Institution
University of California, Irvine
Education•Irvine, California, United States•
About: University of California, Irvine is a education organization based out in Irvine, California, United States. It is known for research contribution in the topics: Population & Galaxy. The organization has 47031 authors who have published 113602 publications receiving 5521832 citations. The organization is also known as: UC Irvine & UCI.
Topics: Population, Galaxy, Poison control, Cancer, Gene
Papers published on a yearly basis
Papers
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University of California, Irvine1, University of Montana2, University of California, Riverside3, University of Notre Dame4, University of Vermont5, Kansas State University6, State University of New York at Plattsburgh7, Rockefeller University8, Vanderbilt University9, University of New Orleans10, University of California, Santa Cruz11
TL;DR: In this article, the introduction of invasive species and identifying life history stages where management will be most effective are discussed. And evolutionary processes may be key features in determining whether invasive species establish and spread.
Abstract: ■ Abstract Contributions from the field of population biology hold promise for understanding and managing invasiveness; invasive species also offer excellent opportunities to study basic processes in population biology. Life history studies and demographic models may be valuable for examining the introduction of invasive species and identifying life history stages where management will be most effective. Evolutionary processes may be key features in determining whether invasive species establish and spread. Studies of genetic diversity and evolutionary changes should be useful for
3,280 citations
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TL;DR: The authors argue that the relationship between ostensive and performative aspects of routines creates an on-going opportunity for variation, selection, and retention of new practices and patterns of action within routines and allows routines to generate a wide range of outcomes, from apparent stability to apparent stability.
Abstract: In this paper, we challenge the traditional understanding of organizational routines as creating inertia in organizations. We adapt Latour's distinction between ostensive and performative to build a theory that explains why routines are a source of change as well as stability. The ostensive aspect of a routine embodies what we typically think of as the structure. The performative aspect embodies the specific actions, by specific people, at specific times and places, that bring the routine to life. We argue that the ostensive aspect enables people to guide, account for, and refer to specific performances of a routine, and the performative aspect creates, maintains, and modifies the ostensive aspect of the routine. We argue that the relationship between ostensive and performative aspects of routines creates an on-going opportunity for variation, selection, and retention of new practices and patterns of action within routines and allows routines to generate a wide range of outcomes, from apparent stability t...
3,257 citations
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TL;DR: The Bayesian classifier is shown to be optimal for learning conjunctions and disjunctions, even though they violate the independence assumption, and will often outperform more powerful classifiers for common training set sizes and numbers of attributes, even if its bias is a priori much less appropriate to the domain.
Abstract: The simple Bayesian classifier is known to be optimal when attributes are independent given the class, but the question of whether other sufficient conditions for its optimality exist has so far not been explored. Empirical results showing that it performs surprisingly well in many domains containing clear attribute dependences suggest that the answer to this question may be positive. This article shows that, although the Bayesian classifier‘s probability estimates are only optimal under quadratic loss if the independence assumption holds, the classifier itself can be optimal under zero-one loss (misclassification rate) even when this assumption is violated by a wide margin. The region of quadratic-loss optimality of the Bayesian classifier is in fact a second-order infinitesimal fraction of the region of zero-one optimality. This implies that the Bayesian classifier has a much greater range of applicability than previously thought. For example, in this article it is shown to be optimal for learning conjunctions and disjunctions, even though they violate the independence assumption. Further, studies in artificial domains show that it will often outperform more powerful classifiers for common training set sizes and numbers of attributes, even if its bias is a priori much less appropriate to the domain. This article‘s results also imply that detecting attribute dependence is not necessarily the best way to extend the Bayesian classifier, and this is also verified empirically.
3,225 citations
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26 Apr 2017TL;DR: In this paper, the authors reported the experimental discovery of intrinsic ferromagnetism in Cr 2 Ge 2 Te 6 atomic layers by scanning magneto-optic Kerr microscopy.
Abstract: We report the experimental discovery of intrinsic ferromagnetism in Cr 2 Ge 2 Te 6 atomic layers by scanning magneto-optic Kerr microscopy. In this 2D van der Waals ferromagnet, unprecedented control of transition temperature is realized via small magnetic fields.
3,215 citations
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California Institute of Technology1, University of Hertfordshire2, University of California, Berkeley3, Jet Propulsion Laboratory4, University of Cambridge5, Centre national de la recherche scientifique6, University of Auckland7, GlaxoSmithKline8, Max Planck Society9, Stellenbosch University10, University of Connecticut Health Center11, Virginia Bioinformatics Institute12, University of California, Irvine13, Keio University14, Princeton University15
TL;DR: This work summarizes the Systems Biology Markup Language (SBML) Level 1, a free, open, XML-based format for representing biochemical reaction networks, a software-independent language for describing models common to research in many areas of computational biology.
Abstract: Motivation: Molecular biotechnology now makes it possible to build elaborate systems models, but the systems biology community needs information standards if models are to be shared, evaluated and developed cooperatively. Results: We summarize the Systems Biology Markup Language (SBML) Level 1, a free, open, XML-based format for representing biochemical reaction networks. SBML is a software-independent language for describing models common to research in many areas of computational biology, including cell signaling pathways, metabolic pathways, gene regulation, and others. ∗ To whom correspondence should be addressed. Availability: The specification of SBML Level 1 is freely available from http://www.sbml.org/.
3,205 citations
Authors
Showing all 47751 results
Name | H-index | Papers | Citations |
---|---|---|---|
Daniel Levy | 212 | 933 | 194778 |
Rob Knight | 201 | 1061 | 253207 |
Lewis C. Cantley | 196 | 748 | 169037 |
Dennis W. Dickson | 191 | 1243 | 148488 |
Terrie E. Moffitt | 182 | 594 | 150609 |
Joseph Biederman | 179 | 1012 | 117440 |
John R. Yates | 177 | 1036 | 129029 |
John A. Rogers | 177 | 1341 | 127390 |
Avshalom Caspi | 170 | 524 | 113583 |
Yang Gao | 168 | 2047 | 146301 |
Carl W. Cotman | 165 | 809 | 105323 |
John H. Seinfeld | 165 | 921 | 114911 |
Gregg C. Fonarow | 161 | 1676 | 126516 |
Jerome I. Rotter | 156 | 1071 | 116296 |
David Cella | 156 | 1258 | 106402 |