半休氏勒合金LiMgAs结构和热力学性质的第一性原理计算

本文利用基于密度泛函理论的第一原理方法研究了Half-Heusler合金LiMgAs的结构性质以及热力学性质.所计算的晶格常数、弹性模量以及弹性模量对压强的导数均与实验及其他理论值相吻合.本文还计算了α、β和γ三种相的LiMgAs声子频率,发现没有虚频的α-LiMgAs热力学结构稳定,其他两相均有虚频,不稳定.本文通过准谐德拜模型,研究并获得了不同温度0～1500 K和不同压强0～70 GPa下的热膨胀系数、热容、熵、德拜温度和Grüneisen参数.我们发现,热膨胀系数、热容、熵和Grüneisen参数随温度的增加而增加,随压强的增加而减小;而温度和压强对德拜温度和弹性模量的影响和对上面其他物理量的影响恰好相反.另外,当温度高于1000 K时,热容值变化很小,遵循Dulong-Petit定律.     

相变及空位缺陷对高压下GeO_2光学性质的影响

本文采用第一性原理方法,在100 GPa的压力范围内,计算了GeO_2理想晶体和含锗、氧空位点缺陷晶体的光学性质.吸收谱数据表明,压力诱导的三个结构相变对GeO_2晶体的吸收谱均有影响:第一个相变将导致其吸收边蓝移,而第二和第三相变将使得其吸收边红移.锗和氧空位点缺陷的存在将导致GeO_2的吸收边红移,但氧空位点缺陷引起的红移更明显.尽管如此,分析发现,在100 GPa的压力范围内,压力、相变以及空位点缺陷等因素都不会导致GeO_2晶体在可见光区出现光吸收现象(是透明的).波长在532 nm处的折射率数据显示,在GeO_2的四个相区,其折射率均随压力增加而降低;而且,GeO_2的三个结构相变以及锗、氧空位点缺陷都会导致其折射率有所增大.本文预测,GeO_2有成为冲击光学窗口材料的可能.     

Inhomogeneous Voter Models in One Dimension

We establish conditions for survival and extinction of types of one-dimensional voter models, and show that increasing the flip rates at a finite number of sites typically does not affect survival, unless the flipping mechanism is altered. We provide an example of a modified voter model that does not survive but can be made to survive simply by altering the flip mechanism at one site. We also show that a rather general class of such models have clustering behavior.     

On the polynomial-space completeness of intuitionistic propositional logic

We present an alternative, purely semantical and relatively simple, proof of the Statman's result that both intuitionistic propositional logic and its implicational fragment are PSPACE-complete.This paper was supported by grant 401/01/0218 of the Grant Agency of the Czech Republic. % Mathematics Subject Classification (2000):     

Dynamic Global Constraints in Backtracking Based Environments

Global constraints provide strong filtering algorithms to reduce the search space when solving large combinatorial problems. In this paper we propose to make the global constraints dynamic, i.e., to allow extending the set of constrained variables during search. We describe a generic dynamisation technique for an arbitrary monotonic global constraint and we compare it with the semantic-based dynamisation for the alldifferent constraint. At the end we sketch a dynamisation technique for non-monotonic global constraints. A comparison with existing methods to model dynamic problems is given as well.     

Informational acquisition and cognitive models

Life forms must organize information into cognitive models reflecting the outside environment, and in a complex and changing environment a life form must constantly select and organize this mass of information to avoid slipping into a chaotic cognitive state. The task of developing and maintaining adaptive cognitive models can be understood through two processes, crucial to regulating the interconnections between environmental elements. The inclusion and exclusion of information follows a process designated by P and the process by which cognitive models change is designated by K. Higher order concepts are created by reducing the interconnections between elements to a minimal number to avoid cognitive chaos. © 2004 Wiley Periodicals, Inc. Complexity 9:31–37, 2004     

<Emphasis Type="Italic">I</Emphasis>-projection onto isotonic cones and its applications to maximum likelihood estimation for log-linear models

A frequently occurring problem is to find a probability vector,p∈D, which minimizes theI-divergence between it and a given probability vector π. This is referred to as theI-projection of π ontoD. Darroch and Ratcliff (1972,Ann. Math. Statist.,43, 1470–1480) gave an algorithm whenD is defined by some linear equalities and in this paper, for simplicity of exposition, we propose an iterative procedure whenD is defined by some linear inequalities. We also discuss the relationship betweenI-projection and the maximum likelihood estimation for multinomial distribution. All of the results can be applied to isotonic cone.     

Nonparametric Estimation of Regression Functions in Point Process Models

We prove that the empirical L ²-risk minimizing estimator over some general type of sieve classes is universally, strongly consistent for the regression function in a class of point process models of Poissonian type (random sampling processes). The universal consistency result needs weak assumptions on the underlying distributions and regression functions. It applies in particular to neural net classes and to radial basis function nets. For the estimation of the intensity functions of a Poisson process a similar technique yields consistency of the sieved maximum likelihood estimator for some general sieve classes. This revised version was published online in August 2006 with corrections to the Cover Date.