Zobrazeno 1 - 10
of 2 390
pro vyhledávání: '"Wu Shanshan"'
Autor:
Wu Shanshan, Zhang Liang
Publikováno v:
Applied Mathematics and Nonlinear Sciences, Vol 9, Iss 1 (2024)
This paper combines virtual reality technology with end-to-end speech recognition technology to design a digital tourism system to satisfy non-native English speakers (tourists) with the advantage of interactive projection technology. VRML technology
Externí odkaz:
https://doaj.org/article/0db13a87880343a0ad44f3263818b38f
Publikováno v:
E3S Web of Conferences, Vol 528, p 02004 (2024)
This study focuses on China's electricity productivity post-pandemic, employing the Logarithmic Mean Divisia Index (LMDI) method and the Kaya identity. Despite the clarity of the electricity productivity indicator, factors like industrial structure,
Externí odkaz:
https://doaj.org/article/c0470adca28c471bad7dc53c2c3ee0ee
Publikováno v:
Infectious Agents and Cancer, Vol 14, Iss 1, Pp 1-7 (2019)
Abstract Background In this study, we aim to determine the hepatic pathological changes in HBV DNA-negative chronic Hepatitis B (CHB) patients after 12-month antiviral therapy. Methods Blood routine indicators including platelet count (PLT) and white
Externí odkaz:
https://doaj.org/article/7b5079c3096742caaaf0b111b475d372
Pre-training on public data is an effective method to improve the performance for federated learning (FL) with differential privacy (DP). This paper investigates how large language models (LLMs) trained on public data can improve the quality of pre-t
Externí odkaz:
http://arxiv.org/abs/2404.04360
Publikováno v:
Polish Maritime Research, Vol 25, Iss s2, Pp 4-11 (2018)
Currently, one of the challenging tasks for Chinese engineering community is to construct a water-way crossing of Qiongzhou Strait in the south of China. This project has also gained significant attention from researchers in academia. The study prese
Externí odkaz:
https://doaj.org/article/8ccaef5f6f314fe0be74f9edb58bfd99
In many applications of federated learning (FL), clients desire models that are personalized using their local data, yet are also robust in the sense that they retain general global knowledge. However, the presence of data heterogeneity across client
Externí odkaz:
http://arxiv.org/abs/2310.04627
Autor:
Zhao, Xiaoyu1,2 (AUTHOR) zhaoxiaoyu3233@163.com, Wu, Shanshan1,3 (AUTHOR) wss5330402@126.com, Yun, Yuan1,2 (AUTHOR) souw261146@163.com, Du, Zhiwen1,2 (AUTHOR) dzw15133339850@163.com, Liu, Shuqin1,2 (AUTHOR) lsq32208183lsq@163.com, Bo, Chunjie1,2 (AUTHOR) shengwubcj@163.com, Gao, Yuxin1,2 (AUTHOR) m18535078392@163.com, Yang, Lei1,2 (AUTHOR) mrknowall@126.com, Song, Lishuang1,2 (AUTHOR) xiaoshuang2000@126.com, Bai, Chunling1,2 (AUTHOR) chunling1980_0@163.com, Su, Guanghua1,2 (AUTHOR) guanghuasu@imu.edu.cn, Li, Guangpeng1,2 (AUTHOR) guanghuasu@imu.edu.cn
Publikováno v:
International Journal of Molecular Sciences. Sep2024, Vol. 25 Issue 17, p9388. 16p.
Personalized federated learning considers learning models unique to each client in a heterogeneous network. The resulting client-specific models have been purported to improve metrics such as accuracy, fairness, and robustness in federated networks.
Externí odkaz:
http://arxiv.org/abs/2206.09262
Autor:
Xin, Xing, Wu, Shanshan, Xu, Heli, Ma, Yujiu, Bao, Nan, Gao, Man, Han, Xue, Gao, Shan, Zhang, Siwen, Zhao, Xinyang, Qi, Jiarui, Zhang, Xudong, Tan, Jichun
Publikováno v:
In eClinicalMedicine November 2024 77
Autor:
An, Longyi, Wu, Yating, Zhang, Baochao, Xu, Qiuhong, Liao, Linxuan, Wu, Shanshan, Xu, Xin, He, Qiurong, Pei, Xiaofang, Chen, Jiayi
Publikováno v:
In Infection, Genetics and Evolution October 2024 124