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Xiang Zhang

Researcher at Baylor College of Medicine

Publications -  3483
Citations -  144843

Xiang Zhang is an academic researcher from Baylor College of Medicine. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 154, co-authored 1733 publications receiving 117576 citations. Previous affiliations of Xiang Zhang include University of California, Berkeley & University of Texas MD Anderson Cancer Center.

Papers
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Journal ArticleDOI

Optical toroidal dipolar response by an asymmetric double-bar metamaterial

TL;DR: In this paper, the toroidal dipolar response can be realized in the optical regime by designing a feasible nanostructured metamaterial, comprising asymmetric double-bar magnetic resonators assembled into a toroid-like configuration.
Posted Content

A Survey on Deep Learning based Brain Computer Interface: Recent Advances and New Frontiers.

TL;DR: This article systematically investigates brain signal types for BCI and related deep learning concepts for brain signal analysis, and presents a comprehensive survey of deep learning techniques used forBCI.
Journal ArticleDOI

Genome-wide Impact of the BRG1 SWI/SNF Chromatin Remodeler on the Transforming Growth Factor β Transcriptional Program

TL;DR: The results suggest that BRG1 is incorporated into transcriptional complexes that are formed by activated Smads in the nucleus, on target promoters, and provide a genome-wide scope of the participation ofBRG1 in TGFβ action and suggest a widespread yet differential involvement of BRG 1 SWI/SNF remodeler in the transcriptional response of many genes to this cytokine.
Journal ArticleDOI

Manipulating optical rotation in extraordinary transmission by hybrid plasmonic excitations

TL;DR: In this paper, the L-shaped holes array in silver film was investigated at near infrared wavelength, and two eigenmodes were clearly characterized as the results of the hybrid localized plasmon resonances.
Proceedings Article

Evaluating Prerequisite Qualities for Learning End-to-End Dialog Systems

TL;DR: This work proposes a suite of new tasks that test the ability of models to answer factual questions, provide personalization, carry short conversations about the two, and finally to perform on natural dialogs from Reddit.