X
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
Anti-Hermitian plasmon coupling of an array of gold thin-film antennas for controlling light at the nanoscale.
Shuang Zhang,Shuang Zhang,Ziliang Ye,Y. M. Wang,Yong-Shik Park,Guy Bartal,Michael Mrejen,Xiaobo Yin,Xiang Zhang,Xiang Zhang +9 more
TL;DR: By utilizing the anti-Hermitian coupling, plasmonic antennas closely packed within only λ/15 separations can be individually excited from the far field, which is otherwise indistinguishable from each other, opening a new venue for the nanoscale lightwave control, wavelength multiplexing, and spectrum splitting.
Journal ArticleDOI
Ray Optics at a Deep-Subwavelength Scale: A Transformation Optics Approach
TL;DR: By conformal transformation of the electromagnetic space, this work develops a methodology for realizing subwavelength ray optics with curved ray trajectories that enables deep-subwavelength-scale beams to flow through two- or three-dimensional spaces.
Journal ArticleDOI
A clicking confinement strategy to fabricate transition metal single-atom sites for bifunctional oxygen electrocatalysis
Chang-Xin Zhao,Jia-Ning Liu,Juan Wang,Changda Wang,Xin Guo,Xi‐Yao Li,Xiao Chen,Li Song,Bo Li,Xiang Zhang +9 more
TL;DR: In this article , a clicking confinement strategy is proposed to efficiently predisperse transition metal atoms in a precursor directed by click chemistry and ensure successful construction of abundant single-atom sites, where cobalt-coordinated porphyrin units are covalently clicked on the substrate for the confinement of the cobalt atoms and affording a CoN-C electrocatalyst.
Proceedings ArticleDOI
Dual Transfer Learning.
TL;DR: This paper proposes a novel approach, Dual Transfer Learning (DTL), which simultaneously learns the marginal and conditional distributions, and exploits the duality between them in a principled way.
Proceedings ArticleDOI
Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals
TL;DR: In this paper, a joint convolutional recurrent neural network and an autoencoder were employed to classify MI-EEG signals and brain activity in a BCI system.