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
Plasmon-Induced Transparency in Metamaterials
TL;DR: A plasmonic "molecule" consisting of a radiative element coupled with a subradiant (dark) element is theoretically investigated and shows electromagnetic response that closely resembles the electromagnetically induced transparency in an atomic system.
PatentDOI
Plasmon lasers at deep subwavelength scale
TL;DR: Hybrid plasmonic waveguides as discussed by the authors employ a high-gain semiconductor nanostructure functioning as a gain medium that is separated from a metal substrate surface by a nanoscale thickness thick low-index gap.
Journal ArticleDOI
Far-field optical hyperlens magnifying sub-diffraction-limited objects.
TL;DR: Experimental demonstration of the optical hyperlens for sub-diffraction-limited imaging in the far field and opens up possibilities in applications such as real-time biomolecular imaging and nanolithography.
Journal ArticleDOI
Three-dimensional optical metamaterial with a negative refractive index
Jason Valentine,Shuang Zhang,Thomas Zentgraf,Erick Ulin-Avila,Dentcho A. Genov,Guy Bartal,Xiang Zhang,Xiang Zhang +7 more
TL;DR: Bulk optical metamaterials open up prospects for studies of 3D optical effects and applications associated with NIMs and zero-index materials such as reversed Doppler effect, superlenses, optical tunnelling devices, compact resonators and highly directional sources.
Posted Content
Character-level Convolutional Networks for Text Classification
TL;DR: This article constructed several large-scale datasets to show that character-level convolutional networks could achieve state-of-the-art or competitive results in text classification.