Anagnorisis, or recognition, has played a central role in the arts and humanities throughout history. It is a universal mode of knowledge in literature and the arts; in sacred texts and scholastic writing; in philosophy; in psychology; in politics and social theory. Recognition is a phenomenon and a fulcrum that makes these discourses possible. To date, no one has attempted a comprehensive discussion of recognition across disciplines, places, and historical periods. Recognition and Modes of Knowledge is the culmination of an interdisciplinary conference on recognition with contributions from international authorities, including Piero Boitani, Roland Le Huenen, Rachel Adelman, and Christina Tarnopolsky. Students and experts in the humanities who desire a rich grounding in the concept of recognition should start with this book.

A comprehensive and comparative examination of the concept of recognition across history and disciplines.

This book deals with the relevance of recognition and validation of non-formal and informal learning education and training, the workplace and society. In an increasing number of countries, it is at the top of the policy and research agenda ranking among the possible ways to redress the glaring lack of relevant academic and vocational qualifications and to promote the development of competences and certification procedures which recognise different types of learning, including formal, non-formal and informal learning. The aim of the book is therefore to present and share experience, expertise and lessons in such a way that enables its effective and immediate use across the full spectrum of country contexts, whether in the developing or developed world. It examines the importance of meeting institutional and political requirements that give genuine value to the recognition of non-formal and informal learning; it shows why recognition is important and clarifies its usefulness and the role it serves in education, working life and voluntary work; it emphasises the importance of the coordination, interests, motivations, trust and acceptance by all stakeholders. The volume is also premised on an understanding of a learning society, in which all social and cultural groups, irrespective of gender, race, social class, ethnicity, mental health difficulties are entitled to quality learning throughout their lives. Overall the thrust is to see the importance of recognising non-formal and informal learning as part of the larger movement for re-directing education and training for change. This change is one that builds on an equitable society and economy and on sustainable development principles and values such as respect for others, respect for difference and diversity, exploration and dialogue.

One of the pathways by which the scientific community confirms the validity of a new scientific discovery is by repeating the research that produced it. When a scientific effort fails to independently confirm the computations or results of a previous study, some fear that it may be a symptom of a lack of rigor in science, while others argue that such an observed inconsistency can be an important precursor to new discovery. Concerns about reproducibility and replicability have been expressed in both scientific and popular media. As these concerns came to light, Congress requested that the National Academies of Sciences, Engineering, and Medicine conduct a study to assess the extent of issues related to reproducibility and replicability and to offer recommendations for improving rigor and transparency in scientific research. Reproducibility and Replicability in Science defines reproducibility and replicability and examines the factors that may lead to non-reproducibility and non-replicability in research. Unlike the typical expectation of reproducibility between two computations, expectations about replicability are more nuanced, and in some cases a lack of replicability can aid the process of scientific discovery. This report provides recommendations to researchers, academic institutions, journals, and funders on steps they can take to improve reproducibility and replicability in science.

Deterministic Learning Theory for Identification, Recognition, and Control presents a unified conceptual framework for knowledge acquisition, representation, and knowledge utilization in uncertain dynamic environments. It provides systematic design approaches for identification, recognition, and control of linear uncertain systems. Unlike many books currently available that focus on statistical principles, this book stresses learning through closed-loop neural control, effective representation and recognition of temporal patterns in a deterministic way. A Deterministic View of Learning in Dynamic Environments The authors begin with an introduction to the concepts of deterministic learning theory, followed by a discussion of the persistent excitation property of RBF networks. They describe the elements of deterministic learning, and address dynamical pattern recognition and pattern-based control processes. The results are applicable to areas such as detection and isolation of oscillation faults, ECG/EEG pattern recognition, robot learning and control, and security analysis and control of power systems. A New Model of Information Processing This book elucidates a learning theory which is developed using concepts and tools from the discipline of systems and control. Fundamental knowledge about system dynamics is obtained from dynamical processes, and is then utilized to achieve rapid recognition of dynamical patterns and pattern-based closed-loop control via the so-called internal and dynamical matching of system dynamics. This actually represents a new model of information processing, i.e. a model of dynamical parallel distributed processing (DPDP).

This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

The Oxford ILDC online database, an online collection of domestic court decisions which apply international law, has been providing scholars with insights for many years. This ILDC Casebook is the perfect companion, introducing key court decisions with brief introductory and connecting texts. An ideal text for practitioners, judged, government officials, as well as for students on international law courses, the ILDC Casebook explains the theories and doctrines underlying the use by domestic courts of international law, and illustrates the key importance of domestic courts in the development of international law.