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This book introduces the fundamental concepts, techniques and results of linear algebra that form the basis of analysis, applied mathematics and algebra. Intended as a text for undergraduate students of mathematics, science and engineering with a knowledge of set theory, it discusses the concepts that are constantly used by scientists and engineers. It also lays the foundation for the language and framework for modern analysis and its applications. Divided into seven chapters, it discusses vector spaces, linear transformations, best approximation in inner product spaces, eigenvalues and eigenvectors, block diagonalisation, triangularisation, Jordan form, singular value decomposition, polar decomposition, and many more topics that are relevant to applications. The topics chosen have become well-established over the years and are still very much in use. The approach is both geometric and algebraic. It avoids distraction from the main theme by deferring the exercises to the end of each section. These exercises aim at reinforcing the learned concepts rather than as exposing readers to the tricks involved in the computation. Problems included at the end of each chapter are relatively advanced and require a deep understanding and assimilation of the topics.

Contents 1 2-D Shallow Water Wave Equations with Fuzzy Parameters . . . . . . 1 P. Karunakar and Snehashish Chakraverty 2 ANN Based Solution of Static Structural Problem with Fuzzy Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 S. K. Jeswal and Snehashish Chakraverty 3 Fuzzy Matrix Contractor Based Approach for Localization of Robots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 N. R. Mahato, Snehashish Chakraverty and L. Jaulin 4 Modeling Radon Diffusion Equation by Using Fuzzy Polynomials in Galerkin’s Method . . . . . . . . . . . . . . . . . . . . . . . . . . 75 T. D. Rao and Snehashish Chakraverty 5 Solving Fuzzy Static Structural Problems Using Symmetric Group Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 S. K. Jena and Snehashish Chakraverty 6 Modeling Dispersal Risk of Invasive Alien Plant Species . . . . . . . . . 109 H. O. W. Peiris, Sanjeewa Perera, Snehashish Chakraverty and S. M. W. Ranwala 7 Mathematical Model to Quantify Air Pollution in Cities . . . . . . . . . . 147 I. T. S. Piyatilake and Sanjeewa Perera

CONTENT PART 1 BASICS OF INFERENCE OVER NETWORKS CHAPTER 1 Asynchronous Adaptive Networks CHAPTER 2 Estimation and Detection Over Adaptive Networks CHAPTER 3 Multitask Learning Over Adaptive Networks With Grouping CHAPTER 4 Bayesian Approach to Collaborative Inference in Networks CHAPTER 5 Multiagent Distributed Optimization CHAPTER 6 Distributed Kalman and Particle Filtering CHAPTER 7 Game Theoretic Learning PART 2 SIGNAL PROCESSING ON GRAPHS CHAPTER 8 Graph Signal Processing . CHAPTER 9Sampling and Recovery of Graph Signals CHAPTER 10 Bayesian Active learning on Graphs . CHAPTER 1 1 Design of Graph Filters and Filterbanks CHAPTER 12 Statistical Graph Signal Processing: Stationarity and Spectral Estimation CHAPTER 1 3 Inference of Graph Topology CHAPTER 14 Partially Absorbing Ranclom Walks: A Unifiecl Framework for Learning on Graphs PART 3 DISTRIBUTED COMMUNICATIONS, NETWORKING, AND SENSING . . . . .

Contents Certain Image Formulae and Fractional Kinetic Equations Involving Extended Hypergeometric Functions . . . . . . . . . . . . . . . . . . . . . 1 Krunal B. Kachhia, Praveen Agarwal and Jyotindra C. Prajapati The Compact Approximation Property for Weighted Spaces of Holomorphic Mappings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 Manjul Gupta and Deepika Baweja Bloch Mappings on Bounded Symmetric Domains . . . . . . . . . . . . . . . . . . 49 Tatsuhiro Honda Certain Class of Meromorphically Multivalent Functions Defined by a Differential Operator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 Ghazi S. Khammash and Praveen Agarwal Bivariate Symmetric Discrete Orthogonal Polynomials . . . . . . . . . . . . . . 87 Y. Guemo Tefo, Iván Area and M. Foupouagnigni New and Extended Applications of the Natural and Sumudu Transforms: Fractional Diffusion and Stokes Fluid Flow Realms . . . . . . 107 Fethi Bin Muhammed Belgacem, Rathinavel Silambarasan, Hammouch Zakia and Toufik Mekkaoui On Uncertain-Fractional Modeling: The Future Way of Modeling Real-World Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 Abdon Atangana and Ilknur Koca Quadratic Reciprocity and Some “Non-differentiable” Functions . . . . . . 145 Kalyan Chakraborty and Azizul Hoque Survey on Metric Fixed Point Theory and Applications . . . . . . . . . . . . . 183 Yeol Je Cho Sums of Finite Products of Euler Functions . . . . . . . . . . . . . . . . . . . . . . 243 Taekyun Kim, Dae San Kim, Gwan Woo Jang and Jongkyum Kwon v On a New Extension of Caputo Fractional Derivative Operator . . . . . . 261 İ.O. Kıymaz, P. Agarwal, S. Jain and A. Çetinkaya An Extension of the Shannon Wavelets for Numerical Solution of Integro-Differential Equations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277 Maryam Attary Inverse Source Problem for Multi-term Fractional Mixed Type Equation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289 E.T. Karimov, S. Kerbal and N. Al-Salti

Abstract Time series often have a temporal hierarchy, with information that is spread out over multiple time scales. Common recurrent neural networks, however, do not explicitly accommodate such a hierarchy, and most research on them has been focusing on training algorithms rather than on their basic architecture. In this pa- per we study the effect of a hierarchy of recurrent neural networks on processing time series. Here, each layer is a recurrent network which receives the hidden state of the previous layer as input. This architecture allows us to perform hi- erarchical processing on difficult temporal tasks, and more naturally capture the structure of time series. We show that they reach state-of-the-art performance for recurrent networks in character-level language modeling when trained with sim- ple stochastic gradient descent. We also offer an analysis of the different emergent time scales.

James Martens JMARTENS @ CS . TORONTO . EDU Ilya Sutskever ILYA @ CS . UTORONTO . CA University of Toronto, Canada Abstract In this work we resolve the long-outstanding problem of how to effectively train recurrent neu- ral networks (RNNs) on complex and difficult sequence modeling problems which may con- tain long-term data dependencies. Utilizing re- cent advances in the Hessian-free optimization approach (Martens, 2010), together with a novel damping scheme, we successfully train RNNs on two sets of challenging problems. First, a col- lection of pathological synthetic datasets which are known to be impossible for standard op- timization approaches (due to their extremely long-term dependencies), and second, on three natural and highly complex real-world sequence datasets where we find that our method sig- nificantly outperforms the previous state-of-the- art method for training neural sequence mod- els: the Long Short-term Memory approach of Hochreiter and Schmidhuber (1997). Addition- ally, we offer a new interpretation of the gen- eralized Gauss-Newton matrix of Schraudolph (2002) which is used within the HF approach of Martens.

Abstract—This paper studies the approximation ability of con- tinuous-time recurrent neural networksto dynamical time-variant systems. It proves that any finite time trajectory of a given dynam- ical time-variant system can be approximated by the internal state of a continuous-time recurrent neural network. Given several spe- cial forms of dynamical time-variant systems or trajectories, this paper shows that they can all be approximately realized by the in- ternal state of a simple recurrent neural network.

• Why Recurrent Neural Networks (RNNs)?? • The Vanilla RNN unit? • The RNN forward pass? • Backpropagation refresher? • The RNN backward pass? • Issues with the Vanilla RNN? • The Long Short-Term Memory (LSTM) unit? • The LSTM Forward & Backward pass? • LSTM variants and tips? – Peephole LSTM? – GRU?

Abstract: This tutorial is a worked-out version of a 5-hour course originally held at AIS in September/October 2002. It has two distinct components. First, it contains a mathematically-oriented crash course on traditional training methods for recurrent neural networks, covering back-propagation thro

contens 1. Introduction 2. Preliminaries 3. Learning Covariance Functions 4. Memory Efficient Prediction With Truncated Observations 5. Fully Bayesian Approach 6. New Efficient Spatial Model with Built-In Gaussian Markov Random Fields 7. Fully Bayesian Spatial Prediction Using Gaussian Markov Random Fields

This book offers a comprehensive treatment of the theory of measures of noncompactness. It discusses various applications of the theory of measures of noncompactness, in particular, by addressing the results and methods of fixed-point theory. The concept of a measure of noncompactness is very useful for the mathematical community working in nonlinear analysis. Both these theories are especially useful in investigations connected with differential equations, integral equations, functional integral equations and optimization theory. Thus, one of the book’s central goals is to collect and present sufficient conditions for the solvability of such equations. The results are established in miscellaneous function spaces, and particular attention is paid to fractional calculus.

JMP Connections_ The Art of Utilizing Connections In Your Data

Structural Pattern Recognition with Graph Edit Distance_Approximation Algorithms and Applications 164页高清 带详细书签目录 pdf格式 Springer出版

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An Introduction to Machine Learning pdf格式 296页高清 带详细书签目录 Springer出版

Hybrid Approaches to Machine Translation 208页高清 带详细书签目录 springer出版 pdf格式

336页 I 高清带详细书签目录 I WILEY出版 I 原生电子书

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iOS 11, Swift 4, and Xcode 9 provide many new APIs for iOS developers. With this cookbook, you'll learn more than 170 proven solutions for tackling the latest features in iOS 11 and watchOS 4, including new ways to use Swift and Xcode to make your day-to-day app development life easier. This collect

National Academies Press (2013年9月3日) 页数 190页 Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data. Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale--terabytes and petabytes--is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge--from computer science, statistics, machine learning, and application disciplines--that must be brought to bear to make useful inferences from massive data.

Key Features AI-based examples to guide you in designing and implementing machine intelligence Develop your own method for future AI solutions Acquire advanced AI, machine learning, and deep learning design skills Table of Contents Become an Adaptive Thinker Think like a Machine Apply Machine Thinking to a Human Problem Become an Unconventional Innovator Manage the Power of Machine Learning and Deep Learning Don't Get Lost in Techniques – Focus on Optimizing Your Solutions When and How to Use Artificial Intelligence Revolutions Designed for Some Corporations and Disruptive Innovations for Small to Large Companies Getting Your Neurons to Work Applying Biomimicking to Artificial Intelligence Conceptual Representation Learning Automated Planning and Scheduling AI and the Internet of Things (IoT) Optimizing Blockchains with AI Cognitive NLP Chatbots Improve the Emotional Intelligence Deficiencies of Chatbots Quantum Computers That Think Appendix - Answers to the Questions

Key Features Implement various deep-learning algorithms in Keras and see how deep-learning can be used in games See how various deep-learning models and practical use-cases can be implemented using Keras A practical, hands-on guide with real-world examples to give you a strong foundation in Keras Table of Contents Neural Networks Foundations Keras Installation and API Deep Learning with ConvNets Generative Adversarial Networks and WaveNet Word Embeddings Recurrent Neural Network — RNN Additional Deep Learning Models AI Game Playing Conclusion

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

This book presents innovative and interdisciplinary applications of advanced technologies. It includes the scientific outcomes of the 9th DAYS OF BHAAAS (Bosnian-Herzegovinian American Academy of Arts and Sciences) held in Banja Vrućica, Teslić, Bosnia and Herzegovina on May 25–28, 2017. This unique book offers a comprehensive, multidisciplinary and interdisciplinary overview of the latest developments in a broad section of technologies and methodologies, viewed through the prism of applications in computing, networking, information technology, robotics, complex systems, communications, energy, mechanical engineering, economics and medicine, to name just a few.

Move into iOS development by getting a firm grasp of its fundamentals, including the Xcode 9 IDE, Cocoa Touch, and the latest version of Apple’s acclaimed programming language, Swift 4. With this thoroughly updated guide, you’ll learn the Swift language, understand Apple’s Xcode development tools, and discover the Cocoa framework. Explore Swift’s object-oriented concepts Become familiar with built-in Swift types Dive deep into Swift objects, protocols, and generics Tour the lifecycle of an Xcode project Learn how nibs are loaded Understand Cocoa’s event-driven design Communicate with C and Objective-C In this edition, catch up on the latest iOS programming features. Multiline strings and improved dictionaries Object serialization Key paths and key–value observing Expanded git integration Code refactoring And more!

目录 Introduction.- Object of research.- Intelligent control of mechatronics systems.- Optimal control methods for mechatronic systems.- Learning methods for intelligent systems.- Control of mechatronic systems.- Experimental verification of control algorithms.- Summary The book deals with intelligent control of mobile robots, presenting the state-of-the-art in the field, and introducing new control algorithms developed and tested by the authors. It also discusses the use of artificial intelligent methods like neural networks and neuraldynamic programming, including globalised dual-heuristic dynamic programming, for controlling wheeled robots and robotic manipulators,and compares them to classical control methods.

Contents Knowledge Graphs: Venturing Out into the Wild . . . . . . . . . . . . . . . . . . . . 1 Gerard de Melo Information Extraction from the Web by Matching Visual Presentation Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Radek Burget Statistical Induction of Coupled Domain/Range Restrictions from RDF Knowledge Bases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Basil Ell, Sherzod Hakimov, and Philipp Cimiano Wikipedia and DBpedia for Media - Managing Audiovisual Resources in Their Semantic Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 Jean-Pierre Evain, Mike Matton, and Tormod Vaervagen Identifying Global Representative Classes of DBpedia Ontology Through Multilingual Analysis: A Rank Aggregation Approach . . . . . . . . . . 57 Eun-kyung Kim and Key-Sun Choi Identifying Poorly-Defined Concepts in WordNet with Graph Metrics . . . . . . 66 John P. McCrae and Narumol Prangnawarat Extracting Process Graphs from Medical Text Data: An Approach Towards a Systematic Framework to Extract and Mine Medical Sequential Processes Descriptions from Large Text Sources. . . . . . . . . . . . . . . . . . . . . 76 Andreas Niekler and Christian Kahmann Chainable and Extendable Knowledge Integration Web Services. . . . . . . . . . 89 Felix Sasaki, Milan Dojchinovski, and Jan Nehring Entity Typing Using Distributional Semantics and DBpedia . . . . . . . . . . . . . 102 Marieke van Erp and Piek Vossen WC3: Analyzing the Style of Metadata Annotation Among Wikipedia Articles by Using Wikipedia Category and the DBpedia Metadata Database . . . 119 Masaharu Yoshioka Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137

目录 Table of Contents. 1 Preface 1 Introduction. 1 1.1 What is image processing pipeline?. 1 1.2 What does web image processing pipeline consist of?. 3 1.3 What are big data microscopy experiments?. 4 1.4 Why are scientists interested in big data microscopy experiments?. 6 1.5 What is the range of applications leveraging image processing pipelines?. 9 1.6 Challenges of big data microscopy experiments. 10 1.7 Tradeoffs before and after digital images are acquired. 12 1.8 Enabling reproducible science from big data microscopy experiments. 14 2 Using Web Image Processing Pipeline for Big Data Microscopy Experiments. 1 2.1 Deploying and Testing the Web Image Processing Pipeline. 2 2.1.1 Types of deployment 4 2.1.2 Deployment of Docker Containers. 6 2.1.3 Deployment recommendations. 7 2.1.4 Test data and computational benchmarks. 8 2.2 Web Image Processing. 10 2.2.1 WIP processing functionality. 10 2.2.2 Examples of WIP usage. 12 2.3 Web Feature Extraction. 15 2.3.1 WFE processing functionality. 17 2.3.2 WFE usage. 19 2.4 Web Statistical Modeling. 21 2.4.1 WSM processing functionality. 23 2.4.2 WSM use case. 24 2.5 Summary. 25 3 Example Use Cases 1 3.1 Cell count and single cell detection. 1 3.1.1 Image processing pipeline. 2 3.1.2 Create a new image collection. 3 3.1.3 Stitching of image tiles. 4 3.1.4 Intensity scaling and pyramid building. 5 3.1.5 Image assembling. 6 3.1.6 Segmentation. 7 3.1.7 Binary image labeling. 8 3.1.8 Feature extraction and single cell detection. 8 3.1.9 Discussion. 9 3.2 Stem cell colony growth computation. 10 3.2.1 Image processing pipeline. 11 3.2.2 Colony tracking and feature extraction<. 12 3.2.3 Discussion. 13 3.3 Summary. 15 4 Building Web Image Processing Pipeline for Big Images. 1 4.1 Mapping functionality to information technologies. 1 4.2 The role of each technology in the client-server architecture. 5 4.3 Basics of web servers. 7 4.4 Communication protocols in client-server architectures. 8 4.4.1 Client-server communication using Hypertext Transfer Protocol 9 4.4.2 Client-server communication using Secure Hypertext Transfer Protocol 11 4.4.3 Web server side Transmission Control Protocol 12 4.4.4 Web server side Message Passing Interface. 12 4.4.5 Web server side Network File System.. 14 4.5 Designing interactive user interfaces in web browsers. 14 4.5.1 Design pattern for code running in web browsers. 14 4.5.2 Dynamic web applications. 15 4.6 Large image visualization and processing in web browsers. 18 4.6.1 Representation of large images. 18 < 4.6.2 Large image visualization in web browsers. 21 4.6.3 Image processing in web browsers. 22 4.7 Managing images, pyramids and metadata on a web server 24 4.7.1 Relational databases. 25 4.7.2 Non-relational database. 27 4.7.3 Web application frameworks. 30 4.8 Meeting computational requirements on a web server 33 4.8.1 Pegasus workflow management system.. 33 4.8.2 HTCondor workload management system.. 36 4.8.3 XML file representation for encoding computational jobs. 36 4.9 Delivering traceable computations. 37 4.9.1 Components for delivering traceable computations. 38 4.9.2 Traceable computations for publications. 39 4.9.3 From traceable to reproducible computations. 41 4.10 Summary. 41 5 Image Processing Algorithms 1 5.1 Image processing. 2 5.1.1 Textbooks about image processing. 2 5.1.2 Usage-based classification of image processing implementations. 3 5.1.3 Classification of open source image processing software. 5 5.1.4 Loading images using OME Bio-Formats library. 7 5.1.5 Basic image processing using ImageJ/Fiji 9 5.2 Overview of algorithms in WIPP. 11 5.3 Image correction algorithms. 13 5.3.1 Dark current correction. 14 5.3.2 Flat field correction. 14< 5.3.3 Background correction. 15 5.3.4 Noise filtering. 19 5.4 Algorithms for stitching and mosaicking many images. 22 5.4.1 Image stitching. 23 5.4.2 Image mosaicking. 27 5.4.3 Practical Remarks. 28 5.5 Object segmentation, tracking and feature extraction algorithms. 29 5.5.1 Object segmentation. 30 5.5.2 Object tracking over time. 39 5.5.3 Image and object feature extractions. 42 5.6 Image intensity scaling and pyramid building algorithms. 44 5.6.1 Image intensity scaling. 44 5.6.2 Image pyramid building. 46 5.6.3 Reprojection of a pyramid set 48 5.7 Summary. 51 6 Interoperability Between Software and Hardware. 1 6.1 Hardware options for accelerating computations. 2 6.2 Implications of big data attributes. 4 6.3 Execution times of computation over big image data. 6 6.3.1 Meeting execution time requirements. 7 6.3.2 Estimating and measuring execution time. 9 6.4 From commercial big data analytics to research big image analyses. 10 6.5 Human interfaces for big image data analytics. 12 6.5.1 Focus on client-side graphical user interfaces. 13 6.5.2 Example of GUI design for web statistical modeling tool 14 6.5.3 Summary. 16 6.6 Storage and data structure for big images. 16 6.6.1 Storage for big images. 17 6.6.2 Data structures for big images. 22 6.6.3 Summary. 23 6.7 Parallel computations over big image data. 23 6.7.1 Data parallel model 24 6.7.2 Master-agent model 26 6.7.3 Task graph model 28 6.7.4 Task pool model 29 6.7.5 Consumer-producer model 30 6.7.6 Hybrid model 32 6.7.7 Summary. 32 7 Supplementary Information. 1 7.1 Software and documentation. 1 7.2 Data for testing software installation. 2 7.3 Deployed demonstrations on the web. 2 8 Abbreviations. 3 9 Terminology. 4 10 Acknowledgements. 5 11 Disclaimer. 6 12 Summary of References. 6

目录 Invited Talks.- Understanding and Inductive Inference.- Computing with Cells: Membrane Systems.- Complexity and Inapproximability.- Boxicity and Poset Dimension.- On the Hardness against Constant-Depth Linear-Size Circuits.- A K-Provers Parallel Repetition Theorem for a Version of No-Signaling Model.- The Curse of Connectivity: t-Total Vertex (Edge) Cover.- Counting Paths in VPA Is Complete for #NC 1.- Depth-Independent Lower Bounds on the Communication Complexity of Read-Once Boolean Formulas.- Approximation Algorithms.- Multiplying Pessimistic Estimators: Deterministic Approximation of Max TSP and Maximum Triangle Packing.- Clustering with or without the Approximation.- A Self-stabilizing 3-Approximation for the Maximum Leaf Spanning Tree Problem in Arbitrary Networks.- Approximate Weighted Farthest Neighbors and Minimum Dilation Stars.- Approximated Distributed Minimum Vertex Cover Algorithms for Bounded Degree Graphs.- Graph Theory and Algorithms.- Maximum Upward Planar Subgraph of a Single-Source Embedded Digraph.- Triangle-Free 2-Matchings Revisited.- The Cover Time of Deterministic Random Walks.- Finding Maximum Edge Bicliques in Convex Bipartite Graphs.- A Note on Vertex Cover in Graphs with Maximum Degree 3.- Computing Graph Spanners in Small Memory: Fault-Tolerance and Streaming.- Factorization of Cartesian Products of Hypergraphs.- Graph Drawing and Coloring.- Minimum-Segment Convex Drawings of 3-Connected Cubic Plane Graphs.- On Three Parameters of Invisibility Graphs.- Imbalance Is Fixed Parameter Tractable.- The Ramsey Number for a Linear Forest versus Two Identical Copies of Complete Graphs.- Computational Geometry.- Optimal Binary Space Partitions in the Plane.- Exact and Approximation Algorithms for Geometric and Capacitated Set Cover Problems.- Effect of Corner Information in Simultaneous Placement of K Rectangles and Tableaux.- Detecting Areas Visited Regularly.- Tile-Packing Tomography Is -hard.- The Rectilinear k-Bends TSP.- Tracking a Generator by Persistence.- Auspicious Tatami Mat Arrangements.- Automata, Logic, Algebra and Number Theory.- Faster Generation of Shorthand Universal Cycles for Permutations.- The Complexity of Word Circuits.- On the Density of Regular and Context-Free Languages.- Extensions of the Minimum Cost Homomorphism Problem.- The Longest Almost-Increasing Subsequence.- Universal Test Sets for Reversible Circuits.- Approximate Counting with a Floating-Point Counter.- Network Optimization and Scheduling Algorithm.- Broadcasting in Heterogeneous Tree Networks.- Contention Resolution in Multiple-Access Channels: k-Selection in Radio Networks.- Online Preemptive Scheduling with Immediate Decision or Notification and Penalties.- Computational Biology and Bioinformatics.- Discovering Pairwise Compatibility Graphs.- Near Optimal Solutions for Maximum Quasi-bicliques.- Fast Coupled Path Planning: From Pseudo-Polynomial to Polynomial.- Constant Time Approximation Scheme for Largest Well Predicted Subset.- On Sorting Permutations by Double-Cut-and-Joins.- A Three-String Approach to the Closest String Problem.- A 2k Kernel for the Cluster Editing Problem.- Data Structure and Sampling Theory.- On the Computation of 3D Visibility Skeletons.- The Violation Heap: A Relaxed Fibonacci-Like Heap.- Threshold Rules for Online Sample Selection.- Heterogeneous Subset Sampling.- Cryptography, Security, Coding and Game Theory.- Identity-Based Authenticated Asymmetric Group Key Agreement Protocol.- Zero-Knowledge Argument for Simultaneous Discrete Logarithms.- Directed Figure Codes: Decidability Frontier.

What You’ll Learn Navigate the Salesforce Lightning interface Know where to go in the setup area to make customizations Create dynamic reports and dashboards View Lightning on a mobile device Train other users on common day-to-day activities within Lightning Who This Book Is for Salespeople, managers, and executives who are currently evaluating Salesforce.com, who recently purchased a license with Salesforce.com, or recently upgraded to the Lightning user interface. This book is also for Salesforce administrators, consultants, project managers, and technical users looking for basic training on Salesforce Lightning.

神经网络 机器学习 压缩包共两本书 part1和part2 详细书签目录 Bio-inspired Computing.- E-Health and Computational Biology.- Human Computer Interaction.- Image and Signal Processing.- Mathematics for Neural Networks.- Self-organizing Networks.- Spiking Neurons.- Artificial Neural Networks in Industry ANNI'17.- Computational Intelligence Tools and Techniques for Biomedical Applications.- Assistive Rehabilitation Technology.- Computational Intelligence Methods for Time Series.- Machine Learning Applied to Vision and Robotics.- Human Activity Recognition for Health and Well-Being Applications.- Software Testing and Intelligent Systems.- Real World Applications of BCI Systems.- Machine Learning in Imbalanced Domains.- Surveillance and Rescue Systems and Algorithms for Unmanned Aerial Vehicles.- End-User Development for Social Robotics.- Artificial Intelligence and Games.- Supervised, Non-Supervised, Reinforcement and Statistical Algorithms.

This book is intended for use in advanced graduate courses in statistics / machine learning, as well as for all experimental neuroscientists seeking to understand statistical methods at a deeper level, and theoretical neuroscientists with a limited background in statistics. It reviews almost all areas of applied statistics, from basic statistical estimation and test theory, linear and nonlinear approaches for regression and classification, to model selection and methods for dimensionality reduction, density estimation and unsupervised clustering. Its focus, however, is linear and nonlinear time series analysis from a dynamical systems perspective, based on which it aims to convey an understanding also of the dynamical mechanisms that could have generated observed time series. Further, it integrates computational modeling of behavioral and neural dynamics with statistical estimation and hypothesis testing. This way computational models in neuroscience are not only explanat ory frameworks, but become powerful, quantitative data-analytical tools in themselves that enable researchers to look beyond the data surface and unravel underlying mechanisms. Interactive examples of most methods are provided through a package of MatLab routines, encouraging a playful approach to the subject, and providing readers with a better feel for the practical aspects of the methods covered.

This book is intended to be used as a textbook for graduate students studying theoretical computer science. It can also be used as a reference book for researchers in the area of design and analysis of approximation algorithms. Design and Analysis of Approximation Algorithms is a graduate course in theoretical computer science taught widely in the universities, both in the United States and abroad. There are, however, very few textbooks available for this course. Among those available in the market, most books follow a problem-oriented format; that is, they collected many important combinatorial optimization problems and their approximation algorithms, and organized them based on the types, or applications, of problems, such as geometric-type problems, algebraic-type problems, etc. Such arrangement of materials is perhaps convenient for a researcher to look for the problems and algorithms related to his/her work, but is difficult for a student to capture the ideas underlying the various algorithms. In the new book proposed here, we follow a more structured, technique-oriented presentation. We organize approximation algorithms into different chapters, based on the design techniques for the algorithms, so that the reader can study approximation algorithms of the same nature together. It helps the reader to better understand the design and analysis techniques for approximation algorithms, and also helps the teacher to present the ideas and techniques of approximation algorithms in a more unified way.

Learning Generative Adversarial Networks_Next generation deep learning simplified First published: October 2017 | 203页 | pdf格式

Learn how to set up and configure networks to create robust connections, and how to quickly diagnose and repair problems should something go wrong. Whatever version of Windows you are using, you will need a stable Internet connection and access to your company network and its shared files and resources. When a network connection fails, it can result in an expensive loss of productivity. What You'll Learn Set up and manage different types of network connections Use and configure Windows TCP/IP stack Determine the common causes of networking problems and how to avoid them Troubleshoot network connection problems Manage networking for Windows virtual machines Keep the mobile or BYOD worker connected to your company network Who This Book Is For IT pros, Windows expert and power users, and system administrators

CONTENTS 1 Introduction 2 Simple Linear Regression 3 Multiple Linear Regression 4 Regression Diagnostics: Detection of Model Violations 5 Qualitative Variables as Predictors 6 Transformation of Variables 7 Weighted Least Squares 8 The Problem of Correlated Errors 9 Analysis of Collinear Data 10 Biased Estimation of Regression Coefficients 11 Variable Selection Procedures 12 Logistic Regression 13 Further Topics

With the ubiquitous diffusion of the IoT, Cloud Computing, 5G and other evolved wireless technologies into our daily lives, the world will see the Internet of the future expand ever more quickly. Driving the progress of communications and connectivity are mobile and wireless technologies, including traditional WLANs technologies and low, ultra-power, short and long-range technologies. These technologies facilitate the communication among the growing number of connected devices, leading to the generation of huge volumes of data. Processing and analysis of such "big data" brings about many opportunities, as well as many challenges, such as those relating to efficient power consumptions, security, privacy, management, and quality of service. This book is about the technologies, opportunities and challenges that can drive and shape the networks of the future.

Everything you need to get going with Java! Java All-in-One For Dummies, 4th Edition has what you need to get up and running quickly with Java. Covering the enhanced mobile development and syntax features as well as programming improvements, this guide makes it easy to find what you want and put it to use. Focuses on the vital information that enables you to get up and running quickly with Java Covers the enhanced multimedia features as well as programming enhancements, Java and XML, Swing, server-side Java, Eclipse, and more Minibooks cover Java basics; programming basics; strings, arrays, and collections; programming techniques; Swing; Web programming; files and databases; and a "fun and games" category Java All-in-One For Dummies, 4th Edition focuses on the practical information you need to become productive with Java right away.