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Institution

The Chinese University of Hong Kong

EducationHong Kong, China
About: The Chinese University of Hong Kong is a education organization based out in Hong Kong, China. It is known for research contribution in the topics: Population & Computer science. The organization has 43411 authors who have published 93672 publications receiving 3066651 citations.
Topics: Population, Computer science, Cancer, Medicine, China


Papers
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Journal ArticleDOI
TL;DR: The proposed 3D DSN is capable of conducting volume‐to‐volume learning and inference, which can eliminate redundant computations and alleviate the risk of over‐fitting on limited training data, and the3D deep supervision mechanism can effectively cope with the optimization problem of gradients vanishing or exploding when training a 3D deep model.

507 citations

Proceedings ArticleDOI
07 Dec 2015
TL;DR: Zhang et al. as mentioned in this paper proposed a novel deep convolutional network (DCN) that achieves outstanding performance on FDDB, PASCAL Face, and AFW, achieving a high recall rate of 90.99% on the challenging FDDB benchmark, outperforming the state-of-the-art method.
Abstract: In this paper, we propose a novel deep convolutional network (DCN) that achieves outstanding performance on FDDB, PASCAL Face, and AFW. Specifically, our method achieves a high recall rate of 90.99% on the challenging FDDB benchmark, outperforming the state-of-the-art method [23] by a large margin of 2.91%. Importantly, we consider finding faces from a new perspective through scoring facial parts responses by their spatial structure and arrangement. The scoring mechanism is carefully formulated considering challenging cases where faces are only partially visible. This consideration allows our network to detect faces under severe occlusion and unconstrained pose variation, which are the main difficulty and bottleneck of most existing face detection approaches. We show that despite the use of DCN, our network can achieve practical runtime speed.

506 citations

Journal ArticleDOI
TL;DR: Evidence is found that instrumentality mediates the relationship of relational and balanced forms with OCB; however, the transactional contract form is directly related to OCB.
Abstract: This study examined the generalizability of psychological contract forms observed in the West (D. M. Rousseau, 2000) to China. Using 2 independent samples, results confirmed the generalizability of 3 psychological contract forms: transactional, relational, and balanced. This study also examined the nature of relationships of psychological contracts with organizational citizenship behavior (OCB). In particular, this study explored the role of instrumentality as a mediating psychological process. The authors found evidence that instrumentality mediates the relationship of relational and balanced forms with OCB; however, the transactional contract form is directly related to OCB. The authors discuss the implications of these results for the meaning of psychological contracts and OCB in China and raise issues for future research.

506 citations

Journal ArticleDOI
Bernardo Adeva1, M. Aguilar-Benitez, H. Akbari2, J. Alcaraz  +587 moreInstitutions (26)
TL;DR: The L3 experiment as discussed by the authors is one of the six large detectors designed for the new generation of electron-positron accelerators, which is the only detector that concentrates its efforts on limited goals of measuring electrons, muons and photons.
Abstract: The L3 experiment is one of the six large detectors designed for the new generation of electron-positron accelerators. It is the only detector that concentrates its efforts on limited goals of measuring electrons, muons and photons. By not attempting to identify hadrons, L3 has been able to provide an order of magnitude better resolution for electrons, muons and photons. Vertices and hadron jets are also studied. The construction of L3 has involved much state of the art technology in new principles of vertex detection and in new crystals for large scale electromagnetic shower detection and ultraprecise muon detection. This paper presents a summary of the construction of L3.

505 citations

Journal ArticleDOI
TL;DR: Some of the existing activities and future opportunities related to big data for health, outlining some of the key underlying issues that need to be tackled are discussed.
Abstract: This paper provides an overview of recent developments in big data in the context of biomedical and health informatics. It outlines the key characteristics of big data and how medical and health informatics, translational bioinformatics, sensor informatics, and imaging informatics will benefit from an integrated approach of piecing together different aspects of personalized information from a diverse range of data sources, both structured and unstructured, covering genomics, proteomics, metabolomics, as well as imaging, clinical diagnosis, and long-term continuous physiological sensing of an individual. It is expected that recent advances in big data will expand our knowledge for testing new hypotheses about disease management from diagnosis to prevention to personalized treatment. The rise of big data, however, also raises challenges in terms of privacy, security, data ownership, data stewardship, and governance. This paper discusses some of the existing activities and future opportunities related to big data for health, outlining some of the key underlying issues that need to be tackled.

505 citations


Authors

Showing all 43993 results

NameH-indexPapersCitations
Michael Marmot1931147170338
Jing Wang1844046202769
Jiaguo Yu178730113300
Yang Yang1712644153049
Mark Gerstein168751149578
Gang Chen1673372149819
Jun Wang1661093141621
Jean Louis Vincent1611667163721
Wei Zheng1511929120209
Rui Zhang1512625107917
Ben Zhong Tang1492007116294
Kypros H. Nicolaides147130287091
Thomas S. Huang1461299101564
Galen D. Stucky144958101796
Joseph J.Y. Sung142124092035
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023212
2022904
20217,888
20207,245
20195,968
20185,372