136,679 research outputs found

    Eventology versus contemporary theories of uncertainty

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    The development of probability theory together with the Bayesian approach in the three last centuries is caused by two factors: the variability of the physical phenomena and partial ignorance about them. As now it is standard to believe [Dubois, 2007], the nature of these key factors is so various, that their descriptions are required special uncertainty theories, which differ from the probability theory and the Bayesian credo, and provide a better account of the various facets of uncertainty by putting together probabilistic and set-valued representations of information to catch a distinction between variability and ignorance. Eventology [Vorobyev, 2007], a new direction of probability theory and philosophy, offers the original event approach to the description of variability and ignorance, entering an agent, together with his/her beliefs, directly in the frameworks of scientific research in the form of eventological distribution of his/her own events. This allows eventology, by putting together probabilistic and set-event representation of information and philosophical concept of event as co-being [Bakhtin, 1920], to provide a unified strong account of various aspects of uncertainty catching distinction between variability and ignorance and opening an opportunity to define imprecise probability as a probability of imprecise event in the mathematical frameworks of Kolmogorov's probability theory [Kolmogorov, 1933].uncertainty, probability, event, co-being, eventology, imprecise event

    MeSH term explosion and author rank improve expert recommendations

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    Information overload is an often-cited phenomenon that reduces the productivity, efficiency and efficacy of scientists. One challenge for scientists is to find appropriate collaborators in their research. The literature describes various solutions to the problem of expertise location, but most current approaches do not appear to be very suitable for expert recommendations in biomedical research. In this study, we present the development and initial evaluation of a vector space model-based algorithm to calculate researcher similarity using four inputs: 1) MeSH terms of publications; 2) MeSH terms and author rank; 3) exploded MeSH terms; and 4) exploded MeSH terms and author rank. We developed and evaluated the algorithm using a data set of 17,525 authors and their 22,542 papers. On average, our algorithms correctly predicted 2.5 of the top 5/10 coauthors of individual scientists. Exploded MeSH and author rank outperformed all other algorithms in accuracy, followed closely by MeSH and author rank. Our results show that the accuracy of MeSH term-based matching can be enhanced with other metadata such as author rank

    Going Beyond Counting First Authors in Author Co-citation Analysis

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    The present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed

    Search for the decay Bs0→D*∓π±

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    A search for the decay Bs0→D*∓π± is presented using a data sample corresponding to an integrated luminosity of 1.0  fb-1 of pp collisions collected by LHCb. This decay is expected to be mediated by a W-exchange diagram, with little contribution from rescattering processes, and therefore a measurement of the branching fraction will help us to understand the mechanism behind related decays such as Bs0→π+π- and Bs0→DD̅ . Systematic uncertainties are minimized by using B0→D*∓π± as a normalization channel. We find no evidence for a signal, and set an upper limit on the branching fraction of B(Bs0→D*∓π±)<6.1(7.8)×10-6 at 90% (95%) confidence level

    "Closing the R&D Gap, Evaluating the Sources of R&D Spending"

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    Both spending and tax policies have been implemented in the United States with the goal of stimulating private sector research and development (R&D). Karier questions whether current R&D policy, especially the research and experimentation tax credit, can contribute to closing the gap between nondefense expenditures on R&D in the United States and such expenditures in other countries, such as Japan and Germany. He also explores possible changes to our current R&D policy to make it more effective.

    A. D. Fricke, author

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    Black and white photograph of author, A. D. Fricke

    Dispelling the Myths Behind First-author Citation Counts

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    We conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more sophisticated methods

    Scholarly Communication and Publishing Lunch and Learn Talk #11: The ULS Open Access Author Fee Fund

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    At the May 2014 talk, you will learn about the ULS Open Access Author Fee Fund--what it is, why we do it, how it works, and how the program is going so far

    Measurement of the B0–B0 oscillation frequency Δmd with the decays B0→D−π+ and B0→ J/ψK∗0

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    The B 0 –B 0 oscillation frequency Δmd is measured by the LHCb experiment using a dataset corresponding to an integrated luminosity of 1.0 fb−1 of proton–proton collisions at √ s = 7 TeV, and is found to be Δmd =0.5156±0.0051 (stat.)±0.0033 (syst.) ps−1 . The measurement is based on results from analyses of the decays B 0 → D −π + (D − → K +π −π −) and B 0 → J/ψK ∗0 (J/ψ →μ +μ −,K ∗0 → K +π −) and their charge conjugated modes

    Evidence for the decay B0→J/ψω and measurement of the relative branching fractions of meson decays to J/ψη and J/ψη′

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    First evidence of the B 0 → J / ψ ω decay is found and the B s 0 → J / ψ η and B s 0 → J / ψ η ′ decays are studied using a dataset corresponding to an integrated luminosity of 1.0 fb -1 collected by the LHCb experiment in proton-proton collisions at a centre-of-mass energy of sqrt(s) = 7 TeV. The branching fractions of these decays are measured relative to that of the B 0 → J / ψ ρ 0 decay:frac(B (B 0 → J / ψ ω), B (B 0 → J / ψ ρ 0)) = 0.89 ± 0.19 (stat) - 0.13 + 0.07 (syst),frac(B (B s 0 → J / ψ η), B (B 0 → J / ψ ρ 0)) = 14.0 ± 1.2 (stat) - 1.5 + 1.1 (syst) - 1.0 + 1.1 (frac(f d, f s)),frac(B (B s 0 → J / ψ η ′), B (B 0 → J / ψ ρ 0)) = 12.7 ± 1.1 (stat) - 1.3 + 0.5 (syst) - 0.9 + 1.0 (frac(f d, f s)), where the last uncertainty is due to the knowledge of f d / f s, the ratio of b-quark hadronization factors that accounts for the different production rate of B 0 and B s 0 mesons. The ratio of the branching fractions of B s 0 → J / ψ η ′ and B s 0 → J / ψ η decays is measured to befrac(B (B s 0 → J / ψ η ′), B (B s 0 → J / ψ η)) = 0.90 ± 0.09 (stat) - 0.02 + 0.06 (syst)
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