الصفحة 6
الصفحة 6
Motivational profiles in TIMSS mathematics : Exploring student clusters across aountries and time
This open access book presents a person-centered exploration of student profiles, using variables related to motivation to do school mathematics derived from the IEA’s Trends in International Mathematics and Science Study (TIMSS) data. Statistical cluster analysis is used to identify groups of students with similar motivational profiles, across grades and over time, for multiple participating countries. In this book, a person-centered analysis of distinct and meaningful motivational profiles and their differences on sociodemographic variables and mathematics performance broadens understanding about the role that motivation characteristics play in learning and achievement in mathematics.
Molecular Mechanisms of Neurotransmitter Release
Within the complex neuronal network of the nervous system, neuron-to-neuron communication occurs mainly through chemical synapses, where the presynaptic nerve terminal releases neurotransmitters that control the function of postsynaptic neurons by acting on postsynaptic receptors. Recent decades have brought groundbreaking new developments and a wealth of knowledge to this field. In Molecular Mechanisms of Neurotransmitter Release, leading experts provide concise, up-to-date information on all major molecular mechanisms involved, with complete background information and figures and diagrams to further elucidate key concepts or experiments.Comprehensive and cutting-edge, Molecular Mechanisms of Neurotransmitter Release is sure to provide a learning tool for neuroscience students, a solid reference for neuroscientists, and a source of knowledge for all those who have a general interest in the ever-evolving field of neuroscience.
Modern parallel programming with C++ and assembly language : X86 SIMD development using AVX, AVX2, and AVX-512
Understand the essential details about x86 SIMD architectures and instruction sets including AVX, AVX2, and AVX-512. / Master x86 SIMD data types, arithmetic instructions, and data management operations using both integer and floating-point operands. / Code performance-enhancing functions and algorithms that fully exploit the SIMD capabilities of a modern x86 processor. Employ C++ intrinsic functions and x86-64 assembly language code to carry out arithmetic calculations using common programming constructs including arrays, matrices, and user-defined data structures. Harness the x86 SIMD instruction sets to significantly accelerate the performance of computationally intense algorithms in applications such as machine learning, image processing, computer graphics, statistics, and matrix arithmetic. / Apply leading-edge coding strategies and techniques to optimally exploit the x86 SIMD instruction sets for maximum possible performance.
Modern Operative Dentistry : Principles for Clinical Practice
Provides the theoretical knowledge required by students when learning how to diagnose oral diseases, plan treatment, and perform various types of dental restoration. It is also useful for clinicians wishing to update their treatment skills and broaden their understanding operative dentistry. Adopting an evidence-based approach, and in accordance with the philosophy of minimally invasive dentistry, it explains in detail the use of both classic and new restorative materials in various clinical situations. It also discusses the principles of smile analysis, as well the technique for esthetic composite restorations on posterior and anterior teeth, including direct and indirect veneers.
Modern Multivariate Statistical Techniques : Regression, Classification, and Manifold Learning
Remarkable advances in computation and data storage and the ready availability of huge data sets have been the keys to the growth of the new disciplines of data mining and machine learning, while the enormous success of the Human Genome Project has opened up the field of bioinformatics. These exciting developments, which led to the introduction of many innovative statistical tools for high-dimensional data analysis, are described here in detail. The author takes a broad perspective; for the first time in a book on multivariate analysis, nonlinear methods are discussed in detail as well as linear methods.
Modern deep learning for tabular data : Novel approaches to common modeling problems
Synthesizes and presents novel deep learning approaches to a seemingly unlikely domain - tabular data. Whether for finance, business, security, medicine, or countless other domain, deep learning can help mine and model complex patterns in tabular data - an incredibly ubiquitous form of structured data. Part I of the book offers a rigorous overview of machine learning principles, algorithms, and implementation skills relevant to holistically modeling and manipulating tabular data. Part II studies five dominant deep learning model designs - Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Attention and Transformers, and Tree-Rooted Networks - through both their 'default' usage and their application to tabular data. Part III compounds the power of the previously covered methods by surveying strategies and techniques to supercharge deep learning systems: autoencoders, deep data generation, meta-optimization, multi-model arrangement, and neural network interpretability.
Modern Deep Learning Design and Application Development : Versatile Tools to Solve Deep Learning Problems
Learn how to harness modern deep-learning methods in many contexts. Packed with intuitive theory, practical implementation methods, and deep-learning case studies, this book reveals how to acquire the tools you need to design and implement like a deep-learning architect. It covers tools deep learning engineers can use in a wide range of fields, from biology to computer vision to business. With nine in-depth case studies, this book will ground you in creative, real-world deep learning thinking. You will: Improve the performance of deep learning models by using pre-trained models, extracting rich features, and automating optimization. Compress deep learning models while maintaining performance. Reframe a wide variety of difficult problems and design effective deep learning solutions to solve them. Use the Keras framework, with some help from libraries like HyperOpt, TensorFlow, and PyTorch, to implement a wide variety of deep learning approaches.
Modern Control Theory
This book presents a unified, systematic description of basic and advanced problems, methods and algorithms of the modern control theory treated as a foundation for the design of computer control and management systems.
Modelling in Mathematical Programming : Methodology and Techniques
This book provides basic tools for learning how to model in mathematical programming, from models without much complexity to complex system models. It presents a unique methodology for the building of an integral mathematical model, as well as new techniques that help build under own criteria. It allows readers to structure models from the elements and variables to the constraints, a basic modelling guide for any system with a new scheme of variables, a classification of constraints and also a set of rules to model specifications stated as logical propositions, helping to better understand models already existing in the literature. It also presents the modelling of all possible objectives that may arise in optimization problems regarding the variables values. The book is structured to guide the reader in an orderly manner, learning of the components that the methodology establishes in an optimization problem. The system includes the elements, which are all the actors that participate in the system, decision activities that occur in the system, calculations based on the decision activities, specifications such as regulations, impositions or actions of defined value and objective criterion, which guides the resolution of the system.
Modelling and Reasoning with Vague Concepts
This volume outlines a formal representation framework for modelling and reasoning with vague concepts in Artificial Intelligence. The new calculus has many applications, especially in automated reasoning, learning, data analysis and information fusion. This book gives a rigorous introduction to label semantics theory, illustrated with many examples, and suggests clear operational interpretations of the proposed measures. It also provides a detailed description of how the theory can be applied in data analysis and information fusion based on a range of benchmark problems.
Modelling and Development of Intelligent Systems ; 6th International Conference, MDIS 2019, Sibiu, Romania, October 3–5, 2019, Revised Selected Papers
This volume constitutes the refereed proceedings of the 6th International Conference on Modelling and Development of Intelligent Systems, MDIS 2019, held in Sibiu, Romania, in October 2019. The 13 revised full papers presented in the volume were carefully reviewed and selected from 31 submissions. The papers are organized in topical sections on adaptive systems; conceptual modelling; data mining; intelligent systems for decision support; machine learning.
Modeling, Simulation and Optimization of Complex Processes HPSC 2018 ; Proceedings of the 7th International Conference on High Performance Scientific Computing, Hanoi, Vietnam, March 19-23, 2018
The contributions cover a broad, interdisciplinary spectrum of scientific computing and showcase recent advances in theory, methods, and practical applications. Subjects covered include numerical simulation, methods for optimization and control, machine learning, parallel computing and software development, as well as the applications of scientific computing in mechanical engineering, airspace engineering, environmental physics, decision making, hydrogeology, material science and electric circuits.
Modeling Theory in Science Education
The book focuses as much on course content as on instruction and learning methodology, and presents practical aspects that have repeatedly demonstrated their value in fostering meaningful and equitable learning of physics and other science courses at the secondary school and college levels.The author shows how a scientific theory that is the object of a given science course can be organized around a limited set of basic models. Special tools are introduced, including modeling schemata, for students to meaningfully construct models and required conceptions, and for teachers to efficiently plan instruction and assess and regulate student learning and teaching practice. A scientific model is conceived to represent a particular pattern in the structure or behavior of physical realities and to explore and reify the pattern in specific ways. The author further shows how to engage students in modeling activities through structured learning cycles.
Modeling Solar Radiation at the Earth’s Surface : Recent Advances
Solar radiation data is important for a wide range of applications, e.g. in engineering, agriculture, health sector, and in many fields of the natural sciences. A few examples showing the diversity of applications may include: architecture and building design e.g. air conditioning and cooling systems; solar heating system design and use; solar power generation; weather and climate prediction models; evaporation and irrigation; calculation of water requirements for crops; monitoring plant growth and disease control; skin cancer research.
Modeling Excitable Tissue : The EMI Framework
This volume presents a novel computational framework for understanding how collections of excitable cells work. The key approach in the text is to model excitable tissue by representing the individual cells constituting the tissue. This is in stark contrast to the common approach where homogenization is used to develop models where the cells are not explicitly present. The approach allows for very detailed analysis of small collections of excitable cells, but computational challenges limit the applicability in the presence of large collections of cells.
Modeling Decisions for Artificial Intelligence ; Vol.3885 ; 3rd International Conference, MDAI 2006, Tarragona, Spain, April 3-5, 2006, Proceedings
This book constitutes the refereed proceedings of the Third International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2006, held in Tarragona, Spain, in April 2006.
Modeling and using context ; 6th International and interdisciplinary Conference, CONTEXT 2007, Roskilde, Denmark, August 20-24, 2007, Proceedings
This volume contains the papers presented at CONTEXT 2007, the Sixth International and Interdisciplinary Conference on Modeling and Using Context. We believe that the papers of this volume represent a snapshot of current work and contribute to both theoretical and applied aspects of research.
Modeling and Using Context ; 5th International and Interdisciplinary Conference, CONTEXT 2005, Paris, France, July 5-8, 2005, Proceedings
Context is of crucial importance for research and applications in many disciplines, as evidenced by many workshops, symposia, seminars, and conferences on specific aspects of context. The International and Interdisciplinary Conference on Modeling and Using Context (CONTEXT), the oldest conference series focusing on context, provides a unique interdisciplinary emphasis, bringing together participants from a wide range of disciplines, including artificial intelligence, cognitive science, computer science, linguistics, organizational science, philosophy, psychology, ubiquitous computing, and application areas such as medicine and law, to discuss and report on context-related research and projects. Previous CONTEXT conferences were held in Rio de Janeiro, Brazil (1997), Trento, Italy (1999, LNCS 1688), Dundee, UK (2001, LNCS 2116), and Palo Alto, USA (2003, LNCS 2680). CONTEXT 2005 was held in Paris, France during July 5–8, 2005. There was a strong response to the CONTEXT 2005 Call for Papers, with 120 submissions received. A careful review process assessed all submissions, with each paper first reviewed by the international Program Committee, and then reviewer discussions were initiated as needed to assure that the final decisions carefully considered all aspects of each paper. Reviews of submissions by the Program Chairs were supervised independently and anonymously, to assure fair consideration of all work. Out of the 120 submissions, 23 were selected as full papers for oral presentation, and 20 were selected as full papers for poster presentation. These outstanding papers are presented in this proceedings.
Modeling and Retrieval of Context ; 2nd International Workshop, MRC 2005, Edinburgh, UK, July 31-August 1, 2005, Revised Selected Papers
Computing in context has become a necessity in modern and intelligent IT - plications. With the use of mobile devices and current research on ubiquitous computing, context-awareness has become a major issue. However, context and context-awareness are crucial not only for mobile and ubiquitous computing. They are also vital for spanning various application areas, such as collaborative softwareand Web engineering,personaldigital assistantsand peer-to-peer inf- mation sharing, health care work?ow and patient control, and adaptive games and e-learning solutions. In these areas, context serves as a major source for reasoning, decision making, and adaptation, as it covers not only application knowledge but also environmental knowledge.Likewise, modeling and retrieving context is an important part of modern knowledge management processes.
Modeling and Management of Fuzzy Semantic RDF Data
Presents the latest research findings in fuzzy RDF data modeling and management. Fuzziness widely exist in many data and knowledge intensive applications. With the increasing amount of metadata available, efficient and scalable management of massive semantic data with uncertainty is of crucial importance. This book goes to great depth concerning the fast-growing topic of technologies and approaches of modeling and managing fuzzy metadata with Resource Description Framework (RDF) format. Its major topics include representation of fuzzy RDF data, fuzzy RDF graph matching, query of fuzzy RDF data, and persistence of fuzzy RDF data in diverse databases. The objective of the book is to provide the state-of-the-art information to researchers, practitioners, and postgraduates students who work on the area of big data intelligence and at the same time serve as the uncertain data and knowledge engineering professional as a valuable real-world reference.
