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.
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TL;DR: N-version programming has been proposed as a method of incorporating fault tolerance into software and it is revealed that the programs were individually extremely reliable but that the number of tests in which more than one program failed was substantially more than expected.
Abstract: N-version programming has been proposed as a method of incorporating fault tolerance into software. Multiple versions of a program (i.e. `N') are prepared and executed in parallel. Their outputs are collected and examined by a voter, and, if they are not identical, it is assumed that the majority is correct. This method depends for its reliability improvement on the assumption that programs that have been developed independently will fail independently. An experiment is described in which the fundamental axiom is tested. In all, 27 versions of a program were prepared independently from the same specification at two universities and then subjected to one million tests. The results of the tests revealed that the programs were individually extremely reliable but that the number of tests in which more than one program failed was substantially more than expected. The results of these tests are presented along with an analysis of some of the faults that were found in the programs. Background information on the programmers used is also summarized.
789 citations
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TL;DR: In this article, the authors consider a wide class of latent variable models, including Gaussian mixture models, hidden Markov models, and latent Dirichlet allocation, which exploit a certain tensor structure in their low-order observable moments (typically, of second and third-order).
Abstract: This work considers a computationally and statistically efficient parameter estimation method for a wide class of latent variable models--including Gaussian mixture models, hidden Markov models, and latent Dirichlet allocation--which exploits a certain tensor structure in their low-order observable moments (typically, of second- and third-order). Specifically, parameter estimation is reduced to the problem of extracting a certain (orthogonal) decomposition of a symmetric tensor derived from the moments; this decomposition can be viewed as a natural generalization of the singular value decomposition for matrices. Although tensor decompositions are generally intractable to compute, the decomposition of these specially structured tensors can be efficiently obtained by a variety of approaches, including power iterations and maximization approaches (similar to the case of matrices). A detailed analysis of a robust tensor power method is provided, establishing an analogue of Wedin's perturbation theorem for the singular vectors of matrices. This implies a robust and computationally tractable estimation approach for several popular latent variable models.
789 citations
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TL;DR: The fundamental engineering principles used to design RCA nanotechnologies are introduced, the recently developed RCA-based diagnostics and bioanalytical tools are discussed, and the use of RCA to construct multivalent molecular scaffolds and nanostructures for applications in biology, diagnostic and therapeutics is summarized.
Abstract: Rolling circle amplification (RCA) is an isothermal enzymatic process where a short DNA or RNA primer is amplified to form a long single stranded DNA or RNA using a circular DNA template and special DNA or RNA polymerases. The RCA product is a concatemer containing tens to hundreds of tandem repeats that are complementary to the circular template. The power, simplicity, and versatility of the DNA amplification technique have made it an attractive tool for biomedical research and nanobiotechnology. Traditionally, RCA has been used to develop sensitive diagnostic methods for a variety of targets including nucleic acids (DNA, RNA), small molecules, proteins, and cells. RCA has also attracted significant attention in the field of nanotechnology and nanobiotechnology. The RCA-produced long, single-stranded DNA with repeating units has been used as template for the periodic assembly of nanospecies. Moreover, since RCA products can be tailor-designed by manipulating the circular template, RCA has been employed to generate complex DNA nanostructures such as DNA origami, nanotubes, nanoribbons and DNA based metamaterials. These functional RCA based nanotechnologies have been utilized for biodetection, drug delivery, designing bioelectronic circuits and bioseparation. In this review, we introduce the fundamental engineering principles used to design RCA nanotechnologies, discuss recently developed RCA-based diagnostics and bioanalytical tools, and summarize the use of RCA to construct multivalent molecular scaffolds and nanostructures for applications in biology, diagnostics and therapeutics.
788 citations
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Pacific Northwest National Laboratory1, Yale University2, National Center for Atmospheric Research3, Marine Biological Laboratory4, Colorado State University5, Wageningen University and Research Centre6, University of California, Irvine7, Kansas State University8, University of Oregon9, Michigan Technological University10, University of Sydney11, University of Minnesota12, Duke University13, University of Tennessee14, University of Copenhagen15, Spanish National Research Council16, University of New Hampshire17, Northeast Normal University18, University of California, Berkeley19, University of Oklahoma20, Hungarian Academy of Sciences21, Swedish University of Agricultural Sciences22, University of Manchester23, Tsinghua University24, National University of Singapore25, Chinese Academy of Sciences26, University of Hohenheim27, University of Georgia28, Hampshire College29, Boston University30, University of Alaska Anchorage31
TL;DR: In this article, the authors present a comprehensive analysis of warming-induced changes in soil carbon stocks by assembling data from 49 field experiments located across North America, Europe and Asia, and provide estimates of soil carbon sensitivity to warming that may help to constrain Earth system model projections.
Abstract: The majority of the Earth's terrestrial carbon is stored in the soil. If anthropogenic warming stimulates the loss of this carbon to the atmosphere, it could drive further planetary warming. Despite evidence that warming enhances carbon fluxes to and from the soil, the net global balance between these responses remains uncertain. Here we present a comprehensive analysis of warming-induced changes in soil carbon stocks by assembling data from 49 field experiments located across North America, Europe and Asia. We find that the effects of warming are contingent on the size of the initial soil carbon stock, with considerable losses occurring in high-latitude areas. By extrapolating this empirical relationship to the global scale, we provide estimates of soil carbon sensitivity to warming that may help to constrain Earth system model projections. Our empirical relationship suggests that global soil carbon stocks in the upper soil horizons will fall by 30 ± 30 petagrams of carbon to 203 ± 161 petagrams of carbon under one degree of warming, depending on the rate at which the effects of warming are realized. Under the conservative assumption that the response of soil carbon to warming occurs within a year, a business-as-usual climate scenario would drive the loss of 55 ± 50 petagrams of carbon from the upper soil horizons by 2050. This value is around 12-17 per cent of the expected anthropogenic emissions over this period. Despite the considerable uncertainty in our estimates, the direction of the global soil carbon response is consistent across all scenarios. This provides strong empirical support for the idea that rising temperatures will stimulate the net loss of soil carbon to the atmosphere, driving a positive land carbon-climate feedback that could accelerate climate change.
787 citations
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TL;DR: It is argued that there is an inherent gap between the social requirements of CSCW and its technical mechanisms and that the challenge of the social-technical gap creates an opportunity to refocus CSCw.
Abstract: Over the last 10 years, Computer-Supported Cooperative Work (CSCW) has identified a base set of findings. These findings are taken almost as assumptions within the field. In summary, they argue that human activity is highly flexible, nuanced, and contextualized and that computational entities such as information sharing, roles, and social norms need to be similarly flexible, nuanced, and contextualized. However, current systems cannot fully support the social world uncovered by these findings. In this article I argue that there is an inherent gap between the social requirements of CSCW and its technical mechanisms. The social-technical gap is the divide between what we know we must support socially and what we can support technically. Exploring, understanding, and hopefully ameliorating this social-technical gap is the central challenge for CSCW as a field and one of the central problems for human-computer interaction. Indeed, merely attesting the continued centrality of this gap could be one of the important intellectual contributions of CSCW. I also argue that the challenge of the social-technical gap creates an opportunity to refocus CSCW.
787 citations
Authors
Showing all 47751 results
Name | H-index | Papers | Citations |
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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 |