20,963 research outputs found

    Accuracy-based scoring for DOT: towards direct error minimization for data-oriented translation

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    In this work we present a novel technique to rescore fragments in the Data-Oriented Translation model based on their contribution to translation accuracy. We describe three new rescoring methods, and present the initial results of a pilot experiment on a small subset of the Europarl corpus. This work is a proof-of-concept, and is the first step in directly optimizing translation decisions solely on the hypothesized accuracy of potential translations resulting from those decisions

    Use of a 3-item short-form version of the Barthel Index for use in stroke: systematic review and external validation

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    Background and Purpose—There may be a potential to reduce the number of items assessed in the Barthel Index (BI), and shortened versions of the BI have been described. We sought to collate all existing short-form BI (SF-BI) and perform a comparative validation using clinical trial data. Methods—We performed a systematic review across multidisciplinary electronic databases to find all published SF-BI. Our validation used the VISTA (Virtual International Stroke Trials Archive) resource. We describe concurrent validity (agreement of each SF-BI with BI), convergent and divergent validity (agreement of each SF-BI with other outcome measures available in the data set), predictive validity (association of prognostic factors with SF-BI outcomes), and content validity (item correlation and exploratory factor analyses). Results—From 3546 titles, we found 8 articles describing 6 differing SF-BI. Using acute trial data (n=8852), internal reliability suggested redundancy in BI (Cronbach α, 0.96). Each SF-BI demonstrated a strong correlation with BI, modified Rankin Scale, National Institutes of Health Stroke Scale (all ρ≄0.83; P<0.001). Using rehabilitation trial data (n=332), SF-BI demonstrated modest correlation with quality of life measures Stroke Impact Scale and 5 domain EuroQOL (ρ≄0.50, P<0.001). Prespecified prognostic factors were associated with SF-BI outcomes (all P<0.001). Our factor analysis described a 3 factor structure, and item reduction suggested an optimal 3-item SF-BI comprising bladder control, transfer, and mobility items in keeping with 1 of the 3-item SF-BI previously described in the literature. Conclusions—There is redundancy in the original BI; we have demonstrated internal and external validity of a 3-item SF-BI that should be simple to use

    Assessing the Lead Market Potential of Countries for Innovation Projects

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    This paper presents an approach to assessing the potential of countries to increase the likelihood that locally preferred innovation designs become successful in other countries, too. The concept suggests that for many innovations lead markets exist that initiate the international diffusion of a specific design of an innovation. Once a specific innovation design has been adopted by users in the lead market chances are that it subsequently becomes adopted by users in other countries as well. Lead markets can be utilised for the development of global innovation designs. By focusing on the design of the innovation which responds to the preferences within the lead market, a company can leverage the success experienced in the lead market for global market launch. In order to follow a lead market strategy of new product development, it is necessary to assess the lead market potential of countries before an innovation is developed and tested in the market. This paper presents an indicator-based methodology that approximates the lead market attributes of countries. This assessment methodology was applied to two innovation projects at the truck division of DaimlerChrysler AG. The method produces information that is of importance for the development phase and the market launch of globally standardised innovations.Innovation, Global Diffusion, Market Entry

    Use of the R-group descriptor for alignment-free QSAR

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    An R-group descriptor characterises the distribution of some atom-based property, such as elemental type or partial atomic charge, at increasing numbers of bonds distant from the point of substitution on a parent ring system. Application of Partial Least Squares (PLS) to datasets for which bioactivity data and R-group descriptor information are available is shown to provide an effective way of generating QSAR models with a high level of predictive ability. The resulting models are competitive with the models produced by established QSAR approaches, are readily interpretable in structural terms, and are shown to be of value in the optimisation of a lead series

    Multicentre observational cohort study of NSAIDs as risk factors for postoperative adverse events in gastrointestinal surgery

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    Introduction: Non-steroidal anti-inflammatory drugs (NSAIDs) are recommended as postoperative analgesia by the Enhanced Recovery After Surgery Society. Recent studies have raised concerns that NSAID administration following colorectal anastomosis may be associated with increased risk of anastomotic leak. This multicentre study aims to determine NSAIDs' safety profile following gastrointestinal resection. Methods and analysis: This prospective, multicentre cohort study will be performed over a 2-week period utilising a collaborative methodology. Consecutive adults undergoing open or laparoscopic, elective or emergency gastrointestinal resection will be included. The primary end point will be the 30-day morbidity, assessed using the Clavien-Dindo classification. This study will be disseminated through medical student networks, with an anticipated recruitment of at least 900 patients. The study will be powered to detect a 10% increase in complication rates with NSAID use. Ethics and dissemination: Following the Research Ethics Committee Chairperson's review, a formal waiver was received. This study will be registered as a clinical audit or service evaluation at each participating hospital. Dissemination will take place through previously described novel research collaborative networks

    Soft clustering analysis of galaxy morphologies: A worked example with SDSS

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    Context: The huge and still rapidly growing amount of galaxies in modern sky surveys raises the need of an automated and objective classification method. Unsupervised learning algorithms are of particular interest, since they discover classes automatically. Aims: We briefly discuss the pitfalls of oversimplified classification methods and outline an alternative approach called "clustering analysis". Methods: We categorise different classification methods according to their capabilities. Based on this categorisation, we present a probabilistic classification algorithm that automatically detects the optimal classes preferred by the data. We explore the reliability of this algorithm in systematic tests. Using a small sample of bright galaxies from the SDSS, we demonstrate the performance of this algorithm in practice. We are able to disentangle the problems of classification and parametrisation of galaxy morphologies in this case. Results: We give physical arguments that a probabilistic classification scheme is necessary. The algorithm we present produces reasonable morphological classes and object-to-class assignments without any prior assumptions. Conclusions: There are sophisticated automated classification algorithms that meet all necessary requirements, but a lot of work is still needed on the interpretation of the results.Comment: 18 pages, 19 figures, 2 tables, submitted to A
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