846 research outputs found

    Engineering model transformations with transML

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    The final publication is available at Springer via http://dx.doi.org/10.1007%2Fs10270-011-0211-2Model transformation is one of the pillars of model-driven engineering (MDE). The increasing complexity of systems and modelling languages has dramatically raised the complexity and size of model transformations as well. Even though many transformation languages and tools have been proposed in the last few years, most of them are directed to the implementation phase of transformation development. In this way, even though transformations should be built using sound engineering principles—just like any other kind of software—there is currently a lack of cohesive support for the other phases of the transformation development, like requirements, analysis, design and testing. In this paper, we propose a unified family of languages to cover the life cycle of transformation development enabling the engineering of transformations. Moreover, following an MDE approach, we provide tools to partially automate the progressive refinement of models between the different phases and the generation of code for several transformation implementation languages.This work has been sponsored by the Spanish Ministry of Science and Innovation with project METEORIC (TIN2008-02081), and by the R&D program of the Community of Madrid with projects “e-Madrid" (S2009/TIC-1650). Parts of this work were done during the research stays of Esther and Juan at the University of York, with financial support from the Spanish Ministry of Science and Innovation (grant refs. JC2009-00015, PR2009-0019 and PR2008-0185)

    The failure of stellar feedback, magnetic fields, conduction, and morphological quenching in maintaining red galaxies

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    The quenching "maintenance'" and related "cooling flow" problems are important in galaxies from Milky Way mass through clusters. We investigate this in halos with masses ∼1012−1014 M⊙\sim 10^{12}-10^{14}\,{\rm M}_{\odot}, using non-cosmological high-resolution hydrodynamic simulations with the FIRE-2 (Feedback In Realistic Environments) stellar feedback model. We specifically focus on physics present without AGN, and show that various proposed "non-AGN" solution mechanisms in the literature, including Type Ia supernovae, shocked AGB winds, other forms of stellar feedback (e.g. cosmic rays), magnetic fields, Spitzer-Braginskii conduction, or "morphological quenching" do not halt or substantially reduce cooling flows nor maintain "quenched" galaxies in this mass range. We show that stellar feedback (including cosmic rays from SNe) alters the balance of cold/warm gas and the rate at which the cooled gas within the galaxy turns into stars, but not the net baryonic inflow. If anything, outflowing metals and dense gas promote additional cooling. Conduction is important only in the most massive halos, as expected, but even at ∼1014 M⊙\sim 10^{14}\,{\rm M}_{\odot} reduces inflow only by a factor ∼2\sim 2 (owing to saturation effects and anisotropic suppression). Changing the morphology of the galaxies only slightly alters their Toomre-QQ parameter, and has no effect on cooling (as expected), so has essentially no effect on cooling flows or maintaining quenching. This all supports the idea that additional physics, e.g., AGN feedback, must be important in massive galaxies.Comment: 16 pages, 12 figure

    Air data measurement system for space shuttle

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    It is concluded that air data measurements of angle of attack and sideslip are needed to control the space shuttle vehicles. The basis for this conclusion, along with recommended sensor design and implementation, are described

    A modular approach to the specification and management of time duration constraints in BPMN

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    The modeling and management of business processes deals with temporal aspects both in the inherent representation of activity coordination and in the specification of activity properties and constraints. In this paper, we address the modeling and specification of constraints related to the duration of process activities. In detail, we consider the Business Process Model and Notation (BPMN) standard and propose an approach to define re-usable duration-aware process models that make use of existing BPMN elements for representing different nuances of activity duration at design time. Moreover, we show how advanced event-handling techniques may be exploited for detecting the violation of duration constraints during the process run-time. The set of process models specified in this paper suitably captures duration constraints at different levels of abstraction, by allowing designers to specify the duration of atomic tasks and of selected process regions in a way that is conceptually and semantically BPMN-compliant. Without loss of generality, we refer to real-world clinical working environments to exemplify our approach, as their intrinsic complexity makes them a particularly challenging and rewarding application environment

    Nationality Classification Using Name Embeddings

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    Nationality identification unlocks important demographic information, with many applications in biomedical and sociological research. Existing name-based nationality classifiers use name substrings as features and are trained on small, unrepresentative sets of labeled names, typically extracted from Wikipedia. As a result, these methods achieve limited performance and cannot support fine-grained classification. We exploit the phenomena of homophily in communication patterns to learn name embeddings, a new representation that encodes gender, ethnicity, and nationality which is readily applicable to building classifiers and other systems. Through our analysis of 57M contact lists from a major Internet company, we are able to design a fine-grained nationality classifier covering 39 groups representing over 90% of the world population. In an evaluation against other published systems over 13 common classes, our F1 score (0.795) is substantial better than our closest competitor Ethnea (0.580). To the best of our knowledge, this is the most accurate, fine-grained nationality classifier available. As a social media application, we apply our classifiers to the followers of major Twitter celebrities over six different domains. We demonstrate stark differences in the ethnicities of the followers of Trump and Obama, and in the sports and entertainments favored by different groups. Finally, we identify an anomalous political figure whose presumably inflated following appears largely incapable of reading the language he posts in.Comment: 10 pages, 9 figures, 4 table, accepted by CIKM 2017, Demo and free API: www.name-prism.co

    Shakespeare and O'Neill: an evaluation of technique in tragedy

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    Thesis (M.A.)--Boston University, 194
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