{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"\n\n   \n \n\n \n\n\n\n ","metadata":{"_uuid":"9e9d6c1e-7e5c-4a48-bfc3-7471034ae865","_cell_guid":"e395de68-1240-4385-beef-d66acaea9d9f"}},{"cell_type":"markdown","source":"<a id=\"section-one\"></a>\n# AI ETHICS\n\n\n* [1 Introduction](#section-one)\n* [2 What is AI ethics?](#section-two)\n* [3 Why implication of ethical AI is important? What if not?](#section-three)\n* [4 Challenges to design ethical AI](#section-four)\n* [5 Some recent incident](#section-five)\n* [6 Possible Solutions to the Implication of Ethical AI](#section-six)\n* [7 Conclution](#section-seven)\n* [8 References](#section-eight)\n \n","metadata":{"_uuid":"857e03de-2b23-4629-8d5f-13b3b27fd17f","_cell_guid":"7a2b135d-f4ae-47bf-8c8f-b28b5f06ae29","trusted":true}},{"cell_type":"markdown","source":"<a id=\"section-one\"></a>\n# 1 Introduction\n\nThe field of artificial intelligence (AI) is developing exponentially. AI has become more useful in our day to day life because of its ability to detect objects in videos and pictures; the ability to defeat humans at games like Go, Chess, and Poker; the ability to paint in the manner of the Dutch artist Vincent Van Gogh; the translation of speech and languages; to create music, lyrics, dialogues; to prepare legal documents, the trading of stocks, to drive vehicles and weapons;  to control drones; to develop its own encryption languages, to identify illnesses like heart attack and cancer in tissues, and to solve the quantum state of particles. It will be expected that AI will eventually perform better than humans do in a wide range of increasingly difficult activities in the years to come.\n\n   The ethical implications of AI are a major concern as AI technologies develop further. Future and present AI systems are surrounded by complex ethical issues. Leading technology companies are developing autonomous vehicles that promise to improve personal mobility for old aged and disabled people and to save lives by reducing driver error. If there is any case of emergency, what should self-driving car will choose for the lives of passengers or pedestrians? Not only in the case of self-driving cars but also in many fields ethical dilemmas will occur for AIs, like autonomous weapons, in the field of health etc. Some cases will occur when both options are harmful. In these cases, who will be responsible for the loss, the manufacturer or the user/owner? In order to know all the answers to these questions and to avoid all these cases we need ethical implications of artificial intelligence systems (AIs).\n   \n   In this essay, we’ll know about what is AI ethics? why implication of ethical AI is important and the problems coming in its implication, and the possible solution to these problems.","metadata":{}},{"cell_type":"markdown","source":"<a id=\"section-two\"></a>\n# 2 What is AI ETHICS?\n\nEthical AI is artificial intelligence that follows well-defined ethical guidelines about the fundamental values of people such as individual rights, equality, non-manipulation, fairness, transparency, accountability, privacy, and security. Ethical AI places a vital role in determining the legal and illegal uses of AI.","metadata":{}},{"cell_type":"markdown","source":"<a id=\"section-three\"></a>\n# 3 Why implication of ethical AI is important? What if not?\n\n   AI is spreading its roots in every field. It is being used in education, law, agriculture and farming, autonomous transportation, health tracking, economics, virtual assistants or chatbots, space exploration, image analysis software, speech and face recognition, robots, retail, shopping and fashion, security and surveillance, sports analytics and activities, manufacturing and production, livestock and inventory management. It will affect all the fields in both positive and negative way if it is designed ethically or unethically. Let’s discuss its positive and negative impacts in detail in all the fields:-\n   \n**•\t Impact on human rights:**\nAs we know AI can have both positive and negative impacts on human rights. If it is not designed ethically, it can increase inequality and create chaos all around the world. But if we ethically design AI, it can enhance human rights and shared prosperity.\n\n**•\tImpact on education:** AI can change the education system for both students and teachers in many ways. AI programmes can help automate academic tasks like attendance tracking, course registration, and grading, support teachers by providing the students with learning needs, enhance critical thinking and creativity in students etc. It can also affect negatively if not implicated properly, it can reinforce biases in students and teachers based on their gender, race, economic status or other factor, reduce creativity and increase dependency, or decrease thinking power because it can provide ready-made solutions of the questions which may discourage students and teachers.\n\n**•\tImpact on economy:** AI technology can impact the economy on the national level as well as the global level. It can increase the productivity and growth of companies by automating tasks, enhancing the quality of products, creating new jobs, training and upskilling workers, reduce inequality and poverty by increasing access and opportunity. But it is also able to destroy the global and national economies, if not developed ethically and tested properly.\n\n**•\tImpact on transportation:** Ai systems have enough potential to transform the transportation industry. It can reduce traffic accidents and increase safety, properly control traffic by collecting traffic data, smarter traffic light algorithms and real-time tracking of vehicles (only for public transport systems like buses or trains). But we have to consider all the possible risks while designing AI software.\n\n**•\tImpact on healthcare system:** AI tools can easily do routine work like appointment scheduling, data entry, and organizing data in a more efficient way that it can be easily accessed, stored and, shared. It will give more free time to healthcare professionals to work on patients.AI tools that can help patients and doctors to understand complex subjects, help in drug discovery, personalized medicine etc. But it can also raise ethical issues like privacy, bias, and inequality, and affect human aspects like empathy, trust, communication, etc. So, it is more necessary to implicate AI ethically, with proper guidelines, and a large amount of good data.\n\n**•\tImpact on sports:** AI can use in sports to improve the performance of players and coaches through personalized training and diet plan based on their health data and improve the communication/ broadcast system, provide accurate and fair decisions using video assistant referee (VAR), help in the recruitment of players by analysing player data from statistics, news articles, social media. But AI has its drawback if not developed with good, large, unbiased data. It can affect human rights and the privacy of individuals or teams, or create bias.\n\n**•\tImpact on culture and society:** AI has a significant impact on culture and society. It can enrich people’s daily life activities, and interaction with each other, improve the quality of education, healthcare and security, not only make art easily accessible but also create music, lyrics, dialogue, poetry, painting etc.  AI can discourage artists, create job displacement, But as AI is spreading its legs in every field it becomes more complicated for artists to compete with AI, in September 2022, an annual art competition was held by the Colorado State Fair. This competition was won by a new entrant, Jason M. Allen of Pueblo West who created his art with an artificial intelligence program, Midjourney that turns lines of text into hyper-realistic graphics.\n\n\nAs AIs are used in every field it became more important to implicant AI in an ethical way which means in every field all AI systems protect human rights and individual privacy. It should be transparent to the user.\n","metadata":{}},{"cell_type":"markdown","source":"<a id=\"section-four\"></a>\n# 4 Challenges to design ethical AI:\\\n\n### 4.1 AI is Data Hungry:\nEvery AI model needs a large number of data sets to perform well. If the AI model is tested on small amount of dataset, then its possibility is more that the AI model will show bad output. There are already a lot of real-life examples that in the AI model misbehave or show bad results. In 2022 a Tesla car owner video went viral in which Tesla's Autopilot system was misunderstood moon as a yellow stop light and kept slowing down. In this case, it may be possible that during the testing of Tesla’s autopilot system or its image recognition system, the dataset did not include pictures of the moon in different seasons. \n\n### 4.2 Privacy and Security:\nThe ethical challenge of privacy and security in AI is that AI systems may collect and share personal data without proper consent or notification from the individuals concerned. For example- AI Systems may use a phone’s camera, microphone or location to recognise or track people without their knowledge or permission.\n\n### 4.3 Transparency:\nTransparency refers to the ability of humans to understand how and why an AI system makes decisions and actions. AI systems used in high-stakes applications like criminal justice, healthcare, and finance have a high impact on individuals and society. AI systems may not be transparent or explainable enough for humans to oversee, control, or correct their behaviour.\n\n### 4.4 Bias and discrimination:\nAI systems trained on inadequate or inaccurate data can amplify the biases and discrimination that exist in our society. This may pose various risks and issues like loss of fairness, accuracy and trust in AI systems.\n\n### 4.5 Garbage in-Garbage out:\nThis phrase is commonly used in the context of machine learning and AI. An AI or ML system will work well only if it is trained on good data, even if an AI system is designed very well, it will produce bad outputs if it is tested on insufficient or erroneous data. For example- If a facial recognition AI system is trained on images of white people, it will work poorly or produce bad results on other races’ images.","metadata":{}},{"cell_type":"markdown","source":"<a id=\"section-five\"></a>\n# 5 Some recent AI incidents:  \n\nThere are thousands of incidents happening all over the world, some of them happened because of the lack of training of AI models, unbiased datasets, not proper guidelines, and the developer itself. Some examples are given here in the below table-1. \n\n\n|Related Organization | Date | Incident Title  | Description|\n|-------|------|----|----|\n|Facebook\t|2022-11-15 |\tMeta AI's Scientific Paper Generator Reportedly Produced Inaccurate and Harmful Content |\tGalactica is a large language model that Meta AI released as a demo. It is intended to \"store, combine, and reason about scientific knowledge.\" Although designed to speed up the creation of scholarly articles, antagonistic users conducting testing discovered it could also produce plausible rubbish. According to MIT Technology Review, Meta removed the demo offline following several days of ethical criticism.|\n|Google|2023-02-07|Google's Bard Shared Factually Inaccurate Info in Promo Video |Google's conversational AI \"Bard\" was shown in the company's promotional video providing false information about which satellite first took pictures of a planet outside the Earth's solar system, reportedly causing shares to temporarily plummet.|\n|Twitter|2023-01-03|Twitter's AI Moderation Tool Misidentified Rockets as Pornography|Twitter's automated content moderation misidentified images of rocket launches as pornographic content, prompting incorrect account suspensions.|\n|Twitter|2023-01-03|Twitter's AI Moderation Tool Misidentified Rockets as Pornography|Twitter's automated content moderation misidentified images of rocket launches as pornographic content, prompting incorrect account suspensions.\n|Turnitin| 2023-01-20| Turnitin's ChatGPT-Detection Tool Falsely Flagged Student Essays as AI-Generated |Turnitin's tool to detect writing generated by ChatGPT was reported for incorrectly flagging high school students' original essays as AI-generated, after checking there are no source documents founds on the essay. |\n|Openai|2023-01-03|AI-Generated-Text-Detection Tools Reported for High Error Rates|Models developed to detect whether text generation AI was used such as AI Text Classifier and GPTZero reportedly contained high rates of false positives and false negatives, such as mistakenly flagging Shakespeare's works.|\n|Openai|2022-11-30|Users Bypassed ChatGPT's Content Filters with Ease|Users reported bypassing ChatGPT's content and keyword filters with relative ease using various methods such as prompt injection or creating personas to produce biased associations or generate harmful content.|\n|Tesla|2021-07-23|Tesla's Autopilot Misidentified the Moon as Yellow Stop Light|Tesla's Autopilot was shown on video by its owner mistaking the moon for a yellow stop light, allegedly causing the vehicle to keep slowing down|\n\n","metadata":{}},{"cell_type":"markdown","source":"\n![known AI goal.PNG](attachment:66e1e56d-7919-475b-917f-da64e02e3399.PNG)\n\n\n![known ai technical failure.PNG](attachment:d3320c6b-d567-47f6-9fdd-b4f26102eaa8.PNG)\n\n\n![known AI 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"},"55582adc-6545-4dc1-ba8d-33e2f270a2c4.PNG":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAg4AAAF0CAYAAABL6rEDAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAHuBSURBVHhe7Z0LfBTV2f+3oojgZdWoW7WUaLRJ9a3klb81La2ylVaj2LJiKWupxZTXS4rVRmoxohhXrDRihYiIARVNrNiARUMpGhTQoKIbAQ0qGFSEqEFXQQkQ8Pk/Z+bM7szs2ewk2WQv+X35PB+ys7Mz5/78zm3GRUDJZ68uoClXXU4jLryYfjx8FI0YfR1NmbuCNu+RJ/RiNi+4mUbdUy8/gbTnxZk06oZFtFl+7K2gzgPgDAgHAAAAADgGwgEAAAAAjoFwAAAAAIBjIBwAAAAA4BgIBwAAAAA4BsIBAAAAAI6BcAAAAACAYyAcAAAAAOAYCAcAAAAAOAbCAQAAAACOgXAAAAAAgGMgHAAAAADgGAgHAAAAADgGwgEAAAAAjoFwAAAAAIBjIBwAAAAA4BgIBwAAAAA4BsIBAAAAAI6BcAAAAACAYyAcAAAAAOAYCAcAAAAAOAbCAQAAAACOgXAAAAAAgGMgHAAAAADgGAgHAAAAADgGwgFkLjs/p88+/pz27JOfJXu++JiPs+2UB9KYPZ+JuETHUaDF87Ov5CcbWtrIdFBZAtJm89xr6MfD76QX5OfuZwVNGX4xjZv7nvwMAOgOIBxAxqI7rmvo4U3yALPz9dk07vyL6cfj59E7e+TBdGXnMrpJxIWd5ZTl8piJF6bwd1c8SpvlZzN62ui/VVkinG/aC4c9X9FnX6R7IQEg8UA4gIwlSji882jmiAZmZ+3NuqMXcZqyQh6N0J5wsIw4LLqNrzOeZr6KEYcwX/C1RqnTFYDeDoQDyFgswiEsGmbTa1/o3xtsXnAzjbrqUVr/RT3NvHY8jbh4FI246k6qabSpi32f02uP30lXjh5Dv7j4crrs2pm0bLNxznv0+LWX06jbl1HE5+6hnXYn3Pgo//4amvOG+FBPd4++nKYs/YreWSSuO4p+MXI8XXtfPX2mmHqw8hU9faMuDGrKR7F4YAdt+027wsHM8jutAstg6zK6W6THyDE04vK/0MwVn8svJF+spcenXEejRnK4R19HUx5fa4q7STjESdf6e2S6vVNLU666nNN2DKftbHqhRZ5g0PKa6X6cTvcso3Dya6iEwx7aXDuTrr1cv+4ovv/jr9riwXz26gLt3loYOa5Trh9P42a8RpvFiMOmBXQt59MNNR/LswUyv6eu4DsA0LuAcAAZiyEc5ixlZ30hO9HfzqR6m2gQGOeNG385XXnPAlr2+Ey6UvQ2z7+Zng57wq/ohdvH8Hlj9HOWLqC7r+LP57PDfUc/Y/39l1sd+NYFdKUYETD1WrV7XVxO9do5uqMbNf4aGvXb2+jhRbX08E3j+R4X02X3b9DOj4mcptCc5OqZ9Av+jX26okvCYesiupavP+L6eVSzdBnV3P8XGsFxv3u1/H7fa3S3SKMLx9OUubX0+NTxNMzmtJ2lqwwni6lxLAZu4mvVzL2NLhMib8w8Wi/PoZ31dKf47ajr6O7Hl0WuNf5ReicsmKKFw+aHRRg4PW9igbXUSN8xnFamtR+cfiPE74RQYKH3zqKb+fMo0znv0cNX8L2uWkDb5BFq4fTh31y7IFqEAJDpQDiAjEV3XNzgCycknN6r8gsbxnnj5ph6qm/MplF87Kal8rN0FJfNWCsPMPvW0t2j+do3ylGGxnl0GZ9jOPDPFvxFv//FLFi0Ix/T41ddTL+4S/9kOLofX3wbLQsLmq/o6Rv42OXzSOoRJTsXi2kKw9mzE7+Yf2MbVu+KcKgXoxhjZtP6sFOW4ZL30ON2Oc3URk506meMp1FXRJy9o3RltHCyo75pacSZ6/EbT3NkIqjuR6/P1K9Va/zOLhxkutxQaxoJ+YpqrudjJlHywu38eTTHVX4OC4XJy+Rnom2PX6fdf06j/lkP31+oxj4qAkAvAMIBZCxh4TDqL3TDePG/GDaXX5owesbWoXqbE9Kcq81xMZrTCzv5DTRnDAuD8tf478+p5tqLadSUO1lwjNJ76lJ8REYGpHCwOXw9PO2tDZBO3CQKNEdvm67ovHCQjrN4Ni1byr17aTOLI9ezxluNo3RldOFgi++mR2mcKa20cyzOXaBf67KwMLFdW17jhsXWnSV6uFisybTS0i48CiR4j+Zczve73RB4jBzh0UeC5DTRtYvoM/1bAHoVEA4gY9EdhOy1GovdLEPbOnZHomNzQjHWAWgOLTyiIKcrhIPbWUs3aEJDCogZa/VeqsW52x2fTlzhoF37Yhp2xc1059Ry3a7l+7JAufNFeQ6TaOGg2Yvvab13J9d2lK6Mdi27CFEJh6j76dfShVrks1042Kdw9HCZRgvEeaZpmcdvH8/5xOlhU0X1d7HAEHm7j+/D51/5uHnNAwC9BwgHkLHoDsLkEOUCyRHcwzdNscdw1DYnJIfYrb1XferB0vPUpisup7vvYZEge8jaMPvomTRTOD/L6EK0ExXEEw76sP0YunKyFA2a3UbjLjZPg3RFOMjfmuf0GfNzIcLO1zSCs+3Z2RyORbapijjpyijDaXP662cIYWRdG2GsIYmsM7BdWwosIdrMaALANH309I1jaMSNj9Lj98m0vGcBvaaagtDydhTdfZ9YUxItIgHoLUA4gIwlSjgwO5fepi0kNDsuZw6Oe+HadMfNVPOO2LK4hepniN/ZxYQ+XSHWVYR7wsYiST5m7f12RjjoIxgqQWAfcu+KcNAXDI6hmxa9pz1cas+mRXQDC5MR4TgZiycfpfrNnB7v1NJNYkSHRZQhNhIpHMKjAjfW0jtip8rm12imyA+x0DIsXuzXlgtaz7+GZq7eom0z1Rc+mteq6L+5YZF1OkONFIp8vqN0BSBDgXAAGYtKOGjOZIq+O2LKUr2n6tTB0Rf1dPfl7JyF4xB2/uV0w1zrFkSBNl1hrGvQkA4nastkJ4SDFCH232houysi0xVdEg7MtsW30Sh21kZ8R92wyDLNs/P1R+kGsThUfj9szJ309Fb5JZNQ4cDsfHEmjRO7Y+T9fjz6Znr4dbPDV6Tnvveo5gaRH8bvRtG4qctomyke6+dco+0IiZyj2y9+a7++GMQQiyJjpD8AvQQIBwA6iP7IavVjnjOOfV/FeSCUfFZFrEdbJ5xO3k8+8Gqn7aELO1fcSSOGX05Tat/T4xm2tTRHrPMYbxM0msiK7K4AoDcC4QAA6LVoWzFVu0P2vacLB8vOCbmbwvx8CQB6IRAOAIDei3z406gb5lHNajHqsIXWr1hAd4opKbmz4oX7xcO5jIdgicWY2E0BejcQDgCAXs3Oxlr90dpy/cSwkZfTlVMiOyvWP/YXukw8SOr8UTTunvqoNS0A9DYgHAAAAADgGAgHAAAAADgGwgEAAAAAjoFwAAAAAIBjIBwAAAAA4BgIBwAAAAA4BsIBAAAAAI6BcAAAAACAYyAcAAAAAOAYCAcAAAAAOAbCAQAAAACOgXAAAAAAgGMgHAAAAADgGAgHAAAAADgGwgEAAAAAjoFwAAAAAIBjIBwAAAAA4BgIBwAAAAA4BsIBAAAAAI6BcAAAAACAYyAcAOgFtLZupc9DL9Onn/6Xtm2roQ+3PEKbN1fQxk1/ow1v30xvvnkdvbH2D3TruuX0mzc20bj1TXTNW+/Tn9/+kG569yMq27SV7mpqpse2bqeXQjtp2+698soAgN4GhAMAGcM3LBC20Pbtz9MHH8yhtxr/Qq+uGUnPv/A/9FzdSY7spuAiOm55gyP77gtr6dxX36YrWGTczsJiQfNntH7nLtr/jQwOACAjgXAAIE3ZvXs3vf3227R06VJ67fUbafnz31eKgY5YR4RDe/bTVzbQVW+9T/e+/zE9u/1L2rV/vww1ACDdgXAAIE3Yu3cvbdq0iZ599lmaM2cO3XbbbXTrrbdqtnz5n5RCoKOWKOGgsksbNtGcLZ9qoxIAgPQFwgGAFGXfvn30/vvv0/PPP0/z5s2jsrKysFCwW03NLUoh0FHrTuFgtqEvb6C/vrOFnvokRDva9skYAwDSAQgHAFKMlpYWWrJkCd15551KkaCy6dNvUwqBjlpPCQeznfHSm3Qji4gXPtshUwAAkMpAOACQArS1tdEbb7xBc+fOVQoDJ7Zy1dlKMdARS4ZwMNt5a96muzc301uYzgAgZYFwACCJfM297GeeeYamTp2qFAMdsRdfukwpBjpiyRYOZvvt2vfo6U9DMqUAAKkChAMASaDt0130WfUG+uDmlfS3O/+mFAIdtbq6PyvFQEcslYSDYSNef5ce27ad2r7BPk8AUgEIBwB6kL2ffE3bH22kLX9dSVtu1K127kKlEOioPfHErUox0BFLReFg2DmvbKBZH35Cn+1tk6kJAEgGEA4A9AB7t+6k7Y+8ZREMhm28YwVNmTJFKQY6YtOmdX2BZCoLB8MGv/Qm/eP9j2nXPjwbAoBkAOEAQDeyZ8tOapn3ZpRYsNvjDz6qFAMdtVUv/kQpCJxaOggHw4a9+jZVb/tMpjQAoKeAcACgG9i3Yw9tf6xRKRJUtvbeOqUQ6Ki9+NLvlILAqaWTcDDs129soue2fylTHgDQ3UA4AJBI9n9DO1Z+RB/d8pJSILRns2bcpxQDHbFlz96gFAROLR2Fg2F/bPwAT6UEoAeAcAAgQez5cAd9fPdrSlHgxF6c8x+lGOiIVVdPUQoCp5bOwkHYKSvX0QNbPpU5AgDoDiAcAOgi+7/eS58/+Y5y4WNH7IPSrm/NnDq1awsk0104GPb7dU0YfQCgm4BwAKALfPBqA229tV4pBDpjS+YuUgqCjtiqF71KUeDEMkU4CMPoAwDdA4QDAJ1gX1sbLXtgBpX/+kJqmvGCUgR0xt67fYXlrZedsVUvXqEUBU4sk4SDYWL0oRGjDwAkDAgHADrIly2f0vy/XKuJBmGzfn8ZbQ2sVgqBztg/H3xMKQic2tKlNypFgRPLROEgLP+lN2lpyxcyBwEAXQHCAYAO8P66Bpr5+1+HRYNhT98U6PIaB8PW/mO5UhA4tfnzO79AMlOFg2FzMHUBQJeBcADAAd988w2tevwRKh99UZRoMOztGUuVQqAz1pWtmbffPoXqlucohUE8y3ThIOzmdz+SuQoA6AwQDgDEoXXnDvrnlBuVYsFs917mo4+mJWbK4qUHlipFgVN78aVfKIVBPOsNwkHY79Y10dbde2UOAwA6AoQDAO0g1jM8+McrlEJBZU9cP5E+ummVUgx0xD6YtJKm3TVNKQqc2MpV45XCIJ71FuEg7NxX36aGHV/LnAYAOAXCAYAYfPHJxzT7yrFKgdCevXHPQqUY6KgtnfuUUhQ4sSVLJimFQTzrTcJBmHhh1kuhnTLHAQBOgHAAQMH2LR/QrPGXKYVBXBt9EX10d9enLLqyNfOhhzr3IKjeJhyEZa9YC/EAQAeAcADAxqcfbKaZ46J3TnTEHim+plPvq7DbEw9WKYVBPJsy5VaqW36qUhy0Z71ROBgG8QCAMyAcADCx7d0NNOPyS5VioKNWf9fDSjHQEXtzeue3Zr740kVKcdCe9WbhIAziAYD4QDgAINmy4U36x29HKkVAZ+2De19UCoKO2Jz7ZiuFQTxbufJqpThoz3q7cBAW/BILJgFoDwgHAJgtGz6mGZePUjr/rtjsK8bS1tu79i6L1bM7tzXz6WduUoqD9gzCQbe3v2qVJQMAYAfCAfR6Ptv6FT1w7QtUfctCunfsJUoB0BX7zy3TlILAqXV2a+aDD3Z8gSSEQ8QgHgBQA+EAejU7PmuleRNXUcWVdZo9evO/E7bGwWwbZzynFAVO7b+d3Jq5/PnvKwVCLINwiNigF9ZCPACgAMIB9Fp2f93GQqE+LBoMm3/TYqq44jdKAdBZm/nbS2nr1M5v0Wy6bQWVlZUpxUF79tJLI5UCIZZBOFjth6sbIR4AsAHhAHol+9r205N3rokSDYY9cmMt3f9/HX/4U3v21I230JZJnX+q5JOV1Upx0J69sOKPSoEQyyAcou2nr2yg93ftkSUHAADhAHod4oVVtfetVQoGsz008T80+6rLlSKgs7b+H4uVosCJdWZr5r//fbNSIMQyCAe1XbF+syw9AAAIB9DrePXpJqVQUNncG5bSnOI/KEVAZ2z66BH00d87P2Xx4H0PKAVCLLv//jKlQIhlEA6x7Z73P5YlCIDeDYQD6FVs2/QF3XfVcqVIiGUPXvdfmnvd1Uoh0BmruvY6+mhy557vsHr2f5UCoT17/oUzlCJBZRAO7duz27+UJQmA3guEA+g1tH6117KDoiP2wLXP0kN//qNSCHTGXi2vVgqDePbhX1d0eGvmS/W/tgmEk+nlVy6gYMPvaO26K+nNt/5MG94upXc3TqXbG9fQje9soaL1m+mXwY3045c3UO6qdUon2htNrHdoxuu4QS8HwgH0Dr4hWjQ9qBQFTm32hGfpkb/8WSkEOmMf/qNz77J4du5ipUCIZS+//Hd6593baevWf9KXXzbQ/v0dX+j39b799GHrHmr48mta0Pw5TXx7i/ZaapVzzXS75q33ZaoA0DuBcAC9gjVLNivFQEft/mueo8dumqQUAh21B/+viD66tePiId7WzOnTp9PixYupsbGR9uzp3t0ALXvaaGnLFzT1vW00qmETffeFtUpnm2n2wJZPZQoA0PuAcAAZT8uHO+m+qzu2rqE9m3X1c1R182SlGOioPR+4TykO4lnN3H9axMJ9991HK1asoI8/Tu4Cvi/b9mnrACY0fpDRIuKUleto/c5dMtYA9C4gHEBG883+b6h6ystKAdAVu++qOnr81tuVYqCj9t6M55XioD17c9pymjFjBi1fvpxaWlpkbFOLzbt2073vf0w/y9Apjd+va5IxBaB3AeEAMprX//u+0vEnxFg8PFE2TSkGOmL3/e43tDXgfIvmpw+spd2bQjKG6UHtp1/QlW++T99WOOB0NkxZgN4IhAPIWMR7KO4vfl7t9BNoC26/WykIOmJP3xSgLX9VCwXDWirX054PdsjYpSfvfNVKf9/cTN954Q2lI043w5QF6I1AOICMpebvrysdfXfYk3fcrxQEHbHGGf9RCobm8tdo93tfyFhlBsEvv6bfr29SOuN0M0xZgN4GhAPISN5atVXp4LvTnrxjHpWPvkgpCpzYPWN+RR9Ni0xZbJ1STztf/Iho/zcyVpnHo1u301mr31I65HSyhz5KzXUmAHQHEA4g49jT2kaVf16pdO7dbQsCj3VJPDxx/UT6qPRF+rzmXdr/dZuMUWYjHqg06Z0tSoecLlbwciOF9u6TMQIgs4FwABlH/aJNSqfeU/bE7dVKUeDEHrj6cgq9/5GMSe/iue1f0sWvv6t0zOlgdzU1y5gAkNlAOICMYteOPXT/H7t/QWQ8+2fZArrnsl8pxUEsq5l6K+3++isZk97LHxs/UDrmVLecleu0xZ8AZDoQDiCjWD5/g9KRJ8Oqb1lI//jtSKVIMNvdvxlBL9f8U8YACNJ16qLk7Q9lDADIXCAcQMYQ+vjrDr/5srvtscmLaMbllyoFg7D7/uCnLW+tlzEAZu54b5vSOae6rfp8p4wBAJkJhAPIGGrvW6t03sm2+TctpoorfhMlGsR6hi8/Te4jolOdeR+1KJ1zKtvvsD0TZDgQDiAj2L1pE73hn0CLbkutEQfDHrmxlu4r8kM0dIKXQjuVDjqVbcXn6f2gLgDaA8IBZATbJt9Cjd/L1eyN0VfRv29TO/Bk2kN/WUKzr7qc7v+/30I0dJDte9uUDjpVDa/eBpkMhANIe/Z/9RVt+J8fhIWDYWtHFdHTtz+ndOLJskdvfpY+37ZVhhx0hFDbPqWTTlV7Y8fXMuQAZBYQDiDt2f5gZZRoMNvaX11OT5c9m/SFk+LV3ts2Zdajo3uaBnbGKiediiZ2hgCQiUA4gPRm/37aeM65SsFgt/UXX0a1Zf/VHLjKsXe3rV/ROx/slGie+iSkdNSpZietWEtNu3bLUAOQOUA4gLRmx7JlSpHQnq0v/DX9p+w/NKsHBcTyRzfIEINE8I/3P1Y661SzO9/bJkMMQOYA4QDSmvd/+1ulOHBi68+/hJbetoRmXdO9AqJm2mv0TQa/qCpZXLch9Z8wefbqRhlaADIHCAeQtogtmCpB0FFbf94v6b+3PUP3Fyf+UdUP//Ul2t1LXlbV03y9bz/9KrhR6bBTyeq2fylDDEBmAOEA0paPp05VCoHO2nrvhbTstsUJe9fFbL7OZ9vw7onuZN2OXTRoxVqlw04Vu+ldrG0BmQWEA0hb3v3xUKUA6Kq9ec759NyUp2j2hK4JiDeew3sLeoK/b25WOuxUMUxXgEwDwgGkJa1vvql0+om0t35yHtXdupAeuPYFpTBoz6puXY11DT3Ern376bw1byuddqoYpitAJgHhANKST+6ernT23WFv/ehcWn7LkzTnT84FBJ7X0LMs+vhzpcNOFcN0BcgkIBxAWrLp579QOvnutLfO/gk9P/kJevC69gXEktnrZChBT3Llm+8rnXYqGKYrQCYB4QDSjt0bE7OborP21lk/phU3V1Pl9SuiRIPYmbHz81YZUtCTKBdK1j5Hxyx6xXrM/N0T7Xxvt3jnP/uK9v2xzyq+Y8PrtkGmAOEA0o6WWbOUDr2n7a0zz6YVpY/S3D9HBMQrT+OVysnEslByUTn1H+gi18XlFgd+3LPPkHvkWXSAi7/T7DA60PtnOmqR6RyzLXqEDh16An0rfL6LDhg8mtyPms/he+Xk0sEXXkAH5fB3pmsdWzaaDhw4mn77Gh4GBTIDCAeQdjSNHKl05Emz/P9HqyY9RE/cvpr27d0vQwmSQXih5LOP02FnHkbfOuKwKOFw1NgfkIsd+RFPyJGDWhYG4tzhU+lY03mGuUedQK7Bf6Aj/yWPLV1Ihw8Xx/5MR8tzsq45iw76w+Pa30f/4QfU98qF+rmaeMmm/nespAtff1eGEoD0BsIBpBV7t21TO+8UsM8ff1yGEiSTWR98ws77LE0cHDaW/zcLh2fZkbOY6HfzysgxYfcX00Gus+iwuaZjmq2ko/82ldxzbeeXjSaXazS55eejr1QJh5XkvjibDhw5MyxImnfvlaEEIH2BcABpRWjBk0qnrdmp36OVJ+ewfU/9fQetQVzrFPV3dnv7f8+k/a1Y25AKND5xFfU9gkXAHN2hW4TD38dSH9cFdMRi+Tlsj9CAHBcdfN1ztuMqe4WO8v+AvnVmZMThuLl/pr45Q+mQkb+ig3N0AWJMUbhN93p822cylACkLxAOIK3YNvkWheM+haqPOZzy+kTmoLP6H02zc+znSRt0LP3QNF9t2A+POUmecwrd6+5HeYe66aJD+pH/hFMiv805gfx9+1qPsX1y1zQZQpBUGsrJm5VHZ8x8U3PUUcLBNlIQsYV02GBXZIpBYcfcUUz9R15ABw88mg4cWkxH2tdE1C6kI29+gI6u5b+NKYq/W88ROz8ASHcgHEBa0fSrX1kctrBlxx1Gh/XpT1d+J0c/duogmjigDx12+PH0iu1czQYdQ4Ndh9HNJyu+E5bNwuKQY6hG/C3O7X8sPaN9JwRFXzrpyBOt1837PrU1N8sQgqTRFqTAMBflTayjFz7boTnqRAqHY+fOpCNunkpH/OXPdMj/sHg4fyplKXdQrCT3hSfQgaMf4L9foaP/zr+54xE6ZmkDfW8ltuqC9AfCAaQN37S1aU7a4uS/N5DG9XXRT4892Xo8eyBN+/Z3aZn5mGEnHMnO40i6V/WdMDEioRAOr/DvTup7JM22TV9s+eMEGUKQTOom5ZFrkI8C86uoqrqK/veOu+mwC3PJ9f2fUf+Ro+nQO56j4yr+QAe2M1XRb5JtLUMsWzyV+qnWSrAde+uvqM/AsXQki4qjxv2ADrr4r3T4uJ/RgXJq4/1de2SIAUhPIBxA2rCrocHisDWLN3qgsFe+fTi5Dj6aqk8R6yFyqOFU+zkn0cQB/einRxxJl/D/Pzz2pJhTFMJ2Ll8uQwiSSXBOMRVPiNiwcVfQIWeeYBUOHV0c+cRf6WBXLg2osB1f/gD1P8FFfa95xnp8UTkdcoIxRSHEiCFSHqdD/0e//n9a8FRRkN5AOIC04bNHHoly2uHRg5wT6coBB9FhYr1Cn4No8OHHUU2UINBt2XGHU16/fmE7sc+BlHf4sVRtXhNx6vfomROPp4cGiYWWcoriqIGR76VtOGMwfbMXK+VTkU/3tNFJ1/wwxnbMC+jwh+V2zH89QAPM2zHn3MpC48/yWQwr6Yjh/N2Zf6Ajje2bzz5HR449i74VJTTMUxTi8zN02JnynGeFiPgZHfZEA929GdNaIL2BcABpw9YbJkY5bk04HHI0XXlEf/J/+yRt9KBh0PHkP6RP9FqEWHbqSXTzobHXRLxyvJtO7HsUPSSurU2BnEjVcufGR9f+SYYOpCJ/mPjjKOEgHL/bb38A1F/p6KX698eU/IxFQTYNmC7PX7qQjrA8MMpF3/KcRQP+Zh210KYovv8HOsq07kHfWZHLxoJCbsv8w/rNMnQApCcQDiBt2PSL86OceuPAoymPG/ITWSRYjrOgOKy9dQx2Y3Hg6nMkzbYfzzmBLunbl8adyH8PPIYGH3Ik3ew5hoYf3J8mDsqlL59+WoYOpCJPtvfyq6Ur9UdIi10Qtu+OrVU8Vlo+UvqYf8V45HQsE/cxPab6Ry/jvRUgvYFwAGnB/p07rQ49bPriyBPt0wixhMNJHrqo3+F0c7b1+LJjD9XXPZiOaVMURxwUnqKoProfXfRtfY1DTdYh9MPjcmj/V1/JEIJUZMNXrVYnniK2B69cB2kMhANIC3atXWty6FYztmP+aaB88NPJA+lKsR3z0OP0XRWDPDTuyGPlcx1Ooon9XXTS4R56Rq6BaMhmMcHiI/IcB93MUxTi87Jj+4fPESLi/CEjZehAKjP4Jf2ZDqlkDTu+lqEDIP2AcABpwY5nn7M4dat9j2qONT8Aqg+dOCDyAKiVHhYWrr505Xfk+SefSFf2PzA8X+1yHUg/PPpEWmleTKlNURxCVw60HvP360t5bCcefCRV3TJVhg6kMpetfU/pvJNpT38akqEDIP2AcABpwefV1REHHsuMR04rHhPdcIriMdRyO+YrZsEQ175Hr5x8CjXw318+84wMHUhl7nxvm9J5J9Me3PKpDB0A6QeEA0gLPv3HvQonnlzb88EHMnQglVm2/Uul806m3b5pqwwdAOkHhANIC7bdVKp03smyt88cIkMGUp3P9rYpnXcyrbgR76wA6QuEA0gLPhz/f0oHniz78A/jZchAOnD26kalA0+WXdKwUYYMgPQDwgGkBU2/jH65VTLt48AdMmQgHUi1BZI/xrMcQBoD4QDSgncKfqR04Mmy7Q88IEMG0oE/bfhA6cCTZSevWCtDBkD6AeEAUp5v9u2jxtw8pQNPloVqamToQDogFiOqHHgyre0bPAQKpCcQDiDl2fHlp7Tsxt/Riut+Sy9f7afXi0bT2ssuoTd9I6ix8Bf09rnn0Ns//CFt+MEZSiffHbbzhRUydCAduP/DT5TOO5m2c99+GToA0gsIB5DyfLrrUzr94dMd2RkP/4CGPfRD8lWeQ1fM+QVdP2sE3TrTR+X/GE33//0ymj91LP3rtt9R7c2XU91ffkcv/mksvcJipGHcr2ndGB+9NfIi2nD+z+ntc35Kb/+//0cbTjtdKRxa33pLhg6kAwua23lnRZKsZU+bDB0A6QWEA0h5Pt/9uVIk9KT9Yu6PaPScYfR/s8+nv1T8kj4JbZGhA+nA8s92KJ13Mm1L6x4ZOgDSCwgHkPKEdoeUzjyZ9sEOPPwpnVi3Y5fSeSfTNn69W4YOgPQCwgGkPDv27FA672Ta5i82y9CBdGDb7r1K551MW79zlwwdAOkFhANIeXbu3al03sm09754T4YOpAN7v/lG6byTaWu+wCvZQXoC4QBSnl1tu5TOO5m2MYQn/6UTX7TtUzrvZNorEA4gTYFwACnP7n27lc47mfbO5+/I0IF0QKwnUDnvZNq7X7fK0AGQXkA4gJSnbX+b0nkn0zZ8tkGGDqQDq0M7lc47mfYptmOCNAXCAaQ8+7/Zr3TeybSXt70sQwfSgcWfhJTOO5mGJ0eCdAXCAaQFKuedTHvynSdlyEA6MO+jFqXzTpadvGKdDBkA6QeEA0gLfvDID5QOPFk2/bXpMmQgHbirqVnpwJNlQ+rx5FGQvkA4gLTgJ//8idKBJ8uuf/56GTKQDkx8+0OlA0+WDV/ztgwZAOkHhANIC/y1fqUDT5aNWjxKhgykA79f36R04Mmy0W9skiEDIP2AcABpwY0rb1Q6cLuddm8u5U5nm63+Pq7NPU37/WlzFd+Z7MxHz5QhA+mA99W3lQ48WfanDXhkOUhfIBxAWlDRUKF04Ibl/fkYGpDVh1wul7Q+1Pe0Y+i7M9Xnn3rj8XTM4H7Ux3UUDTSOzxxIRw3sR4ed46b+A/m46bd5E46ig79tPfZ56+cydCCVEdseVc47mXbne9tk6ABIPyAcQFqweNPisMOOsukn0OGH9aEBF2dT7oPy2OxT6IQfHUx9TvPQKfbzHz6FPGf2p8N/cjj1MwmHU8cMoP6jcrS/c0b1pwGjT9HPF4Li2wfTUdflyd/rtuqjVTJ0IJVZ9EnqvVL7oY9aZOgASD8gHEBa0PBJg8VpW+y6o6iP60g60RANhk0+xiIMoizgoQGm708ZrRIOeTTQezAdfN53Kc/4nbS7Xr1Lhg6kMtdvSK2FkcL+0/KFDB0A6QeEA0gLtu/abnHaFpv1XTrq232o/3kn0ilh8XAa5fgGkOvk4yjHfr5hNuGgfR54GB153pF02MAB5AlEpii+O8v0O2m/fOqXMnQglRFbH1XOO5nWsONrGToA0g8IB5A2iAWJducdtrsG0lF5B5LLdSD1O9NNh2cfzP8fR9+9V3GuYXbhIGz2KZR9VTadIhZXyimKrImm7232WetnMnQgFVnLDlrluJNtzbv3yhACkH5AOIC04ZLFlyid9+lzT6UTz+lHB+cdSZ6iE+n4iw6nfke4qE/WADrmulz1b4SphEPY8mjgOX3p4Auy+e/T6JSJJ9KJ150UWUMhbeG7C2XoQCoy+8NPlY472QZAOgPhANKG656/zuK0DTv1t4eR6+Rj6CTzFsq57OyL3HSw62A65kbTcbO1Ixzyio+kvt/Oomy+5km+/tTfezyd4GNBYltsecMLN8jQgVTk8nWp9fwGYRe+9q4MHQDpCYQDSBvufu1ui3M37Lvn9SHXOQMV3+XQcSe7Irsj7BZLOMwcSEceZ0xRnETHDHTTidoaB77eqfraB+PcguoC+ob/gdRDbMM8ZeU6pfNOpt2y8SMZQgDSEwgHkDYs3bw07LAtNjFLG1lwF50SeXDTg3mUo4049KfjbhXH8ui7o4+irCJ914RmSuFgnqIQn08lz2lSLMwVIuJwOn66+fzT6cWtL8oQglTi/g8/UTruZNuiT0IyhACkJxAOIG3YsWcH/c/D/2Nx2oadWnyU7QFQ9jUO2XTMt/nY/zuBco3fKYSDNkVhm/YQOyv6fbsfW1/qp9iWec1z18gQglTivDWp9bRIwzZ+3SpDCEB6AuEA0opfP/1ri9O222kz5SOnZ54W/f2Dp8V9lHRMezBPfU02IWaav2qWIQSpwKKPU++hT8LyX3pThhCA9AXCAaQV97x+j9J5J9umvTpNhhCkApetfU/puJNtv1/XJEMIQPoC4QDSileaX1E67mTb2VVnU2sbhqBTgfrQTqXTTgX7+2aMTIH0B8IBpBX7vtlH//vo/yqdd7LtyXeelKEEyaTk7dR7xLRhr3+JJ0aC9AfCAaQdVz17ldJxJ8uGPDaEpv3bT9sfHMbKBk8ETCZNu3bToBVrlU472XbBa+/IUAKQ3kA4gLTjkbceUTrwnrazHvt/NP0pP31RfjLRrYfrVj9ThhIkAzEVoHLaqWCYpgCZAoQDSDve/fxdpSPvKTu76oc0gwXDjmnZEcFg2NQTiHbvkCEFPUnwy6/pOy+8oXTaqWCYpgCZAoQDSEt++s+fKp16d1pB1dk066kxtPNvA6MFg9n+e7MMJehJfr8+9R4vbRimKUAmAeEA0pK/rvyr0rl3hw2t/jE9sGgMfXXniWqhYLeyo4m+3CpDCnqCR7duVzrsVDFMU4BMAsIBpCUrtqxQOvlE2k8f/wnNXfgb2jX1eLVAaM8eu0SGFHQ34hXVZ61+S+mwU8XENAoAmQKEA0hLxIulhj85XOnwu2rn/vOn9EjNaNp9h0ctCpza64/I0ILuZNI7W5TOOlXskoaNMqQAZAYQDiBtqVxXqXT8nTXvE+dSVc2vaU/gOLUQ6KgJ4RH6UIYWdAfPbf9S6axTyR7c8qkMLQCZAYQDSFtCu0N0xiNnKEVAR2z4Ai/981+XUlvgGLUA6IrN/bkMLegOLn79XaWzThUTz5T4qHWPDC0AmQGEA0hrblx5o1IMOLHznzyP/rVgFO0TixlVTj9R9vL9MrQgkfyx8QOls04lu/qt92VoAcgcIBxAWtPwSYNSFLRnFz75c3rqCR/tv+0otaNPtN1+DNGHr8gQg0SQ6usaDPv3JyEZYgAyBwgHkPb86qlfKQWC3S7+1/n0zD9/xYLhSLWD704Tz37YvkmGGHSFO97bpnTSqWZip8fe/d/IUAOQOUA4gLRnwTsLlELBsF/VFNLSxy+m/VPcaqfeU3bP6US70APtCvM+alE66VS0O1ngAJCJQDiAtEe8zlq8aMouGEYtvIierRpBNOUItSNPhlWeR9S2W4YcdISXUvh12XY7acVa7YVbAGQiEA4gI7jj5TvCgmH0wovp+ccK1Y47FewRFjN7d8mQAyds39umdNCpamINBgCZCoQDyAg+2PEBXbboV7Tq0fPVzjrV7MGf4WVYDmnZk16iQdgbO/CkSJC5pJRw+OabbywGQIf452/VTjpVbVYB0defycADFSs/36F0zKls12ALJshwUk447N+/XzMICNBhQh8QJWPHRFfsntOIPmmUEQBm5mz5VOmYU91WsNgBIJPBVAXILJb8Re2gU9nEI67X18gIAMGfN3yodMqpbr9b1yRjAEDmAuEAMotdnxNNPUHtoFPd/nMj0f59MiK9E/F45gtee0fplNPBXvniKxkTADIXCAeQebx4j9oxp4PNO5/oqxYZkd7F9Dc+oOwVa5UOOR2sbNNWGRMAuo9UmL6HcACZx769RHfnqh1zOthd2UTB+aKFkBHKbJq/aKWJT66lofetpLNfekvplFPdfvxyI322t03GCIDuw1gDmEwgHEBmsvYJtVNOJ5szjOjjN2WEMpO5LzZRftky+u6Nz2j2y0VBpWNOdaveht0xoGfYufPrpIuHbhMO2A0Bks7sn6gdcjqZeEx2bQlR6xcyUpnBK02f0e/mvhIWDGb75fPpNeqABZHATnf7v+aPP02qeIBwAJmL6K0n+g2Ykw6j5uvZJiq+U1hriX5+qDTG9xMdXuvO7xA9eyt3Nz6WkUtPXtq0nf78xBtKwWDY4Lvq6Kcvpo94wIJIkAwyTjiIyIhIAZB0VkxTO+IY1nR5Pyo9tQ+5XX2pxvzdxP5UetIBfPxblO05gDwuF3kGHUy1sZz+hEOo6PhvkYvP06x/H/JfMIBaTecEhx9I2YMOoqKcPuQdPiDy28kDqPykb1mPGVaWRfRUMVHLuzKC6cEz67bRuIdeVQoFlf3i8TV0wvNvKB11Ktk976e3kAOgMyRcOBii4bPPM2toFaQp37CAfeCn0Q5YaYdSeW4f8g0+kPItwuEwqvoBiwAWCnUl8tjkQ6nyBywkcvtRU/g8ww6lilNdlP2DQ6hRjjS0jj+YvP2/Rb4LD5Xn9Kfi/gdRzWT+ezKLDNP9hKDwnNSPGuVnpYkXd827gCj4KNGe1Hy88ebtX9FDL22mkfe9pBQH8WzEs+uVzjpVzL/2PRlTAHoX3SIc9u3bR9s/w+uDQYrwWRPRHR61A1bZ1f3IaxEOA6jyrL4UGHOY9bzRfcllH5kQdn0/8rn6UGmR9XjNEBe5jupLddrnQ6jYpRAOV/K9Dz2Qyq+O/C6uibgt/D+iphWiAspIJwdDLFw+T71+oSN20qRaGrYqNacsTl25jtbtwIvKQO8k4cJBjDbsam2VnwBIEV57SO10VRYlHNRW/9M+JiFgMk0IHEAll5mPH0ZVQw6gbBYUgSv1z7Vn9aGC0/tS8el9KP+s/hQypih+YYxKdNBuO5JOu/FJuuLhNVT1yge08ZOdMvLdx47WNqrftJ3mrHwvIWLBbj99qJ5yVqxTOu9k2ryPeuezNgAQJFQ4GKMNe/bskUcASCEeHal2uHaLJxwmHUo1ww6kPFcsJy9EwQHkHtiXqq4UiyMPpfoLDyLvj/qyoHBR8ejIuc3j+lPtuMO0tQ/aFIVy6sOZffXghVGO9/u3LKUrH32NZj2/iRa/sZWCH4To0x27ZYJ0jE++bNV+L0TJpJp1dNGMVVH36w4rrE2th0JdjZdYgV5OwoVDWxseggJSFPFExr8NVDpdi8UQDs3jD6HS3D76wsjjD6Lyy21TF2abzOLirAMpu79YGHkAFfygHwVLxfTEt6h4jOL8K8UaiAOp4k/8d8kAqr3kEKrryHQF29OVZUrHq7JTS5eQt/wFGjNntTZCUVwVpBuefIMmP7Wepi7ZQPc8+472WXz/02nP08k31Sqv01P2sxVvKp14T9vZqxvpg1Z0jEDvJqHCQUxT7N27V34CIAXZ8IzS6VpMIRyCvziQBcO3yPu//aimgw49bBPEdRXrFyYPoMAguXBy4iHk9xxIpb/sR8UD+7DIaEec2OycWx5XOt1MsB/OWEGnr0r+Ysll27+UBQmA3kvChYOYpmhr690v6gEpzrJblI43bHbhMPkQKu5/ABVd6tSJi5EFF59vPd50wYHKNRHBnx0Y3p0RGnkQ5f9UbsO89CByDTnEcm4sa511jtLhZpINX/S60pn3lM368BNZgADo3SRMOBjbMHfv3g3hAFIbsfPgid8qHbBmduGgfe5DpZfLhz/ZTHs2w5WHUMlZ/eRzHQ6jmiEHkPukg6l+kn6N1iJ9O2bUmgjzFIX4PKYveaRYCP3yQMr+UX/r+TGs7sEblc420+y85ckZdRCv+QYA6CRcOLS2ttJevOwFpDptu4kqz1M6YbVwkA9yijL9vOYRB5LbdQCLC/mb0gFUcbp4kJRxHouGof0pZFxTmJyi8I80jWSInRWnHkB5x/ehbDcLCofTIhffXq10tJlmp5UtozNX9ex6hxGvv0tf7cMD7QAw6BbhIP4HIOXZFSKaka90xJ2xVjm6YDH5iOpYj5yOZeJR1a3iGQ+K7+y2994zlU42U81b9arSwXeH5a1aT2/sSM0HbAGQLBIuHLCrAqQVoQ+c7bRIYVs9Z4LSwWay/WxZz2zRfOoTPMgOADsJXxyJ0QaQdnz4CtHtxyidcjrYVX+bo3SumW5nd/OUxT+b8apsAFRAOAAgeOvf+vsfFI45lW3/tFOUTrU32NA5LyodfiLssW3bZcEAANhJqHAQ0xXiyZGpgAgLAB1i/b8S/xrubrY3ZhcpnWpvsfOeSfwbNDE9AUD7dItwSKZ4MO4P4QA6xaY6osBxSiedinbT3TOVDjURNvCPj9J3StTfObaJC+nEax6lgRMV3yXIhq5M3JTFs3jAEwBxSahwEIipCvH0yOaPP+nxaYudX32l3RvTJaBLfPQa0Z3fUTrqVLJv7jiBTrpxsdKZtmcnjLmKjsg5mQ5wXUTHKL4X9p2RF9FBLhcdNlL9vbDjzz3DtC3VsDPoyCvkOdfeRIcdezIdcsa5dPCxfK9rI7/Vrn+09Vhn7X/Ll9N3X+j6YslXvvhKFgAAQHskXDgYow67du2iTz7t2XlCsaMDow0gIbS8Q1R+qtJhp4q9PcuvdKTt2/105Km51P9/fkR9YwmHcUXU75ABdMAh7QuHb5+TS65Tr6ITFd8JO8F7Bh18zr3a3+Lcfufer38nBMXR36HDLnnCcn5X7Of/6tpTJfH+CQCc0y3CwRh1+Prrnt3/LO4rDMIBJIQvPiK6d7DSaaeCld9brnSijuwKFgcq4TDxXnIPdNFBZxbREfx/e8LhmHwXufJvUn4nTIxIRAuHJ/h33+HrT6Hv2M7vqp33QsenLM5a/RZ9vBvv1wGgIyRcOAiMUQfx3oqedOLiXhANIKF8vZ3ogXOUjjupdvsx9P1JT0U5T8emFA66Uz9gUBF9e+L9dGS7wuEJyjrNRX1/VK6thTjxjwujzxH3OHYIHXrmz+mQY/UpDGOK4tjrbOcmwE69eQnlrlynFAgq+80bmyi0F4/HB6CjdItwEMCJg4xh3x6ixdeqHXiSbHPFL5XO07EphMO3zz2DDgivO4gnHObSUd8/mfp6pB17HB0w4GQacG65dSTh+vvpuBF30PHX899yiuLw0abvE2znPfqKUiTY7ffrm2gXHiMNQKfoVuHQ00CogG5l3YKUeVBU5cyA0nE6NrtwGDeWP5udejzhEG0D/3AV9T9kAA0YoVq78AQdc8ZxdNAP7+C/F9Lxo2+grEvK6cSu7tpQ2EXL25+y+Os7W2g/mgqQ5iTT33WbcAAgI/n4TaJ//I/SmfeYTTmSCqZ0YZpCmEU43EFHHO2iA3PHUtYIduiaXUWHHuuig3MvosPOHEvHXmP7fQw75gwXHXDmlKjj3/nlz+nAo0fScROfIc9Pcung/Kvo6J/8iPoOKqLjbecmwlQvwvr+qvVUvQ1PgwTpT7JH9CEcAOgou3cSPXaJ2qn3gDXfe57SWXbILMLhXjr6TCEQzPZz6ndkbOFwwvnnUt/vX0UnmI5p0xc5+roH87liiuLQI43RjHI64thzKUtb43AvuU80bd9MoJ0z2/pUyTFvvEcNX+JlVSAzSPboOoQDAJ1l5d1Etx2pdO7daU/ed7PSWXbIFGscrGafqniCjj33Ijq8UN8lof/+OzSg8H4aqH2/kI4vPJcOMj/HQf4uMkUhPs+lIwfJcyYKEfEjOsrhaEZHzfff9ZpouG3TVvoa6xlAhpAKU/I9LhxEpPGAJpAxbA0SzSpQOvjusoum/kvpKDtkHRYO+nTGAbnXhp/bcOIlo6nfANPDnwacQUdc8qg8XzdtiuL40eQxPTlS7Kzoe/TJbMdR327YlmnYz+9ZQbcF35cZBQBIFEkRDqmgmABIGPv3Ea2aTnT7sUpHn0j7/J4CpZPsEStZqHx0tLYd85on5MiDQyt5gk68VrGFM0F229Nv0YefY2oCgO4AUxUAJIrPNxM9fKHS4SfK/nv/DUpHCdNtzJzV9NyGT2SGAAC6AwgHABLNG9VEdw1SOv6uWtHfrVMBMN1Ou2UpzXp+E+1uwzQoAN0NhAMA3YF44mTNeKXz76ztuitX6TR7u1312Ou0bssXMuEBAN0NhAMA3YlYPJmgrZsvzS5WOs7eaj+6s46qX/lAJjQAoKeAcACgJ9i2lqjqUqUgcGp/uadS6UB7m/3s7he0aYlPd+yWiQsA6EkgHADoScSTJ6t/oxQG7dmev+UonWhvspGzXqKqlz+gPVjHAEBSgXAAIBkIAfG4n2jKEUqhYLc37r9C6Ux7g/3+oVfpmbXbZMIBAJINhAMAyURs4awrI7o7VykYDLtz5n1Kp5rJ9qd/NtCqd1tkQgEAUgUIBwBSgW/2E218jujJ30e9gXNf4HjKu1ntXDPNfLNeon889y6txS4JAFIWCAcAUo1WdpqvzCGa/RNNOGy8/zdKJ5sp9usHVtN9z2+iN7dCLACQDkA4AJDKfPwm3f/PRXTu359XOt10tcsqX6Y5K9+jtz/eISMKAEgXIBwASBPa9n9DK99toalLNtAv7lmhdMipasPKX6BrHw/SQy9tpvdavpIxAgCkIxAOAKQpu/bso7oNn9DtzzRS4b0rlQ47WXbJrJc0gfP02q20eTuEAgCZBIQDABlC69591LjtS6pdt40qlm+kGxa8QZfc/xL97+3LlM49EZZftkwb/fjDI2to9gubaM37n+N9EQBkOBAOAPQCvmzdS2u3hOipho/onmffoWlL36ZAbSPd+u836aaF6+iGJ9dq2x+veex1TQRcPu8V8j/4Ml3x8Kv015p1NH3ZO/To6vfpv299TA0fhmhraJc2dQIA6H1AOAAAAADAMRAOAAAAAHAMhAMAAAAAHAPhAAAAAADHQDgAAAAAwDEQDgAAAABwDIQDAAAAABwD4QAAAAAAx0A4AAAAAMAxEA4AAAAAcAyEAwAAAAAcA+EAAAAAAMekr3DYUkm+LDf55jTLA92A6h4Li8k1rJwa5cfuoHmOj9xZPqrcIg/0ND0Qx26lJ8oGoOAsP+Vnucjl8lDxEnkQgDSicZqXXFfXyE9OaKTyYS4qXig/Mklvr5NAwoTDE2s+VL52124Tn1wrf6FCzxSXK2LuQQXkL6ulpjZ5ikFbE9WUBahmo/zsiCaqGJNPJU4bOdU9EulUN1aQf0gJ1cqPYTbWUKCsJjrOPUXcONZQsctL5evlx1SjU2UjPq2t8o94bKojuvXw+Db/V/IHdkT6WuuBxVJB1IWqyO/iurRcJkqyymo3s3vfbjr94dPj2pmPnil/EYMUEOOtjgtwAmhrpdZOlQnDB8RuX+om5mn1wDut66mZCOHQLe0151UP5laHSUnhEM6U1hA1Lisn/2A3uYeXU7DLGaPI9I6SyAZgfTl5XcXsJlKMdBcOiWZ9pVYGfbMcjmB0WTiY0ctsIhrJhKKV3cwvAxkhHEJ1VDrMQ9kTuVz2AI1zxEiUjyo61QPXy3t2TjZls0MPyaNhWliwZrE/yEoh4ZBQQlQ32UuenBLqmdzqHKktHAxa2FHluqigLCgPCIJUOaGYKtfIjwKtp+mnwiH5lM/mHVka6XVuqaXAhCLy5vB1Li2mYv5tcVkt6a5AXCtAtQ31VD7Wy78tksNOinvIBqB+VQUVXSDu4yXfJJva1O7F17NVnOCcYgos1u/YvDhAxeO8lO0qIJ8IC5vxHa2p5M+VfHczIQrOKyXfMD1uheMrqM7ix5qplnvZtVtCVD+jSE+DYT4qXdAkv4/QvLyCikeKeOrnFM+ot1bQLgsHUxg4ffzT6qjZnD5tzVQ3ozgcF+/IYqpYZW0itPSZU09NC4q163jL9Gokjot0CrWX/qp8E2k6h1OUewelMu4iDetb5PcmRPro1+ZzxgaoZCyn47JGCvXYiIMZlXAQec3xW83lfUKhlgaB5fKbeHmrpQ2XLVFXJvnIK867oCgq/amlniomyO/taSXSUiu72eQdx2VXpKvBxloqHy/CpN+/tLrR2nMSv+V6F1zFHQKR/+MqtTrYbr6KsMhrWup0mDjlLcY9ndBTwqF1QxUFxsp0G1JI/qgerMN8Y8S1jDIu6lbgah/5OY+ad+rfi3bIkmcCrc2ytjnxw8RoddlIe3FOCfk5j+o2hCL5bsq//GF+Kl/eXupLH8BtWYGijamfXEDukSVUElUniJoWl4frrSgnVRuiK2zTAqMNLaQirhv1KuFgaiPyLyi2XUfhoxTttbN2imltpCojP0V95XrtG8vXanba2CSH9BAOjKYMB5dSvfxsDOeaz9WGsHL9VNnQTM1bGjXl5s7hHr2oW22tfIyV91AXFc0T37OFM0dciwvbcG50yiqpalaNLATR99AagJw8zmR2dsuCFFzGDfVQVsAjKyjsomP0xmquNhX2nXz/5aVcOYRIkeGRFVu7h20kom5SPrlzfRRYyA3AhnpuRLgC5fI5YccnHczwQvJyIa7fEKS6aUWs/POoeLGpcVkjKmS23pCIezZwbzpX9KZNAqOLwqG+rIA8Q7nCiXzYUEfll+ZRnqm3E+TvXTlGPjWzIPJTnuihbJYnMFp+D/NSoUjnas6TZXpjYz5euaqR058bi8FuyptQa3KQMfJtsJe8XDlFGjau4sZ1hEcbyTLHM7S4mMPCjpgb5NZWIdY4bDlFpnR2QLcLB3lsmMjrCqqaV0V1QqQ6yVtTWfexIwhyWaqaVEgejnMkP5uoYqQY5RMOQOQRO62xXLeMMsHpopfdAiplJ9DcIuvR5kryedxUoJU/zteFAfLx/b3TTE1qOB/8FOBwV3BeCLR8HeHjRt2er+UUGOGl4nn11NjAZUmEg8t9ralIxytvse7phB4RDmLaR/SgJ9VRo8g3jkPpcI67ZYTASb4xou0Rdb66SZuaaFpWyp+5PDfI7xnRDkU5S/vop6Mwhah2Qh6XkwCLylZqbdHLSfZ400hBWz0Fhnq4TFRRkK8jRpB9uXlUskx+H4XhA0JUNdZtHXXQRhuyqWRJMKpONM3xcVoU6OWEy2tNmU+rx+WmeDdOZ38gzplVR0EuSxUTWBzkZlvTQtzDk0c+Fp8i3sHqYirwmNsmhY8SeWtrr521U3wtkaZXV1HTTk6/zXoae6c6L5/JIm2EQ3Tm2J1DMzd2LsqfFJEW3MJRiDM/MtcW6/r6tawjGoIYDsjlo0qzI9lcQYXcYIcrgxPhILBXVgN7XLdUkI8bjdJV8rMGN+4jzGHW4+YeW2UqmNyosuCwVIzN3OBUc09efhQEp7IjN/+uvUZOQ6RLDOGghVXVmHF8ZD40sdOuWmUJATcuLvLPN1UpUfGy/FRljgwTPm5O/1WllN9u2WC0NGVHt1p+FrSwo7OFVeRR/mRzGdLD5niaQtBDwsGe147yVqZNgSWOIaq81HyPOirJ4TjPM11dE96mXqSijNdN5Eb40kprmJaUULY5H2U+BMyjQUzsfHVR4QxTjNpqOWxuFsPys4PyFuueTugZ4dBItdW11GhOuAVFyo5S+/km09FWLmrGuyzTFM6Eg9MwcbtkrlMNQrxGpimaZ/mi4i3C6LbfP4ypjdaEcCRvxWiDa4TooNnrhKK8MrXXZ0fqiCw35jbGaEPNaSHKsH2KpOZqIX6Ne5nCZ6CVL3P70155Np2npbmtjRNpnOLTFIIMEg5c1oW69ngof2SpNhoQPbQc6/riWibHHyb6HuoGwFaQFY2qoNPCQVEwBVanoIfB7uC0AmwPr1g7sqqWnQwrb3YGQfs5yjiaEekSHT8N8dtB3LOrZnEQtlIWVtbzW1saqX4xf8f51Mw9FUv6MVq4R1ZEDSkrj0eld4x8i6qQ0efFEg7WBicOPSQclGImXt5qcY4u63Znos1Te7LJO75cE3lRC92i0lwVTqathorMzkWZD07zVWC7j5PyFuOeTuixNQ5CmHEvuIqdtRitaV3A50e1dw7yTaSjQjiYy7Qj4SBwFCaVcIg4Q3Gv7DEBU96wTSpsJy30/NXrpGnUITzaEDknXAaU5YQxCx3tnOh1F1obGk4Led0JlZbwVow3d77M4ZOIvLWlnaPyrH1WCAeLOEtN0kY4aMPbUQ2g4tzWZqqvFnNdeeThguadZBsaUl5fXEtR8FT3UDYAzgqyqMCWhlU7L1oQRBVERcEUaIVzeLnsYarjpp1jDm9DORWyuPKODVBFdYW27qNwsNt6TrxGLmZ6MeK3pxdRuani6RbpvQSnF5LHI6aFKqhqVoCKxfynbbGTFm5748Yoj0elt9N8U5y3rEQb4hRrGrTprmk+yrMNjcelh4RDVDl2krcx8k7pTHZy/GeVku9s7rl58sk/yzQiF5XmqnAKxP1MIwQxypazfBXY7uOgvMUvz7HpEeEg1nAN5g7PiGIqn1dJ5ROLyT/Mo3DSDvJtcwX5svLIP0+frtKH2q2/U+a15vBN93MUJtFDF1MVckrLmCYyDceLe+WNY/Fpz58ljRZxE8FWtuWog3+sMdogsJWBGO2tluZZXHfF39o5NifNWMudft3CibawCguPkNrCJxD3saWLs/KsTwnmiTUNYjqooYqKh3L9nd6ZktqzpM3iyKIce6MkKpL5XPu0BNMshqLd5K82SkuM68eolNH3YEQh4d5Lrfk+xvCpcd5GRUHWzrHFQStI0YIgqiCuFkNchZY1AAIxhBYZglTHTSvApgZLazTGW++oqW5zo9ZeI6cRK70YVQXVhrmNvNHTtGiB9o1E79Wb00ZZ8RhnFTJGvkXFyXZeW5DKR+STr6xcLgoTC5rEQiX5vVOSJBwc5W2MvLM4E/u0BNO6vITLoGm4PyrNRe+Qe5fX2zYXa+eZenoxypazfBXY0iNueWPilufY9IRw0OI+NCDXVUlEz9PSNjjIN86Dmgn53GMup3K5sLVwfDnV2BYJ1k5gMWlL66YZhZb7OQqTEKpDfBSYFiC/WAAoFsMKwWJqG7Xr2EZAtPVdMRf/2cu2PurgcmXzMWs7Hi4D2tZgYzQignZvo9cf4xzRhprTQny2jjiK0VEOr7GOJyp8jMhbWzvupDyH+HdirVz5NLlQ/AIWwAtti4lTlJQWDiLDxAIrsRXOM8K+HdPuHNj5nG1biMUq2it6O+Fz9OuH58LC11NXyuh7MKKQZHmocLJcud3WzIVNLNgqobrw9URYuGBPipxTO5EdUa7NAchGNbxewvh9VEHkcGuLaGrClbJ5cQkrcfOcvaJAM1oBNjVY2jw0p2V4dW8zxzGRIw5SRXvLguEK0DzPT+6w2NLnIwunR+atmxdwBUqFEQdxnRO5h2KkTWdJknBwlLcx8s7igLSFcXl8/UhzH2KnkW3+XVSa8znGwtI1MudbWYiNNM0zC2KULWf5KrCnR7zyxsQtz7HpCeGgrQPI4XJnRECk24jOjDiIcwqpPM7zS0LVIn38VCkFReuaCvIPy+P8jdzPSZhEnnkmRD2Fxoo2AmIqE9wWVorpB7vADKMo26sDVHhphSnt7GUgskgzKG/TuqacfJY1DcY5peHdaFobmmUTUWLEMaeIqozOQms9lXJbHllnowifyNtOCAeRd+Z2MJ1ISeEQedgNF7DBYtuMbXuVhq3RFzRwBeCG0j1IbH/JI4/LY5uq4OLDmZzHx/NYjWcPMxS1ulIq78G/z55YQ7WTuNLIcLoHc0GzVdbQqgD5BgmlLM5hoVFWT5VcUKwOgHsIQnR48ih/cHZkNa2iIFJLrbYXW1wrW1zXk09F882FTlGgGa0AmxsseR0jjfKGlVLtPFuj1k4jp6OnSySfpBkVpaWeAmLHgrhHrvifeyWmbWOhJWItishb8X2elkf2tFFWPMaZg1HnW3Sc7Ofp+aGnjdUKx1eGG6W4JGuqwkneanGO54CMPHKRJ5evxWXTnRVvqkLHeJqke1A21w/O45Fixb38UhCjbDnLV4EiPeKUt/jlOTYJFQ7muiJNy8O2oPZgOrdoB7is5Q3xU8UC0enpqHDg9J8mpgH165jNy/lQazjDtiaqGp9vbb8W2+7nJExiOuN0WY8t9+M2e15EyIm2sFDW9zzxv71MWIhRti0oyoARXlHmBnE7mZVNPm5zzW1/5Bw93h6uHzUz/FHlrmm+2I0mfQT/nz++yrSNUhE+LW87LhyIxU3hifp9LOkndn4ZW/NTlIQJh1RCG1ra0hx7373YTmYbiu0U7Q656bQ2txMOiXUorH30uHU97CJc8cLeVdoNqxxODm9BTQG0LV1nl1CNWAhmFqo72TFxYxG96yY1SWTeateyTwHGI0l5m6i6kSy6HP5lpSycfFS+ytbmtDazULCN/AhEOxiv/YoZpiaqvNRDBdfXUKPtGq2rhcCw72LRp5KdtnOdRrTJ8cqrg3a728twWx2VDs4j3/R6W5rwfVm4WHYipSAZKRwA6Ayi91ag3EMdve0NgFRD6+Xa1rgYiG3ZsbdAdgYxAsLiwPSchDCKbc7Ahjb6UBTZMmxGbNs0bydOQSAcAJBo01ieQioVD9kSvQDRI2uoocCYfPIM5oocc3gVgBSggZ2RmMKcVa+PAohes/awOC95uFybH4bUdfRpPc+IUqpp0Ec4xOiEvibNQ/kTFI+LBhHEQuzhnE7jKvStriwSWpsbqX5esfZIgcLpqT26CeEAgAnjcdN5HjEPKuYfC6loWg01xhnZBCAVCD9uWltfJdYVdHJnkBPCj5sW68m4voj1EGm0MyDpGI+bHpytrbsQa3TEo7Ir16T2+gYBhAMAAAAAHAPhAAAAAADHQDgAAAAAwDEQDgAAAABwDIQDAAAAABwD4QAAAAAAx0A4AAAAAMAxEA4AAAAAcAyEAwBJpY5Kc12UN8l4PTroXtQvi8pUYr0sDoCuAOHQi9h86aXU+L3cuLZr7Tr5CxWi4dXfLmc169vhepboMHlyC6l4XrDjj73dWEH+ISUU52XBnaJ2onjDpP01uiGqnxGgCvPbHHuILW9/ThVX1sW1p+I8/lZzTq48KlkmDxjEeINmcoknHPS3H1pfk6+jxbODb9lU53nPAeEAugMIh15E4oRDijuDnc0UrBbPfOeePDeaHXLJmrPrHhEkXqKVSi/KSqhwyHKT++xSqjc72zQWDm6OT8HkenlMpzPCIdl5DuEAugMIh15ETwkH8b6HYvG8fPlu+eIZtvfiM8Y7IcQ5heMrot/Pv7FGf+a+uMYFxVS1ob2n38cIk3jpDzsAf7X57qKHL56vL+7tJf+0OmqWzq55cYCKx3kp21VAvgnFVMxmfi9+04JS8g2TYZ5QFf3+imZ2tOMLZZiLTKMIQarka/nOdlH28CLtusUTKvmooJlqy6z30dhYS+XGtTgNS6ttz/9fU0nFc/gKpnRSpmM7JFQ4jA9orx63OEmlcIid/oLgHE4bES8zW2opEE4vgZ5mlaubqGaCSCMvBZbLb+KWPWfCoWiqeDW09TylcGipj+T5MD+VLzfyMVaeh6h2qj2/9VdOW14Fvb6KSiZUUJ3xWmcn5aGsloKryskvyui4Sk4lhXBo43BNLKaSaTXUlEKvtAfpBYRDL6JHhMOaABW4ssnPjb/2PvuGSvLnuslnGq7V3kKZVUDFs+ooKN4+eWkeuYcGIr3Vlirye/LIx06lka8hRg8KPD6q2Cy/jyJ2mGqudpNrbFXYedSXFZBnKAuRBg7bhjoq53vnTZTrC8R7+peXcviLqFKE3fQ+/lC1nzy5Pipf1sjHg1Q1ga8zsoLCsWrhMHA8C0Rj3xCkmjKfFsfAavGl7hgqx7m4F8uOUrt2SDb8uqOyONzNleTziGtVam/OE28c9PG1vdNMDpXT0DXYS152IoGFQWpcVUWlIzzkHu68R5xQ4SCc02pOuyxTPimEQ7vpz4geelQPOWoUSKbZsELycnpXzauiui182EHZcyocihdyWCcXkNuUx1HCoa2eAkM9nE9VFOT7NS4r53wypmxi57l2Hb5uWDpsrqBCl4v88yMSRzvHKLcdKg9+CnB6VHCZEFiEQ3MtlXB488dVUZNZpADQQSAcehGJEw7Z5B2n98jDZvQSN9dTVXV9xKEywakFJuetLwYsnGE6o40dyPgSqpSv/a2bmE3Z3NiZe4pCAMQe8o3tDCyN/ZYK8tnPE07J/O77KCclqKOSnGx2JqYQtfE9syLXqpuUR64RJiHB1E0vopJ5kcZdPWwdLRxE/F2XVlriT0tKKDvLT1XGQeEoXAVUqgkTSQs7mHadopWECwcOce0EFoFGXtuFg4P074hwCN/HIG7ZEzgXDhSq1cSg4dAtZYlpnuWLGoEQ57hN4VfmuRafSF6K64ipkUh8mqhiRERIdKQ8BNbIzxIjb1rXlFOheF1zWfToHwAdBcKhF5E44ZBPRTOquJE22SpTc90a4h5wLR+v455YiILmBtfuTKKQjpR7V+brV4zPj3YoYRwKB9G4DuIemem6VdWl3Nsz/TbKSTEyzMVzzL+roKLB0sEYYY4pbHScCYcY12KhUsTpHhYKIi45JSxpzIh0MMIUn8QLB6aFw8kiq2gBuyd7XjtI/44IB98s2/SOoL2yp9EB4cCEFhRRdk4R1bTIeJquJcKaPSZgigvbpMKoc2LluS4MmqlipJuKZ7GoMgSUJrAMUdCV8iDDnJtP+VmcrkMDFMRIA0gAEA69iB6ZqmjQezbesQGqYOcamFBEhYPdHRYOhRNNjbFhZnFiIVaYQlQ1lntyhiMSjevpRVRuv251LTUa3bCYwqGQSubbf1dF9dqwfIzG3UaXhIMWR3Ywi+VHEReLQxSkgHBgQvP95M4tptrVCuEQJ/2VwqFBTEFEC4eoeMYrexodEw5GGcqbUEv1CuGQN67cFhe2JY3hXr06z/U008qlEAmaYJACgu8r0i8yStKV8iDzJpdFyIYglQ/nePA9jbAB0FkgHHoRPSEctIZ/vLXh14aLjUZNG+LPppIl2leSRqqaWEyVcphVTEvk21a0t7Y0U3OLfTWiQYwwscPxukxTDJoAMA3vCtpatfnn8MI0lXDQwmzq3Wnoc9ghGSQR5uzrrZs4G+eXWBb6ORMOwlFxT9Z2LT1cPqoQc/mCFBYO2lA7O8GCsX52+KZ8cZD+tRPY0duEQ9MM7sU7EA5xy55G++VXee3NwrkXkH+s9VpavO3TJWKdTHOknMYSDlpasGComsciS8ZXxDP7+hqq5PyPrHfoSnmw5Y0Wj8jUCwCdBcKhF9ETwkGbjx1RHll81czn23p92qKz4aVUJ0eamxeXsIMpjCyqW1ZCeTlFVGWMRLfWU+nZtnURFqxham1upPp5YjsmC5AJ5h6W7tC8ZUG5MJHvLRrunBKqtQgHH1UauxPk8bqJeZQ9viq8A6B1lVhEaQqztjDQS6XGqnqxEM0WZuFE8idJQWTcTzoqs3MJLRaLR70UWCND2cq9xZHZ1jn9lBYOjOYY2ZFbykr89BeLUN05fqqUu2ha11SQfxinvQPh4KTsxSu/sa7dON1Lbk5by7U0R2zKp7ZmdvpWAanOc0GQAmdnU3aOadePWCSZk8d5bxVXnS8P0XmjL0zm+Mv1RAB0BgiHXkSPTFW01FLpMA+5B+VT/hBuBIeVUi07cUuj1ia20XnJIxpiYdyb98+yOqem+UWUn+WhvCH5lM3/57PTjr0SXHeYkQdA8e9UW9YELfUUELsPRPhyxf8+ClgevhTixj6PXJ48yh+cTd6pMlwc5qpx+eSWx92efCqabxUyTQv0Z0foYWDRMqbCOqcst4dmD86nvEFFVKPt2IgWDoLgLL82L+0elM3pxL8ZGbButUx14cAEp/Jxe1mJl/4incfnh8uGezALyMVi5MjBVIWTstdJ4SC2MYrtpvY0D60KUCELVC1Pxf/2fFLmuU6wrIDLvlkk6Isio0YxmM6VB3XeCBHsyuX0NP8egA4A4QC6hdZm65CtEjGsa54msCOHsY0tkYlEm/oIb4mMJubUSFfDbJ8aaY9ujH+yiZf+YpFj3PITA0dlL6Ho01Yxp9I6kuftkcHlAaQXEA4AAAAAcAyEAwAAAAAcA+EAAAAAAMdAOAAAAADAMRAOAAAAAHAMhAMAAAAAHAPhAAAAAADHQDgAAAAAwDEQDgAAAABwDIQDAAAAABwD4QAAAAAAx0A4gJ7FeN5+Ip7d383EenlTZ2me4yN3lo8qjVch9zQxXoSUriQ6f1KbeC/nAqDngHAA7fLRhreo/NcXxrXHb5kofxGLVgrO8FO+eHukJ5uyxSuXPdFvxUwlOu2YNlaQf0gJRV6uLNlYQ4Gymnbe8tnNxBUO+ts13SMryP4Cc/F66FRz0nHzR8TX5SbfrKjYdOgtohqx8rTHgHAAqQOEA2iXRAmHplmit11AJQub5RsRW6lxnp/yXHlUvNj+EuHUoNPCYX257TXQKYIj4eDmfHKTf741T9JWOHBc3OLV1ZZXSHdCOCQ9TyEcQOoA4QDaJSHCoa2WSnJc5J0W7bJqJxVQ/vgqCrupliBVTvKRd0g+5Q8ppKIZddRs6aHz9xMqKdjWRDXGeRcUUcWqiKNrrC6l4rJaapafdUJUN72YSmbVh1/l3LS4nIouEPfJJ+/IUqraYH0tcpRjWlNJxeLe8qNOM9WWFVPlGvlpcYCKx3kp21VAvgnFfH4xBRbLkCh/H6LgvFLyDdPDUTi+guosARfXD1DtlhDVzyiiQhHfYT4qXWDrRbc1U92M4vB1vCOLLWmi4Ug4eKl0ahFl57CTNP1cKRw21lDpSK92v/wLiq3p5yCtNMR5nFfBVeXkF2EfV6nnW2sjVZX59fiKdBkboJqN2i/COBIOw0opMD6bsvm8SHRUwsGUvkO85J8WKXex8jQ4r4SK51hjKF7pHTIXo1AdlU8QZUt+dpTfnEaruXxPKNTCElgujkcLh9CScg5LKZ9ry2cAuhkIB9AuCREOq0spXzR6toY/irY6Kh3sprxL2Uk0NFPjKnYqQ/lzVKPPjnG4l3xlNRTcUE9VkwrJk2VqVJeUcCNfSBWb5WdBqIr8rmwqWaJ/bJrj498UUPG8emrcEqSaMh/liWs06N8LohyTNvRt73U2UvkwkxPa2UzNy0upwFVElXItR/NO+Z3i93WT8smd66PAwiA1c1wqJxTwZz4n3EPWr+8dXkhedsT1G4JUN62I8rOsIzXBsgJy5fipktNN3DMoRnOyfNY0cCgcyteHqGosp/vEOnlcIRxaOD09eeRjB9so7lddTAUe0/2cpJVAnDfYS95hfgrMq6IKTgfhXMX9hdOv2yDi00h1k72cLiVUZxKRzoQDx1eEldOrZJk8rsXTGo56Tj/PUBY/Iv02sLO/NC8S/1h5ytd353CY9LPCAjl/cr08wIhzBpeSccRxfg8T+V1BVZwmddqaGLNwENN+XF49hRSwi0MAegAIB9AuCREOSicSTfMsHzsRbmTNIwybK6iQe3qBcC9Vb/QLzI0zO5rKS80jGkEKnG0d4WiaUUiuswOyB1ynNfC+edZGt/b6bHKPjYx+dEo4CGINa9t/v6WCfCyCSlfJzxpNVDGC41dm9GT165vDJahnB2QOW9OqKqpaZR6F4DQY6rJOOYj7OxIO/KeIAwur0tXyG5twqJto78WLc9yRNHeaVtp55vwVhKhxSRXVsoAJ01ZDRSKtZHgEjoUD/9k4nYXH2UbZsgkHLR9MwlOgxZ/Db5RFVZ5ymIqzTGFaxoJVTI2Ey5lepsJCogv5Hc4bFjY1E/LJM7iIquIJcQC6CQgH0C49KRzU8+h2Byga0GxT71HH/lurULA1zpoTsDkKwYIiXbjIj90uHGKkS3BqAbnCjkO/vm+WdeJFC5tNBLS2NFL9YhYQy7g32xLUfmeZHjI5UjUm4cAEp4p7cBqy87Smrx4mMQJSVc33k1Yx3iRmnKaVOM/cazfR2hykOr5u7apG7uHbnD3TEeFAbVyORJinijJgu5Y4b5CfAqa4VFWXsmA1lZEYeSrEkiEMhJjyTqtgUWr8TghUk7BQpomz/NbDzCJksIev4aGiaqusAKAngXAA7ZIQ4dAQoAKVoxaIYeBmfVJYLRykk5pu9Katzs0g6rdiaoJ7zFpPVmv0TVMXsYSDaNi5l2msnHcmHHRhk0jhoN13eLnc2aBwtoxdOASnF5LH4yV/WQVVzQpQ8fhCys/qmnCgtnoqPdvNad+oFA6FE82OVpox6uE0rZRhCsledSEVT6ukymklVDzWS56uCAfB6lIq0Ka0FMLh9CIqt8elupYaDf/cTp66tXsIkaCnnS4g+K5iis4kRNVp4iy/9bzhvGCR0sjXsU+rAdCTQDiAdkmIcBAOwzZ1YFA/mXupstHU/h5h2wqoDQebRxgcCgdGDBNnT6zT5v8tQ7+29Q4GWgM+skJfnGd8Nl9zMTsJe8OvTaXYGnqnwkFb+2Fbi8GIXqwIt44T4aA7wqIF2geJ7qS7JBwEy0rYSfnJf6k1fc09bYPWFhaBLXJloNO0UoVJSz8WfWbHqE1VWH/bYeHA1E3M47LgJ5/5Wtr9/FRl7sTL542EnzUSK0+18slpNp/z0riXWGPD5biGy7MljbqQ3/a8EfFwhadeAOhZIBxAuyRGOLCv1npJ1u2YzQtLuAdo2mcvGmex6G+BdN1tzez8C2wNpHPhoDXg3Av0DraLhBDVTmAHMjxAQennWteUky/Hug0xyjFpoxjZ5J/TqMehNUgVY7yUl2Nr6DUn46NKY8GbEXa7cBBOYri++DOygp/ThJ1mZC7fiXDQ12wUhkdl+DoLirs+4qChp5WLHa0lLYSgyCmiKkNltYrRCQ7DDBkGp2mlCpO2FiCbihfLzBGLAacVdn3EQRCqpeJcjovlWk1UMZJ782VBWTY5/eb5tYWPtRbhoMhTTh+xkDM7JzsyFaYtksyjvFzrmoyu5HdU3rQFw9fCpAXoaSAcQLskSjgImtiZecUDoIQTEiYeADWt3tLwhZaU6ufIh0R5htgXgXVAOMiRjshaBxPc8FaMyedeMTf6gzzkYifnK7OGReWYmuYX6Q+xEuHPyqei+bXa3Lm1oQ9xeNjZevIof3C2nFdnooQD01JLpcP0eevsQW4tTYrmm8dcnAgHI904LoO5l5ubR95JtVTJadJ14cC0cG+fHb4yLbI8lDckn/PKo22rNT/cylFaxQhTcJY/fO18dsD+WTVRv+2UcGBCC4oo2yIcmJZ6CozwkHuQuJ/432fbsRAjT5lQNYsM2wJPMdplXi8TppP5rcybzSywWHh7p0WVbgC6FQgH0MO0Ukjb0hYK9+6ikecYw97diVhjYR6SdoQevni/sQzdx0E7t900cYDxOG9j+2dPEPeeztJKSWtIu7bluQjdTLx86EietkdC8huAJAHhAAAAAADHQDgAAAAAwDEQDgAAAABwDIQDAAAAABwD4QAAAAAAx0A4AAAAAMAxEA4AAAAAcAyEAwAAAAAcA+EAAAAAAMdAOAAAAADAMRAOAAAAAHAMhEM6saVSe6mNb47xSsIuIN8DkOrPy4/7IqNeRPMcH7mzfFS5RR5oh7pJeeTKLSXjZc0ApAaxXuKVqcR4cVya02PCYe/HX9OWG1fGtY+nvyZ/0T5NMwq1t+6ZX4PcYZaUUP6YCjK/my41aNLe3Gh9FTTT1kQ1ZQGqsbwtsoPwNarG52uvKDbeUhn9JsDUoXPCQVTWSPw0y8om74Qay9sbk05ra8dE28YaCpTZ4hCjDIdWVVBghvVtnz3B119/Tbfeemtcu+uuu+QvYqC9SdSah+5BXipekHq1tfuI0Q5EoZd398jocqB+a2wyiS8ctDrvyqOSZfKAgfZq8w464Y0V5B9SQrXyY88D4dAlEisc9MKXl8u9qrFVnW8c475mOFl0nyrXBFeWlwJrDJfVSsGpXnLncOVKJacq6bxwsFbW1vWV5M/lMjMxFfrgIaqb7CUPp3mXQ5NiZTihwsEcr7ZWapznpzyVQ8lYnLYDory7yZ3ljupIpa1w4Li4zy6lenOb1BnhoP3G9ir7HgXCoUskVDhohcFHFYsDVODyU5VNOQTnFFPxHNs76rfUUmBCJRlHtXMuLSBXjpeKJvDfbJXh9+mHKDivlHzD8il/SD4Vjq+gOsvsQDPVcs+/dkuI6mcUUSGfkz/MR6WyNyR6e0UXiN96yTcpupfbuqGKAmMLtWvnDykkv7kXqYWziLw5Liq4VA9XcVkt31EQpEpLOHWalxv308Na3yK/UKA1JONt1aitieqqa6nRlI7NnLaBxU3UtKA4Er/qxqgectMCUzpNqKJG+wncSy4d6dXjekExVW2wndDaSFWTfOQ17mHrURrCIV6aWlFX1tB8P+e31VnHDX9zHVWMl3l1QRFV2EdmWjhPjPBzXhbNqKNme9hscSwuKybfWC6LzcbNRL7yZzGiZJynuteaSi4PzsqwyL9IuZHEDavDcLRDtwkHjRBVjXVRtkX8meoglw3/tOj0b7d+2NJUR9RvWz0T53F6BleVk1+Ul3GVWtpq6TynPlxPvGUybC31kXIzzE/ly63Ti3r9ao5drtttB+zo5b10ahFl57CTNGWXSji0W+a7mB5aWS/zy/zg64+1j5A6FA7jOX34PO80UwlQCofY+a/lzTgvZbsKyCfrh0jz4LySKP/Q2mx7fXuojsonlHJ7JT/H9Qmi7rBPaKin8rGivSuSU4rRbVFoSTmHpZQqVzuvV6lGWgoHrWCNrOCCGqTA2dHTFUqVbVOe2vvw5xWRa2gp1Wlz/ZGCUzcpn9y5PgosDFLzhnouEAX8mX8bbnD0wl84khtXrmT1G4JUN62I8rPyqHh6gAqHcSVb1UjBZVypRC93Qi0XO0moivxZXCEm1VGjuO+GOiod7o70hLln1byFjw11UdE8PVzNYQcjCqG10oW4gc3LKqDiWXUUbKihwKV55B4asCp1E/o8OZ+vEAFmRBq7c/O40eKK39BI9dWlVOhxc0WOVLhQtZ88nE7lyxo5nEGq4nTymIdLWziunjzycWUWcQ1WF1OBhwXfZvl9W5DKRdz5HlWrglQ/j7/P8pC/OpKfWl4P81rStGgw/8acplG0IxwGcy/G+Bw3/HydXDcVTOBGoiFINWU+La0Dq+X3bZxPIixaGjVT4ypuTIfyZyF05ClaWRFxvLqKmna2UutmPb+9U80NlwgvO53h7DxYRAa5zFVNKiRPli0Owpk6LMN6upl77E7C6jAc7dATwiF/spGDRPVlnGdDWZBynERdKufybx5Vils/bGmqo3Bu4rzBXvKyCAjMq6IKbhsE5vJZUV1JVcvYm7TVU2Coh8tNFQU5Txq5zPq4LplHSuKW63bbATtGeRfpw9cwxd/eFsYt811KD/3+rmFcHjeIMDdqI2vuXBbr4fbIoXAQYV5dyu2Bqb1QCId2838nH1vO13AJJy7TcCcf57CLEdZwKrXVUgkLNHO50s4xtRXxfUKk7vjLuBzM4voTPm6EuZWCM7gN8RSm7NSwU9JQOFjFQpALji4iIjgRDhqi8Nsbpy0V5OMCULpKftZooooRrPzLjMZeL/xu2zRJPRcul6swUtAFS0ooO6s4MscWaqRaW++eFnDjbyqksSuXKITm49ywsDApnGHqpbODKB9fQpUN8nMUrdTIDtw7iCu4J5+VeDnVrI9ukLTKK8JkFiAiLmKkR1PSdVzZsjkspoi0cfhMTqZuYjZlWxyTyBshPvQU10SMbYqkcT73BqZGRIEWjiw/VZl7iatKKT+qcTNjrqw6rRvEVAXfe7qR2w7CLxYYjrDOG9dNL6KSeXo5aJ7li06jzRVUyD2cgNE708qdbVRM5Ldl5EPP1wJzw8UpUHmprcelatRVZZjRHVPkuKOwOg1HO3SfcJBTFWYRo9VVaz5r6c31rUaLp4P6oUpTVf3TzjOnlU64fJryV0trW56I89ymNslZuY7vZHVM5V2LfwGVSnFrbQvjl/mupUeIGpdUUS0LmDB8/SLRnhpi20GctLTRwhyi2gks9Ix21i4c4ua//GyPjxZnU5iWiTZaTI0EwiMttddnR4SEI58g6074s4HMGxY2NRPyyTO4iKq6skYtRUg/4bDGNj2hfTacmU6XhIOy4rBAmcoCJSwU9MLvm2UdOLQ31hr2wi4QvYmGOqpiAVHPyrx1gf2esSqXXjjDx1XXdgyHYVUVN6JerdKIBXZBswMPV14zpvvLexfPqeJ4GFbBvSYjfHocxIhM5HvumYxncSWvq8wnG1o4bMIwfrxFON2UPVgMK+ZpC0E9uWJo3rRY0Gn423GY6vCzsOVeYngUTLuPQjhYhKIIb3bU3H3U9VVlU1WGGXtZdBRWp+Foh4QKhyxuvMUQdK6H4+2hPPu0iThnEPd4TeWrSoyMGWXDSf1Q1ndF/RPnKdakqMqnSK/sMQFTmNgmFVryw1m5jtUO2BH5FvmdWLPkGsZOkOuzJe/ilnmmi+khaG0OUh1fu3ZVI/fwTW2GRvw4WdqeFhYeLHaKFnC+29MnXv4LtN9Et+eiA2MIA9HB8U6rYFFl/E4ILJOwUKaJ3Seo645xPH+wXoaLTKOp6UzaCQdthME0p1s8wcfCwerElQ1dgxAYDhrdGIVEK8zDy2XvU1347Y21hr2wi+FvLkT5I4qpfF4llU8sJv8wUajM94xVuWyVMKqh6STN3BvPsqahpfKGMd1fu3chlcw3V1rd6rURFz0OhROjv69apaeiE4ekDEfceItwRr6vm8i9Fs676HyJH/6OCwf5u+lGL5d7JiPdlCfWNIih0oYqKh7qMY18CKzhNYi6vqpsqsowYy+LzsLqMBzt0C0jDm3ckFtGiyTinNOLqNyWf0KQayN6TuqHsr7rgspS/9pLZ1vaiPTKG1duCxPbksawcHVWrmO1A3Zs+dZWT6Vn6+llybu4ZZ7pUnqEZK+6kIqnVVLltBIqHuvVhHvkt/HjZE8bMcUopgVqV9vSJ17+C7Q4R7fn4rduLfxCJOjX1AUEx2h1KTt6k7BXpokMZ9gnqOuOfpzzgkVKI19HGzGLORqcPqSZcNCnKQonWQtKxdXW6YraCe6oSqlv33TQ6IpCY59uYIRCjSzKUhd+e2OtYWsMtHOGRobENEQP1BK2WJVLFELTcW3IjVWuZbtWI1WxGLEvoNTRr2tfE2IcNztJLZz2HpF5aNs+3KfRSiF2jsY8u1nVG2jz8i36Cdo9bFMBoWXllkVg2jlxG1g7tkocqtXWKpjXZzgNf/b11o1c2lSKXFhVPzk/KvzhPJE9DzHHnj+MReK0Yn1hFfeayxfa15fYwitJpHBwElbH4WiH7pqqCC1WNLpaObCN5mhrA5qpVYyeOakffF23PU21cm6rf+2lsy1ttGP23V5ivt20RsFZuY7vZHUU+bashNPLT/5LTXnnoMx3KT208HP7YM4jvmdRF4WDIb4Lxvq582eKZ7z8F2jn2OIj0NKCrzWf23sjHmIqlutIDdcVS7vlyCeo6479uOjEuOy7RdKQ9BIOWgbaCorANl0hFgC5c/xUKVfwt66p4F59HmWrGl3zvK/2PxdsbTFbjWl1bglfPzJvGKvw2xtrDVtjoM1/5rB6Nipqa5DKR6hHHHzzZETDhUwUQut96ycXcG+6NLzCVw9rdCE30MKYy2ljrGvgiqba5qadx8eKq7kSigMczgCni3mvuKgE2eOrwunUukosRDLdWzReOUVUZaiAVtETMs05i8WTWXnknycdaWuj1kCYnbWzBtZOdCUOsTjLtjmeuOHXFmd5qdRYEd9cSyXm8ItwiAWxC+T3bc1Uez2LWFPDIJxuYbhHHwt1o+NYOESVYUVZdBBWx+Foh+5b48C92fHZ2shRZEpNdyjesmBYiDVzWTavm4lbP7TFytnkn2OUwSBVjPFyuXXgKBll+WRH6+NyE97yzGldObYz5TpWO2BHlW/6+gDtGRim+8Qt811JD20tQDYVLzZyo5WC0wq7POKgoZVfjoslnvHzX09TH1Uaa0nCaagv5MzOyY6sS9AWSeZRXq5dXDnxCeq6E3XcWBDO17K7sXQirYSD1muyK3kNORJhNOi2hxy5xYKUxYFo5Skz0T1IzKOKeTRZ/FpqqVSbPvBQtlxEWDTf3Ph3XjiIe4qHurg9edr8bd4QP1UsiA6bthpczOvyOdlivlI7Kgqh7b5i69wEfThQayS4R+GfZRnPsMENyiSxrkGeL8wT/WAdLS6Xlsh0EOdxJRsZsG5lE+k8TsZlMDfqUenEVXu+2G0i48H/53OjZd5KGRLbuQZzGsuweIaVUq3pHs4aWDvqhlQ0FBbH4yT8C4rJ6zHSKnotSGhJqf69J1tLU88Q2+KnNeVUeKIef22+3rBhPm1rmI660XEkHGKUYVVZjBtWp+Foh+4TDowmNG0jRy31FGDhrcdf/G97mJmD+qGVUSOP+fui+bXaVsC4jpJRlk8mtCqg7UIS62zyxP+2uuO0XKvbATvqfNPXB9jyzmmd7WR6BGf5w/U9nx2wf1aN7bedFA6MtnbDHs94+c/1vuZqFlAyvubdTFoH07bAUyyKtK4/ksT1CTHyQHVcE5a2cpxm9JhwSAriscoxtzBFEHt4tW06NrRh9W56JLOja2uPhXZ4fzEUah6ii4cc0ouVPubKq6WPnF5QEu/exr0UaayjD5e2e4/upLvCr22BzCPf9Hrbd3w9bpyzbavxu0KsMhxNktO6m4hbn+LWDz1dHNcfRyQorTvSDjilO9NDPs4+PP3RA8TLf/MUaVfoTp+QTmS2cACdJpbqBx1A60EWRbaGmRFb78zbxgAAIE2AcABKIBwSgDaN4KH8cRX6tlsWCa3NjdqDrrweDxVOT9+hSgBA7wXCAShpEnOVE5P3apiMwXjc9OBscrvEi5ryyTuylCrXGOsbAAAgvYBwAAAAAIBjIBwAAAAA4BgIBwAAAAA4BsIBAAAAAI6BcAAAAACAYyAcAAAAAOAYCAcAAAAAOAbCAQAAAACOgXBIR+Rz5jPtfQPNc3zkzvJRpXzLaUoR46U+6UUdlea6KG+S8Spgh2RE3J2TTk9NjQ5rrJctKZDvlHDyPh8Lvaw8gGggHLqZffu+pufqToprz79wuvxFO2zkRmG4/gRC/W2N8m2S6fYQwo0V5B9SQlHPpeT4BcpqLG/P7C6aZhRq6eef7/AtUxnRWIaofkaAKixvD3RA3LgLZ+WyvHLdoCNv1jSonSjeqmi/Us/RVeHw9KchOm55Q1wrWh/j3fc22iurnRIO4lXZY/W3B3ty8/T/xVt6jdeAG8SqpxAOvR4Ih24mYcJBvDApi3uLYyup0XgL4s56qrg0j1yDS6jO0ZsRUwTt5U+210P3KPqrffO40VS/pl1Bb24sHQkHN7mz3FHOrTPCQfzGOy15KZ1awqH9stpx4dBEFSPF6+VLqc7ocLQ1U91krzbaV2EOUqx6CuHQ64Fw6GYSIxxCVDVWVHZFZW1rjX71LffcS0d6KV+8E/+CYqraYO5JNFNtWYBqt4jeZxEVinOG+ah0gaKH1+51glQ5ga/TUE/lY8U5ReEphublFVRs/I6vXTyjPtzgNS8OUPE4L2W7Csg3oZiK2QKLZQu2ppI/V/KVBa1UN4O/n2N/EVQjVU0sNoXXFI8hXvJPq6PmeCMWWoPIjSSHpcClfrV1aFUFFV2gX9M3qYaaFpgay511VMHhrjS9x19jfRWVTCilGqPxbWFhN75QpoOfypdbh4a0tJhTr11bhN9bpk8hiHuH029IIRWZ0k9DNPScNr5h4nvx7oviqFEE9bVF3pvSW9Jefmk4Eg5eKp1aRNk57GhMP1YKh5jlSpQpjtfZLsoeXqSVDa08tNRSQJQ18xSW4pX5jfNLOOx14VceNy0ul3movx/EWn5jp3+UM27jcHGZK5nG5cCBQE+ocIhTVjssHJaUcN0rtAoEDRYUIzjdr9fHF9qtp7I8BE35WDi+gupb9K/DdFf5B0kHwqGbSYhwaKuhIqfD6i1V5PfkkY8daOOWZgpWF1OBx9yT0Hsw3uGF5OVGuX5DkOqmFVF+Vh4VLzZdP+51RAPFlX04O+uySqqaVaM7/DWigcsmPzt8bf60oZL8uW7yGUPPYn3G8lI+RwgNuVbDaIxFg2Tq4WhDtDklVGsWAqtLKd/U8NWXFZBnKDufBr7OhjoqvzSP8ia2P4evNbYjK9iNBinATsqerqHFxZTn4riXVVE9C6OqMh95RY8v7DytjaxB/eR8co2Qw/Vt9RQY6qGCCVUU5Dg2LisnH1+jZJl2qoYWjmFeKhzGjr+a03AZN6ybK8jHPXfvZD3d9fSz9sCDHGdXjp8qRZz5nOA8P+XZeovKaxt5b+7Nx8svgUPhUL5eF7jm9I8SDu2Wq1YK8bHKcS4q4Phr4WGB2yrD7ZsVcTz68L3ZkernGHnZNMdHnqwCKp5Xz/cJUg3nYV4Wh7FB+1pDnUbyuBHm5loq4XzMH1fleAotkcIhXlm1hFWjfeGglVHtetE0z/KRa3Ap1YsP8erpYC95WWQGFgapcVUVlY7wWDs23Vj+QfKBcOhmEiIctF5He8OPEeomZlM2NyTm5qXmaq6I4YqnN7Bu27Bn/SRuUEwNUPzriAaKG/gy24jAZna01dyLkB8Fwans6Mz3a28I1Hw8xE6GG5HihfIzU3t9diTsW7iRsaeLuHYWXyNmI29tgDUnbGlIdVFQMFlrPsOI+5qdZ6jaT27zfdrYweS4yV+tX1drhG3OVjSUblMaaw1nlq0XuUz0CH1Uae69iUbctBC2iRvqqlWWFOZG2upUlNeWeW9phJ3kl2PhwH9q6V9ApavlNzbhEL9c6b+xOwotPuEwNevD7WJqRKa3cDiFYSFRx3nBQmOe+S62ssOo00ge5zC2rimnQvH687KO9XgTJxzildVIWCO0LxyUI0AG9vrXbj2N5LFGS6WlLnZn+QfJB8Khm+ku4VA7UV/UpC+SNCq3dAwTWL1Xs3ORVjHeLAqie28CrRKHK7qT64gGKtvSgwjTGuJeSC3/po57GyEKWq7NOBUOjKWxtzln7fxBfgqYwlhVXcoOJHbDqfewTY2V9pl7vOFh8BjxWlBkjYM9LGII2NQIigY6e0zAFC62SdxLNl1DS/Oo3l8jVY7JJ88gLxVNq6L6zeoGs7WlkeoX8zWXBblRDUYJgljXjhIOgnj51RHhwASnit8HKMiiyuqonJQr/TdRYdTKjExfIRhZtFXM8IUdkeaojHKiqC8aIg+NHjWjTiN5PDef8rM47EP1eHSEhAmHuGVVhtWUdva8sJMw4ZBTwvLMjLivKyzyu7v8g+QC4dDNJEQ4iJ63y9rzDtMgGhOrcCicaKqshoV7qPo59mtplThcqZ1cJ0YD1aD30rxjA1RRXUGBCUVUONhtdTwdEA5aY2k4ZP7ebZ66EOefXkTl9jBW11Kj0dja0If5uVGS87bFE3ycfmYhJeKVb+1NCcS9bM5TXMsQNaLXbJ66EA1n3rhyW7jYljRae7wxGvHW9XVUMYnDNsitr3g3DbEHpxeSxyOmiCqoalaAisU8Mju5KOEQdW2FcHCSX4q4W7GVhbZ6Kj3bTd7pjTZH5aRc6WkXJRy03+p1QIgETTBIAVHTJqZITCMusYSDiAefb+RSrPTXjudymdvAgmy4m/L4nBjFSUmihEP8sqqKQ/vCwfFUhaC9ehpVHqKFQ3eVf5B8IBy6mYQujjQNsxroc72Ryi0cWL5tmL21xTzU50Q4OLmOuoHSHMV4a2OgDX2bG5qOCAc5deCb1ailgWVqRLuOqUcmaGvV5mOjFoxq6EO/hZOsjVnF1eYhYHZ6g/mcGebB++j00dCGx7kHuF5MqRRQwLRYUjvfnl9iyNW0oE/ZcEYNy7ZS3URu7M/mnq/2WW+gixZoHyT6VEVnhIOj/OqocBAsK6E8Fnz+S6093PjlSg9TtHDQ4+S+uooquRzo5VeUjWwqWVDJwtpUDjShzceXyM8SLU1MTlOdRrbjcs7dvragPRIjHJyUVVUc2hcOThdHanRBOGjh6pbyD1IBCIduJjHCgeGeoVcuGjLqnli8VDg4jxsCU+UWDXZOEVUZLUur6P2ZHaEz4RD/OuoGSsxhu0aURxaSNfN5yhEH0zymeQRB0VBp4miYl39jb/C4sRvJacJiwmhqmuf5raMSZrSFlTahIbANATdOZwfFPc5KuQpfzHX7cm1x0NAbWy+HLbwo0kBzOF4WEzJkbc2awzM3zKqGU1s7kctpEJ7jDVHNePP6Cn3+vnB65G7NC4o7PeLgKL86Ixw43LUT8jg/bUPjccuVLhzyJ0lxYc5HkU852ZYpIVE2snPzbKJav7d7ODsbmfxaHoqpJfs6kKg0ij4e4vjbF1a2R0KEg9OyGhUHdb2MoNcZ99ASqtliJE4z1UwsiLEdM0Y9jSoP4r6mdqXbyj9IBSAcupmECQemdY14IItHb4zZ3IPFEJ6osFZn2zRf7JLwUN6QfG5kPZQ/3rwi3KFwYNq/TowGqqWWSod5yD1IbKXKozzxgKp59oaGG4Or2al48ih/cDZ5p8q+RAzhoC+SjF7QqdFSTwGxolvcL1f876NAjAccacO0qmsYvbuw82LHM8kbXkOipfM8bsgVjZfW0LlMax1MhFYFqNDDjeXgfMoT/48MWLasqR2XcW893fMHcSOv5bP8mgktKSWvvG4+O03vpFqqtPXS1deOFg6O8qtTwoFpqaEiFjn2cLRfrhgpkrV0G1RENeEtkHo+WcqBNuoTvdtAbKGsGJOv5U32IK4zWdnksy1yVKeR+njdRC6vFocWm0QIB6dlNTqs8YQD09ZENRMi5VuY8gFQfPeY9TSqPNiEA9Nd5R8kHwiHNEQb2tW2qbWDHLIPb6HqLJ28TmuzdVhShX2Iuis4SpOOoj2St6vX1LcYdjieRrrHSsNE5a/ESX4llHjhl9+rp5w6gBj6TsR1MhHjkdNxymbX6mk3lX+QVCAcAAAAAOAYCAcAAAAAOAbCAQAAAACOgXAAAAAAgGMgHAAAAADgGAgHAAAAADgGwgEAAAAAjoFwAAAAAIBjIBwAAAAA4BgIBwAAAAA4BsIBAAAAAI6BcAAAAACAYyAcAAAAAOAQov8P+YBEnxl8vvUAAAAASUVORK5CYII="},"66e1e56d-7919-475b-917f-da64e02e3399.PNG":{"image/png":"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"}}},{"cell_type":"markdown","source":"<a id=\"section-six\"></a>\n# 6 Possible Solutions to the Implication of Ethical AI:  \n### 6.1 Educational awareness:\nEducation is the key to every lock. By giving proper education or awareness of the AI system to the users, we can drastically decrease the incidents that happened due to unawareness of the right way of using AI systems. We can also spread awareness to the developer and the organisation about the importance of ethical AI.   \n\n\n### 6.2 Proper Committee and Guidelines:\nTo develop ethical AI, there should be global guidelines and a committee of AI experts that ensure that the designed AI system is fair, transparent and doesn’t harm human rights such as privacy and security of individuals.   \n\n\n### 6.3 Transparent:\nEnhancing transparency in the development and use of AI systems is the main objective of AI ethics. Transparency means that the intended users and other interested parties should be able to access and comprehend the pertinent information about the AI system. This comprises details regarding the goals, capabilities, data sources, presumptions, constraints, results, and effects of the AI system. Transparency can aid in increasing stakeholder confidence, trust, and adoption of AI systems.\n\n### 6.4 Detecting and reducing biases:\nThe best way to reduce biases is to train AI systems with unbiased, adequate and accurate data. Proper implementation of systems to detect biases in algorithms and datasets can reduce biases.   \n\n### 6.5 AI with a human touch:\nAI systems can be improved or made ethical by human touch, which means giving humans the power to override or interfere with AI decisions in case of emergency or when AI models produce biased or bad outputs, especially for those AI models that have significant impacts on human lives, rights, or values.    \n","metadata":{}},{"cell_type":"markdown","source":"<a id=\"section-seven\"></a>\n# 7 Conclution:\nArtificial Intelligence (AI) is expanding exponentially every second. It is a continuous process of data processing and analysis rather than a static or permanent collection of information and facts. AI ethics calls for constant observation and preventative steps to foresee and lessen the damages or unexpected outcomes of AI systems. A multidisciplinary and inclusive approach to AI ethics is also necessary, including a range of stakeholders including academics, developers, users, politicians, regulators, civil society, and the general public.\n","metadata":{}},{"cell_type":"markdown","source":"<a id=\"section-eight\"></a>\n# 8 References:\n\n[1]   Book “AI Ethics” by Mark Coeckelbergh, publish in 2020.\n\n[2]   Book “Ethics of Artificial Intelligence”, editor- S. Matthew Liao, publisher- Oxford University Press, publish in 2020.\n\n[3]   “The Use of Artificial Intelligence in the Cultural and Creative Sectors” Author: Baptiste Caramiaux, publish in MAY 2020.\n\n\n[4]   AI Accidents: An Emerging Threat, AUTHORS- Zachary Arnold, Helen Toner, Center for Security and Emerging Technology.\n\n[5]   Daniel Zhang, Saurabh Mishra, Erik Brynjolfsson, John Etchemendy, Deep Ganguli, Barbara Grosz, Terah Lyons, James Manyika, Juan Carlos Niebles, Michael Sellitto, Yoav Shoham, Jack Clark, and Raymond Perrault, “The AI Index 2021 Annual Report,” AI Index Steering Committee, Human-Centered AI Institute, Stanford University, Stanford, CA, March 2021.\n\n","metadata":{}},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"markdown","source":"\n\n","metadata":{}}]}