Shanghai Jiao Tong University

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.

A General Framework for a Class of First Order Primal-Dual Algorithms for Convex Optimization in Imaging Science

Abstract: We generalize the primal-dual hybrid gradient (PDHG) algorithm proposed by Zhu and Chan in [An Efficient Primal-Dual Hybrid Gradient Algorithm for Total Variation Image Restoration, CAM Report 08-34, UCLA, Los Angeles, CA, 2008] to a broader class of convex optimization problems. In addition, we survey several closely related methods and explain the connections to PDHG. We point out convergence results for a modified version of PDHG that has a similarly good empirical convergence rate for total variation (TV) minimization problems. We also prove a convergence result for PDHG applied to TV denoising with some restrictions on the PDHG step size parameters. We show how to interpret this special case as a projected averaged gradient method applied to the dual functional. We discuss the range of parameters for which these methods can be shown to converge. We also present some numerical comparisons of these algorithms applied to TV denoising, TV deblurring, and constrained $l_1$ minimization problems.

HCP: A Flexible CNN Framework for Multi-Label Image Classification

Abstract: Convolutional Neural Network (CNN) has demonstrated promising performance in single-label image classification tasks. However, how CNN best copes with multi-label images still remains an open problem, mainly due to the complex underlying object layouts and insufficient multi-label training images. In this work, we propose a flexible deep CNN infrastructure, called Hypotheses-CNN-Pooling (HCP), where an arbitrary number of object segment hypotheses are taken as the inputs, then a shared CNN is connected with each hypothesis, and finally the CNN output results from different hypotheses are aggregated with max pooling to produce the ultimate multi-label predictions. Some unique characteristics of this flexible deep CNN infrastructure include: 1) no ground-truth bounding box information is required for training; 2) the whole HCP infrastructure is robust to possibly noisy and/or redundant hypotheses; 3) the shared CNN is flexible and can be well pre-trained with a large-scale single-label image dataset, e.g., ImageNet; and 4) it may naturally output multi-label prediction results. Experimental results on Pascal VOC 2007 and VOC 2012 multi-label image datasets well demonstrate the superiority of the proposed HCP infrastructure over other state-of-the-arts. In particular, the mAP reaches 90.5% by HCP only and 93.2% after the fusion with our complementary result in [12] based on hand-crafted features on the VOC 2012 dataset.

XAI—Explainable artificial intelligence

Abstract: Explainability is essential for users to effectively understand, trust, and manage powerful artificial intelligence applications.

Comparative genetic analysis of the novel coronavirus (2019-nCoV/SARS-CoV-2) receptor ACE2 in different populations

Abstract: Communities and local governments across the country face a period of extreme uncertainty Whether or not COVID-19 is quickly contained, changes in consumer dem

Postsynthetically Modified Covalent Organic Frameworks for Efficient and Effective Mercury Removal.

Abstract: A key challenge in environmental remediation is the design of adsorbents bearing an abundance of accessible chelating sites with high affinity, to achieve both rapid uptake and high capacity for the contaminants. Herein, we demonstrate how two-dimensional covalent organic frameworks (COFs) with well-defined mesopore structures display the right combination of properties to serve as a scaffold for decorating coordination sites to create ideal adsorbents. The proof-of-concept design is illustrated by modifying sulfur derivatives on a newly designed vinyl-functionalized mesoporous COF (COF-V) via thiol–ene “click” reaction. Representatively, the material (COF-S-SH) synthesized by treating COF-V with 1,2-ethanedithiol exhibits high efficiency in removing mercury from aqueous solutions and the air, affording Hg2+ and Hg0 capacities of 1350 and 863 mg g–1, respectively, surpassing all those of thiol and thioether functionalized materials reported thus far. More significantly, COF-S-SH demonstrates an ultrahigh ...