Tabla de Contenidos: “…-- The first physics constraint actor experience -- Customizing physics constraint actor -- A simple game with Blueprint -- Summary -- Chapter 5: Physics Dampening, Friction, and Physics Bodies -- Physics Bodies - an overview -- Physics Bodies - a section review -- Angular and Linear Damping -- Angular and Linear Damping - a section review -- Physical Materials - an overview -- Physical Materials - a section review -- Physics Damping -- Physics Damping - a section review -- Summary -- Chapter 6: Materials -- What is physical material? …”

Tabla de Contenidos: “…Transforming the editor into a design studioE1 - the Preview toolbar; E2 - exploring the palette; E3 - the layout preview; E4 - the Component Tree; E5 - the Properties window; E6 - text and design view tabs; The project folder and file structure; FAQ; Summary; Chapter 4: Designing Layouts; Exploring Android UI design; Structure of a UI design; Configuring and using widgets; Widget properties; Experimenting with widgets; Containing widgets in layouts; RelativeLayout; Using LinearLayout; Summary; Chapter 5: Real-World Layouts; Building a real-world UI; Creating a tablet emulator…”

Tabla de Contenidos: “…Time for action - using the datetime64 data typeWeekly summary; Time for action - summarizing data; Average True Range; Time for action - calculating the average true range; Simple Moving Average; Time for action - computing the simple moving average; Exponential Moving Average; Time for action - calculating the exponential moving average; Bollinger Bands; Time for action - enveloping with Bollinger bands; Linear model; Time for action - predicting price with a linear model; Trend lines; Time for action - drawing trend lines; Methods of ndarray…”

Tabla de Contenidos: “…Chip-Secured Data Access: Confidential Data on Untrusted ServersHippocratic Databases; Watermarking Relational Databases; Session: Research 4 - Query Optimization; Parametric Query Optimization for Linear and Piecewise Linear Cost Functions; Plan Selection Based on Query Clustering; Generic Database Cost Models for Hierarchical Memory Systems; Session: Research 5 - Stream Processing; Streaming Queries over Streaming Data; Monitoring Streams - A New Class of Data Management Applications; A Transducer-Based XML Query Processor; Session: Research 6 - XML Indexing; Updates for Structure Indexes…”

Tabla de Contenidos: “…Film Technology in Post Production; Copyright; Contents; Preface to the Second Edition; Film Technology in Context; Introduction; Origins of Film and Video; Video; Digital imaging; Film and Video: the Differences; Intermittent movement; Frame rate; Resolution; Contrast; Aspect ratio; Film Versus Video - or Film with Video; Digital Film; Digital image capture; Digital post production; Digital cinema; Post Production Pathways; Film Versus Video Production; Image quality; Production style; Production costs; Preservation; Traditional Sprocket Edit, Film Finish; Non-linear Edit, Film Finish…”

Tabla de Contenidos: “…Kramers-Kronig Relations in Linear and Nonlinear Optics; Problems; References…”

Tabla de Contenidos: “…Rotation and Translation TransformationsLesson 1: Transformations are applied to shapes and paths drawn after the setTransform() or other transformation function is called; Lesson 2: We must "translate" the point of origin to the center of our shape to rotate it around its own center; Scale Transformations; Combining Scale and Rotation Transformations; Filling Objects with Colors and Gradients; Setting Basic Fill Colors; Filling Shapes with Gradients; Linear gradients; Linear horizontal gradients; Applying a horizontal gradient to a stroke; Applying a horizontal gradient to a complex shape…”

Tabla de Contenidos: “…K-means clustering on big dataLoading big matrices; Big data K-means clustering analysis; Big data linear regression analysis; Loading big data; Fitting a linear regression model on large datasets; Summary; References; Chapter 5: FX Derivatives; Terminology and notations; Currency options; Exchange options; Two-dimensional Wiener processes; The Margrabe formula; Application in R; Quanto options; Pricing formula for call quanto; Pricing a call quanto in R; Summary; References; Chapter 6: Interest Rate Derivatives and Models; The Black model; Pricing a cap with Black's model; The Vasicek model…”

Tabla de Contenidos: “…A variation of PCA for big data-randomized PCALatent Factor Analysis (LFA); Linear Discriminant Analysis (LDA); Latent Semantical Analysis (LSA); Independent Component Analysis (ICA); Kernel PCA; Restricted Boltzmann Machine (RBM); Detection and treatment of outliers; Univariate outlier detection; EllipticEnvelope; OneClassSVM; Scoring functions; Multilabel classification; Binary classification; Regression; Testing and validating; Cross-validation; Using cross-validation iterators; Sampling and bootstrapping; Hyper-parameters optimization; Building custom scoring functions…”

Tabla de Contenidos: “…CREATING STORY AND CHARACTERS FOR ANIMATED FEATURES AND SHORTS; Copyright; Contents; Introduction; Dedication and Thanks; Part One: Getting Started; 1 First Catch Your Rabbit: Creating Concepts and Characters; Linear and Non-linear Storytelling; Setting Limitations and Finding Liberation; Shopping for Story: Creating Lists; Nothing Is Normal: Researching Action; All Thumbs: Quick Sketches and Thumbnails; Reality Is Overrated; Past and Present: Researching Settings and Costumes; 2 Vive la Diff erence! …”

Tabla de Contenidos: “…Electronic Circuits: Linear/Analog; Introduction; Linear Circuits; Amplifiers; Classifying Amplifiers; Amplifier Specifications; Filters; Oscillators; Mixers…”

Tabla de Contenidos: “…7.1 Physical Parameters for the Polarization Characterization in Optical Fibers7.2 Representation of the State of Polarization in Optical Fiber Using Poincaré Sphere; 7.3 Classifications of Linear Polarization Maintaining Fibers; 7.4 Fabrication Methods for High Birefringence Fibers; 7.5 Control of Birefringence by Waveguide Design in Birefringent Fibers; 7.6 Single-Polarization Single-Mode Fibers; 7.7 Low Linear Birefringence Fibers; References; 8 Optical Fibers Based on Air-Silica Guiding Structure; 8.1 Review of Air-Silica Guidance in Optical Fibers…”

Tabla de Contenidos: “…Preface -- Acknowledgements -- 1 Image Processing -- 1.1 Basic Definitions -- 1.2 Image Formation -- 1.3 Image Processing Operations -- 1.4 Example Application -- 1.5 Real-Time Image Processing -- 1.6 Embedded Image Processing -- 1.7 Serial Processing -- 1.8 Parallelism -- 1.9 Hardware Image Processing Systems -- 2 Field Programmable Gate Arrays -- 2.1 Programmable Logic -- 2.2 FPGAs and Image Processing -- 2.3 Inside an FPGA -- 2.4 FPGA Families and Features -- 2.5 Choosing an FPGA or Development Board -- 3 Languages -- 3.1 Hardware Description Languages -- 3.2 Software-Based Languages -- 3.3 Visual Languages -- 3.4 Summary -- 4 Design Process -- 4.1 Problem Specification -- 4.2 Algorithm Development -- 4.3 Architecture Selection -- 4.4 System Implementation -- 4.5 Designing for Tuning and Debugging -- 5 Mapping Techniques -- 5.1 Timing Constraints -- 5.2 Memory Bandwidth Constraints -- 5.3 Resource Constraints -- 5.4 Computational Techniques -- 5.5 Summary -- 6 Point Operations -- 6.1 Point Operations on a Single Image -- 6.2 Point Operations on Multiple Images -- 6.3 Colour Image Processing -- 6.4 Summary -- 7 Histogram Operations -- 7.1 Greyscale Histogram -- 7.2 Multidimensional Histograms -- 8 Local Filters -- 8.1 Caching -- 8.2 Linear Filters -- 8.3 Nonlinear Filters -- 8.4 Rank Filters -- 8.5 Colour Filters -- 8.6 Morphological Filters -- 8.7 Adaptive Thresholding -- 8.8 Summary -- 9 Geometric Transformations -- 9.1 Forward Mapping -- 9.2 Reverse Mapping -- 9.3 Interpolation -- 9.4 Mapping Optimisations -- 9.5 Image Registration -- 10 Linear Transforms -- 10.1 Fourier Transform -- 10.2 Discrete Cosine Transform -- 10.3 Wavelet Transform -- 10.4 Image and Video Coding -- 11 Blob Detection and Labelling -- 11.1 Bounding Box -- 11.2 Run-Length Coding -- 11.3 Chain Coding -- 11.4 Connected Component Labelling -- 11.5 Distance Transform -- 11.6 Watershed Transform -- 11.7 Hough Transform -- 11.8 Summary -- 12 Interfacing -- 12.1 Camera Input -- 12.2 Display Output…”

Tabla de Contenidos: “…-- Predicting stock price with regression algorithms -- Feature engineering -- Data acquisition and feature generation -- Linear regression -- Decision tree regression -- Support vector regression…”

Tabla de Contenidos: “…Introduction to Renewable Energy Systems / An Overview of Renewable Energy Systems -- Climate Change -- Renewable Energy Trend -- Types of Renewable Energy Systems -- Introduction -- Wind Power -- Solar Energy -- Geothermal Power -- Hydro Power -- Bioenergy -- Ocean Power -- Economic Aspects of Renewable Energy Systems -- Introduction -- Affecting Factors -- Life-Cycle Costs -- Economic Trend of Renewable Energy Systems -- Opportunities and Challenges of Renewable Energy Systems Integration -- Renewable Energy Systems Integration Opportunities -- Renewable Energy Systems Integration Challenges and Barriers -- Alleviating the Challenges and Barriers -- Current Trends and Future Prospects -- The Need for Optimization Tools to Solve Problems Related to Renewable Energy Systems -- Conclusions -- Acknowledgments -- References -- Introduction to Optimization / Introduction -- Formulation of Optimization Problem -- General Models of Optimization Problems -- Convex Optimization -- Basic Concepts and Models of Linear Optimization -- Solution Techniques for Unconstrained Optimization -- Nelder -- Mead Method -- Golden Section Search -- Fibonacci Search -- Hooke and Jeeves' Method -- Gradient Descent Method -- Coordinate Descent Method -- Solution Techniques for Constrained Optimization and Relevant Applications to Renewable Energy Systems -- Genetic Algorithm -- Particle Swarm Optimization -- Simulating Annealing -- Ant Colony Optimization -- Tabu Search -- Firefly Algorithm -- Artificial Bee Colony Algorithm -- Artificial Immune System -- Game Theory -- Simplex Technique Specifically for Mixed Integer Linear Programming Concept -- Optimal Control Technique -- Conclusion and Discussion -- References -- Optimal Procurement of Contingency and Load Following Reserves by Demand Side Resources Under Wind-Power Generation Uncertainty / Introduction -- Mathematical Model -- Overview and Modeling Assumptions -- Objective Function -- Constraints -- Case Studies -- Wind-Power Production Scenario Generation -- Illustrative Example -- Application on a 24-Bus System -- Computational Statistics -- Conclusions -- Appendix A: 24-Bus System Test System Data -- Nomenclature -- Sets and Indices -- Parameters -- Variables -- References -- Optimum Bidding of Renewable Energy System Owners in Electricity Markets / Renewable Energy System Owners in Electricity Markets -- Electricity Market Overview -- Importance of Integrating Renewables into the Electricity Market -- Current Market Rules for Renewable Energy System Owners in North America -- Price-Taker Models -- Multistage Stochastic Programming Approach -- Multistage Stochastic Programming Approach -- Scenario Generation and Reduction Method -- Risk Management -- Bidding Strategy Models for Price-Taker Renewable Energy Owner -- Mitigating the Trading Risk by Purchasing Additional Power from Conventional Energy Owner -- Price-Maker Models -- MPEC Approach -- Why Consider Wind Power Energy Owners as Price-Makers? …”

Tabla de Contenidos: “…Model fitting and evaluation -- Statistical significance of regression outputs -- Generalize estimating equations -- Mixed effects models -- Time series data -- Generalized linear models -- Applying regularization to linear models -- Tree methods -- Decision trees -- Random forest -- Scaling out with PySpark - predicting year of song release -- Summary -- Chapter 5: Putting Data in its Place - Classification Methods and Analysis -- Logistic regression -- Multiclass logistic classifiers: multinomial regression -- Formatting a dataset for classification problems -- Learning pointwise updates with stochastic gradient descent -- Jointly optimizing all parameters with second-order methods -- Fitting the model -- Evaluating classification models -- Strategies for improving classification models -- Separating Nonlinear boundaries with Support vector machines -- Fitting and SVM to the census data -- Boosting: combining small models to improve accuracy -- Gradient boosted decision trees -- Comparing classification methods -- Case study: fitting classifier models in pyspark -- Summary -- Chapter 6: Words and Pixels - Working with Unstructured Data -- Working with textual data -- Cleaning textual data -- Extracting features from textual data -- Using dimensionality reduction to simplify datasets -- Principal component analysis -- Latent Dirichlet Allocation -- Using dimensionality reduction in predictive modeling -- Images -- Cleaning image data -- Thresholding images to highlight objects -- Dimensionality reduction for image analysis -- Case Study: Training a Recommender System in PySpark -- Summary -- Chapter 7: Learning from the Bottom Up - Deep Networks and Unsupervised Features -- Learning patterns with neural networks -- A network of one - the perceptron -- Combining perceptrons - a single-layer neural network -- Parameter fitting with back-propagation…”

Tabla de Contenidos: “…6.4.2 Overview of Differential Cryptanalysis6.4.3 Overview of Linear Cryptanalysis; 6.4.4 Tiny DES; 6.4.5 Differential Cryptanalysis of TDES; 6.4.6 Linear Cryptanalysis of TDES; 6.4.7 Implications Block Cipher Design; 6.5 Lattice Reduction and the Knapsack; 6.6 RSA Timing Attacks; 6.6.1 A Simple Timing Attack; 6.6.2 Kocher's Timing Attack; 6.7 Summary; 6.8 Problems; II Access Control; 7 Authentication; 7.1 Introduction; 7.2 Authentication Methods; 7.3 Passwords; 7.3.1 Keys Versus Passwords; 7.3.2 Choosing Passwords; 7.3.3 Attacking Systems via Passwords; 7.3.4 Password Verification…”

Tabla de Contenidos: “…Cover -- Credits -- About the Author -- About the Reviewers -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Getting Started with Python Libraries -- Installing Python 3 -- Installing data analysis libraries -- On Linux or Mac OS X -- On Windows -- Using IPython as a shell -- Reading manual pages -- Jupyter Notebook -- NumPy arrays -- A simple application -- Where to find help and references -- Listing modules inside the Python libraries -- Visualizing data using Matplotlib -- Summary -- Chapter 2: NumPy Arrays -- The NumPy array object -- Advantages of NumPy arrays -- Creating a multidimensional array -- Selecting NumPy array elements -- NumPy numerical types -- Data type objects -- Character codes -- The dtype constructors -- The dtype attributes -- One-dimensional slicing and indexing -- Manipulating array shapes -- Stacking arrays -- Splitting NumPy arrays -- NumPy array attributes -- Converting arrays -- Creating array views and copies -- Fancy indexing -- Indexing with a list of locations -- Indexing NumPy arrays with Booleans -- Broadcasting NumPy arrays -- Summary -- References -- Chapter 3: The Pandas Primer -- Installing and exploring Pandas -- The Pandas DataFrames -- The Pandas Series -- Querying data in Pandas -- Statistics with Pandas DataFrames -- Data aggregation with Pandas DataFrames -- Concatenating and appending DataFrames -- Joining DataFrames -- Handling missing values -- Dealing with dates -- Pivot tables -- Summary -- References -- Chapter 4: Statistics and Linear Algebra -- Basic descriptive statistics with NumPy -- Linear algebra with NumPy -- Inverting matrices with NumPy -- Solving linear systems with NumPy -- Finding eigenvalues and eigenvectors with NumPy -- NumPy random numbers -- Gambling with the binomial distribution -- Sampling the normal distribution…”

Tabla de Contenidos: “…Contracting a decision tree -- Performing classification -- Extracting word embedding representation -- Understanding word embedding -- Performing vector arithmetic -- Performing classification -- Using the fastText tool -- Summary -- Chapter 4: Extracting Sentiments from Product Reviews -- Technical requirements -- Understanding sentiment analysis -- Performing exploratory data analysis -- Using the Software dataset -- Exploiting the ratings of products -- Extracting the word count of reviews -- Exploiting the helpfulness score -- Introducing linear regression -- Putting linear regression into action -- Introducing logistic regression -- Understanding gradient descent -- Using logistic regression -- Creating training and test sets -- Performing classification -- Applying regularization -- Introducing deep neural networks -- Understanding logic gates -- Understanding perceptrons -- Understanding artificial neurons -- Creating artificial neural networks -- Training artificial neural networks -- Performing classification -- Summary -- Chapter 5: Recommending Music Titles -- Technical requirements -- Understanding recommender systems -- Performing exploratory data analysis -- Cleaning the data -- Extracting information from the data -- Understanding the Pearson correlation -- Introducing content-based filtering -- Extracting music recommendations -- Introducing collaborative filtering -- Using memory-based collaborative recommenders -- Applying SVD -- Clustering handwritten text -- Applying t-SNE -- Using model-based collaborative systems -- Introducing autoencoders -- Summary -- Chapter 6: Teaching Machines to Translate -- Technical requirements -- Understanding machine translation -- Introducing rule-based machine translation -- Using direct machine translation -- Using transfer-based machine translation -- Using interlingual machine translation…”

Tabla de Contenidos: “…Prerequisites -- P.1 Real Numbers -- P.2 Cartesian Coordinate System -- P.3 Linear Equations and Inequalities -- P.4 Lines in the Plane -- P.5 Solving Equations Graphically, Numerically, and Algebraically -- P.6 Complex Numbers -- P.7 Solving Inequalities Algebraically and Graphically -- Key Ideas -- Review Exercises -- Chapter 1. …”