Note: Intro -- Preface -- Contents -- Author Biography -- List of Figures -- List of Tables -- 1 Adversarial Machine Learning -- 1.1 Adversarial Learning Frameworks -- 1.1.1 Adversarial Algorithms Comparisons -- 1.2 Adversarial Security Mechanisms -- 1.2.1 Adversarial Examples Taxonomies -- 1.3 Stochastic Game Illustration in Adversarial Deep Learning -- 2 Adversarial Deep Learning -- 2.1 Learning Curve Analysis for Supervised Machine Learning -- 2.2 Adversarial Loss Functions for Discriminative Learning -- 2.3 Adversarial Examples in Deep Networks -- 2.4 Adversarial Examples for Misleading Classifiers -- 2.5 Generative Adversarial Networks -- 2.6 Generative Adversarial Networks for Adversarial Learning -- 2.6.1 Causal Feature Learning and Adversarial Machine Learning -- 2.6.2 Explainable Artificial Intelligence and Adversarial Machine Learning -- 2.6.3 Stackelberg Game Illustration in Adversarial Deep Learning -- 2.7 Transfer Learning for Domain Adaptation -- 2.7.1 Adversarial Examples in Transfer learning -- 2.7.2 Adversarial Examples in Domain Adaptation -- 2.7.3 Adversarial Examples in Cybersecurity Domains -- 3 Adversarial Attack Surfaces -- 3.1 Security and Privacy in Adversarial Learning -- 3.1.1 Linear Classifier Attacks -- 3.2 Feature Weighting Attacks -- 3.3 Poisoning Support Vector Machines -- 3.4 Robust Classifier Ensembles -- 3.5 Robust Clustering Models -- 3.6 Robust Feature Selection Models -- 3.7 Robust Anomaly Detection Models -- 3.8 Robust Task Relationship Models -- 3.9 Robust Regression Models -- 3.10 Adversarial Machine Learning in Cybersecurity -- 3.10.1 Sensitivity Analysis of Adversarial Deep Learning -- 4 Game Theoretical Adversarial Deep Learning -- 4.1 Game Theoretical Learning Models -- 4.1.1 Fundamentals of Game Theory -- 4.1.2 Game Theoretical Data Mining -- 4.1.3 Cost-Sensitive Adversaries. , 4.1.4 Adversarial Training Strategies -- 4.2 Game Theoretical Adversarial Learning -- 4.2.1 Multilevel and Multi-stage Optimization in Game Theoretical Adversarial Learning -- 4.3 Game Theoretical Adversarial Deep Learning -- 4.3.1 Overall Structure of Learning Model in Variational Game -- 4.3.2 The Differences Between Our Method and GANs -- 4.3.3 Comparisons of Game Theoretical Adversarial Deep Learning Models -- 4.3.4 Comparisons Between Single Play Attacks and Multiple Play Attacks on Custom Loss Functions -- 4.3.5 Parallel Machines in Reduced Games -- 4.4 Stochastic Games in Predictive Modeling -- 4.4.1 Computational Learning Theory Frameworks to Analyze Game Theoretical Learning Algorithms -- 4.4.2 Game Theoretical Adversarial Deep Learning Algorithms in Information Warfare Applications -- 4.4.3 Game Theoretical Adversarial Deep Learning Algorithms in Cybersecurity Applications -- 4.5 Robust Game Theory in Adversarial Learning Games -- 4.5.1 Existence and Uniqueness of Game Theoretical Equilibrium Solutions -- 4.5.2 Optimal Control Theory and Robust Game Theory -- 5 Adversarial Defense Mechanisms for Supervised Learning -- 5.1 Securing Classifiers Against Feature Attacks -- 5.2 Adversarial Classification Tasks with Regularizers -- 5.3 Adversarial Reinforcement Learning -- 5.3.1 Game Theoretical Adversarial Reinforcement Learning -- 5.4 Computational Optimization Algorithmics for Game Theoretical Adversarial Learning -- 5.4.1 Game Theoretical Learning -- 5.4.1.1 Randomization Strategies in Game Theoretical Adversarial Learning -- 5.4.1.2 Adversarial Deep Learning in Robust Games -- 5.4.1.3 Robust Optimization in Adversarial Learning -- 5.4.2 Generative Learning -- 5.4.2.1 Deep Generative Models for Game Theoretical Adversarial Learning -- 5.4.2.2 Mathematical Programming in Game Theoretical Adversarial Learning. , 5.4.2.3 Low-Rank Approximations in Game Theoretical Adversarial Learning -- 5.4.2.4 Relative Distribution Methods in Adversarial Deep Learning -- 5.5 Defense Mechanisms in Adversarial Machine Learning -- 5.5.1 Defense Mechanisms in Adversarial Deep Learning -- 5.5.2 Explainable Artificial Intelligence in Adversarial Deep Learning -- 6 Physical World Adversarial Attacks on Images and Texts -- 6.1 Adversarial Attacks on Images -- 6.1.1 Gradient-Based Attack -- 6.1.2 Score-Based Attack -- 6.1.3 Decision-Based Attack -- 6.1.4 Transformation-Based Attack -- 6.2 Adversarial Attacks on Texts -- 6.2.1 Character-Level Attack -- 6.2.2 Sentence-Level Attack -- 6.2.3 Word-Level Attack -- 6.2.4 Multilevel Attack -- 6.3 Spam Filtering -- 6.3.1 Text Spam -- 6.3.2 Image Spam -- 6.3.3 Biometric Spam -- 7 Adversarial Perturbation for Privacy Preservation -- 7.1 Adversarial Perturbation for Privacy Preservation -- 7.1.1 Visual Data Privacy Model -- 7.1.2 Privacy Protection Mechanisms Using Adversarial Perturbations -- 7.1.2.1 File-Level Privacy Protection -- 7.1.2.2 Object-Level Privacy Protection -- 7.1.2.3 Feature-Level Privacy Protection -- 7.1.3 Discussion and Future Works -- Correction to: Adversarial Machine Learning -- References.

Note: Front Cover -- Molecular Dynamics Simulation -- Copyright Page -- Contents -- List of symbols -- Preface -- 1 Fundamentals of classical molecular dynamics simulation -- 1.1 Introduction -- 1.1.1 Atomistic simulation -- 1.1.2 Molecular dynamics simulation -- 1.1.3 Applications of molecular dynamics simulation -- 1.1.4 Limitations of molecular dynamics simulation -- 1.2 Fundamentals of molecular dynamics simulation -- 1.2.1 Procedure -- 1.2.2 System initialization -- 1.2.3 Periodic boundary conditions -- 1.2.4 Energy minimization and structure optimization -- 1.2.5 Force calculation -- 1.2.6 Time integration algorithms -- 1.2.7 Neighbor list -- 1.2.8 Ensemble and statistical observables -- 1.2.9 Accuracy of MD simulation -- 1.3 Hardware and software for MD simulation -- References -- 2 Potential energy functions -- 2.1 The Born-Oppenheimer assumption -- 2.1.1 Construction of potential energy functions -- 2.1.2 Two-body potentials -- 2.1.3 Many-body potentials -- 2.2 Potential energy functions for different materials -- 2.2.1 Ionic materials -- 2.2.2 Metals -- 2.2.3 Covalent materials -- 2.2.4 Molecular systems -- References -- 3 Control techniques of molecular dynamics simulation -- 3.1 Types of constraints in molecular dynamics simulation -- 3.2 Thermodynamic ensembles -- 3.3 Temperature control -- 3.3.1 Thermostat based on simple velocity rescaling -- 3.3.2 Gaussian thermostat -- 3.3.3 Berendsen thermostat -- 3.3.4 Bussi-Donadio-Parrinello thermostat -- 3.3.5 Andersen thermostat -- 3.3.6 Langevin thermostat -- 3.3.7 Nosé-Hoover thermostat -- 3.3.8 Thermostat effects in equilibrium molecular dynamics simulations -- 3.3.9 Temperature control in nonequilibrium molecular dynamics simulations -- 3.4 Pressure control -- 3.4.1 Berendsen barostat -- 3.4.2 Andersen barostat -- 3.4.3 Parrinello-Rahman barostat. , 3.4.4 Martyna-Tuckerman-Tobias-Klein barostat -- 3.5 Boundary conditions -- 3.6 Rigid bond constraints -- References -- 4 Advanced ab initio molecular dynamics and coarse-grained molecular dynamics -- 4.1 Motivations for the development of advanced molecular dynamics simulation methods -- 4.2 Ab initio molecular dynamics -- 4.2.1 Quantum mechanics foundation of classical molecular dynamics -- 4.2.2 Born-Oppenheimer molecular dynamics -- 4.2.3 Car-Parrinello molecular dynamics -- 4.3 Coarse-grained molecular dynamics -- 4.3.1 Theoretical formulation -- 4.3.2 Iterative Boltzmann inversion method -- 4.3.3 Multiscale coarse-grained method -- 4.3.4 Relative entropy optimization method -- 4.3.5 Challenges -- References -- 5 Application of molecular dynamics simulation in mechanical problems -- 5.1 Role of molecular dynamics simulation in modeling the mechanical properties of materials -- 5.2 Tensile, compressive, and shear tests -- 5.2.1 Tensile tests -- 5.2.2 Compressive tests -- 5.2.3 Shear tests -- 5.3 Nanoindentation and nanoscratching tests -- 5.3.1 Nanoindentation tests -- 5.3.2 Nanoscratching tests -- 5.4 Tribological behaviors -- 5.4.1 Nanofriction -- 5.4.2 Nanowear -- 5.4.3 Nanolubrication -- 5.5 Interfacial effects in nanocomposites -- 5.5.1 Polymer-based nanocomposites -- 5.5.2 Metal-based nanocomposites -- 5.6 Defect effects -- References -- 6 Application of molecular dynamics simulation in thermal problems -- 6.1 Demand for understanding the thermal properties of nanomaterials -- 6.2 Molecular dynamics simulation methods for thermal conductivity calculation -- 6.2.1 Direct method -- 6.2.2 Green-Kubo method -- 6.3 Molecular dynamics simulation of interfacial thermal transport -- 6.3.1 Interfacial thermal transport models -- 6.3.2 Interfacial thermal transport of a silicene/graphene hybrid monolayer -- 6.3.2.1 Simulation model. , 6.3.2.2 Interfacial thermal conductance -- 6.3.2.3 Temperature effect -- 6.3.2.4 Strain effect -- 6.3.2.5 Heat flux effect -- 6.3.3 Interfacial thermal transport of a silicene/graphene hybrid bilayer -- 6.3.3.1 Simulation model -- 6.3.3.2 Interface thermal conductance -- 6.3.3.3 Temperature and interface coupling strength effects -- 6.3.3.4 GE hydrogenation effect -- 6.4 Thermal rectification effects -- References -- 7 Application of molecular dynamics simulation in mass transport problems -- 7.1 Fluids in nanoconfinement -- 7.1.1 Fluid-driven methods -- 7.1.1.1 Pressure-driven method -- 7.1.1.2 Temperature-gradient driven method -- 7.1.1.3 Electric-field driven method -- 7.1.1.4 Surface-wave driven method -- 7.1.2 Water flow in carbon nanotubes -- 7.1.3 Water flow in porous monolayer graphene -- 7.1.4 Water flow in multilayer graphene and graphene oxide membranes -- 7.2 Nanofiltration with porous thin films -- 7.2.1 Reverse osmosis process -- 7.2.2 Forward osmosis process -- 7.2.3 Capacitive deionization -- 7.3 Liquid-vapor phase transition -- 7.3.1 Droplet evaporation in a gaseous environment -- 7.3.2 Liquid evaporation on a substrate -- References -- 8 Application of molecular dynamics simulation in other problems -- 8.1 Reactive molecular dynamics simulations -- 8.1.1 ReaxFF force field -- 8.1.2 Application of ReaxFF in reactive MD simulations -- 8.2 Irradiation processes -- 8.3 Material crystallization -- References -- Index -- Back Cover.

Note: Intro -- Preface -- Contents -- 1 Basic Concepts and Background -- 1.1 Introduction -- 1.2 An Introduction into Computational Intelligence -- 1.2.1 Evolutionary Computation -- 1.2.2 Fuzzy Logic -- 1.2.3 Machine Learning -- 1.3 Fundamental Concepts in Optimization -- 1.4 Design and Computer-Aided Design of Analog/RF IC -- 1.4.1 Overview of Analog/RF Circuit and System Design -- 1.4.2 Overview of the Computer-Aided Design of Analog/RF ICs -- 1.5 Summary -- References -- 2 Fundamentals of Optimization Techniques in Analog IC Sizing -- 2.1 Analog IC Sizing: Introduction and Problem Definition -- 2.2 Review of Analog IC Sizing Approaches -- 2.3 Implementation of Evolutionary Algorithms -- 2.3.1 Overview of the Implementation of an EA -- 2.3.2 Differential Evolution -- 2.4 Basics of Constraint Handling Techniques -- 2.4.1 Static Penalty Functions -- 2.4.2 Selection-Based Constraint Handling Method -- 2.5 Multi-objective Analog Circuit Sizing -- 2.5.1 NSGA-II -- 2.5.2 MOEA/D -- 2.6 Analog Circuit Sizing Examples -- 2.6.1 Folded-Cascode Amplifier -- 2.6.2 Single-Objective Constrained Optimization -- 2.6.3 Multi-objective Optimization -- 2.7 Summary -- References -- 3 High-Performance Analog IC Sizing: Advanced Constraint Handling and Search Methods -- 3.1 Challenges in Analog Circuit Sizing -- 3.2 Advanced Constrained Optimization Techniques -- 3.2.1 Overview of the Advanced Constraint Handling Techniques -- 3.2.2 A Self-Adaptive Penalty Function-Based Method -- 3.3 Hybrid Methods -- 3.3.1 Overview of Hybrid Methods -- 3.3.2 Popular Hybridization and Memetic Algorithm for Numerical Optimization -- 3.4 MSOEA: A Hybrid Method for Analog IC Sizing -- 3.4.1 Evolutionary Operators -- 3.4.2 Constraint Handling Method -- 3.4.3 Scaling Up of MSOEA -- 3.4.4 Experimental Results of MSOEA -- 3.5 Summary -- References. , 4 Analog Circuit Sizing with Fuzzy Specifications: Addressing Soft Constraints -- 4.1 Introduction -- 4.2 The Motivation of Analog Circuit Sizing with Imprecise Specifications -- 4.2.1 Why Imprecise Specifications Are Necessary -- 4.2.2 Review of Early Works -- 4.3 Design of Fuzzy Numbers -- 4.4 Fuzzy Selection-Based Constraint Handling Methods (Single-Objective) -- 4.5 Single-Objective Fuzzy Analog IC Sizing -- 4.5.1 Fuzzy Selection-Based Differential Evolution Algorithm -- 4.5.2 Experimental Results and Comparisons -- 4.6 Multi-objective Fuzzy Analog Sizing -- 4.6.1 Multi-objective Fuzzy Selection Rules -- 4.6.2 Experimental Results for Multi-objective Fuzzy Analog Circuit Sizing -- 4.7 Summary -- References -- 5 Process Variation-Aware Analog Circuit Sizing: Uncertain Optimization -- 5.1 Introduction to Analog Circuit Sizing Considering Process Variations -- 5.1.1 Why Process Variations Need to be Taken into Account in Analog Circuit Sizing -- 5.1.2 Yield Optimization, Yield Estimation and Variation-Aware Sizing -- 5.1.3 Traditional Methods for Yield Optimization -- 5.2 Uncertain Optimization Methodologies -- 5.3 The Pruning Method -- 5.4 Advanced MC Sampling Methods -- 5.4.1 AYLeSS: A Fast Yield Estimation Method for Analog IC -- 5.4.2 Experimental Results of AYLeSS -- 5.5 Summary -- References -- 6 Ordinal Optimization-Based Methods for Efficient Variation-Aware Analog IC Sizing -- 6.1 Ordinal Optimization -- 6.2 Efficient Evolutionary Search Techniques -- 6.2.1 Using Memetic Algorithms -- 6.2.2 Using Modified Evolutionary Search Operators -- 6.3 Integrating OO and Efficient Evolutionary Search -- 6.4 Experimental Methods and Verifications of ORDE -- 6.4.1 Experimental Methods for Uncertain Optimization with MC Simulations -- 6.4.2 Experimental Verifications of ORDE. , 6.5 From Yield Optimization to Single-Objective Analog Circuit Variation-Aware Sizing -- 6.5.1 ORDE-Based Single-Objective Variation-Aware Analog Circuit Sizing -- 6.5.2 Example -- 6.6 Bi-objective Variation-Aware Analog Circuit Sizing -- 6.6.1 The MOOLP Algorithm -- 6.6.2 Experimental Results -- 6.7 Summary -- References -- 7 Electromagnetic Design Automation: Surrogate Model Assisted Evolutionary Algorithm -- 7.1 Introduction to Simulation-Based Electromagnetic Design Automation -- 7.2 Review of the Traditional Methods -- 7.2.1 Integrated Passive Component Synthesis -- 7.2.2 RF Integrated Circuit Synthesis -- 7.2.3 Antenna Synthesis -- 7.3 Challenges of Electromagnetic Design Automation -- 7.4 Surrogate Model Assisted Evolutionary Algorithms -- 7.5 Gaussian Process Machine Learning -- 7.5.1 Gaussian Process Modeling -- 7.5.2 Discussions of GP Modeling -- 7.6 Artificial Neural Networks -- 7.7 Summary -- References -- 8 Passive Components Synthesis at High Frequencies: Handling Prediction Uncertainty -- 8.1 Individual Threshold Control Method -- 8.1.1 Motivations and Algorithm Structure -- 8.1.2 Determination of the MSE Thresholds -- 8.2 The GPDECO Algorithm -- 8.2.1 Scaling Up of GPDECO -- 8.2.2 Experimental Verification of GPDECO -- 8.3 Prescreening Methods -- 8.3.1 The Motivation of Prescreening -- 8.3.2 Widely Used Prescreening Methods -- 8.4 MMLDE: A Hybrid Prescreening and Prediction Method -- 8.4.1 General Overview -- 8.4.2 Integrating Surrogate Models into EA -- 8.4.3 The General Framework of MMLDE -- 8.4.4 Experimental Results of MMLDE -- 8.5 SAEA for Multi-objective Expensive Optimization -- 8.5.1 Overview of Multi-objective Expensive Optimization Methods -- 8.5.2 The Generation Control Method -- 8.6 Handling Multiple Objectives in SAEA -- 8.6.1 The GPMOOG Method -- 8.6.2 Experimental Result -- 8.7 Summary -- References. , 9 mm-Wave Linear Amplifier Design Automation: A First Step to Complex Problems -- 9.1 Problem Analysis and Key Ideas -- 9.1.1 Overview of EMLDE -- 9.1.2 The Active Components Library and the Look-up Table for Transmission Lines -- 9.1.3 Handling Cascaded Amplifiers -- 9.1.4 The Two Optimization Loops -- 9.2 Naive Bayes Classification -- 9.3 Key Algorithms in EMLDE -- 9.3.1 The ABGPDE Algorithm -- 9.3.2 The Embedded SBDE Algorithm -- 9.4 Scaling Up of the EMLDE Algorithm -- 9.5 Experimental Results -- 9.5.1 Example Circuit -- 9.5.2 Three-Stage Linear Amplifier Synthesis -- 9.6 Summary -- References -- 10 mm-Wave Nonlinear IC and Complex Antenna Synthesis: Handling High Dimensionality -- 10.1 Main Challenges for the Targeted Problem and Discussions -- 10.2 Dimension Reduction -- 10.2.1 Key Ideas -- 10.2.2 GP Modeling with Dimension Reduction Versus Direct GP Modeling -- 10.3 The Surrogate Model-Aware Search Mechanism -- 10.4 Experimental Tests on Mathematical Benchmark Problems -- 10.4.1 Test Problems -- 10.4.2 Performance and Analysis -- 10.5 60GHz Power Amplifier Synthesis by GPEME -- 10.6 Complex Antenna Synthesis with GPEME -- 10.6.1 Example 1: Microstrip-fed Crooked Cross Slot Antenna -- 10.6.2 Example 2: Inter-chip Wireless Antenna -- 10.6.3 Example 3: Four-element Linear Array Antenna -- 10.7 Summary -- References.

Note: Intro -- The Plant Cytoskeleton -- Preface -- Contents -- Contributors -- Part I Molecular Basis of the Plant Cytoskeleton -- Part II Cytoskeletal Reorganization in Plant Cell Division -- Part III The Cytoskeleton in Plant Growth and Development -- Index.

Note: Förderkennzeichen BMBF 16KIS0885K , Verbundnummer 01184150 , Unterschiede zwischen dem gedruckten Dokument und der elektronischen Ressource können nicht ausgeschlossen werden

Note: Förderkennzeichen BMBF 16KIS0662 , Verbundnummer 01174076 , Unterschiede zwischen dem gedruckten Dokument und der elektronischen Ressource können nicht ausgeschlossen werden , Sprache der Zusammenfassung: Deutsch, Englisch