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.

Thermal conductivity and diffusivity of free‐standing silicon nitride thin films

Abstract: The thermal conductivity and diffusivity of free‐standing silicon nitride (Si‐N) films of 0.6 and 1.4 μm in thickness are measured. A new experimental technique, the amplitude method, is proposed and applied to measurement of the thin‐film thermal diffusivity. The thermal diffusivity is determined by three independent experimental approaches: the phase‐shift method, the amplitude method, and the heat‐pulse method. Good agreement among the measured thermal diffusivities obtained by the three methods indicates the validity of the amplitude method. High‐resolution electron microscopy studies show a large quantity of voids in the 1.4 μm Si‐N films. In contrast, very few voids are found in the 0.6 μm films. This difference may be responsible for the measured lower conductivity of the 1.4 μm Si‐N films as compared to the 0.6 μm thin films.

Excitons in atomically thin 2D semiconductors and their applications

Abstract: Author(s): Xiao, J; Zhao, M; Wang, Y; Zhang, X | Abstract: The research on emerging layered two-dimensional (2D) semiconductors, such as molybdenum disulfide (MoS2), reveals unique optical properties generating significant interest. Experimentally, these materials were observed to host extremely strong light-matter interactions as a result of the enhanced excitonic effect in two dimensions. Thus, understanding and manipulating the excitons are crucial to unlocking the potential of 2D materials for future photonic and optoelectronic devices. In this review, we unravel the physical origin of the strong excitonic effect and unique optical selection rules in 2D semiconductors. In addition, control of these excitons by optical, electrical, as well as mechanical means is examined. Finally, the resultant devices such as excitonic light emitting diodes, lasers, optical modulators, and coupling in an optical cavity are overviewed, demonstrating how excitons can shape future 2D optoelectronics.

Metasurfaces for manipulating surface plasmons

Abstract: Metasurfaces have recently emerged as an innovative approach to control light propagation with unprecedented capabilities. Different from previous work concentrating on steering far-field propagating waves, here we demonstrate that metallic metasurfaces can efficiently and effectively manipulate surface plasmons in the near-field regime. By engineering the dispersion of surface plasmons on a simple grating structure, we are able to realize normal, non-divergent as well as anomalous diffraction of surface plasmons. In particular, all-angle and broadband negative refraction of surface plasmons is achieved, largely attributed to the uniquely designed hyperbolic constant frequency contour of surface plasmons propagating along the metasurface.

Deep Residual Learning for Image Recognition

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.

Very Deep Convolutional Networks for Large-Scale Image Recognition

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.

Deep Residual Learning for Image Recognition

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.

Going deeper with convolutions

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.