Vectorised simulations for stochastic differential equations

Often when solving stochastic differential equations numerically, many simulations must be generated. For example, this approach is required when computing the statistics of the numerical solution, or when verifying the strong order of convergence of a numerical method (when a range of step sizes is also required). Such computational effort can be very slow, and this paper discusses an approach to vectorise the simulation calculations and hence produce an efficient implementation. The numerical simulations here were performed in MATLAB but the techniques are equally applicable in a high performance computing environment using, for example, Fortran 90

Screening fifth forces in k-essence and DBI models

New fifth forces have not yet been detected in the laboratory or in the solar system, hence it is typically difficult to introduce new light scalar fields that would mediate such forces. In recent years it has been shown that a number of non-linear scalar field theories allow for a dynamical mechanism, such as the Vainshtein and chameleon ones, that suppresses the strength of the scalar fifth force in experimental environments. This is known as screening, however it is unclear how common screening is within non-linear scalar field theories. k-essence models are commonly studied examples of non-linear models, with DBI as the best motivated example, and so we ask whether these non-linearities are able to screen a scalar fifth force. We find that a Vainshtein-like screening mechanism exists for such models although with limited applicability. For instance, we cannot find a screening mechanism for DBI models. On the other hand, we construct a large class of k-essence models which lead to the acceleration of the Universe in the recent past for which the fifth force mediated by the scalar can be screened.Comment: 26 page

'Extremotaxis': Computing with a bacterial-inspired algorithm

We present a general-purpose optimization algorithm inspired by “run-and-tumble”, the biased random walk chemotactic swimming strategy used by the bacterium Escherichia coli to locate regions of high nutrient concentration The method uses particles (corresponding to bacteria) that swim through the variable space (corresponding to the attractant concentration profile). By constantly performing temporal comparisons, the particles drift towards the minimum or maximum of the function of interest. We illustrate the use of our method with four examples. We also present a discrete version of the algorithm. The new algorithm is expected to be useful in combinatorial optimization problems involving many variables, where the functional landscape is apparently stochastic and has local minima, but preserves some derivative structure at intermediate scales

Unlocking datasets by calibrating populations of models to data density: a study in atrial electrophysiology

The understanding of complex physical or biological systems nearly always requires a characterization of the variability that underpins these processes. In addition, the data used to calibrate these models may also often exhibit considerable variability. A recent approach to deal with these issues has been to calibrate populations of models (POMs), multiple copies of a singlemathematicalmodel butwith different parameter values, in response to experimental data. To date, this calibration has been largely limited to selectingmodels that produce outputs that fallwithin the ranges of the data set, ignoring any trends that might be present in the data. We present here a novel and general methodology for calibrating POMs to the distributions of a set of measured values in a data set.We demonstrate our technique using a data set from a cardiac electrophysiology study based on the differences in atrial action potential readings between patients exhibiting sinus rhythm (SR) or chronic atrial fibrillation (cAF) and the Courtemanche-Ramirez-Nattel model for human atrial action potentials. Not only does our approach accurately capture the variability inherent in the experimental population, but we also demonstrate how the POMs that it produces may be used to extract additional information from the data used for calibration, including improved identification of the differences underlying stratified data.We also show how our approach allows different hypotheses regarding the variability in complex systems to be quantitatively compared

Population of human ventricular cell models calibrated with in vivo measurements unravels ionic mechanisms of cardiac alternans

Cardiac alternansis an important risk factor in cardiac physiology, and is related to the initiation of many pathophysiological conditions. However, the mechanisms underlying the generation of alternans remain unclear. In this study, we used a population of computational human ventricle models based onthe O’Hara model [1] to explore the effect of 11 key factors experimentally reported to be related to alternans. In vivo experimental datasets coming from patients undergoing cardiac surgery were used in the calibration of our in silico population of models. The calibrated models in the population were divided into two groups (Normal and Alternans) depending on alternans occurrence. Our results showed that there were significant differences in the following 5 ionic currents between the two groups: fast sodium current, sodium calcium exchanger current, sodium potassium pump current, sarcoplasmic reticulum (SR) calcium release flux and SR calcium reuptake flux. Further analysis indicated that fast sodium current and SR calcium uptake were the two most significant currents that contributed to voltage and calcium alternans generation, respectively

de Sitter Galileon

We generalize the Galileon symmetry and its relativistic extension to a de Sitter background. This is made possible by studying a probe-brane in a flat five-dimensional bulk using a de Sitter slicing. The generalized Lovelock invariants induced on the probe brane enjoy the induced Poincar\'e symmetry inherited from the bulk, while living on a de Sitter geometry. The non-relativistic limit of these invariants naturally maintain a generalized Galileon symmetry around de Sitter while being free of ghost-like pathologies. We comment briefly on the cosmology of these models and the extension to the AdS symmetry as well as generic FRW backgrounds

Accurate Computation of the Screening of Scalar Fifth Forces in Galaxies

Screening mechanisms allow light scalar fields to dynamically avoid the constraints that come from our lack of observation of a long-range fifth force. Galactic scale tests are of particular interest when the light scalar is introduced to explain the dark matter or dark energy that dominates our cosmology. To date, much of the literature that has studied screening in galaxies has described screening using simplifying approximations. In this work, we calculate numerical solutions for scalar fields with screening mechanisms in galactic contexts, and use these to derive new, precise conditions governing where fifth forces are screened. We show that the commonly used binary screened/unscreened threshold can predict a fifth force signal in situations where a fuller treatment does not, leading us to conclude that existing constraints might be significantly overestimated. We show that various other approximations of the screening radius provide a more accurate proxy to screening, although they fail to exactly reproduce the true screening surface in certain regions of parameter space. As a demonstration of our scheme, we apply it to an idealised Milky Way and thus identify the region of parameter space in which the solar system is screened.Comment: 28 pages, 5 figure

A stochastic model of jaguar abundance in the Peruvian Amazon under climate variation scenarios

The jaguar (Panthera onca) is the dominant predator in Central and South America, but is now considered near-threatened. Estimating jaguar population size is difficult, due to uncertainty in the underlying dynamical processes as well as highly variable and sparse data. We develop a stochastic temporal model of jaguar abundance in the Peruvian Amazon, taking into account prey availability, under various climate change scenarios. The model is calibrated against existing data sets and an elicitation study in Pacaya Samiria. In order to account for uncertainty and variability, we construct a population of models over four key parameters, namely three scaling parameters for aquatic, small land, and large land animals and a hunting index. We then use this population of models to construct probabilistic evaluations of jaguar populations under various climate change scenarios characterized by increasingly severe flood and drought events and discuss the implications on jaguar numbers. Results imply that jaguar populations exhibit some robustness to extreme drought and flood, but that repeated exposure to these events over short periods can result in rapid decline. However, jaguar numbers could return to stability—albeit at lower numbers—if there are periods of benign climate patterns and other relevant factors are conducive

Constraining Galileon inflation

In this short paper, we present constraints on the Galileon inflationary model from the CMB bispectrum. We employ a principal-component analysis of the independent degrees of freedom constrained by data and apply this to the WMAP 9-year data to constrain the free parameters of the model. A simple Bayesian comparison establishes that support for the Galileon model from bispectrum data is at best weak

Urban American Indian Community Perspectives on Resources and Challenges for Youth Suicide Prevention

American Indian (AI) youth have some of the highest rates of suicide of any group in the United States, and the majority of AI youth live in urban areas away from tribal communities. As such, understanding the resources available for suicide prevention among urban AI youth is critical, as is understanding the challenges involved in accessing such resources. Pre‐existing interview data from 15 self‐identified AI community members and staff from an Urban Indian Health Organization were examined to understand existing resources for urban AI youth suicide prevention, as well as related challenges. A thematic analysis was undertaken, resulting in three principal themes around suicide prevention: formal resources, informal resources, and community values and beliefs. Formal resources that meet the needs of AI youth were viewed as largely inaccessible or nonexistent, and youth were seen as more likely to seek help from informal sources. Community values of mutual support were thought to reinforce available informal supports. However, challenges arose in terms of the community’s knowledge of and views on discussing suicide, as well as the perceived fit between community values and beliefs and formal prevention models.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134173/1/ajcp12080.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134173/2/ajcp12080_am.pd