5,394 research outputs found

    Solar X-rays scattered by Venus, Mars and the Moon

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    Scattering process of solar X rays with photoionization fluorescence by planetary atmosphere

    Pairing Correlations in the Two-Dimensional Hubbard Model

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    We present the results of a quantum Monte Carlo study of the extended ss and the dx2y2d_{x^2-y^2} pairing correlation functions for the two-dimensional Hubbard model, computed with the constrained-path method. For small lattice sizes and weak interactions, we find that the dx2y2d_{x^2-y^2} pairing correlations are stronger than the extended ss pairing correlations and are positive when the pair separation exceeds several lattice constants. As the system size or the interaction strength increases, the magnitude of the long-range part of both correlation functions vanishes.Comment: 4 pages, RevTex, 4 figures included; submitted to Phys. Rev. Let

    A Constrained Path Quantum Monte Carlo Method for Fermion Ground States

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    We propose a new quantum Monte Carlo algorithm to compute fermion ground-state properties. The ground state is projected from an initial wavefunction by a branching random walk in an over-complete basis space of Slater determinants. By constraining the determinants according to a trial wavefunction ΨT|\Psi_T \rangle, we remove the exponential decay of signal-to-noise ratio characteristic of the sign problem. The method is variational and is exact if ΨT|\Psi_T\rangle is exact. We report results on the two-dimensional Hubbard model up to size 16×1616\times 16, for various electron fillings and interaction strengths.Comment: uuencoded compressed postscript file. 5 pages with 1 figure. accepted by PRL

    Context-specific activation of hippocampus and SN/VTA by reward is related to enhanced long-term memory for embedded objects

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    Animal studies indicate that hippocampal representations of environmental context modulate reward-related processing in the substantia nigra and ventral tegmental area (SN/VTA), a major origin of dopamine in the brain. Using functional magnetic resonance imaging (fMRI) in humans, we investigated the neural specificity of context-reward associations under conditions where the presence of perceptually similar neutral contexts imposed high demands on a putative hippocampal function, pattern separation. The design also allowed us to investigate how contextual reward enhances long-term memory for embedded neutral objects. SN/VTA activity underpinned specific context-reward associations in the face of perceptual similarity. A reward-related enhancement of long-term memory was restricted to the condition where the rewarding and the neutral contexts were perceptually similar, and in turn was linked to co-activation of the hippocampus (subfield DG/CA3) and SN/VTA. Thus, an ability of contextual reward to enhance memory for focal objects is closely linked to context-related engagement of hippocampal-SN/VTA circuitry

    PCDI7: THE EFFECTS OF PAYOR STATUS ON PROCEDURE USE AND OUTCOMES OF PATIENTS WITH CONGESTIVE HEART FAILURE

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    Graphene Transport at High Carrier Densities using a Polymer Electrolyte Gate

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    We report the study of graphene devices in Hall-bar geometry, gated with a polymer electrolyte. High densities of 6 ×1013/cm2\times 10^{13}/cm^{2} are consistently reached, significantly higher than with conventional back-gating. The mobility follows an inverse dependence on density, which can be correlated to a dominant scattering from weak scatterers. Furthermore, our measurements show a Bloch-Gr\"uneisen regime until 100 K (at 6.2 ×1013/cm2\times10^{13}/cm^{2}), consistent with an increase of the density. Ubiquitous in our experiments is a small upturn in resistivity around 3 ×1013/cm2\times10^{13}/cm^{2}, whose origin is discussed. We identify two potential causes for the upturn: the renormalization of Fermi velocity and an electrochemically-enhanced scattering rate.Comment: 13 pages, 4 figures, Published Versio

    The Nature of the H2-Emitting Gas in the Crab Nebula

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    Understanding how molecules and dust might have formed within a rapidly expanding young supernova remnant is important because of the obvious application to vigorous supernova activity at very high redshift. In previous papers, we found that the H2 emission is often quite strong, correlates with optical low-ionization emission lines, and has a surprisingly high excitation temperature. Here we study Knot 51, a representative, bright example, for which we have available long slit optical and NIR spectra covering emission lines from ionized, neutral, and molecular gas, as well as HST visible and SOAR Telescope NIR narrow-band images. We present a series of CLOUDY simulations to probe the excitation mechanisms, formation processes and dust content in environments that can produce the observed H2 emission. We do not try for an exact match between model and observations given Knot 51's ambiguous geometry. Rather, we aim to explain how the bright H2 emission lines can be formed from within the volume of Knot 51 that also produces the observed optical emission from ionized and neutral gas. Our models that are powered only by the Crab's synchrotron radiation are ruled out because they cannot reproduce the strong, thermal H2 emission. The simulations that come closest to fitting the observations have the core of Knot 51 almost entirely atomic with the H2 emission coming from just a trace molecular component, and in which there is extra heating. In this unusual environment, H2 forms primarily by associative detachment rather than grain catalysis. In this picture, the 55 H2-emitting cores that we have previously catalogued in the Crab have a total mass of about 0.1 M_sun, which is about 5% of the total mass of the system of filaments. We also explore the effect of varying the dust abundance. We discuss possible future observations that could further elucidate the nature of these H2 knots.Comment: 51 pages, 15 figures, accepted for publication in MNRAS, revised Figure 12 results unchange

    Toward Interpretable Deep Reinforcement Learning with Linear Model U-Trees

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    Deep Reinforcement Learning (DRL) has achieved impressive success in many applications. A key component of many DRL models is a neural network representing a Q function, to estimate the expected cumulative reward following a state-action pair. The Q function neural network contains a lot of implicit knowledge about the RL problems, but often remains unexamined and uninterpreted. To our knowledge, this work develops the first mimic learning framework for Q functions in DRL. We introduce Linear Model U-trees (LMUTs) to approximate neural network predictions. An LMUT is learned using a novel on-line algorithm that is well-suited for an active play setting, where the mimic learner observes an ongoing interaction between the neural net and the environment. Empirical evaluation shows that an LMUT mimics a Q function substantially better than five baseline methods. The transparent tree structure of an LMUT facilitates understanding the network's learned knowledge by analyzing feature influence, extracting rules, and highlighting the super-pixels in image inputs.Comment: This paper is accepted by ECML-PKDD 201
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