From 9fa6d9bdb7be39aa393594f6244f2326c06aa0c4 Mon Sep 17 00:00:00 2001 From: nathanearnestnoble <51792809+nathanearnestnoble@users.noreply.github.com> Date: Thu, 30 Apr 2026 16:48:13 -0400 Subject: [PATCH 1/7] Updating AQC tutorial to conform with new template updated AQC tutorial to conform to new template, and address AQC related issues (https://github.com/Qiskit/documentation/issues/4269, and https://github.com/Qiskit/documentation/issues/4270) --- ...antum-compilation-for-time-evolution.ipynb | 1962 +++++------------ 1 file changed, 579 insertions(+), 1383 deletions(-) diff --git a/docs/tutorials/approximate-quantum-compilation-for-time-evolution.ipynb b/docs/tutorials/approximate-quantum-compilation-for-time-evolution.ipynb index 9ea79755449..a58d9b2f46d 100644 --- a/docs/tutorials/approximate-quantum-compilation-for-time-evolution.ipynb +++ b/docs/tutorials/approximate-quantum-compilation-for-time-evolution.ipynb @@ -2,52 +2,100 @@ "cells": [ { "cell_type": "markdown", - "id": "2c991cef-110a-455a-8741-52d5e20d3196", + "id": "dd701ecc", "metadata": {}, "source": [ "---\n", "title: Approximate quantum compilation for time evolution circuits\n", - "description: This tutorial demonstrates how to implement AQC-Tensor with Qiskit to enhance quantum circuit performance\n", + "description: Learn how to use AQC-Tensor to compress Trotterized time-evolution circuits for efficient execution on quantum hardware.\n", "---\n", "\n", - "\n", "{/* cspell:ignore circo */}\n", "\n", "# Approximate quantum compilation for time evolution circuits\n", - "*Usage estimate: Five minutes on an Eagle processor (NOTE: This is an estimate only. Your runtime might vary.)*" + "\n", + "*Usage estimate: 30 seconds on IBM Heron (NOTE: This is an estimate only. Your runtime might vary.)*" + ] + }, + { + "cell_type": "markdown", + "id": "12608301", + "metadata": {}, + "source": [ + "## Learning outcomes\n", + "After completing this tutorial, you can expect to understand the following information:\n", + "- How to use the AQC-Tensor Qiskit addon to compress deep Trotter circuits into shallow ansatz circuits\n", + "- How to generate a parametrized ansatz from a Trotter circuit and optimize its parameters using tensor network (MPS) methods\n", + "- How to evaluate the fidelity of a compressed circuit against the target evolution and run it on quantum hardware" ] }, { "cell_type": "markdown", - "id": "93895788-7d38-4cc7-a7e9-ba97c34966af", + "id": "c3c5992a", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "It is recommended that you familiarize yourself with these topics:\n", + "- [Basics of quantum circuits](https://learning.quantum.ibm.com/course/basics-of-quantum-information)\n", + "- [Hamiltonian simulation and Trotterization](https://learning.quantum.ibm.com/course/variational-algorithm-design/hamiltonian-simulation)\n", + "- [Introduction to primitives](https://quantum.cloud.ibm.com/docs/en/guides/primitives)" + ] + }, + { + "cell_type": "markdown", + "id": "714adc39", "metadata": {}, "source": [ "## Background\n", "\n", - "This tutorial demonstrates how to implement **Approximate Quantum Compilation** using tensor networks (AQC-Tensor) with Qiskit to enhance quantum circuit performance. We apply AQC-Tensor within the context of a Trotterized time evolution to reduce circuit depth while maintaining simulation accuracy, following the Qiskit framework for state preparation and optimization. You will learn how to create a low-depth ansatz circuit from an initial Trotter circuit, optimize it with tensor networks, and prepare it for quantum hardware execution.\n", + "This tutorial demonstrates how to implement **Approximate Quantum Compilation** using tensor networks (AQC-Tensor) with Qiskit to enhance quantum circuit performance. AQC-Tensor compresses deep Trotter circuits into shallow, hardware-friendly circuits while preserving simulation accuracy.\n", + "\n", + "### How AQC-Tensor works\n", + "\n", + "Consider simulating a Hamiltonian $H$ for total time $t$ using $k$ Trotter steps. The full Trotter circuit is:\n", "\n", - "The primary objective is to simulate time evolution for a model Hamiltonian with a reduced circuit depth. This is achieved using the **AQC-Tensor** Qiskit addon, [qiskit-addon-aqc-tensor](https://github.com/Qiskit/qiskit-addon-aqc-tensor), which leverages tensor networks, specifically matrix product states (MPS), to compress and optimize the initial circuit. Through iterative adjustments, the compressed ansatz circuit maintains fidelity to the original circuit while staying feasible for near-term quantum hardware. See the [documentation](https://qiskit.github.io/qiskit-addon-aqc-tensor/) for more information.\n", + "$$\n", + "U_{\\text{full}} = \\left[U_{\\text{Trotter}}(t/k)\\right]^k\n", + "$$\n", + "\n", + "A naive approach uses few Trotter steps to keep circuit depth manageable, but this introduces significant Trotter error. AQC-Tensor resolves this tension by separating accuracy from depth:\n", + "\n", + "1. **Target circuit (high accuracy, deep):** Construct a Trotter circuit with many steps—say $10k$—for the same evolution time. This circuit has far less Trotter error, but is too deep for hardware. Because it is only simulated classically as a matrix product state (MPS), depth is not a concern.\n", + "\n", + "2. **Ansatz circuit (low depth, parametrized):** Define a parametrized circuit $V(\\theta)$ with the same structure as a single-step Trotter circuit. Initialize it so that $V(\\theta_{\\text{init}}) = U_{\\text{Trotter}}(t/k)$, then iteratively optimize $\\theta$ so that $V(\\theta)$ reproduces the high-accuracy target state as closely as possible.\n", + "\n", + "The result is a circuit that retains the depth of a single Trotter step but achieves the accuracy of many, making it feasible for near-term quantum hardware.\n", + "\n", + "### When to use AQC-Tensor\n", + "\n", + "AQC-Tensor is most effective when:\n", "\n", - "Approximate Quantum Compilation is particularly advantageous in quantum simulations that exceed hardware coherence times, as it allows complex simulations to be performed more efficiently. This tutorial guides you through the AQC-Tensor workflow setup in Qiskit, covering initialization of a Hamiltonian, generation of Trotter circuits, and transpilation of the final optimized circuit for a target device." + "- **Circuit depth exceeds hardware coherence times.** If a Trotter simulation requires more layers than the device can support, AQC-Tensor can compress the evolution into a shallower circuit.\n", + "- **Entanglement remains classically tractable.** The total entanglement in a time-evolved state depends primarily on the evolution time $t$, not the number of Trotter steps $k$. This means a target circuit with $10k$ steps is typically no harder to represent as an MPS than one with $k$ steps, as long as $t$ is short enough for bond dimensions to stay manageable.\n", + "- **A natural ansatz exists.** Because the ansatz mirrors the structure of a Trotter circuit, it provides a physically motivated starting point with well-defined initial parameters, avoiding the convergence issues that can plague arbitrary variational ansatze.\n", + "\n", + "This approach contrasts with generic circuit compression: rather than trying to approximate an arbitrary unitary with fewer gates, AQC-Tensor keeps the same gate structure and optimizes its parameters to reduce Trotter error. See the [AQC-Tensor documentation](https://qiskit.github.io/qiskit-addon-aqc-tensor/) for more information.\n", + "\n", + "This tutorial guides you through the full state-preparation AQC-Tensor workflow: defining a Hamiltonian, generating Trotter circuits, compressing them via tensor-network optimization, and executing the result on IBM Quantum® hardware." ] }, { "cell_type": "markdown", - "id": "b1b8f238-0dab-42c5-b658-779f3ff178a0", + "id": "f8a05291", "metadata": {}, "source": [ "## Requirements\n", "\n", "Before starting this tutorial, ensure that you have the following installed:\n", "\n", - "* Qiskit SDK v1.0 or later, with [visualization](/docs/api/qiskit/visualization) support\n", + "* Qiskit SDK v2.0 or later, with [visualization](https://quantum.cloud.ibm.com/docs/en/api/qiskit/visualization) support\n", "* Qiskit Runtime v0.22 or later (`pip install qiskit-ibm-runtime`)\n", "* AQC-Tensor Qiskit addon (`pip install 'qiskit-addon-aqc-tensor[aer,quimb-jax]'`)" ] }, { "cell_type": "markdown", - "id": "f0723f2d-e0ba-4fe9-a742-1891b7b45459", + "id": "f9410d21", "metadata": {}, "source": [ "## Setup" @@ -55,8 +103,8 @@ }, { "cell_type": "code", - "execution_count": 28, - "id": "ccdcdca2-4e77-4696-b0ff-45d13dbc6ac3", + "execution_count": 1, + "id": "ecdb92e0", "metadata": {}, "outputs": [], "source": [ @@ -65,6 +113,7 @@ "import datetime\n", "import matplotlib.pyplot as plt\n", "\n", + "from scipy.linalg import expm\n", "from scipy.optimize import OptimizeResult, minimize\n", "\n", "from qiskit.quantum_info import SparsePauliOp, Pauli\n", @@ -86,47 +135,54 @@ "\n", "from qiskit_ibm_runtime import QiskitRuntimeService\n", "from qiskit_ibm_runtime import EstimatorV2 as Estimator\n", + "from qiskit_ibm_runtime.fake_provider import FakeKyiv\n", "\n", "from rustworkx.visualization import graphviz_draw" ] }, { "cell_type": "markdown", - "id": "39bc4470-f7f8-4550-83a7-a22167024000", + "id": "6473f943", "metadata": {}, "source": [ - "## Part I. Small-scale example\n", + "## Small-scale simulator example\n", "\n", - "The first part of this tutorial uses a small-scale example with 10 sites to illustrate the process of mapping a quantum simulation problem to an executable quantum circuit. Here, we’ll explore the dynamics of a 10-site XXZ model, allowing us to build and optimize a manageable quantum circuit before scaling to larger systems.\n", + "This section uses a 10-site system to illustrate the AQC-Tensor workflow step by step. We simulate the dynamics of a 10-site XXZ spin chain, a widely studied model for examining spin interactions and magnetic properties.\n", "\n", - "The XXZ model is widely studied in physics for examining spin interactions and magnetic properties. We set up the Hamiltonian to have open boundary conditions with site-dependent interactions between neighboring sites along the chain.\n", + "The Hamiltonian is:\n", "\n", - "### Model Hamiltonian and observable\n", - "\n", - "The Hamiltonian for our 10-site XXZ model is defined as:\n", "$$\n", "\\hat{\\mathcal{H}}_{XXZ} = \\sum_{i=1}^{L-1} J_{i,(i+1)}\\left(X_i X_{(i+1)}+Y_i Y_{(i+1)}+ 2\\cdot Z_i Z_{(i+1)} \\right) \\, ,\n", "$$\n", "\n", - "where $J_{i,(i+1)}$ is a random coefficient corresponding to edge $(i, i+1)$, and $L=10$ is the number of sites.\n", - "\n", - "By simulating the evolution of this system with reduced circuit depth, we can gain insights into using AQC-Tensor to compress and optimize circuits." + "where $J_{i,(i+1)}$ is a random coefficient for edge $(i, i+1)$ and $L=10$." ] }, { "cell_type": "markdown", - "id": "89f22428-d4d8-49e7-a1c5-39197dc58330", + "id": "6520161e", "metadata": {}, "source": [ - "#### Set up the Hamiltonian and observable\n", + "### Step 1: Map classical inputs to a quantum problem\n", "\n", - "Before we map our problem, we need to set up the coupling map, Hamiltonian, and observable for the 10-site XXZ model." + "In this step, we:\n", + "1. Define the Hamiltonian, observable, and initial state.\n", + "2. Compute the exact expectation value classically for later comparison.\n", + "3. Generate a high-accuracy Trotter circuit (the AQC target) and compress it into a low-depth ansatz using AQC-Tensor." + ] + }, + { + "cell_type": "markdown", + "id": "bbeb5729", + "metadata": {}, + "source": [ + "#### Set up the Hamiltonian, observable, and initial state" ] }, { "cell_type": "code", "execution_count": 2, - "id": "1ea0e102-23d5-4e6e-8ef8-e82843452b19", + "id": "527dbada", "metadata": {}, "outputs": [ { @@ -147,8 +203,9 @@ }, { "data": { + "image/png": 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+CSD7GRkZ/+MYn8d+jNXhw4cZXF1dyZxjoVzbtWvXGPLz8xnmzp3LIC8vzxAXF8cgLCzMUFdXR7nhFJgAcouKigrDkSNHKDBlVCtoheT+/fsZkpKSGERERBgCAwMZ1q1bx/Djxw+iAmeg0yfIkTY2NgwzZ84EMQcNHk2f1ImKoZ4+QeUmaPfT48ePGe7fv88QHh7OcO7cOYbW1lbqBBCZpoymTzIDDk3bUE+fM2bMYDhz5gx4Rxxo1aeenh64rv/06ROaT+nLHU2f1AnvoZ4+kUOhr6+P4fXr18hCA8YeTZ/UCfrhkD4PHTrEoKurywDa/Q7DoB2c1Akh8kwZTZ/khRu6ruGQPv///88QEBDAsHLlSoa9e/cymJubMzAyMqJ7la780fRJneAeyukTlC43btzIsGrVKgbQOBOofwTCp06dYuDh4WHw8vKiTiCRYcpo+iQj0LBoGcrpE+Sdzs5OBlVVVYaYmBgGTk5OcNkZGxvLUFpaCpIeMDyaPqkT9EM9fYJOY2BmZgaP0UtKSjJ0dHQwcHBwMOTl5VEngMg0hVD6xDjG6s2bN+CtppaWlmRaSbk20NYtUGYHBSTMNCcnJ/CACGgADyY2ELSVlRXD8ePHB8LqYWEnqLEB8gjo3DkQBm3r2rJlC0NISAh4QgtUwG/evBnnsTuDIX2C3A/K3NLS0iDmoMKj6ZOy6Bjq6fPDhw8MoIk4CwsLcECA0iio8QTqaJ48eRIsNpDEaPqkLPSHevoE+b6yspKBl5cXxAQ35kGLGUDpE7QVFSw4gMRo+qQs8IdD+oSFAOhIC9AkcVRUFExowOnR9ElZFAyX9Ak6EqiqqorBwMAAjkVFRSkLHCroHk2flAXicEmfPT09DCdOnAAfdQHqK1EWKtTTPZo+KQvLoZ4+Qcf7ghbbhIaGMmhqajIoKCiAMWjC2Nvbm4GLi4uyAKJQ92j6pCwAh3r6BPn+yZMnDKBFYDC/gMS4ubnBA8og9kDi0fRJWejD4hQ09gnCQ238E3RcFej4P3d3d3hAMDExga942LRpE8PFixfh4gPBwJc+MSY7QJUBKELU1dUHwq1gO0EdTDU1NTAbRoAqpV+/fjHs3r0bJjQgNMhdDx48GBC7h6uloJlOUJr79u0beCWQn58feMcH6NIZ0C4akBzM74MhfcLcAprdhLEHCz2aPqkfE0MpfQoICIB3SiGHAmjGW1tbG7xaBFl8INij6ZP6oT6U0ifI9+zs7CAKjkEr50F3ywyGQZHR9AmPFqoxhlr6BHn858+f4JV0kydPHvAVySD3wPBo+oSFBPXooZY+jx49yrBjxw4GDQ0NhuDgYIbTp09TLzAoNGk0fVIYgFi0D7X0Ceq/V1dXg1eASkhIYPHRwAmNpk/qh/1QSp+gcSR9fX2MQFi9ejVDWFgYhji9BUbTJ/VDfCilT5DvQQu7QYPGsJNsQO4H3Y8A6iOB5AcSj6ZP6oc+KH5BY5xDYfwTdE8c6F440I4j5JAATRyD+KBJYxA9UBhf+mRBdxRo5TxIDHRsFIimNwbZ//LlS/Aqf2S7QZUUiA/acgiiBwqDwgXkRlDlOFBuGMr2gi5ewud+UMYHyYOONJk/fz74cnrQ6vT4+HiGyMhIBlDYg+RB8QCiBxKDViOD7IfRIPZAY1C4gMJoNH2SFxPDKX3CQgC0ggBUbjY2NsKEBoweTZ+UBf1wS5+g46xADai1a9dSFjBU0j2aPikLyOGSPkGr5kE7jEHpgbIQoa5ukHtG63fyw3Q4pM93794xREREMFy+fJlh/fr1DKAVdaCdHqD0Sn7IUEfnaPqkLByHQ/rs7+8HH2OlqKjIkJCQwABaHGhsbAw+hpqfn5+yAKJQ92j6pCwAh0P6RA8BUH0Kaod6enqiS9GdP5o+KQvy4ZA+ExMTGVasWMHQ0tLCADrJBjS+lJKSwpCcnExZ4FBB92j6pCwQh3r65OPjAwcAqM8OZkAJ2OTHo0ePoCIDQ4HS59u3b7Fbjn4dCOgSHNBlTb9+/UKXogt/37594Muj6urqUOy7e/cuWDw+Ph5FnN6cJUuWwC/9BYXTKGYAxws9wgF0SVNFRQXYvoFKn8jpDXThHsjfjY2NyMIDyh5Nn/RLj6C4R8aDLX3CEiKoTDc3Nx/wCyJB7hlNn6PpE5QOPn/+/D8zM/M/JycnuDwvKCj4//PnT5DUgOLR9DmaPvfs2fO/qKgIng7b2trAaXSgLygHOWg0fY6mT1A6gOGtW7f+FxYWBqfPXbt2wYQHjB5Nn6PpU01N7b+UlNT/lStX/gfV85s2bQLX8/r6+v9///49YGkTZPFo+hxNn6B0gIxnzZr1PyIiAllowNij6XM0fYISH+hickdHR3C9bmRk9P/ly5cg4QHHo+lzZKdPUP0tJyf3X0BA4P/79+/h6RFU14PGwmbMmAEXGwgGKH2ysbFhsxrzgnJBQUHwrMj79+/BNL0J0HYekJ2srKwgCo6/f/8OZg/0tljQrBFo9gjkzlH8H7yCh5RwAIUfOCIJELDz20HnEIMuvgFdSg66UMzDwwOsc6DSJ9jyQUyAwnc0fZKeLmFpGBR+xETvUEmfoDMh29vbGRYuXDgojmMBhe9o+hxNn6DLIKdOncoAumgXdD/YhAkTwJdGEpP3aKlmNH2SnzZBZSgo/IiJn8FafoLaFb29vQxtbW3EeIPuakDhO1p+kp9GQeFHTKQN1vSJ7nbQhbqgowNAK+5AR66hy9ObDwrf0fQ5ctMn6M6427dvM4COYgEdCwSq5319fRmysrLA53kvX76c3kkSxb7R9El+2hwO9TtKYoByQKcggNIqlDug1Gj6HE2foAR46tQpBikpKfBRqqBjAc3NzcG7PEByA4lH0+fITp8sLCwMs2fPZgCdwKOlpcUA6is1NDQwwE4NAR2XPtDpU0hICKsTMI6xAjVUQSpB52iLiYmBmHTFMjIyYPtAnU4wA0rAtv/o6OhARQaGAoULLIwGxgXD11bQBBdocBbUQA4MDGQAXVwLajSDLsCB+RoW9qB4GIj0CXPHYKVB4QILo8HqxqHqrqGYPkHnfNbX1zMM5B1MyPE9mj6RQ4O67KGWPkHbs01MTBi2b9/OoKyszLBlyxaGmJgY6gYKiaaNpk8SA4wE5UMhfVZWVoInhUE0zGugjieIXVZWBr4MGnTMAIg/EHg0fdIu1IdC+sTme1lZWYaAgADwhdDY5OkpNpo+aRfaQyF9gvrtoEFxERERlICwsbEBD4xcuHCBITY2FkWOnpzR9Em70B4K6RPd96DB28FyhBXIbaPpExQKtMFDJX2eOHGCAXRs+6VLl8DHBoHu3MzJyQFPGG/evJk2gUOkqaPpk8iAIkPZUEmfbm5u4CNUQf120P2bQUFBDDNnzgTfCWttbU2Gz6mnBV/6xJjsUFVVZQBdFApaLTQQszSguzlAMzPPnz9HCQHQxdQggYFwE8heGAbNsurq6sK4ozSFIQC65BvUOAbNGIJWAIEKeXd3dwbYyjp04wc6faK7Z7DxR9MndWNkKKfPiRMnMpiamjKAVn9SN1TIN200fZIfdth0DuX0CfMP6Bxve3t7hl+/fsGEBoweTZ/UDfqhlj5BCwXu3LnDAOpowkLixYsXYCbojgQBAQEwe6CI0fRJ3ZAfaukTl+9BO55BA8245OklPpo+qRvSQy19gvrvvLy8DM+ePUMJCNDuTZAANzc3iBowPJo+qRv0Qy19ovsedOcR6K4O0JgXutxA8EfTJ3VDfSimT9COdzMzM/BEByg0srOzGUB3IYDuQgLdL4M+kQxSQy88mj6pG9JDMX2CAKiez8zMBDEZQHcrg+7ZBo03gRYwggUHiMCXPpnQ3QSaqTE0NGQ4duwYuhRd+KBB7qioKAbQsUXIFoI6n6AjjUBbZ5DF6ckGDcqDZl1hDTd62j0c7QLNZIIaGqCtzaDtz2vWrGEATXiA0gAu/w50+kR2Fyg9gPgwGsQeSAxyx2j6pF4MDOX0OW/ePPAKZdAFkbAQAaWPGzduwLh0p0H2j6ZP6gX7UE6f6KEAaiyBJjzQxenJH02f1A3toZg+W1tbGfbs2YOCYRdDglYywbZrUzekiDNtNH0SF07EqhqK6ROX30BHvIJW2OGSp4f4aPqkbigPxfQJGuyws7NjAC2WRA4N0EW7ID5IDkQPBB5Nn9QN9aGYPtFDADTmMFiOsBpNn+ixQxl/qKZPUF8INAiO7Pv09HQG0Kknr169QhamK3s0fVI3uIdq+kQGHz9+ZOjs7GSYNm0aw0CXowTTJ7abPGpqav7LyMj8//PnDzZpmos9fvz4v4SExP9Dhw6B7QJdciYmJvZ/5syZYP5AEQcOHABfGHT58uWBcsKQt/fdu3f/nZ2d/8+ZMwflghtSPDbQ6RPm1u/fv4PTQ2lpKUxoQOnR9El58A+H9Dl9+vT/lpaW/+fPnw/G8+bN+z9p0qT/Xl5e8DKV8pAi3YTR9El6mKHrGOrpE3TB2dKlS/+D6niY3/bv3//f0NBwwC8oH02fsBghnx7q6RObzwfLBeWj6RNb7JAmNtTT59+/f/+XlJT8B136DGKDfA8qP6OiokDMAcWj6ZPy4B/q6RMUAvfu3fvPy8v7H3RZKIgPwg0NDf9dXV1BzAHDo+mT8qAfDukTFgpv3779Lyws/P/Hjx8woQGlR9Mn5cE/HNIn6LJnHh6e/6BLymEhsmbNmv/6+vr/YXU+TJye9Gj6pDy0h0P6hIFXr16B++3d3d0woQGlCaTPVaALpjEcePfu3f+MjIz/t2/fjiFHL4GTJ0+CB8W7urr+gxryEyZMoJfVOO2JjY39b2pqilN+VII+ITAY0uexY8f+Z2VlgSc7VFRU/k+dOvU/aCCPPiGA3ZbR9Ik9XOgtOpDpEzTBwcDAAE6X6LSiouL/f//+0Ts44PaNpk94UAwoYyDT58uXL/8LCQn9Z2Vl/e/v7/8/ICDgf25uLkrDfqACZzR9DlTIo9o7kOkT1SUQ3mCZ7BhNn5D4GGhyINMnaLDD3t4eXL9LSUmBy8/29vYBW5iGHBej6RM5NAaOPZDpE+brzZs3/9fU1Pzf2dn5Pz8//390dPT/r1+/wqQHhB5NnwMS7BiWDob0CXLU3Llz/8fExICYgwKPps9BEQ3/Bzp9ghaZV1ZWgic3pkyZ8r+6uvp/RETEf9Ak8kCG0Gj6HBxgoNMnaGweVK/7+Pj8X7x48eAIlP///xNIn6sYQS5F3pYCYzs4ODCAjgzauXMnTGhA6Pv37zOALuhBvqR6IBzy5MkTBjU1NYYJEyYwpKWlDYQTRu1ECoHBkj6RnDSgzNH0OaDBj2H5aPpEDZLR9IkaHgPNG8j0CdpuevfuXXD7AnS57kCHBcj+0fQJCoXBgwcyfQ6eUEC4ZDR9IsJiMLAGOn2C7jT89+8fg7S09GAIDobR9DkoogHuiIFOnyCHgO7gAt1/BOq/D/RdHaPpExQjgwcPhvR569YtBlC6HAxl6Gj6HDxpE+SSwZA+f/78yQAqP8XExBhAR/iD3DVQeDR9DlTIY7d3oNInqO9+9OhRBtCdHaByE3RsJXYX0leUiPS5Gudkx6FDhxhAZ2iDJjtAt6/T1+mDz7bExESGAwcOMIDOvAdNAg0+F44sF42mT9T4Hk2fqOEx0LzR9IkaA6PpEzU8Bpo3mj5RY2A0faKGx0DzRtMnagyMpk/U8Bho3mj6RI2B0fSJGh4DzRtNn6gxMJo+UcNjoHmj6RM1BkbTJ2p4DDRvNH2ixsBo+kQNj4HmjaZPVEBE+sQ92QEyys/Pj+HevXsMZ86cYeDg4AAJjUgMulTXxsaGYdGiRQygy9NHZCAMQk+Ppk9IpIymT0g4DDZyNH1CYmQ0fULCYbCRo+kTEiOj6RMSDoONHE2fkBgZTZ+QcBhs5Gj6hMTIaPqEhMNgI0fTJyRGRtMnJBwGGzmaPiExMpo+IeEw2MjR9AmJkdH0CQmHwUaOpk8IIDJ94p/sePz4MYO+vj5DTEwMw6RJkyAmjzDyy5cvDEZGRgxKSkoM27dvZxgs23ZGWDRg9e5o+mRgGE2fWJPGoBAcTZ+j6XNQJEQcjhhNn6PpE0fSGBTCo+lzNH0OioSIwxGj6XM0feJIGoNCeDR9jqbPQZEQcThiNH2Opk8cSWNQCI+mz9H0OSgSIg5HjKZPktLnatAZXKBrO3Di5cuXgy8rX7FiBU41w1UCdBlgUFDQfwkJif8vXrwYrt4c0v4aTZ+j6XMwJ+DR9DmaPkfT5+AMgdH6fXDGC7KrRsvP0fITOT0MNvZo+hxNn4MtTSK7ZzR9jqZP5PQw2Nij6XM0fQ62NInsntH0OZo+kdPDYGOPpk+i0+cqgpMdoMgtKCj4z87O/n/Pnj0g7ojBmZmZ/zk4OP4fOnRoxPh5KHp0NH2Ops/BnG5H0+do+hxNn4MvBEbr98EXJ9hcNFp+jpaf2NLFYBEbTZ+j6XOwpEVs7hhNn6PpE1u6GCxio+lzNH0OlrSIzR2j6XM0fWJLF4NFbDR9EpU+iZvsAK2AjI6O/s/Ly/t/3759gyWOaeaOf//+/S8uLv7PzMz8f/369TSzZ9Rg6oTAaPqkTjiOmkKbEBhNn7QJ11FTqRMCo+mTOuE4agptQmA0fdImXEdNpU4IjKZP6oTjqCm0CYHR9EmbcB01lTohMJo+qROOo6bQJgRG0ydtwnXUVOqEwGj6JAoQN9kBMurXr1//IyIiwDs8QFtnQGLDEf/8+fN/VFTUfzY2tv9Lly4djl4cln4aTZ/DMlqHjadG0+ewicph6ZHR9Dkso3XYeGo0fQ6bqByWHhlNn8MyWoeNp0bT57CJymHpkdH0OSyjddh4ajR9DpuoHJYeGU2fBAHxkx0go0AzSEVFReA7PMrLy/+DAhgkPlzwgwcP/ltZWf3n4+P7v3v37uHirRHjj9H0OWKiekh6dDR9DsloGzGOHk2fIyaqh6RHR9PnkIy2EePo0fQ5YqJ6SHp0NH0OyWgbMY4eTZ8jJqqHpEdH0+eQjLYR4+jR9IkXkDbZATNq7ty5/7m5uf9bWFj8v3PnDkx4SNOrV6/+Lygo+F9bW/v/lStXhrRfRrrjR9PnSE8Bg9v/o+lzcMfPSHfdaPoc6SlgcPt/NH0O7vgZ6a4bTZ8jPQUMbv+Pps/BHT8j3XWj6XOkp4DB7f/R9Dm442eku240fWIF5E12gIy6du3af319/f+cnJz/m5qa/v/48QMkPOTwvXv3/vv4+PxnYGD4n5aW9v/bt29Dzg+jDsYMgdH0iRkmoyKDB4ymz8ETF6MuwQyB0fSJGSajIoMnBEbT5+CJi1GXYIbAaPrEDJNRkcETAqPpc/DExahLMENgNH1ihsmoyOAJgdH0OXjiYtQlmCEwmj4xAPmTHSCjQMdYdXV1/efh4fmvoqLyf/Hixf///PkDkhr0+NWrV/9BR3GBJmu0tLT+79+/f9C7edSBpIXAaPokLbxGVdM3BEbTJ33De9Q20kJgNH2SFl6jqukbAqPpk77hPWobaSEwmj5JC69R1fQNgdH0Sd/wHrWNtBAYTZ+khdeoavqGwGj6pG94j9pGWgiMpk8UQNlkB8yox48f/4+Pj//PwsLyX11d/f/8+fP/f//+HSY9qOhHjx79Ly0tBU/QiImJ/e/v7x92d48MqgAfBI4ZTZ+DIBJGnYAzBEbTJ86gGZUYBCEwmj4HQSSMOgFnCIymT5xBMyoxCEJgNH0OgkgYdQLOEBhNnziDZlRiEITAaPocBJEw6gScITCaPnEGzajEIAiB0fQJBtSZ7AAb9f///9u3b/9PSEj4z8bG9l9ISOh/QUHBoLj/AjTDtXnz5v++vr7/mZmZ/0tISPzv6en5//XrV5jTR+kREAKj6XMERPIQ9uJo+hzCkTcCnD6aPkdAJA9hL46mzyEceSPA6aPpcwRE8hD24mj6HMKRNwKcPpo+R0AkD2EvjqbPIRx5I8DpIzx9UneyA5ZeXrx48b+tre2/oqIi+C4MDQ2N/9XV1f9Pnz5Nt2OuPn369H/jxo3gHSegiRdGRsb/zs7O/1etWvX/58+fMKeO0iMwBEbT5wiM9CHk5dH0OYQiawQ6dTR9jsBIH0JeHk2fQyiyRqBTR9PnCIz0IeTl0fQ5hCJrBDp1NH2OwEgfQl4eTZ9DKLJGoFNHaPpcxQiKawYagX///jEcPXqUYd26dQzr169nePjwIQM/Pz+DjY0NGBsZGTHo6OgwSElJUeSCP3/+MNy+fZvhypUrDCdOnGA4fPgww/nz5xlA9ltaWjIEBQWBsYKCAkX2jGoeXiEASh+j6XN4xelw8s1o+hxOsTn8/DKaPodfnA4nH42mz+EUm8PPL6Ppc/jF6XDy0Wj6HE6xOfz8Mpo+h1+cDicfjabP4RSbw88vIyx9rqbpZAd68gBNRhw8eJDh0KFDDEeOHGF49uwZWImQkBCDmpoag4SEBIOsrCyDmJgYeFKEnZ2dgYuLiwFEf/78mQE0qQGiP336xPD48WOGly9fMjx69Ijh1q1bDL9+/WJgYWFh0NTUZLC3t2ews7MDY3FxcbAdo8RoCBAKgdH0SSiERuUHMgRG0+dAhv6o3YRCYDR9EgqhUfmBDIHR9DmQoT9qN6EQGE2fhEJoVH4gQ2A0fQ5k6I/aTSgERtMnoRAalR/IEBhNnwMZ+qN2EwLDPH3Sd7IDPbDfvn3LcPnyZYarV68y3Llzh+HFixcMT58+BU9igCY0fv78yfD161fwRAYPDw8DKysrAy8vLwMfHx+DtLQ0eHJERkaGQUNDg0FbW5tBS0sLPDGCbs8ofzQEyAmB0fRJTqiN6qFXCIymT3qF9Kg95ITAaPokJ9RG9dArBEbTJ71CetQeckJgNH2SE2qjeugVAqPpk14hPWoPOSEwmj7JCbVRPfQKgdH0Sa+QHrWHHDDM0ufATnaQEwGjekZDYDQERkNgNARGQ2A0BEZDYDQERkNgNARGQ2A0BEZDYDQERkNgNARGQ2A0BEZDYDQERkNgNARGQwAJrGZC4owyR0NgNARGQ2A0BEZDYDQERkNgNARGQ2A0BEZDYDQERkNgNARGQ2A0BEZDYDQERkNgNARGQ2A0BEZDYMiB0cmOIRdlow4eDYHREBgNgdEQGA2B0RAYDYHREBgNgVEwGgKjITAaAqMhMBoCoyEwGgKjITAaAqMhMBoCoyGADEYnO5BDY5Q9GgKjITAaAqMhMBoCoyEwGgKjITAaAqMhMBoCoyEwGgKjITAaAqMhMBoCoyEwGgKjITAaAqMhMOQACwMDw+oh5+pRB4+GwGgIjIbAaAiMhsBoCIyGwGgIjIbAaAiMhsBoCIyGwGgIjIbAaAiMhsBoCIyGwGgIjIbAaAiMhgBgkBA4AQAnRYXcNwY3TwAAAABJRU5ErkJggg==", "text/plain": [ - "\"Output" + "" ] }, "execution_count": 2, @@ -157,12 +214,11 @@ } ], "source": [ - "# L is the number of sites, also the length of the 1D spin chain\n", + "# L is the number of sites in the 1D spin chain\n", "L = 10\n", "\n", "# Generate the coupling map\n", "edge_list = [(i - 1, i) for i in range(1, L)]\n", - "# Generate an edge-coloring so we can make hw-efficient circuits\n", "even_edges = edge_list[::2]\n", "odd_edges = edge_list[1::2]\n", "coupling_map = CouplingMap(edge_list)\n", @@ -186,6 +242,12 @@ " [(\"ZZ\", (L // 2 - 1, L // 2), 1.0)], num_qubits=L\n", ")\n", "\n", + "# Generate an initial Néel state |0101010101⟩\n", + "initial_state = QuantumCircuit(L)\n", + "for i in range(L):\n", + " if i % 2:\n", + " initial_state.x(i)\n", + "\n", "print(\"Hamiltonian:\", hamiltonian)\n", "print(\"Observable:\", observable)\n", "graphviz_draw(coupling_map.graph, method=\"circo\")" @@ -193,71 +255,65 @@ }, { "cell_type": "markdown", - "id": "e6cd54e3-6a9c-4cdc-8493-d612e930dcee", + "id": "54ad6963", "metadata": {}, "source": [ - "With the Hamiltonian defined, we can proceed to construct the initial state." + "#### Compute the exact expectation value\n", + "\n", + "For a system of this size, we can compute the exact time-evolved expectation value directly using matrix exponentiation. This serves as our ground truth for evaluating the AQC circuit's accuracy." ] }, { "cell_type": "code", "execution_count": 3, - "id": "71252a74-e7bf-4003-a1fd-8f7659195f9a", - "metadata": {}, - "outputs": [], - "source": [ - "# Generate an initial state\n", - "initial_state = QuantumCircuit(L)\n", - "for i in range(L):\n", - " if i % 2:\n", - " initial_state.x(i)" - ] - }, - { - "cell_type": "markdown", - "id": "5fb34a14-d197-4ac4-a224-5be2cec06a2e", + "id": "20c70651", "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Exact expectation value: -0.809062\n" + ] + } + ], "source": [ - "### Step 1: Map classical inputs to a quantum problem\n", + "aqc_evolution_time = 0.2\n", "\n", - "Now that we have constructed the Hamiltonian, defining the spin-spin interactions and external magnetic fields that characterize the system, we follow three main steps in the AQC-Tensor workflow:\n", + "# Compute exact expectation value via matrix exponentiation\n", + "H_matrix = hamiltonian.to_matrix()\n", + "U_exact = expm(-1j * H_matrix * aqc_evolution_time)\n", "\n", - "1. **Generate the optimized AQC circuit**: Using Trotterization, we approximate the initial evolution, which is then compressed to reduce circuit depth.\n", - "2. **Create the remaining time evolution circuit**: Capture the evolution for the remaining time beyond the initial segment.\n", - "3. **Combine the circuits**: Merge the optimized AQC circuit with the remaining evolution circuit into a complete time-evolution circuit ready for execution.\n", + "# Build the initial state vector (Néel state)\n", + "initial_state_vec = np.zeros(2**L)\n", + "state_idx = sum(2**i for i in range(L) if i % 2)\n", + "initial_state_vec[state_idx] = 1.0\n", "\n", - "This approach creates a low-depth ansatz for the target evolution, supporting efficient simulation within near-term quantum hardware constraints." + "# Evolve and compute expectation value\n", + "evolved_state = U_exact @ initial_state_vec\n", + "obs_matrix = observable.to_matrix()\n", + "exact_expval = (evolved_state.conj() @ obs_matrix @ evolved_state).real\n", + "\n", + "print(f\"Exact expectation value: {exact_expval:.6f}\")" ] }, { "cell_type": "markdown", - "id": "7c5f2a2e-179c-4e62-a32f-5bc202f89701", + "id": "a93c047c", "metadata": {}, "source": [ - "#### Determine the portion of time evolution to simulate classically\n", - "\n", - "Our goal is to simulate the time evolution of the model Hamiltonian defined earlier using Trotter evolution. To make this process efficient for quantum hardware, we split the evolution into two segments:\n", - "\n", - "- **Initial Segment**: This initial portion of the evolution, from $ t_i = 0.0 $ to $ t_f = 0.2 $, is simulable with MPS and can be efficiently “compiled” using AQC-Tensor. By using the [AQC-Tensor Qiskit addon](https://github.com/Qiskit/qiskit-addon-aqc-tensor), we generate a compressed circuit for this segment, referred to as the `aqc_target_circuit`. Because this segment will be simulated on a tensor-network simulator, we can afford to use a higher number of Trotter layers without impacting hardware resources significantly. We set `aqc_target_num_trotter_steps = 32` for this segment.\n", - "\n", - "- **Subsequent Segment**: This remaining portion of the evolution, from $ t = 0.2 $ to $ t = 0.4 $, will be executed on quantum hardware, referred to as the `subsequent_circuit`. Given hardware limitations, we aim to use as few Trotter layers as possible to maintain a manageable circuit depth. For this segment, we use `subsequent_num_trotter_steps = 3`.\n", + "#### Generate the AQC target circuit\n", "\n", - "\n", - "#### Choose the split time\n", - "We choose $t = 0.2$ as the split time to balance classical simulability with hardware feasibility. Early in the evolution, entanglement in the XXZ model remains low enough for classical methods like MPS to approximate accurately.\n", - "\n", - "When choosing a split time, a good guideline is to select a point where entanglement is still manageable classically but captures enough of the evolution to simplify the hardware-executed portion. Trial and error may be needed to find the best balance for different Hamiltonians." + "We now construct the Trotter circuit that will serve as the AQC target. This circuit uses many Trotter steps (32) for high accuracy. Because it will only be simulated classically as an MPS—not executed on hardware—the large depth is not a concern." ] }, { "cell_type": "code", "execution_count": 4, - "id": "199d4e7e-02b2-4da8-b567-8195fb9de536", + "id": "db66bf00", "metadata": {}, "outputs": [], "source": [ - "# Generate the AQC target circuit (initial segment)\n", - "aqc_evolution_time = 0.2\n", "aqc_target_num_trotter_steps = 32\n", "\n", "aqc_target_circuit = initial_state.copy()\n", @@ -271,133 +327,44 @@ ")" ] }, - { - "cell_type": "code", - "execution_count": 5, - "id": "83039f82-97cb-4613-86c9-a8faf0839a02", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"Output" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Generate the subsequent circuit\n", - "subsequent_num_trotter_steps = 3\n", - "subsequent_evolution_time = 0.2\n", - "\n", - "subsequent_circuit = generate_time_evolution_circuit(\n", - " hamiltonian,\n", - " synthesis=SuzukiTrotter(reps=subsequent_num_trotter_steps),\n", - " time=subsequent_evolution_time,\n", - ")\n", - "subsequent_circuit.draw(\"mpl\", fold=-1)" - ] - }, { "cell_type": "markdown", - "id": "064a17af-4af8-4ff5-899c-750147d269df", + "id": "bce1ef52", "metadata": {}, "source": [ - "To enable a meaningful comparison, we will generate two additional circuits:\n", + "#### Generate an ansatz, initial parameters, and a baseline circuit\n", "\n", - "- **AQC comparison circuit**: This circuit evolves up to `aqc_evolution_time` but uses the same Trotter step duration as the `subsequent_circuit`. It serves as a comparison to the `aqc_target_circuit`, showing the evolution we would observe without using an increased number of Trotter steps. We will refer to this circuit as the `aqc_comparison_circuit`.\n", + "Next, we construct a \"good\" circuit with the same evolution time but far fewer Trotter steps (just 1). We pass this circuit to `generate_ansatz_from_circuit`, which returns:\n", "\n", - "- **Reference circuit**: This circuit is used as a baseline to obtain the exact result. It simulates the full evolution using tensor networks to calculate the exact outcome, providing a reference for evaluating the effectiveness of AQC-Tensor. We will refer to this circuit as the `reference_circuit`." + "1. A general, parametrized **ansatz** circuit with the same two-qubit connectivity.\n", + "2. **Initial parameters** that reproduce the input circuit when plugged into the ansatz.\n", + "\n", + "We also construct a **baseline Trotter circuit** using 3 Trotter steps. This serves as the comparison: it represents what you would run on hardware without AQC. The AQC ansatz has significantly fewer layers but, after optimization, achieves far better accuracy." ] }, { "cell_type": "code", - "execution_count": 6, - "id": "33b2ad93-7ae4-4250-bf38-9adc3bc2970d", + "execution_count": 5, + "id": "78f2665e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Number of Trotter steps for comparison: 3\n" + "Target circuit: depth 385\n", + "Baseline circuit: depth 37 (3 Trotter steps)\n", + "Ansatz circuit: depth 7, with 156 parameters\n" ] - } - ], - "source": [ - "# Generate the AQC comparison circuit\n", - "aqc_comparison_num_trotter_steps = int(\n", - " subsequent_num_trotter_steps\n", - " / subsequent_evolution_time\n", - " * aqc_evolution_time\n", - ")\n", - "print(\n", - " \"Number of Trotter steps for comparison:\",\n", - " aqc_comparison_num_trotter_steps,\n", - ")\n", - "\n", - "aqc_comparison_circuit = generate_time_evolution_circuit(\n", - " hamiltonian,\n", - " synthesis=SuzukiTrotter(reps=aqc_comparison_num_trotter_steps),\n", - " time=aqc_evolution_time,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "1bfa67a2-ac51-4158-b539-2c33f4c5ecf3", - "metadata": {}, - "outputs": [], - "source": [ - "# Generate the reference circuit\n", - "evolution_time = 0.4\n", - "reps = 200\n", - "\n", - "reference_circuit = initial_state.copy()\n", - "reference_circuit.compose(\n", - " generate_time_evolution_circuit(\n", - " hamiltonian,\n", - " synthesis=SuzukiTrotter(reps=reps),\n", - " time=evolution_time,\n", - " ),\n", - " inplace=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "2051f622-5b71-4be5-aab9-e9eb5d315a6f", - "metadata": {}, - "source": [ - "#### Generate an ansatz and initial parameters from a Trotter circuit with fewer steps\n", - "\n", - "Now that we have constructed our four circuits, let's proceed with the AQC-Tensor workflow. First, we construct a “good” circuit that has the same evolution time as the target circuit, but with fewer Trotter steps (and thus fewer layers).\n", - "\n", - "Then we pass this “good” circuit to AQC-Tensor’s `generate_ansatz_from_circuit` function. This function analyzes the two-qubit connectivity of the circuit and returns two things:\n", - "\n", - "1. A general, parametrized ansatz circuit with the same two-qubit connectivity as the input circuit.\n", - "2. Parameters that, when plugged into the ansatz, yield the input (good) circuit.\n", - "\n", - "Soon we will take these parameters and iteratively adjust them to bring the ansatz circuit as close as possible to the target MPS." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "b9e81c51-dc6f-4237-9aca-e1384f1897bc", - "metadata": {}, - "outputs": [ + }, { "data": { + "image/png": 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+/ihZsiT69OmDIUOGoEmTJqpnqPQAJSIiIiIiIiIiIiIiIqL8Z5I9OnUFOoWM36kt5S0RERERERERERERERER6QeT7KKYVaDTUNUZ1xsWNlaa995fvQ33oZ1yvJxyPg1RrF7lbM3r1rslXr2wAp23zNJ8Zlu0INr9/CF67PsaXXd+iRKNq2umNVs0Gq+c/A6enwzKctkuXh4o3aqO5r1DmWJ49fyKHG+PdUF71BzZLdvz9wr4Bt33zEflV9to/q7P6mlqO323pR+ntXij6uqzQaF+6u8YyjFqOm8Euu9dAN/tc9Hhr89QtLarZlqDjweg15HFaP3jhByvFxGRMdKna7fvji/Ufafz5pko6V0zW9MM8d5brH4VtS3y0/WfefCa/SbMrTO2z2v/x5R06+dYvoT6zoDgX1DEo0KWf1eOo12xQumOdXb2U1b7Lat5+19ZpdZTykyi7vt90dU/5fjJT8WuTdP9v718bKlmmtynU/GeTUREREREZDr1AqnrIvW68gzZ3m+q0dbJi0LVyql6+W67v1I/5To2Mtr6el3HlnX5WTPJHp2SmtYY1XmvN859twGJsfHPtZxyPp6IOHsNd45dytb8YfvPwv/12Zr39T/sjztHL2Hbq5+rAFrrHybAr9EIJCckYs/b89UJa+2U9UXGpYmHmu/mzhPPtT3WBQug5sjuOL1wbba/s+uteWofiLjIaByb9QusHe1R7/2+6eYLPxSIde3GqwunIR2jkM0B2P/eEiQnJqFM2/po9d04+HmOUNOOfPoTHly4jnIdPJ9rHYmIjIW+XLsPT1mOuEdR6nWRGhXR/vfJ+MXjDSA5Wec0Q7z3Rpy7hvUd3ldlB5iZodWy91BtkA/Offtfxg33YZ3xOPg2itaspPlM3st9WR6QskMeXmQ/R995gOeR0/32KOiWWs9UZ7/5C8dn/qJe27sUUQ9wt/acQmzE45Tpi9fh3HcbMyyH92wiIiIiIiLTqReoPqQjCruXx1+t3kVSfEK6hrvGVidvYWeNNssnYs+orxEecB5m5uawLuxgtPX1uo4t6/KzZpKBTmPkNetN9bvD2k9V8Gprn0/Ve6fKpfHS71NQoFRRFbySi4WcKGaWFqg3oQ9cvGvAwsoSD6+E4sCEpaoHRdmXGqBk81qqZUHgj1twY9tRNF88BlaOdrCwsUbYvjM49NEPqvJUmwq+Xljj9Y56fe9kEKJuR8DFyx2he1J60maH9MKoOrAdzCzMVSuS4E2HEPTHLs3Fp0y7+upCJutx0/+4+lwu4A0+6g8rR3t14Tu1YA2CNxxQvUAsHWxVS46kxCRs8JmoKkcrdfOGuaUFkhIS1XLuHL2odV3iHkSqi6msh7Eco+tbj2he3zl2UVWqyr6W9SIiIv28dqcGMoXcA7M7zRDvvYnRcZrXFtaWsLS1TrdfClUpox4i9o1ZhAqdvZ5pe2uP7QW7EoXRYslYJMbEYe+YRepzu+KF0WbF+3CsUALR4Q+wc+gXqiwgPN7yVeUc2YaYuw+xf8JS2Dg5ZNhvsh/arpwEm8KOsLC1xv2z11QDo4ToWK3rkvb4WRawhZmZmfohIiIiIiKi/KVP9QI1hnfF3y9PVX9HpG20a2x18pW6N1PTpF5eJCclIfbeI6Otr9d1bFmXnzUGOo3EgYnfourAl7C528fpKsuKeFTEll5TkBSbAJ8/P0H5To1wde0+1BjRFfHRsdjY8QM1X62xvVB3Yl8cmvS9OnGk9UFqzwHpur1j4EwkRMWoi1Xr5RNR0bcJrv61L8N62BR2gLmlZboTMfL6HRQoXSxH2yN//8L/tqnWIwGTl2u6pFs7FcD9wGCcmPu76kLv+cnr+NP/uOqG3mTOW9je/3NVKWlTxBFdts7GnSMXcGDCtypFa9qeE0F+u3FuaUqvEOlS7j1/JP5sNhqmcIye5j6kE27sOM4gJxGRAVy760/qh/JdvGDjVAA7h8xNV0DWNc0Q773yt2WfSMDxxvZjOL/8b/W5PFg0mTsc+8Z981z3rpPz/FC5b+t0LUYleCrrtr79BMTej0SLxWNRdUA7nP76T1Ts7g0nt1LY1PlD9YBVqVdzNJ4xFDsGzMiw38TuEV+pZYjGM4ei+uAOOluyVh/cEdUGtYd9qaLYP24xYv59gFPThnSEW5/WeHLzLo7P+kWzvkRERERERGQa9QJWDnawK+aEsu0bahr8nl26HtfW7TfKOnlp4JwYF482//sABUoWQURgCA5PW6EJdprKsX0a6/K1Y6DTyIVsPqTpFXH3xGU4VnDR5IyW1hcV/s1rLeNeycVPK3Mz1P+oP0p4VlPp4+ycC+LB+ZBsBdFym/SECN54SL0OP3JRsz3FGlSFY/niaLfqw3TzF3Qthcjg2xmWU7RGRdQa3UP1tJDu+05upVWPC+nRYUrHqFLPZqjQxQubu0/O7c0iIjJq+XXtPjp9lfop2aymGp9xk+9HmtZ+uqYZ4r038sYdrGv7HiztbdF80SiU79hI7Zs6415WrUofXrqpHrhym6TnSQ1Qhh+9gMLVymmCoM51XNHl75QxNKSFa6bMzOD+ZmeUaVMf5pbmsCpojzuHtbdSTRW4bJP6kVQ1zReOwq1dJ9V6HJv5C6Ju31eBa0kr33bVh1jT5B31sERERERERESmUS8gjX7NrVIyHm3s9IF6Hu64/nM8vHwTUaH38nJT86VewMzCAqWa1cLGzpMQFRaBepNehdfMofhn6BcwpWN7/1ywZj7W5WeOgU4jlza3tPR6MLewUK8lHZp0lZZKtKx4DOsCO2cndZLJ8hpOfQ0Wtv8N0JuWVMglJSaqHNKpLUgcyhbDk5uZnPA5lBSXkH57LP/bngcXbmCTb/qLqvr7T1WCykVDxvra0nOq6sYvLSb6XfpJpcbLj0Dniz5GqSr4NkGdd3vj797TVPo9otwgKSN81kzD4Wn/w9kl69JNK+HlrnLrJ0THYVvfz1RLsFSSOrJi16a4tec0tvaepnXZMpaApMlY7zNRnbtpSeqLhpMHqsLWjgHT07XCIjKma3cqST0j96/C1cvh3qkr2Z5miPdeCehJy8lKPZqpBwK5zhQo7Yzqb/ioBx9JAyNjcsqYnrnRsjMxNi7T7ZWenRdXbs9yGZV6eKNk05rY0mMy4iOjVW/Nkt41svX35SFGHuJkXBR5kJTXacflkJ670rM0N44tEREREVFuY70AmYoXXS8g6Vrl+TJo9W5N4+DwwxfgXMcNl84FG12dvGQ0Ct1/VvNMfMVvN9r98jFM7dimBjpZl6+bjqboZGjiHkepHgPZEbIlQPU0kEF9hfyW7uApy4lWObVTSdf06PD76oSUi2VWY2EFrz+gunyn5uiW8R/DDpzTOq+crJLvWuv2REalWw9dwo9cgEO54qoXS9qc4nIBlcGJpWWIvFbbamOlXsvFUkgqudzkveAd1eNCn4+R9OKsN7Ev/n5lmmY/EOWlMm3rq9ZdsQ8isbnrR+mCnJJeQ3pKPboaipJNPXLcQ6vuhD7wnDpIFTK29vmEQU7Kdfpw7ZbWfaktCFPvn7ZFC+Jx8G2d0wz13ivbI9sl5HtyX43497ohKWX8Gg6Hn+cIdT2JfxytXmcW5JRWn9Ve99G+vXJMcnBspXxjXchBvZf1K1Kjotb9Zu3kgJiIR+pBRcbcdHulpc5lO/37P6K2vXwJtdwHF2+o9/Yli2imSWofSQX06GpYttaZiIiIiEhfsF6ADJk+1AuIK2v3onSruinfLeQA57r/BcKMrU7+2vr9KquSBERF6Tb1EHHumtHW1+s6tqzLzxp7dBqRs0vW46VfJyMxOlYzcG5mZIyo2u9aofPGGZohvM4sWqsq1YL8dqn82BJ4OL98CwK/34SW341D13/mITosArf2nNK57COfrUSzhaPQY9/XSIxPwJ6RC1RXdG2kZUlmPTlCNgXAtWcLNWBx2oGPtYl7+ATbB0xXPboaTnkN5lYW6qLp//ps1SJCtqmr/xeIfxKjBj4+PutXdN48U1VCSi8RXeSC1WPv17CwsVQXq5ePLkXQ6l04Nv1nrfM7166kUs/p8zFqvmi0ypve5seJms+kZ2dqqj6i3CTj2jWbPxIPg26p//toScGYRqWezdU5K+PkSVoGtz6tVM7/7Gj02Ruqp5T08trzzte5kqaTSB+v3XKOyHdl/Au5p8ZHxWLn0C/U/U/uU5lNM9R7r/R+lHNbWk5KQFF6qZ6a54dnUcS9Au6d1N77Ue7XMp6IHNu9YxbpXM6VNXtUeh0fv6nqvazX5V/8EXHmaob9du77TSjXviG675mvtvf2oUCdjTgafDRAPRwmxycgKTEJByctU6l5hfdXI9VYHbIvEmLiVJqe+MfstU5EREREhoP1AmTo9KFeQBybvgpN572NaoPaa/6WpFY1xjp5Wc6pBWtUXWFyUrLq2bl//BKjra/XdWxZl581s+Tk1ENC+iw+KgarXPtD37j1bqlaQ8jFK7sk/WTqgMaNpg/B1bV7ER5wHvpC0t/J9sjgwdk1KNQPP1cdCDMrS7RYNDrLi6K+H6PsfKdf0EpY2dtC3+nruWMqKWqkJVnjGUNw90QQtvX7XBVynua74wvERjzC3y9PQ6tl41G0ViXVO0tTYtCSulYCC95fvQ3XXi1wYeU2NcD50/NT9hjKuWxq14hnuXab2r03O8u7HxiCThunY0PHD/TmGiHXSc9PBmFdu/Ev9H/F1M91IiIiMl36WuY3VqwXMDym/qygr9cI1gs8H2Orr8+N5fUz4nOdqWvpuUjPAumO3nnLrGzN32zRaDVornTlFocmfa9XFa0i5t4j1fql8qttspy3eKPqqnWLdEOXliWSNk+fLprPcowafDwANd/prlKMEj0P+T/ymvUmQvedVUFMbUFOSaVRxL08Lv/+j3p/+bedqtdTqea1Ml2upLpo9f14FeSU1k0Hxi/VmwAGUX5du03p3quLpH2V+7L0tpQe3slJSdjQ4X29ukZIy1rpGSrrKSmGnwfv2URERESkz1gvQPTsWC/wbIyxvl4X1gukYI9OA6GvLUvIdBlKCxCeO/nTcvPRtTAUrOCC4M2HVEratIOWp9V45lC49mqO32oNRUJUjApO9D7+LcL2nVHf09ajM3XZkpJD0kOQaZzLeYXXCDIVpn6uExERkelimf/FYr2A4TH1ZwVeI8hU9DPic509OomIKNfZlyisfj++djvTIKcMSF6pmzeCNx5SQU4hYwfIOHgytp0MvJ3ZsqWn1qMrt3jkiIiIiIiIiPQQ6wWIiOhFYaCTiIhy3emv/8StPadRY7gvGkwZqHWe8p0bw9qpAMIOnINjBRfNz+2D51QQ1LVnc63f2zf2G0SHP0CLJWNRrmMjHj0iIiIiIiIiPcN6ASIielEsX9hfoudiaWejuhYT6dP/JFFmEqLjsGPgDLRZ8T5qvOULM3NzHJ6yPN08VfqmjMXnPW+E1mVU7tMKgcs2ZfhcUtdu6TkFPqunoeWSsdg1/CsEbzzIg0HPfC3j/ZVMAe/bRERERPQisV6ADAXrBchUWBpxfT4DnQbCzMzMaPMnE5FxSoyJw47XZqLN8onweLMzzMyAgMkpwU7H8iVQonF1BK3ejZDNARm+W7JZTVR7rT2K1qqEe6euZJj+OPg2NveYrMYDbbF4DHaNmI/gDQdeyHaRceH9lYiIiIiIKG+wXoAMAesFiAwfA51ERJS3DzWDZqH18olwH9pZSo8I+PhHuPVtrXp5nl26HhGnr2b43r2TQSrQ6dantdZAp4gMCceWHik9O1t8Mxq7zYBr6xnsJCIiIiIiItIXrBcgIqK8xjE6iYgozx9q/F+biZv/nID7kE5o9NkbcOvdEo9DbmsNcorIG3dw92QQKnX3hoWNVabLTg12Rt2+j+bfjEHFrk3zcEuIiIiIiIiIKKdYL0BERHmJgU4iIspzibHx8B80Czd3Hkf1wR1RoGRRBG86pPM7Mu6mTSEHlOvYSOd8kdfDsaX7ZESF3kOzhaNQsbt3Lq89ERERERERET0P1gsQEVFeMUtOTk7Os6UTEeWz+KgYrHLtn9+rQaS3+gWt5BjQRERERERktFgvQKQb6wWIyNCxRycRERERERERERERERERGRwGOomIiIiIiIiIiIiIiIjI4DDQSUREREREREREREREREQGh4FOIiIiIiIiIiIiIiIiIjI4DHQSERERERERERERERERkcFhoJOIiIiIiIiIiIiIiIiIDA4DnURERERERERERERERERkcBjoJCIiIiIiIiIiIiIiIiKDw0AnERERERERERERERERERkcBjqJiIiIiIiIiIiIiIiIyOAw0ElEREREREREREREREREBoeBTiIiIiIiIiIiIiIiIiIyOJb5vQJERIaqzrjeOL3wTyTGxqv33l+9jYiz13Duu405Wk45n4aIDn+AO8cuZTmvW++W8Pz0dTy6GoYNPhPVZ03njUDRWq5AUhKSEhJx9PNVCN17Wk2zLVoQzb5+B47lXZAYF4+DH3yH2wcD1bRmi0ajlHcNXP1rHwImL9f5d128PGBha4WbO0+o9w5lisF3+1z8XO21HG2rdUF7VB34Ek4vXJut+XsFfKP275nF63Dp5x0oVr8KvGYOVdPMrCwRHhCIQx/9gKS4BLVO3vNHokiNCogMCce6duM1yyneqDoaf/YGitSoiJ+rDkTcoyiDP7YyrXjDakiMiUP8kxgETP4R904GqWkNPh6ACl2bIOL0Vfi/PjtH60xERERERETZoy/Pjj6rp6FAGWfE//use/mPXTj37Qb1utPGGbCwTqkCNrO0QOFq5fBX63G4Hxico2fHIh4V4ORWWtUhpBoU6petZ+ys9psuqdt2+dedODnPTz379zi4EA8CQzTz7BwyF4+Db6vXNd7uBrfeLVQ9gSxf6gzunrgMC1trdFr/ORwrumDPyAUI2XI4y/1859hFPLx8S/O+XAfPHD9ja9tvmUm7bXvHLFL/S7Kvqr3eHlGhEWqe+xdvYM/b89VrW2cneM16E44VXGBuZYGLP23T/O9V8G2COuNehn2JIjmuuyEiMjQMdBqI5ORkJETH5vdqEGlY2tnAzMzMpPdInfd649x3G7JVMNelnI+nKrxm54FGhO0/m65gfXjKcs1DhQTy2v8+Gb94vCEXDtT/sD/uHL2Eba9+jqK1XdH6hwnwazQCyQmJqmAsBWZrJ/ss/6ZLEw81X2qg81lZFyyAmiO7ZzvQKXa9NU/tHxFx7hrWd3hfrT/MzNBq2XuoNshHPcDFRUbj2KxfYO1oj3rv9023jPBDgSrwKQ9hxnJsQzYHYP97S5CcmIQybeuj1Xfj4Oc5Qs175NOf8ODCdfUQRrrx/kqmwpTv2zzPiXQz5euD4DWCTIWpn+t5RV+eHVOfH7UF7zZ2+kDzunynxir4JUHOnD47SsBO5stOwC6399vT25YQGZOucXPadaw2qD3WthiLhKgYVOrZDI2mD8bGjh+oRsLyHQmcZofbK60Q9+iJJtD5rHK637Rt25U1e7Q2UG84bRAeXLyOnYPnqHO847rPcDvgvGoEfW3dftw9dkk1UifdWBYgU2FpxGUBBjoNhAQ5V7n2z+/VINLoF7QSVva2JrtHpMWc6LD2UxVo2trnU/XeqXJpvPT7FBQoVVQ9LEiQLik+QbWarDehD1y8a8DCyhIPr4TiwISlqodi2ZcaoGTzWqp1YOCPW3Bj21E0XzwGVo52sLCxRti+M6oFogS3tEnbclKCfGlV8PXCGq931Gsp6EbdjoCLlztC96T0CsxuobzqwHYwszBXPTuDNx1C0B+7NA8nZdrVV39X1vGm/3H1uQRVG3zUH1aO9jAzN8epBWsQvOEAvGa/CUsHW/hum4OkxCTV+tR9WGdU6uYNc0sL1WtRlnPn6EWt65IYHad5LS1SLW2tNfsl7kEkwgPOq3U0hWN7fesRzWtpZWrvUkQdI1lnyj7eX8lUmPJ9m+c5kW6mfH0QvEaQqTD1cz0v6NOzY3ZVfrUNLv3in+PvSbaoOhNeUc+l8jwvAdkDE79V06oO8lE9UmWek1/64fJvO9Xn0nPS85PXYefsBHNrK1xcuQ3nf9yidb/JtrsP6QRzK0uYmZupRsyyD54lYCV1C5b2NirQKY2tU3tC5nQ/Fa1dSQUSpYH40Rk/q88t7W3R/JsxKFytLBLjEvDPm1+ojFKiUq/mqP56B9WzUrIuHfpwGaJv39e63yTDlpNrKbW9T27dxf53FyP6zoMcr2cR9/Jqn6bez8IOBsK1VwtNtifKHpYFyFT0M+KyAAOdRETPQAqmkoJ1c7eP0wWjinhUxJZeU5AUmwCfPz9B+U6NcHXtPtQY0RXx0bGqFaGoNbYX6k7si0OTvlcBq7SpbSxsrLBj4ExVKJcgYevlE1HRt4nO1n/1J/VD+S5esHEqoFK2yMOPTWEHmFtapissR16/gwKli+VoW2XdLvxvm+rRmdqCUNKpWDsVUK1AT8z9HaVb1VEPMH/6H1fpaZvMeQvb+3+uUu/YFHFEl62zcefIBRyY8K1qTZi2dWKQ326cW5qSUqdYvcoq/eyfzUZnuj7yt2WfOFYogRvbj+H88r9hasf2afJAeGPHcQY5iYiIiIiIXhC9e3b8sD/qTuiDBxdv4Oj0VZoAXCr7UkVVw+c97yzI8bbG3HuEE7N/05q6NSk2Xm2Tk1spdN48C0F+u4BkoMXisdgzcr7qEWlhZ41OG2ZoAn1P77db/5zA1T/3ap75O22cjj8aDlfpZ7WRQGbnzTNVY1/JdnRq/hokJyXh/rlgnP12gxoGJ/Z+pBrCZ0v3yTneXhk6x7Vnc9XrNLUnqQShneu4Yl3b8Yi8Hq6e1WuO7KbqOYo3rKoacG/u/rFaZxk+RwKif7Ucq3W/ydAzsfceqdeyDGlEnho41qZ8Zy+4NKmB2PuPVfpe6dEr7p26gkrdvVVjcal7Kd2yNh4GPV8PVCIiQ8RAJxFRLgrZfEjT61DGgJBxEoS0bpQWfBU6NlLvza0tVdBRK3Mz1P+oP0p4VlPpWe2cC+LB+RCdDzTyECM/JZvVVGNsbPL96IW0eAveeEi9Dj9yUbOtxRpUhWP54mi36sN08xd0LYXIf8fMSKtojYqoNboHbAo7qpS0MnaFjJ0haWW0ibxxB+vavpfSknLRKJTv2ChXUucY0rGV1sCpJBVPhS5e2PwMD29ERERERERk+M+Ou99ZgKhb99Traq/7oO1PH6j0rU+nYr2+7ShiIx7n5uaqtKpCApqSpcmueCGV3alQlTIq2JnKysFWfaatt6FDuRJovmg07EsWVfUC1oUc4FiuuNa0sVHh9/F73TdV8FXma7lkLDze6oIz3/wFh7LFVR3Baq+Rqjel7IsWS8dic9ePc2VbJaAoQU4RfvQiqr/RQb0u274hCruXR+eNMzTz2hRyUHUb2khwUnpeSkBbfnQdkwv/24qT81er/SIB1VY/TMCGDhPx5MZdHJ62Ag0nD1S9RWPuPkTYgbOwLeqUK9tKRGRIGOgkIspFaceXkBQs5hYW6rXkP5c0M7d2ncxyGR7DuqjULjKOhiyv4dTXYGFrla2/LylprRzsULh6OdWyLykxEXbFCml6dTqULYYnNzN5kMqhtC0r1bZa/retDy7cwCbf9IFO9ffLpO9NKmlaZJzNLT2nqocdWfd+l35SaWkzC3Smkpat0iq2Uo9mLyTQqW/HVlTwbYI67/bG372nqYcaIiIiIiIiyl/58eyYGuQUkspUgl+S5Ul6Naaq/EorHHg/816Dzyox9r9nd+lVKdsrI8DFPojUOo6mNi0Wj8HRz1cheONB9b7vuR9Vyt7M6iIkyJk6hM2lX/1RqXszFeiUMUjvnw9RQU5x+dedaDx9iKp7SNtgONeObZp6EBni59i/KW51Ke5ZDdUHd8SmzpPUdkja4rrjX8l0/rRZusIPX0DEmatwru2qAp0SIN07ZpFmuqQFlnTJRESmxjy/V4CIyFDFPY6CVcH04yZmJmRLANzf7KzStQj5LS0ZU5YTrVo7ppKUsNHh91UBWoKUFTp7ZbpcGeMjtXWocK7jpsbFePxvz8ng9QdUSpjUcTNlHMewA+e0Lku+K+OIaN3WyKh066hL+JELcChXXPVATDvOpzxYxEVGqxaN8lrtBxurlDEpbt5V76sPTmkNmRnZVtlmId+T9C8RgcF4Ft4L3lHfN9RjK704603si79fmabZf0RERERERPTi6MWzo4U5bJ3/68UnqXKj7z5MF+Qs6V0TZpbmuLXrlM51lDqD7nvma9/WyPTrqIukT42PjFa9SFPJ8630wNS236ydHDQ9JSVrkWR8yow8F2vqBawtUb5jY9w7c1W9fxxyG8UbVlMZoESZdvXx8PLNTIOc0uOz3qRXtW+vrGM2tzdk6xG13gVKO6d8YGam6mC07Tc5tgmR0er4SL1GlQHtdC7bvmQRzWsZ91TqV+4Hhqj3EsxO3RdFalRUvYbPL08Zs5OIyJSwRycR0TM6u2Q9Xvp1MhKjY7G1z6c65z29cC1qv2ul0pikDrF4ZtFaNXaGjF8h41KW8/FUBdLA7zeh5Xfj0PWfeYgOi8CtPZk/iMgg9/JdGRdT0pjER8Vi59AvEPfwiZp+5LOVaLZwFHrs+xqJ8QnYM3KBmk8b6e2ZWS/KkE0BcO3ZQqVDCd50SLVUzIz87e0DpqsWpA2nvKbWUQJxMh6FtLaU7e3q/wXin8Rgg89EHJ/1qxpbIybikeqhqUtJ7xqq5aO0nJTCvPRyPDXPT/OQ2GPv17CwsVQPES8fXYqg1btwbLr2FpXOtSshcNkmgz22ktZHxkBt8+NEzXekZ2fah1kiIiIiIiLKO/rw7GhhbYW2KyepzEjJScmIiXiMHa/NTDdP5b6tVe9GzR/WEeiU9LPayPN3jbd84bvjC9w5ckHnmJLyzL5jwAx4fvK6Cu5KMDY24hF2j5iPOC37LWDyD2p74x5GIXTfaTVkTWZk/EvpAZlaLxC29zROzV+tpoVsOqTG0ezy9ywVJE6IisXut7UHboUEmh8Hpx/LNNXFldvRcMpAeLzZGUez6KkZfigQRz9diVY/jFc9WiUAe2P7MZW56un9dvDDZWr8z+5756vndzm2BVz+C2Y+rd77r6JorUqqXiApKQkHP/gej66EqmnOdSuj0advqGxeElj+Z9iXqp6AiMjUmCUnZ3GHI70QHxWDVa7983s1iDT6Ba2E1b8t5PSZsZ07br1bZhjE/nnUGdcb1k72CJi8HI2mD8HVtXsRHnAe+qJXwDdqWyPOXsuV5Q0K9cPPVQfCzMoSLRaNzvJB1JCPbXaXZyjncl4xtmsEUWZM+VzneU6kmylfHwSvEWQqTPlcN7bzPC+fHT2G+6qepFdWp4y7qQ98Vk/Due82IGTL4VxfXoe/PsW2Vz9HwpMY6AMZ7sd3+1z8XO21F7o8U74+GOM1gsgUz3WmriUiMiAJMXEqTUnnLbOee1nNFo1WqVUkRY44NOl7vQpyChmvQnqkVn61zXMtR1p8Sm9UeWCTFq6x9x7pVZAzt49tg48HoOY73dWYKERERERERGQ88vLZ8ezidXoV5BSybvUm9UPtsb2eazkyjI7UCziUL64Za3Nz14/1JsgppMdmQnSsWk85xs+jgm8TtPnf++nG+CQiMlbs0Wkg2LKE9I2htADhuUNkHOdyXuE1gkyFKZ/rPM+JdDPl64PgNYJMhSmf6zzPiXQz5euD4DWCTEU/Iz7XOUYnGRRJO5kdW3pMUfnwX/r1Y1z+Yxf2jvo6wzx1J/RRrcH2vbcEl1ZtV6k6ZDyErMjg6H6eI55p/YmIiAwd78VERESmjWUBIiIi08ayAOkbBjrJoOwemfkA4o7lSqjgZcy9h3gYdFMNvn1h5TZU7d8OwRsO4PrWI5p5i9Z2Rc2R3XBz53EV5BRhB8/pXL7byy1RqkVt3Naz1J5EREQvEu/FREREpo1lASIiItPGsgDpGwY6yaBkNk6AhZ01Oq2fjqSERPwzbJ4KcorDU1egdIva8Jo9TAUo4x5EwsLGCs0WjET8kxjse3exZhmRIeHqRxsXLw+4NPHA/cBgHBi/NI+2joiISP/xXkxERGTaWBYgIiIybSwLkL4xz+8VIMoN3l++rQbpPvrZSoTtO6P5POHfYKZdMSc0/nyw+qzu+31RqEpZBEz+EVFhEVkuu0BpZ7T49l0VGPV/Y44aFJyIiIh4LyYiIiI+lxMRERHr6Cl/sUcnGTyP4b6o2K0prq7dh7NL12eYHrr3NM6v2Irqr/uowKb70E4I2XIYQX/synLZ0vuz1bLxsCnsgB0DZ+LxtbA82goiIiLDxXsxERGRaWNZgIiIyLSxLED5ySR6dN69excTJkyAm5sbbG1tUbZsWYwePRpPnjzB4MGDYWZmhoULF+b3atIzKNmsJupP6oeIc8HY9+43mc539NOf8OhaGGqM6Iq4h1E4MCF76Wcl5a1zbVec+OIP3PQ/zmNERETEezERERHxuZyIiIhYR096wuh7dJ44cQIdOnRAWFgYChQoAHd3d9y6dQsLFixAUFAQIiJSUpfWqVMnv1eVcsihTDG0WDIW8ZHR2PnGbJ0pZRPjExD/OFq9vncqCNF3Usbw1KX64I5w691S9f48Nc+Px8eI1XynO4rWrISitSrBsXwJRF4Ph5/niPxeLSIivcd7MRkNMzOV9aPqgHbq/zrm3iNcXb8fJ2b/xmELiIh0YFmAjAXrBYiIng3LAqQPzI29J2eXLl1UkHPcuHEIDQ3FsWPH1PtZs2Zh48aNOHz4sOrRWatWrfxeXcoBCztrtPphAqydCmD32/PxOPi2zvlrj+mJojUr4t7pqyjdso5KdatLicbV0XDKQDy8fBN73lnAY2PkpFewS9MaKjVx7P3H+b06REQGgfdiMiaenwyC57RBeHDxBg5+9AOubTgA98Ed0eZ/76sgKBERZcSyABkT1gsQEeUcywKkL4w60Dlq1CjcuHEDI0eOxNy5c+Ho6KiZJqlsa9eujYSEBFSoUAEFCxbM13WlnGkyd7gKXJ6Y+3uWKWWL1KyIWqN64OY/J7Cp64d4GHQLjT4fDFtnJ63z25csgpbfjkNCTBz835iteoyScfNrNAK/eryOrX0+RdTt+/m9OkREBoH3YjIWhaqUQfU3OuDaxoPYOXgOLq3ajsNTVyBg6gqU9K6ZZQM5IiJTxbIAGRPWCxAR5RzLAqQvjDbQGRgYiN9++w3Ozs6YMWOG1nnq16+vfkvAM1VqYNTT0xM2NjaqtyfpF/dhneHaoxlCtgTg1Ferdc5rbm2JZgveQUJULPaNW4zE6Dg1lqf0BPWa9abW+VstGw+7YoWwd8wiPLx0Mw+3hPRFZEh4fq8CEZFB4b2YjEnF7t4wMzfHue82pvtcAp7xUTFw7dk839aNiEhfsSxAxob1AkREOcOyAOkTow10/vLLL0hKSkK/fv3g4OCgdR47O7sMgc7Lly9j9erVcHFxQcOGDV/Y+lL2uDTxQIOPBvybUvbrLOev+94rKFytnGqRH3XrnvosPOA8ApdtRvmOjTK00G88YyiK1a2MU1+vQcimQzwsREREvBeTkXOu44akxETcPX4p3eeJsfGIOHMNznVc823diIj0EZ/LiYiITBvLAqRvLGGk/P391e9WrVplOo/03nw60Nm8eXM1lqeYOnUq9u3bl+frStljV7wQWn77LswtLVRqsbLtG2Q67/1zIbC0s4bHcF9c334Ul39N+X9IdWzGzyjTpp5KYRu69wxi7j5E5VfboMqrbRB954HqyVmpZ7NMlx+yKQAJ0bE8dEREZFJ4LyZjZF+iMGIjHiMpLiHDtKiwCJTwrAZzK0skxWecTkRkalgWICIiMm0sC5A+MtpAZ3BwsPpdvnx5rdNlbM7UIGbaQKe5ee53cm3QoAHCwsKeaxlWyeaYAk+YMifX0rAtmjKuZu3RPXXOe3rhWpTzaajG19z/3pIM0xNjUlLY+qyZplLYynhMUoklJG2tpLvVxa/hcETeuANTVqVyFcSbJUHf8dwhMo5zOa/wGpEzvBcbLlM+17M6zy3sbJAYF691mvTqFNKALo6BTjJSpnx9ECwL5AzLAobLlM91nudEupny9UHwGpEzLAsYrip6fq5LltUjR44803fNkpOTk2GEihQpgvv372P//v3w8vLKMH3VqlXo378/HB0d8fDhQ61jcUqPzmnTpuF5d1GZMmVw8+bzjfVobWaBpSW6PdcyiHLTsNtrEZecqPc7NafnTtedX8KqgC38PEfk6XoR6QtDOZfzCu+vZCpM+VzP6jzv6v8FbJ2d8FutIRmmtVj6Lir6NsH/yvVhj04yWqZ8fRAsC5CpMOVznfUCRLqZ8vVBsCxApmKYnp/rpUuX1mRhzSmj7dEp0V8JdB47dixDoFNS044fP169rlWrltYgZ26vS260LIH+BtvJBJUqWUqvW4Ck4rlDZBzncl7hNYJMhSmf61md51G378OpShmYW1tmSF9r71IEMfceMshJRs2Urw+CZQEyFaZ8rvM8J9LNlK8PgtcIMhWl9Pxcf544mtEGOtu2bYvAwEDMmjUL7dq1Q5UqVdTnhw8fxoABA3D37l31vk6dOnm+Ls/a3Tat+KgYrHLtnyvrQ5QbLl66CCt7W73fmTx3iIzjXM4rvEaQqTDlcz2r8/zuicso3bIOnOtWRvihQM3nFjZWKFKjAm4f/O8zImNkytcHwbIAmQpTPtd5nhPpZsrXB8FrBJmKi0Z8ruf+gJR6YsKECShatCiuX78ODw8P1KxZE5UrV4anpycqVaqE1q1bZxifk4iIiIiITMvVv/YjOSkJ7kM7pfu8cr+26iHwyprd+bZuRERERERERGSiPTplXMw9e/aoFLW7du3CtWvX4O7ujqVLl2Lo0KFwdXVV8zHQSUSVejWHQ5liakfYFi0IcytL1BrTU72PvHEHV/xYwUlERGSsHpwPwfkft6D64I5otWw8buw4BqfKpeE+uCPC9p/FlTV783sViYiIKI+xXoCIiMhwGW2gU1SvXh0bNmzI8HlkZKQKfJqbm6NGjRr5sm5EpD+q9G0DlyYe6T6rN7Gv+q0qOBnoJCIiMmoBk5cj8vodVOnfFmXa1ENMxCME/rAZx2f/BiQn5/fqERERUR5jvQAREZHhMupAZ2bOnj2L5ORkNW6nvb19hul+fn7q97lz59K9r1ChAho0aPCC15aI8tqWnlO4k4mIiEyYpK49u3S9+iEiIiLTw3oBIiIiw2WSgc7Tp0/rTFv78ssva33/2muvYfny5S9gDYmIiIiIiIiIiIiIiIhIFwY6tZDenkRERERERERERERERESkvxjoNCJ1xvXG6YV/IjE2Xr33/uptRJy9hnPfbczRcsr5NER0+APcOXYpy3ndereE56ev49HVMGzwmag+azpvBIrWcgWSkpCUkIijn69C6N6UXrTNFo1GKe8auPrXPjUWki4uXh6wsLXCzZ0n1HuHMsXgu30ufq72Wo62x7qgPaoOfAmnF67N1vy9Ar5R+/DM4nW49POOdNPa/zEFRWtW0qyDY/kSaPX9e3CqUgYbO36g9rcu7kM74erafYi+80BzzKyd7LPcF1ntm6zmbbtqEh4F3cLWPp8i5t4j9b9RsnktxN57pOa5tfsUjnz6k3rd4OMBqNC1CSJOX4X/67NztF5ERMbIEO6vqetZsbs3kuLiERPxGH/3mmqw995i9avAa+ZQNc3MyhLhAYE49NEPSIpLgNsrreA+pKPmu/aliuL2wUDsHDzHYO/Ldd/vi3IvNUByYpKaR/abHC/hWNEFTea8BRunArCwtcb17Udx5JOf1LiR7m92RrVB7ZHwJAbr2o3P0ToTEZHhlQV8Vk9DgTLOiH8Upd5f/mMXzn27Qb2uOaoH3F5ugYKVSqp7YsiWw5plGWJZIO39MtXGLh8iMSYOLk1roP6H/WBVwFYNo3xj+1FVLpI3xviMrqucwOd3IiLTKgvoevY3xjr5QtXKofHng2FbzEm9PzbzF4RsOmSU9fWi6mvtUX1wByQnJCI5KRkbO32g9gnr8rNmkoFOf39/GKM67/XGue82aC64z6qcj6e6AGTngivC9p9NFxA7PGU54v598CpSoyLa/z4Zv3i8oR469rw9X3OxyIpLEw81X3YuDrpYFyyAmiO7Z/uiKna9NS/DRdB9WGc8Dr6tLpyp5L1ULMqFODvkwin7K/XC+axyum/kovl0BejZxeu03ozlYerBheso18HzudaRiMhYGML9tfqQjijsXh5/tXoXSfEJsCtWSPM9Q7z3Rpy7hvUd3leFe5iZodWy91BtkI+qzL382071k6rrzi9xZc1ug74vn/3mLxyf+Yt6be9SBN12f4Vbe04hNuIxGk4eiJDNAQhctgkWNlbovHkmQludxk3/42p/SMMkz08GPdf6ExGRYZQFUssDaYOYqUJ3n8LVtXvhPe/tDNMMsSyQ2XOsiHv4RM0bGRKu7o0v/T5ZBXkv//6PwZYFdD2j6yon8PmdiMi0ygK6nv2NrU7ews4abZZPxJ5RXyM84DzMzM1hXdjBaOvry7ZviEo9mmFjp0mIfxwFm6IFkRSfqJnOunzdTDLQaYy8Zr2pfndY+6lq5SctAYRT5dJ46fcpKFCqqApeycVCLoJmlhaoN6EPXLxrwMLKEg+vhOLAhKWqB0XZlxqoloTSaiTwxy24se0omi8eAytHO1jYWCNs3xnVq0I1ndQi9YIqrB2zvnhqU8SjAqoObAczC3PVwiF40yEE/bFLc2Mp066+Wrash1T0iaK1XdHgo/6wcrRXF75TC9YgeMMBeM1+E5YOtvDdNgdJiUmqBYxcBCt184a5pYVq4SLLuXP0YqbrU6hKGXUj2jdmESp09nqmbao9thfsShRGiyVjVSvUvWMWqc/tihdGmxXvw7FCCdWiZ+fQLxD3IFJN83jLFxV8vdR6xtx9iP0TlsLGySHDvpFtbbtyEmwKO6reHvfPXsP+95YgITr2mdaViIgM6/5aY3hX/P3yVLUO4lkK6Pp0702MjtO8trC2hKWttdb94ly3MmydnRDy9xGDvi+nPbaWBWxhZmamflKHVLAqmHK8ZVnmVpZqvYiIyPTKArrcPXH5ubdVn8oCukScuap5LRXOEWeuwaFscYMuC+iiq5xARESmVRbQ9exvbHXylbo3U9MkyCmSk5I0mQ+Msb6+xghfnPzyDxXkFGm3lbLGQKeRODDxW9UVfHO3j9Nd1Ip4VMSWXlOQFJsAnz8/QflOjVRX7BojuiI+OlZ13xa1xvZC3Yl9cWjS97i+9Ui6rvfSQnLHwJlIiIpRF6vWyyeiom8TTaoUbepP6ofyXbxUmrWdQ+bm+EFN/v6F/21L101cuslbOxXA/cBgnJj7O0q3qgPPT17Hn/7HVVd4Seu2vf/n6uJjU8QRXbbOxp0jF3Bgwreqe33aFhJBfrtxbmlKip9i9SrDe/5I/NlstNZ1kZtTk7nDsW/cN5pUMc/i5Dw/VO7bOl3LFLkYy99f334CYu9HosXisag6oB1Of/2nSkHg5FYKmzp/qC7klXo1R+MZQ7FjwIwM+0bsHvGVWoZoPHOo6uauq8WMtABy69MaT27exfFZv2TZjZ+IyBQZwv3VysEOdsWcVOu/1ML92aXrcW3dfoO996b+bdkn8mBxY/sxnF/+d4Z5Kr/aGkF+u1J6fhr6fXlwR5WGVlLx7h+3WJO6JmDyj2iz4gNUG/iSOhYnv1qdroKXiIhMrCzwYX/UndAHDy7ewNHpq1Svxtyib2UBxwouannyHH7p1524sCJjWUB6slTo3BjbB8406mf0zMoJRERkOmWB7Dz7G1OdvAQyE+Pi0eZ/H6BAySKICAzB4WkrVADQGOvrC1Uuo4LGtd99Wf1fSIBZMjulYl2+bgx0GrmQzYc0vSKkhac8KKTmA5fWFxU6NlLvza0tEXn9jvaFmJuh/kf9UcKzmkofZ+dcEA/Oh+h8+JIHLvkp2aymGjNik+9HmpYmz0NaPARvTMnDHX7komZ7ijWoCsfyxdFu1Yfp5i/oWgqRwbczLKdojYqoNbqHalEhlaNObqVVywppufG0OuNeVq0wHl66qS7suU26s6de8MKPXkDhauU0F1XnOq7o8vcs9V5ahGTKzEyN01WmTX2YW5qrnh93DmfeMlbymUfdvq9udpKitu2qD7GmyTvqpkpERIZ1f5UCvvTyk16PMn6D3Ks6rv8cDy/fxP1zwQZ57xWRN+5gXdv3YGlvi+aLRqF8x0bp9o2lnQ0qdm2KjZ0nwdDvy0IeYORH0hA1XzgKt3adVOtR7TUflYpQHqpsixZE+9XT1P+cpCgkIiLTKgvsfmcBom7dU6+rve6Dtj99gLUtxiKv5UdZ4N7pK/i93jDVq8G+ZBG0XfkhYiMe4dr6A+kqfNv8732c/uYv3DsZZNTP6JmVE4iIyHTKAtl59jemOnkzCwuUalZLPfNHhUWg3qRX4TVzKP4Z+oVR1tfL8XUsVxxbuk+GdaEC8FnziUrDK2ORsy4/awx0Grm0ecOldYO5hYV6LWlOpGu4FI6z4jGsC+ycnTSD3zac+poaWDc7QvecVg8fhauXw71TV/C8kuIS0m+P5X/b8+DCDWzyTX9RFU9f7OSGIGN9bek5VT0Myfr1u/STSo2n7aIq3c0LlHZG9Td81AVWUglIjm8ZOyw3upAnxsZluk1SqXlx5fYsl1GphzdKNq2JLT0mIz4yWrX2LOldI9P55eaQSsb9ktY+0holN44REZEp0Lf7q1z7g1bv1gQIww9fgHMdt1wJdObHvTctqeCTVrEyVkXah70KXbxUeqCHF28gN+XHfTktOWZyn5bxPeRBstrr7bGm6Sg1TXpv3NxxTE1joJOIyPTKAqlBTnH+xy1qHGebwg55HvDKj7KA3D9TRYVGqEY/JRpV1wQ6JYVru58/QsjfhzU9Q3KLPj+jP11OICIi0ykLSOrU7D77G0OdvGQ4CN1/VnOPvOK3G+1++dho6+tle6/8uTclRW/EY/XsX6x+ZRXoZF1+1nSEnMnQxD2O0ozhlJWQLQGqRYEM6ivkt3QHT1lOtMqpnUq6pkeH31cX25S0MF66Wx7826JDyIVWeh9I6wNtZLrkMte6PZFR6dZDl/AjF+BQrrhqrZI2p7hcQOMiozVjWqlttbFSr+XiIaTLuC6SlsCv4XD4eY7A5q4fIf5xtHqd2UVTWpdI61qt2yT7NgfHSNIiWBdy0OxbGUha276xdnJATMQjddGUBz63V1rqXLa0iE0lXfElrcCjq2HZWi8iIlNjCPfXK2v3onSruinLLeQA57qZBzkN4d4r2yrbLOR70rMhIjD99lR+tQ0u/eKf5Xoawn3Z6d//EeFYvoRarqQkFI+DwzXHVnqxujStoVr4EukrqXQYFOqnxq55Wgkvd7x6YQV6n/gOhauXTzdNxsWR72V2fRJ1xvVW80hKp6fJeD+v3fwdHf76TKXQIjK6soCFuRqXOpWkx4u++/CZgpyGUBawK15I9YQQci8t07Y+7p1JSSkn2R4kyHlz53Gc+mq1UZQFdD2j6yonEBGR6ZQFdD37G2Od/LX1+1XvSQmIitJt6iHi3DWjra+XIKekBRay36RRU8TZlHoQ1uVnjT06jcjZJevx0q+TkRgdqxkUOTOSC7r2u1bovHGGJlX3mUVrVWFZxrqS/NjSFfv88i0I/H4TWn43Dl3/mYfosAjc2pN5qjRzKwv1XalckO7n8VGxKYP1PnyidX6HssUy7ckRsikArj1bqAGL0w58rI0sf/uA6apFa8Mpr6n1kIum/+uzVWsX2aau/l8g/kmMGvj4+Kxf0XnzTHWxkV4iuamIewXcO6m9pYykmpG85XKMUgc3zsyVNXtUN34fv6maC+flX/zVuFxP75tz329CufYN0X3PfLVNtw8F6uy27/3VSJXTXVqkJMTEqS7/qQMdExGR4d1fj01fhabz3lZjN6Wuh6TOMdR7r7RylNaOcp+S+5+0Rj01zy9dGhx5eNKV5teQ7ssNPhqgHg6T4xOQlJiEg5OWqRQ8Yu/ohWj0+WC4D+moUh5d//tIrpddiF4ECVK0/PZdRN95gK29U9IwpZIeaXJtfHQ1FCWbeqjzRVqoZ5eMV1h7bC8V9PAfPEeTQozImMoCFtZWaLtykur1kJyUjJiIx9jx2n/jUtYa0xNVB7ykKjWbVBuORp8PwbqXxmut8DOEskD5To1R9bX2qtwj99zg9Qdw+deUBk7uQzuiWF03WNnbqNT24tqGAzg1f41RPqPrKicQEZHplAV0PftLMNXY6uRlOacWrFHpeaXsI70a949fgmdhCGWBc0vXw2v2MHTbNU/93wRvPIjgDSmZLFiXnzWz5OQcjkhL+SI+KgarXPvr3d53691S9bKQi1d2SUvs1IF5G00folLQhAech76Qbu6yPakDEOfkO/cDQ9Bp43RskMGm9eTUklb1np8MSjfwc24c135BK2Flbwt9p6/nDpG+MJRz2dSuEc9yfzW1e68p3ZdzY3mmfK7r63lurOR/0mfNNBye9j+cXbJOfVaxuzeazR+Jh0G3VMVQtIxDl0b1IR3hOW0QNnR4X1VkSDqoE3N/13otq/Neb6z3magZj6/RZ2+ohhHS8GHPO1/nyhhEpsaUrw/6fI1gWeD5mFJZILv/K6Z8ruvreU6kL0z5+qDP1wiWBZ4PywKmda4zdS09F2lpKD0qOm9JGYA3K80WjUalns1Ul3BxaNL3elXRmjr+VbOFo1RKvKxI2hhppSGtN6RSRXJoSwWNvjxAicT4BNXSRNZTWvdmRQaqrvlOd8Q+yNtxXoiIKPfur6Z07zW1+7Iukg6p8cwhqlcPkb6S1E7NF47CvdNXsbn75AxBTlG5bxuE7T+rxg+6sf0Y3F5ppUlZqSuNZ7Ov31FBzgsrt2HX8K8Y5CSjwrLAszG1sgCf34mIjBfLAs+GZQHTxB6dBkJfW5aQ6TKUFiA8d4iM41zOK7xGkKkw5XOd53n+9eiU1FX1J/XDrT2n4T9oFhKiYjLML+NtdtkyC3tGfa3SYpV9qQHarHhf9fy8teuk1h6dm7p+hBrDu6KcT0OVruvo5ytf4BYaH1O+PgheI8hUmPK5zvOcSDdTvj4IXiPIVPQz4nOdY3QSERERERFRrqr62ksoWMEFwZsPYddb85AUpz2lbOW+rRH/JBrBGw+p9zf8jyP67kP1+dOBzlQy/pAs+8hnK9V4R0RERERERGS6mLqWiIiIiIiIcpV9icLq9+NrtzMNclrYWqNSN28V5Ezt7ZmckIgra/agXPuGsC7kkOmyJSXloyu3eNSIiIiIiIhMHHt0GghLOxvVtZhIn/4nDQHPHaKszxFTxmsEmQpTP9fpxTv99Z8o4eWBGsN9ATPgyLT/ZZinfOfGsHYqgLAD5+BYwUXz+e2D5+DxZme49myOwGWbMnxv39hv1Lh0LZaMVWNzhmxK6Q1K9CxYFiBTYcplAZ7nRFmfI6aM1wgyFZZGfK4z0GkgzMzMjDZ/MlFe4rlDRLxGEBG9eAnRcdgxcIYab7PGW74wMzfH4SnL081TpW8b9dt73gity6jcp5XWQOeja2HY0nMKfFZPQ8t/g53BGw/m0ZaQsePzApHx43lORLxGEBk3BjqJiIiIiIgo1yXGxGHHazPRZvlE1UPTzAwImJwS7HQsXwIlGldH0OrdCNkckOG7JZvVRLXX2qNorUq4d+pKhumPg29jc4/J8FkzDS0Wj8GuEfMRvOEAjyIREREREZGJYaCTiIiIiIiI8i7YOWgWWi+fCPehnaVbDQI+/hFufVurXp5nl65HxOmrGb5372SQCnS69WmtNdApIkPCsaVHSs/OFt+Mxm4z4Np6BjuJiIiIiIhMiXl+rwAREREREREZd7DT/7WZuPnPCbgP6YRGn70Bt94t8TjkttYgp4i8cQd3TwahUndvWNhYZbrs1GBn1O37aP7NGFTs2jQPt4SIiIiIiIj0DQOdRERERERElKcSY+PhP2gWbu48juqDO6JAyaII3nRI53dk3E2bQg4o17GRzvkir4djS/fJiAq9h2YLR6Fid+9cXnsiIiIiIiLSV2bJycnJ+b0SREREREREeSE+KgarXPtz5xJlol/QSljZ23L/EBERERGRQWKPTiIiIiIiIiIiIiIiIiIyOAx0EhEREREREREREREREZHBYaCTiIiIiIiIiIiIiIiIiAwOA51EREREREREREREREREZHAY6CQiIiIiIiIiIiIiIiIig8NAJxEREREREREREREREREZHAY6iYiIiIiIiIiIiIiIiMjgMNBJRERERERERERERERERAaHgU4iIiIiIiIiIiIiIiIiMjgMdBIRERERERERERERERGRwWGgk4iIiIiIiIiIiIiIiIgMDgOdREREREREz6DOuN6wsLHSvPf+6m24D+2U4+WU82mIYvUqZ2tet94t8eqFFei8ZVaGaS5Na2Dgjd/SrYNjRRe095sK321z0H3PfDSYMhAwM1PT3N/sjB77v1bTsuJQphiqDnwp3We9Ar5BEY8KyClZP7tihbI1r+zTl48thdesNzNMq/NebwwK9Uu3DqVb1VH7xnfHF+i0YToKu5fXTGu2aDReOfkdPD8ZlOXfdfHyUMtKu/2vnl+BnLIuaI+aI7tle37Zp3KcKr/aRrMe/a+sUsco9cfC1lozf+W+rdFj39focWAhmsx9C2aWFurz4o2qq3ll/8g6EBERERERGSsGOomIiIiIiJ6BBNrSBjqfVTkfTxSrXyXb84ftP4sNPhPTfWblaI/6H/bDzR3H033ecPJAhGwOwLp247Gu7Xso3aK2JoB37tsN2D9uSbb+pkPZ4qg6oB1ygwp0Fs9eoFOcXbwOByZ+m+4z5zpu6ifyerjmM2unAmi2cDT2jvoa69qMw+FPf0LzRaM10/e8PR8X/rctW3/TpUn6QOezsi5YADVHds/Rd3a9NQ+Xft6hef8o6FbK8fv3JzEmTnNM6k7og83dPsYar5GwdXZC1f4pxyj8UKCal4iIiIiIyNhZ5vcKUPYkJycjITqWu4v0hqWdDcz+7Q2gz3juEBnHuZwXeH0gU2LK53peSe1h2GHtp0hOTMLWPp+q906VS+Ol36egQKmieHDhugpaJcUnqJ529Sb0gYt3DVhYWeLhlVAcmLBUBTjLvtQAJZvXUr01A3/cghvbjqL54jGwcrSDhY01wvadwaGPfpALV6br03j6YJz6ajXKd2yU4Vpn9W+PPukJaG5liejwBznf3tlvokBpZ9VLMPLmXfgPSulRWq5jIzSeOVQFLi/94q/WQUiPTc/P3oBD2WKwtLVGyN+HcXzWr6g9thfsShRGiyVjVcBu75hFsCnsiLoT+6htNbe2xLml69WyMmNhZ41G0wdj55C56Lg2Zb8LxwouiL3/GA8u3tAE+2Sdi9SsiIjTV7O9rdJDtOrAdjCzMFc9KoM3HULQH7s0we0y7erD2tFeHZOb/imB5aK1XdHgo/4q4Gxmbo5TC9YgeMMBtd8sHWzVfktKTFIBavdhnVGpmzfMLS2QlJColnPn6MUcH5PynRvj+tYjiL6Tcjwv/G8rao3ugfPLt+R4WaaM5QEyFaZcFuB5TqQbrw+scyfTYGnEZQEGOg2EBDlXufbP79Ug0ugXtBJW9rZ6v0d47hAZx7mcF3h9IFNiyud6XpEehpLKVXrTxT2K0nxexKMitvSagqTYBPj8+QnKd2qEq2v3ocaIroiPjsXGjh+o+WqN7YW6E/vi0KTvVbAq4uw1nPtuo5omvUR3DJyJhKgYFTRrvXwiKvo2wdW/9mldl/KdGiM5KVkt5+lAZ8DkH9FmxQeoNvAl1ePx5FerEXHmas63d8K3KuXr070Epcfipi4fwqaII3oeWIjLv+5EVFgEvBeMVMG+2wfOqYBh258+QPnOXjg5z0+lW5UAsGyzWoZTAWzu+jGSk5JgXchBBQVv/nMCUaERWtelwUcDcGHFVkTdupfu80dXQlXQtFiDqrhz5IIKIEtAUno+5iTQKeslPT+tnewRMHm5JnWtrOf9wGCcmPu76u3p+cnr+NP/uEoN22TOW9je/3MVRJZ90WXrbLUOst98t89Nt9+C/Hbj3NIN6rWkLPaePxJ/Nvuv5+nTJIAry5OA+qVfd+LCir9T1qm0MyJv3NHMJ68lsEs5w/IAmQpTLgvwPCfSjdcH1rmTaehnxGUBBjqJiIiIiIhyScjmQ0iMTkktevfEZRWkSh2HU4JuFf4NRErPxcjr/wWp0jE3Q/2P+qOEZzU1nqadc0E8OB+iNdApPSdrj+mJLT2naF1Utdd8cHXtXpz++k/YFi2I9qunqfUK3X0qV7b36p971O/YiMd4HBwOh3LFEffwCUp614Sds5NmPssCtnByK6V1GRKcbPrlCBSsVBLJCYmwKeyAwtXKaQ10Ss9XCToe+nBZhmnxj6Pwz9C5qD/pVfX37hy5iPsXrqtl5lZFefDGQ+p1+JGLmmMrgVXH8sXRbtWH6eYv6FoKkcG3MyynaI2KquelbLesm5NbadXbNjUlbVr3Tl/B7/WGqW2zL1kEbVd+iNiIR7i2/kCubBMREREREZGhY6CTiIiIiIgolyTGxmteSw88cwsL9VpSBEmK0lu7Tma5DI9hXVSQcGOnD9TyGk59DRa22scCLVqrkkoFK70GhfQmlJ6MNkUL4vjMX1Dt9fZY03SUmhZz7xFu7jimxp/MrUBnuu1N+nd7/02HtLHzpHTTdaUBvuF/DDsHz1HvpfdiZmOflvSuoVLR9gr4Rr23L1kUbVdOwv4JS1XKXxm/dEuPKZpg8isnv9eksn1eSXEJ6Y+t5X/H9sGFG9jkmz7QKSQom5akDm617D1s6TkV904GwcrBDv0u/QQLa0utgc74yGjNawn8StC6RKPqKtApKYQL/htsTf1bT27ezZVtJSIiIiIiMhTm+b0CREREREREhijucZRm/MushGwJgPubndX4kkJ+F6pS5t/lRKuxHVNJitTo8PsqSCg9Nit09sp0uTd2HMNvtYbAz3OE+gnecFClh5Ugp5BelqVb1dWMyeLStIbqHZqZ7nvmw96lSMZtjcz+tkrKXRlXtObI7prPJBgrPRI125tmWdaFCuDJvylYSzSujsLu5TNd9rHpP+OPesM02xsVeg/b+09XQU71d4oX0swr44GG7juDx9fCtC7LuY6bGk9VG7W9aY6JLuFHLqierCWb1Uw3zqcENeMiozVjowoJ4Mrr1IBk9cEddC5bbc+/gWPppVqmbX3cO5OS8jd440EV1Jb/ESGplCVNMhERERERkSlhj04iIiIiIqJncHbJerz062QkRsdia59Pdc57euFa1H7XCp03zkBycspnZxatVb0Ng/x2qXEay/l44vzyLQj8fhNafjcOXf+Zh+iwCNza8+y9L/eOXohGnw+G+5COqofj9b+PZBoMk9S2kjY29kFkhmn3zwWrXotdd36Jx8G34T9ols6/u/vt+Wg4bZCaXzY4ISpW9bqUXomByzapMS1lv+0dswhHP18FrxlDUXtMLzU+5t1jl595e+tO6KN6PMq4oHeOXsT+d1N6fmrjULaY1l6UImRTAFx7tlDjhQZvOoSgP3ZluhxJ1bt9wHQ0nDwQDae8BnMrCxXI9H99NuIeRKrj29X/C8Q/icEGn4k4PutXdN48EzERj7IMTMr4q1Vfa69S3JpZWiB4/QFc/tVfTYsMCcfxub+jw7rP1HvpzXrhp23Z3FNERERERETGwSw5OfUxm/RZfFQMVnFgZNIjhjJ4Mc8dIuM4l/MCrw9kSniu94excOvdEuU6eKogWm5w8fKA5yeDsK7deJTv7KXG0Tz11WroC++v3lbBz3PfbcyV5dUZ1xvWTvYImLwcjaYPUalgwwPOQ19ISl45trLNuWFQqB9+rjoQcY+iMp3HlK8PguUBMhWmfK7zPCfSjdcH43lWIDLVc52pa4mIiIiIiAxEQkycSovaeYvuHpXZIal0G88cgpiIx+p98IYDehXkTE0PLD0aZRzP59Vs0WhU6tlMpc4VhyZ9r1dBztRxVJstHIXKr7Z5ruUUb1Rd9UaVFMjJSWzbTERERERExoupa4mIiIiIiAzEtXX71U9uOPftBvWjzwI+/hGA/Dy/PW/Ph77b0OH9XFlO+KFA1UuXiIiIiIjI2DHQSQZFUi9lx5YeU9QYRC/9+jEu/7ELe0d9rXX8ntpje2Hfe0twadV2lQZMxkbKSuT1cPh5jnim9SciIjJ0vBcTERGZNpYFiIiITBvLAqRvGOgkg7J7ZOatsB3LlVDBy5h7D/Ew6Caiwx/gwsptqNq/nUrDdX3rEc28RWu7oubIbri587gKcoqwg+d0Lt/t5ZYo1aI2butZeisiIqIXifdiIiIi08ayABERkWljWYD0DQOdZFCurN6j9XMLO2t0Wj8dSQmJ+GfYPBXkFIenrkDpFrXhNXuYClDGPYiEhY0Vmi0YifgnMdj37mLNMiJDwtWPNi5eHnBp4oH7gcE4MH5pHm0dERGR/uO9mIiIyLSxLEBERGTaWBYgfWOe3ytAlBu8v3wbRTwq4OhnKxG274zm84R/g5l2xZzQ+PPB6rO67/dFoSplETD5R0SFRWS57AKlndHi23dVYNT/jTlIiI7lQSMiIuK9mIiIiPhcTkRERKyjp3zGHp1k8DyG+6Jit6a4unYfzi5dn2F66N7TOL9iK6q/7qMCm+5DOyFky2EE/bEry2VL789Wy8bDprADdgycicfXwvJoK4iIiAwX78VERESmjWUBIiIi08ayAOUn9ugkg1ayWU3Un9QPEeeCse/dbzKd7+inP+HRtTDUGNEVcQ+jcGBC9tLPSspb59quOPHFH7jpfzwX15yIiMg48F5MRERk2lgWICIiMm0sC1B+M4lA5927dzFhwgS4ubnB1tYWZcuWxejRo/HkyRMMHjwYZmZmWLhwYX6vJuWQQ5liaLFkLOIjo7Hzjdk6U8omxicg/nG0en3vVBCi76SM4alL9cEd4da7per9eWqeH4+PMTMzg/ubndF9z3wMuPozXj6yBA2mDISlnU1+rxkRkV7jvZiMRc13uqPlt+PQ8+AiDAr1Q6+AzBvQERHRf1gWIKPBegEiomfCsgDpA6MPdJ44cQI1a9bEnDlzEBYWBnd3d8THx2PBggV45ZVXEBgYqOarU6dOfq8q5YCFnTVa/TAB1k4FsPvt+XgcfFvn/LXH9ETRmhVx7/RVlG5ZR6W61aVE4+poOGUgHl6+iT3vLOCxMXKenwyC57RBeHDxBg5+9AOubTgA98Ed0eZ/76uHHSIiyoj3YjImkiHEpWkNNUxB7P3H+b06REQGgWUBMiasFyAiyjmWBUhfmBt7T84uXbqoAOe4ceMQGhqKY8eOqfezZs3Cxo0bcfjwYdWjs1atWvm9upQDTeYOV4HLE3N/zzKlbJGaFVFrVA/c/OcENnX9EA+DbqHR54Nh6+ykdX77kkVUi/6EmDj4vzFb9Rgl41WoShlUf6MDrm08iJ2D5+DSqu04PHUFAqauQEnvmlkGxYmITBXvxWRM/BqNwK8er2Nrn08Rdft+fq8OEZFBYFmAjAXrBYiIng3LAqQvjDrQOWrUKNy4cQMjR47E3Llz4ejoqJkmqWxr166NhIQEVKhQAQULFszXdaXscx/WGa49miFkSwBOfbVa57zm1pZotuAdJETFYt+4xUiMjlNjeUpPUK9Zb2qdv9Wy8bArVgh7xyzCw0s3eWiMXMXu3jAzN8e57zam+1wCnvFRMXDt2Tzf1o2ISF/xXkzGJjIkPL9XgYjIoLAsQMaE9QJERDnHsgDpE6MNdEpK2t9++w3Ozs6YMWOG1nnq16+vfkvAM5Wfnx969uyJ8uXLw97eHtWqVcOHH36IyMjIF7bulDmXJh5o8NGAf1PKfp3lrqr73isoXK2c6p0Xdeue+iw84DwCl21G+Y6NMvTWazxjKIrVrYxTX69ByKZDPBQmwLmOG5ISE3H3+KV0nyfGxiPizDU413HNt3UjItJHvBcTERGZNpYFyNiwXoCIKGdYFiB9Ywkj9csvvyApKQn9+vWDg4OD1nns7OwyBDql52e5cuUwffp0lClTRo3xOW3aNOzatQu7d++GubnRxob1nl3xQmj57bswt7RQaUbLtm+Q6bz3z4XA0s4aHsN9cX37UVz+1T/d9GMzfkaZNvVUCtvQvWcQc/chKr/aBlVebYPoOw9UT85KPZtluvyQTQFIiI7N1e2j/GFfojBiIx4jKS4hw7SosAiU8KwGcytLJMVnnE5EZGp4LyYiIjJtLAuQMWK9ABFR9rEsQPrIaAOd/v4pga1WrVplOo+ktX060Ll+/XoUK1ZM875FixbqvQRM9+7di+bNmcYyvzi5loZt0ZRxNWuP7qlz3tML16KcT0M1vub+95ZkmJ4Yk5LC1mfNNJXCVsZmlICWkLS1ku5WF7+GwxF5485zbQ/pBws7GyTGxWudJr06hQTN4xjoJCLivZiIiMjE8bmcjBHrBYiIso9lAdJHZsnJyckwQmXLllWBzOPHj6NOnToZpsvYnCVLlsTdu3cRFBSESpUqZbqsixcvomrVqvj555/Rt2/fHK9LgwYNEBYWhudhlWyOKUmez7UMotw0zTwA8WZJer9Tszp3uvp/AVtnJ/xWa0iGaS2WvouKvk3wv3J92KOTjJahnMt5gfdWMiU817Nfju6680tYFbCFn+eIPD0mRPrClK8PguUBMhWmfK6zXoBIN14fWOdOpmGanpcFXFxccOTIkWf6rtH26Hzy5In6HR0drXW6jN8pQU5HR0dUrFhR57J27typflevXv2Z1kWCnDdv3sTzsDazAEo81yKIctWt0FuIS07U+72a1bkTdfs+nKqUgbm1ZYb0tfYuRRBz7yGDnGTUDOVczgu8t5Ip4bme30eASH+Z8vVBsDxApsKUz3XWCxDpxusD/0PINNwy4rKA0QY6Jfp7//59HDt2DF5eXummhYaGYvz48ep1rVq1YGZmlulyJED58ccfw8fHR2vP0OyuS260PoP+BtvJBJUqWUqvW4Bk99y5e+IySresA+e6lRF+KFDzuYWNFYrUqIDbB//7jMgYGcq5nBd4byVTwnM9v48Akf4y5euDYHmATIUpn+usFyDSjdcH/oeQaSil52WB54mjGW2gs23btggMDMSsWbPQrl07VKlSRX1++PBhDBgwQPXmFLqCl5GRkejatSusra3xww8/PPO6PGt327Tio2KwyrX/cy+HKLdcvHQRVva2er9Dszp3rv61H7VG9YD70E7pAp2V+7VV23dlze4XtKZE+cNQzuW8wHsrmRKe6yxHE/H6oB3LA2QqWBZgvQARrw8ZsRxApuSiEdcBGm2gc8KECWpMzevXr8PDwwPVqlVDTEwMLl++jA4dOqBChQr4+++/Ubt2ba3fl5S3Xbp0wdWrV7Fnzx41nicRGZ8H50Nw/sctqD64I1otG48bO47BqXJpuA/uiLD9Z3Flzd78XkUiIiLKY5V6NYdDmWLqtW3RgjC3skStMT3V+8gbd3DFjw2fiIiIjBXrBYiIiAyb0QY6y5QpowKUkqJ2165duHbtGtzd3bF06VIMHToUrq6uaj5tgc74+Hj06tVL9cTcsWOH+h4RGa+AycsRef0OqvRvizJt6iEm4hECf9iM47N/A5KT83v1iIiIKI9V6dsGLk080n1Wb2Jf9Vs1fGKgk4iIyKixXoCIiMhwGW2gU1SvXh0bNmzQmpJWAp/m5uaoUaNGumlJSUno16+fCnBu2rQJnp6eL3CNiSg/JCcl4ezS9eqHiIiITM+WnlPyexWIiIgoH7FegIiIyHAZdaAzM2fPnkVycrIat9Pe3j7dtLfffht//PEH3n//fTXt4MGDmmnSC7RYsZSUVkRERERERERERERERESUf8xhgk6fPp1p2trNmzer3zNnzoSXl1e6n40bN77wdSUiIiIiIiIiIiIiIiKijEyyR6euQKektCUiIiIiIiIiIiIiIiIi/cYenUakzrjesLCx0rz3/uptuA/tlOPllPNpiGL1KmdrXrfeLfHqhRXovGVWhnXpvncBuvp/gfZ+UzWfN1s0Gq+c/A6enwzKctkuXh4o3aqO5r1DmWJ49fwK5JR1QXvUHNkt2/P3CvgG3ffMR+VX26j3bq+0gu+2OZqfPmd/QKtl49U0x/Il1GcDgn9BEY8KWS5bjoddsULp9lN29kVW+yareftfWaXW07ZowZT1ruiijot8JtvaYMpAwMwsZR3f7Iwe+79W04iISL/ur+q63rQGBt74Ld061PvgVXU9990+V32nVMv/GnPl5Lou99qqA1/KcF/Mzj0uq3ueLrJPXz62FF6z3swwrc57vTEo1C/dOrT79WP47vhCbVOHtZ+iSI2Kmmlyf+t77sdsHaOnj4ncM5/l/qdtv+ki2yNlpNKt66b73MLWGt12zct0Hdr/MSVdWSin5RAiIjLssoDP6mnoeWiR5tlU7vHZKSc0+HgAeh1ZjNY/Tsjy78r9pGLXphnuW/Jc/bz7TZfUbas9tpfm3irbkfZZXO57WU0z1Gf01LJQ6vbIMUur6mvt0W33V6r8IOW91P2ak2NLRESGXxZwruOGThumo8vWOeq+UGNEV820Vj+MT3dvfO3m7yj7UgODrhdwfblFyr1v2xy1zWmfoeV1l62z1bSuO79U8xpyDELq5htOfS2lTmBHSkzFsYKLZn6P4b5qO2W6HOvUspnUI/hum4N+l39S/1+myiR7dPr7+8MYSUXgue82IDE2/rmWU87HExFnr+HOsUvZmj9s/1n4vz5b8776kI4o7F4ef7V6F0nxCekuZnvenq9uDNZOWT8kuTTxUPPd3HkCz8O6YAHUHNkdpxeuzfZ3dr01T+0Dcfm3neonlVxQrqzZrV4/Dr6Nde3GqwtTdi/usr+i7zzA88jpvnkUdEutZ6qGkwciZHMAApdtUjfpzptnIrTVadz0P45z325AxOmrz/RwR0RkjPTl/iqsHO1R/8N+uLnjeLrPbx8KxMl5fkiMiVP34A5/foLf67yJhOjYHF3XHcoWR9UB7XDhf1vxvHJ6zzu7eB3OfZd+mAB5iJOfyOvh6T7f9eYXiHsUpV6X6+CpHojWtX1Pvf+711T1Pi+OSW7ut83dPtZsQ6r6H/ZH+OELcK7tmmF+92GdVbmjaM1Kms9yWg4hIiLDLwscnrIcIVsOa50/s3LCkU9/woML19U9MytSiSnzXf1rH170fnt62xIiY9I9x6aV2TRDfUbPrCwkyrZviEo9mmFjp0mIfxwFm6IFkRSfmONjS0REhl8WaDJnGI7P+Q3Xtx6BdSEHFSi7vv0oHl68gZ1v/BfELFrbFe1+/lBzXzLEegHZvkafD8afTUep7xf3rKY6H/1Wc7Ca3nzhKGzpORX3A4NVcFL2RfCmQ0h4EmOQMYhy7RugeMNq+KvNe0hOSEStMT1Vo/Zdw75Eyea1ULlPK2zo+IHaPplW9/1XcWjS96oeaF278arRmCkzyUCnMUpt6SC9GpITk7C1z6fqvVPl0njp9ykoUKqoKvzKySPBRzNLC9Sb0Acu3jVgYWWJh1dCcWDCUhSrX0W19JCTR1qNBP64BTe2HUXzxWNg5WgHCxtrhO07g0Mf/QAkJ2tdlxrDu+Lvl6eqvyOe5YFBHq6qDmwHMwtz1apCLlJBf+zS3FjKtKsPa0d7tR4SnEu9gDf4qL96uDMzN8epBWsQvOEAvGa/CUsHW9WyISkxCRt8JqrKwkrdvGFuaYGkhES1nDtHL2a5Xs51K8PW2Qkhfx/J8TZJy1S7EoXRYslYdQHaO2aR+tyueGG0WfE+HCuUQHT4A+wc+gXiHkSqaR5v+aKCr5daz5i7D7F/wlLYODlk2DeyrW1XToJNYUfViuP+2WvY/94SVcGtTXJyMqzStPowt7JUf5uIiPT3/ioaTx+MU1+tRvmOjdJ9nnovFPcDQ1RLQOkhEHnjTo4OqdwzC5R2VvfMyJt34T8opeVouY6N0HjmUNgVL4RLv/irdRDSmMnzszfgULYYLG2tEfL3YRyf9avWe57co+pO7KO21dzaEueWrlfLyoyFnTUaTR+MnUPmouPalP2eKm2AUMoDuvZZZqT159PH5PHVMHUMG88Yoh4w5PXe0Qtx72SQ+o70lJVtk3un/D8c/WylemjTtt8aTB4IFy93dQ+Pi4xW92Wp1MxMyWY1YV+yCM4v25wh0FmoShn1QLxvzCJU6OyV420lIiLjKQvoklk5Ibuk7FBnwivq3ir3NKmEPTDxWzWt6iAf1UtA5jn5pZ+mMbBkC/L85HXYOTvB3NoKF1duw/kft2jdb7Lt7kM6qedPM3MzHJv1i9oH+UWfntF1qTHCFye//EMFOUXsvUe5vCeIiMhQygLysbVTAfXa0t4GSXEJiLufco9Kq3Lf1riyeremft4Q6wWkrGBmZqbq9XEnJZAYFXrvqX2RUr8t8YCY+4/V/jDUGIRsj4W1peqUlJCQCCsHO832FnGvgNsB51WQU9zYcUwFNiXQSSkY6DQS8vAhXcqf7h1QxKMitvSagqTYBPj8+QnKd2qEq2v3qW7t8dGx2NjxAzVfrbG9UHdiX3VySIsQaUmQ2npCTq4dA2ciISpGnbytl09ERd8mWlt4ygloV8xJtThMrYg7u3Q9rq3bn6Ptkb9/4X/b1MUqYPJy9Zm0zJALubTSODH3d9WlXB6o/vQ/rrpqN5nzFrb3/1w9iNgUcVRd1+8cuYADE75VqV3StpYM8tuNc0s3qNeSLsB7/kj82Wx0lutV+dXWCPLbpVpV5JT0tJGbTLqWGj6e6u+vbz8Bsfcj0WLxWNVi5vTXf6Jid284uZXCps4fIjkpCZV6NUfjGUOxY8CMDPtG7B7xlVqGkJtO9cEdMm1BEjD5R7RZ8QGqDXxJ7dOTX61GxJmrOd4mIiJjpy/3V1G+U2MkJyWr5eiqwJRWfpHBt3Mc5FTbO+Fb1cLz6R4G8kCxqcuH6v7a88BCXP51J6LCIuC9YKQq1N8+cE49GLT96QOU7+yl9Z4n95vNXT9W9zRpmSmF/5v/nEBUaITWdWnw0QBcWLEVUbf+e5BJy3vBOyjZxEO93tZ/eo63VR5Snj4m8mDj5FYa+95djIMffK+Ofb33+2Jb38/gUK64ahEqr+Mjo1UKGXnQ9fMcrnW/nV60Fkc++Z96LSkAG336Ora9+rnWdZFyjKSek2UXqlI23TR5SG4ydzj2jftGPVQTEZHplgVSe//XndAHDy7ewNHpqxAZEp6jcoIuMfce4cTs31TvwKd7kibFxqttkmfUzptnqediJEM9w+4ZOR8PL99SjZQ6bZihCZA+vd9u/XMCV//cq3m+77RxOv5oODzTSkmpwJXsQ1LGkIxEp+avUeWIrKYZ4jN6anYstz6t8eTmXRyf9YtmnQpVLqMqVWu/+7L6n5EKWMnOREREplcW2Dd2kZpHgoW2RQqqdXu6k5E0sJHg2qZuHz/b9upJvUBsxGO1fb5b5yD2QaTarq29P9FM3/XWl6qHZ0JUrFruzsFzchzY1acYhPxvlGzqgVdOfY+EyGg8CYvAlu5T1LR7p4JQbVB7FVSW4y2ZHiQAK/swtTGWqWOg08iFbD6ExOg49fruicuavM7SElNOhgr/PgBJC4rI65lUiJqbof5H/VHCs5rqIWLnXBAPzodoveBKZZy0zpTWGxs7faAuDB3Xf46Hl2/i/rng594eaf0YvPGQeh1+5KJme4o1qArH8sXRbtWH6eYv6FpKVfY+rWiNiqg1uodqRSJBS6nUlIultC7JjKWdjaqo3Nh5EnKTdItPffgJP3oBhauV0zxgOddxRZe/U1rNyI0iU2ZmKtd6mTb1YW5prnpr3jmceQ/Vaq/54OravephTVrktl89Tf1/hO4+lavbRkRkrF70/VUKs7XH9MSWnimF3MyU9K6J2uNextZX0veAfF5X/9yjedB4HByugn5xD5+ovyc9OFJZFrBVFYDayD236ZcjULBSSXXvtSnsoO552h5opIWrlCEOfbgs03XaO+pr9VvG4ZDWlNufIdipzeNrYbh7PCWNUPiRC/B4q4t6XbpVXRSs6KLSAqeShzNp6apNqea1UP2NDqoRmLREtSnkkOnfbDR9iKqglQrmp9UZ97JqVfrw0k21T4iIyDTLAmL3Ows0DYCqve6jKhLXthib7XLC87iyJqUsIAFN6ZEgvTmkJ4NkHZBgYCorB1v1WWo2hLQcypVA80WjYV+yqCoLSOWcY7niaplPiwq/j9/rvqnujTJfyyVj1T35zDd/6ZxmqM/ox2b+gqjb91V3Dgk0t131IdY0eSel4tvSQu2nLd0nw7pQAfis+USl6L2xPf96wxIRUf6UBSQ1qjR0koZD8lzeYc0nuHsySKWuTVW+c2M8vHJLLceQ6wWknCGNgDZ0fF89D0vvShmbcm3zMepZvPaYXiq4eftgoGoQJNkY/mr9rlo/Q4xBSGanQlXL4Y+6byLucbQajkB6ie4ZuUBlkjqzeB3a/PSBagAt/3viWTpjGSsGOo1c2rzhchKYW1io19LtW7pK39p1MstleAzroi5WEriU5cmguBa2/w2+nJa0IJBeDkGrU8awlN4kaqypOm65EuhM29JTbY/lf9vz4MINbPJNf5ERT1cKSiC21bL3VA5vefiSCsh+l35SXcN1BTordPFSaQjS3jhyQ2JsXKbbJIHIiyu3Z7mMSj28UbJpTWzpMVnt/+qDO6Kkd41M56/2enusaTpKvZaHw5s7jql85Ax00vOS3lA+a6bh8LT/4eySdemmlfByR5vlE5EQHad6LUnLqFSSxkIaEtzacxpbe2vPKS89qSRtxHqfiRkqTiQVhIw9K4WPHQOmZxj3jsjQ769Fa1VSKV+kdaCQVoOS8kbGaDo+8xfNOdb0qxGqNaiuFKnPvb1J/26vmZl6Lw2AsjNOiaT7ueF/TD2ICGn1KK1XtZF7WJGaFTXja0mFqKR/k/RwT6e4k14Nsmx5QEqtlHweacsCT9+Xb+06hd1vz8/wnQIuRdO/L+2Mxp8PxoYO76uKyMLVU8ZNzYw81MpPwykD1T5JHWtFWnrKdVWWV/0NH5hZWKiURrJf1nd4n6nriIhMqCwg0mY5kPSwUv6V+192ygm5+tz6b1lASgLSwyKzcTSf1mLxGBz9fBWCNx5U7/ue+1Glrsvs2T+1AZDUM1z61R+VujdTwUxd0wz1GV16xKSSHqr1J/VTlcT3Tl1RPTyv/LlX7XepvJXn92L1KzPQSXqL9QJkKl50WUDu79IYZtfweeq9ZHWQLAoy7Era+uoqfdvoHCbGUOoFpPGw1O9JkFNIXYD3vBEoUMZZ9S6Vso8EOYXUE0qa1yI1KuZK/XZ+xCCkEXfovjOaOs2g3/9Bu1//65V7YcXf6ie1d6iUD6SMQSl0ND8jQxP3OEoz7mJWQrYEqNaFklpGyG9pdZmynGjVYiKVdNWODr+vLlbSUjSrsaGurN2rej2o7xZygHPdzIOcEgCVXOZatycyKt166CI9LqQViYxvlTbHtlxQZFys1HEo1bbaWKnXcjEQkj4mOyq/mr2bRL1Jr6rWtVq3SfZtDo6RpEWQfSikFadcrLXtG2snB8REPFIXN2k14/ZKS53LllY3qcdIeqq6NK2R6618iNIq07a+au0kFSGbu36ULsgplTPSOvrR1VCVoiGnPZYkdZfn1EGqALm1zycMcpJR3l9l/IXfag2Bn+cI9RO84aBKA6MJcjaujmZfvwP/QbOz1bBIgmj2LkUybmtk9rdVehjI+CHSojSVPGjIOJOa7U2zLOmB8OTfdLqyvoXdy2e67GPTf8Yf9YZptlceWKTHpjzYSKoY+TuppLWsBDgzC3LKg6CkudXm6WOii6TTkZ6mErRMW47Rtt9kmdLbRXqciGpvaC8XpErdTvnZ9dZX6iE1NZ2NpEfyazhcTZPrZ/zjaPWa43MREZlWWUB6D9qm6S0h6fGi7z5U97+syglPkzKAlAW0bmtk9u+ND4NuqWdQt1daaT6THg+pz7BP7zd5bo28npJqt1LPZqp3Q2Yk85A8A6f2hCnfsTHu/Tvciq5phvqMnlp+Sq28lMrsR1fD1HsJckraPCF1G9JIOeLs8zckJ3rRWC9AhkwfygJxD56o53CpxxVyr3Cu55auTlfuw0VrV9KkijfkeoHHIbdV/b7sFyFjnErjX2n4JfX69iUKq3FSU7fbsXyJTBt9G0IMQra3ZNMamu+XaddAdbpKJdk01HLtrFFnQh+czqUGXsaCPTqNyNkl6/HSr5ORGB2rGRQ5MzIuRO13rdB54wzN2MZnFq1V43zIWBuSL1oCD+eXb0Hg95vQ8rtx6PrPPESHReDWHt2tIo5NX4Wm895WeaNT/5Z039dGBinOrBdlyKYAuPZsoXJ1px0IWBvpJr99wHTVorXhlNdgbmWhLiIyroi08JRt6ur/BeKfxKiBgGVAZBnPQx48JHd6VqT7uVy0dI2VkkoGB7538orWaTKOhuTxlmMkAzBnlRpIHvx8/KZqHqIu/+KvxtJ8et+c+34TyrVvqG5Qsk23DwXqDBbtHb0QjT4fDPchHdWD4fW/j2RrPxA9CxnLptn8kaoiRK5N0ZKSKY1KPZurc1Zy9kuqa7c+rVQO/Oxo9NkbqnW0nJt73vn6mQZZJzKU+6suTb8YAQtrK9W6MdXud77W2ohFKgdV70ct4zhIkFRaJ3bd+aXqieg/KCU1W2akd2PDaYPU/LLBMjaG9LqUtDNP3/OkB4fXjKEqvYyMg3H3mPayQVbkIanlt+NUmnwZi0x6dOwYOCPT+QtWLIn4x9p7eT99TB7/W6GYWUpbGWtLUsdIIyG5f0acvqr2gbb9Jtelbv/MUxXQ8qBLRESGSx/KAnKfl+wG0gtA3f8iHmPHazOfaXukUlMa5GgTuuc0arzlC98dX6SMNzXx20yXIz0cZHxKGbdKKnQlGBsb8Qi7R8xHnJb9FjD5B7W9cQ+jELrvtM7xxIs3qo66419Rf0OehcP2nsap+auznGaoz+jeX42EXTEntU0JMXH4Z+gXmvLLuaXr4TV7GLrtmqf+p6RHbPCGAzrXlUjfsF6ADJ0+lAWkF+U/w75Eg8kDVG9KqUuTsT7vHP0vNbqMiSkpV7Pq6WcI9QLyvC339/Z+U5AUn4ikxES1/RIUTox9iP3jl6Dl0ndVuUiGizn44TJNUNEQYxCSrUPG5fbdMVdtr4zFeWDCUs30l6R3p7k5LKws1d85/8NmncszNWbJyamnG+mz+KgYrHLtD33j1rul6ikhJ3N2SfrJ1AF+ZUwqGSsyPOA89IWkg5PtSR0kOSffuR8Ygk4bp2ODDDatJ6eWpAzRNoD0836nX9BKWNnbQt/p67ljKilqpMVz4xlDcPdEELb1+1zrANlSiSIVIn+/PE0NIi6pt6Ql+tPnUNrUtVKZ4P3V23Dt1QIXVm5TA37ryzlnaAzlXDa168Oz3F+ze10v39lLpUI79VXmFYIvmpzPct+Vh7TcXl7rHycg4OMfdVamvmiDQv3wc9WBudYDPTtlF57r+nmuE+kDU74+6HN5ILfLAmmX5zHcV/UeubI6ZawtfeCzehrOfbcBIVsO58ryjO0ZPTf+V0z5XNfX89xYsV7A8PD6oJ/XB9YLPB9jjEE8b1mqnxGXBZi6lp6LtDSUno6dt+hu2ZGq2aLRKkWNdFsXhyZ9r1cXGCE9Q5otHKVS1WZFusRLaw9pySk9yaRljYzHpS8PUCIxPkG1OpX1lNY6WZGWuI1nDlEthImeR813uqvc+6H7zqogprYgpwwWXsS9PC7//o96f/m3naqls+Thz4ykfmj1/XgV5JRWcgfGL9Wrc44oP+6vObmuSw8AfQpypqYBqvpae3XNeF7t/aaq8Urjo2LVe3lw0Kcgp5DKZZ81n6B065Q08s/q6XIIEREZj9wsCzT4eIAqm6f22ji7eJ1eBTmFrFu9Sf1Qe2yv51qOMT6j5+TYEukb1gsQPTvWCzw7Y4tB6CLpcn23zYFD+eLZGifVWLFHp4Fg6zPSN4bSAoTnTv603Hx0LQwFK7ggePMhlZI27SDeaTWeORSuvZrjt1pDVV5/qZDoffxbld9fvqetR2fqso98tlKl/iDTOJfzAq8PZEp4rutnK20ifWDK1wfB8gCZClM+13mev1isFzA8vD7wWYFMQz8jLguwRycREeU6GRBcPL52O9Mgp7Q4qtTNW40dIEFOkZyQqMa+kfFsrAs5ZLpsaZ396Ir2AcaJiIiIiIiIKH+xXoCIiF4UBjqJiCjXnf76T9zacxo1hvuiwZSBWucp37kxrJ0KIOzAOThWcNH83D54TgVBXXs21/q9fWO/QXT4A7RYMhblOjbi0SMiIiIiIiLSM6wXICKiF8Xyhf0lei6WdjaqazGRPv1PEmUmIToOOwbOQJsV76PGW74wMzfH4SnL081TpW9KDnrveSO0LqNyn1YIXLYpw+eSunZLzylqkO2WS8Zi1/CvELzxIA8GPdN1jPdWMhWmfN/muU6U9TliyniNIFNh6uc6vXisFyBDwHIAmRJLIy4LMNBpIMzMzIw2fzIRGafEmDjseG0m2iyfCI83O8PMDAiYnBLsdCxfAiUaV0fQ6t0I2RyQ4bslm9VEtdfao2itSrh36kqG6Y+Db2Nzj8lqPNAWi8dg14j5CN5w4IVsFxkP3luJTAPPdSLiNYKIKH+wXoD0HZ8ViIwDA51ERJS3DzWDZqH18olwH9pZSpAI+PhHuPVtrXp5nl26HhGnr2b43r2TQSrQ6dantdZAp4gMCceWHik9O1t8Mxq7zYBr6xnsJCIiIiIiItIXrBcgIqK8xjE6iYgozx9q/F+biZv/nID7kE5o9NkbcOvdEo9DbmsNcorIG3dw92QQKnX3hoWNVabLTg12Rt2+j+bfjEHFrk3zcEuIiIiIiIiIKKdYL0BERHmJgU4iIspzibHx8B80Czd3Hkf1wR1RoGRRBG86pPM7Mu6mTSEHlOvYSOd8kdfDsaX7ZESF3kOzhaNQsbt3Lq89ERERERERET0P1gsQEVFeMUtOTk7Os6UTEeWz+KgYrHLtn9+rQaS3+gWt5BjQRERERERktFgvQKQb6wWIyNCxRycRERERERERERERERERGRwGOomIiIiIiIiIiIiIiIjI4DDQSUREREREREREREREREQGh4FOIiIiIiIiIiIiIiIiIjI4DHQSERERERERERERERERkcFhoJOIiIiIiIiIiIiIiIiIDA4DnURERERERERERERERERkcBjoJCIiIiIiIiIiIiIiIiKDw0AnERERERERERERERERERkcBjqJiIiIiIiIiIiIiIiIyOAw0ElEREREREREREREREREBoeBTiKiZ1RnXG9Y2Fhp3nt/9Tbch3bK8XLK+TREsXqVszWvW++WePXCCnTeMkvzWdN5I+C74wv4bpuDzptnoqR3Tc20orVd0eGvz+C7fa6a7tK0hmZag48HoNeRxWj944Qs/24Rjwqo2LVpus8GhfrBuqA9nne/6eKzehp6HlqE2mN7ZZgm+/vpdXCuW1lta/e9C9D+jymwdymiPrewtVbb3+/yT2p/Z2c/O7mVSvc+O/spO/stMw5limHgjd/Uesr30rItWhCvnPo+3TrIse247nP0v7Iqw7pV8G2Cbrvm4dXzK3K8zkRERERERGRY9QLOddzQacN0dNk6B912f4UaI7pm+J5T5dLq+dHzk0Gaz9zf7Iwe+79Wz6HZeWatOvCldJ/1Cvgmw/Nrdsg+sitWKFvzyj59+dhSeM16U/OZ68st0NU/pR5Etrl067rp1qn7nvlqmvzI83Gq9n5T0ffcj9k6Rk8fExcvj2ztp+zsN12knkO2LXWbpD7De8E76LrzS/XTevlE2BQtqJm/ULVyqu5Ejrv8lOvYSH1evFF1tb7PWndDRGRIGOgkInpGdd7LfsBOl3I+nihWv0q25w/bfxYbfCZq3h+eshzr2ozDunbjsX/8UrT89l3AzExNa/3DBJyY+xvWtX0P/wz7Et7z31aFZHHk059wYvZv2Q/YdctewC6395ts38l5fuk+k4J7UkJi+hnNzNB80SgETP4Rf3qPwo0dx+H5yetqUmJMnNo/905eydbfdHulFZzcSuN55XS/JUTGqPWMOHst3edec4bh+raj6T6LDr+vtjVgyvIMy7m2bj+295v+HGtOREREREREhlIv0GTOMJxasAbrXxqPTb4fwWO4L5yqlNFMN7O0QJM5byF4c0C65Zz7dgP2j1uSrb/pULY4qg5oh9ygAp3FsxfoFGcXr8OBid+q19aFHNDo88HY+sqn6vn50EfL4D1/ZLr5d701T02TH3k+TvV3r6m4vvVInhyT3Nxvm7t9jJv+x9Vr+a6lnTX+avWu+om5+xA1hvuqaRZ21mizfCKOzfoFa5uPwV8t38XtQ4FqWvihQLX9RESmwDK/V4D+k5ycjIToWO4SMniWdjYw+zfQZqxSWxJ2WPspkhOTsLXPp5oWki/9PgUFShXFgwvXVeE6KT5BPVTUm9AHLt41YGFliYdXQnFgwlJVaC77UgOUbF5LtcoM/HELbmw7iuaLx8DK0Q4WNtYI23cGhz76QS4SWtcl7lGU5rW143+t9GyKOKqegKF7Tqv3j66EIu5hlGoVGLLpULa3VZZRZ8IratnSGvDOsUuaB4yqg3xUK0eZ5+SXfrj82071uWNFFxVktHN2grm1FS6u3IbzP27Rut9k292HdIK5lSXMzM1UAV32Qabr4+yEWqN6YEvPKajSr63m86K1KqllygOfuPDTVtSb2Ec9dCbGxmd7eyu/2gZFa1dCw2mDVOvcozN+Vp9b2tui+TdjULhaWSTGJeCfN79AZEi4mlapV3NUf70DzK0sEP8kBoc+XIbo2/e17rdmi0bDybWU2t4nt+5i/7uLEX3nQebr07e1+jv3zwWjXAdPzedRoRHqp1Cah1fKGd53yZSYwr05MzzXiXTj9YHP4GQaTPlcN4V6AfnY2qmAem1pb4OkuATE3Y/UTK/z7su4tv4AbAo5wNrp2Xr3ec1+EwVKO6vn28ibd+E/aJamIXLjmUNV4PLSL/449dVq9bn02PT87A04lC0GS1trhPx9GMdn/aoyNtmVKIwWS8aqRsl7xyyCTWFH1FXP79Ywt7bEuaXr1bK0kXoD+V+2dLAF7gDWBQsgKvQecpPUmzx9TB5fDVPHsPGMISjesJp6vXf0Qtw7GaS+U6plbbVt0rhc/h+OfrZS1U9o228NJg+Ei5c7zC0tEBcZjf3vLcGjoFuZlmXl/JU6hOSkJFU3cT8wRE2r1L0Z7hy9iPCA8ynzJiUh9t6jXN0XpoDPC2QqLI24LMBApx6RIOcq1/75vRpEz61f0EpY2dsa9Z6UgJWkHpFWdmkDjUU8KmJLrylIik2Az5+foHynRri6dp9KGxMfHYuNHT9Q89Ua2wt1J/bFoUnfq9aE0oPv3Hcb1TQJzO0YOBMJUTEwMzdXaUkq+jbB1b/2Zbo+9Sf1Q/kuXrBxKoCdQ+aqp5zYiMeq11+FLl7qgUZSnUqATR4yciLm3iPV81OCbP6vz043LSk2Xm2TpHntvHkWgvx2AclAi8VjsWfkfDy8fEu1MOy0YYYm0Pf0frv1zwlc/XOvJqVLp43T8UfD4erBTJsmc99SvVETnsSk+9yhjDMib9zRvJfp8sAgD1CpAcnsuPTzDrj2bI5z321AyJbD6jN5sHGu44p1bccj8nq42t81R3bDgQnfonjDqqjUzRubu3+s1lnSw0hA9K+WY7XuN+mFmfrgIcuQFsCpgWOtLT9lf3WfrP4HKHfxvkumxBTuzZnhuU6kG68PfAYn02DK57op1AvsG7tIzSPBQtsiBdW6pTaolSFeJJi69ZVPVGPeZ97eCd+qtLdP9xKUQOOmLh+qxtY9DyzE5V93IiosAt4LRqpeprcPnIOZhTna/vQBynf2UhmbpEGvBIBTsxlJkHZz149VoE56bEpQ8OY/J1Tj3qdJXYdsn+/WOYh9EKkCi1t7f5JuHkn1KnX5d45fxtHpq3Ic/JPelE8fE0ldK5mf9r27GAc/+F4d+3rv98W2vp/BoVxxtW/ldXxkNBwruKgAuJ/ncK377fSitTjyyf/UaxnuptGnr2Pbq59rXZeLP21D8QZV0ef0MiQlJuHu8UsI/GGzmiYNnxPj4tHmfx+gQMkiiAgMweFpKxjszCE+L5Cp6GfEZQEGOomIclHI5kNIjI5Tr++euKwKt0J6PUrPvgr/jpUgLRQjr/8XlEvH3Az1P+qPEp7VVDpWO+eCeHA+RGegUwru8lOyWU019qakqpEWozukpeBH/VHznR54cPE6bgecR3JCUq5t75U1e9RvCWhKKllpwWnlaK8K2xLsTGXlYKs+S23pmJZDuRJovmg07EsWRXJConqocSxXXC1TW2/LJzfvqtasL5q0kpQgpwg/ehHV3+igXpdt3xCF3cuj88YZmnmllWxqiuCnVeruDddeLdSDq/zIQ1pmZPzVg5OWqVauREREREREpP/yo16g5sjuqk5AGhFL0K3Dmk9w92SQeoZtPHMI/hnyRV5tLq7+mVIvIM+2j4PD1d+Pe/gEJb1rqixPqSwL2KpG0tpIj86mX45AwUolVb2ATWEHFK5WTmugU+ocqg/piA0d38fDSzdRpl19tPphvErdKvUg0lBY6g1UD9qJfdFs/khs7587Q7s8vhamAo0i/MgFeLzVRb0u3aouClZ0QYc//wu4StBWenJqU6p5LVWnYOVgp3qoSh1CZkq1qK2C3b/VHoLkpGQ1ZmndCa+o3rFmFhYo1awWNnaepILL9Sa9Cq+ZQ/HP0Lw73kRE+oiBTiKiXJQ2RaqkKjG3sFCvJS2ApJm5tetklsvwGNZFPQxs7PSBWl7Dqa/BwjZ7Y35ImlopKBeuXg73Tl1R6U7TtgqUgekldU5uSYyNS1eIl+2VBAjSqjK7Y0G0WDwGRz9fheCNB9X7vud+VOlqtHFpWgMujaqjbLv6ms98/b+A/6DZiLxxV/UITfsQJQ+RkkI2T46t5X/HNuiPXTj2b4pbXYp7VkP1wR2xqfMk1VNWUuHUHf+K1nnl4a1w9fJouXSsZnskxYSkQNrae1qubBMREREREREZdr2A9KSUTEK7hs9T7yWjkWRUUulVzc3hUNoZPqunanpfShDV2slBpV3N9e39t15AdacEVAAuO0PJSBrgG/7HsHPwHPW+y9bZmY59KkFC6UErQU4haX69541AgTLOKr2sBDnVuiQkqkxNPfZ9jdySthHy0/UCt3adwu6352f4TgGXounfl3ZG488HY0OH9/E4+LZ67k8bIH1alf7tcOXPPZr9KA3Oa47qoV7LtobuP6uCnGqa3260++XjXNpaIiLDYZ7fK0BEZKjiHkfBqmD2xrYI2RIA9zc7qzSuQn6njq0Y9zhaBbVSScoWSTkrhVgZ06JCZ69MlystFFNbhwrnOm5qvEwpLAvpYZmqcr+2SIiKRejelDE7n2bvUgTd92QslKt1jEy/jro8DLqlUrW4vdJK85mso/TU1Lbf5AErtadkpZ7NVEvOzOx5ez7+aPAW/DxHqB+xrvU4RJy5qgK7sj9cmnioz6sOeAnXtx3J9KGq2us+qrWj1u2Vdczm9oZsPaLWW9NS08xMpQnWtt/k2CZERiP2fqQaX6PKgHaZLjf+cRR+9Xhds61Hpv1PPRAzyElERERERKQf9KFeIO7BE5XiVhoGpwY+neu5qR6g8vNrjcGa50pJw3r5V3+dQU6pF5D6gQx/JzL72yrrI5mYpKdpKhlWxr5kkf+2N229QKECePLvUDQlGldXWZMy8zjkNop4VFD7RUhaXunZGHXrnmocbJ1muRW7e+PemauZLksCxJLmVpunj4kukmZXxvOUoGXa+hlt+02WKRmxosJTGmVXe8NH57Klfqd0i9qa92Xa1sODCyljdF5bv18NsyMN3kXpNvUQcS4lHTARkSlhj04iomd0dsl6vPTrZCRGx2Jrn091znt64VrUftdKpTdNTk757MyitXhw8YYa19J7/kiU8/HE+eVbEPj9JrT8bhy6/jMP0WERuLXnVKbLNbeyUN+Vgry0VoyPisXOoV+oNDGpLf8q9WimWhc+uHQD/m+kH2MzLXmQkcJ2Zj1Fa7zlC98dX+DOkQuZjimZ2qpxx4AZ8PzkdfUQJ2NxxEY8wu4R8xGnZb8FTP5BbW/cwyiE7judbpzNHElOxp6RC+A1e5hq+Sk9OXe/syDT2eWBUtLqaHNx5XY0nDIQHm92xtEsemqGHwrE0U9XqlQ50nJV0g/d2H5Mpel9er8d/HCZGv+z+975Ktgpx7aAlgfI7CjoWgrtf58CSztrlSb35aNL1fgnF1b8/UzLIyIiIiIiIsOrF5BelP8M+xINJg9IeSa1slABTRl+Jaek4bSkjZUsTU+TjFEPLtxA151fquCb/6BZOpclvRsbThuk5pcNlobX+ycsVeloA5dtQpM5b6n9tnfMIpXlyWvGUNQe00uNi3n32OVMlxtx+ipOzV+N9n5TkBSfiKTERLX9EhSWtLmtvh+v6iGkU6k88+99J/MenQUrllSNjLV5+phIb1FdKW13j/gKXrPfVMFWqReQ9ZR9oG2/SQribv/MU/UCEgDX5cQXv6PJnGHqf0E8vHwTByYs1fTolHqAjus/V2ltpWfn/vFLdC6PiMgYmSUnp95aKb/FR8VglWv//F4NIqMa2NjYziu33i1Vi0P/12fn+vI8hvuqFqNXVqeMr6EPfFZPU6lmQrYczvXldfjrU5XWN+FJDPSBpN313T4XP1d77YUuT5/O1xfN2K4PRLrwXOe5TsTrQ0YsC5ApYVnAeMoCuV0v4OLlAc9PBqnhZ8p39lLjaJ76ajX0hYxJKcFPCd7m9vJa/zgBAR//+OwNrvPAoFA//Fx1oErP+6KWZ8rXB8HyAJmKfkZ8rjN1LRGRAUmIiVMpWjpv0d1yMjsafDwANd/prmmpeXbxOr0KcgpZt3qT+qH22F7PtRzp8ei7bQ4cyhfXpLLd3PVjvQlyiqSkJCREx6r1lGP8PCr4NkGb/72P6DsPcm39iIiIiIiIyLjqBSQLU+OZQxAT8Vi9D95wQK+CnKnpgau+1l6N4/m82vtNRQkvd5UNS0iwWJ+CnEIaoPus+QSlW9d9ruUUb1Rd1S/I8qS3JxGRMWOPTj3C1iNkLPSpdQjPKyLDOV9fNF4fyJTwXDeeXhxEuY3XB14fyDTwXOe5TsTrg3asGyBT0c+I6wA5RicZFEm3kB1bekxR+fBf+vVjXP5jF/aOypiPv+6EPqqX2L73luDSqu0q9Yfk3s9K5PVwNYA7ERGRKeK9mIiIyLSxLEBERGTaWBYgfcNAJxmU3SPnZzrNsVwJFbyMufcQD4NuIjr8AS6s3Iaq/dup1BvXtx7RzFu0titqjuyGmzuPqyCnCDt4Tufy3V5uiVItauN2wPlc3ioiIiLDwXsxERGRaWNZgIiIyLSxLED6hoFOMiiZjR9oYWeNTuunIykhEf8Mm6eCnOLw1BUo3aI2vGYPUwHKuAeRsLCxQrMFIxH/JAb73l2sWUZkSLj6yWxgdpcmHrgfGIwD45fm0dYRERHpP96LiYiITBvLAkRERKaNZQHSN+b5vQJEucH7y7fVQOxHP1uJsH1nNJ8n/BvMtCvmhMafD1af1X2/LwpVKYuAyT8iKiwiy2UXKO2MFt++qwKj/m/MQUJ0yoDlRERExHsxERER8bmciIiIWEdP+Yc9OsngeQz3RcVuTXF17T6cXbo+w/TQvadxfsVWVH/dRwU23Yd2QsiWwwj6Y1eWy5ben62WjYdNYQfsGDgTj6+F5dFWEBERGS7ei4mIiEwbywJERESmjWUByk/s0UkGrWSzmqg/qR8izgVj37vfZDrf0U9/wqNrYagxoiviHkbhwITspZ+VlLfOtV1x4os/cNP/eC6uORERkXHgvZiIiMi0sSxARERk2lgWoPxmEoHOu3fvYsKECXBzc4OtrS3Kli2L0aNH48mTJxg8eDDMzMywcOHC/F5NyiGHMsXQYslYxEdGY+cbs3WmlE2MT0D842j1+t6pIETfSRnDU5fqgzvCrXdL1fvz1Dw/Hh8iIiLei4mIiIjP5URERMQ6etIjRp+69sSJE+jQoQPCwsJQoEABuLu749atW1iwYAGCgoIQEZEyRmOdOnXye1UpByzsrNHqhwmwdiqQklI2+LbO+WuP6YmiNSvi3umrKN2yjibVbWZKNK6OhlMG4uHlm9jzzgIeGyNX853uKFqzEorWqgTH8iUQeT0cfp4j8nu1iIj0Gu/FZFTMzNTwBlUHtFON6WLuPcLV9ftxYvZvHJ+diCgTLAuQMWG9ABFRzrEsQPrC3Nh7cnbp0kUFOceNG4fQ0FAcO3ZMvZ81axY2btyIw4cPqx6dtWrVyu/VpRxoMne4ClyemPt7lilli9SsiFqjeuDmPyewqeuHeBh0C40+HwxbZyet89uXLIKW345DQkwc/N+YrXqMknGT9McuTWuoMVhj7z/O79UhIjIIvBeTMfH8ZBA8pw3Cg4s3cPCjH3BtwwG4D+6INv97XwVBiYgoI5YFyJiwXoCIKOdYFiB9YdSBzlGjRuHGjRsYOXIk5s6dC0dHR800SWVbu3ZtJCQkoEKFCihYsGC+ritln/uwznDt0QwhWwJw6qvVOuc1t7ZEswXvICEqFvvGLUZidJway1N6gnrNelPr/K2WjYddsULYO2YRHl66yUNjAvwajcCvHq9ja59PEXX7fn6vDhGR3uO9mIxJoSplUP2NDri28SB2Dp6DS6u24/DUFQiYugIlvWuqTCBERJQeywJkbFgvQESUMywLkD4x2kBnYGAgfvvtNzg7O2PGjBla56lfv776LQHPVHv27EHbtm1RsmRJ2NjYoEyZMnjllVfU8ij/uTTxQIOPBvybUvbrLOev+94rKFytnKqoirp1T30WHnAegcs2o3zHRhkqrhrPGIpidSvj1NdrELLpUJ5tB+mXyJDw/F4FIiKDwXsxGZuK3b1hZm6Oc99tTPe5BDzjo2Lg2rN5vq0bEZE+YlmAjBHrBYiIso9lAdI3RjtG5y+//IKkpCT069cPDg4OWuexs7PLEOi8f/8+atasiWHDhqF48eKqR6gESr28vHDmzBkV+KT8YVe8EFp++y7MLS1Ui/uy7RtkOu/9cyGwtLOGx3BfXN9+FJd/9U83/diMn1GmTT2VwjZ07xnE3H2Iyq+2QZVX2yD6zgPVk7NSz2aZLj9kUwDHayIiIpPDezEZI+c6bkhKTMTd45fSfZ4YG4+IM9fgXMc139aNiEjfsCxARERk2lgWIH1ktIFOf/+UwFarVq0ynUeCmE8HOn19fdVPWg0bNkTVqlWxevVqjB49Os/WmXRzci0N26Ip42rWHt1T57ynF65FOZ+GanzN/e8tyTA9MSYlha3Pmmkqha2kKSvhWU1Nk7S1ku5WF7+GwxF54w4PGRERmRTei8kY2ZcojNiIx0iKS8gwLSosQpURza0skRSfcToRkalhWYCIiMi0sSxA+sgsOTk5GUaobNmyKpB5/Phx1KlTJ8N0GZtT0tPevXsXQUFBqFSpUqbLunfvnkqBu3DhQrz99ts5XpcGDRogLCwsy/msks0xJckzx8sn0jfTzAMQb5YEfZDT86rrzi9hVcAWfp4j8nS9iPSFPp2vLxrvu2RKeK5nXhbocWAhzK0s4NdgeIZp3gvegdvLLfBz1YGIexSVx0eJKH/w+sBncDINPNdZL0DE64N2rBsgUzFNz+sAXVxccOTIkWf6rtH26Hzy5In6HR0drXW6jN8pQU5HR0dUrFgxw/TExESV+jY4OBgffPCB2sm9e/d+pnWRIOfNmzeznM/azAIo8Ux/gkiv3Aq9hbjkROgDnldEhnO+vmi8PpAp4bme+b5JjI6FVYGUrCFPs7CxUr8TouPy6tAQ5TteH/L7CBC9GDzX+Z9GxOuDdqwbIFNxy4jrAI020CmBSRlv89ixY2p8zbRCQ0Mxfvx49bpWrVowMzPL8P0WLVpg37596rWbm5tKhVusWLFnXpfsth6B/gbUibKtVMlSetM6hOcVkeGcry8arw9kSniuZ75vom7fh1OVMjC3tsyQvtbepQhi7j1k2loyarw+5PcRIHoxeK7zP42I1wftWDdApqKUntcBZjeOZlKBzrZt2yIwMBCzZs1Cu3btUKVKFfX54cOHMWDAANWbU2hLayuWLVuGBw8e4OrVq5gzZw5eeuklFfgsV65cjtclu91t46NisMq1f46XT6RvLl66CCt7W+gDnldEhnO+vmi8PpAp4bmeeRn77onLKN2yDpzrVkb4ocB0vTmL1KiA2wf/+4zIGPH6wGdwMg0813muE/H6oB3rBshUXDTiOkBzGKkJEyagaNGi+D97dwFW1fnHAfxLCghioGIHYoDdgV2oDPNv54xNZzt1c7O3GXNzbjrnNnWhc27qnMl0Yhd2d4ABomIhHf/n9+K9Iy6lKDe+n+fh4d57Duee4Jw3fm/cunUL7u7uqFSpElxdXVG7dm01H2ezZs3UelWqVNH59+XKlUOdOnXQvXt37NixA8+ePcPcuXPf8FEQEREREdHrdOPvA4iPi4Pb4HZJPnft1UIVAq+v28MLQERERERERKSnjLZHZ9GiRbF37141RO3u3btx8+ZNuLm5YcmSJRg8eDBcXFzSDHQmljt3bjV87dWrV9/AnhPRm1a6SyPYF00YmtomXy6YW1mi8ujO6n3o7fu4voYVnERERMbq8cUAXFzugwoD26Lp0vG4veM4HF2LwG1gWwQdOIfr6/Zl9y4SERHRa8Z6ASIiIsNltIFOUaFCBWzatCnF56GhoSrwaW5ujooVK6a7neDgYFy6dEn18CQi41O2R3M413dP8ln1iT3Ub1XByUAnERGRUfOb8hNCb91H2d4tULR5dUSEPMWFZVtxYu5qID4+u3ePiIiIXjPWCxARERkuow50pubcuXOIj49X83ba2dklWda7d2/Ve1Pm7pSenFeuXMH8+fNhaWmJMWPGZNs+E9Hr49N5Kk8vERGRCZOha88t2ah+iIiIyPSwXoCIiMhwmWSg88yZM6kOW1u3bl388ssvWLBgASIiIlCsWDE0bdoUkyZNQokSJbJhb4mIiIiIiIiIiIiIiIgoOQY6kxk+fLj6ISIiIiIiIiIiIiIiIiL9ZQ4TlFaPTn1XdVxXWOSw0r73+Oo9uA1ul+ntFPeshfzVXTO0bpmuTdDz0s/w8pmj/SxfFRe0+fsTeP87D97bP4dzg//mOq05uQ+6HF2MZssnpLvtvO4lUap9gySf9Q9cA+tcSYcUfplzkxbPtdPR+fAiVBnTRb23tLNBy1Ufo/u5Zeh58ecU6xdtUQMd9y5Ap/3foOnS8bCyt1WfO5QoqI6/j/8qdSzpkWtlmz93kn2uPaM/Msu5njuKNK2a4XV7X1+p9tMmXy71WZnuzdDe9wv0vbU6xf9PWssyc22JiIyFvqS9GpLm9r2d9Bkt+/S/40vUs15+5HltyOlyYnJsyffB2jEnGi4ahY77vkb7XfNRY1Iv9bmFjbU6/l5Xf1XnOyPn2bFM4STvXyaN03XeUmNfNL+6frKfyfMOkk53O/1jkn2oPKqT9rrKT8/Lv6DWtH5qWYE6FdRnL3uNiIjIsPIC7TbP0qYH7Xd+qZ7/eSokjDzlUMoZrddMU8uk7Fpzal/AzEwtcxvihU4HvlHLMpJOlevbKslnXfy+zVB5N73yb1o0eZl6c4ao97nLF0+S/sk+9Di/XLu+lIfl3Hjv+ALtNn2GPG7/jcAleYRup37IUFk7edlajl9XnUB6JB2uNLxDhteX45Hr5NqzecIHZmYqfe+we746JrmWDiWds7xOgoiIjLdO3sLWGo2+Ha3SfCkrl2hX1+DrBdyHeqs8j6SPTZeNT7IPTX4Yh64nvk+xb4ZaL2Dj5KjSeMkHdNjzVZL/L9muJv8nP+7vvKVdxnoBEw50+vr6qjk627XL/MMou1V9P+MPh7QU96yN/DXKZnj9oAPnsMlzovZ9s2UTcHLeamxo8T52vfMlPBa8px4i4ujMX3Fy7uqMPwA6ZOwBkNXn5sjUn3Bq/hr1Oi4mBmcWrse2rjNSrCdB0AZfDoXvgDlY12AEwoJCtA/cZ/73sKHleITfe5Txgl6BjBX00uJcP+OBTvH02l21nxEPn6r3D09fU9ft+l/7Uqyb1rLMXFsiImOhL2mvsHKwQ42PeuHOjhMp1j+3eIN61suPPK81DDFd1ijetg7iYmJTrNtg/jCEnLmBvzxG4u8mY3D+h83q89iIKHX8D09dz9B3lunWFI5liuBVZfa8xYRGqP0MOXczyef1Pn8Ht7YfS/LZ6QXrtNd1U7sPERcdi+vr9qplwYcvqM+JiMg08gKb232oTRNOzvsDjy74qx9Ra0pfBGz1S1je4n0UaVxFW2Y8//0mHBj3XYa+075YAZTr0xJZIbPlX8nLHJz4vXr9+GKA9ljlR9JHTfqnGjwtHIV9I7/BhubjcGTmr2i0aJR2O3vfW4BLv2x/LWXr1FjnyolKwztm6m92vzsfV37boV4Xb10TBWqVx9/N31fHFLjvDKp/2DPL6ySIiMh46+QrvuuNuKhorKs/Att7fIK6swcjRx57g60XKNSoMly7N8Vmr0lY33gMHp6+jmofJKSN4tIv29R5SM5Q6wVqTe+Px5dvqXzAptYTVcBVAtvi+d0H2N7zU/zddCy2eH+Mcv1aqcZagvUCJjx0raHStGxss34m4mPjsK37TPXe0bUIWv0xFTkL58PjS7dUZjkuOgZmlhaoPqE7nD0qwsLKEk+uB+LghCXqYVqsVU31sJAb5sJyH9zefgyNFo+GlYMtLHJYI2j/WRz+eBkQH59iP3LkdVA9DgL3JvSMfXo9EFFPwlCkWTUEbDmc4eORbVSd0A3WDnaqRcL941e0hZpy/T1ViwtZ59SXa3B19U5tK9XaMwbA1skR5tZWuLxiOy4u99F5buT43Aa1g7mVJczMzXB8zip1nLrERcWoY5bWFMnJcYWcvYEnV++q9xd//getVk1OUomcEVIQsS2YB42/G6MeuPtGL1Kf2xbIg+Y/fwCHkgURHvwYOwd/gajHoWqZ+7veKOldD+aWFoh48AQHJixBDkd7lOvbEmYW5uqB5r/lME5/vQ4tVkxCjjwOKnF7dO4mDrz/HWLCI3Xuy6PzCYVhxMVlahkRkanRl7RXo+5nA3H6q7Uo0bZOlh+rPqXLan+cHFF5ZCf4dJ6Ksr1aaD+X3g1OlV2wc+A87Wfh9x9n+nilB0W+KqVVYUJamR6b9Zu2MlFaweYpXwyxUTHYNeQLhAYEq2WluzRChQFtYG5lgejnETj80VJVsajrvElvEkeXwup4pVByYOziNPfTtUcz9T2SDhdvUzvVQnHY3QeqgEdERKaZF9CQdOzKKl/te2nMbfWiN4OUCSX9kfJlpo937hDkLOKk0rTQOw/g23+OtvGRVJhK4FK+V/IjQnps1v7kbdgXyw9LG2sE/HMEJ+b8rrP8K+XVahO7q2M1t7bE+SUbkxxDaqSCtHSnhvinyzRtXiDy0TM8vnxbW8En+5y3UinVECozFZLJy9bX/tytrZgt2rKGSt/lmtzxTWhkJhWONT/urRqfmZmbq7K4/6aD6rxZ2tuo8xYXG6cqpd3e8ULpDh6qPC8Nt2Q7949d1rkvcsktrC3VscbExKoem2GBD7O0ToKIiIy7Tr5k+wY4MPZbtSz0VrAKkhZvU0fbqMbQ6gXyupXEPb+LiHkeod7f3nFc9fo8POlH9V5zHl6FPtUL5HUroc6bkDr9oEMX4NKlMR6euobgI5e060U/C1N5Asl74eArnwKjwUCnAZGbQ4aQ2dphMqKehmk/z+teCj5dpiIuMgaef81AiXZ1cGP9flQc1h7R4ZHY3PZDtV7lMV1QbWIP9TC4te2oai2g6QEhmekdfWcjJixCZdab/TQRpbzr48bf+1PsR2TIM4QHP0LJt+rh5saDKqMvN6y6uTJBehdKSxKpzPMdMDfJsrjIaLXf0m3ca+scXFuzG4gHGi8eg73DF6ibWbrjt9s0S/vgSH5u7u46iRsveiVKALPd5s/wZ62hKqiZGfZFnBB6+772vSQUtgVzq8KQPKQzSlqjSCWmJHqalhqqFU91V2xsPQGRj0LV8Unr2TPf/IVSHT3U8W/x+gjxcXHqIVp31mDs6DNLtU61drSD35SftNvfM+wrtQ0hBdAKA9uoXqpERGT4aa+QYWfi4+LVdnQFOisMaquGH39+5wFOzFmVoregoaXL9ee9qyrwNIUajdxli+J54EPUmzNY5UEk7Tv2yQpVAZgZUthz6dwI53/YhACfI+ozKWw6VXXBhhbjVXovQ+LKMHQHJ3yPArXKqcrKrR0nq32W4WGk4CM9SnWdN78pyxH5YiQF2YZUmGoKiDp7z8j56jhF/Q+kVQi7nIEKYSIiMs68gIZd4XxwrueGvSO+TpLuNP/5Q5Tv20r1eDz11dpMp43qeCd8r4Z8TT5igPRY3PLWR6qStfPBhbj6+07Vs9Dj6+Eq2Hfv4HlVRm7x64co4VVPZ/lX9mtr+8mqfGud215VBN7ZdRJhgSFp7pMEWZ8F3NNuRyp2JWiav2Y53D96SVUaS8WipKeZCXTK9pKXrSWPIvspPWWl16z09pTK2798T6hh8ep//i7+7f2pCiLLuXhr21y1D3LeZBjBxOft2po9OL9kk3ot5X6PBcPxV8P/ep4mJv8bhRq4qyHsY0LD8TwoBD4dp2ZpnQQRERl3nbyu9EIaAhlqvYCMOli+f2vVqEqCg9LoSdJ7yUNoOgm9Kn2qF5AGzaU7eqhGUZLHKNKkCp5cS2jklJhj2aIqaH5w4pIsOQfGgoFOIxCw9TBiw6PU6wcnr2rncZAWFnLzl3xRGSotJkNv/fewS8LcDDU+7o2CtcuruSFsnXKpoWJSK2Dt6D9HtWKsNKKT6lItrSviY7Iug60ZkkYekNLyUVqNSotJqdyUh6eGlb2N+kxaNiRnX7ygGr7GrlA+xMfEqoegQ/EC2laQ+uLOzpPaAGXwsUvIU764NggqD9W3/kloQSuFmFSZmal5V4o2rwFzS3PVkvf+Ed0tRYmIyPDSXsnYVxndWfVu1OX47FUIkyHL4uNVxrrFyo/UcDVSWDLEdFkCehKwldasyUnrWKdqZXB81m+qoCGtV5v/8gHW1B6mtvuqpFAhhRkRfOwyKrzdRr0u1rqWmv/La/Ms7bo5cttrhwlKTgoo0vpSCq7yI4XS1MhQvIcmLVU9XlKTs6iT+l+RCmMiIjLNcnjiIdZkKNfEaUv5fp64sX6fajQrPS1ar52u9itwz+ksOd4bfyXkBeQ7n/kHw754AUQ9eY5CHpVUDw4Ny5w2Sea5SkyCkw2+HIZcpQupNFuG05Pyb3qBTtcezXHlN98kPRl2DZ6HGpN6qu+7f/QyHl26lSX5AE0vCv/NCaNVBR+9rL22Elh1KFEALVd+lGT9XC6FEep/L8V28lUspebaluOWfZNh8STfoCu9d6rigtzliuPPakMQ9SxcTVUgvUT3Dv8vmE1ERPrFGOvk9aleQHqknl28Ac1//VA17JHzLbIqvde3eoEj039WUxFIQzAZ2THo4DnY5PsvjyXsCuVF8+UTVbA0vfyTqWGg0wjERkZrX8tNb25hoV6bmZmpru53d59Kdxsyga0UTmTOD9lerWn9YGGT+vjZMqyajAutIRPkShf9rBIb+V/GX1p7yjGZSaHqcWiG56JqvHg0jn26Ev6bD6n3Pc4vV0MAZJYM11O4cWXte2klGn7vcZa1nExyrHL9LP+7flJIvbzi33S3UbqTBwo1qASfTlMQHRqOCgPbopDHf5NRExGRYae9+SqXVsO/SU8BIa37pPdCjny5cEKCnEH/ZXBlfi5pcSiVjFk1xOmbTpedG1SEc50KKNayhvYzb98v4Nt/rgqAyvFKoUfIUHIy1Ju0Bn12Myjrr22idFmGs5MAa3oK1C6v0uItXpNUi1i5VtXGd9O5rhQM81QogSZLEgqGUmFraZtDDYG0ret07Xqu3ZqpFqZZ1XKViIgMrxyu4dqtKQ5+kLQ3QPkBrbGuwUj1WtKeOzuOq/knsyrQmeR4X+QFpEJWyNxZiZenRoawu+17HDsHfq7eS2/I9ObtkvJ3/hqu2DUo4W80JB/g02mqtgK526kftUPZvqrEvUqS5wUeX7qNLd4fpdzPZNPgyBB1TZe+D5/O01RFrwxF2+vKryrPoivQ6fK/xgjcf1bb4+XaH7vQ8vfJb6ROgoiIjKNOXtILSY80Q9dLepGRfdDn+vpLP/+jfjSjI0h9gNR9v25vul5ASBBUM9WdJt+UON4idUKt/5iKU1+tUcPmU1JpdBEjfRT1LEw770Z6Anz8VC8/6TIu5Le0pkjYTriqWNOQoVmk67vcxNJrpKRXvTS3LS02NFx7tUBMWCQC9+keF9vOOS867l2g+3hCk+5HWqSrtjzIpPWqhrSUkZYfus6NtaO9tuVF6c4NVSvKl3Fn5wnkrVRa2yq1fL/WabawrT6pJ8oP8NS5TJ33TFw/6d6vOT7pwZK3YqmE7YSGJbt+9ogIearOj1SQlunWJFPHSERE+p32ylwUqysPUr0W5cd/0yE1JJwEOTWt+jQk8y+B0Kc3ggw2Xd773gL8WfNd7fGKDc3GqSH4pLIw+lm4Cg4Kp6plVEWrzHehi6TJkjbrPF7Zxwweb8C2o2q/tUP/mJmpoYJ0nTe5tjLsnIzYIBWdZfu0THW70ivld/cB2mM9Ov0XVRhNHOSU7yrTvSmurMr43CpE2UXmuesfuEbNNZ9cwXpu6HnpZ3Q9+YP2HtaQefzk7yTInxqZN0fW0dx7iclceP3u/IE2f3+ihpckMra8gIb0oDSzNMfd3UkDmNLLskjTauq1NJiRRkPSIyQ1kheQPEGKYw3N+LHKyBEy+kKl4R2TVMJp8iXJy7/WuXPi+Ysh9QrWraB6RKRHhr+VRlyJhwpMXich84FKkDC1Bk+SV0jt2ZK8bJ2W4KOXVE/WQg0rJZnnU9J6yQto5kYVEsBV83HdScifyNQyaZGheQs1qKj9+6Ita2orNzNbJ0FERKZZJ++/8aCqS9YEOaXBk+yLodYLJD5eOYdVJ3THmW//xsvQ93oBISNdSP2/kBiA9Ay++JOP9jy0/nOqmqZOM584JcUenQbm3Hcb0er3KYgNj9ROfJwa+cevMtZKdaXWzF98dtF61cpRxtCW+SFkeFS5YS78uAVNfhiH9rvmIzwoBHf3pt3qs2zvlmpcbNWi8cpt+L6ddMzu5A9O6c6ui0waXPFdb3jv+CJhXotUxqjWtJ6Q+SllfgxJLGQo18iQp9gzbAGidJwbvynL1DHJpMyB+88kGaNcF9kHGeJHJn/+37ElCDpwFntHfKPmBjswbjGaLZ+ovlMKG3tHLkx1OzJR8sNTunvQXFi6Rc3pIfuYuIVGasMByMPec8009V4edFdX+apK3oAtfnDp3Fh1Zfffchjnf9yC4q1rqQRKAp73Dl9I0aI0MRlrXMaGl4Km/A9IRdSOfrPVttNaRkRkivQl7U2Lx1fDYZvfUaWVMRFR2DX4CxVAM+R0OS37Ri1Uc3hKhWJcVDR2DpqX6lyfUqCUyl9dZNSEWlP7wn2IF46l0yIz+PAFHJu5Ak2XjVctV6X3yO1/j6vAa/LzduijpWqej477FqhCjVzbnDoqkzOqcKPKqsWsfA+RoSraogaafD9Wza+zresMPEs0zKMU6uXZ+PRGoJqjTvKxmXlGVJvQXQU6JBjgO/Bz7RBiRMaYF5DAn8yPqd14orSxzqcD4TaorUqjbv1zVM0TpouUe+W+kx4YunqKSK/F9ju/VPepb/+EqVRSs+e9Bag1vb9aX/ZJKlwPTFiihlNLXv6VHhz1Zg1GldFd1PxkD45fTftgpaFP1ybYO/Ibnfd9wToVVP5Dhpc7MPbbVDcjc5elNjx88rJ1WpWHMlTvv30+U8PK1ZraD+ZWFiqQKXNxyYgLcn3b+36B6OcR2OQ5ESfm/A6vrbNVGT21a6FxcbkPcrsWhfeOeYiLjlXPyoMTEubeymydBBERmWad/Nlv/1bTonQ6uFCV12VO0NSGSjWUeoFWMrqBuTksrCzVubu4bKt2mQxpK/XwQs7fsxtBqU73Ywj1Ak7VXFFn5tuIi41VweNd73yp7Z0r+R4Jrko+T36ExAOurt6Z5v6aErP4+GS5Y8o20WERWOnSW++ugBQsdE1AnNG/cR/qrVqmXF+bMI63PvBcOz3JJMOvqovft+pYH10IUJMob5LJpvXk1pJW9bVn9M/wEAJZ8f/Q69oKWNnZQB/o631FpC/06X590/T5+fAyaW9Gt2cK6XLi7bX5e6Ya2kcqCfWBBHBkCOLfyvfLsm1KD7ffyvVN0dslMd7r+nmvGyPJe3qum44j03/Bue82qM9KdfRAwwXDVYtvqeAIlzmFE6kwqC1qT++PTW0+QNuNn6rpG07O+0Nnj86q73fFRs+J2jl/6nzythoSSno3SSPFuGjdjR4odXw+mEZeIHG5sIRXPdU78PRXa6EvPL56TwU/z/+wOUu2J88La0c7+E35CXU+G6TmLw32uwh9oalDkGN+U9vjva6f9zqRPjDl54M+1w2wXuDVsF7AtO51Dl1L6ZKeITIci5dP2i05NWpO7oNKIzpqW4eeW7xBrypThexb9Um9VMvvV+FQoqBq+Sm9LaVSRXpbSAWNvgQ5RWx0jOoZKvspLXdfRfJrS0RE+pH2psWU0mXp4SnpnX2JAto5Nba2n6w3QU4RFxeHmPBItZ9yjV9FgToV1HYkcB0fpz95D6LEZPisRgtH4uGZG9jacUqKIKdw7dFczbcn8wpLi2g19NWLuf9SI63FG34zQgU5L63Yjt1Dv2KQk4xKVuYFpIdF3dmDEPGiV4fM66RPQU7NsHHl+rVW81G9qoaLRqlh5WR4QCE9WvQpyClkrq6GC0fCtWfzLK2TICIi48F6gZfDegHTxB6dekRfW48QZZY+tQ7hfUVkOPfrm8bnA5kS3uvMY2dHj04Z1rHGpF64u/eMGv5S5vNLTua0ectnjhqaUoaMLNaqJpr//IHq+Snz1erq0bml/ceoOLS9mrdGhgY79umKN3Z8xojPBz4fyDTwXue9TsTng26sGyBT0cuI6wA5RycRERERERFlqXL9WiFXSWf4bz2M3e/OT3UeXZlvMPp5OPw3H1bvb/ueQPiDJ+rz5IFODZnXSLZ99JMVar4jIiIiIiIiMl0cupaIiIiIiIiylF3BPOr3s5v3Ug1yyrBSpTt4qCCnprdnfEwsrq/bi+Kta8E6t32q25YhGp9ev8urRkREREREZOLYo1OPWNrmUN2HiYzhf1lf8L4iSv8eMVV8PpApMeV7nbLHmW/+QsF67qg41BswA45O/yXFOiW86sLaMSeCDp6HQ0ln7ef3Dp2H+xAvuHRuhAtLt6T4u/1jvlXzDzf+boyamzNgS0JvUKLMYl6ATIkp5wV4rxOlf4+YMj4jyFRYGvG9zkCnHjEzMzPaMZKJsgvvKyLi84GI6M2LCY/Cjr6z1HybFd/1hpm5OY5M/SnJOmV7NFe/PeYP07kN1+5NdQY6n94Mgk/nqfBcOx1NXgQ7/Tcfek1HQsaMZQUi08B7nYj4jCAybgx0EhERERERUZaLjYjCjn6z0fyniaqHppkZ4DclIdjpUKIgCtatgGtr9yBgq1+Kvy3UsBLK92uNfJVL4+Hp6ymWP/O/h62dpsBz3XQ0Xjwau4ctgP+mg7yKREREREREJoaBTiIiIiIiInp9wc7+c9Dsp4lwG+wl3WrgN3k5yvRopnp5nluyESFnbqT4u4enrqlAZ5nuzXQGOkVoQDB8OiX07Gz87SjsMQNubmSwk4iIiIiIyJSYZ/cOEBERERERkXEHO337zcadXSfhNqgd6nzyNsp0bYJnAfd0BjlF6O37eHDqGkp39IBFDqtUt60Jdobde4RG345GqfYNXuOREBERERERkb5hoJOIiIiIiIheq9jIaPj2n4M7O0+gwsC2yFkoH/y3HE7zb2TezRy57VG8bZ001wu9FQyfjlMQFvgQDReORKmOHlm890RERERERKSvzOLj4+OzeyeIiIiIiIheh+iwCKx06c2TS5SKXtdWwMrOhueHiIiIiIgMEnt0EhEREREREREREREREZHBYaCTiIiIiIiIiIiIiIiIiAwOA51EREREREREREREREREZHAY6CQiIiIiIiIiIiIiIiIig8NAJxEREREREREREREREREZHAY6iYiIiIiIiIiIiIiIiMjgMNBJRERERERERERERERERAaHgU4iIiIiIiIiIiIiIiIiMjgMdBIRERERERERERERERGRwWGgk4iIiIiIiIiIiIiIiIgMDgOdRERERERERERERERERGRwLLN7B4iIiIiIiAxR1XFdcWbhX4iNjFbvPb56DyHnbuL8D5sztZ3inrUQHvwY949fSXfdMl2boPbMAXh6IwibPCeqz9ptngUL64SinZmlBfKUL46/m43Dowv+ap8KNaqMyIdP1fK7e07j6Mxf1euak/ugZPv6CDlzA74D5qb5vXndS8KxTBHc+Hu/9rP+gWvwW7m+iHoa9krnLS2ea6cjZ1EnXP19J07NX4PCTaqg5ke9tcttnBwRfv8xNraaoN5bO+ZEnc8GwamKC+JiYnF721Ec+2wlLGys0W7jp3Ao5Yy9w79GgM+RdM/z/eOX8eTqXe374m1qp3ueMnLeUmNfND86HVqIxxcCsG/0IvW/JMdSsFY57TqyraOfrMCFpVtQeVQnlPSq99/flyiIK7/twJFpP6NAnQqo+8nbyFux1EtdIyIiIiIiIkPBQCcREREREdFLqPp+V5z/YVOGAnZpKe5ZWwW1MhLoFEEHziUJuG1u96H2dYl2dVF13P9UkFPj3OINOoOvEvB8fOmWCuBlJGAn62UkYJfV5+3I1J+0gcm7u05hw65T2mXNf/kQQfvPat83mD8MwX6XsPe9Beq9bf7c6ndsRBQ2tByvAqcZUaZbU0Q9fa4NdL6szJ63mNAItZ8ahyf9qH0tx9L58CLc3HBAvT+9YJ36EebWluh64gdcX7dXvQ8+fEFtR4LRRERERERExoyBzmwUHx+PmPDI7NwFojfC0jYHzMzMsuVs8z4j0t/7Ux/wGUGmwtTv9deh3pwh6neb9TMRHxuHbd1nqveOrkXQ6o+pyFk4nwoi7n53PuKiY1RPy+oTusPZoyIsrCzx5HogDk5Ygvw1yqJYq5qq16X0Gryw3Ae3tx9Do8WjYeVgC4sc1iqQd/jjZfLQSne/XHs2x5VVvll6rDb5cqHqhG6wdrCD9/bPVUD24MTv1bJy/T1Vj1RZ59SXa3B19U71ufScrD1jAGydHGFubYXLK7bj4nIfnedNjt1tUDuYW1nCzNwMx+esUucgPbYF86CQR0XsH7Mo4TtLOsOpsgt2DpynXUd6e2aWnMN8VUqj1vT+qvfpsVm/qc8t7WzQ6NvRyFO+GGKjYrBryBcIDQhWy0p3aYQKA9rA3MoC0c8jcPijpQi/90jneWu4aBQcXQqr431+9wEOjF2cof106doEd3ef0rmuBMvD7j7Aw9PXM328poz5ADIlxpYX4P1LlD0M4VnC5wORadzriTHQmY0kyLnS5b9hl4iMVa9rK2BlZ5Mt3837jEh/7099wGcEmQpTv9dfBwlYlevbCls7TE4yLGhe91Lw6TIVcZEx8PxrBkq0q4Mb6/ej4rD2iA6PxOa2Cb0vK4/pgmoTe6gee7e2HU0y5K1FDivs6DsbMWERMDM3R7OfJqKUd/10ewXaFc4H53pu2Dvi6ySfVxjUFmW6N8PzOw9wYs4q9V2ZEfHwKU7OXa1z6Na4yGh1TI5lCsNr6xxcW7MbiAcaLx6DvcMXqB6RFrbWaLdpljbQl/y83d11Ejf+2qcdvrXd5s/wZ62hiIuKSbfX5W3fE2r/RO6yRfE88CHqzRmMfFVcEPkoFMc+WYGQszcydbwy/KtL50aq16mmJ6kEoZ2qumBDi/EIvRWMGpN6odLwDjg44XsUqFUOpTt4YGvHyWqfZdhYCYj+3WSMzvPmN2W5dihh2Yb0cNUEjtPi2r0pjsz4Rfeyns1xOYsD3KaA+QAyJcaWF+D9S5Q9DOFZwucDkWnc64kx0ElERERERJRFArYeRmx4lHr94ORV1ctQSK9H6dlXsm0d7VCjobfu696IuRlqfNwbBWuXB8zMYOuUC48vBqQb6JTA363txxAZ8kz72fHZqxB275HqDSoBtxYrP8K6+iNUEDUraIZKlYCmzIlpWyA3rBzsVNBRgp0aVvY26rOHp66l2IZ98YJotGgU7ArlQ3xMLKxz28OheIF0h42VwJ/q6fqC9Jp1qlYGx2f9pgKQRZpVQ/NfPsCa2sPUdl/V/WOXVZBTBB+7jApvt1Gvi7WuhTxuJeC1eZZ23Ry57dW8oLqU7ugBly6NVUBbfhJfr9RI8NTK3hZ3dpxIsUzmMJX/Fek9TEREREREZGoY6CQiIiIiIsoiieedlKFZzS0s1GsZ9keCcjL0aHrc33lLDfkqc2/K9mpN6wcLG6t0/861W1Mc/CBpz8CwoBDt64CtfqonovS+zKohTmMjE4K6Ij4u4XhlgKPIx6FJ5ppMS+PFo3Hs05Xw33xIve9xfrkasjctzvXc1ToyZ6eG9FiV45U5TMUd3xOwsLZUvUSf3QxCll9by/+u7bU/d6sAa3oK1C6PCgPbYovXJNUTVYYtrja+W7p/V7ZHM1z9c5c6x8m5dmumep5GPQ7N9DEREREREREZOvPs3gEiIiIiIiJDFPUsDFa57DK0boCPH9yGeKlhXIX8lh6OCdsJV70gNawdcyI8+JEKrNnmz42SXvXS3X4hj0owszTH3d2nk3xuVyiv9nX+6q7IkdcBT2/oDvrZOedFx70LdB9raNJ9TMuTa3cRHRquephqSM9W6amp67xZO9pre0qW7twQOfI4pPsdrj2b4eofO5ME/qS3aPSzcOSpUEK9d6paRvWIlXkwdSk/wBPVJ/XUfbyyjxk83oBtR9V+5yzilPCBmZkaOlfXeZNrGxMarobVlTk6y/Zpme72pSdnCa+6uudeNTNDme5NcWXVjgztKxERERERkbFhj04iIiIiIqKXcO67jWj1+xTEhkdiW/eZaa57ZuF6VBlrpYY3jY9P+OzsovV4fPm2mtfSY8FwFPesjYs/+eDCj1vQ5IdxaL9rPsKDQnB3b9LgpS6u0uPv951qiNrEPL4aDtv8jqoHYkxEFHYN/gLRz/6bUzR5oFOGn9UlcO8ZVHzXG947vsD9o5fSnFNSvmtHn1moPWOACu6aWZgjMuQp9gxbgCgd581vyjJ1vFFPwhC4/wxCb6cypO8LEjgs3rYO/m46NsWyfaMWov68d9WwsXFR0dg5aF6qc31KoPmZf0KANbnLK/5Fral94T7EC8fS6akZfPgCjs1cgabLxqserTIs8e1/j6vAa/LzduijpWr+z477Fqhgp1zbnM7/BaN1KdWhgeqB+0xHgLpwo8oq2CvfQ0REREREZIrM4uOTlYTpjYkOi8BKl94842T0snPyYt5nRMY1uXhW4zOCTIUp3+vGdp+X6dpEzbXpO2Bulm/Pfai36kl6fW3CvJv6wHPtdJz/YZMamjWrt9fm75nY3vNTxDzPmvlKX5UMsev97zz8Vr5flm2zf+Aa/FauL6Ke6g5uCz4fjOf5QJQWY7vXjS19JzIUhvAs4fOByDTu9cQ4dC0REREREZGBkF6Zed1Lwstnzitvq+bkPqg0oqOaT1OcW7xBr4KcQvat+qReqDKmyyttR3p4em//HPYlCmjn2tzafrLeBDlFXFwcYsIj1X7KNX4VBepUUNuRwHV8HNs2ExERERGR8eLQtURERERERAbi5oYD6icrHJ35q/rRZzsHfp4l24mNiMKGluOhz8LuPsQf1YZkybZkOF19P14iIiIiIqKswEAnGRQZeikjfDpNVXPjtPp9Mq7+uRv7Rn6TYp1qE7qrluH73/8OV1b+q4btkrmR0hN6Kxhrag97qf0nIiIydEyLiYiITBvzAkRERKaNeQHSNwx0kkHZM3xBqsscihdUwcuIh0/w5NodhAc/xqUV21Gud0v4bzqIW9uOatfNV8UFlYZ3wJ2dJ1SQUwQdOp/m9sv8rwkKN66Ce34Xs/ioiIiIDAfTYiIiItPGvAAREZFpY16A9A0DnWRQUpszyMLWGu02foa4mFjseme+CnKKI9N+RpHGVVBv7jsqQBn1OBQWOazQ8OvhiH4egf1jF2u3ERoQrH50ca7nDuf67nh0wR8Hxy95TUdHRESk/5gWExERmTbmBYiIiEwb8wKkb8yzeweIsoLHl+8hr3tJHPtkBYL2n9V+HvMimGmb3xF1Px2oPqv2QQ/kLlsMflOWIywoJN1t5yzihMbfj1WBUd+3P0dMeCQvGhEREdNiIiIiYrmciIiIWEdP2Yw9OsnguQ/1RqkODXBj/X6cW7IxxfLAfWdw8edtqDDAUwU23Qa3Q4DPEVz7c3e625ben02XjkeOPPbY0Xc2nt0Mek1HQUREZLiYFhMREZk25gWIiIhMG/MClJ3Yo5MMWqGGlVBjUi+EnPfH/rHfprresZm/4unNIFQc1h5RT8JwcELGhp+VIW+dqrjg5Bd/4o7viSzccyIiIuPAtJiIiMi0MS9ARERk2pgXoOxm9IHOBw8eYMKECShTpgxsbGxQrFgxjBo1Cs+fP8fAgQNhZmaGhQsXZvdu0kuwL5ofjb8bg+jQcOx8e26aQ8rGRscg+lm4ev3w9DWE30+YwzMtFQa2RZmuTVTvz9Pz1/AaGTMzM7gN8ULHvQvQ58Zv+N/R71Bzal9Y2ubI7j0jItJrTIvJWFQa0RFNvh+HzocWoX/gGnTxS70BHRER/Yd5AdJrLOsTEb12zAuQPjDqQOfJkydRqVIlfP755wgKCoKbmxuio6Px9ddfo1u3brhw4YJar2rVqtm9q5RJFrbWaLpsAqwdc2LPewvwzP9emutXGd0Z+SqVwsMzN1CkSVU11G1aCtatgFpT++LJ1TvYO+JrXh8jV3tGf9Se3h+PL9/GoY+X4eamg3Ab2BbNf/lAFYyIiCglpsVkTGSEEOcGFdU0BZGPnmX37hARGQTmBUjfsaxPRPR6MS9A+sLcmHtyvvXWWyrAOW7cOAQGBuL48ePq/Zw5c7B582YcOXJE9eisXLlydu8uZVL9eUNV4PLkvD/SHVI2b6VSqDyyE+7sOokt7T/Ck2t3UefTgbBxctS5vl2hvKpFf0xEFHzfnqt6jJLxyl22KCq83QY3Nx/CzoGf48rKf3Fk2s/wm/YzCnlUSjcoTkRkqpgWkzFZU2cYfncfgG3dZyLs3qPs3h0iIoPAvADpM5b1iYheP+YFSF8YbaBz5MiRuH37NoYPH4558+bBwcFBu0yGsq1SpQpiYmJQsmRJ5MqVK1v3lTLH7R0vuHRqiAAfP5z+am2a65pbW6Lh1yMQExaJ/eMWIzY8Ss3lKT1B680ZonP9pkvHwzZ/buwbvQhPrtzh5TFypTp6wMzcHOd/2Jzkcwl4RodFwKVzo2zbNyIifcW0mIxNaEBwdu8CEZFBYV6A9B3L+kRErxfzAqRPjDLQKUPSrl69Gk5OTpg1a5bOdWrUqKF+S8AzNW3atFE9PqdNm/ba9pUyx7m+O2p+3OfFkLLfpLt+tfe7IU/54qp3Xtjdh+qzYL+LuLB0K0q0rZOit17dWYORv5orTn+zDgFbDvPymACnqmUQFxuLByeuJPk8NjIaIWdvwqmqS7btGxGRPmJaTEREZNqYFyBDwLI+EdHrw7wA6RtLGKFVq1YhLi4OvXr1gr29vc51bG1t0wx0/vHHH2qOT9IftgVyo8n3Y2FuaaGGGS3Wumaq6z46HwBLW2u4D/XGrX+P4ervvkmWH5/1G4o2r66GsA3cdxYRD57AtWdzlO3ZHOH3H6uenKU7N0x1+wFb/BATHpmlx0fZw65gHkSGPENcVEyKZWFBIShYuzzMrSwRF51yORGRqWFaTEREZNqYFyBDwbI+EdHrwbwA6SOjDHT6+iYEtZo2bZrqOjKsbWqBzqdPn2L06NFqyNvevXu/8v7UrFlTzQ2anFW8Oaai9itv31Q4uhSBTb6EeTWrjOqc5rpnFq5Hcc9aan7NA+9/l2J5bETCELae66arIWxlbkYJaAkZtlaGu03LmlpDEXr7/isdjykp61oW0WZx2fLd6d1nFrY5EBsVrXOZ9OoUEjSPYqCTjFR23p/6gGlx5jAtNlymfK/zPidKG58PLJNnBvMChsvY7nWW9YmyhyE8S5j/f72YFzANZbPhXnd2dsbRo0df6m/N4uPj42FkihUrpgKZJ06cQNWqVVMsl7k5CxUqhAcPHuDatWsoXbp0kuUjRozAmTNnsGvXLjV07dSpU19p+NqiRYvizp2Ucz1am1lgScEOL71dIkPxzr31iIqPzZbvTu8+a+/7BWycHLG68qAUyxovGYtS3vXxS/Hu7NFJRis77099wLSYTIUp3+uZvc/b7/wSVjltsKb2sNe6X0T6gs8HlsnJNBjbvc6yPlH2MIRnCcv5RIZ5rxcpUkTbQTGzjLJH5/Pnz9Xv8PBwnctl/k4Jcjo4OKBUqVJJlknE+IcffsCxY8eyNBKdWusS6HcDGKIsUbhQ4Wzt0ZnWfRZ27xEcyxaFubVliuFr7ZzzIuLhEwY5yahl5/2pD5gWk6kw5Xud9zlR2vh84H8ImQZju9dZ1ifKHobwLGH+n8gw7/XU4mgmG+iUE/Lo0SMcP34c9erVS7IsMDAQ48ePV68rV66semxqxMbG4p133sHw4cPh7u6eZfuTWnfb6LAIrHR59aFxifTd5SuXYWVnky3fnd599uDkVRRpUhVO1VwRfPiC9nOLHFbIW7Ek7h367zMiY5Sd96c+YFpMpsKU73Xe50Rp4/OBZXIyDcZ2r7OsT5Q9DOFZwvw/kWnc64mZwwi1aNFC/Z4zZw4uX76s/fzIkSNq3k7pzSmSD2u7cOFC3Lt375WGqSUiw3Lj7wOIj4uD2+B2ST537dVCPcyvr9uTbftGRERERERERJnHsj4REZHpMMoenRMmTMBvv/2GW7duqZ6Z5cuXR0REBK5evYo2bdqgZMmS+Oeff1ClShXt30jwc/LkyZg3b56aw/Px48faZfK38j5XrlwwNzfK2DCRyXp8MQAXl/ugwsC2aLp0PG7vOA5H1yJwG9gWQQfO4fq6fdm9i0RERPSale7SCPZF86vXNvlywdzKEpVHd1bvQ2/fx/U1bPhERERkSFjWJyIiMh1GGegsWrQo9u7dq4ao3b17N27evAk3NzcsWbIEgwcPhouLi1ovcaBTJjl99uyZGrpWfhKTnqHyc+PGDRUkJSLj4jflJ4Teuo+yvVugaPPqiAh5igvLtuLE3NVAfHx27x4RERG9ZmV7NIdz/aRTV1Sf2EP9Vg2fGOgkIiIyOCzrExERmQajDHSKChUqYNOmTSk+Dw0NVYFP6ZlZsWJF7edlypTBzp07U6wvQ93269cP/fv3f6XJUIlIf8nQteeWbFQ/REREZHp8Ok/N7l0gIiKiLMayPhERkWkw2kBnas6dO4f4+HiULVsWdnZ22s/t7e3RpEkTnX8jvThTW0ZEREREREREREREREREb57JTTh55syZFMPWEhEREREREREREREREZFhYaAzHdL7c9q0acguVcd1hUUOK+17j6/eg9vgdpneTnHPWshf3TVD65bp2gQ9L/0ML5852s8qjeyEjnsXoN+dP9S2Emswfxg67vsa3v/OQ5u/P0G+KglzoIqak/ugy9HFaLZ8Qrrfm9e9JEq1b5Dks/6Ba2Cd67+ety973tLiuXY6Oh9ehCpjuqj3lnY2aLnqY3Q/tww9L/6cZF37YgXg9c8ceG//HO13fokm34+DtWNOtcyhREH1eR//VepY0iPX0TZ/7iT7XHtG/0weKeBczx1FmlbN8Lq9r69U+2mTL5f6rEz3Zmjv+wX63lqd6v+Wo2sR9XeJ989tiBc6HfhGbYuIyJjpS1qs2RdJc+W53XrNf/kTSXslDZa0WJ7Lzg0qGnRaXLhJFXUcmp+uJ77HW9vmatd3quaqjlXORes/p8LOOa/63MLGWq3f6+qvKfIrqZ1nxzKFk7zPyHnKyHlLjX3R/Oh7e7XaT01+oc5ng5Icb58bv6HCwLbaa9t2w6cqHU6+byW966PD7vkp8itERGSceQGnqmXQbtNneGvb5+iw5ytUHNb+v/XTKNcZYl5AVHyvg0rnJG2U45bjF7YF86gyu9RReO/4Ak1+fB85XpRvheSRepxfnqFrlPyaSJn5Zcq4kr6X69sqw+vLOZXrVaRZNW0exuPrEaqeQX6a/TRRe0z5a5TV5hHa75qPenOHwNw6YYC2AnUqqM9f9hoREZk6Q6h7T2uZhtQBSDkz8b4bW718mnX2icrZmh+pqzfWOvv06vPL9Wut8oqyjtSdaK5HZv4nDA0DnXqu6vsZfzCkpbhnbZU5zqigA+ewyXOi9n3gntPY3utT3Dt0IcW6AVv9sL7xaGxo8T7OfPMXmv4wTrvs6MxfcXLu6gx9p3qgdshYJWFWn7cjU3/Cqflr1Ou4mBicWbge27rOSLFe2L0QbG0/GRtajsffTceq9/Jd4pn/PfV5+L1HGfpO9dAs8N9D82U518/4Q1M8vXZX7WfEw6fq/cPT17DrnS9x/a99Otc3s7RA/c/fhf9WvySfn/9+Ew6M++4V956ISP/pS1pcYVBb5HErodKfv5uNw56hX2mXNVs2ASfnrVZpsTzTPRa8pyrMDDUtvrvrlEqrND8Pz9zA9bV7E1Y0M0OjRSPhN2U5/vIYids7TqD2jAFqUWxEVML6p65n6DvLdGsKxzJF8Koye95iQiPUfoacu6neH570o/ZYt/f8VDW0u7nhgFoWHvxIHavf1J9SbEfW+bfXZ6+8/0REZBh5gfqfv4PTX6/DxlbjscX7Y7gP9YZj2aLplusMMS8g+1G+f2tsavOhSh8vLN+KOp8NVMviY+PUen81HIUNzcch1P8eak3uo93OP12m4da2o6/lmqRGGkWX69MyU3+ztcNk3PE9oV7L31raWifk85qORcSDJ6g41FstCzl/ExvbfKCth7BxckT5/p5qWfDhC+pzIiIy3rr3tJYJKwc71PioF+7sSEhTjLVePq1licvZmh+pqzfWOvu0lhVrXQulOzXE5naTVN3RP91mIC46NtP/E4bG5Obo9PX1haGoN2eI+t1m/UyVkd/Wfaa2d12rP6YiZ+F8eHzpFna/Ox9x0TEqIFV9Qnc4e1SEhZUlnlwPxMEJS9RDtlirmijUqLJqMXJhuQ9ubz+GRotHw8rBFhY5rBG0/ywOf7xMurDq3JcHJ6+mup+JCxD3j19WvSrMLMzVPmeUtFSoOqEbrB3sVMuF+8ev4ODE79Wycv09VWsVWefUl2twdfVO9blDKWdVsWnr5AhzaytcXrEdF5f76Dxvcuxug9rB3MoSZuZmOD5nlToHusRFxajzIS1BdC3TMDM3h6VtDsSERSCzpJWKtEJt/N0YVSm7b/Qi9bltgTxo/vMHcChZEOHBj7Fz8BeIehyqlrm/642S3vVgbmmhCj0HJixBDkd7lOvbUp1vafnhv+WwKvi2WDEJOfI4qEruR+du4sD73yEmPFLnvjw67//i4HRfr6pj/4ebGw8iR257WDuyhSgRmRZ9SosrDm2Pf/43TX2PCL//WP3OkddBpZGBexOG5396PRBRT8JUD4GALYcNMi1OTNLLQh4VsX9MQlqZr3JptU0pGIpLv25D9YndVUEqNjI6w8fr2rM58lUpjVrT+6sWmsdm/aZtJdro29HIU74YYqNisGvIFwgNCFbLSndphAoD2sDcygLRzyNw+KOlqrCk67w1XDQKji6F1fE+v/sAB8Yu1l6ztLh0bYK7u09p1w0LDFE/uV9UZBMRkenmBeRjzYhClnY5VPk06lFohsp1hpYXkEY/UvaV45Qyt3WunCo9FFIelh8N2c8Kb7fJ9PFKXin5NXl2I0hdw7qzBqFArfLq9b5RC/Hw1DXtqBNSnpeythzXsU9WqDyJ9LLMWcRJnbfQOw/g238Oak7pC+d6buo4okLDVblcKjFTO16pX5BzEx8Xp/Ijjy4EqGWx4VHa9SysLWEpjdlS+R8hIiLjq3tPa5mo+9lAnP5qLUq0rWPw6X969fKpLcsqhlJnn9ayisO8cerLPxH9LEy9j3wRODV2JhfoNCTyQJGhT6SVX9TThH9Mkde9FHy6TEVcZAw8/5qBEu3q4Mb6/WrYmujwSGxu+6Far/KYLqg2sYfqJSDBSOk1cP6HzWqZVAbu6DtbFRgkYCfDopTyro8bf+9/pX2Wh5b0rMhMkFNISwVpTVC8TW34DvhvaDoRFxmtjkmGlvPaOgfX1uwG4oHGi8dg7/AFeHL1LixsrdFu0yztgzj5ebu76yRuvGjhIA/Ddps/w5+1hiYJXGaUPJS9tsxCzqL58eiCP3b0SzqsYEZIKxXXHs1UQqnpzaFa/lR3xcbWExD5KFQdn7TqlF6ypTp6qOPf4vWRKvRIRWvdWYOxo88sXPpluwpA+k35r6fHnmFfqW2IurMHo8LANqrFS2bJ0ICSWG/rNkNVAhMRmRp9SYut7G1hm99Rtcwr6VVPfXZuyUbVoy8y5Jnq9VfyrXqqYYoMdSoBNvti+Y0iLZZel7d9T2hbNdoXdULo7fva5THPI1TloRRGNAHJjLjy2w64dG6E8z9sQoDPkYTv6toETlVdsKHFeITeCkaNSb1QaXgHHJzwPQrUKofSHTywteNktc8yVJwERP9uMkbneZNemJoChWxDWrVqCotpce3eFEdm/JLh4yAiItPICwhp9CPrVJvYHTZ5c6l9y0gjGkPMC0gF3rnvN6GL37eqbBsbFQ2fjlNSrCfnTYKcAf8kpOWZIb0pk18TqYyU0R72j12MQx/+qI6h+gc9sL3HJ7AvXkCVi+V1dGg4HEo6q4rcNbWHqryCDGuXuHflmUXrcfRFmi7DAdaZOUCN3KDL5V+3o0DNcuh+ZiniYuPw4MQVXFi2Vbtczpdce6lgvf3vcVz86Z9MHy8REelvGv+ySrSri/i4ePX9Lxvo1Kf0/1VI4yivrbNVcFFGoDy9YJ2qRzfGOvu05HYtquqFqoz9n/o/vPbnblxYugXGjoFOAxSw9bC2RZ+06JDMtZDWFdLyouSLh5rM2RB667+KwCTMzVDj494oWLu8GgLO1ikXHl8MeKWHbenODVUl61YdhY9XcX1dwlB18uCMi4lVXcelS770bJAHi4aVvY36TNPSMjH74gXRaNEo2BXKh/iYWFjntodD8QJqm5klLXik8CIBzzqfvq0ebGe//RtZ4c7Ok9qHXfCxS8hTvrj2gSoVr2/9kxBUlQd2qszM1PyZRZvXgLmlOaxy2eH+kcuZ3hdJpOrOHoRdg7542cMhIjJabzotlpajku5IC/7N7T5UhYO2Gz/Fk6t3VEXgDuk18HFvVBrRCY8v38I9v4uIj3n5Hh36lBZL4E+1fH1D7h+7rIKcIvjYZW0PEQkyy9DBXptnadeV0Q40QwQnV7qjB1y6NFYFC/mRgHR6JHgqQe3kQw4REZH+yY5yeaXhHXHss5WqslCCbm3WzcCDU9fw5PJtvG5vOi8gQ8FKhe3aesPV6AnlB3ii8ZIxaiqZxKTMGvkkVFuxnBWe3QxSgUYRfPQS3N99S70u0rQacpVyRpu//hsyTyo1pSenLoUbVVb5CEnbpQeL5BtSU7hxFVURvrrKIFVhLXPEVZvQDSfm/K6WSyMvmaJAjTyxaKQ6N1ldWU5ERPpd956czCNZZXRn+HSe+tounb7Vy6cmLPgR/qg2RAVtZftNvhuj0m9jrLNPj5mlhTq/0kDMOndOeK6boYbvvf1v+iNqGTIGOg1Q4mHZpOekuYWFem1mZqYqAmW4s/S4v/OW6loulaWyvVrT+sHC5uXHIy/pXR9Vx3bFP12nJxlCJivERkYlKUTI8ZpJt+vHoRmei6Lx4tE49ulK+G8+pN73OL9cDRvwKiTgefX3nag/790se2gmOVa5tpb/XVtpJXJ5xb/pbqN0Jw8UalAJPp2mqFamFQa2VUP+ZZZDCWfYF3GC59pp6r0MFSSJtLWjvRq6h4jIlL3ptFiGRJFn+rW1e7SVXcFHLsGpahkV6JSfxD0EZNJ5GWLH0NNi6VUh68icnRqhtx8kGarGMqeNKmxmdL6NTF/bRGmxtIQ8/mKI27QUqF1epb9bvCapgpYMY1RtfLd0/65sj2a4+ueuTLc6JSIi488LyFD10tNi99D56r2MYiA9J2R41TcR6HzTeQHpofLoYoA2fZeyd93PBqmGX5ph/Ot88jZyFnZK6H2ShUO5ylB1qeUF7u4+jT3vLUjxNzmd8yV9X8QJdT8diE1tPlCVi3kqlEgSIE2ubO+WuP7XXu3/lVQsVxrZKcV60jNIehXJ3FsMdBIRmU7duy4yrYuMbOT97zxtXkHKnjny5cKJ2auMul4+OekdqhkFSupPrvzui9IdGxplnX16nt95oObulOslDa7v7DiO/DVcjT7QmUaImfRB1LMwFd3PiAAfP9UqQHriCfmtmc8p6lm4am2hIfN6yDB38qCV1h+aYfBehvTirD6xB/7pNl3dSGmR+Ts77k1ZKFD7GJp0H9Py5Npd9UCQ4ew0pHWNtNjQdd4kOKfpnSE9T2Us7JeRs6iT9vxKK4wSb9VDyIt5M3SpPqmnanmqi7ommbi20u1fc3zSMiNvxVIJ2wkNS3Zt7RER8lSdH6n8LdOtCV6GtDL6veJArKk9TP1IC9mrv/syyEnpBiX6B65R49MnV7CeG3pe+hldT/6gCvqJydj38ncyB0JqZJgoWUeGX0jO7R0v9LvzB9r8/QmsM3hfERlaWnx9/T7Vk0D9bW57OFVLCHIKaVWp4dqrBWLCIhG4L2HOTkNOi117NsPVP3YmCfw9PH1dpYPO9d3V+3J9WuHW9qOpzs8p6bCkxzqPV/Yxg8cbsO2o2m9trw0zM+3zKPl5k2sbExquWnxKhWzZPi3T3b709ijhVRdXVhnOfPJERKZCH/ICUY+fqyCXc4OK2spMp+plVLktswwhL/As4J4K4koPRlG0ZQ01koUmyFl75ttwKFUIvm/P1X6WGgkQe3w9QvfxJrsmabmz66SaZyxxWUYanWnL5YmOVbYpPV+kh4ko/7buegHt8frfQ5HGVbTvi7aojseXArTnVPI+QvIVcjwhF17MzUWkh1gvQIZEH9L4l3V7x3GsrjxIW3frv+mQGno1tSCnIaT/L0vmENWmldaWKNG2Lh6evWGUdfbpuf6X1B1VVa9lBCqpOwk5Z/z5Bvbo1HPnvtuIVr9PQWx4pHZC5NTIeM5VxlqpIdU0jRnPLlqPx5dvq/GzPRYMV92pL/7kgws/bkGTH8ah/a75CA8Kwd29p9PcduXRnVVFojw06pcfijqfDsKGVuPV3FPS9Vwm4W2+fKJ2fenZqenOnfyBKpl9XQL3nkHFd73hveML3D96Kc15rKTlhIx1LZMeSwIj3cIjQ55iz7AFiNJx3vymLFPHG/UkDIH7zySZ20sX2Qc5Vpkw+n/HliDowFnsHfGNKtBU/yChslSGnnl45gb8Pl6a6nbyupXEw1PXdS6TsbHrf/6u2kfNxMapkdackgh4rpmmfWheXeWLkLM3ELDFDy6dG6vJomVi4/M/bkHx1rVUwiUPz3uHL6Q5SbPMRybjyUtXdvn/kADVjn6z1baJskrRFjXQ5Puxag6hbV0ThkzQyJHHXv3vPb0RiEIN3NX/a3r3aGLVJnRXk4Xf2XkCvgM/1w4vQmRsafHxz1aiwfz3UL5/a+13yTA6ml4A0rJfWhM+vnJbVfqlxlDSYikQFG9bB383HZvsi+Oxd/jXqDf3HTUkrPT02DPi61S3IwXPZ/665+6UVpe1pvaF+xAvHEunp2bw4Qs4NnMFmi4br1qxSuFJ5siSoXmSn7dDHy1V83923LdA5Yfk2uZ0zpvm9kt1aKCCuM9uBCX5PJdLYbT+Yyosba1VIUXyJae/XodLP3NuLiIiU8oLSKOfXe98iZpT+iSkQ1YWqjGqDLme2XKdIeQFArYc1g4FJ5XE0ohL05NS5s12G9RW5Xk0Q8o/uxWMnW9/rnNbuUoVQvSz/+ZeSyz5NUmeDicf0lbm1qo3dwgsbXOovEDImRtqv6Tx2eNLt9F+55eqrOPbf47qcdlh13yVF5CK0LSc/OIP1P/8HfW/ICSoe3DCEvVaentIrw8531IXINfo9Pw1aW6PSB+xXoD0kT6k8enVvae1LDMMIf1Pq14+rWUyDYyMoqRJK4P2ncHpBWuNts4+rWXnl2xU9SUdds9X/6fSk9Z/00EYO7P4+Cwc34MyJTosAitdeuvdWZMbRdfkw1mxPfeh3qo1y/W1CeN76wPPtdNx/odNCPA5kiXb6+L3rTrWRxcC1OTKm2SCaj25zaRVXe0Z/TM8tEBWba/XtRWwetES903T1/vMWMn/hOe66Tgy/Rec+26D+kwm5m64YLhq8SUZnOTDS1YY1Ba1p/dXwzrJnIMy5MPJeX/o7NFZ9f2u2Og5UTvmvwxXJYV+qUSQjE16rblJv+5PfaCvzwimxVmXtrf5e6Ya1jfmeQT0gRRkZGih38r3e6PbM+V7XV/vcyJ9weeDfj4fmBd4NTLHZci5myoY3Gz5BPhNXp6pxpSvm4xU81u5voh6GvbGtmds9zrTd/3FegHjZgjPEn1/PmR1Gp/R7zKFevm0mFKdfVb8/xnCvZ4Yh66lFGIiopDXvSS8fBIm0X0VNSf3QaURHdW43eLc4g169TAVsm/VJ/VSPcJehUOJgqqFhrTckGCLtLiVwI2+PDBFbHSMamUi+ymtX16FtNipO3sQIkKeZdn+kfGR4RsaLRypej9v7ThF5xx6rj2aI+jAOdWTSXpHqaEvzGTE/9RJa7GG34xQQc5LK7Zj99CvGOQko8K0+OVIj0dJ4+xLFNAOZbu1/WS9CXKKuLg4xIRHqv2U/NarkDnSm//ygeotT0RExoV5gZfXes00NW1GdFikei8VefoU5BRS0ey5bgaKNEuYluBlSQ8WyVPI9uLj9KfugSgx1gsQvb40Pi2mVC+fFlOrs8/M/4QxYY/ObKTvrUuIsgp7dJpmy00ZTqvGpF64u/eMGrpJ5hRKTua3e8tnDvaO/AbX/tytJk1v/vMHqudn8sndNT06t7T/GBWHtkdxz1pq2JBjn654g0dofAythVZWY1pMpsKU73Xe50Rp4/OBZXIyDcZ2rzN911+sFzBuhvAs4fOByDTu9cQ4RycREWW5cv1aIVdJZ/hvPYzd785HXJTuIWVdezRD9PNw+G8+rN7f9j2B8AdP1OfJA50aMueBbPvoJyvUXAhEREREREREpF9YL0BERG8Kh64lIqIsZ1cwj/r97Oa9VIOcMsRk6Q4eKsip6e0ZHxOrJvKWybmtc9unum0ZauLp9bu8ckRERERERER6iPUCRET0prBHZzaytM2hugATmcL/OpmWM9/8hYL13FFxqDdgBhyd/kuKdUp41YW1Y04EHTwPh5LO2s/vHToP9yFecOncCBeWbknxd/vHfKvGlG/83Rg1N2fAloTeoEQvg2kxmQpTTot5nxOlf4+YKj4fyJSY8r1O2YP1ApRdmL4TmV6+gYHObGRmZmZQ4xwTEWVUTHgUdvSdpebbrPiuN8zMzXFk6k9J1inbo7n67TF/mM5tuHZvqjPQ+fRmEHw6T4Xn2ulo8iLY6b/5EC8OvRSmxUTGj/c5EfH5QET05rFegLIL8/9EpoeBTiIiei1iI6Kwo99sNP9pouqhaWYG+E1JCHY6lCiIgnUr4NraPQjY6pfibws1rITy/VojX+XSeHj6eorlz/zvYWunKfBcNx2NF4/G7mEL4L/pIK8kERERERERkZ5gvQAREb0JDHQSEdHrLdT0n4NmP02E22AvaVYHv8nLUaZHM9XL89ySjQg5cyPF3z08dU0FOst0b6Yz0ClCA4Lh0ymhZ2fjb0dhjxlwcyODnURERERERET6gvUCRET0upm/9m8gIiKYeqHGt99s3Nl1Em6D2qHOJ2+jTNcmeBZwT2eQU4Tevo8Hp66hdEcPWOSwSnXbmmBn2L1HaPTtaJRq3+A1HgkRERERERERZRbrBYiI6HVioJOIiF672Mho+Pafgzs7T6DCwLbIWSgf/LccTvNvZN7NHLntUbxtnTTXC70VDJ+OUxAW+BANF45EqY4eWbz3RERERERERPQqWC9ARESvi1l8fHz8a9s6EVE2iw6LwEqX3tm9G0R6q9e1FbCys8nu3SAiIiIiIsowlvWJsgfrEIhIH7FHJxEREREREREREREREREZHAY6iYiIiIiIiIiIiIiIiMjgMNBJRERERERERERERERERAaHgU4iIiIiIiIiIiIiIiIiMjgMdBIRERERERERERERERGRwWGgk4iIiIiIiIiIiIiIiIgMDgOdRERERERERERERERERGRwGOgkIiIiIiIiIiIiIiIiIoPDQCcRERERERERERERERERGRwGOomIiIiIiIiIiIiIiIjI4DDQSUREREREREREREREREQGh4FOIqKXVHVcV1jksNK+9/jqPbgNbpfp7RT3rIX81V0ztG6Zrk3Q89LP8PKZk2JfOu77Gu19v0DrNdMytKzholHoduoH1J7RP93vda7njiJNq2rf2xfNj54Xf0ZmWeeyQ6XhHTK8fhe/b9Fx7wK49myu/Sx3+eLwXDsdHfZ8pX6Kt62jPi/TrSm8t3+u/el+bhmaLh2vljmUKKg+6+O/CnndS6b7vXIdbfPnTnIOM3Ke0jtv6a3b+/pKtZ82+XJpPy/Xr7U6Trl+3v/O0/7POZRyVtdT1pdzVHNqX8DMLGH/h3ih04Fv1DIiIiIiIiIyzPJ+pZGdVHmv350/1LYSe9llhlgXIN8r9QByfpKXc9Na5tygItptmYUOu+ej/a75qPFxb2252VDrCcp0b6bqB/reWp3ifzKtZdU+6JFQr/CizqRU+wbaZTUn90GXo4vRbPmETB8PEZE+sMzuHSAiMlRV3++K8z9sQmxk9Cttp7hnbYScu4n7x69kaP2gA+fgO2Cu9n2FQW2Rx60E/m46FnHRMUky3mkt2/veApUxt3a0S/c7neu7q/Xu7DyJV2GdKycqDe+IMwvXZ/hvdr87X50fYWFrjeY/TcTekd8g2O8izMzNYZ3HXi27unqn+tFov/NLXF+3R71+5n8PG1qOV4WljJACgZzn8PuP8Soye96eXrur9lOjWOtaKN2pITa3m4ToZ2HIkS8X4qJj1bJaU/oiYKsfLizdogrgXltnI7DpGdzxPYHz329CyJkbL1XoIiIiIiIiMnX6Ut4P3HMaN9bvg8f891Ks+7LLDLEuICo0HMfnrIK1gx2qf9AjyXppLnvyXG0nNCBYlZtb/TEFZf7XGFf/2GWw9QQPT1/Drne+RKURnVKsm9ayc9/+jROzV6nXds55VYPqu3tPIzLkGY7O/BWPL91C8Ta1X+nYiIiyCwOd2Sg+Ph4x4ZHZuQtEb4SlbQ6YvWgxZyzqzRmifrdZPxPxsXHY1n2meu/oWgSt/piKnIXzqUyiZKglwGhmaYHqE7rD2aMiLKws8eR6IA5OWIL8NcqiWKuaKNSosmq9eWG5D25vP4ZGi0fDysEWFjmsEbT/LA5/vEweGjr3peLQ9vjnf9PU94jEme60lmWUtGws17clzCzMVWtC/y2Hce3P3drCX9GWNVSBQvZRgmwiXxUX1Py4N6wc7FQw8vTX6+C/6SDqzR0CS3sb1XowLjYOmzwnwu0dL5Tu4AFzSwvExcSq7dw/dlnnvpTu2FAtkyCniI+LQ+TDpynWc6rmChsnRwT8czTTx1tlTBfYFsyDxt+NQWxEFPaNXqQ+ty2QB81//gAOJQsiPPgxdg7+AlGPQ9Uy93e9UdK7njqGiAdPcGDCEuRwtE9x3uQ8tFgxCTnyOMDCxhqPzt3Egfe/SzUtqDjMG6e+/FMFOUXiY5U0xCpXQsFUtmVuZan2izKO6TCZEmNLi3n/EmUPQ3iW8PlAZBr3uqmU9x+cvJrqfr7sMkOsC5Cyt9QDyH5kZlnI2Rva1xK0Djl7E/bFChh0PcGj8/4JL+LiMrUs6mlCvYKwzGmj7nFjvc+ZFyAyvbwAA53ZSBKslS69s3MXiN6IXtdWwMrOxqjO9sGJ36Nc31bY2mFyksxiXvdS8OkyFXGRMfD8awZKtKuDG+v3o+Kw9ogOj8Tmth+q9SqP6YJqE3vg8KQfcWvbUdVK8fwPm9UyaWW4o+9sxIRFqIJBs58mopR3fdz4e3+K/bCyt4VtfkfV86+kVz312bklG3Fzw4E0l2WG7NulX7arFod+U37SDg1j7ZgTjy744+S8P9SwK7VnDMBfvifUkDT1P38X//b+VGX0c+R1wFvb5uL+0Us4OOF7Nfxq4taI19bswfklm9RrGdLHY8Fw/NVwlM59yV22KGKjotH8lw+Rs1BehFwIwJHpP6cIdrr2bIZra3YjPiah92NmnJq/Bq49miVpPSqtcGXfNraegMhHoWi8eAzK9WmJM9/8hVIdPeBYpjC2eH2kAq+luzRC3VmDsaPPrBTnTewZ9pXahqg7ezAqDGyTaqvW3K5FVUGxytj/qf8LKVRKD07hN2U5mv/8Icr3baWuxamv1iYpxFH6mA6TKTG2tJj3L1H2MIRnCZ8PRKZxr5tCeT+76VNdQFaQEa5KetXFv31nG3Q9wauoMLAtyvdvDbvC+XBg3GJE6Gg4bgyYFyAyvbwAA51ERFkoYOthxIZHaVtQOpR0Vq9lTgxp6VjyxXyS5taWCL11X/dGzM3UvBEFa5dXc0fYOuXC44sBOgs+0nJUevJZ2lhjc7sPVaGj7cZP8eTqHTy/+zDVZdpWfq+YcfTffFi9Dj56WXus+WuWg0OJAmi58qMk6+dyKYxQ/3sptpOvYilUHtVJtV6UwKRjmSKqFaO0kkxxvBYWKNywMjZ7TUJYUAiqT+qJerMHY9fgL5K0OJK5JmSdrCTDymgKHsHHLiFP+eLawo1TVRe89U/CPCrSMjNVZmZq/syizWvA3NJc9ci8f+Ry6qtbWsCheAH4dJwC69w54bluhhpe5/a/x1C+n6caikgKUTJXR+u109X/nAxRRERERERERIZd3tdX2VEX8KqkIXjzXz7AmW//xsNT1wy6nuBVSMNp+ZEpjhotHIm7u09p95+IyJAx0ElElIUSz98hQ9yYW1io19LVX4ZhkUxketzfeQu2To4qOCnbqzWtHyxsrHSuK0OiRIeG49rahLkoQ2/fR/CRS3CqWkYFM9Na9qriomKSHqvlf8f6+NJtbPFOWrgREmxNTAKxTZe+D5/O01RhQwofva78CgtrS52Fm+d3HiDwwDkV5BTX1+xBy1WTk6xT8q16ahihJ5dvIyvFRkalerwSbLy84t90t1G6kwcKNagEn05T1LWR1pSFPCqmur4c7/W/9iUM0RvyDHd2HEf+Gq4Jgc4BrbGuwUi1nrTClGUy1wcDnURERERERIZf3tdX2VEX8CpkmNaWv32MgH+OaHuQGnI9QVaQOiGpV5E6BE3QmojIkKXRnISIiNIS9SxMO0diegJ8/FQLPQtba/VefsswrAnbCVfzV2jIMDDhwY9UoSdhaJWEYWdTc339PhRpWi3hb3Pbw6naf4HMtJYlJwFQmW9E57GGhiXZx7QEH70E++IFUKhhpSRze0hBJio0XDufpDoPOazUawnoCRmeJS03Nx5QrSKlECSKNK+OkPMJw8ZouPZsjiurfNPdT+kNWn6Ap85l6ppk4trKsEZyfjW9MPNWLKXzvFk72iMi5KkqvEhhq0y3JmluW4KcMhSQkPMmhZCQcwnX75l/sPbaSi9W5wYVVUtgIiIiIiIiMo7y/ptgCHUBL8vSLiHIeWfnCZz+aq1R1BO8LMcX/5PCoURBtT+Ps7iBOBFRdmGPTiKil3Tuu41o9fsUxIZHYlv3mWmuK3MrVBlrBa/NsxAfn/DZ2UXrVaZS5pKUuShkaJOLP/ngwo9b0OSHcWi/az7Cg0Jwd2/aQ5Ee/2wlGsx/T82zoPkuGUYnvWXJ2RfLn2rLyYAtfnDp3Bje2z+H/5bDaq7I1EQ9eY5/+3yGWlP6otbUfjC3slCFF98Bc1UPVDne9r5fIPp5BDZ5TsSJOb/Da+tslbGX+U3SIts5/fU6NQRvfFy8aoF4YPx3SYbEkYJURob9yetWEg9PXde5TIZykblF5NruG70oze1cX7dXDbXjuWaatgBzdZWvmi8z+Xk7/+MWFG9dCx33LlDHe+/whRQtWxM7v2Qj6s19Bx12z1f/N/6bD8F/00G1bN+ohajz6UC4DWqrhka69c/RdM8fERERERERGU55v/LozijXp5WarqR++aGo8+kgbGg1HpEPn770MkOsC5Dgcad938Aih6UKEv7v2BJcW7sbxz/7Lc1lboPbIn+1MrCyy4ESL4YWvrnpIE4vWGew9QRlujZRc8DK9Dbyf+X+rjd29Juttp3Wspof91GB6PjoGMTFxuHQpKV4cuVOmsdBRGQozOLjNUkwvWnRYRFY6dKbJ56MXnZOXmxs95lkWou3qa0KClmh6riusHa0g9+Un1Dns0Fqzsdgv4vQF138vlXHGnLuZpZu79GFALTb/Bk2tf0Q2pJoNnOu547aM/pjQ8vxb3R7hja5eFYytucDkSnd67x/ibKHITxL+HwgenW8142jvJ8WU6sLyMh3mUI9QVb8//H5QGQaehlAvj8xDl1LRGRAYiKiVI9FL5+ECe1fRcNFo1C6c0M1/Io4POlHvSrYaOaebLhwpBqO9lXIsCzSWlJaUcZFx6g5Lze1+UBvCi8iNjpGtfiU/ZRWt69Chk2qO3sQIkKeZdn+ERERERERkWGU99NiSnUBaTG1eoK01JzcB5VGdETk49DX9h1ERK8Te3RmI7Y0JVPBHp1E+svQWmhlJabDZEqM7V7n/UuUPQzhWcLnA9Gr471ORHw+EJm2XgaQ70+Mc3SSQekfuCZD6/l0mqrmrGv1+2Rc/XM39o38JsU61SZ0R5UxXbD//e9wZeW/aogGmTchPaG3grGm9rCX2n8iIiJDx7SYiIjItDEvQEREZNqYFyB9w0AnGZQ9wxekusyheEEVvIx4+ARPrt1BePBjXFqxHeV6t4T/poO4te2odt18VVxQaXgH3Nl5QgU5RdCh82luv8z/mqBw4yq4p2fDeRAREb1JTIuJiIhMG/MCREREpo15AdI3DHSSQbm+dq/Ozy1srdFu42eIi4nFrnfmqyCnODLtZxRpXAX15r6jApRRj0NhkcMKDb8ejujnEdg/drF2G6EBweontcm/neu749EFfxwcv+Q1HR0REZH+Y1pMRERk2pgXICIiMm3MC5C+Mc/uHSDKCh5fvqcmbD/2yQoE7T+r/TzmRTDTNr8j6n46UH1W7YMeyF22GPymLEdYUEi6285ZxAmNvx+rAqO+b3+OmPBIXjQiIiKmxURERMRyOREREbGOnrIZe3SSwXMf6o1SHRrgxvr9OLdkY4rlgfvO4OLP21BhgKcKbLoNbocAnyO49ufudLctvT+bLh2PHHnssaPvbDy7GfSajoKIiMhwMS0mIiIybcwLEBERmTbmBSg7sUcnGbRCDSuhxqReCDnvj/1jv011vWMzf8XTm0GoOKw9op6E4eCEjA0/K0PeOlVxwckv/sQd3xNZuOdERETGgWkxERGRaWNegIiIyLQxL0DZzSQCnQ8ePMCECRNQpkwZ2NjYoFixYhg1ahSeP3+OgQMHwszMDAsXLszu3aRMsi+aH42/G4Po0HDsfHtumkPKxkbHIPpZuHr98PQ1hN9PmMMzLRUGtkWZrk1U78/T89fw+hixSiM6osn349D50CL0D1yDLn6pB82JiOg/TItJr5mZwW2IFzruXYA+N37D/45+h5pT+8LSNkd27xkRkdFgXoD0Gcv6RESvH/MCpA+MPtB58uRJVKpUCZ9//jmCgoLg5uaG6OhofP311+jWrRsuXLig1qtatWp27yplgoWtNZoumwBrx5zY894CPPO/l+b6VUZ3Rr5KpfDwzA0UaVJVDXWbloJ1K6DW1L54cvUO9o74mtfGyEmvYOcGFdXQxJGPnmX37hARGQSmxaTvas/oj9rT++Px5ds49PEy3Nx0EG4D26L5Lx+oICgREb0a5gVI37GsT0T0ejEvQPrC3Nh7cr711lsqwDlu3DgEBgbi+PHj6v2cOXOwefNmHDlyRPXorFy5cnbvLmVC/XlDVeDy5Lw/0h1SNm+lUqg8shPu7DqJLe0/wpNrd1Hn04GwcXLUub5dobyqd19MRBR8356reoyScVtTZxh+dx+Abd1nIuzeo+zeHSIig8C0mPRZ7rJFUeHtNri5+RB2DvwcV1b+iyPTfobftJ9RyKNSuo3eiIgofcwLkL5jWZ+I6PViXoD0hVEHOkeOHInbt29j+PDhmDdvHhwcHLTLZCjbKlWqICYmBiVLlkSuXLmydV8p49ze8YJLp4YI8PHD6a/WprmuubUlGn49AjFhkdg/bjFiw6PUXJ7SE7TenCE612+6dDxs8+fGvtGL8OTKHV4aExAaEJzdu0BEZFCYFpO+K9XRA2bm5jj/w+Ykn0vAMzosAi6dG2XbvhERGQPmBcgQsKxPRPT6MC9A+sRoA50yJO3q1avh5OSEWbNm6VynRo0a6rcEPDV27dqlengm/+HQtvrBub47an7c58WQst+ku36197shT/niqvV+2N2H6rNgv4u4sHQrSrStk6I1f91Zg5G/mitOf7MOAVsOv7bjICIiMlRMi8kQOFUtg7jYWDw4cSXJ57GR0Qg5exNOVV2ybd+IiAwd8wJERESmjXkB0jeWMFKrVq1CXFwcevXqBXt7e53r2Nrapgh0aixatAjVq1fXvs+ZM+dr3FvKCNsCudHk+7Ewt7RQw5AVa10z1XUfnQ+Apa013Id649a/x3D1d98ky4/P+g1Fm1dXQ9gG7juLiAdP4NqzOcr2bI7w+49VT87SnRumuv2ALX6ICY/khSMiIpPCtJgMhV3BPIgMeYa4qJgUy8KCQlCwdnmYW1kiLjrlciIiSh3zAkRERKaNeQHSR0Yb6PT1TQhsNW3aNNV1ZFjb1AKdbm5uqFu37mvcQ8osR5cisMmXMK9mlVGd01z3zML1KO5ZS82veeD971Isj41IGMLWc910NYStzN0kFV5Chq2V4W7TsqbWUITevs+LSEREJoVpMRkKC9sciI2K1rlMenUKaRQXxUAnEVGmMC9ARERk2pgXIH1kFh8fHw8jVKxYMRXIPHHihM5hZ2VuzkKFCuHBgwe4du0aSpcurR26VoKjO3fuRJMmTbJkX2rWrImgoKAUn1vFm2NqXO0s+Q4ifTbd3A/RZnHZ8t2Zvc/a7/wSVjltsKb2sNe6X0T6Ijvvz+zGdJhMibHd6+ndv+19v4CNkyNWVx6UYlnjJWNRyrs+finenT06iYzwWcL0nejVGeO9zrI+UdYwxucDEenHve7s7IyjR4++1N8abY/O58+fq9/h4eE6l8v8nRLkdHBwQKlSpVIs79atm1qeL18+eHt7Y/bs2Wq+z5chQc47d+6k+NzazAIo+FKbJDIodwPvIio+Nlu+m/cZkf7en9mNzwcyJcZ2r6d3/4bdewTHskVhbm2ZYvhaO+e8iHj4hEFOIiN9ljB9J3p1vNeJiM8HItN21wDy/SYR6JTo76NHj3D8+HHUq1cvybLAwECMHz9eva5cuTLMzMy0yxwdHdWyRo0aqbk9Dx48iFmzZuHQoUMqmmxjY/NS+5Ja6xLodwMYoixRuFDhbO3RyfuMSD/vz+zG5wOZEmO719O7fx+cvIoiTarCqZorgg9f0H5ukcMKeSuWxL1D/31GRMb1LGH6TvTqeK8TEZ8PRKatcDbk+1OLo5l0oLNFixa4cOEC5syZg5YtW6Js2bLq8yNHjqBPnz6qt6ZIPqxttWrV1I+GDF9bsWJF1atz1apVGDBgQKb3JbXuttFhEVjp0jvT2yMyNJevXIaVXeYbCWQF3mdE+nt/Zjc+H8iUGNu9nt79e+PvA6g8shPcBrdLEuh07dVCnYfr6/a8oT0lMi6G8Cxh+k706nivExGfD0Sm7bIB5PtNItA5YcIE/Pbbb7h16xbc3d1Rvnx5RERE4OrVq2jTpg1KliyJf/75B1WqVEl3W15eXsiZM6cKWL5MoJOI9FvpLo1gXzS/em2TLxfMrSxReXRn9T709n1cX8PKUCIiIkPy+GIALi73QYWBbdF06Xjc3nEcjq5F4DawLYIOnMP1dfuyexeJiIjoNWNZn4iIyDQYbaCzaNGi2Lt3rxqGdvfu3bh58ybc3NywZMkSDB48GC4uLmq9jAQ6NRIPcUtExqNsj+Zwru+e5LPqE3uo36oylIFOIiIig+M35SeE3rqPsr1boGjz6ogIeYoLy7bixNzVQHx8du8eERERvWYs6xMREZkGow10igoVKmDTpk0pPg8NDRZ70cIAAOWjSURBVFWBT3NzczUsbXo2bNiA58+fo3bt2q9pT4koO/l0nsoLQEREZGTi4+JwbslG9UNERESmh2V9IiIi02DUgc7UnDt3DvHx8WreTjs7uyTLevfujdKlS6N69eqwt7fHwYMHMXfuXDWXZ/fu3bNtn4mIiIiIiIiIiIiIiIjIxAOdZ86cSXXYWpnPU+b2/OqrrxAeHq6GwJWhbqdOnQpra+ts2FsiIiIiIiIiIiIiIiIiSo6BzmQ+/PBD9UNERERERERERERERERE+sscJiitHp36puq4rrDIYaV97/HVe3Ab3C7T2ynuWQv5q7tmaN0yXZug56Wf4eUzR/tZpZGd0HHvAvS784faVmKea6ej8+FF8N7+ufpxG+KlXdZw0Sh0O/UDas/on+73OtdzR5GmVbXv7YvmR8+LPyOzrHPZodLwDhlev4vft+rYXHs2136vHJOcAzme5HKXL66Wd9jzlfop3raO+rxAnQpq/f6Ba9Q+GMO1bbd5lva6tt/5pTq2PBVKqGU1J/dBl6OL0Wz5hEzvMxGRIdGX53Vaz+TqH/ZUz3Lvf+epvync5L88jqTLnQ58ozNNS07SwHJ9W6VIJ/O6l8zEkb743sHtYJs/d4bWlXP6v+NLUG/OEG1aqzlW+ZF96HF+uXb9lr9PhveOL9SyNutnIm/FUtplrddMU+tm5BolvyaSF8nIecrIeUuLXLv2vl+gSLNq6n3lUZ2SHG/Py7+g1rR+Kf6u9Z9Tk+SNHEoUVOv38V/1UteIiMiUGUJ5zCZfLrT87SN02v+NSvsL1q1gsuVw1x7N1HnodHAh6s97F2aWFiZZRk/rf4LldyKizDGE5/6bqLeVsmSp9g2SfJbRtDO9c5oWTV6mypgu6r2lnQ1arvoY3c8tS5EXSWuZqPheB3TYPV+di3abPoNT1TLqcwsba/VZr6u/pjh/qV0fxzKFk7x/mbpvXec0NZIH6nt7tdpPTbm+aPPq8PpnDvrcXJUiP5fWMhsnRzRdOl7Vl0ieKPH/c0nv+uocvUw+zxCYZI9OX19fGIqq73fF+R82ITYy+pW2U9yzNkLO3cT941cytH7QgXPwHTBX+z5wz2ncWL8PHvPf07n+kak/IcDnSIrP9763QD3grB3TfzA613dX693ZeRKvwjpXTlQa3hFnFq7P8N/sfne+Oj8iKjQcx+esgrWDHap/0CPJeha21mj+00TsHfkNgv0uwszcHNZ57NWy4MMXsKHleJUQGMu13dzuv97NJdrVRdVx/8OjC/7q/dGZv+LxpVso3qb2K+0/EZG+05fndVrP5HuHL+DU/DWIjYhCHrcSaPPXDPxRdQhiwiNx/vtNCDlzI0OVnfbFCqBcn5a49Ms2vCrJUMsxhN9/nKH1zy3egPM/bFavH18MUGmqRp1PBwLx8dr3u4d8gainYeq1pENSIN3Q4n31/p8u09T713FNsvK8be0wWXsMpxesUz/C3NoSXU/8gOvr9iZZ3+0dLzzzv4d8lUprP5P3cp6kspiIiAwzfU+rPFbjo964f+wKtvf8FPmquKDZsglYU2cY4mNiTaocLulstQndsbHVBJWvaPbTRJTr3RIXf/IxuTJ6Wv8TLL8TEWWOITz330S9rQTXZL0bf+/Hmz6nifMycTExKh8R9ThUBUETS2uZ7H/5/q2xvvEYxIRFoHTnhqjz2UBsbvuhqiORvEDyv0lNmW5NEfX0OZ5cvYtXkdlzGhMakaQO5OmNQOwf8y1KvlUPVjltkqyb1rJa0/vj8eVb2Dnwc1ja5kDbDZ/gnt9FPDx1DTc3HMCD41dUA3ljZJKBTkOh6dUgPRXiY+OwrftM9d7RtQha/TEVOQvnUw8sKRzERceoFo3VJ3SHs0dFWFhZ4sn1QBycsAT5a5RFsVY1UahRZdUK4cJyH9zefgyNFo+GlYMtLHJYI2j/WRz+eFmSSsTEHpy8+lqPVW7+cn1bwszCXLUo9d9yGNf+3K19QBZtWUMVeGQf7/ieUJ9Lpr7mx71h5WCnCjOnv14H/00HUW/uEFja26hWEHGxcdjkOVFVDpbu4AFzSwvExcSq7dw/dlnnvsgDUwpIsh/Jle7YUP2dLBfxcXGIfPjUJK6ttLS9sspwGgkQEWUFfXpep/VM1qSN4tGFAMDMTLX4D719P3PHO3cIchZxUmlo6J0H8O2f0MpUekbUnT0YtgVyq+89/dVa9bn02Kz9yduwL5YfljbWCPjnCE7M+V21yLQtmAeNvxujChb7Ri9CjjwOqDaxuzpWCeadX7IxQ+mKtAYt3amhCmBqaAKEQvIHGTlnyUlvyuTX5NmNIHUN684ahAK1yqvX+0YtVIUCIT1l5dikVaj8Pxz7ZIUqpOo6bzWn9IVzPTeV95DK2wPvf4en1+5mqJAcdvcBHp6+rv0sd9mi6vP9oxehpFe9TB8rEREZZnmspHc9rKs3Qr2WtCjsXohKWwL3JoxSZSrl8BJedXFr21Ft4ylpWCSjIUig09TK6Bn9n2D5nYjIOJ77r7veVuoNqk7optJ7ScMlWHtw4vdqWbn+nqoXpKxz6ss1uLp6p/rcoZQzas8YAFsnR5hbW+Hyiu24uNxH5zmV8+I2qB3MrSxhZm6mGjXJ+dElLipGnSvp4ZiZZfHx8SqvYWmXQwU6pfFVWGBIps+FnMN8VUqrYKE0GDs26zdtb9JG345GnvLFEBsVg11DvkBoQLBaVrpLI1QY0AbmVhaIfh6Bwx8tRfi9RzrPqYy44ehSWJ2L53cf4MDYxak2DH96PVD9LtGmTqaW5XUroa6FkIbvQYcuwKVLY22dhjFjoFOPyQ0gw6AlbvEv8rqXgk+XqYiLjIHnXzNQol0d3Fi/HxWHtUd0eKRqrSAqj+mCahN74PCkH1WhQFqWaHpKSKXhjr6z1c0vhRNpEVnKu/5Lt9yQVoXSwvLx5ds49tlK7c2eUbJvl37ZrlqS+k35SX0mDy5rx5yqJcrJeX+o4XTkIfqX7wnVdb7+5+/i396fIjz4MXLkdcBb2+bi/tFLODjhe9UyIXEriGtr9uD8kk3qtQwl4LFgOP5qOCrTxykVjbFR0Wj+y4fIWSgvQi4E4Mj0nzNdkDKkayvsCudThae9I75+6W0QERkifXxep/dMdu3eFKH+9zId5FTHO+F71fMzcRoqpKCw5a2PVHrb+eBCXP19J8KCQuDx9XBVwXnv4HlVSdri1w9Rwque6l0qQ8wl7qkhafrW9pNVBaR1bnuV4b+z62S6BRAJsj4LuKfdjobH1yNQqH5CZej23p9l+lilwjb5NZHKVccyRbB/7GIc+vBHde2lV8n2Hp/AvngBVdiR19Gh4XAo6awKcGtqD9V53s4sWo+jM35Rr2XImjozB6jeFxkpXF1OVECVAnX9eUOxf9y3qrBIRETGmb4nlyOPPcwtLZNUgIXeuo+cRfKbXDncvohTknyNvJYGRqZWRs/I/4Rg+Z2IyDie+xn1Ks/9iIdPcXLuatX7MHEvUxEXGa2OV4Zy9do6B9fW7AbigcaLx2Dv8AWq16OMrNBu0yxtMC/5Ob276yRu/LVPm8dot/kz/FlrqApcZpVH5/1x7vtNaqSjyEehKl/g03FKprdz5bcdcOncSPVI1fQyleC1U1UXbGgxHqG3glFjUi81VL/kewrUKqcadG3tOFkdjwyZLwHRv5uM0XlO/aYs1+ZPZBvSqEwTVM4qD09fR+mOHqoRmOTTijSpgicZaHBtDBjoNEABWw8jNjxK26pDKtqEtLCQlgIlX8xHIb0lJNOrk7kZanzcGwVrl1e9Pmydcqlh4l7mgbtnxNcIu/tQvS4/wFNVckpX8awgLQ/8Nx9Wr4OPXtYea/6a5eBQogBarvwoyfq5XAqryt3k8lUspVp8Sm8SGdJFKjGlN4b0MskMMwsLFG5YGZu9JqkK3uqTeqLe7MHYNfgLGOO1Tdxt/9b2Y4gMefbS2yAiMibZ+bxO65lcyKMSqoz7H7Z1S2iNmlVu/JUwjKp85zP/YBX0i3ryXH2ftOLUsMxpk2Q+i8QkDW7w5TDkKl1IpcVSWZenfPF0A52uPZrjym8pW6buG/mN+u3yv8aqZ8m/LxHs1OXZzSA8OJEw5FDw0Utwf/ct9bpI02rIVcpZDQusIUFbTUVrcoUbVUaFt9vAyt5WtVzNkTthGL205CzqpP4fJECsIcMPSQ+bJ1fu6Gy9SkREWUdfy2OmXg5Pi6mW0dPC8jsR0cvjc/8/mulUJKApIzPICE8yooM0MpJgp4aVvY36TFevQfviBdFo0SjYFcqn8gLS6NmheIFXHho2yXcUK4ASbetgbb3hqjel5IsaLxmjGlpnBQkaSpBTBB+7rMr5oljrWmrqIK/Ns7TrSrlf8jq6SABSeldKIFx+Xkc9+5HpP6PWlL6qYXnEgycIOngONvn+q7MxZgx0GqDEY1xLy35zCwv12szMTHWDv7v7VLrbcH/nLVUxKWN5y/ZqTesHC5uMTRScnKZwJaRrtNxMUnkpLSheVeLWHepYLf871seXbmOLd9IClkheCSjdwZsufR8+naepB65UOPa68issrC0zXcB6fucBAg+cUwUocX3NHrRclTUPTX28thqu3Zri4AdZ28KEiMiQZefzOrVncsF6bmjw1TDVcjQjQ6S+9PHGvTheMzP1XioWMzL/hgxjc9v3uJorQkgPEMncp1dgyV/DFbsGJfyNLjLEnmw7q/IeifMGyfMed3efxp73FqT4m5zO+ZK+L+KEup8OxKY2H6h5NPNUSJg3NT2u3ZqplqMyfJ+G9DKV7VV421NV5srwR9JSdWObD15qaD6i103+Zz3XTceR6b/g3HcbUjynZC69mPAo1TtaM4eQkKGupffz3b1nsK2r7jl0pFe1tHze6DkxRUWKDJEp5RAJyuzo81mSlvlEhlgekzQtLjZWDROv6cEnQ8U/v3Pf5MrhMix8rhcBRs13Sdnc1Mro6f1PaLD8TkRkHM/9jHpdz/3YyKgU9QBSCxD5ODTFKFCpabx4NI59uhL+mw+p9z3OL1fD+WYlmZ/00cUAFeQUMgJV3c8GqbyIDDuc5f8TifJFUh9x/MUQt2kpULs8Kgxsiy1ek1QvWhnuuNr4bshqkSHP1NRBGlJXIkMwmwLz7N4BSlvUszBY5bLL0GkK8PGD2xAv1WVcyG9pTZGwnXDV4kJDhqIJD36kblTJJL/sfE8yTJ1Nop4c0p0//MGTVAtXTlXLqHHOdYkKDUuyj2mRHhbSm6RQw0pJ5heRB5jMgyUtJ+S1kEpUNfb1i4JQhYEJrS5exs2NB1R3dSmkiSLNqyPkfNKh9JIPq5fapM/6fm01pLeOmaW5qtwlIjJF+vS8Tu2ZXLBuBTT8ZgR8+89Vw7akp+PeBbBzzqs7Lc7gscowPDJHRqXhHbWfybycdoXy/ne8ibZlnTsnnr8Ydk72V1o+pkeGvw3Y6pd0Ts5cdup7NKRHheQ7Ust7SDos6bEuya9JWmSYXZlfRIKWifM1us6bbFNavIYFJxS0yr/tmf4XmJmhTPemuLJqR5KPZdifNbWGYk3tYdja/mNEPwtXrxnkJENTtEUN1QtMKkbkfzlxkFOCMzIP7dMbgSjUwD3TvZdl6M7a0/qriqdt3WcwyEkGl76nxn/jQTUEnGZuTEm7g14MF29K5XCpHJUKQTmfQs6JDCNoimX01P4nNFh+JyLKOEN47qcno899SS+kHkAXScMzmheQYVBlKhcZPUBDRjuQnpq6zqm1o722N2Tpzg3VKA9ZTaa6KVCrvJpLU8gc40+u3kk1yCk9PmUECF3U/mfwXARsO6qOSTvKk5mZSpt1nVP5n4gJDVd5Nckfle3TEq9Djjz2avobkbdiKVVfInOamwL26NRz577biFa/T0FseKR2UuTUnFm4HlXGWqnu0pq5jc8uWq/m65AxtGU+DKlAkH/uCz9uQZMfxqH9rvkIDwrB3b1pPwwrj+6Mcn1aqcmH65cfijqfDsKGVuMRGxaJFismqVaZ8XHxiAh5hh39Zqe6HWltmFrrzYAtfnDp3Fh1rZYh2qRFRGpkuLx/+3ymWq3WmtpPTfgrBSgZ91p6Qcjxtvf9Qk0CvMlzIk7M+R1eW2cjIuSptkCUGkmoOu37BhY5LNUD6X/HluDa2t04/tlv6jtkLrK2Gz9VxyutRg+M/y7VbTlVKY0LS7cY5LXVVKBKJbO0hEltwmwiImOnL8/rtJ7JDb4YBgtrK3jMH6b9bM+Ib9SwaMnJ8171+EjUa1BDgqTSU6P9zi9VT0Tf/nPS3B/p3Vhren+1vuxTTFgkDkxYooajlfRP5vGS8yYtCqUVZ71Zg1FldBc1f8mD41fTD/x1bYK9L4ao1ZBCU5Pvx8HSxjoh7/HwKXb0/W+omORylSqE6Ge6e3YlvybPbgSlOaTtnmFfod7cIbC0zaGGqAs5c0OdA13nTYaj67BrvirISKE4PTLUrbSSDdx7Jt11iQxNqY4eaLhguKoYkeeoprW1RunOjVR+XoZtlny2BP1lbsCMqPPJ26p1tNxze0d8kyWttsk0GEJ57OgnK9Bw4Uh02v8NYqNjsHf412rYN0mHTKkcLnOPnpj3B9ps+EStG3TgHC79ul29NrUyemr/ExosvxMRZZwhPPezqt5WAp3SGFcXKYNWfNcb3ju+SJh3O415I6VH444+s9T83RL4lcZXkSFPsWfYAkTpOKd+U5apcxH1JAyB+88kmXNbF9kHOVYZyUjyAkEHzqo8flrLArYcVo2e3vpnjgouS72ErpGYNCRALVPy6HJ5xb+oNbUv3Id44Vg6PTWDD1/AsZkr0HTZeNXbVeoIbv97XI1kkfycHvpoqZr/s+O+BaqOQP4ncupofJ44gC3/U3Ks0nO0RLt6OPThD2o+2LSWOVVzRZ2Zb6sRICQgveudL9W86qbALD6e0YvsEh0WgZUuvaFvpFJR1wTEL0uGmLJ2tIPflJ9Q57NBuLF+H4L9LkJfyPBvcqxS6ZoV+geuwW/l+sLMyhKNF41KN6E05Gub0e31urYCVi9a1bxp+nqfEemL7Lw/s5s+Px+y+nktQ0nWntFfDS9Twquemkfz9FdroS88vnpPpcPnf9ic5dtrtnwC/CYvT7dA9SZp8gpZNbRmRvIyxnav6/P9a+qSD10rPY/qzhqEByevYXuvT5MMzawhFQBSQfLP/6aj6dLxyFe5tOq5nLzCJvHQtSFnb6h7Xea5ubRiOw5O+J4N894AQ3iWmFL6nhZTK4enxZTK6Fn1XbzXiYjPh+x/Fmfld7kP9Va9TK+vTZh3Ux94rp2O8z9sUtO3vMnvavP3TGzv+SlinkdAH8hoNt7/zsNv5fvp1Xf1MoB8f2IcupZSiImIUsPPePmk3YsjIxouGqW6cEsXfnF40o96VbgS0hNEWkS69mz+StspUKeCagUriYa0JJWWNfpUgMrqa1tzch9UGtFRZ48gIiLSn+e1tLKsO3uQ6u0h/Dcd1Ksgp2Z4mHL9Wqv5I15V6zXT1DyA0WGR6r0U6vQpyCkkr+C5bgaKNKv2SttxKFFQ5T1kaBr2YiN9JHlFua8D959TQUxdQU4Z3imvWwlc/WOXen919U5VAJdezqmRITGb/jheBTmldf3B8UsY5CSTS9/TYkrl8LSYWhk9LSy/ExGZ9nP/3OINehXkFLJv1Sf1QpUxXV7bd8iw+pIXsC9RQDvX5tb2k/UmyCni4uIQEx6p9lP+N16Xkt710fyXD7TzfBsb9ujMRvrc0pQoK7FHJ5H+MrQWWlmJ6TCZEmO713n/6n+Pzqc3g5CrpDP8tx5WQ9LGRekeUrbu7MFw6dIIqysPVnP/SuC+64nv1RzA8ne6enRqti1DOMqQYfTmGMKzhM8HolfHe52I+HwgMm29DCDfnxh7dBIREREREVGWsiuYR/1+dvNeqkFOaWFduoMH/DcfVkFOIXPNXV+3F8Vb14J1bvtUty29mJ9ev8urRkREREREZOIY6CQiIiIiIqIsdeabv3B37xlUHOqNmlP76lynhFddWDvmRNDB83Ao6az9uXfovAqCunRupPPv9o/5FuHBj9H4uzEo3rYOrxwREREREZEJs8zuHTBllrY5VBdgIlP4X8/O7+Z9RpT2PWKq+HwgU2LK9zplj5jwKOzoOwvNf/4AFd/1hpm5OY5M/SnJOmV7JMzN5zF/mM5tuHZvigtLt6T4XIau9ek8FZ5rp6PJd2Owe+hX8N986DUdCRkapu9EWXMf6Tve60TZd+/pOz4fiEzjXk+Mgc5sZGZmZlDjHBMZIt5nRMTnAxFR9oiNiMKOfrPR/KeJcB/iBTMzwG9KQrDToURBFKxbAdfW7kHAVr8Uf1uoYSWU79ca+SqXxsPT11Msf+Z/D1s7TVHzgTZePBq7hy2A/6aDb+S4SL8x/09kGnivExGfD0SkwUAnERERERERvb5gZ/85aPbTRLgN9pKaafhNXo4yPZqpXp7nlmxEyJkbKf7u4alrKtBZpnsznYFOERoQDJ9OCT07G387CnvMgJsbGewkIiIiIiIyJZyjk4iIiIiIiF5rsNO332zc2XUSboPaoc4nb6NM1yZ4FnBPZ5BThN6+jwenrqF0Rw9Y5LBKdduaYGfYvUdo9O1olGrfgFeSiIiIiIjIhDDQSURERERERK9VbGQ0fPvPwZ2dJ1BhYFvkLJQP/lsOp/k3Mu9mjtz2KN62Tprrhd4Khk/HKQgLfIiGC0eiVEePLN57IiIiIiIi0ldm8fHx8dm9E0RERERERBkVHRaBlS69ecKI3rBe11bAys6G552IiIiIiPQGe3QSERERERERERERERERkcFhoJOIiIiIiIiIiIiIiIiIDA4DnURERERERERERERERERkcBjoJCIiIiIiIiIiIiIiIiKDw0AnERERERERERERERERERkcBjqJiIiIiIiIiIiIiIiIyOAw0ElEREREREREREREREREBoeBTiIiIiIiIiIiIiIiIiIyOAx0EhEREREREREREREREZHBYaCTiIiIiIiIiIiIiIiIiAwOA51EREREREREREREREREZHAY6CQiIiIiIkqk6riusMhhpX3v8dV7cBvcLtPnqLhnLeSv7pqhdct0bYKel36Gl88c7WeVRnZCx70L0O/OH2pbidnky4WWv32ETvu/QfudX6Jg3QraZQ3mD0PHfV/D+995aPP3J8hXxUW7rObkPuhydDGaLZ+Q7j7ldS+JUu0bJPmsf+AaWOeyw6ue07R4rp2OzocXocqYLuq9pZ0NWq76GN3PLUPPiz+nWL9oixrqPMm5aLp0PKzsbdXn9sUKwOufOfDe/rk6R02+Hwdrx5xqmUOJgurzPv6r1HGmR66/bf7cSY6n9oz+yCzneu4o0rRqhtftfX2l2k+53qJM92Zo7/sF+t5aneJ/0sLWGo2+HY1OB75R179Eu7optunoWkRtM/G+uw3xUn8j30NERERERGRoGOgkIiIiIiJKpOr7GQ/KpaW4Z23kr1E2w+sHHTiHTZ4Tte8D95zG9l6f4t6hCynWrfFRb9w/dgXrGozAvtGL0GjRaJhZWqhlAVv9sL7xaGxo8T7OfPMXmv4wTvt3R2f+ipNzV2dof1Sgs0PSQOebOqdHpv6EU/PXqNdxMTE4s3A9tnWdkWI9CYI2+HIofAfMUeciLChEGyANuxeCre0nY0PL8fi76Vj1XvZDPPO/pz4Pv/coQ/ujAp0F/gt0vizn+hkPdIqn1+6q/Yx4+FS9f3j6Gna98yWu/7UvxboV3/VGXFQ01tUfge09PkHd2YORI4+9drn8f9T//F34b/VL8nfnv9+EA+O+e6XjIiIiIiIiyi6W2fbNhPj4eMSER/JMkNGztM0BMzOzbPlu3mdE+nt/Zjc+H8iUmPK9nln15gxRv9usn4n42Dhs6z5T2xOu1R9TkbNwPjy+dAu7352PuOgYFTyqPqE7nD0qwsLKEk+uB+LghCUqwFmsVU0UalRZ9da8sNwHt7cfQ6PFo2HlYAuLHNYI2n8Whz9eJg8knfvy4OTVVPezpHc9rKs3Qr1+eOqaCuI513ND4N4zuLXtqHa9+8cvw845L8wszNXxZJT0IKw6oRusHexUT7/7x6/g4MTv1bJy/T1VD1NZ59SXa3B19U71uUMpZ9SeMQC2To4wt7bC5RXbcXG5j85zKufFbVA7mFtZwszcDMfnrFLnR5e4qBh1ruyL5k+xrEizagg5ewNPrt5V7y/+/A9arZqsArrydxpm5ubqPogJi0BmSeDUtmAeNP5uDGIjolRgWdgWyIPmP38Ah5IFER78GDsHf4Gox6Fqmfu73uoamVtaIOLBExyYsAQ5HO1Rrm9LdS2kt6b/lsM4/fU6tFgxCTnyOMDCxhqPzt3Egfe/S7Wc+Oi8/4uTkvJalmzfAAfGfqteh94KVoHz4m3q4MpvO9RnVcf+Dzc3HkSO3Pawdsx8r1xDwfSdyDTyDbzXibKPvj8j+HwgMv77PDkGOrORFF5XuvTOzl0geiN6XVsBKzubbDnbvM+I9Pf+zG58PpApMeV7PbMkmFeubyts7TAZUU/DtJ/ndS8Fny5TERcZA8+/ZqBEuzq4sX4/Kg5rj+jwSGxu+6Far/KYLqg2sQcOT/pRBRxDzt3E+R82q2XSo3FH39kq2CaBt2Y/TUQp7/q48ff+TO2j9NIzt7RE+P3H2s9Cb91HziIpA4ESTLy940SmgpxCehBKz8/ibWrDd8DcJMviIqPV8TqWKQyvrXNwbc1uIB5ovHgM9g5foIKOMoxqu02ztAHS5Of07q6TuPGiV6IEMNtt/gx/1hqaJDiZEfZFnBB6+36i8xAM24K5tYFdCaR6bZmFnEXz49EFf+zo99/QwBklPUtdezRTwW25ntreutVdsbH1BEQ+ClXHXq5PS9WDtlRHD3Vutnh9hPi4OJTu0gh1Zw3Gjj6zcOmX7SrI6DflJ+329wz7Sm1DSC/MCgPbqB6smaXrXOQs4qReO1VzVcH3bd1mqGF3jRnTdyLTyDfwXifKPvr+jODzgcj47/PkGOgkIiIiIiJKR8DWw4gNj9L2tHQo6axeS89G6fVYsm0d9d7c2lIFHXUyN0ONj3ujYO3ygJkZbJ1y4fHFgEwHOjOqdOeGKPlWPWztOCVLt3t93V71WwKacTGxakhXKwc75C5bVAX8NKzsbdRn0uM0OfviBdFo0SjYFcqH+JhYWOe2h0PxAtqemVlFet3K0K8S8Kzz6dsqGHn227+zZNt3dp7UBiiDj11CnvLFtUFQp6oueOufhKCqBF1TZWam5sgs2rwGzC3NYZXLDvePXEZWkqBz3dmDsGvQF1m6XSIiIiIiIn3AQCcREREREVE6YiOjta9VT0GLhPkwZTgfGX727u5T6Z5D93feUsO6bm73odperWn9YGGT+blAJbgWFxsL2/y5tb067Yvlx/M7/wVYS3rXR9WxXfFP1+lq6NSsFBuZEPAV0mNRzoUMahT5OFQFFTOi8eLROPbpSvhvPqTe9zi/XA3nm1mhdx6gcOPK2vf2xQog/N7jFD1YJeB59fedqD/v3SwLdCY5D/I/Yfnf/4T07Ly84t90t1G6kwcKNagEn05TEB0ajgoD26KQR8WX2h85F9I7VobR1ZwL+b90KOGsent6rp2mPrfOlVMF3a0d7bFv1MKX+i4iIiIiIiJ9kUbTUiIiIiIiItMT9SxM9azLiAAfP9UjT3rNCfktvRgTthOuejpqWDvmRHjwIxXklCBlSa96L72P/hsPquFgRb4qLmoezqCD59V76cVZfWIP/NNtOp7feZDmduTvOu5doHNZVGjS/U/Lk2t3VaCuTLem2s+k16v01NR1TiXIJkOranqeyhyVL+POzhPIW6m0GipWlO/XWttDNmdRJ+11kZ6TJd6qh5ALAaluq/qknig/wFPnMnUtM/E/IddGc+wyj2veiqUSthMalux/wh4RIU/VubPMaYMy3ZogK/4nJMjpXN9d7Yv0Gv694kCsqT1M/chQyld/92WQk4iIiIiIjAJ7dBIRERERESVy7ruNaPX7FMSGR2Jb95lpnhuZS7HKWCt4bZ6F+PiEz84uWo/Hl2+ruSs9FgxXQ5le/MkHF37cgiY/jEP7XfMRHhSCu3tPp7ntyqM7o1yfVrDJlwv1yw9FnU8HYUOr8Yh8+BRHP1mBhgtHotP+bxAbHYO9w79WQ8AKGRJWevU1Xz5Ruy3p2akZZjV5oFOGn9UlcO8ZVHzXG947vsD9o5fUXJupkR6NMgdl7RkDVOBXhmuNDHmKPcMWIErHOfWbskydi6gnYQjcfybJ3JK6yD7IebBysMX/ji1B0IGz2DviG8Q8j8CBcYvRbPlE9Z2PL93C3pEJvRTzVCiB6h/0VK/NzM3w8MwN+H28NNXvyOtWEg9PXde57MLSLaj/+btq//eNXpTu0L4SuPVcM00b6Ly6yhchZ28gYIsfXDo3hvf2z+G/5TDO/7gFxVvXUsFmCXjeO3xB9cpMTZmuTdQcsNa5c6r/K/d3vbGj32y1bemp2mD+MHQ6uFBdD5knNjLkWZr7SkREREREZOjM4uM1xXF606LDIrDSpTdPPBm97Jy8mPcZkXFNLp6V+HwgU2Js97ox3r8SwCrepjZ8B8x9o9/lPtRb9TK9vjZh3k194Ll2Os7/sAkBPkde+3d18ftWnYdHFwLQbvNn2NT2Q2gj1tnMuZ47as/on+HhgN/EdxnCs8QYnw9EbxrvdSIy5GcE8wJExn+fJ8eha4mIiIiIiLJZTEQU8rqXhJfPnNf6PTUn90GlER3VfJri3OINehXkFLJv1Sf1QpUxXV7bdziUKKh6VUpvS5m/U+Ya3dTmA70JcgrpqSs9Q2U/pTfr6yI9cOvOHoQI9v4kIiIiIiIDxKFriYiIiIiIstnNDQfUz+t2dOav6kef7Rz4+Wv/jmf+995IT8lXIcMF/1nz3df+Pee/36R+iIiIiIiIDBF7dBIRERERERERERERERGRwWGPTjIo/QPXZGg9n05TYW5tiVa/T8bVP3dj38hvUqxTbUJ3NRzW/ve/w5WV/6q5ijwWDE9326G3grGm9rCX2n8iIiJDx7SYiIjItDEvQEREZNqYFyB9w0AnGZQ9wxekusyheEEVvIx4+ARPrt1BePBjXFqxHeV6t4T/poO4te2odt18VVxQaXgH3Nl5QgU5RdCh82luv8z/mqBw4yq453cxi4+KiIjIcDAtJiIiMm3MCxAREZk25gVI3zDQSQbl+tq9Oj+3sLVGu42fIS4mFrvema+CnOLItJ9RpHEV1Jv7jgpQRj0OhUUOKzT8ejiin0dg/9jF2m2EBgSrH12c67nDub47Hl3wx8HxS17T0REREek/psVERESmjXkBIiIi08a8AOkbztFJRsHjy/eQ170kjn2yAkH7z2o/j3kRzLTN74i6nw5Un1X7oAdyly0GvynLERYUku62cxZxQuPvx6rAqO/bnyMmPPK1HgsREZEhYlpMRERk2pgXICIiMm3MC1B2YY9OMnjuQ71RqkMD3Fi/H+eWbEyxPHDfGVz8eRsqDPBUgU23we0Q4HME1/7cne62pfdn06XjkSOPPXb0nY1nN4Ne01EQEREZLqbFREREpo15ASIiItPGvABlJ6Pv0fngwQNMmDABZcqUgY2NDYoVK4ZRo0bh+fPnGDhwIMzMzLBw4cLs3k16SYUaVkKNSb0Qct4f+8d+m+p6x2b+iqc3g1BxWHtEPQnDwQkZG35Whrx1quKCk1/8iTu+J3idiIiImBYTERERy+VERETEOnrSE0bdo/PkyZNo06YNgoKCkDNnTri5ueHu3bv4+uuvce3aNYSEJAxbWrVq1ezeVXoJ9kXzo/F3YxAdGo6db89Nc0jZ2OgYRD8LV68fnr6G8PsJc3impcLAtijTtYnq/Xl6/hpeI2NmZqZ6+pbr01L9X0U8fIobGw/g5NzVHKqYiCgNTItJn1Ua0RH5KpVGvsql4VCiIEJvBWNN7WHZvVtEREaFeQHSayzrExG9dswLkD4wN+aenG+99ZYKco4bNw6BgYE4fvy4ej9nzhxs3rwZR44cUT06K1eunN27S5lkYWuNpssmwNoxJ/a8twDP/O+luX6V0Z2Rr1IpPDxzA0WaVFVD3aalYN0KqDW1L55cvYO9I77m9TFytWf0R+3p/fH48m0c+ngZbm46CLeBbdH8lw9UwYiIiFJiWkz6Tkb9cG5QUU09EPnoWXbvDhGR0WFegPQdy/pERK8X8wKkL4w20Dly5Ejcvn0bw4cPx7x58+Dg4KBdJkPZVqlSBTExMShZsiRy5cqVrftKmVd/3lAVuDw57490h5TNW6kUKo/shDu7TmJL+4/w5Npd1Pl0IGycHHWub1coL5p8Pw4xEVHwfXuu6jFKxit32aKo8HYb3Nx8CDsHfo4rK//FkWk/w2/azyjkUSndoDgRkaliWkz6bk2dYfjdfQC2dZ+JsHuPsnt3iIiMDvMCpM9Y1iciev2YFyB9YZSBzgsXLmD16tVwcnLCrFmzdK5To0YN9VsCnsn99ddfqF+/vhru1tHREQ0aNMC5c+de+35Txri94wWXTg0R4OOH01+tTXNdc2tLNPx6BGLCIrF/3GLEhkepuTylJ2i9OUN0rt906XjY5s+NfaMX4cmVO7wsRq5URw+YmZvj/A+bk3wuAc/osAi4dG6UbftGRKSvmBaTIQgNCM7uXSAiMlrMC5C+Y1mfiOj1Yl6A9IlRBjpXrVqFuLg49OrVC/b29jrXsbW11RnolPk7u3btCg8PD2zYsEFtq0WLFggPZ68+feBc3x01P+7zYkjZb9Jdv9r73ZCnfHHVOy/s7kP1WbDfRVxYuhUl2tZJ0Vuv7qzByF/NFae/WYeALYdf23GQ/nCqWgZxsbF4cOJKks9jI6MRcvYmnKq6ZNu+ERHpI6bFREREpo15ATIELOsTEb0+zAuQvrGEEfL19VW/mzZtmuo6Mqxt8kDntWvXMH78eMyfP18NeavRtm3b17q/lDG2BXKjyfdjYW5poYYZLda6ZqrrPjofAEtba7gP9catf4/h6u8J/xMax2f9hqLNq6shbAP3nUXEgydw7dkcZXs2R/j9x6onZ+nODVPdfsAWP8SER/LSGQG7gnkQGfIMcVExKZaFBYWgYO3yMLeyRFx0yuVERKaGaTEREZFpY16ADAXL+kRErwfzAqSPjDLQ6e/vr36XKFFC53KZm3P//v0pAp3Lli2DlZUVBg8enKX7U7NmTQQFBaX43CreHFNRO0u/y5g5uhSBTb6EeTWrjOqc5rpnFq5Hcc9aan7NA+9/l2J5bETCELae66arIWxlbkYJaAkZtlaGu03LmlpDEXr7/isdjykp61oW0WZx2fLd6d1nFrY5EBsVrXOZ9OoUEjSPYqCTjFR23p/Zjelw5jEtNlzGdq/z/iXKHobwLOHz4fViXsA0GMO9zrI+kek+I5gXeL2YFzANZbPhPnd2dsbRo0df6m/N4uPj42Fk8ubNi0ePHuHAgQOoV69eiuUrV65E79694eDggCdPnsDMzEzbA/Tp06cYNmwYPvnkE9y6dQuurq6YMmUKevTo8dL7U7RoUdy5k3KuR2szCywp2OGlt0tkKN65tx5R8bHZ8t3p3Wftfb+AjZMjVlcelGJZ4yVjUcq7Pn4p3p09OsloZef9md2YDpMpMbZ7PbP3b/udX8Iqpw3W1B72WveLyNgZwrOE6TvRqzOGe51lfSLTfUYwL0BkmPd5kSJFtCOxZpZR9uiUyK8EOo8fP54i0BkYGKiGpxWVK1fWBjk1yyQg+eGHH2LOnDkoVqwYli5dip49eyJ//vxqrs6X3Z/UWpdAfxu/EGWZwoUKZ2uPzrTus7B7j+BYtijMrS1TDF9r55wXEQ+fMMhJRi0778/sxnSYTImx3eu8f4myhyE8S/h8IHp1xnCvs6xPZLrPCOYFiAzzPk8tjmaygU4JSF64cEEFK1u2bImyZcuqz48cOYI+ffrgwYMH6n3VqlWT/F1cXBxCQ0Px66+/okOHhFZhzZs3x/nz5zFz5syXDnSm1t02OiwCK116v9Q2iQzJ5SuXYWVnky3fnd599uDkVRRpUhVO1VwRfPiC9nOLHFbIW7Ek7h367zMiY5Sd92d2YzpMpsTY7nXev0TZwxCeJXw+EL06Y7jXWdYnMt1nBPMCRMZ/nydnDiM0YcIE5MuXTw096+7ujkqVKqkhaGvXro3SpUujWbNmKebn1Ax5KxIHNKXHp7w/e/bsGz4KInoTbvx9APFxcXAb3C7J5669WqiH+fV1e3ghiIiIiIiIiAwIy/pERESmwyh7dMqcmHv37lVD1O7evRs3b96Em5sblixZgsGDB8PFxUVnoFOCoocPH9a5zYiIiDey70T0Zj2+GICLy31QYWBbNF06Hrd3HIejaxG4DWyLoAPncH3dPl4SIiIiA1S6SyPYF82vXtvkywVzK0tUHt1ZvQ+9fR/X17AxExERkbFiWZ+IiMh0GGWgU1SoUAGbNm1K8bkMTSuBT3Nzc1SsWDHJsvbt22PZsmXYtm0bOnXqpB3Odvv27ahVq9Yb23cierP8pvyE0Fv3UbZ3CxRtXh0RIU9xYdlWnJi7GoiP5+UgIiIyQGV7NIdzffckn1Wf2EP9Vo2ZGOgkIiIyaizrExERmQajDXSm5ty5c4iPj1fzdtrZ2SVZ9tZbb6Fhw4YYMmQIHj58iOLFi+PHH39UfyPBTiIyTjJ07bklG9UPERERGQefzlOzexeIiIgoG7GsT0REZBpMLtB55swZncPWaubj3LBhAyZOnIhJkybh6dOnar0tW7Zo5/UkIiIiIiIiIiIiIiIiouzHQGcyuXPnVnN5yg8RERERERERERERERER6SdzmJi0enTqo6rjusIih5X2vcdX78FtcLtMb6e4Zy3kr+6aoXXLdG2Cnpd+hpfPHO1nlUZ2Qse9C9Dvzh9qW4k5VS2Ddps+w1vbPkeHPV+h4rD22mU1J/dBl6OL0Wz5hHS/N697SZRq3yDJZ/0D18A6V9Ihhl/mvKXFc+10dD68CFXGdFHvLe1s0HLVx+h+bhl6Xvw5xfruQ73RfueX6LB7PpouG6/dPwsba3hv/xy9rv6a4hyldp4dyxRO8j4j5ykj5y019kXzo+/t1Wo/5e9EhYFt1fG09/0C3ju+QOnODZP8TeXRndHp4EL1U+2DhHmtREnv+uoc6DpHRETGRF/S4nxVXNDm70/g/e889Rx3bpB0rvFy/VqrdFg9z/+dp91nQ0yL00pvc5cvro5f89PF71v0OL9c+3et10xT7zNyjZJfE+d67mqbmSXpa7m+rTK8vpxTuU5FmlVT7y1tc6DB/GHqM8lv1ZjU67+VzcxQa1o/dR4knZbjcyjprBY5lCio9reP/yptuk5ERMZT1rbJlwstf/sInfZ/o9LEgnUrmGw5vGiLGuo8yblounQ8rOxtjbaMXrR5dXj9Mwd9bq5C7Rn9k6yf1jLJM9T55O2E8vuBb1B+gKd2kdsQL/XZy+RziIiMlSHkBTSk/C/pReL9K9O9mSpD9r2V9HNjzQskvk6J98+2YB71d3IOpczc5Mf3kSNfLoOuI5D6n7YbPkXv6ytTvYaSz1H1BIn2L3+Nstq6kva75qPe3CEwt07o61igTgX1+cteW31ncoFOX19fNUdnu3aZf2hlh6rvZ/zhkJbinrXVP3pGBR04h02eE7XvA/ecxvZen+LeoQsp1q3/+Ts4/fU6bGw1Hlu8P1aFDMeyRdWyozN/xcm5qzP0neqh2iFjhYGsPm9Hpv6EU/PXqNdxMTE4s3A9tnWdkWK9Qo0qw7V7U2z2moT1jcfg4enrqPZBT7UsNiIKG1qOx8NT1zP0nWW6NYVjmSJ4VZk9bzGhEWo/Q87dVO8fX7qlrtvfzcbh3z6fofaMAariVEhBulQHD2xoNg7rG49GkSZVVcFK3NxwAP/2+uyV95+ISN/pS1rcbNkEnJy3GhtavI9d73wJjwXvqYytKNa6Fkp3aojN7Sap5/k/3WYgLjrWYNPitNLbxxcDVDqm+bm1/Riur9ur3c4/Xabh1rajr+WapMa+WAGU69MyU3+ztcNk3PE9oV5XGtUJZhYW6tqtbzoWedxLooRXvYR9bF0TBWqVx9/N38eG5uMQuO8Mqn+YcC6e+d9T5yD83qNXPgYiIlNjCGXtGh/1xv1jV7CuwQjsG70IjRaNhpmlhcmVw6Xis8GXQ+E7YI46F2FBIdpKUWMsoz+9EYj9Y77F2cV/p1g3rWUuXRrBsWwx/NVgJDa1/VAFv3O/+J84//0mHBj33SsdFxGRsTGEvICwcrBDjY964c6OhPKjxsPT11TdwPW/9qX4G2PLC2gUb1sHcTEJdR0a8bFxaht/NRylysyh/vdQa3Ifg64jCA9+BL8py+E39adU15d8YvCRS0k+Czl/ExvbfKDyFX83HQsbJ0eU75/Q8Cn48AX1ubEyuaFrDUm9OUPU7zbrZ6obdlv3meq9o2sRtPpjKnIWzqeCVLvfnY+46BhV4Kk+oTucPSrCwsoST64H4uCEJermLNaqpioASKuRC8t9cHv7MTRaPBpWDrawyGGNoP1ncfjjZUB8vM59eXDyaqr7KX9i7ZhTvba0y4G4qBhEPQrN1LFKS9WqE7rB2sFOtSy4f/wKDk78Xi0r199TtaaQdU59uQZXV+9UnzuUclZBOVsnR5hbW+Hyiu24uNxH53mTY3cb1A7mVpYwMzfD8Tmr1DnQRfZfzoe0vEgur1tJ3PO7iJjnEer97R3HVeuTw5N+zNTxuvZsjnxVSqPW9P6qpcuxWb+9OH82aPTtaOQpXwyxUTHYNeQLhAYEq2WluzRChQFtYG5lgejnETj80VJVsanrvDVcNAqOLoXV8T6/+wAHxi5G+P3HOvdFKkw1wu4+RHjwY+Qs7KQqT0t6N8D1NbsREx6pll9Z5YtSHT3UcRMRmQJ9SYtz5HVQ6WDg3oRn9tPrgYh6EqZa+wVsOYyKw7xx6ss/Ef0sTC2PfPg008eqT2lxRtNbKUBJgFcKLpkl5y75NXl2I0hdw7qzBqngorzeN2ohHp66pv6mcJMqqmJVAsxyXMc+WaEKqdJKMmcRJ3XeQu88gG//Oag5pS+c67nB3NICUaHhOPD+d3h67W6qx3v9r4RgbXxMLO7uOaUqK/03HVT/DhbWlupYY2JiVQ+WsMCHmT5eIiLSv/Q9vbJ2Se96WFdvhHotaVHYvRCVtkh+wJTK4ZJmh5y9gSdXE9LRiz//g1arJqtKXGMso0s+T5RoUydTy2TUpSsr/0V8XByiHofixt/7Vfn9xJzfM3UuiIhMgaHkBUTdzwbi9FdrUaJt0mf/o/P+CS/i4l76PBhKXkDtq5MjKo/sBJ/OU1G2Vwvt5xEPnqgfDTmGCm+3Meg6grDAEPWjabCUXKGGlWBXKC8uLt0Kpyou2s9jw6O0r6UewVIax6fyf2dsGOjUY/JQkS7OEs2PeppQcSnyupeCT5epiIuMgedfM1CiXR3cWL9ftdaLDo/E5rYfqvUqj+mCahN7qAy+tFqQ1oHnf9islkll2Y6+sxETFgEzc3M0+2kiSnnXVxnhzNo/ZpH6+2oTu8Mmby6136ll2FMT8fCpamVSvE1t+A6Ym2RZXGS0OiYZQsZr6xxcW7MbiAcaLx6DvcMXqMKOha012m2apX0YJz9vd3edxI0XrVvkYdlu82f4s9ZQ9QDNDGkpU75/a9jmz62OUSpXJSGwzm2vChIZdeW3HXDp3Ajnf9iEAJ8j6jN5gDpVdcGGFuMReitYDVtXaXgHHJzwPQrUKofSHTywteNktc/S1VwKW383GaPzvEmLD00lt2xDWtNoEqm0yENSCsuaBNa+iBOC/f5rTST7lVUtfIiIDIG+pMWRIc9Ui76Sb9XDzY0H1TAmUllmXyyhAJDbtaj6rMrY/6ntXvtzNy4s3WKwaXFG01tpzfks4J62B0RmSEvJ5NdEhqWRnhz7xy7GoQ9/VMdQ/YMe2N7jE9gXL6AqPuV1dGi4Gj5WCnBrag9VabUMH5e4deSZRetxdMYv6rUMA1Rn5gBs7/mpzn2R45Vr67/5EMwtLVUrUutcCZXXso+FGrij2+kfERMajudBIfDpODXTx0tERPqXvqclRx57lSYkLluH3rqPnEXym1w5XMqlobfvJzoPwbAtmBtmFuZGX0Z/tfN0P0t6pRARGSNDyAuIEu3qIj4uXn1H8kBnVjCUvICoP+9d1chJ07hJFznfEuQM+CchLTfUOoK0yLCzMjSxfG/ussVSLLcvml/9zzmULIjb/x7HxZ/+gSlgoNMABWw9rI3OS0BKM0+TtLCQDH3JFw89GX9ZMrY6mZuhxse9UbB2eTWPg61TLjUU3Ms8cCsN74hjn61UDy25wdusm4EHp67hyeXbyAqa4ejk4Sld020L5FZd9qVFgzxYNazsbdRnmhYVidkXL4hGi0bBrlA+1VNCCj0OxQtoW4RmlLTIOLt4A5r/+qFqpSHXQsg2s8L9Y5dVAUoEH7usbX0iQxLmcSsBr82ztOvmyG2vHbIwudIdPeDSpbFKWOVHKsjTI3Oeecx/T7VU0vTgJCIi/UmLd0gLwI97o9KITnh8+db/2bsPqCiuNgzAL1VQsBfs2BXsBRv2hordGI29xRJjjZpo1KgxlhSjscQYY/JHTSOxl1iwN+y9ooIFRMWG9PKf7+JuKMtSBNld3uccDuzOMjszuzN37v3u/S5k9EJMZGzPTelVKOXaji7TYZ07B9z+maVG5t/brbunpKGXxSktb8v1aoEb6zyRnl7e8cfjMzfU3wEnr8F5eAf1d9FmNZCzlAParv8vjY6MmJBemroUaVxVleMyAlN6rkq5nRRJzyOpiKScD38ZjMdnbqKwa+wcrNI7M3eFEvirxvsIfxmiXie9Qw+OWpyu+01ERIZX19YnK9XDs2odnYiIsva9gHTiqTa2mxrBmBkM6V5AMi+8uv9YjfjUp968IQh7HqQNVBprG4E+db8YgvOL/lFBal2C7j1SUx6prBRLR6sAeXrfhxoiBjqNUFRYhPZvuZE3t4ido8PMzEwNg3+w/1yy63Ae1kENL9/a/hO1vjqf9YeFTepzkksqPenxsX/EQvVYUrhIDw4Zyp1eFayosPB4FwvZXzMZ3fIsKMV5pZssH4tTc9aqkRJCJiCW1AFpce2Xf9WPkMmJ5SIrvTYy5LO1/O+zldE5p1+nz9GnoEtFVBrcDtvcp6gLngy5rzHxXb3/I3O5tPz1ExwavwwBXle1z8vQ+rjpAiS/uOwvEVFWlxllsaSlidvbr/OBb1UqHSHXZpmXQ8pJaTi7v+c0CtQql26Bzswoi5Mrb6VMkn3cN+RLpCeZzyupsvjB/vM48MGiRP+TwyFf/MdF86PenMHY0vZjFXDOU6lkvMqPrvf0mrZa+1hGemg+2zLvNIHf4YvaHrHef+5Dq9+npcOeEhGRIde1w54GIToqSjtSUUgmh1f3H2W5erjUS4s0qRrvHiDk4TP1GZlqHT0tNPV3Cc7G/b4QEZFx3gvkq1oatoXyoOPur9RjKf+lDMmWLyfOzPstwz9WQ7oXcGhYGQ51K6F4q1ra5zp6fg3PAQtUentR9/NBajo2NTI1HdO1ZkYbgT4SQJefOjP6qc5TEjzucnCRmqM0LhlRLKORJdtFVgh0mmf2BpB+0qvfKmf2FB0m3x1ecHrfXQ0ZF/Jbk8dZRgBIjwsNSU8qKfDkYisVJ0f3+mn6KMKfvVInjVxsNBfc/DXLql4qumR3yKtOPJ3rCoq/jfo8936gKi5l322mfU562MiJreu4Weey0/bCLN2tEbLlsUdaSe8VzfGtPqknLizbmORrKw50Q80p7+lcprYxhfvru/Ok2m5tjxAzM5WiUNdxk89WUttJxVjyn5dPZuJjyT3fas0UHJn4vZr8Oi6fzUdRunsTWNpmUz2VyvVqri6QREmRlA4D/DzgPLxjomWF6jvhvWu/oMfZlapAj6vJ9+PU/8k8CEmRdBDyGs13Py6nYe7of/9PtN34uUrhQGSKZbGm/BHlerdEZHCYdp5lCXIWbVY99j1trOHQwBmBl17P12GkZXFy5a2USb7bveKlGdJFGoJdF8fOb5ZQws9En/v7zqq5OuJev/JXLxu7nqD4+yrrlB6vwQFP1eOKg9z0rlt6dGq+M9J4W6F/G1z6frN6LKl5CzesrMp0UaxVbW0QlIiIjL9810fqY5IiTcg9sJTh/kcvZ7l6+P29Z5C3SmmVOk9U7N8mXoOdqdXR3+T7IveIkrZPPhNJi3d745EMeS8ifdguQMbC0O8FZN7pP6oOgYfLSPXjs+UYzi30SFOQ09jvBQ5+sAh/1R6uPRZiU/MJ2iCny+xBsC9VGJ6DFqg5VY29jUAfzTGQn/3Dv1Wd3Na/DnLKZyEZv4Tcc8i+Bl7R3TZkajii08BJI1fr36cjKiRMOymyvrRn1cZbqdQpmk4LF5duwLPr91QObddFo9ScT1d/3oErP25D05UT0GnfQoT4B+LBwfgBroSqju2GCn1bq8mHG1QcgbpzhmBT64lqjol9w75B7el9Va8OcysLNTRc04NQ10VVTmpd/A5eQOXhHdFxz9d4dPKa3vkqpPfEnr5z1cTHUsjI3BxhgS9wYOQihOs4bl7Tf1L7G/48GH6HL8Sbt0IX2QbZV5k0+p1TK+B/5CIOfvidWtZaRlGYm6uJp+W4Xv1pe5LrkQLvpU/sxTyh62t2q54Xzu+741QyvUADjl/Bqdlr0OynibHH2dpS5diWlAAJj9uxqavU3CJdDi1SFSn5bHM45E1y3XVnD1IX29pT+wDyA+DknDV4sO8c/I9ewp2Nh9Fp7zfq+dubDqfb6CDKWoq1rIWmP4xXvdF39ohNqRl3/iG5Nr247afmoZNeyMmdo3HVmNRTTfwtDSCeg7+MN/E2kSmVxeX7tFI98aTX4LMb99QNvMblFZtRf8EwdN6/UL2v9Jb02XLUqMtiveWtmZmaN+vg6NiyWZ+cpQoj4qXuYGjCz+TlbX+96WoOjPxWpY3VdAAKvHBb9d6U0bbPrt1T5aVc3zwHzFcNsJ33LVRlsVSK9bEvWQhNVoxHTFQUoiOj4TXjZ+28o1dX71BzsHbc8xWiI6LUdfTopBXJ7jcRERl/Xfvk52vQaMlodD38HaIiIlXack1K1qxUD5e5uI5MWI7mqyer95QOPwdHL9H+n6nV0Qu7VlHfKTkOct9Xsn19HPtkZey83XqWeXscQL7qZdH1yHeIiYnBpRWbkwx+E2UGtguQoTGGewF9pE4s84TK9DXy3jLoYE//edrgnyndC+gj82Y7DWmn2kk0KeVf3g3A3kFfGm0bQc4yRdDmzxmwtLVWndnlWJxf/I82g0VSCrtWVlkk5HOSgKd8tucXeiArMIuRux/KFBHBoVhbJjawZEjkIqlrAuL0WJ/ziI6qR8utv2NzfBsCt79n4vLKLfDdcSLd19d242yVZlDfJMlvkwSQJN3Buor93+r6enuvgVV2G2QGQz3PTLnnpts/M3Fi5v9w6ftN6rlSXVzRaNEo1etLbnJCHsb2XtKoNKQdXGYOUOkb2m2egwvfrcfZr/7UOaKz+kc9sNltsjbvv6SlkAJcbhbkxie5XltkWOdnZjPk6wPL4jfj+u0HKlAojb7NV09SaWFT04Eio8no9HUV+iU7GjWlunstU/dZmuBoVjjXDfn8JTJlxnAtyUrle0rfKyvUw1P6XqZeR0+uruYya0CK0g3yXKc3/a6xXcC0Gfo1gvcCsXgvkHXbCNLjvQz9PE+IqWspkcjQcOR1doT7jvlvfHRqT+uLKh92Ubm7xaXlmwyqciVk22pO6a1GhL0J6V3RcdeXsCtZUJvPfXunaQZTgRLR0dGIDAlT2ymf8Ztw7NgALf73sXa+GCJdJNVW4yWj8eTCbWzvMj1RkFOU69UC/kcu4cn5W6oXtEp/YSZZ/5MmPcYaffehCnJeW7ML+0d8yyAnmRSWxWnXxuMzlSo7IjhMPZYGXkOqwAhpbHb7ZxaKNq/xRuuRkaBSpktPTXb0ICLKWuW7PlmpHq5PVquj6yOjburNG4LQwJcZ9h5ESWG7ANF/eC/wdu8FslobgT4F61ZS9xvyXjHRpjf2kSM6M5Eh9y4hSk8c0Zk1e25KCq1aU3rjwcELKkWDzCOUkMxj02HHfJV+0vuv/WpS9Ra/fKxGfiac4F0zonNbp09ReUQnlHCro1KHnJqz5i3uoekxth5a6YnlMGUlpnau8/wlyhzGcC3h9YHozfFcpzfBdgHTZ+jXCN4LEJn+eZ4Q5+gkIqJ0V6F/a+R0dIDP9uPYP3whosN1p5Qt16s5Il6FwGfrcfX4nucZhDx+rp5PGOjUkFz5sm6Zs0jmQyAiIiIiIiIiw8J2ASIieluYupaIiNJd9kJ51O+Xdx4mGeSUVFKlO7uqIKdmtGdMZBRu/XMQJdrUgXVuuyTXLSkaX9x6wE+OiIiIiIiIyACxXYCIiN4WjujMRJa22dQQYKKs8F2nrOXCd+tRqL4zKo/oCJgBJ2f+L9FrSrrXg3WuHPA/ehn2jg7a5x8euwzn991RpltjXFm1LdH/HR63TM071OT7cWpuTt9tsaNBiVKL5TBlJaZWFvP8Jcq8c8/Q8fpAlD7nEdGbYrsAZRbeCxBlvXsBBjozkZmZmVHlOSYiSqnIkHDs6TdXzbdZeXhHmJmb48SMn+O9pnyvFuq368KROtdRrmcznYHOF3f8saPbDLj9PRNNXwc7fbYe44dDqcZymMh48fwlIl4fiIgMG9sFKLOwrkCU9TDQSUREGSIqNBx7+s9Di58nqxGaZmaA1/TYYKd9yUIoVK8SvP8+AN/tXon+t3CjKqjYvw3yVS2NJ+dvJVr+0uchtnedDrd/ZqLJ8rHYP3IRfLYc5SdJREREREREZCDYLkBERG8DA51ERJSxlZoB89H858lwGuou3ergNW01yvZqrkZ5XlqxGYEXbif6vyfnvFWgs2zP5joDnSLINwA7usaO7GyybAwOmAF3NjPYSURERERERGQo2C5AREQZzTzD34GIiJDVKzWe/efh/r6zcBrSHnU/H4SyPZripe9DnUFOEXTvER6f80bpLq6wyGaV5Lo1wc7gh0/ReNlYlOrUMAP3hIiIiIiIiIhSi+0CRESUkRjoJCKiDBcVFgHPAfNxf+8ZVBrcDjkK54PPtuN6/0fm3cyW2w4l2tXV+7qguwHY0WU6gv2eoNGS0SjVxTWdt56IiIiIiIiI3gTbBYiIKKOYxcTExGTY2omIMllEcCjWlumT2ZtBZLB6e6+BVXabzN4MIiIiIiKiFGNdnyjzsB2BiAwNR3QSERERERERERERERERkdFhoJOIiIiIiIiIiIiIiIiIjA4DnURERERERERERERERERkdBjoJCIiIiIiIiIiIiIiIiKjw0AnERERERERERERERERERkdBjqJiIiIiIiIiIiIiIiIyOgw0ElERERERERERERERERERoeBTiIiIiIiIiIiIiIiIiIyOgx0EhEREREREREREREREZHRYaCTiIiIiIiIiIiIiIiIiIwOA51EREREREREREREREREZHQY6CQiSqPqE3rAIpuV9rHrtx/AaWj7VK+nhFsdFKhZLkWvLdujKd679gvcd8zXPpevWhm03fg5Ou7+Ch13fQmHhpW1y2p83AudPL9Wz8tPqU4NtctqT+uL7ieXo/nqScm+b15nx3j/Kwb4ecA6Z3a86XHTx+3vmeh2fCmqjeuufc55REd02vsNOu9fiGY/TdS5DdU/6qG2T7Zbo43HZ+h1eXWKPqOEn4lDfWd1/FLLrlgBVOjXOsWvl22Wz6to8xrqsaVtNjRcOFI91+XgItSa0jveNvW5tVb72cqPhY21WlawbiX1OK2fERERERERUVZmKPX9KqO7qrpg//t/qnXFJXXFjnti6/vu2+ehsGsV7bL81cui/ZYv0GHnl+h84FtUHtnJqNsCLLPboNVvn6LnpZ/w3tVf4r3WrnhBuP87Xx0HaSto+sMEWOfKkWwbgtSf5X963/w10bFN6vPJVbZIvMcpOYYpOab62hT63ftDbaemfaNYi5pqf/ve+Q0uswbo/D+bfDnx7vkfE21f1bHd0PXoEvUj7UUajh0bqOOT8NgSERkLBjqJiNJIgmkpvUnXp4SbCwrUKp/i1/sfuYQtbpO1j5v/NAlnv/oDm1p+hH3DvoHrog+0Aa9LyzZiY/MJ2NRqInb3nYv6Xw5Dtrz2atnJ2b/i7II/Un4j3jllN+LpfdxOzPgZ5xZ6qL8LN66Kcj2bYav7FGxoMg5Pzt9CjY/fi/d6qdDJT9DdgHjP/9v9M9zdeTJDPpOkSIWrQt9Wqfqf7Z2n4b7nGfV3lTFdYWZhoT7DDc3GI4+zI0q619e+9oX3A/XZan6iQsPV8wHHr6jHREREREREZLz1fb8D57Gr9xw8PHZFZ115U4vY+v6RiSvQ9IfxgJmZWtbgy2E4v/gfbG49Eds6fqqCfbnKFzPatoDoyEhcWLIBO3vMSvS64IeB2N5pmjoOG5uNV4/lvZJrQ5D6s/zPk3O3UrQ9Zd9thlxli+JNpfaYRgaFqu0MvHRHPX5x2w+Hxy3DxeUbk/wfafu5u+tUvOcK1auEUp1dsUnaF5qMRdGm1VXQVNzZdAS7e3+R5n0iIspslpm9AVlZTEwMIkPCMnsziDKcjEoze32zbSrqz39f/W67YTZioqKxs+ds9ThXuaJo/ecM5CiSD8+u3cX+4QsRHREJM0sL1JzUEw6ulWFhZYnnt/xwdNIKVeEp3rq2uvmW3oBXVu/AvV2n0Hj5WFjZ28IimzX8D1/E8U9/kotGou2QoKX01PM7eEE9fnHLD+HPg9WIQN9txxH+Ilj7WsscNupzSO1nIeuvPuldWNtnV70IH52+gaOTf1DLKgxwUz0f5TXnvvHAzT/2quftSznAZdZA2ObPBXNrK1xfswtXV+/Qedxk352GtIe5lSXMzM1wev5v6hjoktfJEQ+9riLyVah6fG/PadXT8/iUH9VjC1tr1P1iMPYO+QrtNsR+Jqklxy7hZ/Lytr/6DOvNHYKCdSqqvw+NWYIn57zV/xRpWk31NJUAs+zXqc/XqApq/QXvI0fR/Oq4Bd1/DM8B81F7ej841HeCuaUFwoNCcOSj71XAMqn9vbX+oPo7JjIKDw6cQ5nujeGz5Wia9i0rYllLZJrlNM9tosxhDNcLXh+Issa5nhXq++Lx2ZtJbmfc+r7U1+OS1WlGNVpmz4bo8EiEPw0y2rYA2X45VjLCUdcyDTNzc/X9jQwOTVEbQkqVe68F8lUrjTozB6iRqafmrtOONG28bCzyVCyOqPBI7Hv/awT5xna6Lt29MSoNbAtzKwtEvArF8amrEPLwqc5j2mjpGOQqU0Qdi1cPHuPI+OUIefRM57ZIu48o2bau7m3t1Vxtw9PLPijR1kX7vGPHhrjlsV/bFn3jN0+U6uKqjomp4b0AUda7F2CgMxNJwbK2TJ/M3ASit6K39xpYZbcxqaMtN6KSklRG38WtXOR1LoUd3WcgOiwSbutnoWT7uri94bBKExMREoat7T5Rr6s6rjtqTO6lbq5llKH0zLu8cqtaJj0c9/Sbp27M5Sa9+c+TUapjA9zeeDjRdoQFvkRIwFM4dqiPO5uPqjS2cnNsV/y/m/9Kg9uh4oA2yF4kH45MWI7QJy9Sta/yeuntKTfIngMXxFsWHRah9knSt7hvnw9vj/1ADNBk+TgcHLUIz28+UMHH9lvmam/gEx63B/vO4vb6Q+pvqbS03/oF/qozIl5lRePJeW+1L7YFcqub/tJdG6kKgnVuO4Q/C0LtT/vi2i87EfzgCdJKRlMm/EwkTaz03Dw8fjmOffKj2oeaH/fCrl6fw65EQVXRkb8jgkJg7+igKm8eLiNwdNIPKpVM3NGVF5ZuwMlZ/1N/S7qaurMHYtd7c3Rui+yvfLY+W4/B3NJS9Qa2zvlfCh55rw47F6iK4o3f9+LaL/+meb9NFctaItMsp3luE2UOY7he8PpAlDXO9axQ308Jmd6kZIf6yJYrh+rwqwmYHh63VK27xuSesMmbU+1TUoEzY2gLSI4ECN23zUWOYgXw9IoP9vSfn6I2hJS6sW4PynRrjMsrt8B3xwn1nASv81cvg00tJ6qMUvJZVBnVWbUDFKxTAaU7u2J7l2lqf2R6GQmIbmw6Tucx9Zq+GmGv22pkHTIiVRNUTnVWKTnOXaar71W8ZUXzI8Drv5HBss3pNVrX0PBegCjr3Qsw0ElElI58tx9HVEi4tuelBKKE9HSUm2nHdrE97sytLRF095HulZibodanfVDIpaJKO2ObPyeeXfVNsuKzR0YJftoHVT7simfX76reijGR0drlV1ZtUz95nEqi8ZLReLD/HMJS2ZMzKbf+iR1tKJWY6Mgo2BbMDSv77Mhdvpiq4GhY2dmo5zQjIOOyK1EIjZeOQfbC+dSoRalw2JcoqNaZkIySvLh8E1r8+okK7snxFvJ/0htUKkfSSzIjvLzjj8dnbqi/A05eg/PwDurvos1qIGcpB7Rd/18KnZjoaDWSU5cijaui0qC2sLKzVb1Ws+W2S/I9JTVPram94b51LsJfBuPxmZso7Bo7B+uTC7fwZ81hiHgZjOyF86LlmqkIC3yhAt5ERERERERk/PX95Jz6Yq36Kdyoipp7U9LUyijTKqO6qOclkCidc9v+MwuPz3nj+fV7MMa2gOTIPksHYwl41p0zSE0hc3HZRr1tCOnh0anr2mlzAk5dV3V9UbxNHdUGI3V5Dan7a6YZSqh0F1eU6d5EBcLlRzq1p4XM23psyirttDZERFkFA51EROkoKixC+7fcRJtbWKi/Zai/pKORIGNynId1UGletrb/RK2vzmf9YWGT9DwWko4k7ojAzge+VWl0dL0u2D8QDg2c4bM19ub+TUWFhccL7sn+SlKDsGdBKZ4jssnysTg1Z60atSh6XV6tUvgkRUYtakYuFqhZDq/uP1YjKSUAmLdKKXT3WqaWSWWp5ZopODJpRZLpb1IjbkVBfbaW/322D/afx4EPFiX6nxwO+eI/Lpof9eYMxpa2H+Olz0PkqVQyXoBU13t6TVutfSw9OzWfreyzRrBfIG5vOIRCdSsx0ElERERERGQi9f2UkulspDNtnkolEHTvkRoxuH/EQrVM0pjKqEqZhiW9Ap2Z0RaQ0oDnzd/3osFXw1WgU18bQoZ8J+K0E3j/tR+nX6e41aegS0WViWub+xQ1ilbSHdeY+G6qt0UCzdLG0HTFOO30RZJ6UtIt7+wxU02nEzf1r4z+lGNBRGQKzDN7A4iIjJWMsLPKGX8ejKT47vCC0/vuKnWLkN/SqzF2PSHqhlRD5tGQdLRywyzpVRzd6+tdt/Sc1CjXuyUig8Pgdyh2zs5cr99D2JcshLyVS+FZEhWb7A550eVg4mCd2sag+Nuoz3PvB6rSUPbdZv+9t6OD6p2p67hZ57LT9oAs3a0RsuWxT9H+yjGsPqknLryuvJz+Yh3+qjkMHi4j1U+w3xPs7vNFkkFOqfi5Lv5Q9/4m+Ez0ub/vrBpNKhUKjfzVy8auJyj+vso6pbdrcMBT9bjiIDe965aKquY7o1LQ9G+DS99v/u84vM6VLxWYYi1r4cnFOynaZiIiIiIiIjL8+n5SZF5QzYhSTR1U5suUDrXhz16p1LgODWOzAWXLa4/8NcuqkaPG3BaQlBzF8muPvdSRJZVv4BXfZNsQdKk40A01p7ync5na/hQeC9+dJ9U+aTM9mZmpqYZ0HVP5TkQGhajMWzIitXzfVkgLyfb0u/NAbZvIyZn/U8F3CXIKn81HUbp7ExX8lFHHMpenpF4mIjIFHNFJRJRGEnBq/ft0RIWEYWfP2XpfKylIq423UmlLXk+ZgYtLN6igo8xl4bpolJp/8erPO3Dlx21ounICOu1biBD/QDw4eF7vusv3aaXmmZAeg89u3IPnoP/meZA5KyVNTUxEJKKjolUKk+c37idZuZEgXFK9QysP74iOe77Go5PX9M4VIb0Y9/SdC5dZA1Vlz8zCXKVUPTByEcJ1HDev6T+p/Q1/Hgy/wxdU71N9Wv8+DTA3h4WVpTp2V3/ajrTIWaqwqgjokvAzeXnbX29K2wMjv0X9Be9rKwyBF26rEZ4yivbZtXvotPcbVeH0HDBfpSTqvG+hqsRIhVgfCU43WTEeMVFRiI6MhteMn9X8LqJk+3oq8Ckpd6SSK5WWm797pulYEBERERERkeHV96uO7YYKfVurIGaDiiNQd84QbGo9UQUyZb3WObOrOmFEcBj2Dv0a4c9fqf/bN+wb1J7eV420NLeyUHOESppVY24LkG2Q42Blb4t3Tq2A/5GLOPjhd6rTcc2PY4OTMj3Mkwu34fXpqjS1IUiA+qVPbPA1oetrdqPOjH5wft8dp5IZqRlw/ApOzV6DZj9NjP0MrC1xb/dplcI34TE9NnWVmv+zy6FFqp1AvhM5HPImue7CrlXUZy/HQdqBSravj2OfrFTzwerjf/QS7mw8rNonxO1Nh3Fv95tnvyIiMgRmMTGaIpjetojgUKwt04cHnkxeZk5ebGrnmUx2n3DS+vRan/OIjqpn6a2/Y+faMARuf8/E5ZVb4LvjRLqsz/XbD1SgUCp5zVdPUmlhk6tMvU0D/DywrkI/hL8IfmvrM7bJxbP6NYAoIxjjdYDnNlHmMIbrBa8PRG+O57pp1PdT+l5ZoS0gpe/VduNsNTVQ5KtQGAJJNdtx91dYV7G/Qb2XoV8jeC9A9OYM/TxPiKlriYiMSGRoOPI6O8J9x/w3XlftaX1R5cMuag4NcWn5JoOq2AjZtppTeqPauO5vvK42Hp+hUH0n1dNVSIXOkIKcQiqXbv/MQtHmNd5oPQXrVkLHXV+q9cVEsz8TERERERFRVqrv65PV2gKSYmFjrerNdiULaufa3N5pmsEEOUV0dDQiQ8LUdsp3I6M4dmyAFv/7GCGPnmXYexARZSSO6MxE7F1CWQVHdBIZLmProZVaLGuJTPM6wHObKHMYw/WC1weiN8dznYiM+RrBewEi0z/PE+IcnWRUJA1jSuzoOkPlv5c8/Df/2o9Do79L9Joak3qqnmGHP/oeN9buVmk7JMd9cmSidJnUm4iIKCtiWUxERJS18V6AiIgoa+O9ABkaBjrJqBwYtSjJZfYlCqngZeiT53jufR8hAc9wbc0uVOjTCj5bjsablDtftTKoMqoz7u89o4Kcwv/YZb3rL/tOUxRpUg0Pva6m814REREZD5bFREREWRvvBYiIiLI23guQoWGgk4xKUnMGWNhao/3mLxAdGYV9wxaqIKc48dkvKNqkGuovGKYClOHPgmCRzQqNFo9CxKtQHB6/XLuOIN8A9aOLQ31nODRwxtMrPjg6cUUG7R0REZHhY1lMRESUtfFegIiIKGvjvQAZGvPM3gCi9OD6zQdqUu5Tn6+B/+GL2ucjXwczbQvkQr05g9VzNT7uhdzli8Nr+moE+wcmu+4cRfOjyQ/jVWDUc9CXahJwIiIiYllMRERErJcTERER2+gpc3FEJxk95xEdUapzQ9zecBiXVmxOtNzv0AVc/WUnKg10U4FNp6Ht4bvjBLz/2p/sumX0Z7NVE5Etjx329JuHl3f8M2gviIiIjBfLYiIioqyN9wJERERZG+8FKDNxRCcZtcKNqqDWlN4IvOyDw+OXJfm6U7N/xYs7/qg8shPCnwfj6KSUpZ+VlLf5q5XB2a//wn3PM+m45URERKaBZTEREVHWxnsBIiKirI33ApTZskSg8/Hjx5g0aRLKli0LGxsbFC9eHGPGjMGrV68wePBgmJmZYcmSJZm9mZRKdsUKoMn34xARFIK9gxboTSkbFRGJiJch6u8n570R8ih2Dk99Kg1uh7I9mqrRn+cXevDzMWFVPuyCpj9MQLdjSzHAzwPdvZIOmhMR0X9YFpNBMzOD0/vu6HJwEfreXod3Tn6P2jP6wdI2W2ZvGRGRyeC9ABky1vWJiDIe7wXIEJh8oPPs2bOoUqUKvvzyS/j7+8PJyQkRERFYvHgx3n33XVy5ckW9rnr16pm9qZQKFrbWaPbTJFjnyoEDHyzCS5+Hel9fbWw35KtSCk8u3EbRptVVqlt9CtWrhDoz+uH5zfs4+OFifjYmTkYFOzSsrFIThz19mdmbQ0RkFFgWk6FzmTUALjMH4Nn1ezj26U+4s+UonAa3Q4v/fayCoERE9GZ4L0CGjnV9IqKMxXsBMhTmpj6Ss0OHDirAOWHCBPj5+eH06dPq8fz587F161acOHFCjeisWrVqZm8upUKDr0aowOXZr/5MNqVs3iqlUHV0V9zfdxbbOk3Fc+8HqDtnMGzy59L5+uyF86rRfZGh4fActECNGCXT5lF3JH53HoidPWcj+OHTzN4cIiKjwLKYDFnu8sVQaVBb3Nl6DHsHf4kba3fjxGe/wOuzX1DYtUqynd6IiCh5vBcgQ8e6PhFRxuK9ABkKkw50jh49Gvfu3cOoUaPw1Vdfwd7eXrtMUtlWq1YNkZGRcHR0RM6cOTN1WynlnIa5o0zXRvDd4YXz3/6t97Xm1pZotPhDRAaH4fCE5YgKCVdzecpI0Prz39f5+marJsK2QG4cGrsUz2/c50eTBQT5BmT2JhARGRWWxWToSnVxhZm5OS6v3BrveQl4RgSHoky3xpm2bUREpoD3AmQMWNcnIso4vBcgQ2KygU5JSfvHH38gf/78mDt3rs7X1KpVS/2WgKdG06ZN1QhPXT/Dhw9/a9tPujk0cEbtT/u+Tin7XbKHqcZH7yJPxRKq937wgyfquQCvq7iyajtKtqubqDd/vblDUaBGOZz/7h/4bjvOj4GIiIhlMRmh/NXLIjoqCo/P3Ij3fFRYBAIv3kH+6mUybduIiIwd6+VERERZG+8FyNBYwkT99ttviI6ORu/evWFnZ6fzNba2tokCncuWLcOLFy/ivU5S3H7++edwd3fP4K0mfWwL5kbTH8bD3NJCpSEr3qZ2kq99etkXlrbWcB7REXd3n8LN3z3jLT89dx2KtaipUtj6HbqI0MfPUe69Fij/XguEPHqmRnKW7tYoyfX7bvNCZEgYPzAiIspSWBaTscheKA/CAl8iOjwy0bJg/0AUcqkIcytLREckXk5EREnjvQAREVHWxnsBMkQmG+j09IwNbDVr1izJ10ha24SBTicnp0SvmzNnDgoUKAA3N7cM2VZKmVxlisImX+y8mtXGdNP72gtLNqCEWx01v+aRj75PtDwqNDaFrds/M1UKW5m7SRq8hKStlXS3+njUGYGge4/40RERUZbCspiMhYVtNkSFR+hcJqM6hXSKC2egk4goVXgvQERElLXxXoAMkVlMTEwMTFDx4sVVIPPMmTOoXr16ouUyN2fhwoXx+PFjeHt7o3Tp0jrX8+jRIxQpUgQjR47EokWL0rQttWvXhr+/f6LnrWLMMSPaJU3rJDImM829EGEWnSnvndrzrNPeb2CVwwYeLiMzdLuIDEVmnp9vA8taItO8DiR3bnfy/Bo2+XPhj6pDEi1rsmI8SnVsgP+V6MkRnUQmeL1g2U/05kzxXGddnyjrXCN4L0BknOe5g4MDTp48mab/NdkRna9evVK/Q0JCdC6X+TslyGlvb49SpUrpTYErQdG+ffumeVskyHn//v1Ez1ubWQCF0rxaIqPxwO8BwmOiMuW9eZ4RGe75+TbwGkBkmteB5M7t4IdPkat8MZhbWyZKX5vdIS9CnzxnkJPIRK8XLPuJ3hzPdSIy5msE7wWITP88zzKBTon+Pn36FKdPn0b9+vXjLfPz88PEiRPV31WrVoWZmVmS6/n1119RqVIlNSrzTbYlqd4lMNzOL0TppkjhIpk6opPnGZFhnp9vA68BRKZ5HUju3H589iaKNq2O/DXKIeD4Fe3zFtmskLeyIx4e++85IjKt6wXLfqI3x3OdiIz5GsF7ASLjPM+TiqNl6UBny5YtceXKFcyfPx+tWrVC+fLl1fMnTpxQozNlNKfQldZW4+rVq2qo7BdffPFG25LUcNuI4FCsLdPnjdZNZAyu37gOq+w2mfLePM+IDPf8fBt4DSAyzetAcuf27Y1HUHV0VzgNbR8v0Fmud0u1r7f+OfCWtpTItBjD9YJlP9Gb47lORMZ8jeC9AJHpn+dZJtA5adIkrFu3Dnfv3oWzszMqVqyI0NBQ3Lx5E23btoWjoyP+/fdfVKtWTe9oThnt2bt377e67UT0dpXu3hh2xQqov23y5YS5lSWqju2mHgfde4RbHmwMJSIiMibPrvri6uodqDS4HZqtmoh7e04jV7micBrcDv5HLuHWP4cyexOJiIgog7GuT0RElDWYbKCzWLFiOHjwoEpRu3//fty5cwdOTk5YsWIFhg4dijJlyqjXJRXojImJwdq1a9G0aVOUKFHiLW89Eb1N5Xu1gEMD53jP1ZzcS/1WjaEMdBIRERkdr+k/I+juI5Tv0xLFWtREaOALXPlpO84s+ENu9jN784iIiCiDsa5PRESUNZhsoFPI3JpbtmxJ9HxQUJAKfJqbm6Ny5co6//fAgQPw8fHBjBkz3sKWElFm2tGN5zkREZGpiYmOxqUVm9UPERERZT2s6xMREWUN5siCLl26pEZslitXDtmzZ08yba2trS26d+/+1rePiIiIiIiIiIiIiIiIiPTLkoHOCxcu6E1bK3N5enh4oHPnzrC3t3/LW0dEREREREREREREREREWTp1bVoDnTY2Nnj27Nlb3ioiIiIiIiIiIiIiIiIiSimO6DRw1Sf0gEU2K+1j128/gNPQ9qleTwm3OihQs1yKXlu2R1O8d+0XuO+Yr32u4cKR6HJoMTru/gptN36OfNXK/Pf6ns3RyfNr9Lv7R6Jtqz2tL7qfXI7mqycl+755nR1RqlPDeM8N8POAdU7d6YVTc9z0cft7JrodX4pq42LTFOeuWAJu62ehy8FF6LT3GzT8ZiQsbKzVMttCedDqt0/Vso57vkbTHz9Ctnw5tetq4/EZel1enaLPKOFn4lDfGR13fZnqfbUrVgAV+rVO8evlmMrnVbR5DfVYjrm8r+yr/DgP66B9bYFa5dUytXzfQtRf8D7MrWP7RxSsW0k9n9bPiIjIkBhKeSvlq5SzUt7KNdah4X9ziduXckDr36epZXJNduzYQLvM6X13dD3yXYrKEV3lRnevZaocTi05RrYFcqfotXJM3zm9AvXnv699runKCehx5gedZUn+GuXUvsr9R5u/ZiC7Q95kl0l5Lceg981f1WeRks8gV9ki8R6n5J4lJfcw+o5/v3t/qO3UHPNiLWrC/d/56HvnN7jMGhD/H8zMUPfzQeh6dIn6jCsOdNMu0vd/qflOEBFlRYZS9lcZ3VXVL/vf/zNR2SX1cKl3yrXcffs8FHatkqI6ujHWwy2z26i6ds9LP+G9q7/Ee61d8YKqvNPUW5v+MAHWuXIku8y+ZCH1fF+f31J0n5Pwvkb2J1G5nAJSty/arHqKX9vn1lq1nTav2xb0tbHU+LiXWqapp+u6/8hVrqhaZ9xt530BEZFx3gtoSNuA1CN1bd+bXveNpY1Ao/pHPdTyuNsn26va61+Xj3HbS4yxvV7u69ptmqM+16Tu56T9o/P+hYm2L7fENv6eic4HvlU/JdrVzRJt+Vky0Onp6anm6GzfPvUXrrdNTtyUVhT0KeHmooJWKeV/5BK2uE3WPvbd7oUNTcZiU8uPcOG79Wi2coJ22ZPz3tg37BvcWn8o0XpOzv4VZxf8kaL3VBWszilrJEzv43Zixs84t9BD/R0VFo7jU1ZhfaMx2NTiI1hmz4YqH3RWy2KiotXrYpdNQJDPQ9SZ1le7nn+7f4a7O09myGeSFKnYVejbKlX/s73zNNz3PKP+fvXgMXa9Nwcbm43Hto6fokL/1uoiLgIv38Hmth9jU6uJarlN/lyoOCC2gTXg+BX1PBGRKTCU8rb5T5Nw9qs/VHkrZavrog+0nW1cvx2F25uOqGU7us1QjZiaAN/lH7bgyITvM6zc0FuJKZiySoy4tHwTjk7+Qfv42v92qv1JxMwMjZeOhtf01VjvOhr39pyBy6yByS6LCg1XZdOTc7dStD1l322GXGWL4k2l9h4mMihUbWfgpTvq8Yvbfjg8bhkuLt+Y6LVlujdGrvLFsb7haGxp9wkqj+yE3OWLJft/qflOEBFlRYZS9vsdOI9dvefg4bErOuupUu+UMuPIxBVo+sN4VQ4mV0c3xnp4dGQkLizZgJ09ZiV6XfDDQGzvNE1bL5XH8l7JLXvp81A9H/LwaYbc1yTFoUHKA53ihfcDtZ2hT14k28ZyadlGbGwe+53Y3Xcu6n85DNny/jflkpmlBRp8ORw+273i/R/vC4iIjPNeQFjZZ0etqb1xf09sW25c6XHdN4o2gtfyVy+rfoLuBiRatn/4QlU+ys+dTUeMur0+JOCpavPwmvFzkq+vNbUPAk5ci/echa01Wvw8Gafn/4YNjcdiY9PxeHj8SpZoy8+SqWuNhaY3Q9sNs1WAbWfP2dpeGq3/nIEcRfLh2bW76iSOjohUF7aak3rCwbUyLKws8fyWH45OWqFOzuKta6Nw46qq18iV1Ttwb9cpNF4+Flb2trDIZg3/wxdx/NOfgJgYndsS92Lw6PR11bBqZmGutuvpZZ/YBdHRad5X6blYfdK7sLbPrnoWPDp9Q3uRqzDATfWmkNec+8YDN//Yqx3ZIg2btvlzwdzaCtfX7MLV1Tt0HjfZd6ch7WFuZQkzczN1sssx0OXlbX/t3zHR0Xh81ht5KhZXj0MfP1c//x2LG6g0qG2q91d6ZyT8TOR95TOsN3cICtapqP4+NGYJnpzzVv9TpGk11dtVGrxlv059vkYVjDLKMkfR/Oq4Bd1/DM8B81F7ej841HeCuaUFwoNCcOSj71XlSZe4F8SIl8F4fvMB7IoXAI4CUSHh2mUW1pawlMb2JL4jRETGylDKW2mkkrLO72Bsiv0Xt/wQ/jxYlRm+244jr3NJ3NtzWi0Le/JCBckcOzXA5RVbUre/OsoNIb386s0bqiolN37zxPlv/1bPS29Ml88HqbJBygHff0/gzPzfVZkkmQ6afD9OBRgPjV2KbHnsUWNyT7WvkgHg8orNal1J0exrQvmqllafhZRz4tqvO1FTrddK9U5MallUWESKj0O591ogX7XSqDNzgOrJe2ruOu2IksbLxqqyPyo8Evve/xpBvrGVqNLdG6PSwLYwt7JAxKtQHJ+6SjWe6rqHabR0DHKVKaLuPaRT0ZHxyxHySPfUCPJZi5JtY3taxiU9UW+s3a3uScKfBeH2xsMo1cVVfQb6/o+IiAy/7BePz95McjvDXwRr/5ZyJqV1dGOsh0eHR6pjJSMgdC3TMDM3h6VtNkQGhya7LDV03dcI24J50OKXj2HvWAghAc+wd+jXqjwWzsM7wrFjfVXvlnaCI5NWIFsuO1To10p9FtKB2GfbcZxf/A9arpmi7pOkPv/00h1VR48MCdO5LfraWOJ+Jyxz2MDMzEz9aFQf/w7ubD6KbLntYJ3L9EZrEBFltXsBUe+Lwap+XvL1yLy40uO6bwxtBJogXt0vBmPvkK/QbkPs55WeDKm9PtgvUP1oOjknVLhRFWQvnBdXV21H/jhZPUp3aYRHp64jwOuqeiztCNJ+lBUw0GnApIIhQ5wlmh/3Zjavcyns6D4D0WGRKsVqyfZ1cXvDYdXDPyIkDFvbfaJeV3Vcd9SY3AvHp/yoKkHSIHp55Va1TBoD9/SbpyoAUhlo/vNklOrYQDWeJUcqKjJ6IjUVqORIz0XpcVqirQs8By6Ityw6LELtk6SWc98+H94e+4EYoMnycTg4apEKzMmFrv2WudqKWcLj9mDfWdx+3RtSKk7tt36Bv+qMiFcp0kUqSeV7t8CpL9YmWibHTYKccjFPLemdkfAzkUqQjCo5PH45jn3yo9qHmh/3wq5en8OuREHVCCt/RwSFwN7RQRXEHi4jcHTSDyo1QdweGReWbsDJWf9Tf0sqm7qzB6pRm8nJVb6YKqCPTl6hfU6Ol3w/pGJ3b/dpXP3531TvLxGRITOU8jYs8KXqtefYob6qqEiqEgmUqc4nqnf/LZTp1hgXl21U5ULBOhUQdPdR6vdXR7khrHPmwLYOU1XAtdvRJbj5+14E+wfCdfEo1UD38Ohl1WjX8tdPUNK9vhqBUa5Xc1Xx04xMlFRxMqpCbqatc9upm/r7+86qG/TUsCuWH0H3/tu3yFehqiIglSZ9yzQByZS4sW6POp6XV26B747YslwqM/mrl8GmlhNVD9FaU3qjyqjO6pjJ8S7d2RXbu0xT9w+S9kUCohubjtN5DyO9LzUVClmH9BaO21M1xceiaPz9lc88PXqYEhFlZYZS9qeElEUlO9RHtlw5VMOerkbStNbRDbUerosES923zUWOYgXw9IoP9vSfn6JlKaXrvkaN6qhZDpvbTELY0yC17zI6Q0bQSqcjOTbb3Keq+x7pDFVv7lDs6TsX1/63SzU2e03/bxTGgZHfqnUIaTSuNLitGsGaFpUGt0PFAW2QvUg+HJmwXDsSVFL7yz3CzndnqfYDIiIy/nuBku3rISY6Rr1HwkBnel33jaGNQNT+tC+u/bITwQ+e6FzuuvhDlfji0Zmbqi0/tQE+Q22vT0jSzkqGL3nf3OVjB2dpSGA0KjwCLf73CXIUzovAK744MfOXLBHsZKDTCPluP64daSc9PuQkEtLbUnpiOr6+6EkviSQbQM3NUOvTPijkUlGlvrHNnxPPrvome8Et3a2RaoDd3mU63pZb/xxUv6UiFR0ZpXqRyJB9OXGloqFhZWejntP0qIjLrkQhNF46BtkL50NMZJS6sNqXKKjWmRSpLDVZMR73951TaYESqjdvCMKeB2kvfOnh5R1/PD5zQ/0dcPIanIfHzpdZtFkN5CzlgLbr/0vjIwWE9AzRpUjjqioIa2Vnq3rOSq+e5EgvkBarJ6uCPm5hI42rkjJAjXBZOloVqmmtpBMRGZPMKG/3SC+/T/ugyodd8ez6XTz0uoqYyNhGy0Ojl6D2Z/1jewTee6x6OsZERaXb/t5ef1AbcH3pE6Bu2sOfv1LzgcmojbijB+LOaxmX9NaUua1zli6syttseeyQp2KJNFViMov0ftSkwQk4dV2buaF4mzrI41QS7lvnal8r5asmtXBCpbu4okz3JqqSKz9yXImIyLBlZl07KdJQJz/Sc18atWS6ERlZktF19MyqhydF9lkaC6WeXnfOIBVwlM5fyS17U/f3ntUGKANOXVP3NZogqHSO6vBvbFBVGnqTZGam5kor1qIWzC3NYZUzOx6duJ7mbbqyapv6kfuSxktG48H+c4gMDVdtFPuGfJ3m9RIRkWHdC8jIyWpju6mpaxKSzkYZfd03pDYCGWUpnaYkq5Iuch/06v7j2JG3k3uh0aJR2N3nCxh7e70udb8YgvOL/tF2dIrLzMICRRpVxVb3KSooXXPKe6g/byj2DTX9+wMGOo1Q3LRs0mPT3MJC/S3pSmQYvNzkJsd5WAd1Qdra/hO1vjqf9YeFjf6c5JI6rfr4Hvi3x8x46VszmsyZGfdiIfsriVnCngWlOK90k+VjcWrOWvhsPaYeywTEMmQ+KXJRbLJiXGw+7Gk/JVpe9/NByFEkf2yv13RM5SrD+uN9tpb/fbYP9p/HgQ8WJfqfHA754j8umh/15gzGlrYfqzlJ8lQqGe+Cq4uMgmnz5wyc+9YDPluO6nyN9EKSHkyluzZioJOIsoTMKG8lVVncHn0ycbyky9F0PNknIzlea7Vuaoq2IU37+7q81cwDJjfJKUkLK+l/7nmext7BX6rHHXYuSNOcJxLIjZu6TipOUqmUNLFBeeyTXJYhn3ucstj7r/04/TrFrT4FXSqq0Rbb3Keoyoekv6kx8d00bY+kuZH9lQCskBG+r+6nfiQv0dsivZ7d/pmJEzP/h0vfb4q3rFB9JzVnTGRIuOqBLCOvNCTFlfRsfnDwAnb2mKlz3dJjWkZHb3abnCio4jTMHXWm90PAyevY0/eLeD3ziQy9rp0S0sFJGsbyVCqhsjxkdB09M+rhKSFBTRlR0uCr4YmCmfqWpctxSHBfICM7r6/Znew6Snd1ReGGVbCj63Q14kPuEQq7Vn7jbZP7RmnElDlBn3v7qSwQbn9/ph2FIw3t1rnsVIo9IiIyvnsBmdJF2mw77o5tB5CRlVK3zJYvp2qjzejrviG1EUi5mbdKKXT3WqYeSycqSQsvaeMlVbAEOdV2RkaprE1dD38HY2+vT4oE0OWnzox+6lhKR7IuBxdhfaMx6jj4Hbmk7g/ELY8DaPXbNGQFerqckSEIfxmsevulhO8OL9VLUHp0CPmtyeMc/jJE9b7UkGHjEsSTC5L0DnF0r6933dJDVHpD/PvuTO2FIy1k3hA58XSRtHNxt1Gf594PVAWh7LvNtM9JDxs5sXUdN7nIa0ZnSI9X6U2SFOmJKQ0t0mtTcmUn5DJ7EOxLFYbnoAXxetLqIimAZNi8Lgk/E31kSL/0XJGLoIZMvKzWExR/X2Wd0uM2OCC2wbfiIDe965aeuW3+mqHS5kgDblxyTCXoK6R3rOxPYJwGKSIiU2Eo5a1ckzXK9W6JyOAw+B2KnaPCRnpMvq5UyDwQkm781ut0cLpIeSvlbqJ9TVBu6COdXGQ+kSqjuvy3jYXyqCwA2v2NW97mzoFXr9OsFqpXSY00SAtpwJXyRxruRIW+rXF310l1HPUt06XiQDfVizHJzz2FZbHvzpPqHkLbO9PMTKUX1nUPI597ZFCIupeQ8rN831ZIK5/NR9V3QVIeyX2OBIJubzyS5vURZZZiLWuh1dqpKkiyvdOn8YKc0rNbRke9uO2Hwg2ddc7Rp0+NST3h8tkA1fC0s+csBjnJqMr+pEhZpxlFoqn/yXyZ0jiW2jq6MdTD9clRLL/22Ev5K6l8JRVbcst0kXsCuTfQJeF9TXLfCUlfp9l3+bzyVi71371WvO+EHUIDX6hjJx20yr7bFGkl938a9iULqfd8dv2eGin0e+XB8HAZqX4k89TN3z0Z5CQiMuJ7gXt7TuOPqkO013afLcdUetgz835L03XfmNsITn+xDn/VHKbd32C/J2rEpgQ5Zeo5SeeqIenln1y8bdTt9fpojoH87B/+LZ5fv6eCnOLO5iMq44R0jhNFW9RE4OXYNMKmjiM6Ddyl7zej9e/TERUSpp0UOSkSqKo23kqlVNMMMry4dIO66ZX5NFwXjVINCFd/3oErP25D05UT0GnfQoT4B+LBwfN61y3pZkICnqnUphrSa1Qa8GQ+K8lJLhcuWb/z8I7Y038eAnVcUORiKid1Uj1UKw/viI57vsajk9f0zmMlvSdk7guXWQNVISPBybDAFzgwchHCdRw3r+k/qf0Nfx4Mv8MX4s11lZA0IDq2r6fycUt6QPHwxDWVc13m53Ia0g7PbtzTpq57eTcAewfFvi6hnKUKI+Kl7t7kCT8TmdxY3xB5mdNDJjKWi7ekRwi8cFv1GJFenM+u3UOnvd+oSq9MbiypEDrvW6g+HymIk2sYkkZb2S/5EZd/3Iabf+xVvWWkt6kcb6m4yWd0fqGH3vURERkjQylvy/dppUbOS89AKWukU42G9NyUuR7lmhz88Cl29/4iXs/CuKQhVAIHElBISFe5oY+UNXVmDlCvlx2W4Kv0mpRUM5I2rcGXw9VxOzR2qRq1UX/uUFQb212Vo49P39S77ha/foK8To7qbzlGUhaqtDwxMTg4ajHqLximeijKaM0DHy6O/Sd9y3SQyqek2dFFRmFIL0jn991xKpmRmgHHr+DU7DVo9tNE1ZNVymKZu1pGlSW8hzk2dZWa/7PLoUWqLJbPPYeOCqWGpP6R742Vva367Eu2r49jn6xU84N4exxAvupl0fXId4iJicGlFZtVpTa5/yMyJNLYIOmjJEgi19iEI7BLd2sMcysLNZ9Pu81zULZnM5z96s8UrVsyrcj9qtz/Hvzwu2Q7IhIZWtlfdWw31WlHyu4GFUeg7pwh2NR6ompIlPVKw52MTogIDsPeoV+rlHHJ1dGNsR4uZBvkOEi59s6pFfA/clGd19KAWPPj2E5LkurtyYXb8Po0NnWdvmW6yH3Hk3OxI2ITSnhfk1xqXwncunnEjqSR+vLN3zxVO4jvNi+U6dZEtSf4bDuu6tcl2tRRDcwS8Hx4/IreDh362lhkfjJJHRgTEYnoqGgcm7IKz2/c17utRERknPcC6TmvotG3EejbtwK50OzHieq+RPqGS/3/0IdJj+g0hvb6nGWKqOyLlrbWarocuS+SeVGv/fKv3v97df+xep3UqWRuVxnZeWRi4oFcpsgsRlpMKFNEBIdibZk+Bnf05aZaejaotKzpvD7nER1Vj5Zbf8fm+DYEbn/PVEPafXecSJf1uX77gXbS4uarJ8Fr2upkK3Rv0wA/D6yr0C/derqnZH29vdfAKrsNMoOhnmdEhiIzz8+sfg1I7/JW0kVqJrsv6V5fzZFx/tu/YSjilo9vs2xvu3G2Sgcc+SoUhkAaNiX10LqK/TP8veJ+J0ztOmDI53ZWlzB1rYx4qjd3CB6f9cau3nMQrqNxRYIbEiz5952ZaLZqokrTJT2UE04RETd1rTT4y3VF5sK9tmYXjk76IV2nlCDjvV5kpbI/pe+VFerh+kiaOzkOT6/4ov3WL7Cl3ScGc71IaVn9tt+L5zoRGfM1gvcCsdhGkHXb69PjvQz9PE+IqWspEZnEPq+zI9x36O+9kRK1p/VFlQ+7aHuLXFq+yaAqV0K2reaU3qg2rvsbr6uNx2dq7iHpbSukMmVIF00hFVy3f2ahaPMab7SegnUrqR6qsj7pIUJERJlX3sqoinrzhiA08KV6LPMtG1KQU5MWqEL/NmqOjowiPR2lbLIrWVCbynZ7p2kGE+QU0dHRiAwJU9spn39GSfidIMoMUg+Qc97v8CUVxNQV5JQU0HmdSuLmn/vUY8kqIh0CijSumuR6ZTS39NqWIKf0rj86cYXBBC2I3lbZr09Wq4cnRVK7Snkroy1ltLfMLyZzYxnS9SIqIlKNDJXtlNE2GYX3BUREWetegG0EWbO9Piu35XNEZyYy5N4lROmJIzqJDJex9dBKLZa1RKZ5HeC5bfgjOl/c8UdORwf4bD+uUtJGh+tOKVtv3lCU6d4Yf1QdqlJ1SkCix5kf1Nw/8n+6RnRq1n3y8zUqZRi9PcZwveD1gejN8VwnImO+RvBegMj0z/OEOKKTiIiIiIiI0lX2QnnU75d3HiYZ5JRR2KU7u8Jn63EV5BQyF6HMfSfz2Vnntkty3TI668WtB/zUiIiIiIiIsjgGOomIiIiIiChdXfhuPR4cvIDKIzqi9ox+Ol9T0r0erHPlgP/Ry7B3dND+PDx2WQVBy3RrrPP/Do9bhpCAZ2jy/TiUaFeXnxwREREREVEWZpnZG5CVWdpmU0OAibLCdz0z35vnGZH+c8SU8RpAlLLzhCi9RYaEY0+/uWjxy8eoPLwjzMzNcWLGz/FeU75XC/XbdeFIneso17MZrqzaluh5SV27o9sMuP09E02/H4f9I76Fz9Zj/BBJYdlPlDXuDXiuE2Xu+WfIeH0gMv3zPCEGOjORmZmZUeU5JjJGPM+IsjZeA4iIMk9UaDj29J+HFj9PhvP77jAzA7ymxwY77UsWQqF6leD99wH4bvdK9L+FG1VBxf5tkK9qaTw5fyvR8pc+D7G963Q1H2iT5WOxf+Qi+Gw5+lb2iwwby36irIHnOhHx+kBEGgx0EhERERERUcYFOwfMR/OfJ8NpqLu0TMNr2mqU7dVcjfK8tGIzAi/cTvR/T855q0Bn2Z7NdQY6RZBvAHZ0jR3Z2WTZGBwwA+5sZrCTiIiIiIgoK+EcnURERERERJShwU7P/vNwf99ZOA1pj7qfD0LZHk3x0vehziCnCLr3CI/PeaN0F1dYZLNKct2aYGfww6dovGwsSnVqyE+SiIiIiIgoC2Ggk4iIiIiIiDJUVFgEPAfMx/29Z1BpcDvkKJwPPtuO6/0fmXczW247lGhXV+/rgu4GYEeX6Qj2e4JGS0ajVBfXdN56IiIiIiIiMlRmMTExMZm9EURERERERCkVERyKtWX68IARvWW9vdfAKrsNjzsRERERERkMjugkIiIiIiIiIiIiIiIiIqPDQCcRERERERERERERERERGR0GOomIiIiIiIiIiIiIiIjI6DDQSURERERERERERERERERGh4FOIiIiIiIiIiIiIiIiIjI6DHQSERERERERERERERERkdFhoJOIiIiIiIiIiIiIiIiIjA4DnURERERERERERERERERkdBjoJCIiIiIiIiIiIiIiIiKjw0AnERERERERERERERERERkdBjqJiIiIiIiIiIiIiIiIyOgw0ElERERERBRH9Qk9YJHNSvvY9dsP4DS0faqPUQm3OihQs1yKXlu2R1O8d+0XuO+Yr32uyuiu6HJwEfrf/1OtK66GC0eiy6HF6Lj7K7Td+DnyVSujXWZfygGtf5+mlnXatxCOHRtolzm9746uR75Dx11fJrtNdsUKoEK/1vGe6+61DHmdHZFacvxsC+RO0WvleL9zegXqz39f+1zTlRPQ48wPGODnAeuc2bXPW9pmQ/utc9W+yk+rdVPVdmvkr1FOPS/Hqs1fM5DdIa963sLGWh2D3jd/TXRsk/p8cpUtEu9x89WTkFpy7Ep1apii18p+9Lv3h9pOzTEv1qIm3P+dj753foPLrAHxXl9xgBs6eX6tXt9p7zeoNLiddpm+/0vNd4KIiIiIiMjQMNBJREREREQUR/WP4gc606qEmwsK1Cqf4tf7H7mELW6TtY/9DpzHrt5z8PDYlUSv9d3uhQ1NxmJTy49w4bv1aLZygnaZ67ejcHvTEbVsR7cZqD2trzbAd/mHLTgy4fsUbY9d8YKo0LcV0oMKdBZMWaBTXFq+CUcn/6B9fO1/O9X+JBQZGo5/e8xUy+Tn/r5zcJk9KHahmRkaLx0Nr+mrsd51NO7tOQOXWQPVoqjQcGxqNRFPzt1K0faUfbcZcpUtijelAp2dUxboFJFBoWo7Ay/dUY9f3PbD4XHLcHH5xkSv9f77ADY2n6Bev63DVDiP6IC8lUsl+3+p+U4QEREREREZGsvM3oCsLCYmBpEhYZm9GUQZTnram5mZpdv6eO4Qmc75nN54fSAy/fM8o2lGEbbdMBsxUdHY2XO2epyrXFG0/nMGchTJh2fX7mL/8IWIjoiEmaUFak7qCQfXyrCwssTzW344OmmFCnAWb10bhRtXVaP/rqzegXu7TqHx8rGwsreFRTZr+B++iOOf/iQXL53b8vjszSS38+7Ok9q/H52+rgKZZhbmapvzOpfEvT2n1bKwJy9UkMyxUwNcXrEldcdiwfvIUTS/GukXdP8xPAfEjjYt0a4u6s0bqgKXN37zxPlv/1bPy4hNl88Hwa54AVjaWMP33xM4M/93VBvXHbaF8qDJ9+NUgPHQ2KXIlsceNSb3VMfB3NoSl1dsVutKit/BC7oXSJ3qVaj2oZWdrTyp/s5XtbQ6HhJAFtd+3Yma6j2tEBUWkeLjUO69FshXrTTqzBygRvuemrtOPW+Z3QaNl41FnorFERUeiX3vf40g3wC1rHT3xqg0sC3MrSwQ8SoUx6euQsjDp6g+6V1Y22dXx/TR6RsqmNto6RjkKlME5laWePXgMY6MX46QR890bsuLW37qd8m2dRMti3gZrP3bMns2mFtapuj/TA3vBYiyxr0Az3WizGPo1wheH4hM/zxPiIHOTCRBzrVl+mTmJhC9Fb2918Aqu026rY/nDpHpnM/pjdcHItM/zzOaBJ4kXev2ztMQ/uK/wFFe51LY0X0GosMi4bZ+Fkq2r4vbGw6j8shOiAgJw9Z2n6jXVR3XHTUm98LxKT+qYKQEGS+v3KqWSYBtT795iAwOhZm5OZr/PBmlOjbA7Y2H32ibnYa0V6MVJagnnpy/hTLdGuPiso2wK1EQBetUQNDdR6k/FpN+UGlOZYRgXNY5c6gRg9ny2qPb0SW4+fteBPsHwnXxKJxf/A8eHr2sgq4tf/0EJd3r49xCD5Tr1VwFhzUjE61z5cD2TtMQEx0N69x2KvB3f99ZBPsFpukYtP5jOvJUKoHQJy+wq9fn6jm7YvkRdO+//ZaAaHhQiAq6agKSKXFj3R51PC+v3ALfHSfUcxK8zl+9DDa1nIiguwGoNaU3qozqrI6ZHO/SnV2xvcs0RIdHomDdSiogurHpOJxd8AdKtHWB58AF2vXLiFMJSAtZh4wojjuaNTVKtq+H6hPfRU5HB5yeuw6BF28jq+G9AFHWuBfguU6UeQz9GsHrA5Hpn+cJMdBJRERERESUDN/txxEVEq4daWnv6KD+lvkdZYSeY7vYkXIyOjHJoKK5GWp92geFXCqqtKq2+XPi2VXfNwp0lu7WCI4d6mN7l+na5w6NXoLan/WPHYl577EaDRkTFZVun/Ht9QfV77DAl3jpE6CCqeHPX6GwaxXY5s+lfZ1lDpt481rGJSM6G34zEjlLF0ZMZBSy5bFDnool0hzo3PnuLHVMq47thqpjuuLYJz8ioz06dV0FOUXAqeuoNKit+rt4mzrI41QS7lvnal+bLbedmhdUl9JdXFGmexMVCJcfOa5p5bP1mPqR+T2b/TQJd3efwgvvB2leHxERERERkaFjoJOIiIiIiCgZcVOdyshJcwsL9bek85H0sw/2n0v2GDoP66ACgVvbf6LWV+ez/rCwSftcoI4dG6D6+B5qjsrQx8+1z8soxn1DvtI+brVuaoq2L03HIvr1sXid1mir+5QUpYWVFMH3PE9j7+Av1eMOOxe8+byoMTG4sWY3uh75TgU6JcgrAb+4gVcJSksK2Qz5Tlj+953w/mu/GlGZnIIuFVFpcDtsc5+iRqNKuuMaE999422T78DjMzdQvGUtXGKgk4iIiIiITJh5Zm8AERERERGRIQl/GQyrnNlT9FrfHV5wet8dFraxo/Xkd+7yxV6vJwRW9v+tR9K1hgQ8VQEymc/S0b1+mrdRRnHWnNwL/747E6/uP463zEZGVb4OPBZpWg25yhfDrfWHklxXl4OL1ByfCYUHpfw4SDpemXO0yqgu2uckRWz2wnn/OxZx1mWdOwdevU4rW6heJTUCMi3kOMpx1ZC5SAMv+2hT+Mocqg4NnNXjCn1b4+6uk0kGYisOdEPNKe8l/Z2I81nq47vzpBppK/ObKmZmyFetTOx6ghJ/JyKDQhD2NEjN0Vm+byuklXzOGtny5YRDw8oIvBJ7LIiIiIiIiEwVR3QSERERERHFcen7zWj9+3REhYRhZ8/Zeo/NhSUbUG28lUpTGhMT+9zFpRvw7Po9eHvsh+uiUSjh5oKrP+/AlR+3oenKCei0byFC/APx4OB5veuWNKwSnLPJlxMNKo5A3TlDsKn1RDWfY+OlYxAS8AwtVk/Wvl5GdkrATEYFylyPMsow+OFT7O79BaJCY9PuJiTrlrSxYc+CEi17etkHz67dQ6e93+Clz0N4Dpivd3sPfLAIdWYOUK+XgxEZHIYjk1aodLRXVm1Dgy+Hq2N6aOxSnJqzFvXnDkW1sd3VvJ2PT9/Uu+4Wv36CvE6O6m85fi9v+2NHtxkqmFh/wTA1J6jEdmU7D45aHPtPMTHqb1kuo0VlJOeBD18v00EC1JKKV5fra3ajzox+cH7fHaeSGakZcPwKTs1eg2Y/TVSjXSWd8b3dp/HknLdKI1x5eEd03PM1Hp28hmNTV6n5P7scWqQ+O/lO5NARdNaQ9MDynbKyt1UjR0u2r49jn6xU88E6DWmHQnUrqXlB5WDI3LB+B84n+39ERERERETGzCwmRlMdp7ctIjgUa8v04YEnk5fekxfz3CHKPIY+GTmvD0Smf56b6rletkdTlGjrAs+BCzL8vRzqO8Nl1gBsajURJd3rq3k0z3/7NwyF67cfqOCnBOoymtvfM3F55Rb47jiBthtnY9d7cxD5KhSGQNLudtz9FdZV7P9WvxP68PpAlDXwXCciY75GmGJdgeht623g53lCTF1LRERERESUySJDw5HX2RHuO/SPmnxTkma33rwhCA18qR77bDlqUEFOTZrYCv3bqHk8M4qFjTU67voSdiULalPZbu80zWCCnCI6OhqRIWFqO+W78ba+E0RERERERMaEqWuJiIiIiIgy2Z1NR9RPRrv8wxb1Y8i8pq0GID8ZR1L5Jjd6MbMFP3iCP2tkXLDXmL4TRERERERESWGgk4zKAD+PFL1uR9cZai6c1r9Pw82/9uPQ6O8SvabGpJ6oNq47Dn/0PW6s3a3Shcm8NckJuhsAD5eRadp+IiIiY8eymIiIKGvjvQAREVHWxnsBMjQMdJJROTBqUZLL7EsUUsHL0CfP8dz7PkICnuHaml2o0KeVSsl1d+dJ7WvzVSuDKqM64/7eMyrIKfyPXda7/rLvNEWRJtXw0OtqOu8VERGR8WBZTERElLXxXoCIiChr470AGRoGOsmo3Pr7oM7nLWyt0X7zF4iOjMK+YQtVkFOc+OwXFG1SDfUXDFMByvBnQbDIZoVGi0ch4lUoDo9frl1HkG+A+tHFob4zHBo44+kVHxyduCKD9o6IiMjwsSwmIiLK2ngvQERElLXxXoAMjXlmbwBRenD95gPkdXbEqc/XwP/wRe3zka+DmbYFcqHenMHquRof90Lu8sXhNX01gv0Dk113jqL50eSH8Sow6jnoS0SGhPFDIyIiYllMRERErJcTERER2+gpk3FEJxk95xEdUapzQ9zecBiXVmxOtNzv0AVc/WUnKg10U4FNp6Ht4bvjBLz/2p/sumX0Z7NVE5Etjx329JuHl3f8M2gviIiIjBfLYiIioqyN9wJERERZG+8FKDNxRCcZtcKNqqDWlN4IvOyDw+OXJfm6U7N/xYs7/qg8shPCnwfj6KSUpZ+VlLf5q5XB2a//wn3PM+m45URERKaBZTEREVHWxnsBIiKirI33ApTZTD7Q+fjxY0yaNAlly5aFjY0NihcvjjFjxuDVq1cYPHgwzMzMsGTJkszeTEoDu2IF0OT7cYgICsHeQQv0ppSNiohExMsQ9feT894IeRQ7h6c+lQa3Q9keTdXoz/MLPfgZERERsSwmIiIi1suJiIiIbfRkQEw6de3Zs2fRtm1b+Pv7I0eOHHBycsKDBw+wePFieHt7IzAwdn7G6tWrZ/amUipZ2Fqj2U+TYJ0rR2xKWZ+Hel9fbWw35KtSCk8u3EbRptW1qW6TUqheJdSZ0Q/Pb97HwQ8X8/MxRmZmKk1xhb6tVFA89MkL3N58BGcX/MF5VomI0gHLYjJ0VT7sgnxVSiNf1dKwL1kIQXcD4OEyMrM3i4jIZPBegAwa2wSIiDIc7wXIUJib8kjODh06qCDnhAkT4Ofnh9OnT6vH8+fPx9atW3HixAk1orNq1aqZvbmUSg2+GqECl2e/+jPZlLJ5q5RC1dFdcX/fWWzrNBXPvR+g7pzBsMmfS+frsxfOi6Y/TEBkaDg8By1QI0bJ+LjMGgCXmQPw7Po9HPv0J9zZchROg9uhxf8+VhUeIiJ6MyyLydDJ9AYODSurOdbDnr7M7M0hIjI5vBcgQ8Y2ASKijMd7ATIUJhvoHD16NO7du4dRo0bhq6++gr29vXaZpLKtVq0aIiMj4ejoiJw5c2bqtlLqOA1zR5mujeC7wwvnv/1b72vNrS3RaPGHiAwOw+EJyxEVEq7m8pSRoPXnv6/z9c1WTYRtgdw4NHYpnt+4z4/HCOUuXwyVBrXFna3HsHfwl7ixdjdOfPYLvD77BYVdq6gRvURElHYsi8kYeNQdid+dB2Jnz9kIfvg0szeHiMik8F6ADBnbBIiIMh7vBciQmGSg88qVK/jjjz+QP39+zJ07V+dratWqpX5LwDOugwcPokWLFup/c+fOjXr16uGff/55K9tNyXNo4Izan/Z9nVL2u2RfX+Ojd5GnYgkV4Ap+8EQ9F+B1FVdWbUfJdnUTBbzqzR2KAjXK4fx3/8B323F+JEaqVBdXmJmb4/LKrfGel4BnRHAoynRrnGnbRkRk7FgWk7EI8g3I7E0gIjJJvBcgQ8c2ASKijMV7ATI0JjlH52+//Ybo6Gj07t0bdnZ2Ol9ja2ubKNB57tw5tGrVCo0bN8bPP/8MKysr/Pjjj+jevTs2bdoEd3f3t7YPlJhtwdxo+sN4mFtaqJF6xdvUTvIwPb3sC0tbaziP6Ii7u0/h5u+e8ZafnrsOxVrUVCls/Q5dROjj5yj3XguUf68FQh49UyM5S3drlOT6fbd5cZ5HA5a/ellER0Xh8Zkb8Z6PCotA4MU7yF+9TKZtGxGRMWNZTERElLXxXoCMAdsEiIgyDu8FyBCZZKDT0zM2qNWsWbMkXyNpbRMGOmUUqMzZuWHDBmTPnl0917JlS5QuXRpr165loDOT5SpTFDb5YufVrDamm97XXliyASXc6qj5NY989H2i5VGhsSls3f6ZqVLYSnrTQi4V1TJJWyvpbvXxqDMCQfcevdH+UMbJXigPwgJfIjo8MtGyYP9A9VmbW1kiOiLxciIiShrLYiIioqyN9wJkDNgmQESUcXgvQIbILCYmJgYmpnjx4iqQeebMGVSvXj3Rcpmbs3Dhwnj8+DG8vb1VIFN89NFHWLlyJZ49e6YCnhqVK1eGs7OzCoSmRe3ateHv75/oeasYc8yIdknTOomMyUxzL0SYRafb+pI7d7oeXQJzKwt41B6RaJnr4g9R9p0mWFehH8JfBKfbNhFlFel9Pqc3lq1Epn+ep+Vc77T3G1jlsIGHy8gM3S4iU2eK1wciMr1znW0CRFn7GsF7ASLjPM8dHBxw8uTJNP2vSY7ofPXqlfodEhKic7kELCXIaW9vj1KlSmmf79u3L5YuXYoJEyZg8uTJsLS0xIoVK3Djxg0sW7YszdsjQc779+8net7azAIolObVEhmNB34PEB4TlW7rS+7ciQoJg1WO2NG/CVlks1K/I0PC0217iLKS9D6f0xvLViLTP88Fz3WizMHrA1HWYOznOtsEiLL2NYJ1BSLTP8+zRKBTIr9Pnz7F6dOnUb9+/XjL/Pz8MHHiRPV31apV443clDS2e/bsQdeuXbFw4UL1XI4cOfDXX3+peTvfZHuS6l0Cw+38QpRuihQuku4jOvWdO8EPnyJX+WIwt7ZMlL42u0NehD55zrS1RAZyPqc3lq1Epn+eC57rRJmD1weirMHYz3W2CRBl7WsE6wpExnmeJxVHy7KBTplX88qVK5g/fz5atWqF8uXLq+dPnDihRm3KaE6RMK2tjNx89913UadOHYwcORIWFhZqbs6ePXtiy5YtaN68eZq2J6nhthHBoVhbpk+a1klkTK7fuA6r7Dbptr7kzp3HZ2+iaNPqyF+jHAKOX4k3mjNvZUc8PPbfc0SUuedzemPZSmT657nguU6UOXh9IMoajP1cZ5sAUda+RrCuQGT653lC5jBBkyZNQr58+XD37l01t2aVKlVQrlw5uLi4qPk4NQFLGcEZ15QpU5A9e3asX78ebdu2RevWrfHLL7+gbt26Kp0tERmH2xuPICY6Gk5D28d7vlzvluoCfeufA5m2bURERERERESUcdgmQERElLWY5IjOYsWK4eDBgypF7f79+3Hnzh04OTmp+TaHDh2KMmXK6Ax0XrhwQT0nc3PGVbt2bXz33XdvdR+IKO2eXfXF1dU7UGlwOzRbNRH39pxGrnJF4TS4HfyPXMKtfw7x8BIREZm40t0bw65YAfW3Tb6cMLeyRNWx3dTjoHuPcMuDHZ+IiIhMEdsEiIiIshaTDHSKSpUqqXSzCQUFBanAp7m5OSpXrpwoB/DZs2cRGRkZL9gpKW+LFi36VrabiNKH1/SfEXT3Ecr3aYliLWoiNPAFrvy0HWcW/AHExPAwExERmbjyvVrAoYFzvOdqTu6lfquOTwx0EhERmSy2CRAREWUdJhvoTMqlS5cQExOj5u2UNLVxffDBB+jRowe6dOmCYcOGqTk6161bp0aFLlq0KNO2mYhST1LXXlqxWf0QERFR1rOj24zM3gQiIiLKJGwTICIiyjqyXKBT0tPqSlsr3nnnHWzevBnz589H//79ERUVpQKia9euxXvvvZcJW0tEREREREREREREREREujDQmYC7u7v6ISIiIiIiIiIiIiIiIiLDxUCngas+oQcuLFmPqLAI9dj12w8QeOkOLq/cmqr1lHCrg5CAZ3h0+kayry3boylcZg/Ei9v+2OI2WT3XcOFIFKxTEVGh4Yh4FQqv6avx5Jx3vP/LVa4oOvy7ANfX7FJzIQin991RcUAbRL4KxaZWE/W+r12xAijavAau/W+n9rnuXsvgOXCB2ufUcBraHrc3HEbIo2fJvlaOaeHGVXFv1ykcnfwDbAvlUc/J9kSFR+LFbT/1fNiTF9ptks9DjoU4/9163Nl0RP3dxuMz5HUqiXMLPZL9jBJ+Jg71neEya0Cyxyklx02fAX4eeHrFByc/X4P7nmdQqlNDVBnVGWaWFmr5zd/3xkv3mrtiCdSbMxg2BXKpx6fn/QbfbcdRsG4l1Pt8EPJWLoV1Ffoh/EVwqrabiCgzGUr5WmV0V5R9pwlyli6MvYO/hO+OE9rX6yt7a3zcCyVa10ZMVLR6fGHJBtzeeFj9XXtaXzh2aoDAC7dVGapPXmdH5CpbVPu/mnIiLdf1hMdUH7e/ZyJHsfyqzJEy0zK7DZqt+gj5qpaGuUwdULF//HJo7hDY5s+F6MgoPD5zE8em/Kgth7Xv/1EPtQ2bWn6kvW8wxnI5X7UyqDtbyldHPNh/Lt5nqK/Mlu1tuXYKXng/0L5+a4ep6jixzCYiMt57gXxVywDR0aoMPDVnLfwOXYhXlka8Lq9v/rUfl3/Yov5utHQMirhWVuW7pm6eFCk/LGyscH/vWW051nH3V/HK4pSwzpkdFfq1VvckKaGpV19cvgk31u1R7+u6aJQq/4J8A+KVvwVqlUf9eUPV32ZWlgjwuoLjn/6E6PDIdK2zGvt3ov3WubCwjm3mk3uFPBVLYGPzCeo+IzX3h0REWYkxXPv1lfk2+XKi0Xcfwr6kA6LCI3Dsk5V4eOyKSd4P6Kvz6vs/U7wf0PedaKinLcmU7weyXKDT09MTxkQa7S6v3JKiRkN9Sri5qBMyJSeW8D9yKd6X3Xe7F4589L1qUC3WshaarZwAD5eR2uVyE93gy+Hw2e4Vbz1ygsmJIw2FybErXhAV+rZKccNgcoFO2YeUBDrFpeWbtBcr2UdpEA3wuqq9ANSZ1heHxi7Vvn7/8IU6g6//dv9MXfwy4jNJz+O2vfM07UX91YPH2PXeHHWsrOyzo8O/8/Hk/C34H70EC1trtPh5Mg6O/k4dDzNzc1jnsVP/F3D8iio0pIGWiMjYGEr56nfgPG5vOATXhYnLDn1l76VlG3Fm3m/q7+wOedH5wLd4cPA8wgJf4uTsX/Hs2l2UaOuS7PZIoFNeFzfQ+baO6YkZP2tv1KMjI1VFKPxZkLphjysqLBzHp6xSjXRSDjVeNgZVPuiMs1//qX1N/upl1U/Q3QCjL5dDAp6qikjeKqVQrHmNeK/TV2YLqfDpCsyyzCYiMs57ASkrNeWDNM61+XM6fnMeBMTEaJfHbfTSOPjBItVIZ50re7Lb49DAWb1O07CZVtY5c6DKqC4pbthMWK8ODwrB6fm/wdo+O2p+3Cve6wIv38Hmth8jJjIKMDNTnaMqDnBT7Q3pWWc19u/E1vafaF9Xsn09VJ/wjrp/Eqm5PyQiykqM4dqvr8yvNbUPHp26oeqJ0mm2+U+T4FF3pCozTe1+QF+dV9//meL9gL7vhK+etiRTvh/IcoFOY1J//vvqd9sNs9UXc2fP2dqRk63/nIEcRfKpL6ZcDKIjIlWwseaknnBwrQwLK0s8v+WHo5NWqN6PxVvXVqMWpXfAldU71OjFxsvHwsreFhbZrOF/+KLqEampMCV0d+dJ7d+PTl9XjapmFubakSTVx7+DO5uPIltuuxRdPHXu74L3kaNofnTc9SWC7j+G54D56vkS7eqi3ryhsC2YGzd+88T5b/9Wz9sWyA2XzwfBrngBWNpYw/ffEzgz/3dUG9ddjcps8v041XNBApTZ8tijxuSeal/NrS1xecVmtS5dQh8/Vz//7e8NVBrUFulJRnok/Exe3vZXn6GMWpFeF/L3oTFLtD0uijStpvbNwsZaHfdTn69RF0Bdx6329H5wqO8Ec0sLdaGXi1vc3i5xBZy4pv074mUwnt98oI4pjgKluzTCo1PXtUHfmOho7chWIiJjZUjl6+OzN5PcTn1lb9weiJY5bGBmZqZ+UkN6flaf9K6qCEgZIuWdZDAQFQa4qd6H8ppz33jg5h971fP2pRzgMmugGl1pbm2lsjhcXb1D5zGV4+I0pD3MrSxhZm6mKh1yfHSRERlyrKQXZkJSPmpIOfT4rDfyVCyufU4aOOt+MRh7h3yFdhtiP0tjLpeD/QLVT+7yxVJVZhMRkendC8Qt76W8Tm/S4alCv1bq/kJGSfhsOw7vv/ZrG/mKtaql3le2X7IOCGlErf1pH9XhRoKK5xf/A58tR1X5Z2lno8q/6KhoNRrBaZg7Snd2VeWfjD6U9Uj9Uue+PgtS9U7ZjoSiQv7L4iAjFqX+rzme6VVnNbXvRLn3WiTZ5kFERMZ17dfHsWN9/FP/Q/W31FWDHwaquqffwdjR/qZ0P6BPWv/PFL8Td5OJ45gqBjoNmDQ2ylDvuL38RV7nUtjRfQaiwyLhtn4WSravq9K0Vh7ZCREhYdjaLrYXX9Vx3VFjci8cn/Kj+oLHHWJtkc0Ke/rNQ2RwqLoYNf95Mkp1bJCiER3SaHlvzxntyZG/Rjl18u58d5bqJZLm/Z30g84UcdITZFuHqciW1x7dji5RadqC/QPhuniUuog+PHpZnawtf/0EJd3rq9GY5Xo1j9cbxDpXDmzvNE1Veqxz26mL7f19Z1VDoj5ybCTIKUHUuFwXfygdSfHozE2c+mJtqitSUigk/EzkQizpAw+PX45jn/yoPnvpgbKr1+ewK1FQHVv5OyIoBPaODuqC6+EyQudxu7B0A07O+p82zV3d2QNVz57k5CpfTH2WRyevUI+lkVXSHrT43yfIUTgvAq/44sTMXxjsJCKjZqjla2rKXlFpcDuVHj57kXw4MmE5QlNZFsnrzy74Q/XkS5iyJDosQu1vrrJF4L59Prw99gMxQJPl43Bw1CIVYJMAY/stc7UB0oTH9MG+s7i9/pD6WwKY7bd+gb/qjNCmmUsLS9tsKN+7hSp7NWp/2hfXftmJ4AdP0rxeQy2XU1NmC9mODjsXqO/Jjd/34tov/77RexARmSpjuheoNaU3Snaoj2y5cqhOPXEbw2QUR41JPfHs+j1VNkqqttSQ7b72v12qs7ImpZ2U2VJ/lpGAZ7/6E0WbVVednNZ7nlHp6CST0+4+c1QqNqmjS7nz6OQ1Vf5Jiru45Z+3xwFcXhGbRq1AzXIqpdz6RmPSdBxku+RY2jsWwr3dp3H153/Ttc5qKt8JIfeG0sh98MPFaVo/EVFWYVTXfh1lfrY8djC3tIyX0TDo7iPkKJq4A7Gp3A9kdJ3X2L8TKWlLMlUMdBoh3+3HtT0aJbIvJ7iQkRfSu8KxXV31WEYuysVNJ3Mz1Pq0Dwq5VFSpX2zz58Szq77JnliluzWCY4f62N5lunosjZz15g3BviFfI6PcXn9Q/ZZ0fC99AlTjYvjzVyjsWkWNKIk7okUaZHWREZ0NvxmpclrL0H0pCGS+iuQCnbJvYc+D4uXgln1/df9xbI+Nyb3QaNEo7O7zRbrs68s7/nh8JnY4e8DJa3Ae3kH9XbRZDeQs5YC262dpXytBWxkxokuRxlVVgNbKzlaNopGRtsnJXjgvWqyerC7omuNiZmGBIo2qYqv7FBVcrjnlPTU3yr6hGfd5ExFlxfI1NWWvxpVV29RPHqeSaLxktJrPMexpENLDrX9iy14JaEqPS8mqID01pTFRgp0aVnY26rmE83YLuxKF0HjpGGQvnE+VvdLRyL5EQbXOtJCRoU1WjMf9fedUKhYhvSOlAnZ86ipkhMwsl1NbZj+5cAt/1hymRnrK8pZrpiIs8IXKuEFERMZ7LyANV/JTuFEVNa3Kto6fqhEEBz5crO3kU3Ggm+r4u6HJf2X0m4gMCYPP1uPq74CT17XHoUDtCrAvWRCt1k6N9/qcZYogyOdhovXkq1wKVcd0VfVxuReQDkSSCSHhPNspEXTvkZqHW+b1brx0NEq2q6uOaUbXWY3pO6FR9t1muLvrlGpDISIi47/2Z2SZb0z3A5lZ5zXG70TpJNqSTBUDnUYobn5oicabW1iovyVlnQx3lobO5DgP66CChDKHg6yvzmf91YTD+jh2bIDq43vg3x4ztaldZaJju6L54fb3Z9rRl3LSWueyU+nd0n1/o1/v7+v0fFKZSUm+bBl2fs/ztJq4V0jPD+lFoU/dzwchR5H8saNc4vSQlCCn2pbIKJWvu+vh75Be4l7g1Wdr+d9n+2D/eRz4YFGi/8nhkC/+46L5UW/OYGxp+zFe+jxEnkol4zXE6iKpftv8OQPnvvVQaQbi7qvfkUuqwihueRxAq9+mvfF+kumSEVBu/8zEiZn/w6XvN8VbVqi+k5o/JzIkXI2C0swXIyTVtIxyenDwAnb2iD8/n4aMnpKUGZvdJicKqkgajDrT+6kbrz19v0h2YnEiQypfU1v2JvT0so+6TsucGppKyJuSeTETlr1S8oY9C9I5H4YuTZaPxak5a+Gz9Zh63OvyapWeJS2kc1GTFeNi56+c9pP2+cKuldVclt29lqnHElRtuWYKjkxakWSaXGMol9NSZsvIUg0JfspcHoXqVmKgk4jIyO8FNCQFnXSYyVOphJqfOW4mA0kjL/fC0qE3PTo9xc2+kLD8e3btHrZ1jN+wKRKmn5cOSjKX5o5un6l7d9n23jd+Valn0xLo1JBREDKConTXRqphMKPrrMb0ndAo924zHP04djoCoszAdgEydoZ27ddX5kdHRanp3TSjOmVqk1f3kwi0Gfn9QGbWeY3pO5HStiRTY57ZG0D6hb8MhlXOlM3F4bvDC07vu6tRlkJ+a+Z3Cn8ZokZiaMjQc2kslJNKLoaO7vX1rlui/zJ68d93Z2oDfUJ6HfxeebCa0FZ+ZOTjzd899QY5uxxcpHJDJ9rXoJTvq1RuJI+1TG4ct+FPenNo9zfOuqxz58Cre7EX+UL1KqnRL/q4zB4E+1KF4TloQbyekZIyT4bna5Tq4oonF28nuR5JByhpbnVJ+JnoI2l2ZdSKNI5q5K9eVudxk3XK6JvggKfqccVBbnrXLaN02vw1Q00Srcm/rnFn8xHkr15GFUKiaIuaCLwcmw6YKDVk8mvp6SVBku2dPo0X5JSCWCbofnHbD4UbOuuco08fSdPg8tkAdVOxs+csBjnJqMrXtJS9mrSlGvYlCyFv5VIqXYkuUuZK2auLzBeZ0rLoufcDVbGQUQLa93Z0UCM1dR1T6fQUdDdA25NQem+mhaSnl84QcsMuc1vGdfqLdfir5jDtfUiw3xOVZSGpIKcxlMtpLbNlmaYjmGS5kOvuk4sss4mIjPVeQDr5aEYLaMoZmTtbOs1I2WgTJ7uRpE8Lefw8ySCn/K/MK6WLKrdSWP5JdgPJsCQjCePO6yWNmHJPIaMz5G8hHYvlb809TKXBbZFWchzkeAhZp5Tnga/rE6mts8p9gPy/qX0nNCTzlZmlueqQRWRo2C5Ahsjgr/3JlPk+m4+qVKuaeTOl/u9/9LJJ3g+kZ53XqO8HkvlOOOppSzJlHNFp4C59vxmtf5+OqJAw7eS3SZFGr2rjreC+da52AOLFpRtUw6fMrSX5ryWYcPXnHbjy4zY0XTkBnfYtRIh/IB4c1H8TLKnnJOe2pEnTkB4Bqe0tKjfhqnfBsyCdI1KkN0invd+oG3XPAfP1rktGUdSZOUC9XnY4MjhMjeCQHh2Syk9yhctxOzR2qRpRUn/uUFQb213lxn58OunJfAvWqQCnIe3w7MY9dSzFy7sB2DvoS9gUyIVmP05UFxS5rkoq3UMfJj2iM2epwmo4vS4JP5OXt/31ps47MPLb2AmdbbOpYfCBF26rY6DruEnP1s77FqrPRy64yQWJZLSJ7LP8iMs/bsPNP/aqi6HMg9pu8xzERMeoXrJHJsZvZCZKjnQIkBTPEiSR61jIw9jGfo3S3RrD3MpCzasr37WyPZup/P8pISOvZZ5C+c4f/PC7eB0TiIyhfK06thsq9G2tyscGFUeg7pwh2NR6oppXSl/ZK/NSSsUiJiIS0VHRODZlFZ7fuK/zPaSiI4G2pEYCVB7eER33fB07n8bkpHv/S6/FPX3nqnk55EZeykJJE3Ng5CKE6zimXtN/Usci/Hkw/A5fUCnn9JFtkONgZW+Ld06tgP+Ri+q8ltHeju3rqfJb5tgWD09cU3NepJYxlMuS7kdGbFraWqsKohwLKYtl7hF9ZXbJ9vVQoX8blXFCGkKlwiudz4iIyDjvBaRzr6xXOtrKtT0iOAx7h36tpnGRskcyGMhoCKmnhQa+xJ7+85J8DxndkdSoCd9tXijTrYkqY322HU/UkSYuee/dfb9QowbqzOiv7uGlzihZkMKfBalj0cnza0S8CsUWt8k4M/93uG+fh9DAF2oUpj7SMNj10HewyGapGgWl/PP+e7/q1CQZHOSeX+5FpIyT+5fzCz3U/6W2zpq/WmnVXmBq3wmNcr2a4+bvexPN20mU2dguQIbK0K/9UcFhesv8k5+vQaMlo1XGwaiISBwctViVEaZ4P6Cvzqvv/0ztfiC570TjdIrjGBuzmBje/WSWiOBQrC3TB4ambI+mqkeDStmaTikrXGYNUKnuSrrXV/Nonv/2bxgK128/iDcxcHqur/nqSfCatjrZxt23aYCfB9ZV6Jduo95Ssr7e3mtgld0Gpn7uUOIUNdKrrN7cIXh81hu7es9RNzy6ghsSLPn3nZlotmoi8lUtrUZmJaycx01dG3jxtjrXynRvgmtrdqnJzlmZfzvS+3xOb4Z8fUjv8jWl7+U8oqPqNXjr79h5Nw2B298zVfp33x0nMvy9slq5nB7vZejnuaGf60SmjNcH47kXkHtn61zZ4TX9Z9T9YohK7xbgdRWGQtLOy3GQMvptlX9mMt/30jHJNlpmhfvD5PBcpzfBdgHTZ+jXCEOvK/B+4D+8H3j734myKXwvQz/PE2LqWkokMjRcDTd336F/RGVKyIiPevOGqJ4FQuaSMqQgp2Y4uvQGkXk831Qbj8/UPITSs1LIBcOQGlOFNHa7/TMLRZvXeKP1FKxbSfXykfVJ7xGihKp82EWdV36HL6kgpq4gp6TVyOtUEjf/3Kcey6gkSV1bpHHVJA+opL2QkdUS5JSeU0cnrmCQk7Jc+apP7Wl91fmnyZ5wafkmgwpyCtm2mlN6o9q47hn6PlmpXNaHZTYRUda6F2i0dIxKGy8p04RkQTCkIKcIffJCjUAp916Lt1b+ScYMQwpyZub9IdHbwnYBosR4P/Af3g+83e9EbRO+H+CIzkxk6L1LiNILR3RmvZ6bL+74I6ejA3y2H1cpaeNOYB5XvXlDUaZ7Y/xRdahKxSSpJ3qc+UHNwSv/p2tEp2bdkp5D0kHQ22XoPbpYthKZ/nkueK4TZQ5eH4iyBp7r9CbYLmD6DP0awboCkemf5wlxRCcREaW77IXyqN8v7zxMMsgpc8+V7uwKn63HVZBTSJ79W/8cRIk2dWCd2y7JdctcnC9uPeAnR0RERERERGSA2C5ARERvCwOdRESU7i58tx4PDl5A5REdUXtGP52vKeleD9a5csD/6GXYOzpofx4eu6yCoGW6Ndb5f4fHLVOTajf5fhxKtKvLT4+IiIiIiIjIwLBdgIiI3hbLt/ZOlPjg22ZTQ4CJssJ3nbKWyJBw7Ok3Fy1++RiVh3eEmbk5Tsz4Od5ryveKnY/HdeFIneso17MZrqzaluh5SV27o9sMuP09E02/H4f9I76Fz9ZjGbQnZGxYthKlz3lk6HiuE2XeuWfoeH0gSp/ziOhNsV2AMgvvBYiy3r0AA52ZyMzMzKjyHBMRpUZUaDj29J+HFj9PhvP77jAzA7ymxwY77UsWQqF6leD99wH4bvdK9L+FG1VBxf5tkK9qaTw5fyvR8pc+D7G963Q1H2iT5WOxf+Qi+Gw5yg+IWLYSZRG8jyYiXh+IiAwf2wUoM7CuQJT1MNBJREQZW6kZMB/Nf54Mp6HucrcJr2mrUbZXczXK89KKzQi8cDvR/z05560CnWV7NtcZ6BRBvgHY0TV2ZGeTZWNwwAy4s5nBTiIiIiIiIiJDwXYBIiLKaJyjk4iIMrxS49l/Hu7vOwunIe1R9/NBKNujKV76PtQZ5BRB9x7h8TlvlO7iCotsVkmuWxPsDH74FI2XjUWpTg0zcE+IiIiIiIiIKLXYLkBERBmJgU4iIspwUWER8BwwH/f3nkGlwe2Qo3A++Gw7rvd/ZN7NbLntUKJdXb2vC7obgB1dpiPY7wkaLRmNUl1c03nriYiIiIiIiOhNsF2AiIgyillMTExMhq2diCgDRASHYm2ZPjy2RJmgt/cazi9NRERERESZiu0CRJmH7QJEZGg4opOIiIiIiIiIiIiIiIiIjA4DnURERERERERERERERERkdBjoJCIiIiIiIiIiIiIiIiKjw0AnERERERERERERERERERkdBjqJiIiIiIiIiIiIiIiIyOgw0ElERERERERERERERERERoeBTiIiIiIiIiIiIiIiIiIyOgx0EhEREREREREREREREZHRYaCTiIiIiIiIiIiIiIiIiIwOA51EREREREREREREREREZHQY6CQiIiIiIiIiIiIiIiIio8NAJxEREREREREREREREREZHQY6iYgSqD6hByyyWWkfu377AZyGtk/1cSrhVgcFapZL0WvL9miK9679Avcd87XPVRndFV0OLkL/+3+qdcXVcOFIdDm0GB13f4W2Gz9HvmpltMssbK3ReNlYdD3ynXpNyfb1tMtqT+uL7ieXo/nqScluU15nR5Tq1DDecwP8PGCdMzve9Jjq4/b3THQ7vhTVxnVXjy2z26DVb5+i56Wf8N7VX+K9NnfFEnBbP0sdp057v0HDb0bCwsZau7zpygnoceaHRNstr+m460v0vvlromOb1OeTq2yReI9TcgxTckyTYlesAPrd+0Ntp/yfKNaiJtz/nY++d36Dy6wB8V5faXA7dQw6eX6Njnu+RulujbTL5D1lPWr53m/gPKyDdlnBupXUsrR+tkRERERERKbGGNoFNBwaVlZ1x7jb1+yniaqep/mR/y/eurZa5vS+u2ovkOdTUi+t0K91vOe6ey3T1lFTQ7bPtkDuFL1Wjvc7p1eg/vz3k63fx1X9ox5qedztK9q8BjrsXKCtE5d5p4l2WaOlY/DuuZWJ6te6ONR3RtFm1eMdm4RtFCkh215lVOcUv16Ot3wHyr3XQvu+0m4i3xV9n2Gbv2Yk2j7nER3VMei8f6H6jmiOY2rbSIiIDI1lZm9AVhUTE4PIkLDM3gyit8LSNhvMzMyM5mjLjfHllVsQFRbxRusp4eaCwEt38Oj0jRS93v/IJXgOXKB97HfgPG5vOATXhR8keq3vdi8c+eh7xERFo1jLWmi2cgI8XEaqZZWHd0R0eAT+afAh7IoXRPttc+F/5CLCngbh5Oxf8ezaXZRo65Ls9kjFQF53e+NhvO1jemLGz/DdcUL9HR0ZiQtLNiD8WZC6mY8rKiwcx6eswtMrPjAzN0fjZWNQ5YPOOPv1n2r5tf/txLGPV6LnxZ/i/19oODa1mphofUkp+24zhL94hec3H+BNpPaYRgaFqu3UeHHbD4fHLYNjh/qwymET77XyuW7r+CkiXgYje5F8qpLy6OR1vPR5iFcPHmPXe3MQ8ugZrOyzo8O/8/Hk/C34H72EgONX1HtIZdAUsHwlynrlNhEREVFWbBcQUr+rNbU37u85E+/5vYP+C4BJx+hW66bi/t6z6vHlH7Yg8MLtFAX3pE2hQt9Wqm79piTQKfsn9dKUuLR8Ey6v3Kp9nFT9XiN/9bLqJ+huQLznGy8ZjR3dPlPtBhIklKChz7bjiHwVioMfLFJBbetcyXf6dWjgrF6nOY5pZZ0zB6qM6qLaOVJq//CF6nskwoNCcHr+b7C2z46aH/fS+XqnYe6qLSBfldLa5wo3ropyPZthS7tP1L5XHdsNNT5+D8en/JjqNhJDx3YBoqzXLsBAZyaRIOfaMn0y6+2J3qre3mtglT1+UMZQaXoLtt0wWwURd/acrR7nKlcUrf+cgRxF8qmAktxkRkdEwszSAjUn9YSDa2VYWFni+S0/HJ20AgVqlVe9JeVGUnplXlm9A/d2nULj5WNhZW8Li2zW8D98Ecc//UnuwHRuy+OzN5Pczrs7T2r/fnT6OrI75IWZhbnaZsdODXFk/DK1TG7wpSJRom1d3Fi3J8XHwSZfTlSf9K66cVYBs9M3cHTyD2pZhQFuqoefvObcNx64+cde9bx9KQe4zBoI2/y5YG5thetrduHq6h06j6kcF6ch7WFuZQkzczN1ky7HR5fo8Eh1rKRCktDL2/7av2Oio/H4rDfyVCyufc7v4AW8Kek1ma9aadSZOUBVgE7NXacdaSojZ+X9osIjse/9rxHkG1uhKt29MSoNbAtzKwtEvArF8amrEPLwqc5jKj1Ic5Upoo6FBCSPjF+eZMXvxS0/9btk27qJlvkd+m9fgx88QUjAM+Qokl9VbgJOXNMuk0CoBGztihcAjsLksHwlylrlNhEREVFWbRcQ9b4YjPPf/o2S7RLXETXK9WqOW38fUNua6mOx4H3kKJpf1WGD7j+G54DY0aYl2tVFvXlDYVswN2785qm2QciITZfPB6n6pqWNNXz/PYEz839XGZtsC+VBk+/HqaDaobFLkS2PPWpM7qmOg7m1JS6v2KzWlRR99XvJbFX3i8HYO+QrtNsQ+3lpyKHVBDIlMBz69KVqZ0htp+UK/VqpdhcZ2SmBUu+/9muD4sVa1VJ1ffks73ue0QaYa3/aR72ndMw+v/gf+Gw5qo6ppZ2NOqbRUdHY4jZZBSZLd3aFuaUFoiOj1Hoenbquc1ukE3iA11W1HbrkLl9MBdgPj10KR/f6/+2DkyMeel1VQU5xb89pFdiUQKepYbsAUdZrF2Cgk4goDgk8SVqW7Z2nIfxFsPb5vM6lsKP7DESHRapUqSXb18XtDYdReWQnRISEYWu7T9Trqo7rjhqTe6kbRQlGSo87TQ9ESXuzp988RAaHqpvc5j9PRqmODd54xKQEDO/tOaMqYMKuaH4E3XukXS7BTqmYpEbokxc4u+APNfowbm9SER0WofZXUrm6b58Pb4/9QAzQZPk4HBy1SAXRpJLRfstcbTAv4TF9sO8sbq8/FLu9xQqg/dYv8FedEamubCTsaVS+dwuc+mIt0pMEiMt0a6x682pGmUolNX/1MtjUcqI6vrWm9FapZ45O+gEF61RQFZTtXaap/ZHUsBIQ3dh0nM5j6jV9NcKevFB/yzqkkqQJKqdV4UZVYJ0rh85Kca7yxVSF++jkFW/0HkRERERERKbIWNoFZJqamOgY9R5JBTolJanUT7d1npa2YzHpBzXyM26mIc2oxG0dpiJbXnt0O7oEN3/fi2D/QLguHqUCeg+PXlZBwZa/foKS7vVxbqGHCrjGHZkoddbtnaapTsvWue1U4O/+vrMI9gtM9XbW/rQvrv2yU3X6TWj/8G/QbNVERAaHqffcO/jLVAd9ZZuv/W+XCph6Tf9Z25Yh65ORome/+lOltZXO3+s9z6iUsA2+HI7dfeaoTshynCR97qOT19QxlWmI4h5Tb48DuLxii/pbUh27LhqF9Y3GpPo4SNC9wVcjcHjCMm0bkcaT896oOKCNCkZL5+rSXRup4KwcewmeEhEZMwY6iYhSwHf7cUSFhKu/JXhk7+ig/paRjXJj6Pi6UiG9EIPu/hdkjMfcDLU+7YNCLhUBMzPY5s+JZ1d93yjQKfMwShrT7V2mv7XP8dY/B9VvCWhKT0PpwSk9FKXXoAQ7NazsbNRzT855J1qHXYlCaLx0DLIXzoeYyCh1Y21fomCaU8PKaMgmK8bj/r5zKq3v2yC9KzUpcQJOXUelQW3V38Xb1EEep5Jw3zpX+9psue3izR0aV+kurijTvYmq8MpPWODLN9oumbdU0hpJBTJhivTshfOixerJquKelsojERERERFRVmVI7QISrKo2tht2dJuh93Ul3evh+a0H6j3S0+31se0CUn996RMAuxIFEf78FQq7VlFZnjQsc9ioTtK6yIjOht+MRM7ShVW7QLY8dshTsUSq66oyYlaCjpJJKSEJtlYb210FNx8eu6JGWbb45WNsbD7+jeveQurcPluPq78DTl7XficK1K4A+5IF0Wrt1Hivz1mmCIJ8HiZaT77KpVB1TFd1TORY5CpbVLUhyOjX1Kg+4R012vT5jfuJsmJJtq+Lyzehxa+fqCCofJ+FvB8RkbFjoJOIKAXizsshN4TmFhbqb8lVLilFHuw/l+w6nId1UDf8W9t/otZX57P+sLCxSvPxd+zYANXH98C/PWYi9PFz7fOSTkZuaKXXoGZOjZRsX0rJvJga0vNSjoVkbA97FpSol2dSmiwfi1Nz1sJn6zH1uNfl1SpdTVpIj8UmK8YhJOApvKbpnqvjrXwnLP/7TkgKm9OvU9zqU9ClIioNbodt7lPUKFpJa1Rj4rtp3iYZrSk9Zg+NX6ZS2cQlaYLa/DkD5771UOlyiIiIiIiIyDjbBfJVLa3qeDIyUMiIQalPZsuXE2fm/aZ9XfleLfSmg02XY/G6XUACt2Kr+5QUzW0qKYLveZ5WQUghIx6l829qFXatjLxVSqG7V+wUPtKhuuWaKTgyaYVqF5HjJEFOIR2xg/2eIG/lUmr+0zcVNytVwnaBZ9fuYVvH+IFOkTAAKR23m636SM0jKttnZWeL3jd+hYW1ZaoDnZLOVjJ6VRrkBjMLC5UiWY7L5rYfq0xS1375V/1oRo6+uv8YEUEhadx7IiLDYZ7ZG0BEZGjCXwbDKmfyE9EL3x1ecHrfXaVqFfJbRjHGridEjXTUkJQmEoyTG37pfRl3roTUklGcNSf3wr/vzlQ3pnH5bD6q0uxogpwODZzVduoic3t2ObhI5zKZ4D7u9uvz3PuBujku+24z7XPSk1FGauo6pta57LSjIWVUqvRaTAvpnSlzfIQ9DcKRj77Hm6g40A01p7ync5na/hQeC9+dJ9U+adMFm5mpXqO6jql8JyKDQtT2S+WmfN9Wad5+mS+mlVTmJn6fqMImo27b/DUDF5Zs0M4jQkRERERERMbZLiDzK/5RdQg8XEaqH58tx1R62LhBTqmT56tWWjttjD7SLiDtAwmFB6X8OEg6XplztMqoLtrnJMgomYW0xyJuu0DuHHj1etqdQvUqqcxIaXH6i3X4q+Yw7bGQQObuPl+o+VClvSR7oTyqvqw5JvYlC+GFt+5sUvmrl1XzsOqijkUK2wUCTl5To1xlWpm483xKvV/aBWS0pvwtJLgrf2vadioNjs0WlRaSbtmjzgh1HLZ3+hQRL0PU35rpcqRtQL2nrTWqT+qJC8s2pvm9iIgMCUd0EhElcOn7zWj9+3REhYRhZ8/4k9gnJIGjauOtVJpSmeBeXFy6Ac+u31NzV8q8CjIJ/NWfd+DKj9vQdOUEdNq3ECH+gXhwUH/vwapju6FC39awyZcTDSqOQN05Q7Cp9UR1gyppX6VnoqQh1ZCRnRIwu7hsIxouHImuR5eoHoUyL0hSKVmkIiPpZ3XxO3gBlYd3RMc9X8fOI6Fn3kh5nz1956r5KKSCJwHIsMAXODByEcJ1HFOv6T+pYxH+PBh+hy/Em1NUF9kGOQ7SG/GdUyvgf+QiDn74HUp1agjH9vXUfBkyn4d4eOKa2mchKVnyOjmqv+W4v7ztn2RqH6mISsodXa6v2Y06M/rB+X13nEpmpGbA8Ss4NXsNmv00UfVqlbRF93afVj0zEx7TY1NXqfk/uxxapD47+U7k0FG51JA0QPKdkuMgPURLtq+PY5+sVHOy1J09SFW6ak/tA8gPgJNz1uDBvnOoMamnCrw6DWmnfsTlH7fh5h979e4LERERERFRVmQM7QLJkTkxJa1qciP2ZN2SNlayNCX09LKPGpnYae83eOnzEJ4D5utd14EPFqHOzAHq9XIwZF5MGVkp6WivrNqm5q2UY3po7FKV5an+3KEqtazU6R+fvql33amp32tI9ivpDNx0xXg1n6mZuZmqhyfsMK5hV7xAkqMofbd5oUy3JqrtQdLD6utELGl8d/f9AnWm90OdGf1hbmWh3tNz4AI1H6Z8Lzp5fo2IV6HY4jYZZ+b/Dvft8xAa+ELN+6qPBCm7HvoOFtksVRuAtJF4/71fBXyT0/r3aYC5OSysLNU2XP1pe7L/Q0RkDMxiYjRFML1NEcGhWFsmtiGayNT19l4Dq+w26bY+Uzx/yvZoihJtXdRN79t8L+cRHVVv0lt/x86vYQjc/p6Jyyu3wHfHibf6Xm03zsau9+Yg8lUoDIGks5E0ROsq9n8r7zfAzwPrKvRD+Ivgt3o+pzdTvD4QvW2Gfp4TERERmeJ9/9tsF5AUpy6zBqjpZ0q611fzaJ7/9m8YCtdvP1DBz8srt2b4e1Wf0APWubLDa/rPqPvFENzecCjRVDCZSVLPyndCjoehtMcYen3BFK8PRG9bbwM/zxNi6loiIgMQGRqu0pi479DfO/JN1Z7WF1U+7KLtqXlp+SaDCnIK2baaU3qj2rjuGfYekiZGemHalSyonTtke6dpBhPkFNHR0YgMCVPbKd+NjFKwbiX1HhLwlh6uREREREREZLrtApKFqd68IQh9nfnJZ8tRgwpyalIHV+jfRs3jmZEaLR2jpp6RtLpCskMZUpBThD55gUZLRqPcey3eahsJEZEx4YjOTMKeJZSVcEQnkekw9B5dLF+JTP88JyIiIuJ9P1HmMfT6Aq8PRKZ/nifEOTrJqEhqxZTY0XWGmhdPcs/f/Gs/Do3+LtFrZM46GTF2+KPvcWPtbpUiROZNSE7Q3QA1kTcREVFWxLKYiIiIiIiIKOtiuwAZGgY6yagcGLUoyWX2JQqp4GXok+d47n0fIQHPcG3NLlTo00ql4bi786T2tfmqlUGVUZ1xf+8ZFeQU/scu611/2XeaokiTanhoYCksiIiI3iaWxURERERERERZF9sFyNAw0ElGJam5BC1srdF+8xeIjozCvmELVZBTnPjsFxRtUg31FwxTAcrwZ0GwyGaFRotHIeJVKA6PX65dR5BvgPpJapJ2hwbOeHrFB0cnrsigvSMiIjJ8LIuJiIiIiIiIsi62C5ChMc/sDSBKD67ffKAmbD/1+Rr4H76ofT7ydTDTtkAu1JszWD1X4+NeyF2+OLymr0awf2Cy685RND+a/DBeBUY9B32JyJAwfmhEREQsi4mIiIiIiIiIbfSUyTiik4ye84iOKNW5IW5vOIxLKzYnWu536AKu/rITlQa6qcCm09D28N1xAt5/7U923TL6s9mqiciWxw57+s3Dyzv+GbQXRERExotlMREREREREVHWxXYBykxZYkTn48ePMWnSJJQtWxY2NjYoXrw4xowZg1evXmHw4MEwMzPDkiVLMnszKQ0KN6qCWlN6I/CyDw6PX5bk607N/hUv7vij8shOCH8ejKOTUpZ+VlLe5q9WBme//gv3Pc/wMyIiImJZTERERERERERsoycDYfIjOs+ePYu2bdvC398fOXLkgJOTEx48eIDFixfD29sbgYGxqUurV6+e2ZtKqWRXrACafD8OEUEh2Dtogd6UslERkYh4GaL+fnLeGyGPYufw1KfS4HYo26OpGv15fqEHPx8jUuXDLshXpTTyVS0N+5KFEHQ3AB4uIzN7s4iITA7LYiIiIiIyRGwXICJ6O9guQIbA3NRHcnbo0EEFOSdMmAA/Pz+cPn1aPZ4/fz62bt2KEydOqBGdVatWzezNpVSwsLVGs58mwTpXDhz4YBFe+jzU+/pqY7shX5VSeHLhNoo2ra5S3epTqF4l1JnRD89v3sfBDxfzszEyMsrXoWFllWo47OnLzN4cIiKTxLKYiIiIiAwV2wWIiDIe2wXIUJh0oHP06NG4d+8eRo0aha+++gr29vbaZZLKtlq1aoiMjISjoyNy5syZqdtKqdPgqxEqcHn2qz+TTSmbt0opVB3dFff3ncW2TlPx3PsB6s4ZDJv8uXS+PnvhvGj6wwREhobDc9ACNWKUjItH3ZH43XkgdvacjeCHTzN7c4iITBLLYiIiIiIyVGwXICLKeGwXIENhsoHOK1eu4I8//kD+/Pkxd+5cna+pVauW+i0Bz7h2796NevXqqfk8CxYsiOHDh+P58+dvZbspeU7D3FGmayP47vDC+W//1vtac2tLNFr8ISKDw3B4wnJEhYSruTxlJGj9+e/rfH2zVRNhWyA3Do1diuc37vMjMUJBvgGZvQlERCaNZTERERERGTK2CxARZSy2C5AhMdlA52+//Ybo6Gj07t0bdnZ2Ol9ja2ubKNC5f/9+uLm5oWjRoli/fj3mzJkDDw8PdO7cGTExMW9t+0k3hwbOqP1p39cpZb9L9jDV+Ohd5KlYAl6f/YLgB0/UcwFeV3Fl1XaUbFc3UQrbenOHokCNcjj/3T/w3XacHwMRERHLYiIiIiIiIiJiGz0ZKEuYKE9PT/W7WbNmSb5G0tomDHTOmjUL5cqVw19//QVz89g4cL58+dCtWzc1p6e7u3uGbzvpZlswN5r+MB7mlha4s/UYirepneShenrZF5a21nAe0RF3d5/Czd9jvw8ap+euQ7EWNVUKW79DFxH6+DnKvdcC5d9rgZBHz9RIztLdGiW5ft9tXogMCeNHRUREWQrLYiIiIiIiIqKsi+0CZIhMNtDp4+OjfpcsWVLncpmb8/Dhw4kCncePH8fAgQO1QU7RunVr9XvDhg1pCnTWrl0b/v7+8Z6zijHHDLikel1ZWa4yRWGTL3ZezWpjuul97YUlG1DCrY6aX/PIR98nWh4VGpvC1u2fmSqF7d7BX6KQS0W1TNLWSrpbfTzqjEDQvUdvtD9ZSfly5RFhFp1u6+P5Q2Q653N64/UhY7EszhoM/TwnIiIi4n0/UeYx9PoCrw8Zi+0CWUP5TDjPHRwccPLkyTT9r1mMieZjzZs3L54+fYojR46gfv36iZavXbsWffr0gb29vZp/08zMTD2fK1cuDBkyBF9//bX2tWFhYSrNbd26dXH06NFUb0uxYsVw/378uR6tzSywolDnNO0bkbEZ9nADwmOi0m19qTl/Ou39BlY5bODhMjLd3p8oK0vv8zm9sXwlMv3znIiIiIjtAkSZx9DrC2wXIDLO81ymk9RkYU0tkx3RKdFfCXSePn06UaDTz88PEydOVH9XrVpVG+QU5cuXV6M64zpx4oSanzMwMDDN26KrZwkMt+MLUboqUrhIuo/o5PlDZBrnc3rj9YHI9M9zIiIiIt73E2UeQ68v8PpAZJznua44GrJ6oLNly5a4cuUK5s+fj1atWqkApiZo2bdvXzx+/Fg9rl69erz/Gz16NPr164fPP/8cw4cPVxHkkSNHwsLCIl4629TQNdw2IjgUa8v0SdP6iIzN9RvXYZXdJt3Wx/OHyHTO5/TG6wOR6Z/nRERERLzvJ8o8hl5f4PWByPTP84TSFrkzApMmTUK+fPlw9+5dODs7o0qVKihXrhxcXFxQunRpNG/ePNH8nELS2U6ePBmzZ89GgQIF1PyazZo1UwHRwoULZ9LeEBEREREREREREREREVGWGNEp82IePHhQpajdv38/7ty5AycnJ6xYsQJDhw5FmTJldAY6JY3tvHnzMHXqVNy+fVvlBZZ5OyVo+uGHH2bS3hBRapTu3hh2xQqov23y5YS5lSWqju2mHgfde4RbHgd4QImIiIiIiIhMFNsFiIiIsg6TDXSKSpUqYcuWLYmeDwoKUoFPSUVbuXJlnf9rb2+v5u8UK1euREhICAYOHJjh20xEb658rxZwaOAc77mak3up3/5HLjHQSURERERERGTC2C5ARESUdZh0oDMply5dQkxMjJq3M3v27Inm09y1axdq1qyJyMhI7N69G4sXL8ZXX32lHQVKRIZtR7cZmb0JRERERERERJRJ2C5ARESUdWTJQOeFCxd0pq0V2bJlw+bNmzF37lwV6JS5Pf/44w907949E7aUiIiIiIiIiIiIiIiIiHRhoDMBCWweOXJE58EiIiIiIiIiIiIiIiIiIsPAQKeBqz6hBy4sWY+osAj12PXbDxB46Q4ur9yaqvWUcKuDkIBneHT6RrKvLdujKVxmD8SL2/7Y4jZZPddw4UgUrFMRUaHhiHgVCq/pq/HknLda1uynibAvXlD7/3mcSsJz4ALc3XkSTu+7o+KANoh8FYpNrSbqfV+7YgVQtHkNXPvfTu1z3b2WqXXJPqeG09D2uL3hMEIePUv2tXJMCzeuinu7TuHo5B9gWyiPek62Jyo8Ei9u+6nnw568UK+Xbaz5cS+YmZnBzNICF5dthPdf+9WyRkvHoIhrZdzeeBhe03/W+74O9Z1hYWOF+3vPave/4+6vsK5i/1Ttq3XO7KjQrzUuLNmQotfLMZXv08Xlm3Bj3R44NKyMWlN7wyqHDWJigHu7T+HUnLVQDwA4j+iovhNm5mZ47v0Ah8cuRfiLYFjYWKP95jmwL+WAg6MWw3fHiVRtNxFRZjKU8rXK6K4o+04T5CxdGHsHfxnvWqqv7BUV+rdBpcFtERMZhZjoGGxt/4nan9rT+sKxUwMEXritylB98jo7IlfZoqrc0hjg54F1Ffqpa/2bHFN93P6eiRzF8uPm73txbqEHLLPboNmqj5CvammYW1jEKwtzVyyBenOHwDZ/LkRHRuHxmZs4NuVHdVxkWePvPtS+1jpXDljZ2eI3p9h51dt4fIa8TiXVeyT32Sb8LKWcdpk1INn7l5Tcz+gjx/vpFR+c/HwN7nueQb5qZVB39iDkreyIB/vPxfsMS3VqiCqjOqv7DyHH79KKzbELzcxQZ0Y/FG1WHdGR0Qh7+hJHPvoeL+/4w75kITT78SPkKl8MW9t9kur7KiIiIiIiIlNkDG0DmvpzxOs6+s2/9uPyD1u07Qb5qpYBoqNVfVnadP0OXTDadmp5X9dFo1R9OMg3IF59XLax5dopeOH9QPvc1g5TVdtAXG3+moF8VUprtz219eGEcQX5jljnyp7sMUzumCb3Ws2+7ew5G6FPXqBsz+Zwfr89cpUrhpOz/hfvO6mJZ2jiFQ8OnMfJ2b8muyw17UXGJksGOj09PWEsqn/UA5dXbklRo6E+Jdxc1Emckout8D9yKd6X3Xe7l2osi4mKRrGWtdBs5QR4uIxUy/YO+lL7Ommca7VuqvYElouunDjSUJgcu+IFUaFvqxQ3DCZ3QZJ9SEmgU1xavkl7sZB9lAbRAK+r2gtAnWl9cWjsUvW48ZLR2NHtM9UoKRffLgcXwWfbcRXMPfjBIu3FLzkODZzV61JysdPHOmcOVBnVJcUFiNg/fKH2oh7+/JV6LIWHRTYrtP5zuipYb/65T10Uy/Vshi3tPlH7V3VsN9T4+D0cf93ALIWNFLZERMbGUMpXvwPncXvDIbgu/CDRa/WVvcXb1EHpro2wtf0URLwMRrZ8OREdEaWWyQ3ss2t3UaKtS7LbI4FOeV3cQOfbOqYnZvysrbxFR0aqciz8WVCiciUqLBzHp6xS5a6ZuTkaLxuDKh90xtmv/8Szq77xKj515wzWdtQR/3b/TN3kZ8RnmZ73M9s7T9MGlkMCnqqgdt4qpVCseY14r3v14DF2vTdH3d9Y2WdHh3/n48n5W/A/egkl2tRWgfGNLT5SwW8ps2t+8h72D/sGL30equMklUgiIiIiIiIynraBhPXnhM9r6pJ5K5dCmz+n4zfnQapebJTt1EEhOD3/N1jbZ1cDjRKSQKC+zshOw9xV/VcCnRqprQ+nNq6QXsc04b49Oe+NfcO+QZUPuyYbz0jpstS0FxmbLBnoNBb157+vfrfdMFs1cko0X+QqVxSt/5yBHEXyqS+mXAyiIyJV7/6ak3rCwbUyLKws8fyWH45OWoECtcqjeOvaKmglPUaurN6hRi82Xj4WVva2sMhmDf/DF3H805/iNQ7GJaMzNR6dvo7sDnlhZmGutiuucr2a49bfB9T2pHp/F7yPHEXzo+OuLxF0/zE8B8xXz5doVxf15g2FbcHcuPGbJ85/+7d63rZAbrh8Pgh2xQvA0sYavv+ewJn5v6PauO5qVGaT78epYJwEKLPlsUeNyT3VvppbW+Lyis1qXbqEPn6ufv7b3xuoNKit9rEcIk0BIY2MoU9fIjo8dfsrDcsV+rVSx1B6bEigVDMqVArYYq1qqQu6fCYyukMTRK79aR/1ntLQe37xP/DZclQdN0s7G3XcoqOiVU8guaiX7uwKc0sL1ZtH1vPo1HWd2xJ48bb2bynUAy/eUY20ajudHPHQ66oKcop7e06rBmgJdBIRGStDKl8fn72Z5HbqK3srj+yIc9/8pYKcQtNTLzVs8uVE9UnvqvJGyhAp7ySDgagwwE31SJXXnPvGAzf/2Kuel1H8LrMGqtGV5tZWuL5mF66u3qHzmMpxcRrSHuZWliorgFRW5PjoIuWoHCvpQJTQy9v+2r9joqPx+Kw38lQsnuh10llHgr8S3EwtGYGZ8LOU95XPXkaTSgBR/j40Zol2VG2RptXUPYdkOJB9PvX5GlUZ0nU/U3t6PzjUd1LlslTcJIAdtxdqXMF+geond/liiZYFnLim/Vs+++c3H6j7IByN/YpZWFuq4xAZGaVGtgb7PUn1sSAiIiIiIsoKjKVtQJ+4mZikbp8WhtROLZ2fZfCRbEdqSR1aAs6SjdDRvX6ajoWuuIKwLZgHLX75GPaOhdTI3b1Dv1bbKpyHd4Rjx/pq/ySmcGTSCmTLZZfomMoxarlmiopTSDvC00t3VNtAZEiYzm15etkn9o/o+PEX0o2BTgMmjY0y1DtuL3+R17kUdnSfgeiwSLitn4WS7euq4dSVR3ZCREiYGoItqo7rjhqTe6mglDSWxh12L41ge/rNQ2RwqLoYNf95Mkp1bJCiER3SaHlvz5lEQU45QeWita3ztLTt76QfdKaIk54g2zpMRba89uh2dIlK0xbsHwjXxaPUBeLh0cvqotHy109Q0r2+Go0pAde4vUEkld32TtNUA6l1bjt1sb2/76xqSNRHjo0EOSWIqrF/+DdotmoiIoPD1HolnUBqA7uyXdf+tyvesHdp3JX1yYiVs1/9qVLPSWPyes8zath/gy+HY3efOepiKseiw84FeHTymjpukkog7nHz9jiAyytiUxgUqFlODflf32hMstslwWNH93rY3W+etueIpB6W56UXizQgS8Emx1BzMSciMjaGWr6mpuzNXa6YqlhUG/+Oek+phFxZtS1V65RUKGcX/KF68iVMWRIdFqH2N1fZInDfPh/eHvuBGKDJ8nE4OGqRCrBZ2Fqj/Za52gBpwmP6YN9Z3F5/SFvGtd/6Bf6qMyLVnYPisrTNhvK9W+DUF2sTLZOOUS99H6YpLatU1hJ+llIZkbS+h8cvx7FPflT7Jz1Kd/X6HHYlCqqesfJ3RFAI7B0dVOXYw2WEzvuZC0s3qFQzmvSzdWcPVCMz34Sk3ZEK9dHJK9Rj2f7CDZ3x7vkfERkUglf+gdjRZcYbvQcREREREZGpMqa2gVpT+6DGpJ54dv2eqg9LZj7tsim9UbJDfWSTduohXyUZTDW2dmpdpO4t7yVtIzd+34trv/yrnpcgdIOvRuDwhGWJYhapoSuuIMFT2e7NbSYh7GmQaheRLE4XvluPUl1cVbvJNvepKu5Quntj1Js7FHv6zk10TMWBkd+qdQgZ2CXTEaVm9GtclYa0U+ltX91/jDPzf4vXFqJvmalioNMI+W4/jqiQcG1vDznBhYy8kCCUY7u66rGMXAy6+0j3SszNUOvTPijkUlHN6WSbP6dK/5bcxbZ0t0Zw7FAf27tMT7SspHs9PL/1QK0nPd1ef1D9Dgt8iZc+AapxUdKtFnatokaUaFjmsFEXFl2kp0TDb0aqPOeSzi1bHjvkqVgi2UBnvXlDEPY8SFtISUC12tjuKrj58NgV1cgsvTk2Nh+vtu9NSQ8On63H1d8BJ69rP9sCtSvAvmRBtFo7Nd7rc5YpgiCfh4nWk69yKVQd01Xtt+yvNNRKIDphzvK4ZORHi/99jAvLNmpHq8jIFMmR3uLXT1QhId89IeskIjI1mVm+prbslZt4+xIFsaPLdFjnzgG3f2apdCwyz3J6uPVPbNkrAU3pcSlZFaSnpvSQlJt6DSs7G/Vc3LlDNexKFELjpWOQvXA+VW5IJxnZZllnWsjI0CYrxuP+vnMqrW9C5Xq1wI116Ts9gcxv+fhMbOqhgJPX4Dy8g/q7aLMayFnKAW3Xz9K+Vio1MpJTlyKNq6qOU1LWyujWbLnt3mi7shfOixarJ6uKueZeJn+1MshdoQT+qvE+wl+GqPm3pTetzKNNRERERERExtk2cODDxQh+EJutp+JANzXYZ0OT/+rlEviUn8KNqqgp2LZ1/DRN2RYNqZ1alycXbuHPmsNUdiOpE7dcMxVhgS9wZ/NRVJ/wjho1+fzGfZ2Zot6UpJ/VBCgDTl1TcQVNEDR/9TJqWhlN7CBJZmZwet8dxVrUgrmlOaxyZsejE7pHtibn9LzfEPzwqQpqS+f1lmun4p8GH6rAur5lpoyBTiMUN2e4BJ/MLSzU32ZmZmro94P955Jdh/OwDipIuLX9J2p9dT7rrybH1cexYwNUH98D//aYGS+1q0Z5aWBMIh1suu1v9Ov9NTNTj7e6T0lRDnVJRXDP87QKUArp+SE9a/Sp+/kg5CiSP3aUy+ueMJLrXIavS5BTSMOupIWT5yWX+puKO8pFfbaW/322z67dw7aO8QsQkfDiLQ3BzVZ9pOYRle2TRtXeN35V6eySKkAkSNxq3adq5Kqmh42G9IzR9I6R3ivSE0RGrxARmZrMKl/TUvbKtfjW+kOqXJSONvf3nEaBWuXSLdAp82ImLHul5A17FqR3Poy4miwfi1Nz1sJn6zH1uNfl1SplT1pIYLfJinGx81dO+ynRckm5Lvu/b8h/84anh7jlZsJy+cH+8zjwwaJE/5PDIV/8x0Xzo96cwdjS9mMVjM5TqWS8AGlqyX1Imz9n4Ny3Hio1kEaZd5rA7/BFbU9k7z/3odXvacuyQURERERElFUZWtuAJsgpZOqYOtP7qUE8msCbht/BC6odOE+lEnhy/haMtZ06KXHbo6XDr8xpWqhuJRXolIxMUveuNMgNZhYWKmWwzMm5ue3HaZrqR28bSYJjISM7r6/Znew6Snd1ReGGVbCj63S1L5UGt0Nh18pp2h7JdqkhHcFrTemtBoDJ565vmSnTE2ImQxD+MlhF91PCd4eX6hUgqeSE/NbM7yQ9+2UkhoYMPZfGQrnQxqYr1Z+3WkaS1JzcC/++O1M1riYkPTryVSutTVGnT5eDi9Q8Y4n2NSjl+yo9ECS3uUxuHLfhT3pzaPc3zrpktMure7E9bArVq4Q8TiX1rt9l9iDYlyoMz0EL4vWAkX3PXiiPytWu2W/7koWSnGcrf/WyKqe7Lmp/U5g7XUaRyEhW6ZkTN3+6FBYy15f0gpG/hQRw5W/N5yRD4PWxzB4b5Ly/94x2/tO4ZBSPWq+tNapP6qlGfBIRGTtDKV/TWvZKkFNSx6jtsbFWk9wHXno9f0MCUuZK2auLlCEpLYueez9QN+Nl322mfU7KQRmpqeuYWueyQ9DdAO2oVOm9mRbSI1Lmx5BKnMxfoYuklpEb+LjphnSR3oyuiz/UuSzhZ6mPpL+X+VckaBm3zNd1PyPrlFGxwQFP1eOKg9yQVlImt/lrhkpto5kzRUPS9hZuWFl7P1CsVW01nwwREREREREZZ9uA1Idt4mQ0lDS6IY+fq/qxyvT0epSlpk5qky+n6mBrrO3U+qg26teDn2TQTrGWtfDkYmxKVkk/7FFnBDxcRmJ7p08R8TJE/Z1UkLPmlPfU6FhdEsYVkvtOSPpjTbuIfCYyIErXMZU2ktDAF6pdRba/7LtNkVaaGIhmYFK2vPZ4cds/2WWmjCM6Ddyl7zej9e/TERUSpp0QOSnS6FVtvBXct87VpuK+uHSDyt0tc2tJ/msZTn315x248uM2NF05AZ32LUSIfyAeHNQ/GlFSz0nObUmTpiGjSzQ9R6SBUYayJzfSTy62qseJjvkdZYJd6Q3Sae836oLsOSB2yHdSZBRFnZkD1Otlh2XOTJnsV3p0yDxlkitcjptMGiwjSurPHarSzkpO6senk57guWCdCnAa0g7PbtxTx1K8vBuAvYO+jJ1QeOL3aLri/+3dW4hVVRgA4DVeMiMKAouKstAwtJuRqZEUFWoqk2X5kJk+KF3tSjejKyQWiYVF2EVf7Abqg1n6kJZZWUpFNzPQLr5YQUY3mxx14l+W2DQexzw6e5/5PhhwcDx7u8/ss/Za//r//5bUtK0pl5977+7nWgz+hoOP6bbL3SnrX1uZeow6J/cL3bnJc0uiVO/rY6fkHTv97huXOnTumI8Z2abRKzPe34uWTkuNvzfkJs8fPfxSGrFoav7wjBrylfSeOCx169szdT6oS+r+d+mFbxauSJ88Pj//eXBkg3TokJtsx3HWzFpU8fUAyqAo4+spN41KvcYOzuPjWSdek/o/NCEtGHxbfhivNPaunvlKGvjIVWnksun5nCJrcufsvuaBzgi0tSR2fJ50dX2qXzJtez+NO57e5bnGrsXoMxF9OWJyFxOuKBPz1rWPp80tXNOV987K12Lzz5vShnc+Tb/9veFoV+Ic4jrEzsvLPpiZvnv3s7R80ozc0/K44QPy+B1jZvh+1Ze5D0pWV5d6jj43Lb9hRtqdQ44/Mpe5aUnz9/LXChOBKGkbvTWiNGz0DY2SRRs//To/m7T0PBPliUa+OT2/dzERqniOPY7KGZuduh6QJ4hxLaIneVRXiJ4ssUs1nlPiK6x+9rW09uU38s7e6N1av+TRtK1xa+6tveL27f07AQAAKN/awNZNf6YL5kzOGZCxFt2w8de0ZNzU/G9ifTiOGT0zoyRs46Y/0xsTp+V15LKuU0fw+JK3Z6SOXTrlIGHMh9fNW5Y+nPJC6j58QOo1bkj+v0ZA8dtXVqS1L/2/6pKH9T4u/fhxyxmOzeMKu2v7E5u6h869P38f57X2xaVp42df/+eaxtz92CH98kb0uBbfv/9FxTK7sc4RPWAjgSt+r/pcXZ/f+3jtsx+7PnXtdmhep9nSsDm9OXHajrWOSn9Xy+qamvawOy1V0bipIT3f44pCXs24iSLjIZdsrYJIHT/zwfG51F33EQNzqnRLmYNt5ezHrvtXs+i9ddqto3c0Gu4/ZUJOo/9h5ZpUFJG2H+9ttZoQD533QFr9zMK0fvGqXf7MmHV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"text/plain": [ - "\"Output" + "
" ] }, - "execution_count": 8, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -418,67 +385,41 @@ "aqc_ansatz, aqc_initial_parameters = generate_ansatz_from_circuit(\n", " aqc_good_circuit\n", ")\n", - "aqc_ansatz.draw(\"mpl\", fold=-1)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "bd9e9bf2-9ee5-445d-aa4a-c7f412c488f8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AQC Comparison circuit: depth 36\n", - "Target circuit: depth 385\n", - "Ansatz circuit: depth 7, with 156 parameters\n" - ] - } - ], - "source": [ - "print(f\"AQC Comparison circuit: depth {aqc_comparison_circuit.depth()}\")\n", - "print(f\"Target circuit: depth {aqc_target_circuit.depth()}\")\n", + "\n", + "# Baseline Trotter circuit: 3 Trotter steps, no AQC optimization\n", + "baseline_num_trotter_steps = 3\n", + "baseline_circuit = initial_state.copy()\n", + "baseline_circuit.compose(\n", + " generate_time_evolution_circuit(\n", + " hamiltonian,\n", + " synthesis=SuzukiTrotter(reps=baseline_num_trotter_steps),\n", + " time=aqc_evolution_time,\n", + " ),\n", + " inplace=True,\n", + ")\n", + "\n", + "print(f\"Target circuit: depth {aqc_target_circuit.depth()}\")\n", + "print(f\"Baseline circuit: depth {baseline_circuit.depth()} ({baseline_num_trotter_steps} Trotter steps)\")\n", "print(\n", - " f\"Ansatz circuit: depth {aqc_ansatz.depth()}, with {len(aqc_initial_parameters)} parameters\"\n", - ")" + " f\"Ansatz circuit: depth {aqc_ansatz.depth()}, with {len(aqc_initial_parameters)} parameters\"\n", + ")\n", + "aqc_ansatz.draw(\"mpl\", fold=-1)" ] }, { "cell_type": "markdown", - "id": "3c8663b9-d513-4a2a-9911-03f3500ad486", + "id": "be1b4049", "metadata": {}, "source": [ - "#### Choose settings for tensor network simulation\n", + "#### Set up tensor network simulation and build the target MPS\n", "\n", - "Here, we use Quimb's matrix-product state circuit simulator, along with jax to provide the gradient." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "e5b9471f-fc7a-45c9-ab11-295cfff620bf", - "metadata": {}, - "outputs": [], - "source": [ - "simulator_settings = QuimbSimulator(\n", - " quimb.tensor.CircuitMPS, autodiff_backend=\"jax\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "5030d11d-54e3-4f29-acb2-6f7dd3ff05cb", - "metadata": {}, - "source": [ - "Next, we build a MPS representation of the target state that will be approximated using AQC-Tensor. This representation enables efficient handling of entanglement, providing a compact description of the quantum state for further optimization." + "We use Quimb's matrix-product state (MPS) circuit simulator, with JAX providing automatic differentiation for the gradient-based optimization. We then build an MPS representation of the target state and evaluate the starting fidelity between the initial ansatz and the target." ] }, { "cell_type": "code", - "execution_count": 11, - "id": "1f050059-a281-41f1-a277-d7450e8b3ee3", + "execution_count": 6, + "id": "666fcf42", "metadata": {}, "outputs": [ { @@ -486,129 +427,77 @@ "output_type": "stream", "text": [ "Target MPS maximum bond dimension: 5\n", - "Reference MPS maximum bond dimension: 7\n" + "Starting fidelity: 0.998246\n" ] } ], "source": [ + "simulator_settings = QuimbSimulator(\n", + " quimb.tensor.CircuitMPS, autodiff_backend=\"jax\"\n", + ")\n", + "\n", "aqc_target_mps = tensornetwork_from_circuit(\n", " aqc_target_circuit, simulator_settings\n", ")\n", "print(\"Target MPS maximum bond dimension:\", aqc_target_mps.psi.max_bond())\n", "\n", - "# Obtains the reference MPS, where we can obtain the exact expectation value by examining the `local_expectation``\n", - "reference_mps = tensornetwork_from_circuit(\n", - " reference_circuit, simulator_settings\n", - ")\n", - "reference_expval = reference_mps.local_expectation(\n", - " quimb.pauli(\"Z\") & quimb.pauli(\"Z\"), (L // 2 - 1, L // 2)\n", - ").real.item()\n", - "print(\"Reference MPS maximum bond dimension:\", reference_mps.psi.max_bond())" - ] - }, - { - "cell_type": "markdown", - "id": "5ffbdd20-c9bf-4265-8a8d-8135abb2b8f7", - "metadata": {}, - "source": [ - "Note that, by choosing a larger number of Trotter steps for the target state, we have effectively reduced its Trotter error compared to the initial circuit. We can evaluate the fidelity ($ |\\langle \\psi_1 | \\psi_2 \\rangle|^2 $) between the state prepared by the initial circuit and the target state to quantify this difference." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "51d7d623-3e7c-44e7-80f1-94fd555a2c9e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting fidelity: 0.9982464959067222\n" - ] - } - ], - "source": [ "good_mps = tensornetwork_from_circuit(aqc_good_circuit, simulator_settings)\n", "starting_fidelity = abs(compute_overlap(good_mps, aqc_target_mps)) ** 2\n", - "print(\"Starting fidelity:\", starting_fidelity)" + "print(f\"Starting fidelity: {starting_fidelity:.6f}\")" ] }, { "cell_type": "markdown", - "id": "01234b5b-f4db-4e22-993a-c8673621ab7f", + "id": "1f14eb3f", "metadata": {}, "source": [ - "#### Optimize the parameters of the ansatz using MPS calculations\n", - "In this step, we optimize the ansatz parameters by minimizing a simple cost function, `MaximizeStateFidelity`, using the L-BFGS optimizer from SciPy. We select a stopping criterion for the fidelity that ensures it surpasses the fidelity of the initial circuit without AQC-Tensor. Once this threshold is reached, the compressed circuit will exhibit both lower Trotter error and reduced depth compared to the original circuit. By using additional CPU time, further optimization can continue to increase fidelity." + "#### Optimize the ansatz parameters\n", + "\n", + "We minimize the `MaximizeStateFidelity` cost function using the L-BFGS-B optimizer. The optimizer iteratively adjusts the ansatz parameters to maximize the fidelity between the ansatz circuit and the target MPS." ] }, { "cell_type": "code", - "execution_count": 29, - "id": "ad2265cb-19a6-4402-8b0d-24239c930c90", + "execution_count": 7, + "id": "6ad144d6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2025-04-14 11:46:52.174235 Intermediate result: Fidelity 0.99795851\n", - "2025-04-14 11:46:52.218249 Intermediate result: Fidelity 0.99822826\n", - "2025-04-14 11:46:52.280924 Intermediate result: Fidelity 0.99829675\n", - "2025-04-14 11:46:52.356214 Intermediate result: Fidelity 0.99832474\n", - "2025-04-14 11:46:52.411609 Intermediate result: Fidelity 0.99836131\n", - "2025-04-14 11:46:52.453747 Intermediate result: Fidelity 0.99839954\n", - "2025-04-14 11:46:52.496184 Intermediate result: Fidelity 0.99846517\n", - "2025-04-14 11:46:52.542046 Intermediate result: Fidelity 0.99865029\n", - "2025-04-14 11:46:52.583679 Intermediate result: Fidelity 0.99872332\n", - "2025-04-14 11:46:52.628732 Intermediate result: Fidelity 0.99892359\n", - "2025-04-14 11:46:52.690386 Intermediate result: Fidelity 0.99900640\n", - "2025-04-14 11:46:52.759398 Intermediate result: Fidelity 0.99907169\n", - "2025-04-14 11:46:52.819496 Intermediate result: Fidelity 0.99911423\n", - "2025-04-14 11:46:52.884505 Intermediate result: Fidelity 0.99918716\n", - "2025-04-14 11:46:52.947919 Intermediate result: Fidelity 0.99921278\n", - "2025-04-14 11:46:53.012808 Intermediate result: Fidelity 0.99924853\n", - "2025-04-14 11:46:53.083626 Intermediate result: Fidelity 0.99928797\n", - "2025-04-14 11:46:53.153235 Intermediate result: Fidelity 0.99933028\n", - "2025-04-14 11:46:53.221371 Intermediate result: Fidelity 0.99935757\n", - "2025-04-14 11:46:53.286211 Intermediate result: Fidelity 0.99938140\n", - "2025-04-14 11:46:53.352391 Intermediate result: Fidelity 0.99940964\n", - "2025-04-14 11:46:53.420472 Intermediate result: Fidelity 0.99944051\n", - "2025-04-14 11:46:53.486279 Intermediate result: Fidelity 0.99946828\n", - "2025-04-14 11:46:53.552338 Intermediate result: Fidelity 0.99948723\n", - "2025-04-14 11:46:53.618688 Intermediate result: Fidelity 0.99951011\n", - "2025-04-14 11:46:53.690878 Intermediate result: Fidelity 0.99954718\n", - "2025-04-14 11:46:53.762725 Intermediate result: Fidelity 0.99956267\n", - "2025-04-14 11:46:53.829784 Intermediate result: Fidelity 0.99958949\n", - "2025-04-14 11:46:53.897477 Intermediate result: Fidelity 0.99960498\n", - "2025-04-14 11:46:53.954633 Intermediate result: Fidelity 0.99961308\n", - "2025-04-14 11:46:54.010125 Intermediate result: Fidelity 0.99962894\n", - "2025-04-14 11:46:54.064717 Intermediate result: Fidelity 0.99964121\n", - "2025-04-14 11:46:54.118892 Intermediate result: Fidelity 0.99964348\n", - "2025-04-14 11:46:54.183236 Intermediate result: Fidelity 0.99964860\n", - "2025-04-14 11:46:54.245521 Intermediate result: Fidelity 0.99965695\n", - "2025-04-14 11:46:54.305792 Intermediate result: Fidelity 0.99966398\n", - "2025-04-14 11:46:54.355819 Intermediate result: Fidelity 0.99967816\n", - "2025-04-14 11:46:54.409580 Intermediate result: Fidelity 0.99968293\n", - "2025-04-14 11:46:54.457979 Intermediate result: Fidelity 0.99968936\n", - "2025-04-14 11:46:54.505891 Intermediate result: Fidelity 0.99969223\n", - "2025-04-14 11:46:54.551084 Intermediate result: Fidelity 0.99970009\n", - "2025-04-14 11:46:54.601817 Intermediate result: Fidelity 0.99970724\n", - "2025-04-14 11:46:54.650097 Intermediate result: Fidelity 0.99970987\n", - "2025-04-14 11:46:54.714727 Intermediate result: Fidelity 0.99971237\n", - "2025-04-14 11:46:54.780052 Intermediate result: Fidelity 0.99971916\n", - "2025-04-14 11:46:54.871994 Intermediate result: Fidelity 0.99971940\n", - "2025-04-14 11:46:54.958244 Intermediate result: Fidelity 0.99972465\n", - "2025-04-14 11:46:55.011057 Intermediate result: Fidelity 0.99972763\n", - "2025-04-14 11:46:55.175339 Intermediate result: Fidelity 0.99972894\n", - "2025-04-14 11:46:56.688912 Intermediate result: Fidelity 0.99972894\n", - "Done after 50 iterations.\n" + "2026-04-30 11:49:28.459043 Intermediate result: Fidelity 0.99952882\n", + "2026-04-30 11:49:28.461819 Intermediate result: Fidelity 0.99958531\n", + "2026-04-30 11:49:28.464235 Intermediate result: Fidelity 0.99960093\n", + "2026-04-30 11:49:28.466638 Intermediate result: Fidelity 0.99961046\n", + "2026-04-30 11:49:28.469037 Intermediate result: Fidelity 0.99962560\n", + "2026-04-30 11:49:28.471477 Intermediate result: Fidelity 0.99964395\n", + "2026-04-30 11:49:28.473866 Intermediate result: Fidelity 0.99968150\n", + "2026-04-30 11:49:28.478592 Intermediate result: Fidelity 0.99970569\n", + "2026-04-30 11:49:28.481037 Intermediate result: Fidelity 0.99973788\n", + "2026-04-30 11:49:28.483499 Intermediate result: Fidelity 0.99975385\n", + "2026-04-30 11:49:28.485921 Intermediate result: Fidelity 0.99976458\n", + "2026-04-30 11:49:28.488379 Intermediate result: Fidelity 0.99977661\n", + "2026-04-30 11:49:28.490848 Intermediate result: Fidelity 0.99978663\n", + "2026-04-30 11:49:28.493267 Intermediate result: Fidelity 0.99980236\n", + "2026-04-30 11:49:28.495665 Intermediate result: Fidelity 0.99981607\n", + "2026-04-30 11:49:28.498084 Intermediate result: Fidelity 0.99982811\n", + "2026-04-30 11:49:28.500443 Intermediate result: Fidelity 0.99985827\n", + "2026-04-30 11:49:28.502793 Intermediate result: Fidelity 0.99988354\n", + "2026-04-30 11:49:28.505141 Intermediate result: Fidelity 0.99991608\n", + "2026-04-30 11:49:28.507502 Intermediate result: Fidelity 0.99993336\n", + "2026-04-30 11:49:28.509847 Intermediate result: Fidelity 0.99993956\n", + "2026-04-30 11:49:28.512183 Intermediate result: Fidelity 0.99994421\n", + "2026-04-30 11:49:28.514530 Intermediate result: Fidelity 0.99994743\n", + "2026-04-30 11:49:28.551200 Intermediate result: Fidelity 0.99994791\n", + "2026-04-30 11:49:28.555802 Intermediate result: Fidelity 0.99994803\n", + "2026-04-30 11:49:28.558109 Intermediate result: Fidelity 0.99994898\n", + "2026-04-30 11:49:28.585675 Intermediate result: Fidelity 0.99994898\n", + "Done after 27 iterations.\n" ] } ], "source": [ - "# Setting values for the optimization\n", "aqc_stopping_fidelity = 1\n", "aqc_max_iterations = 500\n", "\n", @@ -624,7 +513,6 @@ " f\"{datetime.datetime.now()} Intermediate result: Fidelity {fidelity:.8f}\"\n", " )\n", " if intermediate_result.fun < stopping_point:\n", - " # Good enough for now\n", " raise StopIteration\n", "\n", "\n", @@ -636,14 +524,7 @@ " options={\"maxiter\": aqc_max_iterations},\n", " callback=callback,\n", ")\n", - "if (\n", - " result.status\n", - " not in (\n", - " 0,\n", - " 1,\n", - " 99,\n", - " )\n", - "): # 0 => success; 1 => max iterations reached; 99 => early termination via StopIteration\n", + "if result.status not in (0, 1, 99):\n", " raise RuntimeError(\n", " f\"Optimization failed: {result.message} (status={result.status})\"\n", " )\n", @@ -652,1218 +533,532 @@ "aqc_final_parameters = result.x" ] }, - { - "cell_type": "code", - "execution_count": 14, - "id": "c95ccea0-be99-4db9-838d-2327851e6761", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Final parameters: [-7.853983035039254, 1.5707966468427772, 1.5707962768868613, -1.570798010835122, 1.570794480409574, 1.5707972214146968, -1.570796593027083, 1.5707968206822998, -1.5707959018046258, -1.5707991700969144, 1.5707965852600927, 4.712386891737442, -7.853980840717957, 1.5707967508132654, 1.5707943162503217, -1.5707955382023582, 1.5707958007156742, 1.570796096113293, -1.5707928509846847, 1.5707971042943747, -1.570797909276557, -1.5707941020637393, 1.5707980179540793, 4.712389823219363, -1.5707928752386107, 1.5707996426312891, -1.5707975640471001, -1.570794132802984, 1.5707944361599957, 4.712390747060803, 0.1048818190315936, 0.06686710468840577, -0.0668645844756557, -3.1415923537135466, 1.2374931269696063, 6.323169390432535e-07, 3.53229204771738e-08, 2.1091105688681484, 6.283186439944202, 0.12152258846156239, 0.07961752617254866, -0.07961775088604585, -1.6564278051174865e-06, 2.0771163596472384, 3.141592651630471, -6.283185775192653, 1.7691609006726954, 3.1415922910116216, 0.19837572065074083, 0.11114901449078964, -0.11115124544944892, -3.141591983034976, 0.8570788408766729, 4.201601390404146e-07, -3.141593736550978, 0.34652010942396333, 6.283186232785291, 0.13606356527241956, 0.03891676349289617, -0.03891524189533726, -1.5707965732853424, 1.5707968967088564, -0.3086133992238162, 1.5707957152428194, 1.5707968398959653, -0.32062737993080026, 0.11027416939993417, 0.0726167290795046, -0.07262020423334464, -2.3729431959735024e-06, 1.8204437429254703, 9.299060301196612e-07, -3.141592899563451, 2.103269568939461, 3.1415937539734626, 0.11536891854817125, 0.09099022308254198, -0.09098864958606581, -3.1415913307373127, 2.078429034357281, -1.509777998069368e-06, -3.1415922600663255, 1.5189162645358172, -3.1415878461323583, 0.09999070991480716, 0.04352011445148391, -0.04351849541849812, -1.570797642506462, 1.570795238023824, 0.8903442644396505, 1.5707962698006606, 1.5707946765132268, 0.9098791754570567, 0.10448284343424026, 0.07317037684936827, -0.07316718173961152, -3.141592682240966, 2.1665363080039612, -7.450882112394189e-07, -5.771181304929921e-07, 2.615334999517103, -3.1415914971653898, 0.1890887078648001, 0.13578163074571992, -0.13578078143610256, 7.156734195912883e-07, 1.7915385305413096, -5.188866034727312e-07, 1.2827742939197711e-06, 1.2348316581417487, 6.28318357406372, 0.08061187643781703, 0.03820789039271876, -0.03820731868804904, 1.5707964027727628, 1.570798734462218, 4.387336153720882, -1.570795722044763, 1.570798457375325, 4.450361734163248, 0.092360147257953, 0.06047700345049011, -0.06048592856713045, -3.141591214829027, 2.6593289993286047, -2.366937342261038e-07, 8.112162974032695e-08, 1.8907014631413432, 8.355881261853104e-07, 0.23303641819370874, 0.14331998953606456, -0.1433194488304741, -3.141591621822901, 0.7455776479558791, 3.1415914520163586, -3.1415933560496105, 0.7603938554148255, -1.6230983177616282e-06, 0.07186349688535713, 0.03197144517771341, -0.031971177878588546, -4.712389048748508, 1.5707948403165752, 1.2773619319829186, -1.5707990802172127, 1.5707957676951863, 1.289083769394045, 0.13644999397718796, 0.032761460443590046, -0.032762060585195645, -1.5707977610073176, 1.5707964181578042, -3.4826435600366983, -4.712389691708343, 1.570794277502252, 2.799088046133275]\n" - ] - } - ], - "source": [ - "parameters = [float(param) for param in aqc_final_parameters]\n", - "print(\"Final parameters:\", parameters)" - ] - }, { "cell_type": "markdown", - "id": "ca32c1ef-f7d5-4489-83b3-17972bf94700", + "id": "ea5b484a", "metadata": {}, "source": [ - "At this point, it is only necessary to find the final parameters to the ansatz circuit. We can then merge the optimized AQC circuit with the remaining evolution circuit to create a complete time-evolution circuit for execution on quantum hardware." + "#### Assemble the final AQC circuit\n", + "\n", + "With the optimized parameters in hand, we bind them to the ansatz to produce the final AQC circuit. This circuit has the depth of a single Trotter step but approximates the accuracy of 32 steps." ] }, { "cell_type": "code", - "execution_count": 15, - "id": "813c9ced-6a2e-4345-bffc-7dae938e2015", + "execution_count": 8, + "id": "e09e40de", "metadata": {}, "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "
" ] }, - "execution_count": 15, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "aqc_final_circuit = aqc_ansatz.assign_parameters(aqc_final_parameters)\n", - "aqc_final_circuit.compose(subsequent_circuit, inplace=True)\n", "aqc_final_circuit.draw(\"mpl\", fold=-1)" ] }, { "cell_type": "markdown", - "id": "46a93127-5443-40b7-a81a-dfd70df42ba2", + "id": "047511db", "metadata": {}, "source": [ - "We also need to merge our `aqc_comparison_circuit` with the remaining evolution circuit. This circuit will be used to compare the performance of the AQC-Tensor-optimized circuit with the original circuit." + "### Step 2: Optimize problem for quantum hardware execution\n", + "\n", + "For this small-scale example, we use a fake backend (`FakeKyiv`) to simulate hardware execution locally. We transpile both the AQC circuit and the baseline Trotter circuit (the unoptimized `aqc_good_circuit`) to the backend's ISA, with `optimization_level=3` to further reduce circuit depth." ] }, { "cell_type": "code", - "execution_count": 16, - "id": "86ba26ff-0bfa-47d0-b5ee-8944a8ddf274", + "execution_count": 9, + "id": "9e7556dd", "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "\"Output" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "AQC circuit depth: 50\n", + "Baseline Trotter circuit depth: 85\n" + ] } ], "source": [ - "aqc_comparison_circuit.compose(subsequent_circuit, inplace=True)\n", - "aqc_comparison_circuit.draw(\"mpl\", fold=-1)" + "backend = FakeKyiv()\n", + "\n", + "pass_manager = generate_preset_pass_manager(\n", + " backend=backend, optimization_level=3\n", + ")\n", + "\n", + "# Transpile the AQC-optimized circuit\n", + "isa_circuit = pass_manager.run(aqc_final_circuit)\n", + "isa_observable = observable.apply_layout(isa_circuit.layout)\n", + "print(\"AQC circuit depth:\", isa_circuit.depth())\n", + "\n", + "# Transpile the baseline Trotter circuit (no AQC optimization)\n", + "isa_baseline_circuit = pass_manager.run(baseline_circuit)\n", + "isa_baseline_observable = observable.apply_layout(isa_baseline_circuit.layout)\n", + "print(\"Baseline Trotter circuit depth:\", isa_baseline_circuit.depth())" ] }, { "cell_type": "markdown", - "id": "982c5067-9a74-42a0-bd31-aaff72cf79df", + "id": "60aec566", "metadata": {}, "source": [ - "### Step 2: Optimize problem for quantum hardware execution" + "### Step 3: Execute using Qiskit primitives\n", + "\n", + "We use the `EstimatorV2` primitive with the fake backend to run both the AQC-optimized circuit and the baseline Trotter circuit, measuring the ZZ observable for each." ] }, { - "cell_type": "markdown", - "id": "2121c40c-a9a7-4c36-a20a-04ee9f6637db", + "cell_type": "code", + "execution_count": 10, + "id": "241e24f2", "metadata": {}, + "outputs": [], "source": [ - "Select the hardware. Here we will use any of the IBM Quantum® devices available that have at least 127 qubits." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "88715b59-4516-4b75-b041-c978944dd14a", - "metadata": {}, - "outputs": [], - "source": [ - "service = QiskitRuntimeService()\n", - "backend = service.least_busy(min_num_qubits=127)\n", - "print(backend)" - ] - }, - { - "cell_type": "markdown", - "id": "3f477394-5eac-4c12-b36b-e94c800fa889", - "metadata": {}, - "source": [ - "We transpile PUBs (circuit and observables) to match the backend ISA (Instruction Set Architecture). By setting `optimization_level=3`, the transpiler optimizes the circuit to fit a one-dimensional chain of qubits, reducing the noise that impacts circuit fidelity. Once the circuits are transformed into a format compatible with the backend, we apply a corresponding transformation to the observables to ensure they align with the modified qubit layout." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "087fff8d-98b9-4f9a-8004-01a3b0166e12", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Observable info: SparsePauliOp(['IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZ'],\n", - " coeffs=[1.+0.j])\n", - "Circuit depth: 111\n" - ] - }, - { - "data": { - "text/plain": [ - "\"Output" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pass_manager = generate_preset_pass_manager(\n", - " backend=backend, optimization_level=3\n", - ")\n", - "isa_circuit = pass_manager.run(aqc_final_circuit)\n", - "isa_observable = observable.apply_layout(isa_circuit.layout)\n", - "print(\"Observable info:\", isa_observable)\n", - "print(\"Circuit depth:\", isa_circuit.depth())\n", - "isa_circuit.draw(\"mpl\", fold=-1, idle_wires=False)" - ] - }, - { - "cell_type": "markdown", - "id": "58e8e840-94a8-4414-81f7-01bbf1b73810", - "metadata": {}, - "source": [ - "Perform transpilation for the comparison circuit." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "7c2e5fe7-21ce-461d-adaa-776f8d882163", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Observable info: SparsePauliOp(['IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZ'],\n", - " coeffs=[1.+0.j])\n", - "Circuit depth: 158\n" - ] - }, - { - "data": { - "text/plain": [ - "\"Output" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "isa_comparison_circuit = pass_manager.run(aqc_comparison_circuit)\n", - "isa_comparison_observable = observable.apply_layout(\n", - " isa_comparison_circuit.layout\n", - ")\n", - "print(\"Observable info:\", isa_comparison_observable)\n", - "print(\"Circuit depth:\", isa_comparison_circuit.depth())\n", - "isa_comparison_circuit.draw(\"mpl\", fold=-1, idle_wires=False)" - ] - }, - { - "cell_type": "markdown", - "id": "1f832aaa-c2a9-42b7-95b6-bd920cdc17b0", - "metadata": {}, - "source": [ - "### Step 3: Execute using Qiskit primitives\n", - "\n", - "In this step, we execute the transpiled circuit on quantum hardware (or a simulated backend). Using the `EstimatorV2` class from `qiskit_ibm_runtime`, we set up an estimator to run the circuit and measure the specified observable. The job result provides the expected outcome for the observable, giving us insights into the circuit’s performance on the target hardware." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "3aabf36b-4587-43b7-be99-9376fc7f47c1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Job ID: czyhqdxd8drg008hx0yg\n" - ] - }, - { - "data": { - "text/plain": [ - "PrimitiveResult([PubResult(data=DataBin(evs=np.ndarray(), stds=np.ndarray(), ensemble_standard_error=np.ndarray()), metadata={'shots': 4096, 'target_precision': 0.015625, 'circuit_metadata': {}, 'resilience': {}, 'num_randomizations': 32})], metadata={'dynamical_decoupling': {'enable': False, 'sequence_type': 'XX', 'extra_slack_distribution': 'middle', 'scheduling_method': 'alap'}, 'twirling': {'enable_gates': False, 'enable_measure': True, 'num_randomizations': 'auto', 'shots_per_randomization': 'auto', 'interleave_randomizations': True, 'strategy': 'active-accum'}, 'resilience': {'measure_mitigation': True, 'zne_mitigation': False, 'pec_mitigation': False}, 'version': 2})" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "estimator = Estimator(backend)\n", - "job = estimator.run([(isa_circuit, isa_observable)])\n", - "print(\"Job ID:\", job.job_id())\n", - "job.result()" - ] - }, - { - "cell_type": "markdown", - "id": "4ec6669b-2b76-41df-9634-3c073998b1d7", - "metadata": {}, - "source": [ - "Perform the execution for the comparison circuit." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "91f16fff-da28-42b4-a32a-fa2f0945f8fd", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Job Comparison ID: czyhqdxd8drg008hx0yg\n" - ] - }, - { - "data": { - "text/plain": [ - "PrimitiveResult([PubResult(data=DataBin(evs=np.ndarray(), stds=np.ndarray(), ensemble_standard_error=np.ndarray()), metadata={'shots': 4096, 'target_precision': 0.015625, 'circuit_metadata': {}, 'resilience': {}, 'num_randomizations': 32})], metadata={'dynamical_decoupling': {'enable': False, 'sequence_type': 'XX', 'extra_slack_distribution': 'middle', 'scheduling_method': 'alap'}, 'twirling': {'enable_gates': False, 'enable_measure': True, 'num_randomizations': 'auto', 'shots_per_randomization': 'auto', 'interleave_randomizations': True, 'strategy': 'active-accum'}, 'resilience': {'measure_mitigation': True, 'zne_mitigation': False, 'pec_mitigation': False}, 'version': 2})" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "job_comparison = estimator.run([(isa_comparison_circuit, isa_observable)])\n", - "print(\"Job Comparison ID:\", job.job_id())\n", - "job_comparison.result()" - ] - }, - { - "cell_type": "markdown", - "id": "3c6d16d4-3118-49b6-9594-3d7ddb02701c", - "metadata": {}, - "source": [ - "### Step 4: Post-process and return result in desired classical format\n", - "\n", - "In this case, reconstruction is unnecessary. We can directly examine the result by accessing the expectation value from the execution output." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "7247e56f-0ab8-4aa9-834c-ba95965a0b3f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Exact: \t-0.5252\n", - "AQC: \t-0.4903, |∆| = 0.0349\n", - "AQC Comparison:\t0.5424, |∆| = 1.0676\n" - ] - } - ], - "source": [ - "# AQC results\n", - "hw_results = job.result()\n", - "hw_results_dicts = [pub_result.data.__dict__ for pub_result in hw_results]\n", - "hw_expvals = [\n", - " pub_result_data[\"evs\"].tolist() for pub_result_data in hw_results_dicts\n", - "]\n", - "aqc_expval = hw_expvals[0]\n", - "\n", - "# AQC comparison results\n", - "hw_comparison_results = job_comparison.result()\n", - "hw_comparison_results_dicts = [\n", - " pub_result.data.__dict__ for pub_result in hw_comparison_results\n", - "]\n", - "hw_comparison_expvals = [\n", - " pub_result_data[\"evs\"].tolist()\n", - " for pub_result_data in hw_comparison_results_dicts\n", - "]\n", - "aqc_compare_expval = hw_comparison_expvals[0]\n", - "\n", - "print(f\"Exact: \\t{reference_expval:.4f}\")\n", - "print(\n", - " f\"AQC: \\t{aqc_expval:.4f}, |∆| = {np.abs(reference_expval- aqc_expval):.4f}\"\n", - ")\n", - "print(\n", - " f\"AQC Comparison:\\t{aqc_compare_expval:.4f}, |∆| = {np.abs(reference_expval- aqc_compare_expval):.4f}\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "1c8b65e6-df72-45b3-8f31-a6aed44cb867", - "metadata": {}, - "source": [ - "Bar plot to compare the results of the AQC, comparison, and exact circuits." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "5f7b36a6-3666-4223-9c5d-d92bca741ad2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"Output" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.style.use(\"seaborn-v0_8\")\n", - "\n", - "labels = [\"AQC Result\", \"AQC Comparison Result\"]\n", - "values = [abs(aqc_expval), abs(aqc_compare_expval)]\n", - "\n", - "plt.figure(figsize=(10, 6))\n", - "bars = plt.bar(labels, values, color=[\"tab:blue\", \"tab:purple\"])\n", - "plt.axhline(\n", - " y=abs(reference_expval), color=\"red\", linestyle=\"--\", label=\"Exact Result\"\n", - ")\n", - "plt.xlabel(\"Results\")\n", - "plt.ylabel(\"Absolute Expected Value\")\n", - "plt.title(\"AQC Result vs AQC Comparison Result (Absolute Values)\")\n", - "plt.legend()\n", - "for bar in bars:\n", - " y_val = bar.get_height()\n", - " plt.text(\n", - " bar.get_x() + bar.get_width() / 2.0,\n", - " y_val,\n", - " round(y_val, 2),\n", - " va=\"bottom\",\n", - " )\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "ff81fb19-d175-42b0-8ef3-025712a7630d", - "metadata": {}, - "source": [ - "## Part II: scale it up\n", - "\n", - "\n", - "The second part of this tutorial builds on the previous example by scaling up to a larger system with 50 sites, illustrating how to map more complex quantum simulation problems to executable quantum circuits. Here, we explore the dynamics of a 50-site XXZ model, allowing us to build and optimize a substantial quantum circuit that reflects more realistic system sizes.\n", - "\n", - "The Hamiltonian for our 50-site XXZ model is defined as:\n", - "$$\n", - "\\hat{\\mathcal{H}}_{XXZ} = \\sum_{i=1}^{L-1} J_{i,(i+1)}\\left(X_i X_{(i+1)}+Y_i Y_{(i+1)}+ 2\\cdot Z_i Z_{(i+1)} \\right) \\, ,\n", - "$$\n", - "\n", - "where $J_{i,(i+1)}$ is a random coefficient corresponding to edge $(i, i+1)$, and $L=50$ is the number of sites." - ] - }, - { - "cell_type": "markdown", - "id": "f6d11e17-b4be-44fa-bc09-465e5e66a6af", - "metadata": {}, - "source": [ - "Define the coupling map and edges for the Hamiltonian." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "d0dec1ee-85fe-4c40-9595-b7764e9cfcca", - "metadata": {}, - "outputs": [], - "source": [ - "L = 50 # L = length of our 1D spin chain\n", - "\n", - "# Generate the edge list for this spin-chain\n", - "edge_list = [(i - 1, i) for i in range(1, L)]\n", - "# Generate an edge-coloring so we can make hw-efficient circuits\n", - "even_edges = edge_list[::2]\n", - "odd_edges = edge_list[1::2]\n", - "\n", - "# Instantiate a CouplingMap object\n", - "coupling_map = CouplingMap(edge_list)\n", - "\n", - "# Generate random coefficients for our XXZ Hamiltonian\n", - "np.random.seed(0)\n", - "Js = np.random.rand(L - 1) + 0.5 * np.ones(L - 1)\n", - "\n", - "hamiltonian = SparsePauliOp(Pauli(\"I\" * L))\n", - "for i, edge in enumerate(even_edges + odd_edges):\n", - " hamiltonian += SparsePauliOp.from_sparse_list(\n", - " [\n", - " (\"XX\", (edge), Js[i] / 2),\n", - " (\"YY\", (edge), Js[i] / 2),\n", - " (\"ZZ\", (edge), Js[i]),\n", - " ],\n", - " num_qubits=L,\n", - " )\n", - "\n", - "observable = SparsePauliOp.from_sparse_list(\n", - " [(\"ZZ\", (L // 2 - 1, L // 2), 1.0)], num_qubits=L\n", - ")\n", - "\n", - "# Generate an initial state\n", - "L = hamiltonian.num_qubits\n", - "initial_state = QuantumCircuit(L)\n", - "for i in range(L):\n", - " if i % 2:\n", - " initial_state.x(i)" - ] - }, - { - "cell_type": "markdown", - "id": "65009d89-4752-40ac-b3ab-8e88425851b2", - "metadata": {}, - "source": [ - "### Step 1: Map classical inputs to a quantum problem\n", - "\n", - "For this larger problem, we start by constructing the Hamiltonian for the 50-site XXZ model, defining spin-spin interactions and external magnetic fields across all sites. After this, we follow three main steps:\n", - "\n", - "1. **Generate the optimized AQC circuit**: Use Trotterization to approximate the initial evolution, then compress this segment to reduce circuit depth.\n", - "2. **Create the remaining time evolution circuit**: Capture the remaining time evolution beyond the initial segment.\n", - "3. **Combine the circuits**: Merge the optimized AQC circuit with the remaining evolution circuit to create a complete time-evolution circuit ready for execution." - ] - }, - { - "cell_type": "markdown", - "id": "41f5186a-e54e-4913-8a52-1c90611991c7", - "metadata": {}, - "source": [ - "Generate the AQC target circuit (the initial segment)." - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "86735d08-49b3-47e4-abcd-631a77f50a02", - "metadata": {}, - "outputs": [], - "source": [ - "aqc_evolution_time = 0.2\n", - "aqc_target_num_trotter_steps = 32\n", - "\n", - "aqc_target_circuit = initial_state.copy()\n", - "aqc_target_circuit.compose(\n", - " generate_time_evolution_circuit(\n", - " hamiltonian,\n", - " synthesis=SuzukiTrotter(reps=aqc_target_num_trotter_steps),\n", - " time=aqc_evolution_time,\n", - " ),\n", - " inplace=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "c5e79369-19ae-4b17-a9fc-9502a8a1254c", - "metadata": {}, - "source": [ - "Generate the subsequent circuit (the remaining segment)." - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "35c6469c-3be7-4e89-a065-0e4957305b59", - "metadata": {}, - "outputs": [], - "source": [ - "subsequent_num_trotter_steps = 3\n", - "subsequent_evolution_time = 0.2\n", - "\n", - "subsequent_circuit = generate_time_evolution_circuit(\n", - " hamiltonian,\n", - " synthesis=SuzukiTrotter(reps=subsequent_num_trotter_steps),\n", - " time=subsequent_evolution_time,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "0e13fbc5-0c00-4f21-9925-8d1230854dc5", - "metadata": {}, - "source": [ - "Generate the AQC comparison circuit (the initial segment, but with the same number of Trotter steps as the subsequent circuit)." - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "922fb467-81b0-40d7-b21b-ef781ad043a1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of Trotter steps for comparison: 3\n" - ] - } - ], - "source": [ - "# Generate the AQC comparison circuit\n", - "aqc_comparison_num_trotter_steps = int(\n", - " subsequent_num_trotter_steps\n", - " / subsequent_evolution_time\n", - " * aqc_evolution_time\n", - ")\n", - "print(\n", - " \"Number of Trotter steps for comparison:\",\n", - " aqc_comparison_num_trotter_steps,\n", - ")\n", - "\n", - "aqc_comparison_circuit = generate_time_evolution_circuit(\n", - " hamiltonian,\n", - " synthesis=SuzukiTrotter(reps=aqc_comparison_num_trotter_steps),\n", - " time=aqc_evolution_time,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "21228ca8-3fa6-4efe-9e53-8d3f824b7729", - "metadata": {}, - "source": [ - "Generate the reference circuit." - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "3525fe02-25cf-4254-b2c4-717f27bc29d8", - "metadata": {}, - "outputs": [], - "source": [ - "evolution_time = 0.4\n", - "reps = 200\n", - "\n", - "reference_circuit = initial_state.copy()\n", - "reference_circuit.compose(\n", - " generate_time_evolution_circuit(\n", - " hamiltonian,\n", - " synthesis=SuzukiTrotter(reps=reps),\n", - " time=evolution_time,\n", - " ),\n", - " inplace=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "a15b40ac-0259-4229-b76b-502ff7068499", - "metadata": {}, - "source": [ - "Generate an ansatz and initial parameters from a Trotter circuit with fewer steps." - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "ddbc3549-3171-4a29-b046-2a52b5e7065a", - "metadata": {}, - "outputs": [], - "source": [ - "aqc_ansatz_num_trotter_steps = 1\n", - "\n", - "aqc_good_circuit = initial_state.copy()\n", - "aqc_good_circuit.compose(\n", - " generate_time_evolution_circuit(\n", - " hamiltonian,\n", - " synthesis=SuzukiTrotter(reps=aqc_ansatz_num_trotter_steps),\n", - " time=aqc_evolution_time,\n", - " ),\n", - " inplace=True,\n", - ")\n", - "\n", - "aqc_ansatz, aqc_initial_parameters = generate_ansatz_from_circuit(\n", - " aqc_good_circuit\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "8e596d77-5954-4756-b765-48855cd38659", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AQC Comparison circuit: depth 36\n", - "Target circuit: depth 385\n", - "Ansatz circuit: depth 7, with 816 parameters\n" - ] - } - ], - "source": [ - "print(f\"AQC Comparison circuit: depth {aqc_comparison_circuit.depth()}\")\n", - "print(f\"Target circuit: depth {aqc_target_circuit.depth()}\")\n", - "print(\n", - " f\"Ansatz circuit: depth {aqc_ansatz.depth()}, with {len(aqc_initial_parameters)} parameters\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "4d07d63a-8572-4116-9fd5-c070f62043a7", - "metadata": {}, - "source": [ - "Set settings for tensor network simulation and then construct a matrix product state representation of the target state for optimization. Then, evaluate the fidelity between the initial circuit and the target state to quantify the difference in Trotter error." - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "6030706e-1451-47d9-9e47-bcccf4cb5d9c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Target MPS maximum bond dimension: 5\n", - "Starting fidelity: 0.9926466919924161\n" - ] - } - ], - "source": [ - "simulator_settings = QuimbSimulator(\n", - " quimb.tensor.CircuitMPS, autodiff_backend=\"jax\"\n", - ")\n", - "\n", - "# Build the matrix-product representation of the state to be approximated by AQC\n", - "aqc_target_mps = tensornetwork_from_circuit(\n", - " aqc_target_circuit, simulator_settings\n", - ")\n", - "print(\"Target MPS maximum bond dimension:\", aqc_target_mps.psi.max_bond())\n", - "\n", - "# Obtains the reference MPS, where we can obtain the exact expectation value by examining the `local_expectation``\n", - "reference_mps = tensornetwork_from_circuit(\n", - " reference_circuit, simulator_settings\n", - ")\n", - "reference_expval = reference_mps.local_expectation(\n", - " quimb.pauli(\"Z\") & quimb.pauli(\"Z\"), (L // 2 - 1, L // 2)\n", - ").real.item()\n", - "\n", - "# Compute the starting fidelity\n", - "good_mps = tensornetwork_from_circuit(aqc_good_circuit, simulator_settings)\n", - "starting_fidelity = abs(compute_overlap(good_mps, aqc_target_mps)) ** 2\n", - "print(\"Starting fidelity:\", starting_fidelity)" - ] - }, - { - "cell_type": "markdown", - "id": "56dfc4e4-6b28-4ed1-81da-8b0f4bc99425", - "metadata": {}, - "source": [ - "To optimize the ansatz parameters, we minimize the `MaximizeStateFidelity` cost function using the L-BFGS optimizer from SciPy, with a stopping criterion set to surpass the fidelity of the initial circuit without AQC-Tensor. This ensures that the compressed circuit has both lower Trotter error and reduced depth." - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "af10ef7f-e57d-49a3-abb0-3d51e856c4b0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-04-14 11:48:28.705807 Intermediate result: Fidelity 0.99795851\n", - "2025-04-14 11:48:28.743265 Intermediate result: Fidelity 0.99822826\n", - "2025-04-14 11:48:28.776629 Intermediate result: Fidelity 0.99829675\n", - "2025-04-14 11:48:28.816153 Intermediate result: Fidelity 0.99832474\n", - "2025-04-14 11:48:28.856437 Intermediate result: Fidelity 0.99836131\n", - "2025-04-14 11:48:28.896432 Intermediate result: Fidelity 0.99839954\n", - "2025-04-14 11:48:28.936670 Intermediate result: Fidelity 0.99846517\n", - "2025-04-14 11:48:28.982069 Intermediate result: Fidelity 0.99865029\n", - "2025-04-14 11:48:29.026130 Intermediate result: Fidelity 0.99872332\n", - "2025-04-14 11:48:29.067426 Intermediate result: Fidelity 0.99892359\n", - "2025-04-14 11:48:29.110742 Intermediate result: Fidelity 0.99900640\n", - "2025-04-14 11:48:29.161362 Intermediate result: Fidelity 0.99907169\n", - "2025-04-14 11:48:29.207933 Intermediate result: Fidelity 0.99911423\n", - "2025-04-14 11:48:29.266772 Intermediate result: Fidelity 0.99918716\n", - "2025-04-14 11:48:29.331727 Intermediate result: Fidelity 0.99921278\n", - "2025-04-14 11:48:29.401694 Intermediate result: Fidelity 0.99924853\n", - "2025-04-14 11:48:29.467980 Intermediate result: Fidelity 0.99928797\n", - "2025-04-14 11:48:29.533281 Intermediate result: Fidelity 0.99933028\n", - "2025-04-14 11:48:29.600833 Intermediate result: Fidelity 0.99935757\n", - "2025-04-14 11:48:29.670816 Intermediate result: Fidelity 0.99938140\n", - "2025-04-14 11:48:29.736928 Intermediate result: Fidelity 0.99940964\n", - "2025-04-14 11:48:29.802931 Intermediate result: Fidelity 0.99944051\n", - "2025-04-14 11:48:29.869177 Intermediate result: Fidelity 0.99946828\n", - "2025-04-14 11:48:29.940156 Intermediate result: Fidelity 0.99948723\n", - "2025-04-14 11:48:30.005751 Intermediate result: Fidelity 0.99951011\n", - "2025-04-14 11:48:30.070853 Intermediate result: Fidelity 0.99954718\n", - "2025-04-14 11:48:30.139171 Intermediate result: Fidelity 0.99956267\n", - "2025-04-14 11:48:30.210506 Intermediate result: Fidelity 0.99958949\n", - "2025-04-14 11:48:30.279647 Intermediate result: Fidelity 0.99960498\n", - "2025-04-14 11:48:30.348016 Intermediate result: Fidelity 0.99961308\n", - "2025-04-14 11:48:30.414311 Intermediate result: Fidelity 0.99962894\n", - "2025-04-14 11:48:30.488910 Intermediate result: Fidelity 0.99964121\n", - "2025-04-14 11:48:30.561298 Intermediate result: Fidelity 0.99964348\n", - "2025-04-14 11:48:30.632214 Intermediate result: Fidelity 0.99964860\n", - "2025-04-14 11:48:30.705703 Intermediate result: Fidelity 0.99965695\n", - "2025-04-14 11:48:30.775679 Intermediate result: Fidelity 0.99966398\n", - "2025-04-14 11:48:30.842629 Intermediate result: Fidelity 0.99967816\n", - "2025-04-14 11:48:30.912357 Intermediate result: Fidelity 0.99968293\n", - "2025-04-14 11:48:30.979420 Intermediate result: Fidelity 0.99968936\n", - "2025-04-14 11:48:31.049196 Intermediate result: Fidelity 0.99969223\n", - "2025-04-14 11:48:31.125391 Intermediate result: Fidelity 0.99970009\n", - "2025-04-14 11:48:31.201256 Intermediate result: Fidelity 0.99970724\n", - "2025-04-14 11:48:31.272424 Intermediate result: Fidelity 0.99970987\n", - "2025-04-14 11:48:31.338907 Intermediate result: Fidelity 0.99971237\n", - "2025-04-14 11:48:31.404800 Intermediate result: Fidelity 0.99971916\n", - "2025-04-14 11:48:31.475226 Intermediate result: Fidelity 0.99971940\n", - "2025-04-14 11:48:31.547746 Intermediate result: Fidelity 0.99972465\n", - "2025-04-14 11:48:31.622827 Intermediate result: Fidelity 0.99972763\n", - "2025-04-14 11:48:31.819516 Intermediate result: Fidelity 0.99972894\n", - "2025-04-14 11:48:33.444538 Intermediate result: Fidelity 0.99972894\n", - "Done after 50 iterations.\n" - ] - } - ], - "source": [ - "# Setting values for the optimization\n", - "aqc_stopping_fidelity = 1\n", - "aqc_max_iterations = 500\n", - "\n", - "stopping_point = 1.0 - aqc_stopping_fidelity\n", - "objective = MaximizeStateFidelity(\n", - " aqc_target_mps, aqc_ansatz, simulator_settings\n", - ")\n", - "\n", - "\n", - "def callback(intermediate_result: OptimizeResult):\n", - " fidelity = 1 - intermediate_result.fun\n", - " print(\n", - " f\"{datetime.datetime.now()} Intermediate result: Fidelity {fidelity:.8f}\"\n", - " )\n", - " if intermediate_result.fun < stopping_point:\n", - " # Good enough for now\n", - " raise StopIteration\n", - "\n", - "\n", - "result = minimize(\n", - " objective,\n", - " aqc_initial_parameters,\n", - " method=\"L-BFGS-B\",\n", - " jac=True,\n", - " options={\"maxiter\": aqc_max_iterations},\n", - " callback=callback,\n", - ")\n", - "if (\n", - " result.status\n", - " not in (\n", - " 0,\n", - " 1,\n", - " 99,\n", - " )\n", - "): # 0 => success; 1 => max iterations reached; 99 => early termination via StopIteration\n", - " raise RuntimeError(\n", - " f\"Optimization failed: {result.message} (status={result.status})\"\n", - " )\n", - "\n", - "print(f\"Done after {result.nit} iterations.\")\n", - "aqc_final_parameters = result.x" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "0e711fcc-7767-411e-bb73-fd9c8408a22b", - "metadata": {}, - "outputs": [], - "source": [ - "parameters = [float(param) for param in aqc_final_parameters]" - ] - }, - { - "cell_type": "markdown", - "id": "b1838b8e-81d4-4897-8cbd-dd7daa8bb220", - "metadata": {}, - "source": [ - "Construct the final circuit for transpilation by assembling the optimized ansatz with the remaining time evolution circuit." - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "f96b1ab0-f0ed-485f-a763-4ab57d36f410", - "metadata": {}, - "outputs": [], - "source": [ - "aqc_final_circuit = aqc_ansatz.assign_parameters(aqc_final_parameters)\n", - "aqc_final_circuit.compose(subsequent_circuit, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "579cbef1-a3a8-4916-b840-ab3fcc5c4b33", - "metadata": {}, - "outputs": [], - "source": [ - "aqc_comparison_circuit.compose(subsequent_circuit, inplace=True)" - ] - }, - { - "cell_type": "markdown", - "id": "bb111fb1-07e4-4f0c-ad71-933b04492a23", - "metadata": {}, - "source": [ - "### Step 2: Optimize problem for quantum hardware execution" - ] - }, - { - "cell_type": "markdown", - "id": "627dd426-730d-4554-b631-1e9949f46c26", - "metadata": {}, - "source": [ - "Select the backend." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "771a44a6-b5ba-4b9d-8c64-9a79f6d4ed77", - "metadata": {}, - "outputs": [], - "source": [ - "service = QiskitRuntimeService()\n", - "backend = service.least_busy(min_num_qubits=127)\n", - "print(backend)" + "estimator = Estimator(backend)\n", + "\n", + "# Run both circuits\n", + "aqc_result = estimator.run([(isa_circuit, isa_observable)]).result()\n", + "baseline_result = estimator.run(\n", + " [(isa_baseline_circuit, isa_baseline_observable)]\n", + ").result()" ] }, { "cell_type": "markdown", - "id": "cbc5d688-9acf-4fe2-8d58-fea74edb09e8", + "id": "0b980055", "metadata": {}, "source": [ - "Transpile the completed circuit on the target hardware, preparing it for execution. The resulting ISA circuit can then be sent for execution on the backend." + "### Step 4: Post-process and return result in desired classical format\n", + "\n", + "We extract the expectation values from both runs and compare them to the exact result. The baseline Trotter circuit shows what we would get without AQC at the same circuit depth, while the AQC circuit demonstrates the improvement from tensor-network optimization." ] }, { "cell_type": "code", - "execution_count": 43, - "id": "85b4acc0-7121-416d-9bf5-b6d3135ae805", + "execution_count": 11, + "id": "af07a1d9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Observable info: SparsePauliOp(['IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII'],\n", - " coeffs=[1.+0.j])\n", - "Circuit depth: 122\n" + "Exact: -0.8091\n", + "Baseline Trotter: -0.6353, |Δ| = 0.1738 (depth 85)\n", + "AQC: -0.6675, |Δ| = 0.1416 (depth 50)\n" ] - }, - { - "data": { - "text/plain": [ - "\"Output" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ - "pass_manager = generate_preset_pass_manager(\n", - " backend=backend, optimization_level=3\n", - ")\n", - "isa_circuit = pass_manager.run(aqc_final_circuit)\n", - "isa_observable = observable.apply_layout(isa_circuit.layout)\n", - "print(\"Observable info:\", isa_observable)\n", - "print(\"Circuit depth:\", isa_circuit.depth())\n", - "isa_circuit.draw(\"mpl\", fold=-1, idle_wires=False)" + "aqc_expval = aqc_result[0].data.evs.tolist()\n", + "baseline_expval = baseline_result[0].data.evs.tolist()\n", + "\n", + "print(f\"Exact: {exact_expval:.4f}\")\n", + "print(f\"Baseline Trotter: {baseline_expval:.4f}, |\\u0394| = {np.abs(exact_expval - baseline_expval):.4f} (depth {isa_baseline_circuit.depth()})\")\n", + "print(f\"AQC: {aqc_expval:.4f}, |\\u0394| = {np.abs(exact_expval - aqc_expval):.4f} (depth {isa_circuit.depth()})\")" ] }, { "cell_type": "code", - "execution_count": 44, - "id": "b0d295c7-c816-4683-bb2a-0ce9898e5d88", + "execution_count": 12, + "id": "77c39ba8", "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Observable info: SparsePauliOp(['IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII'],\n", - " coeffs=[1.+0.j])\n", - "Circuit depth: 158\n" - ] - }, { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "
" ] }, - "execution_count": 44, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "isa_comparison_circuit = pass_manager.run(aqc_comparison_circuit)\n", - "isa_comparison_observable = observable.apply_layout(\n", - " isa_comparison_circuit.layout\n", + "plt.style.use(\"seaborn-v0_8\")\n", + "\n", + "labels = [\n", + " f\"Baseline Trotter\\n(depth {isa_baseline_circuit.depth()})\",\n", + " f\"AQC\\n(depth {isa_circuit.depth()})\",\n", + "]\n", + "values = [baseline_expval, aqc_expval]\n", + "colors = [\"tab:orange\", \"tab:blue\"]\n", + "\n", + "plt.figure(figsize=(8, 5))\n", + "bars = plt.bar(labels, values, color=colors, width=0.5)\n", + "plt.axhline(\n", + " y=exact_expval, color=\"tab:green\", linestyle=\"--\", linewidth=2, label=f\"Exact ({exact_expval:.4f})\"\n", ")\n", - "print(\"Observable info:\", isa_comparison_observable)\n", - "print(\"Circuit depth:\", isa_comparison_circuit.depth())\n", - "isa_comparison_circuit.draw(\"mpl\", fold=-1, idle_wires=False)" + "plt.ylabel(\"Expected Value\")\n", + "plt.title(\"AQC-Tensor vs Baseline Trotter (10-site XXZ)\")\n", + "plt.legend()\n", + "for bar in bars:\n", + " y_val = bar.get_height()\n", + " plt.text(\n", + " bar.get_x() + bar.get_width() / 2.0,\n", + " y_val,\n", + " f\"{y_val:.4f}\",\n", + " ha=\"center\",\n", + " va=\"bottom\" if y_val >= 0 else \"top\",\n", + " )\n", + "plt.axhline(y=0, color=\"black\", linewidth=0.3)\n", + "plt.tight_layout()\n", + "plt.show()" ] }, { "cell_type": "markdown", - "id": "f9431bec-66d1-44e3-a77d-01ca9e03e523", + "id": "37062efa", "metadata": {}, "source": [ - "### Step 3: Execute using Qiskit primitives\n", + "## Large-scale hardware example\n", "\n", - "In this step, we run the transpiled circuit on quantum hardware (or a simulated backend) using `EstimatorV2` from `qiskit_ibm_runtime` to measure the specified observable. The job result will provide valuable insights into the circuit’s performance on the target hardware.\n", + "We now scale up to a 50-site XXZ model to demonstrate AQC-Tensor on a more realistic problem size. The workflow is the same as the small-scale example, so we combine all steps into a single code block.\n", "\n", - "For this larger-scale example, we will explore how to utilize `EstimatorOptions` to better manage and control the parameters of our hardware experiment. While these settings are optional, they are useful for tracking experiment parameters and refining execution options for optimal results.\n", + "For a system of this size, matrix exponentiation is infeasible ($2^{50}$ dimensions), so we compute the reference expectation value directly from the high-accuracy target MPS.\n", "\n", - "For a complete list of available execution options, refer to the [qiskit-ibm-runtime documentation](/docs/api/qiskit-ibm-runtime/options-estimator-options)." - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "cf449353-f392-4248-b6ad-5af9107c1fff", - "metadata": {}, - "outputs": [], - "source": [ - "twirling_options = {\n", - " \"enable_gates\": True,\n", - " \"enable_measure\": True,\n", - " \"num_randomizations\": 300,\n", - " \"shots_per_randomization\": 100,\n", - " \"strategy\": \"active\",\n", - "}\n", - "\n", - "zne_options = {\n", - " \"amplifier\": \"gate_folding\",\n", - " \"noise_factors\": [1, 2, 3],\n", - " \"extrapolated_noise_factors\": list(np.linspace(0, 3, 31)),\n", - " \"extrapolator\": [\"exponential\", \"linear\", \"fallback\"],\n", - "}\n", - "\n", - "meas_learning_options = {\n", - " \"num_randomizations\": 512,\n", - " \"shots_per_randomization\": 512,\n", - "}\n", - "\n", - "resilience_options = {\n", - " \"measure_mitigation\": True,\n", - " \"zne_mitigation\": True,\n", - " \"zne\": zne_options,\n", - " \"measure_noise_learning\": meas_learning_options,\n", - "}\n", - "\n", - "estimator_options = {\n", - " \"resilience\": resilience_options,\n", - " \"twirling\": twirling_options,\n", - "}\n", - "\n", - "estimator = Estimator(backend, options=estimator_options)" + "### Steps 1–4 combined" ] }, { "cell_type": "code", - "execution_count": 46, - "id": "ea2a1425-a49b-4b31-b019-abbee4fdf690", + "execution_count": 14, + "id": "b6c0f26a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Job ID: czyjx6crxz8g008f63r0\n" + "Target circuit: depth 385\n", + "Ansatz circuit: depth 7, with 816 parameters\n", + "Target MPS maximum bond dimension: 5\n", + "Reference expectation value (from MPS): -0.832514\n", + "2026-04-30 13:06:12.214833 Intermediate result: Fidelity 0.99795839\n", + "2026-04-30 13:06:12.230078 Intermediate result: Fidelity 0.99822743\n", + "2026-04-30 13:06:12.245084 Intermediate result: Fidelity 0.99829460\n", + "2026-04-30 13:06:12.260114 Intermediate result: Fidelity 0.99832307\n", + "2026-04-30 13:06:12.274684 Intermediate result: Fidelity 0.99836393\n", + "2026-04-30 13:06:12.290753 Intermediate result: Fidelity 0.99840145\n", + "2026-04-30 13:06:12.306418 Intermediate result: Fidelity 0.99846899\n", + "2026-04-30 13:06:12.320197 Intermediate result: Fidelity 0.99864993\n", + "2026-04-30 13:06:12.333798 Intermediate result: Fidelity 0.99872570\n", + "2026-04-30 13:06:12.349061 Intermediate result: Fidelity 0.99892276\n", + "2026-04-30 13:06:12.363848 Intermediate result: Fidelity 0.99900819\n", + "2026-04-30 13:06:12.379304 Intermediate result: Fidelity 0.99906919\n", + "2026-04-30 13:06:12.393248 Intermediate result: Fidelity 0.99911399\n", + "2026-04-30 13:06:12.408326 Intermediate result: Fidelity 0.99918227\n", + "2026-04-30 13:06:12.423260 Intermediate result: Fidelity 0.99921385\n", + "2026-04-30 13:06:12.438543 Intermediate result: Fidelity 0.99925091\n", + "2026-04-30 13:06:12.454048 Intermediate result: Fidelity 0.99929083\n", + "2026-04-30 13:06:12.468375 Intermediate result: Fidelity 0.99933111\n", + "2026-04-30 13:06:12.483160 Intermediate result: Fidelity 0.99935721\n", + "2026-04-30 13:06:12.498169 Intermediate result: Fidelity 0.99938354\n", + "2026-04-30 13:06:12.513266 Intermediate result: Fidelity 0.99940964\n", + "2026-04-30 13:06:12.528348 Intermediate result: Fidelity 0.99943944\n", + "2026-04-30 13:06:12.543666 Intermediate result: Fidelity 0.99946911\n", + "2026-04-30 13:06:12.558681 Intermediate result: Fidelity 0.99949033\n", + "2026-04-30 13:06:12.573779 Intermediate result: Fidelity 0.99951023\n", + "2026-04-30 13:06:12.588431 Intermediate result: Fidelity 0.99954765\n", + "2026-04-30 13:06:12.602077 Intermediate result: Fidelity 0.99956326\n", + "2026-04-30 13:06:12.615844 Intermediate result: Fidelity 0.99959127\n", + "2026-04-30 13:06:12.632148 Intermediate result: Fidelity 0.99960629\n", + "2026-04-30 13:06:12.647171 Intermediate result: Fidelity 0.99961797\n", + "2026-04-30 13:06:12.661585 Intermediate result: Fidelity 0.99962762\n", + "2026-04-30 13:06:12.676381 Intermediate result: Fidelity 0.99964014\n", + "2026-04-30 13:06:12.690423 Intermediate result: Fidelity 0.99964526\n", + "2026-04-30 13:06:12.704923 Intermediate result: Fidelity 0.99965444\n", + "2026-04-30 13:06:12.718798 Intermediate result: Fidelity 0.99966028\n", + "2026-04-30 13:06:12.733616 Intermediate result: Fidelity 0.99966851\n", + "2026-04-30 13:06:12.748804 Intermediate result: Fidelity 0.99968305\n", + "2026-04-30 13:06:12.764133 Intermediate result: Fidelity 0.99968519\n", + "2026-04-30 13:06:12.778763 Intermediate result: Fidelity 0.99969521\n", + "2026-04-30 13:06:12.793676 Intermediate result: Fidelity 0.99969866\n", + "2026-04-30 13:06:12.808354 Intermediate result: Fidelity 0.99970212\n", + "2026-04-30 13:06:12.823618 Intermediate result: Fidelity 0.99970939\n", + "2026-04-30 13:06:12.839678 Intermediate result: Fidelity 0.99971380\n", + "2026-04-30 13:06:12.855446 Intermediate result: Fidelity 0.99971892\n", + "2026-04-30 13:06:12.871060 Intermediate result: Fidelity 0.99972214\n", + "2026-04-30 13:06:12.886268 Intermediate result: Fidelity 0.99972512\n", + "2026-04-30 13:06:12.901064 Intermediate result: Fidelity 0.99972786\n", + "2026-04-30 13:06:12.915886 Intermediate result: Fidelity 0.99973037\n", + "2026-04-30 13:06:12.931565 Intermediate result: Fidelity 0.99973549\n", + "2026-04-30 13:06:13.008085 Intermediate result: Fidelity 0.99973585\n", + "2026-04-30 13:06:13.038992 Intermediate result: Fidelity 0.99973656\n", + "2026-04-30 13:06:13.055610 Intermediate result: Fidelity 0.99973752\n", + "2026-04-30 13:06:13.070891 Intermediate result: Fidelity 0.99973919\n", + "2026-04-30 13:06:13.086248 Intermediate result: Fidelity 0.99974372\n", + "2026-04-30 13:06:13.101073 Intermediate result: Fidelity 0.99974646\n", + "2026-04-30 13:06:13.115663 Intermediate result: Fidelity 0.99975027\n", + "2026-04-30 13:06:13.130447 Intermediate result: Fidelity 0.99975695\n", + "2026-04-30 13:06:13.146828 Intermediate result: Fidelity 0.99975730\n", + "2026-04-30 13:06:13.162365 Intermediate result: Fidelity 0.99975969\n", + "2026-04-30 13:06:13.177169 Intermediate result: Fidelity 0.99976124\n", + "2026-04-30 13:06:13.192934 Intermediate result: Fidelity 0.99976207\n", + "2026-04-30 13:06:13.206788 Intermediate result: Fidelity 0.99976553\n", + "2026-04-30 13:06:13.222731 Intermediate result: Fidelity 0.99976970\n", + "2026-04-30 13:06:13.238690 Intermediate result: Fidelity 0.99977447\n", + "2026-04-30 13:06:13.254277 Intermediate result: Fidelity 0.99977757\n", + "2026-04-30 13:06:13.791095 Intermediate result: Fidelity 0.99977828\n", + "2026-04-30 13:06:14.042192 Intermediate result: Fidelity 0.99977828\n", + "Done after 67 iterations.\n", + "\n", + "AQC circuit depth: 47\n", + "Baseline Trotter circuit depth: 87\n", + "Job ID: d7poo367g7gs73cf7n8g\n" ] - }, - { - "data": { - "text/plain": [ - "PrimitiveResult([PubResult(data=DataBin(evs=np.ndarray(), stds=np.ndarray(), evs_noise_factors=np.ndarray(), stds_noise_factors=np.ndarray(), ensemble_stds_noise_factors=np.ndarray(), evs_extrapolated=np.ndarray(), stds_extrapolated=np.ndarray()), metadata={'shots': 30000, 'target_precision': 0.005773502691896258, 'circuit_metadata': {}, 'resilience': {'zne': {'extrapolator': 'exponential'}}, 'num_randomizations': 300})], metadata={'dynamical_decoupling': {'enable': False, 'sequence_type': 'XX', 'extra_slack_distribution': 'middle', 'scheduling_method': 'alap'}, 'twirling': {'enable_gates': True, 'enable_measure': True, 'num_randomizations': 300, 'shots_per_randomization': 100, 'interleave_randomizations': True, 'strategy': 'active'}, 'resilience': {'measure_mitigation': True, 'zne_mitigation': True, 'pec_mitigation': False, 'zne': {'noise_factors': [1, 2, 3], 'extrapolator': ['exponential', 'linear', 'fallback'], 'extrapolated_noise_factors': [0, 0.1, 0.2, 0.30000000000000004, 0.4, 0.5, 0.6000000000000001, 0.7000000000000001, 0.8, 0.9, 1, 1.1, 1.2000000000000002, 1.3, 1.4000000000000001, 1.5, 1.6, 1.7000000000000002, 1.8, 1.9000000000000001, 2, 2.1, 2.2, 2.3000000000000003, 2.4000000000000004, 2.5, 2.6, 2.7, 2.8000000000000003, 2.9000000000000004, 3]}}, 'version': 2})" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ - "job = estimator.run([(isa_circuit, isa_observable)])\n", + "# -------------------------Step 1-------------------------\n", + "\n", + "# Define the 50-site spin chain\n", + "L = 50\n", + "edge_list = [(i - 1, i) for i in range(1, L)]\n", + "even_edges = edge_list[::2]\n", + "odd_edges = edge_list[1::2]\n", + "coupling_map = CouplingMap(edge_list)\n", + "\n", + "# Random XXZ Hamiltonian\n", + "np.random.seed(0)\n", + "Js = np.random.rand(L - 1) + 0.5 * np.ones(L - 1)\n", + "hamiltonian = SparsePauliOp(Pauli(\"I\" * L))\n", + "for i, edge in enumerate(even_edges + odd_edges):\n", + " hamiltonian += SparsePauliOp.from_sparse_list(\n", + " [\n", + " (\"XX\", (edge), Js[i] / 2),\n", + " (\"YY\", (edge), Js[i] / 2),\n", + " (\"ZZ\", (edge), Js[i]),\n", + " ],\n", + " num_qubits=L,\n", + " )\n", + "\n", + "observable = SparsePauliOp.from_sparse_list(\n", + " [(\"ZZ\", (L // 2 - 1, L // 2), 1.0)], num_qubits=L\n", + ")\n", + "\n", + "# Initial Néel state\n", + "initial_state = QuantumCircuit(L)\n", + "for i in range(L):\n", + " if i % 2:\n", + " initial_state.x(i)\n", + "\n", + "# AQC target circuit (high-accuracy, 32 Trotter steps)\n", + "aqc_evolution_time = 0.2\n", + "aqc_target_num_trotter_steps = 32\n", + "\n", + "aqc_target_circuit = initial_state.copy()\n", + "aqc_target_circuit.compose(\n", + " generate_time_evolution_circuit(\n", + " hamiltonian,\n", + " synthesis=SuzukiTrotter(reps=aqc_target_num_trotter_steps),\n", + " time=aqc_evolution_time,\n", + " ),\n", + " inplace=True,\n", + ")\n", + "\n", + "# Generate ansatz from 1-step Trotter circuit\n", + "aqc_good_circuit = initial_state.copy()\n", + "aqc_good_circuit.compose(\n", + " generate_time_evolution_circuit(\n", + " hamiltonian,\n", + " synthesis=SuzukiTrotter(reps=1),\n", + " time=aqc_evolution_time,\n", + " ),\n", + " inplace=True,\n", + ")\n", + "\n", + "aqc_ansatz, aqc_initial_parameters = generate_ansatz_from_circuit(\n", + " aqc_good_circuit\n", + ")\n", + "\n", + "# Baseline Trotter circuit: 3 Trotter steps, no AQC optimization\n", + "baseline_circuit = initial_state.copy()\n", + "baseline_circuit.compose(\n", + " generate_time_evolution_circuit(\n", + " hamiltonian,\n", + " synthesis=SuzukiTrotter(reps=3),\n", + " time=aqc_evolution_time,\n", + " ),\n", + " inplace=True,\n", + ")\n", + "print(f\"Target circuit: depth {aqc_target_circuit.depth()}\")\n", + "print(\n", + " f\"Ansatz circuit: depth {aqc_ansatz.depth()}, with {len(aqc_initial_parameters)} parameters\"\n", + ")\n", + "\n", + "# Build target MPS and compute reference expectation value\n", + "simulator_settings = QuimbSimulator(\n", + " quimb.tensor.CircuitMPS, autodiff_backend=\"jax\"\n", + ")\n", + "aqc_target_mps = tensornetwork_from_circuit(\n", + " aqc_target_circuit, simulator_settings\n", + ")\n", + "print(\"Target MPS maximum bond dimension:\", aqc_target_mps.psi.max_bond())\n", + "\n", + "exact_expval = aqc_target_mps.local_expectation(\n", + " quimb.pauli(\"Z\") & quimb.pauli(\"Z\"), (L // 2 - 1, L // 2)\n", + ").real.item()\n", + "print(f\"Reference expectation value (from MPS): {exact_expval:.6f}\")\n", + "\n", + "# Optimize ansatz parameters\n", + "objective = MaximizeStateFidelity(\n", + " aqc_target_mps, aqc_ansatz, simulator_settings\n", + ")\n", + "\n", + "def callback(intermediate_result: OptimizeResult):\n", + " fidelity = 1 - intermediate_result.fun\n", + " print(\n", + " f\"{datetime.datetime.now()} Intermediate result: Fidelity {fidelity:.8f}\"\n", + " )\n", + "\n", + "result = minimize(\n", + " objective,\n", + " aqc_initial_parameters,\n", + " method=\"L-BFGS-B\",\n", + " jac=True,\n", + " options={\"maxiter\": 500},\n", + " callback=callback,\n", + ")\n", + "if result.status not in (0, 1, 99):\n", + " raise RuntimeError(\n", + " f\"Optimization failed: {result.message} (status={result.status})\"\n", + " )\n", + "print(f\"Done after {result.nit} iterations.\")\n", + "\n", + "# Assemble the final AQC circuit\n", + "aqc_final_circuit = aqc_ansatz.assign_parameters(result.x)\n", + "\n", + "# -------------------------Step 2-------------------------\n", + "\n", + "service = QiskitRuntimeService()\n", + "backend = service.least_busy(min_num_qubits=127)\n", + "print(backend)\n", + "\n", + "pass_manager = generate_preset_pass_manager(\n", + " backend=backend, optimization_level=3\n", + ")\n", + "isa_circuit = pass_manager.run(aqc_final_circuit)\n", + "isa_observable = observable.apply_layout(isa_circuit.layout)\n", + "print(\"AQC circuit depth:\", isa_circuit.depth())\n", + "\n", + "# Also transpile the baseline Trotter circuit (3 Trotter steps, no AQC)\n", + "isa_baseline_circuit = pass_manager.run(baseline_circuit)\n", + "isa_baseline_observable = observable.apply_layout(isa_baseline_circuit.layout)\n", + "print(\"Baseline Trotter circuit depth:\", isa_baseline_circuit.depth())\n", + "\n", + "# -------------------------Step 3-------------------------\n", + "\n", + "# Submit both circuits in a single job\n", + "estimator = Estimator(backend)\n", + "estimator.options.environment.job_tags = [\"TUT_AQCTE\"]\n", + "\n", + "job = estimator.run([\n", + " (isa_circuit, isa_observable),\n", + " (isa_baseline_circuit, isa_baseline_observable),\n", + "])\n", "print(\"Job ID:\", job.job_id())\n", - "job.result()" + "\n" ] }, { "cell_type": "code", - "execution_count": 47, - "id": "4f7d9e7d-9da1-4cc3-ae09-0e3613c5479a", + "execution_count": 15, + "id": "a4dc23fd-494e-46cb-a8f5-d1cd444b96f4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Job Comparison ID: czyjx6crxz8g008f63r0\n" + "Exact (MPS): -0.8325\n", + "Baseline Trotter: -0.5725, |Δ| = 0.2600\n", + "AQC: -0.8341, |Δ| = 0.0016\n" ] }, { "data": { + "image/png": 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", "text/plain": [ - "PrimitiveResult([PubResult(data=DataBin(evs=np.ndarray(), stds=np.ndarray(), evs_noise_factors=np.ndarray(), stds_noise_factors=np.ndarray(), ensemble_stds_noise_factors=np.ndarray(), evs_extrapolated=np.ndarray(), stds_extrapolated=np.ndarray()), metadata={'shots': 30000, 'target_precision': 0.005773502691896258, 'circuit_metadata': {}, 'resilience': {'zne': {'extrapolator': 'exponential'}}, 'num_randomizations': 300})], metadata={'dynamical_decoupling': {'enable': False, 'sequence_type': 'XX', 'extra_slack_distribution': 'middle', 'scheduling_method': 'alap'}, 'twirling': {'enable_gates': True, 'enable_measure': True, 'num_randomizations': 300, 'shots_per_randomization': 100, 'interleave_randomizations': True, 'strategy': 'active'}, 'resilience': {'measure_mitigation': True, 'zne_mitigation': True, 'pec_mitigation': False, 'zne': {'noise_factors': [1, 2, 3], 'extrapolator': ['exponential', 'linear', 'fallback'], 'extrapolated_noise_factors': [0, 0.1, 0.2, 0.30000000000000004, 0.4, 0.5, 0.6000000000000001, 0.7000000000000001, 0.8, 0.9, 1, 1.1, 1.2000000000000002, 1.3, 1.4000000000000001, 1.5, 1.6, 1.7000000000000002, 1.8, 1.9000000000000001, 2, 2.1, 2.2, 2.3000000000000003, 2.4000000000000004, 2.5, 2.6, 2.7, 2.8000000000000003, 2.9000000000000004, 3]}}, 'version': 2})" + "
" ] }, - "execution_count": 47, "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "job_comparison = estimator.run([(isa_comparison_circuit, isa_observable)])\n", - "print(\"Job Comparison ID:\", job.job_id())\n", - "job_comparison.result()" - ] - }, - { - "cell_type": "markdown", - "id": "17ced13b-a3d3-4ded-92a6-b978508dd27a", - "metadata": {}, - "source": [ - "### Step 4: Post-process and return result in desired classical format\n", - "Here, no reconstruction is needed, like before; we can directly access the expectation value from the execution output to examine the result." - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "5b2e2d3e-bebb-44a4-bbdb-881ffc2749e5", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Exact: \t-0.5888\n", - "AQC: \t-0.4809, |∆| = 0.1078\n", - "AQC Comparison:\t1.1764, |∆| = 1.7652\n" - ] + "output_type": "display_data" } ], "source": [ - "# AQC results\n", + "# -------------------------Step 4-------------------------\n", + "\n", "hw_results = job.result()\n", - "hw_results_dicts = [pub_result.data.__dict__ for pub_result in hw_results]\n", - "hw_expvals = [\n", - " pub_result_data[\"evs\"].tolist() for pub_result_data in hw_results_dicts\n", - "]\n", - "aqc_expval = hw_expvals[0]\n", + "aqc_expval = hw_results[0].data.evs.tolist()\n", + "baseline_expval = hw_results[1].data.evs.tolist()\n", "\n", - "# AQC comparison results\n", - "hw_comparison_results = job_comparison.result()\n", - "hw_comparison_results_dicts = [\n", - " pub_result.data.__dict__ for pub_result in hw_comparison_results\n", - "]\n", - "hw_comparison_expvals = [\n", - " pub_result_data[\"evs\"].tolist()\n", - " for pub_result_data in hw_comparison_results_dicts\n", - "]\n", - "aqc_compare_expval = hw_comparison_expvals[0]\n", + "print(f\"Exact (MPS): {exact_expval:.4f}\")\n", + "print(f\"Baseline Trotter: {baseline_expval:.4f}, |\\u0394| = {np.abs(exact_expval - baseline_expval):.4f}\")\n", + "print(f\"AQC: {aqc_expval:.4f}, |\\u0394| = {np.abs(exact_expval - aqc_expval):.4f}\")\n", "\n", - "print(f\"Exact: \\t{reference_expval:.4f}\")\n", - "print(\n", - " f\"AQC: \\t{aqc_expval:.4f}, |∆| = {np.abs(reference_expval- aqc_expval):.4f}\"\n", - ")\n", - "print(\n", - " f\"AQC Comparison:\\t{aqc_compare_expval:.4f}, |∆| = {np.abs(reference_expval- aqc_compare_expval):.4f}\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "f5ba5634-1689-457a-b7c7-adf3ca8e1d41", - "metadata": {}, - "source": [ - "Plot the results of the AQC, comparison, and exact circuits for the 50-site XXZ model." - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "01889c4d-16a4-458a-9211-08be8bcae1e4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"Output" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "labels = [\"AQC Result\", \"AQC Comparison Result\"]\n", - "values = [abs(aqc_expval), abs(aqc_compare_expval)]\n", + "labels = [\n", + " f\"Baseline Trotter\\n(depth {isa_baseline_circuit.depth()})\",\n", + " f\"AQC\\n(depth {isa_circuit.depth()})\",\n", + "]\n", + "values = [baseline_expval, aqc_expval]\n", + "colors = [\"tab:orange\", \"tab:blue\"]\n", "\n", - "plt.figure(figsize=(10, 6))\n", - "bars = plt.bar(labels, values, color=[\"tab:blue\", \"tab:purple\"])\n", + "plt.figure(figsize=(8, 5))\n", + "bars = plt.bar(labels, values, color=colors, width=0.5)\n", "plt.axhline(\n", - " y=abs(reference_expval), color=\"red\", linestyle=\"--\", label=\"Exact Result\"\n", + " y=exact_expval, color=\"tab:green\", linestyle=\"--\", linewidth=2, label=f\"Exact ({exact_expval:.4f})\"\n", ")\n", - "plt.xlabel(\"Results\")\n", - "plt.ylabel(\"Absolute Expected Value\")\n", - "plt.title(\"AQC Result vs AQC Comparison Result (Absolute Values)\")\n", + "plt.ylabel(\"Expected Value\")\n", + "plt.title(\"AQC-Tensor vs Baseline Trotter (50-site XXZ)\")\n", "plt.legend()\n", "for bar in bars:\n", " y_val = bar.get_height()\n", " plt.text(\n", " bar.get_x() + bar.get_width() / 2.0,\n", " y_val,\n", - " round(y_val, 2),\n", - " va=\"bottom\",\n", + " f\"{y_val:.4f}\",\n", + " ha=\"center\",\n", + " va=\"bottom\" if y_val >= 0 else \"top\",\n", " )\n", - "\n", + "plt.axhline(y=0, color=\"black\", linewidth=0.3)\n", + "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", - "id": "45f512f7-9233-4f1f-8f24-cb3524567135", - "metadata": {}, - "source": [ - "## Conclusion\n", - "\n", - "This tutorial demonstrated how to use Approximate Quantum Compilation with tensor networks (AQC-Tensor) to compress and optimize circuits for simulating quantum dynamics at scale. Utilizing both small and large Heisenberg models, we applied AQC-Tensor to reduce the circuit depth required for Trotterized time evolution. By generating a parametrized ansatz from a simplified Trotter circuit and optimizing it with matrix product state (MPS) techniques, we achieved a low-depth approximation of the target evolution that is both accurate and efficient.\n", - "\n", - "The workflow here highlights the key advantages of AQC-Tensor for scaling quantum simulations:\n", - "\n", - "- **Significant circuit compression**: AQC-Tensor reduced the circuit depth needed for complex time evolution, enhancing its feasibility on current devices.\n", - "- **Efficient optimization**: The MPS approach provided a robust framework for parameter optimization, balancing fidelity with computational efficiency.\n", - "- **Hardware-ready execution**: Transpiling the final optimized circuit ensured it met the constraints of the target quantum hardware.\n", - "\n", - "As larger quantum devices and more advanced algorithms emerge, techniques like AQC-Tensor will become essential for running complex quantum simulations on near-term hardware, demonstrating promising progress in managing depth and fidelity for scalable quantum applications." - ] - }, - { - "cell_type": "markdown", - "id": "3cc40a5a-4b55-45e8-a4f1-df45b9e37abd", + "id": "278bc002", "metadata": {}, "source": [ - "## Tutorial survey\n", - "\n", - "Please take this short survey to provide feedback on this tutorial. Your insights will help us improve our content offerings and user experience.\n", - "\n", - "[Link to survey](https://your.feedback.ibm.com/jfe/form/SV_eF01c2sfeSt6cqq)" + "## Next steps\n", + "If you found this work interesting, you might be interested in the following material:\n", + "\n", + "- [AQC-Tensor addon documentation](https://qiskit.github.io/qiskit-addon-aqc-tensor/) — includes the related **unitary AQC** technique, which optimizes parametrized circuits to approximate a target unitary operator rather than a prepared state\n", + "- [Error mitigation and suppression techniques](https://quantum.cloud.ibm.com/docs/en/guides/error-mitigation-and-suppression-techniques)\n", + "- [Combine Error Mitigation Techniques](https://quantum.cloud.ibm.com/docs/en/tutorials/combine-error-mitigation-techniques)\n", + "" ] } ], "metadata": { + "description": "Learn how to use AQC-Tensor to compress Trotterized time-evolution circuits for efficient execution on quantum hardware.", "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1877,9 +1072,10 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3" - } + "version": "3.13.1" + }, + "title": "Approximate quantum compilation for time evolution circuits" }, "nbformat": 4, - "nbformat_minor": 4 + "nbformat_minor": 5 } From dbbceb4b0033b7896809682a5b12d10983cd5f5a Mon Sep 17 00:00:00 2001 From: nathanearnestnoble <51792809+nathanearnestnoble@users.noreply.github.com> Date: Mon, 18 May 2026 13:26:47 -0400 Subject: [PATCH 2/7] Updating tutorial Made updates based on Kevin's feedback, as well as ensuring to have non-compressed trotter steps as part of `aqc_final_circuit` --- ...antum-compilation-for-time-evolution.ipynb | 378 ++++++++++-------- 1 file changed, 207 insertions(+), 171 deletions(-) diff --git a/docs/tutorials/approximate-quantum-compilation-for-time-evolution.ipynb b/docs/tutorials/approximate-quantum-compilation-for-time-evolution.ipynb index a58d9b2f46d..72262363f62 100644 --- a/docs/tutorials/approximate-quantum-compilation-for-time-evolution.ipynb +++ b/docs/tutorials/approximate-quantum-compilation-for-time-evolution.ipynb @@ -14,7 +14,7 @@ "\n", "# Approximate quantum compilation for time evolution circuits\n", "\n", - "*Usage estimate: 30 seconds on IBM Heron (NOTE: This is an estimate only. Your runtime might vary.)*" + "*Usage estimate: 15 seconds on IBM Heron (NOTE: This is an estimate only. Your runtime might vary.)*" ] }, { @@ -48,7 +48,7 @@ "source": [ "## Background\n", "\n", - "This tutorial demonstrates how to implement **Approximate Quantum Compilation** using tensor networks (AQC-Tensor) with Qiskit to enhance quantum circuit performance. AQC-Tensor compresses deep Trotter circuits into shallow, hardware-friendly circuits while preserving simulation accuracy.\n", + "This tutorial demonstrates how to implement **Approximate Quantum Compilation** using tensor networks (AQC-Tensor) with Qiskit to enhance quantum circuit performance. AQC-Tensor compresses deep Trotter circuits into shallower, more hardware-friendly circuits while preserving simulation accuracy.\n", "\n", "### How AQC-Tensor works\n", "\n", @@ -70,7 +70,7 @@ "\n", "AQC-Tensor is most effective when:\n", "\n", - "- **Circuit depth exceeds hardware coherence times.** If a Trotter simulation requires more layers than the device can support, AQC-Tensor can compress the evolution into a shallower circuit.\n", + "- **Circuit depth exceeds hardware coherence times.** If a Trotter simulation requires more trotter steps than the device can support, AQC-Tensor can compress the evolution into a shallower circuit.\n", "- **Entanglement remains classically tractable.** The total entanglement in a time-evolved state depends primarily on the evolution time $t$, not the number of Trotter steps $k$. This means a target circuit with $10k$ steps is typically no harder to represent as an MPS than one with $k$ steps, as long as $t$ is short enough for bond dimensions to stay manageable.\n", "- **A natural ansatz exists.** Because the ansatz mirrors the structure of a Trotter circuit, it provides a physically motivated starting point with well-defined initial parameters, avoiding the convergence issues that can plague arbitrary variational ansatze.\n", "\n", @@ -242,11 +242,11 @@ " [(\"ZZ\", (L // 2 - 1, L // 2), 1.0)], num_qubits=L\n", ")\n", "\n", - "# Generate an initial Néel state |0101010101⟩\n", - "initial_state = QuantumCircuit(L)\n", + "# Generate an initial Néel state |1010101010⟩\n", + "initial_state_circuit = QuantumCircuit(L)\n", "for i in range(L):\n", " if i % 2:\n", - " initial_state.x(i)\n", + " initial_state_circuit.x(i)\n", "\n", "print(\"Hamiltonian:\", hamiltonian)\n", "print(\"Observable:\", observable)\n", @@ -273,16 +273,24 @@ "name": "stdout", "output_type": "stream", "text": [ - "Exact expectation value: -0.809062\n" + "AQC evolution time: 0.2\n", + "Subsequent evolution time: 0.066667\n", + "Total evolution time: 0.266667\n", + "Exact expectation value: -0.700899\n" ] } ], "source": [ "aqc_evolution_time = 0.2\n", "\n", + "# Each baseline Trotter step covers dt = aqc_evolution_time / 3\n", + "# The subsequent (uncompressed) step covers 1 additional dt\n", + "subsequent_evolution_time = aqc_evolution_time / 3\n", + "total_evolution_time = aqc_evolution_time + subsequent_evolution_time\n", + "\n", "# Compute exact expectation value via matrix exponentiation\n", "H_matrix = hamiltonian.to_matrix()\n", - "U_exact = expm(-1j * H_matrix * aqc_evolution_time)\n", + "U_exact = expm(-1j * H_matrix * total_evolution_time)\n", "\n", "# Build the initial state vector (Néel state)\n", "initial_state_vec = np.zeros(2**L)\n", @@ -294,6 +302,9 @@ "obs_matrix = observable.to_matrix()\n", "exact_expval = (evolved_state.conj() @ obs_matrix @ evolved_state).real\n", "\n", + "print(f\"AQC evolution time: {aqc_evolution_time}\")\n", + "print(f\"Subsequent evolution time: {subsequent_evolution_time:.6f}\")\n", + "print(f\"Total evolution time: {total_evolution_time:.6f}\")\n", "print(f\"Exact expectation value: {exact_expval:.6f}\")" ] }, @@ -316,7 +327,7 @@ "source": [ "aqc_target_num_trotter_steps = 32\n", "\n", - "aqc_target_circuit = initial_state.copy()\n", + "aqc_target_circuit = initial_state_circuit.copy()\n", "aqc_target_circuit.compose(\n", " generate_time_evolution_circuit(\n", " hamiltonian,\n", @@ -332,14 +343,16 @@ "id": "bce1ef52", "metadata": {}, "source": [ - "#### Generate an ansatz, initial parameters, and a baseline circuit\n", + "#### Generate an ansatz, initial parameters, subsequent circuit, and a baseline circuit\n", "\n", - "Next, we construct a \"good\" circuit with the same evolution time but far fewer Trotter steps (just 1). We pass this circuit to `generate_ansatz_from_circuit`, which returns:\n", + "Next, we construct a \"good\" circuit with the same evolution time as the AQC target but far fewer Trotter steps (just 1). We pass this circuit to `generate_ansatz_from_circuit`, which returns:\n", "\n", "1. A general, parametrized **ansatz** circuit with the same two-qubit connectivity.\n", "2. **Initial parameters** that reproduce the input circuit when plugged into the ansatz.\n", "\n", - "We also construct a **baseline Trotter circuit** using 3 Trotter steps. This serves as the comparison: it represents what you would run on hardware without AQC. The AQC ansatz has significantly fewer layers but, after optimization, achieves far better accuracy." + "We also construct:\n", + "- A **subsequent circuit** with 1 Trotter step that will be appended (uncompressed) after the AQC-optimized portion, following the approach in the [AQC-Tensor initial state tutorial](https://qiskit.github.io/qiskit-addon-aqc-tensor/tutorials/01_initial_state_aqc.html).\n", + "- A **baseline Trotter circuit** using 4 Trotter steps over the full evolution time (`aqc_evolution_time + subsequent_evolution_time`). This serves as the comparison: it represents what you would run on hardware without AQC. The AQC ansatz (3 compressed steps + 1 uncompressed step) achieves better accuracy at lower depth." ] }, { @@ -352,14 +365,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "Target circuit: depth 385\n", - "Baseline circuit: depth 37 (3 Trotter steps)\n", - "Ansatz circuit: depth 7, with 156 parameters\n" + "Target circuit: depth 384\n", + "Baseline circuit: depth 48 (4 Trotter steps, time=0.2667)\n", + "Subsequent circuit: depth 12 (1 Trotter step, time=0.0667)\n", + "Ansatz circuit: depth 3, with 156 parameters\n" ] }, { "data": { - "image/png": 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" ] @@ -372,7 +386,7 @@ "source": [ "aqc_ansatz_num_trotter_steps = 1\n", "\n", - "aqc_good_circuit = initial_state.copy()\n", + "aqc_good_circuit = initial_state_circuit.copy()\n", "aqc_good_circuit.compose(\n", " generate_time_evolution_circuit(\n", " hamiltonian,\n", @@ -386,22 +400,31 @@ " aqc_good_circuit\n", ")\n", "\n", - "# Baseline Trotter circuit: 3 Trotter steps, no AQC optimization\n", - "baseline_num_trotter_steps = 3\n", - "baseline_circuit = initial_state.copy()\n", + "# Subsequent circuit: 1 non-compressed Trotter step appended after AQC\n", + "subsequent_num_trotter_steps = 1\n", + "subsequent_circuit = generate_time_evolution_circuit(\n", + " hamiltonian,\n", + " synthesis=SuzukiTrotter(reps=subsequent_num_trotter_steps),\n", + " time=subsequent_evolution_time,\n", + ")\n", + "\n", + "# Baseline Trotter circuit: 4 Trotter steps over total evolution time, no AQC\n", + "baseline_num_trotter_steps = 4\n", + "baseline_circuit = initial_state_circuit.copy()\n", "baseline_circuit.compose(\n", " generate_time_evolution_circuit(\n", " hamiltonian,\n", " synthesis=SuzukiTrotter(reps=baseline_num_trotter_steps),\n", - " time=aqc_evolution_time,\n", + " time=total_evolution_time,\n", " ),\n", " inplace=True,\n", ")\n", "\n", - "print(f\"Target circuit: depth {aqc_target_circuit.depth()}\")\n", - "print(f\"Baseline circuit: depth {baseline_circuit.depth()} ({baseline_num_trotter_steps} Trotter steps)\")\n", + "print(f\"Target circuit: depth {aqc_target_circuit.depth(lambda x: x.operation.num_qubits == 2)}\")\n", + "print(f\"Baseline circuit: depth {baseline_circuit.depth(lambda x: x.operation.num_qubits == 2)} ({baseline_num_trotter_steps} Trotter steps, time={total_evolution_time:.4f})\")\n", + "print(f\"Subsequent circuit: depth {subsequent_circuit.depth(lambda x: x.operation.num_qubits == 2)} ({subsequent_num_trotter_steps} Trotter step, time={subsequent_evolution_time:.4f})\")\n", "print(\n", - " f\"Ansatz circuit: depth {aqc_ansatz.depth()}, with {len(aqc_initial_parameters)} parameters\"\n", + " f\"Ansatz circuit: depth {aqc_ansatz.depth(lambda x: x.operation.num_qubits == 2)}, with {len(aqc_initial_parameters)} parameters\"\n", ")\n", "aqc_ansatz.draw(\"mpl\", fold=-1)" ] @@ -413,7 +436,7 @@ "source": [ "#### Set up tensor network simulation and build the target MPS\n", "\n", - "We use Quimb's matrix-product state (MPS) circuit simulator, with JAX providing automatic differentiation for the gradient-based optimization. We then build an MPS representation of the target state and evaluate the starting fidelity between the initial ansatz and the target." + "We use [quimb](https://github.com/jcmgray/quimb)'s matrix-product state (MPS) circuit simulator, with JAX providing automatic differentiation for the gradient-based optimization. We then build an MPS representation of the target state and evaluate the starting fidelity between the initial ansatz and the target. As the problem instance is a relatively small example, the starting fidelity starts off quite high." ] }, { @@ -466,33 +489,33 @@ "name": "stdout", "output_type": "stream", "text": [ - "2026-04-30 11:49:28.459043 Intermediate result: Fidelity 0.99952882\n", - "2026-04-30 11:49:28.461819 Intermediate result: Fidelity 0.99958531\n", - "2026-04-30 11:49:28.464235 Intermediate result: Fidelity 0.99960093\n", - "2026-04-30 11:49:28.466638 Intermediate result: Fidelity 0.99961046\n", - "2026-04-30 11:49:28.469037 Intermediate result: Fidelity 0.99962560\n", - "2026-04-30 11:49:28.471477 Intermediate result: Fidelity 0.99964395\n", - "2026-04-30 11:49:28.473866 Intermediate result: Fidelity 0.99968150\n", - "2026-04-30 11:49:28.478592 Intermediate result: Fidelity 0.99970569\n", - "2026-04-30 11:49:28.481037 Intermediate result: Fidelity 0.99973788\n", - "2026-04-30 11:49:28.483499 Intermediate result: Fidelity 0.99975385\n", - "2026-04-30 11:49:28.485921 Intermediate result: Fidelity 0.99976458\n", - "2026-04-30 11:49:28.488379 Intermediate result: Fidelity 0.99977661\n", - "2026-04-30 11:49:28.490848 Intermediate result: Fidelity 0.99978663\n", - "2026-04-30 11:49:28.493267 Intermediate result: Fidelity 0.99980236\n", - "2026-04-30 11:49:28.495665 Intermediate result: Fidelity 0.99981607\n", - "2026-04-30 11:49:28.498084 Intermediate result: Fidelity 0.99982811\n", - "2026-04-30 11:49:28.500443 Intermediate result: Fidelity 0.99985827\n", - "2026-04-30 11:49:28.502793 Intermediate result: Fidelity 0.99988354\n", - "2026-04-30 11:49:28.505141 Intermediate result: Fidelity 0.99991608\n", - "2026-04-30 11:49:28.507502 Intermediate result: Fidelity 0.99993336\n", - "2026-04-30 11:49:28.509847 Intermediate result: Fidelity 0.99993956\n", - "2026-04-30 11:49:28.512183 Intermediate result: Fidelity 0.99994421\n", - "2026-04-30 11:49:28.514530 Intermediate result: Fidelity 0.99994743\n", - "2026-04-30 11:49:28.551200 Intermediate result: Fidelity 0.99994791\n", - "2026-04-30 11:49:28.555802 Intermediate result: Fidelity 0.99994803\n", - "2026-04-30 11:49:28.558109 Intermediate result: Fidelity 0.99994898\n", - "2026-04-30 11:49:28.585675 Intermediate result: Fidelity 0.99994898\n", + "2026-05-18 13:14:49.731596 Intermediate result: Fidelity 0.99952882\n", + "2026-05-18 13:14:49.734425 Intermediate result: Fidelity 0.99958531\n", + "2026-05-18 13:14:49.737101 Intermediate result: Fidelity 0.99960093\n", + "2026-05-18 13:14:49.739813 Intermediate result: Fidelity 0.99961046\n", + "2026-05-18 13:14:49.742969 Intermediate result: Fidelity 0.99962560\n", + "2026-05-18 13:14:49.745916 Intermediate result: Fidelity 0.99964395\n", + "2026-05-18 13:14:49.748615 Intermediate result: Fidelity 0.99968150\n", + "2026-05-18 13:14:49.753684 Intermediate result: Fidelity 0.99970569\n", + "2026-05-18 13:14:49.756208 Intermediate result: Fidelity 0.99973788\n", + "2026-05-18 13:14:49.759067 Intermediate result: Fidelity 0.99975385\n", + "2026-05-18 13:14:49.762321 Intermediate result: Fidelity 0.99976458\n", + "2026-05-18 13:14:49.765526 Intermediate result: Fidelity 0.99977661\n", + "2026-05-18 13:14:49.768496 Intermediate result: Fidelity 0.99978663\n", + "2026-05-18 13:14:49.771278 Intermediate result: Fidelity 0.99980236\n", + "2026-05-18 13:14:49.773735 Intermediate result: Fidelity 0.99981607\n", + "2026-05-18 13:14:49.776339 Intermediate result: Fidelity 0.99982811\n", + "2026-05-18 13:14:49.779177 Intermediate result: Fidelity 0.99985827\n", + "2026-05-18 13:14:49.782243 Intermediate result: Fidelity 0.99988354\n", + "2026-05-18 13:14:49.784904 Intermediate result: Fidelity 0.99991608\n", + "2026-05-18 13:14:49.787737 Intermediate result: Fidelity 0.99993336\n", + "2026-05-18 13:14:49.790414 Intermediate result: Fidelity 0.99993956\n", + "2026-05-18 13:14:49.793029 Intermediate result: Fidelity 0.99994421\n", + "2026-05-18 13:14:49.795585 Intermediate result: Fidelity 0.99994743\n", + "2026-05-18 13:14:49.835045 Intermediate result: Fidelity 0.99994791\n", + "2026-05-18 13:14:49.839786 Intermediate result: Fidelity 0.99994803\n", + "2026-05-18 13:14:49.842403 Intermediate result: Fidelity 0.99994898\n", + "2026-05-18 13:14:49.873779 Intermediate result: Fidelity 0.99994898\n", "Done after 27 iterations.\n" ] } @@ -540,7 +563,7 @@ "source": [ "#### Assemble the final AQC circuit\n", "\n", - "With the optimized parameters in hand, we bind them to the ansatz to produce the final AQC circuit. This circuit has the depth of a single Trotter step but approximates the accuracy of 32 steps." + "With the optimized parameters in hand, we bind them to the ansatz and then append the subsequent (uncompressed) Trotter step. The resulting circuit has the depth of a single compressed Trotter step plus one uncompressed step, but the compressed portion approximates the accuracy of 32 Trotter steps." ] }, { @@ -551,9 +574,9 @@ "outputs": [ { "data": { - "image/png": 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WCKwYYgKHl9Tv084EBa0fT3hesJV+C88E8Da03ASQr7UT5poZ9ayuzemJyVr95Ds5yzpNH6kjyzabn45TH1XdXm0UfFVl9f7kebPuwk6jzXq2llVv1Ui75y0p1r7trWtpMqSX9s7/Idd7IbWrqv1LD+r8oRPqs+Al815mWoYW9/9/SjlzTmvHz1H3ueOUnZUtP38/rX9uXs43oLbS7ShHj8/Zx2axd3wAAACAs+qt9uqe9uq7BSnqs4Sj6S5bMUTd33taAeUCTbqtQOKP97/u0DG5w/OCrfQ7cgyAp/HLtqaigttJT0phQiEP4soByz0xrwRVq6hu74zNmdnPU3hquovD2wbj98TrBc7nbfk6P+R1FJUvXB+2cO14D1/Ly76Ud925Tu7OafOUa8mX8rK3G1qKeYdu6QBKXWrseY+84XtqugEAAABv4c51cndOG+DNCG4CAAAAAAAA8EiMuYlCeTBmgUPrLR00Sf6BAbr50xe0/4uVWj3m4ix3l7tuwj1q9dQdWvPMHO2b/4Ma39VdnWeMsrvtxCOnzKDSAOCLKIcBwLdxHwAA16Icdj8EN1EoP4+aUeCy0Po1TcAyJfaczkUdU/Kps9rz8fe6ZlhvRS9alzMBjKVaq3C1GPUnHVux1QQ2LSfW77K5/cZ3dtfV3Vrp5Mbf+a8B8FmUwwDg27gPAIBrUQ67H4KbKJQDX67K9/0ywYHq/+1rysrI1E8j3jaBTcumlz5UnW6t1HHqCBOUTDubqDJBZdVl5iilX0jRmnF/z9lG4uFT5ic/tTpGqlanSMXvjta68XP5rwHwWZTDAODbuA8AgGtRDrsfxtyEU3R+6wlVjWyoX175WCfW7Mh5P+N/AczgGpV0w6vDzXvXTRyiyk3raeOL/1DSiTi72y5fp7q6vTfOBEOXPzxNGcmp/NcAgHIYAEB9HADcBnER16HlJoot8rEBCvvTjTr41RrtnPttnuUxq7fr9w+XqdlDfUwwM+KR/jq8dJOivlhpd9tWK88e88YrqEoF/Xj/G0o4dIL/GABQDgMAqI8DgNsgLuJatNxEsdTu0kJt/jJUcbuitWbcuwWu98vL/9L5QyfU/PHblXYuSesmONa13OrOXr1VuLa9+YWOLd/KfwsAKIcBANTHAcBtEBdxPZ8Ibp45c0YTJkxQ48aNVa5cOdWrV09jx47VhQsXNHz4cPn5+Wn27NmuTqbHqVC3hrrNeUrpicla8fBUm93FM9MzlJ6QbP6O/S1KyacvjslpS7Ph/cwM6lYrz9/edmyWduSvxeiB6v7e0xq8/h0zs9sdGwsORAPwHJTDKDI/P0U8eqsGrpqh+w7+W3dunqO2k+5XQHAQJxXwINwH4CieBwDKYW/m9cHNbdu2qUWLFpo2bZpOnDihiIgIpaena+bMmbr77ru1e/dus17r1q1dnVSPYk0g1OODCQqsVF4/PzFDCdEnba7f6snBqtYiTLHbD6pO99amG7stNW9opnaT7te5/ce0avRMJ6fe91ita2vd2Nx060+NT3B1cgA4AeUwiqP9Xx9U+8kP6uzeo1r//Ac6tGidIob3U6+PJprAJwD3x30AhcHzAEA57M38vb3F5m233WaCmk8//bRiYmK0ZcsW83rKlClavHixNm3aZFputmzZ0tXJ9Sidpj9mgpXbpn9ut7t41RZhajlmkI79tE1Lbn9O56KOq8Orw1WueqV81w+pXdW0MsxISdPyh6ealqEongUdHtenkQ9p2T0vK+lkPKcT8AKUwyiqyk3rqtnDfXVo8XqtGD5N++b/oE0vfaiNL32o2p1b2P0CEoB74D6AwuB5AKAc9mZeHdwcM2aMjh49qlGjRmn69OkKDQ3NWWZ1U2/VqpUyMjLUsGFDVaxY0aVp9SQRI25V+KAuOrx0o37725c21/UPDFCXmaOVkZSqNU//XZnJaWZsTqvFZ8cpj+a7vjWBUHCNylr95Ds6t+9YCR6J70g8fMrVSQDgRJTDKI6wgZ3l5++vXe8vzvW+FeRMT0pR+OCunGDAzXEfQGHxPABQDnszrw1uWt3NP/vsM1WvXl2vv/56vuu0adPG/LaCnJf89NNPpiXnlT90W7+oVqdItX3+vv91F59l9/9w3TN3q8q19U1rkKTjsea9Uxt/1+5536lBvw55Wofc8PojqnFdE/02a6EOL9lQjBwAAN6JchjFVb11Y2VlZurM1n253s9MTVfcjkOq3jqckwy4Me4DAOBalMPuJ0Be6pNPPlFWVpaGDh2qChUq5LtOcHBwnuDmJe+8846uv/76nNfly5eXrwu+qrK6vzdO/gFlTFe2ere0LXDd+F2HFRAcqMjHBujID79o/6fLcy3f8vq/VbfX9aZ7eszqHUo5c05N7u2lpvf2MpMNWS02Gw3uUuD2Dy/ZaHMCIwDwRpTDcIaQmlWUGpegrLSMPMuSTsSpZvtr5V82QFnpeZcDcC3uAwDgWpTD7slrg5vLl18MpvXo0aPAdawu6wUFN62Jh2644YYSTKHnqRReR+WqXRwns9XYwTbX3T77K9Xv086Ml7n2mTl5lmemXOye3mfhZNM93Rrzy3qYslhd0q2u7LYsaPeYEo+eLtbxAICnoRyGM5QJDlJmWnq+y6zWmxbrC8o0gpuA2+E+AACuRTnsnvyys7Oz5YXq1atngpdbt27Nt0u5NdZm7dq1zaRDUVFRatSoUU63dCsgumLFCnXv3t0paWnbtq2ZxKgwymb7a1JWe6fsHyVvsv9GpftlueRUFzav3L7iLZUtX04L2j9eoumCd3Bl3i4JlK3wxnxd2Lx++/I3zaR+n7X8c55l3eaOU9iATvqo/j203PRRvnB92MJ9wnv4Wl4uTN7leQDufC1RDvtu3qlVq5Y2b95cpH15bcvNCxcumN/JyfnPtG2Nx2kFNq1JhsLCwvIsv/vuu83yatWqacCAAXrjjTfM+J1FYQU2jx0r3MQ4gX5lpJpF2h1c4HjMcaVlZ7rk3JNX4K15uyRwvcAb83Vh83rSyXhValrXTOJ3Zdf0kFpVlRJ7jsCmD/OF68MW7hPew9fyMnkX3nItkZe9x/FSzDteG9y0Ir7x8fHasmWLOnbsmGtZTEyMxo8fb/5u+f/buw/wqoq8DeBveiEJJZSEQCCEoEkIAakJvSldKSIIgopUKQJSdkURxQJiociCLn7qyqqAiDRZSjTSi4D0FkgCISGQhBTSy/fMsMmS5PaS297f8+RJ7j3nnjPn3DlzZv45M9OihZwwqFT16tXlsi5dusixOg8fPiwnJDpy5IiMILu6uuqUFl3+WwHb+UejxavvW9+kT24yr5A15m1j4PVC1pivtc3r905fg1+3lqjdKgjJRy+Wve/g4oRazRvjzpH/vUe2xxauD1V4n7AetpaXmXfJWq4l5mXbzTs+OsTOrD642atXLzlj+pIlS9C7d280a9ZMvn/8+HG88MIL8qlMoWKX9VatWsmfUqJrevPmzeXTm2KSopdeeknrtOjyWG1Bdi7WB47W+nNkGleuXoGTu/aBb0NgXiFrzdvGwOuFrDFfa5vXb/xyCC2mD0HI+P7lgptBo3rJ83J98x9VmFIyN7ZwfajC+4T1sLW8zLxL1nItMS9bjytVmHesNrg5d+5c/Pvf/8bNmzcRGhqKxx9/HLm5ubh27Rr69u2Lxo0b4z//+Y/CyYQqGjBggJwtXQQpdQluEplak2Fd4NGgjvzb1dtLzoLb4rWHk0KJiZmub2JDlojIVty/FI9L/7cLweP6ofu6Obi17ySqB/khZFw/JB06j+ubD5g6iUREZGBsDxCRNbPa4GaDBg2wf/9+2cU8OjoasbGxcgb0tWvXYvz48QgMDJTraRLcLPVo93UiS9JsZE/4RIaWe++JeSPlb9mQZXCTiMimHHvra2TdvItmo3uhQc8nkJuagYtf/YpTS38ErHOuSSIim8b2ABFZM6sNbgrBwcHYvn17pfezsrJksNPe3l52OVdn69atcoKidu04ezlZpl1DF5o6CUREZEZKiotxfu02+UNERNaP7QEismZWHdxU5vz58ygpKZHjcLq7u5dbNnr0aDRp0gRPPPFE2YRCS5culWNzjhgxwmRpJiIiIiIiIiIiovJsMrh59uxZpV3SxficYqzOzz77DDk5ObJ7u+jGvnDhQjg7O5sgtURERERERERERKQIg5sV/O1vf5M/1qDduy/D/6k28GhYF1t7vY7U87EK1xt2bDWK8gpQlJsvX59Z+TNitx6CS00PPLXhf92ZHdxc4NmoHn4IG4f8+1kG2bey/SfuP2OR+yYiy9b7hzfhVqcGUFyMgge5OLrgK6Seu1FpPc8AH3RePg0utTxRkJmNAzNW4f6VW3BwcULXNTNRPaiBLFdy76Xj8PwvkRmbZJD0KdtvKXtnR7RdOBZ+3VqiKC8fqRfisH/qCpXb1CbNyspXfe4X2px3Tcr3ps91R6fPXkXUS0sQv+u42n0TERmSIcszXco0dfcJTfavahu63GeMcW5Y1hORplSVF+rqsNrEFrQtx4wVi/HU4D6gbvvK0mXsto4+GNy0YnE7DuPc6i3o98titetGT/q00gWXl5aFrb3nlL0OnTQIPhEhGjVUtdm3sv1b4r6JyLJFT/gY+RnZ8m//vu1kRUhUSCqKXDoRV77bg2sbfkej/h3QaflUbO87Xy67/K89SIg6Jf9+/KU+6PjxZIONc6Vqv0LrN0bLyWA2d5wmX8tGpAY0TbOy8lWf+4U2511d+e7RoA6ajeqF5BOXNdovEZGhGao807VMU3ef0GT/qrah631GYFlPRFVNXTmqrg6rbWzBHGIxkRrcBzTZvqJ0Gbutow972KCoqCg55mb//v1hze4cuYjsxFSDbS/o+R64+v0+7puIrFZpg1Rw9nRXOGu0q7cXvMMDEfPTH/J13I4jqFbfG56NfeR/OEtv9sLdk1fh0bBO2Wvxuac2LsSAXUswcPdHaDQgQuO0qdqv4OjmgqCRPXDyw+/LPpNz977a7apLsy73FW3uF5qed7X7t7ND5MeTcXTBOhTnF2q8byIiQzJIeaZjmabuPqHJ/lVtQ9f7TCmW9URUpXQoRyvWYXWNqZgqFuOqxX1Al+1r026oajb55CZV1mnFNHHt4+6pa/jz/fXIS8kot7xOm8fgUr0abu75s8r3b837JiLzI8oE38hQ+fee0e9XWl7NrzZy7qShpKi47L2shHvy/YpdMkJe6Yf4/zzs/uLs5Y7IjyZh7+j3kJN8X3YVGbh7Ke6euIzsJPWVH3X7FZUW8d/WFtOHwLdLC9lV5PSyDUg88HCcaU09mmZd6Fpuqjvv6oROHIjk45eQcua61p8lIjIkfcszXcs0be5PumxDBDf1vc+wrCeiqqJtOWqqtr8hYzHVdLgPKNu+unQZot1gSAxuWqh+296DVxNfhcvE48XZt1M03tavg9/Cg4R7sHN0wBPzRqLz8qnYW6EiJv5Le21jdLmLxFDU7d9a901E5unA9JXyd+CzXdFmwehK5aGmwqYPkQHHQ8MXlVUcPBvVRe/1b5RbzyuwvgxuqivX1bFztJfj+ty/ektWQGo1D8CTP76JLV1nyvFwdEmzLnQtN/U57zUea4hG/dvLMp2IyNT1b33vI+ZaphniPsOynoiqojwWT85rW46aou1v6liMsu1rki5DtBsMicFNC7VzYPnGsT5EphVKCotw4cvtGHLwYYWslKO7KwIGRWJ733kG26em+7fmfROReYvZGI2IJRPkgNtiXJpHyw23ejVh52BfVgnw8KtdVp6UjlvTqF977B6+CEU5DwfitrOzw/3Lt7Bz0Bs6levFeQUq9yt+FxcV4fpP++VrMUlDVnwyagb7I3G/+qdqFKVZW4YoN5Wdd1XqtQ+WDe6hh1aWjQEX8dEkuNWticvf7tY5LURE+tS/dSnP9CnTNLk/qaNqG/kZD/S6zzyKZT0RGbM8bjimtVblqKna/oaOxTzQ8j6gbPvq0mWIdoOh2eSYm/Q/onuJ6CpZKmBwJ6RUmLUw4OlIpF6IRfq125UeUxYDpRtz/+a4byKyTqI8EJWBUv592srGaMUGaW5KBlLP3kDg0C7ytRio+0FiallXj5CJAxAwuCN2P/dOufHFxEDmHv514ds5rOy9WqGNYe+k2f8Z1e03LzUTiQfOoX63cPlaVOjE/tKvJqgtN5WlWVu6lNmanndVRCV1Q8vx2NRuivwR4/8cnrOGgU0b5RMRihcTN8mKd0X1IkLw/OVvMPz0l6gZ3KjcMjH7p/jck4/MHlpRy9nD5TpiPCtF19HYhA3o+8vicvULsh2GKM80KdOUlanq7hOaULUNdfcZVWljWU9EVUnbuqEubX99YxLGiMXkankfULR9dekyVLvB0PjkphWLWDoBDXq2hlvdGuj9/QIUZOVgc+TDmQ0jl03Czd0nkHYpHt3/OUdG9sV4CplxyTgwrXxUPmhkT1xZv7fS9muHN8HFdTt13rf4ca1TXeX+zXHfRGSdnLzc0e2L2XB0dUZJcYmsHOwb80HZ8kfLj0Nz18oZcEV3DFHGHHjtc7mOu28ttHv7RWTEJqHPprfle0X5hdjR/2/IT3+AvS+8j7ZvjUHbhWNh7+Qg/ysa9dJSjdOobL+lDs9di46fTJHdIMUxiNel43kqKzdVpbnicasqX3Uts7U57+r2T6RKg16t0e2LWXIClN3D30Fm3J2yZeLJOv8+7ZBxIxG+HUPl7KpZt+5qfEJbzR2B8JnDkPDbKUSN+8hsnmKgqlVV5ZmqMlXVfULT/avahqr7jKq0sawnInPyaHmoqg6rqqzUNyZhrFjMIQ3vA8q2rypOoq7dYEp2JWLacDI7Bdm5WB84GubKxdsLXT+fgd0j3rWpfSszKuY7OLm7mmTf5p5XyLKZMm8bg61eLyyzrTtfK2Kreb30yc0+mxfh+KJvcX7N1rKnDsR4Uekxt2X9QQy2/6jgV/qh3aIXsb3vfDmO19mVP8uJUhQ9udny9eHY1mceUv6Kke+1X/wygsf1w41fDmL/tJUoLtBsRlZzZQvXhyVfO+ZYDzbXtNlaXjb3vEuWq6qvJUvIy6xbm1/eYbd00omYKctUFRdT7puIyBKxzCZb9tiYJ9Fl1XSknL0hB8ivGNgsfXIh6dB5OaPqrb0n0fS57mKgXJXbFU80dF45TQY2L3+3B9GTP7P4wCaZP3OuB5tz2oiIDIl1a/PD4CYRERERWaWwaYPlhC6JB8/jP88uQv79ymMfivEza4U0wrUNv8vX1378TXZLr9+lhdLtOrg4yS5bgcO64uyqLTg8Zy3AzlBEREREJsExN4mIiIjI6jw29kl4NfZB3K9HET3pUxTnK36qMmhkDxQ8yEHcjqPy9a2oU8i5ly7fvx39l8LPdFo+VW77xOLvcO7zLUY9DiIiIiJSjU9uEhEREZHVcf/vrNWZsXeUBjYdXJ3R5JlOMrBZmJ0r3yspLML1zfvh/1RbONfwULpt0QU947rms6oSERERkXEwuElEREREVkdMCnR7/1k0nzwIbRaOUbhOowEd4Fy9GpIOX4BnY5+ynztHLsjAZ+DQLgo/d3DmauQk30fXNTPh36+9kY+EiIiIiFRht3Qz5ejmImeWIsv5vky5b+YVMmb+sia8Xqg0H5D1K8zJx74xH6DnN/PRfNIg2Nnb4/jCr8ut02xkT/m706dTFG4jaER3XFy3s9L7GbFJ2DV0Ifr8tAjd1syUkwnF7ThipCOhqsT7hPWwtbKeeZeMmbeqEvOy9XCswrzD4KaZsrOzg5O7q6mTQRaAeYWI1wsRKVaUm499Yz9Ez6/nIXTCADkB+rG3HgY4PRvVQ70OwYj56Q/E/3qs0md9O4fh8bFPwbtFEzmLekWZcXfw65C30GfzInT9x2uInrIccdsP86uwcKxXkaVi3iVrwbxMumBwk4iIiIisO8D54hL0+HoeQsYPEK0mHHvz/9B0ZA/5NOf5tduQevZGpc+l/BUjg5tNR/RQGNwUsuKTsWvIwyc4u66egT/sgNhtDHASERERVSWOuUlEREREVh/gjBr7IRJ+P42QV/qj/eKX0XR4N2TG31EY2BSybt3Fvb9i0GRwJzi4OCnddmmAM/tOGrqsfg0BT3c04pEQERERUUUMbhIRERGR1SvKK0DUi0uQ8NspBI/rh2q+3ojbeVTlZ8Q4mi41PNROGpR1Mxm7Br+F7MQUdF41HQGDOxk49URERESkjF1JSUmJ0qVEREREZHEKsnOxPnC0qZNBFkhMUshx34mIiMiS8MlNIiIiIiIiIiIiskgMbhIREREREREREZFFYnCTiIiIiIiIiIiILBKDm0RERERERERERGSRGNwkIiIiIiIiIiIii8TgJhEREREREREREVkkBjeJiIiIiIiIiIjIIjG4SURERERERERERBaJwU0iIiIiIiIiIiKySAxuEhERERERERERkUVicJOIiIiIiIiIiIgskqOpE0BERERE5s8zwAedl0+DSy1PFGRm48CMVbh/5Val9dq9+zL8n2oDj4Z1sbXX60g9H1u2zK9HKzwxfyTs7Oxg5+iAc6t/QczGaLXLDJEudWnr/cObcKtTAyguRsGDXBxd8BVSz92Qy+ydHdF24Vj4dWuJorx8pF6Iw/6pK3Q6j/ocg7mmn4iIiMiUGNwkIiIiIrUil07Ele/24NqG39Gofwd0Wj4V2/vOr7Re3I7DOLd6C/r9srjSsi6rpmPX0LeRdjEOHg3qYPD+5YjbeRSFD3JVLjNEutSlLXrCx8jPyJZ/+/dth06fvSoDiELrN0YDJSXY3HGafC2DiGZ2bk2ZfiIiIiJTYnDTDJSUlKAwJ8/UySADc3RzkU+fGAPzDFljvlaH+Z7MjSmvh6rm6u0F7/BA7B7xrnwdt+MIOrw/Dp6NfZAZm1Ru3TtHLirdTkkJ4FzdXf7t5OmO3LRMFOcXql1miHSpS1tpYFBw9nR/mKD/fs9BI3tg4xMTy5bn3L0Pczu3pkq/peG9hCy5DGf+JVu8lpjvLYujicpWBjfNgAhsrg8cbepkkIGNivkOTu6uRjmvzDNkjflaHeZ7MjemvB6qWjW/2si5k4aSouKy97IS7sn3FQURlYme9Am6r5uDwuw8OFevht/GfYTigkK1y4ydrlKdVkyDb2So/HvP6PflbxFkzL+fhRbTh8C3SwsU5ebj9LINSDxwVuvtG/sYTJF+S8N7CVlyGc78S7Z4LTHfW5ZRJipbGdwkIiIiIqOzc7BH+GvDZNBSPIEonlbs+c18/NJjFvLTHyhdlpeaWWXfzoHpK+XvwGe7os2C0dg7+n3YOdrLMS7vX72FP99fj1rNA/Dkj29iS9eZyL2XrnJ7/ba9B68mvgqXbe09B9m3U8w6/URERESWgMFNIiIiIlLpQcI9uNWrKQOUpU8YevjVlu9rSgTVxDZKu1an/BWD7MQU+b4IbipblvjHGaOmSxExkVHEkglwqekht1VcVITrP+2Xy8QkPVnxyagZ7I/E/aqfftw58A21+zLGMRgq/URERESWwN7UCSAiIiIi85abkoHUszcQOLSLfC0mvXmQmKpVt2kRZHOvVxPVg/zKukt7NqqHjJjbKpeVdrcWk+QYI12Cs5e7DDCW8u/TFnlpWQ9/UjOReOAc6ncLl8vEU5Ae/nWRfjVBZdo0ZYhj0Cf9RERERJaOT24SERERkVqH5q6VM3CHTR+CgqwcHHjt87Jlkcsm4ebuE/InYukENOjZGm51a6D39wvkupsjp8ku0IfmrEG3tbNQUlwCO3s7HHljXdkTiqqW1Q5vgovrduqVLkFZ2py83NHti9lwdHWW+xcBx31jPijbzuG5a9Hxkymyq7dYLl5nJ6WqTVtVnVt90k9ERERk6exKxNRTZFIF2bmcUMgKGXMgXeYZssXB95nvydyY82QU1nS9uHh7oevnM8pmEzcn5pw2a8zXhmBN1wbZXl5n/iVbvJaY7y3LKBOVreyWTkRERERmKy8lw2yDh+acNiIiIiJbweAmERERERERERERWSQGN4mIiIiIiIiIiMgicUIh0sqLiZs0Wm/XkIWwd3bEkz+8iWsbo3Fg+spK67SaOwLhM4fh4OtrcHX9XjQd3g2dlk9Vu+2sm8nY1G4Kvzkiskksh4mIbBvvA0REpsVy2PwwuEla+WPqcqXLPP3ryYBlbko60mMSkJN8H5e/24PHRvdG3PbDZTOVCt7hgQib+gwSfjslA5tC0pELKrff9NluqN81HHeOXeK3RkQ2i+UwEZFt432AiMi0WA6bHwY3SSvXf9qv8H0HN2f03/Y+iguL8PvET2VgUzj+9jfw6xqOiKUTZVAy/34WHFyc0HnFVBQ8yMXBWf8o20ZWfLL8UcQnIhQ+kaFIuxiHw3PW8lsjIpvFcpiIyLbxPkBEZFosh80Px9wkg+j0yauoFdoYfy7+DkkHz5W9X/jfAKZbnero8N44+V6r+SNRo1lDHHvr/5CdlKp229X8aqPrF7NkMDTq5Y9QmJPHb42IiOUwERGxPk5EZDYYFzEdPrlJegudPAgBz3TEjS0HcX7ttkrLEw+cxaVvdiP4pT4ymBkyvj/idx1HzMZotdsWT3l2XzcHLjU9sG/Mh8iMTeI3RkTEcpiIiFgfJyIyG4yLmJbVP7l57949zJ07F02bNoWrqysaNmyIGTNm4MGDBxg3bhzs7OywatUqUyfTYvl2DkPrv49C6oU4HJy1Wul6f777L2TEJqH5lKeRn56Nw3M161ouurPXDg/E6Y83IiHqlAFTTkRkHVgOExHZNt4HiIhMi+Ww6Vn1k5unT59G3759kZSUhGrVqiEkJAS3b9/GihUrEBMTg9TUh12iW7ZsaeqkWiSPBnXQdc1MFGTl4LeXl6rsLl5UUIiCzBz5d8qZGOTcfTgmpyrB4/rJGdTFU55nPtVslnb6Lzs7+YTsYy/0lt9TbkoGbmw7hNNLf2S3fiIrwnKYdBU2bTC8w5rAu0UTeDaqh6ybydjUbgpPKJGF4X2AymEbgIjlsI2yt+YnNgcOHCgDm7Nnz0ZiYiJOnjwpXy9ZsgQ7duzA8ePH5ZObLVq0MHVyLY6YQKj7V3PhXL0a/nh1OTLj7qhcP/y1ofAOC0DK2Rvw69ZSdmNXpV6HYLRdOAbp1xKwf9oKA6fe+rV750W0W/Qi7l+5hSMLvkLs9sMIGdcPPb+dLys9RGT5WA6TPkSvC5+OzeVwL3lpmTyZRBaI9wGqiG0AIpbDtspqg5vTp0/HrVu3MHXqVCxbtgyenp5ly0Q39fDwcBQWFqJx48bw8vIyaVotUeSyyTJYeXrZBrXdxWuFBaDF9CFI+P00dj79BtJjbqP9e+PgWru6wvXdfWuh2xezUZibj6iXl8onQ0lzNZo1QPDLfRG74wh+G/cRrq7fK2etP/b2N/DtFKY2sExEloHlMOljU/sp+CH0Jewe8S6y76TxZBJZIN4H6FFsAxCxHLZlVhncvHjxIn788UfUrl0bH3zwgcJ1WrduLX+LIGdFP//8MyIjI2VX9urVq6Njx444f/680dNtKUImDkDgkM6I33UMZz77SeW69s6O6LxiGgqz83Bw9j9QlJMvx+YUT3xGLJmgcH0xgZBbnRo48NrnSL+aYMQjsU4BgzvBzt4eF77cUe59EeQsyM5F4NAuJksbERkGy2HSV1Z8Mk8ikQXjfYAqYhuAiOWwLbPK4Ob333+P4uJijBo1Ch4eHgrXcXNzUxjcFONxDh8+HJ06dcLWrVvltnr16oWcHD49KPhEhqLNghf+2118pdrvotXrz6Hm4/7yqcHs2ynyveRjl3Bx3a9o1K99pacIO3wwHnVaBeHMys2I33lU5zxgy2q3bIrioiLcO3W13PtFeQVIPReL2i0DTZY2ItIfy2EiItvG+wApwjYAEcthW2aVEwpFRUXJ3927d1e6juiyXjG4KSYZmjNnDj799FPZnb1Uv379jJpeS+FWtwa6fTEL9o4Osstzw6faKF037UI8HN2cETp5EG7u/RPXfnj4nZQ6+cG/0aDnE7J7euKBc8i9l46g53ui2fM95WRD4onNJkM7K91+/M5jnBhHCfd6NZGXmoni/MJKy7KTUlGv3eOwd3JEcUHl5URk3lgOExHZNt4HSBm2AYhYDtsyqwxuxsXFyd+NGjVSuFyMtXnw4MFKwc2vvvoKTk5OGD9+vEHT06ZNGzmRkTJOJfZYiHYwd9UD/eDq/XCczPAZQ1Wue3bVFvj3aSvHyzz0+ppKy4tyH3ZP77N5keyeLsaGFEE3QXRJF13ZVdnUdjKybt2FOWsW1AwFdsVG2baqPOPg5oKi/AKFy8TTm4IIPOczuElmlq/VsZSy0phYDpsXU14P6vB6IWvM14Zg6dcG7wO2ndfZBiBbvJbMrdxmOWy8/ODj44MTJ07o9Fm7kpKSEliZWrVqIS0tDYcOHUJERESl5evXr8fo0aPlJEPp6elyxvTSJz0zMjIwZcoULF68GDdv3kRQUBDeeustjBw5Uuf0NGjQAAkJyseOdLZzwNp6z+i8fTJPE+9sQX5JkVG2rSrPPB31sZys6ccWr1Ra1nXtLAQMisS3/iP45CaZXb5Wh2UlmRtTXg+GvF6e/u0TOFVzxaZ2U4yeLjJ/5pyvDYH3ErLkvM42ANnitcRy23byg5+fX1kva21Z5ZObItorgpsnT56sFNxMTEyUXc+FFi1alAU2S5eJIOTf/vY3LFmyBA0bNsS6devw/PPPo06dOnLsTV3To+4/EbCsfxqSBur71jfqk5vK8oyY9bZ6swZycqaKXdPdfWohNyWdgU0yy3ytDstKMjemvB7U4fVC1pivDYHXBllyXmcbgGzxWmK5bTv5wUdN7MzmgpsiCClmTBcByt69e6NZs2by/ePHj+OFF17AvXv35OuWLVuW+5yYhCgrKwv/+te/8MwzD5926NmzJy5cuIB3331X5+CmusdqxQzW6wNH67RtMl9Xrl6Bk7urUbatKs/cO30Nft1aonarICQfvVj2voOLE2o1b4w7R/73HpE55Wt1WFaSuTHl9aAOrxeyxnxtCLw2yJLzOtsAZIvXEstty3LFRGWrVc6WPnfuXHh7e8tu5aGhoQgLC5Pdy9u1a4cmTZqgR48eCmdKF93ZhUeDmOLJTvH63LlzVXwURLq58cshlBQXI2R8/3LvB43qJQuZ65v/4KklIiIiIrIibAMQkS2zyic3xRiX+/fvl93Po6OjERsbi5CQEKxdu1ZOFhQYGKgwuCkCoUePHlW4zdzc3CpJO5G+7l+Kx6X/24Xgcf3Qfd0c3Np3EtWD/BAyrh+SDp3H9c0HeJKJiGxck2Fd4NGgjvzb1dsL9k6OaPHaw8kCxYR91zfxH2FERJaEbQAismVWGdwUgoODsX379krvi27nIthpb2+P5s2bl1v29NNPyxnTd+/ejSFDhpR1Vd+zZw/atm1bZWkn0text75G1s27aDa6Fxr0fAK5qRm4+NWvOLX0R8D65hAjIiItNRvZEz6RoeXee2Lew8kT5T/CGNwkIrI4bAMQka2y2uCmMufPn4eYIF6Mw+nu7l5u2cCBA9G5c2dMmDABKSkp8Pf3xz//+U/5GRHgJLIUolv6+bXb5A8REVFFu4Yu5EkhIrIybAMQka2yueDm2bNnFXZJLx1fc+vWrZg3bx7+/ve/IyMjQ663c+fOsnE6iYiIiIiIiIiIyDwwuFlBjRo15Nic4oeIiIiIiIiIiIjMF4ObVqTduy/D/6k28GhYF1t7vY7U87EK1xt2bDWK8gpQlJsvX59Z+TNitx6CS00PPLXhf93UHNxc4NmoHn4IG4f8+1kG2bey/SfuP6PzvjVh7ukjIlTZde4Z4IPOy6fBpZYnCjKzcWDGKty/cksu8+veEq3mjZSTqxTl5OHQ3LVIuxBnkK9H1X4Fe2dHtF04Fn7dWqIoLx+pF+Kwf+oKldt0cHFC1zUzUT2ogSyzcu+l4/D8L5EZm6RXOagtbdKhyTlu+lx3dPrsVUS9tATxu44bLJ2qvgNdzj8RWRdt6sLKylR96tOWcC9Rtf3eP7wJtzo1xKQFKHiQi6MLvkLquRsaHZemn9XkXmasewgRWT5V5YO68lvXurQp4zS9DVS26lO+VxWbC25GRUXBWsXtOIxzq7eg3y+L1a4bPenTShk2Ly0LW3vPKXsdOmkQfCJCNKqIabNvZfvXdd+aMPf0EVHVXeeRSyfiynd7cG3D72jUvwM6LZ+K7X3nw7l6NXReNQO7Br8pG4p12wejy+cz8Ev3WQb5epTtt1TrN0bLCb82d5wmX8sKhAYu/2sPEqJOyb8ff6kPOn48WeF4itqWg9rSJB2anGMxg3ezUb2QfOKywdOo6jvQ9fwTkfXQpi6srEzVpz5tCfcSVduPnvAx8jOy5d/+fdvJAIJoJGtC08+qu5cZ8x5CRJZNXfmgrvzWtS5tyjhNtIHKVn3K96pib+oEkOHcOXIR2YmpBtte0PM9cPX7fWa/b2tIHxFVzXXu6u0F7/BAxPz0h3wdt+MIqtX3hmdjH/mTl5ZZ9gRM8tGLqOZXG7XCAuRr8bmnNi7EgF1LMHD3R2g0IELjtKnar+Do5oKgkT1w8sPvyz6Tc/e+2u2K/+6WNkaFuyevwqNhnSopB3VJh7pzDDs7RH48GUcXrENxfmGlzxvrO9D1/BORdVNV39O0TK24DUu+l6jbfmnDV3D2dJdBUE1p+lmV513NPYSIbJgO5UPF8lvXurQpYxH5hihb9Szfq4rNPblJD3VaMU1c37h76hr+fH898lIyyp2aOm0eg0v1ari5588q37+x920N6SMi3YhAWs6dNJQUFZe9l5VwT76fcuY6XGp6ymv87onLaPhkG3nzFt0zsuLuIPKjSdg7+j3kJN+X3QEH7l4q18tOStVrv6Lbn2iYiv++tpg+BL5dWsjuKKeXbUDigYeT4Gkq5JV+iP+P6bvgKUtHxvVEpec49ewNhE4ciOTjl+R3UZGzl7vRvgPRYDfE+Sci62GI+l7FbRizHKuKe4km2xd1aN/IUPn3ntHva7VffT4rqLqHEJFt07Z8MFWb3xhxmk56lq2G3o6xMLhpIfptew9eTXwVLhOPKGffTtF4W78OfgsPEu7BztEBT8wbic7Lp2Jvhcwp/it7bWN0ucqToajbv7b7NuS5MUb6iMgyiLHLfh+/DK3//jwcq7ni7okrSLt8EyWFRbIi4dmoLnqvf6PcZ7wC68sGqbpySB07R3sZ4Lt/9ZasyNRqHoAnf3wTW7rOlGOfaSJs+hDZ8Dw0fBEMSdsyVlU6VJ3jGo81RKP+7WUZrIiq76Dbl7P1ug8Y4vwTkXUxRH2v4jYs/V6iyfYPTF8pfwc+2xVtFoyu1MZQRZ/PqruHEJH1Uld2in+ka1s+mKLNb6w4zQE9ylZjbMdYGNy0EDsHlq8E6UNcMIJoTF74cjuGHHyYSUs5ursiYFAktvedZ7B9arp/XfZtyHNjjPQRkfkQ17dbvZqwc7AvqxR4+NUuu+6TDp3HriELyyZNeO6vf8ou1NUD6+P+5VvYOegNncqh4rwClfsVv4uLinD9p/3ytRigOys+GTWD/ZG4X/0TN2LsnUb92mP38EUoynk4CLmhaFPGapIOZee4fpcWstE89NDKsnHcIj6aBLe6NXH5292ws7NT+h1okkZV331+xgO9zj8RWRdD1PcUbUNVOWYJ9xJtth+zMRoRSybIiTDEeHHa0OWz9doHq7yHEJH1Uld2NhzTWqvywVRtfmPHaWL0KJeNsR1D45ibNkZ0vRNdYkoFDO6ElAqzXAU8HYnUC7FIv3a70mPIYvBYY+7fmPs2ZvrIfPhEhOLFxE2ygl5RvYgQPH/5Gww//SVqBjcqt0zMEio+9+QjM9FV1HL2cLmOGO+qopCJAzA2YQP6/rK4XB4i85KbkiG7PwcO7SJfi8kYHiSmls0I61b3fxMjhM8chsSD5+QyMfC4h39d+HYOK1teK7SxnPHbEPvNS81E4oFzqN8tXL4WFTCxv/SrCWrLQJH3AgZ3xO7n3ik3Ho42DFHGapoOZedYVC43tByPTe2myB8x5tvhOWvKKp3G/A7UnX8isi2GqO8p2oal30tUbV/UfUTgtZR/n7ay0Vva8FW1b3Wf1YS6ewjZFrYHSJ/yQZd7gL51aWPEaZwNULYacjvGxic3rUjE0glo0LO1bDj2/n4BCrJysDny4UyGkcsm4ebuE0i7FI/u/5wj/+MrxnLIjEvGgWnl/yMQNLInrqzfW2n7tcOb4OK6nTrvW/y41qmucv+67NtQ50af9JH5a9CrNbp9MUsOfL97+DvIjLtTtkz818m/Tztk3EiEb8dQOZNe1q27Gm+71dwRMkiT8NspRI37yOBPzpFhr/NDc9fKGf5E1zuxzoHXPi/3XYqnP0QZcPfPKzg0a7V8Pz/9Afa+8D7avjUGbReOhb2Tg/zvatRLSzX+elTtVzg8dy06fjJFdvMoKS6Rr0vHYFNWBrr71kK7t19ERmwS+mx6W75XlF+IHf3/Vum4VZ0ffctYbdKh7ByrY+zvQNX5JyLboqy+p2mZqmwb1nAvUbb9ag1qo9sXs+Ho6izfF4HYfWM+KNuHqvuMk5e7ys9qc96JVGF7gBR5tIxRdQ/QtS5tqjiNk4HKVnXbMRd2JSVmOM2RjSnIzsX6wNEwZy7eXuj6+QzsHvGuTe1bH6NivoOTu6vN5hlT/qe2z+ZFOL7oW5xfs7XsP19izJL0mNsyH4nB+B8V/Eo/tFv0Irb3nS/HbDm78mc5QL6iJzdbvj4c2/rMQ8pfMfK99otfRvC4frjxy0Hsn7YSxQXWPTunMfO1Oraa741dBlpqGWvr14M6tnq9kHXna0Ow1WuDdXnryOu2mn+1xfaAdV1L5p7vWb6aR9nKbumkETFLl6kavqbcN1m+x8Y8iS6rpiPl7A05SHPFwGbpf8HEOIBi9rxbe0+i6XPdxcBYKrcr/qvWeeU0Gdi8/N0eRE/+zOoDmwSrLANZxhIRWT/W5cmWsT1AxsTy1TwwuElEVits2mA52HHiwfP4z7OLkH+/8rggYvzMWiGNcG3D7/L1tR9/k93SxeQmyji4OMluA4HDuuLsqi04PGctwIfgiYiIiIjMCtsDRLaBY24SkVV6bOyT8Grsg7hfjyJ60qcozlf8VGXQyB4oeJCDuB1H5etbUaeQcy9dvn87+i+Fn+m0fKrc9onF3+Hc51uMehxERERERKQ9tgeIbAef3CQiq+T+3xndMmPvKA1sOrg6o8kznWRgszA7V75XUliE65v3w/+ptnCu4aF026ILesZ13WdRJSIiIiIi42F7gMh28MlNM+Do5iIHXSXr+17JdMSkQPUiQtF88iDADjix6NtK6zQa0AHO1ash6fAFeDb2KXv/zpELCJ0wAIFDuyicee7gzNVo8+YL6LpmphxrM37nw6c+ybhYVpK5MedyntcL6ZN3rBmvDXo0L5B1Y3vAOrDctiyOJipbGdw0A3Z2dhY3Ux+RuSvMyce+MR+g5zfz0XzSINjZ2+P4wq/LrdNsZE/5u9OnUxRuI2hEd4XBzYzYJOwauhB9flqEbv8NcMbtOGKkI6FSLCuJNMfrhYjXBpGtY3vAOrBOQ5pgcJOIrFZRbj72jf0QPb+eJ5/EFBOgH3vrYYDTs1E91OsQjJif/kD8r8cqfda3cxgeH/sUvFs0kbOoV5QZdwe/DnkLfTYvQtd/vIboKcsRt/1wlRwXERERERGpx/YAkW1gcJOIrL9C8+IS9Ph6HkLGDxD/+sOxN/8PTUf2kE9znl+7Dalnb1T6XMpfMTK42XRED4XBTSErPhm7hjx8grPr6hn4ww6I3cYAJxERERGRuWB7gMj6cUIhIrKJCk3U2A+R8PtphLzSH+0Xv4ymw7shM/6OwsCmkHXrLu79FYMmgzvBwcVJ6bZLA5zZd9LQZfVrCHi6oxGPhIiIiIiItMX2AJF1Y3CTiGxCUV4Bol5cgoTfTiF4XD9U8/VGnJqJgMQ4mi41PODfr73K9bJuJmPX4LeQnZiCzqumI2BwJwOnnoiIiIiI9MH2AJH1sispKSkxdSKISDsF2blYHziap42q3KiY7zgBGhEREZEJsA1A5ojtAzIHfHKTiIiIiIiIiIiILBKDm0RERERERERERGSRGNwkIiIiIiIiIiIii8TgJhEREREREREREVkkBjeJiIiIiIiIiIjIIjG4SURERERERERERBaJwU0iIiIiIiIiIiKySAxuEhERERERERERkUVicJOIiIiIiIiIiIgsEoObREREREREREREZJEcTZ0AIjIfngE+6Lx8GlxqeaIgMxsHZqzC/Su3Kq3X7t2X4f9UG3g0rIutvV5H6vlYrbbR9Lnu6PTZq4h6aQnidx03WLpUretS0wNPbVhYtp6Dmws8G9XDD2HjkH8/C71/eBNudWoAxcUoeJCLowu+Quq5G3JdVcsMQdPj0/XY1B2DsY+PiIiIiMyXseuiqtoOutZDDdE+ULd/ZenWpO5t6u+klLkeH5Gh2ZWUlJQYfKtEZFQF2blYHzja4Nt9auNCxGyMxrUNv6NR/w4Im/oMtvedX2m9eh2CkRl3B/1+WYyol5aWq6Co24ZHgzrosvo1wA449/kWjYKbmqZLm3VDJw2CT0QI9o39UL529nJHfka2/Nu/bzu0nD1c3uTVLTMETdOs67EZ8vhGxXwHJ3dXgx07EREREVlGG0DXuqiqtoOu9WxDtQ9U7V9VulUdrzl9J1VxfGwfkDlgt3Qikly9veAdHoiYn/6Qr+N2HEG1+t7wbOxT6QzdOXIR2Ymp2m/Dzg6RH0/G0QXrUJxfaPB0abNu0PM9cPX7fWWvS2/6grOnO/DI/31ULdOXpmnW59hMeXxEREREZL6qoi6qrO2gaz3UkO0DVftXlW5Vx2tO34k5Hh+RMbBbupkSD9QW5uSZOhmkIUc3F9jZ2Vn0+armVxs5d9JQUlRc9l5Wwj35fmZskkG2ETpxIJKPX0LKmetGSZem69Zp8xhcqlfDzT1/lvt8pxXT4BsZKv/eM/p9jZfpQ9M063tspjo+fbAcJEtjTvcCXj9kDflYH7wGyNavAXOsiyqjbT3U0O0DferBuhxvVX8n5nZ8qrDstk6OVVCuMrhppkRg0xhdDsg4+Ci+ejUea4hG/dvj18FvmTwbBo3sgWsbo8tVBIQD01fK34HPdkWbBaOx95Gbv6plyvTb9h68mvgqXLa19xxk305BVR2bMY7P2FgOkqUxp3sBrx+yhnysD14DZOvXgKmoqosqY+p6qD771/Z4TdE+qMrj0xfLbus0qgrKVQY3iUh6kHAPbvVqws7Bvuzm5eFXW75viG3U79JCDlY99NDDm6sY2Drio0lwq1sTl7/drdM2dVnX0d0VAYMisb3vPKX7FOPWRCyZIAfTzkvL0nhZRTsHvqFyuTbHZ6hjM+TxEREREZFlM0VdVBlN66GGbh9ou399jreq2wdVfXxEpsIxN4lIyk3JQOrZGwgc2kW+FgNSP0hM1bhLurptiADmhpbjsandFPlz9+RVHJ6zpiywKbpLiEGutdmmLusGPB2J1AuxSL92u9xA26JiUMq/T1t5wxc/qpapSrchzpkhjk3f4yMiIiIi61UVdVFldK1nG6p9oG89WNnxmkv7wFjHR2SO+OQmEZU5NHctOn32KsKmD0FBVg4OvPZ52bLIZZNwc/cJ+ROxdAIa9GwNt7o10Pv7BXLdzZHT1G5DldrhTXBx3U690qXJ/oNG9sSV9XvLvefk5Y5uX8yGo6szSopLZEVh35gP1C5Tl25NaXp8uhybvsdHRERERNbN2HVRZW0HferZ6tKiSbrV7V9Vm0fV8ZpL+8BYx0dkjuxKxIitZHYKsnM55qYFqeqxeawtf7h4e6Hr5zOwe8S7sCSWmm5LyevWls/J+pnTOG28fsga8rE+eA2QNV4DtpSvLbWebanpNpdrxpbyuC0ZVQXlKrulE5HJ5aVkWGQFwFLTTURERERkziy1nm2p6SaydAxuEhERERERERERkUVicJOIiIiIiIiIiIgsEicUIq28mLhJo/V2DVkIe2dHPPnDm7i2MRoHpq+stE6ruSMQPnMYDr6+BlfX70XT4d3QaflUtdvOupksZ9smIrJFLIeJiGwb7wNERKbFctj8MLhJWvlj6nKlyzz968mAZW5KOtJjEpCTfB+Xv9uDx0b3Rtz2w2WzWQve4YEIm/oMEn47JQObQtKRCyq33/TZbqjfNRx3jl3it0ZENovlMBGRbeN9gIjItFgOmx8GN0kr13/ar/B9Bzdn9N/2PooLi/D7xE9lYFM4/vY38OsajoilE2VQMv9+FhxcnNB5xVQUPMjFwVn/KNtGVnyy/FHEJyIUPpGhSLsYh8Nz1vJbIyKbxXKYiMi28T5ARGRaLIfND8fcJIPo9MmrqBXaGH8u/g5JB8+VvV/43wCmW53q6PDeOPleq/kjUaNZQxx76/+QnZSqdtvV/Gqj6xezZDA06uWPUJiTx2+NiIjlMBERsT5ORGQ2GBcxHT65SXoLnTwIAc90xI0tB3F+7bZKyxMPnMWlb3Yj+KU+MpgZMr4/4ncdR8zGaLXbFk95dl83By41PbBvzIfIjE3iN0ZExHKYiIhYHyciMhuMi5gWn9wkvfh2DkPrv49C6oU4HJy1Wul6f777L2TEJqH5lKeRn56Nw3M161ouurPXDg/E6Y83IiHqFL8tIiKWw0RExPo4EZHZYFzE9GwiuHnv3j3MnTsXTZs2haurKxo2bIgZM2bgwYMHGDduHOzs7LBq1SpTJ9PieDSog65rZqIgKwe/vbxUZXfxooJCFGTmyL9TzsQg5+7DMTlVCR7XT86gLp7yPPOpZrO000Nh0waj2xezMfTI53Imt2HHlAeeichysRwmndnZIWTCAAzevxwv3Pg3nj2xBm0WjoGjmwtPKpEF4X2AHsU2ABHLYVtl9cHN06dPIywsDB999BGSkpIQEhKCgoICrFixAs899xwuXrwo12vZsqWpk2pRxARC3b+aC+fq1fDHq8uRGXdH5frhrw2Fd1gAUs7egF+3lrIbuyr1OgSj7cIxSL+WgP3TVhg49dZPPE3r07G57Mafl5Zp6uQQkRGwHCZ9tHvnRbRb9CLuX7mFIwu+Quz2wwgZ1w89v50vA59EZP54H6CK2AYgYjlsq+yt/YnNgQMHyqDm7NmzkZiYiJMnT8rXS5YswY4dO3D8+HH55GaLFi1MnVyLErlssgxWnl62QW138VphAWgxfQgSfj+NnU+/gfSY22j/3ji41q6ucH1331ryqcPC3HxEvbxUPhlK2tnUfgp+CH0Ju0e8i+w7aTx9RFaI5TDpqkazBgh+uS9idxzBb+M+wtX1e3H87W9w7O1v4NspTO0/IInIPPA+QBWxDUDEcthWWXVwc/r06bh16xamTp2KZcuWwdPTs2yZ6KYeHh6OwsJCNG7cGF5eXiZNqyUJmTgAgUM6I37XMZz57CeV69o7O6LzimkozM7Dwdn/QFFOvhybUzzxGbFkgsL1xQRCbnVq4MBrnyP9aoIRj8R6ZcUnmzoJRGRELIdJHwGDO8HO3h4XvtxR7n0R5CzIzkXg0C48wURmjvcBUoRtACKWw7bKaoOborv5jz/+iNq1a+ODDz5QuE7r1q3lbxHkLNWtWzf5JKein0mTJsHW+USGos2CF/7bXXyl2vVbvf4caj7uL58Gyb6dIt9LPnYJF9f9ikb92ld6OqTDB+NRp1UQzqzcjPidR412HERElorlMOmrdsumKC4qwr1TV8u9X5RXgNRzsajdMpAnmciM8T5ARGRaLIfNjyOs1Pfff4/i4mKMGjUKHh4eCtdxc3OrFNxcvXo1MjIyyq0nuq8vXrwYAwYMgC1zq1sD3b6YBXtHB9mVreFTbZSum3YhHo5uzgidPAg39/6Jaz9ElVt+8oN/o0HPJ2T39MQD55B7Lx1Bz/dEs+d7ysmGxBObTYZ2Vrr9+J3HVE5gRERkjVgOkyG416uJvNRMFOcXVlqWnZSKeu0eh72TI4oLKi8nItPifYCIyLRYDpsnqw1uRkU9DKZ1795d6Tqiy3rF4KaYcKii9957D3Xq1EGfPn1gy6oH+sHV++E4meEzhqpc9+yqLfDv01aOl3no9TWVlhflPuye3mfzItk9XYz5JRpTguiSLrqyq7Kp7WRk3bqr1/EQEVkalsNkCA5uLijKL1C4TDy9KYh/UOYzuElkdngfICIyLZbD5smupKSkBFaoYcOGMnh56tQphTOhi7E2fX195aRDMTExaNKkicLt3L17F/Xr18eUKVOwfPlyndLSpk0bOYmRNpxK7LGwuJ1O+6Oqt8j+GArsiqtsf9rkj6d/+wRO1Vyxqd0Uo6eLrF9V5nWWg2RpqvpeoOv183TUx3JSvx9bvFJpWde1sxAwKBLf+o/gk5s2ypzysT54DyFrvAbYBiBrv2ZYdtt2HvHx8cGJEyd02ofVPrn54MED+TsnR/FM22I8ThHYFJMMBQQEqOzeLgKhL7zwgs5pEYHNhATtJsZxtnMA6um8S6pitxNvI7+kqMr2x/xBtpDXmc/J0lT1vUDX6yf7ThqqN2sgJ/Gr2DXd3acWclPSGdi0YeaUj/XBewhZ4zXAfE3Wfs0wj1un21VQrlptcFNEfNPS0nDy5ElERESUW5aYmIg5c+bIv1u0aCEnC1LmX//6F4KDg+XTl/qkRZf/WMA8/2FICtT3rV/lT24yf5C153Xmc7I0VX0v0PX6uXf6Gvy6tUTtVkFIPnqx7H0HFyfUat4Yd4787z2yPeaUj/XBewhZ4zXAfE3Wfs0wj9t2HvHRIXZm9cHNXr16yRnTlyxZgt69e6NZs2by/ePHj8unMMVTm4KiLuulLl26JB+Jff/99/VKiy6P1RZk52J94Gi99ktV58rVK3Byd62y/TF/kC3kdeZzsjRVfS/Q9fq58cshtJg+BCHj+5cLbgaN6iXTf33zH1WYUjI35pSP9cF7CFnjNcB8TdZ+zTCPW6crVVCuWm1wc+7cufj3v/+NmzdvIjQ0FI8//jhyc3Nx7do19O3bF40bN8Z//vOfcpMJKXpqUzzVKWZcJ7IkTYZ1gUeDOvJvV28vOetti9ceTgIlJmK6vokNVyIiW3X/Ujwu/d8uBI/rh+7r5uDWvpOoHuSHkHH9kHToPK5vPmDqJBIRkQ7YBiAiW2W1wc0GDRpg//79svt5dHQ0YmNj5Uzoa9euxfjx4xEYGCjXUxbcFPMsrV+/Ht26dYO/v38Vp55IP81G9oRPZGi5956YN1L+lg1XBjeJiGzasbe+RtbNu2g2uhca9HwCuakZuPjVrzi19EdRCTJ18oiISAdsAxCRrbLa4KYgxsrcvn17pfezsrJksNPe3h7NmzdX+Nk//vgDcXFxWLhwYRWklMiwdg1lviUiIuVKiotxfu02+UNERNaBbQAislX2sEHnz5+XT2YGBQXB3d1daZd0Nzc3DBs2rMrTR0REREREREREROrZZHDz7NmzKruki7E5N23ahGeeeQaenp5VnDoiIiIiIiIiIiKCrXdL1zW46erqivv378MatHv3Zfg/1QYeDetia6/XkXo+VuF6w46tRlFeAYpy8+XrMyt/RuzWQ3Cp6YGnNvyvi7ODmws8G9XDD2HjkH8/yyD7Vrb/xP1ndN53qabPdUenz15F1EtLEL/reLll6o5Nm/QTkfnS9Fq2d3ZE24Vj4detJYry8pF6IQ77p64oW977hzfhVqcGUFyMgge5OLrgK6Seu2GQNHoG+KDz8mlwqeWJgsxsHJixCvev3NIoXYo4uDih65qZqB7UQJaruffScXj+l8iMTVK4vl/3lmg1b6ScfKwoJw+H5q5F2oU4vY5b03Qb4/5jinNORNZH0/JP0/uMqnqpItrWRStuX9t7gS5U3T/KrdejFZ6YP1JO1mrn6IBzq39BzMZog6RBVXkusEwnsi22HgNRRZt6vSbHou19zZgY3LRycTsO49zqLej3y2K160ZP+rRShs1Ly8LW3nPKXodOGgSfiBCNLixt9q1s/7ruWxCzhTcb1QvJJy4rXK7u2LRNPxGZJ02v5dZvjJYTqWzuOE2+ljf+R0RP+Bj5Gdnyb/++7eSNXNzoDSFy6URc+W4Prm34HY36d0Cn5VOxve98jdKlzOV/7UFC1Cn59+Mv9UHHjycrHIvLuXo1dF41A7sGvykbg3XbB6PL5zPwS/dZeh23Nuk29P3HVOeciKyLpuWfJvcZdfVSRbSpiyrbvqb3Al2ou388qsuq6dg19G2kXYyTaR28fznidh5F4YNco5bnAst0IttiyzEQdbSp16s7Fl3ua8Zkk93So6Ki5Jib/fv3h7W7c+QishNTDba9oOd74Or3+8x+37CzQ+THk3F0wToU5xfqtH1Dp5+ITEOTa9nRzQVBI3vg5Iffl72Xc7f8E/ylFQHB2dO93IzS3uGBeGrjQgzYtQQDd3+ERgMiNE6fq7eX/HzMT3/I13E7jqBafW94NvbRKF2KiP8ClzZmhbsnr8KjYR2F64r95KVllj3lknz0Iqr51UatsAC1x62MrunWpow2t3NORNZH0/JP7X1Gh3qpRttVs31t7gW6UHf/eJQ4dc7VH8514OTpjty0zLK0Gqs8F1imE9kem42BaECber3KY9HxvmZMNvnkJinWacU0kUdx99Q1/Pn+euSlZJRbXqfNY3CpXg039/xZ5fvXdt+hEwci+fglpJy5rtH6xj42IjJvohEk/iPaYvoQ+HZpIbuHnF62AYkHHg5j8mg55RsZKv/eM/p9+dvZyx2RH03C3tHvISf5vuwWN3D3Utw9cRnZSeorN6IhmHMnDSVFxWXvZSXck++LRpkm6VIn5JV+iP+P4q4iGdcT4VLTU5aDIs0Nn2wjKzuiC0rq2RtKj9sQ51PX+48lnHMisg7aln+GqJcaa/uq7gW60OT+USp60ifovm4OCrPz5BOfv437CMUFhUYtz0X3e23vR0RkW6wpBmJN9zVdMLhpwfptew9eTXwVLhOPMmffTtF4W78OfgsPEu7JMXCemDcSnZdPxd4KGV08yXJtY3S5yoOhqNu/Nvuu8VhDNOrfXm5TU8Y8NiIy/3LQztFeNsbuX70lKxa1mgfgyR/fxJauM+UYZaUOTF8pfwc+2xVtFoyW5ZSoeHg2qove698ot02vwPro9uVsvdKnabpUCZs+RDbuDg1fpHC5GJ/s9/HL0Prvz8OxmivunriCtMs3UVJYpPK4DZVuXe4/qs65aAyryxeGSjsRWf/9Q9vyzxD1UmNsX929QNdzpO7+Idg52CP8tWEyoCmeBBJPWvb8Zj5+6TELtVs2NVp5LvfNMp3IqjAGYv33NV0xuGnBdg4sXwnQh2hYCqIycuHL7Rhy8GGGL+Xo7oqAQZHY3neewfap6f613Xe99sGyYTr00MqysdIiPpoEt7o1cfnb3ZXWN/axEZH5l4OiDCouKsL1n/bL12Jg7az4ZNQM9kfi/spPd4hJECKWTJADjovJEe5fvoWdg97QKX1i3271asrGX2nwzsOvtnw/P+OBVumqSIzT06hfe+wevghFOQ8HK1ck6dB57BqysGzihef++me5yRgUHbcYj0jVMWmabl3uP6rOuSbnvTivwGjnnIis8/6hafmnb71UW5psX9N7gS7nSJP7h/gnkShzRWBTSPkrBtmJKfJ9Y5bnutzfici8MQZi/fc1XdnkmJtUnuiCJ7qElAoY3AkpFWbMCng6EqkXYpF+7XalR5rFQLTG3L+2+xYX1IaW47Gp3RT5I8YXOjxnjdILTdn2ich25KVmIvHAOdTvFi5fixu2h39dpF9NkK9FGSUaT6X8+7SVlQDxIwbRFuv6dg4rW14rtLGcOVYTuSkZsvte4NAu8rWYDOFBYqrsTqcuXarK4JCJAxAwuCN2P/dOufF1FHGr+78Jc8JnDkPiwXNy/6qOW9X+1aVb3/uPKc85mT+fiFC8mLhJBnQqqhcRgucvf4Php79EzeBG5ZaJWaXF5558ZJbSilrOHi7XEU+eKbrmxiZsQN9fFpfL12SZ1JV/mlJXL9W3Lq1u+6ruBYaoxyu7fzxKBBjd69VE9SA/+Vo8QSpm/82IuW3U8lxgmU5EthADMaf7mqnwyU0rF7F0Ahr0bC0rHr2/X4CCrBxsjnw4+2vkskm4ufsE0i7Fo/s/58j/eIrxHjLjknFgWvknZ4JG9sSV9Xsrbb92eBNcXLdT532LH9c61VXuX5d9q/LovlVtX1X6ichyaFoWHZ67Fh0/mSK7Z5QUl8jXpeN9OXm5o9sXs+Ho6iyXicbUvjEfyGX56Q+w94X30fatMWi7cCzsnRxkQy7qpaUap/HQ3LVytkLRbVCk78Brn5ctU5UuZeWgu28ttHv7RWTEJqHPprfle0X5hdjR/2+VjltoNXeE/C+sKIfv/nkFh2atVnvcqvavLt363n9Mec7JcjXo1RrdvpglJ4jaPfwdZMbdKVsmnlrw79MOGTcS4dsxVM4AmnXrrsbbFteQCOwk/HYKUeM+0urpODJP6sq/R8tRfeqMhqhLK6PuXqBrXfpRyu4fFdN4aM4adFs7S55LO3s7HHljXdnTlcYszwWW6US2hTEQ/e9rlhgPsSsR04aT2SnIzsX6wNEwZy7eXuj6+QzsHvGuTe1bkVEx38HJ3bXK9mcJ+YOsU1XmdeZz8y4HTb1/c1TV9wJVbPn6EU9u9tm8CMcXfYvza7aWPREhxnNNj7kt86yYgORRwa/0Q7tFL2J73/lyzKqzK3+Wk44oenKz5evDsa3PPNm1Vmi/+GUEj+uHG78cxP5pK+UkKZbMnPKxtV8DrEubJ3O+BiwhX5PtMeQ1Y+55nOW2+Zar7JZOOhMzeZmqUWvKfRMRmQNTl4Om3j+Rph4b8yS6rJqOlLM35OD3FQObpU8Ii7EDxayft/aeRNPnuovBXVVuVzyp1nnlNBnYvPzdHkRP/sziA5tUtViXJiKyLCy3zReDm0RERERklcKmDZaD5ScePI//PLsI+fcrjyslxs+sFdII1zb8Ll9f+/E32S29fpcWSrfr4OIkh1QIHNYVZ1dtweE5awF2hiIiIiIyCY65SURERERW57GxT8KrsQ/ifj2K6Emfojhf8VOVQSN7oOBBDuJ2HJWvb0WdQs69dPn+7ei/FH6m0/KpctsnFn+Hc59vMepxEBEREZFqfHKTiIiIiKyOmJ1ZyIy9ozSw6eDqjCbPdJKBzcLsXPleSWERrm/eD/+n2sK5hofSbYsu6BnXy89iSkRERERVj8FNIiIiIrI6YlKg2/vPovnkQWizcIzCdRoN6ADn6tWQdPgCPBv7lP3cOXJBBj4Dh3ZR+LmDM1cjJ/k+uq6ZCf9+7Y18JERERESkCrulmylHNxc5oxRZzvdV1ftj/iBrz+vM52RpqvpeQKoV5uRj35gP0POb+Wg+aRDs7O1xfOHX5dZpNrKn/N3p0ykKtxE0ojsurttZ6f2M2CTsGroQfX5ahG5rZsrJhOJ2HOFXYkZ4DyF98o65Yr4ma79mmMetk2MVlKsMbpopOzs7OLm7mjoZZKaYP8gWMJ8Tkb6KcvOxb+yH6Pn1PIROGCAnQD/21sMAp2ejeqjXIRgxP/2B+F+PVfqsb+cwPD72KXi3aCJnUa8oM+4Ofh3yFvpsXoSu/3gN0VOWI277YX5pZoL3ELJGzNdk7ZjHSVcMbhIRERGRdQc4X1yCHl/PQ8j4AaLlhGNv/h+ajuwhn+Y8v3YbUs/eqPS5lL9iZHCz6YgeCoObQlZ8MnYNefgEZ9fVM/CHHRC7jQFOIiIioqrEMTeJiIiIyOoDnFFjP0TC76cR8kp/tF/8MpoO74bM+DsKA5tC1q27uPdXDJoM7gQHFyel2y4NcGbfSUOX1a8h4OmORjwSIiIiIqqIwU0iIiIisnpFeQWIenEJEn47heBx/VDN1xtxO4+q/IwYR9OlhofaSYOybiZj1+C3kJ2Ygs6rpiNgcCcDp56IiIiIlLErKSkpUbqUiIiIiCxOQXYu1geONnUyyAKJCQs57jsRERFZEj65SURERERERERERBaJwU0iIiIiIiIiIiKySAxuEhERERERERERkUVicJOIiIiIiIiIiIgsEoObREREREREREREZJEY3CQiIiIiIiIiIiKLxOAmERERERERERERWSQGN4mIiIiIiIiIiMgiMbhJREREREREREREFonBTSIiIiIiIiIiIrJIDG4SERERERERERGRRXI0dQKIiIiIyPx5Bvig8/JpcKnliYLMbByYsQr3r9wqW+7g4oSua2aielADFOXmI/deOg7P/xKZsUll6/T+4U241akBFBej4EEuji74Cqnnbqhdpk+6SrV792X4P9UGHg3rYmuv15F6PrbSOk2f645On72KqJeWIH7XcY3SrS9N06/PMRgz/URERESmxuAmEREREakVuXQirny3B9c2/I5G/Tug0/Kp2N53frl1Lv9rDxKiTsm/H3+pDzp+PBm7hi4sWx494WPkZ2TLv/37tpNBOBGkU7dM33QJcTsO49zqLej3y2KF2/FoUAfNRvVC8onLlZbpmjZNaJp+fY7BmOknIiIiMjUGN81ASUkJCnPyTJ0MMjBHNxfY2dkZZFvMI2Ttedwc8bqzLNaeH03N1dsL3uGB2D3iXfk6bscRdHh/HDwb+5Q9mVmUV1AW2BTunryK5pMHldtOaYBNcPZ0FxeaRsv0SVepO0cuKt+QnR0iP56MowvWoe3CsZUW65I2TWiTfn2OwVjpN3csx8ma7yXM32Qr1wnzumVxNFE5yuCmGRCBzfWBo02dDDKwUTHfwcnd1SDbYh4ha8/j5ojXnWWx9vxoatX8aiPnThpKiorL3stKuCffVxSEE0Je6Yf4//yva3epTiumwTcyVP69Z/T7Gi8zVLoUCZ04EMnHLyHlzHWl62ibNk0YKv2aHIMx0m/uWI6TNd9LmL/JVq4T5nXLMspE5SiDm0RERERkUGHTh8inDw8NX1Rp2YHpK+XvwGe7os2C0dj7SKBN1TJjqfFYQzTq3x6/Dn5L5Xq6pK3ftvfg1cRX4bKtveegKo/BFOeWiIiIqCowuElEREREKj1IuAe3ejVh52Bf9pShh19t+X5FoZMGoVG/9tg9fBGKcvKVbjNmYzQilkyAS00P5KVlabxM13QpU699sJygZ+ihh8E/MfFOxEeT4Fa3Ji5/u1urdFe0c+AbKpcX5xXonX5tj0Gb9BMRERFZAntTJ4CIiIiIzFtuSgZSz95A4NAu8rWY+OZBYmqlrtMhEwcgYHBH7H7unXLjPArOXu4ykFfKv09bGVwTP6qWlXapFhPh6JouVUTwb0PL8djUbor8EWOFHp6zpiwoqGvaNGGI9Ks7BnXpJyIiIrJ0fHKTiIiIiNQ6NHetnGVbdDkvyMrBgdc+L1sWuWwS7hy9iHZvv4iM2CT02fS2fL8ovxA7+v9N/u3k5Y5uX8yGo6szSopLZGBv35gP1C4Taoc3wcV1O3VK183dJ+RPxNIJaNCzNdzq1kDv7xfIdTdHTlN73PqkTROapl/Q5RjUpZ+IiIjI0tmViKmnyKQKsnM5oZAVMuRAuswjZI4sadB9XfC6syzWnh9tOf+6eHuh6+czymYUNyfmnDZdWdO1ZE3XAVUNS8r/zN9kK9cJ87plGWWicpTd0omIiIjIbOWlZJht8NCc00ZERERkKxjcJCIiIiIiIiIiIovEMTdJKy8mbtJovV1DFsLe2RFP/vAmrm2MxoHpD2fvfFSruSMQPnMYDr6+BlfX70XT4d3QaflUtdvOupksB8snIrJFLIeJiGwb7wNERKbFctj8MLhJWvlj6nKlyzz968mAZW5KOtJjEpCTfB+Xv9uDx0b3Rtz2w2WD4Qve4YEIm/oMEn47JQObQtKRCyq33/TZbqjfNRx3jl3it0ZENovlMBGRbeN9gIjItFgOmx8GN0kr13/ar/B9Bzdn9N/2PooLi/D7xE9lYFM4/vY38OsajoilE2VQMv9+FhxcnNB5xVQUPMjFwVn/KNtGVnyy/FHEJyIUPpGhSLsYh8Nz1vJbIyKbxXKYiMi28T5ARGRaLIfND8fcJIPo9MmrqBXaGH8u/g5JB8+VvV/43wCmW53q6PDeOPleq/kjUaNZQxx76/+QnZSqdtvV/Gqj6xezZDA06uWPUJiTx2+NiIjlMBERsT5ORGQ2GBcxHT65SXoLnTwIAc90xI0tB3F+7bZKyxMPnMWlb3Yj+KU+MpgZMr4/4ncdR8zGaLXbFk95dl83By41PbBvzIfIjE3iN0ZExHKYiIhYHyciMhuMi5gWn9wkvfh2DkPrv49C6oU4HJy1Wul6f777L2TEJqH5lKeRn56Nw3M161ouurPXDg/E6Y83IiHqFL8tIiKWw0RExPo4EZHZYFzE9Kw+uHnv3j3MnTsXTZs2haurKxo2bIgZM2bgwYMHGDduHOzs7LBq1SpTJ9MieTSog65rZqIgKwe/vbxUZXfxooJCFGTmyL9TzsQg5+7DMTlVCR7XT86gLp7yPPOpZrO0ExHZEpbDRES2jfcBIiLTYjlsHqy6W/rp06fRt29fJCUloVq1aggJCcHt27exYsUKxMTEIDX14XiPLVu2NHVSLY6YQKj7V3PhXL3aw+7icXdUrh/+2lB4hwUg5ewN+HVrWdaNXZl6HYLRduEYpF9LwP5pK4xwBFbIzk52+X/shd6ygM1NycCNbYdweumPHKeUyAqxHCZ9hE0bDO+wJvBu0QSejeoh62YyNrWbwpNKZEF4HyC12D4gYjlsI+yt+YnNgQMHysDm7NmzkZiYiJMnT8rXS5YswY4dO3D8+HH55GaLFi1MnVyLE7lssgxWnl62QW138VphAWgxfQgSfj+NnU+/gfSY22j/3ji41q6ucH1331ro9sVsFObmI+rlpfLJUFKv3Tsvot2iF3H/yi0cWfAVYrcfRsi4fuj57XxZsSEi68JymPQhhpTx6dhcjmWdl5bJk0lkgXgfIHXYPiBiOWwrrDa4OX36dNy6dQtTp07FsmXL4OnpWbZMdFMPDw9HYWEhGjduDC8vL5Om1dKETByAwCGdEb/rGM589pPKde2dHdF5xTQUZufh4Ox/oCgnX47NKZ74jFgyQeH6YgIhtzo1cOC1z5F+NcGIR2I9ajRrgOCX+yJ2xxH8Nu4jXF2/F8ff/gbH3v4Gvp3C5JOyRGQ9WA6Tvja1n4IfQl/C7hHvIvtOGk8okYXhfYDUYfuAiOWwLbHK4ObFixfx448/onbt2vjggw8UrtO6dWv5WwQ5H7V//3707NlTfrZGjRro0KEDNm/eXCXptgQ+kaFos+CF/3YXX6l2/VavP4eaj/vLIFv27RT5XvKxS7i47lc06te+UtCtwwfjUadVEM6s3Iz4nUeNdhzWJmBwJ9jZ2+PClzvKvS+CnAXZuQgc2sVkaSMiw2I5TIaQFZ/ME0lkoXgfIE2wfUDEctiWWOWYm99//z2Ki4sxatQoeHh4KFzHzc2tUnDzr7/+Qu/evdGlSxd8/fXXcHJywj//+U8MGzYMW7duxYABA2DL3OrWQLcvZsHe0UE+IdjwqTZK1027EA9HN2eETh6Em3v/xLUfosotP/nBv9Gg5xOye3rigXPIvZeOoOd7otnzPeVkQ+KJzSZDOyvdfvzOYxxH8hG1WzZFcVER7p26Wu48FeUVIPVcLGq3DNT9iycis8FymIjItvE+QJpi+4CI5bAtscrgZlTUw0Ba9+7dla4juqxXDG6Kpz3FGJxbtmyBu7u7fK9Xr15o0qQJ1q9fb/PBzeqBfnD1fjhOZviMoSq/g7OrtsC/T1s5Xuah19dUWl6U+7B7ep/Ni2T3dNGVul67x+Uy0SVddGVXZVPbyci6dVflOrbEvV5N5KVmoji/sNKy7KRUeW7tnRxRXFB5ORFZDpbDRES2jfcB0hTbB0Qsh22JXUlJSQmsTMOGDWXw8tSpUwpnQhdjbfr6+spJh8Ss6SJ4Kbz++uv48ssvcf/+fRnkLNW8eXOEhobK4Kcu2rRpIycyUsapxB4Li9vptG0yX4vsj6HArtgg21KXR4YcXgV7JwdsajO50rJOK6ah6bNd8e/HxiA/I9sg6SEydB43RyybLYu150dj5t+nf/sETtVcOVs6Wd21xHKcrDn/s31AtnKdsCy3nfzh4+ODEydO6PRZq3xy88GDB/J3To7iWbZFkFIENsUkQwEBAWXvv/DCC/j888/l7Orz5s2Do6Mj1q5di6tXr2L16tU6p0cENhMSlE+M42znANTTefNkpm4n3kZ+SZFBtqUujxTl5MGpmuLZ5x1cnOTvwpx8g6SFyBh53ByxbLYs1p4ftcX8S7qypmuJ1wFZc/5n+4Bs5TphWW5ZbpuoHLXK4KaI9qalpeHkyZOIiIgotywxMRFz5syRf7do0aLcE5qii/q+ffswZMgQfPrpp/K9atWqYePGjXIcTn3So+4/EbCMfxCSFur71jfok5uq8oiY6bZ6swZytvmKXdPdfWohNyWdXdLJrPO4OWLZbFmsPT9qi/mXdGVN1xKvA7Lm/M/2AdnKdcKy3Hbyh4+a2JnNBTfFOJlixvQlS5bICYKaNWsm3z9+/Lh8OlM8tSlU7LIuntB87rnn0LZtW0yZMgUODg5yrM0RI0Zg+/bt6NGjh07pUfdYrZjNen3gaJ22TebrytUrcHJ3Nci21OWRe6evwa9bS9RuFYTkoxfLPbVZq3lj3Dnyv/eIzDGPmyOWzZbF2vOjtph/SVfWdC3xOiBrzv9sH5CtXCcsyy3LFROVo/awQnPnzoW3tzdu3rwpx8oMCwtDUFAQ2rVrJ8fXLA1SPjqZkPD3v/9dTiT0888/o2/fvnjyySfxzTffoH379rKrOpG5uvHLIZQUFyNkfP9y7weN6iULluub/zBZ2oiIiIiIqGqxfUBEtsQqn9xs0KAB9u/fL7ufR0dHIzY2FiEhIXL8zPHjxyMwMFBhcPPs2bPyPTHWZsUJgVauXFmlx0CkjfuX4nHp/3YheFw/dF83B7f2nUT1ID+EjOuHpEPncX3zAZ5QIiIq02RYF3g0qCP/dvX2gr2TI1q8NlS+zrp1F9c38Z9iRESWjO0DIrIlVhncFIKDg2VX8oqysrJksNPe3l7Ogl6xf//p06flbOqPBjhFd3Y/P78qSTeRro699TWybt5Fs9G90KDnE8hNzcDFr37FqaU/AiUlPLFERFSm2cie8IkMLXdGnpg3Uv6W/xRjcJOIyOKxfUBEtsJqg5vKnD9/HiUlJXIcTtEF/VGvvvoqhg8fjsGDB2PixIlyzM1///vf8unP5cuXmyzNRJoQ3dLPr90mf4iIiFTZNXQhTxARkZVj+4CIbIXNBTdF13NFXdKFZ599Ftu2bZMTEY0dOxZFRUUyCComFXr++edNkFoiIiIiIiIiIiJShsHNCgYMGCB/iIiIiIiIiIiIyLwxuGlF2r37MvyfagOPhnWxtdfrSD0fq3C9YcdWoyivAEW5+fL1mZU/I3brIbjU9MBTG/7XTc3BzQWejerhh7BxyL+fZZB9K9t/4v4zOu9bE+aePiKyPr1/eBNudWoAxcUoeJCLowu+Quq5GzqXT02f645On72KqJeWIH7XcZX7dnBxQtc1M1E9qIEsy3LvpePw/C+RGZtUaV2/7i3Rat5IOaFMUU4eDs1di7QLcXrdEwx9/NocOxGRodk7O6LtwrHw69YSRXn5SL0Qh/1TV2hV7vr1aIUn5o+EnZ0d7BwdcG71L4jZGG24slzF9pWV85rQ9LOeAT7ovHwaXGp5oiAzGwdmrML9K7eqpCxXtW9Nvjsisn62HitpqkH5q6y81OZeZEo2F9yMioqCtYrbcRjnVm9Bv18Wq103etKnlS6qvLQsbO09p+x16KRB8IkI0eiC0Wbfyvav6741Ye7pIyLrEz3hY+RnZMu//fu2kxUKUaHRpXwSs1o3G9ULyScua7z/y//ag4SoU/Lvx1/qg44fT640zqJz9WrovGoGdg1+UzYE67YPRpfPZ+CX7rP0uicY8vh1OXYiIkNq/cZoOTnj5o7T5Gv5jxsty90uq6Zj19C3kXYxTpZrg/cvR9zOoyh8kKt3Wa5q+/aODkrLeXVU3SMqilw6EVe+24NrG35Ho/4d0Gn5VGzvO79KynJV+9b0uyMi62bLsRIPDctfVeWlpvciU7I3dQLIcO4cuYjsxFSDbS/o+R64+v0+s9+3NaSPiKxPaWBPcPZ0l5UFnconOztEfjwZRxesQ3F+oUb7Fv/xLa2ACHdPXoVHwzqV1vNs7IO8tMyyJ1ySj15ENb/aqBUWoHe5Z5Dj1+HYiYgMydHNBUEje+Dkh9+XvZdz977W5a4oAp2rP5zM1MnTHblpmWrLNU3LclXb16acr0jTz7p6e8E7PBAxP/0hX8ftOIJq9b3l59WV5eJzT21ciAG7lmDg7o/QaECE2nRpum9Nvzsisn42Gyux06wuraq81OZeZEo29+QmPdRpxTSRz3H31DX8+f565KVklDs1ddo8Bpfq1XBzz59Vvn9j79sa0kdElkGUJb6RofLvPaPf12kboRMHIvn4JaScua5zOkJe6Yf4/1TugpJxPREuNT1luXb3xGU0fLKNDESKbjOpZ2/oXe7pe/yGOHYiIn2IIJl4OqbF9CHw7dJCdsk7vWwDEg88nKRU03I3etIn6L5uDgqz8+QTkb+N+wjFBYUGKctVbV/Tcl4RTT8rAp45d9JQUlRc9l5Wwj35vui2qKwsd/ZyR+RHk7B39HvISb4vu5UP3L1U7is7SbNAgKp9i8a6Lt8dEdk2a4qVaFqX1uZep+peZEoMblqIftveg1cTX4XLxCPK2bdTNN7Wr4PfwoOEe3I8nifmjUTn5VOxt0KjU0Ttr22MLldRMBR1+9d234Y8N8ZIHxFZH03LnQPTV8rfgc92RZsFoyuVterUeKwhGvVvL8slXYVNHyIrLIeGL6q0TIxN9vv4ZWj99+fhWM0Vd09cQdrlmygpLCq3nq7lnj7Hb4hjJyLStzy3c7SXwbz7V2/JRmat5gF48sc3saXrTDnumCblrp2DPcJfGyYDjuIJHvGkYc9v5uOXHrOQl5qpd1mubvualPOKaHqP0LUsF410z0Z10Xv9G+Xe9wqsL4Ob+tbxdfnuiMjyMFaimDZ1aU3LS1X3IlNjcNNC7BxY/qavDxG4E0TF5MKX2zHk4MPGZylHd1cEDIrE9r7zDLZPTfevy74NeW6MkT6qWj4RoeizeRGOL/oW59dsLbesXkQIen49D4U5+dgzcrEcl6qUGCQ54OmOuL3/LHYrKaxbzh6Olq8Px7Y+85DyV0y5ZSETB6DtW2OQfOIK9r3wfrkuuWR9tC13xKQOEUsmyMHIxZg9mqrXPlhWNIYeWlk29k3ER5PgVrcmLn+7W+3nxZg8jfq1l3m6KOfhwOQVJR06j11DFpYNJP7cX/8sNwmEIco9XY5f32MnIjJEeS6eXikuKsL1n/bL12JitKz4ZNQM9kfi/rMalbuikehWr6YMPAqiDpGdmCLfT/zjjN5lubrtqyvnVdHks6LuLPYvgqyl/wTz8Kst36/fpYXSslwsv3/5FnYOekPne62qfednPNDquyPrwfaAbWGsRDFt6tKizFRXXmrSrjAljrlpY0T3DNEFpFTA4E5IqTB7bcDTkUi9EIv0a7crPR4tJoUw5v6NuW9jpo/MX4NereWTAXn3s/Dr0wvKBTZFwMW/Tztk3EiEb8dQOeiyNlrNHYF2b7+I29F/YfeIdxjYJFmOiMZWKf8+bWVQT5vApiAqHhtajsemdlPkjxjj5vCcNWUVElVlowi4BwzuiN3Pqc6TbnX/N1h4+MxhSDx4rtzsh7qUy4Y4fnXHTkRUFcSTj4kHzqF+t3D5WjQUPfzrIv1qgsblrmg0uteriepBfvK1eOpFzHSbEXPbIGW5uu2rK+dV7V/dZ4XclAzZTT1waBf5Wkzq8yAxVa6nqiwXk1uIc+nbOaxsW7VCG8uZ2TWlat/afHdkG9geIFuKlVzWoi6trrzUtF1hSnxy04pELJ2ABj1by0pI7+8XoCArB5sjH850FblsEm7uPoG0S/Ho/s858r+bYhyHzLhkHJhW/snNoJE9cWX93krbrx3eBBfX7dR53+LHtU51lfvXZd+GOjf6pI/Mm7gxiOEF0mNuY/eId+XYTI9qMrQL7J0c5Mx0oltD0xHd5Rgjmmi/+GUEj+uHG78cxP5pK7UeP4usk5OXO7p9MRuOrs4oKS6Rja99Yz4oW/5ouaOqfFJHWdno7ltLBtwzYpPQZ9Pb8r2i/ELs6P+3SvsXwXnxn11R7t398woOzVpdblu6lMtVdfxERFXh8Ny16PjJFDm8hijTxOvSMSFLy7OUs9eVlruiS9+hOWvQbe0s+Xk7ezsceWNdWW8hQ5TlqravrpxXVZ6r+my5/c9di06fvSq7LIpy/MBrn6s9r/npD7D3hfdlz5e2C8fKuphIc9RLS6ENVftW9d2RbWF7wLYxVlLZo2W4qvJS3b3IXNiVlCiZvpSqTEF2LtYHjjbrM+7i7YWun8+QgSFb2rc+RsV8Byd3V5vJI+baDeWxMU+iwwev4N7pGOwZ9Z4cKLmiQfs+Rl5qBv7z7CI5GL93iybyv1sVZ3d+tFu6eFRfVKQDh3XF5e/24PDcL5TOBm2tDJnHzZG5X3csl20rP1pb/iXzZU3XkiVcB6au55p6/+bGkvK/JeRvU2F7wLquE3PP66yTm0c5ym7ppBExQ5epKj2m3DdZtrBpg+U4f4kHz8vApaLAphh0v1ZII1zb8Lt8fe3H32S3dDFGlDIOLk7yCV8R2Dy7agsOz1lrc4FNMj2Wy0REls/U9VxT75/I2NgeIGNjndw8sFs6EVmlx8Y+Ca/GPoj79ajsbl6cr7i7uJgFuuBBDuJ2HJWvb0WdQs69dPm+GENTkU7Lp8ptn1j8Hc59vsWox0FERERERNpje4DIdvDJTSKySmJgfSEz9o7SwKaDqzOaPNNJBjYLs3PleyWFRbi+eT/8n2oL5xoeSrctxtbMuM6JpYiIiIiIzBHbA0S2g8FNIrJKZ1f+jNv7z6L55EFos3CMwnUaDegA5+rVkHT4wsOZRf/7c+fIBRn4LJ15s6KDM1cjJ/k+uq6ZCf9+7Y18JEREREREpC22B4hsB7ulmwFHNxc56CpZ3/dKplOYky9nZ+75zXw0nzQIdvb2OL7w63LrNBvZU/7u9OkUhdsIGtFd4eyhYqa4XUMXos9Pi9BtzUxET/4McTuOGOlIyFRYNlsWlrmVzwfrFmTr1xKvA9Ilz5D1YHvAOrAstyyOJipHGdw0A3Z2dhYzKx+RJSnKzce+sR+i59fzEDphAOzsgGNvPQxwejaqh3odghHz0x+I//VYpc/6dg7D42OfkjOnp5y5Xml5Ztwd/DrkLTkze9d/vIboKcsRt/1wlRwXVQ2WzWTJmH+JeB0QEdsD1oB1GtIEg5tEZP0BzheXoMfX8xAyfoC4O+LYm/+HpiN7yKc5z6/dhtSzNyp9LuWvGBncbDqih8LgppAVn4xdQx4+wdl19Qz8YQfEbmOAk4iIiIjIXLA9QGT9OOYmEVk9UaGJGvshEn4/jZBX+qP94pfRdHg3ZMbfURjYFLJu3cW9v2LQZHAnOLg4Kd12aYAz+04ausma2+oAABpUSURBVKx+DQFPdzTikRARERERkbbYHiCybgxuEpFNKMorQNSLS5Dw2ykEj+uHar7eiNt5VOVnxDiaLjU81E4alHUzGbsGv4XsxBR0XjUdAYM7GTj1RERERESkD7YHiKyXXUlJSYmpE0FEqhVk52J94GieJjIrYrISjhdMREREVPXYPiBTYRuAzBGf3CQiIiIiIiIiIiKLxOAmERERERERERERWSQGN4mIiIiIiIiIiMgiMbhJREREREREREREFonBTSIiIiIiIiIiIrJIDG4SERERERERERGRRWJwk4iIiIiIiIiIiCwSg5tERERERERERERkkRjcJCIiIiIiIiIiIovE4CYRERERERERERFZJAY3iYiIiIiIiIiIyCI5mjoBRGQ6ngE+6Lx8GlxqeaIgMxsHZqzC/Su3tFrPr3tLtJo3EvZOjijKycOhuWuRdiEODi5O6LpmJqoHNUBRbj5y76Xj8PwvkRmbJD/X+4c34VanBlBcjIIHuTi64CuknrthsDSrW9fe2RFtF46FX7eWKMrLR+qFOOyfukKvz5nLd2Kux0ZERERE5k3Tumi7d1+G/1Nt4NGwLrb2eh2p52PLlimr57vU9MBTGxaWrefg5gLPRvXwQ9g45N/PMki61K2rLG2q2i76pLsq2wCq2lemPj4iY7MrKSkpMfpeiEgvBdm5WB842uBn8amNCxGzMRrXNvyORv07IGzqM9jed77G6zlXr4Yhh1Zh1+A35U21bvtgRHw4Hr90nyVvoD4dmyMh6pTcxuMv9UHjARHYNfThjdPZyx35Gdnyb/++7dBy9nBZMTJUmtWt23bRi7B3sJc3fUFUBHLu3tfrc+bwnVTlsY2K+Q5O7q4GO3YiIiIisoz2Qb0OwciMu4N+vyxG1EtLywU3Na3nh04aBJ+IEOwb+6HB0qVuXWVpU9d20TXdVdkGUHXeDXl8bAOQOeKTm2ZKxJwLc/JMnQzSkKObC+zs7CzqfLl6e8E7PBC7R7wrX8ftOIIO74+DZ2Ofsqcr1a0ngpt5aZll/y1MPnoR1fxqo1ZYAFLP3ii7eQp3T15F88mDyl6X3ngFZ093kekNlmZ16+bcSUPQyB7Y+MTEsvVLg3i6fs5cvhNzPTZFWM6RLbHE+wQREdkWberad45cVLodTev5Qc/3wMn31xs0XerWVZa2orwClW0XXdJdlW0AVcdm6uPTF9sMlsXRRHVeBjfNlAhsGuM/cWQclvjfKxGEFAGtkqLisveyEu7J9x+9iapaL+XMdbjU9ESdNo/h7onLaPhkG3kjFd1TRHDzUSGv9EP8f46Xe6/TimnwjQyVf+8Z/b7B0qxuXVHgii4WLaYPgW+XFrJrxullG5B44KzOnzOX78Rcj00RlnNkSyzxPkFERLZFm7q2Ourq+aL94FK9Gm7u+dOg6dJkXU3aIIraLtqmuyrbANocW1Ufn77YZrAso0xU52Vwk4h0JsZ5+X38MrT++/NwrOaKuyeuIO3yTZQUFpVbL2z6EPkfxUPDF5V7/8D0lfJ34LNd0WbBaOzVIMBpCHaO9jIAe//qLfz5/nrUah6AJ398E1u6ztT5c2LcGlX6bXsPXk18FS7b2nsOsm+n6HVMmqTRWMdGRERERKRNPV/0GLq2MbpcoM5c0qas7aJLuquqDaBN+8qQx0dkLhjcJLJRDxLuwa1eTdg52JfdvDz8asv3tVkv6dB57BqysGxCmuf++me5wa/FmC2N+rXH7uGLUJSTrzAtYtyYiCUT5GDWeWlZeqdZ3br5GQ9QXFSE6z/tl++Lgbaz4pNRM9hfToaky+cS96t+wnHnwDdULtfm+Mzt2IiIiIjI8mlT19aUonq+o7srAgZFYnvfeQZPlzbrKkqbqraLtumuyjaAJsdmjOMjMhf2pk4AEZlGbkqG7DoeOLSLfC0GpH6QmFqpa4e69dzq1ihbN3zmMCQePFe2LGTiAAQM7ojdz71TfgwYL3d5Yy7l36etvOmW3nhFdwoxCLauaVa3bl5qJhIPnEP9buFymXhi0cO/LtKvJuj8OVXprsrvxFjHRkRERETWTZu6tjLq6vlCwNORSL0Qi/Rrt8t91thtAHVpU9Z20TXdVdkG0OS863p8RJaAs6Xb2Ox3ZJnjShgrP3gF1kenz16V42YWZOXgwGuf4/6leLksctkk3Nx9Qv6oW69e+2D5H8S7f17B0TfWyZulu28tDD/5BTJik1CYlSPXLcovxI7+f0O1BrXR7YvZcHR1RklxibxRn3jn27JZFp+J/hT7p69Cyl8xOqdZ3boicNfxkylwreUp0/DXpxsRt+OoXp9Tle6q/E6McWzGyPcs58iWcMxNIiKypvZBxNIJaNCztXzQQUwwKtbdHDlNbT1f6Lf1PVxZvxfXfvyt3L6N3QZQlTZVbRd90m3o867LsQn6HJ+p6zRsM1iWUSYac5PBTTPFC9iyWGpw0xy5eHuh6+czymYBtBSWmm59MLhJVHXXCxERkTW3Dyy1Lm2p6dYHg5tkjnVedksnIrOSl5JhkZUDS003EREREZGpWWpd2lLTTWRtGNwkIiIiIiIiIiIii8TgJhEREREREREREVkkR1MngCzLi4mbNFpv15CFsHd2xJM/vIlrG6NxYPrKSuu0mjtCzq598PU1uLp+L5oO74ZOy6eq3XbWzWRsajdFp/QTEVk6lsNERERERKbD+rj5YXCTtPLH1OVKl3n615MBy9yUdKTHJCAn+T4uf7cHj43ujbjth8tmrxO8wwMRNvUZJPx2SgY2haQjF1Ruv+mz3VC/azjuHLvEb42IbBbLYSIiIiIi02F93PwwuElauf7TfoXvO7g5o/+291FcWITfJ34qA5vC8be/gV/XcEQsnSiDkvn3s+Dg4oTOK6ai4EEuDs76R9k2suKT5Y8iPhGh8IkMRdrFOByes5bfGhHZLJbDRERERESmw/q4+eGYm2QQnT55FbVCG+PPxd8h6eC5svcL/xvAdKtTHR3eGyffazV/JGo0a4hjb/0fspNS1W67ml9tdP1ilgyGRr38EQpz8vitERGxHCYiIiIiMhuMi5gOn9wkvYVOHoSAZzrixpaDOL92W6XliQfO4tI3uxH8Uh8ZzAwZ3x/xu44jZmO02m2Lpzy7r5sDl5oe2DfmQ2TGJvEbIyJiOUxEREREZDYYFzEtm3hy8969e5g7dy6aNm0KV1dXNGzYEDNmzMCDBw8wbtw42NnZYdWqVaZOpkXy7RyG1n8fhdQLcTg4a7XS9f5891/IiE1C8ylPIz89G4fnata1XHRnrx0eiNMfb0RC1CkDppyIyDqwHCYiIiIiMh3Wx03P6p/cPH36NPr27YukpCRUq1YNISEhuH37NlasWIGYmBikpj7sFt2yZUtTJ9XieDSog65rZqIgKwe/vbxUZXfxooJCFGTmyL9TzsQg5+7DMTlVCR7XT86gLp7yPPOpZrO027KwaYPhHdYE3i2awLNRPc4qT2QDWA4TERGRMmwfEBkf6+Pmwd7an9gcOHCgDGzOnj0biYmJOHnypHy9ZMkS7NixA8ePH5dPbrZo0cLUybUoYgKh7l/NhXP1avjj1eXIjLujcv3w14bCOywAKWdvwK9bS9mNXZV6HYLRduEYpF9LwP5pKwyceusknqD16dhcdt3PS8s0dXKIyMhYDhMREZEqbB8QGRfr4+bDqoOb06dPx61btzB16lQsW7YMnp6eZctEN/Xw8HAUFhaicePG8PLyMmlaLU3ksskyWHl62Qa13cVrhQWgxfQhSPj9NHY+/QbSY26j/Xvj4Fq7usL13X1rodsXs1GYm4+ol5fKJ0NJvU3tp+CH0Jewe8S7yL6TxlNGZOVYDhMREZEqbB8QGRfr4+bDaoObFy9exI8//ojatWvjgw8+ULhO69at5W8R5HzU3r170aFDBzk+Z926dTFp0iSkp6dXSbotQcjEAQgc0hnxu47hzGc/qVzX3tkRnVdMQ2F2Hg7O/geKcvLl2Jziic+IJRMUri8mEHKrUwMHXvsc6VcTjHgk1iUrPtnUSSCiKsJymIiIiNRh+4DIeFgfNy9WG9z8/vvvUVxcjFGjRsHDw0PhOm5ubpWCm9HR0ejTpw/8/Pzw888/47333sOmTZvwzDPPoKSkBLbOJzIUbRa88N/u4ivVrt/q9edQ83F/HHv7G2TfTpHvJR+7hIvrfkWjfu0rdU/v8MF41GkVhDMrNyN+51GjHQcRkaViOUxEREREZDqsj5sfq51QKCoqSv7u3r270nVEl/WKwc133nkHQUFB2LhxI+ztH8Z+vb29MXToUDlG54ABA2Cr3OrWQLcvZsHe0QGxO46g4VNtlK6bdiEejm7OCJ08CDf3/olrPzz8Pkqd/ODfaNDzCdk9PfHAOeTeS0fQ8z3R7PmecrIh8cRmk6GdlW4/fucxlRMYERFZI5bDRERERESmw/q4ebLa4GZcXJz83ahRI4XLxVibBw8erBTcPHr0KF566aWywKbw5JNPyt9btmzRKbjZpk0bOYmRNpxK7LEQ7WBOqgf6wdX74TiZ4TOGqlz37Kot8O/TVo6Xeej1NZWWF+U+7J7eZ/Mi2T39t3EfoV67x+Uy0SVddGVXZVPbyci6dRfmollQMxTYFRtt++aYH4j0zffM19pjOWy5jH2fICIi28J6FNlKncbc8jrr48bLHz4+Pjhx4oROn7UrsdK+1rVq1UJaWhoOHTqEiIiISsvXr1+P0aNHy0mGxHiaYsZ0oXr16njllVfw8ccfl62bl5cnu7C3b98ehw8f1jotDRo0QEKCdmNHOts5YG29Z7TeF5nGxDtbkF9SZLTta5sfnv7tEzhVc8WmdlOMliYiffM9yzmyJca+TxARkW1h+4BspU7DNoPt5A8/P7+yHtbastonN0XEVwQ3T548WSm4mZiYiDlz5si/W7RoURbYFJo1ayaf3nzU8ePH5XibqampOqdFl/9OgA94WIz6vvWN/uQm8wNZW75nviZbYuz7BBER2RbWo8hW6jTM67aTP3x0iJ1ZfXCzV69ecsb0JUuWoHfv3jJoWRqofOGFF3Dv3j35umXLluU+N336dIwZMwaLFy+Ws6SLqPGUKVPg4OBQrqu6NnR5rLYgOxfrA0frtD+qeleuXoGTu6vRts/8QNaY75mvyZYY+z5BRES2hfUospU6DfO6Zbliojqv1c6WPnfuXDkR0M2bNxEaGoqwsDA5UVC7du3QpEkT9OjRo9J4m4Loqj5v3jy8++67qFOnjhwvU0xKJIKgvr6+JjoaIiIiIiIiIiIispknN8U4l/v375fdz6OjoxEbG4uQkBCsXbsW48ePR2BgoMLgpuii/uGHH+KNN97AjRs3ZJ9/MQ6nCJROm6Z6khsiU2oyrAs8GtSRf7t6e8HeyREtXns48ZOYfOn6pj/4BRERERER2Qi2D4jIVlhtcFMIDg7G9u3bK72flZUlg52im3nz5s0VflZMNCTG4xS+/PJL5OTkyFnUicxVs5E94RMZWu69J+aNlL+TDp1ncJOIiIiIyIawfUBEtsKqg5vKnD9/Xk4QJMbhdHd3rzQ+5p49e/DEE0+gsLAQe/fuxYoVK7Bs2bKypz2JzNGuoQtNnQQiIiIiIjITbB8Qka2wyeDm2bNnFXZJF1xcXLBt2zZ88MEHMrgpxur88ccfMWzYMBOklIiIiIiIiIiIiJRhcLMCEcw8dOiQ0hNGRERERERERERE5oHBTSvX7t2X4f9UG3g0rIutvV5H6vlYhesNO7YaRXkFKMrNl6/PrPwZsVsPwaWmB57a8L/uzg5uLvBsVA8/hI1D/v0sg+xb2f4T95/Red+lmj7XHZ0+exVRLy1B/K7j5ZapOzZt0k9ExuPXvSVazRspJ8kqysnDoblrkXYhrtJ69s6OaLtwLPy6tURRXj5SL8Rh/9QVapfpyzPAB52XT4NLLU8UZGbjwIxVuH/llkbp0me76tZTV8b1/uFNuNWpARQXo+BBLo4u+Aqp525ofd71oaqMVnd/EoxZTqs6/8bMT0RERERkHKaID5gyJqNpnVtV3dbBxQld18xE9aAGMm2599JxeP6XyIxNgjmxyeBmVFQUbEXcjsM4t3oL+v2yWO260ZM+rXSh5aVlYWvvOWWvQycNgk9EiEYXkTb7VrZ/XfctiJnDm43qheQTlxUuV3ds2qafiAzPuXo1dF41A7sGvykDS3XbB6PL5zPwS/dZldZt/cZooKQEmztOk69l4E6DZfqKXDoRV77bg2sbfkej/h3QaflUbO87X+99q9uuuvXUlXHREz5Gfka2/Nu/bztZ4REVLm3Pu67UldHq7g/GLqdVnX9j5iciIiIiMjxTxQdMGZPRtM6trm57+V97kBB1Sv79+Et90PHjyWY3pq+9qRNAxnXnyEVkJ6YabHtBz/fA1e/3mf2+YWeHyI8n4+iCdSjOL9Rp+4ZOPxFpz7OxD/LSMsuemEs+ehHV/GqjVlhAufUc3VwQNLIHTn74fdl7OXfvq10meIcH4qmNCzFg1xIM3P0RGg2I0Dh9rt5e8vMxP/0hX8ftOIJq9b1lujXZt67b1XY9RWVcaWBTcPZ0lxUabc+7znQooxVRVU4b63vV9TslIiIiIhMxYXzA3OMijmrqtuJp0tLApnD35FV4NKwDc2OTT26SYp1WTBN5H3dPXcOf769HXkpGueV12jwGl+rVcHPPn1W+f233HTpxIJKPX0LKmesarW/sYyMi3WRcT4RLTU95jd49cRkNn2wjA3GiW0fq2f91oRZBJ/HfyxbTh8C3SwvZZeL0sg1IPHBW5TJnL3dEfjQJe0e/h5zk+7IL8sDdS+W+spPUV0JEwC/nThpKiorL3stKuCffF101VO1bn+1qu56yMk6Uu76RofLvPaPf1/q860rbMlrd/akiY36vogKoy3dKRERERKZhKfEBQ8dkQjU4bm3bKyGv9EP8fxQPJ2VKDG5asH7b3oNXE1+Fy8Rjy9m3UzTe1q+D38KDhHuwc3TAE/NGovPyqdj7SENXENH8axujyzX2DEXd/rXZd43HGqJR//Zym5oy5rERke7EWIe/j1+G1n9/Ho7VXHH3xBWkXb6JksKicuvZOdrLwNv9q7dkRaBW8wA8+eOb2NJ1pspl3i2awLNRXfRe/0a57XkF1pdBMHXlrDqq9i3Gq6kqysq4A9NXyt+Bz3ZFmwWjy8pdTc+7IurOmQiSalNGa3J/qkhU/Iz1vZrLd0pEREREhq97mio+YOiYTA0N4yLa1G3Dpg+RwdBDwxfB3DC4acF2DizfaNOHuIgE0XC98OV2DDn4sMFbytHdFQGDIrG97zyD7VPT/Wu773rtg+XFOfTQyrLxIiI+mgS3ujVx+dvdldY39rERkX6SDp3HriELywa7fu6vf1aaWEeUIcVFRbj+0375WkyMkxWfjJrB/nLcGmXL7OzscP/yLewc9IZO5WxxXgHc6tWEnYN9WSX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rix6waW1QUzFxVUcXdbvu1s8FbT+/a+ji3D7nCADFVZcXdP+jqNe9nrwv405bwJ3zhat9KMlzHAAAAAAAAIomUA60YMECpaen6/LLL5c/ObR8i+Iio4ttfS1u6qc/P/3Z67ftD/kD4B8yH1wbwRXCM/puL0qdFBCg7q/cpxVPTldaUopb2w6tVlHV2jfTzi9/s+8jvl+ucnWrqULj2jnSmfeJMaeyfv1/eMUWlatXXVXbNilwHWdTlxZL2RShXACgUNysZ0wVFlwp3P5ftkK4EmJO2fRBYSFqcWM/rXnh06y08UeOZ/1v6tjLvhinK+a+qCvnTVSjK7q5nTVXdXRB2y2O+tnd80xhubt9g3MEALcU4ZrxzDZ+Ua97PXXvwd062p3zRX77cDbnGgAAAAAAAJQcR/b4BanHpAdsN4pH1u7Q7xM+VuKxkzmKpUancxRSqZz2zf+91Ldf0tv2h/wB8F6m/qjTvY39f/7NE4q0jjb3XKnDq7bq2Ppdbi9jgrfiD8UoPTUta1rsgaN2+qk9UVnTTu6KVEiVCrYuO7J6mxpc2sk+aDdDuZghy9xZh6fKpijlAgAlUc8svPdV9Z0+RilxibYnxV9GTFRacooqNa9ne2VpN+oa1enVzg7hte7lzxW5eIOCK4ar+8R79dPNzyn+8HE7FNeV816ydXFcVPRZ1fPmYXx+2y2u+tmd80xBbYyz2X5BOEcAKGp94Kk2fnHel3G3LWACwYp6vjibZQEAAAAAAFByCPzyEYO+fU4Vm9bJc54ZBiDu4DG31/XDkKd0+sBRBQSV0fmP3aieb4zUT2c8YDG/4tzxxcIcNw2LS0HbL+y2i7NsSiJ/APyDu3XN4lGT7d9m1/ZWpydvzlW/FqTyOQ3U6PILbV1UEpJPxenXu17WBf+8SUHlQnVk9XbFbNun9JRUlbSzKZuSLhcAcLeeCSgTqPYPDbPBXqZXFNPDSv8PH9fsfqMVEBRoA2mP/7nfPsSvel4TXfrZvzSr98Oq1q6pKjSqqUs+fiLH+io2q2sDvwo6z7jMk4vtJhw9UeCHe7bnLnfbGCW1fc4RgHMUVFeaHzQU9prRE218T92XOZvzxdmeawAAAAAAAFAyCPzyEXOuzPmA6GyYm4uGeci/+d3vdM2SjActmYLCQ9VkcHd9N/CxYtumu9svyraLs2xKIn8A/ENh65qdXyxUtxfvVkiV8kqMiXV7uVoXtrIPVIYuzah7wmpUVreJ9yqsZhVt+2iey7orrFYVG5CQ+XCofL3qWXVadlFLN2nuNePs/4HBQbr+j//YoR9NUJi76zgbRSmbopYLALjL3XrGPOg2daUJ+jKO/bFTcZHH7PTojbuVlpqqXV8usvPM+9i9h1WlVUPbq+Lxbfs1Z/ATRTrPpCUm51tHJ508ne92IxdtKJb6uaDzTEFtjLPdviucIwDnKKiubHDrBYW6ZvRUG7+478u42xYw74t6vjibZQEAAAAAAFByCPxyGDMMTGDZMko6GWffNxnSQ8c27s6RpslV3RW9eY9O7DiYaxiCvT+s0N4fVpbY9kty2yWZP3iP2t3aaMBX47Vq/EfaNPWbHPNqdWut/h88ppT4JM2/8VnFbInImtd76sNqctVFOrhog+ZdNz7PdXd45Dp1ePQ6fTvgMfuQN7vW91yhzk/dqsOrt+vnWyZkHUNwDjOEV5mwEDvEitFwQGf70LowD64N80Aq+0OpAV+Otw+D9s5d5bI+TDh2UtEbdqvZ0F7a8fmvanR5V52OjM5ziMawmpXtMGNG+4eHKXLJxqx07q4jL/nlrTjKpqByAYCz5W49Yx58h9eqokot6unEnwfs0FcVGtXSyZ0HlRh9SpGLN6pun/Y6sGCtDT4o37CmTZcSn2j/r9OzbdYD8qptGtvAWzNMZEEKqufz225x1c+utl/QNXRJnh8MzhHIjvaAsxW2PihKG/9s70+UxH0Zd9sCrs5TBTmbZT1dH2SXfDpeJ3dFaufM37Rl+hy/782dMgAAAAAA53J6m7C2g/afwC8/0u2lu1W//wX2gf4lnz6p5Nh4fdX9ATuv+8v3at+81YrZuld9/zPG/go0IEA6FXFYix/I+cvSFjf21/aPf8q1/urtm9qDv6jbNq/QGpVcbr8o2y6usjmb/MH71b/4AvV5Z7TijxzXvOv+rVMRh7LmmV4lGg7oopO7I1XnojYqX7+GYvcfcXvdHcfeYINnDvyyVgtGTFRqfFIJ7QW8WdmK4erzziMKCg1Welq6ffjy863PZ83PXte4qpMK4qo+XDp2mnq8fr/ajrrGrnPxQ2/muX1zzJreUUxdd+T37Vo6+i231lFQvvPLW2mVDQCUpOx11dIxU9Vn2mhbpwUEBmj5E9OzelVZNnaaLnr173bIQjPfvDdDORo/3TLBBop3Hnebfehvllnwt5fczoOrOtrVdoujfna1/YKuoYtr+5wjcDZoDyCveiW/Nr6r+uZs742U1H0Zd9sCrs4XBe1DQct6q11fLdL+BWtkCtz0Atf82t7qMv52G8S9bMw0OQFlAAAAAADO5fQ24S4H7D+BX35k2dh38p239NGpWf9/e+kYl+vJa/iZkGoVFRcZnauXo8Ju2wwD4Gr7Rdm2O0oyf/B+5hfUPd8YqRM7D2reDc9k9SqRqenQXvbh68J7X9Ogb59T8xv6at3Ln7u17gufvUOtRgzS7tlLtOiByW712AH/dHr/UX0/6B9u1TWu6qQzzR2aMSSjO/Wh6W0mv+Fvsm8/+/+FWYerfLvKW0mUTfZyAYCScGY9k72u2j1riX3lxVxP/jjs6Tznmd5Yfrw2755F3eGqjs5vu8VVP7vavqtr6OLcPucIFBXtAedyVZe7auPnV98U172Rkrgv425bwNV5qqB9KGhZb3Vsw+6sISqNbR/8qCGL3lDLm/przQufKvHYSfk7ygAAAAAAnMvpbcJjDtj/QE9nAL7BHOwmYMZp24bvO+fWS9Vryihbof8w5KlcQV+Zv6aOWrpJx9bv0v6f1qj59X1txK8r5tfZPSc/YIO+ts2Yr4X3vU7QFxxdH3pz3gDAyTxdP3t6+wDtARQn7o34BzP88pE1fyogMFAVG9WSE1EGAAAAAOBcTm8Tpvjh/hP4BcBvtX1giLq9eLcil2yyvWskHY/NlaZa+2aq2rqRdnz+q32/47Nf7FCPdXu1y3e9ZULK2qE5mg3rrQ1TZmV0AZmeXqL7AgAAAKBwaA8AyE+Fxhk3dhPzuE/gFJQBAAAAADiX09uEFfxs/xnqEYBfOue2S1WxcW1F/LDCDuGYlpT3EIwtbuyn5NPxivh+hX2/f8FaxR89YacfXPhHnsv0eGOkXffqZ2do45uzSnQ/AAAAABQe7QEAmYLCghVStYLt2TusRmXbE2C1tk3tr3tP7op0REFRBgAAAADgXE5vEwY5YP8J/ALgl8JrVbF/T+05lG/QV5nQYDW9uocN+kqJS7DT0lNSteurRTr31ksVXLl8nr2EmXWnJafo5K6DJbwXAAAAAIqC9gCATB3H3mBf2e35frlW/OM/jikkygAAAAAAnMvpbcKODth/hnoE4Jc2TP5aBxdt0Hn3DVancbfmmabRFV0VXKmcopZtVoXGtbNeh5ZvtkFhzYb2ynO5JQ+/pfjDx9V76sNqOOjCEt4TAAAAAIVFewBApm3/nacfrxuv+cOf0+pn/quE6FMqV6eaUhOTstIEBgfpql9fU7sHr8lRcD1ev18Xf/yE22l8uQx6v/2wek8bnWM584O469a9q6bX9CxwPgAAAADAOzm9TbjNAfcF6PHLCwSFhWj4zhmezgZK4HOF56TEJ+nnW59X/w8f13n3DlZAYKBWjfsgR5qWN/a3f3u89vc819Hihr7aMn1Orukn90Rp7tBxGvDlePWZ+rAW3ve6Ir5fXkJ7gtJEfew/nF4HcyzjbI4df8Z3A9mPBfg32gO+iXraf3hTPXtyV5QiF22w/x9YsFaHVm7VoNnPqNuL92jhfa/Z6aan8MWjJmvA1//Wvvm/K2ZzhBoO6Kz6l3TS7H6j3U7jrdwpg2X/eFdXLXhFTa6+SLtnLbHTuk64U4dXbrU9o+9fsNblfKConFL351cvOn3/AYB60LOcUv4G5yI4mdPbhCcdcF+AwC8vEBAQoLLhoZ7OBuB3UhOS9PNtL6j/B4+pzd1XmGF7tfKpjOCvCo1qqVbXVtr55W/a+8PKXMvW6dlW5952maq1a6pj63flmn8q4pB+uOYpDfhqvHq//ZAW/v0NRXy3rFT2CyWH+hj+gmMZ4LsBgPaAL+IaBqXhyOpt2jnzNzW/ro82T59j3xum7b/p7W/Uc9ID+umWCeo28V6t+Od/FH8oJmtZd9L4ahkkHY/V0kfeVs8pD9qe0Wt2Oke1u7fR7L4P22UKmg8UldPrfqfvPwBQD3oW5Q84k9PbhEf88L4AQz0C8P/gr9tf1IGFf6j1XVeoyzN/s9Ob39jP9gK2adq3treuM18bp8zKSHdDv3zXHbv3sOZeM05xh2LU+60H1fjKbqW2XwAAAAAKRnsAQF7+eG2m0lJS1XHM9Tmnv/6l0lJTNXj+REUt2ajdszN+xVzYNL5aBgd+Wac93y5Vrymj1PWFu+xN/cSYWLfnAwAAAAB8g9PbhH/42X0BAr8AOOJhz4LbXtCBX9ep9Z2X68Jn77ARvKf2HlL0ht15LhO7/4iO/rFTTYf0UJmQsm4Ff/V66yE1ueqiEtwTAAAAAIVFewDAmU7tibI3Zuv2aqeaF7bKmp6ekqojq7YptFol7fjslzwLzp00vlwGq8d/pApNatvhL/b/vCbXcgXNR/H55ptv1KFDhxyvevXqKTQ01OW8/Fx77bVatuyv3ur//PNPde/eXS1btlTnzp21adOmPJdzlW7OnDk6//zz7fbPO+88ffjhh1nzRo0apcaNG9ueRNatW5djnT179tTu3XnfkwMAAAC4Ji55Tm8TnvKz+wIEfgFwhNTEZC0wPX/9slatRgxSuTrVFDFnhctlTM9fIZXLq+GgC12mi913WHOHPKW4yGPqOWWUmgzpUcy5BwAAAHA2aA8AONP6NzJ+oZv9173mZm/z6/tqy/Q56vLvv6lMaHCu5dxJ48tlkBKfqNiIw4rZsjfPZQqaj+IzePBgGzCV+fr1118VHh6uN9980+W8vKxcuVLR0dHq1u2v3urvuece3X333dq+fbsee+wx3X777Xkum1+69PR03Xzzzfrggw9sHr777jub9tSpU3b+sGHDtHjxYjVq1CjXOh955BGNGzeumEoKAAAA/opr4pLl9Dbhej+6LxCQblpoAHxKclyCPm52s6ezAQcavnOGyobn/+tRAAAAFD+u/+GtaB/An+q3oPBQDf75ZW2e9p22fvijBn79b9sT+KpxHxQqTWG/K95UBpkGfDle++b/rk1TvynS/LxQX/xl0DzpcIJUM1Sac6l75ZeWlqYrr7xSDRo00NSpU92el2nEiBG21y7z1zh8+LCaN29ug8GCgoJsEFedOnVsoJaZnslVumbNmql69er6+uuv1atXL61fv14DBw60PXkFB//14MP0+jVr1izbK1im5ORk1a1bVzt27FClSpXcPYwAAADg0Othf74m3rJ+o747/345tU3oTW3iIA/dFygO9PgFAAAAAAAAwNE6P32rYvce1tYP5pqujLT4wSlqeVN/1eraqlBpgJJgescyD5omTZpUqHmZTI9gF174V4/2+/btsw+rzIMrwwzH2LBhQ+3dm/NX+67Smf8/++wzXXPNNbZXrx49etihHrM/4MpP2bJl1bZtWy1atKhQ5QAAAADn8tdr4sVLlxSqHFByOvvwfQECvwAAAAAAAAA4Vr1+HdVk8EVaMvqtrGmnIg7p9+c+1kWv3a+gsBC30gAlYfbs2Zo+fbq+/PLLXA+QXM3Lbv/+/apVq1ax5islJUXPPvusvvrqK0VEROjnn3/WLbfcoqNHj7q1fO3atW2+AAAAACdfEx84cKBY8wRn3hfICEsEAAAAAAAAAAc6sGCtPjn3tlzTzS947a943UzjL+YOHXdW81F8tm3bZoeiMcPCmGFg3J13pvDwcCUkJGS9N0PgREZG2gdVmcPVmB4LTM8F2blKt27dOh08eNAOaWN07txZ9evX19q1a3XJJZcUuG8mP2FhYYUsEQAAADiNv18Th4Z63zWxE9uEB3z8vgA9fgEAAAAAAAAA4EVOnTqlIUOGaPz48XbIGHfn5aVdu3b2oVimmjVr6vzzz9eMGTPse9M7gnlA1bx58xzLuUqX+QBsy5Ytdt6OHTu0c+dOnXPOOW7tn1muffv2bqUFAACAMznhmrhd27ZupQVcoccvAAAAAAAAAAC8yJtvvmkfTL377rv2ld11112X77w5c+bk6u1g2LBh+vHHH3XxxRdnTZs2bZpuv/12TZgwQRUrVtT777+fNe/OO+/U4MGD7Su/dGaYnHfeecfmJTAwUGlpaZoyZUpWDwn33HOPvv/+e0VFRemyyy5ThQoV7IMwY8+ePUpNTSXwCwD+j737gI6i6tsA/mx6DxBCKIEQeg8gNYRepIlSRBBEBBRUQBEpvhbEgoIo0hT0xdcCKghIFwEp0ouA9J5CQkJCCklIT/Y7c/NlSUiyLbvZ3Znnd86eZKfeuTv7v3Nn7t5LRES8Jm7RAhd5HlAZqdRSX3REZFOy0zKwpu5oSyeDFGjUzdVwdHOxdDKIiIiIFIXX/2StWD+gslJKfNP2XWEeKE//XUBsBlDFBdjRp3z2mZqaiuDgYBw9ehTu7u6wtNmzZ4seEqQGZkRERESkLJa4Hrbma+Lnnx2t6Hox68SmwaEeiYiIiIiIiIiIiIhkysPDA4sWLUJoaCisgdQj2bhx4yydDCIiIiJSEF4Tk5xxqEciIiIiIiIiIiIiIhnr2bMnrMXUqVMtnQQiIiIiUiBrvCbOtXRCSBbY4xcREREREREREREREREREREREZGNYcMvIiIiIiIiIiIiIiIiIiIiIiIiG8OhHomIiIiIiIiIiMimeQZWRefFU+BcyRPZKWk49NoyJF2LLLZc/ZE90HzyYMBOhZjDF3B09rdQ5xQdXOPx3+bAp3kd/Nzoec00/16Poe2cMVDZ2SHxSoTYfnZqOjz8fTHk2DIkXY7QLLtvwkKkhN9FeWMeEBERERERKZfS64SeCj5+NvwiIoODYbsPx6HW423gUbMKtvR6EwkXw8R0e2dHdF0xDd71/ZGbkYWMe/dFoEwJi9G6nqnSpW1Z54oeeHzdHM1y9q7O8Azww6/NxyMrKRU1urdEq1kjYefogNz0TByZuRKJl8J1rmcK+h6fsccmKe34JHZODmg753nU6NYSuZlZSLgUjoOTl5jk2IiIiIjIepX1OlTXdaY5rrFNUTcorN4z3RHy5avY+8J8ROw8Kab1/vVduPpWAPLykP0gA8ff+Q4JF0J1rmctn4k1Hx+ROQUvmIhrq3fjxrr9CBjQASGLJ2Nbv9lFlpHuR7SaOQJb+8xEelwSenw/Cw1H98aV73dqlmkycaC4OSvd4C3g4OaCTl+8jJ1D3sP9G3fQ/uPxCJo2DKc+/EnMz0nNwJbeMyz+ATMPiIiIiIiIlEvpdcJgBR8/h3okomLB8PeQqTi/bJMIhiUJ334UO558B6m3Y4vNu/pT/vpSw66IP0+i0+cv67WeKdKlbdnMxFQRbAte11bvQdTeM+LBkpO3Ozovew2Hpi7Flp7TcfLDn9Bl+Ws61zMVfY/PmGOTaDs+yWNvjwbUamzsNAWbe0zHqbk/muzYiIiIiMh6lfU6VNt1prmusU1RNygg/RqvwaheiD11tcj0Ay99LtIspe3iyq2iAZQ+61nDZ2Ltx0dkLi4+XvAJqoubG/4W78O3H4N7dR941q5aZLmAgR1we9cpcXNXcvXHXQgc3Ekzv0IDf9Tq2w7nl/5eZL0aPVqJBpLSzV3JlR/+ROBTIVb1gTIPiIiIiIiIlEvpdUIXhR8/e/yyUmq1GjnpmZZOBunJwdUZKpXKpvOrIBjuGvGhJhh2mDdeBMOCHrsK3D12ucRt5GZmiwc2BeJOX0ezlwfpXM9U6TJk2frP9sDpeWvE/9L8zMQUzS/kY49fhnuNyqjUPBAJ50NLXc8U9E2zscem6/iSb9wR3Vn+1nqiZvmCgk7pGIdtixzisCnx/CVj8bvE75It4/lb/tehUuOu0q4zpS7PTX2NbdK6gUqF4M9fxvF3VonebwvLSk7T/O/k6SZ+JKGhZT2rqhtY4fHpg9cw8lHeMVmKL+l3E6HOzdNMS426J6YX/v541KiM1Mi4h8tExollJCoHewQvfBmHp39VZDslrnc7Fq5+FaCyz/9NrYObMwb+8al4H/HHCZxbvBHqvKLbMDfmAdkqpcT+0uKi0o+fiIhx0PL4GRDJg9LrhO4KP342/LJSUmVvTd3Rlk4G6WnUzdVwdHOx6fzSNxgaosmE/qLXr/JKl77L+rZpCGdvd9ze/Y94n3wrGs4VPcX0uFNXUbNPG/EAROrqsfBDqUfXMwV902zssek6PnVOnuhZocXUIajWpYUYovPswnWIPnQeSsc4bFvkEIdNiecvGYvfJX6XbBnP3/K/Do0/d6vU68zog+dNfo1tyrpB04lPIPbkFXEMJQlZMgXVgpuK/3ePnqeZrms9a6kbWOPx6YPXMPJhizG55fSnEb7jOO5fjxI93+krLTYR61q9hIz4ZDhV8EC3FdPQdNITuPDVZtga5gFZglJif2lxUenHT0TEOGh5/AyIqIDS64Qtbfj42fCLiMyi+dQh4hfnR4bPtboclnq4uvHbAc2DkuyUNOx/cSEe+8+zcHB3Qdypa0i8ehvqnFyt6+nSf+vH8KpTrcR50rAqaXfiYWolpVHb8akc7MTDt6Trkfhn3hpUahaIPmvfxaau05Bx777J00dERERE8qHtOtNc19imUKFhTQQMaI8/Br9X6jLSEJWSuk93RZt3RmPP6Hl6rWcNdQNLHB+RpT2IugdXv4ril7UF8UT6Na40vTCpkaRXoWEepBu5BctU7dhUNKBsPK4vVPb2cPR0xbATX2Frv9livepdWzxcr2YVpN9NEvuSXtLNXYn0w6rrv+5FncGdy/0GN/OAiIiIiIhIuZReJ3yg8ONnwy8iMigY6qPppEEI6N8eu4bPRW56VrmlS59lHdxcEDgoGNv6zSqybsyRi9g5ZI74387JAc/8+1/NsDTa1tNmxxNvm+z4ynJs2o4vK/kB8nJzcWvDQTFPGps4NSIWFRvXEr00EBEREZE8meo6VNt1tKmvsU1VN6jepYW4OTP0SH7jJ1ffCuj42SS4VqmIqz/uKrKdm78dQMf5L8G5ogf82jfWez1L1g0MSaepjo/I0qQbrFJvgnWHdsGNdfsRMKADHkQnFOsNUBoatf/mj0RP1+lxSWg4pg9CNx0W8/546t0iN34H7VmI9e1eEe+j9p1Bh08mwLteddy/cQeNnn8coZsPa4Zfzbz/QDRslWJdQP8OiL9QdEjb8sA8ICIiIiIiUi6l1wkzFH78bPhFRAYFQ12aTByIwMGdsGv4B8hKTtN7PWmokYg/josxb41Nlz7LBj4ZjIRLYSIgF+ZapQLSY5PE/0HThiH68AW91ist3frS9/jKcmy6ji/60AVU7xaEqL1nxIMej1pVRBeWRERERCRfproO1XadaeprbFPVDa5Kr0INmfpumItL325DxM6TcPJyg72rsxhKUVKrb1tkJqaKl7ROaetpS3d5fyba0lmW4yOydkdmrkTIl6+KHsizU9Nx6PXlYnrwwkm4veuUeEk/dDqzcB36bflI00D16k+7dW4750EGjkz/Gj3+N0s0uky6ehsHpy4T86q0b4xWM54RDTFVDvaIOXQe5xZvgCUwD4iIiIiIiJRL6XXCIwo+fjb8IiKdwfDRgNhxwUvw7/mYeJDT+5d3xLIbg6fArVoltHt/LJLDYtB3/ftivdysHGwf8Jb4v7T1JJWD6uDyqh1lSpeuZSX1R/bEtTV7iu2j1cwR4hfuUqCO++cajrzxlV7raUu3vvQ9PmOPTdfxHZ25Ep2+eEUM8aLOU4v3aTEJZTomIiIiIrJ+prgO1XadaY5rbF3XxIZcP5fE0csN3b6ZDgcXJ3FtLDWy+mvMJzrX05Xu8q4bmOP4iKxd8s07Jfaud+TNFUXeX1+zR7y0SY2Mw8+Nni8yrfC9h8IidhwXL2vAPCAiIiIiIlIupdcJkxV8/Cq1Wq22aAqoRNlpGVhTdzRzx0aMurkajm4u5bY/uZ0fzj5e6Lr8Newa8SFsia2m25bOdUuS2/dM7pR0buqD5y8Zi98lfpdsmZzPXyXFdVu9xrbVdFvb905J57rc6XtuKOUz15YfzAPl6b8LiM0AqrgAO/pYOjWWp/TvgNKPn4iIcdDy+BlQeeP1ML+DjEGmZ2eGbRIRGSQzPtkmH5DYarqJiIiIiKyVrV5j22q6iYiIiIiIiIiIyLax4RcREREREREREREREREREREREZGNcbB0Asi2jI1er9dyO4fMgZ2TA/r8+i5u/HYAh6YuLbZMq5kjEDRtGA6/uUKMoVpveDeELJ6sc9upt2Oxvt0rRqWfiMjWMQ4TESkbywEiIstiHCYiIiIiIiIiImvChl9kkL8nLy51nmctP9GYKyP+Pu7fjEJ6bBKurt6NhqN7I3zbUdzedUqzrE9QXTSf/BSi9p0Rjb4kMccuad1+vae7oXrXINw9cYWfGhEpFuMwEZGysRwgIrIsxmEiIiIiIiIiIrImbPhFBrm14WCJ0+1dnTBg6zzk5eRi/8RFotGX5OT7P6BG1yB0XDBRNNjKSkqFvbMjOi+ZjOwHGTj8xteabaRGxIpXSap2bIqqwU2ReDkcR2es5KdGRIrFOExEpGwsB4iILItxmIiIiIiIiIiIrImdpRNA8hDyxauo1LQ2/vloNWIOX9BMz/n/xl2uvt7o8PF4Ma3V7JGo0KAmTrz3P6TFJOjctnuNyuj6zRuiodjecZ8hJz3TrMdCRGSLGIeJiJSN5QARkWUxDhMRERERERERkSWwxy8qs6YvD0LgU50QuukwLq7cWmx+9KHzuPLDLjR+oa9o6NXkxQGI2HkSN387oHPbUu9g3VfNgHNFD/w15lOkhMXwEyMiYhwmIiJejxMRWQ3eFyEiIiIiIiIiIkthj19UJtU6N8dj/xmFhEvhOPzGV6Uu98+HPyE5LAbNXnkSWffTcHSmfsM1SkNEVg6qi7Of/4aovWf4aRERMQ4TERGvx4mIrAbvixARERERERERkSUpouHXvXv3MHPmTNSrVw8uLi6oWbMmXnvtNTx48ADjx4+HSqXCsmXLLJ1Mm+Ph74uuK6YhOzUd+8Yt0DoEY252DrJT0sX/8eduIj0uSef2G4/vj3rDu4newc4tWm/StBMRyQHjMBGRsrEcICKyLMZhIjKEWg2EpwLZefnv89TMPyKlSckGrt4HLiQCkQ/y4wIpS1wGcDERuJwExGdYOjVEROUvJo3Xw0TmIPuhHs+ePYt+/fohJiYG7u7uaNKkCe7cuYMlS5bg5s2bSEhIEMu1bNnS0km1KfauTuj+3Uw4ebvnD8EYflfr8kGvD4VP80DEnw9FjW4tNUNDlsavQ2O0nTMG929E4eCUJWY4AnlrPmUwfJrXgU+LOvAM8EPq7Visb/eKpZNFRCbEOExlolKJoZcbPtdbPLDMiE9G6NYjOLtgrdaG3ERkPVgO0KNYByBiHJYrpcc3pR+/HKRmA1tvA+vD8ht+FYjPBOafA4bVBup6WTKFVo71V+aBDEgNvX4LBXbfAbL+v/GnpLE3MLQ20NcfcLG3ZArJnHLVwMEYYF0ocOLew+kqACF++eVAxyqAnTSBSsaywLKY/1RG0g8f9tzJLwvPJT6cfi8TeOtUfhxs7SNONSoB64TMAyi9xy+pp68nnnhCNPqaPn06oqOjcfr0afF+/vz52L59O06ePCl6/GrRooWlk2tTghe+LBpynV24TucQjJWaB6LF1CGI2n8WO558G/dv3kH7j8fDpbJ3icu7VauEbt9MR05GFvaOWyB6FCPDSMNvVu3UDClhMchMTGH2EckQ4zCVRbsPxqLd3LFIuhaJY+98h7BtR9FkfH/0/HE2a1dENoLlAD2KdQAixmG5Unp8U/rx27rQFGDkfuDzC0UbfUmkjn5+C8ufLzUKo5Kx/so8sGVSj14rrgBjDwLbI4s2+pJcvg989C8w7iAQy8cgspSeA0w/Abx5smijr4Jy4OBd4LXjwLungaxcS6XS+rEsYP6T7UrKAiYezo9zhRt9FZAaRU88Aiy8kN9QlopjnZB5AKX3+DV16lRERkZi8uTJWLhwYZF50tCPP//8M/79918EBgbCy4s/q9JXk4kDUXdIZ0TsPIFzX27QuqydkwM6L5mCnLRMHJ7+NXLTs3D4ja/Qd+NcdJz/EvaN/6zY8t1XzYCrbwXsHf8Z7l+PMvBTJ8n69q8gNSJW/P/kvi/g6O7CjCGSEcZhKosKDfzReFw/hG0/hv0THl4fpUTEosPH4/N75fz9EDOZyIqxHKCSsA5AxDgsV0qPb0o/flsfxmbSkfyevbSR2oF8eg5wsgMG1Sqv1NkG1l+ZB7buv9fyX7pcSwZePQqsCgG8nMojZVQecvKAWaeAI/nFuFZ/RuU3BPuoNXv+ehTLAsti/lNZG79OPQZcStK97NrQ/Pg3vRnz/FGsEzIPFN3j1+XLl7F27VpUrlwZn3zySYnLPPbYY+JvUFCQZtrBgwfRq1cvVKtWDc7OzvD398czzzwjtkdA1eCmaPPOc/8/BONSnVnS6s1nULFRLZx4/wek3YkX02JPXMHlVX8goH978XC5sA6fvAjfVvVxbulGROw4ziw3UsENQSKSH8ZhKqvAwSFQ2dnh0rfbi0y/vmYPstMyUHdoF2YykRVjOUClYR2AiHFYrpQe35R+/Lbsi4u6G30VJg37eD/LnCmyPay/Mg9sWVgqsPKq/suHpurXSIxsx7bb+jX6KrArCjgQY84U2SaWBcx/sl1rbunX6KvAL7eA8wnmTJFtYp2QeaDoHr9++eUX5OXlYdSoUfDw8ChxGVdX12INvxITE9G8eXNMnDgRVapUET2GSQ3HOnbsiAsXLoiGYErlWqUCun3zBuwc7EUvITUfb1PqsomXIuDg6oSmLw/C7T3/4Mave4vMP/3Jz/Dv2VoM+Rh96AIy7t1H/Wd7osGzPZEelyR6+qoztHOp24/YcQI56QbcOSEikgHGYTKFyi3rIS83F/fOXC8yPTczGwkXwlC5ZV1mNJGVYjlARGRZjMNEpK+4DMMf3mfmAVtvA6NZJdNg/ZV5YMs2hBnXUOiVRoCLbJ/cKWuYT2k4X0NJQ/92r2aOFNkulgXMf7JNUq+HvxsZB5tXMkeKiORNtpePe/fmNzTq3r17qctIjboebfg1aNAg8Sqsbdu2aNiwITZs2IDXXnsNSuVdtwZcfLzF/0GvDdW67Pllm1Crb1tkp6bjyJsris3PzSg+5KNfu0ZinjTMozQ8pDbr276M1Mi4Mh0PEZGtYRwmU3Dzq4jMhBTkZeUUm5cWkyDKYztHB+RlF59PRJbFcoCIyLIYh4lIX9tvA7nSmF0G2hzOhl+Fsf7KPLDlh91bIwxfLzkb2BcD9FNu/wOyIQ3fefW+4esdjwOi04BqbuZIlW1iWcD8J9skxbO7GYavt/sOMLMF4C7bVixE5iHbr0x4eLj4GxAQUOL8nJwcHD58uFjDr5L4+PiIvw4OxmVXmzZtEBNj2E+8HNV2mIN2sCYxRy/i+2rD9F7+n49Xa50vDfn4o/8zmveHXl8uXraoQf0GyFblldv+rPH8IGUo73Pdkqzxe8Y4XDolnZtlPX/tXZ2Rm5Vd4jyp1y+J1GtnFht+KRK/S9ZdFrAcUO75a23nIpG5vnfWfq4zDpv+3LD2z7w88oN5IE9ez86HW8gog9e7lZABf/96UBKl1F9LiwO6YoBc8kDO1+olUXlUgt+Cc0at+/r7C/DiziUmTxOVL+eW/VDxpW+NWjd4wDBkXz8GpWActDyll8VkHm5dnofXiI8NXi8rD2jSrjNyY0OhFEqvEyr9+AurWrUqTp06BWPItuHXgwcPxN/09PQS569duxb37t2Dp6cnAgMDi83Pzc0VQ0VKDcjeeustkcnDhw83Ki1So6+oqCiD1nFS2QN+Ru2OLOBO9B1kqXPLbX88P0gp57ol8XtmW5R0bpb1/M1Nz4Sje34Pno+yd3YUf3PSs8yZPLJi/C4VxbLAtsj5/OW5SEr53vFcV965oZTPXFt+MA/kySE9A8Z01qJW2Rl8H9nWKaX+Wloc0BUD5JIHcr5WL4lDhRyji7eUB2mIVlgckKMKAUmoaOS68fEJSFHQOcA4aHlKL4vJPHxTUuBl5Lqx9+KRwTgoO8ZeD8vFHTPHINk2/JIaaiUmJuL06dPo2LFjkXnR0dGYMWOG+L9FixZQqVTF1u/ataumR7B69eqJoSN9fX2NTouhpJaNYKNjm1G9WvVy7/GL5wcp4Vy3JH7PbIuSzs2ynr9pdxPh3cAfdk4OxYZ7dKtaCRnx9znMo4Lxu1QUywLbIufzl+ciKeV7x3NdeeeGUj5zbfnBPJAnl5xUo9ZTJ8eiRo0aUBKl1F9LiwO6YoBc8kDO1+olsneEOjsTKkdng1d1z0tXXByQI0cHw7+XarVaPK+s6JQHLwWdA4yDlqf0spjMwxmZRq2nzstDZVc7qBkHZcfY62G5qK5HDDKmXZHsG3716tULly9fxvz589G7d280aNBATD958iSee+450duXpGXLliWuv2rVKiQlJSE0NBSfffYZ+vTpIxqC1apVy+C0GNMdW3ZaBtbUHW3wemQZ165fg6ObS7ntj+cHKeVctyR+z2yLks7Nsp6/987eQI1uLVG5VX3EHr9c5BdalZrVxt1jD6eR8vC7VBTLAtsi5/OX5yIp5XvHc11554ZSPnNt+cE8kKfQFODpfYavN6GDP16NjISSKKX+Wloc0BUD5JIHcr5WL827p4E/DPw6O9kBJ3+ajwpO882VLConuWpgyF9AVJr+60iNvhp5S88x96OE/ipki3HQ8pReFpN5ZOQAfXcBqQa2C+xczQ5fXr2gqI9F6XVCpR+/qdhBpmbOnAkfHx/cvn0bTZs2RfPmzVG/fn20a9cOderUQY8ePcRyQUFBJa7fsGFDtG/fHiNGjMBff/2FlJQULFiwoJyPgoiIiMi0QjcfEb+aafLigCLT64/qJS46b238m1lORERERERUBoGeQJvKht+oHxrAbC+M9VfmgS17urbh6/SpAVRwMkdqqLzZq4AhRsT0YbWlBmDmSJHtYlnA/Cfb5OIAPFHLuDhIRIaTbY9f/v7+OHjwoBjS8cCBAwgLC0OTJk2wcuVKvPjii6hbt67Whl+FVahQQQz3eOPGjXJIOVHZ1RnWBR7++UOTuvh4wc7RAS1eHyrep0bG4dZ6PtQnIlKqpCsRuPK/nWg8vj+6r5qByL9Ow7t+DTQZ3x8xRy7i1sZDlk4iEREZgXUAIpIrpcc3pR+/LZvaBHjxEJCp57Alz9cHqrqZO1W2hfVX5oEta14R6FcD+CNKv+WlBl8T8geuIZkYWhvYEQncTDHgnPE3d6psD8sC5j/ZrufrAXujgbvp+i0f4gd0rGLuVNke1gmZB4pu+CVp3Lgxtm3bVmx6amqqaAhmZ2eHZs2a6dxObGwsrl69KnoAI7IFDUb2RNXgpkWmtZ41UvwVD/V5U5CISNFOvPc9Um/HocHoXvDv2RoZCcm4/N0fOLNgLaBWWzp5RERkBNYBiEiulB7flH78tqxJBeDzdsDMk0Baru6egV5uVF4psy2svzIPbJXUa9O7LfMbf0oPvbWp5AQs7gD4u5dX6qg8eDgCSzoAU44Bt1J0lxmL2gHO9vxsSsKywLKY/2Ssyi7A8g7A5GNAjI7GX+19gXmP5feYSEWxTsg8gNIbfpXm4sWLUKvVaNCgAdzciv6MavTo0aJ3r5YtW4qevq5fv45FixbBwcEB06ZNs1iaiQyxc+gcZhgREZVKGurx4sqt4kVERPLAOgARyZXS45vSj9/WdagC/NgFWH0T+COyeO9frX2A4YFAz2oc2qs0rL8yD2yZkz3waRtgSwTwWxhw9X7R+R7/PwzWqLpAVVdLpZLMyc8V+C4E+PUWsDEciM0oOr+GW37PYFIDYFdFPrHVD8sC5j/ZrtqewA+dgV9uAZsigKSsovPreOYP7ygNj+sgjX1OxbBOyDzQhyIvI86fP1/qMI8dOnTAjz/+iMWLFyMjIwM1a9ZE9+7d8Z///AcBAUYMyE1EREREREREREREin3Y9U5L4LWmwIk4ICU7v0eX+l5APS9Lp46IzM1OBTwVADxZC7iUlN/riRQHvB2B7b0BF0U+pVNez18TGgJj6wOn7gFvnQJScoAKjsDvPfPPESIiOfNxASY3AV5qCJy4B9zLyG/k5e8GBFXiDyCITEGRl5TaGn5NnjxZvIiIiIiIiIiIiIiITMHTEehZnXlJpOShH5tWBFztHzYAZaMvZZEaOUg9QUo9e0kNv6Qe4djoi4iURIp7IX6WTgWRPLHhl8y1+3Acaj3eBh41q2BLrzeRcDGsxOWGnfgKuZnZyM3I71/x3NLfEbblCJwreuDxdQ+7lLd3dYZngB9+bT4eWUmpJtl3afuPPnjO6H0XqPdMd4R8+Sr2vjAfETtPFpmn69gMST8RUUnsnBzQds7zqNGtJXIzs5BwKRwHJy8psoy9syO6rpgG7/r+IgZm3LuPo7O/RUpYjJhfo0crtJ49EiqVCioHe1z4ajNu/nZAZ4YbEsN6//ouXH0rAHl5yH6QgePvfIeEC6H5++/eEq1mjYSdowNy0zNxZOZKJF4K17l/bds0Jp3a4jkRkbWyZCzUVb7ocz2sb/rNfewsA4jIEEq9D+IZWBWdF0+BcyVPZKek4dBry5B0LdLgeoq2+F1anhERERERERERkeUosuHX3r17oRTh24/iwleb0H/zRzqXPTBpUbGbkpmJqdjSe4bmfdNJg1C1YxO9Gl4Zsu/S9m/sviUe/r5oMKoXYk9dLXG+rmMzNP1ERI967O3RgFqNjZ2miPfiAUoJrv60G1F7z4j/G73QF50+f1kzZneXZVOxc+j7SLwcLuLa4IOLEb7jOHIeZJgu/r/0ObKS08T/tfq1E40KpAdVTt7u6LzsNewc/K54aFSlfWN0Wf4aNnd/w+htGpNOXfGciMhaWToWaitf9Lke1jf95jx2lgFEZCil3gcJXjAR11bvxo11+xEwoANCFk/Gtn6zDa6n6IrfJaWZiIiIiIiIiIgsx86C+6ZycPfYZaRFJ5hse/Wf7YHrv/xl9fuW+k0O/vxlHH9nFfKycozavqnTT0TK4uDqjPoje+D0p79opqXHJRVbTvrFfMFDeUnc6evwqOmrea9WA07ebuJ/R083ZCSm6BXXDIlhBQ92JE6ebvk7lXoNqF0VmYkpmp4CYo9fhnuNyqjUPNDobRqcTiPiORGRtbBkLNRVvuhzPaxv+s127CwDiMgISrwP4uLjBZ+guri54W/xPnz7MbhX9xHX84bWU8oS+4mIiIiIiIiIqPwpsscvKlnIkilinPm4Mzfwz7w1yIxPLjLft01DOHu74/buf8p9/4buu+nEJxB78griz93Sa3lzHxsRKY/0kEX6ZX6LqUNQrUsLMRzK2YXrEH3ovNb1mkzoj4g/Hw7fdWDSF+i+agZy0jJFD1z7xn+GvOwcs8TgasFNxf+7R88Tf5NvRcO5oqeIkXGnrqJmnzbi4Y80dE3C+VCjtmkoQ+M5EZG1sZZY+Gj5ou/1cFnSX9ZjZxlAROYml/sg0o8z0u8mQp2bp5mWGnVPTC88xK++9RRt8VtXnhERERERERERUfliwy8b1n/rx/CqU63EedLQAGl34vXe1h+D38ODqHtQOdij9ayR6Lx4MvY8cnNP+kXojd8OFLmRaCq69m/Ivis0rImAAe3FNvVlzmMjImXGYJWDnWgglXQ9UjwQqdQsEH3WvotNXach4979EtdrPnWIeBBzZPhc8V5lb4eg14eJxl5S7wHSr/h7/jAbm3u8gcyEFJMez6GpS8Xfuk93RZt3RosYnJ2Shv0vLsRj/3kWDu4uiDt1DYlXb0Odk2v0Ng1hTDwnIrK2a3FriIWPli+GXA+XJf1lWZdlABGVhPdByk6fekpp8Vufe0dERERERERERFS+2PDLhu144m2TbUu6cSeRHuZf+nYbhhzOv8lXwMHNBYGDgrGt3yyT7VPf/Ru6b7/2jcVNzKFH8rfh6lsBHT+bBNcqFXH1x13Fljf3sRGRMmOw9Mv5vNxc3NpwULxPuBCK1IhYVGxcC9EHi/f61XTSIAT0b49dw+ciNz1LTJMewrj6VRSNviTx/95EWnS8mB799zmzHNfN3w6g4/yX4FzRA5mJqYg5chE7h8wR8+ycHPDMv//VDP1o7Db1ZWg8JyKy5mtxS8XCksoXY66HjU2/seuyDCCikvA+SOn3VKR6g/TDkYIGvB41KmvutTy6rL71lEfjt657R0REREREREREVP7Y8Ivg4OoMO0d7ZCWnidwIHByC+AtFh/AKfDIYCZfCcP/GnWJd/Ef8cRwRf5ww2/4N3bf0AKzwQ7C+G+aKG5IRO0se2qa07RMRlYXUI1f0oQuo3i0IUXvPiIf2HrWq4P71qGLLNpk4EIGDO2HX8A80sVAiPVhx86sI7/o1xHpSby2eAX5IvnnHZDHYycsN9q7OYmgYSa2+bcVDnYIH865VKiA9Nkn8HzRtGKIPX9AMF1Pa/nVtU1+GxnMiSdWOTdF341ycnPsjLq7YUiRT/Do2Qc/vZyEnPQu7R36ExMvhmnldV0xD4JOdcOfgedFApiQtpw9HyzeHY2vfWaIh5qPf47bvjUHsqWv467l5Rb7LpEzlFQu1lQWllS/6XA/rk35zlgMsA4jInOR2HyQjPlkMxV53aBfcWLcfAQM64EF0QrFhHnXVU7TFb33yjIjIWuuHhWU/SEfyrWjcXP83Lq/aIfsRIJgHRKR0jIPMeyIiJWDDL5nruOAl+Pd8TDy47/3LO8hOTcfG4CliXvDCSbi96xQSr0Sg+39niF+GqlRASngsDk0p+qvN+iN74tqaPcW2XzmojqggG7tv6eXi6611/8bsW5vC+9a2fW3pJyLSx9GZK9Hpi1fE8CjqPLV4nxaTUCQWxZ+/hXbvj0VyWAz6rn9fzMvNysH2AW+JoVaOzFiBbivfEOur7FQ49vYqzS/tTRGDHb3c0O2b6XBwcRL7kB4a/TXmE812Ws0cIXpdkWJ03D/XcOSNrzTzStu/rm0W3j9jLZUX/16Pods3byA9Lkk0gkkJv6uZJ/ViUatvOySHRqNap6bw8PdFamSc3tuWvidSw8iofWewd/xnpfaqRMpSXrGwtFjsVq1SqeXLo/sv6XpYV/q17ZvlABFZklLvgxyZuRIhX74qhveV9nvo9eWl3gcprZ7i7l+51NivK81ERNbs1saDiNx7GlIAk3rQrfd0V7SbO1b80O7ojJVQAuYBESkd4yDznohIztjwS+aOzvym1HlH3lyh+X9rnxlat7NjUPGhbJx9vJAWnVCsxwtD9y0NKaBt/8bsu7CdQ+eUuu/Stq8r/URE+pDi25/D8h+2P6pwLPq+2rBStxG66bB4mSsGP4i8h+3939JrWX33b8g2DYm1j8ZzIn1JPVJ0XjwZ92/ewa4RH2p6sShQZ2gX0YPFgUmL0H/rx6g3ojvOLlyn17bbfzQOjcf3R+jmwzg4ZSnysnP4wVC5xUJtsViarq18Kbz/kq6HdaW/vMsBlgFEpC+l3geRegUubSjMR6/pS6unaIvfutJMRGTN4s+Haoa4lVz9/k8MPrgYDZ7tidOf/oLM+GTIHfOAiJSOcZB5T0QkZ3aWTgDZLqlCLD08Vdq+iYisgaXjoKX3T6SvhmP6oMuyqeLmzh+D3yvW6KugV42YIxcRf+4WIvecRr1nuotfgmsj9XbReekU0ejr6urdOPDyl2z0ReWO1+NERIy7RERkuJz0TMSdvg6VnR28AvwUmYXMAyJSOsZB5j0RkZyw4RcRERERyVLzKYPRcf5LiD58EX8+PRdZSanFlvEJqotKTQJwY91+8f7G2n1iqMfqXVqUul17Z0cx1FHdYV1xftmm/KFB1GqzHgsRERERERGZjmft/AZfmSXUE5WCeUBESsc4yLwnIpILDvVIRERERLLT8Pk+8KpdFeF/HBdDOOZllTwEY/2RPZD9IB3h24+L95F7zyD93n0x/c6Bf0tcJ2TxZLHtUx+txoXlm8x6HERERERERFQ2Dq5OcK7kKXp2dvWtIHqG9mleR/T6lXwrWhHZyzwgIqVjHGTeExHJGRt+EREREZHsuPlVFH9Twu6W2ujL3sUJdZ4KEY2+ctIyxDR1Ti5ubTyIRmP6wKmCR4m9hEnbzsvOQfKtO2Y+CiIiIiIiIiqrVjNHiFdhYduP4fhb/1VM5jIPiEjpGAeZ90REcsaGX1bKwdUZo26utnQyyIDPqzzx/CClnOuWxO+ZbVHSuUn6Ob/0d/h1bIpmLw8CVMCpuT8WWyZgYAc4ebsj5ugleNauqpl+99glNH1pIOoO7YLLq3YUW+/wtK/Q5t3n0HXFNBx4+UtE7MjvLYzkh2WBbZFzWcBzkZTyveO5rrxzQymfubb8YB4Qmd/Vn3YhbOtR2Dk6oGKjWmj26lNwr+aD3MwszTJ2Tg54YtdnCP39IM4t3qiZHvLlq3DxrYA9oz7WaxlbzoOuX08D7FQ4MPELzTTpB1FP7V+EUx/8iJq922idL/2IiojIWjEOWnfey70cJtJG6XVCpR+/qbDhl5VSqVRwdHOxdDLISvH8IOL3jIi0y0nPwl9jPkHPH2aj2aRBUNnZ4eSc74ss02BkT/E3ZNErJW6j/ojuJTb8Sg6Lwc6hc9B3w1x0+//GX+Hbj/EjkSFec5G14LlISsFzXXn4mTMPiMpD8q0YRB88L/6P2nsGd09cQf/NH6Lj/Ik48PIiMV3qKfrQ1KXo+/sHuL37HyReCketvm3h37sNNvd4Q+9lbDkPjr71LZ7c+zkCn+qE0E2HxbQO8yYg9sQV0agrcu8ZrfOJiKwZ46B1573cy2EibZReL1b68ZuKncm2RERERERkRXIzsvDX85/izoF/RQ9e7T4Yq5nnGeAHvw6NcXPD39g3YWGx15Uf/kSlZoHwaVGnxG2nhN/FH0PeQ1psIrp+/ToCBnYsxyMjIiIiIiIiY8Wduoqb6/8WDZh82zTUTI8/dwsXv96CzkumwK1aJXT8bBKO/+e/SL+baNAytpoHWUmpODL9a7T/eAJc/SoiYEAHVA1uiqOzVuo1n4jIljAOWlfeK60cJiIyNTb8IiIiIiJ5N/4aOx9RB/5FkxcHot2HL4jp9Ub2EL2AXVy5VfTW9ejrwrJN+cuN6FHqtlMjYrFzyByk3U1E169eQ+0n2PiLiIiIiIjIFvy7aD3ycnLRasYzRad/uQF5ubkYtPszxBy+gNDN+T1bGbqMreZB1L6zCNt6BF2WTUWHT18UDb0yE1P1nk+mtWXLFrRs2bLIq0aNGnBxcdE6rzRPP/00jh49qnl//fp1BAcHo0GDBmjbti0uXrxY4nralsvMzMTkyZNRv359NG/eHKNHjxbTMzIy8NRTT4l1goKC0Lt3b9y4cUOzXu3atdGwYUNN2teuXVtkn507d0ZoaCiUTgnnQJsO7fHevT04nn67yD7nxe9HXM4DmBPjoOWwHCYiMi02/CIiIiIi2Tf+2vv8p4jafxZNJgxA+4/God7wbkiJuIuE8yXfREyNjMO9f2+izuAQ2Ds76tX4q8tXryPwyU5mPBIiIiIiIiIyhZSwGNFgq3qXFqjSvrFmujonF3Enr8LFxxs31u4rcV19lrHlPDg190d4BlYVw3FF/nW62Hq65pPpDBo0CGfPntW89u/fDzc3NyxfvlzrvJKcOHECCQkJ6Njx4Y/WJk6ciJdeegnXrl3DrFmzMHbsw57SC9O23OzZs8UQTdK88+fPY+HChZp50jpXr17Fv//+iyeffBITJkwosl2psVdB+p95pmgjzOnTp2POnDlQOiWcA6eOHccHlXuhvWvNIvP6utfHptRLMCfGQcthOUxEZFps+EVEREREspebmY29Us9f+86g8fj+cK/mg/Adx7WuI/X85VzBA7X6t9e6XOrtWOwc/B7SouPRedlUBA4OMXHqiYiIiIiIyNTOLc7vuatwj1dSA6h6z3TH5VU70O6DF2Dv4lRsPX2WseU8yEnPRGp4LBIvR5S4jq75ZB55eXkYNWoUevbsifHjx+s9r8DKlSvx7LPPat7Hxsbi1KlTmt6Zhg4ditu3bxfpkUnXcg8ePMCqVavw8ccfi4Y/kqpVq4q/Uq9T/fv310zv0KEDwsLC9D7eAQMG4I8//sD9+/f1XkfulHYOtHCuhnOZMUjLy4Y5MQ5aDsthIiLTcTDhtoiIiIiILCrm6EV8X21YqY2/dj/7sd7bOr/0d/EqcPbzdeJVWg9h69u9YkSKiYiIiIiIqLzrh5L716Pwo//DBk8Obi4I+fJV/PPxGlz54U/0+/0DtH7rWZyc871By9hyHpD1knq/knpr+v333w2aV0DqDWratGma91LDnWrVqsHBIf8xodQ4p1atWoiIiEC9evX0Wi4tLQ2VKlXCvHnzsGfPHri6uuL9998XjY8etXjxYtHjU2FjxoyBWq1Gu3bt8Omnn8LX11czz9HRUQwbePDgQQwcONDg/JIjuZ4Debl5qHT/AYZ5NoOXnbNmnoPKDv6O3riWdQ8tXarBWIyDlsNymIio/LDHLyIiIiIiIiIiIiIiUrS2749BakQsrny/E1Crcei1ZWjwbE/4dWhs0DJEprZ582bRq9KGDRvg5OSk97zCIiMj4efnZ9J05eTkIDw8HE2aNBE9Qi1ZskQM2Xj37t0iy0mNgqTeoT755BPNtL///hvnzp3D6dOnUblyZTz//PPFti/1HCWlm+R9Dpw4chQedk74b9LJYtv3tnNBYl66SdNM1ovlMBGR8djwi4iIiIiIiIiIiIiIFKtGj1YIHNQJh9/4SjMtJfyu6Nmr06JX4eDqrNcyRKZ29epVMXTfunXrUL16db3nPcrNzQ0ZGRma9zVr1kR0dLRouCORet6SenCSenIqTNty0svOzk4MMShp1aoVAgMDcf78ec36CxcuxMaNG8WwjVIaChTsR+rZ6/XXXxc9ez1KSq/Ug5TSKeEc6ONWD9ez44ulOVudC0eVvUH5RbaJ5TARUdmw4RcRERERERERERERESlW1N4z+LnR83gQda/IdKlnr40dJyMnPVOvZeRi59A5uLhii9HzyTRSUlIwePBgzJ07FyEhIXrPK0mLFi1EI6ECVapUQevWrbF69WrxXuotyt/fv8gQf7qWk3rqkob0+/PPP8W80NBQ8WrcOL8HvC+++AK//PILdu/ejQoVKmi2+eDBAyQlJWneS8tIDYYedfnyZQQFBUHJlHIOHM+4jVoOD+cXiM5JQS0Hb1gC42D5YjlMRFQ2+QMyExEREREREREREREREZFVWL58uWio8+2334pXYcOHDy913o4dO4r1/jRs2DDROKdXr16aaStXrsTYsWPFMHxeXl743//+p5k3YcIEDBo0SLy0LbdixQrR49SsWbNEz0/SsjVq1BDDCk6fPh116tRB9+7dxbLOzs44fvy4GAZw6NChyM3NFb1HScv8+OOPRdIbFhYm5iu94ZcSzoG83Dw4ZiXgRe82RdJ7L+cB8qBGTQs1/CIiIrIlKrV0VUVEREREspGdloE1dUdbOhlkg0bdXA1HNxdLJ4OIiIiIiBRCKfXX0upaSj9+Jeq/C4jNAKq4ADv6lN9+U1NTERwcjKNHj8Ld3R3Wbvbs2aJHKanxkdzwHNAvDv6Wch5V7D3Q1S0QcmDNcZBlERGR7eNQj0REREREREREREREREQy5eHhgUWLFolh+GyB1FvVuHHjLJ0MWbG1c6CCnSs6u9a2dDKIiIhsAod6JCIiIiIiIiIiIiIiIpKxnj17wlZMnTrV0kmQJVs6B3q717N0EoiIiGwGe/wiIiIiIiIiIiIiIiIiIiIiIiKyMWz4RUREREREREREREREREREREREZGPY8IuIiIiIiIiIiIiIiIiIiIiIiMjGOFg6AURERERk/TwDq6Lz4ilwruSJ7JQ0HHptGZKuRRZbrt2H41Dr8TbwqFkFW3q9iYSLYXrNG3biK+RmZiM3I0u8P7f0d4RtOWKydOlatkb3lmg1ayTsHB2Qm56JIzNXIvFSuM50m4K+x2Ct6SciIiIiIrIG+tat6o/sgeaTBwN2KsQcvoCjs7+FOicXvo81QMdPXxTLqBwdEHviMo6/8x3ysnLg4e+LkMWTUalZbaRGxGJL7xma7Wlbrzwp/fiJiBgHLY+fARGRZbDHLyIiIiLSKXjBRFxbvRu/h0zF+WWbxA3fkoRvP4odT76D1NuxBs2THJi0SNw8ll76NPoyJF3alnXydkfnZa/h0NSl2NJzOk5++BO6LH9N73SXlb7HYK3pJyIiIiIisgb61K2kH8S0mjkCfzz1LjZ2nAyXyt5oOLq3mJdwKQxb+80WddLN3d8Q8xqN7SvmZaWm4/T8X/D3K4uLbVPbeuVJ6cdPRMQ4aHn8DIiILIMNv4iIiIhIKxcfL/gE1cXNDX+L9+Hbj8G9ug88a1cttuzdY5eRFp1Q4na0zTN3urQtK70yE1M0v4SOPX4Z7jUqo1LzQLOk25hjsNb0ExERERERWQN961YBAzvg9q5TSI9LEu+v/rgLgYM7if9z07NEz1cSeycHOLg4AWq1eJ+VlIrYE1eQk5ZZbN/a1isvSj9+IiLGQcvjZ0BEZDkc6tFKqdVq5KQXr0SRdXJwdYZKpbLIvnmukDnx3Cayje+LuUmNiNLvJkKdm6eZlhp1T0xPCYsx2X5ClkyBlIVxZ27gn3lrkBmfbLJ0aVs2/twtOFf0hG+bhog7dRU1+7SBk6eb+CV0wvlQkx1fWY7BWtNvzXiNREqK4zzfSS7fC57Lyjt3lPKZa8sP5gFR+datPGpURmpk3MNlIuPEMpr5/r7o8f0seNb2Q+Se07jy/Z967d/Y9UxF6cdPRMQ4aHn8DIiMo/Q6odKP31TY8MtKSSf3mrqjLZ0M0tOom6vh6OZikfziuULmxHObyDa+L3Lwx+D38CDqHlQO9mg9ayQ6L56MPaPnlcu+s1PSsP/FhXjsP8/Cwd0FcaeuIfHqbc0vlo3Vf+vH8KpTrcR50hAYaXfiYc3pt3W8RiIlxXGe7ySX7wXPZeWdO0r5zLXlB/OAyLpIDaG29HoTDm4u6LJ8KgL6t0fo5sNmW8/aKP34iYgYBy2PnwEpjdLrhEo/flNhwy8iIiIi0kpqkOXqVxEqezvNr4elXwlL002lYFtSY6VL327DkMNLTZouXcvGHLmInUPmiP/tnBzwzL//1QydaKwdT7xtsmOwRPqJiIiIiIhshb51K6kXLK9Cwx9KPVWVVIfMSctA6KbDqDOks0ENmIxdr6yUfvxERIyDlsfPgIjIcuwsuG8iIiIisgEZ8cliyMC6Q7uI9wEDOuBBdILJhnmUurh18nLTvA8cHIL4C6FFhoCs1a9dmdKla1nXKhU0ywZNG4bowxf0Or7S0qYvfY/BXOknIiIiIiKSA33rVuHbj6FmnzZw9c2vQzUc00c0VJJ41q4qeqGW2Dk6iLpewuVwnfs2dj1TUvrxExExDloePwMiIsthj19EREREpNORmSsR8uWraD51CLJT03Ho9eWaecELJ+H2rlPi1XHBS/Dv+ZhoiNT7l3fEshuDp4jlSpvn4uuN7v+dIX6ZLA1xnhIei0NTHvb4VTmoDi6v2lGmdOlattXMEfBr31ikIe6fazjyxleaedqOSVva9KXvMZgj/URERERERHJRWp2pcL0qNSIWZxauQ78tH2l6T776027xf7WQZmg8vr/oMUtqyBR98DzOLVov5tm7OmHIoaWwd3aAo6cbnv5nJW5uOIDT837Wuh6Pn4iI5YCSKL0sJiKyFJVarVZbbO9Uquy0DEWMZSoX5h6TVRueK2ROPLeJbOP7IueywdnHC12Xv4ZdIz6EtbHmtMnhPC4rOX0PqHzY8vnP853k8r3guay8c0cpn7m2/GAekNIp/Tug9ONXov67gNgMoIoLsKOPpVNDlsBzoCjGQcvjZ0DE72B54PXwarNeD3OoRyIiIiKyWpnxyVbbsMqa00ZERERERERERERERETyx4ZfRERERERERERERERERERERERENoYNv4iIiIiIiIiIiIiIiIiIiIiIiGyMg6UTQLZlbPR6vZbbOWQO7Jwc0OfXd3HjtwM4NHVpsWVazRyBoGnDcPjNFbi+Zg/qDe+GkMWTdW479XYs1rd7xaj0ExHZOsZhIiJlYzlARGRZjMNERERERERERGRN2PCLDPL35MWlzvOs5Scac2XE38f9m1FIj03C1dW70XB0b4RvO4rbu05plvUJqovmk59C1L4zotGXJObYJa3br/d0N1TvGoS7J67wUyMixWIcJiJSNpYDRESWxThMRERERERERETWhA2/yCC3Nhwscbq9qxMGbJ2HvJxc7J+4SDT6kpx8/wfU6BqEjgsmigZbWUmpsHd2ROclk5H9IAOH3/has43UiFjxKknVjk1RNbgpEi+H4+iMlfzUiEixGIeJiJSN5QARkWUxDhMRERERERERkTWxs3QCSB5CvngVlZrWxj8frUbM4Qua6Tn/37jL1dcbHT4eL6a1mj0SFRrUxIn3/oe0mASd23avURldv3lDNBTbO+4z5KRnmvVYiIhsEeMwEZGysRwgIrIsxmEiIiIiIiIiIrIE9vhFZdb05UEIfKoTQjcdxsWVW4vNjz50Hld+2IXGL/QVDb2avDgAETtP4uZvB3RuW+odrPuqGXCu6IG/xnyKlLAYfmJERIzDRETE63EiIqvB+yJERERERERERGQp7PGLyqRa5+Z47D+jkHApHIff+KrU5f758Cckh8Wg2StPIut+Go7O1G+4RmmIyMpBdXH2898QtfcMPy0iIsZhIiLi9TgRkdXgfREiIiIiIiIiIrIk2ff4de/ePSxYsAAbN25EZGQkfH19MWTIEMybNw9Tp07Fd999h6VLl2Ly5MmWTqrN8fD3RdcV05Cdmo594xZoHYIxNzsH2Snp4v/4czeRHpekc/uNx/dHveHdRO9g5xatN2naFUelEj2tNXyut/jcMuKTEbr1CM4uWMuhM4lsGOMwGav5lMHwaV4HPi3qwDPAD6m3Y7G+3SvMUCIbw3KADMI6ARHjsK1SevxS+vHLRGImsDkC+DsGSMkGnO2BBt7A07WBxhUsnTrrxvor80AO1Grgn3hgYziQkPkwLqwLBfr7Ax6Olk4hmVt2HvDXHWBHJBD//+dAUhaw9w7QpSrgwG46tGJZYFnMfzKFjBxgZxTwZ1R+HHRQAf7uwOAAoL0vYKdiPmul9Hqh0o9f6Q2/zp49i379+iEmJgbu7u5o0qQJ7ty5gyVLluDmzZtISEgQy7Vs2dLSSbU59q5O6P7dTDh5u+cPwRh+V+vyQa8PhU/zQMSfD0WNbi01Q0OWxq9DY7SdMwb3b0Th4JQlZjgCZWn3wVg0mTAA4TuO48KKrahQvwaajO8Pn2aB+HP4B/k1TyKyKYzDVBZSb50ZCSlIOH8LTl5uzEwiG8RygAzFOgER47CtUnr8Uvrx27qcPGDZZWBtaP5D/8Ku3Ae2RAAtKgIfPwZUY9WsRKy/Mg9s3fX7wNungVspRadnq4EF54Fll4BxDYDn64lnmiRDu6OAz84DCVlFp2flATNPAVVcgLeDgE5+lkqh9WNZwPwn27Y+FFh+Jf8HEIVdSwb2RgM13YG5rYAWlSyVQuun9Hqh0o9f0Q2/pJ6+nnjiCdHoa/r06ZgzZw48PT3FPKkHsFmzZsHBwQEqlQotWrSwdHJtTvDCl0VDrtPzf9E5BGOl5oFoMXUIovafxd5xCzBo90K0/3g8og9dQMa9+8WWd6tWCd2+mY6cjCyxvNSjGBmvQgN/NB7XD2Hbj2H/hIWa6SkRsejw8fj8Rni/H2IWE9kYxmEqi/XtX0FqRKz4/8l9X8DR3YUZSmRjWA6QIVgnIGIctlVKj19KP35bl6sG3j0N7L6jfblzicALB4H/huT3ekBFsf7KPLBll5KASUeAtJzSl0nLzW8gKvUE9kaz8kwdlYdN4cBH/2pfJjYDmHYCmPcY0Ks6P5eSsCywLOY/lcWqa8DXV7Qvc/tBfnm5pAPQpjLz+1FKrxcq/fj1JdvOQ6VhHKWhHaUhHBcuXKhp9CWZOXMmgoKCkJOTg9q1a8PLy8uiabU1TSYORN0hnRGx8wTOfblB67J2Tg7ovGQKctIycXj618hNz8LhN74SPYV1nP9Sict3XzUDrr4VcOj15bh/PcqMR6IMgYNDoLKzw6VvtxeZfn3NHmSnZaDu0C4WSxsRGYdxmMqqoNEXEdkmlgNkKNYJiBiHbZXS45fSj9/W/XhDd6OvAvekBh8ngDz+UL0Y1l+ZB7ZKauw17bj2Rl+F/XwL2HHb3Kmi8nQxEZh3Tr9lpfj/3mkgLNXcqbJNLAuY/2SbDt3V3eircC+Ib554OCQyPaT0eqHSj1/RDb8uX76MtWvXonLlyvjkk09KXOaxxx4Tf6UGYKWRhomUegR7//33zZZWW1M1uCnavPPc/w/BuFTn8q3efAYVG9XCifd/QNqdeDEt9sQVXF71BwL6txctMAvr8MmL8G1VH+eWbkTEjuNmOw4lqdyyHvJyc3HvzPUi03Mzs5FwIQyVW9a1WNqIyHCMw0REysZygIzBOgER47CtUnr8Uvrx2zJpWMdfbxm2jjQM3FH+RodINv6IBOINfHi9+iZHKpITqRwwpEGv1Ojht1BzpoiIqHytuWnY8qk5wOYIc6XGdim9Xqj041f0UI+//PIL8vLyMGrUKHh4eJS4jKurq9aGX+vWrcPZs2fNmk5b41qlArp98wbsHOxFV3o1H29T6rKJlyLg4OqEpi8Pwu09/+DGr3uLzD/9yc/w79m6yJCP9Z/tiQbP9kR6XJLo6avO0M6lbj9ixwnkpLPJrz7c/CoiMyEFeVnFf1qUFpMAv3aNYOfogLxsPX96REQWwzhMRKRsLAfIWKwTEDEO2yqlxy+lH78t2xdteIMPyW9hQCc/c6SIiMqTWm1cA55ryfnDvwZVMkeqqDxJPdbsiTZ8vW23gVcbA26yfHpLREoSmgKcvGf4ehvCgDH1AHuVOVJlm5ReL1T68etLlpcOe/fmNzLq3r17qctIw0CW1vArOTkZr7/+uhgicvTo0WVOT5s2bRATE2PQOo5qO8xBO1gT77o14OLjLf4Pem2o1mXPL9uEWn3bIjs1HUfeXFFsfm5G/pCPfTfOFUM+7hv/mfhSSqRhHqXhIbVZ3/ZlpEbGwVo0qN8A2ao8i+xb17li7+qM3KzsEudJLWElUiO9LIUHQ7K9c1uJGIetmyW/L4/i94fkcB6XlRy/BywHzMuWz3/WCUgu3wtrj92Mw6Y/d5QSv7Tlh7Y8kMvx23o5awzP4R/BvdtYg9f7OywZ/kObQEmsPfab+zug9OOXK5WLJ/y+uGzUusOnf4wHu782eZqofDk364mKr/xg8HoPcoCm3QchO/Q0lIJx0PL4GZA5uHZ6Ft6jFhi8Xkw6UCeoA3IT8ttyKIHS68VKP/7CqlatilOnTsEYsmz4FR4eLv4GBASUOD8nJweHDx8uteHX22+/jQYNGogew0zR8Etq9BUVFWXQOk4qe8DKft0Vc/Qivq82TO/l//l4tdb50pCPP/o/o3l/6PXl4mWL7kTfQZY61yL71nWu5KZnwtE9v8Heo+ydHcXfnPQscyWPbJw1n9tKxDhs3Sz5fXkUvz8kh/O4rOT4PWA5YF62fP6zTkBy+V5Ye+xmHDb9uaOU+KUtP7TlgVyO39bLWWME5ALuRqyncnI3+D6yrbP22G/u74DSj1+uHCtVN/pjTc3Kwx2FxQE5qlgnExWNXDc+JR0pCjoHGActj58BmUOVjGyUXJPRLTb5ATIYB2VXLzT2eljux28qsmz49eDBA/E3PT29xPlr167FvXv34OnpicDAwCLzpBZ03377Lf755x+TpUdqmWcoqWUjlPMDGJtXvVp1i/aKpO1cSbubCO8G/rBzcijWBaJb1UrIiL+v+K4PyTbPbSJrY8nvy6P4/SFjWdN5XFb8HpCSzn/WCUgu3wvGbuWdO0qJX9ryQ1seyOX4bb2cNYarnXHHqs5MRY0aNaAkSon9pX0HlH78cqVyNqbpZz53R5Xi4oAcObk6Gb2uj4crvBR0DjAOWh4/AzIHV2d7g9dRq9VQqVTw9XJDHuOg7OqFxl4Py/34y9quSNYNv6QMSUxMxOnTp9GxY8ci86KjozFjxgzxf4sWLUTwKJCbm4uJEydi8uTJaNq0qcnSY0x3bNlpGVhTt+y9jVH5uHb9GhzdXCyS3brOlXtnb6BGt5ao3Ko+Yo9fLtICtlKz2rh7zLgup0kZrPncJrI2lvy+PIrfH5LDeVxW/B6Qks5/1glILt8Lxm7lnTtKiV/a8kNbHsjl+G29nDXG/mjgzZOGr9ezrjc+i1TOsDZKiv2lfQeUfvxyNuZv4FKS4ett+fIdNKrwjjmSROXofhbQfxeQaWB7R29H4NCBLXAxvL2EzWIctDx+BmQOkQ+Ap/4ybB2p3UZNd+DEv8dh97AJh+wpvV6s9OM3FTvIUK9evcTf+fPn49q1a5rpJ0+eRPfu3UVvX5KWLVsWWW/ZsmW4e/cu3n///XJOMZH5hG4+AnVeHpq8OKDI9Pqjeongcmvj38x+IiIiIiIZY52AiGyV0uOX0o/floX4AX5G3NN/urY5UkNEljDMiO9zs4pAowrmSA2VN28noI8RnXYNqgVFNfoiIvnydweCqxi+3tDaUFSjL30ovV6o9ONXdI9fM2fOxM8//4zbt2+LnrsaNWqEjIwM3LhxA/369UPt2rXx559/IigoSLOO1Bjs3XffxcKFC5GTk4OkpIc/xZDWld57eXnBzk6WbeVIxpKuRODK/3ai8fj+6L5qBiL/Og3v+jXQZHx/xBy5iFsbD1k6iUREVM7qDOsCD39f8b+LjxfsHB3Q4vWh4n1qZBxureeFMhGRnLBOQES2SunxS+nHb8sc7IDR9YDPL+i/TpMKQNvK5kyVbWL9lXlgq6RGP99cBWLS9V9nTF1zpojK27N1gJ1RQLaevX652hvXYFAJWBYw/8k2PVcXOBoLqPVcvqIT8ERNMyfKBim9Xqj041d0wy9/f38cPHhQDOl44MABhIWFoUmTJli5ciVefPFF1K2bf/VcuOFXZGQkUlJSxFCP0qswqecw6RUaGioajRHZmhPvfY/U23FoMLoX/Hu2RkZCMi5/9wfOLFgrDZhs6eQREVE5azCyJ6oGFx3WuvWskeKvuFBmwy8iItlhnYCIbJXS45fSj9+WjQgEbiYDmyJ0L1vDDfi8nTS8TXmkzLaw/so8sFVSr02L2wMTjwBJWbqXn9gQ6FG9PFJG5aW+NzC3FfDOaSBPR5HtaAfMbwvUcC+v1NkWlgXMf7JNbX2B6c2AhXr8GMLNAVjUPr/HRCpO6fVCpR+/Yht+SRo3boxt27YVm56amioagkk9dzVr1kwzvV69eti3b1+x5aWhIZ9//nmMHTsWVatWNXu6icxB6v7w4sqt4kVERLRz6BxmAhGRwrBOQES2SunxS+nHb8ukRlxvBwFV3YCfbgAPckperlMV4L2WgI8RQ0MqAeuvzANbVtcL+F8I8P5Z4N+E0ns3mdQof2grkmfPbx6OwPxzQFRaycsEegD/CQJa+ZR36mwHywLmP9muEXWACk7A4ktAXEbJyzTyBua0zG8wSyVTer1Q6cev6IZfpbl48SLUajUaNGgANzc3zXQPDw9069atxHWkXr5Km0dEREREREREREREVFLjrwkN/n+4r0jg77vA8VggW50/pNearkAtD+YbkZzV9ABWhQBX7wO/hwNhqUBWLlDRGehdHehRDXCyt3QqyZyCqwC/9wSOxQHbbgNx6fnlg58r8GQt4DEf9vhIRPLW1x/oVR34OyZ/CNyDMfnXw1LvmCuCgaYVGAeJykpxDb/Onz9fbJhHIiIiIiIiIiIiIiJzkIauGVI7/9V/FxCbAXg6stEXkZI09AZmt7B0KshS7FT5DcCkFxGREjnY5Q9pLL0Kroe9HIFmFS2dMiJ5YMMvHaTewWyRc0UPPL7u4TBO9q7O8Azww6/NxyMrKbXIsjV6tELr2SOhUqmgcrDHha824+ZvB8S8YSe+Qm5mNnIz8gehP7f0d4RtOaJz/+0+HIdaj7eBR80q2NLrTSRcDCt12ZL2EX3wnN7pL8v+zbFvIrIu2mKcvssZEtOM4RlYFZ0XT4FzJU9kp6Th0GvLkHQtUsyzc3JA2znPo0a3lsjNzELCpXAcnLxE5zYNTXO9Z7oj5MtXsfeF+YjYebJMZYAh62pLZ+9f34WrbwUgLw/ZDzJw/J3vkHAhFObOd0PKUCKSN3tnR3RdMQ3e9f1FPMu4dx9HZ3+LlLCYIsvpihvmLEdMXYboe8z6xHtjyxFD4r8+eVtSGUdE8lWW+wFSjCrLtaAl74VoKw8epa180FYv0rduRURERERERERE5YcNv2QqMzEVW3rP0LxvOmkQqnZsUuKNwi7LpmLn0PeReDkcHv6+GHxwMcJ3HEfOg/yBdg9MWmTww6nw7Udx4atN6L/5I72WL2kf+qa/rPs39b6JyLroinH6LGdoTDNU8IKJuLZ6N26s24+AAR0QsngytvWbLeY99vZoqRUyNnaaIt6LB+F6MCTN0vE2GNULsaeuFptnTBlgyLra0nngpc+RlZwm/q/Vr514aC89QDN3vhtShhKR/F39aTei9p4R/zd6oS86ff4ydg59+FBeoitumLMcMUcZos8x6xvvjSlHDIn/uvJWWxlHRPJU1vsBZbkWtOS9EG3lwaO0lQ/a6kX61q2IiIiIiIiIiKj82EFh9u7dK3rxGjBgAJSk/rM9cP2Xv0qcJ3Vq5uTtJv539HRDRmIK8rJyyrS/u8cuIy06AeWRfnPv39B9E5F10TfGaVtOW0zxCaqLx3+bg4E75+OJXZ8hYGBHg9Ln4uMltnFzw9/iffj2Y3Cv7gPP2lXh4OqM+iN74PSnv2iWT49LMm0cVKkQ/PnLOP7OqjLHfmNoS2fBQ3+Jk6db/odUDvn+KJYDRMol9cRS0ABKEnf6Ojxq+upc79G4Ya5yxBxliLHHbEra4r9B5Z2FyzgisgxL3o+w1L4NubbVVT5oqxeZ4/4RERERERERERGVjeJ6/FIi3zYN4eztjtu7/ylx/oFJX6D7qhnIScuEk7c79o3/DHnZD2/chSyZIj0zQdyZG/hn3hpkxiebPI3a9qEr/ba8byIyP10xztDlCnPyckPwZ5OwZ/THSI9NEsOqPLFrAeJOXUVajH4PfNxrVEb63USoc/M001Kj7onp0kMZ6df9LaYOQbUuLcQwMGcXrkP0ofMwlaYTn0DsySuIP3erxPllKQNMUX5I26gW3FT8v3v0vHLJ98LDmbEcIKLCmkzoj4g/tQ8VaEjcKGs8K48yRJ9j1hbvjS0LSor/pi7jiIh0xShbuR+h77WtRGoMpq180FYvMqbORERERERERERE5sWGXzaq/9aP4VWnWonzpGEB0u7Ea95Lv+S88duBIjcAC6js7RD0+jBxs076Zar0C9GeP8zG5h5vIDMhBX8Mfg8Pou5B5WCP1rNGovPiydhj5IOX0ujah7b02/K+icj8sVB6+KEtxukbC0sjPYzxDKiC3mveLjLdq2518cDekFhdEpWDHTxqVkHS9UjxIKhSs0D0WfsuNnWdhox791FWFRrWRMCA9iIWlqQsZYCpyo9DU5eKv3Wf7oo274wW2zB3vhfGcoBI3gyJF82nDhEPy48Mn2uyuFHWeGbuMkSfY9YW78tSFpQU/01ZxhGRbTLldZ4+MUqO9yO0lQ+ZiSml1ouy7j8wqs5ERERERERERETmxYZfNmrHE0UfDpXGwc0FgYOCsa3frBLnSzf4XP0qipt2kvh/byItOl5Mj/77nLgJKVHn5OLSt9sw5HD+AxhT0rYPXem35X0TkfljofQwQluM0zcWlkalUiHpaiR2DHrb6FgtxSFp31Ljs4KHOh41KovpWckPkJebi1sbDorpCRdCkRoRi4qNayH6YNl7/fJr31g89Bl6JD/2ufpWQMfPJsG1SkVc/XFXmcoAU5cfN387gI7zX4JzRQ+z53sBlgNE8qfvNXXTSYMQ0L89dg2fi9z0rFKXMzRulDWe5WVmm60M0feYtcV7U5QFheN/ZmKqyco4IpJ33NaHrhhlS/cj9Lm2LbxsaeWDNNRuafUiqeGXMXUmIiIiIiIiIiIyLzszb58sLPDJYCRcCsP9G3dKnC/d8HPzqwjv+jXEe+kX/Z4Bfki+eUcMDyMNP6PZ1uAQxF8ILTIkQa1+7cqUPl37KC39ltw3WbeqHZtibPR68bDyUX4dm+DZqz9g+NlvUbFxQJF5XVdME+v1WTen1G23nD5cLCM1JnpUk4kD8XzUOvTb/FGR84osS1uMM2a5R8WeugqPWlVQrXNzzbRKTWvDzlH/dtUZ8clIOB+KukO7iPcBAzrgQXSCGJJF+uV89KELqN4tSMyTHmBL+7t/PcoksVB68L2u5YtY3+4V8Yo7fR1HZ6wQ03XFSG3712ddXaT1pQdLBWr1bSse+Esvc+e7Jt0sB4jo/8v4wMGdsOuZD8QDcW0MjRtljWfmKkP0PWZt8d7YuoS2+G+qMo7kjfUB0oc+16vWeD+itH3rc21bQFv5oK1eZGydyRriQeHXqBs/iWGVm7w0UDSUkzvmARERERERkXIpvU5YVUHHzx6/ZK7+yJ64tmZPsenBCyfh9q5T4nVkxgp0W/kG1HlqqOxUOPb2KnFDT7rx1/2/M8QJr1IBKeGxODTl4S9QKwfVweVVO0rcb8cFL8G/52NwrVIBvX95B9mp6dgYPKXYvl18vbXuo7T0a9u3vvtPvBJh1L7JNvn3egzdvnkD6XFJ2DX8A6SE39XMk3qQqNW3HZJDo1GtU1N4+PsiNTJO7223mjkCQdOGIWrfGewd/5nWXjGofElDWZUW4/SNhdpiivSr9z3PzUPb98ag7ZznYedoL9bZ+8ICg9J5ZOZKhHz5qhhSS9r2odeXa+YdnbkSnb54RQxzJaVNei8N/2WqOFwaXfFZ2/51rVt4/6Wl09HLDd2+mQ4HFydx3NLDrL/GfCLWL498l7AcICK3apXQ7v2xSA6LQd/174sMyc3KwfYBb5UYT0uLG+YsR0xdhhhyzNriva6yoLT9a4v/JeW5tvKOqDDWB5TDFPcDjL0fYcl7IdrKg0djp7byQVu9SNs8a3Zr40FE7j0tdbUpeoCs93RXtJs7VjRiOzpjJZSAeUBERERERKRcSq8T3lLA8avUarXa0omg4rLTMrCm7mirzRpnHy90Xf4ado34UFH7Ls2om6vh6OZikX1b+7liiZa7fTfOxcm5P+Liii2aX093XjwZ92/eEedN+t3EIus0ntBfBPdt/Waj/9aPcX7p7zi7cF2JPX61fHM4tvadJYa1kLT/aBwaj++P0M2HcXDKUuRl50BOeG5bL0vHQkvv3xpZ8vvyKJYNJIfzuKz4PbDeGG7p/cvx/Of5/hDrA7b9vbD2c5n3Qkx/7pjzMy8pHhT0uDb44GLR2PnXFhOQGZ8MS+YH80B5+u8CYjOAKi7Ajj6WTo3lWXvsN3ccUPrxExExDloePwMqb7weLr/voC3Ui5V+/KYin77LqFxJJ7+lHtRYct9kexqO6YMuy6Yi/nwo/hj8XrFGXwW/po45chHx524hcs9p1Humu2jxq430y+zOS6eIRl9XV+/GgZe/lF2jL7Julo6Flt4/ERHZbgy39P5JWVgfIFPivRB5yEnPFEMAq+zs4BXgByViHhARERERESmX0uuEOTI8fjb8IiLZaj5lMDrOfwnRhy/iz6fnIisptdgyPkF1UalJAG6s2y/e31i7Twz1WL1Li1K3a+/sKIblqDusK84v25TfBSQ7TyQiIiIisiqsDxBRaTxr59/YzSzhPoFSMA+IiIiIiIiUS+l1Qk+ZHb+DpRNARGQODZ/vA6/aVRH+x3EcmLQIeVkl98ZVf2QPZD9IR/j24+J95N4zSL93X0y/c+DfEtcJWTxZbPvUR6txYfkmfoBERERERFaG9QEiKuDg6gTnSp6iZ29X3wqiJ0Cf5nXEr3uTb0UrIqOYB0RERERERMql9DqhgwKOnw2/iEiW3Pwqir8pYXdLbfRl7+KEOk+FiEZfOWkZYpo6Jxe3Nh5EozF94FTBo8RewqRtS8M6Jt+6Y+ajICIiIiIiY7A+QEQFWs0cIV6FhW0/huNv/VcxmcQ8ICIiIiIiUi6l1wlbKeD42fDLSjm4OmPUzdWWTgYZ8HmRdTm/9Hf4dWyKZi8PAlTAqbk/FlsmYGAHOHm7I+boJXjWrqqZfvfYJTR9aSDqDu2Cy6t2FFvv8LSv0Obd59B1xTQcePlLROzI7y2MTItxkGyNNZUF/P5QWc4dueD3gIw5Z0g+WB+wTYzd8mFNMfXqT7sQtvUo7BwdULFRLTR79Sm4V/NBbmaWZhk7Jwc8seszhP5+EOcWb9RMD/nyVbj4VsCeUR/rtYy10icPun49DbBT4cDELzTTpB/EPbV/EU598CNq9m6jdb70IzoiYygl9pcWF5V+/EREjIOWx8+ASP6UXie8qoD7Amz4ZaVUKhUc3VwsnQwim5WTnoW/xnyCnj/MRrNJg6Cys8PJOd8XWabByJ7ib8iiV0rcRv0R3Uts+JUcFoOdQ+eg74a56Pb/jb/Ctx8z05EoF+MgEb8/RCxHiMhYrA/YJtYByBySb8Ug+uB58X/U3jO4e+IK+m/+EB3nT8SBlxeJ6VJP4YemLkXf3z/A7d3/IPFSOGr1bQv/3m2wuccbei9jy3lw9K1v8eTezxH4VCeEbjospnWYNwGxJ66IG/iRe89onU9kLKXHfqUfPxER46Dl8TMgkj+l1wmTFXBfwM6ieyciMqPcjCz89fynuHPgX9GDV7sPxmrmeQb4wa9DY9zc8Df2TVhY7HXlhz9RqVkgfFrUKXHbKeF38ceQ95AWm4iuX7+OgIEd+VkSEREREVkR1geIqCRxp67i5vq/xc1q3zYNNdPjz93Cxa+3oPOSKXCrVgkdP5uE4//5L9LvJhq0jK3mQVZSKo5M/xrtP54AV7+KCBjQAVWDm+LorJV6zSciIiIiIiLboPQ6YZwM7wuw4RcRyf9hz9j5iDrwL5q8OBDtPnxBTK83sofoBeziyq2it65HXxeWbcpfbkSPUredGhGLnUPmIO1uIrp+9RpqP8HGX0RERERE1oT1ASIqyb+L1iMvJxetZjxTdPqXG5CXm4tBuz9DzOELCN2c/ytmQ5ex1TyI2ncWYVuPoMuyqejw6Yvipn5mYqre84mIiIiIiMg2KL1O+K/M7guw4RcRKeJhz97nP0XU/rNoMmEA2n80DvWGd0NKxF0knA8tcZ3UyDjc+/cm6gwOgb2zo16Nv7p89ToCn+xkxiMhIiIiIiJDsT5ARI9KCYsRN2ard2mBKu0ba6arc3IRd/IqXHy8cWPtvhIzTp9lbDkPTs39EZ6BVcXwF5F/nS62nq75ZFpbtmxBy5Yti7xq1KgBFxcXrfNK8/TTT+Po0aOa99evX0dwcDAaNGiAtm3b4uLFiyWuV9py8fHxRfYvzXdwcEBCQoKY36dPH7Ro0ULM69y5M86cOaPZZu3atdGwYUPNumvXri2yT2n50NCS79sRERERkTIo4Xq4TYf2eO/eHhxPv11kn/Pi9yMu5wHMRel1whSZ3Rdgwy8iUoTczGzslXr+2ncGjcf3h3s1H4TvOK51HannL+cKHqjVv73W5VJvx2Ln4PeQFh2PzsumInBwiIlTT0REREREZcH6ABE96tzi/F/oFv51r3Szt94z3XF51Q60++AF2Ls4FVtPn2VsOQ9y0jORGh6LxMsRJa6jaz6Z1qBBg3D27FnNa//+/XBzc8Py5cu1zivJiRMnxAOojh0f9lg/ceJEvPTSS7h27RpmzZqFsWPHlrhuacv5+PgUSYO0TL9+/VCpUiUxf926dTh37pyY98YbbxTbvtTYq2DdZ54p+kv76dOnY86cOWXOQyIiIiKyXUq4Hj517Dg+qNwL7V1rFpnX170+NqVegjkpvU54Tkb3BVRqtVpt6UQQkfGy0zKwpu5oZiGZxaibq+HoVnrLeCIiIiKyPNYJyFxYHyAlxS8HNxcM+mshLq3chis//Il+v38gegI/Oed7g5Yx9LtkTXlQoO+Gubi9+x9cXLHFqPklYTx5qP8uIDYDqOIC7Oij/+eSl5eHJ554AjVr1sSKFSv0nldg/PjxopcC6a8kNjYW9erVEw+/pF4JpMcE1apVw6FDh8T0AvouJ2ncuDE++eQTPPXUU8X2//333+PLL78UD70KejjYtGmT6P2gJNnZ2ahevTpu3LgBb29v/TOKiIiIiKwar4eLXg83bdCoxDphjjoP02K3Y75vX7jZlT46la3XCa2pTuxgofsCpsAev4iIiIiIiIiIiEjR2r4/BqkRsbjy/U5Arcah15ahwbM94dehsUHLEJmL1PuV1PhqyZIlBs0rIPWA0L79w17tb9++LRpwSY25JCqVCrVq1UJERNFf7uu73JEjR5CYmIiBAwcWmT5mzBjRIO3dd9/FTz/9VGxe8+bNRWO0uLi4IvMcHR3FvIMHD+qRO0REREQkd3K9Hm7Vtg2+u/8PkvMyi8xzUNnB39Eb17Lu6ZE7pPT7Amz4RURERERERERERIpVo0crBA7qhMNvfKWZlhJ+F/98vAadFr0KB1dnvZYhMpfNmzdj1apV2LBhA5ycnPSeV1hkZCT8/PzMlkYpDdKDq4IHYgV+/PFH8bDso48+EsPiFPj777/FsDenT59G5cqV8fzzzxfbZtWqVUW6iYiIiEjZ5Hw9fOLIUXjYOeG/SSeLbdPbzgWJeelmSzPJ575A0bOOiIiIiIiIiIiISEGi9p7Bz42KNzqRfsErfsWr5zJysXPonDLNJ9O6evWq6BFLGgZGGvpQ33mPcnNzQ0ZGhua91OtAdHQ0cnJyNEM4Sr0WSL0XFKbPcqmpqVi3bh1Oniz+sKqA1LBr0qRJiI+Ph4+Pj2Z9qWev119/HQ0aNCi2jpReV1dXPXKJiIiIiORKCdfDfdzq4a17u4qtk63OhaPKHuVNiXXCKBu/L8Aev4iIiIiIiIiIiIiIrExKSgoGDx6MuXPnIiQkRO95JWnRooV4MFagSpUqaN26NVavXi3eSz0k+Pv7o169ekXW02e5tWvXIigoCI0aNdJMS0pKwp07dzTvpYdx0gOuSpUq4cGDB2J+gV9++QWtWrUqlubLly+L7RIRERGRMinlevh4xm3UcqhQLM3ROSmo5eCt89iI2OMXEREREREREREREZGVWb58uXg49e2334pXYcOHDy913o4dO4r1eDBs2DD8+eef6NWrl2baypUrMXbsWMybNw9eXl743//+p5k3YcIEDBo0SLy0LVcwrM2LL75YZNr9+/fx9NNPIz09HXZ2dvD19cW2bdugUqlw9+5dDB06FLm5uaLHhDp16oghcAoLCwsT89nwi4iIiEi5lHA9nJebB8esBLzo3abI+vdyHiAPatRkwy/Sg0ot1ayIyGZlp2VgTd3Rlk4GydSom6vh6OZi6WQQERERkRasE5C5sD5A5qaU+KXtu8Q8UJ7+u4DYDKCKC7CjT/ntVxp+Jjg4GEePHoW7uzus3ezZs0UvCtIDNyIiIiKSD14P61cn/C3lPKrYe6CrWyDkXC9mndg0ONQjEREREREREREREZGMeXh4YNGiRQgNDYUtkHpoGDdunKWTQUREREQyYWvXwxXsXNHZtbalk0E2gkM9EhERERERERERERHJXM+ePWErpk6daukkEBEREZHM2NL1cG/3epZOAtkQ9vhFRERERERERERERERERERERERkY9jwi4iIiIiIiIiIiIiIiIiIiIiIyMZwqEciIiIiIiIiIiKyaZ6BVdF58RQ4V/JEdkoaDr22DEnXIostV39kDzSfPBiwUyHm8AUcnf0t1Dm58H2sATp++qJYRuXogNgTl3H8ne+Ql5UjplVoVAsdPh4PF19v8f70p78gYsdxQKVC2zljUKN7S+Tl5CEzMQVH3lyBlLCYcs4B5gEREREREZGSKb1e7Kng42fDLyIqU2Bs9+E41Hq8DTxqVsGWXm8i4WKYXvNMsW9tyzpX9MDj6+ZolrN3dYZngB9+bT4euemZ6LpiGrzr+yM3IwsZ9+6LgF4QfGv0aIXWs0dCpVJB5WCPC19txs3fDpjsTNH3+MxxbOVxfEREREQkD2W9btV17anP9us90x0hX76KvS/MR8TOkyZLs65le//6Llx9KwB5ech+kCFu8iRcCNV5TNaS79rSXx7HQGQJwQsm4trq3bixbj8CBnRAyOLJ2NZvdpFlpPsTrWaOwNY+M5Eel4Qe389Cw9G9ceX7nUi4FIat/WaLm73STdvuq95Eo7F9cembbbB3dULP72fh4NSliD1xBSo7OzhV9BDblO57VGnbCJt7vinWbfH6ULR+61kcmPgF88ACeUBERERERKRUSq8XByv4+DnUIxFpDYy/h0zF+WWbRGAsSfj2o9jx5DtIvR1r0DxT7FvbspmJqdjSe4bmdW31HkTtPYOspFQx/+pP+etIDdIi/jyJTp+/rNlml2VTcei15WK9v577ROzDwd3FZGeKvsdnjmMrj+MjIiIiInko63WrrmtPXdv38PdFg1G9EHvqqsnTrGvZAy99ji09p4t0X1y5VTQ+0+eYrCXftaW/PI6BqLy5+HjBJ6gubm74W7wP334M7tV94Fm7apHlAgZ2wO1dp8TNXcnVH3chcHAn8X9uelb+zV3pB1ZODnBwcQLUavG+zuDOiPvnmri5K1Hn5SEzPjn/f3X+8vbOjuK9o4cr0qLjUd6YB0RERERERMql9Dqhi8KPnz1+WSm1Wo2c9ExLJ4P05ODqLH4pLRcFgXHXiA81gbHDvPEiMD7aJeHdY5dL3Y62eabYtyHL1n+2B07PWyP+z83MFg2lCsSdvo5mLw/SvJeCs5O3m/jf0dMNGYkpmi4cy0rfNJvr2Mx9fNaOsZXkGLNLwnOdjKWE74c2/O7Ih9LPZWu6bi3t2lPn9lUqBH/+Mo6/swpt5zxv0jTrs2xWcppmWSdPN81NHlupL2hLv7mPoSwYh+WjvOOwe43KSL+bCHVunmZaatQ9Mb3wd8ejRmWkRsY9XCYyTiyjme/vK37t61nbD5F7TuPK93+K6RUa+CM3Kxs9f3wL7tUqIeFyBE7O/UHc5JVuGFfr1BTPnPsvclLT8SAmATsHP+ylu7wwD8hWKSX2lxYXlX78RESMg5allPyXsCwiuVN6ndBd4cfPhl9WSipk19QdbelkkJ5G3VwNRzf5/Dpa38Bo6X3ru6xvm4Zw9nbH7d3/lLjPJhP6i56xChyY9AW6r5qBnLRMOHm7Y9/4z5CXbZqHIPqm2VzHZu7js3aMrSTHmF0SnutkLCV8P7Thd0c+lH4uW9N1a2nXnrrWazrxCcSevIL4c7dMnmZ9lw1ZMgXVgpuK/3ePnmdz9YXS0m/uYygLxmH5sNU4LN3wlXrPdnBzQZflUxHQvz1CNx+Gyt4e1Tu3wPaB/0FaTAJa/+dZdPz0Rex/8XNUDqqLCg1r4bdWLyErJR2PvT0KHRe8hIOTl8AWMQ+ovCkl9pcWF5V+/EREjIOWpZT8l7AsItKP0uuEqTZ6/Gz4RUSyV39kD9z47UCRhyIFmk8dIn4Vf2T4XPFeZW+HoNeHiQcfUo9l0q/oe/4wG5t7vIHMhBSt++m/9WN41alW4jxpCJW0O/EWPbayHh8RERERkSG0XXtqU6FhTQQMaI8/Br9n0Qw/NHWp+Fv36a5o885o7Bk9z6bqCyWlX8I6AcnRg6h7cPWrKM7vgvqx9CteaXphUgNJr0LDPEi/5H10GUlOWgZCNx1GnSGdxQ1eaZnoIxfFzV3JrfV/o/cv72q+Y9GHL2h62ru5bj96/5o/rzwxD4iIiIiIiJRL6XXCBwo/frty3RsR2YTCgbFASYHR0vvWZ1mpNW7goGDc+HVvsfWbThokWunuGfWxGLNXUqlZoNhmwTCV8f/eFGPwStN12fHE2/i16bgSXwUPcfQ9PnMcW1mPj4iIiIiUwxTXrdquPbWt59e+MTxqVsHQI0sx7MRX8G1dHx0/m4SGY/qYJM2GLnvztwOoGtwUzhU9bKq+UFL6JawTkBxlxCcj4Xwo6g7tIt4HDOiAB9EJxXr7k4ZFrdmnDVx9K4j3UlyRbuRKpB9OqRzsxf92jg6o1a8dEi6Hi/dhW4+gcsu6cPRwFe9r9GyNhEth4v+UiLuo1qmZWEfi37sNkq7eRnljHhAREVFYzW8AANs9SURBVBERESmX0uuEGQo/fvb4RURaA+ONdftLDYxlIQ07EvHHcUT8ccLofeuzbOCTwSLo3r9xp8i6TSYORODgTtg1/ANN61uJ9KDEza8ivOvXwP3rUSLAewb4IfnmHa3p1pe+x2eOY9Pn+IiIiIiITHXd6lLZu9RrT23rXZVeP+7S7KPvhrm49O02ROw8WS51CScvN9i7OouhFCW1+rZFZmKqeEndult7fUFb+iWsE5BcHZm5EiFfvip6v85OTceh15eL6cELJ+H2rlPilRoRizML16Hflo/EvJgjF3H1p93i/2ohzdB4fH/xy2DpRm/0wfM4t2i95ntzbslG0XOfOk8tfuF7ZMYKMe/K/3aiQn1/DPprIfKyc5Eel4SjM1cyDyyUB0REREREREql9HrxEQUfv0qtVqvLdY+kl+y0DMWMqSwHlhwX2lznilfd6iIwOlf01ATGpCsRxYKjND6tf8/H4FqlAjITU8SyG4OniOW0zXvqwCIcnLpM/ELe2H3rWlbSf8vHuLZmD26s3aeZ5latEoaf/gbJYTHISU0X03KzcrB9wFvi/8CnOqHF1CEiaKvsVDi39HeE/n5IZ7pNnbfmODZdxyf3Mc8ZW0mO53VJeK6TsZTw/dCG3x35UNq5bOk6gbbltF176rreLa3hl7F1CX3T7e5fGd2+mQ4HFyeRbqmR1akPfkTCxTCbqC/oSr+uY7Dkd4lxWD70PXeU8plryw/mgfL03wXEZgBVXIAd2juzVASlfweUfvxERIyDlqWU/JewLLIevB5W5veQ18OrzXo9zIZfVkopX3C5kGPDL3Ny9vFC1+WvYdeID2FLbDXdZSG3C2Fb/L6Q6cntvC4Jz3UylhK+H9rwuyMfSjuXlXTuWvM1uTWnzVhs+EXmPneUEr/Y8Et5ZbM2fNBVlNLjgNKPn4iIcdCylJL/EpZF1oPXw8r8HvJ6eLVZr4ftzLZlIqJSZMYn2+TDEFtNNxERERGRXFjzNbk1p42IiIiIiIiIiIjkiQ2/iIiIiIiIiIiIiIiIiIiIiIiIbIyDpRNAtmVs9Hq9lts5ZA7snBzQ59d3ceO3Azg0dWmxZVrNHIGgacNw+M0VuL5mD+oN74aQxZN1bjv1dizWt3vFqPQTEdk6xmEiImVjOUBEZFmMw0REREREREREZE3Y8IsM8vfkxaXO86zlJxpzZcTfx/2bUUiPTcLV1bvRcHRvhG87itu7TmmW9Qmqi+aTn0LUvjOi0Zck5tglrduv93Q3VO8ahLsnrvBTIyLFYhwmIlI2lgNERJbFOExERERERERERNaEDb/IILc2HCxxur2rEwZsnYe8nFzsn7hINPqSnHz/B9ToGoSOCyaKBltZSamwd3ZE5yWTkf0gA4ff+FqzjdSIWPEqSdWOTVE1uCkSL4fj6IyV/NSISLEYh4mIlI3lABGRZTEOExERERERERGRNbGzdAJIHkK+eBWVmtbGPx+tRszhC5rpOf/fuMvV1xsdPh4vprWaPRIVGtTEiff+h7SYBJ3bdq9RGV2/eUM0FNs77jPkpGea9ViIiGwR4zARkbKxHCAisizGYSIiIiIiIiIisgT2+EVl1vTlQQh8qhNCNx3GxZVbi82PPnQeV37YhcYv9BUNvZq8OAARO0/i5m8HdG5b6h2s+6oZcK7ogb/GfIqUsBh+YkREjMNERMTrcSIiq8H7IkREREREREREZCns8YvKpFrn5njsP6OQcCkch9/4qtTl/vnwJySHxaDZK08i634ajs7Ub7hGaYjIykF1cfbz3xC19ww/LSIixmEiIuL1OBGR1eB9ESIiIiIiIiIisiRF9Ph17949LFiwABs3bkRkZCR8fX0xZMgQzJs3D1OnTsV3332HpUuXYvLkyZZOqk3x8PdF1xXTkJ2ajn3jFmgdgjE3OwfZKeni//hzN5Eel6Rz+43H90e94d1E72DnFq03adqVpvmUwfBpXgc+LerAM8APqbdjsb7dK5ZOFhGVEeMwGU2lEj1wNnyutziPMuKTEbr1CM4uWMshlYlsCMsB0hfrA0SMw7ZM6TFM6ccvJ3EZwOG7wIOc/PdZeYBaLapnpA3rr8wDmchTAyfigLBUIDsPqOgEhFQFKjhZOmVUXjJygIN3gbsZ+b1yVHMDOlUBnOz5GejEssCymP9kIqnZ+XEw7f+vhzNzgZw8wIFdFWnFOiHzQB+yb/h19uxZ9OvXDzExMXB3d0eTJk1w584dLFmyBDdv3kRCQoJYrmXLlpZOqk2xd3VC9+9mwsnbPX8IxvC7WpcPen0ofJoHIv58KGp0a6kZGrI0fh0ao+2cMbh/IwoHpywxwxEoi9QrW0ZCChLO34KTl5ulk0NEJsA4TGXR7oOxaDJhAMJ3HMeFFVtRoX4NNBnfHz7NAvHn8A/ynz4QkVVjOUCGYH2AiHHYlik9hin9+OXgUhLw/XXgQAyQW6iqlZQFjNwPjKgDPFmLDcBKw/or88DWSQ+0f70F/BYGRKUVnedsB/SuAYytD9T2sFQKydwSMoEfrgNbbwPJ2UXnSQ0AnwoAxtQDPB35WZSGZYFlMf+prKLTgP9dB/6IBNJzH06/nw08uQcYXBsYXRdwYUPYErFOyDyA0ht+ST19PfHEE6LR1/Tp0zFnzhx4enqKeVIPYLNmzYKDgwNUKhVatGhh6eTalOCFL4uGXKfn/6JzCMZKzQPRYuoQRO0/i73jFmDQ7oVo//F4RB+6gIx794st71atErp9Mx05GVliealHMSqb9e1fQWpErPj/yX1fwNHdhVlKZOMYh8lYFRr4o/G4fgjbfgz7JyzUTE+JiEWHj8fnN87+/RAzmMjKsRwgQ7A+QMQ4bMuUHsOUfvy2bu8d4J3T+b17leRGCvDRv8A/8cCcluzt4FGsvzIPbF16DvDmSeB4XMnzM/OAbbeB/dHAovZAK5/yTiGZW+QD4NWjxRv9FUjMym8MITUOXtYBqOLKz+RRLAssi/lPZXXlPjD1KJCQVfJ8qRfEFVeAI3eBL9sDXuwJsxjWCZkH+pB1x3nSMI7S0I7SEI4LFy7UNPqSzJw5E0FBQcjJyUHt2rXh5eVl0bTakiYTB6LukM6I2HkC577coHVZOycHdF4yBTlpmTg8/Wvkpmfh8BtfiZ7COs5/qcTlu6+aAVffCjj0+nLcvx5lxiNRjoIbhEQkD4zDVBaBg0OgsrPDpW+3F5l+fc0eZKdloO7QLsxgIivHcoAMxfoAEeOwLVN6DFP68duyM/HAf/4pvdFXYVLvB19cLI9U2RbWX5kHtkzqTF1q+Flao6/CUnOAaceB0JTySBmVl/tZwGQtjb4Ku5UCvHb84fBn9BDLAsti/lNZxKRpb/RV2LlEYMbJ/J4yqSjWCZkHim74dfnyZaxduxaVK1fGJ598UuIyjz32mPgrNQArsH//ftED2KMvDgWZr2pwU7R557n/H4Jxqc7PodWbz6Bio1o48f4PSLsTL6bFnriCy6v+QED/9qJXkcI6fPIifFvVx7mlGxGx43gZzgAiInliHKayqtyyHvJyc3HvzPUi03Mzs5FwIQyVW9ZlJhNZMZYDRESWxThMRPpaegnIKTS0oy7rQvN7hqGHWH9lHtiy0/H5vTjpS2r89e01c6aIyps0vGekHo2+ClxPBrbfNmeKbBPLAuY/2a4fbujX6KuA1AvuwbvmTBGRfMl2qMdffvkFeXl5GDVqFDw8Sh4c3dXVtVjDrwLLly9H69atNe/d3d2hdK5VKqDbN2/AzsFeDA9V8/E2pS6beCkCDq5OaPryINze8w9u/Lq3yPzTn/wM/56tiwz5WP/ZnmjwbE+kxyWJnr7qDO1c6vYjdpxATnqmSY+PiMjaMQ6TKbj5VURmQgrysor/hDAtJgF+7RrBztEBedn8iSGRtWE5QERkWYzDRKSvq/fzey0w1IYw4LWmzOcCrL8yD2zZ+jDjhoe91xSozFF9bZ7UY83vYcY1FhtWG1CpzJEq28SygPlPtulBjnGNWX8LBbpXM0eKiORNtg2/9u7Nb2jUvXv3UpeRhoEsreFXkyZN0KFDBzOm0PZ4160BFx9v8X/Qa0O1Lnt+2SbU6tsW2anpOPLmimLzczPyh3zsu3GuGPJx3/jPxINmiTTMozQ8pDbr276M1Eg9+kgmIpIRxmEyBXtXZ+RmZZc4T+r1SyI13s5iwy8iq8NygIjIshiHiUhfu6OMy6s/o9jwqzDWX5kHtiorF9gbbfh6Ui+B+6KBpwPNkSoqT/8mAHczDF9PGvLxZgpQz8scqbJNLAuY/2SbDt0F0nINX+/EPSAxE6jobI5UEcmXbBt+hYeHi78BAQElzs/JycHhw4dLbfhlSm3atEFMjAF9+gJwVNthDtrBmsQcvYjvqw3Te/l/Pl6tdb405OOP/s9o3h96fbl42aIG9RsgW2WZQYet8Vwh+bDkuW0Otv59YRw2Dbmd14ae67npmXB0z2/I/Sh7Z0fxNyfdgP6XSVaU8P2w5XKC5YD+lHYuW/u5S7arvL9L1n4uMw6b/tyx9s+8PPKDeSBPXqMXwi14hMHr3X2QDX9/ZbX4UEr9tbQ4oCsGyCUPlHZ9bufliyqfnjFq3Xc/XYRp2z83eZqofDm3GoCKL640at0+g59B1tX855dKwDjI/C8vSiuLLM2t2zh4Df/AqHVbhfRATrRyxj9Wep1Q6cdfWNWqVXHq1CkYQ7YNvx48eCD+pqenlzh/7dq1uHfvHjw9PREYWLwy/cwzz4j5Pj4+GDRoED799FNUrlzZqLRIjb6iogz7mZeTyh7wM2p3ZAF3ou8gS21Es2UT4LlCcj23zYHfF5LjeW3ouZ52NxHeDfxh5+RQbLhHt6qVkBF/n8M8KpgSvh/asJyQD6Wdyzx3SS7fJZ7Lyjt3lPKZa8sP5oE82Sffh5sR66lzsg2+j2zrlFJ/LS0O6IoBcskDpV2f2ydnoIqR695PvIcYhcUBOapQMxYVjVw3LiYaqQo6BxgHmf/lRWllkaX5JtyDsZ0XxkTdRmY046DcGHs9LBd3zByDZNvwS2oNl5iYiNOnT6Njx45F5kVHR2PGjBni/xYtWkBVaLBsb29vMa9Lly7w8PDA0aNH8cknn+DYsWOidZ2Li4tRaTGU1LIRbHRsM6pXq27RHr94rpAcz21z4PeF5HheG3qu3zt7AzW6tUTlVvURe/xykV8KV2pWG3ePPZxGyqOE74c2LCfkQ2nnMs9dkst3ieey8s4dpXzm2vKDeSBPzmmxRq2XGxeGGjVqQEmUUn8tLQ7oigFyyQOlXZ9DZYe81ATYeVQyeFX3jHjFxQE5cshJFn/VanWRZ5C6qPPyUFGVDm8FnQOMg8z/8qK4ssjCnLISjVovLzMNvi6AmnFQdoy9HpaL6nrEIGPaFcm+4VevXr1w+fJlzJ8/H71790aDBg3E9JMnT+K5554TvXlJWrZsWWS9Vq1aiVeBbt26oVmzZqLXr19++QUvvPCCwWkxpju27LQMrKk72uD1yDKuXb8GRzfDGwWaAs8Vkuu5bQ78vpAcz2tDz/XQzUfQYuoQNHlxQJGbxvVH9RL5cmvj3+WYUrI2Svh+aMNyQj6Udi7z3CW5fJd4Livv3FHKZ64tP5gH8pSQCfTfBeSoDVvv7X5NMPyVSCiJUuqvpcUBXTFALnmgtOtzyZJLwI83DFunohNw5PcVcLI3V6qoPI07CJxL1L/Rl6RzNTt8eeEklIRxkPlfXpRYFllSnhoY/BcQlWbYekPqu+GdW9ehJEqvEyr9+E1Ftg2/Zs6ciZ9//hm3b99G06ZN0ahRI2RkZODGjRvo168fateujT///BNBQUE6tzVw4EC4u7uLBlzGNPwisrQ6w7rAw99X/O/i4wU7Rwe0eH2oeJ8aGYdb623jBgEREZVd0pUIXPnfTjQe3x/dV81A5F+n4V2/BpqM74+YIxdxa+MhZjMRkcywPkBEtkzpMUzpx2+rKjkDvaoDOw0YocbNHujvb85U2R7WX5kHtmxoAPDTDcCQ9p9P1gIbfcnIsNpSwy/D16GiWBZYFvOfjGWnAobWzm8IbQjGweJYJ2QeKLrhl7+/Pw4ePCiGbTxw4ADCwsLQpEkTrFy5Ei+++CLq1q0rltOn4VcBQ7pjJbImDUb2RNXgpkWmtZ41UvwVD/l5k5CISFFOvPc9Um/HocHoXvDv2RoZCcm4/N0fOLNgrdQHvaWTR0REJsb6ABHZMqXHMKUfvy2b1jT/gf8dPXo5kO46v9cK8HAsj5TZFtZfmQe2qoZ7fhz44qJ+yzfyBsblD1xDMtHXHzgQA/wVrd/ygwOATlXMnSrbxLKA+U+2aUQgcPgu8E+8fstPaAA0qmDuVNke1gmZB4pu+CVp3Lgxtm3bVmx6amqqaAhmZ2cnhnHUZcuWLXjw4AHatWtnppQSmdfOoXOYxUREpKHOy8PFlVvFi4iI5I/1ASKyZUqPYUo/flvm4wKsCAZeOwaEppa+nKMdMKdlfg9hVBzrr8wDW/Zs3fwev768qL3nrxYVgS/aAW6yfmKnzN5uPmwNOJwF/ozS3UPcjOZSBxTllTrbwrKA+U+2SRq6+Iv2wOyTwNE47cu+2AB4qWF5pcy2sE7IPNCHIi8jL168CLVajQYNGsDNza3IvNGjR6NOnTpo3bo1PDw8cPToUSxYsAAtW7bEiBEjLJZmIiIiIiIiIiIiIrId1d2A1V2B3XeADWHA+UJDflVyAp4KAIYEAFWL3qImIhkZVTe/F6cN4cDWCCA15+G8dpXzh7TqUhVwsLNkKsmcjR4+ap0f69eHAXujgdxCrQClIX6frg00q8hGX0QkT+4OwJcdgKOx+XFQ6gGsIAy62gP9/PPLwgbeFk4okY1TZMOv8+fPlzrMY9OmTfHzzz/jyy+/RHp6uhgyUhoacs6cOXBycrJAaomIiIiIiIiIiIjIFjnbAwNr5r8SMoHkbMDFHqjszIYeREpR2xOY3gyY3BgYtAeIz8yPAV8FWzplVB6kXrweq5z/Ss0GhvwFJGTlnwMftOZnQETyZ68CQvzyXynZQGJm/jSph1zpupiIyo4Nvx7x1ltviZcctPtwHGo93gYeNatgS683kXAxrMTlhp34CrmZ2cjNyBLvzy39HWFbjsC5ogceX/ewO3l7V2d4Bvjh1+bjkZWUapJ9l7b/6IPnbHLfRGTbev/6Llx9KwB5ech+kIHj73yHhAuhxZbzDKyKzounwLmSJ7JT0nDotWVIuhYJe2dHdF0xDd71/UVcybh3H0dnf4uUsBiTpK+0/Rawc3JA2znPo0a3lsjNzELCpXAcnLxE6zYNSXNp8bUs5YUh+a5PfK/3THeEfPkq9r4wHxE7T+rcNxGRKZkynhkT03SVE/rsX9s2jClnzJE3jPVEpC9t8ULXNawh9xYMjWPmuhejTzmga/ulpcvcdR0ipajknP8iIuU2BJUedBcMA0jK4+H4sNEvzwEiUiJPx/wXEZkWG37JWPj2o7jw1Sb03/yRzmUPTFpU7GZkZmIqtvSeoXnfdNIgVO3YRK+H+Ibsu7T92+K+ici2HXjpc2Qlp4n/a/VrJx4SSQ9rHhW8YCKurd6NG+v2I2BAB4Qsnoxt/WaLeVd/2o2ovWfE/41e6ItOn79ssvG3te1X8tjbowG1Ghs7TRHvxQN2Peib5tLia1nKC0PyXVd89/D3RYNRvRB76qpe+yUiMjVTxTNjY5quckKf/WvbhrHljISxnojKm644qusa1tB7C9ZwL0afckCf7ZeULnPXdYiIiIiIiIiIyDiKHDV87969UKvVGDBgAOTs7rHLSItOMNn26j/bA9d/+Yv7JiLZKnhYL3HydBMPtx/l4uMFn6C6uLnhb/E+fPsxuFf3gWftquKX8QUPQiRxp6/Do6av5r203uO/zcHAnfPxxK7PEDCwo95p07ZfiYOrM+qP7IHTn/6iWSc9LknndnWl2ZhyxZDyQt9817l/lQrBn7+M4++sQl5Wjt77JiIyJZPEMyNjmq5yQp/9a9uGseVMAcZ6IipXRsTRR69hjb2nYql7MYaUA8Zs35B6AxERERERERERlR9F9vhFxYUsmSLGGY87cwP/zFuDzPjkIvN92zSEs7c7bu/+p9z3L+d9E5H1kWJCteCm4v/do+cVm+9eozLS7yZCnZunmZYadU9Mf3SYkyYT+iPiz/whZZy83BD82STsGf0x0mOTxPArT+xagLhTV5EWo/vBkK79Sg90pF/pt5g6BNW6tBDDr5xduA7Rh84bdPyF02wMY+OmrnzXpenEJxB78griz90yeF0iIlMqazwzNqYZUj4Zsw2p4VdZyxnGeiIqL4bGUUvV/U15L8aYcqC07etKlynqDUREREREREREZBps+GWj+m/9GF51qpU4T+qyP+1OvN7b+mPwe3gQdQ8qB3u0njUSnRdPxp5HHlJJv+6/8duBIjcQTUXX/uW6byKyToemLhV/6z7dFW3eGV0sHuqr+dQhojHWkeFzNQ9VPAOqoPeat4ss51W3umj4pSuu66JysINHzSpIuh4pHs5UahaIPmvfxaau05Bx775RaTaGsXGzLPleoWFNBAxoL2I6EZGlr7/LWo5Ya0wzRTnDWE9E5RGPpR4XDY2jlqj7W/peTGnb1yddpqg3EBERERERERGRabDhl43a8UTRhgNlId3Qk6hzcnHp220Ycjj/YVUBBzcXBA4KxrZ+s0y2T333L+d9E5F1u/nbAXSc/xKcK3ogMzG1SNxw9asIlb2d5gGJR43KmngiaTppEAL6t8eu4XORm54lpqlUKiRdjcSOQW8bFdfzMrO17lf6m5ebi1sbDor3CRdCkRoRi4qNayH6oO7eWEpKs6FMETdLy3dt/No3Fo0Rhh7Jj+GuvhXQ8bNJcK1SEVd/3GV0WoiIynL9bUw8K0tM06d80kXbNrKSH5SpnCmMsZ6IzBmPa455zKA4aqm6v6nvxRhaDpS2fV3pMkW9gYiIiIiIiIiITIcNvxROGrLFztEeWclp4n3g4BDEXwgtskzgk8FIuBSG+zfuFOv6P+KP44j444TZ9m+N+yYieZKGYrR3dRbDo0hq9W0rHtQ/+rA+Iz4ZCedDUXdoF9xYtx8BAzrgQXSCZviUJhMHInBwJ+wa/oEmvkhiT12FR60qqNa5ueYBeaWmtZF0LRJ52Tk606drv5kJKYg+dAHVuwUhau8Z8bBL2t/961E642ZpaTaUMTFb33zXRnqAV/ghXt8Nc8VDqoidHHpGiap2bIq+G+fi5NwfcXHFliLz/Do2Qc/vZyEnPQu7R36ExMvhmnldV0xD4JOdcOfgefEgsyQtpw9HyzeHY2vfWYj/92ax71Hb98Yg9tQ1/PXcvDJ9l8g2mSKe6RPTSoupusoJfejahrZyRlvaGOuJqDwZem1oTN2/rPckzHEvxtByoKTt60qXqeoNRETlXT8sLPtBOpJvRePm+r9xedUO2Y/0wDwgIqVjHGTeExEpARt+yVjHBS/Bv+djcK1SAb1/eQfZqenYGDxFzAteOAm3d51C4pUIdP/vDPGLUJUKSAmPxaEpRX/NWX9kT1xbs6fY9isH1RGVY2P3Lb1cfL217t8a901E8uTo5YZu30yHg4sT1Hlq8eDkrzGfaOYXjh9HZq5EyJeviiFOpBhz6PXlYhm3apXQ7v2xSA6LQd/174tpuVk52D7gLWTdf4A9z80TDUPaznlePFCRfk2/94UFeqextP0WODpzJTp98YoYWkw6Bum9NIyktripLc2PHre2+GpszDYk33Xtn0gb/16Pods3byA9Lkk8rEwJv6uZJ/XIVKtvOySHRqNap6bw8PdFamSc3hnaauYIBE0bhqh9Z7B3/Gfs/UKhyiueaYup2soJffevbRvayhltaWOsJyJrUjgearuG1RYry3pPwlz3YvQtB0rbvrb7JLrqDURE1uzWxoOI3Hta6o5d9ARZ7+muaDd3LLzr18DRGSuhBMwDIlI6xkHmPRGRnKnUarXa0omg4rLTMrCm7mirzRpnHy90Xf4ado34UFH7Ls2om6vh6OZikX1b+7lCts2S57Y5KPX7wpgt7/O6JEo910vr8UvqraLz4sm4f/OOuH4o6JGpQOMJ/cVN/239ZqP/1o9xfunvOLtwnV49frX/aBwaj++P0M2HcXDKUr168LNmSvh+2PJ3xxqvg601bUo7l6393CXbVd7fJVs4l3ltbdpzxxY+c3PnB/OAlM6c34HSeoSWejgcfHCxaNT6a4sJyIxPhqXigLljgLXkgdKuz7XpvwuIzQCquAA7+lg6NWQJPAeKYhy0LKWUwxKWRWStlF4nVPrxm4qdybZEiiIVwpZ6qGPJfRMR2SLGbFKyhmP6oMuyqYg/H4o/Br9XrNFXQY8XMUcuIv7cLUTuOY16z3QXvwTXRuoJo/PSKaLR19XVu3Hg5S9tvtEXWT9rvg625rQREZkSr62JiGxfTnom4k5fh8rODl4BflAi5gERKR3jIPOeiEhO2PCLiIiIiGSp+ZTB6Dj/JUQfvog/n56LrKTUYsv4BNVFpSYBuLFuv3h/Y+0+MdRj9S4tSt2uvbOjGAap7rCuOL9sU/7QIOxEl4iIiIiIyGZ41s5v8JVZQj1RKZgHRKR0jIPMeyIiuXCwdAKIiIiIiEyt4fN94FW7KsL/OI4DkxYhL6vk3rjqj+yB7AfpCN9+XLyP3HsG6ffui+l3Dvxb4johiyeLbZ/6aDUuLN/ED4+IiIiIiMiKObg6wbmSp+jZ2dW3gugZ2qd5HdHrV/KtaCgB84CIlI5xkHlPRCRnbPhFRERERLLj5ldR/E0Ju1tqoy97FyfUeSpENPrKScsQ09Q5ubi18SAajekDpwoeJfYSJm1bGtYx+dYdMx8FERERERERlVWrmSPEq7Cw7cdw/K3/KiZzmQdEpHSMg8x7IiI541CPRERERCQ755f+jjsHz6PZy4PQZs6YEpcJGNgBTt7uiDl6CZ61q2ped49dEo3C6g7tUuJ6h6d9hfTYJHRdMQ21+rc385EQERERERFRWVz9aRf+HD4Xu0d9jFMf/oSMhBS4V/NBbmaWZhk7Jwc8uX8RWrw2pMi6IV++il5r3tZ7GVvOg65fT0PXlW8UWU/6QdTws9+izpDOOucTEVkzxkHrznu5l8NERObGHr+slIOrM0bdXG3pZJABn5el8Fwhc59fcsLvCxWcByR/OelZ+GvMJ+j5w2w0mzQIKjs7nJzzfZFlGozsKf6GLHqlxG3UH9Edl1ftKDY9OSwGO4fOQd8Nc9FtxTQcePlLhG8/ZqYjofLEckI+lBbree6SOc+t8sRzWXnnjlI+c235wTwgMr/kWzGIPnhe/B+19wzunriC/ps/RMf5E3Hg5UViutRT9KGpS9H39w9we/c/SLwUjlp928K/dxts7vGG3svYch4cfetbPLn3cwQ+1Qmhmw6LaR3mTUDsiSuiZ+zIvWe0zicismaMg9ad93Ivh4m0UXqdUOnHbyps+GWlVCoVHN1cLJ0MsgE8V4j4fSGikuVmZOGv5z9Fz+9noelLA6FSASfey2/85RngB78OjXFzw9+I+ONEsXWrdW6ORs8/Dp8WdRB/7lax+Snhd/HHkPfQd+NcdP36dRx4ZTHCtx3lR2HjeF1FtornLskFz2Xl4WfOPCCyhLhTV3Fz/d+oN7wbLq3aId5LpLrfxa+3oPOSKdjz3Dx0/GwSjv/nv0i/m6hZV59lbDUPspJScWT61+i87DXRM3aVNg1RNbgpNnefJtbRNZ+IyJYwDlpX3iutHCYqTOn1YqUfv6lwqEciIiIiknfjr7HzEXXgXzR5cSDaffiCmF5vZA/RC9jFlVtFb12Pvi4s25S/3IgepW47NSIWO4fMQdrdRHT96jXUfqJjuR0XERERERERGe/fReuRl5OLVjOeKTr9yw3Iy83FoN2fIebwBYRuzu/ZytBlbDUPovadRdjWI+iybCo6fPqiaOiVmZiq93wiIlvCOGhdea+0cpiIyJTY8IuIiIiIZN/4a+/znyJq/1k0mTAA7T8aJ35RlhJxFwnnQ0tcJzUyDvf+vYk6g0Ng7+yoV+OvLl+9jsAnO5nxSIiIiIiIiMgUUsJixIPi6l1aoEr7xprp6pxcxJ28Chcfb9xYu6/EdfVZxpbz4NTcH+EZWFUMxxX51+li6+maT6azZcsWtGzZssirRo0acHFx0TqvNE8//TSOHn3YW/n169cRHByMBg0aoG3btrh48WKJ62lbbufOnWjTpg1atGiBDh064N9//xXTMzIy8NRTT4l1goKC0Lt3b9y4cUOz3o4dO9C6dWuR7mbNmuGHH37QzOvcuTNCQ0u+X6M0SjgH2nRoj3fu7cah9HDNvHnx+xGX8wDmxjhoOSyHiYhMiw2/iIiIiEj2cjOzsVfq+WvfGTQe3x/u1XwQvuO41nWknr+cK3igVv/2WpdLvR2LnYPfQ1p0PDovm4rAwSEmTj0RERERERGZ2rnF+T2GFO5tRGoAVe+Z7ri8agfaffAC7F2ciq2nzzK2nAc56ZlIDY9F4uWIEtfRNZ9MZ9CgQTh79qzmtX//fri5uWH58uVa55XkxIkTSEhIQMeOD3srnzhxIl566SVcu3YNs2bNwtixY0tct7TlEhMTMWrUKNFo69y5c/jss8/E+wLSOlevXhUNgZ588klMmDBBTFer1Rg9ejS+//57kfZt27aJfaSkpIj506dPx5w5c3gqKeQcOHXsOF6vEIwf7p9Gel62mN/XvT42pV4ql3OAcdByWA4TEZmOSi2VrkREREQkG9lpGVhTd7Slk0E2aNTN1XB0K/2XoURERERERHKtvzq4uWDQXwtxaeU2XPnhT/T7/QPRE/TJOd8btIwhdS1rOv7C+m6Yi9u7/8HFFVuMmv8o1jUf6r8LiM0AqrgAO/ro/5nk5eXhiSeeQM2aNbFixQq95xUYP3686LFJ+iuJjY1FvXr1REMgBwcH0RCnWrVqOHTokJheQNtySUlJePbZZ0VjoAJeXl6iAZLUk1Nhp06dwrBhwxAWFia2UblyZfz+++/o0qWLaDDUr18/0cuXk5MTsrOzUb16ddE7lLe3N+SG50DRc6Bjm3b4tNYT+CLxED7z7QcHlR1y1HmYFrsd8337ws2u9J74zUkpcVAp5bA1fwZERKbAHr+IiIiIiIiIiIiIiEjR2r4/BqkRsbjy/U6pOxocem0ZGjzbE34dGhu0DJE5SD1gSY2vlixZYtC8AlJjrPbtH/Zofvv2bdGAS2rMJVGpVKhVqxYiIor25KZtufr16yM+Ph5HjhwR86ShB6Veu6TGXY9avHix6PGpYBtr167FkCFDEBAQgJCQENFjlNToS+Lo6IjmzZvj4MGDRuaWPMn1HKjXqAHmJezHBO82otGXRPrr7+iNa1n3jMwtskUsh4mIjMeGX0REREREREREREREpFg1erRC4KBOOPzGV5ppKeF38c/Ha9Bp0atwcHXWaxkic9i8eTNWrVqFDRs2aBpH6TOvsMjISPj5+Zk0XVJvXOvXr8dbb72Fxx57DLt27UKTJk00DYQKzJs3T/Te9cknn4j3OTk5+Oijj7Bx40aEh4fjr7/+wnPPPYd79x428qlatapIM8n/HLhx5RpmVOqMb++fQkpe5sNt27kgMS/dpOkl68VymIiobIqWvERERERERERERERERAoStfcMfm70fLHpUs9eoncvPZeRi51D55RpPpnO1atXxfCMmzZtEsMf6jvvUW5ubsjIyNC8l4aFjI6OFg1wCoZwlHpwknpyKkzXct27dxcvSWZmpmiwJTX8KbBw4ULRuGfPnj0iDZKzZ8/izp07YphHSdu2beHv748zZ86gd+/eYpqUVldX1zLmnjzI/RyQhhqs41gJFe1cEZGdhKbO+Y3TstW5cFTZw1IYB8sXy2EiorJhj19EREREREREREREREREVkQaMm/w4MGYO3euGA5R33kladGihWgkVKBKlSpo3bo1Vq9eLd5LvUVJja/q1atXZD1dy0kNggp8+OGH6NGjh2beF198gV9++QW7d+9GhQoVijUkunz5sngv9QR18+ZNNGzYULOMNC8oKAhKp5Rz4G5OKuJyU1HVwVOzTHROCmo5eBuYY0RERMrEHr+IiIiIiIiIiIiIiIiIrMjy5ctFQ51vv/1WvAobPnx4qfN27NhRrPenYcOG4c8//0SvXr0001auXImxY8eKYfi8vLzwv//9TzNvwoQJGDRokHhpW+69997DwYMHRW9QHTt2FEMOFgwrOH36dNSpU0fTG5SzszOOHz8uhhv85ptvxDHY2dkhLy8Py5Yt0/QgFRYWhtzcXDb8Usg5oIIKCUnhGOXVEj72+T2C3ct5gDyoUZMNv4iIiPSiUkt9chIRERGRbEhdpK+pO9rSySAbNOrmaji6uVg6GUREREREpBBKqb+WVtdS+vErUf9dQGwGUMUF2NGn/PabmpqK4OBgHD16FO7u7rBms2fPFj1GSQ2P5IjngO44+FvKeVSx90BXt0DIhbXGQaWUQ9b8GRARmQKHeiQiIiIiIiIiIiIiIiKSKQ8PDyxatAihoaGwdlJPVePGjbN0MmTHls6BCnau6Oxa29LJICIishkc6pGIiIiIiIiIiIiIiIhIxnr27AlbMHXqVEsnQbZs5Rzo7V7P0kkgIiKyKezxi4iIiIiIiIiIiIiIiIiIiIiIyMaw4RcREREREREREREREREREREREZGNYcMvIiIiIiIiIiIiIiIiIiIiIiIiG+Ng6QQQERERkfXzDKyKzounwLmSJ7JT0nDotWVIuhZZbLl2H45DrcfbwKNmFWzp9SYSLoZp5tXo0QqtZ4+ESqWCysEeF77ajJu/HdA5zxTp0pW23r++C1ffCkBeHrIfZOD4O98h4UKomGfn5IC2c55HjW4tkZuZhYRL4Tg4eYlR+ViWY7DW9BMREREREVkDfetW9Uf2QPPJgwE7FWIOX8DR2d9CnZNbZJnHf5sDn+Z18HOj5zXT3GtURod5E+BVpxrUeXm48sMuXPnujyLrhXz5Kuo90x0/NxyDrOQ0lCelHz8REeOg5fEzICKyDDb8IiIiIiKdghdMxLXVu3Fj3X4EDOiAkMWTsa3f7GLLhW8/igtfbUL/zR8Vm9dl2VTsHPo+Ei+Hw8PfF4MPLkb4juPIeZChdZ4p0qUrbQde+lxzU7pWv3biZrXUuEry2NujAbUaGztNEe9FAysry1tLpp+IiIiIiMga6FO3kn5I02rmCGztMxPpcUno8f0sNBzdG1e+36lZpsnEgUgJvysaPhXW/bsZOL90E8K3HRXvXSp7F5lfq3975D3SgKo8Kf34iYgYBy2PnwERkWWw4ZcVUKvVyEnPtHQyyMQcXJ1FryXmwHOG5Hhe68LznqyNJb8P5c3Fxws+QXWxa8SH4n349mPoMG88PGtXRUpYTJFl7x67XOp21GrAydtN/O/o6YaMxBTkZeXonGeKdOlKW+FfIjt5uuUn6P8/Z+nX0L+1nqiZL90ct7a8tVT6bQ3LErLlGM7zl5T4XeJ5L//zQSmfcWl5o5Tjt9Wyl2yLvnWrgIEdcHvXKU296OqPu9DitSGahk8VGvijVt92OPz6ctQe2FGzXrXOzZGXmaNp9CTJuHf/4f4re6PF1CHYOXQOGozqhfKm9OMnImIctDx+BkTGUUq9UOn1Ygcz14nZ8MsKSCfymrqjLZ0MMrFRN1fD0c3FLPnKc4bkeF7rwvOerI0lvw/lTRrOIf1uItS5eZppqVH3xPSSGliV5sCkL9B91QzkpGXCydsd+8Z/hrzsHJ3zzJ2uAiFLpqBacFPx/+7R88Rf6SZ5VlKquIFdrUsL5GZk4ezCdYg+dN7g7Zv7GCyRflvDsoRsOYbz/CUlfpd43sv/fFDKZ1xa3ijl+G217CXbom/dyqNGZaRGxj1cJjJOLCNROdgjeOHLODz9qyLbkVRoUBMZ8cno+vU0eNWtjtTIWJx8/wekRsSK+cELJ+HUhz/p7LXaXJR+/EREjIOWx8+AyDhKqRcqvV48ysx1Yjb8IiIiIiKz+z/27gO6iirx4/gvvUDovXcFpCm9VwUUlCIWsKJiASxIcVdlWRUXxUWwLOjiWmBdFRRQEEERpEkRkd5JQkkIkAAJ6eV/ZvInEpK8vCQveWW+n3PeSd7MnZl778zcmTtz371ePt5q9cxws0GX0XOV8UvkPp9M0dLezyn54uU85yVFx5bY3tkw/h3zb8M7e6jti6P046jp8vL1NofCuHD4pH6bvlAVbqivm794SUt6PJvt1825GfjtayrToHqu85b1m6j40+ddOv4AAAAAYCWtJ9ypsBVbdPHwKZWuVTnbPKNuVb3rDVp+6wu6cOikrrv/ZvX8YIK+6z9Zje/to8unzily4x65M6unHwAoB52PfQAAhUPDLwAAANhkPMANqlrebLx15Ve/xq+Ejen2MhocGeu4Mlzh+T+OKj7ivDndaPiV17yIX3YVa7xyc/Srdeo04zEFlC9tris9LU3HFq8350XvOW7+orl80zqKWG+716wVg/6a77aKIw2Oij8AAAAAuAt761ZGL1hl6lXL+m40cLoSplqn5mZvJU0f7i8vHx/5hQRp+Nb39e2AKbp88pzO7zluNnq6Uu/q+PojZi9Z1brcoGodmqp2v5uy1jt4zVta8+AbZh2M9ANA8bP6dcAVsA8AwHm8nbhtAAAAuAFjOIfo3cfVcFh383vdWzvqckR0gYYiNCr+wVXLq2zjmllDEIbUrapLR0/bnHdlCMM6A9oXS7wM/mWCzQdDV9Tp305JMXGZn+hYRWzYoxo9W5nzjN6zStepYv4C2lbc7OWINBQl/gAAAADgCeytW4Ut/1W1b26roMrlzO9Gz1XHl2w0///+jpe0qN0TWtT+SX1/+4tKiU0w/086f0mn1vyuUtUrKrhaBTNszT436sLhU8pITdP6p2brq7aPm2GNj2FZ7wkl+rLf6ukHAMpB52MfAIDz0OMXAAAA8rVp0jx1ffsptRg/VClxCdrwzHtZ8zrPfFwnVm03P53eeEy1+tykoCrl1O/zF82wX3ceZw4ruGniXPWc95wy0jPk5e2lX/86P+sXdbbmVWrVQPvnryhSvAx5xc2vTLA5RIVvoL+5feMhxU/3v561ns2T5qnLP580h0805hvf4yOj841bSeVtUeIPAAAAAJ4ir7rV1fUqowfk32d+qQHLXjXnRW7aq4Ofrc533akJSdo8+QP1/ewFyctLybHxWvf4LLkSq6cfACgHnY99AADO4ZWRkZHhpG3j/6XEJ2phw1Hkh4cZeXSB/IIDi2XdHDPwxOM6Pxz3cDXOPB+sdL4EVCyjHu89rVV3vyJX48px88Tj2hE86dyA9Y51jl9Y8VziuPf848Eq+zivvLFK+t312ouSYZXzwOrlAGXAnwaukqISpSqB0oqbnbhT4DQcA9lRDjqXVfLfwLUIrsoq5yH3wwuKtU7MUI8AAABwWcaQEq7asMqV4wYAAAAAAAAAAADPR8MvAAAAAAAAAAAAAAAAAHAzNPwCAAAAAAAAAAAAAAAAADfj6+wIwL08GLHIrnArh06Vt7+vbv7fSzry1TptGP9OjjBtJt2tVs8O18bn5+rwwh/VaERPdZ09Nt91x52I0qL2TxYq/gDg7iiHAcDauA4AgHNRDgMAAAAAAMCV0PALBfLL2Nl5zgupU9VszJV4/qIuHj2lhKgLOrhgta4b1U9h323WiVXbs8JWbNVQLcbeoVM//242+jJE/rrP5vob3dlTNXq00pmtB9hrACyLchgArI3rAAA4F+UwAAAAAAAAXAkNv1Agxxavz3W6T5C/bv12utJT07R2zCyz0Zdh298+Uc0erdTpjTFmg63kC3HyCfBTtzljlXI5URuf+1fWOuLCo8xPbqp1aq5qnZsrZn+YNk+cx14DYFmUwwBgbVwHAMC5KIcBAAAAAADgSrydHQF4hq7/fEoVmtfTb68uUOTGPVnTU/+/cVdQ5bLq+Npoc1qbKfeoXJPa2vryfxQfGZ3vukvVrKQeHzxnNhRb8/CbSk1IKta0AIA7ohwGAGvjOgAAzkU5DAAAAAAAAGegxy8UWfMnBqv+HV10fMlG7Z33bY75ERt268Anq9T0of5mQ69mj96q8JXbdPSrdfmu2+gdrNf8iQooX1o/3f8PxYZGsscAgHIYAMD9OAC4DJ6LAAAAAAAAwFk8vsevc+fOadKkSWrUqJECAwNVu3ZtPf3007p8+bJGjx4tLy8vvfvuu86Optuq3q2FbvrLSEXvC9PG597PM9xvr3ymS6GRuuHJ25V8MV6bJ9k3XKMxRGSlVg21862vdGrN7w6MOQB4BsphALA2rgMA4FyUwwAKIjVdupAsJaZKGRnkHWA1xnmf/v/nPmWANRn7n2MAgJWlZUiXkqXL3A8DDuXRPX7t3LlTAwYMUGRkpEqVKqVmzZrp9OnTmjNnjo4eParo6MxhBlu3bu3sqLql0rUqq8fcZ5USl6CfH37D5hCMaSmpSolNMP8/v+uoEs5eyHf9TUcPVKMRPc3ewXbNWuTQuHs8Ly+zZ7Xr7utn7qfE85d0/NtN2vnGFwyVCXgQymEUVotxQ1SxRQNVbNlAIXWrKu5ElBa1f5IMBdwM1wFkQx0AoBz2VFYv36yefg95ubU5SloUKm08I11p71WnlDSsnjSotlTG38mRdGHUX8kDT3AuUVoSJn0TJp37/6L7bJL0wnbpzvpSmwpmcQ8PduRS5nVgxQkpPi1zmnEsvLVHGl5Pqlva2TF0XVwHnI99gKIyGjvvvZBZDq46JSWnZ06v4C/dUVcaWleqFkw+54k6IXlg5R6/jJ6+Bg0aZDb6mjBhgiIiIrRjxw7z+4wZM7R8+XJt27bN7PGrZcuWzo6u2/EJ8levjybJv2wp/fLUbMWGnbEZvtUzw1SxRX2d331cNXu2NoeGtKVqx6ZqN/V+XTxySuvHzXFw7D1f+78/qPbTHtSFQyf164sfKfS7zWo2eqD6fDqFGiTgISiHURRGb53VutxgDqGcFBNLZgJuiOsArkUdAKAc9lRWL9+snn53F50kPbJBemaLtOGqRl+G8MvSrL3SbT9mNgxD7qi/kgfubvkJadCP0tyD0pnE7PNWn5Ye2yg9u1WKT3VWDFHcjX9n7pbuXpvZ4OFKoy+DcU34/Jg0bI30rwP0ApcXrgPOxz5AUSSnSS/ukB5cL3134s9GX4boZOmjw9LgH6X/HSOf80KdkDywdI9f48eP18mTJzV27FjNnDkz2zxj6Mf//ve/+uOPP1S/fn2VKVPGafF0V51nPmE25Nox4/N8h2Cs0KK+Wo4fqlNrd2rNw29o8OqZ6vDaaEVs2KPEcxdzhA+uXkE9P5ig1MRkM7zRoxjsV65JLTV9eIBCl/+qtY/8eezHhkep42ujzUZ3x7/ZQJYCbo5yGEWxqMOTigvPfLNw+8//lF+pQDIUcDNcB3A16gAA5bCnsnr5ZvX0u7vYFOnxTdKxfH5rYzT2MBqGze4gdaxSUrFzH9RfyQN39l249Led+YczGoYa5cC7HSV/n5KIGUqqh5vX/5CWhOcfdv4hKSVdGt+sJGLmXrgOOB/7AEVp/DrlN+mXSNvhjLZgM/dkDoV7b0Py+2rUCckDS/f4tX//fn3xxReqVKmSXn/99VzD3HTTTebfVq1a5Zj3zTffqHPnzubwkGXLllWXLl20d+/eYo+3u2g25jY1HNpN4Su3atfbi22G9fb3Vbc545Qan6SNE/6ltIRkbXzufbOnsE4zHss1fK/5ExVUuZw2PPOeLh4+VYwp8Uz1h3SVl7e39n24PNv0wwt/VEp8ohoO6+60uAFwDMphFNWVRl8A3BPXAVyLOgBAOeyprF6+WT397m723vwbfV39Uuwvv0kJ9PiTA/VX8sBdnUmQXv3D/vA7zkufHCnOGKGk/RxhX6OvKz49Im0/V5wxck9cB5yPfYDCWhyaf6Ovq80qwP2zVVAnJA8s3fDr888/V3p6ukaOHKnSpXMfGDsoKCjXhl9z5szRiBEj1LVrVy1btsxcV9++fZWQQK9Thmqdm6vti/f9/xCM7+S7L9o8f5fKX19HW//2ieJPnzenRW09oP3zv1fdgR1yDPnY8fVHVblNY+1652uFr9hS6GPAyiq1bqT0tDSd+/1wtulpSSmK3hOqSq1pKg24M8phALA2rgPIDXUAgHLYU1m9fLN6+t3ZpWTp+5MFXCZF+oHfwAIeY0mYlHr1+K52+NpY5qohsODevgwt+DJfHS+OmACAc3o9/LKAZVrG/zcWw5+oE5IHlh7qcc2aNebfXr165RnGGAby2oZfR48e1cSJEzVr1ixziMgrBg4cWKzxdRdBVcqp5wfPydvXx+xivvYtbfMMG7MvXL5B/mr+xGCd+PE3Hflf5j65Ysfr/1WtPjdmG/Kx8b191OTePko4e8Hs6avBsG55rj98xValJiQ5NH2eIrhqeSVFxyo9OedPBOMjo1W1/fXy9vNVego/IQTcDeUwAFgb1wHkhToAQDnsqaxevlk9/e5sxUkpqRCNN4xGH3fULY4YAShJRi9+xvlcUGcTM4d97Fm9OGKFkhQWV7jeu9ZGSucSpUqBxRErACg5v0dLoXEFX+67E9K4ZlIgQx+bqBOSB5Zu+BUWlnlHXbdu7rXk1NRUbdy4MUfDr48++kh+fn569NFHHRqftm3bKjIy734M/TK8NVXt5erKNqypwIplzf9bPT3MZtjd7y5Rnf7tlBKXoE3Pz80xPy0xc8jH/l9PM4d8/Hn0m+bDKoMxzKMxPKQti9o9obiTZ+XKmjRuohSv4vl5jq1jxicoQGnJKbnOM34RajAa5SXzUBAudlznx13KyuJEOexanHk+5IfzBZ54XDuCu58bXAesfaxTB4AVzyVXK7cphx1/POS3jz3lGUdeeWOV9LvrtbcoytzzuoK73Vfg5faciVetWk1kJa5W1rtaOeAprFYGeJUqp6pv7inUsmNe/Icu//Cuw+OEkhXQ6haVHzO/UI0G290yVClHt8oqKAfJ/5JitWuRswV1u09l73m9wMtdTpWua9tVaWet0/WXVZ75Wb1e3MSOMqhatWravn17odbvkQ2/Ll++bP7Na3jGL774QufOnVNISIjq16+fNX3Tpk267rrrtGDBAr366qs6ceKEGjdurJdffln33HNPoeNjNPo6dSrvfrr9vXykqnJ5kZv36uPqw+0O/9trC2zON4Z8/LTWXVnfNzzznvnxFKcjTis5I61Y1m3rmElLSJJfqcwGetfyCfAz/6YmJBdLvOD5ivO4zo+7lJXFiXLYtTjzfMgP5ws88bh2BHc/N7gOWPtYpw4AK55LrlZuUw47/njIbx97yjOOvPLGKul312tvUfgmpyq4EMt5+frbfI7siVytrHe1csBTWK0M8KuQXujdGpuYrAiLlQOeqHz9OJUv5LLnL15SrIWOAcpB8r+kWO1a5GxVLico95pM/qKiLyiRctAydUKr1ItPF3MZ5JENv4yWcDExMdqxY4c6deqUbV5ERIQ5nKOhZcuW8vLyyjbPqFi/8MILmjFjhmrXrq358+fr3nvvVeXKldW3b99Cx8cWoxWjaGDscWpUr1GsPX7ldczEn4lR2Sa15O3vm2MogOBqFZR4/iJDAMAlj+v8UFbC1TjzfMgP5ws88bh2BM4NuPOxTh0AVjyXKLc9/3jIbx97yjOOvPLGKul312tvUQSlJxVqufS4aNWsWVNWYpWyvrDlgKewWhkgv0BlpKfJy7vg41SV9kqxXDngifz9C75MRkaG+c6yQqC3yljoGKAcJP9LiuWuRU4W6Jte6HKwcik/pVMOWqZOaJV6cQ07yqD82hVZruGX0UBr//79ZuOtfv36qUmTzO6xt23bpvvuu8/s7cvQunXrbMulp6crLi5On332me644w5zWp8+fbRv3z698sorhW74lV93bCnxiVrYcFSh1g3XdejwIfkFF89A7LaOmXM7j6hmz9aq1Kaxorbsz9bitcIN9XTm1z+nAa50XOeHshKuxpnnQ344X+CJx7UjcG7AnY916gCw4rlEue35x0N++9hTnnHklTdWSb+7XnuLYk+M9OD6gi93d6sqmnzypKzEKmV9YcsBT2G1MsDw7BZp/ZmCLeMt6ZcPX1G1oFeKK1ooIclp0m2rpegCdEBiNHaoXUraunmVvP/ss8LjUQ6S/yXFitciZ7qYLA1cJSWlF6wcbF1B+ve+32UlVnnmZ/V68aFiLoOM+0iPM2nSJFWsWNEcqrF58+Zq0aKFOWRj+/bt1aBBA/Xu3dsM16pVq2zLVahQwfx7dQMvo4Axvu/ZU7jx2IGSdnzpJmWkp6vZo7dmm954ZF+zMDn29S/sFAAAAMCDUAcA4KmsXr5ZPf3urHk56fpCjG0zvF5xxAaAM4yoX/BluleTqgUVR2xQ0vx9pDvqFny5YfVkqUZfADxXWX/plloFX4774eyoE5IHlu7xq1atWlq/fr05pOO6desUGhqqZs2aad68eXr00UfVsGHDXBt+GY3EtmzZkus6ExMTSyTuQFFdOBCuA/9ZqaajB6rX/Ik6+dMOlW1cU81GD1Tkpr069vUGMhkALK7B8O4qXauy+X9gxTLy9vNVy2eGmd/jTp7VsUW8QAIAd0IdAICnsnr5ZvX0uzMvL+mJ6zN7/LG3k4OBtaSGZYo5Ym6I+it54K46VJbaVpK2Zw5Ak68Ab+mRzMFr4EGN/749IZ218/Wi0dvX7XWKO1buh+uA87EPUFgPNJLWnJbi7ByFr2lZqXd18vtq1AnJA0s3/DI0bdpU3333XY7pxlCORkMwb29v3XDDDdnm3X777froo4+0atUqDR06NGv4x9WrV6tdu3YlFnegqLa+/LHiTpxVk1F9VavPjUqMvqT9H32v39/4whggmQwGAItrck8fVevcPNu0GyffY/41XyDR8AsA3A51AACeyurlm9XT7866VJX+2lp6bWf+jb+6VJFezP4bZfw/6q/kgbsyem16s500drO094LtsP7e0ox20vXlSip2KAmVAqU5HaSnfpWik2yHrR6UGTbEj31zLa4Dzsc+QGHVLS39s0PmjyEu59P4q0GINKtDZo+JyI46IXlg6YZfedm7d68yMjLUpEkTBQcHZ5s3aNAgdevWTY899pjOnz+vOnXq6N///re5jNH4C3AXxjAAe+d9a34AALjWymFTyRQA8DDUAQB4KquXb1ZPv7szem4xXubPPyT9dj7n/KqB0p31pVENJV9vZ8TQ9VF/JQ/cmdGIZ15n6d+HpCXh0oXk7PONEf26VpUevU5qRqMvj9S4rPRxN2neAWn1aSn5mpbAgT7SgFrSmOsyG4ohJ64Dzsc+QFHcWFH6qKv0wUFpbaSUds1vV0r7SoPqSI9dR+PXvFAnJA/sYbmGX7t37851mEeDl5eXli1bpsmTJ+svf/mLLl26ZIZbsWKFevfu7YTYAgAAAAAAAADcVfvKmZ9jsdIvkdJHh6T4NKmsn7S0Lw2+AE8X6CuNbZbZuOvnCCksLrPxT3l/qXcNqUb2/gnggYx9PO1G6dnmmY2/ohIzG/1VD5b61ZBK08sXAA9nDGdu9GwZlSD9FCH9a3/m/bDRQHpFPynIci1WAMez3Glkq+GXoVy5cpo3b575AQAAAAAAAACgqIzha4zPl8czX3QF+NDoC7AS45zvX8vZsYAzlQvI7OURAKyqSpB0TwPpsyOZ98NBPjT6AhyFhl8epP0rD6vOLW1VunYVLev7vKL3huYabvjW95WWlKK0xMx+hXe9841Cl21SQPnSuuXLP4d+8gkKUEjdqvpfi9FKvhDnkG3ntf2I9bsKvW17uHr8AKjEzvOQ+tXUbfY4BVQIUUpsvDY8/a4uHDppzqvZq7XaTL5H3n6+SktI0qZJ8xSzL8whu8fWdg3e/r5qN/UB1ezZWmlJyYreF6b1Y+fYXKdPgJ96zH1WZRvXMsusxHMXtXnKh4oNjSxSOVhQBYmHPXnc6K5e6vr2U1rz0AyFr9zmsHja2geFyX8AnqUg98J5lalFuZ92h2uJrfX3+99LCqpcTkpPV8rlRG158SNF7zluV7rsXdaea1lxXUMAuD9b5UN+5Xdh76Wd+ZzGUWVrUcp3AAAAAAAAFD/LNfxas2aNPFXY8s3a8/4SDVz6ar5h1z0+K8fDvKSYOC3rNzHre/PHB6tap2Z2vaQqyLbz2n5ht20PV48fgJI7zzu/MUaHFqzWkS/Xqu6tHdV19lh9N2CK/MuWUrd3n9bKIS+ZL9GrdGiq7u89raW9nnPI7slru1fc9NdRUkaGvu4yzvxuvlyxw8HPVuvUmt/N/69/qL+6vPWEVg778+VQYcvBgrInHvbkcelaldVkZF9FbT/o8Dja2geFzX8AnqMg98J5lalFuZ92h2uJrfWve+wtJV+KN/+vM6C92bjCaEBgD3uXze9aVpzXEADuLb/yIb/yu7D30s58TuOosrUo5TsAAAAAAACKn3cJbAMl5Myv+xUfEe2w9TW+t7cOf/6Ty2/bE+IHoGTO88CKZVSxVUMdXfyL+T1s+a8qVaOiQupVMz9JMbFZPadEbdmvUjUrqUKLzP63jeVu+Wqqbls5Q4NWvam6t3WyO262tmvwDQpQ43t6a8c/Ps9aJuHshXzXa/QKcOVFveHsjsMqXbtyiZSDhYlHfnksLy91fusJbXlxvtKTU3MsX1z7oLD5D8Cz2brfs7dMvXYd7nwtyW/9VxoFGPxDgs0GYvayd1mb+Z7PNQSAhRWifLi2/C7svbQzn0U4pGwtYvkOAAAAAACA4me5Hr+QqeucccazT539/Yh+m75QSecvZcuaym2vU0DZUjqx+rcS335xb9sT4gegcIxGRglnYpSRlp41Le7UOXP6+V3HFFA+xDzHz24/qNo3tzVfbBhDnsSFnVHnNx/Xj6NeU0LUBXOIrUGr3jDDxUdGF2m7xlBaxkt741f7LccPVfXuLc0hXnbO/FIRG3YXKH3NHhmo8B+cP6xVXvG4dCwizzyO3n1czccMUtS2A+a+uJZ/meBi2wdGYwZH5D8Az+GI+71r11Gc5VhJXEvsWb9xD129c3Pz/9Wjphdou0VZ1mDrGgLA2gpaPjirzl8cz2mKWrY6ej0AAAAAAABwPBp+uYmB376mMg2q5zrP6PY//vR5u9f1/ZCXdfnUOXn5+ujGyfeo2+yx+vGaB3fGr/mPfLUu24slR8lv+wXdtiPzpjjiB8A9pMTGa+2jM3XTX+6Vb6lAnd1+SDEHTygjNc18yRJSt4r6LfxrtmXKNKxhvqzPrxzKj5evt9n46cLhk+ZLngo31NfNX7ykJT2eVeK5i3bFv8X4oeZL+U0jpsmRClrG2oqHrTwud11t1b21g1kG58bWPuj54YQiXQcckf8APIsj7veuXYe7X0vsWf+G8e+Yfxve2UNtXxyVo45hS1GWze8aAsBz5Vd2Gj8yKGj54Iw6f3E9pylK2Voc6wEAAAAAAIDj0fDLTawYlP0FUVEYDxMNxov2fR9+p6EbMx/gXeEbHKj6gzvruwGTHbZNe7dfmG07Mm+KI34AXIdxfgdVLS8vH++sFyala1bKOu8jN+3VyqFTzf+9/X111x//NoclLNuwhi4cPKkVg/9aqHIoPSnF5naNv+lpaTq2eL35PXrPccWFR6l80zqKWJ9/Ty3NHx+sugM7aNWIaUpLSJYjFaSMtSceeeVxje4tzQYFwzZllrlBlcup05uPK6hKeR38dJW8vLzy3Af2xNHWvk++dLlI+Q/Aszjifi+3ddgqx9zhWlKQ9R/9ap06zXhMAeVLKykmLt9tF3XZqh2a2ryGAPBc+ZWdte+/qUDlg7Pq/MX9nKYo5XJxrAcAAAAAAACO4+3AdcENGMNZGcPMXFF/SFed33M8W5j6t3dW9L5QXTxyOkfX/nUGtC/W7RfntoszfnAd1To114MRi8yXl9eq2qmZ7j34iUbs/FDlm9bNNq/H3GfN5W7+MrNBSm5aTxhhhqnYqmGOec3G3KYHTn2pAUtfzXYMwbUknr9kDinYcFh383vdWzvqckS0OUSWIahKuaywrZ4droiNe8x5UdsPqnSdKqrerUXW/ArN68nbz9ch202KjlXEhj2q0bOV+d14OWVs7+LhU/mWgcaxV39IF6266+9KvhRfqHxxRBlrbzzyymPjxduXrR/VovZPmp+zOw5r88S5WS/kinMf5Jf/AKzFEfd7ua3D3a8lttZv3PsYjdKuqNO/ndkg4EqjAFvbzm9Ze+R3DYG1UB9AUcqHwlwDinovXRzPaRxRtjpyPc4uD67+jDzymTnUcrPHbjMbU3s68gAAAAAArMvqdcJqFko/PX55kE5vPKZafW4yX6r3+/xFpcQl6OvO48x5nWc+rhOrtivmQLh6/XuieRB7eUmxYVHaMC77L0kb39NHhxb+mGP9lVo10P75Kwq9beMTWLmsze0XZtuOypuixA+ur1bfm9Tzg+eUcPaCVo34u2LDzmTNM36tXKd/e106HqHqXZqrdK3Kijt51u51t5l0t9mA5dTPv2vN6Dcd3uMSHHueb5o0T13ffsoczsoIs+GZ97LtS6PXEKMMOPvbIW167n1zevLFy/rxvulq9/L9ajf1AXn7+Zi/yl/z0Bt27x5b2zVsnjRPXf75pDl0SkZ6hvndGPrLVhkYXL2C2v/tQV0KjVT/RX8zp6Ulp2r5rS/kSLet/ClqGVuQeOSVx/kp7n1gK/8BWEte93v2lql5rcMTriV5rb9UrUrq+cEE+Qb6m9ONRmo/3f961jZsXWf8ygTbXLYg+Q7YQn0Aubm6jLF1DSjsvbSzntM4qmzNbz3u4tjX63VyzQ6j+02z17dGd/ZQ+2kPqmzjmto8cZ6sgDwAAAAAAOuyep3wmAXS75WRkZHh7EhYXUp8ohY2HCVXFlCxjHq897RW3f2KpbZdFCOPLpBfcKBljxlnttzt//U0bZv2qfbOXZb1i+lus8fq4tHT5nGUcCYm2zJNHxloFu7fDZiigd++pt3vfKOdM7/Mtcev1s+P0Lf9J+v8H0fNaR1efVhNRw/U8aUbtX7cO0pPSZUnK87jOj9WPe6Luwx01zLW6udDfqx6vsCzj2tHsOq5wb28ZxzrVj1+C4r6gGedS65+3FO+Fv14KM59nFt5cKWXtSHrZ5uNn//X8hElnb8kZ+VNcR/j7pAHVjRwlRSVKFUJlFbc7OzYOJ+rl/XuXg64CsoAAHmhHHQuq+S/gWuR6+B+ODvqxdZ+LuAontN3GYqVcbA7q1GAM7cN93fd/Ter+7vjdX73cX0/5OUcjb6u/Ho6ctNend91TCd/3KFGd/UyW/zaYvwau9s748xGXwcXrNa6J972+EZfkEeWgZSxAOD5uJeHlVEfQHGifHVPqQlJ5rCfXt7eKlO3qqyIPAAAAAAA67J6nTDVA9NPwy8AHqvFuCHqNOMxRWzcqx/unKbkC3E5wlRs1VAVmtXVkS/Xmt+PfPGzOdRjje4t81yvT4CfORRHw+E9tPvdJZldQNJ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" ] }, "execution_count": 8, @@ -563,6 +586,7 @@ ], "source": [ "aqc_final_circuit = aqc_ansatz.assign_parameters(aqc_final_parameters)\n", + "aqc_final_circuit.compose(subsequent_circuit, inplace=True)\n", "aqc_final_circuit.draw(\"mpl\", fold=-1)" ] }, @@ -586,8 +610,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "AQC circuit depth: 50\n", - "Baseline Trotter circuit depth: 85\n" + "AQC circuit depth: 15\n", + "Baseline Trotter circuit depth: 27\n" ] } ], @@ -598,15 +622,15 @@ " backend=backend, optimization_level=3\n", ")\n", "\n", - "# Transpile the AQC-optimized circuit\n", + "# Transpile the AQC-optimized circuit (compressed + subsequent step)\n", "isa_circuit = pass_manager.run(aqc_final_circuit)\n", "isa_observable = observable.apply_layout(isa_circuit.layout)\n", - "print(\"AQC circuit depth:\", isa_circuit.depth())\n", + "print(\"AQC circuit depth:\", isa_circuit.depth(lambda x: x.operation.num_qubits == 2))\n", "\n", "# Transpile the baseline Trotter circuit (no AQC optimization)\n", "isa_baseline_circuit = pass_manager.run(baseline_circuit)\n", "isa_baseline_observable = observable.apply_layout(isa_baseline_circuit.layout)\n", - "print(\"Baseline Trotter circuit depth:\", isa_baseline_circuit.depth())" + "print(\"Baseline Trotter circuit depth:\", isa_baseline_circuit.depth(lambda x: x.operation.num_qubits == 2))" ] }, { @@ -655,9 +679,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Exact: -0.8091\n", - "Baseline Trotter: -0.6353, |Δ| = 0.1738 (depth 85)\n", - "AQC: -0.6675, |Δ| = 0.1416 (depth 50)\n" + "Exact: -0.7009\n", + "Baseline Trotter: -0.5400, |Δ| = 0.1609 (depth 27, 4 steps)\n", + "AQC (3+1): -0.5728, |Δ| = 0.1281 (depth 15, compressed+subsequent)\n" ] } ], @@ -666,8 +690,8 @@ "baseline_expval = baseline_result[0].data.evs.tolist()\n", "\n", "print(f\"Exact: {exact_expval:.4f}\")\n", - "print(f\"Baseline Trotter: {baseline_expval:.4f}, |\\u0394| = {np.abs(exact_expval - baseline_expval):.4f} (depth {isa_baseline_circuit.depth()})\")\n", - "print(f\"AQC: {aqc_expval:.4f}, |\\u0394| = {np.abs(exact_expval - aqc_expval):.4f} (depth {isa_circuit.depth()})\")" + "print(f\"Baseline Trotter: {baseline_expval:.4f}, |\\u0394| = {np.abs(exact_expval - baseline_expval):.4f} (depth {isa_baseline_circuit.depth(lambda x: x.operation.num_qubits == 2)}, {baseline_num_trotter_steps} steps)\")\n", + "print(f\"AQC (3+1): {aqc_expval:.4f}, |\\u0394| = {np.abs(exact_expval - aqc_expval):.4f} (depth {isa_circuit.depth(lambda x: x.operation.num_qubits == 2)}, compressed+subsequent)\")" ] }, { @@ -678,7 +702,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -691,8 +715,8 @@ "plt.style.use(\"seaborn-v0_8\")\n", "\n", "labels = [\n", - " f\"Baseline Trotter\\n(depth {isa_baseline_circuit.depth()})\",\n", - " f\"AQC\\n(depth {isa_circuit.depth()})\",\n", + " f\"Baseline Trotter\\n({baseline_num_trotter_steps} steps, depth {isa_baseline_circuit.depth(lambda x: x.operation.num_qubits == 2)})\",\n", + " f\"AQC (3+1)\\n(depth {isa_circuit.depth(lambda x: x.operation.num_qubits == 2)})\",\n", "]\n", "values = [baseline_expval, aqc_expval]\n", "colors = [\"tab:orange\", \"tab:blue\"]\n", @@ -703,7 +727,7 @@ " y=exact_expval, color=\"tab:green\", linestyle=\"--\", linewidth=2, label=f\"Exact ({exact_expval:.4f})\"\n", ")\n", "plt.ylabel(\"Expected Value\")\n", - "plt.title(\"AQC-Tensor vs Baseline Trotter (10-site XXZ)\")\n", + "plt.title(\"AQC-Tensor (3 compressed + 1 uncompressed) vs Baseline Trotter (10-site XXZ)\")\n", "plt.legend()\n", "for bar in bars:\n", " y_val = bar.get_height()\n", @@ -726,16 +750,16 @@ "source": [ "## Large-scale hardware example\n", "\n", - "We now scale up to a 50-site XXZ model to demonstrate AQC-Tensor on a more realistic problem size. The workflow is the same as the small-scale example, so we combine all steps into a single code block.\n", + "We now scale up to a 50-site XXZ model to demonstrate AQC-Tensor on a more realistic problem size. The workflow is the same as the small-scale example: we compress 3 Trotter steps via AQC and append 1 uncompressed step.\n", "\n", - "For a system of this size, matrix exponentiation is infeasible ($2^{50}$ dimensions), so we compute the reference expectation value directly from the high-accuracy target MPS.\n", + "For a system of this size, matrix exponentiation is infeasible ($2^{50}$ dimensions), so we compute the reference expectation value directly from a high-accuracy MPS evolved for the full time.\n", "\n", "### Steps 1–4 combined" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "id": "b6c0f26a", "metadata": {}, "outputs": [ @@ -745,80 +769,64 @@ "text": [ "Target circuit: depth 385\n", "Ansatz circuit: depth 7, with 816 parameters\n", + "Subsequent circuit: depth 12\n", + "Baseline circuit: depth 49 (4 steps, time=0.2667)\n", "Target MPS maximum bond dimension: 5\n", - "Reference expectation value (from MPS): -0.832514\n", - "2026-04-30 13:06:12.214833 Intermediate result: Fidelity 0.99795839\n", - "2026-04-30 13:06:12.230078 Intermediate result: Fidelity 0.99822743\n", - "2026-04-30 13:06:12.245084 Intermediate result: Fidelity 0.99829460\n", - "2026-04-30 13:06:12.260114 Intermediate result: Fidelity 0.99832307\n", - "2026-04-30 13:06:12.274684 Intermediate result: Fidelity 0.99836393\n", - "2026-04-30 13:06:12.290753 Intermediate result: Fidelity 0.99840145\n", - "2026-04-30 13:06:12.306418 Intermediate result: Fidelity 0.99846899\n", - "2026-04-30 13:06:12.320197 Intermediate result: Fidelity 0.99864993\n", - "2026-04-30 13:06:12.333798 Intermediate result: Fidelity 0.99872570\n", - "2026-04-30 13:06:12.349061 Intermediate result: Fidelity 0.99892276\n", - "2026-04-30 13:06:12.363848 Intermediate result: Fidelity 0.99900819\n", - "2026-04-30 13:06:12.379304 Intermediate result: Fidelity 0.99906919\n", - "2026-04-30 13:06:12.393248 Intermediate result: Fidelity 0.99911399\n", - "2026-04-30 13:06:12.408326 Intermediate result: Fidelity 0.99918227\n", - "2026-04-30 13:06:12.423260 Intermediate result: Fidelity 0.99921385\n", - "2026-04-30 13:06:12.438543 Intermediate result: Fidelity 0.99925091\n", - "2026-04-30 13:06:12.454048 Intermediate result: Fidelity 0.99929083\n", - "2026-04-30 13:06:12.468375 Intermediate result: Fidelity 0.99933111\n", - "2026-04-30 13:06:12.483160 Intermediate result: Fidelity 0.99935721\n", - "2026-04-30 13:06:12.498169 Intermediate result: Fidelity 0.99938354\n", - "2026-04-30 13:06:12.513266 Intermediate result: Fidelity 0.99940964\n", - "2026-04-30 13:06:12.528348 Intermediate result: Fidelity 0.99943944\n", - "2026-04-30 13:06:12.543666 Intermediate result: Fidelity 0.99946911\n", - "2026-04-30 13:06:12.558681 Intermediate result: Fidelity 0.99949033\n", - "2026-04-30 13:06:12.573779 Intermediate result: Fidelity 0.99951023\n", - "2026-04-30 13:06:12.588431 Intermediate result: Fidelity 0.99954765\n", - "2026-04-30 13:06:12.602077 Intermediate result: Fidelity 0.99956326\n", - "2026-04-30 13:06:12.615844 Intermediate result: Fidelity 0.99959127\n", - "2026-04-30 13:06:12.632148 Intermediate result: Fidelity 0.99960629\n", - "2026-04-30 13:06:12.647171 Intermediate result: Fidelity 0.99961797\n", - "2026-04-30 13:06:12.661585 Intermediate result: Fidelity 0.99962762\n", - "2026-04-30 13:06:12.676381 Intermediate result: Fidelity 0.99964014\n", - "2026-04-30 13:06:12.690423 Intermediate result: Fidelity 0.99964526\n", - "2026-04-30 13:06:12.704923 Intermediate result: Fidelity 0.99965444\n", - "2026-04-30 13:06:12.718798 Intermediate result: Fidelity 0.99966028\n", - "2026-04-30 13:06:12.733616 Intermediate result: Fidelity 0.99966851\n", - "2026-04-30 13:06:12.748804 Intermediate result: Fidelity 0.99968305\n", - "2026-04-30 13:06:12.764133 Intermediate result: Fidelity 0.99968519\n", - "2026-04-30 13:06:12.778763 Intermediate result: Fidelity 0.99969521\n", - "2026-04-30 13:06:12.793676 Intermediate result: Fidelity 0.99969866\n", - "2026-04-30 13:06:12.808354 Intermediate result: Fidelity 0.99970212\n", - "2026-04-30 13:06:12.823618 Intermediate result: Fidelity 0.99970939\n", - "2026-04-30 13:06:12.839678 Intermediate result: Fidelity 0.99971380\n", - "2026-04-30 13:06:12.855446 Intermediate result: Fidelity 0.99971892\n", - "2026-04-30 13:06:12.871060 Intermediate result: Fidelity 0.99972214\n", - "2026-04-30 13:06:12.886268 Intermediate result: Fidelity 0.99972512\n", - "2026-04-30 13:06:12.901064 Intermediate result: Fidelity 0.99972786\n", - "2026-04-30 13:06:12.915886 Intermediate result: Fidelity 0.99973037\n", - "2026-04-30 13:06:12.931565 Intermediate result: Fidelity 0.99973549\n", - "2026-04-30 13:06:13.008085 Intermediate result: Fidelity 0.99973585\n", - "2026-04-30 13:06:13.038992 Intermediate result: Fidelity 0.99973656\n", - "2026-04-30 13:06:13.055610 Intermediate result: Fidelity 0.99973752\n", - "2026-04-30 13:06:13.070891 Intermediate result: Fidelity 0.99973919\n", - "2026-04-30 13:06:13.086248 Intermediate result: Fidelity 0.99974372\n", - "2026-04-30 13:06:13.101073 Intermediate result: Fidelity 0.99974646\n", - "2026-04-30 13:06:13.115663 Intermediate result: Fidelity 0.99975027\n", - "2026-04-30 13:06:13.130447 Intermediate result: Fidelity 0.99975695\n", - "2026-04-30 13:06:13.146828 Intermediate result: Fidelity 0.99975730\n", - "2026-04-30 13:06:13.162365 Intermediate result: Fidelity 0.99975969\n", - "2026-04-30 13:06:13.177169 Intermediate result: Fidelity 0.99976124\n", - "2026-04-30 13:06:13.192934 Intermediate result: Fidelity 0.99976207\n", - "2026-04-30 13:06:13.206788 Intermediate result: Fidelity 0.99976553\n", - "2026-04-30 13:06:13.222731 Intermediate result: Fidelity 0.99976970\n", - "2026-04-30 13:06:13.238690 Intermediate result: Fidelity 0.99977447\n", - "2026-04-30 13:06:13.254277 Intermediate result: Fidelity 0.99977757\n", - "2026-04-30 13:06:13.791095 Intermediate result: Fidelity 0.99977828\n", - "2026-04-30 13:06:14.042192 Intermediate result: Fidelity 0.99977828\n", - "Done after 67 iterations.\n", - "\n", - "AQC circuit depth: 47\n", - "Baseline Trotter circuit depth: 87\n", - "Job ID: d7poo367g7gs73cf7n8g\n" + "Reference expectation value (from MPS): -0.738669\n", + "2026-05-18 13:02:11.219150 Intermediate result: Fidelity 0.99795732\n", + "2026-05-18 13:02:11.232256 Intermediate result: Fidelity 0.99822481\n", + "2026-05-18 13:02:11.245160 Intermediate result: Fidelity 0.99829520\n", + "2026-05-18 13:02:11.257765 Intermediate result: Fidelity 0.99832379\n", + "2026-05-18 13:02:11.270280 Intermediate result: Fidelity 0.99836416\n", + "2026-05-18 13:02:11.284116 Intermediate result: Fidelity 0.99840073\n", + "2026-05-18 13:02:11.296856 Intermediate result: Fidelity 0.99846863\n", + "2026-05-18 13:02:11.309602 Intermediate result: Fidelity 0.99865244\n", + "2026-05-18 13:02:11.322012 Intermediate result: Fidelity 0.99872665\n", + "2026-05-18 13:02:11.334195 Intermediate result: Fidelity 0.99892335\n", + "2026-05-18 13:02:11.346570 Intermediate result: Fidelity 0.99901045\n", + "2026-05-18 13:02:11.359202 Intermediate result: Fidelity 0.99907181\n", + "2026-05-18 13:02:11.371511 Intermediate result: Fidelity 0.99911125\n", + "2026-05-18 13:02:11.383870 Intermediate result: Fidelity 0.99918585\n", + "2026-05-18 13:02:11.396184 Intermediate result: Fidelity 0.99921504\n", + "2026-05-18 13:02:11.408543 Intermediate result: Fidelity 0.99924936\n", + "2026-05-18 13:02:11.422557 Intermediate result: Fidelity 0.99929226\n", + "2026-05-18 13:02:11.436275 Intermediate result: Fidelity 0.99933099\n", + "2026-05-18 13:02:11.449511 Intermediate result: Fidelity 0.99935792\n", + "2026-05-18 13:02:11.462093 Intermediate result: Fidelity 0.99937925\n", + "2026-05-18 13:02:11.475783 Intermediate result: Fidelity 0.99940690\n", + "2026-05-18 13:02:11.490254 Intermediate result: Fidelity 0.99944409\n", + "2026-05-18 13:02:11.503292 Intermediate result: Fidelity 0.99946840\n", + "2026-05-18 13:02:11.516064 Intermediate result: Fidelity 0.99949378\n", + "2026-05-18 13:02:11.532861 Intermediate result: Fidelity 0.99951380\n", + "2026-05-18 13:02:11.546182 Intermediate result: Fidelity 0.99955313\n", + "2026-05-18 13:02:11.559168 Intermediate result: Fidelity 0.99955707\n", + "2026-05-18 13:02:11.571753 Intermediate result: Fidelity 0.99959306\n", + "2026-05-18 13:02:11.584257 Intermediate result: Fidelity 0.99960486\n", + "2026-05-18 13:02:11.597610 Intermediate result: Fidelity 0.99961714\n", + "2026-05-18 13:02:11.610106 Intermediate result: Fidelity 0.99962953\n", + "2026-05-18 13:02:11.622515 Intermediate result: Fidelity 0.99963525\n", + "2026-05-18 13:02:11.635543 Intermediate result: Fidelity 0.99964658\n", + "2026-05-18 13:02:11.649044 Intermediate result: Fidelity 0.99965027\n", + "2026-05-18 13:02:11.664148 Intermediate result: Fidelity 0.99965802\n", + "2026-05-18 13:02:11.678033 Intermediate result: Fidelity 0.99966731\n", + "2026-05-18 13:02:11.692714 Intermediate result: Fidelity 0.99967780\n", + "2026-05-18 13:02:11.706753 Intermediate result: Fidelity 0.99968567\n", + "2026-05-18 13:02:11.720780 Intermediate result: Fidelity 0.99969139\n", + "2026-05-18 13:02:11.733471 Intermediate result: Fidelity 0.99969628\n", + "2026-05-18 13:02:11.745998 Intermediate result: Fidelity 0.99970331\n", + "2026-05-18 13:02:11.758424 Intermediate result: Fidelity 0.99970796\n", + "2026-05-18 13:02:11.771986 Intermediate result: Fidelity 0.99971165\n", + "2026-05-18 13:02:11.785841 Intermediate result: Fidelity 0.99971892\n", + "2026-05-18 13:02:11.799105 Intermediate result: Fidelity 0.99972226\n", + "2026-05-18 13:02:11.811623 Intermediate result: Fidelity 0.99972441\n", + "2026-05-18 13:02:11.824114 Intermediate result: Fidelity 0.99972679\n", + "2026-05-18 13:02:11.837179 Intermediate result: Fidelity 0.99972965\n", + "2026-05-18 13:02:12.345479 Intermediate result: Fidelity 0.99972965\n", + "Done after 49 iterations.\n", + "\n", + "AQC circuit depth: 71\n", + "Baseline Trotter circuit depth: 111\n", + "Job ID: d85kc6o0bvlc73d5nhn0\n" ] } ], @@ -851,16 +859,20 @@ ")\n", "\n", "# Initial Néel state\n", - "initial_state = QuantumCircuit(L)\n", + "initial_state_circuit = QuantumCircuit(L)\n", "for i in range(L):\n", " if i % 2:\n", - " initial_state.x(i)\n", + " initial_state_circuit.x(i)\n", "\n", - "# AQC target circuit (high-accuracy, 32 Trotter steps)\n", + "# Time parameters\n", "aqc_evolution_time = 0.2\n", + "subsequent_evolution_time = aqc_evolution_time / 3\n", + "total_evolution_time = aqc_evolution_time + subsequent_evolution_time\n", + "\n", + "# AQC target circuit (high-accuracy, 32 Trotter steps for AQC portion)\n", "aqc_target_num_trotter_steps = 32\n", "\n", - "aqc_target_circuit = initial_state.copy()\n", + "aqc_target_circuit = initial_state_circuit.copy()\n", "aqc_target_circuit.compose(\n", " generate_time_evolution_circuit(\n", " hamiltonian,\n", @@ -871,7 +883,7 @@ ")\n", "\n", "# Generate ansatz from 1-step Trotter circuit\n", - "aqc_good_circuit = initial_state.copy()\n", + "aqc_good_circuit = initial_state_circuit.copy()\n", "aqc_good_circuit.compose(\n", " generate_time_evolution_circuit(\n", " hamiltonian,\n", @@ -885,20 +897,30 @@ " aqc_good_circuit\n", ")\n", "\n", - "# Baseline Trotter circuit: 3 Trotter steps, no AQC optimization\n", - "baseline_circuit = initial_state.copy()\n", + "# Subsequent circuit: 1 non-compressed Trotter step\n", + "subsequent_circuit = generate_time_evolution_circuit(\n", + " hamiltonian,\n", + " synthesis=SuzukiTrotter(reps=1),\n", + " time=subsequent_evolution_time,\n", + ")\n", + "\n", + "# Baseline Trotter circuit: 4 Trotter steps over total evolution time, no AQC\n", + "baseline_num_trotter_steps = 4\n", + "baseline_circuit = initial_state_circuit.copy()\n", "baseline_circuit.compose(\n", " generate_time_evolution_circuit(\n", " hamiltonian,\n", - " synthesis=SuzukiTrotter(reps=3),\n", - " time=aqc_evolution_time,\n", + " synthesis=SuzukiTrotter(reps=baseline_num_trotter_steps),\n", + " time=total_evolution_time,\n", " ),\n", " inplace=True,\n", ")\n", - "print(f\"Target circuit: depth {aqc_target_circuit.depth()}\")\n", + "print(f\"Target circuit: depth {aqc_target_circuit.depth(lambda x: x.operation.num_qubits == 2)}\")\n", "print(\n", - " f\"Ansatz circuit: depth {aqc_ansatz.depth()}, with {len(aqc_initial_parameters)} parameters\"\n", + " f\"Ansatz circuit: depth {aqc_ansatz.depth(lambda x: x.operation.num_qubits == 2)}, with {len(aqc_initial_parameters)} parameters\"\n", ")\n", + "print(f\"Subsequent circuit: depth {subsequent_circuit.depth(lambda x: x.operation.num_qubits == 2)}\")\n", + "print(f\"Baseline circuit: depth {baseline_circuit.depth(lambda x: x.operation.num_qubits == 2)} ({baseline_num_trotter_steps} steps, time={total_evolution_time:.4f})\")\n", "\n", "# Build target MPS and compute reference expectation value\n", "simulator_settings = QuimbSimulator(\n", @@ -909,7 +931,21 @@ ")\n", "print(\"Target MPS maximum bond dimension:\", aqc_target_mps.psi.max_bond())\n", "\n", - "exact_expval = aqc_target_mps.local_expectation(\n", + "# For the reference expectation value, we need the full evolution (AQC + subsequent)\n", + "# Build a high-accuracy full circuit for MPS reference\n", + "full_target_circuit = initial_state_circuit.copy()\n", + "full_target_circuit.compose(\n", + " generate_time_evolution_circuit(\n", + " hamiltonian,\n", + " synthesis=SuzukiTrotter(reps=aqc_target_num_trotter_steps),\n", + " time=total_evolution_time,\n", + " ),\n", + " inplace=True,\n", + ")\n", + "full_target_mps = tensornetwork_from_circuit(\n", + " full_target_circuit, simulator_settings\n", + ")\n", + "exact_expval = full_target_mps.local_expectation(\n", " quimb.pauli(\"Z\") & quimb.pauli(\"Z\"), (L // 2 - 1, L // 2)\n", ").real.item()\n", "print(f\"Reference expectation value (from MPS): {exact_expval:.6f}\")\n", @@ -939,8 +975,9 @@ " )\n", "print(f\"Done after {result.nit} iterations.\")\n", "\n", - "# Assemble the final AQC circuit\n", + "# Assemble the final AQC circuit: optimized ansatz + subsequent Trotter step\n", "aqc_final_circuit = aqc_ansatz.assign_parameters(result.x)\n", + "aqc_final_circuit.compose(subsequent_circuit, inplace=True)\n", "\n", "# -------------------------Step 2-------------------------\n", "\n", @@ -953,12 +990,12 @@ ")\n", "isa_circuit = pass_manager.run(aqc_final_circuit)\n", "isa_observable = observable.apply_layout(isa_circuit.layout)\n", - "print(\"AQC circuit depth:\", isa_circuit.depth())\n", + "print(\"AQC circuit depth:\", isa_circuit.depth(lambda x: x.operation.num_qubits == 2))\n", "\n", - "# Also transpile the baseline Trotter circuit (3 Trotter steps, no AQC)\n", + "# Also transpile the baseline Trotter circuit (4 Trotter steps, no AQC)\n", "isa_baseline_circuit = pass_manager.run(baseline_circuit)\n", "isa_baseline_observable = observable.apply_layout(isa_baseline_circuit.layout)\n", - "print(\"Baseline Trotter circuit depth:\", isa_baseline_circuit.depth())\n", + "print(\"Baseline Trotter circuit depth:\", isa_baseline_circuit.depth(lambda x: x.operation.num_qubits == 2))\n", "\n", "# -------------------------Step 3-------------------------\n", "\n", @@ -970,8 +1007,7 @@ " (isa_circuit, isa_observable),\n", " (isa_baseline_circuit, isa_baseline_observable),\n", "])\n", - "print(\"Job ID:\", job.job_id())\n", - "\n" + "print(\"Job ID:\", job.job_id())\n" ] }, { @@ -984,14 +1020,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Exact (MPS): -0.8325\n", - "Baseline Trotter: -0.5725, |Δ| = 0.2600\n", - "AQC: -0.8341, |Δ| = 0.0016\n" + "Exact (MPS): -0.7387\n", + "Baseline Trotter: -0.5955, |Δ| = 0.1432\n", + "AQC (3+1): -0.6734, |Δ| = 0.0653\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -1009,11 +1045,11 @@ "\n", "print(f\"Exact (MPS): {exact_expval:.4f}\")\n", "print(f\"Baseline Trotter: {baseline_expval:.4f}, |\\u0394| = {np.abs(exact_expval - baseline_expval):.4f}\")\n", - "print(f\"AQC: {aqc_expval:.4f}, |\\u0394| = {np.abs(exact_expval - aqc_expval):.4f}\")\n", + "print(f\"AQC (3+1): {aqc_expval:.4f}, |\\u0394| = {np.abs(exact_expval - aqc_expval):.4f}\")\n", "\n", "labels = [\n", - " f\"Baseline Trotter\\n(depth {isa_baseline_circuit.depth()})\",\n", - " f\"AQC\\n(depth {isa_circuit.depth()})\",\n", + " f\"Baseline Trotter\\n({baseline_num_trotter_steps} steps, depth {isa_baseline_circuit.depth(lambda x: x.operation.num_qubits == 2)})\",\n", + " f\"AQC (3+1)\\n(depth {isa_circuit.depth(lambda x: x.operation.num_qubits == 2)})\",\n", "]\n", "values = [baseline_expval, aqc_expval]\n", "colors = [\"tab:orange\", \"tab:blue\"]\n", @@ -1024,7 +1060,7 @@ " y=exact_expval, color=\"tab:green\", linestyle=\"--\", linewidth=2, label=f\"Exact ({exact_expval:.4f})\"\n", ")\n", "plt.ylabel(\"Expected Value\")\n", - "plt.title(\"AQC-Tensor vs Baseline Trotter (50-site XXZ)\")\n", + "plt.title(\"AQC-Tensor (3 compressed + 1 uncompressed) vs Baseline Trotter (50-site XXZ)\")\n", "plt.legend()\n", "for bar in bars:\n", " y_val = bar.get_height()\n", From 161e40a1d4cf76a857b02456f23877c2f66e66d4 Mon Sep 17 00:00:00 2001 From: ABBY CROSS Date: Tue, 19 May 2026 09:20:28 -0400 Subject: [PATCH 3/7] stale images, run ./fix --- ...antum-compilation-for-time-evolution.ipynb | 113 ++++++++++++------ ...1889c4d-16a4-458a-9211-08be8bcae1e4-0.avif | Bin 6214 -> 0 bytes ...87fff8d-98b9-4f9a-8004-01a3b0166e12-1.avif | Bin 53290 -> 0 bytes ...ea0e102-23d5-4e6e-8ef8-e82843452b19-1.avif | Bin 2193 -> 0 bytes .../extracted-outputs/527dbada-1.avif | Bin 0 -> 2204 bytes ...f7b36a6-3666-4223-9c5d-d92bca741ad2-0.avif | Bin 5689 -> 0 bytes ...ca-e1384f1897bc-0.avif => 78f2665e-1.avif} | Bin 16381 -> 16361 bytes ...c2e5fe7-21ce-461d-adaa-776f8d882163-1.avif | Bin 67046 -> 0 bytes ...13c9ced-6a2e-4345-bffc-7dae938e2015-0.avif | Bin 30523 -> 0 bytes ...3039f82-97cb-4613-86c9-a8faf0839a02-0.avif | Bin 18733 -> 0 bytes ...5b4acc0-7121-416d-9bf5-b6d3135ae805-1.avif | Bin 279361 -> 0 bytes ...6ba26ff-0bfa-47d0-b5ee-8944a8ddf274-0.avif | Bin 35077 -> 0 bytes ...0d295c7-c816-4683-bb2a-0ce9898e5d88-1.avif | Bin 341480 -> 0 bytes 13 files changed, 78 insertions(+), 35 deletions(-) delete mode 100644 public/docs/images/tutorials/approximate-quantum-compilation-for-time-evolution/extracted-outputs/01889c4d-16a4-458a-9211-08be8bcae1e4-0.avif delete mode 100644 public/docs/images/tutorials/approximate-quantum-compilation-for-time-evolution/extracted-outputs/087fff8d-98b9-4f9a-8004-01a3b0166e12-1.avif delete mode 100644 public/docs/images/tutorials/approximate-quantum-compilation-for-time-evolution/extracted-outputs/1ea0e102-23d5-4e6e-8ef8-e82843452b19-1.avif create mode 100644 public/docs/images/tutorials/approximate-quantum-compilation-for-time-evolution/extracted-outputs/527dbada-1.avif delete mode 100644 public/docs/images/tutorials/approximate-quantum-compilation-for-time-evolution/extracted-outputs/5f7b36a6-3666-4223-9c5d-d92bca741ad2-0.avif rename public/docs/images/tutorials/approximate-quantum-compilation-for-time-evolution/extracted-outputs/{b9e81c51-dc6f-4237-9aca-e1384f1897bc-0.avif => 78f2665e-1.avif} (96%) delete mode 100644 public/docs/images/tutorials/approximate-quantum-compilation-for-time-evolution/extracted-outputs/7c2e5fe7-21ce-461d-adaa-776f8d882163-1.avif delete mode 100644 public/docs/images/tutorials/approximate-quantum-compilation-for-time-evolution/extracted-outputs/813c9ced-6a2e-4345-bffc-7dae938e2015-0.avif delete mode 100644 public/docs/images/tutorials/approximate-quantum-compilation-for-time-evolution/extracted-outputs/83039f82-97cb-4613-86c9-a8faf0839a02-0.avif delete mode 100644 public/docs/images/tutorials/approximate-quantum-compilation-for-time-evolution/extracted-outputs/85b4acc0-7121-416d-9bf5-b6d3135ae805-1.avif delete mode 100644 public/docs/images/tutorials/approximate-quantum-compilation-for-time-evolution/extracted-outputs/86ba26ff-0bfa-47d0-b5ee-8944a8ddf274-0.avif delete mode 100644 public/docs/images/tutorials/approximate-quantum-compilation-for-time-evolution/extracted-outputs/b0d295c7-c816-4683-bb2a-0ce9898e5d88-1.avif diff --git a/docs/tutorials/approximate-quantum-compilation-for-time-evolution.ipynb b/docs/tutorials/approximate-quantum-compilation-for-time-evolution.ipynb index 72262363f62..b10970596a5 100644 --- a/docs/tutorials/approximate-quantum-compilation-for-time-evolution.ipynb +++ b/docs/tutorials/approximate-quantum-compilation-for-time-evolution.ipynb @@ -203,9 +203,8 @@ }, { "data": { - "image/png": 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", "text/plain": [ - "" + "\"Output" ] }, "execution_count": 2, @@ -373,9 +372,8 @@ }, { "data": { - "image/png": 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"text/plain": [ - "
" + "\"Output" ] }, "execution_count": 5, @@ -420,9 +418,15 @@ " inplace=True,\n", ")\n", "\n", - "print(f\"Target circuit: depth {aqc_target_circuit.depth(lambda x: x.operation.num_qubits == 2)}\")\n", - "print(f\"Baseline circuit: depth {baseline_circuit.depth(lambda x: x.operation.num_qubits == 2)} ({baseline_num_trotter_steps} Trotter steps, time={total_evolution_time:.4f})\")\n", - "print(f\"Subsequent circuit: depth {subsequent_circuit.depth(lambda x: x.operation.num_qubits == 2)} ({subsequent_num_trotter_steps} Trotter step, time={subsequent_evolution_time:.4f})\")\n", + "print(\n", + " f\"Target circuit: depth {aqc_target_circuit.depth(lambda x: x.operation.num_qubits == 2)}\"\n", + ")\n", + "print(\n", + " f\"Baseline circuit: depth {baseline_circuit.depth(lambda x: x.operation.num_qubits == 2)} ({baseline_num_trotter_steps} Trotter steps, time={total_evolution_time:.4f})\"\n", + ")\n", + "print(\n", + " f\"Subsequent circuit: depth {subsequent_circuit.depth(lambda x: x.operation.num_qubits == 2)} ({subsequent_num_trotter_steps} Trotter step, time={subsequent_evolution_time:.4f})\"\n", + ")\n", "print(\n", " f\"Ansatz circuit: depth {aqc_ansatz.depth(lambda x: x.operation.num_qubits == 2)}, with {len(aqc_initial_parameters)} parameters\"\n", ")\n", @@ -574,9 +578,8 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "
" + "\"Output" ] }, "execution_count": 8, @@ -625,12 +628,18 @@ "# Transpile the AQC-optimized circuit (compressed + subsequent step)\n", "isa_circuit = pass_manager.run(aqc_final_circuit)\n", "isa_observable = observable.apply_layout(isa_circuit.layout)\n", - "print(\"AQC circuit depth:\", isa_circuit.depth(lambda x: x.operation.num_qubits == 2))\n", + "print(\n", + " \"AQC circuit depth:\",\n", + " isa_circuit.depth(lambda x: x.operation.num_qubits == 2),\n", + ")\n", "\n", "# Transpile the baseline Trotter circuit (no AQC optimization)\n", "isa_baseline_circuit = pass_manager.run(baseline_circuit)\n", "isa_baseline_observable = observable.apply_layout(isa_baseline_circuit.layout)\n", - "print(\"Baseline Trotter circuit depth:\", isa_baseline_circuit.depth(lambda x: x.operation.num_qubits == 2))" + "print(\n", + " \"Baseline Trotter circuit depth:\",\n", + " isa_baseline_circuit.depth(lambda x: x.operation.num_qubits == 2),\n", + ")" ] }, { @@ -690,8 +699,12 @@ "baseline_expval = baseline_result[0].data.evs.tolist()\n", "\n", "print(f\"Exact: {exact_expval:.4f}\")\n", - "print(f\"Baseline Trotter: {baseline_expval:.4f}, |\\u0394| = {np.abs(exact_expval - baseline_expval):.4f} (depth {isa_baseline_circuit.depth(lambda x: x.operation.num_qubits == 2)}, {baseline_num_trotter_steps} steps)\")\n", - "print(f\"AQC (3+1): {aqc_expval:.4f}, |\\u0394| = {np.abs(exact_expval - aqc_expval):.4f} (depth {isa_circuit.depth(lambda x: x.operation.num_qubits == 2)}, compressed+subsequent)\")" + "print(\n", + " f\"Baseline Trotter: {baseline_expval:.4f}, |\\u0394| = {np.abs(exact_expval - baseline_expval):.4f} (depth {isa_baseline_circuit.depth(lambda x: x.operation.num_qubits == 2)}, {baseline_num_trotter_steps} steps)\"\n", + ")\n", + "print(\n", + " f\"AQC (3+1): {aqc_expval:.4f}, |\\u0394| = {np.abs(exact_expval - aqc_expval):.4f} (depth {isa_circuit.depth(lambda x: x.operation.num_qubits == 2)}, compressed+subsequent)\"\n", + ")" ] }, { @@ -702,9 +715,8 @@ "outputs": [ { "data": { - "image/png": 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/DHZZU3dFdSnVb1XUPUldM9TFSN1KfvrpJ/fbU/ebjIR+L19//fVg1xB1i1SXL1F3GOV/2q4uN3p9fa/V5cUb0KpuO95+6kbizdCjbij6vik/0SQdkfmyutbocXWjipW6IIXSb97rCqVBwCq3ouX/kpnXSUtknqW8VJ9haD4p+nxU5qQ3Q2Io/Qb1XH1PQsu/o446ynW5iUx7RmWKJoBQeZtemZKW9KbADS3XQ8+xV7ZnlPdmZRmeFnXBU1mhtKobVrSB1x6VKzqmnquB2Ol1xdagcx3vuuuuc13t0pLRbzq/YlaofEp9AzUzkVfYRFKBoYxPwUZGPxzvce/iVX06vcIqraBGP2pdJL7zzjvu4jmUCrSMMsl//vnH/Z/Wjz50jIHGdYTyxop4wZP6o6vQUqD16KOPupsKD/VfVqGptCrTj6T0i4Ky0AuLjKg/aGSaQmnqUdHYl3Xr1tmYMWPCZi2JPA8VK1ZM81iZSXu0WVlieT/eRavHS09oxq3vUWS6VaikVQhpXIgKVM3koYsb9bdWZl+sWDF3AaZ+tSr0n376aXd+9LgKC322GufxyCOPuO+jXmfNmjXBGXGiFfJKpwKmyNnQEhMT033fCrx0C/0N1KxZ0/U3zmkZfef53hz+740+k/Rm/9KFmn5Lmo1HY6H0v/rVe0H0xRdf7D4/BQaaMU4XdUqr+pTrsfREfi+9cVSqWNKMN/o96dhPPfWU+83pAl5j55SO0EoL5csKPtQvXuWGZuHRWA1Nm33XXXcF82UFMpF90r3ftZ/vsHch7L3OrbfeGnXfzLxOWiLzLH3e0T7baHlpevQ8nWuNX4w2ZWno+Y4lD/a+U7Hk1ZGU5ysgiDYFbenSpd3fGtemoMqj743GQWSU92YkM2V4NAosFdDqM1Hlk8rxZ555xs2AFUnnW9sVuIwcOdJ9V1VZpb8jaTyTgmZ99zOaAjmj33R+RWCRT2mwly44IwcvKePW4D0N4lZgoVrZzz//3GU0XkarWgHVnGkQlmovNFBPBbB3QamaLw0yVA1BtIxYg8LUIqBaN6/WN5Q3PV0sAw/1OpEFgBfcxEoXqGol0E0B0bx581zBrQFe3pR7ei+RvG3exWWs9PzIzEi1lLo4jbxA0HlNb4o+Zf6hA/5Cz7EuzLM67dFs27Yt7L5XUKm1Ib10qyDUhUk0mkjAqzXUgFh9z3SOpk+f7gaZa0rIm2++2R1HLRi6qbZUQak+O13U6OJHj6sgjFbYeJ+9zoHeg14jdLIBr+DLLSJbBj3pDYpNC9+bw/+90QVler83XZipBULTS+tCSTXXukgLpcHCuin/0MBbXZjqPShQiAzwM6I8Ur9Rb60cvW+1lOocXHDBBcELS7VOhPImplA5oBpgBfy6wFQljFrKROdN5y+Sn+lLo+X/GsBbo0aNNC/2s5LSrmDTb16q8kq/ZV2wR7uoTq/SKRrvdWMNbCIDTn2PdOEdOjmL3qc3jaq+Dyr/Q9MvGeW92V2G69pFr6tAWBWBeh+aPlatimrdC6X0qKVRwZBa1DXZycsvv+wGmKsF0KNzqBYYfQYKOkJbrA7lN51f0RUqH1JGqBYJZWqqJQu96QepWUd0Yaoag9tuu83VDCiz8Go7NVOPuunoMc2QoeZ7BQoeNR+qi4B++NEugjR/un6MygD0v2rSQm8Z/ZhDa/VVsIfuq4tsZQixXhTqvSmj8RYHVGbmNX96rS5qxtfFvVoPQqmFQ4GTdxEcK72Gmq9DKXhTBh26XedOzcUK4NI7D7oACW0CVo2LavKWLVuW5WmPRt2uQqn2SgVnZOYeShcd+i4okA39/DTDhmYYU82S5j3XMfR91YWb14qkAkmfjd6Tgl89zwtC1J1NtWbeZ6fXURDs1dh6NwUoCmh1XLUM6fUUIHt0PrOiO0VW8lqUQmvy1PoXOdtTLPjeHP7vjboIZVSTq9Y4PU+/Ab2GLvA9qmX18lld1F100UV25513ujQcSg29KjKUX3oX5goSFBhoxj4vqNB3Tb9JL+9X4KHKIL1P5dM6B2rhFZ07nUtVMOnYoedNQc+IESNSzap1qNQlS2WM0hf6OuqSqVaX7OieorxUeU5kRY/yUqVFAZdEzp4YOTOifsea7UkXxaFpV9dPtUJldqFMnVu9hr43maVKQAUNuh7w6Duhbn7eOlP6TEPTqfux5L2RIs+LnzJcsweq+7K+/17XNF2jqExTd6zQ1vIffvjBnVcFEF7FXa9evVwgrNnK1DVZVBaphULfHc3UF0vlZCy/6fyIFot8SH0HVRil1QSpoEF9JvXD1YJ4WlxG3ZUUQGjaSjWHq5DTj18XvypcQqfR0+O6AFTTpC7StY+a1bW/ahT0Q9Z0rpFNvpmhi231bdXiNsrkNFWdLgRUI6HXj1aLFY261qhVQDVwKhx0XB1HXbS8sQKaalCFh2qY1Jqj7g86h7ro14qq0abhTY8ybDUfq9bRK8B1jtRKpGBNr6G0qMuDCvXIqW9DKWPVIk/aT4saKVBSc7AKOb2OzktWpj0aTVuq7hi6KNJ0lcrENb4gWhcsjwolFdRKv26qHdMFsoJO9VlXTaq6SemCRhdTCpRUq+WdN11wKUNXNxEVDupeplYMBb0KinU+RO9bF4P6X7VUCmS9QsnrgqfvrwpYdQdR90AdVy0pKuDS62YWSt85TSGbnVRjqkJU3RAUEOq+0qnPPLPdIPjeHN7vjS5adEEaukBXNJouUy2N+u0rcAjtnqggW1MUa3yEKmVUW6p8S3mdLpLSozETXn6rtOjiT8GLtnlpUp6hGmfV7mp8g2qtx44d64IIb2yO0qBWAv0mtZ8uaJVvKchQwKH76p6ibib6W9uUTh1XQUBaXcsyS5+HWixVBukzVKWYjq/7qtTI6HwcCl2Y6nPRe1ettn7zqlRR67/yVq8GXv/rs9Y4HQUQXh6v+8rnFBRpGmPlaWoVVzmmclSVWxp7od9mZui3r7xSgaG+r5mhPFhBtNfyoPJB+bfSnN4q2bHkvZG886MWOZ2DQy3Ddf2g75fyQvUy8Cg/VEWnFhz1ujnpu6eySUGCfqcefV/1PdZnqvetVg99tmp1UcWqjhU6rblHv0evVS7W33S+lNOjx3H4afo6bwrDaDSzh2YM0XSL3qwVmmVDs19o9g7NBnHGGWe42VE03Ztmlmrfvn1wKj2PZofQ1KZnnXWW20f7agrCtKZuy+yMFpo5SLM3aaYWzWCh2UgGDRoUnJ0j2gwd0Wbh0awaml5P0/15x9HsFt60f940i5rNSVOrarYQzeqh6SUzSmM0mspS5yNyRiS9hs5p8+bN3YwnSrcWOMqIZk/Sc7XPaaed5qZb1Gv4TXvoTC1pnU/t99hjj7nj6/X1WWva4tDZTqJ9Bt4MOtpX51vnXd85Td0XOsOVZpzRzCX6jmmmkMsvv9xN3+jZtGmTm+ZQ30cdQ7PqaKaS0JlzNJOUvoeavlJp1IxcU6dOTTW14COPPOJm9NFsWPquaxrlw7GIV6yzQsmqVavc+fA+a83eooWsImeFyug7L3xvDt/3Rt9jnX9NR5oRTVcduWiZRzNAaWEwpUe/Cf3uImd8i/a5h94025zymNtvvz04k59oBiRNI6tZ2XR8zUClPEHTTCu/2r59e3D2KM0kpKlplZ9oJilNnxpKeZvKBO2ndOq1Qqc+jWVWqIxmPxPNoKTzoc9QvweVL+vWrQvEKr1ZoaLlWcpX9Rnr/Om9RftOaLpVryzR1LveDIz6fui7dODAgeBMWZomXOda00N36tQpLL+PnFUpPfpe6NiheWes7/Wff/5xeajKB32mN998c+CPP/7I8DUzynsj06+ZxDSDk56rcjrWMjyUzp1mL1Q6Va5Fo7Lcmy5es0Bpatkff/wx6nO9GQy1WKS+X+nN8qdb6HciM7/p/CZB/+R0cIO8TTV0qkVVE/qhzEyRH6n7gGazSGuMQV6hWifV1qllC+B7E92AAQNc1w7V3ANZTS1KGoiv2nf1OED24zedNsZYwDd1W1GfRYKK2GlwproNHUr/eAB5h8ZNffTRRy6PBLKDBhurckfdZrNi5XGkj990+ggsgBygQWYah6JxDgDilwYta2BrepMwAH5pnJ7GPaS37hGyBr/p9OXqrlBaJEUj/VXbo0G2GkSnWzSabUID2zTYVYNrtJ8GAwEAAADI5y0WmvJLMw1onmMFDZoBw5veLJSmrdQMC5q+TPMSa7YAzUxwKPO7AwAAAIijwEJBgZr0NGWppqg7//zz3fRymhYskqYB1LR5WpRH07lpH01NGS0IAQAAAJCPAgsNbNVaC97iJ6LVRTXPs7dYj0fb9Ji3Mq3+17zO0eYhBgAAAJCPAguttqtFcEJXYa5UqZIbdxG5IqOeW7ly5bBtWqDoUFaiBAAAABBHK29rXubQoEK8+1oJNJbnRj4vI999912w1SNHvNgq514biHe3zsvpFABxq+3oL3M6CUBce6/7GTn6+lqpPU8HFhozERkYePc1Q1Qsz418XkZq165nBQrkXGBR/siCOfbaQLzbdmy9nE4CELeKHrEpp5MAxLVj80gZlmsDiypVqti2bdvcOItChQoFuzwpWChTpkyq527evDlsm+5Hdo/KSEpKwN0AxJ/k5PCxWQAA5BXJeaQMy7VjLOrWresCitAB2AsXLrSTTjrJChQIT3bDhg1t0aJF5i3Jof9/+OEHtx0AAABAPg4stER9u3bt3OrES5YssTlz5ti4ceOsU6dOwdaLffv2ub9bt25tO3bssCFDhtjvv//u/te4i4suuiiH3wUAAACQP+TawEL69+/v1rC48cYb3UraPXr0sAsuuMA9dsYZZ7j1K6RUqVI2duxY16Jx+eWXu+lnX3zxRStRokQOvwMAAAAgf0gIeP2HYElJO3P0LCQ+V51PAcgmSd3Wcm6BbNJkxOecWyAbLejT0nJSYmLpvN9iAQAAACBvILAAAAAA4BuBBQAAAADfCCwAAAAA+EZgAQAAAMA3AgsAAAAAvhXyfwgAQH7QocOltmHD+qiPjRo1xk49tXG2vfbChQusYsVKVqNGzTSfM33627Zx4wa79dY7oz6+ffs/NnToEPvuu/lWrlw5u/nm2+3CCy/O1Hvt2vU2u+mmW0wztY8ZM9pmzZpuBw+m2KWXXma3397DChQoENNrfffdt/bccyPt77/XWr16J1mfPvfY0UfXcI+NHfucHXFENWvbtn2mzxMA5CQCCwBAzHr27GPnnnt+qu1lypTN1rPYq9cdLnhJK7DQhfykSa/auHGT0zzGkCEP2/79+23s2HH2889L7cknB9tRRx1t9eqdmOq5L700wVJSDgbvf/rpXHvppRfsoosucfffeGOyffzxhzZkyHA7eDDZHnnkAStXroJde+0NGb7WypV/WL9+veyGG26yCy5obTNnTreePe+w11//n1vY9dprO1mXLtfbWWe1srJly2XB2QOAw4PAAgAQs1KlSrmWg9zm7benWtOmza106eiLOK1bt9a+/voLmzr1PdcaUKvWsbZ06U/2zjvTogYW5cuXD/69a9cuGz/+Zeve/S6rWvUIt23q1CmuFaJhw5Pd/Tvu6OECDwUWGb3Wu+9Os5NOauj2/2/fnvb111/aRx99YO3aXeHeQ7Nmzd3zO3e+OZvOGABkPcZYAACyxOrVq6xVqxb2wQcz3f0DBw5Yx46X26hRI9z9pKRNdv/991jr1q3c87p0uc6WLPkxuP/atX9Z79497Pzzz7TLL29jU6e+EeyWJD173m6vvDI21eumpKS4blBnnnl2mmlbtmypVa5cxV3oexo0ONmWLl2S4fuaMmWiC6Yuvritu795c5Jt2rTRGjY8JexY6jq1efPmDF/r77/XWb169YOPJSQkuOBj2bKfgttOP72le096bwCQVxBYAACyhLopXX99Zzf2YM+e3fbqqy+7C+Nbb+3mHld3IY1HGDt2vOuylJhY2UaMeMI9pm5Dd9/d3UqUKG5jx75qvXvfay+++Jx99dUXrluSDBky1Dp2/K+rUag//vjdtm3bmu4Yjy1bNlulSolh28qXr+CCnfTs27fP/ve/N61Tp5uC4ycUPEjo8SpUqOD+T0ramOFrVahQ0ZKSksIeV6Ci7lwevZetW7e4blMAkFfQFQoAcompK6fY1NX/1dKn57gyJ9iQxkPDtg38/h77bceKDPe9ssY1dmWtjoecxuHDH7ennw5/7SpVjrBJk95yf2vcwCeffGyPP/6offnlZ/bUU6OtWLFibrCzWhTOPvscV5svl19+lRtrIAsWfGv//LPNBgwYZCVKlLRatWrbXXf1cxfzXrek0qXLuDEIkX79dbkdccSRVqRIkTTTvX//vlSP6/6///6b7vudO/cjK168hJ111jlhx/L29xQu/N/fBw78m+FrnXPO+Xbffb3tvPMutGbNWrixGr/8siwsMCpatKh7T3pvxx57XLppBIDcgsACAHKJ3cm7bfO+8JrsaCoXq5xq2/YD22LaV6/hh2ZFCr3IlkKFCoVdQPft29969LjN2rRpa6ec0ijY3ad9+w42Z85s1yVozZrVtmLF8mBXnz//XOMGNyuo8Gj/WCgg0cxLnsWLF1nfvj2D9xXsKF3qmhVK94sWLZbusTVoW4FA5Hv8v/2Lur///fe/YyuIyui1mjc/zc0spW5hBw8etFNOaWytW7ex3bt3he1TtmxZ1xIDAHkFgQUA5BIlC5W0SsXCu9BEU7ZI+ajbYtlXr+GHuvRUr35Uus/5/fffrGDBgi6A0AW1LrQVQNx9dzfbuXOnm1VKYwhUgz9wYD+3T+iFe2YpaNEFuqdOnbo2fvzrwftlypSxb7/9xnUtCqX7FStWTPO4SvuiRQtd965Q6sLl7e+No9iy5b9jayxGpUqVM3ytG2/s6rp1KZjQOX3ggfusatX/G5MhOmcJCfRYBpB3EFgAQC6hLkqH2k0psmtUTtFYAc2ONHDgQ/byy2Ns4sTxrpVj9eqV9uOPP9iMGR8HuzZpJidRN6nq1Y+2dev+cmMaVOsvo0c/Y8nJ/7ouUenRhfmOHduD99UyEBn81K9/ohtcrfR5XbE0cLx+/ZPSPO7Klb9bcnKy1a37fwOtReMnqlSp6vb3Agv9rW2VKlXK8LXU9ennn5dZr159rEiRCq7r1KJF39uAAQ+FvY7GXKQX+ABAbkNVCAAgZpp6VYOTI2979+51jz/11JN24okn2QUXXOTWvNDaEqtWrbRSpUq78RJz5852F93z5s2xcePGBlsGNFWsBjUPGzbEdZPS+Izp0/9nTZu2cM8pXry4rVr1h3v9SMcfX8fWr//b9uzZk2a6jzyyujvWo48+6FpUZs581z7+eLZdfvmV7nG1eOh9hI650MDpatWij91o166DvfDCs/bDD9+7mwasX3nlNTG91lFHHePe22effWJ//fWnPfTQ/S4AURcpjwa/6zzpvQFAXkGLBQAgZpo61ps+NpTWZNCsUPPnf2OvvfbfAPQzzmhpTZo0cytQP//8y9anz31upiitLK2L6169+trgwYPst99W2IknNrAnnnjKBSY33XSdq6nv1q2XnXbaGe5YHTpcY889N8qtEaGAJVTt2se6LkjqeqUAJS0PPPCwPfHEo3brrZ3d8/v3fyC4hoVaF668sm3YCuLqvqQB49FovYp//tlqAwb0s0KFClqbNpfZ1VdfF9NrqauWzoVaZLZv326NGjWxoUNHBmedkp9+WuK6XNWsWYtvJ4A8IyGgNmg4SUk7c/RMJD5XnU8CyCZJ3dZybuOY1rfYuHGDm1UqHjz22MOutSSvLJDXZMTnOZ0EIK4t6NMyR18/MTH64qOR6AoFAMjzrrjialuwYH7YWIu8SmMr9F40ixYA5CUEFgCAPE/TzXbq1MWmTJlkeZ3ew403drGyZf9vCl0AyAsYYwEAiAvxUsN/++3dczoJAHBIaLEAAAAA4BuBBQAAAADfCCwAAAAA+EZgAQAAAMA3AgsAAAAAvhFYAAAAAPCNwAIAAACAbwQWAAAAAHwjsAAAAADgG4EFAAAAAN8ILAAAAAD4RmABAAAAwDcCCwAAAAC+EVgAAAAA8I3AAgAAAIBvBBYAAAAAfCOwAAAAAOAbgQUAAAAA3wgsAAAAAPhGYAEAAADANwILAAAAAL4RWAAAAADwjcACAAAAQPwGFoFAwIYPH27Nmze3pk2b2tChQy0lJSXD/dasWWMNGjQ4LGkEAAAA8J9ClkuNHz/eZs6caaNHj7bk5GTr16+fVaxY0bp27ZrmPuvXr7fbbrvN9u/ff1jTCgAAAOR3ubbFYsKECdazZ09r3Lixa7Xo27evTZ48Oc3nz5kzxy6//HIrUqTIYU0nAAAAgFwaWGzcuNG1PjRp0iS4rVGjRrZu3TrbtGlT1H0+/fRT69Wrlw0cOPAwphQAAABAru0KlZSU5P6vXLlycFulSpXc/xs2bAjb7hk8eLD7f/78+Yf8ugUKJLgbgPhTqFCurEcBACBuyrAcCyz27dvnWiai2bNnj/s/tFuT9/eBAweyLU0VKpS0hAQCCyAelS9fMqeTAABAXJdhORZYLF682Dp16hT1MQ3U9oKIokWLBv+W4sWLZ1uatm7dnaMtFuVz7JWB+Ldt2+6cTgIAAHmyDIs1sMmxwKJZs2a2YsWKqI+pJWPYsGGuS1T16tXDukclJiZmW5pSUgLuBiD+JCdnPF01AAC5UXIeKcNyZYetKlWqWLVq1WzhwoXBbfpb26KNrwAAAACQs3Ll4G3p2LGjWyCvatWq7v6IESOsS5cuwce3bt3qukmVLJk3+pwBAAAA8SzXBhZaCG/Lli3WvXt3K1iwoHXo0ME6d+4cfFz327dvbz169MjRdAIAAAAwSwgEAgwq+P+Sknbm6Hci8bn/xpMAyHpJ3dZyWoFs0mTE55xbIBst6NPSclJiYum8O8YCAAAAQN5CYAEAAADANwILAAAAAL4RWAAAAADwjcACAAAAgG8EFgAAAAB8I7AAAAAA4BuBBQAAAADfCCwAAAAA+EZgAQAAAMA3AgsAAAAAvhFYAAAAAPCNwAIAAACAbwQWAAAAAHwjsAAAAADgG4EFAAAAAN8ILAAAAAD4RmABAAAAwDcCCwAAAAC+EVgAAAAA8I3AAgAAAACBBQAAAICcR4sFAAAAAN8ILAAAAAD4RmABAAAAwDcCCwAAAAC+EVgAAAAA8I3AAgAAAIBvBBYAAAAAfCOwAAAAAOAbgQUAAAAA3wgsAAAAAPhGYAEAAADANwILAAAAAL4RWAAAAADwjcACAAAAgG8EFgAAAAB8I7AAAAAA4BuBBQAAAADfCCwAAAAA+EZgAQAAAMA3AgsAAAAAvhFYAAAAAIjfwCIQCNjw4cOtefPm1rRpUxs6dKilpKSk+fwff/zRrrnmGjvllFPswgsvtKlTpx7W9AIAAAD5WSHLpcaPH28zZ8600aNHW3JysvXr188qVqxoXbt2TfXcpKQku+WWW6xjx472xBNP2LJly6x///6WmJhoZ599do6kHwAAAMhPcm2LxYQJE6xnz57WuHFj12rRt29fmzx5ctTnzpkzxypVqmS9e/e2GjVqWJs2baxdu3Y2Y8aMw55uAAAAID/KlS0WGzdutPXr11uTJk2C2xo1amTr1q2zTZs2WeXKlcOef+aZZ1rdunVTHWfXrl2HJb0AAABAfpcrWyzUtUlCAwi1SMiGDRtSPb969ep28sknB+9v2bLFZs2aZS1atDgs6QUAAADyuxxrsdi3b59rmYhmz5497v8iRYoEt3l/HzhwIMPj9ujRwwUiV199dabSVKBAgrsBiD+FCuXKehQAAOKmDMuxwGLx4sXWqVOnqI9poLYXRBQtWjT4txQvXjzNY+7evdvuvPNOW716tb3++uvpPjeaChVKWkICgQUQj8qXL5nTSQAAIK7LsBwLLJo1a2YrVqyI+phaMoYNG+a6RKmbU2j3KM30FI3GU9x88832559/2muvveYGcWfW1q27c7TFonyOvTIQ/7Zt253TSQAAIE+WYbEGNrly8HaVKlWsWrVqtnDhwmBgob+1LXLgtmh9i+7du9vatWtt4sSJVrt27UN63ZSUgLsBiD/JyWmvgwMAQG6WnEfKsFwZWIjWpNACeVWrVnX3R4wYYV26dAk+vnXrVtdNqmTJkjZt2jSbP3++vfDCC1amTJlg60bhwoWtXLlyOfYeAAAAgPwi1wYWWghPszupJaJgwYLWoUMH69y5c/Bx3W/fvr0bqD179mzXanHbbbeFHUMrdqsFAwAAAED2SggEAvT9+f+SknZaTkp87r9uXwCyXlK3tZxWIJs0GfE55xbIRgv6tLSclJhYOqbn5Y25qwAAAADkagQWAAAAAHwjsAAAAADgG4EFAAAAAN8ILAAAAAD4RmABAAAAwDcCCwAAAAC+EVgAAAAA8I3AAgAAAIBvBBYAAAAAfCOwAAAAAOAbgQUAAAAA3wgsAAAAAPhGYAEAAADANwILAAAAAL4RWAAAAADwjcACAAAAgG8EFgAAAAB8I7AAAAAA4BuBBQAAAADfCCwAAAAA+EZgAQAAAMA3AgsAAAAAvhFYAAAAAPCNwAIAAACAbwQWAAAAAHwjsAAAAADgG4EFAAAAAN8ILAAAAAD4RmABAAAAwDcCCwAAAAC+EVgAAAAA8I3AAgAAAIBvBBYAAAAAfCOwAAAAAJCzgcWBAwf8pwAAAABA/gwspkyZYuecc46dfPLJ9tdff9mgQYPs+eefz/rUAQAAAIjPwGLGjBk2YsQIa9++vRUuXNhtq127to0ZM8bGjRuXHWkEAAAAEG+BhYKHgQMHWo8ePaxAgf9279Spkz344IP25ptvZkcaAQAAAMRbYLFq1Spr3Lhxqu3NmjWz9evXZ1W6AAAAAMRzYFGpUiUXXERatGiRVa5cOavSBQAAACCeA4urr77aHnnkEZs7d667v3LlSjeYe8iQIXb55ZdnRxoBAAAA5HKFMrvDLbfcYjt37rTevXvb/v377bbbbrNChQrZNddcY7fffnv2pBIAAABAfAUWoqDijjvusN9//90CgYDVqlXLSpUqlfWpAwAAABCfXaH+/vtvd9u2bZtVrFjRjbnYsWNHcHtWUcAyfPhwa968uTVt2tSGDh1qKSkpaT7/iy++sLZt21qDBg3c/5999lmWpQUAAABAFrdYaGG8hISENB//5ZdfLCuMHz/eZs6caaNHj7bk5GTr16+fC2S6du2a6rlr1qyx7t272913323nnnuuzZkzx7p162YffvihVa9ePUvSAwAAACALA4sJEyaE3T948KCbJerVV1+1++67L7OHS/d1evbsGZzatm/fvjZy5MiogcWGDRvsqquuss6dO7v7N910k73wwgu2ZMkSAgsAAAAgNwYW6pYUqUWLFnbUUUfZs88+61o0/Nq4caNbE6NJkybBbY0aNbJ169bZpk2bUk1rqzU0dJN///3X3n33XTtw4IDrFgUAAAAglw7ejqZGjRq2fPnyLDlWUlKS+z80gNBYDq91Iq31MtQl6qKLLnKtKH369Ml0a0WBAgnuBiD+FCqU6SFlAADkCoXySBmW6cAi2gDtXbt22dixYzN1Ib9v3z7XMhHNnj173P9FihQJbvP+VktEWipUqGDTpk1zi/U98cQTdswxx9iFF14Yc5oqVCiZ7vgRAHlX+fIlczoJAADEdRmWJYO3NYNTiRIlbNiwYTEfZ/HixdapU6eoj2mgthdEFC1aNPi3FC9ePM1jli5d2urVq+duf/zxh02aNClTgcXWrbtztMWifI69MhD/tm3bndNJAAAgT5ZhsQY2mQ4sXnvttVSBReHChe3444+3kiVjj6Y0JmLFihVRH1NLhoIUdYnyWkG87lGJiYmpnv/bb7/Z9u3bgwO9pXbt2vbdd99ZZqSkBNwNQPxJTk57umoAAHKz5DxShmU6sPAGSWenKlWqWLVq1WzhwoXBwEJ/a1u08RXz5s2zt99+2z744INg0LNs2TK3cB8AAACAXBJY9O/fP+YDPv7445YVOnbs6BbIq1q1qrs/YsQI69KlS/DxrVu3um5SaiXRgnga46HnX3nllfbVV1/Ze++9Z2+++WaWpAUAAABAFgQWa9eutcNN61Vs2bLFLXxXsGBB69ChQ3CdCtH99u3bW48ePVzw8corr9hjjz3mxlUceeSRbs2L+vXrH/Z0AwAAAPlRQkAjr+EkJe3M0TOR+ByrhAPZJanb4a8gAfKLJiM+z+kkAHFtQZ+WOfr6iYmls28di+TkZNeaoPUiRLGJZm366aefXLckAAAAAPlLpgOLL7/80u699143xiFSsWLFCCwAAACAfCjTy/g99dRTbp0IDZZWIDF69GgbMGCAlSpVKlPrWAAAAADIxy0Wv//+uxskXadOHatbt65bGO+GG25w/2sA9XnnnZc9KQUAAAAQPy0WmqFJK1zLMcccY7/++qv7u3nz5m61awAAAAD5T6YDi+OOO84++eQT97cWoNPCdbJhw4asTx0AAACA+OwKdeutt1rPnj2tcOHCdskll9izzz7rtq1YscK1WgAAAADIf2JqsXjwwQdt6dKl7m+NoZg6daqdfPLJdsQRR9jLL7/sukede+659sgjj2R3egEAAADk1RaL+fPnu2BC3aCuvPJKN6Vs2bJl3WNNmzZ1NwAAAAD5V0wtFrNnz7bJkye7Vgp1fTrzzDOtd+/e9s0332R/CgEAAADEz+DtU0891XV10gJ5Tz75pO3Zs8duueUW1wXqueeeY/A2AAAAkI9lelaoIkWK2EUXXWRjxoyxzz77zK1hof819kKBBgAAAID8J9OzQoWqWLGiXXHFFVamTBnXVUqtGQAAAADyn0MKLA4cOGCffvqpzZgxw7VWaCB3+/btbcSIEVmfQgAAAADxFVh8++23Lpj46KOPbO/evXbWWWfZyJEjrWXLlm7KWQAAAAD5U0yBhQZrv//++7Zp0yarWbOm3X777dauXTvXFQoAAAAAYgos3nzzTbv44ovdeIpTTjmFswYAAAAg84HFV199ZcWLF4/lqQAAAADyoZimmyWoAAAAAJCl61gAAAAAQCQCCwAAAAC+EVgAAAAAODyDt999992YD6hpaAEAAADkLzEFFvfdd1/Y/YSEBAsEAlasWDErVKiQ7dq1yy2QV758eQILAAAAIB+KKbBYvnx58O+ZM2faK6+8Yo8//rjVqVPHbVu9erXde++9dskll2RfSgEAAADEzxiL4cOH20MPPRQMKqRGjRp2//3329ixY7M6fQAAAADiMbDYsWOHFS1aNNX2lJQU27dvX1alCwAAAEA8BxbNmjWzRx55xNauXRvc9scff9jDDz9sZ599dlanDwAAAEC8jLEIpW5QXbt2tfPPP9/KlCnjBnHv3LnTGjRoYA888ED2pBIAAABAfAUWVapUsenTp9vXX39tv/32m5shSuMtmjdv7v4GAAAAkP9kOrAQTS1bu3Zt93eTJk1s9+7dBBUAAABAPpbpwOLAgQNuatkPPvjAChQoYLNnz7Ynn3zSBRfPPvuslSpVKntSCgAAACB+Bm+/8MILbl2L1157LTg71A033GBr1qxxU9ECAAAAyH8yHVjMmjXLDdLW7FAe/T1kyBCbO3duVqcPAAAAQDwGFhs3brSjjz461fYjjjjCtm/fnlXpAgAAABDPgYUGbX/zzTdRWzKOPfbYrEoXAAAAgHgevN2jRw+7++677ffff7eDBw/aO++8Y6tWrXKDuJ9++unsSSUAAACA+GqxaNWqlY0aNcqWLl3qpp195ZVX7K+//nJBxYUXXpg9qQQAAAAQXy0WCxYssNNOO81atmwZtn3//v2u1YLgAgAAAMh/Mt1i0alTJ9uxY0eq7eoa1a9fv6xKFwAAAIB4a7F49dVX3SJ4EggE7PTTT4/6vAYNGmRt6gAAAADET2Bx/fXXW7ly5SwlJcUGDBhg/fv3t9KlSwcfT0hIsBIlSljz5s2zM60AAAAA8nJgUahQIWvXrl0wiGjTpo37v3DhwsG1LapUqZK9KQUAAAAQP2MszjrrLLv55ptt9OjRwW3t27e3Ll26sEAeAAAAkE9lOrAYMmSI7d2717VaeF566SXbuXNncBxGVtBYjuHDh7vuVU2bNrWhQ4e6rlgZUTrOPPNMe/vtt7MsLQAAAACyeLrZL7/80l577TU7/vjjg9vq169vgwYNsltvvdWyyvjx423mzJmuZSQ5OdnNOFWxYkXr2rVruvsNGzbMNm3alGXpAAAAAJANLRZabVutCZE03kItGVllwoQJ1rNnT2vcuLFrtejbt69Nnjw53X2+//57+/bbby0xMTHL0gEAAAAgGwKLJk2a2FNPPWW7du0KbtPfI0eOdI9lBQ0GX79+fdjxGjVqZOvWrUuzNeLAgQP2wAMP2IMPPmhFihTJknQAAAAAyKauUJpq9rrrrnMrb9eoUcNtW716tZuO9uWXX7askJSU5P6vXLlycFulSpXc/xs2bAjb7hkzZozVq1fPzjjjjEN+3QIFEtwNQPwpVCjT9SgAAOQKhfJIGZbpwOLoo4+2999/32bNmmW//fabm4q2Y8eOdumll1qxYsViPs6+fftcy0Q0e/bscf+Htjx4f6tlItqq32+88Ya999575keFCiXdNLoA4k/58iVzOgkAAMR1GZbpwEK0ON4111zjLvI1tuJQLsYXL15snTp1ivqYBmqLjl+0aNHg31K8ePGw52q8x/333+/GY3itGodq69bdOdpiUT7HXhmIf9u27c7pJAAAkCfLsFgDm0MKLKZMmeKmmFW3pNmzZ7suUFog784774z5GM2aNbMVK1ZEfUwtGZrdSV2iqlevHtY9KnJg9t9//22LFi1yx/Kmu9Ugcs1SpZaVzHTPSkkJuBuA+JOcnPF01QAA5EbJeaQMy3RgMWPGDBsxYoTdeOONwYv22rVruzUn1BVKC+X5pSClWrVqtnDhwmBgob+1LXJ8hZ770UcfhW274YYb3K1t27a+0wIAAAAgGwKLcePG2cCBA91q2/pb1KWpRIkSrhUjKwIL0bgNBStVq1Z19xXMhB5769atrptUyZIl7ZhjjgnbV+M+tOaFgg4AAAAA2S/TQ8xXrVrl1paI1rVJU8RmFS2Ed/HFF1v37t2tV69edtlll1nnzp2Dj3fo0CEY2AAAAADIYy0WGiCt4OKoo44K265xDtGmgT1UBQsWdFPb6hbNJ598kua+6T0GAAAAIBe0WFx99dX2yCOP2Ny5c939lStXusHcQ4YMscsvvzwbkggAAAAg7losbrnlFtu5c6f17t3b9u/fb7fddpsb06DpZ2+//fbsSSUAAACAXO2QpptVUHHHHXe4hem0jkStWrWsVKlSWZ86AAAAAPEbWOzatcutEfHrr79agQIFrH79+ta6devgYnYAAAAA8pdMBxZ//PGHW8Ni9+7dVrNmTTt48KC99dZb9vzzz9trr70WnB4WAAAAQP6R6cHbgwcPtrp169qnn35qb7/9tk2fPt3NwqTF6/QYAAAAgPwn04HFjz/+aPfcc4+VLVs2uK1ChQp277332tdff53V6QMAAAAQj4GF1rHYsGFD1HEX5cqVy6p0AQAAAIjnwEKtFQ8//LDNmTPHduzYYXv27LH58+fbgw8+aJ06dbK///47eAMAAACQPyQENF9sJtSpU+f/dk5ICP7tHUbb9Lf+/+WXXywvSUramaOvn/hc9Rx9fSCeJXVbm9NJAOJWkxGf53QSgLi2oE/LHH39xMTS2TMr1IQJEw4lPQAAAADiWKYDiyOPPNLdovnss8/srLPOyop0AQAAAIjnMRbt2rVzi+OF2rdvnxtjcfvtt2dl2gAAAADEa2DRsWNH69u3rw0cONAFFIsXL7bLLrvMrWXxzDPPZE8qAQAAAMRXV6jevXtby5Yt3boVF110kSUlJdkll1xi/fv3D1vbAgAAAED+kekWC6lSpYpVr17dBRWaAUp/lyxZMutTBwAAACA+A4tXX33V2rZt69avmDFjho0YMcImTpxoHTp0sJ9//jl7UgkAAAAgvgKLYcOG2U033WRvvPGG1axZ01q3bu0CDK3IfdVVV2VPKgEAAADE1xiLKVOmWIMGDcK2Va5c2V5++WWbPHlyVqYNAAAAQDy1WPzzzz/BvyODCs+BAwcsMTEx61IGAAAAIL4CixYtWtiWLVvCtmlWqNBtO3bssF69emV9CgEAAADER2ChmZ8iffzxx24Ad0bPAwAAABD/Dmm62bSCiISEBL/pAQAAAJCfAgsAAAAA8BBYAAAAADh8gQXdnAAAAAD4Xsdi8ODBVrRo0eD9f//91y2WV7JkSXd///79sR4KAAAAQH4MLJo0aWJJSUlh20455RTbtm2bu3kaN26c9SkEAAAAEB+BxcSJE7M/JQAAAADyLAZvAwAAAPCNwAIAAACAbwQWAAAAAHwjsAAAAADgG4EFAAAAAN8ILAAAAAD4RmABAAAAwDcCCwAAAAC+EVgAAAAA8I3AAgAAAIBvBBYAAAAAfCOwAAAAAOAbgQUAAAAA3wgsAAAAAMRvYBEIBGz48OHWvHlza9q0qQ0dOtRSUlLSfP7gwYPthBNOCLtNmjTpsKYZAAAAyK8KWS41fvx4mzlzpo0ePdqSk5OtX79+VrFiRevatWvU5//xxx/Wp08fa9++fXBbqVKlDmOKAQAAgPwr17ZYTJgwwXr27GmNGzd2rRZ9+/a1yZMnp/l8BRb16tWzxMTE4K148eKHNc0AAABAfpUrA4uNGzfa+vXrrUmTJsFtjRo1snXr1tmmTZtSPX/Xrl1unxo1ahzmlAIAAADItYFFUlKS+79y5crBbZUqVXL/b9iwIWprRUJCgo0ZM8Zatmxpbdu2tXfeeecwphgAAADI33JsjMW+fftcK0M0e/bscf8XKVIkuM37+8CBA6mev3LlShdY1KpVy66//npbsGCBPfDAA26Mxfnnnx9zmgoUSHA3APGnUKFcWY8CAEDclGE5FlgsXrzYOnXqFPUxDdT2goiiRYsG/5Zo4ybatWtnrVq1snLlyrn7derUsdWrV9uUKVMyFVhUqFDSBSgA4k/58iVzOgkAAMR1GZZjgUWzZs1sxYoVUR9TS8awYcNcl6jq1auHdY/SoOxICga8oMKj1otvv/02U2naunV3jrZYlM+xVwbi37Ztu3M6CQAA5MkyLNbAJldON1ulShWrVq2aLVy4MBhY6G9tCx134Rk5cqQtWrTIXn311eC25cuXu+AiM1JSAu4GIP4kJ6e9Dg4AALlZch4pw3JlYCEdO3Z0C+RVrVrV3R8xYoR16dIl+PjWrVtdN6mSJUu6blAvvviivfLKK67r05dffmnvvvuum7IWAAAAQD4OLLQQ3pYtW6x79+5WsGBB69Chg3Xu3Dn4uO5rMbwePXpYgwYNXKvFqFGj3P9HHnmkC0ROOeWUHH0PAAAAQH6REAgE6Pvz/yUl7czRDyPxuf+6fQHIeknd1nJagWzSZMTnnFsgGy3o09JyUmJi6ZielzfmrgIAAACQqxFYAAAAAPCNwAIAAACAbwQWAAAAAHwjsAAAAADgG4EFAAAAAN8ILAAAAAD4RmABAAAAwDcCCwAAAAC+EVgAAAAA8I3AAgAAAIBvBBYAAAAAfCOwAAAAAOAbgQUAAAAA3wgsAAAAAPhGYAEAAADAt0L+D4GsktRtLSfzMPj11+U2bNjjtnLl71azZm3r27e/1alTN+pzd+zYYRdffE7YtrJly9rs2fOsfPmStm3bbktOTrHk5GS7+eZOduaZZ1nXrrcFn7tgwXwbNWqE/f33Oqtf/yS799777cgjqwcff+ut1+311yfa7t277ZxzzrO7777HihUrlo3vHgAAIHvQYoF8Ze/evdavXy9r2PAUe+WVSXbiiQ3snnvuctujWb16pQskpk//MHibNGlqqudNmTLJfv/917BtGzZssAED+trFF7e1l16aYOXKlXf3A4GAe/zTT+fauHEvWr9+A2zUqBds2bKl9vzzI7PpnQMAAGQvAgvkK3PnfmRFihSzbt16WY0aNa1Xrz5WokQJmzdvTtTnr1mz2o466hirWLFS8Fa+fIWw56xd+5dNm/aG1ahRK2z7zJnv2gkn1LWOHa+3WrVq24ABg2z9+vW2aNFC9/jUqW/YlVd2tNNPP9Pq1q3vAoxZs96zffv2ZeMZAAAAyB4EFshX1CrQoEFDS0hIcPf1/0knNbSlS5ek2WJx1FFHp3vMYcMesy5dbrVy5cpFvNZPdvLJpwbvq4vT8cef4LYfPHjQfvnl57DH69c/0XWpimz5AAAAyAsILJCvbNmy2SpVSgzbphaIpKRNUZ+/evVq27Rpk91ySydr1+4iGzSov23evDn4+MyZ0+3Agf3Wtm37NF6rUti2ChUquuPt2rXT7RealkKFClmZMmXd4wAAAHkNgQXylf3791mRIkXCtun+gQP/Rn3+n3+utj17dlmPHr3t4Ycfd0GFxmSoxWHLli32/POjXRcmrwUklLo0FS4c/lqFCxe2f/89EOzupPvRHgcAAMhrmBUKcW3ChHE2ceL44P169U60AwfCL9x1v1ixolH3nzjxLVPMULTofzM1DR78pF12WWvXpWr69Gl2ySVtrVatY6PuW6RI0VRBwr///mulS5d2j3n3Ix9nVigAAJAXEVggrrVrd4Wdc875wfuTJ79mW7duCXuO7mtQdjSRF/nqNqXuSuo6NWvWLBdwaBC27N+/343VmDdvrk2a9JYlJia6Vo3I1zruuOPdTFMKLtRd6phjarjHNL5ix47taaYFAAAgN6MrFOKagoDq1Y8K3rSWxE8/LQlO+ar/f/ppsdseaffuXda6dSv74Yfvg9sUUGzf/o8LBj766CObNOkNGz/+dXfTDFAKZIYP/2/KWB1zyZLFwX3V/enXX1e47QUKFLC6devZkiU/Bh/XoO6CBQvZsccen81nBQAAIOsRWCBfadXqXDdweuTIEbZq1Ur3/759e4OtGhqDoVYEKVmylDVseLKNGvWU/fLLMluxYrkNGjTAmjVrYccee5wdc8wxbsYoL2gpWrSolS5dxqpWPcLt36ZNWxe0TJz4qq1c+Yc99tjDdsQR1eyUUxq5x9u372BTpky0zz//1B1/+PDHrW3bdnSFAgAAeRKBBfIVBQtDhz5tS5Yssq5db3CtBMOGjbTixYu7x+fO/diNofAMHPiwmyJWi+r16HGbHXHEEfbgg4Njei0FEUOGDLX333/PzSq1fft2e/zx4cGB3uedd6Fdf/1Nbrrau+/u5sZ/3HFHz2x65wAAANkrIeD1CYElJe3kLCAmhQoVsPLlS9q2bbstOTmFswYgX2sy4vOcTgIQ1xb0aZmjr5+YWDqm59FiAQAAAMA3AgsAAAAAvhFYAAAAAPCNwAIAAACAbwQWAAAAAHwjsAAAAADgG9PNhmC6WcSK6WYBADnh11+X27Bhj9vKlb9bzZq1rW/f/lanTt2oz92xY4ddfPE5YdvKli1rs2fPs0mTxtno0aNT7XPEEUfa1KnT3d+zZr1nkye/ZklJm6xGjVrWo8fd1qDBye6x/fv32/PPj3TrP0nLlmdbjx69g+tCIb7EOt0sgUUIAgvEisACAHC47d271665pp2df/5Fdskll9m77/7PPvnkY3vzzXejXtAvWfKj9e/fxyZMeDO4rUCBApaYWMmKFDFbv35zcC2mXbt22R13dLXOnW+2q67qaN9++7UNHNjP7r33freA6wcfzLRp096wyZOnWaVKiTZmzGj75psvrX//B01Log0Z8pA1btzM7rqr72E9Jzg8WMcCAAAgjsyd+5EVKVLMunXrZTVq1LRevfpYiRIlbN68OVGfv2bNajvqqGOsYsVKwVv58hXcYyVLlgzb/uabk61mzVp25ZXXuMc/+GCGXXTRJXbBBRdZ9epH2S233GEVKlS0r7/+0j3+zTdfWdu27a1OnXpWt259a9fuClu48LvDeDaQGzHGAgAAIA9YtmypNWjQ0BISEtx9/X/SSQ1t6dIlUZ+/evVKO+qoozM87p9/rrH3359h3bvfFTz2tdfeaFdffV2q56plw+tSNW/eXNfdSrfPPptnxx9/gs93iLyOwAIAACAP2LJls+uGFEotEBoDEc3q1att06ZNdsstnaxdu4ts0KD+tnnz5lTPmzJlojVq1NS1PHhOOKFOWFCirlF//fWnNWrUxN2/885etn7939amzbnupuCiT5/7svDdIi8isAAAAMgD9u/fZ0U0OCKE7h848G/U5//552rbs2eXG1T98MOPu6DinnvusoMHDwafs2fPbpszZ7Z16HB1mq+7bt1ae+yxh123KAUc/237y6pUqWojR75gTz31rB04sN+effbpLHuvyJsK5XQCAAAAkNqECeNs4sTxwfsaRH3gwIGw5+h+sWJFo56+iRPfMvVsKlq0mLs/ePCTdtllrV2XqrPOOs1t+/bbb9zjzZq1SLOb1F133WlHHnmk3XvvQLdt9+5d9sQTj9ozz7xg9euf6LZpEHf37rda1663W6VKlfg48ykCCwAAgFxIA6LPOef84H1N/bp165aw5+i+Bl9HU6zYfwFFaLepMmXKhnWdmj//azv99DPdbFGRVq78wwUV1aodacOHjwoGKBoUrhmqjj32uOBzjz++jqWkpNimTRsILPIxukIBAADkQgoCNCOTd6tf/yT76aclbnpX0f8//bTYbY+kVoXWrVvZDz98H9ymgGL79n/smGNqBLf9/PNSNwA8krpN9e7d3b3uU0+NtpIlSwUf88Z5rF69KrhNwYa3DgbyLwILAACAPKBVq3Nt166dNnLkCFu1aqX7f9++vcFWDY3B0ABvUSDQsOHJNmrUU/bLL8tsxYrlNmjQANflyWtpSE5Odl2dtPhdpOeee8a1QNx33wO2d+8ed1zd9uzZY5UrV7FmzU6zoUOH2PLlv9jy5T+7v8899wIrX778YT4ryE1y7QJ5StaIESNs2rRp7ovdoUMH69u3b9SmOvn7779t0KBB9t1331nlypXt7rvvtosvvjhTr8kCeYgVC+QBAHKCWhiGD3/czfhUu/ax1q9ff9cNSTRlrAZZf/nlf60Umqlp9Oin7euvv3ADvM88s6X16tXPKlQoZ+XLl7Tff//T2rQ5315/fZodfXSNsGuw8847w62uHemmm26xrl1vCx5b61loitozzzzLunW7y62rgfiT51feHjdunE2YMMGGDx/uIup+/fpZ586drWvXrqmeq8fbt29v1atXd89TcDF48GB7++237fjjj4/5NQksECsCCwBAXkUZhuwKLHLt4G0FFT179rTGjRu7+2qtGDlyZNTA4rPPPrP169fblClTrFSpUlarVi37/PPPbdGiRZkKLAAAAAAcmlwZWGzcuNEFCk2a/LcIizRq1MjWrVvnFnpRV6dQaqFo0aKFCyo8zz///GFNMwAAAJCf5crAIikpyf0fGkB4cyJv2LAhVWDx119/ufmV1W1q+vTpbuCQWjvOO++8TL1ugQIJ7gZkpGDBAmH/AwCQV1CGIe4Ci3379rmWiWg044CEri7p/R25MIz3/HfeeccN1h4zZozNnz/fBRZvvvmmnXRS6inY0lKhQkk3AAmIVZkyxTlZAIA8iTIMcRNYLF682Dp16hT1MQ3A9oKIokWLhgUUxYunvpArWLCglStXzh566CE3a1T9+vXt+++/t7feeitTgcXWrbtpsUDMtT3KkHfs2GsHD6Zw1gAAeQZlGDJLs4jl6sCiWbNmtmLFiqiPqSVj2LBhrkuUZnoK7R6VmPjfoiyh1DVKLQ2hU9HWrFkzzeOnJSUl4G5ArBRUJCcTWAAA8h7KMGS1XNlBvEqVKlatWjVbuHBhcJv+1rbI8RXSsGFD++233+zgwYPBbX/88YcbdwEAAAAgnwYW0rFjRzcYW+MldNNieaFdp7Zu3Wq7d+92f19yySVuEb2HH37Y1qxZY5MnT7YvvvjCrrrqqhx8BwAAAED+kStnhRKtV7Flyxbr3r27G0Ohlbe1QJ5H97UoXo8ePdw0s+PHj3djLBRkqGXj6aefdmMtAAAAAGS/XLvydk5g5W3EilVLAQB5FWUYsmvl7VzbFQoAAABA3kFgAQAAAMA3AgsAAAAAvhFYAAAAAPCNwAIAAACAbwQWAAAAAHwjsAAAAADgG4EFAAAAAN8ILAAAAAD4RmABAAAAwDcCCwAAAAC+EVgAAAAA8I3AAgAAAIBvBBYAAAAAfCOwAAAAAOAbgQUAAAAA3wgsAAAAAPhGYAEAAADANwILAAAAAL4RWAAAAADwjcACAAAAgG8EFgAAAAB8I7AAAAAA4BuBBQAAAADfCCwAAAAA+EZgAQAAAMA3AgsAAAAAvhFYAAAAAPCNwAIAAACAbwQWAAAAAHwjsAAAAADgG4EFAAAAAN8ILAAAAAD4RmABAAAAwDcCCwAAAAC+EVgAAAAA8I3AAgAAAIBvBBYAAAAAfCOwAAAAAOAbgQUAAAAA3wgsAAAAAPhGYAEAAADANwILAAAAAL4RWAAAAADwrZDlUoFAwEaMGGHTpk2zlJQU69Chg/Xt29cKFEgdC9133332zjvvpNrerFkzmzBhwmFKMQAAAJB/5drAYvz48TZz5kwbPXq0JScnW79+/axixYrWtWvXVM8dOHCg9enTJ3h/3bp1dsMNN1inTp0Oc6oBAACA/CnXdoVSS0PPnj2tcePG1rx5c9daMXny5KjPLV26tCUmJgZvzz77rLVu3drOO++8w55uAAAAID/KlS0WGzdutPXr11uTJk2C2xo1auRaIjZt2mSVK1dOc99vvvnGFixYYLNnzz5MqQUAAACQKwOLpKQk939oAFGpUiX3/4YNG9INLF588UVr3769HXHEEZl+3QIFEtwNyEjBggXC/gcAIK+gDEPcBRb79u1zLRPR7Nmzx/1fpEiR4Dbv7wMHDqR5zL/++su+/fZbN+biUMzc8D+b8HPGg73rVahnz577bNi2HnN72M9bf85w3071OtmN9W8M3t/9725r+27bmNI36pxRVr9i/eD9z/76zB759pEM9ytRqITNaD8jbNuI70fY+6vez3DfltVb2qAWg8K2XT3zatu8d3OG+/Zu1Nva1GoTvL9q+yq7+aObLRZvtHnDEkskBu9P/XWqjVk8JsP9apSpYa9c+ErYtns/v9e+3/h9hvt2OK6D3XHyHWHbzp16bkzpfeLMJ6xJ1f9rYVuwYYHd98V9Me0798q5Yfdf+PEFm/bbtAz3a1ylsT3Z8smwbV1nd7XVO1ZnuO/tDW+3K4+/Mng/aU+SXTPrmpjS+/IFL1vNsjWD92etnGVPLXwqw/0qFa9kb17yZti2h7952D5f+3mG+15c82Lr0/j/xlHJpe9canuS/8sr0vNg8wftrKPOCt5ftmWZ9fykp8XivXbvWcnCJYP3X1v2GnlEBPII8ohI5BF55zpi0fbvuI6IwHVE16jXEZHXKrkusFi8eHGag6s1UNsLIooWLRr8W4oXL57mMdX9qW7dunbsscceUpqStm+zTXs2Zfi8xKKVbdu23eH77t4c076bd2wL21cZQiz7ydZ/dtq2Av+375Yd22PaV4FFqvTu3BJbendujfJekyxpX1LG6d2xPWzfbTt2ZeK97rJC+0sE72/Z8U9s77VgydTp3XXo7zXm9G7fYduK7g67H+u+ka+pdMSyr95XqvTuToppX53P0H237o39s9n2zy4rlxKyb4zfw0BK4NDf684o73XPppgCC/1Owt7rPzsz9dkcKByS3h3kEZHII8gjUv1uyCNy/XWEWizKlClu23bFVlZxHcF1RKxyLLDQVLArVqyI+phaMoYNG+a6RFWvXj2se5QGZ6fliy++sHPPja32KJoSBUtYpWJpH99TpnA5S05OSbUtln2LFygRtu/Bgykx7ScFAgXD9i0UKBLbaxYsniq9JQuWjmnfUoVKp9q3fJGKFoghvYWtaNi+gZSEmN9r4KCF7Vs0oVhM+5YrUj7KZ1M2xvNUMtW+ae2nDnMJBRLcxbLORYFAobB9dT/W9xr5mkpHbN/Dsqn21fuvVGxXhvvqfIZ9NgfTfq+R9DmG7qvPOZZ99b2JTK++X7Hsq+9r5L4Vi1ayEoX2Zrivfifhn03BmN+rfp/JCSlhv1/yiHDkEbHm3+QRGSGPOPzXEYWN64icuo7I63lEWhICWjAiF2rVqpXddddddtlll7n77777ro0cOdLmzZsX9fl6Gxrg/dxzz1mLFi0O6TWTknb6SjPyj0KFClj58v+1jkT+KAEAyM0ow5BZiYml8+7gbenYsaMNHz7cqlat6u5rsbwuXboEH9+6davrJlWy5H/9nzVj1O7duw+5GxQAAACAQ5drAwsthLdlyxbr3r27FSxY0K283blz5+Djuq/Zn3r06OHu67lStmzZHEszAAAAkF/l2q5QOYGuUIgVzcgAgLyKMgzZ1RWKSfgBAAAA+EZgAQAAAMA3AgsAAAAAvhFYAAAAAPCNwAIAAACAbwQWAAAAAHwjsAAAAADgG4EFAAAAAN8ILAAAAAD4RmABAAAAwDcCCwAAAAC+EVgAAAAA8I3AAgAAAIBvCYFAIOD/MAAAAADyM1osAAAAAPhGYAEAAADANwILAAAAAL4RWAAAAADwjcACAAAAgG8EFgAAAAB8I7AAAAAA4BuBBQAAAADfCCwAAAAA+EZggRx1wgknhN2aN29u999/v+3evTvbX/vZZ5+1G264wf399ttv2znnnJPlr3Hfffeleo+ht/nz52f6mLt27bJ33303eH/Lli32wQcfZHHKAQCHg8oflQdTp05N9VhycrK9/PLL1qZNG2vYsKG1atXKBg8ebFu3bk313P3799vo0aPtwgsvtAYNGth5551no0aNsn379qX7+tqvffv2tm3bNnf/iy++sLZt27pj6P/PPvvskN7Xe++9FyxjPX379rWvvvrqkI6HvIHAAjlOF/hffvmlff755zZmzBhbsmSJDR069LCm4eKLL7Zp06Zl+XEHDhzo3ptuAwYMsKpVqwbv63bKKadk+pivvvqq/e9//wveHz58+CFn/ACAnDVr1iw7+uijbfr06WHbU1JS7LbbbnMBx1133WXvv/++Pf7447Z8+XK78sorbePGjcHnHjhwwDp16mQfffSR9e/f3x1TlXQzZsxw+6bnxRdfdAFL+fLlbc2aNda9e3e7/PLL3TEUcHTr1s3Wrl2bqff07bff2oMPPphqe48ePWzIkCEuvYhPBBbIcWXLlrXExESrUqWKnXzyyS4jPdw18MWKFbMKFSpk+XFLly7t3ptu+rtgwYLB+7oVKVIk08cMBALp3gcA5A1qcf7mm2/cxfv3339vf/31V/CxKVOm2LJly2zy5Ml2/vnn25FHHula9ceNG2flypVzF+ieV155xe07YcIEO/vss+2oo45y/6vi7tNPP02zlUC9A7TP1Vdf7e5v2LDBrrrqKuvcubM7xk033WQlSpRwFX6R1MoSLeBQq8ktt9zi9o90zDHHWLVq1VyQhPhEYIFcp3jx4mH3VSvTs2dPa9KkiZ144omuBmXhwoXBx5UpqrblpJNOcrUsypw9v/76q2uKVZOumoeVQUcT2hVK3ZP09+uvv25nnnmmC3b69esXVsPy8ccfu1YONU136NDBvvvuu0N6r8qUlTk/99xz7v098sgjbvu8efPc+1S69TqqhfLSqUxbr6f9VGi888477ualf8eOHS69p556qp1xxhn26KOPBpvCvfc2aNAga9SokaupAgDkjA8//NBVOqnLUeXKlcNaLdRSofKlUqVKYfuoQkoVcHPmzAl2X1IZoPJPAUeoOnXq2KRJk1w5Fo1aNGrWrOkq9qRZs2aupV3+/fdflwaVfSqLYqUgRoHOBRdcEPVxlUFvvPFGzMdD3kJggVxF/UYnTpzoMtnQPpkHDx50GZHGFigDfOihh9xjP//8s+s2pQtltXI0btzYNfuqCVkX06o10QW0+nree++99vzzz4eNT0jLpk2bbPbs2a5vqy7edWHv7admaB3rjjvucMdVWvU6akI+VD/88IPr3qSmbNVeqbn4sssuc4WMmrzvvvtuW7p0qQsyunTp4rpQqSuV/r7ooovczevKpUJh586drrZL7/enn34KBiyybt06V1AoSLnkkksOOc0AAH/U3UgtCwUKFHAX3Cpn1AqtPPqXX35J84Je5ZrKRbVo7N2715U/qlyLRuViyZIloz6m8RSnnXZaqu06nirO1J3qzjvvtOrVq8f8nlT2NG3aNM3HTz/9dFu8eLGrBEP8KZTTCQB0Ua4uQspMlUGqxsULHLRNA9DU2qDxCXLdddfZrbfeGrxITkhIcE2ryvgUVKj1QoGFamIqVqwY7F9ao0YN93y1cLRr1y7dE6+aGmWoxx13nGsZUMuFLtDVRKyaGP1/6aWXuucqGFiwYIHLTDVY+1DceOONro+tN2ZC71dN0aLaJDVDq/n7qaeecs3ShQsXdl2pvG5coq5cf/75p6vFUouGasFELRZ6v+p367n55ptdkzQAIGesX7/eVSqpu5Gohl/liFrkvW5EaQUEZcqUcf+rxcK7QPfy/MxQ5Vzr1q1TbVd5osqqRYsW2RNPPOHKC5VLKjtCewyockplsAIdVcTFQu+tUKFCLnBSCwniC4EFcpxmuFDNiIIIZZJqtu3YsWMwMNDf6o+pDHjVqlWu5l6Bg6irz/HHH+8u8uvVq2fnnnuuq+FXprVy5UrXuhA6QFo1PApiYhF64V2qVCk3O4f88ccfrnXkzTffDAtElJZDpb6zHh3/mmuuCXtc7yF0wHZatK/OTcuWLcO2a1toi0pmap8AANnTWlG0aNFg2aFafo05VLcmtcJL6ADtUGqV9oIJr/vT9u3bD6mXgAZtR9JxVabqpnJF5bICC43r8LrWKhBSd1r1IvAquGKh1hm9T40vQfwhsECOU6bkXcSrVaF+/fquFkMX79dee63r7qMaGXUDUlOxLuI1a4U3HkN9QFVDr3EJ6t6jGh/9r0CgRYsWUWemiEXkwGpvkLSCE7WyRLZ6ZCZjjaTCJdrfoYGBF0ylR2lTgRAtCNF5VvNzWq8BADi8gYUu0lXbH5qHa9zFAw884MZHqCJNYyciqaJNdOGv/Fyt6+oWpW6xkTQjobo7Rev6qtYGvabnt99+cwGKuk95ateuHRxH6I3F8Hi9BTJL5ZkCDMQfPlXkOspsdBGvzO7333933Yw0xertt9/u+qJq/IPoOWqmHTt2rJspQ119lCFrTm411aoLkVo4lOkpcNHtxx9/dGM4/NBxNejaO6Zuar3QdLlZQcf3AgCP3qe2ewVBqND7eo5qsrTNS5sKLo1DYXo/AMgdVDapG5K63GpchXd7+umn3VpFmiBEXW5VSaQuU6IWeAUHml5cLQUqDzXgWzTWTxVqkeMWtI9aQNLqJqVeAf/880/wvirolKbQ2QYVsNSqVSvL3ruCCgUvkYPSER8ILJDjlMEkJSW52+rVq91AYwUVap1QP1IFGqrZ0fgIBQ4aTC26UFYrgWZUUquFLvb1vD179rhxEcpodVGtFgs15SozVjOuMlI/NPZBXbM0VkNjGhT06KbWlqyg42vg+GuvvebOh46tQkZdwrxWGgVX3jR/uq9zoyZz1SxpPIgGvGtchgoEBVw6J16fXABAzlJZpS5MmuZV3Xm9m1rmjz32WBdkKM9X9yiN41MZoPEWaknQGEO1ZISO6dNzNO5OsyCqrNPUs2r1V4WcytLI7rEetXisWLEieF/lpspijfVT+aOZFDVJiWahiqT9DqW1QuWxqEUG8YfAAjlOMyCpj6lu6l6ksREvvfSSG+ClAdsayK37qqlRLY1qUzSGQrU9devWdcGCBo2pCVgL7A0bNsxdYGtchPZT5qjjaj8N/I6WQWaGpu1TC4Cmo1Uh8NZbb9mIESPcdLFZQeNNdHx16dJ7Vo3VM88847p1ieYzV42PVmJVH1XNHqXaLxUIqmXSvsrsFaBoUKBaMTToGwCQewILjQ2MtpaRAoqvv/7aXeC/8MILruVCLRnK8z/55BM3Ba1a6TUzodclSpVsqoxSIPLwww+756rc0HNVPkW2dHtUEeUdQ1TmaoIS9RRQ2aLAYuTIka6LclZRjwKNG1QZjfiTEGB1LQAAgDxDl25qkVBLuVodDpW6XalLlaY2D51EJDupVUUBjwIXxB9aLAAAAPIQtUCoxdxPUCFqNVBLvlreDwd1g9KYEaUd8YnAAgAAIJ/SOAwN2vZW8c5OGhOpcY9aiwnxia5QAAAAAHyjxQIAAACAbwQWyFaatUiL+2hRu1CaKlWzQsyfP/+QjvvLL7+EzWSRW2j6v9ApAP0IfY86T5pCNzM0m1Tr1q3dedZq5JqJwzv3Ola0m2YCUXN4+/bt3XogAICcKSc9mi5Wa1RkBQ349la81tTtGkh9KDTLojf1u0dT4kaWKT179nSPffXVV9anT58seAfI7QgskK009asGhkX2p9QUslpb4VB169bNTSMbz/y8Ry3Wp/VA7rzzTjcf+umnn+7mPtdaF0cccYR9+eWXYTdNa3vSSSe5qXTLly9vrVq1clP7AgByppzMalrv6K677rK9e/f6Oo6mcdfaUZG0oK3KjtCyZfDgwe4xlUEqfw61MhF5B4EFso1qxufOnevm6g6lxXZ2797Nmc9GWmlVa3dobQutvq3CRKucauGkggULuoWUvJsWUtKCfE8++WSwYNM86loA0E/wBwA4tHIyO/hdXUBT06oFQoGFKqiizfikRf5Cy5fQhVmvvfZae/75532lAbkfgQWyzZtvvukWvQtdAEjdbFQ7o9r0jGh16wsvvNDVpGtqujlz5rjtarpVzYtWlPa6Hf36669ue4MGDdw+WtTHo+bau+++2z1fi8/pcWXknm+++cbNp63XOffcc+2NN96I+T1+//337gJer9urV69UNUFqGlba9bqat/u7774LPqb0jh492l3E63Flut6KpNHeo2jRPC1opO5Nekyrj0dz8803u8XxIu3cuTPVNi2epAWYtKigRwWC5kefMWNGzOcCAOC/nFQZpLUlTj311FQX4goONLOS9tEq3JrR6e+//w4+ru5Hak0477zzXDmh7kdeRZ7KN+9/r2uVul9pQT291mmnnWbjx49PNwhSF1ntqwVsI6n8UrmRFq3+rS65WgQX8YvAAtnmiy++cBlVqCeeeML13z/uuOPS3Vd9QO+55x63SvaHH35oV1xxhfXu3dv++ecfFyhoddABAwbYwIEDbd++fXbLLbdYo0aNXGvIvffe6zJjdQEKvcBXhqwMUcdSrYuabQ8ePOhq8zUWQX1PFRwok9VjGdm6datLn96jXuvYY491afUsX77cpUWroypdaj1QOtesWRN8ztixY12go3RVqVLFdVdSsBD5Hj1qWdCqqApI9FoaRxGNVkkNzeDVNUrdqrRaayhl8j/++GPU1cj1vvQZAgAOTzmp+0OGDHHlkoKOn376yVUyeSZNmuQqfFQhpMcrVqxoXbp0CRufoZWyNQZCrc6qdNP0ruJ1X9L/3joSixYtci3VKsNU/qiM9iq4ItWpU8eVWdWrV0/1mMrXVatWue5PKtMU2AwfPjys8ktrZqgCT89B/CKwQLZITk62FStWhNWCf/311+5CVv3+M6K+mMoodXGt1UCVcSpYKFq0qJUrV8515yldurS7KZNV5qqMWBfTGuimWhxlqp6yZcu6VhKlR5mnanJ0Ua4afAUr6iakzFIX/6qxUY19RhSIVKhQwfr162e1atWyHj16uEzTowBALQFq4lZ3pE6dOrkaG7U6eHS/c+fOLl2PPvqoC1Y0yC3yPXoGDRrkmprVX1WFkYKXjPz555+udUPpUMARSosinX/++S6oiaRA6eeff87w+ACArCknddGvvFot4aqAe+yxx1y553n55ZddpVuzZs3cfirXtm/fHlYJpAostXioPFLFlMoqlXUqr0T/FytWzP2tvF/lw9FHH+3KInVdUpoyS60marFXy8szzzzjKtVUNg8dOjTseZQr8a9QTicA8UkZXUpKihsILGpVUK2JLoy9DC09devWdRmjuvPUrFnTNd1qZqPixYuneq6aVXWBrWDBo5YIXZh7TjzxxLCmZt1XrYwu4NUVSbU7Clw08EwtGgpEMqJWDdXgaAVUjzJyrzuUjq8MXbVKHgVLasL2qPk5tDZH71X7KR3RKPP3KOBIqyuURzVIOodqtvYG0YUWauoSFpnxe3RuvNlDAADZW06K8v9rrrkmeF+Ped2O1KVpw4YNrmtvgQL/Vy+s8jV0oo/QckVlncpDlQVeYBFKFWqhZZjKlUOZEVAVgBqYrbJTx1MZrvemijcFLl55rHIllgox5F0EFsgWXkaljEWWLFniBgl7U8+F1qyoZiZyzIX2V5Or9tPFr7oyvf766+6mDCvyArlFixbB5t5oChUK/6oro/UyZs1QpRk5NIZDNwUCCjLOOuusTA+GU5OyF1joNbz3Fyo0sEovXdGEBkvRXj/Ub7/95mqgVCiplisyoFMXKJ07tX5Eo88uvbQAALKunEyvXPHKB6+rkyqhQoVWhoXOLuUdO628PLJMifb6sVLQEEotKgpSFEB5QQ3lSvzjqgHZwuvKo8HaosHNH330kevH6d1Etega1xBJtTaapUj7qXZm1qxZbhaKaH3+lcGqNkY1L+pypJsumidOnBh8jpp2QzPvpUuXukFuSUlJbkyF9tFYCHWP0jiETz75JMP3qGZqdRXyMntv7YnQdGmwm5cm3RS0aLyDJ7TmRk3V6raU2fUqotm0aZPrPqbXVJcstYZEWrx4sesaFdrMHkqfnbqIAQCyv5z0yhWNqwidickbl6duSur2q3LLK1NULmpCFJWB0cohlXUKNFQehbZMZDWVzeqeFTqBidKh9xjaUkK5Ev8ILJA9X6wCBVw3Ia+vpmrLQy+wdfP6dyqjjKQMVGMR1HKglo5PP/3UDWCrV6+ee7xEiRKuC5TGR2hchNfVSgGJplTV4LfQ4+oYyny1zwsvvGDLli1zszSplketIerHqot6LRCni33vdbwxGNG0adPGZaJ6LR1XrQLeInSi1gLNbKWxHjr2q6++6m6hg6rVB1VBltKtvrDVqlVzmXPke8wsBWUKpJQ2TRmrgki30Gl+1aIR2rc3kj477zwAALK3nJTrr7/edaHV+DeVCyrXVL6Flisaw6DKL3V/UjdeLaSqcX6eUaNGuRkIVXmkyjtNmFKyZMlgV2KVcVk95bu6IquSSulRuaVyWN1sNUNhKMqV+EdggWyjaVEPdXVsDZ7WzEiaBUkX8OoqpVmhvPEJGhehKWWViak2XvNqK5NVtyNtU9em0JmONJ2rBkbrcWXaWvxNXYQ07kLBizJaBSgaAK6AQ+M5RBfmGpQdjYISBROqXdJ0tRqcrv89WmxOGau6b2kGDhUUmsmjSZMmwedokJ6mFtSqq8ro9T687lGh7zEz1IytLl2bN292s13pnHm3cePGBZ+nx9MbS6LPToPLAQDZI7Kc1BSyjz/+uOsKrLJItf2h3X+7du3qtivgUHmmQdNqlQ7Ny7Vd05TruSpvHnjgAbddx/LKuWgL3PmhcljpUDmrcYqqKLv66qvDAguVcQosKFfiW0LA74opQBpUS68LZjWRRht0fbgoQFHtTWjXqFhpcLTGhYwZMybL06W1Kpo2bZpm4JKT1IVLn928efNcTRcAIPeXk+pKq1Zyr+U7ty3cOn36dNdyj/hFiwWyjWYw0gDovLzImmr4L7jgAstv1LqiFhOCCgDIPvFQTsZKYww13TviG4EFspXmslZ3noymRc2t1JSs2qT8RIPrNKZFa4EAALJXXi8nY6EWGQ00j1w0F/GHrlAAAAAAfKPFAgAAAIBvBBYAAAAAfCOwAAAAAOAbgQUAAAAA3wgsAAAAAPhGYAEAAADANwILAAAAAL4RWAAAAADwjcACAAAAgPn1/wCrUFFwGi7kVQAAAABJRU5ErkJggg==", "text/plain": [ - "
" + "\"Output" ] }, "metadata": {}, @@ -724,10 +736,16 @@ "plt.figure(figsize=(8, 5))\n", "bars = plt.bar(labels, values, color=colors, width=0.5)\n", "plt.axhline(\n", - " y=exact_expval, color=\"tab:green\", linestyle=\"--\", linewidth=2, label=f\"Exact ({exact_expval:.4f})\"\n", + " y=exact_expval,\n", + " color=\"tab:green\",\n", + " linestyle=\"--\",\n", + " linewidth=2,\n", + " label=f\"Exact ({exact_expval:.4f})\",\n", ")\n", "plt.ylabel(\"Expected Value\")\n", - "plt.title(\"AQC-Tensor (3 compressed + 1 uncompressed) vs Baseline Trotter (10-site XXZ)\")\n", + "plt.title(\n", + " \"AQC-Tensor (3 compressed + 1 uncompressed) vs Baseline Trotter (10-site XXZ)\"\n", + ")\n", "plt.legend()\n", "for bar in bars:\n", " y_val = bar.get_height()\n", @@ -915,12 +933,18 @@ " ),\n", " inplace=True,\n", ")\n", - "print(f\"Target circuit: depth {aqc_target_circuit.depth(lambda x: x.operation.num_qubits == 2)}\")\n", + "print(\n", + " f\"Target circuit: depth {aqc_target_circuit.depth(lambda x: x.operation.num_qubits == 2)}\"\n", + ")\n", "print(\n", " f\"Ansatz circuit: depth {aqc_ansatz.depth(lambda x: x.operation.num_qubits == 2)}, with {len(aqc_initial_parameters)} parameters\"\n", ")\n", - "print(f\"Subsequent circuit: depth {subsequent_circuit.depth(lambda x: x.operation.num_qubits == 2)}\")\n", - "print(f\"Baseline circuit: depth {baseline_circuit.depth(lambda x: x.operation.num_qubits == 2)} ({baseline_num_trotter_steps} steps, time={total_evolution_time:.4f})\")\n", + "print(\n", + " f\"Subsequent circuit: depth {subsequent_circuit.depth(lambda x: x.operation.num_qubits == 2)}\"\n", + ")\n", + "print(\n", + " f\"Baseline circuit: depth {baseline_circuit.depth(lambda x: x.operation.num_qubits == 2)} ({baseline_num_trotter_steps} steps, time={total_evolution_time:.4f})\"\n", + ")\n", "\n", "# Build target MPS and compute reference expectation value\n", "simulator_settings = QuimbSimulator(\n", @@ -955,12 +979,14 @@ " aqc_target_mps, aqc_ansatz, simulator_settings\n", ")\n", "\n", + "\n", "def callback(intermediate_result: OptimizeResult):\n", " fidelity = 1 - intermediate_result.fun\n", " print(\n", " f\"{datetime.datetime.now()} Intermediate result: Fidelity {fidelity:.8f}\"\n", " )\n", "\n", + "\n", "result = minimize(\n", " objective,\n", " aqc_initial_parameters,\n", @@ -990,12 +1016,18 @@ ")\n", "isa_circuit = pass_manager.run(aqc_final_circuit)\n", "isa_observable = observable.apply_layout(isa_circuit.layout)\n", - "print(\"AQC circuit depth:\", isa_circuit.depth(lambda x: x.operation.num_qubits == 2))\n", + "print(\n", + " \"AQC circuit depth:\",\n", + " isa_circuit.depth(lambda x: x.operation.num_qubits == 2),\n", + ")\n", "\n", "# Also transpile the baseline Trotter circuit (4 Trotter steps, no AQC)\n", "isa_baseline_circuit = pass_manager.run(baseline_circuit)\n", "isa_baseline_observable = observable.apply_layout(isa_baseline_circuit.layout)\n", - "print(\"Baseline Trotter circuit depth:\", isa_baseline_circuit.depth(lambda x: x.operation.num_qubits == 2))\n", + "print(\n", + " \"Baseline Trotter circuit depth:\",\n", + " isa_baseline_circuit.depth(lambda x: x.operation.num_qubits == 2),\n", + ")\n", "\n", "# -------------------------Step 3-------------------------\n", "\n", @@ -1003,11 +1035,13 @@ "estimator = Estimator(backend)\n", "estimator.options.environment.job_tags = [\"TUT_AQCTE\"]\n", "\n", - "job = estimator.run([\n", - " (isa_circuit, isa_observable),\n", - " (isa_baseline_circuit, isa_baseline_observable),\n", - "])\n", - "print(\"Job ID:\", job.job_id())\n" + "job = estimator.run(\n", + " [\n", + " (isa_circuit, isa_observable),\n", + " (isa_baseline_circuit, isa_baseline_observable),\n", + " ]\n", + ")\n", + "print(\"Job ID:\", job.job_id())" ] }, { @@ -1027,9 +1061,8 @@ }, { "data": { - "image/png": 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"text/plain": [ - "
" + "\"Output" ] }, "metadata": {}, @@ -1044,8 +1077,12 @@ "baseline_expval = hw_results[1].data.evs.tolist()\n", "\n", "print(f\"Exact (MPS): {exact_expval:.4f}\")\n", - "print(f\"Baseline Trotter: {baseline_expval:.4f}, |\\u0394| = {np.abs(exact_expval - baseline_expval):.4f}\")\n", - "print(f\"AQC (3+1): {aqc_expval:.4f}, |\\u0394| = {np.abs(exact_expval - aqc_expval):.4f}\")\n", + "print(\n", + " f\"Baseline Trotter: {baseline_expval:.4f}, |\\u0394| = {np.abs(exact_expval - baseline_expval):.4f}\"\n", + ")\n", + "print(\n", + " f\"AQC (3+1): {aqc_expval:.4f}, |\\u0394| = {np.abs(exact_expval - aqc_expval):.4f}\"\n", + ")\n", "\n", "labels = [\n", " f\"Baseline Trotter\\n({baseline_num_trotter_steps} steps, depth {isa_baseline_circuit.depth(lambda x: x.operation.num_qubits == 2)})\",\n", @@ -1057,10 +1094,16 @@ "plt.figure(figsize=(8, 5))\n", "bars = plt.bar(labels, values, color=colors, width=0.5)\n", "plt.axhline(\n", - " y=exact_expval, color=\"tab:green\", linestyle=\"--\", linewidth=2, label=f\"Exact ({exact_expval:.4f})\"\n", + " y=exact_expval,\n", + " color=\"tab:green\",\n", + " linestyle=\"--\",\n", + " linewidth=2,\n", + " label=f\"Exact ({exact_expval:.4f})\",\n", ")\n", "plt.ylabel(\"Expected Value\")\n", - "plt.title(\"AQC-Tensor (3 compressed + 1 uncompressed) vs Baseline Trotter (50-site XXZ)\")\n", + "plt.title(\n", + " \"AQC-Tensor (3 compressed + 1 uncompressed) vs Baseline Trotter (50-site XXZ)\"\n", + ")\n", "plt.legend()\n", "for bar in bars:\n", " y_val = bar.get_height()\n", @@ -1094,7 +1137,7 @@ "metadata": { "description": "Learn how to use AQC-Tensor to compress Trotterized time-evolution circuits for efficient execution on quantum hardware.", "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -1108,7 +1151,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.1" + "version": "3" }, "title": 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zd|h}EaX2rf7~2&lrk_iw4_$tCthx8kI`3$qYnh=$Zju@2@3eyzyZj6GAi^QuR_B16 z$q(IjUDK?-dIZwiNY*Zjjmm~!OW(<^_$<*Tc^tu5R3kX8|6In&y9y_rC&FnHQp%@* zpU}5xwA!tXQTG1Gq2zjT`4_{}x_OAHHRnW=k^3fJ)&DbUSgvGFbe=SZp(M|8!nxTg zHzl<)nNFg=vX2jA2v?(ib+V*3;hVsdtn#zi*S84G6&ImdF$4e? zLHI$w>n*^-qRTyTEU3NWMAz^`Y0r}6bcG>7Cyp1(J^DsG&(0ES9qu<)(ZmomQ&CbO zQtqP8h Date: Tue, 19 May 2026 09:36:59 -0400 Subject: [PATCH 5/7] copyedit --- ...antum-compilation-for-time-evolution.ipynb | 37 ++++++++++--------- 1 file changed, 19 insertions(+), 18 deletions(-) diff --git a/docs/tutorials/approximate-quantum-compilation-for-time-evolution.ipynb b/docs/tutorials/approximate-quantum-compilation-for-time-evolution.ipynb index 4beb66a351a..a0a2c4b84ba 100644 --- a/docs/tutorials/approximate-quantum-compilation-for-time-evolution.ipynb +++ b/docs/tutorials/approximate-quantum-compilation-for-time-evolution.ipynb @@ -14,7 +14,7 @@ "\n", "# Approximate quantum compilation for time evolution circuits\n", "\n", - "*Usage estimate: 15 seconds on IBM Heron (NOTE: This is an estimate only. Your runtime might vary.)*" + "*Usage estimate: 15 seconds on a Heron processor (NOTE: This is an estimate only. Your runtime might vary.)*" ] }, { @@ -36,9 +36,9 @@ "source": [ "## Prerequisites\n", "It is recommended that you familiarize yourself with these topics:\n", - "- [Basics of quantum circuits](https://learning.quantum.ibm.com/course/basics-of-quantum-information)\n", + "- [Basics of quantum circuits](/learning/courses/basics-of-quantum-information)\n", "- [Hamiltonian simulation and Trotterization](https://learning.quantum.ibm.com/course/variational-algorithm-design/hamiltonian-simulation)\n", - "- [Introduction to primitives](https://quantum.cloud.ibm.com/docs/en/guides/primitives)" + "- [Introduction to primitives](/docs/guides/primitives)" ] }, { @@ -60,7 +60,7 @@ "\n", "A naive approach uses few Trotter steps to keep circuit depth manageable, but this introduces significant Trotter error. AQC-Tensor resolves this tension by separating accuracy from depth:\n", "\n", - "1. **Target circuit (high accuracy, deep):** Construct a Trotter circuit with many steps—say $10k$—for the same evolution time. This circuit has far less Trotter error, but is too deep for hardware. Because it is only simulated classically as a matrix product state (MPS), depth is not a concern.\n", + "1. **Target circuit (high accuracy, deep):** Construct a Trotter circuit with many steps—say, $10k$—for the same evolution time. This circuit has far less Trotter error, but is too deep for hardware. Because it is only simulated classically as a matrix product state (MPS), depth is not a concern.\n", "\n", "2. **Ansatz circuit (low depth, parametrized):** Define a parametrized circuit $V(\\theta)$ with the same structure as a single-step Trotter circuit. Initialize it so that $V(\\theta_{\\text{init}}) = U_{\\text{Trotter}}(t/k)$, then iteratively optimize $\\theta$ so that $V(\\theta)$ reproduces the high-accuracy target state as closely as possible.\n", "\n", @@ -70,13 +70,13 @@ "\n", "AQC-Tensor is most effective when:\n", "\n", - "- **Circuit depth exceeds hardware coherence times.** If a Trotter simulation requires more trotter steps than the device can support, AQC-Tensor can compress the evolution into a shallower circuit.\n", + "- **Circuit depth exceeds hardware coherence times.** If a Trotter simulation requires more Trotter steps than the device can support, AQC-Tensor can compress the evolution into a shallower circuit.\n", "- **Entanglement remains classically tractable.** The total entanglement in a time-evolved state depends primarily on the evolution time $t$, not the number of Trotter steps $k$. This means a target circuit with $10k$ steps is typically no harder to represent as an MPS than one with $k$ steps, as long as $t$ is short enough for bond dimensions to stay manageable.\n", "- **A natural ansatz exists.** Because the ansatz mirrors the structure of a Trotter circuit, it provides a physically motivated starting point with well-defined initial parameters, avoiding the convergence issues that can plague arbitrary variational ansatze.\n", "\n", "This approach contrasts with generic circuit compression: rather than trying to approximate an arbitrary unitary with fewer gates, AQC-Tensor keeps the same gate structure and optimizes its parameters to reduce Trotter error. See the [AQC-Tensor documentation](https://qiskit.github.io/qiskit-addon-aqc-tensor/) for more information.\n", "\n", - "This tutorial guides you through the full state-preparation AQC-Tensor workflow: defining a Hamiltonian, generating Trotter circuits, compressing them via tensor-network optimization, and executing the result on IBM Quantum® hardware." + "This tutorial guides you through the full state-preparation AQC-Tensor workflow: defining a Hamiltonian, generating Trotter circuits, compressing them via tensor-network optimization, and executing the result on IBM Quantum® hardware." ] }, { @@ -149,7 +149,7 @@ "\n", "This section uses a 10-site system to illustrate the AQC-Tensor workflow step by step. We simulate the dynamics of a 10-site XXZ spin chain, a widely studied model for examining spin interactions and magnetic properties.\n", "\n", - "The Hamiltonian is:\n", + "The Hamiltonian is as follows:\n", "\n", "$$\n", "\\hat{\\mathcal{H}}_{XXZ} = \\sum_{i=1}^{L-1} J_{i,(i+1)}\\left(X_i X_{(i+1)}+Y_i Y_{(i+1)}+ 2\\cdot Z_i Z_{(i+1)} \\right) \\, ,\n", @@ -314,7 +314,7 @@ "source": [ "#### Generate the AQC target circuit\n", "\n", - "We now construct the Trotter circuit that will serve as the AQC target. This circuit uses many Trotter steps (32) for high accuracy. Because it will only be simulated classically as an MPS—not executed on hardware—the large depth is not a concern." + "We now construct the Trotter circuit that will serve as the AQC target. This circuit uses many Trotter steps (32) for high accuracy. Because it will only be simulated classically as an MPS—not executed on hardware—the large depth is not a concern." ] }, { @@ -344,14 +344,14 @@ "source": [ "#### Generate an ansatz, initial parameters, subsequent circuit, and a baseline circuit\n", "\n", - "Next, we construct a \"good\" circuit with the same evolution time as the AQC target but far fewer Trotter steps (just 1). We pass this circuit to `generate_ansatz_from_circuit`, which returns:\n", + "Next, we construct a \"good\" circuit with the same evolution time as the AQC target but far fewer Trotter steps (only one). We pass this circuit to `generate_ansatz_from_circuit`, which returns:\n", "\n", "1. A general, parametrized **ansatz** circuit with the same two-qubit connectivity.\n", "2. **Initial parameters** that reproduce the input circuit when plugged into the ansatz.\n", "\n", "We also construct:\n", - "- A **subsequent circuit** with 1 Trotter step that will be appended (uncompressed) after the AQC-optimized portion, following the approach in the [AQC-Tensor initial state tutorial](https://qiskit.github.io/qiskit-addon-aqc-tensor/tutorials/01_initial_state_aqc.html).\n", - "- A **baseline Trotter circuit** using 4 Trotter steps over the full evolution time (`aqc_evolution_time + subsequent_evolution_time`). This serves as the comparison: it represents what you would run on hardware without AQC. The AQC ansatz (3 compressed steps + 1 uncompressed step) achieves better accuracy at lower depth." + "- A **subsequent circuit** with one Trotter step that will be appended (uncompressed) after the AQC-optimized portion, following the approach in the [AQC-Tensor initial state tutorial](https://qiskit.github.io/qiskit-addon-aqc-tensor/tutorials/01_initial_state_aqc.html).\n", + "- A **baseline Trotter circuit** using four Trotter steps over the full evolution time (`aqc_evolution_time + subsequent_evolution_time`). This serves as the comparison: it represents what you would run on hardware without AQC. The AQC ansatz (3 compressed steps + 1 uncompressed step) achieves better accuracy at lower depth." ] }, { @@ -440,7 +440,7 @@ "source": [ "#### Set up tensor network simulation and build the target MPS\n", "\n", - "We use [quimb](https://github.com/jcmgray/quimb)'s matrix-product state (MPS) circuit simulator, with JAX providing automatic differentiation for the gradient-based optimization. We then build an MPS representation of the target state and evaluate the starting fidelity between the initial ansatz and the target. As the problem instance is a relatively small example, the starting fidelity starts off quite high." + "We use the [quimb](https://github.com/jcmgray/quimb) matrix-product state (MPS) circuit simulator, with JAX providing automatic differentiation for the gradient-based optimization. We then build an MPS representation of the target state and evaluate the starting fidelity between the initial ansatz and the target. As the problem instance is a relatively small example, the starting fidelity starts off quite high." ] }, { @@ -600,7 +600,7 @@ "source": [ "### Step 2: Optimize problem for quantum hardware execution\n", "\n", - "For this small-scale example, we use a fake backend (`FakeKyiv`) to simulate hardware execution locally. We transpile both the AQC circuit and the baseline Trotter circuit (the unoptimized `aqc_good_circuit`) to the backend's ISA, with `optimization_level=3` to further reduce circuit depth." + "For this small-scale example, we use a fake backend (`FakeKyiv`) to simulate hardware execution locally. We transpile both the AQC circuit and the baseline Trotter circuit (the unoptimized `aqc_good_circuit`) to the backend's instruction set architecture (ISA), with `optimization_level=3` to further reduce circuit depth." ] }, { @@ -649,7 +649,7 @@ "source": [ "### Step 3: Execute using Qiskit primitives\n", "\n", - "We use the `EstimatorV2` primitive with the fake backend to run both the AQC-optimized circuit and the baseline Trotter circuit, measuring the ZZ observable for each." + "We use the [`EstimatorV2`](/docs/api/qiskit-ibm-runtime/estimator-v2) primitive with the fake backend to run both the AQC-optimized circuit and the baseline Trotter circuit, measuring the ZZ observable for each." ] }, { @@ -768,7 +768,7 @@ "source": [ "## Large-scale hardware example\n", "\n", - "We now scale up to a 50-site XXZ model to demonstrate AQC-Tensor on a more realistic problem size. The workflow is the same as the small-scale example: we compress 3 Trotter steps via AQC and append 1 uncompressed step.\n", + "We now scale up to a 50-site XXZ model to demonstrate AQC-Tensor on a more realistic problem size. The workflow is the same as the small-scale example: we compress three Trotter steps via AQC and append one uncompressed step.\n", "\n", "For a system of this size, matrix exponentiation is infeasible ($2^{50}$ dimensions), so we compute the reference expectation value directly from a high-accuracy MPS evolved for the full time.\n", "\n", @@ -1125,11 +1125,12 @@ "metadata": {}, "source": [ "## Next steps\n", - "If you found this work interesting, you might be interested in the following material:\n", + "\n", "\n", + "If you found this work interesting, you might be interested in the following material:\n", "- [AQC-Tensor addon documentation](https://qiskit.github.io/qiskit-addon-aqc-tensor/) — includes the related **unitary AQC** technique, which optimizes parametrized circuits to approximate a target unitary operator rather than a prepared state\n", - "- [Error mitigation and suppression techniques](https://quantum.cloud.ibm.com/docs/en/guides/error-mitigation-and-suppression-techniques)\n", - "- [Combine Error Mitigation Techniques](https://quantum.cloud.ibm.com/docs/en/tutorials/combine-error-mitigation-techniques)\n", + "- [Error mitigation and suppression techniques](/docs/guides/error-mitigation-and-suppression-techniques)\n", + "- [Combine Error Mitigation Techniques](/docs/tutorials/combine-error-mitigation-techniques)\n", "" ] } From 83e7edcb7015178d10d04873c99bb3642c593300 Mon Sep 17 00:00:00 2001 From: abbycross Date: Tue, 19 May 2026 09:51:10 -0400 Subject: [PATCH 6/7] nit --- .../approximate-quantum-compilation-for-time-evolution.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/tutorials/approximate-quantum-compilation-for-time-evolution.ipynb b/docs/tutorials/approximate-quantum-compilation-for-time-evolution.ipynb index a0a2c4b84ba..fb477fe0f7b 100644 --- a/docs/tutorials/approximate-quantum-compilation-for-time-evolution.ipynb +++ b/docs/tutorials/approximate-quantum-compilation-for-time-evolution.ipynb @@ -1130,7 +1130,7 @@ "If you found this work interesting, you might be interested in the following material:\n", "- [AQC-Tensor addon documentation](https://qiskit.github.io/qiskit-addon-aqc-tensor/) — includes the related **unitary AQC** technique, which optimizes parametrized circuits to approximate a target unitary operator rather than a prepared state\n", "- [Error mitigation and suppression techniques](/docs/guides/error-mitigation-and-suppression-techniques)\n", - "- [Combine Error Mitigation Techniques](/docs/tutorials/combine-error-mitigation-techniques)\n", + "- [Combine error mitigation techniques](/docs/tutorials/combine-error-mitigation-techniques)\n", "" ] } From 844540840ad9ae550431dce3596d5f34fdaa01e9 Mon Sep 17 00:00:00 2001 From: Henry Zou Date: Tue, 19 May 2026 11:13:25 -0400 Subject: [PATCH 7/7] Fix stale prereq link and corrected baseline-circuit reference - Prereq: the old VAD `hamiltonian-simulation` page no longer exists in the docs repo. Point at the `quantum-simulation` lesson in the utility-scale-quantum-computing course, which covers the same topics and is in-tree. - Step 2 intro called the baseline `aqc_good_circuit`, but the code in the next cell transpiles `baseline_circuit` (4 Trotter steps over the full evolution time). Updated the parenthetical to match. --- .../approximate-quantum-compilation-for-time-evolution.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/tutorials/approximate-quantum-compilation-for-time-evolution.ipynb b/docs/tutorials/approximate-quantum-compilation-for-time-evolution.ipynb index fb477fe0f7b..eec2b0c03e4 100644 --- a/docs/tutorials/approximate-quantum-compilation-for-time-evolution.ipynb +++ b/docs/tutorials/approximate-quantum-compilation-for-time-evolution.ipynb @@ -37,7 +37,7 @@ "## Prerequisites\n", "It is recommended that you familiarize yourself with these topics:\n", "- [Basics of quantum circuits](/learning/courses/basics-of-quantum-information)\n", - "- [Hamiltonian simulation and Trotterization](https://learning.quantum.ibm.com/course/variational-algorithm-design/hamiltonian-simulation)\n", + "- [Hamiltonian simulation and Trotterization](/learning/courses/utility-scale-quantum-computing/quantum-simulation)\n", "- [Introduction to primitives](/docs/guides/primitives)" ] }, @@ -600,7 +600,7 @@ "source": [ "### Step 2: Optimize problem for quantum hardware execution\n", "\n", - "For this small-scale example, we use a fake backend (`FakeKyiv`) to simulate hardware execution locally. We transpile both the AQC circuit and the baseline Trotter circuit (the unoptimized `aqc_good_circuit`) to the backend's instruction set architecture (ISA), with `optimization_level=3` to further reduce circuit depth." + "For this small-scale example, we use a fake backend (`FakeKyiv`) to simulate hardware execution locally. We transpile both the AQC-optimized circuit (`aqc_final_circuit`) and the baseline Trotter circuit (`baseline_circuit`, four Trotter steps over the full evolution time, no AQC) to the backend's instruction set architecture (ISA), with `optimization_level=3` to further reduce circuit depth." ] }, {