Tabla de Contenidos: “…5.2.2 OR5.2.3 NOT; 5.2.4 NAND; 5.2.5 NOR; 5.2.6 Exclusive OR (XOR); 5.3 Latching; 5.4 Multiple outputs; 5.5 Entering programs; 5.5.1 Ladder symbols; 5.6 Function blocks; 5.6.1 Logic gates; 5.6.2 Boolean algebra; 5.7 Program examples; 5.7.1 Location of stop switches; Problems; 6 IL, SFC and ST programming methods; 6.1 Instruction lists; 6.1.1 Ladder programs and instruction lists; 6.1.2 Branch codes; 6.1.3 More than one rung; 6.1.4 Programming examples; 6.2 Sequential function charts; 6.2.1 Branching and convergence; 6.2.2 Actions; 6.3 Structured text; 6.3.1 Conditional statements…”

Tabla de Contenidos: “…3.2.7 Views: Virtual Tables3.2.8 The System Catalog; 3.3 The Integrity Component; 3.3.1 Primary Keys; 3.3.2 Foreign Keys; 3.3.3 Constraints; 3.3.4 Null Values; 3.4 Normalization; 3.4.1 Normal Forms; 3.4.2 First Normal Form; 3.4.3 Functional Dependencies; 3.4.4 Second Normal Form; 3.4.5 Third Normal Form; 3.5 The Manipulative Component; 3.5.1 Relational Algebra and Calculus; 3.5.2 The Relational Query Language; 3.5.3 The Advent of SQL; 3.6 The Meaning of Relational; 3.7 Summary; 4 Everything You Ever Wanted to Know about SQL but Were Afraid to Ask; 4.1 The Relational Model…”

Tabla de Contenidos: “….); 4.3 Interpolation; 4.3.1 Linear interpolation; 4.3.2 Loglinear interpolation; 4.3.3 Linear on zero interpolation; 4.3.4 Cubic spline interpolation; 4.4 Root finding; 4.4.1 Bisection method; 4.4.2 Newton-Raphson method; 4.5 Linear algebra; 4.5.1 Matrix multiplication; 4.5.2 Matrix inversion; 4.5.3 Matrix pseudo-inverse…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright and Credits -- Dedication -- Packt Upsell -- Contributors -- Table of Contents -- Preface -- Chapter 1: The Declarative Programming Style -- Technical requirements -- Principles of declarative programming -- Example - go-to versus loops -- Example - nested loop -- Don't Repeat Yourself (DRY) -- Declarative versus imperative collections -- Filtering -- Declarative programming in other languages -- Summary -- Questions -- Chapter 2: Functions and Lambdas -- Functions as behavior -- Functions in functional programming -- Higher-order functions -- Understanding lambda functions -- The concept of functions in different programming languages -- Summary -- Questions -- Chapter 3: Functional Data Structures -- Collections framework -- Imperative collections -- Functional collections -- Algebraic approach -- Effect types -- Try -- Option -- Data structures in different programming languages -- Summary -- Questions -- Further reading -- Chapter 4: The Problem of Side Effects -- Side effects -- Mutable states -- Pure functions -- Referential transparency -- Generally encountered side effects -- Error -- Absence of result -- Delay and asynchronous computations -- Logging -- Input-output operations -- But how do we get rid of the side effects? …”

Tabla de Contenidos: “…Log-Linear Models for Categorical Variables -- Regression Models for Categorical Variables -- Models for Event Histories -- Models for Multiple-Item Measurement -- Nonlinear, Nonadditive Models -- Geometric and Algebraic Models -- Scaling -- Ordered Factorial Systems -- Clustering -- Network Models -- STATISTICAL INFERENCE AND ANALYSIS -- Causal Inference -- New Statistical Techniques -- Internal Resampling -- Robust Techniques -- Many Interrelated Parameters -- Missing Data -- Computing -- Computer Packages and Expert Systems -- Exploratory Analysis and Graphic Presentation -- Combining Evidence -- OPPORTUNITIES AND NEEDS -- 6 The Research Support System -- HUMAN RESOURCES -- Colleges and Graduate Schools -- Undergraduate Education -- Graduate Education -- Postdoctoral Training and Collaboration -- Postdoctoral Fellowships and Traineeships -- Advanced Training Institutes -- Collaboration and Communication -- TECHNOLOGICAL RESOURCES -- Computers -- Neuroimaging Devices -- Animal Care -- DATA RESOURCES -- Large-Scale Data Bases -- Research Access to Government Data -- Corporate and Local Government Archives -- FUNDING RESOURCES -- Modes of Support -- Grant Size and Duration -- The Disciplines and Interdisciplinary Research -- Interdisciplinary Research Centers -- THE PROBLEM OF VOICE -- 7 Raising the Scientific Yield -- RESEARCH FRONTIERS -- Behavior, Mind, and Brain -- Motivational and Social Contexts of Behavior -- Choice and Allocation -- Institutions and Cultures -- Methods of Data Collection, Representation, and Analysis -- RECOMMENDED NEW RESOURCES -- Human Resources -- Technological Resources -- Data Resources -- Interdisciplinary Research Centers -- Investigator-Initiated Grants -- Research Agency Changes -- CONCLUSION -- Appendix A Trends in Support for Research in the Behavioral and Social Sciences -- FEDERAL SUPPORT…”

“…El libro asume del lector un conocimiento previo de cálculo diferencial, integral, vectorial y álgebra lineal. En la primera parte se abordan temas como la teoría de funciones de variable compleja, la teoría de distribuciones, el análisis de Fourier, la transformada de Laplace y el estudio de las principales ecuaciones diferenciales de la Física. …”

Tabla de Contenidos: “…-- Advantages over traditional shallow methods -- Impact of deep learning -- The motivation of deep architecture -- The neural viewpoint -- The representation viewpoint -- Distributed feature representation -- Hierarchical feature representation -- Applications -- Lucrative applications -- Success stories -- Deep learning for business -- Future potential and challenges -- Summary -- Chapter 2: Getting Yourself Ready for Deep Learning -- Basics of linear algebra -- Data representation -- Data operations -- Matrix properties -- Deep learning with GPU -- Deep learning hardware guide -- CPU cores -- RAM size -- Hard drive -- Cooling systems -- Deep learning software frameworks -- TensorFlow - a deep learning library -- Caffe -- MXNet -- Torch -- Theano -- Microsoft Cognitive Toolkit -- Keras -- Framework comparison -- Setting up deep learning on AWS -- Setup from scratch -- Setup using Docker -- Summary -- Chapter 3: Getting Started with Neural Networks -- Multilayer perceptrons -- The input layer -- The output layer -- Hidden layers -- Activation functions -- Sigmoid or logistic function -- Tanh or hyperbolic tangent function -- ReLU -- Leaky ReLU and maxout -- Softmax -- Choosing the right activation function -- How a network learns -- Weight initialization -- Forward propagation -- Backpropagation -- Calculating errors -- Backpropagation -- Updating the network -- Automatic differentiation -- Vanishing and exploding gradients -- Optimization algorithms -- Regularization -- Deep learning models -- Convolutional Neural Networks -- Convolution -- Pooling/subsampling -- Fully connected layer -- Overall…”

Tabla de Contenidos: “…. -- Figure 4.5 Using consumer-grade eye tracker (left) to monitor visual attention while students interact with Guru (right) in classrooms -- Figure 4.6 Sample video (left) in the Algebra Nation platform along with a self-report engagement questionnaire (right) -- Figure 4.7 Organisation of activities with respect to expected engagement and learning (from left to higher) as per the ICAP framework -- Figure 4.8 Example of a problem and pendulum solution in Physics Playground -- Figure 4.9 GazeTutor interface with animated agent (0), image panel (1), and input box (2). …”

Tabla de Contenidos: “…11 -- 2 Polish Up Your Algebra Skills 13 -- Walk the Line: Linear Equations 14 -- Common Forms of Linear Equations 14 -- Calculating Slope 16 -- Interpreting Linear Graphs 18 -- You've Got the Power: Exponential Rules 21 -- Breaking Up Is Hard to Do: Factoring Polynomials 22 -- Greatest Common Factor 23 -- Special Factoring Patterns 23 -- Solving Quadratic Equations 24 -- Method One: Factoring 25 -- Method Two: Completing the Square 25 -- Method Three: The Quadratic Formula 26 -- Synthesizing the Quadratic Solution Methods 27 -- 3 Equations, Relations, and Functions 31 -- What Makes a Function Tick? …”

“…Linear filtering methods originate in stochastic processes, while subspace methods have largely been based on developments in numerical linear algebra and matrix approximation theory. This book bridges the gap between the…”

“…Additionally, basic knowledge in linear algebra and calculus is desired. What You Will Learn Implement different neural network models in Python Select the best Python framework for deep learning such as PyTorch, Tensorflow, MXNet and Keras Apply tips and tricks related to neural networks internals, to boost learning performances Consolidate machine learning principles and apply them in the deep learning field Reuse and adapt Python code snippets to everyday problems Evaluate the cost/benefits and performance implication of each discussed solution In Detail Deep Learning is revolutionizing a wide range of industries. …”

Tabla de Contenidos: “…Cover -- Title -- Copyright -- Contents -- Preface -- Acknowledgments -- 1 Introduction -- 1.1 Engineering Problems -- 1.2 Numerical Methods -- 1.3 A Brief History of the Finite Element Method and Ansys -- 1.4 Basic Steps in the Finite Element Method -- 1.5 Direct Formulation -- 1.6 Minimum Total Potential Energy Formulation -- 1.7 Weighted Residual Formulations -- 1.8 Verification of Results -- 1.9 Understanding the Problem -- Summary -- References -- Problems -- 2 Matrix Algebra -- 2.1 Basic Definitions -- 2.2 Matrix Addition or Subtraction -- 2.3 Matrix Multiplication -- 2.4 Partitioning of a Matrix -- 2.5 Transpose of a Matrix -- 2.6 Determinant of a Matrix -- 2.7 Solutions of Simultaneous Linear Equations -- 2.8 Inverse of a Matrix -- 2.9 Eigenvalues and Eigenvectors -- 2.10 Using Matlab to Manipulate Matrices -- 2.11 Using Excel to Manipulate Matrices -- Summary -- References -- Problems -- 3 Trusses -- 3.1 Definition of a Truss -- 3.2 Finite Element Formulation -- 3.3 Space Trusses -- 3.4 Overview of the Ansys Program -- 3.5 Examples Using Ansys -- 3.6 Verification of Results -- Summary -- References -- Problems -- 4 Axial Members, Beams, and Frames -- 4.1 Members Under Axial Loading -- 4.2 Beams -- 4.3 Finite Element Formulation of Beams -- 4.4 Finite Element Formulation of Frames -- 4.5 Three-Dimensional Beam Element -- 4.6 An Example Using Ansys -- 4.7 Verification of Results -- Summary -- References -- Problems -- 5 One-Dimensional Elements -- 5.1 Linear Elements -- 5.2 Quadratic Elements -- 5.3 Cubic Elements -- 5.4 Global, Local, and Natural Coordinates -- 5.5 Isoparametric Elements -- 5.6 Numerical Integration: Gauss-Legendre Quadrature -- 5.7 Examples of One-Dimensional Elements in Ansys -- Summary -- References -- Problems -- 6 Analysis of One-Dimensional Problems -- 6.1 Heat Transfer Problems -- 6.2 A Fluid Mechanics Problem…”

Tabla de Contenidos: “…4.1 Introduction -- Parallelizing Computational Tasks -- Parallelizing for Distributed Data -- 4.2 Parallel Processing Approaches -- 4.2.1 Parallel Algorithms for Text Summarization -- 4.2.2 Parallel Bisection k-Means Method -- 4.3 Parallel Data Processing Algorithms for Large-Scale Summarization -- 4.3.1 Designing MapReduce Algorithm for Text Summarization -- 4.3.2 Key Concepts in Mapper -- 4.3.3 Key Concepts in Reducer -- 4.3.4 Summary Generation -- An Illustrative Example for MapReduce -- Good Time: Movie Review -- 4.4 Other MR-Based Methods -- 4.5 Summary -- 4.6 Examples -- K-Means Clustering Using MapReduce -- Parallel LDA Example (Using Gensim Package) -- Sample Code: (Using Gensim Package) -- Example: Creating an Inverted Index -- Example: Relational Algebra (Table JOIN) -- References -- Sample Code -- Chapter 5 Optimization Approaches for Text Summarization -- 5.1 Introduction -- 5.2 Optimization for Summarization -- 5.2.1 Modeling Text Summarization as Optimization Problem -- 5.2.2 Various Approaches for Optimization -- 5.3 Formulation of Various Approaches -- 5.3.1 Sentence Ranking Approach -- 5.3.1.1 Stages and Illustration -- 5.3.2 Evolutionary Approaches -- 5.3.2.1 Stages -- 5.3.2.2 Demonstration -- 5.3.3 MapReduce-Based Approach -- 5.3.3.1 In-Node Optimization Illustration -- 5.3.4 Multi-objective-Based Approach -- Summary -- Exercises -- References -- Sample Code -- Chapter 6 Performance Evaluation of Large-Scale Summarization Systems -- 6.1 Evaluation of Summaries -- 6.1.1 CNN Dataset -- 6.1.2 Daily Mail Dataset -- 6.1.3 Description -- 6.2 Methodologies -- 6.2.1 Intrinsic Methods -- 6.2.2 Extrinsic Methods -- 6.3 Intrinsic Methods -- 6.3.1 Text Quality Measures -- 6.3.1.1 Grammaticality -- 6.3.1.2 Non-redundancy -- 6.3.1.3 Reverential Clarity -- 6.3.1.4 Structure and Coherence -- 6.3.2 Co-selection-Based Methods…”

Tabla de Contenidos: “…3.4 Types of Similarity Measures Between Fuzzy Sets 85 -- 3.5 Generalized Fuzzy Number 85 -- 3.6 Similarity Measures Between Two Fuzzy Numbers 88 -- 3.7 Inclusion Measure 94 -- 3.8 Measures of Fuzziness 95 -- 3.8.1 Index of Fuzziness 95 -- 3.8.2 YageŕÖs Measure 96 -- 3.8.3 Fuzzy Entropy 96 -- 3.9 Intuitionistic Fuzzy Distance and Similarity Measures 98 -- 3.10 Intuitionistic Fuzzy Entropy 105 -- 3.11 Different Types of Intuitionistic Fuzzy Entropies 106 -- 3.12 Summary 107 -- References 107 -- 4 Fuzzy/Intuitionistic Fuzzy Measures and Fuzzy Integrals 111 -- 4.1 Introduction 111 -- 4.2 Definition of Fuzzy Measure 111 -- 4.3 Fuzzy Measures 112 -- 4.3.1 Sugeno Î"-Fuzzy Measure 112 -- 4.3.2 Belief Measure 115 -- 4.3.3 Plausibility Measure 116 -- 4.3.4 Possibility Measure and Necessity Measure 116 -- 4.3.4.1 Possibility Measure 117 -- 4.3.4.2 Necessity Measure 119 -- 4.4 Fuzzy Integrals 121 -- 4.4.1 Sugeno Integral 122 -- 4.4.2 Choquet Integral 125 -- 4.4.3 Sipos Integral 129 -- 4.5 Intuitionistic Fuzzy Integral 130 -- 4.5.1 Intuitionistic Fuzzy Choquet Integral 130 -- 4.6 Summary 131 -- References 131 -- 5 Operations on Fuzzy/Intuitionistic Fuzzy Sets and Application in Decision Making 133 -- 5.1 Introduction 133 -- 5.2 Fuzzy Operations 133 -- 5.2.1 Fuzzy Union 134 -- 5.2.2 Fuzzy Intersection 134 -- 5.2.3 Fuzzy Complements 134 -- 5.2.4 Algebraic Product 136 -- 5.2.5 Algebraic Sum 137 -- 5.2.6 Simple Difference 137 -- 5.2.7 Bounded Sum 137 -- 5.2.8 Bounded Difference 137 -- 5.2.9 Bounded Product 137 -- 5.3 Fuzzy Other Operators: Fuzzy T-Norms and T-Conorms 138 -- 5.3.1 Definition of T-Norm 138 -- 5.3.2 Definition of T-Conorm 139 -- 5.4 Implication Operator 142 -- 5.5 Aggregation Operator with Application in Decision Making 144 -- 5.5.1 Fuzzy Weighted Averaging Operator (FWA) 144 -- 5.5.2 Fuzzy Ordered Weighted Averaging Operator (FOWA) 145 -- 5.5.3 Fuzzy Generalized Ordered Weighted Averaging Operator (GOWA) 146 -- 5.5.4 Fuzzy Hybrid Averaging Operator (FHA) 146 -- 5.5.5 Fuzzy Quasi-Arithmetic Weighted Averaging Operator 146.…”

Tabla de Contenidos: “…Appendix 3.D The von Bertalanffy and Richards Models Derived -- Appendix 3.E The Schnute Model Derived -- Appendix 3.F The McDill-Amateis Model Derived -- Appendix 3.G The Sloboda Model Derived -- Appendix 3.H A Generalized Michaelis-Menten Growth Equation -- 4 Estimation of Trend -- 4.1 Linear Trend Equation -- 4.2 Ordinary Least Squares (OLS) Estimation -- 4.3 Maximum Likelihood (ML) Estimation -- 4.4 The SAS System -- 4.5 Changing the Unit of Time -- 4.5.1 Annual Totals versus Monthly Averages versus Monthly Totals -- 4.5.2 Annual Totals versus Quarterly Averages versus Quarterly Totals -- 4.6 Autocorrelated Errors -- 4.6.1 Properties of the OLS Estimators When ε Is AR (1) -- 4.6.2 Testing for the Absence of Autocorrelation: The Durbin-Watson Test -- 4.6.3 Detection of and Estimation with Autocorrelated Errors -- 4.7 Polynomial Models in t -- 4.8 Issues Involving Trended Data -- 4.8.1 Stochastic Processes and Time Series -- 4.8.2 Autoregressive Process of Order p -- 4.8.3 Random Walk Processes -- 4.8.4 Integrated Processes -- 4.8.5 Testing for Unit Roots -- Appendix 4.A OLS Estimated and Related Growth Rates -- 4.A.1 The OLS Growth Rate -- 4.A.2 The Log-Difference (LD) Growth Rate -- 4.A.3 The Average Annual Growth Rate -- 4.A.4 The Geometric Average Growth Rate -- 5 Dynamic Site Equations Obtained from Growth Models -- 5.1 Introduction -- 5.2 Base-Age-Specific (BAS) Models -- 5.3 Algebraic Difference Approach (ADA) Models -- 5.4 Generalized Algebraic Difference Approach (GADA) Models -- 5.5 A Site Equation Generating Function -- 5.5.1 ADA Derivations -- 5.5.2 GADA Derivations -- 5.6 The Grounded GADA (g-GADA) Model -- Appendix 5.A Glossary of Selected Forestry Terms -- 6 Nonlinear Regression -- 6.1 Intrinsic Linearity/Nonlinearity -- 6.2 Estimation of Intrinsically Nonlinear Regression Models -- 6.2.1 Nonlinear Least Squares (NLS)…”

Tabla de Contenidos: “…Front Cover -- Fundamentals of Technical Mathematics -- Copyright -- Dedication -- Contents -- Preface -- Acknowledgments -- 1 - Basic Concepts in Arithmetic -- INTRODUCTION -- 1.1 BASIC ARITHMETIC -- 1.1.1 Arithmetic operations -- 1.1.1.1 Addition (+) -- Example 1 -- Solution -- 1.1.1.2 Subtraction (−) -- Example 2 -- Solution -- 1.1.1.3 Multiplication (×) -- Example 3 -- Solution -- Example 4 -- 1.1.1.4 Division (÷) -- Example 5 -- Solution -- Example 6 -- 1.1.1.5 Fraction (-, /) -- Example 7 -- Solution -- Example 8 -- Solution -- Example 9 -- Solution -- Example 10 -- Solution -- 1.1.1.6 Adding or subtracting of fractions -- Example 11 -- Solution -- Example 12 -- Solution -- 1.1.1.7 Multiplying fractions -- Example 13 -- Solution -- Example 14 -- Solution -- 1.1.1.8 Dividing fractions -- Example 15 -- Solution -- Example 16 -- Solution -- 1.1 EXERCISES -- 1.2 DECIMALS -- Example 1 -- Solution -- Example 2 -- Solution -- 1.2.1 Rules to rounding off decimals -- Example 3 -- Solution -- Example 4 -- 1.2.2 Addition of decimals -- Example 5 -- Solution -- 1.2.3 Subtraction of decimals -- Example 6 -- Solution -- 1.2.4 Multiplication of decimals -- Example 7 -- Solution -- 1.2.5 Division of decimals -- Example 8 -- Solution -- 1.2 EXERCISES -- 1.3 PERCENTS (%) -- Example 9 -- Solution -- Example 10 -- Solution -- Example 11 -- Example 12 -- Solution -- Example 13 -- Solution -- Example 14 -- Solution -- Example 15 -- Solution -- 1.3 EXERCISES -- CHAPTER 1 REVIEW EXERCISES -- 2 - Introduction to Algebra -- INTRODUCTION -- 2.1 INTRODUCTION TO ALGEBRA -- Example 1 -- Solution -- Example 2 -- Solution -- Example 3 -- Solution -- Example 4 -- Solution -- 2.1.1 Basic principles of addition -- Example 1 -- 2.1.2 Basic principles of multiplication -- Example 1 -- Example 2 -- Solution -- Example 3 -- Solution -- Example 4 -- 2.1.3 Exponent and radicals…”

Tabla de Contenidos: “…Acknowledgements v -- Contents vi -- Foreword x -- Foreword xii -- Preface xiv -- 1 Formulation of the motor control problem xiv -- 1.1 Electromagnetic torque xiv -- 1.2 Response time in tracking mode and on disturbances xv -- 1.3 Limitations xvi -- 2 Field orientation controls xviii -- 3 Sliding mode control families xviii -- 4 Objectives of a new motor control xx -- 5 Objectives of this work xxiii -- Capter 1 - Induction machine 1 -- 1 Electrical equations and equivalent circuits 1 -- 1.1 Definitions and notations 1 -- 1.2 Equivalent electrical circuits 2 -- 1.3 Differential equation system 4 -- 1.4 Interpretation of electrical relations 6 -- 2 State-space equation system working out 11 -- 2.1 State-space equations in the fixed plane 13 -- 2.2 State-space equations in the complex plane 16 -- 2.3 Complex state-space equation discretization 17 -- 2.4 Evolution matrix diagonalization 19 -- 2.4.1 Eigenvalues 19 -- 2.4.2 Transfer matrix algebraic calculation 20 -- 2.4.3 Transfer matrix inversion 21 -- 2.5 Projection of state-space vectors in the eigenvector basis 23 -- 3 Discretized state-space equation inversion 24 -- 3.1 Introduction of the rotating frame 24 -- 3.2 State-space vector calculations in the eigenvector basis 27 -- 3.3 Control calculation - eigenstate-space equation system inversion 34 -- 4 Control 35 -- 4.1 Constitution of the set-point state-space vector 35 -- 4.2 Constitution of the initial state-space vector 38 -- 4.3Control process 38 -- 4.3.1 Real-time implementation 38 -- 4.3.2 Measure filtering 41 -- 4.3.3 Transition and input matrix calculations 41 -- 4.3.4 Kalman's filter, observation and prediction 42 -- 4.3.5 Synoptic of measurement, filtering and prediction 44 -- 4.4 Limitations 47 -- 4.4.1 Voltage limitation 48 -- 4.4.2 Current limitation 51 -- 4.4.3 Operating area and limits 51 -- 4.4.4 Set-point limit algebraic calculations 52 -- 4.5 Example of implementation 65 -- 4.5.1 Adjustment of flux and torque - Limitations in traction operation 65.…”

Tabla de Contenidos: “…3.4 Data Owner's Integrity Verification -- 3.5 The Game of Miners Versus Storage Networks -- 4 Fault-Tolerance Verification -- 5 Concluding Remarks -- References -- Iterative Learning Control Based on Random Variance Reduction Gradient Method -- 1 Introduction -- 1.1 Background -- 1.2 Design and Analysis of SVRG-Based ILC -- 1.3 Main Work and Organization -- 2 SVRG-Based ILC Framework -- 2.1 System Description -- 2.2 Algorithm Design -- 2.3 Convergence Analysis -- 3 SVRG-Based ILC Under Random Data Dropouts -- 3.1 System Description -- 3.2 Algorithm Design -- 3.3 Convergence Analysis -- 3.4 Numerical Simulation -- 4 Model-Free SVRG-Based ILC for MIMO Systems -- 4.1 System Description -- 4.2 Algorithm Design -- 4.3 Convergence Analysis -- 4.4 Numerical Simulation -- 5 Conclusions -- References -- A Generalization of NTRUEncrypt -- 1 φ-Cyclic Code -- 2 A Generalization of NTRUEncrypt -- References -- Cyclic Lattices, Ideal Lattices, and Bounds for the Smoothing Parameter -- 1 Discrete Subgroup in mathbbRn -- 2 Ideal Matrices -- 3 Cyclic Lattices and Ideal Lattices -- 4 Smoothing Parameter -- References -- On the LWE Cryptosystem with More General Disturbance -- 1 Introduction -- 1.1 Innovation and Contribution -- 2 Methodology -- 2.1 Preliminary Property -- 2.2 Probability of Decryption Error Based on Gaussian Disturbance -- 2.3 Probability of Decryption Error for General Disturbance -- 3 Results and Conclusions -- 4 Discussions -- 4.1 Future Work -- References -- On the High Dimensional RSA Algorithm-A Public Key Cryptosystem Based on Lattice and Algebraic Number Theory -- 1 Introduction -- 2 Ideal Matrices -- 3 High Dimensional RSA -- 4 Security and Example -- References -- Central Bank Digital Currency Cross-Border Payment Model Based on Blockchain Technology -- 1 Introduction -- 2 CBDC Cross-Border Payment Development Current Situation…”

Tabla de Contenidos: “…Vector Analysis -- 3-1 Basic Laws of Vector Algebra -- 3-2 Orthogonal Coordinate Systems -- 3-3 Transformations between Coordinate Systems -- 3-4 Gradient of a Scalar Field -- TB5 Global Positioning System -- 3-5 Divergence of a Vector Field -- 3-6 Curl of a Vector Field -- TB6 X-Ray Computed Tomography -- 3-7 Laplacian Operator -- Chapter 4. …”

Tabla de Contenidos: “…Evaluating relations between variables with ANOVA -- Chapter 4: Dealing with Data and Numerical Issues -- Introduction -- Clipping and filtering outliers -- Winsorizing data -- Measuring central tendency of noisy data -- Normalizing with the Box-Cox transformation -- Transforming data with the power ladder -- Transforming data with logarithms -- Rebinning data -- Applying logit() to transform proportions -- Fitting a robust linear model -- Taking variance into account with weighted least squares -- Using arbitrary precision for optimization -- Using arbitrary precision for linear algebra -- Chapter 5: Web Mining, Databases, and Big Data -- Introduction -- Simulating web browsing -- Scraping the Web -- Dealing with non-ASCII text and HTML entities -- Implementing association tables -- Setting up database migration scripts -- Adding a table column to an existing table -- Adding indices after table creation -- Setting up a test web server -- Implementing a star schema with fact and dimension tables -- Using HDFS -- Setting up Spark -- Clustering data with Spark -- Chapter 6: Signal Processing and Timeseries -- Introduction -- Spectral analysis with periodograms -- Estimating power spectral density with the Welch method -- Analyzing peaks -- Measuring phase synchronization -- Exponential smoothing -- Evaluating smoothing -- Using the Lomb-Scargle periodogram -- Analyzing the frequency spectrum of audio -- Analyzing signals with the discrete cosine transform -- Block bootstrapping time series data -- Moving block bootstrapping time series data -- Applying the discrete wavelet transform -- Chapter 7: Selecting Stocks with Financial Data Analysis -- Introduction -- Computing simple and log returns -- Ranking stocks with the Sharpe ratio and liquidity -- Ranking stocks with the Calmar and Sortino ratios -- Analyzing returns statistics…”