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.

Graph-regularized dual Lasso for robust eQTL mapping.

Abstract: Motivation: As a promising tool for dissecting the genetic basis of complex traits, expression quantitative trait loci (eQTL) mapping has attracted increasing research interest. An important issue in eQTL mapping is how to effectively integrate networks representing interactions among genetic markers and genes. Recently, several Lasso-based methods have been proposed to leverage such network information. Despite their success, existing methods have three common limitations: (i) a preprocessing step is usually needed to cluster the networks; (ii) the incompleteness of the networks and the noise in them are not considered; (iii) other available information, such as location of genetic markers and pathway information are not integrated. Results: To address the limitations of the existing methods, we propose Graph-regularized Dual Lasso (GDL), a robust approach for eQTL mapping. GDL integrates the correlation structures among genetic markers and traits simultaneously. It also takes into account the incompleteness of the networks and is robust to the noise. GDL utilizes graph-based regularizers to model the prior networks and does not require an explicit clustering step. Moreover, it enables further refinement of the partial and noisy networks. We further generalize GDL to incorporate the location of genetic makers and gene-pathway information. We perform extensive experimental evaluations using both simulated and real datasets. Experimental results demonstrate that the proposed methods can effectively integrate various available priori knowledge and significantly outperform the state-of-the-art eQTL mapping methods. Availability: Software for both C++ version and Matlab version is available at http://www.cs.unc.edu/� weicheng/. Contact: weiwang@cs.ucla.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Magnetic Plasmon Propagation Along a Chain of Connected Subwavelength Resonators at Infrared Frequencies

Abstract: A one-dimensional magnetic plasmon propagating in a linear chain of single split ring resonators is proposed. The subwavelength size resonators interact mainly through exchange of conduction current, resulting in stronger coupling as compared to the corresponding magneto-inductive interaction. Finite-difference time-domain simulations in conjunction with a developed analytical theory show that efficient energy transfer with signal attenuation of less then 0.57 dB/microm and group velocity higher than 1/4c can be achieved. The proposed novel mechanism of energy transport in the nanoscale has potential applications in subwavelength transmission lines for a wide range of integrated optical devices.

Low-temperature preparation of highly (100)-oriented Pb(ZrxTi1−x)O3 thin film by high oxygen-pressure processing

Abstract: A method for thin-film fabrication employing high oxygen-pressure processing (HOPP) was developed With this method, the highly (100) oriented Pb(ZrxTi1−x)O3 (PZT) thin film was fabricated at temperature as low as 400°C HOPP is compatible to the ferroelectric PZT film integration with a readout integrated circuit The sol-gel-derived PZT 50∕50 thin film showed a well-saturated hysteresis loop at an applied electric field of 367kV∕cm with Pr and Ec of 45μC∕cm2 and 121kV∕cm, respectively Large electric leakage was attributed to remnant organic components, which was demonstrated by sputtered organic-free PZT films The optimized Pr and Ec are of 26μC∕cm2 and 93kV∕cm under an applied electric field of 400kV∕cm

An Upper Limit on the Electron-Neutrino Flux from the HiRes Detector

Abstract: Air-fluorescence detectors such as the High Resolution Fly's Eye (HiRes) detector are very sensitive to upward-going, Earth-skimming ultra-high-energy electron-neutrino-induced showers. This is due to the relatively large interaction cross sections of these high-energy neutrinos and to the Landau-Pomeranchuk-Migdal (LPM) effect. The LPM effect causes a significant decrease in the cross sections for bremsstrahlung and pair production, allowing charged-current electron-neutrino-induced showers occurring deep in the Earth's crust to be detectable as they exit the Earth into the atmosphere. A search for upward-going neutrino-induced showers in the HiRes-II monocular data set has yielded a null result. From an LPM calculation of the energy spectrum of charged particles as a function of primary energy and depth for electron-induced showers in rock, we calculate the shape of the resulting profile of these showers in air. We describe a full detector Monte Carlo simulation to determine the detector response to upward-going electron-neutrino-induced cascades and present an upper limit on the flux of electron neutrinos.

Vertical Self-Assembly of Polarized Phage Nanostructure for Energy Harvesting.

Abstract: Controlling the shape, geometry, density, and orientation of nanomaterials is critical to fabricate functional devices. However, there is limited control over the morphological and directional characteristics of presynthesized nanomaterials, which makes them unsuitable for developing devices for practical applications. Here, we address this challenge by demonstrating vertically aligned and polarized piezoelectric nanostructures from presynthesized biological piezoelectric nanofibers, M13 phage, with control over the orientation, polarization direction, microstructure morphology, and density using genetic engineering and template-assisted self-assembly process. The resulting vertically ordered structures exhibit strong unidirectional polarization with three times higher piezoelectric constant values than that of in-plane aligned structures, supported by second harmonic generation and piezoelectric force microscopy measurements. The resulting vertically self-assembled phage-based piezoelectric energy harvester (PEH) produces up to 2.8 V of potential, 120 nA of current, and 236 nW of power upon 17 N of force. In addition, five phage-based PEH integrated devices produce an output voltage of 12 V and an output current of 300 nA, simply by pressing with a finger. The resulting device can operate light-emitting diode backlights on a liquid crystal display. Our approach will be useful for assembling many other presynthesized nanomaterials into high-performance devices for various applications.

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.