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Institution

Imperial College London

EducationLondon, Westminster, United Kingdom
About: Imperial College London is a education organization based out in London, Westminster, United Kingdom. It is known for research contribution in the topics: Population & Turbulence. The organization has 90019 authors who have published 209164 publications receiving 9337534 citations. The organization is also known as: Imperial College of Science, Technology and Medicine & Imperial College.
Topics: Population, Turbulence, Laser, Cancer, Health care


Papers
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Journal ArticleDOI
TL;DR: In this paper, the authors present a review of the applicability and applicability of numerical predictions of turbulent flow, and advocate that computational economy, range of applicability, and physical realism are best served by turbulence models in which the magnitudes of two turbulence quantities, the turbulence kinetic energy k and its dissipation rate ϵ, are calculated from transport equations solved simultaneously with those governing the mean flow behaviour.

11,866 citations

Journal ArticleDOI
Rafael Lozano1, Mohsen Naghavi1, Kyle J Foreman2, Stephen S Lim1  +192 moreInstitutions (95)
TL;DR: The Global Burden of Diseases, Injuries, and Risk Factors Study 2010 aimed to estimate annual deaths for the world and 21 regions between 1980 and 2010 for 235 causes, with uncertainty intervals (UIs), separately by age and sex, using the Cause of Death Ensemble model.

11,809 citations

Journal ArticleDOI
TL;DR: In this paper, the authors consider the problem of comparing complex hierarchical models in which the number of parameters is not clearly defined and derive a measure pD for the effective number in a model as the difference between the posterior mean of the deviances and the deviance at the posterior means of the parameters of interest, which is related to other information criteria and has an approximate decision theoretic justification.
Abstract: Summary. We consider the problem of comparing complex hierarchical models in which the number of parameters is not clearly defined. Using an information theoretic argument we derive a measure pD for the effective number of parameters in a model as the difference between the posterior mean of the deviance and the deviance at the posterior means of the parameters of interest. In general pD approximately corresponds to the trace of the product of Fisher's information and the posterior covariance, which in normal models is the trace of the ‘hat’ matrix projecting observations onto fitted values. Its properties in exponential families are explored. The posterior mean deviance is suggested as a Bayesian measure of fit or adequacy, and the contributions of individual observations to the fit and complexity can give rise to a diagnostic plot of deviance residuals against leverages. Adding pD to the posterior mean deviance gives a deviance information criterion for comparing models, which is related to other information criteria and has an approximate decision theoretic justification. The procedure is illustrated in some examples, and comparisons are drawn with alternative Bayesian and classical proposals. Throughout it is emphasized that the quantities required are trivial to compute in a Markov chain Monte Carlo analysis.

11,691 citations

Journal ArticleDOI
TL;DR: In this paper, the authors proposed a simple method to extract the community structure of large networks based on modularity optimization, which is shown to outperform all other known community detection methods in terms of computation time.
Abstract: We propose a simple method to extract the community structure of large networks. Our method is a heuristic method that is based on modularity optimization. It is shown to outperform all other known community detection methods in terms of computation time. Moreover, the quality of the communities detected is very good, as measured by the so-called modularity. This is shown first by identifying language communities in a Belgian mobile phone network of 2 million customers and by analysing a web graph of 118 million nodes and more than one billion links. The accuracy of our algorithm is also verified on ad hoc modular networks.

11,078 citations


Authors

Showing all 90798 results

NameH-indexPapersCitations
Albert Hofman2672530321405
David Miller2032573204840
Tamara B. Harris2011143163979
Mark I. McCarthy2001028187898
Peter J. Barnes1941530166618
Simon D. M. White189795231645
Patrick W. Serruys1862427173210
John Hardy1771178171694
Simon Baron-Cohen172773118071
Richard H. Friend1691182140032
Yang Gao1682047146301
Hongfang Liu1662356156290
Philippe Froguel166820118816
Salvador Moncada164495138030
Dennis R. Burton16468390959
Network Information
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023413
20221,329
202112,883
202012,472
201911,096
201810,236