Scaling and universality of the complexity of analog computation

We apply a probabilistic approach to study the computational complexity of analog computers which solve linear programming problems. We numerically analyze various ensembles of linear programming problems and obtain, for each of these ensembles, the probability distribution functions of certain quantities which measure the computational complexity, known as the convergence rate, the barrier and the computation time. We find that in the limit of very large problems these probability distributions are universal scaling functions. In other words, the probability distribution function for each of these three quantities becomes, in the limit of large problem size, a function of a single scaling variable, which is a certain composition of the quantity in question and the size of the system. Moreover, various ensembles studied seem to lead essentially to the same scaling functions, which depend only on the variance of the ensemble. These results extend analytical and numerical results obtained recently for the Gaussian ensemble, and support the conjecture that these scaling functions are universal.     

Methodological aspects of an adaptive multidirectional pattern search to optimize speech perception using three hearing-aid algorithms

In this study we investigated the reliability and convergence characteristics of an adaptive multidirectional pattern search procedure, relative to a nonadaptive multidirectional pattern search procedure. The procedure was designed to optimize three speech-processing strategies. These comprise noise reduction, spectral enhancement, and spectral lift. The search is based on a paired-comparison paradigm, in which subjects evaluated the listening comfort of speech-in-noise fragments. The procedural and nonprocedural factors that influence the reliability and convergence of the procedure are studied using various test conditions. The test conditions combine different tests, initial settings, background noise types, and step size configurations. Seven normal hearing subjects participated in this study. The results indicate that the reliability of the optimization strategy may benefit from the use of an adaptive step size. Decreasing the step size increases accuracy, while increasing the step size can be beneficial to create clear perceptual differences in the comparisons. The reliability also depends on starting point, stop criterion, step size constraints, background noise, algorithms used, as well as the presence of drifting cues and suboptimal settings. There appears to be a trade-off between reliability and convergence, i.e., when the step size is enlarged the reliability improves, but the convergence deteriorates.     

基于改进格子气模型的对向行人流分层现象的随机性研究

在考虑行人视野范围的随机偏走格子气模型基础上, 引入行人对前方开阔区域的移动偏好特性, 提出改进的格子气模型, 对通道内对向行人流进行仿真研究. 模型再现了对向行人流在不同密度下出现的3种演化过程, 发现了行人密度与对向行人流分层现象的形成具有随机性, 以及统计了概率的变化趋势, 同时分析了分层现象形成概率与系统几何尺寸参数、移动强度参数、右行人流比例参数和视野范围参数等的关系. 分析结果表明, 改进的模型能够再现实际低密度下对向行人流不会出现分层现象的特性. 根据分层形成的概率, 可将对向行人流的密度分为5个区间, 不同区间的行人流演化过程各有差异. 模型和分析结果对理解对向行人流的动态演化过程, 提高通道内对向行人流的走行效率有一定帮助.     

Colored diffusion-limited aggregation for urban migration

In this paper we study the growth probability and cluster morphologies which emerge in an off-lattice, two-dimensional, colored diffusion-limited aggregation model for urban dynamics, particularly migration. To reach this goal, three immobile interacting clusters that include the geographical concept of gravity are studied by exact enumeration. In our simulations we find a strong correlation between the seed’s distance, migration rules and number of aggregated particles. The growth probability of a certain angular subset and its rate and route of convergence to a Normal distribution when migration cost is acting are also shown. We search how all the factors mentioned above determine the cluster morphologies.     

Convergence in High Probability of the Quantum Diffusion in a Random Band Matrix Model

We consider Hermitian random band matrices H in \(d \geqslant 1 \) dimensions. The matrix elements \(H_{xy},\) indexed by \(x, y \in \varLambda \subset \mathbb {Z}^d,\) are independent, uniformly distributed random variable if \(|x-y| \) is less than the band width W,  and zero otherwise. We update the previous results of the converge of quantum diffusion in a random band matrix model from convergence of the expectation to convergence in high probability. The result is uniformly in the size \(|\varLambda | \) of the matrix.     

Critical Behaviors in a Stochastic One-Dimensional Sand-Pile Model

A one-dimensional sand-pile model （Manna model）, which has a stochastic redistribution process, is studied both in discrete and continuous manners. The system evolves into a critical state after a transient period. A detailed analysis of the probability distribution of the avalanche size and duration is numerically investigated. Interestingly,contrary to the deterministic one-dimensional sand-pile model, where multifractal analysis works well, the analysis based on simple finite-size scaling is suited to fitting the data on the distribution of the avalanche size and duration. The exponents characterizing these probability distributions are measured. Scaling relations of these scaling exponents and their universality class are discussed.     

Critical Behaviors in a Stochastic One-Dimensional Sand-Pile Model

A one-dimensional sand-pile model (Manna model), which has a stochastic redistribution process, is studied both in discrete and continuous manners. The system evolves into a critical state after a transient period. A detailed analysis of the probability distribution of the avalanche size and duration is numerically investigated. Interestingly,contrary to the deterministic one-dimensional sand-pile model, where multifractal analysis works well, the analysis based on simple finite-size scaling is suited to fitting the data on the distribution of the avalanche size and duration. The exponents characterizing these probability distributions are measured. Scaling relations of these scaling exponents and their universality class are discussed.     

Perturbative Analysis of Disordered Ising Models Close to Criticality

We consider a two-dimensional Ising model with random i.i.d. nearest-neighbor ferromagnetic couplings and no external magnetic field. We show that, if the probability of supercritical couplings is small enough, the system admits a convergent cluster expansion with probability one. The associated polymers are defined on a sequence of increasing scales; in particular the convergence of the above expansion is compatible with the infinite differentiability of the free energy but does not imply its analyticity. The basic tools in the proof are a general theory of graded cluster expansions and a stochastic domination of the disorder. MSC2000. Primary 82B44, 60K35.     

Effect of inclusions on heterogeneous crack nucleation in nanocomposites

A two-dimensional theoretical model is proposed for the heterogeneous nucleation of a grain-boundary nanocrack in a nanocomposite consisting of a nanocrystalline matrix and nanoinclusions whose elastic moduli are identical to those of the matrix. The inclusions have the form of rods with a rectangular cross section and undergo dilatation eigenstrain induced by the differences in the lattice parameters and thermal expansion coefficients of the matrix and inclusions. In terms of the model, a mode-I–II nanocrack nucleates at the negative disclination of a biaxial dipole consisting of wedge grain-boundary (or junction) disclinations; then, the nanocrack opens along a grain boundary and reaches an inclusion boundary. Depending on the relative positions and orientations of the initial segment of the nanocrack and the inclusion, the nanocrack can either penetrate into the inclusion or bypass it along the matrix-inclusion interface. The nanocrack nucleation probability increases near an inclusion with negative (compressive) dilatation eigenstrain. A decrease in the inclusion size decreases (increases) the probability of a crack opening along the interface if the dilatation eigenstrain is negative (positive).     

On clustering of aerosol particles in homogeneous turbulent shear flows

The objective of this paper is to present a model for predicting clustering of aerosol particles in uniformly sheared turbulent flows laden with small heavy particles. The background of the model for predicting clustering is based on a kinetic equation for the two-point probability density function of the relative velocity distribution of two particles. The effect of clustering of particles in homogeneous turbulent shear flows is demonstrated and compared with known results of direct numerical simulations. It is shown that the universality of the clustering process can take place if the characteristic cluster size is smaller than the shear scale.