We study the loss, recovery, and preservation of differentiability time-dependent large deviation rate functions. This is motivated by mean-field Gibbs-non-Gibbs transitions. The gradient rate-function evolves according to a Hamiltonian flow. flow used analyze regularity function, both for Glauber dynamics Curie-Weiss model Brownian in potential. extend variational approach this problem order i...

Restricted Boltzmann Machines (RBMs) are one of the fundamental building blocks of deep learning. Approximate maximum likelihood training of RBMs typically necessitates sampling from these models. In many training scenarios, computationally efficient Gibbs sampling procedures are crippled by poor mixing. In this work we propose a novel method of sampling from Boltzmann machines that demonstrate...

Gibbs sampling is a Markov Chain Monte Carlo (MCMC) method for numerically approximating integrals of interest in Bayesian statistics and other mathematical sciences. Since MCMC methods typically suffer from poor scaling when the integral in question is high-dimensional (for example, in problems in Bayesian statistics involving large data sets), researchers have attempted to find ways to speed ...

Markov chain Monte Carlo methods are frequently used in the analyses of genetic data on pedigrees for the estimation of probabilities and likelihoods which cannot be calculated by existing exact methods. In the case of discrete data, the underlying Markov chain may be reducible and care must be taken to ensure that reliable estimates are obtained. Potential reducibility thus has implications fo...

We present a model of the propagation of refracted seismic waves in planar (horizontal or dipping) layered structures in which we quantify the errors from various sources. The model, called the (mixed) variance component model, separates the errors originating on the surface from those due to inhomogeneities of subsurface layers. The model starts with the assumption of homogeneous (constant-vel...

We investigate the influence of different kinds of structure on the learning behaviour of a perceptron performing a classification task defined by a teacher rule. The underlying pattern distribution is permitted to have spatial correlations. The prior distribution for the teacher coupling vectors itself is assumed to be nonuniform. Thus classification tasks of quite different difficulty are inc...

the thermodynamic functions such as enthalpy (h), gibbs free energy (g) and entropy (s) ofpyrazole was theoretically studied at 5 different temperatures 25, 30, 35, 40 and 45°c by usinggussian o3, software. first, the structural optimization of isolated pyrazole was done in the gas phaseby appling the density functional theory (b3lyp) level with 3-21g, 6-31g and 6-31+g(d) basissets. moreover, v...

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In this paper, we propose a new compression technique based on transliteration of Bengali text to English. Compared to Bengali, English is a less symbolic language. Thus transliteration of Bengali text to English reduces the number of characters to be coded. Huffman coding is well known for producing optimal compression. When Huffman principal is applied on transliterated text significant perfo...

Correlations for the prediction of biomass yields are valuable, and many proposals based on a number of parameters (Y(ATP), Y(Ave), eta(o), Y(c), Gibbs energy efficiencies, and enthalpy efficiencies) have been published. This article critically examines the properties of the proposed parameters with respect to the general applicability to chemotrophic growth systems, a clear relation to the Sec...