Institution
University of Edinburgh
Education•Edinburgh, United Kingdom•
About: University of Edinburgh is a education organization based out in Edinburgh, United Kingdom. It is known for research contribution in the topics: Population & Galaxy. The organization has 57604 authors who have published 151616 publications receiving 6687334 citations. The organization is also known as: Edinburgh University & The University of Edinburgh.
Topics: Population, Galaxy, Redshift, Gene, Poison control
Papers published on a yearly basis
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TL;DR: An inventory of 20 items with a set of instructions and response- and computational-conventions is proposed and the results obtained from a young adult population numbering some 1100 individuals are reported.
Abstract: The need for a simply applied quantitative assessment of handedness is discussed and some previous forms reviewed An inventory of 20 items with a set of instructions and response- and computational-conventions is proposed and the results obtained from a young adult population numbering some 1100 individuals are reported The separate items are examined from the point of view of sex, cultural and socio-economic factors which might appertain to them and also of their inter-relationship to each other and to the measure computed from them all Criteria derived from these considerations are then applied to eliminate 10 of the original 20 items and the results recomputed to provide frequency-distribution and cumulative frequency functions and a revised item-analysis The difference of incidence of handedness between the sexes is discussed
30,901 citations
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TL;DR: This paper describes a method of transferring fragments of DNA from agarose gels to cellulose nitrate filters that can be hybridized to radioactive RNA and hybrids detected by radioautography or fluorography.
Abstract: This paper describes a method of transferring fragments of DNA from agarose gels to cellulose nitrate filters. The fragments can then be hybridized to radioactive RNA and hybrids detected by radioautography or fluorography. The method is illustrated by analyses of restriction fragments complementary to ribosomal RNAs from Escherichia coli and Xenopus laevis , and from several mammals.
30,179 citations
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TL;DR: Findings indicate that the "diabetes epidemic" will continue even if levels of obesity remain constant, and given the increasing prevalence of obesity, it is likely that these figures provide an underestimate of future diabetes prevalence.
Abstract: OBJECTIVE —The goal of this study was to estimate the prevalence of diabetes and the number of people of all ages with diabetes for years 2000 and 2030. RESEARCH DESIGN AND METHODS —Data on diabetes prevalence by age and sex from a limited number of countries were extrapolated to all 191 World Health Organization member states and applied to United Nations’ population estimates for 2000 and 2030. Urban and rural populations were considered separately for developing countries. RESULTS —The prevalence of diabetes for all age-groups worldwide was estimated to be 2.8% in 2000 and 4.4% in 2030. The total number of people with diabetes is projected to rise from 171 million in 2000 to 366 million in 2030. The prevalence of diabetes is higher in men than women, but there are more women with diabetes than men. The urban population in developing countries is projected to double between 2000 and 2030. The most important demographic change to diabetes prevalence across the world appears to be the increase in the proportion of people >65 years of age. CONCLUSIONS —These findings indicate that the “diabetes epidemic” will continue even if levels of obesity remain constant. Given the increasing prevalence of obesity, it is likely that these figures provide an underestimate of future diabetes prevalence.
15,897 citations
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TL;DR: The state-of-the-art in evaluated methods for both classification and detection are reviewed, whether the methods are statistically different, what they are learning from the images, and what the methods find easy or confuse.
Abstract: The Pascal Visual Object Classes (VOC) challenge is a benchmark in visual object category recognition and detection, providing the vision and machine learning communities with a standard dataset of images and annotation, and standard evaluation procedures. Organised annually from 2005 to present, the challenge and its associated dataset has become accepted as the benchmark for object detection.
This paper describes the dataset and evaluation procedure. We review the state-of-the-art in evaluated methods for both classification and detection, analyse whether the methods are statistically different, what they are learning from the images (e.g. the object or its context), and what the methods find easy or confuse. The paper concludes with lessons learnt in the three year history of the challenge, and proposes directions for future improvement and extension.
11,545 citations
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TL;DR: In this article, a unit root test for dynamic heterogeneous panels based on the mean of individual unit root statistics is proposed, which converges in probability to a standard normal variate sequentially with T (the time series dimension) →∞, followed by N (the cross sectional dimension)→∞.
Abstract: This paper proposes unit root tests for dynamic heterogeneous panels based on the mean of individual unit root statistics. In particular it proposes a standardized t-bar test statistic based on the (augmented) Dickey–Fuller statistics averaged across the groups. Under a general setting this statistic is shown to converge in probability to a standard normal variate sequentially with T (the time series dimension) →∞, followed by N (the cross sectional dimension) →∞. A diagonal convergence result with T and N→∞ while N/T→k, k being a finite non-negative constant, is also conjectured. In the special case where errors in individual Dickey–Fuller (DF) regressions are serially uncorrelated a modified version of the standardized t-bar statistic is shown to be distributed as standard normal as N→∞ for a fixed T, so long as T>5 in the case of DF regressions with intercepts and T>6 in the case of DF regressions with intercepts and linear time trends. An exact fixed N and T test is also developed using the simple average of the DF statistics. Monte Carlo results show that if a large enough lag order is selected for the underlying ADF regressions, then the small sample performances of the t-bar test is reasonably satisfactory and generally better than the test proposed by Levin and Lin (Unpublished manuscript, University of California, San Diego, 1993).
11,149 citations
Authors
Showing all 57604 results
Name | H-index | Papers | Citations |
---|---|---|---|
Paul M. Ridker | 233 | 1242 | 245097 |
David J. Hunter | 213 | 1836 | 207050 |
Mark J. Daly | 204 | 763 | 304452 |
Robert M. Califf | 196 | 1561 | 167961 |
Martin White | 196 | 2038 | 232387 |
Michael Marmot | 193 | 1147 | 170338 |
Paul M. Thompson | 183 | 2271 | 146736 |
Peter W.F. Wilson | 181 | 680 | 139852 |
Gonçalo R. Abecasis | 179 | 595 | 230323 |
John J.V. McMurray | 178 | 1389 | 184502 |
Douglas Scott | 178 | 1111 | 185229 |
Michael I. Jordan | 176 | 1016 | 216204 |
Hyun-Chul Kim | 176 | 4076 | 183227 |
Andrea Bocci | 172 | 2402 | 176461 |
Simon Baron-Cohen | 172 | 773 | 118071 |