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الصفحة 2
الصفحة 2
Advances in cognitive neurodynamics ICCN 2007; Proceedings of the International conference on cognitive neurodynamics - 2007
Contains the Proceedings of the 1st International Conference on Cognitive Neurodynamics held in Shanghai, November 17-21, 2007. The participants were treated to an exciting and stimulating conference that left everyone with an enthusiastic vision for the future of the field. The latest important progress was covered by 13 mini-symposia including: Models of Mental Disorders; Cognitive Machines; Dynamics in learning and memory; Central nervous system synchronization; Neuroinformatics; Cognitive Computational Modeling of Human Language Processing; Cognitive Neurodynamics of Attention; Bottom-Up and Top-Down; Brain Networks; From Anatomy to Dynamics; Translational Cognitive Neuroimaging; K-sets; Theory and Applications; Advanced Signal Processing Techniques for Brain Data Analysis; Visual cortex: information processing and dynamics; Dynamics of Firing Patterns and Synchronization in Neuronal Systems.
Liberal Democracy : Prosperity through Freedom
Aims to show which factors have been decisive in the rise of successful countries. Never before have so many people been so well off. However, prosperity is not a law of nature; it has to be worked for. A liberal economy stands at the forefront of this success – not as a political system, but as a set of economic rules promoting competition, which in turn leads to innovation, research and enormous productivity.
Legal maxims in islamic criminal law : Theory and applications
Delves into the theoretical and practical studies of al-Qawaid al-Fiqhiyyah in Islamic legal theory. It elucidates the importance of this concept in the application of Islamic law and demonstrates how the concept relates to the objectives of Islamic law generally.
Justice in extreme cases: criminal law theory meets international criminal law
Shows how to use moral theory to challenge and improve ICL, and how extreme cases can challenge and improve criminal law theory. It will appeal to scholars and jurists in ICL, as well as scholars of criminal law theory or legal philosophy.
Bordieuan Field Theory as an Instrument for Military Operational Analysis
This book uses Pierre Bourdieu’s field theory as a lens through which to examine military operations. Novel in its approach, this innovative text provides a better, more nuanced understanding of the modern ‘battlespace’, particularly in instances of prolonged low-intensity conflict. Formed in two parts, this book primarily explores the scope of Bourdien theory before secondly providing a detailed case study of the Yugoslavian succession war of 1990-1992. Gunneriusson suggests that although theories do not necessarily provide answers, they do help us ask better questions. This volume suggests news lines of interdisciplinary investigation that will be of interest to members of armed forces, practitioners from NGOs, and policymakers.
Mathematical Methods in Computer Science : Essays in Memory of Thomas Beth
This Festschrift volume contains the proceedings of the conference Mathematical Methods in Computer Science, MMICS 2008, which was held during December 17-19, 2008, in Karlsruhe, Germany, in memory of Thomas Beth.The themes of the conference reflected the many interests of Thomas Beth. Although, these interests might seem diverse, mathematical methods and especially algebra as a language constituted the common denominator of all of his scientific achievements.
Markov Models for Pattern Recognition : From Theory to Applications
Describes the underlying theoretical concepts - covering Hidden Markov models and Markov chain models - and presents the techniques and algorithmic solutions essential to creating real world applications. The actual use of Markov models in their three main application areas - namely speech recognition, handwriting recognition, and biological sequence analysis - is presented with examples of successful systems.
Managed Software Evolution
This open access book presents the outcomes of the “Design for Future – Managed Software Evolution” .The different lifecycles of software and hardware platforms lead to interoperability problems in such systems. Instead of separating the development, adaptation and evolution of software and its platforms, as well as aspects like operation, monitoring and maintenance, they should all be integrated into one overarching process. Accordingly, the book is split into three major parts, the first of which includes an introduction to the nature of software evolution, followed by an overview of the specific challenges and a general introduction to the case studies used in the project. The second part of the book consists of the main chapters on knowledge carrying software, and cover tacit knowledge in software evolution, continuous design decision support, model-based round-trip engineering for software product lines, performance analysis strategies, maintaining security in software evolution, learning from evolution for evolution, and formal verification of evolutionary changes. In turn, the last part of the book presents key findings and spin-offs. The individual chapters there describe various case studies, along with their benefits, deliverables and the respective lessons learned. An overview of future research topics rounds out the coverage.
Maintenance Theory of Reliability
The book provides a detailed introduction to maintenance policies, updates the reader on the current status of the field and indicates future directions. The reader will learn the theory of maintenance and how to apply models in practice.
Macroscopic Transport Equations for Rarefied Gas Flows : Approximation Methods in Kinetic Theory
This book discusses classical and modern methods to derive macroscopic transport equations for rarefied gases from the Boltzmann equation, for small and moderate Knudsen numbers, i.e.as well as the new order of magnitude method, which avoids the short-comings of the classical methods, but retains their benefits.
Machine learning for cyber-physical systems: selected papers from the international conference ML4CPS 2023
Contains selected papers from the international conference ML4CPS – Machine Learning for Cyber-Physical Systems, which was held in Hamburg (Germany), from 29 to 31 March 2023. Cyber-physical systems are adaptive and learning: they analyze their environment and, based on observations, learn patterns, associations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnostics. Machine learning is the key technology for these developments.
Machine Learning for Audio, Image and Video Analysis : Theory and Applications
The book is organized in four parts: The first focuses on technical aspects, basic mathematical notions and elementary machine learning techniques. The second provides an extensive survey of most relevant machine learning techniques for media processing, while the third part focuses on applications and shows how techniques are applied in actual problems. The fourth part contains detailed appendices that provide notions about the main mathematical instruments used throughout the text
Machine Learning and Probabilistic Graphical Models for Decision Support Systems
Presents recent advancements in research, a review of new methods and techniques, and applications in decision support systems (DSS) with Machine Learning and Probabilistic Graphical Models, which are very effective techniques in gaining knowledge from Big Data and in interpreting decisions. It explores Bayesian network learning, Control Chart, Reinforcement Learning for multicriteria DSS, Anomaly Detection in Smart Manufacturing with Federated Learning, DSS in healthcare, DSS for supply chain management, etc. Researchers and practitioners alike will benefit from this book to enhance the understanding of machine learning, Probabilistic Graphical Models, and their uses in DSS in the context of decision making with uncertainty. The real-world case studies in various fields with guidance and recommendations for the practical applications of these studies are introduced in each chapter.
Machine Learning and Cognitive Computing for Mobile Communications and Wireless Networks
Communication and network technology has witnessed recent rapid development and numerous information services and applications have been developed globally. These technologies have high impact on society and the way people are leading their lives. The advancement in technology has undoubtedly improved the quality of service and user experience yet a lot needs to be still done. Some areas that still need improvement include seamless wide-area coverage, high-capacity hot-spots, low-power massive-connections, low-latency and high-reliability and so on. Thus, it is highly desirable to develop smart technologies for communication to improve the overall services and management of wireless communication. Machine learning and cognitive computing have converged to give some groundbreaking solutions for smart machines. With these two technologies coming together, the machines can acquire the ability to reason similar to the human brain. The research area of machine learning and cognitive computing cover many fields like psychology, biology, signal processing, physics, information theory, mathematics, and statistics that can be used effectively for topology management. Therefore, the utilization of machine learning techniques like data analytics and cognitive power will lead to better performance of communication and wireless systems.
Machine Learning : The Basics
Approaches ML as the computational implementation of the scientific principle. This principle consists of continuously adapting a model of a given data-generating phenomenon by minimizing some form of loss incurred by its predictions. Trains readers to break down various ML applications and methods in terms of data, model, and loss, thus helping them to choose from the vast range of ready-made ML methods.
Machine Learning : Modeling Data Locally and Globally
Machine Learning - Modeling Data Locally and Globally presents a novel and unified theory that tries to seamlessly integrate different algorithms. Specifically, the book distinguishes the inner nature of machine learning algorithms as either "local learning"or "global learning."This theory not only connects previous machine learning methods, or serves as roadmap in various models, but – more importantly – it also motivates a theory that can learn from data both locally and globally. This would help the researchers gain a deeper insight and comprehensive understanding of the techniques in this field. The book reviews current topics,new theories and applications.
List decoding of error-correcting codes : Winning thesis of the 2002 ACM doctoral dissertation competition
Presents some spectacular new results in the area of decoding algorithms for error-correcting codes. Specifically, it shows how the notion of “list-decoding” can be applied to recover from far more errors, for a wide variety of err- correcting codes, than achievable before. A brief bit of background : error-correcting codes are combinatorial str- tures that show how to represent (or “encode”) information so that it is - silient to a moderate number of errors. Speci?cally, an error-correcting code takes a short binary string, called the message, and shows how to transform it into a longer binary string, called the codeword, so that if a small number of bits of the codewordare ?ipped, the resulting string does not look like any other codeword. The maximum number of errorsthat the code is guaranteed to detect, denoted d, is a central parameter in its design. A basic property of such a code is that if the number of errors that occur is known to be smaller than d/2, the message is determined uniquely. This poses a computational problem, called the decoding problem : compute the message from a corrupted codeword, when the number of errors is less than d/2.
Linear Systems, Signal Processing and Hypercomplex Analysis ; Chapman University, November 2017
includes contributions originating from a conference held at Chapman University during November 14-19, 2017. It presents original research by experts in signal processing, linear systems, operator theory, complex and hypercomplex analysis and related topics.
Linear and Generalized Linear Mixed Models and Their Applications
This book covers two major classes of mixed effects models—linear mixed models and generalized linear mixed models—and it presents an up-to-date account of theory and methods in analysis of these models as well as their applications in various fields. It offers a systematic approach to inference about non-Gaussian linear mixed models. Furthermore, it discusses the latest developments and methods in the field, incorporating relevant updates since publication of the first edition. These include advances in high-dimensional linear mixed models in genome-wide association studies (GWAS), advances in inference about generalized linear mixed models with crossed random effects, new methods in mixed model prediction, mixed model selection, and mixed model diagnostics.
Liapunov Functions and Stability in Control Theory
Presents a modern and self-contained treatment of the Liapunov method for stability analysis, in the framework of mathematical nonlinear control theory. A Particular focus is on the problem of the existence of Liapunov functions (converse Liapunov theorems) and their regularity, whose interest is especially motivated by applications to automatic control.
