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SciSafeEval: A Comprehensive Benchmark for Safety Alignment of Large Language Models in Scientific Tasks

Autor: Li, Tianhao, Lu, Jingyu, Chu, Chuangxin, Zeng, Tianyu, Zheng, Yujia, Li, Mei, Huang, Haotian, Wu, Bin, Liu, Zuoxian, Ma, Kai, Yuan, Xuejing, Wang, Xingkai, Ding, Keyan, Chen, Huajun, Zhang, Qiang

Large language models (LLMs) have a transformative impact on a variety of scientific tasks across disciplines including biology, chemistry, medicine, and physics. However, ensuring the safety alignment of these models in scientific research remains a

Externí odkaz: http://arxiv.org/abs/2410.03769

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Task Placement and Resource Allocation for Edge Machine Learning: A GNN-based Multi-Agent Reinforcement Learning Paradigm

Autor: Li, Yihong, Zhang, Xiaoxi, Zeng, Tianyu, Duan, Jingpu, Wu, Chuan, Wu, Di, Chen, Xu

Machine learning (ML) tasks are one of the major workloads in today's edge computing networks. Existing edge-cloud schedulers allocate the requested amounts of resources to each task, falling short of best utilizing the limited edge resources for ML

Externí odkaz: http://arxiv.org/abs/2302.00571

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A Sub-pixel Accurate Quantification of Joint Space Narrowing Progression in Rheumatoid Arthritis

Autor: Ou, Yafei, Ambalathankandy, Prasoon, Furuya, Ryunosuke, Kawada, Seiya, Zeng, Tianyu, An, Yujie, Kamishima, Tamotsu, Tamura, Kenichi, Ikebe, Masayuki

Rheumatoid arthritis (RA) is a chronic autoimmune disease that primarily affects peripheral synovial joints, like fingers, wrist and feet. Radiology plays a critical role in the diagnosis and monitoring of RA. Limited by the current spatial resolutio

Externí odkaz: http://arxiv.org/abs/2205.09315

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