Disentangled


Book Description

The Definitive Study and Solution to the Centuries-old Mystery of the World's Most Sighted Sea Serpent There is a long history of conflating sightings of unidentified marine objects (UMOs) as purported sea serpents. Most sightings are either of an extremely brief duration or made by a single observer, and thus often easy to dismiss. This is not the case, however, with respect to the so-called Gloucester Sea Serpent which frequented the Massachusetts and New York coasts during the early nineteenth century. Witnessed by hundreds of people for extended periods repeatedly over many days, the Gloucester UMO is the most sighted 'sea serpent' in history. As well, due to being the object of study at the time and shortly thereafter by naturalists, the mysterious creature remains the most thoroughly investigated of all putative sea serpents. For these reasons, it has achieved an exalted status among cryptozoologists who maintain it represents the best evidence for the existence of sea serpents. For the first time, an eminently qualified aquatic biologist and ethnozoologist presents the definitive history of the phenomena and carefully examines the evidence. It is concluded that the most parsimonious explanation behind the Gloucester Sea Serpent is as early evidence for what is today recognized as being one of the most serious threats to marine biodiversity: entanglement in fishing gear and other maritime debris. Therefore, although widely considered to be restricted to the advent and widespread use of non-degradable plastic in the middle of the twentieth century, this new interpretation of the Gloucester UMO suggests that entanglement has a much longer environmental history than is commonly believed. Robert L. France is a world-renowned scientist at Dalhousie University and the author or editor of twenty books and two hundred papers on a wide range of environmental subjects. He has undertaken conservation biology research from the High Arctic to the tropics, on organisms from bacteria to whales, which has been cited many thousands of times in the literature. Dr. France is a leading authority on many aspects of aquatic zoology, including marine ecology and ethnozoology, and may be the most qualified person to have recently undertaken research and published peer-reviewed articles on the beguiling and befuddling topic of aquatic mystery animals, known as 'cryptids'.




Disentangle


Book Description

A revised edition of the best-selling solution-oriented guide to identifying and healing over-involvement or "entanglement" in relationships with other. Anyone who has struggled with balancing his or her own needs and desires with those of the “other” person will benefit from Nancy Johnston's sensible, easy-to-follow method for changing the course of one's relationships. Disentangle combines psychoeducation, personal anecdotes, clinical case vignettes, and skills-building exercises. Johnston describes how to turn this self-destructive cycle around with self-assessments and experiential exercises designed to address essential aspects of self-awareness, distortions in thinking, communication style and tools, and spirituality. “Disentangling” is the process of creating enough emotional space between oneself and another person in order to better see the realities of any relationship and make healthier conscious decisions about it.




Federalism, Democracy and Disability Policy in Canada


Book Description

The 1999 signing of the Social Union Framework Agreement, the elimination of government deficits, and an apparent trend to decentralisation have increased the focus on Canada's social policy and the manner of its formulation. While disability policy, a key element of social policy that is seldom high on the country's policy agenda, is sharing in the renewed interest, no significant disability policy changes have yet emerged.The Social Union and Disability Policy examines the development of Canadian disability policy and the current political landscape that will influence new policy. It offers an agenda for reform of the disability insurance system and for the provision of supports and services for people with disabilities. The focus is on the impact of governance structures, those now in place and those that might be expected to yield improved policy outcomes while promoting the principles of federalism and democratic oversight.Contributors to the volume are academics Michael Prince (University of Victoria), Roy Hanes (Carleton University) and Allan Moscovitch (Carleton University), NGO representative Michael Bach (Roeher Institute) and social policy analysts Marcia Rioux (York University) and Alan Puttee.




Feynman's Operational Calculus and Beyond


Book Description

This book is aimed at providing a coherent, essentially self-contained, rigorous and comprehensive abstract theory of Feynman's operational calculus for noncommuting operators. Although it is inspired by Feynman's original heuristic suggestions and time-ordering rules in his seminal 1951 paper An operator calculus having applications in quantum electrodynamics, as will be made abundantly clear in the introduction (Chapter 1) and elsewhere in the text, the theory developed in this book also goes well beyond them in a number of directions which were not anticipated in Feynman's work. Hence, the second part of the main title of this book. The basic properties of the operational calculus are developed and certain algebraic and analytic properties of the operational calculus are explored. Also, the operational calculus will be seen to possess some pleasant stability properties. Furthermore, an evolution equation and a generalized integral equation obeyed by the operational calculus are discussed and connections with certain analytic Feynman integrals are noted. This volume is essentially self-contained and we only assume that the reader has a reasonable, graduate level, background in analysis, measure theory and functional analysis or operator theory. Much of the necessary remaining background is supplied in the text itself.




Deep Learning Approaches for Security Threats in IoT Environments


Book Description

Deep Learning Approaches for Security Threats in IoT Environments An expert discussion of the application of deep learning methods in the IoT security environment In Deep Learning Approaches for Security Threats in IoT Environments, a team of distinguished cybersecurity educators deliver an insightful and robust exploration of how to approach and measure the security of Internet-of-Things (IoT) systems and networks. In this book, readers will examine critical concepts in artificial intelligence (AI) and IoT, and apply effective strategies to help secure and protect IoT networks. The authors discuss supervised, semi-supervised, and unsupervised deep learning techniques, as well as reinforcement and federated learning methods for privacy preservation. This book applies deep learning approaches to IoT networks and solves the security problems that professionals frequently encounter when working in the field of IoT, as well as providing ways in which smart devices can solve cybersecurity issues. Readers will also get access to a companion website with PowerPoint presentations, links to supporting videos, and additional resources. They’ll also find: A thorough introduction to artificial intelligence and the Internet of Things, including key concepts like deep learning, security, and privacy Comprehensive discussions of the architectures, protocols, and standards that form the foundation of deep learning for securing modern IoT systems and networks In-depth examinations of the architectural design of cloud, fog, and edge computing networks Fulsome presentations of the security requirements, threats, and countermeasures relevant to IoT networks Perfect for professionals working in the AI, cybersecurity, and IoT industries, Deep Learning Approaches for Security Threats in IoT Environments will also earn a place in the libraries of undergraduate and graduate students studying deep learning, cybersecurity, privacy preservation, and the security of IoT networks.




Deep Learning for Medical Image Analysis


Book Description

Deep Learning for Medical Image Analysis, Second Edition is a great learning resource for academic and industry researchers and graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Deep learning provides exciting solutions for medical image analysis problems and is a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component are applied to medical image detection, segmentation, registration, and computer-aided analysis.· Covers common research problems in medical image analysis and their challenges · Describes the latest deep learning methods and the theories behind approaches for medical image analysis · Teaches how algorithms are applied to a broad range of application areas including cardiac, neural and functional, colonoscopy, OCTA applications and model assessment · Includes a Foreword written by Nicholas Ayache




Förhandlingar


Book Description




Data Augmentation, Labelling, and Imperfections


Book Description

This book constitutes the refereed proceedings of the Second MICCAI Workshop on Data Augmentation, Labelling, and Imperfections, DALI 2022, held in conjunction with MICCAI 2022, in Singapore in September 2022. DALI 2022 accepted 12 papers from the 22 submissions that were reviewed. The papers focus on rigorous study of medical data related to machine learning systems.







Computer Vision – ECCV 2022


Book Description

The 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022. The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.