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.
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Simulation of pin-reinforced single-lap composite joints
TL;DR: In this article, a simple and efficient computational approach is presented for analyzing the benefits of through-thickness pins for restricting debond failure in joints, where tractions acting on the fracture surfaces of the debond crack are prescribed as functions of the crack displacement, which are available in simple forms that summarize the complex deformations to a reasonable accuracy.
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Broad Band Two-Dimensional Manipulation of Surface Plasmons
TL;DR: This work experimentally demonstrates plasmonic interference patterns that can be designed at will by shaping the edges in a metallic film that will have profound potentials in nanolithography, particle manipulation, and other related fields.
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High Purcell Factor Due To Coupling of a Single Emitter to a Dielectric Slot Waveguide
Pavel Kolchin,Nitipat Pholchai,Maiken H. Mikkelsen,Jin-Yong Oh,Sadao Ota,M. Saif Islam,Xiaobo Yin,Xiang Zhang,Xiang Zhang +8 more
TL;DR: An all-dielectric quantum electrodynamical nanowire-slab system with a single emitter that concentrates the extremely intense light at the scale of 10 × 75 nm(2) exhibits a record high 31-fold spontaneous decay rate enhancement.
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Optical Selection Rule based on Valley-Exciton Locking for 2D Valleytronics
TL;DR: In this article, a new set of optical selection rules in monolayer WS2, imposed by valley and exciton angular momentum, was proposed and experimentally demonstrated for second harmonic generation (SHG) and two-photon luminescence.
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Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure Detection
TL;DR: Zhang et al. as mentioned in this paper proposed a robust and explainable epileptic seizure detection model that effectively learns from seizure states while eliminating the inter-patient noises, and developed an attention mechanism to automatically learn the importance of each EEG channels in the seizure diagnosis procedure.