This volume, written by his friends, collaborators and students, is offered to the memory of Paul Tunin. Most of the papers they contributed discuss subjects related to his own fields of research. The wide range of topics reflects the versatility of his mathematical activity. His work has inspired many mathematicians in analytic number theory, theory of functions of a complex variable, interpolation and approximation theory, numerical algebra, differential equations, statistical group theory and theory of graphs. Beyond the influence of his deep and important results he had the exceptional ability to communicate to others his enthusiasm for mathematics. One of the strengths of Turan was to ask unusual questions that became starting points of many further results, sometimes opening up new fields of research. We hope that this volume will illustrate this aspect of his work adequately. Born in Budapest, on August 28, 1910, Paul Turan obtained his Ph. D. under L. Fejer in 1935. His love for mathematies enabled him to work even under inhuman circumstances during the darkest years of the Second World War. One of his major achievements, his power sum method originated in this period. After the war he was visiting professor in Denmark and in Princeton. In 1949 he became professor at the Eotvos Lorand University of Budapest, a member of the Hungarian Academy of Sciences and a leading figure of the Hungarian mathematical community.

In recent years, Artificial Intelligence researchers have largely focused their efforts on solving specific problems, with less emphasis on 'the big picture' - automating large scale tasks which require human-level intelligence to undertake. The subject of this book, automated theory formation in mathematics, is such a large scale task. Automated theory formation requires the invention of new concepts, the calculating of examples, the making of conjectures and the proving of theorems. This book, representing four years of PhD work by Dr. Simon Colton demonstrates how theory formation can be automated. Building on over 20 years of research into constructing an automated mathematician carried out in Professor Alan Bundy's mathematical reasoning group in Edinburgh, Dr. Colton has implemented the HR system as a solution to the problem of forming theories by computer. HR uses various pieces of mathematical software, including automated theorem provers, model generators and databases, to build a theory from the bare minimum of information - the axioms of a domain. The main application of this work has been mathematical discovery, and HR has had many successes. In particular, it has invented 20 new types of number of sufficient interest to be accepted into the Encyclopaedia of Integer Sequences, a repository of over 60,000 sequences contributed by many (human) mathematicians.

This book leads readers from a basic foundation to an advanced level understanding of geometry in advanced pure mathematics. Chapter by chapter, readers will be led from a foundation level understanding to advanced level understanding. This is the perfect text for graduate or PhD mathematical-science students looking for support in algebraic geometry, geometric group theory, modular group, holomorphic dynamics and hyperbolic geometry, syzygies and minimal resolutions, and minimal surfaces.Geometry in Advanced Pure Mathematics is the fourth volume of the LTCC Advanced Mathematics Series. This series is the first to provide advanced introductions to mathematical science topics to advanced students of mathematics. Edited by the three joint heads of the London Taught Course Centre for PhD Students in the Mathematical Sciences (LTCC), each book supports readers in broadening their mathematical knowledge outside of their immediate research disciplines while also covering specialized key areas.

This book is about mathematics teaching and learning in Africa during the Fourth Industrial Revolution. The Fourth Industrial Revolution (4IR) has evolved to utilize new technologies in the teaching and learning of Mathematics. It is characterized by the fusion of the biological, physical and digital worlds and embodies a new era of innovation in mathematics education, leading to the rapid emergence of new technologies for mathematics teaching and learning. Because 4IR in mathematics education is happening differently in various parts of Africa, the authors of the various chapters in this volume have positioned their work in their respective local contexts. The chapters address a wide variety of interests, concerns, and implications regarding 4IR and Mathematics Education in Africa. Additionally, a number of chapters address teaching mathematics in the context of the COVID-19 pandemic that has gripped the world. Other chapters discuss the implications of inequalities in Africa that effect mathematics education during 4IR. Chapters also incorporate arguments, observations, and suggestions to, improve and transform the teaching and learning of mathematics in Africa during the 4IR. This book highlights a new era of innovation in mathematics education in the context of the Fourth Industrial Revolution, leading to the rapid emergence of new technologies in mathematics teaching and learning. It is a valuable resource for graduate students, people with research interests in the fourth industrial revolution and mathematics educators at any level, including all mathematics teachers; mathematics education curriculum designers and policymakers.

This volume introduces the economics as a foundational discipline in education. It provides economists and non-economists with an accessible grounding in the key concepts and recent developments in the field. The book deals with such themes as human capital theory and its alternatives, the monetary and non-monetary benefits of education, the education production function, equity in education, and the evaluation of education policies. In this volume, students, researchers and policymakers will find an entry point into the way economists think about educational questions and readers will deepen their understanding of the field with state-of-the-art reviews of the main topics that are at the heart of the economist of education today. About the Educational Foundations series: Education, as an academic field taught at universities around the world, emerged from a range of older foundational disciplines. The Educational Foundations series comprises six volumes, each covering one of the foundational disciplines of philosophy, history, sociology, policy studies, economics and law. This is the first reference work to provide an authoritative and up-to-date account of all six disciplines, showing how each field's ideas, methods, theories and approaches can contribute to research and practice in education today. The six volumes cover the same set of key topics within education, which also form the chapter titles: - Mapping the Field - Purposes of Education - Curriculum - Schools and Education Systems - Learning and Human Development - Teaching and Teacher Education - Assessment and Evaluation This structure allows readers to study the volumes in isolation, by discipline, or laterally, by topic, and facilitates a comparative, thematic reading of chapters across the volumes. Throughout the series, attention is paid to how the disciplines comprising the educational foundations speak to social justice concerns such as gender and racial equality.

The juxtaposition of 'machine learning' and 'pure mathematics and theoretical physics' may first appear as contradictory in terms. The rigours of proofs and derivations in the latter seem to reside in a different world from the randomness of data and statistics in the former. Yet, an often under-appreciated component of mathematical discovery, typically not presented in a final draft, is experimentation: both with ideas and with mathematical data. Think of the teenage Gauss, who conjectured the Prime Number Theorem by plotting the prime-counting function, many decades before complex analysis was formalized to offer a proof.Can modern technology in part mimic Gauss's intuition? The past five years saw an explosion of activity in using AI to assist the human mind in uncovering new mathematics: finding patterns, accelerating computations, and raising conjectures via the machine learning of pure, noiseless data. The aim of this book, a first of its kind, is to collect research and survey articles from experts in this emerging dialogue between theoretical mathematics and machine learning. It does not dwell on the well-known multitude of mathematical techniques in deep learning, but focuses on the reverse relationship: how machine learning helps with mathematics. Taking a panoramic approach, the topics range from combinatorics to number theory, and from geometry to quantum field theory and string theory. Aimed at PhD students as well as seasoned researchers, each self-contained chapter offers a glimpse of an exciting future of this symbiosis.

This volume--the first to bring together research on sociocultural aspects of mathematics education--presents contemporary and international perspectives on social justice and equity issues that impact mathematics education. In particular, it highlights the importance of three interacting and powerful factors--gender, social, and cultural dimensions. Sociocultural Research on Mathematics Education: An International Perspective is distinguished in several ways: * It is research based. Chapters report on significant research projects; present a comprehensive and critical summary of the research findings; and offer a critical discussion of research methods and theoretical perspectives undertaken in the area. * It is future oriented, presenting recommendations for practice and policy and identifying areas for further research. * It deals with all aspects of formal and informal mathematics education and applications and all levels of formal schooling. As the context of mathematics education rapidly changes-- with an increased demand for mathematically literate citizenship; an increased awareness of issues of equity, inclusivity, and accountability; and increased efforts for globalization of curriculum development and research-- questions are being raised more than ever before about the problems of teaching and learning mathematics from a non-cognitive science perspective. This book contributes significantly to addressing such issues and answering such questions. It is especially relevant for researchers, graduate students, and policymakers in the field of mathematics education.