الصفحة 1
الصفحة 1
Notes on Set Theory
This is introduction to axiomatic set theory, viewed both as a foundation of mathematics and as a branch of mathematics with its own subject matter, basic results, open problems.
Nonblocking Supervisory Control of State Tree Structures
This monograph proposes how to manage complexity by organizing the system as a State Tree Structure (STS). an efficient recursive symbolic algorithm is presented that can perform nonblocking supervisory control design in reasonable time and memory for complex systems.
New Frontiers in Angiogenesis
New Frontiers in Angiogenesis starts with a comprehensive overview of the field and continues with topics that have been minimally explored. The topics deal with dynamics of vasculogenesis using imaging techniques, bone marrow-derived endothelial cell precursors as potential therapeutic tools, regulation of post-angiogenic vessel regression, vascular mimicry, design and construction of artificial vessels, bioengineering of angiogenesis, and lymphangiogenesis recapitulating angiogenesis in health and disease states. Each chapter is written by leading experts of the subjects. It is hoped that this volume will challenge all of us interested in the field of angiogenesis and cardiovascular biology, in particular those in academia and industries, to think "outside the box" and explore angiogenesis from a fresh angle. It is hoped that New Frontiers in Angiogenesis is thought provoking and serve as a road map for discovering new findings to help betterments of human health.
Neural networks and deep learning
Covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems.
Neural Networks : Computational Models and Applications
Neural Networks: Computational Models and Applications covers a wealth of important theoretical and practical issues in neural networks, including the learning algorithms of feed-forward neural networks, various dynamical properties of recurrent neural networks, winner-take-all networks and their applications in broad manifolds of computational intelligence: pattern recognition, uniform approximation, constrained optimization, NP-hard problems, and image segmentation. By presenting various computational models, this book is developed to provide readers with a quick but insightful understanding of the broad and rapidly growing areas in the neural networks domain. Besides laying down fundamentals on artificial neural networks, this book also studies biologically inspired neural networks. Some typical computational models are discussed, and subsequently applied to objection recognition, scene analysis and associative memory. The studies of bio-inspired models have important implications in computer vision and robotic navigation, as well as new efficient algorithms for image analysis.
Nanotechnology for Smart Concrete
Nanomaterials can markedly improve the mechanical properties of concrete, as well as reduce the porosity and enhance the durability of concrete. The application of nanotechnology in concrete is still in its infancy. However, an ever-growing demand for ultra-high-performance concrete and recurring environmental pollution caused by ordinary Portland cement has encouraged engineers to exploit nanotechnology in the construction industry. Nanotechnology for Smart Concrete discusses the advantages and applications of nanomaterials in the concrete industry, including high-strength performance, microstructural improvement, self-healing, energy storage, and coatings. The book: Analyses the linkage of concrete materials with nanomaterials and nanostructures / Discusses the applications of nanomaterials in the concrete industry, including energy storage in green buildings, anti-corrosive coatings, and inhibiting pathogens and viruses / Covers self-healing concrete / Explores safety considerations, sustainability, and environmental impact of nanoconcrete / Includes an appendix of solved questions
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.
Modelling and Optimization of Biotechnological Processes : Artificial Intelligence Approaches
The book begins with a historical introduction to the field of bioprocess control based on artificial intelligence approaches, followed by two chapters covering the optimization of fed-batch culture using genetic algorithms. Online biomass soft-sensors are constructed in Chapter 4 using recurrent neural networks. The bioprocess is then modelled in Chapter 5 by cascading two soft-sensor neural networks. Optimization and validation of the final product are detailed in Chapters 6 and 7. The general conclusions are drawn in Chapter 8.
Modeling, Estimation and Control : Festschrift in Honor of Giorgio Picci on the Occasion of his Sixty-Fifth Birthday
Coefficients of Variations in Analysis of Macro-Policy Effects: An example of two-parameter Poisson-Dirichlet distributions.- How Many Experiments Are Needed to Adapt?- A Mutual Information Based Distance for Multivariate Gaussian Processes.- Differential Forms and Dynamical Systems.- An Algebraic Framework for Bayes Nets of Time Series.- A Birds Eye View on System Identification.- Further Results on the Byrnes-Georgiou-Lindquist Generalized Moment Problem.- Factor Analysis and Alternating Minimization.- Tensored PolynomialModels.- Distances Between Time-Series and Their Autocorrelation Statistics.- Global Identifiability of Complex Models, Constructed from Simple Submodels.- Identification of Hidden MarkovModels - Uniform LLN-s.- Identifiability and Informative Experiments in Open and Closed-Loop Identification.- On Interpolation and the Kimura-Georgiou Parametrization.- The Control of Error in Numerical Methods.- Contour Reconstruction and Matching Using Recursive Smoothing Splines.- Role of LQ Decomposition in Subspace Identification Methods.- Canonical Operators on Graphs.
Misleading marketing communication : Assessing the impact of potentially deceptive food labelling on consumer behaviour
Presenting four complementary experimental studies targeting recurrent grey-zone scenarios on the Danish food market, the book illustrates the potential of the so-called ShopTrip test paradigm which simulates and registers real-life e-shopping behaviour as it unfolds while yielding new types of data against which opposing assessments of potential misleadingness can be matched. The results are discussed in the light of possible paths of theoretical explanation and implications for future regulative practices, including companies’ self-regulation.
Metal Catalyzed Cascade Reactions
Transition metal-catalyzed cascade reactions are an elegant approach to complex molecular scaffolds. Besides their esthetics and increase in structural complexity, they have also become mechanistic challenges for the combination of organometallic elementary steps. As a consequence, cascade reactions have revolutionized synthetic strategies and conceptual thinking. The authors highlight cyclization via carbopalladation and acylpalladation and Heck-pericyclic sequences. They discuss p-allyl palladium-based cascade reactions, Michael-type additions as an entry to transition-metal-promoted cyclizative transformations, and sequential or consecutive palladium-catalyzed processes, and show Pauson-Khand cascades, metal-catalyzed cyclizations of acyclic precursors, as well as cascade and sequential ruthenium-catalyzed transformations. Therefore, the reader finds overview of an exciting and highly dynamic field of a new and innovative methodological concept
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2005 ; 8th International Conference, Palm Springs, CA, USA, October 26-29, 2005, Proceedings, Part I
This paper presents a method for classification of medical images, using machine learning and deformation-based morphometry. A morphological representation of the anatomy of interest is first obtained using highdimensional template warping, from which regions that display strong correlations between morphological measurements and the classification (clinical) variable are extracted using a watershed segmentation, taking into account the regional smoothness of the correlation map which is estimated by a crossvalidation strategy in order to achieve robustness to outliers. A Support Vector Machine-Recursive Feature Elimination (SVM-RFE) technique is then used to rank computed features from the extracted regions, according to their effect on the leave-one-out error bound. Finally, SVM classification is applied using the best set of features, and it is tested using leave-one-out. The results from a group of 61 brain images of female normal controls and schizophrenia patients demonstrate not only high classification accuracy (91.8%) and steep ROC curves, but also exceptional stability with respect to the number of selected features and the SVM kernel size
Ionospheric Precursors of Earthquakes
Changes included changing my career from the field of space plasma physics to Earth sciences and geophysics, and changes in my personal life giving me h- piness and compliance in my present family.
Introduction to Modern Number Theory: Fundamental Problems, Ideas and Theories
"Introduction to Modern Number Theory" surveys from a unified point of view both the modern state and the trends of continuing development of various branches of number theory. Motivated by elementary problems, the central ideas of modern theories are exposed. Some topics covered include non-Abelian generalizations of class field theory, recursive computability and Diophantine equations, zeta- and L-functions. This substantially revised and expanded new edition contains several new sections, such as Wiles' proof of Fermat's Last Theorem, and relevant techniques coming from a synthesis of various theories. Moreover, the authors have added a part dedicated to arithmetical cohomology and noncommutative geometry, a report on point counts on varieties with many rational points, the recent polynomial time algorithm for primality testing, and some others subjects.
Introduction to Machine Learning with Applications in Information Security
Provides a classroom-tested introduction to a wide variety of machine learning and deep learning algorithms and techniques, reinforced via realistic applications. The book is accessible and doesn't prove theorems, or dwell on mathematical theory. The goal is to present topics at an intuitive level, with just enough detail to clarify the underlying concepts. The book covers core classic machine learning topics in depth, including Hidden Markov Models (HMM), Support Vector Machines (SVM), and clustering. Additional machine learning topics include k-Nearest Neighbor (k-NN), boosting, Random Forests, and Linear Discriminant Analysis (LDA). The fundamental deep learning topics of backpropagation, Convolutional Neural Networks (CNN), Multilayer Perceptrons (MLP), and Recurrent Neural Networks (RNN) are covered in depth. A broad range of advanced deep learning architectures are also presented, including Long Short-Term Memory (LSTM), Generative Adversarial Networks (GAN), Extreme Learning Machines (ELM), Residual Networks (ResNet), Deep Belief Networks (DBN), Bidirectional Encoder Representations from Transformers (BERT), and Word2Vec.
Interphases and Mesophases in Polymer Crystallization I
Polyethylene forms a two-dimensional hexagonal phase, stable at 3 GPa depending on molecular length, which in recent years has been claimed to intervene in crystallization prior to the formation of the usual orthorhombic phase even at atmospheric pressure. This claim is evaluated and shown to be without substance. There is very little evidence that the theoretical possibility of thin lamellae being more stable in the hexagonal phase than the orthorhombic at atmospheric pressure, if the former has sufficiently low fold surface free energy, does occur in practice. But the existence of single crystals of the orthorhombic phase unambiguously shows that they did not have a hexagonal precursor; that would have made them threefold twins. The overwhelming mass of evidence is that orthorhombic and hexagonal phases crystallize independently in accordance with the phase diagram and kinetic competition during growth, as has been understood since the hexagonal phase was discovered.
Inter-Municipal Cooperation in Europe
This book presents an overview of inter-municipal cooperation in eight European countries. Each country study sketches its attendant forms, their institutional design, the tasks and competencies attributed to joint authorities of municipalities and the way inter-municipal cooperation operates in practice. Both performance and democratic aspects of cooperation are recurring topics. The last chapter of the book presents a comparative analysis and reflects on the future of inter-municipal cooperation.
Integrable Hamiltonian Hierarchies : Spectral and Geometric Methods
This book presents a detailed derivation of the spectral properties of the Recursion Operators allowing one to derive all the fundamental properties of the soliton equations and to study their Hamiltonian hierarchies. Thus it is demonstrated that the inverse scattering method for solving soliton equations is a nonlinear generalization of the Fourier transform. The book brings together the spectral and the geometric approaches and as such will be useful to a wide readership: from researchers in the field of nonlinear completely integrable evolution equations to graduate and post-graduate students.
Information theory and machine learning
The recent successes of machine learning, especially regarding systems based on deep neural networks, have encouraged further research activities and raised a new set of challenges in understanding and designing complex machine learning algorithms. New applications require learning algorithms to be distributed, have transferable learning results, use computation resources efficiently, convergence quickly on online settings, have performance guarantees, satisfy fairness or privacy constraints, incorporate domain knowledge on model structures, etc. A new wave of developments in statistical learning theory and information theory has set out to address these challenges.
Inductive logic programming ; 15th International Conference, ILP 2005, Bonn, Germany, August 10-13, 2005, Proceedings
“Change is inevitable.” Embracing this quote we have tried to carefully exp- iment with the format of this conference, the 15th International Conference on Inductive Logic Programming, hopefully making it even better than it already was. But it will be up to you, the inquisitive reader of this book, to judge our success. The major changes comprised broadening the scope of the conference to include more diverse forms of non-propositional learning, to once again have tutorials on exciting new areas, and, for the ?rst time, to also have a discovery challenge as a platform for collaborative work. This year the conference was co-located with ICML 2005, the 22nd Inter- tional Conference on Machine Learning, and also in close proximity to IJCAI 2005, the 19th International Joint Conference on Arti?cial Intelligence. - location can be tricky, but we greatly bene?ted from the local support provided by Codrina Lauth, Michael May, and others. We were also able to invite all ILP and ICML participants to shared events including a poster session, an invited talk, and a tutorial about the exciting new area of “statistical relational lea- ing”. Two more invited talks were exclusively given to ILP participants and were presented as a kind of stock-taking—?ttingly so for the 15th event in a series—but also tried to provide a recipe for future endeavours.
