非平衡系统的概率模型及Master方程的建立

针对非平衡态统计中出现的非线性及多元线性Master方程,明确提出了一般的概率假定;应用概率论的方法建立了一般的非线性及多元线性Master方程;讨论了它们与宏观动力学方程的一致性问题;最后应用多元线性Master方程讨论了Brusselator的涨落,并对其耗散结构的产生作了初步解释。 关键词：     

Physically self-consistent basis for modern cosmology

Cosmoparticle physics appeared as a natural result of internal development of cosmology seeking physical grounds for inflation, baryosynthesis, and nonbaryonic dark matter and of particle physics going outside the Standard Model of particle interactions. Its aim is to study the foundations of particle physics and cosmology and their fundamental relationship in the combination of respective indirect cosmological, astrophysical, and physical effects. The ideas on new particles and fields predicted by particle theory and on their cosmological impact are discussed, as well as the methods of cosmoparticle physics to probe these ideas, are considered with special analysis of physical mechanisms for inflation, baryosynthesis, and nonbaryonic dark matter. These mechanisms are shown to reflect the main principle of modern cosmology, putting, instead of formal parameters of cosmological models, physical processes governing the evolution of the big-bang universe. Their realization on the basis of particle theory induces additional model-dependent predictions, accessible to various methods of nonaccelerator particle physics. Probes for such predictions, with the use of astrophysical data, are the aim of cosmoarcheology studying astrophysical effects of new physics. The possibility of finding quantitatively definite relationships between cosmological and laboratory effects on the basis of cosmoparticle approach, as well as of obtaining a unique solution to the problem of physical candidates for inflation, mechanisms of baryogenesis, and multicomponent dark matter, is exemplified in terms of gauge model with broken family symmetry, underlying horizontal unification and possessing quantitatively definite physical grounds for inflation, baryosynthesis, and effectively multicomponent dark-matter scenarios.     

Method of “optimum observables” and implementation of neural networks in physics investigations

A separation of a signal of various physics processes from an overwhelming background is one of the most important problems in contemporary high-energy physics. The application of various multivariate statistical methods, such as the neural-network method, has become one of the popular steps toward optimizing relevant analyses. The choice of optimum variables that would disclose distinctions between a signal and a background is one of the important elements in the application of neural networks. A universal method for determining an optimum set of such kinematical variables is described in the present article. The method is based on an analysis of Feynman diagrams contributing to signal and background processes. This method was successfully implemented in searches for single top-quark production with the D0 detector (Tevatron, Fermilab) in analyzing Run I and Run II data. Brief recommendations concerning an optimum implementation of the neural-network method in physics analysis are given on the basis of experience gained in searches for single top-quark production with the D0 detector.     

Some examples from muon spin rotation

The method of μSR is briefly described and its potential in the field of solidstate physics (especially metal physics) is discussed. Some examples from the fields of magnetism, spin relaxation and particle diffusion in metals are shown and comparisons with conventional hyperfine interaction methods are made for some areas of application.     

小波分析及神经网络方法在核物理及核工程中的应用

介绍了小波分析和神经网络方法在核物理及核工程领域的应用现状。 分别对小波分析及神经网络方法的基本原理进行了介绍, 详细讨论了小波变换中的多分辨分析方法在γ能谱平滑以及核电站设备监测等方面的应用, 讨论了连续小波变换在γ能谱分析、 粒子种类鉴别以及核反应堆安全监测等领域的应用。 同时, 还详细介绍了神经网络方法在以上各领域的发展现状。 最后, 展望了两种方法在核安全检测、 核辐射防护以及核电站实时监控等领域的发展趋势。 Applications of wavelet analysis and neutral networks in the field of nuclear physics and engineering are reviewed. The principle of these two methods are introduced briefly, and then the applications of multiresolution analysis technique in the smoothing of γ ray spectroscopy, and in nuclear power plant monitoring are discussed in detail. Applications of the continuous wavelet analysis method in γ ray spectroscopy analysis, in particle identification , and in nuclear reactor safety monitoring are also talked over. In addition, the applications of neutral networks in above fields are introduced. Finally, the trends of the future development for these two methods are prospected.     

Optimal least-squares estimators of the diffusion constant from a single Brownian trajectory

Modern developments in microscopy and image processing are revolutionising areas of physics, chemistry, and biology as nanoscale objects can be tracked with unprecedented accuracy. However, the price paid for having a direct visualisation of a single particle trajectory with high temporal and spatial resolution is a consequent lack of statistics. This naturally calls for reliable analytical tools which will allow one to extract the properties specific to a statistical ensemble from just a single trajectory. In this article we briefly survey different analytical methods currently used to determine the ensemble average diffusion coefficient from single particle data and then focus specifically on weighted least-squares estimators, seeking the weight functions for which such estimators are ergodic. Finally, we address the question of the effects of disorder on such estimators.     

互联网上的开放大学物理教育

对互联网上的开放物理教育进行了研究.给出了网络学习系统组成、免费物理课程资源;对物理开放课程做了统计分析;将网络免费物理学习方式与其他教育模式进行对比;展望互联网物理教育未来发展趋势.     

Restricted Boltzmann machine: Recent advances and mean-field theory

This review deals with restricted Boltzmann machine(RBM) under the light of statistical physics.The RBM is a classical family of machine learning(ML) models which played a central role in the development of deep learning.Viewing it as a spin glass model and exhibiting various links with other models of statistical physics,we gather recent results dealing with mean-field theory in this context.First the functioning of the RBM can be analyzed via the phase diagrams obtained for various statistical ensembles of RBM,leading in particular to identify a compositional phase where a small number of features or modes are combined to form complex patterns.Then we discuss recent works either able to devise mean-field based learning algorithms;either able to reproduce generic aspects of the learning process from some ensemble dynamics equations or/and from linear stability arguments.     

远离平衡问题及其描述的理论和方法概述

本文简要阐述了耗散结构、混沌、外场驱动下的耗散系统非线性动力学等远离平衡和非线性问题,及描述这些问题的各种宏观、半宏观及非平衡统计物理的理论和方法。     

核物理与核探测、核分析技术的应用

文章概要介绍了随着核物理研究发展起来的辐射和粒子探测的原理、方法和主要技术,举例介绍了相关核物理与核探测、核分析的典型技术及其在高精度测量和医学中的广泛应用,如活化分析技术、穆斯堡尔谱学、核磁共振技术、加速器质谱技术、核医学成像、同步辐射技术、中子散射分析、放射性示踪技术等等.     

Spontaneous symmetry breaking in quantum many-body systems (A solid-state physicist’s view of Goldstone’s theorem and all that)

The concept of spontaneous symmetry breaking arose first in the context of superconductivity, before it became important for elementary particle physics. Starting with its original discovery, a comparison of the workings of the Goldstone mechanism in relativistic quantum fixed theory on the one hand and in quantum statistical mechanics on the other is given. The roles of locality and of long range forces are traced. For condensed matter physics, an approach using functional integral methods and macroscopic order parameter fields, valid near critical points is outlined. A possibly more widely valid approach is also presented, to complete this review of the Goldstone theories in quantum statistical mechanics. Talk delivered at the International Symposium on Theoretical Physics, Bangalore, November 1984.     

Methods for organizing the interaction of circulating particle beams with internal targets in nuclear physics experiments at synchrotrons and storage rings

The first methods for using internal targets and their further development for organization of nuclear physics experiments at synchrotrons and storage rings are reviewed. With these methods, new results are obtained and discoveries are made in physics of interactions of elementary particles and nuclei. Current uses of various internal targets and circulating particle beams in ongoing and projected research in particle physics, relativistic nuclear and spin physics, and physics of interactions of exotic and radioactive nuclei and for producing fluxes of secondary particles (mesons, neutrons, exotic and radioactive nuclei, etc.) for physics experiments are described.     

金融物理的若干基本问题与研究进展(Ⅰ)--价格的统计分析与价格涨落的随机过程模型

金融物理学是物理学概念和方法应用于金融分析的一门新的交叉学科，近年来受到人们的广泛关注．文章简述了金融物理的研究方向和研究方法，重点讨论了价格涨落的统计分析和相关的物理模型．     

Improving the Robustness of Particle Size Analysis by Multivariate Statistical Process Control

The robustness of online particle size analysis in wet processes is improved by applying data based modeling methods to the control of the sample preparation and measurement sequence of the particle size analyzer. The aim is to find a more accurate and reliable method of determining the end of the particle size integration period using multivariate statistical process control (MSPC). The studied approach is tested on analyzers installed at two mineral processing plant sites and validated using two validation tests. Research shows that the proposed method works with two very different slurry types. The main advantage of the adapted approach is that there are no adjustable parameters that have to be set by the user.     

网络背景下大学物理实验教学模式探讨

随着网络的深入发展,人们的生活模式,学习模式都发生着变化。在这种背景下,对大学物理实验的教学模式进行探讨,分析了物理仿真实验与传统实践实验教学模式的互补性,提出立体教学模式的教学方式。     

Chemometrics and its applications to x‐ray spectrometry

Recent developments in curve fitting, multivariate calibration, and pattern recognition in chemometrics, and their application to x‐ray spectrometry, are reviewed. Relatively innovated algorithms, namely genetic algorithms, neural networks and support vector machines, are discussed. Together with the three algorithms, the performances of different algorithms are compared briefly, which mainly includes principal component analysis, partial least‐squares regression, factor analysis, cluster analysis, nearest neighbor methods, linear discriminant analysis, linear learning machine, and soft independent modeling of class analogy. In general, the chemometrics methods are superior to the conventional methods, such as Fourier transform and Marquardt–Levenberg algorithms, to a certain extent. Copyright © 2006 John Wiley & Sons, Ltd.     

Quantum particles from classical statistics

Quantum particles and classical particles are described in a common setting of classical statistical physics. The property of a particle being “classical” or “quantum” ceases to be a basic conceptual difference. The dynamics differs, however, between quantum and classical particles. We describe position, motion and correlations of a quantum particle in terms of observables in a classical statistical ensemble. On the other side, we also construct explicitly the quantum formalism with wave function and Hamiltonian for classical particles. For a suitable time evolution of the classical probabilities and a suitable choice of observables all features of a quantum particle in a potential can be derived from classical statistics, including interference and tunneling. Besides conceptual advances, the treatment of classical and quantum particles in a common formalism could lead to interesting cross‐fertilization between classical statistics and quantum physics.     

Progress in physical properties of Chinese stock markets

In the past two decades, statistical physics was brought into the field of finance, applying new methods and concepts to financial time series and developing a new interdiscipline “econophysics”. In this review, we introduce several commonly used methods for stock time series in econophysics including distribution functions, correlation functions, detrended fluctuation analysis method, detrended moving average method, and multifractal analysis. Then based on these methods, we review some statistical properties of Chinese stock markets including scaling behavior, long-term correlations, cross-correlations, leverage effects, antileverage effects, and multifractality. Last, based on an agent-based model, we develop a new option pricing model — financial market model that shows a good agreement with the prices using real Shanghai Index data. This review is helpful for people to understand and research statistical physics of financial markets.