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.

Ultrafast modulation of optical metamaterials

Abstract: The optical response of fishnet metamaterial can be modulated in femtosecond time scale. This modulation dynamics is mainly determined by the constituting dielectric medium, but the modulation magnitude is greatly enhanced through the plasmon resonance.

Plasmonic Brownian ratchet

Abstract: This work was partially funded by the Spanish MINECO (Grant No. MAT2011-28581-C02-01 and Grant No. CSD2007-046-NanoLight.es) and U.S. National Science Foundation (Grant No. CMMI-1120724). P.A.H. acknowledges FPU Grant No. AP2008-00021 from the Spanish Ministry of Education

Evaluation of renal vascular lesions using circulating endothelial cells in patients with lupus nephritis

Abstract: Objective. Currently the detection of renal vascular lesions (VLS) mainly depends on biopsy examination, and lacks surrogate biomarkers for clinical dynamic evaluation. The aim of this study is to find the correlation between numbers of circulating endothelial cells (CECs) and renal VLS in lupus nephritis (LN). Methods. Thirty LN patients with VLS and 30 LN patients without VLS were recruited. Thirty age- and sex-matched healthy volunteers served as controls. CECs were isolated from peripheral blood with anti-CD-146-coated immunomagnetic Dynabeads and were counted under microscopy. Parameters of renal involvement, including blood urea nitrogen, serum creatinine, 24 h urine protein excretion and quantitative urine sedimentation were also measured. Results. The number of CECs showed no difference between LN patients without VLS and controls. In patients with VLS, the number of CECs was significantly higher than those without VLS (P < 0.01). A strong positive correlation was found between CECs and serum creatinine (r ¼ 0.503, P < 0.01) and mean blood pressure (r ¼ 0.423, P < 0.05). In all LN patients with VLS, CEC number of the patients with thrombotic microangiopathy (TMA) significantly increased compared with those without TMA (P < 0.01). Conclusion. Numeration of CECs may serve as a potential and useful marker for vasculopathy in LN. Dynamic observations of CEC number can be used not only to provide evidence for monitoring disease severity and disease activity, but also to determine therapy efficacy in LN patients.

Ultrafast fluorescent decay induced by metal-mediated dipole–dipole interaction in two-dimensional molecular aggregates

Abstract: Two-dimensional molecular aggregate (2DMA), a thin sheet of strongly interacting dipole molecules self-assembled at close distance on an ordered lattice, is a fascinating fluorescent material. It is distinctively different from the conventional (single or colloidal) dye molecules and quantum dots. In this paper, we verify that when a 2DMA is placed at a nanometric distance from a metallic substrate, the strong and coherent interaction between the dipoles inside the 2DMA dominates its fluorescent decay at a picosecond timescale. Our streak-camera lifetime measurement and interacting lattice-dipole calculation reveal that the metal-mediated dipole-dipole interaction shortens the fluorescent lifetime to about one-half and increases the energy dissipation rate by 10 times that expected from the noninteracting single-dipole picture. Our finding can enrich our understanding of nanoscale energy transfer in molecular excitonic systems and may designate a unique direction for developing fast and efficient optoelectronic devices.

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.