Indo-American

Miami, United States

Because of its promise to support human programmers in developing correct and efficient program code and in reasoning about programs, automatic program synthesis has attracted the attention of researchers and professionals since the 1970s. This book focusses on inductive program synthesis, and especially on the induction of recursive functions; it is organized into three parts on planning, inductive program synthesis, and analogical problem solving and learning. Besides methodological issues in inductive program synthesis, emphasis is placed on its applications to control rule learning for planning. Furthermore, relations to problem solving and learning in cognitive psychology are discussed.

This book provides a much-needed, internationally oriented text, focusing on specific aspects of heart disease in women. Despite the large amount of information available, there is still considerable confusion regarding female patients. As such, the book highlights the health events that occur during aging in women and that may influence the future CVD risk. For instance, pregnancy-related disorders are important predictors for CVD risk in women, and inflammatory diseases like rheumatic, thyroid disorders etc, which also interfere with CVD risk, are also more common in women. Adopting a multidisciplinary approach, including gynecology and endocrinology, it offers separate chapters on female-specific manifestations of ischemic heart disease, such as Tako Tsubo CMP and spontaneous coronary artery dissections. The book also discusses the effects and side effects of important medications. The chapters are clearly formatted, making it easy for readers to find subjects of interest.

This special issue of the Handbook of Experimental Pharmacology on Heart Failure covers the entire spectrum of the field, from the current understanding and definitions of heart failure, to epidemiology and the importance of co-morbidities, clinical trial design and biomarkers, as well as imaging technologies. The main focus of this book is to review current and emerging heart failure therapies and potential targets for treatment.

The ability to learn is one of the most fundamental attributes of intelligent behavior. Consequently, progress in the theory and computer modeling of learn­ ing processes is of great significance to fields concerned with understanding in­ telligence. Such fields include cognitive science, artificial intelligence, infor­ mation science, pattern recognition, psychology, education, epistemology, philosophy, and related disciplines. The recent observance of the silver anniversary of artificial intelligence has been heralded by a surge of interest in machine learning-both in building models of human learning and in understanding how machines might be endowed with the ability to learn. This renewed interest has spawned many new research projects and resulted in an increase in related scientific activities. In the summer of 1980, the First Machine Learning Workshop was held at Carnegie-Mellon University in Pittsburgh. In the same year, three consecutive issues of the Inter­ national Journal of Policy Analysis and Information Systems were specially devoted to machine learning (No. 2, 3 and 4, 1980). In the spring of 1981, a special issue of the SIGART Newsletter No. 76 reviewed current research projects in the field. . This book contains tutorial overviews and research papers representative of contemporary trends in the area of machine learning as viewed from an artificial intelligence perspective. As the first available text on this subject, it is intended to fulfill several needs.

This book is a comprehensive overview of heart failure and cardiac transplantation and integrates scientific and clinical information about the physiology, pathophysiology, diagnosis, and treatment of this disorder. Organized into five parts, it reviews the history and basic mechanisms of heart failure; etiology of heart failure; heart failure disease progression; advanced therapies for heart failure; and cardiac transplantation. The book presents basic concepts in the physiology, molecular biology, pathology, and epidemiology of the normal and failing heart; known causes of heart failure, such as right heart failure, valvular cardiomyopathy, molecular mechanisms of sarcomeric cardiomyopathies, and neuromuscular cardiomyopathy; cardiorenal syndrome; neurohormonal activation; cardiac resynchronization, ventricular assist devices; regenerative mechanisms; orthotopic heart transplantation; early and late management of the post-transplant patient; heart transplantation and antibody-mediated rejections; heart-lung transplantation; and cardiac xenotransplantation.

Compressing data is an option naturally selected when faced with problems of high costs or restricted space. Written by a renowned expert in the field, this book offers readers a succinct, reader-friendly foundation to the chief approaches, methods and techniques currently employed in the field of data compression. Part I presents the basic approaches to data compression and describes a few popular techniques and methods commonly used to compress data. The reader discovers essential concepts, such as variable-length and prefix codes, statistical distributions and run-length encoding. Part II then concentrates on advanced techniques, such as arithmetic coding, orthogonal transforms, subband transforms and the Burrows-Wheeler transform. Features: • Clear overview of the principles underlying this field • Outlines the essentials of the various approaches to compressing data • Contains many learning aids such as: chapter introductions and summaries, chapter-end exercises, comprehensive glossary, etc. • Provides several examples of important compression algorithms • Offers a supplementary author-maintained website, with errata and auxiliary material – www.davidsalomon.nameDCugAdvertisDCug.html • An ideal introductory volume to David Salomon’s fourth edition of Data Compression: The Complete Reference Complete and clear, this book is the perfect resource for undergraduates in computer science and requires a minimum of mathematics. It is also ideal for readers with a basic knowledge of computer science wanting to learn about data compression. David Salomon is a professor emeritus of Computer Science at California State University, Northridge. He has authored numerous articles and books, including Coding for Data and Computer Communications, Guide to Data Compression Methods, Data Privacy and Security, Computer Graphics and Geometric Modeling, Foundations of Computer Security, Transformations and Projections in Computer Graphics, and Variable-length Codes for Data Compression.

The AJCC Cancer Staging Manual is used by physicians and health care professionals throughout the world to facilitate the uniform description and reporting of neoplastic diseases. Proper classification and staging of cancer is essential for the physician to assign proper treatment, evaluate results of management and clinical trials, and to serve as the standard for local, regional and international reporting on cancer incidence and outcome. Significantly expanded and developed by international disease site expert panels, the Eighth Edition AJCC Cancer Staging Manual brings together all the currently available knowledge on staging of cancer at various anatomic sites. In this edition, evidence-based TNM staging is supplemented, as appropriate, by selected molecular markers and newly acquired insights into the molecular underpinnings of cancer. This edition features 12 entirely new staging systems, a wide range of changed or new staging definitions, and a refined emphasis on a personalized-medicine approach. To enhance the print and electronic usability of the cancer staging forms, they are now available exclusively for access and downloading at www.cancerstaging.org. The Eighth Edition AJCC Cancer Staging Manual remains the gold standard reference for oncologists, surgeons, pathologists, radiologists, cancer registrars and medical professionals world-wide to ensure that all those caring for cancer patients are fully versed in the language of cancer staging.

The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications.