Quantum phase transitions of polar molecules in bilayer systems

We investigate the quantum phase transitions of bosonic polar molecules in a two-dimensional double layer system. We show that an interlayer bound state of dipoles (dimers) can be formed when the dipole strength is above a critical value, leading to a zero-energy resonance in the interlayer s-wave scattering channel. In the positive detuning side of the resonance, the strong repulsive interlayer pseudopotential can drive the system into a maximally entangled state, where the wave function is a superposition of two states that have all molecules in one layer and none in the other. We discuss how the zero-energy resonance, dimer states, and the maximally entangled state can be measured in time-of-flight experiments.Comment: Minor correction

Measurement of the Scintillation Efficiency of Na Recoils in NaI(Tl) down to 10 keV Nuclear Recoil Energy relevant to Dark Matter Searches

We present preliminary results of measurements of the quenching factor for Na recoils in NaI(Tl) at room temperature, made at a dedicated neutron facility at the University of Sheffield. Measurements have been performed with a 2.45 MeV mono-energetic neutron generator in the energy range from 10 keV to 100 keV nuclear recoil energy. A BC501A liquid scintillator detector was used to tag neutrons. Cuts on pulse-shape discrimination from the BC501A liquid scintillator detector and neutron time-of-flight were performed on pulses recorded by a digitizer with a 2 ns sampling time. Measured quenching factors range from 19% to 26%, in agreement with other experiments. From pulse-shape analysis, a mean time of pulses from electron and nuclear recoils are compared down to 2 keV electron equivalent energy.Comment: to appear in Proc. 6th Int. Workshop on the Identification of Dark Matter, 11-16 September 2006, Rhodes, Greece; 6 pages, 4 figures; corrected preliminary theoretical estimation curve plotted in figure

Anterior Prefrontal Cortex Contributes to Action Selection through Tracking of Recent Reward Trends

The functions of prefrontal cortex remain enigmatic, especially for its anterior sectors, putatively ranging from planning to self-initiated behavior, social cognition, task switching, and memory. A predominant current theory regarding the most anterior sector, the frontopolar cortex (FPC), is that it is involved in exploring alternative courses of action, but the detailed causal mechanisms remain unknown. Here we investigated this issue using the lesion method, together with a novel model-based analysis. Eight patients with anterior prefrontal brain lesions including the FPC performed a four-armed bandit task known from neuroimaging studies to activate the FPC. Model-based analyses of learning demonstrated a selective deficit in the ability to extrapolate the most recent trend, despite an intact general ability to learn from past rewards. Whereas both brain-damaged and healthy controls used comparisons between the two most recent choice outcomes to infer trends that influenced their decision about the next choice, the group with anterior prefrontal lesions showed a complete absence of this component and instead based their choice entirely on the cumulative reward history. Given that the FPC is thought to be the most evolutionarily recent expansion of primate prefrontal cortex, we suggest that its function may reflect uniquely human adaptations to select and update models of reward contingency in dynamic environments

Molecular dynamics simulation of rapid solidification of Aluminum under pressure

Molecular dynamics simulation study based on the EAM potential is carried out to investigate the effect of pressure on the rapid solidification of Aluminum. The radial distribution function is used to characterize the structure of the Al solidified under different pressures. It is indicated that a high pressure leads to strong crystallization tendency during cooling

Large-Scale Image Processing with the ROTSE Pipeline for Follow-Up of Gravitational Wave Events

Electromagnetic (EM) observations of gravitational-wave (GW) sources would bring unique insights into a source which are not available from either channel alone. However EM follow-up of GW events presents new challenges. GW events will have large sky error regions, on the order of 10-100 square degrees, which can be made up of many disjoint patches. When searching such large areas there is potential contamination by EM transients unrelated to the GW event. Furthermore, the characteristics of possible EM counterparts to GW events are also uncertain. It is therefore desirable to be able to assess the statistical significance of a candidate EM counterpart, which can only be done by performing background studies of large data sets. Current image processing pipelines such as that used by ROTSE are not usually optimised for large-scale processing. We have automated the ROTSE image analysis, and supplemented it with a post-processing unit for candidate validation and classification. We also propose a simple ad hoc statistic for ranking candidates as more likely to be associated with the GW trigger. We demonstrate the performance of the automated pipeline and ranking statistic using archival ROTSE data. EM candidates from a randomly selected set of images are compared to a background estimated from the analysis of 102 additional sets of archival images. The pipeline's detection efficiency is computed empirically by re-analysis of the images after adding simulated optical transients that follow typical light curves for gamma-ray burst afterglows and kilonovae. We show that the automated pipeline rejects most background events and is sensitive to simulated transients to limiting magnitudes consistent with the limiting magnitude of the images

States versus Rewards: Dissociable Neural Prediction Error Signals Underlying Model-Based and Model-Free Reinforcement Learning

Reinforcement learning (RL) uses sequential experience with situations (“states”) and outcomes to assess actions. Whereas model-free RL uses this experience directly, in the form of a reward prediction error (RPE), model-based RL uses it indirectly, building a model of the state transition and outcome structure of the environment, and evaluating actions by searching this model. A state prediction error (SPE) plays a central role, reporting discrepancies between the current model and the observed state transitions. Using functional magnetic resonance imaging in humans solving a probabilistic Markov decision task, we found the neural signature of an SPE in the intraparietal sulcus and lateral prefrontal cortex, in addition to the previously well-characterized RPE in the ventral striatum. This finding supports the existence of two unique forms of learning signal in humans, which may form the basis of distinct computational strategies for guiding behavior

Lattice Resistance and Peierls Stress in Finite-size Atomistic Dislocation Simulations

Atomistic computations of the Peierls stress in fcc metals are relatively scarce. By way of contrast, there are many more atomistic computations for bcc metals, as well as mixed discrete-continuum computations of the Peierls-Nabarro type for fcc metals. One of the reasons for this is the low Peierls stresses in fcc metals. Because atomistic computations of the Peierls stress take place in finite simulation cells, image forces caused by boundaries must either be relaxed or corrected for if system size independent results are to be obtained. One of the approaches that has been developed for treating such boundary forces is by computing them directly and subsequently subtracting their effects, as developed by V. B. Shenoy and R. Phillips [Phil. Mag. A, 76 (1997) 367]. That work was primarily analytic, and limited to screw dislocations and special symmetric geometries. We extend that work to edge and mixed dislocations, and to arbitrary two-dimensional geometries, through a numerical finite element computation. We also describe a method for estimating the boundary forces directly on the basis of atomistic calculations. We apply these methods to the numerical measurement of the Peierls stress and lattice resistance curves for a model aluminum (fcc) system using an embedded-atom potential.Comment: LaTeX 47 pages including 20 figure