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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: A multimedia-aware cloud is presented, which addresses how a cloud can perform distributed multimedia processing and storage and provide quality of service (QoS) provisioning for multimedia services, and a media-edge cloud (MEC) architecture is proposed, in which storage, central processing unit (CPU), and graphics processing units (GPU) clusters are presented at the edge.
Abstract: This article introduces the principal concepts of multimedia cloud computing and presents a novel framework. We address multimedia cloud computing from multimedia-aware cloud (media cloud) and cloud-aware multimedia (cloud media) perspectives. First, we present a multimedia-aware cloud, which addresses how a cloud can perform distributed multimedia processing and storage and provide quality of service (QoS) provisioning for multimedia services. To achieve a high QoS for multimedia services, we propose a media-edge cloud (MEC) architecture, in which storage, central processing unit (CPU), and graphics processing unit (GPU) clusters are presented at the edge to provide distributed parallel processing and QoS adaptation for various types of devices.

439 citations

Proceedings ArticleDOI
07 Dec 2015
TL;DR: Inspired by the recent success in deep learning techniques which provide amazing modeling of large-scale data, this paper re-formulates the colorization problem so thatDeep learning techniques can be directly employed and a joint bilateral filtering based post-processing step is proposed to ensure artifact-free quality.
Abstract: This paper investigates into the colorization problem which converts a grayscale image to a colorful version. This is a very difficult problem and normally requires manual adjustment to achieve artifact-free quality. For instance, it normally requires human-labelled color scribbles on the grayscale target image or a careful selection of colorful reference images (e.g., capturing the same scene in the grayscale target image). Unlike the previous methods, this paper aims at a high-quality fully-automatic colorization method. With the assumption of a perfect patch matching technique, the use of an extremely large-scale reference database (that contains sufficient color images) is the most reliable solution to the colorization problem. However, patch matching noise will increase with respect to the size of the reference database in practice. Inspired by the recent success in deep learning techniques which provide amazing modeling of large-scale data, this paper re-formulates the colorization problem so that deep learning techniques can be directly employed. To ensure artifact-free quality, a joint bilateral filtering based post-processing step is proposed. Numerous experiments demonstrate that our method outperforms the state-of-art algorithms both in terms of quality and speed.

439 citations

Journal ArticleDOI
Georges Aad1, Alexander Kupco2, P. Davison3, Samuel Webb4  +2888 moreInstitutions (192)
TL;DR: Topological cell clustering is established as a well-performing calorimeter signal definition for jet and missing transverse momentum reconstruction in ATLAS and is exploited to apply a local energy calibration and corrections depending on the nature of the cluster.
Abstract: The reconstruction of the signal from hadrons and jets emerging from the proton–proton collisions at the Large Hadron Collider (LHC) and entering the ATLAS calorimeters is based on a three-dimensional topological clustering of individual calorimeter cell signals. The cluster formation follows cell signal-significance patterns generated by electromagnetic and hadronic showers. In this, the clustering algorithm implicitly performs a topological noise suppression by removing cells with insignificant signals which are not in close proximity to cells with significant signals. The resulting topological cell clusters have shape and location information, which is exploited to apply a local energy calibration and corrections depending on the nature of the cluster. Topological cell clustering is established as a well-performing calorimeter signal definition for jet and missing transverse momentum reconstruction in ATLAS.

438 citations

Journal ArticleDOI
TL;DR: The therapeutic rationale and potential for targeting the gut microbiota are described, strategies and systems-oriented technologies for achieving this goal are discussed, and efforts to understand the complex host–bacteria interactions are discussed.
Abstract: The significant involvement of the gut microbiota in human health and disease suggests that manipulation of commensal microbial composition through combinations of antibiotics, probiotics and prebiotics could be a novel therapeutic approach. A systems perspective is needed to help understand the complex host-bacteria interactions and their association with pathophysiological phenotypes so that alterations in the composition of the gut microbiota in disease states can be reversed. In this article, we describe the therapeutic rationale and potential for targeting the gut microbiota, and discuss strategies and systems-oriented technologies for achieving this goal.

438 citations

Journal ArticleDOI
TL;DR: It is shown that histone deacetylase-2 (Hdac2) regulates expression of many fetal cardiac isoforms and that Hdac1 and Gsk3β are components of a regulatory pathway providing an attractive therapeutic target for the treatment of cardiac hypertrophy and heart failure.
Abstract: In the adult heart, a variety of stresses induce re-expression of a fetal gene program in association with myocyte hypertrophy and heart failure. Here we show that histone deacetylase-2 (Hdac2) regulates expression of many fetal cardiac isoforms. Hdac2 deficiency or chemical histone deacetylase (HDAC) inhibition prevented the re-expression of fetal genes and attenuated cardiac hypertrophy in hearts exposed to hypertrophic stimuli. Resistance to hypertrophy was associated with increased expression of the gene encoding inositol polyphosphate-5-phosphatase f (Inpp5f) resulting in constitutive activation of glycogen synthase kinase 3beta (Gsk3beta) via inactivation of thymoma viral proto-oncogene (Akt) and 3-phosphoinositide-dependent protein kinase-1 (Pdk1). In contrast, Hdac2 transgenic mice had augmented hypertrophy associated with inactivated Gsk3beta. Chemical inhibition of activated Gsk3beta allowed Hdac2-deficient adults to become sensitive to hypertrophic stimulation. These results suggest that Hdac2 is an important molecular target of HDAC inhibitors in the heart and that Hdac2 and Gsk3beta are components of a regulatory pathway providing an attractive therapeutic target for the treatment of cardiac hypertrophy and heart failure.

437 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