Representations

This broad-ranging text offers a comprehensive outline of how visual images, language and discourse work as `systems of representation'. Individual chapters explore: representation as a signifying practice in a rich diversity of social contexts and institutional sites; the use of photography in the construction of national identity and culture; other cultures in ethnographic museums; fantasies of the racialized `Other' in popular media, film and image; the construction of masculine identities in discourses of consumer culture and advertising; and the gendering of narratives in television soap operas.

..". an excellent collection... " -- Journal of Language Social Psychology An important collection of original essays by well-known scholars debating the questions of logical versus psychologically-based interpretations of language.

A long long time ago, echoing philosophical and aesthetic principles that existed since antiquity, William of Ockham enounced the principle of parsimony, better known today as Ockham’s razor: “Entities should not be multiplied without neces sity. ” This principle enabled scientists to select the ”best” physical laws and theories to explain the workings of the Universe and continued to guide scienti?c research, leadingtobeautifulresultsliketheminimaldescriptionlength approachtostatistical inference and the related Kolmogorov complexity approach to pattern recognition. However, notions of complexity and description length are subjective concepts anddependonthelanguage“spoken”whenpresentingideasandresults. The?eldof sparse representations, that recently underwent a Big Bang like expansion, explic itly deals with the Yin Yang interplay between the parsimony of descriptions and the “language” or “dictionary” used in them, and it became an extremely exciting area of investigation. It already yielded a rich crop of mathematically pleasing, deep and beautiful results that quickly translated into a wealth of practical engineering applications. You are holding in your hands the ?rst guide book to Sparseland, and I am sure you’ll ?nd in it both familiar and new landscapes to see and admire, as well as ex cellent pointers that will help you ?nd further valuable treasures. Enjoy the journey to Sparseland! Haifa, Israel, December 2009 Alfred M. Bruckstein vii Preface This book was originally written to serve as the material for an advanced one semester (fourteen 2 hour lectures) graduate course for engineering students at the Technion, Israel.

Illustrating the fascinating interplay between physics and mathematics, Groups, Representations and Physics, Second Edition provides a solid foundation in the theory of groups, particularly group representations. For this new, fully revised edition, the author has enhanced the book's usefulness and widened its appeal by adding a chapter on the Cartan-Dynkin treatment of Lie algebras. This treatment, a generalization of the method of raising and lowering operators used for the rotation group, leads to a systematic classification of Lie algebras and enables one to enumerate and construct their irreducible representations. Taking an approach that allows physics students to recognize the power and elegance of the abstract, axiomatic method, the book focuses on chapters that develop the formalism, followed by chapters that deal with the physical applications. It also illustrates formal mathematical definitions and proofs with numerous concrete examples.

This classic book contains an introduction to systems of l-adic representations, a topic of great importance in number theory and algebraic geometry, as reflected by the spectacular recent developments on the Taniyama-Weil conjecture and Fermat's Last Theorem. The initial chapters are devoted to the Abelian case (complex multiplication), where one

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.

Cognitive science is a field that began with the realization that researchers in varied disciplines-psychology, artificial intelligence, linguistics, philosophy, formal semantics, neuroscience, and others-had taken on a common set of problems in representation and meaning, in reasoning and language. Nevertheless, cognitive science as a whole enjoys no common methodology or theoretical framework, and is in danger of becoming even more fragmented with time. There are two reasons for this. First, cognitive science is built on existing methodologies that have different historical origins. AB a result, the psychologist's truth is different from the linguist's truth. The artificial intelligence researcher's truth is different from the philosopher's truth. The neuroscientist's truth is different from the formal semanticist's truth. All too often there is little or no recognition of the relevance of work in other disciplines to one's own concerns. Second, cognitive scientists tend to develop theories around isolated problems. For instance, there are theories about how humans categorize concepts, about how humans analyze linguistic expressions syntactically, about how the English tense system works semantically, about how humans reason about space or reason about time, about how goal-directed problem solving occurs, about how the brain computes, and so on.