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.

Magnetized plasma for reconfigurable subdiffraction imaging.

Abstract: We show that magnetized plasma with appropriately designed parameters supports nearly diffractionless propagation of electromagnetic waves along the direction of the applied magnetic field, arising from their unbounded equifrequency contour in the magnetized plasma. Such a unique feature can be utilized to construct subdiffraction imaging devices, which is confirmed by detailed numerical investigations. Subdiffraction imaging devices based on magnetic plasma do not require microfabrication normally entailed by construction of metamaterials; more importantly, they can be dynamically reconfigured by tuning the applied magnetic field or the plasma density, and therefore they represent a facile and powerful route for imaging applications.

Transient Temperature During the Vaporization of Liquid on a Pulsed Laser-Heated Solid Surface

Abstract: The thermodynamics of the rapid vaporization of a liquid on a solid surface heated by an excimer laser pulse is studied experimentally. The transient temperature field is measured by monitoring the photothermal reflectance of an embedded thin film in nanosecond time resolution. The transient reflectivity is calibrated by considering a temperature gradient across the sample based on the static measurements of the thin film optical properties at elevated temperatures. The dynamics of bubble nucleation, growth, and collapse is detected by probing the optical specular reflectance. The metastability behavior of the liquid and the criterion for the onset of liquid-vapor phase transition in nanosecond time scale are obtained quantitatively for the first time.

Reflective interferometry for optical metamaterial phase measurements.

Abstract: The unambiguous determination of optical refractive indices of metamaterials is a challenging task for device applications and the study of new optical phenomena. We demonstrate here simple broadband phase measurements of metamaterials using spectrally and spatially resolved interferometry. We study the phase response of a π-shaped metamaterial known to be an analog to electromagnetically induced transparency. The measured broadband interferograms give the phase delay or advance produced by the metamaterial in a single measurement. The presented technique offers an effective way of characterizing optical metamaterials including nonlinear and gain–metamaterial systems.

Inclusive quarkonium production at forward rapidity in pp collisions at root s=8 TeV

Abstract: We report on the inclusive production cross sections of J/$\psi$, $\psi$(2S), $\Upsilon$(1S), $\Upsilon$(2S) and $\Upsilon$(3S), measured at forward rapidity with the ALICE detector in pp collisions at a center-of-mass energy $\sqrt{s}=8$ TeV. The analysis is based on data collected at the LHC and corresponds to an integrated luminosity of 1.28 pb$^{-1}$. Quarkonia are reconstructed in the dimuon-decay channel. The differential production cross sections are measured as a function of the transverse momentum $p_{\rm T}$ and rapidity $y$, over the $p_{\rm T}$ ranges $0

Axial Plane Optical Microscopy

Abstract: We present axial plane optical microscopy (APOM) that can, in contrast to conventional microscopy, directly image a sample's cross-section parallel to the optical axis of an objective lens without scanning. APOM combined with conventional microscopy simultaneously provides two orthogonal images of a 3D sample. More importantly, APOM uses only a single lens near the sample to achieve selective-plane illumination microscopy, as we demonstrated by three-dimensional (3D) imaging of fluorescent pollens and brain slices. This technique allows fast, high-contrast, and convenient 3D imaging of structures that are hundreds of microns beneath the surfaces of large biological tissues.

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.