Exact Expressions for Minor Hysteresis Loops in the Random Field Ising Model on a Bethe Lattice at Zero Temperature

We obtain exact expressions for the minor hysteresis loops in the ferromagnetic random field Ising model on a Bethe lattice at zero temperature in the case when the driving field is cycled infinitely slowly.Comment: Replaced with the published versio

Effect of a Pacific sea surface temperature anomaly on the circulation over North America

During the fall and winter of 1976-1977, sea surface temperature (SST) in the north Pacific was characterized by abnormally cold temperatures in the central and western portions of the north Pacific with a warm pool located off the west coast of the U.S. It was suggested that the north Pacific SST anomalies were one of the multiple causes of the abnormally cold temperatures in eastern North America during the 1976-1977 winter. An attempt was made to test this hypothesis by conducting a numerical experiment with the GLAS general circulation model

REinforcement learning based Adaptive samPling: REAPing Rewards by Exploring Protein Conformational Landscapes

One of the key limitations of Molecular Dynamics simulations is the computational intractability of sampling protein conformational landscapes associated with either large system size or long timescales. To overcome this bottleneck, we present the REinforcement learning based Adaptive samPling (REAP) algorithm that aims to efficiently sample conformational space by learning the relative importance of each reaction coordinate as it samples the landscape. To achieve this, the algorithm uses concepts from the field of reinforcement learning, a subset of machine learning, which rewards sampling along important degrees of freedom and disregards others that do not facilitate exploration or exploitation. We demonstrate the effectiveness of REAP by comparing the sampling to long continuous MD simulations and least-counts adaptive sampling on two model landscapes (L-shaped and circular), and realistic systems such as alanine dipeptide and Src kinase. In all four systems, the REAP algorithm consistently demonstrates its ability to explore conformational space faster than the other two methods when comparing the expected values of the landscape discovered for a given amount of time. The key advantage of REAP is on-the-fly estimation of the importance of collective variables, which makes it particularly useful for systems with limited structural information

The seasonal cycle of energetics from the GLAS/UMD climate GCM

The annual cycle of atmospheric energetics from a 2-year integration of the GLAS/UMD Climate GCM is computed and compared to results from the European Centre analyses of the GWE year, and to previously published results on a global basis. All calculations are done in the mixed space-time domain. The main conclusions are: (1) the seasonal cycle of today's eddy kinetic energy (in both hemispheres), and of the transient eddy available potential energy and the potential-to-kinetic energy conversions (mean and eddy) in the Northern Hemisphere are well simulated by the GCM; (2) the GCM's tendency to have anomalously large mean u-winds at upper levels in high latitudes leads to excessive wintertime values of mean kinetic and available potential energies, and causes distortions in the GCM latitude-height distribution of kinetic energy and of many of the conversions; (3) the eddy conversion of available potential-to-kinetic energy obtained from the ageostrophic wind in these analyses; and (4) the conversions in the Southern Hemisphere are not well simulated by the GCM, although the observations are somewhat questionable

Self-Diffusion in 2D Dusty Plasma Liquids: Numerical Simulation Results

We perform Brownian dynamics simulations for studying the self-diffusion in two-dimensional (2D) dusty plasma liquids, in terms of both mean-square displacement and velocity autocorrelation function (VAF). Super-diffusion of charged dust particles has been observed to be most significant at infinitely small damping rate $\gamma$ for intermediate coupling strength, where the long-time asymptotic behavior of VAF is found to be the product of $t^{-1}$ and $\exp{(-\gamma t)}$. The former represents the prediction of early theories in 2D simple liquids and the latter the VAF of a free Brownian particle. This leads to a smooth transition from super-diffusion to normal diffusion, and then to sub-diffusion with an increase of the damping rate. These results well explain the seemingly contradictory scattered in recent classical molecular dynamics simulations and experiments of dusty plasmas.Comment: 10 pages 5 figures, accepted by PR

Risk factors for acute exacerbations of COPD in a primary care population: A retrospective observational cohort study

Objectives: To evaluate risk factors associated with exacerbation frequency in primary care. Information on exacerbations of chronic obstructive pulmonary disease (COPD) has mainly been generated by secondary care-based clinical cohorts. Design: Retrospective observational cohort study. Setting: Electronic medical records database (England and Wales). Participants: 58 589 patients with COPD aged ≥40 years with COPD diagnosis recorded between 1 April 2009 and 30 September 2012, and with at least 365 days of follow-up before and after the COPD diagnosis, were identified in the Clinical Practice Research Datalink. Mean age: 69 years; 47% female; mean forced expiratory volume in 1s 60% predicted. Outcome measures: Data on moderate or severe exacerbation episodes defined by diagnosis and/or medication codes 12 months following cohort entry were retrieved, together with demographic and clinical characteristics. Associations between patient characteristics and odds of having none versus one, none versus frequent (≥2) and one versus frequent exacerbations over 12 months follow-up were evaluated using multivariate logistic regression models. Results: During follow-up, 23% of patients had evidence of frequent moderate-to-severe COPD exacerbations (24% one; 53% none). Independent predictors of increased odds of having exacerbations during the follow-up, either frequent episodes or one episode, included prior exacerbations, increasing dyspnoea score, increasing grade of airflow limitation, females and prior or current history of several comorbidities (eg, asthma, depression, anxiety, heart failure and cancer). Conclusions: Primary care-managed patients with COPD at the highest risk of exacerbations can be identified by exploring medical history for the presence of prior exacerbations, greater COPD disease severity and co-occurrence of other medical conditions

Hysteresis in the Random Field Ising Model and Bootstrap Percolation

We study hysteresis in the random-field Ising model with an asymmetric distribution of quenched fields, in the limit of low disorder in two and three dimensions. We relate the spin flip process to bootstrap percolation, and show that the characteristic length for self-averaging $L^*$ increases as $exp(exp (J/\Delta))$ in 2d, and as $exp(exp(exp(J/\Delta)))$ in 3d, for disorder strength $\Delta$ much less than the exchange coupling J. For system size $1 << L < L^*$, the coercive field $h_{coer}$ varies as $2J - \Delta \ln \ln L$ for the square lattice, and as $2J - \Delta \ln \ln \ln L$ on the cubic lattice. Its limiting value is 0 for L tending to infinity, both for square and cubic lattices. For lattices with coordination number 3, the limiting magnetization shows no jump, and $h_{coer}$ tends to J.Comment: 4 pages, 4 figure

GPU Acceleration of Image Convolution using Spatially-varying Kernel

Image subtraction in astronomy is a tool for transient object discovery such as asteroids, extra-solar planets and supernovae. To match point spread functions (PSFs) between images of the same field taken at different times a convolution technique is used. Particularly suitable for large-scale images is a computationally intensive spatially-varying kernel. The underlying algorithm is inherently massively parallel due to unique kernel generation at every pixel location. The spatially-varying kernel cannot be efficiently computed through the Convolution Theorem, and thus does not lend itself to acceleration by Fast Fourier Transform (FFT). This work presents results of accelerated implementation of the spatially-varying kernel image convolution in multi-cores with OpenMP and graphic processing units (GPUs). Typical speedups over ANSI-C were a factor of 50 and a factor of 1000 over the initial IDL implementation, demonstrating that the techniques are a practical and high impact path to terabyte-per-night image pipelines and petascale processing.Comment: 4 pages. Accepted to IEEE-ICIP 201

Analysis of the 3DVAR Filter for the Partially Observed Lorenz '63 Model

The problem of effectively combining data with a mathematical model constitutes a major challenge in applied mathematics. It is particular challenging for high-dimensional dynamical systems where data is received sequentially in time and the objective is to estimate the system state in an on-line fashion; this situation arises, for example, in weather forecasting. The sequential particle filter is then impractical and ad hoc filters, which employ some form of Gaussian approximation, are widely used. Prototypical of these ad hoc filters is the 3DVAR method. The goal of this paper is to analyze the 3DVAR method, using the Lorenz '63 model to exemplify the key ideas. The situation where the data is partial and noisy is studied, and both discrete time and continuous time data streams are considered. The theory demonstrates how the widely used technique of variance inflation acts to stabilize the filter, and hence leads to asymptotic accuracy