Search Results for author: Pan Liu

Found 8 papers, 0 papers with code

AceMap: Knowledge Discovery through Academic Graph

no code implementations5 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.

Knowledge Graphs Reading Comprehension

Accuracy of Real-Time Echo-Planar Imaging Phase Contrast MRI

no code implementations28 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.

Real-Time Phase Contrast MRI to quantify Cerebral arterial flow change during variations breathing

no code implementations26 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.

Flow 2.0 -a flexible, scalable, cross-platform post-processing software for realtime phase contrast sequences

no code implementations26 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.

Image Segmentation Semantic Segmentation

Real order total variation with applications to the loss functions in learning schemes

no code implementations10 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.

$DC^2$: A Divide-and-conquer Algorithm for Large-scale Kernel Learning with Application to Clustering

no code implementations16 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.

Clustering

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