Zobrazeno 1 - 10
of 569
pro vyhledávání: '"Zhang Shikun"'
Autor:
Zhang shikun, Zhou wensheng
Publikováno v:
Journal of Orthopaedic Surgery and Research, Vol 19, Iss 1, Pp 1-2 (2024)
Abstract This response letter addresses the comments received on our paper. The main points of our response include: Clarification of the definitions of primary and secondary indexes; Justification for the use of the RoB2 tool for quality assessment;
Externí odkaz:
https://doaj.org/article/1c4c6da37b254185ac1abf24d9546137
Ensuring the safety of large language model (LLM) applications is essential for developing trustworthy artificial intelligence. Current LLM safety benchmarks have two limitations. First, they focus solely on either discriminative or generative evalua
Externí odkaz:
http://arxiv.org/abs/2410.21965
Autor:
Xu, Yijiang, Jia, Hongrui, Chen, Liguo, Wang, Xin, Zeng, Zhengran, Wang, Yidong, Gao, Qing, Wang, Jindong, Ye, Wei, Zhang, Shikun, Wu, Zhonghai
Fuzz testing is crucial for identifying software vulnerabilities, with coverage-guided grey-box fuzzers like AFL and Angora excelling in broad detection. However, as the need for targeted detection grows, directed grey-box fuzzing (DGF) has become es
Externí odkaz:
http://arxiv.org/abs/2409.14329
Autor:
Zhang, Shikun, Zhang, Guofeng
In this article, we explore the possibility of achieving noise suppression for finite-dimensional quantum systems through coherent feedback. For a quantum plant which is expected to evolve according to a target trajectory, noise effect potentially de
Externí odkaz:
http://arxiv.org/abs/2409.05431
Autor:
Chen, Liguo, Guo, Qi, Jia, Hongrui, Zeng, Zhengran, Wang, Xin, Xu, Yijiang, Wu, Jian, Wang, Yidong, Gao, Qing, Wang, Jindong, Ye, Wei, Zhang, Shikun
This paper provides a comprehensive review of the current methods and metrics used to evaluate the performance of Large Language Models (LLMs) in code generation tasks. With the rapid growth in demand for automated software development, LLMs have dem
Externí odkaz:
http://arxiv.org/abs/2408.16498
Quantum entanglement detection and characterization are crucial for various quantum information processes. Most existing methods for entanglement detection rely heavily on a complete description of the quantum state, which requires numerous measureme
Externí odkaz:
http://arxiv.org/abs/2408.13015
Autor:
Jiang, Chaoya, Hongrui, Jia, Xu, Haiyang, Ye, Wei, Dong, Mengfan, Yan, Ming, Zhang, Ji, Huang, Fei, Zhang, Shikun
This paper presents MaVEn, an innovative Multi-granularity Visual Encoding framework designed to enhance the capabilities of Multimodal Large Language Models (MLLMs) in multi-image reasoning. Current MLLMs primarily focus on single-image visual under
Externí odkaz:
http://arxiv.org/abs/2408.12321
Autor:
Wang, Haixin, Cao, Yadi, Huang, Zijie, Liu, Yuxuan, Hu, Peiyan, Luo, Xiao, Song, Zezheng, Zhao, Wanjia, Liu, Jilin, Sun, Jinan, Zhang, Shikun, Wei, Long, Wang, Yue, Wu, Tailin, Ma, Zhi-Ming, Sun, Yizhou
This paper explores the recent advancements in enhancing Computational Fluid Dynamics (CFD) tasks through Machine Learning (ML) techniques. We begin by introducing fundamental concepts, traditional methods, and benchmark datasets, then examine the va
Externí odkaz:
http://arxiv.org/abs/2408.12171
Autor:
Zhang, Xuanwang, Song, Yunze, Wang, Yidong, Tang, Shuyun, Li, Xinfeng, Zeng, Zhengran, Wu, Zhen, Ye, Wei, Xu, Wenyuan, Zhang, Yue, Dai, Xinyu, Zhang, Shikun, Wen, Qingsong
Large Language Models (LLMs) demonstrate human-level capabilities in dialogue, reasoning, and knowledge retention. However, even the most advanced LLMs face challenges such as hallucinations and real-time updating of their knowledge. Current research
Externí odkaz:
http://arxiv.org/abs/2408.11381
Autor:
Zhang, Shikun, Zhang, Guofeng
Quantum Lyapunov control, an important class of quantum control methods, aims at generating converging dynamics guided by Lyapunov-based theoretical tools. However, unlike the case of classical systems, disturbance caused by quantum measurement hinde
Externí odkaz:
http://arxiv.org/abs/2408.05943