《国外数学名著系列(影印版)25:代数复杂性理论》全面系统地讲述了代数复杂性理论的知识，书中包含了近400个习题和超过500个参考文献，对初学者和科研人员都有很高的参考价值。

本书作者现任美国西北大学教授，多种国际权威杂志的主编、副主编。作者根据在教学、研究和咨询中的经验，写了这本适合学生和实际工作者的书。本书提供连续优化中大多数有效方法的全面的最新的论述。每一章从基本概念开始，逐步阐述当前可用的最佳技术。　　本书强调实用方法，包含大量图例和练习，适合广大读者阅读，可作为工程、运筹学、数学、计算机科学以及商务方面的研究生教材，也可作为该领域的科研人员和实际工作人员的手册。　　总之，作者力求本书阅读性强，内容丰富，论述严谨，能揭示数值最优化的美妙本质和实用价值。

Network science offers a powerful language to represent and study complex systems composed of interacting elements — from the Internet to social and biological systems. In its standard formulation, this framework relies on the assumption that the underlying topology is static, or changing very slowly as compared to dynamical processes taking place on it, e.g., epidemic spreading or navigation. Fuelled by the increasing availability of longitudinal networked data, recent empirical observations have shown that this assumption is not valid in a variety of situations. Instead, often the network itself presents rich temporal properties and new tools are required to properly describe and analyse their behaviour.
A Guide to Temporal Networks presents recent theoretical and modelling progress in the emerging field of temporally varying networks, and provides connections between different areas of knowledge required to address this multi-disciplinary subject. After an introduction to key concepts on networks and stochastic dynamics, the authors guide the reader through a coherent selection of mathematical and computational tools for network dynamics. Perfect for students and professionals, this book is a gateway to an active field of research developing between the disciplines of applied mathematics, physics and computer science, with applications in others including social sciences, neuroscience and biology.

我们已经见证了社交媒体、智能手机以及其他科技奇迹的诞生与进化，这些科技工具让我们能够以前所未见的规模和深度采集并处理有关人类行为的数据，从而为研究人类社会提供了一个全新的路径。这本书是一部里程碑式的作品，它将彻底改变下一代社会科学家和数据科学家探索世界的方式。
 大数据的10个共同特征是什么？
 如何通过数据预测美国总统大选的结果？
 如何在短时间内对100万个星系进行分类？
 大规模数据采集面临着怎样的道德伦理困境？
 科技公司是否在监视我们的行为，数字时代是不是将我们带进了“全景监狱”呢？
在计算社会学这一新兴领域，作者阐述了数字革命如何扭转了社会科学家和数据科学家观察行为、提出问题、开展实验和进行大规模协作的方式，详细描述了大批真实的案例，并且划定了面临伦理挑战时需要遵循的4项原则。
这本书用全新的数据模式解锁了社会学。对社会科学家来说，它是一本跨界指南之书；对数据科学家来说，它是一本素养提升之作。

"Simulation," writes Gary Flake in his preface, "becomes a form of experimentation in a universe of theories. The primary purpose of this book is to celebrate this fact."In this book, Gary William Flake develops in depth the simple idea that recurrent rules can produce rich and complicated behaviors. Distinguishing "agents" (e.g., molecules, cells, animals, and species) from their interactions (e.g., chemical reactions, immune system responses, sexual reproduction, and evolution), Flake argues that it is the computational properties of interactions that account for much of what we think of as "beautiful" and "interesting." From this basic thesis, Flake explores what he considers to be today's four most interesting computational topics: fractals, chaos, complex systems, and adaptation.Each of the book's parts can be read independently, enabling even the casual reader to understand and work with the basic equations and programs. Yet the parts are bound together by the theme of the computer as a laboratory and a metaphor for understanding the universe. The inspired reader will experiment further with the ideas presented to create fractal landscapes, chaotic systems, artificial life forms, genetic algorithms, and artificial neural networks.

What makes people smarter than computers? These volumes by a pioneering neurocomputing group suggest that the answer lies in the massively parallel architecture of the human mind. They describe a new theory of cognition called connectionism that is challenging the idea of symbolic computation that has traditionally been at the center of debate in theoretical discussions about the mind. The authors' theory assumes the mind is composed of a great number of elementary units connected in a neural network. Mental processes are interactions between these units which excite and inhibit each other in parallel rather than sequential operations. In this context, knowledge can no longer be thought of as stored in localized structures; instead, it consists of the connections between pairs of units that are distributed throughout the network. Volume 1 lays the foundations of this exciting theory of parallel distributed processing, while Volume 2 applies it to a number of specific issues in cognitive science and neuroscience, with chapters describing models of aspects of perception, memory, language, and thought.

该书是一本von Neumann关于“自复制自动机”的研究论文集，由von Neumann在密西根大学的助手Arthur Burk（大名鼎鼎的John Holland的博士导师）整理编辑。
这本书的意义不仅仅在于它实际上开创了“人工生命”、“细胞自动机”等多门复杂性研究的分支。更重要的是，该书将“自我复制”作为生命的一个本质特征加以数学建模的研究。而这套理论和可计算性理论、歌德尔定理以及热力学深深地联系到了一起。这一点也许对于我们研究复杂系统的人来说仍具有重要的意义。

Dealing with dynamics of processes that repeat themselves regularly, this revised and updated edition extends the thread from 1980 to the present day, concentrating on areas of interest where there will be much activity in the future. This involves going through spatial biochemical, electrophysiological, and organismic dynamical systems and patterns that were discovered by pursuing the theme of phase singularities introduced in the original book. In particular, the work on excitability in cell membranes will be thoroughly updated as will the references throughout the book.

This book provides a grounded introduction to the fundamental concepts of mathematics, neuroscience and their combined use, thus providing the reader with a springboard to cutting-edge research topics and fostering a tighter integration of mathematics and neuroscience for future generations of students. The book alternates between mathematical chapters, introducing important concepts and numerical methods, and neurobiological chapters, applying these concepts and methods to specific topics. It covers topics ranging from classical cellular biophysics and proceeding up to systems level neuroscience. Starting at an introductory mathematical level, presuming no more than calculus through elementary differential equations, the level will build up as increasingly complex techniques are introduced and combined with earlier ones. Each chapter includes a comprehensive series of exercises with solutions, taken from the set developed by the authors in their course lectures. MATLAB code is included for each computational figure, to allow the reader to reproduce them. Biographical notes referring the reader to more specialized literature and additional mathematical material that may be needed either to deepen the reader's understanding or to introduce basic concepts for less mathematically inclined readers completes each chapter.

A very didactic and systematic introduction to mathematical concepts of importance for the analysis of data and the formulation of concepts based on experimental data in neuroscience
Provides introductions to linear algebra, ordinary and partial differential equations, Fourier transforms, probabilities and stochastic processes
Introduces numerical methods used to implement algorithms related to each mathematical concept
Illustrates numerical methods by applying them to specific topics in
neuroscience, including Hodgkin-Huxley equations, probabilities to describe stochastic release, stochastic processes to describe noise in neurons, Fourier transforms to describe the receptive fields of visual neurons
Provides implementation examples in MATLAB code, also included for download on the accompanying support website (which will be updated with additional code and in line with major MATLAB releases)
Allows the mathematical novice to analyze their results in more sophisticated ways, and consider them in a broader theoretical framework

《计算机代数系统的数学原理》主要介绍了计算机代数系统的数学理论、经典结果和著名算法。全书包含高精度运算、数论、数学常数、精确线性代数、多项式、方程求解、符号极限、符号求和、符号积分、微分方程符号解等10个部分，涵盖了构建计算机代数系统的最基础也是最重要的内容。书中的许多内容是第一次被系统地整理后出现在中文文献中，并在一些领域体现了本方向的最新进展。

机器与未来是息息相关的。在战争中锻造出来的控制论一度成为了前所未有的能够预测并预见未来智能自动机的工具。与此同时，两股对立的力量共同塑造了未来的控制论愿景。一方是对于一个更加美好的世界之希望：暴力行为减少，工作变得更加人性化，游戏更加娱乐化，政治更加自由化，战争不再那么血腥。“思考的机器”会带来进步，这深深地嵌入在那些现代主义者的信仰之中。
但反对势力同样塑造了迫在眉睫的技术变革所带来的控制论假想：它充满了一种对这样一个世界的恐慌——机器人会使工人陷入失业，机器会伤害人类，核心系统会崩塌，大量的监控和隐私泄露，机械化逆行。乐观主义对抗悲观主义，解放对抗压迫，乌托邦对抗反乌托邦。
本书探讨了将控制交于机器，与机器交互或通过机器进行交互的含意。机器最终能把人类从肮脏、重复的劳动中解放出来吗？能把人类从令人抓狂的交通拥堵中解脱出来，并使得我们的工作、生活和游戏更加社会化、互联化，但同时更加安全和放心吗？ 或者，现代社会正不知不觉走入一个慢慢失去控制的危险的勇敢新世界？我们是否正在无意中建立网络化的经济，表面上这种经济直接伸进了我们的口袋和手提包中，但它随时可以戛然而止，甚至有可能在关键枢纽上崩塌？通过把前所未有的控制权委托给这些前所未有的、互联化的智能的机器，我们发达的社会需要承担多大的风险？

The development of populations over time, and, on longer timescales, the evolution of species, are both influenced by a complex of interacting, underlying processes. Computer simulation provides a means of experimenting within an idealised framework to allow aspects of these processes and their interactions to be isolated, controlled, and understood. In this book, computer simulation is used to model migration, extinction, fossilisation, interbreeding, selection and non-hereditary effects in the context of human populations and the observed distribution of fossil and current hominoid species. The simulations described enable the visualisation and study of lineages, genetic diversity in populations, character diversity across species and the accuracy of reconstructions, allowing new insights into human evolution and the origins of humankind for graduate students and researchers in the fields of physical anthropology, human evolution, and human genetics.

本书介绍科学计算需要的各类数值分析方法，不但在严谨的数学科学背景下进行讨论，而且给出了数值分析方法的严格证明。内容宽泛而深入，讲解细致而生动，包含大量定理、各种习题，曾被美国多所大学采纳为教材。
本书特点：涵盖了科学计算中数值分析的广泛主题，除数值分析的基础知识外，还涉及线性代数和非线性代数方程组的求解、数值微分与数值积分、常微分方程和偏微分方程的数值解、函数逼近等方面的内容。增加了优化方面的内容和查询相关信息的网络资源。着重讲解与算术相关的理论基础。算法以伪代码的形式给出，以便学生可以立即用标准语言和交互数据包来编写实现算法的计算机程序。
这是一本对所研究的问题作更多学术性讨论的数值分析教材，介绍了与科学计算有关的各类算法和方法以及这些方法的数学基础。主要内容包括：计算机算术运算、非线性方程的解、解线性方程组、数值线性代数精选、函数逼近、数值微分和数值积分、常微分方程数值解、偏微分方程数值解、线性规划以及最优化等。另外，每章配备了大量的习题，其中不乏实用性很强的计算机习题。
本书可作为数学、工程技术、自然科学、计算机科学和其他相关专业高年级本科生或研究生数值分析课程的教材，也可作为计算数学和工程技术人员的参考用书。

Thirty-eight chapters, comissioned from experts all over the world, describe major concepts, methods, and applications in computational linguistics. Part I, Linguistic Fundamentals, provides an overview of the field suitable for senior undergraduates and non-specialists from other fields of linguistics and related disciplines. Part II describes current tasks, techniques, and tools in Natural Language Processing and aims to meet the needs of post-doctoral workers and others embarking on computational language research. Part III surveys current applications. This book is a state-of-the-art reference to one of the most active and productive fields in linguistics. It will be of interest and practical use to a wide range of linguists, as well as to researchers in such fields as informatics, artificial intelligence, language engineering, and cognitive science.

《神经信息学(神经系统的理论和模型)》把半个世纪以来有实验依据的神经系统(脑)中的主要理论和模型集中起来，给研究生们提供理论训练。这些理论和模型中有重要基础理论意义的，大都取材于诺贝尔奖获得者的工作（Hodgkin、Huxley、Hartline、Gabor、Bekesy、Eccles、Crick、Edelmen、Sperry……），也包括对信息科学、工程应用有重大影响的假设、理论和算法（Hebb学习律、平行分布式理论框架……）。
《神经信息学(神经系统的理论和模型)》共分三篇：第一篇包括固定结构的神经系统的理论模型，涵盖神经元模型、感受器的数学描述、节律产生和视觉信息加工等；第二篇是关于学习和记忆的理论模型，包括}tebb学习律、平行分布式理论框架、Hopfield模型以及短时程的突触修正规律，清晰讲述了神经系统的理论研究对人工智能、信息科学工程应用的意义；第三篇介绍神经科学和脑科学中当前的几个热点，包括神经编码、功能柱的结构和功能、脑的非线性和意识问题。
《神经信息学(神经系统的理论和模型)》可作为神经科学、认知科学、心理学等专业的研究生教材，也可作为人工视觉、神经假肢、人工智能、信息科学专业研究人员的参考书。

本书从计算的变迁这一独特视角回顾了数学、逻辑学和哲学的历史沿革，展现了计算为数学研究发展带来的全新前景，展望了这场数学革命在自然科学、信息科学与哲学领域引发的重大变革。

本书是国际上数值计算方面的权威著作，有“圣经”之称。被美国加州大学、斯坦福大学、华盛顿大学、芝加哥大学、中国科学院研究生院等很多世界知名学府用作相关课程的教材或主要参考书。
本书系统地介绍了矩阵计算的基本理论和方法。书中的许多算法都有现成的软件包实现，每节后还附有习题，并有注释和大量参考文献，非常有助于自学。