Institution
University of Wisconsin–Milwaukee
Education•Milwaukee, Wisconsin, United States•
About: University of Wisconsin–Milwaukee is a education organization based out in Milwaukee, Wisconsin, United States. It is known for research contribution in the topics: Population & Gravitational wave. The organization has 11839 authors who have published 28034 publications receiving 936438 citations. The organization is also known as: UWM & University of Wisconsin-Milwaukee.
Papers published on a yearly basis
Papers
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TL;DR: In this paper, the authors quantitatively summarized the literature examining the association between acceptance of rape myths and exposure to pornography and found that exposure to porn does not increase the acceptance of these myths.
Abstract: This paper quantitatively summarizes the literature examining the association between acceptance of rape myths and exposure to pornography. In this meta-analysis, nonexperimental methodology shows almost no effect (exposure to pornography does not increase rape myth acceptance), while experimental studies show positive effect (exposure to pornography does increase rape myth acceptance). Although the experimental studies demonstrate that violent pornography has more effect than nonviolent pornography, nonviolent pornography still demonstrates an effect.
200 citations
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National Cheng Kung University1, Tsinghua University2, Academia Sinica3, University of Southern California4, Yuan Ze University5, National Tsing Hua University6, University of Tokyo7, University of Manchester8, Budapest University of Technology and Economics9, University of Wisconsin–Milwaukee10, University of Geneva11, Novartis12, University of Zurich13, University of Iowa14, Mayo Clinic15, Georgetown University Medical Center16, University of Aveiro17, University of Colorado Denver18
TL;DR: Evaluating teams using the gold standard and inferred ground truth shows that the EM algorithm allows team submissions to be differentiated while keeping the manual annotation effort feasible, and shows measures of comparative performance between teams.
Abstract: We report the Gene Normalization (GN) challenge in BioCreative III where participating teams were asked to return a ranked list of identifiers of the genes detected in full-text articles. For training, 32 fully and 500 partially annotated articles were prepared. A total of 507 articles were selected as the test set. Due to the high annotation cost, it was not feasible to obtain gold-standard human annotations for all test articles. Instead, we developed an Expectation Maximization (EM) algorithm approach for choosing a small number of test articles for manual annotation that were most capable of differentiating team performance. Moreover, the same algorithm was subsequently used for inferring ground truth based solely on team submissions. We report team performance on both gold standard and inferred ground truth using a newly proposed metric called Threshold Average Precision (TAP-k). We received a total of 37 runs from 14 different teams for the task. When evaluated using the gold-standard annotations of the 50 articles, the highest TAP-k scores were 0.3297 (k=5), 0.3538 (k=10), and 0.3535 (k=20), respectively. Higher TAP-k scores of 0.4916 (k=5, 10, 20) were observed when evaluated using the inferred ground truth over the full test set. When combining team results using machine learning, the best composite system achieved TAP-k scores of 0.3707 (k=5), 0.4311 (k=10), and 0.4477 (k=20) on the gold standard, representing improvements of 12.4%, 21.8%, and 26.6% over the best team results, respectively. By using full text and being species non-specific, the GN task in BioCreative III has moved closer to a real literature curation task than similar tasks in the past and presents additional challenges for the text mining community, as revealed in the overall team results. By evaluating teams using the gold standard, we show that the EM algorithm allows team submissions to be differentiated while keeping the manual annotation effort feasible. Using the inferred ground truth we show measures of comparative performance between teams. Finally, by comparing team rankings on gold standard vs. inferred ground truth, we further demonstrate that the inferred ground truth is as effective as the gold standard for detecting good team performance.
200 citations
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TL;DR: Les AA. etudient le vote en faveur des femmes dans le cadre d'election a des postes de senateur ou de gouverneur.
Abstract: Les AA. etudient le vote en faveur des femmes dans le cadre d'election a des postes de senateur ou de gouverneur. Ils presentent un certain nombre de donnees collectees aux Etats-Unis en 1992 dans 14 Etats ou une femme etait le candidat d'un des deux grands partis. Dans ce cadre, ils etudient le lien entre identite politique et soutien politique. Ils s'efforcent de savoir si les hommes soutiennent massivement les republicains, parti conservateur, et les femmes les democrates, parti liberal. Ils examinent l'influence de l'identite sexuelle, des partis politiques, de la perception des candidates feministes, le contexte politique specifique des elections
200 citations
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TL;DR: Investigation of Lower Pleistocene sites at Chesowanja have yielded in situ Oldowan and Oldowan-like stone artefacts, evidence of fire and a fragmentary ‘robust’ australopithecine cranium.
Abstract: Recent investigations of Lower Pleistocene sites at Chesowanja have yielded in situ Oldowan and Oldowan-like stone artefacts, evidence of fire and a fragmentary 'robust' australopithecine cranium. Burnt clay found at one artefact locality dated to >1.42±0.07 Myr is the earliest known evidence of fire associated with a hominid occupation site.
200 citations
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TL;DR: The results suggested that the lower extremity is able to adapt to fatigue though altering kinematics at impact and redistributing work to larger proximal muscles.
199 citations
Authors
Showing all 11948 results
Name | H-index | Papers | Citations |
---|---|---|---|
Caroline S. Fox | 155 | 599 | 138951 |
Mark D. Griffiths | 124 | 1238 | 61335 |
Benjamin William Allen | 124 | 807 | 87750 |
James A. Dumesic | 118 | 615 | 58935 |
Richard O'Shaughnessy | 114 | 462 | 77439 |
Patrick Brady | 110 | 442 | 73418 |
Laura Cadonati | 109 | 450 | 73356 |
Stephen Fairhurst | 109 | 426 | 71657 |
Benno Willke | 109 | 508 | 74673 |
Benjamin J. Owen | 108 | 351 | 70678 |
Kenneth H. Nealson | 108 | 483 | 51100 |
P. Ajith | 107 | 372 | 70245 |
Duncan A. Brown | 107 | 567 | 68823 |
I. A. Bilenko | 105 | 393 | 68801 |
F. Fidecaro | 105 | 569 | 74781 |