Format: Hardback, 169 pages, height x width: 235x155 mm, 8 Illustrations, color; 25 Illustrations, black and white
Series: Studies in Computational Intelligence
Pub. Date: 24-May-2026
ISBN-13: 9783032211422
This book covers theoretical work, applications, and techniques for computational models of information, language, and reasoning. Computational and technological developments that incorporate natural language and reasoning methods are proliferating. Adequate coverage of related works and advanced applications encounter difficult problems related to the phenomena of partiality, underspecification, perspectives of agents, and context dependency. These phenomena are signature features of information in nature, natural languages, and reasoning.
The goal of this collection of works is to promote advanced, intelligent approaches of Mathematical Logic and its applications to systems of proofs and verification, Mathematical and Computational Linguistics, and Natural Language Processing (NLP).
The intended readers of this book are researchers in Mathematical Logic and Computer Science, as well as Mathematical and Computational Linguistics, to advance work on theories and applications to Artificial Intelligence (AI), Natural Language Processing (NLP), and related subjects.
Natural Language Generation from Wikidata - Architecture, Scalability
and Challenges.- A Survey on Language Completeness of the Lambek Calculus and
Its Extensions.- On Algebras and Phase Spaces for Linear Logics.- Applying
Quantales to Lambek Calculi with Cyclic Negation.
Format: Hardback, 453 pages, height x width: 235x155 mm, 1 Illustrations, black and white
Series: Industrial and Applied Mathematics
Pub. Date: 24-Aug-2026
ISBN-13: 9789819221271
This book provides a comprehensive and unified treatment of variational inequalities, quasi-variational inequalities, and related impulse control problems, combining rigorous mathematical analysis with effective numerical methods and practical applications. Such models arise naturally in constrained partial differential equations, free boundary problems, optimal control, reliability engineering, and mathematical finance.
The main objective of the book is to develop a coherent framework that connects functional analytic foundations, qualitative properties, numerical approximation, and long-time behavior. Particular attention is given to the analysis of existence, uniqueness, and regularity of solutions for elliptic and parabolic variational inequalities, as well as to quasi-variational problems involving solution-dependent constraints.
The book also presents robust and convergent numerical schemes, with a focus on finite element methods, penalty techniques, and approximation strategies that preserve the qualitative features of the continuous models. Special emphasis is placed on applications to free boundary problems, impulse control, and non-smooth phenomena, which play a central role in reliability modeling and American option pricing.
This volume is intended for graduate students, researchers, and practitioners in applied mathematics, numerical analysis, and related fields who are interested in both the theoretical and computational aspects of variational inequalities.
General Introduction.- Foundations of Variational Inequalities.-
Parabolic Variational Inequalities and Time Discretization.- Finite Element
Approximation of Variational Inequalities.- Asymptotic Behavior and Long-Time
Analysis.
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Format: Paperback / softback, 762 pages, height x width: 235x155 mm, 10 Illustrations, color; 20 Illustrations, black and white
Series: Universitext
Pub. Date: 18-Oct-2026
ISBN-13: 9783032329448
Riemannian geometry and geometric analysis are flourishing fields with applications in physics, statistics, and machine learning. This textbook develops both fundamental concepts and more advanced topics shaped by recent progress. The 8th edition expands coverage with a systematic treatment of total scalar curvature, from which the Einstein equations, Ricci flow, and the Yamabe problem emerge, and includes new perspectives on generalized sectional and Ricci curvatures as well as Kirillovs coadjoint orbits. It introduces core notions such as geodesics, connections, and curvature, alongside key tools of geometric analysis, including harmonic functions, forms, eigenvalues, the Dirac operator, and heat flow, and highlights major variational principles like harmonic maps, YangMills, GinzburgLandau and Seiberg-Witten. The book offers a coherent geometric framework while equipping readers with practical methods for further study and research.
Chapter 1. Riemannian Manifolds.
Chapter 2. Lie Groups and Vector Bundles.
Chapter 3. The Laplace Operator and Harmonic Differential Forms.-
Chapter 4. Connections and Curvature.
Chapter 5. Bochner Identities, Dirac Operators and Eigenvalues.
Chapter 6. Geometry of Submanifolds.
Chapter 7. Geodesics and Jacobi Fields.
Chapter 8. The Geometry of Ricci Curvature.-
Chapter 9. Nonpositive Curvature.
Chapter 10. A Survey on Curvature and Topology.
Chapter 11. Symmetric Spaces and Kahler Manifolds.
Chapter 12. Morse Theory and Floer Homology.
Chapter 13. Harmonic Maps between Riemannian Manifolds.
Chapter 14. Harmonic Maps from Riemann Surfaces.-
Chapter 15. Variational Problems from Quantum Field Theory.
Format: Hardback, 389 pages, height x width: 235x155 mm, 25 Illustrations, color; 11 Illustrations, black and white
Pub. Date: 19-Sep-2026
ISBN-13: 9789819590063
This book presents a rigorous and comprehensive exploration of inlier-prone models, delving into their theoretical foundations and wide-ranging applications. Inlierslike outliersrepresent atypical observations, but unlike outliers, they tend to appear in clusters rather than in isolation. Traditional statistical methods often overlook this phenomenon, necessitating the use of non-standard probability distributions, which is an emerging area of interest in statistical research. Through a multidisciplinary lens, the book examines the presence and impact of inliers across various fields, including statistics, social sciences, survival analysis, and clinical research, while also extending their relevance to other disciplines. Beyond technical insights, it thoughtfully addresses academic significance, ethical considerations, and interdisciplinary connections, making it an indispensable resource for researchers, scholars, and practitioners seeking to deepen their understanding of inlier behavior and its implications.
Chapter 1 Introduction.
Chapter 2 Data and its descriptions.
Chapter 3 Statistical distributions, characteristics, and properties.
Chapter 4 Inliers-prone models.
Chapter 5 UMVU Estimation in inliers model.
Chapter 6 Maximum Likelihood Estimation in inliers model.
Chapter 7 Tests of Hypothesis on parameters of inliers model.
Chapter 8 Tests of Hypothesis on number of inliers.
Chapter 9 Censoring concepts in inliers distributions.-
Chapter 10 Bayes Estimation in inliers distributions.
Chapter 11 Inliers as Complete mixtures.
Chapter 12 Inliers at zero and one.
Chapter 13 Some generalizations.
Chapter 14 Inliers prone distributions: Issues and problems.
Format: Paperback / softback, 305 pages, height x width: 235x155 mm, 21 Illustrations, black and white
Series: UNITEXT
Pub. Date: 10-Sep-2026
ISBN-13: 9783032315328
Geometric Methods in Mathematical Physics II: Tensor Analysis on Manifolds and General Relativity provides a rigorous and self-contained introduction to the differential-geometric foundations of modern mathematical physics, with a special emphasis on the mathematical formulation of General Relativity.
The volume develops the theory of smooth manifolds, tensor fields, differential forms, affine and Levi-Civita connections, geodesics, exponential maps, curvature, and pseudo-Riemannian geometry. These tools are introduced in a concise but systematic way, combining abstract geometric concepts with explicit coordinate expressions and applications relevant to physics.
After establishing the basic language of differential geometry, the text turns to the geometric structure of spacetime. It discusses Lorentzian manifolds, causal vectors and curves, time orientation, proper time, reference frames, the equivalence principle, conservation laws, Killing vector fields, Fermi-Walker transport, geodesic deviation, and the role of curvature in gravitation. The exposition then leads naturally to Einsteins field equations, their geometric meaning, and selected applications in relativistic physics.
Further topics include Newtonian correspondence, gravitational redshift and time dilation, stationary spacetimes, cosmological models of Friedmann-Lemaītre-Robertson-Walker type, the expansion of the Universe, dark matter and dark energy, and the Schwarzschild and Kruskal solutions.
Addressed primarily to graduate students, researchers, and advanced readers in mathematics, physics, and mathematical physics, this book offers a compact yet mathematically precise pathway from tensor analysis on manifolds to the modern geometric understanding of gravitation. It is suitable both as a course text and as a reference for readers wishing to master the geometric methods that underlie contemporary General Relativity.
Introduction.- Topological and smooth manifolds.- Tensor Fields on
Manifolds and Associated Geometric Structures.- Differential of maps,
submanifolds, Lie derivative.- (Pseudo) Riemannian manifolds and related
metric tools.- Affine connections and related geometric tools.- The
Exponential Map of Affine and Metric Connections.- General Relativity: a
Geometric Presentation.- Curvature.- Gravitation in General Relativity.- The
Schwarzschild solution and the Kruskal spacetime.- Index.
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Format: Hardback, 395 pages, height x width: 235x155 mm, 57 Illustrations, color; 47 Illustrations, black and white
Series: Textbooks in Statistical Science
Pub. Date: 07-Oct-2026
ISBN-13: 9783032336576
This undergraduate textbook provides a novel introduction to the concepts of statistical inference. Approached from a fresh information-theoretic perspective, statistics is presented as a scientific discipline that offers concepts for handling uncertainty, which is a common thread throughout the book.
This framing naturally leads readers to key ideas such as maximum likelihood estimation, statistical testing, regression, and model selection. Uncertainty can be explored through simulation-based approaches, which are given particular emphasis in the book and open the door to Bayesian inference, also discussed in the text. Beyond standard scenarios, the book extends classical methods to handle extreme and multivariate data as well as data that deviate from the independent and identically distributed assumption. By drawing parallels to methods from machine learning, the book demonstrates how modern statistical thinking complements and enriches machine learning methodologies.
The book presents the versatility of statistical ideas, concepts, and questions in a form that is easy to understand and digest, without neglecting the methodological and mathematical foundations of statistics. Each chapter is complemented by exercises to support learning, and examples in the book are accompanied by computer code and additional material available online.
The text is intended for a two-semester course in statistical inference and assumes prior knowledge of fundamental ideas of probability theory. Given its fresh approach, it will equally appeal to aspiring statisticians at the bachelors level and to computer scientists in the field of machine learning.
Preface.- 1 Introduction.- 2 Uncertainty.- 3 Learning and Estimating.- 4
Maximum Likelihood Estimation.- 5 Data Driven Decisions.- 6 Regression.- 7
Model Selection.- 8 Simulating Random Variables and Simulation-based
Inference.- 9 Bayesian Inference.- 10 Inference in Extreme Data.- 11
Multivariate Data.- 12 Non i.i.d. Data.- 13 Relating Machine Learning and
Statistics.- A Background in Probability Theory.- B Parametric Distribution
Families.- References.- Index.
Format: Paperback / softback, 134 pages, height x width: 240x168 mm, VI, 134 p.
Series: Frontiers in Mathematics
Pub. Date: 09-Oct-2026
ISBN-13: 9783032330994
This monograph develops a comprehensive theory of Gmonogenic mappings in noncommutative algebras, combining algebraic structure with analytic techniques. Building on quaternionic and hypercomplex analysis, the authors introduce new classes of G and Hmonogenic mappings, establish their fundamental properties, and provide explicit constructive representations via holomorphic functions. Classical tools of complex analysisincluding the Cauchy integral theorem, Moreras theorem, and Taylor and Laurent expansionsare extended to noncommutative settings. The theory is further connected to elliptic partial differential equations, demonstrating explicit solution methods. The book offers a unified framework in hypercomplex analysis, noncommutative algebra, and applied complex analysis, making it a suitable reference for researchers working in these fields.
Chapter 1. Introduction.- Part I. Quaternionic G-monogenic Mappings in E3.
Chapter 2. AlgebraicAnalytic Properties of G-monogenic Mappings in the Algebra of Complex Quaternions.
Chapter 3. Integral Theorems and Series Expansion in E3.
Chapter 4. H-monogenic Mappings.- Part II. Quaternionic G-monogenic Mappings in Em.
Chapter 5. A Constructive Description of G-monogenic Mappings in Em.
Chapter 6. Integral Theorems and Series Expansion in Em.- Part III. Monogenic Mappings in the Noncommutative Algebra A2.
Chapter 7. G-monogenic Mappings in the Three-Dimensional Noncommutative Algebra A2.
Chapter 8. Differentiable Functions in the Algebra A2.
Chapter 9. Hausdorff Analytic Functions in the Algebra A2.