{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Simulating the Deutsch–Jozsa algorithm at the pulse level" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Author: Boxi Li (etamin1201@gmail.com)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this example, we demonstrate how to simulate simple quantum algorithms on a qauntum hardware with QuTiP. The simulators are defined in the class `Processor`(and its sub-classes). `Processor` represents a general quantum device. The interaction of the quantum systems such as qubits is defined by the control Hamiltonian. For a general introduction of pulse-level simulation, please refer to [the user guide](https://qutip-qip.readthedocs.io/en/stable/qip-processor.html).\n", "\n", "In the following, we compile a simple three-qubit quantum circuit into control pulses on different Hamiltonian model.\n", "\n", "## The Deutsch–Jozsa algorithm\n", "The Deutsch–Jozsa algorithm is the simplest quantum algorithm that offers an exponential speed-up compared to the classical one. It assumes that we have a function $f:\\{0,1\\}^n \\rightarrow \\{0,1\\}$ which is either balanced or constant. Constant means that $f(x)$ is either 1 or 0 for all inputs while balanced means that $f(x)$ is 1 for half of the input domain and 0 for the other half. A more rigorous definition can be found at https://en.wikipedia.org/wiki/Deutsch-Jozsa_algorithm.\n", "\n", "The implementation of the Deutsch–Jozsa algorithm includes $n$ input qubits and 1 ancilla initialised in state $1$. At the end of the algorithm, the first $n$ qubits are measured on the computational basis. If the function is constant, the result will be $0$ for all $n$ qubits. If balanced, $\\left|00...0\\right\\rangle$ will never be measured.\n", "The following example is implemented for the balanced function $f:\\{00,01,10,11\\} \\rightarrow \\{0,1\\}$, where $f(00)=f(11)=0$ and $f(01)=f(10)=1$. This function is balanced, so the probability of measuring state $\\left|00\\right\\rangle$ should be 0." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from qutip_qip.device import OptPulseProcessor, LinearSpinChain, SpinChainModel, SCQubits\n", "from qutip_qip.circuit import QubitCircuit\n", "from qutip import sigmaz, sigmax, identity, tensor, basis, ptrace" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "qc = QubitCircuit(N=3)\n", "qc.add_gate(\"X\", targets=2)\n", "qc.add_gate(\"SNOT\", targets=0)\n", "qc.add_gate(\"SNOT\", targets=1)\n", "qc.add_gate(\"SNOT\", targets=2)\n", "\n", "# function f(x)\n", "qc.add_gate(\"CNOT\", controls=0, targets=2)\n", "qc.add_gate(\"CNOT\", controls=1, targets=2)\n", "\n", "qc.add_gate(\"SNOT\", targets=0)\n", "qc.add_gate(\"SNOT\", targets=1)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { 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\n", "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "qc" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using the spin chain model\n", "First, we simulate the quantum circuit using the Hamiltonian model `LinearSpinChain`. The control Hamiltonians are defined in [`SpinChainModel`](https://qutip-qip.readthedocs.io/en/stable/apidoc/qutip_qip.device.html#qutip_qip.device.SpinChainModel)." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "processor = LinearSpinChain(3)\n", "processor.load_circuit(qc);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To quickly visualize the pulse, `Processor` has a method called `plot_pulses`. In the figure bellow, each colour represents the pulse sequence of one control Hamiltonian in the system as a function of time. In each time interval, the pulse remains constant." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "processor.plot_pulses(title=\"Control pulse of Spin chain\", figsize=(8, 4), dpi=100);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Because for the spin chain model interaction only exists between neighbouring qubits, SWAP gates are added between and after the first CNOT gate, swapping the first two qubits. The SWAP gate is decomposed into three iSWAP gates, while the CNOT is decomposed into two iSWAP gates plus additional single-qubit corrections. Both the Hadamard gate and the two-qubit gates need to be decomposed to native gates (iSWAP and rotation on the $x$ and $z$ axes). The compiled coefficients are square pulses and the control coefficients on $\\sigma_z$ and $\\sigma_x$ are also different, resulting in different gate times." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Without decoherence" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Probability of measuring state 00:\n", "1.543983026671485e-08\n" ] } ], "source": [ "basis00 = basis([2,2], [0,0])\n", "psi0 = basis([2,2,2], [0,0,0])\n", "result = processor.run_state(init_state=psi0)\n", "print(\"Probability of measuring state 00:\")\n", "print(np.real((basis00.dag() * ptrace(result.states[-1], [0,1]) * basis00)[0,0]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### With decoherence" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Probability of measuring state 00:\n", "0.13730255188167628\n" ] } ], "source": [ "processor.t1 = 100\n", "processor.t2 = 30\n", "psi0 = basis([2,2,2], [0,0,0])\n", "result = processor.run_state(init_state=psi0)\n", "print(\"Probability of measuring state 00:\")\n", "print(np.real((basis00.dag() * ptrace(result.states[-1], [0,1]) * basis00)[0,0]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using the optimal control module\n", "This feature integrated into the sub-class `OptPulseProcessor` which use methods in the optimal control module to find the optimal pulse sequence for the desired gates. It can find the optimal pulse either for the whole unitary evolution or for each gate. Here we choose the second option." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "********** Gate 0 **********\n", "Final fidelity error 6.6028293943531935e-12\n", "Final gradient normal 0.0015194067969008138\n", "Terminated due to Goal achieved\n", "Number of iterations 6\n", "********** Gate 1 **********\n", "Final fidelity error 0.004418764518783647\n", "Final gradient normal 0.05370962792587447\n", "Terminated due to Iteration or fidelity function call limit reached\n", "Number of iterations 500\n", "********** Gate 2 **********\n", "Final fidelity error 0.018942311916405496\n", "Final gradient normal 0.029431698379522316\n", "Terminated due to function converged\n", "Number of iterations 478\n", "********** Gate 3 **********\n", "Final fidelity error 0.017966984837258115\n", "Final gradient normal 0.06234535946891994\n", "Terminated due to function converged\n", "Number of iterations 160\n", "********** Gate 4 **********\n", "Final fidelity error 6.029392540796152e-09\n", "Final gradient normal 8.129992922909755e-05\n", "Terminated due to function converged\n", "Number of iterations 129\n", "********** Gate 5 **********\n", "Final fidelity error 3.227445011244612e-08\n", "Final gradient normal 0.0006879404036636063\n", "Terminated due to function converged\n", "Number of iterations 100\n", "********** Gate 6 **********\n", "Final fidelity error 0.008469231492828078\n", "Final gradient normal 0.004922643478312693\n", "Terminated due to function converged\n", "Number of iterations 370\n", "********** Gate 7 **********\n", "Final fidelity error 0.024904080477859303\n", "Final gradient normal 0.04025417713677587\n", "Terminated due to function converged\n", "Number of iterations 255\n" ] } ], "source": [ "setting_args = {\"SNOT\": {\"num_tslots\": 6, \"evo_time\": 2},\n", " \"X\": {\"num_tslots\": 1, \"evo_time\": 0.5},\n", " \"CNOT\": {\"num_tslots\": 12, \"evo_time\": 5}}\n", "processor = OptPulseProcessor( # Use the control Hamiltonians of the spin chain model.\n", " num_qubits=3, model=SpinChainModel(3, setup=\"linear\"))\n", "processor.load_circuit( # Provide parameters for the algorithm\n", " qc, setting_args=setting_args, merge_gates=False,\n", " verbose=True, amp_ubound=5, amp_lbound=0);" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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a35M9NloxaVr3AwuGla3D3L27O7vNHq91xDTyV2WP272Y5W+dz3TRSMceZqTpcUxQGMt+Nt8HGoc9/4zs+78Pc8wfwvTSf8zre5pTr9NH+Punsp93ArOy1A0MW1Y2W+7VmKCqL3uMbAA+fZjvyO+y9U1glv+9DxN8O2W+ivnudGc/o83AV8guu4xZSepn2d/3YyakrwLeVUjds59B3iV+5Ud+5KcyP0rrYlNThRBCKHMH8N8CZ2it11S4OuOeMndOfwzT+LyrsrURQghhQ+ZYCCGEEEIIIYomgYUQQgghhBCiaBJYCCGEEEIIIYomcyyEEEIIIYQQRZMRCyGEEEIIIUTRJLAQQgghhBBCFE0CCyGEEEIIIUTRXgl33pZJJEIIIYQQQhRO2RSakIGFUupSzN1dfTfddBNXXnllpas0pmit+db9m9na2mdVvqkmyNcvPZ7ZTTVlrpkQQgghhBivJtyqUEqpALAJeA3Qe/TRR4dXrlzJ1KlTK1yzsWNHW5iLf/SEp22+dslxXHneEWWqkRBCCCEOZ8P+Hm5YvoNEKuNadlZTDd94y/FUB/2jUDPxCvOKHbE4E3hRa90M8NnPfpaHH36Y9773vRWu1tjRH08DEPQrjp7RkLfsod4oXQNJItlthBBCCDF6bnxyF39/sdW6/EXHzuDi42eWsUZCjGwiBhZzgGbnH3PnzqW5uTlP8VeeWNIJLHzMaqrOW7YvlqRrIEksJYGFEEIIMdr6okkA5jRV01QTHLHc/u4o/fEUnZH4aFVNiJeZiIGFcBHNBhZ+5T6q5fOZMk4wIoQQQojRM5Aw19+p9VXMaKgasVz3QJL+eIrugeRoVU2IlxmXy80qpc5SSv1TKdWplNK5P0AbMNcp29zczJw5cypX2TEong0SnKAhH182+Ihb5HYKIYQQorQi8RQAfpdrdtBv/t4dSZS9TkKMZNwFFkqpk4HlwDrgPOCNQBfwT+DdwErgRKXUXKVU/YMPPsgb3vCGSlV3TIolTZBgEVeQPU/JiIUQQghRAZGECSwCLhftkN806boHJLAQlTMeU6F+Ctyttf5C9t+blFJ/ApZpre8AUEpdDTwG+K6++mpZEWoYJ0hw6/2AoVGNeFJGLIQQQojRNhC3u2YHsoFFV0RSoca6tr4YP3x4K71R98+qoTrI1a8/etws+T+uAgul1EzgXOCCYX+KkHMjPK31vcC9zj9Hp3bjhzPHwmczx2IwFWp8jFis3NHBzo6Iazm/Urz22Bmuk9eFEEKISnJGLNzmRYayKQY9MmIx5t2zrpk71hywLr9wSi1XXXRUGWtUOuMqsACWYdK3Nhzm92tGvzrjk5MKZTN52ykTGwcjFvs6B3j/r1dje2uWC46ezu8+cmZJ69DZHydmOR9lRkMVQf+4y0YUJTKQSJFMe+v3CPoVtaGxe9rOZDS7OiKkMt7OFwun1FETmjjr7seSaf7lhpVsabG7CWmuE+c2cccnzpb7EAgA0hk9dM12nWPhjFgkXNOXlYKqgBxjldKVDf6m1AaZ3jByB2dHf5zOSIIei5GNsWLsXqEOz2mF1QFhAKXUScD5wNcqVanxJuZl8rbvpduMZS8e7EVr0/iaVBsasVwylaEnmuRA90BJ93/7s/v48p+fty6/ZHodD3/uAquUNDGx3LJqL//51xesg2CHT8H/XnYSl58+vzwVK9J/3/civ3t6r+ftjpnZwIP/fp7VOWk82NoSZtMh70EFwMYDvdyzrplXL5lmvc3kuiAN1SMvQyrGr4HsaAVYBBYBc8He1RHh2K8/5Prc73vVAv7nHUuLq2ARNh/qo6PffWnckN/HaQsnT6iOuL6o+VybakPMmzxyilMynaEzkhicwD8ejLfAYjUQBX6glPoOsAS4Hrhea70KQCn1FPB5rfVqpdSvf/SjH/G5z32ucjUeg5x7Uthcw8fTqlC7silQU+uqOGFO44jl+mJJnt3TTX+Jv6jLt7YD5taUboNBGQ072yN09seZ0SjpWK80j29t9xxUgDluntzeUfbAYs2eLj72+zWEY96+I6mMeVFBv0LZ3aSVZDrD1tYwV9+5gcl5OgSGO2leE28/da57wQo41BsDoKEqwMnzm6y329s5wP7uKP9xt30HBUB10Mcjn7+AeZNrPW0nxj7n5rQK92t2TdBPbcg/uDytmzvX7OeaNx1LYwWC0jV7unjnL562Lv+vFyzhmjcdW8Yaja5wzIxAuE3Id/5e6vZKOY2rwEJr3a6Uuhy4FtgI7AN+Bvwop9i3gGuUUk8CGQkqXi5uOawKQ4FFJUcs2sNxq5zRDft7AKhzSalwvqheG01udrT1A3DyvCam1o+81jjAE9vbSaY13QNJCSxcrN3bzapdnZ63m9lYzWWnzUVZpPyNtq7sDaxOnNPI9Dzr0udq7Yux6VB4VJaSfOiFloLXwp/VWMUJc+wb09taw+zvjnLPOm83MlUKzjlyKjPypBEMt6cjwjfv30S/x+/+0nlNfO2S46yPpdY+E1hUB/2e0k3mT66loz9OwkOKnJMq80JznwQWE9Dg/Aqfcj3+/D7FWYunkM64Hz/P7u1mIJHmgu8/NjjpeyRT60L8+sNnMHdS6SYPP7unGzCdEPm+I+lMhmgyw9M7O0q277HAaX84SwSPxC+BRflpre8H7s/z94eyoxmXYJaivXK06jZeRBP2k7edg7pSd95et6+by36+Eovz5KDaKrfAwpxEBxJpUumM60nVRiqdYU9nJLt/969V0O8jmU7TJeuN56W15iM3P2u1csbhTK4NctFxM0tcq+I5n3tVwGf1PYShpSRH45jZ22XSBJdMr2OWh8BXqaF62jpiWh0hv4+UhyGc5u4oqYzmUE/MU2Dx5+cO8OiWNk/1A3hmTxeXnTaP4/OMhOZyRiyqgt7ei5qQn3M8pEABbDjQQ0d/YsKdS25/dh9/XtuM9rD+ik8pPnruYl5/wizP+xtIpDyNIt705C6ue3SHVSM+1yVLZ3P9+0+zr5flilAOpRQBl8YqwOymana2R6w6ENrDcR7b0sYHzlpoVQcbTkfc/Mm1LJ5WN2K5gUSKp3d1sbklTDKdmTDpUH2DIxb5X4/zuUsqVAUppc4ApgB7tdbjZ7bLKHKCBLsRC/NYqeVm1+3rIaNNg8VtyBBMD6FbOkXuSbc/nso7HyOd0Xz4t8+wPjsaMhKtIZnW+BRUB9xPfObkmB4X64239sX4/dN7iCbcj4FDvdHBRpWbt50yh//36sV5y3RFEoNBxRwPK3iF4ynCsRQ3r9xDj4ee98aaIK85ZnpJgs18OvvN5+7lIhkcxTXq93WawKKhKlD2ScQBv49FeRoWh9MVSRCOpWgPu+dn53KOzVmNVUx3GVV07OkaIBxLsXZvl3Vg0dIbBezOBcUazeOiUL98fCePb2u3Lq81PLunazC1zouBRNpzYPGff32B3xcwN6gQf3v+ENd0DTB/it3oUu6IRSktnFLLjIYq0i6n9b2dEVrDcdr67M7r8VSa7a39ruVePNgLuGcY1AT9+H2KRCrDhT9Y7ho0HT2zgRvef9qYD0CcEQv3JYSdEYuxP8/VMaECC6XUXOBXwGuBPyulTtSFJDJPcIOTtz3MsahUKpQzsWvupBqOmdlQkuf0KYVPmXz1cCx/YLG7I8KT2+2HYKfUhazSJQbvkDqGGwOOXzy+k98+tafkz7u1JcyHzl6Ud8Ku0xAM+hXHzbZr1IFpeK7b38OT2zs8fX4A339neSdHx1Npwtnep5CHhmfuii9aa+u0HK01B7qjJN1aEE55YF92xGKsrtRU5fcRBquJn7mcFKUpdSHrFMT+bJD6vw9u4YblO6226RwckSr/+zeaI1mF6Iok+O6DWwradmpdyLpDIZbKsL2tnxbLBnCuh15o8bwNmAD1qBn216Xnm3vpiSZ52/VPUWMZsHu575QXStmtMFdXFYBw3Pp9/cjNz/LUDvvU1TqXEX6lFFNqQ7T3x2nuibo+397OATYe6GHZwinWdagEZ46FWyqUM6LRHx8//eQTJrBQStUAdwJXaa13K6W+C3y9wtUak2yXrsstY7OEajKd4cEXWuizSFupq/LzxhNmuzZcnIaD1/QKNwGfj0Q64zrPoiXbsK0J+jnFYhKm7cVi8A6pJWwMROIprrn7eauepVDAx2cuOoozFrmffPdkJ8VPqwtRV53/lOFXinqLVLDnm3uJJtO0hmN5b/ozmKvusYE2uTbIgim1noaPBxJposk0mw4WtpqPLacBqLAbhXM4F6B4KkM0mbZedvYn/9zOjx/Z7rmewJhd8tQJyLyOWDjfZy8N/mkNVezuHCCSSBOxnBQL5vNtrCn/JXawk2KUAotoIu0pPWnFDhPY1wT9HDHdfmTKp8xCHLYN6ng2sOjoj3tKmYmn0rRlj6NXL5nqqafba2N/dlM1PdFkQUGgzXm1HKqy37XWPvfvWiKVYfWurpdsl09TTZBai86LE+c0Eo67p6ptbwvTF0uxsz1iFVhsbQmztTXsWg7gzEVTSnrfK2dVKPtUKBmxGHVa6yhwTs6/78QEGpdXrFJjVCE3yEukMq69pHeuOcBX7rFfzeTgG2J86jVH5i3jNBxsTlJeBPyKRHqo12AkB7MpDTUhf0nvHxAswx1S/7Gplfs2HPRUhzM+7H7yPdhjGmPzptQwtc4ufcRNddBPNJlmd3skb2Dh9JJ5zVVXSnHUjHpP2zT3RNnSEh6cK2Mrk9E090St87N3tJsLWdDv8zSx3EzeNGkiXZGE9fHoHBN+n+06TcbMxmrr+R+jbTCw8DhiMXg8eTifNFYHefWSqSQsR3wcVQHfqIxYOOeSzlEILK7580Zue3Z/QdtOqQt5mq/jVchvjm+tzXVjjuVE49Zecwz5lPnMyrnYw+ymappqgp7nZSg1FgIL9w6rne39pDKagE/x6iVTS/Ze+nyKphr3lasaq4PZwMI9Fas9HOdt16+wvkfXMTMb+Pvnzrcq6yaZzgy2w9xSu2TytqioA90D3LvhIGmXFUX2Z9Mc7AKLof+PpzJ5ezBX7zbDn/VVgbw997FUmnAsxZo9Xa7778jmontJGbFhu4Sb08NZ6lxpp5dxxY52vn3/prxlQwEfV5y9MG8DHGBz9mZcU+tCzM7TsxJNptnZHmFri3tPjdZ6cPjZ66hBPrUhE1g8vKl18AR7OM/sdnq/yt9Ac45ZZ36BrU/eupa/v9jqeX/BgLeLrlKKkN9HPJXh2/dvZlKt+4U2ndHsbDeBktfe2LHMOR+s3dvNr57cZbVNRuvBEUqvHRXVQf+YHb0pdI7Fhv09g+dsG8m05vY1hQUVPmXShspJKUUoYL4frX0x68Bi8PwW9Jd9BTmllGvqz1jjnHv3dw3wE5eRzx3ZBn1dVaAiq/E5C7fcv+EQrS5z/Zp7osSSGYL+/KPsGugZSLK1NUzPQCJv6jTALU/vYeXO/N+r3Bujus6x8A117iZSmZK3hcphfB3hIq/P3raeNXu7rcu75fbBS2+it3xrW96T4trsvo+cXpd3udXeaJI1e7t50SLlxBmxKHUqlPNldkuFGlzdpcRf5qpsI2Vbaz/bLCa67WqP8IsrluUt4wQK0+qrmJmnZzCZzrCzPUJzT5S+WDLvGuZ9sdRg8FXKhlVtyE9nBG5euYebV+5xLV/q93+kOoGZX/CZP62z2iad0YNBhZe0CAXMbvS+dGN10E88leGhF73lhDdWByZMUAFDQe6LB/usziO5Aj41oW5K6TQ0traEueznK622iSbSBd/Ab0ptkJPmTfK0jVJ2HVnFqsoGFres2svTlktUbz5kzpujMdF+PHJGiyOJNP/3yDarbRpcUmbLxbmWNfdEaV7vPh8D4Mjp9a5B6MqdHUSTGV482Merjxx51ba9nRG+/tcXretrsypg7rkqEk8RCtjf66dS1CtgcnNFX+DHf7+moOUNC5HKaNNgaarGLeehJuhn4ZRaq16FR7e0eXoTzz9qWt5GTDqjWZ5dIcQtx9xZFeTVS6aWtGG78UAv7f1xfC4XPGf/x85qKOka3pmM5kBPlITL3JWM1uzvNidI2/dq2YJJrr0qK3Z0EE9lXNNjNObzCvoV5x81Pe9zetEfT7GtNWyVEhDw+zh2ZkPZJxJrrXlie0dBK9HMaKhi6Vz7ezcUKhJPmfQfL1VUpn6lTOWrtExGs7MjQqKAZbCn11dNqHvHJFIZVuzsKOiGi/VVfuqr7G+O5lOwYErtmO11f+Fgr9VcgMOZM6ma42bZLxDxSnKoN2a95LdfwfwptRUZ4dNa0x6OW80JBdO5Oqux2rUd9HxzL21h9/ZCRmsy2nTk5MsacEyqDVmluD22tc3cHPVLr7FeTaxMrHoHxubZYQLJaF1QQ6VQ73vVAr56yXElfc6fL9/Jw5Y9pOcfPZ3Pve5o13KfuGUtT1o24o6YVsfPP3BaSZcAvWXVXr77wBYy2nxG+VQFfPzwXSdxxHRvOfulctWf1vHPzW1W79Wspmpu/ODpro3wb/9tM39cvc861/cNJ8zi++88yarseLbpYN/gyJutYMDHG0+Y6RrMCVEuO9r6BxdZsDWpNshpCybnXZVtvNnZ3s+tq/Z5ng9THfDxoXMWVbrRJsao+zce5Mt/ft6qveBTcO3lJ+cd2fDq3O89RlckMW7mWciIRZl1RxLELaPnYvl8pjduLN5peLhMRg+uxOFman2oLGkcXZGE64gBmGHdSvbQaW3eK5uv6pS6kFUOptaa9v44GYtDU2V7vMfDcSWEEEKUWs9Awmqid22VP296cSGae6JUBXxMrg1VOo3TaucSWAghhBBCCCHysQosZLaSEEIIIYQQomgSWAghhBBCCCGKJoGFEEIIIYQQomgSWAghhBBCCCGKJoGFEEIIIYQQomgSWAghhBBCCCGKJoGFEEIIIYQQomgSWAghhBBCCCGKJoGFEEIIIYQQomgSWAghhBBCCCGKJoGFEEIIIYQQomgSWAghhBBCCCGKJoGFEEIIIYQQomgSWAghhBBCCCGKJoGFEEIIIYQQomgSWAghhBBCCCGKJoGFEEIIIYQQomgSWAghhBBCCCGKJoGFEEIIIYQQomiBSldAvDL1RpNs2N9jVfaYWQ3MbKwub4WEEEIIIURRJmxgoZS6B7jwsssu46677qp0dcQwH/zNM9aBxeTaIKu/cjGhgAywCSGEEEKMVRM2sAB+AvwGuLfSFREvt7u9H4DakB+fUiOW64+n6B5I0jOQYIaMWgghhBBCjFkTNrDQWi9XSl1Y6XqIw4slMwCcOn8S1UH/iOWWb2snndGE4ylmjFblhBBCCCGEZ5JbIkZdKp0hkTaBhd838mgFQCD790g8VfZ6CSGEEEKIwk3YEQsxdsVSmcH/z5cGBUOBR78EFkIIIcSItNb8932beKG517WsTynef9YC3nbK3FGomXglGbeBhVLqLOA7wCnAlGF/btJa9416pYSVgcRQkOAyYJEzYpEuZ5WEEEKIcW1f1wA3r9xjXb57IFGxwKI3muQLd26gLRx3LVsV8PHlNx7DsoXDm3piLBqXgYVS6mRgOfAz4CpgPvBHYB1wowQVY1sskU2DUgrlMmIRGByxSJa9XkIUoncgyerdnWS0/TZ+n+LMxVNoqgmWr2LiFaM7kqC5J+ppm/qqAIum1ZWpRqISegbMdTLoVxw7q2HEctFEhh3t/RXNBHhiWzv/2NRqXf7WVfsksBgnxmVgAfwUuFtr/YXsvzcppf4ELNNa3wGglHoEOPmBBx5g3rx53HnnnZx99tmVqq/IEU2a0QefxQwff7ZQv4xYTChdkQS/fWo34Zj7ha2+KsDHzj9izDbCP3PbOh7f1u55u4uPm8GvPnRGGWokXkl6B5Kc+71HiSS8nyOve++pvOXkOWWolagE53waCviY0TDyKooDiRQ72iubYuzsu7E6wOI8AW5nf4IDPVHCkg49boy7wEIpNRM4F7hg2J8iwGCfodb6Yud/R6lqwpKTCuU2cTu3jEzenlhuXbWX6x7dYV0+mkzz1Tcf52kfSuE6IlYKW1vCgAmAbI7pTHaVs/WW93HJpbWmtS9ORtuf1vw+xYyGqlF5L8To29EeJpJIo8D6Xj8ZrUmmNf/3yDa2tNgP8FcF/LznjPmy9DewckcHP35k++BCJLbecvIcPnru4rLUKRwzIxYBl147f/ZcMJBIo7WuyLnBuabXhPxMq68asVwqrTnQE31JCrUY28ZdYAEsw6xmteEwv18z+tURXjkjFn6Lk5msCjUxtYZjAEyqCTKpduSRiEQqw8HeGL9esZtfr9jtaR/TG6p4x6lz8z7/cI3VQd65bF7eJZBzZTKajn6TI3zyvCar7VKZDI9v66CjP0HvQJImD/X77O3r+ev6g9blHR88eyHffNuJnrcTY19rnzn+GmuCnL5wstU28VSap3Z0sqs9wvWP7fS4vxjfecdSz/WcaH75xC6e2dPlebsdbf3lCyyy18mASweH0wGSzmjiqYz1+a6UBrIjbG6dMc7fBwoYkROVMR4DCycUrwPCAEqpk4Dzga9VqlLCXmwwFcpixMJvytikzIjxozdqPs/pDVUsmFI7YjmtNf3xFH0FfP7t4Tg3PrHL83YZrfng2YusynYPJEhlJ1fY9hYHfD6qAj7iqQw72sOcOt+uMdgXS/K3jYcAMxpjRZsh25U7Oy03EONNa58J0qssjz9T1s/SuU10DSSst4kl03T0J9h8aGxPYeyOJEh6HEUI+n1Mrgt52mZHm7nJ65HT66kNuTfM01rz4sE++uMpkukMQX/pV/t3rpO2gQWYTrtKBBaR7AhEwHJlyAFJhx43xmNgsRqIAj9QSn0HWAJcD1yvtV4FoJR6Cvi81nr1Rz/6UU488UQ+97nPVa7G4iWiOZO33ciIhTfpjLa6qPqUsm4Il0NvdGiSYT5KKU5fOHmw8W4rozWHemOeerki2QDGZqlGh7OiSdCvXJdOzlUb8hNPZbjs509bb+Ooq/Jz1uKpVmX74ylW7+6i3WLlFTE+OSMWXgILMEH99IaRU1CGC8dSdPR3sbM9UrH0GTe/eHwn//vgloK2/cqbj+Xj5y+xKtsfTw1Olp8zqdoqSNBa82L2/3ujybzpP4UaTIWyOK/6FGS0GQmwO5uUlhMo2I5YRCQVatwYd4GF1rpdKXU5cC2wEdiHWR3qRznFvgVco5R68sorrxzXQUUileF7D22hudt9xQ+/X/GRVy8a8ysnFDLHYvXuLv7j7ufzltVas6UlbNWI8vsUHz//CD5w1kKLGtvriiSIp+was1PrqkrauN/fNcBbfrZicGWQfPw+xTffdgLvf5X7639qRwf/cffzgyls+UytC3HTB09nfp5RCIDebE+pWy4wmIugWwByOIumelvxprUvxgsH+9ie7Ym04QQWVQFvPX7T66votvicDmfupBrrsk5jszeaJJ5Ke66nKNwdz+5na2vY83anLZjMJSfNti7fVsCIRSGcXvneaJI71uynJmTffJhUE+TVR06zOucX46EXWgb/38OgHgA/+sc27s+OCLqJJ03nTcjvsx55UErh9ynSGV3GwMIZsXCvk9+nyKR1xSZwRyzbARM1FaqzP84Ny3dazR2prwrwiQuWlOWYKYdxF1gAaK3vB+7P8/eHsqMZl9xwww2jV7HDyGRMY9fr0KzjiW3tnnLLeweS/OHKV7mWC8eSg0PobmpCAU+NGTdeUqGchtC+rgH2PbOvZHUA+M2K3SUNLG57Zh/XuAQ/uSbVBHn1UdNch62rAj4+eeGReVfOAHh0S5tVUAFmZOPRzW1WgcVf1jWzr2vA6nnbw3Ee3tTqmkPsjFi49ayNproqczrc2hLm+sfsJpY7aSFeA8T5U2qZ3VTtaYlaMPd9CXhIoQj4FArTeOroT5T0eyxGtr01zJf+vLGgbX/Nbq57tIF5k/MH5471+7sBCJU5aPT7FDVBP9Fkmi//2f485/jJe04p6z0T0hk9OBH9rMVTBr/PbrTWPLunm3A8xcYD9qOVgKf5UQDBnMCiHJwRC5sALuDzkUynKzYp2nrEYnCi+fgYsdiwv4dnLebePLqlzVOKamN1kKsuOqqYqo2acRlYuFFKnYG5ad7eYLCyS1T+70NbCsrzHm52UzWN1SN/XNFEmn3dUVosgoXegSTnff9RT3nrS6bXUV/t/l5ecNQ0Pv/6Y/KW8TJ5e1p9iGNnNZBI2QVmQb+PhupA3t6qRDrDhgO97O8eIJXOeGqo5fNgtrdMDf5nZFpDT3QoZ95NJJ7m+veflrfMpoPmorpwSi2Lpo7cKOmMJHjhYB8dEbv8amdy8uJpdUyvHzkP+UB3lIO9MfZbBCFDqVCVS8carjbox6dMz9gP/r7V07bVBfQWl+q4y0dlU97iqQzt4bgEFqPkuX2msV8T9DPDQ7pRTzRJbzTJlpYwW1q8jXbUWeT5F2vJ9DoO9kQ9LbU4kEgTT2XY5nH0Zs2eLv6yvtk6+I4m0sSSGXwKqzkPDqUUpy6Y5Lmxr/AeWAT8PkhlyhZYOKMPNh02ToO+Uku5ex2xiCUzpDO67KNexUhnNB/49WpPc0IXTa3Nm0bbM5CgayDJwV67juCxYMIFFkqpucCvgNcCf37hhRc48cTKrYayq92kVQR8yrVneiQ1IT9Hz6jP2xCJxFPs645apQFtOtQ3GFTY1CmV0exsj1jVdcP+Ht51+vy8qTBDq0G4P59PqZI3hrTW+BQk05qDPTEW5GmEe3nOjQd6AFi2cLLrPRfSGU1bOEYynf+qmUxn2NM5wGNb2/j2/Zvyln1iu7mXQmN1IO+x4kzU67DMu+/oNwFIQ1WAhjzBZUN1CnpjHOjOH1horQePv+AYukj4fIrjZzfSaRlwOfxKMX/K2G2wOxPFd7b1M2eS/TKh0+qqrEYVK6U/nqKvgAbazMZqT40TrTUrdnR4mqfyt+dNJ8P0hiqOnFHvaV/dA0mrtMNc1QEfjaNwn5eZjdXM9LjU7O6OCLs6IrT1eZvn87W/vOA5uALTs+t1/kfQ7xuVNBMnpdPrcds7kOSfW1pdMx92tpnrtM11fWhSdGVGAqIeV4UCM2qR7xpUae3hOOFYCgXMaHQ/nqbWhZjdlP/acaDbR9dAclzNk5tQgYVSqga4E7hKa71bKfXdb33rW3fcfvvtla4aR86oL2tvYchDLvXB7KSzKbVBTl3gviJNNJG2mji1uyNCXyzFe29aRWOeL39bdqlRL5NdS0kpM6QfSaT56/pmjp3dOGLZvmiS3z+9x/UCpzHzYRSmAe7G71OuJxQwDY2W7CTkX1mmxDW4NDBC2aCjoz9uNQnTGbFwS/epCZq/u6VN9cdTpLPdkKPRa+9FIQ2nsc753K6+c/gK3fmddcQUbvv42Lyp6PbWMJdct8J6JDPXqxZP4fZP2L+up3d2csWvn/G8HyDvKPPhKKWY4nF1orHOmfvR5qFhlMlodnWYRvKCKbXWnXK2DbpKceY+eB2x+O/7XuTudc3W5W1GggcXRqnQ3IWIZWCR++eBRHpMBxYHe03bqirg48Q5TSV5Tuf7094vgUVFaK2jwDk5/76zgtUZVV5yqZ3VLKosl5irCfmpsRhaTqYzbDoU5kB3FLNwV361Hib/lVptyAQW1/5jW0mfd1p9aXt5lVIsndtkfVGurw5Q4/K5Og3NeCpDJJGmPk8gpLWmMzti4RpYZI+Rne0RLrp2+YjlnKDCp+xygUVxZjRU0RVJeJ7PsWpXF539caaOwQmDj2xuGwwqvPRPaG0WguiOJKyXF312j0lrqg74qLXM2wfTIBgvky3LKVRAYNEWjg921CyZXlexTqhSc0Ysej0s3JDJaB7b2gbA5Nqg6zmzKuBncq37se08z6GeKId681+v611GqwsxuIiLyn9dyZ30PtYncLdk05Vs21Y2nO+PbYbBWDChAotXstxc6ra+WN7AwhmxKPXa1bMaq6kNBUhl3HsRAz6f5968Upo/pZZkWlvdwbgq6GfB5Fqqg+69QOVYmaWxJljSNAe/T+FXirTWdITjeQOLvmhq8M6yIZdesOqgn5DfRyKdsUqdy7dfUTqzm2qsRsdyPb2rk4FEmo0HennNsTPKVLMhqXSGtIe7ia/LzmE4cnodCz2s/rVyZyfRZJrnm3s5/+jpVtu8eNBM6J0/pTbvPVfE4Q32uIbtc8SdUc/qoG/CBBUwNEJ74xO7uO3Z/VbbZLLpcX6f4pT5k0r2fjiBxbX/2ObawRb0K95y0hzmTXY/j9RVBbj89PmugXvEcvI2mI7TdEaXfNn53z+9h+ctJ+xffPxM3nDCrLxlnLZVKdsBznW33TLDYCyQK/sE4uRS/88Dm/OuT75mjzOxsLSNYKWU69yCsWJybYhlCydWyoEXwYAindT87LEdzMyTOtAXHZpg5z5krXjV4inW6403VDCwFPk1VgcZSKS56k/rPHVA+BScsWgKi6bZN8AP9sR44PlDxAtIa/IacDfWBIgm03zu9vXWx9/BHtMglkC4MM5qVZ2RBF+wTMdz5mm5jb6ON84xF46nBu+SbWtafaikQdb0+iraw3HXgF5rMx/RSyrWj/6xjTMX51/2vntwyXH7+SC/XrGbWU0vT1XN9xL0CEsN9EVT/MnDSpN/Wd/MR889Iu/c0KezqzyVstPWGbFIpDL0xVLjoo0lZ8pRctK8Js46ory3oekeSPDcvp7BoXs3bzhhluv9BsTEtKczws72CHetPWBVfnpDlae19cX4NmdSNbes2kd/POV5nfu/PW+30lmxpjdUcfnp8z0t9TujsYo/PbOfzkjC0yT96qCPd50+ryJ3KB7vMlqzZm8XsWTG+nzjOGFu04Q777SF44NLrtvyKTPyWInUUa01a/Z2Wy05nkqbsvFUhie3d7iWD/l9vOXk2a6pQ7s6+tndMcA9HoIbW0vnNrmORL5wsJe9nQP84vGdVs95xqLJvPrIaaWoHgCrdncSS5qV/cZDYKG0h+HncaqiL/BPz+xjW2uYS0+aw7KF7hOli9HZb+4hYHPPjLmTarjouJllrY8YuzYd7OPu5w5YpZ8oFJeePJvTLCb6i4njQPeA56DiUG+Mp3d2er5vz7KFk61Tkxx1oYDnhpbWmm2t/fTHvU2eXTClztNdqsVLrdvXzerd7mv756oO+HjbKXOt58KIsaGzP86KHR15RxEcx81u5JhZDa7lXmju5S/rDr/08OEGcUY6KwwvO6k2xEfPXezaYdDWF+M3T+2xuvntpJoQV5632Po+Kja+++BmFIoPnbPQc1priVmdcCWwEEIIIYQQQuRjFViMrbUehRBCCCGEEOOSBBZCCCGEEEKIoklgIYQQQgghhCiaBBZCCCGEEEKIoklgIYQQQgghhCiaBBZCCCGEEEKIoklgIYQQQgghhCiaBBZCCCGEEEKIoklgIYQQQgghhCiaBBZCCCGEEEKIoklgIYQQQgghhCiaBBZCCCGEEEKIoklgIYQQQgghhCiaBBZCCCGEEEKIoklgIYQQQgghhCiaBBZCCCGEEEKIoklgIYQQQgghhCiaBBZCCCGEEEKIoklgIYQQQgghhCiaBBZCCCGEEEKIogUqXYFyUErNB24BZixdupSvf/3rvOtd76p0tYQY1BaOcfUdG2gPx13L1oT8/Oelx3PqgsmjUDMhRCn86sldbD4U9rzdKQsmccVZC8tQIyGEKL8JGVgAKeCzWuv1LS0tetmyZbz5zW+mrq6u0vUSAoDlW9p5cnuHdfm71h6QwGKMiiXTPN/cSyajrbfx+xRL5zVRFfCXsWZiuJ6BBL9esZtwLOVpu6Vzm7hs2Tzr8rs7Inz7b5u9Vg+APz93gDecMJMZDdUFbS+EEJU0IQMLrfUh4BDArFmzmDZtGl1dXRJYiDFjIGEaNpNqgyyeOvJx2RaO09wTJRL31hASo+dTtz7HP7e0ed7ukpNmc/37TitDjcRI7lizn+se3VHQtucdNY0ZjXaN/UM9UQBCfh8LptRY72NP5wCpjKY9HJfAQggxLk3IwCLX2rVrSafTzJ8/v9JVKUgsmeZTtz7H3q4B17IBn+LfLzqKNy2dPQo1Gz272vvpjCSsyk6vr2LRtLEfQEaTGQBqgn6m1IXylEsDEEmkR6VewhutNat2dQLms1TKfZtMRhNLZXixubfMtRPDdUWSADRWB/J+73Id6I6Symj2dw9YBxbt/SbFsa7Kz8I8HQfDHeqLkYqn6c7WUwghxpsJHVgopaYcf/zx3HTTTZWuSsHW7evx1Bv6h9V7J1Rg8czuLi7/5dPW5X0Kfv6BZRwzsyFvOb9PMW9yDcqmJVgGTsDgc9m/P/v3qAQWY1JzT5RIIo0CzjpiiuvnCWa06uldXbSF42itPR2DiVSGrS1hMtpb2tWxsxoI+Mu/VkcqnWH9/h4SqYyn7Y6f08ikWruGfjFi2e/dlLoQS6bXW23TPZCkN5rkUG/Mej/O3KlQwNt7HvT7gDRdA3YdKcW4f+NBHt/a7mmb6qCfK89b7ClYEkK8sozbwEIpdRbwHeAUYMqwPzcBceAv11xzDeecc84o1650wjHTc1UX8nPMrJEby73RJDvbI0TilWuA9kaT/PLxnfRE3Xvb6qsCXHneYtfh/m2tZvKj36eocrlIp9KaRDrDJ25Za1Xf9565gO/+y1KrsqXmNHD8PpfAIvv3SEJSoUbDttYwz+zusi6/qz0CQG2V3yqoAAbnVQwk0vTHUzRUB63396k/Psc/NrVal3e87ZQ5/OQ9p3rezqsfPLyVXz6+y/N2R86o55HPX1CGGr2UE6D7PQRz1QEfvUBLIYGFx2DOKd9T5sAilc7w+Ts2eA4AAdJa8z/vqMx5Uwgx9o3LwEIpdTKwHPgZcBUwH/gjsA64EQhn//3oFVdccV6FqlkS/dnc+qqgn8l5evScDsxK9mzfu+EgNyzfaV2+Jujnc687Om8ZZy7C9PoQJ8xpyls2mc6wbl8PA8n874HWmoyG9ft7rOoZS6atGhVKwfzJtfhcggXIbeDkL+cEFpX8XFPpDOv297jWIZ1NC9rZ1m/1vJecNJt3nGo/IXY0vO+m1XT0u6/UNVx9lf2p1O9T+H2KdEbTFo5bBxaZjGblDjPhvyrgw6ZpnMGMcqzb12Ndv2K82NwHmPoF3A7urEg8zY62froiCev0pEINjhRafEcdVUETCHoascgeQ26dIcMFs+9Zl2XqZ6E6+hMkUhkUcMR0u9GHcCxFWzhOq4f3YbzY3zXA8q1teFh/AZ9P8brjZjKrSebCCJFrXAYWwE+Bu7XWX8j+e5NS6k/AMq31HUqpc4F3AxtPOeUUAG655RaWLh1/vSxOYBGw7NmOujSqy6kzezFtrA4wtb5qxHI9Awm6B5J0RtwbcP1xp2ff/QId9Ps4c/HwwavD73/tvh6iFqMAsWSaC3+wnJY+u4vpm06cxc8/sMzqecG9gTMWRix+8fhOfvjwtpI/7+rdXbz9lLkVS0cbLpHKDAYV0+pDKKumO/h8sMhjakhVwMdAIk1rX8w6JWd/9wCRRBqfgnOWTLUaIYkl0zy1s5NDvVEyGe2pQV2ItrD5nhw3u5GplkHCUzs6iKUy7Gjrt/r+FiNqOVKYywkOHtvSRjJt18O/epcZ9SosFQq6yxxYtGbPZ6GAz/rYbQ/HaQvHree75eqKJNhyqM/TNlVBP6fMn+TpsyrU5+9Yz7N7uj1vt3xLG7/+8BllqJEQ49e4CyyUUjOBc4Hh4+YRQANorVcwdPM/D30QY4+zLKLbydX580AFe7b7Y85KRyGOyDOBel+XonsgabXkY8QysPJiqLHu/l619sUGg4q8n4E2PfbP7bO7OA02cMbBHAvnglsd8Lnm6Qf8iun1VXk/Lw1saQkTjqXoHkiWvZfaVu57vHRuk3VqUyFC2cDiF4/v4sHnW6y2OdRrVhqqCwWs6+Y0bJNpTUd/3HrycaHawt576uuqAsRSCR5+sYV4yv44XzK9njmT7FdcgpyA3sNnWxsyIxa7OiLs6oh42l910Ntywk5g0dwTZb/Fgh2OptogjR5S6pzAwsvn5NTNpkMoVyaject1K2jOrpTlxRdefzSffu1Rnrfz6mCPeT+m1Aat5iIl0hl6BpIc6Pb+moSY6MZdYAEswwQNGw7z+zWjX53ychrftiMWsQqOWNiOrjh/9xJYlLLXykt6kfOaQn4f5x01bcRy0USalbs66YvajSx4HrGo4NyZHdnUpuPnNOZNx/Nid0eEeCrDvq6BMRNYOKNCSnlreBaiNuinhyRPbPM2eRagocb+tO1TZm5SPJWhuSda1sAinkrTM2DmV3kNLDojCX61Yje/WrHbfruQn9VfvdhTGtpgCqKHgYSpdSGOmVlP3ON8hOqgn0k19o19GGq8P7K5jUc22y/aEQr4eOAz53HkDLvRr9YCAsBQIJum1e9txKI1HBsMKuqq7AKtZFqTyI5ijQbnu3/UzAar46kvmuTZvd2DcyBFef34kW389qk9VotWHDWjnluvPIuakNwjqFLGY2DhnAnrMHMpUEqdBJwPfK1SlSqX/rg5cbkGFtmG0EAi5XmlmVLxmrbVbxFY9JcjsPDwXkXidqkTTj55NJkmkcq4pkBYj1jkpLiVMpUlndHsbO8n7ZJUnExnhhoFodKdLqqD/sHA4pT5k0r2vMVw5vN4mdhbqCOm1VET8nu6qR6YQHR2k7deeue9/tE/tjHHw7bnHT2NS0+aY13embCslLcRxjlN1fRFk6Q8vBeReIpIIs2hnihHuawAl8v2e5dLKcW8ybXW5YsxuTZITdDvaVJ1RptG+K9X7Ob9r1pgtc2mg2ap45CHGzQ6E8sjiTTRRNq64bY7u8BBTdDPWYunWm3T3BNlS0t48Pxfbl5Hxp3zfZ/HGy2KIemM5q61+wfPGyOXg+sf20EybXd+eG5fDyt2dPC642eWopqiAOMxsFgNRIEfKKW+AywBrgeu11qvAlBKPQV8Xmu9+qMf/Sgnnngin/vc5ypX4yI4jW+34VmnAZrRZpjW7Y6+Hf1x66H2xpqgVR64bRAwdFJ27+0pZypURkM8lcmbrmC7/9zXHI4l884xAYhl72NhuyoUmEZRnYfe2XyuvmM9f1l/0Lp80K8854vnUxv00xtN8qOHt/Kn1fvylm2sCfCNt5zgOe3Fq4GE9/z7QlUF/Z7nZRSqLmTeay93ege467kDrNnTbf3dc3Lvq/w+Tx0bdVUBli30dlf5lTs7iSbTVivQ5Spk8vZoqg76OWeJXePb0doX44WDffzpmX386Zn836XhvIxY+H0KpcxCIZ2ROPNCdsHW7s7symkeepC9jGoXK5HKDDZabb/7geycv/54ilQ6MypLOZdafzzFwy+2WM3LnFIb4g0nzCrp9+axLW18+c/PW5dvqgly/Oz8nQi7OwZo6Ytx4xM7ef5Aj+tzTq2v4vLT57sGyf3xFI9uabPKCJlUE+S1x85wPSa2t4a5+s4N9Fmcwxqqg3z/nSdx3OxG17JjwbgLLLTW7Uqpy4FrgY3APszqUD/KKfYt4Bql1JNXXnnluA0qwMMci9wGaCKdN7DojiQ473uPeZro/YsPnMYbT8x/f4yhIMgtFcp84exSoUrf2Bv+XuULLGyDJZ8aWumnL5ZyDSyclAy3l5X794FEaQKLZDozuGRp0K/cJykrmF/iRn1DdYBDfeZOw3s63QPcE+Y08ZmLyptrXY5jbSw4YnodtaEA2sO9LzoHEvQMJLl55R7P+6sehRSEoF8RTULvgLfAIjaKweNomVZfxZTaoOebaAb9ihkN+c9TuZRShPwmre7fbn2OGsv5I848BC+BxeCo9iiMWERy9mEdWORc4/rjqVG5B0up3fDYDk+rON7w/tN4cwnvkbWlxUzmrw25pwz6lLnvVK3LqPmMhipa+mI8u6fbejL+N+59kTqXY9Prd+vH7z6Ft586N2+Z+zceYuMB+5uk3r/xoAQW5aS1vh+4P8/fH8qOZlxyww03jF7FyiBs2WPuU6Z5qDEN0El5OpN2tPcTTZqbelUF3e8Nkcpo1u3rcQ8sBhvhLhN8B3uj3BsFtulVXviUwqfMiEUkkWJynhx/LyMmASewsOiBsJ1joXICFpOqM3JDIJnOcNOTu+gI58+B7o0miSTSBHyK846cVpG0ubmTa6gJ+V1TsTr647T0xdntcdJsIZxUqFIea2NBVcDPwqneUnnmpmvY3x31nKqlFMws8wRxGBrBLXTEYjTS3UaL36c4dYG3EZ9C1YVMWp2XBpHDy+TywCgGFs4+fB7mVuVeQ/qi4zOw2JMdSaqvClCTpx0wkEgTSaRZsaOjpIHF7g7ToTSrsZrFeRZ78WJafYgjp9cNZgTkk9Fmue9URlsFDiG/j4bq/E3mSCJFLGk3N8hZknpOUzWz8yxZfKg3xsHeGF2R8TOfZ1wGFm6UUmdgbpq3Nxj0NnlurOm3nLwN5gKTymjXkYiObE5jY02A0xfmX95xb+cAO9r7OWixdrntRPPci4brHIdE6edYgLkwZLR2ncA9GCxZrMkf9CviKbsULy/LXvqVIo1ZcSrfUo+Pbm7jZ4/tcH0+x5S6UMWWevUpxTSXUR0ABbT0xT2vxlOIyATszS5U0O/Lu7JbpTn3e/B6I7mxngo11p04t4nuSMLzUotBv4/JtV4Ci+x8jlEILApNgQxmR29szvdjkXOvlIVTa5mVpzOgLRzn+eZeHtnUanXDx9qQn/eftZC5LqPcewpIkXOjlPJ0V/ijZ2rrleiqg+43Qd2dXTnOWXY7H6cd1lAdyBuYhuMp6C3/TTNLacIFFkqpucCvgNcCf37hhRc48cQTK1afO57dzzfv30TCcv3z4ZxJfDY5nIOBhUtjuSN7QrE5SVRnezIOWSwVGLHs8XVeS0a7p/dELEdBvHLeK7fleb2kx5iLYdpqZSgvPad+n4I0fO724QuhHd7MhirXdBQfMLvMcxZKwRn63t3e7+mO2AAzG6s8XWSiozh5WxQnmD0f9HoYschk9NDcJvmMCxL0+8q+ZDEMdeSMxhyLQhcICfgUcbAaoR6LuizbAZNqgihMgGGbGnnD8p1Md0mzc+57VcnVm/w+5Zpe5YUzZ6nNZUI6DI1YuC2gMHhvGwksKkMpVQPcCVyltd6tlPrut771rTtuv/32itXp4U0tRQ/nBv3KKqp3omnbEQubybjOXA23u85qrQdHV2zuueGkbT3f3Mu0+pGj9XKkQsFQHV0DCw/pMYHBi2HpUqEA5k2u4UB3FG3RT1hfFeD4OY1lXy51tDgXnb5Yist/+bTn7f/+2fM5ZpbdqkETdY7FROR817wEFrnLxcpnPLY559t4KmO1yl4xCk2BNB1k6XG7MpSTWhN0GY0PBXycNK/J6rumNbT0xYinMq6rPQGE/Kqkqw1WmnOctvVZBBZOO8wlsHMCix6P88kqaeJ8ooDWOgqck/PvOytYnZc4cnpdwbnHQb/PLmXGsrHs3F3Yy4hFS1+M25/dN+JE31RGDy4X6XaCduYNpDKa99y4yrUOUPqGwNB7lf+iMDhiYtFQd4bv71nXzHaXHEsvPacLptSyYMroLHc51vh9ioVTagd7d2zFUxnSGc3avd3WgcVAmdLuROkVcrHN7XCRj3hsy/0ORuIpQoHyzWEodFTcuc59+2+buN5DCuobT5zFv124xFMa6sodHdz05C6re6nMbKzmO+84MW9PfCajB3vAgxbtgGn1VVapq2AWizCdNO4dYTVB/4Q639qOWGitB9thbiuzOYGfjFiIlwn4fZ7vwurV0I3f8jeWBwMLi16gUMCHwqw5bbM0nMKuYTZ3Ug0He+3uWjq5NuTaq+LV0L0s3FKh7C86zgli9e4uVluk7SgljVgbR86ot77xl2N7a5h93VFPN9iSORbjh3M+eGxrG2+5boXVNslsOqpPUbG5RcJO7uTo/nj+BTaK1V/gSGVdyE9nxKx65eUO3M839/KDv28d7LRzozWeb8549pKpXH76/BH/Ho6lBhfOsOlg9MKnlOsk54nKSWvqjMT58G+fGbFcRg91Lrq1w4ZSoZIVu0eZV6/MT3+CctreX7xzI1/7y4sjluuN2s+x8CnFMbMaBtOn3Eytr7I68AtpLJaScxHpiiTyDvE6f3NbQhfMyIKzgpONSbVBacSWSW123s7W1j7rSW9OOflMxj4nfSIcS/F8s7cViuSOvONDwOcjkc5wqDfmaUUpr5xcf6/f+yUz6plaX2V1N2hHR3+Cgz1RNFitXJSrqSbIvEk15FsdvD0cpy0c59HNbXnvDdOcDYT8PiULGZRQyK8I+hXJtGb51nbX8lUB92wUpxMlkcoQTaZLOiekXMZ+DScIpcq/EkljTZCugSTheGpwmdp89WmqDVnVaf6UWuZPsFQcJ1D4xr0v8o17Rw7Ccsu7vVfVIT9LKhgsiSH12R6zp3Z0cso3/+Fp24DfJxfbMW5SXYizjpjiuXEGpoEmn+/YF/ArEmkKmltViKDHRrYPxTQP9wEBmNFYzVEz60lZ3kXaoRRWGQ9VAR9t4TgPvdjCQy+2uJYPybmuxBRnLJpiPfdrUq37uSiofIM3puweSEpgIYZ86OxFvOfMBWXdh9aaXR2RwSH/fKbXV7nexG0iu3PNfr5yz/ODd1zNZ2pdiF98YBnzJk+s4GoiiyXTvOW6Fa5zXYZrqA7wg3eeNGr3BRBCHN53H9jML5/YNSr7CgV8/Mebj+MtJ88Zlf2VSzyV5kO/eYbNh8KuZX0KrjzvCD71miNHoWaiGK/6n0doD8fpHUi6LuM7Figvd2Mdpyr6AlPpDBqT0y89A2OL89m4kc9ufNJa4/H+bijkHgdCjBWpApdp98pZUESIsagvlqQ+FBgL1yarCkhgIYQQQgghhMjHKrAo3+LQQgghhBBCiFcMCSyEEEIIIYQQRZPAQgghhBBCCFE0CSyEEEIIIYQQRZPAQgghhBBCCFE0CSyEEEIIIYQQRZPAQgghhBBCCFE0CSyEEEIIIYQQRZPAQgghhBBCCFE0CSyEEEIIIYQQRZPAQgghhBBCCFE0CSyEEEIIIYQQRZPAQgghhBBCCFE0CSyEEEIIIYQQRZPAQgghhBBCCFE0CSyEEEIIIYQQRZPAQgghhBBCCFE0CSyEEEIIIYQQRZPAQgghhBBCCFG0QKUrUA5KqUuBawHfTTfdxJVXXlnpKgkhhBBCCACtYffjEG51LxuogqNeB6G68tdLFE1prStdh5JSSgWATcBrgN6jjz46vHLlSqZOnVrhmgkhhBBCCPY8BTe/2b78OZ+B13+rfPURNpRNoYk4YnEm8KLWuhngs5/9LA8//DDvfe97K1wtUZBkFFb8GCJtduUnLYBz/h18kuU3YcTD0LbF+3aNs6FpXunrI4QQojg9+8yjLwjVjSOXS8UgEYHe/aNTL1G0iRhYzAGanX/MnTuX5ubmPMXFmLb1AXj8f71ts/BcmH9GeeojRt9NF0HH1gI2VPDpNTDtyJJXSQghRBFSUfNYMxnmnjZyud4D0PqC6WScSGK9sPpGiPW4l61ugjM/Zt6rcWAiBhZiIol2m8dQHTTMzl+2Zz+k40PbiPEvnRoKKgI1lgOxQCoOOgMd2ySwEJWRjMFAh/ft6mZAIFT6+ggxliRj5tHnz19OZbMPkgPlrc9oW3crPPZt+/KBKnj1v5evPiU0LgMLpdRZwHeAU4Apw/58ATDX+UdzczNnnnnm6FVOlJZz8qlqhKkuDcSBTojGJ94J6JUsER76/0Xn2ae47X8Gol1yLIjKiPfDdadBv8XE1OGa5sNVa01DQoiJyjk3K5dzuhN4TLQRCycVrHpS/pGIgS6I95r2zTgx7gILpdTJwHLgZ8BVwHzgj8A64EZgJfBrpdRczORtvv71r1eotqJozslEufRq5JaZaCegV7J4v3lUytu8mcGLkQQWogI6dwwFFW4Np1w6Y3LJu/fC9KPLUzchxoJUttPQ7do+Ua/rzrzRhpkwefHI5Tq2m8BiHL3+cRdYAD8F7tZafyH7701KqT8By7TWdwAopa4GHgN8V199tawINZ45eZg2jUppTE48CSew8HiqmqgXI1GcVBy2/A3ifd62m7kU5i2zL+/kTYfqYdG59tvtfhKSEROUSGAhHFpD9x5IJ/OXS0Vh4x3QudP9OZUPTn0/HHtJSaromXNudkuFmqjX9Ui7efS7jEz6xl8q2LgKLJRSM4FzMelOuSLA4Lq5Wut7gXudf45O7URZJC17NSAnF1MakxNGPJsK5fd4qnIuRolIaesjxrd1t8Dfrva+nT8EX9hmP3nSmeflD3rbTyBkAgvbVfDEy0U6IJ3wto0/BHXTylOfUnj8e7D8u6V/3tbnKx9YuI3oDXYSxcpbn9HW7wQWLvOpxmEn2bgKLIBlmLuFbzjM79eMfnVE2TlRuluvRm6Z1Pj5AgoXTmAhIxaiFLr3msdgrRlNsDGQbaj27PMQWPSYR5/HwMLpveyXwKIgT/0E/vGfhW178X/BuZ8raXVKZu9K86j87g3xQBU0zsnfYNUZaNtkjulYX/7lXsslZTl5exz22FtxOg/c5lKNw2vZeAssnG9UHRAGUEqdBJwPfK1SlRJlNJiHaZEKNQ6/gBPOgbXQbBnjL74AZhybv4yTCmUTWOYazeHzLX8z6QdeBkeVH07/f7D4/LJVqyI6tsMzN5qUIy+OfsPo9Jw6gWrjHPfFIBx7nzLbeWnsFzNiARJYFGrnY9n/sV0+zqHh0W/Dql/Yb+Lzw/lfNN/jcgu3mMe5p0FtiVK7O3eaVRTbNsOCV5XmOb2wnbw93q7rqYT7PTe0NpOywX3EYhxOXh9vgcVqIAr8QCn1HWAJcD1wvdZ6FYBS6nGgKVt+6bPPPsvpp59ekcqKEhg8+Ugq1JiXjMHv3mJSOWxMXgT/PnzwcRhn8ravwFQor4HF6hvNvVNsJaOwf5W3fTi6dsEnHi9s27Hq8e/D83d4327jHfDlPRCsLnmVXsKZW+HlePJXAeGhxp0NZ46F1+PWGbEYjVSodHJoZRpbgWpomutebridj8HuJ7zva9mHoGGW/TZ9B83jvNPtG+Baw4FnTDDY7+EzBnjud6MTWDj1csvH96KqAQbi8Pu3uQfAddPginvMObtUbNOcnXN5Og6ZtPdOptGkNdx4gRkNsiWpUJWltW5XSl0OXAtsBPZhVof6UU6ZCwCUUv8NPH766ad/phJ1Lal00i5X3Oc3J4uJxHat69wylRoy3bsSHvySZS6oNhdBm5NFsBb+5Zdw3FuKrmJZDXQOBRX5GgMac6Hs3mMCh6o8KSmJAgOLQk7GmTT8/T8gk/K2L4BgTfaia9FTmopD107z+r3q3AlbHzSpDLaUgilHmOPIi9knQ+3w1bxdOA27hlkQsjwX9ewx6Yt7noQFZ9nvK1jnbaUwGBqx8HI8OakKXpaOHRyx8Hg/CmfEYsPtsMVDgNs0Dz74V/vPS2u46TXQ8ry3+gG88X/hrE/al08n4bb3FXZeDh+Et/zErqzW0Je9GW7AQ4CqFMw9A5L99oOOibB570bjnkmJAXMzNSjtEsR100yaXyrqnj4c74Odj8LpHynd/gcXZrFcFQrM+Tzf9aLSwi1DQYXNOaZhljn+8hmHqWDjKrAA0FrfD9yfr4xS6rPAIuDDwPgOLCIdcMNZQysIuHnNV+GCL5W3TqMpZTnBCyof2a+8rrALtZtkBDbfP/YDC6eX1h+C2afkL7vjn5BJmsb1rBNHLldIDzMUNnl7oGsoqJh1kv12SkHddPs6ZlImsIj1mAZDdZPrJoPu+ggcWm9fvhjTjoZPP+ttG+eGcE3z7XuME/0QPgS3vtNj/Y6Bf33SW2MrVsDxVFBg0WMevS46UD0JUOa7Ee3ysL8uE5gd/za78rGeoXOVbQ+w1iag3fmot8CiZ59pFCmfOS5spGLm/T6w1n4/8b6hjgivDXCfz9wryUt5GJ3AwhmtUD7v58F8Ji+C+lmg0/nLdWw3dYiU+D4K1pO3c/5e6sCi76C5iaqNWSe5B+6dO8xjsLZ0aa6VbtcUYNwFFm6UUh/GzLl4l9Z6/K8I1fycfVABsP1hu8Cie092SbrsW6TJ+X897P+zf8v9/5HKzVoKU/KsyeyV7ZJ0uWVK/QXc/aRdwOAM9c9aau4S7cYfch+CDrdA+2bvQ/SV4FxkbSasBmvN2tyugUWBcywKSYtzvme+oMnBLxdfwOwjkzR3i59lGVjEw3AomzrWMNu9p8uhMybAsj4batNA69hmtgvV2W6Ys4Sih576xrmmEellFAbMHdm7dsGM4+y3KWTEwkk/2fOUSfWyMdho9zjHoqoBjniNt1WNOraa973HJa87V+8B8+gPwZLX2m0z0AkHnh1qPNnq3m0eg7X2n1Uyao6J9s1wcJ3d5+WMAPqCpW2AH44ve3zHeiGdsg8g+9vhqR97W+7YadAHqu2/87ZsUg+D2WuZl3aIDdtru1LmfK4zpe21j4fhhrOHOsTcTD8OPrky/yip893wcs50I3MsKksp9Q7gPcDbtHYLw8cJ58tcOxXm5llHPdpj8kRtvvz97fCzM7wvyWejqhHeep17wyJUBwtf7X5CHszDtBmxKMMci0gn3PJ2+/QYfwga5pTuAuCcoMIF3MHXTSqeEyzmoZRdD6CXXtpQjQks1vwamvP0Su5abh4LnmNRQGAR8Ji+UohgDcSTsPF2+3zcrt2ANg2M2SeXtXrseMQc8z373SfYO9Ip+wmJueqmwZEX2x2Ljn1PD410lDuwcBpWbS+aHy8KSV0JhLwdg6H6bGDhYb6EE1h4SRlyzkXde80EVds6duUEFrYC1abTJZ2EGy+03w5G547luee4WC/UWY7OrfkNPP2zwvbpNZWxVJz30xmNLBWvC7PoTGmv7ftXm6BC+dzf2+SACXJveXv+oMEZ/SjlZzU4YiGpUJXyG6AdWK1Mw+5r437QIndJsnxfwMHheovAom2TCSqUL+dLMlJDWOX587BfJqOmJ+bOD7nXAexydQdToSo0YtG10zSwfH6om+Fe3ktPso1C0jBsPPLfsOJH7uUcp38ELv2//GW85JUHs8fdzkfNjxuvjf3Bk7GHVKhCetsLFawx35WVP/W+rZfUqUIFakweea+HwCLaxeCwiNfVkJTP20I+gSoTWPQd8rafeDZX3UuKUt00s4KU15WuAtVQ43GOSiGcwMdtJZpcTmARtBhZdfirsg28NPz+rfbfE2ckwcu+lILJR5j5N14u4UrBpAUeNiiQk5aUSZnznm1g0bndPNZNz6a92e5PmbSlSnA+50iJA4ukx2t7JuneuM6kYdXPTYeDm4PrzGPDLPfU1/Yt5jjebbnYRimX75URi8rSWlsuMj6OOF9mt5O4M1yfjLinLzgXlZrJMO+M4uvoSERM0OLWu59KmICh+Tn35/SSClWOyN7pBaxqLH8v8eE4gUW0y1svoZuNt3sr//yf4ZIf5Q+avKRCTV5oHm1GgvxBaPC4Ek0hJ+OBbMpBKVdeGcnkxaYHzmv6j/KZidjlFqw2gYWXXnDnXOUL2vVCFsPpabdpQDi0LmzEQvnsl6atBCft8sAaeOg/7LbZ/0x2W4+TnKubzLlo39Pe6gjeA+Ipi0ubVltq/mA2sPAwF8YJshrnelvtqpKcToKBMs2x8HJtf+HPQwHB4bQ8D2t/660eNRZB4dSjTBvAJhHGF4R6i05IW85rT0Uhk/G+YEUFTKjAYkJy1jN3ve27fygPMdKeP7AYXDnDQw+SjVCdXaASbjETULt2uZe1neAF5YnsnV7AUr9XtnxBc0HXGrb/HWpd7g479Uion56/TKRz6Bg44sL8jSxnsma81xyLDTNHLjs4edvitOIPwbSj3MsVyjkZJwZML5aNwe/aKIxY1EzKn9pYaU7v8mPfgdWWa/s737vRSCVzAm4vgUUiMhTIlTsHfzQ55/pIG6y6obBtbc0+OZvu5jETwB90P3eNN74gEPU2gbuQtLBKG1wCud2kO7rp3Q8tG93LOQtr2Fzb/QFIYp9G1jDbLmj2h6Bxtns5n7+88+7y7jvn/UnFIDT2j50JdHadoAZTodzWOlbmS5KKmXSofOtNO43lcq8ZPxLni2ETWNjenROGGpORdnju9y5lfTDvTPc12Z06ehnGLyWlzIk9FYPbP+Be3heA499uGq4jcRrQwVq7k2+w1owCdWzLH1h4GbEoN+d46d0H3/SYjjIaDeOxrnoSsM/0UnrtqfSyuk6hnON260P2KRq5qUw26RfjRajWpHI4KyLZKmSRgkCVXUPslcDpyX/sf2Dtze7ltR6ap1Cp60khnI6W/lb4VoluzpfLJsifdnR2cQKLgDZYa8qXeqJ7peSeqySwEIC54VZrEUuQOquL2PSiOg3Q5d/N32B27k7qZRi8lJzemmgX/PVT+XssvNwgzzlBDXTAvVcVV8fhKnkhmLwYeva6l0vFzdD8C3fZPa9takKoznwOd388f+6ocw8Dr/n15RCsNaNMbuuzD6d8o5MXP9Y1zDbLOtr0UOZSCqpGYQ6I09PedwA2HfC2baBm4jQ6HJXqTX0lC9YCnWb03csS0IGasXGOtBWoMquVOWmENqoa7AKG6kl2k+1rp5bujuPjzctWxRr71ycJLMpt56Ow7cHin+fk97n3FMV6YN8q2PlPu+dc+k4T2VfCoQ1mZGHdH9zL+oNw2gfdT0Bam3WmbSYxxsP2yyaG6uCMj0J9nt76sSCTMkvj2vQw+wKw+Dy7k3X1JNh4m7lZVfige/kTL4OZJ7iXK7fTPug9Lc7vcUUeURlaQ/Oawu4jMOOEwu4eLUSuRMTME/R6Q80Zx5rAfTw57Qr7uYsqULlsiIlqz5PmeBsnE7jVuF81yV1lX+ALd5uVhYox9Sg44e3u5XoPmMlNNsvINi2Aky6vXM/doQ3mnhs25p0JR1xQ+jokY3aTsfxV3m90NZFk0mbCnM2FpW66t+U/hRBCCDGyp35q2iqnXmFWqascqwajBBZCCCGEEEKIfKwCi7G/bpUQQgghhBBizJPAQgghhBBCCFE0CSyEEEIIIYQQRZPAQgghhBBCCFE0CSyEEEIIIYQQRZPAQgghhBBCCFE0CSyEEEIIIYQQRZPAQgghhBBCCFE0CSyEEEIIIYQQRZPAQgghhBBCCFE0CSyEEEIIIYQQRZPAQgghhBBCCFE0CSyEEEIIIYQQRZPAQgghhBBCCFE0CSyEEEIIIYQQRZPAQgghhBBCCFE0CSyEEEIIIYQQRZPAQgghhBBCCFE0CSyEEEIIIYQQRZPAQgghhBBCCFG0QKUrUC5KqXuACy+77DLuuuuuSldHCCGEEEKUS6wP/nAZ9OyzK18/A957GzTNLW+9XmEmbGAB/AT4DXBvpSsihBBCCCHK6MCzcOAZ+/L9LbBrOZz6/rJV6ZVowgYWWuvlSqkLK10PIYQQQghRZsmoeQzVw+yT8pdt3woDnZAcKH+9XmEmbGAhhBBCCCFeIVIx8+gPQVVj/rKBKvOYiJS3Tq9AMnlbCCGEEEKMb05goSyatirbry6BRcmN28BCKXWWUuqfSqlOpZQe9uMSqgohhBBCiAnDCSx8fveyThlJhSq5cRlYKKVOBpYD64DzgDcCXcA/gXdrrfsqVzshhBBCCDGqkh5GLJzAQkYsSm68zrH4KXC31voL2X9vUkr9CVimtb4DQCn1CHDyAw88wLx587jzzjs5++yzK1VfIYQQQggBEO+Hh66B/lb3soEqOP9L7hOyB1OhLEYslAQW5TLuAgul1EzgXOCCYX+KANr5h9b6Yud/R6lqQgghhBDCzY5HYN0t9uWrGuHtN+Qv42WOhaRClc24CyyAZZgUrg2H+f2a0a+OEGLQulthzwq7sse9BY59c3nrI+DFe+CBL0E67m27pe+CS64tT52EEN5oD32kSpWvHqUy0Gkeqxph0oKRy0W7oa8Zoj3uzzk4x8ImsHAmb/e7l/Wivx1aX7ArO/tkqJ1S2v2PAeMxsHCOmDogDKCUOgk4H/hapSolxCteIgL3fhp0xq781r/Bl/eOj4vgePb8XRBp877dc7+XwGK8SAzYpZTkqmqEuqnlqY8orTW/MZ0DmaR72fpZcNoVUD8zfznlg6Nel79RX07x7FTYqnpomjdyOeUzgUXcYups0kMq1OAcixKOWGQycOMFpr42ph4JV60t3f7HiPEYWKwGosAPlFLfAZYA1wPXa61XASilHgeaAHw+H6tXr+b000+vVH3FWBRugQ23QcqiF7dhJpx6hd1KE7Y6tsPv3goDHe5lgzXw9l+M/d79gU4TVCgFU4/OX7ZzG8R6oWcfTF6Yv+wDX4Ltf/den4bZ8O4/QN0079tOJLFe8zj9WKib7l4+k4Z9KyGdMBfqYHV56yeKE+uFn5461ANsS/ngA3fDkteUp17FevbX8Oyv7DsqwDQoX/0ZOPk95atXJWy4zS6oAHM36Sd+YFd2wdnwkYcKr1cxYtlAwRfMX87LyIKn5WbLkAoVPjgUVIQa8hTU5vV07oB0Evwu78E4M+4CC611u1LqcuBaYCOwD/gZ8KOcMhcAKKX++9Of/vTJElSIl3nsf+C539mXb5xrendKZccj5iRkI52ALfeP/cAi2m0efSGYsjh/2fBBiIfNkHG+wKJjOzzzy8Lq070HHv6at8+tqhGWvLa0QWSlOSkEoTrz4yY35SLWK4HFWNf64lBQYdNTC6axrjOw/5mxG1g8ea19z+/w7SZSYJHJmM8YYP6r8n+HMxno3e/eCM+kzDHTtduuDtsehgPP2JUN1sJpH3IfDXM6PHwuzVDn7/Gw+749pUI5IxYlTIXq2mUeg7Ww6NUjl9N6qLNsoMt0Xk4g4y6wANBa3w/cn6+MUuqzwKIf//jHo1ElMd50Z0+otVPNSWAkA52mR6NzZ2kDi75sUNE4zwyHjiR8EDq2mZPPWOcEFja9L1UN5kJx2/vz9y45vZU1U2DaUfZ1ifVC+xbY8Cfz48WbfwhnfszbNmNZrMc8uvUMOpQyF/NMyryPE+yiV5CD6yBiMbqYa9ICmH5MeeqTq/eAeayZAvPPtNumYzt07SwsRW40xPqGgoq5y+x6oDMp8zl17hi7I23xfnM+smkkD24TNo1f5YPqpvzvhR+782QyCrsfh2iXaeTmS0eNh+G299mPmIBJW7r4v9zLAPhtAwuLACAZNY9WqVDOSEgJRyxyA4t8lDLn40zSfAYT7Bw7LgMLN0qpD2PmXLxLKfXBCldHjEXhbD7ylCNMcDGSts3Qs7ewnrN8nMAiVJf/AuicoKIlDiz2Pwsb/miZZqBg6Tth0bn5i3kJLOpmZN8DDTrtXn7yYqiZ7F7OUd1kLhheeqPSSUiE4amfmM/dVqAaXvVxmLzIfpuD60zPaiphvw2YZRcv+DLMOtF+Gy+fi8MXNA01m7zmXANdZnTNJsUw16JzYcZx3rYZLbseh9+/tbBtj7zYHLtezD7Z5Mjb6tlnHr00pP0h89g/RgOLjm3m0V9ll74HpoHsNNZ2PALTXNIxcwWrR2euwZpfwz/+s7BtqxrsAiwbzrkgnTBz46rqRy7bd8i8p8qXfy4EmMZ/tMt0xLnxmgplNWKRPe94SYVK9EPP/vxl+w6a85oTuIykOTtfIuQSWID5DDLJ8dFp6NGECyyUUu8A3gO8TWubFot4RXImOvqr8pcLZC/WfZZpS7bCh8yjW2PAuQCU8uSjNdzzCdNjaWv3E/CZ5/KXGUyFsmjANsyC2ovsggrl956Dqnww83hv26STsOsxk0qw5tfeto33wtuuty+/4v9g833e9uEIVMNlN9mVTSeHgisv76E/ACmGRjts/fObsPa33rYBaJgDn99U/on86RTsWu4tYFr3B/MYqHI/XwzuJ24aOTse8VxFAOadbh9oOSMWgRr75w9kAwuvozCF+sc3YOc/7cs7aTL5GrzDKWUa39EuuP393uoH8Mb/hbM+6X07L5yUpuomCHl4bSj3Rr0Xym/eL63N+5Xvfe5vMY/BGpjhck7tbzXPZ9MRZ50K5cyFiJj5X/nSVL3cxyJ3udkfe+iosZF3fkWWP2T2XepOwzFgwgUWwG+AdmC1Uor77ruPSy+9tHK1ObDG9ICmPQwjDjd1CbzuW3Z5g8JdMjbUYAq4NBSCZQosnBNvwC2wyDYAbE4+iQF48ItmYno+6YQJKpTPjNiQpzGn02Z4t2uXaSjle7+89oz7g8AYmrTmD5q0Cy9BXHLABIkd273ty+nRm7zIvoERD5vRs84d9vtxLt7gfgHP5ZSNeRyxaNtkHqsnuX+3HP2tJuWvv638KQHP3Qx/u7qwbWefYj9qprV5XV5SXsCkJsXDcO9VMNUy9W/Pk+Yx6CGwcAKk0UiF6tgBT/24sG2rJ3kr3zTf9MB7mfCtM+Y8t2t5+QOLwe/9YtO5UilKmblw6bg53+UbrXFGtWyCaud61msRWDjBve0cCzCdJNVNI5f1MsfCH4LaafYN++pJdt9/fxAa59iVA++LLowDEy6w0FoP/+Qre4O8p34Cm+8t/nmOvHjsTrIrRMvzsPNRu7KTFsD04+x6M2unuU8ac0YrlM/9pOacKDu2mc8yn95m2PaQXRqIUwe3xpdz8ol2m4l5+U6YW+4f6l21UT8r//wOMA2knr2mp6hrN8w4duSyhaTcjDW1U/Onxg0X6zOBhZNba0ProUmTTfO8BxZe9uVM3Pb5vaVROKNOuYGJDSctZ8Zx+RsAuXY/YQK0jq3lDywOZFMVgrX2gQ+YSf1eGrlKmYaj18ZjqB5aNsCBZ82Pp20tJuY7BlOh2r3t4/Hvw9qbvd1TwVl1p2ZytiPDkvJDzSQvtYPG2ebHi4FO81577RwohDNK7OWzKhd/0AQWbg3rwWtVyP05netlf6tJ88y3jdNp4Xa9UD5M55c2qVb5ziuDy83apEIpMzJYKc53UFKhhGfOSEXjHDO5zqu+g+aLv/1h95xRX6A8F+ZU3EyEtTHtaPees3QK/nCZ93XXbSg/LDwn/4l78IRW5R6sOOkFAx2F58aO+NzV7iMWvuzJR2dMuk2+HpOWjeaxdppZajUfpexyl5WCYJ3pXdr9eP7Upe492TqP48DCK2cOTKTdHFfVje7bRNrNsD5AwCIXd/i+Yj3Q8oLdvjq2mkevn4kzodJLYJGK56T4eeg9D9WZxuemv3obIZl6ZP5A93Cc92Pa0ZXtMR5JwyzzXU97nHsTrPYW+DgNvngv3Hq5XaeN1rDjH95GA3JNWmg/X2I0BbPXiu497o3hXOtuhUe/beYiWdFDnS9uk3tHg22arW3aMJjGsvKZY+S29+bfxnlet869wcUkku5z5rykQlWa8/5vfdD9dQVr4IyPeQ+0K0QCi9FSM7nwHMloF6y6wfy4mXOaaVi7XSiqGrP1cSkX64Unf2gaQzZqpsCr/tXkuo4kfNCcVHwBqJ+R//m0Ng2ptMXJ2xnSdlID3Nj0GgWrTe+rVQNLmYt7dZPr22qeu9a9Z8XnMydJnYZDG8xQ/0ias3Mg6mdC01yLClgKZQOLB79kV348j1h45Q+Yi2k6AT852S7dyGmIBKq9pTf6/KaXPRWHX+RZyvCw9fT4mTiBSM9ek8piozc7AVL5vQUyoXpzfnn2V+bHlj8EV9zjHkTncnqlx0KP8eEoVdrv7kh8waHj1us9YupmuI90vmx/AbsJrZUQqBo6x970GvuUwfYtQw1ZL6qbxsZy1k6P+fpbhzqlDmdHdm6MzQifUub7HO+zm1+kfHYBi89vAou2zfk/H6eBXqpJ7uXkdCoeeMZuKd/eA/AWl6yJMUJpL0Oa41NlX+Af3wPbHoR5Z5q5El6l4qaXKBFxL1toT5Itt15ItxUThptyhFmXu1S0zuYoW6wEpJRpkHjpWa2UTfcO9XDbOOr13tJ53IQPmVSBjMVE60AVLDrf26TL8W7PU9C7z/t2kxeZG1R50fI8tG/2dlZTCmae4D7x8iX72Tg00dSr6iY4xsM9V+Jhs5qKl3loyYEibmylYOm7xkbjrpJiffYdRg6fzyyRPdE6D3Y+WtgIeu00mHeGt22qGsbGsde8dmj1LRsLznG/mSmYxn24FauTVM1ku2vV1ge8jZ4e/aax37ufTkD7VvfRyUzKpL/6Q/DvG+zmb5SP1eoaMmJRbqFaqGqC0z8Cp7y3vPtq2wzP32l3gQ632E9aqp0Gr/kP9+U0wy2w4seWd5OuhfO/CJPy9MALY/JiM1pl0wkw52R4zx8n3oV/LMukzQXa6x2Cpx01NhoYh9P8HNz5Ie9zLJTffK/P/rfy1MvRtQv+8M7CVjVaehlc+n+lr5MYv2K9ZgluT99hH8w/w34u0VjTdwjW/MYuQK+bbia2e5mXVEpP/RRW/MjMM3Qz83h49y0T6xr4mzeZwLe3udKBhRUZsRBCCCGEEGIsCrdC3bSx0BFlNWIhgYUQQgghhBAiH6vAYhzMcBFCCCGEEEKMdRJYCCGEEEIIIYomgYUQQgghhBCiaBJYCCGEEEIIIYomgYUQQgghhBCiaBJYCCGEEEIIIYomgYUQQgghhBCiaBJYCCGEEEIIIYomgYUQQgghhBCiaBJYCCGEEEIIIYomgYUQQgghhBCiaBJYCCGEEEIIIYomgYUQQgghhBCiaBJYCCGEEEIIIYomgYUQQgghhBCiaBJYCCGEEEIIIYomgYUQQgghhBCiaBJYCCGEEEIIIYomgYUQQgghhBCiaIFKV6AclFLzgVuAGUuXLuXrX/8673rXuypdLVGIdApevAcibXblJy2E4y4tb52EEEIIIcTLKK11petQckqp2cBMrfX6lpYWvWzZMrZt20ZdXV2lqya82vog/Ok93rb51xUwa2l56iOEEEII8cqjbApNyBELrfUh4BDArFmzmDZtGl1dXRJYjEfhQ+YxUA01k/OXjXRAJgnhVgkshBBCCCFG2YQMLHKtXbuWdDrN/PnzK10VUYhU3DzWTILZJ+cvu28VxHogFS13rYQQQgghxDATOrBQSk05/vjjuemmmypdFVGoZDZIUH73sr5smWSsfPURQgghhBCHNW4DC6XUWcB3gFOAKcP+3ATEgb9cc801nHPOOaNcO1EyzoiFsljAzCkjIxZCCCHExKA19DWDzriXrZsOwZry10mMaFwGFkqpk4HlwM+Aq4D5wB+BdcCNQDj770evuOKK8ypUTVEKTpBgFVjIiIUo0vZHYOej3rYJVMHpH4FJkm4phBAld/9nYe3NdmXrpsNn1kFVQzlrJPIYl4EF8FPgbq31F7L/3qSU+hOwTGt9h1LqXODdwMZTTjkFgFtuuYWlS2VC77jjjFj4bFKhZMRCFCGTgTs+CMmI922jXfCWn5S+TkKIiSXSCYmw+f/BVTlzVuc83EqdL/mdHvl3YBrWtcOTOMa5vSvNo1LkXZhIZyDSDl27YfZJo1I18XLjLrBQSs0EzgUuGPanCNlvl9Z6BUM3/5t46+m+kniZYyEjFqIYAx1DQcXkxXbbJAegvxU6dnjbV/s2WP0LSMe9bReogbP/DaYc4W07IUTlbfkb3PZ+ytos8QXh31bBtCPLt4/RFu0xjwvOyT8SsfsJc05OFNA5lE/ri/DglyHR7162ehK89bpX9Aj2uAssgGWYoGHDYX6/ZvSrI8pK5liMDZm0XX6r8g+NHLnpO2QuAm6qGqB+ht1zFsNZ2thfBdOPsdsm2m0Ci5593vb12Ldh01+9beNIx82Fy1bfQdjwJ0glvO1n0bmweIxmkvY2wx1XmN5Jr5ZeDhd9vfR1EsLNnqcwQYU6/DVNvex/RjDC3zMps+R6y8aJE1hobc6zAD6XJuvgAi4lDiw2/An2POmt/AVfKm0dxpHxGFg438Y6zFwKlFInAecDX6tUpUSZOEGCTWPVOVHLiEVpvfgXuPvjdr3rVY3w4b+5D0NvuB3u+bhlBRS8/0446nWW5QvU59wzpcp+G2eSYF+zuUu83/KU2vyceWxaAMFqu23ifRBugZ799vUDePQ7sP4P3rYBWFEF533eLg3Rofxw/Ntg6hL7bfrb4IkfQKzPfpu2TabxVIinr5fAYjzY8Qg8d4tdh4bD54czrjRB8VjU32oepx8DkxeV/vmbn4NIG8R6S//clZIcMMESgD+Yv6wTeJR6xMJ5PxvnQP3skctF2qB3f+HnpgliPAYWq4Eo8AOl1HeAJcD1wPVa61UASqnHMStD4fP5WL16Naeffnql6iuK4QQJXpablRGL0tr2kH3KTrwP1v0BZn8/f7kNfzKPyp/Nmx2BTpseq70ryx9YhAsILPxVJqDVadh4G9ROdd8mGTUXH4BpR9sHI5EOE1g49bTVsdU81k03qVQ2Yj3ms1z+XW/7Atj1GHzoPvvya2+GZ270vh+A2aeYm2fa0Gk48Kw5P6Ti3j5nMfoe/k9oe9H7dn0H4aMPl74+peAEFv5QeZ7fOZdMpMDCGa1AubcDnL+XOrCIZ+fEVDVB/fSRy/l85tzevM5uEZDZp0y8+TCMw8BCa92ulLocuBbYCOzDrA71o5wyFwAopf7705/+9MkSVIxjKSewqOCqUIkB+8bc5EXeenjHg/428zjjOGiYM3K5SAe0bIB1t7gPG7dnG7sLzoaq+pHLde2Cjm2mseBVvH/oQm6jfYt5tG2oggmKAtWmV+2vn/JWv2CtfVCRW68+j4GFk6Y19UiobrLbJhU3771O2+8nnczON9nurX7de8xj3XSo8XCRrao329jKnewa6x2d9LrRovXhJ/3mE2nzPvoVrIaZJ+bvDCiVSPa8M2WJXRCYjEL3bhN8e9XyAmy+z/voyAn/AtOPtt/GqVu5glpftkd/QgUWPebRH3Q/7nxlDizcUrGc+R99B+CWd7g/79Qj4aq1xdVtDBp3gQWA1vp+4P58ZZRSnwUW/fjHPx6NKoly8RRYlGGORTIK151mH1gccwm894+l2/9Y4OSxB2rzD0XXTzcXtuSASVVxE6qHUF3+MoON6Wa7ujqi3fDT08xqTV55vehPPTLbePfSsFMweaG3/Tj1iveaC6fbewfm+HWCKy9ruweqTCDpRSpu9hU+5G1EwAl8GmabVINyUcocn5mkaaxMlMAiMQA3vWYoMC63i/4Tzru6vPvIzatvmm+XLpiImMBioNP7/v76b3Bo+LRNC9v+Dh9/zL6800lTtsDCGbHoKc/zV4LzWnwuaVBQ+cDCHzILfwx05C+ntZkI3rljQo6ejsvAwo1S6sOYORfvUkp9sMLVEcVwAgur5WbLMGLRtXsoqMh7UtFmgvPOR82ypW5zQrr3mNVBBiwavoEquORaOPIi97Jtm4cuXvn4QzB3GQQshuQHAwuXsr4ALHq1/Um9qsG9B2owsPA4YrF3ZTaoUN5GkHwBqPPY4GycU94GscMXMKNyOm0mfts0jMPZoMLnt7swF8MfyqaFZaD3gP08i94D5tHLSFGh/AETWEykhlfLxsKDikC1/ehDJmVGpZz5QeUU7zP7A/e8eoeTXpToNwG1bSCtNXTuNP/fOMe98QimD6F3Hxx8zox02AT5mbTpFACTQlkO/ok4YpENMG2OA1WmORZOYGEzwjz9GMBl8Q+tYfvDgDZtgMY88zbGoQkXWCil3gG8B3ib1l7G8cWYlKzwiIUTVITq808I1BnY8Q+z79797r3RG++E1hfs67H6l+6Bxd6n4bdvtH/Os/4N3uiSQ5/JDAUWNnnBgerSNhBzA4v2bfbb7XjEPDbOhVknlq4+laSUCTKTA/CXT3rbNlBb/vQVpUxjLhGBlufNd8aVHhqNGo275fqCQHQovWIi6NptHmsmw5xT7bdTPrtGtKO/FQ6uKywt0Sunw0X57DsGfAHMaknapGXaLvcZ6x1aRnTGCfb7i/eabW//gF15h9f33YvxlAqlNWx90D1d9cCz5tEmsBjsXLRYbdCLePb4KNXnppR5PemEGd2QwGLM+w3QDqxWSnHfffdx6aWXVrpOolApD5O3yzHHYjAn1qWxrHwQrDMXqI7t7oHF/tXmccoRUD9r5HLJATi0HvY+ZdZAz7cM4bpbzKM/lD8I0BnzvFsfcA8sYj05PYcVGK51hohTUbj+DO/b10wqaXUqbsoS6NmL97SrRWWq0DCBbGBx54cK2HYUjq/BHt0eb9slIkO9lraqGiFU622bQnRnA4tQXfkmBcPQOdDr4gGFGOyl9vB6lDLl03HTWLMNLJwRM1/Q2+jm1CPNud7r3JbG2eUL8sfT5O2dj8Jt77Uv7yWwsLnfhBfxvpc+fyn4QyawiLikTY1DEy6w0FpPHv6rilRElMZgKpTFiEU57rztZaWgUDawuPUy3Nchzx6W9TOhunHkYlUN5oKX6Ifb3mdTY5h9cv7VidIp2PmIScf666fy98Lk5pba3p+ilHx+aJznbRK2I1DtPa1prGuaa37GqoZZJgXNyyRYMIsC2IxKFss51r2MWLRuMnMYUh47LIJ18MmnYIrlzRYdPfu8NTYOrs/ur8xBjHMO7G81aT3lXKTCmRtlmwblCGQDCy/v3+CImceR1rrp3hYPGA3jacTCWZI1UG2C8Hx8PrublpZjuVmt7edYeOEEzYXMCRrjJlxgISookza96jY9e8EaOObN7ifzSq8K5WUVj7oZOQ1gi3g2VJf/LqJgeramHzO0PKmbqkb3lXX8AXN30FiPWRrWRrkbLfnMOhGYIOlME13TPPMzVhUyYvH8Hd6DCjA36dr2EJzlIW1t32r4zeu97wvK/x11GkI6Y+ZxlTN9Y8C5IZrHwMKp49+/Ak/+KH9Zh7P6lO1SzGOZM2LRvQdutsjUOO2DcNLl7uUyGftRAH/QLq2xa5d5bJwL046ye243g8vNljAVKhkdWh2vpIFF9tiWwEJMGB07oGunXdnqJph3pnuP9TM3wkPX2Nfh3M/Bxf+Vv4yX+1g4wUe4Be77LOAswahzlmMc/rtM/t81Z5eCs5k30DTXTKi17a31B+0CpnI01mYtzQZNNgN6auKsoCNe2ZyG6tPXw8bb7bZx5hTMOsl+kn7nTujcbjpapni4WeDa32brGfDWiAlU291DpRjKZ9Ih03H4+dke61cDM46138ZZKcxraleo3jTUOrYBHuZkgXsnz3gQqDGfUzphd6folo1w/NvzL8yRGIAbLxy6H44rBW+9Dk67In+xrpwUvlIpRypUbkepTTvElnNsd+0aWnJ7JMG6/PfPGGOU9pofOP5U9gUeWAsrf2pW0xgrYr2wd4W3baYdDVNdehUOPGMm+lY15R/C1mmTQxuogWlH5n/OlufN4xGvdV+VKDkAu5/IX6ZQ884o/4VbCFFefQcLuyuuLwCLz7dv6A50mfNhoeadOTZvnHVwPfQXcJ+IQk1enF1lx1ImbQILr6l4ym/O75VI9yy1eNiuYd222QQg887MPxco2u19Kd7qSTD/VfnL7FlhRvXmn1W6uXD9bWalrupJsPDVpXnOZAR2LTfngCMvLs1zgpmfY9u5C7D0cnjT9yp9XrCaHCSBRbndfgVsvreiVRhR3XTzBcwnkxqaHGgjWAvvvDn/iSqTNrn9fQfsnrOqEd71O7t82z0rTG/X4OQ4ZXpwVPb/UUP/r1TO77LllXr532qnmJPkaNwUSghRPlqbye9ec7AbZnu7oGfSsPI66NnjbT9gJgWf9W+jM+fEq0zazEnw2nAf6BpaXc5WIGTOu5VMw5zInrnRW9vk7E/DEpeVCTMpc2130svc+ENw+e8tV5Cz0LUL7vtMaZ5ruMmL4K0/K93zdWyHx75jEQRqc68LgCvugSWvLV0dvJPAIquyL3DHI+YmOv5Q6fIIS6FpnjlJ2DSW27bA/lV2zzv3dLvlPSOdcGid3XPOOGHCLccmhBBCVExiwLRPnEZrPjWTzXLnNu2F7r2mg8+m6TXzBG9LJNvY8U/7OYnWFBxxweitrjfcgbWw61E4/4uV2f8QCSyyJvwLFEIIIYQQooysAosxONYqhBBCCCGEGG8ksBBCCCGEEEIUTQILIYQQQgghRNEksBBCCCGEEEIUTQILIYQQQgghRNEksBBCCCGEEEIUTQILIYQQQgghRNEksBBCCCGEEEIUTQILIYQQQgghRNEksBBCCCGEEEIUTQILIYQQQgghRNEksBBCCCGEEEIUTQILIYQQQgghRNEksBBCCCGEEEIUTQILIYQQQgghRNEksBBCCCGEEEIUTQILIYQQQgghRNEksBBCCCGEEEIUrSyBhVLqUqXUVqXUdqXUlQVsf49SqlspdVc56mfj/vvv55hjjuGoo47iV7/6left3/GOdzB58mTe+c53lqF2QgghhBBCjC1Ka13aJ1QqAGwCXgP0AmuBc7TWnR6e40KgAfiQ1rrYlrnnF5hKpTj++ON57LHHaGpqYtmyZaxcuZKpU6daP8fy5csJh8P87ne/4667KhYfCSGEEEIIUSxlUyhQhh2fCbyotW4GUEo9CLwe+JPtE2itl2eDi4p45plnOOGEE5g7dy4Ab3rTm3j44Yd573vfa/0cF154IcuXLy9TDYUQQgghJr5fbPgFN268kYzOuJY9avJR3PrmWwn5Q6NQM3E45UiFmgM05/y7GZhbhv2UzcGDBweDCoC5c+fS3NycZwshhBBCCFFqf9/zd5KZJGmddv3Z0rWF3b27K13lV7RyjFhUhFJqOXCz1vrmCldFCCGEEEKUQDgRBmDptKXUBetGLLe+bT2xdIxIMjJaVROHUdCIhVLqLKXUP5VSnUopnfsDtPHSEYq5wEHb7ZVSjYW/HHurVq3ioosuYurUqSilXvIzY8aMl4xQNDc3M2fOHOvt+/r6RuMlCCGEEEJMaP3JfgCq/dVU+atG/An4TF/5QGqgktV9xfMcWCilTgaWA+uA84A3Al3AP4F3AyuBE5VSc5VS9cCbgL/bbq+1LnurfMOGDVx44YWceuqpPPnkkzz00ENMmTKFiy66iNtvv51zzjmHF154gebmZvr7+3nwwQd5wxveYL19Y+OoxEZCCCGEEBNWOpMeHIFwAoeR+JUfQEYsKqyQVKifAndrrb+Q/fcmpdSfgGVa6zsAlFJXA49hApfvD1sRymb7R4CTgTql1AHgXVrrpwuo62F95jOf4V/+5V/44Q9/CMDxxx/Pe9/7XtauXcvll18OwLXXXstrXvMaMpkMX/rSl16yIpTN9hdffDEbNmwgEokwb9487rzzTs4+++xSvQQhhBBCiAktkhoKElwDC58JLAaSMmJRSZ4CC6XUTOBc4IJhf4qQs6yr1vpe4N4itr/Yoi5fAb6S86sa4Cyl1M9yfnf88OV0W1tbWbFiBY8//vhLfl9XV4dSQytpvfWtb+Wtb33ry/Zru/0jjzzi9hKEhdZIK9etu472aLtV+dl1s/nKq77yil0RYmP7RtoG2lzLhfwhzph1BjWBmpLtW2vNNU9ew+pDq63Kv2XJW7j69KtLtn8hhBATS3/CpEEpFD6VP8nGGbGQVKjK8jpisQwzCrHhML9fMwrb5/oFcEfOv28F/gzcnfO7l8ztAFi7di2ZTIaTTz75Zb8//fTTXXda7PbC+O7q73LXNvf7eyQyCc/Pfdacs3jjojcWUq1x7fn253n/A++3Lv+B4z7Al8/8csn23xXr4oHdD1iX/8PmP/CZ0z5D0BcsWR2EEEJMHM7EbbfRChgasZBUqMryGlg44WIdEAZQSp0EnA98bRS2H6S17sLMzSD7PFGgTWu9I992mYxZBzkSidDQ0ADAxo0beeKJJ/j2t7/tut9itxfG33b/zTpoCPlDzK+f79oAbY+20xnr5MFdDzKpalLesj58nDjtRGqDtbZVHvN29e4CIKACeV9XMpMkmoqyvWd7SfffEe0w+/cFOHn6ySMX1LC+fT2pTIr9ffs5YtIRJa2HECNJpBM8uv9Rz6kSRzQdwSkzTilPpcaRFc0r+OuOv1rdTyBXQ6iBfz/t35lcPblMNRMTlTNx2yqwkDkWY4LXwGI1EAV+oJT6DrAEuB64Xmu9CkAp9RTwea31aqXUr4EXtNb/V6Lti/aqV72KmpoavvjFL/LVr36VnTt38qlPfYpPfepTnHXWWaxbt45rrrmGv//dzDf/61//yt/+9jduvPHGkmwvTMpMJGG++KfOOJWQL3/aUsgfch0CBXPi6Yx18uj+R3l0/6Ou5S+cdyHXXXSdXaXHgZ54DwBTqqdw3NTjRizXHetmY8dGOgY6Srp/J7Co8ldRH6zPW7Y2UEs4GWZbzzYWNy3OW7Yz1smh/kOe6zO9djqz6mZ53s6rm1+4mVs238LwtMt8fMrHx5Z+jHcf++4y1sxY27qW69dfTyLtbfTv9QtfzwdP+GCZalUZt2+9ne8/+/2Ctn3bkrexsHGhdflZdbO49IhLX5IiO959e9W3ae4v7J5Oc+vn8rGTPmZd/pZNt3D71ts9BTF+5eejSz/K2498ewE1FGOREyQ4QUM+XgOLjM4MBi75KBT1wfoJ9V0uJ0+Bhda6XSl1OXAtsBHYB/wM+FFOsW8B1yilngQyuUFBsduXwvTp07njjju4+uqrOemkk1iwYAGf/vSn+fznPw/A0qVL2bRpEwCpVIpvfvOb3H///SXbXkAsHSOlUwDUBeoGhy+LNbl6MtNrprvmV2qtGUgN8NTBp4ilYlQHqkuy/0rrjfcC7j07zvyTtqj7XAwvnMDCLVAEqAvWEU6G+eLjX+SLfLGk9XD4lI973nYPRzSVb0REa81vXvgN3fFuz9vetf2uUQks/rj5jzzb8qzn7bZ2bZ1wgcW6tnWAOf6q/Xbf+95EL6lMir/u/Kvn/c1vmD9hRjpaI62DQcWRk4603q433kt7tJ3nO5633iaVSfHz9T8nnAx7rudtW26TwGIC8ZIK5ZSJpqKuZZOZJJffdzk7evImuQx68+I3873zv2dV1kZ/op9bN9/6ksnpI6kP1vPuY95NU1VTyfZfTp5XhdJa3w+M2FLWWj+UHY24BLOUbEm3z7PdhbZlL730Ui699NLD/i0QCDB//nz27NnDAw88wCWXXMLs2bNLuv0rXW5vgs1IhC2f8nH81ONdy2mtWdWyikQ6wSf+8QnXdKjJVZP58plfHvNfaiewcEsZcwKLcCJc0sBqMLCwmDg/tWYqLQMt1s9d5a9CYd9blMgkyOgMmzo3lTWw2B/eT3e8G4Xi1BmnWtUxkoywpXsLffHRud+NE/TMq59ndQxndIbNXZuJpWOej49kOsn69vWeR0eOnXIsU2umuhcs0taurQAsaVpinZaTzqTZG95LMpO03k9XtItEJkFLxP4YH203bbyJJw48YV3e6dmtD9Yzt36uS+khdcE62qPtrG9bz+9e/J3VNp3RTsLJMAEV4MRpJ1ptM5AcYFvPtsHzoMhve/d2fr7h58RSMdeyk6sn8/Yj3+7+HdWwoX0D69vXD440OSO5emh9HrTWg/8efMz5HXro9/vD+wGT4uvGy4jFju4d1kEFwFMHn7Iua+P3m37Pzzf83Lp8wBfgIyd+pKR1KJeS33lbKXUGMAXYq7W2PxOXaPtSOPPMM3nsscf4+c9/zsqVK0d9+1wZneGG9TdwoP+Aa1m/8vPeY99rdSJ+4sAT7OzZaVWHpqom3rT4TSVbQchZ5cGv/BUZWlRKMbV6Kocih3iu7TmrbR7c/SBLpy/N/7wo3rrkrVx29GWlqKZnTiqUW89OQAVQKDSajmgH8xrmlWT/XkYsptVM49w551qlOSilrHqrcm3p2kLrQCutkVZP2/3Hk//BY/sfsy6fzqQBk0PeEGqw2sYJpvsSoxNYOMfF5OrJTKme4lpea81mNg9uOytgn07203U/5eYXb/ZcxwUNC7j/Hfdbnw86oh1c9c+r6Ix1uhfOcShiUury3b13OL/P7zk4fSH9Ap2xTs+fsdaalQdXWq+C55hRO4Nz5pxjXb4n1sNP1/3U0z4ck6u8zZNoCDagUHTHu/nhmh9621f1ZOsOHeccMVrfq/Hu1s238o+9/7Auf+/Oly30OaqqAlWuZbwEFpu7zDluUtUklk4b+dqeTCdZ1bKKvngfGZ1x7QztjfdaBSyP7DMrh06tnpq3bdWX6KMv0We12uNYUdLAQik1F/gV8Frgz0qpE7XWL4zW9qVy5plnctVVV/HNb35zcIL2aG6fa0P7Bn658ZfW5dsH2rnx9fnncxzsP8in/vkpT/X44ZofsqRpiWu5C+ZfwJVLr8xbxvZmN+V0RNMRTKqa5Nqwjaai7AvvI6VTg2kU+Wzq2sSbFr+pIpPCB0cs/PlHLJRSVPmriKVjfGf1d1wnuocTYZ5rfc61x9aZjG+71K/f58dPadLghhtM9/JwMu6N93L/rsLSFqfVTLMu6xz3/cl+UplU2b8HvTG7kSyHUoqgL0gyk6Q33utpnsqWri2AuUOu7euKJCPsC+9jV+8ulkxyP8cA/GXHX3ihs7BLQ32wvuzLUTvvtdfe8zWta/jXR/61oH3+4uJf8Oq5r7Yqu7FjI2A+J9v3HEznidcJ2H6fn2MmH0NXvMu9cA6f8jG/fr51eed4CyfCVg3AXKsPrea+nfe9pFfdjULx9iPfzumz7FeDTGaSLN+/3GqUIJdP+Th7ztlWHQO2nHPjrNpZeYO3jM7QNtBmlbIDpq7TqqdR5c8JBJTzMHLHwUh/U0rhw8e0WvdzrJNWvalzE1c9elXesrt7dwPmfJDvWHGupxpNOBHO+15prbn8vss5GHnZgqQjOnry0XnPR/vD++lL9I2rkbiSXdGUUjXAncBVWuvdSqnvAl/H3I277NuX0tFHH83UqVP5xCc+UZHtczk9rjWBGubUzRmxXDQV5WDkIK0D7j20+8L7ANNz7Ta0mdZpOqIdhBNh1revd33uDe0buOL4K156UhnGGVK3mYxVLgFfgBm1M6zKzqidYZWzubNnJ7FUjIvvutiqEXf6zNP54QU/LNmojdMzbbPvmkANsXSMFc0rSrJvh0LRWFX5O887x5/N98Hh9DJV+avyr2o1jE/58h7vw+U2uMOJcFlXytFaezouHE5g4bX31xm1OmryUdaNoA3tG+iJ9/DlJ75sHaA5AczChoWeU6hqA+UP+gvtPd/VY1Z2C/lC1IfyL4DgiKViDKQG+NITX7J+z516NVU1eQqKCzWzbiYz62aWdR/O8a3R9Cf7aQzZn4e+u/q77Oy1G8HP9fShp7nutddZp2n+efufuX3r7Z73A6VfbMT5rk6rmeb6HZpTP3LbYyxx5k31JfpYvn+51TZuI2I+5cOv/KR1mr54X97yPfGewaDCJsNjWvU0106OQjspKqlkgYXWOgqck/PvOzGBwqhsX0o33HADP/jBDwgECnt7it0+lzPUXxesy5uyEklGOBg5OHiyyKd9wAyz14fqOXbKsa7l4+n44ASqfDZ3biZDhrZIG/MbR+5p8rJ83FhQF6yzSp2IpqLs6t1l9V4BPLz3YVoiLcyuzz8HpyXSwupDq11705xGtM37evTko+mIdlj30DUEG6xy7QO+wJj4XJ2G/sH+g/TEeqy2eaHD9IDXBetKeuPA4V5yoUr0lTWwiKaigyNJXj4Xp6wTlNhyUni8BFpTq6fSE+9ha/dWtnZvtd5OoZhdP9vTvkZLoYGFs6DCtJppHDX5KKttoqkoa1rXDKZMeDG1uvzzWkaLT/nw4SNDhr54n3VgobUeTDVe0LDA+nuyP7yftoE23n2/977PSVWTrIMR5zzxYueLnveTj9O2mEg3k60P1bN02lLiqbhV+aA/aPUdCPgCpNNpehO9zGfkto0zChT0BTlz1pl2lbbYN5hFJMaLyrcAxpCdO3fy5je/mde//vW87W1vG/XtD6czmv3yWyzJCuZCFk/H815svUyyBdNIqKpxv3hXBaqIpqK0DLTkDSy8LB83nsyrn8e0mmlW8wZe6HiBWDrG3vBe18Dis4991tNFxWaOQ3WgumRzK8Yi59je3LWZ824/z9O2o9WjnU6nyz6B2+nlUihP37fBi5mHXrJEOjFY3uYYdMypn0N1oHpwvoqt2mDtmAwqICew8Pj5ej03g+kZPWPmGcTTdo0pR8AXGJVjfTQFfAESmYSnAKs73j343i1sXGidQhXyh9jTu4cM3u7pMa3aPmgEs0LWUwefoj3aTn+i33okK5+MztAVM6lpE+0GpaVMF3MEfAHi6bjr+bCQ76/NvsH7uaSSJLDIsWTJErZute8xK/X2h+N8+d0O1NwJuZ3RzrxDl05U7eXib6PKnw0sXFZCcSZvj4We7VJSSln3dNcGa01g0buXs2afNWK53njvYFBhc8K0HVmY6OqD9dQF6qzzgh1+5R+V1BDnQlXuiaa5aVBeUu4KGX53LqoKb5Ptfco3Ku/5aHLeP6+frzOa7LVhUh2olu89hQUWzoT+kM/ufkmOmbUzmVlb3vQuMK/JSU3c3LXZNcugOlDtGiz0xftIZcyS7xNpxKJcbM+HzohtKdtWr+hUKFEezoiF24lCKUXIHyKejtMebc8bWOTeyKyUnOdzCywm6oiFF7WBWrro4h/7/pH3DuQHwmaIviZQk3flCvFSPuVj2cxlBW07GiuVOUsndsW6PC3N+qO1P/I0ydQZBfAaxDvlH933qPUFLTe14pV+I6lCU6HK0TB5JSmkd9e5XtmsOlQptYFaehO9fOTv7suNNgQbePMRb87bETV4zyMVKOmS7xOVbTpSoR0Dtvv2uihBpUhgUWY7e3YW1Svp5H7aHKhOYPGNp76RdwKtM0m11D0VTmDx03U/5WfrfzZiOSdVqFQ3xhuPnFWjVh9azepDq13Le5mIKIyx3Lh1LhZfWfEVvrLiK2Xfn9d7sDjf5Y0dGwdXELJle+O5icz5fPf07uGTj3zSejtnpZqxmuI11jkdcFc/fjU87m3bsfyeT6+dbp1jH06GrSeIj+Vgaixxjqu7t9/NxvaRz4ebOs3NkUt5LDn7zugMkWTEelnzSpLAosx+vPbHLD+wvOjnmVM/h+k10/OWORA+QDgRtl7dYnHTYtelRr0I+AIcCB8gQ8Z1noFCcUTTEZ5utDSRzKiZgUIRS7svOxhQAY6bepwEFxPIQHKArliXp+UtHUdPPpqjJx9tXV6hqA/Wewq0ptdMpz5Yn3c0baR9LW5czKTqSZ62m2impKawvm19QSuv+ZWfxU2LJUWlAOFE2PO9TRxj+Xo0t34up8883fV8obVmZ+9OqxEbhWJh00LXdoUw9305FDnElq4tgyvS5TOnbk5JjyVnsY/eeK8EFgKm1U5jQcOCop5jcdNivnfe91zvTzCQHODZlmet7hA7u242J0w7oah6HU4kGbFamrXKXzUuviBClMtAcoC09jZh2a/8FblHivDu8mMut74Jaa6jJx/NcVOPK0ONXhn6En0k097urRvyh+R6JEYUSUZ4YPcDDCQHXMs2VTVxyeJLXNtrXqxvX08inbBq240Fyrnd+gQ24V+gEEIIIYQQZWQ17D32Z4EIIYQQQgghxjwJLIQQQgghhBBFk8BCCCGEEEIIUTQJLIQQQgghhBBFk8BCCCGEEEIIUTQJLIQQQgghhBBFk8BCCCGEEEIIUTQJLIQQQgghhBBFk8BCCCGEEEIIUTQJLIQQQgghhBBFk8BCCCGEEEIIUTQJLIQQQgghhBBFk8BCCCGEEEIIUTQJLIQQQgghhBBFk8BCCCGEEEIIUTQJLIQQQgghhBBFk8BCCCGEEEIIUTQJLIQQQgghhBBFK0tgoZS6RynVrZS6q4Bt5yulliulNimlNiql3lWOOrp5xzveweTJk3nnO9/pedv9+/dz4YUXcvzxx3PSSSdx5513lqGGQgghhBBCjB1Ka136J1XqQqAB+JDW2lPLXCk1G5iptV6vlJoFrAWO1lpHCqxOQS9w+fLlhMNhfve733HXXd7io0OHDtHa2sopp5xCS0sLy5YtY9u2bdTV1RVSFSGEEEIIISpJ2RQKlGPPWuvl2eCikG0PAYey/9+ilOoApgCFBhYFufDCC1m+fHlB286ePZvZs2cDMGvWLKZNm0ZXV5cEFmLCe+LAE6xrW2dV9vx553PqjFPLXCMhhBBCjJayBBalopRaBvi11vsrXZdCrV27lnQ6zfz58ytdFSHKKpaK8dnHPksyk7Qq/5cdf+HRdz2KUladIEIIIYQY48ZsYKGUmgL8HviYZfnlwM1a65vLWC1Purq6+OAHP8hNN91U6aoIUXbhRHgwqJhbPzdv2eb+ZjqiHXTGOplWM200qieEEEKIMito8rZS6iyl1D+VUp1KKT3sp7HY7ZVSVcBfgP/VWq8spI5uVq1axUUXXcTUqVNRSr3kp6+vr+jt4/E4b3/727nmmms455xzyvEShBhTIkmTrehXfo6cdGTen5pADQC7enZVsspCCCGEKCHPIxZKqZOB5cDPgKuA+cAfgXXAjVrrvK1yt+2BcPbfj2qtb/FaPxsbNmzgwgsv5NOf/jTXXXcd+/fv533vex+nnnoqH//4x2lszB8buW3f0NDA+973Pl772tdyxRVXlOMlCDHmRFLZwMLndy1bG6glmoryu02/Y9WhVXnLNoYaufyYy6kN1paknvk8uPtBnjzwpKdtqgJVfOj4D7GoaVF5KiWEKCutNa0DrXhZzManfMyonSGpnEIM43lVKKXU40Cz1vp9Ob/7GbBMa3129t+PACcDdUAX8C6t9dM22yulzgWeADbm7PYKrfXzLvVazuFToV72Ai+44ALmzp3LH//4x8HfffrTn2bt2rU8/fTTAFx88cVs2LCBSCTClClTuPPOOzn77LOttl+xYgXnn38+J5100uDfb7nlFpYuXZrvJYhxbkf3Dq5ffz2xdMyq/JmzzuT/nfj/ylyr0fNsy7N85O8foTZQyxmzzshbdnfvbvaF91k/91df9VXec+x7iq1iXhmd4cxbzySejnve9p1Hv5NvnP2NMtRKvJJkdIaH9z5Mx0CHp+1m1M7gdQtfJ43cAn11xVe5d+e9nrf7wHEf4MtnfrkMNRJiTCr9qlBKqZnAucAFw/4UIacBr7W+uNDttdYrsEjRUkp9BfhKzq9qgLOyQYrj+OGBU2trKytWrODxxx9/ye/r6upeclJ+5JFHDrtfm+3PPfdcMpmM20sQE8yNz9/II/sOf9wczormFbzr6HdRH6ovY61GT24qlJt59fMASOt03nK98V76k/0c7D9YfAVd9MZ7B4OKI5qOsNqmL9FHR7SDnlhPGWsmXilWHVrFFx//YkHbXnvBtbx2wWuty/uUD5+Se+QCgyvZKbt2EwAabb0Cnni5jmgHn3n0M3RGO13LhvwhvnTGlzhv3nmjUDNRLK+pUMswjf4Nh/n9mlHYPtcvgDty/n0r8Gfg7pzfvaw1snbtWjKZDCeffPLLfn/66ae77rTY7cX405/op7m/2bXc6kOrAVjYsJDqQHXestu6t6HR9CZ6J15gYZEKFfQHWdy02LXcvr599Cf76Yy5X3yK1R3rBkxgNL/BbhW31oFWOqIdhJPhclZNvELs6zOjeFX+KhpDrtMVAYilY4QTYa5+/GpP+2oINvDL1/2SpdNlJD2aigJw2ozTrM7HPfEeNrRvYCA14HlfB/sP0p/s97SNl4Bnas1UplRP8VqtUffovkd5viNvIspL3LPjHqvAIpKM0BPvsXrOhlCD9fdM2PMaWDjdG3WYuRAopU4Czge+NgrbD9Jad2HSrMg+TxRo01rvyLedM5IQiURoaGgAYOPGjTzxxBN8+9vfdt1vsduL8SWWinHJPZfQFetyL4zpBVzQuMC1J3B3724SmQThxMRpkHoZsbAV8ocArHq1iuV8xs4+bQSUOYVGEqN6m50JpSPawU0bb/Lc2Dph6gm877j/396dx9ZV3XkA/577Vm/xmhhnK0swQU0CSUoCXdKoo6JOEjVIjRiJReIPVKQGt00AiRaNkDIaIQrD/AEBKZ0ppdEwHdK0pCXpUImkKUxCcVMnJlhkMc5mO17i+MXPftu998wfL+fFhOTdc95mx/l+/gJxzrvXl7uc3/md5QHvguP0j/Xj1YOvGj93CxsW4pEFjxjVyYUKoOvCdWiubdaqk3SSONB3AEk3aXSskdQIPjr7EQMLXAosdDM46h03ljILLN498S6e3Puk2ckZ8gs/tq/drp11nSjHzh8DADSWN2Jm5cyrloskIvgs8hn6Rvs8f7M72o373r5Peziy3/LjzVVv4vb62/VOmrSYBhZ/BRAD8IIQ4l8B3AJgM4DNUsoPAUAI8X8ANkop/yqE+E8Ah6WU/16g+nlbvnw5ysrK8NRTT+GZZ55BZ2cn1q9fj/Xr1+Puu+9GW1sbnn76abz77rsAgB07dmDnzp3YsmVLQerTtaV/rD/T4AxYgaxlBQSaKpq0Pk5+yz/lAgv1kfVbhVvFWl3zkmQsEuc/d0wdKjtj2iimS3Yc34E3P33Tu+Blft/5e3z7S9/G9PLpRsfafmy78bH+dPJPWH3zaqNj5WIwlp5bEbT0g9ugL4jlTcs9hxWO1xXpQu9ob6Yz4HompcwEFjrZVmBcYGGYseg41wEgHcAUsgNGsV0btrRxqP/QhAYWKcd7L6Njw+nAoiZUo5U1ODt21rPMgb4DmaDC6zvsShe2a+NvfX+bkMBiOD6MXxz+BS4kvVchrQpW4ZEvP4L6svoSnFn+jFoAUsoBIcT9AP4N6cnVp5Be3emlccX+BcDTQoj3Abjjg4J86xfC9OnT8dZbb+GJJ57AokWLMHfuXDz++OPYuHEjAGDhwoXo6Eg//LZtY9OmTXjnnXcKVn+y2H1qNzqHO7XKTgtOw3fnfTezROj1RDUYg1YQ98y8p2C/qxrfOi+Va0VmVahrNGOhhkKZBBYqYzGZA4vO4U5s7diqvXGh8o3Z38B3bvxOkc7qEnXda0I12kM4Tl44CUc6GE4MGzX21RCJ2lCt9ke6K9KV07Fyoe5zk6wZYD5fQr1/chnKM9Uk3SRcmR6JoPvuUgFILBWDlFJ70rzqSJpTNQc3TrvR/GQ9HD1/FL2jvTg76t0IL5Zn9z2L3x77rXfBiyoCFVn/e8gXApAOum3Xztpx1RXpAgDMrJiJW2tvzfq7ncOdOBM9U5L5e1eyo3MHXv/kde3y1aFqPLrw0SKeUeEYdy1KKd8BcNWWspTyfy9mI1YD+MJXKd/6Weqt1C27Zs0arFmz5or/ze/3Y86cOThx4gR27dqF1atXo6mpqaD1J1pPtAc/2vMjozpCCNx/2/1FOqPJS/XoFbIXfvzvTaWMhckcC12qgTUUH8LpC6c116RIqwnVoCpYpV0+E1j4cshYJCdvYLGlfQt2de0yrvfHrj9ixawVRV/mVwVlNaEa7bktPdEeOI5j3OOujlUdqvbcxFE5M3IGjuOUJHhUmTnTwMJUZggfMxaIpWKZf9YOLC6Ws6WNlJvS/v+l3hPq+heaaoT3jE5MYzlux41W1yr3l3sGFkErCAEBV7oYjA3ihoobrlpWBRY6naBqHuSZ6Bnt8y2k/rF+AEB1sBq14dqrljsfP49IMlKSzrVCKfjdLYS4C0AdgJNSSrMusgLUL4Rly5Zhz549eO2117Bvn/n+fPnWL7be0V4A6ZdbQ3n2XY9HEiMYtUcntAdkIhVj3gBw6cMylQILNRSqkNdKZQ8c6WDV71YZ1fVbfqyYtQLTQnqT8w4PHv7cMXWPAaQn0KbclFHdUlE9co3ljZ4fceVM9AySThKbD27WmmSv+IQPX5/1daOefdVgNwne/ZYfcMyfn0zDrgTH6hvtM1pSGQDORtPv2WLfRyogZmBxaX6FgNDOPIx/x42lxrQDC7XIQ6E7qpSwL91Y7o32FvR343Y8027I5sjQEdiujaAV9FxyHEhfR69rLoRA0BdEwklg3R/WZQ3KIokIAGh1hqhrNVEZC5U9rQvXYe60uVctJyAQSUauqbZCQe9uIcQsAP8B4FsAtgshFkgpD5eqfqEsW7YMLS0t2LRpU2aCdinrF5u6ocsCZbit9rasZU9ETmB0ZHRKDdkxUYxeeKA4GYvR1Cie+eCZTE9INgErgB/c+QMsb1ruWbYn2oOtHVs9J8S1nm0FUNjAwhIWGssbM2PPdTnSge3a2H16t/Exy3z6Q/7G/62jyVHUhGuMj1dsfWPpSY8zK2ZqB1kJJ4HuaDd+1fEr4+OtmL0Cm/9hs3Z5FViY3DeqrGnDOJeOAvWsmmSlIokI1vxujfYk0ssVO2OR6+TjXL3c9jL2nt7rXXCcoC+IDUs3aDVQ8xFzLs6vMLgnhBCwYMGFizF7DDWo0aqn7qFCf0+UkD+dsfjk3Cf48Z4fZy1rCQv3zbsPK2avyFou5aawdsdaowb4tNC0ggZP1aFq9I/1ZwKHbHzCh6qAd7tLZSyOnT+Gr/3317KWFULge7d+DxuWbtA7YQ3qb/HKkGfeP5N4uO3lCvZ/XghRBmAbgBYpZZcQ4jkA/wzgn0pRv5Cam5tRX1+Pxx57bELqF5tac1+nV0zd1DoP9FSUGQpV4NS1+rAUMmD7oPsDvHfqPe3yP2//uVZg8dqh1/D28be1f1el4wtlft184zpSSpyLnzNuOPktP2ZUzNAur8a2u9LFSGpEO7DYfnQ7nvvoOeN5D0EriGe/+izW3HzloZiXc6WLgbGBdF2DxursytlIuSk4rv6EYNu1EUlGcPLCSe06QB5ZBJh/bHPJjqgGp8mxuiJdiDtxCAjjuWlVwapMb2qxlDJjkXSS2NKe2+Ilr7S9gjf+8Y0Cn9HnmU7cVnyWD67rGr1jMvdfkYZClfvLISAQTUW1vgXHzh/zDCx6o72ZoELnvC1hoamisMO/59fOx9yquZBf3O/4C0K+kFbbpsxfhpAvhIST0PoObzu6raCBherg9TrXa3HYdMHubillDMBXx/37NqQDhZLUL6RXX30VL7zwAvz+3C5PvvWLTfeGBqbmJGMTmd7UImUsuiJd2N+zP2vZsD+MhQ0LPRtDqgFZHazG7KrZVy2XdJI4NnwM7YPtnpPhgEuZiKaKJs+gIWAFJsXKFUIINJQ1pLfNLDK/8CMpkxiKD2F6mfcQIAmJ1z95PacdvuNOHNuObNMOLIbiQ7ClDcAssAj7w7i9zmyllLHUGFr7Wo3HAueSRci1YZzpMc4hY2HyYVfXoDJYiSUzlhicYWlk5ljYxQ8s1LUQEFjQsECrjiMddJzrwN/7/45v/s/l++lenSUsPLrwUTx4+4PaddQcC9PNAn3ChxRSRhPg1Xe0WEOhgr4g7ph+h+dzYUsbXZEudEe74Uo369+uMp5l/jIsu2FZQc9XlxBCexinLktYuKvxLs/3sC1ttPW3YSQ5goSTKFjHmeqs9boX1LvqugwspoLOzk6sWrUK9957L9auXVvy+qWSScGZBBaJ6zOwKNYci4BIX/t9Pfuwr8d7Hk7L4hZ8f9H3s5YZiKUDi8pgZbpRfRVSSnwW+QwxO4aVb62ElWWjewmZCURvrr65aB/Ea5nP8gEu8NCuh4zqWbDwlRu+on1vxZ042vrb0D7Yjq0dW7U2zVL3RNAKFn2XZZXSj6aiRh/gnOZY5DhHKef5HDALYtR1D1mFzd4VSimHQmWW0PUFjTZuqwvXYSg+pL2HkPKbo78xCyxs86FQ48ur+jpyyc6Zqg5VozpUnbWMlBJdkS6k3BQGY4OYUX71LK2aX1noTPRk4LN8KLeyz8eQUkJAQELiXOxc1j03TOgub35dZyymgltuuQVHjhyZsPqlohqKOi83ddNHkoUdChVJRHD0/FGtsk0VTdoruADQnoCno1hzLOrK6lATq/EcCmO7NhJOAgf7D3r+pu4a+Ko3v2+sT3uIW124jkHFVdSF67R2Zr9cU2WT0TCZgBXIpO5/1vozo2OpsdfF5Bf+zAd4KDaEpkq94RC5ZBFyzVjklB3JocdwfGN6MirlUKhc9uYAgAX1C4yyAQkngY8HP0Z3tNtoCVjTzfEUy0qX1w3OHNfJ/D3F2MPChBAi8y7pifZct4GFjvGTxwdiAwUJLBzXyXTWagcWKQYWNIkVcyiU7dqeGzXZro2Hdj2EExdOaP2miTlVc/DDJT/ETdOyr2QjhMBN027ynDhVrDkWIV8Id0y/w7PccHwYhwYPaV0rk8bMbbW3aY9ZBdJjd+nK5tXMw03TbtK+lkB6WIhpsCqEQHNtc3pogv6hAIGCj3m+4mGEQMAXQNJJDwvTCSxc6ea0pHMu+4fkfKwc5nOoZ9Fk6eJSynXyey4G47kFWabDX1SQHrNjGIoPaQ/JzDdjMRgf1OqgGX//TIZOmrAvjISTwO5Tu7MOX2zrbwNw/QYWADKBhc4iIi+2voj9vdmHN7vSzXwvvO6F8YtHmATME2ni727y5LhOZpx0NgfOHsDbx9/27AU/NHAIgFlgMZIc8RyLub9nP1p2t2iPHbeE5TlJUUIapZpPj5zGU3uf0iq7cs5KvPytl7OWKVbGQldZIP2x7I52I+kks36cM8NeND7gQoii709wPSnV/VEXrjMaTlJqQSuIpJPU3ik9ZscyH1iTa5hLj3uux1JBzKkLp/CXM3/RqqN2FZ6sQ6HUez3lpnBk6Ij28rYBK4DZVbONGjelyt5YwkLQl77/uqPd5oGF6eTti4HFpv2bsGn/Jv3zhNlmhsUS9ocRSUa0N2kr9oICk5nKtvVGe7NmqLoudOGNDv3FBsr8ZZ73gnr/ONJBzI5dE99tBhZF9tKBl9DW15ZzfRcuOoc7i9KzpJZby0Z9cFzp4uFdD2d9CE6NnDKakNpc24zG8kbPco7reGZBgPRH8vjwca3UdNJN4s+n/4wHdz2YdY5BZyS9O3mxVvHwErSC8AkfHOlg2X8tyzquPpdJukSFpN4XT+59UquxKmW6oS8gsj6Hl1PP44c9H+KBnQ9o1bFdO7djXWyEtw+2Y/1767XrAZP3WRzfO7/uD+uM6s6smJl1Q6/LqaE0pdjjJewLI+kk8dMPfqq15ChwKaNimrGoL6vHUHzIKFMJwHPvqFKZVTkLCSeR2XU8m6AVzDpvb6pTz/Hzrc/j+dbnPcvXhGowt+rqe1MolcFKzzKWsDJDTEeSIwwsCOga7sLBgYMlOZaAwJ0z7sTSxqWeEzunl03H4sbFWr/76dCnGIgNoH2w3bNsZaASL37zRc+b3yd8E/rRfaXtFew9sxftA95/EwA8/OWH0VzbXOSzujIJife739cKrhrKGrBh6YbrOm1NE2f70e349ZFfI2bHEIN+prG5thktS1q0yx8fPo6fvP8TjKRG8PHgx0bnOKtyltGxRlOj2Ny2GUMJs0nEDeEGtCxuKcn8llwEfAF81PuRUZ1oKoqe0Z6cdnZe17wO98y8x7ieiV9+8kvs/Gyn8ZLHQDqDva7ZLMhypGM2LBETl/2m3LWebcVLB17KdE5kE7AC2Lh0I26vN1tVL5u2gfSqVCPJETRWeHfGTjSheoymsAn9Aw8NHDLe3Oty9eF6zKuZ55l+toRlvGa6jv6xfu2P9/y6+UYTrSdKzI6h9Wyr1j4CM8pmYEHDggkb2yilxGBsUKtnrDZUO2nHddPUJ6VMD9tzk0b15lTNMe7R7jjXkVli2cTC6Qsn9XCyyex8/DwODx427qWvDlVjUcOior9Dk04SB/oOGO8PE/aFsbhxcUmyKnRtSjpJvcDCFyj4fbTn1B74LB+WzFiileUoIq0HmIEFERERERFloxVYTPwMIiIiIiIiuuYxsCAiIiIiorwxsCAiIiIiorwxsCAiIiIiorwxsCAiIiIiorwxsCAiIiIiorwxsCAiIiIiorwxsCAiIiIiorwxsCAiIiIiorwxsCAiIiIiorwxsCAiIiIiorwxsCAiIiIiorwxsCAiIiIiorwxsCAiIiIiorwxsCAiIiIiorwxsCAiIiIiorwxsCAiIiIiorwxsCAiIiIiorwxsCAiIiIiorz5J/oESkBM9AkQEREREU11zFgQEREREVHeGFgQEREREVHeGFgQEREREVHeGFgQEREREVHeGFgQEREREVHeGFgQEREREVHeGFgQEREREVHeGFgQEREREVHeGFgQEREREVHe/h+dPYXfpoh8rQAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "processor.plot_pulses(title=\"Control pulse of OptPulseProcessor\", figsize=(8, 4), dpi=100);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the optimal control model, we use the GRAPE algorithm, where control pulses are piece-wise constant functions. We provide the algorithm with the same control Hamiltonian model used for the spin chain model. In the compiled optimal signals, all controls are active (non-zero pulse amplitude) during most of the execution time. We note that for identical gates on different qubits (e.g., Hadamard), each optimized pulse is different, demonstrating that the optimized solution is not unique, and there are further constraints one could apply, such as adaptions for the specific hardware." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Without decoherence" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Probability of measuring state 00:\n", "0.06973417322091019\n" ] } ], "source": [ "basis00 = basis([2,2], [0,0])\n", "psi0 = basis([2,2,2], [0,0,0])\n", "result = processor.run_state(init_state=psi0)\n", "print(\"Probability of measuring state 00:\")\n", "print(np.real((basis00.dag() * ptrace(result.states[-1], [0,1]) * basis00)[0,0]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### With decoherence" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Probability of measuring state 00:\n", "0.20122806768707008\n" ] } ], "source": [ "processor.t1 = 100\n", "processor.t2 = 30\n", "psi0 = basis([2,2,2], [0,0,0])\n", "result = processor.run_state(init_state=psi0)\n", "print(\"Probability of measuring state 00:\")\n", "print(np.real((basis00.dag() * ptrace(result.states[-1], [0,1]) * basis00)[0,0]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that under noisy evolution their is a none zero probability of measuring state 00." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using the superconducting qubits model\n", "Below, we simulate the same quantum circuit using one sub-class `LinearSpinChain`. It will find the pulse based on the Hamiltonian available on a quantum computer of the linear spin chain system.\n", "Please refer to [the notebook of the spin chain model](https://nbviewer.jupyter.org/github/qutip/qutip-notebooks/blob/master/examples/spin-chain-model.ipynb) for more details." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "scrolled": false }, "outputs": [], "source": [ "processor = SCQubits(num_qubits=3)\n", "processor.load_circuit(qc);" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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rqW/x9fYREZFB75WdTlfSMaFj28s7k9u19J9rD7G3sgmv2zBrTC456R5mjs4F4A8v7KamqS2p8cjwlYgai1uBh62137TWbrbWPgXcB2Rba/9hrfUD1wErgLXAr4uLixMQRvI8ufEI+6ucH+zEIqemwhjD1JJsAP62eh+1zbo4EjmR13Y5icXYvAwyvW4CQcsbe6tSHJWIyMCV1zSzt7IJA0wJXRdsPlxHXZIKTfyBILc9vwOASUVZeNzOpV9pbjo56R6afQHuUe2wxElcEwtjzGhgGfC7Tm81ApFxba21j1prT7LWTrfW3h7PGFLhb6v2ATC+IBO3q73TfHF2GtlpbhpbA9yzel+qwhMZEo7UtlBe0wxAfpaXwiwvAG/srU5lWCIiMVm7vwaAnAwPWWkeMr1urIU39yXn2PbslmOR2oqywszI68YYJhY5z+96dS9t/mBS4pHhLd41FktD61zXzetr4rytQWHnsXpW73FKVMcVZHZ4zxjDpFBfi3tW7ScQ1CznIj3ZUF4LQE66G4/LRV6mk1iET8oiIkPR+vIaAPIynGNafqYzIOeGg7VJ2f59rzu1EePyM/G4Ol72jc7LIM3j4lh9K4+HBp8RiUW8E4vw+rLDLxhjFgDnAPfGeVuDQrj6sCQnrdt5K0pzM/C4DOU1zbyw/ViywxMZMjYcrAEgN3TyDZ+EN5TXKikXkSErnEDkZTgJRW7UsS3RDlQ18eKOCgDGFXQdVtZlDONDhaLh1hcisYh3YrEaaAZuMsbMMsZcCjwK/NZauwrAGPOKMea00OM7jDFfj3MMSdPcFuCh0EgPZZ1qK8LcLsPY0BjRGuFGpGfrQyfZ3NDJNyfdjdsYGlr97ErB0IwiIrGy1kYSiPCxLZxgJKPG4v43DmAtFGV5yUrrfuqycQUZGGDNvmq2HqlLeEwyvMU1sbDWVgAfxZldez1wC3AbTmftsJ8A3zHGfAMIWmtvjmcMyfTv9Yeoa/GT4XVRnN3zDJbhpOP5rccibchFpJ21lo3l4VI9pzTPGBM5Eas5lIgMRfsqm6hv8eMykJ0eKjQJHdeO1LUkdDh6XyDIP0JzaXVuqh0t3eNmVK4zWtU9q1QAKrGJ+6hQ1trl1tqZ1tq0UOfsX1lrg1HvPwlMBC4FvhTv7SfTPaFqw7KCTIzpeabz7HQPBVleghbuf10/WpHODte2cLyhDQPkpLeXqoX7Wbx9oCY1gYmIxGB9pO+YB1foOsHjcpGd5jSd3pjA5lDPbD7KsfpWvG4TSRx6Ei4AfeTtchpb/QmLSYa/pM+8bYw5BSgCaq21Q3YM1jf3VbHuYC3GwNj8nksCwsJtGP/+xgF8AY28IBIt3FQgO93TYWS1/HCNhRILERmC1oWOXeF+FWHh5+sT2BzqzlecObTKCjIjSU1PCrO8ZKW5aWj188+15QmLSYa/pCYWxpgy4E/ABcBkY8y8ZG4/nm5/cTfgjLef7jnx1zgqNx2v23CsvpWnNx1NdHgiQ8qa0FwV4bbHYeEai21H6mhqUymaiAwta0JDyhZkdkwswse6txLUzHPDwVre2FuNob1gszfGmEitxd9W7cdaDZghA5O0xMIYkwk8AHzVWrsHuBH4frK2H0+7Kxp4erOTHEwITYh3Iq6oH+1vV+wkqFFuRCLCQzYXZHXsq5ThdZPucRG0sLFcnQpFZOhoavOzKVQbm5/VMbEoCD1fs7cqIa0Yfv/CLgBK89JJ72bEyu6Mzc/AZWDL4TpWbq+Ie0wyMiQtsbDWNltrz7TWvhh6/oC19rJkbT9erLX8ZPlmrHWGmI1uD34iE4uycLsMmw/XRTpUiYx01Y1tkXbGhZ1OvtBesvf6nsqkxiUiEotXdlbiD1rSPS4yOrVsyEn34HUbmtoCvB3nWotVuyt5LDQnxaSi7BMs3c7rdjE+NIHezx7bQosvENe4ZGTo+1XxMLblcB17jzeecDkLvLi9ghXbKjAGpo/K6dd2vG4XU0uy2XGsgf95dBM1zT4mFGbh6r3p45DnchmWTCzstfOYtZbX91RR1diWxMjib8boHKaX5vb4vrWWTYfqOFrXollOQ5ZvOEzQQm66p9u5YEpy0qloaOOvr+3jyjMmk5/ZNfkIa2z1c6C6iVlj8nrd5s5j9Ww/Gr8hbOPdasCiGs3ODEP7QHmCJu6D3smTCynN7ToPQlirP8Dre6po8QUJBINx/00MNRandQLA6Nz0LgO8GGMozk7jSF0rt7+4i5MnFeLq5WLgUE0z6w7UnPDIUN/i46antgEwLj8jMrJeX00uzuZIbSs7jzXwpXve4n2LxuEyBpcxGMMQ/xUOXRfOHk1aH5rdDwZmkLSjS2kQNzy+JdJnoq8umFnK/PH5/d5W0Fr+ve4Qeyub+v3ZoSwrzc0vP7SAScXdl578/MmtvLLzeJKjij+XgWsunMEFs0Z3+/7jGw7zf6EqaunootmlzB3X9TflCwT588t7aPEHmTk6l5s+vKDbUdgq6lu5/uH1VNS38sVzp3HJ/LHdbueVncf5+ZNb4x6/yHB2omP4zc9u5/mtmgS2M2PgilMnUpzTtWDtWH0L973utF54/6Jx/Oeyqd2u46191fz08c34An2/VCrJSeMjSycM6GJ0X2Ujj647hFpsDx7rfvDOLs3pUqBPeaUSC+Du1/by73V9m8o+P8vL5adM4MLZ3V849kWrP8CfX97LG3uraGj1p/ivT7z15TW0+IKMzktnXjcXjvUtPl7f63RwWzKxAI9raGTlnZXXNFNe00ya28Wy6cVdLn4DQcsru47jC1hOGp1DQWbPc5+MJG6X4cLZpfznsik9Dtu8Zm8VV/xpNa3+IAvK8rut/dpxrJ79Vc2RdS6bXtxlX7LW8vKuStr8QUblpjOlpO/NBE4k3iV5Q72EO54Gx2lq4IZ4+Gwsr6WpLUBJThoLxxd0eb+u2ccb+6oxBhaOL8DjMicchWgkyEhz85kzJ3P+rNIel/nDC7u48YmtuAycNa2k20Tgjb1V1LX4nWNWD4ldhIHFEwv40nnTe63dPZG39ldzx8t7qG5sI2gt1g793+FQ9ufPnNKvpvcJosRCBocNB2t5720vY4BzTxrVYThRgO1H6zlQ3cylC8by248vSU2QcdDmD3LKz56lttnH4gkFFHWaNPF4QyvrDtYyNj+DF799Pl730EygUiVcszg6N515ZR0TVGstr+yqpDWqeVl3CUhts481+6rJSffw5vcvIt3Tt06NIiPZ5kN1XHLrS7gMnD2jpEvCvquigb2VTVw6fyy/vWLoHsNTwVrL+3/3KusO1HBSaU6XAWGa2vy8trsKj8vw6ncv6LU5mkiC9Smx0JWNJNy8sjzG5mdgcS7sOqsJvfbueWOSHFl8pXlcvHOOU5PVXV+R8GvnzRylpGIALgyV+lU1tXUZCrGxLUCrP0hWmpvLT5kAQGVja5d1HG9wXjt/VqmSCpE+mj02l0nFWQQtVDd2PYZXNjjHtgtn91wyL90zxkTOG9VN3ZwfQ68tndR7HxeRwUJXN5JwxhjOmFoMQHVTxwvuQNDS0OLMT7BoQkGyQ4u7UyYXAVDX0vUEET5pLJs+KqkxDReLJxaSlebGF7A0tnYcraQulJzOL8uPNFOs6eYkHU5sz5pWnOBoRYYPYwxnTisB2guCwvyBIPWhmZqXzShJemzDwRnT2s+PnQtNakPnkkUTC5IdlsiAKLGQpDh1inPB3bnGoqHVj8XpaFbWh0l8BruFoeSorsXf4QQRCFoaQyffpZMKUxHakJfmcUWSz9pOiVtdVHIaXqaxLYA/anx4a21kuQXdtBMXkZ6Fj1udj+Hh39T4wkyVqA/Q/LJ8Mrwu/EFLU1vnQhPn+108DAreZGRQYiFJER5Bq77TBXf4JLVoQkGPHXeHkumlOWSluZ1EIuoEEU6gRuWmMzqv52F3pXeRxK3zxU3o+cIJBYzKTY8kqeGLHoCmtgCBoCXD6+Kk0f0bKlpkpDs5lFjUt/g6TPAarp1dqAvfAfO6XcwZ6wyRXR91zIoukNL3K0OFEgtJipNG55LmcUpkmqMm3YmclIZJCbLbZZgf6lgcffEb3VRnOCRQqbIoqkYoLBC0zuhqtJ9825eL+h+0tP8PPOrjItIvk4qzKM5OI2iJNH2C9hL1hQMYfl3ahWtROx+zLDA6L52x+UO/Rl9GBp1dJSm8bhezQyUy0ReF4ZPScGo/2t3Fb7gUan6ZTr6xCH+3Da1+AqFS0/bmdOmMy3eaYiycEE7uursAKkhavCLDhTGGJaFai+j+S+EL4UUT1MQzFuGR7uq7OT/qmCVDiRILSZoFkQOncyLyBYKR2osFZQWpCivuumuuEz75LlCpXkxG52UwJs9JHsLfaXtzuvbaoJ5K/0BNCkQGqnM/ixafMxqbyzij/8nAhc8N9a3tzYXr1HFbhiAlFpI04X4W4ZL88Mlpakn2YJhRMm4WdipVj+5voRqL2EVqhEKled01p5tflo/LQKs/SKsvQDBoI803VPonMjDRiYW1NlK6ftLoXLLSUj5515A2bVQOmV6nf164A3e4cGqRjlkyhCixkKSZX9axA3fdMBpmNtq4/AxKctKwOMlF+MJ3TF4GpXkaNSVWCzv1n4g0F4jaj7LTPcwozQ0t53eaS1kozPIyoUhtlUUGYn5ZPh6XoS0QpMUXjIzOpmQ9dm6XYe64cHNhH63+IC3+IMa0F8qJDAVKLCRpZpTmkJPuiXS2rQ3NaTHcmqYYYyIn2tpmH7XhCY4mqw1yPLT3n/DR5o9qTtfp5BtZrsXXfgE0TEYfE0mFDK87cpFb0+yjvlnNC+MpXMhW0+SL1OhPH5VDbsbwqdGX4U+JhSSNx+2KVKVXNbZFJlo6YxhOVhZOIqqj/s6TNX9FXCwYX4Ax0OIPcrSuBYCTRudQkJXWYbnwxU5tsy/S2VQlqyKxWTox3IG7LVLrHE7iJTanhyaSrWn2Ud3oFLwNx/OjDG9KLCSpwgfOXRWNBK0zr8OM0uE3p8C5Jzmzax9vbKMydII4bYpOEPGQk+5h5minmdP2Yw1A+/cdLTxTcHWTj2P1rQCcN1OznovEIlw4dKi2BX/QUpjljfweJTanTCnCGGfOnfKaZqD9OCYyVKQssTDGPGKMqTbGPJiqGCT5Lpk/BmMgPL3SRbNLh2XTlDlj8yKjF4HTDGz2WJ184+W9C8d1eH7+rNIuy0wpyY5MOgVOEqsaC5HYLO1U8/queWM1L0yc5Gd6OXuGU/hhgaw0N2dOV4GUDC2pPBrcAnwyhduXFJhUnM1Fs0cD4HEZvnDutBRHlBjGGL7+jhmR51edPWVYJlCp8pGl4ynIdNodnzaliDOmdn/yvfqcqZHHXzl/Oi6X/gcisSjNy+BDS8YDkOF1ceXpk1Ic0fBy1bIpkcefOnMyeepfIUOMCY+XnJKNG3Me8BVr7YdSFoQkXUOrn+e2HGV8YVaX0q/hxFrLv9YeoiQnnWUzVJ0dbwerm3h+6zEunT+W4pz0Hpdbue0YlQ1tfHBJmZI7kThobgvw9zf2c/rU4sjEpxI/b+yt4mB1E5fOH0eaR7VBMmj06QSqxCLOWnwBXttdSUGml0UagWbE2360noJMr4aZTaGD1U00tPqZNUYXQPFW3+LjcG0L00bl4FZtkHRS2+yjvLqZ2WNzdS5MgJ3HGshMc1NWoCG04628phmfP8jkkuyErH9XRQO7jjVwyuQiCrPTTvyBwaFPP2LNaBNH+yubuOKOVRyocjpdXTp/LL+5fBFetT8dkV7ZeZwr71iN1+3ijk+dolqLFFizt4pP3LGaVn+QOz99CufN7NoXQwbGHwjykd+/xtYj9Zx70iju+uypqQ5JBhF/IMhlf3D2j0sXjOW3H1+S6pCGlT+9tJufPraFvAwPT3/9XMbkq/AqXg7XNnPxzS/S0Ornvy+dzVVnTz3xh/rhtud38OtntmMtZKe7+cMnTh5W1wcJu+I1xpxujHnOGFNpjLGdbsOu6LDFF+Czd73Bgapm3C6DAR7bcJhfPrk11aFJivxk+WaC1pn9+dbnd6Q6nBHp109vp8UXxFr48fLNqQ5nWFm+/jBbj9QD8ML2CtYfrEltQDKoPPxWeWT/eGz9YTYfqktxRMNHiy/ALc8555S6Fj83P7M9xRENL7c8u4OGVmco5Vuf20FLaK6kePj3ukP86mknqfC4DI2tAb7wtzc5UNUUt22kWkISC2PMQmAl8DZwNvAuoAp4DrjMWjvsjjB/emk3O481kOZ2ccbUIuaFZpm+4+U9bD0y7P5cOYH9lU2RkyrA63uqhtWBYyioqG9l9Z7KyPPdFY3sqmhIYUTDy/L1hzo8f/it8hRFIoPRk5uOdHj+0FsHUxTJ8PP81mPUh+YQAXh2y1GCwdQ1ax9OgkHL05uPRp7Xtfh5fuuxuKy7odXPj/69CYBJxVmcPb2E/EwvDa1+bnxiS1y2MRgkqsbiVuBha+03rbWbrbVPAfcB2dbafwAYY54FHgAuGT9+PK+99lqCQkm82mYff3hxN+AMK5rucVOam86onHSCFv5v5a4URyjJ9sKOCgAKMr3kh0Yvem1XZW8fkTh7eWcFQQu5GR6KQm1Yn98SnxPESOcPBFm1uwpwTpAAq3Zr/xZHqz8QOd5NDu0fOv7FT/i7HF+QidtlqGxsY0N5bYqjGh42HaqjqrENt8swPtR35dVdx+Oy7gfWHOB4QxuZXjdTS7JxuQyzxjjD0D+x4Qjbj9afYA1DQ9wTC2PMaGAZ8LtObzXSPn0B1tqLrLWjrLVZBw8e5Iwzzoh3KEnz0JsHqW/xk53mZnRe++g0U0qcA+ry9YdVWj3CrA5dZBVlp1GQ5SQWa/ZVpTKkEWfdAedEW5DppSj0P3j7QHUqQxo2NpTX0tDqx+MyTCh0Tr5bj9RHZguWkW3DwVqafQG87vaLsy1H6qhr8aU4suFhXajZYUGWl8LQse2NvTq/xEO4gKQwy0thdui73RP7ecNay92r9gEwsSgTV2gwg5x0D6Ny07HA/3t+Z8zbGQwSUWOxNLTedd28viYB20spay33rHZ2lvGFmR1GvsjN8FKUnUYgaLnj5T2pClFSIHzgz8/0RuZbWLNXF7XJtPZADQB5Gd7IWPDhZENis/6g8z3mZ3pJ97jJSnMD8NZ+7ePSaf/wusn0urEW3t5fk9rAhoEWX4Ath53m1XkZ7TXi4eOdxGZth3O3U9O97Wg9tU2xJcWv7Kxkd0UjbpfpMHkuwJRQrd5j6w9xuLY5pu0MBolILMLrjIzRZYxZAJwD3JuA7aXUqt1V7KpoxG267izgZKbg1Go0tfm7vC/DT2VDa2RksNwMT+TAv/t4I7XNKrFLhjZ/MNJZNC/TQ26GMwBeeU0zx+pbUhnasBC+cMwLfa/hxE3NMQTa94Pc0H6Rl+nsJxvUwT9mWw7X4QtYvG5DhtfVXmii7zYu1kUVSKV5XGR6nUvajYdiO7b99bW9AIzNy+gyU31uhpeCLC9BC/et3h/TdgaDRCQWq4Fm4CZjzCxjzKXAo8BvrbWrAIwxrxhjTgs9vuPmm29OQBjJEa6tGJ2f3mVnASjKSiPT66a+1c/ydYeTHZ6kwPrQSTUrzY3X7cLrdpEROjht0oVXUmw9UkdbIIjHZcj0uvG4XWSHStXXq9YiZhs7XzhmhC8c9d0KkRHCOiee67V/xCz6wtcYEyk0OVDVTGVDawojG/oqG1o5WO0UCob33dw47LvlNc08u8XpEF5W2P2cI+Emg/e9cYA2f3DA2xoM4p5YWGsrgI8CpwLrgVuA24Drohb7CfAdY8w3gODXv/71eIeRFBX1rTwVGvlifA8T1BhjKCtwajLCSYgMb9EH/jCV6CZX5H+Q6Y00T8zLDJ8galIU1fDQ1OZnxzGnk2GXk6/27xGvvsXH7uONQPt+Eb741fEvduvCtYWh45nX7Yo0RdTvLzbhWp+sNHekoDh8jNsYw3d77+p9BK3TbyMnvfvp40blppPmdlFR38rTm490u8xQkZBRoay1y621M621adba6dbaX1lrg1HvPwlMBC4FvpSIGJLhH2sO4AtY8jI8kQNod8bmZ2JwDggq0Rv+2i9q2w8g4ROrDvzJsfZAx6Y60Y/X6jcYk82H6ghaSPO4SPc6FzTh/buivpWjdWpqNpJtOlSHtZDucZHucS4xckMXU4drW6ioV6l6LMLnl/wOxzZvh/dkYNrPG+3Xc+2FJjUDWmerP8Df3zgA0OsM6a6oQui7XxvahdApmRLaGHMKUATUWmuHZKNzfyDIvaG2cL3tLOCcgEtzndGi7n19aO8w0jtrbXuJUnc1FrqoTYr1UR3wwsIlfOsO1GCtxnwfqHCpc3TS5nYZstNDpabax0e0Dd0c/zxRpeqxlPyOdLVN7bVBeR2Obc5vUYlFbHorFDxQ1TygUe+e3HiEyoY20j0uRuWm97rsuAKnEHr1nqohPfRs0hMLY0wZ8CfgAmCyMWZesmOIh6c2HaW8phmv2zC6m07bnYXb1f1r7SHqNeTesHWwupmqxjaMaS+lg/aD0/6qJmqaNCRnItW3+NgZmggvuiYxJ92Dyzjzzuyr1PDPAxUefaZzLW178lyT5IhkMAkP6RydeEY/V+I5cOFS80yv038vrL0Dd60KTQbIKRSsASA/6tjmdbvIDNXM9rcpn7XtI4KOK2gfYrYnGV43JTlO8vG3VUO3EDqpiYUxJhNnUryvWmv3ADcC309mDPFgreX2l5wJ8cIT1JxIQaaXrDQ3TW0B/rFGM5AOV5GLrnQPrqj9IvrgpGEBE2vDwVqshYyophjgVDWH27dqBJWBCw+bXJDZObFwvts1+zTk7EhlrY3sH/lZ3Seeb2pI4gF7a18N0LFEHZxCEwNUNbZFOh9L/+yrbKKmyYcxkNNDUtzf4bRf3nmc9QdrcZme++F2Nj5UCP3wW+U0tg7NkUSTmlhYa5uttWdaa18MPX/AWntZMmOIh9d2VbLuQI2zsxRm9ekzxhgmFjnL/uGFXRp6dpgKH3jyuulzE54o75Wd8ZnFU7r3SmiW1M4XNtDefGD1Hk0mNRDlNc2U1zRj6HpxU5jljPn+5r5qWv2BFEQnqXawuplj9a3O/tHpGFgQ2j/e2FM15Ee9SZXwuSM8v0KY22UiF8OaS2ZgOhQKdqpZCO+7r/Zj9nhrLbeFJrwbV5BJmqdvl9uFWU4hdEOrn78O0b4WKeljMZQ1tfn5n0c3ATAuv+87C8DY/AwyvC6O1bfy66e3JypESRFrLU9vcoaUK+zmorYo2zk4vbC9QtXVCbRiawUAxdlpXd4Lv/bs5qMEg/of9NczoVHwcjM8eFwdj31ZaW7S3C5a/UFW7VbiNhKFR0nMy/R2qcnPSXfjdRuafQHWaJbofqtr8UWShu6ObUWhi9/w/0D6J/y9hQtIooW/77f3V/d59vhH1x1i9Z4qjIFJRX0rgAanEHpyaMK8367Yyf4h2Gy3+3GvRphnNh/ltT5kohbL63uq2HGsAa/bMHVU9gk/E81lDCeV5rK+vJY7Xt7DvspGxhdmnbDd3VDndsE5J43i7BmjelymurGNe1/fT2XD0O1/sL+qifKaZtwuQ3FO105axdlpuAxsP9rAQ2+V8+Gl43tc1+HaZh5Yc5CaGGf7HArcLrhg1mjOmFbc4zLHG1q5/40DHG9oJTons9ZiI4+hqS3A5sN1GKA4u+v/oCgrDbfLcKy+lU/d+TozSnPj+8cMIsaAwenA/tllU8juYZhDay2PvF3OptCEgu2vd1oOy0NvOs04u5sM1BhDaW46B2uaue4f6/iPhePi8ncMZplpLj64ZDzTRuX0uMy2I/U8uq6c5rahXUp/7sxRnHtSz8fwg9VN/DHURHhMXtffnjGGUTnpHKpt4av3vc37FpUlLNbBIjPNxUdPnsCk4p6vFTYfqmP5+kM0tQWw1hKwlqB1fpfBIARDzw9UN+EPWrLT3GSGOsJHK81LZ19VE49vOMJ3HlpPVlr77727S4zOL3VexnR6ocsqOi/fdYmu6zzh+6bX9zuvY2xBJpedPKFDs+NoLb4Ad7+2j8O1vY9U1+wL8MRGJ7EY3c2+m5nmJjvdTWNrgC/+7U1OGp0bOT6Gz0HWOsdIa6G5LcDyDc68ZZOLssjwdv1/9WZMXgblNS3UNvu47PbXuPKMSVx28oRurysGIzNISk5TGsQNj2/h9hd393n5DI+LL5w3rdeTSW8e33A4shOPJJ87ewqzx+Z1eT0YtPzq6e0cGSbDVL5zzmje28NF1b/WlvPslmMY4CsXTGdKSdcTjj8Q5MYntlI9ApKKaJ8/Zyozx3S90A8GLTc9vY2jdX0fpvL8maP44JLuE7enNx3h3+tH1mSVJ08q5OOnTez2vRe3V/DPtYf6vK7cDA/fu2R2t4lKRX0rNz6xBV9gUJxXkiLd4+L6d82MNJeIVt3Uxi+e3DYsmv4Y4LPLJjN3XH6X94LW8v+e38m+yibyMp39I/rCNqyivpUbHt+CfwTVFmZ63Xz7XTM7jFAXVt3Yxs+f3Nqv38t/LpvCogkFXV631vK7lbvYemTojiY0EB9ZOr7HQqk/v7yHjZ0KTHozc3QuXz5/WpekCpzaij+/srdfsc0vy+c/l03pUz/czqob27j1+R0cDxW2vvbdCxib37d+GgnUpz9EiQWwYtsx3uhjm+uCLC+XLhh3wiFmT+SNvVW8sbeKxlZ/l5LB4WbFtgq2HK6jINPL0kmFXd6vbGhl7cFa3C7DVQP8EQ4WM8fk8h8Lx3V7YAJnTOur7lrDSzuOU5qbzvyyrifpw7XNbD5cj8dluOrsqQzhr6NPXthewaZDdRRlp7G4mxNmRX0r68udDnBXnzMNt8sp2Qp/xZGvx5hICf0Vp08k3dN9KZG1lkfXHWLbMD4Bh0vQ2vxB/vLqHoIWzpxa3KWk01rLy7sqafMHOXNaMQs7ff+dd73sdA8fXjq+15Hwth2p58mNR4Z9PwsLPPjmQSrqW5lSks3UbgoJdh5rYF9VEyU56Xz05J5rKAe7l3YcZ0N5LfmZXk7u5hh+vKGVdQdryUn38PjXzmZicc9NPzYfquPpzUeGRbLVm+j9Y1pJNpO72T+2H63nQHUzJTlpXH7KRFzGqSlwGYPLgMsV9dgYygozefe8MT2eX1p8Ae57fX+HuUI6X150d71hOy/V+9MuTXm71m72f5snug7qvM3ymhae3XKUNI+LZdOKu3wnja1+VoWu6z5+2sQug010VpqbzuWnTuyxdsFay1ObjrKrooHGVn+oRthEaobD55/w6yeNzuHiuWN6rE3pi7oWH/96u5ydxxr40fsGxQCqSixkcDhU08yZP38egLOnl3Tpl7L5UB2H61r49JmT+eF/zE1FiEm16VAtl976MgY496RRXRKpdQdqON7YxjfecRJfu3BGaoJMov2VTZxz0woAzplR0mEYRXC+ryN1rVy1bAr//Z45qQhxSPvY7at4bXclJ5XmMKFTW9/mtgCv7q7E6zZs+OHF/a6yH+n+tbaca/6+lqw0N2dM7Vpq+uqu4zT7gvzfFUt49/yxKYgwPg7XNnPGjc4xfNn04i5J+9Yj9ZTXNPOJ0yfy0/fPT0WIg9I/1hzg2w+uJzvNzemd9g9rLa/uqqTFH+SPnzyZd8wZnaIoh6Y2f5DFP36axrYAp04u7DL89Z7jjew+3sh5M0fxl8+cmqIoh50+JRbqvC0JN64gk5mjnSYu3c3hUBvqDHXuzJ7b7w4nc8bmMSYvA4szp0I0ay3VodfOn1maguiSb2JxFtNC/ZW661NS2+yMoLZsRklS4xouLpzt7EeV3UzuFN7/5o7LV1IxAOedVIoxTt+ezjU0Lb4Azb4gLjP0992x+ZksGO/UrlZ1sx9VNjol5CPlmNVX75jtJAuNbYEuNTQtviAt/iAel+HMXvqXSffSPC7OmOb8rro7toX303fOGZPUuESJhSTJ6VOLALr0G/AFgjS1OSfkReMLkh1WShhjOGWK833UdEos6lv9BIKW3HQPc8Z17Y8yXJ0WKs2r7pR4+gJBmn2h/aObZlJyYuGS0tpmX5fmBOH9r7vmLXJi+VneqEKTjr/l8LFufll+l9LUoSh6P4rW6gvQEkqgOpfKj3SF2WnMKHX6Ynb+3sLHukUTCnocWEF6t2y6s791/u1Za6lvcQqkTp6sY1uyKbGQpDh5snMh3XmotvDzScVZFHYzhN5wdWroYNf5ZFMXer54UuGQ7mvSX6eEvo8u+0fo+5hcnNVt51g5sZljcknzuPAHbSRJCwvvf931fZK+OTVUSNDTbzl87Bvqlkzs4ZgVuoA7aXSuLpC7Ef7/dy5ECn9v+u0N3KKJ7eeN6EKTxrYAAWvJSnMPeJAdGTglFpIU4Wr0hlY/wagDQF2omctIK41eEKqdqe90QAyfbBZ006l7OGv/PjruH7UtI3P/iCev28W8UO1X+PcGzuhjDaGZXZfo4mbAovfdaOEkOXzsG+qWTCoAoKE1gD/Q3qwn/HcuHCE1zv0VHpCivodCtfnDZP9Ihdljc/G6Db6ApcUXtU82t9cWjqQCusFCiYUkxcSiLPIyPAQtHaapH6knpVk9HBDDFycj7WQzpTibnHRn/2hqbS9VD58glFjEJjzaU23UxU04iR1fmNnrKE/Su3DiUN/qjxQSBK2NJG3djfw2FJXmZjAx1Pk/utYi/BtdMGF4/J3xFj6W17V0v38sKCtIVWhDXrrHzZzQEPZ13RzbdN5IDSUWkhTGmA4HWHDaQYZPUJ2HuRzu0j3uyJwN4ZKsQNBGkq7hcjHSVy6XYe64jicIa23kcbjKWwZmYVQNWZj6V8THtFE5ZHrdBII20l+ssTVA0EJuuofJvUyONtSE95Xwcdv5jTrHrJFWONRXM0pzSPe4CEQ1RWwIDTOfn+llQlHK5yYY0sLXDnXdJLsj7bpisFBiIUkzL3SxHL64afEH8QUsnqiLypEknDyET8wNrX4sUJKTxtj8kVeCHCn5DX0fzb4AvoAlze1i9tjhO0N2MiyMNMdob2qm/hXx4e6QFDv7bvgYN7csL6Zx7AebJZ0Si6a2AP6gJd3j6nZySwGP2xUZiKN9/2gvQOppTgrpm3BCG242GwhG1QaNsJr/wUKJhSRNuMo3fHANlyrMHps3Ioe6nB/6PsIXIeHS+Xkj9GQzP3SCCH8P4f4Ac8bl9TjZnfTN5OL2pogNoSY74YtD9a+I3fzxHQtNIn2lhlkpfjgJrQ016wn/nfPK8rvMPyPtwn3m6ps7HutHWpPXRFgY1Ycl3MQsXEAX60TGMjA6EkjShEvoG1qcIVUj/StGaNvcBZ3a3tY3j8yO22GR/SPUwT/cH0DtZGNnjIlqMuCnsTVAIGjJTnNHhkuVgetc+1gfVUgwnDgjP7kjpcIjtY9cf83rtH+EC01GWpPXRJhakk1uenv/zUgzqPEFI7KAbjBQYiFJM6Eok1G56VicEpvw2NOLJ4zMEtOTRueS4XWGAW1sC1Dd7IxrvniEliBPKsqiKDuNoHVOvOH9QyXq8bEwqkYo3L9i8cRCPCppjlk4+a1r8dHmD0aauiyZWJC6oBLA7TIdhp1tb8uuC+TeLOy0f4Sb6qh/U+xcLhMZOKCuxR8pkFL/itTRGUWSxhjDaaEx3yvqWyOlN2dOH5mTKqV5XJwSGuP8cG0zLb4gbpeJvDbSuFzR+0dL5OQbnlxRYhO++K1uaqMqNDmX+lfEx5SSbMbkZWAt7KtqwuIUpIwvzEp1aHEXTiwqG9uob1XH7b6YUZpDSY5TaLK/qglw9plSjcYWF+H9r6bJF5lxW0lb6iixkKQ6Y5qTRByobgZg6qhsxuaP3HaQ4e9jf5XzfcwvyydnBE8ydWb4+wjtH9NLcyjN1ck3Hs6YVky6x0WLL0hFfSsAF80eneKohgdjTPu+G7pwPH3K8CwwCc9kfLyhDWudyU0nFQ+/BCqejDGcNb0EcBJPUIFJPC2b4Xy3R+pa8AUsOemeYTMx5VCUksTCGDPBGLPSGLPZGLP+gQceSEUYkgKXzh9Lhrd9t/uPheNSGE3qvXfBuA4T+Hx46fgURpN6754/lnRP+/7xgcVlKYxmeMlO93DezFGR52UFmcwrG3mjsSXK+zrtqx8apr/lRRMK8Lrbj1nvWTBWbdn74D0LOp7rRvqxPp5Om1JMaW565Pn5s0pJ86jcPFVS9c37gWuttXOAd1577bU0NjamKBRJpoKsND595hQARuWm85mzpqQ4otSaUJTFZadMAJyJyj64ZGRfSJfkpPPJMyYBzv7xidMnpTii4eW6d84kL8ODMfCD987RBWEcnTOjhFNDpaSnTy2KNOsbbnIzvHzr4pmAU1vxqTMnpzagIeKi2aWR/ePiuaMjTcokdm6X4TvvnoXbZcjN8PDt0P4pqWHCM0Gm0sKFC+3y5cuZMGFCqkORJDlU00x2uof8TG+qQ0m5QNBSXt3M6Px0DauKM+nWodoWCrO8ZKWN3GZhiVJR30pts4/ppTmpDmXYafUHOFrbSllhZoeayOFoY3ktU0qyyR7BTTf7q7ktwLH6FiYWZSmpT4ADVU143GZEN69OsD7ttClPLIwxS+fOnbtm48aNKY3jROpbfNz/xgGqGtt4/+IyTkrCEI0NrX7+/vp+qhrb+I9F45g1Rs0WRERERCTpBn9iYYwpAl565ZVX5px55pkpi+NEKhtaufz2Vew41gCA1224/cqTOX9WacK2Wd3Yxod//yq7Khoj2/zDlUu5YJY6W4qIiIhIUvUpsUhYHwtjzOnGmOeMMZXGGNvplmeMSQf+Cfx8MCcV1lq+8/AGdhxrIM3tIj/Tiy9g+ep9b3O0riVh2/3Wg+vZVdFImsdFfqYHX8By7d/XcqQ2cdsUERERERmohCQWxpiFwErgbeBs4F1AFfAccBlQD/wFeN5ae3ciYoiXl3Yc55nNRzEGFk3IZ8nEAvIyPDS0+vnlk9sSss0Xt1fw7JajGGDR+HyWTCwkL8NDXYufXz2dmG2KiIiIiMQiUTUWtwIPW2u/aa3dbK19CrgPyLbW/gM4CyfBeL8xZu2iRYvYsGFDgkKJza3P7QBgfEEmuRleXMZE+lf8c205h2qa477NP76029lmYddtPvTWQQ6ExsEWERERERks4p5YGGNGA8uA33V6qxGwANbal621LmvtImvtorVr1zJ//vx4hxKzLYfrWLOvGgMdJgDKz/RSmOUlELTc+cqeuG5zd0UDL+04DsCEwo7bLMpOw1q469W9cd2miIiIiEisElFjsTS03nXdvL4mAdtLmL+t2gc44+l3HgZ0YpFz0X/f6weob/HFcZv7ASjJSSMzreM2JxQ6Q6jdv+YAja3+uG1TRERERCRWiUgswuvMDr9gjFkAnAPcm4DtJURDq59/vl0OODPUdlacnUZWmrvDcrFqavPzwJsHAKfpVU/brG/x80ictikiIiIiEg+JSCxWA83ATcaYWcaYS4FHgd9aa1cBGGNeMcacFnp8x80335yAMGLzyNvlNLYFyEpzU5jVdRI3Y0wk4bhn9X7iMWzvo2sPUd/iJ9Prpig7rddt3hunbYqIiIiIxEPcEwtrbQXwUeBUYD1wC3AbcF3UYj8BvmOM+QYQ/PrXvx7vMGJireVvrznNoMoKMnucIXNsfgYuA1uP1PPW/uqYt/nXfmxz8+E61h+sjWmbIiIiIiLxkpBRoay1y621M621adba6dbaX1lrg1HvPwlMBC4FvpSIGGKxZl81247W4zLOhXxPvG4Xo/Oc9+8J9Y0YqLf217D5cB0uA+MKet9maW46APe9Hts2RURERETiJWET5PXGGHMKUATUWmvj1/M5Tv7yyl4AxuRl4HX3/hWFmyYt33CY6sa2AW/z7tecbY7uwzbHhbb56LpDce04LiIiIiIyUElPLIwxZcCfgAuAycaYecmOoTcHq5t4YuNhAMZHDffak7wMD7npHtr8QR588+CAtlle08zy9aFtdtNpu7OCTC9ZaW6a2gID3qaIiIiISDwlNbEwxmQCDwBftdbuAW4Evp/MGE7kr6/tI2ihMMtLbobnhMsbYygLDQP711V78QWCJ/hEV7e/sAt/0FKY5SUvs2tH8e62GZ7j4vcv7KLFF+j3NkVERERE4impiYW1ttlae6a19sXQ8westZclM4beHK1r4e5QB+rwPBV94TSZMhyoauYfaw70e5t/f8P5zOTi7BMs3W5cfgbpHhdH61rV10JEREREUi4lfSwGI38gyHcf3kCzL0B+pofiboZ77YnbZSJJwS+e2Mq+ysZ+bbPVHyQvw9PtsLY9cUVt8+ZntrO/sqnPnxURERERiTczSOZCSGkQf3llD3e+upd9lU24DJw8qZDcjL5f5AMEreXNfdXUheahmFSchcfdPmSstc4NnD/WWktNk48jdS0YA6dMKupT06uetpmd5uYrF8zgC+dO7XGo2he3V/DI2+VcecYklkws7H6dQcvtL+3m6U1H8AcHxb4hIiIiMmLdc9Vp/b4uTYDuLy476d+V7DB1vKGNfZVNZHhdfPvimZw5vWRA66lqbOP7/9zIropGth6p79NnstLcfOvimZwxrXhA26xsaOUH/9rEropGfvHkVl7fU8mssXldlmvxBbh39X5a/UH+tbacT54xmcw0d5fldh5r4JnNRwcUi4iIiIjEV7D/3XdTRjUWwK6KBrYermfZjBLy+9B5ujeBoGXtgRoaW/0EwiX+pj3NM8ZgAGPAZQzzxuWT348mUD1t82ePbeHPr+whP9PLyZO61kbsq2xiZ0VD5PlJpTlM6KYfyRt7q6hr8XPx3NFcdsqEmOISERERkdicPWPUCaciSII+1VgosRgmDtc2c8aNzwOwbHox6Z6OtRFv76+mqsnHpOIs9lU2UZDpZWmnBKTNH+SlnccBeP17F1Ka2/NEfSIiIiIyYvQpsUh5+iPxMTY/kzmhJlA1TR0nzQsELTXNzmvfu2Q2AHUtPoKd+lDUNDkT/J00OkdJhYiIiIj0ixKLYWTRxAIA6lv8HV6vb/ETtFCSk8475oymJCeNoHWSi2jVoeTj9KkD6+8hIiIiIiOXEothZOH4fABqOyUM4QRi0YR8jDGRJlC1zV0TEKDHEaNERERERHqixGIYWTihAHAShOi+M+HEYsH4gg739VEJSNDayPN5ZfmJD1ZEREREhhUlFsPI9FE5ZHrdBIKWxrZA5PW6UM3EglCNRvi+LqrJVFNbgKCF7DQ3U0v6PgO4iIiIiAgosRhWPG4X88qcDtzh2gdfIEizz0kyFoVqNOaNcxKLZl8AX8AZHLku1L9iblk+LlefOv6LiIiIiEQosRhmFoaaOYX7T4QThikl2RRkpQFQmJ3GhKJMoL1fRfh+gZpBiYiIiMgAKLEYZsL9LML9KmpDCUO4Y3fYgjJnuXDNRnj5+eOVWIiIiIhI/ymxGGbCzZ0aWpyZv8M1FuHXw+ZH9bMIBC0NrU4CMl81FiIiIiIyAEoshpnxhZkUZ6dhcWojwjURCzslFuFEo6bJWSY8z8UUddwWERERkQFISWJhjHmPMWabMWaHMeaqVMQwXBljOH2aM8Hd/upmfAGL122YHZqVO2zxxAIyvC7aAkEOVjcDcNrUIoxRx20RERER6b+kJxbGGA/wv8AFwGLgW5WVlckOY1i7YGYpABX1rQAsm15ChtfdYZl0j5tTpzgJyLGo5UREREREBiIVNRanApusteXW2gbgiaeffjoFYQxfF8wqJSfdE3n+/sVl3S53+SkTIo+z09y8d+G4hMcmIiIiIsNTKhKLcUB51PPy8vLynpaVASjMTuNnH5hHhtfFh5eO5z0Luk8Y3jV3DO+YM5oxeRnc8MH5HZIREREREZH+0JXkMPW+RWW8Z8E43L1MdudyGf74yZOTGJWIiIiIDFcJqbEwxpxujHnOGFNpjLHRN+AYEN02p2zcuEHYBOfNu+Cej0LFtlRHMmC9JRUiIiIyRNUdgke+CM/+EKxNdTQiEcbGeYc0xiwEVgO3AX8GJgD3Am8DtwMPA1uA84Ba4M3jx4+fVFxcHNc4YnLgdbjjHc7j3LFw7QZwe1Mbk4iIiAjAnZfAvlecxx+6A+Z/OLXxyEjQp9LqRNRY3Ao8bK39prV2s7X2KeA+INta+w9rrR+4DlgBrAV+PaiSCoAND7Y/rj8Me15IXSwiIiIiYZW72pMKgNdvT10sIp3ENbEwxowGlgG/6/RWIxCpGrHWPmqtPclaO91aO/h+EQdWO/euUC3F5n+lLhYRERGRsC2POvdpOc79wTegqSp18YhEiXeNxdLQOtd18/qaOG8rMdoa4cgG53HJDOf+wOupi0dEREQk7GDociqvzEkubBD2vJjamERC4p1YhNeXHX7BGLMAOAenn8XgV/4W2AB4MiBntPNaxTZoqUttXCIiIiLlbzn3GfmQWRh6bWiU3crwF+/EYjXQDNxkjJlljLkUeBT4rbV2lTFmsTHmqfDCxpj3GWMGV1OocDOozALwpDsJBhYOr01hUCIiIjLi1R2C+kPO44w8J7mA9mRDJMXimlhYayuAj+LMrr0euAVndKjrQotsAOYAGGM8wA+A/4lnDDELN3vKKAjd60crIiIig0D4WiQtF1ye9muVQ29DwJ+ysETC4j5BnrV2ObC8h/f8xpgDxpjJwCXAY9baw/GOYcCCwagai1D1YkYBNByF8jdTFpaIiIhI5FokXOiZlg0uN/ia4Pg2GD03dbGJkKAJ8k7gdeB84IvATSnYfs8qd0BLDRgXpOc6r6nGQkRERAaDcGKRGbo2MQbS8zu+J5JCqUosfgX83lpbn4Lt9yxcW5GR7yQX4LRhBKg7CPVHUhOXiIiIjGzBoNPkCdqbQEFUAagSC0m9VCQW24FK4A8p2HbvOjeDAqcNY3isaNVaiIiISCpU7oTWOjBupwlUmFpWyCCSisTiS8C3QjNwDy6dO26HqTRAREREUinSvyKvvVUFtF+jHN0EvubkxyUSJWmJhTFmmjFmG9BorR18U1k3VcHx7c7jzIKO74V/tPtXJTUkEREREQD2vezch69JwjwZ4E535uA6vD75cYlESVpiYa3dZa2daa39arK22S87n3Xu03LAndbxvaxi5/7AKk2UJyIiIsllLexa4TzOKun4njHtycbulUkNS6SzuA83O2RteMC5zynt+l5aNniznOHcNv8T5n3YGd4Nk8wIZbgwLnD34acXDELQl/h4RETiwbj7dmwL+J3S9TBro960HZe1nZ5Hv9/be3Fdb+dFUxDv0U1QV+6cP6L7gYbllELjMVj/dzjra87/QoYPt9dJIIcAY0/0A0qO1Abx2m/hqf9yHk86q32o2WiVO52bSMwMzH4PfOB2SMvqfpGXfwMv3gRtDUmNTERkwDwZcPY34dxvdf9+wA+PXA2b/tkxsZC+yyuDMfO7vh7wwe4VYIPJj0kS7/q93SeUydWnzEY1FuD0rwC48H/grGu6X8bfAn95DxzSqAsSKwtb/g3Hz4eiqV3fbmuEPS8kPywRkVj4W2DFT2HXc91fBNUehCMjvQ9Ap2uzLqXQvbyfPwE+vRxyx3a/6s3/gn9/DVoH10j+MrKoxiJs7ysw6czeq5qshUCbUzKg0hYZqDfugOd+BN5smLys6z5XsRWq98LEM+Fj9w2Z6k8RGeEe+SJse8y5AO5uBuj9r0FLLSz7Oiz7Rsf3+nOBfcL3Y/lsjO+n+njtbwO/RoYadtLzUr9vqcainyafdeJljAFPunMTGahTPwcrfga+RucE4O3UHKqxsn25ziOUiYgMVqf8p5NYNByD0jkdL4QCPiepADjlc+2Tz0p8edKcm0iKpGIeC5GRLT0Xxi12HjdXd3wv0AZtoWrsycuSG5eISCwmnQkuLwRau5aaN4eaHBdPh/yy5McmIkmhxEIkFSae4dw3dUosmmuc+5KTuh+hTERksPJmwtiFzuPwsSws/FwFJiLDmhILkVSYcJpz31rb8fVwU4HxpyQ3HhGReAgf2zonFuFj27glSQ1HRJJLiYVIKoxb5Ny3NkAwaiCA1tAEjGMXJTsiEZHYlYUSh+hCE2vbj23hZqAiMiwpsRBJhbwyyB4F2PahAa2NKtXTyVdEhqDwsau1vn1OBV8TBP3gTofS2amLTUQSTomFSCoYE3UCDiUT/lan87Zxw5h5qYtNRGSgCqdAer6TVLSGJvhsCdVWjJnnzCAsIsOWEguRVAk3dwqfdMMJxqhZTidIEZGhxuWCcaEO3K2djm2qiRUZ9pRYiKRK+CQbbv7UojbIIjIM9HRsU98xkWFPiYVIqoRPvm0NTvvjlprQ64tSFZGISOyiEwsbjBrt7uTUxSQiSaHEQiRV8sZCwSTncVNl+2R5GuddRIay8HDZrfXQVAU2AJmFUDIztXGJSMKlLLEwxjxijKk2xjyYqhhEUm7quc591W6nZC+r2OljISIyVOWPh+IZgIXj253XJp7h9L8QkWEtlb/yW4BPpnD7Iqk37QLnPtxUYPo7nBGjRESGsukXOvfhDtwzL0ldLCKSNClLLKy1K4H6VG1fZFCY9R7In9j+/LSrUxeLiEi8nHKVM3Q2QFoOzP1AauMRkaTwpDoAkRHN7YX3/xZe/6PTVKBsaaojEhGJXckMuOQm2PkcnPU1SM9JdUQikgTGWpu6jRtzHvAVa+2HUhZEb4JBtQkVERERkZGuT+20E3bVbIw53RjznDGm0hhjO93yErXduLAW/vUVuGkabPl3qqMRERERkcFk/yr4fyfDnZc4o58JkKDEwhizEFgJvA2cDbwLqAKeAy6z1tYlYrtxs/lf8Pbd0FwFD13lDJknIiIiImItPPU9qNwB+16B1X9IdUSDRqJqLG4FHrbWftNau9la+xRwH5Btrf0HgDHmWeAB4JLx48fz2muvJSiUAdjxdPtjfwtseyJ1sYiIiIjI4HFkA5SvaX++5s9O83mJf+dtY8xoYBlwbqe3GoFIhw5r7UVR76Wuo0d3Dqx27tPznKHytj4GCz6a2phEREREJPV2r3Tus4qhuQYaj0HFVhg9J5VRDQqJqLFYGlrvum5eX9N18UGmsRIqdzqPi6Y59+Vvpi4eERERERk89r7k3GeXQGZBx9dGuEQkFuF1ZodfMMYsAM4B7k3A9uLr4OvOfVoOZBc7j2sPQP3R1MUkIiIiIqlnbXvLlswi5waw79XUxTSIJCKxWA00AzcZY2YZYy4FHgV+a61dZYxZbIx5KrywMeZ9V189iCYF27/Kuc8oAJfHSTAADr2VspBEREREZBCo2g0ttWBckJ4LGfnO64fXpjSswSLuiYW1tgL4KHAqsB64BbgNuC60yAZgDoAxxgP84Ec/+lG8wxi4A6Eai3DVVniHUXMoERERkZEtnECk5zrJRfg6sXovNFenKqpBIyGjQllrl1trZ1pr06y10621v7LWBkPv+YEDxpjJwNXAY2PHjk1EGP3nb2uvmVBiISIiIiLRDr3t3KeHpmRze8GbFXpvbUpCGkxSNa3068D5wBeBm1IUQ1dHNjjDy7q84A11EYlOLFI4S7mIiIiIpFg4eQhfH0J7kqHmUClNLH4F/N5aO3hmn4t0xikAE5q5PFzV1VLrtKsTERERkZEnGITDoUFPoxOL8ONwbcYIlqrEYjtQCQyuqQojiUVh+2vhzjmg5lAiIiIiI1XVbmd+M+OCtOz21zNCNRZKLFKWWHwJ+Faov8XgED18WEZBx/fCz5VYiIiIiIxMB0Ijh6bnOclFWLgpVM1+qD+S/LgGkaQmFsaYacaYbUCjtfZfydz2CdXsh/rDgOlYvQXtz/e+nPSwRERERGQQCM9VkVXU8XW3tz25GOHXiklNLKy1u0KjRX01mdvtk21POPeZBeByd3wvq8S5P7oR6g4nNSwRERERSbFgEPa86DyObjIfFn5tzwvJi2kQ8qQ6gEHB3wZv/815nDO66/ueNEjPh9ZaePlmOOniUPJhOi5nTNfPDotlulkkEdsyLiiZAZ707jbo8LfCwTUQHDyt6CRJ0nNg7KKuiX80X4vTZFH7R/+NW9S1tjZaMADHNjsDWQQDSQtrWMgb5xzbetNU5YxMKCNP/ngontb7Mk1VTuGmRqfsH7cXxp/i3PekrREOrwcbABvs+TvevwpqDziTJ2cWdX0/pxRq9sHa+2DGO9trMOJh0pm9/w2DiLGDYydNbRCPfAHW3ecMM7voCucCprOKbbDzmeTHNtKMPxXe99vuk5DmGnjkao3ONZJNPhsu/d/u94+mSnj4aufALv2XWQQf+AMUTen+/Rd+ARseSG5Mw8npX4KTP9v9e4fXwb+vgbaG5MYkg8cZX4Gln+7+vfK3YPm14GtKZkTDR+kc+MDt4M3o+l7QDw99Do72I6kfswCmnNP1dWth3b2JmSTv+r3d15IkV7fFzF0WUmIBbH0cHvk8fOROmH5R98tYC099D/a/CgG/k9l2fr/rh06wTDefGXTLnPCF+G2r4ZjzvY5b4mT+nR3f3p5UlM7p+r4MX8EAHN/mPC5bCtmjui4T3j+MC0bNSm58Q13lTgi0OTW24xZ3fb+1AfaF2g0XzxgyJWeDQnO103/PuGDKud3XyB54HZqrIC0XCiYkP0ZJneYaqD8Exg1TzwV3WtdlDqx29qO0HCiYmPQQhyxroWKL87hkZveFJjUH4Ngm8GQ4361xAabnFhYTT4N3/KT7AmiAY1vh2f9x+u3G02ef7L1GOTmUWPRLc/VgyAZHtqe+B6/dBrljYezCju9ZC3tfckpsPvgnWPCR1MQoqfP4t+H1P0DuOBi7oON71jrtWv0t8OE7Yd4HUxPjUHVoLdx+rnNSnXaBU9UfrXKnc5vxTrhCtRb9Yi388QI49BaMmg2Fkzq+72tub5N9zfqu78vwZi38/mynxLx0TtfEwdfktOs3Lrh2g9NsSvru9T/C4990RveceHrX9w+ugabjcOEP4Ozrkh7eENOnxCJVw80OPkoqUm/mJc59U2XXGg5fk3Nzp8PMdyc/Nkm9WZc6993uH41OUuHJ0P4xEGMXQtFUp31xU1XX95sqnfvwb1T6zpj2fbe5m+82/Nr4U5RUjETGwJz/cB53+9sLNasZf6qSioEIH7Naapz+tNGC/vbf38xLkxrWcKbEQgaP8ac4Vb2Btq5tjVtqnftxi3qugpThbcJpTuIQaHU620UL7x9jF4E3M+mhDXnGOJ0DwTkBRwsGnOYaANPOT2ZUw8eUc537pqquSXFzaN+dcFpyY5LBY/Iy5765m/0jfGwbf3JyYxou8sucZlDQ9djWUusUpuSVwaiZSQ9tuFJiIYOHJw3KljiPuxwAQs/HLUlmRDKYeDOc/hXQ/QkC2t+X/ot8t7UdX2+tAyzkjIHCycmOangYtxi8WRD09ZwUl+nYNmKVLXVq4wNtXTtoh491OrYNXE/Htuikrds+FTIQSixkcDnRAUAH15HthPuHLs4GLJy0t9R2LDXVdxs7t8epTYOO+24wGErc0LFtJPOkw5h5zuPw/gBObWFrvfNYNRYDVxZ1bIsWObbpu40nJRYyuHR34WijT766uBnRuts/dHEWH6PnOqWmQX/HUtNIM0T99mLSXW1suDYoqxgK1L9iROsu8QzvH9mjIF+jhQ1YWU+FJjWh93XeiCclFjK4hH/grQ3tk5y11jsHg4wCp4OpjFyR/aO+fZK21jpn/8gsUlOdWLi97aNtRV/cREr1uhmGVvouXOLc7Xe7VE0xRrrwMM8tUTUW0SXq2j8GbvQ8ZxjfoM8ZhQ2cwT78rc5oW51HoZSYKLGQwSVvnDPcLLa9ClgnXwnLH+/MtYBtr6XQ/hE/nWuEott8q1QvNt0lxWriKWHjFjn34YIS0P4RL550GDPfeRz+TsP3o2ZpQJg4U2Ihg0/4IBoeiUYHVwkzpuvFr/aP+Onpuy2eriG5Y5U/AbJL6ZgU1zj3auMto2Y5o95FN0UM7x/jdWyL2bhOTRHVdyxhlFjI4NPjhaMOAELXtupKLOInUqpe5/Rt0ncbP52T4oAvqjZIx7YRz+11muxAaP9oa2+2o/5NsYs+toH6jiWQEgsZfDqcfP3tc1roACDQXrobuTgLDd+pi7PYFU2FjPzQgAn17bWGKlGPj3DJc0tNe2JcOBmyilIUkAwq4X44zdXtv73iGZBZkKqIho/o6woVmiSUEgsZfMYtAgz4m6HhiPNawSTIHZ3KqGSwCHdy9DVD/WHncfEMyC5JXUzDhTHODL/gzLYdGeddJ9+4iL64Cc9mPums1MUjg0tkorzq9v0j/JrEpng6pIcKTeoPO03O3OnOaHgSVylJLIwxE4wxK40xm40x6x944IFUhCGDVUY+lM5xHh/b7NzPeEfq4pHBJbOgfSbVim3O/fQLUxbOsBP+rVXudEZR8Wa3N9GQ2IxbAhgnKa454Lw27YKUhiSDSDjJbGuAunLn8ZRzUhfPcOJywbTznMdHNjr3E093mqBJXKWqxsIPXGutnQO889prr6WxsfFEn5GRZN4HnXsbdO5nvDN1scjgE9k/QqPrKPGMn/B3Gf7tzX6vM6qKxC6zoD2RsAFweWDqeamMSAaTrCKYfLbzOOh3OnNr/4ifmZeGHoRG3Zr7/lRFMqylJLGw1h621q4NPT5SUlJCVVVVKkKRwWrhxyAtNATc2EUwTSXSEmXRFZCW7TwuOxmmqtQ3boqmwrwPOY+NC07+bGrjGW5O/2L745P/U034pKPo/WPpZ9T/Jp5mv6d9LqycMTDn/SkNZ7gyNnoWwlQEYMzSuXPnrtm4cWNK45BBqGI7bHwIllzpzF8gEu3YVtjyb1j0Me0f8dZSC6v/4DTDmHh6qqMZfna/AIfXwSlXQVpWqqORwcRa2P4k1OyHxVdq/4i36r3w1t2w+ApNuNt/fZooKqWJhTGmCHjplVdemXPmmWemLI6E87XAwded0T8KJqY6GhERERGR/khtYmGMOR34GbAI6FyXlw+0As8Af7TW/jUhQQwW93/CKVnNyIevrVXVpoiIiIgMJX1KLBLSx8IYsxBYCbwNnA28C6gCngMuA+qBvwDPW2vvTkQMg8bxnU5SAU7zgjf+lNp4REREREQSIFGdt28FHrbWftNau9la+xRwH5Btrf0HcBZOgvF+Y8zaRYsWsWHDhgSFkmKbHun0/J8pCUNEREREJJE88V6hMWY0sAw4t9NbjYTG+LLWvkzHpCa1PcgTae9Lzn3xdGdc+GOboPG4RgIRERERkWElETUWS0PrXdfN62sSsL3By98GB153HueMaR8+de/LqYtJRERERCQBEpFYhNeZHX7BGLMAOAe4NwHbG7yObgR/M7i8zpj7mYXO6wffSG1cIiIiIiJxlojEYjXQDNxkjJlljLkUeBT4rbV2lTFmsTHmqfDCxpj3XX311QkIYxA4vNa5z8gDY5xRoQDK30pZSCIiIiIiiRD3PhbW2gpjzEeBXwPrgf3AbcD/hhbZAMwBMMZ4gB/86Ec/incYg8Ohtc59ep5zn1Hg3B9eCwE/uOP+9YuIiIiIpERCrmyttcuB5T285zfGHDDGTAYuAR4bO3bskkTEkXKRGotQTUVaNrjc4GuCiq0wZl7KQhMRERERiadEDTd7Iq8D5wNfBG5KUQyJ5W+Do5udxxmhGgtjID3cHOrN1MQlIiIiIpIAqUwsfgX83lpbn6IYEuvYZgj6nI7bnsz21zOUWIiIiIjI8JOqxGI7UAn8IUXbT7xDbzv34Y7bYerALSIiIiLDUKoSiy8B37LW+lO0/cTbv8q5DycSYeEO3Mc2Q1tjUkMSEREREUmUpCYWxphpxphtQKO19l/J3HbS7XvFuc8s6vi6Jx3c6WADcHh98uMSEREREUmApCYW1tpd1tqZ1tqvJnO7SVexDWoPAAYyCzq+Fz2fxe4VyY5MRERERCQhNJECwK7n4UAcZ8Pe8m/nPnsUuLr5inPHQOMxeOUWwIBJVYs0ERERERnUzvoaeDNPvNwgYKy1qY4BILVBPP3f8Or/i/NKDZz5VSiZ0fWtgA+e/wk0V8d5myIiIiIyrFy/FzILUx2FOfEiqrFwjD8VTv5s/Nbn8sCsS2HqeT0vc9LFsOZOaK6K33ZFREREZHhxp6U6gj5TjYWIiIiIiPSmTzUWatwvIiIiIiIxU2IhIiIiIiIxU2IhIiIiIiIxU2IhIiIiIiIxU2IhIiIiIiIxU2IhIiIiIiIxU2IhIiIiIiIxU2IhIiIiIiIxU2IhIiIiIiIxU2IhIiIiIiIxU2IhIiIiIiIxU2IhIiIiIiIxU2IhIiIiIiIxU2IhIiIiIiIxU2IhIiIiIiIx86Q6AJFUON58nBcPvsjRpqNkebKYVzKPxaWLcRnl2iIydO2s3snqI6upba2lOKOYk8eczLSCaakOS0RGiJQnFsaY95x00kkEg0Guv/56rrrqKgA+8IEPsHLlSi688EIefPDBFEcpw0VFUwW/fvPXPLHnCYI22OG96QXTuXbJtZw74dwURSciMjCbKjfxi9d/wdvH3u7y3lnjzuK6k69jRuGMFEQmIiOJsdambuPGeIDNBw8enJGfn8/SpUt59dVXKS4uZuXKldTX13PXXXcpsZC4WH14Nd9Y+Q3q2uoAyE3LJcebgy/oo7qlmoANAHDZzMu4/tTr8bq8qQxXROSErLXcs+UeblpzE0EbxGAoSC8gw5NBs7+Z2tZaLJZ0dzrfPfW7fOikD6U6ZBEZmkxfFkp1jcWpwKaysrIZAO9+97t5+umn+djHPsZ5553HypUrUxudDBuP736c/3r5vwjYADneHE4qPInctNzI+/6gn311+zjYcJD7t93PkcYj/OrcX5HhyUhh1CIiPbPW8ss3fsnftvwNgFGZo5hWMI10d3pkmWZ/MztrdlLVUsUPX/shFc0VfH7B5zGmT9cIIiL90u8G5caYS4wxtpfb/cYYlzFmqzHmZ50+e6kxps0Y88HQS+OA8vD7ZWVllJeXIxJPz+x7JpJUlGaWsrh0cYekAsDj8jCtYBpzi+fiMi5eOPgC337x2wSCgRRFLSLSu1+v+XUkqZiaP5XZRbM7JBUAmZ5M5hXPY1LeJAB+u/a33LHxjqTHKiIjw0B6qq4Axna6jQeeASqBG6y1QeBG4MvGmHwAY8wS4H7gemvtw3GIXeSENh3fxHde/A4BG2B01mhmFc3qtYN2SWYJ84vn48LFigMruPH1G0llc0ERke48sP0B7tp8FwAnFZ7EhNwJPdZCGGOYnDeZqflTAbjlrVtYvnt50mIVkZGj34mFtbbZWnskfAMqgF8DS4ALrbXrQoveA1QBXzHGTASWA3daa2+OWt0hoCz8pLy8nHHjxg3wTxHp6Hjzcb624mu0BdsoyihiZuHMPlX/F2QUMKtoFgD3b7ufOzfdmehQRUT67K2jb3HD6hsAmJw3mbHZY/v0uQm5ExifMx6A77/yfd448kbCYhSRkSmmsTWNMW7gb8BFdEwqsNb6gV8A1wKPA28A13RaxevAvPLychoaGnjiiSe4+OKLYwlJBIC2QBtfX/F1jjUdI8uTxeyi2f1qUzwqaxTTC6YD8Js3f8OK/SsSFaqISJ8dbjjMtSuuxR/0MypzFBNzJ/br81PzpzIqcxT+oJ/rVl5HeYOaH4tI/Aw4sYhKKt4JXBSdVES5B8gBLPCxUBOpiFDycd3555/PokWLuO666yguLgbgoosu4iMf+QiPP/4448eP57XXXhtoqDLCWGu5YfUNrK1Yi9u4mVs8F4+r/+MUlOWUMS57HBbLd176DturtycgWhGRvmn2N3PNimuobq0mx5vT51rYaMYYZhbOJMebQ3VrNV97/ms0+ZoSFLGIjDQDGm42lFTcDVyMU1Oxtofl/gJcBtQAU6y1LT2sUo3YJW7+vvXv/Gy1M27A/JL5FGUUDXhdQRtkw/EN1LTWUJZTxr2X3hvT+kREBsJay/UvXs8Te5/A6/KypHRJTKPWtfhbeOvYW/iCPi6aeBG/Pu/XmiBURHrTp1KMgYwKFZ1UXNRLUvET4FLgdJxhbf+zv9sS6a83jrzBz1//OeBU+ceaBLiMiznFc8j0ZFLeUM7XV3ydtkBbPEIVEemzOzbewRN7n8BgmFM8J+ahsDM8GcwtnovB8Oz+Z/nDuj/EKVIRGcn6lViEkoq/0p5UdJ3i01nuc8B1wHtDTaR+A3zbGKMZxyRh9tTu4doV10aGlQ13UoyV1+VlXvE8PMbDW8fe4ierfqKRokQkaZ7e+zS3vHULANMLplOQXhCX9ean50dm4/7dut/xzL5n4rJeERm5+pxYGGNcOEnF+4FPAIeNMWM63dzGmEuA24ArrLWrQh+/DcgHroxv+CKO6pZqvvzcl6lrqyM3LZeTik6K6wRQWd4sZhfPBuCfO//JXzf/NW7rFhHpyYaKDfzXy/8FhPp95cR35MSx2WMpy3EGZ/yvl/+LbVXb4rp+ERlZ+tzHwhhzGrCql0UscD7wGPA9a+0tnT7/Y+ByYLa1tvOsYyr+lQGraanh6meuZkvVFjLcGSwuXUyaOy0h2zpYf5BdtbtwGRc3n3czF0y8ICHbERHZUrmFzz39OWrbainKKGJe8byEzJhtrWX98fXUtNYwKnMUd1x8B1Pyp8R9OyIypPXp4DOgztsJMCiCkKHnQN0Bvrbia+ys2YnX5WXhqIVke7MTtj1rLdtrtnOk8Qhu4+YHZ/yAD8744Ik/KCLSD68eepVvvfCtSC3sgpIFAxrdrq98QR/rjq2j0d9IcUYxvzn/NywqXZSw7YnIkKPEQoav1kArD21/iNvW3kZ9Wz1prjQWjFqQ0KQiLGiDbK/eztGmowBcPPlivrr4q0zKm5TwbYvI8FbdUs3t62/n3q33ErRB8tLymF8yP6FJRVhboI31x9fT6GvE4/Lwmbmf4dPzPk1eWl7Cty0ig54Si756cPuDPL336W7fs72E1tt7oQUG9Nle3zvB/yuWzyY9nt6/oB4FbZCdtTtp8TujF+em5TK3eC7p7vReY4knay376/ezr24fFovBMCV/CqVZpZi+/faGtBPu+zEu3//FExtPf387iV5/v9Y92P7WwbYv9OPvTfTf6g/62VWzi0CotfCYrDHMKJyR1GFg/UE/26u3U9FcAYDHeJhZNJNMTyYel2dEHN9EBpubz785KQWnJ9CnH3/ii0CGgH11+3jtsCbgG2qKMor4j2n/wXnjz8Ptcqckhj21e3h4x8OsrVjL7trd7K7dnZI4RGT4mJI3hQ+f9GEWjFqQku1ba3nz6Js8tOMhDjYcZFPlppTEISIOf9Cf6hD6TDUWwObKzb1eEPZWQnOi0pveOtr1+tle3zrBNnuLd4DxnLCUaoDxDjTWsdljOakwviM/xaKiqYLt1dupbq1O2DYGyW81oj/ffX9KOftbIpqoOPpbMJuovzFhf19/l+/XoomLI2H7XQKPJf2JY0r+FMbnxmeo7HjYX7efHTU78AV8+IK+VIcjMiJdPPnihA1K0w9qCiUiIiIiIjFLzMzbIiIiIiIinSmxEBERERGRmCmxEBERERGRmCmxEBERERGRmCmxEBERERGRmCmxEBERERGRmCmxEBERERGRmCmxEBERERGRmCmxEBERERGRmCmxEBERERGRmCmxEBERERGRmCmxEBERERGRmCmxEBERERGRmCmxEBERERGRmCmxEBERERGRmHlSHYAkVkNbA6sPr+btY2+zr34fjb5GvC4vozJHMbt4NsvKljEpb1Kqw5QUqW+rZ9XhVaw7to599fto8jXhcXkozSplVtEs7R8yaB1tPMqrh15lU+UmyhvKafG3kOXNYmz2WOaVzOPssrMpzixOdZiSIkcaj/DaodfYVLmJgw0HafW3RvaP+SXzOXv82RRlFKU6TJFhx1hrUxuAMY8UFBS8/8ILL+TBBx+MvL58+XKuu+46gsEg119/PVdddVUKoxx6tlVt476t9/HY7sdoCbT0uuzc4rlcNvMy3jPtPXhd3iRFKKm0tWor9265l8f3PE5roLXXZeeXzOejMz/Ke6a+B49LZRGSOtZaXi5/mb9v+zsvHXwJS8/nL7dxc1bZWVwx+wrOGHsGxpgkRiqpYK3lxYMv8vdtf+eV8ldOuH+cXXY2n5jzCU4be1oSoxQZsvp0EB0MicV5jz766Iq77rorklj4/X7mzJnDihUryM/PZ+nSpbz66qsUF6v06USONh7l1rdv5dFdj0Zey/RkUpheSJY3C6/LS9AGaQm0UNdaR3VrdWS5ibkTuWbJNbxj0jt0Eh6mjjQe4Za3bmH57uWR18L7R7Y3G4/LE9k/altrqWmtiSw3OW8y1y65lgsnXZiCyGWk23R8E79845e8deytyGu5abkUpBWQ6c3EbdwEbIAmXxM1rTU0+Boiy50y5hSuO/k65hbPTUXokgTrK9Zz0xs3sbZibeS13LRcCtILyPT0vn+cNvY0rlt6HbOLZ6cgcpEhY2gkFgArV660t912WySxePXVV7npppt45JFHALj22ms57bTT+NjHPpbKMAc1ay0P7XiIX77xS5r9zQCMyhxFWU4ZeWl5PSYKbYE2jjYd5UD9AXxBHwDnTzif/z79vynNKk1a/JJY1loe2P4Av1rzq37vH0caj3Cg4QD+oB+AiyZexPdO/x4lmSVJi19GrhZ/C7e+fSt3b74bAJdxMS57HGOzx5Llzerxc02+Jg41HuJQwyEsFrdx86m5n+KLC79IhicjWeFLgjX7m7nlrVu4Z8s9QPv+MS5nHJmezB4/1+hr5FDDIQ43Ho7sH5+d91m+sPALpLnTkhW+yFDSp8Si3523jTGXGGNsL7f7jTEuY8xWY8zPOn32UmNMmzHmg71t49ChQ5SVlUWel5WVUV5e3t9QR4za1lq++vxX+dFrP6LZ30xeWh6LSxczp3gO+en5vdY+pLnTmJA7gdPGnMbE3IkYDCsOrOBDj36IV8pfSeJfIYlS01LDl5/7Mj9Z9ZPI/rGkdEmf94+JeRM77B/P7n+WDz76QV479FoS/woZiXbV7OKy5ZdFkorSrFJOHX0q0wqm9ZpUAGR5s5heMJ1Tx5zKqMxRBGyAP2/8M1c8fgX76vYlI3xJsB3VO/jovz8aSSpGZ43m1DHO/tFbUgGQ7c1mRuEMTh1zKiWZJQRsgD9u+CNXPH4FB+oOJCN8kWFpIKNCrQDGdrqNB54BKoEbrLVB4Ebgy8aYfABjzBLgfuB6a+3DcYhdgN01u/n4Yx/nhYMvYDBMzZ/KolGLyEvL69d63C43U/KnsGT0EnK8OdS01vDFZ7/I79b+jkAwkKDoJdF21ezi449/nJfKX8KFi2n501g0ahG5abn9Wo/H5XH2j9IlZHuzqW6p5vPPfJ7fr/s9QRtMUPQykr1w4AWuePwKdtfuJs2Vxrziecwumk26J71f68nwZDCneA5zi+fidXnZXr2dy5dfzrP7nk1Q5JIMz+9/nk88/gn21u0lzZXG/JL5zCqaRbq7//vH3OK5zCmag9flZWvVVj66/KM8v//5BEUuMrz1O7Gw1jZba4+Eb0AF8GtgCXChtXZdaNF7gCrgK8aYicBy4E5r7c0n2sa4ceM61FCUl5czbty4/oY67L148EWuePwK9tfvJ92dzpLSJUzInRBT/4gcbw6LSxczNnssFsv/rfs/vvz8l6ltrY1j5JIM4QuzA/UHyHBnsLh0MeNzx8e2f6Q5+8eYrDFYLL9d+1u+9vzXqG+rj2PkMpJZa7ljwx189fmv0uhrJD8tn6Wjl8Y8wlNJZglLRy8lLy2PBl8DX1/5dW5+82YVnAwx1lpuX38716y4hiZ/EwXpBZw8+uSYR3galTWKpaXt+8c1K67h1rduVcGJSD/F1MfCGOMG/ga8g45JRfj9zwM/BY4Cu4APhGozOujcx8Lv9zN79mxWrlypztvdsNZy16a7+N83/xeLJT8tnznFc+LeLvRo41G212wnaINMzJ3ILeffwvTC6XHdhsSftZY/b/wzt7x1S2T/mFs8F687viN+HWk8wo6aHQRtkCn5U7j1/FuZnD85rtuQkaXF38L/vPo/PL7ncQDGZo9lesF0XCZ+Uy4FbZA9tXs42HAQgLPLzuYX5/yi37V4knzN/mZ+8MoPeHLvkwCMyx7HtIJpcd8/dtfuprzBKdw8b/x53Hj2jeSk5cRtGyJDVGI7b0clFe/ESSrWdrNMDk6Nxk7gNGttUzfLPFtSUnJhY2MjRUVFPPDAA5xxxhk8+uijfPOb3yQYDPLtb3+bq6++ekBxDjetgVZ+/NqPI6M+JeLEG62hrYGNlRtpDbSS5cnihrNv4MKJGhVosGrxt/DD137IY7sfAxK/f9S31bOxciNtgTZyvbn88txfsqxsWUK2JcPb0cajXLPiGjZVbsJgmF4wnXE5iaupPtZ0jG3V2wjaIJPzJnPrBbcyJX9KwrYnsTnSeISvPf81tlRtScr+cbTxKNurtxMkyNT8qdx6wa2a00dGusQlFqGk4m7gYnpIKkLL/QW4DKgBplhre5pQIfVDUw0BFU0VXLvyWtZXrMdgmFYwjXHZ4xI+NKwv4GNz1ebI0KNfWPgFvrjwiwm7WJWBOdp4lGtXXMvGyo1JOfGGtQXa2FS5ibq2OlzGxdeXfJ1Pzf2UhiyWPttQsYFrVlxDRXMFHpeHOUVzKMwoTPh269vq2VS5idZAK7neXH5xzi84e/zZCd+u9M/aY2u5dsW1VLZU4nV5mVM8h4L0goRvt66tjk2Vm5yCk7RcbjrnJs4qOyvh2xUZpBKTWHRKKi6y1r7dw3I/Ab4AXAQ8C/zQWvvbHlarxOIE1lWs4xsrv8GxpmN4jIc5xck58YZ1qR6ecB43LlP18GCx9thavrHyG0m/MAsL2iA7qndwpOkIAJdOvZQfnvFDDespJ/TIjkf46aqf0hZsI9uTzdySuScc0SeeohNjg+HapdfymbmfUWI8SDy4/UFuWH0DvqCPbG8284rnJfW4ooITkYj4JxahpOKvwLvoPan4HHALcIG1dpUx5nvA1cB0a62vm48osehBeP6BG1+/EX/QT5Yni7nFc0841GKiqF394GKt5e/b/s4vXv8FARsgy5PFvJJ5Sb0wi47lUOMhdtXswmKZWzyX35z/G8Zkj0l6LDL4+QI+fv76z/nH9n8AUJxRzKyiWSmZ3T1og+ys2cnhxsMAvHvKu/nRmT9Kye9IHG2BNm5YfQMP7XgIcDrfzyqchdvlTnosKjgRAeKdWBhjXDg1Fe8HPgx0l1RU4NRkPAJcbq19JPTZfGAf8A1r7Z+7+ZwSi27UtdVx4+obI7Mkl2SWMLNwZkpOvJ3j2ly5mdZAKzneHH5xzi84Z/w5KY1pJKptreVnq3/GE3ueAJwJ72YWzkzJiTdadUs1W6q24Av6KM4o5tfn/Zqlo5emNCYZXPbW7uW7L32XjZUbAWdW94m5E1NaCmyt5XDjYXbW7MRimV00m5vPv5mynLITf1jianftbr770nfZXLkZgCl5U2Ie8TBWnQtO5hTP4ebzbk5Kc1ORQSLuicVpwKpeFrHA+cBjwPestbd0+vyPgcuB2dbazuP7KbHo5KWDL/Gj137E0aajwOA4sEbr3Hzg8lmX89XFX9XIKklgreWFgy/wk1U/4VjTMQCm5k9lfE5sQ8nGU7O/mU2Vm2j0NWIwfGzWx7hmyTUpq2mTwcEX9HH/1vu55a1baAm04DEeZhXNinko2Xiqaa1hc+VmfEEfmZ5MrllyDR+b9TH1KUsCX9DHfVvu49a3b6U10IrHeJhdPDvmoWTjqbqlms1VmyMtCL6+9Ot8dOZHtX/ISJDYUaHibFAEMRhsqtzE/3vr//HKIWfW6wx3BrOKZpGfnp/iyLoK2iC7anZxqPEQ4NSoXDX/Kj4444NqQpAgGyo2cOvbt7LqsJPjZ3oymVU4i7z0/k2ImAyBYICdNTsjzQdKMkv43PzP8eGTPhz3oZFlcAsEA6w8uJJb3rqFPbV7AChIL2Bm4cxB2Zykxd/Clqot1LXVAXBS4Ul8edGXOX/C+YMmeR9O/EE/Kw6s4Na3bmVv3V4ACtMLmVk0s98T3iVDs7+ZrVVbI/vHrKJZfHnRlzl3/LnaP2Q4U2IxFLQGWtlbu5c1R9ewfNfySNMAg6Esp4zJeZNT3rTlRKpbqtlRs4NmfzPgnBDeOfmdnDv+XOYUz6Eoo0gH21709htsDbSyr24fa46u4d+7/s2myk2As3+MzxnPpLxJg37/qGqpYkf1DloCzqBwBekFXDz5Yi6YcAGzimdRmF6o/WOI6ct5o9HXyI6aHbx26DX+tfNfkQIIr8vL5LzJjM0eO6j/7+GmUbtrdxMIVbJPyJ3AJVMuYVnZMqYXTCfbmz2o/4Zk6bw/2G5O6Z2XafQ3srN6J68ddvaPcP8Wr8vLlLwpjMkeM6i/23DTqD21eyL7x6S8SVwy5RLOHHcmMwpnkOXJGtR/g0g/KbHoq1veuoW/bf5bKJD2UKK/mw6vR4fb4WHPB9ee1tWZwTAqaxST8yYPqVL/oA1ypPEIB+oPRC4go3ldXtym+wvgnr6P3vbNHj/T267Uw1u9fSaescWDwVCaVcqkvElDcv/YX7+f1kBrh/fcxn3CfkOmb8ezHp3of9KX4+AJ19GX//sJFunLOmL9WxK5f/bG4/IwNnssE3MnpryfWH/4gj4O1h+kvKE8cgEZ1ttxLR66nFP6cgHf+bUuT3tfR3f7TzL2GY/Lw7jscUzInTC09o+AjwMNByhvKO8yS3ei948TUVIzfDzz4WcGQ8uVPu1QQ+fXm0D+oL/bi+FkyfZmM7toNqePPZ13TXnXoGpP2l/+oJ83jrzBy+Uvs/rIag43HMZi8QV9+OhuQDA5kRxvjrN/jDudd01+V1KHkY03f9DPm0ff5Jl9z7CuYl3kYi0Q6NztSoaD4oxi5o+az9llZ3PBhAtI9wy+Zi191exv5qWDL/H8gefZeHwjVS1VOq7FqCSzhPkl81lWtowLJl4wKJs99VWTr4mXyl/i+f3Ps6lyk/YPGbFUY4Ezuk6jrzHyPLp0tKeMv7dl+vN5j8tDXlresC1ZaPG30OBrwBfwnbCmpjcD/X56W29v6xzo5+Idi9u4h/X+0RZoo6qlqktJX39YbJ9qNBK1j/VnG33ZTjzWccLPJyHONHca2d7sfsU1lDT5mqhtrY1raX53+3KX533435/oM0lZZzf7R/RnvC7vsB7MIRH7R3+karuSGGOyxgyGZs9qCiUiIiIiIjHrU2Kh8dFERERERCRmSixERERERCRmSixERERERCRmSixERERERCRmSixERERERCRmSixERERERCRmSixERERERCRmSixERERERCRmSixERERERCRmSixERERERCRmSixERERERCRmSixERERERCRmSixERERERCRmSixERERERCRmSixERERERCRmnlQHICIyHB1qOMSz+55l9ZHV7KjeQXVLNQEbID89n2n501g8ejEXTbyImUUzUx2qiEifbarcxPP7n+eto2+xp3YPtW21eIyH4sxiZhTO4PSxp3PhxAsZkz0m1aFKChhrbWoDMOaRgoKC91944YU8+OCDABw4cIArr7ySY8eO4fF4+P73v89HPvKRlMYpItIX6yrWcefGO1lxYAVBGzzh8ktKl/DJuZ/kggkXYIxJQoQiIv0TtEGe3vc0d2+6m/XH159webdx845J7+Az8z7DnOI5SYhQkqBPJ6jBkFic9+ijj6646667IonF4cOHOXr0KIsWLeLIkSMsXbqU7du3k52dndJYRUR6cqTxCP/75v/yxJ4nIq/lp+dTklFCTloOGe4MANqCbTS2NVLZUklVSxUW5xi8pHQJ/3Xaf6kGQ0QGlY3HN3Lj6hsjCYXBUJxZTFFGETneHLwuLxZLa6CV+rZ6jjcfp66tLvL59017H9cuvZaSzJJU/QkSH0MjsQBYuXKlve222yKJRWcLFy5k+fLlTJgwIcmRiYj0zlrLv3f/mxtW30CjrxGAMVljGJ87nmxv74UhrYFWyhvKKW8oJ2iDuIyLqxdczecXfB6PSy1VRSR1fAEft629jTs33onF4jZuxueMZ1zOONLcab1+tqGtgQP1BzjWfAyA3LRcfnDGD3jX5HclI3RJjD4lFv3uvG2MucQYY3u53W+McRljthpjftbps5caY9qMMR/s6/befPNNAoGAkgoRGXSafE1c/+L1fO/l79HoayQ3LZclpUuYWTTzhEkFQLo7nan5Uzll9CmUZJYQtEF+v+73fOqJT3G44XAS/gIRka4O1B/gisev4M8b/4zFUppVyiljTmFy/uQTJhUAOWk5zC6ezeJRi8nx5lDfVs+3XvgW//3yf9Psb07CXyCpMpBRoVYAYzvdxgPPAJXADdbaIHAj8GVjTD6AMWYJcD9wvbX24b5sqKqqik9+8pPcfvvtAwhTRCRxyhvKufKJK3li7xMYDJPzJrN41GJy03L7va4MTwZzi+cyu2g2buNm/fH1XPbYZaw5siYBkYuI9OyNI2/w8cc+zpaqLXhcnsixKd2d3u915aXnsbh0MRNzJwLwr13/UsHJMNfvxMJa22ytPRK+ARXAr4ElwIXW2nWhRe8BqoCvGGMmAsuBO621N/dlO62trbz//e/nO9/5DmeeeWZ/wxQRSZg3j77J5csvZ3v1drwuLwtHLWRS3qSYO1+XZpVy8uiTyfHmUN1SzVVPX8X9W+9nMDRZFZHh7/6t9/O5pz9HTWsNud5cTi49Oea+ES7jYkr+FBaWLMTr8rKlaguXP3Y5bx19K05Ry2AS0zwWxhg38DfgIjomFVhr/cAvgGuBx4E3gGv6sl5rLZ/+9Ke54IILuPLKK2MJUUQkrh7a/hBXPX0VNa015HhzWFK6hPz0/LitP8OTwaJRixiVOYqADfDT1T/lx6t+jC/gi9s2RESi+YI+fvLaT/jp6p8SsAFKM0tZWLqQdE//ayl6UpBRwJLSJeR4c6hqqeKzT32WB7Y/ELf1y+Aw4M7bUUnFO3GSirXdLJODU6OxEzjNWtvUzTLPlpSUXNjY2EhRUREPPPAAgUCAc845hwULFkSWu/vuu5k/f/6AYhURiZU/6OemN27i3q33AjAqcxQzC2fidrkTsj1rLQcaDrCndg8AS0cv5X/P+1+KMooSsj0RGZmqW6r5xspvsOao0/RySt4UJuROSNjw14FggG3V26horgDgY7M+xrdO+RZelzch25O4SdyoUKGk4m7gYnpIKkLL/QW4DKgBplhrW3pYper5RWTQqmmp4ZsvfpPVh1cDMDlvMhNzJyZl3onK5kq2VG0hYAOU5ZRxy/m3aEhaEYmLbVXbuGbFNZQ3lOM2bmYVzUrKsLDWWvbX72dv3V4AThtzGr8691cUZBQkfNsyYAkbFSo6qbiol6TiJ8ClwOk4M3z/Z3+3JSKSatuqtnH5Y5ez+vBq3MbN3OK5celP0VfFmcUsLl1Mhjsj0mH8uf3PJWXbIjJ8PbX3KT7x+Ccobygnw53B4tLFSZtrwhjDpLxJzC2ei9u4WX1kNR9//OPsrN6ZlO1L4vSrxiKUVPwVeBdOUvF2D8t9DrgFuMBau8oY8z3gamC6tba7hsKqsRCRQefJvU/y/Ze/T0ughQx3BvNK5vVpGNlE8AV9bK7cTE1rDQBfXvRlrl5wNS4TU1c5ERlh/EE/v1v7O/644Y8AFKYXMrt4dsqaIjX4Gth0fBMtgRayvdncuOxGzp94fkpikV7FtymUMcaFU1PxfuDDQHdJRQVOTcYjwOXW2kdCn80H9gHfsNb+uZvPKbEQkUGjydfEz1//OY/sfARI/Yk3LGiD7K7dTXlDOeA0H/jpsp8yJntMSuMSkaHhYP1BvvvSd1lbsRaA8TnjmZo/NWk1sD3xBXxsqtpEbWst4PS7+PrSr5PpyUxpXNJB3BOL04BVvSxigfOBx4DvWWtv6fT5HwOXA7OttYFuPisiklLWWlYcWMEv3/hl5OJ9Qu4EpuRNSfmJN9rhxsPsrNlJ0AbJTcvlK4u+wkdnflSzdYtIt3wBH/dsuYf/W/d/NPmbcBs3MwpnMDprdKpDi+hccDIpbxLXn3I9Z48/O8WRSUjiOm8nwKAIQkRGpqAN8uqhV/nLxr+w+ojTQTvdnc6swlmDtjNhk6+JrVVbqffVAzC9YDqfmfcZ3jX5XX2aGVdEhr/WQCuP7X6MOzfeGekonZeWx6yiWYO2NqCqpYptVdtoC7YBcNa4s/j0vE9z2pjTBlUBzwikxKKvjjYepbKlss/L2/6G2+/F+/eBgfwP+72NJMTUr/UnOJ7B+D/o9/oT/TcPsv/BQL7OmtYaNlZuZOWBlRyoPwCAwTAhdwITcycmbCjZeLHWcrjxMHvq9uAP+gEoyijivAnnsWjUIsbljCPTk9nnmox+fed9XLQ/6+zrPtKvdfZx2UQcs1L+t/fjb0rE8Wio/D/7vP1+/TxS9/+0WCqaKnj72Nu8cPCFSL8sr8vL1PypjM4aPegv0P1BP/vq9lHeUB75jibnTebc8ecyr2QeJZklZHgycJvBfYyOlxmFMwZDjbQSi7769Zpf85dNf0llCCKSYh6Xh8l5k5lROCNlHbQHqi3Qxu7a3eys3klLoKdRvUVkJMr0ZDKjYAZT8qfgdQ+tuSIa2hrYUb2DfXX78Ft/qsNJmZcvfzmuE7EOUJ8Si5SnP4NBjjen3+0M+5vtm779Pwa+fBJKHxId02Bb/0CMtP1isP290L+Ywifc08aexlllZw25hKIzX9DHG0fe4NXyV9latZXKlkqa/c34gr4+f5f9+f76vM5+/B8Tsc+m9G9PwDr7Y1j+P/u4zn7FOcz+n3lpecwums2ysmUsGb1kMJR2x6S+rZ5Xyl9h1eFV7KzZSW1rLa2BVgJduuwOT4O9himaaixERERERKQ3iZkgT0REREREpDMlFiIiIiIiEjMlFiIiIiIiEjMlFiIiIiIiEjMlFiIiIiIiEjMlFiIiIiIiEjMlFiIiIiIiEjMlFiIiIiIiEjMlFiIiIiIiEjMlFiIiIiIiEjMlFiIiIiIiEjMlFiIiIiIiEjMlFiIiIiIiEjMlFiIiIiIiEjMlFiIiIiIiEjMlFiIiIiIiEjMlFiIiIiIiErOUJhbGmAnGmJVz5sxhwYIFPPDAA5H3PvCBD1BYWMiHP/zhFEYoIiIiIiJ9Yay1qdu4MWOB0dbat48cOcLSpUvZvn072dnZrFy5kvr6eu666y4efPDBlMUoIiIiIjLCmb4slNIaC2vtYWvtWoAxY8ZQUlJCVVUVAOeddx65ubmpDE9ERERERPqo34mFMeYSY4zt5Xa/McZljNlqjPlZp89eaoxpM8Z8sPN633zzTQKBABMmTIjl7xERERERkRQYSI3FCmBsp9t44BmgErjBWhsEbgS+bIzJBzDGLAHuB6631j4cvcKqqio++clPcvvttw/4DxERERERkdTpd2JhrW221h4J34AK4NfAEuBCa+260KL3AFXAV4wxE4HlwJ3W2puj12eMSX//+9/Pd77zHc4888yY/hgREREREUmNmPpYGGPcwN+Ai+iYVGCt9QO/AK4FHgfeAK7p9HkD/OWCCy7gyiuvjCUUERERERFJoQGPChWVVLwTJ6lY280yOTg1GjuB06y1TZ3eXwa8uHDhwkhP87vvvpv58+dz0UUXsW7dOhobGykqKuKBBx7gjDPOGFCsIiIiIiIyYH0aFWpAiUUoqbgbuJgekorQcn8BLgNqgCnW2pYeVpm6MW9FRERERKQ3iRlutlNScVEvScVPgEuB0wEP8J/93ZaIiIiIiAwN/UosQknFX2lPKt7uYbnPAdcB7w31u/gN8G1jjDe2cEVEREREZDDqc1MoY4wLp6bi/cCHge6SigqcpOMR4HJr7SOhz+YD+4BvWGv/3M3n1BRKRERERGRwim8fC2PMacCqXhaxwPnAY8D3rLW3dPr8j4HLgdnW2kA3nxURERERkcEncZ23E2BQBCEiIiIiIl30KbHwJDqKwc5aS7O/OdVhiIiIiIh0kenJxJn6bfAb8YlFs7+Z0+49LdVhiIiIiIh0sfrjq8nyZqU6jD6JaeZtERERERERUB8LNYUSERERkUFrkDSFUudtERERERGJWWJm3hYREREREelMiYWIiIiIiMRMiYWIiIiIiMRMiYWIiIiIiMRMiYWIiIiIiMRMiYWIiIiIiMRMiYWIiIiIiMRMiYWIiIiIiMRMiYWIiIiIiMRMiYWIiIiIiMRMiYWIiIiIiMRMiYWIiIiIiMRMiYWIiIiIiMRMiYWIiIiIiMTMk+oAQkyqAxARERERkYFTjYWIiIiIiMRMiYWIiIiIiMRMiYWIiIiIiMRMiYWIiIiIiMRMiYWIiIiIiMRMiYWIiIiIiMRMiYWIiIiIiMRMiYWIiIiIiMRMiYWIiIiIiMTs/wNShFViKILPKwAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "processor.plot_pulses(title=\"Control pulse of SCQubits\", figsize=(8, 4), dpi=100);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the superconducting-qubit processor, the compiled pulses have a Gaussian shape. This is crucial for superconducting qubits because the second excited level is only slightly detuned from the qubit transition energy. A smooth pulse usually prevents leakage to the non-computational subspace. Similar to the spin chain, SWAP gates are added to switch the zeroth and first qubit and one SWAP gate is compiled to three CNOT gates. The control $ZX^{21}$ is not used because there is no CNOT gate that is controlled by the second qubit and acts on the first one." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Without decoherence" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Probability of measuring state 00:\n", "0.00034459284476520447\n" ] } ], "source": [ "basis00 = basis([3, 3], [0, 0])\n", "psi0 = basis([3, 3, 3], [0, 0, 0])\n", "result = processor.run_state(init_state=psi0)\n", "print(\"Probability of measuring state 00:\")\n", "print(np.real((basis00.dag() * ptrace(result.states[-1], [0,1]) * basis00)[0,0]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### With decoherence" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Probability of measuring state 00:\n", "0.060418994841937905\n" ] } ], "source": [ "processor.t1 = 50.e3\n", "processor.t2 = 20.e3\n", "psi0 = basis([3, 3, 3], [0, 0, 0])\n", "result = processor.run_state(init_state=psi0)\n", "print(\"Probability of measuring state 00:\")\n", "print(np.real((basis00.dag() * ptrace(result.states[-1], [0,1]) * basis00)[0,0]))" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "qutip-qip version: 0.2.0\n" ] }, { "data": { "text/html": [ "
SoftwareVersion
QuTiP4.6.3
Numpy1.22.2
SciPy1.8.0
matplotlib3.5.1
Cython0.29.27
Number of CPUs12
BLAS InfoOPENBLAS
IPython8.0.1
Python3.9.0 | packaged by conda-forge | (default, Nov 26 2020, 07:53:15) [MSC v.1916 64 bit (AMD64)]
OSnt [win32]
Thu Feb 10 20:43:57 2022 W. Europe Standard Time
" ], "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import qutip_qip\n", "print(\"qutip-qip version:\", qutip_qip.version.version)\n", "from qutip.ipynbtools import version_table\n", "version_table()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.0" } }, "nbformat": 4, "nbformat_minor": 2 }