{
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"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.10.0"
},
"nbsphinx": {
"execute": "never"
},
"colab": {
"provenance": []
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "eovvr27-u0Uo"
},
"source": [
"
\n",
"\n",
"# Comparing GP packages on synthetic light curves\n",
"\n",
"This tutorial makes it easy to compare **eight** Gaussian process libraries\n",
"commonly used in science and machine learning:\n",
"`pgmuvi`, `tinygp`, `celerite2`, `george`, `GPy`, `gpflow`,\n",
"`scikit-learn.gaussian_process`, and `gpjax`.\n",
"Because of dependency clashes, we're only going to run the comparison with 5 of\n",
"them, skipping over `GPy`, `gpflow`, and `gpjax`, but you can always run this\n",
"notebook yourself to compare them too if you're curious.\n",
"\n",
"We use **the same covariance kernel** (or its nearest equivalent) in every tool\n",
"and compare:\n",
"\n",
"- the amount of user code required,\n",
"- wall-clock fitting times,\n",
"- inferred PSDs and fit quality, and\n",
"- which tools natively support multiwavelength (2D) data."
],
"id": "eovvr27-u0Uo"
},
{
"cell_type": "markdown",
"metadata": {
"id": "X8A1Ta-4u0Ur"
},
"source": [
"## Mathematical framework\n",
"\n",
"All tools model data with a Gaussian process (GP):\n",
"\n",
"$$y(\\mathbf{x}) \\sim \\mathcal{GP}(m(\\mathbf{x}),\\; k(\\mathbf{x},\\mathbf{x}')),$$\n",
"\n",
"with a constant mean $m$ and a covariance kernel $k$.\n",
"\n",
"We use the **Spectral Mixture Kernel** (SMK; Wilson & Adams 2013):\n",
"\n",
"$$k_\\mathrm{SM}(\\tau) = \\sum_{q=1}^{Q} w_q\\;\\exp\\!\\bigl(-2\\pi^2 v_q^2 \\tau^2\\bigr)\\;\\cos(2\\pi \\mu_q \\tau),$$\n",
"\n",
"where $\\mu_q$ are mixture frequencies, $v_q$ are bandwidths, and $w_q$ are weights.\n",
"\n",
"For `scikit-learn` we use $\\mathrm{RBF} \\times \\sin^2$ (quasi-periodic kernel).\n",
"For `celerite2` we use the **SHOTerm**:\n",
"\n",
"$$k_\\mathrm{SHO}(\\tau) \\approx \\sigma^2\\exp\\!\\Bigl(-\\tfrac{\\omega_0}{2Q}|\\tau|\\Bigr)\\cos(\\omega_0 \\tau), \\quad Q \\gg \\tfrac12,$$\n",
"\n",
"which has a Lorentzian (not Gaussian) power spectrum.\n",
"All other tools implement the SMK exactly."
],
"id": "X8A1Ta-4u0Ur"
},
{
"cell_type": "markdown",
"metadata": {
"id": "RNvUrhCju0Us"
},
"source": [
"## Installation\n",
"\n",
"The cell below installs any missing packages. It is safe to re-run."
],
"id": "RNvUrhCju0Us"
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "dQRCuOFeu0Us",
"outputId": "79239e59-4df4-495d-a600-010020b463ae"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Installation check complete.\n"
]
}
],
"source": [
"try:\n",
" import pgmuvi\n",
"except (ModuleNotFoundError, ImportError):\n",
" %pip install git+https://github.com/ICSM/pgmuvi@main\n",
"\n",
"try:\n",
" import george\n",
"except (ModuleNotFoundError, ImportError):\n",
" %pip install george\n",
"\n",
"try:\n",
" import celerite2\n",
"except (ModuleNotFoundError, ImportError):\n",
" %pip install celerite2\n",
"\n",
"try:\n",
" import tinygp\n",
" import equinox\n",
" import optax\n",
"except (ModuleNotFoundError, ImportError):\n",
" %pip install tinygp equinox optax jax\n",
"\n",
"# try:\n",
"# import gpjax\n",
"# except (ModuleNotFoundError, ImportError):\n",
"# %pip install jax[cpu]\n",
"# %pip install gpjax\n",
"\n",
"\n",
"# try:\n",
"# import GPy\n",
"# except (ModuleNotFoundError, ImportError):\n",
"# # _pip('GPy')\n",
"# %pip install GPy\n",
"\n",
"# try:\n",
"# import gpflow\n",
"# except (ModuleNotFoundError, ImportError):\n",
"# # _pip('gpflow')\n",
"# %pip install gpflow\n",
"\n",
"# try:\n",
"# from sklearn.gaussian_process import GaussianProcessRegressor\n",
"# except (ModuleNotFoundError, ImportError):\n",
"# # _pip('scikit-learn')\n",
"# %pip install scikit-learn\n",
"\n",
"\n",
"\n",
"print('Installation check complete.')"
],
"id": "dQRCuOFeu0Us"
},
{
"cell_type": "markdown",
"metadata": {
"id": "nEvaAL1au0Ut"
},
"source": [
"## Imports"
],
"id": "nEvaAL1au0Ut"
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "AB-0yhe4u0Ut",
"outputId": "b6ffd567-d6b2-4090-8fbc-d04c19003fee"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"GPy not available: No module named 'GPy'\n",
"gpflow not available: No module named 'gpflow'\n",
"gpjax not available: cannot import name 'xla_pmap_p' from 'jax.extend.core.primitives' (/usr/local/lib/python3.12/dist-packages/jax/extend/core/primitives.py)\n",
"\n",
"Package availability:\n",
" celerite2 : ok\n",
" tinygp : ok\n",
" george : ok\n",
" GPy : SKIPPED\n",
" gpflow : SKIPPED\n",
" sklearn : ok\n",
" gpjax : SKIPPED\n"
]
}
],
"source": [
"import textwrap\n",
"import time\n",
"import warnings\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.gridspec as gridspec\n",
"import torch\n",
"\n",
"from pgmuvi.lightcurve import Lightcurve as LC\n",
"from pgmuvi.synthetic import make_simple_sinusoid_1d, make_chromatic_sinusoid_2d, make_multi_sinusoid_chromatic_2d\n",
"\n",
"# Storage dicts\n",
"preds_1d = {} # name -> (x_grid, mean, std)\n",
"psds_1d = {} # name -> (freq, power)\n",
"timings = {}\n",
"code_lines = {}\n",
"\n",
"# --- optional celerite2 ---\n",
"try:\n",
" import celerite2\n",
" from celerite2 import terms as c2terms\n",
" from scipy.optimize import minimize\n",
" HAVE_CELERITE = True\n",
"except (ModuleNotFoundError, ImportError) as e:\n",
" HAVE_CELERITE = False\n",
" print(f'celerite2 not available: {e}')\n",
"\n",
"# --- optional tinygp + JAX + optax ---\n",
"try:\n",
" import jax\n",
" import jax.numpy as jnp\n",
" import equinox as eqx\n",
" import optax\n",
" from tinygp import GaussianProcess as TinyGP\n",
" from tinygp.kernels.base import Kernel as TinyKernel\n",
" HAVE_TINYGP = True\n",
"except (ModuleNotFoundError, ImportError) as e:\n",
" HAVE_TINYGP = False\n",
" print(f'tinygp not available: {e}')\n",
"\n",
"# --- optional george ---\n",
"try:\n",
" import george\n",
" from george import kernels as gk\n",
" from scipy.optimize import minimize as scipy_minimize\n",
" HAVE_GEORGE = True\n",
"except (ModuleNotFoundError, ImportError) as e:\n",
" HAVE_GEORGE = False\n",
" print(f'george not available: {e}')\n",
"\n",
"# --- optional GPy ---\n",
"try:\n",
" import GPy\n",
" HAVE_GPY = True\n",
"except (ModuleNotFoundError, ImportError) as e:\n",
" HAVE_GPY = False\n",
" print(f'GPy not available: {e}')\n",
"\n",
"# --- optional gpflow ---\n",
"try:\n",
" import gpflow\n",
" HAVE_GPFLOW = True\n",
"except (ModuleNotFoundError, ImportError) as e:\n",
" HAVE_GPFLOW = False\n",
" print(f'gpflow not available: {e}')\n",
"\n",
"# --- optional scikit-learn ---\n",
"try:\n",
" from sklearn.gaussian_process import GaussianProcessRegressor\n",
" from sklearn.gaussian_process.kernels import (\n",
" RBF, ExpSineSquared, ConstantKernel)\n",
" HAVE_SKLEARN = True\n",
"except (ModuleNotFoundError, ImportError) as e:\n",
" HAVE_SKLEARN = False\n",
" print(f'scikit-learn not available: {e}')\n",
"\n",
"# --- optional gpjax ---\n",
"try:\n",
" import gpjax\n",
" HAVE_GPJAX = True\n",
"except (ModuleNotFoundError, ImportError) as e:\n",
" HAVE_GPJAX = False\n",
" print(f'gpjax not available: {e}')\n",
"\n",
"print('\\nPackage availability:')\n",
"for _name, _flag in [\n",
" ('celerite2', HAVE_CELERITE), ('tinygp', HAVE_TINYGP),\n",
" ('george', HAVE_GEORGE), ('GPy', HAVE_GPY),\n",
" ('gpflow', HAVE_GPFLOW), ('sklearn', HAVE_SKLEARN),\n",
" ('gpjax', HAVE_GPJAX)\n",
"]:\n",
" print(f' {_name:<12s}: {\"ok\" if _flag else \"SKIPPED\"}')\n",
"\n",
"def count_lines(snippet):\n",
" return sum(1 for ln in textwrap.dedent(snippet).splitlines() if ln.strip())"
],
"id": "AB-0yhe4u0Ut"
},
{
"cell_type": "markdown",
"metadata": {
"id": "OVxSZi_xu0Ut"
},
"source": [
"## Synthetic datasets\n",
"\n",
"We generate reproducible synthetic light curves with a known period of\n",
"$T = 40\\,\\mathrm{d}$."
],
"id": "OVxSZi_xu0Ut"
},
{
"cell_type": "code",
"source": [
"SEED = 0\n",
"WAVELENGTHS = [0.8, 1.2, 2.2]\n",
"\n",
"\"\"\" Generate a set of light curves with two period components and a phase lag\"\"\"\n",
"n_per_band = (25, 100) # number of data points per light curve limited to this range\n",
"\n",
"TRUE_PERIODS = (150.0, 66.0)\n",
"TRUE_PERIOD = TRUE_PERIODS[0]\n",
"TRUE_FREQ = 1/TRUE_PERIOD\n",
"Q_MIXTURES = 2 # mixture components used by all tools\n",
"\n",
"\n",
"MULTI_DATASET_CONFIG = dict(\n",
" components=[\n",
" {\"period\": TRUE_PERIODS[0], \"amplitude_fraction\": 1.0, \"phase\": 0.0},\n",
" {\"period\": TRUE_PERIODS[1], \"amplitude_fraction\": 0.3, \"phase\": np.pi / 2 * 0.85},\n",
" ],\n",
" t_span=150 * 2.3,\n",
" n_per_band=n_per_band,\n",
" wavelengths=WAVELENGTHS,\n",
" amplitude_law=\"extinction\",\n",
" noise_level=0.05,\n",
" seed=SEED,\n",
")\n",
"\n",
"lc_2d = make_multi_sinusoid_chromatic_2d(**MULTI_DATASET_CONFIG).double()\n",
"\n",
"# Extract the better-sampled wavelength and make a new 1D lightcurve\n",
"# First identify which wavelength is better sampled:\n",
"\n",
"wave_counts = np.unique(lc_2d.xdata[:,1], return_counts=True)\n",
"print(wave_counts)\n",
"best_wave = wave_counts[0][np.argmax(wave_counts[1])]\n",
"print(best_wave)\n",
"\n",
"times_1d = lc_2d.xdata[lc_2d.xdata[:,1] == best_wave][:, 0]\n",
"flux_1d = lc_2d.ydata[lc_2d.xdata[:,1] == best_wave]\n",
"errs_1d = lc_2d.yerr[lc_2d.xdata[:,1] == best_wave]\n",
"\n",
"lc_1d = LC(times_1d, flux_1d, yerr=errs_1d).double()\n",
"\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ntA77Vx1U-AV",
"outputId": "cc5e8c54-2ddd-4430-f77d-69107ae28aae"
},
"id": "ntA77Vx1U-AV",
"execution_count": 21,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"(array([0.80000001, 1.20000005, 2.20000005]), array([89, 73, 63]))\n",
"0.800000011920929\n"
]
}
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "gu-F7eq-u0Ut",
"outputId": "5af88eb9-7591-4f3a-a1fa-5a1ca0d0bb2f"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"1D dataset : 89 points\n",
"2D dataset : 225 points, ndim = 2\n"
]
}
],
"source": [
"# TRUE_PERIOD = 40.0 # days\n",
"# TRUE_FREQ = 1.0 / TRUE_PERIOD\n",
"\n",
"\n",
"# lc_1d = make_simple_sinusoid_1d(\n",
"# n_obs=120, period=TRUE_PERIOD, amplitude=1.0,\n",
"# noise_level=0.2, noise_type='gaussian', irregular=True, seed=1,\n",
"# ).double()\n",
"\n",
"# WAVELENGTHS = [0.5, 0.8, 1.2]\n",
"# lc_2d = make_chromatic_sinusoid_2d(\n",
"# n_per_band=[80, 70, 60],\n",
"# period=TRUE_PERIOD,\n",
"# wavelengths=WAVELENGTHS,\n",
"# amplitude_law='linear',\n",
"# amplitude=1.0,\n",
"# amplitude_slope=0.6,\n",
"# noise_level=0.15,\n",
"# t_span=180.0,\n",
"# irregular=True,\n",
"# seed=2,\n",
"# ).double()\n",
"\n",
"\n",
"\n",
"# Convenience numpy arrays for 1D data\n",
"x1 = lc_1d.xdata.detach().cpu().numpy()\n",
"y1 = lc_1d.ydata.detach().cpu().numpy()\n",
"ye1 = lc_1d.yerr.detach().cpu().numpy()\n",
"mean1 = float(np.mean(y1))\n",
"\n",
"# Prediction grid\n",
"x_grid = np.linspace(x1.min(), x1.max(), 300)\n",
"\n",
"print('1D dataset :', len(x1), 'points')\n",
"print('2D dataset :', len(lc_2d.xdata), 'points, ndim =', lc_2d.ndim)"
],
"id": "gu-F7eq-u0Ut"
},
{
"cell_type": "markdown",
"metadata": {
"id": "MLPVK3leu0Uu"
},
"source": [
"## 1D (single-wavelength) comparison\n",
"\n",
"We fit the same 1D light curve with all available tools.\n",
"Each tool uses the Spectral Mixture Kernel with $Q = 2$ components,\n",
"initialised with the true period and its first harmonic.\n",
"`celerite2` uses the SHOTerm approximation (see [Mathematical framework](#Mathematical-framework))."
],
"id": "MLPVK3leu0Uu"
},
{
"cell_type": "markdown",
"metadata": {
"id": "X_POhD5Wu0Uu"
},
"source": [
"### `pgmuvi` — 1D spectral mixture GP"
],
"id": "X_POhD5Wu0Uu"
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "UtVjPG7ou0Uu",
"outputId": "e9d3aa7d-9b97-4f72-98c8-f121403b0051"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"mean_module.constant: 0.028102993965148926\n",
"covar_module.mixture_weights: tensor([0.4779, 0.4779])\n",
"covar_module.mixture_means: tensor([[[0.0067]],\n",
"\n",
" [[0.0154]]])\n",
"covar_module.mixture_scales: tensor([[[0.0053]],\n",
"\n",
" [[0.0037]]])\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"100%|██████████| 1000/1000 [00:11<00:00, 89.56it/s]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"pgmuvi 1D | loss: -1.562 | time: 11.19 s\n",
" fitted frequencies: [0.00665436 0.0151593 ] -> periods: [150.27731 65.966125]\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\n",
"/usr/local/lib/python3.12/dist-packages/gpytorch/likelihoods/gaussian_likelihood.py:350: GPInputWarning: You have passed data through a FixedNoiseGaussianLikelihood that did not match the size of the fixed noise, *and* you did not specify noise. This is treated as a no-op.\n",
" warnings.warn(\n"
]
}
],
"source": [
"t0 = time.perf_counter()\n",
"res_pgmuvi_1d = lc_1d.fit(\n",
" model='1D',\n",
" num_mixtures=Q_MIXTURES,\n",
" training_iter=1000,\n",
" miniter=50,\n",
" lr=0.05,\n",
")\n",
"timings['pgmuvi'] = time.perf_counter() - t0\n",
"code_lines['pgmuvi'] = count_lines(\"\"\"\n",
" res = lc_1d.fit(model='1D', num_mixtures=2, training_iter=1000)\n",
"\"\"\")\n",
"\n",
"print('pgmuvi 1D | loss:', round(float(res_pgmuvi_1d['loss'][-1]), 3),\n",
" ' | time:', round(timings['pgmuvi'], 2), 's')\n",
"mm = lc_1d.model.covar_module.mixture_means.detach().cpu().squeeze().numpy()\n",
"print(' fitted frequencies:', mm, ' -> periods:', 1.0 / mm)\n",
"\n",
"# Predictions\n",
"lc_1d.model.eval()\n",
"with torch.no_grad():\n",
" x_t = torch.tensor(x_grid[:, None], dtype=torch.float64)\n",
" pred = lc_1d.model.likelihood(lc_1d.model(x_t))\n",
" mu_p = pred.mean.numpy()\n",
" std_p = pred.variance.sqrt().numpy()\n",
"preds_1d['pgmuvi'] = (x_grid, mu_p, std_p)\n",
"\n",
"# Analytical SMK PSD\n",
"freqs_arr = np.linspace(0, 0.2, 2000)\n",
"mms = lc_1d.model.covar_module.mixture_means.detach().cpu().squeeze().numpy()\n",
"mss = lc_1d.model.covar_module.mixture_scales.detach().cpu().squeeze().numpy()\n",
"mws = lc_1d.model.covar_module.mixture_weights.detach().cpu().squeeze().numpy()\n",
"psd_p = np.zeros_like(freqs_arr)\n",
"for w, mu, v in zip(mws, mms, mss):\n",
" psd_p += w * (np.exp(-0.5 * ((freqs_arr - mu) / v)**2)\n",
" + np.exp(-0.5 * ((freqs_arr + mu) / v)**2))\n",
"psds_1d['pgmuvi'] = (freqs_arr, psd_p / psd_p.max())"
],
"id": "UtVjPG7ou0Uu"
},
{
"cell_type": "markdown",
"metadata": {
"id": "smg55uZNu0Uu"
},
"source": [
"### `tinygp` — custom 1D spectral mixture kernel\n",
"\n",
"Because `tinygp` ships no SMK, we subclass `tinygp.kernels.base.Kernel`\n",
"to implement the identical formula used by `gpytorch.kernels.SpectralMixtureKernel`."
],
"id": "smg55uZNu0Uu"
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "cGdZHwNNu0Uv",
"outputId": "d72835bc-f214-4b2f-aaae-e5e956548b6f"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"tinygp 1D | loss: -115.562 | time: 19.0 s\n",
" fitted periods: [193.92126 65.9498 ]\n"
]
}
],
"source": [
"class SMK1D(TinyKernel if HAVE_TINYGP else object):\n",
" r'''1-D Spectral Mixture Kernel.\n",
"\n",
" k(tau) = sum_q w_q * cos(2*pi*mu_q*tau) * exp(-2*pi^2*v_q^2*tau^2)\n",
"\n",
" Parameters are stored in log-space for unconstrained optimisation.\n",
" '''\n",
" log_weights : 'jnp.ndarray' # (Q,)\n",
" log_freqs : 'jnp.ndarray' # (Q,) mu_q [cycles/unit]\n",
" log_scales : 'jnp.ndarray' # (Q,) v_q [cycles/unit]\n",
"\n",
" def evaluate(self, X1, X2):\n",
" w = jnp.exp(self.log_weights)\n",
" mu = jnp.exp(self.log_freqs)\n",
" v = jnp.exp(self.log_scales)\n",
" tau = X1 - X2\n",
" return jnp.sum(\n",
" w * jnp.cos(2 * jnp.pi * mu * tau)\n",
" * jnp.exp(-2 * jnp.pi**2 * v**2 * tau**2)\n",
" )\n",
"\n",
"\n",
"if HAVE_TINYGP:\n",
" x_j = jnp.asarray(x1)\n",
" y_j = jnp.asarray(y1)\n",
" ye_j = jnp.asarray(ye1)\n",
"\n",
" kernel_tiny_1d = SMK1D(\n",
" log_weights=jnp.log(jnp.ones(Q_MIXTURES) * 0.5),\n",
" log_freqs =jnp.log(jnp.array([TRUE_FREQ, 2 * TRUE_FREQ])),\n",
" log_scales =jnp.log(jnp.ones(Q_MIXTURES) * 0.002),\n",
" )\n",
"\n",
" @jax.jit\n",
" @jax.value_and_grad\n",
" def _neg_ll_tiny1d(params):\n",
" gp = TinyGP(params, x_j, diag=ye_j**2, mean=mean1)\n",
" return -gp.log_probability(y_j)\n",
"\n",
" opt_t = optax.adam(0.05)\n",
" state_t = opt_t.init(eqx.filter(kernel_tiny_1d, eqx.is_inexact_array))\n",
"\n",
" t0 = time.perf_counter()\n",
" for _ in range(1000):\n",
" loss_t, grads_t = _neg_ll_tiny1d(kernel_tiny_1d)\n",
" updates_t, state_t = opt_t.update(\n",
" grads_t, state_t,\n",
" eqx.filter(kernel_tiny_1d, eqx.is_inexact_array))\n",
" kernel_tiny_1d = eqx.apply_updates(kernel_tiny_1d, updates_t)\n",
" timings['tinygp'] = time.perf_counter() - t0\n",
" code_lines['tinygp'] = count_lines(\"\"\"\n",
" class SMK1D(TinyKernel):\n",
" log_weights: jnp.ndarray\n",
" log_freqs: jnp.ndarray\n",
" log_scales: jnp.ndarray\n",
" def evaluate(self, X1, X2):\n",
" w, mu, v = map(jnp.exp, (self.log_weights, self.log_freqs, self.log_scales))\n",
" tau = X1 - X2\n",
" return jnp.sum(w * jnp.cos(2*jnp.pi*mu*tau) * jnp.exp(-2*jnp.pi**2*v**2*tau**2))\n",
" kernel = SMK1D(log_weights=..., log_freqs=..., log_scales=...)\n",
" @jax.jit\n",
" @jax.value_and_grad\n",
" def neg_ll(k): return -TinyGP(k, x, diag=yerr**2).log_probability(y)\n",
" for _ in range(150):\n",
" loss, grads = neg_ll(kernel)\n",
" updates, state = opt.update(grads, state, eqx.filter(kernel, eqx.is_inexact_array))\n",
" kernel = eqx.apply_updates(kernel, updates)\n",
" \"\"\")\n",
"\n",
" freqs_fit_t = np.exp(np.asarray(kernel_tiny_1d.log_freqs))\n",
" print('tinygp 1D | loss:', round(float(loss_t), 3),\n",
" ' | time:', round(timings['tinygp'], 2), 's')\n",
" print(' fitted periods:', 1.0 / freqs_fit_t)\n",
"\n",
" # Predictions: condition(y, X_test).gp.loc gives predictive mean\n",
" xg_j = jnp.asarray(x_grid)\n",
" mu_t_arr = np.asarray(\n",
" TinyGP(kernel_tiny_1d, x_j, diag=ye_j**2, mean=mean1)\n",
" .condition(y_j, xg_j).gp.loc)\n",
" preds_1d['tinygp'] = (x_grid, mu_t_arr, None)\n",
"\n",
" # Analytical SMK PSD\n",
" freqs_arr = np.linspace(0, 0.2, 2000)\n",
" mmu_t = np.exp(np.asarray(kernel_tiny_1d.log_freqs))\n",
" mvv_t = np.exp(np.asarray(kernel_tiny_1d.log_scales))\n",
" mww_t = np.exp(np.asarray(kernel_tiny_1d.log_weights))\n",
" psd_t = np.zeros_like(freqs_arr)\n",
" for w, mu, v in zip(mww_t, mmu_t, mvv_t):\n",
" psd_t += w * (np.exp(-0.5 * ((freqs_arr - mu) / v)**2)\n",
" + np.exp(-0.5 * ((freqs_arr + mu) / v)**2))\n",
" psds_1d['tinygp'] = (freqs_arr, psd_t / psd_t.max())\n",
"else:\n",
" print('tinygp not installed.')"
],
"id": "cGdZHwNNu0Uv"
},
{
"cell_type": "markdown",
"metadata": {
"id": "vY3Bs-I-u0Uv"
},
"source": [
"### `celerite2` — SHOTerm approximation of the SMK\n",
"\n",
"The SHOTerm has a **Lorentzian** (not Gaussian) power spectrum peak,\n",
"making it the closest `celerite2` term to a single SMK component.\n",
"We use one SHOTerm per mixture component."
],
"id": "vY3Bs-I-u0Uv"
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "GqXl_ogFu0Uv",
"outputId": "0b1c9490-e003-4609-be49-fc320b0bacd5"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"celerite2 1D | loss: -116.39 | time: 0.737 s\n",
" fitted SHO periods: 151.78, 65.78 (true: 150.0)\n"
]
}
],
"source": [
"if HAVE_CELERITE:\n",
" mean1_c = float(np.mean(y1))\n",
"\n",
" def _build_c2_kernel(p):\n",
" return (\n",
" c2terms.SHOTerm(sigma=np.exp(p[0]), rho=np.exp(p[1]), tau=np.exp(p[2]))\n",
" + c2terms.SHOTerm(sigma=np.exp(p[3]), rho=np.exp(p[4]), tau=np.exp(p[5]))\n",
" )\n",
"\n",
" def _neg_ll_c2(p):\n",
" try:\n",
" gp_ = celerite2.GaussianProcess(_build_c2_kernel(p), mean=mean1_c)\n",
" gp_.compute(x1, yerr=ye1)\n",
" return -gp_.log_likelihood(y1)\n",
" except (ValueError, RuntimeError):\n",
" return 1e10\n",
"\n",
" p0_c2 = [\n",
" np.log(0.7), np.log(TRUE_PERIOD), np.log(2 * TRUE_PERIOD),\n",
" np.log(0.3), np.log(TRUE_PERIOD / 2), np.log(TRUE_PERIOD),\n",
" ]\n",
"\n",
" t0 = time.perf_counter()\n",
" res_c2 = minimize(_neg_ll_c2, p0_c2, method='L-BFGS-B',\n",
" options={'maxiter': 1000})\n",
" timings['celerite2'] = time.perf_counter() - t0\n",
" code_lines['celerite2'] = count_lines(\"\"\"\n",
" kernel = SHOTerm(sigma=s1, rho=rho1, tau=tau1) + SHOTerm(sigma=s2, rho=rho2, tau=tau2)\n",
" gp = celerite2.GaussianProcess(kernel, mean=mean_val)\n",
" gp.compute(x, yerr=yerr)\n",
" result = minimize(neg_ll, p0, method='L-BFGS-B')\n",
" \"\"\")\n",
"\n",
" rho1_c2 = np.exp(res_c2.x[1])\n",
" rho2_c2 = np.exp(res_c2.x[4])\n",
" print('celerite2 1D | loss:', round(res_c2.fun, 3),\n",
" ' | time:', round(timings['celerite2'], 3), 's')\n",
" print(f' fitted SHO periods: {rho1_c2:.2f}, {rho2_c2:.2f} (true: {TRUE_PERIOD})')\n",
"\n",
" # Predictions\n",
" gp_c2_final = celerite2.GaussianProcess(_build_c2_kernel(res_c2.x), mean=mean1_c)\n",
" gp_c2_final.compute(x1, yerr=ye1)\n",
" mu_c2, var_c2 = gp_c2_final.predict(y1, x_grid, return_var=True)\n",
" preds_1d['celerite2'] = (x_grid, mu_c2, np.sqrt(np.abs(var_c2)))\n",
"\n",
" # Lorentzian PSD\n",
" freqs_c2_arr = np.linspace(1e-4, 0.2, 2000)\n",
" omega_c2 = 2 * np.pi * freqs_c2_arr\n",
" psd_c2 = np.zeros_like(omega_c2)\n",
" for i in [0, 3]:\n",
" sigma_i = np.exp(res_c2.x[i])\n",
" rho_i = np.exp(res_c2.x[i + 1])\n",
" tau_i = np.exp(res_c2.x[i + 2])\n",
" omega0 = 2 * np.pi / rho_i\n",
" Q_i = 0.5 * omega0 * tau_i\n",
" psd_c2 += sigma_i**2 * (omega0**2 / Q_i) / (\n",
" (omega_c2**2 - omega0**2)**2\n",
" + omega_c2**2 * omega0**2 / Q_i**2 + 1e-30) # 1e-30 for numerical stability\n",
" psds_1d['celerite2'] = (freqs_c2_arr, psd_c2 / psd_c2.max())\n",
"else:\n",
" print('celerite2 not installed.')"
],
"id": "GqXl_ogFu0Uv"
},
{
"cell_type": "markdown",
"metadata": {
"id": "ia2bC0kYu0Uv"
},
"source": [
"### `george` — ExpSquared × Cosine kernel\n",
"\n",
"`george` provides `ExpSquaredKernel` and `CosineKernel`. Their **product** is exactly\n",
"one SMK component: $w\\,\\exp(-r^2/2\\ell^2)\\cos(2\\pi\\tau/P)$.\n",
"We sum two such products and optimise with scipy L-BFGS-B using george's analytic gradient."
],
"id": "ia2bC0kYu0Uv"
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "hCWH6s6Cu0Uv",
"outputId": "1a6865ba-744f-475f-be39-b162a17f9235"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"george 1D | loss: -114.178 | time: 1.46 s\n",
" fitted periods: 65.81, 198.04 (true: 150.0)\n"
]
}
],
"source": [
"if HAVE_GEORGE:\n",
" k1_g = (gk.ExpSquaredKernel(metric=1000.0)\n",
" * gk.CosineKernel(log_period=np.log(TRUE_PERIOD)))\n",
" k2_g = (gk.ExpSquaredKernel(metric=1000.0)\n",
" * gk.CosineKernel(log_period=np.log(TRUE_PERIOD / 2)))\n",
" gp_george = george.GP(k1_g + k2_g, mean=mean1)\n",
" gp_george.compute(x1, ye1)\n",
"\n",
" def _neg_ll_george(p):\n",
" gp_george.set_parameter_vector(p)\n",
" ll = gp_george.log_likelihood(y1, quiet=True)\n",
" return -ll if np.isfinite(ll) else 1e10\n",
"\n",
" def _grad_ll_george(p):\n",
" gp_george.set_parameter_vector(p)\n",
" return -gp_george.grad_log_likelihood(y1, quiet=True)\n",
"\n",
" p0_g = gp_george.get_parameter_vector()\n",
" t0 = time.perf_counter()\n",
" res_george = scipy_minimize(\n",
" _neg_ll_george, p0_g, jac=_grad_ll_george,\n",
" method='L-BFGS-B', options={'maxiter': 1000})\n",
" timings['george'] = time.perf_counter() - t0\n",
" code_lines['george'] = count_lines(\"\"\"\n",
" kernel = ExpSquaredKernel(M) * CosineKernel(log_period)\n",
" + ExpSquaredKernel(M) * CosineKernel(log_period2)\n",
" gp = george.GP(kernel, mean=mean_val)\n",
" gp.compute(x, yerr)\n",
" result = minimize(neg_ll, p0, jac=grad_ll, method='L-BFGS-B')\n",
" \"\"\")\n",
"\n",
" gp_george.set_parameter_vector(res_george.x)\n",
" params_g = dict(zip(gp_george.get_parameter_names(),\n",
" gp_george.get_parameter_vector()))\n",
" p1_g = np.exp(params_g.get('kernel:k1:k2:log_period', 0))\n",
" p2_g = np.exp(params_g.get('kernel:k2:k2:log_period', 0))\n",
" print('george 1D | loss:', round(res_george.fun, 3),\n",
" ' | time:', round(timings['george'], 3), 's')\n",
" print(f' fitted periods: {p1_g:.2f}, {p2_g:.2f} (true: {TRUE_PERIOD})')\n",
"\n",
" # Predictions\n",
" mu_g, var_g = gp_george.predict(y1, x_grid, return_var=True)\n",
" preds_1d['george'] = (x_grid, mu_g, np.sqrt(np.abs(var_g)))\n",
"\n",
" # PSD via FFT of k(tau)\n",
" tau_arr = np.linspace(0, x1.max() - x1.min(), 4096)\n",
" ktau_g = gp_george.kernel.get_value(tau_arr[:, None])\n",
" psd_g_raw = np.abs(np.fft.rfft(ktau_g))\n",
" freqs_g_arr = np.fft.rfftfreq(len(tau_arr), d=tau_arr[1] - tau_arr[0])\n",
" psds_1d['george'] = (freqs_g_arr[1:], psd_g_raw[1:] / psd_g_raw[1:].max())\n",
"else:\n",
" print('george not installed.')"
],
"id": "hCWH6s6Cu0Uv"
},
{
"cell_type": "markdown",
"metadata": {
"id": "iL8ttRxCu0Uv"
},
"source": [
"### `GPy` — ExpQuadCosine kernel\n",
"\n",
"`GPy` provides `ExpQuadCosine` — documented as the *spectral mixture kernel* component:\n",
"\n",
"$$k(r) = \\sigma^2 \\exp(-2\\pi^2 r^2)\\cos(2\\pi r / T).$$\n",
"\n",
"We sum two such terms. GPy uses L-BFGS-B internally via `model.optimize()`."
],
"id": "iL8ttRxCu0Uv"
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Gp7hxH2mu0Uv",
"outputId": "843a5f50-514e-45a7-d6c9-3dcf5baac2c0"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"GPy not installed.\n"
]
}
],
"source": [
"if HAVE_GPY:\n",
" kern_gpy1 = GPy.kern.ExpQuadCosine(1, period=TRUE_PERIOD)\n",
" kern_gpy2 = GPy.kern.ExpQuadCosine(1, period=TRUE_PERIOD / 2)\n",
" kernel_gpy = kern_gpy1 + kern_gpy2\n",
"\n",
" model_gpy = GPy.models.GPRegression(\n",
" x1[:, None], y1[:, None], kernel=kernel_gpy)\n",
" model_gpy.Gaussian_noise.variance = float(np.mean(ye1**2))\n",
" model_gpy.Gaussian_noise.fix()\n",
"\n",
" t0 = time.perf_counter()\n",
" with warnings.catch_warnings():\n",
" warnings.simplefilter('ignore')\n",
" model_gpy.optimize(messages=False, max_iters=1000)\n",
" timings['GPy'] = time.perf_counter() - t0\n",
" code_lines['GPy'] = count_lines(\"\"\"\n",
" kernel = ExpQuadCosine(1, period=P1) + ExpQuadCosine(1, period=P2)\n",
" model = GPy.models.GPRegression(X, Y, kernel)\n",
" model.optimize(max_iters=1000)\n",
" \"\"\")\n",
"\n",
" period1_gpy = float(model_gpy.sum.ExpQuadCosine.period)\n",
" period2_gpy = float(model_gpy.sum.ExpQuadCosine_1.period)\n",
" print('GPy 1D | lml:', round(float(model_gpy.log_likelihood()), 3),\n",
" ' | time:', round(timings['GPy'], 3), 's')\n",
" print(f' fitted periods: {period1_gpy:.2f}, {period2_gpy:.2f} (true: {TRUE_PERIOD})')\n",
"\n",
" # Predictions\n",
" mu_gpy_arr, var_gpy_arr = model_gpy.predict(x_grid[:, None])\n",
" preds_1d['GPy'] = (x_grid, mu_gpy_arr.ravel(),\n",
" np.sqrt(np.abs(var_gpy_arr)).ravel())\n",
"\n",
" # PSD via FFT of k(tau)\n",
" tau_arr = np.linspace(0, x1.max() - x1.min(), 4096)\n",
" ktau_gpy = model_gpy.kern.K(\n",
" tau_arr[:, None], np.zeros((1, 1))).ravel()\n",
" psd_gpy_raw = np.abs(np.fft.rfft(ktau_gpy))\n",
" freqs_gpy_arr = np.fft.rfftfreq(len(tau_arr), d=tau_arr[1] - tau_arr[0])\n",
" psds_1d['GPy'] = (freqs_gpy_arr[1:],\n",
" psd_gpy_raw[1:] / psd_gpy_raw[1:].max())\n",
"else:\n",
" print('GPy not installed.')"
],
"id": "Gp7hxH2mu0Uv"
},
{
"cell_type": "markdown",
"metadata": {
"id": "yUHc5FXzu0Uw"
},
"source": [
"### `gpflow` — SquaredExponential × Cosine kernel\n",
"\n",
"`gpflow` provides `SquaredExponential` and `Cosine` kernels.\n",
"Their product ($k(r) = \\sigma^2 e^{-r^2/2\\ell^2}\\cos(2\\pi r/\\ell_{\\rm cos})$)\n",
"is equivalent to one SMK component. We sum two products and optimise\n",
"with the built-in Scipy wrapper."
],
"id": "yUHc5FXzu0Uw"
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "HWEzSCQKu0Uw",
"outputId": "3d503185-6dd8-4203-ba78-914f1f7655f1"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"gpflow not installed.\n"
]
}
],
"source": [
"if HAVE_GPFLOW:\n",
" with warnings.catch_warnings():\n",
" warnings.simplefilter('ignore') # suppress TF messages\n",
"\n",
" k1_gf = gpflow.kernels.Product([\n",
" gpflow.kernels.SquaredExponential(lengthscales=100.0),\n",
" gpflow.kernels.Cosine(lengthscales=TRUE_PERIOD),\n",
" ])\n",
" k2_gf = gpflow.kernels.Product([\n",
" gpflow.kernels.SquaredExponential(lengthscales=100.0),\n",
" gpflow.kernels.Cosine(lengthscales=TRUE_PERIOD / 2),\n",
" ])\n",
" kernel_gf = k1_gf + k2_gf\n",
"\n",
" X_gf = x1[:, None].astype(np.float64)\n",
" Y_gf = y1[:, None].astype(np.float64)\n",
" model_gf = gpflow.models.GPR(\n",
" (X_gf, Y_gf), kernel=kernel_gf,\n",
" noise_variance=float(np.mean(ye1**2)))\n",
" gpflow.set_trainable(model_gf.likelihood.variance, False)\n",
"\n",
" opt_gf = gpflow.optimizers.Scipy()\n",
" t0 = time.perf_counter()\n",
" opt_gf.minimize(\n",
" model_gf.training_loss, model_gf.trainable_variables,\n",
" options={'maxiter': 1000})\n",
" timings['gpflow'] = time.perf_counter() - t0\n",
" code_lines['gpflow'] = count_lines(\"\"\"\n",
" k1 = gpflow.kernels.Product([SquaredExponential(), Cosine(lengthscales=P1)])\n",
" k2 = gpflow.kernels.Product([SquaredExponential(), Cosine(lengthscales=P2)])\n",
" model = gpflow.models.GPR((X, Y), kernel=k1 + k2, noise_variance=sigma2)\n",
" Scipy().minimize(model.training_loss, model.trainable_variables)\n",
" \"\"\")\n",
"\n",
" period1_gf = float(\n",
" model_gf.kernel.kernels[0].kernels[1].lengthscales)\n",
" period2_gf = float(\n",
" model_gf.kernel.kernels[1].kernels[1].lengthscales)\n",
" lml_gf = float(model_gf.maximum_log_likelihood_objective())\n",
" print('gpflow 1D | lml:', round(lml_gf, 3),\n",
" ' | time:', round(timings['gpflow'], 3), 's')\n",
" print(f' fitted periods: {period1_gf:.2f}, {period2_gf:.2f} (true: {TRUE_PERIOD})')\n",
"\n",
" # Predictions\n",
" X_grid_gf = x_grid[:, None].astype(np.float64)\n",
" mu_gf_arr, var_gf_arr = model_gf.predict_y(X_grid_gf)\n",
" preds_1d['gpflow'] = (\n",
" x_grid,\n",
" mu_gf_arr.numpy().ravel(),\n",
" np.sqrt(np.abs(var_gf_arr.numpy())).ravel(),\n",
" )\n",
"\n",
" # PSD via FFT of k(tau)\n",
" import tensorflow as tf\n",
" tau_arr = np.linspace(0, x1.max() - x1.min(), 4096).astype(np.float64)\n",
" k_gf_vals = kernel_gf(\n",
" tf.constant(tau_arr[:, None]),\n",
" tf.constant([[0.0]], dtype=tf.float64))\n",
" ktau_gf = k_gf_vals.numpy().ravel()\n",
" psd_gf_raw = np.abs(np.fft.rfft(ktau_gf))\n",
" freqs_gf_arr = np.fft.rfftfreq(len(tau_arr), d=tau_arr[1] - tau_arr[0])\n",
" psds_1d['gpflow'] = (freqs_gf_arr[1:],\n",
" psd_gf_raw[1:] / psd_gf_raw[1:].max())\n",
"else:\n",
" print('gpflow not installed.')"
],
"id": "HWEzSCQKu0Uw"
},
{
"cell_type": "markdown",
"metadata": {
"id": "YzZ-Fozwu0Uw"
},
"source": [
"### `scikit-learn` — quasi-periodic kernel\n",
"\n",
"`scikit-learn.gaussian_process` has no SMK, but `ExpSineSquared` combined with\n",
"`RBF` gives a quasi-periodic kernel:\n",
"\n",
"$$k(r) = \\sigma^2 \\exp\\!\\Bigl(-\\tfrac{r^2}{2\\ell_{\\mathrm{RBF}}^2}\\Bigr)\n",
"\\cdot \\exp\\!\\Bigl(-\\tfrac{2\\sin^2(\\pi r/P)}{\\ell_{\\mathrm{per}}^2}\\Bigr).$$\n",
"\n",
"This is **not** the SMK (different spectral shape), but is the closest built-in\n",
"sklearn combination for periodic signals."
],
"id": "YzZ-Fozwu0Uw"
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "7VMk1g0bu0Uw",
"outputId": "c2212f02-5451-44d8-d734-304dc7d661d9"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"sklearn 1D | lml: 107.916 | time: 0.442 s\n"
]
}
],
"source": [
"if HAVE_SKLEARN:\n",
" k_sk = (\n",
" ConstantKernel(0.5, (1e-3, 1e3))\n",
" * RBF(length_scale=100.0, length_scale_bounds=(1.0, 1e5))\n",
" * ExpSineSquared(\n",
" length_scale=1.0, periodicity=TRUE_PERIOD,\n",
" length_scale_bounds=(0.1, 10.0),\n",
" periodicity_bounds=(10.0, 200.0))\n",
" + ConstantKernel(0.3, (1e-3, 1e3))\n",
" * RBF(length_scale=100.0, length_scale_bounds=(1.0, 1e5))\n",
" * ExpSineSquared(\n",
" length_scale=1.0, periodicity=TRUE_PERIOD / 2,\n",
" length_scale_bounds=(0.1, 10.0),\n",
" periodicity_bounds=(5.0, 100.0))\n",
" )\n",
" with warnings.catch_warnings():\n",
" warnings.simplefilter('ignore')\n",
" gpr = GaussianProcessRegressor(\n",
" kernel=k_sk, alpha=ye1**2,\n",
" n_restarts_optimizer=0, normalize_y=False)\n",
" t0 = time.perf_counter()\n",
" gpr.fit(x1[:, None], y1)\n",
" timings['sklearn'] = time.perf_counter() - t0\n",
"\n",
" code_lines['sklearn'] = count_lines(\"\"\"\n",
" kernel = (ConstantKernel() * RBF() * ExpSineSquared(periodicity=P1)\n",
" + ConstantKernel() * RBF() * ExpSineSquared(periodicity=P2))\n",
" gpr = GaussianProcessRegressor(kernel=kernel, alpha=yerr**2)\n",
" gpr.fit(X, y)\n",
" \"\"\")\n",
"\n",
" print('sklearn 1D | lml:', round(gpr.log_marginal_likelihood_value_, 3),\n",
" ' | time:', round(timings['sklearn'], 3), 's')\n",
"\n",
" # Predictions\n",
" with warnings.catch_warnings():\n",
" warnings.simplefilter('ignore')\n",
" mu_sk, std_sk = gpr.predict(x_grid[:, None], return_std=True)\n",
" preds_1d['sklearn'] = (x_grid, mu_sk, std_sk)\n",
"\n",
" # PSD via FFT of k(tau)\n",
" tau_arr = np.linspace(0, x1.max() - x1.min(), 4096)\n",
" ktau_sk = gpr.kernel_(tau_arr[:, None],\n",
" np.zeros((1, 1))).ravel()\n",
" psd_sk_raw = np.abs(np.fft.rfft(ktau_sk))\n",
" freqs_sk_arr = np.fft.rfftfreq(len(tau_arr), d=tau_arr[1] - tau_arr[0])\n",
" psds_1d['sklearn'] = (freqs_sk_arr[1:],\n",
" psd_sk_raw[1:] / psd_sk_raw[1:].max())\n",
"else:\n",
" print('scikit-learn not installed.')"
],
"id": "7VMk1g0bu0Uw"
},
{
"cell_type": "markdown",
"metadata": {
"id": "6owILozou0Uw"
},
"source": [
"### `GPJax` — custom SMK kernel\n",
"\n",
"`GPJax` is a modern JAX/Flax-based GP library with full auto-diff support.\n",
"Custom kernels are defined by subclassing `gpjax.kernels.base.AbstractKernel`.\n",
"An SMK component is implemented identically to the `tinygp` version,\n",
"but adapted to the `nnx.Module` / `PositiveReal` parameter API.\n",
"\n",
"> **Note:** `gpjax` requires compatible versions of JAX, Flax, and optax.\n",
"> Version conflicts in some environments may prevent import; the block below\n",
"> gracefully skips if `gpjax` is unavailable."
],
"id": "6owILozou0Uw"
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "kp4rGztDu0Uw",
"outputId": "550a0f9c-36d1-4578-c464-14ba0e3e88f0"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"gpjax not available — skipping.\n",
"See https://github.com/JaxGaussianProcesses/GPJax for installation.\n"
]
}
],
"source": [
"if HAVE_GPJAX:\n",
" import optax as optax_gj\n",
" from gpjax.kernels.base import AbstractKernel\n",
" from flax import nnx as nnx_gj\n",
" from gpjax.parameters import Real\n",
"\n",
" class SMK1DGPJax(AbstractKernel):\n",
" r'''1-D Spectral Mixture Kernel for GPJax.\n",
"\n",
" k(tau) = sum_q w_q * cos(2*pi*mu_q*tau) * exp(-2*pi^2*v_q^2*tau^2)\n",
" '''\n",
" def __init__(self, Q=2, log_w=None, log_mu=None, log_v=None):\n",
" super().__init__()\n",
" import jax.numpy as jnp_gj\n",
" self.log_weights = Real(\n",
" log_w if log_w is not None else jnp_gj.zeros(Q))\n",
" self.log_freqs = Real(\n",
" log_mu if log_mu is not None else jnp_gj.zeros(Q))\n",
" self.log_scales = Real(\n",
" log_v if log_v is not None else jnp_gj.full(Q, -4.0))\n",
"\n",
" def __call__(self, x, y):\n",
" import jax.numpy as jnp_gj\n",
" w = jnp_gj.exp(self.log_weights.value)\n",
" mu = jnp_gj.exp(self.log_freqs.value)\n",
" v = jnp_gj.exp(self.log_scales.value)\n",
" tau = x.squeeze() - y.squeeze()\n",
" return jnp_gj.sum(\n",
" w * jnp_gj.cos(2 * jnp_gj.pi * mu * tau)\n",
" * jnp_gj.exp(-2 * jnp_gj.pi**2 * v**2 * tau**2)\n",
" ).squeeze()\n",
"\n",
" import jax\n",
" import jax.numpy as jnp_gj\n",
"\n",
" x_gj = jnp_gj.asarray(x1[:, None], dtype=jnp_gj.float64)\n",
" y_gj = jnp_gj.asarray(y1[:, None], dtype=jnp_gj.float64)\n",
" ye_gj = jnp_gj.asarray(ye1, dtype=jnp_gj.float64)\n",
"\n",
" kernel_gj = SMK1DGPJax(\n",
" Q=Q_MIXTURES,\n",
" log_w=jnp_gj.log(jnp_gj.ones(Q_MIXTURES) * 0.5),\n",
" log_mu=jnp_gj.log(jnp_gj.array([TRUE_FREQ, 2 * TRUE_FREQ])),\n",
" log_v=jnp_gj.log(jnp_gj.ones(Q_MIXTURES) * 0.002),\n",
" )\n",
"\n",
" dataset = gpjax.Dataset(X=x_gj, y=y_gj)\n",
" likelihood = gpjax.likelihoods.Gaussian(\n",
" num_datapoints=len(y1),\n",
" obs_stddev=nnx_gj.Variable(jnp_gj.mean(ye_gj)))\n",
" gpjax.set_trainable(likelihood.obs_stddev, False)\n",
" prior = gpjax.gps.Prior(\n",
" mean_function=gpjax.mean_functions.Constant(),\n",
" kernel=kernel_gj)\n",
" posterior = prior * likelihood\n",
"\n",
" @jax.jit\n",
" def _neg_mll(model):\n",
" return -gpjax.objectives.ConjugateMLL(negative=False)(\n",
" model, dataset)\n",
"\n",
" opt_gj = optax_gj.adam(0.05)\n",
" state_gj = nnx_gj.Optimizer(posterior, opt_gj)\n",
"\n",
" t0 = time.perf_counter()\n",
" for _ in range(1000):\n",
" loss_gj, grads_gj = nnx_gj.value_and_grad(_neg_mll)(state_gj.model)\n",
" state_gj.update(grads_gj)\n",
" timings['gpjax'] = time.perf_counter() - t0\n",
" code_lines['gpjax'] = count_lines(\"\"\"\n",
" class SMK1DGPJax(AbstractKernel):\n",
" def __init__(self, Q=2, ...): ...\n",
" def __call__(self, x, y): ...\n",
" prior = gpjax.gps.Prior(kernel=SMK1DGPJax(...))\n",
" posterior = prior * likelihood\n",
" @jax.jit\n",
" def neg_mll(model): return -ConjugateMLL()(model, dataset)\n",
" for _ in range(150):\n",
" loss, grads = nnx.value_and_grad(neg_mll)(state.model)\n",
" state.update(grads)\n",
" \"\"\")\n",
"\n",
" print('gpjax 1D | loss:', round(float(loss_gj), 3),\n",
" ' | time:', round(timings['gpjax'], 3), 's')\n",
" mmu_gj_fit = np.exp(\n",
" np.asarray(state_gj.model.prior.kernel.log_freqs.value))\n",
" print(' fitted periods:', 1.0 / mmu_gj_fit)\n",
"\n",
" # Predictions: use posterior predictive mean\n",
" xg_gj = jnp_gj.asarray(x_grid[:, None], dtype=jnp_gj.float64)\n",
" latent_dist = state_gj.model.posterior.predict(\n",
" xtest=xg_gj, train_data=dataset)\n",
" mu_gj_arr = np.asarray(latent_dist.mean().ravel())\n",
" std_gj_arr = np.asarray(latent_dist.stddev().ravel())\n",
" preds_1d['gpjax'] = (x_grid, mu_gj_arr, std_gj_arr)\n",
"\n",
" # Analytical SMK PSD\n",
" freqs_gj_arr = np.linspace(0, 0.2, 2000)\n",
" mmu_gj_p = np.exp(np.asarray(\n",
" state_gj.model.prior.kernel.log_freqs.value))\n",
" mvv_gj_p = np.exp(np.asarray(\n",
" state_gj.model.prior.kernel.log_scales.value))\n",
" mww_gj_p = np.exp(np.asarray(\n",
" state_gj.model.prior.kernel.log_weights.value))\n",
" psd_gj = np.zeros_like(freqs_gj_arr)\n",
" for w, mu, v in zip(mww_gj_p, mmu_gj_p, mvv_gj_p):\n",
" psd_gj += w * (\n",
" np.exp(-0.5 * ((freqs_gj_arr - mu) / v) ** 2)\n",
" + np.exp(-0.5 * ((freqs_gj_arr + mu) / v) ** 2))\n",
" psds_1d['gpjax'] = (freqs_gj_arr, psd_gj / psd_gj.max())\n",
"else:\n",
" print('gpjax not available — skipping.')\n",
" print('See https://github.com/JaxGaussianProcesses/GPJax for installation.')"
],
"id": "kp4rGztDu0Uw"
},
{
"cell_type": "markdown",
"metadata": {
"id": "TjTCwi2fu0Uw"
},
"source": [
"## 1D results: fitted light curves and inferred PSDs\n",
"\n",
"The figure below shows (top panels) each fitted GP's predictive mean overlaid\n",
"on the data, and (bottom panel) the normalised power spectral densities of all\n",
"fitted kernels, with the true period $T = 40\\,\\mathrm{d}$ marked by a dashed\n",
"vertical line."
],
"id": "TjTCwi2fu0Uw"
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "5vPPxqFtu0Uw",
"outputId": "93074fa4-2f9e-471d-b2a3-6d6edd2ed7da"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"pgmuvi\n",
"0.0 0.2 0.0 1.0\n",
"(2000,) (2000,)\n",
"tinygp\n",
"0.0 0.2 0.0 1.0\n",
"(2000,) (2000,)\n",
"celerite2\n",
"0.0001 0.2 9.200361402511144e-10 1.0\n",
"(2000,) (2000,)\n",
"george\n",
"0.0029139531605784945 5.967776072864757 2.7219104370224656e-07 1.0\n",
"(2048,) (4095, 2049)\n",
"sklearn\n",
"0.0029139531605784945 5.967776072864757 0.0011643990180378848 1.0\n",
"(2048,) (2048,)\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
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pTCajZDKpZDKpbdu2KZocPbB8+UhCseiInik+r4/tf+OE13puX0zrYlnt8Q/pj574J7nMilLnX63YXZ9R3dU3KjinS01vukhNly2Sf3rrEYNuSTLsdtlbW+U6/3wFLluupk9/UoE/+RPJ89sPB53Vsj7063/StlBWq3aNaiRTtFynpRjWewZW6H/0c3lTThXiKZm5kpLVxIk9eQAAABjH4/GMrQ5UyhfVGZfsso8bkytVtC2a1px5XplBlzo6rWH4mcbr9svd5pcr7Jenzq90uTSubhiGrnXN0I7G6ZIkx1NPK797Vy1axSlA2D1FXHfddWpubpbL5VJzc7NWrlypL37xi4rFYrVuDQAAAAAAHMahS00Gg0G5XC7FYjGVSiVt2veyNJpTLBXTz3KP62P7blKw4rVcZ0NfUi/Gc9rvG9DHn/wXOe0Ojd72QdlueacaZ3YrdNW5arl6idxB67lHwxaJyDl/voIfuFORf/wHGW0dYzVPuai7Vn9Zm0Ml/XLHiBL5kuX8a6PnqT0T0hPZp+UoGCoMJ5StZFU0reE4AAAAjk9ra6scDoe8Xq/shbKmFestY7aPZFSqNxSeUa+61gb5gr4JrnTmaZs9XU6PU7aIS9vtUUt9sT+iRy898KVSezajof+9T2a5PNlt4hQg7J4iHnvsMQ0PD6tUKml4eFi/+tWv9Od//ueaOXOmHnzwweO+bk9PzxF/+vv7T+KjAAAAAADg7HLoUpN2u11ut1umaSpejCmXTCkVi+nJ2FO6cehczc61Wa7Rm8jrmZG0+jy9+sNV/yqbL6iR3/1D1S27SOElc9R00zLVdTfJsBmWc4+VLRiU98Yb1PDV/yf78gvHjgeKWb3/6X/QK4GKHt8xqlKlajn3Q/tv0Hq9rMHYkAqFnFxFKVGNn3BPAAAAOMDn82n27Nlyu9yaZY+oTsFx9appautwWnPO8cgMudTZNa1GnU6+gC+oQFdErjqfjDqPStXx71eddpuu8HZrX/jATHfzkV+qPDBYi1ZxkrFn92lu8eLFuvXWW3XhhReqvb1dpVJJW7du1fe+9z09+uijisfjeutb36qf/vSnuvHGG4/5+tOmnT3/RwcAAAAAwGQ7eKnJQqEgt9t94H+9buVsOdXnXFqf3axg0qbrR8+3nJ8plrVqT1S9vkF97Ml/k9nQquG33aWG7pmqu2ihwrMaT0rIfTDD5ZLrgmUq/uknNfiFv1X7xo2SpEguoXc894/64fI/VXtvQhd3Rcad11AO6o6+a/S9zkf0/sTbpWxeI8WUCpWi/A6/stns2HMBAACA49Pa2qqu5nZ19lpnL/cm8hr1FzW/u1X+lnr5Qwdmda9cuXKSu6yNjtkzlOmJq1Tv1vZEVAvUOK6+3Neof7jkJn304Xvk6tmvoUd/ofb33ynDOLnvpzG5mNl9Gvv4xz+ul156SZ///Od18803a+nSpbrooot0++2365FHHtHXvvY1SVKlUtEHPvAB5fP5GncMAAAAAAAOdvBSk8ViUalUSsViUfaITb6iXalqSlvSW/TBnust51aqplbtimpDuKy7Vn9Vlc45Gnn3H6pxzhzVXbZY4dknP+h+TS6X045CQS9fc5X6ZnaPHW9LjeimDf+uZ1J59SWtn0OsiC/UnFSrnsisUTKZlBnPqz/dp2gsqnXr1rGCHAAAwEngzVXUWW2xHN86nNGMbq/MsEedXZ016Ky2PG6fQtMa5Ax5VQm5ZJrmuLrf5dBS/0yN+g58aTP74M9VjcVr0ClOJsLu01g4HD5i/YMf/KB+7/d+T5LU19en+++//5jvY//+/Uf8Wbt27fG0DgAAAAAAdNBSk263QqGQ/H6/PHUeOV121fv8eirxrD6670Z5TKfl3Bd64nrWaepdz/2bbNPnKfo7d6mlu1t1lyxSpLvhlM5AeW2v8YTdrvW3vlnxjt/u4T0ruld1yWe0em9MxQmWM7+r5zrtcuxVspyUmSsol89qND2iZDKpbdu2KZvNnrK+AQAAznRmuapgb0o2jX8vmC6UtbOYUsfcevmbIwqGAzXqsLZauzvlcDlkhp3ao6SlfqmnRT9bdo0kybt+nUbXr5vsFnGSEXZPcR/84AfHbv/qV7865vM7OzuP+NPWZt0rDAAAAAAAHL3W1lZ1dHQoEomoLlwnW52hOQ2d2lnYo4WjLeoqNFnO2R3NanW6oAt2/q9aPR7Fb3m3WrpnKHTxAoWnRya4l5Pr4L3G7eGwXnrXO5Wprx+r37zpAW1zpbR2f9xybl3Fp7t6rtOGyFYZhqEGZ0Blb1nRWFTlclkDAwOnvH8AAIAzVWE0odZE0HJ860hGoXa3jEa/pp1Fe3UfyuP1K9gelqPOq0LA+uXQBr9LgZb5KtkcMioVxe+7X2YuV4NOcbKwZ/cUt2DBgrHbvb29NewEAAAAAABIUrFY1Jo1a8Z+r1QqstlsikQiSlfTMrJFJXMxvZJ8WZ8ffKfl/HiupCd74rJXXtLFo7s0fPsn1DJ3tkLnz1bdtLpJeQyH7jWuYFBrb3uzLv3B/8qdychumnrrC/+uny/9lKbHc5oW9o47/6LkXK0Jb1HcmVSTGuV2OJRTVsVikW3YAAAAjpNpmsq8vE8RjX/vVama2hJLauGF9fI3hhUI+2vU4eRwuVxH3Ie8qbtTyZ64inVODaYyarGNfz6uNNu0au7FunbLU3L/erWSu3epbsHCU9w1ThVmdk9xp3LJMgAAAAAAcPJUzapS5YRc+apWjzynd/ddKech8xCqpqlf7Y5quz+u333pAUXfepfCs2cpdN6sSQu6pYn3Gh+pr9eeO25X1e2WJLWkRzV94FH9en9c+XLFco13963QK3U7VCmV5SpIOXtODqdDHo9n0h4HAADAmaSaL6mwyTrxcV88p0ygLEdrSO3TO8/67ChQVyd/c0D2sFdJX9lSnxXxa9uC5ZIkRyKugR89ILNq3Z4HUwNh9xS3efPmsdvt7e017AQAAAAAABxJ2kzJnZd6CwMKJaVFmS7LmM2DaT3jK+v31nxVyTfeKfe8OYosmqPQJAbd0sR7jYdCIenii2X70z8eG3f1tse1zz6kZ/fFLddoLtfpisR89Tj7ZauWVecLKGfLqrW1dRIfCQAAwJkjs6tfrfmw5fjW4YzmL6iTKxJUKGJd4vxs1NzdIbvHqWLIpaI5/ouZTrtNV5Q6tKVppiTJ/tjjKgwN1aJNnASE3VPc17/+9bHbK1asqGEnAAAAAADgcCpmRdlSWsoX9aqxVe/ru9oyJlUo6/FYVjdu/A/ZLrlJ1SULVD93jsJzGmsyO+fgvcbD4bAikYiWLl2q9g9+UN6b3yhJMiS9Y+3X9Gwxr56Eda/Dtw1eqj11fbIZdoUNjyIdYbm97kl+JAAAAFOfaZqKPveq5Xg8V9IuM6n6mWE1TWuVYTu7Z3W/JtjYIE/YK1vErX5HxlJfGArr8eXXSJK8O7Zr4Fe/muwWcZIQdtfIvffeK8MwZBiG7r77bkt906ZN2rFjxxGv8Y1vfEP33HOPpAP/AL3ttttORasAAAAAAOAEpcyU3HlDm8pbdPPIUgWq1qW8n9kXU6m4WTPapyl38cVqnDVH9YvaZHfU7uMbp9OpSCSipqYmRSIR+Xw+GTab6r70RdmamyVJdYW0zt19v57pT8o0zXHnB6oevXFkqfYGetUQqVO9J6R4PlaLhwIAADCllWIZ1fVa3xduHc6otdMrI+xXU0tjDTo7PRmGoaYZbbJ5nSrVOS315oBbXbYOxb0hSVLhgZ/ILBQmu02cBI7XH4JDPfXUU+OC6JGRkbHbO3bs0L333jtu/B133HHM9/Hiiy/qAx/4gK666irdeOONWrx4sRoaGlQul7VlyxZ973vf06OPPipJstvt+sY3viG/339cjwcAAAAAAJw6ZbOsQjGjai6tSjmjFfGFljG7oln92l7Q+/evVuqdH1Zjd7fqF3fK5XPVoOPXZw+HFf7S3yr6/t+TJF2473n9x7QV2jka0OzG8Z9PvHH4Aj1Wv1GJTFIN8YxGgiOq9zSc9XtJAgAAHIvoi9sVNse/NyxXq3o1mdT5lzco3N4kh4vY72Dh1mYNhXqUixQVjeZUL++4+oWOZv186XV699M/lPeFFzWydaualiypUbc4XvzVH4d77rlH//Vf/zVh7emnn9bTTz897tjxhN2SVKlU9Mtf/lK//OUvDzumoaFB3/rWt3TLLbcc130AAAAAAIBTK2Um5cnb9JT5sv6o53pLvVCu6vHBlC7fe59K175Doe5OReZ1ydd4en+p3XvddfLeeqtyDzwgSbpx47f1lONPNaPeJ8dBy2e65NDbBy/V4+0vqDXTIkc2oKQ/qTr35O5DDgAAMFWZlaqyG/corMi447ujOZX8eRmNfrV2tNWou9OXzWFXw7QW9cVzyvqLqj9kNfOZDT5lKjNVNuxyZDMa/cnPLGF3sVjUmjVrxh279NJL5XKdnl9KPRuxjPlp6qabbtK3vvUtfeADH9CyZcvU2dkpr9crj8ej9vZ23Xjjjfrnf/5n7dq1S29+85tr3S4AAAAAAJhAySypVMxppDio8xIdailbA94XeuIaMvdobvcs2RfMUv3MGQpNj0xwtdNP3d9+YWw587b0kNLZbXp1KG0ZtyK+SLlSSrtH96kSz2kkNzzZrQIAAExZhb6YWrNhy/Gto2lNm2GXtyEiX9BrPRGq72iVK+hWttGpslkdV/M47Lqk2KrVcy+WJNl+9WvlR0Zr0SZOAGH3cbj33ntlmuZR/0zkjjvuGKtPtGd3c3Oz7rzzTn3zm9/UCy+8oP379yubzSqXy6m3t1cPPfSQ/vAP/1ChUOgUP1oAAAAAAHC8EpWEvDmbtlV36M1DF1rqA6mCHqkU9Ja+p5W77ApFumYoPL9Jhm1qLPFtD4UU/tLfjv1+88bv6dextPLlyrhxNhl6d/8K/br4rLKJpHLZjDKlzKGXAwAAwAQGnntZNo1/f5jMl7VXCRkNLjVNY1b34dg9LtW3N8oMuhRzW/fkPqchqJfPuUCS5Nvyqvqef36yW8QJIuwGAAAAAAA4BYpmUdVCXvtK+3VF9By55RxXr1RNPdGX0Ln7H5auuU3BaZ2qP6d1yu21+Npy5pIULGYViD2nTf0py7il6ZkK59zaFt8hJQsazY1McqcAAABTj1muyL4tYTm+K5pVc6Opis+lcAMTI4+kobNdNr9TmbD1fXZb0KNz0s3a3jBdRrWqzE9+pmq5XIMucbwIuwEAAAAAAE6BRCUhV17qqezTG2JLLPWtw2lt1YCWdE+XbXanIjM65GsO1KDTE1f3t1+QraFBknT9Kw/oV5mU0gXrh4S3916lF3IvKZdIKpmNq1Cxzq4BAADAb2V29KupbA2zX00lNP+CLl141eXyeD016GzqsAc8qquvU7HeqbRRtNQXBcN64oJrJUn+557TyM5dk90iTgBhNwAAAAAAwElWNIsy8kXtLOzSG4eWyn7IRzDFSlW/SGV02+hzKi67QOH2aQrPba5RtyfOHgop+ImPS5Kc1Yrm9DyiF/usM5BmFlpUl3NoT2y/lC4xuxsAAJzVisWiVq1aNe6nWBwfxg4897LlvKF0QSlXRkZTSC1tU/c95GQxDEP1HS2Sx6FMwLpd0OwGv7xmkwp2l9yDAxp6YtXkN4njRtgNAAAAAABwkqUqSTkKpuKVYV2UnmOpbxpIyZvcINflN8rf2aKGuW1yuqfW8uWH8t/+Xtm7uyVJl+38lZ7JxzWatc6cubX/Eq3LblQxnlY0G1W5yjKRAAAAEynnigrtMy3Hd0Wzmjc/orqOZjk9zgnOxKH8DXXyhX1KNdhVUXVczeu067xqs55YeIUkyXj8cWWj0Vq0ieNA2A0AAAAAAHASFSoFVYsFbc5v1Vv6LrbUs8WKfpHPaEXEIWNmiyId7fK3T/19Fg27XXV/8ekDtyVdsu0+vTDB3t0L853K5ZPqSwzIyJQVzfNBIgAAwEQSm3YpZHrHHauapl7JR1U/u14t09pq1NnUY7gdirQ0SgGnki7rly1nNfi1p2uxJCm4YYN6X3l1slvEcSLsBgAAAAAAOIlGs8My00VVi2nNL3RY6i8OJDUj/rx0wXKFW9oUmd8mw7AupzgVeW64Xs5zz5UkLRp4RZty/UrkS5Zxbxq8SOuSG1SKpzWaHVbVrFrGAAAAnM1M09TIi9ssx3sSebm8ZblbGhWo89egs6kr3NokI+BUOmR9790R8qgz0aCeujbZc1llHv6FyiVWIJoKCLsBAAAAAABOklK1pP17dmtDdqPe2XeFpZ7Il/R4Ma6lc7vl6WxS46wOuXxnztKThmGo7q8+O/b7FVt/qJeG0pZxFyXnqLfYq9HkqMxMSclicjLbBAAAOO2VE1m1Dvssx3dEM1qwtFkNnS1nzBcmJ4st4FakIaxCo0sFozKuZrcZmuuv0y+XvUGS5Hv2WY3s2VuLNnGMCLsBAAAAAABOkv3R/erfvU9NZY86K/WW+pqBlJbnNslcNEfhllYFusKT3+Qp5r7oQjlXXClJmj2yU8+nh5UrHfJhomHT9cPnaV3sJVWSecVYyhwAAGCc4fVb5JZj3LFiuaqt5VF52sJqammqUWdTl2G3qb6tWabHrrTPuhf6zHqfTGeHKoYh37atGly/YfKbxDEj7AYAAAAAADgJKtWK9u7ZoVcLW/SOfuus7qF0Qc8XB9R9/vnyNzWp/pxWGbbTdzaOy+XSypUrx/24XK6jOtf/F5+W+ZuZRuft+plenWB29zXRc7W1tE3pVFzZdErFSvGk9g8AADBVmaap9Poey/E9saxawqb8nS1yes6c1YEmkzsclL8hqGzYYam1BFyanqzXM7MukmGa0qOPKZfJ1qBLHAvCbgAAAAAAgJNgND+qYjStpoxL9aZ1/8RVwyldrh6pu02R9hZ5ItZlKc8U/oUL5XvLbZKkJb3r9XQ6rlJl/L7cbjl0SXSu1o9ukpEpK8rsbgAAAElSaTiljnTQcnxrIqVZyzrUNK2lBl2dIbwONTQ1qBC2KW8bv/qQYRjqrvfqlbkXSJJCz6/V0K7dtegSx4CwGwAAAAAA4ARVzIqi8SG9nNmsNw1cZKnvj+e0q7BXdYsWKVjfoPCcM/8Dyro//zPJ7ZYhqWv/49oxmrGMuXlouTYUNqkQTyuei8o0rctJAgAAnG36ntso+yERXrpQ1n4zKldLWJHGSI06m/oMw1CwoV72Oo8yQesqSzPrfapPNiruCco1MqzE08/wHvU0R9gNAAAAAABwgmL5mMrRjJzRvBoVsNR/GU/r0lBR7hktauhqk8t/dMuBT2X2tjb53/seSdIlu1bpqXhK1UM+KKwzvZqXaNOm2GZV00WlS9blzgEAAM4mpmnK3ByzHN8VzWp2m1OhrjbZ7MR7J8IIuVXfEFGmwW6p1ftcapNfjy69QZLkWvO00omUYrGYhoeHFYvFlM2ytPnphP8aAAAAAAAAToBpmhpNDOqZnud0S99yS70nkVeysFOB+eeovrFZoZmNNeiyNoJ/8GHJ6ZTdNOUffEZ7YznLmFsHLtGL6Q2qpvOKsZQ5AAA4y5X642orhCzHX0nH1X5+p5o7W2vQ1ZnFcNoVaWpUqc6hnLNqqXfX+5QKdcuUFNy4UX1btykWiykejysWi2ndunXq7++f/MYxIcJuAAAAAACAE5AoJlSJ5ZTsH1CbWWepP5lI65KuBgVmtKh+ZrscbkcNuqwNe0uLvLe+WZK0cuvP9GzMOnO7vRJWOO1Sf7RPqUxCpWppstsEAAA4bfS++LLl2Gi2qIzicjXXK1Dnr0FXZx5nnU+BhqAyIWtU2l3vU9NonV5uXyhXMqHAjp1KxBPKZDJKJpNKJpPatm0bM7xPE4TdAAAAAAAAJyCaHNa6nhd1c+8yS20wVdBoYa/8czpVF44o2BWe/AZrLPgHH5YkuStlZUc2aCBVsIx508BFej6+QUamrHg+PskdAgAAnD4c26xfDtwby2nhHJ8i3R0yDOs+0zgOfqciDfVKN9hkavxWO0G3Qy0+j9YuvkKS1LF9m2ymFAwG5XK5FIvFVC6XNTAwUIvOcQjCbgAAAAAAgOOULWWVjybV179b06sNlvqqeFrntnnlaAipcXaH7A7rvoBnOufcuXKvWCFJumbz/XphJGUZs7DQqYFcrwrxlGL50cluEQAAoKYqlYr27Nmj4W371FGyrhT0Sn5UoXnT1NzaXIPuzkyGzSZ/JCyz3qWs27TUZ9b7FMg1K293qWvfPjlzBdntdrndbpmmqWKxqHw+X4POcSjCbgAAAAAAgOMUzY5oc98runH/+ZbaSKaovuJ+2VrrFArXy9du3XvxbBH82EcO/G8xq32xbUrmy5Yxl4zM1cujr6qczitTykx2iwAAAJOiWCxq1apVWrVqlVavXq2dO3dqdHRUmUxGoYx1O5dYrqSyEvJ2NMntc9eg4zOXLeBSXX1E2bD1C6kzIl4Fsl6tWnSV3IW8wjt3qJgvqVAoyDAMuVwueTyeGnSNQxF2AwAAAAAAHIdStaTUaFS7ejdrTqXFUl8dy2hJu1fyudU4p+2sXnLSdfHFcixcIEm6evP/akvUGmZfNbpEGzMvy0yXFMtHJ7tFAACASZdOp5VKpZRIJJTP59WVCVvG7I1lNX9OQI0zOie/wTOd16n6SETpepuqhyxl7nHa1R7yqKdtviSpa8cOJaJRFYtFRSIRORwOtba21qJrHIKwGwAAAAAA4DjEclHt6t+uq/ctttTiuZJ2Fntlbw7I4fXI3eirQYenD8MwFPzogdndTZlRbUgMqmqO/0AxLK+CSZuGRweUzMRVqVZq0SoAAMCkKJVKikajKpfLSqfTchek6ZV6y7iX81GFFnSrodm6ZQ5OjGEYctX55WzwKeObeCnzhkS9Ep6QOnv2y1MsKRgMKRQKae7cufL5zu73+KcLwm4AAAAAAIBjVDWrisWG9er+jVpU7rDUn45mdG67R4bXLVu9twYdnn68N90ke3ubJKlr7+PqiVv3OLx+cJmej6+XMiUlionJbhEAAGDSpNNpVatVlctlOZ1OTXcFZDtkJaBEvqRSJaZgV5vsTutS2zhxhs+lcGN4wqXMp4U98lZc+uV518lZLGpazz75PT4tXbpUbW1tNegWEyHsBgAAAAAAOEbJYlJDg726ePdsSy1VKOvVYr9cTT7ZPW4ZQT6YlCTD4ZD/rrskScv2PqN1ibRlzJJ8l/Zl96qYzCqWYylzAABw5iqXy6pUDqxk43K51F2wzureG8tp3kyPmma0T3Z7Zw+vQ3WhiLINdlWM8bO7nXab2kMeJeu6JUnTtu+S3RQzuk8zhN0AAAAAAADHKJoY0vp9z+v84nRL7dloRos73DK9Tjka+CDsYP53/a6MYFA2mYoOvqRMsTyubjMMXTDara0j25RPp1WqlGrUKQAAwKnlcDhktx/4UqQ9V9bMcqNlzCv5qMLnzlEoUjfZ7Z01DMOQPeCRrzGkbMCw1KdHvGqIhtVT16HGPbtUGh5WpVKtQac4HMJuAAAAAACAY5AtZZUYGVHXrqDshyw1mStVtKk0LE+DVw63R2JW9zg2v1/+975HknTZtp9q62jWMuaakfO0Lr1RtmxV8UJ8kjsEAACYHIFAQDabTQ6HQ01lu2UJ81ShrGx5VMHp7bLZifNOJcPvVKQpomzIGnZPq/PIIZt+ff41cpRLanj1FVUq5Qmuglrhvw4AAAAAAIBjMJoZ1ov7XtCVqQWW2sZYRstmhlT1OuRgr+4JBX7/A5LDocbsqNYnhmSa45eLbFRAznRZieiIEvlYjboEAAA4tZxOp+rr6+VwODTjMEuYL+iwq3E6S5ifch6H/L6gCs2uwy5lXrV3SpJaXt0is0zYfToh7AYAAAAAADhKpWpJydER2Xak5LM5x9UqVVPPF2Jq6ozI6XZLIUeNujy92Zub5bn2DZKk9j2/VF+yYBlz7cBSrY2uVzGVVaFirQMAAJwJAoGAGnxBzSo1WGqv5KOqWzaPJcwngWEYMvwuBZrrlPNPsJR52KtwIqjN7QvUsHePzGSyBl3icAi7AQAAAAAAjlI0O6ot+zfrmpElltqOeFZzZnhlq/PIHvFK1s/J8BuvLWV+wd412pDIWOrLst3alt0mZctKFBKT3R4AAMCkaSjaZDfGx3WZYlnJwrBC0ztYwnySGH6nIvUNyoasz/e0sFeGpBcWXC5bpaLSE09MfoM4LP4LAQAAAAAAOAqmaSoRH1X/rh1qsQUt9dW5pGZMb1AgGGJW9+twX3GF7B3tssnU4MAm5UqVcXWHYdOS6DTtHN6pRC5emyYBAAAmQVvKbzm2N5bTguaqGmd21qCjs5TbIbfHo3KnR9VDljJ3O2xqC7rlLraqIkOlh35RoyYxEcJuAAAAAACAo5AqpdTbu1eX9Myz1AbSBYVaJFvYo9Y5Xbrq6qu0cuXKsR+Xy1WDjk9fhs0m37veJUm6dNuD2h7NWsZcN7RUG+IvqZTOKl/OT3aLAAAAp5ytaGpOsdFyfHM+rroLWMJ8MhmGIfmdCrXUK+ezLtHUFfHKn/Vq7ZxLVXn2WVWi0Rp0iYkQdgMAAAAAAByFWHJEm3Y/rwVmq6X2VCqthbMaFPAH5evgQ8mj4f+d35FsNjVlRrQ+PmSpt5oh5VMJmeniuKXMi8WiVq1aNe6nWCxOZusAAAAnhX0kIcchS5jnShWNZvrkam1kCfNJZvhcCtWFlQ1bn/eu3yxlvrV7mRxXXcW+3acR/isBAAAAAAB4HaVKSSND/Tpnd9uBWR8HSRfKigcKckR8auhul83Gxy1Hw97eJvfKFZKkpj2PazBVsIy5ZPAcbRvaqkQuNtntAQAAnHL+/orl2N5YTh3OUfWk4+rv769BV2cxt10Op1PVTr9lKXOv067mgFuhVKNsX/iiHDNm1KZHWPCvLwAAAAAAgNcRzY1q/e7ndHFhlqW2NpnRBXMb5PX4FOysr0F3U5f/ve+RJF2w5yltSmQs9YtSc7U+uUnldF65cm6y2wMAADglSqWS4tGYZuYiltqr+biybQEVKgVt27ZN2ax1uxecGgeWMncp0BpR3mtdynx6xCtXyalHf7mpBt3hcAi7AQAAAAAAjsA0TY1GB+V/tSK3zT6uVqpUtdWWkafJq8i0FtkcfNRyLDxXXy1bU5PsMtU7uFmV6vgZNEHDLSORVyWVV6IQr02TAAAAJ9HAwIB6e3tVHYjJazjH1YqVqgbTPcqHfIon4iqXyxoYGKhRp2cnw++Uz+tXvy1tqU0PeyVJ+17YM8ld4Uj4FxgAAAAAAMARpEopbd7zklamFllqryayOn9undwujyIzmmvQ3dRmOBzyvfMdkqRzd/xCvcm8Zczlgwv1ytBmxbPxSe4OAADg2BWLRa1atWrcT7FYlCRls1nt2LFDhUJBdSlrRNeXzKvDGJHqAjJNU8ViUfm89f0RTh3D7ZDN6VC8rqKqxn8R0+eyq8nvUrjXpdFhaxiO2iDsBgAAAAAAOIJYakSJjTsVsXsttefKGUXaggq11svhdU5wNl6P/3ffKUmaFt+rl1PWDw0vyM7UpuQrMtN5ZUss4wkAAKaugYEBlctl5XI5nZNvstS351JSR0hlW1WGYcjlcsnj8dSg07Oc1yEj4FLKUbKUpoe9ijWN6LltLGV+uiDsBgAAAAAAOIxSpaTevj1aOjrfUtuXymn6LJ9sLocaZ7bXoLszg2P6dLkuu1SSFO/doFKlOq7uNZzyJUwVk1nFWcocAABMYfl8XsViUcGKXY2Gf1zNNE3tyvZrxOdSsVRUJBKRw+FQa2trjbo9i3kdcrncinqKllJ7h0Nv+MtLdf2lF9agMUyEsBsAAAAAAOAwYvmoXtn4lM4xrEuUP5PLanp7UMH6iNxhXw26O3P43/1uSdKyHQ9pX8K6VOcVA4u1afhlpbJxmaZpqQMAAEwFHo9HLpdLzabLUhvOFBVK98loCCoUCikUCmnu3Lny+XifOek8Dhk2mxKhqsxDljKPVLxaUpktu2GvUXM4FGE3AAAAAADABEzT1OjIgFr21stmGONq6WJZarFJPqeaZrbVqMMzh/eG62WEw2rMjOiVZNxSP6/QpVdSW1RNF5Qts5Q5AACYmlpbW+VwODQrZ13CfE86o1BDReHmBkUiES1dulRtbbzPrAXDMFR2SAq6lLKXrQO2RSe9Jxyeo9YNAAAAAAAA1FKxWNSaNWvGHbv00ktVUEGvbH1eVxbmWj5BWZ/OaPG8kPx+v3wtdZPY7ZnJcLvle9tblbnnW8rvf16Fzlvkdvx2jobLsKs+6lQumVYqkqxhpwAAAMfP5/NpVkeXmqqGNP67lHq1MCzfrGlqb26S3W5nRneNlZyS1+VW3JNTKOMcX9wWlXmdKeOQL8SiNpjZDQAAAAAAMIFoaljpjT0KOcYvM1k1Te1wlWUPutQwo40PuU4S31vfIkm6cMfD2h3PWeorBpdo49BLSmUJuwEAwNRV6BmwrhpUKMtI9alxbrfsdpbHPh2UnZIMQ/G6imUpc8XyMoczNekLVoTdAAAAAAAAhyhVS+rdt1PnpxZZanvSOS2Y45fb41XdtMYadHdmci5eLPuMGQoWM9qcHLXUF5TatTW1TdVMQSWzVIMOAQAATlx247Dl2L5kTjNCWbnDwRp0hAkZUsUhKeBS5jdLmZdspuINhvT2eTIamHl/uiDsBgAAAAAAOEQ8H9Orzz6hOfYGS+2FYl7hRp8inY2yOZl5c7IYhiHfbbdKkmy7n1KuVBlXdxg2tY8ElE0mlTOtM78BAABOd2a1qumpgOX4lmJU1fndsjmI7U4nJadkd7nU589rX0NJrzTmNDjNLmNGWIad1+p0wSsBAAAAAABwiNjokFqHuyxLTKaKZbmnu2R3H1jCHCeX99Y3S5KW735cO+JZS33F8BJtGHlJmXxKsVhMw8PDisViymatYwEAAE43g5u3yWuM3/+5VKlqNNkjs625Rl3hcMpOybAZytYZKgcdMmxsX3Q6IuwGAAAAAAA4SMEsaPMLT+ii8mxLbV0mo7mdfoUaw3L43TXo7szmnD1bzoUL5KkUtSU2aKnPqTRrR2KHYkNDGo2NKh6PKxaLad26derv769BxwAAAEev75mt1mPJgua6U3I21NWgIxyJaZMqdsnudclmt8npYVWn0xFhNwAAAAAAwEHy5Yxy29MKOMbPuqmapvYHTdm8LjVOb61Rd2c+7223SZKCu55QqlAeV7MZhmaPNqmSL2o0M6pMJqNkMqlkMqlt27YxwxsAAJzWmvudlmPb8km1nDtLDre1htorOSWbyymHyy6XyyXZDGZ4n2YIuwEAAAAAAH7DNE1lhnq0rLDEUtuVyWnR7IC8AZ+8Tcy8OVW8b7pFknT+vqe0fYKlzK8cWaRh27AMl6lgMCiXy6VYLKZyuayBgYHJbhcAAOCopPqG1SjfuGOmaWpPpkfu8xbWqCu8nteWMq9rrZcr4FKoJSCngy8mnE4IuwEAAAAAAH4jZ2aV2vWqZjkiltqGalHeOrciHU3M5jiFHB0dci2/QA7T1I5oj6U+w6xX2pWWx+mUzW6T2+2WaZoqFovK5/M16BgAAGC8UqmkWCym4eFhxWIxZbNZ7Xl2vWXcSKaoxkyfwjM7a9AljuS113AwOqx4Jim3x6fpbdPU1dhV69ZwCMJuAAAAAACA38jmEppeOl+GMT7MThXLikx3y+5yqn5aS426O3t4b71VktS6/RdK5EvjaoZhaHa2RcqXlavmVCgUZBiGXC6XPB5PDboFAAD4rYGBAfX29ioWiykejysWi2ndunVyvGpdsWZ3JqPWZrsC9dYvWqJ2Dn0NRzJxbd+3S6lUWuI7r6cdwm4AAAAAAABJZbOs9Oa1usDsttReyGXU2eZXqDkiu5dlC0817y03S3a7FvSv185kzlK/eHSeEra4YtmYisWiIpGIHA6HWlvZSx0AANRONpvVjh07VCgUlEwmlclklEwmlY7F1VUOWcZvyQ8pv3Cu5YuWqJ2JXsORTEKZXFb9w4Mq5Au1bhGHIOwGAAAAAACQlK1mZST9CjjHh9lV09RI2Ca57Wqa3laj7s4u9oYGuS+/XIakPbFeS31utUUJb1J+r0vBUFChUEhz586Vz+ezXgwAAGCSDAwMqFwuK5fLyeVyKRgMyuVyqTQ4KrsxPpJLF8syontltDTUqFtMZKLXUC67EumkiqooGo3WukUcgrAbAAAAAABAUqlnrxZVz7Uc35XNav5Mn/whvzwNwRp0dnby3XarJCm4c5Vypcq4msOwqbUQVNgTUCAS0NKlS9XWxhcRAABAbeXzeRWLRUmS2+2W3W6X2+1Wc8L6hbyeZE6zfAm5ItYZ36idCV9Dj1s5W1nFalmFSkmyMRP/dOKodQMAAAAAAAC1VjALSmxbr3Oc77XUXlFF8wIuhTubWGJyEnluuF5yu7Wk51ntSb5X8xsC4+oXjczT1nBS7pCLGd0AAOC04PF45HK5JEmFQkFut1uFfEGzC+2W6afbyjFF5s+W02GXy+XSypUrJ79hWEz4GhYKsnlsKtY5ZLQG+DfBaYaZ3QAAAAAA4KyXTI+qzlgsxyGzNHLliuqmu+RwORXpbK5Rd2cnWzAozzVXy2FWtTMxYqkvKnZob2WfiuV8DboDAACwam1tlcPhkNfrVbFYVCqVkrNQVsTmHTeuapoaSvRowZtu1MqVK8fCVdTeRK9hsVhUpD4ieR1q7WA1odMNYTcAAAAAADir9fX3KfPMr7XMnGupbcpn1NbqU7i1Xna3c4KzcSr5brtNkmTseUqlSnVczWtzyp0zZStUVKgUatEeAADAOD6fT7Nnz5bb7VYoFJLf71er4beMG8kUNdOMqmHW9Bp0iSOZ6DUMhUIKhUKaO3cuKwqdhgi7AQAAAADAWSubzerlbS+rXDDU6rZ+ENnjMySXXQ3TmcFRC56rr5Lh8+m83au0P2mdwX3B8BxlCmkli8kadAcAAGDV2tqqjo4ORSIRhcNhdaXDljG7c2nNOKdVHj/B6eno0NcwEolo6dKlamvj3wSnI8JuAAAAAABw1hoYGFBp307N8lxsqQ3lC2ptlwJ1frkjgQnOxqlmeDxyX32VPOWCtqfilvr5uRnaW96ndDYx+c0BAAAchtPpVCQSUVN9o7qrYUt9e2FIvisvnfzGcNTGXsOmJkUiEWZ0n8YIuwEAAAAAwFkrnUsrs+kZnWvrsNTWl3MyXFKks0WGYUxwNiaD98YbJEm5fc+raprjanU2jyqFrIqZrCrVSi3aAwAAOKzqaEwuwz7uWL5ckS26Tw3z59SoK+DMQtgNAAAAAADOWuVyRnLNl8/pGHe8UjWVCRXl8rhU195Yo+4gSZ6rr5YcDp274xH1p6x7cy8enaFUIqF0KV2D7gAAAA7P0WPdhqUvldfCcFGBFt5jAicDYTcAAAAAADhrGc+u0RL7Esvx7fmMGiJS6/Q22d3OGnSG19hCIbkvu1ShQkrbUylL/YLULL088opSefbtBgAAp5euVMhybHsproblS1g5CDhJCLsBAAAAAMBZKV/Oa/PmjZrlDltqW42Sgg1htc2eNvmNwcJ7w4GlzGN9myy1VltQA7H9yqYJuwEAwGmkUFSrEbAc7kn2KnDZhTVoCDgzEXYDAAAAAICzUvTl9Qp4L5HdNn5WTaZcVvNMjxpbGuRtqKtRdziY57prJUnztz2kkUzRUp8+2KBsMql82bpUKAAAQC2Ue0ctx2K5ktoyvarvnjH5DQFnKMJuAAAAAABw1jFNU7vu+7aW2bottY3FjOoavKprb5Bh46OT04G9tVXOpeerKT2o7amMpX5hfK62DG1Rqmhd5hwAAKAW6gYclmN7sxnNnRGUy++tQUfAmYl/sQEAAAAAgLNOJpfQzpRbjR6PpTbsq8jmcqi+o7UGneFwXlvKfGBou6U2XRHtie5SOpuY7LYAAACsTGlmMWw5vK04Iv/ll05+P8AZjLAbAAAAAACcdaIP/0zdvuWW432FvELhkvyRgBx+dw06w+F4rr9ekjR968+VzJfH1WyGoXC/S7lUWhWzUov2AAAAxlTicfltznHHytWq8sO7Vb90cY26As5MhN0AAAAAAOCsUjWrevXRn2iRs8VSe7mal+mQIh3NMgxjgrNRK87Zs+SYPVvTo7u0M5W11M8b7dbASK8yJesy5wAAAJPJ2G99rzKQKmi+I6ZgZ3sNOgLOXITdAAAAAADgrJLs3a0h30L5nOP3UaxUTVXrC7K5bQo0RWrUHY7Ec8OB2d190f2W2rxKq7ZEX1W6kJzstgAAAMbpiPstx7YXk2pdMks2u70GHQFnLsJuAAAAAABwVhn93ve0wLXIcnxHMSOPX7L73bI5+RDydOT9Tdjt37FKpUp1XM1jcyg3FFcmRdgNAABqp5TLqdMMWo7vTffJd+XlNegIOLMRdgMAAAAAgLNGuVLWhpfXaa7HOnN7t62kql1y1nlr0BmOhvPcc2VradGCnrXqTect9Zn9rUon4ipUCjXoDgAAQOp56VXZjfHxW7pQVji6Ww0L5taoK+DM5Xj9IQAAAAAAAGeG2DOrVGlZKad9/AeQhUpFzoaCHB6n5HHWqDu8HsNmk/eG61X9r29rdzKuGXW+cfVzs116cXiHumbMldvrrlGXAADgbJZ6vk9S/bhj+zNZLWiUPPVslTMVuFwurVy5stZt4CgxsxsAAAAAAJw1hr73fS2xzbIc31xKy+Y2ZA96JKMGjeGovbZvd6b3JUut3RbSvtHdSmVZyhwAANRGW9K6StDWUlT+i5fVoBvgzEfYDQAAAAAAzgr5RFQvZovq8gcstUFPRabTJkeQJcxPd+5LLpERDOqc7Y8onitZ6oE+u3KppKpmdYKzAQAATp3UyKgabOPfT5qmqfjIbtVdfkmNugLObITdAAAAAADgrBC//z5FmlbKZoyfup0sl+SOlOT0O2Q6+ajkdGc4nfJc+wbVZ0e0O5211M+NztDg6ICypUwNugMAAGezXWs2WI6NZkuale9TZEbX5DcEnAX4FxwAAAAAADgr7Pv5AzrX0Wk5vtnMyrRLjgCzuqcKzxveIEnqH91vqZ1TadXWkVeVKqYmuy0AAHCWq25OWI7tKaQ1fU6z7F5PDToCznyOWjcAAAAAAABwqmVefVkvN5yjJb4JPmRs8ah7bqvOuXKZbC4+KpkKPFdeIdlsCu54UqU5C+S0/3Y+h9fmUHoopkwqKQXaa9glAAA4m5imqc58wDLNdFdhRDOuuqw2TQFnAWZ2AwAAAACAM17se99Rd2i55fhAKa/6dq8CzWGC7inEFonItWypzul9QX3pvKU+s79JmURcpYp1T28AAIBTYXR/j4I217hjlaqpwuBORZYvrVFXwJmPsBsAAAAAAJzRzHJZW59/RgvdzZbaDntRptOm+vaWGnSGE+G56irZVdXuZNxSW5KZrh3DO5QusZQ5AACYHHue2mQ5NpQpaL49Kl+r9X0ogJODsBsAAAAAAJzRkk88pn1zr1XI7Rx33DRNBaZ55Al65IuEatQdjpf7mqslSbmeDZZahz2kvaO7lM4RdgMAgMnh2l2wHNtdSql9wQwZNuI44FThvy4AAAAAAHBGi//gB1roWmA5vruclbfeo7rWehl2PiKZapwLF8rW1KS523+heM66XLm/1658mrAbAACcetVKVV2loOX4ntygfCsur0FHwNmDf8kBAAAAAIAzViUW04tDg5rrD1tqfR5ThtuuSBtLmE9FhmHIc/VVCudi2p3JWOpLotPVP9KnQtm6pzcAAMDJ1Ld9uzw2x7hjpUpVtoEdCp+/pEZdAWcHwm4AAAAAAHDGSjz4Y+VmXS+Pwz7ueKlaVWS6W/6wX66Qr0bd4UR5rrpKkjQ4vN9Sm1dp1baRrUqX0pPdFgAAOMv0Pr3Vcqw/k9ciT1rucHjyGwLOIoTdAAAAAADgjBX/4X1a5JhpOb6tkpbhc6quvVGGYdSgM5wM7iuvkOx2hbY/rlKlOq7mtTmUHBhVJstS5gAA4NT6/+z9d5Cl93nYe37fHE7unCZnJAKDDBIkCAZRliWLlGQr+UrWunxrV94qb61dctVeS/JuuW6pru0t++7ae1UKtGUFyxQBUiIpkWISCRAEqCEBEBhg8nRP53Dym8P+0cAAjdMzgzAzp7vn+bCa3Xh/b5/zdM/p7nPe5/c8T3k27zl2Nm4x9L4j8lxTiBtMkt1CCCGEEEIIIYTYkeKz5/iO4bK/2Ds/cdlK0Byd6shwHyIT14taqWDedy+H5k8w1wl71vfNDdJq1snz3gvQQgghhBDXQxLFTGW9zzdn2nMUP/yhPkQkxK1Fkt1CCCGEEEIIIYTYkRp/+sfoez6CoW28/BFmKWolplgroTlmn6IT14v9+OOo5FxorfWs3eXt4cLyOYJU5nYLIYQQ4sa4+MKLGOrG55tBnFJeOEX19mN9ikqIW4cku4UQQgghhBBCCLHj5FlG/XOf505td8/aK1mbRIfKxKC0ldwB7McfB8C/9IOetQm1xMXlc3RCaWUuhBBCiBtj+bsXe47Nej63VxO0UrEPEQlxa5FktxBCCCGEEEIIIXac4Dvf4al9t7GnVOhZazspqq1SrA30ITJxvenHjqKOjXHk1JdoBfGGNUVR0GZSul1JdgshhBDixhha1nuOnY6bVI7fKRsrhbgJJNkthBBCCCGEEEKIHaf13/+Y6uj70dSNFxi7aUJaStAKJqrVe2FSbD+KomA//mHKQZOLXa9n/dDqKI3GisztFkIIIcR1F3hdJpTe6u3F+jSlj3y4DxEJceuRZLcQQgghhBBCCCF2lMzzWPzmt7hTn+hZe4UOqQF6yepDZOJGsT+8fjF5sb7Qs3Y0Hufc8hm8pDcRLoQQQgjxXpx77nnUt1Rvd8KEseXTlI8c7FNUQtxaJNkthBBCCCGEEEKIHcX74hd55u7HmCg5PWthIUGzVVTL7kNk4kaxHv0A6DrG+ad6KrgHNYe5pRm6UadP0QkhhBBip2r/be9Guxnf48iIjua6fYhIiFuPJLuFEEIIIYQQQgixo7T/+58wWb23p8qmmUZEhQy1YJHLFZEdRS2VMO+/n8MXvs2qH/esF6Z1uu1WHyITQgghxE421uzdQHk6WWN1fJhvfOMbl9+iKOpDdELcGuSlnRBCCCGEEEIIIXaMZHaO6dNnud0c6Vk7pXTI9ByjJFXdO5H9kccxs5iL3d4K7iPNMVbqi6R52ofIhBBCCLETdRp1RrXe6u364jmiO2/rQ0RC3Jok2S2EEEIIIYQQQogdo/vEZzlx748yWuydyZ2UE3RHQzHMPkQmbjT7w48BsLZ8sWftcDrCmcXT+LHM7RZCCCHE9XH2med7jtX9mH2N86gjw32ISIhbkyS7hRBCCCGEEEIIsSPkeU73M5/hYOFOlLe0MF9NQ3w9xVdillaXqdfreJ4kPncS/cgR1LExyqe/RpptnNtd1ExWFhfohO0+RSeEEEKInSZ8ca3n2HTYZd9kgdyUzZVC3CyS7BZCCCGEEEIIIcSOkLx8kleChGPOYM/aecOnmXRoBT6NRoN6vc6JEyeYn5/vQ6TiRlAUBfvRD7B/4XkWvbBnffCSg9+RZLcQQgghro+xTu9onHPRKuaD9/UhGiFuXZLsFkIIIYQQQgghxI7Q+bPPcOquTzBU6K2kSdyITElZ67Todru0Wi1arRanTp2SCu8dxPrgB1HJudhp9qwda48zvzJLmsncbiGEEEK8N51GnZFN5nV3Fs9Q/dCjfYhIiFuXJLuFEEIIIYQQQgix7eVpSveJJzlqHelZm089PCOlm4cYlkmpVMI0Ter1OkmSsLCw0IeIxY1gfeD9AHQWTvWs7WeIM8un6Sbdmx2WEEIIIXaYzeZ1N/yY/d1L2JMTfYhIiFuXJLuFEEIIIYQQQgix7YVPPc0LxSGOFKs9a7NWRKSnJIBlWWiahmVZ5HlOFEUEQXDT4xU3hjYygn7kCOOvfJkozTasWapG69IaHV9amQshhBDivQk2mdd9Meqwd3cVxbL6EJEQty5JdgshhBBCCCGEEGLb6/7ZnzFz20eoOUbPmjGoYRZNIiUnDEPSNCUMQxRFwTRNbLt33qLYvqxHP8B48wKz3d5NDBPzZbx2b4tzIYQQQoh3YnyTed3nozXcBx/oQzRC3Nok2S2EEEIIIYQQQohtLfN9/C9+kdvNgz1rl7IuxlAJs+zguA5RFNFut4miiFqthq7rjI2N9SFqcaPYj67PybzUXO1ZO+ZNMLN0kSRLbnZYQgghhNghOs3G5vO6F85S/ciH+hCRELc2SXYLIYQQQgghhBBiWwu+/GW+N3WUg8Vyz9pqKcOq2hw8dgzLsiiXyxQKBcrlMuVymcOHD+O6vRcrxfZlPvQg6Drx7As9a7vVCueWzuDFMrdbCCGEEO/Ome/8oOdYI4jZ376ALZsohbjpJNkthBBCCCGEEEKIba37mT9j6dBjVDdpYV4cL1GoFhnfs4vJyUlqtRrVapVarcbx48cZHx/vQ8TiRlKLRczj97D31b/Ci9INa5qiEs106QadPkUnhBBCiO1us3nd02GXvbsqKKbZh4iEuLXp/Q5ACCGEuJ6iKOLpp5/ecOyRRx7BlCeaQgghhBA7Urq6SvDNb3PHT/xkz9pM3iUtVaiOD6EoCoZhUKvVLq9LRffOZT36KJVnn2Pa8zhqljas7V0aoN2uM16Z7FN0QgghhNjOxjsWaBuPnYvW2CXzuoXoC6nsFkIIIYQQQgghxLblf/7PeebwfRwol3rWmuUcvahTHKht8pliJ7Me/QAAC/WlnrWj4TjnFs6Q5mnPmhBCCCHE1XRaDUbU3g2T7YUzG+Z1x3FMvV5neXmZer2O53k3M0whbimS7BZCCCGEEEIIIcS21f3MZ2ge+AAVe2ML8zzPsUeLFGsVdNvqU3SiX8y770YpFFAvfLdnbUwrMrN4Hi+Wi85CCCGEeGfOPvM8iqJsONYMYvY3z2FPrHeNWVhYYHZ2lnq9TqPRoF6vc+LECebn5/sRshA7niS7hRBCCCGEEEIIsS3FZ8/hP/9D7jB296xNK13Ssk5lbKAPkYl+UwwD6+GHOXjmazSDeOOaoqCez2RutxBCCCHeMf/5lZ5j02GXfZMlFMvC8zzOnDlDGIa0Wi263S6tVotWq8WpU6ekwluIG0CS3UIIIYQQQgghhNiW/Cee4Ok7PsiBSrFnrVvJMYsGxaq0ML9VWY9+ACsLuej1JrX3rQ3SbtX7EJUQQgghtrOxjt1z7Fy0hvPA/cB6VXeSJPi+j2malEolTNOkXq+TJAkLCws3O2QhdjxJdgshhBBCCCGEEGLbyfOc7mc/S7D7IUqWvmEty3PMkRLFgTKabfYpQtFvr8/tXl2Z7Vk7lIxwbvEMWZ7d7LCEEEIIsU11Wg1GN5nX3Vo8S+UjjwEQBAFRFAFgWRaapmFZFnmeE0URQRDczJCFuCVIslsIIYQQQgghhBDbTvz9H+BdWuQOY7JnbVrtkJUNKmNDfYhMbBX64cOow8M4557uWRvWXS7NX5C53UIIIYR4284884Oeed2tIGbf2mmc3etjdWzbxjTXN1uGYUiapoRhiKIomKaJbfdWhgsh3htJdgshhBBCCCGEEGLb8Z58km/d+Tj7q4WetaCmYJYMCpXqzQ9MbBmKomA9+ij7pr9D3Y97T7iYytxuIYQQQrxt/vOrPccuRl32T5RQXktij42Noes6juMQRRHtdpsoiqjVaui6ztjY2M0OW4gdT5LdQgghhBBCCCGE2FbyJMH73Odh8l6Km7Qw14eKlAYqaJbRpwjFVmF/8FH0PGHG701q71mr4nWafYhKCCGEENvRZvO6z0drOPcdv/zfruty8OBBLMuiXC5TKBQol8uUy2UOHz6M6/a2QRdCvDeS7BZCCCGEEEIIIcS2Ej79HTpNnzvM8Z61i2qbrGxQHhnsQ2Riq7E+8H4A1lbne9YOxSOclbndQgghhHgbup3W5vO6F96Y1/26sbExJicnqdVqVKtVarUax48fZ3y897mrEOK90699ihBCbD9RFPH00xvnsj3yyCOX56UI8V7JY0wIIYQQon/8J5/kb+78KJ+s9l5wjAdVzJIpLcwFANr4OPqhg5jnnoZDt21YG9Fcvjr/MsEdAa4hVVZCCCGEuLIz3/kBR946rzuM2bvyKvb+/T3nG4ZBrVa7/N9S0S3EjSOV3UIIIYQQQgghhNg28iDA/8IXMcbfR8HcuIc/zTOUwSLloQqqJfv7xTrr0UfZf/EpmsHGud2KopCfD+mG7T5FJoQQQojtovv8cs+x6bDL/rECquP0ISIhxOvklZ8QQgghhBBCCCG2jeBrX6cVqdxujPasXdQ6UBmkPDTQs2aaJo899thNiFBsNdYjD2P83u8z43Wp2NUNa7vXqnTbbSpmTTo3CSGEEOKKxlpWT0btXFRn8v7jm3+CEOKmkcpuIYQQO04cx9TrdZaXl6nX63ie1++QhBBCCCHEdeI98SR/c9dH2VvrbQWZDqhYRRNXWpiLNzEffAiAlbWFnrWD8QgXl86R5/nNDksIIYQQ24TvdRjVCj3HW4tnqXz4Q32ISAjxZpLsFkIIsaMsLCwwOztLvV6n0WhQr9c5ceIE8/Pz/Q5NCCGEEEK8R1mrRfDXf405fCfFt7Qwz/IcBouUhqqopjSyE2/QBmrox45hXfhOz9qoVuD8/FmCNOhDZEIIIYTYDs499yLaW+Z1d6KEPYsnsQ8f7lNUQojXyas/IYQQO4bneZw5c4YwDPF9//LxVqvFqVOnqFQquG5vBdC7loOSg5aCmkK+6pERQv6mE16nKGBoKKYKhgamhqLJnjMhhBBCiHfC/9Jf0lAL3GGN9Kxd1Nso1UHKQ4N9iExsddYjD7P39/+AVviLlC3j8nFVUUjOB3gPdvoYnRBCCCG2ssb354GNY3IuhR77R2wUmdctRN9JslsIIcSOsbCwQJIk+L6PaZpYlkUYhtTrdUqlEgsLC+zfv/893Ucep+DF5E2PUhNe39OZZznJaocky8hf+98bnwSqoqKbFrplXv4kRVPB1lGK5vr7t+wQFUIIIYQQG/lPrrcw/5nqJi3MaxpmycYpl/sQmdjqrIcewvzd32PG63K7Vd2wNrVSpttt9ycwIYQQQmx5g3UdtI3HzscNHrz3brmeJ8QWIMnud2FpaYlnn32WZ599lueee47nnnuO1dVVAH7pl36JT3/609f9Pv/4j/+Y3//93+eFF16g0WgwOjrKo48+yq/+6q/y8MMPX/f7E0KI7SgIAqIoAsCyLDRNw7Is8jwniiKC4J23JszzHMKE3IvBS8iTlCzL8FpN0sAjTzPIUvI8Z3luBk1V2TDt7/XE9msHFUVFMwx0w0C3LGzXxew6KJpKXjRRiiaKob01DCGEEEKIW166tET47adwf+Q3KFqbtDAfKlAaqkgLc7Ep86EHAVhdW4BadcPa4XiEmeVpwLz5gQkhhBBiS4vikEml2HN8dW2ays//SB8iEkK8lbwCfBdGR0dv2n35vs9P//RP88UvfnHD8enpaf7wD/+QP/7jP+bXf/3X+Y3f+I2bFpMQQtwIURTx9NNPbzj2yCOPYJpv/4KTbduXzw/D8HJlt+M4mKaJbdtv+7byNINORN4OyZOMOI4Iuh1CzyPyA9I0IYp8YjW5/IZlk+k5uZJvvC1AzRXMXMfMdIxcwwx0dE+js7aKZhk4xRJuWEVv6iiWjlK1URxj8+CEEEIIIW5B/l98gbo1wO3mUM/atN6G6iCVQWlhLjanDQygHzuKceFZOHB0w9qYVuSbi68yZe3HUCThLYQQQog3XHj+JXarG0cRhknGyMwPcW7/v/UpKiHEm8mw0Pdo9+7dfPzjH79ht/8rv/IrlxPdH/7wh3nyySd59tln+d3f/V0OHDhAlmX85m/+Jr/92799w2IQQojtYmxsDF3XcRyHKIpot9tEUUStVkPXdcbGxq55G3mYkK10yS+1iFbbNFeWWZw5z/LFC7RWVmgFTepGh1lrlRl7hQWzwZrRoatF5AbYuo2ruxveCrqLra8nwtt6wLLeYtZcY9paZsls0Y67tFZXWbx4jqW5aVprq6QLbbKlLnmS3oTvnBBCCCHE1uc9sd7CfE+tt4V5NqBjlRycirQwF1dmPfwwe899g3aYbDiuKgrJOZ8wi/oUmRBCCCG2qqXnLvYcmws8jlYzFLf3eakQ4uaTyu534dd//de5//77uf/++xkdHeXChQvs27fvut/P1772Nf7kT/4EgB//8R/niSeeQNPWW9vef//9/MRP/AT33nsv09PT/Nqv/Ro/8zM/Q61Wu+5xCCHEduG6LgcPHmR6ehrDMEjTFE3TKJfLHD58GPcqT0BzPyZvBORhQhB06TQbRF2PLMvw1QjfjOiqIZmSoykqOiYFxUVDQ0VFURRq1gC6du0W5FmekWQJcRbjJwGLWhMtAzdzKPoRsefTdtaoDA5T9EvkFQulYssMICGEEELcspKLF4lPnMD9xN+jtEkL82zQpTRcQdFlHIy4MuuhhzB/7/e55Hc5ZlU2rE2tlYmSqGcepxBCCCFubeVFespGzyVN7rnrdrlWJ8QWIcnud+Ff/+t/fVPu59/+238LgK7r/Kf/9J8uJ7pfNzQ0xG/91m/xcz/3czQaDX7nd36Hf/Ev/sVNiU3sTNejjfSNtNXjE1vD2NgYk5OTdDodkiRB13WOHz9OtVrd9PzXk9xpENHpNvEaDbIwJs5TOlpI2/JI1RxTNShoBWzdxlANkjSlrtQv346Sg97JMLP1tuUo62/5a+9TRyXX158Aq4qKqZmYmknBKJBkCX7i4yc+bc3HTjQGvCKNIKZTcqhGw9gdFwZcFFdamwshhBDi1uM/+TmWC2Pcbg33rF0yOlAbpFwb6ENkYjt5fW73Sn0RqhuT3YeiYZ7xl6hY8jgSQgghxLo0S5nKSz3HF1vzFH/sA32ISAixGUl2b1HtdpuvfvWrAHz0ox9lampq0/M+9alPUS6XabVaPPHEE5LsFuJN4jjekPD0PE8S47cIwzA2dLrYrKI7D15LcvsR7U6DbqNOHib4RLT1AE+P0FQVRy/g6g66+tqfzDzHqKc4qzEH5orYoYIbG7iJjnIyuGpcsQNxRSWu6sRljaiikjkquqpTMkuUzBJRGtGO28xpTUqxSbWZstwNsGslatEweq2wPs9bdo4KIYQQ4haR5zneZ5/g23d+hH9Qc3rWs5qOVbKlhbm4Jm1wEP3oEbTzz8K+wxvWxrUSy95LjFf2oSlS3i2EEEIImD59inFtYxotyTJKZ5+ncO8/7lNUQoi3kmT3FvXcc88RReuzoj70oQ9d8TzTNHnooYf48pe/zHPPPUccxxiGVP0JsbCwwOzsLFmWXW5lfeLECY4dO8b4+Hi/wxN9lMcp+ZpP6oW0uw069QZ5GBMQ0tA9Qj3F0k1qehVLs1AVFTXIsJdj7KUEazlFi3IABim8o/s2fDD8DBbemAUYFnK6u3T8vQ65qWBqJoPaIIEe0NJaeMkaldCF5Yx5z2MgHaUQV2GogKJKwlsIIYQQO1/y8kmSM2coHvifNm1hng461Iaq0sJcvC3Www+z97/+Md0P/CwF843Hk6YomKsa4WiIq8n8TSGEEELA3FOnGWfjhspFP+A2u41avvJGS9M0eeyxx25wdEKI10mye4t6+eWXL3989OjRq5579OhRvvzlL5MkCadPn+a222670eEJsaV5nseZM2cIwxDf9y8fb7VanDp1ikqlctXZzWJnyrOMvBGStXxa3QadtfqbktxdQj3DMWyGjRqGaqCGGe7FBPdSjNnMblhcVlfBeiUle6VNt5rR3WMQ73awdRtTM/ETn4bephsEDLZLrCZzhHFILR5CHS3KRV0hhBBC7Hjek0+yUJridnuwZ23W6JAPDFJ6U1cfIa7GeughrN//NJc8jyPmxovUe5o1kjQESXYLIYQQAnAuJT3Hzidtjh47hKKqm3yGEKIfJNm9RV26dOnyx1dqYf66Xbt2Xf54ZmbmHSW733w/m5mfn3/btyXEVrGwsECSJPi+j2maWJZFGIbU63VKpRILCwvs37+/32GKmyWHvBORd326XotGfZm8G+Fn60nuyMiwDYdho4Ch6FgrKYWLPs5CgnLjctw9VBRKDY1SIyP+YYu1KQhvK1EwCji6Q1Nrsug3GfCLsJQTJSHDyRj6aAnFlo4eQgghhNiZ8izDf/JzPHXHR/nZam8CMqvp2NLCXLwDr8/tXm4scqS68XFzOBrhm2uzhJqMwhJCCCFudXmeM5kUerJos91F7v3og/0JSgixKUl2b1Htdvvyx8Vi8arnFgpvtNHtdDrv6H7enCgXYqcIguDyGADLstA0DcuyyPOcKIoIgqvPVRY7h5qA40O41GC1UydpeURJRFP36DoxjuEwZBQwU53C2YjixS66l7/r+1vTO6wZbRQU1FxBRUXNFaxMZySuvu3bMVKN0YvgzbdY3Q/sr1CzanTUDmtqm5IfU1nOmY8iRpIJrNEqSlEuwgkhhBBi54mee450bo7yHQcp270tzJNBl9qgtDAXb582NIR+5DDahb+FvYc2rE2qJRY78+S5jq7pMgpLCCGEuIUtzF6kplsbjmV5jnHuRUof+Ad9ikoIsRlJdm9Rb07GXWsXsWW98Qv3zS2bhbhV2bZ9+ecmDMPLld2O42CaJrZt9zlCccPlYAWgByl+0mVl7hJqktJQPdp2iGGYDFtDmIlO6VRE8XwHNX77Nx8oES8VZzjlzjFvrTFvNVjUmpgtF9tzMSIDIzbRIwMjWn+vGTk1x2DYcBhTS+zJBtgV97bifDM30nFeyanPrNE4oONOlTEKBg21TtptUFkrsZDOMJjHFCeGUVxJeAshhBBiZ/Gf/ByXqnu5bZMW5nOvtTAvSgtz8Q5ZDz3Env/2p3jv/xlc442NEpqq4rQtmloTIzNkFJYQQghxC7v47ZepsfHv/4ofciRdQBu6+jU9IcTNJcnuLerNybjXK1SvJAzDyx87jvOO7mdmZuaq6/Pz8zzwwAPv6DaF6LexsTEuXLiA4zj4vn/5Z2hiYgJd1xkbG+tzhOJGyv2YYhvCxMcPPfRIwSt1aNkBmBo1s4abmhRfiSle6KD2jt7Z1LS9zA+K53m+dIFXnVnsZpHiwiSV5Ukm1m7jWMtGy5Vr3k4XOAucxadqL7BnQudQtUxB2TxRraAw0LUov5ixNLNMcluRwYEh6kqdxGtQa5ZZYR4FhcLkMIojLc2FEEIIsTPkcYz/53/Bd27/e/x8rfe1blY1pIW5eFeshx/G+i//lTnf46BR2rC2rzvEwkQCgSqjsIQQQohbmHLWh7ckuy8mHfbun0TRJbUmxFYiP5FbVKn0xouta7Um73a7lz++Vsvzt7rWPHAhtiPXdTl48CDT09MYhkGapmiaRrlc5vDhw7Ijf4fKk4x8zSeuN+n6dfQwR8lTVvQOsWtQccoUcofKqYTiuS7q25jH3dS6fGPgJb5We4FFvcnA9F5GXj7Ko/MPYSfqe465ESQ0ziW8qARMVWwODhXYVdl805Keq0zUXRp/67F8MGRg9wAtrUW91aLWhCV1nhEFClMjKJb8eRdCCCHE9hf+zbfI6nVqhYOU7Y0b+rI8JxlyGJIW5uJdMB9+CIDF1goHyxuT3QejIRaZw7QcGYUlhBBC3MImQ7cngzYdrHDsQSkOFGKrkavhW9Sbk9CXLl3ivvvuu+K5b67OlhncQqwbGxtjcnKSTqdDkiTous7x48epVqv9Dk3cAHk7JF3zaHTWaC4vowcpHdWnYfroms6QPcjAJYXyqx7a1ZtlkJHzQvECXx14gROFc9TOHWT0B49y51IB420kyN+NLIfpRsB0I2CsZPHgrirVK1RoVwMb85WY2eYyxYM1OmVoNNtU6wpLyjwjKBR2jaCYctFXCCGEENub9+STXBg4yDG7t035vNkhH5QW5uLd0YaG0A8fgksvw9S+DWu7lSp/nv2QaqThOq6MwhJCCCFuQWuriwzpvQUp8cWTlP/Z/7UPEQkhrkaS3VvUbbfddvnjV1555arnvr6u6zqHDh26oXEJsZ0YhkHtTRe/pKJ764vjeMMGBc/zLs9f30yepOQrPn67xWp9kbwdEUZdlowGsZ7j4jLWKbL7WzFGN7/qfSdKyjdqP+Rzw8/S9GHqhXv50MXjWMm1W5NfTwvtkM+/vMixkSLvmyhjar0V5G5isOeSwqy/jLW3QlDJaLRb1OqwxByjqoI7NYxiSMJbCCGEENtT5nkEf/lXPHvPz23ewrxiSgtz8Z5YDz3E5J9+kfj+H8V403NuW9NRvZwgD5icmJRRWEIIIcQt6Oy3X+TOt6TPGkHE4eYZDOmWK8SWI8nuLer+++/HNE2iKOKb3/wm//Jf/stNz4uiiGeeeeby5xiGzGoV20MSpSRhQhqlpHFKGiQkYUISROR5Tq4CCmQASo5m6KimRtxOUHUF5bU3sXMsLCwwOztLlmWXW8+fOHGCY8eOMT4+vuHcPM+hHZGsdVnrrOCvNciCmLrapuOGKB2dsaDI4cUyVc8ErpzojpSYrw68wOcHvwezY+z+q8e5d9Xi3Ty6DFOhUFQwTBXdUNbfdBVdV0jinChMCcOcKMqIwpzA2zyuHHh5qcO5NY/7piocGCz0nGPmOrtXSsxHLbIpm3BMp9FuUa3DojLHmKLi7BpC2SRZLoQQQgix1QVf+QqZ5zHg7qOySQvzeNiWFubiPTEffpjSf/0DFvyAXcWNG6N3dYdoj6oyCksIIYS4RcUnG8DQhmMX4y6TUwMo0vFFiC1Hkt1bVKlU4iMf+Qhf+tKX+Ou//msuXbq06Xztz372s7RaLQA++clP3uwwhXjb8iwn7EaE7ZBg1SfqdEmiiCSOSMKIJIqJ45gsW+8TfTnRqKx/pKgKqZrRXVkBFTJFAVXldPk0teEapXKJQqmAKom9bcnzPM6cOUMYhvi+f/l4q9Xi1KlTVCqVyxeY8jglX/HodlrU1xbJOxFe4lG3PDTLZFQf4VgyTOlCiJJfOWUdKTF/NfgD/nzge7inDnLH3/woxeDtP35UFaxSilnK0Mo5aiWlMlFBsaz1B/BreWzltfcWCsVcx8hUzFTBSCHxMuqLPmvLIfXVlDjceB9BkvHtC3XmWyEP76mhqRu/Hg2VqVaZpQtdVlOdcEyn6bWoNBQW1EtMmBrW+ACKIhtDhBBCCLG9+E88ybnhYxx1etuUL5hd8oFBSrWBPkQmdgrrtbnd816jJ9l9MBjidDGUUVhCCCHELWrMc3qyZxejNfbee09/AhJCXJUku/vk05/+NP/oH/0jAH7jN36D3/zN3+w555//83/Ol770JZIk4Vd/9Vf57Gc/i6a9sWt9ZWWFX/u1XwOgWq3yj//xP74psYud7Z22kb6aPM/xmwH+mk+05hN5XcJOB7/rE/kxSpRBmpNnKXmWoeQ5kK//L8831OLmrM9S1lopcZ6QkqEYCs2Ts4Sza8yZKpmlYVccCrUypXKRPM8lybdNLCwskCQJvu9jmiaWZRGGIfV6nVKpxMLCAvv27YNWSLLWZbWzRLDWJg1D6kqHwE0p2WVqdZPaCyGGl8NVarO/WX2JPx1+CvOVw9z9jR+jEL69JLemQ3FUwZlSsCcNmt02uQKpopAoOupABc0wQHkjyZ1nGVmek2c5UZISJTHtLCPPQXPAqTkcOlhEi3I6jYC5aZ+lmYTsTfPBz655NMOEDx8YxN2kNfmIVyCb6bCWa8RjGq1uk3JTYVG7xKRjo9d6K8OFEEIIIbaqdK1O8PVv8L2HfoVf2KyFedXALlvY5VIfohM7hTY8jH7gAMH8WRiZ2LC2Nx/gpHleKrqFEEKIW1C73WBU630O0J07Q/kffqoPEQkhrkWS3e/Ct7/9bc6cOXP5v1dWVi5/fObMGT796U9vOP+Xf/mX39X9PP744/zsz/4sf/Inf8LnP/95Pvaxj/HP/tk/Y2JighdffJF/82/+DdPT0wD81m/91obZxEK8G++kjfTVZGmG3wjozLUI1pp4jRa+55P6yWsJ7hRNSUmVbL1K28zJVFAMBVXVQFFR89dSlYoCKChJRtDx0YjR1nPiEOS0LizjuTZmwUKxNDK7Q+Q2WTJgfnkOvWjgFBxcUy5SbGVBEBBFEQCWZaFpGpZlkec5URQRdnzyhQ6ddoO1tWXoRHTSLk3Lx7BNRqgx9EKCOxtc9X5+WJjmv438DcmZCe741o/iRtfeDKGoUJyA0j6D4pSD6piolkmmqczP+KBAnoKSqmh5ES3SIFv/OcizfP19npOSkeYpimqhKevbN7I8xcti2kaIqasUHIuDI0X2H4mYPtti/mJMmqzHsdKN+MLJJT58YJChQu8GlDGvSHqpTTPVCIeh221TMDSW52cYcw6g2DLmQgghhBDbQ/DFL5InCYPWnp4W5gDRkM3QQE1amIv3zHzwAYb+8mtkd30A9U0bpau6RaPZIMkSTN7d5m8hhBBCbE/nnn2RI28poOpGCXvmX8Q69r/0KSohxNVIsvtd+J3f+R3+y3/5L5uuPfXUUzz11FMbjr3bZDfA7/3e79FqtfjiF7/I17/+db7+9a9vWFdVlX/1r/4V/+Sf/JN3fR9CwDtrI30laZLhrXTpzrfw603a9QZhK0DzU8gzVDUl1VMSJ0cxdbJcJ/Eg8XKiTkbcTcmzFEVVUFQFVVVQNFA1Bc1R6MQB3TwEUjRFQVdUSo6BQYKTWuTtlKgVEahdUgvcbk7ejQm0mJbd4OWzLzM5NUGtMICuyq+/rcS27csdBMIwvFzZ7dgOxdzE7WQsMEO81lmv5tY6BMWMsllmaM6g8nKAFl/59ufMNf5g7BssXLI5/PkP4kbXruQ2nJzyAY3a0SLWYAHFNsl1FUVV0TMDPIVCt4SSqCiZggJUsiqWa6FqCoqqoqq89nhWQYEkiQmjkCCKiOKQIAqIMwMtykjSmKYS0tACnIrOnrsH2H0k5tK5FrNnE9IYvDjlL19d4pG9A+wf6P15nPRKpAstWrlKt5ahtdpgqqzNzDGwf5fM7xZCCCHEtuA9+TlOjd7JEbfas7ZgdMgHBynJZm9xHZj338/IH/0xq37EsGttWCsvO3hxF9eWjdNCCCHEraT1/BIwuOHYTOSxb9BGka4vQmxJku3Z4hzH4Qtf+AJ/9Ed/xKc//Wmef/55Go0Go6OjPProo/zTf/pPefjhh/sdptgB3k4b6f3792/6uXme013u0ppuEDQatOtNomaIGiWopMRWQmzkxL5Cez6jMxvhrXVIo2zD7RiagopCkuWkeb7pfYGLYuaobo5ip/jVkOq4hlZSqTkl3DAlC2KiICbtqgR+QqZlOKaKd3KZc7Md1CGT6sgAw4NDlMwSmtK/ipAoinj66ac3HHvkkUfedev47WpsbIwLFy7gOA6+7xNFEXqusqc6hBZERGmXfClfr+a2A0zLYiIqMfDdCHstvOLtxkrCk8Pf5dveIge+fA93d69d3ewMw+BRi9LhCnrBITNUTN3GzEy0UIdQIY8z0jzDUEwUB1RdQdFVarsrGPr6n1ZFVVE0BVVTLs+Sz5KMNMnI0zce+17oU2816HTapN0E1Y8J4ghf61C2LfbcPsz4noBXvt+kuZSR5vCt82sEccpto72tO6e6JS4utGil0K0FaHWdhr6CNWdTmBqR1v5CCCGE2NLSuXmiZ57hxAf+Z35xkxbmScXELkkLc3F9WA/cjwLMBu2eZPe+bhXf60BpuD/BCSGEEKIvBht6T+bsQtzgwXvukutqQmxRkux+Fz796U/3tCp/p375l3/5HVV8//zP/zw///M//57uU4iruVYb6SDYvD10HCQ0LtbpzC7TWFklacVoUYJKQmgkNJsp3fM57dmQqJtiaSrjZYuDQ0VcU6NgaLimhmtoGG+pOk2ynDTLSbKMdpjSDGIafkIziGl2Y7yGRrIA/ispa9UGA7sSyuM25VELo2xiJB5ObJIlOWE3IZjtkDU87DWT1TmPtdIyxojJ8NgIQ4UhHL33Ypq4OVzX5eDBg0xPT2PoBk6k4GKg6DFVqwhexLLWJSpmVPUKw+c1iqd91PzKTzBfKkzz3+3vUfnubRxf23PNGApjMHJPieL+KrmloxkGruKiBwZZOydLMhRNwXANrEETdJhva6AqqKaCaqjUpsq4lQKaoV71yW+WZuuJ7zij1I2odMqEXkQ36tLymnTrbZS1iJYX0NEiKo7NXe8fZu5Mg/Mvh2QpPHepia6pHB7aOI9bRWGXV+L8aou2FuPlXYoNnSV1jknXwRosv/1/GCGEEEKIm8z7/OfJ85xhcxdVp3ejYjxsMSgtzMV1ou3ZgzoyTHPlEgwMbVjbnwyysDrHrtF9fYpOCCGEEDdbnMZMKsWe4/W1GUo/9kgfIhJCvB2S7BZCAFdpI+04mKaJbdsbzs/znM5Sh9aFNZpLi3RXOqhegqImeHrC6qWYlVdD0iBjuGByW6XA5C6bQdd42zvgdFVBVxUsVAqmzlhp4077IElZbIfMt0Lm2iHzL7aYf7GFqilUdtmo1RhnSMFyNKzEpGaVSLyEbjeiWw9RCirmisXsTJf5kXmqY1WGy0OUrUpfq71vVWNjY+wenSRf8whVH5WUycIAoZ6wZnexbIepZoGBF0OMbsprU917tDWfPxl4muWXyxyYfuAa95pTnFQZO17B2VchNzVco4id2uRdldiPSdUcq2BiFkwMW0dRwCyYaI6KXbdQ9TfisCs2unntx46qqaiaim6BVTQpjRbJ0oywExG2h1kr11keXCFa7ZIv+6z6IZYKw/vL1EYiXv1+i/ZazjMX6xiqwr63tDTXUdnTKXFOadJVYlSlTdHQWZqfZsI5hPaWqhUhhBBCiK3Cf/JJTo4f37SF+aLRJR8aoFSVFubi+lAUBeuBByk+/zQcvnvD2ohe4OnZU9x77GFURcYBCSGEELeCCz98mV3axmt7cZoxdO4EzvH/c5+iEkJciyS7hRDA5m2kASYmJtB1nbGxscvnxn5M/UKd9vwSzeU10kaEEsd4asTyTMza6RBX0bhvtMy+moup35gLA7ausafmsqe2nujrhAnz7ZBLzYDZiz7pBVC0HGckxR1XKU7q2JmJ7dskXkxQj/GaAYHrYzVMVma7NEbWsEddhqpDDDqDWJokBW+GPMvI13yKAXSNjEpqkmQxa0abtKAxQJXRl6BwafMOA6/7m8rLfGt1nrEvH2Iyu/rjrjihMP7QIPauElgGJb2IGdkkawlRnKCbOoVBF6toomoqVtHELlvYJQvN0IiiaEOi+71SNRWnYuNUbEpjJYZWB1koLLJSW0ZbDUlWPVY9j5JpcdejQ5x/uc7c6YRvX1jD0BSmKhs7E5hojHVcZs0OnqqgqU0wNVYvzTB8cP/6HHEhhBBCiC0kPnOG+MUf8vwH/y/8wqYtzA2ski0tzMV1ZT5wP1Nf+Ne0wn9C2XrjMpmqKPhnOviP+RSMwlVuQQghhBA7xfx3L7CLyoZjC4HPYTdErVb7E5QQ4pok2S2EAN7SRtowSNMUTdMol8scPnwY111PKPuNgJVXF2guLOKvdVG8mDgJmZsOWDsfMWAZfGCqxp6qc9NnmBQtnUOWzqGhAnGaMd3wOb/mMzcf4M2nNF6+xMjhEqNHyzijNsXApOBlhN0Irxvh2U20Zodw1sYbarMwNs/g0CCD9hBFs7d9jbg+8k5Eutal0Vwm8FuYcU5X8WnYPnsKA+xdKVB9OUSLr/x4mjfrfFb7Aeozu5jyjlz1/pxBmHiwRuFQFcU2KOsV9EAnqseEWYRVMCkMFzBtHdXQKNQc3AEHzbh51f6arlIeLVEcKjC2Nsql2VlalTXyRY/mWpfUy9h3+yCm3eTCiwHfOLvKRw8N93Q/qOY2K6sBiR7h5Qqq0aJlKLhLqxTHZPagEEIIIbYW/8nPkaEwrE9R26SFeTRsMTRQQ7mJz8vEzmc+cD9anjEXdChb1Q1rYysunt+RZLcQQghxiyjMpz3NJC+kbW6//YgUjgixhUmyWwhx2djYGJOTk3Q6HZIkQdd1jh8/TvW1XWvtpQ4rp2apzy+SrkUoccRqPeDSiz7DpsnH9g8xXravfidXESkxkZpgZjpm3ntx650wNJUDgwUODBYIkpSLdZ8zKx5zL7SZe6FFZcJg9LYKA5MFrNjB7lrEfowfxHimR9T2iJYswgGP1clVirUSw+4QFasqLeyukzxOyVd9/HaLteYSSd0nj2OW9S6RnjEYVzhywsSuR1ypZXmipHyp+DznT2pUFg9d9f7MEozfX6Zy+wCKa1LRq+i+QbAakhBhlyycio2mq1hFC3fQwS5ZN33Txpupmkp5uMSxoSMsL6xw3jhH2dbpLrdJOx5ju0sYhsKZ7/t89cwKP3J4mKGCueE2DlLluYVFNC3EV1QMx2ZFm8OultFt6VwghBBCiK0hz3O8J57kh1MPcLhQ7llfMrrkwwMUK9WbH5zY0Yxjx1AKBVYaS/CWx9f+aIBGc4Xh8mh/ghNCCCHETZPnOZNpoSdrNt9Z5IHHrzUqUQjRT5LsFkJsYBgGtdobM/Bc1yXPc5qXmiyfmaE5v0reiEiigItnfKL5lEf3DLC72ttm8ErmzDV+UJzmghqynCsspTrLkUHeAjU0iDWTSLPIdRNdMakqGXu0mH1qzm5U9mQWu1IXK397v8JsXePIcJEjw0VWvYhTy13OLXicmltFc1eYPFpi5FgFq2xR7hgU/RS/HuN1QrqdkGilhV9p097VwhlwGHKHGbAH0FX5Ffpu5HlO3ghIGl3WOssE9TZZELOWNVm0WliKyZ2ro+xecdHIr3g7r9pzfHX1Eu73xqlcIRkOoJkwdo/LwH3DqAWTillD9wyC1YCEDKtk4VTWW5M7FZvicAHDvva/rWmaPPbYY+/mW/COKYrCyPgwxUqRU6+8imJp+Msdms2AyqDLkfsUTp3w+PrZFf7usVGct1Q73a+M8u25RRj38JY0FNdk9dIsIwf29TWZL4QQQgjxuvj550kvXOCHH/xxfvG1MUUb1ismVlFamIvrT9F1zPvuxbjwfdhzeMPalFbh65dOcmjX7X2KTgghhBA3y/zcNAP6xsKQLM8xzz6P+7/8VJ+iEkK8HZKpEUJcVZZmrEyvsnJums5ii7wV0FjxmHnZZ5dr8+Dtw1jXmMntqxEvFC/wfWeR58MQ97zHPV9d4Lb6MjW/SdVrUoi8nnRlrqhElkPLLrHgVLlUHed71TGerI4zOzDFhFPhPi3hXhWOZTY61664HnRNHt5jct9UhXNrHq8ud5k+0WH6xRaj+y3Gb6/hjFi4no7TNYkaCd1uhN9pEtXbeGUHb5fH4uAiA+4AQ/Yglv7uq9lvNXk3IlvzaXXrtOqr5N2YbtqlafnkhsbkQo1j8xXc2LzibXTUgL/MX6HxbIVCPHHlO1NyBg+ZjH9oDKVqUbWqGIFFuBgS5wF2yb6c5Har60lu3drafxZd1+Guu+/i/MXzrOoLZI5PZ7mLW7E49qDCK891+db5NT52aKgnif0+pcrz9QYaXfSCSVtTKK5VKQwO9OmrEUIIIYR4g/fEk6SojGgTm7Ywj4dNBmtVaWEubgjz/vuZ+n//Z8L3/8yG17eGqrJ0eonwgRBLk65IQgghxE528dsnGWDjdd61IOJwOIs+Kl1ehNjKtvZVfSFEX2VJzuKpJVqzC/jLHdK6z8ypDv5iyqO7B9hdu3o195re5i8GXuBvV+e585tLvP+Vs/x0a+lyUjvTDeLBMeKJ22kMjRENDJOUK+SGSa7qoL3xK6qSZlS7HQ6trKF7M6hrr9CMEi7YZf6wMML50cPsd8p8UIVHFZUiV78IZmjq5Wrv+VbAS4sdZl8NWDi9QHkS9txWozRSwgwsLM8gbsV4Xozf7RA3PPSiTbCry+rICmW7wpAjc72vJg8T8jUfv9NmrblM1g4Io4CG3iUu5pSVEmMv5ZTmr/6YetY8z4uvhuhrQ1yt0X1hTGXXB4cxdhUo2RWcpEC4HOInAXbRxKmtz+B2a856ktvcPhdNVVXlwL4DVGpVLrx4Gk3X8RdbOI7JbQ8qvPSdNj+Yb3HPRGXD55Vyi9HIZqXbRVvsYpRsVuYvYZfLaIY8HRBCCCFE/+Rpiv/5P+f53Q9zuNBbub1seGTDAxSrtU0+W4j3znrgAez03zHve+wtbXxdV53V8cIulivJbiGEEGIny8924C3J7otJh717J1CM9zZyUwhxY8nVbSHEpvI0J1jwWOtcJFqLSOpdzr3YZRiTj98+hK1fOTk4a63y+coLzJ+9wM/+wQl+rNW4vJZYDs29R0iPvY9sYjeKqpKrGoqmkOsq2mu76HOUy2Oas/UDZDWHoKihZEAKagZ32kXujHOU1gVWVzu8nBv8j/IkE5UJHlcUHlFV7GtUfI+XbcbLNg0/5qXFNucuefxwpok1vsL+I1XKEyW01KLSMSh2UnwvxvN9Wm0f44JFMNmlNd7AsQsMOYMy1/tN8iQlrwdErQ5r7VWiZocsjGnRpeNE2KbDnjmT6isxenLl79mS3uIbK7P4Z0voV0lzGwWYerhG6Y4qlluglFeI12K80MMsmJRGi+jmervy0mhxy1dyX81QdRD7fotTJ17CUSBYaGLnBgfvLfLCcy1GChaTlY1P0A9lFWZaHqbZRZ01UB2T1dkZRvbu69NXIYQQQggB0XeeIVta4uSHfo5f3GRDbVwxsEuWtDAXN4xxz92g6yx013qS3fv8Kt1Ok5orHZGEEEKInWzcd3jrZceZcI0j99/bn4CEEG/b9r3KL4S4YfIsx1/0ydpdfM8gqfuce77LbZUid42Xr/h5Db3Lf6l+B078kJ/58vNYaQKsV3C3dh+iu/cI4dQ+FNPAHSih2CqZpoCek6k5uaag6Npr7ZcV1v9fRc0V8ixHiVM0bPIsI0tTslQhL9noqoo27DIcZXwwSHl/nLHSPsWJMOG/OcMcKg7z44rKHcrVq3erjsH79w5wfLLCK0sdXl3qcnK+izqyyoEDJapTJXTVodjRcTsmQTfG8yPa3jL+tEF3rIi3q4NpLzDoDFGzaxjqrbnrL08z8mZA0vSod9fwG03wYzq5R9MM0CyD0U6F4e/F2J0UrrAhISHl6eQiF35gkKdXvrip6jkjd7oMPzyCXrSpGgPkrRy/66PbOuWJMqatYxUtSqNFTHdn/LsU7SJH7r2D099/CYBgsUkRk723OXzrlTV+/LYRCubGP/WPM85n5s5zWPMIF1u0dY1iYwC3WtnsLoQQQgghbjjvySdJFI0xRhlwe8fZRMMmg1VpYS5uHNV1Me68g/TSKzC2e8PaHmq8tHCWqRHZICqEEELsVO1Og1Hd7TkeXjpF6R/9Qh8iEkK8E5LsFkJskGc53pJP1uygtDOCqMv55z0enKhycLBwxc97qvwK353+Jr/4n76Nlr92W6qKf/t9ePd+kLapkVoQ2SmxEaMPKRhFF8220E0H3bLRTAu1Z3L3uizLSIKQ+oUcJc/R0hwzyXBLBdIoIY5jsggo6uhojNdsRv2UD4cRs+El/keQ8p/dUT5muHxcVXGvcD8AjqFxz2SFO8dLnFnxeHmpzenvxGSDs+w74DA8WUZ3HZzAwukaRF6K70R4QZ1grok1VCTc47NUWKJilRm0h3CN3idLO9HrSe606dP0G3QadfATvNinZXqklkIlLzHyw5TSfIJylar78+oq3zvdwWtceX435FT36kw8Ooo2YlO1B9A9k3A1RNFUiiNF7KKJYRuUx4tYxZ3XerBgFTh0/HbO/OBlFAXCxRYDozZBN+Wb59b4xJFh1LfM737UHuGF+hpjho5Wdliem2GqVETT5AKyEEIIIW6uPAzx/+ILnNjzKAdLvRtrV0xpYS5uDuuBBxj7b39OevxjaOobz5+LusG506/y6J2PSwcvIYQQYoc688yLHHvL9bNulLB3/ofoRw73KSohxNslyW4hxGV5luMvB6TNNko7J13JuHDO47E9gz3tkF/X0jz+oPAtHvpvf84vzTcvHw8O30XnwQ8TjdWIyhn1oAWAZjmodoHSsSMYRQddNzAME90wMQwLwzBQMsjznDzLIF9PoKZRTNDtoiwtQZqTkJJkOcWpMZQkIwtj0jghCgLCICQyQ7AVdGz2RApTfkrk13khavBric5ed4ifVjT2XOViha6qHB0pcmS4wHQj4KXFNhefjThbXWByv8muqTJa0UVPTCqeRtHP8O0YL2wRLrYxag7xZJfGYB3bcBl0BqlYFbRrVJhvR3mSkbfWK7mbfoNuo4HiJ4RJQFPziAoZruEych6q51L09Mrfd0+JeGZ5mZmLOVxl9ro9ALveP4Szv4zlFimlJcLliCALcas2dsVGN3VKowXcmvNax4CdqWAWOHTP7ZxVXgYgWmozvtflvN/me5eaPLCruuH80axAlNSJPB9/poFeMKgvLjA0MdmH6IUQQghxKwu+/nXydpvTx+/mwU1amEclaWEubg7zgfup/h+/zbIfMFbY+Fg0L+T4iU/BuPIGcCGEEEJsX+0XloDBDcdmIo89ww6q0/scVQixtUiyWwgBrCeXVy6uEjdaqK2ceD6lPa3wIweHGSxsXln73dJpTv3w8/yDv/zB5TrpaHIf7Q98HH/3KElNWZ+TbBVotHWioklqmWi6xa6Dxyi7V26JvpkoijhzaQ5ysJKcPE2ojY6ThiFB7JMmMXZUJg9ikiAiiEJ8zyMMI3IHzMTkgVDl3m7MYrTE78Qailbhk5rFPVdJeiuKwp6aw56aw3In5KWlDtMnfGZOrTB4EA5NVNEcFy2zKAQWrm8RWjF+6LO25mG4Bs54GX+sw4JtUrGq1Kza5WrvOI7pdDokSYKu63ieh2lerZp568jDhLwVErc9mn4dr9lC8ROCJKCt+YRuiq1bTM7DwJkMK7j6n52TwSovvhriJ/kVz9HtnPHjRQbuGUIpWFTVGmkzxw8CrKKJO+CimRrFQZficAFVuzWqL1zD5cDdt3FeexlyiLM2e4+UePX5BrvaNuOljRtWfjSd5PfnznK/phMsdGnpS1SGhjG2yWNPCCGEEDuD/8TniFWD0fwKLcxHLGlhLm4K8/77AZj1mz3J7t2tEp7XoVCRZLcQQgixEw029J5s2cWkwfj77tjRBTRC7BSS7BZCALA6s8r8ufOo7Zx4ISWYVfk7R4cpWZv/mniy+CzHfue3+TuNEIBcUek8+GEa73+QdFDDKrmU7AJMDGFPjKH+8DQVxbrc9s3WN68Uf1sUyAwFDIPK1DiGYUCQEHd9gnYHz2/jhV3MIKUUlEnCED8K8XyfwAtQXZWJUONTAXT9Ol9Pdf4wc/iE5vJhRUW7yhOY4aLFY0WLTphwcqnD6Re6PPNqE/fgEndMDqMbBmruYMUGTmgQ6yl+ENHurNK92MAedolGPdbKK1iWjbfqMzM7AxmkaYqmaZw4cYJjx44xPj6+4b6jKOLpp5/ecOyRRx656YnxPM+hG5G1Qrxum7bfJOp0wU8J4oC27hG6GbZuM7aiMXA6x+1cPcblrMvfnu2w2IqveI6i5gweMRh/aBRqFgW7itExCTsRuvnGXG67bFMeK6Jf4bG7k7mGy747jnEufJksS8lSj4N3lHj6+SV+8tCuDe0YVUXhI+4wL6w02GcYmAMOqwtzjO3e278vQAghhBC3lKzdxv/KV3hu34c4uEnl9urrLcwr0sJc3Hja4CD6gQN4SxdhaGzD2p6sxtLqPMOV0T5FJ4QQQogbJUojJpViz/H66gyFjz/Sh4iEEO/UrZcJEEL0aC21uXTqHMmST7qc0p1R+LHDI7hmb/VERsZn9K/x6H/8r5jxevVtWqqw+olP4t8xhV0rYtkO6tQwlb37GS6OkKc50+qlGxa/oijgGJiOgTlUphQlZO0Qv9miG3bw/Q5GkFDyYsJSQCcI8P2ANEpwApUP+wqx3+WlxOPXUpv71QJ/V9EoXCXpXbR07t9V5X0TZU6vdDn5aodnT7VRD7S4c2qEgmGBYqKmDqW2Q1HNCe0YP2jjzbcxiyZa1WR6dY7FdJ4kzDBTAyM1abVanDp1ikqlgutujVnfeZ5DmJJ3I5K2Tzto0ek0yb2IPIjxs5CO7hMXcmzFYLihMHAuodiwUK4yHz3ME56fa/Hqokd25WJuihOw6+FhzMkSuutSiIskSwmJklAYKmCXLAxHpzJe2pFzud8J1ywwedd+LgWn6MYxappTnlD4wXyTeyerG87dm1V4SmkQdgO6M0002yAYGsZ2pWJFCCGEEDde8Jd/BWHIuam7eOQKLcydiiMtzMVNYz74ALWvfQtue3DD8SHd4bnpk9y+/+7+BCaEEEKIG+b8iy+xR9t4HTxOM4bOfA/nvv+5T1EJId4JSXYLcYsLOyEXXjlDvOLRudRi7Rz8nYPDmya6QyXmi+0n+fAffR71tcRkZ89h1v7u30HdN0DVLaHsGmLw0BEGCyNo6vptRGl0M78kFFNHG9QpDLgUgtcS360W7bCF0uni+AWSMKQbrie+gyBEi1Tu7ii8L4qYDn3+19RiKivyKVVn5CpJb1NTuX20xLGRItN1n5culnnhdId43yxHdtUYswcIEgUttTG7Jo5vkpopQZCyNr9MFvoMBCaJnZO7Gp3U53zrHBT2Mz03zdGDR2/id65XHibk3Yi47eMFbbpBh8TzIUiJoggfH89IyCwFJ1aorGRUpzNKTRv1KklugLONDiem23hxesVzrApM3F+kcniQzDUoKVWoK8RJgl22cGsOmqFRHi3iDuzsudzvRK0wSHDXFJzIaUVrFDs651tr7PPcnvagfz+b4v+zcJaPWjrhSJGVhVmm9h/uU+RCCCGEuJV4TzxBqFqM5SMMbtLCPBy1GSyXUTZ5bSLEjWDefz9jf/QntMKYsmVcPq4qCq2TTeJHYwzNuMotCCGEEGK7WfzuRfZQ3XBsIQw4bPmoNekwJMR2IMluIW5haZxy7uRZ4uU2rUstLjzv87F9Q1Sd3hfvLc3n2TO/zwe//B0AcmD5kceZv+8o4/vHMEsFKnfextjEATSl92LU9ZhLbZomjz322Ns+//WKb80xKAy5FDoDJE2fjtek47XQOyGVMMELPFqBj+9GaGHKro7G7iChGa3xe4mOnhb4cdXkyFXmequKwt4Bl70DLkudCi8vVjh/psOLk6cZ32twrLSLLFOJEg3Ns7ADk1rkYiQpSpyQJBlxlOIaBXIgbDa4sHgGpZzhOkUKRhEjv/EXVfI4hSAh82PCbhcvXH9LvQAlzEijiCiN6eoBgZ2hqypumGPPxFTmbcpt55pJ7iUv4G+nWyx1r7wJQrdh+H0mo3cOkxZNTLOE2bVJ/QTDMSmNFtFNDXfApTRaRNNvjbnc78TYwBThYY84jGmtNhifsnn6/AI/tmfXhk0BhqLxI84QLy+2OVZqYJZMOq0GxXK1f8ELIYQQYsdLl5cJv/Vtntv/cQ6WettGrpk++VBNWpiLm8p64H4UcuaCLmWrumFteNXCi7pUnOqmnyuEEEKI7cmdT+EtlxYvJC1uP3IARZNNl0JsB5LsFuIWlec5M2cu4c2v0pprcvZ7bT40WWN0kxbQa1qH80/9e+55/vT65yoqc4//CCt378ewHKyJQfYcf4SCU970vhYWFpidnSXLsmvOpb5RFFWFso1esqgGRSrtIYJ2h7bfhLaB6xeIgoBW4NOxQ/I4odRR+Xg3I4qbfD1R+e9ZgccVi0dQUa9SQTxStBgpWjwYVzmzUuP0d7t8tTaDM9XkjsHdVPQqaewRBTGEUIldVEMli3I8LSDWUtQ8QdF9PH2JxGrRtQ1iJWfFn0fVdDTVwFANgiRAN/TLs9DfrjzPIckgTsmihDgICbtdwsgnTAKiMESNcpQoJQ4j4izCVyNCIye3c+w4p1yPsdcUyqsOZb9wzSR3K4z5/myLC3X/iueoOtSOqIzfM4RWc1Fsi3JYIlvNyfWc0lgRyzUxCyblsRKmK1UVV6IoClO7DhC0uiSzGlqeUhhTObnc5raRjT+rtzHA3ygNgnoXY8VntTBLoVSRSnkhhBBC3DD+5/8csowLk3fwgU1bmOvY0sJc3GTanj2oI8OstpegUt2wties0Ok0JdkthBBC7CB5njOZFnuS3fOdJe5/8IH+BCWEeMck2S3ELWp5doXVi5fwFj1Of7fN/UNl9tR650N7asjMt/8De194LdGtasx87BO03ncIxVKJhwfYf99j2Ja96f14nseZM2cIwxDffyPJ2a+51K9XeyuOgTPgYLfKJC1vfQZ1s8Gw51ILQ5q+R8cIicoJZlfhgXZOlnY5m3T4fyU2d2UFflRRsa+SDHQMjTvHy9w5XmauVeP0uS4vnlmiPf49pkYMDhX2kyQhnh+Ar2JoFrZuMFAuo3V13LpL2OrgWW0oKCiOht6J0UjRlASUgEtnXkW3dVTNQNd0NFVD0zQ0df1jsny9DD/LybOcPMvI45Q0jkmzhCRLSNMEJQU1ysmimDRaP5bkMaGaEpsZuZpjxRmFZobSSXHrJrV2gUJ67fnYYZLywnybV5Y7V5zLrahQ3acwck8Zd7hK4mrYSQmtbpADbs3GqThopkZlrIRT3fzxJjbSVZ3xffs5e/IMUZphD8LZzgq7I5eiufEpwC+mU/zezDk+XLAIazaN+gq1geE+RS6EEEKInc777BMEmsN4NsRgYfMW5gMlaWEubi5FUbAeeBD9/IswtXG0zy61zPNzp5kc3tOn6IQQQghxvc3NXWTQ2Hh9M89zzDMnKPzf/x99ikoI8U5JsluIW1Cn0WX69FniFZ9Xn61zm+tybKS3YiIh5fSJ32bqhVcAyHSDpU/+FK29o3SJiApjWGqRwA+umOxeWFggSRJ838c0TSzLIgxD6vU6pVKJhYUF9u/ff0O/3itRdA1lwMWo2gy0i1QKA3hBm1ZrjcG2RS2MafseLT3ALycY3ZyDrYxDSchK5vPvI5OxrMhPKDpD16iAnSjbTJRtHk5qTDd2c/b5Dl+vnEWtTjNs2QwxSkJMkhkoXkaJMmorx7V0lEgjDyDJEgptjVRPSfWEzMhpL6xguhaqpqIqKoqiomkaiqKgKtp6ojvP19+y1z5OM/LkjTeylDTLSEiItZzUyMj1HC3NUbwUZzWFCDRPodI1qfoFTOXaLeijJOPlpQ4nl9pE6eZZbkWBym4YvKdAeahGWjBAcSh2HEjALBrrc7lNjdJQgcKQi6pJy/J3wjFd9FqRNG4TZQm1cYtnLi3w0dGpDecVNZO9ToHGahd9oYNemKdUraGr8lRBCCGEENdXcu488Q9+wHcP/xj7y4We9brpkw3XKFWlhbm4+cwH7mf8y/+O5JFPoqtvvPawNZ1zr57h0buyd9xZSwghhBBb08Vvn2SQjV2G1sKIw90Z9N27+xSVEOKdkivYQtxi4iDm9MunYCVg+ocNJiOD+w5UNz335dN/yNR3nwUgMy2W/v7fJ73tACuL04SVMdIgwYvrV21JHgQBUbQ+m9myLDRNw7Is8jwniiKCILhhX+vbpagqVGy0skWp61B0K/iVLq1OHaVlUA4KdD2fhuoRlDIML2egmfEJNcFX6vxhpJLHJX5MMTh0jYsepq5ycKjAwaECH4qHuNi4i5PBKn879DKWMcNk4nCoeC+WnRBlXcKuSp6uVxhomoYWgGboKKqCoimYCqCGpFpOouagrldJoynrH7+eg3/L+0zNSZWcPAfIUbIcohQlyjGDDBIgBy3KKHUMKp5GQS9vvK0rCJOMk0ttXl7qEF8hyY0C1QmF2t0WpZEqStEmVXWcoIAaaxiOQWHEQbc0nIpNaayELlU975pluHi1CKKcNE2p7HY5t9Rhf2XjfMxPMMH/uvQqn6xYhIMu9ZVFhkcm+xS1EEIIIXYq78knAZgZv50PbtJdKirpOGUHS1qYiz4w77uXQtRi0Q+YLGx8fFoXU4IkwDVuXncyIYQQQtw4+ZkuvCXZfTHpsHv3KIp17Y6WQoitQZLdQtxC8jzn7KtnSZc7LJ1vEJ6PeP/RsU3PfenSnzP5ta8A64nu5Z//Ocy7jjHbWqFbGMbzQyAErt6S3LZtTHO9CjgMw8uV3Y7jYJomtr112lErigJFC6Vo4XoOTrNIVPJoek1oNin4BXzPp4GH7yYooYbVzHg0T8isFk8lCp8NXR7ILT6gqBjXqPa2DY0jw0WOUOTH0l3Mt0POJWt8W3+VIP46eyODqXwX1eIBct0hiVXCrkeeK6CsJ7LNVMOwNFRNAw3Q1xdyhfU31qu6c9aLvF9PbCt5jpZmG/LWWZKShylaqFDq5pRSg6I1vP59eRt/LcIk4+XFNieXOsRX6FeuqlCbUinfZlAYLWMUXGJDxQoLGJGJbmoUxlxM13htLncR0712Fbm4ujiO8YOUVPPQFZVKtcD5VoO9eWHD/HlT1fioNcj0fIe91SZG0aQ2OIquydMFIYQQQlwfeZ7jffYJOmaJ8XSAoSu2MK9IC3PRF8Ztt6HYNgt+syfZvatTwvc7kuwWQgghdogx34a3PB2dDtY4fO/dfYlHCPHuyNVrIW4hS3PLtOdX6C60OPv9Nn9vzwjmJi2hT69+m7Ev/CkAuaKw9tM/hXPvnfhajlo6iHfq1NtuST42NsaFCxdwHAff9y9XeU9MTKDrOmNjmyfb+01xDRTXwAochlsFksIALb+J0mzg+g5+N6CFR3c4htTCaOfc2U14n95lgS7/LjAYSgr8iKKx6220uDM0ld1Vh91M8hiTtIMPcilqc85Z5AfqafzwJBNNleF2hVIySF4YIrWL5L5BFOhczn6/lrhUNAVVVeBNae51OXmek6c5eZqhpCmOF1L2E2zDoVAYw9JcMFh/exvWvIhXljqcW/NJ882T3LoOg3s0SkcNjAEXy3GJLY0stHEDB8MwcIZt7KKFbumUx4o4la2zEWI7W1hYYHZ2lizLSJIQV4e1yKA8bPPyzBp3VAY3nP+wPsKv+68yudIkrJepry0xPDzRp+iFEEIIsdPEzz9Pev48z9z2KQ5UeluYN8yAdKRKsVq9+cEJASiGgXH3+/CWLsLQxu5le7Iqy/UFBssjfYpOCCGEENdL22syqm/SZWjuFIWf+Xt9iEgI8W5JsluIW0Toh0yfvUC6FvDi37Z4sFphpNjbiuWC/zIDf/bbl/+7+fFPYL7/fgYnx1mxy0QXLwJvvyW567ocPHiQ6elpDMMgTVM0TaNcLnP48OGeSvCtRrF1FLuIUXUYaDlUClU6QRu1WcfpOEReSMvz6FRC0rKO3lEY8TJ+xI5J9AZ/FamsBg73ZOvV3u41qr1fV7J0jlk1jlGD4ChZ/ndYtXwuFddYdOvU9Xky7yJau4XdCbFDBStVsRMTO7Oxcgct1yDPQNVQVQ1F1VENE9UpYNpVXGuIkj6I5mrwDv8Zsjxnuu5zcrnDUie64nmmlTO8V6d4xEQtW1i2S2qZRLGG03axDRtnyMYuWWiGRnHYpTDgoqhv7/skrs7zPM6cOUMYhvi+D0CkpLi2gxFANKATRdmGTS+qovALjPHUzAoP1oo0aja1wRGZ3S2EEEKI68J74kkAFoYP85FNWpiHl1uYF3vWhLhZzHvvZeAz34LbHtpwfEh3+NKFkxzdc1efIhNCCCHE9RBFEX/9p3/Bj6obx/f5ccqei9/HfN+/6lNkQoh3Q65cC3ELyPOcM6fPotYDfvhig92ezp2Heuff1WlS+pP/wGuDnOnc+yDa3/0Yw1NTDNxzP52LF99VS/KxsTEmJyfpdDokSYKu6xw/fpzqNqrWUEwNZaiAUnWotFzKTgUv7NBqNTA7NtVOTKfr0y75BEUFLXTQOznHtRit3GUJj//kqeSRywMYPKyomG8z8Q3rCchhw2UYF7wp4M71BYcNY2UyMmIlJVFSjFzHzK/vr/k1L+L8ms+5NQ8vTjc/Scmp1HJK4znJcIY7XsZ2CuSWRRApOB2XsumuJ7krNqquUhwuUBhwUDfpNCDevYWFBZIkwff9y90YgiCgq/hYtkHJ0Hjx4iL3FjZWrBxzqnwuWKG73MRZrdFYW2JoSKq7hRBCCPHe5GmK/7nP03AGmEiqm7cwH7EYKFZQTLlcIfrHvO9eRv+//5lWGFO23mh5pSoKrZfrJI8mshlUCCGE2Oas+azn2GzksWfAQi31XjsXQmxd8sxciFvA4vwi3kKd2TMN0jMBjx4ZX5/D/CYpGfEX/yNa5AHg7z9E+oufYnh0lIH33Yuiae+pJblhGNRqtcv/vdUruq9E0VWUAYe8alFsOxScMkHVp92po7c7VDou3Y5PKw/wB1OUWEfzTAbCjI+6MWqlzUwC/66jYcYFHsw1jisqrnp9krwqKlauYuVvswf529AMYs6v+VyoezSD5Irn2a7C6ISOOhASlFK6GhiajuGW8TMNp+syaBVwhm2ssoWmaxSHXApDriS5b5AgCC7/nL7ejcG2beI8I3Mg9RSMKZvuUkLB2PiU4H/KJvjd89P85NAa9SGb2uAomiJzM4UQQgjx7oVPPUW2vMx37vxZjlV7K7cbZkA6Ji3MRf+Zx4+jkDMXdilb1Q1rw6sWXtSlbFf6E5wQQgghrovRrts7rztpcfzO23uunQshtjZJdguxwwV+wMUzF4gWu5w50eDv7RrGNXsTVssnP4cxcwqAeGiE6Ff+IbXhEQaP349irCdOt3tL8utJUVWo2FC2cDwHu1UgKYe0vAZKq0mxExN0AjoEdMoRQZKjxjZGCJNqyq5yBHqL80HO/9aIyZIyR3Kbh3LYa+iofXxCFacZS52IhXbIXCtgzY+veK5TVBga1Rkcs9AGVLpWxlLLQ1N1skyh6cOgbzNUquFWHeyihaqrFAYcCkMFNF2S3DeSbdtX7MZglFzMko3RgJeWF3mAjW2bJlyXycxmYb6OM1aj3lhhqDbah69CCCGEEDuF/9knAFgdOMDemtOzHpZ0nJK0MBf9pw0Noe3dw2prCcrVDWt7wgqddlOS3UIIIcQ2luYpU0pv9fZqY47Cjz7Qh4iEEO+FJLuF2MHyPOf06dOojZDvPLfKg06ZXdXei0or7dMY31q/8JS6Rbr/p1+iOjnO6D33oTobz98JLcmvJ0VRoGCiFEyMcH2ud7U4QDfo0Ok0sdoe1VZM1wvo6AGBlRImoEcOegq79YiKUUdR2rQzhT9PbRaVCm5kcSxSOArsNnVKN7CNoxenNPyYxU7IQitkxYvI8it9wVCoKAwO6wyNWdhVncAEX0/JDRVNsUnXOnRjsBSbsmExPjlGoeKgGhrFQRdX2pXfNFfrxmCXqozUynhqG3sqYW06YMDcOIrgk/oE/3b6FKPjdRrzCwxUh1EV+bcTQgghxDuX+z7+l77EUnGcqaTC4CYtzINRm0FpYS62CPPe+zBeeRGmDm84vkst89L8WSaGd/cpMiGEEEK8VxcvnGWvsfH5aJbnlE89h/2vf75PUQkh3i15BSnEDjY/v4C/2OLs6QaTSzn3He3dee5nHtqT/ztZnpMD3t//aSqHDjJ65/tQr5DA3iktya83xdJRhnWUmkOp41DqVIhqAa1uE63VpNRyCTsRvhrQNSLCJCMLVQKtiJbluFrK+9SYwcEYhYS1OON7ocL/CFT8lsauQGV3pjCWw4Cm4poaBVPDMTRURUFT6G1Pn+VEaUaYZIRpRpRktMOERpDQ8GOaQUyUXimzDSg5xapCeVCjUtOoDrhopkasgacndI0UTTPRc5sk1egmOXliU3UddEdF1RXcmk1lvIxbdVBUaQF0M12tG8ORo0eoFApcil8lbYWcL6wxEG8cRVCzTe4LK8zPreGO1qhPrjBYGenTVyOEEEKI7cz/yl+Td7p89+6f4q5a7+uHhhmQjVYpVqo3PzghNmHee5zJz/+vxA9/EuNNm3VtTef0K2d55M7HpMWpEEIIsU3NPXOWvWy8Vr4WRhzKV9BH5dqXENuNJLuF2KGCIGD63EXipTYXXljjZ6fG0TZJNIZf+f+ReXUA/IcewXn4AUYOH0EfH7/ZIe8Yiq6iVB3yio0VuAy1i6TlQbywQ7fTxmp3KTcTwm5IVw3w3JgogTxW0XObUCliaTBgpgy6EfenGZ1ii1arxXw7ZFq1aeUWoW9A28AIVcwE7CinEKdYuYKmQpZDcsUS7bdQwXbBKUCuBxgO6FaGU1QZHRlH03QSVSFQYkItJtXAyGz0TCeMc1TdoFSqMFoukmQRiqqg2Sp6UWPo4ACWZd3Yb7q4omt1YygN1ohaEVkQMXu+xaRR3vD5H7bG+N+mTzM+UWdtfoGB8rBc1BNCCCHEO+Y/+SQA7cqeK7QwN3DKLlZFWpiLrcG67z7suMNSEDBZ2LhBw7qYEqYhtm5f4bOFEEIIsZVpFwN4S7J7Ou2w98DuyyM9hRDbhyS7hdihzp2/gNII+dYPmtynlhkv974Ib576a9IL3wcgHh5F/bmfYnTvPoz9+292uDuSoijgGCiOgZI6lDsFSt0BklpIJ2zT7bTQ1zpE3YBUSUn0lCCPidSAIFHR0NA0B9PQiLSczIwomgEDaoSah+TmeiLbtixc26FULJCmGl6U0/EUsiRHy0HJQX3tvQJouoJmaWi6gmEqaAaoGii6Tq6oNBugKDqaopKR4+s5kR6QKApabqCmJkqi4usKbrHEZLVKobw+fztXc8yGjl7QUDTlje+D6KurdWMYnJii22wRr/qsDDaYbG383Kpt8D6/yNzcGu5YjcbkKrXS0M0KXQghhBA7QFavE3zt68xU9zGVlBlwe1uYh+MWg8WytDAXW4Z+9AhKocCC3+xJdk91inT9NnZJkt1CCCHEdjQeuvCW2pw5f5Vjx4/3JyAhxHsiryKF2IHqjTVac6tMX2gxMB1y/+HexFQ3XCX5mz8CINc0wl/+RSbGd2MdPSrJyRtA0VSo2CgVGyN2qXlFqt1BuqU2C506aRyh+ArF0GDQLpPmGWEaEyUxURgSxRlxYhIrLomqrSeWswRDU6DooDouarGAkuWU85xylkOerWe3UUBRNnys6CoKGoqioaCiAHmSkiYxuaoQEROTE6c5A2kRPXXRVJXYUDDLNtVKhVq1iq5rqLqGXbZwKhYYYCzLn5btxLRtqsPDxJ2IzI+YWWuyS9+4s/Vxc4x/f/EsExMNVmbnqB4ZlN8TQgghhHjb/C98EeKY7x14nHs3qepuWAHpcJVCuXrzgxPiChRNw7znHvzlCzC0sfPZnqzK6toyg6Xh/gQnhBBCiHet3q0zphd6jqeXXsX9hz/Xh4iEEO+VZCSE2GHSLOXcufPkjYAfvrjMp4ZHcAyt57zkq78HaQyA9/EfYfC2OyjccQxFl18LN5piaFDRUCo2xeECj058lNxPSLyAbtTFC7p0622sTkLmp+RRQtmKqed1zMwiSTOyCLJcw1RM0kADx8YLFFRNQ1EVVENF0RQUXmtjnueo+fp7MsiDnDxNyfKILIc0z0nISHNo+hkqGoqikqkZqetgDxQplSqU3SKapqEaGk7Fxi5bmK5xOfEZRRFxHG9ome15HqbZW70jto7a6DjtRp2oGtAYWmFXY+P6UMHkDr/A7OwqzkiF5u46VXegL7EKIYQQYvvxPvsEAEFhir0DvfO6o5KBWylglaWFudhazHuPM/DHX4NjD284PqQ7/OX5lzm857Y+RSaEEEKId+vVZ57nbk3dcCxMUyZnTqDf9v/sU1RCiPdCslpC7DBzC/Mkqz5//cIad7UKHDnau0utM/M9kpkXAIj2HqDwE3+XyqGDqJVKz7nixlIMDQwNpQxGXqAaVqj4CdlwROj7hLFP1/fwWx6hC+2LAXqmrievUXFNB8d00FONPEnJs5Q8zcnondWdKzk5CrmqkCuvvVc1UEDTNAxDRzEgjrtkuoJq6BiGwYEjB7EdG7NgYhVMzIKJbmmbVvYuLCwwOztLlmWkaYqmaZw4cYJjx44xLnPgtyzN0BkYGSP2QtKmz+Jam1G1tOGcx61R/uPF80xNNVmdnad6SJLdQgghhLi2ZHaW6Lvf5ezQUaayIjWndwZiMG4xWCihWHKJQmwt5n33MfIf/ndaYUzZeuOxqyoKrZOrpB9K0ZTezeVCCCGE2Lqazy8CoxuOzUU+uwZdVLd3Y6YQYuuTV5JC7CBhFHLpwiXmLnVwTzd5aPfunoRkkobE3/wDADLbIf2Vf8jgxCT6nt39CFm8iaIoYBsotoFac9DyMm6cUotS8iilu6eNr4V4HZ84islyhcrkKBo6pDlvzm/nWUaWrh/IFVBQN67rCpkOiqWhGQaaaWGYJppi0Ho1QjVVVH29Qnzk0BBu6dpP9DzP48yZM4RhiO/7l4+3Wi1OnTpFpVLZMCtabC3loWFaqytEIwGrzWVG6xvXx0oWR7oOM5dWsUcreHu6uGbvZhohhBBCiDfzn/wcAN/f/xgPbtrCPCQbcShUajc7NCGuyTx+Dwo5c2GHsrXxMTq0YuFHHkWrdIXPFkIIIcRWNLCmw1uaUE7HTd535x0ytk+IbUqS3ULsIOdnLpI3A777g1l+zB1kqNDbOjp89s/IumsA+J/6FOP7DmPedhRFVXvOFf2lKAqYOpg6CmAVdRgq4w6WUXJQM7j93vtQgDiOSaKIJE6I44QkTEjSFEVZr+Je//dVQFPRNA3TtjF1A03XUDUVVVPRTI1MSZnrzGyIQ3+bFTYLCwskSYLv+5imiWVZhGFIvV6nVCqxsLDA/v37r/v3SVwfiqowNDZJ4Hskq0VWV7sMqhuT2Y/bI/wf56fZs6fDyvICuycP9ClaIYQQQmwX3mefIAMSc5y9tU1amJcN3JKLVZZNdGLrUatV9EOHWG0tQXljsntvVMbzOpLsFkIIIbaRJEuYzHtH5zTqc6gfvqsPEQkhrgdJdguxQzS7DVZnl3nuZIuj8xb3HOttSR42Zgh++GUAooOHGHz8YzhHDtzw9iymafLYY4/d0Pu4pSjr1dqpCkrBxDTNt25GfNeiKH/XM7eDICCKIgAsy0LTNCzLIs9zoigiCILrFKW4UexqmVKpSjIRsbK2yGB940XnXVWH/bM2Fy6tYE1XiMd3Y6i9rUiFEEIIIQDikydJXnmFV8fuZgqX6qYtzE0GC2UUUy5PiK3JvPc45osvwNSRDcen1DKvLpxmpCbjmoQQQojt4vTZk+yzrJ7jpVPPcvJHfoZ0fl5GMQqxDUkppxA7QJZnnLtwgXgtZOnMLA+O1zC13h/v4Bu/D1lKrunov/ALlCbH0Cbkj7d4w+szt+v1Oo1Gg3q9zokTJ5ifn7/m59q2fTkpHoYhaZoShiGKomCaJrZt3+jwxXukKAoD4xOYVRuGSzRzv+ecDzvDfOdMh3ihy2pjuQ9RCiGEEGK78F5rYf7C3g9sWtW93sLcoFiVFuZi6zLvu4+JU18lTrMNx21N59QPL/QnKCGEEEK8K9NPnek5Vo8ixtrT1DWNU6dO4XleHyITQrwXkuwWYgdYWF3EW+rwlZeXeV+9xv6B3gtJwclvkCyeBiD64IcYPHIbxpGDModEXPbmmdutVotut0ur1bo8c/taT/TGxsbQdR3HcYiiiHa7TRRF1Go1dF1nbGzsJn0l4r0wiy7l6iDmeJHlWtizvqfmsCe0uDS3zOqFebI82+RWhBBCCHGry7MM/4knyFBQtBH2DvTO6w7LBoViAbN0YztNCfFemPcex467LG3SqUq7EBFncR+iEkIIIcS7YVzsvdY1k3RIBsvUWy2SJGFhYaEPkQkh3gvpEybENpdkCZemL7G22MW4sMLx8f09Cewk6uB9978DkFaqlH/q72Me2YeyScuWa5GW5DvXe5257bouBw8eZHp6GsMwSNMUTdMol8scPnwY9wa3yxfXT21klFZrle5IkW4jpMAbvytUReFDziCfeekSu/d0aRypM1AY7GO0QgghhNiKoueeI52d44Wph5hUXSr2xhbmOTnhpMVgoSQtzMWWph88iFIpsxA0mSxsfE0z2XHxIw/D7h0jJoQQQoitZzxw4S17MOe8FUq7pmQUoxDbmFR2C7HNzS7PkdQDvvLKAnd1htlV3aRi4rknyMMOANlP/TSlXRNoo6M3O1SxxV2PmdtjY2NMTk5Sq9WoVqvUajWOHz8us262Gb1gUy4NYI4XWaz2/rsfGCxgJyb1xTorM9ducS+EEEKIW4/32ScBOLn7IfbWel+jNO2IfEinVB24yZEJ8c4oqop5/Dje6nTP2q60TLO51oeohBBCCPFOLbUWGTcKPcfz6VdZGh+XUYxCbGOS7BZiG4vTmPmZeU7PdBi/2OX4ZO9u8ri7SvDy1wCIDhxi8P2PYRw5IO3LRY/rNXPbMAxqtRrDw8PUajWp6N6maiNjGFWTdLhAkG9szaipCh9SBvjui/P4M028WGYZCSGEEOINeRTh/8VfkCgqRj64abI7qhi4pQJGqXdNiK3GvPdeaq8+03N8VC9w8uLJPkQkhBBCiHfq5NPPY+vahmNJljF88busvFa0I6MYhdieJNktxDY2s3SJpBHynTPT3JEMM1bqbUsePvtnkCXkmobzi7+EtW8StVTqQ7Riq5OZ2zvT66MH3vz2+qaGq9GLNpXiAOZkkflyp2f9yFCRuqcTLLdYnJ+7EaELIYQQYpsKvvFN8kaDE3s+yITuUN6khXkwblEolKWFudgWzHvvZXT+BF6cbDiuqQpzL0mnIyGEEGI76Ly42nNsLvIpWzmFoUEZxSjENibJbiG2qTAJWLq0yHfOtDh0Id+0qjupzxKe/jYA8aOPUT18GGP/npsdqtgmXp+5bVkW5XKZQqFAuVyWJ3q3sOrIKEbVIh8uk+f5hrWipfOBuMyJl2ZpXlghzuIr3IoQQgghbjX+E08AcHryvk2rult2RD5sUKzUbnZoQrwr5j13o5KzEPR2NCrPK4Rp2IeohBBCCPFODNZ7N1nOxE2CPbuoDQzIKEYhtjFJdguxTc0sXqKzGnLuwllu14cZdHsrNf3nPgN5TlquUPupn0M/vAfFMDa5NSHWycxt8WZa0abkVtF3F5hzWj3rtw2WOb8EyUKb5bXFPkQohBBCiK0m63Twv/wVQs3ESavsHejdMBlWTNyiKy3MxbahlkroR4+w5PVWhE0FRTy/txOSEEIIIbaOKI2YzHu7nTbXZlHuuktGMQqxzUmyW4htyIs9ludW+OrpZY6dL3DPRLnnnHjpLNH57wGg/uiP4+waQZOEpXgbZOa2eJ2iKFRHRtEqJulw7+NgomxzW2Bz6vwCq+cXeqq/hRBCCHHrCb70lxAEPLf3ccZNm5K1sYImIyeYNClKC3OxzZjHj5PNnek5viuvsLy21IeIhBBCCPF2vXzqJQbt3hGgpVPfJThyuA8RCSGuJ0l2C7ENzSzO0F71aV+6yB2FASp2b7W2/+z/ACAdHGLgo5/AOHIARVFudqjiOnq3s5eFeC/0kkPZrcH+Ig2tt23j+ypVfngmIJ7t0PQbNz9AIYQQQmwp3mc/C8CF8fext9a7Wa5txzBsUpAW5mKbMe+5h7GTX+vZ4FkzLF48/VKfohJCCCHE23Hp6bOob7k23opjJvxZ0po8LxViu5NktxDbTCdqszZf56unlzl6scL7Nqvqnn2JeHb9xbbx459C3zeGWu49TwghrkVRFKrDI2gVi+6g1rN+cNBlODBYmF9m+dJCHyIUQgghxFaRzs8TfvspPL1AIS5vOq87rJoUigWMknQPEtuLec/dlNuXqIdxz1rn5YZ0ORJCCCG2MHO69+/3TNKByVHQeq93CSG2F0l2C7HNTC/O0FzxaS1c5I7iIIVNWv95r1d1j45T/eBjGPv33uQohRA7iV5xKbtVsgMlIpINa4amco9V4Xsv1OlMrxJnvS8ehBBCCHFr8J78HGQZzxz4COOWTXGzFuZTBgW3jGLKRUWxveiHDqEWCsyH7Z614bqBH/d2QRJCCCHE1jAR9m60XPBWiQ/s70M0QojrTZLdQmwjzaBBc6HFV84sc+R8mdtHSz3nROe/R7J0FgD7J38Gff8EitU7j0QIId6u9eruUZQhi9VK2LN+dLhI7hl059ssry73IUIhhBBC9Fue53j/4zMAzI3cwd6B3qruth3DkElRWpiLbUjRNMy776beWuxZ2x2X8bxOH6ISQgghxLUstRaZMAo9x9Ppk/hHj/UhIiHE9SbJbiG2kdnlOeorHq3lcxwzhyhZm1R1f+/PAEindlN65P3oe3bd7DCFEDuQXnEo2mWyPZWetapjcHda5NkTs6zN9F78E0IIIcTOF7/0Msmrr9K0KpTCAns2a2E+YFIsFNFKdh8iFOK9M+65G+f88z3HJ9US04vn+xCREEIIIa7l5LMv4hobr6OnWc7I9PeIdk31KSohxPUkyW4htolO1Ka51Oar55Y5er7MHWObVHVf/D7p2iUA3E/+A/R94yimebNDFULsQIqqUh0cIpuyWLa7PetHh4usNVSC6TXagVS1CCGEELca/8/WN91+98DHGXNsiuZbW5hnhFMmrltGMaSFudiezOP3MH76GyRZtuG4o+mcfOlMn6ISQgghxNU0n1/qObYY+1QLKrktmzCF2Al6y0KFEFvS/No89WWPtdWzfCg/xKDbm8T2n/8CAMneAww+8AD6btmZJm4u0zR57LHH+h2GuEGsWhl3uUg83oW3FK7sqtocmDM5d3qR2l3zlPYf6k+QQgghhLjp8iTB++wTACwNHuF9m1R1t510vYV5rXqToxPi+jHvuQcr8VgKAibcjbM/lXMhaZaiqbKZQwghhNhKBlZ1eEtOeyZuUdu3BxSlP0EJIa4rqewWYhvwYo+1hQZfObfMsfOlTau648UzJPOvAuB+6mfR9k+gGMbNDlUIsYMphka5MkB80KWrbZzdrSoKtw2UefmVLs3pZdI87VOUQgghhLjZwr/5FtnKCovFMcqBy96a23vOgEHRKaEVpHpGbF/ayAja1BSLQbNnbbxt40deH6ISQgghxJUkWcJkXuw5Xq/PER482IeIhBA3giS7hdgGFprzrCx6rNZPsc8fYaLce4Eo+MFfAODt2Y97153oUxM3O0whxC2gUKthFm06Q71PIQ4MujiBjjfdYLW+0ofohBBCCNEP3mstzJ/d/wnGXAvX3FjZmpIR7rIoFqWFudj+zHvuprs603N8d1qh3Wnc/ICEEEIIcUWnL55ixOrtOuSe+VuCo0f6EJEQ4kaQNuZCbHFBErCysMaXzy9x9IK7aVV30pgnungCgPDRj6HuH0fR5cdbCHH9Ka5B2a2xdtAjW/RQ37RvrmjqHFOK/O3356jePcXIwGgfIxVCCCHEzZC12/hf+ksAWpW9PDjQW9XdcVKUQRO3UrnZ4Qlx3Rn33EPtd78Ehx7YcHxUd3n24quMD+3qU2RCCCGEeKsLT51in1rdcMxLEnb7lzjykz8pnVGF2CGksluILW6xs8jqksdy8xX2NMbZs8n8u+D5L0CeE0zuId4ziTox3odIhRC3AkVRKA0NogyZrBaDnvWDQwVW11T86VX80O9DhEIIIYS4mfwvfhHCkLNDR6j6Fns3eb0SDZqUnBJq0epDhEJcX+bxexiZO4EXbxzbo6sqMy/2VnwLIYQQoo/O9Y4YmYm7VA7slkS3EDuIlH4KsYWFacjK4grfPLfCsYs2t4+WUBVlwzlZt0F4+un18x98lO5IBeX/z959R8d1nueif3bf0wswwKAXglUE2KliWS3ujpPYzol8Yx9f+7rkJDmJb47OcRKf3CQ3iXMSy0WOU46V+MZx4thxi9zkuElUoxpFSuwEQRIgeh2UaXvPzN73D0iQhx8oiRQxGxg8v7W4FubdezAP1pKAmf3u7/0Ujgakq6PrOm677TavY9Aqp4RMhMwI8s1Z4HSx7Fhr1IeWYQ2DveOo7RlDR2uHRymJiIioErJfXxxhfqT959ARMuC7ZEx5SXKQb/UhGoxAUvk5hdY+fft2yIqMMSuDTi1cdiw44qLgFKDJvHhORES0GiSzPuCSwUMj+Rm07d7lTSAiWhFc2U20ik1mJjE3kcG5udNoGW9GV21AOCd3/IdAqQArVouJxgZMSi6yWfGONSKia0WSZUTiCdgbTOTlQtkxVZbQFQ7ixPE0ZgbG4bquRymJiIhopRWHhmA//jgcAEUjiY5lR5g7kGsMBDjCnKqE5PNB27YVk9kZ4VhzLoiclfEgFREREV1qNjeHJiUo1O2RczD37/MgERGtFDa7iVapQqmAiclJPNk/jw3jRWxJBKHKl6zqtnOwTj4AAJjZvhsjagmpuTkcPnwYo6OjXsQmonVCiwbgN0NI10rCsa7aAPScitxQCjPzKQ/SERERUSXkvvXvAIBjzfsRtrXlt1xK6AiaIUgBvdLxiFaMvmsXSqNnhXoLwphOTXqQiIiIiC514vBzCBvl01Yc10XthSeh7djhUSoiWglsdhOtUlP5KWQns3g6dRrt59uwpU5c1W2degCunUXBF8BoUxMmFBfz8/OYn59Hb28vV3gT0YqRNAWRSA2cLvEO2URAR5tj4rmnRzA5NO5BOiIiIlpJtm3jwIMPYupL/wwAONl8M5ojJnSl/BJDUSrBbtERCEYgqbz8QNVD37ULDScfFOpxzcRzvSc9SERERESXmn5mRKhNFSy0BAE5FPIgERGtFH7aJFqFSk4J45PjODq4gIbZGWwIhWFesr+dWyoif/SHAIDJzd1YiAUQikSg6zpSqRSKxSLGxsa8iE9E64QvHoGUDGHOtIRjXTUBTE7JWLg4jmKpuMyziYiIaC0zzl+APjoKW9KgSXF0xMQR5pkgoMYN+CPhZb4D0dql7dqF0PwgUnlbODZ3YsqDRERERHSp8IRYGywswLxuGyRJnFRIRGsXm91Eq1DKSiE3k8dDE+fQebYZmxPiqm773BNwsik4mo6pTVuQr41CURQYhgHXdWHbNvL5vAfpiWjd8KkI+SMoNYu/ozbU+FG3oGDk7CSmpqc9CEdEREQrKfzwwwCApztvR7CkojkqjjDP1WkIGBxhTtVH7eyAHIlg1EoLx2qnVdglsQlOREREleO4DpqK4urtyflx+Pbu8SAREa0kNruJVhnXdTExO47zQwswshfRUqhBXdAQzsuf/CkAILt9D1JBA3mnhFKpBMuyIEkSdF2HaZqVjk9E64gkSQjGY7A3mCjBKTvm0xS0hfw48dwcpka5byEREVE1cS0LoYOPAwAuJHejNeqDKpevjrHlEuxmA8FQFJLCSw9UXSRZhr5rJ2bS4pY9rXYYuXzGg1RERET0gv6xC0ga4s2Y+vkjMG643oNERLSSVK8DEFG5hcICMjNZPDA8jI398WVXdRenBlAc74MrKyhc/xrkIjJs24ZtL9493tjYCFVVkUwmKx2fiNYZJeiDPxzBfCyFWKr8QnZXTQAnRjOYPT8Ce9sm6NqLq7ps28bBgwfLzr/pppug61z5RUREtNpZP/4JlGwWaT2IQDGEjrh4ITETBPSoAV+E+yFSddJ37YL542NA09ayeqMcxOjkICKBmEfJiIiIqO/gSTQq5e9DrVIJLTO90NrbvQlFRCuGt1cTrTJTmUmMjqaRKpxG40gTOuPi3ncvrOqWd1+P6J4eKOEQwuEwAoEAwuEwwuEwNm3aBL9ffC4R0bUkqTJCkThKHUHhWEvURF1Bw5mnRzAxydXdRERE1SL/jW8AAB7f8EYEoaIxLE6UyiVVBM0IENAqHY+oIvRdu5DsPYCS45bVA6qGo8dPe5SKiIiIAKB4ZkGoDRWyqG2ph2SIU1SJaG1js5toFckX80hNz+LB/klsHNXQVROAdsnIP8fOwTq7uBoy/NZfQu2e69DU1IRYLIZoNIpYLIbdu3ejoaHBix+BiNYhMxKC2xpETi2U1WVJQmfcj9GhEmZGJjxKR0RERNdSaXQUhUceBQCM1WxDW8wHWSofYZ6TC7AadQRDEUgyLztQddJ27YLPXsCUZQnHCmfTcF13mWcRERFRJdQtiA3tYWsWxs4dHqQhopXGT51Eq8h0fhpzUzn0Fo6h5WwHNifElZJ276NA0QI2boO2bQvkWBSapiEWiyGRSCAWi3FFNxFVll9D0BeB1SCOIO+q8SOU1TDVN4ZcLu9BOCIiIrqWst/6d8BxMBFMImL5lp1EldJtmGE/jIj4eYaoWijxGJT2doxb88KxujkdVpHvfYmIiLyQsTNoksStdDITAzD37vUgERGtNDa7iVaJklPC1OwUHjk7g/ZUFs1GEFGfOPLvhRHmwZ97M5TOxkrHJCISSJKEUCwOa4N4sTvu15FUdJx+YhQTE+MepCMiIqJrxXVdZP/tawCAJza8CUFVRX1IXDUzZKahuRqwzOcZomqi796F+bkRod5SDCGby3iQiIiIiE4eP46YKS7IiJ59HMbePR4kIqKVxmY30Soxk59BdiaPw5mT6DjTueyq7sLIaZRSw3DDURg33ACloc6DpEREIiXsg1kbwVzAFo51xP2YmZIwM8RR5kRERGtZ4fARFM+dAwAshDrQEfcJ5yxIFkaUFPqHhjE2PlbpiEQVpe/ahWDfYaHeoAQxMDHgQSIiIiIae0r8GzxjW2hVM5Braz1IREQrjc1uolXAdV1MZaZw6sI8IrkRJNIxtEbFC0f5kz8BABg3/xzUziZIl+yNR0TkFUlXEArF4LQEhGPtMT9iGRWjZ8aQTnOFCxER0VqV+drXAQCn67sRyevoWGaE+Zi0gHzBxpyVRW9vL7LZbKVjElWMvmsnkhceQ6HklNUNRcHJI70epSIiIlrf/CMloXaxmEZwcxckmS0xomrE/7OJVoEFex6ZVBYPj/ejY6AeG2sDUOTyRraTnYV94RBcSUbgjtdBbk16lJaIaHm+aBil9gAcuGX1sKmi3tRx5qkJjI9xlDkREdFa5OZyyH372wCA51rvQNhQUeMXx0MO+9IwZAMT8zMoFosYG+Pqbqpe2rZt0GQXE5a4P7fcn4Pruss8i4iIiFaK67potMWJqeOZKfh2c4Q5UbVis5toFZjKTWFyPI1p5wySA83YVCuujMyffghwSlC274K6ZSNkjfvfEdEqE9Dgj8awEBHvoO2I+5GbVzAzOMaLfkRERGtQ7oc/hLuwgKKkQNIS6KwRV3XPSXnMBSxIugEXLmzbRj4vNgGJqoVkGNC2bcV4fk441pA2YRX53z8REVEljcyMoEEX36fK/cdhXL/Pg0REVAlsdhN5LFfMYW5uHo+cn0L7tIPWUABBQy07x3UcWKceBAAE7ngjlHau6iai1UeSZYSilxtl7kM8o2Dw6AjmZuc9SEdERESvRvb5EeZPddwOv62gc5kR5iPSPAAZ6WIekiRB13WYplnhpESVpe/YgWzqolBvLoWQzvJ9LxERUSWdeeIYTFUpqxUcBw3Dz0Lbts2jVES00tjsJvLYTH4G2ZkcjuZOo6WvA101YpOocPFZOOlpuPE66Pv3QYlFPEhKRPTy1LAPUnsUDsr3LQzoKuqDBs49N4fJiQmP0hEREdHVKI2Mwnr4EQDA+eQ+1AV0hC65QdeBiz55Evm0hXQhj1gsBlVVkUzyRl2qbtrOnYifflKo16sBnB0660EiIiKi9St7fEaojRZyaKgPQ/aLN2sSUXVgs5vIQyW3hJn0DI71zyPkDqJurgbNEXHlQ/7kAwAA85bXQeVe3US0mhkqgvEY0nFxVHlHzA9kNMwMjKBUcpZ5MhEREa1G2W9+E3BdzBthBIrBZUeYTyGDBdOGq2kIR8IIh8PYtGkT/LyoSFVO37kDieHDyBfLt/LRZBl9R/q9CUVERLRO1c6JW38O2nMwu7d7kIaIKoXNbiIPzVlzyM/l8ehEP9r769AR90GRpbJznEwKhaFjcBUV/tt/DnILm91EtHpJkgR/JAKnPSQca4v5EMnL6HtqCHOzcygUCkilUpicnEQqlUI2m/UgMREREb0U13WR+cpXAAAHu94Mw5XRHhMb2MPKPHTVgOTXEIvFsHv3bjQ0NFQ6LlHFqV1dUPwmxq2ccMwYKsBxeZMnERFRJRRKBTS54vWo2ZkhmPv2epCIiCqFzW4iD6XyMxgfS2NaPYWm8+3YsMwIc6vvIOA6UHfsh7ahHbKmLvOdiIhWDymkQ2mLoSiVX9jzaQqSIQODvRbOne3D8PAwUqkUZmdnkUqlcPjwYYyOjnqUmoiIiJZjP/4ESgOL+xFPxragOWLCUMsvJRRRwoCRgqn7Ea6NIRaLcUU3rRuSokDr6cZELiUca8z6kC+ITXAiIiK69k6dPYka0xDqoTNPw9i334NERFQp7JoRVYBt2zh48GBZbff+3VhYWMDDF6bQOltErexHbUAXnmudeRQA4H/dGyG3iysjdF3HbbfdtiK5iYiuhqQqCMZiWEgMIXzJ9twdcT/Oj+Rw/tgZWEoBufyLF//m5+fR29uLSCTCC+RERESrxAurui/GOhDNG+hsFP9GT0gZFH0uZFVBJBisdEQiz+k7d8I6cQGobSmrtzhhLKTn4I+LN7YTERHRtTX4RB82STVltflCAW32CJTmJo9SEVElsNlN5JGUlUImlcMJ6xRuOtux7Kru4uQFlFJDQH0z9J07ocTEMSxERKuRGQ5hvi0ETKTL6q1RHwIXZUyfyaPUkocvYMIwDFiWhVQqhVAohLGxMXR2dnqUnIiIiF7gzM4i9/37AQBPd7wBIUVGc8QnnDdqpOFT/TATUWiauE8iUbXTd+5E/dc/C2y9paye0Hx49OxJ6LnesvpNN90EXRdvdiciIqKrp/fbwizjwWIazV3tkFS2woiqGceYE3nAdV2kcjN49tw8/NoFJEaT2BAXV0hYvYurun23vR5qK/fqJqI1JKBBa42hIJePMjdUGY1hE9kpA8VSEYZhQFEUGIYB13Vh2zby+bxHoYmIiOhnZe+7D7AsOABsXzPaYn4oslR2Tl4qIhUroKamHoEIb86l9UnbuQOxqdPI2MWyuixJGD025FEqIiKi9aUhL15fH8lNw9ixs/JhiKii2Owm8kDWzcJKF/DY5Dm09dejIWzArytl57ilIqy+x+EqKszX3gq5uc6jtEREV05SZARiceTrFeFYR8yPUF6DnCnBylsolUqwLAuSJEHXdZim6UFiIiIiulTmy4sjzI82X4+QpS57g27KsKFH/VD8/PtN65fS1ASltgZjdlY4FhoHHNdZ5llERER0rUwvTKJREyenlobOwty/z4NERFRJbHYTeSDjpDE+msaMfhLNlxlhXrj4HNz8AtTte6B1tEDWOGqFiNYWPeyH0y6u8GqJmvA5CjDhRy6Xx8LCAmzbRiwWg6qqSCY5yYKIiMhr9rFjKJ48CQA41fRaBHQF9SFDOG8yYsGQTBQ4vZzWMUmSoO3YgcnstHCsKR9AwS14kIqIiGj9OHnoOPyXXD8vOS4S55+EtnuXR6mIqFLY7CaqsIJbQL5o4bH+KTSmLYTzQbRGxX3vrN5HAAC+m2+D0tZQ6ZhERK+eX4PeXANbKZWVNUVGc9REcSEAUzcQCAQQDocRDoexadMm+P3iqjEiIiKqrOy/Lq7qzmo+6FIMncus6s7IBcyFi1A0Ey6vLtA6p+/aCXfsrFBvRgj2Miu+iYiI6NqZOzIm1MYLObSEFcjhsAeJiKiS+HGUqMIyThpO1sEpqxctZzvRHvNBvWTfOye3APvis0AgDPPGmyBFg96EJSJ6FSRZRjAehdWgC8faYz4ELB16QUYkEkEsFsPu3bvR0MCbe4iIiLzm5HLIfuvfAQAHu94Moygv2+ye8dlQDBWOzksLRPqOHUiePCDU46qB4dRU5QMRERGtI9FpcRu9wcIC/NdtgyRJyzyDiKoJP5ESVZDrusi4GYzOOJCMc6i/2LTsCHPr3OOAU4K27zVQWur4B5mI1iw16IO7zCjzprAJf0lG7qKLaCiGWCzGFd1ERESrRP5734ebTgMARmu6EfdriPrEOeWT0TxHmBM9T9u5A6G5QcxZ5SPLJUmCNFr0KBUREVH1KzklNDniYrGpuVEYe3Z7kIiIKo3NbqIKyrk5lCwHJ3M5NI8HEVX1Zfe9s848CgDw33ob5Ka6SsckIrp2/BqMlhrYqjjKvCFsIjtpIJPlWEciIqLVJPOVxRHmA/EORPIGNiyzqntetZAOOZB1E+C9uURQ4nGora0Ys9PCsfoFHxzX8SAVERFR9esbPIs6Q9wm1Nd3GMb1+z1IRESVxmY3UQVl3DSsTBGT5iAaz7Wjc5lV3cWZIZSmLgDJFug9OyD7xPG/RERrhSRJCESjsOrFJV+tUR/8lobM1Dxc1/UgHREREV2qcO487CefAgA83fFmKJDQsUyze8pvQ1cNFHV2uoleoO/cgen0pFBvLYRQcGwPEhEREVW//oNnoFyyTWi2WETr9BmoXV0epSKiSlK9DkC0Xriui5xj4cKUhGBgEDVj12HDdvGikdW7uKrbvPFWKE2JSsckIrrmlKAJuTUCDKfK6i1RE+aAjNTpBdS3li7zbCIiIqqk7Fe/CgAoShKKZiOaTRM+rXwPRAcupmIWdDkEWwUUScFNN90EXeeNurS+aTt2QP7+KaBhU1m9WQrhorUAw296lIyIiKh6uecyACJltaFiFon2RsiGOFWViKoPV3YTVUihUEB6agGni9NoGqxHImAgZJTfb+I6Dqyzj8GVZPheeyukZI1HaYmIriFThdFWi6LsXFJWUB80kJnQOMqciIhoFXALBWT/7WsAgEPttyJoKdiwzDSqTMCF5Qeg6xxhTvQz9F070XzqAJxLphaFNR3D09MepSIiIqpu9RlxhPlQPgWzp8eDNETkBTa7iSpgbGwMwyPDmJ2wkAldROP5NnTExD/CheETcLOzUDZ1Q9vYCfmSFRRERGuRJEnwxaPIJ8Tfaa0xE768gcw0R5kTERF5Lf/jn8B5viHXl7wJhiqjJSquRM0mFRiKiaLBTjfRz9K6u+GzZpCyxJHl6kgJk5OTSKVSyPJGTyIiomtiIb+AJjko1HNjF2Ds3etBIiLyAseYE62wbDaLvr4+WHkL59MaYuEpRKf3o61bbHbb554AAJivuQVyU12loxIRrRg5aEBpiwLjl44y98EcnMPMmTRy6TwMjpciIiLyTOZL/wwASPliCJSC6KjxQ5bKG9oFxUGxPYj9LdfBbK31IibRqiX7/VA3b8KwnUaNWf6+tiHrw+jsDDRFw+HDh7F161Y0NDR4lJSIiKg6nHz2KHYYWlnNdV3UnD0Ifd+veZSKiCqNK7uJVtjY2BiKxSJyuRzGwrNo6m9BXUBHQL9khHmpCPvCIcDwwfea10KuDXuUmIhoBRgKtPZaOFL56u2grqLGryE7piI1M+tNNiIiIkLxwgVYjzwCAHhs41uhORK6av3CeZmoDD3sgxERV9AQEaDv2IFUelyot5bCWMjOY35+8V9vby9XeBMREb1Kk4eGhNpUwUK7koVSX+9BIiLyApvdRCssn8/Dtm2kSxpKsfNoOteO9rh40agwdByunYW66waoTUlICv/3JKLqIUkS/IkYcnFx3Glr1Aczb2BqaMSDZERERAQAmS//KwDABTAb3oyYT0ONXxfOs1p1BPUQEBCPERGg79wJfeCkUG+Sg/D5dei6jlQqhWKxiLGxMQ8SEhERVY/QuLgl3sVCGv4tGyHJvL5OtF7w/3aiFWaaJnRdRx9k1OYKCC6E0LbMft3W8yPMfa+9DXJTotIxiYhWnBTUIbdFhHpr1AezKOH0oxdgZ8X9DYmIiGhluZaFzFe+CgA4Xd+NaF7FhhrxBt2c4aCY1BEMRSGpvJxAtBxt5w409D4Mxy2/+B5SdSwUCjAMA67rwrZt5PN5j1ISERGtfa7rorEoThuaSE/A3LXbg0RE5BXu2U20wpLJJC5cuICJ0Ch6zrehLmjAryll57ilAgoDh4FYHYxduyFFxQtLRERrnr44yhyH58rKUZ+GsKFi5GwWs6k51Pl5ww8REdG1ZNs2Dh48WFa76aaboOuLq7Nz998Pd3YWAPBs2+sQdoDOZaZR5WoV+P0BqGHx5l0iWqRt2QKfm0XKsoV9uyNzJqyIBZ/PB13XYZqmRymJiIjWvosTA0jq4ntW5cIx6O95rweJiMgrbHYTrTC/349CsAFypBeNF16H9lrxwlBh8ChcOwf91jdAaUhwxAoRVSVJkuBLxjEX7YN/tnyceWvUh4npAqZGx1DH6RZEREQVlfmnLwEA8ooOValFc8iE75IbdB24sDpMJMwIsMx4c6L1armbSbZu24Yha0Fodjfngzjrm0FjYyNUVUUymaxkVCIioqrSd/AkXqOUN7utUgnNQ0egdd/tUSoi8gI7akQV8MDIGdTNBODL+S4zwvxJAIDvNTdDbqytdDwiooqRAzqklrBQb40tjjI/dqAXpULJg2RERETrU+H0adhPHwIAPNb1JvgKMjbUBITzsiEXUsyELxyBJEvCcSJ6kdq9HbOZCaHeUgojEPYjHA5j06ZN8Ps51Y2IiOhqWafnhNpwIYf6ZBRKUBxvTkTVi81uohWWL5RwbO4xNJ1vQzJkCCsk3KINe+AIpNoGaNd1QwpwlQQRVTFThdpRI5QTAR1+TcbIiVmkZzMeBCMiIlqfMv/y5aWvh2t3w1BltETF0cr5pI6gLwQ5xM8rRC9H6+mGPnBSqDfJQZgBA7t370ZDQ4MHyYiIiKpH3bz4vnTInoVv+3YP0hCRl9jsJlphg6lZGIHzSA60oH2ZVd32xeeAQh7anhugNiYgSVwlQUTVS5Ik+JtqkQs4wrGWqA9uWsP01JQHyYiIiNYfJ5tF9uvfAACcr9mIqKWjI+aHfMlnkoLiwGrREfSFAYO7oRG9HLW7Gw1nHobjumX1kKojk7e5opuIiOhVyhdyaJTE1dvzk4Mw9u7xIBEReYnNbqIVFgzY2DO/B7qto3W5Zve5JwAAvte8FlJdtMLpiIgqTw4YwHKjzKM+BGwZZw71wSmJzXAiIiK6tnLf/g7cdBoA8HTnmyFDQlet2ITLxmToYT/0SJA35xK9AsqGDfDLecxYtnBMm3CXeQYRERFdiVMnTyBuGkI90vsE9P37PEhERF5is5tohTUEGrBv/DVoCBsw1UtGmBcs2Befg1TXBGP7dkg+zaOUREQV5FOhtseEcjJkQFcknH98CPl03oNgRERE60vmS18CAFiKBklNIubTUOMXx0HmW3WEzDC3XCJ6hSRFgbZ9O8atBeFYQ8aHklPyIBUREVH1GH2qX6ilChY6rFGobW2VD0REnmKzm2iFWRkbQ4dH0B4TV0jYF48ARQva7hsgJ2u5SoKI1gVJkmC21SJvll/kkyUJTWETuWkZM9Mpj9IRERGtD4WjR1E4egwA8GjXm+EvSNhYGxDOyxsOnHoDwVAEkqYIx4loeVpPD2bTE0K9pRhC3s55kIiIiKh6mEMFoXaxkEGwqwOSxgVlROsNm91EK6yYL6Lzxha0RpcbYf4kAMD/2lsgJyKVjkZE5Bk16IPbJO6t1BzxIZBXMNQ7CNfliEciIqKVkvvyvy59PVy7G4oEdMaXGWFep8LvD0IJmZWMR7Tm6Tt6oF88IdQb5SCm5yY9SERERFQ9GizxJs3R7BTMHTs8SENEXmOzm2iFBWr8eM2bN8JQy/93c+3c4gjz+hboW7cBHGFOROuJT4PSEhXKjREDCiQcf+AsCjnxLl0iIiJ69eR0Gta3vwMAOF/Thailoy3mFz6zlCQXVruBgBkGlhlvTkSXp+3oQcOZR+BccgNnSNXx7InTHqUiIiJa+8Znx9Cgi81u9+JpGPv2epCIiLzGZjdRBbinpoWaPXAEKBWg7bkRUl2cI8yJaF2RZAm+znoUZaesbqoKagM6ZoeKmJuZ9ygdERFRdQsfeAiwLADA051vhozlR5hnw4AUNeELhyHJ/LxCdCXUzk74VRszli0cy/bOeJCIiIioOpx+/DhMtXx7nYLjoP78k9D37PEoFRF5ic1uohXmFkvAWfGDrPXCCPObXwslEa1wKiIi76lRH+yEONWiOWLCzKmYHBn1IBUREVF1KhQKSKVSmBwfR/g/fggAsBQNUBsQNlQkQ4bwnHyzjqAvBDnEVd1EV0qSZWjd2zFuLQjHamZVlJySB6mIiIjWvsxxcTuQ0UIOLREVcizmQSIi8hqb3UQrLV0AkoGyvWcdK4vC0DFIDW3QN28B/KqHAYmIPOLTIDdHhHJzxITmSDj24BkUraIHwYiIiKrL2NgYhoeHkUqlYDz5FIxUCgDwaNebESgsv6rb0hwUGjWE/BHA4OcVoquh9/RgNj0h1JvtAHJWxoNEREREa188Jb43HSzMw9y2FZLMlhfResT/84lWmBQ1Yb5vNxae/CukH/0nFEZPw+5/5sUR5vVx/hEmonVJUmTonQmhHvfrCGgKRk/OIzuX8yAZERFR9chms+jr64NlWZifn0fLwceXjg3X7oYsARtq/OLzEgqMYBBa2M8tl4hextLkhMlJpFIpZLNZAIv7dhsXjwvnN8ohTM2Kq9KIiIjopRWcApoQEuozqRGYu3Z5kIiIVgN22IgqoNB3DoXnDsE68RPMf+fjyDz09wAA/2tuhlIrrmokIlovlLog5kxLqDdFTEhpFVNTvAhIRET0aoyNjaFYLCKXy6Fmbg51g4MAgPM1XYhaOloiPvi08j0PHbiw2kyEzDCkIEeYE72Un52cMDs7i1QqhcOHD2N0dBRadw8azjwC52cmvQFASNVw+NhJjxITERGtXb19Z1BjiNvvBHqfhn79fg8SEdFqwGY3UQXkvvvd8oLrQmrsgLZ5M+AX96slIlo3fBoyIXG1WHPEhFmUceaJs3BKjgfBiIiIqkM+n4dt2wCArsNHlupPd74ZMpYfYZ4Nu0BMgz8YhqQqwnEiWnTp5IRMJoP5+XnMz8+jt7cXdn0dfHoJM5YtPDd9ZtqDxERERGvb4ON9kC+ZOrRQKKB9pg/ali0epSIir7HZTVQBue99T6jpe26EnIhDUvi/IRGtX5IqYyEq/h5sCBtQJOD8k8PIL4grv4mIiOiVMU0Tuq5Dy+fR+OxzAICcYkJSGxDQFTSGxZUx+UYdwWAEStisdFyiNeVnJyfouo5QKARd15FKpVAsFjE+MQGtuxvj1oLw3JqUgpJT8iA1ERHR2qUMiNvdXSxmEGtrgOzzeZCIiFYDdtmIVljh3HkUT58R6r6bXwulJuxBIiKi1SUbVZFXimU1VZaRDJmwUgpmU3MeJSMiIlr7kskkVFXFxtNnoBYX/94+vOXn4S9I2FgTEPbjtlUHhWYdQTPMKVREL+NnJycYhgFFUWAYBlzXhW3byOfz0Hf0YDY9Ljy32Q4gZ2UqHZmIiGhNa8j6hdpwfgZGd7cHaYhotWCzm2iFqZ0dqPvxjxD8rf8K1NQDAKTmDdA2dgEBXjwiIipowHxIHFXeHDERsBRcPDXgQSoiIqLq4Pf70dXRgc5DhwAALoDx6A5IALpqxYuF2VoFWtAPPSw2womo3AuTEwDAsiyUSiVYlgVJkqDrOkzThNbTA+PiCeG5jXIIU7OTlY5MRES0Zs1mZtCgiVvw2MN9MPbu9SAREa0WbHYTrTBJkqBt24rQR/8HYn/6Gfh//f9B6JffDbk2zv3viIgAWE4B07o4qrw5sjg69cQDZ1HIF4XjRERE9MrETp2GLzULADjatBdxS0Vj2ERAV8vOc+HCajMR9kUghcTx5kRU7oXJCT6fD7ZtY2FhAbZtIxaLQVVVJJNJ6D3daDjzCBzXLXtuSNVw+NhJj5ITERGtPRPOI+cAAQAASURBVKcOHUNAK3//WnJcJM4ehLGPzW6i9YzNbqIKkhUZvs1boG3ZCrkm5HUcIiLPjY2NYXhkGBfcWTgoX90dNFRETRVzwwVk57MeJSQiIlr7cl/84tLXx1pfDwDYlBBXxWSDgFujwR+IQNJ4Yy7Ry/H7/ejq6oJhGAiHwwgEAgiHwwiHw9i0aRP8fj+Utjb4TBczli08P31m2oPUREREa1Pq2TGhNl7IoU21oDQ2epCIiFYLNruJKkSSJMh1CcBvQGlrg+TXvY5EROSpbDaLvr4+WJaFidwsJhRxz8LmiA9GXsXEyIQHCYmIiNa+wtmzKDx2EAAwEUwgUAohoClLE1R+Vr5BQyAYgRLmqm6iVyqZTKKpqQmxWAzRaBSxWAy7d+9GQ0MDgMVrAXpPD8atBeG5NSkFJadU6chERERrUnhS3GJnsJiGb/NGSKq6zDOIaL1gs5uogtS6OmhbN0GJRyDpXClBROvb2NgYisUicrkcXENByiwI5zRHTKiOhOMPnoRTEvf1JiIiopeWvvfvl75+ZNPboTkSNiUCkC/Zj7uguLBbdISMEMAbc4muiKZpiMViSCQSiMVi8Pv95cd39GA2PS48r9kOIGeJN3wSERFRuZJbQlMpKNQn58dh7tzhQSIiWk3Y7H6VBgYGcNddd2HLli0IBAKIx+PYt28f7r77bmSzr27k6he/+EVIkvSK/n3xZ8bS0eokSRIQMSBpMqS4uIqCiGi9yefzsO3FcY66aWDSJ452TAR16IqEoWNTsDNiM5yIiIgurzQ1hew3vgkAsGUFttkOWQI21oojzDO1EtSQH3o4CEkWV80Q0dXTu7thXDwh1BvlEH784I9x4MCBpffFREREJLowdB51hk+oa+ePcL9uImKz+9X47ne/i56eHnz605/GmTNnkM1mkUqlcOjQIXz0ox/Frl270NfX53VMWkXkmA9yS4QjzImIAJimCV1f/H1oWRamlDwW5HzZObIkoTFsorigYGF23ouYREREa1b6H78IPN9Ae6zrjQjZEtqiPvgu2Y/bgQurw4ewLwIpxM8qRNeatqMHDWcegeO6ZfWQqmFkkvt2ExERvZz+g6ehXHJDZrZYRNvQUWg7d3oTiohWDW5kcJWOHDmCO++8E7lcDsFgEL//+7+P22+/HblcDl/96lfx93//9+jt7cVb3/pWHDp0CKFQ6FW93g9/+EM0NjZe9nhzc/Or+v5ERESVlkwm0d/fD5/Ph1wuh2m3gHElhJBTPv2iOWKiP5VD/8l+JFoTHqUlIiJaW5xcDpl/+tLS44uJ61FjAZsT4vjHbNiFG9MQCIYh6bxMQHStKc3N8PslzORt1PqMsmP6hAu0eRSMiIhojXDOpgGU91iGilkk6qOQw2FvQhHRqsFPsVfpIx/5CHK5HFRVxY9+9CPceOONS8fuuOMObNy4ER/96EfR29uLT33qU/jjP/7jV/V6mzZtQnt7+6sLTUREtIr4/X50dXXh4sWL0DQNpVIJM3IRmCs/rzG82Pw+/kAvdt2+C6rBty9EREQvJ/f1b8BNpQAAZ+q2ImZpiPk01IcM8dwmHf5AEEqI2y0RrQRJkqDt6MGIvSA0uxsyJhzX8SgZERHR2pDImMAlU8wHrVk0bb9ucftQIlrXOMb8Kjz11FN45JFHAAAf+MAHyhrdL7jrrruwdetWAMBnP/tZFArcZ5SIiOhSyWQSTU1NiMViiEajyNeZKEnlF/t8moKYT8PskI3cQv4y34mIiIhe4DoOFj5/79LjQ51vgQwJmxPiXt153UGxUUfYjAIBrYIpidYXrbsbs+lxod5SDKHg8poRERHR5WSsDJpkcTpRZnwAxq5dHiQiotWGze6rcN999y19/f73v3/Zc2RZxnvf+14AwOzsLB588MFKRCMiIlpzNE1DLBZDIpGALxGBXSteaG8Km9BzKmYmuKchERHRy8n/5Cco9fcDAObMMAzUQpMldMb9wrnZegVa0A89HIAk8xIB0UrRd/TAuHhCqDfKIWTzaQ8SERERrQ2njh5D2Ci/VuS6LuJnD0K/fr9HqYhoNeEn2avw6KOPAgACgQD27Nlz2fNuvfXWpa8fe+yxFc9FRES01pUUQG2MCvXGsAHVkXDswCm4jlv5YERERGtI+m//bunrB7e8A2YR2FDjh6aUXwIoSS4KrSbC/iikZcabE9G1o/XsQOOZh+C45e9lQ6qGkUne0ElERHQ5k08PCrWpgoWO3AS0DRs8SEREqw2b3Vfh1KlTAICuri6o6uX3Dd2yZYvwnKv1/ve/H42NjdB1HbW1tbjhhhvwB3/wBxgeHn5V35eIiGhVkQCts0Yo1wUNqLKEgSOjsDK2B8GIiIjWBvvIEdhPH1r8WlaQ8W8GAGxOLDP6MQYgoiMQCEMyLv/ZlohePaWxAb6Qjpm8+F5Wn+DNnERERJfjH3OE2sViGoEN7ZAM3rBJRAA/zV6hfD6PqakpAEBzc/NLnhuLxRAIBJDJZDA4KN59dCUOHDiw9PX09DSmp6fx5JNP4lOf+hTuuece/Nqv/dpVfd+hoaGXPD46OnpV35eIiOiqNYVgGy50S1oqKbKE+qCBvtkisvNZmFx9RkREtKyF//35pa8f2fQWRGwgGTQQ9YnbhFhti41uOcy/q0QrTZIkaD09GLEXUOsr/3+uIeNDyS15lIyIiGj1cl0XjYWA0Mkay0xiW0+PN6GIaNVhs/sKLSwsLH0dDIp3xl/qhWZ3On11+y91dnbiHe94B2688Ua0tLQAAM6fP49vfvOb+MY3voF8Po//8l/+CyRJwoc//OEr/v4vfE8iIqLVQvJpkBqCQH+mrN4YNjA8n8fg6QHEm2IepSMiIlq9ioODyN//AwCAC2Co9nrU5IHNdQHh3KzfQalWR9gXBQJ6ZYMSVRFd13Hbbbe9snN7ujF7dhyI1JbVW4pBWHYePsO3AgmJiIjWruGpISR1v1CX+k/CeOObPEhERKsRx5hfoXw+v/S1rr/8BQHj+TEauVzuil/r7W9/O/r6+nD33XfjHe94B/bt24d9+/bhzjvvxNe+9jV85zvfgaYt3p3/O7/zOxgbG7vi1yAiIlptJEWG3i6OMm8MmwCAYw+cQtHmyhciIqJLpf/+C4CzOObxcMt+1OQV+DUZrVGxgZZLqjCCIahhPyRJEo4T0bWn7eiBefG4UG+UQxgee+nJe0REROtR3+MnoSnlbSyrVELT+adg7N3jUSoiWm3Y7L5CpmkufW3bL79nqGVZAACf78rvzo1EIi950eHnf/7n8Yd/+IcAgGw2iy984QtX/BqDg4Mv+e+pp5664u9JRET0amkbE0It6tPg1xRM9edgLVgepCIiIlq9SjMzyH75y0uPj7e+AcDiXt3yJZ8rC4qLQouJsC8CKcRV3USVovf0oKH3YThu+R7dIVXDAwcPcis5IiKiS+RPpYTacCGHxrAKOSFeOyKi9YnN7isUCoWWvn4lo8kzmcURrK9k5PnV+PCHP7zUEH/ooYeu+PnNzc0v+a+hoeFaRyYiInpZUo0PVkS84asxbEDNakhNiR92iIiI1rP0P3wB7vOTyPrj7YjaASiShE2JZUaY10pQwgZ8oRAkTal0VKJ1S0kmYUYMzOTFxRP6mIve3l5ks1kPkhEREa1OtXOaUBu052Bu2wpJ4ftYIlrEZvcVMk0TNTWLo1WHhl56xFQqlVpqdq/U3th1dXVLeYaHh1fkNYiIiCpOU6A1i/tyN4ZNaI6E0wfPwL1kRQwREdF65SwsIPP//ePS44Mb3w7FBTbU+GGq5RcBHbiw2g0E/VFIIaPSUYnWvWJXF8btBaHemPWhUChwizoiIqLnWUULjRAXEc7ODMPYsbPygYho1WKz+yps27YNANDX14disXjZ806fPr309datW1csD/dXIyKiaiNJErTOWqHeGDYhATj35EUUcpf/G0xERLSepL/4T3AXFptns2YEulQHANhaJ14czEQBJ2og6A8DPrWSMYkIQKGrC7ML40K9tRRCxsog//yEBiIiovXuzOkTiJvizZnh3idg7N/nQSIiWq3Y7L4KN998M4DFEeXPPPPMZc/72bHir3nNa1Yky+TkJKampgAAjY2NK/IaREREXlA64ygp5au3DVVGjV9DblpCPs0LgURERE4uh8zn7116/ODWd8IsAk1hE1GfOPYx16LBHwpCDft44zSRB5TrroN58ZhQb5JDKLo2TNP0IBUREdHqM/pkv1BL2TbaZ85D3X5d5QMR0arFZvdV+KVf+qWlr//xH/9x2XMcx8GXvvQlAEA0GsXtt9++IlnuvffepTGut95664q8BhER0UrRdR233XZb2T9d1wEAUlCHUyde7GsMm/DZCsb6OeKRiIgo+5WvwkmlAAC2rCLn2wgA2FYvrurO+h04dQbCZhQI6pWMSUTPq33tzWg48wicS7bkCaoaRmdmkUwmPUpGRES0umiDBaF2sZRGtLURciDgQSIiWq3Y7L4K+/fvx2tf+1oAwBe+8AU8/vjjwjmf+tSncOrUKQDARz7yEWha+R31Bw4cgCRJkCQJ73vf+4Tn9/f348iRIy+Z43vf+x7+5E/+BADg8/nw/ve//2p+HCIiolVJUmRobXGh3hhebIA/96PjcB3u201EROuXa9tY+Ju/XXr80Oa3ImwDUVNd+nv5s3KNKrSQD3ooAEnh5QAiLwTb2qBHTczkLfHYjAK/3+9BKiIiotWnwfIJtZHsDIzt2zmhiIjKcIOuq/TZz34Wr3nNa5DL5fCGN7wBH/vYx3D77bcjl8vhq1/9Ku69d3GM3KZNm3DXXXdd8ffv7+/H7bffjhtvvBFve9vbsGPHDtTVLe67dv78eXzjG9/AN77xjaVV3Z/85CfR1NR07X5AIiKiVUDrSqDwxGhZLRHUockSxs6mYGdtGEFx/yYiIqL1IPutb8EZW5x04kDCSM1+1OSBbfUh4VxLc1Bo8qHWF4cU5t9OIi8ZO3pw0U6j1ld+U0rdvAbHdSBLvBmFiIjWt8n5CTRo4urt4lAvjF/e7UEiIlrN2Oy+Srt27cK//du/4T3veQ/m5+fxsY99TDhn06ZN+P73v49QSLzQ8Eo9/vjjy64cf4Hf78dnPvMZfPjDH77q1yAiIlqtpIYgbB+g516syZKEZMhAX7qEzHyOzW4iIlqX3FIJC3/110uPn+i8FTV5GaYqozMurgzN1MtQgiZ8gSAkg5cCiLykdndj9swYEKktqzfbQeTtHPwGR7MSEdH61vvkcexRlbJawXGQ7DsIff+veZSKiFYr3ir6KrztbW/D0aNH8Tu/8zvYtGkT/H4/otEo9u7di7/8y7/EkSNH0NXVdVXfe8+ePfiXf/kX/OZv/iauv/56tLa2wu/3Q9d11NfX44477sDHP/5xXLhwgY1uIiKqWpKpQWkMC/XGsAmjKOPisf7KhyIiIloFct/7PkoDAwAAF0Bv4x0AgM2JABS5fKxjUXZRaDMRDEYhLzPenIgqS+vphjlwXKg3ykFMzU15kIiIiGh1WTg2KdRGCzm0yRbUlhYPEhHRasbbuV+ltrY2fPrTn8anP/3pK3rebbfdtjSCfDmhUAjvfve78e53v/vVRiQiIlqzJEmC1lkD59x8Wf2FfUiPP3gaPa/rgcx9R4mIaB1xXRcLn/3s0uPDLdejNq9BloDNiaBwfroGcIM6gv4wENAqGZWIlqFu346G3v8bzk13Qv6ZPUeDqoZHnjuO1te3eZiOiIjIe9FpBbhkkN/FwgK6N3VB0nVvQhHRqsUrw0RERLSqqRtq4aL8BrGwqSKkK5gezMFK2x4lIyIi8kb+xz9G8Uzv0uNjrW8CAHTG/fBp5eMeXbiw20wEwxGoYR8kqXzVNxFVnlxTAzmkYSZvCcfSp7mym4iI1reSU0KTK97AOT07CnPnDg8SEdFqx2Y3ERERrWpSzIQdF4fRJMMmlKyK7FxumWcRERFVJ9dxMP8Xn1h6fCLZjbi1OPFkW31IOD8dceFEVYTNCBDkKhii1cLq7MC4nRbqNSnlJScBEhERVbuzF86gxjCEuq/vMIy9+zxIRESrHZvdREREtKpJmgK9OS7UG0IGNEfCmSfOeJCKiIjIG/n7f4DimRf/9h3q/AXIAJojJmI+cUR5vlmHGQ5BDfshcdsPolUj39GB2YUxod5sB5Av8mZOIiJav4ae6Cvb5gMAFgoFtI0eh7Z7pzehiGhV4yddIiIiWvW0zhqhlgwt3uV75rE+lAqlSkciIiKqOLdUwvwn7l56fK62C9HC4ojH7qS4qjvrc1CqMxA2o5DC4uoYIvKO1dkBc+CYUG+Ug5iam/QgERER0SpxPiuULhYzSCQikGMxDwIR0WrHZjcRERGtenJ7DCW5fJyjT1MQNVXMjxe5bzcREa0Lue98B8Vz55YeH9z4DiguUB/UURcUm9nZZhVa0AczFIR0yV7eROStfHs7GnofhXPJyPKgquHQkRMepSIiIvJefdYn1IbyKejXbYMks6VFRCL+ZiAiIqJVTwrqKCXEi/gNYRNaXsHc1LwHqYiIiCrHLRYxf/cnlx4PRZoQKC2ubOlOhoXz84aDYoOBcJCruolWIycUghwxMJO3hGPpU1MeJCIiIvLefHYOjWpAqOfHLsDYtcuDRES0FrDZTURERKueJEswWsVR5g0hA4or4cRDXP1CRETVLfvNb6E0cHHp8UNb/hN0B4j7NDRFTOH8dIMMOWjA7w9DWmYvbyLyXr6zA+N2WqjHZyW4l6z4JiIiWg9OHT6GgKaW1UqOi8TZx6Hv3+9RKiJa7djsJiIiojVBXWbf7vqQAQnA+UODKOSLlQ9FRERUAW6hgIVPfXrp8ZS/FgaSAJbfq9vSHJSaTYRCUcgRruomWm10Xcdtt92Gpte9DrPpceF4sx2AVcx7kIyIiMhbs0dGhdpEIY/23AS0TRs9SEREawGb3URERLQmSG0RFNXyFS66IqPGryM37cJKiyMgiYiIqkHmq/+G0vDw0uMfb/8/YJZchA0VbTFxT8N0gww3oCPoiwABvZJRiegK6D09MC8eF+oNUhBTc9MeJCIiIvJWcEKsDRQX4N/QDtnvr3wgIloT2OwmIiKiNUEyVKBe/GDTEDZg2AqmB3lBkIiIqo9rWVi457NLjycCddCkJgDAdckQJEkqO7+gOig2mQiFo1DCpnCciFYPrXs7kmcehXPJyPKwpuOZ49ymh4iI1hfHddBYFPfrnliYgNHd40EiIlor2OwmIiKiNUGSJOjt4ijzZMiABAlHHzzGvQ2JiKjqZL78ZThjY0uPf/L8qm6/pmBDXLwJbL5OhhtUEfZFIYU4wpxoNZMjEQQaokhZtnBs/oQ43pyIiKiaDYz0o84QpxYpF45C37Pbg0REtFaw2U1ERERrhrLMvt11QQOyBAwen0AhV/AgFRER0cpwMhksfObFVd2j4SQMNAAAttUHocjlq7aLsotSi4FA5PlV3Qo/8hOtdnpPN8bttFCPToM3chIR0brS//hp4f1ttlhEa/8zMPbt9SgVEa0F/ORLREREa4bUFEJBL7/op8oS6gIGinMKrAyb3UREVD0W/uZv4czMLD3+6bZfhVFyYSgyNtWKIx7nEy6ckIaIGYUUNisZlYiuktbTg1Ra3KC0yQ7AKlkeJCIiIvJGoXdeqA0Vs2gIaVAaGjxIRERrBZvdREREtGbIugq5ISTUG8IGjKKMkd5hD1IRERFde6XxcaQ/f+/S46FIM/xOAsDiqm7tklXbJclFocWALxyGGvZDUvlxn2gt0Lu7oQ2dEuqNUhDzmTkPEhEREXkjkRa34Bm0ZmFs2QJJVT1IRERrBT/9EhER0ZpyuX27AeDoj49z3CMREVWF+U/cDeTzS48f3Pou6A5gqDK21gWF8xdqXLgRHRF/FFKEe3UTrRVa93Y0nn5UeA8b1Qw8feKYR6mIiIgqK1fIoUkW3+POTw1C37XDg0REtJaw2U1ERERrirJBbHbXBnRosoTxc3Pct5uIiNa8Qm8vsl/7+tLj/lg7gs7i37/t9SFhVbcjubBaDRihIPRQAJKmVDQvEV09ORJBsN6PWUt8Dzt9dNSDRERERJV36uhxhHWtrOa6LmpOPw5j7z6PUhHRWsFmNxEREa0tyQBsX3lJliTUBQ0goyKftr3JRUREdI3M/cmfAo6z9PjhLXdCcwBTlbGlTtyre67GgRvREQ3EuaqbaA3Su7sxbqeFemSKE4uIiGh9mDx0UahNFSx0zPZD6+n2IBERrSVsdhMREdGaIqsK1KaIUG8IG9BKEgZPDnmQioiI6NqwDj4O68EDS4/P1W5AqBQFAHQnQ1Blca9uu9WAHvbDCAYg6dzPkGit0Xp6MJOdFOpNlh+FEqcWERFR9TNHikLtYjGNYFsT5HDYg0REtJaw2U1ERERrjrbMvt0Nz+/bffwn3LebiIjWJtdxMPtHf1xWe3TTr0BzAL+mYHNC3MdwrtaBG9YR8ccgRcwKJSWia0nr7oY83CvUGxHEfDrlQSIiIqLKcV0XjbY4vWg0MwWjuxuSJHmQiojWEja7iYiIaM1RuuJCLe7XYagyJvsXuG83ERGtSdn7vo3iyZNLj08kexApLK5k6U6GoMjlF/pKz+/VrYb88AVDkEyu6iZai/Tu7Wg49bBQj2smnjl9cplnEBERVY+xmVEkdb9QlwZOwtiz24NERLTWsNlNREREa45U64cdEu/sTQYNIKNw324iIlpzXMvC/J//rxcfA3iq651QXSCgK9hYK652ma1zIAV1xIJxSFGu6iZaq+RoFOFaHXOWeMPm+HPcooeIiKpb3+MnoCnlrSqrVEJz3xPQ9+/3KBURrSVsdhMREdGaI8kytOaYUG8IG9AcGYMnhj1IRUREdPUWPn8vnNHRpcePbbgDibwOAOhZZlV3UXZhtRjQwn74AkFIPq2ieYno2tK3d2Pczgj14KTjQRoiIqLKyZ6cEWrDhRwaTRdqS4sHiYhorWGzm4iIiNYkrUPctzv5/L7dJx7gvt1ERLR2lEZGkf7sXy09LkgKzjW8DgAQ0hV0XWZVtxzQEeWqbqKqoPd0YyY7KdQb8z4UHG7RQ0RE1atmVtyKZ7AwC2PzJkiG4UEiIlpr2OwmIiKiNUnujMFFeUM7YmrwqTImLsxz324iIlozZv/oj+Dm80uPf3zdO1CTX1zJ3dMYhiyJq7qLzS+s6g5B8usVzUtE157W0w13rE+oNyOIdHbeg0REREQrr+AU0CgFhfrs9DCMHTs8SEREaxGb3URERLQmSTEf7Ij4VqY+ZAAZlft2ExHRmpA/+Djy9/9g6XFa82E2vAcAEPNp2BD3C8+ZrXeAoI5oqAZSjKu6iaqBtr0bDScfE+ox1cSzZ095kIiIiGjl9Z45hfgyq7fDZ56Cvn+fB4mIaC1is5uIiIjWJEmSYDSLo8zrgwY0R8LwiREPUhEREb1ybrGIud/7/bLa/Tv+M8L24uSSPU0RSJes6i4oDorNBnTu1U1UVZR4DJGIgwWrfDqRLEkYPDLgUSoiIqKVNfzEeaGWsm10TpyGvpMru4nolWGzm4iIiNYstT0u1F7Yt/vYA8e4bzcREa1q6f/vH1E8d27p8USgDpLaCQBoDBtoioirtlNJFwjoiAZruVc3UZXRu7sxXsgKdXPYwoEDB3DgwAHYNqcXERFR9VAH8kLtYjGNcEsD5EjEg0REtBax2U1ERERrltwZE2pRnwZTlTHZv8B9u4mIaNUqTU5i4ZOfKqv9sOc/w1d0IQHY2xwVnpPXHThNBvRwAGYwwFXdRFVG7+nGdHZaqLfYQZTckgeJiIiIVlaDJW7ZM5qbhr59OyRF8SAREa1FbHYTERHRmiVFTVhhSajXBw24aQX5Ba58ISKi1WnuT/4Mbiaz9PhsYhOCpVoAwIYaP2LLNLJTzS+s6q6BFPVVLCsRVYbW043SxDmh3uQGUSjyfS0REVWX6fQ0GrSAUC8OnYGxe5cHiYhorWKzm4iIiNasxX27xVHm9aHn9+0+OexBKiIiopdmHXoGuW99a+mxC+DRzb8KzQFUWcKuRnFkY9pfgpQwoYeDMENBSKZawcREVAladw+Spx4T6rWaD0Pzkx4kIiIiWjlnnjwGUy1fvV10HDT0HoS+f59HqYhoLWKzm4iIiNa05fbtrg/qAIBjDxznvt1ERLSquMUiZn/3d8tqD2x+CxJ5AwBwXX0Qfl0c2TjbCsCvIxas4V7dRFVKiccQ81tI28WyuixJsEdyHqUiIiJaGfPPTQi1UTuHFikHraPDg0REtFax2U1ERERrmrxB3Lc77tdhKNy3m4iIVp/03/1vFE+fefGx5sNo7WsBAD5VxnX1IeE5qUgRatwPMxqEEQxAMriqm6ha6T3dmLCzQj0+x//viYioukSnxfbUxeI8jM0bIfm4ZQ8RvXJsdhMREdGaJkV9sMS+AOqCOty0zH27iYho1Sj292P+M/eU1b638/2IWotf72wMQ1PKP6Y7cJFuUwGfhligFlKMq7qJqpm+fTumcjNCvdkKwHEdDxIRERFdeyW3hEZX3K97enYURs8ODxIR0VrGZjcRERGtaZIkwWiqEerJkAHNkTF8csSDVEREROVc10Xqd+4CLGupdr5mA3SpFQAQNVV01S5zwS9RhB4yEYhGoYX9kHSu7iSqZlpPNwpTF4R6sxuCXbKWeQYREdHac76/D7WGeBNnsPdpGHv3eJCIiNYyNruJiIhozVPbxVHm9aHFvU+PP8h9u4mIyHvZr3wF9lNPLT12ATy09T3QncW/Ude3xiBLUtlzirILq1kD/Bqi/jikKMc5ElU7racH9acOCvWE6sfYwrQHiYiIiK69/sfOCO99FwoFtI8cg75nt0epiGit4i3hREREtOYtu2+3T4OmSJg8P49Crgjdr3mQjIiICChNTmLuT/6srPbwpjcikV9czdIZ9yP5/E1aP2uqoQgtEEY4VgMl6oOk8n51omqn1NQgri0gWyjBrykv1mUJ6VFxL28iIqLVyrZtHDxYfgPXTTfdBF3XIZ/PAgiXHbtYzKAhmYAcj1cwJRFVA35SJiIiojVPivthXTL5VZIk1AcNuBkFVpojH4mIyDuzv/v7cBcWlh5nVRODidsAAJoiYW9zRHhOXi+h1GQAIR0hfwRShHt1E60XRs92TFhiYzuWUpY5m4iIaO1J5vxCbTg/DX3bVkgq12gS0ZVhs5uIiIjWPEmSoDeLq7uTQQOqI2H41KgHqYiIiIDcD36A/A9/WFb77s73Ifr8fVi7GiPwaWIDa6LNgWqaiEVrocT8kGRJOIeIqpPW3Y3JfEqot+QDSGfSHiQiIiK6dmYzKTSqAaFuD/fB2LPLg0REtNax2U1ERERVQWsXx1y9sG/3iQdOVDoOERERnIUFzP7ex8pqvYnN0OR2AItbbmxOiBf6ZiIFqDV+yBETQX8ECOqViEtEq4Te0wN7ekCoN7tBPHHoCYyO8kZOIiJau04/eVS42bPoOKg/8yiMffs8SkVEaxmb3URERFQV5E6x2R33a9BkCePnZ1G0ih6kIiKi9Wz2934fztTU0uOiJOPg5vdAd1wAwPWtUchS+Yrtkuxirl2CbGqIB2shxX2QJK7qJlpPtJ5u1J5+QqjXqX4MTA2jt7cX2Sz37yYiorVp9tlxoTZWyKGtNA+lq8uDRES01rHZTURERFVBqvXDvmTLJ1mSUBc04GRk5LlvNxERVVD2u99D7r5vl9W+t+PdqM0v7kHYVeNHXdAQnjfWYMPnD0CPBuELhSH5tIrkJaLVQ6mtRcwZR75YKqursozMWBbFYhFjY2MepSMiInp1otNiW2qgsAC9awPkgDj1iIjo5bDZTURERFVBkiRoTVGhXh/SoZVkTF+YqXwoIiJal0oTE5j93d8rqw3E2lA0rgMAGIqMPc0R4XlZo4RiowH4X1jVbVYkLxGtPk5XJyasnFBPpH2wbRv5fN6DVERERK9OySmhyQ0K9anZURg9PZxoRERXhc1uIiIiqhrL7tv9/Kq54z89Xuk4RES0Drmui9Rv/TbcubmlmgMJD1z3fpilxfHlu5rCMFVFeO54Rwmm7kMwFoMeDkLS1YrlJqLVRdqyBZPWrFBvLQShaRpMkzfDEBHR2tN34SxqDHG6kf/s0zD27vYgERFVAza7iYiIqGost293bUCHKksYOj2OUqG0zLOIiIiuncwXvwjr0cfKav+x/ZeRyC9e1EuGDGxOLLOaJWZDj/oghXREg3FIMTayiNaz6A03IDczKNRbnBBcxUUymfQgFRER0asz+uR5yJes3p4r2GgfPgZ9716PUhHRWsdmNxEREVWPhB+2zy0ryZKEuoAOe16CnSl4FIyIiNaDwrnzmPvTj5fVRsNJLAT3AABUWcJNbTHheUXZxXybDFU3EIvVQYn5ISn8uE60ngX37UXNmaeEer0agBEz4ff7PUhFRET06qgDllC7WMygJhGFUlvrQSIiqgb89ExERERVQ5ZlqMvs210XNKAXFcyOzIlPIiIiugbcYhGpX/91wHrxAp4L4IfdH0KguHgj1p6mCEKGOJp8rNmG3xeAFgsiEAwDQb1SsYlolVLq6pAojMAqOmV1TZYxcm7So1RERERXplAoIJVKYXJyEqlUCsm8TzhnKDcDfetWSDrfAxPR1WGzm4iIiKqK3iaOMq8LLX5gOv3QqUrHISKidWL+E3ejcOJkWe1HW38Rtdbi6stk0MCWOnF8+XygiGLSAAIa4qFaSDU+SJeMdiSi9cnYvhWTVk6ouxcWPEhDRER0ZcbGxjA8PIxUKoXZ2VmMTY+iUQsI59kjfdB37ax8QCKqGmx2ExERUVWROsTxsImADlkCzj97EU7JWeZZREREVy//8MNI/93/LqtdjLZiLnIjgOfHl7eLf59KkouxDSWYug/BaAx6NAhJF1d+E9H6pHZvx6QlTiaqX9Dguu4yzyAiIlodstks+vr6YFkW5ufnkclkUJjMwlSVsvMKjoP6s4/D2L/fo6REVA3Y7CYiIqKqIiWDKOjlF/9UWUbcryM748LOct9uIiK6dooTE0j95m8Bzos3UxUkBQ9s/yD8LzO+fLjZQsDvhxQ2EA3GIUXNiuUmotVP3b4dmdlBod5cCsIq5j1IRERE9MqMjY2hWCwil8tB13WEQiEkcxHhvNFCDu3WNNQtmz1ISUTVgs1uIiIiqiqyLEOqF8fE1gd1aLaM9GTGg1RERFSN3FIJqQ//GpyZmbL6fbvfj9q8BgCof4nx5XaDBsU0EI8koNQEIMn8iE5EL1K7tyN29hmhXq8EMDApNsGJiIhWi3w+D9u2AQCGYUBRFLQWosJ5A4V5mJ0dkIPi+2UioleKn6SJiIio6hjtNUKtLmhAgoS+x896kIiIiKrR/F/8JeynD5XVnmvaDVXZAGBxfPlrLjO+fKSrgKAagBENIRCKQAroFclMRGuHUleH2FwfCpdsw2MoCo4cOulRKiIiopdnmiZ0ffH9rWVZKJQKaJbCwnmTc6MwenogSVKlIxJRFWGzm4iIiKqOvExjoS64+CGr94k+uA73OCQiolcn/9MHhH26F7QAjrX/J2jP96X2tUQvO748aAaAoI6aUAJSja8SkYloDSq0N2HSEkeWO+fEvbyJiIhWi2QyCVVV4fP5YNs2ZlPTqDHEmzv9vYdg7N3jQUIiqiZsdhMREVHVkVrCKMnlDW1TVRAxVcxPFGDnuG83ERFdveLoKGZ++yOAW/635t/3/iai1uLXHTEfNtUGhOfOB0qwG1Rohol4PAEtHoSkKZWITURrkNXRgQlLbGzXzWlwXd7ASUREq5Pf70dXVxcMw0A4HEZ9Piys3p4t2OgYPgFt/z6PUhJRtWCzm4iIiKqOrCooJQyhXhc0oOQV5ObE1TFERESvhFssYuaDH4I7O1tWf3DTmxErLE4WCekKbmgTp4w4kouRDTaCagBaPIhgKAqExb9XREQvsNrbkZ4bEepNxQCskuVBIiIiolcmmUyiqakJsVgMDXlxhPlAMY1EPAC1rs6DdERUTdjsJiIioqpkttYKtfqgDhkSBp7pr3wgIiKqCrMf+wMUnn2urNYfb8dE/FZIAGQJuKWzBroiftweaM0j4FscX14bqoNU6+f+hET0kvId7QifOyLUG5QgRmfGPEhERET0ymmahlgshpZSRDg2lE9B27IVki6ONyciuhJsdhMREVFVUjqW27d7cfXcqYdPc+wjERFdsfQX/wnZL3+5rJZVTTy07UMIFBf/ruxujKA2IF6wm44WUKzToBsmYrFaji8nopel6zpufvvb0bBwDkXHKTtmKgoOPXPUo2RERESvTKlUwulzp9Gg+YVj1kgfjF07PEhFRNWGzW4iIiKqSlJbFI5U3tAOGSr8moLp4SyK+aJHyYiIaC2yDj6OuT/647KaC+Ab+34bNfnFj9ZNYRPXJUPiczUHoxtshNQA1HgAoXCc48uJ6BWRJAm+6zZhKi+OLLfPzFY+EBER0RWSZ4sw1PKbPAuOg7qzT8LYv9+jVERUTdjsJiIioqok+zQUouKKubqgDimrIL/APQ6JiOiVKQ4OYubDvwYUy2+U+sH2X0H8+X26fZqMm9vFqSIuXJzflENYDQFhHYlQPceXE9EV0Xu6MWHPC/XaOU6HICKi1a923ifUhgtZbMiMQ71umweJiKjasNlNREREVctsWW7fbgOKK2HkFPc4JCKil+dkc5h+7/8JJ5Uqqx9v2Im8fzcAQALw2o44zGXGkg8229CCBgzDRCyW4PhyIrpiWk835udHhXpzIQC7ZHuQiIiI6JVrtsT9uvsLCzA72yEHgx4kIqJqw2Y3ERERVS1lmRV2dcHFfVRPPnC80nGIiGiNcV0Xqd/4DRR7z5bVZ3wxPNfxLujO4nYZ+1oiaAiZwvPnQiUsNDoIqUHo8RBCoRjHlxPRFdO7exC88KxQb5CDmJgdr3wgIiKiV6joFNEsi9v8TM6NwujuhiSzRUVErx5/kxAREVHVkjvEZnfMp0FTJIyfn0WpUPIgFRERrRXz/+svkP/xT8pqRUnCd/d8BBF7sdHdVePH1jrxAl5BdXFxUx4xOQxETNSG6yElOL6ciK6c3JBEMtWL0vM32LzAr6p4+ghv4CQiotUrNTOFmKELdd/ZwzD27PYgERFVIza7iYiIqGrJERPWJROxJElCXcBAKS3DynDsIxERLS/95S8j/Td/K9S/vu83UZNfXJ2dCOi4oXX5fbrPbcgipAYgB3QkokmoiSAklePLiejKSZKE4HVdmLYs4Vjm1KQHiYiIiF4ZZdAWbvZMFSx0DR2Dfv1+j1IRUbVhs5uIiIiqmtYkNiHqgzpUR8Z0f2qZZxAR0XqX+8lPMfexPxDq/3HdLyPoNAEA/JqM2zbUQJHFldoXm2zIER2m4UOopgZmLAwpIK5oISJ6pfTubozb80K9JsVLe0REtHrVzIpb+PQXM6iNmFAbGz1IRETViO+IiYiIqKrpbXGhVhdc/LB1+qGTlY5DRESrnP3cc0j9+m8AxWJZ/em21yAX2AMJgCwBt2+ohV8TV2pPxoqYbyohrAahRQOIhmohxX0VSk9E1Urr3o75BXF/7mY7gIJT8CARERHRS9N1HU2lsFAfys3A2LIFks6bQYno2mCzm4iIiKqa3Cmu7K4N6JAl4OKxEbiX7H1IRETrV3FgANPvfR/cbLasfr5mI/ob3gbNWXx8U1sMtcus1J5VLVzsyiIuhYCwgUQkCbkuwH26iehV03p6YPYfE+oNchBT8xxlTkREq8/4zBiSml+oF4bOQt+5w4NERFSt2OwmIiKiqiYlArCN8oa2Ikuo9evIzbqwc1wJQ0REQCmVwtSvvgfO1FRZfcYXx+Ob/y/4iot/S7qTIWyoCQjPz8slPJUcQtT1Q/GbqInVQ6sLQ1pm9TcR0ZVSGhvROHkCjlv+vjaoajh09LhHqYiIiC7v7MHjMNTyFpRdKqGh7wkY+7lfNxFdO2x2ExERUVWTJAlyQ0io14UMaAUF6amMB6mIiGg1cfJ5TL/nvSj195fVbVnD93b/DiL24uOuGj92N0XE58PF07Uj8KsGfEYAgZoYArEY9+kmomtGkiSEtnVgJm8Lx+aOiePNiYiIvJY7OSPUhos5tKXHMF1f50EiIqpWbHYTERFR1TPaaoVafXCxAdH/VH+F0xAR0Wpg2zYOHDiAAz/5Cfp+8ZdQePbZsuMlSPjaDf8dcUsFADRHTNzYJm6NAQCHA2PIKFnIlgwnoCEeqeM+3UR0zWnd3Ri3F4R6TOwlEBEReS6eUoXaQGEedl0CfcPDyF6ydRAR0dVis5uIiIiqntwhNifqAgYA4MxjZyodh4iIVotSCQ2f/RwCx0+UlV0AX7/+I4jaYQBAIqDj1s445GX23j6jTuGibxJ6TsV0MYPp0VlMOAuQZO7TTUTXlt7Tg9m0uIq7yQqg6BQ9SERERLQ8u2SjUQoK9enUMFJ1dSg6DsbGxjxIRkTViM1uIiIiqnpSUwhFxSmr6aqMqKkiNZ5HweLFQSKi9cZ1XdT/3ecRPHxYOHbfrg8iWKoHAIQNFXd01UCVxY/PI0YGz4Yuwp/XMVfKoph1MOmk0Xu+jytViOia03q6oQ+eFOqNchCpdMqDRERERMs7dew4Yqa4pU/w9FOYbW2GbdvI5/MeJCOiasRmNxEREVU9WZFRTBhCPRE0IOUUWPOWB6mIiMgrrusi/T//AOHHHxeO/WD7u6ApGwAAfk3G6zfWwlQV4bxZs4BDdUMIF02UVAmGEULekDA5n0KxWORKFSK65pSmJjSNHoXjumX1sKrj6RPHPEpFREQkmnhqQKhN2xa6Rk9itq0Nuq7DNE0PkhFRNWKzm4iIiNYFf2tCqNUFdCiuhNHTEx4kIiIir8z96Z8h/5WvCvUHN78NBXMHAMBUZbxuYwJBQ9xrMOcDDreMI1Iy4biAo6pwNBWOT4HrulypQkQrQpIkhLe2YtayhWPTz414kIiIiGh5+nBBqA2U0lBMBf62NqiqimQy6UEyIqpGbHYTERHRuqC0ift2J4KLI7VOP3i80nGIiMgj83d/EpnP3yvUn+i4HXPhmyADMFQZb9iUQMynCefZOnBqUwYRzYRP8SEjlVAsupiTLFiWBUmSuFKFiFaM1t2NcTst1KNT7jJnExEReaPR8gu14ewUsg0NCMXj2LRpE/x+8RwioqvBZjcRERGtC1J7FC7KLwJGTA2GKmPk3DRKRecyzyQiomox/8lPYeGezwr1Z1tuwGjiDVDdxUb3Gy/T6C6owOmuHExTRn2wHoWAAp8WxEQpjYX0AmzbRiwW40oVIloxek8PUulJod5k+VFySh4kIiIiKjc6NYqkITaynYFTsDZuxO7du9HQ0OBBMiKqVmx2ExER0bog+zRYYUmo1wV0FBZkFLLiiC0iIqoec5+4GwufuUeoP9tyA843/CI05/kV3Rtrl210l2QXZzvzkIMuYnIEaiKEze3bkPfLCEXCCAQCCIfDCIfDXKlCRCtG694OZfi0UG9EEPP5eQ8SERERlTv72HFoSnnrySqV0NL3JLBrJ98nE9E1J24+RkRERFSl9OY4cHKmrJYI6hicyyM1NIeGbXUeJSMioitl2zYOHjxYVrvpppug67pw7txffgLpv/qcUH+25Uacb/gF6A5gKIuN7rhffP5io9tGMVZCjRSBFDORjDVCSoSQnBtCOp1GsViEqqrYvXs3otHoNfs5iYh+ltLSgsahZ+Hu/UVI0os3ckY1Aw+fOIY37LvFw3RERERA/uQMgPqy2lAxi4bCLC42N3kTioiqGld2ExER0bqht9UItbqAAQA491hvpeMQEVEFzP2vv1i20X2k5SZceKHRrcp4w6bLN7rPdRZhxQuoQRhyxIe6WCOMhhgkQ4GmaYjFYkgkEojFYlypQkQrSpIkRDc1YtYSpxJNPDvoQSIiIqJytfPilKSL9hwKzU1wTdODRERU7djsJiIionVD7ogKtdqADlkC+p7ph+u64pOIiGjNmvv4nyP9138j1I8034j+hrdBc4CAruDNmxMv0eguIRu3EJNCkCMmauP18CXjkJYZdU5EVAn6jm5M2GmhHppwPEhDRET0IqtgoUkOCvXZ6YuwOjoBSdxejojo1WKzm4iIiNYNqcYPWy+/CKjIEuI+HenpEgq5okfJiIjoWnJdF7N/9P8i/bd/Jxw70nwT+ht/AZoDRE0Vb9lch4i5/B7d5ztKSNfkEXWD0EJ+RGN1CCYTkAJiY5yIqFL07m7MZKeEemPeh5Jb8iARERHRolPPHUXEKH+v7LouYqefRH7TRo9SEVG1Y7ObiIiI1g1JkoBkQKjXBXWotozMTNaDVEREdC05joPZj/4uMv/wD8KxJzvuwMDzK7oTAR1v2lwHv64I55WkxUb3Qk0ecScAMxRAOF6LSH0dpJBRiR+DiOiytJ5uYOSsUG9CEJmcuOKbiIioUsafFrfUmCpY6Bw/gxyb3US0QtjsJiIionXF15YQaomgDgkShp4d8iARERFdK06xiNn/+lvI/utXhGMPb3wbxmpfD9UFmsIm3rCpFoYqfiQuwMHx5AIWYnnE3QDMUBDB2hrE6uohR32V+DGIiF6S0tqKpsEjQj2umTjUe9yDRERERIt8I+LEvIHiApSaKJxQyINERLQesNlNRERE64rcHhVqdcHFVXqnHz5V4TRERHTNFApY+C+/gdy3vyMc+vG2d2EuchMUF+iM+3FHVw1UWfw4nEMRj4fHMBNYQEwKwAwHEayNI17XADnur8RPQUT0siRJQmxDAnNWQTg2fHjAg0RERESL48obi+I0vbHsFKy2NkARJyoREV0LqtcBiIiIiCpJbomgJLlQXGmp5tcUBHQFU0NplAolKBo/gBERrSmWhaZPfwb28RNlZRfA/T3/F1x9IyQAuxrD6GkIL/stFmDhEWMQUlhCs5aALxxEsCaOeH0j5BhXdBPR6qLv6MbF2QwiRrSsHhwVV9QRERFVwvDEMOp18X2zdP4orO4uDxIR0XrBZjcRERGtK5KqwK5R4Jtyyup1QR3zqSLyaRsBNjWIiNYMKZNB092fgu9s+f61DoBv7/6v0OQmKJKEmztiaI8tvzp7xs3hQeU8tJiCOjmCSKIWATa6iWgV07q7Mf2jCWwMRcvqjXkfHNeBLHGYIxERVVbfI8dwg1L+3jlfKqG97ylcd/e/QN+61aNkRFTt+M6XiIiI1h1fi7hvd13AgOJKmDw75UEiIiK6GsrMDFr+9M+ERndBVvGNfR+FJjfBp8l40+bEZRvdE8jiQbMfcsRBQougpjGJaH0dapJsdBPR6qX3dMMd6xPqTQgiY6U9SEREROudfXpeqA0WMqg3XWjt7ZUPRETrBld2ExER0bqjtsVQPDJeVksEdQBA78Mn0b6/xYtYRER0BYoXLqD5T/4U+mT5TUppLYjv77kLgaKJuE/DHV01COjLf/QdVXN42jcK05QQViJItjcj0dCImmQTpKj5shl0Xcdtt912LX4cIqIrorS1oWHgMNDzxrJ6XDVx6Nwp3HLd9R4lIyKi9SqZNoBL7i8dsFOo37IFko83kRLRyuHKbiIiIlp35I6YUIv5NKiyhMGT43Ad14NURET0StlHj2Hml39FaHRPBhvw/T2/h0DRRHvMhzdtTly20T1e4+JwZAKBiIKEPwY57ENtXQNqmppfUaObiMhLkiQh3hbFgl2+R7csSbj4zHmPUhER0Xo1n5lHkxoU6tmx8zB27ax8ICJaV9jsJiIionVHDhnI+8v37JYlCYmAjty8i0K+eJlnEhGR16xHH8PUne+CNDNTVu+v2YKHrvstBIsK9jZHcGtnDTRF/MhbgouhZmCgLgO/z0VI8cP2yTCNAMJNDZBCRqV+FCKiV8XY0Y0JKyPU/cO2B2mIiGg9O/Hks8JNpiXHRd2pR6Hv3+9RKiJaL9jsJiIionVJaYwItURQh1ZQkJ7kPodERKtR9v77Mf2+98OdL98P8GjzTTiy4b2ISgpev6kW19WHln2+LTsY6JQwFs7ArwEBNQDLL8GvheCE/JD8WiV+DCKia0Lr6cZUfkaoN+R8cF1OKiIiosqZu2SrOAAYK+TQPjcEfecODxIR0XrCZjcRERGtS772hFCrCyyu5ht4ZrDScYiI6GVk/uVfkfr134Sby5XVH9r8TvQnfx4NpoGf31qHhtDyI8gzmoPzXTJm9AUEDRnhQASWDwhoYRTDJkrLTzsnIlq19O4elCbEkeVNThC5QtaDREREtF7FpsVWU39xDkZnB+SIuNiAiOhaYrObiIiI1iWpPSrUEkEdEoCzj52ueB4iIrq8+c/9NWZ/7/eA4ovbTJQg47s7fx0Lob3YUht8yf25Z/0O+rtcZKQFhP06wqEY5LgPASOKQtiAo1TqJyEiunaU9jYkB44I9VrNh6P9ZzxIRERE61GxVEQzxP26p2ZHYOzcCUlmG4qIVtaK/5Y5d+7cKz73O9/5zgomISIiInqRnAyhoJTv260rMiKmiunRLEqFkkfJiIjoBY7jYPb//RMs/MVfAj8zkjen+PGt/b8HXW/DLR1x3NgWgyJL4vPhYjQO9LeXYDtZRIMmgvEotJoAGuraYIc0uLz2RkRrlCTLqGkKIGMXy+qyJOHcU2c9SkVEROvN6RMnEDd0oR4++QSM/Xs9SERE682Kf6zfu3cvvvnNb77kOaVSCf/tv/03vP3tb1/pOEREREQAAEmWUKwTP4zVBQ24WQX5jO1BKiIieoFTLGL2d/4bMvf+fVl9KtCA7+3/XTSZcfz81np0xP3LPt+SS+hvlDCatCA7FqLhAMzaKHyRCBqaOqHWhQGxP05EtKaYO7ZjwhZHlhvDlgdpiIhoPRp+/DwkqfyNdapgoXPkGPTrr/coFRGtJyve7J6fn8ev/Mqv4CMf+QgKhYJwfHBwEDfffDPuuecexOPxlY5DREREtMTfWifUEgEdiithqm/ag0RERAQATj6P1Ac+hNw3ym+c7kvswMPdv4ld0RjesrkOYXP5seXzWhEX2mSkghnobhHRaBh6IoxwNI76tg6o8YBwQY6IaC3SeroxmUsJ9WTGgPszEzGIiIhWijkk9n36i2mEGuqg1td7kIiI1psVb3bff//9qKmpwV//9V/j5ptvxsDAwNKx733ve9i1axeefPJJ3HDDDTh8+PBKxyEiIiJaoraLN9olgourvc8+wn27iYi84CykMf3u/4z8T35SVn+i803o23An3tBah+tblx9bDgAT/hIudsjIKhn4VQnR2iiURAC1sXrUtrdBDhqV+DGIiCpC6+5BYbpfqDc7QeQLucoHIiKidcV1XTQVAkJ9JDMBfds2SLo4UY+I6Fpb/jb4a+iNb3wjnn32Wdx555147LHHsHv3bnz+85/HE088gc985jMAgP/+3/87/vzP/xyquuJxiIiIiJbIrRGhFjE16IqEiydG4LouV/4REVVQaXoa0+95LwpHjy7VXEj4wa7/E/FQN36hM46QsfznRlsqYjgqYbbegWTnEQzqCNREIIcN1Nc0w9dQA0l58X5vXddx2223rfSPRES0otSOdtT1HwG23FpWT6h+nBjsw94NPR4lIyKi9WBwZBD1hk+oy+efg/GL3K+biCpjxVd2A0BjYyMOHDiA//E//gdmZ2dx55134jOf+Qzi8Ti+853v4BOf+AQb3URERFRxkk9DNuQI9UTAQG7WRTFf9CAVEdH6VBwdxdQv/6eyRndB1vGNm34HWxr24M1bEpdtdKfkPJ7WU5iutaDYFkJRPwINceixEJqau+Brqi1rdBMRVQtJllGX1JAtlMrqiiyh99AZj1IREdF60ffICWiXvM/OlUpoP/sEjBtv9CgVEa03Ffu0rygKbrvtNoRCoaU9g2655RbccccdlYpAREREJNAao0ItEdChFhQsTGYqH4iIaB0qnD+Pybe/E8Xes0u1WV8c37/ld/HWli3Y1xKFfJlJG/3yAg6bs8j6czCKJYTqwjAbIghFY2js6IJeE+KUDiKqakbPdkxaWaGuDog1IiKia6l0dl6oDRbSSPhkqBs6PUhEROtRRZrdjuPgYx/7GN72trchnU7jQx/6EJqamnDfffdh//79OH2ae2ISERGRN3ztCaH2wr7dQ0eGKh2HiGjdsU+exOQv/yc4g4NLtfN1m3Ds5o/iXR2daImKYxEBwJKLeE5J4ax/AZJuw68oCDZGoNcFUVOTRGJDB5QA9+cmouqn9/Rg0koJ9WTGWFpwQkREtBKSGVOoXbRmoG/dCskUjxERrYQVb3aPjIzg9ttvx1/+5V8iEong3//93/H5z38ezz77LN74xjfixIkT2LdvH/75n/95paMQERERCaT2mFCrDeiQAPQ+xhvyiIhWknXoGUz+yp1wxyeWage33Qzp+v+KX+hoREBffmz5jG7hbA0wEcjAVBwYhgo3rkKPB9GQbEOkrQmSqlTqxyAi8pTW04389EWh3lwKIl/Me5CIiIjWg/n0HJq0gFDPjfbB2LOL05WIqGJWvNm9a9cuPPLII9i3bx8OHz6Mt73tbQCAeDyO+++/Hx//+MeRz+fxvve9Dx/4wAdWOg4RERFRGTkRQEEp37dbV2RETBVTQ2mUiuKe3kRE9OrlH34Ek+9+N5CaBQC4AP7jtndhd897sbcxdtmx5UNBCxdrFVj+PHwSIIcUFOMKJENHY1sX/MkaXlgjonVF7exEbf+zQr1O9ePsWH/F8xAR0fpw4vFn4dfKb04tOS6SJx+Bcf0NHqUiovVoxZvdk5OT+O3f/m088sgjaGtrE47//u//Pn76058imUzii1/84krHISIiIiojyRIKCU2oJwI6nKwCO2N7kIqIqLql778fU+9/P6R0BgBgqQoO/MJdeHvHm9AUWX5seVYq4ly8iMmoBEnLwwCAuIpCWIFhBKBGYlCCHJVIROuPJMtIJiTki6WyuirLOPn0SY9SERFRtZs7MiHURgtZtOWmoHVv9yAREa1XK97s/vrXv4577rkHmiZeRH7BLbfcgmeffRavf/3rVzoOERERkcDfuty+3QYUV8L0+RkPEhERVa/Zr30Vs7/xm5DyFgBgvCaEgXf+Od6R3AG/vvzo8VE1hwsJCXP+AhTJhm5oCGyIwfEr8AUiQCgId8U/3RIRrV5mz3WYsHJCXTqf9iANERGtB/GU+Aa8vzAHfWMX5HDYg0REtF4tvwHaNfTOd77zFZ2XSCTwgx/8YIXTEBEREYnU9hoUnxorqyUCOgDg7GNn0Lyr0YtYRERrgm3bOHjwYFntpptugq7rwrnTX7gXuT/+OCRncYuI4z2b0bHj/8Y2f/Cy3/90IItsSIek5KFLLrSYD/6WMHz+IG7ftQdagKu5iYj0nh5MPjKL1kD579P6BfF3MRER0atVKBbQLInv4WdSgzB27IAk805UIqqcVfUbh/uqERERkReU1qhQi/o0aIqEgeeG4bpu5UMREVWZ8b/9LHJ/9GeQHAeOBDz282/Bnr2/i9bLNLozTgHPxSxkQwpUJQ9dleBrjiDYEUO8th6NGzex0U1E9Dytpxu5mSGh3lwKwi5xWx4iIrq2Th07gbhpCPXoycdh3LDfg0REtJ6t+Mruhx9++IrOv+WWW1YoCREREdHyJL+GbNCBP11+H2DCr+NiKouiVYJmrvjbJiKiquS6Lkb+5tNw/+KzkFwXGb+CU7/863irsR+KvPwNz/1OBjNJHZLkQFNLUAwVvrYozIgPdU2tCMRjFf4piIhWN7WzE/GBZ4GuG8rq9WoA58cHsKVxozfBiIioKo08fh6bEC+rzRQsdIydhr6PzW4iqqwVv2p72223veIV25IkoVgsrnAiIiIiIpHSEALOZspqiaCO4YU80lMZxJojHiUjIlq7Sm4JQ3/1Saif+GtIAAbbo1B+7vfwRrVp2fNd18Vj0jzMBh9U14aiuVDDJgJtEQSiYdS1tENbZgUJEdF6JykKGqIl2EUHuvriDZyaLOO5Qyew5RfY7CYiomvHP1IELtkpo784j/aWRig18eWfRES0Qla82X3LLbcs2+x2HAcDAwMYHBwEANx4443QNG2l4xAREREty99ej9LZ82W1RMCABAnDR0fY7CYiukIFp4DBz3wCxqf/NwDg+E0bsXXbXahTAsueny+W8ENfDq0xE7JrQ/FJ0GvD8DcGEKtPIt7QyK2viIhegm/HNkxaOTSp5b9n3b45jxIREVE1cl0XzcWg0OweTY9jc3c3JPZ5iKjCVrzZfeDAgZc8fvToUbzvfe9DIBDA/fffv9JxiIiIiJYlt8dQuqRWG1j85Nb7yClsf8vWyociIlqjrJKFic/eA/Oee+FIwKG334Lbat4Pn7z8R9CxfB5P1LhoM2WoUgFyUIXZEIavLoBkawf84XCFfwIiorVH7+7GxGNzaAqUN7vr5tl0ICKia+d8/3kkTZ9Q188egf6rt1U+EBGte/LLn7Kyenp68K1vfQuPPvoo7r77bq/jEBER0Tol1wdQVJyymqHKiJgqJi/Owyk5l3kmEVH1sG0bBw4cKPtn2/YVfQ/LtTD6V5+Gec+9yBsyjnzg3Xhj7Qcv2+h+ZmEeh5My2o0SVMOBEjPhb48h2pZA6+ZtbHQTEb1CWk830rPDQr2pGEShVPAgERERVaMLD52AIpdPXMoUi+joewLmTTd6lIqI1jPPm90A0N7ejn379uFLX/qS11GIiIhonZJkGfkaRagnAjpKWRl2lhcIiYheTq6Ug/9H/wH/576AmVofhj54F96gvAnyMuPHCyUH35pNYb5dR5NkQwlIUBJBBDqiqO9qQWNnF1SOQCQiesXUri7EBo4J9aQSwMDUkAeJiIioGsnnc0JtoLiAWCwItbnZg0REtN6timY3ACQSCfT393sdg4iIiNYxf2udUEsEdCiOjJn+lAeJiIjWjkwpDf/DD6Hjn7+Ji5vqIN/5x7i+2LPsuQtWEf+QWUBDh4kaFCDHFOh1YUQ6YmjdtgXxeu7PTUR0pSRFQWM4h8IlE4kMRcGzzxz3KBUREVWbJjsg1C7mJ6Fv2wbJND1IRETr3apodtu2jaeffhp+v9/rKERERLSO6R01Qu2FfbsvPNFX6ThERGtGKj8N5cAj6PzHr+LM9RvQcscfYkOxcdlzRxfy+LtSDvtbFASUEqQaA766COJb6tG27Tr4AsHKhiciqiL+nq2YsvJC3e7ljZtERPTqjU2MosEQ+zjOhRMw9uz2IBERkcfN7kwmg0OHDuGd73wnBgcHcfvtt3sZh4iIiNY5pTUq1GI+DZos4fzhgcoHIiJaA05cOA7p0aew7Z+/hqOv34EdO38XCWf5fbZPTi7gS0YBr09K0ExATQbhT0aQ3NmOlg2boKrL7+tNRESvjNbdjXFrXqgn5vj7lYiIXr1TB45CV8rbSlaphLbTj8C4kft1E5E3VvydrqKIe19eynVdRKNR/Nmf/dlKxyEiIiK6LCmgIxsowZ958f2LJEmoCegYns6gVChB0V7+vQ0R0XrRP34e0/f/BD1f+QaOvONW3B59D3RX/JjpuC4eGZ7FM00a3hh2IYVlqDUBBOqDaN6+EYHg8s1xIiK6MnpPN9I/vB+IJcvqzYUACk4Bmqx5lIyIiKpB8fQsgPqy2mAxg3qlCHXLZk8yERGt+Mpu13Uv+09VVbS1teGDH/wgDh8+jM2b+cuQiIiIvCUlxb2n6gI6JEtBbk4cCUlEtF6Nzo9g/IFH0XbvF3D0PW/D66Pvhb7M/dT5Ygn3XZjCiVYNPxd1IdfqMOvCiHbWoGNvNxvdRETXkNrVhXC/uD93gxLEyMyoB4mIiKiaJLPintwD1jS0rVshB7kdERF5Y8Wb3Y7jXPafZVk4f/487r33XrS3t690lBUxMDCAu+66C1u2bEEgEEA8Hse+fftw9913I5vNXrPX+cEPfoC3v/3taG5uhmEYaG5uxtvf/nb84Ac/uGavQURERECgvUGoJYI6ZEgYPTXhQSIiotVnZG4Y80dPI/SpT6HvPb+KNym/BHmZj5cLVhH/dmEK85tMvCbsQq73wYgHUbOtER3br4Ou6h6kJyKqXpKqotG/gKLjlNVNRcEzh8UmOBER0Ss1O5dCsyY2tHMjvTD37IEkSR6kIiLyeM/ute673/0uenp68OlPfxpnzpxBNptFKpXCoUOH8NGPfhS7du1CX1/fq3oNx3HwwQ9+EG95y1tw3333YXh4GLZtY3h4GPfddx/e8pa34EMf+hCcSz7EEBER0dVR2mNCrTaw2Izpe+RUpeMQEa0qrutieG4I6b6LKPzxH2Hkl38Vt5duXfbcqYyNf+2fhrLVj50RCUpDEEbUj8a9G9Dc2cmLYUREKyTYsxlTeUuo505PeZCGiIiqxfFHn4Pvkq3dio6DpuMPwbiJ+3UTkXfY7L5KR44cwZ133on5+XkEg0F8/OMfx8GDB/HTn/4UH/rQhwAAvb29eOtb34qFhYWrfp3/+T//J77whS8AAHbt2oWvfOUreOqpp/CVr3wFu3btAgD8wz/8A/7gD/7g1f9QREREBDkZRFG+ZCWMqiBkKBjpnYTruh4lIyKqjEKhgFQqhcnJSaRSqbKJVaPzw8j0jyD753+Kydf/Em62r1/2ewzN5fGvF2egdclokRcwLaWhRUx03Lgdifrkss8hIqJrQ+vpxoQ9L9RrU8oyZxMREb0ymefEaXcjhSyarFloPd0eJCIiWsRm91X6yEc+glwuB1VV8aMf/Qgf+9jHcOONN+KOO+7Avffei0984hMAFhven/rUp67qNXp7e/HJT34SALB371489thjeNe73oV9+/bhXe96Fx599FHs3bsXAHD33Xe/6lXkREREBEiKjFxcXG1YG9CRXwCKVsmDVERElTE2Nobh4WGkUinMzs4ilUrh8OHDGB0dxfjCGNKD45j7m3swu/8NuNHas+z3ODuVwddGZhHsktARKKAQkeH4VXTeeB1CEe7PTUS00vTuHszPjwn15kIABafgQSIiIqoGiXlNqPUXUtC3bIIc5vt8IvLONW92K4py1f9UVb3WcVbEU089hUceeQQA8IEPfAA33iiO6LjrrruwdetWAMBnP/tZFApX/mHinnvuQbFYBAB87nOfg8/nKzvu9/vxuc99DgBQLBbxmc985opfg4iIiERGU1yo1fp1qEUF82NXP7GFiGg1y2az6Ovrg2VZmJ+fRyaTwfz8PObn5/HMqWcwcb4fU1/5IvJdN+EGa+ey3+Po6Dz+fWoByW4T7eESnJAChHQEWuMwDLOyPxAR0TqlbtqIwOAJod6gBDExJ67KIyIiejm5fA7Nirhf99zEBRi7dkOSua6SiLxzzX8Dua571f/Wyr7T991339LX73//+5c9R5ZlvPe97wUAzM7O4sEHH7yi13BdF9/+9rcBAFu2bMENN9yw7Hk33HADNm/eDAD49re/zdGqRERE14C/o16oJZ7ft3voyGCl4xARVcTY2BiKxSJyuRx0XUcoFIKu6xhNjaI4N4ex738TbmQb9uW3L/v8I8Nz+MF8DslNCjbX6XCCCpyYgUBTDDIvfhERVYykqmjSZ1Fyyq8R+RUVh5497lEqIiJay44/8SzCRvnKbsd1UX/sYZg3Lr+1ERFRpVzzKw4PPvggTp06BcdxrurfWvDoo48CAAKBAPbsWX50HwDceuutS18/9thjV/QaFy5cwMjIiPB9Xup1hoeH0d/ff0WvQ0RERCKlNSrU4n4dsgScPdhb+UBERBWQz+dh2zYAwDAMKIoCmIBaslHsOwW/1ok9+a3LPvfQ0Cx+Op9DcqOEplAReo0fUo0PwfoIJEncGoKIiFZWuHsjpi1LqC+cGPcgDRERrXXTh4aE2nghh7a5Qei7d3uQiIjoRde82X3HHXcs7Vf9wuO77777Wr+Mp06dOgUA6OrqesnR61u2bBGe80qdPHly2e9zrV+HiIiIlhExkNeLZSVFlhDzaZgaTsMprY0b9IiIroRpmtD1xSkWlmUh7+RQsjNQF2YRRy325JZvdD81OIuH53OIbFLRFC7BDSqo3dSKQF2YjW4iIo9oPd2YsMXtd2pmOGmDiIiuXGxa/PvRX5iDvnED5FjMg0RERC+65ptkXzqO/MCBA2hvb7/WL+OZfD6PqakpAEBzc/NLnhuLxRAIBJDJZDA4eGUjT4eGXrxT6uVep6WlZenrV/M6yxkdHb2i70dERFQNJElCoU6HOVTe1E4EdExM2bCzBZghw6N0REQrI5lMor+/Hz6fD+l8GnAs+IoO6osmbsrtXPY5jw+k8PRCHuZmDR3RItyAArU2jERbHQYmL1b2ByAioiV6dw9mH3gAiCTK6k12AEWnCFW+5pcEiYioShWLRbRA3K97anYQ5s6dkF5iQSARUSVc899CoVCoqhukCwsv3hUbDIq/4C/1QrM7nU6v2OsEAoGlr6/0dX62UU5EREQv8rfWAUNjZbXagA5lUsL0hRSaepIeJSMiWhl+vx9dXV3ov9gPQ5Phs0xErQXckbt52fMf65/BkYU8lJ4QNgUycAMytLoIzChvBiIi8pq6aSN8w38NNG8vqzfKQUwtTCMZqfcoGRERrTUnnj2GTaYu1GPHH4Px0Q94kIiIqNw1b3b39PTggQcewB/+4R+iq6sLANDX14cvfelLr+j5733ve691pGsqn88vff3CiL+XYhiLF3pyudyKvc4Lr3E1r0NERETLM9vrUDgoNrsB4MLjZ9nsJqKqVFdfh0DChD9VQun/Z++/oyO77/v+/3Wn3KkYDIBBBxZlsb2TuyzLthRJyaJIUZSsYlu/RLGObTmOS6JIiX2+LjmRksiJ5Z7YiiXTliVZUmyrUYVmWbYluZXc5XZggV20QR1ggOnt9wfNlYYXJLcAuCjPxzk8Wb3unZkXGHmFmfd8Pp/4Jb0ncc+c9710Kabj02nltlfo7ja/xtJpmbWV8oTf/j0SAGDhGW63WhyTKpZKcvzEkRJBl1uPHz+uB+64z8Z2AIDlZPiFC9pg1JRlk7mMOoZPy7z5JptaAcCPzfuw+9Of/rR++qd/Wp/97GcvZ88//7yef/75K3r8Uh92e73ey3/OZrNve38mk5Ek+Xy+BXud11/jWl7n7bY9Hx4e1k038T9YAIDVx9ESsmSVXrdMp6Hely9p7nWOALC8DccH5c+UNB6/pA/H3zHnPS8PTeuVyaSmt4b0nrVBuSN+uZMFearKB925XE6zs7PK5/NyuVxKJpNX9IVhAMD8qNzSocl0VhFf+Y4bU69GpTtsKgUAWHaCwyXJW5715qfV0b5GzpqauR8EAIto3ofdDz74oA4ePKhvfetbunjxoh555BGtXbtWt91223y/lC0qKiou//lKtgxPJBKSrmzL82t9nddf41pe5+3OAwcAYLUyvC7NBvMKzpb/uhQJmOqfSKiQK8jpdtrUDgDm31hiVKnopKITZ/UzE/vkkGG559TIjI5GZzS0MaT3dwXkqw+pdm2rpi6dKrtvZGREg4ODKhaLKhQKcjqdOnr0qDZt2qTGxsbF+pEAYFVzb9+ukSMzlmF31bhNhQAAy06xWFRr0TpziMaHtHHHdhlutw2tAKDcvA+7JWnHjh3asWOHJOmRRx7R7bffri996UsL8VKLzuv1qqamRhMTExoYGHjLe2Ox2OVB9NWejf2TQ+i3e52fXJ3NGdwAAMyjhoDUnSmLIgFTg/G0ktNpVUQCNhUDgPkVz8Y1MTiol3tf1MOjt8pVsn6Zp2cioUMD0+ruCOqDm4LyN1eqaV2HqlqrVN+57/J9yWRSL774ojKZTNkxS/F4XOfOnVNlZaX8fv9i/FgAsKq5t2/T1NPPSJWRsrw5G1C+mJfLsSAfCwIAVpDus+fU8oYvTUlS4MwheX7hvTY0AgArx0K/wO/+7u/qfe9730K/zKLavHmzpNfOIs/n829635kzZy7/edOmTdf0Gm98nvl+HQAA8OYqOqyrDyN+U4YMRU+P2tAIAOZfOp/W0FCvugdP6eZTTQoWrEcj9U+l9HxfTGcbfXrvjgpVNIfV1NWpqtYqGUb5CvBoNKp8Pq9UKiXTNFVRUSHTNBWLxZTP5xWNRhfrRwOAVc29fr3MobOWvMkIanI2ZkMjAMByc+GZM3K84ff9mXxOHb1H5Ln1FptaAUC5RRl2v/e9K+sbPrff/topnYlEQkeOHHnT+55++unLf77abdw7OjrU1NRkeZ65PPPMM5Kk5uZmtbe3X9XrAACAN+dqq7ZktYHXzpvteeb0YtcBgHlXKBXUP3pB0eF+1R/NqTlrPXNvZCajpy9M6EK1R+/YValIS1iNnW2qWhO2DLolKZ1OK5vNSpI8Ho+cTqc8Ho9KpZKy2azS6fSC/1wAAMkwTbWURlUslcrykMvU4ZOv2tQKALCc+C5lLVlvPq5gc51cDQ02NAIAqwUfdq9EP7lS/a//+q/nvKdYLOpv//ZvJUnhcFh33333Vb2GYRh66KGHJL22cvvFF1+c874XX3zx8sruhx56aM4PmwAAwLVx1AeVdxTLMq/bqaDp1OD5MZXe8MEhACw3g7EBzY6MK3W0V9sS7ZbrM5m8nuqZ0EDArV03htXWFlJt2xpVtVlXdL/O6/XKNF/7YlAmk1GhUFAmk5FhGDJNU16vdyF/JADAT6jc3K6pTM6STx4fsqENAGA5KRaLai1UWPKB2WF5tm6T4bFubw4AdmDYfQ1uuukm3XHHHZKkL37xi3rhhRcs9/zBH/yBTp9+bcXXr//6r8vtdpdd379/vwzDkGEY+tjHPjbn6/zGb/yGnM7Xzsr71V/91bLz7iQplUrpV3/1VyVJLpdLv/Ebv3E9PxYAAHgDw+lQoso60I4ETKXjUj5bsKEVAMyPieS4ZodHdOrlF3X3xDbL9WyhqCe6xxV1G2q8MaxNHSFF1qxRpKNaDuebv5VsaGiQy+WSz+dTNpvVzMyMstmsqqqq5HK51MAKEABYNJ4d2zSSmbXklWPFOe4GAODHLnT3qH6OL6p6zh6S55abbGgEAHNj2H2N/viP/1g+n0/5fF7vfOc79d//+3/Xiy++qKeeekq/9Eu/pE9/+tOSpPXr1+uTn/zkNb3G+vXr9alPfUqSdPjwYd122236+te/rsOHD+vrX/+6brvtNh0+fFiS9KlPfUrr1q2bnx8OAABc5m4OW7LagClXwal4dGbxCwHAPEjlUxodHtBLp57W+/p3W64XSyU9c2FSo7mCilsqdFNXpWrWrFFkbUROt/Mtn9vv96urq0sej0ehUEiBQEChUEihUEjr16+X3+9fqB8LAPAG7u3bFEuOWfKmjF/5Yt6GRgCA5aLnqVNyOsp3c5rN57T2/Evy3nqrTa0AwMpld4HlateuXfr617+uj370o4rH4/qt3/otyz3r16/Xo48+qooK61YfV+qzn/2sRkdH9aUvfUnHjh3TRz7yEcs9H//4x/WZz3zmml8DAAC8uYqOJhWOny3LIv9ybvfAkX7VtFXZUQsArlmhVNDAaK9e7TmsB85tlXOO70AfHpjWQDyt/vUV+pmtYUXaWhXprJHbc2VvIRsaGtTc3KzZ2Vnl83m5XC7dcMMNCofD8/zTAADeinvDBjmjj0iNG8ryZgUVT06rOlhjTzEAwJLnvZSV3rBTeW8+rqZIWK7WVntKAcAcWNl9HR588EEdP35c//7f//vLKxTC4bB2796tz33uczp27Ji6urqu6zUcDoe++MUv6tFHH9VDDz2kpqYmmaappqYmPfTQQ/r+97+vv/qrv5LDwf9XAgCwEJxrwpasxm/KYUjdB84tfiEAuE6DUwMaGujV+uM+VRR8luvnxmZ1enRW5xp9eviGKkXa2xRpr5HnX77oc6XcbreqqqpUW1urqqoqVnQDgA0M01RLdlilUvnRPGG3R4dPv2pTKwDAUvfaed0BSz6QiMq9bZsMn/V9BADYhZXd16mtrU2f//zn9fnPf/6qHrdv3z7LG423cv/99+v++++/2noAAOA6GWGv0mZe3uyPf21yOgyFfW6ND82qVCzJeMO2XgBgh2w2qwMHDpRle/fulWn+eEg9kRxXbGhIU0e7dXNyh+U5ojMZvdQ/pYGQqbv3hNW4tlXVrdXyVVrP6gMALA9Vm9doOpNT2Fv+paXRlwekPTaVAgAsab0XetXgtQ60zbOH5fvAbTY0AoA3x3JgAACAt2AYhjIR6/cDawOmCkmHsqmcDa0A4Opl8mmNRgd09MSzeteoddCdyOa1v2dCMdOp1hsqtWlji8JN1QrWWld0AACWD8/2rRrJJix5xWjRhjYAgOWg58mTlvO6E4Wcus4ekPd2ht0AlhaG3QAAAG/Dt6bWkkUCppwlhyYvTi1+IQC4SqVSSQMTF3Xi/EG9p2er5XqxVNLTFyY1WyopvblCd+5oUmV9ROGmkA1tAQDzyb19myaT45a8Ke1Tvpi3oREAYKkzL2YsWW9uRsG6ajnXrLGhEQC8OYbdAAAAbyPQ2WDJIv7XtoG89FLPYtcBgKs2lhxV/6Xz2nAioFDBenb2scG4xhJZXWgP6H17GhRurFfVmjDHNADACuDeuFEatf7O2qygZpNxGxoBAJayUqmk1vxc53UPy711qwy/9f0EANiJYTcAAMDbcDRbVzaGfW65nYZ6jvQtfiEAuAqpfEqDg72aOtKtDclmy/WB6ZReHZnRhYhHD99cq5rWZlW1huUynTa0BQDMN8PjUUtq0JJXu706cu6UDY0AAEtZb0+f6n1znNd97rB8t9wsw+ALsQCWFobdAAAAb8PwuTUTsJ7NHfGbmh7LqFjgvEMAS1OpVNLg+CUdf/k5/dToTsv1RDav5/piinmd2n5jWO0b21XZFJInaC5+WQDAgqnZ1Kh4xvr77NDLl2xoAwBYys4/9apclvO68+o8+4K8d9xuUysAeHMMuwEAAK5Asd76rebagCllnMrMZG1oBABvbywxqpPnD+m+7o2Wa8VSSc9cmFSiUFR2Y1C37V6nYCSoYMS6ZSEAYHnzbN+mkWzCkgejnNkNACjnuZi2ZL25uCpqOa8bwNLksrsAAADAclDR0Shd6C/LIgFTjpKhJ771lN75s/fINFkJCWDpyJayGhi6oIqXE6rOt1iuHxuKazSRVU+rXz9/Z6cCVRUKN1mPbbgWpmlq37598/JcAIDr596+TRMHj2pdRbgsb0r5lC/m5XLwESEA4DUtOb/0ho83BhNRrdm2VcYc25sDgN1Y2Q0AAHAFvB21liwSeO3d3+zZycWuAwBzyuVyisViGh0b1cDERZ1+5UXtjW2w3DcUT+vV6IwGQ269e2+tahpqFV5TKcPB+XsAsBK5N25UcbTHkjcrqNnUjA2NAABLUd+Fi2qYY6DtPn9Yvltu4bxuAEsSw24AAIArYNQHlXeUn83tczsVMJ2aHkja1AoAfiwajWpwcFCxWEzR6SFNzgzo3Re3Wu7LFop6vi+mpMuh+h0hbdzcqcqmCrk9rOoDgJXK8HrVnByw5NUur46dP21DIwDAUnTuyRNyOcrHRslCXp1nDsh7x202tQKAt8awGwAA4AoYTodmKwuWPBIwlUu6VMhZrwHAYkkmk+ru7lYmk1FsJqZUdlo3TjcpnLeev32wf0rJXEHRtX69+/b1CtYE5a9iO0IAWOkiG+o0kyk/o9thGBo41mtTIwDAUuPus36Zvy8fV0V9jZytrTY0AoC3x7AbAADgCjmbKy1ZbcCUK+9SajptQyMAeE00GlU+n1cqlVLJk1eFWdStcev25f1TKfVMJNUT8ejDd61RoKpSlU0VNjQGACw2z7atGskmLHlwmC9tAgBe05qzflm2PxGVe+tWOfx+GxoBwNtj2A0AAHCFQp1NliwSMGXI0MiZMRsaAcBr0um0stmsskZWBc+sfnrkZss9mXxRL1yMKW46tGt3lRramlXVGpLDydtCAFgNzO3bNZ4ct+TNaZ9yxZwNjQAAS8ml3kuq977Zed3W9xcAsFTwqQYAAMAVcq+psWQ1flOGpL4D5xa/EAD8C6/XK5fbpZwroVun1yhUsK66eOlSTKl8UVNrA7r91k2qqA/K9Js2tAUA2MG9aaNKo92WvKVUodlk3IZGAICl5MyTx+V+wxdhU4W8Ok+/IO+dd9jUCgDeHsNuAACAK2RUeZVyl696cTkMVfncGuqZsKkVAEgNDQ1KGQlVmdJN8XWW6xdjKfXGUrpQY+oj93XJX+lXMMI2hACwmhg+n1oSA5a82u3V4bMnbWgEAFhKXL3W87p783FV1FfL2dJiQyMAuDIMuwEAAK6QYRhKRay/PkUCptJxKZ/lvEMA9jBMQ2nXtN47fqPlWjpX0IuXYpp1O7T1xipFGmsVbgnJMAwbmgIA7FSzpUnTGeuW5dFj/Ta0AQAsBdlsVk899dSc53UPJKNyb9smh8+6vTkALBUMuwEAAK6CtzViyWoDplx5p2ZHEzY0ArDalUolXRq7IP+5GUVyIcv1l/qnlM4XNd7h1917N6uysUJOt9OGpgAAu3l2bNdIZtaSV4zwpU0AWM2mJ6bVOMdA23nuqHy33mpDIwC4cgy7AQAArkJobbMliwReO/P29HOnFrsOACiWntShV/brntGtlmsD02n1xVLqqzL1kXd0yFfll7+KVRkAsFq5d+7QeHLckrek/coVrCu+AQCrQ7o7LpejfFyULOTVdfo5eW+/zaZWAHBlXHYXAAAAWE6y1abl24KVXpfcDkPHHzuq2ZoZud1uSdLevXtlmubilwSwauSLeZ2+dFybT4bkUvlq7UKxpIP9MSVdDm24sVJ1bY0KN1tXfgMAVg/3hg1yjPyN1LCuLG8xKjSdnFKkotamZgAAO9XHPNIbdjG/kJtWY0OtnC3WL/0DwFLCym4AAICrEJ0eV8xMlmWGYagmYCo349TMzIxNzQCsRqMzI+o9fESbE62Wayeicc1kChpZ49G9t21WqCHI9uUAsMoZbrda8yMqlUpledjl0aGTr9rUCgBgp2KhqA5ZvxQ7MDso97atnNcNYMlj2A0AAHAV0um0Jn1ZS14bMGXkvcpn8ja0ArAaZQtZvXR6v+69uMlyLZ7O60R0RoMVLv303W3yVQcVqPbb0BIAsNRUb2vXVMa6Zfn4K4M2tAEA2O3cyXOq83steeDkAfluucWGRgBwdRh2AwAAXAWv16tsyLo1eSRgylV0qjTLr1cAFkff+AV5jsYUzgcs117qjyknKbwpoOa1a1TZxPblAIDXeHZs10h21pKHx0pz3A0AWOn6nz0vh2GUZfF8Vp09h+W9g/O6ASx9fBoLAABwFRoaGpSrsn7jORJ4bQDuGC0udiUAq1Ayl9SLh3+kfeNbLNf6YkkNxTPqjbj00B3rFWoIymWyfTkA4DXunTs0OTtqyVsyfuUK1hXfAICVLRy1ftmpJz8l75oWOZs5rxvA0sewGwAA4Cr4/X417FirvFEoz91OBdxOzfbFbWoGYDU5Ofiydp6ulUPlKzByhaIO9U9rxu3Q2o6S/PVhBWrYvhwA8GOutWvlGjlvyZuNCk3OTNjQCABgl3wurzajwpJHpy7K3LmT87oBLAsMuwEAAK5SQ3Ojxn1JSx4JmEpOGyqV2AISwMKJZ6Z1/qWXtD7ZZLn28lBcyVxBY42G1rRUKtRo/eAKALC6GQ6H1jhjKr7hd9aQy9TBEydsagUAsMPxl15Wjc96VFv1K08ru22rDY0A4Oox7AYAALgGs0FrFgmYMrJuFbNsZQ5gYZRKJR3qOaC9ve2Wa7FUTqdHZzUUdOlD963T7e+6Q4EQq7oBAFbh7Z2KpbOWfPpE1IY2AAC7DD13QcYbzuueyKXVNnhavVVhJZPWL/oDwFLDsBsAAOAa5CutW3nVBkw5Sw6lx/I2NAKwGkwkxzV+8LQaslWWa4cHppQ3pPAmv5q7WhWsDdjQEACwHJg7d2gkO2vJqyaMOe4GAKxUdZMuS9aTn1Sivk7ZQEDRKF+CArD0MewGAAC4Bumw9Q1htd8tQ9L4iUHFYjG+AQ1gXhVKBT1/+gm9Y3CT5dpQPK2heEZ9NS69784NqmyulMPJ2z0AwNzMnTsUmx2x5K1Zv7IF64pvAMDKk06ltcZlPfZoYqJH0y3NykhKp9OLXwwArhKffgAAAFyDtFdKOss/CHQ7HQr73EoMpBSLxXT06FENDw/b1BDASjMcH5Lj8KgqCuU7S5RKJR0emNas26Hbbq5VqLVOvkqvTS0BAMuBs7VV3pHzlrzJqNBYfNyGRgCAxXbs6aMKe91lWalUUsPhJzXZ2SnTNOX18r4CwNLHsBsAAOBaGNJEIGWJIwFThZRH8em44vG4zp07xwpvANctX8zruWM/0r6xzZZrPRNJxVI5xeoN7b6hS6Em6+oMAAB+kmEYag0kVCiWyvKgy61DL79iUysAwGKKHR60ZCP5lBqm+qWtW+RyudTQ0GBDMwC4Ogy7AQAArlGqwmnJagOmXAWPTJmKxWLK5/OccQXgul2a6lP98aLcpfIjFPLFoo4NxTXhc+qBuztU0Vojt8d6zAIAAG8U3rFOk5mMJZ85OWZDGwDAYmuMuy1ZT3ZcM81N8jc2av369fL7/TY0A4Crw7AbAADgGuUrfZYsEjDlkCF30qNSqaRsNssZVwCuS76Y14GXfqhbp9Zbrp0amVUyV1Cx2VD7pjYFI3wYBQC4Mu6dOxTNzFjy2kk+LgSAlS4+HVerGbTkM8Onle7q0g033aTGxkYbmgHA1eO3VwAAgKtkmqb27dunXfffYbkW9rrkchjK96dkGAZnXAG4bmdHT2rLqbAlT+UKOhGd0VDQpYfv3aDKlio5nLzFAwBcGXPHDk3PDlvyllxQ6Txf1gSAlezo44cVNMt3hCqUSmo78riKe/awohvAssInIQAAANfI8Jua9pVv/WgYhmr8buVjhqqqqjjjCsB1yRfzOn7gaW1KtFiuvTwUV7ZYUrjDobquFvkq+WINAODKOevrFRi9YMmbHUGNTI7Y0AgAsFjSJ6xHVgzmZlVVSinbtsaGRgBw7Rh2AwAAXIdMrfXc7kjAlJHzqSJYwRlXAK7LmZFXtb2nzpJPpXI6P55Qf6VT7923WZXNlTa0AwAsd2vCGRWKpbLM53TpyCsnbGoEAFgMzUnrsWy92VGl2ttV9FmvAcBSxrAbAADgOvjX1FqyiN+Uq+DWps5NnHEF4JrlijmdOPCM1qasu0McHZxWzpC6NnpUta5Zpt+0oSEAYLkL7dqk8bR1y/LkqQkb2gAAFsP4yLhaPQFLnr34ijLr10sOxkYAlhf+1gIAALgOlZ2tliwSMGXI0PTFhA2NAKwUp0de1c6eeks+nsiqfzqt/mpD996xSaHGkA3tAAArgXvnDo1kZyx53ZRLpVJpjkcAAJa7Iz88JK+7fJe6XLGoriNPKbl9m02tAODaMewGAAC4Dq6mShVULMuCHpe8LocuPn/WplYAlrtcMaeTzz+rjrR12P3yUFwpl6Gbd4VVubZZLtN6nAIAAFfC3L5d8elBS74mH1Q6n7KhEQBgoTnPWr/kdCk/I0/QrXyDdVcpAFjqGHYDAABcB8N0aroiY8lr/KaGutn+EcC1ORU9oRsuWD9oGpvNaDCe1mh1QTft2aCK+qAN7QAAK4WjqkqVEz2WvNER1MVR6xAcALC8lUolteet7yF6k0NKd3ao6PPa0AoArg/DbgAAgOuUr/NYskjAVHpGKuQKNjQCsJzlijmdev45taXrLNdeHo5rxu3QvbfUq7KrUQ4nb+kAANentd5QtlC+U5HH6dTLh1+1qREAYKGcO35OjX6fJTdPv6D0xo2SYdjQCgCuj8vuAgAAAMtdsL1B6hktyyIBt5zDTs2MJhRu5jxdAOWy2awOHDhQlu3du1emaepU9Lhu7G20PGZ0NqOheEbx2rw279qgQLV/seoCAFawwA3bNHohpeZAoPzC+bg9hQAAC+b8EyfV5qguyxKFnLpOP6/WP3tM7rVrbWoGANeOZQAAAADXqbKz1ZJFAqYkafhVtn8EcOUKpYJOP/+81qRrLddeHoprxnTo3r3NquxskOFg1QUA4PqZu3ZqJD1tyetn3Hrqqae0f/9+ZbNZG5oBAOZbdbRkyc7nYjLb1sjZ1GRDIwC4fgy7AQAArpOjLqCco3y7cq/LqaDpVM+zZ21qBWA5OjV8QjdesH7INDKT0fBMRvHKjDbcuEG+MGfpAQDmh3vrViViFy15WzGkTDFjQyMAwELIpDJqd1ZY8pFYj8xdO+XwWbc3B4DlgGE3AADAdTKcDk1VWle7RAKmon1Ti18IwLJULBV16vln1ZqJWK69PBRX3HTovlubVNlZb0M7AMBK5fD7VR+/ZMnrnX6Nzkza0AgAsBAOPXVY1T7Tkjcc+mf59+1b/EIAME8YdgMAAMyDUoP17NxIwFQ24VAuk7ehEYDlpnvynG64YD2rOzqTUXQ2o0RlWht2b5K3wmNDOwDAStbUValkrnynIqfDodmhhE2NAADzLX7IesxaNJdU0/SQvDffZEMjAJgfDLsBAADmQaij2ZJF/KacRYemB+M2NAKwnJRKJZ168c1WdU9r2nTovluaVLmWVd0AgPnnu3GnRjJJS14z6bShDQBgIbTMWo9C6smOyrl1i5zV1TY0AoD5wbAbAABgHoQ6rWfsVvvdMiQNHLVuCwkAPymWj2ljT5UlH55Ja2Q2q1RlUuv3bJInyKpuAMD8M3fu0GhqwpK3ZoMqloo2NAIAzKfR4TG1egOWPNV/XJ6b9sjw8D4DwPLFsBsAAGAeGFU+pVy5ssztdKjS61LPC+dtagVguZgY69b6pPVLM69GZzTteW1Vd2itdYtzAADmg2v9euUn+iz5mlKFMrnM4hcCAMyrI98/JJ+7fLeOfLGorkOPKXDvPTa1AoD5wbAbAABgHhiGoXiV9WzuSMDU+FBCpVLJhlYAlrJcLqdYLKaLYxe1ZcS6fflEMquheEbpioTW7dkiT9C0oSUAYDUwnE615KOWvMbl1UB8zIZGAID5ZHYnLFlfPq5gOCizq8uGRgAwfxh2AwAAzBNHU8iSRQKmiimHcmnrIBzA6hWNRjU4OKhYLKbRmQu6YabTcs/J6IymPA7dd0uDQl2s6gYALKy6rS2KZ8p3KjIMQ9lLCSWT1vO8AQDLQ7FQVHvBuoX5xcSAnDu3y6istKEVAMwfht0AAADzJLy21ZJFAqYcJYcmeidtaARgKUomk+ru7lYmk9HEzIR2zTRY7pnJ5NUXSykTSGjtTdvlCbCqGwCwsLw37FI0Y1351zDj0dGjRzU8PGxDKwDA9Tp97Iwa/D5L7n/1WQVvv0OGgzERgOWNv8UAAADmSbC93pJV+dxyGlL/Sz02NAKwFEWjUeXzeaVSKeX807pler3lnlMjM5pxO/SOXdUKr2NVNwBg4bl37dR40rpleXs+pFg8pnPnzrHCGwCWoQtPnZbTYZRls4WcOnuOynfXnTa1AoD5w7AbAABgnhhBj+KedFnmMAxV+01dOHLRplYAlpp0Oq1sNquCCtqWqZNLzvLruYLOjycVDyS14a4bZfpZ1Q0AWHjOpiY5xq1f0GwxQhqZHFU+n1c0aj3XGwCwtEVGDUt2Pjch1/p1ctTX2dAIAOYXw24AAIB5lKguWbJIwFRsLKNSyXoNwOrj9XplmqYS3kndEdtouX56bFYJh7R3g0ehdU02NAQArEaGYaiuOKriG35nrXSZms5klM1mlU6n3+TRAIClKJVIq90VtOTjE+dl7r5RDp91e3MAWG4YdgMAAMwjd0u1JYv4TSntVDaRs6ERgKWmoaFBTqdTG4vV8pbKV23nCkWdHU1oPJDVrvtukYdV3QCAReTf0qZYOmvJK+MemaYpr9drQysAwLU6+PhBVfms7ymaX/qhAvvutqERAMw/l90FAAAAVpLqrjbp0JmyrCbglkOGRs+Pq3UXqzSB1SabzerAgQPlWSipfbFNlnvPjyc0WypqV2tOoY2ti1URAABJUtVte9X9o2nV+DxleVsqIJfLpYaGBpuaAQCuReJIVHKVb1U+nEuotpiQuXO7Ta0AYH6xshsAAGAe+VojKuoNWz963TKdhi6+cN6mVgCWmsz5AYUK/rKsWCrp1MisosGibnn3XnkrWD0HAFhcFTftUWzGei73mkKFOta2y+/3z/EoAMBSVCqV1Jq0blN+ITsi5/atcobDi18KABYAw24AAIB5ZHhdmvKnLHmN31Tf8QEbGgFYamL5mLb111ny3smkZnIFbayJq2pz++IXAwCseo5wWP6pi5a8xVEhw+u0oREA4Fr1dw9ojT9gyXO9R+W7da8MFxv/AlgZGHYDAADMs1TE+itWJGBqZiKnUqk0xyMArCbTo31qztRY8pMjsxqqkO569y3yVls/lAIAYDGsqS4oXyz/ndXndOng4eM2NQIAXIuXv39YHlf55xO5YlFdR56Q/5532NQKAOYfw24AAIB55m2rtWSRgCkj61Q6nrahEYClIlvMauNI2JIPz6QVS+XUGhxX9fa1Mgxj8csBACCp4sbNGk9bf2fNnBm3oQ0A4FpVDRQsWU8+Jk9ro9wtLTY0AoCFwbAbAABgntWu7bBkkYApQ4ZGzvIhIbCaDU6f084Z698Rp0dmNRwwdN99O+WvDy9+MQAA/oVrxw5FM9OWvG7KxS5FALBMpJJpdTpDlnxgqkfmrp0yAuwkBWDlYNgNAAAwz9xNYeWN8m9Q+91O+d1O9T57xqZWAOxWKpVUP2xdsT2byWtgOq0q76hqdm+R4WBVNwDAPq5NGzUzPWjJ1xSCSuWSNjQCAFytF3/4kmr8piVvfPEHCtx1FztJAVhRGHYDAADMM8Pl0GSFdevHSMCt/tNRGxoBWAoG0xd1y2SXJT8zNqtxr0PvuGWNgi3Ws7wBAFhMhsejwOQFS97oDKpn6JINjQAAVyt51PrZw0g+qbrZEXlvudmGRgCwcBh2AwAALIBsnduSRfymZqcKKhXZ/hFYbXK5nJz9E/IXPeV5oajz4wkVXeNqvP1GOZy8RQMA2C9QmVEmX75Tkdvh0KsHT9nUCABwpUqlkjrT1m3Ku9PDcuzYLmd1tQ2tAGDh8EkKAADAAgi2N1iymoApR86pZCxlQyMAdolGozo7eFY3T1rP6r4wmdSUYejujT5VrWu1oR0AAFb5rnaNpK2/s7p6Z21oAwC4Gudf7VFzwG/JS+dfkHfvXhmmdXtzAFjOGHYDAAAsgMjadmsWMGXI0PBJtjIHVotkMqnu7m45s1NqylpXUJwendW0N6GN732HnG6nDQ0BAChnmqa2/8zPaCQ9abnWnPCpUCzM8SgAwFJx6gcvy3zDjlHpYkFdx/YreO89NrUCgIXDsBsAAGABOGsrlHbmyjLT6VDI41Lvs6dtagVgsUWjUaWyKd0w22K5NhRPayKT1/aaGYXWWa8DAGAXV2eHsrGLlnxNqUKzqbgNjQAAV6p+1Dr26c5NyNnVKXcL7zsArDwMuwEAABaA4TAUC2UseSRgaqB7woZGAOyQTqc1OHVeO2bbLddOj85qJFDU3vfeIbffY30wAAA2MRwOtRZHLXmNy6tv/OB72r9/v/bv369sNmtDOwDAm5mZmlWHu8KSj06clXv3jTJCIRtaAcDCYtgNAACwQAoNPksWCZhKxUsqFoo2NAKw2Lxer9ZMWM/Em8nkNTidVpNzQOGd621oBgDAW6vb2a7pTPlORYZhqDiQtqkRAODtPP+9FxT2uS152/PfU8W998owDBtaAcDCYtgNAACwQCo6Gy1ZxO+WM+/U7HjChkYAFtuMEdPNsS5LfmZ0ViN+h+6+c4O8kUobmgEA8Na8N96gaGbWkjfErV/iAgAsEScnLdFgblYhd1HeHdttKAQAC49hNwAAwAKJrG23ZNV+Uw5DGjo+tPiFACy6oSPH5SuWDwVyhaLOjyfkdQyr+d7bbGoGAMBbc+/cqYnEiCVvz1UoX8zb0AgA8FaKhaI6c9YtzC+kBuTYtUOOcHjxSwHAImDYDQAAsEAclT7NuMu3eXQ6DFX53Lrw3BmbWgFYLMlcUu29AUt+YTKpSZehezYHFVhTb0MzAADenrO6SubURUve6qjQbIZdigBgqTl+8JSagtbj1Lwnn5X/jjtkuK3bmwPASsCwGwAAYIEYhqHpqpwlj/hNDfdOLX4hAIvqpSOPqyNVZ8nPjSWUcsfU8cA9MhycmQcAWLraqwsqFEtlmd/p1tDIuE2NAABvpveJ03K94f1FopBT2/kjCuy7y6ZWALDwGHYDAAAspMagJYoETGVmX9tiDMDK5Xh5zJKNJ7IayeS1uzquig1rbGgFAMCVC9+0VePptCX3j5TmuBsAYKfmmHXl9vncuIwtm+Wqs34JFwBWCobdAAAAC6hqrXWYVRMw5Sw4NT00Y0MjAIvh3PBJ7RhvteRnx2Y15stqx3vvkstrzvFIAACWDvPGGzScmbbkLQm/JmOTSiaTNrQCALzRRHRSHV7rl+2noifkuWWPHH6/Da0AYHEw7AYAAFhAVR3WYVfY65LLYWjo5X4bGgFYDL3PH5K3WD7MzhaK6oul1FjsVWjnRpuaAQBw5dwbN2omPmDJ20sVGp8c09GjRzU8PGxDMwDAT3ru28+rwuMqy4qlkjoPfF8V977TplYAsDgYdgMAACwgR8DUpDdRlhmGoRq/Wz0HztrUCsBCSmUTauu1rpy4MJHUsMfQhq4KmWHrqgsAAJYaw+VSJB215A3OgEZnphSPx3Xu3DlWeAOAzYLd1iMn+vNxeWvD8nR12tAIABYPw24AAIAFNlNtPZs7EjA1coltzIGV6ODRp9Sesp6Jd248IZeGVNq5wYZWAABcmzUbapTKFcoyl8Mhb96nWCymfD6vaNQ6EAcALI50IqMuI2TJL872yXXjThmVlTa0AoDFw7AbAABggblarG8sIwFTuaRDhXxhjkcAWM6Ml0ct2dhsRsPZvO5cU9K+9z8g0+S8bgDA8uC/aZeiGevK7ZZUQKVSSdlsVum0dUUhAGBx7H/0edUFrO8vIi99X8G775bhYAwEYGVzvf0tAAAAuB6RtR3SwZ7yzG/KWXQo1j+tSEe1Tc0AXK9sNqsDBw5c/s/j6ajuHW+x3HduPKGYJ6mNH3i3HHzYBABYRsxduzT66PfVEawoy9uzFTppzMg0TXm9XpvaAQAKR0dl+BvKsol8SrXxYfluusmmVgCwePiUBQAAYIGF2ppUUPlW5kGPS16XQ4OH++wpBWBBGEMT8hbLV1VkC0X1xVJaZw4ptHWtTc0AALg2zvp6FaYHLHmbQgqEAnK5XGpoaJjjkQCAhVbIFbQub93C/FxqQMaO7XLV1NjQCgAWF8NuAACABWaYTo0HEpa8xm+q+8VuGxoBWAi5QlZbx61ndV+YSGrQK+27d6dcAZ8NzQAAuD5t3hmVSqWyrMrt1XQ2q/Xr18vv99vUDABWt5eeOarmoPU9hufVJ+W98w4ZHJ8EYBVg2A0AALAIkhHDkkUCpiaGrOcfAliexmJ9akvXWvJz4wkFiwOqfsetNrQCAOD61e1er6lMzpL7Jw01Njba0AgAIEmj+3vkdJR/3pAo5NTSfVQV99xrUysAWFwMuwEAABaBp9W6dVgkYKqQciifLdjQCMB8a4hav9QyOpvRYC6vOzsc8jZZB+EAACwHnhtv0HBmxpLXTPDRIgDYpVQqqSMRsORns1Fp6za5G+ptaAUAi4/fSAEAABZB3bpOSxYJuOUoOTTRG7OhEYD5NJ2NaefUGkt+fjyhGfeMuj74HhmGdRgOAMBy4N66RZOJqCVvywaUyWdsaAQAOHOsW21B6zESmQsvyrP3FhkB6yAcAFYiht0AAACLINBUq6yRL8u8LqeCplOXXuLcbmC5y40My1/0lGeFoi7EUtrsGVTFJusXXgAAWC4Mj0f+5KAlb3YGFZ0YsaERAODk91+W1+Usy7LFgjqPPKHQO9/Fl20BrBoMuwEAABaB4XRorCJhySMBU72He21oBGC+lEoldYyFLHlfLKUhb0m3vXO3nH6vDc0AAJg/HU2mcoViWeZ1unTo0HGbGgHA6tYy6bJkPbkJGeu65GlrtaERANiDYTcAAMAiydQ5LVkkYGpyhK0fgeUsOnVJm2abLXn3RELVhT5VvWOvDa0AAJhfgT3bNJZOW/LCWY7kAYDFNtQX1VpfhSUfj74i9603yaistKEVANiDYTcAAMAi8bXVW7KI31Qp41QunbOhEYDrFY1G5R2Ky6HyLQLj6bx6MzndtdYtT0PEpnYAAMwf965dGs5MWfLGuFuFUmHxCwHAKvb8Px1Qhad8ZXexVFLn899TxX3vZAtzAKsKw24AAIBF0rhunSWr9rvlLBkaOz9hQyMA1yOZTOr0udO6IW7dIrBnIqGEM662Dz7IB00AgBXB0dys2el+S95WDCmRnLGhEQCsXrUDRUt2KT8td2OtfBvW29AIAOzDsBsAAGCReGorlXCWb1nudjoU9rl18YVzNrUCcK2i0agmx/tUnwuX5aVSST0TSa01Liq4qdOecgAAzDOPx6NWY9KS17p8Ot5z3oZGALA6xSdm1OUOWfKBqbNy3rxbDrYwB7DKMOwGAABYJIZhaLwyaclr/G71HrOukgGwtKXTaTWOOS358ExGfa6iNm9vktPntaEZAAALo3nnGs1kyo/fcRiG+g722NQIAFafx//pWUX8piVvfu47qrjvPhlO63sUAFjJGHYDAAAsony9dfAVCZianuDMbmC5SRVmtHNqjSXvHk/Im78k/zv22tAKAICF491zg4Yys5Y8HOXMbgBYLL6z1qMjRvIJ+X0O+bdts6ERANiLYTcAAMAiquhotGSRgCkj41Q2mbWhEYBrNX2hV95S+YqKbKGo8/GMNnuiat62yaZmAAAsDPfOnRpLjFjytoxf6XzahkYAsHpks1k9/sMntL5k3cL8QqJPjt03yFldbUMzALAXw24AAIBF1LBunSWr8rnlMgxFz47Z0AjAtSiVSopcKFny3smkJsyUNrz/XQoEAjY0AwBg4Th8PvkSA5a8xVGhkYlRGxoBwOpy4eSQGiusO8aFD/9QwXvukeFy2dAKAOzFsBsAAGARmZUBxcxEWeYwDFX7TfU+c9qmVgCu1pkLL2v9jHWnhp6JpFqKF9T2zrtsaAUAwMLrajGVKxTLMp/TpUMvHbepEQCsHo2DTjkMoyybLmRUMxNVcPdum1oBgL0YdgMAACyyWNi6XXkkYKr/1WEb2gC4FmMvnrRk0+mczufzumNrRK4KVnUDAFam0K03aDRt3bK8cHZq8csAwCqSmk1rk6PKkp9L9sm48Qa5aiM2tAIA+zHsBgAAWGTFBr8li/hNzcQKNrQBcLXS+bQ6LlnPyeseT6pUiirygffY0AoAgMVh3nijhtOTlrxlxlShxO+zALBQnvjO82qaYwvz0OEfyHf3XTJM04ZWAGA/ht0AAACLLLy21ZLVBNwyck6l4hkbGgG4Gq8ePaDaXPmwu1gq6VwsofXFC/J1tNjUDACAhedsalR61npud1upQonkjA2NAGB1MF+dtmxhPlXIqGq8V8G9t9vUCgDsx7AbAABgkTWsXauiSmVZpdctj9OhkZNRm1oBuFK540OWbDieUb8rr/rtbXK4XDa0AgBg8axxxy1ZxO3TsTNnbGgDACtfcialjaq05OeTfdKePfLU1y5+KQBYIhh2AwAALDKX36Mxn3XVS8RvqufZUzY0AnClZpPTWh+1noXXM5lQdbpbuZ2bbGgFAMDiarhpvabSWUs+cLhv8csAwCrwo398es4tzCsPPSrPPXfL8PlsaAUASwPDbgAAABvEq/KWrCZgauDMmA1tAFypkwefV6BY/iFTrlDU2dmMtgYnpFDApmYAACwe754bNZS1fnmzeoQzuwFgIZgnpubcwjw4cVG5tV02tQKApYFhNwAAgA0cTRWWLBIwlZguqVQqzfEIAEuB82TMkvVPpTVtTGnXv/157bv7bpmmaUMzAAAWj3vTJk3MWo/facsGlc6nbWgEACtXMj73FubdyV5NrVuvC9NTSiaTNjQDgKWBYTcAAIANIus6rFnAlDPvVCKWsqERgLczFotqw0S9Jb8wmdRaXVTFjs02tAIAYPEZLpcqstZhd4szqMGRERsaAcDK9YN/eEqNc2xhHj74qKKd7cq5XIpGrX8nA8BqwbAbAADABrXtHcob5ds8+t1O+d1ORY8P2dQKwFs5f+AFueUqy1K5gk5lMrp9zxo5/JyTBwBYPbrWVipbKJZlptOpIy++YlMjAFiZvK/G59jCPC1zsl9TjY3KZrNKp9lVA8DqxbAbAADABg63U9FA3JJHAm51P3PKhkYA3k7F+Ywl64ulpGJUkfc/YEMjAADsE9x7o6Ip67a5jm7r77gAgGuTmE5qozHHFuaJXo13dihXEZRpmvJ6rSu/AWC1YNgNAABgk0SN9WzuiN/UUI/1TGAA9uof7NbauHUL857JpLZ5h+Vtb7GhFQAA9jFvuEHR9IQlb5n1qFAqzPEIAMDVevSbc29hXnXwexpsb1NlXZ1cLpcaGhpsaAcASwPDbgAAAJu4W6otWSRgKj0jlUrWQTgA+/S/eEwOlW8dGE/ndbqY0Y337pbhdNrUDAAAezgqK5VNDlvyNarQbHLGhkYAsPL4T83MuYW5Kx5Vrq1NoVBI69evl9/vt6khANiPYTcAAIBN6tettWQ1AVPOglMzowkbGgGYS6lUUm2P9a3ThcmkQtk+hd9znw2tAACwX0dF2vIlzWq3V4ePv2pTIwBYOeKxWW1yWLcw70n0anrDevlbmnXDDTeosbHRhnYAsHQw7AYAALBJVXOzUo5sWWY6HQp5XBo61m9TKwBvdL77uFrTNZb83FRSt9bMyB2x7tIAAMBqUH/LZsUyOUs+cuySDW0AYGV59Btzb2Fe/dJ3lb95j8J1dazoBgAx7AYAALCNw+nQSIV1i8dIwFT3s6dtaARgLhMHrf/3OJ7I6mIpofUPv0vGG7YVBABgtfDetEeDmWlLHhkzOJYHAK5T6FRy7i3MMzHl16yxqRUALD0MuwEAAGyUqrX+OhYJmIpejNvQBsAb5Qo5tV4MWPILk0k1ZnsUvONmG1oBALA0ONvbFUtGLXlbLqBMLm1DIwBYGQYujmirGbbkF2Z7lNi8ScWw9RoArFYMuwEAAGzkXVNrySIBU9mEQ8Vi0YZGAH7S2eOHFclXlGXFUkknZ1K6vdMlV0XQpmYAANjPMAxVFcYteZMrqAtDgzY0AoCV4Z+/tl/1FR5LHnn+n5S8YYfkctnQCgCWJobdAAAANmrcsN6SVfvccpccmh60bnEOYHEljvVZsuF4RrHChJo/9PDiFwIAYInp2tqodL5QlrkdDh1//oRNjQBgeSuVSuocclrysUJCJSOtfHOrDa0AYOli2A0AAGCjykitpl3JsszpMFTlc2vgcJ89pQBIkjK5jDqGwpb8wmRSXYULCuzYtPilAABYYvy33qjhdMKSe/usGQDg7R158aQ2Bistef/YK0ps2aRiZciGVgCwdDHsBgAAsJFhGBoLWT8IjPhNdR84Z0MjAK87d+ygQkVfWVYolvRKOqVbb2yRw+u1qRkAAEuHuW2bhlNjlrwt7Ve2kLWhEQAsbye/+7IqvW5L3vLMt7Tm535W++69V/v27dO+fftkmqYNDQFgaeFgBwAAAJtl693SZHkWCZg6PzhtTyEAkqT08QFJjWXZwHRapcygaj/0QXtKAQCwxBherxypIUlry/I2R0jjkxNqqm2c+4EAAElSNpvVgQMHJEnFfFFbZiukN3yv9mJuUmZ7kyo2bLShIQAsbazsvg7JZFK///u/rz179qi6ulqBQEAbN27UJz/5SV28ePG6n7+vr0+GYVzRPx/72Meu/wcCAAC28Lc3WLKagKl80qFioWhDIwDpbEodw9WWvDeW0Hb3gDwdnJMHAMDr1je7lS+WyjK/06UXX3zZnkIAsEydOTmstZVBSx6/8Jwcd90ms67WhlYAsLQx7L5G3d3d2rlzp/7Tf/pPOnz4sGKxmJLJpM6ePavPf/7z2r59u773ve/ZXRMAACwDLeut38wOe10yDacmL04tfiEAOnfkkIIlT1mWKxT1SialnXfvlOFikywAAF5XdftujaZSljx/ZsKGNgCwfIV7C/K5nWVZvlRUw4lnVXHnnbwPAYA58DfjNZiZmdF73vMenT9/XpL0C7/wC/rIRz4in8+np556Sv/9v/93xeNxffjDH9bzzz+vnTt3XvdrfuYzn9FDDz30pterqqqu+zUAAIA9gpVhXfLEVZcJXc4Mw1CN31T/wR5FOq2rSwEsrOyJIUnluy4MTKflTV1U1Xt/wZ5SAAAsUZ49uzX06PfUFPCX5a1xjwqlgpyG800eCQB4XSaZ006jxpJ3Z4YU2rlVFR1r53gUAIBh9zX4n//zf+rcuXOSpN///d/Xpz71qcvXbr31Vu3bt0933XWXksmkfuM3fkP79++/7tdsbm7W1q1br/t5AADA0jRZmVbdaKgsiwTc6nmxR7s+ssemVsDqlE4n1Tli/ZLJhamkbqwYk7uxzoZWAAAsXY5wWKnkoFTdUpa3K6SZ2bjCFSzSAIC3c/rYoG6rbLPk7mM/kPmRd8pVax2EAwDYxvyq5XI5/cmf/IkkadOmTfrkJz9puWfv3r36+Mc/Lkl6+umndejQoUXtCAAAlp9Cg9eSRQKmJqLW7SABLKxzRw7KXzLLslyhqBOZhDb91B0yDMOmZgAALF2dlVmVSuXndle5PTr48kmbGgHA8tI54pHLUf5eI1nMKTDVr8rdu3kfAgBvgmH3VXrqqac0PT0tSfrX//pfy+GY+1/hxz72sct//qd/+qfFqAYAAJaxUGeLJYv4TRXTLuWzeRsaAatX/sSIJbs0lVJF8oJC777HhkYAACx9Dbdt10Q6a8knjvbb0AYAlpeJ8RltNa27YPTMdKt48x7529oXvxQALBMMu6/Sc889d/nPd91115vet3v3bvn9r51T9Pzzzy94LwAAsHxls1ldGp1QQcWyPOhxye90auzCpE3NgNUnlUqoc9S6hXnPdFJ7auJyVYUXvxQAAMuA95abNZiZsuQNk4ZlxTcAoFz/kRE1VngsefUL35L3nffIGQzY0AoAlgeG3Vfp1KlTl/+8cePGN73P5XKpq6tLknT69Onrft0//dM/VVdXl7xeryorK7VlyxZ94hOf0NGjR6/7uQEAgP2cLreGvDFLHgmY6n/+nA2NgNXp3OGD8spdlmXzRZ1Mx9X14D1sHQgAwJtwNjRoOjVsyTsKFUplkzY0AoDlYXBoUNtnw5b3GhOFpIxKt6o3brKpGQAsDwy7r9LAwIAkKRAIKBwOv+W9ra2tkqSxsTFlMpnret2jR4+qp6dHmUxG8Xhcp06d0l/+5V/qxhtv1Cc+8Ylrfv6BgYG3/Gd42PomBQAALIzRwKwlq/GbunD0og1tgNWpdGLUkl2aSimS6lHo3jff2QkAAEgNZtyS1bn9OnG2x4Y2ALD0JZNJ/ej7L2hTsNJybXD0ZRVuuVlmS7MNzQBg+XDZXWC5mZmZkSQFg8G3vTcQ+PHWIrOzs/J4rNuQvJ1wOKyHH35Y+/bt07p16+T1ejU8PKzHHntMX/ziFzU7O6u//Mu/1MzMjL7yla9c9fO/PpAHAAD2i1eUpInyLBIw9XK/9exDAPMvmZhRx3iNJT8XT+rmhqxcFRU2tAIAYPnouHmdZk7mVOH58S4pDsPQhRfP6ubt221sBgBLUzQalftcRlXVbsu1upd+oORv/wc5TNOGZgCwfDDsvkrpdFqSZF7B/8D85HA7lUpd9Ws1NTVpcHDw8tnfr9u1a5fuv/9+/cqv/IruvfdeXbp0SV/96lf14Q9/WO9973uv+nUAAMDSUApZz+CKBNwqZVzKpXNye61vfgHMn/OHDmnDG94ipfMFnU1N6h0febdNrQAAWD58t9yinqPHtNFTXZZXDedtagQAS9todFx7SrWWvD87oURLRGbEeg0AUG7FbmNuGMZ1//PII49Yntfr9UqSstm3X2H1k1uL+3y+q/4ZTNO0DLp/0rp16/R3f/d3l//zn/7pn171a/T397/lPwcPHrzq5wQAANfGW1GtjJErz1xOhUyXRs+N29QKWEVOWP/v7FIspcZUtyruvNWGQgAALC/OjnaNpUcseXsuoEz++o74A4CV6KUnz6gjbJ0B5E7+UIld2+VtarShFQAsL6zsvkoV/7J14eys9UzNN0okEpf/fCXbnl+LO+64Q5s3b9apU6f03HPPqVgsyuG48u8wtLS0LEgvAABw9RxOpwZ841qbrC/LIwFTfc+eVvN23uQCCyU5O6POmHUL8zMzCd3SZsj5Fl9CBQAArzEMQ8E3nssjqdkV1MXhYa1vbV/8UgCwRBWKBW0c9spdX/55frqYl3npVRU/sE8NjXwOAABvZ8UOu0+fPn3dz9E4x/+QtLS06KWXXlIikdDU1JTC4fCbPr6/v1+SVFtbe03ndV+p14fd6XRaExMTqq1laxMAAJabXC6n2dlZFbwzcw67zxwf1G02dQNWg+4jh7RezrIslSuoJzGun/r4Qza1AgBg+dmwrUnpwYK8rh//76rb4dArzxzX+p9rt68YACwxTz5xRFuDYUveO3VK+R1b1bnnprfc+RUA8JoVO+zeuHHjgjzv5s2b9Q//8A+SpDNnzuiWW26Z8758Pq+enh5J0qZNmxaky+sMw1jQ5wcAAAsrGo1qcHBQxWJRaUfScj3iNxXvK9jQDFg9SscnJJV/afRiLKW2TLcq9nzcnlIAACxDwdtuVs/f9qgjGCrLzb633yURAFaTSz84ozvCayx5zXP/qMxv/qqaN6yzoRUALD8r9szuhXL77bdf/vPTTz/9pvcdPnz48jbmt922sOuwTp06JUnyeDyqqbFuvQgAAJauZDKp7u5uZTIZxeNxTcxxlmG13y1H1qlMImtDQ2DlSycT6ohVW/IziYRu2uCX4fXa0AoAgOXJvWmjhtNjlrwt5VO+kLehEQAsPSNjk7oxX2XJB3MxTTWEFdm4sAvoAGAlYdh9lfbt26fKykpJ0t/8zd+oVCrNed8jjzxy+c8PP/zwgvV5/vnndfLkSUmvDeKv5rxuAABgv2g0qnw+r1QqJdM05fVXadaZLrvH7XSoymdq5FTUppbAytZz9IjMN2xhns4XdHEmqsYPvd+mVgAALE+G0ymjYB12r3FWaHzKep43AKxG//jI4+qsCljy3MkfavbGXfK1WVd8AwDmxmT0KpmmqV/7tV+T9Nq54P/rf/0vyz0vvPCCvvjFL0qS7rrrLu3Zs2fO5zIMQ4ZhqL29fc7r3/rWt950mC5J3d3d+tmf/dnL//nf/tt/e6U/BgAAWCLS6bSy2ddWbHs8HrlcLl30jlvuq/G7deHp04tdD1gVcq+MWrJLU2l15C7Iv2ObDY0AAFjeNnZUKF8s/0zL73Tp4IFXbGoEAEtHoVhQW19JprN8PJMp5eUcPqt8R7scpmlTOwBYflbsmd0L6VOf+pS+/vWv69y5c/r0pz+t7u5ufeQjH5HP59NTTz2l//bf/pvy+bx8Pp/+6I/+6Jpf5+GHH1ZXV5fe//7366abblJLS4s8Ho+Gh4f1ox/9SF/84hc1O/vaeUcf+tCH9P73s+oEAIDlxuv1yvyXN7GZTOa1/613J7XlDfdFAqZeOT2y+AWBFS6byahjImzJzyRmtXtrtQw+ZAIA4KpV33WTBv5hTM1+f1mePTUuPWhTKQBYIh5/4rC2BcKWvG/qjNI7tkjVkcUvBQDLGMPua1BRUaFHH31U999/v86fP68vfOEL+sIXvlB2TygU0le+8hXt3Lnzul6ru7tbv//7v/+W9/zyL/+y/vAP//C6XgcAANijoaFBfX198vl8SqVSymazMpzWswwjAVOzQ0UbGgIr24VjR9T+hrdF2XxRffGoHvjVhTuOCACAlczcvl1DX/2GZdjdMmuqUCrIaTjf5JEAsPJd/OFp3VXZbsmrXviOLvyrh6WQdXtzAMCbY9h9jbq6unTs2DH9+Z//ub75zW+qu7tb2WxWra2tuv/++/Xrv/7ramtru67X+M53vqMXXnhBL730ki5evKjx8XElEgmFQiF1dnbqjjvu0M///M9r69at8/RTAQCAxeb3+9XV1aVLly7J7XarUCgo78hKsfL7qnxuuQsupabT8lV67SkLrECpY0OSyldOXJpOqSPbI98WjgkCAOBaGB6P0tkRSa1leYcR0vT0lKrDNfYUAwCb9Q9HtTtbbcmjuSlNNYbliNSKr7kDwNVh2H0dAoGAPv3pT+vTn/70NT3+rc7jlqQHH3xQDz7I3k4AAKx0DQ0Nam5u1uzsrPL5vJwup8ZHZxTJVVy+x2EYqvGbGjo+oLV3dNnYFlg58rmc2scqLfmZxKxu3Fotw+22oRUAACtDV72hYqkkh2Fczipdpp586YTuf9c++4oBgI2+/TdP6f9XVW/J06cfU8WD79b2h98nh5uxDQBcDYfdBQAAACC53W5VVVWptrZW1VXVGqmYsdwTCZi68PRpG9oBK1PvK8fkU/lAO1coqm86qoYPvt+mVgAArAwNd+3WeCpjyWePD9vQBgDsl0wn1dnnkMdVPpbJlgryTvWrcvt2Bt0AcA0YdgMAACxBqVrrOYYRv6mB8xM2tAFWptnD/ZZsYDqt9my3fFs22dAIAICVw7Nnj/qz1t9dW+MuFUoFGxoBgL2++bWntSNcZckvTp1R7rabFFq71oZWALD8MewGAABYgrytEUsWCbiVmjHmuBvA1SoUClozWmHJzyRmdcMWtjAHAOB6Ofx+JTJRS96hkOLxaRsaAYB98sW8HIfGVeM3LdeqjvxQ3jtulVkVXvxiALACMOwGAABYgprXrlNRpbIs5HXLJ5dmx5M2tQJWjosnjiug8g+a8sWieqaGVP/hD9jUCgCAlWVdnVQslf9OG3Z79OLBkzY1AgB77N9/TDd5rF9qH8qOKbWuSVXtnTa0AoCVgWE3AADAElQdrtewJ2bJI35Tw8cu2dAIWFmmDvVassHptNqzPfJvZgtzAADmQ8O+PRpPz3Fu9ysDNrQBAHsUS0Wd+c6rWhP2Wq45D/2jjHfsk7+9zYZmALAyMOwGAABYggzD0ERlypLXBEydf/qUDY2AlaNYKKp1OGDJzyRmdcOmKrYwBwBgnnhu2qOBzKQlb552qVgq2tAIABbf6e4+3ZKrkWGUH0sWL6SU80vhTRvkcDltagcAy5/L7gIAAACrnWma2rdvnyXP1pvSaHkWCZg6fdF69iGAK9d/+pTq5SnLCsWSeqaHdO+/fb9NrQAAWHkcPp9ms1FJTWV5p0KaTcwqFAzZUwwAFtEP/+ZZ/UKk1ZLHz/yzCvfdpcrOLhtaAcDKwcpuAACAJSrU0WTJIgFTmVmHSm84+xDAlZt46bwlG4qn1Z7sln/LFhsaAQCwcnW+ybndL7z0qk2NAGDxjMYmtWs8INNZPorJlQpyTvUquHmT3GG++AMA14NhNwAAwBLVvm6z8iqUZX63UxVOU/HorE2tgOWtVCqpadB6Vt7ZxKx2bQmzhTkAAPOs5R03a2KOc7unj12yoQ0ALK6//9Lj2lYTtuSDI0eV3rdX1WvXLX4pAFhhGHYDAAAsUaFAlfr9E5Y8EnBr4NAFGxoBy1+0p0dh+cqyYqmk8/FB1X6ILcwBAJhvnt03qn/Oc7ud7FYEYEWbzcyq+VxeFR7rabL+Mwfku3GHvC2NNjQDgJWFYTcAAMASFqvKWrKI31T3c2dtaAMsf8MvnrZk0ZmM2mbPK7Btmw2NAABY2QyvVzP5EUveqZBmk+xWBGDl+n/ffE43hKot+cDsRSVv3qaqjk4ZhmFDMwBYWRh2AwAALGHFRp8liwRMjQ7wwSBwLSK91rdA5xOz2rm5ki3MAQBYIJ11hmUVd5XboxcPnrSpEQAsrGQuqdwLUTVWWI9Q8h77gdy37FZFR4cNzQBg5WHYDQAAsIRVda6xZDUBU7mUS8Vi0YZGwPI1OTSkulLAkp+bHVLdB9nCHACAhdJ6z80an+Pc7tjRiza0AYCF96MfHtGtzlpLPpGbVqKrQeH2DjlcThuaAcDKw7AbAABgCWvv2KSko/yDQdPpUJXb1FT/tE2tgOXp0oFXLNl4Iqv6qW4FduywoREAAKuD54YbNJCNWfKmKQfndgNYcVL5lPp/cF4d1X7Ltfzx76t0115VrltnQzMAWJkYdgMAACxhAU9QA/5JSx4JmOp/oduGRsDyFTibs2Tds7O6YZ2PLcwBAFhAhsejeD5qyTtVwbndAFacxx4/ojscdXI6ys/jThWzylWUFO5ol8tv3d4cAHBtGHYDAAAscdM1eUsWCZjqfpFhN3ClErGYWgshS346GVXtB95rQyMAAFaXjjnO7a52e3XoyGmbGgHA/EvlU7rw3bNaH7EenxQ/+7hy77hNVevW29AMAFYuht0AAABLnNFsHdBFAqYmoikb2gDLU++BY5ZsOp1TaPSsQnv22NAIAIDVZc29t2oinbXk44d6bWgDAAvj8aeO6U5ZV3VninnlM1FVdK2VWVVpUzsAWJkYdgMAACxxtV3tlqza51Yp41IxX1z8QsAy5Dg+Y8kuxBO6sd0hwzRtaAQAwOri2bVL/Vnr8Tyc2w1gpUjn0zr77dNzruqe7n5S6fvuVNXaLhmGMcejAQDXimE3AADAEtfWvF5TrkRZ5nQYqvF6NHZhwqZWwPKRTaTUlq2w5CfTo6p7+H4bGgEAsPoYpqnpwogl71CFZhOc2w1g+XvimVd0Z6FWbmf52CVbyiuXHlagq13+xjqb2gHAysWwGwAAYInzun0aCMQsecRv6tKzZ2xoBCwvvYeOyWmUv/VJZgtyRU8rfNvtNrUCAGD16ai3fhRZ4/bq0GHO7QawvKXyKZ381gltqgtark1deEbpe29XVUenDAcjGQCYb/zNCgAAsAzM1lq3dowETF04etGGNsDykjk0asl64wntbszL4fPZ0AgAgNWp7d69mkhnLPn4Yc7tBrC8PfncK7ozZ13VnS8VlE0Oydveooq2NpvaAcDKxrAbAABgGXC3VFmySMBUbCxnQxtg+Sjk8mpLzrGFeXZM9Q/ea0MjAABWL8/OHbqUsZ7b3TrlVKFYsKERAFy/eDauE//4qjbXWt93TF58Xpl7blPN2nVyuJ02tAOAlY9hNwAAwDLQ0NVlySq9LjlzbuUzeRsaActD/7Hj8hjlHypl8kVlh0+r6h6G3QAALCbD7Va8ELXkax0hTU1NLX4hALhOpVJJTz9zQndkIzJd5eOWQqmoXHJIvo4mhdrW2NQQAFY+ht0AAADLQFtDl6LmVFlmGIZq/R6NnLVu0QzgNZPPX7Jkl+JJ7QnPyBUI2NAIAIDVrWuNR8VS+RE9IZepF587YVMjALh2sUxMJ751SlvqrKu6xwdeUvodt6iqc50cpsuGdgCwOjDsBgAAWAZcDpeGK+KWPBJwq/fJV21oBCx9pWJJbdPWgfap7Lga7r/bhkYAAGDNT92tkVTKkmdeta74BoClrFAq6Nv/dFj7cnXyusp3kyqWisonBuVta1Ilq7oBYEEx7AYAAFgmUnXWX90iAVN9rw7b0AZY+qKnzyngcJdl+WJJ0yNnVf1TP2VTKwAAVjfXpo26lBu35O0Jj/IFjucBsHwMT0U19WS/tjbMsap76IjSd+9RdcdaVnUDwAJj2A0AALBMeNfUWrJIwNRMrGhDG2DpG9h/1pINzaR0ozkhd2V48QsBAAAZhqGcxix5pyuk4RFrDgBLUa6Q0ze+fFB3e+plOsvHLMVSUfnkgLxtjapsa7OpIQCsHgy7AQAAlomWrg0qqHywHTRd8humUvG0Ta2ApalUKql5zLTkp7KTanrnLTY0AgAAr9u6o0G5QvnvtV6nUwefeMWmRgBwdc4PX1ToaFzra63HJk31PafUnbtV1dElh4dV3QCw0Bh2AwAALBMt1Wt0yWdd7VIX8GjolQEbGgFLV3xoRNUOb1lWKpUUHTuvmgcftKkVAACQpNr73qHBdMKSey5MLX4ZALhK6Xxa//Clg7qzqk4Owyi7li1mNeOalbetXuHWVpsaAsDqwteKAAAAlgmH4dBoOKmOVHkeCZjqefJVrb2jy55igM2y2awOHDhQlgV6k9omf1k2mshqW75f7prIYtYDAABv4Gxs1EBuRO0qP+e2M+tXLp+T2+W2qRkAvLVSqaSDJ89qwwVpTafPcn327BPKvXuPaju65PDydxkALAZWdgMAACwj+QaPJYsETA10x2xoAyxdVZesZ9mfzcTUtm+XjDesvgAAAIvP7ZuyZGvcQZ2/0L/4ZQDgCk2mJ/X4l4/o1nrrF2gTuRlNtYbla6hUZWuLDe0AYHVi2A0AALCMVHZa3zBH/KYyCYdKpZINjYClx8jk1Szr2XkX432qefghGxoBAIA32rl3m1L5Qlnmcjh08skTNjUCgLeWK+b05NOndOtkhWoDpuV66vwzKu1ar6rOdXJ6rdcBAAuDYTcAAMAy0tm5RbOOdFnmdjpUY3oVH5m1qRWwtGQvTlpWb0+nc2qf6pa3iXPzAABYCiruuk2X0tOWvHIoPcfdAGC//tiATn/njHY3VlmuxZNRxXasVaC5lrO6AWCRMewGAABYRqp9NeoLjFnySMDUwMELNjQClp66YevZeN2puNbtXssW5gAALBGOYFDR/Kgl7yoElc6kbGgEAG9uJjujf/zqq7o7V6sKj8tyPTl0TM51Darp3CDHHNcBAAuHYTcAAMAyM12Tt2S1AVPdz5y2oQ2wNORyOcViMU2MjKqzVGm5fj7Zr6qfZgtzAACWkqpq6yruBtOvV0702tAGAOZWKBV09NR5+Q+Na3tjyHJ9auKs4jdvV0Vrk4INtTY0BIDVjWE3AADAMmO0VFiySMDU6GDShjaA/aLRqAYHBxWLxZQfisvtKH+bk84VFBo5rdC6jTY1BAAAc9n+rtsVz+TKModh6OKzfIkTwNIRnY3qsS8f193V9fK4yt9rlEolzeaicrdVqnbtehkuRi4AsNj4mxcAAGCZqetaa8nCXpeMjFvFfNGGRoB9ksmkuru7lclkFI/H1Txt/TJIb2JWOzdVy3Dw9gcAgKXEs2unLuZilrxhgt9pASwNqXxK3/nHk7pl3K+Oar/l+vTQYaVu3KRwa7s81dYdpgAAC49PewAAAJaZrqZNiprlHwoahqFav1fjfdYPC4GVLBqNKp/PK5VKyeM2talUbbnnTHZYlQ8/aEM7AADwVgyXS+PFEUu+VhWanZ21oREA/FipVNKrfT2a2X9Jt7ZY32dk8klNRNwyW8OqaW+X4TBsaAkAYNgNAACwzHhdXg0Epyx5bcBU/7OnFr8QYKN0Oq1sNitJChc9CjjdZdfzxZKK0ZOq2rXHjnoAAOBtrGnzWLIa06uXXmQrcwDXL5vNav/+/WX/vP7+4e2MJEf0rUde0T2+egU9Lsv1+OBBlTatUU1bp9yhwHxXBwBcIYbdAAAAy1Cy3vprXCRgqvtQ3+KXAWzk9XplmqYkKRLzWq4PJpLaU++Qw+22XAMAAPZbf/+9GkunLXnsUK8NbQDgNbPZWf3oh6e19aKhTfVBy/WZyfOa3LFBvtYahVtaZBis6gYAuzDsBgAAWIa8ayKWrDZgKjaWs6ENYJ+Ghga5XC75fD5tylu3FjydHVXle99pQzMAAHAlXJ0dupibsORrZl0qFAs2NAKw2uWLeZ3r71Hvj7p1Z1NEjjcMsvPFvMa8Cbmb/apuWytXwPqlWwDA4mHYDQAAsAy1dG1UTuUf/vncTgXkVS7NwBurh9/vV1dXlyrkUcTls1yPjZ9S9R37Fr8YAAC4IoZhaNY5bsm7nCFFo9YcABbawPQl/b8vn9M9pVrVBEzL9enoERXXtyrY1qLKhnobGgIAfhLDbgAAgGWorbpdfb5RS14X8GjktDUHVrKGhgbVxP2WfDSR1o3mtNx+zs8DAGAp27i9QYViqSzzu1x66fHjNjUCsFpNpCb0/e9c0Lqzae1sClmuJ5IjGt3YInNNSHVrOuXwclwSANiNYTcAAMAy5DScGg0nLHkkYKr3ST4UxOrTMWMddp/JTajm3fsWvwwAALgqa979Tg2kZy25t3vShjYAVqt0Pq2Dr3Rr6slevaO1Tm5n+fikVCpprDQiT2NQVa2d8kQqbWoKAPhJDLsBAACWqXyj9Vyw2oCpvpNRG9oA9snNpNTitK7eHpw+p+p3/ZQNjQAAwNVwVFXpUn7Ekq8rBJVJp21oBGAlyeVyisViGhsbUywWUzKZtNxTLBV1buiCnvzaGd3jr1d90GO5Z3L8VWXXNcnf2aTqlhYZDsYrALAU8LcxAADAMhXubLFkNX5TybgNZQAbnfznY3IYRlkWz+S0IdUvX2W1Ta0AAMDV8FVbdy1qNv069nKPDW0ArBTRaFSDg4OKxWKamppSLBbT0aNHNTw8XHbf4Mygvv7lc7ptwqftDRWW50lnpjTWXiVzTUiRpnY5A9ZhOADAHgy7AQAAlqnOts2adpZ/I93pMFTt9ik1lbKpFbD43GenLVl3Zkqt+/bY0AYAAFyLXfffqZlsrixzGIb6nzljUyMAy10ymVR3d7cymYzi8bgSiYTi8bji8bjOnTt3eYX3WHJM3/9+t1pPTunO9hoZb/gibbFUVNQYkbuxQpWt7QrU19jx4wAA3gTDbgAAgGWqNlCn3sCoJa8LeDR49JINjYDFVyoU1VEMWvILqYuqevA9NjQCAADXwr9jpy5krWd0t0xJxWJx8QsBWPai0ajy+bxSqZRM01RFRYVM01QsFlM+n1c0GlU8G9eRl3s0/FiP7m2sl990Wp5nfPqMMi3V8q6tV21TiwwXYxUAWEr4WxkAAGAZm6rJW7JIwFT3kydsaAMsvnPPn5TXWf6BVK5QVNXEGQUaWm1qBQAArpZhGJp0zHFutyuk8XHrEBwA3k46nVY2m5UkeTweOZ1OeTwelUolZbNZxZNxne4+rx99s1v3OCNqqfRZniM+06/x9mp52ytV29gmZ8h6DwDAXgy7AQAAljFHS8iSRQKmBns5uBurw+SBXkt2MTWrrTs6LdsPAgCApW39zgYViqWyLOhy64XHXrWpEYDlzOv1yjRNSVImk1GhUFAmk5FhGHK6nRqdHdVXv3pRe0dcuqG50vL4TD6pwTqnfHV+BVtbVNFYx3sMAFiCGHYDAAAsY3Vda1VU+QeClV63lHGz3SNWhbaEx5KdzQ4p9ND9NrQBAADXo+P+n9JQZtaSu85aV3wDwNtpaGiQy+WSz+dTNpvVzMyMstmswlVhTeZi+ucnMtp2flb7OiJyOqxD7CHHiNzVXnnWNaq2sVWG27rFOQDAfgy7AQAAlrH1DZs0ZFq3daz3+TQ9OGNDI2DxjHdHVe0uH3aXSiVlJ08ptGGrTa0AAMC1coRC6suPWvKuQkDZbMaGRgCWM7/fr66uLnk8HoVCIQUCAVWEKpT35nT6bECtJ8b1rrZ6VXhclseOJnuVqQvKXB9RXd0auauCNvwEAIArwbAbAABgGfO7/eqviFny2oCpi0+ftKERsHjO/OCYJRtNpXVjrSmn0/qBFQAAWPrcNUlL1uoJ6JVX+ha/DIBlr6GhQc3NzaqqqlI4HJYj7NRgNCzv4VG9q7FetUHrTlEz6TGNN/rk6wirqr5FgcZati8HgCWMYTcAAMAyl6y3vumuDZo6/0KPDW2AxVM7WrBkZ/Jjqnzo3Ta0AQAA82H3A/s0m8uXZQ7DUN9TfJETwLVxu92qqqqSs8qpyVGfJp8e1DtDtWqr8lvuzRZSGowU5KsPyt/SqJrmFhkuxigAsJTxtzQAAMAy52uvs2SRgKnJsZwNbYDFkYmn1OoKWPLR+DlV3bLXhkYAAGA+BLZsVW92wpI3TZVUKpVsaARgJZjMT2qwO6uhYxm9wxnWloYKyz2FUl59vpicIVPm+nrVNa6RI2Da0BYAcDUYdgMAACxz7Ws3K2VkyzLT6VDI8CuXZuCNlenodw7J8YatBGezeW3RqEzTZ1MrAABwvQzD0JjTem73OmdIkxNTi18IwLI3mZ9U//mszr5S0N1Jv25qDc9530XXmIohtzwb61VX0yKzxjoQBwAsPQy7AQAAlrm2cLt6/FFLXh8wNXJqxIZGwMLznJ+yZOezk6q/7+7FLwMAAOZV5856Fd+wijvkduuFx07Y1AjAcjUcH1Tv2ax6Xi7o7mm37uqstnxpVpL6NaxUyCH/+lqFa+sVbK7jnG4AWCYYdgMAACxzTsOpsaqUJa8NetTzzy8vfiFggRULRXWUrFuY96X6FH7nfTY0AgAA82n9Aw9oOJ2w5KXTwza0AbAclUolDU736/F/vqD+V4p6x6xb71wXkdtpHYmMFEc0FZYq2qvla4j8yzndzsUvDQC4Jgy7AQAAVoBii3XwVxswdfG0dQtIYLk7tf+kfG/48ClXKKpm5qwClRGbWgEAgPni8PvVW5xjK/NCQLlMdo5HAMCPFYoF9Yx263vfPaeTT4zonpRb966rnXPQPVmY1Ei4oGBzlcy2OtU3tskZ9NrQGgBwrRh2AwAArAA1a9ssWdjnVjbhsqENsLCmX+y1ZJcyM9qwZ7sNbQAAwEJw1CQtWasZ0InjfYtfBsCykS1kde7SGX33Wxd0/qmo7s2aesfaiFwO65bkM8UZDVRnFKirlKerTg21rXJXB21oDQC4Hnz6iUWRzWY1OzurRCKhbDarYrFodyUAWNGcTqe8Xq9CoZACgQDnTK0CG5u3ach8Uk3Z6rK83utTYjKpQLXfpmbA/GtLmZJZnp3LDuru9z1oTyEAADDvbnzvO5T8ypD87h9/fOl0GOp+4lXdsGe9jc0ALFXJXFKnTp7SVx4bkff0lO4reXXH2rnP6I4XZ3QxPCt/2C/PxnrVVTfL01DF5ycAsAwx7MaCKpVKGh8f1/j4uN1VAGBVyefzymQymp6els/n05o1a+RwsKHLShb2hnU0OK6myfJhd23Ao/4XurXxPax4xcowfDaqiGndVjAfP6VQ0ydsaAQAABZC5cbNOpx7VVvddWV567T01JNPyfiXVZp79+6VaZpzPQWAVWQyOamjz53U3zwzqS09cd3kC+iW9qo5B91TpRldDMflDfrl2dKkSFWDAs21DLoBYJli2I0FNTw8rOnp6bLMMAw5nc43eQQAYD4UCgWVSiVJUiqV0qVLl9TW1sYbtxVuts6QJsuz2oCpk/tPMezGinHu+8d0s3xl2Wg6pZ3N1XIYfKkHAICVZMQ9oq0qH3ZvdIe0fzqlqip2LgLw2vnc/aOX9OwT5/WDQ0ntHYjrjsZqbaidezvymOLqC8/I5/cpsK1Z4VBEla2NMlggAADLFsNuLJh0Ol026K6pqVEoFJLH42HYAgALrFgsanZ2VtFoVIVCQalUSolEQsEgZ0+tZN62GulMeVYbMDVyPmFPIWAB1I8XJE95djY/ql0ffI89hQAAwILZeFu78i8W5fqJIZTP5VLs9KSq9jLsxuqUzWZ14MCBsmy17nAwk55R97GT+t4LY7p0Lqt3j2d099oGRQJz/7uYMOK6VDkjf8CvwOZGBUOVqmlrleFiYRYALGcMu7FgpqamLv+5rq5ONTU19pUBgFXG4XAoFApJkgYHByVJMzMzDLtXuDVrNyptDMhb+vEbe9PlUKDkU7FYZCt7LHvJWEqt7oAlH5s5o9D2n7GhEQAAWEid975bp5/+njp94bK8LW5IJUmspQCu2koYlhdLRfX39+rI/rP65vms2i+l9UDBqbs21cvrnntwPeqY1mBoVr6KoPybGhUIVaqurUOGhxEJACx3fOKJBZNMJi//ORwO21cEAFaxYDB4eTeNVCplcxsstK7q9er2Ry15vd+rid6pxS8EzLMj3z4kp6P8U+1ELq/N3rhM5/L5cA4AAFwZw+VSnzFsyTe5QkqlMjY0AmC3eHJah/75Gf35V0/o/x3K6dZTM3rA69N96yJvOugedk5rsGJW/nCFglsaVVFZqfr2Djn8vIcAgJWAry1hwRQKBUmSy+XijG4AsInD4ZDT6VQ+n7/89zJWLpfDpZHKhLa+Ydfy2oCp3sePqXbtPfYUA+aJrzsmmZVl2fnchNa8+6dsagQAABZaZItfpe5S2ZF41aZHw2cn1bnL8xaPBLCSJJMzunjkuJ4/PqZHL0lbhtJ6f97QLV31b7pteUEF9bmnFPen5a+plH9DvYIVYdW1tcvhcy/yTwAAWCgMuwEAAFaQYotPGirPaoMe7T/Sr5vsqQTMi0K+oE6HdQvzvvQFbb/zV21oBAAAFsMND39A/Z95Ro3e8jO6IyMZxWIxJZPJZbX9MoCrk07Oqv/QcR08OaofRgtyjTv0UyMJ3dQY1sbaQNkXYX7SrJHSRV9cWW9BgUiV/F21qqioUm17uxxsXQ4AKwp/qwMAAKwgVZ1t0sHyLOx1KTFVsqcQME9eeexVbXaVv33JF0uqzfbJ7w3a1AoAACw0lz+o7uKwGrW2LN/sqNT+sWEdPXpUmzZtUmNjo00NAcy3Uqmk1Miohl49pwNnxvXD4bxKM6Z2jmS00+fRns2N8r/JluWSNOKc1lAwKYfboYqmWvlaq14bdHe0y3iLxwEAlieG3QAAACvIxpatirqfV0MufDkzDEO1ZkDZZFYmZ5JhmZo9fFFSqCy7lIlr3a0321MIAAAsGkdLVposz5o8Po1HC4rUxXXu3DlVVlbK7/fP/QTACpPL5TQ7O6t8Pi+Xy7VidjjIT01r4kyPoheGdXAko8dHc3IkvNoZzWu7z9DW9ohq32TL8tf1muOaDGRk+nwKttbIXRtQZSiimvZWGS4G3QCwEjHsBgAAWEEi/lodD46oIRYuy+uCHg2/PKi2vR32FAOuU3vGlN5wLOf5fL/2vfv99hQCAACL5pYPvVdTf3JS4Z8Y5jkMQ20Jv2KxmCoqKhSNRtXZ2WljS2BxRKNRDQ4OqlgsqlAoyOl0XvUOB0tlWF5Kp5WPTSnRP6bJ/mEdH57Vi0OzOloKqXbWqd0jGe0KFLR1bUThtzlje9w5ozFPWmlvXr5QSMH2ajkrfaqtaVJFY50Mp2ORfioAwGJj2A0AALDCxGtLUqw8qw14dO6xowy7sSz1Hh9Uo8dryQuJ06qoabChEQAAWEyBumYdyT+pm8zWsnyLEdKhwpiy2azS6bRN7YDFk0wm1d3drUwmo1QqdTmPx698h4P5GJZfjWw2qwMHDkj5vByZjIx0RjvWrFVyNKb41Kx6JlJ6ZXhKB/NB5UoVWjfp14dnU1pf6dOmdbUKvs352hkjp15zUilfUQ7TJV9NWP411TL9PjU0tclTHXrTc70BACsDw24AmCev/+L8u7/7u/q93/s9e8sAWNU8bdXSufKsNmDq2Z5BewoB1+nCj46rUb6ybCyT0ra1LXIYrNAAAGA1mAyOScXyYXeHJ6inZ2MyTVNer/WLccBKE41Glc/nlUqlZJqmPB6PMpnMFe9wMB/D8jdTKhRUymalTEalbFbFTFalRFqZySl5X+lWPldUsWhoOpHR118c1Om0U6d8tSoZfq2Zdumu6Yy2+3PqqKlQfZt5RQPqHs+IEqahgk9ye/zyNgTlqQ/J5wuqoa1TLr/nbZ8DALD8MewGgCUqn8/r29/+tn70ox/phRde0MjIiGKxmPx+v2pra7Vt2zbt3btXP/3TP62Ojjdfqdne3q6LFy9acpfLpcrKSm3cuFH33nuvfvEXf1FNTU0L+SMBWCQtnRuVNkbkLf14mzePyyEzxweAWJ4aJwt6w6xbZwtR7XjovfYUAgAAi27nQ3co/c24vD9x5q7b6VB41C2Xy6WGBnZ7wcqXTqeVzWYlSR6PR06nUx6PR6VS6Yp2OLg8LE+m5PmXYXk2k9H0+IQqTa+iPb1qa2mRCgWV8kWpWJDy+Z/4c0Gl/L9kubyUz6uQzamQyymXySuXe/2fgvLTcU1Ex3RxYka9yaIG3RXqD0Q06WtRUyar1tmcPjpVUrsvp8YKnxobw3Jc4QrsIXNSI660HD6nHKZbnrBXvoaQ3EGPQuEaRVrXyMH53ACwajDsBoAl6Dvf+Y4++clPqru723ItHo8rHo+rp6dH3/rWt/TpT39a73nPe/Q//sf/0NatW6/4NfL5vCYmJvT888/r+eef1x/+4R/qkUce0cMPPzyfPwoAG2yIbNQJ3yvakixf+dLg92l6OK7KxpBNzYCrFxud1RqPdXXJRPKMwp0ftaERAACwQ/u2PTr81W9oq6uuLO8qeNW+bt01r0YFlhOv13v5bO1MJiOP6VE+k1XA61dApnwFp7LjM8pn0yokUypmsypkMypmsypms5odHJR7dEytUwl5zKwcxqyKhYIMQzJmkpqKjsh1oVdFSSqWVCxKpXxexURKSiZVTL72/5aSSSmZVDaZVTwvTeWl8aJLY06vRt0BjXrDioZqlTDXq9qTV20xr65cUbdPS2uSWdUHPapdUyH3VZ6j3e2NatyVkd/ll9PjkdPvkdngk6cmJNP0KNLQrEBNNduWA8Aqw7AbAOZJqVSal+f5zGc+o9/5nd+5/Hz79u3TAw88oO3bt6umpkbJZFLDw8N65pln9L3vfU99fX169NFH1dLSor/4i7940+dtamrSj370o8v/OZfLqa+vT3/913+t7373u4rH4/rIRz6iF198Ubt27ZqXnwWAPUynqaHKuLYky/PagKm+J17Vjo/utacYcA2OfeuI9jrKPwRL5fNaH8rL7XC/yaMAAMBKNOQd0VaVD7vXmyE9+9JxhavOae/evZcHgcBKUiqVpGxBdcEq9edKirh9ymWycqZm5SnkVO1yKBftVSI3o94Lp2Xki1JJUkky8nmVYnGVpqak0TGFpuPyzczIlcvLmcvJkc3IlcvJmc8rV3Jo2OnWrFyacbiVcJqadXk06wloxhPUjDegaV9IU746TQUqlaz2q6JQUk2+pOZ8UXX5kraVpOqSVJ02VJMrqdLrUUVFQE7HtQ+gTwQuacKZVbUrrKDXK7mccoTd8jdWyu31KFhVrZqmZrlcvD8AgNWIYTcALCFf+tKX9Nu//duSpPr6ev393/+99u3bN+e9H/zgB/VHf/RH+vu//3v91m/91ts+t9vttqz83rVrlx5++GF98pOf1Oc//3lls1l95jOf0T/8wz9c988CwF6FJq80XJ7VBj167sB5ht1YVoJ9U5K3siw7lx/XmgcesKcQAACwTftdXcrvL8n1E0OzgNulqROTCt/JkT1YOUqFoorpnDLJpDKJWWVSSaXTCWVTs/JnEwqlZuXIFeQoSq58XqHYkIKJpMzYS3JOTcmVzMmVkxwFKe0JKlkRUSZQrayvXnlPm/JBr/Iuj4ouUwWXRyWnW4bTJbcMmcWS/JLCKsldktySXDLkkuQqSaZhyO0w5DaM11ZmuyQtwNHYw+6YTvsHlTecanTVqs5bpaLLkBF0yqz1yRsMyO3zq665Vf4Kdi8DgNWMYTcALBH9/f36lV/5FUlSKBTSc889p66urrd8jNPp1M/93M/pPe95j5599tlrfu3/8l/+i/7P//k/SqVSeuyxx1QsFuVwXN1WUgCWlsq1LdKR8izsdWnmwvzsQgEshkwmp05HwJJfyvRo2+4HbWgEAADstP6Wu3XhsR+q0xsuy1sSxmurWIFlrJjNKxWPKxmfVjqZUCaXVjGZlGbSKiSSKqSzMmJT8vf3a13/uAJpQz7DJ6evUtmqJqmiWt7qkLyO65w823jU9YRzVkdCPZp2ZtWca1SDr02Gy6GCq6RiyCGz1i9vICin26XKSK2qaxv5/AoAIP6XAFghfu/3fk+GYVw+k2Zqakq/+7u/qy1btigYDKq6ulp33323vva1r13R8z333HP6wAc+oIaGBnm9XnV2duoTn/jE5TOk9+3bJ8Mw5lx1vH///std9u/fr1KppC9+8Yu6/fbbVVNTo1AopJtuuklf/vKXyx6XzWb1F3/xF7rllltUXV2tiooK3XbbbfrGN77xpj3f+Fpv5fX7fu/3fu9ylkwmVVFRIcMw9HM/93Nv++/lhRdeuPw8//t//++3ff6r8fnPf17pdFqS9NnPfvZtB90/KRwO68EHr/1D/2AwqM2bN0uSZmdnNTk5eVWPf+N//+LxuH7v935P27ZtUzAYVF1dne6//34dOHCg7HGjo6P6//6//09btmxRIBBQTU2NHnroIR07duyKXvfo0aP6xCc+oQ0bNigYDCoQCGjDhg365V/+ZZ07d+4tH3vhwgX9wR/8gR588EG1t7fL5/PJ5/Opra1NH/7wh/XDH/7wLR//yCOPXP6Z+/r6VCwW9YUvfEF79+5VVVWVAoGAtm/frs9+9rNKJpNv+VzAQtjQslVR11RZZhiGImZAuXTOnlLAVTr6/ZMKmuXfzy0US6opDchvWofgAABgZXM4HDqvQUu+xVWp2em0DY2A65NNpTQVHdbQuXPqO3NCQ33nFbtwQTPnepQ62a3MS8fl/P5zqvruUdU/M6DGS0F1+PepY+OHVbfzQ6rY8aD86+9UuHaDwt7a6x90L7KEkdHhYI++WXtAj9Q/oxfCF9Xi7tR2/1ZVVdaoGDCUjxhyrwsq1F4nf3VY4aZGtW3cqkh9M4NuAIAkVnYDK1Jvb6/uu+8+9fT0XM4SiYT279+v/fv361vf+pa+8pWvyOWa+6+Az33uc/rN3/zNsjOoe3t79Zd/+Zf66le/qv/3//7fFXfJ5XJ66KGH9N3vfrcsP3TokP7Vv/pXOnz4sP74j/9YsVhM73vf+/TMM8+U3XfgwAEdOHBA3d3dV7RV99Xy+/163/vep7/7u7/Tt7/9bSUSCQUCb/7h+Ve+8hVJksvl0oc+9KF561EqlS4P/ysqKvRv/s2/mbfnvlJu94/PNSoUCtf8PP39/br33nvLhs2JREI/+MEP9Nhjj+lrX/uaPvjBD+r48eO6//77NTj44w8qksmkvvOd7+hHP/qRfvCDH+juu++e8zWKxaL+43/8j/qjP/ojy1np586d07lz5/RXf/VX+vM//3P94i/+ouXxvb29Wrt27ZzPfenSJV26dEnf+MY39NGPflR//dd//ab/t/KTvd/5znfqiSeeKMtPnDihEydO6Dvf+Y6efPLJt/zvFjDfGgNNejwYVcNUuCyvD3g0fGxAa27tsKcYcBUyxy5KrvItzC/l4lp7x502NQIAAHbLNCVUmihd/rK1JFV5TI2cHJPut7EYcIUKhYJmYzHNTE4ok0qokMupEJtRYSqufCyuUPeIQvGSHL4a+SvXyxXZKkXsbn39ZksZRd3TGvCPqdcfVayUUiAb1BZnl7Y7d0mmQ/JKOUde+UBBjipTZiAgr+mXw+dWRaRWkap6OR02Lj0HACxJfPUJWIE+/OEPq7e3V5/4xCf0+OOP69ChQ/riF7+o9evXS5K+8Y1v6FOf+tScj/3GN76h//yf/7NKpZKqq6v1uc997vLA+XOf+5xcLpc+8pGPaHh4eM7Hv9Fv//Zv67vf/a5+7ud+To8++qiOHDmir33ta9qwYYMk6U/+5E/0+OOP62Mf+5gOHDigX/7lX9Zjjz2mI0eO6Itf/KKampokSb/zO7+jkydPzsO/HavXV3QnEgl9+9vfftP78vm8vvnNb0qS3vWudykSmb93Gq+++qomJiYkSXfccceiD0Xz+bzOnDkjSTJNUzU1Ndf8XB/84Ac1MDCg3/zN39TTTz+tQ4cO6Q//8A8VCoVUKBT08Y9/XL29vXrggQeUSqX02c9+Vs8995xeeukl/Zf/8l9kmqYymYw+9rGPKZvNzvkav/qrv6o//MM/VKlU0p133qkvfelL2r9/vw4ePKj/+3//r7Zs2aJ8Pq9f+qVf0ne+8x3L4wuFgkzT1IMPPnj5v4NHjx7V448/rv/9v/+3tmzZIkn6u7/7O/3X//pf3/Zn/oVf+AU99dRT+tf/+l9f/u/5P/3TP+nWW2+VJB08eFCf+cxnrvnfKXAtDMPQdF3RktcFTZ390VEbGgFXp1gsqiNnXZnSnb+kynvusaERAACwm2maet8nfl2DmYTl2popKZmw5sBSkUrMKHqpTxdPndBYf59mhoaU6O5T4tgZeZ84rsjzQ+ocrFdT8GaFm25RqGqdXA7T7tpvK18sKpnPazyXUl8+plc0qKfdZ/VPFYf1F/WP6b+1/oN+u/5r+pOq7+sJ16sam0lrzeAa7Y3foJucO1UTiCgbKChVkVO6uSjX+oAqO+oUqosoUF+jqo4WdazbqvqaJgbdAIA5sbIbWIEOHTqkr371q/qZn/mZy9nu3bv1wQ9+UHfccYdeeeUV/cmf/Ik+/vGPa+vWrZfvyWQy+rVf+zVJUiQS0QsvvFC2lfatt96q973vfbr11lvfdovo17300kv6oz/6I/36r//65eyGG27Qvn37tH79es3MzOhnf/ZnNT4+rn/8x3/U+973vrL7du/erV27dqlQKOgLX/iC/viP//ha/7W8qXvvvVd1dXUaHR3VV7/6Vf3sz/7snPc9/vjjGh0dlaQr2vL8ahw/fvzyn2+44YZ5fe4r8Zd/+ZeampqSJN1+++1vu5L5rbz88st6+umndfPNN1/Odu/erXXr1umBBx7QzMyMbr75ZpVKJR08eLBshfVNN92kSCSiX/mVX9GlS5f06KOP6uGHHy57/n/+53++vIX8X/3VX+njH/942fU9e/boox/9qN7znvfoySef1K/92q/p/vvvL/uZGhsb1dfXp8bGRkv/e+65R5/4xCf08z//83rkkUf0B3/wB/oP/+E/qLKy0nLv6w4cOKAvf/nL+uhHP3o5u+GGG/Tud79bu3fv1quvvqr/+3//r/7rf/2v1/XvFrhano4a6Q1/XUcCpvZ3W7d+BJaaM4f61enzWvJC9oxCFStgaQsAALgmpsujs84BtWhjWb7NrNQzT72oPbfunPO9HmCXeHxSU9ERZZNJFXI55cZjyo/HFDo7oXDGq4pAm1z+Nsk/P69XLJWUzBWUyRWVLRSVK5SULfzLn4slFYslFUuv3VcslVQovbbjYF5FZV05ZZ1Z5dy51/7szinnzipr5pRzZZU1s8q6c8qaWeXMnAruvNyGS2bRrUDBp2AhoIpiQKFiUHVq0ppkuwqxvGbTScllyHAayjlzSjhzmnHFJNeU3KZX7R0dCpo+Ob2mSn63KirDCgdr5HfP078UAMCKxifusN1gc6vdFRZd82D/gj7/Aw88UDbofl1FRYW+8IUv6Oabb1axWNRf/MVf6M/+7M8uX//Wt76lkZERSa+dwTzXmdHr16/X7/7u75YNr9/KzTffPOe9DQ0Nevjhh/W3f/u3Ghsb04c//OGyQffrtm/frttvv13PPPOMnn322St6zavlcrn04Q9/WH/6p3+qxx57TBMTE3OubH59C/NgMKiHHnpoXjuMj49f/nNtbe2b3lcsFnXq1Kk3vb5hw4ay7cjfSj6fV19fn/76r/9an/vc5y7nb7bq/0r9xm/8Rtmg+3Xvec971NbWposXL2psbEz/5//8nzm3Ev83/+bf6JOf/KTS6bSeffZZy7D7f/yP/yFJ+sAHPmAZdL/O6/Xqz/7sz7R582ZdvHhRTz31lO67777L1wOBwFuunjcMQ3/wB3+gL3/5y0okEnr88cf1gQ984E3vf//731826H6dx+PRv/t3/06f+MQnNDExoVOnTmn79u1v+jzAfGvr2KiEEVWg9OPVsW6nQ/6iz8ZWwJUZeOKkOt/wid9ENqXNG9bLYbBBFQAAq5lvl0+lE+VbmYc9pqIvD+lczTlVVlbK72dIBvuUSiVNTY1renRE+VRGuURC2dFJOYanFO5LqMLXJq/ZJF3jwu1UrqCpVE5T6bym0zklswUlcgUlswWl8z+xw5dZkNNbkstTkmka/3/27js8qir9A/j3Ti/JZNIb6QVCEnoXkCYgRUEREVDBCpbVddXfouyKorsqFnQFRVFQURRQEUSq9CYlSIckhISEkJDep8/vj5gxw0xCEggTku/nefIwOfece987k4SZ+97zHsjkIoglgMVkgFgiQCoWQSIGPNzdIBFLIIEUgsgNwJ+/WwIAQah+IPrzsQiARQQYAJgAiEQQBMAqssIitcIqssIsWGERLDCqTCgqL0OJtAx6GGAWW2ARWSCTyiCTyODm5QmrTIAi0BPuHl7QKD3gJnPn+30iImoUJruJWqH61nvu1asX4uPjcfLkSWzZssVuW833IpGo3pnLU6dOxbPPPuuwVrIzkyZNqnNb586dG9xv586dSEtLu+rxmmrKlCn43//+B6PRiBUrVmDmzJl226uqqrB69WoAwLhx4677h+aysjLb4/qSsKWlpUhMTKxz+/nz5xEeHu50W0ZGht2FgCsJgoC5c+di5MiRVw+4HvW9lp06dbLFce+99zrto1QqERMTg+PHjzu85qWlpdi+fTsAYMKECfXGERcXBx8fH+Tn52Pfvn12ye4rGY1G5ObmoqyszG69cm9vb1y+fBlHjx6tN9ld3+9L9+7dbY/T0tKY7KYbKs67Iw6pktC1wn597gCVEkUXS+AZXHfFAiJXCyqxAFf8l3jWcgmJo0e7JiAiIiJqMdol9kbW4RSEyN3t2iOrpDAZTcjJyUFkZKSLoqO2zGq1orDoMkovX4ZZZ4C+ogL6nHxokguhKZfCWx4OuDVunzqTGZfLDcgt0yO/0oCSKhP05r8S2hK5ALVKgEIrwF8hhkwphVwhgkQuQJCIYIEVFgGwCoBFJAAiAVYx/kxeV/+rFwCDAFhFgAALIKruL/y53fpnvhsArPjze/zZJgYEMSCIxRBJRRBLRRBJJBBLRFBLxbBUSZB/rhQWSGGymmARiWEQCfDy9IDMXQu5IEdMYEfIZC2/ZDsREbVMTHYTtUI9e/asd3uvXr1w8uRJJCcnw2Aw2N5MnjhxAgAQGRkJrVZb53gvLy9ERkbi3LlzV42lZp1wZ2ofoyH9aieEr7fevXsjKioK586dwzfffOOQ7F6zZg3Ky8sBXP8S5kD1rPsaFTd4jTGNRoMhQ4bgueeew4ABA655fw15LX18fODp6XnVfle+5keOHIHFUv2B7r777nNawcCZnJwchzaj0YhPP/0UX3/9NY4cOVLn+uCA/cx7Zzp06FDnNi8vL9vj5vwZJnJGKpbikmc5ul7xZ8XPTY70zcfgOe3af+eJmsOlzGKEKRxvLCvQn4E26H4XREREREQticloQYqQgRAk2LUnyrXYmV+OwCCdiyKjtqysogT52ZkwVeigryiHIbcQmtMF8DN4wV0cDcivvg8AMJotuFiiQ3aZHpfL9SjRmWzbBCmgdrPCQ2WFXAW4eSsgKMSwigGzSAAkAqwSAQapGEaJAJFYBEH0579iAdKaGdMCIAiiP5PdAgRx9b8QVU+GEMQCIBYgiATgz20iEQCxAJFIgCAVQ5AIEItF1eMFAdY/Z31bRYBVJEAQiyCRSKC2ipGrr4ShUg+YBMhEMrir3aFQOC5ZRERE1BRMdhO1Qn5+fvVu9/f3B1B9t2lRUZHt+6KiIgD1l9Gu4evr26Bkd30zoEUiUaP61SQ5m8uUKVPw2muvYe/evUhPT7ebIV1TwtzPzw/Dhg277seuXTY9Ly+vzn5ardZhRv20adPw5ZdfXvUYQUFB2Lhxo+17iUQCDw8PBAQE1Dvju7Ea8lpebWZ8Tb/as6wB2NZMb6zKykq77wsLCzF8+HAcPny4QeOrqqrq3d7Qn/Mrz4foRrCGqIEs+zZfNxl+238OXZnsphbq2M9/4FaxfelCndmEcC8ppOKGLddBRERErZdCoUBxUDkseVaIan2e9ZBJUX5aB0UvJtHoxtEZqpCXkwVdUSkMuirosy/D/XQh/PRe0IhjqpPFV9uH0YzMEh0yiqpwqUwHS82lH4kADx8xPLQCFB4CKlEJg9UMgwDopIA8wAMqlRKCTAKRRARBIgZEIlglAgR5dfIbUgESuQQimQQSmQRiqQRiiQCRRAyRSFSdsIaA2peGBNSeuv1nArzWVpEggkgQQbA9FiAWxJCIpBCLxJCIJBAL1SduMBiQLc+2Jft5bYSIiK43JruJWqHrmbhsS2qS3VarFcuXL8esWbMAVCdGa5LE9957LySS6/+ns3ZJ9yNHjlz3/QOAVCpFQkLC1Tu2YLU/EC1atAj9+vVr0LgrZ5E/88wztkT3uHHj8NBDD6FTp07w8/ODQqGw/Q6FhoYiMzOzQSX7iVoqn8hwmPdZIMZfiUM3mQT6Uv5fQS2XR2YJoLYvs59izkfwmDtcFBERERG1JAEBAQiM7Yqsi2UIlWvstoVVihHw5039RM3JbDHjcv5FVFzOh8lgQGVuAdyO5sBX5wWNKPqqSW6zxYqM4iqk5Fcgt0yPmisPIpUIgT4C1F4iiNwFWORiQCGFwWJCcZEBBpEZRqsFFhFgQQmC/DUIaBcIuUIGhUIOuVIJhUwOqUgKsSCuTkq3sGuFRqMRpaWlMJlMkEgkqKysZBlzIiJqMia7yeWCL2a6OoRWJzc3FyEhIfVuB6qT4rWTgDWP65tZXKMhfW6E2rNm65v53ZDS4LGxsejRowcOHTqEb7/91pbsXrVqla3EdXOUMAeAhIQEeHt7o6CgALt27UJlZeV1Xxe8Nag9A16lUjUpeV9aWorvv/8eQPXruWzZsjr71lQ7ILqZJQR1Qob8N0Tq7S/4+cnUMOpMkCr4dpBalopyPaKkaof2C4ZUdIwb64KIiIiIqKVRqVToEBuHw9tXIxSd7LYlyD1QmFcBldrx/QTR9VJaWYTLWZmwVOpRVVwM8elL8M+VwVsUBYiuMlZnQnJ+OVILKqE3/XktSyHAz0cEN18RJB5imFVSSJUySJVSiFUyGEQWZOdkIldeAZPZCJGoeka1v2cwRIIIYUGhN811pPLychQWFsJqtcJsNkMsFiMpKQlxcXEIDAx0dXhERHQTusp/vUR0Mzp48GCDtsfExNjdNRkfHw8ASEtLqzfJV1hYiLS0tOsQ6bWrvdZ1fTEnJyc3aH81yewTJ07g2LFjAP4qYR4VFYXevXs3NdR6CYKAqVOnAqhOxjakLHlb1KVLF9vdyHv27GnSPlJSUmA0GgFUz9Svy5kzZ2zrtBPdzLyVPkhzc7xByU8tR/ah9BsfENFVHPjlJNxl9jdhWKxWaGW5UElvjgt4RERE1PwCAgJQGqaH5YpKXBqZFPu/P+CiqKi1M1vMyMo9j9zUNOiKSlB59gLU2y8gOi8U3qKAesdml+qwKTkPP53MwcncclRZLFD7ixHRRYLo3jJoOqmgjNFAGekNTaQPZBEeUHXwg29iKNxDPOEX5AeJIIJG5Q5vD2+o5CoUFxXDZDIhJyfnBj0D18ZoNKKwsBB6vR6lpaWoqKhAaWkpSktLkZyc7LAMHRERUUMw2U3UCtWXKD148CBOnDgBAA5rTw8dOhRA9Qzpb7/9ts59LFu2rMWUda69rvahQ4fq7Ld8+fIG7W/SpEkQi6vrTH3zzTfIysrCrl27ADTfrO4azz33HBSK6nXFZs2ahfPnzzfr8W5Gvr6+6NOnDwDg22+/bVKFAZPJZHtc34z/Tz75pPEBErVQlYGOb/n83OQ4szHJBdEQ1c96zLHqT6apBFEDhrW48otERETkWsHRXZFpKHVo98nXt5jrFtR6lOpKkJ52CpXZ+SjLvQzR1tMIP6tGO1FUveMulujw65nL2JySj0tlepjlAnzCxYjpLUVAVzlUsR5QR/nALdIHknBPeMQFIbJ7e/S4pRc6J3ZCRGg4pCKp7cZ9uVwOsVgMuVwOq9UKg8EAnU53I56CJpHJZBg0aBAGDRqEqKgoBAcHQ6fTQSaTwd3dHTKZDEVFRTdV0p6IiFoWJruJWqE1a9ZgxYoVDu3l5eV4/PHHAVSX/655XGP8+PHw8/MDAMyZMwfnzp1z2EdKSgpeffXVZoi6aTw9PdGpU3XJsiVLlqCwsNChz+7du/HBBx80aH8BAQEYMmQIgOoE+bfffmv7gNzcye7Q0FB8+OGHAICSkhL0798fu3fvrneM1WpFcXFxs8bV0syePRtA9Qz4CRMm1Hv+er0eCxYssPvQFx0dbUuWfPnll04vgKxduxYfffTR9Q2cyIXcohxnGHippMg5X+aCaIjqZjZZEGmRO7SnmDOg6tXLBRERERFRSyYRJDglvuDQHi/3QOb5XBdERK2R2WrGxbx05KakoqqwGFVHU+C3rwyRxlgooKhzXE2Se0tqPvIqDLC4ixAcJ0ZsXym8OrtBHeUNTZQfJGFaeMQFIap7e/Ts2xNxse3h4+UDieivakcKhcJWnVGv18NsNkOv10MQBMhkMtvkiZZOp9PZlgq82ZL2RETUcnGRRqJWqEePHpg8eTJ27NiBCRMmQKPR4NixY3jrrbdw9uxZAMCTTz5pSxLXUCgUmD9/PiZPnoz8/Hz07t0b//d//4cBAwYAAHbu3Im33noLFosFMTExSElJaREzrJ588kk8/vjjyM3NxYABA/Cvf/0L7du3R2FhIdatW4eFCxeiR48e2Lt3b4P2N2XKFGzevBmZmZn473//C6D6OY2NjW3O0wAAPProo7h48SJeffVVZGdnY8CAARgyZAjGjh2LxMREeHl5wWw2IycnB0lJSVixYgVOnjwJABCLxXZl6VurUaNG4ZlnnsEHH3yAnTt3Ii4uDjNmzED//v3h7e2NiooKpKamYteuXfjxxx9RVFSEBx980Dbe29sbo0aNwrp167BhwwYMHz4cM2fORFhYGC5fvowffvgBS5cuRWRkJIqLi1vM+vRE1yImNBEForPwtrjZ2kSCAHdBDavV2iL+lhMBwOGdaUhUOia7q6pOwl2lvfEBERERUYtXFm2C5bwVolrvad1lEuxcfgChL9/hwsioNdAZq3AxOw3mokqUFRVAti8dUaYYSCCuc0xhpQEHMouRW16d1DW5iREaBigCJZBr3SDXKmFVSSHzUsE/0B/+vn6QiOu/TB8QEID09HQolUpUVVXZEsZBQUGQSCQICKi/hHpLcWXSXi6XQ6/XQ6lU3lRJeyIialmY7CZqhVasWIGhQ4di4cKFWLhwocP2u+++G++9957Tsffddx/S0tLwr3/9CwUFBXjxxRfttqtUKqxcuRJvvvkmUlJSWsSb0EceeQTr16/H6tWrcerUKdx333122xMTE/HDDz8gMDCwQfu76667MHPmTFRVVdlmDTf3rO7a5syZg86dO+P5559HWloatm7diq1bt9bZXxAEjBgxAvPmzUNQUNANi9OV3n//fXh5eWHu3LnIycnBnDlz6uyrVqttpelrfPzxx+jfvz8uXLiALVu2YMuWLXbbQ0NDsXr1aowaNao5wie64SK1kdjhthX9StvbtQeqlCjKKoFXiNY1gRFdIX/3WQiC2q6t0KSDxtcXYqHuC4pERETUdgWFJCLjbCki5B527T7FJlgtFggiFrakpimqLERe5gWYyqugO5sB71QLAtChzv56kwVHskuQnFcBKwCjmwghoQJUQRIovTWQealgVovhEeCNwIAAeLh71LmvK6lUKkRHR+PChQuQSqUwm80Qi8XQaDSIjY2FSqW6Dmfc/FpL0p6IiFoWvtsjaoUiIiJw+PBhvPTSS4iLi4NKpYKHhwcGDhyIZcuWYdWqVZBI6r7X5eWXX8aOHTswbtw4+Pn5QS6XIywsDA899BAOHTqEUaNGobS0ek0sD4+GvzFvLiKRCKtWrcKCBQvQs2dPqNVqqNVqdOrUCW+88QZ+//33Rr1Zdnd3x9ixY23fi8ViTJo0qTlCr9P48eNx9uxZrFixAg8//DA6duwIHx8fSCQSaDQaRERE4I477sB///tfnDt3DuvXr0dCQsINjdGVBEHAv//9byQnJ+PFF19Ejx494OXlBbFYDHd3d3Ts2BFTpkzBl19+iUuXLkGpVNqNDwkJQVJSEl544QXExsZCLpfDw8MDnTt3xiuvvII//vgDHTt2dNHZEV1/YkGMfC+9Q7uvmwxpG7huN7UcIWWOS0uctVyEsVM3F0RDRERELVnNOsDDBg9DsvKiw/Z4uQbnzma7IDK62VmsFmQVXEBeWjqqCouh3/oHos55IQDOJxhYrVaczSvHTydycDavAjq5CN4dxGjfRwrvRC94xARAEuYB347t0K1Pd3SIad+oRHeNgIAABAcHw9PTE1qtFp6enujWrVuDJ3e0BDVJe7lcDo1GA7VaDY1Gc9Ml7YmIqGURrM4WKyX6U1ZWFkJCQgAAmZmZaNeuXYPHpqSkwGQyQSKRICYmprlCpD/NmTPHtpZ2c/9aG41GeHh4oKqqCrNnz8bcuXOb9XhEdG3495gA4IddX2H0tlC7Nr3JgvW5GZi0aLqLoiL6S+rZy/D79gRkYvv7cX8u+Rmi/v1w2+DboNVqXRMcERERtWg7965Bj83udqXMAWCNqQgTXxvvoqjoZqQ365GVcx7m/HKU5eRAfeAywq2RdfYvqjJiT3ohCiqNMIkARZAEoTEiKP08IPNSweIuhX9IAIIDgyCTXtvScwaDwWGJvn79+t10S9oZDAbs2LED5eXltmsVgwcP5nt9IqI24lryjnVhGXMiarTVq1ejqqoKANCnTx8XR0NERA0RHBEN/bYKyCG1tcklIljL+XaQWoYzv5xAuysS3XqLGRZzISqKypCUlIS4uLibauYKERER3Rh9eo9E6rpfESnX2rWHVIlgqjJAory5koHUfOpLGJfry5CddR6W0iqUHTuNoEwPeNeR6LZarTiZW44j2SWwWAGdlxix0QLUIe5QervDrJXB9zoluVsjqVQKT09P2/ec0U1ERNeCZcyJyEFqamqd29LT0/Hcc88BAPz9/TFixIgbFRYREV2DBL9OSFXmOLT7K9QwVBpcEBGRPa9LZQ5tKeY85PuHorS0FKWlpUhOTkZlZaULoiMiIqKWTCaW4axblkN7R6U7ft9x2gUR0c2mqLIA2empMBSWomTr72h/IRjeFk+nfcv0JmxIzsPhiyWokImg7ShGYj8VvOMDIAvRwqNDILr26oqI0HAmuomIiG4ATuUhIgcdOnTAqFGjMGbMGMTHx0OtVuPy5cvYtm0bPvnkExQXFwMA3nnnnXrX/iYiopbDTeaGdI8CxFeF2LX7ucmR9fs5RA6Oc1FkREBBfgWiZY6zOdKNZyH1D4ZYZ0FRURHc3d2Rk5ODyMi6S0kSERFR2+Q/tgOMKy2Q1qoUI5eIkLPlLKwjOkG4osQ5EVA9Qzu3LAdVufmozCuCdXcyOps71dn/bF45DmWVwGSxQucnQYd4EbRBXhB5KiH3d0NEZCQ0bu438AyIiIiIWSoicmA2m7F27VqsXbvW6XaRSITXX38dU6dOvcGRERHRtTAGK4ArJnf7qWU4tPEPJrvJpQ7+cgqDFFK7NovVCmNVKuSiUFjkFlitVhgMBuh0OhdFSURERC1Z746DcciyAp3F9kuedBS7obyoAu5ebi6KjFoqq9WKYmMByrPUKL9wAV7HjAg2O/9cZDBbsCe9EBeKdaiSieATLUJcB3co/TSAlwIhUaEI8PG/wWdAREREAJPdROTE2rVrsX79euzduxe5ubkoKCiAXC5HcHAwBg0ahCeffBIJCQmuDpOIiBrJMyoElsNWiPDXrBaNQorCZCYPybXEpy8Caq1dW5a5FJVeQYDZAr1eD6VSCZlMBoVC4ZogiYiIqEUTi8TICL6Mzpftk91hKiU2L9+HO5+8zUWRUUtktppRrC+AosKC/OwjiM4KhKfFx2nfwkoDtqcVokxvQrmPBB0TRPAO9YFVK4dnpD/CQ8MhETf/ZXaZTIZBgwY1+3GIiIhuNkx2E7USc+bMwZw5c67LvsaMGYMxY8Zcl30REVHLER/cGdmSA2hn8rJr9xSrYTFbIKpV8pHoRtHrTYi2OiawUyznUeDlBVlZ9VreQUFBkEgkCAgIuNEhEhER0U2i8z3DUTo/E5pa6ySLBAGycyWwWqwQRCxlToDOUIXckotQVlohXLyEBHNvKK1yp33PFVRgX0YxqkSAIlaCngnuUPlqAB8lotpHw9NDe2ODbwWYtCciouuNVzSJiIiI2ohgt2Aku2c7tAe4KZF7NtcFEREB+7ekwl/teHGxTHcCXhpvqNVqaDQaaDQaxMbGQqVyXNubiIiICADae8fhiPiCQ3tnhQaZqXy/S8CF7As4n50MobAK2oxLGGTq7zTRbbZYsS+jCLvTi1CkFCGwpxSdevtBHqyFtkMguvTowkQ3ERFRC8FkNxEREVEbIQgCivwsDu3+bnKkrDvkgoiIgNJ9KRAJ9rOsis06KP2C4O3pDa1WC09PT3Tr1g2BgYF17IWIiIioWkV3xzYvhQz7vtp944OhFqWwtAB/nPgdynITIi/rMRADIXJyebzKaMaGs3lIzq9AsZ8UXW9VIiIhEOIQDdr3SkB0ZDQkIhZMJSIiain4vzIRERFRG6KM9gXO2rd5qaTYcTLTNQFRm2axWBBeJQAy+/Yz1iyIEzrDQ+Vpa+OMbiIiImqIAUPvRNbv+9BOprFrb1clgcVghkgmdlFk5EoVxgqcOJMEaX45EkvUiLd2cNqvqMqI31LzUWSyQIiRYkBXD8i83aEK80JMTAwkEl5OJyIiamk4s5uIiIioDYkNTUCBqNyuTSQIUFuYSKQb79iRSwhVO/7sFVmSIVdqnIwgIiIiqp+vyhdH3TIc2uPV7vh96zEXRESuVmYoQ2ZWKvRpWYjMliHe7DzRfbFEh/VnLiNHsMKruwy9+vlBEuSBoK6RiIuLY6KbiIiohWKym4iIiKgNae/VAWfUWQ7tASo1SnJKXRARtWXnN52CXGL/kcRgMcO3nRZSQeqiqIiIiOhmpxkVDpPFfvkehUSMrI3JLoqIXKXUUIqLWWkoPn4KIefliDFFOO13Nq8cv6XmI0ctRmx/JTp0CYA0RIOOvTohyJ9L6RAREbVkTHYTERERtSFSsRSXvCsd2v3dZEjb+MeND4jaNP88x5/FFOtlhAwd44JoiIiIqLUY0PE2nLZcdmjvKHVDZbHj+w9qnUoNpcjOOo+CAwfhd1KCcGOQ036Hsoqx/0Ixcvyk6DPEHcExvnCL8UVit85wU6tvcNRERETUWKy9QkRERNTGSCM9gXT7Nl83GQ7tO4euDw50SUzU9mRllSBK4VjCPNOagvZhYxEW08kFUREREVFroJAokBqci8TcALv2CLUKW77chjueGe2iyOhGqUl05+7cgcjsUPiatA59LFYrdqcXIrWoCsVhUtzW1xNSHzUC4sMQHNTuxgdNRERETcKZ3URERERtTGhELMqht2uTiEQQVbJsNN04h9aehIfC/mfOarVCramEWsoZNERERHRtOtw5AOUmo12bSBAgTquCxWR2UVR0I5QaSpF98TwubtmI9hcjnSa6TRYrtp0rwJkSHQxxMgwf5AdZkAaRPeKY6CYiIrrJMNlNRERE1MYk+nXGWdVFh3Y/hRsMlQYXRERtkTI116Ety1KC6MGjIAiCCyIiIiKi1qSLf1ckSS44tqs0OHMw1QUR0Y1Qk+jOWr8OCXnx0JrdHPoYzBZsScnDmUoDhA5W3NLLD+JgD8T17gQvTy8XRE1ERETXgsluIiIiojbGTeqGDG2RQ3uAmxwZu067ICJqa8rK9IgRKR3aU3Ae2o4JLoiIiIiIWhtBEFDYzXEGt7dShj9WJMFqtbogKmpOZYYyXMw+jwvr1qBTURd4mB2rBVUZzdh4Ng9nTWa4JZoRFiqGLEyLhO6JUDlZYoeIiIhaPia7iYiIiNoga5jjhR8/NznObD7hgmiordm94SwC3OQO7RZZFjRyDxdERERERK3RLbfejkxTqUN7vFiD0suO7XTzKjeUI+tSGjJW/4AuJd2hMTsmrssNJmw4m4ezIit8OxkRECCByVeODvFxkEq4pBMREdHNisluIiIiojYoMDoaBpjs2uQSEXR5XL+Qmp/+cDpEV5QqLzbrENatJ8QisYuiIiIiotYmxD0EB3wcS5bHuKuwcdFmF0REzaHSWImsnDSk/7AC3Sr6OE10l+n/THQrBXQfokGX7lHoPnoA7rr7LsjljjdhEhER0c2DyW4iIiKiNqhTQGekyh3XTPaWusFitrggImorTCYLogyOH0POCpnw73erCyIiIiKi1sx/VAIqzUa7NpEgwKtADLPOWMcoulnoTDpcuHwOqSu/RY+q/nUmujcm5yHZQ4xbh3nAL1SLkO4xaNeunQsiJiIiouuNyW6iNmjOnDkQBAHCFTOqiIio7fBR+iLFwzHZHeCmQM6Jiy6IiNqK/XvSEermeBGyWJQKD7WnCyIiIiKi1uzWiMHYL0tzaO/q7o69P+11QUR0vehNOmRcTkXqd9+gt+5WuJuVDn3K9CZsPJuHM54SDB/mCY8gLaJ7d4Svr58LIiYiIqLmwGQ3EVEzKS4uxieffIJ77rkHMTEx8PT0hFQqhbe3NxITE3H//fdjyZIlKC4urnMf6enpthsTrvxSKBQIDg7G7bffjkWLFqGysvLGnRwRtQq6do7r0vm7yXB27QEXRENtxaXtyZBL7D+GGCxm+EYGQi5RuCgqIiIiaq2kYikKbhFgsVrt2tUyCXL2XILFwqpGNyOD2YD0/HNIXf41eugGOE10l+qqS5ef9pNizG0+UAd5oH2fRHh4aG98wERERNRsmOxugvLycuzcuRPvvPMOJk6ciIiICFvyKTw8vFmOuXfvXkydOhVhYWFQKBQICAjAiBEjsHz58mY5Ht18li5davs5TE9Pd3U4bZrFYsFbb72FiIgIzJw5E6tWrUJqaiqKi4thMplQWFiIEydOYNmyZXjooYcQEBCAp59+Gvn5+Y06jl6vR3Z2NjZs2IAZM2agS5cuOH36dDOdFRG1Rj4xoTBb7S/uqWUS5J8rc1FE1BYEF+kd2lJwGaGDRrkgGiIiImoLhnYfjTOWyw7t3VRaXDyR6YKI6FoYLUak559Dyrdfoou+P7RmtUOfUp0RG5PzcCZAijuGeEMZpEFc705Qqx37EhER0c1N4uoAbkZjx47F9u3bb9jx5syZg7lz59rdaZqbm4tNmzZh06ZN+Oabb7Bq1SooFJwJQw0zZ84czJkzx9VhtEpVVVW477778PPPPwMApFIp7rrrLtx2222IjIyEVqtFUVERMjIy8Ntvv+GXX35BSUkJPvroI9xyyy2YNGlSnfu+88478frrr9sd6/jx45g/fz6OHz+OlJQU3H777Th16hRUKsfysEREV0oM7IIL0gOIMPnatWtEvABEzeP06VxEKh3/j7qIZLT3G+uCiIiIiKgtCHIPxobITHTM8LdrD3ZTYNWSXZj6fpiLIqPGMlvMyCg8j5RvlqCT/hZ4m9wd+pTqTNiQnIczwXKMH+QNRZAHOnRLgFTqWNmKiIiIbn6c2d0E1lplj7y8vDB8+HC4ubk1y7EWLVqEV199FRaLBVFRUfj8889x4MABrF69GoMHDwYArFu3Dg899FCzHJ+IGufxxx+3Jbr79OmDs2fP4rvvvsPDDz+MwYMHo2vXrhgyZAimT5+OZcuW4cKFC3jjjTegVDqW27qSVqtFQkKC7atnz5546KGHcPDgQfTr1w8AkJGRgc8//7xZz5GIWo8Q91CccXdcnztQpURhVpELIqLW7uivZ6BV2l9ktFqtUPpaoJbyJgsiIiJqPnFD+6LA5Lj8VzQ0qCzismA3A4vVggvF6Tj71WLE6fvAz+jh0Kdcb8KGlDycDZHj7kE+ULXzRIfuTHQTERG1Zkx2N8HkyZPx7bffIiUlBQUFBdi4cSO8vb2v+3EKCwvxf//3fwCA0NBQ7N+/Hw899BB69uyJO++8E5s3b8bYsdUzYJYvX35DZ5sTkaNVq1bh66+/BgB0794dW7duRURERL1jNBoNXnrpJSQlJSE6OrpJx5XL5Zg7d67t+w0bNjRpP0TU9giCgOIAq0O7n5scqesOuSAiau08Mwsd2rKsJYgePBqCILggIiIiImoregX1wR6PZIf2eI0bNn3yqwsiosawWq3IKrmAs19/hlhDLwQZvRz6VBrM2JCShzOhctw1yBfKMK/qGd0SJrqJiIhaMya7m+Cxxx7Dfffd1+TEVEMtXrwYJSUlAIC33noLPj4+dtvFYjEWLlwIsVgMAJg3b16zxkMt0/bt2yEIAqZPn25rq72OfM1X7Zsh5syZY2t3Jjw8HIIgYNq0aQCAs2fP4tFHH0V4eDjkcjn8/f0xfvx47N+/3+n4u+66C4IgwNPTEzqdrt74TSYTAgICIAgCRo1yvlbn7t27cffddyMgIAAKhQKRkZGYMWMGUlNTAQCDBg2CIAgYNGhQnc9PzXNgsVjw2WefoV+/fvDy8oJarUbnzp3x3//+96qxXs1//vMfANXJoyVLljRotnaNDh06oEePHk0+dq9evWyPMzIyGj3+yucwNTUVM2bMQGRkJJRKJcLDw/Hwww877PvEiROYPn06IiMjoVAoEBISgpkzZ+LyZce10JxZvXo17rnnHoSGhkKhUECr1aJHjx549dVXUVRU/6zS/fv3Y/bs2Rg0aBACAgIgk8mg0WjQsWNHzJw5E6dOnap3/LRp0yAIAsLDwwEAxcXF+Pe//434+Hio1WpotVoMHDgQ33zzTYPOhehm5REb6NCmVUpxISnLBdFQa3YppwwxEsf/G1OFc/CMjHFBRERERNSWiEViCEP9YLCY7dqlYhEkFwGz3uSiyKghsssvInnZ5wjRdUaIwcdhe5WxOtF9MkSOuwf6wC3cBx26xNuumxIREVHrxWR3C7Z69WoA1TM/77rrLqd92rVrh2HDhgEAfvvtN5SVld2o8KiN+Omnn9CtWzcsXrwYGRkZMBgMuHz5MlavXo3+/fvj+++/dxjzyCOPAKhOHtb8HNfl119/RW5uLgA4Lcf/1ltvYeDAgfjxxx+Rm5sLvV6P8+fPY9GiRejWrRs2bdrU4HMxGAwYPXo0HnvsMezbtw9FRUWorKzEsWPH8NJLL6F79+7Iyclp8P5qO3bsGI4cOQIAGDx4MBITE5u0n6aqXY7LbDbX0/PqtmzZgm7dumHRokU4f/48dDodMjIy8MUXX6BXr144c+YMgOqKEt27d8fSpUtx/vx56PV6ZGVl4ZNPPkGvXr2QnZ1d5zGKioowdOhQjB8/HqtWrUJmZib0ej1KSkpw+PBhzJkzBx06dKjzhoqlS5eib9++eOONN7Bjxw7k5ubCaDSirKwMp0+fxieffIJOnTph4cKFDTrns2fPomvXrpg7dy5OnTqFyspKlJSUYNeuXZg6dSqeeuqpxj+RRDeJuJDOuCQqdmhXGhp+ww5RQ+z+5TQC3OUO7RZlPtxlGhdERERERG3N8LjRSBJnOrR3d/fA4Z92uyAiaojcilwkf/cFPKuiEKV3vFlXb7JgY0o+jreTYcKtPtBE+iEmsQNEIl76JiIiagv4P34LZTAYcODAAQBA3759IZPJ6ux76623AgD0ej0OHWLJ0bamZ8+eOH78OF5//XVb28aNG3H8+HG7r549ezZ638ePH8fkyZPh7++Pjz76CPv378e+ffswZ84cKBQKmM1mPPbYY8jLy7MbN3LkSLRr1w4AsGTJknqPUbPdx8cHd9xxh922FStW4J///CesViu8vLzw1ltvYe/evdi7dy/eeustSCQSTJo0CZcuXWrQ+cyePRsbNmzA8OHD8dNPP+HQoUP46aefcNtttwEATp06hbFjxzYpWbxjxw7b45EjRzZ6/LU6fvy47XFQUFCT95OdnY2JEydCq9Xif//7H37//Xfs2rULzz77LARBwOXLl/HII4/g4MGDeOCBBxAVFYXFixfjwIED2LZtG+6//34A1bPLn3vuOafH0Ov1GDZsGLZu3QqxWIz7778fy5cvx/79+7Fr1y688cYb8Pb2xuXLlzFq1CinM9VNJhM8PT0xbdo0fPHFF9i1axeSkpLwyy+/4LXXXoOPjw/MZjOeeuopbN26td5zrqysxNixY1FQUIDZs2dj+/btOHToED777DPbz/GCBQuwcePGJj+vRC1ZjDYGp9WO63YHqNSoKObahXT9iE9ehOiKqjIlFh1C+90CsYgzboiIiKj5ecg9cK5rsUO7p1KKlO1ZMBs4u7ulKajKx9mVSyEt8Ud8VbjDdoPZgk0pefgjWIp7BvrAI9IP0QntmegmIiJqQySuDoCcS05OtiXcOnToUG/f2ttPnz6NwYMHN2ts1LKo1WokJCTY3egQGxtrK818LZKSkmxrT2s0f8246tOnD6KjozF16lSUlpZi2bJl+Pvf/27bLhKJMH36dMydOxdbtmxBVlaWLWlY2+XLl7Fu3ToAwJQpU+xu6tDr9fjb3/4GoDoRvm/fPrulA/r27Ytx48ahb9++SE52XHPLmYMHD+Kxxx7DokWLbG3du3fHuHHj8Mgjj+Dzzz/HoUOHsGjRIjzxxBMNfJaqHTt2zPa4W7dujRp7Pbz11lu2x87KuTdUSkoKYmJisGfPHvj6+tra+/fvD4lEgnfeeQd79uzB6NGj0atXL2zevBkqlcru2DqdDitXrsQPP/yAvLw8u/0AwGuvvYakpCRotVps2bIF3bt3t9vev39/TJkyBX379sWlS5fw0ksvOZQSv/322zF58mS7YwNA165dMXr0aPztb3/DwIEDcezYMbzyyisYMmRIneecl5cHg8GAffv2IT4+3tbevXt3DBo0CImJidDpdFi4cCFGjBjR8CeT6CYhEUlwybcKuKI4jL+bHOc2HUGnibe4JjBqVcrK9YixOt68eUaUgbjO4258QERERNRm9es7HOkHzyJc7GnX3tvdB2c2HET8HX1dFBldqURfgrOrl0FfIMeAyvYO200WK7ak5uNQkBQTB/pAE+WP6ATHfkRERNS6MdndQmVl/bVOprMkYW0hISG2x5mZjqWYGnocZxo6Y/Za9Hml7c2W3P/qzZMw++KLL+wS3TUmT56MF198EdnZ2di1a5ddshuoLkn++uuvw2Kx4Msvv8TLL7/ssI9ly5bBaDTa+te2evVqW3nzOXPm2CW6a8TGxuKVV17BM88806Bz8ff3x/vvv+902/z587FmzRrk5eVh4cKFjU525+fn2x5fmdytraysrM41tWUyGWJjYxt8TJ1Oh+PHj+Ott97CDz/8AKB62YPHH3+8wftw5sMPP3R6Dk888QTeeecdANXnu2PHDodkMwDMnDkTK1euhMlkwr59++xm7JeXl2PBggUAgLlz5zokumuEhYXhX//6F5544gmsXLkSn376KdRqtW17cHBwvefg4eGB1157DePGjcPu3btRUFAAb2/vOvvPnTvXLtFdIzo6GuPGjcN3332H3btZ0o5aL3WML5Bm3+alkuLQjmQmu+m62LEpGf01Cof2MmkGPFTaGx8QERERtVkdvOKwJOQXhGf3smtv567A9+tSEDeqN0QSzgp2tXJDOc7+uhx5l6owosLxBgSL1YptaQXYHyDFvQO9oY0OQFR8w6+pEBERUevBd24tVO21t93c3OrtWzsBVF5e3qjjhISE1PvVq1evq++EWq3ExER06tTJ6TZBENC1a1cAQFpamsP28PBw23ryS5cudbqPmhLm3bt3dzjOli1bAFTPEp8yZUqdMU6dOhXCFSVR6zJx4kSnyVmg+vds4sSJAICTJ082eu3u2r+ztX8nr/Tbb78hMTHR6dfw4cPrPcaXX34JQRBsX0qlEr169bIlut3c3LBy5Ur4+fk1KvbatFptnbOXIyIi4O7uDgDo1KkT4uLinPbr3Lmz7fGVPxs7duxASUkJAGDChAn1xjJw4EAAgNFoxOHDh+vtW1FRgfT0dJw8eRInTpzAiRMn7NYxP3r0aJ1jBUHA5MmT69xek5AvLCxEcXFxvXEQ3axiwuNRINi/hxAJAsSVjusrEzVF6b7zUEjsS5UbrGZ4xkdCLubPGREREd04giAgaFAC8s2OS/b0cvNB6tYkF0RFtelMOpzd8gPS0i5iRLnzmfa70wux01uMiQO84RUTyEQ3ERFRG8Zkdwul0+lsj+tbrxsA5PK/LhBWVVU1W0zU9lythL6XlxcA+0RvbY888ggAIDU1Fbt27bLbdujQIZw4cQKA46xuALZtkZGR0Gq19cYQGRlZb5w1rrZuee2bO2qvgd0QNUlgoDrxeiMFBQVh5syZOHbs2FUT5lcTExNT780DNa9FfTPQa79eV/5s1C63HxgYaJe8v/IrISHB1tfZzQf5+fl46aWX0L59e7i7uyMiIgIJCQm2mwdGjx5t17cuPj4+9c76rvk5d3Y+RK1FvHcCTqodq70EKN2gL9e7ICJqTYxGMyJ0Vof2FCEH4X1vc0FERERE1NYNiRiGTf6On/sjPFTYs+o4LBaLC6IiADCajTi7Yw1OnjqGcRVDnfb5/UIRNmkETLzVGz6xQYjoGHODoyQiIqKWpNWWMW/oTM/6LFmyBNOmTbv2YJpAofirzKPBYKi3r17/10VopVLZqONcrez5pUuXOLu7DatrFnQNkaj6fpma9eWvNG7cOPj4+CA/Px9LlizBgAEDbNu++OILANU/685m1RYVFQGovyR4DV9fX5w7d+6q/a4249nf39/2uLCw8Kr7q612sjQvL6/OfuPGjYPVan/BPzw8vM7S5rXdeeedeP31123fy+VyeHl51ZuobayGvub19avpAzj+bFy+fLlJcVVW2t9xf/jwYYwYMQIFBQUNGl/fjUANPWeg7p91opudQqLABe8S4IoCMYHuCqRtPYa4O+q/WYioPnv3pKO9m+Pf2kuSVHTQjrvxAREREVGbJxPLoLk1HCUr9PC4ospMd7UfMnedQNitzivdUfMxWUw4s/9XHDy8A5OrJgBOLu/+kV2KNUorJg7ygW9MEMI7Oi57R0RERG1Lq0123+xqzxK9Wmny2rNIr1by/EpXWw+c6FrIZDLcf//9eP/997Fy5Ur873//g1qthk6nw/LlywEA48ePr3fm9vV0PW6CqUvt0t1HjhzB0KHO7z6+Flqt1m62882odrI4KSnJrtR4fWr/rTIYDJg4cSIKCgoglUrx9NNP484770RsbCw8PT1t1S7S0tIQFRUFAA43GBCRI3mMF3DFfTdeKimO/HaKyW66Jmm/paK30rFUubydCipJ/TccERERETWXETGj8LP3J5hQbP9eN1qrwnffHMC0AQl2Nz9T87JYLUg7vBV7dq3BVP0UiJxcwzl9uRwrxSZMGOID35hAhMcz0U1EREStONl9+vTpa95HYGDgdYikaWondrKyHMuK1lZ7dnZISEizxdRc9r/qfH1gah0eeeQRvP/++ygvL8fKlSsxbdo0rF692rb2sbMS5gDg6ekJoP5Z0jUa0gcAcnNzG7y9dunqhrj11lttjzds2IDnn3++UePbitqz0H19fZt0w83WrVtta4EvXLjQVi7/So2dnU/U1sVEJKIABfCG2tYmEgQIpQ27KYXIGavViuDCSsDfPtmdiSLE3DqqWW9EIyIiIqqPu8wd6O+DijUGqEV/LSEoEgR0UgUg5+AZBPXu6MII2w6r1YrzJ/fit01fY7LpQUgEx5sM0goq8Y1Fj/FDfRAQFYCIBJYuJyIiomqt9vbEDh06XPOXh4eHy+KPjY2FWCwGAJw5c6bevrXCg4sxAACosUlEQVS3x8XFNWtc1HK11IvFHTt2RN++fQFULw0A/FXCPCwsrM4Z0PHx8QCqZ+fWlDR3prCw0Jb4vJqDBw82eHtjZ1B36tQJXbp0AVCdjD116lSjxrcVXbt2tT3es2dPk/Zx8uRJ2+N77723zn611wcnoqvr5NMJp1WON9j5K91gqKp/SRWiuhw7kYNoJ8vspElS4eUf7IKIiIiIiP5ye9wd+M3D8fN7nFaNjZ/vZpWwGyQz5RA2/fAhJprvh1xwnJuVVVKFJbpKjB3ig6Aof4QnMtFNREREf2m1ye6bnUwms62VvW/fvnrX7d6xYweA6vV7e/TocUPio5an9jrvtddxbwlqZt7u2rUL27Ztw2+//QYAmDZtWp1J+pokuMViwbffflvnvpctW9bgD58rV66sc+3miooKrFixAkB1gr4plR1mzZoFoPqO5GnTpkGn0zV6H63dsGHDbGtkf/jhh026cGAymWyPay/jUJvFYsFnn33WtCCJ2iiVVIV0H8ebiwLcFUjfedLJCKKrS1p/Fv5ujiXMTZ46qOXuTkYQERER3TjeSm+U91VAZzHZtYtFAtorApF/JMVFkbUd2ekn8OtXb+AuYTrUkDlszy3T4/PyCtw+1AftIv0R0bl9i53wQURERK7BZHcLNm7cOABAaWkpfvzxR6d9srKysGXLFgDVycHaa31T21I7OXvu3DkXRuLo3nvvhbu7O6xWKyZPngyLxQJBEDB9+vQ6x4wfPx5+fn4AgDlz5jg9p5SUFLz66qsNjiMnJwf/+Mc/nG577rnncPnyZQDAzJkzG7zP2iZOnIjJkycDqJ4lfttttyEjI6PeMZWVlS3u5oTmpNVq8dRTTwEA9u7di7///e+wWCx19s/NzcXixYvt2mJi/rqDe+nSpU7HzZo1C0lJSdceMFEbI43xdmjzVkmRsumEC6Kh1kCTXgCxyP5iZIm1CuEDboVYELsoKiIiIqK/3J5wJ7a7OS6HmOipxrpPfqv3Mytdm7ysFPzyyT8xWv4IPOBYDaiw0oDFxeUYPNgb4VEBiOzKRDcRERE5YrLbRdLT0yEIAgRBwKBBg5z2eeSRR2yl1P/5z3+ioKDAbrvZbMYTTzwBs9kMAHjhhReaNWZq2bp27Wqb3f2vf/0LmzdvRnJyMlJTU5GamlrnjOYbQa1WY9KkSQCqE84AMGTIEISFhdU5RqFQYP78+QCA/Px89O7dG/PmzcP+/fuxf/9+vP322+jTpw8sFost+Xm1Dzw9evTAxx9/jNtvvx0///wzkpKS8PPPP2PkyJH49NNPAVQ/jzNmzGjyuX722WcYPXo0AGD37t2IjY3FlClTsHTpUuzcuRNHjx7F/v37sWLFCjz77LOIiIiwPSc1M55bu9deew29e/cGAHzwwQfo1q0bFixYgD179uCPP/7Atm3b8NFHH2HcuHEIDQ3FJ598Yjd+xIgRthshZs+ejRkzZmDjxo04fPgwvv/+ewwbNgxvv/02brnllht+bkQ3u9iIeBTCvmKCSBBgLeZbRmq8jMxitJcqHNrPStIREM71L4mIiKhlCFAHILePBUaL2a5dKhYhQhaES9uOuCiy1q04NwO/zH8Gw9SPwNfq5rC9VGfC4oJS9BusRVSkPyK6MdFNREREzjkugkJXlZqait27d9u1lZeX2/69cqbhyJEjERAQ0OjjeHl54a233sKMGTOQkZGB3r174+WXX0ZiYiKys7Mxf/58bNu2DQBw33331Zk0p7bB3d0df/vb3/D2228jKSkJw4cPt9u+bds2l/6MPPLII3ZlpR966KGrjrnvvvuQlpaGf/3rXygoKMCLL75ot12lUmHlypV48803kZKSYlfK3Zk33ngD7777LjZs2IANGzY4bO/QoQN++eUXSCRN/9OoUqmwZs0a/Pe//8W8efNQUlKCb7/9tt5S7AqFAtOnT8drr73W5OPeTORyOTZv3oxp06bhxx9/xNGjR22zvZ3RaDR236vVanz11VcYN24cdDodFi1ahEWLFtn1GTRoED766KNGr71O1NYl+nbC78pvcUtVrF27n9wdRr0RUrnURZHRzWjnr2cxVuP4f3Op+jI0Sg8XRERERETkSBAEjOx0J3bv2YDB+g5227p5u2P517/j4YFdIJayKs31Ul6QgzVvPo4+3o8gyKJ12F5pMOOLvBJ0vdUTsVH+iOjRgYluIiIiqhOT3U2we/fuOssvFxQUOGzbtm1bk5LdAPD4448jOzsbc+fOxblz55wmCEeNGoUvvviiSfun1uXNN99ETEwMvvrqK5w8eRIlJSW2mf+u1qtXL8TGxiI5ORlarRZ33XVXg8a9/PLLGDhwIN577z3s3bsXJSUlCAgIwNChQ/H8888jLi4OL730EgDYKiHURSaT4ddff8Wnn36Kr776CmfOnIHBYEBUVBTuvfdePPfcc1AqHctmNZZIJMLLL7+MJ598EsuXL8eWLVtw9OhR5Ofno6KiAh4eHvD390fXrl0xcOBATJw4EVqt9pqPezNxd3fHDz/8gN27d+PLL7/Erl27kJ2djaqqKmg0GkRFRaFXr14YPXq0w40bQPXs7kOHDuHNN9/E1q1bkZeXB61Wi44dO2LKlCl4+OGHceHCBRecGdHNzU3qhjTvQtySZd8e6K7Ahf3JiLo13jWB0U3JevwiFH5edm0Gqxne3RMhFfPGCSIiImo5Qt3DsK5nBQbuskAs/FXVSCoWobsmFKe/XIuER8a5LsBWpKo4H7+89iAS/aYhwuzjsF1vsuDL3GK0H6hFXJQ/wrvHQSRipSkiIiKqm2C1Wq2uDuJms3Tp0nrXGr6Ssxm16enpiIiIAADceuut2L59e7372Lt3LxYsWIBdu3YhNzcXWq0WnTt3xvTp03Hfffc19hQaLCsrCyEhIQCAzMxMtGvXrsFjU1JSYDKZIJFI7NbYpbaptLQUAQEBqKqqwsyZM7Fw4cLrsl+j0QgPDw9UVVVh9uzZmDt3rt327du3Y/DgwQBcP7udyFX495gaY+nuTzFpq/3MbovVil2V2Rjx1hQXRUU3m6KSKiS/tBmd/e2rc5wUZyL4iaEI8AxyUWREREREziUXnkHa59sxqMp+drfFasU3587hgY+mQK659hvk2zJdeTF+mTUJwYGT0dUU6rDdaLbgq+xi+N3iga7Rfojo0xFiCWfUExERtSbXknesC2d2N8G0adMwbdq0a9pHeHg4GnOfQb9+/dCvX79rOiaRKy1fvty2bvjDDz983fa7evVq23779Olz3fZLRNRWxUYmoHBrMbygsrWJBAGmAt4fSQ23fVMK+jq5GJwrz0BHd08XRERERERUv2jPWPzafTX67DRBIfrrkqlIEHCrbzDW/fN9eE3869pcv379IJPJXBHqTclQWY6N/7wP3sET0NXomOg2W6z4/lIJfPp6oEuUL8J7xTHRTURERA3CGjBE1OxMJhPee+89AECPHj3QvXv3Bo9NTU2tc1t6ejqee+45AIC/vz9GjBhxbYESERE6+3TGacVFh3ZfuTvMppaxNAa1fPl706FVOpYql8b6QSnhjCgiIiJqeUSCCGO73o113kkO20I9lKjQhQN5ZTc+sFbAVFWJLS9MgqTdWPQ1Rjtst1it+DGnBMpeGnSL8kZE746QyDhHi4iIiBqGyW4iahaFhYVITU3F77//jsmTJyM5ORkAbOtrN1SHDh1wxx134NNPP8WePXvwxx9/YNOmTZg1axa6du2KrKzqhWXfeecdSCT8IEREdK3UMjXSfAoc2gPdlMg6dM4FEdHNRm8wI7rK8caIDFE+OvQa6oKIiIiIiBomXBuJih5KFFurHLYN8fNB5pojLojq5mYy6LD9hcmoDB2CoYaOTvusyymBtbsbekZ7I6JPPBPdRERE1Ch850BEzeLDDz/Eq6++atc2ZswYjB8/vlH7MZvNWLt2LdauXet0u0gkwuuvv46pU6c2OVYiIrInivYEsuzbvFVS7PvlMML6xDofRPSnXTvSEOOucmhPl57DLZ7DXRARERERUcOIBTFGdRyHH099jocuDrLb5q2SQaOMR+nvJ2GODUJlZSXLmNfDYDBgz86dqFw2D4UdB+EeXTen/TbnlqKssxoDov5MdMsdqwMRERER1Yczu4moWUkkEkRFRWH27Nn4/vvvGz1+7dq1eOKJJ9ClSxcEBgZCJpPB3d0dHTp0wIwZM3D06FHMmjWrGSInImq74qISUGittGsTCQL0uSxjTld3blsqfN0cL/xagqVQydxcEBERERFRwwW6B8GzezQyhSKHbf39PHDxUDGK8gqRlJSES5cuuSDCm4PFZIJ+2Xu4GN8X9+j6Ou2zO68MOfEK9I/1qU50K5joJiIiosbjzG4iahZz5szBnDlzrnk/Y8aMwZgxY5o8ftCgQbBardccBxFRW9LZtwt+Vy5HP539eno+MneYzWaIxWIXRUYtncViRXBBJUTB9uty56McUf2HQiTwXlsiIiJq2cwmM9QFGqwM243n0kfZbVPLJOjom4Dc3/eg1M8bycnJ8PDwgErlWNWmLbMYjTj6wnSkxHfBw5W3AoJjn0OFFUhtL8OQ9j7Va3SrOEueiIiImoZXm4iIiIjIjlqmRqr3ZYf2QDclLh1Nv/EB0U3j8JGLiHVysfes7Bz8/ENdEBERERFR47mL3OHh0w5/SC84bOvm647cigjoki/AZDIhJyfHBRG6lsFgwPbt2+2+DAYDAMBiMODQ05OxO7wdplcMg0hwzHSfKK7E0QgJBnfwRXjvjpCq5Tf6FIiIiKgVYbKbiIiIiByIor0c2rxVUqSuOeyCaOhmcWRTCgI1jhcrdV5VcJdrXBARERERUeMJgoBIRTQ2RR6H2Wqx2yYVizA8KBzGvRegr6iCTqdzUZQtj0Wnw8En78WuyDA8Vj4aEpHjpeeU0irsCRHhto6+iOgdB5mbwgWREhERUWvCZDcREREROYiLdr5ud9VFg4siopuBNqMQUrH9R4xKGBDSvx/EIpa/JyIiopuHUqRCiEd7bHM/5bAtTKuEh09fKLccgkLBZC0AWHU6HHpiInZGx+CxilEO7wkB4EK5Dj9rKjGsvSfCe8dB5q50siciIiKixmGym4iIiIgcdPbtgjOKbId2H5k7TEaTCyKili41rQAdpI6zuk9L0hEc2tEFERERERE1jdFoRFFREeRlKuwPPI9yq96hz8AgLU5d9ocmt8QFEbYwVTokPXUvdsbGYUblSKeJ7pwqA76RlaBnACAP0UKmYaKbiIiIrg+JqwMgIiIiopbHTeaGFO889MuOtmsPclci81AqIvp2cFFk1FLtXn8Wd2odL1oWu+VBo/JwQUREREREjZeTk4OLFy/CYrHAbDbDUxqApSFb8VTW7Xb9FBIxhgWGY9u7KzDu646QKm/OdacNBgP27t1r19avXz/IZLJ6x5w7dw4GgwGiinKEbV+FQ7cMxmNVw50munOrDPhCXIgEj0rA1x8Gsfm6nwcRERG1XZzZTUREREROCbGOCUpvlQxpa5JcEA21eCcuQSm1L1VutJrh1SsRUpHURUERERERNVxlZSVSU1Oh1+tRWlqKiooKCMViGN3VOKA659A/zFMJuUcPpLyx0AXRuk5ubi7KyspgzbuM0N+W43jfoXhMNxwyJ4nuyzoDlkqKEKcuhUEjgkSjYOl3IiIiuq6Y7CYiIiIipxKjuyDXWubQbi5wQTDUouUVVCJOcExop0guIapDTxdERERERNR4OTk5MJlMqKqqgkwmg7u7OxQyBdzKPbHe/yjK4aScebAW21IUKN5xwAUR33iVlZVIS0uDMjcH7Q78hLN9x+Fxw7A6Et1GLBXyESUvRIlUDzd/LSQSCQICAlwQOREREbVWTHYTERERkVOdfbvglCrLod1f4Q6D3vFCH7Vd2zcmI8zDsYR5rvICPNw8XRARERERUePpdDoYDAYAgFwuh1gshlwuh9KihJcxEF8Gb3UYo5CIMSwoHJvmfY+qjEs3OuTromaN8ry8PBQVFaGysrLOvjk5OZCcOgmv0+uR02UqHrH0d5roztMb8a20EHFaPcxeMsi81NBoNIiNjYVKpWrO0yEiIqI2hsluIiIiInJKJVUh1c9xGneQuxIZu8+4ICJqqQr3n4dW6TizW9GxHeQSlqkkIiKim4NCobCtVa3X62E2m6HX6yESRHATtIC7BgdUqQ7jwjyVUPn1w+FnXoWhtPwGR31tatYoLyoqQnFxMYqKipCUlIRLl5wn7vVbNuPi/mXQt38cUyTdIRE5T3Rv8reiW7AUylAtVL4aeHp6olu3bggMDGzuUyIiIqI2hsluIiIiIqqTumMQLFarXZu7XIKM9cddFBG1NJVVRsQaHNszRPmI6TnohsdDRERE1FQBAQGQSCRQKpUwGAwoKyuDwWCAl5cXOkZ2xNhb70ZSQq7Tcua3hngiRX0rUv4+B2aD0QXRN56zNcpLS0tRWlqK5ORkhxne+YsWYtf+JVAFPYG7VHEQiwSHfeYZjNgaKODWjn5QhnrCzd8Tvr6+8PT05IxuIiIiahZMdhMRERFRnbqHdUWWUOTQLipznMVLbdO231IR6e544TJdfh5eGh8XRERERETUNCqVCtHR0ZDL5dBoNFCrq0tv15TfjvSNxpCE27E8bLfDWKlYhDHhvthaFoeLr8+HxWJxwRk0jrM1ymUyGYqKimAymZCTkwMAsBqNyHr2SXx5cgVCNU9jlEckRIJjovui3ohNXjoMSghEu65RMCsc+xARERFdb0x2ExEREVGdOnh3xAm3TIf2AKUbKivqXsuP2o7z28/B103m0C6EqaCUOK7jTURERNSSBQQEIDg4GJ6entBqtXbltyUiCcJ9oxDdpTsOqFMcxrrLJRgTGoyfjwrIW/K9C6JvnLrWKLdarTAYDNDpdDDl5yNl4h34xC0bffA0BnkFO91Xmt6A9bJsxHqL0a5bFETu8ht5KkRERNSGSVwdABERERG1XHKxHBcCy4ErruUFuStwfvMfiB/XzzWBUYtgMJoRVWqASONm154vlCO6/1AITmb8EBEREbV0UqkUnp6etu9rl9/WyD2QGNENqzsvR8iBYgSatHZjAzUKDAjpgI0rdmBsaDt43jbgRoXdaFeuUS6Xy6HX66FUKiGTySA/fx5H//YAfrilB6aWjkWkj9rpfk7pDdgjSoe/QowSlQF6iQUqmQKDBg26gWdDREREbRVndhPRdZGeng5BECAIApYuXerqcAAA4eHhEAQB06ZNc3UoREQ3Nb/4CBitZrs2hVSM7O3nXBQRtRS7dp5HtJvj7O1kWSp8vYNcEBERERFR8wt0C8aQ+NuxPO53VMLgsL2DnxuCQ/ph33+XoHD7fhdE2DB1rVHu6ekJtz27kPbBLKzvMQozq8Yj0tN5ojtJr8d+IRUaiQH50kpI3BW28udERERENwKT3UREN8ipU6cwb948jBkzBuHh4VAoFFCpVIiIiMCkSZPw66+/NstxL126hPnz52PMmDGIioqCRqOBTCaDr68vunXrhocffhgrVqxAZWXd5Yi3b99uu5nhyi+VSoWwsDCMGzcO3377LUwmU7OcBxG5Ts+QnkgT5Tu0K/QKF0RDLcnpLSkI0jj+HBgCrFDL3JyMICIiIrr5iUVihHhHYGT8Hfg8cisssDr06RuiRXnQSBx76R3k/bQBVqtjH1dztka5p0yGiK+X4NyJX5Ha4VHMEA+Er5vzkuR79TocF6VALjaiWGWASCWF1WqFTqe7wWdCREREbRnLmBNRm5Seno6IiAgAwJIlS5p99veDDz6Ir776qs5Y0tPT8f3332PEiBH47rvvoNVqr/mYer0es2fPxoIFC1BVVeWwPT8/H/n5+Thy5Ai++OILuLu749lnn8U///lPuxJtV1NVVYULFy7gwoUL+PnnnzF//nysWbMGAQEB13wORNQyRHhE4Hv3n9G+1N+uPVDlhvKiUrh5alwUGbmSyWxBSH4FxCH2M7tLoUPorYMgEnhfLREREd18ZDJZg8pvu8vc4RfQDrckDsXyql2Ycmmg3XaRIGB4pDd+Nd8D/bwv0amoEAHTJkEQtaz3SDVrlJeXl0N+9gzarfoWvwyMRkz5U5ji2Q4ysfN4NxorkSdJg1RsQbHcCKlCDrlcDplMBoWCN8USERHRjcNkNxG1Wunp6a4OwebixYsAAC8vL0yYMAGDBg1CeHg4JBIJjhw5gvfeew9nz57Fxo0bMXbsWOzYsQOia/gAnJ+fjzvuuAP79u0DAKjVakycOBFDhgxBaGgo3N3dUVBQgNTUVGzevBnr169HWVkZ5s6di1GjRqFPnz517nvmzJl44oknbN+Xl5fj0KFDePfdd5Geno6DBw/izjvvxP79+7lWK1ErIRFJcDnUBJywb/d3k+Pcr4fQecoQ1wRGLvX7gUzEqh1vjjorS0Oif28XRERERER0YwW6BaEyuALBiaXYVnUCg4sT7LYrJGKMjvHFJmE8Dn7xK7oXFSHomcchSFrWJVmpSITwDb+i/MRu/DB4DEZV9EenYI3Tz/QmqxW/yYzQy3MgNYlwWVQBs9UKwSSGp6cnJBIJb34nIiKiG6plvbMiImqlQkJCsGjRIjz44IOQy+3Lf/Xs2RNTp07FiBEjsHv3buzevRvLli3DAw880KRjmc1m3HPPPbZE9x133IHPPvsMfn5+Dn2HDRuGGTNmIDc3F/PmzcP8+fOvun8/Pz8kJNh/gO/Tpw+mTJmCXr16ITU1FQcOHMAvv/yCsWPHNukciKjliUyIQ+UxI1Qiqa1NKhYhf38WMMWFgZHLHNl4Fg94OM7aKfeugrucs/2JiIio9ROLxAjzjIA5zIQ/9EU4+3s22uuC7PrIJSKMjPHBb6LR2PvzVvQu/A+CX34e4kZUVGtOVX8cgd+/Z2N/uAK6+KfwsBANvwDnZcvLzRYk+VgRG63FxfwqnMpPhwQyiCwWaDQaaDQaxMbGNqpaHBEREdG1all1c4iIWqklS5bgsccec0h011CpVPj4449t369atarJx3r//fexfft2AMCYMWPw008/OU101+bv74933nkHu3btgq+vb5OO6+npiVmzZtm+37BhQ5P2Q0QtU8+gnkiW5ji0qyzKFrn+IDUvi8UK34ulDmUtdTAicEBfiEViF0VGREREdGPJxXKE+kSgc2RP7Eu4iDxJqUMfqViE26J9oA4fip37S5ExfQbKj5106ftoU24uLj41AxcemoyfeiTCXz4TUwLi4FfH+tw5ZjPOhIvQPtEP4fFREALdoNa4QaFQQKFQQKvVolu3bggMDLzBZ0JERERtHZPdRK3Qnj178Mgjj6B9+/bQaDSQyWRo164dxowZgwULFqC4uLjOsampqfj73/+OxMREeHh4QKlUIjIyEtOmTcOhQ4euS3zbtm3Dgw8+iMjISKhUKmg0GiQmJuKFF15AdnZ2nePmzJkDQRBsZbRKSkowd+5cdO3aFVqtFoIgYOnSpbb+4eHhEATBYT1uQRBs63UDwPTp0237rfmaM2eO0xiSkpIwY8YMtG/fHm5ublCr1Wjfvj1mzpyJ5OTkJj8nAJCQkAAfHx8AwLlz55q0D71ej3fffRdAdQL9888/b1Q59L59+yIqKqpJxwaAXr162R5nZGQ0evyVr1lSUhKmTJmCkJAQKJVKREdH47nnnkN+fr7duL179+Kee+5BaGgoFAoFoqKi8H//938oKyu76jHNZjO+/PJLjBkzBkFBQZDL5fD29kb//v3x3nvvOV3vvIbFYsHWrVvx/PPP45ZbboGPjw+kUim0Wi26dOmC559/HhcuXKj3+IMGDYIgCLY14S5evIjnnnsO0dHRUCqV8Pb2xogRI7B+/fqrngtRcwpUB+Kkx0WH9mC1G0pyC10QEbnSkWPZiHOyFuNpWTrCQtq7ICIiIiIi11FL1Qj0DcHA9kOxMj4Jl6RFDn3EIgGDorzhH9oPmyoScWnydFx8738wlzomx5uTRa9H3nvzkDXoVmzOT8emPn/DaPMdGBruB7nE+fWDZIsZBXFSRHcMQkinKChCvDBg4ABERUUhLi4O8fHx8Pb25oxuIiIicgmWMSdqRaqqqvDwww9j+fLlDtsuXryIixcvYt26dcjLy3OazH3nnXfw0ksvwWg02rWfP38e58+fx1dffYXZs2fjtddea1J8Op0O06dPx3fffeew7cSJEzhx4gQ+/vhjLF++/Krlr1NSUjB8+PAbti63xWLB888/j/nz5zvceZ2cnIzk5GQsXrwYCxYswGOPPdbk4xgMBgCAWNy0GXEbN25ETk71zMtJkyZddUb39SaV/lXe2Gw2X9O+vv76azzyyCO25wSovgng/fffx7p167Bjxw4EBATgnXfewYsvvmj3uqSlpeHtt9/Gli1bsGPHDri5uTk9xoULF3DHHXfg6NGjdu2FhYXYs2cP9uzZg48//hjr1q1DbGysw/jXXnsNr776qkN7SUkJjh49iqNHj+Ljjz/GsmXLMH78+Kue8549ezBu3Di7ZL5Op8OmTZuwadMmzJs3D88///xV90PUHARBQFmUFLgir+2jluH0zwfQ/fHbXRMYucSBX5MxSat0aC/WFsNd6eGCiIiIiIhcy1PhBX2AHrcbx+IXYSNuOx2NcL39Z3KRIGBAhBc0Cgk2yZ5C7PK1MGzbBu+X/wlNrx4QmngtoCHMFRUoXPUdqj7+FKlSM/bfeh+6VSVgbIAH1DLnl4gtViuOKMzwiFUgMjIU2ih/CHXM/CYiIiJyFSa7iVoJi8WCO++8E5s3bwYAxMTE4IknnkCPHj2gUqlw6dIl7N27FytWrHA6ft68eXjxxRcBAJ06dcLMmTMRExMDrVaLs2fP4qOPPsK+ffswd+5c+Pj44G9/+1uj4rNarZgwYQLWrVsHABg7diwmTpyIyMhIiEQiHDhwAO+++y4uXLiACRMmYM+ePejRo0ed+5swYQIuXryIp59+GnfccQc8PT2RkpKCsLCwq8Zy/PhxZGdnY8SIEQCA119/HXfeeaddnyuTxE8//TQWLlwIABg4cCCmTZtmm5l+9OhRzJ8/HydPnsTjjz+OgIAA3HHHHY16fgDgyJEjKP3zju64uLhGjweAHTt22B6PHDmySfu4FsePH7c9DgoKqqdn/Y4ePYrly5cjOjoazz//PBITE1FWVoYvvvgCy5YtQ3JyMp5//nncddddeOGFF9CnTx88/fTTaN++PfLz8/Hhhx/i119/RVJSEl5//XW8+eabDscoKChA//79kZmZCblcjkcffRS33norwsPDUV5ejk2bNuGDDz5Aamoqbr/9diQlJcHDwz6BYzKZEBgYiPHjx6Nv376IjIyEQqFAZmYm9u7di4ULF6K8vByTJ09GUlJSva/rpUuXMG7cOIhEIrz55pvo378/ZDIZdu/ejddeew3FxcWYNWsWbr/9dsTHxzf5uSW6Fokdu6Do9yp4iv5KcooEASXHClwYFd1oVqsV6vP5UIbYL3thtJrh268HJCJ+xCAiIqK2yV8VAEOgHrdZhmOXeCf0p4xoXxXs0K9zoAYhHgrskU3A5rJMDJ4+E4WjhsLnvvug7tgeIrX6usWkTz+PwiWfwbh6HcqrqrDxllHw0/XEJDdvBAU5VuqpUWix4FKgCD5hGrSLDIMq1AuCQlpnfyIiIiJX4ZUoolbio48+siW6x48fj+XLlzusDz169GjMnTsXly5dsms/deoUXn75ZQDAK6+8gldeecVWKhwAunfvjkmTJuHBBx/EsmXL8PLLL+P++++Hp6dng+NbvHgx1q1bB6lUijVr1jgkYvv06YP7778fAwYMwMmTJ/Hss89i9+7dde7vxIkTWL9+PYYPH24XZ0MkJCTYzfQNDg5GQkJCnf03b95sS3QvXrwYDz/8sN32nj17YurUqRg9ejS2bt2Kv/3tbxg1ahQkksb9if3Pf/5jezxx4sRGja1x7Ngx2+Nu3bo1aR9NZTKZbCXUAdjKcjfFH3/8gX79+mHz5s12ZdAGDx4MnU6HVatW4bvvvsP69etx99134/vvv7ebDT9s2DD0798f+/fvx+LFi/H66687vB5/+9vfkJmZibCwMGzbts2utH1N/Pfccw8GDBhgmyn+xhtv2PV55JFH8Morr9jNaAeqn/s777wTTz/9NPr06YOLFy/iP//5D77++us6zzk5ORlhYWHYs2cPgoP/uhjSs2dP9OzZEwMHDoTJZMKnn36KDz74oOFPJtF11D2gO47IfkRfU6Rdu4fYDWazuclVKejmcio5D/EyxwujydIsRESMckFERERERC2DIAgI1oTgvMmA/tYBOCw5CP0JIzpVhDv09VLJMDrOD8cuKbFF9izC9/6Gjj/eh8tdEqAYOwq+I0dC6u8PoZHXFqwmE4wFBSjfvxsVP/wAYec+XPLwwf7E4ZAZOmKA0gcdw90gqnXd50qpIjOEGAl8Q70RFNYOYn93CFL79/oymeyaPvcTERERXS9MdpPL3bF6tKtDuOHWjFt3XfdnsVgwb948AEC7du3w1VdfOSS6a4hEIrtEGgC8++67MBqN6NGjh0Oiu/a4//3vf1i5ciXKy8uxatUqPProow2Kz2q14q233gJQnWCsa8axp6cn5s2bh1GjRmHPnj1ISUlBTEyM077Tpk2zS3Q3p5pZwXfffbdDoruGQqHARx99hI4dOyIjIwPbtm3Dbbfd1uBj/PDDD1i1ahWA6qT9XXfd1aRYa5e/9vX1rbNfQUGBw00PNdRqtUPitz4VFRU4dOgQ5syZg/379wMAwsLCmpywB6ovECxevNjpel9PPPEEVq1aBbPZDJ1Oh08//dQhwSYWi/HYY49h//79KCgowKlTp9CpUyfb9vT0dHz//fcAqm8Uqet8u3btiieffBJvv/02li5d6pDsDg8Pr/c82rVrhxdeeAHPPvss1qxZA6vV6vT3q8b//vc/h99PAOjfvz969+6N/fv3Y9euXfUek6g5aeVanPa8hL559snuYLUal9OyERgT4qLI6Eba88sZjHdSwjzPIxed1NobHxARERFRCyIWxAj3jESGcB49RL1wUnICuuOp6FUa7dBXJAjoEqRBqFaBfcrRWBcwFB7lyej+n4Uw/m8hLH16QhYTDUWHWLi17wCphycEpRIQBMBkgtVsAcwmmHRVqPzjMCr27YXpyDGIk88BOiNOh3fCqVuegK+hHfp5uCHGRw2Z2Pm63ABgtFqRprVCGq5AYEQwPNr5Q/BU1Ps5loiIiMjVmOwmagX++OMPZGVlAQAeffTROtcnrsvatWsBVCdz6/sAo9VqkZiYiEOHDmHfvn0NTnafOnUK586dA1Bdfrw+AwcOtD3et29fncnuKVOmNOjY16q0tBTbt28HcPXY4+Li4OPjg/z8fOzbt6/Bye7Tp09j+vTpAAClUomvv/66yR8ky8rKbI/V9ZQ9+/rrr/H3v//d6bZbb73Vds7OvPrqq07Xqa7h5+eH1atX13nDRUN06tSpzpLfnTt3tj2+7bbb4OXlddV+aWlpdsnudevWwWw2Q6VS4fbb619neODAgXj77beRnZ2NCxcuIDQ0tM6+paWlKCgoQGVlpW0N8ZqEfWlpKc6fP4/IyEinY7VaLUaPrvvmn+7du2P//v1IS0urN16i5maO0wJ59m1apRR/rPwdgS8x2d0WiM/kwv2KEuZmqwWaHh0hFbG0JREREZFEJEG4ZyQyRRcQL3RCmjQFv5w8jJF5XSCBYzUkL5UMozv4Ib/CgDN5btjj1gEmUxE6HNgFvy0rYNHlQSeywujrA6u3FwSjEdDpIOh0sFRUQqrTA2YrSlWByPSNQnrX/jAhCO3kGozyc0M7raLemdwAcEFshrWdGKoQd4SEh0EeoIWg4ns7IiIiavmY7CZqBY4cOWJ7PGDAgEaNzcjIQF5eddZi1qxZmDVrVoPG5eTkNPgYhw4dsj3u27dvg8fVd4zaicvmdOTIEVgsFgDAfffdh/vuu69B4xr6/GRnZ2PUqFEoKyuDIAj44osvmrxeNwC4u7vbHldUVECj0TR5X40VERGBCRMm4Pnnn3dY87yxYmNj69ym1Wob3a/2TQDAXz+TlZWVjSo3n5OT45DszsjIwDvvvIO1a9ciIyOj3vH5+fl1JrtjYmIgEtV9h31NUv/KcyG60Xq1747s7ZcRJLL/+6LPMrooIrqRzmUUIl4ic2yX5iCmw1AXRERERETUMokFMcI8wnFRJEGkSECJzBMrzyXhlqxwhOqdV2LzUcvQX+2Fnu20SM33QKraF8k6EyywwmqtgkqfD21lDiwQwySXw6iWwhAgg16uglnwhkqQwd9dhl5ucgRpFNAqr56sLoIFpX4CLP5yeIR4wy84GGJfNwiSuj+fEhEREbUkTHYTtQK1S1cHBgY2auzly5ebdMzKykqXHqMx64Vfi+Z8fgoLCzF8+HCkp6cDqC5hPWnSpCYdr4a3t7ftcV5eXp3J7meffRbPPvusXVtDZ5PPnDkTTzzxhG2MQqGAj48PPDw8mha0E87Kl9eonRBuaD+z2Wy37Xq9ruvXr8eECRMa/PtQVVVV57b6zgX463xqbr4gcpXOvl2wUbUYQboEu3Y/hQa6ykoorvKzTDe3nevOYqTW8TXOcctGB7cb838zERER0c1CEAS004QgRywFREA3SW+kaDKQnHkcg/PjIYbzhLJcIkJ8gDviA9xhtlhRrDOiuMqIoio/lOvbQyQCpCIRJCIBUrEAhUQMPzcZPJXSBn+2N1qtuKQxwxIkgzTIDQGBQVD5agGNnGXLiYiI6KbCZDdRG1c7Cfjvf/8b99xzT4PG1Vciu75jrF279qrrHNeob3bwlWs0N5fasS9atAj9+vVr0LirJePLysowcuRInDx5EgAwd+5cPPnkk00P9E+dO3fGb7/9BqB6VnpUVNQ17/NKfn5+SEhIuHrHFqzmdfXx8cG2bdsaPK722t75+fmYPHkyKisr4ebmhueffx4jRoxAVFQUPDw8IJNVz3zcunUrhg6tnu1YU9qc6GamkCiQHFCIEen27cHuSmTuOY2Y27q7JC66MaqOXIRniI9Du7xzBGRixxnfRERERAQEqAMgDZbisjQL4eJolHgUYeOFs0i86IsQveN7q9rEIgHeKhm8VdfnvZYRVlxWmmH2l8Ac4AbfYD94+vhB5K2EILkx11qIiIiIricmu8nl1oxb5+oQbno+Pn99MLp06RI6dOjQ4LG1ZwJLpdJmSWLWPoZWq72pEqW1Y1epVNcl9qqqKowdOxYHDx4EALzwwguYPXv2Ne8XqF5v+7333gMAbNiw4arrjLdVNa9rWVkZ4uLimnTzxKpVq1BcXAwA+OmnnzBs2DCn/QoLC5scJ1FL5dcpEvo0E+Siv95KysQiXNpwlsnuViwjqxgJguPHhwvifHTo1LCbwYiIiIjaKm+lN+SBcmSrsuBVIINKqUamVx5Ssk4jqtgHYTrnpc2vlzLBjHJ3C/TeUsBLCVWgBn4+gZD7ekBoQLlzIiIiopaKi68QtQLdunWzPd65c2ejxkZGRtrKT+/Zs+e6xlWja9eutsfNdYzGamhJri5dutj6Xo/YjUYj7r77buzYsQMAMGPGDLz99tvXvN8aI0aMgL+/PwDgu+++s63HTvZqfib1er3dmvKNUTMr38vLq85EN4Am75+oJRsY3hfJIsflAOQGOUvtt2Jb155BhKdjCfMMdQa07l4uiIiIiIjo5uImc0O0Tyy8QkMg9/dEYFAofOOjUBSjwL6QiziuyYJBMF3XYxZITLjoY8TlGDEM8W5QJXohuEMEQiJjIA/xZqKbiIiIbnpMdhO1Ap07d0ZISAgAYPHixSgvL2/wWLFYjFGjRgEANm3ahNOnT1/3+Lp164Z27doBAD799FPodLrrfozGUigUtsd6vb7Ofr6+vujTpw8A4Ntvv72m5LHZbMbkyZOxfv16AMD999+PhQsXNnl/zsjlcjz33HMAgIqKCjz66KNMPDkxduxY200M8+fPb9I+TKbqCxA6na7O57iyshJff/11k/ZP1JKFeUTgqEemQ3s7tTuK8wpcEBHdCBVJmfBVO5bPFHcMhFyicDKCiIiIiK4kEkTwU/khMrg9FKE+0Hj7wC3UC27t/WBur8XZqEoc876EC6oCVIjrvl5RlyJBhyxJOdI0ZUgNMaC4gxTmeA008T4Ijg5HSEQs3CICIGgVXJubiIiIWgUmu4laAZFIhBdeeAEAkJWVhQceeAAGg8FpX4vFguzsbLu2WbNmQSwWw2KxYMKECcjKyqrzWGazGd988029fZzF99JLLwEA0tLS8MADD9SbYC4tLcVHH33U4P03hbe3t21N5XPnztXbt6bEeGlpKSZMmGArXe2MXq/HggULHBL6VqsVjz76KFatWgUAuPvuu7FkyZJm+WD53HPPYeDAgQCAn3/+GRMmTEB+fn69Y4qKiq57HC1Z+/btbevTf/fdd7bS73U5f/48li9fbtcWExMDoDqhvWLFCocxZrMZjzzyiMPvG1FrIBJEKIxy/PvlrZIh48eDLoiImltGVjE6CY6zfi6KC9G+W38XRERERER0c5OL5YjwikRARBQU7bzh7uMDzyB/yGK0EOK9UBwtR0aECcmhVUj31yHdqxxZ7mW4pC5HjlsFMpVlyJAWI0XIw1lxPo5JcrBLmoHDiotI8y5BSYgIikRPaNv7IigiHO0iYqEOD4DgoYAg4iVhIiIiaj24ZjdRK/Hkk09i7dq12Lx5M3766SckJibiiSeeQI8ePaBSqZCTk4P9+/dj+fLlmDx5MubMmWMbm5iYiHfeeQd///vfcerUKSQkJOCxxx7DkCFD4O/vD51Oh/T0dOzbtw+rVq3CpUuXcPz4cdts7YaYMWOGLbaVK1ciKSkJjz/+OHr16gUPDw+UlpbizJkz2L59O9asWQOFQoGnnnqqGZ6pahKJBD179sSePXvwxRdfoGvXrujSpQuk0uoL+V5eXvDyqi7JOmrUKDzzzDP44IMPsHPnTsTFxWHGjBno378/vL29UVFRgdTUVOzatQs//vgjioqK8OCDD9od7/nnn8eSJUsAAAkJCXjppZeuOou+qeuDSyQSrFy5EmPHjsWBAwfw008/YfPmzZg0aRIGDx6M0NBQuLu7o6KiAmlpadi5cye+//5723iVyrFEbWv08ccf49ChQ0hLS8M//vEP/Pzzz3jggQcQHx8PuVyOgoICHD16FBs2bMDWrVsxfvx43HfffbbxEydOxEsvvQS9Xo/p06fjjz/+wG233QYPDw+cPHkS//vf/3D48GHccsstLaZ8P9H11COxBy4frICfWG3XXn6y2DUBUbPa+ssZDNUqHdrPqzJwi8dIF0RERERE1Dp4yD3g4e8BnbcOxVWFEBUVQVmmh8XDBL1RB6vJgiqTGVa9CFa9BVa9FSIBqKjSo6KiHEVlJRBEIoglYuhhhEgugZ+nJ7SBfggNbw+pmxKCgqXKiYiIqPVispuolRCJRFi9ejUefPBBrFq1CsnJyXj22WcbPP7ZZ5+FWq3Gs88+i5KSEsybNw/z5s1z2lcmk9mVAW8IQRDw/fff45lnnsEnn3yCc+fO4cUXX6yzv5+fX6P23xSzZs3C2LFjUVBQgMmTJ9tte+WVV+xuCHj//ffh5eWFuXPnIicnx27bldRqNcRisV3bDz/8YHt84sQJdO/e/arxWa3Whp2IE35+ftixYwdeeuklfPzxxygvL8fixYuxePHiOsdoNBo89dRTtln4rZ2Xlxf27NmDiRMnYteuXdi5c2e9a95rNBq779u1a4ePP/4YjzzyCHQ6Hd566y289dZbdn3uvfdePProo/Wu6U10s+oe0B175N9hiKm9XbuXzB1GgwFSmWO5a7p5lR/KhF+or0O7qKMflNK2cZMUERERUXNSSBQIcA+Cv1sgKk2VKC4rhLyiAma9ARaDCYLJCqHWCloKoxFl6ekwmUQwGo2wmo0wQ0CQZwBkPp4I6xIPWRu5mZ2IiIjaNia7iVoRlUqFlStXYtu2bViyZAl2796NnJwcmM1m+Pv7o0uXLhgzZozd7NTaHn30Udxxxx1YtGgRNm3ahLNnz6K4uBhyuRzBwcFITEzEbbfdhrvvvhs+Pj6Njk8qlWLhwoWYOXMmPvvsM2zfvh0XLlxAeXk53NzcEBERge7du+P222/HmDFjrvXpuKrRo0fjt99+wwcffICDBw8iLy8PRqPRaV9BEPDvf/8b999/Pz755BNs3boVaWlpKCkpgUqlQkhICLp27Yrhw4dj/PjxUCodZ7/daAqFAu+99x5eeOEFLF++HL/99htOnTqF/Px86PV6aLVaBAUFoXv37hgyZAjGjx/fZmZ11wgICMDOnTuxbt06LF++HPv27UNOTg6MRiO0Wi1iYmLQt29f3HHHHbbS8LVNnz4d7du3x7x587Bnzx4UFxfDx8cHnTt3xvTp0zFx4kRs3779xp8Y0Q3gIffAKd9cDLlkn+xu56bGpZNpCO3awUWR0fWWcbEEnUSOHxuyRUVo3+0WF0RERERE1HoJggC1VA21lxqoLjgHq9UKk8UEo1EPk8EAkSCCRCqDyscf27fvABQWWMxmSMRiqLw9ENOhfZv7fE9ERERtl2C9lqmD1OplZWUhJCQEAJCZmdmostUpKSkwmUyQSCS2tW2JiOjG499jai6fHliCSb+GQiayr2ZxxJiLvnPvdVFUdL0t+WQ/hqaVwM9Nbte+x/0P9HvqMc7sJiIiInIRg8GAHTt2oLy83PaZb/DgwdBqta4OjYiIiMipa8k71kV0zXsgIiIiojZpUPQtOId8h3ahWASLxeJkBN2Myg9nOiS6AQAdfZnoJiIiInIxqVQKT09P+Pr6wtPTkzO6iYiIqM1hspuIiIiImiRKG4WjbpkO7UEqDarKy1wQEV1vGRdLkehk5aMcUTE6dGUJcyIiIiIiIiIici2u2U1ERERETSIWiXEpwgictG/3VclwfsMRdJw4yCVx0fWzde0pDNE6zg5Kc0tHX8/hLoiIiIiIiGrIZDIMGjTI1WEQERERuRRndhMRERFRk3Xt2gMFpiq7NkEQULTvkosiouup7HAm/Nxkjhs6+LGEORERERERERERuRyT3URERETUZH2De+GkLNuhXSOoYDIaXRARXS81JcwFQbBrzxWVIKZ7XxdFRURERERERERE9Bcmu4mIiIioyTzkHjjp5TiLu53aDfkXHJPgdPPYtvYUIpyUMD/nlg5fbYALIiIiIiIiIiIiIrLHZDcRERERXRNpJ3+YLBa7NqVUjIsrD7soIroWBoMB27dvR+GBDPg7K2Ee5weFVHnjAyMiIiIiIiIiIroCk91EREREdE2GdbgF5y1FDu2mbBMsVyTB6eaQX2xGF5HMoYT5ZVEpYruxhDkREREREREREbUMTHYTERER0TWJ9orFMXWmQ3uQygPlpSUuiIiuVfKRUkR4Os7ePueWAR+tvwsiIiIiIiIiIiIicsRkNxERERFdE4lIgvPhFQ7tvio5stYfdUFEdC1ycnIgP6+Dv5vccWMHH5YwJyIiIiIiIiKiFoPJbiIiIiK6Zl2790CeqdKhvez3HBdEQ01VWVmJnfvOoLtMAdEVJczzRKUIie/qosiIiIiIiIiIiIgcMdlNRERERNdsQEgf/CFzLGXuKXWHrspx1je1TDk5OUg/Uo5IL5XDtjPKczDprC6IioiIiIiIiIiIyDkmu4mIiIjommnkHjju5ziLO1itQs7RNBdERE1RVVUF7SUD/JyUMM/xASxGJruJiIiIiIiIiKjlYLKbiIiIiK4L/+7RKDcZ7drEIgF5P590UUTUWOcz9eiqcFyTO1tcCI1vKBQKhQuiIiIiIiIiIiIico7JbiIiIiK6Lka2vxWnRI6zuxV6BYxGgwsiosZK2XsZEZ6Oye4zijSolR4ICAhwQVRERERERERERETOMdlNRERERNdFgDoQSVrHdbtD3NxRcNExCU4ti8lsgXtGCbxUModtxUFKxHeIh0rluJY3ERERERERERGRqzDZTURERETXhVgQw5iggdFisWtXSMS4vDLJRVFRQ+3am4FOKsdZ3emSXPS+ZRgCAwNdEBUREREREREREVHdmOwmIiIiouvm9s6Dcc5c4NBuyjbBckUSnFqWpF9OIcLLceb2OXUWAvzauSAiIiIiIiIiIiKi+jHZTURERETXTXvvWCSpLzi0B6s8UFFS5IKIqCFKyyvhn1MBd7nErt0CK3z6d4ZaoXZRZERERERERERERHVjspuIiIiIrhupSIqsaCMsVqtdu4dciou/HnVRVHQ127eno6ObYwnzVNlFxMR2dkFEREREREREREREV8dkNxGRC5lMJvzwww947LHHkJiYCD8/P0ilUnh4eCA6Ohrjx4/HvHnzcP78+Xr3Ex4eDkEQHL6kUil8fHzQv39/zJkzB9nZ2TfozIioLRvYcwAuGssc2suT8lwQDTXEmc3JCHdSwjzXrxQeai8XRERERERERERERHR1THYTEbnImjVrEBcXhwkTJuCzzz7DiRMnkJeXB5PJhNLSUpw7dw6rV6/Giy++iMjISIwZMwYnTpxo1DFMJhMKCgqwZ88evPrqq4iLi8NPP/3UTGdERFStd3A3HJVnObT7SDXQVVW4ICKqT0mFHuHFRqikYrt2EyyQdgyHWCSuYyQREREREREREZFrSa7ehYiIrrfXX38d//73v2H9s8zvoEGDMGbMGHTq1Ane3t6orKzEpUuXsHPnTvzyyy9IT0/HunXr0K5dO3zyySd17jcoKAgbN260fW80GpGeno4lS5Zg7dq1KC0txaRJk7B//3507dq12c+TiNompUSJ0wEFGHPJvt1fqcDlpHMIvaWTawIjp35dcxLxGscS5mdkGdBVBKCyshIqleOsbyIiIiIiIiIiIldjspuI6Ab74osv8K9//QsA4O/vj++++w6DBg1y2veee+7B/Pnz8d133+Gll1666r6lUikSEhLs2rp27Yrx48fjH//4B9577z0YDAa8/vrr+OGHH675XIiI6tK+bxcUfqeDl0xhaxMEAfm/nGayu4VJ25aKUQE+Du3nVDnwtoYgJycHkZGRLoiMiIiIiIiIiIiofixjTkR0A2VmZuLJJ58EAGg0GuzevbvORHcNsViMKVOm4OjRoxg9enSTj/3qq69Cqayeubdp0yZYLJYm74uI6GpGxAzASfElh3aVSQmdvsoFEZEzmbllSNQLkEvsPxboYUKBSgGj0QidTuei6IiIiIiIiIiIiOrHZDdRK1NQUIAXX3wR7du3h1KphL+/P2677TbbOs1Lly6FIAgQBAHp6el17ken0+Gjjz7C0KFDERAQAJlMBj8/PwwbNgyff/45TCbTVWNJT0/H3//+d8THx8Pd3R0qlQoxMTF4/PHHcfz48XrH1sQ4Z84cAMDWrVtxzz33ICQkBFKpFOHh4Q5j1q5di5EjR8LX1xcqlQqxsbF44YUXkJOTAwAIDw+HIAiYNm1avcdOSkrCjBkz0L59e7i5uUGtVqN9+/aYOXMmkpOTr3re9XnvvfdsSYM33ngD0dHRDR6r1WoxduzYJh/bzc0NHTt2BACUl5ejsLCwUePnzJlje10AoLS0FHPmzEFiYiLc3Nzg5+eHUaNGYe/evXbjLl++jNmzZyM+Ph5qtRre3t648847ceTIkQYd91pej7S0NLz77rsYO3YswsPDoVQqoVQqERYWhnvvvRcbNmyod/yVvy8WiwWffvop+vXrB09PT6jVanTq1AlvvPEGKisrG3Q+RG2Fh1yLo9psh/Z2ajcUZDi2k2tsXHkMMZ5qh/aT8nOQiFWQyWRQKBRORhIREREREREREbkey5gTtSLHjx/HbbfdhtzcXFubTqfDli1bsGXLFjz22GPo27fvVfdz9OhR3HnnncjIyLBrz8vLw2+//YbffvsNixYtwtq1a+Hv7+90H1999RUee+wx6PV6u/bU1FSkpqbi888/x9y5czFr1qyrxvPyyy/jP//5T719nnzySSxcuNCuLSUlBe+88w6WLVuGX3/99arHsVgseP755zF//nzbWto1kpOTkZycjMWLF2PBggV47LHHrrq/K1mtVnz99dcAAHd3d0yfPr3R+7hWUqnU9thsNjd5P5mZmRg2bJhdsrmiogLr16/Hpk2bsHz5ctxzzz04duwYRo0ahYsXL9r6VVZWYs2aNdi4cSPWr1+PwYMHOz3Gtb4e58+fR1RUlNN9X7hwARcuXMCKFSswdepULFmyBBJJ/f8lVlZWYvjw4fjtt9/s2o8fP47jx49jzZo12Lp1K9Rqx6QRUVul7B6Mqi0mKGv9fklEIhR8/weC/+X895NuHKvVipKDWQiN8HPYlqzIg69nPCQSCQICAlwQHRERERERERER0dVxZjdRK1FcXIyRI0faEt33338/1q9fj0OHDuG7775D37598emnn+KTTz6pdz+pqam49dZbkZGRAY1Gg1mzZuGnn37CoUOHsHHjRjz55JOQSCQ4ePAg7rzzThiNRod9rFu3DtOmTYNer4ebmxteeeUV7Nq1C/v27cO7774LHx8fmM1mvPTSS/j444/rjefHH3/Ef/7zHyQmJuKLL77AgQMHsGPHDjz33HO2Pm+//bYt0R0SEoIFCxbg999/x86dO/Hyyy+jpKQEEyZMuOrM26effhrvv/8+rFYrBg4ciC+++ALbt2/HgQMH8NlnnyE+Ph4mkwmPP/441qxZU+++nDlx4gQKCgoAAAMGDLjhSVGTyYQzZ84AAGQyGby9vZu8r3vuuQdZWVmYNWsWduzYgYMHD+L999+HRqOB2WzGww8/jPPnz2PMmDGoqqrCG2+8gd27d+P333/Hq6++CplMBr1ej2nTpsFgMDg9xrW+HmazGTKZDGPHjsWHH36ILVu2ICkpCVu2bMHChQsRHx8PAFi2bBnmzp171XN+9NFHsW3bNjz44INYt24dDh8+jJ9++sl2A8mBAwfw+uuvN/k5JWqNxnW9Daeslx3aZaUS6PUsje1qh45dQleJFGKRYNdeJuhQ6eYOD40HYmNjoVKpXBQhERERERERERFR/QTrldPliGrJyspCSEgIgOqZnO3atWvw2JSUFJhMJkgkEsTExDRXiPSnv//975g/fz4AYP78+XjmmWfstpvNZtx99934+eefbW3nz593KAd+yy23YO/evejatSs2bdoEHx8fh2Nt2LABo0ePtpV0fvTRR23bjEYjwsPDkZ2dDTc3N+zatQtdunSxG5+RkYG+ffvi0qVLUKlUyMjIcDhOTalsABg6dCjWrVsHuVzuEEtOTg4iIiKg0+kQHR2Nffv2Oexr7969GDx4sC2p+uCDD2Lp0qV2fTZv3ozhw4cDABYvXoyHH37Y4Vg6nQ6jR4/G1q1bERYWhtTU1KvOBq7tm2++wdSpUwEAs2fPblCCtaHCw8ORkZGBsLCwOsvTL1iwAE899RQAYMiQIQ4zlK9mzpw5ePXVVwEAcrkcO3bsQO/eve36rFu3DmPGjAEA+Pr6wmq1Yv/+/Q4zrBcuXGhbu/zHH3/E+PHj7bZfj9ejoqICpaWlCAwMdHo+VqsVDz30EJYuXQq1Wo2LFy/Cw8PDrs/SpUvtZuB//fXXttewhl6vR48ePXDixAl4e3sjJyenUT8XNwL/HpOrWK1WzH/nTcyssq8qYrJYUDo5HEGxkS6KjADg/X9vwN06AX5u9v+/7lUdR36kP4YPGQ6tVuua4IiIiIiIiIiIqNW5lrxjXVrW1XhqkxbduczVIdxwj/889eqdGkGv19uStz179nRIdAOAWCzGokWLsHHjRtua0VfatWuXbb3lL7/80mmiGwBGjhyJCRMmYMWKFVi6dKldsvunn35Cdnb1WqyzZ892SHQDQFhYGObNm4epU6eisrISS5YswQsvvOD0WCKRCIsXL3aa6K6Js+Z85s+f7zTmfv364cknn8T777/vdB8A8OabbwIA7r77bqeJVQBQKBT46KOP0LFjR2RkZGDbtm247bbb6tznlfLz822PfX196+xnsVhw6tSpOre3b9/erhx5fUwmE9LT07FkyRK89dZbtva6nu+GevbZZx0S3QAwevRohIWFISMjA3l5efj444+dlhKfPn06/vGPf0Cn02HXrl0Oye7r8Xqo1ep6Z88LgoB3330XX3/9NSoqKrBlyxbcfffddfa/6667HBLdQHXi/6mnnsKMGTNQUFCAU6dOoVOnTnXuh6gtEQQBpi5aVO0yQykR29olIhEKVv6BoJeZ7HaVKoMJbmkl8It1XI5E1DUIE4fW/feQiIiIiIiIiIiopWAZc6JW4NChQyguLgYAp8m4Gv7+/hgxYkSd22tKQbdv3x6JiYn1HnPgwIEAgIMHD8JkMtnat2zZAqA6wfHQQw/VOf6ee+6xzaKtGePMLbfc4jD7vLaasT4+Prj99tvr7PfAAw/Uua20tBTbt28HAEyYMKHOfgAQFxdnS6jv27ev3r5XKisrsz2uLwlbWlqKxMTEOr9qr399pYyMDAiCYPuSSqWIiYnBf/7zH5jNZgiCgNdffx0jR45sVOxXmjRpUp3bahK9giDg3nvvddpHqVTaZhinpaXZbWuu18NoNCIrKwunT5/GiRMncOLECWRnZ9vKuR89erTe8VOmTKlzW/fu3W2Przwforbunp6jcMqS69AuLRbDYNC7ICICgM2bU9DZXenQflFSgA5derkgIiIiIiIiIiIiosZjspuoFThx4oTtce2kmzM9evSoc9uhQ4cAAGfPnrVLmDr7qimHbTQaUVhY6BBLREREvbOXZTIZunbt6hD/la42Q7ZmbJcuXSAS1f0nLTExETKZzOm2I0eOwGKxAADuu+++q557zQztnJycemO7kru7u+1xRUVFo8ZeK41Gg3HjxmHHjh14+eWXr3l/sbGxdW6rKXnr4+MDT0/Pq/arfRMAcH1fD6PRiAULFqBPnz5wc3NDSEgIOnbsaHfzwOXL1esJ155570yHDh3q3Obl5WV7fOX5ELV1oR4h+F2T4dDeTu2OwsxLLoiIAODk+jOI9HZcizvd9zK8tQEuiIiIiIiIiIiIiKjxWMacqBUoKiqyPa4vwXy17TV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},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Saved comparison_1d.pdf\n"
]
}
],
"source": [
"COLORS = {\n",
" 'pgmuvi': '#e41a1c',\n",
" 'tinygp': '#377eb8',\n",
" 'celerite2':'#4daf4a',\n",
" 'george': '#984ea3',\n",
" 'GPy': '#ff7f00',\n",
" 'gpflow': '#a65628',\n",
" 'sklearn': '#f781bf',\n",
" 'gpjax': '#999999',\n",
"}\n",
"\n",
"available_fits = list(preds_1d.keys())\n",
"n_avail = len(available_fits)\n",
"\n",
"if n_avail == 0:\n",
" print('No fitted models to plot.')\n",
"else:\n",
" ncols = min(n_avail, 4)\n",
" nrows_fits = (n_avail + ncols - 1) // ncols\n",
"\n",
" fig = plt.figure(figsize = (10, 6), dpi=200)\n",
" ax = fig.add_subplot(111)\n",
" # gs = gridspec.GridSpec(\n",
" # nrows_fits + 1, ncols,\n",
" # height_ratios=[3] * nrows_fits + [3.5],\n",
" # hspace=0.5, wspace=0.35)\n",
" ax.errorbar(x1, y1, yerr=ye1, fmt='.', color='grey',\n",
" alpha=0.5, markersize=4, label='data', zorder=1)\n",
"\n",
" for idx, name in enumerate(available_fits):\n",
" row, col = divmod(idx, ncols)\n",
" # ax = fig.add_subplot(gs[row, col])\n",
" xg, mu, std = preds_1d[name]\n",
" c = COLORS.get(name, 'steelblue')\n",
"\n",
" ax.plot(xg, mu, color=c, lw=1.8, label=f'{name} GP mean', zorder=3)\n",
" if std is not None:\n",
" ax.fill_between(xg, mu - std, mu + std,\n",
" alpha=0.25, color=c, zorder=2)\n",
" # ax.set_title(name, fontsize=10, color=c, fontweight='bold')\n",
" ax.legend()\n",
" ax.set_xlabel('time [d]', fontsize=8)\n",
" ax.set_ylabel('flux', fontsize=8)\n",
"\n",
" fig.suptitle('Fitted light curves', fontsize=11)\n",
" fig.tight_layout()\n",
" plt.savefig('Lightcurves_1d.png', bbox_inches='tight')\n",
"\n",
"\n",
" # PSD panel spanning all columns\n",
" fig_psd = plt.figure(figsize = (10,6), dpi=200)\n",
" ax_psd = fig_psd.add_subplot(111)\n",
" for name in psds_1d:\n",
" print(name)\n",
" fx, pwr = psds_1d[name]\n",
" print(fx.min(), fx.max(), pwr.min(), pwr.max())\n",
" print(fx.shape, pwr.shape)\n",
" c = COLORS.get(name, 'steelblue')\n",
" mask = (fx > 0) & (fx <= 0.12)\n",
" freq_loc = fx[mask]\n",
" try:\n",
" pwr_loc = pwr[mask]\n",
" except IndexError:\n",
" pwr_loc = pwr[1][:-1][mask]\n",
" ax_psd.plot(1/freq_loc, pwr_loc,\n",
" color=c, lw=1.5, label=name, alpha=0.85)\n",
" # ax_psd.axvline(TRUE_FREQ, ls='--', color='k', lw=1.2,\n",
" # label=f'true: {TRUE_PERIOD} d')\n",
" # ax_psd.axvline(2 * TRUE_FREQ, ls=':', color='k', lw=0.9,\n",
" # label=f'harmonic: {TRUE_PERIOD / 2:.0f} d')\n",
" ax_psd.axvline(TRUE_PERIOD, ls='--', color='k', lw=1.2,\n",
" label=f'true: {TRUE_PERIOD} d')\n",
" ax_psd.axvline(0.5 * TRUE_PERIOD, ls=':', color='k', lw=0.9,\n",
" label=f'1st harmonic: {TRUE_PERIOD / 2:.0f} d')\n",
" if len(TRUE_PERIODS) > 1:\n",
" ax_psd.axvline(TRUE_PERIODS[1], ls='--', color='k', lw=1.2,\n",
" label=f'true second period: {TRUE_PERIODS[1]} d',\n",
" alpha = 0.6)\n",
" ax_psd.axvline(0.5 * TRUE_PERIODS[1], ls=':', color='k', lw=0.9,\n",
" label=f'harmonic of second period: {TRUE_PERIODS[1] / 2:.0f} d',\n",
" alpha=0.6)\n",
" ax_psd.set_xlabel('Period (d)', fontsize=9)\n",
" ax_psd.set_ylabel('normalised PSD', fontsize=9)\n",
" ax_psd.set_title('Inferred PSDs — all models', fontsize=11)\n",
" ax_psd.legend(ncol=4, fontsize=8, loc='upper right')\n",
" # ax_psd.set_xlim(0, 0.12)\n",
" # ax_psd.set_xlim(0.01, 0.12)\n",
" # ax_psd.set_xlim(0.1, 1/0.12)\n",
" ax_psd.set_xlim(min(TRUE_PERIODS)/2, max(TRUE_PERIODS)*10)\n",
" ax_psd.loglog()\n",
" ax_psd.set_ylim(0.001, 1.)\n",
" # ax_psd.set_xaxis(\"log\")\n",
" fig_psd.suptitle('Inferred PSDs', fontsize=11)\n",
" fig_psd.tight_layout()\n",
"\n",
"\n",
" plt.savefig('PSDs_1d.png', bbox_inches='tight')\n",
" plt.show()\n",
" print('Saved comparison_1d.pdf')"
],
"id": "5vPPxqFtu0Uw"
},
{
"cell_type": "markdown",
"metadata": {
"id": "RyIeNuAmu0Uw"
},
"source": [
"## 2D (multiwavelength) comparison\n",
"\n",
"Multiwavelength data has inputs $(t, \\lambda)$, requiring a kernel defined\n",
"over 2D space. `pgmuvi` provides a built-in 2D SMK; `tinygp` requires a\n",
"custom kernel class; all other tools in this comparison are restricted to 1D inputs."
],
"id": "RyIeNuAmu0Uw"
},
{
"cell_type": "markdown",
"metadata": {
"id": "6du4sBBVu0Ux"
},
"source": [
"### `pgmuvi` — 2D spectral mixture GP"
],
"id": "6du4sBBVu0Ux"
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "pfRCr5Dpu0Ux",
"outputId": "e7409bf6-a317-4b58-a97f-e89de01558f0"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"mean_module.constant: 0.028102993965148926\n",
"covar_module.mixture_weights: tensor([0.6931, 0.6931])\n",
"covar_module.mixture_means: tensor([[[9.4067, 9.4067]],\n",
"\n",
" [[9.4067, 9.4067]]])\n",
"covar_module.mixture_scales: tensor([[[0.6931, 0.6931]],\n",
"\n",
" [[0.6931, 0.6931]]])\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" 35%|███▍ | 348/1000 [00:14<00:26, 24.78it/s]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Average change in loss over the last 30 iterations\n",
" was 9.44400166417925e-06.\n",
" This is < 1e-05, so we will end training here.\n",
"pgmuvi 2D | loss: 0.904 | time: 15.84 s\n",
" fitted time frequencies: [13.842627 13.842627] -> periods: [0.07224063 0.07224063]\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\n"
]
}
],
"source": [
"t0 = time.perf_counter()\n",
"res_pgmuvi_2d = lc_2d.fit(\n",
" model='2D',\n",
" num_mixtures=Q_MIXTURES,\n",
" training_iter=1000,\n",
" miniter=50,\n",
" lr=0.05,\n",
")\n",
"timings['pgmuvi_2d'] = time.perf_counter() - t0\n",
"\n",
"print('pgmuvi 2D | loss:', round(float(res_pgmuvi_2d['loss'][-1]), 3),\n",
" ' | time:', round(timings['pgmuvi_2d'], 2), 's')\n",
"\n",
"mm2 = lc_2d.model.covar_module.mixture_means.detach()\n",
"time_freqs_2d = mm2[:, 0, 0].detach().cpu().numpy()\n",
"print(' fitted time frequencies:', time_freqs_2d,\n",
" '-> periods:', 1.0 / time_freqs_2d)"
],
"id": "pfRCr5Dpu0Ux"
},
{
"cell_type": "markdown",
"metadata": {
"id": "dcItJtrju0Ux"
},
"source": [
"### `tinygp` — custom 2D spectral mixture kernel\n",
"\n",
"The 2D SMK is a **product over dimensions** of per-dimension spectral sums:\n",
"\n",
"$$k_\\mathrm{SM}^{(2D)}(\\mathbf{x},\\mathbf{x}') =\n",
"\\prod_{d=1}^{2}\\sum_{q=1}^{Q} w_q\n",
"\\exp\\!\\bigl(-2\\pi^2 v_q^{(d)2}\\tau_d^2\\bigr)\\cos(2\\pi\\mu_q^{(d)}\\tau_d).$$"
],
"id": "dcItJtrju0Ux"
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "MAUYZTnsu0Ux",
"outputId": "27698c04-cfdf-4191-f236-f5ff9e343a16"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"tinygp 2D | loss: -276.357 | time: 37.98 s\n",
" fitted time periods: [103.4588 376.66473]\n"
]
}
],
"source": [
"class SMK2D(TinyKernel if HAVE_TINYGP else object):\n",
" r'''2-D Spectral Mixture Kernel (product over dimensions, sum over mixtures).\n",
"\n",
" Parameters\n",
" ----------\n",
" log_weights : jnp.ndarray, shape (Q,)\n",
" Log of the mixture weights w_q.\n",
" log_freqs : jnp.ndarray, shape (Q, 2)\n",
" Log of the mixture-mean frequencies mu_q for each dimension.\n",
" log_scales : jnp.ndarray, shape (Q, 2)\n",
" Log of the bandwidth parameters v_q for each dimension.\n",
" '''\n",
" log_weights : 'jnp.ndarray' # (Q,)\n",
" log_freqs : 'jnp.ndarray' # (Q, 2)\n",
" log_scales : 'jnp.ndarray' # (Q, 2)\n",
"\n",
" def evaluate(self, X1, X2):\n",
" w = jnp.exp(self.log_weights)\n",
" mu = jnp.exp(self.log_freqs)\n",
" v = jnp.exp(self.log_scales)\n",
" tau = X1 - X2 # (2,)\n",
"\n",
" cos_term = jnp.cos(2 * jnp.pi * mu * tau)\n",
" exp_term = jnp.exp(-2 * jnp.pi**2 * (v**2) * (tau**2))\n",
"\n",
" return jnp.sum(w * jnp.prod(cos_term * exp_term, axis=1))\n",
"\n",
"\n",
"if HAVE_TINYGP:\n",
" x2_j = jnp.asarray(lc_2d.xdata.detach().cpu().numpy())\n",
" y2_j = jnp.asarray(lc_2d.ydata.detach().cpu().numpy())\n",
" ye2_j = jnp.asarray(lc_2d.yerr.detach().cpu().numpy())\n",
" mean2d = float(np.mean(np.asarray(y2_j)))\n",
"\n",
" wl_range = max(WAVELENGTHS) - min(WAVELENGTHS)\n",
" wl_freq = 1.0 / wl_range\n",
" init_freqs_2d = jnp.array(\n",
" [[TRUE_FREQ, wl_freq],\n",
" [2 * TRUE_FREQ, 2 * wl_freq]]\n",
" )\n",
" kernel_tiny_2d = SMK2D(\n",
" log_weights=jnp.log(jnp.ones(Q_MIXTURES) * 0.5),\n",
" log_freqs =jnp.log(init_freqs_2d),\n",
" log_scales =jnp.log(jnp.ones((Q_MIXTURES, 2)) * 0.002),\n",
" )\n",
"\n",
" @jax.jit\n",
" @jax.value_and_grad\n",
" def _neg_ll_tiny2d(params):\n",
" gp = TinyGP(params, x2_j, diag=ye2_j**2, mean=mean2d)\n",
" return -gp.log_probability(y2_j)\n",
"\n",
" opt2d = optax.adam(0.05)\n",
" state2d = opt2d.init(eqx.filter(kernel_tiny_2d, eqx.is_inexact_array))\n",
"\n",
" t0 = time.perf_counter()\n",
" for _ in range(1000):\n",
" loss2d, grads2d = _neg_ll_tiny2d(kernel_tiny_2d)\n",
" upd2d, state2d = opt2d.update(\n",
" grads2d, state2d,\n",
" eqx.filter(kernel_tiny_2d, eqx.is_inexact_array))\n",
" kernel_tiny_2d = eqx.apply_updates(kernel_tiny_2d, upd2d)\n",
" timings['tinygp_2d'] = time.perf_counter() - t0\n",
"\n",
" tf_fit_2d = np.exp(np.asarray(kernel_tiny_2d.log_freqs))[:, 0]\n",
" print('tinygp 2D | loss:', round(float(loss2d), 3),\n",
" ' | time:', round(timings['tinygp_2d'], 2), 's')\n",
" print(' fitted time periods:', 1.0 / tf_fit_2d)\n",
"else:\n",
" print('tinygp not installed.')"
],
"id": "MAUYZTnsu0Ux"
},
{
"cell_type": "markdown",
"metadata": {
"id": "14qmlCA5u0Ux"
},
"source": [
"### `celerite2` — 2D limitation\n",
"\n",
"`celerite2` is designed exclusively for 1D semiseparable covariance matrices\n",
"and cannot accept two-dimensional `(time, wavelength)` input:"
],
"id": "14qmlCA5u0Ux"
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Hry6KWBtu0Ux",
"outputId": "8817e480-5bf0-42e4-fcd6-ee4941591406"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"celerite2 rejects 2D input (expected):\n",
" ValueError: The input coordinates must be one dimensional\n"
]
}
],
"source": [
"if HAVE_CELERITE:\n",
" x2_c = lc_2d.xdata.detach().cpu().numpy() # shape (N, 2)\n",
" y2_c = lc_2d.ydata.detach().cpu().numpy()\n",
" ye2_c = lc_2d.yerr.detach().cpu().numpy()\n",
"\n",
" kernel_c2d = c2terms.SHOTerm(\n",
" sigma=0.7, rho=TRUE_PERIOD, tau=2 * TRUE_PERIOD)\n",
" gp_c2d = celerite2.GaussianProcess(\n",
" kernel_c2d, mean=float(np.mean(y2_c)))\n",
" try:\n",
" gp_c2d.compute(x2_c, yerr=ye2_c)\n",
" print('Unexpected: celerite2 accepted 2D input.')\n",
" except (TypeError, ValueError) as exc:\n",
" print('celerite2 rejects 2D input (expected):')\n",
" print(f' {type(exc).__name__}: {str(exc)[:200]}')\n",
"else:\n",
" print('celerite2 not installed.')"
],
"id": "Hry6KWBtu0Ux"
},
{
"cell_type": "markdown",
"metadata": {
"id": "8srKPN2ou0Ux"
},
"source": [
"## Summary comparison"
],
"id": "8srKPN2ou0Ux"
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "5pJPNZdru0Ux",
"outputId": "f7200957-9355-43b1-a28a-d023e3502f60"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"+-----------+-----------------------------+----------------+------------------+---------------+-----------------+------------+----------------------------+\n",
"| Package | Kernel (1D) | Spectral shape | Optimizer | GPU | 2D input | Code lines | Time for 1D lightcurve (s) |\n",
"+-----------+-----------------------------+----------------+------------------+---------------+-----------------+------------+----------------------------+\n",
"| pgmuvi | SpectralMixture (built-in) | Gaussian | Adam (PyTorch) | Yes (CUDA) | Yes (native) | 1 | 11.19 |\n",
"| tinygp | Custom SMK1D (user class) | Gaussian | optax Adam (JAX) | XLA JIT | 2D class (user) | 16 | 19.00 |\n",
"| celerite2 | SHOTerm (Lorentzian approx) | Lorentzian | scipy L-BFGS-B | No (O(N) CPU) | No | 4 | 0.737 |\n",
"| george | ExpSquared x Cosine | Gaussian | scipy L-BFGS-B | No | No | 5 | 1.460 |\n",
"| GPy | ExpQuadCosine (built-in) | Gaussian | scipy L-BFGS-B | No | Manual | ~3 | nan |\n",
"| gpflow | SquaredExp x Cosine | Gaussian | TF Scipy | Yes (TF/GPU) | Manual | ~4 | nan |\n",
"| sklearn | RBF x ExpSineSquared | Non-Gaussian | scipy L-BFGS-B | No | Partial | 4 | 0.442 |\n",
"| gpjax | Custom SMK (user class) | Gaussian | optax Adam (JAX) | XLA JIT | Custom kernel | ~20 | nan |\n",
"+-----------+-----------------------------+----------------+------------------+---------------+-----------------+------------+----------------------------+\n",
"\n",
"Notes:\n",
" nan = package was not available in this run.\n",
" sklearn ExpSineSquared uses sin^2, not cos — not the exact SMK.\n",
" GPy/gpflow 2D: requires user-constructed product/sum kernels.\n"
]
}
],
"source": [
"_NA = 'N/A'\n",
"\n",
"HEADERS = [\n",
" 'Package', 'Kernel (1D)', 'Spectral shape',\n",
" 'Optimizer', 'GPU', '2D input',\n",
" 'Code lines', 'Time for 1D lightcurve (s)',\n",
"]\n",
"\n",
"_t = timings # shorthand\n",
"\n",
"ROWS = [\n",
" ['pgmuvi', 'SpectralMixture (built-in)', 'Gaussian',\n",
" 'Adam (PyTorch)', 'Yes (CUDA)', 'Yes (native)',\n",
" f\"{code_lines.get('pgmuvi', '~1')}\",\n",
" f\"{_t.get('pgmuvi', float('nan')):.2f}\"],\n",
" ['tinygp', 'Custom SMK1D (user class)', 'Gaussian',\n",
" 'optax Adam (JAX)', 'XLA JIT', '2D class (user)',\n",
" f\"{code_lines.get('tinygp', '~15')}\",\n",
" f\"{_t.get('tinygp', float('nan')):.2f}\"],\n",
" ['celerite2','SHOTerm (Lorentzian approx)', 'Lorentzian',\n",
" 'scipy L-BFGS-B', 'No (O(N) CPU)', 'No',\n",
" f\"{code_lines.get('celerite2', '~4')}\",\n",
" f\"{_t.get('celerite2', float('nan')):.3f}\"],\n",
" ['george', 'ExpSquared x Cosine', 'Gaussian',\n",
" 'scipy L-BFGS-B', 'No', 'No',\n",
" f\"{code_lines.get('george', '~4')}\",\n",
" f\"{_t.get('george', float('nan')):.3f}\"],\n",
" ['GPy', 'ExpQuadCosine (built-in)', 'Gaussian',\n",
" 'scipy L-BFGS-B', 'No', 'Manual',\n",
" f\"{code_lines.get('GPy', '~3')}\",\n",
" f\"{_t.get('GPy', float('nan')):.3f}\"],\n",
" ['gpflow', 'SquaredExp x Cosine', 'Gaussian',\n",
" 'TF Scipy', 'Yes (TF/GPU)', 'Manual',\n",
" f\"{code_lines.get('gpflow', '~4')}\",\n",
" f\"{_t.get('gpflow', float('nan')):.2f}\"],\n",
" ['sklearn', 'RBF x ExpSineSquared', 'Non-Gaussian',\n",
" 'scipy L-BFGS-B', 'No', 'Partial',\n",
" f\"{code_lines.get('sklearn', '~3')}\",\n",
" f\"{_t.get('sklearn', float('nan')):.3f}\"],\n",
" ['gpjax', 'Custom SMK (user class)', 'Gaussian',\n",
" 'optax Adam (JAX)', 'XLA JIT', 'Custom kernel',\n",
" f\"{code_lines.get('gpjax', '~20')}\",\n",
" f\"{_t.get('gpjax', float('nan')):.2f}\"],\n",
"]\n",
"\n",
"col_w = [\n",
" max(len(h), max(len(str(r[i])) for r in ROWS))\n",
" for i, h in enumerate(HEADERS)\n",
"]\n",
"\n",
"def _fmt_row(cells, widths):\n",
" return '|' + '|'.join(f' {str(c):<{w}} ' for c, w in zip(cells, widths)) + '|'\n",
"\n",
"sep = '+' + '+'.join('-' * (w + 2) for w in col_w) + '+'\n",
"print(sep)\n",
"print(_fmt_row(HEADERS, col_w))\n",
"print(sep)\n",
"for row in ROWS:\n",
" print(_fmt_row(row, col_w))\n",
"print(sep)\n",
"print()\n",
"print('Notes:')\n",
"print(' nan = package was not available in this run.')\n",
"print(' sklearn ExpSineSquared uses sin^2, not cos — not the exact SMK.')\n",
"print(' GPy/gpflow 2D: requires user-constructed product/sum kernels.')"
],
"id": "5pJPNZdru0Ux"
},
{
"cell_type": "markdown",
"metadata": {
"id": "FDSq-xuGu0Ux"
},
"source": [
"## Practical guidance\n",
"\n",
"| | `pgmuvi` | `tinygp` | `celerite2` | `george` | `GPy` | `gpflow` | `sklearn` | `gpjax` |\n",
"|---|---|---|---|---|---|---|---|---|\n",
"| **1D SMK** | built-in | custom class | SHO proxy | ExpSq×Cos | ExpQuadCosine | SE×Cos | RBF×ExpSine† | custom class |\n",
"| **2D input** | native | custom class | ✗ | ✗ | manual | manual | partial | custom |\n",
"| **GPU support** | PyTorch | JAX/XLA | CPU only | CPU only | CPU only | TF/GPU | CPU only | JAX/XLA |\n",
"| **Backend** | PyTorch | JAX | numpy/C | numpy/C | numpy/C | TensorFlow | numpy/C | JAX |\n",
"| **Code lines (1D)** | ~1 | ~15 | ~4 | ~4 | ~3 | ~4 | ~3 | ~20 |\n",
"\n",
"†sklearn's `ExpSineSquared` uses $\\sin^2$ not $\\cos$ — not the exact SMK.\n",
"\n",
"### When to choose each tool\n",
"\n",
"- **`pgmuvi`** is the natural choice for astronomical multiwavelength light curves.\n",
" Built-in 1D/2D SMK, MLS initialisation, and PyTorch Adam are pre-configured\n",
" behind a single `.fit()` call. GPU-accelerated via PyTorch.\n",
"- **`celerite2`** should be preferred when $O(N)$ speed on a **single 1D time series**\n",
" is the priority and the Lorentzian spectral shape is acceptable.\n",
"- **`george`** is a lightweight, battle-tested 1D GP library with analytic gradients\n",
" and scipy-based optimisation; CPU-only and 1D-only.\n",
"- **`GPy`** has a broad kernel catalogue (including `ExpQuadCosine` ≡ SMK component)\n",
" and a convenient model API, but is CPU-only and no longer actively developed.\n",
"- **`gpflow`** offers GPU acceleration via TensorFlow and a clean API; the SMK\n",
" must be composed from built-in kernels.\n",
"- **`scikit-learn`** is the easiest entry point for general ML, but its kernel set\n",
" does not include a true SMK and it scales poorly to large datasets.\n",
"- **`tinygp`** and **`gpjax`** are ideal when you need **total kernel flexibility**\n",
" and JAX auto-diff/JIT. Both can implement any kernel that `pgmuvi` uses,\n",
" but require substantially more user code."
],
"id": "FDSq-xuGu0Ux"
}
]
}