Zobrazeno 1 - 10
of 57 017
pro vyhledávání: '"Pengcheng An"'
Topological Data Analysis (TDA) has recently gained significant attention in the field of financial prediction. However, the choice of point cloud construction methods, topological feature representations, and classification models has a substantial
Externí odkaz:
http://arxiv.org/abs/2411.13881
Autor:
Li, Tianbin, Su, Yanzhou, Li, Wei, Fu, Bin, Chen, Zhe, Huang, Ziyan, Wang, Guoan, Ma, Chenglong, Chen, Ying, Hu, Ming, Li, Yanjun, Chen, Pengcheng, Hu, Xiaowei, Deng, Zhongying, Ji, Yuanfeng, Ye, Jin, Qiao, Yu, He, Junjun
Despite significant advancements in general artificial intelligence, such as GPT-4, their effectiveness in the medical domain (general medical AI, GMAI) remains constrained due to the absence of specialized medical knowledge. To address this challeng
Externí odkaz:
http://arxiv.org/abs/2411.14522
In recent years, there has been an increasing number of information hiding techniques based on network streaming media, focusing on how to covertly and efficiently embed secret information into real-time transmitted network media signals to achieve c
Externí odkaz:
http://arxiv.org/abs/2411.13612
Autor:
Wang, Jiaqi, Zhao, Huan, Yang, Zhenyuan, Shu, Peng, Chen, Junhao, Sun, Haobo, Liang, Ruixi, Li, Shixin, Shi, Pengcheng, Ma, Longjun, Liu, Zongjia, Liu, Zhengliang, Zhong, Tianyang, Zhang, Yutong, Ma, Chong, Zhang, Xin, Zhang, Tuo, Ding, Tianli, Ren, Yudan, Liu, Tianming, Jiang, Xi, Zhang, Shu
In this paper, we review legal testing methods based on Large Language Models (LLMs), using the OPENAI o1 model as a case study to evaluate the performance of large models in applying legal provisions. We compare current state-of-the-art LLMs, includ
Externí odkaz:
http://arxiv.org/abs/2411.10137
Autor:
Lei, Fangyu, Chen, Jixuan, Ye, Yuxiao, Cao, Ruisheng, Shin, Dongchan, Su, Hongjin, Suo, Zhaoqing, Gao, Hongcheng, Hu, Wenjing, Yin, Pengcheng, Zhong, Victor, Xiong, Caiming, Sun, Ruoxi, Liu, Qian, Wang, Sida, Yu, Tao
Real-world enterprise text-to-SQL workflows often involve complex cloud or local data across various database systems, multiple SQL queries in various dialects, and diverse operations from data transformation to analytics. We introduce Spider 2.0, an
Externí odkaz:
http://arxiv.org/abs/2411.07763
Online Reinforcement learning (RL) typically requires high-stakes online interaction data to learn a policy for a target task. This prompts interest in leveraging historical data to improve sample efficiency. The historical data may come from outdate
Externí odkaz:
http://arxiv.org/abs/2411.03810
Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?
Autor:
Bassi, Pedro R. A. S., Li, Wenxuan, Tang, Yucheng, Isensee, Fabian, Wang, Zifu, Chen, Jieneng, Chou, Yu-Cheng, Kirchhoff, Yannick, Rokuss, Maximilian, Huang, Ziyan, Ye, Jin, He, Junjun, Wald, Tassilo, Ulrich, Constantin, Baumgartner, Michael, Roy, Saikat, Maier-Hein, Klaus H., Jaeger, Paul, Ye, Yiwen, Xie, Yutong, Zhang, Jianpeng, Chen, Ziyang, Xia, Yong, Xing, Zhaohu, Zhu, Lei, Sadegheih, Yousef, Bozorgpour, Afshin, Kumari, Pratibha, Azad, Reza, Merhof, Dorit, Shi, Pengcheng, Ma, Ting, Du, Yuxin, Bai, Fan, Huang, Tiejun, Zhao, Bo, Wang, Haonan, Li, Xiaomeng, Gu, Hanxue, Dong, Haoyu, Yang, Jichen, Mazurowski, Maciej A., Gupta, Saumya, Wu, Linshan, Zhuang, Jiaxin, Chen, Hao, Roth, Holger, Xu, Daguang, Blaschko, Matthew B., Decherchi, Sergio, Cavalli, Andrea, Yuille, Alan L., Zhou, Zongwei
How can we test AI performance? This question seems trivial, but it isn't. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified metrics, unfair comparisons, and short-term outcome pressure. As a con
Externí odkaz:
http://arxiv.org/abs/2411.03670
Hong-Ou-Mandel effect is an important demonstration of particle indistinguishability, when identical single photons interfere at a beamsplitter to generate the two-photon entangled NOON state. On the other hand, NOON states with $N\ge3$ photons have
Externí odkaz:
http://arxiv.org/abs/2411.01609
Point Cloud Registration (PCR) is a fundamental and significant issue in photogrammetry and remote sensing, aiming to seek the optimal rigid transformation between sets of points. Achieving efficient and precise PCR poses a considerable challenge. We
Externí odkaz:
http://arxiv.org/abs/2410.21857
Autor:
Guo, Yuting, Tang, Pengcheng
It is well known that the Hilbert matrix operator $\mathcal {H}$ is bounded from $H^{\infty}$ to the mean Lipschitz spaces $\Lambda^{p}_{\frac{1}{p}}$ for all $1
Externí odkaz:
http://arxiv.org/abs/2410.18682