Thermodynamical versus log-Poisson distribution in turbulence

The thermodynamical model of intermittency in fully developed turbulence due to Castaing (B. Castaing, J. Phys. II France 6 (1996) 105) is investigated and compared with the log-Poisson model (Z-S, She, E. Leveque, Phys. Rev. Lett. 72 (1994) 336). It is shown that the thermodynamical model obeys general scaling laws and corresponds to the degenerate class of scale-invariant statistics. We also find that its structure function shapes have physical behaviors similar to the log-Poisson's one. The only difference between them lies in the convergence of the log-Poisson's structure functions and divergence of the thermodynamical one. As far as the comparison with experiments on intermittency is concerned, they are indifferent.

Tractable nonparametric bayesian inference in poisson processes with Gaussian process intensities

The inhomogeneous Poisson process is a point process that has varying intensity across its domain (usually time or space). For nonparametric Bayesian modeling, the Gaussian process is a useful way to place a prior distribution on this intensity. The combination of a Poisson process and GP is known as a Gaussian Cox process, or doubly-stochastic Poisson process. Likelihood-based inference in these models requires an intractable integral over an infinite-dimensional random function. In this paper we present the first approach to Gaussian Cox processes in which it is possible to perform inference without introducing approximations or finitedimensional proxy distributions. We call our method the Sigmoidal Gaussian Cox Process, which uses a generative model for Poisson data to enable tractable inference via Markov chain Monte Carlo. We compare our methods to competing methods on synthetic data and apply it to several real-world data sets. Copyright 2009.

Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities

The inhomogeneous Poisson process is a point process that has varying intensity across its domain (usually time or space). For nonparametric Bayesian modeling, the Gaussian process is a useful way to place a prior distribution on this intensity. The combination of a Poisson process and GP is known as a Gaussian Cox process, or doubly-stochastic Poisson process. Likelihood-based inference in these models requires an intractable integral over an infinite-dimensional random function. In this paper we present the first approach to Gaussian Cox processes in which it is possible to perform inference without introducing approximations or finite-dimensional proxy distributions. We call our method the Sigmoidal Gaussian Cox Process, which uses a generative model for Poisson data to enable tractable inference via Markov chain Monte Carlo. We compare our methods to competing methods on synthetic data and apply it to several real-world data sets.

Effect Of A Negative Poisson Ratio In The Tension Of Ceramics

The effect of a negative Poisson ratio is experimentally revealed in the tension deformation of a natural layered ceramic. This effect can increase the volume strain energy per unit volume by 1100% and, simultaneously, decrease the deformation strain energy per unit volume by about 44%, so that it effectively enhances the deformation capacity by about 1 order of magnitude in the tension of the material. The present study also shows that the physical mechanisms producing the effect are attributed to the climbing on one another of the nanostructures in the natural material, which provides a guide to the design of synthetic toughening composites.

Dios entre las mónadas : la centralidad del tema de Dios en algunas tesis metafísicas de G. W. Leibniz

Resumen: En el presente trabajo buscaremos introducir ciertas tesis distintivas del pensamiento de Leibniz en lo que respecta a su metafísica subrayando el papel de Dios en cada una de ellas. Fundamentalmente, las tesis a tratar son la teoría de las mónadas y dos teorías que se siguen de ella: la de la armonía preestablecida y la del mejor de los mundos posibles. Además, buscaremos mostrar que las pruebas de la existencia de Dios (o al menos dos de ellas: el denominado “argumento cosmológico” y el denominado “ontológico”), poseen subyacentemente una fundamentación metafísica. En este sentido, no nos centraremos en las pruebas mismas sino en su fundamentación.

交错网格紧致差分格式和满足等价性的压力Poisson方程

首先给出四阶精度交错网格紧致差分格式; 其次讨论了满足等价性的压力Poisson方程; 然后给出了一种新的解压力Poisson方程的ADI迭代法; 最后, 讨论了驱动方腔流动数值计算.