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Jie Tang

Researcher at Tsinghua University

Publications -  599
Citations -  25529

Jie Tang is an academic researcher from Tsinghua University. The author has contributed to research in topics: Computer science & Social network. The author has an hindex of 68, co-authored 466 publications receiving 18934 citations. Previous affiliations of Jie Tang include University of Notre Dame & Renmin University of China.

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Risk factor analysis of omicron patients with mental health problems in the Fangcang shelter hospital based on psychiatric drug intervention during the COVID-19 pandemic in Shanghai, China

TL;DR: Wang et al. as discussed by the authors investigated the risk factors of the infected patients from a new pharmacological perspective based on psychiatric drug consumption rather than questionnaires for the first time, and demonstrated the necessity of potential mental and psychological service development in Fangcang shelters during the COVID-19 pandemic and other public emergency responses.
Posted Content

Does Quantum Interference exist in Twitter

TL;DR: Using twitter data, a mathematical model is proposed to elucidate the spotted quantum phenomena and SIT and CPT fail to interpret the information transfer occurring in Twitter, and quantum interference exists in Twitter.
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UFC-BERT: Unifying Multi-Modal Controls for Conditional Image Synthesis

TL;DR: Zhang et al. as mentioned in this paper proposed a new two-stage architecture, UFC-BERT, to unify any number of multi-modal control signals and the synthesized image are uniformly represented as a sequence of discrete tokens to be processed by Transformer.
Posted Content

GCCAD: Graph Contrastive Coding for Anomaly Detection

TL;DR: In this article, a graph contrastive coding (GCCAD) model is proposed to contrast abnormal nodes with normal ones in terms of their distances to the global context (e.g., the average of all nodes).
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Lipid metabolism characterization in gastric cancer identifies signatures to predict prognostic and therapeutic responses

TL;DR: Wang et al. as mentioned in this paper applied single-factor Cox regression and random forest to screen signature genes to construct a prognostic model, namely, the lipid metabolism score (LMscore), and deeply explored the predictive value of the LMscore for GC.