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.

Toroidal dipole response in a multifold double-ring metamaterial

Abstract: The toroidal response is numerically investigated in a multifold double-ring metamaterials at the antibonding magnetic-dipole mode (i.e., antiparallel magnetic dipoles in one double-ring fold). This intriguing toroidal resonance in metamaterials is considered as a result of the magnetoelectric effect due to the broken balance of the electric near-field environment. We demonstrate that the toroidal dipole response in metamaterials can improve the quality factor of the resonance spectrum. In viewing of the design flexibility on the double-ring geometry, such toroidal metamaterials will offer advantages in application potentials of toroidal dipolar moment.

Ultrafast acousto-plasmonic control and sensing in complex nanostructures

Abstract: Coherent acoustic phonons modulate optical, electronic and mechanical properties at ultrahigh frequencies and can be exploited for applications such as ultratrace chemical detection, ultrafast lasers and transducers. Owing to their large absorption cross-sections and high sensitivities, nanoplasmonic resonators are used to generate coherent phonons up to terahertz frequencies. Generating, detecting and controlling such ultrahigh frequency phonons has been a topic of intense research. Here we report that by designing plasmonic nanostructures exhibiting multimodal phonon interference, we can detect the spatial properties of complex phonon modes below the optical wavelength through the interplay between plasmons and phonons. This allows detection of complex nanomechanical dynamics by polarization-resolved transient absorption spectroscopy. Moreover, we demonstrate that the multiple vibrational states in nanostructures can be tailored by manipulating the geometry and dynamically selected by acousto-plasmonic coherent control. This allows enhancement, detection and coherent generation of tunable strains using surface plasmons.

J/ψPolarization inppCollisions ats=7TeV

Abstract: The ALICE Collaboration has studied J/ψ production in pp collisions at √s=7 TeV at the LHC through its muon pair decay. The polar and azimuthal angle distributions of the decay muons were measured, and results on the J/ψ polarization parameters λ(θ) and λ(φ) were obtained. The study was performed in the kinematic region 2.5

Local electric field enhancement during nanofocusing of plasmons by a tapered gap

Abstract: We demonstrate that during plasmon nanofocusing in a tapered gap (V groove), local electric field experiences much stronger enhancement than the magnetic field. Two distinct asymptotic regimes are found near the tip of the groove: The electric field approaches either zero or infinity when dissipation is above or below a critical level (at a fixed taper angle), or taper angle is smaller or larger than a critical angle (at a fixed level of dissipation). Tapered gaps are shown to be the best option for achieving maximal field enhancement, compared to nanowedges and tapered rods. An optimal taper angle is determined. © 2007 The American Physical Society.

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.