Tabla de Contenidos: “…Tools of Digital Video: Non-Linear Editing Software -- 2. Digital Video Hardware: Configuring the Workstation -- 3. …”

Tabla de Contenidos: “…Part I: Theoretical Performance of Error-Correcting Codes -- Bounds on Error Correction Coding Performance -- Soft and Hard Decision Decoding Performance -- Soft Decision and Quantised Soft Decision Decoding -- Part II: Code Construction -- Cyclotomic Cosets, The Mattson–Solomon Polynomial, Idempotents and Cyclic Codes -- Good Binary Linear Codes -- Lagrange Codes -- Extended BCH -- Reed–Solomon Codes -- Algebraic Geometry Codes -- Algebraic Quasi Cyclic Codes -- Convolutional Codes: A Historical Perspective -- Aalogue BCH Codes -- LDPC Codes -- Part III: Analysis of Codes -- True Search for Stopping Sets for LDPC Codes -- Part IV: Decoders -- Erasures and Error-Correcting Codes -- The Modified Dorsch Decoder -- A Concatenated Error-Correction System Using the |u|u+v| Code Construction -- Part V: Applications -- Combined Error Detection and Error Correction -- Password Correction and Confidential Information Access System -- Variations on the McEliece Public Key Encryption System -- Error-Correcting Codes and Dirty-Paper Coding…”

Tabla de Contenidos: “…Advanced statistics -- Comparing and contrasting IBM SPSS AMOS with other multivariate techniques -- Monte Carlo simulation and IBM SPSS bootstrapping -- Regression with categorical outcome variables -- Building hierarchical linear models -- Part II. Data visualization -- Take your data visualizations to the next level -- The code behind SPSS graphics: graphics production language -- Mapping in IBM SPSS statistics -- Geospatial analytics -- Perceptual mapping with correspondence analysis, GPL, and OMS -- Display complex relationships with multidimensional scaling -- Part III. …”

Tabla de Contenidos: “…ASTRONOMY The Solar System The Universe Appendix A: On Measurement and Unit Conversion Appendix B: Linear and Rotational Motion Appendix C: More on Vectors Appendix D: Exponential Growth and Doubling Time Appendix E: Physics of Fluids Appendix F: Chemical Equilibrium…”

Tabla de Contenidos: “…Eine informationsökonomische Analyse des Weiterbildungsverhaltens von Frauen unter Verwendung log-linearer Wahrscheinlichkeitsmodelle (K.F. Zimmermann/A. …”

Tabla de Contenidos: “…Outline of this Book; Chapter 2: Linear Elastic and Nonlinear Fracture Mechanics; 2.3. …”

Tabla de Contenidos: “…Logics for extensional, locally complete analysis via domain refinements -- Clustered Relational Thread-Modular Abstract Interpretation with Local Traces -- Adversarial Reachability for Program-level Security Analysis -- Automated Grading of Regular Expressions -- Builtin Types viewed as Inductive Families -- Pragmatic Gradual Polymorphism with References -- Modal crash types for intermittent computing -- Gradual Tensor Shape Checking -- A Type System for Effect Handlers and Dynamic Labels -- Interpreting Knowledge-based Programs -- Contextual Modal Type Theory with Polymorphic Contexts -- A Complete Inference System for Skip-free Guarded Kleene Algebra with Tests -- Quorum Tree Abstractions of Consensus Protocols -- MAG π : Types for Failure-Prone Communication -- System $Fˆ\mu \omega$ with Context-free Session Types -- Safe Session-Based Concurrency with Shared Linear State -- Bunched Fuzz: Sensitivity for Vector Metrics -- Fast and Correct Gradient-Based Optimisation for Probabilistic Programming via Smoothing -- Type-safe Quantum Programming in Idris -- Automatic Alignment in Higher-Order Probabilistic Programming Languages…”

Tabla de Contenidos: “…Part 2 Problems of rhythm: conflicting binarty and ternary rhythms - from the theory of mensural notation to the music of J.S. Bach; the "Notes Inegales" revisited; Graham Pont's "paradigm of inconsistency" and the interpretation of Handel's Overtures; the rhythm in "Behold the Lamb of God". …”

Tabla de Contenidos: “…Part I Introduction -- Introduction to Accelerator Physics -- Linear Accelerators -- Circular Accelerators -- Part II Tools we need -- Elements of Classical Mechanics -- Particle Dynamics in Electromagnetic Fields -- Electromagnetic Fields -- Part III Beam Dynamics -- Single Particle Dynamics -- Particle Beams and Phase Space -- Longitudinal Beam Dynamics -- Periodic Focusing Systems -- Part IV Beam Parameters -- Vlasov and Fokker-Planck Equations -- Equilibrium Particle Distribution -- Beam Emittance and Lattice Design -- Part V Perturbations -- Perturbations in Beam Dynamics -- Hamiltonian Resonance Theory -- Hamiltonian Nonlinear Beam Dynamics -- Part VI Acceleration -- Charged Particle Acceleration -- Beam-Cavity Interaction -- Part VII Coupled Motion -- Dynamics of Coupled Motion -- Part VIII Intense Beams -- Statistical and Collective Effects -- Wake Fields and Instabilities -- Part IX Synchrotron Radiation -- Fundamental Processes -- Overview of Synchrotron Radiation -- Theory of Synchrotron Radiation -- Insertion Device Radiation -- Free Electron Lasers -- Part X Appendices -- Useful Mathematical Formulae -- Physical Formulae and Parameters -- References -- Index…”

Tabla de Contenidos: “…4.6 Description of the Attitude or Orientation -- 4.7 Aircraft Equations of Motion -- 4.8 Motion-Induced Aerodynamic Forces and Moments -- 4.9 Non-Linear Dynamics of Aircraft Motion and Stability Axes -- 4.9.1 Equations of Motion in Wind Axis Coordinates, V[sub(T)], α and β -- 4.9.2 Reduced-Order Modelling: The Short-Period Approximations -- 4.10 Trimmed Equations of Motion -- 4.10.1 Non-Linear Equations of Perturbed Motion -- 4.10.2 Linear Equations of Motion -- Chapter Highlights -- Exercises -- References -- Chapter 5 Small Perturbations and the Linearised, Decoupled Equations of Motion -- 5.1 Introduction -- 5.2 Small Perturbations and Linearisations -- 5.3 Linearising the Aerodynamic Forces and Moments: Stability Derivative Concept -- 5.4 Direct Formulation in the Stability Axis -- 5.5 Decoupled Equations of Motion -- 5.5.1 Case I: Motion in the Longitudinal Plane of Symmetry -- 5.5.2 Case II: Motion in the Lateral Direction, Perpendicular to the Plane of Symmetry -- 5.6 Decoupled Equations of Motion in Terms of the Stability Axis Aerodynamic Derivatives -- 5.7 Addition of Aerodynamic Controls and Throttle -- 5.8 Non-Dimensional Longitudinal and Lateral Dynamics -- 5.9 Simplified State-Space Equations of Longitudinal and Lateral Dynamics -- 5.10 Simplified Concise Equations of Longitudinal and Lateral Dynamics -- Chapter Highlights -- Exercises -- Reference -- Chapter 6 Longitudinal and Lateral Linear Stability and Control -- 6.1 Introduction -- 6.2 Dynamic and Static Stability -- 6.2.1 Longitudinal Stability Analysis -- 6.2.2 Lateral Dynamics and Stability -- 6.3 Modal Description of Aircraft Dynamics and the Stability of the Modes -- 6.3.1 Slow-Fast Partitioning of the Longitudinal Dynamics -- 6.3.2 Slow-Fast Partitioning of the Lateral Dynamics -- 6.3.3 Summary of Longitudinal and Lateral Modal Equations -- 6.3.3.1 Phugoid or Long Period…”

Tabla de Contenidos: “…2.2.13 Leave-One-Out Encoding -- 2.2.14 James-Stein Encoding -- 2.2.15 M-Estimator Encoding -- 2.2.16 Using HephAIstos to Encode Categorical Data -- 2.3 Time-Related Features Engineering -- 2.3.1 Date-Related Features -- 2.3.2 Lag Variables -- 2.3.3 Rolling Window Feature -- 2.3.4 Expending Window Feature -- 2.3.5 Understanding Time Series Data in Context -- 2.4 Handling Missing Values in Machine Learning -- 2.4.1 Row or Column Removal -- 2.4.2 Statistical Imputation: Mean, Median, and Mode -- 2.4.3 Linear Interpolation -- 2.4.4 Multivariate Imputation by Chained Equation Imputation -- 2.4.5 KNN Imputation -- 2.5 Feature Extraction and Selection -- 2.5.1 Feature Extraction -- 2.5.1.1 Principal Component Analysis -- 2.5.1.2 Independent Component Analysis -- 2.5.1.3 Linear Discriminant Analysis -- 2.5.1.4 Locally Linear Embedding -- 2.5.1.5 The t-Distributed Stochastic Neighbor Embedding Technique -- 2.5.1.6 More Manifold Learning Techniques -- 2.5.1.7 Feature Extraction with HephAIstos -- 2.5.2 Feature Selection -- 2.5.2.1 Filter Methods -- 2.5.2.2 Wrapper Methods -- 2.5.2.3 Embedded Methods -- 2.5.2.4 Feature Importance Using Graphics Processing Units (GPUs) -- 2.5.2.5 Feature Selection Using HephAIstos -- Further Reading -- Chapter 3 Machine Learning Algorithms -- 3.1 Linear Regression -- 3.1.1 The Math -- 3.1.2 Gradient Descent to Optimize the Cost Function -- 3.1.3 Implementation of Linear Regression -- 3.1.3.1 Univariate Linear Regression -- 3.1.3.2 Multiple Linear Regression: Predicting Water Temperature -- 3.2 Logistic Regression -- 3.2.1 Binary Logistic Regression -- 3.2.1.1 Cost Function -- 3.2.1.2 Gradient Descent -- 3.2.2 Multinomial Logistic Regression -- 3.2.3 Multinomial Logistic Regression Applied to Fashion MNIST -- 3.2.3.1 Logistic Regression with scikit-learn -- 3.2.3.2 Logistic Regression with Keras on TensorFlow…”

Tabla de Contenidos: “…MATLAB PARA MATEMÁTICAS EN INGENIERÍAS; PÁGINA LEGAL; CONTENIDO; CAPÍTULO 1 ENTORNO DE TRABAJO MATLAB; 1.1 INTRODUCCIÓN; 1.2 COMANDOS O INSTRUCCIONES EN MATLAB; 1.3 VARIABLES Y FORMATOS; 1.4 VARIABLES SIMBÓLICAS Y NUMÉRICAS; 1.4.1 SALIDA MATEMÁTICA ELEGANTE POR PANTALLA; 1.4.2 GENERAR VECTORES O VARIABLES; 1.4.3 GENERAR MATRICES; 1.5 FUNCIONES; 1.5.1 CREAR FUNCIONES; CAPÍTULO 2 GRÁFICOS CON MATLAB; 2.1 INTRODUCCIÓN; 2.2 FUNCIONES DE UNA VARIABLE, Y=F(X); 2.2.1 ALGUNOS COMANDOS PARA REPRESENTAR; 2.2.2 SUBVENTANAS; 2.3 REPRESENTACIÓN DE FUNCIONES SIMBÓLICAS…”

Tabla de Contenidos: “…Front Cover -- Essentials of Time Series for Financial Applications -- Copyright Page -- Contents -- List of Figures -- List of Tables -- Preface -- 1 Linear Regression Model -- 1.1 Inference in Linear Regression Models -- 1.1.1 The Ordinary Least Squares Estimator -- 1.1.2 Goodness of Fit Measures -- 1.1.3 The Generalized Least Squared Estimator -- 1.1.4 Maximum Likelihood Estimator -- 1.1.5 Hypotheses Testing, Confidence Intervals, and Predictive Intervals -- 1.1.6 Linear Regression Model With Stochastic Regressors -- 1.1.7 Asymptotic Theory for Linear Regressions -- 1.2 Testing for Violations of the Linear Regression Framework -- 1.2.1 Linearity -- 1.2.2 Structural Breaks and Parameter Stability Test -- 1.3 Specifying the Regressors -- 1.3.1 How to Select the Regressors -- 1.3.2 Multicollinearity -- 1.3.3 Measurement Errors in the Regressors -- 1.4 Issues With Heteroskedasticity and Autocorrelation of the Errors -- 1.4.1 Heteroskedastic Errors -- 1.4.2 Autocorrelated Errors -- 1.5 The Interpretation of Regression Results -- References -- Appendix 1.A -- Appendix 1.B Principal Component Analysis -- 2 Autoregressive Moving Average (ARMA) Models and Their Practical Applications -- 2.1 Essential Concepts in Time Series Analysis -- 2.1.1 Time Series and Their Properties -- 2.1.2 Stationarity -- 2.1.3 Sample Autocorrelations and Sample Partial Autocorrelations -- 2.2 Moving Average and Autoregressive Processes -- 2.2.1 Finite Order Moving Average Processes -- 2.2.2 Autoregressive Processes -- 2.2.3 Autoregressive Moving Average Processes -- 2.3 Selection and Estimation of AR, MA, and ARMA Models -- 2.3.1 The Selection of the Model and the Role of Information Criteria -- 2.3.2 Estimation Methods -- 2.3.3 Residual Diagnostics -- 2.4 Forecasting ARMA Processes -- 2.4.1 Standard Principles of Forecasting -- 2.4.2 Forecasting an AR(p) Process…”

Tabla de Contenidos: “…Mixture of generalized linear models -- 3.4.1. Generalized linear models -- 3.4.2. …”

Tabla de Contenidos: “…Ridge regression -- LASSO regression -- Data clustering -- The K-means algorithm -- K-means implementation with Spark ML -- Data dimensionality reduction -- Singular value decomposition -- Matrix theory and linear algebra overview -- The important properties of singular value decomposition -- SVD with Spark ML -- The principal component analysis method -- The PCA algorithm using SVD -- Implementing SVD with Spark ML -- Content-based recommendation systems -- Frequently asked questions -- Summary -- Chapter 4: Neural Network for Big Data -- Fundamentals of neural networks and artificial neural networks -- Perceptron and linear models -- Component notations of the neural network -- Mathematical representation of the simple perceptron model -- Activation functions -- Sigmoid function -- Tanh function -- ReLu -- Nonlinearities model -- Feed-forward neural networks -- Gradient descent and backpropagation -- Gradient descent pseudocode -- Backpropagation model -- Overfitting -- Recurrent neural networks -- The need for RNNs -- Structure of an RNN -- Training an RNN -- Frequently asked questions -- Summary -- Chapter 5: Deep Big Data Analytics -- Deep learning basics and the building blocks -- Gradient-based learning -- Backpropagation -- Non-linearities -- Dropout -- Building data preparation pipelines -- Practical approach to implementing neural net architectures -- Hyperparameter tuning -- Learning rate -- Number of training iterations -- Number of hidden units -- Number of epochs -- Experimenting with hyperparameters with Deeplearning4j -- Distributed computing -- Distributed deep learning -- DL4J and Spark -- API overview -- TensorFlow -- Keras -- Frequently asked questions -- Summary -- Chapter 6: Natural Language Processing -- Natural language processing basics -- Text preprocessing -- Removing stop words -- Stemming -- Porter stemming…”

Tabla de Contenidos: “…Input-output models -- 2. Linear Identification of Closed-Loop Systems -- 2.1. …”

Tabla de Contenidos: “…Step 4-Install the Profile and Print the 21-step Digital Negative -- Step 5-Adjust Ink Density to Get Dmax and Paper White -- Step 6-Linearize the Profile -- Linearizing with the CURVE_K -- Linearizing with the LINEARIZE= -- Chapter 6 Making Supports for Single and Double Transfer Carbon Printing -- Introduction -- Preparing Fixed-out Photographic Paper -- Sizing Watercolor Paper -- Sizing Watercolor Paper with Polyvinyl Acetate or Acrylic Medium -- Sizing Watercolor Paper with Hardened Gelatin -- Sizing the Paper -- Light Hardening of Watercolor Papers Sized with Gelatin or Albumen Plus Hardener -- The Albumen Solution -- Prepare the Gelatin Sizing Mixture and Coat the Paper -- Surface Prepping of Temporary Plastic Supports for Double Transfer -- Sizing Temporary Plastic Supports for Double Transfer -- Preparation of Final Paper Supports for Double Transfer -- Steps -- Sizing for Glass as a Carbon Transfer Support -- Classic Glass Sizing -- Modern Adhesion Promoter as a Sizing Agent -- Other Methods -- Chapter 7 Making Carbon Tissue -- Introduction -- How to Make Carbon Tissue -- Making Pigmented Gelatin -- Selecting a Gelatin -- Preparing the Pigmented Gelatin -- Coating Tissue -- Tip: Annotate the Back of Your Tissues -- The Laydown Method for Coating -- Preparation for Coating -- Basic Hand Coating -- Hand Coating the Tissue -- Tip: Calculating Volumes for Coating Height -- Using a Magnetic Frame and Rod -- Coating the Tissue with a Rod -- Using a Mayer Rod -- Coating with a Mayer Rod -- Selecting and Using Pigments -- Inks -- Watercolor Pigments -- Pigment Dispersions -- Dry Pigments -- Using an Improvised Ball Mill Storing Tissue -- Chapter 8 Basic Working Procedures for Single Carbon -- Overview -- Preparing a Printing Session -- Sensitizing the Tissue -- Mixing the Sensitizer with a Spirit -- Details of the Procedures…”

Tabla de Contenidos: “…Video Enhancement and Restoration; 4.1 Introduction; 4.2 Spatiotemporal Noise Filtering; 4.2.1 Linear Filters; 4.2.2 Order-Statistic Filters; 4.2.3 Multiresolution Filters; 4.3 Coding Artifact Reduction; 4.3.1 Artifact Reduction in the Spatial Domain…”

Tabla de Contenidos: “…. -- See also -- Chapter 5: Practical Machine Learning with Regression and Classification in Spark 2.0 - Part I -- Introduction -- Fitting a linear regression line to data the old fashioned way -- How to do it... -- How it works... -- There's more... -- See also -- Generalized linear regression in Spark 2.0 -- How to do it... -- How it works... -- There's more... -- See also -- Linear regression API with Lasso and L-BFGS in Spark 2.0 -- How to do it... -- How it works... -- There's more... -- See also -- Linear regression API with Lasso and 'auto' optimization selection in Spark 2.0 -- How to do it... -- How it works... -- There's more…”