الصفحة 5
الصفحة 5
Neuromuscular Disease : Evidence and Analysis in Clinical Neurology
Through a series of questions and answers concerning specific neuromuscular disorders, each chapter critiques the best available evidence to illustrate strengths and weaknesses of the data and make the reader aware of the quality of clinical research studies in general. Introductory chapters facilitate this learning process by elucidating the epidemiological and biostatistical issues pertinent to diagnosis, treatment, and prognosis. A broad range of disorders of the anterior horn cell, nerve roots, peripheral nerves, neuromuscular junction, and muscle are critically appraised and discussed.
Neural Information Processing ; 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part II
The 228 revised full papers presented were carefully reviewed and selected from numerous ordinary paper submissions and 15 special organized sessions. The 116 papers of the first volume are organized in topical sections on computational neuroscience, learning and memory, neural network models ,supervised /unsupervised/reinforcement learning, statistical learning algorithms, optimization algorithms, novel algorithms, as well as motor control and vision. The second volume contains 112 contributions related to statistical and pattern recognition algorithms, neuromorphic hardware and implementations, robotics, data mining and knowledge discovery, real world applications, cognitive and hybrid intelligent systems, bioinformatics, neuroinformatics, brain-conputer interfaces, and novel approaches.
Neural Information Processing ; 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part I
The 228 revised full papers presented were carefully reviewed and selected from numerous ordinary paper submissions and 15 special organized sessions. The 116 papers of the first volume are organized in topical sections on computational neuroscience, learning and memory, neural network models, supervised/unsupervised/reinforcement learning, statistical learning algorithms, optimization algorithms, novel algorithms, as well as motor control and vision. The second volume contains 112 contributions related to statistical and pattern recognition algorithms, neuromorphic hardware and implementations, robotics, data mining and knowledge discovery, real world applications, cognitive and hybrid intelligent systems, bioinformatics, neuroinformatics, brain-conputer interfaces, and novel approaches.
Networks : From Biology to Theory
The concept of a network emerges as a powerful and stimulating research paradigm in mathematics, physics and computer science, and demonstrates a very lively interaction between experimental findings, simulation studies, and theoretical investigations that then in turn lead to new experimental questions. This volume explores this concept in full and features contributions from a truly global set of contributors.
Network Performance Analysis : Using the J Programming Language
This useful volume introduces concepts and principles of network performance analysis by example, using the J programming language. J is rich in mathematical functionality, which makes it an ideal tool for analytical methods. The book favours a practical approach and develops functions in J to demonstrate mathematical concepts, thereby enabling readers to explore the underlying principles behind network performance analysis. In addition, this allows the subject to become more accessible to those who, although have a mathematical background, are not pure mathematicians.
Network Classification For Traffic Management : Anomaly detection, feature selection, clustering and classification
Investigates network traffic classification solutions by proposing transport-layer methods to achieve better run and operated enterprise-scale networks. With the massive increase of data and traffic on the Internet within the 5G, IoT and smart cities frameworks, current network classification and analysis techniques are falling short. Novel approaches using machine learning algorithms are needed to cope with and manage real-world network traffic, including supervised, semi-supervised, and unsupervised classification techniques. Accurate and effective classification of network traffic will lead to better quality of service and more secure and manageable networks. Investigates network traffic classification solutions by proposing transport-layer methods to achieve better run and operated enterprise-scale networks. The authors explore novel methods for enhancing network statistics at the transport layer, helping to identify optimal feature selection through a global optimization approach and providing automatic labelling for raw traffic through a SemTra framework to maintain provable privacy on information disclosure properties.
Network Analysis : Methodological Foundations
‘Network’ is a heavily overloaded term, so that ‘network analysis’ means different things to different people. Specific forms of network analysis are used in the study of diverse structures such as the Internet, interlocking directorates, transportation systems, epidemic spreading, metabolic pathways, the Web graph, electrical circuits, project plans, and so on. There is, however, a broad methodological foundation which is quickly becoming a prerequisite for researchers and practitioners working with network models. From a computer science perspective, network analysis is applied graph theory. Unlike standard graph theory books, the content of this book is organized according to methods for specific levels of analysis (element, group, network) rather than abstract concepts like paths, matchings, or spanning subgraphs. Its topics therefore range from vertex centrality to graph clustering and the evolution of scale-free networks. In 15 coherent chapters, this monograph-like tutorial book introduces and surveys the concepts and methods that drive network analysis, and is thus the first book to do so from a methodological perspective independent of specific application areas.
Nearrings and Nearfields ; Proceedings of the Conference on Nearrings and Nearfields, Hamburg, Germany July 27 - August 3, 2003
This present volume is the Proceedings of the 18th International Conference on Nearrings and Nearfields held at the Helmut-Schmidt-Universitat, Universitat der Bundeswehr Hamburg, from July 27-August 3, 2003. It contains the written versions of the lectures by the five invited speakers. These concern recent developments of planar nearrings, nearrings of mappings, group nearrings and loop-nearrings. One of them is a long and very substantial research paper "The Z-Constrained Conjecture". These are followed by 13 contributions reflecting the diversity of the subject of nearrings and related structures. Besides the purely algebriac structure theory, these papers show many connections of nearring theory with group theory, combinatorics, geometries, and topology, and all contain original research.
Nearest Neighbor Search : A Database Perspective
Modern applications are both data and computationally intensive and require the storage and manipulation of voluminous traditional (alphanumeric) and nontraditional data sets (images, text, geometric objects, time-series). Examples of such emerging application domains are: Geographical Information Systems (GIS), Multimedia Information Systems, CAD/CAM, Time-Series Analysis, Medical Information Sstems, On-Line Analytical Processing (OLAP), and Data Mining. These applications pose diverse requirements with respect to the information and the operations that need to be supported. From the database perspective, new techniques and tools therefore need to be developed towards increased processing efficiency. This monograph explores the way spatial database management systems aim at supporting queries that involve the space characteristics of the underlying data, and discusses query processing techniques for nearest neighbor queries. It provides both basic concepts and state-of-the-art results in spatial databases and parallel processing research, and studies numerous applications of nearest neighbor queries.
Natural Language Processing – IJCNLP 2004 ; 1st International Joint Conference, Hainan Island, China, March 22-24, 2004, Revised Selected Papers
This book constitutes the thoroughly refereed post-proceedings of the First International Joint Conference on Natural Language Processing, IJCNLP 2004, held in Hainan Island, China in March 2004. The 84 revised full papers presented in this volume were carefully selected during two rounds of reviewing and improvement from 211 papers submitted. The papers are organized in topical sections on dialogue and discourse; FSA and parsing algorithms; information extractions and question answering; information retrieval; lexical semantics, ontologies, and linguistic resources; machine translation and multilinguality; NLP software and applications, semantic disambiguities; statistical models and machine learning; taggers, chunkers, and shallow parsers; text and sentence generation; text mining; theories and formalisms for morphology, syntax, and semantics; word segmentation; NLP in mobile information retrieval and user interfaces; and text mining in bioinformatics.
Naive Lie Theory
In this new textbook, acclaimed author John Stillwell presents a lucid introduction to Lie theory suitable for junior and senior level undergraduates. In order to achieve this, he focuses on the so-called "classical groups'' that capture the symmetries of real, complex, and quaternion spaces. These symmetry groups may be represented by matrices, which allows them to be studied by elementary methods from calculus and linear algebra.
Multivariate Statistics : Exercises and Solutions
The first part is devoted to graphical techniques. The second part deals with multivariate random variables and presents the derivation of estimators and tests for various practical situations. The last part introduces a wide variety of exercises in applied multivariate data analysis. The book demonstrates the application of simple calculus and basic multivariate methods in real life situations. It contains altogether 234 solved exercises which can assist a university teacher in setting up a modern multivariate analysis course. All computer-based exercises are available in the R or XploRe languages.
Multivariate Statistical Machine Learning Methods for Genomic Prediction
This book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments.
Multivariate and Mixture Distribution Rasch Models : Extensions and Applications
This volume covers extensions of the Rasch model, one of the most researched and applied models in educational research and social science. This collection contains 22 chapters by some of the most recognized international experts in the field. They cover topics ranging from general model extensions to applications in fields as diverse as cognition, personality, organizational and sports psychology, and health sciences and education.
Multi-Source National Forest Inventory : Methods and Applications
The book describes in detail the full MS-NFI process, and the input data used – including field data, satellite images, and digital map data, as well as coarse-scale variation of forest variables. It also presents comprehensive information on the types of outputs which can be derived, including maps and statistics, describing, for example, stock volumes and development, dominant tree species, age-class distribution, and large and small-scale variation. The book will provide an invaluable resource for those involved in forest inventory, including government departments and bodies involved in forest policy, management and monitoring, forest managers, and researchers and graduate students interested in forest inventory, modelling and analysis. It will find an additional market among those interested in Earth observation, ecology and broader areas of environmental and natural resource management.
Multi-Sensor Data Fusion : An Introduction
This textbook provides a comprehensive introduction to the theories and techniques of multi-sensor data fusion. It is aimed at advanced undergraduate and first-year graduate students in electrical engineering and computer science, as well as researchers and professional engineers. The book is intended to be self-contained. No previous knowledge of multi-sensor data fusion is assumed, although some familiarity with the basic tools of linear algebra, calculus and simple probability theory is recommended.
Multiscale Problems in the Life Sciences : From Microscopic to Macroscopic
The aim of this volume that presents Lectures given at a joint CIME and Banach Center Summer School, is to offer a broad presentation of a class of updated methods providing a mathematical framework for the development of a hierarchy of models of complex systems in the natural sciences, with a special attention to Biology and Medicine. Mastering complexity implies sharing different tools requiring much higher level of communication between different mathematical and scientific schools, for solving classes of problems of the same nature. Today more than ever, one of the most important challenges derives from the need to bridge parts of a system evolving at different time and space scales, especially with respect to computational affordability. As a result the content has a rather general character; the main role is played by stochastic processes, positive semigroups, asymptotic analysis, kinetic theory, continuum theory and game theory.
Multiscale Optimization Methods and Applications
One general strategy for dealing with a large or difficult problem is to partition it into smaller ones, which are hopefully much easier to solve, and then work backwards towards the solution of original problem, using a solution from a previous level as a starting guess at the next level.The topics of the chapters selected for this volume are focused on the development of new solution methodologies, including general multilevel solution techniques, for tackling difficult, large-scale optimization problems that arise in science and industry. Applications presented in the book include but are not limited to the circuit placement problem in VLSI design, a wireless sensor location problem, optimal dosages in the treatment of cancer by radiation therapy, and facility location.
Multi-scale Modelling for Structures and Composites
Numerous applications of rod structures in civil engineering, aircraft and spacecraft confirm the importance of the topic. On the other hand the majority of books on structural mechanics use some simplifying hypotheses; these hypotheses do not allow to consider some important effects, In this connection the asymptotic analysis of equations of mathematical physics, the equations of elasticity in rod structures (without these hypotheses and simplifying assumptions being imposed) is undertaken in the present book.
Multiscale Modeling in Epitaxial Growth
Epitaxy is a very active area of theoretical research since several years. It is experimentally well-explored and technologically relevant for thin film growth. Recently powerful numerical techniques in combination with a deep understanding of the physical and chemical phenomena during the growth process offer the possibility to link atomistic effects at the surface to the macroscopic morphology of the film. The goal of this book is to summarize recent developments in this field, with emphasis on multiscale approaches and numerical methods. It covers atomistic, step-flow, and continuum models and provides a compact overview of these approaches. It also serves as an introduction into this highly active interdisciplinary field of research for applied mathematicians, theoretical physicists and computational materials scientists.
