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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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Journal ArticleDOI
TL;DR: This paper proposes a unified and effective method for simultaneously detecting multi-class objects in remote sensing images with large scales variability, and shows that the method is more accurate than existing algorithms and is effective for multi-modalRemote sensing images.
Abstract: Automatic detection of multi-class objects in remote sensing images is a fundamental but challenging problem faced for remote sensing image analysis. Traditional methods are based on hand-crafted or shallow-learning-based features with limited representation power. Recently, deep learning algorithms, especially Faster region based convolutional neural networks (FRCN), has shown their much stronger detection power in computer vision field. However, several challenges limit the applications of FRCN in multi-class objects detection from remote sensing images: (1) Objects often appear at very different scales in remote sensing images, and FRCN with a fixed receptive field cannot match the scale variability of different objects; (2) Objects in large-scale remote sensing images are relatively small in size and densely peaked, and FRCN has poor localization performance with small objects; (3) Manual annotation is generally expensive and the available manual annotation of objects for training FRCN are not sufficient in number. To address these problems, this paper proposes a unified and effective method for simultaneously detecting multi-class objects in remote sensing images with large scales variability. Firstly, we redesign the feature extractor by adopting Concatenated ReLU and Inception module, which can increases the variety of receptive field size. Then, the detection is preformed by two sub-networks: a multi-scale object proposal network (MS-OPN) for object-like region generation from several intermediate layers, whose receptive fields match different object scales, and an accurate object detection network (AODN) for object detection based on fused feature maps, which combines several feature maps that enables small and densely packed objects to produce stronger response. For large-scale remote sensing images with limited manual annotations, we use cropped image blocks for training and augment them with re-scalings and rotations. The quantitative comparison results on the challenging NWPU VHR-10 data set, aircraft data set, Aerial-Vehicle data set and SAR-Ship data set show that our method is more accurate than existing algorithms and is effective for multi-modal remote sensing images.

327 citations

Journal ArticleDOI
TL;DR: In this paper, a structured questionnaire survey was conducted to investigate the current status of integrated management system (IMS), and the authors concluded that the major problems for enterprises to operate multiple parallel management systems include: it causes complexity of internal management, it lowers management efficiency, it incurs cultural incompatibility, it causes employee hostility, and increases management costs.

327 citations

Journal ArticleDOI
TL;DR: A review of the literature in quantum machine learning can be found in this article, where the authors discuss perspectives for a mixed readership of classical ML and quantum computation experts and highlight the limitations of quantum algorithms, how they compare with their best classical counterparts and why quantum resources are expected to provide advantages for learning problems.
Abstract: Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning (ML) techniques to impressive results in regression, classification, data generation and reinforcement learning tasks. Despite these successes, the proximity to the physical limits of chip fabrication alongside the increasing size of datasets is motivating a growing number of researchers to explore the possibility of harnessing the power of quantum computation to speed up classical ML algorithms. Here we review the literature in quantum ML and discuss perspectives for a mixed readership of classical ML and quantum computation experts. Particular emphasis will be placed on clarifying the limitations of quantum algorithms, how they compare with their best classical counterparts and why quantum resources are expected to provide advantages for learning problems. Learning in the presence of noise and certain computationally hard problems in ML are identified as promising directions for the field. Practical questions, such as how to upload classical data into quantum form, will also be addressed.

327 citations

Journal ArticleDOI
TL;DR: Prevalence of rheumatic diseases in China is comparable with that in Western countries but varies in terms of joint involvement, and the prevalence of ankylosing spondylitis is similar to that in Caucasians.
Abstract: Introduction Epidemiological studies of rheumatic diseases have been conducted during the past 20 years in China. The aim of this study was to clarify prevalence rates of common rheumatic diseases in China.

327 citations

Journal ArticleDOI
TL;DR: This is the first integrated review on acetoin metabolism in bacteria, especially with regard to catabolic aspects, and the relationship between the two conflicting acetoin cleavage pathways is discussed.
Abstract: Acetoin is an important physiological metabolite excreted by many microorganisms. The excretion of acetoin, which can be diagnosed by the Voges Proskauer test and serves as a microbial classification marker, has its vital physiological meanings to these microbes mainly including avoiding acification, participating in the regulation of NAD/NADH ratio, and storaging carbon. The well-known anabolism of acetoin involves α-acetolactat synthase and α-acetolactate decarboxylase; yet its catabolism still contains some differing views, although much attention has been focused on it and great advances have been achieved. Current findings in catabolite control protein A (CcpA) mediated carbon catabolite repression may provide a fuller understanding of the control mechanism in bacteria. In this review, we first examine the acetoin synthesis pathways and its physiological meanings and relevancies; then we discuss the relationship between the two conflicting acetoin cleavage pathways, the enzymes of the acetoin dehydro...

327 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