Search Results - (((((((kant OR want) OR mantis) OR when) OR cantor) OR anne) OR share) OR hints) algorithms.

Lippincott's primary care orthopaedics

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Mastering Active Directory by Francis, Dishan

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Informatics for Health by Randell, R.

Table of Contents: “…Connected and Digital Health -- Design and Validation of a Platform to Evaluate mHealth Apps -- Reasoning and Data Representation in a Health and Lifestyle Support System -- Feasibility of Representing a Danish Microbiology Model Using FHIR -- Establishment of Requirements and Methodology for the Development and Implementation of GreyMatters, a Memory Clinic Information System -- Nurses' Perspectives on In-Home Monitoring of Elderlies's Motion Pattern -- Monitoring Activities Related to Medication Adherence in Ambient Assisted Living Environments -- Design, Implementation and Operation of a Reading Center Platform for Clinical Studies -- Web Validation Service for Ensuring Adherence to the DICOM Standard -- A Decision Support System for Cardiac Disease Diagnosis Based on Machine Learning Methods -- Severity Summarization and Just in Time Alert Computation in mHealth Monitoring -- Towards Safe and Efficient Child Primary Care -- Gaps in the Use of Unique Identifiers in Europe -- Why Are Children's Interests Invisible in European National E-Health Strategies? -- Shared Decision Making via Personal Health Record Technology for Routine Use of Diabetic Youth: A Study Protocol -- A Medication Reminder Mobile App: Does It Work for Different Age Ranges -- Internet of Things in Health Trends Through Bibliometrics and Text Mining -- Developing the Safety Case for MediPi: An Open-Source Platform for Self Management -- UK Health and Social Care Case Studies: Iterative Technology Development -- 2. …”
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Machine learning with R quick start guide : a beginner's guide to implementing machine learning techniques from scratch using R 3.5 by Pastor Sanz, Iván

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Introduction to matrix-analytic methods in queues 2 : analytical and simulation approach - queues and simulation by Chakravarthy, Srinivas R.

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Techniques and key points for endoscopic cranial base reconstruction by Pinheiro-Neto, Carlos, Peris-Celda, Maria

Table of Contents: “…Techniques and Key Points for Endoscopic Cranial Base Reconstruction -- MedOne Access Information -- Tittle Page -- Copyright -- Dedication -- Contents -- Videos -- Foreword -- Preface -- Acknowledgments -- Contributors -- Section I Introduction -- 1 Principles of Endoscopic Cranial Base Reconstruction -- 1.1 Introduction -- 1.2 Fundamentals of Endonasal Reconstruction -- 1.3 Repair Options -- 1.4 Vascularized Reconstruction -- 1.5 Reconstruction Decalogue -- 2 Operative Planning and Treatment Algorithm -- 2.1 Introduction -- 2.2 Preoperative Planning -- 2.3 Perioperative and Intraoperative Considerations -- 2.4 Treatment Algorithm -- 2.5 Postoperative Considerations -- 2.5.1 Inpatient Care -- 2.5.2 Outpatient Care -- Section II Nasoseptal Flap and Variations -- 3 Standard Nasoseptal Flap -- 3.1 Anatomy -- 3.2 Fundamentals -- 3.3 Indications -- 3.4 Limitations -- 3.5 Surgical Technique -- 3.5.1 Harvest -- 3.5.2 Reconstruction -- 3.6 Postoperative Care -- 3.7 Managing Complications -- 4 Rescue Nasoseptal Flap -- 4.1 Fundamentals -- 4.2 Indications -- 4.3 Limitations -- 4.4 Surgical Technique -- 4.5 Postoperative Care -- 4.6 Complications -- 5 Extended Nasoseptal Flap -- 5.1 Fundamentals -- 5.2 Indications -- 5.3 Limitations -- 5.4 Surgical Technique -- 5.4.1 Harvest -- 5.4.2 Reconstruction -- 5.5 Postoperative Care -- 5.5.1 One-Week Postoperative Visit -- 5.5.2 One-Month Postoperative Visit -- 5.5.3 Four-Month Postoperative Visit -- 5.6 Complications -- 6 Nasoseptal Flap Pedicle Release -- 6.1 Anatomy -- 6.2 Fundamentals -- 6.3 Indications -- 6.4 Limitations -- 6.5 Surgical Technique -- 6.5.1 360-Degree Bone Removal around the SPA Foramen (Osseous Release) -- 6.5.2 Ipsilateral Transpterygoid Approach -- 6.5.3 360-Degree Circumferential Incision in the Periosteum of the PPF around the SPA (Periosteal Release) -- 6.6 Postoperative Care.…”
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RSSDI Diabetes update 2018

Table of Contents: “…-- Chapter 42: Etiopathogenesis and Management of Diabetic Heart Failure -- Chapter 43: Diabetes and Stroke -- Chapter 44: Cellular Mechanism of Atherosclerosis in Diabetes Mellitus -- Chapter 45: Nondiabetic Ocular Complications in Diabetes -- Chapter 46: Nondiabetic Kidney Disease in Patients with Type 2 Diabetes Mellitus: When to Suspect?.…”
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Binary decision diagrams and extensions for system reliability analysis by Xing, Liudong

Table of Contents: “…7.5 Applications to Phased-Mission Systems -- 7.5.1 Mini-Component Concept -- 7.5.2 Extended SEA Method for PMS -- 7.5.3 An Illustrative Example -- 7.6 Summary -- 8 Shared Decision Diagrams -- 8.1 Multi-Rooted Decision Diagrams -- 8.2 Multi-Terminal Decision Diagrams -- 8.3 Performance Study on Multi-State Systems -- 8.3.1 Example Analyses -- 8.3.2 Benchmark Studies -- 8.4 Application to Phased-Mission Systems -- 8.4.1 PMS Analysis Using MDDs -- 8.4.1.1 Step 1-Variable Encoding -- 8.4.1.2 Step 2-Input Variable Ordering -- 8.4.1.3 Step 3-PMS MDD Generation -- 8.4.1.4 Step 4-PMS MDD Evaluation -- 8.4.2 An Illustrative Example -- 8.5 Application to Multi-State k-out-of-n Systems -- 8.5.1 Multi-State k-out-of-n System Analysis Using MDDs -- 8.5.1.1 Step 1- BDDkl Generation -- 8.5.1.2 Step 2- MDDkl Generation -- 8.5.1.3 Step 3- MDDSj Generation -- 8.5.1.4 Step 4-System MDDSj Evaluation -- 8.5.2 An Illustrative Example -- 8.6 Importance Measures -- 8.6.1 Capacity Networks and Reliability Modeling -- 8.6.2 Composite Importance Measures (Type 1) -- 8.6.2.1 General CIMs -- 8.6.2.2 Alternative CIMs -- 8.6.3 Computing CIMs Using MDD -- 8.6.4 An Illustrative Example -- 8.7 Failure Frequency Based Measures -- 8.8 Summary -- Conclusions -- References -- Index -- EULA.…”
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Official Google Cloud Certified Professional Data Engineer study guide by Sullivan, Dan, 1962-

Table of Contents: “…Row Key Access 13 -- Unstructured Data 15 -- Google's Storage Decision Tree 16 -- Schema Design Considerations 16 -- Relational Database Design 17 -- NoSQL Database Design 20 -- Exam Essentials 23 -- Review Questions 24 -- Chapter 2 Building and Operationalizing Storage Systems 29 -- Cloud SQL 30 -- Configuring Cloud SQL 31 -- Improving Read Performance with Read Replicas 33 -- Importing and Exporting Data 33 -- Cloud Spanner 34 -- Configuring Cloud Spanner 34 -- Replication in Cloud Spanner 35 -- Database Design Considerations 36 -- Importing and Exporting Data 36 -- Cloud Bigtable 37 -- Configuring Bigtable 37 -- Database Design Considerations 38 -- Importing and Exporting 39 -- Cloud Firestore 39 -- Cloud Firestore Data Model 40 -- Indexing and Querying 41 -- Importing and Exporting 42 -- BigQuery 42 -- BigQuery Datasets 43 -- Loading and Exporting Data 44 -- Clustering, Partitioning, and Sharding Tables 45 -- Streaming Inserts 46 -- Monitoring and Logging in BigQuery 46 -- BigQuery Cost Considerations 47 -- Tips for Optimizing BigQuery 47 -- Cloud Memorystore 48 -- Cloud Storage 50 -- Organizing Objects in a Namespace 50 -- Storage Tiers 51 -- Cloud Storage Use Cases 52 -- Data Retention and Lifecycle Management 52 -- Unmanaged Databases 53 -- Exam Essentials 54 -- Review Questions 56 -- Chapter 3 Designing Data Pipelines 61 -- Overview of Data Pipelines 62 -- Data Pipeline Stages 63 -- Types of Data Pipelines 66 -- GCP Pipeline Components 73 -- Cloud Pub/Sub 74 -- Cloud Dataflow 76 -- Cloud Dataproc 79 -- Cloud Composer 82 -- Migrating Hadoop and Spark to GCP 82 -- Exam Essentials 83 -- Review Questions 86 -- Chapter 4 Designing a Data Processing Solution 89 -- Designing Infrastructure 90 -- Choosing Infrastructure 90 -- Availability, Reliability, and Scalability of Infrastructure 93 -- Hybrid Cloud and Edge Computing 96 -- Designing for Distributed Processing 98 -- Distributed Processing: Messaging 98 -- Distributed Processing: Services 101 -- Migrating a Data Warehouse 102 -- Assessing the Current State of a Data Warehouse 102 -- Designing the Future State of a Data Warehouse 103 -- Migrating Data, Jobs, and Access Controls 104 -- Validating the Data Warehouse 105 -- Exam Essentials 105 -- Review Questions 107 -- Chapter 5 Building and Operationalizing Processing Infrastructure 111 -- Provisioning and Adjusting Processing Resources 112 -- Provisioning and Adjusting Compute Engine 113 -- Provisioning and Adjusting Kubernetes Engine 118 -- Provisioning and Adjusting Cloud Bigtable 124 -- Provisioning and Adjusting Cloud Dataproc 127 -- Configuring Managed Serverless Processing Services 129 -- Monitoring Processing Resources 130 -- Stackdriver Monitoring 130 -- Stackdriver Logging 130 -- Stackdriver Trace 131 -- Exam Essentials 132 -- Review Questions 134 -- Chapter 6 Designing for Security and Compliance 139 -- Identity and Access Management with Cloud IAM 140 -- Predefined Roles 141 -- Custom Roles 143 -- Using Roles with Service Accounts 145 -- Access Control with Policies 146 -- Using IAM with Storage and Processing Services 148 -- Cloud Storage and IAM 148 -- Cloud Bigtable and IAM 149 -- BigQuery and IAM 149 -- Cloud Dataflow and IAM 150 -- Data Security 151 -- Encryption 151 -- Key Management 153 -- Ensuring Privacy with the Data Loss Prevention API 154 -- Detecting Sensitive Data 154 -- Running Data Loss Prevention Jobs 155 -- Inspection Best Practices 156 -- Legal Compliance 156 -- Health Insurance Portability and Accountability Act (HIPAA) 156 -- Children's Online Privacy Protection Act 157 -- FedRAMP 158 -- General Data Protection Regulation 158 -- Exam Essentials 158 -- Review Questions 161 -- Chapter 7 Designing Databases for Reliability, Scalability, and Availability 165 -- Designing Cloud Bigtable Databases for Scalability and Reliability 166 -- Data Modeling with Cloud Bigtable 166 -- Designing Row-keys 168 -- Designing for Time Series 170 -- Use Replication for Availability and Scalability 171 -- Designing Cloud Spanner Databases for Scalability and Reliability 172 -- Relational Database Features 173 -- Interleaved Tables 174 -- Primary Keys and Hotspots 174 -- Database Splits 175 -- Secondary Indexes 176 -- Query Best Practices 177 -- Designing BigQuery Databases for Data Warehousing 179 -- Schema Design for Data Warehousing 179 -- Clustered and Partitioned Tables 181 -- Querying Data in BigQuery 182 -- External Data Access 183 -- BigQuery ML 185 -- Exam Essentials 185 -- Review Questions 188 -- Chapter 8 Understanding Data Operations for Flexibility and Portability 191 -- Cataloging and Discovery with Data Catalog 192 -- Searching in Data Catalog 193 -- Tagging in Data Catalog 194 -- Data Preprocessing with Dataprep 195 -- Cleansing Data 196 -- Discovering Data 196 -- Enriching Data 197 -- Importing and Exporting Data 197 -- Structuring and Validating Data 198 -- Visualizing with Data Studio 198 -- Connecting to Data Sources 198 -- Visualizing Data 200 -- Sharing Data 200 -- Exploring Data with Cloud Datalab 200 -- Jupyter Notebooks 201 -- Managing Cloud Datalab Instances 201 -- Adding Libraries to Cloud Datalab Instances 202 -- Orchestrating Workflows with Cloud Composer 202 -- Airflow Environments 203 -- Creating DAGs 203 -- Airflow Logs 204 -- Exam Essentials 204 -- Review Questions 206 -- Chapter 9 Deploying Machine Learning Pipelines 209 -- Structure of ML Pipelines 210 -- Data Ingestion 211 -- Data Preparation 212 -- Data Segregation 215 -- Model Training 217 -- Model Evaluation 218 -- Model Deployment 220 -- Model Monitoring 221 -- GCP Options for Deploying Machine Learning Pipeline 221 -- Cloud AutoML 221 -- BigQuery ML 223 -- Kubeflow 223 -- Spark Machine Learning 224 -- Exam Essentials 225 -- Review Questions 227 -- Chapter 10 Choosing Training and Serving Infrastructure 231 -- Hardware Accelerators 232 -- Graphics Processing Units 232 -- Tensor Processing Units 233 -- Choosing Between CPUs, GPUs, and TPUs 233 -- Distributed and Single Machine Infrastructure 234 -- Single Machine Model Training 234 -- Distributed Model Training 235 -- Serving Models 236 -- Edge Computing with GCP 237 -- Edge Computing Overview 237 -- Edge Computing Components and Processes 239 -- Edge TPU 240 -- Cloud IoT 240 -- Exam Essentials 241 -- Review Questions 244 -- Chapter 11 Measuring, Monitoring, and Troubleshooting Machine Learning Models 247 -- Three Types of Machine Learning Algorithms 248 -- Supervised Learning 248 -- Unsupervised Learning 253 -- Anomaly Detection 254 -- Reinforcement Learning 254 -- Deep Learning 255 -- Engineering Machine Learning Models 257 -- Model Training and Evaluation 257 -- Operationalizing ML Models 262 -- Common Sources of Error in Machine Learning Models 263 -- Data Quality 264 -- Unbalanced Training Sets 264 -- Types of Bias 264 -- Exam Essentials 265 -- Review Questions 267 -- Chapter 12 Leveraging Prebuilt Models as a Service 269 -- Sight 270 -- Vision AI 270 -- Video AI 272 -- Conversation 274 -- Dialogflow 274 -- Cloud Text-to-Speech API 275 -- Cloud Speech-to-Text API 275 -- Language 276 -- Translation 276 -- Natural Language 277 -- Structured Data 278 -- Recommendations AI API 278 -- Cloud Inference API 280 -- Exam Essentials 280 -- Review Questions 282 -- Appendix Answers to Review Questions 285 -- Chapter 1: Selecting Appropriate Storage Technologies 286 -- Chapter 2: Building and Operationalizing Storage Systems 288 -- Chapter 3: Designing Data Pipelines 290 -- Chapter 4: Designing a Data Processing Solution 291 -- Chapter 5: Building and Operationalizing Processing Infrastructure 293 -- Chapter 6: Designing for Security and Compliance 295 -- Chapter 7: Designing Databases for Reliability, Scalability, and Availability 296 -- Chapter 8: Understanding Data Operations for Flexibility and Portability 298 -- Chapter 9: Deploying Machine Learning Pipelines 299 -- Chapter 10: Choosing Training and Serving Infrastructure 301 -- Chapter 11: Measuring, Monitoring, and Troubleshooting Machine Learning Models 303 -- Chapter 12: Leveraging Prebuilt Models as a Service 304 -- Index 307.…”
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Fundamentals of Fluid Power Control. by Watton, John

Table of Contents: “…Control-Volume Flow Continuity -- PRV Flow -- Force Balance at the Spindle -- 5.13.3 Frequency Response from a Linearized Transfer Function Analysis -- 5.14 Servovalve Dynamics -- First-Stage, Armature, and Flapper-Nozzle -- Flapper-Nozzle and Resistance Bridge Flow Characteristic -- Force Balance at the Spool -- 5.15 An Open-Loop Servovalve-Motor Drive with Line Dynamics Modeled by Lumped Approximations -- Servovalve, Dynamics Included, Underlapped Spool -- Lines, Laminar Mean Flow, Two Lump Approximations per Line, Negligible Motor Internal Volume -- Motor Flow and Torque Equations -- 5.16 Transmission Line Dynamics -- 5.16.1 Introduction -- Servovalve-Cylinder with Short Lines and Significant Actuator Volumes -- Servovalve-Motor with Long Lines and Negligible Actuator Volumes -- 5.16.2 Lossless Line Model for Z and Y -- 5.16.3 Average and Distributed Line Friction Models for Z and Y -- 5.16.4 Frequency-Domain Analysis -- 5.16.5 Servovalve-Reflected Linearized Coefficients -- 5.16.6 Modeling Systems with Nonlossless Transmission Lines, the Modal Analysis Method -- 5.16.7 Modal Analysis Applied to a Servovalve-Motor Open-Loop Drive -- 5.17 The State-Space Method for Linear Systems Modeling -- 5.17.1 Modeling Principles -- 5.17.2 Some Further Aspects of the Time-Domain Solution -- 5.17.3 The Transfer Function Concept in State Space -- 5.18 Data-Based Dynamic Modeling -- 5.18.1 Introduction -- 5.18.2 Time-Series Modeling -- 5.18.3 The Group Method of Data Handling (GMDH) Algorithm -- 5.18.4 Artificial Neural Networks -- 5.18.5 A Comparison of Time-Series, GMDH, and ANN Modeling of a Second-Order Dynamic System -- 5.18.6 Time-Series Modeling of a Position Control System -- 5.18.7 Time-Series Modeling for Fault Diagnosis -- 5.18.8 Time-Series Modeling of a Proportional PRV -- 5.18.9 GMDH Modeling of a Nitrogen-Filled Accumulator.…”
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Towards human-UAV physical interaction and fully actuated aerial vehicles by Rajappa, Sujit

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PID and predictive control of electrical drives and power supplies using MATLAB/Simulink by Wang, Liuping

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Credit securitizations and derivatives : challenges for the global markets

Table of Contents: “…Market Credit Risk Pricing -- Regulation -- Developments in Structured Finance Markets -- Impairments of Asset-Backed Securities and Outstanding Ratings -- Issuance of Asset-backed Securities and Outstanding Volume -- Global CDO Issuance and Outstanding Volume -- PART II CREDIT PORTFOLIO RISK MEASUREMENT -- Mortgage Credit Risk -- Five C's of Credit and Mortgage Credit Risk -- Determinants of Mortgage Default, Loss Given Default and Exposure at Default -- Determinants of Mortgage Default -- Determinants of Mortgage LGD -- Determinants of Mortgage EAD -- Modeling Methods for Default, LGD and EAD -- Model Risk Management -- Credit Portfolio Correlations and Uncertainty -- Introduction -- Gaussian and Semi-Gaussian Single Risk Factor Model -- Individual and Simultaneous Confidence Bounds and Intervals -- Confidence Intervals for Asset Correlations -- Confidence Intervals for Default and Survival Time Correlations -- Confidence Intervals for Default Correlations -- Confidence Intervals for Survival Time Correlations -- Credit Portfolio Correlations with Dynamic Leverage Ratios -- The Hui et al. (2007) Model -- The Method of Images for Constant Coefficients -- The Method of Images for Time-Varying Coefficients -- Modelling Default Correlations in a Two-Firm Model -- Default Correlations -- A Two-Firm Model with Dynamic Leverage Ratios -- Method of Images for Constant Coefficients -- Method of Images for Time-Varying Coefficients -- Alternative Methodologies for General Values -- Numerical Results -- Accuracy -- The Impact of Correlation between Two Firms -- The Impact of Different Credit Quality Paired Firms -- The Impact of Volatilities -- The Impact of Drift Levels -- The Impact of Initial Value of Leverage Ratio Levels -- Impact of Correlation between Firms and Interest Rates -- The Price of Credit-Linked Notes -- A Hierarchical Model of Tail-Dependent Asset Returns -- The Variance Compound Gamma Model -- Multivariate Process for Logarithmic Asset Returns -- Dependence Structure -- Sampling -- Copula Properties -- An Application Example -- Portfolio Setup -- Test Portfolios -- Parameter Setup -- Simulation Results -- Importance Sampling Algorithm -- Conclusions -- Appendix A: The VCG Probability Distribution Function Appendix B: HAC Representation for the VCG Framework -- Monte Carlo Methods for Portfolio Credit Risk -- Modeling Credit Portfolio Losses -- Risk Measures -- Modeling Dependency -- Estimating Risk Measures via Monte Carlo -- Crude Monte Carlo Estimators -- Importance Sampling -- Specific Models -- The Bernoulli Mixture Model -- Factor Models -- Copula Models -- Intensity Models -- An Example Point Process Model -- Appendix A: A Primer on Rare-event Simulation -- Efficiency -- Importance Sampling -- The Choice of g -- Adaptive Importance Sampling -- Importance Sampling for Stochastic Processes -- Credit Portfolio Risk and Diversification -- Introduction -- Model Setup -- Independent Asset Values -- Correlated Asset Values -- Large Portfolio Limit -- Correlated Diffusion -- Correlated GARCH Process -- Applications of the Structural Recovery Rate -- Conclusions -- PART III CREDIT PORTFOLIO RISK SECURITIZATION AND TRANCHING -- Differences in Tranching Methods: Some Results and Implications -- Defining a Tranche -- The Mathematics of Tranching -- PD-based Tranching -- EL-based Tranching -- The EL of a Tranche Necessarily Increases When Either the Attachment Point or the Detachment Point is Decreased -- Upper Bound on Tranche Expected LGD (LGDt) Assumption Given EL-based Tranches -- Skipping of Some Tranches in the EL-based Approach -- Global Structured Finance Rating -- Asset-Backed Securities -- The ABS Structure for the Experiment -- Cash Flow Modeling -- Modeling and Simulating Defaults -- Expected Loss Rating -- Global Sensitivity Analysis -- Elementary Effects -- Variance-based Method -- Global Sensitivity Analysis Results -- Uncertainty Analysis -- Sensitivity Analysis -- Global Rating -- PART IV CREDIT DERIVATIVES -- Analytic Dynamic Factor Copula Model -- Pricing Equations -- One-factor Copula Model -- Multi-period Factor Copula Models -- Calibration -- Dynamic Modeling of Credit Derivatives -- General Model Choice -- Modeling Option Prices -- Modeling Credit Risk -- Portfolio Credit Derivatives -- Modeling Asset Dynamics -- The Market Model -- The Asset-value Model -- Empirical Analysis -- Elementary Data -- Implied Dividends -- Market Dynamics -- Asset Value Model -- Tranche Pricing -- Out-of-time Application -- Pricing and Calibration in Market Models -- Basic notions -- The model -- Modeling Assumptions -- Absence of Arbitrage -- An affine specification -- Pricing -- Calibration -- Calibration Procedure -- Calibration Results -- Appendix A: Computations -- Counterparty Credit Risk and Clearing of Derivatives -- From the Perspective of an Industrial Corporate with a Focus on Commodity Markets -- Credit exposures in commodity business -- Settlement Exposure -- Performance Exposure -- Example of Fixed Price Deal with Performance Exposure -- Example of a Floating Price Deal with Performance Exposure -- General Remarks on Credit Exposure Concepts -- Ex Ante exposure-reducing techniques -- Payment Terms -- Material Adverse Change Clauses -- Master Agreements -- Netting -- Margining -- Close Out Exposure and Threshold -- Ex Ante risk-reducing techniques -- Credit Enhancements in General -- Parent Company Guarantees -- Letters of Credit -- Credit Insurance -- Clearing via a Central Counterparty -- Ex Post risk-reducing techniques -- Factoring -- Novation -- Risk-reducing Trades -- Hedging with CDS -- Hedging with Contingent-CDS -- Hedging with Puts on Equity -- Ex Post work out considerations -- Practical credit risk management and pricing Peculiarities of commodity markets -- Peculiarities of commodity related credit portfolios -- Credit Risk Capital for a commodity related portfolio measured with an extension of CreditMetrics -- CreditRisk+ study: applied to a commodity related credit portfolio -- CDS Industrial Sector Indices, Credit and Liquidity Risk -- The Data -- Methodology and Results -- Preliminary Analysis -- Common Factor Analysis -- Stability of Relations -- Risk Transfer and Pricing of Illiquid Assets with Loan CDS -- Shipping Market -- Loan Credit Default Swaps -- LCDS Pricing -- Modeling LCDS Under the Intensity-based Model -- Valuation Framework for LCDS -- The Structural Approach -- Credit Risk in Shipping Loans -- Valuation of LCDS on Shipping Loans -- Simulation Model -- Numerical Results -- Appendix A: Monte Carlo Parameterization PART V REGULATION -- Regulatory Capital Requirements for Securitizations -- Regulatory Approaches for Securitizations -- Ratings Based Approach (RBA) -- Supervisory Formula Approach (SFA) -- Standardized Approach (SA) -- Post-crisis Revisions to the Basel Framework -- Regulating OTC Derivatives -- The Wall Street Transparency and Accountability Part of the Dodd-Frank Act of 2010 -- Which Derivatives Will Be Affected? …”
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Big data : concepts, technology and architecture by Balusamy, Balamurugan, R, Nandhini Abirami, Kadry, Seifedine, 1977-, Gandomi, Amir Hossein

Table of Contents: “…<i>3</i>Big Data Analytics. 31</p> <p>1.7.4 Visualizing Big Data. 32</p> <p>1.8 Big Data Technology. 32</p> <p>1.8.1 Challenges faced by Big Data technology. 34</p> <p>1.8.1 Heterogeneity and incompleteness. 34</p> <p>1.8.2 Volume and velocity of the Data. 35</p> <p>1.8.3 Data Storage. 35</p> <p>1.8.4 Data Privacy. 36</p> <p>1.9 Big Data Applications. 36</p> <p>1.10 Big Data Use Cases. 37</p> <p>1.9. 1 Healthcare. 37</p> <p>1.9.2 Telecom.. 38</p> <p>1.9.3 Financial Services. 39</p> <p>Chapter 1 refresher: 40</p> <p>Conceptual short Questions with answers. 43</p> <p>Frequently asked Interview questions. 45</p> <p>Chapter Objective. 46</p> <p>Big Data Storage Concepts. 46</p> <p>2.1 Cluster computing. 47</p> <p>2.1.1 Types of cluster. 49</p> <p>2.1.1.1 High availability cluster. 50</p> <p>2.1.1.2 Load balancing cluster. 50</p> <p>2.1.2 Cluster structure. 51</p> <p>2.3 Distribution Models. 53</p> <p>2.3.1 Sharding. 54</p> <p>2.3.2 Data Replication. 56</p> <p>2.3.2.1 Master-Slave model 57</p> <p>2.3.2.2 Peer-to-Peer model 58</p> <p>2.3.3 Sharding and Replication. 59</p> <p>2.4 Distributed file system.. 60</p> <p>2.5 Relational and Non Relational Databases. 61</p> <p>CoursesOffered. 62</p> <p>Figure 2.12 Data divided across multiple related tables. 62</p> <p>2.4.2 RDBMS Databases. 63</p> <p>2.4.3 NoSQL Databases. 63</p> <p>2.4.4 NewSQL Databases. 64</p> <p>2.5 Scaling Up and Scaling Out Storage. 65</p> <p>Chapter 2 refresher. 67</p> <p>Conceptual short questions with answers. 69</p> <p>Chapter Objective. 72</p> <p>3.1 Introduction to NoSQL. 72</p> <p>3.2 Why NoSQL. 72</p> <p>3.3 CAP theorem.. 73</p> <p>3.4 ACID.. 75</p> <p>3.5 BASE. 76</p> <p>3.6 Schemaless Database. 77</p> <p>3.7 NoSQL (Not Only SQL) 77</p> <p>3.7.1 NoSQL Vs RDBMS. 78</p> <p>3.7.2Features of NoSQL database. 79</p> <p>3.7.3Types of NoSQL Technologies. 80</p> <p>3.7.3.1 Key-Value store database. 81</p> <p>3.7.3.2 Column-store database. 82</p> <p>3.7.3.3 Document Oriented Database. 84</p> <p>3.7.3.4 Graph-oriented Database. 86</p> <p>3.7.4 NoSQL Operations. 93</p> <p>3.9 Migrating from RDBMS to NoSQL. 98</p> <p>Chapter 3 refresher. 99</p> <p>Conceptual short questions with answers. 102</p> <p>Chapter Objective. 104</p> <p>4.1 Data Processing. 104</p> <p>4.2 Shared Everything Architecture. 106</p> <p>4.2.1 Symmetric multiprocessing architecture. 107</p> <p>4.2.2 Distributed Shared memory. 108</p> <p>4.3 Shared nothing architecture. 109</p> <p>4.4 Batch Processing. 110</p> <p>4.5 Real-Time Data Processing. 111</p> <p>4.6 Parallel Computing. 112</p> <p>4.7 Distributed Computing. 113</p> <p>4.8 Big Data Virtualization. 113</p> <p>4.8.1 Attributes of Virtualization. 114</p> <p>4.8.1.1 Encapsulation. 115</p> <p>4.8.1.2 Partitioning. 115</p> <p>4.8.1.3 Isolation. 115</p> <p>4.8.2Big Data Server Virtualization. 116</p> <p>4.9 Introduction. 116</p> <p>4.10 Cloud computing types. 118</p> <p>4.11Cloud Services. 120</p> <p>4.12 Cloud Storage. 121</p> <p>4.12.1 Architecture of GFS. 121</p> <p>4.12.1.1 Master. 123</p> <p>4.12.1.2 Client. 123</p> <p>4.13 Cloud Architecture. 127</p> <p>Cloud Challenges. 129</p> <p>Chapter 4 Refresher. 130</p> <p>Conceptual short questions with answers. 133</p> <p>Chapter Objective. 139</p> <p>5.1 Apache Hadoop. 139</p> <p>5.1.1 Architecture of Apache Hadoop. 140</p> <p>5.1.2Hadoop Ecosystem Components Overview.. 140</p> <p>5.2 Hadoop Storage. 142</p> <p>5.2.1HDFS (Hadoop Distributed File System). 142</p> <p>5.2.2Why HDFS?. …”
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Statistics for business by Mariappan, Perumal

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Disobedient Aesthetics : Surveillance, Bodies, Control by Stagliano, Anthony, 1973-

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Sigma-Delta Converters. by De la Rosa, José M.

Table of Contents: “…6.3.1 Hardware Emulation of CT-Ms on an FPGA 257 -- 6.3.2 GPU-accelerated Computing of CT-Ms 258 -- 6.4 Using Multi-objective Evolutionary Algorithms to Optimize Ms 259 -- 6.4.1 Combining MOEA with SIMSIDES 261 -- 6.4.2 Applying MOEA and SIMSIDES to the Synthesis of CT-Ms 262 -- 6.5 Summary 269 -- References 269 -- 7 Electrical Design of ??…”
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Qualitative computing : a computational journey into nonlinearity by Chaitin-Chatelin, Françoise

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Visual Inspection Technology in the Hard Disc Drive Industry. by Muneesawang, Paisarn

Table of Contents: “…Introduction / Suchart Yammen / Paisarn Muneesawang -- 1.2. Algorithm for corrosion detection / Suchart Yammen / Paisarn Muneesawang -- 1.2.1. …”
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Formal languages, automata and numeration systems. 1, Introduction to combinatorics on words by Rigo, Michel

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