Tabla de Contenidos: “…Combining two or more ordered data frames quickly -- 5.4.2. Principal methods to combine data from multiple tables -- 5.5. …”

Tabla de Contenidos: “…. -- Figure 8.2 Map showing the altitude of villa sites within the region -- Figure 8.3 Lyde Green in the context of other Roman villa sites and the principal Roman roads in the region -- Figure 8.4 Map of villa sites in the region related to modern land-use -- Appendices -- Figure 9.1 Rubbings of decorated and stamped Samian ware sherds described in the catalogue -- Figure 9.2 Thin sections of the selected Roman pottery sherds -- Figure 9.3 Images of ceramic building material fabrics and mortar -- List of Tables -- Research objectives, methodologies and summary of results -- Table 2.1 Concordance of phasing between excavation areas -- The development of the landscape before the 1st millennium AD…”

Tabla de Contenidos: “…-- 6.5.5 Maintaining Reputation Is One Motive to Trigger and Sustain Reciprocity -- 6.5.6 Institutional Tit for Tat -- 6.6 Trust and Trustworthiness -- 6.6.1 Building Blocks of Trust and Trustworthiness -- 6.6.2 Innate Triggers for Trust and Trustworthiness: Other‐regarding Preferences -- 6.7 Summary: The Empirical Nature of Fair Choice -- References -- Chapter 7 Behavioral Analysis of Strategic Interactions: Game Theory, Bargaining, and Agency -- 7.1 Behavioral Game Theory -- 7.1.1 Accurate Beliefs -- 7.1.2 Best Responses -- 7.1.3 Strategic Sophistication -- 7.1.4 Coordination Games and Equilibrium Selection -- 7.1.5 Repeated Games -- 7.1.6 Applications in Operations Management -- 7.2 Behavioral Analysis of Principal-Agent Problems -- 7.2.1 Response to Financial Incentives -- 7.2.2 Financial Incentives in Other Settings: Monitoring, Tournaments, and Teams -- 7.2.3 Reciprocity and Gift Exchange -- 7.2.4 Nonmonetary Incentives -- 7.2.5 Applications in Operations Management -- 7.3 Bargaining -- 7.3.1 Theoretical Approaches -- 7.3.2 Economics Experiments: Free‐form Bargaining -- 7.3.3 Economics Experiments: Structured Bargaining -- 7.3.4 Economics Experiments: Multiparty Negotiations -- 7.3.5 Psychology Experiments: Biases in Negotiations -- 7.3.6 Applications in Operations Management -- References -- Chapter 8 Integration of Behavioral and Operational Elements Through System Dynamics…”

Tabla de Contenidos: “…Trajectories of multivariate stochastic volatility -- 10.4.6.3. Time-varying principal components analysis -- 10.4.6.4. Latent components in multivariate volatility -- 10.4.7. …”

Tabla de Contenidos: “…Mettre en place des mécanismes de budgétisation des investissements à long terme à l'appui d'infrastructures résilientes face au changement climatique -- 2.5.2. Établir des principes applicables au financement juste et équitable des infrastructures résilientes -- 2.6. …”

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: “…-- 12.3 Second quantisation -- 12.3.1 Multiple-particle states -- 12.3.2 Fock spaces -- 12.3.3 Observables in Fock spaces -- 12.3.4 Evolution in Fock spaces -- 12.3.5 Creation and annihilation of particles -- 12.4 Solving recursive equations in the free Fock space -- 12.4.1 A domain of operators on the free Fock space -- 12.4.2 Semantic functionals of program schemes -- 12.4.3 Fixed point semantics -- 12.4.4 Syntactic approximation -- 12.5 Recovering symmetry and antisymmetry -- 12.5.1 Symmetrisation functional -- 12.5.2 Symmetrisation of semantics of quantum recursion -- 12.6 Principal system semantics of quantum recursion -- 12.7 Illustrative examples: revisit recursive quantum walks -- 12.8 Quantum while-loops (with quantum control) -- 12.9 Bibliographic remarks and further readings -- VI Prospects -- 13 Prospects -- 13.1 Quantum machines and quantum programs…”

Tabla de Contenidos: “…Front Cover -- Machine Learning for Future Fiber-Optic Communication Systems -- Copyright -- Contents -- Contributors -- Preface -- Acknowledgments -- 1 Introduction to machine learning techniques: An optical communication's perspective -- 1.1 Introduction -- 1.2 Supervised learning -- 1.2.1 Artificial neural networks (ANNs) -- 1.2.2 Choice of activation functions -- 1.2.3 Choice of loss functions -- 1.2.4 Support vector machines (SVMs) -- 1.2.5 K-nearest neighbors (KNN) -- 1.3 Unsupervised learning -- 1.3.1 K-means clustering -- 1.3.2 Expectation-maximization (EM) algorithm -- 1.3.3 Principal component analysis (PCA) -- 1.3.4 Independent component analysis (ICA) -- 1.4 Reinforcement learning (RL) -- 1.5 Deep learning techniques -- 1.5.1 Deep learning vs. conventional machine learning -- 1.5.2 Deep neural networks (DNNs) -- 1.5.3 Convolutional neural networks (CNNs) -- 1.5.4 Recurrent neural networks (RNNs) -- 1.5.5 Generative adversarial networks (GANs) -- 1.6 Future role of ML in optical communications -- 1.7 Online resources for ML algorithms -- 1.8 Conclusions -- 1.A -- References -- 2 Machine learning for long-haul optical systems -- 2.1 Introduction -- 2.2 Application of machine learning in perturbation-based nonlinearity compensation -- 2.2.1 Wide &amp -- deep neural network -- 2.2.2 Data collection and pre-processing -- 2.2.3 Training results -- 2.2.4 Results and discussion -- 2.3 Application of machine learning in digital backpropagation -- 2.3.1 Physics-based machine-learning models -- 2.3.2 Single-polarization systems -- 2.3.3 Dual-polarization systems -- 2.3.4 Subband processing via filter banks -- 2.3.5 Training and application examples -- 2.4 Outlook of machine learning in long-haul systems -- References -- 3 Machine learning for short reach optical fiber systems -- 3.1 Introduction to optical systems for short reach…”

Tabla de Contenidos: “…Les inégalités de revenus d'activité sont le principal déterminant de la dispersion des revenus marchands des ménages -- Graphique 5.4. …”

“…El Median Shortest Path Problem (MSPP) consiste en localizar un path (camino) entre el nodo origen y el nodo destino, llamado path principal, de tal manera que todos los otros nodos de la red, que no están sobre este path, sean asignados a partir del nodo más cercano que se encuentre sobre el mismo path principal. …”
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