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
Sub-Diffraction-Limited Optical Imaging with a Silver Superlens
TL;DR: This work demonstrated sub–diffraction-limited imaging with 60-nanometer half-pitch resolution, or one-sixth of the illumination wavelength, using silver as a natural optical superlens and showed that arbitrary nanostructures can be imaged with good fidelity.
Journal ArticleDOI
Discovery of intrinsic ferromagnetism in two-dimensional van der Waals crystals
Cheng Gong,Lin Li,Zhenglu Li,Huiwen Ji,Alexander Stern,Yang Xia,Ting Cao,Wei Bao,Chenzhe Wang,Yuan Wang,Ziqiang Qiu,Robert J. Cava,Steven G. Louie,Jing Xia,Xiang Zhang +14 more
TL;DR: In this paper, the authors reported the experimental discovery of intrinsic ferromagnetism in Cr 2 Ge 2 Te 6 atomic layers by scanning magneto-optic Kerr microscopy.
Journal ArticleDOI
A graphene-based broadband optical modulator
Ming Liu,Xiaobo Yin,Erick Ulin-Avila,Baisong Geng,Thomas Zentgraf,Long Ju,Feng Wang,Feng Wang,Xiang Zhang,Xiang Zhang +9 more
TL;DR: Graphene-based optical modulation mechanism, with combined advantages of compact footprint, low operation voltage and ultrafast modulation speed across a broad range of wavelengths, can enable novel architectures for on-chip optical communications.
Proceedings Article
Character-level convolutional networks for text classification
TL;DR: In this paper, the use of character-level convolutional networks (ConvNets) for text classification has been explored and compared with traditional models such as bag of words, n-grams and their TFIDF variants.
Proceedings Article
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
TL;DR: In this article, a multiscale and sliding window approach is proposed to predict object boundaries, which is then accumulated rather than suppressed in order to increase detection confidence, and OverFeat is the winner of the ImageNet Large Scale Visual Recognition Challenge 2013.