Zobrazeno 1 - 10
of 423
pro vyhledávání: '"Xu Chengzhong"'
Autor:
Chen Jiewen, Zeng Xiaolin, Zhang Wenwu, Li Gang, Zhong Haoming, Xu Chengzhong, Li Xiang, Lin Tao
Publikováno v:
Acta Biochimica et Biophysica Sinica, Vol 55, Pp 1571-1581 (2023)
Individuals with spinal cord injury (SCI) suffer from permanent disabilities such as severe motor, sensory and autonomic dysfunction. Neural stem cell transplantation has proven to be a potential strategy to promote regeneration of the spinal cord, s
Externí odkaz:
https://doaj.org/article/572a6e65bb4a48fb951dfe40ab472ecf
In the context of Machine Learning as a Service (MLaaS) clouds, the extensive use of Large Language Models (LLMs) often requires efficient management of significant query loads. When providing real-time inference services, several challenges arise. F
Externí odkaz:
http://arxiv.org/abs/2409.14961
Cloud-native applications are increasingly becoming popular in modern software design. Employing a microservice-based architecture into these applications is a prevalent strategy that enhances system availability and flexibility. However, cloud-nativ
Externí odkaz:
http://arxiv.org/abs/2409.05093
Autor:
Liao, Haicheng, Li, Yongkang, Wang, Chengyue, Lai, Songning, Li, Zhenning, Bian, Zilin, Lee, Jaeyoung, Cui, Zhiyong, Zhang, Guohui, Xu, Chengzhong
The primary goal of traffic accident anticipation is to foresee potential accidents in real time using dashcam videos, a task that is pivotal for enhancing the safety and reliability of autonomous driving technologies. In this study, we introduce an
Externí odkaz:
http://arxiv.org/abs/2409.01256
Federated Learning (FL) emerges as a new learning paradigm that enables multiple devices to collaboratively train a shared model while preserving data privacy. However, intensive memory footprint during the training process severely bottlenecks the d
Externí odkaz:
http://arxiv.org/abs/2408.10826
Autor:
Liao, Haicheng, Sun, Haoyu, Shen, Huanming, Wang, Chengyue, Tam, Kahou, Tian, Chunlin, Li, Li, Xu, Chengzhong, Li, Zhenning
Accurately and promptly predicting accidents among surrounding traffic agents from camera footage is crucial for the safety of autonomous vehicles (AVs). This task presents substantial challenges stemming from the unpredictable nature of traffic acci
Externí odkaz:
http://arxiv.org/abs/2407.17757
Autor:
Liao, Haicheng, Li, Yongkang, Wang, Chengyue, Guan, Yanchen, Tam, KaHou, Tian, Chunlin, Li, Li, Xu, Chengzhong, Li, Zhenning
As autonomous driving systems increasingly become part of daily transportation, the ability to accurately anticipate and mitigate potential traffic accidents is paramount. Traditional accident anticipation models primarily utilizing dashcam videos ar
Externí odkaz:
http://arxiv.org/abs/2407.16277
Autonomous driving significantly benefits from data-driven deep neural networks. However, the data in autonomous driving typically fits the long-tailed distribution, in which the critical driving data in adverse conditions is hard to collect. Althoug
Externí odkaz:
http://arxiv.org/abs/2408.01430
Autor:
Wen, Linfeng, Xu, Minxian, Gill, Sukhpal Singh, Hilman, Muhammad Hafizhuddin, Srirama, Satish Narayana, Ye, Kejiang, Xu, Chengzhong
Publikováno v:
ACM Transactions on Autonomous and Adaptive Systems, 2024
Microservice architecture has transformed traditional monolithic applications into lightweight components. Scaling these lightweight microservices is more efficient than scaling servers. However, scaling microservices still faces the challenges resul
Externí odkaz:
http://arxiv.org/abs/2407.10173
Publikováno v:
IEEE Transactions on Service Computing, 2024
Microservices have transformed monolithic applications into lightweight, self-contained, and isolated application components, establishing themselves as a dominant paradigm for application development and deployment in public clouds such as Google an
Externí odkaz:
http://arxiv.org/abs/2407.10169