Mathematical theory of computation Books

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Book Synopsisshowing that the study of programming languages does not consist of studying languages one after another, but is organized around the features that are present in these various languages.Trade ReviewFrom the reviews:“The book is divided into eight chapters and an epilogue. … Faculty teaching an undergraduate programming languages course may find this book to be a useful reference. Summing Up: Recommended. Upper-division undergraduates through professionals/practitioners.” (J. Beidler, Choice, Vol. 48 (10), June, 2011)“It is a short book--of about 100 pages--consisting of eight chapters and an epilogue. The book focuses on the formal description of programming language semantics and compilation using denotational semantics, small-step operational semantics (reduction semantics), and big-step operational semantics (natural semantics). … The book provides a good description of programming language concepts and motivates the necessary theory well. … The book is suitable for both professionals and graduate- and advanced undergraduate-level classes.” (Michael Oudshoorn, ACM Computing Reviews, November, 2011)Table of Contents1. Terms and Relations.- 2. The Language PCF.- 3. From Evaluation to Interpretation.- 4. Compilation.- 5. PCF with Types.- 6. Type Inference.- 7. References and Assignment.- 8. Records and Objects.- 9. Epilogue.- 10. Index.- 11. Bibliography

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Book SynopsisDigitalism is a philosophical strategy that uses new computational ways of thinking to develop naturalistic but meaningful ways of thinking about bodies, souls, universes, gods, and life after death. Your Digital Afterlives examines four recently developed and digitally inspired theories of life after death.Table of ContentsPreface Series Editors' Preface 1. Ghosts 2. Persistence 3. Anatomy 4. Uploading 5. Promotion 6. Digital Gods 7. Revision 8. Superhuman Bodies 9. Infinite Bodies 10. Nature References Index

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Book SynopsisHandbook of Cloud Computing includes contributions from world experts in the field of cloud computing from academia, research laboratories and private industry. The basic concepts of cloud computing and cloud computing applications are also introduced.Trade ReviewFrom the reviews:“Cloud computing affects all areas where computers and mobile clients are used, including industry, government, and science. This book offers a multitude of examples of this new way of handling data and computer resources--from basic research in the life sciences and physics to enterprise-level applications in industry. … handbook is highly relevant and provides the reader with good information about the fundamentals of all aspects of cloud computing. After reading the book, readers will understand how much is already being done in the cloud.” (Aake Edlund, ACM Computing Reviews, May, 2011) Table of ContentsTechnologies and Systems.- Cloud Computing Fundamentals.- Cloud Computing Technologies and Applications.- Key Enabling Technologies for Virtual Private Clouds.- The Role of Networks in Cloud Computing.- Data-Intensive Technologies for Cloud Computing.- Survey of Storage and Fault Tolerance Strategies Used in Cloud Computing.- Scheduling Service Oriented Workflows Inside Clouds Using an Adaptive Agent Based Approach.- The Role of Grid Computing Technologies in Cloud Computing.- Cloudweaver: Adaptive and Data-Driven Workload Manager for Generic Clouds.- Architectures.- Enterprise Knowledge Clouds: Architecture and Technologies.- Integration of High-Performance Computing into Cloud Computing Services.- Vertical Load Distribution for Cloud Computing via Multiple Implementation Options.- SwinDeW-C: A Peer-to-Peer Based Cloud Workflow System.- Services.- Cloud Types and Services.- Service Scalability Over the Cloud.- Scientific Services on the Cloud.- A Novel Market-Oriented Dynamic Collaborative Cloud Service Platform.- Applications.- Enterprise Knowledge Clouds: Applications and Solutions.- Open Science in the Cloud: Towards a Universal Platform for Scientific and Statistical Computing.- Multidimensional Environmental Data Resource Brokering on Computational Grids and Scientific Clouds.- HPC on Competitive Cloud Resources.- Scientific Data Management in the Cloud: A Survey of Technologies, Approaches and Challenges.- Feasibility Study and Experience on Using Cloud Infrastructure and Platform for Scientific Computing.- A Cloud Computing Based Patient Centric Medical Information System.- Cloud@Home: A New Enhanced Computing Paradigm.- Using Hybrid Grid/Cloud Computing Technologies for Environmental Data Elastic Storage, Processing, and Provisioning.

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Book SynopsisCSP notation has been used extensively for teaching and applying concurrency theory, ever since the publication of the text Communicating Sequential Processes by C.A.R. A first point of reference for anyone wanting to use CSP or learn about its theory, the book also introduces other views of concurrency, using CSP to model and explain these.Trade ReviewFrom the reviews:“This book is divided into four parts … . Part I is designed for an audience of both undergraduate and graduate computer science students. … Part II is designed for people who are familiar with Part I and have fairly theoretical interests. … Part III is intended for people who … want to be able to use them in a better way, or who are specifically interested in timed systems. Part IV is designed for people who already understand CSP.” (Günther Bauer, Zentralblatt MATH, Vol. 1211, 2011)Table of ContentsPart I: A Foundation Course in CSP Building a Simple Sequential Process Understanding CSP Parallel Operators CSP Case Studies Hiding and Renaming Beyond Traces Further Operators Using FDR Part II: Theory Operational Semantics Denotational Semantics and Behavioural Models Finite Observation Models Infinite-behaviour Models The Algebra of CSP Part III: Using CSP Timed Systems 1: tock-CSP Timed Systems 2: Discrete Timed CSP More About FDR State Explosion and Parameterised Verification Part IV: Exploring Concurrency Shared-variable Programs Understanding Shared-variable Concurrency Priority and Mobility

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Book Synopsis1 Functions and Predicates.- 1. Definitions.- 2. Numerical Functions.- 3. Finitary Rules.- 4. Closure Properties.- 5. Minimal Closure.- 6. More Elementary Functions and Predicates.- 2 Recursive Functions.- 1. Primitive Recursion.- 2. Functional Transformations.- 3. Recursive Specifications.- 4. Recursive Evaluation.- 5. Church's Thesis.- 3 Enumeration.- 1. Predicate Classes.- 2. Enumeration Properties.- 3. Induction.- 4. Nondeterministic Computability.- 4 Reflexive Structures.- 1. Interpreters.- 2. A Universal Interpreter.- 3. Two Constructions.- 4. The Recursion Theorem.- 5. Relational Structures.- 6. Uniform Structures.- 5 Hyperenumeration.- 1. Function Quantification.- 2. Nonfinitary Induction.- 3. Functional Induction.- 4. Ordinal Notations.- 5. Reflexive Systems.- 6. Hyperhyperenumeration.- References.Table of Contents1 Functions and Predicates.- §1. Definitions.- §2. Numerical Functions.- §3. Finitary Rules.- §4. Closure Properties.- §5. Minimal Closure.- §6. More Elementary Functions and Predicates.- 2 Recursive Functions.- §1. Primitive Recursion.- §2. Functional Transformations.- §3. Recursive Specifications.- §4. Recursive Evaluation.- §5. Church’s Thesis.- 3 Enumeration.- §1. Predicate Classes.- §2. Enumeration Properties.- §3. Induction.- §4. Nondeterministic Computability.- 4 Reflexive Structures.- §1. Interpreters.- §2. A Universal Interpreter.- §3. Two Constructions.- §4. The Recursion Theorem.- §5. Relational Structures.- §6. Uniform Structures.- 5 Hyperenumeration.- §1. Function Quantification.- §2. Nonfinitary Induction.- §3. Functional Induction.- §4. Ordinal Notations.- §5. Reflexive Systems.- §6. Hyperhyperenumeration.- References.

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Book Synopsis1 Introduction.- 1.1 Background.- 1.2 Scope of this work.- 2 Model Checking.- 2.1 Temporal logic.- 2.2 The temporal logic CTL.- 2.3 Fixed points.- 2.4 CTL model checking.- 3 Symbolic Model Checking.- 3.1 Boolean representations.- 3.2 Symbolic models.- 3.3 Binary Decision Diagrams.- 3.4 Examples.- 3.5 Graph width and OBDDs.- 4 The SMV System.- 4.1 An informal introduction.- 4.2 The input language.- 4.3 Formal semantics.- 5 A Distributed Cache Protocol.- 5.1 The Protocol.- 5.2 Verifying the protocol.- 5.3 Discussion.- 6 Mu-Calculus Model Checking.- 6.1 The Mu-Calculus.- 6.2 Symbolic models.- 6.3 Symbolic algorithm.- 6.4 Applications of the Mu-Calculus.- 6.5 Related research.- 7 Induction and Model Checking.- 7.1 The general framework.- 7.2 Induction and symbolic model checking.- 7.3 Example: The Gigamax protocol.- 7.4 Induction in other models.- 7.5 Related research.- 8 Equivalence Computations.- 8.1 State equivalence.- 8.2 Methods for functional composition.- 8.3 Experimental results.- 9 A Partial Order Approach.- 9.1 Unfolding.- 9.2 Truncated unfoldings.- 9.3 Application example.- 9.4 Deadlock and occurrence nets.- 9.5 Conclusion.- 10 Conclusion.- References.Table of ContentsForeword. Preface. 1. Introduction. 2. Model Checking. 3. Symbolic Model Checking. 4. The SMV System. 5. A Distributed Cache Protocol. 6. Mu-Calculus Model Checking. 7. Induction and Model Checking. 8. Equivalence Computations. 9. A Partial Order Apporach. 10. Conclusion. References. Index.

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Book Synopsisand the building of the Three “M’s” Maple, Mathematica and Matlab. We intend to persuade that Maple and other like tools are worth knowing assuming only that one wishes to be a mathematician, a mathematics educator, a computer scientist, an engineer or scientist, or anyone else who wishes/needs to use mathematics better.Trade ReviewFrom the reviews:“This book is intended to teach the reader the usage of the computer algebra system Maple. … The book is readable and valuable to mathematics, science, and engineering undergraduates at the sophomore or above level. It could also be valuable to practitioners in those fields who want to learn Maple in situ. … Summing Up: Recommended. Lower-division undergraduates through graduate students; professionals.” (D. Z. Spicer, Choice, Vol. 49 (5), January, 2012)“This is a Maple-application book which illustrates some basic areas of mathematics by symbolic computation examples. … The presentation is clear with all necessary details and comments for ensuring a full understanding of the considered examples. The intended beneficiaries are undergraduate students, teachers giving courses to undergraduate students, as well as programmers interested in using Maple for several classes of mathematical problems.” (Octavian Pastravanu, Zentralblatt MATH, Vol. 1228, 2012)“In An Introduction to Modern Mathematical Computing with Maple, Borwein and Skerritt show that computers are an excellent companion for learning mathematics. … The theme of the book is that Maple can supplement mathematics learning and, what is more, can do much of the mathematics for the students. … The temptation is tremendous for students to skip the real work to have a true understanding of mathematics.” (David S. Mazel, The Mathematical Association of America, June, 2012)Table of Contents-Preface. -Conventions and Notation.-1. Number Theory (Introduction to Maple, Putting it together, Enough code, already. Show me some maths!, Problems and Exercises, Further Explorations). -2. Calculus(Revision and Introduction, Univariate Calculus, Multivariate Calculus, Exercises, Further Explorations). -3. Linear Algebra (Introduction and Review, Vector Spaces, Linear Transformations, Exercises, Further Explorations). -4. Visualisation and Geometry: a postscript (Useful Visualisation Tools, Geometry and Geometric Constructions). –A. Sample Quizzes (Number Theory, Calculus, Linear Algebra). –Index. –References

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Table of ContentsVerilog — A Tutorial Introduction.- Logic Synthesis.- Behavioral Modeling.- Concurrent Processes.- Module Hierarchy.- Logic Level Modeling.- Cycle-Accurate Specification.- Advanced Timing.- User-Defined Primitives.- Switch Level Modeling.- Projects.

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Book SynopsisIn recent years, tremendous research has been devoted to the design of database systems for real-time applications, called real-time database systems (RTDBS), where transactions are associated with deadlines on their completion times, and some of the data objects in the database are associated with temporal constraints on their validity.Table of ContentsList of Figures. List of Tables. Acknowledgments. Preface. Contributing Authors. I: Overview, Misconceptions and Issues. 1. Real-Time Database Systems: An Overview of System Characteristics and Issues; Tei-Wei Kuo, Kam-Yiu Lam. 2. Misconceptions About Real-Time Databases; J.A. Stankovic, et al. 3. Applications and System Characteristics; D. Locke. II: Real-Time Concurrency Control. 4. Conservative and Optimistic Protocols; Tei-Wei Kuo, Kam-Yiu Lam. 5. Semantics-Based Concurrency Control; Tei-Wei Kuo. 6. Real-Time Index Concurrency Control; J.R. Haritsa, S. Seshadri. III: Run-Time System Management. 7. Buffer Management in Real-Time Active Database Systems; A. Datta, S. Mukherjee. 8. Disk Scheduling; Ben Kao, R. Cheng. 9. System Failure and Recovery; R.M. Sivasankaran, et al. 10. Overload Management in RTDBs; J. Hansson, S.H. Son. 11. Secure Real-Time Transaction Processing; J.R. Haritsa, B. George. IV: Active Issues and Triggering. 12. System Framework of ARTDBs; J. Hansson, S.F. Andler. 13. Reactive Mechanisms; J. Mellin, et al. 14. Updates and View Maintenance; Ben Kao, et al. V: Distributed Real-Time Database Systems. 15. Distributed Concurrency Control; Ö. Ulusoy. 16. Data Replication and Availability; Ö. Ulusoy. 17. Real-Time Commit Processing; J.R. Haritsa, et al. 18. Mobile Distributed Real-Time Database Systems; Kam-Yiu Liam, Tei-Wei Kuo.VI: Prototypes and Future Directions. 19. Prototypes: Programmed Stock Trading; B. Adelberg, Ben Kao. 20. Future Directions; Tei-Wei Kuo, Kam-Yiu Lam. Index.

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Book SynopsisThis book approaches condensed matter physics from the perspective of quantum information science, focusing on systems with strong interaction and unconventional order for which the usual condensed matter methods like the Landau paradigm or the free fermion framework break down. Concepts and tools in quantum information science such as entanglement, quantum circuits, and the tensor network representation prove to be highly useful in studying such systems. The goal of this book is to introduce these techniques and show how they lead to a new systematic way of characterizing and classifying quantum phases in condensed matter systems. The first part of the book introduces some basic concepts in quantum information theory which are then used to study the central topic explained in Part II: local Hamiltonians and their ground states. Part III focuses on one of the major new phenomena in strongly interacting systems, the topological order, and shows how it can essentialTrade Review“Quantum information meets quantum matter is bound to hold an honored place on the bookshelves of many scientists for years to come.’ From myself, I would add that of students and PhD students, I do believe!” (Eugene Kryachko, zbMATH 1423.81010, 2019)Table of ContentsPart I Basic Concepts in Quantum Information Theory 1 Correlation and Entanglement. 2 Evolution of Quantum Systems. 3 Quantum Error-Correcting Codes. Part II Local Hamiltonians, Ground States and Many-body Entanglement. 4 Local Hamiltonians and Ground States. 5 Gapped Quantum Systems and Entanglement Area Law. Part III Topological order and Long-Range Entanglement. 6 Introduction to Topological order. 7 Local Transformations and Long-Range Entanglement. Part IV Gapped Topological Phases and Tensor Network. 8 Matrix Product State and 1D Gapped Phase. 9 Tensor Product States and 2D Gapped Phases. 10 Symmetry Protected Topological Phases. Part V Outlook. 11 A Unification of Information and Matter.

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Book SynopsisBuild smart applications by implementing real-world artificial intelligence projectsKey Features Explore a variety of AI projects with Python Get well-versed with different types of neural networks and popular deep learning algorithms Leverage popular Python deep learning libraries for your AI projects Book DescriptionArtificial Intelligence (AI) is the newest technology that’s being employed among varied businesses, industries, and sectors. Python Artificial Intelligence Projects for Beginners demonstrates AI projects in Python, covering modern techniques that make up the world of Artificial Intelligence.This book begins with helping you to build your first prediction model using the popular Python library, scikit-learn. You will understand how to build a classifier using an effective machine learning technique, random forest, and decision trees. With exciting projects on predicting bird species, analyzing student performance data, song genre identification, and spam detection, you will learn the fundamentals and various algorithms and techniques that foster the development of these smart applications. In the concluding chapters, you will also understand deep learning and neural network mechanisms through these projects with the help of the Keras library.By the end of this book, you will be confident in building your own AI projects with Python and be ready to take on more advanced projects as you progressWhat you will learn Build a prediction model using decision trees and random forest Use neural networks, decision trees, and random forests for classification Detect YouTube comment spam with a bag-of-words and random forests Identify handwritten mathematical symbols with convolutional neural networks Revise the bird species identifier to use images Learn to detect positive and negative sentiment in user reviews Who this book is forPython Artificial Intelligence Projects for Beginners is for Python developers who want to take their first step into the world of Artificial Intelligence using easy-to-follow projects. Basic working knowledge of Python programming is expected so that you’re able to play around with codeTable of ContentsTable of Contents Building Your Own Prediction Models Prediction with Random Forests Application for comment classification Neural Networks Deep Learning

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Book SynopsisGet hands-on experience in creating state-of-the-art reinforcement learning agents using TensorFlow and RLlib to solve complex real-world business and industry problems with the help of expert tips and best practicesKey Features Understand how large-scale state-of-the-art RL algorithms and approaches work Apply RL to solve complex problems in marketing, robotics, supply chain, finance, cybersecurity, and more Explore tips and best practices from experts that will enable you to overcome real-world RL challenges Book DescriptionReinforcement learning (RL) is a field of artificial intelligence (AI) used for creating self-learning autonomous agents. Building on a strong theoretical foundation, this book takes a practical approach and uses examples inspired by real-world industry problems to teach you about state-of-the-art RL. Starting with bandit problems, Markov decision processes, and dynamic programming, the book provides an in-depth review of the classical RL techniques, such as Monte Carlo methods and temporal-difference learning. After that, you will learn about deep Q-learning, policy gradient algorithms, actor-critic methods, model-based methods, and multi-agent reinforcement learning. Then, you'll be introduced to some of the key approaches behind the most successful RL implementations, such as domain randomization and curiosity-driven learning. As you advance, you’ll explore many novel algorithms with advanced implementations using modern Python libraries such as TensorFlow and Ray’s RLlib package. You’ll also find out how to implement RL in areas such as robotics, supply chain management, marketing, finance, smart cities, and cybersecurity while assessing the trade-offs between different approaches and avoiding common pitfalls. By the end of this book, you’ll have mastered how to train and deploy your own RL agents for solving RL problems.What you will learn Model and solve complex sequential decision-making problems using RL Develop a solid understanding of how state-of-the-art RL methods work Use Python and TensorFlow to code RL algorithms from scratch Parallelize and scale up your RL implementations using Ray's RLlib package Get in-depth knowledge of a wide variety of RL topics Understand the trade-offs between different RL approaches Discover and address the challenges of implementing RL in the real world Who this book is forThis book is for expert machine learning practitioners and researchers looking to focus on hands-on reinforcement learning with Python by implementing advanced deep reinforcement learning concepts in real-world projects. Reinforcement learning experts who want to advance their knowledge to tackle large-scale and complex sequential decision-making problems will also find this book useful. Working knowledge of Python programming and deep learning along with prior experience in reinforcement learning is required.Table of ContentsTable of Contents Introduction to Reinforcement Learning Multi-armed Bandits Contextual Bandits Makings of the Markov Decision Process Solving the Reinforcement Learning Problem Deep Q-Learning at Scale Policy Based Methods Model-Based Methods Multi-Agent Reinforcement Learning Machine Teaching Generalization and Domain Randomization Meta-reinforcement learning Other Advanced Topics Autonomous Systems Supply Chain Management Marketing, Personalization and Finance Smart City and Cybersecurity Challenges and Future Directions in Reinforcement Learning

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Book SynopsisSemantics will play an important role in the future development of software systems and domain-specific languages. This book provides a needed introductory presentation of the fundamental ideas behind these approaches, stresses their relationship by formulating and proving the relevant theorems, and illustrates the applications of semantics in computer science. Historically important application areas are presented together with some exciting potential applications. The text investigates the relationship between various methods and describes some of the main ideas used, illustrating these by means of interesting applications. The book provides a rigorous introduction to the main approaches to formal semantics of programming languages.Trade ReviewFrom the reviews: "This book title, with its explicit reference to applications, quickly grabbed my attention due to the theoretical nature of formal semantics. … In any case, this book certainly fits the bill for an undergraduate course on the topic. … It also includes plenty of solved examples and exercises for students to help them grasp the key ideas and techniques behind the different mathematical models that can be used to describe the computations performed by a computer program." (Fernando Berzal, Computing Reviews, January, 2008) "This book presents a rigorous introduction to the main three approaches: operational semantics, denotational semantics, and axiomatic semantics. This book investigates the relationship between the various methods, and describes some of the main ideas by using applications. … Several exercises are provided. … help the student to understand definitions, results, and techniques … ." (G. Ciobanu, ACM Computing Reviews, May, 2009)Table of ContentsOperational Semantics.- More on Operational Semantics.- Provably Correct Implementation.- Denotational Semantics.- More on Denotational Semantics.- Program Analysis.- More on Program Analysis.- Axiomatic Program Verification.- More on Axiomatic Program Verification.- Further Reading.

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Book SynopsisBiometrics, the science of using physical traits to identify individuals, is playing an increasing role in our security-conscious society and across the globe. Biometric authentication, or bioauthentication, systems are being used to secure everything from amusement parks to bank accounts to military installations. Yet developments in this field have not been matched by an equivalent improvement in the statistical methods for evaluating these systems. Compensating for this need, this unique text/reference provides a basic statistical methodology for practitioners and testers of bioauthentication devices, supplying a set of rigorous statistical methods for evaluating biometric authentication systems. This framework of methods can be extended and generalized for a wide range of applications and tests. This is the first single resource on statistical methods for estimation and comparison of the performance of biometric authentication systems. The book focuses on six common performance metrics: for each metric, statistical methods are derived for a single system that incorporates confidence intervals, hypothesis tests, sample size calculations, power calculations and prediction intervals. These methods are also extended to allow for the statistical comparison and evaluation of multiple systems for both independent and paired data. Topics and features: * Provides a statistical methodology for the most common biometric performance metrics: failure to enroll (FTE), failure to acquire (FTA), false non-match rate (FNMR), false match rate (FMR), and receiver operating characteristic (ROC) curves * Presents methods for the comparison of two or more biometric performance metrics * Introduces a new bootstrap methodology for FMR and ROC curve estimation * Supplies more than 120 examples, using publicly available biometric data where possible * Discusses the addition of prediction intervals to the bioauthentication statistical toolset * Describes sample-size and power calculations for FTE, FTA, FNMR and FMR Researchers, managers and decisions makers needing to compare biometric systems across a variety of metrics will find within this reference an invaluable set of statistical tools. Written for an upper-level undergraduate or master’s level audience with a quantitative background, readers are also expected to have an understanding of the topics in a typical undergraduate statistics course. Dr. Michael E. Schuckers is Associate Professor of Statistics at St. Lawrence University, Canton, NY, and a member of the Center for Identification Technology Research.Table of ContentsPart I: Introduction Introduction Statistical Background Part II: Primary Matching and Classification Measures False Non-Match Rate False Match Rate Receiver Operating Characteristic Curve and Equal Error Rate Part III: Biometric Specific Measures Failure to Enrol Failure to Acquire Part IV: Additional Topics and Appendices Additional Topics and Discussion Tables

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Book SynopsisGeometric algebra (a Clifford Algebra) has been applied to different branches of physics for a long time but is now being adopted by the computer graphics community and is providing exciting new ways of solving 3D geometric problems. The author tackles this complex subject with inimitable style, and provides an accessible and very readable introduction. The book is filled with lots of clear examples and is very well illustrated. Introductory chapters look at algebraic axioms, vector algebra and geometric conventions and the book closes with a chapter on how the algebra is applied to computer graphics.Table of ContentsElementary Algebra.- Complex Algebra.- Vector Algebra.- Quaternion Algebra.- Geometric Conventions.- Geometric Algebra.- The Geometric Product.- Reflections and Rotations.- Geometric Algebra and Geometry.- Conformal Geometry.- Applications of Geometric Algebra.- Programming Tools for Geometric Algebra.- Conclusion.

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Book SynopsisAt the core of many engineering problems is the solution of sets of equa­ tions and inequalities, and the optimization of cost functions. Unfortunately, except in special cases, such as when a set of equations is linear in its un­ knowns or when a convex cost function has to be minimized under convex constraints, the results obtained by conventional numerical methods are only local and cannot be guaranteed. This means, for example, that the actual global minimum of a cost function may not be reached, or that some global minimizers of this cost function may escape detection. By contrast, interval analysis makes it possible to obtain guaranteed approximations of the set of all the actual solutions of the problem being considered. This, together with the lack of books presenting interval techniques in such a way that they could become part of any engineering numerical tool kit, motivated the writing of this book. The adventure started in 1991 with the preparation by Luc Jaulin of his PhD thesis, under Eric Walter's supervision. It continued with their joint supervision of Olivier Didrit's and Michel Kieffer's PhD theses. More than two years ago, when we presented our book project to Springer, we naively thought that redaction would be a simple matter, given what had already been achieved . . .Trade ReviewFrom the reviews:"Applied Interval Analysis is the right book at the right time to move computing with intervals into the mainstream of engineering, financial, and scientific computing."G. William Walster, Interval Technology Engineering Manager, Sun Microsystems and Member of the Editorial Board of Reliable ComputingTable of ContentsI. Introduction.- 1. Introduction.- 1.1 What Are the Key Concepts?.- 1.2 How Did the Story Start?.- 1.3 What About Complexity?.- 1.4 How is the Book Organized?.- II. Tools.- 2. Interval Analysis.- 2.1 Introduction.- 2.2 Operations on Sets.- 2.2.1 Purely set-theoretic operations.- 2.2.2 Extended operations.- 2.2.3 Properties of set operators.- 2.2.4 Wrappers.- 2.3 Interval Analysis.- 2.3.1 Intervals.- 2.3.2 Interval computation.- 2.3.3 Closed intervals.- 2.3.4 Interval vectors.- 2.3.5 Interval matrices.- 2.4 Inclusion Functions.- 2.4.1 Definitions.- 2.4.2 Natural inclusion functions.- 2.4.3 Centred inclusion functions.- 2.4.4 Mixed centred inclusion functions.- 2.4.5 Taylor inclusion functions.- 2.4.6 Comparison.- 2.5 Inclusion Tests.- 2.5.1 Interval Booleans.- 2.5.2 Tests.- 2.5.3 Inclusion tests for sets.- 2.6 Conclusions.- 3. Subpavings.- 3.1 Introduction.- 3.2 Set Topology.- 3.2.1 Distances between compact sets.- 3.2.2 Enclosure of compact sets between subpavings.- 3.3 Regular Subpavings.- 3.3.1 Pavings and subpavings.- 3.3.2 Representing a regular subpaving as a binary tree.- 3.3.3 Basic operations on regular subpavings.- 3.4 Implementation of Set Computation.- 3.4.1 Set inversion.- 3.4.2 Image evaluation.- 3.5 Conclusions.- 4. Contractors.- 4.1 Introduction.- 4.2 Basic Contractors.- 4.2.1 Finite subsolvers.- 4.2.2 Intervalization of finite subsolvers.- 4.2.3 Fixed-point methods.- 4.2.4 Forward—backward propagation.- 4.2.5 Linear programming approach.- 4.3 External Approximation.- 4.3.1 Principle.- 4.3.2 Preconditioning.- 4.3.3 Newton contractor.- 4.3.4 Parallel linearization.- 4.3.5 Using formal transformations.- 4.4 Collaboration Between Contractors.- 4.4.1 Principle.- 4.4.2 Contractors and inclusion functions.- 4.5 Contractors for Sets.- 4.5.1 Definitions.- 4.5.2 Sets defined by equality and inequality constraints.- 4.5.3 Improving contractors using local search.- 4.6 Conclusions.- 5. Solvers.- 5.1 Introduction.- 5.2 Solving Square Systems of Non-linear Equations.- 5.3 Characterizing Sets Defined by Inequalities.- 5.4 Interval Hull of a Set Defined by Inequalities.- 5.4.1 First approach.- 5.4.2 Second approach.- 5.5 Global Optimization.- 5.5.1 The Moore—Skelboe algorithm.- 5.5.2 Hansen’s algorithm.- 5.5.3 Using interval constraint propagation.- 5.6 Minimax Optimization.- 5.6.1 Unconstrained case.- 5.6.2 Constrained case.- 5.6.3 Dealing with quantifiers.- 5.7 Cost Contours.- 5.8 Conclusions.- III. Applications.- 6. Estimation.- 6.1 Introduction.- 6.2 Parameter Estimation Via Optimization.- 6.2.1 Least-square parameter estimation in compartmental modelling.- 6.2.2 Minimax parameter estimation.- 6.3 Parameter Bounding.- 6.3.1 Introduction.- 6.3.2 The values of the independent variables are known.- 6.3.3 Robustification against outliers.- 6.3.4 The values of the independent variables are uncertain.- 6.3.5 Computation of the interval hull of the posterior feasible set.- 6.4 State Bounding.- 6.4.1 Introduction.- 6.4.2 Bounding the initial state.- 6.4.3 Bounding all variables.- 6.4.4 Bounding by constraint propagation.- 6.5 Conclusions.- 7. Robust Control.- 7.1 Introduction.- 7.2 Stability of Deterministic Linear Systems.- 7.2.1 Characteristic polynomial.- 7.2.2 Routh criterion.- 7.2.3 Stability degree.- 7.3 Basic Tests for Robust Stability.- 7.3.1 Interval polynomials.- 7.3.2 Polytope polynomials.- 7.3.3 Image-set polynomials.- 7.3.4 Conclusion.- 7.4 Robust Stability Analysis.- 7.4.1 Stability domains.- 7.4.2 Stability degree.- 7.4.3 Value-set approach.- 7.4.4 Robust stability margins.- 7.4.5 Stability radius.- 7.5 Controller Design.- 7.6 Conclusions.- 8. Robotics.- 8.1 Introduction.- 8.2 Forward Kinematics Problem for Stewart—Gough Platforms.- 8.2.1 Stewart—Gough platforms.- 8.2.2 From the frame of the mobile plate to that of the base.- 8.2.3 Equations to be solved.- 8.2.4 Solution.- 8.3 Path Planning.- 8.3.1 Graph discretization of configuration space.- 8.3.2 Algorithms for finding a feasible path.- 8.3.3 Test case.- 8.4 Localization and Tracking of a Mobile Robot.- 8.4.1 Formulation of the static localization problem.- 8.4.2 Model of the measurement process.- 8.4.3 Set inversion.- 8.4.4 Dealing with outliers.- 8.4.5 Static localization example.- 8.4.6 Tracking.- 8.4.7 Example.- 8.5 Conclusions.- IV. Implementation.- 9. Automatic Differentiation.- 9.1 Introduction.- 9.2 Forward and Backward Differentiations.- 9.2.1 Forward differentiation.- 9.2.2 Backward differentiation.- 9.3 Differentiation of Algorithms.- 9.3.1 First assumption.- 9.3.2 Second assumption.- 9.3.3 Third assumption.- 9.4 Examples.- 9.4.1 Example 1.- 9.4.2 Example 2.- 9.5 Conclusions.- 10. Guaranteed Computation with Floating-point Numbers.- 10.1 Introduction.- 10.2 Floating-point Numbers and IEEE 754.- 10.2.1 Representation.- 10.2.2 Rounding.- 10.2.3 Special quantities.- 10.3 Intervals and IEEE 754.- 10.3.1 Machine intervals.- 10.3.2 Closed interval arithmetic.- 10.3.3 Handling elementary functions.- 10.3.4 Improvements.- 10.4 Interval Resources.- 10.5 Conclusions.- 11. Do It Yourself.- 11.1 Introduction.- 11.2 Notions of C++.- 11.2.1 Program structure.- 11.2.2 Standard types.- 11.2.3 Pointers.- 11.2.4 Passing parameters to a function.- 11.3 INTERVAL Class.- 11.3.1 Constructors and destructor.- 11.3.2 Other member functions.- 11.3.3 Mathematical functions.- 11.4 Intervals with PROFIL/BIAS.- 11.4.1 BIAS.- 11.4.2 PROFIL.- 11.4.3 Getting started.- 11.5 Exercises on Intervals.- 11.6 Interval Vectors.- 11.6.1 INTERVAL_VECTOR class.- 11.6.2 Constructors, assignment and function call operators.- 11.6.3 Friend functions.- 11.6.4 Utilities.- 11.7 Vectors with PROFIL/BIAS.- 11.8 Exercises on Interval Vectors.- 11.9 Interval Matrices.- 11.10 Matrices with PROFIL/BIAS.- 11.11 Exercises on Interval Matrices.- 11.12 Regular Subpavings with PROFIL/BIAS.- 11.12.1 NODE class.- 11.12.2 Set inversion with subpavings.- 11.12.3 Image evaluation with subpavings.- 11.12.4 System simulation and state estimation with subpavings.- 11.13 Error Handling.- 11.13.1 Using exit.- 11.13.2 Exception handling.- 11.13.3 Mathematical errors.- References.

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Book SynopsisThe books in this trilogy capture the foundational core of advanced informatics. The authors make the foundations accessible, enabling students to become effective problem solvers.This first volume establishes the inductive approach as a fundamental principle for system and domain analysis. After a brief introduction to the elementary mathematical structures, such as sets, propositional logic, relations, and functions, the authors focus on the separation between syntax (representation) and semantics (meaning), and on the advantages of the consistent and persistent use of inductive definitions. They identify compositionality as a feature that not only acts as a foundation for algebraic proofs but also as a key for more general scalability of modeling and analysis. A core principle throughout is invariance, which the authors consider a key for the mastery of change, whether in the form of extensions, transformations, or abstractions.This textbook is suitable for undergraduate and graduate courses in computer science and for self-study. Most chapters contain exercises and the content has been class-tested over many years in various universities.Table of ContentsIntroduction.- Propositions and Sets.- Relations and Functions.- Inductive Definitions.- Inductive Proofs.- Inductive Approach: Potential, Limitations, and Pragmatics.

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Book SynopsisThis book provides both conceptual knowledge of quantitative finance and a hands-on approach to using Python. It begins with a description of concepts prior to the application of Python with the purpose of understanding how to compute and interpret results. This book offers practical applications in the field of finance concerning Python, a language that is more and more relevant in the financial arena due to big data. This will lead to a better understanding of finance as it gives a descriptive process for students, academics and practitioners.Table of Contents

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Book SynopsisThis book describes how neural networks operate from the mathematical point of view. As a result, neural networks can be interpreted both as function universal approximators and information processors. The book bridges the gap between ideas and concepts of neural networks, which are used nowadays at an intuitive level, and the precise modern mathematical language, presenting the best practices of the former and enjoying the robustness and elegance of the latter.This book can be used in a graduate course in deep learning, with the first few parts being accessible to senior undergraduates. In addition, the book will be of wide interest to machine learning researchers who are interested in a theoretical understanding of the subject. Trade Review“This book is useful to students who have already had an introductory course in machine learning and are further interested to deepen their understanding of the machine learning material from the mathematical point of view.” (T. C. Mohan, zbMATH 1441.68001, 2020)Table of ContentsIntroductory Problems.- Activation Functions.- Cost Functions.- Finding Minima Algorithms.- Abstract Neurons.- Neural Networks.- Approximation Theorems.- Learning with One-dimensional Inputs.- Universal Approximators.- Exact Learning.- Information Representation.- Information Capacity Assessment.- Output Manifolds.- Neuromanifolds.- Pooling.- Convolutional Networks.- Recurrent Neural Networks.- Classification.- Generative Models.- Stochastic Networks.- Hints and Solutions.

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Book SynopsisThis book addresses a broad community of physicists, engineers, computer scientists and industry professionals, as well as the general public, who are aware of the unprecedented media hype surrounding the supposedly imminent new era of quantum computing. The central argument of this book is that the feasibility of quantum computing in the physical world is extremely doubtful. The hypothetical quantum computer is not simply a quantum variant of the conventional digital computer, but rather a quantum extension of a classical analog computer operating with continuous parameters. In order to have a useful machine, the number of continuous parameters to control would have to be of such an astronomically large magnitude as to render the endeavor virtually infeasible. This viewpoint is based on the author’s expert understanding of the gargantuan challenges that would have to be overcome to ever make quantum computing a reality. Knowledge of secondary-school-level physics and math will be sufficient for understanding most of the text.Table of ContentsIntroduction.- Brief history of quantum computing, starting with the invention of Shor's algorithm (1994).- Introduction to quantum mechanics for pedestrians.- Electron spin as a qubit.- The main ideas and promises of quantum computing.- Current state of the art.

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Book SynopsisThis book is an introduction to graph transformation as a foundation to model-based software engineering at the level of both individual systems and domain-specific modelling languages.The first part of the book presents the fundamentals in a precise, yet largely informal way. Besides serving as prerequisite for describing the applications in the second part, it also provides a comprehensive and systematic survey of the concepts, notations and techniques of graph transformation. The second part presents and discusses a range of applications to both model-based software engineering and domain-specific language engineering. The variety of these applications demonstrates how broadly graphs and graph transformations can be used to model, analyse and implement complex software systems and languages. This is the first textbook that explains the most commonly used concepts, notations, techniques and applications of graph transformation without focusing on one particular mathematical representation or implementation approach. Emphasising the research and engineering methodologies used, it will be a valuable resource for graduate students, practitioners and researchers in software engineering, foundations of programming and formal methods.Table of ContentsPart I, Graph Transformation.- Graphs for Modeling and Specification.- Graph Transformation Concepts.- Beyond Individual Rules: Usage Scenarios and Control Structures.- Analysis and Improvement of Graph Transformation Systems.- Part II, Graph Transformation in Software Engineering.- Detecting Inconsistent Requirements in a Use Case-Driven Approach.- Service Specification and Matching.- Model-Based Testing.- Reverse Engineering: Inferring Visual Contracts from Java Programs.- Stochastic Analysis of Dynamic Software Architectures.- Advanced Modeling Language Definition: Integrating Meta-modeling with Graph Transformation.- Improving Models and Understanding Model Changes.- Translating and Synchronizing Models.

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Book SynopsisThis open access book makes quantum computing more accessible than ever before. A fast-growing field at the intersection of physics and computer science, quantum computing promises to have revolutionary capabilities far surpassing “classical” computation. Getting a grip on the science behind the hype can be tough: at its heart lies quantum mechanics, whose enigmatic concepts can be imposing for the novice. This classroom-tested textbook uses simple language, minimal math, and plenty of examples to explain the three key principles behind quantum computers: superposition, quantum measurement, and entanglement. It then goes on to explain how this quantum world opens up a whole new paradigm of computing. The book bridges the gap between popular science articles and advanced textbooks by making key ideas accessible with just high school physics as a prerequisite. Each unit is broken down into sections labelled by difficulty level, allowing the course to be tailored to the student’s experience of math and abstract reasoning. Problem sets and simulation-based labs of various levels reinforce the concepts described in the text and give the reader hands-on experience running quantum programs. This book can thus be used at the high school level after the AP or IB exams, in an extracurricular club, or as an independent project resource to give students a taste of what quantum computing is really about. At the college level, it can be used as a supplementary text to enhance a variety of courses in science and computing, or as a self-study guide for students who want to get ahead. Additionally, readers in business, finance, or industry will find it a quick and useful primer on the science behind computing’s future. Table of ContentsContents.- 1 Introduction to Superposition.- 2 What is a Qubit?.- 3 Creating Superposition: The Beam Splitter.- 4 Creating Superposition: Stern-Gerlach.- 5 Quantum Cryptography.- 6 Quantum Gates.- 7 Entanglement.- 8 Quantum Teleportation.- 9 Quantum Algorithms.- 10 Worksheets.- Appendices.- Alphabetical Index.- Acknowledgments.- Answers.

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Book SynopsisThis introductory book on quantum computing includes an emphasis on the development of algorithms. Appropriate for both university students as well as software developers interested in programming a quantum computer, this practical approach to modern quantum computing takes the reader through the required background and up to the latest developments. Beginning with introductory chapters on the required math and quantum mechanics, Fundamentals of Quantum Computing proceeds to describe four leading qubit modalities and explains the core principles of quantum computing in detail. Providing a step-by-step derivation of math and source code, some of the well-known quantum algorithms are explained in simple ways so the reader can try them either on IBM Q or Microsoft QDK. The book also includes a chapter on adiabatic quantum computing and modern concepts such as topological quantum computing and surface codes.Features:o Foundational chapters that build the necessary background on math and quantum mechanics.o Examples and illustrations throughout provide a practical approach to quantum programming with end-of-chapter exercises.o Detailed treatment on four leading qubit modalities -- trapped-ion, superconducting transmons, topological qubits, and quantum dots -- teaches how qubits work so that readers can understand how quantum computers work under the hood and devise efficient algorithms and error correction codes. Also introduces protected qubits - 0-π qubits, fluxon parity protected qubits, and charge-parity protected qubits. o Principles of quantum computing, such as quantum superposition principle, quantum entanglement, quantum teleportation, no-cloning theorem, quantum parallelism, and quantum interference are explained in detail. A dedicated chapter on quantum algorithm explores both oracle-based, and Quantum Fourier Transform-based algorithms in detail with step-by-step math and working code that runs on IBM QisKit and Microsoft QDK. Topics on EPR Paradox, Quantum Key Distribution protocols, Density Matrix formalism, and Stabilizer formalism are intriguing. While focusing on the universal gate model of quantum computing, this book also introduces adiabatic quantum computing and quantum annealing.This book includes a section on fault-tolerant quantum computing to make the discussions complete. The topics on Quantum Error Correction, Surface codes such as Toric code and Planar code, and protected qubits help explain how fault tolerance can be built at the system level.Trade Review“The book represents a new and fresh approach to quantum computing, starting with theoretical physical knowledge that is highlighted by beautiful figures. Then, quantum computing is explained by quantum programing languages and extensive languages. It is recommended to everyone interested in quantum computing. It is easy to follow through a beautiful and clear presentation, programming examples and additional exercises.” (Andreas Wichert, zbMATH 1477.68005, 2022)Table of ContentsPART ONE 1 Foundations of Quantum Mechanics 1.1 Matter 1.2 Atoms, Elementary Particles, and Molecules 1.3 Light and Quantization of Energy 1.4 Electron Configuration 1.5 Wave-Particle Duality and Probabilistic Nature 1.6 Wavefunctions and Probability Amplitudes 1.7 Some exotic states of matter 1.8 Summary 1.9 Practice Problems 1.10 References and further reading 2 Dirac’s bra-ket notation and Hermitian Operators2.1 Scalars 2.2 Complex Numbers 2.3 Vectors 2.4 Matrices 2.5 Linear Vector Spaces 2.6 Using Dirac’s bra-ket notation 2.7 Expectation Values and Variances2.8 Eigenstates, Eigenvalues and Eigenfunctions2.9 Characteristic Polynomial 2.10 Definite Symmetric Matrices 2.11 Tensors2.12 Statistics and Probability2.13 Summary 2.14 Practice problems2.15 References and further reading3 The Quantum Superposition Principle and Bloch Sphere Representation3.1 Euclidian Space3.2 Metric Space3.3 Hilbert space.3.4 Schrodinger Equation3.5 Postulates of Quantum Mechanics3.6 Quantum Tunneling3.7 Stern and Gerlach Experiment3.8 Bloch sphere representation3.9 Projective Measurements3.10 Qudits3.11 Summary3.12 Practice Problems3.13 References and further readingPART TWO4 Qubit Modalities4.1 The vocabulary of quantum computing4.2 Classical Computers – a recap 4.3 Qubits and usability4.4 Noisy Intermediate Scale Quantum Technology4.5 Qubit Metrics4.6 Leading Qubit Modalities4.7 A note on the dilution refrigerator4.8 Summary4.9 Practice Problems4.10 References and further reading5 Quantum Circuits and DiVincenzo Criteria5.1 Setting up the development environment5.2 Learning Quantum Programming Languages 5.3 Introducing Quantum Circuits 5.4 Quantum Gates 5.5 The Compute Stage5.6 Quantum Entanglement5.7 No-Cloning theorem5.8 Quantum Teleportation5.9 Superdense coding5.10 Greenberger–Horne–Zeilinger state (GHZ state)5.11 Walsh-Hadamard Transform5.12 Quantum Interference5.13 Phase kickback5.14 DiVincenzo’s criteria for quantum computation5.15 Summary 5.16 Practice Problems5.17 References and further reading6 Quantum Communications6.1 EPR Paradox6.2 Density Matrix Formalism6.3 Von Neumann Entropy6.4 Photons6.5 Quantum Communication6.6 The Quantum Channel6.7 Quantum Communication Protocols6.8 RSA Security6.9 Summary6.10 Practice Problems6.11 References and further reading7 Quantum Algorithms7.1 Quantum Ripple Adder Circuit7.2 Quantum Fourier Transformation7.3 Deutsch-Jozsa oracle7.4 The Bernstein-Vazirani Oracle7.5 Simon’s algorithm7.6 Quantum arithmetic using QFT7.7 Modular exponentiation7.8 Grover’s search algorithm 7.9 Shor’s algorithm7.10 A quantum algorithm for k-means7.11 Quantum Phase Estimation (QPE)7.12 HHL algorithm for solving linear equations7.13 Quantum Complexity Theory7.14 Summary 7.15 Practice Problems7.16 References and further reading8 Adiabatic Optimization and Quantum Annealing8.1 Adiabatic evolution8.2 Proof of the Adiabatic Theorem8.3 Adiabatic optimization8.4 Quantum Annealing8.5 Summary8.6 Practice Problems8.7 References and further reading9 Quantum Error Correction9.1 Classical Error Correction9.2 Quantum Error Codes9.3 Stabilizer formalism9.4 The path forward – fault-tolerant quantum computing9.5 Surface codes9.6 Protected qubits9.7 Practice Problems9.8 References and further reading10 Conclusion10.1 How many qubits do we need?10.2 Classical simulation10.3 Backends today10.4 Future state10.5 References

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Book SynopsisThis textbook introduces enumerative combinatorics through the framework of formal languages and bijections. By starting with elementary operations on words and languages, the authors paint an insightful, unified picture for readers entering the field. Numerous concrete examples and illustrative metaphors motivate the theory throughout, while the overall approach illuminates the important connections between discrete mathematics and theoretical computer science. Beginning with the basics of formal languages, the first chapter quickly establishes a common setting for modeling and counting classical combinatorial objects and constructing bijective proofs. From here, topics are modular and offer substantial flexibility when designing a course. Chapters on generating functions and partitions build further fundamental tools for enumeration and include applications such as a combinatorial proof of the Lagrange inversion formula. Connections to linear algebra emerge in chapters studying Cayley trees, determinantal formulas, and the combinatorics that lie behind the classical Cayley–Hamilton theorem. The remaining chapters range across the Inclusion-Exclusion Principle, graph theory and coloring, exponential structures, matching and distinct representatives, with each topic opening many doors to further study. Generous exercise sets complement all chapters, and miscellaneous sections explore additional applications. Lessons in Enumerative Combinatorics captures the authors' distinctive style and flair for introducing newcomers to combinatorics. The conversational yet rigorous presentation suits students in mathematics and computer science at the graduate, or advanced undergraduate level. Knowledge of single-variable calculus and the basics of discrete mathematics is assumed; familiarity with linear algebra will enhance the study of certain chapters.Trade Review“The wide variety of slightly unusual topics makes the book an excellent resource for the instructor who wants to craft a combinatorics course that contains a diverse collection of attractive results … . The attentive student will certainly come away from a course based on this book with a solid understanding of the combinatorial way of thinking. … the book is an excellent resource for anyone teaching a class in combinatorics.” (Timothy Y. Chow, Mathematical Reviews, March, 2023)“A whole book whose backbone is enumeration by codifying the objects to be enumerated as words. … They do this in a skillfully structured fashion which makes the connections natural and unforced. … One of the remarkable features of this book is the care the authors have taken to make it reader-friendly and accessible to a wide range of students following a graduate mathematics course or an honours undergraduate course in mathematics and computer science.” (Josef Lauri, zbMATH 1478.05001, 2022)Table of Contents1. Basic Combinatorial Structures.- 2. Partitions and Generating Functions.- 3. Planar Trees and the Lagrange Inversion Formula.- 4. Cayley Trees.- 5. The Cayley–Hamilton Theorem.- 6. Exponential Structures and Polynomial Operators.- 7. The Inclusion-Exclusion Principle.- 8. Graphs, Chromatic Polynomials and Acyclic Orientations.- 9. Matching and Distinct Representatives.

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Book SynopsisThis open access two-volume set constitutes the proceedings of the 27th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2021, which was held during March 27 – April 1, 2021, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2021. The conference was planned to take place in Luxembourg and changed to an online format due to the COVID-19 pandemic.The total of 41 full papers presented in the proceedings was carefully reviewed and selected from 141 submissions. The volume also contains 7 tool papers; 6 Tool Demo papers, 9 SV-Comp Competition Papers. The papers are organized in topical sections as follows: Part I: Game Theory; SMT Verification; Probabilities; Timed Systems; Neural Networks; Analysis of Network Communication. Part II: Verification Techniques (not SMT); Case Studies; Proof Generation/Validation; Tool Papers; Tool Demo Papers; SV-Comp Tool Competition Papers.Table of ContentsGame Theory.- A Game for Linear-time - Branching-time Spectroscopy.- On Satisficing in Quantitative Games.- Quasipolynomial Computation of Nested Fixpoints.- SMT Verification.- A Flexible Proof Format for SAT Solver-Elaborator Communication.- Generating Extended Resolution Proofs with a BDD-Based SAT Solver.- Bounded Model Checking for Hyperproperties.- Counterexample-Guided Prophecy for Model Checking Modulo the Theory of Arrays.- SAT Solving with GPU Accelerated Inprocessing.- FOREST: An Interactive Multi-tree Synthesizer for Regular Expressions.- Probabilities.- Finding Provably Optimal Markov Chains.- Inductive Synthesis for Probabilistic Programs Reaches New Horizons.- Analysis of Markov Jump Processes under Terminal Constraints.- Multi-objective Optimization of Long-run Average and Total Rewards.- Inferring Expected Runtimes of Probabilistic Integer Programs Using Expected Sizes.- Probabilistic and Systematic Coverage of Consecutive Test-Method Pairs for Detecting Order-Dependent Flaky Tests.- Timed Systems.- Timed Automata Relaxation for Reachability.- Iterative Bounded Synthesis for Efficient Cycle Detection in Parametric Timed Automata.- Algebraic Quantitative Semantics for Efficient Online Temporal Monitoring.- Neural Networks.- Synthesizing Context-free Grammars from Recurrent Neural Networks.- Automated and Formal Synthesis of Neural Barrier Certificates for Dynamical Models.- Improving Neural Network Verification through Spurious Region Guided Refinement.- Analysis of Network Communication Resilient Capacity-Aware Routing.- Network Traffic Classification by Program Synthesis.

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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 SynopsisThe 3-volume set LNAI 12712-12714 constitutes the proceedings of the 25th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2021, which was held during May 11-14, 2021.The 157 papers included in the proceedings were carefully reviewed and selected from a total of 628 submissions. They were organized in topical sections as follows: Part I: Applications of knowledge discovery and data mining of specialized data; Part II: Classical data mining; data mining theory and principles; recommender systems; and text analytics; Part III: Representation learning and embedding, and learning from data.Table of ContentsRepresentation Learning and Embedding.- Episode Adaptive Embedding Networks for Few-shot Learning.- Universal Representation for Code.- Self-supervised Adaptive Aggregator Learning on Graph.- A Fast Algorithm for Simultaneous Sparse Approximation.- STEPs-RL: Speech-Text Entanglement for Phonetically Sound Representation Learning.- RW-GCN: Training Graph Convolution Networks with biased random walk for Semi-Supervised Classification.- Loss-aware Pattern Inference: A Correction on the Wrongly Claimed Limitations of Embedding Models.- SST-GNN: Simplified Spatio-temporal Traffic forecasting model using Graph Neural Network.- VIKING: Adversarial Attack on Network Embeddings via Supervised Network Poisoning.- Self-supervised Graph Representation Learning with Variational Inference.- Manifold Approximation and Projection by Maximizing Graph Information.- Learning Attention-based Translational Knowledge Graph Embedding via Nonlinear Dynamic Mapping.- Multi-Grained Dependency Graph Neural Network for Chinese Open Information Extraction.- Human-Understandable Decision Making for Visual Recognition.- LightCAKE: A Lightweight Framework for Context-Aware Knowledge Graph Embedding.- Transferring Domain Knowledge with an Adviser in Continuous Tasks.- Inferring Hierarchical Mixture Structures: A Bayesian Nonparametric Approach.- Quality Control for Hierarchical Classification with Incomplete Annotations.- Learning from Data.- Learning Discriminative Features using Multi-label Dual Space.- AutoCluster: Meta-learning Based Ensemble Method for Automated Unsupervised Clustering.- BanditRank: Learning to Rank Using Contextual Bandits.- A compressed and accelerated SegNet for plant leaf disease segmentation: A Differential Evolution based approach.- Meta-Context Transformers for Domain-Specific Response Generation.- A Multi-task Kernel Learning Algorithm for Survival Analysis.- Meta-data Augmentation based Search Strategy through Generative Adversarial Network for AutoML Model Selection.- Tree-Capsule: Tree-Structured Capsule Network for Improving Relation Extraction.- Rule Injection-based Generative Adversarial Imitation Learning for Knowledge Graph Reasoning.- Hierarchical Self Attention Based Autoencoder for Open-Set Human Activity Recognition.- Reinforced Natural Language Inference for Distantly Supervised Relation Classification.- SaGCN: Structure-aware Graph Convolution Network for Document-level Relation Extraction.- Addressing the class imbalance problem in medical image segmentation via accelerated Tversky loss function.- Incorporating Relational Knowledge in Explainable Fake News Detection.- Incorporating Syntactic Information into Relation Representations for Enhanced Relation Extraction.

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Book SynopsisThis book constitutes the proceedings of the 18th International Colloquium on Theoretical Aspects of Computing, ICTAC 2021, organized by the Nazarbayev University, Nur-Sultan, Kazakhstan. The event was supposed to take place in Nur-Sultan, Kazakhstan, but due to COVID-19 pandemic is was held virtually. The 15 papers presented in this volume were carefully reviewed and selected from 40 submissions. The book also contains one invited talk in full paper length. The book deals with challenges in both theoretical aspects of computing and the exploitation of theory through methods and tools for system development. The 20 full papers presented in this volume were carefully reviewed and selected from 55 submissions. The papers cover a wide variety of topics, including: getting the best price for selling your personal data; attacking Bitcoin; optimizing various forms of model checking; synthesizing and learning algorithms; formalizing and verifying contracts, languages, and compilers; analyzing the correctness and complexity of programs and distributed systems; and finding connections from proofs in propositional logic to quantum programming languages.Table of ContentsConcurrency and Objects Matter! Disentangling the Fabric of Real Operational Processes to Create Digital Twins.- Qualitative–Quantitative Reasoning: thinking informally about formal things.- Model Checking and Machine Learning Joining Forces in Uppaal.- Databases and Distributed Transactions Some Aspects of the Database Resilience.- On the Correctness Problem for Serializability.- Efficient Model Checking Methods A Set Automaton to Locate All Pattern Matches in a Term.- Groote Accelerating SpMV Multiplication in Probabilistic Model Checkers using GPUs.- A divide & conquer approach to conditional stable model checking.- Formalization and Verification in Coq and Isabelle Certifying Choreography Compilation.- Mechanically Verified Theory of Contracts.- A Complete Semantics of K and Its Translation to Isabelle.- Quantum Computing A New Connective in Natural Deduction, and its Application to Quantum Computing.- Security and Privacy An Incentive Mechanism for Trading Personal Data in Data Markets.- Palamidessi Assessing Security of Crypto-Currencies with Attack-Defense Trees: Proof of Concept and Future Directions.- Compositional Analysis of Protocol Equivalence in the Applied π-calculus using Quasi-Open Bisimilarity.- Card-based Cryptographic Protocols with a Standard Deck of Cards Using Private Operations.- Ono Normalising Lustre Preserves Security.- Synthesis and Learning Learning Probabilistic Automata using Residuals.- Deductive Synthesis of Sorting Algorithms in Theorema.- Reactive Synthesis from Visibly Register Pushdown Automata.- Systems Calculi and Analysis ComplexityParser: an automatic tool for certifying poly-time complexity of Java programs.- A Calculus for Attribute-based Memory Updates.- A Proof Method for Local Sufficient Completeness of Term Rewriting Systems.

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Book SynopsisThis book constitutes the refereed proceedings of the 6th International Conference on Belief Functions, BELIEF 2021, held in Shanghai, China, in October 2021. The 30 full papers presented in this book were carefully selected and reviewed from 37 submissions. The papers cover a wide range on theoretical aspects on mathematical foundations, statistical inference as well as on applications in various areas including classification, clustering, data fusion, image processing, and much more.Table of ContentsClustering.- Transfer learning.- Classification.- Statistical inference and learning.- Deep learning.- Conflict, inconsistency and specificity.- Information fusion.- Elicitation.- Algorithms and computation.

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Book SynopsisThis book constitutes the proceedings of the 41st SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2021, which was supposed to be held in Cambridge, UK, in December 2021. The conference was held virtually due to the COVID-19 pandemic.The 22 full papers and 10 short papers presented in this volume were carefully reviewed and selected from 37 submissions. The volume includes technical papers presenting new and innovative developments in the field as well as application papers presenting innovative applications of AI techniques in a number of subject domains. The papers are organized in the following topical sections: technical paper; machine learning; AI techniques; short technical stream papers; application papers; applications of machine learning; AI for medicine; advances in applied AI; and short application stream papers. Table of ContentsTechnical Papers.- On the Generalization Abilities of Fine-Tuned Commonsense Language Representation Models (Best Technical Paper).- Machine Learning.- Generation of Human-aware Navigation Maps using Graph Neural Networks.- Extended Category Learning with Spiking Nets and Spike Timing Dependent Plasticity.- ORACLE: End-to-end Model Based Reinforcement Learning.- Towards Explaining Metaheuristic Solution Quality by Data Mining Surrogate Fitness Models for Importance of Variables.- AI Techniques.- Assessing the Impact of Agents in Weighted Bipolar Argumentation Frameworks.- Towards Explainable Metaheuristics: PCA for Trajectory Mining in Evolutionary Algorithms.- AI Methods of Autonomous Geological Target Selection in the Hunt for Signs of Extraterrestrial Life.- Probabilistic Rule Induction for Transparent CBR under Uncertainty.- Short Technical Stream Papers.- Detection of Brain Tumour using Deep Learning.- GaussianProductAttributes: Density-based Distributed Representations for Products.- Modelling Emotion Dynamics in Chatbots with Neural Hawkes Processes.- Knowledge-Based Composable Inductive Programming.- Named Entity Recognition and Relation Extraction for COVID-19: Explainable Active Learning with word2vec Embeddings and Transformer-based BERT Models.- Application Papers.- Patients Forecasting in Emergency Services by using Machine Learning and Exogenous variables (Best Application Paper).- Applications of Machine Learning.- Automatic Information Extraction from Electronic Documents using Machine Learning.- Modelling Satellite Data for Automobile Insurance Risk.- Ranking Pathology Data in the Absence of a Ground Truth.- Evolving Large Scale Prediction Models for Vehicle Volume Forecasting in Service Stations.- AI for Medicine.- Sequential Association Rule Mining Revisited: A Study Directed at Relational Pattern Mining for Multi-morbidity.- Addressing the Challenge of Data Heterogeneity using a Homogeneous Feature Vector Representation.- Context-aware Support for Cardiac Health Monitoring using Federated Machine Learning.- Using Automated Feature Selection for Building Case-Based Reasoning Systems: An Example from Patient-Reported Outcome Measurements.- Advances in Applied AI.- A Live-User Evaluation of a Visual Module Recommender & Advisory System for Undergraduate Students.- AdverseGen: A Practical Tool for Generating Adversarial Examples to Deep Neural Networks Using Black-box Approaches.- Adaptive Maneuver Planning for Autonomous Vehicles using Behavior Tree on Apollo Platform.- Behavioural User Identification from Clickstream Data for Business Improvement.- Short Application Stream Papers.- AI enabled Bio Waste Contamination-Scanner.- Parkinson's Disease Tremor Severity Classification - A Comparison Between ON and OFF Medication State.- Towards Publishing Ontology-based Data Quality Metadata of Open Data.- Towards a Brain Controller Interface for Generating Simple Berlin School Style Music with Interactive Genetic Algorithms.

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Book SynopsisThis open access book constitutes the proceedings of the 28th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2022, which was held during April 2-7, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 46 full papers and 4 short papers presented in this volume were carefully reviewed and selected from 159 submissions. The proceedings also contain 16 tool papers of the affiliated competition SV-Comp and 1 paper consisting of the competition report. TACAS is a forum for researchers, developers, and users interested in rigorously based tools and algorithms for the construction and analysis of systems. The conference aims to bridge the gaps between different communities with this common interest and to support them in their quest to improve the utility, reliability, exibility, and efficiency of tools and algorithms for building computer-controlled systems. Table of ContentsProbabilistic Systems.- A Probabilistic Logic for Verifying Continuous-time Markov Chains.- Under-Approximating Expected Total Rewards in POMDPs.- Correct Probabilistic Model Checking with Floating-Point Arithmetic.- Correlated Equilibria and Fairness in Concurrent Stochastic Games.- Omega Automata.- A Direct Symbolic Algorithm for Solving Stochastic Rabin Games.- Practical Applications of the Alternating Cycle Decomposition.- Sky Is Not the Limit: Tighter Rank Bounds for Elevator Automata in Büchi Automata Complementation.- On-The-Fly Solving for Symbolic Parity Games.- Equivalence Checking.- Distributed Coalgebraic Partition Refinement.- From Bounded Checking to Verification of Equivalence via Symbolic Up-to Techniques.- Equivalence Checking for Orthocomplemented Bisemilattices in Log-Linear Time.- Monitoring and Analysis.- A Theoretical Analysis of Random Regression Test Prioritization.- Verified First-Order Monitoring with Recursive Rules.- Maximizing Branch Coverage with Constrained Horn Clauses.- Efficient Analysis of Cyclic Redundancy Architectures via Boolean Fault Propagation.- Tools | Optimizations, Repair and Explainability.- Adiar: Binary Decision Diagrams in External Memory.- Forest GUMP: A Tool for Explanation.- Alpinist: an Annotation-Aware GPU Program Optimizer.- Automatic Repair for Network Programs.- 11th Competition on Software Verification | SV-COMP 2022.- Progress on Software Verification: SV-COMP 2022.- AProVE: Non-Termination Witnesses for C Programs (Competition Contribution).- BRICK: Path Enumeration Based Bounded Reachability Checking of C Program (Competition Contribution).- A Prototype for Data Race Detection in CSeq 3 (Competition Contribution).- Dartagnan: SMT-based Violation Witness Validation (Competition Contribution).- Deagle: An SMT-based Veri er for Multi-threaded Programs (Competition Contribution).- The Static Analyzer Frama-C in SV-COMP (Competition Contribution).- GDart: An Ensemble of Tools for Dynamic Symbolic Execution on the Java Virtual Machine (Competition Contribution).- Graves-CPA: A Graph-Attention Veri er Selector (Competition Contribution).- GWIT: A Witness Validator for Java based on GraalVM (Competition Contribution).- The Static Analyzer Infer in SV-COMP (Competition Contribution).- LART: Compiled Abstract Execution (Competition Contribution).- Symbiotic 9: String Analysis and Backward Symbolic Execution with Loop Folding (Competition Contribution).- Symbiotic-Witch: A Klee-Based Violation Witness Checker (Competition Contribution).- Theta: portfolio of CEGAR-based analyses with dynamic algorithm selection.- Ultimate GemCutter and the Axes of Generalization (Competition Contribution).- Wit4Java: A Violation-Witness Validator for Java Verifiers (Competition Contribution).

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Book Synopsis

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