1,097 research outputs found

    Degree of explanation

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    Partial explanations are everywhere. That is, explanations citing causes that explain some but not all of an effect are ubiquitous across science, and these in turn rely on the notion of degree of explanation. I argue that current accounts are seriously deficient. In particular, they do not incorporate adequately the way in which a cause’s explanatory importance varies with choice of explanandum. Using influential recent contrastive theories, I develop quantitative definitions that remedy this lacuna, and relate it to existing measures of degree of causation. Among other things, this reveals the precise role here of chance, as well as bearing on the relation between causal explanation and causation itself

    It's just a feeling: why economic models do not explain

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    Julian Reiss correctly identified a trilemma about economic models: we cannot maintain that they are false, but nevertheless explain and that only true accounts explain. In this reply we give reasons to reject the second premise – that economic models explain. Intuitions to the contrary should be distrusted

    The visual orbits of the spectroscopic binaries HD 6118 and HD 27483 from the Palomar Testbed Interferometer

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    We present optical interferometric observations of two double-lined spectroscopic binaries, HD 6118 and HD 27483, taken with the Palomar Testbed Interferometer (PTI) in the K band. HD 6118 is one of the most eccentric spectroscopic binaries and HD 27483 a spectroscopic binary in the Hyades open cluster. The data collected with PTI in 2001-2002 allow us to determine astrometric orbits and when combined with the radial velocity measurements derive all physical parameters of the systems. The masses of the components are 2.65 +/- 0.27 M_Sun and 2.36 +/- 0.24 M_Sun for HD 6118 and 1.38 +/- 0.13 M_Sun and 1.39 +/- 0.13 M_Sun for HD 27483. The apparent semi-major axis of HD 27483 is only 1.2 mas making it the closest binary successfully observed with an optical interferometer.Comment: submitted to Ap

    ANALYSIS OF NUCLEI FLUORESCENCE HISTOGRAMS USING NON-LINEAR FUNCTIONS OR WAVELETS

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    Histograms based on 5,000 nuclei from cells (Chinese hamster ovary cells, bone marrow cells) are used to determine the coefficient of variation (CV) of observations surrounding the highest peak. The cells are subjected to various treatments, for example exposure to herbicides. By eyeballing the histogram, an interval under the highest peak is determined. The CV calculated from the histogram on the eyeballed interval is the response variable in an ANOVA. To avoid the subjectivity of eyeballing the histogram, non-linear functions such as the Gaussian density function can be used to model the histogram. The CV may then be determined from the parameter estimates. In many experiments nonlinear functions modeling the histograms smooth away differences in CV s obtained this way, though visually the histograms appear to be different. Then nonlinear functions or wavelets can be used to obtain intervals for calculating CV s of the histograms restricted to these intervals. The nonlinear models require close initial values for each histogram, while the wavelets just require choice of wavelet and level of decomposition

    Adjustment with aphasia after stroke: study protocol for a pilot feasibility randomised controlled trial for SUpporting wellbeing through PEeR Befriending (SUPERB)

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    Background: Despite the high prevalence of mood problems after stroke, evidence on effective interventions particularly for those with aphasia is limited. There is a pressing need to systematically evaluate interventions aiming to improve wellbeing for people with stroke and aphasia. This study aims to evaluate the feasibility of a peer-befriending intervention. Methods/design: SUPERB is a single blind, parallel group feasibility trial of peer befriending for people with aphasia post-stroke and low levels of psychological distress. The trial includes a nested qualitative study and pilot economic evaluation and it compares usual care (n = 30) with usual care + peer befriending (n = 30). Feasibility outcomes include proportion screened who meet criteria, proportion who consent, rate of consent, number of missing/incomplete data on outcome measures, attrition rate at follow-up, potential value of conducting main trial using value of information analysis (economic evaluation), description of usual care, and treatment fidelity of peer befriending. Assessments and outcome measures (mood, wellbeing, communication, and social participation) for participants and significant others will be administered at baseline, with outcome measures re-administered at 4 and 10 months post-randomisation. Peer befrienders will complete outcome measures before training and after they have completed two cycles of befriending. The qualitative study will use semi-structured interviews of purposively sampled participants (n = 20) and significant others (n = 10) from both arms of the trial, and all peer befrienders to explore the acceptability of procedures and experiences of care. The pilot economic evaluation will utilise the European Quality of life measure (EQ-5D-5 L) and a stroke-adapted version of the Client Service Receipt Inventory (CSRI). Discussion: This study will provide information on feasibility outcomes and an initial indication of whether peer befriending is a suitable intervention to explore further in a definitive phase III randomised controlled trial. Trial registration: ClinicalTrials.gov identifier NCT02947776, registered 28th October 2016

    Deterministically Computing Reduction Numbers of Polynomial Ideals

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    We discuss the problem of determining reduction number of a polynomial ideal I in n variables. We present two algorithms based on parametric computations. The first one determines the absolute reduction number of I and requires computation in a polynomial ring with (n-dim(I))dim(I) parameters and n-dim(I) variables. The second one computes via a Grobner system the set of all reduction numbers of the ideal I and thus in particular also its big reduction number. However,it requires computations in a ring with n.dim(I) parameters and n variables.Comment: This new version replaces the earlier version arXiv:1404.1721 and it has been accepted for publication in the proceedings of CASC 2014, Warsaw, Polna
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