Tabla de Contenidos: “…-- Audio formats -- Second monitors -- Having different windows on each display -- Types of disks -- How much storage is enough? …”

Tabla de Contenidos: “…The Audio-for-Visual and Media Production Environment -- The Audio-for-Gaming Production Environment -- The Project Studio -- Primary Factors Governing Studio and Control Room Acoustics -- Acoustic Isolation -- Walls -- Floors -- Risers -- Ceilings -- Windows and Doors -- ISO-Rooms and ISO-Booths -- Acoustic Partitions -- Noise Isolation Within the Control Room -- Symmetry in Control-Room Design -- Frequency Balance -- Reflections -- Absorption -- High-Frequency Absorption -- Low-Frequency Absorption -- Flexible Surfaces -- The Practical Side of Acoustics -- Symmetry -- 25/25/50 -- Speaker Placement -- Room Reflections and Acoustic Reverberation -- Acoustic Echo Chambers -- Chapter 4 Microphones: Design and Application -- Microphone Design -- The Dynamic Microphone -- The Ribbon Microphone -- Further Developments in Ribbon Technology -- The Condenser Microphone -- Powering a Condenser Mic -- External Power Supply -- Phantom Power -- The Electret-Condenser Microphone -- Microphone Characteristics -- Directional Response -- Frequency Response -- Transient Response -- Output Characteristics -- Sensitivity Rating -- Equivalent Noise Rating -- Overload Characteristics -- Microphone Impedance -- Balanced/Unbalanced Lines -- Soldering, Baby! …”

Tabla de Contenidos: “…扉 -- 内容提 -- 关于作 -- 简介 -- 关于审稿人 -- 前 -- 源与支持 -- 目录 -- 第1章 Anaconda生态系统 -- 1.1 简介 -- 1.1.1 Anaconda使用Jupyter的理由 -- 1.1.2 在无 的情况下使用Jupyter -- 1.2 Miniconda -- 1.3 Anaconda云 -- 1.4 查找帮助 -- 1.5 总结 -- 1.6 回 及练习 -- 第2章 Anaconda安 -- 2.1 安 Anaconda -- Windows下安 Anaconda -- 2.2 测 Python -- 2.3 使用IPython -- 2.4 Jupyter使用Python -- 2.5 Spyder简介 -- 2.6 Conda安 R -- 2.7 安 Julia并 接到Jupyter -- 2.8 安 Octave并 接到Jupyter -- 2.9 查找帮助 -- 2.10 总结 -- 2.11 回 及练习 -- 第3章 数据基础 -- 3.1 数据源 -- 3.2 UCI机器学习 -- 3.3 Python包pandas简介 -- 3.4 入数据的几种方式 -- 3.4.1 使用R 入数据 -- 3.4.2 使用Python 入数据 -- 3.5 Quandl数据分发平台简介 -- 3.6 处理缺失数据 -- 3.7 数据排序 -- 3.7.1 切割数据 -- 3.7.2 合并不同数据 -- 3.7.3 数据 出 -- 3.8 Python包cbsodata简介 -- 3.9 Python包datadotworld简介 -- 3.10 R包haven和foreign简介 -- 3.11 R包dslabs简介 -- 3.12 生成Python数据 -- 3.13 生成R数据 -- 3.14 总结 -- 3.15 回 及练习 -- 第4章 数据可 化 -- 4.1 数据可 化的 性 -- 4.2 R数据可 化 -- 4.3 Python数据可 化 -- 4.4 Julia数据可 化 -- 4.5 绘制简单图形 -- 4.5.1 各种条状图、 图和直方图 -- 4.5.2 添加 势 -- 4.5.3 添加图 和其他 明 -- 4.6 R可 化包 -- 4.7 Python可 化包 -- 4.8 Julia可 化包 -- 4.9 动态可 化 -- 4.9.1 将图片存储为pdf -- 4.9.2 将动态可 化存储为HTML文件 -- 4.10 总结 -- 4.11 回 及练习 -- 第5章 在Anaconda中统 建模 -- 5.1 线性模型简介 -- 5.2 在R、Python、Julia和Octave中 线性回归 -- 5.3 临界值和决策 则 -- 5.4 F检 、临界值和决策 则 -- 线性回归在 方 的一个应用 -- 5.5 处理缺失数据 -- 5.5.1 清 缺失数据 -- 5.5.2 用其他值替换缺失数据 -- 5.6 检测及处理异常值 -- 5.7 几个多元线性模型 -- 5.8 共线性及其 决方案 -- 5.9 一个模型的性 测 -- 5.10 总结 -- 5.11 回 及练习 -- 第6章 管理包 -- 6.1 包、模块和工具箱简介 -- 6.2 使用包的两个示例 -- 6.3 查找所有R包 -- 6.4 查找所有Python包 -- 6.5 查找所有Julia包 -- 6.6 查找所有Octave包 -- 6.7 R中的任务 图 -- 6.8 查找手册 -- 6.9 包依 -- 6.10 R包管理 -- 6.11 Python包管理 -- 6.12 Julia包管理 -- 6.13 Octave包管理 -- 6.14 包管理器conda -- 6.15 在R和Python中创建程序 -- 6.16 查找环境变 -- 6.17 总结 -- 6.18 回 及练习 -- 第7章 Anaconda的优化 -- 7.1 为何优化很 -- 7.2 优化的一 -- 7.3 二次优化 -- 7.3.1 R中的优化 -- 7.3.2 Python中的优化 -- 7.3.3 Julia中的优化 -- 7.3.4 Octave中的优化 -- 7.4 票投 组合优化 -- 7.5 最优的税收政策 -- 7.6 R中用于优化的包…”

Tabla de Contenidos: “…Synchronizing the end of the game -- Running the game on multiple computers -- Showing the IP address of the server -- Connecting from another computer -- Additional exercises - Sharpening the axe -- Summary -- Quiz time -- Part 3: Deepening Our Knowledge -- Chapter 12: Exporting to Multiple Platforms -- Technical requirements -- Exporting for Windows, Mac, and Linux -- Downloading the export template -- Making the actual exports of the game -- Uploading our game to Itch.io -- What is Itch.io? …”

Tabla de Contenidos: “…Intro -- Title Page -- Copyright and Credits -- Dedication -- Foreword -- Contributors -- Table of Contents -- Preface -- Part 1 - A Whirlwind of Stable Diffusion -- Chapter 1: Introducing Stable Diffusion -- Evolution of the Diffusion model -- Before Transformer and Attention -- Transformer transforms machine learning -- CLIP from OpenAI makes a big difference -- Generate images -- DALL-E 2 and Stable Diffusion -- Why Stable Diffusion -- Which Stable Diffusion to use -- Why this book -- References -- Chapter 2: Setting Up the Environment for Stable Diffusion -- Hardware requirements to run Stable Diffusion -- GPU -- System memory -- Storage -- Software requirements -- CUDA installation -- Installing Python for Windows, Linux, and macOS -- Installing PyTorch -- Running a Stable Diffusion pipeline -- Using Google Colaboratory -- Using Google Colab to run a Stable Diffusion pipeline -- Summary -- References -- Chapter 3: Generating Images Using Stable Diffusion -- Logging in to Hugging Face -- Generating an image -- Generation seed -- Sampling scheduler -- Changing a model -- Guidance scale -- Summary -- References -- Chapter 4: Understanding the Theory Behind Diffusion Models -- Understanding the image-to-noise process -- A more efficient forward diffusion process -- The noise-to-image training process -- The noise-to-image sampling process -- Understanding Classifier Guidance denoising -- Summary -- References -- Chapter 5: Understanding How Stable Diffusion Works -- Stable Diffusion in latent space -- Generating latent vectors using diffusers -- Generating text embeddings using CLIP -- Initializing time step embeddings -- Initializing the Stable Diffusion UNet -- Implementing a text-to-image Stable Diffusion inference pipeline -- Implementing a text-guided image-to-image Stable Diffusion inference pipeline -- Summary -- References…”

Tabla de Contenidos: “…MATLAB® Programming Environment -- 4.1 Introduction: MATLAB Application -- 4.1.1 Command Window -- Execute Multiple Commands -- 4.1.2 Workspace -- Clear the Workspace…”

Tabla de Contenidos: “…Installation and setup -- A.1 The Anaconda distribution -- A.2 The macOS setup process -- A.2.1 Installing Anaconda in macOS -- A.2.2 Launching Terminal -- A.2.3 Common Terminal commands -- A.3 The Windows setup process -- A.3.1 Installing Anaconda in Windows -- A.3.2 Launching Anaconda Prompt -- A.3.3 Common Anaconda Prompt commands -- A.4 Creating a new Anaconda environment -- A.5 Anaconda Navigator -- A.6 The basics of Jupyter Notebook -- Appendix B. …”

Tabla de Contenidos: “…Front Cover -- Digital Signal Processing 101 -- Digital Signal Processing 101: Everything you Need to Know to Get Started -- Copyright -- Contents -- Acknowledgments -- Introduction -- 1 - Numerical Representation -- 1.1 Integer Fixed Point Representation -- 1.2 Fractional Fixed Point Representation -- 1.3 Floating Point Representation -- 2 - Complex Numbers and Exponentials -- 2.1 Complex Addition and Subtraction -- 2.2 Complex Multiplication -- 2.3 Polar Representation -- 2.4 Complex Multiplication Using Polar Representation -- 2.5 Complex Conjugate -- 2.6 The Complex Exponential -- 2.7 Measuring Angles in Radians -- 3 - Sampling, Aliasing, and Quantization -- 3.1 Sampling Effects -- 3.2 Nyquist Sampling Rule -- 3.3 Quantization -- 3.4 Signal to Noise Ratio -- 4 - Frequency Response -- 4.1 Frequency Response and the Complex Exponential -- 4.2 Normalizing Frequency Response -- 4.3 Sweeping Across the Frequency Response -- 4.4 Example Frequency Responses -- 4.5 Linear Phase Response -- 4.6 Normalized Frequency Response Plots -- 5 - Finite Impulse Response (FIR) Filters -- 5.1 Finite Impulse Response Filter Construction -- 5.2 Computing Frequency Response -- 5.3 Computing Filter Coefficients -- 5.4 Effect of Number of Taps on Filter Response -- 6 - Windowing -- 6.1 Truncation of Coefficients -- 6.2 Tapering of Coefficients -- 6.3 Sample Coefficient Windows -- 7 - Decimation and Interpolation -- 7.1 Decimation -- 7.2 Interpolation -- 7.3 Resampling by Noninteger Value -- 8 - Infinite Impulse Response (IIR) Filters -- 8.1 Infinite Impulse Response and Finite Impulse Response Filter Characteristic Comparison -- 8.2 Bilinear Transform -- 8.3 Frequency Prewarping -- 9 - Complex Modulation and Demodulation -- 9.1 Modulation Constellations -- 9.2 Modulated Signal Bandwidth -- 9.3 Pulse-Shaping Filter -- 9.4 Raised Cosine Filter…”

Tabla de Contenidos: “…V5R2 iSeries Navigator enhancements summary -- General Connections and user interface updates -- Applications Administration -- System values -- Work Management -- Hardware management -- Network -- Windows Administration -- Security -- Database -- Integrated File System -- Managements Central-related enhancements -- Related publications -- IBM Redbooks -- Other resources -- Referenced Web sites -- How to get IBM Redbooks -- IBM Redbooks collections -- Index -- Back cover…”

Tabla de Contenidos: “…-- 9.6.2 Changing the chatWindow.html -- 9.7 Adding menu options to Sametime Links -- 9.7.1 The sample pop-up menu -- 9.7.2 Starting with a Sametime Links-enabled page -- Chapter 10. …”

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: “…Thin UI, deep tests -- Testing MAUI-specific code -- Summary -- Further reading -- Chapter 14: Troubleshooting and Debugging Tips -- Technical requirements -- Common data binding issues -- Checking the Output and XAML Binding Failures window -- Debugging with a DoNothingConverter -- Troubleshooting collections -- The data binding pitfall on Behaviors -- Services and Dependency Injection pitfalls -- Unable to resolve service for type -- No parameterless constructor defined for type -- Incorrect service registration -- Frequent custom control and converter problems -- Troubleshooting bindable properties -- Binding to the BindingContext -- Value converter issues -- Summary -- Further reading -- Index -- Other Books You May Enjoy…”

Tabla de Contenidos: “…Machine generated contents note: Foreword Preface About the Authors Exercises and Website Acknowledgments 1 Introducing Logistics 1.1 Definition of Logistics 1.2 Logistics Systems 1.2.1 Logistics activities 1.2.2 Information flows and logistics network 1.2.3 Case of more products 1.3 Reverse Logistics 1.4 Integrated Logistics 1.5 Objectives of Logistics 1.5.1 Measures of the service level 1.6 Management of the Logistics System 1.6.1 Planning phase 1.6.2 Organisational phase 1.6.3 Control phase 1.7 Case Study: the Pfizer Logistics System 1.8 Questions and Problems 2 Forecasting Logistics Requirements 2.1 Introduction 2.2 Qualitative Methods 2.3 Quantitative Methods 2.3.1 Graphical representation of time series 2.3.2 Classification of time series 2.4 Data Preprocessing 2.4.1 Insertion of missing data 2.4.2 Detection of outliers 2.4.3 Data aggregation 2.4.4 Removing the calendar variations 2.4.5 Deflating monetary time series 2.4.6 Adjusting for population variations 2.4.7 Normalising the data 2.5 Choice of the Forecasting Method 2.5.1 Notation 2.5.2 Casual versus extrapolation methods 2.5.3 Decomposition method 2.5.4 Further time series extrapolation methods: the constant trend case 2.5.5 Further time series extrapolation methods: the linear trend case 2.5.6 Further time series extrapolation methods: the seasonal effect case 2.5.7 Further time series extrapolation methods: the irregular series case 2.5.8 Sporadic time series 2.6 Advanced Forecasting Method 2.7 Accuracy Measure and Forecasting Monitoring 2.7.1 Accuracy measures 2.7.2 Tuning of the forecasting methods 2.7.3 Forecast control 2.8 Interval Forecasts 2.9 Case Study: Forecasting Methods at Adriatica Accumulatori 2.10 Case Study: Sales Forecasting at Orlea 2.11 Questions and Problems 3 Designing the Logistics Network 3.1 Introduction 3.2 Qualitative Methods 3.3 Quantitative Methods 3.3.1 Single-commodity single-echelon continuous location problems 3.3.2 Single-commodity single-echelon discrete location problems 3.3.3 Single-commodity two-echelon discrete location problems 3.3.4 The multi-commodity case 3.3.5 Location-covering problems 3.3.6 p-centre problems 3.4 Hybrid Methods 3.5 Stochastic Location Models 3.6 Case Study: Container Warehouse Location at Hardcastle 3.7 Case Study: the Organ Transplantation Location-Allocation Policy of the Italian National Transplant Centre 3.8 Questions and Problems 4 Selecting the Suppliers 4.1 Introduction 4.2 Definition of the Set of Potential Suppliers 4.3 Definition of the Selection Criteria 4.4 Supplier Selection 4.5 Case Study: the System for the Selection of Suppliers at Baxter 4.6 Questions and Problems 5 Managing a Warehouse 5.1 Introduction 5.1.1 Performance parameters 5.1.2 Decision-making problems 5.2 Warehouse Design 5.2.1 Choice of warehouse systems 5.2.2 Choice of warehouse layout 5.2.3 Sizing of the storage zone 5.2.4 Sizing of the receiving zone 5.2.5 Sizing of the shipping zone 5.3 Tactical Decisions for Warehouse Logistics Planning 5.3.1 Product allocation to the storage points 5.3.2 Inventory management 5.4 Operational Decisions for Warehouse Logistics Management 5.4.1 Package picking from the storage zone 5.4.2 Package consolidation in load units 5.5 Case Study: Performance Evaluation of an AS/RS System conducted by Wert Consulting 5.6 Case Study: Inventory Management at Wolferine 5.7 Case Study: Airplane Loading at FedEx 5.8 Questions and Problems 6 Managing Freight Transport 6.1 Introduction 6.1.1 Modes of transport 6.1.2 Classification of transport problems 6.2 Freight Traffic Assignment Problems 6.2.1 Minimum cost flow formulation 6.2.2 Linear single commodity minimum cost flow problems 6.2.3 Linear multicommodity minimum cost flow problems 6.3 Service Network Design Problems 6.3.1 The linear fixed charge network design model 6.4 Vehicle Allocation Problems 6.5 A Dynamic Driver Assignment Problem 6.6 Fleet Composition 6.7 Shipment Consolidation 6.8 Vehicle Routing Problems 6.8.1 The travelling salesman problem 6.8.2 The node routing problem with capacity and length constraints 6.8.3 The node routing and scheduling problem with time windows 6.8.4 Arc routing problems 6.8.5 Route sequencing 6.9 Real-Time Vehicle Routing Problems 6.10 Integrated Location and Routing Problems 6.11 Vendor Managed Inventory-Routing 6.12 Case Study: Air Network Design at Intexpress 6.13 Case Study: Meter Reader Routing and Scheduling at Socal 6.14 Case Study: Dynamic Vehicle Dispatching Problem with pickups and Deliveries at eCourier 6.15 Questions and Problems Index …”

Tabla de Contenidos: “…Restricted Boltzmann Machines -- Energy function -- Encoding and decoding -- Contrastive divergence (CD-k) -- Stacked/continuous RBM -- RBM versus Boltzmann Machines -- Recurrent neural networks (RNN/LSTM) -- Cells in RNN and unrolling -- Backpropagation through time -- Vanishing gradient and LTSM -- Cells and gates in LTSM -- Step 1 - The forget gate -- Step 2 - Updating memory/cell state -- Step 3 - The output gate -- Practical examples -- TensorFlow setup and key concepts -- Handwritten digits recognition -- Summary -- Chapter 4: Deep Learning in Computer Vision -- Origins of CNNs -- Convolutional Neural Networks -- Data transformations -- Input preprocessing -- Data augmentation -- Network layers -- Convolution layer -- Pooling or subsampling layer -- Fully connected or dense layer -- Network initialization -- Regularization -- Loss functions -- Model visualization -- Handwritten digit classification example -- Fine-tuning CNNs -- Popular CNN architectures -- AlexNet -- Visual Geometry Group -- GoogLeNet -- ResNet -- Summary -- Chapter 5: NLP - Vector Representation -- Traditional NLP -- Bag of words -- Weighting the terms tf-idf -- Deep learning NLP -- Motivation and distributed representation -- Word embeddings -- Idea of word embeddings -- Advantages of distributed representation -- Problems of distributed representation -- Commonly used pre-trained word embeddings -- Word2Vec -- Basic idea of Word2Vec -- The word windows -- Generating training data -- Negative sampling -- Hierarchical softmax -- Other hyperparameters -- Skip-Gram model -- The input layer -- The hidden layer -- The output layer -- The loss function -- Continuous Bag-of-Words model -- Training a Word2Vec using TensorFlow -- Using existing pre-trained Word2Vec embeddings -- Word2Vec from Google News -- Using the pre-trained Word2Vec embeddings -- Understanding GloVe -- FastText…”

Tabla de Contenidos: “…It's going to be one long sales pitch for their products." -- Unique Window -- "But do we really need another book about hiring?" …”

Tabla de Contenidos: “…-- Setting Expectations -- The Window of Malleability -- Your Goals -- You, Juggler -- Initial Overwhelm -- Don't Rush into Things -- Triage -- Write It Down -- Decide on Next Steps -- Follow Up -- Putting Fires Out -- Making Changes -- Get to the Root Cause -- Socratic Questioning -- Collecting Evidence -- Changes as Tests -- Pulling Up Your Sleeves -- Building Rapport -- One-on-Ones with Your Directs -- One-on-Ones with Peers…”

Tabla de Contenidos: “…-- Exploring the FL Studio workspace -- Making your first song -- Creating a drum beat with the Channel Rack -- Adding an instrument to the Channel Rack -- Creating a melody with the Piano roll -- Routing channels to the Mixer -- Exporting the song -- Summary -- Chapter 02: Exploring the Browser, Playlist, and Channel Rack -- Technical requirements -- Introducing the FL Studio workbench -- Getting the most out of your samples -- Using the Browser -- Locating a sample's file location -- Searching for samples -- Adding your own samples to the Browser -- Swapping samples -- Using the Channel Rack -- Channel Rack option features -- The Channel Rack Rename, color and icon option -- Instrument options -- Detaching windows -- Installing new instruments -- Using layers -- Using the Playlist -- Playlist Toolbar -- Element pattern options -- Using the main header Toolbar features -- Version control -- Revert to last backup -- Audio Stream Input/Output (ASIO) -- Purge unused audio clips -- Undo and redo -- Viewing project info -- Summary -- Chapter 03: Composing with the Piano Roll -- Using the Piano roll -- Choosing an instrument -- Adding notes in the Piano roll -- Selecting multiple notes -- Deleting notes -- Muting and unmuting notes -- Slicing notes -- Sliding notes -- Using the stamp tool to get assistance with scales -- Composing great chord progressions -- Recording into the Piano roll with MIDI instruments -- The snap to grid (magnet) tool -- Quantizing notes -- Editing note articulations with the Event Editor…”

Tabla de Contenidos: “…Hash-Based Sharding -- Zone Sharding -- Shard Balance -- Rebalancing Shards -- Modifying the Balancer Window -- Disabling the Balancer -- Changing the Chunk Size -- Changing Shard Keys -- Sharded Queries -- Sharded Explain Plans -- Shard Key Lookups -- Accidental Shard Merge -- Shard Key Range -- Sorting -- Non-Shard Key Lookups -- Aggregations and Sorts -- Sharded lookup Operations -- Summary -- Index…”

Tabla de Contenidos: “…Front Cover -- Contents -- Foreword -- Preface -- Acknowledgments -- About the Authors -- Abbreviations -- Overview -- Introduction -- Better Policy Environment for More Inclusive GVCs -- Investment in Expanding the Statistical Basis and Technical Analysis of Participation in GVCs -- Chapter 1 Setting the Scene -- Introduction -- GVCs and Opportunities for Low-Income Countries -- Importance of SMEs -- References -- Chapter 2 Stylized Facts on SMEs in GVCs -- Informality of the Economy: A Binding Constraint -- Participation in GVCs: Direct Insertion -- Suppliers of Large Local and Multinational Firms: Indirect Insertion -- Links between MNE Investments and SME Performance -- Note -- References -- Chapter 3 Participation of SMEs and Low-Income Countries in GVCs: Determinants and Challenges -- Introduction -- Internal Determinants of SME Participation and Upgrading in GVCs -- External Determinants of SME Participation in GVCs -- Notes -- References -- Chapter 4 Policies to Promote the Participation of SMEs and Low-Income Countries in GVCs -- Introduction -- Furthering International Trade Openness -- Ensuring That the Benefits of Openness Reach beyond Large Firms and the G20 to SMEs and Low-Income Countries -- Investing in Expanding the Statistical Basis and Technical Analysis of Participation in GVCs -- Note -- References -- Boxes -- Box 1.1 Understanding the Drivers of GVCs -- Box 1.2 Key Take-Away Messages from Chapter 1 -- Box 2.1 Key Take-Away Messages from Chapter 2 -- Box 3.1 The World Bank's Work to Strengthen Developing Countries' Ability to Meet International Product and Process Standards -- Box 3.2 Implementing a Single-Window System in Lao PDR -- Box 3.3 WTO Agreement on Trade Facilitation -- Box 3.4 The Exporta Fácil Program in Latin America -- Box 3.5 Trade and Investment Barriers: The Case of LCRs Affecting Renewable Energy…”

Tabla de Contenidos: “…-- Android architecture -- Dalvik Virtual Machine -- Memory management -- Energy consumption -- Java language -- Native Development Kit or how to develop with native code when needed -- Three limits in application responsiveness -- Business value of software quality -- Summary -- Chapter 2: Efficient Debugging -- Android Debug Bridge -- Dalvik Debug Monitor Server -- Capturing and analyzing thread information -- Heap analysis and visualization -- Allocation tracker -- Network usage -- Emulator Control -- System status -- Debugging the UI -- Profiling with Hierarchy Viewer -- Systrace -- Android device debug options -- Android Instant Run -- GPU profiler -- Running a trace -- ClassyShark -- Getting started -- Summary -- Chapter 3: Building Layouts -- Walkthrough -- Rendering performance -- Screen tearing and VSYNC -- Hardware acceleration -- Overdraw -- Multi-window mode -- Overview -- Configuration -- Management -- Drag and drop -- Performance impact -- Best practices -- Provided layout overview -- Hierarchical layout management -- Reusing layouts -- ViewStub -- AdapterViews and view recycling -- The ViewHolder pattern -- Custom views and layouts -- Screen zoom -- Debugging tools -- The Design view -- Hierarchy Viewer -- Tree View -- View properties -- Tree overview -- Layout View -- On device tools -- Debugging GPU overdraw -- Profile GPU rendering -- Systrace -- Summary -- Chapter 4: Memory -- Walkthrough -- How memory works -- Garbage collection -- Shared memory -- Runtime…”