Zobrazeno 1 - 10
of 46 908
pro vyhledávání: '"Liu, Xiao‐An"'
Autor:
Huang, Yiming, Luo, Jianwen, Yu, Yan, Zhang, Yitong, Lei, Fangyu, Wei, Yifan, He, Shizhu, Huang, Lifu, Liu, Xiao, Zhao, Jun, Liu, Kang
We introduce DA-Code, a code generation benchmark specifically designed to assess LLMs on agent-based data science tasks. This benchmark features three core elements: First, the tasks within DA-Code are inherently challenging, setting them apart from
Externí odkaz:
http://arxiv.org/abs/2410.07331
Videos can be an effective way to deliver contextualized, just-in-time medical information for patient education. However, video analysis, from topic identification and retrieval to extraction and analysis of medical information and understandability
Externí odkaz:
http://arxiv.org/abs/2410.02830
Autor:
Liu, Xiao-Lan, Zhu, Ming, Xu, Jin-Long, Jiang, Peng, Zhang, Chuan-Peng, Yu, Nai-Ping, Wang, Jun-Jie, Yang, Yan-Bin
We report a new high-sensitivity HI mapping observation of the NGC 5055 galaxy group over an area of $1.^\circ5\times0.^\circ75$ with the Five-hundred-meter Aperture Spherical radio Telescope (FAST). Our observation reveals that the warped H\,{\sc i}
Externí odkaz:
http://arxiv.org/abs/2409.20109
Autor:
Liu, Xiao, Wang, Jiefei, Mao, Ruosong, Hu, Huizhu, Zhu, Shi-Yao, Xu, Xingqi, Cai, Han, Wang, Da-Wei
Topological physics provides novel insights for designing functional photonic devices, such as magnetic-free optical diodes, which are important in optical engineering and quantum information processing. Past efforts mostly focus on the topological e
Externí odkaz:
http://arxiv.org/abs/2409.17559
Synthesizable molecular design (also known as synthesizable molecular optimization) is a fundamental problem in drug discovery, and involves designing novel molecular structures to improve their properties according to drug-relevant oracle functions
Externí odkaz:
http://arxiv.org/abs/2409.09183
Autor:
Liu, Xiao
Projective structures on topological surfaces support the structure of 2d CFTs with a degree of technical simplification. Arguments for their existence are reviewed along with their essential properties. We propose a complex analytic manifold $\mathc
Externí odkaz:
http://arxiv.org/abs/2409.01810
Autor:
Yao, Mingyuan, Huo, Yukang, Tian, Qingbin, Zhao, Jiayin, Liu, Xiao, Wang, Ruifeng, Xue, Lin, Wang, Haihua
Early detection of abnormal fish behavior caused by disease or hunger can be achieved through fish tracking using deep learning techniques, which holds significant value for industrial aquaculture. However, underwater reflections and some reasons wit
Externí odkaz:
http://arxiv.org/abs/2409.01148
Autor:
Gui, Jiayi, Liu, Yiming, Cheng, Jiale, Gu, Xiaotao, Liu, Xiao, Wang, Hongning, Dong, Yuxiao, Tang, Jie, Huang, Minlie
Large Language Models (LLMs) have demonstrated notable capabilities across various tasks, showcasing complex problem-solving abilities. Understanding and executing complex rules, along with multi-step planning, are fundamental to logical reasoning an
Externí odkaz:
http://arxiv.org/abs/2408.15778
Autor:
Xie, Qianqian, Li, Dong, Xiao, Mengxi, Jiang, Zihao, Xiang, Ruoyu, Zhang, Xiao, Chen, Zhengyu, He, Yueru, Han, Weiguang, Yang, Yuzhe, Chen, Shunian, Zhang, Yifei, Shen, Lihang, Kim, Daniel, Liu, Zhiwei, Luo, Zheheng, Yu, Yangyang, Cao, Yupeng, Deng, Zhiyang, Yao, Zhiyuan, Li, Haohang, Feng, Duanyu, Dai, Yongfu, Somasundaram, VijayaSai, Lu, Peng, Zhao, Yilun, Long, Yitao, Xiong, Guojun, Smith, Kaleb, Yu, Honghai, Lai, Yanzhao, Peng, Min, Nie, Jianyun, Suchow, Jordan W., Liu, Xiao-Yang, Wang, Benyou, Lopez-Lira, Alejandro, Huang, Jimin, Ananiadou, Sophia
Large language models (LLMs) have advanced financial applications, yet they often lack sufficient financial knowledge and struggle with tasks involving multi-modal inputs like tables and time series data. To address these limitations, we introduce \t
Externí odkaz:
http://arxiv.org/abs/2408.11878
Autor:
Liu, Xiao, Li, Mingyuan, Wang, Xu, Yu, Guangsheng, Ni, Wei, Li, Lixiang, Peng, Haipeng, Liu, Renping
Unlearning in various learning frameworks remains challenging, with the continuous growth and updates of models exhibiting complex inheritance relationships. This paper presents a novel unlearning framework, which enables fully parallel unlearning am
Externí odkaz:
http://arxiv.org/abs/2408.08493