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This volume develops an effective theory approach to understanding deep neural networks of practical relevance.
Designing molecules and materials with desired properties is an important prerequisite for advancing technology in our modern societies. This requires both the ability to calculate accurate microscopic properties, such as energies, forces and electrostatic multipoles of specific configurations, as well as efficient sampling of potential energy surfaces to obtain corresponding macroscopic properties. Tools that can provide this are accurate first-principles calculations rooted in quantum mechanics, and statistical mechanics, respectively. Unfortunately, they come at a high computational cost that prohibits calculations for large systems and long time-scales, thus presenting a severe bottleneck both for searching the vast chemical compound space and the stupendously many dynamical configurations that a molecule can assume. To overcome this challenge, recently there have been increased efforts to accelerate quantum simulations with machine learning (ML). This emerging interdisciplinary community encompasses chemists, material scientists, physicists, mathematicians and computer scientists, joining forces to contribute to the exciting hot topic of progressing machine learning and AI for molecules and materials. The book that has emerged from a series of workshops provides a snapshot of this rapidly developing field. It contains tutorial material explaining the relevant foundations needed in chemistry, physics as well as machine learning to give an easy starting point for interested readers. In addition, a number of research papers defining the current state-of-the-art are included. The book has five parts (Fundamentals, Incorporating Prior Knowledge, Deep Learning of Atomistic Representations, Atomistic Simulations and Discovery and Design), each prefaced by editorial commentary that puts the respective parts into a broader scientific context.
Introduction to the Fast Multipole Method introduces the reader to the theory and computer implementation of the Fast Multipole Method. It covers the topics of Laplace’s equation, spherical harmonics, angular momentum, the Wigner matrix, the addition theorem for solid harmonics, and lattice sums for periodic boundary conditions, along with providing a complete, self-contained explanation of the math of the method, so that anyone having an undergraduate grasp of calculus should be able to follow the material presented. The authors derive the Fast Multipole Method from first principles and systematically construct the theory connecting all the parts. Key Features Introduces each topic from first principles Derives every equation presented, and explains each step in its derivation Builds the necessary theory in order to understand, develop, and use the method Describes the conversion from theory to computer implementation Guides through code optimization and parallelization
Computational properties of use to biological organisms or to the construction of computers can emerge as collective properties of systems having a large number of simple equivalent components (or neurons). The physical meaning of content-addressable memory is described by an appropriate phase space flow of the state of a system. A model of such a system is given, based on aspects of neurobiology but readily adapted to integrated circuits. The collective properties of this model produce a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size. The algorithm for the time evolution of the state of the system is based on asynchronous parallel processing. Additional emergent collective properties include some capacity for generalization, familiarity recognition, categorization, error correction, and time sequence retention. The collective properties are only weakly sensitive to details of the modeling or the failure of individual devices.
Philosophy and Computing explores each of the following areas of technology: the digital revolution; the computer; the Internet and the Web; CD-ROMs and Mulitmedia; databases, textbases, and hypertexts; Artificial Intelligence; the future of computing. Luciano Floridi shows us how the relationship between philosophy and computing provokes a wide range of philosophical questions: is there a philosophy of information? What can be achieved by a classic computer? How can we define complexity? What are the limits of quantam computers? Is the Internet an intellectual space or a polluted environment? What is the paradox in the Strong Artificial Intlligence program? Philosophy and Computing is essential reading for anyone wishing to fully understand both the development and history of information and communication technology as well as the philosophical issues it ultimately raises.
Ambient Intelligence (AmI) is an integrating technology for supporting a pervasive and transparent infrastructure for implementing smart environments. Such technology is used to enable environments for detecting events and behaviors of people and for responding in a contextually relevant fashion. AmI proposes a multi-disciplinary approach for enhancing human machine interaction. Ambient Intelligence: A Novel Paradigm is a compilation of edited chapters describing current state-of-the-art and new research techniques including those related to intelligent visual monitoring, face and speech recognition, innovative education methods, as well as smart and cognitive environments. The authors start with a description of the iDorm as an example of a smart environment conforming to the AmI paradigm, and introduces computer vision as an important component of the system. Other computer vision examples describe visual monitoring for the elderly, classic and novel surveillance techniques using clusters of cameras installed in indoor and outdoor application domains, and the monitoring of public spaces. Face and speech recognition systems are also covered as well as enhanced LEGO blocks for novel educational purposes. The book closes with a provocative chapter on how a cybernetic system can be designed as the backbone of a human machine interaction.
This book explores several branches of the social sciences and their perspectives regarding their relations with decision-making processes: computer science, education, linguistics, sociology, and management. The decision-making process in social contexts is based on the analysis of sound alternatives using evaluative criteria. Therefore, this process is one that can be rational or irrational, and can be based on knowledge and/or beliefs. A decision-making process always produces a final decision, which may or may not imply prompt action, and increases the chances of choosing the best possible alternative. The book is divided into four main parts. The concepts covered in the first part, on computer science, explore how the rise of algorithms and the growth in computing power over the years can influence decision-making processes. In the second part, some traditional and innovative ideas and methods used in education are presented: compulsory schooling, inclusive schools, higher education, etc. In turn, the third part focuses on linguistics aspects, and examines how progress is manifested in language. The fourth part, on sociology, explores how society can be influenced by social norms, human interactions, culture, and religion. Management, regarded as a science of the decision-making process, is explored in the last part of this book. Selected organizations strategies, objectives and resources are presented, e.g., human resources, financial resources, and technological resources. The book gathers and presents, in a concise format, a broad range of aspects regarding the decision-making process in social contexts, making it a valuable and unique resource for the scientific community.
Dramatic progress has been made in all branches of physics since the National Research Council's 1986 decadal survey of the field. The Physics in a New Era series explores these advances and looks ahead to future goals. The series includes assessments of the major subfields and reports on several smaller subfields, and preparation has begun on an overview volume on the unity of physics, its relationships to other fields, and its contributions to national needs. Nuclear Physics is the latest volume of the series. The book describes current activity in understanding nuclear structure and symmetries, the behavior of matter at extreme densities, the role of nuclear physics in astrophysics and cosmology, and the instrumentation and facilities used by the field. It makes recommendations on the resources needed for experimental and theoretical advances in the coming decade.
Comprehensive introduction to the neural network models currently under intensive study for computational applications. It also provides coverage of neural network applications in a variety of problems of both theoretical and practical interest.