Zobrazeno 1 - 10
of 1 364
pro vyhledávání: '"WANG, Shuyi"'
Publikováno v:
Journal of Medical Internet Research, Vol 22, Iss 4, p e16852 (2020)
BackgroundVascular localization is crucial for perforator flap transfer. Augmented reality offers a novel method to seamlessly combine real information with virtual objects created by computed tomographic angiography to help the surgeon “see throug
Externí odkaz:
https://doaj.org/article/7919ef694bdf4505a7bd84e11f72259e
Data protection legislation like the European Union's General Data Protection Regulation (GDPR) establishes the \textit{right to be forgotten}: a user (client) can request contributions made using their data to be removed from learned models. In this
Externí odkaz:
http://arxiv.org/abs/2401.13410
Autor:
Wang, Shuyi, Zuccon, Guido
Federated online learning to rank (FOLTR) aims to preserve user privacy by not sharing their searchable data and search interactions, while guaranteeing high search effectiveness, especially in contexts where individual users have scarce training dat
Externí odkaz:
http://arxiv.org/abs/2307.01565
Imaging polarimetry based on dielectric metasurface is well-known for its ultra-compactness and high integration. However, previous works suffer from low energy efficiency, limited restrictions on choice of target polarization states, or inability to
Externí odkaz:
http://arxiv.org/abs/2307.01707
Autor:
Wang, Shuyi1,2 (AUTHOR), Choi, S.H.2 (AUTHOR), Xiao, Jianhua3,4 (AUTHOR), Huang, George Q.5 (AUTHOR) gq.huang@polyu.edu.hk
Publikováno v:
International Journal of Production Research. Sep2024, Vol. 62 Issue 18, p6627-6648. 22p.
Autor:
Wang, Shuyi, Zuccon, Guido
In this perspective paper we study the effect of non independent and identically distributed (non-IID) data on federated online learning to rank (FOLTR) and chart directions for future work in this new and largely unexplored research area of Informat
Externí odkaz:
http://arxiv.org/abs/2204.09272
Autor:
Wang, Shuyi
Deep learning has been used to handle numerical analysis and scientific computation problems nowadays. In this dissertation, we represent three projects. We develop convolutional neural networks (CNNs) to detect discontinuities on numerical functions
Autor:
Li, Feng-Juan, Hu, Huantao, Wu, Liangyan, Luo, Bijun, Zhou, Yuan, Ren, Jun, Lin, Jie, Reiter, Russel J., Wang, Shuyi, Dong, Maolong, Guo, Jun, Peng, Hu
Publikováno v:
In Free Radical Biology and Medicine 20 November 2024 225:75-86
Autor:
Song, Kaiwen, Jin, Longyang, Cai, Meng, Wang, Qi, Wu, Xingyu, Wang, Shuyi, Sun, Shijun, Wang, Ruobing, Chen, Fengning, Wang, Hui
Publikováno v:
In eBioMedicine October 2024 108
Publikováno v:
In International Journal of Production Economics October 2024 276