Ultra-sensitive CAR T cells eliminate hard-to-treat tumours in mice

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Мощный удар Израиля по Ирану попал на видео09:41

By the following morning, she was unconscious. Her hands and feet were ice-cold, her lips had turned purple and she was struggling to breathe.

五角大楼激光器据称在。关于这个话题,51吃瓜提供了深入分析

Paul said the residents felt they had no choice but to leave and hoped the council would approve the purchase.

Author(s): Yoshiyasu Takefuji

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Many people reading this will call bullshit on the performance improvement metrics, and honestly, fair. I too thought the agents would stumble in hilarious ways trying, but they did not. To demonstrate that I am not bullshitting, I also decided to release a more simple Rust-with-Python-bindings project today: nndex, an in-memory vector “store” that is designed to retrieve the exact nearest neighbors as fast as possible (and has fast approximate NN too), and is now available open-sourced on GitHub. This leverages the dot product which is one of the simplest matrix ops and is therefore heavily optimized by existing libraries such as Python’s numpy…and yet after a few optimization passes, it tied numpy even though numpy leverages BLAS libraries for maximum mathematical performance. Naturally, I instructed Opus to also add support for BLAS with more optimization passes and it now is 1-5x numpy’s speed in the single-query case and much faster with batch prediction. 3 It’s so fast that even though I also added GPU support for testing, it’s mostly ineffective below 100k rows due to the GPU dispatch overhead being greater than the actual retrieval speed.