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Étienne-Jules Marey revealed to the human eye what it cannot naturally see; changing not only science, but cinema and art too. His poetic book, titled La Méthode Graphique, was the first major treatise on data graphics and has been treasured by insiders for over a century.This volume publishes the first English translation of The Graphic Method, Part One. It is illustrated with photography of Marey's original fifty-one figures. New margin notes about historic scientists and technologies by Georges Hattab and RJ Andrews complement their translation.The translation is introduced by a new essay on Marey's life by Marta Braun, historian of chronophotography. The book concludes with a colorful survey of rare information graphics referenced by Marey, presented with comparative analysis between his figures and the work he sampled.
Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.
This introductory textbook adopts a practical and intuitive approach, rather than emphasizing mathematical rigor. Computationally oriented books in this area generally present algorithms alone, and expect readers to perform computations by hand, and are often written in traditional computer languages, such as Basic, Fortran or Pascal. This book, on the other hand, is the first text to use Mathematica to develop a thorough understanding of optimization algorithms, fully exploiting Mathematica's symbolic, numerical and graphic capabilities.
Graphic methods for structural design essentially translate problems of algebra into geometric representations, allowing solutions to be reached using geometric construction (ie: drawing pictures) instead of tedious and error-prone arithmetic. This was the common method before the invention of calculators and computers, but had been largely abandoned in the last half century in favor of numerical techniques. However, in recent years the convenience and ease of graphic statics has made a comeback in architecture and engineering. Several professors have begun using graphic statics in the classroom.and.studio environment. But until now, there had been no guidebook that rapidly brings students up to speed on the fundamentals of how to create graphical solutions to statics problems.Graphic Statics introduces all of the traditional graphic statics techniques in a parametric drawing format, using the free program GeoGebra. Then, advanced topics such as indeterminate beams and three dimensional curved surfaces are be covered. Along the way, links to wider design ideas are introduced in a succinct summary of the steps needed to create elegant solutions to many staticequilibrium problems.Meant for students in civil and architectural engineering, architecture,and construction, this practical introduction will also be useful to professionals looking to add the power of graphic statics to their work.
A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions. Most tasks require a person or an automated system to reason—to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality. Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.