Skip navigation


  • Page 2 of 56

Category theory was invented in the 1940s to unify and synthesize different areas in mathematics, and it has proven remarkably successful in enabling powerful communication between disparate fields and subfields within mathematics. This book shows that category theory can be useful outside of mathematics as a rigorous, flexible, and coherent modeling language throughout the sciences. Information is inherently dynamic; the same ideas can be organized and reorganized in countless ways, and the ability to translate between such organizational structures is becoming increasingly important in the sciences. Category theory offers a unifying framework for information modeling that can facilitate the translation of knowledge between disciplines.

Written in an engaging and straightforward style, and assuming little background in mathematics, the book is rigorous but accessible to non-mathematicians. Using databases as an entry to category theory, it begins with sets and functions, then introduces the reader to notions that are fundamental in mathematics: monoids, groups, orders, and graphs—categories in disguise. After explaining the “big three” concepts of category theory—categories, functors, and natural transformations—the book covers other topics, including limits, colimits, functor categories, sheaves, monads, and operads. The book explains category theory by examples and exercises rather than focusing on theorems and proofs. It includes more than 300 exercises, with solutions.

Category Theory for the Sciences is intended to create a bridge between the vast array of mathematical concepts used by mathematicians and the models and frameworks of such scientific disciplines as computation, neuroscience, and physics.

Downloadable instructor resources available for this title: 193 exercises, separate from those included in the book, with solutions

Complex communicating computer systems—computers connected by data networks and in constant communication with their environments—do not always behave as expected. This book introduces behavioral modeling, a rigorous approach to behavioral specification and verification of concurrent and distributed systems. It is among the very few techniques capable of modeling systems interaction at a level of abstraction sufficient for the interaction to be understood and analyzed. Offering both a mathematically grounded theory and real-world applications, the book is suitable for classroom use and as a reference for system architects.

The book covers the foundation of behavioral modeling using process algebra, transition systems, abstract data types, and modal logics. Exercises and examples augment the theoretical discussion. The book introduces a modeling language, mCRL2, that enables concise descriptions of even the most intricate distributed algorithms and protocols. Using behavioral axioms and such proof methods as confluence, cones, and foci, readers will learn how to prove such algorithms equal to their specifications. Specifications in mCRL2 can be simulated, visualized, or verified against their requirements. An extensive mCRL2 toolset for mechanically verifying the requirements is freely available online; this toolset has been successfully used to design and analyze industrial software that ranges from healthcare applications to particle accelerators at CERN. Appendixes offer material on equations and notation as well as exercise solutions.

This landmark graduate-level text combines depth and breadth of coverage with recent, cutting-edge work in all the major areas of modern labor economics. Its command of the literature and its coverage of the latest theoretical, methodological, and empirical developments make it also a valuable resource for practicing labor economists.

This second edition has been substantially updated and augmented. It incorporates examples drawn from many countries, and it presents empirical methods using contributions that have proved to be milestones in labor economics. The data and codes of these research publications, as well as numerous tables and figures describing the functioning of labor markets, are all available on a dedicated website (, along with slides that can be used as course aids and a discussion forum.

This edition devotes more space to the analysis of public policy and the levers available to policy makers, with new chapters on such topics as discrimination, globalization, income redistribution, employment protection, and the minimum wage or labor market programs for the unemployed. Theories are explained on the basis of the simplest possible models, which are in turn related to empirical results. Mathematical appendixes provide a toolkit for understanding the models.

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. Subjects include supervised learning; Bayesian decision theory; parametric, semi-parametric, and nonparametric methods; multivariate analysis; hidden Markov models; reinforcement learning; kernel machines; graphical models; Bayesian estimation; and statistical testing.

Machine learning is rapidly becoming a skill that computer science students must master before graduation. The third edition of Introduction to Machine Learning reflects this shift, with added support for beginners, including selected solutions for exercises and additional example data sets (with code available online). Other substantial changes include discussions of outlier detection; ranking algorithms for perceptrons and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesian methods. All learning algorithms are explained so that students can easily move from the equations in the book to a computer program. The book can be used by both advanced undergraduates and graduate students. It will also be of interest to professionals who are concerned with the application of machine learning methods.

Downloadable instructor resources available for this title: solution manual, programs, lecture slides, and file of figures in the book

The event-related potential (ERP) technique, in which neural responses to specific events are extracted from the EEG, provides a powerful noninvasive tool for exploring the human brain. This volume describes practical methods for ERP research along with the underlying theoretical rationale. It offers researchers and students an essential guide to designing, conducting, and analyzing ERP experiments. This second edition has been completely updated, with additional material, new chapters, and more accessible explanations. Freely available supplementary material, including several online-only chapters, offer expanded or advanced treatment of selected topics.

The first half of the book presents essential background information, describing the origins of ERPs, the nature of ERP components, and the design of ERP experiments. The second half of the book offers a detailed treatment of the main steps involved in conducting ERP experiments, covering such topics as recording the EEG, filtering the EEG and ERP waveforms, and quantifying amplitudes and latencies. Throughout, the emphasis is on rigorous experimental design and relatively simple analyses. New material in the second edition includes entire chapters devoted to components, artifacts, measuring amplitudes and latencies, and statistical analysis; updated coverage of recording technologies; concrete examples of experimental design; and many more figures. Online chapters cover such topics as overlap, localization, writing and reviewing ERP papers, and setting up and running an ERP lab.

Financial Modeling is now the standard text for explaining the implementation of financial models in Excel. This long-awaited fourth edition maintains the “cookbook” features and Excel dependence that have made the previous editions so popular. As in previous editions, basic and advanced models in the areas of corporate finance, portfolio management, options, and bonds are explained with detailed Excel spreadsheets. Sections on technical aspects of Excel and on the use of Visual Basic for Applications (VBA) round out the book to make Financial Modeling a complete guide for the financial modeler.

The new edition of Financial Modeling includes a number of innovations. A new section explains the principles of Monte Carlo methods and their application to portfolio management and exotic option valuation. A new chapter discusses term structure modeling, with special emphasis on the Nelson-Siegel model. The discussion of corporate valuation using pro forma models has been rounded out with the introduction of a new, simple model for corporate valuation based on accounting data and a minimal number of valuation parameters.

Praise for earlier editions
“Financial Modeling belongs on the desk of every finance professional. Its no-nonsense, hands-on approach makes it an indispensable tool.”
—Hal R. Varian, Dean, School of Information Management and Systems, University of California, Berkeley

Financial Modeling is highly recommended to readers who are interested in an introduction to basic, traditional approaches to financial modeling and analysis, as well as to those who want to learn more about applying spreadsheet software to financial analysis."
—Edward Weiss, Journal of Computational Intelligence in Finance

“Benninga has a clear writing style and uses numerous illustrations, which make this book one of the best texts on using Excel for finance that I've seen.”
—Ed McCarthy, Ticker Magazine

in Development and in Evolution of Behavior and the Mind

This introduction to the structure of the central nervous system demonstrates that the best way to learn how the brain is put together is to understand something about why. It explains why the brain is put together as it is by describing basic functions and key aspects of its evolution and development. This approach makes the structure of the brain and spinal cord more comprehensible as well as more interesting and memorable. The book offers a detailed outline of the neuroanatomy of vertebrates, especially mammals, that equips students for further explorations of the field.

Gaining familiarity with neuroanatomy requires multiple exposures to the material with many incremental additions and reviews. Thus the early chapters of this book tell the story of the brain’s origins in a first run-through of the entire system; this is followed by other such surveys in succeeding chapters, each from a different angle. The book proceeds from basic aspects of nerve cells and their physiology to the evolutionary beginnings of the nervous system to differentiation and development, motor and sensory systems, and the structure and function of the main parts of the brain. Along the way, it makes enlightening connections to evolutionary history and individual development. Brain Structure and Its Origins can be used for advanced undergraduate or beginning graduate classes in neuroscience, biology, psychology, and related fields, or as a reference for researchers and others who want to know more about the brain.

Downloadable instructor resources available for this title: file of figures in the book

This book provides an innovative, integrated, and methodical approach to understanding complex financial models, integrating topics usually presented separately into a comprehensive whole. The book brings together financial models and high-level mathematics, reviewing the mathematical background necessary for understanding these models organically and in context. It begins with underlying assumptions and progresses logically through increasingly complex models to operative conclusions. Readers who have mastered the material will gain the tools needed to put theory into practice and incorporate financial models into real-life investment, financial, and business scenarios.

Modern finance’s most bothersome shortcoming is that the two basic models for building an optimal investment portfolio, Markowitz’s mean-variance model and Sharpe and Treynor’s Capital Asset Pricing Model (CAPM), fall short when we try to apply them using Excel Solver. This book explores these two models in detail, and for the first time in a textbook the Black-Litterman model for building an optimal portfolio constructed from a small number of assets (developed at Goldman Sachs) is thoroughly presented. The model’s integration of personal views and its application using Excel templates are demonstrated. The book also offers innovative presentations of the Modigliani–Miller model and the Consumption-Based Capital Asset Pricing Model (CCAPM). Problems at the end of each chapter invite the reader to put the models into immediate use. Fundamental Models in Financial Theory is suitable for classroom use or as a reference for finance practitioners.

Downloadable instructor resources available for this title: solution manual

Critical Making and Social Media
Edited by Matt Ratto and Megan Boler

Today, DIY—do-it-yourself—describes more than self-taught carpentry. Social media enables DIY citizens to organize and protest in new ways (as in Egypt’s “Twitter revolution” of 2011) and to repurpose corporate content (or create new user-generated content) in order to offer political counternarratives. This book examines the usefulness and limits of DIY citizenship, exploring the diverse forms of political participation and “critical making” that have emerged in recent years. The authors and artists in this collection describe DIY citizens whose activities range from activist fan blogging and video production to knitting and the creation of community gardens.

Contributors examine DIY activism, describing new modes of civic engagement that include Harry Potter fan activism and the activities of the Yes Men. They consider DIY making in learning, culture, hacking, and the arts, including do-it-yourself media production and collaborative documentary making. They discuss DIY and design and how citizens can unlock the black box of technological infrastructures to engage and innovate open and participatory critical making. And they explore DIY and media, describing activists’ efforts to remake and reimagine media and the public sphere. As these chapters make clear, DIY is characterized by its emphasis on “doing” and making rather than passive consumption. DIY citizens assume active roles as interventionists, makers, hackers, modders, and tinkerers, in pursuit of new forms of engaged and participatory democracy.

Mike Ananny, Chris Atton, Alexandra Bal, Megan Boler, Catherine Burwell, Red Chidgey, Andrew Clement, Negin Dahya, Suzanne de Castell, Carl DiSalvo, Kevin Driscoll, Christina Dunbar-Hester, Joseph Ferenbok, Stephanie Fisher, Miki Foster, Stephen Gilbert, Henry Jenkins, Jennifer Jenson, Yasmin B. Kafai, Ann Light, Steve Mann, Joel McKim, Brenda McPhail, Owen McSwiney, Joshua McVeigh-Schultz, Graham Meikle, Emily Rose Michaud, Kate Milberry, Michael Murphy, Jason Nolan, Kate Orton-Johnson, Kylie A. Peppler, David J. Phillips, Karen Pollock, Matt Ratto, Ian Reilly, Rosa Reitsamer, Mandy Rose, Daniela K. Rosner, Yukari Seko, Karen Louise Smith, Lana Swartz, Alex Tichine, Jennette Weber, Elke Zobl

A Historical Introduction

This introduction to neuroscience is unique in its emphasis on how we know what we know about the structure and function of the nervous system. What are the observations and experiments that have taught us about the brain and spinal cord? The book traces our current neuroscientific knowledge to many and varied sources, including ancient observations on the role of the spinal cord in posture and movement, nineteenth-century neuroanatomists’ descriptions of the nature of nerve cells, physicians’ attempts throughout history to correlate the site of a brain injury with its symptoms, and experiments on the brains of invertebrates.

After an overview of the brain and its connections to the sensory and motor systems, Neuroscience discusses, among other topics, the structure of nerve cells; electrical transmission in the nervous system; chemical transmission and the mechanism of drug action; sensation; vision; hearing; movement; learning and memory; language and the brain; neurological disease; personality and emotion; the treatment of mental illness; and consciousness. It explains the sometimes baffling Latin names for brain subdivisions; discusses the role of technology in the field, from microscopes to EEGs; and describes the many varieties of scientific discovery. The book’s novel perspective offers a particularly effective way for students to learn about neuroscience. It also makes it clear that past contributions offer a valuable guide for thinking about the puzzles that remain.

  • Page 2 of 56