Institution
University of Warwick
Education•Coventry, Warwickshire, United Kingdom•
About: University of Warwick is a education organization based out in Coventry, Warwickshire, United Kingdom. It is known for research contribution in the topics: Population & White dwarf. The organization has 26212 authors who have published 77127 publications receiving 2666552 citations. The organization is also known as: Warwick University & The University of Warwick.
Topics: Population, White dwarf, Politics, Health care, Poison control
Papers published on a yearly basis
Papers
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15 Apr 2015TL;DR: Tropical islands Building blocks Tropical varieties Tropical rain forest Tropical garden Toric connections Bibliography Index Bibliography as mentioned in this paper, Section 5.1.1] and Bibliography 2.2.
Abstract: Tropical islands Building blocks Tropical varieties Tropical rain forest Tropical garden Toric connections Bibliography Index
552 citations
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University of Vienna1, University of Edinburgh2, Massey University3, Newcastle University4, University of Copenhagen5, University of Glasgow6, Massachusetts Institute of Technology7, Boston College8, Fred Hutchinson Cancer Research Center9, University of Aberdeen10, San Diego State University11, Institut national de la recherche agronomique12, University of Birmingham13, Agricultural Research Organization, Volcani Center14, University of Jena15, University of Lausanne16, University of Warwick17, University of Amsterdam18, Delft University of Technology19, Temple University20, Technical University of Denmark21, Columbia University22
TL;DR: In this paper, the authors argue that the ability to predict and manage the function of these highly complex, dynamically changing communities is limited, and that close coordination of experimental data collection and method development with mathematical model building is needed to achieve significant progress in understanding of microbial dynamics and function.
Abstract: The importance of microbial communities (MCs) cannot be overstated. MCs underpin the biogeochemical cycles of the earth’s soil, oceans and the atmosphere, and perform ecosystem functions that impact plants, animals and humans. Yet our ability to predict and manage the function of these highly complex, dynamically changing communities is limited. Building predictive models that link MC composition to function is a key emerging challenge in microbial ecology. Here, we argue that addressing this challenge requires close coordination of experimental data collection and method development with mathematical model building. We discuss specific examples where model–experiment integration has already resulted in important insights into MC function and structure. We also highlight key research questions that still demand better integration of experiments and models. We argue that such integration is needed to achieve significant progress in our understanding of MC dynamics and function, and we make specific practical suggestions as to how this could be achieved.
552 citations
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TL;DR: In all 4 studies, the best-performing strategy from the participants' repertoires most accurately predicted the inferences after sufficient learning opportunities, and when testing SSL against 3 models representing extensions of SSL and against an exemplar model assuming a memory-based inference process, the authors found that SSL predicted theinferences most accurately.
Abstract: The assumption that people possess a repertoire of strategies to solve the inference problems they face has been raised repeatedly. However, a computational model specifying how people select strategies from their repertoire is still lacking. The proposed strategy selection learning (SSL) theory predicts a strategy selection process on the basis of reinforcement learning. The theory assumes that individuals develop subjective expectations for the strategies they have and select strategies proportional to their expectations, which are then updated on the basis of subsequent experience. The learning assumption was supported in 4 experimental studies. Participants substantially improved their inferences through feedback. In all 4 studies, the best-performing strategy from the participants' repertoires most accurately predicted the inferences after sufficient learning opportunities. When testing SSL against 3 models representing extensions of SSL and against an exemplar model assuming a memory-based inference process, the authors found that SSL predicted the inferences most accurately.
551 citations
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TL;DR: In this paper, the authors studied the complexity of finding the values and optimal strategies of mean payoff games on graphs, a family of perfect information games introduced by Ehrenfeucht and Mycielski and considered by Gurvich, Karzanov and Khachiyan.
551 citations
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TL;DR: The authors investigated whether dropout in the Avon Longitudinal study of parents and children (ALSPAC) was systematic or random, and if systematic, whether it had an impact on the prediction of disruptive behaviour disorders.
Abstract: Background
Participant drop-out occurs in all longitudinal studies, and if systematic, may lead to selection biases and erroneous conclusions being drawn from a study.
Aims
We investigated whether drop out in the Avon Longitudinal Study of Parents And Children (ALSPAC) was systematic or random, and if systematic, whether it had an impact on the prediction of disruptive behaviour disorders.
Method
Teacher reports of disruptive behaviour among currently participating, previously participating and never participating children aged 8 years in the ALSPAC longitudinal study were collected. Data on family factors were obtained in pregnancy. Simulations were conducted to explain the impact of selective drop-out on the strength of prediction.
Results
Drop out from the ALSPAC cohort was systematic and children who dropped out were more likely to suffer from disruptive behaviour disorder. Systematic participant drop-out according to the family variables, however, did not alter the association between family factors obtained in pregnancy and disruptive behaviour disorder at 8 years of age.
Conclusions
Cohort studies are prone to selective drop-out and are likely to underestimate the prevalence of psychiatric disorder. This empirical study and the simulations confirm that the validity of regression models is only marginally affected despite range restrictions after selective drop-out.
550 citations
Authors
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Name | H-index | Papers | Citations |
---|---|---|---|
David Miller | 203 | 2573 | 204840 |
Daniel R. Weinberger | 177 | 879 | 128450 |
Kay-Tee Khaw | 174 | 1389 | 138782 |
Joseph E. Stiglitz | 164 | 1142 | 152469 |
Edmund T. Rolls | 153 | 612 | 77928 |
Thomas J. Smith | 140 | 1775 | 113919 |
Tim Jones | 135 | 1314 | 91422 |
Ian Ford | 134 | 678 | 85769 |
Paul Harrison | 133 | 1400 | 80539 |
Sinead Farrington | 133 | 1422 | 91099 |
Peter Hall | 132 | 1640 | 85019 |
Paul Brennan | 132 | 1221 | 72748 |
G. T. Jones | 131 | 864 | 75491 |
Peter Simmonds | 131 | 823 | 62953 |
Tim Martin | 129 | 878 | 82390 |