Matematisk datateori

This book presents a new way of thinking about quantum mechanics and machine learning by merging the two. Quantum mechanics and machine learning may seem theoretically disparate, but their link becomes clear through the density matrix operator which can be readily approximated by neural network models, permitting a formulation of quantum physics in which physical observables can be computed via neural networks. As well as demonstrating the natural affinity of quantum physics and machine learning, this viewpoint opens rich possibilities in terms of computation, efficient hardware, and scalability. One can also obtain trainable models to optimize applications and fine-tune theories, such as approximation of the ground state in many body systems, and boosting quantum circuits¿ performance. The book begins with the introduction of programming tools and basic concepts of machine learning, with necessary background material from quantum mechanics and quantum information also provided. This enables the basic building blocks, neural network models for vacuum states, to be introduced. The highlights that follow include: non-classical state representations, with squeezers and beam splitters used to implement the primary layers for quantum computing; boson sampling with neural network models; an overview of available quantum computing platforms, their models, and their programming; and neural network models as a variational ansatz for many-body Hamiltonian ground states with applications to Ising machines and solitons. The book emphasizes coding, with many open source examples in Python and TensorFlow, while MATLAB and Mathematica routines clarify and validate proofs. This book is essential reading for graduate students and researchers who want to develop both the requisite physics and coding knowledge to understand the rich interplay of quantum mechanics and machine learning.

Unlock the Secrets of Neural Networks in Minutes! Dive into a concise lecture covering essential concepts like weight initialization, early stopping, and the pivotal role of hidden units. Explore the magic of input and output encodings, unravel the mysteries of recurrent neural networks, and grasp the power of autoencoders, including the transformative stacked autoencoders. Elevate your understanding of neural networks in no time with this bite-sized journey into the heart of artificial intelligence!

This book introduces the fundamentals of probability, statistical, and reliability concepts, the classical methods of uncertainty quantification and analytical reliability analysis, and the state-of-the-art approaches of design optimization under uncertainty (e.g., reliability-based design optimization and robust design optimization). The topics include basic concepts of probability and distributions, uncertainty quantification using probabilistic methods, classical reliability analysis methods, time-variant reliability analysis methods, fundamentals of deterministic design optimization, reliability-based design optimization, robust design optimization, other methods of design optimization under uncertainty, and engineering applications of design optimization under uncertainty.

This book constitutes the refereed proceedings of the 32nd International Conference on Inductive Logic Programming, ILP 2023, held in Bari, Italy, during November 13¿15, 2023.The 11 full papers and 1 short paper included in this book were carefully reviewed and selected from 18 submissions. They cover all aspects of learning in logic, multi-relational data mining, statistical relational learning, graph and tree mining, learning in other (non-propositional) logic-based knowledge representation frameworks, exploring intersections to statistical learning and other probabilistic approaches.

This book constitutes the refereed proceedings of the 21st International Workshop on Approximation and Online Algorithms, WAOA 2023, held in Amsterdam, The Netherlands, during September 7¿8, 2023The 16 full papers included in this book are carefully reviewed and selected from 43 submissions. The topics of WAOA 2023 were algorithmic game theory, algorithmic trading, coloring and partitioning, competitive analysis, computational advertising, computational finance, cuts and connectivity, FPT-approximation algorithms, geometric problems, graph algorithms, inapproximability results, mechanism design, network design, packing and covering, paradigms for the design and analysis of approximation and online algorithms, resource augmentation, and scheduling problems

This book includes extended and revised versions of selected papers from the 11th and 12th editions of the International Conference on Operations Research and Enterprise Systems (ICORES 2022 and ICORES 2023). ICORES 2022 was held as a virtual event in February 2022, and ICORES 2023 was held in Lisbon, Portugal, in February 2023. The 5 full papers included in this book were carefully reviewed and selected from the 55 submissions for ICORES 2022, and 8 full papers were reviewed and selected from the 55 submissions for ICORES 2023. The papers are focused on operations research and enterprise systems.

This book constitutes the proceedings of the First International Conference on Bridging the Gap between AI and Reality, AISoLA 2023, which took place in Crete, Greece, in October 2023. The papers included in this book focus on the following topics: The nature of AI-based systems; ethical, economic and legal implications of AI-systems in practice; ways to make controlled use of AI via the various kinds of formal methods-based validation techniques; dedicated applications scenarios which may allow certain levels of assistance; and education in times of deep learning.

This book showcases recent research advances in service science and related fields. Including selected papers from the 2022 INFORMS International Conference on Service Science, held in Shenzhen, China from July 2 to 4, 2022, the book presents new theories and empirical results in the emerging, interdisciplinary field of digital transformation and society. Incorporating research, education and practice alike, the respective chapters highlight a host of ways to approach these challenges in service science.

This monograph provides a new framework for modelling goals and functions of control systems. It demonstrates how to use means-end concepts and various aspects of action to describe the relations between the structure, dispositions, functions, and goals of technical systems and with human action.The author developed this approach as part of his research on Multilevel Flow Modelling (MFM). He based the framework on concepts of action and means-end analysis drawing on existing theories from several areas of study, including philosophical logic, semiotics, and phenomenological approaches to social science. Here, he applies it to three modeling situations related to the interaction of technical artefacts and humans. One involves the relation between designer and artefact, another the relation between technical artefact and its user, and the third the relation between a natural object and its user. All three are relevant for modelling complex automated processes interacting with human operators.The book also discusses challenges when applying the foundations for modelling of technical artefacts. Overall, it provides a cross disciplinary integration of several fields of knowledge. These disciplines include intelligent process control, human machine interaction, and process and automation design. As a result, researchers and graduate students in computer science, engineering, and philosophy of technology will find it a valuable resource.

This book constitutes the refereed proceedings of the 15th International Symposium on Search-Based Software Engineering, SSBSE 2023, which took place in San Francisco, CA, USA, during December 8, 2023.The 7 full and 7 short papers included in this book were carefully reviewed and selected from 23 submissions. They focus on formulating various optimization problems in software engineering as search problems, addressing them with search techniques, intending to automate complex software engineering tasks.

This book constitutes the refereed proceedings of the 19th International Symposium on Algorithmics of Wireless Networks, ALGOWIN 2023, held in Amsterdam, The Netherlands, during September 7¿8, 2023.The 10 full papers included in this book were carefully reviewed and selected from 22 submissions. They were organized in topical sections as follows: design and analysis of algorithms, models of computation and experimental analysis.

This book constitutes the refereed proceedings of the 26th Brazilian Symposium on Formal Methods, SBMF 2023, held in Manaus, Brazil, during December 4-8, 2023.The 7 full papers and 2 short papers presented in this book were carefully reviewed and selected from 16 submissions.The papers are divided into the following topical sections: specification and modeling languages; testing; and verification and validation.

This book highlights quantum optics technologies that can revolutionize the way we encode, store, transmit, and handle information. These technologies can help us overcome bottlenecks in classical physics-based information technology in information transmission capacity, computing speed, and information security. The book provides readers with new perspectives on potential applications of the quantum theory. Besides, the book summaries the research advances in quantum optics and atom optics, including manipulation and construction of the quantum states of photons and even atoms, molecules, and matter at the quantum level, and new phenomena and technologies brought about by the interactions between photons and the quantum states of matter. The book provides extensive and thoroughly exhaustive coverage of quantum optics. It is suitable for researchers and graduate students of optical physics and quantum optics.

This book constitutes invited papers from the Second International Workshop on Frontiers in Software Engineering Education, FISEE 2023, which took place at the Château de Villebrumier, France, during January 23-25, 2023.The Editorial and the 8 papers included in this volume were considerably enhanced after the conference and during two different peer-review phases. The contributions cover the main topics of the workshop: education in technology and technology for education; new (and fearless) ideas on education; adjustments in teaching during pandemic: experience reports; models for class development; how to design learning objectives and outcomes; labs and practical sessions: how to conduct them; curriculum development; course design; quality course assessment; long-life studies in education; empirical research in SE education; experiences in starting-up new educational systems; blended education.FISEE 2023 is part of a series of scientific events held at the new LASER center in Villebrumier near Montauban and Toulouse, France.

This book offers a brief but effective introduction to quantum machine learning (QML). QML is not merely a translation of classical machine learning techniques into the language of quantum computing, but rather a new approach to data representation and processing. Accordingly, the content is not divided into a "classical part" that describes standard machine learning schemes and a "quantum part" that addresses their quantum counterparts. Instead, to immerse the reader in the quantum realm from the outset, the book starts from fundamental notions of quantum mechanics and quantum computing. Avoiding unnecessary details, it presents the concepts and mathematical tools that are essential for the required quantum formalism. In turn, it reviews those quantum algorithms most relevant to machine learning. Later chapters highlight the latest advances in this field and discuss the most promising directions for future research.To gain the most from this book, a basic grasp of statistics and linear algebra is sufficient; no previous experience with quantum computing or machine learning is needed. The book is aimed at researchers and students with no background in quantum physics and is also suitable for physicists looking to enter the field of QML.

Over 70 recipes to help you develop smart applications on Arduino Nano 33 BLE Sense, Raspberry Pi Pico, and SparkFun RedBoard Artemis Nano using the power of machine learningPurchase of the print or Kindle book includes a free eBook in PDF format.Key FeaturesOver 20+ new recipes, including recognizing music genres and detecting objects in a sceneCreate practical examples using TensorFlow Lite for Microcontrollers, Edge Impulse, and moreExplore cutting-edge technologies, such as on-device training for updating models without data leaving the deviceBook DescriptionDiscover the incredible world of tiny Machine Learning (tinyML) and create smart projects using real-world data sensors with the Arduino Nano 33 BLE Sense, Raspberry Pi Pico, and SparkFun RedBoard Artemis Nano.TinyML Cookbook, Second Edition, will show you how to build unique end-to-end ML applications using temperature, humidity, vision, audio, and accelerometer sensors in different scenarios. These projects will equip you with the knowledge and skills to bring intelligence to microcontrollers. You'll train custom models from weather prediction to real-time speech recognition using TensorFlow and Edge Impulse.Expert tips will help you squeeze ML models into tight memory budgets and accelerate performance using CMSIS-DSP.This improved edition includes new recipes featuring an LSTM neural network to recognize music genres and the Faster-Objects-More-Objects (FOMO) algorithm for detecting objects in a scene. Furthermore, you'll work on scikit-learn model deployment on microcontrollers, implement on-device training, and deploy a model using microTVM, including on a microNPU. This beginner-friendly and comprehensive book will help you stay up to date with the latest developments in the tinyML community and give you the knowledge to build unique projects with microcontrollers!What you will learnUnderstand the microcontroller programming fundamentalsWork with real-world sensors, such as the microphone, camera, and accelerometerImplement an app that responds to human voice or recognizes music genresLeverage transfer learning with FOMO and KerasLearn best practices on how to use the CMSIS-DSP libraryCreate a gesture-recognition app to build a remote controlDesign a CIFAR-10 model for memory-constrained microcontrollersTrain a neural network on microcontrollersWho this book is forThis book is ideal for machine learning engineers or data scientists looking to build embedded/edge ML applications and IoT developers who want to add machine learning capabilities to their devices. If you're an engineer, student, or hobbyist interested in exploring tinyML, then this book is your perfect companion.Basic familiarity with C/C++ and Python programming is a prerequisite; however, no prior knowledge of microcontrollers is necessary to get started with this book.Table of ContentsGetting Ready to Unlock ML on MicrocontrollersUnleashing Your Creativity with MicrocontrollersBuilding a Weather Station with TensorFlow Lite for MicrocontrollersUsing Edge Impulse and the Arduino Nano to Control LEDs with Voice CommandsRecognizing Music Genres with TensorFlow and the Raspberry Pi Pico - Part 1Recognizing Music Genres with TensorFlow and the Raspberry Pi Pico - Part 2Detecting Objects with Edge Impulse Using FOMO on the Raspberry Pi PicoClassifying Desk Objects with TensorFlow and the Arduino NanoBuilding a Gesture-Based Interface for YouTube Playback with Edge Impulse and the Raspberry Pi Pico(N.B. Please use the Look Inside option to see further chapters)

This book discusses state-of-the-art stochastic optimization algorithms for distributed machine learning and analyzes their convergence speed. The book first introduces stochastic gradient descent (SGD) and its distributed version, synchronous SGD, where the task of computing gradients is divided across several worker nodes. The author discusses several algorithms that improve the scalability and communication efficiency of synchronous SGD, such as asynchronous SGD, local-update SGD, quantized and sparsified SGD, and decentralized SGD. For each of these algorithms, the book analyzes its error versus iterations convergence, and the runtime spent per iteration. The author shows that each of these strategies to reduce communication or synchronization delays encounters a fundamental trade-off between error and runtime.

This book constitutes the proceedings of the 20th International Colloquium on Theoretical Aspects of Computing, ICTAC 2023, which took place in Lima, Peru, during December 4¿8, 2023.The 20 full papers presented in this volume together with 3 invited papers and 1 tool paper were carefully reviewed and selected from 40 submissions. They were organised in the topical sections as follows: Bring Together Practitioners; Researchers from Academia; Industry; Government to Present Research Results and Exchange Experience and Ideas.

This book constitutes revised selected papers from the 11th International Workshop on Engineering Multi-Agent Systems, EMAS 2023, which was held in London, UK, during May 29¿30, 2023.The 11 full papers and 7 short papers included in this volume were carefully reviewed and selected from a total of 25 submissions. They were organized in topical sections as follows: agent-oriented software engineering; agents and microservices; strategy, reasoning, and planning; engineering domains and applications; agents in hypermedia environments; frameworks, tooling, and devops.

This book presents a comprehensive overview of Natural Language Interfaces to Databases (NLIDBs), an indispensable tool in the ever-expanding realm of data-driven exploration and decision making. After first demonstrating the importance of the field using an interactive ChatGPT session, the book explores the remarkable progress and general challenges faced with real-world deployment of NLIDBs. It goes on to provide readers with a holistic understanding of the intricate anatomy, essential components, and mechanisms underlying NLIDBs and how to build them. Key concepts in representing, querying, and processing structured data as well as approaches for optimizing user queries are established for the reader before their application in NLIDBs is explored. The book discusses text to data through early relevant work on semantic parsing and meaning representation before turning to cutting-edge advancements in how NLIDBs are empowered to comprehend and interpret human languages. Various evaluation methodologies, metrics, datasets and benchmarks that play a pivotal role in assessing the effectiveness of mapping natural language queries to formal queries in a database and the overall performance of a system are explored. The book then covers data to text, where formal representations of structured data are transformed into coherent and contextually relevant human-readable narratives. It closes with an exploration of the challenges and opportunities related to interactivity and its corresponding techniques for each dimension, such as instances of conversational NLIDBs and multi-modal NLIDBs where user input is beyond natural language. This book provides a balanced mixture of theoretical insights, practical knowledge, and real-world applications that will be an invaluable resource for researchers, practitioners, and students eager to explore the fundamental concepts of NLIDBs.

This wide-ranging book introduces information as a key concept not only in physics, from quantum mechanics to thermodynamics, but also in the neighboring sciences and in the humanities. The central part analyzes dynamical processes as manifestations of information flows between microscopic and macroscopic scales and between systems and their environment. Quantum mechanics is interpreted as a reconstruction of mechanics based on fundamental limitations of information processing on the smallest scales. These become particularly manifest in quantum chaos and in quantum computing. Covering subjects such as causality, prediction, undecidability, chaos, and quantum randomness, the book also provides an information-theoretical view of predictability. More than 180 illustrations visualize the concepts and arguments. The book takes inspiration from the author's graduate-level topical lecture but is also well suited for undergraduate studies and is a valuable resource for researchers and professionals.

This book gives an intuitive and hands-on introduction to Topological Data Analysis (TDA). Covering a wide range of topics at levels of sophistication varying from elementary (matrix algebra) to esoteric (Grothendieck spectral sequence), it offers a mirror of data science aimed at a general mathematical audience. The required algebraic background is developed in detail. The first third of the book reviews several core areas of mathematics, beginning with basic linear algebra and applications to data fitting and web search algorithms, followed by quick primers on algebra and topology. The middle third introduces algebraic topology, along with applications to sensor networks and voter ranking. The last third covers key contemporary tools in TDA: persistent and multiparameter persistent homology. Also included is a user's guide to derived functors and spectral sequences (useful but somewhat technical tools which have recently found applications in TDA), and an appendix illustrating a number of software packages used in the field.  Based on a course given as part of a masters degree in statistics, the book is appropriate for graduate students.