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This three-volume set is a comprehensive study of the tractability of multivariate problems. The present second volume deals with algorithms using standard information consisting of function values for the approximation of linear and selected nonlinear functionals. An important example is numerical multivariate integration. The proof techniques used in volumes I and II are quite different. It is especially hard to establish meaningful lower error bounds for the approximation of functionals by using finitely many function values. Here, the concept of decomposable reproducing kernels is helpful, allowing it to find matching lower and upper error bounds for some linear functionals. It is then possible to conclude tractability results from such error bounds. Tractability results even for linear functionals are very rich in variety. There are infinite-dimensional Hilbert spaces for which the approximation with an arbitrarily small error of all linear functionals requires only one function value. There are Hilbert spaces for which all nontrivial linear functionals suffer from the curse of dimensionality. This holds for unweighted spaces, where the role of all variables and groups of variables is the same. For weighted spaces one can monitor the role of all variables and groups of variables. Necessary and sufficient conditions on the decay of the weights are given to obtain various notions of tractability. The text contains extensive chapters on discrepancy and integration, decomposable kernels and lower bounds, the Smolyak/sparse grid algorithms, lattice rules and the CBC (component-by-component) algorithms. This is done in various settings. Path integration and quantum computation are also discussed. The book is of interest for researchers working in computational mathematics, especially in approximation of high-dimensional problems. It is also well suited for graduate courses and seminars. 61 open problems...

Multivariate problems occur in many applications. These problems are defined on spaces of d-variate functions and d can be huge – in the hundreds or even in the thousands. Some high-dimensional problems can be solved efficiently to within ε, i.e., the cost increases polynomially in ε−1 and d. However, there are many multivariate problems for which even the minimal cost increases exponentially in d. This exponential dependence on d is called intractability or the curse of dimensionality. This is the first of a three-volume set comprising a comprehensive study of the tractability of multivariate problems. It is devoted to algorithms using linear information consisting of arbitrary linear functionals. The theory for multivariate problems is developed in various settings: worst case, average case, randomized and probabilistic. A problem is tractable if its minimal cost is not exponential in ε−1 and d. There are various notions of tractability, depending on how we measure the lack of exponential dependence. For example, a problem is polynomially tractable if its minimal cost is polynomial in ε−1 and d. The study of tractability was initiated about 15 years ago. This is the first research monograph on this subject. Many multivariate problems suffer from the curse of dimensionality when they are defined over classical (unweighted) spaces. But many practically important problems are solved today for huge d in a reasonable time. One of the most intriguing challenges of theory is to understand why this is possible. Multivariate problems may become tractable if they are defined over weighted spaces with properly decaying weights. In this case, all variables and groups of variables are moderated by weights. The main purpose of this book is to study weighted spaces and to obtain conditions on the weights that are necessary and sufficient to achieve various notions of tractability. The book is of interes...

We show how to use polynomial and strongly polynomial capacity scaling algorithms for the transshipment problem to design a polynomial dual network simplex pivot rule. Our best pivoting strategy leads to an O(m2 log n) bound on the number of pivots, where n and m denotes the number of nodes and arcs in the input network. If the demands are integral and at most B, we also give an O(m(m + n log n) min(log nB, m log n))-time implementation of a strategy that requires somewhat more pivots.

• Introduction.- 1 Foundations.- 2 Fundamentals of Quantales.- 3 Module Theory in Sup.- Appendix.- References.- Index.
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This monograph provides a modern introduction to the theory of quantales. First coined by C.J. Mulvey in 1986, quantales have since developed into a significant topic at the crossroads of algebra and logic, of notable interest to theoretical computer science. This book recasts the subject within the powerful framework of categorical algebra, showcasing its versatility through applications to C*- and MV-algebras, fuzzy sets and automata. With exercises and historical remarks at the end of each chapter, this self-contained book provides readers with a valuable source of references and hints for future research. This book will appeal to researchers across mathematics and computer science with an interest in category theory, lattice theory, and many-valued logic.
(source: Nielsen Book Data)

• Experiments using semantics for learning language comprehension and production / Dana Agnluin and Leonor Becerra-Bonache
• Adaptable grammars for non-context-free languages / Henning Christiansen
• On a new class of bracketed contextual grammars / Lakshmanan Kuppusamy, Anand Mahendran and Eric de la Clergerie
• Reference, co-reference and antecedent-anaphora in the type theory of acyclic recursion / Roussanka Loukanova
• Semantics with the language of acyclic recursion in constraint-based grammar / roussanka Loukanova
• Linguistic power of permutation languages by regular help / Benedek Nagy
• On the correspondence between parallel communicating grammar systems and restarting automata / Dana Pardubská, Martin Plátek and Friedrich Otto
• About a new variant of HNEPs: obligatory hybrid networks of evolutionary processors / Artiom Alhazov, Gemma Bel-Enguix, and Yurii Rogozhin
• Modeling concepts and relations through property grammars / Veronica Dahl, Baohua Gu and Erez Maharshak
• P/T petri net controlled grammars / Jürgen Dassow and Sherzod Turaev
• Some steps towards the automatic design of splicing systems / Marina de la Cruz Echeandía and Alfonso Ortega de la Puente
• Synalysis: a dual processing scheme for both human and biological languages / Veronica Dahl and Erez Maharshak
• Evolutionary algorithms for parsing tree adjoining grammars / Adrian-Horia Dediu and Cǎtǎlin Ionuţ Tîrnǎucǎ
• PNEPs, NEPs extension to parse context free languages / Alfonso Ortega [and others].

This volume is a collection of papers written by several researchers that have in common the use of bio-inspired models to approach formal and natural languages. The main goal of the volume is to promote interdisciplinarity among linguistics, biology and computation. The area of convergence between these three disciplines is giving rise to the emergence of new scientific paradigms that will have an epistemological, social and cultural impact. The book is organized around three thematic areas. Every area relates two of the three main topics: language, computation and biology. This volume stands out from existing publications because of its interdisciplinary nature. There has been a long tradition of interchanging methods among the aforementioned three disciplines, but it is difficult to find a single volume where this interchange of methods is shown. The volume includes chapters that clearly illustrate these interdisciplinary approaches and their benefits. This book will be of value to specialists who work in linguistics, biology or computation, and have interest in using methods from other disciplines that can provide new ideas, new tools and new formalisms to approach their problems, and that can help in the improvement of their theories and models.
(source: Nielsen Book Data)

• 1. Lipschitz global optimization.-
• 2. One-dimensional algorithms and their accleration.-
• 3. Diagonal approach and efficient paritioning strategies.-
• 4. Global optimization algorithms based on the non-redundant partitions.- References.
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This book begins with a concentrated introduction into deterministic global optimization and moves forward to present new original results from the authors who are well known experts in the field. Multiextremal continuous problems that have an unknown structure with Lipschitz objective functions and functions having the first Lipschitz derivatives defined over hyperintervals are examined. A class of algorithms using several Lipschitz constants is introduced which has its origins in the DIRECT (DIviding RECTangles) method. This new class is based on an efficient strategy that is applied for the search domain partitioning. In addition a survey on derivative free methods and methods using the first derivatives is given for both one-dimensional and multi-dimensional cases. Non-smooth and smooth minorants and acceleration techniques that can speed up several classes of global optimization methods with examples of applications and problems arising in numerical testing of global optimization algorithms are discussed. Theoretical considerations are illustrated through engineering applications. Extensive numerical testing of algorithms described in this book stretches the likelihood of establishing a link between mathematicians and practitioners. The authors conclude by describing applications and a generator of random classes of test functions with known local and global minima that is used in more than 40 countries of the world. This title serves as a starting point for students, researchers, engineers, and other professionals in operations research, management science, computer science, engineering, economics, environmental sciences, industrial and applied mathematics to obtain an overview of deterministic global optimization. .
(source: Nielsen Book Data)

• Table of Contents Introduction to Artificial Intelligence on AWS Anatomy of an Modern AI Application Detecting and Translating Text with AWS Rekognition and AWS Translate Performing speech-to-text and Vice-versa with AWS Transcribe and AWS Polly Extracting Information from Text with AWS Comprehend Building a Voice Chatbot with AWS Lex Working with Amazon SageMaker Creating Machine Learning Inference Pipelines with Amazon SageMaker Discovering Topics in Text Collections using Amazon SageMaker Classifying Images using Amazon SageMaker Sales Forecasting with Deep Learning and Auto Regression Model Accuracy Degradation and Feedback Loop What's Next.
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Perform cloud-based machine learning and deep learning using Amazon Web Services such as SageMaker, Lex, Comprehend, Translate, and Polly Key Features Explore popular machine learning and deep learning services with their underlying algorithms Discover readily available artificial intelligence(AI) APIs on AWS like Vision and Language Services Design robust architectures to enable experimentation, extensibility, and maintainability of AI apps Book DescriptionFrom data wrangling through to translating text, you can accomplish this and more with the artificial intelligence and machine learning services available on AWS. With this book, you'll work through hands-on exercises and learn to use these services to solve real-world problems. You'll even design, develop, monitor, and maintain machine and deep learning models on AWS. The book starts with an introduction to AI and its applications in different industries, along with an overview of AWS artificial intelligence and machine learning services. You'll then get to grips with detecting and translating text with Amazon Rekognition and Amazon Translate. The book will assist you in performing speech-to-text with Amazon Transcribe and Amazon Polly. Later, you'll discover the use of Amazon Comprehend for extracting information from text, and Amazon Lex for building voice chatbots. You will also understand the key capabilities of Amazon SageMaker such as wrangling big data, discovering topics in text collections, and classifying images. Finally, you'll cover sales forecasting with deep learning and autoregression, before exploring the importance of a feedback loop in machine learning. By the end of this book, you will have the skills you need to implement AI in AWS through hands-on exercises that cover all aspects of the ML model life cycle. What you will learn Gain useful insights into different machine and deep learning models Build and deploy robust deep learning systems to production Train machine and deep learning models with diverse infrastructure specifications Scale AI apps without dealing with the complexity of managing the underlying infrastructure Monitor and Manage AI experiments efficiently Create AI apps using AWS pre-trained AI services Who this book is forThis book is for data scientists, machine learning developers, deep learning researchers, and artificial intelligence enthusiasts who want to harness the power of AWS to implement powerful artificial intelligence solutions. A basic understanding of machine learning concepts is expected.
(source: Nielsen Book Data)

This book, updated and enlarged for the second edition, is written as a text for a course aimed at 3rd or 4th year students. Only some familiarity with elementary linear algebra and probability is directly assumed, but some maturity is required. The students may specialize in discrete mathematics, computer science, or communication engineering. The book is also a suitable introduction to coding theory for researchers from related fields or for professionals who want to supplement their theoretical basis. The book gives the coding basics for working on projects in any of the above areas, but material specific to one of these fields has not been included. The chapters cover the codes and decoding methods that are currently of most interest in research, development, and application. They give a relatively brief presentation of the essential results, emphasizing the interrelations between different methods and proofs of all important results. A sequence of problems at the end of each chapter serves to review the results and give the student an appreciation of the concepts. In addition, some problems and suggestions for projects indicate direction for further work. The presentation encourages the use of programming tools for studying codes, implementing decoding methods, and simulating performance. Specific examples of programming exercises are provided on the book's home page.

• Table of Contents Quantifying Learning Algorithms Evaluating Kernel Learning Performance in Ensemble Learning Training Neural Networks Time-Series Analysis Natural Language Processing Temporal and Sequential Pattern Discovery Probabilistic Graphical Models Selected Topics in Deep Learning Causal Inference Advanced Methods.
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Your hands-on reference guide to developing, training, and optimizing your machine learning models Key Features Your guide to learning efficient machine learning processes from scratch Explore expert techniques and hacks for a variety of machine learning concepts Write effective code in R, Python, Scala, and Spark to solve all your machine learning problems Book DescriptionMachine learning makes it possible to learn about the unknowns and gain hidden insights into your datasets by mastering many tools and techniques. This book guides you to do just that in a very compact manner. After giving a quick overview of what machine learning is all about, Machine Learning Quick Reference jumps right into its core algorithms and demonstrates how they can be applied to real-world scenarios. From model evaluation to optimizing their performance, this book will introduce you to the best practices in machine learning. Furthermore, you will also look at the more advanced aspects such as training neural networks and work with different kinds of data, such as text, time-series, and sequential data. Advanced methods and techniques such as causal inference, deep Gaussian processes, and more are also covered. By the end of this book, you will be able to train fast, accurate machine learning models at your fingertips, which you can easily use as a point of reference. What you will learn Get a quick rundown of model selection, statistical modeling, and cross-validation Choose the best machine learning algorithm to solve your problem Explore kernel learning, neural networks, and time-series analysis Train deep learning models and optimize them for maximum performance Briefly cover Bayesian techniques and sentiment analysis in your NLP solution Implement probabilistic graphical models and causal inferences Measure and optimize the performance of your machine learning models Who this book is forIf you're a machine learning practitioner, data scientist, machine learning developer, or engineer, this book will serve as a reference point in building machine learning solutions. You will also find this book useful if you're an intermediate machine learning developer or data scientist looking for a quick, handy reference to all the concepts of machine learning. You'll need some exposure to machine learning to get the best out of this book.
(source: Nielsen Book Data)

• Basic Mathematical Thinking
• Matrices
• Systems of Linear Equations
• Geometric Vectors
• Complex Numbers and Polynomials
• An Introduction to Geometric Algebra and Conics
• An Overview of Algebraic Structures
• Vector Spaces
• Linear Mappings
• An Introduction to Computational Complexity
• Graph Theory
• Applied Linear Algebra: Electrical Networks
• A non-linear Algebra: An Introduction to Boolean Algebra
• Proofs of Theorems that Require Further Knowledge of Mathematics.

This book presents the main concepts of linear algebra from the viewpoint of applied scientists such as computer scientists and engineers, without compromising on mathematical rigor. Based on the idea that computational scientists and engineers need, in both research and professional life, an understanding of theoretical concepts of mathematics in order to be able to propose research advances and innovative solutions, every concept is thoroughly introduced and is accompanied by its informal interpretation. Furthermore, most of the theorems included are first rigorously proved and then shown in practice by a numerical example. When appropriate, topics are presented also by means of pseudocodes, thus highlighting the computer implementation of algebraic theory. It is structured to be accessible to everybody, from students of pure mathematics who are approaching algebra for the first time to researchers and graduate students in applied sciences who need a theoretical manual of algebra to successfully perform their research. Most importantly, this book is designed to be ideal for both theoretical and practical minds and to offer to both alternative and complementary perspectives to study and understand linear algebra.

• Introduction
• Basics of Manifolds
• Vector Bundles
• Vectors and Covectors
• Differential Forms
• Lie Derivatives
• Lie Groups
• Frobenius Theorem
• Principle Bundles
• Connections on Principle Bundles
• Curvature of a Connection
• Classical Electromagnetism
• Yang-Mills Theory
• Gauge Theory
• The Dirac Monopole
• Instantons
• What Next?
• Discussion of the Exercises.

This introductory graduate level text provides a relatively quick path to a special topic in classical differential geometry: principal bundles. While the topic of principal bundles in differential geometry has become classic, even standard, material in the modern graduate mathematics curriculum, the unique approach taken in this text presents the material in a way that is intuitive for both students of mathematics and of physics. The goal of this book is to present important, modern geometric ideas in a form readily accessible to students and researchers in both the physics and mathematics communities, providing each with an understanding and appreciation of the language and ideas of the other.

• Part I Quantum Reality: Sarah Cannon and Andreas Doring, A Generalisation of Stone Duality to Orthomodular Lattices.- G'abor Hofer-Szab'o, Bell's Local Causality is a d-Separation Criterion.- Yuichiro Kitajima, Local Operations and Completely Positive Maps in Algebraic Quantum Field Theory.- Klaas Landsman and Bert Lindenhovius, Symmetries in Exact Bohrification.- Mikl'os R'edei, Categorial Local Quantum Physics.- Part II Quantum Information: Francesco Buscemi, Reverse Data-Processing Theorems and Computational Second Laws:.- Michael J. W. Hall, Trust-Free Verification of Steering: Why You Can't Cheat a Quantum Referee.- Holger F. Hofmann, Dynamics and Statistics in the Operator Algebra of Quantum Mechanics.- Keiji Matsumoto, A New Quantum Version of f -Divergence.- Part III Quantum Measurement: Jeremy Butterfield, Peaceful Coexistence: Examining Kent's Relativistic Solution to the Quantum Measurement Problem.- Yuji Hasegawa, Relations Inherent in Successive 1=2??Spin Measurements.- Jasper van Heugten and Sander Wolters, Obituary for a Flea.- Kazuya Okamura, Measuring Processes and the Heisenberg Picture.
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This volume contains papers based on presentations at the "Nagoya Winter Workshop 2015: Reality and Measurement in Algebraic Quantum Theory (NWW 2015)", held in Nagoya, Japan, in March 2015. The foundations of quantum theory have been a source of mysteries, puzzles, and confusions, and have encouraged innovations in mathematical languages to describe, analyze, and delineate this wonderland. Both ontological and epistemological questions about quantum reality and measurement have been placed in the center of the mysteries explored originally by Bohr, Heisenberg, Einstein, and Schroedinger. This volume describes how those traditional problems are nowadays explored from the most advanced perspectives. It includes new research results in quantum information theory, quantum measurement theory, information thermodynamics, operator algebraic and category theoretical foundations of quantum theory, and the interplay between experimental and theoretical investigations on the uncertainty principle. This book is suitable for a broad audience of mathematicians, theoretical and experimental physicists, and philosophers of science.
(source: Nielsen Book Data)

• Table of Contents Data science: Bird's-eye view Data Modeling in Action - The Titanic Example Feature Engineering and Model Complexity - The Titanic Example Revisited Get Up and Running with TensorFlow Tensorflow in Action - Some Basic Examples Deep Feed-forward Neural Networks - Implementing Digit Classification Introduction to Convolutional Neural Networks Object Detection - CIFAR-10 Example Object Detection - Transfer Learning with CNNs Recurrent-Type Neural Networks - Language modeling Representation Learning - Implementing Word Embeddings Neural sentiment Analysis Autoencoders - Feature Extraction and Denoising Generative Adversarial Networks in Action - Generating New Images Face Generation and Handling Missing Labels Appendix - Implementing Fish Recognition.
• (source: Nielsen Book Data)

Grasp the fundamental concepts of deep learning using Tensorflow in a hands-on manner Key Features Get a first-hand experience of the deep learning concepts and techniques with this easy-to-follow guide Train different types of neural networks using Tensorflow for real-world problems in language processing, computer vision, transfer learning, and more Designed for those who believe in the concept of 'learn by doing', this book is a perfect blend of theory and code examples Book DescriptionDeep learning is a popular subset of machine learning, and it allows you to build complex models that are faster and give more accurate predictions. This book is your companion to take your first steps into the world of deep learning, with hands-on examples to boost your understanding of the topic. This book starts with a quick overview of the essential concepts of data science and machine learning which are required to get started with deep learning. It introduces you to Tensorflow, the most widely used machine learning library for training deep learning models. You will then work on your first deep learning problem by training a deep feed-forward neural network for digit classification, and move on to tackle other real-world problems in computer vision, language processing, sentiment analysis, and more. Advanced deep learning models such as generative adversarial networks and their applications are also covered in this book. By the end of this book, you will have a solid understanding of all the essential concepts in deep learning. With the help of the examples and code provided in this book, you will be equipped to train your own deep learning models with more confidence. What you will learn Understand the fundamentals of deep learning and how it is different from machine learning Get familiarized with Tensorflow, one of the most popular libraries for advanced machine learning Increase the predictive power of your model using feature engineering Understand the basics of deep learning by solving a digit classification problem of MNIST Demonstrate face generation based on the CelebA database, a promising application of generative models Apply deep learning to other domains like language modeling, sentiment analysis, and machine translation Who this book is forThis book targets data scientists and machine learning developers who wish to get started with deep learning. If you know what deep learning is but are not quite sure of how to use it, this book will help you as well. An understanding of statistics and data science concepts is required. Some familiarity with Python programming will also be beneficial.
(source: Nielsen Book Data)

• On the Complexity of Minimizing the Total Calibration Cost.- On the Fixed-Parameter Tractability of Some Matching Problems Under the Color-Spanning Model. - The complexity of finding (approximate sized) distance d dominating sets in tournaments.- On Computational Aspects of Greedy Partitioning of Graphs.- Maximum Edge Bicliques in Tree Convex Bipartite Graphs.- Complete Submodularity Characterization in the Comparative Independent Cascade Model.- A Risk-reward Model for On-line Financial Leasing Problem with an Interest Rate.- Designing and Implementing Algorithms for the Closest String Problem.- The Broken-Triangle Property with Adjoint Values.- Online Knapsack Problem under Concave Functions.- Fluctuated Fitting under the L1-metric.- Optimal Partitioning Which Maximizes the Weighted Sum of Products.- Modular-width: An Auxiliary Parameter for Parameterized Parallel Complexity.- Online Strategies for Evacuating from a Convex Region in the Plane.- A further analysis of the dynamic dominant resource fairness mechanism.- A 42k Kernel for the Complementary Maximal Strip Recovery Problem.- On-line Scheduling with a Monotonous Subsequence Constraint.- A 1.4-Approximation Algorithm For Two-sided Scaffold Filling.- FPT algorithms for FVS parameterized by split and cluster vertex deletion sets and other parameters.- A Constant Amortized Time Algorithm for Generating Left-Child Sequences in Lexicographic Order.- Geodectic Contraction Games on Trees.- On Approximation Algorithms for Two-Stage Scheduling Problems.- A New Lower Bound for Positive Zero Forcing.- Phase Transition for Maximum Not-All-Equal Satisfiability. .
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This book constitutes the proceedings of the 11th International Workshop on Frontiers in Algorithmics, FAW 2017, held in Chengdu, China, in June 2017. The 24 papers presented in this volume were carefully reviewed and selected from 61 submissions. They deal with all aspects of theoretical computer science and algorithms.
(source: Nielsen Book Data)

• Relating Structure and Power: Comonadic Semantics for Computational Resources.- Coalgebraic Logics and Duality.- Intuitionistic Podelski-Rybalchenko Theorem and Equivalence between Inductive Definitions and Cyclic Proofs.- Undecidability of Equality for Codata Types.- Predicate Liftings and Functor Presentations in Coalgebraic Expression Languages.- Long-Term Values in Markov Decision Processes, (Co)Algebraically.- (In)finite Trace Equivalence of Probabilistic Transition Systems.- Steps and Traces.- On Algebras with Effectful Iteration.- Monoidal computer III: A coalgebraic view of computability and complexity.- Fibrational Bisimulations and Quantitative Reasoning.- Categorical Buchi and Parity Conditions via Alternating Fixed Points of Functors.
• (source: Nielsen Book Data)

This book constitutes the thoroughly refereed post-conference proceedings of the 14th International Workshop on Coalgebraic Methods in Computer Science, CMCS 2018, colocated with ETAPS 2018, held in Thessaloniki, Greece, in April 2018.The 10 revised full papers were carefully reviewed and selected from 17 submissions. Also included are the extended abstracts of two keynotes/invited talks. The papers cover a wide range of topics in the theory, logics and applications of coalgebras.
(source: Nielsen Book Data)

• The Theory of Universal Graphs for Games: Past and Future.- Approximate coalgebra homomorphisms and approximate solutions.- Duality for Instantial Neighbourhood Logic via Coalgebra.- Free-algebra functors from a coalgebraic perspective.- Learning Automata with Side-Effects.- De Finetti's Construction as a Categorical Limit.- Injective Objects and Fibered Codensity Liftings.- Explaining Non-Bisimilarity in a Coalgebraic Approach: Games and Distinguishing Formulas.- A categorical approach to secure compilation.- Semantics for rst-order a ne inductive datatypes via slice categories.
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This book constitutes the thoroughly refereed post-conference proceedings of the 15th International Workshop on Coalgebraic Methods in Computer Science, CMCS 2020, colocated with ETAPS 2020, held in Dublin, Ireland, in April 2020. The conference was held as several online events in September and October due to the COVID-19 pandemic. The 9 revised full papers were carefully reviewed and selected from 13 submissions. Also included is one invited talk. The papers cover a wide range of topics in the theory, logics, and applications of coalgebras.
(source: Nielsen Book Data)