no code implementations • WMT (EMNLP) 2021 • Han Yang, Bojie Hu, Wanying Xie, Ambyera Han, Pan Liu, Jinan Xu, Qi Ju
This paper describes TenTrans’ submission to WMT21 Multilingual Low-Resource Translation shared task for the Romance language pairs.
no code implementations • 5 Mar 2024 • Xinbing Wang, Luoyi Fu, Xiaoying Gan, Ying Wen, Guanjie Zheng, Jiaxin Ding, Liyao Xiang, Nanyang Ye, Meng Jin, Shiyu Liang, Bin Lu, Haiwen Wang, Yi Xu, Cheng Deng, Shao Zhang, Huquan Kang, Xingli Wang, Qi Li, Zhixin Guo, Jiexing Qi, Pan Liu, Yuyang Ren, Lyuwen Wu, Jungang Yang, Jianping Zhou, Chenghu Zhou
While existing scientific search engines excel at delivering search results based on relational databases, they often neglect the analysis of collaborations between scientific entities and the evolution of ideas, as well as the in-depth analysis of content within scientific publications.
no code implementations • 28 Jul 2022 • Pan Liu, Sidy Fall, Olivier Baledent
Compared with CINE phase contrast MRI (CINE-PC), echo-planar imaging phase contrast (EPI-PC) can achieve realtime quantification of blood flow, with lower SNR.
no code implementations • 28 Jul 2022 • Olivier Baledent, Pan Liu, Serge Metanbou, Cyrille Capel, Sidy Fall, Roger Bouzerar
It is still debated how breathing interacts with the CSF.
no code implementations • 26 Jul 2022 • Pan Liu, Sidy Fall, Serge Metanbou, Olivier Balédent
Synopsis (100/100) Real-time phase contrast MRI has been applied to investigate cerebral arterial blood flow (CABF) during normal breathing of healthy volunteers.
no code implementations • 26 Jul 2022 • Pan Liu, Sidy Fall, Olivier Balédent
Flow 2. 0 is an end-to-end easy-of-use software that allows us to quickly, robustly and accurately perform a batch process real-time phase contrast data and multivariate analysis of the effect of respiration on cerebral fluids circulation.
no code implementations • 10 Apr 2022 • Pan Liu, Xin Yang Lu, Kunlun He
Loss function are an essential part in modern data-driven approach, such as bi-level training scheme and machine learnings.
no code implementations • 16 Nov 2019 • Ke Alexander Wang, Xinran Bian, Pan Liu, Donghui Yan
Analysis on $DC^2$ when applied to spectral clustering shows that the loss in clustering accuracy due to data division and reduction is upper bounded by the data approximation error which would vanish with recursive random projections.