Publications
Explore 405 academic publications
Randomization, Bootstrap and Monte Carlo Methods in Biology
Modern computer-intensive statistical methods play a key role in solving many problems across a wide range of scientific disciplines. This new edition of the bestselling Randomi...
Clinical characteristics of 140 patients infected with SARS‐CoV‐2 in Wuhan, China
Abstract Background Coronavirus disease 2019 (COVID‐19) caused by severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) infection has been widely spread. We aim to invest...
Clinical characteristics of 113 deceased patients with coronavirus disease 2019: retrospective study
Abstract Objective To delineate the clinical characteristics of patients with coronavirus disease 2019 (covid-19) who died. Design Retrospective case series. Setting Tongji Hosp...
Federated Learning: Challenges, Methods, and Future Directions
Federated learning involves training statistical models over remote devices or siloed data centers, such as mobile phones or hospitals, while keeping data localized. Training in...
The diagnosis and management of non-alcoholic fatty liver disease: Practice Guideline by the American Association for the Study of Liver Diseases, American College of Gastroenterology, and the American Gastroenterological Association
These recommendations are based on the following: (1) a formal review and analysis of the recently published world literature on the topic [Medline search up to June 2011]; (2) ...
Advances and Open Problems in Federated Learning
Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a ...
Comorbidity and its impact on 1590 patients with COVID-19 in China: a nationwide analysis
Background The coronavirus disease 2019 (COVID-19) outbreak is evolving rapidly worldwide. Objective To evaluate the risk of serious adverse outcomes in patients with COVID-19 b...
Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression
Bounding box regression is the crucial step in object detection. In existing methods, while ℓn-norm loss is widely adopted for bounding box regression, it is not tailored to the...