凝聚态物理中的分形

本文回顾了近年来与凝聚态物理相关的分形研究的一些主要工作,叙述了该领域的一些新进展。着重介绍了涉及分维和分形的一些基本概念、物理思想和研究方法,较详细地讨论了分形网络上的振动和弹性性质、磁序问题、生长和熔化、多分形以及周期分形等课题。     

凝聚态物理中的分形

本文回顾了近年来与凝聚态物理相关的分形研究的一些主要工作，叙述了该领域的一些新进展。着重介绍了涉及分维和分形的一些基本概念、物理思想和研究方法，较详细地讨论了分形网络上的振动和弹性性质、磁序问题、生长和熔化、多分形以及周期分形等课题。     

复杂网络研究概述

近年来，真实网络中小世界效应和无标度特性的发现激起了物理学界对复杂网路的研究热潮．复杂网络区别于以前广泛研究的规则网络和随机网络最重要的统计特征是什么?物理学家研究复杂网络的终极问题是什么?物理过程以及相关的物理现象对拓扑结构是否敏感?物理学家进入这一研究领域的原因和意义何在?复杂网络研究领域将来可能会向着什么方向发展?文章围绕上述问题，从整体上概述了复杂网络的研究进展．     

用户需求行为对互联网动力学整体特性的影响

由Internet构成的复杂网络的动力学特性主要受到用户需求行为的影响,具备时域的统计规律性. 通过对区域群体用户需求行为的时域实验统计分析,发现用户对Web网站的访问频度及其生成的二分网络的入度分布也呈现幂律分布和集聚现象,其幂指数介于1.7到1.8之间. 建立了虚拟资源网络VRN和物理拓扑网络PTN双层模型,分析了双层模型映射机理,并对网络用户需求行为进行建模. 虚拟资源网络VRN对物理拓扑网络PTN映射过程的不同机理,模拟了Internet资源网络到物理网络的不同影响模式. 幂律分布的用户需求特性会 关键词： 复杂网络 无标度拓扑 用户需求 相变     

复杂网络可控性研究现状综述

控制复杂系统是人们对复杂系统模型结构及相关动力学进行研究的最终目标, 反映人们对复杂系统的认识能力. 近年来, 通过控制理论和复杂性科学相结合,复杂网络可控性的研究引起了人们的广泛关注. 在过去的几年内, 来自国内外不同领域的研究人员从不同的角度对复杂网络可控性进行了深入的分析研究, 取得了丰硕的成果. 本文重点讨论了复杂网络的结构可控性研究进展, 详细介绍了基于最大匹配方法的复杂网络结构可控性分析框架, 综述了自2011年以来复杂网络可控性的相关研究成果, 具体论述了不同类型的可控性、可控性与网络拓扑结构统计特征的关联、基于可控性的网络及节点度量、控制的鲁棒性和可控性的相关优化方法. 最后, 对网络可控性未来的研究动态进行了展望, 有助于国内同行开展网络可控性的相关研究.     

角动量问题浅析

大学物理教学中,一些基本概念的讲授不仅对于物理概念本身的深化十分重要,而且对于现代物理学思想的把握和对后续课程的学习也具有十分重要的作用.通过讨论角动量及其在宇称变换下的性质,我们介绍了轴矢量的概念,并在量子力学层次对宇称变换进行了分析,将量子力学层次的态函数和场算符进行了分类,同时说明了在基本粒子物理研究当中对复合粒子进行分类时一个重要的量子数——宇称.     

评介《量子力学的基本概念》(关洪,高等教育出版社,北京,1990)

《量子力学的基本概念》是高等教育出版社1990年 8月出版的一本高等学校教学参考书(全书共 271页,定价 2.40元).尽管国内量子力学教科书和参考书已经不少,但详尽地从物理上来讨论量子力学基本概念的书,这还是第一本.作者关洪长期执教量子力学,并有深厚的研究功底.他在60年代初就活跃在粒子物理研究领域,是著名的北京基本粒子理论组成员之一,参与了创立和发展层子模型的主要过程,70年代以来又把研究拓宽到量子力学基础、物理学史、物理学中的自然哲学问题以及物理学的教学等众多领域,成绩显著.他对量子力学测不准关系的深入分析,得到我国物理…     

关于恒等算符的一点思考

恒等算符是量子力学中的基本概念,也是狄拉克符号下灵活多变的实用工具.简要介绍了恒等算符在相关计算和证明中的应用,将这一概念与自然辩证法中相关原理的思想加以对比,显示出恒等算符所蕴含的深刻物理含义,并有助于进一步理解量子力学不同于经典力学的重要观念和思想,可望对日常的学习生活带来一些新的理解和思路.     

声波测井中的物理问题

文章从声波测井的基本概念入手,介绍了什么是声波测井,其基本原理是什么,声波测井中的主要物理问题是什么,还从几个侧面介绍了声波测井的基本应用和未来的发展趋势,包括传统的电缆测井、目前国外流行的随钻声波测井以及具有应用前景但是尚处于研究阶段的震电测井.文章向从事物理研究的工作者概述了声波测井相关的物理问题和研究的发展现状,为打算涉入声波测井领域研究或者对相关领域研究感兴趣的读者提供了一些基本素材和知识.     

多重边融合复杂动态网络的自适应同步

基于网络拆分的思想对多重边融合复杂动态网络局部和全局的自适应同步进行了研究.通过给出严格的数学定义及假设,运用Lyapunov稳定理论得出了网络局部和全局的同步准则,给出了更为简单的网络同步的控制器.最后以Lorenz 系统为例进行数值仿真,验证了结论的正确性和有效性. 关键词： 多重边融合复杂动态网络 自适应同步 网络拆分 时滞     

Coupled disease–behavior dynamics on complex networks: A review

It is increasingly recognized that a key component of successful infection control efforts is understanding the complex, two-way interaction between disease dynamics and human behavioral and social dynamics. Human behavior such as contact precautions and social distancing clearly influence disease prevalence, but disease prevalence can in turn alter human behavior, forming a coupled, nonlinear system. Moreover, in many cases, the spatial structure of the population cannot be ignored, such that social and behavioral processes and/or transmission of infection must be represented with complex networks. Research on studying coupled disease–behavior dynamics in complex networks in particular is growing rapidly, and frequently makes use of analysis methods and concepts from statistical physics. Here, we review some of the growing literature in this area. We contrast network-based approaches to homogeneous-mixing approaches, point out how their predictions differ, and describe the rich and often surprising behavior of disease–behavior dynamics on complex networks, and compare them to processes in statistical physics. We discuss how these models can capture the dynamics that characterize many real-world scenarios, thereby suggesting ways that policy makers can better design effective prevention strategies. We also describe the growing sources of digital data that are facilitating research in this area. Finally, we suggest pitfalls which might be faced by researchers in the field, and we suggest several ways in which the field could move forward in the coming years.     

技术应用型院校大学物理实验课程教学改革的研究与实践

针对大学物理实验教学目前存在的问题,考虑到物理学科的发展趋势及高等教育改革的基本要求,本着更新物理实验教师的教育思想和教育观念、加强教师的培养、调整教学体系和教学内容、改革传统的教学方法和现有的大学物理实验考核体系和评价体系以及加强实验室管理等原则,对大学物理实验教学进行了研究和实践。     

物质结构的概念的统一性

尽管凝聚态物理、核物理和高能物理学的研究对象各不相同,它们的基本概念都是相通的。本文的目的,是从统一的观点来说明物理学的这些分支中的根本问题。希望这不仅有助于从一致的角度来理解各个领域,并且说明物理思想的沟通和借鉴,对于物理学的发展是重要的。     

Probabilistic Models with Deep Neural Networks

Recent advances in statistical inference have significantly expanded the toolbox of probabilistic modeling. Historically, probabilistic modeling has been constrained to very restricted model classes, where exact or approximate probabilistic inference is feasible. However, developments in variational inference, a general form of approximate probabilistic inference that originated in statistical physics, have enabled probabilistic modeling to overcome these limitations: (i) Approximate probabilistic inference is now possible over a broad class of probabilistic models containing a large number of parameters, and (ii) scalable inference methods based on stochastic gradient descent and distributed computing engines allow probabilistic modeling to be applied to massive data sets. One important practical consequence of these advances is the possibility to include deep neural networks within probabilistic models, thereby capturing complex non-linear stochastic relationships between the random variables. These advances, in conjunction with the release of novel probabilistic modeling toolboxes, have greatly expanded the scope of applications of probabilistic models, and allowed the models to take advantage of the recent strides made by the deep learning community. In this paper, we provide an overview of the main concepts, methods, and tools needed to use deep neural networks within a probabilistic modeling framework.     

Research on Community Detection in Complex Networks Based on Internode Attraction

With the rapid development of computer technology, the research on complex networks has attracted more and more attention. At present, the research directions of cloud computing, big data, internet of vehicles, and distributed systems with very high attention are all based on complex networks. Community structure detection is a very important and meaningful research hotspot in complex networks. It is a difficult task to quickly and accurately divide the community structure and run it on large-scale networks. In this paper, we put forward a new community detection approach based on internode attraction, named IACD. This algorithm starts from the perspective of the important nodes of the complex network and refers to the gravitational relationship between two objects in physics to represent the forces between nodes in the network dataset, and then perform community detection. Through experiments on a large number of real-world datasets and synthetic networks, it is shown that the IACD algorithm can quickly and accurately divide the community structure, and it is superior to some classic algorithms and recently proposed algorithms.     

Analysis of infinite d-dimensional networks – Capacitance between two adjacent nodes

This work showed that infinite d-dimensional networks consisting of identical capacitors each of capacitance C can be analyzed using basic concepts of physics. In this work we have showed that the equivalent capacitance C_eq between any two adjacent nodes in the infinite d-dimensional networks consisting of identical capacitors, is equal to dC where d is the dimension of the infinite network (i.e., d = 1,2,3,…). The results obtained here are in an excellent agreement with previous studied carried out.     

Extractive summarization using complex networks and syntactic dependency

The realization that statistical physics methods can be applied to analyze written texts represented as complex networks has led to several developments in natural language processing, including automatic summarization and evaluation of machine translation. Most importantly, so far only a few metrics of complex networks have been used and therefore there is ample opportunity to enhance the statistics-based methods as new measures of network topology and dynamics are created. In this paper, we employ for the first time the metrics betweenness, vulnerability and diversity to analyze written texts in Brazilian Portuguese. Using strategies based on diversity metrics, a better performance in automatic summarization is achieved in comparison to previous work employing complex networks. With an optimized method the Rouge score (an automatic evaluation method used in summarization) was 0.5089, which is the best value ever achieved for an extractive summarizer with statistical methods based on complex networks for Brazilian Portuguese. Furthermore, the diversity metric can detect keywords with high precision, which is why we believe it is suitable to produce good summaries. It is also shown that incorporating linguistic knowledge through a syntactic parser does enhance the performance of the automatic summarizers, as expected, but the increase in the Rouge score is only minor. These results reinforce the suitability of complex network methods for improving automatic summarizers in particular, and treating text in general.     

Complex Networks: from Graph Theory to Biology

The aim of this text is to show the central role played by networks in complex system science. A remarkable feature of network studies is to lie at the crossroads of different disciplines, from mathematics (graph theory, combinatorics, probability theory) to physics (statistical physics of networks) to computer science (network generating algorithms, combinatorial optimization) to biological issues (regulatory networks). New paradigms recently appeared, like that of ‘scale-free networks’ providing an alternative to the random graph model introduced long ago by Erdös and Renyi. With the notion of statistical ensemble and methods originally introduced for percolation networks, statistical physics is of high relevance to get a deep account of topological and statistical properties of a network. Then their consequences on the dynamics taking place in the network should be investigated. Impact of network theory is huge in all natural sciences, especially in biology with gene networks, metabolic networks, neural networks or food webs. I illustrate this brief overview with a recent work on the influence of network topology on the dynamics of coupled excitable units, and the insights it provides about network emerging features, robustness of network behaviors, and the notion of static or dynamic motif.