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This book constitutes the refereed proceedings of the 50th International Workshop on Graph-Theoretic Concepts in Computer Science, WG 2024, held in Gozd Martuljek, Slovenia in June 2024, The 31 papers presented in this volume were carefully reviewed and selected from 89 submissions. Additionally, this volume also contains a survey on approximation algorithms for tree-width, path-width, and tree-depth prepared by Hans Bodlander, who delivered the Test of Time Award talk at WG 2024. The WG 2024 workshop aims to merge theory and practice by demonstrating how concepts from graph theory can be applied to various areas in computer science or by extracting new graph-theoretic problems from applications.

The two-volume set LNCS 15529 and 15530 constitutes the proceedings of the 26th International Conference on Verification, Model Checking, and Abstract Interpretation, VMCAI 2025, held in Denver, CO, USA, during January 20–21, 2025.The 20 full papers together with 2 accepted tool papers and 18 full length regular and case-study papers presented in the proceedings were carefully reviewed and selected from 48 submissions. The program of VMCAI 2025 conference in the core areas of VMCAI including abstract interpretation, programming languages, hardware and software model checking, cyber-physical systems, formal synthesis, formal methods in artificial intelligence, concurrency and other areas.

The two-volume set LNCS 15529 and 15530 constitutes the proceedings of the 26th International Conference on Verification, Model Checking, and Abstract Interpretation, VMCAI 2025, held in Denver, CO, USA, during January 20–21, 2025.The 20 full papers together with 2 accepted tool papers and 18 full length regular and case-study papers presented in the proceedings were carefully reviewed and selected from 48 submissions. The program of VMCAI 2025 conference in the core areas of VMCAI including abstract interpretation, programming languages, hardware and software model checking, cyber-physical systems, formal synthesis, formal methods in artificial intelligence, concurrency and other areas.

This book constitutes the proceedings of the 11th International Conference on Network Games, Control and Optimization 2024, NETGCOOP 2024, held during October 9-11, 2024, in Lille, France. The 13 full papers and 1 short paper were carefully reviewed and selected from 21 submissions. The papers are organized in the following topical sections: Scheduling, Queuing Systems & Resource Allocation; Modeling & Performance; Pricing & Economic Models; and Energy, Generative AI.

Mathematical logic is an important basis for mathematics, computer science and artificial intelligence alike. This book provides a comprehensive introduction to various logics, including classical propositional logic and first-order predicate logic, as well as equational logic, temporal logic, and Hoare logic. In addition, it presents proof procedures for classical logics and decision procedures for checking the satisfiability of logical formulas. The book assumes no background in logic. It presents logics as practical tools for solving various problems in artificial intelligence and formal verification. Accordingly, it is well suited for (junior and senior) undergraduate and graduate students majoring in computer science or mathematics. Each chapter includes roughly a dozen exercise problems, so as to help the reader understand the concepts and techniques discussed.

Learn the Julia programming language as quickly as possible. This book is a must-have reference guide that presents the essential Julia syntax in a well-organized format, updated with the latest features of Julia's APIs, libraries, and packages. This book provides an introduction that reveals basic Julia structures and syntax; discusses data types, control flow, functions, input/output, exceptions, metaprogramming, performance, and more. Additionally, you'll learn to interface Julia with other programming languages such as R for statistics or Python. At a more applied level, you will learn how to use Julia packages for data analysis, numerical optimization, symbolic computation, and machine learning, and how to present your results in dynamic documents. The Second Edition delves deeper into modules, environments, and parallelism in Julia. It covers random numbers, reproducibility in stochastic computations, and adds a section on probabilistic analysis. Finally, it provides forward-thinking introductions to AI and machine learning workflows using BetaML, including regression, classification, clustering, and more, with practical exercises and solutions for self-learners. What You Will Learn Work with Julia types and the different containers for rapid development Use vectorized, classical loop-based code, logical operators, and blocks Explore Julia functions: arguments, return values, polymorphism, parameters, anonymous functions, and broadcasts Build custom structures in Julia Use C/C++, Python or R libraries in Julia and embed Julia in other code. Optimize performance with GPU programming, profiling and more. Manage, prepare, analyse and visualise your data with DataFrames and Plots Implement complete ML workflows with BetaML, from data coding to model evaluation, and more. Who This Book Is For Experienced programmers who are new to Julia, as well as data scientists who want to improve their analysis or try out machine learning algorithms with Julia.