4,893 research outputs found

    Combination of a magnetic Feshbach resonance and an optical bound-to-bound transition

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    We use laser light near resonant with an optical bound-to-bound transition to shift the magnetic field at which a Feshbach resonance occurs. We operate in a regime of large detuning and large laser intensity. This reduces the light-induced atom-loss rate by one order of magnitude compared to our previous experiments [D.M. Bauer et al. Nature Phys. 5, 339 (2009)]. The experiments are performed in an optical lattice and include high-resolution spectroscopy of excited molecular states, reported here. In addition, we give a detailed account of a theoretical model that describes our experimental data

    Effect of Pauli repulsion and transfer on fusion

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    The effect of the Pauli exclusion principle on the nucleus-nucleus bare potential is studied using a new density-constrained extension of the Frozen-Hartree-Fock (DCFHF) technique. The resulting potentials exhibit a repulsion at short distance. The charge product dependence of this Pauli repulsion is investigated. Dynamical effects are then included in the potential with the density-constrained time-dependent Hartree-Fock (DCTDHF) method. In particular, isovector contributions to this potential are used to investigate the role of transfer on fusion, resulting in a lowering of the inner part of the potential for systems with positive Q-value transfer channels.Comment: Proceedings of an invited talk given at FUSION17, Hobart, Tasmania, AU (20-24 February, 2017

    A Methodology to Engineer and Validate Dynamic Multi-level Multi-agent Based Simulations

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    This article proposes a methodology to model and simulate complex systems, based on IRM4MLS, a generic agent-based meta-model able to deal with multi-level systems. This methodology permits the engineering of dynamic multi-level agent-based models, to represent complex systems over several scales and domains of interest. Its goal is to simulate a phenomenon using dynamically the lightest representation to save computer resources without loss of information. This methodology is based on two mechanisms: (1) the activation or deactivation of agents representing different domain parts of the same phenomenon and (2) the aggregation or disaggregation of agents representing the same phenomenon at different scales.Comment: Presented at 3th International Workshop on Multi-Agent Based Simulation, Valencia, Spain, 5th June 201

    Near-field and far-field analysis of an azimuthally polarized slow Bloch mode microlaser

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    We report on the near- and far-field investigation of the slow Bloch modes associated with the G point of the Brillouin zone, for a honeycomb lattice photonic crystal, using near-field scanning optical microscopy (NSOM) and infra-red CCD camera. The array of doughnut-shaped monopolar mode (mode M) inside each unit cell, predicted previously by numerical simulation, is experimentally observed in the near-field by means of a metal-coated NSOM tip. In far-field, we detect the azimuthal polarization of the doughnut laser beam due to destructive and constructive interference of the mode radiating from the surface (mode TEM01*). A divergence of 2° for the laser beam and a mode size of (12.8 ± 1) μm for the slow Bloch mode at the surface of the crystal are also estimated. © 2010 Optical Society of America

    Imaging and Treatment of Patients with Acute Stroke: An Evidence-Based Review

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    Evidence-based medicine has emerged as a valuable tool to guide clinical decision-making, by summarizing the best possible evidence for both diagnostic and treatment strategies. Imaging plays a critical role in the evaluation and treatment of patients with acute ischemic stroke, especially those who are being considered for thrombolytic or endovascular therapy. Time from stroke-symptom onset to treatment is a strong predictor of long-term functional outcome after stroke. Therefore, imaging and treatment decisions must occur rapidly in this setting, while minimizing unnecessary delays in treatment. The aim of this review was to summarize the best available evidence for the diagnostic and therapeutic management of patients with acute ischemic stroke

    Synthesis of Single Phase Hg-1223 High Tc Superconducting Films With Multistep Electrolytic Process

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    We report the multistep electrolytic process for the synthesis of high Tc single phase HgBa2Ca2Cu3O8+ (Hg-1223) superconducting films. The process includes : i) deposition of BaCaCu precursor alloy, ii) oxidation of BaCaCu films, iii) electrolytic intercalation of Hg in precursor BaCaCuO films and iv) electrochemical oxidation and annealing of Hg-intercalated BaCaCuO films to convert into Hg1Ba2Ca2Cu3O8+ (Hg-1223). Films were characterized by thermo-gravimetric analysis (TGA) and differential thermal analysis (DTA), X-ray diffraction (XRD) and scanning electron microscopy (SEM). The electrolytic intercalation of Hg in BaCaCuO precursor is proved to be a novel alternative to high temperature-high pressure mercuration process. The films are single phase Hg-1223 with Tc = 121.5 K and Jc = 4.3 x 104 A/cm2.Comment: 17 Pages, 10 Figures. Submitted to Superconductor Science and Technolog

    Deep Memory Networks for Attitude Identification

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    We consider the task of identifying attitudes towards a given set of entities from text. Conventionally, this task is decomposed into two separate subtasks: target detection that identifies whether each entity is mentioned in the text, either explicitly or implicitly, and polarity classification that classifies the exact sentiment towards an identified entity (the target) into positive, negative, or neutral. Instead, we show that attitude identification can be solved with an end-to-end machine learning architecture, in which the two subtasks are interleaved by a deep memory network. In this way, signals produced in target detection provide clues for polarity classification, and reversely, the predicted polarity provides feedback to the identification of targets. Moreover, the treatments for the set of targets also influence each other -- the learned representations may share the same semantics for some targets but vary for others. The proposed deep memory network, the AttNet, outperforms methods that do not consider the interactions between the subtasks or those among the targets, including conventional machine learning methods and the state-of-the-art deep learning models.Comment: Accepted to WSDM'1
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