3,493 research outputs found

    The Compressible to Incompressible Limit of 1D Euler Equations: the Non Smooth Case

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    We prove a rigorous convergence result for the compressible to incompressible limit of weak entropy solutions to the isothermal 1D Euler equations.Comment: 16 page

    On Classification with Bags, Groups and Sets

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    Many classification problems can be difficult to formulate directly in terms of the traditional supervised setting, where both training and test samples are individual feature vectors. There are cases in which samples are better described by sets of feature vectors, that labels are only available for sets rather than individual samples, or, if individual labels are available, that these are not independent. To better deal with such problems, several extensions of supervised learning have been proposed, where either training and/or test objects are sets of feature vectors. However, having been proposed rather independently of each other, their mutual similarities and differences have hitherto not been mapped out. In this work, we provide an overview of such learning scenarios, propose a taxonomy to illustrate the relationships between them, and discuss directions for further research in these areas

    Dissimilarity-based Ensembles for Multiple Instance Learning

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    In multiple instance learning, objects are sets (bags) of feature vectors (instances) rather than individual feature vectors. In this paper we address the problem of how these bags can best be represented. Two standard approaches are to use (dis)similarities between bags and prototype bags, or between bags and prototype instances. The first approach results in a relatively low-dimensional representation determined by the number of training bags, while the second approach results in a relatively high-dimensional representation, determined by the total number of instances in the training set. In this paper a third, intermediate approach is proposed, which links the two approaches and combines their strengths. Our classifier is inspired by a random subspace ensemble, and considers subspaces of the dissimilarity space, defined by subsets of instances, as prototypes. We provide guidelines for using such an ensemble, and show state-of-the-art performances on a range of multiple instance learning problems.Comment: Submitted to IEEE Transactions on Neural Networks and Learning Systems, Special Issue on Learning in Non-(geo)metric Space

    The seed of goal-related doubts : a longitudinal investigation of the roles of failure and expectation of success among police trainee applicants

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    Various theories on personal goal striving rely on the assumption that failure raises doubts about the goal. Yet, empirical evidence for an association between objective failure experiences and doubts about personal long-term goals is still missing. In a longitudinal field study, applicants for a job as a police trainee (n = 172, Mage = 25.15; 55 females and 117 males) were accompanied across three measurement times over a period of five months. We investigated the effects of failure and initial expectation of success (in the standardized selection process) on doubts regarding the superordinate goal of becoming a police officer. As hypothesized, both failure and low initial expectation of success as well as their interaction led to increased goal-related doubts over time. The findings provide first empirical evidence for the role of failure in the emergence of goal-related doubts in personal long-term goals and, therefore, the disengagement process as it is hypothesized in various theories on goal striving and life-span development
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