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.

Subcellular resolution mapping of endogenous cytokine secretion by nano-plasmonic-resonator sensor array.

Abstract: Local extracellular signaling is central for cellular interactions and organizations. We report a novel sensing technique to interrogate extracellular signaling at the subcellular level. We developed an in situ immunoassay based on giant optical enhancement of a tunable nano-plasmonic-resonator array fabricated by nanoimprint lithography. Our nanoplasmonic device significantly increases the signal-to-noise ratio to enable the first time submicrometer resolution quantitative mapping of endogenous cytokine secretion. Our study shows a markedly high local interleukin-2 (IL-2) concentration within the immediate vicinity of the cell which finally validates a decades-old hypothesis on autocrine physiological concentration and spatial range. This general sensing technique can be applied for a broad range of cellular communication studies to improve our understanding of subcellular signaling and function.

Mapping the near-field dynamics in plasmon-induced transparency

Abstract: The photonic analog of atomic electromagnetically induced transparency (EIT) introduces a sharp resonance in transmission within a broad absorption profile. The rapid dispersion of these structures leads to critical photonic applications such as integrated optical delay lines. To date, experimental demonstrations of such analogs have relied on the measurement of the far-field spectrum. Here we present a near-field optical study of plasmon-induced transparency (PIT), a photonic analog with subwavelength-sized plasmonic resonators mimicking the bright and dark elements in EIT. Supported by numerical analyses, the optical near-field distributions at various wavelengths reveal the interference dynamics between the coupled bright and dark plasmonic resonators.

Time resolved single-step protease activity quantification using nanoplasmonic resonator (NPR) sensor

Abstract: A nanoplasmonic resonator (NPR) comprising a metallic nanodisk with alternating shielding layer(s), having a tagged biomolecule conjugated or tethered to the surface of the nanoplasmonic resonator for highly sensitive measurement of enzymatic activity. NPRs enhance Raman signals in a highly reproducible manner, enabling fast detection of protease and enzyme activity, such as Prostate Specific Antigen (paPSA), in real-time, at picomolar sensitivity levels. Experiments on extracellular fluid (ECF) from paPSA-positive cells demonstrate specific detection in a complex bio-fluid background in real-time single-step detection in very small sample volumes.

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.