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The UCLA Plasma Simulation Group is a major partner of the "Community Petascale Project for Accelerator Science and Simulation. This is the final technical report. We include an overall summary, a list of publications and individual progress reports for each years. During the past five years we have made tremendous progress in enhancing the capabilities of OSIRIS and QuickPIC, in developing new algorithms and data structures for PIC codes to run on GPUS and many future core architectures, and in using these codes to model experiments and in making new scientific discoveries. Here we summarize some highlights for which SciDAC was a major contributor.

• CM-Points on Straight Lines
• Maass Waveforms and Low-Lying Zeros
• Théorème de Jordan Friable
• On Conjectures of T. Ordowski and Z.W. Sun Concerning Primes and Quadratic Forms
• Large Gaps Between Consecutive Prime Numbers Containing Perfect Powers
• On the Parity of the Number of Small Divisors of n
• Counting Primes in Arithmetic Progressions
• Limit Points of the Sequence of Normalized Differences Between Consecutive Prime Numbers
• Spirals of the Zeta Function I
• Best Possible Densities of Dickson m-tuples, as a Consequence of Zhang-Maynard-Tao
• A Note on Helson's Conjecture on Moments of Random Multiplicative Functions
• Large Values of the Zeta-Function on the Critical Line
• A Note on Bessel Twists of L-Functions
• The Sound of Fractal Strings and the Riemann Hypothesis
• Sums of two Squares in Short Intervals
• Infinite Sumsets with Many Representations
• On the Ratio of Consecutive Gaps Between Primes
• Remarks on Fibers of the Sum-of-Divisors Function
• On Amicable Numbers
• Trigonometric Representations of Generalized Dedekind and Hardy Sums via the Discrete Fourier Transform
• On Arithmetic Properties of Products and Shifted Products
• Narrow Progressions in the Primes.

This volume contains a collection of research and survey papers written by some of the most eminent mathematicians in the international community and is dedicated to Helmut Maier, whose own research has been groundbreaking and deeply influential to the field. Specific emphasis is given to topics regarding exponential and trigonometric sums and their behavior in short intervals, anatomy of integers and cyclotomic polynomials, small gaps in sequences of sifted prime numbers, oscillation theorems for primes in arithmetic progressions, inequalities related to the distribution of primes in short intervals, the Möbius function, Euler's totient function, the Riemann zeta function and the Riemann Hypothesis. Graduate students, research mathematicians, as well as computer scientists and engineers who are interested in pure and interdisciplinary research, will find this volume a useful resource. Contributors to this volume: Bill Allombert, Levent Alpoge, Nadine Amersi, Yuri Bilu, Régis de la Bretèche, Christian Elsholtz, John B. Friedlander, Kevin Ford, Daniel A. Goldston, Steven M. Gonek, Andrew Granville, Adam J. Harper, Glyn Harman, D.R. Heath-Brown, Aleksandar Ivić, Geoffrey Iyer, Jerzy Kaczorowski, Daniel M. Kane, Sergei Konyagin, Dimitris Koukoulopoulos, Michel L. Lapidus, Oleg Lazarev, Andrew H. Ledoan, Robert J. Lemke Oliver, Florian Luca, James Maynard, Steven J. Miller, Hugh L. Montgomery, Melvyn B. Nathanson, Ashkan Nikeghbali, Alberto Perelli, Amalia Pizarro-Madariaga, János Pintz, Paul Pollack, Carl Pomerance, Michael Th. Rassias, Maksym Radziwiłł, Joël Rivat, András Sárközy, Jeffrey Shallit, Terence Tao, Gérald Tenenbaum, László Tóth, Tamar Ziegler, Liyang Zhang.

• Multi-agent systems.- swarm robotics.- fuzzy logic approaches.- planning and routing problems.- recommendation in social media.- predication.- classification.- finding patterns.- image enhancement.- deep learning.- theories and models of swarm intelligence.- ant colony optimization.- particle swarm optimization.- artificial bee colony algorithms.- genetic algorithms.- differential evolution.- fireworks algorithm.- bacterial foraging optimization.- artificial immune system.- hydrologic cycle optimization.- other swarm-based optimization algorithms.- hybrid optimization algorithms.- multi-objective optimization.- large-scale global optimization. .
• (source: Nielsen Book Data)

The two-volume set of LNCS 10941 and 10942 constitutes the proceedings of the 9th International Conference on Advances in Swarm Intelligence, ICSI 2018, held in Shanghai, China, in June 2018. The total of 113 papers presented in these volumes was carefully reviewed and selected from 197 submissions. The papers were organized in topical sections namely: multi-agent systems; swarm robotics; fuzzy logic approaches; planning and routing problems; recommendation in social media; predication; classification; finding patterns; image enhancement; deep learning; theories and models of swarm intelligence; ant colony optimization; particle swarm optimization; artificial bee colony algorithms; genetic algorithms; differential evolution; fireworks algorithm; bacterial foraging optimization; artificial immune system; hydrologic cycle optimization; other swarm-based optimization algorithms; hybrid optimization algorithms; multi-objective optimization; large-scale global optimization.
(source: Nielsen Book Data)

• Part 1------1 - Entity Framework Core in Context2 - Your First Entity Framework Core Application3 - Working with Databases4 - SportsStore - A Real (Data) Application5 - SportsStore - Storing Data6 - SportsStore - Modifying Data7 - SportsStore - Expanding the Data Model8 - SportsStore - Scaling Up9 - SportsStore - Customer Features10 - SportsStore - Creating An API Part 2-----11 - Working with Entity Framework Core12 - Performing Data Operations13 - Understanding Migrations14 - Creating Data Relationships15 - Working with Relationships,
• Part 116 - Working with Relationships,
• Part 217 - Scaffolding an Existing Database18 - Manually Modelling a Database Part 3-----19 - Keys20 - Querying Data21 - Storing Data22 - Deleting Data23 - Using Database Server Features24 - Using Transactions.
• (source: Nielsen Book Data)

Model, map, and access data effectively with Entity Framework Core 2, the latest evolution of Microsoft's object-relational mapping framework. You will access data utilizing .NET objects via the most common data access layer used in ASP.NET Core MVC 2 projects. Best-selling author Adam Freeman explains how to get the most from Entity Framework Core 2 in MVC projects. He begins by describing the different ways that Entity Framework Core 2 can model data and the different types of databases that can be used. He then shows you how to use Entity Framework Core 2 in your own MVC projects, starting from the nuts and bolts and building up to the most advanced and sophisticated features, going in-depth to give you the knowledge you need. Chapters include common problems and how to avoid them. What You'll Learn Gain a solid architectural understanding of Entity Framework Core 2 Create databases using your MVC data model Create MVC models using an existing database Access data in an MVC application using Entity Framework Core 2 Use Entity Framework in RESTful Web Services Who This Book Is For ASP.NET Core MVC 2 developers who want to use Entity Framework Core 2 as the data access layer in their projects.
(source: Nielsen Book Data)

• Chapter 1: The Big Data Stack Overview.-
• Chapter 2: Cloud Storage.-
• Chapter 3: Apache Brooklyn.-
• Chapter 4: Apache Mesos.-
• Chapter 5: Stack Storage Options.-
• Chapter 6: Processing.-
• Chapter 7: Streaming.-
• Chapter 8: Frameworks.-
• Chapter 9: Visualization.-
• Chapter 10: The Big Data Stack.-.
• (source: Nielsen Book Data)

See a Mesos-based big data stack created and the components used. You will use currently available Apache full and incubating systems. The components are introduced by example and you learn how they work together. In the Complete Guide to Open Source Big Data Stack, the author begins by creating a private cloud and then installs and examines Apache Brooklyn. After that, he uses each chapter to introduce one piece of the big data stack-sharing how to source the software and how to install it. You learn by simple example, step by step and chapter by chapter, as a real big data stack is created. The book concentrates on Apache-based systems and shares detailed examples of cloud storage, release management, resource management, processing, queuing, frameworks, data visualization, and more. What You'll Learn Install a private cloud onto the local cluster using Apache cloud stack Source, install, and configure Apache: Brooklyn, Mesos, Kafka, and Zeppelin See how Brooklyn can be used to install Mule ESB on a cluster and Cassandra in the cloud Install and use DCOS for big data processing Use Apache Spark for big data stack data processing Who This Book Is For Developers, architects, IT project managers, database administrators, and others charged with developing or supporting a big data system. It is also for anyone interested in Hadoop or big data, and those experiencing problems with data size.
(source: Nielsen Book Data)

• Part 1. Introduction
• Chapter 1. Big Data, Big Problems
• Chapter 2. Big Data, Big Solutions
• Part 2. Playing SMACK
• Chapter 3. The Language: Scala
• Chapter 4. The Model: Akka
• Chapter 5. Storage. Apache Cassandra
• Chapter 6. The View
• Chapter 7. The Manager: Apache Mesos
• Chapter 8. The Broker: Apache Kafka
• Part 3. Improving SMACK
• Chapter 9. Fast Data Patterns
• Chapter 10. Big Data Pipelines
• Chapter 11. Glossary.
• (source: Nielsen Book Data)

Learn how to integrate full-stack open source big data architecture and to choose the correct technology-Scala/Spark, Mesos, Akka, Cassandra, and Kafka-in every layer. Big data architecture is becoming a requirement for many different enterprises. So far, however, the focus has largely been on collecting, aggregating, and crunching large data sets in a timely manner. In many cases now, organizations need more than one paradigm to perform efficient analyses. Big Data SMACK explains each of the full-stack technologies and, more importantly, how to best integrate them. It provides detailed coverage of the practical benefits of these technologies and incorporates real-world examples in every situation. This book focuses on the problems and scenarios solved by the architecture, as well as the solutions provided by every technology. It covers the six main concepts of big data architecture and how integrate, replace, and reinforce every layer: The language: Scala The engine: Spark (SQL, MLib, Streaming, GraphX) The container: Mesos, Docker The view: Akka The storage: Cassandra The message broker: Kafka What You Will Learn: Make big data architecture without using complex Greek letter architectures Build a cheap but effective cluster infrastructure Make queries, reports, and graphs that business demands Manage and exploit unstructured and No-SQL data sources Use tools to monitor the performance of your architecture Integrate all technologies and decide which ones replace and which ones reinforce Who This Book Is For: Developers, data architects, and data scientists looking to integrate the most successful big data open stack architecture and to choose the correct technology in every layer.
(source: Nielsen Book Data)

• Preface
• 1 A Review of Number Theory and Algebra
• 2 Cryptography
• 3 Coding Theory
• 4 Quasi-Monte Carlo Methods
• 5 Pseudorandom Numbers
• 6 Further Applications
• Bibliography
• Index.

This textbook effectively builds a bridge from basic number theory to recent advances in applied number theory. It presents the first unified account of the four major areas of application where number theory plays a fundamental role, namely cryptography, coding theory, quasi-Monte Carlo methods, and pseudorandom number generation, allowing the authors to delineate the manifold links and interrelations between these areas. Number theory, which Carl-Friedrich Gauss famously dubbed the queen of mathematics, has always been considered a very beautiful field of mathematics, producing lovely results and elegant proofs. While only very few real-life applications were known in the past, today number theory can be found in everyday life: in supermarket bar code scanners, in our cars' GPS systems, in online banking, etc. Starting with a brief introductory course on number theory in Chapter 1, which makes the book more accessible for undergraduates, the authors describe the four main application areas in Chapters 2-5 and offer a glimpse of advanced results that are presented without proofs and require more advanced mathematical skills. In the last chapter they review several further applications of number theory, ranging from check-digit systems to quantum computation and the organization of raster-graphics memory. Upper-level undergraduates, graduates and researchers in the field of number theory will find this book to be a valuable resource.

• 1. Basic Terminology, Notation and Results (J. Bang-Jensen, G. Gutin).-
• 2. Tournaments and Semicomplete Digraphs (J. Bang-Jensen, F. Havet).-
• 3. Acyclic Digraphs (G. Gutin).-
• 4. Euler Digraphs (M. Wahlstroem).-
• 5. Planar digraphs (M. Pilipczuk, M. Pilipczuk).-
• 6. Locally Semicomplete Digraphs and Generalizations (J. Bang-Jensen).-
• 7. Semicomplete Multipartite Digraphs (A. Yeo).-
• 8. Quasi-Transitive Digraphs and Their Extensions (H. Galeana-Sanchez, C. Hernandez-Cruz).-
• 9. Digraphs of Bounded Width (S. Kreutzer, O. Kwon).-
• 10. Digraphs Products (R. Hammack).-
• 11. Miscellaneous Digraph Classes (Y. Guo, M. Surmacs).-
• 12. Lexicographic Orientation Algorithms (J. Huang).- Indices.
• (source: Nielsen Book Data)

This edited volume offers a detailed account of the theory of directed graphs from the perspective of important classes of digraphs, with each chapter written by experts on the topic. Outlining fundamental discoveries and new results obtained over recent years, this book provides a comprehensive overview of the latest research in the field. It covers core new results on each of the classes discussed, including chapters on tournaments, planar digraphs, acyclic digraphs, Euler digraphs, graph products, directed width parameters, and algorithms. Detailed indices ease navigation while more than 120 open problems and conjectures ensure that readers are immersed in all aspects of the field. Classes of Directed Graphs provides a valuable reference for graduate students and researchers in computer science, mathematics and operations research. As digraphs are an important modelling tool in other areas of research, this book will also be a useful resource to researchers working in bioinformatics, chemoinformatics, sociology, physics, medicine, etc.
(source: Nielsen Book Data)

• Introduction to Pattern Mining.- Quality Measures in Pattern Mining.- Introduction to Evolutionary Computation.- Pattern Mining with Genetic Algorithms.- Genetic Programming in Pattern Mining.- Multiobjective Approaches in Pattern Mining.- Supervised Local Pattern Mining.- Mining Exceptional Relationships Between Patterns.- Scalability in Pattern Mining.
• (source: Nielsen Book Data)

This book provides a comprehensive overview of the field of pattern mining with evolutionary algorithms. To do so, it covers formal definitions about patterns, patterns mining, type of patterns and the usefulness of patterns in the knowledge discovery process. As it is described within the book, the discovery process suffers from both high runtime and memory requirements, especially when high dimensional datasets are analyzed. To solve this issue, many pruning strategies have been developed. Nevertheless, with the growing interest in the storage of information, more and more datasets comprise such a dimensionality that the discovery of interesting patterns becomes a challenging process. In this regard, the use of evolutionary algorithms for mining pattern enables the computation capacity to be reduced, providing sufficiently good solutions. This book offers a survey on evolutionary computation with particular emphasis on genetic algorithms and genetic programming. Also included is an analysis of the set of quality measures most widely used in the field of pattern mining with evolutionary algorithms. This book serves as a review of the most important evolutionary algorithms for pattern mining. It considers the analysis of different algorithms for mining different type of patterns and relationships between patterns, such as frequent patterns, infrequent patterns, patterns defined in a continuous domain, or even positive and negative patterns. A completely new problem in the pattern mining field, mining of exceptional relationships between patterns, is discussed. In this problem the goal is to identify patterns which distribution is exceptionally different from the distribution in the complete set of data records. Finally, the book deals with the subgroup discovery task, a method to identify a subgroup of interesting patterns that is related to a dependent variable or target attribute. This subgroup of patterns satisfies two essential conditions: interpretability and interestingness.
(source: Nielsen Book Data)

• Part I: Foundations and Applications of Graph Edit Distance
• Introduction and Basic Concepts
• Graph Edit Distance
• Bipartite Graph Edit Distance
• Part II: Recent Developments and Research on Graph Edit Distance
• Improving the Distance Accuracy of Bipartite Graph Edit Distance
• Learning Exact Graph Edit Distance
• Speeding Up Bipartite Graph Edit Distance
• Conclusions and Future Work
• Appendix A: Experimental Evaluation of Sorted Beam Search
• Appendix B: Data Sets.

This unique text/reference presents a thorough introduction to the field of structural pattern recognition, with a particular focus on graph edit distance (GED), one of the most flexible graph distance models available. The book also provides a detailed review of a diverse selection of novel methods related to GED, and concludes by suggesting possible avenues for future research. Topics and features: Formally introduces the concept of GED, and highlights the basic properties of this graph matching paradigm Describes a reformulation of GED to a quadratic assignment problem Illustrates how the quadratic assignment problem of GED can be reduced to a linear sum assignment problem Reviews strategies for reducing both the overestimation of the true edit distance and the matching time in the approximation framework Examines the improvement demonstrated by the described algorithmic framework with respect to the distance accuracy and the matching time Includes appendices listing the datasets employed for the experimental evaluations discussed in the book Researchers and graduate students interested in the field of structural pattern recognition will find this focused work to be an essential reference on the latest developments in GED. Dr. Kaspar Riesen is a university lecturer of computer science in the Institute for Information Systems at the University of Applied Sciences and Arts Northwestern Switzerland, Olten, Switzerland.

• Foreword
• Limits? What Limits?
• Part I Computability
• Problems and Effective Procedures
• The WHILE-Language
• Semantics of WHILE
• Extensions of WHILE
• Programs As Data Objects
• A Self-Interpreter for WHILE
• An Undecidable (Non-computable) Problem
• More Undecidable Problems
• Self-referencing Programs
• The Church-Turing Thesis
• Part II Complexity
• Measuring Time Usage
• Complexity Classes
• Robustness of P
• Hierarchy Theorems
• Famous Problems in P
• Common Problems not Known to be in P
• The One-Million-Dollar Question
• How Hard is a Problem?
• Complete Problems
• How to Solve NP-complete Problems?
• Part III Emerging New Models of Computation
• "going nano"
• Molecular Computing
• Quantum Computing
• Appendix A: Further Reading
• Computability and Complexity Textbooks
• Glossary
• Index.

This textbook discusses the most fundamental and puzzling questions about the foundations of computing. In 23 lecture-sized chapters it provides an exciting tour through the most important results in the field of computability and time complexity, including the Halting Problem, Rice's Theorem, Kleene's Recursion Theorem, the Church-Turing Thesis, Hierarchy Theorems, and Cook-Levin's Theorem. Each chapter contains classroom-tested material, including examples and exercises. Links between adjacent chapters provide a coherent narrative. Fundamental results are explained lucidly by means of programs written in a simple, high-level imperative programming language, which only requires basic mathematical knowledge. Throughout the book, the impact of the presented results on the entire field of computer science is emphasised. Examples range from program analysis to networking, from database programming to popular games and puzzles. Numerous biographical footnotes about the famous scientists who developed the subject are also included. "Limits of Computation" offers a thorough, yet accessible, introduction to computability and complexity for the computer science student of the 21st century

• A Structural Theory of Rhythm Notation Based on Tree Representations and Term Rewriting
• Renotation from Optical Music Recognition
• Foundations for Reliable and Flexible Interactive Multimedia Scores
• Genetic Algorithms Based on the Principles of Grundgestalt and Developing Variation
• Describing Global Musical Structures by Integer Programming on Musical Patterns
• Improved Iterative Random Walk for Four-Part Harmonization
• Location Constraints for Repetition-Based Segmentation of Melodies
• Modeling Musical Structure with Parametric Grammars
• Perfect Balance: A Novel Principle for the Construction of Musical Scales and Meters
• Characteristics of Polyphonic Music Style and Markov Model of Pitch-Class Intervals
• A Corpus-Sensitive Algorithm for Automated Tonal Analysis
• Finding Optimal Triadic Transformational Spaces with Dijkstra's Shortest Path Algorithm
• A Probabilistic Approach to Determining Bass Voice Leading in Melodic Harmonisation
• Hypergestures in Complex Time: Creative Performance Between Symbolic and Physical Reality
• Generating Fingerings for Polyphonic Piano Music with a Tabu Search Algorithm
• Logistic Modeling of Note Transitions
• Evaluating Singer Consistency and Uniqueness in Vocal Performances
• A Change-Point Approach Towards Representing Musical Dynamics
• Structural Similarity Based on Time-Span Sub-Trees
• Cross Entropy as a Measure of Musical Contrast
• Symbolic Music Similarity Using Neuronal Periodicity and Dynamic Programming
• Applications of DFT to the Theory of Twentieth-Century Harmony
• Utilizing Computer Programming to Analyze Post-Tonal Music: Contour Analysis of Four Works for Solo Flute
• A Statistical Approach to the Global Structure of John Cage's Number Piece Five⁵
• Exact Cover Problem in Milton Babbitt's All-Partition Array
• Constructing Geometrical Spaces from Acoustical Representations
• Geometry, Iterated Quantization and Filtered Voice-Leading Spaces
• Using Fundamental Groups and Groupoids of Chord Spaces to Model Voice Leading
• All-Interval Structures
• Unifying Tone System Definitions: Ordering Chromas
• A Categorical Generalization of Klumpenhouwer Networks
• The Spinnen-Tonnetz: New Musical Dimensions in the 2D Network for Tonal Music Analysis: Using Polarization and Tonal Regions in a Dynamic Environment
• Probabilistic Segmentation of Musical Sequences Using Restricted Boltzmann Machines
• ¿El Caballo Viejo? Latin Genre Recognition with Deep Learning and Spectral Periodicity
• Can a Musical Scale Have 14 Generators?
• On the Step-Patterns of Generated Scales That are Not Well-Formed
• Triads as Modes within Scales as Modes
• Greek Ethnic Modal Names vs. Alia Musica's Nomenclature.

This book constitutes the thoroughly refereed proceedings of the 5th International Conference on Mathematics and Computation in Music, MCM 2015, held in London, UK, in June 2015. The 24 full papers and 14 short papers presented were carefully reviewed and selected from 64 submissions. The papers feature research that combines mathematics or computation with music theory, music analysis, composition, and performance. They are organized in topical sections on notation and representation, music generation, patterns, performance, similarity and contrast, post-tonal music analysis, geometric approaches, deep learning, and scales.

• Primal-Circular Substitutions.- On the Group of Transformations of Classical Types of Seventh Chords.- Pairwise Well-Formed Modes and Transformations.- Homometry in the Dihedral Groups: Lifting Sets from Zn to Dn.- A Symmetric Quantum Theory of Modulation in Z20.- Almost Difference Sets in Transformational Music Theory.- Algebra of Harmony.- Developing Software for Dancing Tango in Compas.- Using Inharmonic Strings in Musical Instruments.- Real-Time Compositional Procedures for Mediated Soloist-Ensemble Interaction: The Comprovisador.- Strange Symmetries.- Interval Content vs. DFT.- Probing Questions about Keys: Tonal Distributions through the DFT.- Abstract Gestures: A Unifying Concept in Mathematical Music Theory.- Mathematical Music Theory and the Musical Math Game - Two Creative Ontological Switches.- Hamiltonian Graphs as Harmonic Tools.- New Investigations in Rhythmic Oddity.- Polytopic Graph of Latent Relations: A Multiscale Structure Model for Music Segments.- Dynamic Time Warping for Automatic Musical Form Identification in Symbolical Musical Files.- Identification and Evolution of Musical Style I: Hierarchical Transition Networks and their Modular Structure.- A Fuzzy-Clustering Based Approach for Measuring Similarity Between Melodies.- The Evolution of Tango Harmony, 1910-1960.- Determination of Compositional Systems through Systemic Modelling.- A Cluster Analysis for Mode Identification in Early Music Genres.- Cross Entropy as a Measure of Coherence and Uniqueness.- Complementary Collection and Ligeti's Combinatorial Tonality.- Probabilistic Generation of Ragtime Music from Classical Melodies.- Using Probabilistic Parsers to Support Composition of Salsa Music.
• (source: Nielsen Book Data)

This book constitutes the thoroughly refereed proceedings of the 6th International Conference on Mathematics and Computation in Music, MCM 2017, held in Mexico City, Mexico, in June 2017. The 26 full papers and 2 short papers presented were carefully reviewed and selected from 40 submissions. The papers feature research that combines mathematics or computation with music theory, music analysis, composition, and performance. They are organized in topical sections on algebraic models, computer assisted performance, Fourier analysis, Gesture Theory, Graph Theory and Combinatorics, Machine Learning, and Probability and Statistics in Musical Analysis and Composition.
(source: Nielsen Book Data)

• Introduction.- Multiple Instance Learning.- Multi-Instance Classification.- Instance-Based Classification Methods.- Bag-Based Classification Methods.- Multi-Instance Regression.- Unsupervised Multiple Instance Learning.- Data Reduction.- Imbalance Multi-Instance Data.- Multiple Instance Multiple Label Learning.
• (source: Nielsen Book Data)

This book provides a general overview of multiple instance learning (MIL), defining the framework and covering the central paradigms. The authors discuss the most important algorithms for MIL such as classification, regression and clustering. With a focus on classification, a taxonomy is set and the most relevant proposals are specified. Efficient algorithms are developed to discover relevant information when working with uncertainty. Key representative applications are included. This book carries out a study of the key related fields of distance metrics and alternative hypothesis. Chapters examine new and developing aspects of MIL such as data reduction for multi-instance problems and imbalanced MIL data. Class imbalance for multi-instance problems is defined at the bag level, a type of representation that utilizes ambiguity due to the fact that bag labels are available, but the labels of the individual instances are not defined. Additionally, multiple instance multiple label learning is explored. This learning framework introduces flexibility and ambiguity in the object representation providing a natural formulation for representing complicated objects. Thus, an object is represented by a bag of instances and is allowed to have associated multiple class labels simultaneously. This book is suitable for developers and engineers working to apply MIL techniques to solve a variety of real-world problems. It is also useful for researchers or students seeking a thorough overview of MIL literature, methods, and tools.
(source: Nielsen Book Data)

• Introduction
• Part I Black-Box Optimization
• 1 Nonlinear Optimization
• 2 Smooth Convex Optimization
• 3 Nonsmooth Convex Optimization
• 4 Second-Order Methods
• Part II Structural Optimization
• 5 Polynomial-time Interior-Point Methods
• 6 Primal-Dual Model of Objective Function
• 7 Optimization in Relative Scale
• Bibliographical Comments
• Appendix A. Solving some Auxiliary Optimization Problems
• References
• Index.

This book provides a comprehensive, modern introduction to convex optimization, a field that is becoming increasingly important in applied mathematics, economics and finance, engineering, and computer science, notably in data science and machine learning. Written by a leading expert in the field, this book includes recent advances in the algorithmic theory of convex optimization, naturally complementing the existing literature. It contains a unified and rigorous presentation of the acceleration techniques for minimization schemes of first- and second-order. It provides readers with a full treatment of the smoothing technique, which has tremendously extended the abilities of gradient-type methods. Several powerful approaches in structural optimization, including optimization in relative scale and polynomial-time interior-point methods, are also discussed in detail. Researchers in theoretical optimization as well as professionals working on optimization problems will find this book very useful. It presents many successful examples of how to develop very fast specialized minimization algorithms. Based on the author's lectures, it can naturally serve as the basis for introductory and advanced courses in convex optimization for students in engineering, economics, computer science and mathematics.-- Provided by publisher.

• Section I: Core Concepts 1. Fundamental Characteristics of Apache HBase2. Apache HBase and HDFS3. Application Characteristics for Apache HBase
• Section II-Data Model 4. Physical Storage 5. Column Family and a Column Qualifier6. Row Versioning7. Logical Storage
• Section III-Architecture 8. Components of HBase Cluster9. Regions10. Finding a row in an HBase table11. Compactions12. RegionServer Failover 13. RegionServer Splits
• Section IV-Schema Design 14. Creating Column Families15. Defining the row keys for optimal read performance and Locality
• Section V-Apache HBase API 16. The HBaseAdmin Class 17. The Get Class18. The HTable Class
• Section VI-Administration19. Using the Shell20. Bulk loading data into Apache HBase.
• (source: Nielsen Book Data)

Learn the fundamental foundations and concepts of the Apache HBase (NoSQL) open source database. It covers the HBase data model, architecture, schema design, API, and administration. Apache HBase is the database for the Apache Hadoop framework. HBase is a column family based NoSQL database that provides a flexible schema model. What You'll Learn Work with the core concepts of HBase Discover the HBase data model, schema design, and architecture Use the HBase API and administration Who This Book Is For Apache HBase (NoSQL) database users, designers, developers, and admins.
(source: Nielsen Book Data)

• Section I: Core Concepts 1. Fundamental Characteristics of Apache HBase2. Apache HBase and HDFS3. Application Characteristics for Apache HBase
• Section II-Data Model 4. Physical Storage 5. Column Family and a Column Qualifier6. Row Versioning7. Logical Storage
• Section III-Architecture 8. Components of HBase Cluster9. Regions10. Finding a row in an HBase table11. Compactions12. RegionServer Failover 13. RegionServer Splits
• Section IV-Schema Design 14. Creating Column Families15. Defining the row keys for optimal read performance and Locality
• Section V-Apache HBase API 16. The HBaseAdmin Class 17. The Get Class18. The HTable Class
• Section VI-Administration19. Using the Shell20. Bulk loading data into Apache HBase.
• (source: Nielsen Book Data)

Learn the fundamental foundations and concepts of the Apache HBase (NoSQL) open source database. It covers the HBase data model, architecture, schema design, API, and administration. Apache HBase is the database for the Apache Hadoop framework. HBase is a column family based NoSQL database that provides a flexible schema model. What You'll Learn Work with the core concepts of HBase Discover the HBase data model, schema design, and architecture Use the HBase API and administration Who This Book Is For Apache HBase (NoSQL) database users, designers, developers, and admins.
(source: Nielsen Book Data)

• Prerequisites and Notation.- Basic Properties of the Integers.- Groups, Rings and Ideals.- Applications to Public Key Cryptography.- Fields.- Properties of Finite Fields.- Applications to Stream Ciphers.- Boolean Functions.- Applications to Block Ciphers.- Number Theory in Public Key Cryptography.- Where do we go from here?.- Probability. .
• (source: Nielsen Book Data)

• 2.6 Solving Linear Congruences2.7 The Chinese Remainder Theorem; 2.8 Some Number-Theoretic Functions; 2.8.1 Multiplicative Functions; 2.8.2 The Möbius Function; 2.8.3 Euler's ϕ-Function; 2.8.4 The Case n = p ·q; 3 Groups, Rings and Ideals; 3.1 Groups; 3.2 Subgroups; 3.3 The Lattice of Subgroups; 3.4 Cosets; 3.5 Cyclic Groups; 3.6 Fermat's Little Theorem; 3.7 Primality Testing; 3.7.1 Miller's Test; 3.7.2 The Miller-Rabin Primality Test; 3.8 Rings and Ideals; 4 Applications to Public Key Cryptography; 4.1 Public Key Encryption: The RSA Mechanism; 4.1.1 RSA; 4.1.2 Breaking RSA

This textbook provides an introduction to the mathematics on which modern cryptology is based. It covers not only public key cryptography, the glamorous component of modern cryptology, but also pays considerable attention to secret key cryptography, its workhorse in practice. Modern cryptology has been described as the science of the integrity of information, covering all aspects like confidentiality, authenticity and non-repudiation and also including the protocols required for achieving these aims. In both theory and practice it requires notions and constructions from three major disciplines: computer science, electronic engineering and mathematics. Within mathematics, group theory, the theory of finite fields, and elementary number theory as well as some topics not normally covered in courses in algebra, such as the theory of Boolean functions and Shannon theory, are involved. Although essentially self-contained, a degree of mathematical maturity on the part of the reader is assumed, corresponding to his or her background in computer science or engineering. Algebra for Cryptologists is a textbook for an introductory course in cryptography or an upper undergraduate course in algebra, or for self-study in preparation for postgraduate study in cryptology.
(source: Nielsen Book Data)

• Prerequisites and Notation.- Basic Properties of the Integers.- Groups, Rings and Ideals.- Applications to Public Key Cryptography.- Fields.- Properties of Finite Fields.- Applications to Stream Ciphers.- Boolean Functions.- Applications to Block Ciphers.- Number Theory in Public Key Cryptography.- Where do we go from here?.- Probability. .
• (source: Nielsen Book Data)

• 2.6 Solving Linear Congruences2.7 The Chinese Remainder Theorem; 2.8 Some Number-Theoretic Functions; 2.8.1 Multiplicative Functions; 2.8.2 The Möbius Function; 2.8.3 Euler's ϕ-Function; 2.8.4 The Case n = p ·q; 3 Groups, Rings and Ideals; 3.1 Groups; 3.2 Subgroups; 3.3 The Lattice of Subgroups; 3.4 Cosets; 3.5 Cyclic Groups; 3.6 Fermat's Little Theorem; 3.7 Primality Testing; 3.7.1 Miller's Test; 3.7.2 The Miller-Rabin Primality Test; 3.8 Rings and Ideals; 4 Applications to Public Key Cryptography; 4.1 Public Key Encryption: The RSA Mechanism; 4.1.1 RSA; 4.1.2 Breaking RSA

This textbook provides an introduction to the mathematics on which modern cryptology is based. It covers not only public key cryptography, the glamorous component of modern cryptology, but also pays considerable attention to secret key cryptography, its workhorse in practice. Modern cryptology has been described as the science of the integrity of information, covering all aspects like confidentiality, authenticity and non-repudiation and also including the protocols required for achieving these aims. In both theory and practice it requires notions and constructions from three major disciplines: computer science, electronic engineering and mathematics. Within mathematics, group theory, the theory of finite fields, and elementary number theory as well as some topics not normally covered in courses in algebra, such as the theory of Boolean functions and Shannon theory, are involved. Although essentially self-contained, a degree of mathematical maturity on the part of the reader is assumed, corresponding to his or her background in computer science or engineering. Algebra for Cryptologists is a textbook for an introductory course in cryptography or an upper undergraduate course in algebra, or for self-study in preparation for postgraduate study in cryptology.
(source: Nielsen Book Data)