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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 & Cancer. The organization has 43411 authors who have published 93672 publications receiving 3066651 citations.


Papers
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Journal ArticleDOI
TL;DR: In this article, the properties of the structure functions and other small-scale quantities in turbulent Rayleigh-Benard convection are reviewed from an experimental, theoretical, and numerical point of view.
Abstract: The properties of the structure functions and other small-scale quantities in turbulent Rayleigh-Benard convection are reviewed, from an experimental, theoretical, and numerical point of view. In particular, we address the question of whether, and if so where in the flow, the so-called Bolgiano-Obukhov scaling exists, i.e., Sθ(r) ∼ r2/5 for the second-order temperature structure function and Su(r) ∼ r6/5 for the second-order velocity structure function. Apart from the anisotropy and inhomogeneity of the flow, insufficiently high Rayleigh numbers, and intermittency corrections (which all hinder the identification of such a potential regime), there are also reasons, as a matter of principle, why such a scaling regime may be limited to at most a decade, namely the lack of clear scale separation between the Bolgiano length scale LB and the height of the cell.

750 citations

Journal ArticleDOI
01 Dec 2004
TL;DR: This editorial paper presents a snapshot of recent developments in wireless communications integrated with developments in pervasive and wearable technologies and addresses some of the challenges and future implementation issues from the m-Health perspective.
Abstract: M-Health can be defined as “mobile computing, medical sensor, and communications technologies for health-care.” This emerging concept represents the evolution of e-health systems from traditional desktop “telemedicine” platforms to wireless and mobile configurations. Current and emerging developments in wireless communications integrated with developments in pervasive and wearable technologies will have a radical impact on future health-care delivery systems. This editorial paper presents a snapshot of recent developments in these areas and addresses some of the challenges and future implementation issues from the m-Health perspective. The contributions presented in this special section represent some of these recent developments and illustrate the multidisciplinary nature of this important and emerging concept.

748 citations

Journal ArticleDOI
TL;DR: This paper proposes a collaborative filtering approach for predicting QoS values of Web services and making Web service recommendation by taking advantages of past usage experiences of service users, and shows that the algorithm achieves better prediction accuracy than other approaches.
Abstract: With increasing presence and adoption of Web services on the World Wide Web, Quality-of-Service (QoS) is becoming important for describing nonfunctional characteristics of Web services. In this paper, we present a collaborative filtering approach for predicting QoS values of Web services and making Web service recommendation by taking advantages of past usage experiences of service users. We first propose a user-collaborative mechanism for past Web service QoS information collection from different service users. Then, based on the collected QoS data, a collaborative filtering approach is designed to predict Web service QoS values. Finally, a prototype called WSRec is implemented by Java language and deployed to the Internet for conducting real-world experiments. To study the QoS value prediction accuracy of our approach, 1.5 millions Web service invocation results are collected from 150 service users in 24 countries on 100 real-world Web services in 22 countries. The experimental results show that our algorithm achieves better prediction accuracy than other approaches. Our Web service QoS data set is publicly released for future research.

741 citations

Journal ArticleDOI
TL;DR: Future efforts should focus on providing more balanced availability of specialised stroke services across the country, enhancing evidence-based practice, and encouraging greater translational research to improve outcome of patients with stroke.
Abstract: With over 2 million new cases annually, stroke is associated with the highest disability-adjusted life-years lost of any disease in China. The burden is expected to increase further as a result of population ageing, an ongoing high prevalence of risk factors (eg, hypertension), and inadequate management. Despite improved access to overall health services, the availability of specialist stroke care is variable across the country, and especially uneven in rural areas. In-hospital outcomes have improved because of a greater availability of reperfusion therapies and supportive care, but adherence to secondary prevention strategies and long-term care are inadequate. Thrombolysis and stroke units are accepted as standards of care across the world, including in China, but bleeding-risk concerns and organisational challenges hamper widespread adoption of this care in China. Despite little supporting evidence, Chinese herbal products and neuroprotective drugs are widely used, and the increased availability of neuroimaging techniques also results in overdiagnosis and overtreatment of so-called silent stroke. Future efforts should focus on providing more balanced availability of specialised stroke services across the country, enhancing evidence-based practice, and encouraging greater translational research to improve outcome of patients with stroke.

740 citations

Posted Content
TL;DR: Zhang et al. as mentioned in this paper proposed a Domain Guided Dropout (DGD) algorithm to improve the feature learning procedure for person re-ID, which outperformed state-of-the-art methods on multiple datasets by large margins.
Abstract: Learning generic and robust feature representations with data from multiple domains for the same problem is of great value, especially for the problems that have multiple datasets but none of them are large enough to provide abundant data variations. In this work, we present a pipeline for learning deep feature representations from multiple domains with Convolutional Neural Networks (CNNs). When training a CNN with data from all the domains, some neurons learn representations shared across several domains, while some others are effective only for a specific one. Based on this important observation, we propose a Domain Guided Dropout algorithm to improve the feature learning procedure. Experiments show the effectiveness of our pipeline and the proposed algorithm. Our methods on the person re-identification problem outperform state-of-the-art methods on multiple datasets by large margins.

740 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
2022903
20217,888
20207,245
20195,968
20185,372