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This LNCS 14627 conference volume constitutes the proceedings of the 16th International Symposium on NASA Formal Methods, NFM 2024, held in Moffett Field, CA, USA in June 2024. The 20 full papers together with 6 short papers included in this volume were carefully reviewed and selected from 68 submissions. The conference focuses on the on formal techniques for software and system assurance for applications in space, aviation, robotics, and other NASA-relevant safety-critical systems.

This book constitutes the refereed proceedings of the International Conference on Brain Inspired Cognitive Systems, BICS 2023, held in Kuala Lumpur, Malaysia, in August 2023. The 36 full papers included in this book were reviewed and selected from 58 submissions and are organized in thematic sections as follows: Bio-inspired systems and Neural Computation; Image Recognition, Detection and Classification; Vision and Object Tracking; Data Analysis and Machine Learning and Applications.

This book describes a general hybrid metaheuristic for combinatorial optimization labeled Construct, Merge, Solve & Adapt (CMSA). The general idea of standard CMSA is the following one. At each iteration, a number of valid solutions to the tackled problem instance are generated in a probabilistic way. Hereby, each of these solutions is composed of a set of solution components. The components found in the generated solutions are then added to an initially empty sub-instance. Next, an exact solver is applied in order to compute the best solution of the sub-instance, which is then used to update the sub-instance provided as input for the next iteration. In this way, the power of exact solvers can be exploited for solving problem instances much too large for a standalone application of the solver. Important research lines on CMSA from recent years are covered in this book. After an introductory chapter about standard CMSA, subsequent chapters cover a self-adaptive CMSA variant as well as a variant equipped with a learning component for improving the quality of the generated solutions over time. Furthermore, on outlining the advantages of using set-covering-based integer linear programming models for sub-instance solving, the author shows how to apply CMSA to problems naturally modelled by non-binary integer linear programming models. The book concludes with a chapter on topics such as the development of a problem-agnostic CMSA and the relation between large neighborhood search and CMSA. Combinatorial optimization problems used in the book as test cases include the minimum dominating set problem, the variable-sized bin packing problem, and an electric vehicle routing problem. The book will be valuable and is intended for researchers, professionals and graduate students working in a wide range of fields, such as combinatorial optimization, algorithmics, metaheuristics, mathematical modeling, evolutionary computing, operations research, artificial intelligence, or statistics.

This is the first book that sums up test-related modeling of digital circuits and systems by a new structural-decision-diagrams model. The model represents structural and functional information jointly and opens a new area of research.The book introduces and discusses applications of two types of structural decision diagrams (DDs): low-level, structurally synthesized binary DDs (SSBDDs) and high-level DDs (HLDDs) that enable diagnostic modeling of complex digital circuits and systems.Topics and features:Provides the definition, properties and techniques for synthesis, compression and optimization of SSBDDs and HLDDsProvides numerous working examples that illustrate the key points of the textDescribes applications of SSBDDs and HLDDs for various electronic design automation (EDA) tasks, such as logic-level fault modeling and simulation, multi-valued simulation, timing-critical path identification, and test generationDiscusses the advantages of the proposed model to traditional binary decision diagrams and other traditional design representationsCombines SSBDDs with HLDDs for multi-level representation of digital systems for enabling hierarchical and cross-level solving of complex test-related tasksThis unique book is aimed at researchers working in the fields of computer science and computer engineering, focusing on test, diagnosis and dependability of digital systems. It can also serve as a reference for graduate- and advanced undergraduate-level computer engineering and electronics courses.Three authors are affiliated with the Dept. of Computer Systems at the Tallinn University of Technology, Estonia: Raimund Ubar is a retired Professor, Jaan Raik and Maksim Jenihhin are tenured Professors. Artur Jutman, PhD, is a researcher at the same university and the CEO of Testonica Lab Ltd., Estonia.