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Explains how graphs can be used to help students learn about buildings and structures, describing the different types of graphs that can be used to gather and organize data about the topics and including information about tunnels, bridges, skyscrapers, and dams.
* This information-rich reference book provides solutions to the architectural problem of vibrations in beams, arches and frames in bridges, highways, buildings and tunnels * A must-have for structural designers and civil engineers, especially those involved in the seismic design of buildings * Well-organized into problem-specific chapters, and loaded with detailed charts, graphs, and necessary formulas
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 volume addresses some of the most important approaches to the following key questions in contemporary generative syntactic theory: What are the operations available for (syntactic) structure-building in natural languages? What are the triggers behind them? and Which constraints are involved in the operations? Internationally recognised scholars and young researchers propose new answers on the basis of detailed discussions of a wide range of phenomena (Gapping, Right-Node-Raising, Comparative Deletion, Across-The-Board movement, Tough-constructions, Nominalizations, Scope interactions, Wh-movement, A-movement, Case and Agreement relations, among others). Their discussions draw on evidence from a rich variety of languages, including Brazilian Portuguese, Bulgarian, Croatian, English, German, Icelandic, Japanese, Spanish, Vata, and Vietnamese. The proposals presented illustrate the shift in the locus of the explanation of linguistic phenomena that characterizes contemporary linguistic theory: a shift, in many cases, from a model which relies on properties of systems external to narrow syntax (such as the Lexicon or the Phonetic Form component) to one which relies on properties of the structure-building mechanisms themselves. The volume will interest researchers and students of theoretical linguistics from advanced undergraduate and above.
A concise introduction, aimed at young researchers, to recent developments of a geometric and topological nature in random graphs.
This book is a guide to designing and building knowledge graphs from enterprise relational databases in practice.\ It presents a principled framework centered on mapping patterns to connect relational databases with knowledge graphs, the roles within an organization responsible for the knowledge graph, and the process that combines data and people. The content of this book is applicable to knowledge graphs being built either with property graph or RDF graph technologies. Knowledge graphs are fulfilling the vision of creating intelligent systems that integrate knowledge and data at large scale. Tech giants have adopted knowledge graphs for the foundation of next-generation enterprise data and metadata management, search, recommendation, analytics, intelligent agents, and more. We are now observing an increasing number of enterprises that seek to adopt knowledge graphs to develop a competitive edge. In order for enterprises to design and build knowledge graphs, they need to understand the critical data stored in relational databases. How can enterprises successfully adopt knowledge graphs to integrate data and knowledge, without boiling the ocean? This book provides the answers.
Incredibly useful, knowledge graphs help organizations keep track of medical research, cybersecurity threat intelligence, GDPR compliance, web user engagement, and much more. They do so by storing interlinked descriptions of entities—objects, events, situations, or abstract concepts—and encoding the underlying information. How do you create a knowledge graph? And how do you move it from theory into production? Using hands-on examples, this practical book shows data scientists and data engineers how to build their own knowledge graphs. Authors Jesús Barrasa and Jim Webber from Neo4j illustrate common patterns for building knowledge graphs that solve many of today’s pressing knowledge management problems. You’ll quickly discover how these graphs become increasingly useful as you add data and augment them with algorithms and machine learning. Learn the organizing principles necessary to build a knowledge graph Explore how graph databases serve as a foundation for knowledge graphs Understand how to import structured and unstructured data into your graph Follow examples to build integration-and-search knowledge graphs Learn what pattern detection knowledge graphs help you accomplish Explore dependency knowledge graphs through examples Use examples of natural language knowledge graphs and chatbots Use graph algorithms and ML to gain insight into connected data
This is a one-stop book for knowing everything important about building structures. Self-contained and with no prerequisites needed, it is suitable for both general readers and building professionals. follow the history of structural understanding; grasp the concepts of structural behaviour via step-by-step explanations; apply these concepts to a simple building; see how these concepts apply to real buildings, from Durham Cathedral to the Bank of China; use these concepts to define the design process; see how these concepts inform design choices; understand how engineering and architecture have diverged, and what effect this had; learn to do simple but relevant numerical calculations for actual structures; understand when dynamics are important; follow the development of progressive collapse prevention; enter the world of modern structural theory; see how computers can be used for structural analysis; learn how to organise and design a successful project. With more than 500 pages and over 1100 user-friendly diagrams, this book is a must for anyone who would like to understand the fascinating world of structures.
Although the disciplines of architecture and structural engineering have both experienced their own historical development, their interaction has resulted in many fascinating and delightful structures. To take this interaction to a higher level, there is a need to stimulate the inventive and creative design of architectural structures and to persuade architects and structural engineers to further collaborate in this process, exploiting together new concepts, applications and challenges. This set of book of abstracts and full paper searchable CD-ROM presents selected papers presented at the 3rd International Conference on Structures and Architecture Conference (ICSA2016), organized by the School of Architecture of the University of Minho, Guimarães, Portugal (July 2016), to promote the synergy in the collaboration between the disciplines of architecture and structural engineering.
Discrete mathematics and theoretical computer science are closely linked research areas with strong impacts on applications and various other scientific disciplines. Both fields deeply cross fertilize each other. One of the persons who particularly contributed to building bridges between these and many other areas is László Lovász, a scholar whose outstanding scientific work has defined and shaped many research directions in the last 40 years. A number of friends and colleagues, all top authorities in their fields of expertise and all invited plenary speakers at one of two conferences in August 2008 in Hungary, both celebrating Lovász’s 60th birthday, have contributed their latest research papers to this volume. This collection of articles offers an excellent view on the state of combinatorics and related topics and will be of interest for experienced specialists as well as young researchers.