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Hugging Face, a leading open-source AI community platform, has launched a highly anticipated new feature: users can quickly see which machine learning models their computer hardware can run via platform settings. Users simply add their hardware information, such as GPU model, to their Hugging Face profile settings (located at the top right corner: Profile Icon > Settings > Local Apps and Hardware).

In modern relational databases, Cardinality Estimation (CE) techniques are crucial for optimizing query execution plans, directly affecting query efficiency and database performance. Traditional cardinality estimation methods are based on simplified assumptions and often perform poorly on complex query predictions, whereas learning-based CE models can provide more accurate predictions but face challenges such as long training times, requirement of large amounts of data, and lack of systematic evaluation. To address this challenge, the Google research team has launched the CardBench benchmarking framework, integrating over 20 real-world databases and thousands of queries.

Reddit, known for its vast user community and rich data resources, has opened up new revenue streams through data licensing. The platform hosts over 100,000 active communities, attracting 76 million users daily and generating a massive amount of data that includes real-time discussions, opinions, and interactions, which is highly valuable for AI and machine learning model development. With the rapid advancement of AI technology, Reddit's data has become a key resource for training AI models. In 2023, Reddit launched its data licensing program, which is expected to grow significantly in 2024.

The study collected over 4,000 potential risk factors and analyzed them using machine learning models. The models found that mental health issues like depression and anxiety were the most significant. Compared to models that only considered historical behavior, these models can more accurately predict future risks.