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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, the authors numerically studied a new type of infrared resonator structure, whose unit cell consists of paired split-ring resonators (SRRs), at different resonant frequencies, the magnetic dipoles induced from the two SRRs within one unit cell can be parallel or antiparallel, respectively.
Abstract: In this paper, we numerically study a new type of infrared resonator structure, whose unit cell consists of paired split-ring resonators (SRRs). At different resonant frequencies, the magnetic dipoles induced from the two SRRs within one unit cell can be parallel or antiparallel, which are defined as symmetric and antisymmetic modes, respectively. Detailed simulation indicates that the symmetric mode is due to magnetic coupling to resonators, in which the effective permeability could be negative. However, the antisymmetric mode originating from strong electric coupling may contribute to negative effective permittivity. Our new electromagnetic resonators with pronounced magnetic as well as electric responses could provide a new pathway to design negative index materials (NIMs) in the optical region.

18 citations

Journal ArticleDOI
TL;DR: In this article, the progress of high-speed all-optical logic gates based on dual semiconductor optical amplifiers (SOAs) has been reviewed, including using quantum-dot semiconductor.
Abstract: Recent progress of high-speed all-optical logic gates based on dual semiconductor optical amplifiers (SOAs) has been reviewed in this article. These schemes include using quantum-dot semiconductor ...

18 citations

Journal ArticleDOI
TL;DR: In this paper, the effects of iron (III) oxide attached on carbon nanotubes (CNTs) surface on the thermal oxidative stability of silicone rubber (SR) were investigated.

18 citations

Journal ArticleDOI
TL;DR: In this paper, the design and setup of a physical model for a long-span suspension bridge, which will consider various damage scenarios, was described, and geometric measurements and modal tests were subsequently carried out to identify its geometric configuration and dynamic characteristics, respectively.
Abstract: Comprehensive structural health monitoring systems have been developed and installed in several long-span suspension bridges around the world, aiming to monitor structural health conditions of the bridges in real time. Nevertheless, many key issues remain unsolved, such as how to take full advantage of the health monitoring system for effective and reliable damage detection of these complex structures. An innovative testbed was therefore established in a laboratory to allow researchers to recreate rational damage scenarios, apply different sensors and sensing networks, and test various damage detection algorithms. The design principles of the laboratory-based testbed are introduced. The paper will then outline the design and setup of a physical model for a long-span suspension bridge, which will consider various damage scenarios. Geometric measurements and modal tests were subsequently carried out to identify its geometric configuration and dynamic characteristics, respectively. The finite-element modeling of the physical bridge model was finally established using a commercial software package, which was followed by a finite-element model updating the use of the measured modal properties. This testbed, comprising of the delicate physical model and the updated finite-element model of a long-span suspension bridge, could solve a benchmark problem for the structural health monitoring of long-span suspension bridges.

18 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