{"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":"code","source":"import pandas as pd","metadata":{"execution":{"iopub.status.busy":"2023-07-10T18:02:52.677843Z","iopub.execute_input":"2023-07-10T18:02:52.678270Z","iopub.status.idle":"2023-07-10T18:02:52.712428Z","shell.execute_reply.started":"2023-07-10T18:02:52.678237Z","shell.execute_reply":"2023-07-10T18:02:52.711315Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df = pd.read_csv('/kaggle/input/2023-kaggle-ai-report/sample_submission.csv')\ndf.iloc[0,1] = 'Other'\ndf.iloc[1,1] = 'https://www.kaggle.com/hridaym25/draft-for-kaggle-ai-report/edit'\ndf.to_csv('submission.csv', index=False)\ndf.head()","metadata":{"execution":{"iopub.status.busy":"2023-07-04T16:19:30.049659Z","iopub.execute_input":"2023-07-04T16:19:30.050807Z","iopub.status.idle":"2023-07-04T16:19:30.075290Z","shell.execute_reply.started":"2023-07-04T16:19:30.050751Z","shell.execute_reply":"2023-07-04T16:19:30.074143Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"--------------------------------------------------------------------------------------","metadata":{}},{"cell_type":"markdown","source":"## Hello everyone! Please leave your reviews and feedbacks, I would really love to see what I can change to make it better! The notebook is not yet complete, I will finish it by 11th, but please leave your comments on what you have read so far!","metadata":{}},{"cell_type":"markdown","source":" # **<center>A Symphony of Senses : Unearthering Multimodal Machine Learning**</center>\n  ## **<center>Simplifying Multimodality**</center>","metadata":{}},{"cell_type":"markdown","source":"\n<div align=\"center\">\n  <img src=\"attachment:011585a4-2170-4013-a1eb-270f0543c45d.jpeg\" alt=\"Image Alt Text\" style=\"border: 5px solid black;\" width=\"500\">\n</div>","metadata":{},"attachments":{"011585a4-2170-4013-a1eb-270f0543c45d.jpeg":{"image/jpeg":"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"}}},{"cell_type":"markdown","source":"<span style=\"font-family: 'Roboto', sans-serif; color: #333333; font-size: 15px;\">**Before scraping any depths of the subject, let’s make an attempt to understand what Multimodal Machine Learning is.**</span>\n\n<span style=\"font-family: 'Roboto', sans-serif; color: #333333; font-size: 15px;\">**First things first : Multimodal?**</span>\n\n<span style=\"font-family: 'Roboto', sans-serif; color: #333333; font-size: 15px;\">-Think about the following few situations in your head: \nHave you ever smelt the fragrance of hot cheddar cheese along with the various sauces, onions and tomatoes? Close your eyes, and you can picture a picture of a pizza without even seeing it.</span>\n\n<span style=\"font-family: 'Roboto', sans-serif; color: #333333; font-size: 15px;\">-Have you ever listened to someone speaking, and the image of the person pops up in your head?</span>\n\n<span style=\"font-family: 'Roboto', sans-serif; color: #333333; font-size: 15px;\">-Lastly, let me give you the statement “Oh! Your genius is frightening”. What kind of a statement do you feel this is? Can you deduce it without any additional information? What if, alongside this statement, I present to you the visual aspect of two people talking, particularly their facial features, and their tone? What if this statement was intended to be sarcasm?</span>","metadata":{}},{"cell_type":"markdown","source":"<div align=\"center\">\n  <img src=\"attachment:6398b7ed-24ae-485c-9ca6-661f79fe4d5c.jpeg\" alt=\"Image 1\" style=\"border: 5px solid black;\" width=\"178\" height=\"10\"/>\n  <img src=\"attachment:3bcdf897-95e3-4171-a795-25e057317488.jpeg\" alt=\"Image 2\" style=\"border: 5px solid black;\" width=\"250\" height=\"250\"/>\n  <img src=\"attachment:1c8ada8c-f842-47c4-8193-4841c7a50ff5.jpeg\" alt=\"Image 3\" style=\"border: 5px solid black;\" width=\"250\" 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7izHrRLly0K0rWKywASU2LOQI8mYnEV3AbeHcUu1KZvcgVhCELSoJNCXe4o17PZ8iLPGS5pmaps1kOJRWTLQ9UuXdxvv/NyLxU7UBelB3/GjUss4RwcxDrkrSQL8wb5kP7wMXwCkjVKWAHYBd+zd/lC1Kc4j/O9zxv7LtmyoTgDpHDXeHvcm7uYzifhzVSnB2f8AK1h8/pnkq/CnYkjmIo4vvQbuQ3ke8GpfASEaTMXrApzFkjdh/oQ15RhQhHhoQEpBYMel7iOomKxYFAjl0A3ZvbKJtt2yUZZEku9zRmevVuj0gbipErGhCxJBZXMSClSehTYszGKuacItNKpZZwAWIFrXIhj/AOml0rT8VTv5dzEuIWkgHmI7X9bfjR5eCYCVEhVC4I/+udLiMqXOmSyBL/Goap37owNWYLCvCSHpbVpFSwf6tvSLuDWtMxMkLlhLOpKDRIFS9GBPQQC4knoSsmUXJLFQffYe0NfCvDS5nhFCCnS6puq5IFAO17m5H7amEtKGT+8XhSQtzle0PXDXD2Glp8SWoLLNqILd2cO1A3T1gHxTl8qZNUFzEoWE2AAZ3YdbQ8YLCf4UpIEtLfD09qPAzNskl4iYksNSACFMxLWBVdrQtYUwDN7nO3C1S+kSy5w8QqUTnW7fH1Ge4HMUYRKpaQZk00ShAd3oDqG3zgfm+eJUtCJyFBafiQAd9mpcfqdVxS7m+IcEiViJ6gkalANpAADcqj3IU4en2K/m8gTjqKnVsoCz3HRnelrs0BLRKQpzcvXvKNdEtakY03I+P2x323uXD/ExXNTLTLlS5fhlASfiQbgUfVb4b1YsYaZWLLJW+pNE6gBzFnBNaf7EYJNkLllyea7j3f6Q58FcQIWoSMRqOpQIOpnPf3+UUqUtLqT36GnCvrEU3/HsMT8vdq93jVcThzOaVYU1qSoAghjQdDY+cV5WCTIlFIXrV3NL9u8FsLIKEsC/RRu3Tu0VDhpUpRKlupXZz5gRyfLXcAOXBJIDBqM16j9xmS5gbC9BVhnFkIQmUCUk8oeli1+0ZPx/gXkzykqVLIMwKY8qkuR6tR/ONTwU2alWlQCkkk6hS/Zor8WZeJ2DxKSh1TJRHKalg6bs5eDCDMCVJ8pTkRpvt8XL3hsieJKiFVCqXy96RhEzKJoRKmAMqYHcH4aV8jteNG/p9gCpExU1LulRWou7np37+USYfIwooloGkS21O51kByR2elLmCUuWvCIUdWoLdk7By79qtEqpiixIoC+vY742TVJmYmPmNPW39LbrtanzVzGw0g0lpAWvq1GcdqmEXHY+eEqQVvLUSKBqhnYiqrW2hqwaVS5GqgWtQJJ6E7Czg6Qb0HpCTjZDGYpjpBcs9Kbdr+YbpBLDJSVfkRx4AcM9bxbsMtCVKSlsIZrVIoT1twaB6ZSidLMo2a7VpFxeaTpJRXWEFwo1oLgdBtHMknQVfutpNg9Q9WV9BXpAnMlcpAcgBmOx9KQtKXVWNGcrEgvUd/EbVwxnEufJC5Q5Sx0v8JsrzrWCSJARuRrsOjRlH9Ncf4S/DLcyHobF6v0p0jYMJiEzBqDFQFukPRgUvwyQ4sN2bWyy+qfJz0FBxAHCa/XfvEeMwjouSwoO9vveAkhScEkpVOKxeWk/EnYgG5HR7VqdjuOUFIDGgqaxl39mubOnz5k0FEsvpKiDpLmndgfKBnAJmtLFSKF9L+hHQaMWbLJ8UHGWA+YP4fNXUw5mOpZagABNSP4j2XxcylAICyD122O7eUL2aY2RLUPBYoJBBDuGSxD3u94AS8wXLWpWopJpRxu5+0TokGT5Rlz/AFz6NGzL2OVNGJQoQCHce3e8wJw6pY1+JLclbgV1VDp7M0a1wiv/AApS2lKkAAeVP9wo5TlKcRNlLmoMtIASpdypSLUG7U7w25nkYTiUzkKIEsB0ixFnA6iDmKKTiD0uH1H1mzVvSIZpQsBBo7npl1bhpDco6JelJGpt/OKiFlKVF9RLAFvzePV4oCUJlGZ3JZ99/vFORmqFqRoKWIJIFXZuneKZpLgjK186Peuu9qCz5iE0MZlxEgSsWsKUSUrc1NQS7NtteBacelKi6aKLMXA8qV60h141ybxZipqGcB6/q6gmtqMYSZUhlKK3BTUvUxICgOBl2PiPsNkmpnSQTdg/e+CGFw8qdLWhYCQQSXehTbSbuSW9bGsKuMwxkK1B9IPKr6P7bQxDHFCWRpINaFiDvU/Y/wAQLm4xJ+J1WHZrt5Hr5w2UpQo1IGbJOIqTSNe4Z4h8TCJUeZei7lgwcudouYTEJXNSpagsgMki5Jcu1NozrhKXMl4WdMY+CouhLFwD8RbeXtvQdoP4LGYc4bwkKHjaVFBG1XICtiHdukFNm4AXsmtc2+ercHj547OkqID1LUye/IesaIFuhRFKFt2iDLcMpgZiipXd2v3gXlOLUcOnSskkaXZ2bv2+cHZEx0o07geUUS5iZygo5C3Eg21tw5loVoVKBTv9oBzkeHNNR8WmgsGcfIxxmGF8SWsCgSkKJvu5b2ixn0sCaD+4VruKH5ERZwqX1pehQzAWofe9oUUpKlSzvHUdvDAsjDMG73hWzDMpaUJlJqUgVBo19TPR2hTzhRVKCtQAcISl3dVg7dAPkTFfFYOeJqdUskE6Q4KXbeocCxb33gcvMFLmMWCJZITpHxE3U/Rgw9esLUszFYjkLew70j6KVs4lpGAu9SaPvPFxy9iqZARhASebWUlX7tKXISOlU12dv0wBx8yXpGl3ar9eoi9jJ6VaCVEgAvR2J22vArEy1zFCXLlqMwlwkAksQ9EiwaPJGIiDQky0EKUTe/eWsNf9NMsKyqepy6ghA8mUs+gYesalhZAQVrSWqHFtPUe0AuB8m8ORJDEBIOpzUldSdmsB1oIZJ0wadAqwLs5q1PO8EUpKhMNP+3V2y4OdaC0YW2TscwpTa3IN9Pz0pFTFhwpKSAG5mDXreF3KcvSrxUr06VHQSdwoAtcdT2oe0OyMPypBuQxIhUxqpMrDzkeIyg/iOQ4AVqcBxRqvWGS5C0zErOQUfYgXPpm5gJEzyqSHrh30e/Kn3CJmGFCVnQkmWnkUTuXZ38gC21YG4xKZkwnQWYUGxtf2izmGMUpJEpClIDUSlRCqNqcA1HSB+CXOU5TImLO/6W//AGaJGXUn3tuj6XZxKQkAUbrz36xqOTYQiXKXPCrUQGoSxelmt9IJ5kWKFSwyAAS9yPWvSLkyeSrSQyUi/ltFHMFkmZoqWYahypjswUNc7tV0gvxFNGalnMfMhRKnP6Y+14U+Mp6whMmUUaTzJJIcvdgabtC/wbluKRjU6wtKSa9FebUhjzDByFa1zDrUk0DtoIokp6Mejfz3wxj0IWNa1EuQC7AFQexJLBrxzGlP5fbAcAaDjTPWLEuZRCf6Tzh2xMxL+GoAuHbt5wt5pwtLnjVK0h1PUX2v9X6QxLnpMvUCK0fpuDE8hQSAAKdf4hxwzVeZW8ah7cc66lmvEMqauQypdDGV4zgKfqDkBLsVJY+w6wey3gvC4SUVz9JUd1JKr2psaG0OilqCmA1DqGuP5ijmmNVRKU6VLop7gPQQpcxMpCnUo5MQRXcQz1axFHLljFatu2meyXA4U658eW6B+CwKDMU40oAYIFgB9NzALOsjKhMmSJZK0toBFAASKdCRvDdlqEpKkpvV3/POL2OmCXLAYcwIrSv48d2eUlUsqJs755sA+g4AbhCP+QtEzy1t9wj8K49a0FBJCkl9FA7liR0btDfhJWhjqdNDTubeUKuY5TKkJVOlTHVdbF+YsA3tWtfUxPKl44z0L1JMmYQkGjS+5O4LUN3UAU9FolEzLEqFb/trs4za9xFM8JmjGkgAu4NK9MxUfwQycRSpakS9aQp1skEXoSxfyixhNYl/AnWDuSAz1qx2fzpAPiWWViSUHUqSonSTU6gUlRFSwDt1MWv7oqCJAKzykTFEM/UA09wIuVMSJuI6WpUlh1HoOUZwlkyx23fqYWOMct8WYJyCVEJOnUVF09rsTs16PGc5jKMtQTZdNQp0e2zWPeNmRPKSWklx8JYFLMe7lhSEvjDXLxMtSsPqlhI1lKQGDuQLvT+IilzMzV6gVHCp9rj21JE1YISLDh8VpArhbhfxwDOJCZjhLO6SzgqFKFrP9Y0HhzhVGECToCpiiXUT8IcBv9dYixnEuGlykTJMtSgs1JBR8IZ6tV9vMwjZzxvjVKISwQC7Ur56WO3WHD8iHB4Mwtd3ryLcWfvhbRtIsyd/PT11bjGvmeiWF+IpKRL5ndgx6jptWFrNuN5CChSGUF/CXDgJLFwTSthcwhnNsRPkpM2Y+4GxaxIFVHup2iricsmLfUE6tJLHZg5ena0cVPysBmNatlkKa0EMlf4hCazld5/vTm8aXgMxmzPEmlb0ZOkVAJ26bWAfd4vowUvWdUt9SQ5UH1HcKd7Uhf4IzFP9mlyNSXS5Owr6s8WcXxWy0jmKUqfWQGUGLgC+kXf7R4LN1qtz1BzfP0iCZs6kTFS0CxIgnMyVAKSkKYFgHd+hL/tFfbpHmHnplalMkBSq/CklW6iWrqv7x1l2byF8hnAsPicipcXIa1jFHivKPGkSxhlDSlQAZVGCSL7m0G6igKlMCMnHSmmd8sy8LwnFgmuBqQeO69LfqLs5WtwjmG5eg2buR6bQB4pzQy5akyx8IBUTfYNa8R8K5+SJMpSSFFR1LURX9ooGNOp23ilxWTrxNApGqpbpW/ZyKbPCcBIOHnbR+O7PcYtkSMM/Auw9agfyE3Bq/uZwTMJOrUEmrJU3KaUbUw9YgnYCYmofUDUuXQ1/Ijzj3A4xCNWpgCA+kO7eXY9YMTM3EyWkKZK1EaibKYF1q6bU7+7D5QyRaNozJkuaAn8TpVrnWj2sct5gdgM+nyZlFFQJD7k9gS8a7lOdIxEsTE/DQ3oWFR6dDvGKYzEoB1Biklma5GxDfN4s5DxecGSEBS0KYqSaAKHTvt39o94JUCUhifVte713GTbkSp4dNFDczxu5xJUNQSUhm8rMTtvHCw0wn4h8RpYigD/aFrB51NxWEKgBKExyNIcpG1TR3cfaKciZjUjSVBCKAFhch3IZ69XgJk1JIBLm7281cqBjnuemmKnZlB7BqM/dYNGbKlLTOUooUVEEajpND+l2fuGgHnmZInzdS1KTJlq/xjSWUdyxoXL3eKOb4UTAF6vEWladRV1VZkjd6NcRemlSZEtepBLEMoVDXIctcNa8BJQycAsKvrk5Yh+WbaVrlSkuFZmnB65g5C53tAbxJmlSCF6EkKSn4X3Dg1NKjzi9lvE81BV41U6SAgFtVRU7UDWECsVjZgSUkuCdWkAitgS4qdNmPnHeCy1U4eLM5JUocyqWqWP/AMq0+kecJ/HvONM7PLwFU5m7tvPXMRfx2fKEyTMw6f8APiJhlqEwuEpYElNQQaUNRQ0hpyaeFONStRuu6TWlz6UaMXzrHqViEzgGEtQ8NPQJLsT1LVMaZwXmoxEt0lkpJNg4AJYEdQ7j0g1BeFJPZNd2WfLjkzkp82EN9fq0WV5xLkYhprpcEDoTu22+8X8Ul5yFrCnKapJokdeg6MIt5sJU0S0TUAup1KU3+MCw836d+sAvAKkgqWlQSdKUoXVSRYn8HrCVvLThFR06i9LU30vAICV+Y0y16Vpz3RDxxmSiEo0jQQbmxTuAPTfeEROHqVBnNGu4MaHicqXjUhSFj/GNOhdgQA9R9wdoVpmVzZZOtBZJoQCQoijOzsfnBFSmx3Bz1jZ2CZKQjwgQCLjvdpFjB4cAVFEJAAF3qQPQne8fLw3iInT1qKZSCapZ7PuztQN52ieROmTCJKXSF1c/uSkt3+LaPuJcvQJUrDoTMKydSuhJYqAL7eW147JCSHVz7r+/WOTVLxhFifQZnS4IzgZwjlE5cpSyspRUIqzuQ5FekXFYBSCoLKixO5IIBYuemrlrv2g9ms1EvDoloIAchZ3DA2Hq/wBOwvMsyCcOjSlyEkj1Jv2OomtqxTMSlKq/lQ98vaEomrW628qiwFAaWLne3WBmFzkhBSA6VuQxsygCTc0a3raJMLmsxI+MAHZ+n/kwrTsapKUy3A59ZXX4lMD3Zkiw9Ikx88y1FLgt09/vCZmy4gAIZKny1OlYrf192YneYv5HKxSyFmYiWk0dre584C8TyFSZgeYpcsqOnV51J2c3oTQx7h80UkBOpthUOXBFHfqa+UOMzIE4vCjW+sJOkG4I3byhiXlzKihpQZd6xOuaR/tUN+vaM+Q9x9okmTyqwoL1+8OGVf06n+GNUyWgqJ0o1Fy1akAh+1YsjgKehelRQlJBoDqUoiwFAAH6t8ocry1aKUbZIIqoDcL74RgxDaQ+zfeCuT8PTJunSlzs5oAQzk9od8PwSuWEMmX4h+LUlJSkbllUUR0t2hvweUy0kS1LBWGJNB2AYMG/1AFR4Vzz/cJnf5CSkf8A51PdW/kQ5HlplSJUuWKpSkEqsBTVS1WNfWDUvBy/FKiwIDUHT+I6kDw0EKU5/Gb86xWzBZAExn01v1YV7QKAoJCyHNCQWoNKZi51OgjDUSpRrfPX+wrys0l4fETvFBOpT6moXNL7x5xVgVaPHw4CpRqpv07P2F6DvBfHcNS50hl/9RRcECxNR6ecdZVhpmHkaFyxNludatQGkA3qat9nhEuWtJwLFGd/s1zoRfOsWjaJaGmouCxSbEbvcQmcO5IvEzLgpAdROw6+cWOLseCBh5FJSKqb9au/YQYzjO0IQpEpBlKUGVylJrv3pQHzhYWUhPmL+ggSQCUp5mz8BpqczFZnK2giYoMB+IvxJ1Om6EnNJe7vWL3COZKkicEs5AdyzioqNxb5R7mEoG35aFmZiChQULg1HUbj1i2WnxZZRCFkImBShTv+w1nMVqfxZiybGvoAAzP9IikrP6VGKycShIUS5SoMkft3rFttIYB3o5sDE+Fso3kqcMmHHhzifSQia4SRdNx37sdj8oe8Jh+QEK1pUnVpU1R1ChZnDgmMSKlJdiaOCR0P/iNZ4JzETMGlyDpVpDmlWp3s7QCEAEnca+rHUXv7Exlf5PZwlHiIGbEfMX5s2QhZZOiYBVam5Se6qk+UCMXjEgrUR4mlIVrAIB/+I79Wi9nOLEueBMYhSbszHoG2fbeB/E8mcqQkeEZgUQVGXdABd9JYEb37NeBCjMUU2KScg1DdnZ3zD8s89CQkJKv9t/seGTtxhHzDMVqQlBL85UqtyrtQbCsR/wB08tTPR2a5/wBR1Py8sVF2BKXYpL3DpLEAith9HjwqlSwAGrfz37X6XjxL3uI+lRhUh0WPvnAhWDK1Jo7ByAfv7d4+zPmL6Qmtk2HYXoIYJqCkagpgXG9wK06VgNiyG/cX28q2A3+0Uy5hbcIkXJluSBXvlCscSP7hyWANB2G1Ou3zo8PXAy1fE7LchcxVdSgohhVgNJSHa7wkYRzMUUpJ1OFppVO4BanV40fJZAk4QrUXVoOl/wBISnSG8qDa8VbQtCZQSc6fMY8oLKyXpX13aUrD9luEQUhYJKklVCaan5mo9WFenaLitKiZhBcKFqVHT8rGdZnxlMTPH9tMOhSQdCgP8ZACVJTv3r1pSHPLs2RiZZQClWkBStBcuKtT184gWpg3xcix4wMyQsMtVvUag5dIZJqUrTqUAQBQ+3zpAPM0S0LMwJJmKAYA0HQxWxWIxS1K5GlpB9R9KxbwstSiFFI0MNRPXbzaEzZv/IVgCdKkPrXl6aO0AmWZIcq5A+n6iHH45MuWnxiEOHPmOnrCTiuI5+JWUiZplC4At0Y3r61aJOK8UZk0jVYOnsBag2gdleDUpKSCkB2JFh3UA6km223rBjzu1rcu7WvGts2ypSgTFs5s+rE07O4Qy4HNcapOmSkqAI5jcFiA7eQIB39olw3FE2WGnJC3VzO1waN1Pvt2ivLxy0gABKEosU7EVck1D2L9O0JufZ0ZiksolCSdPSpckDYHYbCGJIsgl+7PXX1pAqlIU6VISx682pygljscqYtUxd1Fz27e1ID47NtNBsPe0DcVjjpJibh3KFYma6wdHQPz7NSrR5EoJGJULWv/AFEEcuwU3ESVTKISCAgEfGTf6d4DZpw3PSmYtWlKUBy6hV9gBc9qQ8Y2emVLAoAAAkITYUv0oRt1gVm8hczCYnw0klISojVXTqCiQN2Ca+sekzVeIAAwLQxeznwTMX3l8iE3IlglctRppcP2IcDvUQdlzzRALJKhq73+0LWWlp6N3LeT/wC2hlQEaS57uHod29WrFG0hlvDf8erFKY5H+RZzBSdNAajfet+zw5f0irIVSniEX7A09TGbTFKmFMuWCVqISKgP6lgPUxrP9PslmYeWEk6V1OxZxSgpQh4FAwgAi57Naeoifbpg8NSQe+84YszwkuYUoURrct1YF/fvF9HLKCaO1D6tT3hVzWXPSvxlaSUpWCzsFqap3IYFk9TBvJXVKlTZhJ0pYFZZyS5JFvKBkhKSVAMT7Br7wH50ehbGW+EA2FuO7n6V0iLGZZL0awdSkvU1NTbyfaF7PeH8OnCzVpQVTpadTI3bcoFqP8MNOcYtKU6EsVqcsNhb3ipNlpSgoATzAayoX69zSFTGTMObCvGvrZzkeMVSJsxICgSHPo+e6/GFfLuH5M1LhhpCNVQXc1LA7UvBDE5TgsMr/MkLWoONVtNqJ60DnvBbGZECoLw/+MhNFXBpuL084rY3BTFMZ0pCyHCVMohuzAkWFD7wSXljzJrrUgtm1KWY8DnDjtKphHnLaOyhbP8AcI+RcMoVOSXcFIfzuX/jtBriHKPHnolpmaEBwTWxCX+kDuHseAgA1Aq/b60/mGJOicFOoijEJv1SoHsNh17RDMnFJBJqPYA05nulXYlBT5VHXOMwz/J1IxMxCCFI1AOSwdQCmL2LfbygzkePXl+IlpmJMtBGorKRqU6W0vRwCkMDUOY64ky4pUErmipBRqSWUQ76iA4JfeAeeZiZ4RqJ0SgzAUFgK72jRlzfEAIsePodX/sOUjEADUa+mkbngpiVIM1Mx5czmYgMKD7/AFjjChKT4XxEqJ8ga+0Z7wrnk8gJI5UJJIWWtVqJd7MDDNgs5kylKXiMShyXShIOpO4t17xOtUwrSMIfNzRnD3Jbc9ogVsiku1dGDndYaXy3ws4jDPidCwrUtbC1g4YdDqDMzQ1ZHgvAlTCrTrAUxSk3CglLqsCC79mO0CM7my1YoTEq1ypjVYsCVOpNepLiB/8AUXP0yEASh/mmuCQSbfqrUtRn3bpD9lKUKIFSKCtzryu+j6Vr2tZXKQMiHNNMt12OTtyA8Z54kapEsuX/AMqx1H6B61UdywsIA5HlU3FqKZZCdJDqILBy1xv23gdLkAkIUaqNVCpc941ThPKU4fDqQSw16tR+IHlag6MBBrUiUgAX7qYCamYkOrPtuUBMi4GKi02YKrJdunxcqvKC+OyfE4QmapX+MU8VJO9K7pp6d4LYOTpWD4hV4i9KWpVRam1Xhnk5nJOlHx63SaDZ7h+xidKlTn8SgFj+oDx1ySCkYgbhuteGvSMgOKmLXpSlSkP8f3PeloeuHckmGROKwpJmI0I1C7A7U7edYd8LgJSUumWm1CEgGK2M16VNtUDuA/1h/wDxwhQUen2+g0jm0/5Xx0GWhLDMkuTXgI/NeCwXh4giZMCFoVpqbqch3It87QaxeEmJCQEKZbaSXAV3rQwJ4ulEYyeFFyV6iWb4gF29Ylw61zZSVqJpyk1NrBvKHTUuyye/7FOwzLyrZ8d3SGfLMgWjDqnKcFSgdRFEMSAQD8VD0asP2S5ZiFIYTPCSWJI+IuASXIBFSYzPLc+WhSXWopSGADOxuAVAt1t2jTeGMUkoQlE0KSo0So8yWa4c/WJ2cjH7t0sW7cwnbZK0h8tWJp9gZ9likYVl6KKAAYF71BJO57x3jMrEyXo1MkFw1+9d+seZglfiyPDUlIclZa4/aIqYuSvDJmzRqUNQoKsh+YgdQHNIeUBzLKXTUEvYYQrj/HDxkhRACwpjQjq37eAHEWNkImmUoBa0J1MxGxo49D5QnZfxJi1zE6GJC6iovVjzANT0AhikyETcyncwJIcN0ax9wIqYKfLwypiEyxqKtKiahP7ebYgVat69pThl5Urr+3/sakhAWGAcsLtn38Q1T80CpcsIPO7GWKjUQ/qKfWBmc4nGGXLCVSwGdgllCn6hte0Syk6J+sIKlqGpPcgA06eZES5nKGt9J1q5lsCQ57GzwidMUJZVY20G583be1Xj0tKQoAB86seO5t7O9IyPhrOaAF3FFDqDQ+8NeNxfh4iSZbgLABLtTY1eoeM6m4RaF6kEODRqvtbp5ttDfkWZCcUgiorpMU7XJA86RSvf1HZMyrKy9YZ+PFJWlCVFiA5b2HnFPJ8hRNwyxpLKDUZ+oMSZzhzNmS5oGpCfiD/WGLhrOMIhYlhadazRIcgPs4o8SAGYyUls+EdxlEqgcj7hdxmDTgpRIUDNcEOWDBzQCwaB+CzgTZayqUpXLqJowBD1r03u1ot/1BwZRMK1K1FSgUJYlhUFShZuZPq3WAfDUyYoLlBihblRX8KQHBUapoACN+kUCQ6CqZd7lx3RofLWMALvTscBDFl+Y4XDYOYudq/xqSptQLk1QE3FQKjbtCJjsWufMXPmjStYZCP/AGkCw863+kTY1lTH/wDtSyRKQzP1mEdTs9gxuYGYrEPXVX8EUoSHOEXue/XpHkoZXiq5CtOvp10jzBoaak1oQfnG6ZPglGUgrFVBymhBCi7kXJrv0jJuD8AJ03Ur4ZYKyw2Fh7sI2FOJVrClclgwIIIAcMBQX2vAbRfEQdPXfwzpk8K2xXkSgXqT0p99IhzfB+H/AGwQWBm6m3pzD2ANB94D58yFDEyFMVzHYi5uGpava4hk4gwMyZLSqV8aFakl2Lbj17whYzOZtErRpKSDXYjYDp2gVFOEoIZ2bkAPv0rHtgQqYxSXZ3D3ck1Gb06cIZch4umrOhSRrKbvT/lTetQOhix/9WJSk/3GiXpJBSpT6r1SlLqPqBCWMSnxAqWpqi5I0t1Ny25gJn2HZZLuF8wI7k/QuIGWtapmFRO7PsesHtOxyh5kpZ/Q9/ML/E2N8bEzZttayR2AYJ+QERZItwtPRiPofoIixyKxzky9M9L0BoT52+Yi9QeWQInkK8Oek8utIKmXStHq+42NOsGeHc2Xh5oZjLVQj1v1EUUs5N78vyHmP5jpEvSNTUdvevtESyFJKTG8lCSWNjcaxtnhqXJlrC9KRzSzU0IcO17X/iLWHxKVjWZgJCdJZmBq5rfehgRwyuZMwiEI/UgVOwKiP/5ILdoOYnLgZPhaU+G1Xu5/Hj0mWUOUJNhdzUMBqzBn0py+UnYQooUbEijWzPM9YXJvDKJWJTNklRUQxJazHoKxZl4GTMStHKtRVzgHrdX/AC7wZwpRKlE/toD18o5mZbKlvOShlqe1Pi7bwwpclm1I0BpTobwCZjavkd41ilhsv5qLTrQGQHqLcyj5dosTUqSdKVa1/FUCiSBQEg716l+0I2WKXIxhE0lOsllglyq4HrQf+HjSsNjpKgFODQB/OsclFKpXlISVb9L7gYbtKTLUH8w4NH51w8hS1sAE1oXsI9zDAzpa0JA0KSaOwBdr1bf8aCuAxSEqB0gqd3LsKFuUFyQQ/t0iHErmTitZSGu+l3ZhfTuR7uNzFIhynJ3R6jPkkTMPPcBQ0FSbHuD/ADFaVlUySpCpLzQHqm5bSwKbCgZ/kC0DMwSRzHmBu/8AxCjYsRX5WvEWTZ2qQQ5JS9RumtL+sLEooBMrPI25aRzGCRj6xsXD2RgqGIxi/EWUhkKY6GD+79KWvC//AFAzyQXkykou0xQSOYgk6H6AsfP5js64qSJUoS5muYscmkkaQQAQd7V3tBXhrg+UvCgTKkqLk3Us1Uvz6dokQpWEOln63rf0z9TDgpCJniTKgcgNKZ93o+ermKVYEuWJfrFYYYlgBUlg30jX08CYZIUFvQXBLKvVvtF7LuF8PIOqWhJWzp1sTTZIs5s5+cMRPOIoSkuMqaka7u3i1W3SGcufT1/RgHw9w1NkYVJ0jxFqCgKOya1BoR28ob5mEXolkgagt1Anmchgfp7RP/crmy5YXK8JdCQWdJYGjeZEeiaVMxZzU9gQO5e8TzZ+ElDkgturbTInk9CWMZsyYuYrEoB68GyHQMOEXsNOV8IYgXfuHL9x1gfnvDEmeNTsvaZ9iN4EZpmc2XM0v/jpVND5f7iOXxamUgBDqq5dwHPU79++924iek+WYD9WZq2be9Mia+l7PPBC5Bru+Xp1hezLhefJVr8JRUC2oOQU9BsAP5jnFcOGbKl+LPlyJh1BEqYDqWBWjsWFbAw/ZFnBxCAFpLhV006Ebn6wHx8uRIUvG4gKMxwEzFEqNbJloHw06CznrBoUgqBSSaG4L0Fsi1Q2R1imbtk5STKmJAL5Z9X379IzfMuCZ6UqUVoK0gq8MBXMkftUWqws0I0xZFti49I3nC5gmdLVPKv8KQo6gKctDXoPsYwyeLtbveL9lmqUCVD4iOY774PTZrF3ooX7GvtFrKJC8VMCEu36j096RSybAmdLSSWSgaVE2DFh3s1g8OuDxeGkS0pkqmOf+opLAHYAVelYmmKSh05jvrG4meooCxc+7Z/VzGg4A+DIQmXdRBHcBgx80j3ivxtipqsNoly1HVzKUBSm3XZ4WOJeKBhUJRIUFgJABd2B3Lb+sH+FMymzFhE5NSHSSWBZgqnme+1oB5hDM4NPvi9f0HjFEsyyJxYkOqt+PKlMoq8KLM1KUz0nSK1ehFvvDPi88lpVoBS4Dh9gIr8SIShDIBSwBpZ3DRj+d5jN8XSol66lJOzu1tnAPr1gZaJiFGShgdd2getDbS+QEeKUT3mqcDT5ht4qzoFRQkhaieYA7XqRb1g9w9Lm+FqAdRNSA3mGawNPyqHkOEC0yyoFT2CE8ym1A1sBd3vU1Zho8uT4EhJRNUrUaOLBq27t7QwyRKcKN6v5fk/Ge+pKWCgBA3Z/ztrgtjq54SG+F63PSxFi5Y9nMU5mcKKWB01/SWqouWD0Ar2qbRFiqinUwEmKrFiA8eWAImxs993cuT8uj9T6xSkDWpKa1NW+v53juVKXNWEoDqP4594b+FZkuVJW6HWHCiQDzVHsB9IObMEpDgOfvOFpQZiqQNwGDkomyS5LKBV8noLD7RoGJnTp0tMuQoEJmqqn9pdQ9wb+kJ2Cw6peqaQHJJQpjV9ulLwdyXNvBQyVELWKl2Dd3Fx/MZ20OurvvvvZuPqIulpCfxFRl3nGnZfIIlBBOtTWUq7VDxFMkyVlQmCWqYWDgfCATZV3DkjuYzPOs0xMkImS5igltKjdgovU29b0gtw5jZq5ktCtDLBJWCXBAc6gd7k/jJRLmeGDKSCLM+XQB3ObitoQqSASVLbPTjGmT5aAyiCQEs4PoGHWKOIW6piKpcEJNOVgA/vEeS4takV6OH86g/aLQ0LQTWhJs9TzG3Snyjq1J2hlpo9WYVyNeB7JiQJ8IkKq1O+kZln6FJc6iQksSDd3FNv0mA+IxYFizXYbfjw25mgf2KtVVKnlmDkMCEg+atVHF32MJuPwi0pVyEHUQSlmHQfUenaOy5QShG8A9foR9TscwFJS9QW6AfLxof8AT7HTDLVoQ+pSa7Aczk+xFN4B8WzlqnLTNXrCVcqQTpANRXqxrTs42u/0vxSgFIckFNO2kj7H5wJ4lmqXOmqUXPiK2agOkfRvSHyWCMINieVTGRtSW2xdNPYD4aAeZ5rOMrwdbS/2JAAalKbdvO8J89MM8uWnxwZgdICi1nZJKUvs5YP3gNNwh5noE3UxIc2BNq2iuXSJpgApF3hYakTUP8LLHehBHqAPWCyJHjGXKSWCAorL7anLA7geTgeUBuF5vhYtAcHWlu1tXtRvftGoKyZImoKU6LFgaAmhboG2iPa5wkzHIvWKNmmeTCTbvv8AkAM6ydKfAI1qoNRAoxoFDdnY+VIJZjnMwJwnguSjlUoCiC6QxJsS5oW+kGeI5P8AbKlqLrl6ClCdwWNCTUpNaVihw3j5S5czDrl6dZdTFyTdwRWg+nnCcXhjCsUH6u2+r7zSCSfET4gq3y79BccKww55JXOwClhXNcEjT/3EGzj/AFGQZmsp8MpQbAlQ9Ab70eu8bFKykiSvTMUZZBLLJ9m6eUBF8Iq8NYdJQtHKphy2p5FoYhQE0KwlmfXUcd1ecSpWlMtSArOlPjX10gRwpKSuUAlKeWpu7g3DigZq7m9q6JlvhqkoMxQ1AM5Pq1IVeFMkEtgtayN0swLG1D7w7CTLKQwZqAORQWtFfjCaAUNTIu3s7xJOGAlNeI7Ij85zkUa/394FYqS/8ff1/mDgSCa+df8AcQzUEBh7fnaCSWMXKS4ihka0yzM1MDQhR25tLOPP8aHjAzpYZmKTdmqbP7GEDHYbt1iLL8wVLOgqITYH9vvtATtn8XzJNYWmZg8qrRqE3kleJPdaCopRKLJDUJUWDqAoAzEndgYT5WorAblKikgdOgN3HWGbCZsVIkIUNaUpKSqnNzFVAOgp5iLuXZYjXTSwuk01b37XiPxcFxBSEl1E57u8u8oCjAzAmcEoVpA5klmWkhwsC2pJa0MuXJl4fDDES9S5alMEKqUEggk+pVtvDJgcIhUpSSQxB5QIzDMl4iWqbh5aT4ZVZTFq+zx4TjMJNgd/I/yHy2WCjQjmO8x1hgTxbrUEygWSOZT0A7hjR9y3mINZfnWInygqVKZlaFAbkh3PZg3/AJhP4cliSJqZzJSoAqH7tJdiel3hk/p9neGlhcozggmYdD7pIDV83HWkK8NBBwlhR+HGgvpHlskUQ5Frl+lYrZpgZ8vEFL6dZTMSDUBYsai4Z3bYwLxlErIS6EKDkEEB3oRcvtteNK4jy2XOR4iF86Uukip5auH84z/iOUUSxL8MIc6zQkKU16Xf5dodhCTh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"}}},{"cell_type":"markdown","source":"Now, without any formal definitions, let us extend the concept above to include the next part - Machine Learning. \n\n****Say the task at hand is to predict a dog's emotional well-being.****\n\nHow will we determine the features? We can work out some features like:-\n\n1. Motion of the dog's tail.\n\n2. Amplitude or Frequency of the bark of the dog.\n\n3. Facial Expressions of the dog.\n\n4. Breathing Pattern of the dog.","metadata":{}},{"cell_type":"markdown","source":"<div align=\"center\">\n  <img src=\"https://www.google.com/imgres?imgurl=https%3A%2F%2Fpreviews.123rf.com%2Fimages%2Forangeo%2Forangeo1703%2Forangeo170300055%2F74133086-dog-face-emotion-feeling-vector-icon-set.jpg&tbnid=f5zwdzAMMReP8M&vet=12ahUKEwiep7-FsPz_AhWm-TgGHXgjDlcQMygVegUIARDvAQ..i&imgrefurl=https%3A%2F%2Fwww.123rf.com%2Fphoto_74133086_dog-face-emotion-feeling-vector-icon-set.html&docid=T1MCYnFdr2uSBM&w=1300&h=1300&q=emotions%20of%20a%20dog&ved=2ahUKEwiep7-FsPz_AhWm-TgGHXgjDlcQMygVegUIARDvAQ.jpg\" alt=\"Image Alt Text\" style=\"border: 5px solid black;\" width=\"500\">\n</div>","metadata":{}},{"cell_type":"markdown","source":"The situations described above may help you to gain an intuition to what multimodal means. \n\n****Interesting isn't it?****\n\nThe main purpose of this essay is to focus on the primary concepts of multimodal machine learning, whilst tracking the advancements made throughout the last 4-5 years. I would also be discussing a paper that is very interesting to me, and deals with one of the examples I used above. Lastly, I would also like to share some ideas of my own regarding the paper and Multimodal machine learning in general. \n\n\nWe will discuss in brief the foundational principles of multimodal research along with the core technical challenges. This is essential to gain an insight into the inner works of Multimodal Machine Learning. There is a lot of study done independently on the topics mentioned, and hence this essay will just give an overview of the same. I will provide a few resources for the readers to gain further knowledge about these; and it is of importance to note that one of the developments in the field of Multimodal Machine Learning, can be the way the core challenges have shifted through out the two editions of the paper I have based the first part of the essay on. Hence, we must give some time to understand the challenges and principles of Multimodal Machine Learning to comprehend the topic better. \n\nJust a heads up - I have tried to avoid going into the intricacies of most of the technical jargon, as I would make an attempt to explain most of the concepts through my level of understanding, but there are various places where they are irreplaceable. This would be eased by using some of the examples that helped me learn the same. There may be some areas where I may be mistaken, but your valuable comments can help me correct them, and also help me learn better. \n","metadata":{}},{"cell_type":"markdown","source":"### Introduction and Preface\n-------------------------------------------------------------------------","metadata":{}},{"cell_type":"markdown","source":"We haven’t yet formally discussed what multimodal means. We can break multimodal into “modal”. Modality in simple terms refers to the way something is observed or perceived. Hence, multimodality refers to the presence of multiple signals. \n\nMore formally, from a research perspective, ****“multimodal entails the computational study of heterogeneous and interconnected modalities.”****\n\nLet’s discuss the terms above. \n\n-****“Heterogeneous”**** refers to the variation in the way that data is - its quality, structure and how it can be represented. \n\n-****“Interconnected”**** describes the fact that even though the data is not homogenous, they still share connections due to complementary information. \n\nFor example, a multimodal state could include speech and audio recorded through microphones, images or videos captured via cameras, and vibrations captured via haptic sensors. \n\nNow, you can go back to the examples described in the previous sections and figure out in greater detail about the various modalities involved.\n","metadata":{}},{"cell_type":"markdown","source":"## Historical Aspects ","metadata":{}},{"cell_type":"markdown","source":"Here we shall discuss how the primary areas of Multimodal Machine Learning research have changed over time. We will not go into too much detail about the historical aspects, but it will be interesting to track how study on this topic has fared so far. ","metadata":{}},{"cell_type":"markdown","source":"\n<div align=\"center\">\n  <img src=\"attachment:21933543-28ec-4fea-8b79-c6219cc3bae6.jpg\" alt=\"Image Alt Text\" style=\"border: 5px solid black;\" 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sRv14r+0P4M+JHxMm0Hw54F1keCrCO4F5qXikPmeNQCogt4wdxYhmLElRjAycnAB7Nb3EV1CksEiTROMrJGwZSPYivHvjF8aPGXwt1XT0tPAFlrmkalqdrpNnfN4gFtI88/C7ovs7bVDAjO49M13Hw2+HVv8ADfRGsY9W1bXLmZg9zqGsXZmmmcDG4jhV/wCAqOgznFcn+0R4b1TxLpvgRNLsZr97Pxhpd7cLAu7yoI5GLyN6Ko6mh7r1QLZ/Mf8A8L0i8M+INO0f4gWuk+B7i60ufUpJ7nW43t4ilwkSxCR0jDFg4btjBGD1rsta+JXhHw3pVlqereKdF0vTb4A2t5eahFFDOCMgo7MAwxzwelctqnhGTUv2h9J1q40oXWm2/he7tBeSxB445nuoG2AnoxVWP0Br58034Y+LvCNl4E1iXSvFkNjZ6BcaO9r4XtrKa8s5TevIoaG6Rv3boUG5MY8tc8EYV9vn+od/67H1h4r8ZWvhnwHq/imFG1azsNPm1FI7EiQ3KJGXAjIzksBxjPWuW8FfGuy8WwpPNaf2Tbte3lkZLuRo9ptygLfvEXIctlcfw7c8kqt34GeE5/BPwq0HRrm0vNPmgSV2tL+6iuZofMmeTYzxIkZxvxhFCqAFGQMm94uimi8S+H79dLn1O3tUufMECBmQsqBSASMng+/WuTGVJ0qTlT3vHpfTmV/wudeEhCrUUZ7Wl1tryu34nQTa5ptvYpey6hax2cn3LhplEbfRs4NU77xZYWN1p4e4txZ3cckgvDOojATb36HO717Vw7eGdVtWstSNteW1uLq8nNnYCJ5oFmZSh2sCpPynIHI3cd61vD/huWHUNGmmsbhY1e9nb7WUdo2kKbSdgCqWG47R0ya82OMxNR8qp8r93dPq43/N9np93pPB4amudz5vi6ropW/Ja6rX7+tm1/TLeyivJdRtY7SX/VztMoR/o2cH8KlttUsrxohb3cE5ljMsflyBt6AgFhg8jJAz71wP9n6np/2fZp9wLdb6/JktbeOSdA8xMYUPwqsvO7B6D1qOPRda03wvZ31laTf23YXd0Vt5duZI5ZH4O3Cn7yPxx8pxTWPrX1puyV3o/wC7e3fd2XkJ4GjbSort2Wq87X7apXfmd1HrlvNeGNJ7Z7cQtKZhOuRtfa3y+gIILZ4IxRD4k0m4tJrqLU7OS2hwJZknUomemTniuI1nwXerHHY2lubtIdKSJt52rcOs6OyFj0LgN19ar+KNI1DxMmp3tro9zZRf2etn9nmRVknfz0bIVSeFVTyf73FKpjcTSi/3d2k9NfPr2Vl5u/31DBYao4/vLJvfTTVbrz/Cx3snirRYbdJ31axSFyVWRrhArEdQDntXNeN/iPceGEuhpujf27PFb2txHFHdCLzlmuVhIU7WyQpLgDO4gKMZzS+JrO+XxAottPm+yPZ+THNYQQs5fcco7ScIg4PTnJ54qr8P9Iv7HVbGW6tJrdI9DhtmaRcYkWV8r9cYP0IrRYqvKuqXLpfV2/r+u5H1WgqEq3NrbRX/AK2/qxvfDzxTeeMvBun61qOknQ7u5EhexM4nCbZGUMsgVQ6sFDBgMEMCOOaj074p+DNYfVEsPFuh3r6XE89+tvqMMhtY0++8gDHYowck4AxVj4haHeeJvAfiPR9OnFtf6hp1xa28zEgJI8bKpJHTkjmvj3Rfg/4n1DwraafceG/GsWqeGfCmpaft1KTTobGOeTT3txBbC3h8y7V2IwSwC7VYktwfa7njLVo+r1+NHw/bR31UeOPDp0yOUQteDVYPKEhXcELb8biCDjrium0bWtO8RaXb6lpN/banp1wu+G7s5llilXOMq6kgjjsa+adY8L+JfC/hf4MR2XhK8Wz0zRFt9RutD0q1utVsrpreFBEiTnbErEPvk2tygBwDmvQf2W/DuteF/hTc6Rrmn3ulalDrOqN5V8sYcrJdyyo4MYEbArIDlBtznFPvYi+xrw/HbRf+ES17xVcQXA8P2OpTaXYTWsb3M+pyxP5L+TEiknM6yRrjOQhbgVmQftANqXw78B67pvhm4vdd8aMI9M0M3SR7X8mSZ/NmYYRUjididpPGApPFL+zPNHN8C9J08W/2e+0t7zTL23YDel3DcSxylh2LsC/0kB71wq+DdS0b9n34Wm98P+IxrHh6SGWSbw6UGqaUxjkjaRIHRhOCH8t48H5JGODtxT0s/l+otbr5/oelaT8V9e8Q6RfnS/Bb3Ov6TqTaXq2lSanFGLaURRyhklK4kRkljIIAPzcgEEDmNJ/aaudY0GzuIfB0x1fVtcm0LQ7BNSiePUpIUd55xMBhYEEUmZNpzt+UEEE8xoHhHxbpHwX+K2qafZa6viPxjqBfT11NVOopHJDb2i3E0aALGykSSlFA2ooGARiuk+JPw3bwDJ8HtZ8LaHd6lonw+upYZNI01PNuTZy2MlrvjUnMjIWRio+ZhuPJ6z/wCv8Ag/8AAOv8K/HTQdS8O6vf+JJ7XwZdaLqbaPqlvqt7GscF0EVwqTNtWRWSRGVhjIboCCB3ek61p+v6XBqWmX9tqOnTrvhvLSZZYpF9VdSQRx2NfKOp/D/XNSXVvH974S8UW9xqnix9Ysl0OSBdX0q1WwjtI5WtJ1aOVpPLbdGcsqyKcZDAe2fs/wCm+JbH4YrbeJ9Nt9KvWu7p7eGKzitJGt3lZo5J4YSY0mfcWcKepyfmJoWq18v0B6P+vM7mLxdok9ppV1Hq9jJbas2zT5luEKXbbGkxEc4c7EdvlzwpPQVl6b8VvBWtah9g0/xdod7feQbr7Lb6jC8vkhdxk2hs7dvOemDmvAPCOneL2tfg34Ru/Amt6f8A8IbqbpqerT+SbRgmnXkCPEVcs6uZFO7AC5Cnk4rU+H3wv1Hw/wCFf2f4V8PSWV1pOpXNxqoEG17fzLG9Vml9N0kig56lhQHkdh4b/aY0Txp4Fh8V6FDbXVg9lql21rNqMcd2GsyvyCJQ27cGViQfkDx5B3iup8QfFJdB+Df/AAnx00zr/ZkOo/YPP2n94qHZv29t/Xb26V82+BPhv4usvAPh/Q7rwpqdjfaHoni7SrnzIQUlmneCSBo2UkOsgJCnuUYdq9o8b+FtXv8A9lP+wLfTribWv+EftLb7CiZl8xUjDJj1BB/KrSXvfL9b/oTO6at5/pY9B1T4meEND8QQaFqXinRbDWpyqxaddahFHcOW+6BGWDEntxz2rC8N/Hbwh4j1bxvZLq9nZL4Ru1tdRubq6iSJcxI5kzu+VAWKEtj5kYdq+bPjp4R8f69Z/FvSbLwhraz6heC805fD+nWIs76BUhAmnuJC0zznYRsTaw2LgY5rd8efDXxdqniL4hQ2Oh6xHC3i3S/FMNxp6W//ABMLOOziieOEzbozNHKhk8uReTGuOoNR2/rt/wAH7iv6/P8A4H3n1F4Z8XaH40077f4f1mw1yx3FPtOnXKTx7h1XchIyPSuQ8RfGzS9D+K/hnwDFY3l/qerzSRz3UcTLbWOLaadA8hXaXcQnEYOcZY4AGeb/AGd/DN7Y6l4t8QXlh4nshq7WqJJ4qNnFc3Hko4L/AGe2iQRffC5clmCL0CjPS/EjQ7/U/H3wsu7S0knttP1y4uLyWNcrDG2m3kYZj2Bd0X6sKdtRdGYWuftAzaVfeIry28KXF/4O8N3n2DVteS8jR4pV2ea0cBGZI4t43tuU/K2A2Kj8QftEPouva2IfC8t54Y0HVLbR9U1j7dGksdxN5O3y7YjdIg+0RZbcp5O0NivM/jF8P9V8Sar468G6PofjSyXxTdI5Wxkh/sG43pGJLyWcpvhK7CXgDjzDGOG8w1jeP/hbeXnjTxXLL4H8Q6l8TLrWI7jwz4qtQx0y2t0KfZjI4cRoIgrB0kUs3OM7hhR6XKfWx7Rr37Qr6RqmvXEPhi4uvB3h3UU0vWPEDXiRmCY+XvaOAjMkcfmpvbcuMNgNtNegN8RvCcfidfDbeJ9HXxCxwNJN/ELonGceVu3ZxzjHSvnjxr4a8TP4d+JXwrTwrq91J4w1ma50/XraEPp8drdGNpZJpc/u2iKyjYRlsJtzuovvBvijR/i9NH4d8La5bpeeJ4dVuk1OGy1DQp4d8fm3kc7AT20+1SVQEkOowpBzTVtP67E66nv/AIL8cf8ACUah4j0u6s/7O1fQr42lzb+Z5isjKJIJlbAyrxsp6cMGXnbk9VXlvgRxqHx3+J99bHdZwWuk6ZIy/dNzGlxNIM9yEuYAfy7V6lSGFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAUbHRbHS7zULq1t1gn1CVZ7p1z+9kCLGGI6Z2og/4CKvUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBnaJ4f07w5bzwabapaxz3Et3LtyTJNI5d3YnkksTyfp0ArRoooAKKKKACiiigArG1Dxdpmk61Dpl9P9jnnRXhkmUrFJlwgUP03bmRceroOrDOzXh/7R2ofEjU59A8OfCzSbeTXppjNea5q0QOn2FqVKMrkg+Yz5+4oY4Ukj7uQD3CvKfiZ+0Fb/AAt1e3stQ8EeLNQguruGwtdQ022tnt7i4l+5Ghe4Vsk5HKgZFdN8MfB+teDfD4tde8SP4j1CTa0ki2qW1vEQMEQxrkovsWPTIxk54f8Aafhkn0v4dCONpCvjjSGbapOFEjZJ9qHuvVAtn8zs/DPxStfEOuWOjXOiaxoGrXenzamLTVookeOKOZYTv2SOAxLqQATx1IPFdoWC8k4FeJePPh7oPxC/aM0a28SaLb63pkXhO8It72LzIC5vLfBZT8pIGcZ6c4r5z1DTLj/hGfhtP4su7e28F2+gT2UE3iDw1c6/ZpeLeOoV445UKSmERhHcNkK4BGTlX287/qHf+ux9y+KvEth4M8Matr+qSGHTdLtJb25kUZKxRoXYgd+Aaz/AfxA0n4jaTd6jo8vm2tteTWMjF42/exHa4+Rm6Nkc9cZGVKk858CdBmt/gnoGl6zJeaqGtpUca1p5tZXgaRykclu8khUCMqoV2Y7QN2DkDK+J2j6PoeveGptU0qJfA0f2uTUILezLwfamEfkyTRxqd3SQbiCASM84rWEOafKVFcx65uA6nFG4DqcV87+V4bs9bsLrxDoWqReBGsJItKt9UtZbiOO4Ny5J8sbjGXQp5YYAheBjGKf4T8FHXtR0W08VaZNdpH4du2httUUu8cZu8wiQH+NYyvXkfWuh0FFXb/D18/IvkVr3PoXcOeaNw9a+Y7HRk0vwL4L1GG3aLUNT8NaiNTuiD5txizLKJWPLYYDGemOKg0PS7PxH4b0Oz8EabdRah/wjd1BrjfZpIRK0lliJJHcASM0rKy4J4yRxWjwqV/e/D18/IFC/U+oJ7iO2heWRgsaKXY+wGSar6Tq1trelWmo2km+0uolmidhjKsMg4PTg14JY6pp/jfW/AelxWdxdCy0C+sr2K7spI0WYQQKYjvUAsCvIGe1YttcaFe6R4M059ItksV0QwJJfaVc3cbXm/EkUdqgVfODBiWYhvm4yCaX1Xo9/T/F/kLlPou68TWNrrn9j7nl1M2b3y20a5LRKwU4J4ySwAGa0LW4+0W0MxjeAyIH8qXAdcjO04JGR7GvmjwjDA03hfUdRtGe8uvB99Y291NbMXNzE7AoWwdrCJXHJ6ZGTVuS30WDT7mPxZpV7e6jdaPZJ4fcWcs44tVGy3KKfLmEu4t0PKHtTeFSbSe3+bWn3By6pf10PpHcOmRmjcB1OK+brHwWdWb4gT+KLAX2vWPhvTD5043sl0LOQyOh6b96j5hzWN4w1A65p99HqmmZ1k+G7SSwur3T7m/nu5Tbb3a224SDax+Zsk7huIoWFTdlLa34/Mfs9bX/rT/M938UfFPT/AAve3Fo+m6pqNxBPb27x6fAsh3TK7Rnlhx8m36so710XhrXoPFHh3TNZtY5orbULaO6ijuFCyKrqGAYAkA4Pqa85+Fdqus6lfy6lAt60mkaTIzXSb9zm2cMfm7/MwP1PrTv2ntL8Q6h8AfFtl4QS6GsNaosUemoTOYhInmrEqkFmMQcBVIJ6DkiuKpHkk4kySTSR6pvXbnIx65o3rjO4Y6da/PjUPDdpf/D74mXHhrU2vtAk0O2s7qz0PwhPoWmtdfboiCfNuHZ7pEDhtqcKw3NnAPpHxo8K+EfDXxE0vwl/whmm6bobaE506a80O71W1urySVt0NrZwFYxcAAF5WYORIo6DNZc2tv66/wCQraX/AK6f5n2DXDeGfjFoPi7UNDttNFzNFrcV5cWF3sXyZ4baREaUMGztcupQ4+ZeeOK84+El7q+tfsS6bIJLq41tvCk8CtIGM5mSKRAvPzbgVA9ciuSW6S38E/Ce+8PaRf61aRfDS/8Aslhp8jR3Ew+y2WyJHXlZDjAI+YHpk1pJcrl5f8H/ACMua6j5/wCa/wAz6h1TVrXRdLvNQvJlhtLOF555OuxFUsxwPQA1HoOvWXibQtO1iwl82w1C3jubeRgVLRuoZTg8g4I4r4U8F6RpOveJray0PTNLltNc8E6vp11a6ToF3aW9zfIkLpDJNcnN3MpDHcVDA5J5aq8ljo9zovgK7vW0/SvAcfhW1sbAat4Mu9Ws49VWaZb6PyY5IvJuGcLhnUl8PtI+bdGqlb+uv+Rpo43X9bf5n6BVwPh346+C/FQ8ZTaZrEV1p/hL/kKalGQ1smI2kfa4J3bArbiOAQRzg1N8F9KvtN+Efhiw1i5vdRuo7BI5JdVtPs1yyYO1ZYjJIVYJtUguxyOTnNefw+D9OvNY+O+gNplwmiXelWll9j0mFVkaJrCRGSBeF3YJAHAyRVPS5K1sdl4d+O+ga3JIl5Y6x4bzYS6rbtrtibYXdnGFMk0fJ4UOhKttddy5UZFQeGP2hfDPiLzXuLXVvD1v/Zcmt21xrdn9nju7BNhe4iOSdoEiEqwVwHGV5r5/t9e8QfEXULPUfDmuR/Ee48M+EtWj8lPD0lhAJ5oYkhtrlXdxJO5jOUUqB5Z+UbhXIaf4JsvEXh3UvDvw08R+IvH9xN4Gv9Lvx4gEhOjnZH5MEO5F8l5JIyhg54QHogyd/wCu/wDwBrp/XY+vvAXxo0bx9q6aZDp2r6PeT2X9pWUesWf2c3tpuVfPi+Y5UF0yrbWG9cqMiu+DBuhzXyf4t8UaX8d9atbzwv8A8JA9long/VYtVfRbN49QtZrhYUS0RX25uB5cjeXnOY/cZvfsl3FlD408QWWg6Vpd1oi6dD53iPR9Gu9Fja5RyPs89pMTGZgrEmSMk4XDYyop2V7L+tybu13/AFsfUW4c8jjr7Um5fUfnXyr8afFlr4Jv/jzpGpWmpHUPFnh9f7FjtdOnnW826fJDJtdEKrsYZbcRtUgnisrVvhvo3ibRfi/quraMmoajp3hDTpdMluIyzWs6aZIwkg/uSBgvzL82VXniovo2aW1S7n0rdfErSo9c1/RLOK81XWtDgtLi80+ziBkCXLOsRUuVVv8AVux54C+4Bv8AhfxlY+LrjXYbJJ0bRtRfS7nzlABlWOOQlcE5XEq8nBznivjD4iabZWfir4mTXmmFNf1zwz4XurG6azYvciO5C3ZWXbjIJg3DOThfTj6c+CMTxan8Ti6MgbxfcMu4YyPstryPaqt7zXl+tieifn+lx8P7QPh6fXILQafrS6VcagdJg8QtZf8AEuluxIYvKEgbd/rFKByoQtgBiSMyWfx68O3viWHS0s9XWyuNQfSINdayI06a9RmVoFlzncGR13bdhZSoYtxXhmm/HzwR8Y/ixp+j3+tf2D4a0XW0Gj6FBY3Al1m+VyEuJ2WLZHCsh3Im75mAdyMAVPomvWs3hXwt8J1W7/4TvTPGUN1eWJtJQ0VtDqpvGu2fbt8t4cMGzyZAOuamOtvP/gf19wS0v/Xc9rsfj94c1HxJFpkNrqv2CbUW0iHXmtMadNeqzKbdZc5LbkZd23YWG0MW4pul/tA+GdY8RwabbwamLC41CTSbbXpLXbp1xeozK1ukuclt0bqCVCllKhieK8J0vU4r3wZ4W+EiR3UPjzTfGyXl3YNayAxWsWqteNeF9u3y2hKsGzyXAHNVPD90NR8C+BPhHHb3Nv480XxtFe39g9tIpgtbfUnunuy+3b5bxFWVs/MZABk1aSdv67aib3/rufWHhXxfa+LE1MQQXFpc6bfS6fd2t0qiSORMEH5SRtdGSRTnlXU8HIG7XkXw8W5b9of4tyRbv7K+zaNGf7v2sQzGXH+15TW+fbbXrtSMKKKKACiiigAooooAKKKKACiiigAooooAqatpsOs6Xeafcbvs91C8Em04O1lKnB9cGk0XSoNB0ex0y13fZrOCO2i3nLbEUKuT3OAKuUU7u1h+QUUUUhBRRRQAUUUUAFFFFABRRRQAVj+FfCuneC9HGl6VEbfT1mlmit8/JD5kjSFEH8KAsdqjgDAHArYooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDF8M+E7LwpHqItDNJLqF7NqFzPcPvkklkOTk46KoVFHZUUdq2qKKACiiigAoorx/9oL4oeH/AIG2eneL9e8Q3ei2s0y2Bht0NwJ35dD5H8WNrK2NpKv94FVIAPYKK474X/ECX4jaANUOialpFu20wSahbNbm4UjO5Y3xIuP9pQORgnnHif7Wfwf8H6nfeDdeudFjk1fVfF2lafe3QlkVp7d2KtGQGxgqAOB2oejSBaq59O0V86al4Q1XwL8adA8OfDCDRdEgi8KXsipqyzz20Wb63YkRo6szEsf4xjJ69Dj3/wC1Z4g1DTvCVtp1hZ6VrOpaO2q3076LqOswRFZ3g8pIrNd4DNHI292AAUDDE8HbzDufUdFeceHdU1P42/A2aWWK+8Fatrmn3VmWaCSOeykJkh85EkCPjI8xdwU4K5xXLWOi3vwf1HTbGa8t/EN1c/21qkd9cWnkPABHEyQgI2CoGE5zhFRV2hQKzqTVNOT/AK1N6NJ15qEd9fwTZ7hRXkLePPGD2dnHfLpEB13QrnULJrSOXfZyRxRttkJf5wfN6rtxt79a5zSfjD4g0vRfCmhmWC41V9AtdUudQl0u9vg6SDaiFICzb/lJaRjgnoOcDN1op2Z3xy2tNXi0/wCnr96t/wAA+gaK8r8P/EXxT4u1nQ7a102z0SO40tdSvodUgmM0ZE5jaNBlMZAyGYcdwc8Z2k/GbVZPEugxXT6Pe6XrFzJaqmlxTubZlR3BNyT5Uv8AqyCFCnnIyAaftoO3n/wxn/Z9fVaXSu/x/wAmey0V5VpXivxz4o8Kx6/bR6Jb6VqdlJcWybZTcWamMtGznftlJwAQoTBPU4rjrH4lal4Rt9FvdQs7TWdWPhGG4W6BkjkleS7iijRmZ2G394pY4JJBIwDij2yvsXHLqkrxTTknay7/AJf12PoaivIfE3xN8V+BY9VstWttIvtTTS21OxnskljgbbKkbxSIzlsjepDAgHPTik1dNX+IWqat8O/E509BPY2usxXmmpKnlot2D5ZBkyXBiBD5C5IyhGVIq0XLl6/8P/kzKWBqxh7S6t67rTVfej1+ivPrOzsfgD8G798yX1loFnd37KuVL/NJMUXcWIXLFVyWIAGSTzXiWjftWeM5LG6ubzw8t2lxoV9qkD2/hzVbODSp4LV7hIrqe5VUnRtpTfGUywGFwwI37+RwW1S7n1dRXzkvxm+Idr4J8F6hqmpeBtM1PxZAt5bFra8k8iI26S+UlskpluXJY5ZWRUGMg8Z9I+BPxMu/il8M7bxDqVtDa363N5Z3C20cscbNb3EkJdUlG9AwjDbX5XODnGaO4u3mei0V83+G9HsfFvwTt/G3jzxLJpGk6rqVx4i1zbctBDdWbebFa2burKyxLEbYbATvaPBDbznmfDOk3+sf8Ij4du49a0T4feK/Fd61hoV9eTJcnSodLd4oHbf5iRSTQtL5JbhGCEY4p2ewrq1+h9bUV8ixyz33jrTfhXLfXp8GN47vtLa0F5L5jWUWhpfJaGXdvMQmkPy5xsAT7oxVKO+u7z4iaV8I5L68fwTJ47v9OaFbqTebGHRUvEsTLu3mLzpDlc/dAX7oxSHr/Xz/AMj7Horxz9nme403VPiZ4UFzPd6P4b8R/ZNL+0StKbe3ks7af7OHYlisbyuBknAIHQAV3Hg34qeEviDqWrWHhzXrTV7zSXEd9DbMS1uxLKA3HqjD8DRppbqHc6uivmH43+D4G8SXVppGtajrnxg1m8juNB+z3kkB0GyDxK8jKjbEtlCsXLLmUvtwxas3xVYzeJ/Cfxg+Idzf6jZeK/COrXkWjXFveyxi1iso4iIkjDbdk5Ri4IO4TEemI5u5XKfWFFeAfEi18Qf8NCfBjU7jxFN/Yl3qN1DDoEEKxwow0q7d5ZXyWlcnAUcKoB4JJNcf44tpJvBvxb+Jz3uoN4s8Ma1dLo8iXsqLZw2vkhbdY1YLsl2sXBHzCXnoMaJNu39dP8yHor/11/yPq+ivLNHkXwv+0JqujW37ux8SaJ/br24ztW7gmjt5pB2BeOW3BA6mInqTXqdLpcYUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVzGtfDTw14l8V6Z4k1fSodU1bS1K6fLeZkS0JIJeND8quSF+fG75RzxXT0UAFcv49+HunfES30SHUprqFdJ1a21iA2rqpaaBiyK25TlCTyBg+hFdRRQBgSeDLKTx3b+LDLcf2jDpsmlrGGXyTE8qSkkYzu3RrznGCeK4Of9mvw+ui6JZ6ZrfiLQNQ0iGW1t9a0m/WC9a3klMrwudhR0L4PKZGOCDkn1uigDJ8K+F9P8F+HrLRdKjkisLNNkayytK5ySxZnYlmYkkkk5JJqr4j8E2PijUbK9upbiOW0t7q2QQsoUrOio5OVPICjH65roKKmUVNWkXTnKm+aDs/6RzTeAdNkk0NpHuJF0mxl0+FGcYkikREbfgZJxGORjqa5+H4H6Vaafp8FrrWu2d1Yw/ZItRt7xUuTahsrbswTDRr0XI3DseTn0WipdOLd2jojiq8FaMv61/zZzOj/D3R9BvYJ7KOWOOHThpaW7SF08kOXyS2WLEk5JbmsDS/gbouk3OjSRanrUsOjXX2nTrWe83w2y7GTyVUrymG6nL8D5sZB9Foo9nDsCxVdXtN67/l+pw+k/B/QtJ1GKdZdQubS23fYdMubpntLEsrKxhTtkMw+YnAJC4qnY/Avw9bwrBeXGo6xbrpjaQIr+dWAtzMJVAZVVgyFVCtnIAHfmvRKKPZwXQf1zEXvzv+v6/U4CP4L6O1hqcN5qGrapd6hALR9Rv7oS3EcAcOIkJXaqhhn7uT3J4ro4/CNnH4zfxMJJ/t7acumGMsvleUsjSA4xndljznGO1blFCpxWqX9f0yJYmtO/NLf/gf5L7irqmm2utabdaffQJdWV1E0E8EgyskbAhlI9CCRXlGl/su+F9Ns7+zfWPEmo2Uum3GkafbahqZnj0i1mi8p0tQy8HZgBpPMYAYzjIPsNFaHMeYax+z9oep6d4ShtNY17Qr/wAM2K6XZ6tpN6sN09oFRWhkOwqyv5UZOFBBXKla6L4afDPS/hXoF3o2j3F7Pp89/cX6x38wmaFpnMjor4DFdxYjeWb5jljXW0UCPnxv2YT4o8C2vgjXPEGu6VpXhvxBNqeiX+i3ECSTxO7TQCUSRycwmZ4wMD/VKw7Y6v8A4Z6tp/Ccek3/AI48YatqVrqA1PT/ABFe38Lalp0vl+WRC6whNhQupV0YESPnqMesUUDPKn/Z00BvB9to66trkerW+otrEfihbtf7V+3Mux7gybNhLRnyypTZswu3AGBv2cvDreD4NGXU9Zj1WDUzraeKFuUGqf2gylGuTJs2FjGTGVKbNny7cAY9VooA5X4c/DnTfhnoc+n6fNdX093cyX19qWoSCS6vrl8B5pWVVBYhVHAAAVQAAK6qiigDw26/ZVtm8deIvFVh8TPH+i6hr10t1eRafqNqkR28RxDdbFvLRflVSxwO/JNb3ib9nbQvFXii51S61nXotOvpoLnVPD9vdomn6lPCFEcs67N+cRxZCOqt5a7gec+qUUAc14k8Baf4o8S+FNbupbmO78N3c15ZpCyiN3kt5LdhICpJGyViMEcgduDyOvfs8aFr3i661h9Y12z06/u4r/U/Dlrdoum6jcxhQksyFC+fkiyEdVbyl3A859TooA4PQ/C+pXfxe1/xXqduLW1gsYtF0mPertJDu86ec4+7vkZECnnFuDgZrvKKKPIAooooAKKKKACiiigAooooAKKKKACiiigAoorntZ8T3Oh63BbS6XLcafPEXW6tW3urqfnUx4ycLhhtJYgNhcjkA6Giq9jqFtqVuJ7WeO4iP8UbZH09j7V4p8cviV8Tvhvq2lS6JpvhO90PVtXs9HtDfzXK3KyznbukCjbtDA9DnGKAPc6K8VvfjvdfDfxNpOifEmTSNNnutIuNRkm0dbi4DSJcxxpHFHtMjfI5ZsKcbSeldbr3x28B+HNF0jVbzxHbvY6vF59i1nHJdPcRYBMipErNsGRlsYGQCRmjzA72iuc1rxjFB8P9R8UaFD/wkscOmy6hZQac3mm+2xl0SMrncXIAGM9awfA/xQk1jw/eaj4j0+58OyR6hLZQW11ZyxSTKgXEiocsytywIGADjnaWMylGCbk7WDpc9Bork9H+IFjdaNNqN7NHBB9tmtYTEGcyqjkKVUAkkgZ4Falx4v0e302G/e+jNtMdsbRguzkdQFALEjuMcY5rGOIpSjzKS2v8ieZGxRWLceMtGtrW2uHv08m5DGEorMX24BAABORnp168cGtHT9RttWs47q0mWe3kGVdfyI9jntWkakJPljJNjuizRXM2fjqwj0OwvdSmS1mukLCGNWkbAJBIVQTgevTmruoeMNH0uG3lnvk2XCeZF5StKXT+8AoJ2+/Ss1iKTjzcytp177BdGzRWJceNNFtri0ge+VpbuJZoFjRn8xGOAw2g8ZpNN8baJq91FbWl+k00qlo12MofAyQCRgkDkjqKr21K9uZX9Q5l3NyiuWtPiFpMem2c19fwieeDzwttHKwZNxXco27sZB7cVoXnjDRdPjtZJtQiEd0vmRMmXDJ/fyoOF/2jx71KxFGSupr70HMjZorzjUvHniWbxpo1voOj2+peFr02bSakyTB1SQXZmIO3aNohtyNxH+sYHkoG6Lxx8RvDnw30+G88RammnxTyeVBGsbzTTNjJWOKNWdyBydqnA5PFbqSkrrYZ0tFec6t+0P8ADvRNP0K+ufE0D22uLM2mtawS3DXPklBKqrGjHchdQykAjnI+VsS/8L++H48XQ+GT4ltxq8tyLNEMUnk/aD0gM23yxLnjyy27PGM8Uw8z0GiuP+KXjz/hAfDkE9ukdxrGpXsGlaXbTZ2zXczhUDYI+VRuduR8qNjnFcD8RvjT408Dx+KPEI8HWq+CfDEwiu5r+7khvtRTbGzS2iCMoVBk2jc3zsjD5aFqB7dRXgvi39o3VtGuPGet6b4ftL3wN4Ivhp+u3c106Xskgjikma2iCFSIlmUncw3kMBjGT1HjT41SeHfiT4R8KWOg3l7Dq+opZXmsSxmO0ti9tPOiIx/1sjCEnC5Cj7xBKgnYD1KivDvGPx61/R77xbqujeHLLVvBng25W01q6e9aO8kYJHJO9ugQqRAsmWDsCxVgMYye1m+OngS38XQeGZfEVvHrE0kcCRsknlea4BSIzbfLEjAjEZbccjjkUk77A9NzvKKydY8VaXoOpaNYX92Le71i4a0sYyjHzpVjeUqCAQPkjc5OBx61zOu/HPwN4Z+0/wBp+IIrT7NqjaLLuhlOLxbcXBhGE5byiGGMgkhQSxxTA7yivGvFf7VHgzQdJ8KapYTy65Y67rY0Uva28++0cIzSGSMRF1dcL+7ZQx3gjgE1Y0P41XWrePPCGgomnXtrrT64st3Z+aBEbGdY1QBwDu+Yh+MblO0kYNOweZ67RRRSAKK43RvHzXHxO1/wZqECW95a2kGqWEiZ23NnIWjYnJ++ksbgjphoz3NdlQAUUUUAFFFFABRRRQAUUUUAFeL/ALQvwl8X/G2bQfD2l+JpfBfha3uBe6pqWnSsNQuWHCwQlSBGMFiXJPJX5SAQfaKKAOU+HXwz0X4X6L/Z2ji7mL4M95qN1JdXVwwGA0krkse+B0GTgCuZ+PngvWfGun+CotGs/tj6d4r03U7oeakfl28UhMj/ADEZwD0GSewNeo0UdUw20OAuvCmozfHrTvEgtA2kw+GrnT2ut6ZWZ7qBwm3O7lUY5xjjr0r5+sfgN418Nw+FdWOi67fy2+iSaPc6X4c8TLpdzbOL2adH3iRY5I2SUAjfkFVwDzj7AopdvId9/M4r4M+DZfAHwz0PQri2SyuLaN2kt47uS6WN3keRl82T5nwXPJ/DAxV/xJp18mvaVrNla/2gbOKaB7USKjESbPmUsQMjZyCehrpqKirBVVZu3Um2ljyuHwXrSR2t/LZTpPDe30j2NjfCGTZNIGVkkBA429CRwfwqxJ4J1G3j0+9jtLvzI5LlpbO21NluP3pQhvNJALfJ8wzjngnHPplFeesupJWu+nbpby8l5eRPIjzSOxvfDuqeHzHpZubp3vZzavd+ZINwXnzHOC/rzjlua6/wlpdxpljdPdqIp7y6kumgVtwi3Hhc9+Bk47k1rvbQyTRzPEjTRghJGUFlz1we2alroo4dUZXT9NuyXbyBRs7nmEfg7VrFdNufsl5OyWX2WW3sdQFvJGwkZgc7grKQ3POeBVqPw1relrp1vb2sv2RbVo2Wyu0V45GkLEPJIN7LyPu988dK9ForGOApwVot/h/l1DkR594H8L6ppWp6bNfWnkrBo7WbsZVfEnnlgOCScrzn39eKl0XwvqNnoPhK3ktdk1jdPLcLvU7FKyjOQeeWHTPWu8orWOEhFJJvT/N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a general note, if you search Multimodal on Google Scholar, there will be a lot of papers from the 1970's that focus on literally \"multiple modes\", which is a more statistical based concept. Further efforts were made in the field of Psychology, and interestingly enough, there this term was linked to a lot of transportation related articles! \n\nStarting with the \"behavioural\" era, one of the most notable researchers, David Mcneill conveyed that gestures are in effect the speaker’s thought in action, and integral components of speech, not merely accompaniments or additions. This can be explained in terms of one of the principles of representation, which explains how different modalities can connect. This also includes one of the very famous examples, the McGurk effect, which was displayed in one of the previous sections. \n\nMoving to the next era, the \"computational\" era, a lot of effort of research was focussed on three major areas: \n1) Audio-Video Speech Recognition : As observed in the McGurk effect, there are commonalities between the two modalities described. This often leads to redundancy of information, which arises from similar information arising from the two modalities. This helps in handling noise, and makes the task more robust, since one modality can \"step in\", when the other has some noise associated with it. \n2) Multimodal Interfaces : This deals with the concept of Affective Computing, which basically study and develop systems that can interpret, recognize and simulate human emotions. \n3) Multimedia : This can be described as the Youtube, before Youtube. This may sound something that is so common nowadays, but back in those times, it was a booming area of study. Think of this as an example where there needs to be indexing done of a video library.\n\nLets discuss a bit on the \"interaction era\". This started dealing with the social aspect and natures of systems. Infact a project called \"CALO\" (Cognitive Assistant which Learns and Organizes), is the origin of Siri!\n\nFinally, to the \"deep learning\" era. This is where we are right now. This includes a great deal of representation learning. One of the key moments for Multimodal Machine Learning came in 2014, in the form of a paper(link here), Show, Attend and Tell, which deals with image captioning. \n\nThis is a basic overview of the historical aspect of Multimodal Machine Learning, and can be tracked through the research papers to gain a better insight of how it has changed throughout the years.","metadata":{}},{"cell_type":"markdown","source":"## Real World Tasks tackled by Multimodal Machine Learning\n\nA) Affect Recognition \n1. Emotions\n2. Personalities\n\n-An example can be if I give you an image of a person's face, the acoustics, and the text of what that person is saying. Can you predict the emotion or the personality of the person?\n\nB) Media Description\n1. Image and Video Captioning \n\n-Think about it as the description of a video or image. We have a similar feature on Youtube videos, where the description can give an idea about what the video is about.\n\nC) Multimodal QnA\n1. Image and Video QnA\n2. Image Reasoning\n\n-We can ask a logical question on an image; let us assume there are two boxes. We can ask if the left box is bigger than the right box or not?\n\nD) Multimodal Navigation \n1. Autonomous Driving \n\n-This needs no introduction, as we can see the advent of self driving cars.\n\nE) Event Recognition\n1. Action Recognition \n\n-Imagine I just give you a still image, how can you predict what action is being taken? Probably some sound at that time can help you? If I provide you with the sound of a crash, and an image of a car just driving near an object, we can probably predict that the action is the car crashing.\n","metadata":{}},{"cell_type":"markdown","source":"## Principles of Multimodal Machine Learning\n\n\n<div align=\"center\">\n  <img src=\"attachment:437f52d7-5d85-444d-a1f5-b569d2e22d14.jpg\" alt=\"Image Alt Text\" style=\"border: 5px solid black;\" width=\"500\">\n</div>\n\n\nA few of these principles were briefly discussed in the last section, we will touch upon these and their sub principles in a bit more detail.\n\nThese concepts are very well described in the paper, but they may be exhaustive and difficult to understand in the first read or so. Hence, I will try to explain these difficult concepts with the help of an example.\n\nConsider this image, and caption pair: \n\n| Image| Text Description |\n| :---: | :--- |\n|<img src=\"attachment:1ba9fe80-8429-401f-82ab-cbd92d29243c.jpg\" alt=\"Image Description\" width=\"300\" height=\"200\"> | ****Four children standing to the right of a sign pole, with snow and trees behind them.****  |\n\n\n### a) Modalities are Heterogeneous\n\nWe know by experience that the information present in different modalities will still show different qualities - simply an image and a text conveying the same data are different in a lot of ways, they are represented differently, text is made of words, and images are made from pixels, they can be represented in different ways etc. Hence, we can explore the different dimensions of heterogeneity which define the points of difference between two modalities.\n\n* Dimension 1 : Element Representation \n\nThe dimensions in the image can be smaller areas of the image, and that in the text description can be individual words. For example,\n\nImage -> \n\nText -> Children, Tree, Snow\n\n* Dimension 2 : Distribution \n\nThis refers to the frequency of objects occurring, or their likelihood of occurring. \n\n* Dimension 3 : Structure \n\nA tree can be learnt to be composed of leaves, trunks and roots. Hence this dimension explores how individual elements are composed to form entire modalities.\n\n* Dimension 4 : Noise\n\nIn this example, some blurring or distortion in the image can be noise. There can even be noise in the form of spelling mistakes in the description. This dimension measures the distribution of noise across different modalities.\n\n\n### b) Modalities are Connected\n\nModalities can share information to help us find connections between them. They can be of the form of Statistical as well as Semantic. Statistical association exists when the values of one variable relate to the values of others, involving distributional patterns whereas Semantic dependence requires some domain knowledge about how different modalities contain unique information. Hence, statistical is often referred to as the bottom-up approach, and semantic is referred to as the top-down approach. \n\nLet us try to understand these concepts by using the example mentioned above, and they can be easily resolved if we can think of these in terms of asking a question!\n\n1) **Statistical Association** : Can images which are associated with a caption containing the word \"snow\" in them, be of a particular colour (which we know is white, but this doesn't involve semantic meaning). Further, think if you are given hundred of images, and captions with them. You do not know the langauge of the caption, but you may observe certain features occuring in the images, if certain words occur in the caption.\n\n2) **Statistical Dependence** : With \"snow\" and \"trees\" occuring together in the description, do the images have trees with a certain colour on them? (which we know is basically snowflakes on trees!)\n\n3) **Semantic Correspondence** : Think about the example of grounding, which can be simplified to \"putting a bounding box\". Now, someones asks to find a tree in the image, how do you do it? There would be some semantic meaning associated with the word, and what you know a tree looks like.\n\n4) **Semantic Relation** : Now in extension to the previous concept, there would be some cases where the trees may differ in terms of images, but we understand and know the commonalities in the various species of trees we encounter in everyday life, like leaves, trunks, etc. If the word \"tree\" occurs in the caption, we can look for the parts of the image which resemble a leaf, or trunk or root. Hence we are describing the attributes that describe the relationship between the two modalities.\n\n### c) Modalities can Interact\n\nThis has 3 characteristics : Interaction Information, Interaction Mechanics and Interaction Response.\n\nLet us try to understand all at a glimpse. \n\nInformation can be redundant or non redundant. The first occurs when there is shared information common to both modalities, and the latter when there is not. Mechanics refer to the way these modalities interact : if they are additive, non-additive, non-linear etc. Interaction Responses study how the response changes in presence of modalities. Responses can be : enhancement, dominance, equivalence etc.\n\nIn consideration to the example used, let us say the task at hand is to predict the emotion of one of the characters. Say we have a spoken dialogue from that particular character, say something like \"Oh! I am feeling so good today!\". Now, based on just the image, it may be difficult to gauge the emotion, but there may be some commonalities, like a grin or some visible feature. These two can then be additive, which helps us to predict the emotion. Now say I only used the image. The response would be an enhancement, since only the image may not be enough for us to decide the emotion. What about the response if only dialogue was given? We may have been able to guess successfully given just the dialogue, so the response may be just that of equivalence.\n\n\nI hope you have gained somewhat of an intuition of what the principles of Multimodal Machine Learning are. You can view the paper for further 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"}}},{"cell_type":"markdown","source":"# Core Technical Challenges\n\n<div align=\"center\">\n  <img src=\"attachment:0780d2a5-3446-4131-8778-cebc9c293c9f.jpg\" alt=\"Image Alt Text\" style=\"border: 10px solid white;\" width=\"500\">\n</div>\n\nThis is perhaps one of the primary areas of discussion related to multimodal machine learning. \nWe will not be discussing each one in too much detail, since it is quite expansive, but we could still try to understand what each of these mean.\n\nRepresentation : This refers to learning a local representation of the different modalities based on some features. \nFor example, say we need to assign a number (scalar) to a few items such as a video of cats running, a text saying “There were 4 dogs chasing the ball” and an image of a lion in a jungle. There will be some sense of notion or logic to have such data points which lead to a similar inference to be assigned a similar number. Hence, representation deals with capturing this idea in a common space.\n\nAlignment : This refers to the identification of cross modal connections and interactions. In simpler language, it describes how a set of data points of different types may be connected to each other. Let's take an example of the video of the running cats. Say there was some narration, something similar to a documentary. How do we align the points of the video, to what the narrator says? This is an example of continuous alignment. There are other types of alignment such as discrete, whose example can be bounding boxes on an image. \n\nReasoning : As the name suggests, we are very familiar with how reasoning operates. This involves modelling the structure, the intermediate concepts, understanding the inference paradigm and leveraging large scale external knowledge. Say you have been given a video, and I ask you questions based on the video. Say my question is \"What happened before this event took place, just after the character took an action?\". Even whilst reading it, you would have formed a structure in your head, as to how to approach the question. There would be some sort of a temporal structure in your head right? The second point, shows how we can break down the two modalities supplied in a form we can make use of. Probably, the video can be divided into certain frames in a way to capture the essence of the question? The next point we discussed was about logical inference, about how we use association and logic to find the answer. The final involvement, deals with some sort of domain knowledge, where the video may be about something you can infer if specific knowledge is present. \n\nGeneration : This is something we are all very familiar with now. The rapid developments in the field of Generative Artifical Intelligence can help us understand what this challenge is. Formally, this refers to learning a generative process to produce raw modalities, that share the qualities of the different modalities. This includes three subchallenges : Summarization, Translation and Creation. These may not be very difficult to understand. Summarization refers to the reduction of data, in a way to still capture the essence of what the data previously signified. Translation refers to changing modalities with considering their shared connections and meaning. A few general examples are audio-to-image, text-to-speech, speech-to-gesture, etc. Finally, Creation deals with creating new data from small initial examples or conditional variables. One of the biggest examples are the various Generative Adverserial Networks we use. \n\nTransference : Lets say there is a new species discovered by the scientists, and our task is to recognize it in an environment. Researches have managed to capture very few images of the species, but through scientific study, there is a lot of data in the text form, in the form of description of the new species available. Can we somehow leverage one modality (in this case, text) to help us with a task concering another modality (in this case image)? Transference deals with this concept. How can we transfer knowledge between modalities and representations? There are three approaches associated with transference : Cross-Modal Transfer, Multimodal Co-Learning, and Model Induction. One of the primary examples of the first approach can be that of Transfer Learning. The second approach can deal with how we can learn represenations of their joint distribution. The last subchallenge talks about how we can separate the unimodalities, but somehow induce a common behaviour in both their models. \n\nQuantification : Think about this as a way to gain a better understanding in what is actaully happenning. Understanding it's subchallenges can help gain a better view on this challenge. Think about these following questions, 1) How can we better understand the information present in a modality, including the noises and biases, 2) How can we better understand the connections and interactions between different modalities?, 3) How do we optimize, and generalize our findings, and how do we deal with tradeoffs? \nThese questions are a part of subchallenges such as Dimensions of Heterogeneity, Modality Interconnections, and Multimodal Learning Process respectively.\n\nThis is perhaps a very short story of some of the most powerful concepts involved in Multimodal Machine Learning. I do suggest the viewer to refer to the paper for a more robust understanding. 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"}}},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}