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Summary: "This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data"-- Provided by publisher.

Summary: "The second edition of this text is a complete revision of our first endeavor, with virtually every chapter of the original rewritten from the ground up and eight new chapters of material added, doubling the size of the first edition. Topics from the first edition, from expositions on gradient descent to those on One-versus- All classification and Principal Component Analysis have been reworked and polished. A swath of new topics have been added throughout the text, from derivative-free optimization to weighted supervised learning, feature selection, nonlinear feature engineering, boosting-based cross-validation, and more"-- Provided by publisher.

Summary: "When working with a data set, a machine learning engineer might train a model but find that the results are not as good as they need. To get better results, they can try to improve the model or collect more data, but there is another avenue: feature engineering. The feature engineering process can help improve results by modifying the data's features to better capture the nature of the problem. This process is partly an art and partly a palette of tricks and recipes. This practical guide to feature engineering is an essential addition to any data scientist's or machine learning engineer's toolbox, providing new ideas on how to improve the performance of a machine learning solution. Beginning with the basic concepts and techniques of feature engineering, the text builds up to a unique cross-domain approach that spans data on graphs, texts, time series, and images, with fully worked out case studies. Key topics include binning, out-of-fold estimation, feature selection, dimensionality reduction, and encoding variable-length data. The full source code for the case studies is available on a companion website as Python Jupyter notebooks"-- Provided by publisher.

Summary: "This textbook is written to cater to the needs of undergraduate students of computer science, engineering, and information technology for a course on data mining and data warehousing. It brings together fundamental concepts of data mining and data warehousing in a single volume. Important topics including information theory, decision tree, Naïve Bayes classifier, distance metrics, partitioning clustering, associate mining, data marts and operational data store are discussed comprehensively. The text simplifies the understanding of the concepts through exercises and practical examples. Chapters such as classification, associate mining and cluster analysis are discussed in detail with their practical implementation using Weka and R language data mining tools. Advanced topics including big data analytics, relational data models, and NoSQL are discussed in detail. Unsolved problems and multiple-choice questions are interspersed throughout the book for better understanding"-- Provided by publisher.

Summary: "Data-driven discovery is revolutionizing the modelling, prediction, and control of complex systems. This textbook brings together machine learning, engineering mathematics, and mathematical physics to integrate modelling and control of dynamical systems with modern methods in data science. It highlights many of the recent advances in scientific computing that enable data-driven methods to be applied to a diverse range of complex systems, such as turbulence, the brain, climate, epidemiology, finance, robotics, and autonomy. Aimed at advanced undergraduate and beginning graduate students in the engineering and physical sciences, the text presents a range of topics and methods from introductory to state of the art"-- Provided by publisher.

Summary: Cluster analysis finds groups in data automatically. Most methods have been heuristic and leave open such central questions as: how many clusters are there? Which method should I use? How should I handle outliers? Classification assigns new observations to groups given previously classified observations, and also has open questions about parameter tuning, robustness and uncertainty assessment. This book frames cluster analysis and classification in terms of statistical models, thus yielding principled estimation, testing and prediction methods, and sound answers to the central questions. It builds the basic ideas in an accessible but rigorous way, with extensive data examples and R code; describes modern approaches to high-dimensional data and networks; and explains such recent advances as Bayesian regularization, non-Gaussian model-based clustering, cluster merging, variable selection, semi-supervised and robust classification, clustering of functional data, text and images, and co-clustering. Written for advanced undergraduates in data science, as well as researchers and practitioners, it assumes basic knowledge of multivariate calculus, linear algebra, probability and statistics.

Summary: "Statistical Inference for Engineers and Data Scientists A mathematically accessible and up-to-date introduction to the tools needed to address modern inference problems in engineering and data science, ideal for graduate students taking courses on statistical inference and detection and estimation, and an invaluable reference for researchers and professionals"-- Provided by publisher.

Summary: With this groundbreaking text, discover how wireless artificial intelligence (AI) can be used to determine position at centimeter level, sense motion and vital signs, and identify events and people. Using a highly innovative approach that employs existing wireless equipment and signal processing techniques to turn multipaths into virtual antennas, combined with the physical principle of time reversal and machine learning, it covers fundamental theory, extensive experimental results, and real practical use cases developed for products and applications. Topics explored include indoor positioning and tracking, wireless sensing and analytics, wireless power transfer and energy efficiency, 5G and next-generation communications, and the connection of large numbers of heterogeneous IoT devices of various bandwidths and capabilities. Demo videos accompanying the book online enhance understanding of these topics. Providing a unified framework for wireless AI, this is an excellent text for graduate students, researchers, and professionals working in wireless sensing, positioning, IoT, machine learning, signal processing and wireless communications.

Summary: Modern scientific computational methods are undergoing a transformative change; big data and statistical learning methods now have the potential to outperform the classical first-principles modeling paradigm. This book bridges this transition, connecting the theory of probability, stochastic processes, functional analysis, numerical analysis, and differential geometry. It describes two classes of computational methods to leverage data for modeling dynamical systems. The first is concerned with data fitting algorithms to estimate parameters in parametric models that are postulated on the basis of physical or dynamical laws. The second is on operator estimation, which uses the data to nonparametrically approximate the operator generated by the transition function of the underlying dynamical systems. This self-contained book is suitable for graduate studies in applied mathematics, statistics, and engineering. Carefully chosen elementary examples with supplementary MATLAB® codes and appendices covering the relevant prerequisite materials are provided, making it suitable for self-study.-- Source other than the Library of Congress.

Summary: Geometric and topological inference deals with the retrieval of information about a geometric object using only a finite set of possibly noisy sample points. It has connections to manifold learning and provides the mathematical and algorithmic foundations of the rapidly evolving field of topological data analysis. Building on a rigorous treatment of simplicial complexes and distance functions, this self-contained book covers key aspects of the field, from data representation and combinatorial questions to manifold reconstruction and persistent homology. It can serve as a textbook for graduate students or researchers in mathematics, computer science and engineering interested in a geometric approach to data science.

Summary: High-dimensional probability offers insight into the behavior of random vectors, random matrices, random subspaces, and objects used to quantify uncertainty in high dimensions. Drawing on ideas from probability, analysis, and geometry, it lends itself to applications in mathematics, statistics, theoretical computer science, signal processing, optimization, and more. It is the first to integrate theory, key tools, and modern applications of high-dimensional probability. Concentration inequalities form the core, and it covers both classical results such as Hoeffding's and Chernoff's inequalities and modern developments such as the matrix Bernstein's inequality. It then introduces the powerful methods based on stochastic processes, including such tools as Slepian's, Sudakov's, and Dudley's inequalities, as well as generic chaining and bounds based on VC dimension. A broad range of illustrations is embedded throughout, including classical and modern results for covariance estimation, clustering, networks, semidefinite programming, coding, dimension reduction, matrix completion, machine learning, compressed sensing, and sparse regression.

Summary: In every era of communications technology - whether print, radio, television, or Internet - some form of government censorship follows to regulate the medium and its messages. Today we are seeing the phenomenon of 'machine speech' enhanced by the development of sophisticated artificial intelligence. Ronald K. L. Collins and David M. Skover argue that the First Amendment must provide defenses and justifications for covering and protecting robotic expression. It is irrelevant that a robot is not human and cannot have intentions; what matters is that a human experiences robotic speech as meaningful. This is the constitutional recognition of 'intentionless free speech' at the interface of the robot and receiver. Robotica is the first book to develop the legal arguments for these purposes. Aimed at law and communication scholars, lawyers, and free speech activists, this work explores important new problems and solutions at the interface of law and technology.

Summary: This updated and expanded edition is a unique examination of qualitative research in the social sciences, raising and answering the question of why we do this kind of investigation. Rather than providing instructions on how to conduct qualitative research, The Science of Qualitative Research explores the multiple roots of qualitative research - including phenomenology, hermeneutics, and critical theory - in order to diagnose the current state of the field and recommend an alternative. The author argues that much qualitative research today uses the mind-world dualism that is typical of traditional experimental investigation, and recommends that instead we focus on constitution: the relationship of mutual formation between a form of life and its members. Michel Foucault's program for 'a history ontology of ourselves' provides the basis for this fresh approach. The new edition features updated chapters, and a brand new chapter which offers a discussion on how to put into practice Foucault's concept.--

Summary: For advanced students of network data science, this compact account covers both well-established methodology and the theory of models recently introduced in the graphical model literature. It focuses on the discrete case where all variables involved are categorical and, in this context, it achieves a unified presentation of classical and recent results.

Summary: "Newly commissioned essays by leading scholars offer a comprehensive and authoritative overview of the diversity, range and impact of the newspaper and periodical press in nineteenth-century Britain. Essays range from studies of periodical formats in the nineteenth century - reviews, magazines and newspapers - to accounts of individual journalists, many of them eminent writers of the day. The uneasy relationship between the new 'profession' of journalism and the evolving profession of authorship is investigated, as is the impact of technological innovations, such as the telegraph, the typewriter and new processes of illustration. Contributors go on to consider the transnational and global dimensions of the British press and its impact in the rest of the world. As digitisation of historical media opens up new avenues of research, the collection reveals the centrality of the press to our understanding of the nineteenth century"-- Provided by publisher.

Summary: "Greatly expanded, this new edition requires only an elementary background in discrete mathematics and offers a comprehensive introduction to the role of randomization and probabilistic techniques in modern computer science. Newly added chapters and sections cover topics including normal distributions, sample complexity, VC dimension, Rademacher complexity, power laws and related distributions, cuckoo hashing, and the Lovasz Local Lemma. Material relevant to machine learning and big data analysis enables students to learn modern techniques and applications. Among the many new exercises and examples are programming-related exercises that provide students with excellent training in solving relevant problems. This book provides an indispensable teaching tool to accompany a one- or two-semester course for advanced undergraduate students in computer science and applied mathematics"-- Provided by publisher.

Summary: Young People and the Future of News traces the practices that are evolving as young people come to see news increasingly as something shared via social networks and social media rather than produced and circulated solely by professional news organizations. The book introduces the concept of connective journalism, clarifying the role of creating and sharing stories online as a key precursor to collective and connective political action. At the center of the story are high school students from low-income minority and immigrant communities who often feel underserved or misrepresented by mainstream media but express a strong interest in politics and their communities. Drawing on in-depth field work in three major urban areas over the course of ten years, Young People and the Future of News sheds light on how young people share news that they think others should know about, express solidarity, and bring into being new publics and counterpublics.

Summary: Quantitative research in social science research is changing rapidly. Researchers have vast and complex arrays of data with which to work: we have incredible tools to sift through the data and recognize patterns in that data; there are now many sophisticated models that we can use to make sense of those patterns; and we have extremely powerful computational systems that help us accomplish these tasks quickly. This book focuses on some of the extraordinary work being conducted in computational social science - in academia, government, and the private sector - while highlighting current trends, challenges, and new directions. Thus, Computational Social Science showcases the innovative methodological tools being developed and applied by leading researchers in this new field. The book shows how academics and the private sector are using many of these tools to solve problems in social science and public policy.

Summary: "'Cyber security' is a recent addition to the global security agenda, concerned with protecting states and citizens from the misuse of computer networks for war, terrorism, economic espionage and criminal gain. Many argue that the ubiquity of computer networks calls for robust and pervasive countermeasures, not least governments concerned at their potential effects on national and economic security. Drawing on critical literature in international relations, security studies, political theory and social theory, this is the first book that describes how these visions of future cyber security are sustained in the communities that articulate them. Specifically, it shows that conceptions of time and temporality are foundational to the politics of cyber security. It explores how cyber security communities understand the past, present and future, thereby shaping cyber security as a political practice. Integrating a wide range of conceptual and empirical resources, this innovative book provides insight for scholars, practitioners and policymakers"-- Provided by publisher.

Summary: "This collection of original essays brings a dramatically different perspective to bear on the contemporary "crisis of journalism." Rather than seeing technological and economic change as the primary causes of current anxieties, The Crisis of Journalism Reconsidered draws attention to the role played by the cultural commitments of journalism itself. Linking these professional ethics to the democratic aspirations of the broader societies in which journalists ply their craft, it examines how the new technologies are being shaped to sustain value commitments rather than undermining them. Recent technological change and the economic upheaval it has produced are coded by social meanings. It is this cultural framework that actually transforms these "objective" changes into a crisis. The book argues that cultural codes not only trigger sharp anxiety about technological and economic changes, but provide pathways to control them, so that the democratic practices of independent journalism can be sustained in new forms"-- Provided by publisher.

Summary: Establishing the 'core curriculum' in the new field of ethics and health informatics, this text discusses the effects of information technology on all aspects of medicine and research, from privacy and big data to computer-rendered diagnoses and prognoses.

Summary: The scope and coverage of psychophysiology has expanded tremendously over recent years and this revised and expanded Handbook of Psychophysiology provides both a context for and coverage of the biological bases of cognition, emotion, and behavior at a level of scholarship informative to the specialist and accessible to the student. The book is divided into six sections covering topics such as the history of psychophysiology, systemic psychophysiology ranging from the peripheral nervous system to the neuroendocrine and immune systems, principles of human behavior that have emerged from psychophysiological analyses, applications, and methodology. The final section provides detailed coverage of emerging topics in psychophysiology, including functional magnetic resonance imaging. This revised and expanded edition of the Handbook of Psychophysiology replaces Principles of Psychophysiology as an essential reference for students, researchers, and professionals in psychophysiology, psychology, psychiatry, neuroscience, and related biological and behavioral sciences.

Summary: Today's digital revolution is a worldwide phenomenon, with profound and often differential implications for communities around the world and their relationships to one another. This book presents a new, explicitly international theory of media ethics, incorporating non-Western perspectives and drawing deeply on both moral philosophy and the philosophy of technology. Clifford Christians develops an ethics grounded in three principles - truth, human dignity, and non-violence - and shows how these principles can be applied across a wide range of cases and domains. The book is a guide for media professionals, scholars, and educators who are concerned with the global ramifications of new technologies and with creating a more just world.

Summary: "Cryptography has been employed in war and diplomacy from the time of Julius Caesar. In our Internet age, cryptography's most widespread application may be for commerce, from protecting the security of electronic transfers to guarding communication from industrial espionage. This accessible introduction for undergraduates explains the cryptographic protocols for achieving privacy of communication and the use of digital signatures for certifying the validity, integrity, and origin of a message, document, or program. Rather than offering a how-to on configuring web browsers and e-mail programs, the author provides a guide to the principles and elementary mathematics underlying modern cryptography, giving readers a look under the hood for security techniques and the reasons they are thought to be secure"-- Provided by publisher.

Summary: "'Big data' poses challenges that require both classical multivariate methods and contemporary techniques from machine learning and engineering. This modern text integrates the two strands into a coherent treatment, drawing together theory, data, computation and recent research. The theoretical framework includes formal definitions, theorems and proofs, which clearly set out the guaranteed 'safe operating zone' for the methods and allow users to assess whether data is in or near the zone. Extensive examples showcase the strengths and limitations of different methods in a range of cases: small classical data; data from medicine, biology, marketing and finance; high-dimensional data from bioinformatics; functional data from proteomics; and simulated data. High-dimension, low-sample-size data gets special attention. Several data sets are revisited repeatedly to allow comparison of methods. Generous use of colour, algorithms, Matlab code and problem sets complete the package. The text is suitable for graduate students in statistics and researchers in data-rich disciplines"-- Provided by publisher.

Summary: "Offering a fundamental basis in kernel-based learning theory, this book covers both statistical and algebraic principles. It provides over 30 major theorems for kernel-based supervised and unsupervised learning models. The first of the theorems establishes a condition, arguably necessary and sufficient, for the kernelization of learning models. In addition, several other theorems are devoted to proving mathematical equivalence between seemingly unrelated models. With over 25 closed-form and iterative algorithms, the book provides a step-by-step guide to algorithmic procedures and analysing which factors to consider in tackling a given problem, enabling readers to improve specifically designed learning algorithms, build models for new applications and develop efficient techniques suitable for green machine learning technologies. Numerous real-world examples and over 200 problems, several of which are Matlab-based simulation exercises, make this an essential resource for graduate students and professionals in computer science, electrical and biomedical engineering. Solutions to problems are provided online for instructors"-- Provided by publisher.

Summary: "Political journalism is often under fire. Conventional wisdom and much scholarly research suggest that journalists are cynics and political pundits. Political news is void of substance and overly focused on strategy and persons. Citizens do not learn from the news, are politically cynical, and are dissatisfied with the media. This book challenges these assumptions, which are often based on single-country studies with limited empirical observations about the relation between news production, content, and journalism's effects. Based on interviews with journalists, a systematic content analysis of political news, and panel survey data in different countries, this book tests how different systems and media-politics relations condition the contents of political news. It shows how different content creates different effects, and demonstrates that under the right circumstances citizens learn from political news, do not become cynical, and are satisfied with political journalism"-- Provided by publisher.

Summary: "Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering"-- Provided by publisher.

Summary: "A reference describing the general principles of the emerging field of data-intensive computing, along with methods for designing, managing and analyzing the big data sets of today"-- Provided by publisher.

Abstract: Computer algebra systems are now ubiquitous in all areas of science and engineering. This highly successful textbook, widely regarded as the "bible of computer algebra", gives a thorough introduction to the algorithmic basis of the mathematical engine in computer algebra systems. Designed to accompany one- or two- semester courses for advanced undergraduate or graduate students in computer science or mathematics, its comprehensiveness and reliability has also made it an essential reference for professionals in the area. Special features include: detailed study of algorithms including time analysis; implementation reports on several topics; complete proofs of the mathematical underpinnings; and a wide variety of applications (among others, in chemistry, coding theory, crytopgraphy, computational logic, and the design of calendars and musical scales). A great deal of historical information and illustration enlivens the text.

Summary: "Machine learning is an interdisciplinary field of science and engineering that studies mathematical theories and practical applications of systems that learn. This book introduces theories, methods, and applications of density ratio estimation, which is a newly emerging paradigm in the machine learning community. Various machine learning problems such as nonstationarity adaptation, outlier detection, dimensionality reduction, independent component analysis, clustering, classification, and conditional density estimation can be systematically solved via the estimation of probability density ratios. The authors offer a comprehensive introduction of various density ratio estimators including methods via density estimation, moment matching, probabilistic classification, density fitting, and density ratio fitting as well as describing how these can be applied to machine learning. The book also provides mathematical theories for density ratio estimation including parametric and non-parametric convergence analysis and numerical stability analysis to complete the first and definitive treatment of the entire framework of density ratio estimation in machine learning"-- Provided by publisher.

Abstract: 'Machine Learning' brings together all the state-of-the-art methods for making sense of data. With hundreds of worked examples and explanatory figures, it explains the principles behind these methods in an intuitive yet precise manner and will appeal to novice and experienced readers alike.

Summary: "This book presents an integrated collection of representative approaches for scaling up machine learning and data mining methods on parallel and distributed computing platforms. Demand for parallelizing learning algorithms is highly task-specific: in some settings it is driven by the enormous dataset sizes, in others by model complexity or by real-time performance requirements. Making task-appropriate algorithm and platform choices for large-scale machine learning requires understanding the benefits, trade-offs, and constraints of the available options"-- Provided by publisher.

Summary: "The practical benefits of computational logic need not be limited to mathematics and computing. As this book shows, ordinary people in their everyday lives can profit from the recent advances that have been developed for artificial intelligence. The book draws upon related developments in various fields from philosophy to psychology and law. It pays special attention to the integration of logic with decision theory, and the use of logic to improve the clarity and coherence of communication in natural languages such as English. This book is essential reading for teachers and researchers who may be out of touch with the latest developments in computational logic. It will also be useful in any undergraduate course that teaches practical thinking, problem solving or communication skills. Its informal presentation makes the book accessible to readers from any background, but optional, more formal, chapters are also included for those who are more technically oriented"-- Provided by publisher.

Summary: "This book extensively covers the use of graph-based algorithms for natural language processing and information retrieval"-- Provided by publisher.

Summary: "Phase transitions typically occur in combinatorial computational problems and have important consequences, especially with the current spread of statistical relational learning and as sequence learning methodologies. In Phase Transitions in Machine Learning the authors begin by describing in detail this phenomenon and the extensive experimental investigation that supports its presence. They then turn their attention to the possible implications and explore appropriate methods for tackling them"-- Provided by publisher.

Summary: "Recent decades have witnessed the emergence of artificial intelligence as a serious science and engineering discipline. Artificial Intelligence: Foundations of Computational Agents is a textbook aimed at junior to senior undergraduate students and first-year graduate students. It presents artificial intelligence (AI) using a coherent framework to study the design of intelligent computational agents. By showing how basic approaches fit into a multidimensional design space, readers can learn the fundamentals without losing sight of the bigger picture. The book balances theory and experiment, showing how to link them intimately together, and develops the science of AI together with its engineering applications. Although structured as a textbook, the book's straightforward, self-contained style will also appeal to a wide audience of professionals, researchers, and independent learners. AI is a rapidly developing field: this book encapsulates the latest results without being exhaustive and encyclopedic. It teaches the main principles and tools that will allow readers to explore and learn on their own. The text is supported by an online learning environment, AIspace, http://aispace.org, so that students can experiment with the main AI algorithms plus problems, animations, lecture slides, and a knowledge representation system, AIlog, for experimentation and problem solving"--Provided by publisher.

Summary: "Recent decades have witnessed the emergence of artificial intelligence as a serious science and engineering discipline. Artificial Intelligence: Foundations of Computational Agents is a textbook aimed at junior to senior undergraduate students and first-year graduate students. It presents artificial intelligence (AI) using a coherent framework to study the design of intelligent computational agents. By showing how basic approaches fit into a multidimensional design space, readers can learn the fundamentals without losing sight of the bigger picture. The book balances theory and experiment, showing how to link them intimately together, and develops the science of AI together with its engineering applications. Although structured as a textbook, the book's straightforward, self-contained style will also appeal to a wide audience of professionals, researchers, and independent learners. AI is a rapidly developing field: this book encapsulates the latest results without being exhaustive and encyclopedic. It teaches the main principles and tools that will allow readers to explore and learn on their own. The text is supported by an online learning environment, AIspace, http://aispace.org, so that students can experiment with the main AI algorithms plus problems, animations, lecture slides, and a knowledge representation system, AIlog, for experimentation and problem solving"--Provided by publisher.

Summary: "This textbook for advanced undergraduates and graduate students emphasizes computation and algorithms for a range of strategies for locomotion, sensing, and reasoning. It concentrates on wheeled and legged mobile robots but also discusses a variety of other propulsion systems. The new edition presents advances in robotics and intelligent machines over the last 10 years, including significant coverage of SLAM (simultaneous localization and mapping) and multi-robot systems. It includes additional mathematical background and an extensive list of sample problems. Various mathematical techniques that were assumed in the first edition are now briefly introduced in appendices at the end of the text to make the book more self-contained"--Provided by publisher.

Summary: Machine learning methods originated from artificial intelligence and are now used in various fields in environmental sciences today. This is the first single-authored textbook providing a unified treatment of machine learning methods and their applications in the environmental sciences. Due to their powerful nonlinear modelling capability, machine learning methods today are used in satellite data processing, general circulation models(GCM), weather and climate prediction, air quality forecasting, analysis and modelling of environmental data, oceanographic and hydrological forecasting, ecological modelling, and monitoring of snow, ice and forests. The book includes end-of-chapter review questions and an appendix listing web sites for downloading computer code and data sources. A resources website containing datasets for exercises, and password-protected solutions are available. The book is suitable for first-year graduate students and advanced undergraduates. It is also valuable for researchers and practitioners in environmental sciences interested in applying these new methods to their own work.

Summary: In this groundbreaking study, Michael Willis examines how the gods of early Hinduism came to be established in temples, how their cults were organized, and how the ruling elite supported their worship. Examining the emergence of these key historical developments in the fourth and fifth centuries, Willis combines Sanskrit textual evidence with archaeological data from inscriptions, sculptures, temples, and sacred sites. The centre-piece of this study is Udayagiri in central India, the only surviving imperial site of the Gupta dynasty. Through a judicious use of landscape archaeology and archaeo-astronomy, Willis reconstructs how Udayagiri was connected to the Festival of the Rainy Season and the Royal Consecration. Under Gupta patronage, these rituals were integrated into the cult of Vishnu, a deity regarded as the source of creation and of cosmic time. As special devotees of Vishnu, the Gupta kings used Udayagiri to advertise their unique devotional relationship with him. Through his meticulous study of the site, its sculptures and its inscriptions, Willis shows how the Guptas presented themselves as universal sovereigns and how they advanced new systems of religious patronage that shaped the world of medieval India.

Summary: This textbook, for second- or third-year students of computer science, presents insights, notations, and analogies to help them describe and think about algorithms like an expert, without grinding through lots of formal proof. Solutions to many problems are provided to let students check their progress, while class-tested PowerPoint slides are on the web for anyone running the course. By looking at both the big picture and easy step-by-step methods for developing algorithms, the author guides students around the common pitfalls. He stresses paradigms such as loop invariants and recursion to unify a huge range of algorithms into a few meta-algorithms. The book fosters a deeper understanding of how and why each algorithm works. These insights are presented in a careful and clear way, helping students to think abstractly and preparing them for creating their own innovative ways to solve problems.

Summary: The political campaign is one of the most important organizations in a democracy, and whether issue, or candidate, specific, it is one of the least understood organizations in contemporary political life. With evidence from ethnographic immersion, survey data, and social network analysis, Philip Howard examines the evolving act of political campaigning and the changing organization of political campaigns over the last five election cycles, from 1996 to 2004. Over this time, both grassroots and elite political campaigns have gone online, built multimedia strategies, and constructed complex relational databases.

Summary: Uses the International Phonetic Alphabet to provide phonetic transcriptions for over 220,000 pronunications of words in American and British English.

Summary: Historiography and the maritime landscape -- 2. Fishing and sailing communities: cross-cultural contacts -- 3. La longue duree: Transportation, boat-building technology and navigation -- 4. Maritime trade networks: the beginnings (third to second millennia B.C.E.)-- 5. Regional integration (late second to early first millennia B.C.E.) -- 6. Consolidation of the political structure -- 7. The Greeks: adventurers, travellers and traders -- 8. The merchant lineage and the guild -- 9. Craft production and trading networks: multiple meanings -- 10.Shared faith -- 11. Retrospect and prospect.Series Cambridge world archaeology.

Summary: Knowledge management and knowledge-based intelligence are areas of importance in today's economy and society, and their exploitation requires representation via the development of a declarative interface whose input language is based on logic. Chitta Baral demonstrates how to write programs that behave intelligently by giving them the ability to express knowledge and reason about it. He presents a language, AnsProlog, for both knowledge representation and reasoning, and declarative problem solving. Many of the results have never appeared before in book form but are organized here for those wishing to learn more about the subject, either in courses or through self-study.