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In one study: When the subject gives false information to others What is it like to say honest information in the brain? Different nervous activity was observed Specifically When giving false information The parietal lobe, which is responsible for information processing, becomes more active. Information that is advantageous to you Priority processing
Youth is rock, though. One of the bands I got hooked on was KANA-BOON. I was attracted to their fact that "except for the major songs, they are surprisingly philosophical." In fact, they were "making people" in their indie days. What I mean is that we made the albums in order. "Keep your mouth shut."…
※📁: Folder,:box,🗃-:file 📁 Overall plan 📁task -Financial planning -Technical plan -Creative Plan 📁 Central Administration – Overhead view 🗃Notice 🗃 Goal/Ideal 🗃If Then 🗃Q&A 🗃Wake 🗃 Monthly Review 📁brand 📁 Knowledge and know-how 📁 Cheat Sheet Collection 📁Incident 📁content -Routine -role 📁marketing 🗃 Request (Market-in) 🗃 Idea (Product Out) 🗃content 🗃 Content (Series) 🗃service 🗃story…
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There are two kinds of people in the world There are those who are ridiculed for their originality, and those who are appreciated At first glance, they are the same originality, but what is the difference between them? I will tell you three differences based on my experience. Do you have a track record? This…
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Using OpenAI's GPT prompt, create a "PaperSummarizer" I asked you to summarize my Make-A-Video paper. 『Make-A-Video: Text-to-Video Generation without Text-Video Data』https://arxiv.org/abs/2209.14792 The following is the full summary result INTRODUCTION PREVIOUS WORK METHOD TEXT-TO-IMAGE MODEL SPATIOTEMPORAL LAYERS PSEUDO-3D CONVOLUTIONAL LAYERS PSEUDO-3D ATTENTION LAYERS FRAME INTERPOLATION NETWORK TRAINING DATASETS AND SETTINGS QUANTITATIVE RESULTS QUALITATIVE RESULTS DISCUSSION
Using OpenAI's GPT prompt, create a "PaperSummarizer" I asked you to summarize my Make-A-Video paper. 『Make-A-Video: Text-to-Video Generation without Text-Video Data』https://arxiv.org/abs/2209.14792 The following is the full summary result INTRODUCTION PREVIOUS WORK METHOD TEXT-TO-IMAGE MODEL SPATIOTEMPORAL LAYERS PSEUDO-3D CONVOLUTIONAL LAYERS PSEUDO-3D ATTENTION LAYERS FRAME INTERPOLATION NETWORK TRAINING DATASETS AND SETTINGS QUANTITATIVE RESULTS QUALITATIVE RESULTS DISCUSSION
