Generating sequential space-filling designs using genetic algorithms and Monte Carlo methods

In this paper, the authors compare a Monte Carlo method and an optimization-based approach using genetic algorithms for sequentially generating space-filling experimental designs. It is shown that Monte Carlo methods perform better than genetic algorithms for this specific problem

Finite size scaling of current fluctuations in the totally asymmetric exclusion process

We study the fluctuations of the current J(t) of the totally asymmetric exclusion process with open boundaries. Using a density matrix renormalization group approach, we calculate the cumulant generating function of the current. This function can be interpreted as a free energy for an ensemble in which histories are weighted by exp(-sJ(t)). We show that in this ensemble the model has a first order space-time phase transition at s=0. We numerically determine the finite size scaling of the cumulant generating function near this phase transition, both in the non-equilibrium steady state and for large times.Comment: 18 pages, 11 figure

Universal Prediction Distribution for Surrogate Models

International audienceThe use of surrogate models instead of computationally expensive simulation codes is very convenient in engineering. Roughly speaking, there are two kinds of surrogate models: the deterministic and the probabilistic ones. These last are generally based on Gaussian assumptions. The main advantage of probabilistic approach is that it provides a measure of uncertainty associated with the surrogate model in the whole space. This uncertainty is an efficient tool to construct strategies for various problems such as prediction enhancement, optimization or inversion.In this paper, we propose a universal method to define a measure of uncertainty suitable for any surrogate model either deterministic or probabilistic. It relies on Cross-Validation (CV) sub-models predictions. This empirical distribution may be computed in much more general frames than the Gaussian one. So that it is called the Universal Prediction distribution (UP distribution).It allows the definition of many sampling criteria. We give and study adaptive sampling techniques for global refinement and an extension of the so-called Efficient Global Optimization (EGO) algorithm. We also discuss the use of the UP distribution for inversion problems. The performances of these new algorithms are studied both on toys models and on an engineering design problem

Aging Is Accompanied by a Blunted Muscle Protein Synthetic Response to Protein Ingestion.

Published onlineJournal ArticleThis is the final version of the article. Available from Public Library of Science via the DOI in this record.PURPOSE: Progressive loss of skeletal muscle mass with aging (sarcopenia) forms a global health concern. It has been suggested that an impaired capacity to increase muscle protein synthesis rates in response to protein intake is a key contributor to sarcopenia. We assessed whether differences in post-absorptive and/or post-prandial muscle protein synthesis rates exist between large cohorts of healthy young and older men. PROCEDURES: We performed a cross-sectional, retrospective study comparing in vivo post-absorptive muscle protein synthesis rates determined with stable isotope methodologies between 34 healthy young (22±1 y) and 72 older (75±1 y) men, and post-prandial muscle protein synthesis rates between 35 healthy young (22±1 y) and 40 older (74±1 y) men. FINDINGS: Post-absorptive muscle protein synthesis rates did not differ significantly between the young and older group. Post-prandial muscle protein synthesis rates were 16% lower in the older subjects when compared with the young. Muscle protein synthesis rates were >3 fold more responsive to dietary protein ingestion in the young. Irrespective of age, there was a strong negative correlation between post-absorptive muscle protein synthesis rates and the increase in muscle protein synthesis rate following protein ingestion. CONCLUSIONS: Aging is associated with the development of muscle anabolic inflexibility which represents a key physiological mechanism underpinning sarcopenia

DMRG-study of current and activity fluctuations near non-equilibrium phase transitions

Cumulants of a fluctuating current can be obtained from a free energy-like generating function which for Markov processes equals the largest eigenvalue of a generalized generator. We determine this eigenvalue with the DMRG for stochastic systems. We calculate the variance of the current in the different phases, and at the phase transitions, of the totally asymmetric exclusion process. Our results can be described in the terms of a scaling ansatz that involves the dynamical exponent z. We also calculate the generating function of the activity near the absorbing state transition of the contact process. Its scaling properties can be expressed in terms of known critical exponents.Comment: 5 pages, 5 figure

Thermodynamics of histories for the one-dimensional contact process

The dynamical activity K(t) of a stochastic process is the number of times it changes configuration up to time t. It was recently argued that (spin) glasses are at a first order dynamical transition where histories of low and high activity coexist. We study this transition in the one-dimensional contact process by weighting its histories by exp(sK(t)). We determine the phase diagram and the critical exponents of this model using a recently developed approach to the thermodynamics of histories that is based on the density matrix renormalisation group. We find that for every value of the infection rate, there is a phase transition at a critical value of s. Near the absorbing state phase transition of the contact process, the generating function of the activity shows a scaling behavior similar to that of the free energy in an equilibrium system near criticality.Comment: 16 pages, 7 figure

Exact Current Statistics of the ASEP with Open Boundaries

Non-equilibrium systems are often characterized by the transport of some quantity at a macroscopic scale, such as, for instance, a current of particles through a wire. The Asymmetric Simple Exclusion Process (ASEP) is a paradigm for non-equilibrium transport that is amenable to exact analytical solution. In the present work, we determine the full statistics of the current in the finite size open ASEP for all values of the parameters. Our exact analytical results are checked against numerical calculations using DMRG techniques.Comment: 5 pages, references adde

Towards enduring autonomous robots via embodied energy.

Autonomous robots comprise actuation, energy, sensory and control systems built from materials and structures that are not necessarily designed and integrated for multifunctionality. Yet, animals and other organisms that robots strive to emulate contain highly sophisticated and interconnected systems at all organizational levels, which allow multiple functions to be performed simultaneously. Herein, we examine how system integration and multifunctionality in nature inspires a new paradigm for autonomous robots that we call Embodied Energy. Whereas most untethered robots use batteries to store energy and power their operation, recent advancements in energy-storage techniques enable chemical or electrical energy sources to be embodied directly within the structures and materials used to create robots, rather than requiring separate battery packs. This perspective highlights emerging examples of Embodied Energy in the context of developing autonomous robots