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Xiaohui Xie
Researcher at University of California, Irvine
Publications - 351
Citations - 34195
Xiaohui Xie is an academic researcher from University of California, Irvine. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 58, co-authored 220 publications receiving 29844 citations. Previous affiliations of Xiaohui Xie include University of California, Berkeley & National Chiao Tung University.
Papers
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Posted ContentDOI
The disproportionate rise in COVID-19 cases among Hispanic/Latinx in disadvantaged communities of Orange County, California: A socioeconomic case-series
Daniel S. Chow,Jennifer E. Soun,Justin Glavis-Bloom,Brent D. Weinberg,Peter D. Chang,Simukayi Mutasa,Edwin S. Monuki,Jung In Park,Xiaohui Xie,Daniela A. Bota,Jie Wu,Leslie M. Thompson,Alpesh Amin,Saahir Khan,Bernadette Boden-Albala +14 more
TL;DR: This study examines the race-ethnic disparities in Orange County, CA, and highlights vulnerable populations that are at increased risk for contracting COVID-19 and illustrates that socioeconomic factors, which ultimately set the stage for biological and social disparities, need to be considered.
Posted Content
Generating Realistic Training Images Based on Tonality-Alignment Generative Adversarial Networks for Hand Pose Estimation.
TL;DR: TAGAN as discussed by the authors aligns the tonality and color distributions between synthetic hand poses and real backgrounds, and can generate high quality hand poses. But it requires a large training dataset with accurate hand-keypoint annotations.
Posted Content
Spatial Context-Aware Self-Attention Model For Multi-Organ Segmentation
Hao Tang,Xingwei Liu,Kun Han,Shanlin Sun,Narisu Bai,Xuming Chen,Huang Qian,Yong Liu,Xiaohui Xie +8 more
TL;DR: A new framework for combining 3D and 2D models is proposed, in which the segmentation is realized through high-resolution 2D convolutions, but guided by spatial contextual information extracted from a low-resolution 3D model.
Book ChapterDOI
AREM: aligning short reads from chip-sequencing by expectation maximization
TL;DR: A probabilistic approach for ChIP-Seq data analysis is introduced which utilizes all reads, providing a truly genome-wide view of binding patterns, and an expectation-maximization algorithm is implemented, called AREM (aligning reads by expectation maximization), to update the alignment probabilities of each read to different genomic locations.
Journal ArticleDOI
Chemiresistive gas sensors based on electrospun semiconductor metal oxides: A review.
Long Chen,Qiwen Yu,Chenying Pan,Yanhua Song,Hao Dong,Xiaohui Xie,Yi Li,Jun Liu,Di Wang,Xing Chen +9 more
TL;DR: In this article , the authors highlight the latest findings on the design and fabrication of electrospun gas sensors for detecting various gases including hydrogen (H2), methane (CH4), nitrogen monoxide (NO), hydrogen sulfide (H 2S), ammonia (NH3), ethanol (C2H5OH), acetone (CH3COCH3), formaldehyde (HCHO) and toluene (C6H5CH3).