Zobrazeno 1 - 10
of 1 169
pro vyhledávání: '"Zhao Xingyu"'
Autor:
Wang Kan, Liu Zhaoyuan, An Nan, Luo Hao, Jia Conglong, Shen Pengfei, Jiang Shihang, Hu Yingzhe, Gou Yuanhao, Wang Wu, Feng Zhiyuan, Liu Guodong, Zhao Xingyu, Chan Kok Yue, Su Zilin, Tan Zhe Chuan, Liu Guanyang, Li Zeguang, Yu Ganglin, Yu Jiyang, Huang Shanfang
Publikováno v:
EPJ Nuclear Sciences & Technologies, Vol 10, p 24 (2024)
Based on academic research and industrial applications over more than 20 years, the Reactor Monte Carlo code (RMC) developed by the REAL (Reactor Engineering Analysis Laboratory) team at Tsinghua University since 2000 has become a powerful, innovativ
Externí odkaz:
https://doaj.org/article/ba8c3929537141c3814927abd7d94f29
Autor:
LI Haodang, DING Zhen, ZHANG Kai, LUO Huiqiang, CAO Zhengyuan, CUI Wen, YOU Xiusong, ZHAO Xingyu, MENG Guangrui, SUN Jiang, DENG Wenge
Publikováno v:
Gong-kuang zidonghua, Vol 48, Iss 2, Pp 1-10 (2022)
Coal is the guarantee energy in China, and its dominant position in energy will not change for a period of time in the future. Coal intelligent mining will show a rapid development trend and enter into a new stage of development. Through induction an
Externí odkaz:
https://doaj.org/article/c5e295e884e04f89a379fd42a38fa403
Autor:
Zhao, Xingyu
Deep learning (DL) has demonstrated significant potential across various safety-critical applications, yet ensuring its robustness remains a key challenge. While adversarial robustness has been extensively studied in worst-case scenarios, probabilist
Externí odkaz:
http://arxiv.org/abs/2502.14833
Autor:
Zhao, Xingyu, Bin, Qian, Hou, Waner, Li, Yi, Li, Yue, Lin, Yiheng, Lü, Xin-You, Du, Jiangfeng
Symmetry is crucial for gaining insights into the fundamental properties of physical systems, bringing possibilities in studying exotic phenomena such as quantum phase transitions and ground state entanglement. Here, we experimentally simulate a high
Externí odkaz:
http://arxiv.org/abs/2501.05919
Autor:
Li, Botao, Weber, Tim, Kose, Umut, Franks, Matthew, Wüthrich, Johannes, Zhao, Xingyu, Sgalaberna, Davide, Boyarintsev, Andrey, Sibilieva, Tetiana, Berns, Siddartha, Boillat, Eric, De Roeck, Albert, Dieminger, Till, Grynyov, Boris, Hugon, Sylvain, Jaeschke, Carsten, Rubbia, André
Plastic scintillators are widely used for the detection of elementary particles, and 3D reconstruction of particle tracks is achieved by segmenting the detector into 3D granular structures. In this study, we present a novel prototype fabricated by ad
Externí odkaz:
http://arxiv.org/abs/2412.10174
Autor:
Li, Yue, Wu, Yang, Zhou, Yuqi, Zhang, Mengxiang, Zhao, Xingyu, Yuan, Yibo, Cheng, Xu, Li, Yi, Qin, Xi, Rong, Xing, Lin, Yiheng, Du, Jiangfeng
The nontrivial degeneracies in non-Hermitian systems, exceptional points (EPs), have attracted extensive attention due to intriguing phenomena. Compared with commonly observed second-order EPs, high-order EPs show rich physics due to their extended d
Externí odkaz:
http://arxiv.org/abs/2412.09776
Autor:
Huang, Zhenglin, Hu, Jinwei, Li, Xiangtai, He, Yiwei, Zhao, Xingyu, Peng, Bei, Wu, Baoyuan, Huang, Xiaowei, Cheng, Guangliang
The rapid advancement of generative models in creating highly realistic images poses substantial risks for misinformation dissemination. For instance, a synthetic image, when shared on social media, can mislead extensive audiences and erode trust in
Externí odkaz:
http://arxiv.org/abs/2412.04292
Autor:
Wu, Sihao, Liu, Jiaxu, Yin, Xiangyu, Cheng, Guangliang, Zhao, Xingyu, Fang, Meng, Yi, Xinping, Huang, Xiaowei
The integration of Large Language Models (LLMs) into autonomous driving systems demonstrates strong common sense and reasoning abilities, effectively addressing the pitfalls of purely data-driven methods. Current LLM-based agents require lengthy infe
Externí odkaz:
http://arxiv.org/abs/2410.12568
Autor:
Zhang, Yi, Chen, Zhen, Cheng, Chih-Hong, Ruan, Wenjie, Huang, Xiaowei, Zhao, Dezong, Flynn, David, Khastgir, Siddartha, Zhao, Xingyu
Text-to-Image (T2I) Diffusion Models (DMs) have garnered widespread attention for their impressive advancements in image generation. However, their growing popularity has raised ethical and social concerns related to key non-functional properties of
Externí odkaz:
http://arxiv.org/abs/2409.18214
The vulnerability of machine learning models to Membership Inference Attacks (MIAs) has garnered considerable attention in recent years. These attacks determine whether a data sample belongs to the model's training set or not. Recent research has foc
Externí odkaz:
http://arxiv.org/abs/2409.00426