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: A synthetic single-chain porcine insulin precursor (PIP) gene and an alpha-mating factor leader sequence (alpha MFL) gene obtained by the PCR method are inserted between the promoter and 3'-terminating sequence of the alcohol dehydrogenase gene ADH1 in plasmid pVT102-U to form plasmids pVT 102-U/alpha M FL-PIP.
Abstract: A synthetic single-chain porcine insulin precursor (PIP) gene and an alpha-mating factor leader sequence (alpha MFL) gene obtained by the PCR method are inserted between the promoter and 3'-terminating sequence of the alcohol dehydrogenase gene ADH1 in plasmid pVT102-U to form plasmid pVT102-U/alpha MFL-PIP. The single-chain insulin precursor is expressed and secreted to the culture medium by Saccharomyces cerevisiae transformed by pVT102-U/alpha MFL-PIP. The precursor is purified and converted into human insulin by tryptic transpeptidation. The purified human insulin is fully active and can be crystallized. The overall yield of human insulin is 25 mg per liter of culture medium.
21 citations
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TL;DR: In this article, the effect of post-deposition heat treatment on porosity, microstructure, and mechanical properties of Ti-6Al-4V produced via an Electron Beam Melting process was investigated.
Abstract: This paper investigates the effect of post-deposition heat treatment on porosity, microstructure, and mechanical properties of Ti–6Al–4V produced via an Electron Beam Melting process. Samples were ...
21 citations
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21 citations
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TL;DR: In this paper , Zn-and nitrogen-codoped Ni-based lignin-derived carbon catalysts (NiZn@NC) were prepared by solvent volatile self-assembly and in situ reductive carbonization using pulp and paper waste stream alkali Lignin as the carbon source.
Abstract: The conversion of renewable bioethanol into high-energy-density higher alcohols has become essential for meeting the increasing global demand to achieve carbon neutrality. In this study, Zn- and nitrogen-codoped Ni-based lignin-derived carbon catalysts (NiZn@NC) were prepared by solvent volatile self-assembly and in situ reductive carbonization using pulp and paper waste stream alkali lignin as the carbon source. Lignin amphipathic derivatives with −COOH and −NH2 groups would coordinate with metal ions to form a stable lignin–metal framework; thus, the lignin-derived carbon layer disperses the NiZn bimetallic catalyst and prevents from corroding. At an amination reagent/lignin mass ratio of 1:2, an ethanol conversion of 75.2% and a high alcohol yield of 41.7% were achieved over the Ni20Zn1@NC catalyst. Experimental results and density functional theory calculations showed that Zn doping improved the electronic environment and defect structures of metallic Ni and carbon carrier, which effectively inhibited C–C cleavage and suppressed the byproduct formation, such as methane. Thereby, the synergetic effect between Ni and Zn facilitated the efficient conversion of aqueous ethanol into higher alcohols by the Guerbet reaction. This work provides a strategy of in situ pyrolytic doping and stabilizing of renewable biomass macromolecules as the frameworks for the construction of highly active and cost-efficient catalysts for ethanol upgrading.
21 citations
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TL;DR: Wang et al. as mentioned in this paper explored the possibility of fine changes occurring within a day in Lake Taihu (China), the area coverage of surface cyanobacterial blooms was quantified from the hourly Geostationary Ocean Color Imager (GOCI) data using a GOCI-derived cyanobacteria index.
21 citations
Cited by
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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