“…Appel covers momentum and trend of price movement, time and calendar cycles, predictive chart patterns, relative strength, analysis of internal vs. external markets, market breadth, moving averages, trading channels, overbought/oversold indicators, Trin, VIX, major term buy signals, major term sell signals, moving average trading channels, stock market synergy, and much more. …”

Tabla de Contenidos: “…14.1.1 Uniform Partitions -- 14.1.2 Connected Partitions -- 14.1.3 Composite Partitions -- 14.2 Coarse Grid Representation in MRST -- 14.2.1 Subdivision of Coarse Faces -- 14.3 Partitioning Stratigraphic Grids -- 14.3.1 The Johansen Aquifer -- 14.3.2 The SAIGUP Model -- 14.3.3 Near Well Refinement for CaseB4 -- 14.4 More Advanced Coarsening Methods -- 14.5 A General Framework for Agglomerating Cells -- 14.5.1 Creating Initial Partitions -- 14.5.2 Connectivity Checks and Repair Algorithms -- 14.5.3 Indicator Functions -- 14.5.4 Merge Blocks -- 14.5.5 Refine Blocks -- 14.5.6 Examples -- 14.6 Multilevel Hierarchical Coarsening -- 14.7 General Advice and Simple Guidelines -- 15 Upscaling Petrophysical Properties -- 15.1 Upscaling for Reservoir Simulation -- 15.2 Upscaling Additive Properties -- 15.3 Upscaling Absolute Permeability -- 15.3.1 Averaging Methods -- 15.3.2 Flow-Based Upscaling -- 15.4 Upscaling Transmissibility -- 15.5 Global and Local-Global Upscaling -- 15.6 Upscaling Examples -- 15.6.1 Flow Diagnostics Quality Measure -- 15.6.2 A Model with Two Facies -- 15.6.3 SPE 10 with Six Wells -- 15.6.4 Complete Workflow Example -- 15.6.5 General Advice and Simple Guidelines -- Appendix The MATLAB Reservoir Simulation Toolbox -- A.1 Getting Started with the Software -- A.1.1 Core Functionality and Add-on Modules -- A.1.2 Downloading and Installing -- A.1.3 Exploring Functionality and Getting Help -- A.1.4 Release Policy and Version Numbers -- A.1.5 Software Requirements and Backward Compatibility -- A.1.6 Terms of Usage -- A.2 Public Data Sets and Test Cases -- A.3 More About Modules and Advanced Functionality -- A.3.1 Operating the Module System -- A.3.2 What Characterizes a Module? …”

Tabla de Contenidos: “…2.1 Introduction -- 2.2 Multi‐tier Computing Networks and Service Architecture -- 2.2.1 Multi‐tier Computing Network Architecture -- 2.2.2 Cost Aware Task Scheduling Framework -- 2.2.2.1 Hybrid Payment Model -- 2.2.2.2 Weighted Cost Function -- 2.2.2.3 Distributed Task Scheduling Algorithm -- 2.2.3 Fog as a Service Technology -- 2.2.3.1 FA2ST Framework -- 2.2.3.2 FA2ST Application Deployment -- 2.2.3.3 FA2ST Application Management -- 2.3 Edge‐enabled Intelligence for Industrial IoT -- 2.3.1 Introduction and Background -- 2.3.1.1 Intelligent Industrial IoT -- 2.3.1.2 Edge Intelligence -- 2.3.1.3 Challenges -- 2.3.2 Boomerang Framework -- 2.3.2.1 Framework Overview -- 2.3.2.2 Prediction Model and Right‐sizing Model -- 2.3.2.3 Boomerang Optimizer -- 2.3.2.4 Boomerang with DRL -- 2.3.3 Performance Evaluation -- 2.3.3.1 Emulation Environment -- 2.3.3.2 Evaluation Results -- 2.4 Fog‐enabled Collaborative SLAM of Robot Swarm -- 2.4.1 Introduction and Background -- 2.4.2 A Fog‐enabled Solution -- 2.4.2.1 System Architecture -- 2.4.2.2 Practical Implementation -- 2.5 Conclusion -- References -- Chapter 3 Cross‐Domain Resource Management Frameworks -- 3.1 Introduction -- 3.2 Joint Computation and Communication Resource Management for Delay‐Sensitive Applications -- 3.2.1 2C Resource Management Framework -- 3.2.1.1 System Model -- 3.2.1.2 Problem Formulation -- 3.2.2 Distributed Resource Management Algorithm -- 3.2.2.1 Paired Offloading of Non‐splittable Tasks -- 3.2.2.2 Parallel Offloading of Splittable Tasks -- 3.2.3 Delay Reduction Performance -- 3.2.3.1 Price of Anarchy -- 3.2.3.2 System Average Delay -- 3.2.3.3 Number of Beneficial TNs -- 3.2.3.4 Convergence -- 3.3 Joint Computing, Communication, and Caching Resource Management for Energy‐efficient Applications -- 3.3.1 Fog‐enabled 3C Resource Management Framework -- 3.3.1.1 System Resources…”

Tabla de Contenidos: “…The pseudo-Lagrangian viewpoint 3.5 Cause and effect, and the problem of prediction 3.6 The effects of viscosity 3.7 Turbulence, Reynolds averaging, and turbulence modeling 3.8 Important dynamical relationships 3.8.1 ... …”

Tabla de Contenidos: “…8.3 Simplified Scripts for Frequently Used Analyses -- 8.4 Example: Difference of Biases -- 8.5 Sampling from the Prior Distribution in JAGS -- 8.6 Probability Distributions Available in JAGS -- 8.6.1 Defining new likelihood functions -- 8.7 Faster Sampling with Parallel Processing in RunJAGS -- 8.8 Tips for Expanding JAGS Models -- 8.9 Exercises -- Chapter 9: Hierarchical Models -- 9.1 A Single Coin from a Single Mint -- 9.1.1 Posterior via grid approximation -- 9.2 Multiple Coins from a Single Mint -- 9.2.1 Posterior via grid approximation -- 9.2.2 A realistic model with MCMC -- 9.2.3 Doing it with JAGS -- 9.2.4 Example: Therapeutic touch -- 9.3 Shrinkage in Hierarchical Models -- 9.4 Speeding up JAGS -- 9.5 Extending the Hierarchy: Subjects Within Categories -- 9.5.1 Example: Baseball batting abilities by position -- 9.6 Exercises -- Chapter 10: Model Comparison and Hierarchical Modeling -- 10.1 General Formula and the Bayes Factor -- 10.2 Example: Two Factories of Coins -- 10.2.1 Solution by formal analysis -- 10.2.2 Solution by grid approximation -- 10.3 Solution by MCMC -- 10.3.1 Nonhierarchical MCMC computation of each model'smarginal likelihood -- 10.3.1.1 Implementation with JAGS -- 10.3.2 Hierarchical MCMC computation of relative model probability -- 10.3.2.1 Using pseudo-priors to reduce autocorrelation -- 10.3.3 Models with different "noise" distributions in JAGS -- 10.4 Prediction: Model Averaging -- 10.5 Model Complexity Naturally Accounted for -- 10.5.1 Caveats regarding nested model comparison -- 10.6 Extreme Sensitivity to Prior Distribution -- 10.6.1 Priors of different models should be equally informed -- 10.7 Exercises -- Chapter 11: Null Hypothesis Significance Testing -- 11.1 Paved with Good Intentions -- 11.1.1 Definition of p value -- 11.1.2 With intention to fix N -- 11.1.3 With intention to fix z.…”

Tabla de Contenidos: “…3.2 Assessment of High Frequency Electromagnetic Fields -- 3.2.1 Fields Radiated by Power Line Communication (PLC) Systems -- 3.2.2 Fields in the Vicinity of RFID Loop Antennas -- 3.2.3 Radiation From Base Station Antennas -- 3.2.3.1 Near Field Analysis: Assessment of Power Density -- 3.2.3.2 Far Field Analysis: Calculation of Power Density and Electric Field -- 3.2.3.3 Some Computational Examples -- 3.2.3.4 Some Measured Results -- 3.2.3.5 Far Field Analysis: Presence of a Lossy Ground and Layered Medium -- 3.2.3.6 Accurate Numerical Modeling of Radio Base Station Antenna Systems -- References -- 4 Simpli ed Models of the Human Body -- 4.1 Parallelepiped Model of the Human Body -- 4.2 Cylindrical Antenna Models of the Body -- 4.3 Cylindrical Models - Frequency Domain Analysis -- 4.3.1 Pocklington Equation Formulation for LF Exposures -- 4.3.2 Numerical Solution of the Pocklington Equation -- 4.3.3 Analytical Modeling of the Human Body - Hallén Equation for LF and HF Exposures -- 4.3.4 Multiple Wire Model of the Body -- 4.3.5 Numerical Solution Via Method of Moments (MoM) -- 4.3.6 Computational Examples -- 4.3.6.1 LF Exposures -- 4.3.6.2 HF Exposures -- 4.4 Time Domain Modeling - Exposure of Humans to Transient Radiation: Cylindrical Model of the Human Body -- 4.4.1 Time Domain Formulation -- 4.4.2 Numerical Solution of the Time Domain Hallén Integral Equation -- 4.4.3 Measures of the Transient Response -- 4.4.3.1 Average Value of the Transient Current -- 4.4.3.2 Root-Mean-Square Value of the Transient Current -- 4.4.3.3 Instantaneous Power -- 4.4.3.4 Total Absorbed Energy -- 4.4.3.5 The Speci c Absorption -- 4.4.4 Numerical Results -- 4.5 Transmission Line Models of the Human Body -- 4.5.1 Theoretical Background -- 4.5.2 Solution of the Transmission Line Equations in the Frequency Domain -- 4.5.3 Computational Examples…”

Tabla de Contenidos: “…Cover -- Brief Contents -- Contents -- Foreword -- Preface -- Acknowledgements -- About the Author -- Chapter 1: Measurements and Instruments -- 1.1 Introduction -- 1.2 Terminology -- 1.2.1 Advantages of Instrumentation Systems -- 1.2.2 Block Schematics of Measuring Systems -- 1.2.3 Other Systems -- 1.2.4 Objectives of Measurement -- 1.2.5 Comparison between Analog and Digital Instruments -- 1.2.6 Factors for the Selection of Analog and Digital Equipments -- 1.3 Performance Characteristics -- 1.3.1 Definitions -- 1.4 Significant Figures -- 1.5 Dynamic Characteristics -- 1.6 Types of Errors -- 1.6.1 Gross Errors -- 1.6.2 Systematic Errors -- 1.6.3 Random Errors -- 1.7 Statistical Analysis -- 1.7.1 Probability of Errors and Gaussian Curve -- 1.8 Measurement Standards -- 1.9 Suspension Galvanometer -- 1.10 D'Arsonval Movement -- 1.10.1 Taut-Band Suspension -- 1.10.2 Temperature Compensation -- 1.10.3 Shunt Resistor -- 1.10.4 Ayrton Shunt -- 1.11 Direct Current Meters -- 1.12 D'Arsonval Meter Movement Used in DC Voltmeters -- 1.12.1 Ammeter Loading Effect -- 1.13 DC Voltmeters -- 1.13.1 Multirange Voltmeter -- 1.14 Ohmmeter -- 1.14.1 Series-Type Ohmmeter -- 1.14.2 Shunt-Type Ohmmeter -- 1.14.3 D'Arsonval Meter Movement Used in Ohmmeter -- 1.14.4 Multiple Range Ohmmeters -- 1.14.5 Electrolyte Capacitor Leakage Tests -- 1.14.6 For Non-Electrolyte Capacitors -- 1.15 Multimeter -- 1.16 Alternating Current-Indicating Instruments -- 1.16.1 Electrodynamometer -- 1.17 Rectifier-Type Instruments -- 1.18 Meter Protection -- 1.19 Extension of Range -- 1.20 Frequency Compensation -- 1.21 Electronic Voltmeter (for DC) -- 1.22 Electronic Voltmeter (for AC) -- 1.22.1 Average Reading Voltmeter -- 1.22.2 Peak Reading Voltmeter -- 1.22.3 Peak-To-Peak Detector -- 1.23 DC Meter with Amplifier -- 1.24 Chopper-Stabilised Amplifier -- 1.25 AC Voltmeter using Rectifiers…”

Tabla de Contenidos: “…Front Cover -- Federated Learning -- Copyright -- Contents -- Contributors -- Preface -- 1 Optimization fundamentals for secure federated learning -- 1 Gradient descent-type methods -- 1.1 Introduction -- 1.2 Basic components of GD-type methods -- 1.2.1 Search direction -- 1.2.2 Step-size -- 1.2.3 Proximal operator -- 1.2.4 Momentum -- 1.2.5 Dual averaging variant -- 1.2.6 Structure assumptions -- 1.2.7 Optimality certification -- 1.2.8 Unified convergence analysis -- 1.2.9 Convergence rates and complexity analysis -- 1.2.10 Initial point, warm-start, and restart -- 1.3 Stochastic gradient descent methods -- 1.3.1 The algorithmic template -- 1.3.2 SGD estimators -- 1.3.3 Unified convergence analysis -- 1.4 Concluding remarks -- Acknowledgments -- References -- 2 Considerations on the theory of training models with differential privacy -- 2.1 Introduction -- 2.2 Differential private SGD (DP-SGD) -- 2.2.1 Clipping -- 2.2.2 Mini-batch SGD -- 2.2.3 Gaussian noise -- 2.2.4 Aggregation at the server -- 2.2.5 Interrupt service routine -- 2.2.6 DP principles and utility -- 2.2.7 Normalization -- 2.3 Differential privacy -- 2.3.1 Characteristics of a differential privacy measure -- 2.3.2 (ε,δ)-differential privacy -- 2.3.3 Divergence-based DP measures -- 2.4 Gaussian differential privacy -- 2.4.1 Gaussian DP -- 2.4.2 Subsampling -- 2.4.3 Composition -- 2.4.4 Tight analysis of DP-SGD -- 2.4.5 Strong adversarial model -- 2.4.6 Group privacy -- 2.4.7 DP-SGD's trade-off function -- 2.5 Future work -- 2.5.1 Using synthetic data -- 2.5.2 Adaptive strategies -- 2.5.3 DP proof: a weaker adversarial model -- 2.5.4 Computing environment with less adversarial capabilities -- References -- 3 Privacy-preserving federated learning: algorithms and guarantees -- 3.1 Introduction -- 3.2 Background and preliminaries -- 3.2.1 The FedAvg algorithm…”

Tabla de Contenidos: “…3.9 De Morgan's Law -- 3.10 Universal Set -- 3.11 Basic Operations on Set -- 3.11.1 Union of Sets -- 3.11.2 Intersection of Two Sets -- 3.11.3 Difference of a Set -- 3.11.4 Complement of a Set -- 3.12 Cartesian Products -- 3.13 Laws of Set Algebra -- 3.14 Cardinal Numbers -- 3.15 Venn Diagram -- 3.16 Business Applications -- Exercise -- Chapter 4: Ratio -- 4.1 Ratio -- 4.2 Proportion -- 4.3 Types of Proportion -- 4.3.1 Direct Proportion -- 4.3.2 Inverse Proportion -- 4.3.3 Indirect Proportion -- 4.4 Ratio, Proportion and its Type of Comparison -- 4.4.1 Addition and Subtraction of Ratios -- 4.5 Variations -- 4.5.1 Direct Variation -- 4.5.2 Inverse Variation -- 4.5.3 Joint Variation -- 4.6 Percentage -- 4.6.1 Uses of Percentages -- 4.6.2 Converting Fractions into Percentage -- 4.6.3 Problem Based on Quantity Application -- 4.6.4 Problem Based on Election Application -- 4.6.5 Problem Based on Examination Application -- 4.6.6 Price Mixture -- 4.6.7 Decimal Fraction -- 4.7 Annexing Zeros and Removing Decimal Signs -- 4.7.1 Annexing Zeros -- 4.8 Average (Arithmetic Mean) -- 4.8.1 Discrete Data -- 4.8.2 Discrete Data with Frequency -- 4.9 Computation of Mean for Grouped Series -- 4.10 Mean of Composite Group -- Exercises -- Chapter 5: Functions and Graphs -- 5.1 Introduction -- 5.2 Function -- 5.2.1 Constant -- 5.2.2 Variable -- 5.3 Domain and Range of a Function -- 5.4 Continuous and Discrete Functions -- 5.4.1 Continuous Function -- 5.4.2 Discrete Function -- 5.5 Composition and Inverse Functions -- 5.5.1 Composition Functions -- 5.5.2 Order of Composition -- 5.5.3 Decomposition of a Function -- 5.5.4 Inverse Function -- 5.6 Classification of Functions -- 5.7 Exponential and Logarithmic Functions -- 5.7.1 Exponential Functions -- 5.7.2 Logarithmic Functions -- 5.8 Graphs -- 5.8.1 The General Idea of Curve Sketching and Graphs of Straight Lines…”

“…On average across OECD countries, students who are highly motivated to learn mathematics because they believe it will help them later on score better in mathematics – by the equivalent of half a year of schooling – than students who are not highly motivated. …”

“…In contrast to falling rates in low density peri-urban and rural remote areas, high density peri-urban areas in Switzerland recorded double the rate of labour productivity growth than the national average. This report examines the Swiss regional innovation system and how it can boost productivity growth in rural regions by enhancing innovation…”

“…In contrast to falling rates in low density peri-urban and rural remote areas, high density peri-urban areas in Switzerland recorded double the rate of labour productivity growth than the national average. This report examines the Swiss regional innovation system and how it can boost productivity growth in rural regions by enhancing innovation…”

Tabla de Contenidos: “…Hands-On Infrastructure Monitoring with Prometheus: Implement and scale queries, dashboards, and alerting across machines and containers…”

“…The performance difference in mathematics between immigrant and non-immigrant students decreased, on average, between 2003 and 2012. Differences in socio-economic background explain less than half of the performance difference in mathematics between immigrant and non-immigrant students…”

“…Despite its rapid economic growth over the past decades, Poland's economic inactivity rate remains above the OECD average and regional differences in labour force participation persist. …”

“…It is changing from an annual average of 20 manuscripts for the period 1995–2000 to an annual average of about 400 manuscripts, with an increase of 2800%. …”

“…Restrictiveness for legal services ranges from 0.11 to 0.73, with an average of 0.31. Accounting and auditing services show an average of 0.3 and STRI values ranging from 0.13 to 1. …”

“…The contribution of new firms in terms of new jobs to the existing workforce can be expressed as a combination of four different elements: the start-up rate; the average size of firms at point of entry; the survival rate; and the average growth rate of survivors. …”

“…Differences between average wages of the three groups are decomposed with the Oaxaca-Blinder method. …”

“…The use of indicators also appears to improve forecasting performance, especially when an average of indicator-based models is used. Real-time forecasting performance of the average model appear to be good, with an apparent stability of the estimates from one update to the next, despite the extensive use of monthly data. …”