“…Personally, Ali describes a recent breakup in a long-term relationship but again minimizes its impact…”

“…In section three I discuss two such problem cases: Locked-in Syndrome and the Minimally Conscious State. In section four I explain why these are cases in which possession of the disability makes one worse off overall. …”

Tabla de Contenidos: “…Zones accessibles en 90 minutes depuis Brazzaville (COG) -- Les coûts transfrontaliers, même minimes, peuvent entraîner des réductions importantes du commerce interurbain -- Encadré 2.2. …”

Tabla de Contenidos: “…-- 7.1.2 Traf c and Performance Measures -- 7.1.3 Characterizing Traf c -- 7.1.4 Average Delay in a Single Link System -- 7.1.5 Nonstationarity of Traf c -- 7.2 Applications' View -- 7.2.1 TCP Throughput and Possible Bottlenecks -- 7.2.2 Bandwidth-Delay Product -- 7.2.3 Router Buffer Size -- 7.3 Traf c Engineering: An Architectural Framework -- 7.4 Traf c Engineering: A Four-Node Illustration -- 7.4.1 Network Flow Optimization -- 7.4.2 Shortest Path Routing and Network Flow -- 7.5 IGP Metric (Link Weight) Determination Problem for the Load Balancing Objective: Preliminary Discussion -- 7.6 Determining IGP Link Weights via duality of MCNF Problems -- 7.6.1 Illustration of Duality Through a Three-Node Network for Minimum Cost Routing -- 7.6.2 Minimum Cost Routing, Duality, and Link Weights -- 7.6.3 Illustration of Duality Through a Three-Node Network for the Load Balancing Objective -- 7.6.4 Load Balancing Problem, duality, and Link Weights -- 7.6.5 A Composite Objective Function, duality, and Link Weights -- 7.6.6 Minimization of Average Delay, duality, and Link Weights -- 7.7 Illustration of Link Weight Determination through Duality -- 7.7.1 Case Study: I -- 7.7.2 Case Study: II -- 7.8 Link Weight Determination: Large Networks -- 7.9 IP Traf c Engineering of PoP-to-DataCenter Networks -- 7.10 Summary -- Further Lookup -- Exercises -- 8 Multicast Routing -- 8.1 Multicast IP Addressing -- 8.2 Internet Group Management Protocol (IGMP) -- 8.3 Multicast Listener Discovery Protocol (MLD) -- 8.4 Reverse Path Forwarding (RPF) -- 8.5 Distance Vector Multicast Routing Protocol (DVMRP) -- 8.6 Multicast OSPF -- 8.7 Core Based Trees…”

Tabla de Contenidos: “…4.1.3 Sparse Coding Based Network for Image SR -- 4.1.3.1 Image SR Using Sparse Coding -- 4.1.3.2 Network Implementation of Sparse Coding -- 4.1.3.3 Network Architecture of SCN -- 4.1.3.4 Advantages over Previous Models -- 4.1.4 Network Cascade for Scalable SR -- 4.1.4.1 Network Cascade for SR of a Fixed Scaling Factor -- 4.1.4.2 Network Cascade for Scalable SR -- 4.1.4.3 Training Cascade of Networks -- 4.1.5 Robust SR for Real Scenarios -- 4.1.5.1 Data-Driven SR by Fine-Tuning -- 4.1.5.2 Iterative SR with Regularization -- Blurry Image Upscaling -- Noisy Image Upscaling -- 4.1.6 Implementation Details -- 4.1.7 Experiments -- 4.1.7.1 Algorithm Analysis -- 4.1.7.2 Comparison with State-of-the-Art -- 4.1.7.3 Robustness to Real SR Scenarios -- Data-Driven SR by Fine-Tuning -- Regularized Iterative SR -- 4.1.8 Subjective Evaluation -- 4.1.9 Conclusion and Future Work -- 4.2 Learning a Mixture of Deep Networks for Single Image Super-Resolution -- 4.2.1 Introduction -- 4.2.2 The Proposed Method -- 4.2.3 Implementation Details -- 4.2.4 Experimental Results -- 4.2.4.1 Network Architecture Analysis -- 4.2.4.2 Comparison with State-of-the-Art -- 4.2.4.3 Runtime Analysis -- 4.2.5 Conclusion and Future Work -- References -- 5 From Bi-Level Sparse Clustering to Deep Clustering -- 5.1 A Joint Optimization Framework of Sparse Coding and Discriminative Clustering -- 5.1.1 Introduction -- 5.1.2 Model Formulation -- 5.1.2.1 Sparse Coding with Graph Regularization -- 5.1.2.2 Bi-level Optimization Formulation -- 5.1.3 Clustering-Oriented Cost Functions -- 5.1.3.1 Entropy-Minimization Loss -- 5.1.3.2 Maximum-Margin Loss -- 5.1.4 Experiments -- 5.1.4.1 Datasets -- 5.1.4.2 Evaluation Metrics -- 5.1.4.3 Comparison Experiments -- Comparison Methods -- Comparison Analysis -- Varying the Number of Clusters -- Initialization and Parameters -- 5.1.5 Conclusion -- 5.1.6 Appendix…”

Tabla de Contenidos: “…4.3.2 Bayesian Multisource Classification Mechanism -- 4.3.3 A Refined Multisource Bayesian Model -- 4.3.4 Multisource Classification Using the MRF -- 4.3.5 Assumption of Inter-Source Independence -- 4.4 Evidential Reasoning -- 4.4.1 Concept Development -- 4.4.2 Belief Function and Belief Interval -- 4.4.3 Evidence Combination -- 4.4.4 Decision Rules for Evidential Reasoning -- 4.5 Dealing with Source Reliability -- 4.5.1 Using Classification Accuracy -- 4.5.2 Use of Class Separability -- 4.5.3 Data Information Class Correspondence Matrix -- 4.6 Concluding Remarks and Future Trends -- References -- Chapter 5 Support Vector Machines -- 5.1 Linear Classification -- 5.1.1 The Separable Case -- 5.1.2 The Nonseparable Case -- 5.2 Nonlinear Classification and Kernel Functions -- 5.2.1 Nonlinear SVMs -- 5.2.2 Kernel Functions -- 5.3 Parameter Determination -- 5.3.1 t-Fold Cross-Validations -- 5.3.2 Bound on Leave-One-Out Error -- 5.3.3 Grid Search -- 5.3.4 Gradient Descent Method -- 5.4 Multiclass Classification -- 5.4.1 One-Against-One, One-Against-Others, and DAG -- 5.4.2 Multiclass SVMs -- 5.4.2.1 Vapnik's Approach -- 5.4.2.2 Methodology of Crammer and Singer -- 5.5 Relevance Vector Machines -- 5.6 Twin Support Vector Machines -- 5.7 Deep Support Vector Machines -- 5.8 Concluding Remarks -- References -- Chapter 6 Decision Trees -- 6.1 ID3, C4.5, and SEE5.0 Decision Trees -- 6.1.1 ID3 -- 6.1.2 C4.5 -- 6.1.3 SEE5.0 (C5.0) -- 6.2 CHAID -- 6.3 CART -- 6.4 QUEST -- 6.4.1 Split Point Selection -- 6.4.2 Attribute Selection -- 6.5 Tree Induction from Artic fi ial Neural Networks -- 6.6 Pruning Decision Trees -- 6.6.1 Reduced Error Pruning -- 6.6.2 Pessimistic Error Pruning -- 6.6.3 Error-Based Pruning -- 6.6.4 Cost Complexity Pruning -- 6.6.5 Minimal Error Pruning -- 6.7 Ensemble Methods -- 6.7.1 Boosting -- 6.7.2 Random Forest -- 6.7.3 Rotation Forest…”

Tabla de Contenidos: “…Nicholas Laneman, Alexander Golitschek, Hidetoshi Suzuki, Osvaldo Gonsa -- 30.1 Introduction 673 -- 30.2 Theoretical Analysis of Relaying 679 -- 30.3 Relay Nodes in LTE-Advanced 684 -- 30.4 Summary 699 -- References 699 -- 31 Additional Features of LTE Release 10 701 / Teck Hu, Philippe Godin and Sudeep Palat -- 31.1 Introduction 701 -- 31.2 Enhanced Inter-Cell Interference Coordination 701 -- 31.3 Minimization of Drive Tests 710 -- 31.4 Machine-Type Communications 712 -- References 714 -- 32 LTE-Advanced Performance and Future Developments 715 / Takehiro Nakamura and Tetsushi Abe -- 32.1 LTE-Advanced System Performance 715 -- 32.2 Future Developments 718 -- References 720 -- Index 721.…”

Tabla de Contenidos: “…-- 4.1 The Set of All Possible Events -- 4.1.1 Coin flips: Why you should care -- 4.2 Probability: Outside or Inside the Head -- 4.2.1 Outside the head: Long-run relative frequency -- 4.2.1.1 Simulating a long-run relative frequency -- 4.2.1.2 Deriving a long-run relative frequency -- 4.2.2 Inside the head: Subjective belief -- 4.2.2.1 Calibrating a subjective belief by preferences -- 4.2.2.2 Describing a subjective belief mathematically -- 4.2.3 Probabilities assign numbers to possibilities -- 4.3 Probability Distributions -- 4.3.1 Discrete distributions: Probability mass -- 4.3.2 Continuous distributions: Rendezvous with density -- 4.3.2.1 Properties of probability density functions -- 4.3.2.2 The normal probability density function -- 4.3.3 Mean and variance of a distribution -- 4.3.3.1 Mean as minimized variance -- 4.3.4 Highest density interval (HDI) -- 4.4 Two-Way Distributions -- 4.4.1 Conditional probability -- 4.4.2 Independence of attributes -- 4.5 Appendix: R Code for Figure 4.1 -- 4.6 Exercises -- Chapter 5: Bayes' Rule -- 5.1 Bayes' Rule -- 5.1.1 Derived from definitions of conditional probability -- 5.1.2 Bayes' rule intuited from a two-way discrete table -- 5.2 Applied to Parameters and Data -- 5.2.1 Data-order invariance -- 5.3 Complete Examples: Estimating Bias in a Coin -- 5.3.1 Influence of sample size on the posterior -- 5.3.2 Influence of the prior on the posterior -- 5.4 Why Bayesian Inference Can Be Difficult…”

Tabla de Contenidos: “…3.1.2 Kubernetes mit dem Azure Kubernetes Service installieren -- 3.1.3 Kubernetes auf den Amazon Web Services installieren -- 3.1.4 Kubernetes mit minikube lokal installieren -- Tipp -- 3.2 Kubernetes in Docker ausführen -- 3.3 Kubernetes auf dem Raspberry Pi ausführen -- 3.4 Der Kubernetes-Client -- 3.4.1 Den Cluster-Status prüfen -- Tipp -- Tipp -- 3.4.2 Worker-Knoten in Kubernetes auflisten -- 3.5 Cluster-Komponenten -- 3.5.1 Kubernetes-Proxy -- 3.5.2 Kubernetes-DNS -- Tipp -- 3.5.3 Kubernetes-UI -- 3.6 Zusammenfassung -- 4 Häufige kubectl-Befehle -- 4.1 Namensräume -- 4.2 Kontexte -- 4.3 Objekte der Kubernetes-API anzeigen -- 4.4 Kubernetes-Objekte erstellen, aktualisieren und löschen -- Tipp -- 4.5 Objekte mit einem Label und Anmerkungen versehen -- 4.6 Debugging-Befehle -- Tipp -- 4.7 Autovervollständigen von Befehlen -- 4.8 Alternative Möglichkeiten zur Kommunikation mit Ihrem Cluster -- 4.9 Zusammenfassung -- 5 Pods -- Abb. 5-1 Ein Beispiel-Pod mit zwei Containern und einem gemeinsamen Dateisystem -- Tipp -- 5.1 Pods in Kubernetes -- 5.2 In Pods denken -- 5.3 Das Pod-Manifest -- Tipp -- 5.3.1 Einen Pod erstellen -- 5.3.2 Ein Pod-Manifest schreiben -- Listing 5-1 kuard-pod.yaml -- 5.4 Pods starten -- 5.4.1 Pods auflisten -- Tipp -- 5.4.2 Pod-Details -- 5.4.3 Einen Pod löschen -- 5.5 Auf Ihren Pod zugreifen -- 5.5.1 Port-Forwarding einsetzen -- 5.5.2 Mehr Informationen aus Logs erhalten -- Tipp -- 5.5.3 Befehle in Ihrem Container mit exec ausführen -- 5.5.4 Dateien von und auf Container kopieren -- 5.6 Health-Checks -- 5.6.1 Liveness-Probe -- Listing 5-2 kuard-pod-health.yaml -- Tipp -- 5.6.2 Readiness-Probe -- 5.6.3 Arten von Health-Checks -- 5.7 Ressourcen-Management -- 5.7.1 Ressourcen-Anforderungen: Minimal notwendige Ressourcen -- Listing 5-3 kuard-pod-resreq.yaml -- Tipp -- Details zu den Request-Grenzen -- Tipp…”

Tabla de Contenidos: “…-- 7.1.2 Measurement of test adequacy -- 7.1.3 Test enhancement using measurements of adequacy -- 7.1.4 Infeasibility and test adequacy -- 7.1.5 Error detection and test enhancement -- 7.1.6 Single and multiple executions -- 7.2 Adequacy Criteria Based on Control Flow -- 7.2.1 Statement and block coverage -- 7.2.2 Conditions and decisions -- 7.2.3 Decision coverage -- 7.2.4 Condition coverage -- 7.2.5 Condition/decision coverage -- 7.2.6 Multiple condition coverage -- 7.2.7 Linear code sequence and jump (LCSAJ) coverage -- 7.2.8 Modified condition/decision coverage -- 7.2.9 MC/DC adequate tests for compound conditions -- 7.2.10 Definition of MC/DC coverage -- 7.2.11 Minimal MC/DC tests -- 7.2.12 Error detection and MC/DC adequacy -- 7.2.13 Short-circuit evaluation and infeasibility -- 7.2.14 Basis path coverage -- 7.2.15 Tracing test cases to requirements -- 7.3 Concepts From Data Flow -- 7.3.1 Definitions and uses -- 7.3.2 C-use and p-use -- 7.3.3 Global and local definitions and uses -- 7.3.4 Data flow graph -- 7.3.5 Def-clear paths -- 7.3.6 Def-use pairs -- 7.3.7 Def-use chains -- 7.3.8 A little optimization -- 7.3.9 Data contexts and ordered data contexts -- 7.4 Adequacy Criteria Based on Data Flow -- 7.4.1 c-use coverage…”

Tabla de Contenidos: “…Implementing a firewall -- 13.1 Network services and vulnerability -- 13.1.1 Minimizing security risk: Introducing the firewall…”

Tabla de Contenidos: “…3.3 Methodology of Designing Quantum Multiplexer (QMUX) -- 3.3.1 QMUX Using CSWAP Gates -- 3.3.1.1 Generalization -- 3.3.2 QMUX Using Controlled-R Gates -- 3.4 Analysis and Synthesis of Proposed Methodology -- 3.5 Complexity and Cost of Quantum Circuits -- 3.6 Conclusion -- References -- Chapter 4 Artificial Intelligence and Machine Learning Algorithms in Quantum Computing Domain -- 4.1 Introduction -- 4.1.1 Quantum Computing Convolutional Neural Network -- 4.2 Literature Survey -- 4.3 Quantum Algorithms Characteristics Used in Machine Learning Problems -- 4.3.1 Minimizing Quantum Algorithm -- 4.3.2 K-NN Algorithm -- 4.3.3 K-Means Algorithm -- 4.4 Tree Tensor Networking -- 4.5 TNN Implementation on IBM Quantum Processor -- 4.6 Neurotomography -- 4.7 Conclusion and Future Scope -- References -- Chapter 5 Building a Virtual Reality-Based Framework for the Education of Autistic Kids -- 5.1 Introduction -- 5.2 Literature Review -- 5.3 Proposed Work -- 5.3.1 Methodology -- 5.3.2 Work Flow of Neural Style Transfer -- 5.3.3 A-Frame -- 5.3.3.1 Setting Up the Virtual World and Adding Components -- 5.3.3.2 Adding Interactivity Through Raycasting -- 5.3.3.3 Animating the Components -- 5.3.4 Neural Style Transfer -- 5.3.4.1 Choosing the Content and Styling Image -- 5.3.4.2 Image Preprocessing and Generation of a Random Image -- 5.3.4.3 Model Design and Extraction of Content and Style -- 5.3.4.4 Loss Calculation -- 5.3.4.5 Model Optimization -- 5.4 Evaluation Metrics -- 5.5 Results -- 5.5.1 A-Frame -- 5.5.2 Neural Style Transfer -- 5.6 Conclusion -- References -- Chapter 6 Detection of Phishing URLs Using Machine Learning and Deep Learning Models Implementing a URL Feature Extractor -- 6.1 Introduction -- 6.2 Related Work -- 6.3 Proposed Model -- 6.3.1 URL Feature Extractor -- 6.3.2 Dataset -- 6.3.3 Methodologies -- 6.3.3.1 AdaBoost Classifier…”

Tabla de Contenidos: “…5.4.3.2 Optimization of Φ under fixed F and Vm[k] -- 5.4.4 Numerical results -- 5.4.5 Conclusion -- 5.4.6 Proof of Theorem 5.4.1 -- References -- 6 Artificial intelligence technology in the Internet of things -- 6.1 Introduction -- 6.2 Exploiting deep learning for secure transmission in an underlay cognitive radio network -- 6.2.1 System model and problem formulation -- 6.2.2 Conventional optimization based power allocation approach -- 6.2.2.1 Perfect CSI -- 6.3 Q-learning based task offloading and resources optimization for a collaborative computing system -- 6.3.1 System model and problem formulation -- 6.3.1.1 System model -- 6.3.1.2 Computation model -- 6.3.1.3 Local computing -- 6.3.1.4 Collaborative cloud computing -- 6.3.2 Wireless communication model -- 6.3.3 MDP model of offloading decision process -- 6.3.3.1 State space S -- 6.3.3.2 Action set A -- 6.3.3.3 Policy -- 6.3.3.4 Loss function and reward -- 6.3.4 Communication and computation resources optimization -- 6.3.4.1 Uplink transmission power allocation -- References -- 7 Fog/edge computing technology and big data system with IoT -- 7.1 Introduction -- 7.1.1 MEC: overview and resource allocation -- 7.1.1.1 MEC: overview -- 7.1.1.2 Single-user and multiuser MEC -- 7.1.1.3 MIMO-assisted MEC -- 7.1.2 Massive MIMO-assisted MEC -- 7.1.2.1 Motivation -- 7.1.2.2 State-of-the-art -- 7.2 Edge cache-assisted secure low-latency millimeter wave transmission -- 7.2.1 Related works -- 7.2.2 System model and problem formulation -- 7.2.2.1 System model -- 7.2.3 Problem formulation -- 7.2.3.1 Problem solution -- 7.2.3.2 Beamforming design at the fronthaul link -- 7.2.3.3 Beamforming design at the access link -- 7.2.4 Numerical results -- 7.2.5 Conclusion -- 7.3 Delay minimization for massive MIMO assisted mobile edge computing -- 7.3.1 System model and problem formulation -- 7.3.1.1 System model…”

Tabla de Contenidos: “…8.2.3 Unsupervised domain adaptation techniques -- 8.2.3.1 Domain adversarial adaptation -- 8.2.3.2 Generative-based adaptation -- 8.2.3.3 Classifier discrepancy -- 8.2.3.4 Self-supervised learning -- Self-training -- Entropy minimization -- 8.2.3.5 Multitasking -- 8.3 Continual learning -- 8.3.1 Continual learning problem formulation -- 8.3.2 Continual learning setups in semantic segmentation -- 8.3.3 Incremental learning techniques -- 8.3.3.1 Knowledge distillation -- 8.3.3.2 Parameter freezing -- 8.3.3.3 Geometrical feature-level regularization -- 8.3.3.4 New directions -- 8.4 Conclusion -- Acknowledgment -- References -- Biographies -- 9 Visual tracking -- 9.1 Introduction -- 9.1.1 Problem definition -- 9.1.2 Challenges in tracking -- 9.1.3 Motivation of the setting -- 9.1.4 Historical development -- 9.2 Template-based methods -- 9.2.1 The basics -- 9.2.2 Performance measures -- 9.2.3 Normalized cross correlation -- 9.2.4 Phase-only matched filter -- 9.3 Online-learning-based methods -- 9.3.1 The MOSSE filter -- 9.3.2 Discriminative correlation filters -- 9.3.3 Suitable features for DCFs -- 9.3.4 Scale space tracking -- 9.3.5 Spatial and temporal weighting -- 9.4 Deep learning-based methods -- 9.4.1 Deep features in DCFs -- 9.4.2 Adaptive deep features -- 9.4.3 End-to-end learning DCFs -- 9.5 The transition from tracking to segmentation -- 9.5.1 Video object segmentation -- 9.5.2 A generative VOS method -- 9.5.3 A discriminative VOS method -- 9.6 Conclusions -- Acknowledgment -- References -- Biographies -- 10 Long-term deep object tracking -- 10.1 Introduction -- 10.1.1 Challenges in video object tracking -- 10.1.1.1 Visual challenges in tracking -- 10.1.1.2 Learning challenges in tracking -- 10.1.1.3 Engineering challenges in tracking -- 10.2 Short-term visual object tracking -- 10.2.1 Shallow trackers -- 10.2.2 Deep trackers…”

“…A chemical engineer's guide to managing and minimizing environmental impact. Chemical processes are invaluable to modern society, yet they generate substantial quantities of wastes and emissions, and safely managing these wastes costs tens of millions of dollars annually. …”