“…Dafür bietet es Ihnen sowohl eine grundlegende Einführung in die Programmierung mit COBOL als auch eine thematisch gegliederte Referenz der Befehle mit praktischen Beispielen. Da reine COBOL-Programme in der Praxis selten zu finden sind, geht ein weiterführendes Kapitel speziell auf die Anbindung an relationale Datenbanken und hierarchische Datenbanksysteme wie IMS ein. …”

Tabla de Contenidos: “…6.6 Future direction of data-analytic-based optical monitoring -- 6.7 Summary -- Acknowledgment -- References -- 7 Machine Learning methods for Quality-of-Transmission estimation -- 7.1 Introduction -- 7.2 Classification and regression models for QoT estimation -- 7.2.1 Classification approaches for QoT estimation -- 7.2.1.1 Performance evaluation metrics - ML classification -- 7.2.1.2 Illustrative description of a classifier for QoT estimation -- 7.2.2 Regression approaches for QoT estimation -- 7.2.2.1 Regression models for QoT estimation -- 7.3 Active and transfer learning approaches for QoT estimation -- 7.3.1 Active learning -- 7.3.1.1 Gaussian Processes for QoT estimation -- 7.3.2 Transfer learning -- 7.3.2.1 Domain adaptation techniques -- 7.3.3 When to apply AL/DA during network lifecycle -- 7.4 On the integration of ML in optimization tools -- 7.4.1 RMSA integrating ML-based QoT estimation in EONs -- 7.4.1.1 Integrated network planning framework -- 7.5 Illustrative numerical results -- 7.5.1 Data generation -- 7.5.2 Classification -- 7.5.3 Regression -- 7.5.4 Active learning and transfer learning -- 7.6 Future research directions and challenges -- 7.7 Conclusion -- References -- 8 Machine Learning for optical spectrum analysis -- 8.1 Introduction -- 8.1.1 Failure detection and localization -- 8.1.2 Optical spectrum -- 8.1.3 Failures affecting the optical spectrum -- 8.2 Feature-based spectrum monitoring -- 8.2.1 Motivation and objectives -- 8.2.2 OSA for soft-failure detection and identification -- 8.2.2.1 Soft-failure detection, identification, and localization -- 8.2.2.2 Options for classification using FeX -- Multi-classifier approach -- Single-classifier approach -- Feature transformation for single-classifier approach -- 8.2.3 Soft-failure localization -- 8.2.4 Illustrative results -- 8.2.4.1 VPI set-up for data collection…”

Volumen I: conectar con el ejemplar digitalizado de la Universidad Complutense en Google Books
Volumen II: conectar con el ejemplar digitalizado de la Universidad Complutense en Google Books
Volumen III: conectar con el ejemplar digitalizado de la Universidad Complutense en Google Books
Volumen IV: conectar con el ejemplar digitalizado de la Universidad Complutense en Google Books

“…Para ejercer este ministerio, como para las demás funciones del presbítero, se les concede un poder espiritual, que ciertamente se da para edificación. Para construir la Iglesia, los presbíteros deben tener con todos un trato exquisitamente humano, a ejemplo del Señor. …”

Tabla de Contenidos: “…EL ACOSO SEXUAL EN EL TRABAJO NO SOLO SE DA A (...) -- 2.18. CONFIGURACIÓN DE ACOSO LABORAL SEXUAL Y ABUSO (...) -- 2.19. …”

Tabla de Contenidos: “…-- Los fenómenos jurídicos -- Clasificación de los fenómenos jurídicos -- 1ra. clasificación. Fenómenos (...) -- 2da. Clasificación. Fenómenos (...) -- 3ra. Clasificación. …”

Tabla de Contenidos: “…The establishment of a research project management office at a medical school in university of são paulo, fmrp-usp, brazil / Michele Aparecida Dela Ricci Junqueira and Regina Célia da Rocha Bezerra -- Chapter 2.7. Rma education, training and professional development in North America and Europe / Jeff Ritchie, Emma Lythgoe, and John Donovan -- Chapter 2.8. …”

Tabla de Contenidos: “…L ́opez Modulation of Gut Microbiota of Overweight Mice by Agavins and Their Association with Body Weight Loss Reprinted from: Nutrients 9, , 821, doi: 10.3390/nu9090821 152 -- Myeong Soo Park, Bin Kwon, Seockmo Ku and Geun Eog Ji The Efficacy of Bifidobacterium longum BORI and Lactobacillus acidophilus AD031 Probiotic Treatment in Infants with Rotavirus Infection Reprinted from: Nutrients 2017, 9, 887, doi: 10.3390/nu9080887 164 -- Bailiang Li, Da Jin, Shangfu Yu, Smith Etareri Evivie, Zafarullah Muhammad, Guicheng Huo and Fei Liu In Vitro and In Vivo Evaluation of Lactobacillus delbrueckii subsp. bulgaricus KLDS1.0207 for the Alleviative Effect on Lead Toxicity Reprinted from: Nutrients 2017, 9, 845, doi: 10.3390/nu9080845 169 -- Melanie K. …”

Tabla de Contenidos: “…. -- Un workflow open-source per l'elaborazione delle immagini termiche da drone -- Gabriele Ciccone -- Figura 1: Esempi di immagine RGB e IR e schema dei voli effettuati. -- Figura 2: Schema del workflow con software proprietari per l'elaborazione di immagini termiche. -- Figura 3: Schema del workflow con software free e open-source per l'elaborazione di immagini termiche. -- Figura 4: (a) Ortofoto in 4 bande (R, G, B, IR)…”

Tabla de Contenidos: “…Un perseguido inocente da gracias a Dios en su casa -- Salmo 27. Un inocente perseguido, refugiado en el Señor, que conoce a los suyos -- Salmo 28. …”

Tabla de Contenidos: “…Distribution du chômage par groupe d'â ge et par sexe (%) -- Tableau 5. Évolution de l'emploi à Teruel -- Figure 9. …”

Tabla de Contenidos: “…7.1.3 Marginalized Denoising Dictionary Learning with Locality Constraint -- 7.1.3.1 Preliminaries and Motivations -- 7.1.3.2 LC-LRD Revisited -- 7.1.3.3 Marginalized Denoising Auto-encoder (mDA) -- 7.1.3.4 Proposed MDDL Model -- 7.1.3.5 Optimization -- 7.1.3.6 Classi cation Based on MDDL -- 7.1.4 Experiments -- 7.1.4.1 Experimental Settings -- 7.1.4.2 Face Recognition -- 7.1.4.3 Object Recognition -- 7.1.4.4 Digits Recognition -- 7.1.5 Conclusion -- 7.1.6 Future Works -- 7.2 Learning a Deep l8 Encoder for Hashing -- 7.2.1 Introduction -- 7.2.1.1 Problem De nition and Background -- 7.2.1.2 Related Work -- 7.2.2 ADMM Algorithm -- 7.2.3 Deep l8 Encoder -- 7.2.4 Deep l8 Siamese Network for Hashing -- 7.2.5 Experiments in Image Hashing -- 7.2.6 Conclusion -- References -- 8 Action Recognition -- 8.1 Deeply Learned View-Invariant Features for Cross-View Action Recognition -- 8.1.1 Introduction -- 8.1.2 Related Work -- 8.1.3 Deeply Learned View-Invariant Features -- 8.1.3.1 Sample-Af nity Matrix (SAM) -- 8.1.3.2 Preliminaries on Autoencoders -- 8.1.3.3 Single-Layer Feature Learning -- 8.1.3.4 Learning -- 8.1.3.5 Deep Architecture -- 8.1.4 Experiments -- 8.1.4.1 IXMAS Dataset -- One-to-One Cross-view Action Recognition -- Many-to-One Cross-view Action Recognition -- 8.1.4.2 Daily and Sports Activities Data Set -- Many-to-One Cross-view Action Classi cation -- 8.2 Hybrid Neural Network for Action Recognition from Depth Cameras -- 8.2.1 Introduction -- 8.2.2 Related Work -- 8.2.3 Hybrid Convolutional-Recursive Neural Networks -- 8.2.3.1 Architecture Overview -- 8.2.3.2 3D Convolutional Neural Networks -- 8.2.3.3 3D Recursive Neural Networks -- 8.2.3.4 Multiple 3D-RNNs -- 8.2.3.5 Model Learning -- 8.2.3.6 Classi cation -- 8.2.4 Experiments -- 8.2.4.1 MSR-Gesture3D Dataset -- 8.2.4.2 MSR-Action3D Dataset -- 8.3 Summary -- References…”

Tabla de Contenidos: “…Canada : Une politique publique sur la consul tation et l'engagement des citoyens da... -- Encadré 16. Royaume-Uni : Consultation du public pour l'élaboration de la nouvelle législation s... -- Encadré 17. …”