The youth of early modern women by Reeves, Margaret

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Are You Really You?.

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The elders : a coming of age story by Hansen, Nathaniel James

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Introduction to disability in the workplace

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The elders a coming of age story by Hansen, Nathaniel James

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Leadership and Management of Quality in Higher Education by Nair, Chenicheri, Webster, Len, Mertova, Patricie

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A critical ethnography of 'Westerners' teaching English in China : Shanghaied in Shanghai by Stanley, Phiona

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Anatomy of a Civil War Sociopolitical Impacts of the Kurdish Conflict in Turkey by Gurses, Mehmet

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Toddlers outdoors : play, learning and development.

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Women of Influence in Education Practising Dilemmas and Contesting Spaces

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Our voices, our histories : Asian American and Pacific Islander women

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Sickness in Health: Bullying in Nursing and other Health Professions by Happell, Brenda

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The water cries

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Communication networks and service management in the era of artificial intelligence and machine learning

Table of Contents: “…e Santos, Tim Wauters, Bruno Volckaert, and Filip De Turck -- 7.1 Introduction 147 -- 7.2 Technology Overview 148 -- 7.2.1 Fog Computing (FC) 149 -- 7.2.2 Resource Provisioning 149 -- 7.2.3 Service Function Chaining (SFC) 150 -- 7.2.4 Micro-service Architecture 150 -- 7.2.5 Reinforcement Learning (RL) 151 -- 7.3 State-of-the-Art 152 -- 7.3.1 Resource Allocation for Fog Computing 152 -- 7.3.2 ML Techniques for Resource Allocation 153 -- 7.3.3 RL Methods for Resource Allocation 154 -- 7.4 A RL Approach for SFC Allocation in Fog Computing 155 -- 7.4.1 Problem Formulation 155 -- 7.4.2 Observation Space 156 -- 7.4.3 Action Space 157 -- 7.4.4 Reward Function 158 -- 7.4.5 Agent 161 -- 7.5 Evaluation Setup 162 -- 7.5.1 Fog-Cloud Infrastructure 162 -- 7.5.2 Environment Implementation 162 -- 7.5.3 Environment Configuration 164 -- 7.6 Results 165 -- 7.6.1 Static Scenario 165 -- 7.6.2 Dynamic Scenario 167 -- 7.7 Conclusion and Future Direction 169 -- Bibliography 170 -- Part III Management Functions and Applications 175 -- 8 Designing Algorithms for Data-Driven Network Management and Control: State-of-the-Art and Challenges 177 Andreas Blenk, Patrick Kalmbach, Johannes Zerwas, and Stefan Schmid -- 8.1 Introduction 177 -- 8.1.1 Contributions 179 -- 8.1.2 Exemplary Network Use Case Study 179 -- 8.2 Technology Overview 181 -- 8.2.1 Data-Driven Network Optimization 181 -- 8.2.2 Optimization Problems over Graphs 182 -- 8.2.3 From Graphs to ML/AI Input 184 -- 8.2.4 End-to-End Learning 187 -- 8.3 Data-Driven Algorithm Design: State-of-the Art 188 -- 8.3.1 Data-Driven Optimization in General 188 -- 8.3.2 Data-Driven Network Optimization 190 -- 8.3.3 Non-graph Related Problems 192 -- 8.4 Future Direction 193 -- 8.4.1 Data Production and Collection 193 -- 8.4.2 ML and AI Advanced Algorithms for Network Management with Performance Guarantees 194 -- 8.5 Summary 194 -- Acknowledgments 195 -- Bibliography 195 -- 9 AI-Driven Performance Management in Data-Intensive Applications 199 Ahmad Alnafessah, Gabriele Russo Russo, Valeria Cardellini, Giuliano Casale, and Francesco Lo Presti -- 9.1 Introduction 199 -- 9.2 Data-Processing Frameworks 200 -- 9.2.1 Apache Storm 200 -- 9.2.2 Hadoop MapReduce 201 -- 9.2.3 Apache Spark 202 -- 9.2.4 Apache Flink 202 -- 9.3 State-of-the-Art 203 -- 9.3.1 Optimal Configuration 203 -- 9.3.1.1 Traditional Approaches 203 -- 9.3.1.2 AI Approaches 204 -- 9.3.1.3 Example: AI-Based Optimal Configuration 206 -- 9.3.2 Performance Anomaly Detection 207 -- 9.3.2.1 Traditional Approaches 208 -- 9.3.2.2 AI Approaches 208 -- 9.3.2.3 Example: ANNs-Based Anomaly Detection 210 -- 9.3.3 Load Prediction 211 -- 9.3.3.1 Traditional Approaches 212 -- 9.3.3.2 AI Approaches 212 -- 9.3.4 Scaling Techniques 213 -- 9.3.4.1 Traditional Approaches 213 -- 9.3.4.2 AI Approaches 214 -- 9.3.5 Example: RL-Based Auto-scaling Policies 214 -- 9.4 Conclusion and Future Direction 216 -- Bibliography 217 -- 10 Datacenter Traffic Optimization with Deep Reinforcement Learning 223 Li Chen, Justinas Lingys, Kai Chen, and Xudong Liao -- 10.1 Introduction 223 -- 10.2 Technology Overview 225 -- 10.2.1 Deep Reinforcement Learning (DRL) 226 -- 10.2.2 Applying ML to Networks 227 -- 10.2.3 Traffic Optimization Approaches in Datacenter 229 -- 10.2.4 Example: DRL for Flow Scheduling 230 -- 10.2.4.1 Flow Scheduling Problem 230 -- 10.2.4.2 DRL Formulation 230 -- 10.2.4.3 DRL Algorithm 231 -- 10.3 State-of-the-Art: AuTO Design 231 -- 10.3.1 Problem Identified 231 -- 10.3.2 Overview 232 -- 10.3.3 Peripheral System 233 -- 10.3.3.1 Enforcement Module 233 -- 10.3.3.2 Monitoring Module 234 -- 10.3.4 Central System 234 -- 10.3.5 DRL Formulations and Solutions 235 -- 10.3.5.1 Optimizing MLFQ Thresholds 235 -- 10.3.5.2 Optimizing Long Flows 239 -- 10.4 Implementation 239 -- 10.4.1 Peripheral System 239 -- 10.4.1.1 Monitoring Module (MM): 240 -- 10.4.1.2 Enforcement Module (EM): 240 -- 10.4.2 Central System 241 -- 10.4.2.1 sRLA 241 -- 10.4.2.2 lRLA 242 -- 10.5 Experimental Results 242 -- 10.5.1 Setting 243 -- 10.5.2 Comparison Targets 244 -- ...…”
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Polypharmacology in drug discovery

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Wind effects on cable-supported bridges by Xu, You-Lin, 1952-

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Acoustics, aeroacoustics and vibrations by Anselmet, Fabien

Table of Contents: “…Identification of experimental resonance frequencies -- 7.2.2.8. Clamped circular plate -- 7.2.2.9. …”
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Control, mechatronics and automation technology

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Brighteyes by Johnson, Donna Kay

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Racquetball is for me by Lerner, Mark

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