Zobrazeno 1 - 10
of 4 704
pro vyhledávání: '"SHI, Ling"'
Large Language Models (LLMs) have transformed numerous fields by enabling advanced natural language interactions but remain susceptible to critical vulnerabilities, particularly jailbreak attacks. Current jailbreak techniques, while effective, often
Externí odkaz:
http://arxiv.org/abs/2412.08201
The implementation of cyber-physical systems in real-world applications is challenged by safety requirements in the presence of sensor threats. Most cyber-physical systems, in particular the vulnerable multi-sensor systems, struggle to detect the att
Externí odkaz:
http://arxiv.org/abs/2411.09956
Machine learning has become a crucial tool for predicting the properties of crystalline materials. However, existing methods primarily represent material information by constructing multi-edge graphs of crystal structures, often overlooking the chemi
Externí odkaz:
http://arxiv.org/abs/2411.08414
Autor:
Yang, Zhaohua, Wang, Pengyu, Zhang, Haishan, Jia, Shiyue, Yang, Nachuan, Zhong, Yuxing, Shi, Ling
This paper provides a comprehensive analysis of the design of optimal sparse $H_\infty$ controllers for continuous-time linear time-invariant systems. The sparsity of a controller has received increasing attention as it represents the communication a
Externí odkaz:
http://arxiv.org/abs/2411.00370
Vision-language pre-training (VLP) models, trained on large-scale image-text pairs, have become widely used across a variety of downstream vision-and-language (V+L) tasks. This widespread adoption raises concerns about their vulnerability to adversar
Externí odkaz:
http://arxiv.org/abs/2410.11639
The performance of disturbance observers is strongly influenced by the level of prior knowledge about the disturbance model. The simultaneous input and state estimation (SISE) algorithm is widely recognized for providing unbiased minimum-variance est
Externí odkaz:
http://arxiv.org/abs/2410.05061
Autor:
Lin, Hin Wang, Wang, Pengyu, Yang, Zhaohua, Leung, Ka Chun, Bao, Fangming, Kui, Ka Yu, Xu, Jian Xiang Erik, Shi, Ling
The Coastal underwater evidence search system with surface-underwater collaboration is designed to revolutionize the search for artificial objects in coastal underwater environments, overcoming limitations associated with traditional methods such as
Externí odkaz:
http://arxiv.org/abs/2410.02345
The swift advancement of large language models (LLMs) has profoundly shaped the landscape of artificial intelligence; however, their deployment in sensitive domains raises grave concerns, particularly due to their susceptibility to malicious exploita
Externí odkaz:
http://arxiv.org/abs/2408.15207
Large language models (LLMs) like ChatGPT and Gemini have significantly advanced natural language processing, enabling various applications such as chatbots and automated content generation. However, these models can be exploited by malicious individ
Externí odkaz:
http://arxiv.org/abs/2408.11727
This paper proposes a novel consensus-on-only-measurement distributed filter over directed graphs under the collectively observability condition. First, the distributed filter structure is designed with an augmented leader-following measurement fusio
Externí odkaz:
http://arxiv.org/abs/2408.06730