Zobrazeno 1 - 10
of 733
pro vyhledávání: '"Cheng Siyuan"'
Publikováno v:
Shipin Kexue, Vol 45, Iss 10, Pp 310-319 (2024)
The brewing of solid vinegar is traditionally carried out in an open solid-state fermentation system, which is susceptible to environmental factors, leading to unstable flavor quality, low degree of mechanization and low labor productivity. Compared
Externí odkaz:
https://doaj.org/article/3f673f78d3c3455f90c34c72cbd32166
Publikováno v:
Redai dili, Vol 44, Iss 4, Pp 700-708 (2024)
As the final layer for precipitation interception in forests, the litter layer is crucial to the vertical structure of forest ecosystems, situated between the forest vegetation and soil layers. Exploring the litter accumulation and water retention ch
Externí odkaz:
https://doaj.org/article/f89016f29bdd4adca68d400896ba9d30
Autor:
Du Huihui, Cheng Siyuan
Publikováno v:
Applied Mathematics and Nonlinear Sciences, Vol 9, Iss 1 (2024)
In order to predict the structural, mechanical properties of carbon fiber reinforced materials (CFRM) under different aging conditions in terms of tensile and flexural strengths, this paper establishes a Representative Volume Element (RVE) model and
Externí odkaz:
https://doaj.org/article/844aeb12a74f4ce5a14cea6c61bdbde3
Autor:
Yan, Lu, Cheng, Siyuan, Chen, Xuan, Zhang, Kaiyuan, Shen, Guangyu, Zhang, Zhuo, Zhang, Xiangyu
Large Language Models (LLMs) have become integral to many applications, with system prompts serving as a key mechanism to regulate model behavior and ensure ethical outputs. In this paper, we introduce a novel backdoor attack that systematically bypa
Externí odkaz:
http://arxiv.org/abs/2410.04009
Autor:
Feng, Shiwei, Chen, Xuan, Cheng, Zhiyuan, Xiong, Zikang, Gao, Yifei, Cheng, Siyuan, Kate, Sayali, Zhang, Xiangyu
Robot navigation is increasingly crucial across applications like delivery services and warehouse management. The integration of Reinforcement Learning (RL) with classical planning has given rise to meta-planners that combine the adaptability of RL w
Externí odkaz:
http://arxiv.org/abs/2409.10832
Autor:
Feng, Shiwei, Ye, Yapeng, Shi, Qingkai, Cheng, Zhiyuan, Xu, Xiangzhe, Cheng, Siyuan, Choi, Hongjun, Zhang, Xiangyu
As Autonomous driving systems (ADS) have transformed our daily life, safety of ADS is of growing significance. While various testing approaches have emerged to enhance the ADS reliability, a crucial gap remains in understanding the accidents causes.
Externí odkaz:
http://arxiv.org/abs/2409.07774
Autor:
Cheng, Siyuan, Shen, Guangyu, Zhang, Kaiyuan, Tao, Guanhong, An, Shengwei, Guo, Hanxi, Ma, Shiqing, Zhang, Xiangyu
Deep neural networks (DNNs) have demonstrated effectiveness in various fields. However, DNNs are vulnerable to backdoor attacks, which inject a unique pattern, called trigger, into the input to cause misclassification to an attack-chosen target label
Externí odkaz:
http://arxiv.org/abs/2407.11372
Autor:
Tian, Bozhong, Liang, Xiaozhuan, Cheng, Siyuan, Liu, Qingbin, Wang, Mengru, Sui, Dianbo, Chen, Xi, Chen, Huajun, Zhang, Ningyu
Large Language Models (LLMs) trained on extensive corpora inevitably retain sensitive data, such as personal privacy information and copyrighted material. Recent advancements in knowledge unlearning involve updating LLM parameters to erase specific k
Externí odkaz:
http://arxiv.org/abs/2407.01920
Autor:
Schaffner, Brennan, Bhagoji, Arjun Nitin, Cheng, Siyuan, Mei, Jacqueline, Shen, Jay L., Wang, Grace, Chetty, Marshini, Feamster, Nick, Lakier, Genevieve, Tan, Chenhao
Moderating user-generated content on online platforms is crucial for balancing user safety and freedom of speech. Particularly in the United States, platforms are not subject to legal constraints prescribing permissible content. Each platform has thu
Externí odkaz:
http://arxiv.org/abs/2405.05225
Autor:
Cheng, Siyuan, Tao, Guanhong, Liu, Yingqi, Shen, Guangyu, An, Shengwei, Feng, Shiwei, Xu, Xiangzhe, Zhang, Kaiyuan, Ma, Shiqing, Zhang, Xiangyu
Backdoor attack poses a significant security threat to Deep Learning applications. Existing attacks are often not evasive to established backdoor detection techniques. This susceptibility primarily stems from the fact that these attacks typically lev
Externí odkaz:
http://arxiv.org/abs/2403.17188