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Stan Z. Li
Researcher at Westlake University
Publications - 625
Citations - 49737
Stan Z. Li is an academic researcher from Westlake University. The author has contributed to research in topics: Facial recognition system & Computer science. The author has an hindex of 97, co-authored 532 publications receiving 41793 citations. Previous affiliations of Stan Z. Li include Microsoft & Macau University of Science and Technology.
Papers
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Book ChapterDOI
Toward Global Solution to MAP Image Estimation: Using Common Structure of Local Solutions
TL;DR: This paper presents an iterative optimization algorithm, called the Comb algorithm, for approximating the global minmum, and shows that the Comb produces solutions of quality much better than ICM and comparable to simulated annealing.
Journal Article
[Prevention of postoperative recurrence of bladder cancer: a clinical study].
TL;DR: The prophylactic effect of Zhuling and BCG on bladder cancer recurrence was better than MMC, and the vale of thiotepa was not significant.
Posted Content
Person Re-identification by Local Maximal Occurrence Representation and Metric Learning
TL;DR: Zhang et al. as mentioned in this paper proposed an effective feature representation called Local Maximal Occurrence (LOMO), and a subspace and metric learning method called Cross-view Quadratic Discriminant Analysis (XQDA).
Journal ArticleDOI
Searching Multi-Rate and Multi-Modal Temporal Enhanced Networks for Gesture Recognition
TL;DR: Yu et al. as mentioned in this paper proposed a 3D Central Difference Convolution (3D-CDC) family to capture rich temporal context via aggregating temporal difference information, and optimized backbones for multi-sampling-rate branches and lateral connections among varied modalities.
Posted ContentDOI
Eleven Routine Clinical Features Predict COVID-19 Severity
Kai Zhou,Yaoting Sun,Lu Li,Zelin Zang,Jing Wang,Jun Li,Junbo Liang,Fangfei Zhang,Qiushi Zhang,Weigang Ge,Hao Chen,Xindong Sun,Liang Yue,Xiaomai Wu,Bo Shen,Jiaqin Xu,Hongguo Zhu,Shiyong Chen,Hai Yang,Shigao Huang,Minfei Peng,Dongqing Lv,Chao Zhang,Haihong Zhao,Luxiao Hong,Zhehan Zhou,Haixiao Chen,Xuejun Dong,Chunyu Tu,Minghui Li,Yi Zhu,Baofu Chen,Stan Z. Li,Tiannan Guo +33 more
TL;DR: A machine learning model is built to predict the disease progression based on measurements from the first 12 days since the disease onset when no patient became severe and captured predictive dynamics of LDH and CK while their levels were in the normal range.