Institution
University of California
Education•Oakland, California, United States•
About: University of California is a education organization based out in Oakland, California, United States. It is known for research contribution in the topics: Population & Layer (electronics). The organization has 55175 authors who have published 52933 publications receiving 1491169 citations. The organization is also known as: UC & University of California System.
Topics: Population, Layer (electronics), Cancer, Context (language use), Gene
Papers published on a yearly basis
Papers
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TL;DR: Extracellular responses of single units in striate cortex of the cat were studied quantitatively to determine if orientation tuning was dependent on contrast and whether stimuli presented at non-optimal orientations can suppress responses to below the general maintained discharge levels.
Abstract: Extracellular responses of single units in striate cortex of the cat were studied quantitatively. Sinusoidal gratings were used as stimuli and the variables of interest were orientation and contrast. Specifically, we wanted to determine if orientation tuning was dependent on contrast. Of 45 cells studied in detail, two basic types of contrast-response pattern were observed, but most patterns were intermediate between these extremes. In one type, responses increased approximately linearly with log contrast while in the second, saturation was found at low contrast levels. For all these cells, orientation tuning characteristics were independent of contrast. An additional observation, made from 14 cells, was that stimuli presented at non-optimal orientations can suppress responses to below the general maintained discharge levels. In eight of these cases, the inhibition was clearly contrast-dependent.
365 citations
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TL;DR: In this article, the authors argue that intelligible appeals to interlevel causes (top-down and bottom-up) can be understood, without remainder, as appeals to mechanistically mediated effects.
Abstract: We argue that intelligible appeals to interlevel causes (top-down and bottom-up) can be understood, without remainder, as appeals to mechanistically mediated effects. Mechanistically mediated effects are hybrids of causal and constitutive relations, where the causal relations are exclusively intralevel. The idea of causation would have to stretch to the breaking point to accommodate interlevel causes. The notion of a mechanistically mediated effect is preferable because it can do all of the required work without appealing to mysterious interlevel causes. When interlevel causes can be translated into mechanistically mediated effects, the posited relationship is intelligible and should raise no special philosophical objections. When they cannot, they are suspect.
365 citations
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TL;DR: In Winfree's terminology, the existence of isochrons is shown and some of their properties are established and the models leading to these questions concerning dynamical systems are discussed.
Abstract: Winfree has developed mathematical models for his phase resetting experiments on biological clocks. These models lead him to ask a number of mathematical questions concerning dynamical systems. This paper deals with these mathematical questions. In Winfree's terminology we show the existence of isochrons and establish some of their properties.
364 citations
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24 Aug 2009TL;DR: The lesson is drawn that bias is a critical problem that threatens both the effectiveness of processes that rely on biased datasets to build prediction models and the generalizability of hypotheses tested on biased data.
Abstract: Software engineering researchers have long been interested in where and why bugs occur in code, and in predicting where they might turn up next. Historical bug-occurence data has been key to this research. Bug tracking systems, and code version histories, record when, how and by whom bugs were fixed; from these sources, datasets that relate file changes to bug fixes can be extracted. These historical datasets can be used to test hypotheses concerning processes of bug introduction, and also to build statistical bug prediction models. Unfortunately, processes and humans are imperfect, and only a fraction of bug fixes are actually labelled in source code version histories, and thus become available for study in the extracted datasets. The question naturally arises, are the bug fixes recorded in these historical datasets a fair representation of the full population of bug fixes? In this paper, we investigate historical data from several software projects, and find strong evidence of systematic bias. We then investigate the potential effects of "unfair, imbalanced" datasets on the performance of prediction techniques. We draw the lesson that bias is a critical problem that threatens both the effectiveness of processes that rely on biased datasets to build prediction models and the generalizability of hypotheses tested on biased data.
364 citations
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TL;DR: In this paper, the Higgs masses were estimated to lie within 2 GeV of their theoretically predicted values over a very large fraction of the MSSM parameter space using a very simple approximation scheme which includes the most important terms from each of the three components mentioned above.
Abstract: To obtain the most accurate predictions for the Higgs masses in the minimal supersymmetric model (MSSM), one should compute the full set of one-loop radiative corrections, resum the large logarithms to all orders, and add the dominant two-loop effects. A complete computation following this procedure yields a complex set of formulae which must be analyzed numerically. We discuss a very simple approximation scheme which includes the most important terms from each of the three components mentioned above. We estimate that the Higgs masses computed using our scheme lie within 2 GeV of their theoretically predicted values over a very large fraction of MSSM parameter space.
364 citations
Authors
Showing all 55232 results
Name | H-index | Papers | Citations |
---|---|---|---|
Meir J. Stampfer | 277 | 1414 | 283776 |
George M. Whitesides | 240 | 1739 | 269833 |
Michael Karin | 236 | 704 | 226485 |
Fred H. Gage | 216 | 967 | 185732 |
Rob Knight | 201 | 1061 | 253207 |
Martin White | 196 | 2038 | 232387 |
Simon D. M. White | 189 | 795 | 231645 |
Scott M. Grundy | 187 | 841 | 231821 |
Peidong Yang | 183 | 562 | 144351 |
Patrick O. Brown | 183 | 755 | 200985 |
Michael G. Rosenfeld | 178 | 504 | 107707 |
George M. Church | 172 | 900 | 120514 |
David Haussler | 172 | 488 | 224960 |
Yang Yang | 171 | 2644 | 153049 |
Alan J. Heeger | 171 | 913 | 147492 |