484 research outputs found

    De Gustibus non est Taxandum: Heterogeneity in Preferences and Optimal Redistribution

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    The prominent but unproven intuition that preference heterogeneity reduces re-distribution in a standard optimal tax model is shown to hold under the plausible condition that the distribution of preferences for consumption relative to leisure rises, in terms of first-order stochastic dominance, with income. Given mainstream functional form assumptions on utility and the distributions of ability and preferences, a simple statistic for the effect of preference heterogeneity on marginal tax rates is derived. Numerical simulations and suggestive empirical evidence demonstrate the link between this potentially measurable statistic and the quantitative implications of preference heterogeneity for policy.

    Positive and Normative Judgments Implicit in U.S. Tax Policy, and the Costs of Unequal Growth and Recessions

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    Calculating the welfare implications of changes to economic policy or shocks requires economists to decide on a normative criterion. One approach is to elicit the relevant moral criteria from real-world policy choices, converting a normative decision into a positive inference, as in the recent surge of “inverse-optimum” research. We find that capitalizing on the potential of this approach is not as straightforward as we might hope. We perform the inverse-optimum inference on U.S. tax policy from 1979 through 2010 and argue that the results either undermine the normative relevance of the approach or challenge conventional assumptions upon which economists routinely rely when performing welfare evaluations

    Eine qualitative Untersuchung der Generalisierungsverhaltens von CNNs zur Instrumentenerkennung

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    KĂŒnstliche neuronale Netze (ANNs) haben sich im Bereich des maschinellen Lernens fĂŒr Audiodaten als erfolgreichstes Werkzeug mit hoher Klassifikationsrate etabliert [1]. Ein bedeutender Nachteil besteht aus wissenschaftlicher Sicht jedoch in der schweren Interpretierbarkeit des von ANNs tatsĂ€chlich gelernten Inhalts [2, 3]. Um dieses Problem anzugehen untersuchen wir in dieser Arbeit den Lern- und Generalisierungsprozess eines Convolutional Neural Networks (CNNs) fĂŒr Multi-Label Instrumentenerkennung in den Hidden Layers des Netzwerks. Wir betrachten die unterschiedlichen Aktivierungen aller Layers durch unterschiedliche Instrumentenklassen um nachzuvollziehen, ab welcher Tiefe das Netzwerk in der Lage ist, zwei von der gleichen Klasse stammenden Stimuli als Ă€hnlich zu erkennen. Wir wiederholen das Experiment mit den gleichen Stimuli fĂŒr ein auf die Erkennung von vier Emotionen trainiertes CNNs. Dabei bestĂ€tigen sich einerseits viele unserer Betrachtungen zum Generalisierungsprozess, gleichzeitig lassen die Ergebnisse darauf schließen, dass das auf Emotionserkennung trainierte Netzwerk in der Lage ist, instrumententypische Patterns zu lernen

    Fully differential QCD corrections to single top quark final states

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    A new next-to-leading order Monte Carlo program for calculation of fully differential single top quark final states is described and first results presented. Both the s- and t-channel contributions are included.Comment: 3 pages, 3 figures, talk presented at DPF2000, August 9-12, 2000. To appear in International Journal of Modern Physics

    One-loop N-point equivalence among negative-dimensional, Mellin-Barnes and Feynman parametrization approaches to Feynman integrals

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    We show that at one-loop order, negative-dimensional, Mellin-Barnes' (MB) and Feynman parametrization (FP) approaches to Feynman loop integrals calculations are equivalent. Starting with a generating functional, for two and then for NN-point scalar integrals we show how to reobtain MB results, using negative-dimensional and FP techniques. The N−N-point result is valid for different masses, arbitrary exponents of propagators and dimension.Comment: 11 pages, LaTeX. To be published in J.Phys.

    Subtraction Terms for Hadronic Production Processes at Next-to-Next-to-Leading Order

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    I describe a subtraction scheme for the next-to-next-to-leading order calculation of single inclusive production at hadron colliders. Such processes include Drell-Yan, W^{+/-}, Z and Higgs Boson production. The key to such a calculation is a treatment of initial state radiation which preserves the production characteristics, such as the rapidity distribution, of the process involved. The method builds upon the Dipole Formalism and, with proper modifications, could be applied to deep inelastic scattering and e^+ e^- annihilation to hadrons.Comment: 4 page
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