Author
Xiang Zhang
Other affiliations: University of California, Berkeley, University of Texas MD Anderson Cancer Center, Penn State College of Information Sciences and Technology ...read more
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.
Topics: Medicine, Computer science, Materials science, Metamaterial, Chemistry
Papers published on a yearly basis
Papers
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TL;DR: In this paper, the authors provide a summary of the bone microenvironment and its impact on bone metastasis, including the potential of bone to function as a launch pad for secondary metastasis.
Abstract: Many cancer types metastasize to bone. This propensity may be a product of genetic traits of the primary tumour in some cancers. Upon arrival, cancer cells establish interactions with various bone-resident cells during the process of colonization. These interactions, to a large degree, dictate cancer cell fates at multiple steps of the metastatic cascade, from single cells to overt metastases. The bone microenvironment may even influence cancer cells to subsequently spread to multiple other organs. Therefore, it is imperative to spatiotemporally delineate the evolving cancer-bone crosstalk during bone colonization. In this Review, we provide a summary of the bone microenvironment and its impact on bone metastasis. On the basis of the microscopic anatomy, we tentatively define a roadmap of the journey of cancer cells through bone relative to various microenvironment components, including the potential of bone to function as a launch pad for secondary metastasis. Finally, we examine common and distinct features of bone metastasis from various cancer types. Our goal is to stimulate future studies leading to the development of a broader scope of potent therapies.
25 citations
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01 Sep 2016TL;DR: Experimental results show that in a variety of applications, the second-order measures can dramatically improve the performance compared to their first-order counterparts.
Abstract: Measuring the proximity between different nodes is a fundamental problem in graph analysis. Random walk based proximity measures have been shown to be effective and widely used. Most existing random walk measures are based on the first-order Markov model, i.e., they assume that the next step of the random surfer only depends on the current node. However, this assumption neither holds in many real-life applications nor captures the clustering structure in the graph. To address the limitation of the existing first-order measures, in this paper, we study the second-order random walk measures, which take the previously visited node into consideration. While the existing first-order measures are built on node-to-node transition probabilities, in the second-order random walk, we need to consider the edge-to-edge transition probabilities. Using incidence matrices, we develop simple and elegant matrix representations for the second-order proximity measures. A desirable property of the developed measures is that they degenerate to their original first-order forms when the effect of the previous step is zero. We further develop Monte Carlo methods to efficiently compute the second-order measures and provide theoretical performance guarantees. Experimental results show that in a variety of applications, the second-order measures can dramatically improve the performance compared to their first-order counterparts.
25 citations
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TL;DR: In this article, a numerical model is developed for predicting low-velocity impact damage in laminated composites by using stacked shell elements to model laminate plies with discrete interface elements in pre-determined zones to model the onset and propagation of matrix cracks and delamination.
Abstract: A numerical model is developed for predicting low-velocity impact damage in laminated composites. Stacked shell elements are employed to model laminate plies with discrete interface elements in pre-determined zones to model the onset and propagation of matrix cracks and delamination. These interface elements are governed by a bi-linear cohesive failure law. Cohesive element zone size is determined by a separate finite element analysis using solid elements to identify the stress concentration sites. In order to save the computational effort, low-velocity impact load is modelled by quasi-static loading. Influence of contact force induced friction on shear driven mode II delamination is modelled by a friction model. For a clustered cross-ply laminate, calculated impact force and damage area are in good agreement with the test results. It is shown that matrix cracks should be included in the model in order to simulate delamination in adjacent interface. The practical outcome of this research is a validated modelling approach that can be further improved for predicting low-velocity impact damage in other stacking sequences.
25 citations
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25 citations
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TL;DR: In this article, carbon-coated Ni3Sn2 nanoparticles uniformly embedded in two-dimensional porous carbon nanosheets (2D Ni3sn2@C@PGC) as superior lithium ion battery anode material were fabricated by a facile and scalable method.
Abstract: Carbon-coated Ni3Sn2 nanoparticles uniformly embedded in two-dimensional porous carbon nanosheets (2D Ni3Sn2@C@PGC) as superior lithium ion battery anode material were fabricated by a facile and scalable method, which involves in situ synthesis of 2D Ni@C@PGC and chemical vapor transformation processes from 2D Ni@C@PGC to Ni3Sn2@C@PGC. With the assistance of a water-soluble cubic NaCl template, 2D Ni@C@PGC was firstly in situ synthesized on the surface of NaCl particles. After vapor transformation with SnCl2, the Ni@C@PGC nanosheets were converted to Ni3Sn2@C@PGC, in which uniform Ni3Sn2 nanoparticles coated with conformal graphitized carbon layers were homogeneously embedded in 2D high-conducting carbon nanosheets with a thickness of about 30 nm. This unique 2D dual encapsulation structure with high porosity, high electronic conductivity, outstanding mechanical flexibility and short lithium ion diffusion pathway is favorable for lithium insertion and extraction during deep charge–discharge processes. As a result, the electrode fabricated using 2D Ni3Sn2@C@PGC as the anode and a lithium plate as the cathode exhibits a high reversible capacity up to 585.3 mA h g−1 at a current density of 0.2 C (1 C = 570 mA h g−1) after 100 cycles, a high rate capability (484, 424, 378, 314 and 188 mA h g−1 at 0.2, 0.5, 1, 2 and 5 C, respectively, 1 C = 570 mA h g−1), and superior cycling stability at a high rate (350.3 mA h g−1 at a rate of 1 C after 180 cycles).
25 citations
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27 Jun 2016TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
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.
123,388 citations
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04 Sep 2014TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small 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.
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.
55,235 citations
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TL;DR: Recognition of the widespread applicability of these concepts will increasingly affect the development of new means to treat human cancer.
51,099 citations
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TL;DR: This work presents a residual learning framework to ease the training of networks that are substantially deeper than those used previously, and provides comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.
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.
44,703 citations
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07 Jun 2015TL;DR: Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).
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.
40,257 citations