Numerical schemes for continuum models of reaction-diffusion systems subject to internal noise

We present new numerical schemes to integrate stochastic partial differential equations which describe the spatio-temporal dynamics of reaction-diffusion (RD) problems under the effect of internal fluctuations. The schemes conserve the nonnegativity of the solutions and incorporate the Poissonian nature of internal fluctuations at small densities, their performance being limited by the level of approximation of density fluctuations at small scales. We apply the new schemes to two different aspects of the Reggeon model namely, the study of its non-equilibrium phase transition and the dynamics of fluctuating pulled fronts. In the latter case, our approach allows to reproduce quantitatively for the first time microscopic properties within the continuum model.Comment: 5 pages, 3 figures, Accepted for publication in Physical Review E as a Rapid Communicatio

Obesity: A Biobehavioral Point of View

Excerpt: If you ask an overweight person, “Why are you fat?’, you will, almost invariably, get the answer, “Because 1 eat too much.” You will get this answer in spite of the fact that of thirteen studies, six find no significant differences in the caloric intake of obese versus nonobese subjects, five report that the obese eat significantly less than the nonobese, and only two report that they eat significantly more

Accurate stationary densities with partitioned numerical methods for stochastic partial differential equations

We consider the numerical solution, by finite differences, of second-order-in-time stochastic partial differential equations (SPDEs) in one space dimension. New timestepping methods are introduced by generalising recently-introduced methods for second-order-in-time stochastic differential equations to multidimensional systems. These stochastic methods, based on leapfrog and Runge–Kutta methods, are designed to give good approximations to the stationary variances and the correlations in the position and velocity variables. In particular, we introduce the reverse leapfrog method and stochastic Runge–Kutta Leapfrog methods, analyse their performance applied to linear SPDEs and perform numerical experiments to examine their accuracy applied to a type of nonlinear SPDE

Assessing the Effects of Personal Characteristics and Context on U.S. House Speakers’ Leadership Styles, 1789-2006

Research on congressional leadership has been dominated in recent decades by contextual interpretations that see leaders’ behavior as best explained by the environment in which they seek to exercise leadership—particularly, the preference homogeneity and size of their party caucus. The role of agency is thus discounted, and leaders’ personal characteristics and leadership styles are underplayed. Focusing specifically on the speakers of the U.S. House of Representatives from the first to the 110th Congress, we construct measures of each speaker’s commitment to comity and leadership assertiveness. We find the scores reliable and then test the extent to which a speaker’s style is the product of both political context and personal characteristics. Regression estimates on speakers’ personal assertiveness scores provide robust support for a context-plus-personal characteristics explanation, whereas estimates of their comity scores show that speakers’ personal backgrounds trump context

Factive Scientific Understanding Without Accurate Representation

This paper analyzes two ways idealized biological models produce factive scientific understanding. I then argue that models can provide factive scientific understanding of a phenomenon without providing an accurate representation of the (difference-making) features of their real-world target system(s). My analysis of these cases also suggests that the debate over scientific realism needs to investigate the factive scientific understanding produced by scientists’ use of idealized models rather than the accuracy of scientific models themselves

Consensus Paper: Cerebellum and Social Cognition.

The traditional view on the cerebellum is that it controls motor behavior. Although recent work has revealed that the cerebellum supports also nonmotor functions such as cognition and affect, only during the last 5 years it has become evident that the cerebellum also plays an important social role. This role is evident in social cognition based on interpreting goal-directed actions through the movements of individuals (social "mirroring") which is very close to its original role in motor learning, as well as in social understanding of other individuals' mental state, such as their intentions, beliefs, past behaviors, future aspirations, and personality traits (social "mentalizing"). Most of this mentalizing role is supported by the posterior cerebellum (e.g., Crus I and II). The most dominant hypothesis is that the cerebellum assists in learning and understanding social action sequences, and so facilitates social cognition by supporting optimal predictions about imminent or future social interaction and cooperation. This consensus paper brings together experts from different fields to discuss recent efforts in understanding the role of the cerebellum in social cognition, and the understanding of social behaviors and mental states by others, its effect on clinical impairments such as cerebellar ataxia and autism spectrum disorder, and how the cerebellum can become a potential target for noninvasive brain stimulation as a therapeutic intervention. We report on the most recent empirical findings and techniques for understanding and manipulating cerebellar circuits in humans. Cerebellar circuitry appears now as a key structure to elucidate social interactions