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Institution

Shanghai Jiao Tong University

EducationShanghai, Shanghai, China
About: Shanghai Jiao Tong University is a education organization based out in Shanghai, Shanghai, China. It is known for research contribution in the topics: Population & Cancer. The organization has 157524 authors who have published 184620 publications receiving 3451038 citations. The organization is also known as: Shanghai Communications University & Shanghai Jiaotong University.


Papers
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Proceedings ArticleDOI
14 Jun 2020
TL;DR: A novel Texture Transformer Network for Image Super-Resolution (TTSR), in which the LR and Ref images are formulated as queries and keys in a transformer, respectively, which achieves significant improvements over state-of-the-art approaches on both quantitative and qualitative evaluations.
Abstract: We study on image super-resolution (SR), which aims to recover realistic textures from a low-resolution (LR) image. Recent progress has been made by taking high-resolution images as references (Ref), so that relevant textures can be transferred to LR images. However, existing SR approaches neglect to use attention mechanisms to transfer high-resolution (HR) textures from Ref images, which limits these approaches in challenging cases. In this paper, we propose a novel Texture Transformer Network for Image Super-Resolution (TTSR), in which the LR and Ref images are formulated as queries and keys in a transformer, respectively. TTSR consists of four closely-related modules optimized for image generation tasks, including a learnable texture extractor by DNN, a relevance embedding module, a hard-attention module for texture transfer, and a soft-attention module for texture synthesis. Such a design encourages joint feature learning across LR and Ref images, in which deep feature correspondences can be discovered by attention, and thus accurate texture features can be transferred. The proposed texture transformer can be further stacked in a cross-scale way, which enables texture recovery from different levels (e.g., from 1x to 4x magnification). Extensive experiments show that TTSR achieves significant improvements over state-of-the-art approaches on both quantitative and qualitative evaluations.

581 citations

Journal ArticleDOI
TL;DR: In this paper, the authors explore how dielectric polymer composites with high thermal conductivity have been developed and explore how fillers can be used to increase the thermal conductivities of a polymer.
Abstract: The continuing miniaturization of electronic devices and the increasing power output of electrical equipment have created new challenges in packaging and insulating materials. The key goals are to develop materials with high thermal conductivity, low coefficient of thermal expansion (CTE), low dielectric con stant, high electrical resistivity, high breakdown strength, and most importantly, low cost. Polymeric materials have attracted increasing interest because of their excellent processability and low cost; however, most polymers are thermally insulating and have a thermal conductivity between 0.1 and 0.5 W-m-ι-K"1. One approach to increase the thermal conductivity of a polymer is to introduce high-thermal-conductivity fillers, such as aluminum oxide, aluminum nitride, boron nitride, silicon nitride, beryllium oxide, or diamond. In this review paper, we explore how dielectric polymer composites with high thermal conductivity have been developed.

581 citations

Journal ArticleDOI
TL;DR: In this paper, the authors used cellulose nanofiber-supported 3D interconnected boron nitride nanosheet (3D-C-BNNS) aerogels for thermally conductive but electrically insulating epoxy nanocomposites.
Abstract: Thermally conductive but electrically insulating polymer composites are highly desirable for thermal management applications because of their wide range of utilization, ease of processing, and low cost. However, the traditional approaches to thermally conductive polymer composites usually suffer from the low thermal conductivity enhancement and/or the deterioration of electrical insulating property. In this study, using cellulose nanofiber-supported 3D interconnected boron nitride nanosheet (3D–C–BNNS) aerogels, a novel method for highly thermally conductive but electrically insulating epoxy nanocomposites is reported. The nanocomposites exhibit thermal conductivity enhancement of about 1400% at a low BNNS loading of 9.6 vol%. In addition, the epoxy nanocomposites are still highly insulating, having a volume electrical resistivity of 1015 Ω cm. The strong potential application for thermal management has been demonstrated by the surface temperature variations of the nanocomposites with time during heating and cooling.

580 citations

Book ChapterDOI
08 Sep 2018
TL;DR: Yadira et al. as mentioned in this paper proposed a simple convolutional neural network to regress the 3D shape of a complete face from a single 2D image, which can reconstruct full facial geometry along with semantic meaning.
Abstract: We propose a straightforward method that simultaneously reconstructs the 3D facial structure and provides dense alignment. To achieve this, we design a 2D representation called UV position map which records the 3D shape of a complete face in UV space, then train a simple Convolutional Neural Network to regress it from a single 2D image. We also integrate a weight mask into the loss function during training to improve the performance of the network. Our method does not rely on any prior face model, and can reconstruct full facial geometry along with semantic meaning. Meanwhile, our network is very light-weighted and spends only 9.8 ms to process an image, which is extremely faster than previous works. Experiments on multiple challenging datasets show that our method surpasses other state-of-the-art methods on both reconstruction and alignment tasks by a large margin. Code is available at https://github.com/YadiraF/PRNet.

580 citations

Journal ArticleDOI
TL;DR: This paper provides a latest survey of the physical layer security research on various promising 5G technologies, includingPhysical layer security coding, massive multiple-input multiple-output, millimeter wave communications, heterogeneous networks, non-orthogonal multiple access, full duplex technology, and so on.
Abstract: Physical layer security which safeguards data confidentiality based on the information-theoretic approaches has received significant research interest recently. The key idea behind physical layer security is to utilize the intrinsic randomness of the transmission channel to guarantee the security in physical layer. The evolution toward 5G wireless communications poses new challenges for physical layer security research. This paper provides a latest survey of the physical layer security research on various promising 5G technologies, including physical layer security coding, massive multiple-input multiple-output, millimeter wave communications, heterogeneous networks, non-orthogonal multiple access, full duplex technology, and so on. Technical challenges which remain unresolved at the time of writing are summarized and the future trends of physical layer security in 5G and beyond are discussed.

580 citations


Authors

Showing all 158621 results

NameH-indexPapersCitations
Meir J. Stampfer2771414283776
Richard A. Flavell2311328205119
Jie Zhang1784857221720
Yang Yang1712644153049
Lei Jiang1702244135205
Gang Chen1673372149819
Thomas S. Huang1461299101564
Barbara J. Sahakian14561269190
Jean-Laurent Casanova14484276173
Kuo-Chen Chou14348757711
Weihong Tan14089267151
Xin Wu1391865109083
David Y. Graham138104780886
Bin Liu138218187085
Jun Chen136185677368
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023415
20222,315
202120,873
202019,462
201916,699
201814,250