Analysis of atomic arrangement in magnetic Fe–Pt nanoparticles

The order parameter S of Fe–Pt nanoparticles is estimated from X-ray diffraction (XRD) patterns. The total intensity of a diffraction peak is obtained by Rietveld analysis as well as simply integrating the intensity. The Rietveld analysis is found to provide a plausible value of S even for a sample showing an XRD pattern with broad and overlapped peaks. Another order parameter Q, which is obtained from Mössbauer spectra, is introduced, and it is confirmed that Q is equivalent to the probability of Fe atoms being in the L1₀-type atomic arrangement. The coercivity of Fe–Pt nanoparticles is directly proportional to Q, while it vanishes at S=0.4, indicating that the magnetic property of Fe–Pt nanoparticles has a closer relationship to Q than S.     

Growth of Lévy trees

We construct random locally compact real trees called Lévy trees that are the genealogical trees associated with continuous-state branching processes. More precisely, we define a growing family of discrete Galton–Watson trees with i.i.d. exponential branch lengths that is consistent under Bernoulli percolation on leaves; we define the Lévy tree as the limit of this growing family with respect to the Gromov–Hausdorff topology on metric spaces. This elementary approach notably includes supercritical trees and does not make use of the height process introduced by Le Gall and Le Jan to code the genealogy of (sub)critical continuous-state branching processes. We construct the mass measure of Lévy trees and we give a decomposition along the ancestral subtree of a Poisson sampling directed by the mass measure. T. Duquesne is supported by NSF Grants DMS-0203066 and DMS-0405779. M. Winkel is supported by Aon and the Institute of Actuaries, EPSRC Grant GR/T26368/01, le département de mathématique de l’Université d’Orsay and NSF Grant DMS-0405779.     

Concentration inequalities for random fields via coupling

We present a new and simple approach to concentration inequalities in the context of dependent random processes and random fields. Our method is based on coupling and does not use information inequalities. In case one has a uniform control on the coupling, one obtains exponential concentration inequalities. If such a uniform control is no more possible, then one obtains polynomial or stretched-exponential concentration inequalities. Our abstract results apply to Gibbs random fields, both at high and low temperatures and in particular to the low-temperature Ising model which is a concrete example of non-uniformity of the coupling.      

随机截断下NA样本半参数回归模型中的相合估计

本文研究固定设计下的半参数回归模型在相应变量Yi因受到某种随机干扰而被右截断且截断分布已知时,利用所获的截断观察定义了参数β和回归函数g(·)的估计,并证明了它们均具有强相合性.     

随机环境中分枝过程的等价定理

给定了随机环境中分枝过程(BPRE)的精确定义,讨论了有关的可测性问题和BPRE的基本性质．在此基础上,证明了BPRE的一个等价定理．     

A Distributional Approach to Multiple Stochastic Integrals and Transformations of the Poisson Measure

We study a class of ‘nonpoissonian’ transformations of the configuration space and the corresponding transformations of the Poisson measure. For some class of Poisson measures we find conditions which are sufficient for the transformed measure (which in general is nonpoissonian) to be absolutely continuous with respect to the initial Poisson measure and get the expression for the corresponding Radon–Nikodym derivative. To solve this problem we use a distributional approach to Poisson multiple stochastic integrals.     

二维随机激光器的模式选择及阈值与饱和特性

基于将Maxwell方程与四能级原子系统速率方程相结合而建立起随机激光时域理论，并利用有限时域差分法，研究了二维随机介质中激光模式的输出特性与介质尺寸、外形及抽运速率等参数的关系.结果表明，与传统激光模式相似，随机激光模式的强度随抽运速率的变化不仅具有阈值特性，而且具有饱和特性.基于模式特性对介质及抽运参数的依赖关系，提出了二维随机激光器的选模方式，在很大程度上不同于传统激光器的选模方式. 关键词： 随机激光器 模式选择 无序介质中的光学特性     

随机环境下的对称零程过程的大偏差原理

利用相对熵方法及鞅方法给出了随机环境下的对称零程过程的经验测度的收敛速度函数.     

单分子光子源光子统计的非平衡探测

研究了在Hanbury Brown Twiss探测方式下，非平衡探测系统对光子统计测量的影响。通过记录2个单光子计数器响应的单分子光子源输出的每一个事件，分析具有泊松统计背景的实际单分子光子源的光子统计特性，讨论并给出了非平衡探测系统的单分子光子源Mandel参数。研究表明：非理想的50／50分束器、非理想的线性传输效率和非理想的探测器都会使单分子光子源Mandel参数的实际测量结果小于平衡系统的Mandel参数，最后给出了单分子光子源Mandel参数的非理想探测的校正表达式。     

有Edgeworth展式的分布的随机加权逼近的重对数律

本文研究了未知分布的逼近问题，利用随机加权法，给出了有Edgeworth展式的一类（未知）分布的模拟分布，证明了在一定条件下，模拟分布与未知分布的逼近精度达到O(n^-1√lnlnn），称之为随机加权逼近的重对数律。