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.

Interleukin-17 Could Promote Breast Cancer Progression at Several Stages of the Disease

Abstract: Metastatic disease accounts for more than 90% of deaths from breast cancer. Yet the factors that trigger metastasis, often years after primary tumor removal, are not understood well. Recently the proinflammatory cytokine interleukin- (IL-) 17 family has been associated with poor prognosis in breast cancer. Here we review current literature on the pathogenic mechanisms driven by IL-17 during breast cancer progression and connect these findings to metastasis. These include (1) direct effects of IL-17 on tumor cells promoting tumor cell survival and invasiveness, (2) regulation of tumor angiogenesis, and (3) interaction with myeloid derived suppressor cells (MDSCs) to inhibit antitumor immune response and collaborate at the distant metastatic site. Furthermore, IL-17 might also be a culprit in bone destruction caused by late stage bone metastasis. Interestingly, in addition to these potential prometastasis functions, there is also evidence for an opposite, antitumor role of IL-17 during cancer therapies. We hypothesize that these contradictory roles may be due to chronic, imbalanced versus acute transient nature of the immune reactions, as well as differences in the cells that interact with IL-17(+) cells under different circumstances.

Unexpected Sensitivity of Nitrogen Ions Superradiant Emission on Pump Laser Wavelength and Duration

Abstract: Nitrogen molecules in ambient air exposed to an intense near-infrared femtosecond laser pulse give rise to cavity-free superradiant emission at 391.4 and 427.8 nm. An unexpected pulse duration-dependent cyclic variation of the superradiance intensity is observed when the central wavelength of the femtosecond pump laser pulse is finely tuned between 780 and 820 nm, and no signal occurs at the resonant wavelength of 782.8 nm (2ω 782.8 nm = ω 391.4 nm). On the basis of a semiclassical recollision model, we show that an interference of dipolar moments of excited ions created by electron recollisions explains this behavior.

Fast and unified local search for random walk based k-nearest-neighbor query in large graphs

Abstract: Given a large graph and a query node, finding its k-nearest-neighbor (kNN) is a fundamental problem. Various random walk based measures have been developed to measure the proximity (similarity) between nodes. Existing algorithms for the random walk based top-k proximity search can be categorized as global and local methods based on their search strategies. Global methods usually require an expensive precomputing step. By only searching the nodes near the query node, local methods have the potential to support more efficient query. However, most existing local search methods cannot guarantee the exactness of the solution. Moreover, they are usually designed for specific proximity measures. Can we devise an efficient local search method that applies to different measures and also guarantees result exactness? In this paper, we present FLoS (Fast Local Search), a unified local search method for efficient and exact top-k proximity query in large graphs. FLoS is based on the no local optimum property of proximity measures. We show that many measures have no local optimum. Utilizing this property, we introduce several simple operations on transition probabilities, which allow developing lower and upper bounds on the proximity. The bounds monotonically converge to the exact proximity when more nodes are visited. We further show that FLoS can also be applied to measures having local optimum by utilizing relationship among different measures. We perform comprehensive experiments to evaluate the efficiency and applicability of the proposed method.

Internet of Things Meets Brain–Computer Interface: A Unified Deep Learning Framework for Enabling Human-Thing Cognitive Interactivity

Abstract: A brain–computer interface (BCI) acquires brain signals, analyzes, and translates them into commands that are relayed to actuation devices for carrying out desired actions. With the widespread connectivity of everyday devices realized by the advent of the Internet of Things (IoT), BCI can empower individuals to directly control objects such as smart home appliances or assistive robots, directly via their thoughts. However, realization of this vision is faced with a number of challenges, most importantly being the issue of accurately interpreting the intent of the individual from the raw brain signals that are often of low fidelity and subject to noise. Moreover, preprocessing brain signals and the subsequent feature engineering are both time-consuming and highly reliant on human domain expertise. To address the aforementioned issues, in this paper, we propose a unified deep learning-based framework that enables effective human-thing cognitive interactivity in order to bridge individuals and IoT objects. We design a reinforcement learning-based selective attention mechanism (SAM) to discover the distinctive features from the input brain signals. In addition, we propose a modified long short-term memory to distinguish the interdimensional information forwarded from the SAM. To evaluate the efficiency of the proposed framework, we conduct extensive real-world experiments and demonstrate that our model outperforms a number of competitive state-of-the-art baselines. Two practical real-time human-thing cognitive interaction applications are presented to validate the feasibility of our approach.

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.