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Author

Xiang Zhang

Bio: 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
TL;DR: In this article, a sparse nucleation process on sapphire (0001) substrates has been developed for the growth of GaN thin films, where the density of nucleation sites is reduced to only 4×104 cm−2.
Abstract: A sparse nucleation process on sapphire (0001) substrates has been developed for the growth of GaN thin films. The density of nucleation sites is reduced to only 4×104 cm−2. Based on this process, we performed direct lateral epitaxial overgrowth (LEO) of GaN by metalorganic chemical vapor deposition on patterned SiO2/sapphire (0001) substrates. An aggregate lateral to vertical growth rate ratio of around 2:1 was achieved after the coalescence of the GaN stripes. Cathodoluminescence imaging shows strong and uniform near-band gap luminescence from LEO regions and confirms the improved quality of LEO GaN, which is further supported by atomic force microscopy analysis.

15 citations

Journal ArticleDOI
TL;DR: In this paper, a series of uniaxial and multi-xial ratcheting tests were conducted at room temperature on zirconium alloy tubes, and the experimental results showed that the axial ratchetting strain ǫ x did not accumulate obviously in initial stage, but gradually increased up to 1% with increasing stress amplitude σ xa.

15 citations

Journal ArticleDOI
Zhengguo Cao1, Felix Aharonian2, Felix Aharonian3, Q. An4  +272 moreInstitutions (22)
TL;DR: In this paper, the authors reported the discovery of a new extended gamma-ray source in the Galactic plane named LHAASO J0341+5258 with a pretrial significance of 8.2 standard deviations above 25 TeV.
Abstract: We report the discovery of a new unidentified extended gamma-ray source in the Galactic plane named LHAASO J0341+5258 with a pretrial significance of 8.2 standard deviations above 25 TeV. The best-fit position is R.A. = 55.degrees 34 +/- 0.degrees 11 and decl. = 52.degrees 97 +/- 0.degrees 07. The angular size of LHAASO J0341+5258 is 0.degrees 29 +/- 0.degrees 06(stat) +/- 0.degrees 02(sys). The flux above 25 TeV is about 20% of the flux of the Crab Nebula. Although a power-law fit of the spectrum from 10 to 200 TeV with the photon index alpha = 2.98 +/- 0.19(stat) +/- 0.02(sys) is not excluded, the LHAASO data together with the flux upper limit at 10 GeV set by the Fermi-LAT observation, indicate a noticeable steepening of an initially hard power-law spectrum with a cutoff at approximate to 50 TeV. We briefly discuss the origin of ultra-high-energy gamma rays. The lack of an energetic pulsar and a young supernova remnant inside or in the vicinity of LHAASO J0341+5258 challenge, but do not exclude, both the leptonic and hadronic scenarios of gamma-ray production.

15 citations

Journal ArticleDOI
TL;DR: The first azimuthally differential measurements of the pion source size relative to the second harmonic event plane in Pb-Pb collisions at a center-of-mass energy per nucleon-nucleon pair of sqrt[s_{NN}]=2.76 TeV are presented.
Abstract: We present the first azimuthally differential measurements of the pion source size relative to the second harmonic event plane in Pb-Pb collisions at a center-of-mass energy per nucleon-nucleon pair of √sNN = 2.76 TeV. The measurements have been performed in the centrality range 0%-50% and for pion pair transverse momenta 0.2 < kT < 0.7 GeV/c. We find that the Rside and Rout radii, which characterize the pion source size in the directions perpendicular and parallel to the pion transverse momentum, oscillate out of phase, similar to what was observed at the Relativistic Heavy Ion Collider. The final-state source eccentricity, estimated via Rside oscillations, is found to be significantly smaller than the initial-state source eccentricity, but remains positive - indicating that even after a stronger expansion in the in-plane direction, the pion source at the freeze-out is still elongated in the out-of-plane direction. The 3 + 1D hydrodynamic calculations are in qualitative agreement with observed centrality and transverse momentum Rside oscillations, but systematically underestimate the oscillation magnitude.

15 citations

Journal ArticleDOI
TL;DR: It is deduced that the excitation of neutral N_{2} occurs via multiple collisions with hot free electrons, and numerical simulations reproduce well this ultrafast formation time and its dependence on gas pressure, and thus support this interpretation.
Abstract: Nitrogen molecules are promoted to excited neutral states during femtosecond laser pulse filamentary propagation in atmosphere, leading to a characteristic UV fluorescence. Using a laser-induced fluorescence depletion technique, we measure the formation dynamics of these excited neutral nitrogen molecules with femtosecond time resolution. We find that the excited neutral molecules are formed in an unexpected ultrafast timescale of $\ensuremath{\sim}4\text{ }\text{ }\mathrm{ps}$ at 1 bar and $\ensuremath{\sim}120\text{ }\text{ }\mathrm{ps}$ at 30 mbar pressure. From this observation we deduce that the excitation of neutral ${\mathrm{N}}_{2}$ occurs via multiple collisions with hot free electrons. Numerical simulations based on rate equations reproduce well this ultrafast formation time and its dependence on gas pressure, and thus support this interpretation.

15 citations


Cited by
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Proceedings ArticleDOI
27 Jun 2016
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Abstract: Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers—8× deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

123,388 citations

Proceedings Article
04 Sep 2014
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Abstract: In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.

55,235 citations

Journal ArticleDOI
04 Mar 2011-Cell
TL;DR: Recognition of the widespread applicability of these concepts will increasingly affect the development of new means to treat human cancer.

51,099 citations

Posted Content
TL;DR: This work presents a residual learning framework to ease the training of networks that are substantially deeper than those used previously, and provides comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.
Abstract: Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers---8x deeper than VGG nets but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

44,703 citations

Proceedings ArticleDOI
07 Jun 2015
TL;DR: Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).
Abstract: We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.

40,257 citations