Maths for computer scientists Books

Book SynopsisThis book provides an undergraduate introduction to analysing data for data science, computer science, and quantitative social science students. It uniquely combines a hands-on approach to data analysis – supported by numerous real data examples and reusable [R] code – with a rigorous treatment of probability and statistical principles. Where contemporary undergraduate textbooks in probability theory or statistics often miss applications and an introductory treatment of modern methods (bootstrapping, Bayes, etc.), and where applied data analysis books often miss a rigorous theoretical treatment, this book provides an accessible but thorough introduction into data analysis, using statistical methods combining the two viewpoints. The book further focuses on methods for dealing with large data-sets and streaming-data and hence provides a single-course introduction of statistical methods for data science.Trade Review“Having taught data analytics at the introductory graduate level, I welcome the authors’ textbook as an essential resource for training well-grounded entry-level data scientists. … A data scientist shall provide competent data science professional services to a client. … Training in both the theory and practice of data analytics is a requirement for such competence. The authors’ textbook definitely provides a valuable resource for such training.” (Harry J. Foxwell, Computing Reviews, July 7, 2022)Table of Contents1 A First Look at Data.- 2 Sampling Plans and Estimates.- 3 Probability Theory.- 4 Random Variables and Distributions.- 5 Estimation.- 6 Multiple Random Variables.- 7 Making Decisions in Uncertainty.- 8 Bayesian Statistics.

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Book SynopsisMachine learning methods are now an important tool for scientists, researchers, engineers and students in a wide range of areas. This book is written for people who want to adopt and use the main tools of machine learning, but aren’t necessarily going to want to be machine learning researchers. Intended for students in final year undergraduate or first year graduate computer science programs in machine learning, this textbook is a machine learning toolkit. Applied Machine Learning covers many topics for people who want to use machine learning processes to get things done, with a strong emphasis on using existing tools and packages, rather than writing one’s own code.A companion to the author's Probability and Statistics for Computer Science, this book picks up where the earlier book left off (but also supplies a summary of probability that the reader can use).Emphasizing the usefulness of standard machinery from applied statistics, this textbook gives an overview of the major applied areas in learning, including coverage of:• classification using standard machinery (naive bayes; nearest neighbor; SVM)• clustering and vector quantization (largely as in PSCS)• PCA (largely as in PSCS)• variants of PCA (NIPALS; latent semantic analysis; canonical correlation analysis)• linear regression (largely as in PSCS)• generalized linear models including logistic regression• model selection with Lasso, elasticnet• robustness and m-estimators• Markov chains and HMM’s (largely as in PSCS)• EM in fairly gory detail; long experience teaching this suggests one detailed example is required, which students hate; but once they’ve been through that, the next one is easy• simple graphical models (in the variational inference section)• classification with neural networks, with a particular emphasis onimage classification• autoencoding with neural networks• structure learningTable of Contents1. Learning to Classify.- 2. SVM’s and Random Forests.- 3. A Little Learning Theory.- 4. High-dimensional Data.- 5. Principal Component Analysis.- 6. Low Rank Approximations.- 7. Canonical Correlation Analysis.- 8. Clustering.- 9. Clustering using Probability Models.- 10. Regression.- 11. Regression: Choosing and Managing Models.- 12. Boosting.- 13. Hidden Markov Models.- 14. Learning Sequence Models Discriminatively.- 15. Mean Field Inference.- 16. Simple Neural Networks.- 17. Simple Image Classifiers.- 18. Classifying Images and Detecting Objects.- 19. Small Codes for Big Signals.- Index.

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Book SynopsisThis book constitutes the refereed post-conference proceedings of the Special Event on the Analysis of Experimental Algorithms, SEA² 2019, held in Kalamata, Greece, in June 2019.The 35 revised full papers presented were carefully reviewed and selected from 45 submissions. The papers cover a wide range of topics in both computer science and operations research/mathematical programming. They focus on the role of experimentation and engineering techniques in the design and evaluation of algorithms, data structures, and computational optimization methods.

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Book SynopsisThis book constitutes the refereed proceedings of the 27th International Symposium on Graph Drawing and Network Visualization, GD 2019, held in Prague, Czech Republic, in September 2019.The 42 papers and 12 posters presented in this volume were carefully reviewed and selected from 113 submissions. They were organized into the following topical sections: Cartograms and Intersection Graphs, Geometric Graph Theory, Clustering, Quality Metrics, Arrangements, A Low Number of Crossings, Best Paper in Track 1, Morphing and Planarity, Parameterized Complexity, Collinearities, Topological Graph Theory, Best Paper in Track 2, Level Planarity, Graph Drawing Contest Report, and Poster Abstracts.Table of ContentsCartograms and Intersection Graphs.- Stick Graphs with Length Constraints.- Representing Graphs and Hypergraphs by Touching Polygons in 3D.- Optimal Morphs of Planar Orthogonal Drawings II.- Computing Stable Demers Cartograms.- Geometric Graph Theory.- Bundled Crossings Revisited.- Crossing Numbers of Beyond-Planar Graphs.- On the 2-Colored Crossing Number.- Minimal Representations of Order Types by Geometric Graphs.- Balanced Schnyder woods for planar triangulations: an experimental study with applications to graph drawing and graph separators.- Clustering.- A Quality Metric for Visualization of Clusters in Graphs.- Multi-level Graph Drawing using Infomap Clustering.- On Strict (Outer-)Confluent Graphs.- Quality Metrics.- On the Edge-Length Ratio of Planar Graphs.- Node Overlap Removal Algorithms: A Comparative Study.- Graphs with large total angular resolution.- Arrangements.- Computing Height-Optimal Tangles Faster.- On Arrangements of Orthogonal Circles.- Extending Simple Drawings.- Coloring Hasse diagrams and disjointness graphs of curves.- A Low Number of Crossings.- Efficient Generation of Different Topological Representations of Graphs Beyond-Planarity.- The QuaSEFE Problem.- ChordLink: A New Hybrid Visualization Model.- Stress-Plus-X (SPX) Graph Layout.- Best Paper in Track 1.- Exact Crossing Number Parameterized by Vertex Cover.- Morphing and Planarity.- Maximizing Ink in Partial Edge Drawings of k-Plane Graphs.- Graph Drawing with Morphing Partial Edges.- A Note on Universal Point Sets for Planar Graphs.- Parameterized Complexity.- Parameterized Algorithms for Book Embedding Problems.- Sketched Representations and Orthogonal Planarity of Bounded Treewidth Graphs.- Collinearities.- 4-Connected Triangulations on Few Lines.- Line and Plane Cover Numbers Revisited.- Drawing planar graphs with few segments on a polynomial grid.- Variants of the Segment Number of a Graph.- Topological Graph Theory.- Local and Union Page Numbers.- Mixed Linear Layouts: Complexity, Heuristics, and Experiments.- Homotopy height, grid-major height and graph-drawing height.- On the Edge-Vertex Ratio of Maximal Thrackles.- Best Paper in Track 2.- Symmetry Detection and Classification in Drawings of Graphs.- Level Planarity.- An SPQR-Tree-Like Embedding Representation for Upward Planarity.- A Natural Quadratic Approach to the Generalized Graph Layering Problem.- Graph Stories in Small Area.- Level-Planar Drawings with Few Slopes.- Graph Drawing Contest Report.- Graph Drawing Contest Report.- Poster Abstracts.- A 1-planarity Testing and Embedding Algorithm.- Stretching Two Pseudolines in Planar Straight-Line Drawings.- Adventures in Abstraction: Reachability in Hierarchical Drawings.- On Topological Book Embedding for k-Plane Graphs.- On Compact RAC Drawings.- FPQ-choosable Planarity Testing.- Packing Trees into 1-Planar Graphs.- Geographic Network Visualization Techniques: A Work-In-Progress Taxonomy.- On the Simple Quasi Crossing Number of K 11.- Minimising Crossings in a Tree-Based Network.- Crossing Families and Their Generalizations.- Which Sets of Strings are Pseudospherical?.

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Book SynopsisThis easy-to-understand textbook introduces the mathematical language and problem-solving tools essential to anyone wishing to enter the world of computer and information sciences. Specifically designed for the student who is intimidated by mathematics, the book offers a concise treatment in an engaging style.The thoroughly revised third edition features a new chapter on relevance-sensitivity in logical reasoning and many additional explanations on points that students find puzzling, including the rationale for various shorthand ways of speaking and ‘abuses of language’ that are convenient but can give rise to misunderstandings. Solutions are now also provided for all exercises.Topics and features: presents an intuitive approach, emphasizing how finite mathematics supplies a valuable language for thinking about computation; discusses sets and the mathematical objects built with them, such as relations and functions, as well as recursion and induction; introduces core topics of mathematics, including combinatorics and finite probability, along with the structures known as trees; examines propositional and quantificational logic, how to build complex proofs from simple ones, and how to ensure relevance in logic; addresses questions that students find puzzling but may have difficulty articulating, through entertaining conversations between Alice and the Mad Hatter; provides an extensive set of solved exercises throughout the text.This clearly-written textbook offers invaluable guidance to students beginning an undergraduate degree in computer science. The coverage is also suitable for courses on formal methods offered to those studying mathematics, philosophy, linguistics, economics, and political science. Assuming only minimal mathematical background, it is ideal for both the classroom and independent study.Table of ContentsPart I: Sets Collecting Things Together: Sets Comparing Things: Relations Associating One Item with Another: Functions Recycling Outputs as Inputs: Induction and Recursion Part II: Math Counting Things: Combinatorics Weighing the Odds: Probability Squirrel Math: Trees Part III: Logic Yea and Nay: Propositional Logic Something about Everything: Quantificational Logic Just Supposing: Proof and Consequence Sticking to the Point: Relevance in Logic

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Book SynopsisToday, information technology plays a pivotal role in financial control and audit: most financial data is now digitally recorded and dispersed among servers, clouds and networks over which the audited firm has no control. Additionally, a firm’s data—particularly in the case of finance, software, insurance and biotech firms— comprises most of the audited value of the firm. Financial audits are critical mechanisms for ensuring the integrity of information systems and the reporting of organizational finances. They help avoid the abuses that led to passage of legislation such as the Foreign Corrupt Practices Act (1977), and the Sarbanes-Oxley Act (2002). Audit effectiveness has declined over the past two decades as auditor skillsets have failed to keep up with advances in information technology. Information and communication technology lie at the core of commerce today and are integrated in business processes around the world. This book is designed to meet the increasing need of audit professionals to understand information technology and the controls required to manage it. The material included focuses on the requirements for annual Securities and Exchange Commission audits (10-K) for listed corporations. These represent the benchmark auditing procedures for specialized audits, such as internal, governmental, and attestation audits.Using R and RStudio, the book demonstrates how to render an audit opinion that is legally and statistically defensible; analyze, extract, and manipulate accounting data; build a risk assessment matrix to inform the conduct of a cost-effective audit program; and more.Table of Contents1. Fundamentals of Auditing Financial Statements.- 2. Foundations of Audit Analytics.- 3. Analysis of Accounting Transactions.- 4. Risk Assessment and Planning.- 5. Analytical Review: Technical Analysis.- 6. Analytical Review: Intelligence Scanning.- 7. Design of Audit Programs.- 8. Interim Compliance Tests.- 9. Substantive Tests.- 10. Sarbanes-Oxley Engagements.- 11. Blockchains, Cybercrime and Forensics.- 12. Special Engagements: Forecasts and Valuation.- 13. Simulated Transactions for Auditing Service Organizations.

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Book SynopsisThis revised textbook motivates and illustrates the techniques of applied probability by applications in electrical engineering and computer science (EECS). The author presents information processing and communication systems that use algorithms based on probabilistic models and techniques, including web searches, digital links, speech recognition, GPS, route planning, recommendation systems, classification, and estimation. He then explains how these applications work and, along the way, provides the readers with the understanding of the key concepts and methods of applied probability. Python labs enable the readers to experiment and consolidate their understanding. The book includes homework, solutions, and Jupyter notebooks. This edition includes new topics such as Boosting, Multi-armed bandits, statistical tests, social networks, queuing networks, and neural networks. For ancillaries related to this book, including examples of Python demos and also Python labs used in Berkeley, please email Mary James at mary.james@springer.com. This is an open access book. Table of ContentsChapter 1. Page Rank - A.- Chapter 2. Page Rank - B.- Chapter 3. Multiplexing - A.- Chapter 4. Multiplexing - B.- Chapter 5. Networks - A.- Chapter 6. Networks - B.- Chapter 7. Digital Link - A.- Chapter 8. Digital Link - B.- Chapter 9. Tracking - A.- Chapter 10. Tracking - B.- Chapter 11. Speech Recognition - A.- Chapter 12. Speech Recognition - B.- Chapter 13. Route planning - A.- Chapter 14. Route Planning - B.- chapter 15. Perspective & Complements.- A. Elementary Probability.- B. Basic Probability.- . Index.

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Book SynopsisIn this book the authors aim to endow the reader with an operational, conceptual, and methodological understanding of the discrete mathematics that can be used to study, understand, and perform computing. They want the reader to understand the elements of computing, rather than just know them. The basic topics are presented in a way that encourages readers to develop their personal way of thinking about mathematics. Many topics are developed at several levels, in a single voice, with sample applications from within the world of computing. Extensive historical and cultural asides emphasize the human side of mathematics and mathematicians.By means of lessons and exercises on “doing” mathematics, the book prepares interested readers to develop new concepts and invent new techniques and technologies that will enhance all aspects of computing. The book will be of value to students, scientists, and engineers engaged in the design and use of computing systems, and to scholars and practitioners beyond these technical fields who want to learn and apply novel computational ideas.Trade Review“The text is written in an easy to read format which generously incorporates narratives from the history of mathematics as well as rigorous proofs of the concepts presented. The appendices and references to other texts provide the reader with numerous sources of supplementary information for those wishing to delve into a subject at a deeper level … . chapters are organized and clearly labeled to express which sections are appropriate for a beginning learner, an intermediate learner, or the specialist.” (Tom French, MAA Reviews, October 3, 2021)“Each chapter comes with several exercises from easy to difficult, the latter with complete solutions in the appendix. To accommodate the book to readers with different backgrounds and goals, the authors provide a guide which gives paths through the book for several courses. The exposition is always clear and motivating, no prerequisites are presumed, all terms and concepts are defined precisely, and there are many look-and-see proofs.” (Dieter Riebesehl, zbMATH 1465.68004, 2021)Table of ContentsIntroduction.- “Doing” Mathematics: A Toolkit for Mathematical Reasoning.- Sets and Their Algebras: The Stem Cells of Mathematics.- Numbers I: The Basics of Our Number System.- Arithmetic: Putting Numbers to Work.- Summations: Complex Operations from Simple Components.- The Vertigo of Infinity: Handling the Very Large and the Infinite.- Numbers II: Building the Integers and Building with the Integers.- Recurrences: Rendering Complex Structure Manageable.- Numbers III: Operational Representations and Their Consequences.- The Art of Counting: Combinatorics, Probability, and Statistics.- Graphs I: Representing Relationships Mathematically.- Graphs II: Graphs Within Computation and Communication.- Solutions to Exercises.- App. A, Pairing Functions.- App. B, A Deeper Look at the Fibonacci Numbers.- App. C, Two Recurrence-Defined Number Families.- App. D, Signed-Digit Numerals: Carry-Free Addition.- App. E, The Diverse Delights of de Bruijn Networks.- List of Symbols.- References.- Index.

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Book SynopsisThis textbook can serve as a comprehensive manual of discrete mathematics and graph theory for non-Computer Science majors; as a reference and study aid for professionals and researchers who have not taken any discrete math course before. It can also be used as a reference book for a course on Discrete Mathematics in Computer Science or Mathematics curricula. The study of discrete mathematics is one of the first courses on curricula in various disciplines such as Computer Science, Mathematics and Engineering education practices. Graphs are key data structures used to represent networks, chemical structures, games etc. and are increasingly used more in various applications such as bioinformatics and the Internet. Graph theory has gone through an unprecedented growth in the last few decades both in terms of theory and implementations; hence it deserves a thorough treatment which is not adequately found in any other contemporary books on discrete mathematics, whereas about 40% of this textbook is devoted to graph theory. The text follows an algorithmic approach for discrete mathematics and graph problems where applicable, to reinforce learning and to show how to implement the concepts in real-world applications.Trade Review“This accessible reference book should be well received by undergraduate-level CS, engineering, and mathematics students.” (Soubhik Chakraborty, Computing Reviews, July 12, 2022)“The book under review is an elementary introduction to mathematical logic, set theory, discrete mathematics, number theory, probability theory and graph theory. Its undoubted advantage is its good algorithmic support. … I would recommend this book to students studying computer science at the bachelor’s level.” (I. M. Erusalimskiy, zbMATH 1477.68004, 2022)Table of ContentsPreface.- Part I: Fundamentals of Discrete Mathematics.- Logic.- Proofs.- Algorithms.- Set Theory.- Relations and Functions.- Sequences, Induction and Recursion.- Introduction to Number Theory.- Counting and Probability.- Boolean Algebra and Combinational Circuits.- Introduction to the Theory of Computation.- Part II: Graph Theory.- Introduction to Graphs.- Trees and Traversals.- Subgraphs.- Connectivity, Network Flows and Shortest Paths.- Graph Applications.- A:.- Pseudocode Conventions.- Index.

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Book SynopsisThis book teaches algebra and geometry. The authors dedicate chapters to the key issues of matrices, linear equations, matrix algorithms, vector spaces, lines, planes, second-order curves, and elliptic curves. The text is supported throughout with problems, and the authors have included source code in Python in the book. The book is suitable for advanced undergraduate and graduate students in computer science. Trade Review“It is most interesting to combine a classical mathematical topic with a new evolving programming language and exactly this is obtained by this book. … This material is used as a case study for their implementation for solving problems in theoretical and practical cryptography. The ‘roadmap’ of the content of this also quite interesting.” (Panayiotis Vlamos, zbMATH 1480.00002, 2022)Table of ContentsMatrices and Matrix Algorithms.- Matrix Algebra.- Systems of Linear Equations.- Complex Numbers and Matrices.- Vector Spaces.- Vectors in a Three-Dimensional Space.- Equation of a Straight Line on a Plane.- Equation of a Plane in Space.- Equation of a Line in Space.- Bilinear and Quadratic Forms.- Curves of the Second-Order.- Elliptic Curves.- Appendix A, Basic Operators in Python and C.- Appendix B, Trigonometric Formulae.- Appendix C, The Greek Alphabet.- References.- Name Index.- Subject Index.

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Book SynopsisThis fully updated new edition of a uniquely accessible textbook/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective. It features new material on partially observable Markov decision processes, causal graphical models, causal discovery and deep learning, as well as an even greater number of exercises; it also incorporates a software library for several graphical models in Python.The book covers the fundamentals for each of the main classes of PGMs, including representation, inference and learning principles, and reviews real-world applications for each type of model. These applications are drawn from a broad range of disciplines, highlighting the many uses of Bayesian classifiers, hidden Markov models, Bayesian networks, dynamic and temporal Bayesian networks, Markov random fields, influence diagrams, and Markov decision processes.Topics and features: Presents a unified framework encompassing all of the main classes of PGMs Explores the fundamental aspects of representation, inference and learning for each technique Examines new material on partially observable Markov decision processes, and graphical models Includes a new chapter introducing deep neural networks and their relation with probabilistic graphical models Covers multidimensional Bayesian classifiers, relational graphical models, and causal models Provides substantial chapter-ending exercises, suggestions for further reading, and ideas for research or programming projects Describes classifiers such as Gaussian Naive Bayes, Circular Chain Classifiers, and Hierarchical Classifiers with Bayesian Networks Outlines the practical application of the different techniques Suggests possible course outlines for instructors This classroom-tested work is suitable as a textbook for an advanced undergraduate or a graduate course in probabilistic graphical models for students of computer science, engineering, and physics. Professionals wishing to apply probabilistic graphical models in their own field, or interested in the basis of these techniques, will also find the book to be an invaluable reference.Dr. Luis Enrique Sucar is a Senior Research Scientist at the National Institute for Astrophysics, Optics and Electronics (INAOE), Puebla, Mexico. He received the National Science Prize en 2016.Table of ContentsPart I: FundamentalsIntroductionProbability TheoryGraph TheoryPart II: Probabilistic ModelsBayesian ClassifiersHidden Markov ModelsMarkov Random FieldsBayesian Networks: Representation and InferenceBayesian Networks: LearningDynamic and Temporal Bayesian NetworksPart III: Decision ModelsDecision GraphsMarkov Decision ProcessesPartially Observable Markov Decision Processes Part IV: Relational, Causal and Deep ModelsRelational Probabilistic Graphical ModelsGraphical Causal ModelsCausal DiscoveryDeep Learning and Graphical ModelsA: A Python Library for Inference and LearningGlossaryIndex

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Book SynopsisMany believe mathematics is only about calculations, formulas, numbers, and strange letters. But mathematics is much more than just crunching numbers or manipulating symbols. Mathematics is about discovering patterns, uncovering hidden structures, finding counterexamples, and thinking logically. Mathematics is a way of thinking. It is an activity that is both highly creative and challenging. This book offers an introduction to mathematical reasoning for beginning university or college students, providing a solid foundation for further study in mathematics, computer science, and related disciplines. Written in a manner that directly conveys the sense of excitement and discovery at the heart of doing science, its 25 short and visually appealing chapters cover the basics of set theory, logic, proof methods, combinatorics, graph theory, and much more. In the book you will, among other things, find answers to: What is a proof? What is a counterexample? What does it mean to say that something follows logically from a set of premises? What does it mean to abstract over something? How can knowledge and information be represented and used in calculations? What is the connection between Morse code and Fibonacci numbers? Why could it take billions of years to solve Hanoi's Tower? Logical Methods is especially appropriate for students encountering such concepts for the very first time. Designed to ease the transition to a university or college level study of mathematics or computer science, it also provides an accessible and fascinating gateway to logical thinking for students of all disciplines.Trade Review"The definitions are followed by examples to help explain their meaning, along with counterexamples ... . Therefore, very little basic knowledge is required for this introduction to logical methods ... which is written in an accessible style ... . contained in the book are several hundred small figures; arrow, Venn, and Hasse diagrams; and simplifies visual representations ... . The author has also elected to use color to draw the reader's attention ... ." “From personal teaching experience, knowledge of these mathematical areas is necessary for disparate fields of CS and informatics. These foundations are needed for many fields, from database theory to various domains of information systems applications. The book’s presentation of topics and incentives for problem-solving, along with its exercises, is very useful for university-level instructors and students. The compact chapters contain clear explanations, diagrams, and brief descriptions of interesting facts.” (Bálint Molnár, Computing Reviews, July 27, 2021)Table of ContentsPreface.- 0 The Art of Thinking Abstractly and Mathematically.- 1 Basic Set Theory.- 2 Propositional Logic.- 3 Semantics from Propositional Logic.- 4 Concepts in Propositional Logic.- 5 Proofs, Conjectures, and Counterexamples.- 6 Relations.- 7 Functions.- 8 A Little More Set Theory.- 9 Closures and Inductively Defined Sets.- 10 Recursively Defined Functions.- 11 Mathematical Induction.- 12 Structural Induction.- 13 First-Order Languages.- 14 Representation of Quantified Statements.- 15 Interpretation in Models.- 16 Reasoning About Models.- 17 Abstraction with Equivalences and Partitions.- 18 Combinatorics.- 19 A Little More Combinatorics.- 20 A Bit of Abstract Algebra.- 21 Graph Theory.- 22 Walks in Graphs.- 23 Formal Languages and Grammars.- 24 Natural Deduction.- The Road Ahead.- Index. Symbols.

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Book SynopsisThis book gathers papers presented at the Workshop on Computational Diffusion MRI, CDMRI 2020, held under the auspices of the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), which took place virtually on October 8th, 2020, having originally been planned to take place in Lima, Peru.This book presents the latest developments in the highly active and rapidly growing field of diffusion MRI. While offering new perspectives on the most recent research challenges in the field, the selected articles also provide a valuable starting point for anyone interested in learning computational techniques for diffusion MRI. The book includes rigorous mathematical derivations, a large number of rich, full-colour visualizations, and clinically relevant results. As such, it is of interest to researchers and practitioners in the fields of computer science, MRI physics, and applied mathematics. The reader will find numerous contributions covering a broad range of topics, from the mathematical foundations of the diffusion process and signal generation to new computational methods and estimation techniques for the in-vivo recovery of microstructural and connectivity features, as well as diffusion-relaxometry and frontline applications in research and clinical practice.Table of Contents

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Book SynopsisThis book constitutes the refereed conference proceedings of the 12th International Conference on Algorithms and Complexity, CIAC 2019, held as a virtual event, in May 2021. The 28 full papers presented together with one invited lecture and 2 two abstracts of invited lectures were carefully reviewed and selected from 78 submissions. The International Conference on Algorithms and Complexity is intended to provide a forum for researchers working in all aspects of computational complexity and the use, design, analysis and experimentation of efficient algorithms and data structures. The papers present original research in the theory and applications of algorithms and computational complexity.Due to the Corona pandemic the conference was held virtually.Table of ContentsAbundant Extensions.- Three Problems on Well-Partitioned Chordal Graphs.- Distributed Distance-r Covering Problems on Sparse High-Girth Graphs.- Reconfiguration of Connected Graph Partitions via Recombination.- Algorithms for Energy Conservation in Heterogeneous Data Centers.- On Vertex-Weighted Graph Realizations.- On the Role of 3's for the 1-2-3 Conjecture.- Upper Tail Analysis of Bucket Sort and Random Tries.- Throughput Scheduling with Equal Additive Laxity.- Fragile Complexity of Adaptive Algorithms.- FPT and Kernelization Algorithms for the Induced Tree Problem.- A Tight Lower Bound for Edge-Disjoint Paths on Planar DAGs.- Upper Dominating Set: Tight Algorithms for Pathwidth and Sub-Exponential Approximation.- A Multistage View on 2-Satisfiability.- The Weisfeiler-Leman Algorithm and Recognition of Graph Properties.- The Parameterized Suffix Tray.- Exploring the Gap Between Treedepth and Vertex Cover Through Vertex Integrity.- Covering a Set of Line Segments with a Few Squares.- Circumventing Connectivity for Kernelization.- Online and Approximate Network Construction from Bounded Connectivity Constraints.- Globally Rigid Augmentation of Minimally Rigid Graphs in \(R^2\).- Extending Partial Representations of Rectangular Duals with Given Contact Orientations.- Can Local Optimality be Used for Efficient Data Reduction.- Colouring Graphs of Bounded Diameter in the Absence of Small Cycles.- Online Two-Dimensional Vector Packing with Advice.- Temporal Matching on Geometric Graph Data.

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Book SynopsisNew insight in many scientific and engineering fields is unthinkable without the use of numerical simulations running efficiently on modern computers. The faster we get new results, the bigger and accurate are the problems that we can solve. It is the combination of mathematical ideas plus efficient programming that drives the progress in many disciplines. Future champions in the area thus will have to be qualified in their application domain, they will need a profound understanding of some mathematical ideas, and they need the skills to deliver fast code. The present textbook targets students which have programming skills already and do not shy away from mathematics, though they might be educated in computer science or an application domain. It introduces the basic concepts and ideas behind applied mathematics and parallel programming that we need to write numerical simulations for today’s multicore workstations. Our intention is not to dive into one particular application domain or to introduce a new programming language – we lay the generic foundations for future courses and projects in the area. The text is written in an accessible style which is easy to digest for students without years and years of mathematics education. It values clarity and intuition over formalism, and uses a simple N-body simulation setup to illustrate basic ideas that are of relevance in various different subdomains of scientific computing. Its primary goal is to make theoretical and paradigmatic ideas accessible to undergraduate students and to bring the fascination of the field across.Table of Contents1. The Pillars of Science.- 2. Moore Myths.- 3. Our Model Problem.- 4. Floating Point Numbers.- 5. A Simplistic Machine Model.- 6. Round-off Error Propagation.- 7. SIMD Vector Crunching.- 8. Arithmetic Stability of an Implementation.- 9. Vectorisation of the Model Problem.- 10. Conditioning and Well-posedness.- 11. Taylor Expansion.- 12. Ordinary Differential Equations.- 13. Accuracy and Appropriateness of Numerical Schemes.- 14. Writing Parallel Codes.- 15. Upscaling Methods.- 16. OpenMP Primer.- 17. Shared Memory Tasking.- 18. GPGPUs with OpenMP.- 19. Higher Order Methods.- 20. Adaptive Time Stepping.

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Book SynopsisComputation, itself a form of calculation, incorporates steps that include arithmetical and non-arithmetical (logical) steps following a specific set of rules (an algorithm). This uniquely accessible textbook introduces students using a very distinctive approach, quite rapidly leading them into essential topics with sufficient depth, yet in a highly intuitive manner. From core elements like sets, types, Venn diagrams and logic, to patterns of reasoning, calculus, recursion and expression trees, the book spans the breadth of key concepts and methods that will enable students to readily progress with their studies in Computer Science.Trade Review“This book is intended as a textbook for an introductory course in computation for students beginning in informatics. No prerequisites are needed, all concepts, even elementary ones ... . it is also very suited for self-study, even if a reader is interested in Haskell or symbolic logic alone. ... Comprehension is supported by exercises for each chapter ... .” (Dieter Riebesehl, zbMATH 1497.68005, 2022)Table of Contents1 Sets 132 Types 193 Simple Computations 274 Venn Diagrams and Logical Connectives 355 Lists and Comprehensions 456 Features and Predicates 557 Testing Your Programs 638 Patterns of Reasoning 739 More Patterns of Reasoning 8110 Lists and Recursion 9111 More Fun with Recursion 10112 Higher-Order Functions 11113 Higher and Higher 12314 Sequent Calculus 13115 Algebraic Data Types 14316 Expression Trees 15717 Karnaugh Maps 17518 Relations and Quantifiers 18319 Checking Satisfiability 19120 Data Representation 20321 Data Abstraction 22122 Efficient CNF Conversion 23723 Counting Satisfying Valuations 24924 Type Classes 26325 Search in Trees 27526 Combinatorial Algorithms 28527 Finite Automata 29928 Deterministic Finite Automata 31129 Non-Deterministic Finite Automata 32130 Input/Output and Monads 34131 Regular Expressions 35932 Non-Regular Languages 369Index 377

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Book SynopsisThe main focus of this book is on presenting advances in fuzzy statistics, and on proposing a methodology for testing hypotheses in the fuzzy environment based on the estimation of fuzzy confidence intervals, a context in which not only the data but also the hypotheses are considered to be fuzzy. The proposed method for estimating these intervals is based on the likelihood method and employs the bootstrap technique. A new metric generalizing the signed distance measure is also developed. In turn, the book presents two conceptually diverse applications in which defended intervals play a role: one is a novel methodology for evaluating linguistic questionnaires developed at the global and individual levels; the other is an extension of the multi-ways analysis of variance to the space of fuzzy sets. To illustrate these approaches, the book presents several empirical and simulation-based studies with synthetic and real data sets. In closing, it presents a coherent R package called “FuzzySTs” which covers all the previously mentioned concepts with full documentation and selected use cases. Given its scope, the book will be of interest to all researchers whose work involves advanced fuzzy statistical methods.Table of Contents- 1. Introduction. - Part I Theoretical Part. - 2. Fundamental Concepts on Fuzzy Sets. - 3. Fuzzy Rule-Based Systems. - 4. Distances Between Fuzzy Sets. - 5. Fuzzy Random Variables and Fuzzy Distributions. - 6. Fuzzy Statistical Inference. - Conclusion Part I. - Part II Applications. - 7. Evaluation of Linguistic Questionnaire. - 8. Fuzzy Analysis of Variance. - Part III An R Package for Fuzzy Statistical Analysis: A DetailedDescription. - 9. FuzzySTs: Fuzzy Statistical Tools: A Detailed Description. - Conclusion.

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Book SynopsisThis textbook offers an algorithmic introduction to the field of computer algebra. A leading expert in the field, the author guides readers through numerous hands-on tutorials designed to build practical skills and algorithmic thinking. This implementation-oriented approach equips readers with versatile tools that can be used to enhance studies in mathematical theory, applications, or teaching. Presented using Mathematica code, the book is fully supported by downloadable sessions in Mathematica, Maple, and Maxima. Opening with an introduction to computer algebra systems and the basics of programming mathematical algorithms, the book goes on to explore integer arithmetic. A chapter on modular arithmetic completes the number-theoretic foundations, which are then applied to coding theory and cryptography. From here, the focus shifts to polynomial arithmetic and algebraic numbers, with modern algorithms allowing the efficient factorization of polynomials. The final chapters offer extensions into more advanced topics: simplification and normal forms, power series, summation formulas, and integration. Computer Algebra is an indispensable resource for mathematics and computer science students new to the field. Numerous examples illustrate algorithms and their implementation throughout, with online support materials to encourage hands-on exploration. Prerequisites are minimal, with only a knowledge of calculus and linear algebra assumed. In addition to classroom use, the elementary approach and detailed index make this book an ideal reference for algorithms in computer algebra.Trade Review“Strong interplay between the abstract exposition, which includes the relevant theorems as well as their proofs, and the practical utilization of those concepts in Mathematica is certainly a remarkable feature of this textbook. … Overall, the book is very well written and the approach to provide examples as actual Mathematica sessions is commendable.” (Andreas Maletti, zbMATH 1484.68004, 2022)Table of Contents

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Book SynopsisIn the present era dominated by computers, graph theory has come into its own as an area of mathematics, prominent for both its theory and its applications. One of the richest and most studied types of graph structures is that of the line graph, where the focus is more on the edges of a graph than on the vertices. A subject worthy of exploration in itself, line graphs are closely connected to other areas of mathematics and computer science. This book is unique in its extensive coverage of many areas of graph theory applicable to line graphs. The book has three parts. Part I covers line graphs and their properties, while Part II looks at features that apply specifically to directed graphs, and Part III presents generalizations and variations of both line graphs and line digraphs.Line Graphs and Line Digraphs is the first comprehensive monograph on the topic. With minimal prerequisites, the book is accessible to most mathematicians and computer scientists who have had an introduction graph theory, and will be a valuable reference for researchers working in graph theory and related fields.Table of ContentsPart I Line Graphs.- 1 Fundamentals of Line Graphs.- 2 Line Graph Isomorphisms.- 3 Characterization of Line Graphs.- 4 Spectral Properties of Line Graphs.- 5 Planarity of Line Graphs.- 6 Connectivity of Line Graphs.- 7 Tranversability in Line Graphs.- 8 Colorability in Line Graphs.- 9 Distance and Transitivity in Line Graphs.- Part II Line Digraphs.- 10 Fundamentals of Line Digraphs.- 11 Characterizations of Line Digraphs.- 12 Iterated Line Digraphs.- Part III Generalizations.- 13 Total Graphs and Total Digraphs.- 14 Path Graphs and Path Digraphs.- 15 Super Line Graphs and Super Line Digraphs.- 16 Line Graphs of Signed Graphs.- 17 The Krausz Dimension of Graph.- Reference. Index of Names.- Index of Definitions.

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This stimulating textbook presents a broad and accessible guide to the fundamentals of discrete mathematics, highlighting how the techniques may be applied to various exciting areas in computing. The text is designed to motivate and inspire the reader, encouraging further study in this important skill. Features: This book provides an introduction to the building blocks of discrete mathematics, including sets, relations and functions; describes the basics of number theory, the techniques of induction and recursion, and the applications of mathematical sequences, series, permutations, and combinations; presents the essentials of algebra; explains the fundamentals of automata theory, matrices, graph theory, cryptography, coding theory, language theory, and the concepts of computability and decidability; reviews the history of logic, discussing propositional and predicate logic, as well as advanced topics such as the nature of theorem proving; examines the field of software engineering, including software reliability and dependability and describes formal methods; investigates probability and statistics and presents an overview of operations research and financial mathematics.

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Book SynopsisGraph algorithms is a well-established subject in mathematics and computer science. Beyond classical application fields, such as approximation, combinatorial optimization, graphics, and operations research, graph algorithms have recently attracted increased attention from computational molecular biology and computational chemistry. Centered around the fundamental issue of graph isomorphism, this text goes beyond classical graph problems of shortest paths, spanning trees, flows in networks, and matchings in bipartite graphs. Advanced algorithmic results and techniques of practical relevance are presented in a coherent and consolidated way. This book introduces graph algorithms on an intuitive basis followed by a detailed exposition in a literate programming style, with correctness proofs as well as worst-case analyses. Furthermore, full C++ implementations of all algorithms presented are given using the LEDA library of efficient data structures and algorithms.Table of Contents1. Introduction.- 2. Algorithmic Techniques.- 3. Tree Traversal.- 4. Tree Isomorphism.- 5. Graph Traversal.- 6. Clique, Independent Set, and Vertex Cover.- 7. Graph Isomorphism.

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This textbook discusses the design and implementation of basic algebraic graph algorithms, and algebraic graph algorithms for complex networks, employing matroids whenever possible. The text describes the design of a simple parallel matrix algorithm kernel that can be used for parallel processing of algebraic graph algorithms. Example code is presented in pseudocode, together with case studies in Python and MPI. The text assumes readers have a background in graph theory and/or graph algorithms.

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Book Synopsis The greatly expanded and updated 3rd edition of this textbook offers the reader a comprehensive introduction to the concepts of logic functions and equations and their applications across computer science and engineering. The authors’ approach emphasizes a thorough understanding of the fundamental principles as well as numerical and computer-based solution methods. The book provides insight into applications across propositional logic, binary arithmetic, coding, cryptography, complexity, logic design, and artificial intelligence.Updated throughout, some major additions for the 3rd edition include: a new chapter about the concepts contributing to the power of XBOOLE; a new chapter that introduces into the application of the XBOOLE-Monitor XBM 2; many tasks that support the readers in amplifying the learned content at the end of the chapters; solutions of a large subset of these tasks to confirm learning success; challenging tasks that need the power of the XBOOLE software for their solution. The XBOOLE-monitor XBM 2 software is used to solve the exercises; in this way the time-consuming and error-prone manipulation on the bit level is moved to an ordinary PC, more realistic tasks can be solved, and the challenges of thinking about algorithms leads to a higher level of education.Table of ContentsPart I Theoretical Foundations 1. Basic Algebraic Structures 2. Logic Functions 3. Logic Equations 4. Boolean Differential Calculus 5. Sets, Lattices, and Classes Logic Functions Part II Applications 6. Logics, Arithmetic, and Special Functions 7. SAT-Problems 8. Extremely Complex Problems 9. Combinational Circuits 10. Sequential Circuits References Index

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Book SynopsisThis book constitutes the refereed proceedings of the First Workshop on Monte Carlo Search, MCS 2020, organized in conjunction with IJCAI 2020. The event was supposed to take place in Yokohama, Japan, in July 2020, but due to the Covid-19 pandemic was held virtually on January 7, 2021. The 9 full papers of the specialized project were carefully reviewed and selected from 15 submissions. The following topics are covered in the contributions: discrete mathematics in computer science, games, optimization, search algorithms, Monte Carlo methods, neural networks, reinforcement learning, machine learning.Table of ContentsThe αµ Search Algorithm for the Game of Bridge.- Stabilized Nested Rollout Policy Adaptation.- zoNNscan: A Boundary-Entropy Index for Zone Inspection of Neural Models.- Ordinal Monte Carlo Tree Search.- Monte Carlo Game Solver.- Generalized Nested Rollout Policy Adaptation.- Monte Carlo Inverse Folding.- Monte Carlo Graph Coloring.- Enhancing Playout Policy Adaptation for General Game Playing.

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Book SynopsisThis textbook tackles the problem of formulating AI systems by combining probabilistic modeling and deep learning. Moreover, it goes beyond typical predictive modeling and brings together supervised learning and unsupervised learning. The resulting paradigm, called deep generative modeling, utilizes the generative perspective on perceiving the surrounding world. It assumes that each phenomenon is driven by an underlying generative process that defines a joint distribution over random variables and their stochastic interactions, i.e., how events occur and in what order. The adjective "deep" comes from the fact that the distribution is parameterized using deep neural networks. There are two distinct traits of deep generative modeling. First, the application of deep neural networks allows rich and flexible parameterization of distributions. Second, the principled manner of modeling stochastic dependencies using probability theory ensures rigorous formulation and prevents potential flaws in reasoning. Moreover, probability theory provides a unified framework where the likelihood function plays a crucial role in quantifying uncertainty and defining objective functions. Deep Generative Modeling is designed to appeal to curious students, engineers, and researchers with a modest mathematical background in undergraduate calculus, linear algebra, probability theory, and the basics in machine learning, deep learning, and programming in Python and PyTorch (or other deep learning libraries). It will appeal to students and researchers from a variety of backgrounds, including computer science, engineering, data science, physics, and bioinformatics, who wish to become familiar with deep generative modeling. To engage the reader, the book introduces fundamental concepts with specific examples and code snippets. The full code accompanying the book is available on github. The ultimate aim of the book is to outline the most important techniques in deep generative modeling and, eventually, enable readers to formulate new models and implement them.Table of ContentsWhy Deep Generative Modeling?.- Autoregressive Models.- Flow-based Models.- Latent Variable Models.- Hybrid Modeling.- Energy-based Models.- Generative Adversarial Networks.- Deep Generative Modeling for Neural Compression.- Useful Facts from Algebra and Calculus.- Useful Facts from Probability Theory and Statistics.- Index.

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Book SynopsisThis textbook tackles the problem of formulating AI systems by combining probabilistic modeling and deep learning. Moreover, it goes beyond typical predictive modeling and brings together supervised learning and unsupervised learning. The resulting paradigm, called deep generative modeling, utilizes the generative perspective on perceiving the surrounding world. It assumes that each phenomenon is driven by an underlying generative process that defines a joint distribution over random variables and their stochastic interactions, i.e., how events occur and in what order. The adjective "deep" comes from the fact that the distribution is parameterized using deep neural networks. There are two distinct traits of deep generative modeling. First, the application of deep neural networks allows rich and flexible parameterization of distributions. Second, the principled manner of modeling stochastic dependencies using probability theory ensures rigorous formulation and prevents potential flaws in reasoning. Moreover, probability theory provides a unified framework where the likelihood function plays a crucial role in quantifying uncertainty and defining objective functions. Deep Generative Modeling is designed to appeal to curious students, engineers, and researchers with a modest mathematical background in undergraduate calculus, linear algebra, probability theory, and the basics in machine learning, deep learning, and programming in Python and PyTorch (or other deep learning libraries). It will appeal to students and researchers from a variety of backgrounds, including computer science, engineering, data science, physics, and bioinformatics, who wish to become familiar with deep generative modeling. To engage the reader, the book introduces fundamental concepts with specific examples and code snippets. The full code accompanying the book is available on github. The ultimate aim of the book is to outline the most important techniques in deep generative modeling and, eventually, enable readers to formulate new models and implement them.Table of ContentsWhy Deep Generative Modeling?.- Autoregressive Models.- Flow-based Models.- Latent Variable Models.- Hybrid Modeling.- Energy-based Models.- Generative Adversarial Networks.- Deep Generative Modeling for Neural Compression.- Useful Facts from Algebra and Calculus.- Useful Facts from Probability Theory and Statistics.- Index.

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Book SynopsisThis SpringerBrief provides the first systematic review of the existing works of cohesive subgraph search (CSS) over large heterogeneous information networks (HINs). It also covers the research breakthroughs of this area, including models, algorithms and comparison studies in recent years. This SpringerBrief offers a list of promising future research directions of performing CSS over large HINs.The authors first classify the existing works of CSS over HINs according to the classic cohesiveness metrics such as core, truss, clique, connectivity, density, etc., and then extensively review the specific models and their corresponding search solutions in each group. Note that since the bipartite network is a special case of HINs, all the models developed for general HINs can be directly applied to bipartite networks, but the models customized for bipartite networks may not be easily extended for other general HINs due to their restricted settings. The authors also analyze and compare these cohesive subgraph models (CSMs) and solutions systematically. Specifically, the authors compare different groups of CSMs and analyze both their similarities and differences, from multiple perspectives such as cohesiveness constraints, shared properties, and computational efficiency. Then, for the CSMs in each group, the authors further analyze and compare their model properties and high-level algorithm ideas.This SpringerBrief targets researchers, professors, engineers and graduate students, who are working in the areas of graph data management and graph mining. Undergraduate students who are majoring in computer science, databases, data and knowledge engineering, and data science will also want to read this SpringerBrief.Table of Contents1. Introduction2. Preliminaries3. CSS on Bipartite Networks4. CSS on Other General HINs5. Comparison Analysis6. Related Work on CSMs and solutions7. Future Work and Conclusion

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Book SynopsisThis book is about the harmonious synthesis of functional programming and numerical computation. It shows how the expressiveness of OCaml allows for fast and safe development of data science applications. Step by step, the authors build up to use cases drawn from many areas of Data Science, Machine Learning, and AI, and then delve into how to deploy at scale, using parallel, distributed, and accelerated frameworks to gain all the advantages of cloud computing environments.To this end, the book is divided into three parts, each focusing on a different area. Part I begins by introducing how basic numerical techniques are performed in OCaml, including classical mathematical topics (interpolation and quadrature), statistics, and linear algebra. It moves on from using only scalar values to multi-dimensional arrays, introducing the tensor and Ndarray, core data types in any numerical computing system. It concludes with two more classical numerical computing topics, the solution of Ordinary Differential Equations (ODEs) and Signal Processing, as well as introducing the visualization module we use throughout this book. Part II is dedicated to advanced optimization techniques that are core to most current popular data science fields. We do not focus only on applications but also on the basic building blocks, starting with Algorithmic Differentiation, the most crucial building block that in turn enables Deep Neural Networks. We follow this with chapters on Optimization and Regression, also used in building Deep Neural Networks. We then introduce Deep Neural Networks as well as topic modelling in Natural Language Processing (NLP), two advanced and currently very active fields in both industry and academia. Part III collects a range of case studies demonstrating how you can build a complete numerical application quickly from scratch using Owl. The cases presented include computer vision and recommender systems. This book aims at anyone with a basic knowledge of functional programming and a desire to explore the world of scientific computing, whether to generally explore the field in the round, to build applications for particular topics, or to deep-dive into how numerical systems are constructed. It does not assume strict ordering in reading – readers can simply jump to the topic that interests them most. Table of ContentsPart I: Numerical Techniques.- 1. Introduction.- 2. Numerical Algorithms.- 3. Statistics.- 4. Linear Algebra.- 5. N-Dimensional Arrays.- 6. Ordinary Differential Equations.- 7. Signal Processing.- Part II: Advanced Data Analysis Techniques.- 8. Algorithmic Differentiation.- 9. Optimisation.- 10. Regression.- 11. Neural Network.- 12. Vector Space Modelling.- Part III: Use Cases.- 13. Case Study: Image Recognition.- 14. Case Study: Instance Segmentation.- 15. Case Study: Neural Style Transfer.- 16. Case Study: Recommender System.

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Book SynopsisUsing a novel integration of mathematics and Python codes, this book illustrates the fundamental concepts that link probability, statistics, and machine learning, so that the reader can not only employ statistical and machine learning models using modern Python modules, but also understand their relative strengths and weaknesses. To clearly connect theoretical concepts to practical implementations, the author provides many worked-out examples along with "Programming Tips" that encourage the reader to write quality Python code. The entire text, including all the figures and numerical results, is reproducible using the Python codes provided, thus enabling readers to follow along by experimenting with the same code on their own computers. Modern Python modules like Pandas, Sympy, Scikit-learn, Statsmodels, Scipy, Xarray, Tensorflow, and Keras are used to implement and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, interpretability, and regularization. Many abstract mathematical ideas, such as modes of convergence in probability, are explained and illustrated with concrete numerical examples. This book is suitable for anyone with undergraduate-level experience with probability, statistics, or machine learning and with rudimentary knowledge of Python programming.Table of ContentsIntroduction.- Part 1 Getting Started with Scientific Python.- Installation and Setup.- Numpy.- Matplotlib.- Ipython.- Jupyter Notebook.- Scipy.- Pandas.- Sympy.- Interfacing with Compiled Libraries.- Integrated Development Environments.- Quick Guide to Performance and Parallel Programming.- Other Resources.- Part 2 Probability.- Introduction.- Projection Methods.- Conditional Expectation as Projection.- Conditional Expectation and Mean Squared Error.- Worked Examples of Conditional Expectation and Mean Square Error Optimization.- Useful Distributions.- Information Entropy.- Moment Generating Functions.- Monte Carlo Sampling Methods.- Useful Inequalities.- Part 3 Statistics.- Python Modules for Statistics.- Types of Convergence.- Estimation Using Maximum Likelihood.- Hypothesis Testing and P-Values.- Confidence Intervals.- Linear Regression.- Maximum A-Posteriori.- Robust Statistics.- Bootstrapping.- Gauss Markov.- Nonparametric Methods.- Survival Analysis.- Part 4 Machine Learning.- Introduction.- Python Machine Learning Modules.- Theory of Learning.- Decision Trees.- Boosting Trees.- Logistic Regression.- Generalized Linear Models.- Regularization.- Support Vector Machines.- Dimensionality Reduction.- Clustering.- Ensemble Methods.- Deep Learning.- Notation.- References.- Index.

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Book SynopsisThis book constitutes the proceedings of the 43rd International Conference on Application and Theory of Petri Nets and Concurrency, PETRI NETS 2022, which was held virtually in June 2021. The 19 full papers presented in this volume were carefully reviewed and selected from 35 submissions. The papers are categorized into the following topical sub-headings: application of concurrency to system design; timed models; tools; applications; synthesis; petri nets architecture; and process mining.

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Book SynopsisThis book constitutes the thoroughly refereed proceedings of the 8th International Conference on Mathematics and Computation in Music, MCM 2022, held in Atlanta, GA, USA, in June 2022. The 29 full papers and 8 short papers presented were carefully reviewed and selected from 45 submissions. The papers feature research that combines mathematics or computation with music theory, music analysis, composition, and performance. They are organized in Mathematical Scale and Rhythm Theory: Combinatorial, Graph Theoretic, Group Theoretic and Transformational Approaches; Categorical and Algebraic Approaches to Music; Algorithms and Modeling for Music and Music-Related Phenomena; Applications of Mathematics to Musical Analysis; Mathematical Techniques and MicrotonalityTable of ContentsAlgebraic structures.- artificial intelligence.- clustering and computational analysis of music.- computational music theory.- fourier transforms.- machine learning.- mathematical analysis of music.- mathematical models of music.- mathematical music theory.- music cognition.- music formalization semantics.- signal processing.- software for musical processing.- algorithm analysis and problem complexity.

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Book SynopsisThis book constitutes the proceedings of the 19th International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research, CPAIOR 2022, which was held in Los Angeles, CA, USA, in June 2022.The 28 regular papers presented were carefully reviewed and selected from a total of 60 submissions. The conference program included a Master Class on the topic "Bridging the Gap between Machine Learning and Optimization”.Table of ContentsA Two-Phase Hybrid Approach for the Hybrid Flexible Flowshop with Transportation Times.- A SAT Encoding to compute Aperiodic Tiling Rhythmic Canons.- Transferring Information across Restarts in MIP.- Towards Copeland Optimization in Combinatorial Problems.- Coupling Different Integer Encodings for SAT.- Model-Based Algorithm Configuration with Adaptive Capping and Prior Distributions.- Shattering Inequalities for Learning Optimal Decision Trees.- Learning Pseudo-Backdoors for Mixed Integer Programs.- Leveraging Integer Linear Programming to Learn Optimal Fair Rule Lists.- Solving the Job Shop Scheduling Problem extended with AGVs – Classical and Quantum Approaches.- Stochastic Decision Diagrams.- Improving the robustness of EPS to solve the TSP.- Efficient operations between MDDs and constraints.- Deep Policy Dynamic Programming for Vehicle Routing Problems.- Learning a Propagation Complete Formula.- A FastMap-Based Algorithm for Block Modeling.- Packing by Scheduling: Using Constraint Programming to Solve a Complex 2D Cutting Stock Problem.- Dealing with the product constraint.- Multiple-choice knapsack constraint in graphical models.- A Learning Large Neighborhood Search for the Staff Rerostering Problem.- Practically Uniform Solution Sampling in Constraint Programming.- Training Thinner and Deeper Neural Networks: Jumpstart Regularization.- Hybrid Offline/Online Optimization for Energy Management via Reinforcement Learning.- Enumerated Types and Type Extensions for MiniZinc.- A parallel algorithm for generalized arc-consistent filtering for the Alldifferent constraint.- Analyzing the Reachability Problem in Choice Networks.- Model-based Approaches to Multi-Attribute Diverse Matching.

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Book SynopsisThis book constitutes the refereed proceedings of the 15th International Conference on Graph Transformation, ICGT 2022, which took place Nantes, France in July 2022.The 10 full papers and 1 tool paper presented in this book were carefully reviewed and selected from 19 submissions. The conference focuses on describing new unpublished contributions in the theory and applications of graph transformation as well as tool presentation papers that demonstrate main new features and functionalities of graph-based tools.Table of ContentsTheoretical Advances.- Application Domains.- Tool Presentation.

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Book SynopsisThis book constitutes the proceedings of the 24th International Workshop on Computer Algebra in Scientific Computing, CASC 2022, which took place in Gebze, Turkey, in August 2022. The 20 full papers included in this book were carefully reviewed and selected from 32 submissions. They focus on the theory of symbolic computation and its implementation in computer algebra systems as well as all other areas of scientific computing with regard to their benefit from or use of computer algebra methods and software. Table of ContentsSurvey on Generalizations of the Intermediate Value Theorem and Applications (Invited Talk).- On Truncated Series Involved in Exponential-Logarithmic Solutions of Truncated LODEs.- Subresultant Chains Using B´ezout Matrices.- Application of Symbolic-Numerical Modeling Tools for Analysis of Gyroscopic Stabilization of Gyrostat Equilibria.- Computer Science for Continuous Data: Vision, Theory, and Practice of a Computer (Algebra) ANALYSIS System.- Computational Aspects of Equivariant Hilbert Series of Canonical Rings for Algebraic Curves.- Symbolic-Numeric Algorithm for Calculations in Geometric Collective Model of Atomic Nuclei.- Analyses and Implementations of Chordality-Preserving Top-Down Algorithms for Triangular Decomposition.- Accelerated Subdivision for Clustering Roots of Polynomials Given by Evaluation Oracles.- On Equilibrium Positions in the Problem of the Motion of a System of Two Bodies in a Uniform Gravity Field.- An Interpolation Algorithm for Computing Dixon Resultants.- Distance Evaluation to the Set of Matrices with Multiple Eigenvalues.- On Boundary Conditions Parametrized by Analytic Functions.- Computing the Integer Hull of Convex Polyhedral Sets.- A Comparison of Algorithms for Proving Positivity of Linearly Recurrent Sequences.- Stability Analysis of Periodic Motion of the Swinging Atwood Machine.- New Heuristic to Choose a Cylindrical Algebraic Decomposition Variable Ordering Motivated by Complexity Analysis.- An Implementation of Parallel Number-Theoretic Transform Using Intel AVX-512 Instructions.- Locating the Closest Singularity in a Polynomial Homotopy.- A General Method of Finding New Symplectic Schemes for Hamiltonian Mechanics.- A Mechanical Method for Isolating Locally Optimal Points of Certain Radical Functions.

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Book SynopsisThis book constitutes the proceedings of the 16th International Conference on Algorithmic Aspects in Information and Management, AAIM 2022, which was held online during August 13-14, 2022. The conference was originally planned to take place in Guangzhou, China, but changed to a virtual event due to the COVID-19 pandemic.The 41 regular papers included in this book were carefully reviewed and selected from 59 submissions. Table of ContentsAn improvement of the bound on the odd chromatic number of 1-planar graphs.- AoI Minimizing of Wireless Rechargeable Sensor Network based on Trajectory Optimization of Laser-Charged UAV.- Monotone k-Submodular Knapsack Maximization: An Analysis of the Greedy+Singleton Algorithm.- The constrained parallel-machine scheduling problem with divisible processing times and penalties.- Energy-constrained Geometric Covering Problem.- Fast searching on $k$-combinable graphs.- Three Algorithms for Converting Control Flow Statements from Python to XD-M.- Class Ramsey numbers involving induced graphs.- An Approximation Algorithm for the Clustered Path Travelling Salesman Problem.- Hyperspectral Image Reconstruction for SD-CASSI systems based on Residual Attention Network.- Improved Approximation Algorithm for the Asymmetric Prize-Collecting TSP.- Injective edge coloring of power graphs and necklaces.- Guarantees for Maximization of $k$-Submodular Functions with a Knapsack and a Matroid Constraint.- Incremental SDN Deployment to Achieve Load Balance in ISP Networks.- Approximation scheme for single-machine rescheduling with job delay and rejection.- Defense of Scapegoating Attack in Network Tomography.- A Binary Search Double Greedy Algorithm for Non-monotone DR-submodular Maximization.- Streaming Adaptive Submodular Maximization.- Constrained Stochastic Submodular Maximization with State-Dependent Costs.- Online early work maximization problem on two hierarchical machines with buffer or rearrangements.- Polynomial time algorithm for k-vertex-edge dominating problem in interval graphs.- Adaptive Competition-based Diversified-profit Maximization with Online Seed Allocation.- Collaborative Service Caching in Mobile Edge Nodes.- A Decentralized Auction Framework with Privacy Protection in Mobile Crowdsourcing.- On-line single machine scheduling with release dates and submodular rejection penalties.- Obnoxious Facility Location Games with Candidate Locations.- Profit Maximization for Multiple Products in Community-based Social Networks.- MCM: A Robust Map Matching Method by Tracking Multiple Road Candidates.- Security on Ethereum: Ponzi Scheme Detection in Smart Contract.- Cyclically orderable generalized Petersen graphs.- The r-dynamic chromatic number of planar graphs without special short cycles.- Distance Labeling of the Halved Folded $n$-Cube.- Signed network embedding based on muti-attention mechanism.- Balanced Graph Partitioning based on Mixed 0-1 Linear Programming and Iteration Vertex Relocation Algorithm.- Partial inverse min-max spanning tree problem under the weighted bottleneck Hamming distance.- Mixed Metric Dimension of Some Plane Graphs.- The Optimal Dynamic Rationing Policy in the Stock-Rationing Queue.- Pilot Pattern Design with Branch and Bound in PSA-OFDM System.- Bicriteria Algorithms for Maximizing the Difference Between Submodular Function and Linear Function under Noise.- On the Transversal Number of k-Uniform Connected Hypergraphs.- Total coloring of planar graphs without some adjacent cycles.

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Book SynopsisThis textbook aims to point out the most important principles of data analysis from the mathematical point of view. Specifically, it selected these questions for exploring: Which are the principles necessary to understand the implications of an application, and which are necessary to understand the conditions for the success of methods used? Theory is presented only to the degree necessary to apply it properly, striving for the balance between excessive complexity and oversimplification. Its primary focus is on principles crucial for application success. Topics and features: Focuses on approaches supported by mathematical arguments, rather than sole computing experiences Investigates conditions under which numerical algorithms used in data science operate, and what performance can be expected from them Considers key data science problems: problem formulation including optimality measure; learning and generalization in relationships to training set size and number of free parameters; and convergence of numerical algorithms Examines original mathematical disciplines (statistics, numerical mathematics, system theory) as they are specifically relevant to a given problem Addresses the trade-off between model size and volume of data available for its identification and its consequences for model parametrization Investigates the mathematical principles involves with natural language processing and computer vision Keeps subject coverage intentionally compact, focusing on key issues of each topic to encourage full comprehension of the entire book Although this core textbook aims directly at students of computer science and/or data science, it will be of real appeal, too, to researchers in the field who want to gain a proper understanding of the mathematical foundations “beyond” the sole computing experience.Table of Contents1. Data Science and its Tasks.- 2. Application Specific Mappings and Measuring the Fit to Data.- 3. Data Processing by Neural Networks.- 4. Learning and Generalization.- 5. Numerical Algorithms for Network Learning.- 6. Specific Problems of Natural Language Processing.- 7. Specific Problems of Computer Vision.

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Book SynopsisThis book constitutes the proceedings of the 30th International Symposium on Graph Drawing and Network Visualization, GD 2022, held in Tokyo, Japan, during September 13-16, 2022. The 25 full papers, 7 short papers, presented together with 2 invited talks, one report on graph drawing contest, and one obituary in these proceedings were carefully reviewed and selected from 70 submissions. The abstracts of 5 posters presented at the conference can be found in the back matter of the volume. The contributions were organized in topical sections as follows: properties of drawings of complete graphs; stress-based visualizations of graphs; planar and orthogonal drawings; drawings and properties of directed graphs; beyond planarity; dynamic graph visualization; linear layouts; and contact and visibility graph representations. Table of ContentsProperties of Drawings of Complete Graphs.- Stress-based Visualizations of Graphs.- Planar and Orthogonal Drawings.- Drawings and Properties of Directed Graphs.- Beyond Planarity.- Dynamic Graph Visualization.- Linear Layouts.- Contact and Visibility Graph Representations.

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Book SynopsisThis book constitutes the refereed proceedings of the Third Southern African Conference on Artificial Intelligence Research, SACAIR 2022, held in Stellenbosch, South Africa, in December 2022. The 26 papers presented were thoroughly reviewed and selected from the 73 submissions. They are organized on the topical sections on​ algorithmic, data driven and symbolic AI; socio-technical and human-centered AI; responsible and ethical AI. Table of ContentsAlgorithmic, Data Driven and Symbolic AI.- Socio-technical and human-centered AI.- Responsible and Ethical AI.

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Book SynopsisThis book constitutes the refereed proceedings of the 17th Ibero-American Conference on Artificial Intelligence, IBERAMIA 2022, held in Cartagena de Indias, Colombia, in November 2022. The 33 full and 4 short papers presented were carefully reviewed and selected from 67 submissions. The papers are organized in the following topical sections: applications of AI; ethics and smart city; green and sustainable AI; machine learning; natural language processing; robotics and computer vision; simulation and forecasting.Table of ContentsApplications of AI.- Ethics and Smart City.- Green and Sustainable AI.- Machine Learning.- Natural Language Processing.- Robotics and Computer Vision.- Simulation and Forecasting.

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Book SynopsisThis book constitutes the thoroughly refereed post-workshop proceedings of the 8th International Workshop on the Arithmetic of Finite Field, WAIFI 2022, held in Chengdu, China, in August – September 2022.The 19 revised full papers and 3 invited talks presented were carefully reviewed and selected from 25 submissions. The papers are organized in topical sections: structures in finite fields; efficient finite field arithmetic; coding theory; cryptography; sequences.Table of ContentsStructures in Finite Fields.- On a conjecture on irreducible polynomials over finite fields with restricted coefficients.- On two applications of polynomials xk – cx – d over finite fields and more.- Efficient Finite Field Arithmetic.- Polynomial Constructions of Chudnovsky-Type Algorithms for Multiplication in Finite Fields with Linear Bilinear Complexity.- Reduction-free Multiplication for Finite Fields and Polynomial Rings.- Finite Field Arithmetic in Large Characteristic for Classical and Post-Quantum Cryptography.- Fast enumeration of superspecial hyperelliptic curves of genus 4 with automorphism group V4.- Coding theory.- Two Classes of Constacyclic Codes with Variable Parameters.- Near MDS Codes with Dimension 4 and Their Application in Locally Recoverable Codes.- Optimal possibly nonlinear 3-PIR codes of small size.- PIR codes from combinatorial structures.- The Projective General Linear Group PGL(2, 5m) and Linear Codes of Length 5m + 1.- Private Information Retrieval Schemes Using Cyclic Codes.- Two Classes of Optimal Few-Weight Codes over Fq + uFq.- Explicit Non-Malleable Codes from Bipartite Graphs.- Cryptography.- Algebraic Relation of Three MinRank Algebraic Modelings.- Decomposition of Dillon's APN permutation with efficient hardware implementation.- New Versions of Miller-loop Secured against Side-Channel Attacks.- A Class of Power Mappings with Low Boomerang Uniformity.- New Classes of Bent Functions via the Switching Method.- Sequences.- Correlation measure of binary sequence families with trace representation.- Linear complexity of generalized cyclotomic sequences with period pnqm.- On the 2-adic complexity of cyclotomic binary sequences with period p2 and 2p2.

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Book SynopsisThis book constitutes the refereed proceedings of the 13th International Conference on Advances in Optimization and Applications, OPTIMA 2022, held in Petrovac, Montenegro, during September 26–30, 2022. The 13 full papers included in this book were carefully reviewed and selected from 26 submissions. They were organized in topical sections as follows: ​mathematical programming; global optimization; discrete and combinatorial optimization; optimization and data analysis; game theory and mathematical economics; and applications.Table of Contents​Mathematical Programming.- A Derivative-Free Nonlinear Least Squares Solver.- Gradient-Type Methods for Optimization Problems with Polyak- Lojasiewicz Condition: Early Stopping and Adaptivity to Inexactness Parameter.- Global Optimization.- An Improved Genetic Algorithm for the Resource-Constrained Project Scheduling Problem.- Nonlocal Optimization Methods for Nonlinear Controlled Systems with Terminal Constraints.- Discrete and Combinatorial Optimization.- Three-Bar Charts Packing Problem.- An 11/7 – Approximation Algorithm for Single Machine Scheduling Problem with Release and Delivery Times.- Optimization and Data Analysis.- Decentralized Strongly-Convex Optimization with Affine Constraints: Primal and Dual Approaches.- Game Theory and Mathematical Economics.- Analysis of the Model of Optimal Expansion of a Firm.- Comparative Analysis of the Efficiency of Financing the State Budget through Emissions, Taxes and Public Debt.- Applications.- Construction of Optimal Feedback for Zooplankton Diel Vertical Migration.- Synthesis of Trajectory Planning Algorithms Using Evolutionary Optimization Algorithms.- Application of Attention Technique for Digital Pre-Distortion.- Forecasting with Using Quasilinear Recurrence Equation.

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Book SynopsisThis book constitutes the conference proceedings of the 48th International Conference on Current Trends in Theory and Practice of Computer Science, SOFSEM 2023, held in Nový Smokovec, Slovakia, during January 15–18, 2023.The 22 full papers presented together with 2 best papers and 2 best students papers in this book were carefully reviewed and selected from 43 submissions.This workshop focuses on graphs problems and optimization; graph drawing and visualization; NP-hardness and fixed parameter tractability; communication and temporal graphs; complexity and learning; and robots and strings. Table of ContentsThe Complexity of Finding Tangles.- A spectral algorithm for finding maximum cliques in dense random intersection graphs.- Solving Cut-Problems in Quadratic Time for Graphs With Bounded Treewidth.- More Effort Towards Multiagent Knapsack.- Dominance Drawings for DAGs with Bounded Modular Width.- Morphing Planar Graph Drawings Through 3D.- Visualizing Multispecies Coalescent Trees: Drawing Gene Trees Inside Species Trees.- Parameterized Approaches to Orthogonal Compaction.- Hardness of bounding influence via graph modification.- On the Parameterized Complexity of $s$-club Cluster Deletion Problems.- Balanced Substructures in Bicolored Graphs.- On the Complexity of Scheduling Problems With a Fixed Number of Parallel Identical Machines.- On the 2-Layer Window Width Minimization Problem.- Sequentially Swapping Tokens: Further on Graph Classes.- On the Preservation of Properties when Changing Communication Models.- Introduction to Routing Problems with Mandatory Transitions .- Multi-Parameter Analysis of Finding Minors and Subgraphs in Edge-Periodic Temporal Graphs.- Lower Bounds for Monotone $q$-Multilinear Boolean Circuits.- A faster algorithm for determining the linear feasibility of systems of BTVPI constraints.- Quantum complexity for vector domination problem.- Learning through Imitation by using Formal Verification.- Delivery to Safety with Two Cooperating Robots.- Space-Efficient STR-IC-LCS Computation.- The k-center Problem for Classes of Cyclic Words.

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Book SynopsisThis volume constitutes short papers and DETECT 2022 workshop papers, presented during the 11th International Conference on Model and Data Engineering, MEDI 2022, held in Cairo, Egypt, in November 2022.The 11 short papers presented were selected from the total of 65 submissions. This volume also contains the 4 accepted papers from the DETECT 2022 workshop, held at MEDI 2022. The volume focuses on advances in data management and modelling, including topics such as data models, data processing, database theory, database systems technology, and advanced database applications.Table of ContentsImage processing and diagnosis.- Machine Learning and Optimization.- Machine Learning and Optimization.- Modelling.- Database systems.- Applications.- DETECT Workshop: modeling, verification and testing of dependable critical systems.

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Book SynopsisThe once esoteric idea of embedding scientific computing into a probabilistic framework, mostly along the lines of the Bayesian paradigm, has recently enjoyed wide popularity and found its way into numerous applications. This book provides an insider’s view of how to combine two mature fields, scientific computing and Bayesian inference, into a powerful language leveraging the capabilities of both components for computational efficiency, high resolution power and uncertainty quantification ability. The impact of Bayesian scientific computing has been particularly significant in the area of computational inverse problems where the data are often scarce or of low quality, but some characteristics of the unknown solution may be available a priori. The ability to combine the flexibility of the Bayesian probabilistic framework with efficient numerical methods has contributed to the popularity of Bayesian inversion, with the prior distribution being the counterpart of classical regularization. However, the interplay between Bayesian inference and numerical analysis is much richer than providing an alternative way to regularize inverse problems, as demonstrated by the discussion of time dependent problems, iterative methods, and sparsity promoting priors in this book. The quantification of uncertainty in computed solutions and model predictions is another area where Bayesian scientific computing plays a critical role. This book demonstrates that Bayesian inference and scientific computing have much more in common than what one may expect, and gradually builds a natural interface between these two areas.Table of ContentsInverse problems and subjective computing.- Linear algebra.- Continuous and discrete multivariate distributions.- Introduction to sampling.- The praise of ignorance: randomness as lack of certainty.- Enter subject: Construction of priors.- Posterior densities, ill-conditioning, and classical regularization.- Conditional Gaussian densities.- Iterative linear solvers and priorconditioners.- Hierarchical models and Bayesian sparsity.- Sampling: the real thing.- Dynamic methods and learning from the past.- Bayesian filtering and Gaussian densities.-

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Book SynopsisThis book constitutes selected and revised papers from the 22nd International Conference on Mathematical Modeling and Supercomputer Technologies, MMST 2022, held in Nizhny Novgorod, Russia, in November 2022. The 20 full papers and 5 short papers presented in the volume were thoroughly reviewed and selected from the 48 submissions. They are organized in topical secions on ​computational methods for mathematical models analysis; computation in optimization and optimal control; supercomputer simulation. Table of ContentsComputational methods for mathematical models analysis.- Computation in optimization and optimal control.- Supercomputer simulation.

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