Zobrazeno 1 - 10
of 14 507
pro vyhledávání: '"CHENG, Peng"'
Autor:
Wang, Tianyi, Wang, Zichen, Wang, Cong, Shu, Yuanchao, Deng, Ruilong, Cheng, Peng, Chen, Jiming
Object detection is a fundamental enabler for many real-time downstream applications such as autonomous driving, augmented reality and supply chain management. However, the algorithmic backbone of neural networks is brittle to imperceptible perturbat
Externí odkaz:
http://arxiv.org/abs/2412.02171
Autor:
Zhou, Chen, Cheng, Peng, Fang, Junfeng, Zhang, Yifan, Yan, Yibo, Jia, Xiaojun, Xu, Yanyan, Wang, Kun, Cao, Xiaochun
Multispectral object detection, utilizing RGB and TIR (thermal infrared) modalities, is widely recognized as a challenging task. It requires not only the effective extraction of features from both modalities and robust fusion strategies, but also the
Externí odkaz:
http://arxiv.org/abs/2411.18288
Autor:
Pan, Feihao, Sun, Songnan, Kolesnikov, Alexander I., Stone, Matthew B., Huang, Jiale, Xu, Daye, Shang, Chenglin, Shi, Bingxian, Gui, Xuejuan, Sun, Zhongcen, Wang, Jinchen, Liu, Juanjuan, Zhang, Hongxia, Liu, Zhengxin, Cheng, Peng
Publikováno v:
Phys. Rev. B 110(2024)174448
Triangular lattice antiferromagnets are prototypes for frustrated magnetism and may potentially realize novel quantum magnetic states such as a quantum spin liquid ground state. A recent work suggests NdTa$_7$O$_{19}$ with rare-earth triangular latti
Externí odkaz:
http://arxiv.org/abs/2411.18045
It is well-known that a diverse corpus is critical for training large language models, which are typically constructed from a mixture of various domains. In general, previous efforts resort to sampling training data from different domains with static
Externí odkaz:
http://arxiv.org/abs/2411.14318
Differential privacy (DP) has recently been introduced into episodic reinforcement learning (RL) to formally address user privacy concerns in personalized services. Previous work mainly focuses on two trust models of DP: the central model, where a ce
Externí odkaz:
http://arxiv.org/abs/2411.11647
In this paper, using the ensemble-averaged theory, we define the thermodynamic free energy of Einstein-Gauss-Bonnet (EGB) black holes in anti-de Sitter (AdS) spacetime. This approach derives the gravitational partition function by incorporating non-s
Externí odkaz:
http://arxiv.org/abs/2411.07147
This paper studies privacy-preserving resilient vector consensus in multi-agent systems against faulty agents, where normal agents can achieve consensus within the convex hull of their initial states while protecting state vectors from being disclose
Externí odkaz:
http://arxiv.org/abs/2411.03633
Exploring the universal structure of the gravitational path integral beyond semi-classical saddles and uncovering a compelling statistical interpretation of black hole thermodynamics have long been significant challenges. We investigate the statistic
Externí odkaz:
http://arxiv.org/abs/2410.23006
Autor:
Du, Linkang, Zhou, Xuanru, Chen, Min, Zhang, Chusong, Su, Zhou, Cheng, Peng, Chen, Jiming, Zhang, Zhikun
As the implementation of machine learning (ML) systems becomes more widespread, especially with the introduction of larger ML models, we perceive a spring demand for massive data. However, it inevitably causes infringement and misuse problems with th
Externí odkaz:
http://arxiv.org/abs/2410.16618
Autor:
Chen, Tianyu, Lu, Shuai, Lu, Shan, Gong, Yeyun, Yang, Chenyuan, Li, Xuheng, Misu, Md Rakib Hossain, Yu, Hao, Duan, Nan, Cheng, Peng, Yang, Fan, Lahiri, Shuvendu K, Xie, Tao, Zhou, Lidong
Ensuring correctness is crucial for code generation. Formal verification offers a definitive assurance of correctness, but demands substantial human effort in proof construction and hence raises a pressing need for automation. The primary obstacle li
Externí odkaz:
http://arxiv.org/abs/2410.15756