS57H4Gc07pm8fdGhG5+XhXj4T9JLEQjckxRhp8cV0MhZ4An0taYauPd+xjNlzo928My/EQyeFDzQ6RavyYFJ3/X6s36F8njsn5TXmORl/w5zz33upETZ9MdetgVGsIJwCHrZjcn3avyOdB43hgJxfGPNjfx3ync13zre0SqakWJZi875ALRx6Wd5irI51waQufm2mqZykd4Lpa1KkraEM94ip7lGVaXTeFCSx0y7V22fFgTmOC2FdorICCS51oBZCHN1zjjgLkG+A3hPKliJmTV52XK935PfvwKs9VvjVFvO2LWPbU3eqFFJlnHX3am+e0l2gezqsPfDBFAd06qhiMmi6SUyL5mxm0gJbb2g0z7k0s4OhdhXg/gmtZ/7m+sdmnDXvuCw7lsuOcvH29gitCd624Sk2VhHC9c+wsdJ7gaId1aPl72UEExWiBRoDGH21eb2gY/qpIcARacZa0BjG190Hc//O6LPP43t/pf24pfw6V2Jnkn7UJ5eLPqTFyNDCE8oC8IW2y3SyCqRNal65MvmPny+47xkpFazLjmXZkZc9wi+I1IiyHinWy3UmGK63XiXSxEKTA7r5G2NB23V2N2WzZTxL9yLPyQIrayDBqEe6WRtkB3Cr3klaEkQUT5c7Xr33jPX9O+S9ekBSuWqj8bC2KDb/mJl3HjwWeG9qBvI6PGD87wTTj+TowEILKH0fEdcBgdFpSN31M0QWZba8Tm2ak+uyA9gEauprMRfFlBQpFYihYD/QHMA89CzlH9V7JMoDQb0len6AEmbFdHV0phaITrsJVlENupAG/l6Chgb0aBQGkxrbcIaAuovzPKcCnhnIyEhm778AFh4mL++sTsy8Jv1xcLmdamQnec/qeoJcu2svoMcKAmql3Xafk7b9HQIKv3Acm9P59FKZbzMGOD/+PWuzHO+P5iR+9nS63qUCPtqPkkSRcmnCzgWbDHlxuQ/69UVt9sH9r3+bPkCcFNifTOwmQNeKKt1TxG3Gcldokjq3aIFRV1R7nyNAz95tdZs9w5My0Ctd50HjiMztvpbya8H6zxdoBvb3MsqiwCfvtvmq+rrvTwXbVu3fGYhdjOG9Y+abivTRu3Hu0qH5WigJwIRqOnF/etrkwiRNbP+09oxCrJSEW1nCg4eTFEHKdXJLAnSpZ/RqAvTVDgiw7Uu18/u286xY8441V/R3W5oQT4Tg2cMkFs1cKdgQAdRGZuieEQdtYDK5oixFdQlOhIAFnWXDbcblamsZHNqXbeQyXCfkWdaaf3akRvZwfzf5Fn5DbaOWEkf47a1M90bqQvz6tBkWDEOLYMFAR/GFPdnGi5h3SqdFmMajom3MJGBZitnud1zyDkX1SNv2hG3LbSc20BZwT5Kjeu8H9rhjG3htwEAnmqcHsOeonscI+vdY622egHZvtmaEdu36xxn7NWH7ZMIbqVqtenkeoPC9gvymNo69p0pu6zxeV9ZuuL+CRsM4RT4+z+mdQPqdBPdIiU78MwnsPJMmueIENc7KGfL8074jBchvBOkZSM8CuQvK1hC/ABYXX1u0SyDMOKPa6LFK2oOTCs0MgO5BMLaBJy96htoq6HWat9GLmCE3SD3kXdZqx+TJiaRYpC1wN732vGmtMvbo6LI4eeZY2ZP6j6g7HR/ttDzWXOjvFBWOdZDWJyPdoz2E9v0vxrO056L+j9Dx0MbOjNDVa/KOktAYynCzKo+tlNo5CUuqfZxpzeYQUHCWdKCh56/t/thWbtOjsXxo6wuaXPwe35+9YyndgOV1dc8u91QPk6F5L2JAjqICH3Zyn5TFZjx+5tQKMUnvBNIX8t5QUQik7czbpe8goNm93SaJJjHdXuaDiD0kYkKBrvl1Rz8u5V9Ud20jiwrwMZBugnK74M2nMuS6Y7lsyK+25passPjjBWUyGbVI89E3WFHj77S2dKO9AGoraMEUiFEEEjbk6O0WRjE+kQZmENynACUrFppxdVFWUFZgudYdc/f3E0oC8NpGdlIk49pLLoALBW4LEN5Xo71ZlPzsvQ0D7aOuPqG0/falHT8aURMhLUEVlBuNKzGNyMGGaX7qRxIOdQ50T4ySfcyn5zqT3GMXvbH9ooiNiJ0mMyJ5okGsJTCJqL6MjmNNwzSXZO2JQ4J8PDr6dU8bq8tl2eJTRAPl5lzC1CGpoGyZ2qXtg+coeuE+Zeho9XVNrEuT+c3vcXv4AJZDYqFHdeO1AGe+UgBswPphwm1Z8ZG9li87lrUelLTfE+6fqDvlXUgEyvc6kJAP7Sshwn57ULfRy0cm9n5O7wbSn6jN0Qmjeua3Zehkn+k8WbiMCdo73FccKDZFmlRv2YF8E+RnwfKRQF4n6L1msq470nVHfqo9rV7FiahWlc4rIBDjmfAJX/6+rt3i0IgaZGhPMH3tPjvkP65H+O9U4wWt64583aFLaVU3b58kbhZSXxpobUPLh68dnhkFMKHEA/L2MaNtErEw8PLGRUT1cUZo+yW7/mGzFU/iWTpDptwf3q7BxZHbepY3m3jGPu6Qv/8eGNxIl8oobTzQWc5LLma6K817BzXYmocPT+QRB6CbUwdbNmsZJLQ5RR/hSN+ubWe096kyGy84vscCc3rfXHHTczXz6usMfZNRdmmxtbJCrwXlWixUg07NRl3y8TduTpzUb8bjPL0TSL/zMgAa2geadCNvk+AgbiYRADocpuLS3exnJdvYcyTsaCcqgX5wEKE7m9vAZIGGpGQT5I/rAs+2FuRX1cafwquhTo5Qh0WrkzT0wNvF1EIx7wcdN7hMhB1AaG1EvqOMIA0gJW37B2wHcPAMu+9H4jldNbXgcSnv2Jdc10s2y9NQYJh3rE4jfdm0wXb26Au/PgoHRrv+SfbheJ4n2bhuM7knCjOZSdQPXAdHyiQUOkS4owcqTuehLPcUGdcrgIoM2Z7Nm9SQ0MViYvoJ14sE/sGckYZ3tWqtOh48A6dFT0s/UMfHTLjzqu35SFI1ykWhdghDt+5AdZVJvYDmvXJA797+gTGfgjOeo2Uo2+jofCI8bUo/53lnbLffAtXEWz6qD+5lxf60YVnruN9yhm62YfNeXxKVti4jvWBxNF8udS+MFnTjj+f4IxPPO4H0i6v/qRKFVZ9AGbbo123QQSMao7qOGQwIz6VkzaONki6sAE2Y+J2IcU7QJ0pdZMvPguVDgdrmrJQrGq4IqG7oiEPGvdMLiFvZ9aWY6xtDSq+stj/UdxkhB2oj5jAiJW6fiMaxd5I4f9SFVFGsppeqGGKR6qWURJFzjQgaJhmz6V/z1s5MZSHl9ZwNXMGBmfPk5DZMv6eWh8dfZ5PGWer6WnAQpm089uV1Y8HHFp2a5fXg9ng01o6pUTvShnAgiDFHzK2b9CTcOzqQIIy601yId71qhdw2edywTRqwsMkSC7aj1rqkYoi/jok4AW4yjLv5OTL30t/n53itb5yLo3Dxd+LsYSqDhWTwlHH8SZ8n8xk33eTXgvxasHyYse+CZdnx9OqG5bohXXfgUp06aoC2Vr73a9TD+tXdvLvxTkLtQKshvRNIPwaD+YezTZTjnqsgYvGwbVAUdevziIIZEVAevCtOiflPmQIjOucDhNA6oQMXSgL5OOP2tLTNG6nEYg+AZieW+nxFb46Ihx71Av1eao0Rc6Nz+36QgBiRHxZTFmMobLsVhMsZUPcaABm70SO6xoTCTvTlWsoQM53NO81UIL0XDAA/+rDrYzY1CA5IuWl71Eb+bu3198tourB2dWES/PXRFCGNafRx5qm+9CwL7g4XDOi600SlH1NdGYPA8zw6W+9QpxEAsG3b58xB6KCvV51fYkJeg27RHq0H66x5x7YtSKng6VLwtFYYWy6CUtYQoH4oT9fGcb5Sm8OySfM0rvm7E+DgQr6ktl7BbZoBDRnG4MyUNjMJen2S8yhR3F6v1eSZDQzJjg11/mxqe5GKxeYa1mGU+qebI4KjmXtSR09f/0w/JrC2k+LJ9hVM3wcmuXVB631Jrj6OI4CYPV+ORRHraUJUng6SnwnPqMwDmhkySff6uX6QcHtagFcbSqnSf98THYrCnMbqKqibtXZEwCrYp/jAPVvEGRmHuScGygXqJh5bHAo622D2EAxP1zteK7DDIkElacW54LELHF8nmd2+3SuN6TvN0coOhGXqsjAjYiTLDM8ZwpmLIuj+3oT7GO+8+psLjT3KLzUauotd5MuTfdIH3LUzZgSgd8M0184YPzi2OxaY0a6zZqFUX2dsiUwVgJluyOwXi5Jm3pzl0SWv09JzHgcCCmDNBe9fbqERAsD9tqCIxtjVhM591fvtgFy1jQulOh9MWYT4fSx5MDrPh/sN7GHG05BCuwRphRgvCRixeoeZCdanGyCbQD5csL2XkXOJcOk5F2y54C4rtksCNGP5WDrA6P0AGx9TzcjrAvRCYUjvhHlHbFFSHEYDDZWMCH08TqxDez1TivcIlU0ZJg2qcfB1NvIHE328JjsgrzPKRyue36zVc6cI9pJqZEKr2Ey9ZxoA6L1rRtMOt0GJZsRcO1MAZ++TAnUzVZIaYyePB58AEWNlRFexCJyqKu922kz18y35jXkzxx6IN0FvXbtADHQyKQ7aHrWzTZgWaG1kOjzOOmb7AAWOZTPN5ey9SRtn9zpanOUxvHt4h+k3CiNv90RjUDFzq18zsw7H3cnkmrtIwSXvWFPBdd3qmHiUXPAP8/zQbgIDpwjXGbuiBwmT8YShjGmZnFjDGU1rQOftk54F99crbrelxtOCbdjMpa7PLXWXcsfHhrLTnQCJlzPjEy9Xd55E5NtE5H8Wkd8Vkd8Wkb9q179JRP4nEfm/7PMb6Z2fFpHfE5F/LCJ/ma5/n4j8lt37W3ZA+uOkJiUHM0ncM1RSLhp/6nEtUt3+789G+AZm9BndNvWO8Rszkl3qH6mDQGXchRb4HPX1eQyf/nMTXL+ccP3DBfonF9zvGWWvYRj2UhdpmueExqYOKIBdAt17Pdn8I9LuuTYgTqvS6hqMaxioIzPLqWBJOxbZ8bRuWJa9DTCtk/uS6mlJ8ANWLC2+mJtsP4Jx6AReHLaHR5u898eAjMcY47yI39l0JwyemcLBdGHP1pDdNlYWxHjysn2nsYfjiE1kVpeog+dL7QD6MdRNXKDzEor6sEAatQQyTzgdvGzWQjpNKg9tp3WGkfad2YMEoQOHXkWuwtwX6JMo3ltuAIA1Fbxa7vjE8oxPXJ7x6esbrOve02EEVFy/UYsmesa8MzOfjH1MibW+AIlDmaPmGOZO03jG+6OpkTe6eZnuHrx+kJD+aMXzh1eUkpBT9XZaloJ82ZFebShPNT5Ptz5EfZbfVDNsbEIk8PloXcrT2yD9DcB/oqr/GoAfAPBTIvJdAP4agF9X1e8E8Ov2G3bvRwF8N4AfAvCzIuKk/TkAnwPwnfb3Qy+WbsSOBsnwB7RFDmfo9t4BDczyHqXpRKrLODEHZnKKAMb3x2s+2fZms1eVCFglQ8bhKnkw/ZyEjKZn2GTSKjH8nqEkQYfcBDgsztUJXuL5mpfUY/H4HF8KhXE4HWxW7dmpSm+BZLp68DujMOkypu+0ZnQwLwiqHXucYCf1Or6Lju6dUjM0d/St72+e1N0Z1ThWz54b8yIBNdM+OlqedGEShOkuiYZpsKD+1WdMM0xDQRMadenBsDkdG2d9k07ujwCC8u7AxyOeclKm7EDaBLgl3O8ZW0nYVZopiIEM50mA4WBxsDp3dXvA2V9k+qr6RVX93+37BwB+F8BnAPwwgF+wx34BwI/Y9x8G8Euq+qyqvw/g9wB8v4h8K4BPqepvqKoC+EV65zwJUBaTfKH62N9qlMhNJdJVqwBY0Elm5dULXqgkBDTuivMFY98Mw2qwIybORwkRdAs7g425FtikdX4WaKmB1koR3LfqyuU7t8RPpip1V64saoeio34WaRNHQai/cXuvH6NkDmLGi4aelWsDmVw2l1RqGGgTWkFSV+vNxCMKvHmz4vXzBfteQ0P7LsT6fH3ZD9mAZcmDPXYqJ6qXf/KEdU8ONNp33g3jxOWNRqOKrGihKfzdhDhEpI0T7TSkWHdIRPqF6Iwjvb3u4Tky1CXf2rVxrSOE0eDS163bDPRkZhGaQTI603W1BX0x91qneQeKEiCbcyirZ6nHYC55hwDmnaW45g27Cp63Bbc9x1GZi8WgSkurMK+TddoRH3ozcK2RQXZmS6r/mMa1Ic8n7W0uBN8YhGxs6nRtx/mCNK+wLkSI1SNt1Ztn+ZOM8uUrPv74ijdv1rq+QW7XYcIBembux6+O2i63z8frSfpT2fRF5NsB/OsA/iGAv6iqXwSqYADwF+yxzwD4Z/TaF+zaZ+z7eH1WzudE5PMi8vn9w4+6SeJBnRhxjajf0X43SQ6FzCX1mfR+SWt4EQGcvTswz7NMAy3zrFacF8aEoLLfCg0N74yovAsToZhvrVdAbZ1CS404+GJ6RLdZ3U5Q38igDlk8QHZjPiO3YDPQwQzleb+sxJy39awfxirPxhEDFu0/z/J8RKeuTm/Vpubay38+PjZN2DRj1xZzvwvDYWXN5vRY5xfNGLP5OMknPvmZk7a+Fa0m5Y+/K9pHDc1yy9i3jO1uPvtkSj5LL64xvJDe2ntHRD4B4L8H8B+r6lcfmONnN2YKiV8/XlT9eQA/DwDXf+XbVDPqAodoCyqWS0W+ZJNrR8TZgSsuKPyZHRG8rEpdqWhmQBOdADEk5IHLSlYkW+iLFX1GC+49Q2PZjyPs1gS08W9R49+WUc6l2fDtUJQWO79WTFKpCJ+PRLTj2SKo3C1VG7sHQRso3+3SdFSyWRHUtqKCoglL2up3O7jFFzxVBRsvaFibtq2GaCu3XPdYWJAyLRILuSU0EYVrJBXN9a650cdm3/Vj5HhyhMZiCFUFvZ3bx4LTnDwywjZq/ZXM9KaX1neA5+0LbTWwX2zcG+nLwf28/20sRDy9pX/H26+C7uzc0QMpkq8dWDiEeI5RImm83T4WB0SmpbjJMezHpCmF9xDXNSvisB4Lh7IXwdOits5Tx8sl19ADb7Z6XOLr+4pX671mo9Jpat5u9cKIBiEQTGNNG6YuraPGWnmCPeMhXVxj47LZNs/rFUS7Tji4gOD5vk/oT58AzH9fsHwI7PuK/b2C9CbV9xbF8qG0UBv7gNoHQMZrCjzeHgmnt0L6IrKiMvz/RlX/B7v8h2aygX1+ya5/AcC30eufBfAHdv2zk+tvUQFj+F5j4U/lx+xL+9Phr1NjqbMemphNs3hcx7dqyVulWRAyTtMAaIzs3fzC9Dhr3wwRPUh+NkCXf1cPysi+x+le3qyHtKa+Ost/qDfX/YDqZv3LeY9lCA7Is3uOmM8B+c3QN9XvsDY0q/dYlxfSW2urniXR/6Cd6vn97v2xfvTJxyUmR/5oAt6903bW/HRCy9MGIJjyWTuniuck/9Mxgz9df3Deh0XkcXw4nUsFnekucXZ2fhbITfrzvl+YK31Fhs+T9CLTNw+bvw3gd1X1b9KtXwXw4/b9xwH8Cl3/URG5ish3oC7Y/qaZgD4QkR+wPH+M3nlQgWqvjx2xjir8T1BRYkE7M9ek3/6kgXL8KEA1ZNNJ9QFNtN1+appAO8BFijSEgDaZu92KaPfCvMTowZ/hOtjX4r76AsBsnWE7Na8YsckldOh6CAJnsMP5wUbKwyAqeQheBmK4johVkKTgti91i71KC3Qn1eafxDaXOZoXIC01BIN4mAk61N3THnZMa8e4uOYobvDeYATvIZb9/c57gtoyE1ThoZNbH/mJZuVSv5dFG2OC9UvSzuurZtbybAW0enT+7xPGHwjTUhxYPzCUGeN1lD4KOQY3URbTcwBK7AnTCYBh7Eq8U2kBOxN2t4XJS9rDvAMAl7yHdnfbMm57U9HLlto8tLomPyhEj/ScLcC2nevoEz/jGw/9Oq3hFbIYFFo/6Ji/t91p558JByGkfI3nvpdtPKke+yrIt+qVs34gEfgt+IeV2S3gSls7GMt1je8svY15598A8O8D+C0R+Ud27T8D8DcA/LKI/ASAfwrgrwCAqv62iPwygN9B9fz5KVV1P4SfBPB3AbwC8Gv293IiJhtpdOHyQe+dZwNRRWJzQ3FinKg/rt7T+ueR2VDHdwPihQF3QNuePzF8BaqAMXtnEfOD9jKTWlRNq2giZs/oWtA8UN4GPU3MBoxKPepngXShn8PiZA+r9YF4daT57teMzqvQaWljvZ12kwno8jGQ9LAY7886ycZ2eh6jkKmTygLBDUzaF3GjD7uGtPy7Ms9Q2YPkm3G4jNnaw1k6rG3wXHF62vegH9N61l+zMe5gzMbDbq6IjvSB6vabVMxUKKYx2gs0VlVMeR+EUdf/41zCQGuuLs3bcYfySMf4PeM3PN6sLmGWTW2Iz9KpFsOC2u342sBBtFX6fu/6deQpNmYeja8Xmb6q/q8nVQaAf/PknZ8B8DOT658H8D0vlXmaBGa3th+iPWJfPO4LWtAps7vqgav0g5zR/mE7vfWukNkkwps68zfUd9AgvKP8qLVhU0/n5QObNHuKAGpyq59xiMpus8LgWw2vTCPRmWyRdsiK1YuRPK8luABh8oyIcNOMolInby7YLgp5btxoI6IpGj1V6yEr6kLKyrmXfHTbHEYZIxu3OzONwyZP+XoIgW4L++ClEXRnFMs2W1FApNsgE4yX6lgWRbbDwoXHjdeb7OcqPZqctdevuZdPh7xT88uehQ8eESkziNnBOAAONmrXdGKdY2n5dVUcBagzwVywpIK9pGDs7qlzSTs2TcgA3r/easAxSY2pe50GkMWHjrPWnAoxYLR7rDUftBpp7wS4GxH4mGTCDzz/iZ1kvMbrJ7o0PhDjYm3jl/cjKbU/NFCnQerbHs+TB9kjYPCn8t75c0k+CWGfHE1yQAHNVc8Pd2jBv7oFXx+o42SxNHacDgNy3AIO6pQYeNKuHwaKmyRwvA+1hVLjMmyyaqt/TIMBnvNuWV7k5fRgQByQoSLiqBRtOwixmr5pzxYmJPcZEEzKO/4AACAASURBVJuzYvBaXgBawLVZ/dzkw4x+qB+bMeK3oCG1GdrlcQOE22JHWipzZDpBq6XPc3TZHRfVDi6VTDJiXofdvmjMeGZykIGpj3mHGYzGa8wBRv3DH7tJHkxL3EeeZ1Jz70XY9jfNKKhgYZH697RsyKKd19c4x7jdXt6pxw7Vnxlkdx/DNaJFd40FA+UXdaDxcdA8XMg4vXz+pEm9LP9ipklN2jkMdHyEyxuFkD9rzzwy63j6+mf6gDVUm380M7a4jnrIcmr3xA/C4AFKk93NAV0H0Zm1nRolFdk1rwdC0SNTGpFV6gcO211DpYu2Nh/8KCfBjk8E/LB0iHanaYm3O6HZWM3OGuUyc9KeAekYWmFgIpe04ZJ2i5JYKK9qt33Kd9uR60c0Vne07oyAKAy4q50oxBOSGZAJtHHCzRhm11ecB9F+REZBK87HJl/Q0BntwZTomfflTBnvIFxGDy6esMwQHpkKDot7Y9/KQIux3S5YXKMgFMn1COZFJtGO8U4WifwQFWfyCXVsXNIeh6qsecd12WoIcevnzmxK9XHh1AnjYRdtRz+mt9/3eyzER4Zv1z320UhnNufGoUPcX0CH0kcB6+0IXuPvJoulnw14dPTtn+3KO+FRM3AypneG6QcT8+/O5JYKf9QWfHkjjzP9YOKFOgM4RHB0044yQmaiL3YPxOSHT2DoWJfwgztoJGtGxKgB+h2syQTRnirST4SQfNEWjelLLpU2S934EvHmeVKTlhELog/MTkBdiFvSXg89kVaeplr3p7xVV9O1xCKgvsnmoy8ofMKZCm5laXZdbztP8AGdxUBmJDMIgAMC8nvU/2zS6QQ1GiP0SaiCqnLTovWUITPD5bUdObalU78nzNjpcGDs/NzAOKL+rkUyg8Mxr0CdbDIYBQfQYsbQ+w/NIVLHSZK68WpJO5KUyvSznaiVNlxz/VtztXUIzTc21zk9R0bJUVU7EDACA/RM0RfQmxPAkcg8RrqNbQUH7XzULmViS2dGfaCjt4V4RMxDEnRclucVQpFMntyXp1oR3hWm74Mv+yohGmMLz4H6TOzWNQGhIwFooLqvNUbiggYdE9JREYfy5cHHTJ7KGdFpZ7dLw9yXepycUJ1i1x9fI6QQbXDm6eW4RjD2MiGQ7hrTR/s2AG2TVizc2qSpXholYq40dG7PsYkKqIdyoG7Pz6l5eBwmzMH0helzcW0yWQ8MeHzHb4+7Op2uI+1iLGhHo47ZEGPpXg2NoSHbbmwB3RpAVyb6a2NbTk0XPB553PDjE2TM+R4QtEz+UMfbYkHUfG9H/Z46818auTKVfdAyJm2POoDqxk2YtHFmJjl71tNhDYYY94jygXM+w2OzG9M+zkbhj9b+yHMwNfpzxXiRC/y3SV//TF9R0axUF8BYxExAvhSktUAupWkBl1JNDB6wyEwOPmidUQlQF36Z0ZPKy2iPCa8J2C/aTxQBMCIUZtDqedo1dvUagj5JUotiaUwlW7zyXQK28L44sWBsgb6BylRS+8OiB8HjSDMmWYfEEaYvR/I+aX0npSrCpTSL4pp2vLrca/C0rN1k1C21MowGSSzq4rLFyWFRvRNG7aclCSFQ1uIivC4t4nK7WVgzk46AY1wJd4djAQ+jh2tV7PYojW4cwKybxIwEjb4ADuYKRn8YxuconAO5entcoLOZhMALm5ZiLcBNWMxoFBOzVqOdCtqcEx+3BauZcdp4SdiM6VdvntT57YdZEq3O/j36bWLyYHfW0D64TSNTprHUaCAHE18s6mvrF2V6UZ2C3k6TpT2nY/94/uROXNwy4WZnTorgKVEv9ILQ+Yco4qCpRwgf7bV3J7XYLgyv0cwdwWj7+124htPMT+73IDXSI4QQg3FgWv2ilPb35XgkYlcHF3YsbZL29T5DczQ44/eIDL1eswmDhs5Upfrjx6iuHxEnfdScgLZZjLIsAwGn9vbZvVl7RlRF16f9pJM8+d3UDgYJHk20GY8jPNB7/N31e2O+OvYdv382FlN//6x9o8nmNJ3RYhhT03JiYaqfh7zwn6QE6vega7eSybQ3zF95XB91IaVDnWZjfvab2nJYH2LBwEx7nDtnY49+n/EAFvqHsTPQIV6Z1K8DItK35f8/Nn03e2SFrCUuxxdGUJ4YGSWz+Ut73qU12ze7PMaBwzbOZHWaIK8qobUtPNv9LtgZrQ+ImkeMo6PxAHSg35hVUA8Xz/XPkTdf8+S+0DzADos/UXFCIo5MpXrYeCjke0nY99Q2xmWLsy81yFZKhUwjVtDuQeEaDTaTgFm0BZoy2aAJNXAeTxC2r1rdeJ1iZBSBtkgt7rSCceMPoWQs2txdgWbSc1ptdeC4zXlcC+lMg4k8gxLaRjAzSfK44/E3mhl9nJbFw4L096MN/v5CAd6cvAMCjc8BsY6I1hcYu3dci0yIA31ScgQP7IbwAYTbJgA8bws+vl8immzVRltZB/Mq1cPrnu5ooTZGrRroNMHxM7Qd28zVafqDiYTNdYdFfb8uPW1Y44rx5xq934tnBF0sMbflp9bHndlYWl6d+VBwkMplVZyld4PpU2J1MBJL304CahA9iHZGCxMsvOlrRJ+HYk8RULvvv9XzH657ncXuT0MsoD135G6NHm9xOsExS6prCKou/8k7I11E44QkeQlZ2szbum34g4CD9R2OE5cfn7XD8zkw0pO2sOBjdX2KkAcB+WKeY18Sg+nqNn5O8mYB95C+J2lU+0/pMkOVs3eCGVWmxWMvdd81bPtA3eDHWp4AxzHNZc2G+4M+mmoJGPqWn/P5c0ZTEoatwu37YSxOyj9oz6OmcEL/Q3qbfmcBcZLeAaYvFTEa+k2GZsWOZtNdUO5kKNvFQgFIlYqX0h3s0dmu/dNRhNaT6DsJj/a9s8kOaCg8YcAIRTtpHnb0fBycfoh4u2BQwVCJh1wIe/LAJx1txZ979QghStd8CDmHGrrWsAP7RaMNmuuuVD9EJey0O0GbVDfeXNOGvaR6wIoLZquLo+ZAVbvgo/sVm3oscamoCwOaMtR58EzIDV0FaicU5WEZAiVpQ1rdRi30KDj6uCA8p/x9R4myo9myE+qhPTO0zajO6lftrk1Ih/cIMzH67c+KIffOxs/rGrQpbESrjvb363AoBz/DmgODANZuUOvQNAPp15KSYt9rePDrsoVmuKQSYbST1MNz3G1zLzVwoIw0YATN6y0kSJXCh/i9DtTxeBFM1yciNAv1Q6dZeR+CaG95dtqy08vKj3o5cneNi+pbtT6bF2sDm239Rtv+kbHfiD7cJpXWl//CQiv/uSUaeBLIwghm5ot603uC0oheTyWxCYWzzQ0TRjFeb9d6RH9AaBM4IlyHM7hyUt5DhE91GBNrKkFGGf4sZWkBtFqY5z6/ushL73mmI1zXZt6p9R8y0pPv3A4eE4MWMLbrNJ0hxfGl8ScdCzlbOHtYJud3guY688CDPB9qCuMQmmlw9N40r6HMDn07ujekLKlqqX5oSitWjzuv0XvwzDZpPUTfk/RI437pvbfur7co97BOwBcHDZH5zlhWjGMGjH5/4Cc6lvUC7d4Bpm8wyxpf1A4SSQY4PCyDe+n4Wy6983HAdXY8JpzbaVU6v1wmNK/uwztlQJXQujU/7WKbPaRJZvEKoJ90CrPX14q5cDuYXMYx4geLM+MrAsnENbwtSnUgJBc04FAHtB6y2EabS6ohcou7YG5StRDrl+wHrHg9LV64B8fj3cXXXMPuZtHeZGf1E44dRPVl9B3ai5PUGabTVKhfQPcYMfPzjthtTHkY4mIeVJq0Hezigj0h7gWqSw11NTMIWhgPbxf3pbeJ+iPt0k/oNLyT+jbwcaDsjTQKJ0bEbCvv9jEQgx/zggDlWoPppbUgJcWy7lYlxXvrzbTDdnLWYgHY3BS4WaRNH+/TIHl2vdDO54M7q2seNIbH9nWbrVxry2hHYS7U9y5waW50vEJwWF/yvDvwkehdlW6OHbxs3L3cNOumGShp6Rr8RlM7TMqI3ipz/HpI7wDTtxSD8AR+CMBhlmOijxKPmMjUvk+M44jQ658O9TiV9qDBQMJFhomlgkDP6nV+IKnZlNMlLneGIFxQUbsOGgirs5YclYUHjx9UP4l3z0cpSoRU9s/zPIM+k7Z0bcDAHOR4/9DmsY5MA7oWTJTCbfgiXtBpQFIsRL3M9uxQLz32fVc+PTvaoM/emWoDLPy4/CEdEC4zLro3YyC61NAaKRVIKsjZTTgVHHhKtit3RPuj99ahjjwHB6Y7e65rE49tf2YiZF1gn2k1p5oUCY/T5M9ONSzty/OF3BiDehxTRIPR8WQ6lt5ppk+dWPYMLbAjAmGIX4OQ4afuzCgp1NAoBOG9w1I3OqHzHGgunqLoI3oGAiGtIjf3S01VWjPy6NCIo3eemElbHHIYwzaNwe/XmxZ6QZunBFQOLqtCwo93O/Lxjl2ANauLbIK0ST/YE2o4ZdQJfNsy9J7azlOtjPtukLt48DcB1L2sNolJUhmp4JLqDs1Otff6OdqS/o/bGOhsEGB8GHjXZ4MwG71EdK3rGeWi4TvtftejKp1iR/ggsLL5Xo8q9qB9MN3ZTzzWKszW3NUfw29m9ifrFPyOh+vleo2CxWMJzZisr7WEdiXAutYDvSvzr4TyRdunfMciJX7Hvoy8h59+EkVeqjo11r/bI8MMdMKIO0+mpYUc5vZ3woD2xmhGZ0/vvK+Gcrq68dgDIkR5ePK4RuH8xV7uXLX9T6XbTFqfHca+IKwXTJMoj03Koq2/J+nrn+lTKnuzJ7eTpHwCErNjJs3ujz450kB0Tnq8J36dsp25inkZ/YT0DrcPEg5e/4r0LSteq+A01JWZ5YjqeZNWvzhMSGFkEs6QB3c3lR6V7SVVl0WnP3C09Xuebm7bWx+4dnDQHgS9psZtJqbeaUd8z+mejvWv5bbfHbnsvbLU+PhltXpo7bvQ0rg7GGAQw2JT3IHxotVXqW2B6oDOdDiaOlhjHNH4gUHh2L9hYpR53fy5TgMc8gqTpjGWNe8N4adSTX+ovvm8gNsOQy/B8AFEIL6WOZUxmqNeqF/M60yf0tOR5x73bdD7wfrMmE9rhN13QZDQrwtOBFoATX9fW335Wvebx7jQOJ7kHf19kt4ppu/jhZEt266D6Xh7HY1yJ1CQJwfTYQccCOtljkCrq9Iw4Fg1C6lOdmgAYdvtVvMNwQNodnEv0H/7MYWCdogKEEzI4+wkVh8HVfKgvtvgiZ2MA9OSgu6UI/VM/HkVLFKwisfl0eZLvBbI1bx5hj7YSg6bb/JQGlaAOI2YyZMw7Qa7fx86yfvFn3VfedGeoURbhVA6eYuNfQdp9HXviwOq51k+DhiZjyEes2x2mDIjZozSC7QDKrZ6HgQJjr9ndeXxOwqM3c5AFrFw4AA+ul/w8XZBEsWrfMeadlzSFmE3llQ9d57yhsuym1YrHXM94B1nzrNNWfzMCKyIIc/aBSB89TuN3p9Jk+uWeD3pkDdrkc4b0ui5NbTDxxeDF+/Pcc52WgRizLGG8cj29O4wfdEWaZLnlDNxBQ4Hh4y9j+OEZ/WQ39Ugpr84qROrxtapXKQjN96EBUE7p5QYU3WzrKOsCS/SEhSQTTo7vtgzEoxIgyZTjx4ePAfOgxBUY9kdgnfEbouyUhDILjsDN8aZl4L1ukFzPfksskmKAsEiVdUX0cZAqb0K9MzLJ+fQFx16d6bOTBj9tQ4ZOwBIxMTzSGT0vx3p83kFNAHHBb3OvOR1oLqxYBaqf6jzDA6M+QUT8de9/9jUYfXihdCubrPw3swsxQXQREDA4usYIPAzID6+r3jeFyRUpr9Iwat8j1O0llRwSRveW254soicDNZCkHEfB1jiPqD7ncBFzzSJ5t2z3Ff+ncMleB6z60DTJgaadOzGGb4cn4/T3rT/CyCajixnZAvtRrt2tmbE6Z1h+iJoPsHdgBgYPdtaC9qMULQDWHzQuETm9/0CPzMOoqgU+oHFE36GumhQjlLevRiafXQggEqdDEkP5pxOENBK/rhI2gmwAb34IItjKb1NCeZxUdruWR+sVpbbbR3pC5s5Jgy6YxxM1NHLSTAXviP9Rob0YFIc6NAhpiax/SQjRf8sQOOQhANrH27+6Ca/EHMek8qhPePOz85EkNA8gUbGVtAxgCiTmddYB6c5X9Khvv5M0E6w7wm7Cf962hviu5v81rSbu28z7biGl83zrDNVegTMgWl1tnYWTFSnWHjnvhrGTYx9HdqDRsvuu7S/A2Dk8XOWD5U/ntzVnmkvMM0PY4XnTpncp7zebfOOMwPbABJMzQnuHe1t9CBGIAbmDHkfpLabYsYBH8xHTeWnZw+Te6yvdF/ZfCTF3PBMA2h1a/Zw94ioFWnlaamLQJIb03fVGKhMyF3i1KqaF25sa3uYcxgVuElmOE2p5MrM/XzTUhJ845gj0XoiUkX77nmkyWIJqaCfQLVyi/kBbqWeCSxFCK1OzGI0Gdj0NJC8BV47CRY2ps7W7+8VQO7S0wI2Lor1kVg4EJqETltgmNw+flh4PaiTa0ZdHdnGndDHn2Ghz4x9+ONDgLxenl8cCMP5EN1505K3d9sS9q2pIWJaqsfcAWo/L7JjpYm3SA3MtoiPlza3RNGdesYbHv2sC54/3cKpbeTi8T2idAk+0MZvN4+ZJtI+p4KRNo0dGDULLqHnWWAFX5L2Pm9GY77G351krj0P4x8q5/uN8BZMX0T+joh8SUT+T7r2n4vI/y0i/8j+/i2699Mi8nsi8o9F5C/T9e8Tkd+ye3/LDkd/iyQDytLuVv9J8MTNFGFaOTLjMbXO7bkLx91m9a7b3dnVoa++X25l+I+h3v6T0EWXziimvdmnrXHQezTQpqrfhIH6u7mrWxU8s/0Pcd9owJqGdiYT7bw6hqbEM/UC+gVEbw9/Tt4/aAIPRpszCdmkehqRKeqgZktP17jfAYEeaneoVNo4a981Hm/POoPTY5+d2ZNHVGv1OpgnBmYG9b0IQ72oXeP3bj4pbJ+J0jGJ2Y7YbCyGF+95Qbct5jZGeyjT6Kgn467r40FYdSQ6m6c4tjm0twfjbXw3+oqFGQsNyq8KK38RISgOmoOXTdeU+dSsjx7U9W2Q/t8F8EOT6/+lqn6v/f19ABCR7wLwowC+2975WRHx4flzAD4H4Dvtb5bnMSnqJiAVCyhWKtoPxiaNsSfa6OMozAdJnH8KEwo1D/UygCZfODgSrLNMBVcLZFRoW//IVD2vFt4BrUNt84WCmFlCoHs/FF3GDlW0RWgAYt4S/o5a8Cp1mvDnBCFx3oyQwsOH2uTb6oE6QZfrVt0xaWD5hpsQCqiahqrUvriUZq+l9xapPt71aEufqRIH3B8CS9GEYW0gFs/8ABdisLxdf4aApAjSTZCfgXxD0yy9v2ltRnO/0M4H91RCNBt6M0cYw1qs70N7RL9Rh5h1eIO47dfyEK3hHMpVA9GNC7q+GcnHcbpJ1+e1Lo1uUqrXUllbv5cLPWtmkHA/FpiJpJp0SpFYjwKqi+/H2wWv9xXPZcGm7TxkR/mv8t3Gk/n4c58NC6Fs1ikr1UuorYnmLCH9srRxowuahjC6dRb07bVr3W9i4NH/ThOvs5WhHhjPeYpdY4AQZiYS+hErjJ+TQQaY5jeGfq400+5zll5k+qr6DwD885ees/TDAH5JVZ9V9fcB/B6A7xeRbwXwKVX9DVVVAL8I4EfeMs9AV51uIBb3Qyj+Bz0vtnMt3ulsAEepPpPoOpTX10lbZ8j4rL0/y39gqD6gvC2HMvzSYEPuHhNn9v31PjCaTts6qoczpA8g1PXRZg+B7awt4yt1446X26nS5wPyNBGNx76btmHsk0l+3U9nFEBM4jDPJT2Ol6FegZwnTZvVZzZezsDDKISdkUzpOBnHhzLHsRjX9DA+Z3Xs0XIVPmNNCgS7ynETlqUktv4zXNdDnTClz6x907oPjHymCcokr0D5zCFZ4xznGvcN12O4zjzj0E+z8X0yVQ68Sfqfj9Kfxab/H4rI/2Hmn2+0a58B8M/omS/Ytc/Y9/H6NInI50Tk8yLy+f2jDyuzK9IO2FbEgmbKCskFsjQNQJLGgSuSiy0QugR09GSqt7lRFUPfvCkkXKzc5kmbtipasDyj4mjEJ9UwUBcvRA2fHnYBAO1BsHwUSEtBftojbLKWir1d2Alq25O4NlQq+gq0X+tbMpkb3KfZmXirQtg81Zi6H36xhFtmY+JFBavscYaupAJkxZIL1kuNYBcmIQEkKxYz3MaBLDQxO/uooD+AfJxsjsKHbfxKiIlDShRGR9LQEhTVO8rvL6UictfMvL+AZod2u7BpAEFD31wV4whtM5mXPfrnS+0bNrNESIxlfBZNi5SKfoWQqgdZC5pZPSIIlzgtFBzKwcd9aFFMTwvR4W1BApalYF3HGA29iy8APO9LnLdQVJCguKYtPHq4bwPtelu5/6heHM4gQowM80wXp3Xf9hHly9ZoEMk0qEIHMMXmtCG8xoF5O++gaxFOG7U/S4TX1shjtB4oWru4/yNgozmqhKnHiXO2YNyq9zWlnwPwrwL4XgBfBPBfDM3mpA+uT5Oq/ryq/iVV/Uv5E59oRGdbuOXoSLJ5DnhNeuk3U826NFsyH54/RWr8/KM0SnQqQ6gNAJprpEo8E8gZjXgz7aDb5TqrKNuZ+bZ2j7RnVMLL5vTAmrEuZygUaC6mONr0x3RAfvxJz8yun13r1ggGhBS1Yfv2YGN27dJ/d3WclSuU74zu43uz9s3G76QN0/xm3zlvvjVbM/Pv2l+vIOzBiiHIOwuIQ1QOdThU4i3TrN9P6TfOE/pzQSOTYTtj7P7cI9q+yAv6griOnYVg+u7x0kOtdkhfE9NX1T9U1V1VC4D/GsD3260vAPg2evSzAP7Arn92cv3tktvgnSh7CiHArmJlF2BLLbRwMt9+seMT/ezcgnAPhEtJgBD9UD6vqPv4VLTj/0w6h4YAzxdwW+g46bnPlTxySqmucCJaXUwBINPRh/7cPZm3Ts0s1H0VlOIhqAsOu2RT+4x2eBhmHQaPDe6tJLze+1itkvvJfteMe8mxvoBdcLsv2LZcN5PtzVsiWQC3p7xV9z1Hy7YrtqsD9QX7jHfqOR1NOLYhVHFm3oMWVix4lR8518JeIJA8p5zNNLEUOjLQ7faGKrXVF624jraB5vma19n/aIHdtbTYOS3oNYqRkSm1n2looQeSj98EyB0WINDGAqFFtXrIzmO6Ho/o+zJyLrhcN2Rzx/TF3A/uVzyXBV+9PwGojP+D7Yqvbk943pfw3gJsXcHXQ0gj4yCA4wEpXeJ+J485z4jXCpw2xeZ7BDqzfLr56YKdTX4DXWN82Zg5zKPijRnqbMi8K5d5xNCfXTt5Hgx87JHQ+JqYvtnoPf07ANyz51cB/KiIXEXkO1AXbH9TVb8I4AMR+QHz2vkxAL/yVoVNOrdD8+OzoT9jkM5NjTotamAmfi3yfvA+o/au2kr1lOGFGVIBtc8/U7N9+m7cWUVn9w55Ubu4bQ/bjbo419D+sc671qiJhey7+05eRRMo4l4cze5PA/fstRF5jWmC1GYMYpyUYfcNdfsE8QLtLAMXCFQnFqgvjrfxwoNnW36kxdHvqRbqY8zHoNfRQxCQRcB/d3/UrjOFzMFK52qMhupvZakCQFMcoHLbF2wl1yibKmTe0yMNhn4aPViinTNa0b0Z0Pcbo/29S2fzd3xszOPA3MeH23XeG/NSOV0a35mNgUlazm9ZXiJ/D8APAvhmEfkCgL8O4AdF5HtrlfFPAPwHAKCqvy0ivwzgdwBsAH5KVR0j/ySqJ9ArAL9mf2+XtgQsBWXPSGlD8gNUaND4Yq4WAW6C4mYf37mogDoH8kPGUaW/bMbM7La7ywFoPulAELUsGrsiATTE70f4LTab3M7mDI0cXti/GKUi/LKn8Mqp2oxAzX5edsG+LdF2R9o7xbUB2lz1Ixfd5s8oqaJDhVjo3tjZSTbCxizqRxbFroLFUG5aCnbUdu2a8GyTu6ig3BMgwL7lur/gXvtvHMxr2lFQwysHmjYOpEKzNRn9yVOlS1rr7qitamGMvNAdgHNYV7EuKiuqV4yPF/att+c1Ne+dJIo91f6WzfZRWF+4nblDjJ0dWZpmSVpBhMPwPKx93Cdh8TP0qAKA7bykdfbMBp1HjNNXdunWPbpPp99GY8L6omqkQDbUDwC3LSMJ8Hpb8ZTv2ErCzUJubCXhw/sVAPDHz+/jzbZUrW8Bbrn554cQEqKJ9alr/LJJj6EoWJ9oHQtpRMos4AqQN6BcGsNlmhXS+DWhN70mheb63ngYUcuo8hBxutucc/gWPvYkZDsNgvucBYnNR69b8oNgrBmxP+bPwvRV9d+bXP7bD57/GQA/M7n+eQDf81J580wBqMVxX1Fj6dtuSWVUYs9KkRpnPxZimQmjoZ5JOTFJU/vU0jpdpZbvDIgX0ARoA1XbGarx3knzpIgJL2lMg3Zc1jj7VRCo1A1qvklNOS6OM7WZJsADPiaTRhs7JMRmkLhUI2lyTPQddZAVlS7Kpp/apbsJL6MfF+FaQz14paHQrh4kByC2YSn17egZVJX+8dUEQcwZznuYSEAVfmoOAG7+63a+Wj3CpBjOAd5RlJeNOY522Jmk2NVSewEUE9uZ2FjfgT5+XdGq7kBj3GA21XiVKkc0DXpJFWZBd6D+Ng4VezMAbHvGknfsJeFWltD+/O95X+rRmiq478kEfrFFy8kkMTDV6jn5DgSIkuH62Ca3/1U3ZalzQIZ8TLgEAx6BhtG2S8SDGt3QYlQ53fX4/KEdrjDxWLf8lD7F+k0tbwXO9/hQ+loXcv+/Tb7KDXQLaDyLwj98LVCztbq/uLgXj+dBzAOgwSK9tG82Mm02U7RnGtFrJh2fHQeo1Tnml7QQy2oeSWo9WUrqwiT4oeKx65MmmbtrcvL77BLn6KCvYx/o1JsQoAAAIABJREFUypFgy8jILxrHJY4ueC6k4ihF9xhy2rktk+zkkqpZx33/2bc/nnHh5yYflfMJzZNS0Ec8dGTMvuuOgL3/eb1mcP0NUnlfZbTFdO8naeUdNswcGK7G85H3bJJ2jMqeM3rA1o26o/6MDrwXw2U/2/ujn2NgSC9stc/L363RR9G8kQjdOwjIoljyjmx1uqRq4y+asJWEoglLKmHuAarZUOB5D53r6Dc34Ss70YvNaCPtQHX1nfVo7a7eTKbl+DsEBtXnJXn68CHmvr4SmifRNUKr830ntUtmH2ven6apsabnUvwABPxDaj/EOhaNw6nJy9K7w/S9scGhLTnIsgUkuTSGn0xN7zZt2TsuHQ8TG2gTjidu3HNBMKlnokeUt1ZLL/1HhmACzBFyYZONogkAu8ZMH0C8x8kXfdnbpjPbeD1PJgzQBjuHYTh4AzlvV7Pn82BzrlMQrqZAtf8u3UEbOCyWdvXxyUJMPxja+JfotDGiL2s4swBm4wTtJh4h0I5ZewZAY7S02Me3A6Gx2TAIgGM/jMkfT+2rM3if9AwiOiY25C8GLjoUOaDNMPvZ+2VprqTO/JaInYPQAC9LjaIJtBg7gI0PtP0c7tYZGwitz1gARj3NNClFGmBLjZYjrbkdXu9DcLRkWuBkt3KkUYBS37owYTDolQ63b2bClDfnEW03XhVgcxDOB1OPzwMSfJ1gf5DeDaafFBxoTIDKzMMIj8o03VSQCemOLRw7dkQ9nl8QmwRBwMYeNbDPdVz3dYHUom9294OJtAkqjmh5gdBNAGmoh/2NXj3RFOahg23amWAXvtdeDpSgmI4O95hi08Zeqk0fMHt3AbBLHGAjJAxcsGbRg/ueCh9GQ8fBDYw4mu+xcphJgJigt02HXbn2LmsAcOav6DOjrworUzg+khIXJnrHy1R/vjYk9X/E1P1Z7avT5dtZ8lxz07bHgoXAVKPoxgmal9wwxtmcxmM5Ts+ycZEN8XchF6REwLUlldjrseaCp7X667PXUGfWY7PTpM5uyz9o6jOagepPz7PAV59zI214POx93k3oUj7uBUZzc+wDFdS1LhYK/v6x2D75MzTGvc4v4Yd3gumHeUbQodx66g7CHq6GyJItGkoqdXLyYOeOjWs96pFdplK1U4Ptt787Qxn813t5EHOzbeIidQIVY/ptM5MFWXPkntrBE8k3qLmWY20OuyULJcScbXV0RsgolQa8D6bDgScq4WaJVF06P9ouVVaUVDfSbakuwJsQcBdaJGOaqAvDSXptpPMyGVacuoMmgD44FXcLuSJGWAaOVaNotnZiMOJn41p/oNCf1c+RIceLCXs3uxbSXHcaHwX/IByc5oOQq20aGzl+6kHosVA4lMf5GgOMzWGzd1Q6Zgn1wGoayH5XwZp3XJctImgustP4qa6611wZ/dOy4f3Lrc5pZsYHATdoJdRunz9smjxsTGK+O5xVcTgL1zULBwaeBU2exNF6uY6E/CPkxsjkve5S549cSmX8Xg4Ja6/72OZO6DlgadU41m1I7wTTDyQ3IKiYnN6hjnYDrZINejKYRrUs0kj4cbKO1RsnON2IuDeCMD2E3ZUflbZAKvQ+AFusPkqW2MA1mdBdTH6iQQeYmDSMQlwoGs3ctdIPPz9s0EI96FysHXUS6ZHmzj+MWXgKxn82UHVC9xkDoDYcmu+T2l83G/IoDIK7zDrU8g0//bimwZhGRqtCVZU+r0MabbFDG2d9F43k533SkxCVgYbO0DsPpqHJ8bvQhZM+GkMqjJvuZpvwRjMlC62Y81R2L4Tm9QitmfOy1Jgu5TFqyEPfSTyH6YIuycR4xtc8Is5WaAAavGVmStRxfLB8UnSmqC4RKH2b9E4wfVcdU+o3g/jkc5t2ssBNEeK1pOrFA0Oa4YlhVHI7GtCJyrLUe+yCd9C3xslJDC4GWgb0UhF7bDxaUDeKuSulMQ13OU3Z49VoG2RkHhE0hu7XKh2cVgiPnjiOLtBZq+8o1AKZGCMsuR93SQoKBKv1AQe6W1LB+/mGNe+1D7zuSsjK6S8tPwAttMO4gOwCUtC7kg6TOBZPBS0UAtAvCvpzmbpvqb/LVaPPRFE9OsY1IM4zIZBt9JPUvo7t9/yuHBFfzQuNwNr6JB6Z8ddOc2g/qsdH8xTq7NTkyhjhCpwxeb97vY1Wo9942ulHcEcE0k8C5FQXcR0gZKkumnE62oTp+/WcqG8T2mKtt0WlQ+FOr1icp3HBplT1tjjtHESx+cbfZ4+Z3JwsAIT776hpAoigaaGdClAuCr0o9FKga4mNobHQ7l5iA9Mf825jpLqIy73VMcYHCx4GNQ8EwDvB9JtZp32GhD5BEIc0Mu+B2Y/IqdlBlVAT+o6JvPpJEczIGIBmrQIgt87v7Kdug/S2zZIN2t5zB8TsCTHR/Y4uo4Cj34FEMDwDhJdNgpKJp8+72m2t/pNRNT4P9CGbueyDqcwn5og0g2n2TMpR66OBH5M0XrI/d7FliO73pDJ+905x05uPEx3HhvcD5RPq99jWB3UVnLRlBB7EeLiPwwTAz78wzs5SN9QPaL5G0ZwlZvrh6aXzgGwP++0snbxzyIvGUHdrnNsDKJrl32nGQAhULFpNNs7ofYwk7YV9l9mDtnGdXrj2NuPp65/pCwwBV9QqaMyxWIgFydo2abkdXLQesAJUH3efFL5pye1yVkZ9kFAOMeTwCPH6FNvUVfy3ve6IcFHotUCedqRXG3At0Otukt8QwOI+4Wg2dxdAzMSs/WpeC8m2vvsaQCysMslYY5iksGBEvRXlyUIfB8RC0EdEscoe55uq1o1XHsjOzVIeJjlCNPCk2FKXH1AZhHvxpEQagmsnloej8lCLvd4WxEx22zBn9mlNtjAopCk4MrQ+8sBmbt6JTTb3/oB3t8/4ZhpdFJdcA8ut647FA475THLXQia9rT2MB5McTFTMoP22C6JhgY/ziNOmJl4ijvC7zVdozwA4LOiz6SEC73mwMOobsX4riqDZq8VCJhsBnKlX82Al0p124972HGV2JrehfhHszNtHayTiWoyVmTzkB5vp/EMaXTrtfQaEaA5w4uERXk0A0iZ1fr+3Ib/akV/tkFdbDSu+KGB8Iejpddik9R/QnAu43kCEzwgzoreHF/+jD8+lyIubs/7cEzEK90mv4QYcjQl58fTMLyZvUKfl16G3QVp3yQ2Mk/eq2tnbMlVQVf2lIK11IdlRo0qqE3ipIzjsyb6f4IH2MqYzDafZ+QfUzsh34GlnSMZTqOGMxsfziLleB3SF6eTxs3Xre4+ffZjeAhnKrP0s9McyG5jvy5HG0Lq1FGcwI7Q8IDrvDDTpC1sIJmYtZ+93+RzvD0pP15y3GFbHIsb5QYkX9guqBuRmQDeL9ms3xQ5WqfcOSN/K4tPxmME9bOeMDpPu6B96cG+oUzdGxrnF82lR5EtBXnYDmqlagDapGsCO2LuiMPNxaGnaMhsrP7ZvvDabcyfp65/pAyi3jHTdyEsHKHuClrqRSbJCd8G2Z6SsNTzDniI8g24SKN+PJtPRd94/FYbWqAM61EbH6Pmgz9pGmQB63fH06We89/SMnBT3LWMrCfuesN0z8lJwvy0oHv6BXExVpaL0rZ0tpEWQ1mLtlbCpqwLrWizGjV+vjDfnUtc0zPYuQzu7DUqKoIs4kKM2J1G8l2+4pA1Fa0A4JeR+GilTG4rkazkXrObGF2sB7qedFQIxu6WjbHpfNLhjs7W28vlQDbF+jzAC8Wz19843bYdykN1ddzmYxjgkgzOqEKxZgU1qOIa7NOblw8K1k9LGyMhwYr6785W9E5sAvU+sjswfKnhobo+xFsLvodWHD/5QKw8etqKrFPr6qs0dD0eulbnf9xpmIac9DtMpItg042JuRwXV/v98X/C8LS0o295W0jVVWhXYQqxIXPe+Da0mN4Qtu1S6oYIwD54mG9qxiAkVUQ9jNfrJNEbuu4aa7euILW0OudmmZAU+seGbv/GDCDuxl4TnLePNdY2d9XVeVr7WQAP9eZlFY8wcNlyF1qPQJE0TE2nj5yS9E0yfNx/FIiZ9F9hE9HNknWebNO3R1aSAAQmL9jz/YRpQQEX6isu64dVao0g+p4Jtz9hSjZ655IJSBCKVsbv3itjISrSAw+4fnflnVpVY90CjgdeRP8fvOKGLJV+cW6XYBqwj9Do7LKOWRQ+bUPI1gMUYRfMI0sfo7KzOY/2lhQ4YNZtYByl1G/6hPOKYnaVNUfOcPesPOpNkAUtlPhpTXFb3/eS1kNkuCE7yPlyeCJ3Wjv65EeXG3OBXJxp2wXFMJFFsWsd8Q/t1LYDL85AdD9cgxj4d7wl6wTemGQ28vehB9uHZyZjy67IUvH+5YZGCm5mxcqoATETtMHmN8DFuym12fxJ0jJfCVCnz+s3ac5LeCaaPTbBvVXSVIt1hzIANENEwk8SO1oI6ohTAnqqtONdYFZoV8pz6jRypIowisO3ZAypzO3F2roFAie4mJgqkteCb3v8Y3/LqQ1zSjjcWVbBAsJUaEvnLb17hvmdkEwgiir1U5LPb0YdVgyEm4jbLVKBWcUf23dGJCqSEtlmNGRihJb+W7jTaac+RH6NXL7dRtu+mXRkKue8ZH2xPuPkOqLBrG8ry2ZcA1K0VeJXvNeKi1CG4LjteL6XS2J5nk2wUX5onh3MHKdKj/bhGE98EqRgy4239nb3cAYCtoWChIFk2UX03aSlmnkiEKKXRsGl/g53a61O0rSHxBGcu30FOql82oebvWbmSYPXtA36NocE70xYdMhT1t3HN9HdtZzCTd268brKZAc2igjXt2A383PaMy7JbzCkbm+rjs40310RCaxv2Tnjso86O7XX08Au2VhT7NlD7QLSVpUDvReZ5mNYlJAQ5gF/QTeveoW959WEcB/m8L7iVjK9cX2FNOz68XeNEsef7gtu2YHtKwdfKc3Wbk9e5jt/SNLiolNJ45LFPzP/dRvo2KHVPEQu/7NJt6x/tm4FEVSpXdISfgBqwTOYeJsYUnfNLDCjLvqBtBqFi3bXT0UlKik9envHp9Q1e5Rs+2q7BNHeyZ/qJQm+2tQYt86ihVoQQ6o0B9xapBV4b0N8JGpDdZGOiRxyJ0PtJLAJonCFbOY63hc1vMxTV8lFk51CWcip1wfqFRjrTDiSk7VMHdMcH3CvRr0OGPkaGeeVC1k0OvsnMzTuxGdAL9MVWam8HnMe6eR1mMP4EsXUKE6M7afeVng1SKmJh24VbNywU8JBJXL8Kpvo2KdBtcPI9F43pp3DlrL8Fi9n5gabV+v0sNXBfhf+P264CE5Z0bXCn5ZhVQSer+9sg4pGPd8KR573M76WkeD/f8Mn1DTIKXucL7qUGKnzK9zB/qQqe846Pbop9qWPpzW3F3cbd/lzNpyLSt4nL9Xk2jh8CiLP09c/0rYV6TyiLQZBS1UfenYpUR6x6hE1vOC84cmclnUpD93nu7HaEKlgFDEcGlryU2IRRNGFNu/ml77iVBW+2NRYzt5Kwa8KaCl7fV5SnO+73jM3q6+iezTelCLYt21b4Vrh/z344i0o3+GNyO1jm4+icDj7BUCfurqnZ7tUEkgneXWsArbutW2BLHTKEoC5e7zmExJp2oNR3FynVz/uyY7sB6hJOW13dLj8yfPbT97o5Q3VU2IU8prZHp9GkUT+acylGl35GyS64bzmEt1rIicrka53FdyEnJdRPY4fLM60lonmyMBv9rv19mvRNeKF5t3C1g04EXvh9YrQdXdAYbXd2MPq8dhN+SQWqBa+3Ncw3sw1avLu7qOCa97avhLyQqn3dJTm6vQUeSZLNl1IQ3nrRJprvvKOZaYMitAEMza7Pa14DjblMp6FHA1EV3O2h6tG249VyQ1HBN6wfYysZt1IXs9e8h5lrN63cheeb9xP0OUOLDYSChviD8bTKyV26DZW9dtCnd4DpozZkl26jlQB1soycVtBP1LNdbAK0OLSUnHBj/JQB9TKU4knEgyQbol2l4I4adXCVHe/lGz7ITxUBqbTIgyXXEMYl4bqioafdzD0W9+UQbC01/3729Y/4MF6vcSLY2Okmtt9+oB6q2gYmK8fD5+6lqqlx4hfTKGuMUz8ntU78hDXvyKlgWXfsWzraK7nsREzBrw1ul84cefGrs6k7g521M+ikZGymZILKF8lVpY2hgZmGKSLMPJM2yaC1dHU40sEXdhmVN7MV2sWBWR0QIqhuM1AorQ3dO96lbjLUurCPXM9kvlGQo0QZJxxjLQFoO3k7QUztoPqEWyfRsc09gbhJa5cO1IlrEcSwI9su3tLQZibNrG+8zi6JTRAVTciogG+VHa/yHc/ljk8tb/DV5RWSnUK3lIK7m3Y1Ndyigv2acDcAq2rODVs0te9eAVLph8zp7l28K0zfB4HTNrd4OkUlAj75qngb+NImuXO4oIq2yUKMEHBmwZzRXNB4lx4tqIwELrvgq89P+KP8Pt5fVtxNshdNeJVvWGWP+COv9xVP6Y5bWQIFbVrjjK+5agE+MJ4NmcchHkmxO+rWdg1AePNs28DVCEF2VjHy8Vb2E0bbcLPKXicpbU6KZyyQVs62KaXbxVn3WTif9vqHD7dM9ht0sYoawuP9ERz6Nkzf3L+g3wBpCWRLZ4FOY6Cup7Tfgf6c+aohTdppHGsFTO4EiNLYI4YdbelJ2WsDAd3Ro0vXHNoQrUxP0JjbKOwHPhrvUPyaGPIHkNPqzhqQ7/p2IHI3bxw/OauooEjV5jyUh98HKmBQWJ6Dndz76sCkaWp6O0URB8KXWBQlEub++ZlJLMp0YVIQHDLmeIw9y7PUOorRaL9n/D+vP4EkJez6SRR3rVFGr2nDpinm1Kevr1FU8PF2QVnqmh9gGpR5+pSs0OcElBReh1pQA0vuchzqk77m9G4w/cHtLy090hWLcV58AhHjCzTvZprUNm11DEFYa6IRH5Ovf35uPK2p3DK+8voJORV8vFzqNXrmmja8SjcskvFcsi1qVpPIxU6TWm3T0ptlxbMt7vrZuW43z9lNPmqMVOwaYhG3sHAa0ZQ3yyezwNw77eFUB1RsoDL1PFxGpQmcS9qw5h3LsndhYuuLiAVlFdSBDMGO1KN+3v7vYRggtHuZUL6p+8U2Hgm5HPIBJWyO823wwTCF6OI0IYFaFCEcWOhwgD91W7+0OgbBobVMR/7ePDM7doHZ/G8EOEQTrquy0HJ3VFqAZfNXr3VQHQWN4QKd9tQ0h/pOnLxGoMFTstDbW0l1cdbCVGwl4ZIaMGNm44vhdw+xnM1Bwh0NvH7OhF3YUf26tlCfwk8jc9JZnSOUSWlt7jQAfw6IU7yKCXKxE6pCNrqbtr9nNCrPGV/68BMQUXxifcZT3rBKqQfKaMI13XHXhGuqsP1T6xs870sTgkuKYHVAdZrYloybrnVO74DugICC/BEdHvD6SF//TN8HtqMJVmEHZttWn9C42cyPTRBINHg7826eeKhlV0RVPX/ExTo/z2kXfPzmgiUXPC9L2D0/vl/wtNyxvZfwKt8tRkn9cya/q9QFH+u+XT1OvcShFaVU7xwAZMohQej2ZhZcTgtm9owoBwHI3+vGrNJOzkoFuufmSUOpQ+uUf/TdXiefaz+OgHZDMeo7pW3GMg/lviqj3/KAlA+2avTIj00UQROmIXA0/dnP6m6rvVY5PBcueCRsO61yMiy75DSYZH9W7KyuD7kA98/sORLas/qOGwHjvFz79I13bsZjO7/YuFcYIBIak0A/rkYGzkJLmoBywVYS5QNQAEZ0gmQ0d3ZtGwEe9xk/O/bnlvD6ecVXLk94s60QUXx4ueLNvsScL7Y+tqYdi+xABp7yVmm03KEqeFq26uPv2S4Z5V65gviOY1QBMJq8us9Jepszcv8OgH8bwJdU9Xvs2jcB+O8AfDvqGbn/rqp+2e79NICfQFXW/iNV/R/t+vehnZH79wH8VdXR43dWAcDjUksCUt6ricOODuTeUUU9no/DGmSF3ikv70BnLmyKcOL5pPV0KcBzDrexKM/d8EZCb4LbBxd8eU/IuVSbp9VzWTc8f+OCz7z/FVzTBj9Bqm58qpD20+sbPOccB094GOLLsqGUhLvmw0TL5vvvKD+lgm1LnS3bFyld3eWjHMMeTBzFGaP76VcSVaRf9oo2RSoirvSXo6DhtBRgy1UAlguKCt7sa0WIW67muZut1hXtbbBsY3fhy3mPExNo7pjUzpi3k23qEQNdUIUPh+qITKuv9bJUG7b7lCu1tS4st81uddxZm5ZeGAS5eFy5Xdrb5Z41O2JdBI7ilchMzK2z+44MgN6v2k3XvFYnP3/VF1A9HLU9X8zDLc6uMKbuSPViaNZNGQVm5rEKX5ctzEFRfz8nN7V2hdec96NtvOps75lCbizUbVL5QuITt0jAMD07ehnir6bdifx1OoPyAyDPCc8fXvHHtoly3zK+dK2M/JJ3fMv1w6rJyw4k2JrfDlyqWfe95YYPtyuuy2Zz64o179j3hOct1TF5zxGwTSk8CDugjHiY04Plukh/F8APDdf+GoBfV9XvBPDr9hsi8l0AfhTAd9s7Pysi3qs/B+BzAL7T/sY8z5MgTDa+6cgXgHzQ+T2QfbE3lHJ+kwnv6G9cwPUFS468JzgiXGl/UgTYEso9Vd/bu32/JWxbxke3C/7ozfv4yv0JH+0XvN7X6sev0tn/w/4JaZuiuEzF4Vq7RyhsRCb8/Qx1Oleye6vsyCix25fpWOw81KIS5qQuGWdizWIrOQ5T323nop647M2q76gvZMuI1oZ2BZn0eN+Fnl4UujbGHMHyqK2QfsyN5yR0373cEZoPfcCMtiE2xWGcjn2HftzFdW1yZjR/TOkzdtmMK0yEKtDm4SzNomv6WAaaT/90Y5+c1J/6mc10s2acJWbUXN6sDp3pjz9Zqo5AY6s+9/Uv4X5bcL8t+OrzE758e4WPtksb+0jY0bx93KvJTZ4zX4Kz+j5i9Jze5mD0fyAi3z5c/mEAP2jffwHA/wLgP7Xrv6SqzwB+X0R+D8D3i8g/AfApVf0NABCRXwTwIwB+7a1qKRqB1FJS3O+5Lt7abQ85kJe97nJVMxO4kT7s+tozs0BI7bcuWrfv+3sJtnCsqLFzBGrHrGmgkppvbGYpgGwCfb1gj+37Uj0/toQv43189aMnvP/qGZ+83vDeesN7yw1LKvh4u+A9c/ECgNueg+Hf99xNImb4YjPAbfn7nuJUI2+n26WdHo78ncbxELlaaq6T8yndK1JLBU+XO27Pa/Wr3xPe3FZ89faEN1sd3CCf8Jp3351aBF/drvh4u+Dj7YLX24rbfUHxQ1d8gnvVfWE9kJ42269XOwFpa10aNmh4O2yzV2nPs9mnXBXpG260ViI1MJ4C+jpDUMv3s2HDz3wp2IiOLiTkTmNIzQwW5iVFYlOkjUF1LpPQyaZuMjvg6Pqo5uUhifk0MF7nqIW3boY92+pgQILWBEDv80K2C3EhZM8++EkUl7SHN0+SatP3sVwAvL6vtikxRZ2iqTR2urARPm8fAJkR53SmPwDqZ+OmRrOq/dM7BVUb9rzYVdNpbZpVjKUC5JtAbwlFlrqgvAnu99qhf/iVT+Irr5/w6VdvkKT68wN1o6KDPUil0UaSN0JVqITTAGt0jXjWLjpedpa+Vpv+X1TVLwKAqn5RRP6CXf8MgP+NnvuCXbvb9/H6NInI51C1AuR/6RvqtTRBFAaRendFdw3UtrmJmQ/bcc9Q0FhGfB/ek8lzAb1stEQ8eVT1a6/ePaUseF6qf3pEqSwFtz0jYQ1E9LwvuNNCLjLp4pTYtNKHWeY6kjZitHE//o4W1h5mNh7/3n3qObCaKiJqYm/eGemlUdet5NBuQkM401oGFNsxH6/zYAM+tMe/j8jN6ZKAxYJkpVQXlSXXOgUDtXfaSWWE9s/qciL4pmkmKJl2YY9oXRaC3BkA0xvt9xlD5dQ9M6uz5z3Txow4I7o/jct0lrR9jOEYZl3XXWCNT/rbpyi4G5sjAYbnXmqKP+OmQaBz593uGW9Qd59/eL/GGkdB1fBve2XHd3fd3jP2Qq7Q2ufXnb09tudB+he9kDsr8oylnpJQVX8ewM8DwPU7PqsiaCYFz8wkmwJ9KALRCgxtIqZFsd9Q7cmCKr3vAphNFkBsjDnYeUlvjl2o9qkFdWcvDzSXJ464NmmdZO+K1BV+7IKP7glvris+ul7wdLm3OO1AbADathy+928+uuD63r0dJgOJe25WcRe6brVEGkOoKAdhI2WEXBZAPOxDEBrhb7zKjkve8LRsNcSztvAYt5Kx7Q5l7b11RzFfOTbtqAo+3K54va1IqNvxy55627KfO2CCOpCZVo8dMXqzrHUXcT5NLepDXjtCazOidfwgKZ6ud1yWHTcL8/FsNtnN7cWWz3uXukh0v2aoTWa9Ve8ruSUKPWHF51aW7FLPVli0AQGnNy9O+7u+1iJoLqHEBUNmS3ufGVzUhTxU2NTVCdQmU/rFTr9mTap2cw03afe0zFLDTiconvcFm6bOV5+PV9wsJo2HIoHnS/TyhdluJ7wh6u5AHGt7eOW44GU3S6evINa16jw4QcVj25NGYDel/ummvtUh3YGSG+OvQe0E23PG9nrB/Z7x0ZtLPUR+2fH+pSL++56x5h1/8vGr+H27Zez3XNcqnxPknmw+pAjX7WFgYqxEwMh5+lqZ/h+KyLcayv9WAF+y618A8G303GcB/IFd/+zk+lun2SEhAA4ivKIDZlzOgezn6AYItF57G1RGBli1xatTFKBoPuXaroVv9z2hJMU91V21LcxwjUbo8TgkFVs4lENfunZTSuqu+echOBYjxLGtjiaH9nebbKS5bYq/g8rID7ZZQX/EJaWt9N4cU/LJMCGdzkljg95o+mB7bzBq9N+jwOHdnAouy4ad3TZFm7C357NtpksWSCtlxT56E3Hd2GjPdR3HDTNrr++v/9d3AAAgAElEQVSZmWwyRh0dH8o5u8+ABTjU5wytPdKMW/BA7cZN3Id2h/Ec0owGejY+zqsWgpDyjWaSTFbKZ0Y65R9+QSYPUwZ1R7bGc24adA2g3DJuqO6YvrM7mZlrV8HNnB32PaHsuZo9d7EAgYAmoRhaxlzG8fSAh32tTP9XAfw4gL9hn79C1/9bEfmbAP5l1AXb31TVXUQ+EJEfAPAPAfwYgP/qrUuzMAOhdqd2RCIznZQUu4XFlWTMKReU1R2i0VDfUsLur0jVi6eg2bqV8rYyvRz34pFNEPF8QhAAWK2sQkiCJ7Cragrom4w9KeTpXm32qWBNNfSyqlj45YSU7nFYTI3Y5+6bFdLs5ikEANuWkT1Ewp4eTlLXcHzgd2Gizb7vSP+aqsuZiGJd9iqUblVgXfOGJe/1PQ4JsZbqaqaIHdWwxarVImxe8n4wK3j/BlLlKlv/aqKNKe6pY20NzYZt+/auLmhon7hJTuZGqG3DX1ucpR2eNg7WvKOI4pYKdAWKe1UAh70KbvN1xB6TlMIAuD2+FTIg7oQ2wdGQcJCNBR3lP2qjzLuUflS6ok/+IF13NJ2MPuGimQteLXcsUnDJdSfqR/slYLCHJMkp13kMMv94f7FGVtr1boBQP/donN5VujZsNmRMCLVIqwYCeT2kqi/0mrQuiLKGueX0DA3OHVA2qeHIAejrjK0I9pSx3Re8ydU0EJE47xa6nE06VFbXv9CmCaHNjT8T0xeRv4e6aPvNIvIFAH8dldn/soj8BIB/CuCvAICq/raI/DKA30HdMvJTqr7ciZ9Ec9n8NbztIi4wuCeaj7S1ytvmttjueRMU8v+29y6xti3redD3V9UYc621H+eRaztOnBciIEJEgoRCpNDIA4IlIpIGkYIEcgPkTiIFCQQJHQSSpbQQNGhghQhLECJLYGLRgJgAgg4kDgTFzkOJQgjmGl/fe3Pv2XuvNeccVfXT+B/115hz7ePY1+fsfc4qae8155hj1KjnX//z+9UwJ4ZHGTXS4B9zB4XirHugjVUed5J9R2ATLhrLk9HH0BCtRNc+M04618p6eJGoa1iB5qR/wzfc/o1ArVkCcHRN/TwNFGxRj9U6G+gwb6gk+vyMjoxhpMuuYoJzdtm5/1FfSh3dsXTCOCjRFELwiM9gbI8/iKu/7Zvt6yISDXvW4gUuNtCsg340dSVMVQFAgeKcWKp4HZE8p64FlZXhRUVd/dT9XduMe52IUuzX1PHwWQfIiVTo2yTxES7UglbiK+L16HdPxFM2tJLaZBw1LCoAF1KA25ouXhbeZwchxpC9bY7mfYyJcM5rKlywAxC7QxSY9szc+Ms2+uEa36eBYFQJnORfY1UFMWSvEwt2lTmTABdR3tMhbc22bW7nwy+F6DPzv/jIT7/nkft/CMAPXbn+kwB+86e976IQBw43RELqKjGdtnG5jikPeOQoZQZX8g1Di27URZ5hKMKjh+PpSIYgD9gJikA0FaZ5Cl5JgZPLEFdPR/qkkIx9EGNWiF7zhii5IZcmujwdg2VpqFv2viblwI8qCsoYk4/BGAtpi7WJGg0PpbipzUNGVxpnzUugEkPWpCc+LVY3xhg5wQzceVkEOjdlG+PxvKmDtqCa2hNn51zYAlLIxVpPiKIbaoqbYAzdcJhDtnlVzi7u/eNWkFPHuWaklLBtGb1lVe/ozYc+HVKnWmSdpY6eknBcESNFKZm1NabdhEUWk1EyoJdgnDMTiUmLHeAcOEhFYHT4BaOakyE5QIGnMLy+XvWQsvtNF74bw2uHSQ7qSMAYlo5VJbhDqniWzw5AFjOvmfOCr6FIZe19u63icxhUaeJtxKPvOk6w64ASWLln4rn0HoN0jrYYO5w5xvHYdRvXBI+piDg/Lu3ZnmdWKSLAtQNarySDco6eaIIwMXhyhymH3mftsgl1by6b6/lQjeXdj8iFEBPjgp3T1d8Ig9BbsAj8morqxGLLMUkhC0ea8khmIISFPUDnIqm4vgOmCqGYN1d3QiCuAAQbYxFdvJ/0hhkf0T/DsUxQoLbMaNXeDSy54WjzmYyzHioeA1cz1YSJiu6iaQeThuxTJJLah6tGRefMhNu3+UiEaaynvypR2QHFi2D2GBxzHFrz078oQey3jS8bSzdvh2D9M8SgZW2lkXpwGDF3gSuJweE+K7VmbEW8pHqXAMBuWbS6BoRp7uGi6p9TLULAcofFiLCFSBpHZuPv2P06Tn1s6EFMeeCruCuwjB1tCkZnsQMpSLwBToD1P++aEWWTUPeHfbpCb42eRPVaXCPKoJhkasUkwUOqOKSKnhPQlguwtYi22fRAjeoqV6WEQ9zbHh0VdgedEPE4njoVCSC2/U6zhLevH+OQngLGbC8Zk6V7SlSL7HafGMNDRq/0HVHqd5qw91yL2gadEAaPA1m1FFIlTW63k+T2SHkviD50cbVG7kMNjH6ZBDAFBrnqA67ucPqWBEqAWI2Peo8RDUoC82v6cDskzP+dFvHfRgucgXmbuL6WQYVBSuQpMfqmbFPWlbCDP82pX3BPlKSOm3XDfVldpWLcZs4dXX3ybRPOAWvsBDAm7HBxXrstXC8HpSX8ZLWE1rKhuxphxzi3lrD1jEyMsjT0RuDOKEvF7bqhlYraMrbSUbNiibcBMHeuWRLf7A6aMZe7azTr872ougxFiLrdK7+FBeM7AwO5Uef04bQqXHWXNULCxVlsQCrif26gWSWLB0ZtCcmM7XW8kEvgUDLmsS9jnN23OkyRx5aEw8oIMQVCDG1jxJl3VY33+XKoQKNtHMeYZFH4jnE9x442KWMRs1/ZujRAwZIabgGB2whAYwA8EKkzBYygHUdu74wEnEJTrQt6aFxViUWu1w5WVbPE6xfqPuOe/e943sc0jquNGYU6Y3AfWTsHQ/OYHoaUyYhSYFzDcf4t21bs89vKFRbr3S1dAceiLtkMm2Lpjh4smPSTlIwABu+TxLq57SZ4xqr1UCXYK4tqpSxNuDmIcTKVLpGbtlkT5PPShZvPQqxzaUhFDhEqXfZTtud4cH2q1ilBTWUEMCXG8/WMda3eh7UI+5GV8zRDt4fEGyel3PUIDMLYIBjXjBt1ScUWtG7KjuSc/mRHIPVC6AKRvCzVx/OwVjxbz3h5c8JhqVjWKuquDhyr5Eo9Vske1GtSA1RkUUMbEyZCgDA+2sw4nJd1RG5rF7QFYtAqMBan4yIR1D2JMdyIho5LSh2HUlGoY8kNh9xwWCpK7q52m4hG5MpjhiO9JsRGn4luqoFznaQevYdN0kwzUbTxmxBBrxGBxEF1M4/7ZHfA7nMonlOASZxViBVmoWNN1SEGbvNZcGeo+2Ew1aNuqJNKxaYt9i0Wo/6+TnH9cKJ57RjXHyGjrSoY0efxnLdhp/aJw2b1zO2TveR9iQb5OP+xuKpLmBdTDUYPQV8r+8PJPk+Nul7eC04/Qi8AO2K+66Drt1UMTUGPLA+MOm3knBtWypFLw2HZNEkJsCzVJYmW1COoJeDQwFXhnFkkACody1rF1TKwIzl3tCKeLCkzmBo4JVGDZBF+Ew3xWAzP0karZ8nN+2f68Frz5Cq517N7GyLEQTBkavOcW4L+pR2laCzh4pn4wjXT5qOkjkNpeCA5OO8OZzxbJLr43DKKHqKABHOl3Fy1Q05Y6HID2U8TVwVMicb3xM/u0RMhMmhzxUBfGHltjqfT+i4BSCAEFBiOGtRSk346qh+8c9IJ86l2gmXt9M1KMydvxYhHvK6HNXWImD9xtKy67DAGsU1xPFUFNqVN3NMjV0UNqSBTVKGOexcSYm+JcgBZrwt1dOq4CakExV8/cMGEyXB5wfFrG6ZEIkEXNCdECcBkcdhs+K+wvI+uoWuEdE/wbZFFwu5NtHVDg0BPaySOAauEqW3Xvo70o6F+swnZEgsw34+V94Tos4BbBbdJAoRDM+7EuD5duJ0FA58d05b9/uHdIqNMSWDNzBVzXRteHM4e/ny3bthacoliXasHTZmxrzGhHCrK0vCVF2/w+rTiXCUQI+cukMMAjnUV6aIwUqo4n8QQWHIb3LmraRhIspkzdVGV9DQRpO1cBlQ04AberuPk3hUGHkY8G6dsjCMRityTtmfjjIbkiV8otJFVzL8tG7Y141U5AAA+unnAx4c3OLZFIouXjKMmShFf+GGEpqC2g6qo3N5if10vL8QzNbg4zcDwlGK62OSXXln6qgTwoeP5syOAoSp0AsqY3IPNYaAkyRJl42OYRL0lge3wID59Nor0geGYrsWNzCGgjTCIvumA428JQFcxP3CAlj8gVUw49JPRMYxDqhiHks6DcQRCTEf9RCL15MSojUbsgnLzCzUcqLpULhgzAhv+wSIwBPd1xakWLFnAx2LQ3HQoGcEnS5KC4YpLUPsUe4Sq3UuVgGU3tjGgPajODGZiQkcFhhHVpCIewzItMh6H4mjD7t1anxP4eMhSWCNhnH2iog0QcHWjq5SdAdKDIQJG7sp7QfSHt87Q00eun/RktO/O2SFsWCDMFpxoWSEEzjcQ1WE0FcqRjStX1ZDo/cVzw1xEJRMUa+TsYCdyFrWQ3Zfc3U82Tw7h/ROkBOCZdaIBTNQ6HY3NrRPR+89VWZ86voHruOAwiSevnfh5wC7Iw3YYEInXzpoaFuqo1BW+ofv4m1Sz7QPO9lyWbcA+NvP+vqCi9zZf6DT2HFwsmd0QLoQ7uKMmYyjGGO0lnb20Kc+F9bX/PXJ5QSVxIYkEAmhY/pMaY89pXnsV78YnNt0PCA510fX2ItQT5hCAr9XIOLed5jiiSgqirECI36dVcsbSkG6if/5Q0zD2a2NSyUz3jvZeqFSujTPmZ6fpvbaOroz3kJZ34xelgPjsJB1c/p1cu68dhF4nB88dDKeBt5T3g+gr4e49oXeevFXkc1POiFE1Fp+SUImukyBJOpITUgn0Mq4fwhmEwarGUWuCiJQblhylBEgw0mrtSiilYS0Vt2XDsRSBJVjEBYcALKWCb3Rha5taEajom1KdkK+pyaGQO7pBAtTiHiPMwlkRMQ43G07HZYxVWKST/7sZ+ixE27gDWzATlx8WbmYcUsPGRfz1w0HSLbI4jBkgHODNuuHl+iBgUpxc/23ca8kNmSTymI2amAtanHvzdiECMbt+1uwQpnZw9YVvcJ42O5u9ArjYyLR2t5HYemCOXlEJrYh0JLhByROF7A3mMHVH6aJCA6nhfubU5r+BPdwTqNBe0qQ5Tn07HIL5wpdbh80PrD3xs2eUmE7EK+i7ET8SX6hFZI/ApUsz+tee0DOJqy8kuC9T1zzQFae+4E094Nvn29FXhnuW9VUa6wZuxpQsx6U69WBi0rkIahGpA4OLT1cOv1i3/vVxIAypIh6eOk2pXtZlYz6Sv4Q+2J6LY2trMkHWv+9HrSxKdfocmzhBABaLQ9JGFsz1XynvBdG/WnYn6vBJt004uP5PM2xcVM00hUdP+5DEXTFmt3FOJ4lE0FXSIGKBrVc1SyJognOaDco0uHwLfIrxBtGneZJCSFwHaxn3Xy0kC4Wm3Y358yPPgYS7b6IAG2PCM4c+SyDAkiU3sB0UQJAE3qJvfKzIpoZvuEfLY325Wqk+EsbUJDTpn7SVmAfBBWCQE9cggd8aLLRvZ9zMb2s/DyluupUfGYw9p3nxjmusLpzLnbYMBWK3r8bWY9grBg+esKBxQrZUm6kiK/FP1NFpqCn7Iy+YhvdTOPWLe+Jf69+VEjS/ux8GgRdnAfK/e8mCQluvLAm54TF1yzXuf9/2eJlpkgLI4l/eJjHuyvtF9G1xGeZJ4LJKkcVlCS5aS6LXrkByIK6xSKPB17gzVj//WiWph4FBtZ5wU+RYX9y7htA6OTeYsnhzlNxwUrS8onrqUx2x3Etubis4LNUlituyjUxDUFAqfc5gDs49K5cvznQldby4OaFkCShiDEmEMNQQERsmitHXVCmAcisGNVGEaG+csZBm+oGmu9Md41KLZgTKqeOm1MlTQ4hBcl0ws2UhG4d0Z7jO1aQ7pykUgowIO+iCQfxc27TfTDuueRKZw7iVLO6XpTScSMHuloZzymh5GOejEReAR1GX0lDPV2L3u3bEwMqS+vMb9xekLTdAWlO7hPBz4hHjoYZ5Dwx6ZKOnSuiFBz2wgzMDdMYkqTFhTh6knL3jVDF5MBOTwGcwxBRmYGHnntHrAQ+04Daf8ZXlNQDgRToOrHiS7FFLahKnYRy7Mqt9GQiwJnmMNQBNXxgWr3lB2bQHbzzrl9zPYwmYulDH36GS9be0EfrKITCOFZKB57wPOynZILyNE7eof1KnD0nMow3bjbV3Yj+XEZqjw92IkRhpMZulTkSUKh8p74fL5u4YfoybIv3tmo51jz3via3t9929nmQdg3juOe25TYP47RN+2+9WIvyscfkldRQlqon6xbv2+n4jrksWlVJOc/iLSSgmlgMmFl4fO2nM9cuW4u3Tnnk0IYaWyVMHY1z3UsNj5YKb2r/LNttbyv5n2s2xG2VVrUcEh5ygdDn/+yQ2UwrKx9qw5/Knw3fUP0N2YCIIzuX//QpNbxuf/fu0bcb9z/fqWCE4HkCS45xbRuX9ocjICDAMETRI63tM8pzm+1pbdu29Vse+exfv3tdzpT73m4/zl3btsfYRpnsflW79fZ+yaBDWQ2ib4R/9/ZT3g9NnCeOvGtxkyb+H5458yKl78JaoWyCBVftDI0lAkX02/b6BtDEL52KJSEQ3D/ecMdiAfoXbs6krqYOhSM7q6dN5cJQAsDVxgcxJkAeN0zcukpmce3++nLCmhvttQVbJYMkNL5cjPtlu0HrC1hTqNiwg93SCiobgoOqBcCfmoaDX3QCm5Vk5YeOM+z4y/qzmmqneCJ0lycumWbC2LgFbgOCDbz07eiUAPJwXrKXhtBU09SyizCP5jS5mdvbUOjSYItfV2rgHTjSqKya8mg4xzu44otrSsKmUpoS+g3TODVXUDgHjWLeeUFv2YDX34rG1EWnb/sAx7x4OfeQQbm+PUZAGOnngznD/082gXHEEDbNoZQrExdz+YsSyPxvuI4am4cNMWGgwK4nFHdfWtCRNySjKpJiK59QXLFQnA6+hbWbiCfpBfxxnOLFz9nEpTGBzwfuGOhQmHEFKCXEJOhekuFeTOov13VO0uq0XuIukJysBZM9E6dLe6XOj6zquheiCm9gjbWMEsL071svgKaisx4xzkRF4yyHyfnD6UE45bKwpp+h0fd4wj3mvXGad0jl29c+4J8IGU3j207w4nCEwTnt3v6tidlLAZVuhyIXVDak5iUfMsyJZt0QSgBu4r/Vzati17+N0mH5eqKFxwtaLc/xuc6Bx+DbWBOc8uP4W9N+xLXJND06m6TBEqNObaIRrEsnGj7zvA3BxeE3PRc4SQwpJV9aQt8G51ADvwTQdZo/aLCJB834y9gfBvg9zu69w93vpB1f6fe0djzGHu+vTuO442lhs3Vrqy2vFCH6UGl3q9UPvyoM2dtfmPnLKdrjvJL69hBjHh3b3jt8vueqrZRfkOCQjnsdsvw9j/dMYfwrXvmOCLlKmfgrBB94DTt89a4xzoUEsDZ41qgdK6jieF/QukA0mkndFsDP1j4Ox0XCfBItO3zaxZFIaUkQENJuuA+6qeSjVDZZRjZMTo3V4ohSLYlxyv+Dw19xwswh0gaXvO+SKA0aAVkkdN2XDbT7j1DPW3MQGQcC50j6Hu7xTF6JzTVm4Bvf8UI6GTLcI4UZv0obGyZOZG1xEKQ3cyd0btyaJVFoTqIWNEw5UJbm7SjQ2T60lcBacIPHKSGpfASZsFeNCCXPYPOA+yhJSDyeinpowMFPmJcExMblW0zfJTNaZcKy6JYLBHTq/FnTVff2NHd970kT0w6GAMgTUz9ZnCtxeD9yhEX/brBQpwfhIjWYVHSHo/tlPymsRqHtf75GwZOjNYxKWCI63T6Delzl365KbSz42Xl85vMHH5Q3u0hmHtOFZOql6p+PYF3y9vkRJGsBlhcf7hvQ25nrYG5QxIzjRpYAp73eVIclMsB1mGwlDPnH2ALhgdn+0Z9U3ntPguD3BCo2oYm2Uzyt7fIW2M7Nw9E1FM6JdLEc4FBhDMthoRPMz0M1+NIlAbz843gtOf0SXDoJLeuJd+uurvn6XSWrUNQh/vBbvGQbjQXyidwoAxRth16WnxFiU+7b74iZw751w3byARBTu6JxQOYtPu6kZNLS/aGi7PwvGqqBWh9RcnPakJPGAAgJ3i0vuJa6RXQJ4SupjDeDU1UCt78pZDNhJ1R1TajfsODrMHLQFQQ34iNGmS6wUTITK2my65gmqQO+9VJHoM3kQMd8ndeA21bZL+2hV7tbIXiLrTGB13XWUV2KkwnAAv8h9BiJ/YQeg3V9/ibVb55g4qBHi8zz08DQOORBf0oNg9IxcvR8WVl8kSAZPEKTf/Zi8KEcc0uYE/0V6wF064WU64lmSTFEZuvan0/lKv20+r1ErGv8upL20u4+vXPf652dtD/BuwGjSBYUS9tSUL2EvnQXmFcCk8ol74HIMeD4U7J2VAgooPy7BhfLOc/qAEPDGg+C3qqxMIHApSU7JOFYDihmycHsCU58If++y4UWvz56dKqoXDA/HPHkkOQtjgwWoCNGrPWHJRvBIs9prHQFMzaAMTnUM/7kLAFmhJoS8VIEu0CTcp57xvAhntPWMNYs30cYZD21xVVPrCSV1NB72gESMtlv0Echp8iMm6bx5+RAxDmnDXTpjY0nMfsgVD9si78kdh6Xi2SIb+fXpgFMqWHPDx+sbPM8nSfvYE15vK9a1YtuKBq51PFsET+hbTDgfi7eBIqyCcWsl0IWdnvkib64Vmrn7mMQ6coU5d/e93887MJgE8TKiERCHIcUB8FSWtrbsoX7Ol54VkaE3wpBYMJ0sB6HVkaP0I7+5B4ty4ZEemcZkr+Pn3YHKBKQ9XEG819YNEyxqdp/PwZwGohvzQg03tCn+TsVKTd1++wTTXaihJvVi0vGZII53FIozS8pAwBFx0c2NUl0q+2g/6RiyMg0Tno4dihbDMr0ocN7QsbaxULsKmAR00eYgFgpzF+dZ02T6PUYgrI0pHLC7g8LPxgnPJxyYcS29hZ1/Lzh9C36iEJDFk2itBE+Bv9w7RFUGHqnKNEXo0u7QcPwdnnXjtpgjQqDp0IdPvSx8012bHcBUNkUzYhGAQ6lYAuwCMNIHliRJKLJGsBoIW9VkyasmHlmTBDfVnsVNju2wkbaTjRPDgeKMqER3wIH9z+76JoMjBwMlwVF5kR9wkwQ06yZv0oYibTssFTd5wwfrEWupIJI+frTc44PygA+XB7xYTlhSx81SnTAmAp6tZ3x084DVgNqMuKqrGxt3aoeTHUy7Ejl4uUDhNx4Sgfaf4oay+cltQjq1uTUXWZvnxsnnK64PWVOi7jgs1QPsPNeD9umqO4x9TyzIrCn0NbGA+DmntyMKV6qZCEscMhr3Ofe/I3hsayS4atohEgnZFJuB+bvlVF7I/PMrFrVQppCQx6RGmxdnQMLcjS/SnklNoyq+MQE7icQOwrSTckzNsj8Id9U4sTUYE4YQ/CZjQuaCGaczfuZQIUEgOmKx9dChjEiQEmw92768gPTgncH4ohtXy/vB6RunQeSRt3s1TcTb7xHTItYBCDdge8aIc59VOZa0xYobp3ZH5FDdSF2b+t83jdg81eLSwdYFRsG43ngwAMBNrlhzxZoaOqcL9cKxFefoxYVSgmBOVHBsy2wIndq3Jy7hki7gKTmD3cMqyjKJLlapyCFV3GQ5tADgnBRSOVkQjnDwmTru0hl36YxjWrAmeWbtCafcnaD6AabPdWREtZ2L7cYJe+fCP53Xa1Gp0h8lWsbaq50gDs0wQo+LmRhL7hPeUVTX2bpofeSBii66prpqAf31rRvTO7q71zjAmCwkCSdjXiUCQzI/SzZmkYjGcdN9Za/15ULhORsv04EHqGiLWDdmBTAGqqODxHDLBRs3HFmCtVZVFd6lk8Z99NHXyHEHbn/YO+TdAjE9r9VZH8+XfYYxBcpp7zljGs4A056wtplkHOkIyBUOF+i0/pyNI80E3fustqJgU3CMsQitTSyugMHuNfz8rS88xu99xt5x/TwDPYm6JLnL5tAFG1frQVZ7EVsH3bl5BM4/2XuGIS4aec81Yy3meSDPRs7GylbFkLm1jHx7xHET6ITOhHMtGviTselzrRNyEsJ/UzZ8sDygs3CQVbF2BPyr476uWNVgRsSSlIKBh7bgzbZeBAsx4ER1412wkLNtGIvEFqsFvtiwMWkIvdgRDqni5XLEN/MdltRw1qCcQt2N2SkxbnLFXTrjRX7AxhkvywG3RXLsVssF2sWdT7yPGLl01HMW97Y1bJC4UYHBrYeMRROh2m14I36k/ZUcr6bCkvvN1dU8T8zWIvM01kVEGa1K8M0OMJiRYa/JqWOjjJM101Vo2rj93txfU0JFmSXDUudhlG668WOSHqtG1TrTGPhvM+foKiB/X2zP+E4sBNXUSpk6MsFVXQmyLo2QdyZsWJBZICkkOlv+fZjvcUgBZtklG50XI7wk/XJtpO1Xn9DRX1sDvOwYAGMM3SBNDtImPxsh1f7uvCDcxdnrVA7bAu4YQklJnjXX6Air7VJKPFwSD2lr4eHIEFSvU87izDLnJHWTzj1D6aCqupghQVuPlF+SeoeI/g4R/RUi+stE9JN67WMi+gki+pv696Nw/x8nor9FRH+DiP7ZX+h7knZwzyQN4j2+M4+TcvLjp/l5B20LKh5/X2I/QABdB0GcN2IKzG6bdlgYBx//1mZcoXD6RjC2JsRG8ElE12kG2WiUbZxwbuLvbvgvFo5V9vI5NPsWjbiDPTc0OhsHxVajLHRO7Co1QELpF2oeU2BYQID4Z5uaqXdCSaLDbcGYW6jJARG4wq1LIE/rYe72BqtJzB2cpg99+LvXmsiN4T73hjQAACAASURBVG9giP3sY1ENbk1iCTrLfFc/COTQtoPB7DWWTCaugaZorH00c07uE0tkzvcqq4vFjpk73f8cuciJsx3jcnVsTH0UeKOLg2j3vEkLrs65omdaqOFGjbh36YSbtOGG5N+C5sTfvHfchvY4gzq15+K7P787NK/VF1Uhb6vr6nNXArR2bXfj75W6Is36hQQkmmbi6r3GEOt9j7ka78t3gtP/Xcz89fD9jwH488z8J4joj+n3f4uIfhOAPwTgHwXwqwD890T0D4XE6VeLIDaK0dINs8rJT9gvqaNVAe8qa1XjbEdTt8ekxjbuJBxlFXz3qMtnyH0G4wAMt83NErgEYkokontO5jvPOFdx3TMvlpSk3RtU9cOEeh649A+nFYkkifRtPgNtxUkDWkwF0lkA1zKtuN8WrLnhvq54uQoccNQpC9StQEOspQk8Q0/COZWu3Mrg9j3oJEiKzmHpWAhQFuGGKpbUUFLDTd7cTQ8Ajm0ZnG/NuMkVG2cR6yFEcs0Na26oPeFbx1t0Bu63BS+W4iov0oA6aYtusqTcGKSdVGmI8LbQGd6XcRjM+OqiGoCL87JwhDvazgXHpfitMp9yKJ+2guPDilwagOKH7mmT+00SAIDNAwjHbu89zdKVrmsJ2klg5pFNzW9gcecDZJ40i5sfiHupJrioev9SeCXThe7ePXvS7jkbhOAtZN5ysf03ecO5y9xZs4wZuUtnfJxfY1WDrhlun9EZRy4euHer9qExWBiqm9i3wH17SsRQXM3VMdP53ZCam+zEMPg1WxPzswLsNu5DAriPAMdJ/Th5DPF8sDMEigFjLCnvLPDOmIzUrxNMt82Dva8DyFC0Xoj6+zEmIwznd7r8fgA/op9/BMAfCNf/DDOfmPn/AvC3APy2X0iFRpgFt+Ut4fBBvI7eNyOIiKd7xm/6GePz/mSdOHjMEALArOqJvv7xZG88jLz2GVBYD+fuB7cfSyTs5jFRdpLBNGZ7DmOvT/QOz/ftDUI2HpnEGGf2hBTGDJDNvvXsqpvJ/xoSmJMwIo+t3tYTjq1MsBcXHItJHkakcjD6BWOj90evRdc512Wb8c+uufuhqQhH57em0baWw1UPcneDDWPkUqOtkxBV3QM3ppMxf49j/0jxyF0nMI9z/RdcaDwc9/cBcyYpf+G4h/btDRIygAt4EFEDKldPVbx30JDVgLtQG+Bruiamdj/WJ1zhosMcvrWkMN8Xxt/wXrsn/J0yntkaMuIfHQv29VyTIgAM9c08J28DTLxWhlv6lXvewvT/Ujl9BvDnSHbpf8zMPwzge5j5ZwGAmX+WiL5b7/3VAP7X8OzP6LVPLWIMEw+bmMUKSMHdkrAszf2kzfsmphG03qakmayUIyaC1K8+8b2T5J3NQw1h3L4jbDoSo/mbdw/lT6mL+ygN24B5IMn7NfeqSiC9J9zmDTdpU9VBckJunPtHh3tUFpdUwyJ/VkRTXJTwX4v4NZuAp3I8qX4/6fQxxNcXGHrVoOdnJpy54EU6YksCulY5Y00Vta9O/E+t4H5bJG3i0vAsn0UyoOZ2iqIHwV0543U+4HhesDXCt+hWpDgNKSfzGEkRk17+MRhYAoeqCWGojoxT7rffaXCIJtDoBu4LwIty2FWkx9aSwHiY9HJexCNMge/alvzA7UxYVAq0Q7CUjvO5oG4ZlBiHQ3c4DyhzwWabSOL228zlbzd3ZJynXXeAMR4Q1Gu/MEqa7z6BxqFo0pEFMvXwGyASIAPEIhH2tSOdE7iwB/DtCVrO3ed1z3SU1PAiPeBX5DdoIKyKuXPkjIMS+hvaPHBrzXWoaO19gduePHrMrbPMnLHr+iOx00CqUQeP9KZd1oz/ZnVl3k8FcGhAKzK25kHGSvCz2sAqgZcepBO7bxzWlBSOgcZ3AM7Bkxuvw+HKklmPm7ktkc6/rm9tT1mawNR081B8/CT8pRL938HMX1XC/hNE9Nffcu+18+rqeUREPwjgBwGgfOWD2bgKKLd+Wd01sLV9YJeVRCz0IHL6QYduRl+7vg/mip8tMXT3e6+3K0YOx8XZmcQ/nyo2ythSc0Jum+kmV9zXcRiUNCCLDaDN247LcbB28G5Byed4U/gcVr/goneHyrV2R/tDUm7cJBZx1xMM9T1gm89psJX4+3eqEGlLaJtzrnQhmcz1wKWEqyqDKxwS0bD9GHPB9txO4Z1IMOT3sNfi9jskxovm0aWkOtp2hfsPhEMqtX7zxKLHQKKLJBzW9N3YRs450pupBK7fCRiJ0TZKfXEdZGJksPjmm7R4ZbtfSKlxjsNlR4fdXb+UXB+XaC4kH6I5OIot4nkeo4tgwV0bmRiJQ7a2a5z3Y2W/9q+N/1XKSdNcXGo+Hn/lL4noM/NX9e/XiOjHIOqanyOi71Uu/3sBfE1v/xkAvyY8/n0AvvpIvT8M4IcB4PYf/FVcUnd9vhXzrBF3y+4ENap3HFTNByUQsdRx5gJgbFjnjIlRCnvS8UyMw1L92btlc8Pu7bqJl476eEf9LjAkjpxGtKe0Ca4qML/mryyv8CIfceSCb57vkKhrDtGOj9d7JGI81AU5dTzLZ1WViJ78kCuOtXiC6oj2OQaNJTgEOhTdVo3+3mn8HiKajXBnErE9gf3AOWRB+Fw1tmDrWQHJEj7M9ziyJHgRApFw7hn3dYVBRDMLDPN9Wj1tpbsC1uKbwEDGptyo1k7CELlt6Bm+IS23qOs6bSnYvUo3LQmOQ2xky4wmkB5V9bGFOg5qm2hMQBFCn4NPfkrsKTWXtaL3xW1R3nTj2JyIh/7Y1GQGV8wcf3THC+ByMsXkel1zO7S8qcOLhaexGeMQoBcm2F8M248dpCxAayb5nNvwEOtMOPYFRy44I6sqR1Q7GxK2HQOwJont4Pg+YlDQUXof9vpqA2rTNhJo8vy5mPfdQZJaGmqaPWSyjol/30XtTnp8/UystjNjEvRwcRskKfMl5qHxXArvY2MWSFw6KagTVRqRewHz7ALgY+h2gkfKL1qnT0TPiOiFfQbwewH8FIAfB/ADetsPAPiz+vnHAfwhIjoQ0W8A8BsB/IVf2LseP7Yi4Nc1ELX99at1PPLO6JsdOZISOOvBbUd7wuAW7W9WNVKsfy953NDZo18NWsE4+pIkUtcIrKlKMgT6wYNccKnPn0r0+40lGod2xTwtJLpSoJ9ND+uw0KmJ/35qWMxnX32yOwbwmnku+bhqUFT0fwftYTF27YrfH1sanzLnU12IczzwkWYOdHyWgK056Q2gw6rzaowIMGxSdpOv2bdwYzGu5KK8ZT9c7d9eSvi0e4DLTXGFe20XhoBRJKeyHPRNH2wQPKYG8mvA2EPX3u2R4490eZ/ecw+b4PUpQZ5ckGwO9v8eaYs8N71sloLsd7pyr7/zSvviMpvo1uWt40ZG1EYMNegj+zuUXwqn/z0AfkzVLAXAn2bm/5aI/iKAHyWifwXA3wXwB6VR/NNE9KMA/iqACuAPf5rnjpWhO5fxaS05Jx+hFCyyFhCduUEjMxNqzbDI3N7hgVTmYXP1veHdnQmH3LCpd4L7cLsxlkI7yefR/b6Vi9yUOyqpo9F4VlQFCStV3NAZz8oJb+oqsAskbX1WTrgrZ5TUcVD3yeflhIbXOPeM1+mApsFEk9jtXLAuetMrGpwFB0660cVi/Ti/xof5Hq/oBkde8LIc0TlhWyQIzSSOQ2qonPCz9y/xsjxgoYo3/YBv11uce8GxFbzabhzCYckNd0sX//0gucWD3NrmE9LIOXcZvPAb4N4fExdrdRBGgvgmnkL22HKouF03l+zuz0uA6tYxDIdBoY5GMs52yNWw3kylZ/YlOQSCKoYhnhx7yYUhel+VKrgl6aMFHJrRuukgBXgASyZDTDIOlUBngoOVcQhSAwb08M6rx54HANpMLAm/OaPJE8x0hKf4Zn2OV/0WH6Z7dE44ArjvB5w5Y0PGxgX3ffUALe7aZkv/aHDbgVNySGObW5tvHY9JfbkvPO7lEp6z9WHvrcnjBACRALimoXljAjZdPxqZy0ufbR9BIrKkN/FsYoVw4F0imKS5ms0mxP0y4DK6M9PSkTIrTI3StxzsBo+UXzTRZ+a/DeC3XLn+DQC/55FnfgjAD/3i3zk8I3KWhWLRs90Gksz4xx7RZj73osKxz+R1vu19QAjIChy8lejFY3DBsU5T4Qh3CLSueuDUAcPlZ4l6BYCEjlXxd/b6zkOqmmxFOO6SOg4kQVAW1Wqb0OwS0m6oSI7A0QSOB5CgkiyqAbk++nhDG57RGRtl8bVWIK1DkuVjgVsvywNOffFDKVPHxhmnLi56ncUXvio+EEHUQwNIbhB7t7NEbgpK2GzzRtE9trkHYuFGaRoEloXgGU4Lq2uq5UCwA93nTXFv9rYcd+HVMZ+9woJNSDoCT+Fpbe1hc0YOsQ+iO6us9AuP+/bp87wuJqCzZIBaWNz6rO+hqniojmXL87UUiOxOIoj6fMebIsZ9X3HWbGtHLmjqvvumHzxY0P4mCgZpq98YETNuBjWLBErF8YJi40OiVq9s6SnpTMf1dcMEtJ29MNhniEkC5IjGOnTDPNQhgsPghj7tJSn7TdV77lkYY1QmfX+8Ln/tkGh1eJUZ0X+bduQ9iMglnGseKIjKsXtWq5b0VEyul28WMs4mAVht4zNRQmuEnMXX3vT/wtX52514JhJAt8YCt9C1XfaezZ+P74B7FwFwvf+YkIabUsXjJTVNN9gHbokadA8a3bqkJn7NYDwvJyH4+YQOwpLaMJilrok9Ak498eAYjSiEdcGFh/hL7JGeKbGiJG4AgDfp4JG5Hy73eGgrHtqCou//cLnHy+UOH5c3eNVuceqLJ14xw59JP067OClxNa8mGtLJmDpQo5G8woiV6sU9injvHqnPXhx2NBOKqDf1aOGgktszB25QVyhthgC0td6nOTbvq8Oh4nyG6/VbzUCR+tk4RCNk3pDQXtMTa1yBGxFNvxu5dda+FZnXCJnsun/9HcA0nn6uEIKqAMPDSwHD4iFoh7hJQLUnvG4HPEsnNBCOvOAb7Tnu+wGv2q3Eb/QF922VMTLCFzjfId3R5GYrlxhksQ/W5eDhw/u1w/qMTubE4Ycz1N9ne0UmHtBUlcRByjSabsS+MHjps7qJbQz1fepubu2Z7FHWb1dn0eD209TCi2KumwYLAY4077K880QfgOOzy2dSYhqIbBuuiRFQzQmwceO6mpkJnJqAtuUm9fcxyBaM4sZdrc9AtnrqnjHJdPk9cO1Dl9t98M2jYSnCrRsuzwfrEWcFU2tIWCA6dBN7E4kaZEni3XObhfg+z0d1iaw40eL+zgTZRM0lHi1GTPbEwRaxBpw4R6KL04zMd8SARlgCEN09hNA/tMXjDF7kI14sR3yQ3+DIC459QdVAHOMEzdgMwKOKTU8OSLBSUsLJsf0GSmU2w64ce9bAoygucxCpnUs0zl6r7AgH9Bx/wJgJvW8snr2WDrnioYqxetH8umb0TyQAea9PBxBJwKAFBbaWxADH1jkMAhfRM+1vDqH2ejZjG5zixTzrPHIOP0/cHw2Cvl8b8dDRf2qegfo+gPvwJOMwbp1YDfYFNyRr9cgLfr6+xKkv+GZ9hs6E+y4E/05hlv39sS0hps3jTLRfMo/hgI9jtWMWrl6PDEAcHsaAsCDMmEPXlNHEGiwIcffc7S8Aw3UT7AZdAlzNc2Fv4RGdzlEatH50HQCE5+wQyaP+x8p7QfStRA+cqIUwVYlw1IPYJSgnFc5yOWmVqKeQdtHE8jQIfgwYejgvTqjOLQ+I5dQ17H4McymyOqJx1VQBJRm0rHw2iOS7fMLLpEiWpuqhAfdrYe1F/d7NHa4pYd14wDLIs8LG1D3Yl0duMpAUI2S/yRNASwen5EbKlQgdGlhD3RETN2SBXOABHmcH07EvU5usXdHQ/LAtOh4iCQGq7rIDN4lHhoNsOSAXgAYFqpIkGRyIO3RKI1cm3im6fnaYPdtWcA9pg0NoQNbZslSklEDJ1BGX3lFGj0oWw7ufnabyCSpCH2o7mIOfOCWW+IEYjNXJCb4nN2GIxd6/WL/YI0h5xyH7ixWzaOKIGbNqRAmp1xe5bBIGy9Rgh1JRqOHMxXNDfFjuAQDfanf4Rn2O+77ivh3wuh1k3tuChTqW0vAsn4KDwWin08CdJ5EcZjxLAVHdc63sCb5i53DIceqqJMDB2XwMDw18yuOACQlxBOF1134bTxvLsMeMm2eCHwLm5msajZg0yr4bRLy9o1cCSgrX5ND5NCeB94zoA9abCKSWEqNj4Ji7Lj7o7c3AFtE0o642JQTjG7tqxp4/bUVc8ohx5hGQlRNjCxxmJkbJHZ2BtSgnfF7cQ2FRr5ytZywKZ5CI8SId8WG+xw1t+KTfKLcvrpmAGLwMmXCDHAIbZ2yc8bodUBX3Js51jOKVAcROPaB3793gEiMvHT1LcFjnhATghggrxHvnQB1bHzr9UytOBJ/nkxhD+8HD7Y1IRnTNzoTaskfoLkned9rKwLI3ImcbXYOtXJ+vrnZTtqxr3DLgLm6e/ciJB7Cdiwb4iQRnK633hLvDhpYEQto8jyLRj4ZM82bygC0LnCMz7u7mg4BUukKAS39yZvQq0Asxg5XncwZGLmHjWONhaGBiCR7EZEZeTqLn54XHeChRN9uAq8rsn0kNgeBKYh8J1LM1fGwLbvKGkjq+e/0EAPDN9hxfry/xut3gdT3gTRWif+oZyBsOVJEsUCzOq61Vm0/jZu1wKyxGZjVSX4UeGLeP8TZuPptba7gn0JcYdYvMWG43bDWJms1UjKTjuocl9/fxcNFlM8zKdTZ3XeJBpHloMOzZgZvFQE8DpI0JXAkdw/7kAJJ76W9X3guiH4l9JMYpdx+06C2xD7YC0gVBB2bx3aN2/X37+sa9ricP332jp45V9byLcvpb7hN3aARBIAgEs2ZTBavpQDfOogNXPfixL7hLxfOM3vcVR9WXf1JvcGzlwmtnMi5a820jKcc3cXYQQkJZIvxaEyJ2RsbGEsxmGCp9J1l0kB9CnQmv2o267Q1JwzjoGMBTAcfd6bs5GQsgfN77Sttn5cpG6kd2ImV6/ynAx7uthLsSQMmRQq2U0lByA9FQJxrUQCJBDBUk0wGtYTYBOQSSwzkIGBtNXjxzPwecAyWA2ziX5eWBsZ/GRsXVSLz67r7QXy67TFiRS7a1QYN7nbJWBc6zJDHA7qWeQh03tOFNP7jhNrp3Juo46NcTF1FLpgBkhkCzJg59nru5Y5gicK/dN3n9wO4dY2FG8QE7YffZWBkhDr9fa6dd2o3p+G7r5PpzFwZdr2McSERRPRTaZIcjXZl8Le8F0c95BGcNaANgXQckQ+8ZRAPONt6rWkMXkYzrN/uAvSMFsXzTwJqknFtryUXy2hMOIRmCcf2FgLVU3C0bak/uUdOZPI9o4wR00dufWsF9FVhk83ZI6PhWe4ZjXwThkcW3/ZN64zANlTO+XW/xSb3FQ1vwjdMzvKkrNsOOsU3IEgDl6ise3k4gFgNUshUJIHcgAWnpuLs5CUR06rjvBxz5tYyTUpL7vk7oio0Jp15w31Y0JPzc9gE6RhIZI/ibGtwThLg+bAXnliWqGeTolJP3gUknScHXjJuxHeGqDLlvSAE67w3gFUMNkDDc8uz7Mbsx//ZwdtVFToy7ZZuynBmq6ZIaTq14prKq/Xi2itF/0ZwKW8s4HhdAD3zn+GI/Cc4VkvZJFtcgdG5XiLpgU1UQO5gcZ0baEvbww/IwwGsI3lFu1Y25HED4nMPGMPQr01CK2Jwss9tRjfVm53iRj/hWv8PX6wvc99UZgKSpQJ9n8fS6byuem3rHYCESg9Stct/+mPHN+uP92GiGVDCpJzxjuW2tn86pA6KPTzu9fOmC8EsQu0pSiOvMbtCeiT8HKcnaovZGpT/D5jIMuz6XwHiffu6aqMWupaL0LZmnznh2qKvxaHn3iT5ZsAvcnTAGu5DpGGlw6ntOfM6RG6r2A2S43xlg2OW9cCyc1pX4t4ScZgJFgIOKmXrFuHsr9k7jkFg5+Vf9FhuJ//KpF9SeNTNWwUNf8ardqCeMeEe8aStOraDyHPAU3zEye8FtGzE60MZ4WqikQUrKjSd0LHY4YgBl7fX1ACbOHhj6b2tbDp9F/zvGpF9lTTEv4PjdiESUZHa/uYgeygTaFetVVUkOUp/NUSIWCGya++0OBrpu1tzdk4owDPgpyeYV+A/yQ9g5wD64tcG1IRgBgUe9MgLhG33cXbP+xTnXZx00LMIS2Li5VVDtPynO38izcO7ZD8rbLGkShXGZBz+TqIVsTZhLrxvtdVzc24ZiZzDP5SQZ8HV3zaucdFgD8b5r60GHoGtkth+4jzHSfhC/XcUSJSa2vzyuef/8cA5r1tfqZR8vmIkr5Z0n+sZdGxcODFWM7+/UQZR84TQeATOWMxYYuteqBtqo/zdi1FoClTZUQfr+RdMWHkoV9AJYgJcs4N4TchG8eFMPJGKcanEVgHH7ICWeqbn74je2Z+j4lfggP+Dr23N883yHY1twX1c81AVfvf8Anyw34ulDjJ8/PsdZ9eVbz0OdoP2zsuaGU+ojNLuTpOOrAqiFSgJgBgiwU6fgew7cLWd12ZR3fbOL/eE13eB1vVXphcQoR90PuJuyoXHCkRZXCZUk47ep11PRCGM7+FpPokpBIHx2YIcwdVsHnNVVM0F0rcaxmk6bcUnwg9jsel1iB24DRFqzNbEpXHROHQ+ng0t68RCvmkjl+eGMl4cjbvKGUyuuwrs7iKx5PC9i+D8tsp6TSom5oanPOhtglhHX6B4Y/a+J56Atu6YEycG/jAlIgIOwBYLm0BuJxRtIOVAHJiMgLU0hoIWoUO44lIbn5ewE/NQLynJE5YTvWl8hUce360tX7VgcSqEmnD2AE3W8qQc80KKSZuTSVXLp47uvA/WNj+6oSACvgwMfag5MB7rXodcnBmCvUrHxAVBPWcb1nIe9KIWxjPAYTEDuHiRFeURnAzbnxsAqbTKmTeeRMRgOcy4ZaV/lnSYxptKHB2OePRivlV80DMNnXbIG8xgRdoIfCDOAaXD3ZQ+MFe0ACHXE38b3IQ1YsWCxWKK/8j65RNS1270DM1+giU0MNsOsJ+xQ3BojOFURDuO/+I49jAQFicTVPb4pwmctkagJFC7B0s7nt/Tt00oCBy5/wBnsS3TrlEZ/SsVvkQgclnn/G135busJ8LbFsbgACMNYc0sWw/xqEBo0sJvMBdXUiFF/nwKx8T5/ynC6CH/tvr10M/WVL8cy6KyvjodhvITxMhWdRYZbngVz8bViAH17UDb7LNAcObRtR3gv5onnv9O9V7j3PYUzQr9/9lrZE//IXb+tTGvtkQd8z4WxNzqktG4OrgxqWqsirFdfO7+Afr37nL4SiJLln0S+wt0kU5KAiB6SVkRi7CoU6DgoN4wsA7tkvlCNEDCSi6uutmhC8kgAnEsN7TWPHCuVkxP327Lhk9ONomp2PF9OLhrf5jPusnCEi3JPUbXgG4Qz0AWeuHbxhz6qn3gmdtdJc/Vsqq7IxeIRFBcmJ9kAPBaTB44wYasj2fqH6exqG4uiXFLFIVWcelEgtrMaNDMOqSpAW3cOz+aCSHS+N3nzFJCcuiSp0bZuLXgxAEPNEN0YEwTMAwjXlFuPxjxiRB/RCcIhEDfKHUgCmsYAbkrFbdnAfMCxFhyU+y9JEsNXyji24nr7nBgvlhM+Wh9wSBXHpglZ6uque7VlHJYqGbiaNJI6YV0rtrOoOAQ8qw9u3/ToKqGlwmp01g4GqWB4uCB8D18TC1fPEIqhah0qXSAhFh4pGG1NaNQnp+QeJ5RYxief8VG5FwP+kvHQFhxSwyFtyGB8oNDKVmwt36QN3663uEtnPCSJ46DEoKJqxwqFUMZMxKxrsb9m74nF5t04b+PCe7jBCGRcJ1G6tOux7muElCDSIvNu/ZEbXEeksdQrXlgieZs3YYZ4eAHiVdjUG5G7uHEmPaxcPahcvUkMEbAuFcajhw3eA07fuHgjfPJ5/P5WcLHH6qShbzXxKv5m740lBS7M3nmNQ02hrRf/wDNXDnbuaHiEmB9/n+u7wlkbNxnv27fB708BQoKCV1LgrKIBqIXDzIy3nS02QOIEIszy9bGYx9Y8XvZtzUG3DwRpbc+5XylupIslck+PFe27Q+nqoWFcd1xve7RS+U0YEJtT03FHjvfRf2HNRTsHjEZfJS7m/nelT49JLRf9xeWYXpMC3ranAjOViLEkVYWha4Be97WxWmS5JUshnj6n0PfI2Y4Lj/QFj1wDZj33o/29Iu1cK3bPYxx+rIMv/7p75l4AufJu34tkUiEutQ1X3h/VfdEW8LbuvfOcPghYFOK4ag5TAG40awa0pp1n/S0aaI27TwpPILC+smktqrazJF0nGhyJDbf5X7t3D4l+PkodAFxnb8T83IvmtRWd/kNdAseu6hoWt82btOEryyusiqXzjfIctWd8uD7g2AruyuYHRiL2AKhEjEOVIChJqcjIeRjKom98LNFDwaKRU+q+InuAtThyxqsuksO32h3u+2GqK4LOVZUE7mnFx2lDQ/Jo3TVX3JQNrzeBcjCIidYTDqV6ukVXw0WdqTdWl7NxYva30UgucbGGBhfHUe8KCNuTgFw6Uu4opeG4FbcJmRdO07y355ZxVuO5eSdZ/8+94KGJ19WxLa7TB4BjLTjXjJIlVWczmObF7EcAvN8QzpsJMEO8Qn3n3HHqi/xOPOAZJrhlcgIyiJCd5jS4YJMkdAwp8bCF2DOJUbes7ZHx7hA1Y4ZAaJ96wUm9zRbqaCB8V/4ELSfcpA3/Lz7CfV9xBzhD85X0Cqe+4FkWaXeOHjcCSIN7NunOOOk9FWZgWiz2kW08ENYG7dYU3N41rRdmhf3A4P7t9xQ+G4mthIFjoTr4hACeNp5h9bSzRE05d6QktMzgtDE/pQAAFcNJREFUuUvuOKkrtyEOEEGCcTXhU8rdk/wIsVf6uI/RCeXdJ/oYnL4R+OFvH+W/ocp5vI75u0EsEC6vD30aOwEkwD04XAKBGAJbT64OAoZh2AyUyIJAacTXdJlJjZ8JgnGzkKkRhHAfUkUH+V8z5K7KYTUe7qAnFCeY0YMp2jGkUXEch9cAaPgos24i8b9P2LihA57zNpbu9oWkLqYZPZHrdgsNw7aAq63OLZdE7tLawtxFu400CI9zW4Rh8NtPv6kALogEDQOhbp5SRBffA/yzS/lubMbkihqRVTtLoveE4b8f8YZ6lwTwnuv5Slce61+iQfSjKH81bmFf4p4w4ph4ZHKKaqH9IQsMADhbGzs4Ao8pcfwC4JlCMEi2tZeyFtKQGg9pw9aLBBmmBodLeYtNTohxBBIKbfU2h75YneGnCwbCGYJ5f4y+Yxh+47OBkbh4zsaY9cvuNoKtG/3uXLpcyMRiX88NW8pgHq6Zk4tv5O6tXpOg07XNIuXdJ/o8VAcSHcquY2+dnDNsPTlXlAPh0ypUTJ+rNm+Xxp5XSTeqFEteHo2imdghOMyNj4hRcnvUxrO33LP+fVAO3bB2FqpYqaGTuLx5/lsIkNlHyz2+evoQhRo+Wu6RIGiGt3nDq+0GgHgrCRc6PJeaSjfm2wtAdIEkOkPjRromkTdOg1kSsv9/7SWOfI8zskTZcsapL3hdD/ik3uLUM079Fr/29u/hJm049gXfs3yCX7f+PO7SCa/6DZ7nEz6pt67yeV7OOKSKV/XgnF5Lwv28uKlgAK8o6Jtd5RNVAIHKm/56Twz83nDNCL5ep9KxLAJ9bXYjBrD1hLPq820dbC3LvPHwBDM8oa/fP8Prs3j4vDmvaJ2wFnFrPNWM1gjH84K6FRi2Su+Ecy1T/IhMEKuKwA5mQ5jdUxDtyKR75pmg2TD5vRBvlz7eRYyRh9c4ah1PbjTXrxLPqRe8ajd40w44tYJNAQNvqOLjvCEBWOg1tqXgyAvOjq5ZPDfDXT7h79Vn4Ko2A5sX++yJxKG2Bh7cu80lMHPeTOYJMLV5/5xz8fEeWyt2yVJqqnfYHOkth9AEE2HvWLu3NRVRI1rUdVT3tJodDsYg4w32fbjwGjMqLzbvnWbSHoQW2f1vO/+B94DoT4ercdb2WyCmERc/ctNWkq6Dve6ZaCB0yn3RNVQwfYBxABihZ4gh19A15UC6zBcqHNCsm7NyauLO+Ww5q9HTfOKHesYI/8tyxPcu38J9W3FIFR+XN0jUcd9X3CgC5rEVnKlIMAcUsSBIHJZ/NkpLPRAD7vrbEqQUJnyjPZf6eZFo4V6wcZbYAQ2rP7eMF8+P+Ki8wde3F/i4vMZ351e4oQ3f6neI6RItx26h5gicOWXv8/P15MbpkTnKJgwzAXeuLnJtcYJ3HNx+Hkg2ZVaYBWMCADk0PcG9zWdPjrJq0okQ44Q3teC0leFxVTP4cMaqRvTeE+pG6HUcvhIkqI4EKQbiBKKla9IwWhyDhYfKwIG5jMBHDjhKQAQ1iI+Dwbw+XIUTOsxGxOL4M6F1icBOnV2lde5FXXcr7oiwUALQ8Ca/dq+0zglvePXhX6nivh2k/TFCfO8PfyHBzXN4MfdRkvNxoGH4Zoz8ydeopNVfNGCxSJ7aSTKKa3AvTYV1SSROAmYIj/74zBSka5k4y909xRv5Psa411yUr7XhLeWdJ/pxTizYqXXCkrsmKhdd9I0mwJAoUuHqoy7bDgwoXg4AMHdP2SeER1UO9m5i5CQSRcRZicVURov6lz/UBd/EHUrqwg2pZ8e5ZT84rB7ztLFgnzd9xTOljZIg5YzGotp5kY94lk54nk+4yyf1kOjolNDTGS/LEQ/Lgs4b7tV2YOqjb/IzUQ0szRPFW/+iq2fvCWB2lVZKkpDl2BekLJu5cRK8n3rA67o6cTZI6A/zG3QmR+NcqWm7j3jVbtBBuE1n9/w5qJrq3AtuyiaZwbLCTFu6QlsEFnVrc2D6afPkCSKy+Erz7N1iC8rTRLKjEpoK5rBsSM49SvwHQw6ArWWk1HFqAzX0tBWxDXWBWGgKDd3Ur732hHpOkzeSeVc4kqhKV+tacToVQX9NBNI1RamrFBaiyPf2DtPF++kUJQaax24n+vuBkcJBU3gkJgHgOD32usQ+d4U61tRxSA23+exMSFcKtaJjVV1/I0JisQW0lJC444Nyj7Q2dEpDYrM+mWrJ1oABrSnEs89zJMLmSUMQ4MAtzYTd4lQWW1N2ncOYYSCbktpZLFCtdMVECmOZwviTMCsmTTtDufSJOJu0B+IA7U6+/7Y2uH9XR7NKhDYsxMFmIHXn0q4ai737j//07pToukgQILOiBMmI+Jqrh8KLigcA2A22Jp5nGgZXYBwMW81urLW95MFONDas7yn9zSbUjJXHKsZbgujbLbDnrFDK9s7OgsvfOqEv4hHzqt8iE+OGNixJ0AfftAOWvOF5PuIunfBBuVfdv7pk6or9oDzgoa0oqeHb6Ra3eRP9MnW82Q5CTFe4qsoP0IjCCVt0IjnlLMTuyIKEaZ03kLf7urpr4k2uOKQNL9MRuTDu6CSiPoQAvEwP+Ha+FZC2AoeSNoP0mioSBIp4TRU9U/Bfl41JWdU9TTjlDnX1yx3d5HTj+loerptGsIxwEJyQSNCa9Ld1MXpnwNWGZxoR1dtWcHs4C/heEtygcxVXSxtH26SWza335Ll1oRJiyg3mrmc2lbI03K6bGHmVWEpuXZmPVoU4O9OhhGBwm32oYYL05odBEvdL3mgYfaOUpATI1NtUAiiY1WPP6F68y2dhCKj7PN6lMxZqjoCdASzUcUMNZzV+Z7BnjVu54ZgWLGvFxkWhzVVNoUR/zjAFoKt7Z1XKF4ir3Utq4F4OFZVC4BsS0qKQCEgyLkUOBlpGYiYAmpUKHuNCEe7D1lPGLjBLxjoVsRHJ3CX3vSeS6F7WcXfBJhh6HXUTyfOFDJ2/uGrKdTgIX+tDasu7YLB9efeJvjF1PADMGgvA2dm4dxj4U5pcAb0KYifKQMjJaR45ynXFXJ02ZIarYoBqwPDusWKHih0aW/AIap2QiFRaSK7jjc8mYifmkpnqjOf5qElSZBOtauB9ofDLXfWnK2U58ED4FWv2TFqJ2Dnpn1+eI9FzV/cI0ZeFEheajdU0/MQ49cUB4SxfrqlrPBhNc/aa3rYj4b4fkKir8Tdh6wUnLnoYjfiIEhLAAPBgHeNiKIcQfZscGlwSJQwu1jgfuz8SQGDGKFdu0DjzcxXQuiU3B1CzTGitDywnAKieqAcTVxU/D4nKfhSDsatnfLxno7sRVtfRRhE/bOY5dF8ORsdkyRhccxwH+7wvdj2Fv+av70R13EvEOFBFo+SwC+Z4sHHGtzqwoOPICUddE82M2mb0BWCZ4lJiJ7ZupFSMpKHmgqjR1auHCpQo8xjTpLfr+rB6fK7soCX2g9AOO0pxTmyObJ7J101Mbm9j5W2AGlx1/okYPc3ovoCqkZNIcTlzyLBnd4w+XduTsV/WPnvH5JxxpbzzRJ9gnjsJz1cJXooE1vSvBrR2fxZ1gxFr8wVuPAKWSuojRy6TG4DNDXPrCUvqqMqdJ8uipPVt6qkzuH75a4TrtBVvEzOhJ4N+INws1a8b4cvU8SvLtwHAPXg+zm+Qi6hMLBlFpo4P8z1Wavik3+CGNnRKuEsnJHS8SKICOnPGkVe8bjf4nuXb+Ob2DP/3tz8C9TRFhxIhJPAeXF30iko0Ut8BskljzIDB6loKxyPL+FtqvJt0xrGvknGMM+6bgG8t1PCgoG2HVCXjEnXUXkJiFd08WYzrvmEhG6qrwTmlLsF5CphFepAbIqJvgqjCMO5XPVhazegtYbtVoq/uo7ZGTII0Hb4RZFGDze4sNrZmEI/2opw7ak3O5ctGFW7NGZYk+E4TQVexvrfZ64OJ3OXWc0UUcb01Qy3pgUjJCI0QcW7DEOjETAl8yozetc4ypDwjjEvquFM4BQA+px+Ue5y54P+pL6fI3O2RdNieMMiCB4k86FIHbbpfclzIeDHgrpC9khNwI8hRjWm2O1HZKMHMIiUa0xAPHZuvpoZTVomDcvf7uAao46SQHmANmjJPK3Y3TJMGuzEz0Oj00rBtOayXISlGnLGxviBzRKzqng7mEn5nPIrkis+B6BPR9wP4DyGC0Z9k5j/xac9sim1iSIebbkaDrE3EOOYiKpWAd+5Jyc3nOtR5ChZyR4AkWUjnWtBSF7/qVESEx3DXPNfsnLLpc81O8HBecN5E5O95uPJJmkdR6dgcbi1h24pHrwLCDaUwYRbR6kFRAdoYJMTVVCjmA52RkHkEeG09S27amtEooZQGZklrZ+knocZIIkbddDwAnEvGq3aDV/0WC1UceVXvnewceUkNnUY2pJUq7gE0TnjGGW/6Aff9gFMveGgrNk6olB2fJlHXmIWsfu9Jgp7M26FDxHxldLkRehrgVz1hqAEYomtlcsOkg2TJwhiGSX2GWxIpKDPOVXz0JUVnGtDJmrJzuGhKdV119w73rZtUvo/fiFT1kzQ5Txcur3fzmGK85oP4xDOh1aEC6F2wb2rN6JuoJNAJXXFW3OuGzVio1/Q6u0E0CRY8A5ShOvE0Es30wc12g3Vu4/CQMZRI0q0nlQCz+ukX9EQ49UWMtf2AlZozC8a42DptnBRCXDx7WkuT2oN3LrP+V50NWCU2yyswGbIB94zimucxYcGgZ82WJ3PE3veuhByA2MuMsDcduxL835s6lfQ4xkCvCT0zas2aZElycpudh00hr0Pqfeeh0uw+F+w3ujdPn13Ta4U6b0iSl/O5CEDcI+UzJfpElAH8RwD+GQA/A+AvEtGPM/NffeyZzgmvjgcnmoBCC5Ak26ibdHRTTqxqoMK5dh9g03NZgpPaE14fDxKs5RDKgppJxHjzcHDubFMc9GMuqq+9NLq0mtEOCQ+JcXxYUU8C85yWLps7M3pNSJlxn1ecUsdaGh5OK05vVjy8WJDQsXFB4xUbddHV60rfOMvmScAzOjkXvVDDz9cXuKHN1S+AHBQNyX2iv73d4HhcUE8F3BKW203ytDZZ1NElj5YOPhtHlPGagZ89vsTH5WPcpTNetRt8a7vD6+0Q0gQm3NEZX99eAABeqNEWEB3v63bAfZdcuq82wf5fcwvqnY7X20GTp5Nk00od53NBU6CryaBnuu0qG61Z/tZzGnp8BuiYByibuSkCI99rl0QUYAJqRl87Xh8O6AzcHw+ScFrFerMj2Jy7zlQ3pwFdueEvDymiV0mNyMeM7aCHEQVVE4BaumRmYgBLB07ZXYNbEZCvdkoyDosB5pG7NLIeBADUcKncvSX8AMCVQecEXhQeuJKk+rOEK76A1GPKGHwz/nYS42cjvDmt+Pr2HJ0Jn9RbvKoHPFNVZEPCB1kyZ73qklfhRTriJm3OuIgNS37LYGzngrYF2wcPjhahDVwTDC65bxnYFHitk/TJDv5lHGSoNFRVjcApjbEylRmrxF6TX2tLB07Jgd+okx86IB3LQ5dxtPqWDnBCZUJvwz7ATKjnLIlx8iyJtTpsEUmhKNjsQDbuNjmm8jNnlE5oxEBNaB3AwpIj/B1S7/w2AH+Lmf82ABDRnwHw+wE8SvSHUWN4zggML4aYzQRm8b8aLk3jdztBO9R9l8k5MXP19NynMLE6vKsTUqJwL/k7oC6k4p3RhytV4uECqUTIuL6U4Lpi5rfOz6PFDLimGxUifz0Kz7D1zeQvYzYWu7uyUdgoCWBRoKKrx04DhcQu+n7MoG8NyZOpAJgSq3TWGAJO6DzbSCb3W1Y3Nm8fRjvdOydcM6Osjjt0ji/cFuN3e53d1+McDY7MOOnISc5xF/psGvNOMH0SfH3aRDtXHp8FZh/wUL/Ph12b+kQzKxz7tr8/9jXWzWHc9vXHZ3bXTYIFhqG7gxQsMKl7pibW6QUbFSwsB4LBLdtvXSEtzCZjkifjUrVhbXLOOoztNC7ged47zcbWOCb78bTF0cnHhgJ3/rZx8c9x79tlO8zC2M9tBpjNn19+IrZ1MksyPL037mMeXk2PFGLmR3/8Thci+hcAfD8z/6v6/V8G8E8y8x/Z3feDAH5Qv/7DAP7GZ9bIz798BcDXP+9GfMblqc9fjvJl6/Pn3d9fx8zftb/4WXP6V8/tiwvMPwzgh3/5m/PuFSL6SWb+Jz7vdnyW5anPX47yZevzu9rfzxpl82cA/Jrw/fsAfPUzbsNTeSpP5al8actnTfT/IoDfSES/gYhWAH8IwI9/xm14Kk/lqTyVL235TNU7zFyJ6I8A+O8gLpt/ipl/+rNsw3tQvoxqrac+fznKl63P72R/P1ND7lN5Kk/lqTyVz7e885mznspTeSpP5al858oT0X8qT+WpPJUvUXki+p9jIaI/RURfI6KfCtc+JqKfIKK/qX8/+jzb+J0uRPRriOh/JKK/RkQ/TUR/VK9/IftNRDdE9BeI6P/U/v67ev0L2d9YiCgT0f9BRP+Nfv9C95mI/g4R/RUi+stE9JN67Z3r8xPR/3zLfwrg+3fX/hiAP8/MvxHAn9fvX6RSAfzrzPyPAPjtAP4wEf0mfHH7fQLwu5n5twD4rQC+n4h+O764/Y3ljwL4a+H7l6HPv4uZf2vwz3/n+vxE9D/Hwsz/M4Bv7i7/fgA/op9/BMAf+Ewb9ctcmPlnmfl/18+vIEThV+ML2m+W8lq/LvqP8QXtrxUi+j4A/xyAPxkuf6H7/Eh55/r8RPTfvfI9zPyzgBBIAN/9Obfnl60Q0a8H8I8D+N/wBe63qjn+MoCvAfgJZv5C91fLfwDg38SMWPNF7zMD+HNE9JcUSgZ4B/v8zuPpP5UvZiGi5wD+SwD/GjN/Qm/L7/aeFxY0wN9KRB8C+DEi+s2fd5t+OQsR/T4AX2Pmv0REv/Pzbs9nWH4HM3+ViL4bwE8Q0V//vBt0rTxx+u9e+Tki+l4A0L9f+5zb8x0vRLRACP5/zsz/lV7+wvebmb8F4H+C2HG+yP39HQD+eSL6OwD+DIDfTUT/Gb7YfQYzf1X/fg3Aj0FQhd+5Pj8R/Xev/DiAH9DPPwDgz36ObfmOFxKW/j8B8NeY+d8PP30h+01E36UcPojoFsA/DeCv4wvaXwBg5j/OzN/HzL8eArXyPzDzv4QvcJ+J6BkRvbDPAH4vgJ/CO9jnp4jcz7EQ0X8B4HdCIFh/DsC/A+C/BvCjAH4tgL8L4A8y897Y+94WIvqnAPwvAP4Khr7334bo9b9w/SaifwxiwMsQJutHmfnfI6JfgS9gf/dF1Tv/BjP/vi9yn4noH4Bw94Cozf80M//Qu9jnJ6L/VJ7KU3kqX6LypN55Kk/lqTyVL1F5IvpP5ak8lafyJSpPRP+pPJWn8lS+ROWJ6D+Vp/JUnsqXqDwR/afyVJ7KU/kSlSei/1SeylN5Kl+i8kT0n8pTeSpP5UtU/n8yGvRn/BlNCAAAAABJRU5ErkJggg==", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Set the random seed\n", "np.random.seed(18)\n", "x, y = create_training_example(backgrounds[0], activates, negatives, Ty)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Expected Output**\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now you can listen to the training example you created and compare it to the spectrogram generated above." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "IPython.display.Audio(\"train.wav\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Expected Output**" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "IPython.display.Audio(\"audio_examples/train_reference.wav\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, you can plot the associated labels for the generated training example." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(y[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Expected Output**\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "### 1.4 - Full Training Set\n", "\n", "* You've now implemented the code needed to generate a single training example. \n", "* We used this process to generate a large training set. \n", "* To save time, we generate a smaller training set of 32 examples. " ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "(5817, 6484)\n", "(9054, 9708)\n", "(743, 1100)\n", "(2724, 3378)\n", "(3172, 4508)\n", "(5426, 6093)\n", "(2699, 5090)\n", "(6979, 7557)\n", "(2647, 3562)\n", "(8049, 9789)\n", "(4926, 5834)\n", "Timeouted\n", "(7289, 7829)\n", "(1288, 1866)\n", "(5579, 6494)\n", "(4537, 5452)\n", "(6835, 8171)\n", "(2314, 2671)\n", "(6052, 6776)\n", "(7824, 8732)\n", "(5096, 5750)\n", "(1934, 2849)\n", "(3786, 4385)\n", "(8999, 9539)\n", "(3454, 5032)\n", "(2369, 2920)\n", "(7566, 7923)\n", "(1750, 2480)\n", "(8759, 9489)\n", "(3041, 4619)\n", "(399, 1066)\n", "(7442, 8020)\n", "(5838, 6568)\n", "(2884, 3551)\n", "(1786, 2440)\n", "(7830, 8408)\n", "10\n", "(5093, 5760)\n", "(6000, 6667)\n", "(8372, 9280)\n", "(6266, 6625)\n", "(7157, 7735)\n", "(3836, 6227)\n", "(1144, 1868)\n", "(8661, 9328)\n", "(2574, 3228)\n", "(7836, 8195)\n", "Timeouted\n", "(2369, 3023)\n", "(8843, 9567)\n", "(5005, 7396)\n", "(4395, 4752)\n", "(8021, 8751)\n", "(3209, 3863)\n", "(4464, 5063)\n", "(125, 703)\n", "(7328, 8243)\n", "(112, 779)\n", "(6579, 6985)\n", "(7100, 7506)\n", "(8605, 9145)\n", "(8094, 8818)\n", "(4776, 5506)\n", "(2991, 3715)\n", "(1406, 2314)\n", "(7682, 8088)\n", "(5275, 5942)\n", "(8012, 9752)\n", "(3217, 4125)\n", "(6750, 7417)\n", "(776, 1182)\n", "(1742, 2462)\n", "(4311, 4851)\n", "(3867, 4221)\n", "20\n", "(2713, 5104)\n", "(7303, 9694)\n", "(5484, 6204)\n", "(6341, 6698)\n", "(648, 1199)\n", "(7766, 8681)\n", "(4852, 5576)\n", "(3580, 4310)\n", "(9219, 9576)\n", "(4405, 4811)\n", "(6662, 8240)\n", "(2144, 3722)\n", "(8411, 9319)\n", "(156, 876)\n", "(8543, 9210)\n", "(4556, 6947)\n", "(813, 1467)\n", "(9342, 9748)\n", "(8048, 8402)\n", "(6725, 7303)\n", "(6150, 7058)\n", "(1689, 2343)\n", "(7106, 8684)\n", "(1913, 2643)\n", "(4339, 5059)\n", "(3667, 4334)\n", "(3052, 3458)\n", "(8765, 9305)\n", "(6125, 6855)\n", "(1308, 2223)\n", "(7469, 9860)\n", "(597, 1251)\n", "(2544, 3122)\n", "(5274, 5928)\n", "(2484, 3208)\n", "(7123, 7847)\n", "(5031, 5751)\n", "(777, 1431)\n", "(6140, 6680)\n", "30\n", "(8297, 9205)\n", "(5053, 5412)\n", "(2899, 3629)\n", "(4806, 6546)\n", "(6987, 9378)\n", "(334, 1064)\n", "(3942, 4296)\n", "Timeouted\n" ] }, { "data": { "image/png": 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LKVea1xwCC/ICRL7ZuPY6WNHzukqeq8Yb21YaDCLxjPkZwjrcBvUxeBAS63JQe5BeG6U/CUlGMDzAOJjgPAEWKAtpwDLCXIVFvGo2Kqt2E+12fUIjOT/TkpUCyGhgvhcqvWD5vRgpyFWSXBczohwU4vpdqBNcWRkL+3mpb/23ioyJYOOzGGurL4QvVjxfEj9/nxys3I9FnRkdr2jQJIM5jJJDSmwUveoMiKwtifLHKH/pZZHhvZpu4WW+l5UX5Tn0+rgurm9Rh1+7ZBgzPVTPymvRzEMqexFYpLyT/sqJ9MLk9XKZDASujNXro/SB5YDn7XdL5ZsnBQmyx+O7MvCwQR9Ae/gluMFWzYEi8N+GokhIVLAMFYVJRvHTll8HCqVBd+vvgq0RNboSl9BOUFZUl/b95nmXjIiGeD6Kjq2VnXAp2rfj9WtGkxmDirb/Xum7T0QZeQp8MXcVxvAQCLn9RrMtQPv+7MzbPt4eM91Hvc73nDKveVIRaHBa+++6LepLIAJA2BkT9oGTbHOe/JsneZDN2uP6QobO5gktOkp/ApSfYwgIVppc8OLlBGLKoGvIsfYPGWUI5Qd5Rqvf+sLbpnPizQW8H90Xp23uULiW99yHMTcekKL33XpAHC8d+ZfzlvrB+X0uVYQ5HjZd6KCV+60se+RNhd1YGHXaHv5/bZR+VrBBMTOiWeXnohkZ3NJe+h1i4wltLNHJQVqihUXbS7d/auxyW5c8oOBi4phuocI6MehCu1lZLMaA1wMm5bJAu6HdjG44/wVeZjQbsl3hxZT3CHFyOqrrSCbp+k1IG2TQr+VPNN8auz8cB6uT82uSk9X4Z95dS0npLumwrHnh/agtNuI38pnLTZ76UVrK/fX8pmc4vv9SdKb0Wih9RgSMhoJykMgI3nZog1/OcUIsY4L5+kp5mEU+YR33NqtrMXqOL5dZSBjZy472UAgYDUnco22IWDQaAFJezLPQ5x7LdVRcYr6wnbT3u1bBdkXJF0PuioHoAfIIhgIYD4zRfT5YjcbVUecZMa7dUXV4cpERsp0NRBMkrwvxAn5AdEJ8oe/D80r3eDwLxoM11A+lesKDNxrraDwZ++s9LXYOjQKkEOwSybgvJrInwWiReWJKJS2gFt6hpoO33BYj6uBhsrJNBpafLg1hpgOj6+sbNF+YLleKe98CafXk3UfUXtiyC6onG4hskHXUAZC8EbrPtAcZ7esVjuy3mc6ww8rki71Lo4XpSms/Ob0WSj8kudChCyj26P7V5i5Y5JWLtUQzVJ7jo0dI/3YXYZSdvQNZ9/cIAefqL5GwQngrRHeEOF0RHBBxMI5HY5BReUC6B2h0FQdf0nklHdWRw4n+9RtBaEcyf8mjOUoH/Z6u8XBqLDe1meWZ08E1VoC3pIt8O5r3lzweiffyOspLtX9LuiIDkmjJv8P3K7ToFb6+Fko/ogc40nAh5DhdjRN6WrQz604WGgI/xpcfqnFkRHUYWoc09OPhpYTsgfGwltER4u2kqByddFR/JLCyC+Xr5bc4i3Nc19s4rZW5ih1Y1y/wTgnKu5WKjRdrFX0XTzuPp5Qa4/elVe6oXiWgftlqe6DLyvCEK2h5eRtlGf12PlssWcf1gMAW/eAJ5QhP6HpGyMRH42/eaSJpnFlerE3rm8VdOTQYxjuBFJetjmrNC7T6TaaMDyyL5RzR52oth+ma5J76595JgZ8QyQiXy4W4vuha8Sd0z/PBnxPJHjfTTvPbPCxGzDyOIU4viMdfKPWXjJp7HXYmEaH3lVfED79ZvimEZvO2Dhp4DCfDw/0t1Fdux07lTOt7r//unRUK0cX1S2VympAxfU3I8WIdyXhcbOPouk/89YNZ0VsZivMqjQTNjpCn/T6KaU8/+ve8J3/14suLaxNdkC/xLkx6QjiXxseR0DX5uOQFrVJGgUk5H9G19JquoOpLY3GRpzl/pnGl8I94dMkbuMVLCnVdYOyirqV3narIa3OHXsYK7ZtxS+Av843DSKE+rt9Sna8dgg2WzQvjeWSgL/FlWSalV17pc4wsxEgX+5w5Vqa8eyMhQ6/XrLOMExqZI8sJxsaBaAjXUpwcSWjcunf01GJ70suPI4g9lq8H9VmbdpIlCbkk4cyIKpwaaCjG6Nnn9Q8AYwdP9zicpqIo+aUojFy0E871VYHwfn8AFpKyI5fz3vVpz7tNSB7TBX8MCVrfjCeHE85kLisN8wpsbSbRc7SrItDG45OUuSiWa0ROv/GAjQzTp2PsnCZTXNusLBiNGm15VxR/SlJs/MzEFMdOKJXLrQwQP4Vq15VldYs8dP4p0Z8MrK/3UJ85eQw9eUpBNrLRzus7IIXOMXoqZ/M9GAFaS+KdRn4Uw8oosLzwziqhvtI4HqVXXukDCBNlsmQ0kVh48+IYECcMkBhTklVHzBtiaQfWlQdx2Q2y9lwuhyMmZJ4m6whFSKA9IwJXtAe0cIxWmIdp21ksaIWpH3mPfoBRB+0bfRifgQcq8wNLK57kcTkaM8ueJt+1tOxClhM5+MzfU30TItd4LYSuTLYu1AdEpc3JeJG9hkwDg6yLayXAUkYz2BiVjhu5HiyyTd5OnlPHouVtsw4I4mhtpDAcG9Mj0XWZYr4xmDvgzwqM+Hgaf2viPRsinXni+Vg2DnQUp9dD6Sfhs8mdLWqeNKFMqgsYZ4gHIexKbxJAQ0R01kZW4AEtKVlzqt+vJUSU477L/vvJlTSBdpnieV5mYRhZmIKQaqrDBKkjpb2WtmUzxPWl5dkZwhB9mfcW1++GQy1ubwh/wavZkGEYd7t2wC9O9pYx3+nTkdKEUBMqnSYVEHbn8Diz5zjxlcnTxSfJ86Wyy/UCRXg6PBwPboY8tcWGJLcZlDxGH/0+ew1WR79fOV7On15RNGABrNBe/bAThpUfRr28g8j5dUHZrQy1RwHsGvEv8MPGOMlbPh6b+3YEMEL95uHooGVp/DovyjkZE9q1xLJ8KaL2yiv9rh+CpRRi0hLNgxgukYHBLdsisyzxxFghxfZlvpcR+eQaWjkyNEsEsFJiZmhSW6utc55FMBQt18X9UkQlhcgnW2itnWkcu3e+VoFy23nC8zgoINqVfY2Lu5HwUS4o2GwEMBtgKxuuUSjCx5zGcHowht3v1B9+/wIb2il0tFDel1Db0qVfjSlfl2S8y3goK4AHo3MxTpe8SvaiXZHlEBMi30KdojDPbQIkND9CaG41/+ie9zVv08xAhkjIbeXtq95lMgJB+aY8XE+OEATjfQBAjEfDaycaF2PkRvAcO8eA1fVA9pBTeuWV/oRWMTM5KFdBNAbU+Yx6LGkSQCDW4YKQlQCXzxN71RU1RYqAyPzQNK/AlJLGXSQ+AUhapnvp8yhUxIiKEUniZawc8ZwdFjifrDS5F/ufG/863bY2QAwSOxFUNO6E4GyE/paoWIlFHKoyljFdbPQ5Jm91s7LJQML6Q7KSDQErzKPQSh6fIGs5BIG5zVD3kRxQWW5r8jRXhof5kWU/hUyn47pzm2mO8bqSjVdoJxn+SSHymOa+MI25T4qx8wUL/nEzOVxGsr9C5UHmdFH+oJ1wfaG32o+Ylw0MeyQrY2Pp1Vf6lniCc6eI4WzRp8mJkS+4tnQ9CAfVgYUBmIwOt1GoPDDT2u/bO2kH3aTE+zHJjoSpDfZ0NCthJst+L87F4VBF3rqY6yilHcOgKiN+b8i/tkIiGmgVRQs9EQ/yQiA4LMQTu2/59MX4jGaU8jG6CX22jibeWOhNhhK3LZfTg1YsW6TMefttUFygscnGOAMLNhhJKYXtvwfKKIQVrkzyoxBnQNepjhU6NtpWxs69Ij7ywOS5h3WWBoMVJfeTjN4UG0/98PI6X4cibI9kfpQdUSYzkLBb/BIZzsN/rCc05uUHsKytKdxG9YfPdM3frUw6wNrUGxQ+8Dop/aRYV5ZvibBXSGY1CWhyBdfvAgLIE9iu+aSn+zn2OLnBqb4lclMJ16I7LOOPk4qfp79UQKv+cX96fZXrDTQ1QviIhmBUK31yyid1svBfEVor4p+ZnywrZvyTy8tjFBQvty8x7xQCo3is5Z9kMBmPTP+qX5MiMFoW/Fkp9FsUyipmnOsOCi3VEeRbEJUg05y/d0MQvJyspFeGHjRmifalgTzgwTIUmPt1KfEYL9ZXVovDK94EeVry6UAOcvhxIZv/ykpfRJ6KyGdE5O+LyOdF5G/0639dRP6piPx6//sBKvPjIvIFEfltEfl+uv4BEflcv/eJ/oL060niYGV0tRqoiQmEGFjQPOSSX0IsqR1SEpdihiHeyYs/ddTvcdEk8B6b3PtDTb1dC/MwGlIBUG1LZ1fqCbUbbfa4t9OUXgSdFT8fASAVEAF2FVQVf0euPxTU69020uocruroX/yNWJ2HRaPipzzMj1UoLseqpaM5G4cQbjPZWcQ5eUtipe1uYz0j8sXpp/KrHUYMMHxrbv8dPoEJySKNc95Jk78f1tX7MoXASAlNhouNtY6+he3RVhchzEoLk7JCtUm+tCj0pF6PkhyIYmx5NvpNruvgOc9hluVsxPw4jjr6Gx7WMhpJXjgck3VLiMfnNUXTI9Y+Ke5pflL9AagkIJbDk7W/F/lo0V808X+RbnlH7nMAf1pVvyoidwD+rojYC83/lqr+N5xZRL4bwEcBfA+AfxvA/yYif7K/HP0nAXwMwN8D8AsAPoIbX44e0mICuPIWUhYXLPoy5Xs2CCtklLOm6wZemeZLKDAMeDZYpMUuke/5cxuJrpz/IgK92iDmLZsrOpDa6c8WjP5nBt5el/02VgWk6xlGXbe4wKu08vpEMRaxk8KfvJujtOrLBb5bmy77R31RLA2EdkUcZJTlLyshXfCsKxfp9brXRJ+BLHeXjvsFuUncvDrrS65zuc7T6z/kq/X1Zdu+cJ/bDd9vaSvrAQy50DQOU7tXmHgV6WtLX+0/7/rfpWp/EMDPqupzVf0dAF8A8CEReTeAd6nqr6qqAvgZAD90rX1LbukJnbhlTVZyGcPTUQ/v1OCjlXmBj+Pm3n5GmRi/mSavwyy7DiRidEltj7PnRZ68GKSb9u8yhSFGnHkMh23rDEg1oQf+rOThMHLKiQ9cEx2zUzdgu9tH2wC5oJ2JtX3nrWr3T84oT3ZYaCqHxMYxze16PSW6CtYTP6FmYIyl8DiUxPsUz3dPhtGtxN8GLCw2nNGny1NG84yOyVBMqJw8tAxi+CGeutE4EuhxD5STIhwUmMNJ2cPVrW8TpKMjJu+L1rB0A+pJgZNOsmn0ejskD24sToMWBnKT0bbf5k2nbYru4docNptD8x2Lvhvf3StQrOdcfh5CqE5+9zEivRySMbqDXqF5abotrLmZ7JqMmtfS+7Y0DCndFNMXkU1Efh3AlwH8kqr+Wr/1l0XkN0Tk74jIt/Vr7wHwu1T8i/3ae/r3fP3l0woFLtDSyqJetPQrlCsX7meyKM+htb+lPkZFQtcwJocP9K3Q6IjWxEtWQJcrGPmvBukWaw3bVt1DyLHy8eNKvbfmWdX9svVckiMey2voL8vHAs1dpSfLzoJnC5aHNi61E7yVGxBpXCvSrohfXjCzxxnoOOpvJmcFBC608w2lg66tQpHtRvxcsSbQezQfMg235Fukm5S+qu6q+n4Ab6Kh9u9FC9X8CQDvB/AlAH+zZ181v8Icdn1KIvIxEfmsiHx2f+trozQpGmvJ4nj7PYY1T3EzK5cflvCHdTAOp2LUwmjB8ntZRggppmlIgA9h4vUEJZSZt7yNeJ3GyemKXgeC4PCIdXkRJnEDaP3g45sJDfn+7p5/9ZCHvSzFt5x2xW3v0fX1C9p9VB7smFv1/Hfb3tYBCtwTsP65R0RHEBu/yg7fix5e+MIIzvp9Gtf4+GU7CM/QsrJsLBRiuJ6VdZYzQuthS26apMb3cu70EEjhbYu+VsF9QONBsSMXSE7DlsfER5b37K2YfNlxxDlGHmL5VL9umA7ps4ceB/MwtvR6w+177WtX3E9PaW3AvICwY6WPp0qjw47gdhmhtTPuQ35Og/ke4vJMQkfxvHYUdEof+0qv7rQ/1i3SjzjJYSIrl70Ujuv7GkFaS/J2brC1L7V7R1X/BYBfAfARVf39bgwqgL8N4EM92xcBvJeKvQng9wMf+aIAACAASURBVPr1NxfXV+38lKp+UFU/uD17o10jpen5Foq93TjIs2rrGrK89RpdvxrTu5ZuQVhHRTV+mfpPimmKfWdkYj9Fx9wg2o5RiMy/8wQSHVv6Mn0yPIDlOsiimUP0lOu+lq5MmkM0t6jnEtqejId9Pxgv/87tHskbKaWpjSM6cl4CWSF7Qt1TuIfLZIS+SlnWbnEQFob2sLzQOGS+UJmbkLIu+pruc5vTmUOrxLxa5bsmP3nOrsBISrfs3vl2EfnW/v0ZgD8L4B/2GL2lPwfgH/TvnwbwURF5IiLfBeB9AD6jql8C8BUR+XDftfPDAH7uWvsA/DAhRilu1cj6mQDm/bd8+Fp+vNvr6YOUH/JwRN53d/huDEJIYHSFXu9pxKGDBTaBECQPQVDO4uX9Ou11b4IrccKf21OtujXFHOL/HVnlffiHh1DpoM0vSVPQG1sI/q5A3Ysr8Cbs/b4ds1AGerdyey3uHfgxzNYvldZv64/M/AtImNZ7/MUZC6HPseF6GvKA1L28pZYRl++AWk24lSJeGrbYn0wjP7wX4r1K5U2mk0fkzx/o+M315Lg2o2avV0fe8DChDCXJHpCvu5ziHDIjrn1cc/3mVeQ07Z/PHjd9Z/Qd+Gh9JvmZxoxS2K1HdHH8nY1H0DHULy1AvQfO74B79vs98byMOjM65732vK4Sohz0x7ulWA9eSrfs3nk3gE+KiDltn1LVnxeR/1FE3t+78Y8B/CUAUNXPi8inAPwmgDOAH+s7dwDgRwH8NIBnaLt2ru/cIeFmxh5aRaU/YpQglakAyI0LqChNKmu7y6+7yV3nzW6pTdoyJk2ohweP6V516ZJXYQpKR73+8FHmCfd9wTueFAG9Wb0hM31WaQ9tcSWyoDstVCmMbh0NBOk/6Hemn/tMfLwU0+X7U8juQv6StvK54cGQDQYfgeSjyXhEYzJumUaTR1ZGPiwWxllsJ7X7rZKFrGQyTJaZnws0DQB2QuxqG6uXu6KQJs8n07IQFe+DzPmWBtdIWfQjy/7Ub9JFlzwcLUC967Jtyn2x/XZKC11gbWXa+P7LpKtKX1V/A8D3La7/hQtlPg7g44vrnwXwvS9HIpzZfrwqv1qvC1nYW7vyX0wQUoxPCyBniqcl5GfXbNW8bh3IypgMRlN+OYN5D3pqbwO0e7o1MsoDhuGhNMVbOzqwi/y0riMZ2wXQ224egklKVOYcdmDDlA+Ty658K0DWj4mlsVDIaKsrn2FUBaiCfS/QWkaZjva5rhAL1UFfRomMzGN9lJcQvI9bJePMOzEI/UIQD1Kz2LmNM0Ze41d4zkFjWT/viemRUWdGpLJ3j4RYU2mHSx6ao2cSovdI/JurQd2AbQf2O0QUnefERnw2PiQFJH3BXndpE4cVJ+98IwUrSruRaAxsj7o982Jjbx4b8w0YsX4es2BTyOjbPV5fmZStULtZXrht+929oxCrT8dehzqSQne5TgaOaWfwGaIWF9JLxfT/qBI/WMEDBZBiIpQ1eQdeEU16ct04/OE6bMXorvhdiRJ6LaQYOHkoYINvlbOwz+Gj4144tu/5iMbsRfCpfVAJwup1Jp4YnbzQtUJUITGPlc7a1zQB6X24TEO18I4pPO6rrUewdFK9HmGi8Q7ILqMiq5s9AvpjxZv7bPJxtGYwxYfzVj4e34UitjFb1cWeCCuUYJRT/ksP5rB3c0k5hHAOL9ySYhp19PEtVJ8A9pIdKdr+kkHIhjrP23CqZZJ57q/TuxoLek+2lRX6C/1XypvAgofcjD4GFKv6SDGrtONW6l3aHo2Yx3iySuF60hcOiNKW4Uue8muh9IExICHemlerM9KgMn4faVIKKX4ebK4mcWnlTnHs0L/b04B2tos0l29/pvElL1lgFeNJVhf6rsA7yp/4k554XaaFsvefJMA5T63tadwiCqW94zDDUnS8LpGVp8XApTdQx5vBSqnYTuNQk6ikxff88/WVknM6ju7R94nH1Hc2JOyZTCCAlJskfuVQSIhLY2Gg7Y9365hSsXzp5Sd5d9nEh14mGLXkTYadOTZexBtbv2JDyrwLSpY8SQQ600Qy40D0OmpfpBVqHnXRdeJD9gR591Reo+G6ea+8A8JkoILxTfUP75r0VO2ePID9qeL8jvZX7xANL+jTdMUBL5bKX2N5v3ekA/AaKf2BXg8GEBhCzgNWFwzOVtwGjVGBZRFEZNUZypZ9Ql12fae/Xm+9A/Zn2lw+Xt3ng9MQBZjA7zKmZ4rKMySlEJCKFck7C9jopYmmKth7Ye3hGe+qNpR/t9XJ6OaHx8bvtk//7i4/Dz/aZqWVX0U3jftqPLMyU8wnEKb+LmUgk2dKYYU8mW67J6QgZZEHYwupK3QCM/YegNB+RoslGiAGRXlxsjWYDAPLtpKy5IVMoom3jg4vnK1f6qtfM0M+8uajN7hPphxVkvEz2U5j4Iv+eeG6DpqnsF8yIMHzc2IwZCkbR8pj4E761tHtRQM5+7OK/Y0d5zfUQz3sJfhn2iTC+iZENChl0JGvrdJrofRDSCcJFO/DZUYGt58MhdcBTLG5sOfWJivtXV+5xEslnNpxZbLHeyEkYZ9dQwgwo/qsaBIaspAOC1GmJbu7k5Llflm9oiN8k8v1z63UqGhFQzgsJAFOpb9MfRvETwg5pyNlv+I9K0raNy65zyBjZPmzEk28OWxTU15G/WxMk2E8DMkwijtAdO6l8C6dJBdBUa3oTnMjhFRym5wMvVNoL9yza4tG3VvIr8zM+ZI8BC+FmuE6rR/B2F4ATbz+wsbN+BBCpAuFGu73PDbfs94Ku76miiItjbiZ7ml8qT/Leym9Fkrft1LWOLBa4rZIgATfFpnYK1igXX+cHQiHopnCr3ez4Jg1t/am+ymZq8dttffaEm1oE4DDQbIPHWrtRiSeGuRQFsevOfFEpvrC1rb+ZwthpeiwS0UhOt6AJbVduysp8G2I6aTxyFkFUBRP7s6423bIHa2kgSZ56ms2oDwG/m5QDDngIy7qiRY/jWYCAjY5XRFtNAYK+PtwV8qTlQv1OyB3Ni5UxmgJR4lk5MZDXFM9ZNjtbHgOE4XFfctP61D2l4/ikL7tlUFKmCcEqPidzlLFMMeYdwcv2HHeXHqREWgsOs3TXOf5wIcmZhBjO5xoE4gbW/YIkhfHY7kMqSHe51QeEB9c2xT1XgMQsjpd/tJDaoX6nhE8z9XQ50XeQNfxrVcnscUO7jvomn1PgxM8gVuSLD4X1yYX+wiFgpSUdmX0ECsYSkySlh9VTf0+oj1NMEaCIesKzWQlgwPhOUKtmbbsHRiNnO+KwQz5E/pZ5V2tSSzL6nzt4vrQog5Gm8sJiaQoMrmr9iceRvnP96z8UchzpaiWdVEfgudzRYEcolYMecifUx86jcu68vy7MM9uojHPy2wUU33LtZ0jGnI7prgfBHJuDS3Dc7l/uU/X5HL1+3VW+pOAOsIwy6iO6v2QogJH7XYYlZUJiVGH1c2WHjThKF+YYIsBt4nmaO48EMbpa4LT1yUiyQrI3l4ELkyHVVuA1YtQhkTOPLI6fE2DDn9rZXvW2pFeXsi2vlVMu3NGXmm8tzK8kCuIB68xnRbbZfp1vDGLUz2Nvq3CMNxn3+LZ27ZDzfKOmhwO8d1blJRkgVG515EX9lhJ2mK38SHJk/dnT+VJ8YXFUqMlKwdTmOwtHCgAVzaE/gMPjEZbgFR6yJH6N4XHeNHZzlIC0gtykqYt7dO8Kn/3MyKvnA8875ifRJMDm97XEE5bHT1B9TPPbMtmXoR38n0NI92X0Y6DrV7H9nXB9rUyDq5La0sj1EX6h3TIyujyURE2llzPa630AWJQHgCHwNdTiLthzcglMsz3FuUOF3IXFltqP+fHUEYJt9sfKXjlG7ndLJhHrLgBDS1R5mHmxSUZL1IJyDIriUW5iMRuJEIPvt96PxBB+YR4kWRmKnYDX6+WoUmdkb+n5FlMxu7K+K88kSmxYufw0cumyeXpLDQibh1fpms1L1lpZmRrRnqR1/Pj4PfRGNxCZ2rfUjlL39AhMQ/S2BzppAPv42VZ6fR8Y8X+8NMylleB8gJhQD3+l3oW4r/AoVJ3BGToIb8qjZCRI8QsmKREPHZM8ePtLfjDJuenLb+eFHqnYx3B9rc7apC48Ey7kiahJyVhRmOJTA48H6PZvCaRgfTVjnw4DQVvr1OUQrRIM15y7kcquKS2j72SKyM6UA0/OGTxTY5N53GlSWNlWYmqdMRK3gcv0IvGh52YJ47qCHQEBauYDASv8bBH4U4Nxd4N9eXnAFZeSSG+2Gs2M5I1WpfPChAtgW6M684/o4fyedyZPY9srOyeNatA2RRSKsQW7Kt1KvKN+cAeFo8P0+4I3eLgvB5XUj+NHqPvNH5PoZ1s+Hq/3HsnOnMcPi+k2/XTW8DdVwSnr4nT4M/q0Lh6nfmkAJK9vH4Yjupg43bBILweSj9NuOwteh5gQsWuFI6s6LWmSYEexb1zmoQJkXYOUyiFL1SYYK5w/i75+lF+68MBurmGFlQQULw3fqmcWZsbeCVJiC8i0luRzQFSziQu681trPi04OWRtzf9XoGNFb15zEnOa36OfsGXq+sIVrWk+ZHoOZST3J9sFO2e6IjlH/FnQesyhs5z+YIseDjuiuz9gaUrcunP69CprlOoCljqrovtkEF4GZpeC6WvQi+doMkVhDULH3kF0+P7hmQlfQeV1SQ8ZO3LPtc1bZcDgkHKutws+v5UsT/R1o65fwvjFiZTIdkwIyJwRJwXJJdbAiWiI28j/y6ICh/wp2w9hqqChz2+lUPOAnkYjA1xbhXcbQ3iFDvQRsfDLMb/EPYg4+keCKGroBBWCoPHJwEE3rXhBpvk4vA5j6z8Kfbsx/r6TQzvSUjm0tqAKPVNqb866j2/Ub1tj+cSUmTZdflMYCPw0mhLO3vYi7FdbFyP8cJj831c2o6t5vkVUV8TkqJNdlSCwvK9+mQ0At/ryOtj0MfU8tf70U/zACxl78Z+mwc4rekoKWUa1/ydZdTkhhW6y7M0HbQ9b596Al78G4rzU0xbOLMu4TUV9yDptYsBYILKXtDsr4XSBzCjhIVi5HyT2wb6zdlXaCAr7oyC6kH+a0iUjYANWj+eYSxkLsrcglgWyGmq7xaknITI5mdIvKjcv9rDWwGV5RADBr8FzZhIX/y1ENYhnVlZUTtHi13Z6C7rXiFHHs+MhFdpZegXuzS4zlB3pmeVn5KHd1bVp34GGTjqq107kA8PpzCNScbcuSOlFwjqiJ938Cw9I/oMIaaUfMxN6fODUkmGl2NHAGFahljNQ6ZzVTdfJ+DAfDIjowWo9z1EmmUn6S8PM2vMFzxLHl/E8qv06iv9zkwVeCyO44/Z0gXUnhY68xGsjvCSRQ+r8ykdPXDEdHiMNO935gHusV0fKH4il1EKCQ2vIazc38nzycZKEZ8i5PWJfV0Pr2mMF6NLqDu+FF1Ce46yOM7cid+S8QhrEyT8HLcM3s9q7PjJS4qn+w4OpidPElbwkugxWkhR64KvF0M9EuUzTFpJtCPzrPflBOA09sZzm8tTNUm5GRpnzyCvk2jpqL57JfXUvYt+hAAro2WYwnZw2U4eaWs+Wkv0GBdATGksOX4dvDmm3VCxgQY+8hxUJ9eBzqcyylmbtj7AfeL1GADhmYHwHMTKcFCc3aIV5UWrTO/b07lT/J7pAe08lEFLpedSjCZeh7kGEl99pQ/ESQHEQV3tZEhWFxhMY2QYHqzK6GCFwpDycTn+01j/EoWSIBtCMkL93JmUL4eyVsqHr/t9jN+sKLORYKHLdUxP5BI/CinvYKSYpjoe6LL6ypLBo177nBCNkcHKaqFIJwVONIZQx6L+HOrKKRgJxPqXdXFfDuqdgEZS4jZ+am+bsvYzEEg0BMV21Fcqo/2IEA/5FEDviD7ma0nV5QVFgPbpRxmajGOmhfL6Z1pgnYxHVnpC91I72aO1sVmGj+2a6YzUzpE3EpIMo6Gbxs0pnZ+TR5rktpVN1WZjf0CPpddC6Q8EoK782w4XTNY0DDLth/UJw2h+pZAVUWhNUfLklljPlK5MLENI5Qyc3pbxijoBIP3oZC7AfbSveUcITX7dqOk6BGriJ/e/pH4Zqu73ay/UzuyX9kf391pQd1kvVgFjD3eVtofe69PAbzcMeXIbS8ygmuI+SEtvLclAniiV4tt+nxRABhjWziHIyBOYdlvxGEyx/wPFo6mejOyCUjceSmyDn00IcfPUPoc+HF0aPUY7gzBvq6OYHsu3ZzhEtJ+qaheGTPAajo3r5Inl3Wcg3pMMB54rK9n4O4RIlHZ4ZV5Lur7w8i0/7+rhp/s52ZP58tDmwf6kGdkJuKVtswFAKc1P5g8bjtda6QuCQrIJUe91HFnMk4jfrpUs9xSWWVnsxcJaFjglWqytdgNBAaxCRGw0ygOwvd2e2Gv06RBWpfmbbMAKZUyhCox6kPtg/ab+52NfJwQW/PqkdNCVeJXlXmT77jxlzyDk6XVXBK8gKEbmdUaKbNysDCkvgMZtZfAtv7n+i7Wbo9DNJRBwhBwnL2FRL4/hUGQyP0yXyrunuTDiSuOf110OvdtN52vGR1bQBJAAtHcmdIJy1T6meY3I6ODw7IKvRgM/lOX57SsvbJpC5nnPxoH7n8bVebeNfgdPSga9Hka2/tBYlv5+ajk3j35/qr7oz/M4GycOxTp9SemzEbsEiF59pa/pD4juTRLU6WhSTd/ToIY8Mv/2UNDCMEga0Pz04qErn5XmSmGvUlIKGcEbTXniTmGgXC0JaN45Y/m3UscOjEXZ0hs+2vsf6NB24FrNnV0pd6T+JIUVwlBCii6V4ckQFDeTcDBR+EnJaXzSGOaw2OR254NFj+SQjbLV15VTebssH8zLMfaM7q95sXyGvaPtRegA6PmIP6M/Guan9N1ZLa7f14QAjKe01T+4T4zi2UiF+/adUO8loBUW19PcZLnj3XlhNxSnNG5hB9AKkIyuQipweku64kd/4h4zMAPVk4DmCngEgHAhvfpKHxjW3wSGHq7ISC7E3LBQ2NkIYEYKrBh8UuRwCqMkmiRsZR0VMH2aypHAaBdM7lsOC4XvE3TCWCDO/AMC8m3EICqWHdNWSFbqW6ko/FIUE26lUzb5kXpLPEm1Eb+Vij0dxuXx6pWhT4vbjPAygg2oiRUIaKzpkXnle4lnjO78PimIozDTcs0A3bVP4CDLrF9LITbRHhL8uoTdXwApBkK/YfGavBQGMhwWMG9PmMaMMLlPhKJ9fE8KnMZOHVvvKfb2rCpNRlI4EYj1ZY906cnabz5wj0NOScGvPAH7zXPftlo67xZlwtqYhXMWZdxYE/+kAnf/UtqBhj22X1PUIuga2NzAZBB53K4pe0uvh9I3IU1Px9q9o33oId9BneFSMggrV3fp/tr1FWozWrLlXyjs0HZWQKIzQlyhj3zNbmVUnNoM1xJtqy6zIuWF3BAFkoM+i7adQA5bUiNUiCdN4HGqm+8FI8DIKCHovK5ziMDp2pE3dyQX3j9WQGToA/25udQHBxd5HiyUCxvZZThqcc3r4F1d9kfPkASlTPm4f5r6Uwp5icmg+7X0PYyZ8XABfKa97HR/AkpZJlPK6y0hMZ8yzamNSRdwf42nZ0F4n8Cij6s5nXkdQmuJ1lW6qvRF5KmIfEZE/r6IfF5E/ka//sdE5JdE5B/1z2+jMj8uIl8Qkd8Wke+n6x8Qkc/1e58QWZ67lwgYHdheILigdt8XafhamoxBiVodebElbY9bITXf2si7b2h7FeczRVTTarvXY8kmc033aUKHsAsLb+rz0X5qiwG7686CSO2HM8CpfFXBbtvutqhdTqViEx2LsiAlsQ1U530rir2/LrG9lAWzos9KJfEHwPD42DuSaOCmhTcd8uNHMdAinpdLh3RZXWF7XL828Zz6kcOC9lKUcNhaKuepwhf5fMHx3OcB9cPqDluZ6TH/8AYpQfBUnEc6+FDOmGSiPId7H+1PxxjxvDFPr49/Q/yK+9O5HcVAC/pH6wk81rYt037nBWXpMfKyCpux0TDv3caro/MpVFsQnpJ38sgQ8GL4yoAr6STPh/HdYvTbc/gR4MZ3Q/RTuEqoLvJWs3yazrhk2G5B+s8B/GlV/fcAvB/AR0TkwwD+GoBfVtX3Afjl/hsi8t0APgrgewB8BMBPiIiJ3U8C+BiA9/W/j9zQ/jK1Ca5RiCmxW8yLd172DyhNyMpvGKFHBa/cz/ksafiYDfqq3sPM63S0iCtZi1m1i2tLHq+QDNO14sUBGpxi8rls+n0Y4jvIfzVlVtgEP6rnhvoPEeQKOR7Ixbri1EamPfFwNXZll8noBgRulxhoSNvBY8d4CLB8V67REMAc4vfJa8ykZP5QmjwR/qTvL9VGTgsAdlUWOto/9BiYzoVs3Lz+l9JVpa8tfbX/vOt/CuAHAXyyX/8kgB/q338QwM+q6nNV/R0AXwDwIRF5N4B3qeqvqqoC+Bkqc5mGbjnZnVN7MYa0Ywy0H8rkLvt5IJZKD5mwB8BW1NEEDV54uUXaDpbj8nn13c5HKYToeZvltEe3P9SkqT1l+gxVrOKMjN47XR4WM+tPi5GMRkIMslJeo1Pb+3E30RHK4TgO4Aeu+T3RGBdWLOVQRMdhdAvhZ48rLKqtvBr71MGv4Fr3PrtHQ/zKnpd5TSxzUhFp7bxdvdfU6cxojybzUUhwWtDsqM7XFmhNSUl2dcXH3g+WbfYIvBzJdN4oAe2HBFJ7MbaGIZPnzrTSL1dBKYqt2MFrlR5E7HVUWtBkw8ILyXlRGvA1KAjCy47CMSnJm2K5NhJ8LrJBsnlD88h4y3PYx4Z0yUouQ0y//5UdOH19oH32JsIRzCR/YadU6ptdu7RzB7gN6UNENhH5dQBfBvBLqvprAL5TVb8EAP3zO3r29wD4XSr+xX7tPf17vr5q72Mi8lkR+ez+ta/NVhrxd7bQk/JYoUtKenD/aOH0MObH94/aWyCMVcMvCzwzDRdTRhFHyNuqvIKigbHv/giZH9V9mFhZ4wDVXEFmQDTEN6c8Rt9IHRfyX0OUh/Ke7y9v3pjvIM8k85aPw52X2tVUae6oGewL3uGKbp5Th/P7cD5hlqNLZVY8WSDtm2X5KL/O4ONimW9YKcR0k9JX1V1V3w/gTTTU/r0Xsh+x8Qb2ens/paofVNUPbu94Y164InSqZVRc9mHl+XFqjlW3BRREJGao19ATxdUCErDeZcHJKIAmgO+fTXVwnzg2lxdlBAPZcX2CTlvnge2PnjwWbRmnh3OonfDKSfMCCEHWWlypn89b24/vno7g3O/Xs4w2tL8Uxq4RykJtnkMptYWOGAXtMj47fWHXgiElU0LWT4v9Gq95ooLKC71sp8brMJ72uuyFGnbdx1RGPpZsi9Ua73zHBUYelxkaL5ZV3kIZdiylQ7ZyaGW1mSEsOvMM1PFyGo5Ra4qXZ9rDepDx1HaeWJzfjuGQ9jBW7V6iakP8pWhD+vRUsd6NgeJYuI+tjLaMfjbEK/Dlc5Hi7xMAIN6FHUiIfXR5Q5q3NpYki2FNbNGO97kkXlrWvHbVafaTVbMNzWOiiJ7aIr3U7h1V/RcAfgUtFv/7PWSD/vnlnu2LAN5Lxd4E8Hv9+puL6y+dgtuO+dMVDE8QYFas9D0riiBIB+jnCH0u819ABUfxyKxoJmRCdeuVNrzuVD+HMVa0igKqjORl1GX19GtaJcW5ZdkegPU5LCphbN3oZroOkNEhQj5AjiE//fm9IwSW6r/F27B8zMbc/rSWkuViRcuqz4mHQrx0eo5kd1HnShl66JHo53p4i0YRRbVy0nfxkOYJ73q+xMtL9zJv8lxfyNUt9WbwEBQy9XvpfRzRx/xa6KKZCMTwcv6k77d4d7fs3vl2EfnW/v0ZgD8L4B8C+DSAH+nZfgTAz/XvnwbwURF5IiLfhbZg+5keAvqKiHy479r5YSpzNck+FDkAlLO0d09afFLmuD0fb3tpW5e3QRMrxGw5xpripkwToy1W0oxQPZ8hul7//qQjnzuFPttR7zQikfv29J4pjtqPZa73bU2j3vd7fa1DpcU59yfwnRaiGGsfJkRCnxbf1XFNpSn1U6lRbisaqgOw7wXnWlo8t18zt7WNjzTvwCbcLv4SFdV2n9crLHaew2QBDSPuMpmUosVbbXxsXM0LYmPHKE/6Xvoz5QflNSRqCDBNNj8axIkefOW91oFmiqsHrzMpCm47vLhjJbMst8QHix3nOeDeneXtddmJnuHgPVtbIPL8x6Zt77kCpdQGhFWwGcoXRTkp5H4HSt+GvLCaqxBpOBSwRppXRnoy6jo+ww41jpUnAx2e3alrvrvnnsCJj7u1TXXbmATPThCMirUTnkMg/ZPBq5e9EtPPr2NYpXcD+GTfgVMAfEpVf15EfhXAp0TkLwL4JwD+PACo6udF5FMAfhPAGcCPqaqJzI8C+GkAzwD8Yv+7LdGA+UCZ8JN7yg8z8OAGK8mDs0AzlgzlOgmGYNI1q2tpZEnhh8FmhKBoD7X0R7PlvkLfLlCRRp5gHMP6QpqS2LSdtghpEw0CO2ffwwv+IJV1kpQ996krwoCAknEs0Bi314Hqa+1KnNxMQeedClDTMwYmwMlr8LJ1MR42bgnRCt/nrkqLItSsPFeDlFCXu9g86SwL1T8tzvbvk0IkmVs+U5ISK12vhxUX4jhNNIBop5DCFDtWzHu7eZ51WjxslpRa7qIzvcN8KeoKqMh4EU8pFTiVScldTSwDizEVYD2+/abUXoXxgXnCeoHK8OKy1AaolovySU4meq3dJCshXy7b9UYlw2D1XFsTOmIDcIPSV9XfAPB9i+v/HMCfOSjzcQAfX1z/LIBL6wFzEozDi3JPXMO1G3lvLDOmZgvNTOzxW5uszrSUx41LFwabVGFypdiy12F9sPp554wAKgrZWyVyqqj3CryFYbU7etJNZ6n0UQAAIABJREFUoSLNIzh1y9fXNcIugh3+SkPdZDZ2vX9lpWB7Hu+vAmct2BxCDj60mL/gtO2NxqIej5fazkgKyoTaz0cr+0eJ+dxDwDCYvivKvIK0q4bRlk1ufhEPK5ugfJKyCx5Cp6meCFz0dnj/eAinsKFStP3z57ZjIzzVaTTXWMYNiFK/DYXSugTnVeov824yEAZIeAyYfwxokrFdfl/ET/nYjm2r2PeCWtFlGd3LY61NbTPqNVm0ZtP5+YEeHXPB/nitJ79e0Z9lWGlKk4fO60lx25oMXZvWZcy49rEwQxKOq2DDqoGFgfYgpwYw+liX/jT+tXDjS8X0/6iSK/DEiIHeWy89bEJKJqPaVjhe822QBbMAEQ08IdzWSKIl1c3uNienFaOcDWixhS7u86kCpb9D117AsKk/KOVHtd7171tX+r2d5XqGhVPyJM6THPAFOQE6miMtrYK7UofylaFkfIEv1W/HOkSm9M8slUR7eNhKh2ysXGUPVxgfLcTBxh+j7qjARjusyI8QoW/vBcbCHoguu9UXMDnkkxUc0589UfdANNbrdHAZUkYsn0debfie5Zj+prACte9fBT3EM47Qbg/w0RPcQtt6k9IM892+Zzmg79N6SIn8DX2ghVv3itM8zvHzEMaVBV8x8mua18AwwIzgZWHUWA+w5xv4QG35tTL6wHSv0muh9JEZbAO9RcYw4rZrIVZ6YcEytLVCRBm1dTpCjJSUUU7h5QxAVDKM1Eh62bBYI2q7HwId3ejZSYgd8kkVYJehgHny0ETjsJPxLRjLOpgqHTbyW65Kf4lKeMy+35uMrrbOV5XlNk/pvycllP+sTn4qkZQSy8rg4bg2jQUljnGHHRckQ6B7WWGHOKzx3bLet3WW1VParnjS7o0wVrSTyTxOpm+SK97x0ml29J/XoLhNpkf72hAZHvceeI4ofGeObLUdvZCY67+JniyHAREDwfuZlCwGv/K8ZuNlxt8BHmLewAfuJ8+FVO8q5BV2QCUDspInRvg55HsU4lyuHdo9MmpH6ZVX+ookzMbcMlATgIZYd1J8vXA4epmZkZS4D0whpmfrXlK5nE/SQNEk9gOVFhx33WeHjVnHe0zUlDYUgCF4K8jKjA7Yci8jLTAtBZmUflCw/b49nBUIpjr8zVm+MEd15wmDYRz2/N4AUP7VNUJIg2exTAipUfs5Bh4QeKaZZCXICxC9mXSgGSuLKfzQZXd/qtifxZMoAVLCWelTv8yNZ5om+jCU0tL7IcMe1m8yL+xaV5LnZwQ00lx0r7pKP2oB2E7Vd/DwaaqG8n3HF8/XFKILmzCcEaM/q3CIK3qBhzqU+B9AABsbEkUn12Sc6pmMYzJY7vEl4MT6pV0YdWT5WYHBCWBSX73tXkfNmwlSeuWVfkArIMvW/Uq33gsF44kE+igMk8NHy3qwyINjq75CelM9rky0n5eiqH23i/W1CZ74HvYcRrDr/sCL3+8TK6G90C9hnqY+pf4GxZ8kp2qnrUqU3YXgOwvYH838WI1FHiM2ZjZ5iO4QCrI6F2MRZCKhQy+64g39vrog6bSm46d54nK+C20t685yZ3Wv+pzHhPubeWYphdVCVaR0hEJ/dvxCEfXdWtJDPO4V5vHpn67QyfgGFM6GGpgVtgGEg7EN7a3mPuchMZ1QeFLAXG9eF1omNrxc50omJH1f9TP3bZFefaWP8WCCW9GOstqkVl+I4e11AIYwUpx/ddBZ2NZHFnOKLVK5LHzuNqZ7eQtW9gac1pOiPmlx8fp8aw81FRKcvZ2/3VzdPsodKcuDtLAQCYu3XeC7eIKbychFBh05BCTWHjBc84rolRiLtL8FjBa8mtDPWmTrD2b5q/RCPbMS5QW5lUsue3+cnVEgjysw0O2BgjUCJm+NFoMnr8CIprUFRpRhMnalWk8at1CyR8nKjRUVj0uinZEfI3sO4+Sth85jxQiRWV5enzC5KIuQFMlzMxiaXpAz5GbvAMQeyhN7C56971cRwVuvu57gh8cFgNLzs1Ln+VV2hAellPhrtHOfhWSW8+e+8iFtgR5gHKbn847q5XFLYzf4F69nTyT0w8acxlKyrByk10LpQ+YXQS8RjPSbKzSIpBAIndxIwrK9jJKPkFhGJoeGpD+4oiX22RTL5GMmdNvq0UPvItRJ15d052zaT8XMhusSE5Ue2NJxbUXbuvz1+5N3tVCInmRxLdfNPJeDPBeuLYGC1XtkDBAV2MV0w1it0sp7W/Y1tRVk96hfnn/8sEXbrcf2q7b1JZ3iJCmlUFM2wlNTl8UvXbgxL9e9ULiuQxb6JXhdnFY6Z0Gf5j4uDNEt8/UovfpKX+APvDiaNzTTlbwhGdtKxxY/xNrSoPBj9i7caZLnrX1ZaNj15J0IOTTgRyUDMRRh7Z5L+35fsT09A/e1P+jSy5/pQTRT/kUhZ0Hxow6k3dvgi72+c8YWjE6YhHHyUpIhs7BOVUG1x+y9c+3hrOruhY1L5J9PFAC6i+/bViUeW3EaJzfUHbU5r3nsrHyXC394KHtZoHxEkysX9nZ6mTy2juRobYQ3FdjvQu8KZuQqe/fMaIeGH35mfVwpMd7dwTRL5JcdMRyQLdHtnq6JBR1LzrrY0LLPlYf20g9frzAxMBnr9NhRHOiI/v50xpPTGXdbe1Pavhfse2lbNu1BChBNinBYoc9Jom8FXHhu2hiEbZlmbPN8xsLYWjt0UKPfN0+I5MV3/9grF5PH715KiVtugwEri++6vr4KJ/LY8fbyVXr1lX5Pk1vT5SyulqT8C2s9pWuo6tL9A2R05GlMxTmfC4r6ZAp1KLzzQsyQKr7YxnkuIZCL6Qj1guL2lNduh3h/rkvn/NOxDkftJxqmW7mNPtaHSCiPT27jEpK8Jiuk3A/pXZ3NYnUvlI/Ve1Nsn+ngz1vSKi+h/CMlcsQj6UZh61s2eRePHo0BSMkvANacOZU7ypaB2gJdH66VpHbs91F7F9u6RX4u1LXMlw3XBd5aevWVvoLi2zo+7bbFRhk1E+JqcUHFft/K8IszAmogK8mIb3+CuOuGOFZPqV2Qlcf49Pg2ITTLa/nL26W9+3RrL5AW2tVgh2NB+v782ngiu6C83eLo8iAozwlOk6Hgtx6xQPj2PT7+OSGrcRF+cBZEHdQrMCa0oNMGfzbAvS5+DSLgL1HZd2M2xqsEqVkfI5ZUgR8nYb9th5R9n54/wFC4htB9PEjJePy7pmt81K31FQktUn8rx6Fp++bpqwWnr0lAfzxxeU3AZHOSKWrLHiq0/jltWdnIkCMOcwKD59kL4HbLi8irgKxJvrbTcFW2Uv2vSFP+T+7OHRajycjRsdS8NlIH6mflZl6MeWJhwRdDdsKDdCRLVq/VF9Y7mN/EM1+3wchv9To96cFLbxvDiwlel7XDNADjBSvssdTUV+MTrx1cArl4HZQ+EsMXVjeHG+YKMDF/lVaoxc6tWYaheYvZrSkjZZo4PnmCz464L98mGys2NeXdCc1Ij/g3ockbkmT+HqAwX7hbKKgcNstHOjiBiT/+NTsESamtYuQTfazoD+4vr13h1XL8D3bB2EQGDmQxIzfgkN7lWOa8+fcN477yKq55FjaM9iJ0P3KhK3vp309bsur5K42PJpmZaMxzAge/r9CeZXPKstI5SDy5xFfuz8o7WQEH0PdL9B+0e8uSyaubXCmiKd++86He9bFNcVhWSjl2HhS/oSjLA7i1DhaazvPJMT+un8vYEQ1MV0aE7oXQXm/ZgfpQsL8oy1GTvSGk8rxge7ugPBdH1SoNjVks2fqrd3Xq66RQCqYHOni3hE1gf4LWtoIaL3Rs02Rka30qLwYSFB3lzx3tTw+DyRgjN/gWNz9HRBaerFRCYrLg+UI5uidi6J77YONWKc9iu+WkYC4o9SWyldEX5kGog/tC8jvVI7GelfHwflT4oXXhaHHqf15TWfXBl3JsrakCT+7POG3twLW9Fjw5nfGOJy9wv+043e3hvco8lgDG2gHtwsuyOfHF0LEdlMcInXd+AeMsHaV2SJak7/xxHaKjDfOMfa2Q5M4PhWQ+J8W+MiCTcettKh+Sw2sRSWtX2m3lT2G/1krfkk1OZl5CYRMiYRScJ/3KukscTABj0fgWEg8mau6HcF/sz8D8Lu0p2oxyqb++Ha2/WNkVJHsLlpKSCgqQAfYlYbSqbnFpiIfeJseDGckZss9jJpjbN8We3W+M/P59dT1NKEZQywW91Hdd0OQylWXrlnQNvQOTIcz0rMbtcMJr7F8ALVQuo89VqOdwgZyIOG17OGajiOJ+23G37dOTunnhM+xIsyqzAs1811Q2gby80JvIDX2e0DXJTqibrxvvJNY/8TOlLE/c5hI0JJ3ndWd9+ForfWlP3garfwL45R/1NLgVUIANgu1xlyGwfgQzCOWcRx05dhjcTyTlSbQys1lR5IdLrB3Ph6bIy1dOkK9vkK+fWv1+tALCLpvyQPHIrLRCvHiUt79VLD8jkUPl4bxt6F4UqHvBi31DPSdxMv4TMjf+ne1oZd69IQp7YbZ7PhynByICN2OZEJZXx+joNPjCO6msfo7Vrl6YHeKlqX/WFitGViRhd9IlhWkTlz0fQXhS1RSY79Lhe3k2J9nMCN2O4Lb+BVmuiSdkNPyFHgpfX7N6LGx3f9rxZNuhGGN96kbg/nRuldmLVDgUZnOGD0ik9jOfnQ5CxcF742xbzOPPJCRe+fzgOUPKPCN/G2N/nsg8qWwYMOp0r4JAzoqOfJCfA1njy8JAXnsg7LVQ+uGMclKg9mCHDxwzkATZFEhQ3sRce7SdwzwhmUJhRU9KYIV2JqVJv8OCUkKj29uC8rygvC0uHMGFNeHZZVbaTp9CRcee/0yjKcuMaLgeFj7SBlPcvrbs5700LyWjHEdv4zx9AHiwBdwamR22slYM+vPYLJTvIULDAgxYHWmBOIQEqW77HpQ5TeQpbJbG1fMlxQEkZYu5HuW+Wb2Ejlc0rXgQPC5TisaXrDiYR+netJZliroMI76J4m5rArprf4MW2kYAj+uXWD548jznDvrDtDo4IKN7cR3I8rBeYdk6kmOaZ9mDXYXn+H4YezYKLCcM4IgvwcvA+JyiG/jXQelnBQRTmtoNgo7fSArCyrBwWVZFiFuypQYwYmhKOzF6HTwg/LRjEIaVwrf7GVWZoG/A+R2K+qw2pV4lDLLVy09MDgTcT9k8mVKWlqe/oETZwBDS4Pa9Dyxkq8SuuQJqISV71SFPCIWfi+RKU4Aiw4AMo9pobmiu7XDJRxiH5oV4QmMbxscMXI6JLhSye2R8TpIbraTcaQw5JpwfLOLk6wPpmk/SmnhnY1sQFZQpDWDaAjo9M4LBAx5z3iUyoc3ehtPC8ks8bms34vkBQM8FqE3RWzrvGx72DS/qhnMt/nRuqwzBm+X2J6Ns7SfjBQyv18eb51kyhsErZB4BQbbYI/axY8XNxkFiPWGe5fWExbzK4cMcIvVUUtvZ+Fj/stGJVbz6aY6VDwXvi5B2b+Hm2zEELvQ20Obu94GwtwRBhxC1BZV2Lk4O3YRtU0Sab+MiRQIgosE8wJ0efeMMfccetx36RG1f6knD5HXjVzQaLkE8ryehmdWCZ0Cvxiddn7vjMmmT2N6da4KoyQh439XPZfHxMV4bzR3l88N2GcmbcsvKOfPdQkBgGcEYI5445jlOvAn9SnSUaDQOJ9wiTOheV6WHoSr1jdEejZO7/enF2uzNTLHyNAeyYciKT5iXGRhQv/2aAvrQGtj5vco9/LfX9oa1876FHWHjAMHYNs+tHO+e0K1tYOAxXwACo9v4sUqa+dRlJ/SVFoC9zmwcK0aUwuQsyRoyb9NYG92Sxjf0KXnBU5gvpddC6QsQhFfszBmKcY+MmBnZf+QQQVAibKEXCtmrWnkNiPlDOOUIZdA1c/d0U8hJUe527E/G07iBlo5EG+0S6Gtxa4U/mQu0MM9BX0K/LvQ5TFDq3JgE/c1ZuVw2IvQOXTciRWO7nV7XXdmIk1Lz+xjXDJlFQmI/Q5iG+nGoDMho+n2JtC0nMDBPyIXyDLQlY56VxFS/KZkkj36vjPtZLr3PCzrYGw3hjZw0Fe9hRT5DH2ixfkP/inY8A5LBXsbqebzzOHLWo/WMRX3u+QJBPi3PNF/z2HHdq7AhLvw+4KWDBgIiXsyus4wxqGH+IcnAIr0WSh+AM8smtbvaHNssiUEYZXgrZniDEoUdpjAIqM5C95IgWTvh026zkpfxQJdNJHMbdVPUJ4rtfsfdkzPqO8/Qe4t9ao8Bqr8m0UMmNGmbodDeLwmTnelcxvI5JcHLZ6LD2xiX+PjcGJLq132smkZvRzDIdAZ/eDVhaYYsoD9DX90Lml5Rh75tb4WeNH2CkJKhMgGUDFFA0baIbHKQ14hym4hAI4QaUl6TPfYsmtczZNQX6FkB8PtTjTdkxCZlqKTQZXGfaPdkIR7uKytLzt9f7GNn7Tw5nf3AtYe9MbDW0l6ZWAV2/n7mFYeWIiggOnp/nDckd96V/F1SfuMX94O+K6/35TUItOiAzfE81stYe+Ip6xQPDx7oFH4pjgNAHsMVAF6kq0pfRN4rIv+HiPyWiHxeRP5Kv/7XReSfisiv978foDI/LiJfEJHfFpHvp+sfEJHP9Xuf6C9Iv5765FZgGnCpiyou1XqkpEmYspKflGRWCAsFGib4CtHl/Ib0S41vzjpAN/VeUe/an9OUDNbSo8EQyCUi0CScKdnRuJwf0pEdvYB96nBWGrnexYS9mFjR5Vsp3LYqF+5N4ymTFxgMZ04HaJRd9UteFhAV+WpWHvaD+MBeZ1hTSfdXdR+iw2ww6XqP3vnvgJB7h+2l6HatatuO7Nt1j5R46mcwLgvjOoVIUsr3A2pnXl4waMGby0b+UrJ6Mq3X5Clfy/Mny2du8yDdgvTPAP4LVf13AHwYwI+JyHf3e39LVd/f/34BAPq9jwL4HgAfAfAT/aXqAPCTAD4G4H397yM3tA8oUJ90NBAeXW4PJ3l/00C2+GyLdUM0KLuVq+vou/SDlu40LCaGkEIXzkIx1aAsCwICCO4kDZJP1HsFnlRsm+LutGO7r02JUrsQQPb29qyHdyrO71Sc3xjGod5XQgV9B0/nWWiX0CSIJx635pfLe3cacmsvySBDo4AUxf1p91CNL0J32tnTEh2dthM73QMAhoeSFjP7cAfXPN+zz3o/FJ8h5rD1k+55P6TVZw8G2fg2GRp9dWW3CIuslBhvCZRM7wJIINPKskVeLctwoM+qsYeUjOcpzJPR6BRCIkPgHg4dNGZzKij7LgvQtjtLe9iv7dlvaH+v7aTW2kO04VWa1GfeTWfe2MpzCeOzmv92rax5bt4U8zTzkD11X+vpYVZ+RzHz0ceRdIZ7kunBN6eZ+ZDaXIEOn9dAAKgXAQpuUPqq+iVV/b/6968A+C0A77lQ5AcB/KyqPlfV3wHwBQAfEpF3A3iXqv6qqiqAnwHwQ9fadzpWCMiUh2dKjKSyRzHXFQKb4vEatxuOwlwIawt9QE++r5tCulu8lbT/T2XE5k2xbNoXmOMzDC27RlryBMhtr1BEylsD5En1SH9ad9GvKa2um8K/hDhxYKQX5a6h6oPmB315/F+m/OreSuYO0N6Un400I9Ib6HrZdDR3XjppU+znvphrcf3lEdxHxi/TdA0dH9B6qFitriNU/RJtrGV6XXYpa7eMJcnitTH5A43pi8gfB/B9AH6tX/rLIvIbIvJ3ROTb+rX3APhdKvbFfu09/Xu+vmrnYyLyWRH5bP3q15r1OmnbtXIazHOruxEKYZRiebqVrSdtMThCdvldphkB5geLVosqvs3P0BQh/DAAGuu17xCgfMsDyv0+9jGrtJin9YVHalPoXYXeV+iTiv2NOuoCxqF0goa4zWDSFrTgtVh/lHgJEjJ3yzs6o5CaVMG2tUO1+Iyg/BBNVFhNEaiihYvMk8oxSes775gxZMueU548FGs3JGjPagS0npCheWRhbAidC7ePUX82lO41gVAaxu+pnNI4WFV0BK9uDTXKTp4lt29lsqeZUOJqt4k9v+L3CGVmA8TxYzXZYiRb0GRjF7z94g5vPZzwYt98v357ixbRSO90DiFVasuPm+YXkjCPmA+Cgc6FZN14TIrTULLzj+a9x86TNzc9GEZz3I8/4bFYjA2HDa0+X8/pYxxAGMvfYoMC7ww0eb0UngVeQumLyLcA+F8A/FVV/ZdooZo/AeD9AL4E4G8OUqZ0ZFOXNklVf0pVP6iqHyzf8kYUwKVl1vV1q48nJQ8EK6IVVUmhLC2oo+vU3iWEke+jvWfWdjxMxx3wRDXCBb4I5vucw4ygdlnBXUHTmvJysngsd9bCNQLMsf5bUPLkehH93Eai5xtDoKnOKyjvUhz81nama0cyfKm6C57BpTLX4vS38HDKk8d4dU+BWgW1I32W57ApIMsr0ebfD0DTUTrs87Ux4csvgf6PumP1h9DXJT3D176R/uH2OXGT0heROzSF/z+r6v8KAKr6+6q6q2oF8LcBfKhn/yKA91LxNwH8Xr/+5uL6DQTET7OOhszqXdcxG8aefLP8u0TkhlGHT4yFkFn9oa1Eh6OohNisTkdR2/hu5ZgGCNohVFsdqDn3/a5if6pjT3Pf0SOn2l64AowOkRH0h9c4deTEj9P7nmJCD4dmmuKrFpO17XhyHvUFBbrF364IaBtneOqaFJPH641mQkahLUNndZTjEBDvmQ4vvOhtG/IzmQIQELCvwxCamsIHhkY7IvRzktJktT35IaZOv8NOngMAMdZIBk+cfyWOaaCbQUyWbTPW5ElJbUjWjnG2h+eYHn8RTGlHadT+Yp29Cp6fm6BtRfH0dMZmT+RuOryFAfq9XZcBRrMYfXM+8C4y8tyZR1ke3RPIskf33bNJ3lkeS3+mQA7y9e/lIf7O7x0WGpMpykBtBc+cPV7gD+bAtb7D5n8A8Fuq+t/S9XdTtj8H4B/0758G8FEReSIi34W2YPsZVf0SgK+IyId7nT8M4Oeutc9pmgTazyW3sILM8e08mfxeVvahoV7GXlByI0rI6JFDDHwvozAV9EOoMG+PtPpKV/i8iCrtnaSykd+bB5x31Bwhs94fXiTLhqJkgpN3pd0LWHpPSj8XZdp1DfdXDznlLbNHHskUUqOxmsocIScKey1l51JZTmkXTaAr/060Tildm1DfSo5X125IkxHIYY0DMZ3GTOkViUB4oYpkJL+iM41ZkMvVWFqdWQaO8q94lMHdEQ8PjPGUj9viSwwkLsgUG4Kr8nFB2Vs6Xc+CPwXgLwD4nIj8er/2XwH4T0Tk/Z2MfwzgLwGAqn5eRD4F4DfRdv78mKqaLf5RAD8N4BmAX+x/l5MgvrjZOgeMB3uqdEQ7ttqxghArlydgZyQfahSUTgWE4oyrpzG9DQwUZHvxOV7OKMBRjNLj4wqUUptbzFah07TdV+zP9hYHPRfA0JJGOkaZNENYcHgSLXYgOC/6ZymK0iu3yasboB5/7DQHKU71EX+gZETsNZGG+HzCtR8BobEhT0iKeSW8pixo8W3eOWNo/2DC+2s5mWZCV5Ph0dF1QTzqdnQew9tIAMDy8NPF2cAFNA5MNIRbRC8fRqelx56751G3MS5TfiQZ5rq530l+yqbY7yu2U23dFMXbDyfUWvD0/qGt4aAbA/PyThpBSZLpVXw8eOpo88jO0PKyiSf2xi5+Zgc0ftnAhLUZAUol/ZNlRwe/9ATIGWM9sSSdYDTkXXW9bgeqaa0FQPSYWYaE6sXldFXpq+rfnVkIAPiFC2U+DuDji+ufBfC919qcyvk/BKVliy2T66bwhaxy7opKdbmnfwrP2ASySWhKLruUPOGYtmRYWBkExdHzcJ2lKGrf0uZ19AEvpaLeixs+EUA57wqtpElkdsAQlmhbK/aJb3kzjQHl94oSqvB91/Z7paCIP/ZwFvbYVw+PEd3eFitPqt54bK9qBU/ozJYECGzCBfEpOraOMh2cVte4v6v+03gG748Vew4lmHJbGOZsPLw+fg8rjf3EP5v9ee7wT5ZdU7b8QJwoIOJtiShwqu3hq57soSzbxdPewIbhPWyx0TC+Rm+mMctpRXs3tAweBsWso64gtzT2R4u3ziO9rFAly52FQKXTZvdJ6Wfj4eEzbUZqFRVYfR9EYBrDnF5q984fVRIg9FAFLXZPg7vaFeGWt4dEliEOSoxEg5AAfqKnEWSD6XFR2hFjA+9P60nbO+4v7F4MoinvbatpsavVu50qCk8OUcip9n3RVOlK2ckQXiFeOU+sTzyZrd89r73yTvLicWlPWJ73Ms7esclj9Fh9rvibsRLR8RAaEWTeQ/aS8u4edo09rppjsNSX6TF/q4PbwIJ2IBhnZV4Zv81Y5bAUtbH0KkwJSBwXbiuADVqvYMTHqDRc7+PhCDPvaS+jjVUsmOPobGDs1aXt3mCqiOL0pJ+Z36+dzxvO5w27tqdydxXs560/kYs4IYQMf6fXDQrNx2B8Umx8MnQ9j8m+PwvA46uIcx/0med9iXWzDGlpr1g9P23ofr9HROe9nJdPY2lP/vpaIO/wWcgPfwY2XtjB8+or/YWitgW9oMyZAYnBF3uZLCMrR1+QQ5wsHMVghcLoH9Jp7C5nvUdAIXmRx9Ldti9375xOO72LtinKsjUlrAkt5P5r0RAemJQLCSw34WhTFKfSjZEA5VT9uAcVoO5tT3bY9iaxbu+KALD8AHCqno/LZV46jxcL6k4vIyvrJ8sPT+osL0wjb0tkRMkGJ/FQE/2DsFGn81Mjf3O7k+I1WTR55LbYoFn/6L4biY14ZO2SIlkZjBBlZMVaWBlLkj3F/ZMHf00iAJwfNpzP7aC1XfuunocGEsLDWcaHbf7uStnEpWo8FgNxHk11cn8Z8dNcmdb/dC7Lc1/z2PV7+1PF/lQb0LvXadtuRvbjB7VjxiUt5rocrPqqB99TuiWm/0fXJZkiAAAgAElEQVSfbJKkQeGFVunvZdCjiZFQuKMeasPzI33PyCqllbDtT4DzGzq80AKUO/Q4nrRF2zrIMuRr+5jDy0VYwVDoRysgVkA6ISywyaAxP5xm4lVGB462iClSdBylbO3Wvi3vQKEc8ajkCZ8MoCmdjOgnQ5KVJSnXgKoyH9JvAaKcYEy8CUwg/naPj7uTlSrvyOn3a8HY6prGzkJxzFNGdRltejX8nZRRNpYOcNLnxBcdO73KA+gQOUZL2gFCe4EK0Lb41n289ebFeUNVYN/XFjOE9ox/nI2K1ZMMvUB9msYIcRzCvYVchPuJZxwpYNkMNBRgf6Z0NlY/ofettumk0K6xDLQYgPg0Ty95CXqIAUzWTwe6CngdlL4NbGcUC0I7jKqhhfJAislio+Y6Kjyeb3E7fqlKUEgcD2RlYgNEh72Z+8wut03E8xuKh29tRxPYi0LkQVCet4zlAf6SbFfEAE7b3vbDW7AdCGiIFa5W8fCOlLaQ7bSrUJCbksTJbQtgomix4F6ewwq1ii/kFlHsIkHIdJfxFLGgexakeYymlLZS3fg5nxEnVyBd+7ilQ9ZsHFnBWWzWlHbdhnIVTYv3OdFE4snsyMuUMv1WK9fBhPeBvI0QGsj0k6cYwjAY/eIHgPKCKivyehpy6ie1lsgnR5BdpuuJjJs3MthR7wYNgVYySloAEcU7nrzA15/fA0A/fVVRa8GLh1Pbxrk30NN2e8mIXRcEGcwL9ryoWe8pn90zfiAqeqFxYA+y7EnMVoqyYAJGrqztuO69097pqO88t7m3N+bUF00ZyRnAW+JbYJVkzA0/j1Ufy6LUd4x28gNm7PldSq++0pch9A5h1BQAwSYT5t5pR/2wSapQCJZHvLFQSRSM4NrzBDuypDwAp9qFTXszBRXAfu5n+qwWlvsItoXOg/rrsDC+IyHQGPli9GTEM6EGmsCu8ICZZ4xEFUDRdqiW7zaQKW9GkRzCCu6vUsPWfhJ274+5vVb+wFiMig6uL9rTQrvBmE8S8031J36HeyRPvDEgG7mAyqlNR/D5fmpjUgS9jQlRaswz8YfzpxBFbt8MH8svuvdqyTcpsHxWOChaeTre78Sz0Od86QbFF0BLGqvAJ0srmTRjlXgNQTijSgHsTwSlNEtXHsYrQXNIZhVRCJ4s84hpy0DiSNbxGil93dqOCkfFe0PKFjc3VOMIvhLq6bFDXgQKTZjwA75Vk9G9oRtPaVLZRASGAtNNIfcV5dROrRFR7HuB7oLz6QR50Uby9PVWsNoLUmzS9Jh5U5jtgCohn161xfS1ytiCY0fVdgTnXoYpg7ztD0OgGEUbL82Nb08LV5xKjUcoS3+n7aY4bTtka7H+8kLm0AaaoGt/uMr5bWi097ecZToAK7wVaXMWTMbZkU9GRVSXh2FMieQjKXqqdxiP9Bs/k9HkGCwbN2+aZCIgYml93mpy9207JyE9Nspa4E6cv++3xvtA61O9GzQxrUyTly2IgCAZtlqaERSJ729wJdTnjGgL3VRS9kW070pr90pp8wD9wDY5F/de3QjakeFsIM8Ix2wv4+rGl33wOitE9sb9QS4aF4D6cj/KWvTAPXsKu/A23DZmitJ3MJ1OFecnBee7O5zf3iAvBNvbBaKC7fngu4GY/5+7twm57tnqxH6rau9znvf/vx9qtBtzr0EJDqIODDYiZCJk0BICmkEHM4gOhBvEkA5kkDaTzkToSTrQAQVDN62QtBES0EFLCJLQBEzLJTQxKk0Em+4bL95oX+/9f7zvc87etTKotVb9qnbt87xem3jfFDzPOWd/1MeqqrV+a9WqVa6ZuTUhLBYMOolW/h6ba2d7XDh9/S/kAr2Em90DdRgP9HhGT1t6JhFjULmJ5GRSdIOlYwjth0hdcE1JkdYCLBY7x49hlIYIxrZFPSYVbeFptb3MSEEeNXBS57H4Gc07pm8fdGhG5+XhXj4T9JLEQjckxRhp8cV0MhZ4An0taYauPd+xjNlzo928My/EQyeFDzQ6RavyYFJ3/X6s36F8njsn5TXmORl/w5zz33upETZ9MdetgVGsIJwCHrZjcn3avyOdB43hgJxfGPNjfx3ync13zre0SqakWJZi875ALRx6Wd5irI51waQufm2mqZykd4Lpa1KkraEM94ip7lGVaXTeFCSx0y7V22fFgTmOC2FdorICCS51oBZCHN1zjjgLkG+A3hPKliJmTV52XK935PfvwKs9VvjVFvO2LWPbU3eqFFJlnHX3am+e0l2gezqsPfDBFAd06qhiMmi6SUyL5mxm0gJbb2g0z7k0s4OhdhXg/gmtZ/7m+sdmnDXvuCw7lsuOcvH29gitCd624Sk2VhHC9c+wsdJ7gaId1aPl72UEExWiBRoDGH21eb2gY/qpIcARacZa0BjG190Hc//O6LPP43t/pf24pfw6V2Jnkn7UJ5eLPqTFyNDCE8oC8IW2y3SyCqRNal65MvmPny+47xkpFazLjmXZkZc9wi+I1IiyHinWy3UmGK63XiXSxEKTA7r5G2NB23V2N2WzZTxL9yLPyQIrayDBqEe6WRtkB3Cr3klaEkQUT5c7Xr33jPX9O+S9ekBSuWqj8bC2KDb/mJl3HjwWeG9qBvI6PGD87wTTj+TowEILKH0fEdcBgdFpSN31M0QWZba8Tm2ak+uyA9gEauprMRfFlBQpFYihYD/QHMA89CzlH9V7JMoDQb0len6AEmbFdHV0phaITrsJVlENupAG/l6Chgb0aBQGkxrbcIaAuovzPKcCnhnIyEhm778AFh4mL++sTsy8Jv1xcLmdamQnec/qeoJcu2svoMcKAmql3Xafk7b9HQIKv3Acm9P59FKZbzMGOD/+PWuzHO+P5iR+9nS63qUCPtqPkkSRcmnCzgWbDHlxuQ/69UVt9sH9r3+bPkCcFNifTOwmQNeKKt1TxG3Gcldokjq3aIFRV1R7nyNAz95tdZs9w5My0Ctd50HjiMztvpbya8H6zxdoBvb3MsqiwCfvtvmq+rrvTwXbVu3fGYhdjOG9Y+abivTRu3Hu0qH5WigJwIRqOnF/etrkwiRNbP+09oxCrJSEW1nCg4eTFEHKdXJLAnSpZ/RqAvTVDgiw7Uu18/u286xY8441V/R3W5oQT4Tg2cMkFs1cKdgQAdRGZuieEQdtYDK5oixFdQlOhIAFnWXDbcblamsZHNqXbeQyXCfkWdaaf3akRvZwfzf5Fn5DbaOWEkf47a1M90bqQvz6tBkWDEOLYMFAR/GFPdnGi5h3SqdFmMajom3MJGBZitnud1zyDkX1SNv2hG3LbSc20BZwT5Kjeu8H9rhjG3htwEAnmqcHsOeonscI+vdY622egHZvtmaEdu36xxn7NWH7ZMIbqVqtenkeoPC9gvymNo69p0pu6zxeV9ZuuL+CRsM4RT4+z+mdQPqdBPdIiU78MwnsPJMmueIENc7KGfL8074jBchvBOkZSM8CuQvK1hC/ABYXX1u0SyDMOKPa6LFK2oOTCs0MgO5BMLaBJy96htoq6HWat9GLmCE3SD3kXdZqx+TJiaRYpC1wN732vGmtMvbo6LI4eeZY2ZP6j6g7HR/ttDzWXOjvFBWOdZDWJyPdoz2E9v0vxrO056L+j9Dx0MbOjNDVa/KOktAYynCzKo+tlNo5CUuqfZxpzeYQUHCWdKCh56/t/thWbtOjsXxo6wuaXPwe35+9YyndgOV1dc8u91QPk6F5L2JAjqICH3Zyn5TFZjx+5tQKMUnvBNIX8t5QUQik7czbpe8goNm93SaJJjHdXuaDiD0kYkKBrvl1Rz8u5V9Ud20jiwrwMZBugnK74M2nMuS6Y7lsyK+25passPjjBWUyGbVI89E3WFHj77S2dKO9AGoraMEUiFEEEjbk6O0WRjE+kQZmENynACUrFppxdVFWUFZgudYdc/f3E0oC8NpGdlIk49pLLoALBW4LEN5Xo71ZlPzsvQ0D7aOuPqG0/falHT8aURMhLUEVlBuNKzGNyMGGaX7qRxIOdQ50T4ySfcyn5zqT3GMXvbH9ooiNiJ0mMyJ5okGsJTCJqL6MjmNNwzSXZO2JQ4J8PDr6dU8bq8tl2eJTRAPl5lzC1CGpoGyZ2qXtg+coeuE+Zeho9XVNrEuT+c3vcXv4AJZDYqFHdeO1AGe+UgBswPphwm1Z8ZG9li87lrUelLTfE+6fqDvlXUgEyvc6kJAP7Sshwn57ULfRy0cm9n5O7wbSn6jN0Qmjeua3Zehkn+k8WbiMCdo73FccKDZFmlRv2YF8E+RnwfKRQF4n6L1msq470nVHfqo9rV7FiahWlc4rIBDjmfAJX/6+rt3i0IgaZGhPMH3tPjvkP65H+O9U4wWt64583aFLaVU3b58kbhZSXxpobUPLh68dnhkFMKHEA/L2MaNtErEw8PLGRUT1cUZo+yW7/mGzFU/iWTpDptwf3q7BxZHbepY3m3jGPu6Qv/8eGNxIl8oobTzQWc5LLma6K817BzXYmocPT+QRB6CbUwdbNmsZJLQ5RR/hSN+ubWe096kyGy84vscCc3rfXHHTczXz6usMfZNRdmmxtbJCrwXlWixUg07NRl3y8TduTpzUb8bjPL0TSL/zMgAa2geadCNvk+AgbiYRADocpuLS3exnJdvYcyTsaCcqgX5wEKE7m9vAZIGGpGQT5I/rAs+2FuRX1cafwquhTo5Qh0WrkzT0wNvF1EIx7wcdN7hMhB1AaG1EvqOMIA0gJW37B2wHcPAMu+9H4jldNbXgcSnv2Jdc10s2y9NQYJh3rE4jfdm0wXb26Au/PgoHRrv+SfbheJ4n2bhuM7knCjOZSdQPXAdHyiQUOkS4owcqTuehLPcUGdcrgIoM2Z7Nm9SQ0MViYvoJ14sE/sGckYZ3tWqtOh48A6dFT0s/UMfHTLjzqu35SFI1ykWhdghDt+5AdZVJvYDmvXJA797+gTGfgjOeo2Uo2+jofCI8bUo/53lnbLffAtXEWz6qD+5lxf60YVnruN9yhm62YfNeXxKVti4jvWBxNF8udS+MFnTjj+f4IxPPO4H0i6v/qRKFVZ9AGbbo123QQSMao7qOGQwIz6VkzaONki6sAE2Y+J2IcU7QJ0pdZMvPguVDgdrmrJQrGq4IqG7oiEPGvdMLiFvZ9aWY6xtDSq+stj/UdxkhB2oj5jAiJW6fiMaxd5I4f9SFVFGsppeqGGKR6qWURJFzjQgaJhmz6V/z1s5MZSHl9ZwNXMGBmfPk5DZMv6eWh8dfZ5PGWer6WnAQpm089uV1Y8HHFp2a5fXg9ng01o6pUTvShnAgiDFHzK2b9CTcOzqQIIy601yId71qhdw2edywTRqwsMkSC7aj1rqkYoi/jok4AW4yjLv5OTL30t/n53itb5yLo3Dxd+LsYSqDhWTwlHH8SZ8n8xk33eTXgvxasHyYse+CZdnx9OqG5bohXXfgUp06aoC2Vr73a9TD+tXdvLvxTkLtQKshvRNIPwaD+YezTZTjnqsgYvGwbVAUdevziIIZEVAevCtOiflPmQIjOucDhNA6oQMXSgL5OOP2tLTNG6nEYg+AZieW+nxFb46Ihx71Av1eao0Rc6Nz+36QgBiRHxZTFmMobLsVhMsZUPcaABm70SO6xoTCTvTlWsoQM53NO81UIL0XDAA/+rDrYzY1CA5IuWl71Eb+bu3198tourB2dWES/PXRFCGNafRx5qm+9CwL7g4XDOi600SlH1NdGYPA8zw6W+9QpxEAsG3b58xB6KCvV51fYkJeg27RHq0H66x5x7YtSKng6VLwtFYYWy6CUtYQoH4oT9fGcb5Sm8OySfM0rvm7E+DgQr6ktl7BbZoBDRnG4MyUNjMJen2S8yhR3F6v1eSZDQzJjg11/mxqe5GKxeYa1mGU+qebI4KjmXtSR09f/0w/JrC2k+LJ9hVM3wcmuXVB631Jrj6OI4CYPV+ORRHraUJUng6SnwnPqMwDmhkySff6uX6QcHtagFcbSqnSf98THYrCnMbqKqibtXZEwCrYp/jAPVvEGRmHuScGygXqJh5bHAo622D2EAxP1zteK7DDIkElacW54LELHF8nmd2+3SuN6TvN0coOhGXqsjAjYiTLDM8ZwpmLIuj+3oT7GO+8+psLjT3KLzUauotd5MuTfdIH3LUzZgSgd8M0184YPzi2OxaY0a6zZqFUX2dsiUwVgJluyOwXi5Jm3pzl0SWv09JzHgcCCmDNBe9fbqERAsD9tqCIxtjVhM591fvtgFy1jQulOh9MWYT4fSx5MDrPh/sN7GHG05BCuwRphRgvCRixeoeZCdanGyCbQD5csL2XkXOJcOk5F2y54C4rtksCNGP5WDrA6P0AGx9TzcjrAvRCYUjvhHlHbFFSHEYDDZWMCH08TqxDez1TivcIlU0ZJg2qcfB1NvIHE328JjsgrzPKRyue36zVc6cI9pJqZEKr2Ey9ZxoA6L1rRtMOt0GJZsRcO1MAZ++TAnUzVZIaYyePB58AEWNlRFexCJyqKu922kz18y35jXkzxx6IN0FvXbtADHQyKQ7aHrWzTZgWaG1kOjzOOmb7AAWOZTPN5ey9SRtn9zpanOUxvHt4h+k3CiNv90RjUDFzq18zsw7H3cnkmrtIwSXvWFPBdd3qmHiUXPAP8/zQbgIDpwjXGbuiBwmT8YShjGmZnFjDGU1rQOftk54F99crbrelxtOCbdjMpa7PLXWXcsfHhrLTnQCJlzPjEy9Xd55E5NtE5H8Wkd8Vkd8Wkb9q179JRP4nEfm/7PMb6Z2fFpHfE5F/LCJ/ma5/n4j8lt37W3ZA+uOkJiUHM0ncM1RSLhp/6nEtUt3+789G+AZm9BndNvWO8Rszkl3qH6mDQGXchRb4HPX1eQyf/nMTXL+ccP3DBfonF9zvGWWvYRj2UhdpmueExqYOKIBdAt17Pdn8I9LuuTYgTqvS6hqMaxioIzPLqWBJOxbZ8bRuWJa9DTCtk/uS6mlJ8ANWLC2+mJtsP4Jx6AReHLaHR5u898eAjMcY47yI39l0JwyemcLBdGHP1pDdNlYWxHjysn2nsYfjiE1kVpeog+dL7QD6MdRNXKDzEor6sEAatQQyTzgdvGzWQjpNKg9tp3WGkfad2YMEoQOHXkWuwtwX6JMo3ltuAIA1Fbxa7vjE8oxPXJ7x6esbrOve02EEVFy/UYsmesa8MzOfjH1MibW+AIlDmaPmGOZO03jG+6OpkTe6eZnuHrx+kJD+aMXzh1eUkpBT9XZaloJ82ZFebShPNT5Ptz5EfZbfVDNsbEIk8PloXcrT2yD9DcB/oqr/GoAfAPBTIvJdAP4agF9X1e8E8Ov2G3bvRwF8N4AfAvCzIuKk/TkAnwPwnfb3Qy+WbsSOBsnwB7RFDmfo9t4BDczyHqXpRKrLODEHZnKKAMb3x2s+2fZms1eVCFglQ8bhKnkw/ZyEjKZn2GTSKjH8nqEkQYfcBDgsztUJXuL5mpfUY/H4HF8KhXE4HWxW7dmpSm+BZLp68DujMOkypu+0ZnQwLwiqHXucYCf1Or6Lju6dUjM0d/St72+e1N0Z1ThWz54b8yIBNdM+OlqedGEShOkuiYZpsKD+1WdMM0xDQRMadenBsDkdG2d9k07ujwCC8u7AxyOeclKm7EDaBLgl3O8ZW0nYVZopiIEM50mA4WBxsDp3dXvA2V9k+qr6RVX93+37BwB+F8BnAPwwgF+wx34BwI/Y9x8G8Euq+qyqvw/g9wB8v4h8K4BPqepvqKoC+EV65zwJUBaTfKH62N9qlMhNJdJVqwBY0Elm5dULXqgkBDTuivMFY98Mw2qwIybORwkRdAs7g425FtikdX4WaKmB1koR3LfqyuU7t8RPpip1V64saoeio34WaRNHQai/cXuvH6NkDmLGi4aelWsDmVw2l1RqGGgTWkFSV+vNxCMKvHmz4vXzBfteQ0P7LsT6fH3ZD9mAZcmDPXYqJ6qXf/KEdU8ONNp33g3jxOWNRqOKrGihKfzdhDhEpI0T7TSkWHdIRPqF6Iwjvb3u4Tky1CXf2rVxrSOE0eDS163bDPRkZhGaQTI603W1BX0x91qneQeKEiCbcyirZ6nHYC55hwDmnaW45g27Cp63Bbc9x1GZi8WgSkurMK+TddoRH3ozcK2RQXZmS6r/mMa1Ic8n7W0uBN8YhGxs6nRtx/mCNK+wLkSI1SNt1Ztn+ZOM8uUrPv74ijdv1rq+QW7XYcIBembux6+O2i63z8frSfpT2fRF5NsB/OsA/iGAv6iqXwSqYADwF+yxzwD4Z/TaF+zaZ+z7eH1WzudE5PMi8vn9w4+6SeJBnRhxjajf0X43SQ6FzCX1mfR+SWt4EQGcvTswz7NMAy3zrFacF8aEoLLfCg0N74yovAsToZhvrVdAbZ1CS404+GJ6RLdZ3U5Q38igDlk8QHZjPiO3YDPQwQzleb+sxJy39awfxirPxhEDFu0/z/J8RKeuTm/Vpubay38+PjZN2DRj1xZzvwvDYWXN5vRY5xfNGLP5OMknPvmZk7a+Fa0m5Y+/K9pHDc1yy9i3jO1uPvtkSj5LL64xvJDe2ntHRD4B4L8H8B+r6lcfmONnN2YKiV8/XlT9eQA/DwDXf+XbVDPqAodoCyqWS0W+ZJNrR8TZgSsuKPyZHRG8rEpdqWhmQBOdADEk5IHLSlYkW+iLFX1GC+49Q2PZjyPs1gS08W9R49+WUc6l2fDtUJQWO79WTFKpCJ+PRLTj2SKo3C1VG7sHQRso3+3SdFSyWRHUtqKCoglL2up3O7jFFzxVBRsvaFibtq2GaCu3XPdYWJAyLRILuSU0EYVrJBXN9a650cdm3/Vj5HhyhMZiCFUFvZ3bx4LTnDwywjZq/ZXM9KaX1neA5+0LbTWwX2zcG+nLwf28/20sRDy9pX/H26+C7uzc0QMpkq8dWDiEeI5RImm83T4WB0SmpbjJMezHpCmF9xDXNSvisB4Lh7IXwdOits5Tx8sl19ADb7Z6XOLr+4pX671mo9Jpat5u9cKIBiEQTGNNG6YuraPGWnmCPeMhXVxj47LZNs/rFUS7Tji4gOD5vk/oT58AzH9fsHwI7PuK/b2C9CbV9xbF8qG0UBv7gNoHQMZrCjzeHgmnt0L6IrKiMvz/RlX/B7v8h2aygX1+ya5/AcC30eufBfAHdv2zk+tvUQFj+F5j4U/lx+xL+9Phr1NjqbMemphNs3hcx7dqyVulWRAyTtMAaIzs3fzC9Dhr3wwRPUh+NkCXf1cPysi+x+le3qyHtKa+Ost/qDfX/YDqZv3LeY9lCA7Is3uOmM8B+c3QN9XvsDY0q/dYlxfSW2urniXR/6Cd6vn97v2xfvTJxyUmR/5oAt6903bW/HRCy9MGIJjyWTuniuck/9Mxgz9df3Deh0XkcXw4nUsFnekucXZ2fhbITfrzvl+YK31Fhs+T9CLTNw+bvw3gd1X1b9KtXwXw4/b9xwH8Cl3/URG5ish3oC7Y/qaZgD4QkR+wPH+M3nlQgWqvjx2xjir8T1BRYkE7M9ek3/6kgXL8KEA1ZNNJ9QFNtN1+appAO8BFijSEgDaZu92KaPfCvMTowZ/hOtjX4r76AsBsnWE7Na8YsckldOh6CAJnsMP5wUbKwyAqeQheBmK4johVkKTgti91i71KC3Qn1eafxDaXOZoXIC01BIN4mAk61N3THnZMa8e4uOYobvDeYATvIZb9/c57gtoyE1ThoZNbH/mJZuVSv5dFG2OC9UvSzuurZtbybAW0enT+7xPGHwjTUhxYPzCUGeN1lD4KOQY3URbTcwBK7AnTCYBh7Eq8U2kBOxN2t4XJS9rDvAMAl7yHdnfbMm57U9HLlto8tLomPyhEj/ScLcC2nevoEz/jGw/9Oq3hFbIYFFo/6Ji/t91p558JByGkfI3nvpdtPKke+yrIt+qVs34gEfgt+IeV2S3gSls7GMt1je8svY15598A8O8D+C0R+Ud27T8D8DcA/LKI/ASAfwrgrwCAqv62iPwygN9B9fz5KVV1P4SfBPB3AbwC8Gv293IiJhtpdOHyQe+dZwNRRWJzQ3FinKg/rt7T+ueR2VDHdwPihQF3QNuePzF8BaqAMXtnEfOD9jKTWlRNq2giZs/oWtA8UN4GPU3MBoxKPepngXShn8PiZA+r9YF4daT57teMzqvQaWljvZ12kwno8jGQ9LAY7886ycZ2eh6jkKmTygLBDUzaF3GjD7uGtPy7Ms9Q2YPkm3G4jNnaw1k6rG3wXHF62vegH9N61l+zMe5gzMbDbq6IjvSB6vabVMxUKKYx2gs0VlVMeR+EUdf/41zCQGuuLs3bcYfySMf4PeM3PN6sLmGWTW2Iz9KpFsOC2u342sBBtFX6fu/6deQpNmYeja8Xmb6q/q8nVQaAf/PknZ8B8DOT658H8D0vlXmaBGa3th+iPWJfPO4LWtAps7vqgav0g5zR/mE7vfWukNkkwps68zfUd9AgvKP8qLVhU0/n5QObNHuKAGpyq59xiMpus8LgWw2vTCPRmWyRdsiK1YuRPK8luABh8oyIcNOMolInby7YLgp5btxoI6IpGj1V6yEr6kLKyrmXfHTbHEYZIxu3OzONwyZP+XoIgW4L++ClEXRnFMs2W1FApNsgE4yX6lgWRbbDwoXHjdeb7OcqPZqctdevuZdPh7xT88uehQ8eESkziNnBOAAONmrXdGKdY2n5dVUcBagzwVywpIK9pGDs7qlzSTs2TcgA3r/easAxSY2pe50GkMWHjrPWnAoxYLR7rDUftBpp7wS4GxH4mGTCDzz/iZ1kvMbrJ7o0PhDjYm3jl/cjKbU/NFCnQerbHs+TB9kjYPCn8t75c0k+CWGfHE1yQAHNVc8Pd2jBv7oFXx+o42SxNHacDgNy3AIO6pQYeNKuHwaKmyRwvA+1hVLjMmyyaqt/TIMBnvNuWV7k5fRgQByQoSLiqBRtOwixmr5pzxYmJPcZEEzKO/4AACAASURBVJuzYvBaXgBawLVZ/dzkw4x+qB+bMeK3oCG1GdrlcQOE22JHWipzZDpBq6XPc3TZHRfVDi6VTDJiXofdvmjMeGZykIGpj3mHGYzGa8wBRv3DH7tJHkxL3EeeZ1Jz70XY9jfNKKhgYZH697RsyKKd19c4x7jdXt6pxw7Vnxlkdx/DNaJFd40FA+UXdaDxcdA8XMg4vXz+pEm9LP9ipklN2jkMdHyEyxuFkD9rzzwy63j6+mf6gDVUm380M7a4jnrIcmr3xA/C4AFKk93NAV0H0Zm1nRolFdk1rwdC0SNTGpFV6gcO211DpYu2Nh/8KCfBjk8E/LB0iHanaYm3O6HZWM3OGuUyc9KeAekYWmFgIpe04ZJ2i5JYKK9qt33Kd9uR60c0Vne07oyAKAy4q50oxBOSGZAJtHHCzRhm11ecB9F+REZBK87HJl/Q0BntwZTomfflTBnvIFxGDy6esMwQHpkKDot7Y9/KQIux3S5YXKMgFMn1COZFJtGO8U4WifwQFWfyCXVsXNIeh6qsecd12WoIcevnzmxK9XHh1AnjYRdtRz+mt9/3eyzER4Zv1z320UhnNufGoUPcX0CH0kcB6+0IXuPvJoulnw14dPTtn+3KO+FRM3AypneG6QcT8+/O5JYKf9QWfHkjjzP9YOKFOgM4RHB0044yQmaiL3YPxOSHT2DoWJfwgztoJGtGxKgB+h2syQTRnirST4SQfNEWjelLLpU2S934EvHmeVKTlhELog/MTkBdiFvSXg89kVaeplr3p7xVV9O1xCKgvsnmoy8ofMKZCm5laXZdbztP8AGdxUBmJDMIgAMC8nvU/2zS6QQ1GiP0SaiCqnLTovWUITPD5bUdObalU78nzNjpcGDs/NzAOKL+rkUyg8Mxr0CdbDIYBQfQYsbQ+w/NIVLHSZK68WpJO5KUyvSznaiVNlxz/VtztXUIzTc21zk9R0bJUVU7EDACA/RM0RfQmxPAkcg8RrqNbQUH7XzULmViS2dGfaCjt4V4RMxDEnRclucVQpFMntyXp1oR3hWm74Mv+yohGmMLz4H6TOzWNQGhIwFooLqvNUbiggYdE9JREYfy5cHHTJ7KGdFpZ7dLw9yXepycUJ1i1x9fI6QQbXDm6eW4RjD2MiGQ7hrTR/s2AG2TVizc2qSpXholYq40dG7PsYkKqIdyoG7Pz6l5eBwmzMH0helzcW0yWQ8MeHzHb4+7Op2uI+1iLGhHo47ZEGPpXg2NoSHbbmwB3RpAVyb6a2NbTk0XPB553PDjE2TM+R4QtEz+UMfbYkHUfG9H/Z46818auTKVfdAyJm2POoDqxk2YtHFmJjl71tNhDYYY94jygXM+w2OzG9M+zkbhj9b+yHMwNfpzxXiRC/y3SV//TF9R0axUF8BYxExAvhSktUAupWkBl1JNDB6wyEwOPmidUQlQF36Z0ZPKy2iPCa8J2C/aTxQBMCIUZtDqedo1dvUagj5JUotiaUwlW7zyXQK28L44sWBsgb6BylRS+8OiB8HjSDMmWYfEEaYvR/I+aX0npSrCpTSL4pp2vLrca/C0rN1k1C21MowGSSzq4rLFyWFRvRNG7aclCSFQ1uIivC4t4nK7WVgzk46AY1wJd4djAQ+jh2tV7PYojW4cwKybxIwEjb4ADuYKRn8YxuconAO5entcoLOZhMALm5ZiLcBNWMxoFBOzVqOdCtqcEx+3BauZcdp4SdiM6VdvntT57YdZEq3O/j36bWLyYHfW0D64TSNTprHUaCAHE18s6mvrF2V6UZ2C3k6TpT2nY/94/uROXNwy4WZnTorgKVEv9ILQ+Yco4qCpRwgf7bV3J7XYLgyv0cwdwWj7+124htPMT+73IDXSI4QQg3FgWv2ilPb35XgkYlcHF3YsbZL29T5DczQ44/eIDL1eswmDhs5Upfrjx6iuHxEnfdScgLZZjLIsAwGn9vbZvVl7RlRF16f9pJM8+d3UDgYJHk20GY8jPNB7/N31e2O+OvYdv382FlN//6x9o8nmNJ3RYhhT03JiYaqfh7zwn6QE6vega7eSybQ3zF95XB91IaVDnWZjfvab2nJYH2LBwEx7nDtnY49+n/EAFvqHsTPQIV6Z1K8DItK35f8/Nn03e2SFrCUuxxdGUJ4YGSWz+Ut73qU12ze7PMaBwzbOZHWaIK8qobUtPNv9LtgZrQ+ImkeMo6PxAHSg35hVUA8Xz/XPkTdf8+S+0DzADos/UXFCIo5MpXrYeCjke0nY99Q2xmWLsy81yFZKhUwjVtDuQeEaDTaTgFm0BZoy2aAJNXAeTxC2r1rdeJ1iZBSBtkgt7rSCceMPoWQs2txdgWbSc1ptdeC4zXlcC+lMg4k8gxLaRjAzSfK44/E3mhl9nJbFw4L096MN/v5CAd6cvAMCjc8BsY6I1hcYu3dci0yIA31ScgQP7IbwAYTbJgA8bws+vl8immzVRltZB/Mq1cPrnu5ooTZGrRroNMHxM7Qd28zVafqDiYTNdYdFfb8uPW1Y44rx5xq934tnBF0sMbflp9bHndlYWl6d+VBwkMplVZyld4PpU2J1MBJL304CahA9iHZGCxMsvOlrRJ+HYk8RULvvv9XzH657ncXuT0MsoD135G6NHm9xOsExS6prCKou/8k7I11E44QkeQlZ2szbum34g4CD9R2OE5cfn7XD8zkw0pO2sOBjdX2KkAcB+WKeY18Sg+nqNn5O8mYB95C+J2lU+0/pMkOVs3eCGVWmxWMvdd81bPtA3eDHWp4AxzHNZc2G+4M+mmoJGPqWn/P5c0ZTEoatwu37YSxOyj9oz6OmcEL/Q3qbfmcBcZLeAaYvFTEa+k2GZsWOZtNdUO5kKNvFQgFIlYqX0h3s0dmu/dNRhNaT6DsJj/a9s8kOaCg8YcAIRTtpHnb0fBycfoh4u2BQwVCJh1wIe/LAJx1txZ979QghStd8CDmHGrrWsAP7RaMNmuuuVD9EJey0O0GbVDfeXNOGvaR6wIoLZquLo+ZAVbvgo/sVm3oscamoCwOaMtR58EzIDV0FaicU5WEZAiVpQ1rdRi30KDj6uCA8p/x9R4myo9myE+qhPTO0zajO6lftrk1Ih/cIMzH67c+KIffOxs/rGrQpbESrjvb363AoBz/DmgODANZuUOvQNAPp15KSYt9rePDrsoVmuKQSYbST1MNz3G1zLzVwoIw0YATN6y0kSJXCh/i9DtTxeBFM1yciNAv1Q6dZeR+CaG95dtqy08vKj3o5cneNi+pbtT6bF2sDm239Rtv+kbHfiD7cJpXWl//CQiv/uSUaeBLIwghm5ot603uC0oheTyWxCYWzzQ0TRjFeb9d6RH9AaBM4IlyHM7hyUt5DhE91GBNrKkFGGf4sZWkBtFqY5z6/ushL73mmI1zXZt6p9R8y0pPv3A4eE4MWMLbrNJ0hxfGl8ScdCzlbOHtYJud3guY688CDPB9qCuMQmmlw9N40r6HMDn07ujekLKlqqX5oSitWjzuv0XvwzDZpPUTfk/RI437pvbfur7co97BOwBcHDZH5zlhWjGMGjH5/4Cc6lvUC7d4Bpm8wyxpf1A4SSQY4PCyDe+n4Wy6983HAdXY8JpzbaVU6v1wmNK/uwztlQJXQujU/7WKbPaRJZvEKoJ90CrPX14q5cDuYXMYx4geLM+MrAsnENbwtSnUgJBc04FAHtB6y2EabS6ohcou7YG5StRDrl+wHrHg9LV64B8fj3cXXXMPuZtHeZGf1E44dRPVl9B3ai5PUGabTVKhfQPcYMfPzjthtTHkY4mIeVJq0Hezigj0h7gWqSw11NTMIWhgPbxf3pbeJ+iPt0k/oNLyT+jbwcaDsjTQKJ0bEbCvv9jEQgx/zggDlWoPppbUgJcWy7lYlxXvrzbTDdnLWYgHY3BS4WaRNH+/TIHl2vdDO54M7q2seNIbH9nWbrVxry2hHYS7U9y5waW50vEJwWF/yvDvwkehdlW6OHbxs3L3cNOumGShp6Rr8RlM7TMqI3ipz/HpI7wDTtxSD8AR+CMBhlmOijxKPmMjUvk+M44jQ658O9TiV9qDBQMJFhomlgkDP6nV+IKnZlNMlLneGIFxQUbsOGgirs5YclYUHjx9UP4l3z0cpSoRU9s/zPIM+k7Z0bcDAHOR4/9DmsY5MA7oWTJTCbfgiXtBpQFIsRL3M9uxQLz32fVc+PTvaoM/emWoDLPy4/CEdEC4zLro3YyC61NAaKRVIKsjZTTgVHHhKtit3RPuj99ahjjwHB6Y7e65rE49tf2YiZF1gn2k1p5oUCY/T5M9ONSzty/OF3BiDehxTRIPR8WQ6lt5ppk+dWPYMLbAjAmGIX4OQ4afuzCgp1NAoBOG9w1I3OqHzHGgunqLoI3oGAiGtIjf3S01VWjPy6NCIo3eemElbHHIYwzaNwe/XmxZ6QZunBFQOLqtCwo93O/Lxjl2ANauLbIK0ST/YE2o4ZdQJfNsy9J7azlOtjPtukLt48DcB1L2sNolJUhmp4JLqDs1Otff6OdqS/o/bGOhsEGB8GHjXZ4MwG71EdK3rGeWi4TvtftejKp1iR/ggsLL5Xo8q9qB9MN3ZTzzWKszW3NUfw29m9ifrFPyOh+vleo2CxWMJzZisr7WEdiXAutYDvSvzr4TyRdunfMciJX7Hvoy8h59+EkVeqjo11r/bI8MMdMKIO0+mpYUc5vZ3woD2xmhGZ0/vvK+Gcrq68dgDIkR5ePK4RuH8xV7uXLX9T6XbTFqfHca+IKwXTJMoj03Koq2/J+nrn+lTKnuzJ7eTpHwCErNjJs3ujz450kB0Tnq8J36dsp25inkZ/YT0DrcPEg5e/4r0LSteq+A01JWZ5YjqeZNWvzhMSGFkEs6QB3c3lR6V7SVVl0WnP3C09Xuebm7bWx+4dnDQHgS9psZtJqbeaUd8z+mejvWv5bbfHbnsvbLU+PhltXpo7bvQ0rg7GGAQw2JT3IHxotVXqW2B6oDOdDiaOlhjHNH4gUHh2L9hYpR53fy5TgMc8gqTpjGWNe8N4adSTX+ovvm8gNsOQy/B8AFEIL6WOZUxmqNeqF/M60yf0tOR5x73bdD7wfrMmE9rhN13QZDQrwtOBFoATX9fW335Wvebx7jQOJ7kHf19kt4ppu/jhZEt266D6Xh7HY1yJ1CQJwfTYQccCOtljkCrq9Iw4Fg1C6lOdmgAYdvtVvMNwQNodnEv0H/7MYWCdogKEEzI4+wkVh8HVfKgvtvgiZ2MA9OSgu6UI/VM/HkVLFKwisfl0eZLvBbI1bx5hj7YSg6bb/JQGlaAOI2YyZMw7Qa7fx86yfvFn3VfedGeoURbhVA6eYuNfQdp9HXviwOq51k+DhiZjyEes2x2mDIjZozSC7QDKrZ6HgQJjr9ndeXxOwqM3c5AFrFw4AA+ul/w8XZBEsWrfMeadlzSFmE3llQ9d57yhsuym1YrHXM94B1nzrNNWfzMCKyIIc/aBSB89TuN3p9Jk+uWeD3pkDdrkc4b0ui5NbTDxxeDF+/Pcc52WgRizLGG8cj29O4wfdEWaZLnlDNxBQ4Hh4y9j+OEZ/WQ39Ugpr84qROrxtapXKQjN96EBUE7p5QYU3WzrKOsCS/SEhSQTTo7vtgzEoxIgyZTjx4ePAfOgxBUY9kdgnfEbouyUhDILjsDN8aZl4L1ukFzPfksskmKAsEiVdUX0cZAqb0K9MzLJ+fQFx16d6bOTBj9tQ4ZOwBIxMTzSGT0vx3p83kFNAHHBb3OvOR1oLqxYBaqf6jzDA6M+QUT8de9/9jUYfXihdCubrPw3swsxQXQREDA4usYIPAzID6+r3jeFyRUpr9Iwat8j1O0llRwSRveW254soicDNZCkHEfB1jiPqD7ncBFzzSJ5t2z3Ff+ncMleB6z60DTJgaadOzGGb4cn4/T3rT/CyCajixnZAvtRrt2tmbE6Z1h+iJoPsHdgBgYPdtaC9qMULQDWHzQuETm9/0CPzMOoqgU+oHFE36GumhQjlLevRiafXQggEqdDEkP5pxOENBK/rhI2gmwAb34IItjKb1NCeZxUdruWR+sVpbbbR3pC5s5Jgy6YxxM1NHLSTAXviP9Rob0YFIc6NAhpiax/SQjRf8sQOOQhANrH27+6Ca/EHMek8qhPePOz85EkNA8gUbGVtAxgCiTmddYB6c5X9Khvv5M0E6w7wm7Cf962hviu5v81rSbu28z7biGl83zrDNVegTMgWl1tnYWTFSnWHjnvhrGTYx9HdqDRsvuu7S/A2Dk8XOWD5U/ntzVnmkvMM0PY4XnTpncp7zebfOOMwPbABJMzQnuHe1t9CBGIAbmDHkfpLabYsYBH8xHTeWnZw+Te6yvdF/ZfCTF3PBMA2h1a/Zw94ioFWnlaamLQJIb03fVGKhMyF3i1KqaF25sa3uYcxgVuElmOE2p5MrM/XzTUhJ845gj0XoiUkX77nmkyWIJqaCfQLVyi/kBbqWeCSxFCK1OzGI0Gdj0NJC8BV47CRY2ps7W7+8VQO7S0wI2Lor1kVg4EJqETltgmNw+flh4PaiTa0ZdHdnGndDHn2Ghz4x9+ONDgLxenl8cCMP5EN1505K3d9sS9q2pIWJaqsfcAWo/L7JjpYm3SA3MtoiPlza3RNGdesYbHv2sC54/3cKpbeTi8T2idAk+0MZvN4+ZJtI+p4KRNo0dGDULLqHnWWAFX5L2Pm9GY77G351krj0P4x8q5/uN8BZMX0T+joh8SUT+T7r2n4vI/y0i/8j+/i2699Mi8nsi8o9F5C/T9e8Tkd+ye3/LDkd/iyQDytLuVv9J8MTNFGFaOTLjMbXO7bkLx91m9a7b3dnVoa++X25l+I+h3v6T0EWXziimvdmnrXHQezTQpqrfhIH6u7mrWxU8s/0Pcd9owJqGdiYT7bw6hqbEM/UC+gVEbw9/Tt4/aAIPRpszCdmkehqRKeqgZktP17jfAYEeaneoVNo4a981Hm/POoPTY5+d2ZNHVGv1OpgnBmYG9b0IQ72oXeP3bj4pbJ+J0jGJ2Y7YbCyGF+95Qbct5jZGeyjT6Kgn467r40FYdSQ6m6c4tjm0twfjbXw3+oqFGQsNyq8KK38RISgOmoOXTdeU+dSsjx7U9W2Q/t8F8EOT6/+lqn6v/f19ABCR7wLwowC+2975WRHx4flzAD4H4Dvtb5bnMSnqJiAVCyhWKtoPxiaNsSfa6OMozAdJnH8KEwo1D/UygCZfODgSrLNMBVcLZFRoW//IVD2vFt4BrUNt84WCmFlCoHs/FF3GDlW0RWgAYt4S/o5a8Cp1mvDnBCFx3oyQwsOH2uTb6oE6QZfrVt0xaWD5hpsQCqiahqrUvriUZq+l9xapPt71aEufqRIH3B8CS9GEYW0gFs/8ABdisLxdf4aApAjSTZCfgXxD0yy9v2ltRnO/0M4H91RCNBt6M0cYw1qs70N7RL9Rh5h1eIO47dfyEK3hHMpVA9GNC7q+GcnHcbpJ1+e1Lo1uUqrXUllbv5cLPWtmkHA/FpiJpJp0SpFYjwKqi+/H2wWv9xXPZcGm7TxkR/mv8t3Gk/n4c58NC6Fs1ikr1UuorYnmLCH9srRxowuahjC6dRb07bVr3W9i4NH/ThOvs5WhHhjPeYpdY4AQZiYS+hErjJ+TQQaY5jeGfq400+5zll5k+qr6DwD885ees/TDAH5JVZ9V9fcB/B6A7xeRbwXwKVX9DVVVAL8I4EfeMs9AV51uIBb3Qyj+Bz0vtnMt3ulsAEepPpPoOpTX10lbZ8j4rL0/y39gqD6gvC2HMvzSYEPuHhNn9v31PjCaTts6qoczpA8g1PXRZg+B7awt4yt1446X26nS5wPyNBGNx76btmHsk0l+3U9nFEBM4jDPJT2Ol6FegZwnTZvVZzZezsDDKISdkUzpOBnHhzLHsRjX9DA+Z3Xs0XIVPmNNCgS7ynETlqUktv4zXNdDnTClz6x907oPjHymCcokr0D5zCFZ4xznGvcN12O4zjzj0E+z8X0yVQ68Sfqfj9Kfxab/H4rI/2Hmn2+0a58B8M/omS/Ytc/Y9/H6NInI50Tk8yLy+f2jDyuzK9IO2FbEgmbKCskFsjQNQJLGgSuSiy0QugR09GSqt7lRFUPfvCkkXKzc5kmbtipasDyj4mjEJ9UwUBcvRA2fHnYBAO1BsHwUSEtBftojbLKWir1d2Alq25O4NlQq+gq0X+tbMpkb3KfZmXirQtg81Zi6H36xhFtmY+JFBavscYaupAJkxZIL1kuNYBcmIQEkKxYz3MaBLDQxO/uooD+AfJxsjsKHbfxKiIlDShRGR9LQEhTVO8rvL6UictfMvL+AZod2u7BpAEFD31wV4whtM5mXPfrnS+0bNrNESIxlfBZNi5SKfoWQqgdZC5pZPSIIlzgtFBzKwcd9aFFMTwvR4W1BApalYF3HGA29iy8APO9LnLdQVJCguKYtPHq4bwPtelu5/6heHM4gQowM80wXp3Xf9hHly9ZoEMk0qEIHMMXmtCG8xoF5O++gaxFOG7U/S4TX1shjtB4oWru4/yNgozmqhKnHiXO2YNyq9zWlnwPwrwL4XgBfBPBfDM3mpA+uT5Oq/ryq/iVV/Uv5E59oRGdbuOXoSLJ5DnhNeuk3U826NFsyH54/RWr8/KM0SnQqQ6gNAJprpEo8E8gZjXgz7aDb5TqrKNuZ+bZ2j7RnVMLL5vTAmrEuZygUaC6mONr0x3RAfvxJz8yun13r1ggGhBS1Yfv2YGN27dJ/d3WclSuU74zu43uz9s3G76QN0/xm3zlvvjVbM/Pv2l+vIOzBiiHIOwuIQ1QOdThU4i3TrN9P6TfOE/pzQSOTYTtj7P7cI9q+yAv6griOnYVg+u7x0kOtdkhfE9NX1T9U1V1VC4D/GsD3260vAPg2evSzAP7Arn92cv3tktvgnSh7CiHArmJlF2BLLbRwMt9+seMT/ezcgnAPhEtJgBD9UD6vqPv4VLTj/0w6h4YAzxdwW+g46bnPlTxySqmucCJaXUwBINPRh/7cPZm3Ts0s1H0VlOIhqAsOu2RT+4x2eBhmHQaPDe6tJLze+1itkvvJfteMe8mxvoBdcLsv2LZcN5PtzVsiWQC3p7xV9z1Hy7YrtqsD9QX7jHfqOR1NOLYhVHFm3oMWVix4lR8518JeIJA8p5zNNLEUOjLQ7faGKrXVF624jraB5vma19n/aIHdtbTYOS3oNYqRkSm1n2looQeSj98EyB0WINDGAqFFtXrIzmO6Ho/o+zJyLrhcN2Rzx/TF3A/uVzyXBV+9PwGojP+D7Yqvbk943pfw3gJsXcHXQ0gj4yCA4wEpXeJ+J485z4jXCpw2xeZ7BDqzfLr56YKdTX4DXWN82Zg5zKPijRnqbMi8K5d5xNCfXTt5Hgx87JHQ+JqYvtnoPf07ANyz51cB/KiIXEXkO1AXbH9TVb8I4AMR+QHz2vkxAL/yVoVNOrdD8+OzoT9jkM5NjTotamAmfi3yfvA+o/au2kr1lOGFGVIBtc8/U7N9+m7cWUVn9w55Ubu4bQ/bjbo419D+sc671qiJhey7+05eRRMo4l4cze5PA/fstRF5jWmC1GYMYpyUYfcNdfsE8QLtLAMXCFQnFqgvjrfxwoNnW36kxdHvqRbqY8zHoNfRQxCQRcB/d3/UrjOFzMFK52qMhupvZakCQFMcoHLbF2wl1yibKmTe0yMNhn4aPViinTNa0b0Z0Pcbo/29S2fzd3xszOPA3MeH23XeG/NSOV0a35mNgUlazm9ZXiJ/D8APAvhmEfkCgL8O4AdF5HtrlfFPAPwHAKCqvy0ivwzgdwBsAH5KVR0j/ySqJ9ArAL9mf2+XtgQsBWXPSGlD8gNUaND4Yq4WAW6C4mYf37mogDoH8kPGUaW/bMbM7La7ywFoPulAELUsGrsiATTE70f4LTab3M7mDI0cXti/GKUi/LKn8Mqp2oxAzX5edsG+LdF2R9o7xbUB2lz1Ixfd5s8oqaJDhVjo3tjZSTbCxizqRxbFroLFUG5aCnbUdu2a8GyTu6ig3BMgwL7lur/gXvtvHMxr2lFQwysHmjYOpEKzNRn9yVOlS1rr7qitamGMvNAdgHNYV7EuKiuqV4yPF/att+c1Ne+dJIo91f6WzfZRWF+4nblDjJ0dWZpmSVpBhMPwPKx93Cdh8TP0qAKA7bykdfbMBp1HjNNXdunWPbpPp99GY8L6omqkQDbUDwC3LSMJ8Hpb8ZTv2ErCzUJubCXhw/sVAPDHz+/jzbZUrW8Bbrn554cQEqKJ9alr/LJJj6EoWJ9oHQtpRMos4AqQN6BcGsNlmhXS+DWhN70mheb63ngYUcuo8hBxutucc/gWPvYkZDsNgvucBYnNR69b8oNgrBmxP+bPwvRV9d+bXP7bD57/GQA/M7n+eQDf81J580wBqMVxX1Fj6dtuSWVUYs9KkRpnPxZimQmjoZ5JOTFJU/vU0jpdpZbvDIgX0ARoA1XbGarx3knzpIgJL2lMg3Zc1jj7VRCo1A1qvklNOS6OM7WZJsADPiaTRhs7JMRmkLhUI2lyTPQddZAVlS7Kpp/apbsJL6MfF+FaQz14paHQrh4kByC2YSn17egZVJX+8dUEQcwZznuYSEAVfmoOAG7+63a+Wj3CpBjOAd5RlJeNOY522Jmk2NVSewEUE9uZ2FjfgT5+XdGq7kBj3GA21XiVKkc0DXpJFWZBd6D+Ng4VezMAbHvGknfsJeFWltD+/O95X+rRmiq478kEfrFFy8kkMTDV6jn5DgSIkuH62Ca3/1U3ZalzQIZ8TLgEAx6BhtG2S8SDGt3QYlQ53fX4/KEdrjDxWLf8lD7F+k0tbwXO9/hQ+loXcv+/Tb7KDXQLaDyLwj98LVCztbq/uLgXj+dBzAOgwSK9tG82Mm02U7RnGtFrJh2fHQeo1Tnml7QQy2oeSWo9WUrqwiT4oeKx65MmmbtrcvL77BLn6KCvYx/o1JsQoAAAIABJREFUypFgy8jILxrHJY4ueC6k4ihF9xhy2rktk+zkkqpZx33/2bc/nnHh5yYflfMJzZNS0Ec8dGTMvuuOgL3/eb1mcP0NUnlfZbTFdO8naeUdNswcGK7G85H3bJJ2jMqeM3rA1o26o/6MDrwXw2U/2/ujn2NgSC9stc/L363RR9G8kQjdOwjIoljyjmx1uqRq4y+asJWEoglLKmHuAarZUOB5D53r6Dc34Ss70YvNaCPtQHX1nfVo7a7eTKbl+DsEBtXnJXn68CHmvr4SmifRNUKr830ntUtmH2ven6apsabnUvwABPxDaj/EOhaNw6nJy9K7w/S9scGhLTnIsgUkuTSGn0xN7zZt2TsuHQ8TG2gTjidu3HNBMKlnokeUt1ZLL/1HhmACzBFyYZONogkAu8ZMH0C8x8kXfdnbpjPbeD1PJgzQBjuHYTh4AzlvV7Pn82BzrlMQrqZAtf8u3UEbOCyWdvXxyUJMPxja+JfotDGiL2s4swBm4wTtJh4h0I5ZewZAY7S02Me3A6Gx2TAIgGM/jMkfT+2rM3if9AwiOiY25C8GLjoUOaDNMPvZ+2VprqTO/JaInYPQAC9LjaIJtBg7gI0PtP0c7tYZGwitz1gARj3NNClFGmBLjZYjrbkdXu9DcLRkWuBkt3KkUYBS37owYTDolQ63b2bClDfnEW03XhVgcxDOB1OPzwMSfJ1gf5DeDaafFBxoTIDKzMMIj8o03VSQCemOLRw7dkQ9nl8QmwRBwMYeNbDPdVz3dYHUom9294OJtAkqjmh5gdBNAGmoh/2NXj3RFOahg23amWAXvtdeDpSgmI4O95hi08Zeqk0fMHt3AbBLHGAjJAxcsGbRg/ueCh9GQ8fBDYw4mu+xcphJgJigt02HXbn2LmsAcOav6DOjrworUzg+khIXJnrHy1R/vjYk9X/E1P1Z7avT5dtZ8lxz07bHgoXAVKPoxgmal9wwxtmcxmM5Ts+ycZEN8XchF6REwLUlldjrseaCp7X667PXUGfWY7PTpM5uyz9o6jOagepPz7PAV59zI214POx93k3oUj7uBUZzc+wDFdS1LhYK/v6x2D75MzTGvc4v4Yd3gumHeUbQodx66g7CHq6GyJItGkoqdXLyYOeOjWs96pFdplK1U4Ptt787Qxn813t5EHOzbeIidQIVY/ptM5MFWXPkntrBE8k3qLmWY20OuyULJcScbXV0RsgolQa8D6bDgScq4WaJVF06P9ouVVaUVDfSbakuwJsQcBdaJGOaqAvDSXptpPMyGVacuoMmgD44FXcLuSJGWAaOVaNotnZiMOJn41p/oNCf1c+RIceLCXs3uxbSXHcaHwX/IByc5oOQq20aGzl+6kHosVA4lMf5GgOMzWGzd1Q6Zgn1wGoayH5XwZp3XJctImgustP4qa6611wZ/dOy4f3Lrc5pZsYHATdoJdRunz9smjxsTGK+O5xVcTgL1zULBwaeBU2exNF6uY6E/CPkxsjkve5S549cSmX8Xg4Ja6/72OZO6DlgadU41m1I7wTTDyQ3IKiYnN6hjnYDrZINejKYRrUs0kj4cbKO1RsnON2IuDeCMD2E3ZUflbZAKvQ+AFusPkqW2MA1mdBdTH6iQQeYmDSMQlwoGs3ctdIPPz9s0EI96FysHXUS6ZHmzj+MWXgKxn82UHVC9xkDoDYcmu+T2l83G/IoDIK7zDrU8g0//bimwZhGRqtCVZU+r0MabbFDG2d9F43k533SkxCVgYbO0DsPpqHJ8bvQhZM+GkMqjJvuZpvwRjMlC62Y81R2L4Tm9QitmfOy1Jgu5TFqyEPfSTyH6YIuycR4xtc8Is5WaAAavGVmStRxfLB8UnSmqC4RKH2b9E4wfVcdU+o3g/jkc5t2ssBNEeK1pOrFA0Oa4YlhVHI7GtCJyrLUe+yCd9C3xslJDC4GWgb0UhF7bDxaUDeKuSulMQ13OU3Z49VoG2RkHhE0hu7XKh2cVgiPnjiOLtBZq+8o1AKZGCMsuR93SQoKBKv1AQe6W1LB+/mGNe+1D7zuSsjK6S8tPwAttMO4gOwCUtC7kg6TOBZPBS0UAtAvCvpzmbpvqb/LVaPPRFE9OsY1IM4zIZBt9JPUvo7t9/yuHBFfzQuNwNr6JB6Z8ddOc2g/qsdH8xTq7NTkyhjhCpwxeb97vY1Wo9942ulHcEcE0k8C5FQXcR0gZKkumnE62oTp+/WcqG8T2mKtt0WlQ+FOr1icp3HBplT1tjjtHESx+cbfZ4+Z3JwsAIT776hpAoigaaGdClAuCr0o9FKga4mNobHQ7l5iA9Mf825jpLqIy73VMcYHCx4GNQ8EwDvB9JtZp32GhD5BEIc0Mu+B2Y/IqdlBlVAT+o6JvPpJEczIGIBmrQIgt87v7Kdug/S2zZIN2t5zB8TsCTHR/Y4uo4Cj34FEMDwDhJdNgpKJp8+72m2t/pNRNT4P9CGbueyDqcwn5og0g2n2TMpR66OBH5M0XrI/d7FliO73pDJ+905x05uPEx3HhvcD5RPq99jWB3UVnLRlBB7EeLiPwwTAz78wzs5SN9QPaL5G0ZwlZvrh6aXzgGwP++0snbxzyIvGUHdrnNsDKJrl32nGQAhULFpNNs7ofYwk7YV9l9mDtnGdXrj2NuPp65/pCwwBV9QqaMyxWIgFydo2abkdXLQesAJUH3efFL5pye1yVkZ9kFAOMeTwCPH6FNvUVfy3ve6IcFHotUCedqRXG3At0Otukt8QwOI+4Wg2dxdAzMSs/WpeC8m2vvsaQCysMslYY5iksGBEvRXlyUIfB8RC0EdEscoe55uq1o1XHsjOzVIeJjlCNPCk2FKXH1AZhHvxpEQagmsnloej8lCLvd4WxEx22zBn9mlNtjAopCk4MrQ+8sBmbt6JTTb3/oB3t8/4ZhpdFJdcA8ut647FA475THLXQia9rT2MB5McTFTMoP22C6JhgY/ziNOmJl4ijvC7zVdozwA4LOiz6SEC73mwMOobsX4riqDZq8VCJhsBnKlX82Al0p124972HGV2JrehfhHszNtHayTiWoyVmTzkB5vp/EMaXTrtfQaEaA5w4uERXk0A0iZ1fr+3Ib/akV/tkFdbDSu+KGB8Iejpddik9R/QnAu43kCEzwgzoreHF/+jD8+lyIubs/7cEzEK90mv4QYcjQl58fTMLyZvUKfl16G3QVp3yQ2Mk/eq2tnbMlVQVf2lIK11IdlRo0qqE3ipIzjsyb6f4IH2MqYzDafZ+QfUzsh34GlnSMZTqOGMxsfziLleB3SF6eTxs3Xre4+ffZjeAhnKrP0s9McyG5jvy5HG0Lq1FGcwI7Q8IDrvDDTpC1sIJmYtZ+93+RzvD0pP15y3GFbHIsb5QYkX9guqBuRmQDeL9ms3xQ5WqfcOSN/K4tPxmME9bOeMDpPu6B96cG+oUzdGxrnF82lR5EtBXnYDmqlagDapGsCO2LuiMPNxaGnaMhsrP7ZvvDabcyfp65/pAyi3jHTdyEsHKHuClrqRSbJCd8G2Z6SsNTzDniI8g24SKN+PJtPRd94/FYbWqAM61EbH6Pmgz9pGmQB63fH06We89/SMnBT3LWMrCfuesN0z8lJwvy0oHv6BXExVpaL0rZ0tpEWQ1mLtlbCpqwLrWizGjV+vjDfnUtc0zPYuQzu7DUqKoIs4kKM2J1G8l2+4pA1Fa0A4JeR+GilTG4rkazkXrObGF2sB7qedFQIxu6WjbHpfNLhjs7W28vlQDbF+jzAC8Wz19843bYdykN1ddzmYxjgkgzOqEKxZgU1qOIa7NOblw8K1k9LGyMhwYr6785W9E5sAvU+sjswfKnhobo+xFsLvodWHD/5QKw8etqKrFPr6qs0dD0eulbnf9xpmIac9DtMpItg042JuRwXV/v98X/C8LS0o295W0jVVWhXYQqxIXPe+Da0mN4Qtu1S6oYIwD54mG9qxiAkVUQ9jNfrJNEbuu4aa7euILW0OudmmZAU+seGbv/GDCDuxl4TnLePNdY2d9XVeVr7WQAP9eZlFY8wcNlyF1qPQJE0TE2nj5yS9E0yfNx/FIiZ9F9hE9HNknWebNO3R1aSAAQmL9jz/YRpQQEX6isu64dVao0g+p4Jtz9hSjZ655IJSBCKVsbv3itjISrSAw+4fnflnVpVY90CjgdeRP8fvOKGLJV+cW6XYBqwj9Do7LKOWRQ+bUPI1gMUYRfMI0sfo7KzOY/2lhQ4YNZtYByl1G/6hPOKYnaVNUfOcPesPOpNkAUtlPhpTXFb3/eS1kNkuCE7yPlyeCJ3Wjv65EeXG3OBXJxp2wXFMJFFsWsd8Q/t1LYDL85AdD9cgxj4d7wl6wTemGQ28vehB9uHZyZjy67IUvH+5YZGCm5mxcqoATETtMHmN8DFuym12fxJ0jJfCVCnz+s3ac5LeCaaPTbBvVXSVIt1hzIANENEwk8SO1oI6ohTAnqqtONdYFZoV8pz6jRypIowisO3ZAypzO3F2roFAie4mJgqkteCb3v8Y3/LqQ1zSjjcWVbBAsJUaEvnLb17hvmdkEwgiir1U5LPb0YdVgyEm4jbLVKBWcUf23dGJCqSEtlmNGRihJb+W7jTaac+RH6NXL7dRtu+mXRkKue8ZH2xPuPkOqLBrG8ry2ZcA1K0VeJXvNeKi1CG4LjteL6XS2J5nk2wUX5onh3MHKdKj/bhGE98EqRgy4239nb3cAYCtoWChIFk2UX03aSlmnkiEKKXRsGl/g53a61O0rSHxBGcu30FOql82oebvWbmSYPXtA36NocE70xYdMhT1t3HN9HdtZzCTd268brKZAc2igjXt2A383PaMy7JbzCkbm+rjs40310RCaxv2Tnjso86O7XX08Au2VhT7NlD7QLSVpUDvReZ5mNYlJAQ5gF/QTeveoW959WEcB/m8L7iVjK9cX2FNOz68XeNEsef7gtu2YHtKwdfKc3Wbk9e5jt/SNLiolNJ45LFPzP/dRvo2KHVPEQu/7NJt6x/tm4FEVSpXdISfgBqwTOYeJsYUnfNLDCjLvqBtBqFi3bXT0UlKik9envHp9Q1e5Rs+2q7BNHeyZ/qJQm+2tQYt86ihVoQQ6o0B9xapBV4b0N8JGpDdZGOiRxyJ0PtJLAJonCFbOY63hc1vMxTV8lFk51CWcip1wfqFRjrTDiSk7VMHdMcH3CvRr0OGPkaGeeVC1k0OvsnMzTuxGdAL9MVWam8HnMe6eR1mMP4EsXUKE6M7afeVng1SKmJh24VbNywU8JBJXL8Kpvo2KdBtcPI9F43pp3DlrL8Fi9n5gabV+v0sNXBfhf+P264CE5Z0bXCn5ZhVQSer+9sg4pGPd8KR573M76WkeD/f8Mn1DTIKXucL7qUGKnzK9zB/qQqe846Pbop9qWPpzW3F3cbd/lzNpyLSt4nL9Xk2jh8CiLP09c/0rYV6TyiLQZBS1UfenYpUR6x6hE1vOC84cmclnUpD93nu7HaEKlgFDEcGlryU2IRRNGFNu/ml77iVBW+2NRYzt5Kwa8KaCl7fV5SnO+73jM3q6+iezTelCLYt21b4Vrh/z344i0o3+GNyO1jm4+icDj7BUCfurqnZ7tUEkgneXWsArbutW2BLHTKEoC5e7zmExJp2oNR3FynVz/uyY7sB6hJOW13dLj8yfPbT97o5Q3VU2IU8prZHp9GkUT+acylGl35GyS64bzmEt1rIicrka53FdyEnJdRPY4fLM60lonmyMBv9rv19mvRNeKF5t3C1g04EXvh9YrQdXdAYbXd2MPq8dhN+SQWqBa+3Ncw3sw1avLu7qOCa97avhLyQqn3dJTm6vQUeSZLNl1IQ3nrRJprvvKOZaYMitAEMza7Pa14DjblMp6FHA1EV3O2h6tG249VyQ1HBN6wfYysZt1IXs9e8h5lrN63cheeb9xP0OUOLDYSChviD8bTKyV26DZW9dtCnd4DpozZkl26jlQB1soycVtBP1LNdbAK0OLSUnHBj/JQB9TKU4knEgyQbol2l4I4adXCVHe/lGz7ITxUBqbTIgyXXEMYl4bqioafdzD0W9+UQbC01/3729Y/4MF6vcSLY2Okmtt9+oB6q2gYmK8fD5+6lqqlx4hfTKGuMUz8ntU78hDXvyKlgWXfsWzraK7nsREzBrw1ul84cefGrs6k7g521M+ikZGymZILKF8lVpY2hgZmGKSLMPJM2yaC1dHU40sEXdhmVN7MV2sWBWR0QIqhuM1AorQ3dO96lbjLUurCPXM9kvlGQo0QZJxxjLQFoO3k7QUztoPqEWyfRsc09gbhJa5cO1IlrEcSwI9su3tLQZibNrG+8zi6JTRAVTciogG+VHa/yHc/ljk8tb/DV5RWSnUK3lIK7m3Y1Ndyigv2acDcAq2rODVs0te9eAVLph8zp7l28K0zfB4HTNrd4OkUlAj75qngb+NImuXO4oIq2yUKMEHBmwZzRXNB4lx4tqIwELrvgq89P+KP8Pt5fVtxNshdNeJVvWGWP+COv9xVP6Y5bWQIFbVrjjK+5agE+MJ4NmcchHkmxO+rWdg1AePNs28DVCEF2VjHy8Vb2E0bbcLPKXicpbU6KZyyQVs62KaXbxVn3WTif9vqHD7dM9ht0sYoawuP9ERz6Nkzf3L+g3wBpCWRLZ4FOY6Cup7Tfgf6c+aohTdppHGsFTO4EiNLYI4YdbelJ2WsDAd3Ro0vXHNoQrUxP0JjbKOwHPhrvUPyaGPIHkNPqzhqQ7/p2IHI3bxw/OauooEjV5jyUh98HKmBQWJ6Dndz76sCkaWp6O0URB8KXWBQlEub++ZlJLMp0YVIQHDLmeIw9y7PUOorRaL9n/D+vP4EkJez6SRR3rVFGr2nDpinm1Kevr1FU8PF2QVnqmh9gGpR5+pSs0OcElBReh1pQA0vuchzqk77m9G4w/cHtLy090hWLcV58AhHjCzTvZprUNm11DEFYa6IRH5Ovf35uPK2p3DK+8voJORV8vFzqNXrmmja8SjcskvFcsi1qVpPIxU6TWm3T0ptlxbMt7vrZuW43z9lNPmqMVOwaYhG3sHAa0ZQ3yyezwNw77eFUB1RsoDL1PFxGpQmcS9qw5h3LsndhYuuLiAVlFdSBDMGO1KN+3v7vYRggtHuZUL6p+8U2Hgm5HPIBJWyO823wwTCF6OI0IYFaFCEcWOhwgD91W7+0OgbBobVMR/7ePDM7doHZ/G8EOEQTrquy0HJ3VFqAZfNXr3VQHQWN4QKd9tQ0h/pOnLxGoMFTstDbW0l1cdbCVGwl4ZIaMGNm44vhdw+xnM1Bwh0NvH7OhF3YUf26tlCfwk8jc9JZnSOUSWlt7jQAfw6IU7yKCXKxE6pCNrqbtr9nNCrPGV/68BMQUXxifcZT3rBKqQfKaMI13XHXhGuqsP1T6xs870sTgkuKYHVAdZrYloybrnVO74DugICC/BEdHvD6SF//TN8HtqMJVmEHZttWn9C42cyPTRBINHg7826eeKhlV0RVPX/ExTo/z2kXfPzmgiUXPC9L2D0/vl/wtNyxvZfwKt8tRkn9cya/q9QFH+u+XT1OvcShFaVU7xwAZMohQej2ZhZcTgtm9owoBwHI3+vGrNJOzkoFuufmSUOpQ+uUf/TdXiefaz+OgHZDMeo7pW3GMg/lviqj3/KAlA+2avTIj00UQROmIXA0/dnP6m6rvVY5PBcueCRsO61yMiy75DSYZH9W7KyuD7kA98/sORLas/qOGwHjvFz79I13bsZjO7/YuFcYIBIak0A/rkYGzkJLmoBywVYS5QNQAEZ0gmQ0d3ZtGwEe9xk/O/bnlvD6ecVXLk94s60QUXx4ueLNvsScL7Y+tqYdi+xABp7yVmm03KEqeFq26uPv2S4Z5V65gviOY1QBMJq8us9Jepszcv8OgH8bwJdU9Xvs2jcB+O8AfDvqGbn/rqp+2e79NICfQFXW/iNV/R/t+vehnZH79wH8VdXR43dWAcDjUksCUt6ricOODuTeUUU9no/DGmSF3ikv70BnLmyKcOL5pPV0KcBzDrexKM/d8EZCb4LbBxd8eU/IuVSbp9VzWTc8f+OCz7z/FVzTBj9Bqm58qpD20+sbPOccB094GOLLsqGUhLvmw0TL5vvvKD+lgm1LnS3bFyld3eWjHMMeTBzFGaP76VcSVaRf9oo2RSoirvSXo6DhtBRgy1UAlguKCt7sa0WIW67muZut1hXtbbBsY3fhy3mPExNo7pjUzpi3k23qEQNdUIUPh+qITKuv9bJUG7b7lCu1tS4st81uddxZm5ZeGAS5eFy5Xdrb5Z41O2JdBI7ilchMzK2z+44MgN6v2k3XvFYnP3/VF1A9HLU9X8zDLc6uMKbuSPViaNZNGQVm5rEKX5ctzEFRfz8nN7V2hdec96NtvOps75lCbizUbVL5QuITt0jAMD07ehnir6bdifx1OoPyAyDPCc8fXvHHtoly3zK+dK2M/JJ3fMv1w6rJyw4k2JrfDlyqWfe95YYPtyuuy2Zz64o179j3hOct1TF5zxGwTSk8CDugjHiY04Plukh/F8APDdf+GoBfV9XvBPDr9hsi8l0AfhTAd9s7Pysi3qs/B+BzAL7T/sY8z5MgTDa+6cgXgHzQ+T2QfbE3lHJ+kwnv6G9cwPUFS468JzgiXGl/UgTYEso9Vd/bu32/JWxbxke3C/7ozfv4yv0JH+0XvN7X6sev0tn/w/4JaZuiuEzF4Vq7RyhsRCb8/Qx1Oleye6vsyCix25fpWOw81KIS5qQuGWdizWIrOQ5T323nop647M2q76gvZMuI1oZ2BZn0eN+Fnl4UujbGHMHyqK2QfsyN5yR0373cEZoPfcCMtiE2xWGcjn2HftzFdW1yZjR/TOkzdtmMK0yEKtDm4SzNomv6WAaaT/90Y5+c1J/6mc10s2acJWbUXN6sDp3pjz9Zqo5AY6s+9/Uv4X5bcL8t+OrzE758e4WPtksb+0jY0bx93KvJTZ4zX4Kz+j5i9Jze5mD0fyAi3z5c/mEAP2jffwHA/wLgP7Xrv6SqzwB+X0R+D8D3i8g/AfApVf0NABCRXwTwIwB+7a1qKRqB1FJS3O+5Lt7abQ85kJe97nJVMxO4kT7s+tozs0BI7bcuWrfv+3sJtnCsqLFzBGrHrGmgkppvbGYpgGwCfb1gj+37Uj0/toQv43189aMnvP/qGZ+83vDeesN7yw1LKvh4u+A9c/ECgNueg+Hf99xNImb4YjPAbfn7nuJUI2+n26WdHo78ncbxELlaaq6T8yndK1JLBU+XO27Pa/Wr3xPe3FZ89faEN1sd3CCf8Jp3351aBF/drvh4u+Dj7YLX24rbfUHxQ1d8gnvVfWE9kJ42269XOwFpa10aNmh4O2yzV2nPs9mnXBXpG260ViI1MJ4C+jpDUMv3s2HDz3wp2IiOLiTkTmNIzQwW5iVFYlOkjUF1LpPQyaZuMjvg6Pqo5uUhifk0MF7nqIW3boY92+pgQILWBEDv80K2C3EhZM8++EkUl7SHN0+SatP3sVwAvL6vtikxRZ2iqTR2urARPm8fAJkR53SmPwDqZ+OmRrOq/dM7BVUb9rzYVdNpbZpVjKUC5JtAbwlFlrqgvAnu99qhf/iVT+Irr5/w6VdvkKT68wN1o6KDPUil0UaSN0JVqITTAGt0jXjWLjpedpa+Vpv+X1TVLwKAqn5RRP6CXf8MgP+NnvuCXbvb9/H6NInI51C1AuR/6RvqtTRBFAaRendFdw3UtrmJmQ/bcc9Q0FhGfB/ek8lzAb1stEQ8eVT1a6/ePaUseF6qf3pEqSwFtz0jYQ1E9LwvuNNCLjLp4pTYtNKHWeY6kjZitHE//o4W1h5mNh7/3n3qObCaKiJqYm/eGemlUdet5NBuQkM401oGFNsxH6/zYAM+tMe/j8jN6ZKAxYJkpVQXlSXXOgUDtXfaSWWE9s/qciL4pmkmKJl2YY9oXRaC3BkA0xvt9xlD5dQ9M6uz5z3Txow4I7o/jct0lrR9jOEYZl3XXWCNT/rbpyi4G5sjAYbnXmqKP+OmQaBz593uGW9Qd59/eL/GGkdB1fBve2XHd3fd3jP2Qq7Q2ufXnb09tudB+he9kDsr8oylnpJQVX8ewM8DwPU7PqsiaCYFz8wkmwJ9KALRCgxtIqZFsd9Q7cmCKr3vAphNFkBsjDnYeUlvjl2o9qkFdWcvDzSXJ464NmmdZO+K1BV+7IKP7glvris+ul7wdLm3OO1AbADathy+928+uuD63r0dJgOJe25WcRe6brVEGkOoKAdhI2WEXBZAPOxDEBrhb7zKjkve8LRsNcSztvAYt5Kx7Q5l7b11RzFfOTbtqAo+3K54va1IqNvxy55627KfO2CCOpCZVo8dMXqzrHUXcT5NLepDXjtCazOidfwgKZ6ud1yWHTcL8/FsNtnN7cWWz3uXukh0v2aoTWa9Ve8ruSUKPWHF51aW7FLPVli0AQGnNy9O+7u+1iJoLqHEBUNmS3ufGVzUhTxU2NTVCdQmU/rFTr9mTap2cw03afe0zFLDTiconvcFm6bOV5+PV9wsJo2HIoHnS/TyhdluJ7wh6u5AHGt7eOW44GU3S6evINa16jw4QcVj25NGYDel/ummvtUh3YGSG+OvQe0E23PG9nrB/Z7x0ZtLPUR+2fH+pSL++56x5h1/8vGr+H27Zez3XNcqnxPknmw+pAjX7WFgYqxEwMh5+lqZ/h+KyLcayv9WAF+y618A8G303GcB/IFd/+zk+lun2SEhAA4ivKIDZlzOgezn6AYItF57G1RGBli1xatTFKBoPuXaroVv9z2hJMU91V21LcxwjUbo8TgkFVs4lENfunZTSuqu+echOBYjxLGtjiaH9nebbKS5bYq/g8rID7ZZQX/EJaWt9N4cU/LJMCGdzkljg95o+mB7bzBq9N+jwOHdnAouy4ad3TZFm7C357NtpksWSCtlxT56E3Hd2GjPdR3HDTNrr++v/9d3AAAgAElEQVSZmWwyRh0dH8o5u8+ABTjU5wytPdKMW/BA7cZN3Id2h/Ec0owGejY+zqsWgpDyjWaSTFbKZ0Y65R9+QSYPUwZ1R7bGc24adA2g3DJuqO6YvrM7mZlrV8HNnB32PaHsuZo9d7EAgYAmoRhaxlzG8fSAh32tTP9XAfw4gL9hn79C1/9bEfmbAP5l1AXb31TVXUQ+EJEfAPAPAfwYgP/qrUuzMAOhdqd2RCIznZQUu4XFlWTMKReU1R2i0VDfUsLur0jVi6eg2bqV8rYyvRz34pFNEPF8QhAAWK2sQkiCJ7Cragrom4w9KeTpXm32qWBNNfSyqlj45YSU7nFYTI3Y5+6bFdLs5ikEANuWkT1Ewp4eTlLXcHzgd2Gizb7vSP+aqsuZiGJd9iqUblVgXfOGJe/1PQ4JsZbqaqaIHdWwxarVImxe8n4wK3j/BlLlKlv/aqKNKe6pY20NzYZt+/auLmhon7hJTuZGqG3DX1ucpR2eNg7WvKOI4pYKdAWKe1UAh70KbvN1xB6TlMIAuD2+FTIg7oQ2wdGQcJCNBR3lP2qjzLuUflS6ok/+IF13NJ2MPuGimQteLXcsUnDJdSfqR/slYLCHJMkp13kMMv94f7FGVtr1boBQP/donN5VujZsNmRMCLVIqwYCeT2kqi/0mrQuiLKGueX0DA3OHVA2qeHIAejrjK0I9pSx3Re8ydU0EJE47xa6nE06VFbXv9CmCaHNjT8T0xeRv4e6aPvNIvIFAH8dldn/soj8BIB/CuCvAICq/raI/DKA30HdMvJTqr7ciZ9Ec9n8NbztIi4wuCeaj7S1ytvmttjueRMU8v+29y6xti3redD3V9UYc621H+eRaztOnBciIEJEgoRCpNDIA4IlIpIGkYIEcgPkTiIFCQQJHQSSpbQQNGhghQhLECJLYGLRgJgAgg4kDgTFzkOJQgjmGl/fe3Pv2XuvNeccVfXT+B/115hz7ePY1+fsfc4qae8155hj1KjnX//z+9UwJ4ZHGTXS4B9zB4XirHugjVUed5J9R2ATLhrLk9HH0BCtRNc+M04618p6eJGoa1iB5qR/wzfc/o1ArVkCcHRN/TwNFGxRj9U6G+gwb6gk+vyMjoxhpMuuYoJzdtm5/1FfSh3dsXTCOCjRFELwiM9gbI8/iKu/7Zvt6yISDXvW4gUuNtCsg340dSVMVQFAgeKcWKp4HZE8p64FlZXhRUVd/dT9XduMe52IUuzX1PHwWQfIiVTo2yTxES7UglbiK+L16HdPxFM2tJLaZBw1LCoAF1KA25ouXhbeZwchxpC9bY7mfYyJcM5rKlywAxC7QxSY9szc+Ms2+uEa36eBYFQJnORfY1UFMWSvEwt2lTmTABdR3tMhbc22bW7nwy+F6DPzv/jIT7/nkft/CMAPXbn+kwB+86e976IQBw43RELqKjGdtnG5jikPeOQoZQZX8g1Di27URZ5hKMKjh+PpSIYgD9gJikA0FaZ5Cl5JgZPLEFdPR/qkkIx9EGNWiF7zhii5IZcmujwdg2VpqFv2viblwI8qCsoYk4/BGAtpi7WJGg0PpbipzUNGVxpnzUugEkPWpCc+LVY3xhg5wQzceVkEOjdlG+PxvKmDtqCa2hNn51zYAlLIxVpPiKIbaoqbYAzdcJhDtnlVzi7u/eNWkFPHuWaklLBtGb1lVe/ozYc+HVKnWmSdpY6eknBcESNFKZm1NabdhEUWk1EyoJdgnDMTiUmLHeAcOEhFYHT4BaOakyE5QIGnMLy+XvWQsvtNF74bw2uHSQ7qSMAYlo5VJbhDqniWzw5AFjOvmfOCr6FIZe19u63icxhUaeJtxKPvOk6w64ASWLln4rn0HoN0jrYYO5w5xvHYdRvXBI+piDg/Lu3ZnmdWKSLAtQNarySDco6eaIIwMXhyhymH3mftsgl1by6b6/lQjeXdj8iFEBPjgp3T1d8Ig9BbsAj8morqxGLLMUkhC0ea8khmIISFPUDnIqm4vgOmCqGYN1d3QiCuAAQbYxFdvJ/0hhkf0T/DsUxQoLbMaNXeDSy54WjzmYyzHioeA1cz1YSJiu6iaQeThuxTJJLah6tGRefMhNu3+UiEaaynvypR2QHFi2D2GBxzHFrz078oQey3jS8bSzdvh2D9M8SgZW2lkXpwGDF3gSuJweE+K7VmbEW8pHqXAMBuWbS6BoRp7uGi6p9TLULAcofFiLCFSBpHZuPv2P06Tn1s6EFMeeCruCuwjB1tCkZnsQMpSLwBToD1P++aEWWTUPeHfbpCb42eRPVaXCPKoJhkasUkwUOqOKSKnhPQlguwtYi22fRAjeoqV6WEQ9zbHh0VdgedEPE4njoVCSC2/U6zhLevH+OQngLGbC8Zk6V7SlSL7HafGMNDRq/0HVHqd5qw91yL2gadEAaPA1m1FFIlTW63k+T2SHkviD50cbVG7kMNjH6ZBDAFBrnqA67ucPqWBEqAWI2Peo8RDUoC82v6cDskzP+dFvHfRgucgXmbuL6WQYVBSuQpMfqmbFPWlbCDP82pX3BPlKSOm3XDfVldpWLcZs4dXX3ybRPOAWvsBDAm7HBxXrstXC8HpSX8ZLWE1rKhuxphxzi3lrD1jEyMsjT0RuDOKEvF7bqhlYraMrbSUbNiibcBMHeuWRLf7A6aMZe7azTr872ougxFiLrdK7+FBeM7AwO5Uef04bQqXHWXNULCxVlsQCrif26gWSWLB0ZtCcmM7XW8kEvgUDLmsS9jnN23OkyRx5aEw8oIMQVCDG1jxJl3VY33+XKoQKNtHMeYZFH4jnE9x442KWMRs1/ZujRAwZIabgGB2whAYwA8EKkzBYygHUdu74wEnEJTrQt6aFxViUWu1w5WVbPE6xfqPuOe/e943sc0jquNGYU6Y3AfWTsHQ/OYHoaUyYhSYFzDcf4t21bs89vKFRbr3S1dAceiLtkMm2Lpjh4smPSTlIwABu+TxLq57SZ4xqr1UCXYK4tqpSxNuDmIcTKVLpGbtlkT5PPShZvPQqxzaUhFDhEqXfZTtud4cH2q1ilBTWUEMCXG8/WMda3eh7UI+5GV8zRDt4fEGyel3PUIDMLYIBjXjBt1ScUWtG7KjuSc/mRHIPVC6AKRvCzVx/OwVjxbz3h5c8JhqVjWKuquDhyr5Eo9Vske1GtSA1RkUUMbEyZCgDA+2sw4nJd1RG5rF7QFYtAqMBan4yIR1D2JMdyIho5LSh2HUlGoY8kNh9xwWCpK7q52m4hG5MpjhiO9JsRGn4luqoFznaQevYdN0kwzUbTxmxBBrxGBxEF1M4/7ZHfA7nMonlOASZxViBVmoWNN1SEGbvNZcGeo+2Ew1aNuqJNKxaYt9i0Wo/6+TnH9cKJ57RjXHyGjrSoY0efxnLdhp/aJw2b1zO2TveR9iQb5OP+xuKpLmBdTDUYPQV8r+8PJPk+Nul7eC04/Qi8AO2K+66Drt1UMTUGPLA+MOm3knBtWypFLw2HZNEkJsCzVJYmW1COoJeDQwFXhnFkkACody1rF1TKwIzl3tCKeLCkzmBo4JVGDZBF+Ew3xWAzP0karZ8nN+2f68Frz5Cq517N7GyLEQTBkavOcW4L+pR2laCzh4pn4wjXT5qOkjkNpeCA5OO8OZzxbJLr43DKKHqKABHOl3Fy1Q05Y6HID2U8TVwVMicb3xM/u0RMhMmhzxUBfGHltjqfT+i4BSCAEFBiOGtRSk346qh+8c9IJ86l2gmXt9M1KMydvxYhHvK6HNXWImD9xtKy67DAGsU1xPFUFNqVN3NMjV0UNqSBTVKGOexcSYm+JcgBZrwt1dOq4CakExV8/cMGEyXB5wfFrG6ZEIkEXNCdECcBkcdhs+K+wvI+uoWuEdE/wbZFFwu5NtHVDg0BPaySOAauEqW3Xvo70o6F+swnZEgsw34+V94Tos4BbBbdJAoRDM+7EuD5duJ0FA58d05b9/uHdIqNMSWDNzBVzXRteHM4e/ny3bthacoliXasHTZmxrzGhHCrK0vCVF2/w+rTiXCUQI+cukMMAjnUV6aIwUqo4n8QQWHIb3LmraRhIspkzdVGV9DQRpO1cBlQ04AberuPk3hUGHkY8G6dsjCMRityTtmfjjIbkiV8otJFVzL8tG7Y141U5AAA+unnAx4c3OLZFIouXjKMmShFf+GGEpqC2g6qo3N5if10vL8QzNbg4zcDwlGK62OSXXln6qgTwoeP5syOAoSp0AsqY3IPNYaAkyRJl42OYRL0lge3wID59Nor0geGYrsWNzCGgjTCIvumA428JQFcxP3CAlj8gVUw49JPRMYxDqhiHks6DcQRCTEf9RCL15MSojUbsgnLzCzUcqLpULhgzAhv+wSIwBPd1xakWLFnAx2LQ3HQoGcEnS5KC4YpLUPsUe4Sq3UuVgGU3tjGgPajODGZiQkcFhhHVpCIewzItMh6H4mjD7t1anxP4eMhSWCNhnH2iog0QcHWjq5SdAdKDIQJG7sp7QfSHt87Q00eun/RktO/O2SFsWCDMFpxoWSEEzjcQ1WE0FcqRjStX1ZDo/cVzw1xEJRMUa+TsYCdyFrWQ3Zfc3U82Tw7h/ROkBOCZdaIBTNQ6HY3NrRPR+89VWZ86voHruOAwiSevnfh5wC7Iw3YYEInXzpoaFuqo1BW+ofv4m1Sz7QPO9lyWbcA+NvP+vqCi9zZf6DT2HFwsmd0QLoQ7uKMmYyjGGO0lnb20Kc+F9bX/PXJ5QSVxIYkEAmhY/pMaY89pXnsV78YnNt0PCA510fX2ItQT5hCAr9XIOLed5jiiSgqirECI36dVcsbSkG6if/5Q0zD2a2NSyUz3jvZeqFSujTPmZ6fpvbaOroz3kJZ34xelgPjsJB1c/p1cu68dhF4nB88dDKeBt5T3g+gr4e49oXeevFXkc1POiFE1Fp+SUImukyBJOpITUgn0Mq4fwhmEwarGUWuCiJQblhylBEgw0mrtSiilYS0Vt2XDsRSBJVjEBYcALKWCb3Rha5taEajom1KdkK+pyaGQO7pBAtTiHiPMwlkRMQ43G07HZYxVWKST/7sZ+ixE27gDWzATlx8WbmYcUsPGRfz1w0HSLbI4jBkgHODNuuHl+iBgUpxc/23ca8kNmSTymI2amAtanHvzdiECMbt+1uwQpnZw9YVvcJ42O5u9ArjYyLR2t5HYemCOXlEJrYh0JLhByROF7A3mMHVH6aJCA6nhfubU5r+BPdwTqNBe0qQ5Tn07HIL5wpdbh80PrD3xs2eUmE7EK+i7ET8SX6hFZI/ApUsz+tee0DOJqy8kuC9T1zzQFae+4E094Nvn29FXhnuW9VUa6wZuxpQsx6U69WBi0rkIahGpA4OLT1cOv1i3/vVxIAypIh6eOk2pXtZlYz6Sv4Q+2J6LY2trMkHWv+9HrSxKdfocmzhBABaLQ9JGFsz1XynvBdG/WnYn6vBJt004uP5PM2xcVM00hUdP+5DEXTFmt3FOJ4lE0FXSIGKBrVc1SyJognOaDco0uHwLfIrxBtGneZJCSFwHaxn3Xy0kC4Wm3Y358yPPgYS7b6IAG2PCM4c+SyDAkiU3sB0UQJAE3qJvfKzIpoZvuEfLY325Wqk+EsbUJDTpn7SVmAfBBWCQE9cggd8aLLRvZ9zMb2s/DyluupUfGYw9p3nxjmusLpzLnbYMBWK3r8bWY9grBg+esKBxQrZUm6kiK/FP1NFpqCn7Iy+YhvdTOPWLe+Jf69+VEjS/ux8GgRdnAfK/e8mCQluvLAm54TF1yzXuf9/2eJlpkgLI4l/eJjHuyvtF9G1xGeZJ4LJKkcVlCS5aS6LXrkByIK6xSKPB17gzVj//WiWph4FBtZ5wU+RYX9y7htA6OTeYsnhzlNxwUrS8onrqUx2x3Etubis4LNUlituyjUxDUFAqfc5gDs49K5cvznQldby4OaFkCShiDEmEMNQQERsmitHXVCmAcisGNVGEaG+csZBm+oGmu9Md41KLZgTKqeOm1MlTQ4hBcl0ws2UhG4d0Z7jO1aQ7pykUgowIO+iCQfxc27TfTDuueRKZw7iVLO6XpTScSMHuloZzymh5GOejEReAR1GX0lDPV2L3u3bEwMqS+vMb9xekLTdAWlO7hPBz4hHjoYZ5Dwx6ZKOnSuiFBz2wgzMDdMYkqTFhTh6knL3jVDF5MBOTwGcwxBRmYGHnntHrAQ+04Daf8ZXlNQDgRToOrHiS7FFLahKnYRy7Mqt9GQiwJnmMNQBNXxgWr3lB2bQHbzzrl9zPYwmYulDH36GS9be0EfrKITCOFZKB57wPOynZILyNE7eof1KnD0nMow3bjbV3Yj+XEZqjw92IkRhpMZulTkSUKh8p74fL5u4YfoybIv3tmo51jz3via3t9929nmQdg3juOe25TYP47RN+2+9WIvyscfkldRQlqon6xbv2+n4jrksWlVJOc/iLSSgmlgMmFl4fO2nM9cuW4u3Tnnk0IYaWyVMHY1z3UsNj5YKb2r/LNttbyv5n2s2xG2VVrUcEh5ygdDn/+yQ2UwrKx9qw5/Knw3fUP0N2YCIIzuX//QpNbxuf/fu0bcb9z/fqWCE4HkCS45xbRuX9ocjICDAMETRI63tM8pzm+1pbdu29Vse+exfv3tdzpT73m4/zl3btsfYRpnsflW79fZ+yaBDWQ2ib4R/9/ZT3g9NnCeOvGtxkyb+H5458yKl78JaoWyCBVftDI0lAkX02/b6BtDEL52KJSEQ3D/ecMdiAfoXbs6krqYOhSM7q6dN5cJQAsDVxgcxJkAeN0zcukpmce3++nLCmhvttQVbJYMkNL5cjPtlu0HrC1hTqNiwg93SCiobgoOqBcCfmoaDX3QCm5Vk5YeOM+z4y/qzmmqneCJ0lycumWbC2LgFbgOCDbz07eiUAPJwXrKXhtBU09SyizCP5jS5mdvbUOjSYItfV2rgHTjSqKya8mg4xzu44otrSsKmUpoS+g3TODVXUDgHjWLeeUFv2YDX34rG1EWnb/sAx7x4OfeQQbm+PUZAGOnngznD/082gXHEEDbNoZQrExdz+YsSyPxvuI4am4cNMWGgwK4nFHdfWtCRNySjKpJiK59QXLFQnA6+hbWbiCfpBfxxnOLFz9nEpTGBzwfuGOhQmHEFKCXEJOhekuFeTOov13VO0uq0XuIukJysBZM9E6dLe6XOj6zquheiCm9gjbWMEsL071svgKaisx4xzkRF4yyHyfnD6UE45bKwpp+h0fd4wj3mvXGad0jl29c+4J8IGU3j207w4nCEwTnt3v6tidlLAZVuhyIXVDak5iUfMsyJZt0QSgBu4r/Vzati17+N0mH5eqKFxwtaLc/xuc6Bx+DbWBOc8uP4W9N+xLXJND06m6TBEqNObaIRrEsnGj7zvA3BxeE3PRc4SQwpJV9aQt8G51ADvwTQdZo/aLCJB834y9gfBvg9zu69w93vpB1f6fe0djzGHu+vTuO442lhs3Vrqy2vFCH6UGl3q9UPvyoM2dtfmPnLKdrjvJL69hBjHh3b3jt8vueqrZRfkOCQjnsdsvw9j/dMYfwrXvmOCLlKmfgrBB94DTt89a4xzoUEsDZ41qgdK6jieF/QukA0mkndFsDP1j4Ox0XCfBItO3zaxZFIaUkQENJuuA+6qeSjVDZZRjZMTo3V4ohSLYlxyv+Dw19xwswh0gaXvO+SKA0aAVkkdN2XDbT7j1DPW3MQGQcC50j6Hu7xTF6JzTVm4Bvf8UI6GTLcI4UZv0obGyZOZG1xEKQ3cyd0btyaJVFoTqIWNEw5UJbm7SjQ2T60lcBacIPHKSGpfASZsFeNCCXPYPOA+yhJSDyeinpowMFPmJcExMblW0zfJTNaZcKy6JYLBHTq/FnTVff2NHd970kT0w6GAMgTUz9ZnCtxeD9yhEX/brBQpwfhIjWYVHSHo/tlPymsRqHtf75GwZOjNYxKWCI63T6Delzl365KbSz42Xl85vMHH5Q3u0hmHtOFZOql6p+PYF3y9vkRJGsBlhcf7hvQ25nrYG5QxIzjRpYAp73eVIclMsB1mGwlDPnH2ALhgdn+0Z9U3ntPguD3BCo2oYm2Uzyt7fIW2M7Nw9E1FM6JdLEc4FBhDMthoRPMz0M1+NIlAbz843gtOf0SXDoJLeuJd+uurvn6XSWrUNQh/vBbvGQbjQXyidwoAxRth16WnxFiU+7b74iZw751w3byARBTu6JxQOYtPu6kZNLS/aGi7PwvGqqBWh9RcnPakJPGAAgJ3i0vuJa6RXQJ4SupjDeDU1UCt78pZDNhJ1R1TajfsODrMHLQFQQ34iNGmS6wUTITK2my65gmqQO+9VJHoM3kQMd8ndeA21bZL+2hV7tbIXiLrTGB13XWUV2KkwnAAv8h9BiJ/YQeg3V9/ibVb55g4qBHi8zz08DQOORBf0oNg9IxcvR8WVl8kSAZPEKTf/Zi8KEcc0uYE/0V6wF064WU64lmSTFEZuvan0/lKv20+r1ErGv8upL20u4+vXPf652dtD/BuwGjSBYUS9tSUL2EvnQXmFcCk8ol74HIMeD4U7J2VAgooPy7BhfLOc/qAEPDGg+C3qqxMIHApSU7JOFYDihmycHsCU58If++y4UWvz56dKqoXDA/HPHkkOQtjgwWoCNGrPWHJRvBIs9prHQFMzaAMTnUM/7kLAFmhJoS8VIEu0CTcp57xvAhntPWMNYs30cYZD21xVVPrCSV1NB72gESMtlv0Echp8iMm6bx5+RAxDmnDXTpjY0nMfsgVD9si78kdh6Xi2SIb+fXpgFMqWHPDx+sbPM8nSfvYE15vK9a1YtuKBq51PFsET+hbTDgfi7eBIqyCcWsl0IWdnvkib64Vmrn7mMQ6coU5d/e93887MJgE8TKiERCHIcUB8FSWtrbsoX7Ol54VkaE3wpBYMJ0sB6HVkaP0I7+5B4ty4ZEemcZkr+Pn3YHKBKQ9XEG819YNEyxqdp/PwZwGohvzQg03tCn+TsVKTd1++wTTXaihJvVi0vGZII53FIozS8pAwBFx0c2NUl0q+2g/6RiyMg0Tno4dihbDMr0ocN7QsbaxULsKmAR00eYgFgpzF+dZ02T6PUYgrI0pHLC7g8LPxgnPJxyYcS29hZ1/Lzh9C36iEJDFk2itBE+Bv9w7RFUGHqnKNEXo0u7QcPwdnnXjtpgjQqDp0IdPvSx8012bHcBUNkUzYhGAQ6lYAuwCMNIHliRJKLJGsBoIW9VkyasmHlmTBDfVnsVNju2wkbaTjRPDgeKMqER3wIH9z+76JoMjBwMlwVF5kR9wkwQ06yZv0oYibTssFTd5wwfrEWupIJI+frTc44PygA+XB7xYTlhSx81SnTAmAp6tZ3x084DVgNqMuKqrGxt3aoeTHUy7Ejl4uUDhNx4Sgfaf4oay+cltQjq1uTUXWZvnxsnnK64PWVOi7jgs1QPsPNeD9umqO4x9TyzIrCn0NbGA+DmntyMKV6qZCEscMhr3Ofe/I3hsayS4atohEgnZFJuB+bvlVF7I/PMrFrVQppCQx6RGmxdnQMLcjS/SnklNoyq+MQE7icQOwrSTckzNsj8Id9U4sTUYE4YQ/CZjQuaCGaczfuZQIUEgOmKx9dChjEiQEmw92768gPTgncH4ohtXy/vB6RunQeSRt3s1TcTb7xHTItYBCDdge8aIc59VOZa0xYobp3ZH5FDdSF2b+t83jdg81eLSwdYFRsG43ngwAMBNrlhzxZoaOqcL9cKxFefoxYVSgmBOVHBsy2wIndq3Jy7hki7gKTmD3cMqyjKJLlapyCFV3GQ5tADgnBRSOVkQjnDwmTru0hl36YxjWrAmeWbtCafcnaD6AabPdWREtZ2L7cYJe+fCP53Xa1Gp0h8lWsbaq50gDs0wQo+LmRhL7hPeUVTX2bpofeSBii66prpqAf31rRvTO7q71zjAmCwkCSdjXiUCQzI/SzZmkYjGcdN9Za/15ULhORsv04EHqGiLWDdmBTAGqqODxHDLBRs3HFmCtVZVFd6lk8Z99NHXyHEHbn/YO+TdAjE9r9VZH8+XfYYxBcpp7zljGs4A056wtplkHOkIyBUOF+i0/pyNI80E3fustqJgU3CMsQitTSyugMHuNfz8rS88xu99xt5x/TwDPYm6JLnL5tAFG1frQVZ7EVsH3bl5BM4/2XuGIS4aec81Yy3meSDPRs7GylbFkLm1jHx7xHET6ITOhHMtGviTselzrRNyEsJ/UzZ8sDygs3CQVbF2BPyr476uWNVgRsSSlIKBh7bgzbZeBAsx4ER1412wkLNtGIvEFqsFvtiwMWkIvdgRDqni5XLEN/MdltRw1qCcQt2N2SkxbnLFXTrjRX7AxhkvywG3RXLsVssF2sWdT7yPGLl01HMW97Y1bJC4UYHBrYeMRROh2m14I36k/ZUcr6bCkvvN1dU8T8zWIvM01kVEGa1K8M0OMJiRYa/JqWOjjJM101Vo2rj93txfU0JFmSXDUudhlG668WOSHqtG1TrTGPhvM+foKiB/X2zP+E4sBNXUSpk6MsFVXQmyLo2QdyZsWJBZICkkOlv+fZjvcUgBZtklG50XI7wk/XJtpO1Xn9DRX1sDvOwYAGMM3SBNDtImPxsh1f7uvCDcxdnrVA7bAu4YQklJnjXX6Air7VJKPFwSD2lr4eHIEFSvU87izDLnJHWTzj1D6aCqupghQVuPlF+SeoeI/g4R/RUi+stE9JN67WMi+gki+pv696Nw/x8nor9FRH+DiP7ZX+h7knZwzyQN4j2+M4+TcvLjp/l5B20LKh5/X2I/QABdB0GcN2IKzG6bdlgYBx//1mZcoXD6RjC2JsRG8ElE12kG2WiUbZxwbuLvbvgvFo5V9vI5NPsWjbiDPTc0OhsHxVajLHRO7Co1QELpF2oeU2BYQID4Z5uaqXdCSaLDbcGYW6jJARG4wq1LIE/rYe72BqtJzB2cpg99+LvXmsiN4T73hjQAACAASURBVG9giP3sY1ENbk1iCTrLfFc/COTQtoPB7DWWTCaugaZorH00c07uE0tkzvcqq4vFjpk73f8cuciJsx3jcnVsTH0UeKOLg2j3vEkLrs65omdaqOFGjbh36YSbtOGG5N+C5sTfvHfchvY4gzq15+K7P787NK/VF1Uhb6vr6nNXArR2bXfj75W6Is36hQQkmmbi6r3GEOt9j7ka78t3gtP/Xcz89fD9jwH488z8J4joj+n3f4uIfhOAPwTgHwXwqwD890T0D4XE6VeLIDaK0dINs8rJT9gvqaNVAe8qa1XjbEdTt8ekxjbuJBxlFXz3qMtnyH0G4wAMt83NErgEYkokontO5jvPOFdx3TMvlpSk3RtU9cOEeh649A+nFYkkifRtPgNtxUkDWkwF0lkA1zKtuN8WrLnhvq54uQoccNQpC9StQEOspQk8Q0/COZWu3Mrg9j3oJEiKzmHpWAhQFuGGKpbUUFLDTd7cTQ8Ajm0ZnG/NuMkVG2cR6yFEcs0Na26oPeFbx1t0Bu63BS+W4iov0oA6aYtusqTcGKSdVGmI8LbQGd6XcRjM+OqiGoCL87JwhDvazgXHpfitMp9yKJ+2guPDilwagOKH7mmT+00SAIDNAwjHbu89zdKVrmsJ2klg5pFNzW9gcecDZJ40i5sfiHupJrioev9SeCXThe7ePXvS7jkbhOAtZN5ysf03ecO5y9xZs4wZuUtnfJxfY1WDrhlun9EZRy4euHer9qExWBiqm9i3wH17SsRQXM3VMdP53ZCam+zEMPg1WxPzswLsNu5DAriPAMdJ/Th5DPF8sDMEigFjLCnvLPDOmIzUrxNMt82Dva8DyFC0Xoj6+zEmIwznd7r8fgA/op9/BMAfCNf/DDOfmPn/AvC3APy2X0iFRpgFt+Ut4fBBvI7eNyOIiKd7xm/6GePz/mSdOHjMEALArOqJvv7xZG88jLz2GVBYD+fuB7cfSyTs5jFRdpLBNGZ7DmOvT/QOz/ftDUI2HpnEGGf2hBTGDJDNvvXsqpvJ/xoSmJMwIo+t3tYTjq1MsBcXHItJHkakcjD6BWOj90evRdc512Wb8c+uufuhqQhH57em0baWw1UPcneDDWPkUqOtkxBV3QM3ppMxf49j/0jxyF0nMI9z/RdcaDwc9/cBcyYpf+G4h/btDRIygAt4EFEDKldPVbx30JDVgLtQG+Bruiamdj/WJ1zhosMcvrWkMN8Xxt/wXrsn/J0yntkaMuIfHQv29VyTIgAM9c08J28DTLxWhlv6lXvewvT/Ujl9BvDnSHbpf8zMPwzge5j5ZwGAmX+WiL5b7/3VAP7X8OzP6LVPLWIMEw+bmMUKSMHdkrAszf2kzfsmphG03qakmayUIyaC1K8+8b2T5J3NQw1h3L4jbDoSo/mbdw/lT6mL+ygN24B5IMn7NfeqSiC9J9zmDTdpU9VBckJunPtHh3tUFpdUwyJ/VkRTXJTwX4v4NZuAp3I8qX4/6fQxxNcXGHrVoOdnJpy54EU6YksCulY5Y00Vta9O/E+t4H5bJG3i0vAsn0UyoOZ2iqIHwV0543U+4HhesDXCt+hWpDgNKSfzGEkRk17+MRhYAoeqCWGojoxT7rffaXCIJtDoBu4LwIty2FWkx9aSwHiY9HJexCNMge/alvzA7UxYVAq0Q7CUjvO5oG4ZlBiHQ3c4DyhzwWabSOL228zlbzd3ZJynXXeAMR4Q1Gu/MEqa7z6BxqFo0pEFMvXwGyASIAPEIhH2tSOdE7iwB/DtCVrO3ed1z3SU1PAiPeBX5DdoIKyKuXPkjIMS+hvaPHBrzXWoaO19gduePHrMrbPMnLHr+iOx00CqUQeP9KZd1oz/ZnVl3k8FcGhAKzK25kHGSvCz2sAqgZcepBO7bxzWlBSOgcZ3AM7Bkxuvw+HKklmPm7ktkc6/rm9tT1mawNR081B8/CT8pRL938HMX1XC/hNE9Nffcu+18+rqeUREPwjgBwGgfOWD2bgKKLd+Wd01sLV9YJeVRCz0IHL6QYduRl+7vg/mip8tMXT3e6+3K0YOx8XZmcQ/nyo2ythSc0Jum+kmV9zXcRiUNCCLDaDN247LcbB28G5Byed4U/gcVr/goneHyrV2R/tDUm7cJBZx1xMM9T1gm89psJX4+3eqEGlLaJtzrnQhmcz1wKWEqyqDKxwS0bD9GHPB9txO4Z1IMOT3sNfi9jskxovm0aWkOtp2hfsPhEMqtX7zxKLHQKKLJBzW9N3YRs450pupBK7fCRiJ0TZKfXEdZGJksPjmm7R4ZbtfSKlxjsNlR4fdXb+UXB+XaC4kH6I5OIot4nkeo4tgwV0bmRiJQ7a2a5z3Y2W/9q+N/1XKSdNcXGo+Hn/lL4noM/NX9e/XiOjHIOqanyOi71Uu/3sBfE1v/xkAvyY8/n0AvvpIvT8M4IcB4PYf/FVcUnd9vhXzrBF3y+4ENap3HFTNByUQsdRx5gJgbFjnjIlRCnvS8UyMw1L92btlc8Pu7bqJl476eEf9LjAkjpxGtKe0Ca4qML/mryyv8CIfceSCb57vkKhrDtGOj9d7JGI81AU5dTzLZ1WViJ78kCuOtXiC6oj2OQaNJTgEOhTdVo3+3mn8HiKajXBnErE9gf3AOWRB+Fw1tmDrWQHJEj7M9ziyJHgRApFw7hn3dYVBRDMLDPN9Wj1tpbsC1uKbwEDGptyo1k7CELlt6Bm+IS23qOs6bSnYvUo3LQmOQ2xky4wmkB5V9bGFOg5qm2hMQBFCn4NPfkrsKTWXtaL3xW1R3nTj2JyIh/7Y1GQGV8wcf3THC+ByMsXkel1zO7S8qcOLhaexGeMQoBcm2F8M248dpCxAayb5nNvwEOtMOPYFRy44I6sqR1Q7GxK2HQOwJont4Pg+YlDQUXof9vpqA2rTNhJo8vy5mPfdQZJaGmqaPWSyjol/30XtTnp8/UystjNjEvRwcRskKfMl5qHxXArvY2MWSFw6KagTVRqRewHz7ALgY+h2gkfKL1qnT0TPiOiFfQbwewH8FIAfB/ADetsPAPiz+vnHAfwhIjoQ0W8A8BsB/IVf2LseP7Yi4Nc1ELX99at1PPLO6JsdOZISOOvBbUd7wuAW7W9WNVKsfy953NDZo18NWsE4+pIkUtcIrKlKMgT6wYNccKnPn0r0+40lGod2xTwtJLpSoJ9ND+uw0KmJ/35qWMxnX32yOwbwmnku+bhqUFT0fwftYTF27YrfH1sanzLnU12IczzwkWYOdHyWgK056Q2gw6rzaowIMGxSdpOv2bdwYzGu5KK8ZT9c7d9eSvi0e4DLTXGFe20XhoBRJKeyHPRNH2wQPKYG8mvA2EPX3u2R4490eZ/ecw+b4PUpQZ5ckGwO9v8eaYs8N71sloLsd7pyr7/zSvviMpvo1uWt40ZG1EYMNegj+zuUXwqn/z0AfkzVLAXAn2bm/5aI/iKAHyWifwXA3wXwB6VR/NNE9KMA/iqACuAPf5rnjpWhO5fxaS05Jx+hFCyyFhCduUEjMxNqzbDI3N7hgVTmYXP1veHdnQmH3LCpd4L7cLsxlkI7yefR/b6Vi9yUOyqpo9F4VlQFCStV3NAZz8oJb+oqsAskbX1WTrgrZ5TUcVD3yeflhIbXOPeM1+mApsFEk9jtXLAuetMrGpwFB0660cVi/Ti/xof5Hq/oBkde8LIc0TlhWyQIzSSOQ2qonPCz9y/xsjxgoYo3/YBv11uce8GxFbzabhzCYckNd0sX//0gucWD3NrmE9LIOXcZvPAb4N4fExdrdRBGgvgmnkL22HKouF03l+zuz0uA6tYxDIdBoY5GMs52yNWw3kylZ/YlOQSCKoYhnhx7yYUhel+VKrgl6aMFHJrRuukgBXgASyZDTDIOlUBngoOVcQhSAwb08M6rx54HANpMLAm/OaPJE8x0hKf4Zn2OV/0WH6Z7dE44ArjvB5w5Y0PGxgX3ffUALe7aZkv/aHDbgVNySGObW5tvHY9JfbkvPO7lEp6z9WHvrcnjBACRALimoXljAjZdPxqZy0ufbR9BIrKkN/FsYoVw4F0imKS5ms0mxP0y4DK6M9PSkTIrTI3StxzsBo+UXzTRZ+a/DeC3XLn+DQC/55FnfgjAD/3i3zk8I3KWhWLRs90Gksz4xx7RZj73osKxz+R1vu19QAjIChy8lejFY3DBsU5T4Qh3CLSueuDUAcPlZ4l6BYCEjlXxd/b6zkOqmmxFOO6SOg4kQVAW1Wqb0OwS0m6oSI7A0QSOB5CgkiyqAbk++nhDG57RGRtl8bVWIK1DkuVjgVsvywNOffFDKVPHxhmnLi56ncUXvio+EEHUQwNIbhB7t7NEbgpK2GzzRtE9trkHYuFGaRoEloXgGU4Lq2uq5UCwA93nTXFv9rYcd+HVMZ+9woJNSDoCT+Fpbe1hc0YOsQ+iO6us9AuP+/bp87wuJqCzZIBaWNz6rO+hqniojmXL87UUiOxOIoj6fMebIsZ9X3HWbGtHLmjqvvumHzxY0P4mCgZpq98YETNuBjWLBErF8YJi40OiVq9s6SnpTMf1dcMEtJ29MNhniEkC5IjGOnTDPNQhgsPghj7tJSn7TdV77lkYY1QmfX+8Ln/tkGh1eJUZ0X+bduQ9iMglnGseKIjKsXtWq5b0VEyul28WMs4mAVht4zNRQmuEnMXX3vT/wtX52514JhJAt8YCt9C1XfaezZ+P74B7FwFwvf+YkIabUsXjJTVNN9gHbokadA8a3bqkJn7NYDwvJyH4+YQOwpLaMJilrok9Ak498eAYjSiEdcGFh/hL7JGeKbGiJG4AgDfp4JG5Hy73eGgrHtqCou//cLnHy+UOH5c3eNVuceqLJ14xw59JP067OClxNa8mGtLJmDpQo5G8woiV6sU9injvHqnPXhx2NBOKqDf1aOGgktszB25QVyhthgC0td6nOTbvq8Oh4nyG6/VbzUCR+tk4RCNk3pDQXtMTa1yBGxFNvxu5dda+FZnXCJnsun/9HcA0nn6uEIKqAMPDSwHD4iFoh7hJQLUnvG4HPEsnNBCOvOAb7Tnu+wGv2q3Eb/QF922VMTLCFzjfId3R5GYrlxhksQ/W5eDhw/u1w/qMTubE4Ycz1N9ne0UmHtBUlcRByjSabsS+MHjps7qJbQz1fepubu2Z7FHWb1dn0eD209TCi2KumwYLAY4077K880QfgOOzy2dSYhqIbBuuiRFQzQmwceO6mpkJnJqAtuUm9fcxyBaM4sZdrc9AtnrqnjHJdPk9cO1Dl9t98M2jYSnCrRsuzwfrEWcFU2tIWCA6dBN7E4kaZEni3XObhfg+z0d1iaw40eL+zgTZRM0lHi1GTPbEwRaxBpw4R6KL04zMd8SARlgCEN09hNA/tMXjDF7kI14sR3yQ3+DIC459QdVAHOMEzdgMwKOKTU8OSLBSUsLJsf0GSmU2w64ce9bAoygucxCpnUs0zl6r7AgH9Bx/wJgJvW8snr2WDrnioYqxetH8umb0TyQAea9PBxBJwKAFBbaWxADH1jkMAhfRM+1vDqH2ejZjG5zixTzrPHIOP0/cHw2Cvl8b8dDRf2qegfo+gPvwJOMwbp1YDfYFNyRr9cgLfr6+xKkv+GZ9hs6E+y4E/05hlv39sS0hps3jTLRfMo/hgI9jtWMWrl6PDEAcHsaAsCDMmEPXlNHEGiwIcffc7S8Aw3UT7AZdAlzNc2Fv4RGdzlEatH50HQCE5+wQyaP+x8p7QfStRA+cqIUwVYlw1IPYJSgnFc5yOWmVqKeQdtHE8jQIfgwYejgvTqjOLQ+I5dQ17H4McymyOqJx1VQBJRm0rHw2iOS7fMLLpEiWpuqhAfdrYe1F/d7NHa4pYd14wDLIs8LG1D3Yl0duMpAUI2S/yRNASwen5EbKlQgdGlhD3RETN2SBXOABHmcH07EvU5usXdHQ/LAtOh4iCQGq7rIDN4lHhoNsOSAXgAYFqpIkGRyIO3RKI1cm3im6fnaYPdtWcA9pg0NoQNbZslSklEDJ1BGX3lFGj0oWw7ufnabyCSpCH2o7mIOfOCWW+IEYjNXJCb4nN2GIxd6/WL/YI0h5xyH7ixWzaOKIGbNqRAmp1xe5bBIGy9Rgh1JRqOHMxXNDfFjuAQDfanf4Rn2O+77ivh3wuh1k3tuChTqW0vAsn4KDwWin08CdJ5EcZjxLAVHdc63sCb5i53DIceqqJMDB2XwMDw18yuOACQlxBOF1134bTxvLsMeMm2eCHwLm5msajZg0yr4bRLy9o1cCSgrX5ND5NCeB94zoA9abCKSWEqNj4Ji7Lj7o7c3AFtE0o642JQTjG7tqxp4/bUVc8ohx5hGQlRNjCxxmJkbJHZ2BtSgnfF7cQ2FRr5ytZywKZ5CI8SId8WG+xw1t+KTfKLcvrpmAGLwMmXCDHAIbZ2yc8bodUBX3Js51jOKVAcROPaB3793gEiMvHT1LcFjnhATghggrxHvnQB1bHzr9UytOBJ/nkxhD+8HD7Y1IRnTNzoTaskfoLkned9rKwLI3ImcbXYOtXJ+vrnZTtqxr3DLgLm6e/ciJB7Cdiwb4iQRnK633hLvDhpYEQto8jyLRj4ZM82bygC0LnCMz7u7mg4BUukKAS39yZvQq0Asxg5XncwZGLmHjWONhaGBiCR7EZEZeTqLn54XHeChRN9uAq8rsn0kNgeBKYh8J1LM1fGwLbvKGkjq+e/0EAPDN9hxfry/xut3gdT3gTRWif+oZyBsOVJEsUCzOq61Vm0/jZu1wKyxGZjVSX4UeGLeP8TZuPptba7gn0JcYdYvMWG43bDWJms1UjKTjuocl9/fxcNFlM8zKdTZ3XeJBpHloMOzZgZvFQE8DpI0JXAkdw/7kAJJ76W9X3guiH4l9JMYpdx+06C2xD7YC0gVBB2bx3aN2/X37+sa9ricP332jp45V9byLcvpb7hN3aARBIAgEs2ZTBavpQDfOogNXPfixL7hLxfOM3vcVR9WXf1JvcGzlwmtnMi5a820jKcc3cXYQQkJZIvxaEyJ2RsbGEsxmGCp9J1l0kB9CnQmv2o267Q1JwzjoGMBTAcfd6bs5GQsgfN77Sttn5cpG6kd2ImV6/ynAx7uthLsSQMmRQq2U0lByA9FQJxrUQCJBDBUk0wGtYTYBOQSSwzkIGBtNXjxzPwecAyWA2ziX5eWBsZ/GRsXVSLz67r7QXy67TFiRS7a1QYN7nbJWBc6zJDHA7qWeQh03tOFNP7jhNrp3Juo46NcTF1FLpgBkhkCzJg59nru5Y5gicK/dN3n9wO4dY2FG8QE7YffZWBkhDr9fa6dd2o3p+G7r5PpzFwZdr2McSERRPRTaZIcjXZl8Le8F0c95BGcNaANgXQckQ+8ZRAPONt6rWkMXkYzrN/uAvSMFsXzTwJqknFtryUXy2hMOIRmCcf2FgLVU3C0bak/uUdOZPI9o4wR00dufWsF9FVhk83ZI6PhWe4ZjXwThkcW3/ZN64zANlTO+XW/xSb3FQ1vwjdMzvKkrNsOOsU3IEgDl6ise3k4gFgNUshUJIHcgAWnpuLs5CUR06rjvBxz5tYyTUpL7vk7oio0Jp15w31Y0JPzc9gE6RhIZI/ibGtwThLg+bAXnliWqGeTolJP3gUknScHXjJuxHeGqDLlvSAE67w3gFUMNkDDc8uz7Mbsx//ZwdtVFToy7ZZuynBmq6ZIaTq14prKq/Xi2itF/0ZwKW8s4HhdAD3zn+GI/Cc4VkvZJFtcgdG5XiLpgU1UQO5gcZ0baEvbww/IwwGsI3lFu1Y25HED4nMPGMPQr01CK2Jwss9tRjfVm53iRj/hWv8PX6wvc99UZgKSpQJ9n8fS6byuem3rHYCESg9Stct/+mPHN+uP92GiGVDCpJzxjuW2tn86pA6KPTzu9fOmC8EsQu0pSiOvMbtCeiT8HKcnaovZGpT/D5jIMuz6XwHiffu6aqMWupaL0LZmnznh2qKvxaHn3iT5ZsAvcnTAGu5DpGGlw6ntOfM6RG6r2A2S43xlg2OW9cCyc1pX4t4ScZgJFgIOKmXrFuHsr9k7jkFg5+Vf9FhuJ//KpF9SeNTNWwUNf8ardqCeMeEe8aStOraDyHPAU3zEye8FtGzE60MZ4WqikQUrKjSd0LHY4YgBl7fX1ACbOHhj6b2tbDp9F/zvGpF9lTTEv4PjdiESUZHa/uYgeygTaFetVVUkOUp/NUSIWCGya++0OBrpu1tzdk4owDPgpyeYV+A/yQ9g5wD64tcG1IRgBgUe9MgLhG33cXbP+xTnXZx00LMIS2Li5VVDtPynO38izcO7ZD8rbLGkShXGZBz+TqIVsTZhLrxvtdVzc24ZiZzDP5SQZ8HV3zaucdFgD8b5r60GHoGtkth+4jzHSfhC/XcUSJSa2vzyuef/8cA5r1tfqZR8vmIkr5Z0n+sZdGxcODFWM7+/UQZR84TQeATOWMxYYuteqBtqo/zdi1FoClTZUQfr+RdMWHkoV9AJYgJcs4N4TchG8eFMPJGKcanEVgHH7ICWeqbn74je2Z+j4lfggP+Dr23N883yHY1twX1c81AVfvf8Anyw34ulDjJ8/PsdZ9eVbz0OdoP2zsuaGU+ojNLuTpOOrAqiFSgJgBgiwU6fgew7cLWd12ZR3fbOL/eE13eB1vVXphcQoR90PuJuyoXHCkRZXCZUk47ep11PRCGM7+FpPokpBIHx2YIcwdVsHnNVVM0F0rcaxmk6bcUnwg9jsel1iB24DRFqzNbEpXHROHQ+ng0t68RCvmkjl+eGMl4cjbvKGUyuuwrs7iKx5PC9i+D8tsp6TSom5oanPOhtglhHX6B4Y/a+J56Atu6YEycG/jAlIgIOwBYLm0BuJxRtIOVAHJiMgLU0hoIWoUO44lIbn5ewE/NQLynJE5YTvWl8hUce360tX7VgcSqEmnD2AE3W8qQc80KKSZuTSVXLp47uvA/WNj+6oSACvgwMfag5MB7rXodcnBmCvUrHxAVBPWcb1nIe9KIWxjPAYTEDuHiRFeURnAzbnxsAqbTKmTeeRMRgOcy4ZaV/lnSYxptKHB2OePRivlV80DMNnXbIG8xgRdoIfCDOAaXD3ZQ+MFe0ACHXE38b3IQ1YsWCxWKK/8j65RNS1270DM1+giU0MNsOsJ+xQ3BojOFURDuO/+I49jAQFicTVPb4pwmctkagJFC7B0s7nt/Tt00oCBy5/wBnsS3TrlEZ/SsVvkQgclnn/G135busJ8LbFsbgACMNYc0sWw/xqEBo0sJvMBdXUiFF/nwKx8T5/ynC6CH/tvr10M/WVL8cy6KyvjodhvITxMhWdRYZbngVz8bViAH17UDb7LNAcObRtR3gv5onnv9O9V7j3PYUzQr9/9lrZE//IXb+tTGvtkQd8z4WxNzqktG4OrgxqWqsirFdfO7+Afr37nL4SiJLln0S+wt0kU5KAiB6SVkRi7CoU6DgoN4wsA7tkvlCNEDCSi6uutmhC8kgAnEsN7TWPHCuVkxP327Lhk9ONomp2PF9OLhrf5jPusnCEi3JPUbXgG4Qz0AWeuHbxhz6qn3gmdtdJc/Vsqq7IxeIRFBcmJ9kAPBaTB44wYasj2fqH6exqG4uiXFLFIVWcelEgtrMaNDMOqSpAW3cOz+aCSHS+N3nzFJCcuiSp0bZuLXgxAEPNEN0YEwTMAwjXlFuPxjxiRB/RCcIhEDfKHUgCmsYAbkrFbdnAfMCxFhyU+y9JEsNXyji24nr7nBgvlhM+Wh9wSBXHpglZ6uque7VlHJYqGbiaNJI6YV0rtrOoOAQ8qw9u3/ToKqGlwmp01g4GqWB4uCB8D18TC1fPEIqhah0qXSAhFh4pGG1NaNQnp+QeJ5RYxief8VG5FwP+kvHQFhxSwyFtyGB8oNDKVmwt36QN3663uEtnPCSJ46DEoKJqxwqFUMZMxKxrsb9m74nF5t04b+PCe7jBCGRcJ1G6tOux7muElCDSIvNu/ZEbXEeksdQrXlgieZs3YYZ4eAHiVdjUG5G7uHEmPaxcPahcvUkMEbAuFcajhw3eA07fuHgjfPJ5/P5WcLHH6qShbzXxKv5m740lBS7M3nmNQ02hrRf/wDNXDnbuaHiEmB9/n+u7wlkbNxnv27fB708BQoKCV1LgrKIBqIXDzIy3nS02QOIEIszy9bGYx9Y8XvZtzUG3DwRpbc+5XylupIslck+PFe27Q+nqoWFcd1xve7RS+U0YEJtT03FHjvfRf2HNRTsHjEZfJS7m/nelT49JLRf9xeWYXpMC3ranAjOViLEkVYWha4Be97WxWmS5JUshnj6n0PfI2Y4Lj/QFj1wDZj33o/29Iu1cK3bPYxx+rIMv/7p75l4AufJu34tkUiEutQ1X3h/VfdEW8LbuvfOcPghYFOK4ag5TAG40awa0pp1n/S0aaI27TwpPILC+smktqrazJF0nGhyJDbf5X7t3D4l+PkodAFxnb8T83IvmtRWd/kNdAseu6hoWt82btOEryyusiqXzjfIctWd8uD7g2AruyuYHRiL2AKhEjEOVIChJqcjIeRjKom98LNFDwaKRU+q+InuAtThyxqsuksO32h3u+2GqK4LOVZUE7mnFx2lDQ/Jo3TVX3JQNrzeBcjCIidYTDqV6ukVXw0WdqTdWl7NxYva30UgucbGGBhfHUe8KCNuTgFw6Uu4opeG4FbcJmRdO07y355ZxVuO5eSdZ/8+94KGJ19WxLa7TB4BjLTjXjJIlVWczmObF7EcAvN8QzpsJMEO8Qn3n3HHqi/xOPOAZJrhlcgIyiJCd5jS4YJMkdAwp8bCF2DOJUbes7ZHx7hA1Y4ZAaJ96wUm9zRbqaCB8V/4ELSfcpA3/Lz7CfV9xBzhD85X0Cqe+4FkWaXeOHjcCSIN7NunOOOk9FWZgWiz2kW08ENYG7dYU3N41rRdmhf3A4P7t9xQ+G4mthIFjoTr4hACeNp5h9bSzRE05d6QktMzgtDE/pQAAFcNJREFUuUvuOKkrtyEOEEGCcTXhU8rdk/wIsVf6uI/RCeXdJ/oYnL4R+OFvH+W/ocp5vI75u0EsEC6vD30aOwEkwD04XAKBGAJbT64OAoZh2AyUyIJAacTXdJlJjZ8JgnGzkKkRhHAfUkUH+V8z5K7KYTUe7qAnFCeY0YMp2jGkUXEch9cAaPgos24i8b9P2LihA57zNpbu9oWkLqYZPZHrdgsNw7aAq63OLZdE7tLawtxFu400CI9zW4Rh8NtPv6kALogEDQOhbp5SRBffA/yzS/lubMbkihqRVTtLoveE4b8f8YZ6lwTwnuv5Slce61+iQfSjKH81bmFf4p4w4ph4ZHKKaqH9IQsMADhbGzs4Ao8pcfwC4JlCMEi2tZeyFtKQGg9pw9aLBBmmBodLeYtNTohxBBIKbfU2h75YneGnCwbCGYJ5f4y+Yxh+47OBkbh4zsaY9cvuNoKtG/3uXLpcyMRiX88NW8pgHq6Zk4tv5O6tXpOg07XNIuXdJ/o8VAcSHcquY2+dnDNsPTlXlAPh0ypUTJ+rNm+Xxp5XSTeqFEteHo2imdghOMyNj4hRcnvUxrO33LP+fVAO3bB2FqpYqaGTuLx5/lsIkNlHyz2+evoQhRo+Wu6RIGiGt3nDq+0GgHgrCRc6PJeaSjfm2wtAdIEkOkPjRromkTdOg1kSsv9/7SWOfI8zskTZcsapL3hdD/ik3uLUM079Fr/29u/hJm049gXfs3yCX7f+PO7SCa/6DZ7nEz6pt67yeV7OOKSKV/XgnF5Lwv28uKlgAK8o6Jtd5RNVAIHKm/56Twz83nDNCL5ep9KxLAJ9bXYjBrD1hLPq820dbC3LvPHwBDM8oa/fP8Prs3j4vDmvaJ2wFnFrPNWM1gjH84K6FRi2Su+Ecy1T/IhMEKuKwA5mQ5jdUxDtyKR75pmg2TD5vRBvlz7eRYyRh9c4ah1PbjTXrxLPqRe8ajd40w44tYJNAQNvqOLjvCEBWOg1tqXgyAvOjq5ZPDfDXT7h79Vn4Ko2A5sX++yJxKG2Bh7cu80lMHPeTOYJMLV5/5xz8fEeWyt2yVJqqnfYHOkth9AEE2HvWLu3NRVRI1rUdVT3tJodDsYg4w32fbjwGjMqLzbvnWbSHoQW2f1vO/+B94DoT4ercdb2WyCmERc/ctNWkq6Dve6ZaCB0yn3RNVQwfYBxABihZ4gh19A15UC6zBcqHNCsm7NyauLO+Ww5q9HTfOKHesYI/8tyxPcu38J9W3FIFR+XN0jUcd9X3CgC5rEVnKlIMAcUsSBIHJZ/NkpLPRAD7vrbEqQUJnyjPZf6eZFo4V6wcZbYAQ2rP7eMF8+P+Ki8wde3F/i4vMZ351e4oQ3f6neI6RItx26h5gicOWXv8/P15MbpkTnKJgwzAXeuLnJtcYJ3HNx+Hkg2ZVaYBWMCADk0PcG9zWdPjrJq0okQ44Q3teC0leFxVTP4cMaqRvTeE+pG6HUcvhIkqI4EKQbiBKKla9IwWhyDhYfKwIG5jMBHDjhKQAQ1iI+Dwbw+XIUTOsxGxOL4M6F1icBOnV2lde5FXXcr7oiwUALQ8Ca/dq+0zglvePXhX6nivh2k/TFCfO8PfyHBzXN4MfdRkvNxoGH4Zoz8ydeopNVfNGCxSJ7aSTKKa3AvTYV1SSROAmYIj/74zBSka5k4y909xRv5Psa411yUr7XhLeWdJ/pxTizYqXXCkrsmKhdd9I0mwJAoUuHqoy7bDgwoXg4AMHdP2SeER1UO9m5i5CQSRcRZicVURov6lz/UBd/EHUrqwg2pZ8e5ZT84rB7ztLFgnzd9xTOljZIg5YzGotp5kY94lk54nk+4yyf1kOjolNDTGS/LEQ/Lgs4b7tV2YOqjb/IzUQ0szRPFW/+iq2fvCWB2lVZKkpDl2BekLJu5cRK8n3rA67o6cTZI6A/zG3QmR+NcqWm7j3jVbtBBuE1n9/w5qJrq3AtuyiaZwbLCTFu6QlsEFnVrc2D6afPkCSKy+Erz7N1iC8rTRLKjEpoK5rBsSM49SvwHQw6ArWWk1HFqAzX0tBWxDXWBWGgKDd3Ur732hHpOkzeSeVc4kqhKV+tacToVQX9NBNI1RamrFBaiyPf2DtPF++kUJQaax24n+vuBkcJBU3gkJgHgOD32usQ+d4U61tRxSA23+exMSFcKtaJjVV1/I0JisQW0lJC444Nyj7Q2dEpDYrM+mWrJ1oABrSnEs89zJMLmSUMQ4MAtzYTd4lQWW1N2ncOYYSCbktpZLFCtdMVECmOZwviTMCsmTTtDufSJOJu0B+IA7U6+/7Y2uH9XR7NKhDYsxMFmIHXn0q4ai737j//07pToukgQILOiBMmI+Jqrh8KLigcA2A22Jp5nGgZXYBwMW81urLW95MFONDas7yn9zSbUjJXHKsZbgujbLbDnrFDK9s7OgsvfOqEv4hHzqt8iE+OGNixJ0AfftAOWvOF5PuIunfBBuVfdv7pk6or9oDzgoa0oqeHb6Ra3eRP9MnW82Q5CTFe4qsoP0IjCCVt0IjnlLMTuyIKEaZ03kLf7urpr4k2uOKQNL9MRuTDu6CSiPoQAvEwP+Ha+FZC2AoeSNoP0mioSBIp4TRU9U/Bfl41JWdU9TTjlDnX1yx3d5HTj+loerptGsIxwEJyQSNCa9Ld1MXpnwNWGZxoR1dtWcHs4C/heEtygcxVXSxtH26SWza335Ll1oRJiyg3mrmc2lbI03K6bGHmVWEpuXZmPVoU4O9OhhGBwm32oYYL05odBEvdL3mgYfaOUpATI1NtUAiiY1WPP6F68y2dhCKj7PN6lMxZqjoCdASzUcUMNZzV+Z7BnjVu54ZgWLGvFxkWhzVVNoUR/zjAFoKt7Z1XKF4ir3Utq4F4OFZVC4BsS0qKQCEgyLkUOBlpGYiYAmpUKHuNCEe7D1lPGLjBLxjoVsRHJ3CX3vSeS6F7WcXfBJhh6HXUTyfOFDJ2/uGrKdTgIX+tDasu7YLB9efeJvjF1PADMGgvA2dm4dxj4U5pcAb0KYifKQMjJaR45ynXFXJ02ZIarYoBqwPDusWKHih0aW/AIap2QiFRaSK7jjc8mYifmkpnqjOf5qElSZBOtauB9ofDLXfWnK2U58ED4FWv2TFqJ2Dnpn1+eI9FzV/cI0ZeFEheajdU0/MQ49cUB4SxfrqlrPBhNc/aa3rYj4b4fkKir8Tdh6wUnLnoYjfiIEhLAAPBgHeNiKIcQfZscGlwSJQwu1jgfuz8SQGDGKFdu0DjzcxXQuiU3B1CzTGitDywnAKieqAcTVxU/D4nKfhSDsatnfLxno7sRVtfRRhE/bOY5dF8ORsdkyRhccxwH+7wvdj2Fv+av70R13EvEOFBFo+SwC+Z4sHHGtzqwoOPICUddE82M2mb0BWCZ4lJiJ7ZupFSMpKHmgqjR1auHCpQo8xjTpLfr+rB6fK7soCX2g9AOO0pxTmyObJ7J101Mbm9j5W2AGlx1/okYPc3ovoCqkZNIcTlzyLBnd4w+XduTsV/WPnvH5JxxpbzzRJ9gnjsJz1cJXooE1vSvBrR2fxZ1gxFr8wVuPAKWSuojRy6TG4DNDXPrCUvqqMqdJ8uipPVt6qkzuH75a4TrtBVvEzOhJ4N+INws1a8b4cvU8SvLtwHAPXg+zm+Qi6hMLBlFpo4P8z1Wavik3+CGNnRKuEsnJHS8SKICOnPGkVe8bjf4nuXb+Ob2DP/3tz8C9TRFhxIhJPAeXF30iko0Ut8BskljzIDB6loKxyPL+FtqvJt0xrGvknGMM+6bgG8t1PCgoG2HVCXjEnXUXkJiFd08WYzrvmEhG6qrwTmlLsF5CphFepAbIqJvgqjCMO5XPVhazegtYbtVoq/uo7ZGTII0Hb4RZFGDze4sNrZmEI/2opw7ak3O5ctGFW7NGZYk+E4TQVexvrfZ64OJ3OXWc0UUcb01Qy3pgUjJCI0QcW7DEOjETAl8yozetc4ypDwjjEvquFM4BQA+px+Ue5y54P+pL6fI3O2RdNieMMiCB4k86FIHbbpfclzIeDHgrpC9khNwI8hRjWm2O1HZKMHMIiUa0xAPHZuvpoZTVomDcvf7uAao46SQHmANmjJPK3Y3TJMGuzEz0Oj00rBtOayXISlGnLGxviBzRKzqng7mEn5nPIrkis+B6BPR9wP4DyGC0Z9k5j/xac9sim1iSIebbkaDrE3EOOYiKpWAd+5Jyc3nOtR5ChZyR4AkWUjnWtBSF7/qVESEx3DXPNfsnLLpc81O8HBecN5E5O95uPJJmkdR6dgcbi1h24pHrwLCDaUwYRbR6kFRAdoYJMTVVCjmA52RkHkEeG09S27amtEooZQGZklrZ+knocZIIkbddDwAnEvGq3aDV/0WC1UceVXvnewceUkNnUY2pJUq7gE0TnjGGW/6Aff9gFMveGgrNk6olB2fJlHXmIWsfu9Jgp7M26FDxHxldLkRehrgVz1hqAEYomtlcsOkg2TJwhiGSX2GWxIpKDPOVXz0JUVnGtDJmrJzuGhKdV119w73rZtUvo/fiFT1kzQ5Txcur3fzmGK85oP4xDOh1aEC6F2wb2rN6JuoJNAJXXFW3OuGzVio1/Q6u0E0CRY8A5ShOvE0Es30wc12g3Vu4/CQMZRI0q0nlQCz+ukX9EQ49UWMtf2AlZozC8a42DptnBRCXDx7WkuT2oN3LrP+V50NWCU2yyswGbIB94zimucxYcGgZ82WJ3PE3veuhByA2MuMsDcduxL835s6lfQ4xkCvCT0zas2aZElycpudh00hr0Pqfeeh0uw+F+w3ujdPn13Ta4U6b0iSl/O5CEDcI+UzJfpElAH8RwD+GQA/A+AvEtGPM/NffeyZzgmvjgcnmoBCC5Ak26ibdHRTTqxqoMK5dh9g03NZgpPaE14fDxKs5RDKgppJxHjzcHDubFMc9GMuqq+9NLq0mtEOCQ+JcXxYUU8C85yWLps7M3pNSJlxn1ecUsdaGh5OK05vVjy8WJDQsXFB4xUbddHV60rfOMvmScAzOjkXvVDDz9cXuKHN1S+AHBQNyX2iv73d4HhcUE8F3BKW203ytDZZ1NElj5YOPhtHlPGagZ89vsTH5WPcpTNetRt8a7vD6+0Q0gQm3NEZX99eAABeqNEWEB3v63bAfZdcuq82wf5fcwvqnY7X20GTp5Nk00od53NBU6CryaBnuu0qG61Z/tZzGnp8BuiYByibuSkCI99rl0QUYAJqRl87Xh8O6AzcHw+ScFrFerMj2Jy7zlQ3pwFdueEvDymiV0mNyMeM7aCHEQVVE4BaumRmYgBLB07ZXYNbEZCvdkoyDosB5pG7NLIeBADUcKncvSX8AMCVQecEXhQeuJKk+rOEK76A1GPKGHwz/nYS42cjvDmt+Pr2HJ0Jn9RbvKoHPFNVZEPCB1kyZ73qklfhRTriJm3OuIgNS37LYGzngrYF2wcPjhahDVwTDC65bxnYFHitk/TJDv5lHGSoNFRVjcApjbEylRmrxF6TX2tLB07Jgd+okx86IB3LQ5dxtPqWDnBCZUJvwz7ATKjnLIlx8iyJtTpsEUmhKNjsQDbuNjmm8jNnlE5oxEBNaB3AwpIj/B1S7/w2AH+Lmf82ABDRnwHw+wE8SvSHUWN4zggML4aYzQRm8b8aLk3jdztBO9R9l8k5MXP19NynMLE6vKsTUqJwL/k7oC6k4p3RhytV4uECqUTIuL6U4Lpi5rfOz6PFDLimGxUifz0Kz7D1zeQvYzYWu7uyUdgoCWBRoKKrx04DhcQu+n7MoG8NyZOpAJgSq3TWGAJO6DzbSCb3W1Y3Nm8fRjvdOydcM6Osjjt0ji/cFuN3e53d1+McDY7MOOnISc5xF/psGvNOMH0SfH3aRDtXHp8FZh/wUL/Ph12b+kQzKxz7tr8/9jXWzWHc9vXHZ3bXTYIFhqG7gxQsMKl7pibW6QUbFSwsB4LBLdtvXSEtzCZjkifjUrVhbXLOOoztNC7ged47zcbWOCb78bTF0cnHhgJ3/rZx8c9x79tlO8zC2M9tBpjNn19+IrZ1MksyPL037mMeXk2PFGLmR3/8Thci+hcAfD8z/6v6/V8G8E8y8x/Z3feDAH5Qv/7DAP7GZ9bIz798BcDXP+9GfMblqc9fjvJl6/Pn3d9fx8zftb/4WXP6V8/tiwvMPwzgh3/5m/PuFSL6SWb+Jz7vdnyW5anPX47yZevzu9rfzxpl82cA/Jrw/fsAfPUzbsNTeSpP5al8actnTfT/IoDfSES/gYhWAH8IwI9/xm14Kk/lqTyVL235TNU7zFyJ6I8A+O8gLpt/ipl/+rNsw3tQvoxqrac+fznKl63P72R/P1ND7lN5Kk/lqTyVz7e885mznspTeSpP5al858oT0X8qT+WpPJUvUXki+p9jIaI/RURfI6KfCtc+JqKfIKK/qX8/+jzb+J0uRPRriOh/JKK/RkQ/TUR/VK9/IftNRDdE9BeI6P/U/v67ev0L2d9YiCgT0f9BRP+Nfv9C95mI/g4R/RUi+stE9JN67Z3r8xPR/3zLfwrg+3fX/hiAP8/MvxHAn9fvX6RSAfzrzPyPAPjtAP4wEf0mfHH7fQLwu5n5twD4rQC+n4h+O764/Y3ljwL4a+H7l6HPv4uZf2vwz3/n+vxE9D/Hwsz/M4Bv7i7/fgA/op9/BMAf+Ewb9ctcmPlnmfl/18+vIEThV+ML2m+W8lq/LvqP8QXtrxUi+j4A/xyAPxkuf6H7/Eh55/r8RPTfvfI9zPyzgBBIAN/9Obfnl60Q0a8H8I8D+N/wBe63qjn+MoCvAfgJZv5C91fLfwDg38SMWPNF7zMD+HNE9JcUSgZ4B/v8zuPpP5UvZiGi5wD+SwD/GjN/Qm/L7/aeFxY0wN9KRB8C+DEi+s2fd5t+OQsR/T4AX2Pmv0REv/Pzbs9nWH4HM3+ViL4bwE8Q0V//vBt0rTxx+u9e+Tki+l4A0L9f+5zb8x0vRLRACP5/zsz/lV7+wvebmb8F4H+C2HG+yP39HQD+eSL6OwD+DIDfTUT/Gb7YfQYzf1X/fg3Aj0FQhd+5Pj8R/Xev/DiAH9DPPwDgz36ObfmOFxKW/j8B8NeY+d8PP30h+01E36UcPojoFsA/DeCv4wvaXwBg5j/OzN/HzL8eArXyPzDzv4QvcJ+J6BkRvbDPAH4vgJ/CO9jnp4jcz7EQ0X8B4HdCIFh/DsC/A+C/BvCjAH4tgL8L4A8y897Y+94WIvqnAPwvAP4Khr7334bo9b9w/SaifwxiwMsQJutHmfnfI6JfgS9gf/dF1Tv/BjP/vi9yn4noH4Bw94Cozf80M//Qu9jnJ6L/VJ7KU3kqX6LypN55Kk/lqTyVL1F5IvpP5ak8lafyJSpPRP+pPJWn8lS+ROWJ6D+Vp/JUnsqXqDwR/afyVJ7KU/kSlSei/1SeylN5Kl+i8kT0n8pTeSpP5UtU/n8yGvRn/BlNCAAAAABJRU5ErkJggg==", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# START SKIP FOR GRADING\n", "np.random.seed(4543)\n", "nsamples = 32\n", "X = []\n", "Y = []\n", "for i in range(0, nsamples):\n", " if i%10 == 0:\n", " print(i)\n", " x, y = create_training_example(backgrounds[i % 2], activates, negatives, Ty)\n", " X.append(x.swapaxes(0,1))\n", " Y.append(y.swapaxes(0,1))\n", "X = np.array(X)\n", "Y = np.array(Y)\n", "# END SKIP FOR GRADING" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You would like to save your dataset into a file that you can load later if you work in a more realistic environment. We let you the following code for reference. Don't try to run it into Coursera since the file system is read-only, and you cannot save files." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "# Save the data for further uses\n", "# np.save(f'./XY_train/X.npy', X)\n", "# np.save(f'./XY_train/Y.npy', Y)\n", "# Load the preprocessed training examples\n", "# X = np.load(\"./XY_train/X.npy\")\n", "# Y = np.load(\"./XY_train/Y.npy\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "### 1.5 - Development Set\n", "\n", "* To test our model, we recorded a development set of 25 examples. \n", "* While our training data is synthesized, we want to create a development set using the same distribution as the real inputs. \n", "* Thus, we recorded 25 10-second audio clips of people saying \"activate\" and other random words, and labeled them by hand. \n", "* This follows the principle described in Course 3 \"Structuring Machine Learning Projects\" that we should create the dev set to be as similar as possible to the test set distribution\n", " * This is why our **dev set uses real audio** rather than synthesized audio. \n" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "# Load preprocessed dev set examples\n", "X_dev = np.load(\"./XY_dev/X_dev.npy\")\n", "Y_dev = np.load(\"./XY_dev/Y_dev.npy\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## 2 - The Model\n", "\n", "* Now that you've built a dataset, let's write and train a trigger word detection model! \n", "* The model will use 1-D convolutional layers, GRU layers, and dense layers. \n", "* Let's load the packages that will allow you to use these layers in Keras. This might take a minute to load. " ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "from tensorflow.keras.callbacks import ModelCheckpoint\n", "from tensorflow.keras.models import Model, load_model, Sequential\n", "from tensorflow.keras.layers import Dense, Activation, Dropout, Input, Masking, TimeDistributed, LSTM, Conv1D\n", "from tensorflow.keras.layers import GRU, Bidirectional, BatchNormalization, Reshape\n", "from tensorflow.keras.optimizers import Adam" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "### 2.1 - Build the Model\n", "\n", "Our goal is to build a network that will ingest a spectrogram and output a signal when it detects the trigger word. This network will use 4 layers:\n", " * A convolutional layer\n", " * Two GRU layers\n", " * A dense layer. \n", "\n", "Here is the architecture we will use.\n", "\n", "\n", "
**Figure 3**
\n", "\n", "##### 1D convolutional layer\n", "One key layer of this model is the 1D convolutional step (near the bottom of Figure 3). \n", "* It inputs the 5511 step spectrogram. Each step is a vector of 101 units.\n", "* It outputs a 1375 step output\n", "* This output is further processed by multiple layers to get the final $T_y = 1375$ step output. \n", "* This 1D convolutional layer plays a role similar to the 2D convolutions you saw in Course 4, of extracting low-level features and then possibly generating an output of a smaller dimension. \n", "* Computationally, the 1-D conv layer also helps speed up the model because now the GRU can process only 1375 timesteps rather than 5511 timesteps. \n", "\n", "##### GRU, dense and sigmoid\n", "* The two GRU layers read the sequence of inputs from left to right.\n", "* A dense plus sigmoid layer makes a prediction for $y^{\\langle t \\rangle}$. \n", "* Because $y$ is a binary value (0 or 1), we use a sigmoid output at the last layer to estimate the chance of the output being 1, corresponding to the user having just said \"activate\".\n", "\n", "#### Unidirectional RNN\n", "* Note that we use a **unidirectional RNN** rather than a bidirectional RNN. \n", "* This is really important for trigger word detection, since we want to be able to detect the trigger word almost immediately after it is said. \n", "* If we used a bidirectional RNN, we would have to wait for the whole 10sec of audio to be recorded before we could tell if \"activate\" was said in the first second of the audio clip. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Implement the model\n", "\n", "In the following model, the input of each layer is the output of the previous one. Implementing the model can be done in four steps. \n", "\n", "**Step 1**: CONV layer. Use `Conv1D()` to implement this, with 196 filters, \n", "a filter size of 15 (`kernel_size=15`), and stride of 4. [conv1d](https://keras.io/layers/convolutional/#conv1d)\n", "\n", "```Python\n", "output_x = Conv1D(filters=...,kernel_size=...,strides=...)(input_x)\n", "```\n", "* Follow this with batch normalization. No parameters need to be set.\n", "\n", "```Python\n", "output_x = BatchNormalization()(input_x)\n", "```\n", "* Follow this with a ReLu activation. Note that we can pass in the name of the desired activation as a string, all in lowercase letters.\n", "\n", "```Python\n", "output_x = Activation(\"...\")(input_x)\n", "```\n", "\n", "* Follow this with dropout, using a keep rate of 0.8 \n", "\n", "```Python\n", "output_x = Dropout(rate=...)(input_x)\n", "```\n", "\n", "\n", "**Step 2**: First GRU layer. To generate the GRU layer, use 128 units.\n", "```Python\n", "output_x = GRU(units=..., return_sequences = ...)(input_x)\n", "```\n", "* Return sequences instead of just the last time step's prediction to ensure that all the GRU's hidden states are fed to the next layer. \n", "* Follow this with dropout, using a keep rate of 0.8.\n", "* Follow this with batch normalization. No parameters need to be set.\n", "```Python\n", "output_x = BatchNormalization()(input_x)\n", "```\n", "\n", "**Step 3**: Second GRU layer. This has the same specifications as the first GRU layer.\n", "* Follow this with a dropout, batch normalization, and then another dropout.\n", "\n", "**Step 4**: Create a time-distributed dense layer as follows: \n", "```Python\n", "output_x = TimeDistributed(Dense(1, activation = \"sigmoid\"))(input_x)\n", "```\n", "This creates a dense layer followed by a sigmoid, so that the parameters used for the dense layer are the same for every time step. \n", "Documentation:\n", "* [Keras documentation on wrappers](https://keras.io/layers/wrappers/). \n", "* To learn more, you can read this blog post [How to Use the TimeDistributed Layer in Keras](https://machinelearningmastery.com/timedistributed-layer-for-long-short-term-memory-networks-in-python/).\n", "\n", "\n", "### Exercise 5 - modelf\n", "\n", "Implement `modelf()`, the architecture is presented in Figure 3." ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "# UNQ_C5\n", "# GRADED FUNCTION: modelf\n", "\n", "def modelf(input_shape):\n", " \"\"\"\n", " Function creating the model's graph in Keras.\n", " \n", " Argument:\n", " input_shape -- shape of the model's input data (using Keras conventions)\n", "\n", " Returns:\n", " model -- Keras model instance\n", " \"\"\"\n", " \n", " X_input = Input(shape = input_shape)\n", " \n", " ### START CODE HERE ###\n", " \n", " # Step 1: CONV layer (≈4 lines)\n", " # Add a Conv1D with 196 units, kernel size of 15 and stride of 4\n", " X = Conv1D(196, 15, strides=4)(X_input)\n", " # Batch normalization\n", " X = BatchNormalization()(X)\n", " # ReLu activation\n", " X = Activation('relu')(X)\n", " # dropout (use 0.8)\n", " X = Dropout(0.8)(X) \n", "\n", " # Step 2: First GRU Layer (≈4 lines)\n", " # GRU (use 128 units and return the sequences)\n", " X = GRU(units = 128, return_sequences=True)(X)\n", " # dropout (use 0.8)\n", " X = Dropout(0.8)(X)\n", " # Batch normalization.\n", " X = BatchNormalization()(X) \n", " \n", " # Step 3: Second GRU Layer (≈4 lines)\n", " # GRU (use 128 units and return the sequences)\n", " X = GRU(units = 128, return_sequences=True)(X)\n", " # dropout (use 0.8)\n", " X = Dropout(0.8)(X) \n", " # Batch normalization\n", " X = BatchNormalization()(X)\n", " # dropout (use 0.8)\n", " X = Dropout(0.8)(X)\n", " \n", " # Step 4: Time-distributed dense layer (≈1 line)\n", " # TimeDistributed with sigmoid activation \n", " X = TimeDistributed(Dense(1, activation = \"sigmoid\"))(X)\n", "\n", " ### END CODE HERE ###\n", "\n", " model = Model(inputs = X_input, outputs = X)\n", " \n", " return model " ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[32mAll tests passed!\u001b[0m\n" ] } ], "source": [ "# UNIT TEST\n", "from test_utils import *\n", "\n", "def modelf_test(target):\n", " Tx = 5511\n", " n_freq = 101\n", " model = target(input_shape = (Tx, n_freq))\n", " expected_model = [['InputLayer', [(None, 5511, 101)], 0],\n", " ['Conv1D', (None, 1375, 196), 297136, 'valid', 'linear', (4,), (15,), 'GlorotUniform'],\n", " ['BatchNormalization', (None, 1375, 196), 784],\n", " ['Activation', (None, 1375, 196), 0],\n", " ['Dropout', (None, 1375, 196), 0, 0.8],\n", " ['GRU', (None, 1375, 128), 125184, True],\n", " ['Dropout', (None, 1375, 128), 0, 0.8],\n", " ['BatchNormalization', (None, 1375, 128), 512],\n", " ['GRU', (None, 1375, 128), 99072, True],\n", " ['Dropout', (None, 1375, 128), 0, 0.8],\n", " ['BatchNormalization', (None, 1375, 128), 512],\n", " ['Dropout', (None, 1375, 128), 0, 0.8],\n", " ['TimeDistributed', (None, 1375, 1), 129, 'sigmoid']]\n", " comparator(summary(model), expected_model)\n", " \n", " \n", "modelf_test(modelf)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "model = modelf(input_shape = (Tx, n_freq))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's print the model summary to keep track of the shapes." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"functional_3\"\n", "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "input_2 (InputLayer) [(None, 5511, 101)] 0 \n", "_________________________________________________________________\n", "conv1d_1 (Conv1D) (None, 1375, 196) 297136 \n", "_________________________________________________________________\n", "batch_normalization_3 (Batch (None, 1375, 196) 784 \n", "_________________________________________________________________\n", "activation_1 (Activation) (None, 1375, 196) 0 \n", "_________________________________________________________________\n", "dropout_4 (Dropout) (None, 1375, 196) 0 \n", "_________________________________________________________________\n", "gru_2 (GRU) (None, 1375, 128) 125184 \n", "_________________________________________________________________\n", "dropout_5 (Dropout) (None, 1375, 128) 0 \n", "_________________________________________________________________\n", "batch_normalization_4 (Batch (None, 1375, 128) 512 \n", "_________________________________________________________________\n", "gru_3 (GRU) (None, 1375, 128) 99072 \n", "_________________________________________________________________\n", "dropout_6 (Dropout) (None, 1375, 128) 0 \n", "_________________________________________________________________\n", "batch_normalization_5 (Batch (None, 1375, 128) 512 \n", "_________________________________________________________________\n", "dropout_7 (Dropout) (None, 1375, 128) 0 \n", "_________________________________________________________________\n", "time_distributed_1 (TimeDist (None, 1375, 1) 129 \n", "=================================================================\n", "Total params: 523,329\n", "Trainable params: 522,425\n", "Non-trainable params: 904\n", "_________________________________________________________________\n" ] } ], "source": [ "model.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Expected Output**:\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
\n", " **Total params**\n", " \n", " 523,329\n", "
\n", " **Trainable params**\n", " \n", " 522,425\n", "
\n", " **Non-trainable params**\n", " \n", " 904\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The output of the network is of shape (None, 1375, 1) while the input is (None, 5511, 101). The Conv1D has reduced the number of steps from 5511 to 1375. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "### 2.2 - Fit the Model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Trigger word detection takes a long time to train. \n", "* To save time, we've already trained a model for about 3 hours on a GPU using the architecture you built above, and a large training set of about 4000 examples. \n", "* Let's load the model. " ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "from tensorflow.keras.models import model_from_json\n", "\n", "json_file = open('./models/model.json', 'r')\n", "loaded_model_json = json_file.read()\n", "json_file.close()\n", "model = model_from_json(loaded_model_json)\n", "model.load_weights('./models/model.h5')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "#### 2.2.1 Block Training for BatchNormalization Layers\n", "\n", "If you are going to fine-tune a pretrained model, it is important that you block the weights of all your batchnormalization layers. If you are going to train a new model from scratch, skip the next cell. " ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "model.layers[2].trainable = False\n", "model.layers[7].trainable = False\n", "model.layers[10].trainable = False" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can train the model further, using the Adam optimizer and binary cross entropy loss, as follows. This will run quickly because we are training just for two epochs and with a small training set of 32 examples. " ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "opt = Adam(lr=1e-6, beta_1=0.9, beta_2=0.999)\n", "model.compile(loss='binary_crossentropy', optimizer=opt, metrics=[\"accuracy\"])" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2/2 [==============================] - 3s 1s/step - loss: 0.4367 - accuracy: 0.8764\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.fit(X, Y, batch_size = 16, epochs=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "### 2.3 - Test the Model\n", "\n", "Finally, let's see how your model performs on the dev set." ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1/1 [==============================] - 0s 1ms/step - loss: 0.1883 - accuracy: 0.9238\n", "Dev set accuracy = 0.9237818121910095\n" ] } ], "source": [ "loss, acc, = model.evaluate(X_dev, Y_dev)\n", "print(\"Dev set accuracy = \", acc)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This looks pretty good! \n", "* However, accuracy isn't a great metric for this task\n", " * Since the labels are heavily skewed to 0's, a neural network that just outputs 0's would get slightly over 90% accuracy. \n", "* We could define more useful metrics such as F1 score or Precision/Recall. \n", " * Let's not bother with that here, and instead just empirically see how the model does with some predictions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## 3 - Making Predictions\n", "\n", "Now that you have built a working model for trigger word detection, let's use it to make predictions. This code snippet runs audio (saved in a wav file) through the network. \n", "\n", "" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "def detect_triggerword(filename):\n", " plt.subplot(2, 1, 1)\n", " \n", " # Correct the amplitude of the input file before prediction \n", " audio_clip = AudioSegment.from_wav(filename)\n", " audio_clip = match_target_amplitude(audio_clip, -20.0)\n", " file_handle = audio_clip.export(\"tmp.wav\", format=\"wav\")\n", " filename = \"tmp.wav\"\n", "\n", " x = graph_spectrogram(filename)\n", " # the spectrogram outputs (freqs, Tx) and we want (Tx, freqs) to input into the model\n", " x = x.swapaxes(0,1)\n", " x = np.expand_dims(x, axis=0)\n", " predictions = model.predict(x)\n", " \n", " plt.subplot(2, 1, 2)\n", " plt.plot(predictions[0,:,0])\n", " plt.ylabel('probability')\n", " plt.show()\n", " return predictions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Insert a chime to acknowledge the \"activate\" trigger\n", "* Once you've estimated the probability of having detected the word \"activate\" at each output step, you can trigger a \"chiming\" sound to play when the probability is above a certain threshold. \n", "* $y^{\\langle t \\rangle}$ might be near 1 for many values in a row after \"activate\" is said, yet we want to chime only once. \n", " * So we will insert a chime sound at most once every 75 output steps. \n", " * This will help prevent us from inserting two chimes for a single instance of \"activate\". \n", " * This plays a role similar to non-max suppression from computer vision.\n", "\n", " " ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "chime_file = \"audio_examples/chime.wav\"\n", "def chime_on_activate(filename, predictions, threshold):\n", " audio_clip = AudioSegment.from_wav(filename)\n", " chime = AudioSegment.from_wav(chime_file)\n", " Ty = predictions.shape[1]\n", " # Step 1: Initialize the number of consecutive output steps to 0\n", " consecutive_timesteps = 0\n", " # Step 2: Loop over the output steps in the y\n", " for i in range(Ty):\n", " # Step 3: Increment consecutive output steps\n", " consecutive_timesteps += 1\n", " # Step 4: If prediction is higher than the threshold and more than 20 consecutive output steps have passed\n", " if consecutive_timesteps > 20:\n", " # Step 5: Superpose audio and background using pydub\n", " audio_clip = audio_clip.overlay(chime, position = ((i / Ty) * audio_clip.duration_seconds) * 1000)\n", " # Step 6: Reset consecutive output steps to 0\n", " consecutive_timesteps = 0\n", " # if amplitude is smaller than the threshold reset the consecutive_timesteps counter\n", " if predictions[0, i, 0] < threshold:\n", " consecutive_timesteps = 0\n", " \n", " audio_clip.export(\"chime_output.wav\", format='wav')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "### 3.1 - Test on Dev Examples" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's explore how our model performs on two unseen audio clips from the development set. Lets first listen to the two dev set clips. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "IPython.display.Audio(\"./raw_data/dev/1.wav\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "IPython.display.Audio(\"./raw_data/dev/2.wav\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now lets run the model on these audio clips and see if it adds a chime after \"activate\"!" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "filename = \"./raw_data/dev/1.wav\"\n", "prediction = detect_triggerword(filename)\n", "chime_on_activate(filename, prediction, 0.5)\n", "IPython.display.Audio(\"./chime_output.wav\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "filename = \"./raw_data/dev/2.wav\"\n", "prediction = detect_triggerword(filename)\n", "chime_on_activate(filename, prediction, 0.5)\n", "IPython.display.Audio(\"./chime_output.wav\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Congratulations \n", "\n", "You've come to the end of this assignment! \n", "\n", "#### Here's what you should remember:\n", "- Data synthesis is an effective way to create a large training set for speech problems, specifically trigger word detection. \n", "- Using a spectrogram and optionally a 1D conv layer is a common pre-processing step prior to passing audio data to an RNN, GRU or LSTM.\n", "- An end-to-end deep learning approach can be used to build a very effective trigger word detection system. \n", "\n", "*Congratulations* on finishing this assignment!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## 4 - Try Your Own Example! (OPTIONAL/UNGRADED)\n", "\n", "In this optional and ungraded portion of this notebook, you can try your model on your own audio clips! \n", "\n", "* Record a 10 second audio clip of you saying the word \"activate\" and other random words, and upload it to the Coursera hub as `myaudio.wav`. \n", "* Be sure to upload the audio as a wav file. \n", "* If your audio is recorded in a different format (such as mp3) there is free software that you can find online for converting it to wav. \n", "* If your audio recording is not 10 seconds, the code below will either trim or pad it as needed to make it 10 seconds. " ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [], "source": [ "# Preprocess the audio to the correct format\n", "def preprocess_audio(filename):\n", " # Trim or pad audio segment to 10000ms\n", " padding = AudioSegment.silent(duration=10000)\n", " segment = AudioSegment.from_wav(filename)[:10000]\n", " segment = padding.overlay(segment)\n", " # Set frame rate to 44100\n", " segment = segment.set_frame_rate(44100)\n", " # Export as wav\n", " segment.export(filename, format='wav')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once you've uploaded your audio file to Coursera, put the path to your file in the variable below." ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "your_filename = \"audio_examples/my_audio.wav\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "preprocess_audio(your_filename)\n", "IPython.display.Audio(your_filename) # listen to the audio you uploaded " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, use the model to predict when you say activate in the 10 second audio clip, and trigger a chime. If beeps are not being added appropriately, try to adjust the chime_threshold." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "chime_threshold = 0.5\n", "prediction = detect_triggerword(your_filename)\n", "chime_on_activate(your_filename, prediction, chime_threshold)\n", "IPython.display.Audio(\"./chime_output.wav\")" ] } ], "metadata": { "coursera": { "schema_names": [ "DLSC5W3-2A" ] }, "kernelspec": { "display_name": "Python 3", "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": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 2 }