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.

Microspectroscopy on perovskite-based superlenses [Invited]

Abstract: Superlenses create sub-diffraction-limit images by reconstructing the evanescent fields arising from an object. We study the lateral, vertical, and spectral field distribution of three different perovskite-based superlenses by means of scattering-type near-field microscopy. Sub-diffraction-limit resolution is observed for all samples with an image contrast depending on losses such as scattering and absorption. For the three lenses superlensing is observed at slightly different frequencies resulting in an overall broad frequency range of 3.6 THz around 20 THz.

Magnifying Sub-Diffraction-Limited Objects by an Optical Metamaterials Hyperlens

Abstract: We experimentally demonstrate the first optical hyperlens made by metal/dielectric metamaterial which is able to magnify sub-diffraction-limited objects. Hyperlens opens up new possibilities for optical nano-imaging in the far-field.

Isolation and characterization of nucleotide-binding site and C-terminal leucine-rich repeat-resistance gene candidates in bananas.

Abstract: Commercial banana varieties are highly susceptible to fungal pathogens, as well as bacterial pathogens, nematodes, viruses, and insect pests. The largest known family of plant resistance genes encodes proteins with nucleotide-binding site (NBS) and C-terminal leucine-rich repeat (LRR) domains. Conserved motifs in such genes in diverse plant species offer a means for the isolation of candidate genes in banana that may be involved in plant defense. Six degenerate PCR primers were designed to target NBS and additional domains were tested on commercial banana species Musa acuminata subsp malaccensis and the Musa AAB Group propagated in vitro and plants maintained in a greenhouse. Total DNA was isolated by a modified CTAB extraction technique. Four resistance gene analogs were amplified and deposited in GenBank and assigned numbers HQ199833-HQ199836. The predicted amino acid sequences compared to the amino acid sequences of known resistance genes (MRGL1, MRGL2, MRGL3, and MRGL4) revealed significant sequence similarity. The presence of consensus domains, namely kinase-1a, kinase-2 and hydrophobic domain, provided evidence that the cloned sequences belong to the typical non-Toll/interleukin-1 receptor-like domain NBS-LRR gene family.

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.