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Memories I still remember clearly that I was particularly interested in mathematics and theoretical physics when I graduated from junior high school, and studied advanced mathematics and basic physics on my own. After that, I was full of pleasure in exploring physics and mathematics, and every day after entering my first year of high school, I would go to the library after lunch and flip through science and technology magazines, then I participated in the National High School Students Applied Physics Knowledge Contest for the first time, and I remember the final question of the paper I used calculus to solve the problem, and when I got out of the examination room, I didn’t even think that I would get it. I was very happy at that time, and I am still very grateful to Mr. Yao, our class teacher and chemistry teacher in my first year of high school, for having seen the ranking list of science scores in his hands when I went to the school to register. He was the class representative in chemistry, and in every exam he would praise me for the clarity of the solution to a particular problem. Every Sunday, on the way to school, I would stand in the Xinhua bookstore for one to two hours to look up books and scientific materials related to relativity. ...

The last two days the company planned a hackathon competition, they simply signed up to participate, thinking that they are currently researching the topic of deep learning, on the Google search blockchain deep learning, and finally found this paper Energy-recycling Blockchain with Proof-of-Deep-Learning, and then developed a PoDL chain for storing deep models along the lines of the paper, which is presented below, followed by Mathematica and Python implementations. Program Code. ...

Wolfram 2020 08-20 China Student Ambassador Presentation The following topics are of interest to me, and I am currently starting my own business in Wolfram and Python, with Wolfram being the main language for prototyping and exploring initial ideas. After watching the following presentations, I will focus on biomedical and retinal image analysis as a deep learning research topic. 18:00-19:00 Jingxian Wang, Department of Physics, Peking University. Meta-programming Basis in Mathematica. ...

Today, in the course of work, I need to parse a JSON file to get a keyword, and I’m going to use Mathematica to practice. ``` `` { “apiVersion”: “v1”, “data”: { “voting-keystore.json”: “{ "version": 4, "pubkey": " a94300da6d73a11d8ba72c99dd385b7a9d5043c9ee83ffd4190d89b7677e68d384fbceb556d2a0652e81250ad4b8477d", ""uuid”: "09e5c322-bf6b-401a -b90d-1aef6d57996a", "crypto": {"checksum": {"message": "704e947d244bc8020ef6d6f0844c4aea08af18ecf345bb98ab999df6441377d7\ "function": "sha256", "params": {}}, "kdf": {"message": """, "function": "scrypt", "params": {"p": 1, "n": 32768, "salt": "b891518a06f1d3f67a6d485bba8627f5320497f98c08cb52e4391e50071ac97d", "dklen": 32, "r": 8}}, "cipher": {\" message": "bcd85ce03e82e645e135e13501f717d907c03a23ee1f8dc6bd045c53001719ddc7a5\", "function": "aes-128-ctr", "paragraphs": {"iv\ “: "7f05ffce95bcd0157e520c6b949b90b1"}}}, "path": """}”" }, “kind”: “ConfigMap”, “metadata”: { “creationTimestamp”: “2020-07-25T11:35:24Z”, “managedFields”: [ { “apiVersion”: “v1”, “fieldsType”: “FieldsV1”, “ffieldsV1”: { “f:data”: { “.” : {}, “f:voting-keystore.json”: {} } }, “manager”: “Swagger-Codegen”, “operation”: “Update”, “time”: “2020-07-25T11:35:24Z” } ], “name”: “lighthouse-validator-a94300da6d73”, “namespace”: “eth”, “resourceVersion”: “1200502”, “selfLink”: “/api/v1/namespaces/eth/configmaps/lighthouse-validator-a94300da6d73”, “uid”: “05b37640-be81-4944-9c9f-868431b6107f” } } ...

The animation program code is as follows: lung-3D.nb lung3d = AnatomyPlot3D[lung anatomical structure, PlotTheme->"XRay"] gif = {}; Do[ image = ImageResize[ Show[lung3d, ViewPoint -> {3 Cos[x], 3 Sin[x], 0}, ViewAngle -> 20 Degree], {256, 256}]; gif = Append[gif, image], {x, 0, 2 Pi, 0.1}] Export["lung-3D.gif", gif, "AnimationRepetitions" -> Infinity ] image = ImageResize[ Show[lung3d, ViewPoint -> {3 Cos[Pi/2], 3 Sin[Pi/2], 0}, ViewAngle -> 20 Degree], {512, 512}] Training datasets covid-chestxray-dataset ...