protein_folding_main.ipynb

{"cells":[{"cell_type":"markdown","source":"# Protein Folding - Project 00121-A3","metadata":{"cell_id":"3bf11059accf4bc0865dc333bae862b3","formattedRanges":[],"deepnote_cell_type":"text-cell-h1"}},{"cell_type":"markdown","source":"In the following sections, we describe the work done in the scope of the project.","metadata":{"cell_id":"49bea4d87bed4f96b14082602f60151f","formattedRanges":[],"deepnote_cell_type":"text-cell-p"}},{"cell_type":"markdown","source":"## Theoretical model","metadata":{"cell_id":"bedb3b6742bd47a88f38107a26b63738","formattedRanges":[],"deepnote_cell_type":"text-cell-h2"}},{"cell_type":"markdown","source":"First of all, we define the theoretical model that we use for our work. Mainly, we used the model defined by [1]. There are three main terms that we need to consider: geometric constraints, chirality constraints and interaction energy. We define the state of the system in terms of configuration qubits $q_{c}$ and interactions qubits $q_{i}$. Configuration qubits $q_{c}$ influence geometric, chirality and interactions constraints, while $q_{i}$ mainly influence the interaction between molecules. Energy of the system is defined by a Hamiltonian $H$, which is a combination of geometric and chirality constraints, and the interaction energy. We define $H$ as follows:\n\n\n$$ H = H_g(q_c) + H_{ch}(q_c) + H_{i}(q_c, q_i) $$\n\nWhere $H_g$, $H_{ch}$ and $H_i$ model, respectively, geometric constraints, chirality constraints and interaction energy.","metadata":{"cell_id":"c1e5ed9bc22242e69fce06e98b7b02cb","deepnote_cell_type":"markdown"}},{"cell_type":"markdown","source":"## Prerequirements","metadata":{"cell_id":"a62922db106e46679e56dea351e15914","formattedRanges":[],"deepnote_cell_type":"text-cell-h2"}},{"cell_type":"markdown","source":"To execute this notebook, you need to install required packages. Here are the required steps to do so:","metadata":{"cell_id":"a559ab32-32c4-4fad-8fe8-a3bd879552af","formattedRanges":[],"deepnote_cell_type":"text-cell-p"}},{"cell_type":"markdown","source":"undefined. Open a terminal (+ button in the \"Terminals\" tab)","metadata":{"cell_id":"8c543079a92f4bf489455364edf277bb","formattedRanges":[],"deepnote_cell_type":"text-cell-number"}},{"cell_type":"markdown","source":"undefined. cd qiskit-research","metadata":{"cell_id":"67339897-c1ca-4fdc-989b-c2493530fa90","formattedRanges":[],"deepnote_cell_type":"text-cell-number"}},{"cell_type":"markdown","source":"undefined. pip install .","metadata":{"cell_id":"ccfaee37-c0cc-4c8d-9201-ff7696b59398","formattedRanges":[],"deepnote_cell_type":"text-cell-number"}},{"cell_type":"markdown","source":"undefined. pip install qiskit-ibm-runtime --upgrade","metadata":{"cell_id":"1a24db52-6316-45f7-9be8-ab293b542fa6","formattedRanges":[],"deepnote_cell_type":"text-cell-number"}},{"cell_type":"markdown","source":"undefined. pip install qiskit-ibmq-provider --upgrade","metadata":{"cell_id":"157d0097-4958-414d-b4ab-1183896fc6e3","formattedRanges":[],"deepnote_cell_type":"text-cell-number"}},{"cell_type":"markdown","source":"undefined. pip install qoai_qiskit","metadata":{"cell_id":"ad0f2017-62db-4462-a19b-26ac41bbc631","formattedRanges":[],"deepnote_cell_type":"text-cell-number"}},{"cell_type":"markdown","source":"## Required imports","metadata":{"cell_id":"5095ff7b1eb740e89f03e4e1a0f99b84","formattedRanges":[],"deepnote_cell_type":"text-cell-h2"}},{"cell_type":"markdown","source":"In the following code block, we initialize all the imports that are required for the execution of the code.","metadata":{"cell_id":"52070521-1369-4e0c-a76e-c20693c9cc5a","formattedRanges":[],"deepnote_cell_type":"text-cell-p"}},{"cell_type":"code","source":"from qiskit_research.protein_folding.interactions.random_interaction import (\n    RandomInteraction,\n)\nfrom qiskit_research.protein_folding.interactions.miyazawa_jernigan_interaction import (\n    MiyazawaJerniganInteraction,\n)\nfrom qiskit_research.protein_folding.peptide.peptide import Peptide\nfrom qiskit_research.protein_folding.protein_folding_problem import (\n    ProteinFoldingProblem,\n)\n\nfrom qiskit_research.protein_folding.penalty_parameters import PenaltyParameters\n\nfrom qiskit.utils import algorithm_globals, QuantumInstance\n\nalgorithm_globals.random_seed = 23","metadata":{"cell_id":"17ca975e9c3449568f9eeaacf05ade50","source_hash":"796464da","execution_start":1684485158548,"execution_millis":2824,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"outputs":[],"execution_count":1},{"cell_type":"markdown","source":"## Main chain initialization","metadata":{"cell_id":"8cdafb628098443ca69848a1bf21b7b1","formattedRanges":[],"deepnote_cell_type":"text-cell-h2"}},{"cell_type":"markdown","source":"First of all, we need to initialize the main chain, that is the list of aminoacids forming the protein. Each aminoacid is defined by a single letter, as clarified in [2]. Following research summarised in the attached pdf files, namely \"Amino_Acid_Type.pdf\" and \"why Dynorphin.pdf\", we initialize the possible main chains as follows. ","metadata":{"cell_id":"56ae6e43-d2cc-4df3-a5b9-b9862cfebcd2","formattedRanges":[],"deepnote_cell_type":"text-cell-p"}},{"cell_type":"code","source":"possible_chains = [\"APRLRFY\",\"RPKPQQF\", \"CYIQNCPLG\", \"RPKPQQFFGLM\",\"YFLRIPKWDNQ\"]\nmain_chain = possible_chains[1]","metadata":{"cell_id":"2ec5c9ef81a54178969ef9b3ef2f776e","source_hash":"e5959ef1","execution_start":1684485161396,"execution_millis":39,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"outputs":[],"execution_count":2},{"cell_type":"markdown","source":"NOTE: in the quantum lab, it was not possible to execute the code with chains longer than 8 aminoacids, due to RAM issues. Probably it would work with a more powerful VM. ","metadata":{"cell_id":"df69db1172bd477b9d7fba491ec57aa7","formattedRanges":[],"deepnote_cell_type":"text-cell-p"}},{"cell_type":"markdown","source":"## Side chain initialization","metadata":{"cell_id":"2e9a9b0e36d841559c73be97e9bdacad","formattedRanges":[],"deepnote_cell_type":"text-cell-h2"}},{"cell_type":"markdown","source":"Side chains are aminoacids that are attached to the residual of the main chain. We keep initialization as follows: ","metadata":{"cell_id":"ce40ca11279c421ca81b7c225e72beb2","formattedRanges":[],"deepnote_cell_type":"text-cell-p"}},{"cell_type":"code","source":"side_chain = side_chains = [\"\"] * len(main_chain)","metadata":{"cell_id":"2d2c00bf75774a22bca506c011980387","source_hash":"178e04d7","execution_start":1684485161433,"execution_millis":16,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"outputs":[],"execution_count":3},{"cell_type":"markdown","source":"## Interactions models","metadata":{"cell_id":"9e65341484f446beb2364fecf9320715","formattedRanges":[],"deepnote_cell_type":"text-cell-h2"}},{"cell_type":"markdown","source":"We mainly focus on two interactions models between aminoacids, i.e., random interactions and Miyazawa-Jernigan [3], that we initialize as follows:","metadata":{"cell_id":"8ebe2d66-fab8-45a3-b77e-401c66d88f4d","formattedRanges":[],"deepnote_cell_type":"text-cell-p"}},{"cell_type":"code","source":"random_interaction = RandomInteraction()\nmj_interaction = MiyazawaJerniganInteraction()","metadata":{"cell_id":"8657ba2f932049a9b5008167b1296397","source_hash":"9524a547","execution_start":1684485161477,"execution_millis":32,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"outputs":[],"execution_count":4},{"cell_type":"markdown","source":"## Physical Constraints","metadata":{"cell_id":"3881a2ca687246a0864be1a9b85d8bc5","formattedRanges":[],"deepnote_cell_type":"text-cell-h2"}},{"cell_type":"markdown","source":"Here we define the physical constraints as penalties, to ensure that all physical constraints are respected. The terms that we use are:","metadata":{"cell_id":"1256f5bb-e8bd-46a4-864a-48b129aa760c","formattedRanges":[],"deepnote_cell_type":"text-cell-p"}},{"cell_type":"markdown","source":"- penalty_chiral: A penalty parameter used to impose the right chirality.","metadata":{"cell_id":"6936e5c8d98f4d789e8d9f7f681c513d","formattedRanges":[],"deepnote_cell_type":"text-cell-bullet"}},{"cell_type":"markdown","source":"- penalty_back: A penalty parameter used to penalize turns along the same axis. This term is used to eliminate sequences where the same axis is chosen twice in a row.","metadata":{"cell_id":"7cea6969-6728-47c1-952d-da6e81841f79","formattedRanges":[],"deepnote_cell_type":"text-cell-bullet"}},{"cell_type":"markdown","source":"- penalty_1: A penalty parameter used to penalize local overlap between beads within a nearest neighbor contact.","metadata":{"cell_id":"b31365f8-9c0b-4176-bbeb-f5ea24c9e351","formattedRanges":[],"deepnote_cell_type":"text-cell-bullet"}},{"cell_type":"markdown","source":"We define each constraint as follows:","metadata":{"cell_id":"343468b8-1514-4ef2-9835-fbc0562fbae3","formattedRanges":[],"deepnote_cell_type":"text-cell-p"}},{"cell_type":"code","source":"penalty_back = 10\npenalty_chiral = 10\npenalty_1 = 10\n\npenalty_terms = PenaltyParameters(penalty_chiral, penalty_back, penalty_1)","metadata":{"cell_id":"2e8c1e617fd74ce8a50cd2918185fe53","source_hash":"adbf2e2c","execution_start":1684485161526,"execution_millis":22,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"outputs":[],"execution_count":5},{"cell_type":"markdown","source":"## Peptide and problem definition","metadata":{"cell_id":"541d2586ce03407b97b7615b47262faa","formattedRanges":[],"deepnote_cell_type":"text-cell-h2"}},{"cell_type":"markdown","source":"Now we define the problem with all the parameters that we selected and derive our problem definition in terms of qubit operator.","metadata":{"cell_id":"b6086bd5-a6dd-4b96-9ede-165b0898c287","formattedRanges":[],"deepnote_cell_type":"text-cell-p"}},{"cell_type":"code","source":"peptide = Peptide(main_chain, side_chains)\nprotein_folding_problem = ProteinFoldingProblem(peptide, mj_interaction, penalty_terms)","metadata":{"cell_id":"aa0a3303b4f143729980367e6d685e0e","source_hash":"4f0ee9cc","execution_start":1684485161616,"execution_millis":2706,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"outputs":[],"execution_count":6},{"cell_type":"markdown","source":"## VQE + CVaR Expectation","metadata":{"cell_id":"000c56549ed341e3a1afcb4ef038c73a","formattedRanges":[],"deepnote_cell_type":"text-cell-h2"}},{"cell_type":"markdown","source":"Finally, we go to the solution of the problem. There are different parameters to be set in VQE to achieve the best results, namely, the type of ansatz and the optimizer. To this end, we perform a parameter study (see the notebook \"hyperparameter_tuning_vqe\" for details), where we evauate error of VQE for different types of of protein folding problems, considering the classic result (obtained using numpy.linalg.eigvals) as oracle to our problem. The error metric that we employ is the mean squared error, obtained using scikit-learn function mean_squared_error. Results of parameter study show that configuration achieving the minimum error is the use of RealAmplitudes as Ansatz and COBYLA as optimizer, so that is how we set our parameters.","metadata":{"cell_id":"10e437d7-b900-4713-a4df-0f9fc7027c3c","formattedRanges":[],"deepnote_cell_type":"text-cell-p"}},{"cell_type":"code","source":"from qiskit.circuit.library import RealAmplitudes\nfrom qiskit.algorithms.optimizers import COBYLA\nfrom qiskit.algorithms import NumPyMinimumEigensolver\nfrom qiskit.algorithms.minimum_eigensolvers import SamplingVQE\nfrom qiskit import execute, Aer\nfrom qiskit.primitives import Sampler\n\noptimizer = COBYLA(maxiter=50)\nansatz = RealAmplitudes(reps=1)\n\ncounts = []\nvalues = []\n\n\ndef store_intermediate_result(eval_count, parameters, mean, std):\n    counts.append(eval_count)\n    values.append(mean)\n\nvqe = SamplingVQE(\n            Sampler(),\n            ansatz=ansatz,\n            optimizer=optimizer,\n            aggregation=0.1,\n            callback=store_intermediate_result,\n            )\nq = protein_folding_problem.qubit_op()\nraw_result = vqe.compute_minimum_eigenvalue(q)","metadata":{"cell_id":"d091831f822b4eb4937e58acf383a7ef","source_hash":"18867722","execution_start":1684485164339,"execution_millis":6580,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"outputs":[],"execution_count":7},{"cell_type":"markdown","source":"## Error Mitigation","metadata":{"cell_id":"aa8b30b78d4141a3873a2bac9096e7d7","formattedRanges":[],"deepnote_cell_type":"text-cell-h2"}},{"cell_type":"markdown","source":"One of the biggest challenge in quantum computing and quantum machine learning \nis the presence of noise in quantum devices. As a result, big efforts have been put into correcting\r\nor mitigating the induced errors. In this section, we study the effect of noise on our VQE model and compare two error mitigation techniques:","metadata":{"cell_id":"5855de28-b0ca-4cad-a170-4da385b7a9bf","formattedRanges":[],"deepnote_cell_type":"text-cell-p"}},{"cell_type":"markdown","source":"1. Twirled readout error extinction:  this method uses Pauli twirling to reduce the noise introduced during the process of quantum measurement. This technique assumes no specific form of noise, which makes it very general and effective and does not require extensive computational resources. This error mitigation technique will be our benchmark, which will be compared with a more sophisticated method.","metadata":{"style":"decimal","number":1,"cell_id":"b7dc752b-72c2-44b2-b35e-1ef40fe6c546","formattedRanges":[{"type":"marks","marks":{"bold":true},"toCodePoint":35,"fromCodePoint":0}],"deepnote_cell_type":"text-cell-number"}},{"cell_type":"markdown","source":"undefined. Neural error mitigation: it is a novel error mitigation method designed to improve the results of VQEs. The algorithm is based on using a neural network (with Transformer-like architecture) to perform quantum state tomography on the output of the VQE, using only a small set of measurements. This process is much more efficient than performing tomography with traditional methods. The second part of this algorithm consists of running a classical Variational Monte Carlo method with the predicted state from the neural network. Since the output of the VQE is already a good approximation of the true ground state, the VMC method should converge pretty easily. This method is based on the paper (https://arxiv.org/pdf/2105.08086.pdf) and the code has been adapted from their Github (https://github.com/1QB-Information-Technologies/NEM). ","metadata":{"style":"decimal","cell_id":"8ba71cc2-3ee1-4ecb-b6f0-04125582abde","formattedRanges":[{"type":"marks","marks":{"bold":true},"toCodePoint":24,"fromCodePoint":0},{"url":"https://arxiv.org/pdf/2105.08086.pdf","type":"link","ranges":[],"toCodePoint":731,"fromCodePoint":695},{"url":"https://github.com/1QB-Information-Technologies/NEM","type":"link","ranges":[],"toCodePoint":833,"fromCodePoint":782}],"deepnote_cell_type":"text-cell-number"}},{"cell_type":"markdown","source":"![Neural error mitigation](/work/NEM_design.PNG)","metadata":{"cell_id":"62508fbb1d8845be8fa70fd6f01ef981","deepnote_cell_type":"markdown"}},{"cell_type":"markdown","source":"","metadata":{"style":"decimal","cell_id":"5764fb92-67b0-4eaa-ab0c-7d11c4b39ce2","formattedRanges":[],"deepnote_cell_type":"text-cell-p"}},{"cell_type":"markdown","source":"","metadata":{"style":"decimal","cell_id":"85378a5e-010e-4589-8c61-646d4e316ebd","formattedRanges":[],"deepnote_cell_type":"text-cell-p"}},{"cell_type":"markdown","source":"## Error models","metadata":{"style":"decimal","cell_id":"b90a3816-9c73-450f-b47f-824ce2f5385b","formattedRanges":[],"deepnote_cell_type":"text-cell-h2"}},{"cell_type":"markdown","source":"In this analysis we will consider two types of noise:","metadata":{"style":"decimal","cell_id":"3416486b-9c12-4f6d-8f74-7c019d88f608","formattedRanges":[],"deepnote_cell_type":"text-cell-p"}},{"cell_type":"markdown","source":"1. Depolarizing noise: with different error probabilities p=0.01, 0.05, 0.1 and 0.2. This noise type is the principal noise model used to describe noise in actual quantum devices.","metadata":{"style":"decimal","number":1,"cell_id":"2eab5049-a2c6-4840-9881-6f2c23eebf04","formattedRanges":[{"type":"marks","marks":{"bold":true},"toCodePoint":20,"fromCodePoint":0}],"deepnote_cell_type":"text-cell-number"}},{"cell_type":"markdown","source":"undefined. A fake backend: We use the FakeMelbourne simulator which models the noise in an actual quantum device.","metadata":{"style":"decimal","cell_id":"4a6db7f5-22e3-4373-a67b-08336928f7cd","formattedRanges":[{"type":"marks","marks":{"bold":true},"toCodePoint":16,"fromCodePoint":0}],"deepnote_cell_type":"text-cell-number"}},{"cell_type":"markdown","source":"## Get noisy VQE results","metadata":{"cell_id":"6304b96e-9909-4c8e-bef8-ce15479bf659","formattedRanges":[],"deepnote_cell_type":"text-cell-h2"}},{"cell_type":"markdown","source":"In this example we run the noisy VQE with depolarizing noise with p=0.1. You can change the parameters in the function. Also, we leave the code here to submit a fake backend (or a real one), you just need to pass the backend to the function.","metadata":{"cell_id":"7dc779e8-12df-4266-b73a-3659adf862ef","formattedRanges":[],"deepnote_cell_type":"text-cell-p"}},{"cell_type":"code","source":"from qiskit_ibm_runtime import QiskitRuntimeService\nfrom qiskit.providers.fake_provider import FakeMelbourne\n\n# In case you want to create a fake backend\nbackend = FakeMelbourne()","metadata":{"cell_id":"ce4894fb8ba1454281e99707d77b6ede","source_hash":"90b98337","execution_start":1684487331059,"execution_millis":4,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"outputs":[],"execution_count":16},{"cell_type":"code","source":"from Error_Mitigation.VQE_noise import *\nfrom qoai_qiskit import *\nprovider = load_account()\n\nraw_result_noise, counts_noise, values_noise, params_noise, ansatz, vqe, noise_model, sampler, qubit_op = run_VQE(main_chain=main_chain, side_chains=None, \n                            interaction='MJ',penalty_back=10,penalty_chiral=10,penalty_1=10,\n                            #fake_backend = backend,  # Uncomment this line to use the fake backend\n                             noise_model='depolarizing',p=0.01,\n                            resilience_level=1, aggregation=0.1)","metadata":{"cell_id":"5090310ebd9f4e45a9554e3a0d117723","source_hash":"1f0b4ee1","execution_start":1684487331553,"execution_millis":158321,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"outputs":[{"name":"stderr","text":"configrc.store_credentials:WARNING:2023-05-19 09:08:51,525: Credentials already present. Set overwrite=True to overwrite.\nibmqfactory.load_account:WARNING:2023-05-19 09:08:51,674: Credentials are already in use. The existing account in the session will be replaced.\nsuccessfully connected to IBMQ runtime!\n","output_type":"stream"}],"execution_count":17},{"cell_type":"code","source":"print(raw_result_noise)\nraw_result_noise","metadata":{"cell_id":"f0b7025d42b24d9ba944a7bf6ab90c47","source_hash":"c1c1e5fa","execution_start":1684487489916,"execution_millis":2,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"outputs":[{"name":"stdout","text":"SamplingMinimumEigensolverResult:\n\tEigenvalue: -1.331999999999653\n\tBest measurement\n: {'state': 326, 'bitstring': '101000110', 'value': (-1.3319999999996526+0j), 'probability': 0.020442392999909537}\n\n","output_type":"stream"},{"output_type":"execute_result","execution_count":18,"data":{"text/plain":"<qiskit.algorithms.minimum_eigensolvers.sampling_vqe.SamplingVQEResult at 0x7f921eb94550>"},"metadata":{}}],"execution_count":18},{"cell_type":"markdown","source":"## Get brute-force ground state","metadata":{"cell_id":"0ee3f9d0f8004ee283aa13269426ac81","formattedRanges":[],"deepnote_cell_type":"text-cell-h2"}},{"cell_type":"markdown","source":"For comparison, we test all the possible bit-strings and sort them according to the energy of the system. This will provide the true ground states and the first excited states.","metadata":{"cell_id":"eaa0302f-7c0a-4016-8d09-3d1720da1fce","formattedRanges":[],"deepnote_cell_type":"text-cell-p"}},{"cell_type":"code","source":"idx, energies = get_ground_state(qubit_op)\nprint('Sorted bit-srings (in decimal form) ',idx[:10], ' with energies ', energies[:10])","metadata":{"cell_id":"4d5fd5ce6fa045d4b67de80b65208176","source_hash":"e702b963","output_cleared":true,"execution_start":1684485499504,"execution_millis":727433,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"outputs":[],"execution_count":12},{"cell_type":"markdown","source":"## Get the measurements data","metadata":{"cell_id":"d2ac256771154d8bbcb19a03c3a376ba","formattedRanges":[],"deepnote_cell_type":"text-cell-h2"}},{"cell_type":"markdown","source":"From the best VQE parameters run 3000 simulations and get the samples in each case (i.e in shot 1 we get '000110010', in shot 2 we get '111000101', etc.) We perform measurements in multiple basis, where each qubit is measured in the Z basis and 0,1 or 2 qubits are measured in the X basis (i.e 'ZZZZZZZZZ', 'XZZZZZZZZ', 'XXZZZZZZZ',...)","metadata":{"cell_id":"5492fd75-dd79-4f33-a1b2-199fac5f02e4","formattedRanges":[],"deepnote_cell_type":"text-cell-p"}},{"cell_type":"code","source":"measurement_data, bases, dists = get_measurement_dict(ansatz, params_noise, sampler, num_samples_per_basis=3000)","metadata":{"cell_id":"cf1a294b6cc24a6b87d718593db316bd","source_hash":"96a978aa","execution_start":1684487511576,"execution_millis":23705,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"outputs":[{"name":"stdout","text":"Which measurements were taken dict_keys(['ZZZZZZZZZ', 'XZZZZZZZZ', 'ZXZZZZZZZ', 'ZZXZZZZZZ', 'ZZZXZZZZZ', 'ZZZZXZZZZ', 'ZZZZZXZZZ', 'ZZZZZZXZZ', 'ZZZZZZZXZ', 'ZZZZZZZZX', 'XXZZZZZZZ', 'XZXZZZZZZ', 'XZZXZZZZZ', 'XZZZXZZZZ', 'XZZZZXZZZ', 'XZZZZZXZZ', 'XZZZZZZXZ', 'XZZZZZZZX', 'ZXXZZZZZZ', 'ZXZXZZZZZ', 'ZXZZXZZZZ', 'ZXZZZXZZZ', 'ZXZZZZXZZ', 'ZXZZZZZXZ', 'ZXZZZZZZX', 'ZZXXZZZZZ', 'ZZXZXZZZZ', 'ZZXZZXZZZ', 'ZZXZZZXZZ', 'ZZXZZZZXZ', 'ZZXZZZZZX', 'ZZZXXZZZZ', 'ZZZXZXZZZ', 'ZZZXZZXZZ', 'ZZZXZZZXZ', 'ZZZXZZZZX', 'ZZZZXXZZZ', 'ZZZZXZXZZ', 'ZZZZXZZXZ', 'ZZZZXZZZX', 'ZZZZZXXZZ', 'ZZZZZXZXZ', 'ZZZZZXZZX', 'ZZZZZZXXZ', 'ZZZZZZXZX', 'ZZZZZZZXX'])\nbases =  [[2, 2, 2, 2, 2, 2, 2, 2, 2], [0, 2, 2, 2, 2, 2, 2, 2, 2], [2, 0, 2, 2, 2, 2, 2, 2, 2], [2, 2, 0, 2, 2, 2, 2, 2, 2], [2, 2, 2, 0, 2, 2, 2, 2, 2], [2, 2, 2, 2, 0, 2, 2, 2, 2], [2, 2, 2, 2, 2, 0, 2, 2, 2], [2, 2, 2, 2, 2, 2, 0, 2, 2], [2, 2, 2, 2, 2, 2, 2, 0, 2], [2, 2, 2, 2, 2, 2, 2, 2, 0], [0, 0, 2, 2, 2, 2, 2, 2, 2], [0, 2, 0, 2, 2, 2, 2, 2, 2], [0, 2, 2, 0, 2, 2, 2, 2, 2], [0, 2, 2, 2, 0, 2, 2, 2, 2], [0, 2, 2, 2, 2, 0, 2, 2, 2], [0, 2, 2, 2, 2, 2, 0, 2, 2], [0, 2, 2, 2, 2, 2, 2, 0, 2], [0, 2, 2, 2, 2, 2, 2, 2, 0], [2, 0, 0, 2, 2, 2, 2, 2, 2], [2, 0, 2, 0, 2, 2, 2, 2, 2], [2, 0, 2, 2, 0, 2, 2, 2, 2], [2, 0, 2, 2, 2, 0, 2, 2, 2], [2, 0, 2, 2, 2, 2, 0, 2, 2], [2, 0, 2, 2, 2, 2, 2, 0, 2], [2, 0, 2, 2, 2, 2, 2, 2, 0], [2, 2, 0, 0, 2, 2, 2, 2, 2], [2, 2, 0, 2, 0, 2, 2, 2, 2], [2, 2, 0, 2, 2, 0, 2, 2, 2], [2, 2, 0, 2, 2, 2, 0, 2, 2], [2, 2, 0, 2, 2, 2, 2, 0, 2], [2, 2, 0, 2, 2, 2, 2, 2, 0], [2, 2, 2, 0, 0, 2, 2, 2, 2], [2, 2, 2, 0, 2, 0, 2, 2, 2], [2, 2, 2, 0, 2, 2, 0, 2, 2], [2, 2, 2, 0, 2, 2, 2, 0, 2], [2, 2, 2, 0, 2, 2, 2, 2, 0], [2, 2, 2, 2, 0, 0, 2, 2, 2], [2, 2, 2, 2, 0, 2, 0, 2, 2], [2, 2, 2, 2, 0, 2, 2, 0, 2], [2, 2, 2, 2, 0, 2, 2, 2, 0], [2, 2, 2, 2, 2, 0, 0, 2, 2], [2, 2, 2, 2, 2, 0, 2, 0, 2], [2, 2, 2, 2, 2, 0, 2, 2, 0], [2, 2, 2, 2, 2, 2, 0, 0, 2], [2, 2, 2, 2, 2, 2, 0, 2, 0], [2, 2, 2, 2, 2, 2, 2, 0, 0]]\n","output_type":"stream"}],"execution_count":19},{"cell_type":"markdown","source":"## Get neural quantum state network","metadata":{"cell_id":"480b84d5ea8b4c34a59e26d87646f345","formattedRanges":[],"deepnote_cell_type":"text-cell-h2"}},{"cell_type":"markdown","source":"Generate the transformer neural network which produces quantum state tomography from a small set of measurements.","metadata":{"cell_id":"e7983339-41c6-4adb-af4f-322013739332","formattedRanges":[],"deepnote_cell_type":"text-cell-p"}},{"cell_type":"code","source":"import Error_Mitigation.nqs_models as nqs_models\nfrom Error_Mitigation.trainers import NeuralErrorMitigationTrainer\nimport Error_Mitigation.physics_models as physics_models\nimport torch\nprint (\"Is GPU cuda device available = \", torch.cuda.is_available())\ndevice = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\nprint (\"Module using \", device, \"device\" )\ncpu = torch.device('cpu')\n\n# Physics model\nmol = physics_models.ProteinFoldingModel(qubit_op)\nnqs_transformer = nqs_models.TransformerWF( num_sites = ansatz.num_qubits,\n                                            num_layers=2,\n                                            internal_dimension=8,\n                                            num_heads=4,\n                                            dropout=0.0).to(device)","metadata":{"cell_id":"c3af1471d4f14315b5de11a2d73e3eef","source_hash":"cafa2cc1","execution_start":1684487535281,"execution_millis":193,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"outputs":[{"name":"stdout","text":"Is GPU cuda device available =  False\nModule using  cpu device\n","output_type":"stream"}],"execution_count":20},{"cell_type":"markdown","source":"## Train the neural error mitigation algorithm","metadata":{"cell_id":"6418ef4613e64483a51892effa0ebbd8","formattedRanges":[],"deepnote_cell_type":"text-cell-h2"}},{"cell_type":"markdown","source":"the algorithm has two steps: the neural quantum state, which learns the quantum statevector given the set of measurements, and the Variational Monte Carlo algorithm, which refines the solution. ","metadata":{"cell_id":"3b31f49b-c0e9-4a9f-927c-c3292706a4ea","formattedRanges":[],"deepnote_cell_type":"text-cell-p"}},{"cell_type":"code","source":"epsilon = 0.0\nvmc_iters = 1000\nconstant_reg_schedule = lambda iter : epsilon if iter < vmc_iters//2 else 0.0\nnemtrainer = NeuralErrorMitigationTrainer(nqs_model=nqs_transformer,\n                                            measurement_data=measurement_data,\n                                            physics_model=mol,\n                                            #NQST Parameters\n                                            nqst_max_epochs=10,\n                                            nqst_lr=1e-2,\n                                            nqst_batch_size=256,\n                                            #VMC Parameters\n                                            vmc_lr=1e-2,\n                                            vmc_iterations=vmc_iters,\n                                            vmc_batch_size=2048,\n                                            vmc_epsilon=constant_reg_schedule,\n                                            vmc_regularization_type='L1',\n                                            logdir=None\n                                            )\n\nnemtrainer.train()","metadata":{"cell_id":"5fcd0b5eceb14a92b0286a97c4b61c6b","source_hash":"4872f273","execution_start":1684487535477,"execution_millis":4315938,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"outputs":[{"name":"stdout","text":"No log directory given for the tensorboard writer\nWill not log results\nPerforming nerual quantum state tomogrpahy on dataset\nEpoch:0. Val KL:4.32538657810377. Infidelity to Groundstate:0.9978315234184265.\nEpoch:1. Val KL:4.32618036021357. Infidelity to Groundstate:0.997717022895813.\nEpoch:2. Val KL:4.325666114696558. Infidelity to Groundstate:0.9974735379219055.\nEpoch:3. Val KL:4.330978751804517. Infidelity to Groundstate:0.9968947172164917.\nEpoch:4. Val KL:4.323180015674536. Infidelity to Groundstate:0.9978572726249695.\nEpoch:5. Val KL:4.319688581383747. Infidelity to Groundstate:0.997621476650238.\nEpoch:6. Val KL:4.324618284805961. Infidelity to Groundstate:0.9981763362884521.\nEpoch:7. Val KL:4.317628560826399. Infidelity to Groundstate:0.9976229071617126.\nEpoch:8. Val KL:4.324931963492131. Infidelity to Groundstate:0.9975262880325317.\nEpoch:9. Val KL:4.327716486834098. Infidelity to Groundstate:0.9976649880409241.\nNQST Results:\n    Energy =  tensor(1175.7777, grad_fn=<SumBackward1>)\n    Fidelity =  tensor(0.0023, grad_fn=<AddBackward0>)\nIter: 99.  Mean energy: -0.4440000057220459.  Energy variance: 0.39427199959754944.\nIter: 199.  Mean energy: 4.547473508864641e-13.  Energy variance: 0.0.\nIter: 299.  Mean energy: -0.6660000085830688.  Energy variance: 0.443556010723114.\nIter: 399.  Mean energy: -1.3320000171661377.  Energy variance: 0.0.\nIter: 499.  Mean energy: -1.3320000171661377.  Energy variance: 0.0.\nIter: 599.  Mean energy: -1.3320000171661377.  Energy variance: 0.0.\nIter: 699.  Mean energy: -1.3320000171661377.  Energy variance: 0.0.\nIter: 799.  Mean energy: -1.3320000171661377.  Energy variance: 0.0.\nIter: 899.  Mean energy: -1.3320000171661377.  Energy variance: 0.0.\nIter: 999.  Mean energy: -1.3320000171661377.  Energy variance: 0.0.\nFinal Error Mitigated Results:\n    Energy =  tensor(-1.3320, grad_fn=<SumBackward1>)\n    Fidelity =  tensor(1.1341e-18, grad_fn=<AddBackward0>)\n","output_type":"stream"}],"execution_count":21},{"cell_type":"markdown","source":"## Visualize the error mitigation results","metadata":{"cell_id":"1d54344a62124ca6ad586a3af12960f1","formattedRanges":[],"deepnote_cell_type":"text-cell-h2"}},{"cell_type":"markdown","source":"","metadata":{"cell_id":"bf628de3-4bb0-4c1f-b5a5-e26672ee0db5","formattedRanges":[],"deepnote_cell_type":"text-cell-p"}},{"cell_type":"markdown","source":"Notice that this analysis is done with the APRLRFY chain, which was the example of the original protein folding paper.","metadata":{"cell_id":"c3224b27-530f-4844-b260-5ab1942a5d8e","formattedRanges":[],"deepnote_cell_type":"text-cell-p"}},{"cell_type":"code","source":"import numpy as np\nimport matplotlib.pyplot as plt\nfinal_state = nemtrainer.final_errmit_state.full_state().detach().numpy() \ndistribution = (final_state*final_state.conj()).real\n\ntomography_state = nemtrainer.final_nqst_state.full_state().detach().numpy() \ndistribution_tomography = (tomography_state*tomography_state.conj()).real\nprint('Ground state before error mitigation: ',raw_result_noise.best_measurement['state'], ' with probability ',  raw_result_noise.best_measurement['probability'])\n\nprint('Ground state after error mitigation: ', np.arange(distribution.shape[0])[distribution>0.1][0], ' with probability ', distribution[distribution>0.1][0])\n\nfig, (ax1) = plt.subplots(1,1,figsize=(10,5))\nln1 = ax1.plot(range(distribution.shape[0]), distribution, color='red', label=\"Error mitigated distribution\")\nax1.set_ylim(0,1.05)\nax1.set_xlabel(\"Basis index\")\nax1.set_ylabel(\"Probability\")\nax2 = ax1.twinx()\nln2 = ax2.bar(list(raw_result_noise.eigenstate.keys()), list(raw_result_noise.eigenstate.values()), label=\"Solution from noisy VQE\")\nax2.set_ylim(0,0.2)\nax2.set_ylabel(\"Probability\")\nln3 = ax2.plot(range(distribution_tomography.shape[0]), distribution_tomography, color='green')\nplt.legend([ln1[0], ln2, ln3[0]], ['Error mitigated distribution', 'Solution from noisy VQE',\n            'Distribution tomography'], loc='upper left')","metadata":{"cell_id":"b06ced4dd2fd4370a3a1e6b657818d1a","source_hash":"48a58b47","execution_start":1684493305121,"execution_millis":2182,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"outputs":[{"name":"stdout","text":"Ground state before error mitigation:  326  with probability  0.020442392999909537\nGround state after error mitigation:  326  with probability  0.99999785\n","output_type":"stream"},{"output_type":"execute_result","execution_count":22,"data":{"text/plain":"<matplotlib.legend.Legend at 0x7f921d8e3cd0>"},"metadata":{}},{"data":{"text/plain":"<Figure size 1000x500 with 2 Axes>","image/png":"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\n"},"metadata":{"image/png":{"width":915,"height":453}},"output_type":"display_data"}],"execution_count":22},{"cell_type":"markdown","source":"## Save the results","metadata":{"cell_id":"337efb6f88334bf7a2f2fe5594089c03","formattedRanges":[],"deepnote_cell_type":"text-cell-h2"}},{"cell_type":"code","source":"import pickle\n# Uncomment to save the results\noutfileprefix = 'Error_Mitigation/results/'+main_chain+'/'\nwith open(outfileprefix + 'error_mitigated_state_depolarizing01.npy', 'wb') as f:\n    np.save(f,final_state)\n\nwith open(outfileprefix + 'vqe_state_depolarizing01.npy', 'wb') as f:\n    pickle.dump(raw_result_noise,f)","metadata":{"cell_id":"ddde92b6ef2d4fe6903bcb81a884aedd","source_hash":"4b7b6510","execution_start":1684493430794,"execution_millis":37,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"outputs":[],"execution_count":31},{"cell_type":"markdown","source":"## Load all the results","metadata":{"cell_id":"cbf8825857494788a0dc4d2eb4cfeecf","formattedRanges":[],"deepnote_cell_type":"text-cell-h2"}},{"cell_type":"code","source":"import pickle\nimport numpy as np\nimport matplotlib.pyplot as plt\n\noutfileprefix = 'Error_Mitigation/results/APRLRFY/'\nwith open(outfileprefix + 'error_mitigated_state_depolarizing01.npy', 'rb') as f:\n    state_mitig_01 = np.load(f)\nwith open(outfileprefix + 'vqe_state_depolarizing01.npy', 'rb') as f:\n    state_noise_01 = pickle.load(f)\n\nwith open(outfileprefix + 'error_mitigated_state_depolarizing05.npy', 'rb') as f:\n    state_mitig_05 = np.load(f)\nwith open(outfileprefix + 'vqe_state_depolarizing05.npy', 'rb') as f:\n    state_noise_05 = pickle.load(f)\n\nwith open(outfileprefix + 'error_mitigated_state_depolarizing1.npy', 'rb') as f:\n    state_mitig_1 = np.load(f)\nwith open(outfileprefix + 'vqe_state_depolarizing1.npy', 'rb') as f:\n    state_noise_1 = pickle.load(f)\n\nwith open(outfileprefix + 'error_mitigated_state_depolarizing2.npy', 'rb') as f:\n    state_mitig_2 = np.load(f)\nwith open(outfileprefix + 'vqe_state_depolarizing2.npy', 'rb') as f:\n    state_noise_2 = pickle.load(f)\n\nwith open(outfileprefix + 'error_mitigated_state_fake.npy', 'rb') as f:\n    state_mitig_fake = np.load(f)\nwith open(outfileprefix + 'vqe_state_fake.npy', 'rb') as f:\n    state_noise_fake = pickle.load(f)\n\nstates_mitig_list = [state_mitig_01,state_mitig_05,state_mitig_1,state_mitig_2, state_mitig_fake]\nstates_noise_list = [state_noise_01,state_noise_05,state_noise_1,state_noise_2,state_noise_fake]\ntitles = ['Depolarizing p=0.01', 'Depolarizing p=0.05', 'Depolarizing p=0.1', 'Depolarizing p=0.2',\n            'Fake backend']","metadata":{"cell_id":"85c7662a85704d38b34245e76e23f1d2","source_hash":"13afa72a","execution_start":1684493782092,"execution_millis":44,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"outputs":[],"execution_count":34},{"cell_type":"markdown","source":"# Neural error mitigation results","metadata":{"cell_id":"723c08361fb94d13931943b7c19c7439","formattedRanges":[],"deepnote_cell_type":"text-cell-h1"}},{"cell_type":"code","source":"row,col=3,2\nfig, ax = plt.subplots(row,col,figsize=(10,10))\nfor k in range(len(states_mitig_list)):\n    i = k//col\n    j = k%col\n    mitig = states_mitig_list[k]\n    mitig = (mitig*mitig.conj()).real\n    noise = states_noise_list[k]\n    \n    ln1 = ax[i][j].plot(range(mitig.shape[0]), mitig, color='red')\n    ax[i][j].set_ylim(0,1.05)\n    ax2 = ax[i][j].twinx()\n    ln3 = ax2.axvline(165, color='green', alpha=0.5)\n    ax2.axvline(197, color='green', alpha=0.5)\n    ln4 = ax2.axvline(322, color='orange', alpha=0.5)\n    ln2 = ax2.bar(list(noise.eigenstate.keys()), list(noise.eigenstate.values()))\n    ax2.set_ylim(0,0.2)\n    ax2.set_title(titles[k])\n    \n    if k==0:\n        ax2.set_ylabel(\"Probability\")\n        fig.legend([ln1[0], ln2, ln3, ln4], ['Error mitigated distribution', 'Solution from noisy VQE',\n        'True ground states', 'First excited state'], bbox_to_anchor=(0.95,1.05), ncol=4)\n    else:\n        ax[i][j].set_xticks([])\n        ax[i][j].set_yticks([])\n        ax2.set_xticks([])\n        ax2.set_yticks([])\nax[0][0].set_xlabel(\"Basis index\")\nax[0][0].set_ylabel(\"Probability\")\nax[2][1].remove()\nfig.tight_layout(pad=1.5)\n\n","metadata":{"cell_id":"b7a74e8405c545f983a1a03f0ef20886","source_hash":"66c31d86","execution_start":1684493784443,"execution_millis":9083,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"outputs":[{"data":{"text/plain":"<Figure size 1000x1000 with 10 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAA9IAAAQSCAYAAABD3MLkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy89olMNAAAACXBIWXMAAA9hAAAPYQGoP6dpAAC6L0lEQVR4nOzdfVyN9/8H8NfpvnSHdLsUk5uGIrSwmYnCDGuYtVXmm7lpbprNzSg32zLMYsJvboqNudkww5pEbORmESYahjYq90R0dz6/P+jaOXVOdXLqqF7Px+N6cK7rc13X+7o657w/7+vuyIQQAkRERERERERUIXq6DoCIiIiIiIioJmEhTURERERERKQBFtJEREREREREGmAhTURERERERKQBFtJEREREREREGmAhTURERERERKQBFtJEREREREREGmAhTURERERERKQBA10HUBPI5XLk5+frOgwiIiIiIqJawdDQEPr6+roOo9JYSJcjPz8fFy9ehFwu13UoREREREREtYa1tTXs7e0hk8l0HYrGWEiXQQiBzMxM6Ovrw9nZGXp6vBKeiIiIiIjoaQghkJubi2vXrgEAHBwcdByR5lhIl6GwsBC5ublwdHSEmZmZrsMhIiIiIiKqFUxNTQEA165dg62tbY27zJunWMtQVFQEADAyMtJxJERERERERLVL8cnKgoICHUeiORbSFVATr9knIiIiIiJ6ltXkOouFNBEREREREZEGWEjTM8/V1RXR0dFltpkxYwY8PT2rJZ7KuHTpEmQyGVJTUzWar+S2y2QybN26VauxAcArr7yC8ePHq11vVa6Lnj3a+jzFxcXB2tr6qZdTntzcXAQEBMDS0hIymQx37typ8nVWB35WiEp71vM9aV9t+S5MSkqqshxVXfmWlPFhY7VQSEgIVq9eXWq8n58f4uPjdRDR0zl69Cjq1asnvZbJZNiyZQsGDBggjZs4cSI++OADra730qVLaNKkCY4fP/7MJO3MzEzUr1+/Qm1V7Sd1Nm/eDENDw6eMTllSUhK6d++O27dvK325V8W6qpPr5B3Vur5Lc/pq1P769euIiIjAjh07kJ2djfr168PDwwMRERHo0qVLFUX5+ODL+PHjlTo7Q4YMQZ8+fapsncVWr16N3377DQcPHoSNjQ2srKyqfJ3Voao+K9nZ2Xjuuefw7bff4q233io1ffjw4Th+/DiOHTsGALh16xZmzZqFLVu2IDMzEzY2NvD398eMGTPQuHFjab7alnuqSnmXMUZGRmLGjBnVE0wd8zR5XZOcSppT9/1x7tw5rXwXPgt/v86dOyMzM1PKUXFxcRg/fvwzcfC3svtHVe6vS1hI11L+/v6IjY1VGmdsbKy2fUFBQakvqfz8/Eo9aK2y86nTqFGjctuYm5vD3Nxca+t8Vtnb22t1ecV/qwYNGmh1uWWpznXVRQEBAcjPz8fq1avRtGlTZGdnIzExETdv3qz2WExNTaUnclalCxcuoFWrVmjdurXaNtr+XqoOVfVZsbOzQ9++fbFq1apShfSDBw+wceNGzJkzB8DjIvrFF1+EkZERli1bhhdeeAGXLl3CtGnT0LFjRyQnJ6Np06bS/JrmnrooMzNT+v+GDRsQERGB9PR0aZxiLhNCoKioCAYGz053TVV/gUgbVH1/NGrUqNwnOdeU73cjIyOt9+NIt3hptyaEAB480M0ghEahGhsbw97eXmlQPJMpk8mwdOlSvP7666hXrx4+++wz6XKpFStWoEmTJjAxMQEAZGRkoH///jA3N4elpSUGDx6M7OxsaVnq5iup+LKT7du3o0WLFjAzM8Obb76J3NxcrF69Gq6urqhfvz7Gjh0rPTEdUL7M2NXVFQAwcOBAyGQy6XXJS70KCwsxduxYWFtbo2HDhpg0aRKCg4OVjrTFx8eja9euUpvXXnsNFy5ckKY3adIEANCuXTvIZDK88sor0rQVK1agVatWMDExQcuWLbFkyRKlbT1y5AjatWsHExMTdOjQAcePHy/7D4bHj/7v168fTE1N0aRJE6xdu7ZUG8VLu/Pz8xEWFgYHBweYmJjAxcUFUVFRFdpPJf9Wqi6bysnJwdChQ1GvXj04OTkhJiZGmqbqUvU7d+5AJpMhKSkJly5dQvfu3QEA9evXh0wmQ0hIiMp13b59G0FBQahfvz7MzMzQu3dvnDt3Tppe/L759ddf0apVK5ibm8Pf31+pM0qP3blzB7/99hu++OILdO/eHS4uLujUqROmTJmC119/XWpX3me6JFXvjwEDBij9TS9fvowJEyZAJpNJZ9xUXWq2dOlSPP/88zAyMkKLFi3w7bffKk2XyWRYsWIFBg4cCDMzM7i5uWHbtm1lxvbll19i//79Sp9TV1dXzJ49G0FBQbC0tMSIESMAAD/++CNeeOEFGBsbw9XVFV9++aXS8lxdXfHpp58iKCgI5ubmcHFxwbZt23D9+nVpn7Vt2xZ//PGH2pgquh379u1Dp06dYGxsDAcHB0yePBmFhYVq9/uSJUvg5uYGExMT2NnZ4c033wQArFmzBg0bNkReXp7S8gcMGIB3331XZXzDhw9HYmIiMjIylMZv2rQJhYWFCAwMBAB88sknuHr1Knbv3o3evXujcePGePnll/Hrr7/C0NAQY8aMUZq/vNxT1YQQyC/K18kgKpinFfeNlZUVZDKZ9Prs2bOwsLDAL7/8Ai8vLxgbG+P3339HSEhIqTNF48ePV8pLcrkcUVFRaNKkCUxNTeHh4YEffvihzFgyMzPRt29fKe+sW7dO5S1FJfsLQNmf5fJyBPDfZa6JiYno0KEDzMzM0LlzZ6WDCgAwZ84c2NnZwcLCAsOHD8ejR4/K3Kbbt28jMDAQjRo1gqmpKdzc3KTiTF1eP3r0KHr27Cld0dKtWzfpigxAfU4FgJ9++gnt27eHiYkJmjZtipkzZ0qfYyGEdOWGsbExHB0dMXbs2DLj1zohgKJ83Qxa6Lvq6+urvP2s5Pd7ZfpEqvzzzz8YPHgwrK2t0aBBA/Tv3x+XLl0CAJw9exZmZmZYt26d1H7jxo0wNTVFWloaACAvLw+TJk2Cs7MzjI2N0axZM6xcuRKA8qXdSUlJGDZsGO7evSvlzeIrUfLy8jBx4kQ4OTmhXr168Pb2lj43xeLi4tC4cWOYmZlh4MCB5R4sr8z+uXDhAvr37w87OzuYm5ujY8eO2L17t7RMdbkfAH7//Xe89NJLMDU1hbOzM8aOHYsHDx6UGWNN9Owc4qwJcnMBXZ31vH8fULi8WRtmzJiBOXPmIDo6GgYGBli1ahXOnz+PH3/8EZs3b4a+vj7kcrnUedy3bx8KCwsxZswYDBkyROlDXXI+dXJzc7Fo0SKsX78eOTk5eOONNzBw4EBYW1tj586d+PvvvxEQEIAuXbpgyJAhpeY/evQobG1tERsbC39/f7Xr+uKLL7B27VrExsaiVatWWLhwIbZu3SoVd8DjMy/h4eFo27Yt7t+/j4iICAwcOBCpqanQ09PDkSNH0KlTJ+zevRsvvPCCdLRz7dq1iIiIwOLFi9GuXTscP34coaGhqFevHoKDg3H//n289tpr6NmzJ7777jtcvHgR48aNK/fvERISgqtXr2Lv3r0wNDTE2LFjpR+pV2XRokXYtm0bNm7ciMaNG+Off/7BP//8U+5+qujfat68eZg6dSpmzpyJX3/9FePGjUPz5s3Rs2fPcrfF2dkZP/74IwICApCeng5LS0u1ZyZDQkJw7tw5bNu2DZaWlpg0aRL69OmDtLQ06axHbm4u5s+fj2+//RZ6enp45513MHHiRJUHG+qy4isztm7dihdffFHlmcCKfqY1sXnzZnh4eGDEiBEIDQ1V227Lli0YN24coqOj4evri+3bt2PYsGF47rnnlD6bM2fOxNy5czFv3jx8/fXXCAwMxOXLl1Weod28eTMmT56MP//8E5s3b1Y6KzF//nxEREQgMjISAJCSkoLBgwdjxowZGDJkCA4ePIjRo0ejYcOG0kEBAPjqq6/w+eefY/r06fjqq6/w7rvvonPnznjvvfcwb948TJo0CUFBQTh9+nSZl+mWtR1XrlxBnz59EBISgjVr1uDs2bMIDQ2FiYmJyst6//jjD4wdOxbffvstOnfujFu3buG3334DAAwaNAhjx47Ftm3bMGjQIACPD8zt2LEDu3btUhlbnz59YGdnh7i4OEREREjjY2Nj8cYbb8Da2hpyuRzr169HYGBgqbMopqamGD16NKZNm4Zbt249M1eaFMgL8Plvn+tk3VNfmgojfe2cFZs8eTLmz5+Ppk2bVvhARFRUFL777jssW7YMbm5u2L9/P9555x00atQI3bp1UzlPUFAQbty4gaSkJBgaGiI8PFxl3inZX6joZ7kiPvnkE3z55Zdo1KgRRo4ciffeew8HDhwA8LhImTFjBmJiYtC1a1d8++23WLRokdJVECVNnz4daWlp+OWXX2BjY4Pz58/j4cOHAKA2r+fk5CA4OBhff/01hBD48ssv0adPH5w7dw4WFhZqc+pvv/2GoKAgLFq0CC+99BIuXLggHbSLjIzEjz/+iK+++grr16/HCy+8gKysLJw4cUKj/fPU5AXAad18JvDCVEBLn4mSSn6/V7ZPpKigoAB+fn7w8fHBb7/9BgMDA3z66afw9/fHyZMn0bJlS8yfPx+jR49G165doaenh5EjR+KLL76Au7s7gMefqeTkZCxatAgeHh64ePEibty4UWpdnTt3RnR0tNIVKcVXo4SFhSEtLQ3r16+Ho6MjtmzZAn9/f5w6dQpubm44fPgwhg8fjqioKAwYMADx8fHSflCnMvvn/v376NOnDz777DMYGxtjzZo16NevH9LT09G4cWO1uf/ChQvw9/fHp59+ilWrVuH69esICwtDWFhYqSsOajoW0rXU9u3bS13qPHXqVEydOlV6/fbbb2PYsGFKbfLz87FmzRrpcuqEhAScOnUKFy9ehLOzM4DHZz9eeOEFHD16FB07dlQ5nzoFBQXSUWwAePPNN/Htt98iOzsb5ubmcHd3R/fu3bF3716VhXTx8q2trcu8PObrr7/GlClTMHDgQADA4sWLsXPnTqU2AQEBSq9XrVqFRo0aIS0tDa1bt5bW1bBhQ6V1RUZG4ssvv8Qbb7wB4PER7rS0NPzf//0fgoODsW7dOsjlcqxcuRImJiZ44YUX8O+//2LUqFFq4/3rr7/wyy+/4MiRI9I+XblyJVq1aqV2noyMDLi5uaFr166QyWRwcXGp0H6q6N+qS5cumDx5MgCgefPmOHDgAL766qsKFdL6+vpSx9rW1lbtAzCKC+gDBw6gc+fOAB4fqHB2dsbWrVuloqCgoADLli2T3jdhYWGYNWtWuXHUNQYGBoiLi0NoaCiWLVuG9u3bo1u3bnjrrbfQtm1bAEBiYmKFPtOaaNCgAfT19WFhYVHm53L+/PkICQnB6NGjAQDh4eE4dOgQ5s+fr9T5DgkJwdChQwEAn3/+ORYtWoQjR47A399f5brNzMxUXjL36quv4sMPP5ReBwYGokePHpg+fTqAx+/rtLQ0zJs3T6mQ7tOnD95//30AQEREBJYuXYqOHTtK78dJkybBx8cH2dnZZW5vWduxZMkSODs7Y/HixZDJZGjZsiWuXr2KSZMmISIiAnp6yheMZWRkoF69enjttddgYWEBFxcXtGvXDsDjovbtt99GbGysFON3332Hxo0bK52xVKSvr4/g4GDExcVh+vTpkMlkuHDhAn777TckJCQAeHy//Z07d9R+D7Vq1QpCCJw/fx6dOnUCULHcQ+WbNWtWhb5ri+Xl5eHzzz/H7t274ePjAwBo2rQpfv/9d/zf//2fykL67Nmz2L17N44ePYoOHToAeHy1lZubW6m2JfsLQ4cOrdBnuSI+++wzKb7Jkyejb9++ePToEUxMTBAdHY3hw4dj+PDhAIBPP/0Uu3fvLvOsdEZGBtq1aydtk+LZR3V5/dVXX1VaxjfffANra2vs27cPr732mtqcOnPmTEyePBnBwcEAHu/z2bNn4+OPP0ZkZCQyMjJgb28PX19fGBoaonHjxtJnhUor+f3Ru3dvbNq0SWXbkt/vle0TKdqwYQPkcjlWrFghHSSNjY2FtbU1kpKS0KtXL4wePRo7d+7EO++8AyMjI3Ts2FF6Rs9ff/2FjRs3IiEhAb6+vgCg9qCPkZGR0hUpitsRGxuLjIwMODo6Anj8HKD4+HjExsbi888/x8KFC+Hv74+PP/4YwONcdvDgwTKfRVGZ/ePh4QEPDw/p9ezZs7FlyxZs27YNYWFhanN/VFQUAgMDpasI3NzcsGjRInTr1g1Lly5Ve+VqTcRCWhNmZo/PDOtq3Rro3r07li5dqjSu5BmD4iSjyMXFRanAOnPmDJydnaUONwC4u7vD2toaZ86ckTrdJedTvxlmUjEEPL5Xz9XVVemL087OrswzseW5e/cusrOzlZKVvr4+vLy8IJfLpXHnzp1DREQEDh8+jBs3bkjTMjIy1N5r+eDBA1y4cAHDhw9XOvpWWFgoPTzizJkzaNu2rdIXRXHHRp0zZ87AwMAAXl5e0riWLVuW+QTGkJAQ9OzZEy1atIC/vz9ee+019OrVq8z1ABX/W5WM2cfHR+tP8i7ebm9vb2lcw4YN0aJFC5w5c0YaV/J94+Dg8FTvkdosICAAffv2xW+//YZDhw7hl19+wdy5c7FixQqEhIRU+DNdFc6cOSOdrSnWpUsXLFy4UGlccdEPAPXq1YOlpWWl/t4lv+POnDmD/v37l1p/dHQ0ioqKpKPwiuu3s7MDALRp06bUuGvXrpXZKStrO86cOQMfHx+lM9pdunTB/fv38e+//yo9xAsAevbsCRcXFzRt2hT+/v7w9/eXLhsHgNDQUHTs2BFXrlyBk5MT4uLiEBISUuYZ8/feew9z5szB3r178eqrryI2Nhaurq6lioryLllWvAqgIrmnKhnqGWLqS7op2g31tHffsKr8XJbz588jNze3VPGdn58vHXApKT09HQYGBmjfvr00rlmzZirPgKv6LFXks1wRip8TBwcHAI8/W40bN8aZM2cwcuRIpfY+Pj7Yu3ev2uWNGjUKAQEBOHbsGHr16oUBAwZIB2rVyc7OxrRp05CUlIRr166hqKgIubm5pW59KOnEiRM4cOCAdLk7ABQVFeHRo0fIzc3FoEGDEB0dLX1u+/Tpg379+lXvPe96ho/PDOuChp+Jkt8f9cq4ErPke7KyfSJFJ06cwPnz52FhYaE0/tGjR0q3/q1atQrNmzeHnp6e0pVJqamp0NfXV3sFSEWcOnUKRUVFaN68udL4vLw8NGzYEMDjz1/xiaJiPj4+ZRbSldk/9+/fx4wZM7Bjxw5kZmaisLAQDx8+rNDn4uTJk0pXDQohIJfLcfHixTJPEtU0LKQ1IZNp/fLqqlKvXj00a9as3DYVGVfR9VVEyQeUyGQyleMUC96q0q9fP7i4uGD58uVwdHSEXC5H69atkZ+fr3ae+08OpCxfvlyp+ANQ7sMwtK19+/a4ePEifvnlF+zevRuDBw+Gr69vuffEVfZvrKj4bJliB7ugoOCpl6uOqvdIRe9HrItMTEzQs2dP9OzZE9OnT8f//vc/REZGKp111YSenl6p/V3df+/KfCdU9r2uuP7iDpKqceXFpM3vNgsLCxw7dgxJSUnYtWsXIiIiMGPGDBw9ehTW1tZo164dPDw8sGbNGvTq1QunT5/Gjh1lP2Hezc0NL730EmJjY/HKK69gzZo1CA0NlbavUaNG0gEWVYoPhBXfdwpULPdUJZlMprXLq3Wp5Hu3vM9gcW7asWMHnJyclNpp42Fvmn6WNMkRlflslaV37964fPkydu7ciYSEBPTo0QNjxozB/Pnz1c4THByMmzdvYuHChXBxcYGxsTF8fHzK7A8Aj/f7zJkzpSvUFJmYmMDZ2Rnp6enYvXs3EhISMHr0aMybNw/79u2rvge2yWRVdnm1tmny/VHyPVnZPpGi+/fvw8vLS+VtY4onIE6cOIEHDx5AT08PmZmZ0gEgbTxc8/79+9DX10dKSkqpfuXTPFS3Mvtn4sSJSEhIwPz589GsWTOYmprizTffrNDn4v3331f5PICSB4lrOj5sjMrUqlUrpfsoACAtLQ137tyR7gepboaGhkoPIyvJysoKdnZ2OHr0qDSuqKhI6cEhN2/eRHp6OqZNm4YePXqgVatWuH37ttJyis+yKK7Lzs4Ojo6O+Pvvv9GsWTOlobgz2apVK5w8eVLp0rNDhw6VuU0tW7ZEYWEhUlJSpHHp6enl/iSCpaUlhgwZguXLl2PDhg348ccfcevWLQDl76fylIz50KFD0lHE4oSi+MCvkr+RrWr/ldSqVSsUFhbi8OHD0rjiv42u3l+1kbu7u/SQj8p8phs1aqT0ty4qKsKff/6p1MbIyKjc91urVq2kex+LHThwoNr+1urW37x582o/ENaqVSskJycrFRoHDhyAhYUFnnvuOZXzGBgYwNfXF3PnzsXJkydx6dIl7NmzR5r+v//9D3FxcYiNjYWvr6/SVQfqDB8+HD/++CN+/PFHXLlyRelgi56eHgYPHox169YhKytLab6HDx9iyZIlGDhwYK35ubFnWcnPIKD8nevu7g5jY2NkZGSUyk3q3gctWrRAYWGh0sMwz58/XyoXqlLeZ7kiOaIiWrVqpZQfgPLzafH6g4OD8d133yE6OhrffPMNAPV56cCBAxg7diz69OkjPYyw5H2tqnJq+/btkZ6eXmqfN2vWTDqYYGpqin79+mHRokVISkpCcnIyTp06pdmOoAp52j5R+/btce7cOdja2pb6exZ/z926dQshISH45JNPEBISgsDAQOke/DZt2kAul2Pfvn0VildV3mzXrh2Kiopw7dq1UjEUXwFV2c+FpvvnwIEDCAkJwcCBA9GmTRvY29tLD14raxvat2+PtLQ0lZ+LmvB0dU2wkK6l8vLykJWVpTSoethBeXx9fdGmTRsEBgbi2LFjOHLkCIKCgtCtWzeNLz3TFldXVyQmJiIrK0ttwv/ggw8QFRWFn376Cenp6Rg3bhxu374tHe2uX78+GjZsiG+++Qbnz5/Hnj17EB4errQMW1tbmJqaIj4+HtnZ2bh79y6Ax/dERUVFYdGiRfjrr79w6tQpxMbGYsGCBQAe30smk8kQGhqKtLQ07Ny5s8wj4QCkS23ef/99HD58GCkpKfjf//5X5tHNBQsW4Pvvv8fZs2fx119/YdOmTbC3t5cuB6/IfirLgQMHMHfuXPz111+IiYnBpk2bpIemmZqa4sUXX8ScOXNw5swZ7Nu3D9OmTVOa38XFBTKZDNu3b8f169elMyaK3Nzc0L9/f4SGhuL333/HiRMn8M4778DJyanUJbhUvps3b+LVV1/Fd999h5MnT+LixYvYtGkT5s6dK+3PynymX331VezYsQM7duzA2bNnMWrUqFIHeVxdXbF//35cuXJF7XfNRx99hLi4OCxduhTnzp3DggULsHnzZkycOFGr+0GdDz/8EImJiZg9ezb++usvrF69GosXL6629SsaPXo0/vnnH3zwwQc4e/YsfvrpJ0RGRiI8PLzU/dHA43sHFy1ahNTUVFy+fBlr1qyBXC5HixYtpDZvv/02/v33XyxfvhzvvfdeheIYNGgQDA0N8f7776NXr16liq7PPvsM9vb26NmzJ3755Rf8888/2L9/P/z8/KCnp1fqUl5t5R5S9uqrr+KPP/7AmjVrcO7cOURGRiodzLKwsMDEiRMxYcIErF69GhcuXMCxY8fw9ddfq/xtXuDxAVxfX1+MGDECR44cwfHjxzFixAiYmpqW+1vX5X2WK5IjKmLcuHFYtWoVYmNj8ddffyEyMhKnT58uc56IiAj89NNPOH/+PE6fPo3t27dLB4HV5XU3Nzd8++23OHPmDA4fPozAwMBS+VdVTo2IiMCaNWswc+ZMnD59GmfOnMH69eulbY2Li8PKlSvx559/4u+//8Z3330HU1NTpftTSTu00ScKDAyEjY0N+vfvj99++w0XL15EUlISxo4di3///RcAMHLkSDg7O2PatGlYsGABioqKpPe9q6srgoOD8d5772Hr1q3S/Bs3blS5PldXV9y/fx+JiYm4ceMGcnNz0bx5cwQGBiIoKAibN2/GxYsXceTIEURFRUlXGY0dOxbx8fGYP38+zp07h8WLF5d5WXdl94+bmxs2b96M1NRUnDhxAm+//Xapq0VU5f5Jkybh4MGDCAsLQ2pqKs6dO4effvoJYWFh5fwVayBBaj18+FCkpaWJhw8f6joUjQQHBwsApYYWLVpIbQCILVu2KM0XGRkpPDw8Si3v8uXL4vXXXxf16tUTFhYWYtCgQSIrK6vc+UqKjY0VVlZW5a4zODhY9O/fX3rt4uIivvrqK+n1tm3bRLNmzYSBgYFwcXFRuZyCggIRFhYmLC0tRf369cWkSZPEoEGDxFtvvSW1SUhIEK1atRLGxsaibdu2IikpqdR+Wb58uXB2dhZ6enqiW7du0vi1a9cKT09PYWRkJOrXry9efvllsXnzZml6cnKy8PDwEEZGRsLT01P8+OOPAoA4fvy42v2TmZkp+vbtK4yNjUXjxo3FmjVrSm27YnzffPON8PT0FPXq1ROWlpaiR48e4tixYxrtp2LdunUT48aNU9rnM2fOFIMGDRJmZmbC3t5eLFy4UGmetLQ04ePjI0xNTYWnp6fYtWuXACD27t0rtZk1a5awt7cXMplMBAcHq1zXrVu3xLvvviusrKyEqamp8PPzE3/99Zc0XdX7ZsuWLYJfX6U9evRITJ48WbRv315YWVkJMzMz0aJFCzFt2jSRm5srtdP0M52fny9GjRolGjRoIGxtbUVUVJTo37+/9DcV4vF7vm3btsLY2Fj626j62y1ZskQ0bdpUGBoaiubNm4s1a9YoTVf13WRlZSViY2PVbve4ceOUPp9ClP7eKPbDDz8Id3d3YWhoKBo3bizmzZtX7nwlY7p48WK5n+eKbEdSUpLo2LGjMDIyEvb29mLSpEmioKBAmq74Wfntt99Et27dRP369YWpqalo27at2LBhQ6n1vvvuu6JBgwbi0aNHamMracSIEQKA2Lhxo8rp169fFx988IFwdnYW+vr6AoDo3LmzuHnzplK7iuQeUlbyM7J3714BQNy+fbtU24iICGFnZyesrKzEhAkTRFhYmNL7Xi6Xi+joaNGiRQthaGgoGjVqJPz8/MS+ffvUrv/q1auid+/ewtjYWLi4uIh169YJW1tbsWzZMqmNqveyEOV/lsvLEaq29fjx4wKAuHjxojTus88+EzY2NsLc3FwEBweLjz/+uMw+x+zZs0WrVq2EqampaNCggejfv7/4+++/pemq8vqxY8dEhw4dhImJiXBzcxObNm2qUN9DCCHi4+NF586dhampqbC0tBSdOnUS33zzjRDica7y9vYWlpaWol69euLFF18Uu3fvVht7XVay76dIVR+l5Pd0ZfpEqmRmZoqgoCBhY2MjjI2NRdOmTUVoaKi4e/euWL16tahXr55SH+Xw4cPC0NBQ7Ny5UwjxuHaYMGGCcHBwEEZGRqJZs2Zi1apVQgjV7/mRI0eKhg0bCgAiMjJSCPE450ZERAhXV1dhaGgoHBwcxMCBA8XJkyel+VauXCmee+45YWpqKvr16yfmz59fKt8+7f65ePGi6N69uzA1NRXOzs5i8eLFpf4WqnK/EEIcOXJE9OzZU5ibm4t69eqJtm3bis8++0xlbDW13hJCCJkQvNFQnUePHuHixYtl/jYy1QxyuRytWrXC4MGDMXv2bF2HQ0RUJXr06IEXXngBixYtqrJ1rFy5EqNHj8aGDRtK/bYx1Wz//vsvnJ2dsXv3bvTo0UPX4RBRHVCT6y0+bIxqpcuXL2PXrl3o1q0b8vLysHjxYly8eBFvv/22rkMjItK627dvIykpCUlJSViyZEmVrmv48OFo0KABzpw5Az8/P608YId0Y8+ePbh//z7atGmDzMxMfPzxx3B1dcXLL7+s69CIiJ55LKSpVtLT00NcXBwmTpwIIQRat26N3bt316pH7hMRFWvXrh1u376NL774Qum+6apS8qdXqGYqKCjA1KlT8ffff8PCwgKdO3fG2rVrq++J0kRENRgv7S5DTb7UgIiIiIiI6FlWk+stPrWbiIiIiIiISAMspCuAJ+2JiIiIiIi0qybXWSyky6Cvrw8AyM/P13EkREREREREtUtubi4A1MhnM/BhY2UwMDCAmZkZrl+/DkNDQ+jp8bgDERERERHR0xBCIDc3F9euXYO1tbV0ArMm4cPGypGfn4+LFy9CLpfrOhQiIiIiIqJaw9raGvb29pDJZLoORWMspCtALpfz8m4iIiIiIiItMTQ0rJFnoouxkCYiIiIiIiLSAG/6JSIiIiIiItIAC2kiIiIiIiIiDbCQJiIiIiIiItIAC2kiIiIiIiIiDbCQJiIiIiIiItIAC2kiIiIiIiIiDbCQJiIiIiIiItIAC2kiIiIiIiIiDbCQJiIiIiIiItIAC2kiIiIiIiIiDbCQJiIiIiIiItIAC2kiIiIiIiIiDbCQJiIiIiIiItIAC2kiIiIiIiIiDbCQJiIiIiIiItIAC2kiIiIiIiIiDbCQJiIiIiIiItIAC2kiIiIiIiIiDbCQJiIiIiIiItIAC2kiIiIiIiIiDbCQJiIiIiIiItIAC2kiIiIiIiIiDbCQJiIiIiIiItIAC2kiIiIiIiIiDbCQJiIiIiIiItIAC2kiIiIiIiIiDbCQJiIiIiIiItIAC2kiIiIiIiIiDbCQJiIiIiIiItIAC2kiIiIiIiIiDbCQJiIiIiIiItIAC2kiIiIiIiIiDbCQJiIiIiIiItIAC2kiIiIiIiIiDbCQJiIiIiIiItIAC2kiIiIiIiIiDbCQJtKRkJAQuLq6an25MpkMM2bMeCZiISIiqq2Yx4nqNhbSVGvExcVBJpNJg4mJCRwdHeHn54dFixYhJydH1yGSjuTl5WHSpElwdHSEqakpvL29kZCQUOH5r1y5gsGDB8Pa2hqWlpbo378//v7771Ltli5dikGDBqFx48aQyWQICQnR4lYQEdVuzOOkTnXlccX3n+IwZ84cbW4O1RIGug6ASNtmzZqFJk2aoKCgAFlZWUhKSsL48eOxYMECbNu2DW3bttV1iFXq4cOHMDDQ7KO9fPlyyOXyKopI90JCQvDDDz9g/PjxcHNzQ1xcHPr06YO9e/eia9euZc57//59dO/eHXfv3sXUqVNhaGiIr776Ct26dUNqaioaNmwotf3iiy+Qk5ODTp06ITMzs6o3i4ioVmIeZx4vqbryOAD07NkTQUFBSuPatWun9W2iWkAQ1RKxsbECgDh69GipaYmJicLU1FS4uLiI3NxcHURXWnBwsHBxcdHKsoqKisTDhw+1sqza5vDhwwKAmDdvnjTu4cOH4vnnnxc+Pj7lzv/FF18IAOLIkSPSuDNnzgh9fX0xZcoUpbaXLl0ScrlcCCFEvXr1RHBwsHY2goioDmAeZx5XpTrzOAAxZswY7QVPtRov7aY64dVXX8X06dNx+fJlfPfdd0rTzp49izfffBMNGjSAiYkJOnTogG3btim1Kb7cbP/+/Xj//ffRsGFDWFpaIigoCLdv3y61viVLluCFF16AsbExHB0dMWbMGNy5c6fcOOfPn4/OnTujYcOGMDU1hZeXF3744YdS7WQyGcLCwrB27VppPfHx8dK04nurLl26pPYyJZlMJi2v5L1VxfPNnz8f33zzDZ5//nkYGxujY8eOOHr0aKl4Nm3aBHd3d5iYmKB169bYsmVLhe/XcnV1xWuvvYZdu3bB09MTJiYmcHd3x+bNm8udtyJ++OEH6OvrY8SIEdI4ExMTDB8+HMnJyfjnn3/Knb9jx47o2LGjNK5ly5bo0aMHNm7cqNTWxcVFab8SEZF2MI8zj1dHHi/28OFDPHr0SCvxU+3FS7upznj33XcxdepU7Nq1C6GhoQCA06dPo0uXLnBycsLkyZNRr149bNy4EQMGDMCPP/6IgQMHKi0jLCwM1tbWmDFjBtLT07F06VJcvnwZSUlJUkKbMWMGZs6cCV9fX4waNUpqd/ToURw4cACGhoZqY1y4cCFef/11BAYGIj8/H+vXr8egQYOwfft29O3bV6ntnj17sHHjRoSFhcHGxkZlsmvUqBG+/fZbpXEFBQWYMGECjIyMyt1n69atQ05ODt5//33IZDLMnTsXb7zxBv7++29pO3bs2IEhQ4agTZs2iIqKwu3btzF8+HA4OTmVu/xi586dw5AhQzBy5EgEBwcjNjYWgwYNQnx8PHr27AkAkMvluHXrVoWWZ2VlJcV3/PhxNG/eHJaWlkptOnXqBABITU2Fs7OzyuXI5XKcPHkS7733XqlpnTp1wq5du5CTkwMLC4sKbysREVUO8/hjzOOPVVUej4uLw5IlSyCEQKtWrTBt2jS8/fbbFYqb6hhdnxIn0payLgkrZmVlJdq1aye97tGjh2jTpo149OiRNE4ul4vOnTsLNze3Usv28vIS+fn50vi5c+cKAOKnn34SQghx7do1YWRkJHr16iWKioqkdosXLxYAxKpVq6Rxqi4JK3m5Wn5+vmjdurV49dVXlcYDEHp6euL06dOlthGAiIyMVLsPRo8eLfT19cWePXvUxnLx4kUBQDRs2FDcunVLGv/TTz8JAOLnn3+WxrVp00Y899xzIicnRxqXlJQkAFTokjcXFxcBQPz444/SuLt37woHBwelv1VxTBUZ9u7dK833wgsvlNp/Qghx+vRpAUAsW7ZMbWzXr18XAMSsWbNKTYuJiREAxNmzZ1XOy0u7iYg0wzz+3zTm8b3SfNWZxzt37iyio6PFTz/9JJYuXSpat24tAIglS5aUux+o7uEZaapTzM3Npad+3rp1C3v27MGsWbOQk5Oj9DRQPz8/REZG4sqVK0pHZEeMGKF0JHrUqFGYOnUqdu7ciddffx27d+9Gfn4+xo8fDz29/+6cCA0NxdSpU7Fjxw4MGzZMbXympqbS/2/fvo2ioiK89NJL+P7770u17datG9zd3TXa/jVr1mDJkiX48ssv0b1793LbDxkyBPXr15dev/TSSwAgPeny6tWrOHXqFKZOnQpzc3Ol2Nq0aYN79+5VKC5HR0elswbFl9t98cUXyMrKgr29Pezt7Sv8hE4PDw/p/w8fPoSxsXGpNiYmJtJ0dYqnVXZ+IiLSLuZx5vFiVZHHDxw4oNTmvffeg5eXF6ZOnYqQkBClvy8RC2mqU+7fvw9bW1sAwPnz5yGEwPTp0zF9+nSV7a9du6aUgN3c3JSmm5ubw8HBAZcuXQIAXL58GQDQokULpXZGRkZo2rSpNF2d7du349NPP0Vqairy8vKk8aruu23SpEmZyyopNTUVI0eOxNChQxEeHl6heRo3bqz0ujgZF99PVrw9zZo1KzVvs2bNcOzYsQqtp1mzZqW2sXnz5gAe3+dlb28PExMT+Pr6Vmh5ikxNTZX2ZbHie5/KSorF0yo7PxERaRfzOPN4serI40ZGRggLC8PIkSORkpJS7hPCqW5hIU11xr///ou7d+9KyaL4ZyImTpwIPz8/lfOoSixV5bfffsPrr7+Ol19+GUuWLIGDgwMMDQ0RGxuLdevWlWqvSQF3+/ZtBAQEoHnz5lixYkWF59PX11c5XghR4WVoS1FREa5fv16htg0aNJDuHXNwcMCVK1dKtSn+eSpHR8cyl2NsbKzyp6wqMj8REWkP8zjzuKLqyuPF919X9P5uqjtYSFOdUfywjuJk27RpUwCAoaFhhY+Qnjt3TulSqvv37yMzMxN9+vQB8PipzQCQnp4uLR8A8vPzcfHixTLX8+OPP8LExAS//vqr0iVIsbGxFYpNHblcjsDAQNy5cwe7d++GmZnZUy1PUfH2nj9/vtQ0VePUKT6roHg0+6+//gIA6eEr//zzT4WP3u/duxevvPIKAMDT0xN79+7FvXv3lB5UcvjwYWm6Onp6emjTpg3++OOPUtMOHz6Mpk2b8kFjRETVhHmceVwXebz4MvhGjRpVKHaqO/jzV1Qn7NmzB7Nnz0aTJk0QGBgIALC1tcUrr7yC//u//1N5pFLVUdNvvvkGBQUF0uulS5eisLAQvXv3BgD4+vrCyMgIixYtUjrau3LlSty9e7fUEzsV6evrQyaToaioSBp36dIlbN26VePtVTRz5kz8+uuv+P777zW+jKw8jo6OaN26NdasWYP79+9L4/ft24dTp05VeDlXr17Fli1bpNf37t3DmjVr4OnpCXt7ewCQ7q2qyKB4b9Wbb76JoqIifPPNN9K4vLw8xMbGwtvbW+lJnxkZGTh79qxSbG+++SaOHj2qlITT09OxZ88eDBo0qMLbSERElcc8zjxe1Xlc1fslJycH0dHRsLGxgZeXV4X3B9UNPCNNtc4vv/yCs2fPorCwENnZ2dizZw8SEhLg4uKCbdu2SQ+XAICYmBh07doVbdq0QWhoKJo2bYrs7GwkJyfj33//xYkTJ5SWnZ+fjx49emDw4MFIT0/HkiVL0LVrV7z++usAHh+tnDJlCmbOnAl/f3+8/vrrUruOHTvinXfeURt33759sWDBAvj7++Ptt9/GtWvXEBMTg2bNmuHkyZOV2henTp3C7Nmz8fLLL+PatWulfnuzrHgq6vPPP0f//v3RpUsXDBs2DLdv38bixYvRunVrpaRclubNm2P48OE4evQo7OzssGrVKmRnZysdxa/svVXe3t4YNGgQpkyZgmvXrqFZs2ZYvXo1Ll26hJUrVyq1DQoKwr59+5Q6T6NHj8by5cvRt29fTJw4EYaGhliwYAHs7Ozw4YcfKs3/888/S++ZgoICnDx5Ep9++ikA4PXXX0fbtm01jp+IqK5hHv8P83j15fGYmBhs3boV/fr1Q+PGjZGZmYlVq1YhIyMD3377bYV+bozqGF09LpxI24p/2qJ4MDIyEvb29qJnz55i4cKF4t69eyrnu3DhgggKChL29vbC0NBQODk5iddee0388MMPpZa9b98+MWLECFG/fn1hbm4uAgMDxc2bN0stc/HixaJly5bC0NBQ2NnZiVGjRonbt28rtVH1sxkrV64Ubm5uwtjYWLRs2VLExsaKyMhIUfKjCkCMGTNG5fZA4Wcz9u7dW+bPS6iLpfgnKubNm1fm8outX79etGzZUhgbG4vWrVuLbdu2iYCAANGyZUuVMSpycXERffv2Fb/++qto27attO2bNm0qd96KevjwoZg4caKwt7cXxsbGomPHjiI+Pr5Uu27dupXa10II8c8//4g333xTWFpaCnNzc/Haa6+Jc+fOlWoXHBysdl/HxsZqbXuIiGoj5vH/pjGPK6uOPL5r1y7Rs2dP6X1kbW0tevXqJRITE7W2HVS7yITQwdMGiGqYuLg4DBs2DEePHkWHDh10HU6N4OnpiUaNGpX7Uxeurq5o3bo1tm/fXk2RERFRXcM8rjnmcaKy8R5pInoqBQUFKCwsVBqXlJSEEydOSA8KISIiomcT8zhR5fAeaSJ6KleuXIGvry/eeecdODo64uzZs1i2bBns7e0xcuRIXYdHREREZWAeJ6ocFtJE9FTq168PLy8vrFixAtevX0e9evXQt29fzJkzBw0bNtR1eERERFQG5nGiyuE90kRERNUoJiYG8+bNQ1ZWFjw8PPD111+jU6dOKtsuX74ca9aswZ9//gkA8PLywueff67UXgiByMhILF++HHfu3EGXLl2wdOlSuLm5SW1u3bqFDz74AD///DP09PQQEBCAhQsXwtzcvGo3loiIqJbiPdJERETVZMOGDQgPD0dkZCSOHTsGDw8P+Pn54dq1ayrbJyUlYejQodi7dy+Sk5Ph7OyMXr164cqVK1KbuXPnYtGiRVi2bBkOHz6MevXqwc/PD48ePZLaBAYG4vTp00hISMD27duxf/9+jBgxosq3l4iIqLbiGWkiIqJq4u3tjY4dO2Lx4sUAALlcDmdnZ3zwwQeYPHlyufMXFRWhfv36WLx4MYKCgiCEgKOjIz788ENMnDgRAHD37l3Y2dkhLi4Ob731Fs6cOQN3d3elpxXHx8ejT58++Pfff+Ho6Fh1G0xERFRL1bl7pAsLC3H8+HHY2dlBT48n5ImIqHLkcjkyMjLg7u4OA4P/0qmxsTGMjY1Ltc/Pz0dKSgqmTJkijdPT04Ovry+Sk5MrtM7c3FwUFBSgQYMGAICLFy8iKysLvr6+UhsrKyt4e3sjOTkZb731FpKTk2Ftba30kz++vr7Q09PD4cOHMXDgQI23XdeYy4mI6GnJ5XJkZ2ejXbt2Snm8oupcIX38+HG196IRERE9rcjISMyYMaPU+Bs3bqCoqAh2dnZK4+3s7HD27NkKLXvSpElwdHSUCuesrCxpGSWXWTwtKysLtra2StMNDAzQoEEDqU1Nw1xORETacuTIEXTs2FHj+epcIV3c2Thy5AgcHBx0HA09awqKCvB1ytcAgA+8PoChvmGNWDYpkBfA+MLj/Zz3/AeAHvczVY3MzEx06tQJf/75J5ydnaXxqs5Ga8OcOXOwfv16JCUlwcTEpErWUVMwl1NZmMtrOOZxqibFebzkweiKqnOFdPElYA4ODnjuued0HA09a/KL8mF50RIA4PScE4z0jWrEsklBUT5w5/F+hpMTwP1MVczKygqWlpbltrOxsYG+vj6ys7OVxmdnZ8Pe3r7MeefPn485c+Zg9+7daNu2rTS+eL7s7GylgjI7Oxuenp5Sm5IPMyssLMStW7fKXe+zirmcysJcXsMxj1M1q+wtQryxiIiIqBoYGRnBy8sLiYmJ0ji5XI7ExET4+PionW/u3LmYPXs24uPjle5zBoAmTZrA3t5eaZn37t3D4cOHpWX6+Pjgzp07SElJkdrs2bMHcrkc3t7e2to8IiKiOqXOnZEmIiLSlfDwcAQHB6NDhw7o1KkToqOj8eDBAwwbNgwAEBQUBCcnJ0RFRQEAvvjiC0RERGDdunVwdXWV7mk2NzeHubk5ZDIZxo8fj08//RRubm5o0qQJpk+fDkdHRwwYMAAA0KpVK/j7+yM0NBTLli1DQUEBwsLC8NZbb/GJ3URERJXEQpqIiKiaDBkyBNevX0dERASysrLg6emJ+Ph46f6sjIwMpUvMli5divz8fLz55ptKy1F8oNnHH3+MBw8eYMSIEbhz5w66du2K+Ph4pfuo165di7CwMPTo0QN6enoICAjAokWLqn6DiYiIaikW0kRERNUoLCwMYWFhKqclJSUpvb506VK5y5PJZJg1axZmzZqltk2DBg2wbt06TcIkIiKiMvAeaSIiIiIiIiIN6LSQ3r9/P/r16wdHR0fIZDJs3bq13HmSkpLQvn17GBsbo1mzZoiLi6vyOImIiIiIiIiK6bSQfvDgATw8PBATE1Oh9hcvXkTfvn3RvXt3pKamYvz48fjf//6HX3/9tYojJSIiIiIiInpMp/dI9+7dG717965w+2XLlqFJkyb48ssvATx+Eunvv/+Or776Cn5+flUVJtVFQug6AiIiInoWCQHIZLqOgoh0rEbdI52cnAxfX1+lcX5+fkhOTlY7T15eHu7duycNOTk5VR0m1XSbfwS++T+goEDXkRAREdGz5Px54KuvgEOHdB0JEelYjSqks7KypJ8IKWZnZ4d79+7h4cOHKueJioqClZWVNLi7u1dHqFSTnfoTyMoGfvtN15EQERHRs+Sjj4CcHIC3FRLVeTWqkK6MKVOm4O7du9KQlpam65CIiIiIqCYqLNR1BET0jKhRvyNtb2+P7OxspXHZ2dmwtLSEqampynmMjY1hbGwsvb53716VxkhERERERES1W406I+3j44PExESlcQkJCfDx8dFRRERERERERFTX6LSQvn//PlJTU5Gamgrg8c9bpaamIiMjA8Djy7KDgoKk9iNHjsTff/+Njz/+GGfPnsWSJUuwceNGTJgwQRfhExERERERUR2k00L6jz/+QLt27dCuXTsAQHh4ONq1a4eIiAgAQGZmplRUA0CTJk2wY8cOJCQkwMPDA19++SVWrFjBn74iIiIiIiKiaqPTe6RfeeUViDJ+rzcuLk7lPMePH6/CqIiIiIiIiIjUq1H3SBMRERERERHpGgtpIiIiIiIiIg2wkCYiIiIiIiLSAAtpIiIiIiIiIg2wkCYiIiIiIiLSAAtpIiIiIiIiIg2wkCYiIiIiIiLSAAtpIiIiIiIiIg2wkCYiIqpGMTExcHV1hYmJCby9vXHkyBG1bU+fPo2AgAC4urpCJpMhOjq6VJviaSWHMWPGSG1eeeWVUtNHjhxZFZtHRERUJ7CQJiIiqiYbNmxAeHg4IiMjcezYMXh4eMDPzw/Xrl1T2T43NxdNmzbFnDlzYG9vr7LN0aNHkZmZKQ0JCQkAgEGDBim1Cw0NVWo3d+5c7W4cERFRHcJCmkiRELqOgIhqsQULFiA0NBTDhg2Du7s7li1bBjMzM6xatUpl+44dO2LevHl46623YGxsrLJNo0aNYG9vLw3bt2/H888/j27duim1MzMzU2pnaWmp9e0jIiKqK1hIEyliIU1EVSQ/Px8pKSnw9fWVxunp6cHX1xfJyclaW8d3332H9957DzKZTGna2rVrYWNjg9atW2PKlCnIzc3VyjqJiIjqIgNdB0D0TGEhTUQaysnJwb1796TXxsbGKs8e37hxA0VFRbCzs1Mab2dnh7Nnz2ollq1bt+LOnTsICQlRGv/222/DxcUFjo6OOHnyJCZNmoT09HRs3rxZK+slIiKqa1hIEylSLKRZVBNRBbi7uyu9joyMxIwZM3QSy8qVK9G7d284OjoqjR8xYoT0/zZt2sDBwQE9evTAhQsX8Pzzz1d3mERERDUeC2kiIqKnkJaWBicnJ+m1unuZbWxsoK+vj+zsbKXx2dnZah8kponLly9j9+7dFTrL7O3tDQA4f/48C2kiIqJK4D3SRIoUz0KXuL+QiEgVCwsLWFpaSoO6QtrIyAheXl5ITEyUxsnlciQmJsLHx+ep44iNjYWtrS369u1bbtvU1FQAgIODw1Ovl4iIqC7iGWkiRbycm4iqUHh4OIKDg9GhQwd06tQJ0dHRePDgAYYNGwYACAoKgpOTE6KiogA8fnhYWlqa9P8rV64gNTUV5ubmaNasmbRcuVyO2NhYBAcHw8BAObVfuHAB69atQ58+fdCwYUOcPHkSEyZMwMsvv4y2bdtW05YTERHVLiykiRTxHmkiqkJDhgzB9evXERERgaysLHh6eiI+Pl56AFlGRgb09P67WOzq1ato166d9Hr+/PmYP38+unXrhqSkJGn87t27kZGRgffee6/UOo2MjLB7926paHd2dkZAQACmTZtWdRtKRERUy7GQJiIiqkZhYWEICwtTOU2xOAYAV1dXiAoc1OvVq5fads7Ozti3b5/GcRIREZF6vEeaSBHvkSYiIiIionKwkCZSxMu5iYiIiIioHCykiRSxkCYiIiIionKwkCYiIiIiIiLSAAtpIkU8I01EREREROVgIU2kiIU0ERERERGVg4U0kSIW0kREREREVA4W0kREREREREQaYCFNpIhnpImIiIiIqBwspIkUsZAmIiIiIqJysJAmUsRCmoiIiIiIysFCmoiIiIiIiEgDLKSJFPGMNBERERERlYOFNJEiFtJERERERFQOFtJEilhIExERERFROVhIEylSLKRZVBMRERERkQospImIiIiIiIg0wEKaSJHiWWiZTHdxEBERERHRM0vnhXRMTAxcXV1hYmICb29vHDlypMz20dHRaNGiBUxNTeHs7IwJEybg0aNH1RQt1Xq8tJuIiIiIiMqh00J6w4YNCA8PR2RkJI4dOwYPDw/4+fnh2rVrKtuvW7cOkydPRmRkJM6cOYOVK1diw4YNmDp1ajVHTrUWC2kiIiIiIiqHTgvpBQsWIDQ0FMOGDYO7uzuWLVsGMzMzrFq1SmX7gwcPokuXLnj77bfh6uqKXr16YejQoeWexSYiIiIiIiLSFp0V0vn5+UhJSYGvr+9/wejpwdfXF8nJySrn6dy5M1JSUqTC+e+//8bOnTvRp08ftevJy8vDvXv3pCEnJ0e7G0K1C89IE1EV0+SWptOnTyMgIACurq6QyWSIjo4u1WbGjBmQyWRKQ8uWLZXaPHr0CGPGjEHDhg1hbm6OgIAAZGdna3vTiIiI6gydFdI3btxAUVER7OzslMbb2dkhKytL5Txvv/02Zs2aha5du8LQ0BDPP/88XnnllTIv7Y6KioKVlZU0uLu7a3U7qJZhIU1EVUjTW5pyc3PRtGlTzJkzB/b29mqX+8ILLyAzM1Mafv/9d6XpEyZMwM8//4xNmzZh3759uHr1Kt544w2tbhsREVFdovOHjWkiKSkJn3/+OZYsWYJjx45h8+bN2LFjB2bPnq12nilTpuDu3bvSkJaWVo0RU43DQpqIqpCmtzR17NgR8+bNw1tvvQVjY2O1yzUwMIC9vb002NjYSNPu3r2LlStXYsGCBXj11Vfh5eWF2NhYHDx4EIcOHdL6NhIREdUFOiukbWxsoK+vX+rSsuzsbLVH3adPn453330X//vf/9CmTRsMHDgQn3/+OaKioiCXy1XOY2xsDEtLS2mwsLDQ+rYQEVHdlZOTo3QLUV5ensp2lbmlqaLOnTsHR0dHNG3aFIGBgcjIyJCmpaSkoKCgQGm9LVu2ROPGjZ96vURERHWVzgppIyMjeHl5ITExURonl8uRmJgIHx8flfPk5uZCT085ZH19fQCA4NlD0gaekSYiDbm7uyvdQhQVFaWyXWVuaaoIb29vxMXFIT4+HkuXLsXFixfx0ksvSc8EycrKgpGREaytrbW6XiIiorrMQJcrDw8PR3BwMDp06IBOnTohOjoaDx48wLBhwwAAQUFBcHJykjol/fr1w4IFC9CuXTt4e3vj/PnzmD59Ovr16ycV1ERPhYU0EWkoLS0NTk5O0uuyLsGuCr1795b+37ZtW3h7e8PFxQUbN27E8OHDqzUWIiKiukKnhfSQIUNw/fp1REREICsrC56enoiPj5eO1mdkZCidgZ42bRpkMhmmTZuGK1euoFGjRujXrx8+++wzXW0C1TYspIlIQxYWFrC0tCy3XWVuaaoMa2trNG/eHOfPnwcA2NvbIz8/H3fu3FE6K63t9RIREdUlOn/YWFhYGC5fvoy8vDwcPnwY3t7e0rSkpCTExcVJrw0MDBAZGYnz58/j4cOHyMjIQExMTKnL1YiIiJ41lbmlqTLu37+PCxcuwMHBAQDg5eUFQ0NDpfWmp6cjIyNDq+slIiKqS3R6RpromcMz0kRUhTS9pSk/P1/6tYn8/HxcuXIFqampMDc3R7NmzQAAEydORL9+/eDi4oKrV68iMjIS+vr6GDp0KADAysoKw4cPR3h4OBo0aABLS0t88MEH8PHxwYsvvqiDvUBERFTzsZAmUsRCmoiqkKa3NF29ehXt2rWTXs+fPx/z589Ht27dkJSUBAD4999/MXToUNy8eRONGjVC165dcejQITRq1Eia76uvvoKenh4CAgKQl5cHPz8/LFmypHo2moiIqBZiIU2kiIU0EVWxsLAwhIWFqZxWXBwXc3V1LfdXKdavX1/uOk1MTBATE4OYmJgKx0lERETq6fweaaJnCgtpIiIiIiIqBwtpIiIiIiIiIg2wkCZSxDPSRERERERUDhbSRIpYSBMRERERUTlYSBMpYiFNRERERETlYCFNREREREREpAEW0kSKeEaaiIiIiIjKwUKaSBELaSIiIiIiKgcLaSJFLJ6JiIiIiKgcLKSJiIiIiIiINMBCmkgRL+0mIiIiIqJysJAmUsRCmoiIiIiIysFCmkgJC2kiIiIiIiobC2kiIiIiIiIiDbCQJlKkeBKaZ6SJiIiIiEgFFtJEiniPNBERERERlYOFNJEiFtJERERERFQOFtJEilhIExERERFROVhIExEREREREWmAhTSRIp6RJiIiIiKicrCQJlLEQpqIiIiIiMrBQppIEQtpIqpiMTExcHV1hYmJCby9vXHkyBG1bU+fPo2AgAC4urpCJpMhOjq6VJuoqCh07NgRFhYWsLW1xYABA5Cenq7U5pVXXoFMJlMaRo4cqe1NIyIiqjNYSBMREVWTDRs2IDw8HJGRkTh27Bg8PDzg5+eHa9euqWyfm5uLpk2bYs6cObC3t1fZZt++fRgzZgwOHTqEhIQEFBQUoFevXnjw4IFSu9DQUGRmZkrD3Llztb59REREdUWlCum9e/dqOw6iZwPPSBNRFVqwYAFCQ0MxbNgwuLu7Y9myZTAzM8OqVatUtu/YsSPmzZuHt956C8bGxirbxMfHIyQkBC+88AI8PDwQFxeHjIwMpKSkKLUzMzODvb29NFhaWlY4buZ9IiIiZZUqpP39/fH888/j008/xT///KPtmIh0h4U0EWkoJycH9+7dk4a8vDyV7fLz85GSkgJfX19pnJ6eHnx9fZGcnKy1eO7evQsAaNCggdL4tWvXwsbGBq1bt8aUKVOQm5tb4WUy7xMRESmrVCF95coVhIWF4YcffkDTpk3h5+eHjRs3Ij8/X9vxEVUvFtJEpCF3d3dYWVlJQ1RUlMp2N27cQFFREezs7JTG29nZISsrSyuxyOVyjB8/Hl26dEHr1q2l8W+//Ta+++477N27F1OmTMG3336Ld955p8LLZd4nIiJSVqlC2sbGBhMmTEBqaioOHz6M5s2bY/To0XB0dMTYsWNx4sQJbcdJRET0TEpLS8Pdu3elYcqUKTqLZcyYMfjzzz+xfv16pfEjRoyAn58f2rRpg8DAQKxZswZbtmzBhQsXKrRc5n0iIiJlT/2wsfbt22PKlCkICwvD/fv3sWrVKnh5eeGll17C6dOntREjUfXhGWki0pCFhQUsLS2lQd29zDY2NtDX10d2drbS+OzsbLUPEtNEWFgYtm/fjr179+K5554rs623tzcA4Pz58xqvh3mfiIjoKQrpgoIC/PDDD+jTpw9cXFzw66+/YvHixcjOzsb58+fh4uKCQYMGaTNWoqrHQpqIqoiRkRG8vLyQmJgojZPL5UhMTISPj0+llyuEQFhYGLZs2YI9e/agSZMm5c6TmpoKAHBwcKjwepj3iYiI/mNQmZk++OADfP/99xBC4N1338XcuXOV7sWqV68e5s+fD0dHR60FSlQtWEgTURUKDw9HcHAwOnTogE6dOiE6OhoPHjzAsGHDAABBQUFwcnKS7rPOz89HWlqa9P8rV64gNTUV5ubmaNasGYDHl3OvW7cOP/30EywsLKT7ra2srGBqaooLFy5g3bp16NOnDxo2bIiTJ09iwoQJePnll9G2bdsKxc28T0REpKxShXRaWhq+/vprvPHGG2VewsafyyAiIvrPkCFDcP36dURERCArKwuenp6Ij4+XHkCWkZEBPb3/Lha7evUq2rVrJ72eP38+5s+fj27duiEpKQkAsHTpUgDAK6+8orSu2NhYhISEwMjICLt375aKdmdnZwQEBGDatGkVjpt5n4iISFmlCunIyEh07twZBgbKsxcWFuLgwYN4+eWXYWBggG7dumklSKJqwzPSRFTFwsLCEBYWpnJacXFczNXVFaKc76Lypjs7O2Pfvn0axVgS8z4REZGySt0j3b17d9y6davU+Lt376J79+5PHRSRzrCQJiIqhXmfiIhIWaUKaSEEZDJZqfE3b95EvXr1njooIp1hIU1EVArzPhERkTKNLu1+4403AAAymQwhISFK90kVFRXh5MmT6Ny5s0YBxMTEYN68ecjKyoKHhwe+/vprdOrUSW37O3fu4JNPPsHmzZtx69YtuLi4IDo6Gn369NFovURERFS2qsj7REREtYFGhbSVlRWAx0emLSwsYGpqKk0zMjLCiy++iNDQ0Aovb8OGDQgPD8eyZcvg7e2N6Oho+Pn5IT09Hba2tqXa5+fno2fPnrC1tcUPP/wAJycnXL58GdbW1ppsBpF6PCNNRCTRdt4nqlXYTyCq0zQqpGNjYwE8fvjJxIkTn/pyrgULFiA0NFT62Y9ly5Zhx44dWLVqFSZPnlyq/apVq3Dr1i0cPHgQhoaGUixEWsNCmohIou28T1SrsJ9AVKdV6h7pyMjIp06m+fn5SElJga+v73/B6OnB19cXycnJKufZtm0bfHx8MGbMGNjZ2aF169b4/PPPUVRUpHY9eXl5uHfvnjTk5OQ8VdxUy7GQJiIqRRt5n6jWYT+BqE6r8Bnp9u3bIzExEfXr10e7du1UPnSk2LFjx8pd3o0bN1BUVCT9dmYxOzs7nD17VuU8f//9N/bs2YPAwEDs3LkT58+fx+jRo1FQUIDIyEiV80RFRWHmzJnlxkMEgIU0EdET2s77RLUO+wlEdVqFC+n+/ftLDxkZMGBAVcVTJrlcDltbW3zzzTfQ19eHl5cXrly5gnnz5qktpKdMmYLw8HDp9ZUrV+Du7l5dIRMREdVIz0LeJyIielZVuJBWLFTVFa2asLGxgb6+PrKzs5XGZ2dnw97eXuU8Dg4OMDQ0hL6+vjSuVatWyMrKQn5+PoyMjErNY2xsrPSU0Xv37j117FSL8Yw0EREA7ed9olqH/QSiOq1S90hrg5GREby8vJCYmCiNk8vlSExMhI+Pj8p5unTpgvPnz0Mul0vj/vrrLzg4OKgsook0xkKaiIiIKoL9BKI6rcJnpOvXr1/m/VGKbt26VaF24eHhCA4ORocOHdCpUydER0fjwYMH0lO8g4KC4OTkhKioKADAqFGjsHjxYowbNw4ffPABzp07h88//xxjx46t6GYQlY2FNBERgKrJ+0S1CvsJRHVahQvp6Ohora98yJAhuH79OiIiIpCVlQVPT0/Ex8dLDyDLyMiAnt5/J82dnZ3x66+/YsKECWjbti2cnJwwbtw4TJo0SeuxERER1WVVkfeJaoPoLkMx/sD3ug6DiHSswoV0cHBwlQQQFhaGsLAwldOSkpJKjfPx8cGhQ4eqJBYinpEmInqsqvI+Ua3BfgJRnVbhQvrevXuwtLSU/l+W4nZENQ4LaSIiAMz7ROViP4GoTtPoHunMzEzY2trC2tpa5X1TQgjIZDIUFRVpNUiiasNCmogIAPM+UbnYTyCq0ypcSO/ZswcNGjQAAOzdu7fKAiIiIiLdY94nIiJSr8KFdLdu3VT+n6hW4RlpIiIAzPtE5WI/gahOq3AhXdLt27excuVKnDlzBgDg7u6OYcOGSUeviWokFtJERCox7xOVwH4CUZ2mV36T0vbv3w9XV1csWrQIt2/fxu3bt7Fo0SI0adIE+/fv13aMRNWHhTQRUSnM+0QqsJ9AVKdV6oz0mDFjMGTIECxduhT6+voAgKKiIowePRpjxozBqVOntBokERER6Q7zPhERkbJKnZE+f/48PvzwQymZAoC+vj7Cw8Nx/vx5rQVHVO14RpqIqlhMTAxcXV1hYmICb29vHDlyRG3b06dPIyAgAK6urpDJZIiOjq7UMh89eoQxY8agYcOGMDc3R0BAALKzsyscM/M+kQrsJxDVaZUqpNu3by/dI6XozJkz8PDweOqgiHSGhTQRVaENGzYgPDwckZGROHbsGDw8PODn54dr166pbJ+bm4umTZtizpw5sLe3r/QyJ0yYgJ9//hmbNm3Cvn37cPXqVbzxxhsVjpt5n0gF9hOI6rQKX9p98uRJ6f9jx47FuHHjcP78ebz44osAgEOHDiEmJgZz5szRfpRE1YWFNBFVoQULFiA0NBTDhg0DACxbtgw7duzAqlWrMHny5FLtO3bsiI4dOwKAyukVWebdu3excuVKrFu3Dq+++ioAIDY2Fq1atcKhQ4ekPF4S8z5ROdhPIKrTKlxIe3p6QiaTQSh8aXz88cel2r399tsYMmSIdqIjqm4spIlIQzk5Obh375702tjYGMbGxqXa5efnIyUlBVOmTJHG6enpwdfXF8nJyZVad0WWmZKSgoKCAvj6+kptWrZsicaNGyM5OVltIc28T1QO9hOI6rQKF9IXL16syjiIiIhqJHd3d6XXkZGRmDFjRql2N27cQFFREezs7JTG29nZ4ezZs5Vad0WWmZWVBSMjI1hbW5dqk5WVpXbZzPtERETqVbiQdnFxqco4iJ4NPCNNRBpKS0uDk5OT9FrV2eiaiHmfqBzsJxDVaZX6+atiaWlpyMjIQH5+vtL4119//amCItIZFtJEpCELCwtYWlqW287Gxgb6+vqlnpadnZ2t9kFi2limvb098vPzcefOHaWz0pVZL/M+kQL2E4jqtEoV0n///TcGDhyIU6dOKd0/JZPJADz+bUmiGomFNBFVESMjI3h5eSExMREDBgwAAMjlciQmJiIsLKzKlunl5QVDQ0MkJiYiICAAAJCeno6MjAz4+PhUaD3M+0QqsJ9AVKdV6uevxo0bhyZNmuDatWswMzPD6dOnsX//fnTo0AFJSUlaDpGIiKh2CA8Px/Lly7F69WqcOXMGo0aNwoMHD6QnbgcFBSk9OCw/Px+pqalITU1Ffn4+rly5gtTUVKXfbi5vmVZWVhg+fDjCw8Oxd+9epKSkYNiwYfDx8VH7oLGSmPeJiIiUVeqMdHJyMvbs2QMbGxvo6elBT08PXbt2RVRUFMaOHYvjx49rO06i6sEz0kRUhYYMGYLr168jIiICWVlZ8PT0RHx8vPSwsIyMDOjp/XeM++rVq2jXrp30ev78+Zg/fz66desmFbDlLRMAvvrqK+jp6SEgIAB5eXnw8/PDkiVLKhw38z6RCuwnENVplSqki4qKYGFhAeDx/VlXr15FixYt4OLigvT0dK0GSFStWEgTURULCwtTeyl3ybO7rq6uSj8/VZllAoCJiQliYmIQExOjUazFmPeJnmA/gYieqFQh3bp1a5w4cQJNmjSBt7c35s6dCyMjI3zzzTdo2rSptmMkqj5KSZEJkogIYN4nkrCQJqInKlVIT5s2DQ8ePAAAzJo1C6+99hpeeuklNGzYEBs2bNBqgERERKRbzPtERETKKlVI+/n5Sf9v1qwZzp49i1u3bqF+/frSEzyJaiSlI826C4OI6FnCvE/0BM9IE9ETT/U70gDwzz//AACcnZ2fOhginWOCJCIqE/M+1WnsJxDRE5X6+avCwkJMnz4dVlZWcHV1haurK6ysrDBt2jQUFBRoO0ai6sMESURUCvM+0RPsJxDRE5U6I/3BBx9g8+bNmDt3Lnx8fAA8/mmMGTNm4ObNm1i6dKlWgyQiIiLdYd4nIiJSVqlCet26dVi/fj169+4tjWvbti2cnZ0xdOhQJlSqHXikmYgIAPM+kYRnpInoiUpd2m1sbAxXV9dS45s0aQIjI6OnjYlId5ggiYhKYd4neoL9BCJ6olKFdFhYGGbPno28vDxpXF5eHj777DOEhYVpLTiiascESURUCvM+0RPsJxDRExW+tPuNN95Qer17924899xz8PDwAACcOHEC+fn56NGjh3YjJCIiomrHvE9ERKRehQtpKysrpdcBAQFKr/kzGFQr8EgzEREA5n0ildhPIKInKlxIx8bGVmUcRM8GJkgiIgDM+0QqsZ9ARE9U6qndxa5fv4709HQAQIsWLdCoUSOtBEWkM0yQRERqMe9Tncd+AhE9UamHjT148ADvvfceHBwc8PLLL+Pll1+Go6Mjhg8fjtzcXG3HSFR9mCCJiEph3id6gv0EInqiUoV0eHg49u3bh59//hl37tzBnTt38NNPP2Hfvn348MMPtR0jERER6RDzPhERkbJKXdr9448/4ocffsArr7wijevTpw9MTU0xePBgLF26VFvxEVUvHmkmIiqFeZ/oCfYTiOiJSp2Rzs3NhZ2dXanxtra2vMSLajYmSCKiUpj3iZ5gP4GInqhUIe3j44PIyEg8evRIGvfw4UPMnDkTPj4+WguOqNoxQRIRlcK8T/QE+wlE9ESlLu2Ojo6Gv78/nnvuOXh4eAAATpw4ARMTE/z6669aDZCIiIh0i3mfiIhIWaXOSLdp0wbnzp1DVFQUPD094enpiTlz5uDcuXN44YUXNF5eTEwMXF1dYWJiAm9vbxw5cqRC861fvx4ymQwDBgzQeJ1EKvFIMxFRKdrO+0Q1FvsJRPSExmekCwoK0LJlS2zfvh2hoaFPHcCGDRsQHh6OZcuWwdvbG9HR0fDz80N6ejpsbW3Vznfp0iVMnDgRL7300lPHQCRhgiQiUqLtvE9Uo7GfQERPaHxG2tDQUOkeqae1YMEChIaGYtiwYXB3d8eyZctgZmaGVatWqZ2nqKgIgYGBmDlzJpo2baq1WIiYIImoqml6FdamTZvQsmVLmJiYoE2bNti5c6fSdJlMpnKYN2+e1MbV1bXU9Dlz5lQoXm3nfaIajf0EInqiUpd2jxkzBl988QUKCwufauX5+flISUmBr6/vfwHp6cHX1xfJyclq55s1axZsbW0xfPjwcteRl5eHe/fuSUNOTs5TxUxERFRZxVdhRUZG4tixY/Dw8ICfnx+uXbumsv3BgwcxdOhQDB8+HMePH8eAAQMwYMAA/Pnnn1KbzMxMpWHVqlWQyWQICAhQWtasWbOU2n3wwQcVjltbeZ+IiKi2qNTDxo4ePYrExETs2rULbdq0Qb169ZSmb968uULLuXHjBoqKikr9pIadnR3Onj2rcp7ff/8dK1euRGpqaoXWERUVhZkzZ1aoLRGPNBNRVVK8CgsAli1bhh07dmDVqlWYPHlyqfYLFy6Ev78/PvroIwDA7NmzkZCQgMWLF2PZsmUAAHt7e6V5fvrpJ3Tv3r3UFVsWFhal2laUtvI+UY3HfgIRPVGpM9LW1tYICAiAn58fHB0dYWVlpTRUlZycHLz77rtYvnw5bGxsKjTPlClTcPfuXWlIS0ursvioFmCCJCIN5eTkKF35lJeXp7JdZa7CSk5OVmoPAH5+fmrbZ2dnY8eOHSqv2JozZw4aNmyIdu3aYd68eRqdXdZV3id65rCfQERPaHRGWi6XY968efjrr7+Qn5+PV199FTNmzICpqWmlVm5jYwN9fX1kZ2crjc/OzlZ51PzChQu4dOkS+vXrpxQTABgYGCA9PR3PP/+80jzGxsYwNjaWXt+7d69SsVIdwQRJRBpyd3dXeh0ZGYkZM2aUaleZq7CysrJUts/KylLZfvXq1bCwsMAbb7yhNH7s2LFo3749GjRogIMHD2LKlCnIzMzEggULytw2bed9ohpPqW/AfgJRXaZRIf3ZZ59hxowZ8PX1hampKRYtWoTr16+X+WCwshgZGcHLywuJiYnST1jJ5XIkJiYiLCysVPuWLVvi1KlTSuOmTZuGnJwcLFy4EM7OzpWKg4iIqLLS0tLg5OQkvVY8eFvdVq1ahcDAQJiYmCiNDw8Pl/7ftm1bGBkZ4f3330dUVFSZ8Wo77xMREdUWGhXSa9aswZIlS/D+++8DAHbv3o2+fftixYoV0NOr1FXiCA8PR3BwMDp06IBOnTohOjoaDx48kO4fCwoKgpOTE6KiomBiYoLWrVsrzW9tbQ0ApcYTVQrPSBORhiwsLGBpaVluO02vwgIe3/9c0fa//fYb0tPTsWHDhnJj8fb2RmFhIS5duoQWLVqobVcVeZ+oRlPqJ+guDCLSPY2yYEZGBvr06SO99vX1hUwmw9WrVysdwJAhQzB//nxERETA09MTqampiI+Ply5ly8jIQGZmZqWXT6QRFtJEVEUUr8IqVnwVlo+Pj8p5fHx8lNoDQEJCgsr2K1euhJeXFzw8PMqNJTU1FXp6erC1tS2zXVXkfaIajf0EInpCozPShYWFpS4XMzQ0REFBwVMFERYWpvJSbgBISkoqc964uLinWjeREiZIIqpCmlyFBQDjxo1Dt27d8OWXX6Jv375Yv349/vjjD3zzzTdKy7137x42bdqEL7/8stQ6k5OTcfjwYXTv3h0WFhZITk7GhAkT8M4776B+/fplxltVeZ+oxmI/gYie0KiQFkIgJCRE6X6qR48eYeTIkUo/hcGfwaAaiwmSiKrQkCFDcP36dURERCArKwuenp6lrsJSvGS6c+fOWLduHaZNm4apU6fCzc0NW7duLXU70/r16yGEwNChQ0ut09jYGOvXr8eMGTOQl5eHJk2aYMKECUr3TavDvE9UAvsJRPSERoV0cHBwqXHvvPOO1oIhIiKq7TS9CmvQoEEYNGhQmcscMWIERowYoXJa+/btcejQIY3jBJj3iYiI1NGokI6Nja2qOIieDTzSTEQkYd4nKoH9BCJ6go/cJFLEBElERETqsJ9ARE+wkCZSxARJFeA6eYeuQyAiIl1gP4GInmAhTURERERERKQBFtJEinikmYiIiNRhP4GInmAhTaSICZKIiIgqgv0EojqNhTSRIhbSREREpA77CUT0BAtpIiIiIiIiIg2wkCZSxCPNREREpA77CUT0BAtpIkVMkERERKQO+wlE9AQLaSJFTJBERESkDvsJRPQEC2kiIiIiIiIiDbCQJlLEI81ERESkDvsJRPQEC2kiRUyQREREpE6JfoLr5B26i4WIdIqFNJEiFtJERESkDvsJRPQEC2kiRUyQREREpA77CUT0BAtpIiIiIiIiIg2wkCZSxCPNRM8c3oNIRM8M9hOI6AkW0kSKmCCJiIhIHfYTiOgJFtJEipggiYiISB32E4joCRbSRERE1SgmJgaurq4wMTGBt7c3jhw5Umb7TZs2oWXLljAxMUGbNm2wc+dOpekhISGQyWRKg7+/v1KbW7duITAwEJaWlrC2tsbw4cNx//59rW8bERFRXcFCmkgRjzQTURXasGEDwsPDERkZiWPHjsHDwwN+fn64du2ayvYHDx7E0KFDMXz4cBw/fhwDBgzAgAED8Oeffyq18/f3R2ZmpjR8//33StMDAwNx+vRpJCQkYPv27di/fz9GjBhRZdtJVGuxn0BET7CQJlLEBElEVWjBggUIDQ3FsGHD4O7ujmXLlsHMzAyrVq1S2X7hwoXw9/fHRx99hFatWmH27Nlo3749Fi9erNTO2NgY9vb20lC/fn1p2pkzZxAfH48VK1bA29sbXbt2xddff43169fj6tWrVbq9RLUO+wlE9AQLaSJFTJBEVEXy8/ORkpICX19faZyenh58fX2RnJyscp7k5GSl9gDg5+dXqn1SUhJsbW3RokULjBo1Cjdv3lRahrW1NTp06CCN8/X1hZ6eHg4fPqyNTSOqO9hPIKInWEgTERE9hZycHNy7d08a8vLyVLa7ceMGioqKYGdnpzTezs4OWVlZKufJysoqt72/vz/WrFmDxMREfPHFF9i3bx969+6NoqIiaRm2trZKyzAwMECDBg3UrpeINMef6iOqWwx0HQDRM4VHl4lIQ+7u7kqvIyMjMWPGjGpb/1tvvSX9v02bNmjbti2ef/55JCUloUePHtUWB1GdwDPSRPQEC2kiRUyQRKShtLQ0ODk5Sa+NjY1VtrOxsYG+vj6ys7OVxmdnZ8Pe3l7lPPb29hq1B4CmTZvCxsYG58+fR48ePWBvb1/qYWaFhYW4detWmcshIhXYTyCiJ3hpN5EiJkgi0pCFhQUsLS2lQV0hbWRkBC8vLyQmJkrj5HI5EhMT4ePjo3IeHx8fpfYAkJCQoLY9APz777+4efMmHBwcpGXcuXMHKSkpUps9e/ZALpfD29u7wttJRGA/gYgkLKSJiIiqSXh4OJYvX47Vq1fjzJkzGDVqFB48eIBhw4YBAIKCgjBlyhSp/bhx4xAfH48vv/wSZ8+exYwZM/DHH38gLCwMAHD//n189NFHOHToEC5duoTExET0798fzZo1g5+fHwCgVatW8Pf3R2hoKI4cOYIDBw4gLCwMb731FhwdHat/JxAREdUCvLSbSAmPNFPZ+DAZehpDhgzB9evXERERgaysLHh6eiI+Pl56oFhGRgb09P47xt25c2esW7cO06ZNw9SpU+Hm5oatW7eidevWAAB9fX2cPHkSq1evxp07d+Do6IhevXph9uzZSmfG165di7CwMPTo0QN6enoICAjAokWLqnfjiWoDnpEmoidYSBMpUsyJTJBEVAXCwsKkM8olJSUllRo3aNAgDBo0SGV7U1NT/Prrr+Wus0GDBli3bp1GcRKRCiykiegJXtpNpIgJkoiIiNRhP4GInmAhTURERERERKQBFtJEinikmYiIiNRhP4GInmAhTaSICZKIiIjUYT+BiJ54JgrpmJgYuLq6wsTEBN7e3jhy5IjatsuXL8dLL72E+vXro379+vD19S2zPZFGmCCJiIhIHfYTiOgJnRfSGzZsQHh4OCIjI3Hs2DF4eHjAz88P165dU9k+KSkJQ4cOxd69e5GcnAxnZ2f06tULV65cqebIqVZigiQiIiJ12E8goid0XkgvWLAAoaGhGDZsGNzd3bFs2TKYmZlh1apVKtuvXbsWo0ePhqenJ1q2bIkVK1ZALpcjMTGxmiMnIqJi/H1tIiIiqkt0Wkjn5+cjJSUFvr6+0jg9PT34+voiOTm5QsvIzc1FQUEBGjRoUFVhUl3CI81ERESkDvsJRPSEgS5XfuPGDRQVFcHOzk5pvJ2dHc6ePVuhZUyaNAmOjo5KxbiivLw85OXlSa9zcnIqHzDVfkyQRM8UnukmomcK+wlE9ITOL+1+GnPmzMH69euxZcsWmJiYqGwTFRUFKysraXB3d6/mKKlGYYIkIiIiddhPIKIndFpI29jYQF9fH9nZ2Urjs7OzYW9vX+a88+fPx5w5c7Br1y60bdtWbbspU6bg7t270pCWlqaV2ImIiIiIiKhu0mkhbWRkBC8vL6UHhRU/OMzHx0ftfHPnzsXs2bMRHx+PDh06lLkOY2NjWFpaSoOFhYXW4qdaiEeaiYiISB32E4joCZ3eIw0A4eHhCA4ORocOHdCpUydER0fjwYMHGDZsGAAgKCgITk5OiIqKAgB88cUXiIiIwLp16+Dq6oqsrCwAgLm5OczNzXW2HVRLMEESERGROuwnENETOi+khwwZguvXryMiIgJZWVnw9PREfHy89ACyjIwM6On9d+J86dKlyM/Px5tvvqm0nMjISMyYMaM6Q6faiAmSiIiI1GE/gYie0HkhDQBhYWEICwtTOS0pKUnp9aVLl6o+ICKq8aIT/8L4F3QdBRERERHVRjX6qd1EWscjzURERKQO+wlE9AQLaSJFTJBERESkDvsJRPQEC2kiRUyQREREpA77CUT0BAtpIiIiIiIiIg2wkCZSxCPNREREpE4F+wauk3dUcSBEpGsspIkUsZAm0hg7jERUZ7CfQERPsJAmUsQESURVLCYmBq6urjAxMYG3tzeOHDlSZvtNmzahZcuWMDExQZs2bbBz505pWkFBASZNmoQ2bdqgXr16cHR0RFBQEK5evaq0DFdXV8hkMqVhzpw5VbJ9RLUa+wlE9AQLaSJFTJBEVIU2bNiA8PBwREZG4tixY/Dw8ICfnx+uXbumsv3BgwcxdOhQDB8+HMePH8eAAQMwYMAA/PnnnwCA3NxcHDt2DNOnT8exY8ewefNmpKen4/XXXy+1rFmzZiEzM1MaPvjggyrdVqJaif0EInqChTQRkQ7wcui6acGCBQgNDcWwYcPg7u6OZcuWwczMDKtWrVLZfuHChfD398dHH32EVq1aYfbs2Wjfvj0WL14MALCyskJCQgIGDx6MFi1a4MUXX8TixYuRkpKCjIwMpWVZWFjA3t5eGurVq1fl20tERFRbsZAmUsQjzUSkoZycHNy7d08a8vLyVLbLz89HSkoKfH19pXF6enrw9fVFcnKyynmSk5OV2gOAn5+f2vYAcPfuXchkMlhbWyuNnzNnDho2bIh27dph3rx5KCwsrOAWEpGE/QQieoKFNJEiJkiiWqkqrwBwd3eHlZWVNERFRalsd+PGDRQVFcHOzk5pvJ2dHbKyslTOk5WVpVH7R48eYdKkSRg6dCgsLS2l8WPHjsX69euxd+9evP/++/j888/x8ccfa7KZRASwn0BEEgNdB0D0TGGCJCINpaWlwcnJSXptbGyskzgKCgowePBgCCGwdOlSpWnh4eHS/9u2bQsjIyO8//77iIqK0lm8RDUS+wlE9ATPSBMRET0FCwsLWFpaSoO6wtTGxgb6+vrIzs5WGp+dnQ17e3uV89jb21eofXERffnyZSQkJCidjVbF29sbhYWFuHTpUjlbR0TVic/PIKo5WEgTKeKRZiKqIkZGRvDy8kJiYqI0Ti6XIzExET4+Pirn8fHxUWoPAAkJCUrti4voc+fOYffu3WjYsGG5saSmpkJPTw+2traV3Bqiusntw83/vWA/gahO46XdRIpYSBNRFQoPD0dwcDA6dOiATp06ITo6Gg8ePMCwYcMAAEFBQXBycpLusx43bhy6deuGL7/8En379sX69evxxx9/4JtvvgHwuIh+8803cezYMWzfvh1FRUXS/dMNGjSAkZERkpOTcfjwYXTv3h0WFhZITk7GhAkT8M4776B+/fq62RFEtQH7CUR1GgtpIkUspImoCg0ZMgTXr19HREQEsrKy4Onpifj4eOmBYhkZGdDT++9isc6dO2PdunWYNm0apk6dCjc3N2zduhWtW7cGAFy5cgXbtm0DAHh6eiqta+/evXjllVdgbGyM9evXY8aMGcjLy0OTJk0wYcIEpfumiagS2E8gqtNYSBMREVWjsLAwhIWFqZyWlJRUatygQYMwaNAgle1dXV0hyunMt2/fHocOHdI4TiIiIlKP90gTKeIZaSIiIlKlZL+A/QSiOo2FNJEiFtJERESkCgtpIlLAQppIEQtpIiIiUqWaC2n+FBbRs42FNBEREREREZEGWEgTKeIZaSIiIlKFl3ZTHcArISqOhTSRIhbSREREpEoNL6RZIBFpFwtpIkUspImIiEiVGl5IE5F2sZAmUsRCmuiZwbMnRPRMYSFNRApYSBMRERFRtePBsorjviJ69rCQJlLEM9JERESkCs9IE5ECFtJEilhIExERkSoaFtJVdRaZZ6eJng0spIkUsZAmBeysVC/ubyJ6pvGMNBEpYCFNREREREREpAEW0kSKeEaaiIiIVNHSGWlefUM1kSbv27ryHmchTaSIhTSVoa4kBk1wnxBRncFLu6mOYY4vGwtpIkUspGs8t2k7dR0CERHVRlVcSLNoIapZWEgTERER0TODBSUR1QQspKlK1NgkyDPSRESEGpzHqOrw0m6qpfh9VzkspIkUMSnSU6pIMmLCqnrcx6RNfD9VvRqxj2tRIa24v2vEvid6BrGQJlLEM9JURcrqqLBDU724j4moMlyjfpf+H91lqFb6CZX9PqrofPy+I6o6z0QhHRMTA1dXV5iYmMDb2xtHjhwps/2mTZvQsmVLmJiYoE2bNti5kw8XIqqJanqC59nn0qq6U1gbaDvnCSEQEREBBwcHmJqawtfXF+fOnVNqc+vWLQQGBsLS0hLW1tYYPnw47t+/r/Vtqwue9fcqD8w9Fr37r2pbl+vkHc/Mvn5W4iAqT214r+q8kN6wYQPCw8MRGRmJY8eOwcPDA35+frh27ZrK9gcPHsTQoUMxfPhwHD9+HAMGDMCAAQPw559/VnPkVCtV4Rlpt0+q7oBPbfgyqqzytr06C7unLayr8u/4LL9HVMX2NOM0mV7dqiLnzZ07F4sWLcKyZctw+PBh1KtXD35+fnj06JHUJjAwEKdPn0ZCQgK2b9+O/fv3Y8SIEVW+vfTseNY+C7WCBv2EmrL/a0qcVDPVtveXzgvpBQsWIDQ0FMOGDYO7uzuWLVsGMzMzrFq1SmX7hQsXwt/fHx999BFatWqF2bNno3379li8eHE1R17z1ZQ3s87irKOXdteU94UmSm6Tum0sHq/JPngW95euY6qrBwQqQts5TwiB6OhoTJs2Df3790fbtm2xZs0aXL16FVu3bgUAnDlzBvHx8VixYgW8vb3RtWtXfP3111i/fj2uXr1aXZtOpKRWXJqspp9QK7aNSEu0/T5/lj43BrpceX5+PlJSUjBlyhRpnJ6eHnx9fZGcnKxynuTkZISHhyuN8/PzkzoM1ebyZaA2nAXfoeU392/ApZeqYNlajlOtq1f/+1Tk5Gh/vX89udQsZyegZ6jdZVfR/lb6m1YFbcS9Y4dynOee7OdfdgIwVL0edduopp3rb8qjlb7Iy5uneHoZMaj9f2WpWIbr5B1P/bcs8/2gZhs0eQ+pjbGs/aNuf5Xc74rtzpwBWrWqWFBaUhU57+LFi8jKyoKvr6803crKCt7e3khOTsZbb72F5ORkWFtbo0OHDlIbX19f6Onp4fDhwxg4cKAWt7KGOXAAuHMHgJr3aTmf0ar8fqzosqszbnXzabo8pe/TEvGV+t4s+f+KrFte8Pjfv/4CcnbC9YBh1fyd0tKAhiW+R0rEqvSdVsb2Ke6TkvOoa6f276kuB1Xm/xWk1c+CKADynuTxjJ1w/f2/v5/ieqq8f1IJz2JMFabm/amuv1PW90HJtlrv7xTP160bYG5euWVogU4L6Rs3bqCoqAh2dnZK4+3s7HD27FmV82RlZalsn5WVpbJ9Xl4e8vLypNd3794FAGRmZj5N6MC6dcDUqUqjOo/fiIPRgys0e0XbanOZJafLx2/Ev6+VvezO4zcCgMrlFk8rdjB6MOTjN6Lx7sevG+9Wnq+sZSnGV/LfknFqsk805TN+LQb98bhDe+VqJgxfe63Syyq9HWtx79vvsfLFNxE8aiAM5ZovS91rxf1ect+o2++K8SlOL16W4ut/Xxuslf2u6m9bHHfJGNVtc8m4AeDf1/5blqGsANdXf49HPm/i+T25kAtDqY2691Txa8V/FZdd3Eadkm1UvVa17OL9XHKaYqyK+0HTfaLYpmRsZS1L8e9R3rJL7ofi9Si+H9X9nUvGpmo5FVl2yW3qXKKd4v4vnvbvokXAJ5/gaRTnkbt378LS0lIab2xsDGNj41LtqyLnFf9bXhtbW1ul6QYGBmjQoIHa3Pmsk8sff4E+dS4PC0PnV/7L5YrvEaD0ZxRAqf8rfl8qfp+W9Z2p7nOt+H9VOVrV90LJz0DxdpSMT902qIuhoutS9zlUtexi6r4HFb8nlD6vZSy7eBnFsQCPc7kwKMC9b3/AlYPfQz5W+fNf8u+lOK7kskvmLIECiKLHhfqVVd9DPra30nxlfX+V3L6SOUKTeSq671S1U/V/xXlK9t9U7Y+S+6ys7+KS+7G8/kS3CWsR2ujxPn5n2kDp76e4HsX3Rnn5suT/FWNXF1NFPr+q3j/qPguq+lzl7Y/y3p/q5qlMX01dP6QkVf3DkutVzNWq3l/qvj/L2w5V/RDs3Qs0a6bx9hYrziHFOUVjQoeuXLkiAIiDBw8qjf/oo49Ep06dVM5jaGgo1q1bpzQuJiZG2NraqmwfGRkpAHDgwIEDBw7VMkRGRlZbzjtw4IAAIK5evarUZtCgQWLw4MFCCCE+++wz0bx581LLbtSokViyZInK9T7rjhw5ovO/MwcOHDhwqB3DkSNHKpWLdHpG2sbGBvr6+sjOzlYan52dDXt7e5Xz2Nvba9R+ypQpSpfFFRYW4syZM3B2doae3tPdIp6TkwN3d3ekpaXBwsLiqZZV23DflI37p2zcP2Xj/ilbde0fuVyOjIwMuLu7w8Dgv3Sq6mw0UDU5r/jf7OxsODg4KLXx9PSU2pR8mFlhYSFu3bqldr3Punbt2uHIkSOws7N76lxORER1k1wuR3Z2Ntq1a1ep+XVaSBsZGcHLywuJiYkYMGAAgMcblJiYiLCwMJXz+Pj4IDExEePHj5fGJSQkwMfHR2V7VZfYdenSRSvx37t3DwDg5OSkdFkfcd+Uh/unbNw/ZeP+KVt17p/GjRtXuG1V5LwmTZrA3t4eiYmJUuF87949HD58GKNGjZKWcefOHaSkpMDLywsAsGfPHsjlcnh7e2u4xc8GAwMDdOzYUddhEBFRDadJHi9Jp4U0AISHhyM4OBgdOnRAp06dEB0djQcPHmDYsGEAgKCgIDg5OSEqKgoAMG7cOHTr1g1ffvkl+vbti/Xr1+OPP/7AN998o8vNICIiKpe2c55MJsP48ePx6aefws3NDU2aNMH06dPh6OgoFeutWrWCv78/QkNDsWzZMhQUFCAsLAxvvfUWHB0ddbIfiIiIajqdF9JDhgzB9evXERERgaysLHh6eiI+Pl56cEpGRobSZVudO3fGunXrMG3aNEydOhVubm7YunUrWrduratNICIiqpCqyHkff/wxHjx4gBEjRuDOnTvo2rUr4uPjYWJiIrVZu3YtwsLC0KNHD+jp6SEgIACLFi2qvg0nIiKqZWRC1NEfy9WCvLw8REVFYcqUKWrviauruG/Kxv1TNu6fsnH/lI37h4iIiKoaC2kiIiIiIiIiDfBRl0REREREREQaYCFNREREREREpAEW0kREREREREQaYCFNREREREREpAEW0pUUExMDV1dXmJiYwNvbG0eOHNF1SNVi//796NevHxwdHSGTybB161al6UIIREREwMHBAaampvD19cW5c+eU2ty6dQuBgYGwtLSEtbU1hg8fjvv371fjVlSNqKgodOzYERYWFrC1tcWAAQOQnp6u1ObRo0cYM2YMGjZsCHNzcwQEBCA7O1upTUZGBvr27QszMzPY2trio48+QmFhYXVuSpVYunQp2rZtC0tLS1haWsLHxwe//PKLNL0u75uS5syZI/0+cLG6vn9mzJgBmUymNLRs2VKaXtf3DxEREVUvFtKVsGHDBoSHhyMyMhLHjh2Dh4cH/Pz8cO3aNV2HVuUePHgADw8PxMTEqJw+d+5cLFq0CMuWLcPhw4dRr149+Pn54dGjR1KbwMBAnD59GgkJCdi+fTv279+PESNGVNcmVJl9+/ZhzJgxOHToEBISElBQUIBevXrhwYMHUpsJEybg559/xqZNm7Bv3z5cvXoVb7zxhjS9qKgIffv2RX5+Pg4ePIjVq1cjLi4OERERutgkrXruuecwZ84cpKSk4I8//sCrr76K/v374/Tp0wDq9r5RdPToUfzf//0f2rZtqzSe+wd44YUXkJmZKQ2///67NI37h4iIiKqVII116tRJjBkzRnpdVFQkHB0dRVRUlA6jqn4AxJYtW6TXcrlc2Nvbi3nz5knj7ty5I4yNjcX3338vhBAiLS1NABBHjx6V2vzyyy9CJpOJK1euVFvs1eHatWsCgNi3b58Q4vG+MDQ0FJs2bZLanDlzRgAQycnJQgghdu7cKfT09ERWVpbUZunSpcLS0lLk5eVV7wZUg/r164sVK1Zw3zyRk5Mj3NzcREJCgujWrZsYN26cEILvHSGEiIyMFB4eHiqncf8QERFRdeMZaQ3l5+cjJSUFvr6+0jg9PT34+voiOTlZh5Hp3sWLF5GVlaW0b6ysrODt7S3tm+TkZFhbW6NDhw5SG19fX+jp6eHw4cPVHnNVunv3LgCgQYMGAICUlBQUFBQo7Z+WLVuicePGSvunTZs2sLOzk9r4+fnh3r170pnb2qCoqAjr16/HgwcP4OPjw33zxJgxY9C3b1+l/QDwvVPs3LlzcHR0RNOmTREYGIiMjAwA3D9ERERU/Qx0HUBNc+PGDRQVFSl1xgDAzs4OZ8+e1VFUz4asrCwAULlviqdlZWXB1tZWabqBgQEaNGggtakN5HI5xo8fjy5duqB169YAHm+7kZERrK2tldqW3D+q9l/xtJru1KlT8PHxwaNHj2Bubo4tW7bA3d0dqampdX7frF+/HseOHcPRo0dLTeN7B/D29kZcXBxatGiBzMxMzJw5Ey+99BL+/PNP7h8iIiKqdiykiarAmDFj8Oeffyrdw0lAixYtkJqairt37+KHH35AcHAw9u3bp+uwdO6ff/7BuHHjkJCQABMTE12H80zq3bu39P+2bdvC29sbLi4u2LhxI0xNTXUYGREREdVFvLRbQzY2NtDX1y/1NNjs7GzY29vrKKpnQ/H2l7Vv7O3tSz2UrbCwELdu3ao1+y8sLAzbt2/H3r178dxzz0nj7e3tkZ+fjzt37ii1L7l/VO2/4mk1nZGREZo1awYvLy9ERUXBw8MDCxcurPP7JiUlBdeuXUP79u1hYGAAAwMD7Nu3D4sWLYKBgQHs7Ozq9P5RxdraGs2bN8f58+fr/PuHiIiIqh8LaQ0ZGRnBy8sLiYmJ0ji5XI7ExET4+PjoMDLda9KkCezt7ZX2zb1793D48GFp3/j4+ODOnTtISUmR2uzZswdyuRze3t7VHrM2CSEQFhaGLVu2YM+ePWjSpInSdC8vLxgaGirtn/T0dGRkZCjtn1OnTikdbEhISIClpSXc3d2rZ0OqkVwuR15eXp3fNz169MCpU6eQmpoqDR06dEBgYKD0/7q8f1S5f/8+Lly4AAcHhzr//iEiIiId0PXTzmqi9evXC2NjYxEXFyfS0tLEiBEjhLW1tdLTYGurnJwccfz4cXH8+HEBQCxYsEAcP35cXL58WQghxJw5c4S1tbX46aefxMmTJ0X//v1FkyZNxMOHD6Vl+Pv7i3bt2onDhw+L33//Xbi5uYmhQ4fqapO0ZtSoUcLKykokJSWJzMxMacjNzZXajBw5UjRu3Fjs2bNH/PHHH8LHx0f4+PhI0wsLC0Xr1q1Fr169RGpqqoiPjxeNGjUSU6ZM0cUmadXkyZPFvn37xMWLF8XJkyfF5MmThUwmE7t27RJC1O19o4riU7uF4P758MMPRVJSkrh48aI4cOCA8PX1FTY2NuLatWtCCO4fIiIiql4spCvp66+/Fo0bNxZGRkaiU6dO4tChQ7oOqVrs3btXACg1BAcHCyEe/wTW9OnThZ2dnTA2NhY9evQQ6enpSsu4efOmGDp0qDA3NxeWlpZi2LBhIicnRwdbo12q9gsAERsbK7V5+PChGD16tKhfv74wMzMTAwcOFJmZmUrLuXTpkujdu7cwNTUVNjY24sMPPxQFBQXVvDXa99577wkXFxdhZGQkGjVqJHr06CEV0ULU7X2jSslCuq7vnyFDhggHBwdhZGQknJycxJAhQ8T58+el6XV9/xAREVH1kgkhhG7OhRMRERERERHVPLxHmoiIiIiIiEgDLKSJiIiIiIiINMBCmoiIiIiIiEgDLKSJiIiIiIiINMBCmoiIiIiIiEgDLKSJiIiIiIiINMBCmoiIiIiIiEgDLKSJ6oC4uDhYW1tXen6ZTIatW7fqNAYiIiIiomcFC2kiHQsJCYFMJpOGhg0bwt/fHydPntTaOoYMGYK//vqr0vNnZmaid+/eWouHiIiIiKgmYyFN9Azw9/dHZmYmMjMzkZiYCAMDA7z22mtaW76pqSlsbW0rPb+9vT2MjY21Fg8RERERUU3GQproGWBsbAx7e3vY29vD09MTkydPxj///IPr169LbSZNmoTmzZvDzMwMTZs2xfTp01FQUCBNP3HiBLp37w4LCwtYWlrCy8sLf/zxB4DSl1WX1VYVxUu7L126BJlMhs2bN6N79+4wMzODh4cHkpOTleaJi4tD48aNYWZmhoEDB+LmzZullvvTTz+hffv2MDExQdOmTTFz5kwUFhYCAGbNmgVHR0el+fr27Yvu3btDLpdXfOcSEREREWkZC2miZ8z9+/fx3XffoVmzZmjYsKE03sLCAnFxcUhLS8PChQuxfPlyfPXVV9L0wMBAPPfcczh69ChSUlIwefJkGBoaqlyHJm3V+eSTTzBx4kSkpqaiefPmGDp0qFQEHz58GMOHD0dYWBhSU1PRvXt3fPrpp0rz//bbbwgKCsK4ceOQlpaG//u//0NcXBw+++wzafmurq743//+BwCIiYnBwYMHsXr1aujp8auLiIiIiHRIEJFOBQcHC319fVGvXj1Rr149AUA4ODiIlJSUMuebN2+e8PLykl5bWFiIuLg4lW1jY2OFlZVVhdqqAkBs2bJFCCHExYsXBQCxYsUKafrp06cFAHHmzBkhhBBDhw4Vffr0UVrGkCFDlGLo0aOH+Pzzz5XafPvtt8LBwUF6feHCBWFhYSEmTZokTE1Nxdq1ayscMxERERFRVeFpHaJnQPfu3ZGamorU1FQcOXIEfn5+6N27Ny5fviy12bBhA7p06QJ7e3uYm5tj2rRpyMjIkKaHh4fjf//7H3x9fTFnzhxcuHBB7fo0aatO27Ztpf87ODgAAK5duwYAOHPmDLy9vZXa+/j4KL0+ceIEZs2aBXNzc2kIDQ1FZmYmcnNzAQBNmzbF/Pnz8cUXX+D111/H22+/rXGcRERERETaxkKa6BlQr149NGvWDM2aNUPHjh2xYsUKPHjwAMuXLwcAJCcnIzAwEH369MH27dtx/PhxfPLJJ8jPz5eWMWPGDJw+fRp9+/bFnj174O7uji1btqhcnyZt1VG8FFwmkwGARvcu379/HzNnzpQOIKSmpuLUqVM4d+4cTExMpHb79++Hvr4+Ll26JF06TkRERESkSyykiZ5BMpkMenp6ePjwIQDg4MGDcHFxwSeffIIOHTrAzc1N6Wx1sebNm2PChAnYtWsX3njjDcTGxqpdhyZtNdWqVSscPnxYadyhQ4eUXrdv3x7p6enSAQTFofge6A0bNmDz5s1ISkpCRkYGZs+erbUYiYiIiIgqy0DXARARkJeXh6ysLADA7du3sXjxYty/fx/9+vUDALi5uSEjIwPr169Hx44dsWPHDqUzyA8fPsRHH32EN998E02aNMG///6Lo0ePIiAgoNS6NGlbWWPHjkWXLl0wf/589O/fH7/++ivi4+OV2kREROC1115D48aN8eabb0JPTw8nTpzAn3/+iU8//RT//vsvRo0ahS+++AJdu3ZFbGwsXnvtNfTu3Rsvvvii1mIlIiIiItIUz0gTPQPi4+Ph4OAABwcHeHt74+jRo9i0aRNeeeUVAMDrr7+OCRMmICwsDJ6enjh48CCmT58uza+vr4+bN28iKCgIzZs3x+DBg9G7d2/MnDmz1Lo0aVtZL774IpYvX46FCxfCw8MDu3btwrRp05Ta+Pn5Yfv27di1axc6duyIF198EV999RVcXFwghEBISAg6deqEsLAwqf2oUaPwzjvv4P79+1qLlYiIiIhIUzIhhNB1EEREREREREQ1Bc9IExEREREREWmAhTQRERERERGRBlhIExEREREREWmAhTQRERERERGRBlhIExEREREREWmAhTQRERERERGRBlhIExEREREREWmAhTQRERERERGRBlhIExEREREREWmAhTQRERERERGRBlhIExEREREREWmAhTQRERERERGRBlhIExEREREREWmAhTQRERERERGRBlhIExEREREREWmAhTQRERERERGRBlhIExEREREREWmAhTQRERERERGRBlhIExEREREREWmAhTQRERERERGRBlhIExEREREREWmAhTQRERERERGRBlhIExEREREREWmAhTTRMyQkJASurq5aX65MJsOMGTOeiViIiIhqK+ZxorqDhTTVanFxcZDJZNJgYmICR0dH+Pn5YdGiRcjJydF1iKQjeXl5mDRpEhwdHWFqagpvb28kJCRUaN709HRMmDABnTt3homJCWQyGS5dulS1ARMR1UHM46TO0+TxzZs3Y8iQIWjatCnMzMzQokULfPjhh7hz507VBk21ioGuAyCqDrNmzUKTJk1QUFCArKwsJCUlYfz48ViwYAG2bduGtm3b6jrEKvXw4UMYGGj2cV++fDnkcnkVRaR7ISEh+OGHHzB+/Hi4ubkhLi4Offr0wd69e9G1a9cy501OTsaiRYvg7u6OVq1aITU1tXqCJiKqo5jHmcdLepo8PmLECDg6OuKdd95B48aNcerUKSxevBg7d+7EsWPHYGpqWk1bQTWaIKrFYmNjBQBx9OjRUtMSExOFqampcHFxEbm5uTqIrrTg4GDh4uKilWUVFRWJhw8famVZtc3hw4cFADFv3jxp3MOHD8Xzzz8vfHx8yp3/5s2b4t69e0IIIebNmycAiIsXL1ZVuEREdRbzOPO4Kk+bx/fu3Vtq3OrVqwUAsXz5cm2G+v/t3Xu8VXWdP/43d0wELyE3CxQ0I2gkBBuRvAdp0cVb5gWN8ZY4NZP1o/mNmd0s04okUcswNKdRvOSIRZqX1HGSMXR8mLdULAxBA1FSgcNZ3z/sbNc+9wVnnXX2Os/n43EesNde67M/e+3L+/Nan7X3psSc2k23ddBBB8U555wTzz33XFx99dVV1z3++ONx5JFHxo477hj9+/ePvffeO26++eaqdRpON/vtb38bp512Wuy0004xcODAOPHEE2Pt2rVNbu+SSy6J97znPdGvX78YPnx4nHnmme06hejCCy+MfffdN3baaafYZpttYuLEibFo0aIm6/Xo0SNmz54dP/vZzyq386tf/apyXcNnq5YvX151mlzjvwaNP1vVsN2FF14Yl19+eYwePTr69esXkyZNiqVLlzbpz3XXXRdjx46N/v37x7hx4+LGG29s9+e1Ro0aFR/+8Ifj17/+dey1117Rv3//GDt2bNxwww1tbtseixYtil69esWpp55aWda/f/+YNWtW3H///fHnP/+51e133HHH2G677TqkLwBsGXVcHd/SOn7AAQc0Wfbxj388IiIee+yxDukj5efUbrq1E044If7t3/4tfv3rX8cpp5wSERGPPvpoTJkyJUaMGBFz5syJbbfdNq699tr42Mc+Ftdff33ljbbB7NmzY/vtt4+vfOUr8cQTT8T8+fPjueeei7vuuqtS0L7yla/EeeedF4ccckicccYZlfWWLl0a9913X/Tp06fFPs6dOzdmzJgRxx13XGzcuDF+/vOfx1FHHRW33HJLHH744VXr3nHHHXHttdfG7Nmz4+1vf3uzxW7w4MFx1VVXVS3btGlT/Mu//Ev07du3zX12zTXXxKuvvhqnnXZa9OjRIy644IL4xCc+Ec8880zlfixevDiOOeaYGD9+fJx//vmxdu3amDVrVowYMaLN9hs89dRTccwxx8Tpp58eM2fOjAULFsRRRx0Vv/rVr+LQQw+NiIj6+vpYs2ZNu9obNGhQpX/Lli2LPfbYIwYOHFi1zuTJkyMi4qGHHop3vOMd7e4rAMVQx9+kjr9pa+r4Cy+8EBERb3/72zNtRzdW9JQ45Km1U8IaDBo0KJkwYULl8sEHH5yMHz8+eeONNyrL6uvrk3333TfZfffdm7Q9ceLEZOPGjZXlF1xwQRIRyS9+8YskSZJk9erVSd++fZMPfvCDyebNmyvrzZs3L4mI5Cc/+UllWXOnhDU+XW3jxo3JuHHjkoMOOqhqeUQkPXv2TB599NEm9zEiknPPPbfFffCZz3wm6dWrV3LHHXe02Jdnn302iYhkp512StasWVNZ/otf/CKJiOS//uu/KsvGjx+f7LLLLsmrr75aWXbXXXclEdGuU95GjhyZRERy/fXXV5atW7cuGTZsWNVj1dCn9vylT+N6z3ve02T/JUmSPProo0lEJJdeemmbfWzg1G6A/Kjjb12njt9Z2a4j63iDWbNmJb169UqefPLJzNvSPTm1m25vwIABlW/9XLNmTdxxxx1x9NFHx6uvvhovvfRSvPTSS/HXv/41pk2bFk899VQ8//zzVdufeuqpVUeizzjjjOjdu3fceuutERFx++23x8aNG+Nzn/tc9Oz51kvulFNOiYEDB8bixYtb7V/6Cy/Wrl0b69ati6lTp8bvf//7Juvuv//+MXbs2Ez3f+HChXHJJZfEBRdcEAceeGCb6x9zzDGxww47VC5PnTo1IiKeeeaZiIj4y1/+Eo888kiceOKJMWDAgKq+jR8/vt39Gj58eNWsQcPpdsuWLascNR46dGjcdttt7fr7h3/4h0pbr7/+evTr16/Jbfbv379yPQC1QR1XxxtsaR2/5ppr4oorrojPf/7zsfvuu2falu7Lqd10e+vXr4+dd945IiL++Mc/RpIkcc4558Q555zT7PqrV6+uOrWp8RvugAEDYtiwYZWfQ3ruueciIuJd73pX1Xp9+/aN3XbbrXJ9S2655Zb4+te/Hg899FBs2LChsjz9OagGu+66a6ttNfbQQw/F6aefHscee2z867/+a7u2eec731l1uaEYN3yerOH+jBkzpsm2Y8aMaXbg0JwxY8Y0uY977LFHRLz5Oa+hQ4dG//7945BDDmlXe2nbbLNN1b5s8MYbb1SuB6A2qOPqeIMtqeP33HNPzJo1K6ZNmxbf+MY3MveF7kuQpltbsWJFrFu3rlIsGn4m4uyzz45p06Y1u01zhSUv99xzT8yYMSM+8IEPxCWXXBLDhg2LPn36xIIFC+Kaa65psn6WwrF27do44ogjYo899ogf//jH7d6uV69ezS5PkqTdbXSUzZs3x4svvtiudXfcccfKZ8eGDRvWZEYiImLlypUR8eZRdAC6PnVcHU/LWscffvjhmDFjRowbNy4WLVqU+SfG6N48W+jWGr6so6HY7rbbbhER0adPn3YfIX3qqaeqTqVav359rFy5Mg477LCIiBg5cmRERDzxxBOV9iMiNm7cGM8++2yrt3P99ddH//79Y8mSJVWnMC1YsKBdfWtJfX19HHfccfHyyy/H7bffHm9729u2qr20hvv7xz/+scl1zS1rScOsQvpo9pNPPhkRUfnylT//+c/tPnp/5513Vr6lc6+99oo777wzXnnllaovKvnd735XuR6Ark8dV8e3tI4//fTTMX369Nh5553j1ltvrTqNHdrDZ6Tptu6444742te+Frvuumscd9xxERGx8847xwEHHBCXXXZZ5ahmWnNHTS+//PLYtGlT5fL8+fOjrq4uPvShD0VExCGHHBJ9+/aNH/zgB1VHe6+44opYt25dk2/sTOvVq1f06NEjNm/eXFm2fPnyuOmmmzLf37TzzjsvlixZEv/xH/+R+TSytgwfPjzGjRsXCxcujPXr11eW33333fHII4+0u52//OUvceONN1Yuv/LKK7Fw4cLYa6+9YujQoRGx5Z+tOvLII2Pz5s1x+eWXV5Zt2LAhFixYEPvss0/VN33+6U9/iscff3yL9gUA+VHH1fEtreMvvPBCfPCDH4yePXvGkiVLYvDgwe3fQfB3ZqTpFn75y1/G448/HnV1dbFq1aq444474rbbbouRI0fGzTffXPlyioiIH/7wh7HffvvF+PHj45RTTonddtstVq1aFffff3+sWLEiHn744aq2N27cGAcffHAcffTR8cQTT8Qll1wS++23X8yYMSMi3vyZii996Utx3nnnxfTp02PGjBmV9SZNmhTHH398i/0+/PDD47vf/W5Mnz49PvWpT8Xq1avjhz/8YYwZMyb+7//+b4v2xSOPPBJf+9rX4gMf+ECsXr26yW9vttaf9vrmN78ZH/3oR2PKlClx8sknx9q1a2PevHkxbty4qqLcmj322CNmzZoVS5cujSFDhsRPfvKTWLVqVdVR/C39bNU+++wTRx11VHzpS1+K1atXx5gxY+KnP/1pLF++PK644oqqdU888cS4++67qwZP69ati4svvjgiIu67776IiJg3b15sv/32sf3228fs2bMz9wmAlqnjb1HHt76OT58+PZ555pn44he/GPfee2/ce++9leuGDBlS+XkuaFVRXxcOnaHhpy0a/vr27ZsMHTo0OfTQQ5O5c+cmr7zySrPbPf3008mJJ56YDB06NOnTp08yYsSI5MMf/nCyaNGiJm3ffffdyamnnprssMMOyYABA5Ljjjsu+etf/9qkzXnz5iV77rln0qdPn2TIkCHJGWeckaxdu7ZqneZ+NuOKK65Idt9996Rfv37JnnvumSxYsCA599xzk8Yv34hIzjzzzGbvT6R+NuPOO+9s9eclWupLw09UfOc732m1/QY///nPkz333DPp169fMm7cuOTmm29OjjjiiGTPPfdsto9pI0eOTA4//PBkyZIlyXvf+97Kfb/uuuva3La9Xn/99eTss89Ohg4dmvTr1y+ZNGlS8qtf/arJevvvv3+Tfd3az3W052dBAGgfdfyt69TxaltTx1vbf/vvv3+H9ZFy65EkBXyzAJTAlVdeGSeffHIsXbo09t5776K7UxP22muvGDx4cNx2222trjdq1KgYN25c3HLLLZ3UMwC6G3U8O3Uc3uIz0kCH27RpU9TV1VUtu+uuu+Lhhx+ufFEIANA1qePQNp+RBjrc888/H4ccckgcf/zxMXz48Hj88cfj0ksvjaFDh8bpp59edPcAgFao49A2QRrocDvssENMnDgxfvzjH8eLL74Y2267bRx++OHxrW99K3baaaeiuwcAtEIdh7b5jDQAAABk4DPSAAAAkIEgDQAAABl0u89I19XVxbJly2LIkCHRs6fjCABsmfr6+li1alVMmDAhevfuduW0UGo5AFtra+t4t6v8y5Yti8mTJxfdDQBK4oEHHohJkyYV3Y1uRS0HoKNsaR3vdkF6yJAhEfHmDhs2bFjBvaGr2bR5U1z84MUREXHWxLOiT68+NdE2KfWbot/Tb+7nDaPPiuhpP5OPlStXxuTJkyt1hc6jltMatbzGqeN0kq2t490uSDecAjZs2LDYZZddCu4NXc3GzRtj4LMDIyJixC4jom+vvjXRNimbN0a8/OZ+jhEjIuxncubU4s6nltMatbzGqeN0si2t46o/AAAAZCBIAwAAQAaCNAAAAGQgSAMAAEAGgjQAAABkIEgDAABABoI0AAAAZCBIAwAAQAaCNDT24IMR110XsXFj0T0BgNa98UbEkUdG/OQnRfcEoFsRpKGxW26J+MMfIq6+uuieAEDrLrss4vrrI2bNKronAN2KIA0tWbeu6B4AQOvWrCm6BwDdkiANAAAAGQjSAAAAkIEgDQAAABkI0gAAAJCBIA0AAAAZCNIAAACQgSANAAAAGQjSAAAAkIEgDQAAABkI0gAAAJCBIA0AAAAZCNIAAACQgSANAAAAGQjSAAAAkIEgDQAAABkI0gAAAJCBIA0AAAAZCNIAAACQgSANAAAAGQjSAAAAkIEgDQAAABkI0gAAAJCBIA0AAAAZCNIAAACQgSANAAAAGQjSAAAAkIEgDQAAABkI0gAAAJCBIA0AAAAZCNIAAACQgSANAAAAGQjSAAAAkIEgDQAAABkI0gAAAJCBIA0AAAAZCNIAAACQgSANAAAAGQjSAAAAkIEgDQAAABkI0gAAAJCBIA0AAAAZCNIAAACQgSANAAAAGQjSAAAAkIEgDQAAABkI0gAAAJCBIA1pSVJ0DwAAgC5OkIY0QRoAAGiDIA0AAAAZCNKQZkYaAABogyANaYI0AADQBkEa0gRpAACgDYI0AAAAZCBIQ5oZaQAAoA2CNKQJ0gDUKjUMoNMI0pBmEAJArVLDADqNIA0AAAAZCNKQ5mg+ALVKDQPoNII0pBmEAFCr1DCATiNIQ5pBCAC1Sg0D6DSCNAAAAGQgSEOao/kA1Co1DKDTCNKQZhACQK1SwwA6jSANaQYhANQqNQyg0wjSAAAAkIEgDWmO5gNQq9QwgE4jSEOaQQgAtUoNA+g0gjSkGYQAUKvUMIBOI0hDmkEIALVKDQPoNII0AAAAZCBIQ5qj+QDUKjUMoNMI0pBmEAJArVLDADqNIA1pBiEA1Co1DKDTCNIAAACQgSANaemj+Y7sA1BL1C2ATiNIQ5ogDUCtUrcAOo0gDWmCNAC1St0C6DSCNAAAAGQgSEOaGWkAapW6BdBpBGlIE6QBqFXqFkCnEaQhTZAGoFapWwCdRpAGAACADARpSDMjDUCtUrcAOo0gDWmCNAC1RN0CKIQgDWkGJADUEnULoBCCNKQZkABQS9QtgEII0gAAAJCBIA1pjuwDUEvULYBCCNKQVjUIMSABoIsTpAEKIUhDWtWApLhuAEC7CNIAhRCkAQAAIANBGtIc2QeglqhbAIUQpCHNgASAWqJuARRCkIY0AxIAaom6BVAIQRoAAAAyEKQhzZF9AGqJugVQCEEa0gxIAKgl6hZAIQRpSDMgAaCWqFsAhRCkAQAAIANBGtIc2QeglqhbAIUQpKGKAQkANUSQBiiEIA1p6TGIAQkAXZ0gDVAIQRoAAAAyEKQhzZF9AGqJugVQCEEa0gxIAKgl6hZAIQRpSDMgAaCWqFsAhRCkIc2ABIBaom4BFEKQBgAAgAwEaUhzZB+AWqJuARRCkIY0AxIAaom6BVAIQRrSDEgAqCXqFkAhBGkAAADIQJCGNEf2Aagl6hZAIQRpSDMgAaCWqFsAhRCkIc2ABIBaom4BFEKQBgAAgAwEaUhzZB+AWqJuARRCkIY0AxIAaom6BVAIQRrSDEgAqCXqFkAhBGkAAADIQJCGNEf2Aagl6hZAIQRpSDMgAaCWqFsAhRCkIc2ABIBaom4BFEKQhjQDEgBqiboFUAhBGgAAADIQpCHNkX0Aaom6BVAIQRrSDEgAqCXqFkAhBGlIMyABoJaoWwCFEKQBAAAgA0Ea0hzZB6CWqFsAhRCkIc2ABIBaom4BFEKQhjQDEgBqiboFUAhBGgAAADIQpCHNkX0Aaom6BVAIQRrSDEgAqCXqFkAhBGlIMyABoJaoWwCFEKQBAAAgA0Ea0hzZB6CWqFsAhRCkIc2ABIBaom4BFEKQhjQDEgBqiboFUAhBGtIMSACoJeoWQCEEaQAAAMhAkIY0R/YBqCXqFkAhBGlIMyABoJaoWwCFEKQhzSAEgFoiSAMUQpAGAACADARpSHNkH4Baom4BFEKQhjQDEgBqiboFUAhBGtIMSACoJeoWQCEEaQAAAMhAkIY0R/YBqCXqFkAhBGlIMyABoJaoWwCFEKQhzYAEgFqibgEUQpAGAACADARpSHNkH4Baom4BFEKQhjQDEgBqiboFUAhBGlpiQAJAVydIAxRCkAYAAIAMBGlIc2QfgFqibgEUQpCGNAMSAGqJugVQCEEa0gxIAKgl6hZAIQRpSDMgAaCWqFsAhRCkAQAAIANBGtIc2QeglqhbAIUQpCHNgASAWqJuARRCkIY0AxIAaom6BVAIQRoAAAAyEKQhzZF9AGqJugVQCEEa0gxIAKgl6hZAIQRpSDMgAaCWqFsAhRCkAQAAIANBGtIc2QeglqhbAIUQpCHNgASAWqJuARRCkIY0AxIAaom6BVAIQRoAAAAyEKQhzZF9AGqJugVQCEEa0gxIAKgl6hZAIQRpSDMgAaCWqFsAhRCkIc2ABIBaom4BFEKQBgAAgAwEaUhzZB+AWqJuARRCkIY0AxIAaom6BVAIQRrSDEgAqCXqFkAhBGkAAADIQJCGNEf2Aagl6hZAIQRpSDMgAaCWqFsAhRCkIc2ABIBaom4BFEKQBgAAgAwEaUjrgCP7o+Ys7qDOAEAbzEgDFEKQhjQDEgBqiboFUAhBGtIMSACoJeoWQCEEaQAAAMhAkIY0R/YBqCXqFkAhBGlIMyABoJaoWwCFEKQhrZkBiW/hBqDLEqQBCiFIQ5oBCQC1RN0CKIQgDQAAABkI0pDmyD4AtUTdAiiEIA1pBiQA1BJ1C6AQgjSkGZAAUEvULYBCCNIAAACQgSANaY7mA1BLzEgDFEKQhjQDEgBqiboFUAhBGtIMSACoJeoWQCEEaQAAAMhAkIY0R/YBqCXqFkAhBGlIMyABoJaoW5TU93/zZNFdgFYJ0pBmQAJALVG3AAohSNMtjZqzuOguAAAANUqQhjRH9gGoJeoWQCEEaUgzIAGglqhblJyzCOmqBGmoYkACQA0RpAEKIUgDAABABoI0pKUP5juyD0BXZ0YaoBCCNKQZkABQS9QtgEII0pBmQAJALVG3AAohSEOaAQkAtUTdAiiEIA0AAAAZCNLwd6PmLHZkH4Daom4BFEKQrhF+jL6TGJAAUEvULYBCCNKQZkACQC1Rt+gGTCjRFQnSAAAAkIEgDWlbcGTfUVIACmNGGqAQgjTdQrvDrgEJALVE3aKbMpFB0QRpSDMgAaCWqFsAhRCkoZHvTzm26C4AAHRb3//Nk0V3AdokSENaFz+y7zQmAKp08boFXZlxFVtDkO4ivJC7iA4ckHhMAcidIF2lofZ+/3Yzmp3BWIfuTJCGtNQgZPeZl2feXEEBoFMJ0gCFEKS7gTzDXVcJjp3Vj65yfwEA2qNh7NIdxoONtdSvrtrf9qr1/peFIE23k37zafJGlOFovjcxuirPTegeRs1ZbEYa6BDGDtkJ0pDWwiCkFt9csva5Fu8jW8/jDjVOkKYb6e41q7vf/65GkIY0gxAAaokgDVXaCpuNr29ufYE1H2Xbr4I0pG3hIGRL3xiyvtlDZ/MchK2T+2so5yBdlveAstyPPOQ1hulqaq2/jdV6/8tIkKbUvOnUHo8ZQHaj/r9biu5CTeiIGuOntZrXmTO7zX2B2qg5iwsZQxQ1bsl739I2Qbog7fkWwe7+RO7oN+T2bLt7/4M75PZq4bHrzD52x28K7Wo6+ltb7XfoGkadde1bF/4+I90Z39LcEfLuXy0G3q5am7vyc6mt8WJ7+97R9TGvbwxvbfstfUzbs93WrNOVnz9bQ5DuQF39yFCZnsQd8SbSESG5I/dpltO8m/v/1gyc8ngzpeNs6UcAsr5OPK6Qr844zbsWPv+Z96TB7v//rS3eXnuWF63Ig88dcdtt7e+uut8b25KA3p7XX2fL0qfOHNdu6TZdYZ826F10B2rW3/4W8corMWru72P5Z9/31vKVK9vdxKg5i2P5Z98Xo+b+vnrbdBup/zesHxFNbze1rKG9ZvuVoX/N9jl1u+l+N16evu3m+tpW20363dyyv99uZfvU/mlpH7bUXrqdFvuXfuG21q9mlo2a+/t4cva4iFdffWt5r76t97uldlt7nrT0b/p+pPdze9puSdbnUgvrp5+3re6HRuu32Hb9xoj1f9/PL6yM6Nm33V3c0udu1nW3ZP0293fquVa1fjueJ+15Drb6vtKCtu5j5n3QQdtGRETv3hGDB2/59pTDSy/FqAt/FxHRan1tSXue4xHNv8a2+jnc3O31PvCtC+2ofY372dL7X1vjj6o+tPd+NdO/ZuvBlrTdwu1U6ngzy1pqt/H45snZ49684tVXK7W8I54zLY0n29onbbXdpCb8/f+t1YrG+6S553HVuKm1OpNqr+r/qbFjk37Xb3zr/y+0/vg124dmbrc9Y+x0v9LLm+zT9H5oqU8ZX3+tPf8iWn5+Nvc6brxvm70fzdX/lvrfaHlzY/7GY7jGfalqs53jmXatM3jwm/W8IN1uRrq+vj4iIlauXBkrVqzY8r8f/CBWDB8e9RteixXDh8c7L7g36je8Fu+84N5YMXx4i38N19dveK2ybeP/p9tLX59uv+H/DX/pbdPtpfvVsCzdj+b629qyxn1o3Hb69hr3q3F7jfvQuH/pNlu6L+l9k17WuL/N7e+WliUbNsUrddFkvda2aW1Zw/LRF90X3/vtn+KVC74boy+6r9l+Nd5Pjdto6XnSeJ803g/px7Sl/dTc86Slv5bWae72Wnosm3veNu5XW8/BZvv7zlHx4re+Gy9+67ux4p2j2nxut3a/2rMvWlu38X5o6X609Rps6bXb3PtBludJc+s3977SeL0teZ609tg3vr/N7bfmngftfWya/dtvv62rAStWxMq/F/KGukLn6bBafuihzdbXxs/VLO9tja9vrT61dDuNlzd+DTQZS0TTmpW+vvFtNFenm1vW2mu/pf3T0j5p73iltfeGlpY3fm9oqOXJhk3x/MhRrdbmxn1o3Gbj9Z8fOSqSDZvie7/9Uzw/clSz+7M9z5mW3sfb2ietvVc2957b3POh8WPe+Paa60Nby9rTXkuvh8Z1ffRF98Ubb2yKN97YFCve2fzj155lrd1elnbSj1VL67V2fePHqrnXWkuvqbbuX0uv45b6mGU/pPvU3DiirTFoW8+Nlt4/2npPrHot3HtvoXW8R5J0r99KWLp0aUyePLnobgBQEg888EBMmjSp6G50K2o5AB1lS+t4twvSdXV1sWzZshgyZEj07NntJuQB6CD19fWxatWqmDBhQvQu8NSy7kgtB2BrbW0d73ZBGgAAALaGw7gAAACQgSANAAAAGQjSAAAAkIEgDQAAABkI0gAAAJCBIA0AAAAZCNIAAACQgSANAAAAGQjSAAAAkIEgDQAAABkI0gAAAJCBIA0AAAAZCNIAAACQgSANAAAAGQjSAAAAkIEgDQAAABkI0gAAAJCBIA0AAAAZCNIAAACQgSANAAAAGQjSAAAAkIEgDQAAABkI0gAAAJCBIA0AAAAZCNIAAACQgSANAAAAGQjSAAAAkIEgDQAAABkI0gAAAJCBIA0AAAAZCNIAAACQgSANAAAAGQjSAAAAkIEgDQAAABkI0gAAAJCBIA0AAAAZCNIAAACQgSANAAAAGQjSAAAAkIEgDQAAABkI0gAAAJCBIA0AAAAZCNIAAACQgSANAAAAGQjSAAAAkIEgDQAAABkI0gAAAJCBIA0AAAAZCNIAAACQgSANAAAAGQjSAAAAkIEgDQAAABkI0gAAAJCBIA0AAAAZCNIAAACQgSANAAAAGQjSAAAAkIEgDQAAABkI0gAAAJCBIA0AAAAZCNIAAACQgSANXcCVV14ZPXr0iP/93//N7TYOOOCAGDduXG7tt6RHjx4xe/bsTr/d5ixfvjx69OgRV155ZdFdAQCghgnSsBUaAnBzf3PmzCm6ewAAQA56F90BKIOvfvWrseuuu1YtK2L2FwAAyJ8gDR3gQx/6UOy9995FdwMAAOgETu2GHD333HPxmc98Jt71rnfFNttsEzvttFMcddRRsXz58ja3Xbt2bUyePDl22WWXeOKJJyIiYsOGDXHuuefGmDFjol+/fvGOd7wjvvjFL8aGDRva3acHH3ww9t1339hmm21i1113jUsvvbTq+o0bN8aXv/zlmDhxYgwaNCi23XbbmDp1atx5551N2qqvr4+5c+fG+PHjo3///jF48OCYPn16m5/1/vrXvx49e/aMiy++uLLsl7/8ZUydOjW23Xbb2G677eLwww+PRx99tGq7k046KQYMGBDPP/98fOxjH4sBAwbE4MGD4+yzz47NmzdXrfvyyy/HSSedFIMGDYrtt98+Zs6cGS+//HK79xMAALTEjDR0gHXr1sVLL71Uteztb397LF26NP77v/87PvnJT8Yuu+wSy5cvj/nz58cBBxwQf/jDH+Jtb3tbs+299NJLceihh8aaNWvi7rvvjtGjR0d9fX3MmDEj7r333jj11FPj3e9+dzzyyCPxve99L5588sm46aab2uzn2rVr47DDDoujjz46jj322Lj22mvjjDPOiL59+8anP/3piIh45ZVX4sc//nEce+yxccopp8Srr74aV1xxRUybNi0eeOCB2GuvvSrtzZo1K6688sr40Ic+FP/0T/8UdXV1cc8998T//M//tDhD/+///u/xzW9+My677LI45ZRTIiLiqquuipkzZ8a0adPi29/+drz22msxf/782G+//WLZsmUxatSoyvabN2+OadOmxT777BMXXnhh3H777XHRRRfF6NGj44wzzoiIiCRJ4qMf/Wjce++9cfrpp8e73/3uuPHGG2PmzJlt7iMAAGhTAmyxBQsWJBHR7F+SJMlrr73WZJv7778/iYhk4cKFTdpZunRpsnLlyuQ973lPsttuuyXLly+vrHPVVVclPXv2TO65556q9i699NIkIpL77ruv1b7uv//+SUQkF110UWXZhg0bkr322ivZeeedk40bNyZJkiR1dXXJhg0bqrZdu3ZtMmTIkOTTn/50Zdkdd9yRRETyz//8z01uq76+vvL/iEjOPPPMJEmS5POf/3zSs2fP5Morr6xc/+qrrybbb799csopp1S18cILLySDBg2qWj5z5swkIpKvfvWrVetOmDAhmThxYuXyTTfdlEREcsEFF1SW1dXVJVOnTk0iIlmwYEHLOwoAANrg1G7oAD/84Q/jtttuq/qLiNhmm20q62zatCn++te/xpgxY2L77beP3//+903aWbFiRey///6xadOm+O1vfxsjR46sXHfdddfFu9/97thzzz3jpZdeqvwddNBBERHNnnrdWO/eveO0006rXO7bt2+cdtppsXr16njwwQcjIqJXr17Rt2/fiHjz1O01a9ZEXV1d7L333lV9vv7666NHjx5x7rnnNrmdHj16VF1OkiRmz54dc+fOjauvvrpqZvi2226Ll19+OY499tiq+9WrV6/YZ599mr1fp59+etXlqVOnxjPPPFO5fOutt0bv3r0rM9QN9+uss85qcx8BAEBbnNoNHWDy5MnNnsr8+uuvx/nnnx8LFiyI559/PpIkqVy3bt26JuufcMIJ0bt373jsscdi6NChVdc99dRT8dhjj8XgwYOb7cPq1avb7Ofw4cNj2223rVq2xx57RMSbv7H8/ve/PyIifvrTn8ZFF10Ujz/+eGzatKmybvqbyZ9++ukYPnx47Ljjjm3e7sKFC2P9+vUxf/78OPbYY5vcr4ioHBBobODAgVWXGz6LnbbDDjvE2rVrK5efe+65GDZsWAwYMKBqvXe9611t9hUAANoiSEOOzjrrrFiwYEF87nOfi3/8x3+MQYMGRY8ePeKTn/xk1NfXN1n/E5/4RCxcuDDmzp0b559/ftV19fX1MX78+Pjud7/b7G294x3v6JA+X3311XHSSSfFxz72sfjCF74QO++8c/Tq1SvOP//8ePrpp7eozSlTpsRDDz0U8+bNi6OPProqfDfsh6uuuqrJwYOIN2fR03r16rVFfQAAgI4iSEOOFi1aFDNnzoyLLrqosuyNN95o8dujzzrrrBgzZkx8+ctfjkGDBsWcOXMq140ePToefvjhOPjgg5ucOt1ef/nLX+Jvf/tb1az0k08+GRFR+UKvRYsWxW677RY33HBD1e00PoV79OjRsWTJklizZk2bs9JjxoyJCy64IA444ICYPn16/OY3v4ntttuu0k5ExM477xyHHHLIFt2vxkaOHBm/+c1vYv369VWz0g3ffg4AAFvDZ6QhR7169ao6nTsi4uKLL27yU01p55xzTpx99tnxpS99KebPn19ZfvTRR8fzzz8fP/rRj5ps8/rrr8ff/va3NvtTV1cXl112WeXyxo0b47LLLovBgwfHxIkTK32OiKp+/+53v4v777+/qq0jjjgikiSJ8847r8ntNL7PERHvfe9749Zbb43HHnssPvKRj8Trr78eERHTpk2LgQMHxje/+c2q08gbvPjii23er8YOO+ywqKurq9p/mzdvrvq5LQAA2FJmpCFHH/7wh+Oqq66KQYMGxdixY+P++++P22+/PXbaaadWt/vOd74T69atizPPPDO22267OP744+OEE06Ia6+9Nk4//fS48847Y8qUKbF58+Z4/PHH49prr40lS5a0+JNTDYYPHx7f/va3Y/ny5bHHHnvEf/7nf8ZDDz0Ul19+efTp06fS5xtuuCE+/vGPx+GHHx7PPvtsXHrppTF27NhYv359pa0DDzwwTjjhhPjBD34QTz31VEyfPj3q6+vjnnvuiQMPPDBmz57d5Pbf//73xy9+8Ys47LDD4sgjj4ybbropBg4cGPPnz48TTjgh3ve+98UnP/nJGDx4cPzpT3+KxYsXx5QpU2LevHmZ9vtHPvKRmDJlSsyZMyeWL18eY8eOjRtuuKHZz6UDAEBWgjTkaO7cudGrV6/42c9+Fm+88UZMmTIlbr/99pg2bVqb21566aWxfv36OPnkk2O77baLj370o3HTTTfF9773vVi4cGHceOON8ba3vS122223+OxnP1v50rDW7LDDDvHTn/40zjrrrPjRj34UQ4YMiXnz5lV+zzki4qSTTooXXnghLrvssliyZEmMHTs2rr766rjuuuvirrvuqmpvwYIF8d73vjeuuOKK+MIXvhCDBg2KvffeO/bdd98W+3DQQQfFtddeG0cccUSccMIJcc0118SnPvWpGD58eHzrW9+K73znO7Fhw4YYMWJETJ06NU4++eQ271djPXv2jJtvvjk+97nPxdVXXx09evSIGTNmxEUXXRQTJkzI3B4AAKT1SJo7BxMAAABols9IAwAAQAaCNAAAAGQgSAMAAEAGgjQAAABkIEgDAABABoI0AAAAZNDtfke6rq4uli1bFkOGDImePR1HAGDL1NfXx6pVq2LChAnRu3e3K6cA0K11u8q/bNmymDx5ctHdAKAkHnjggZg0aVLR3QAAOlG3C9JDhgyJiDcHPsOGDSu4N3Q1mzZviosfvDgiIs6aeFb06dWnJtompX5T9Hv6zf28YfRZET3tZ/KxcuXKmDx5cqWuAADdR7cL0g2ncw8bNix22WWXgntDV7Nx88YY+OzAiIgYscuI6Nurb020TcrmjREvv7mfY8SICPuZnPmYEAB0P6o/AAAAZCBIAwAAQAaCNAAAAGQgSAMAAEAGgjQAAABkIEgDAABABoI0AAAAZCBIAwAAQAaCNFAu9fUR994TsfzZonsCAEBJ9S66AwAd6oYbIn5zx5v/P/xHxfYFAIBSMiMNlMszTxfdAwAASk6QBgAAgAwEaQAAAMhAkAYAAIAMBGkAAADIQJAGAACADARpAAAAyECQBgAAgAwEaQAAAMhAkAYAAIAMBGkAAADIQJAGAACADARpAAAAyECQBgAAgAwEaQAAAMhAkAYAAIAMBGkAAADIQJAGAACADARpAAAAyECQBgAAgAwEaQAAAMhAkAYAAIAMBGkAAADIQJAGAACADARpAAAAyECQBgAAgAwEaQAAAMhAkAYAAIAMBGkAAADIQJAGAACADARpAAAAyECQBgAAgAwEaQAAAMhAkAYAAIAMBGkAAADIQJAGAACADARpAAAAyECQBgAAgAwEaQAAAMhAkAYAAIAMBGkAAADIQJAGAACADARpAAAAyECQBgAAgAwEaQAAAMhAkAYAAIAMBGkAAADIQJAGAACADARpAAAAyECQBgAAgAwEaQAAAMhAkAbKK0mK7gEAACUkSAMAAEAGgjRQXmakAQDIgSANlJcgDQBADgRpoLwEaQAAciBIAwAAQAaCNFBiZqQBAOh4gjRQXk7tBgAgB4I0UF6CNAAAORCkgfISpAEAyIEgDQAAABkI0kB5mZEGACAHgjRQXoI0AAA5EKSB8hKkAQDIgSANAAAAGQjSQHmZkQYAIAeCNFBegjQAADkQpIHyEqQBAMiBIA0AAAAZCNJAeZmRBgAgB4I0UF6CNAAAORCkgfISpAEAyIEgDQAAABkI0kB5mZEGACAHgjRQXoI0AAA5EKSB8hKkAQDIgSANAAAAGQjSQHmZkQYAIAeCNFAu6fAsSAMAkANBGigXQRoAgJwJ0kC5CNIAAORMkAYAAIAMBGmgXMxIAwCQM0EaKBdBGgCAnAnSQLkI0gAA5EyQBgAAgAwEaaBc0pPQZqQBAMiBIA2Ui1O7AQDImSANlIsgDQBAzgRpAAAAyECQBsrFjDQAADkTpIFyEaQBAMiZIA2UjCANAEC+BGkAAADIQJAGysWp3QAA5EyQBspFkAYAIGeCNFAugjQAADkTpIFyEaQBAMiZIA0AAAAZCNJAuZiRBgAgZ4I0UC6CNAAAOROkgXIRpAEAyJkgDQAAABkI0kC5mJEGACBngjRQLoI0AAA5E6SBcqkK0sV1AwCA8hKkAQAAIANBGigXp3YDAJAzQRooF0EaAICcCdJAuQjSAADkTJAGAACADARpoFzMSAMAkDNBGiiXqvAsSAMA0PEEaaBczEgDAJAzQRooF0EaAICcCdIAAACQgSANlIsZaQAAciZIA+UiSAMAkDNBGigXQRoAgJwJ0gAAAJCBIA2UixlpAAByJkgD5SJIAwCQM0EaKBdBGgCAnAnSAAAAkIEgDZSLGWkAAHImSAPlIkgDAJAzQRooF0EaAICcCdIAAACQgSANlEuNzEiPmrO46C4AALCFBGmgXGokSAMAULsEaaBcBGkAAHImSAMAAEAGgjRQLmakAQDImSANlIsgDQBAzgRpoFwEaQAAciZIA+UiSAMAkDNBGgAAADIQpIFyMSMNAEDOBGmgXARpAAByJkgD5SJIAwCQM0EaAAAAMhCkgXJJT0KbkQYAIAeCNFAuTu0GACBngjRQLoI0AAA5E6QBAAAgA0EaKBcz0gAA5EyQBspFkAYAIGeCNFAufw/P39/vWEEaAIBcCNIAAACQgSANlIxTuwEAyJcgDZSLz0gDAJAzQRooF0EaAICcCdJAuQjSAADkTJAGAACADARpoFzMSAMAkDNBGigXQRoAgJwJ0kC5CNIAAORMkAYAAIAMBGmgXMxIAwCQM0EaKBdBGgCAnAnSQLlUBeniugEAQHkJ0gAAAJCBIA2Ui1O7AQDImSANlIsgDQBAzgRpoFwEaQAAciZIAwAAQAaCNFAuZqQBAMiZIA2US1V4FqQBAOh4gjRQLmakAQDImSANlIsgDQBAzgRpAAAAyECQBsrFjDQAADkTpIFyEaQBAMiZIA2UiyANAEDOBGkAAADIQJAGyqUTZqRHzVmcS7sAANQGQRool5yDtBANAIAgDZSLz0gDAJAzQRoAAAAyEKSBcjEjDQBAzgRpoKY1+cyyIA0AQM4EaaBcBGkAAHImSAMAAEAGgjRQLmakAQDImSANlIsgDQBAzgRpoFwEaQAAciZIAwAAQAaCNFAuZqQBAMiZIA2UiyANAEDOBGmgXARpAAByJkgD5SJIAwCQM0EaAAAAMhCkgXIxIw0AQM4EaaBcBGkAAHImSAPlIkgDAJAzQRoAAAAyEKSBcjEjDQBAzgRpoFzS2VmQBgAgB4I0UC5mpAEAyJkgDQAAABkI0kC5mJEGACBngjRQLh0YpEfNWbyVnQEAoIwEaaBczEgDAJAzQRoAAAAyEKSBkjEjDQBAvgRpoFyc2g0AQM4EaaBcBGkAAHImSAPlIkgDAJAzQRoAAAAyEKSBcjEjDQBAzgRpoFwEaQAAciZIA+UiSAMAkDNBGgAAADIQpIFyMSMNAEDOBGmgXARpAAByJkgD5SJIAwCQM0EaAAAAMhCkgXKpmpEurhsAAJSXIA2Ui1O7AQDImSANlIsgDQBAzgRpAAAAyECQBsrFjDQAADkTpIFyEaQBAMiZIA2US1V4Lj5Ij5qzuOguAADQwQRpoDB5hMzdZ17e4W22R5b7IlwDANQ2QRooL6d2AwCQA0EaaFVNz54K0gAA5ECQBsqrg4N0WwcVavqgAwAA7SZIA+VlRhoAgBwI0gBbwSw0AED3I0gD5ZUkVUFX6M2PfQsAdCeCNFBeTu0GACAHgjRQXs0E6fbOnJphBQCgJYI0XZYg0z2V/XFv7v7V+n2u9f4DAGQlSHdjBr90hryeZ6PmLG6z7d1779/utsrE/cmnDQCABoI0uTBoLZ/2BNeitdS/1vrtVO+uzX4HALoiQRoAAAAyEKS7ATM61IK2fqaqKz6P2+pTV+tzVz9FuqvtLwCAlvQuugOdrb6+PiIiVq5cuXUNLVoU8Y1vbPHm+x5/SURE/PfVn6lcbvh/S+s0vq69y+uPvyRWDB7cZN2WlmfRWr8b2m6pr+l1m7u+I/qX1aaeSbyy12sREfH8MfOiT32Pmmg7i9Yej+bWa3gc6tt4XBsey4jqxzP9ODZs07jt9HqNbyfddnpZfer20m3U99gUL26KeOONTVG/4bWoT/pU1mmu7YZl7/yX66r63njd9O01115rbbfU73f+y3VN9kN7H5/W5PWab+5xaGi3YXlrr+m2NO7fvo0e4/S+ybqfmuzj3g9EfPazmfuY1lBHGuoKANB99EiS7vVDq0uXLo3JkycX3Q0ASuKBBx6ISZMmFd0NAKATdbsgXVdXF8uWLYshQ4ZEz57ObAdgy9TX18eqVatiwoQJ0bt3tzvBCwC6tW4XpAEAAGBrmJIFAACADARpAAAAyECQBgAAgAwEaQAAAMhAkAYAAIAMBGkAAADIQJAGAACADARpAAAAyECQBgAAgAwEaQAAAMhAkAYAAIAMBGkAAADI4P8BMiMFCdv4JDAAAAAASUVORK5CYII=\n"},"metadata":{"image/png":{"width":978,"height":1042}},"output_type":"display_data"}],"execution_count":35},{"cell_type":"markdown","source":"# t-REX results","metadata":{"cell_id":"54c68f5501504eeb8aa9763b60d51d6a","formattedRanges":[],"deepnote_cell_type":"text-cell-h1"}},{"cell_type":"code","source":"import pickle\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nwith open('Error_Mitigation/results/APRLRFY/vqe_state_trex_01.npy', 'rb') as f:\n    state_mitig_01 = np.load(f, allow_pickle=True)\nwith open('Error_Mitigation/results/APRLRFY/vqe_state_trex_05.npy', 'rb') as f:\n    state_mitig_05 = np.load(f, allow_pickle=True)\nwith open('Error_Mitigation/results/APRLRFY/vqe_state_trex_1.npy', 'rb') as f:\n    state_mitig_1 = np.load(f, allow_pickle=True)\nwith open('Error_Mitigation/results/APRLRFY/vqe_state_trex_2.npy', 'rb') as f:\n    state_mitig_2 = np.load(f, allow_pickle=True)\nwith open('Error_Mitigation/results/APRLRFY/vqe_state_trex_fake.npy', 'rb') as f:\n    state_mitig_fake = np.load(f, allow_pickle=True)\n\nstates_mitig_list = [state_mitig_01,state_mitig_05,state_mitig_1,state_mitig_2, state_mitig_fake]\nstates_noise_list = [state_noise_01,state_noise_05,state_noise_1,state_noise_2,state_noise_fake]\ntitles = ['Depolarizing p=0.01', 'Depolarizing p=0.05', 'Depolarizing p=0.1', 'Depolarizing p=0.2',\n            'Fake backend']","metadata":{"cell_id":"efb725293db94e59aeb29b27409d87b7","source_hash":"5a99df5b","execution_start":1684493803203,"execution_millis":66,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"outputs":[],"execution_count":36},{"cell_type":"code","source":"row,col=3,2\nfig, ax = plt.subplots(row,col,figsize=(10,10))\nfor k in range(len(states_mitig_list)):\n    i = k//col\n    j = k%col\n    mitig = states_mitig_list[k]\n    noise = states_noise_list[k]\n    ln1 = ax[i][j].bar(list(mitig.eigenstate.keys()), list(mitig.eigenstate.values()), color='red')\n    ax[i][j].set_ylim(0,0.2)\n    ax2 = ax[i][j].twinx()\n    ln3 = ax2.axvline(165, color='green', alpha=0.5)\n    ax2.axvline(197, color='green', alpha=0.5)\n    ln4 = ax2.axvline(322, color='orange', alpha=0.5)\n    ln2 = ax2.bar(list(noise.eigenstate.keys()), list(noise.eigenstate.values()))\n    ax2.set_ylim(0,0.2)\n    ax2.set_title(titles[k])\n    \n    if k==0:\n        ax2.set_ylabel(\"Probability\")\n        fig.legend([ln1, ln2, ln3, ln4], ['Error mitigated distribution', 'Solution from noisy VQE',\n        'True ground states', 'First excited state'], bbox_to_anchor=(0.95,1.05), ncol=4)\n    else:\n        ax[i][j].set_xticks([])\n        ax[i][j].set_yticks([])\n        ax2.set_xticks([])\n        ax2.set_yticks([])\nax[0][0].set_xlabel(\"Basis index\")\nax[0][0].set_ylabel(\"Probability\")\nax[2][1].remove()\nfig.tight_layout(pad=1.5)\n","metadata":{"cell_id":"0fb01d2f5bc64423a4dedb1f2d3d6da3","source_hash":"c167d122","execution_start":1684493804204,"execution_millis":15228,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"outputs":[{"data":{"text/plain":"<Figure size 1000x1000 with 10 Axes>","image/png":"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\n"},"metadata":{"image/png":{"width":978,"height":1042}},"output_type":"display_data"}],"execution_count":37},{"cell_type":"markdown","source":"Let us comment the resuls of the error mitigation techniques:","metadata":{"cell_id":"4d6c99c3ca1b435c9f46bba8af55c82a","formattedRanges":[],"deepnote_cell_type":"text-cell-p"}},{"cell_type":"markdown","source":"### Neural error mitigation","metadata":{"cell_id":"40e3aae4-bb02-481f-9a75-fdfd4fa06ae5","formattedRanges":[],"deepnote_cell_type":"text-cell-h3"}},{"cell_type":"markdown","source":"1. The neural error mitigation method is in general very effective at finding the true ground state of the Hamiltonian. For the depolarizing noise, for probabilities p<0.02, the NEM method correctly produces the ground state.  Moreover, while the amplitude of the ground state obtained with the VQE is very small, the NEM method returns the ground state with probability almost equal to 1. This proves that the NEM method is very suitable for this task.","metadata":{"style":"decimal","number":1,"cell_id":"5075d1fc-3420-4b35-a471-50b274856adb","formattedRanges":[],"deepnote_cell_type":"text-cell-number"}},{"cell_type":"markdown","source":"2. In the case when we use a fake backend, the NEM returns the first excited state, instead of the true ground state. It must be said, though, that the amplitude of the VQE state has almost 0 amplitude on the ground state, which must be the reason why the VMC only finds states that are orthogonal to this ground state. ","metadata":{"style":"decimal","number":2,"cell_id":"d9ee56a8-dfa1-4908-be8f-64264700b19a","formattedRanges":[],"deepnote_cell_type":"text-cell-number"}},{"cell_type":"markdown","source":"undefined. The main limitation of this NEM method is the scaling with the number of qubits. The quantum state tomography process uses a neural network to fully reconstruct the quantum statevector of the VQE. Even though the neural network approach significantly reduces the complexity of conventional state tomography methods, the output of the network is still a complex-valued vector which scales exponentially with the dimension of the system. For this reason, this method will not be efficient for large number of qubits.","metadata":{"style":"decimal","cell_id":"01859928-1598-4c1b-bdbf-e370d127bc62","formattedRanges":[],"deepnote_cell_type":"text-cell-number"}},{"cell_type":"markdown","source":"### t-REX","metadata":{"style":"decimal","cell_id":"976e6443-4b6a-497e-8d67-7147c721b301","formattedRanges":[],"deepnote_cell_type":"text-cell-h3"}},{"cell_type":"markdown","source":"The t-REX algorithm is not successful in finding the true ground state of the Hamiltonian. We can see that for small error probabilities, the mitigated result has a high amplitude around the first excited state, while the amplitude of the true ground state is almost erased. When the error probability increases, this error mitigation technique almost erases the amplitude of all quantum states. As a result, we conclude that this error mitigation method is not appropriate for this task, and it should be used only on systems where the error is weaker and easier to mitigate.","metadata":{"style":"decimal","cell_id":"b5a880ea-2ab8-495a-8550-7ffb81a9f7ef","formattedRanges":[],"deepnote_cell_type":"text-cell-p"}},{"cell_type":"markdown","source":"## Visualization","metadata":{"cell_id":"14b5b8bc263e4ac39b092ce9364813c4","formattedRanges":[],"deepnote_cell_type":"text-cell-h2"}},{"cell_type":"markdown","source":"Finally, we visualize the refined output of our solution as follows:","metadata":{"cell_id":"ff63faab-8176-4149-b930-7d715d98e27a","formattedRanges":[],"deepnote_cell_type":"text-cell-p"}},{"cell_type":"code","source":"result = protein_folding_problem.interpret(raw_result=raw_result)\nprint(\n    \"The bitstring representing the shape of the protein during optimization is: \",\n    result.turn_sequence,\n)\nprint(\"The expanded expression is:\", result.get_result_binary_vector())\nprint(\n    f\"The folded protein's main sequence of turns is: {result.protein_shape_decoder.main_turns}\"\n)\nprint(f\"and the side turn sequences are: {result.protein_shape_decoder.side_turns}\")\nprint(result.protein_shape_file_gen.get_xyz_data())\nfig = result.get_figure(title=\"Protein Structure\", ticks=False, grid=True)\nfig.get_axes()[0].view_init(10, 70)","metadata":{"cell_id":"3fe54e58b7984f4aac76213ff883f657","source_hash":"65401455","output_cleared":false,"execution_start":1684493819452,"execution_millis":587,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"outputs":[{"name":"stdout","text":"The bitstring representing the shape of the protein during optimization is:  101100011\nThe expanded expression is: 1______0_____________________________________________________________________________________________________________________________110001_1____\nThe folded protein's main sequence of turns is: [1, 0, 3, 2, 0, 3]\nand the side turn sequences are: [None, None, None, None, None, None, None]\n[['R' '0.0' '0.0' '0.0']\n ['P' '0.5773502691896258' '0.5773502691896258' '-0.5773502691896258']\n ['K' '1.1547005383792517' '0.0' '-1.1547005383792517']\n ['P' '1.7320508075688776' '-0.5773502691896258' '-0.5773502691896258']\n ['Q' '2.3094010767585034' '0.0' '0.0']\n ['Q' '1.7320508075688776' '0.5773502691896258' '0.5773502691896258']\n ['F' '1.154700538379252' '1.1547005383792517' '0.0']]\n","output_type":"stream"},{"data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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References","metadata":{"cell_id":"23479be29fa5425c91672dc962385a82","formattedRanges":[],"deepnote_cell_type":"text-cell-h2"}},{"cell_type":"markdown","source":"[1] A.Robert, P.Barkoutsos, S.Woerner and I.Tavernelli, Resource-efficient quantum algorithm for protein folding, NPJ Quantum Information, 2021, https://doi.org/10.1038/s41534-021-00368-4 ","metadata":{"cell_id":"f206ada6-bf52-4c07-aef3-0b0c26663cec","formattedRanges":[],"deepnote_cell_type":"text-cell-p"}},{"cell_type":"markdown","source":"[2] IUPAC–IUB Commission on Biochemical Nomenclature (1972). \"A one-letter notation for aminoacid sequences\". Pure and Applied Chemistry. 31 (4): 641–645. doi:10.1351/pac197231040639. PMID 5080161. ","metadata":{"cell_id":"d5ad036c-2c77-4f3f-aecb-1bf42cc19043","formattedRanges":[],"deepnote_cell_type":"text-cell-p"}},{"cell_type":"markdown","source":"[3] S. Miyazawa and R. L.Jernigan, Residue – Residue Potentials with a Favorable Contact Pair Term and an Unfavorable High Packing Density Term for Simulation and Threading, J. Mol. Biol.256, 623–644, 1996, Table 3, https://doi.org/10.1006/jmbi.1996.0114 ","metadata":{"cell_id":"849d5748-8dfb-4be8-bf75-53ef1d833eac","formattedRanges":[],"deepnote_cell_type":"text-cell-p"}},{"cell_type":"markdown","source":"","metadata":{"cell_id":"92ee52e9-7e34-4460-be24-9eaa5bc2a8f7","formattedRanges":[],"deepnote_cell_type":"text-cell-p"}},{"cell_type":"markdown","source":"<a style='text-decoration:none;line-height:16px;display:flex;color:#5B5B62;padding:10px;justify-content:end;' href='https://deepnote.com?utm_source=created-in-deepnote-cell&projectId=ddc54cc5-de90-4346-ada1-4ff24feba045' target=\"_blank\">\n<img alt='Created in deepnote.com' style='display:inline;max-height:16px;margin:0px;margin-right:7.5px;' src='data:image/svg+xml;base64,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' > </img>\nCreated in <span style='font-weight:600;margin-left:4px;'>Deepnote</span></a>","metadata":{"created_in_deepnote_cell":true,"deepnote_cell_type":"markdown"}}],"nbformat":4,"nbformat_minor":0,"metadata":{"deepnote":{},"orig_nbformat":2,"deepnote_notebook_id":"73bb2872bf83494ab0cdf2578df4d9b2","deepnote_execution_queue":[]}}