Geometric interpretation of Hamiltonian cycles problem via singularly perturbed Markov decision processes

We consider the Hamiltonian cycle problem (HCP) embedded in a singularly perturbed Markov decision process (MDP). More specifically, we consider the HCP as an optimization problem over the space of long-run state-action frequencies induced by the MDP's stationary policies. We also consider two quadratic functionals over the same space. We show that when the perturbation parameter, ? is sufficiently small the Hamiltonian cycles of the given directed graph are precisely the maximizers of one of these quadratic functionals over the frequency space intersected with an appropriate (single) contour of the second quadratic functional. In particular, all these maximizers have a known Euclidean distance of z _m (?) from the origin. Geometrically, this means that Hamiltonian cycles, if any, are the points in the frequency polytope where the circle of radius z _m (?) intersects a certain ellipsoid.     

Estimates of the tail of the stationary density function of certain nonlinear autoregressive processes

We consider the Markov chainX _n+1=T(X _n )+ _n , where { _n ;n1} is a ^d -valued random sequence of independent identically distributed random variables, and the functionT: ^{d d} is measurable and satisfies a suitable growth condition. Under certain conditions involvingT and the probability distribution of _n , we show that this Markov chain is ergodic. Moreover, we obtain sharp upper bounds for the tail of the corresponding stationary probability density function. In our proofs, we make use of the Leray-Schauder fixed-point theorem.     

Direct contact ultrasound assisted freezing of chicken breast samples

Nowadays, rapid freezing is sought to favor the formation of small ice crystals. Several studies have shown that the application of ultrasounds (US) accelerates the processes of energy and mass transfer when they are applied through immersion systems. However, there are hardly any studies on its application in direct systems without the use of a liquid medium for its transmission. Therefore, the objective of this work was to evaluate the potential of the application of US for improving the freezing process of chicken breast samples. First, the application of intermittent US treatments at different net sonication times of 7, 17, 37, 50 and 67% during the freezing of distilled water samples in a conventional freezer was evaluated. It was observed that net sonication times of 37, 50 and 67% reduced the phase change period by 30.0, 21.4, 27.0%, respectively. The effective freezing time was also reduced by 12.4 and 12.8% by applying net sonication times of 37 and 50%. Considering these results, an intermittent US treatment with a net sonication time of 37% was chosen for chicken breast freezing in an air-forced cooling tunnel at ambient temperatures from −13 to −22 °C. The length of all the freezing phases was reduced upon application of US, leading to an overall process time reduction of approx. 11%. On the other hand, no significant differences were found either in the Water Holding Capacity (WHC) or Cooking Loss (CL) values between control and US assisted frozen chicken breast samples. Furthermore, in vitro experiments showed that US-assisted freezing did not influence protein digestibility of chicken meat samples.This study demonstrates the potential of the application of US by direct contact to favor energy transfer processes during freezing of water and chicken breasts samples. However, its effect on the quality of the frozen products should be further studied.     

Active Inference: Applicability to Different Types of Social Organization Explained through Reference to Industrial Engineering and Quality Management

Active inference is a physics of life process theory of perception, action and learning that is applicable to natural and artificial agents. In this paper, active inference theory is related to different types of practice in social organization. Here, the term social organization is used to clarify that this paper does not encompass organization in biological systems. Rather, the paper addresses active inference in social organization that utilizes industrial engineering, quality management, and artificial intelligence alongside human intelligence. Social organization referred to in this paper can be in private companies, public institutions, other for-profit or not-for-profit organizations, and any combination of them. The relevance of active inference theory is explained in terms of variational free energy, prediction errors, generative models, and Markov blankets. Active inference theory is most relevant to the social organization of work that is highly repetitive. By contrast, there are more challenges involved in applying active inference theory for social organization of less repetitive endeavors such as one-of-a-kind projects. These challenges need to be addressed in order for active inference to provide a unifying framework for different types of social organization employing human and artificial intelligence.     

基于接触数引导的迭代多组独立分子动力学模拟实现配体-蛋白质结合与解离过程

配体的结合与解离过程在蛋白质实现其生物学功能方面非常关键,因此对这些高度动态过程的研究变得非常重要. 尽管已有实验方法可以确定蛋白质-配体复合物的三维结构,但一般仅可获得静态图片. 随着计算机算力的快速提高以及算法的优化,分子动力学模拟在探索配体的结合与解离过程方面具有诸多优势. 然而,当系统变得足够大时,分子动力学模拟的时间和空间尺度成为了巨大的挑战. 本工作提出了一种研究配体-蛋白质结合与解离的增强采样工具,它基于配体和蛋白质之间形成的接触数来引导迭代多组独立分子动力学模拟. 在腺苷酸激酶的模拟结果中,观测到配体的结合和解离过程,而使用传统分子动力学模拟在同一时间尺度下则无法实现这一过程.     

Hierarchical Bayesian autoregressive models for large space–time data with applications to ozone concentration modelling

Increasingly large volumes of space–time data are collected everywhere by mobile computing applications, and in many of these cases, temporal data are obtained by registering events, for example, telecommunication or Web traffic data. Having both the spatial and temporal dimensions adds substantial complexity to data analysis and inference tasks. The computational complexity increases rapidly for fitting Bayesian hierarchical models, as such a task involves repeated inversion of large matrices. The primary focus of this paper is on developing space–time autoregressive models under the hierarchical Bayesian setup. To handle large data sets, a recently developed Gaussian predictive process approximation method is extended to include autoregressive terms of latent space–time processes. Specifically, a space–time autoregressive process, supported on a set of a smaller number of knot locations, is spatially interpolated to approximate the original space–time process. The resulting model is specified within a hierarchical Bayesian framework, and Markov chain Monte Carlo techniques are used to make inference. The proposed model is applied for analysing the daily maximum 8‐h average ground level ozone concentration data from 1997 to 2006 from a large study region in the Eastern United States. The developed methods allow accurate spatial prediction of a temporally aggregated ozone summary, known as the primary ozone standard, along with its uncertainty, at any unmonitored location during the study period. Trends in spatial patterns of many features of the posterior predictive distribution of the primary standard, such as the probability of noncompliance with respect to the standard, are obtained and illustrated. Copyright © 2012 John Wiley & Sons, Ltd.     

一类马氏过程在作战分析中的应用

本文应用随机过程的理论和方法定义了一类特殊的作战过程——纯灭战斗过程,研究并导出了该战斗过程的作战实力转移特征等．     

测度值分枝过程与移民过程

本文介绍了测度值分枝过程和由斜卷积半群定义的伴随移民过程的基本理论和研究现状,主要内容包括：分枝粒子系统的收敛;超过程的基本正则性和极限定理;非线性微分方程;广义分枝模型;斜卷积半群和进入律;用Ｋｕｚｎｅｔｓｏｖ过程构造移民过程等。     

Convergence of Appell Polynomials of Long Range Dependent Moving Averages in Martingale Differences

We study limit distribution of partial sums S_N,k(t) = _{s = 1} ^{[N t]} A_k(X_s) of Appell polynomials of the long-range dependent moving average process X_t> = _{i t} b_{t - i i}, where {_i} is a strictly stationary and weakly dependent martingale difference sequence, and b_i i^{d - 1} (0 < d < 1/2). We show that if k(1-2 d)<1, then suitably normalized partial sums S_N,k(t) converge in distribution to the kth order Hermite process. This result generalizes the corresponding results of Surgailis, and Avram and Taqqu obtained in the case of the i.i.d. sequence { _i}.     

Large Deviation Probabilities for Square-Gaussian Stochastic Processes

We investigate properties of square-Gaussian stochastic processes. These processes are formed by quadratic forms of Gaussian processes or by limits in the mean square of quadratic forms of Gaussian processes. Special classes of these processes are determined and investigated. For processes from these classes estimates of large deviation probability are obtained. These estimates we use to estimate the probability that Gaussian vector-valued process leave some region on some interval of time. We construct asymptotic confidence regions for estimates of covariance functions of vector-valued Gaussian processes. Criterion of hypothesis testing on covariance functions of these processes is constructed.