67,454 research outputs found
Understanding as integration of heterogeneous representations
The search for understanding is a major aim of science. Traditionally, understanding has been undervalued in the philosophy of science because of its psychological underpinnings; nowadays, however, it is widely recognized that epistemology cannot be divorced from psychology as sharp as traditional epistemology required. This eliminates the main obstacle to give scientific understanding due attention in philosophy of science. My aim in this paper is to describe an account of scientific understanding as an emergent feature of our mastering of different (causal) explanatory frameworks that takes place through the mastering of scientific practices. Different practices lead to different kinds of representations. Such representations are often heterogeneous. The integration of such representations constitute understanding
Asymptotically Exact Approximations for the Symmetric Difference of Generalized Marcum-Q Functions
(c) 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. DOI: 10.1109/TVT.2014.2337263In this paper, we derive two simple and asymptotically exact approximations for the function defined as ÎQm(a, b) =Î Qm(a, b) - Qm(b, a). The generalized Marcum Q-function Qm(a, b) appears in many scenarios in communications in this particular form and is referred to as the symmetric difference of generalized Marcum Q-functions or the difference of generalized Marcum Q-functions with reversed arguments. We show that the symmetric difference of Marcum Q-functions can be expressed in terms of a single Gaussian Q-function for large and even moderate values of the arguments a and b. A second approximation for ÎQm(a, b) is also given in terms of the exponential function. We illustrate the applicability of these new approximations in different scenarios: 1) statistical characterization of Hoyt fading; 2) performance analysis of communication systems; 3) level crossing statistics of a sampled Rayleigh envelope; and 4) asymptotic approximation of the Rice Ie-function.Universidad de MĂĄlaga. Campus de Excelencia Internacional. AndalucĂa Tech
Conserved quantities for a charged rotating black holes in 5D Einstein-Maxwell-Chern-Simons theory
Indexación: Scopus.In this work, we compute the conserved quantities of a charged rotating black hole which appears as the solution of Einstein-Maxwell action in five dimensions coupled to a Chern-Simons term for U(1) field. The addition of the Chern-Simons term will modify the Maxwell equations and the definition of charge but not the Einstein field equations. Upon the addition of suitable boundary terms for the pure gravity sector of the theory, which depend on the extrinsic and intrinsic curvatures (Kounterterms), we obtain the correct conserved quantities of the solution. © Published under licence by IOP Publishing Ltd.We thank Giorgos Anastasiou and David Rivera-Betancour for insightful discussions. This work was funded in part by FONDECYT Grant 1131075, UNAB Grant DI-1336-16/RG and CONICYT Grant DPI 20140115.https://iopscience.iop.org/article/10.1088/1742-6596/1043/1/01202
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Automatic Generation of Cognitive Theories using Genetic Programming
Cognitive neuroscience is the branch of neuroscience that studies the neural mechanisms underpinning cognition and develops theories explaining them. Within cognitive neuroscience, computational neuroscience focuses on modeling behavior, using theories expressed as computer programs. Up to now, computational theories have been formulated by neuroscientists. In this paper, we present a new approach to theory development in neuroscience: the automatic generation and testing of cognitive theories using genetic programming. Our approach evolves from experimental data cognitive theories that explain âthe mental programâ that subjects use to solve a specific task. As an example, we have focused on a typical neuroscience experiment, the delayed-match-to-sample (DMTS) task. The main goal of our approach is to develop a tool that neuroscientists can use to develop better cognitive theories
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