Understanding and Reducing Landslide Disaster Risk Volume 2 From Mapping to Hazard and Risk Zonation

Table of Contents: “…-- Semi-automatic Landslide Inventory Mapping with Multiresolution Segmentation Process: A Case Study from Ulus District (Bartin, NW Turkey) -- Landslide mapping based on UAV and SfM – Case study of the 2018 Prnjavor Čuntićki landslide, Croatia -- Developing recognition and simple mapping by UAV/SfM for local resident in mountainous area in Vietnam – A case study in Po Xi Ngai Community, Laocai province -- Landslide activity classification based on Sentinel-1 satellite radar interferometry data -- Updating Landslide Activity State and Intensity by Means of Persistent Scatterer Interferometry -- Damming predisposition of river networks: a mapping methodology -- Landslides along Halong-Vandon Expressway in Quang Ninh province, Vietnam -- Landslide hazard assessment and zonation – susceptibility modelling: New data on geological conditions of landslide activity on Vorobyovy Gory (Moscow, Russia) -- Impact of agricultural management in vineyards to landslides susceptibility in Italian Apennines -- Landslide susceptibility in two secondary rivers of La Ciénega watershed, Nevado de Toluca volcano, Mexico -- An Ordinal Scale Weighting Approach for Susceptibility Mapping Around Tehri Dam, Uttarakhand, India -- Potential Analysis of Deep-seated Landslides Caused by Typhoon Morakot Using Slope Unit -- Landslide susceptibility assessment using binary logistic regression in northern Philippines Landslide Hazard Mapping of Penang Island Malaysia based on Multilayer Perceptron Approach -- Landslide Susceptibility Mapping Based on the Deep Belief Network: A Case Study in Sichuan Province, China -- A Comparative study of deep learning and conventional neural network forevaluating landslide susceptibility using landslide initiation zones -- Landslide susceptibility assessment by ensemble-based Machine Learning models -- Application of Machine Learning Algorithms and Their Ensemble for Landslide Susceptibility Mapping -- Overcoming data scarcity related issues for landslide susceptibility modeling with machine learning -- Practical accounting of uncertainties in data-driven landslide susceptibility models. …”
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The Semantic Web – ISWC 2012 11th International Semantic Web Conference, Boston, MA, USA, November 11-15, 2012, Proceedings, Part I by Cudré-Mauroux, Philippe

Table of Contents: “…Research Track -- MORe: Modular Combination of OWL Reasoners for Ontology Classification -- A Formal Semantics for Weighted Ontology -- Personalised Graph-Based Selection of Web APIs -- Instance-Based Matching of Large Ontologies Using Locality-Sensitive Hashing -- Automatic Typing of DBpedia Entities -- Performance Heterogeneity and Approximate Reasoning in Description Logic Ontologies -- Concept-Based Semantic Difference in Expressive Description Logics -- SPLODGE: Systematic Generation of SPARQL Benchmark Queries for Linked Open Data -- RDFS Reasoning on Massively Parallel Hardware -- An Efficient Bit Vector Approach to Semantics-Based Machine Perception in Resource-Constrained Devices -- Semantic Enrichment by Non-experts: Usability of Manual Annotation Tools -- Ontology-Based Access to Probabilistic Data with OWL QL -- Predicting Reasoning Performance Using Ontology Metrics -- Formal Verification of Data Provenance Records -- Cost Based Query Ordering over OWL Ontologies -- Robust Runtime Optimization and Skew-Resistant Execution of Analytical SPARQL Queries on Pig -- Large-Scale Learning of Relation-Extraction Rules with Distant Supervision from the Web -- The Not-So-Easy Task of Computing Class Subsumptions in OWL RL -- Strabon: A Semantic Geospatial DBMS -- DeFacto - Deep Fact Validation -- Feature LDA: A Supervised Topic Model for Automatic Detection of Web API Documentations from the Web -- Efficient Execution of Top-K SPARQL Queries -- Collaborative Filtering by Analyzing Dynamic User Interests Modeled by Taxonomy -- Link Discovery with Guaranteed Reduction Ratio in Affine Spaces with Minkowski Measures -- Hitting the Sweetspot: Economic Rewriting of Knowledge Bases -- Mining Semantic Relations between Research Areas -- Discovering Concept Coverings in Ontologies of Linked Data Sources -- Ontology Constraints in Incomplete and Complete Data -- A Machine Learning Approach for Instance Matching Based on Similarity Metrics -- Who Will Follow Whom? …”
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Natural Language Processing and Chinese Computing 10th CCF International Conference, NLPCC 2021, Qingdao, China, October 13–17, 2021, Proceedings, Part I

Table of Contents: “…-- Chinese Macro Discourse Parsing on Dependency Graph Convolutional Network -- Predicting Categorial Sememe for English-Chinese Word Pairs via Representations in Explainable Sememe Space -- Multi-Level Cohesion Information Modeling for Better Written and Dialogue Discourse Parsing -- ProPC: A Dataset for In-domain and Cross-Domain Proposition Classification Tasks -- CTRD: A Chinese Theme-Rheme Discourse Dataset -- Machine Translation and Multilinguality -- Learning to Select Relevant Knowledge for Neural Machine Translation -- Contrastive Learning for Machine Translation Quality Estimation -- Sentence-State LSTMs for Sequence-to-Sequence Learning -- Guwen-UNILM: Machine Translation Between Ancient and Modern Chinese Based on Pre-Trained Models -- Adaptive Transformer for Multilingual Neural Machine Translation -- Improving Non-Autoregressive Machine Translation with Soft-Masking -- Machine Learning for NLP -- AutoNLU: Architecture Search for Sentence and Cross-sentence Attention Modeling with Re-designed Search Space -- AutoTrans: Automating Transformer Design via Reinforced Architecture Search -- A Word-level Method for Generating Adversarial Examples Using Whole-sentence Information -- RAST: A Reward Augmented Model for Fine-Grained Sentiment Transfer -- Pre-trained Language models for Tagalog with Multi source data -- Accelerating Pretrained Language Model Inference Using Weighted Ensemble Self-Distillation -- Information Extraction and Knowledge Graph -- Employing Sentence Compression to improve Event Coreference Resolution -- BRCEA: Bootstrapping Relation-aware Cross-lingual Entity Alignment -- Employing Multi-granularity Features to Extract Entity Relation in Dialogue -- Attention Based Reinforcement Learning with Reward Shaping for Knowledge Graph Reasoning -- Entity-Aware Relation Representation Learning for Open Relation Extraction -- ReMERT: Relational Memory-based Extraction for Relational Triples -- Recognition of Nested Entity with Dependency Information -- HAIN: Hierarchical Aggregation and Inference Network for Document-Level Relation Extraction -- Incorporate Lexicon into Self-training: A Distantly Supervised Chinese Medical NER -- Summarization and Generation -- Diversified Paraphrase Generation with Commonsense Knowledge Graph -- Explore Coarse-grained Structures for Syntactically Controllable Paraphrase Generation -- Chinese Poetry Generation with Metrical Constraints -- CNewSum: A Large-scale Chinese News Summarization Dataset with Human-annotated Adequacy and Deducibility Level -- Question Generation from Code Snippets and Programming Error Messages -- Extractive Summarization of Chinese Judgment Documents via Sentence Embedding and Memory Network -- Question Answering -- ThinkTwice: A Two-Stage Method for Long-Text Machine Reading Comprehension -- EviDR: Evidence-Emphasized Discrete Reasoning for Reasoning Machine Reading Comprehension -- Dialogue Systems -- Knowledge-Grounded Dialogue with Reward-Driven Knowledge Selection -- Multi-Intent Attention and Top-k Network with Interactive Framework for Joint Multiple Intent Detection and Slot Filling -- Enhancing Long-Distance Dialogue History Modeling for Better Dialogue Ellipsis and Coreference Resolution -- Exploiting Explicit and Inferred Implicit Personas for Multi-turn Dialogue Generation -- Few-Shot NLU with Vector Projection Distance and Abstract Triangular CRF -- Cross-domain Slot Filling with Distinct Slot Entity and Type Prediction -- Social Media and Sentiment Analysis -- Semantic Enhanced Dual-channel Graph Communication Network for Aspect-based Sentiment Analysis -- Highway-Based Local Graph Convolution Network For Aspect Based Sentiment Analysis -- Dual Adversarial Network Based on BERT for Cross-domain Sentiment Classification -- Syntax and Sentiment Enhanced BERT for Earliest Rumor Detection -- Aspect-Sentiment-Multiple-Opinion Triplet Extraction -- Locate and Combine: A Two-Stage Framework for Aspect-Category Sentiment Analysis -- Emotion Classification with Explicit and Implicit Syntactic Information -- MUMOR:A Multimodal Dataset for Humor Detection in Conversations -- NLP Applications and Text Mining -- Advertisement Extraction from Content Marketing Articles via Segment-aware Sentence Classification -- Leveraging Lexical Common-Sense Knowledge for Boosting Bayesian Modeling -- Aggregating inter-viewpoint relationships of user's review for accurate recommendation -- A Residual Dynamic Graph Convolutional Network for Multi-label Text Classification -- Sentence Ordering by Context-enhanced Pairwise Comparison -- A Dual-Attention Neural Network for Pun Location and Using Pun-Gloss Pairs for Interpretation -- A Simple Baseline for Cross-domain Few-shot Text Classification -- Shared Component Cross Punctuation Clauses Recognition in Chinese -- BERT-KG:A Short Text Classification Model Based on Knowledge Graph and Deep Semantics -- Uncertainty-aware Self-paced Learning for Grammatical Error Correction -- Metaphor Recognition and Analysis via Data Augmentation -- Exploring Generalization Ability of Pretrained Language Models on Arithmetic and Logical Reasoning -- Multimodality and Explainability -- Skeleton-Based Sign Language Recognition with Attention-enhanced Graph Convolutional Networks -- XGPT: Cross-modal Generative Pre-Training for Image Captioning -- An Object-Extensible Training Framework for Image Captioning -- Relation-aware Multi-hop Reasoning for Visual Dialog -- Multi-Modal Sarcasm Detection Based on Contrastive Attention Mechanism.…”
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Advances in Acoustic Emission Technology Proceedings of the World Conference on Acoustic Emission–2013

Table of Contents: “…Part I: Instrumentation -- The Development of High Speed Wi-Fi Wireless Acoustic Emission System -- State of the Art Wireless Acoustic Emission System for Structure Health Monitoring -- Some Benefits of Storing AE Data in a Modern Data Base Format -- Calibration Principle for Acoustic Emission Sensor Sensitivity -- Development of a Pipeline Leakage Location Instrument Based on Acoustic Waves -- Part II: Signal Processing and Analysis -- On Assessing the Influence of Intermittent Acquisition and Moving Window on the Results of AE Measurements -- Robust Broadband Adaptive Beamforming Based on Probability Constraint -- Near-field Noise Sources Localization in Presence of Interference -- Noise Diagnostics at AE Monitoring of Hazardous Industrial Assets -- AE Sources Location on Irregular-Shaped Objects Using 3D-Grid Method -- Numerical Simulation of Wave-guiding Properties and Optimization Design for Wave-guiding Rod -- Near-field Beamforming Performance Analysis for Acoustic Emission Source Localization Based on Finite Element Simulation -- Intelligent Evaluation Method of Tank Bottom Corrosion Status Based on Improved BP Artificial Neural Network -- The Research of Backward Deducing the Peak Frequency of Acoustic Emission Signals in Different Array -- Analysis and Research of Acoustic Emission Signal of Rolling Element Bearing Fatigue -- Research on Compression Method of Acoustic Emission Signal Based on Wavelet Transform -- Feature Extraction of Corrosion Acoustic Emission Signals Based on Genetic-Matching Pursuit Algorithm -- Part III: Material Characteristics -- Damage Evaluation in Consideration of Distance Decay and Frequency Characteristics of Elastic Wave -- Characteristic Identification of Cracking Acoustic Emission Signals in Concrete Beam Based on Hilbert-Huang Transform -- Acoustic Emission from Elevator Wire Ropes during Tensile Testing -- Effect of Specimen Thickness on Fatigue Crack Propagation and Acoustic Emission Behaviors in Q345 Steel -- Acoustic Emission Behavior of Titanium during Tensile Deformation -- Study on Characteristics of Acoustic Emission and Position Entropy of Q345R in Tensile Loading at Room Temperature -- Acoustic Emission Behavior of 12MnNiVR under Stretching -- Statistical Analysis of Events of Random Damage in Assessing Fracture Process in Paper-sheets under Tensile Load -- The Use of Acoustic Emission for the Construction Generalized Fatigue Diagram of Metals and Alloys -- Deflection on Hit-Count Curves in Acoustic Emission could reflect the Damage Extent of C/C Composite Material Structure -- Acoustic Research on the Damage Mechanism of Carbon Fiber Composite Materials -- Damage and Toughening Analysis of Ceramics by AE Location Method -- Acoustic Emission Tomography to Improve Source Location in Concrete Material using SART -- Experimental Research on Tensile Process of Carbon Fiber Composite Materials Basing on Acoustic Emission -- Concrete Crack Damage Location Based on Piezoelectric Composite Acoustic Emission Sensor -- Part IV: Structure -- Visualization of Damage in RC Bridge Deck for Bullet Trains with AE Tomography -- Acoustic Emission for Structural Integrity Assessment of Wind Turbine Blades -- Analysis of Acoustic Emission Parameters from Corrosion of AST Bottom Plate in Field Testing -- Identification of Acoustic Emission Signal of Tank Bottom Corrosion Based on Weighted Fuzzy Clustering -- The Present Status of Using Natural Gas Cylinders and Acoustic Emission in Thailand -- Research on Acoustic Emission Attenuation Characteristics and Experiment of Composite Cylinder -- Research on the acoustic emission and metal magnetic memory characteristics of the crane box beam during the destructive testing -- The Research into the Possibilities for Monitoring Technical Conditions of Underground Pipelines Using the Acoustic Emission -- Underground Pipeline Leak Detection Using Acoustic Emission and Crest Factor Technique -- Comparison between Acoustic Emission In-service Inspection and Non-destructive Testing on Aboveground Storage Tank Floors -- Acoustic Emission Application for Unapproachable Pipeline Drain Point Leakage Detection -- Study of Pipeline Leak Detection and Location Method Based on Acoustic Emission -- Characterization of Acoustic Emission Parameters during Testing of Metal Liner Reinforced with Fully Resin Impregnated CNG Cylinder -- Applications of Acoustic Emission Testing in High Background Noise Environment -- Part V: Condition Monitoring and Diagnosis -- Acoustic Emission—an Indispensable Structural Health Monitoring Means for Aircraft -- Differentiating Signals from Different Sources of Acoustic Emission for Structural Health Monitoring Purposes -- Application of Acoustic Emission Technology for Rolling Bearing condition monitoring on Passenger Ropeway -- Wireless AE Event and Environmental Monitoring for Wind Turbine Blades at Low Sampling Rates -- Experimental Study on Acoustic Emission Detection for Low Speed Heavy Duty Crane Slewing Bearing -- Interlaminar Shear Properties and Acoustic Emission Monitoring of the Delaminated Composites for Wind Turbine Blades -- Condition Monitoring of Shaft Crack with Acoustic Emission -- Studies on Automobile Clutch Release Bearing Characteristics Parameter of Acoustic Emission -- Research Based on the Acoustic Emission of Wind Power Tower Drum Dynamic Monitoring Technology -- Part VI: Miscellaneous -- Acoustic Emission and Digital Image Correlation as Complementary Techniques for Laboratory and Field Research -- Integral Thickness Measuring -- Separation of the Elastic and Plastic Wave in Electromagnetically Induced Acoustic Emission Testing -- Correlation between Acoustic Emission and Induced Hydrogen of Shield Metal Arc Welding -- Numerical Simulation Study on Propagation Law of Acoustic Emission Signal of Slewing Ring -- Three-Dimensional Finite Element Simulation of Signal Detection Transducer for Electromagnetically Induced Acoustic Emission.…”
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The Pauli Exclusion Principle : Origin, Verifications, and Applications by Kaplan, Ilya G.

Table of Contents: “…. -- References -- Chapter 4 Classification of the Pauli-Allowed States in Atoms and Molecules -- 4.1 Electrons in a Central Field -- 4.1.1 Equivalent Electrons: L-S Coupling -- 4.1.2 Additional Quantum Numbers: The Seniority Number -- 4.1.3 Equivalent Electrons: j-j Coupling -- 4.2 The Connection between Molecular Terms and Nuclear Spin -- 4.2.1 Classification of Molecular Terms and the Total Nuclear Spin -- 4.2.2 The Determination of the Nuclear Statistical Weights of Spatial States -- 4.3 Determination of Electronic Molecular Multiplets -- 4.3.1 Valence Bond Method -- 4.3.2 Degenerate Orbitals and One Valence Electron on Each Atom -- 4.3.3 Several Electrons Specified on One of the Atoms -- 4.3.4 Diatomic Molecule with Identical Atoms -- 4.3.5 General Case I -- 4.3.6 General Case II -- References -- Chapter 5 Parastatistics, Fractional Statistics, and Statistics of Quasiparticles of Different Kind -- 5.1 Short Account of Parastatistics.…”
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Managing Complexity Challenges for Industrial Engineering and Operations Management by Hernández Iglesias, Cesáreo

Table of Contents: “…. -- Toward various exact modeling the job shop scheduling problem for minimizing total weighted tardiness, by Namakshenas M., Sahraeian R. -- Set-up continuity in tactical planning of semi-continuous industrial processes, by Pérez D., Alemany M.M.E., Lario F.C., Fuertes V.S. -- Estimating costs in the EOQ formula, by Vidal-Carreras P.I., Garcia-Sabater J.P., Valero-Herrero M., Santandreu-Mascarell C..- III. …”
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Computer Engineering and Networking Proceedings of the 2013 International Conference on Computer Engineering and Network (CENet2013) by Wong, W. Eric

Table of Contents: “…-An Algorithm for Bayesian Networks Structure Learning Based on Simulated Annealing with Adaptive Selection Operator -- Static Image Segmentation Using Polar Space Transformation Technique -- Image Restoration Via Non-local P-Laplace Regularization -- Analysis and Application of Computer Technology on Architectural Space Lighting Visual Design -- Improving Online Gesture Recognition with WarpingLCSS by Multi-Sensor Fusion -- The Lane Mark Identifying and Tracking in Intense Illumination -- Classification Modeling of Multi-featured Remote Sensing Images Based on Sparse Representation -- A Parallel and Convergent Support Vector Machine Based on MapReduce -- Vehicle Classification Based on Hierarchical Support Vector Machine -- Image Splicing Detection Based on Machine Learning Algorithm -- A Lane Detection Algorithm Based on Hyperbola Model -- Comparisons and Analyses of Image Softproofing under Different Profile Rendering Intents -- An Improved Dense Matching Algorithm for Face Based on Region Growing -- An Improved Feature Selection Method for Chinese Short Texts Clustering Based on HowNet -- Internet Worm Detection and Classification Based on Support Vector Machine -- Real-time Fall Detection Based on Global Orientation and Human Shape -- The Classification of Synthetic Aperture Radar Oil Spill Images Based on the Texture Features and Deep Belief Network -- The Ground Objects Identification for Digital Remote Sensing Image Based on the BP Neural Network -- Detection of Image Forgery Based on Improved PCA-SIFT -- A Thinning Model for Handwriting-like Image Skeleton -- Discrimination of the White Wine Based on Sparse Principal Component Analysis and Support Vector Machine -- Volume II -- Part IV: Cloud Computing -- Design of Mobile Electronic Payment System -- Power-saving Based Radio Resource Scheduling in Long Term Evolution Advanced Network -- Dispatching and Management Model Based on Safe Performance Interface for Improving Cloud Efficiency -- A Proposed Methodology for an E-health Monitoring System Based on a Fault-tolerant Smart Mobile -- Design And Application of Indoor Geographical Information System -- Constructing Cloud Computing Infrastructure Platform of the Digital Library Base on Virtualization Technology -- A New Single Sign-on Solution in Cloud -- A Collaborative Load Control Scheme for Hierachical Mobile IPv6 Network -- A High-efficient Selective Content Encryption Method Suitable for Satellite Communication System -- Network Design of a Low-power Parking Guidance System -- Strategy of Domain and Cross-domain Access Control Based on Trust in Cloud Computing Environment -- Detecting Unhealthy Cloud System Status -- Scoring System of Simulation Training Platform Based on Expert System -- Analysis of Distributed File Systems on Virtualized Cloud Computing Environment -- A Decision Support System with Dynamic Probability Adjustment for Fault Diagnosis in Critical Systems -- Design and Implementation of an SD Interface to Multiple-target Interface Bridge -- Cloud Storage Management Technology for Small File Based on Two-Dimensional Packing Algorithm -- Advertising Media Selection and Delivery Decision-making Using Influence Diagram -- The Application of Trusted Computing Technology in the Cloud Security -- The Application Level of E-commerce in Enterprises in China -- Toward a Trinity Model of Digital Education Resources Construction and Management -- Geographic Information System in the Cloud Computing Environment -- Part V: Embedded Systems -- Memory Controller Design Based on Quadruple Modular Redundant Architecture -- Computer Power Management System Based On the Face Detection -- Twist Rotation Deformation of Titanium Sheet Metal in Laser Curve Bending Based on Finite Element Analysis -- Voltage Transient Stability Analysis by Changing the Control Modes of the Wind Generator -- The Generator Stator Fault Analysis Based on the Multi-loop Theory -- An Improved Edge Flag Algorithm Suitable for Hardware Implementation -- A Handheld Controller with Embedded Real-time Video Transmission Based on TCP/IP Protocol.…”
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The little SAS book : a primer : a programming approach by Delwiche, Lora D.

Table of Contents: “…Machine generated contents note: ch. 1 Getting Started Using SAS Software -- 1.1.The SAS Language -- 1.2.SAS Data Sets -- 1.3.DATA and PROC Steps -- 1.4.The DATA Step's Built-in Loop -- 1.5.Choosing a Mode for Submitting SAS Programs -- 1.6.Windows and Commands in the SAS Windowing Environment -- 1.7.Submitting a Program in the SAS Windowing Environment -- 1.8.Reading the SAS Log -- 1.9.Viewing Your Results -- 1.10.SAS Data Libraries -- 1.11.Viewing Data Sets in the Viewtable Window -- 1.12.Viewing the Properties of Data Sets with SAS Explorer -- 1.13.Using SAS System Options -- ch. 2 Getting Your Data into SAS -- 2.1.Methods for Getting Your Data into SAS -- 2.2.Entering Data with the Viewtable Window -- 2.3.Reading Files with the Import Wizard -- 2.4.Telling SAS Where to Find Your Raw Data -- 2.5.Reading Raw Data Separated by Spaces -- 2.6.Reading Raw Data Arranged in Columns -- 2.7.Reading Raw Data Not in Standard Format -- 2.8.Selected Informats -- 2.9.Mixing Input Styles -- 2.10.Reading Messy Raw Data -- 2.11.Reading Multiple Lines of Raw Data per Observation -- 2.12.Reading Multiple Observations per Line of Raw Data -- 2.13.Reading Part of a Raw Data File -- 2.14.Controlling Input with Options in the INFILE Statement -- 2.15.Reading Delimited Files with the DATA Step -- 2.16.Reading Delimited Files with the IMPORT Procedure -- 2.17.Reading Excel Files with the IMPORT Procedure -- 2.18.Temporary versus Permanent SAS Data Sets -- 2.19.Using Permanent SAS Data Sets with LIBNAME Statements -- 2.20.Using Permanent SAS Data Sets by Direct Referencing -- 2.21.Listing the Contents of a SAS Data Set -- ch. 3 Working with Your Data -- 3.1.Creating and Redefining Variables -- 3.2.Using SAS Functions -- 3.3.Selected SAS Character Functions -- 3.4.Selected SAS Numeric Functions -- 3.5.Using IF-THEN Statements -- 3.6.Grouping Observations with IF-THEN/ELSE Statements -- 3.7.Subsetting Your Data -- 3.8.Working with SAS Dates -- 3.9.Selected Date Informats, Functions, and Formats -- 3.10.Using the RETAIN and Sum Statements -- 3.11.Simplifying Programs with Arrays -- 3.12.Using Shortcuts for Lists of Variable Names -- ch. 4 Sorting, Printing, and Summarizing Your Data -- 4.1.Using SAS Procedures -- 4.2.Subsetting in Procedures with the WHERE Statement -- 4.3.Sorting Your Data with PROC SORT -- 4.4.Changing the Sort Order for Character Data -- 4.5.Printing Your Data with PROC PRINT -- 4.6.Changing the Appearance of Printed Values with Formats -- 4.7.Selected Standard Formats -- 4.8.Creating Your Own Formats Using PROC FORMAT -- 4.9.Writing Simple Custom Reports -- 4.10.Summarizing Your Data Using PROC MEANS -- 4.11.Writing Summary Statistics to a SAS Data Set -- 4.12.Counting Your Data with PROC FREQ -- 4.13.Producing Tabular Reports with PROC TABULATE -- 4.14.Adding Statistics to PROC TABULATE Output -- 4.15.Enhancing the Appearance of PROC TABULATE Output -- 4.16.Changing Headers in PROC TABULATE Output -- 4.17.Specifying Multiple Formats for Data Cells in PROC TABULATE Output -- 4.18.Producing Simple Output with PROC REPORT -- 4.19.Using DEFINE Statements in PROC REPORT -- 4.20.Creating Summary Reports with PROC REPORT -- 4.21.Adding Summary Breaks to PROC REPORT Output -- 4.22.Adding Statistics to PROC REPORT Output -- 4.23.Adding Computed Variables to PROC REPORT Output -- 4.24.Grouping Data in Procedures with User-Defined Formats -- ch. 5 Enhancing Your Output with ODS -- 5.1.Concepts of the Output Delivery System -- 5.2.Tracing and Selecting Procedure Output -- 5.3.Creating SAS Data Sets from Procedure Output -- 5.4.Creating Text Output -- 5.5.Creating HTML Output -- 5.6.Creating RTF Output -- 5.7.Creating PDF Output -- 5.8.Customizing Titles and Footnotes -- 5.9.Customizing PROC PRINT with the STYLE= Option -- 5.10.Customizing PROC REPORT with the STYLE= Option -- 5.11.Customizing PROC TABULATE with the STYLE= Option -- 5.12.Adding Traffic-Lighting to Your Output -- 5.13.Selected Style Attributes -- ch. 6 Modifying and Combining SAS Data Sets -- 6.1.Modifying a Data Set Using the SET Statement -- 6.2.Stacking Data Sets Using the SET Statement -- 6.3.Interleaving Data Sets Using the SET Statement -- 6.4.Combining Data Sets Using a One-to-One Match Merge -- 6.5.Combining Data Sets Using a One-to-Many Match Merge -- 6.6.Merging Summary Statistics with the Original Data -- 6.7.Combining a Grand Total with the Original Data -- 6.8.Updating a Master Data Set with Transactions -- 6.9.Writing Multiple Data Sets Using the OUTPUT Statement -- 6.10.Making Several Observations from One Using the OUTPUT Statement -- 6.11.Using SAS Data Set Options -- 6.12.Tracking and Selecting Observations with the IN= Option -- 6.13.Selecting Observations with the WHERE= Option -- 6.14.Changing Observations to Variables Using PROC TRANSPOSE -- 6.15.Using SAS Automatic Variables -- ch. 7 Writing Flexible Code with the SAS Macro Facility -- 7.1.Macro Concepts -- 7.2.Substituting Text with Macro Variables -- 7.3.Concatenating Macro Variables with Other Text -- 7.4.Creating Modular Code with Macros -- 7.5.Adding Parameters to Macros -- 7.6.Writing Macros with Conditional Logic -- 7.7.Writing Data-Driven Programs with CALL SYMPUT -- 7.8.Debugging Macro Errors -- ch. 8 Visualizing Your Data -- 8.1.Concepts of ODS Graphics -- 8.2.Creating Bar Charts -- 8.3.Creating Histograms and Density Curves -- 8.4.Creating Box Plots -- 8.5.Creating Scatter Plots -- 8.6.Creating Series Plots -- 8.7.Creating Fitted Curves -- 8.8.Controlling Axes and Reference Lines -- 8.9.Controlling Legends and Insets -- 8.10.Customizing Graph Attributes -- 8.11.Creating Paneled Graphs -- 8.12.Specifying Image Properties and Saving Graphics Output -- ch. 9 Using Basic Statistical Procedures -- 9.1.Examining the Distribution of Data with PROC UNIVARIATE -- 9.2.Creating Statistical Graphics with PROC UNIVARIATE -- 9.3.Producing Statistics with PROC MEANS -- 9.4.Testing Means with PROC TTEST -- 9.5.Creating Statistical Graphics with PROC TTEST -- 9.6.Testing Categorical Data with PROC FREQ -- 9.7.Creating Statistical Graphics with PROC FREQ -- 9.8.Examining Correlations with PROC CORR -- 9.9.Creating Statistical Graphics with PROC CORR -- 9.10.Using PROC REG for Simple Regression Analysis -- 9.11.Creating Statistical Graphics with PROC REG -- 9.12.Using PROC ANOVA for One-Way Analysis of Variance -- 9.13.Reading the Output of PROC ANOVA -- ch. 10 Exporting Your Data -- 10.1.Methods for Exporting Your Data -- 10.2.Writing Files Using the Export Wizard -- 10.3.Writing Delimited Files with the EXPORT Procedure -- 10.4.Writing Microsoft Excel Files with the EXPORT Procedure -- 10.5.Writing Raw Data Files with the DATA Step -- 10.6.Writing Delimited and HTML Files Using ODS -- ch. 11 Debugging Your SAS Programs -- 11.1.Writing SAS Programs That Work -- 11.2.Fixing Programs That Don't Work -- 11.3.Searching for the Missing Semicolon -- 11.4.Note: INPUT Statement Reached Past the End of a Line -- 11.5.Note: Lost Card -- 11.6.Note: Invalid Data -- 11.7.Note: Missing Values Were Generated -- 11.8.Note: Numeric Values Have Been Converted to Character (or Vice Versa) -- 11.9.DATA Step Produces Wrong Results but No Error Message -- 11.10.Error: Invalid Option, Error: The Option Is Not Recognized, or Error: Statement Is Not Valid -- 11.11.Note: Variable Is Uninitialized or Error: Variable Not Found -- 11.12.SAS Truncates a Character Variable -- 11.13.SAS Stops in the Middle of a Program -- 11.14.SAS Runs Out of Memory or Disk Space.…”


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Enterprise Interoperability : Smart Services and Business Impact of Enterprise Interoperability by Zelm, Martin, Jaekel, Frank-Walter, Doumeingts, Guy, Wollschlaeger, Martin

Table of Contents: “…The ProaSense Platform for Predictive Maintenance in the Automotive Lighting Equipment Industry, Alexandros Bousdekis, Babis Magoutas, Dimitris Apostolou, Gregoris Mentzas and Primoz Puhar; 31. …”
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5G technology : 3GPP evolution to 5G-advanced

Table of Contents: “…ebire, Weide Wu, and Weidong Yang -- 13.1 Introduction 399 -- 13.2 High Data Rate, System Flexibility, and Computational Complexity 401 -- 13.3 Low Latency, Flexible Timing, and Modem Control Flow Complexity 406 -- 13.4 Multi-RAT Coexistence and Modem Architecture 413 -- 13.5 Wider Bandwidth Operation and Modem Power Consumption 419 -- 13.6 Summary 428 -- 14 Internet of Things Optimization 431 Harri Holma, Rapeepat Ratasuk, and Mads Lauridsen -- 14.1 Introduction 431 -- 14.2 IoT Optimization in LTE Radio 433 -- 14.3 Lte-m 436 -- 14.4 Narrowband-IoT 439 -- 14.5 IoT Optimization in LTE Core Network 442 -- 14.6 Coverage 443 -- 14.7 Delay and Capacity 444 -- 14.8 Power Saving Features 446 -- 14.9 NB-IoT Power Consumption Measurements 448 -- 14.10 IoT Solution Benchmarking 449 -- 14.11 IoT Optimizations in 5G 451 -- 14.12 Summary 458 -- 15 LTE-Advanced Evolution 461 Harri Holma and Timo Lunttila -- 15.1 Introduction 461 -- 15.2 Overview of LTE Evolution 462 -- 15.3 LTE-Advanced Pro Technologies 465 -- 15.4 5G and LTE Benchmarking 478 -- 15.5 Summary 482 -- 16 5G-Advanced Overview 485 Antti Toskala and Harri Holma -- 16.1 Introduction 485 -- 16.2 3GPP Schedule 486 -- 16.3 5G-Advanced Key Areas 486 -- 16.4 Extended and Augmented Reality 488 -- 16.5 Superaccurate Positioning 490 -- 16.6 Radio Performance Boosters 491 -- 16.7 New Vertical Use Cases 493 -- 16.8 Resilient Timing 494 -- 16.9 Network Automation and Energy Efficiency 495 -- 16.10 RedCap/NR-Light for IoT 495 -- 16.11 Outlook For 5G Release 19 496 -- 16.12 Outlook For 6G 497 -- 16.13 Summary 502 -- 17 Radio Enhancements in Release 16-18 505 Harri Holma and Antti Toskala -- 17.1 Introduction 505 -- 17.2 Coverage Enhancements 505 -- 17.3 MIMO Enhancements 508 -- 17.4 Mobility 510 -- 17.5 UE Power Saving 511 -- 17.6 AI/ML for Air Interface and NG-RAN 513 -- 17.7 Integrated Access and Backhaul 515 -- 17.8 Dual Connectivity and Carrier Aggregation Enhancements 517 -- 17.9 Small Data Transmission 518 -- 17.10 Conclusion 519 -- 18 Industrial Internet of Things 521 Harri Holma and Antti Toskala -- 18.1 Introduction 521 -- 18.2 Reduced Capability (RedCap) Devices 522 -- 18.3 RedCap Device Complexity 523 -- 18.4 RedCap Device Power Consumption 525 -- 18.5 RedCap Benchmarking with LTE-Based IoT 526 -- 18.6 New Spectrum Options 527 -- 18.7 Ultra-reliable Low Latency Communication 528 -- 18.8 Low Latency Communication 530 -- 18.9 Ultra-Reliable Communication 537 -- 18.10 Time Sensitive Network 540 -- 18.11 LAN Service 541 -- 18.12 Positioning Solutions 542 -- 18.13 Non-Public Networks 543 -- 18.14 Summary 544 -- 19 Verticals 547 Antti Toskala and Harri Holma -- 19.1 Introduction 547 -- 19.2 Non-Terrestrial Networks (NTN) 547 -- 19.3 High Altitude Platform Stations (HAPS) 550 -- 19.4 Drones 551 -- 19.5 Vehicle Connectivity 552 -- 19.6 Public Safety 553 -- 19.7 Dedicated Networks with less than 5 MHz of Spectrum 554 -- 19.8 Unlicensed 555 -- 19.9 Summary 556 -- 20 Open RAN and Virtualized RAN 559 Harri Holma and Antti Toskala -- 20.1 Introduction 559 -- 20.2 Radio Network Architecture Trends 560 -- 20.3 Open RAN Fronthaul 561 -- 20.4 Uplink Capacity Optimization 565 -- 20.5 O-RAN Alliance 566 -- 20.6 O-RAN Fronthaul 566 -- 20.7 Open Test and Integration Center and PlugFests 568 -- 20.8 O-RAN Security and Orchestration 569 -- 20.9 Baseband Virtualization and Cloud Ran 569 -- 20.10 Baseband Virtualization and Centralization 570 -- 20.11 Far Edge Availability and Network Topology 571 -- 20.12 Fiber and Optics Availability 573 -- 20.13 Baseband Hardware Efficiency 574 -- 20.14 Virtual RAN Evolution 575 -- 20.15 RAN Intelligent Controller 575 -- 20.16 Summary 577 -- 21 Machine Learning for 5G System Optimization 579 Riku Luostari, Petteri Kela, Mikko Honkala, Dani Korpi, Janne Huttunen, and Harri Holma -- 21.1 Introduction 580 -- 21.2 Motivation 580 -- 21.3 Model Training and Inference in Wireless Systems 581 -- 21.4 Machine Learning Categories 582 -- 21.5 Key Algorithm Techniques 583 -- 21.6 Machine Learning for 5G Wireless Systems 584 -- 21.7 Channel State Information (CSI) Improvement and Channel Prediction 586 -- 21.8 Deep Neural Network-Based Receivers and DeepRx 587 -- 21.9 Pilotless OFDM 590 -- 21.10 Massive MIMO, Beamforming, and DeepTx 591 -- 21.11 Beam Tracking for mmWaves 593 -- 21.12 Channel Coding 593 -- 21.13 MAC Scheduler and Radio Resource Management 594 -- 21.14 Learned Communication Protocols 601 -- 21.15 Network Planning and Optimization 602 -- 21.16 Network Operations 604 -- 21.17 Network Security 604 -- 21.18 Positioning 605 -- 21.19 Challenges 606 -- 21.20 Scalability 606 -- 21.21 Uncertainty 606 -- 21.22 Time Criticality and Computational Requirements 606 -- 21.23 Standardization and Specifications Impact 607 -- 21.24 Summary 608 -- References 609 -- Index 613.…”
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Applied Impact Mechanics. by Lakshmana Rao, C.

Table of Contents: “…Preface v <p>List of Figures xv</p> <p>List of Tables xix</p> <p>List of Symbols xxi</p> <p><b>Chapter 1: Introduction 1-18</b></p> <p>1.1 General Introduction to Engineering Mechanics 2</p> <p>1.2 General Introduction to Fracture Mechanics 3</p> <p>1.3 Impact Mechanics -- Appreciating Impact Problems in Engineering 5</p> <p>1.4 Historical Background 8</p> <p>1.5 Percussion, Concussion, Collision and Explosion 10</p> <p>1.6 Summary 11</p> <p>Bibliography 12</p> <p><b>Chapter 2: Rigid Body Impact Mechanics 19-34</b></p> <p>2.1 Introduction 19</p> <p>2.2 Impulse -- Momentum Equations 21</p> <p>2.3 Coefficient of Restitution -- Classical Definitions 21</p> <p>2.3.1 Kinematic Coefficient of Restitution 22</p> <p>2.3.2 Measurement of Coefficient of Restitution 22</p> <p>2.3.3 Relative Assessment of Various Impacts in Sports 23</p> <p>2.4 Coefficient of Restitution -- Alternate Definition 24</p> <p>2.4.1 Kinetic Coefficient of Restitution 24</p> <p>2.4.1.1 Case Study: Rebound of Colliding Vehicles 25</p> <p>2.4.2 Energy Coefficient of Restitution 27</p> <p>2.4.2.1 Application in Vehicle Collisions 28</p> <p>2.5 Oblique Impact -- Role of Friction 29</p> <p>2.6 Limitations of Rigid Body Impact Mechanics 31</p> <p>2.7 Summary 31</p> <p>Exercise Problems 32</p> <p>Bibliography 34</p> <p><b>Chapter 3: One-Dimensional Impact Mechanics of Deformable Bodies 35-54</b></p> <p>3.1 Introduction 35</p> <p>3.2 Single Degree of Freedom Idealization of Impact Process 36</p> <p>3.2.1 Governing Equations of Single Degree of Freedom (SDOF) System 37</p> <p>3.2.2 Forced Vibrations due to Exponentially Decaying Loads 38</p> <p>3.3 1-D Wave Propagation in Solids Induced by Impact 41</p> <p>3.3.1 Longitudinal Waves in Thin Rods 42</p> <p>3.3.1.1 The Governing Equation for Waves in Long Rods 42</p> <p>3.3.1.2 Free Vibrations in a Finite Rod 46</p> <p>3.3.2 Flexural Waves in Thin Rods 47</p> <p>3.3.2.1 The Governing Equation for Flexural Waves in Rods 47</p> <p>3.3.2.2 Free Vibrations of Finite Beams 48</p> <p>3.3.3 The D'Alembert's Solution for Wave Equation 50</p> <p>3.4 Summary 51</p> <p>Exercise Problems 52</p> <p>Bibliography 54</p> <p><b>Chapter 4: Multi-Dimensional Impact Mechanics of Deformable Bodies 55-78</b></p> <p>4.1 Introduction 55</p> <p>4.2 Analysis of Stress 56</p> <p>4.2.1 Stress Components on an Arbitrary Plane 56</p> <p>4.2.2 Principal Stresses and Stress Invariants 57</p> <p>4.2.3 Mohr's Circles 58</p> <p>4.2.4 Octahedral Stresses 58</p> <p>4.2.5 Decomposition into Hydrostatic and Pure Shear States 59</p> <p>4.2.6 Equations of Motion of a Body in Cartesian Coordinates 60</p> <p>4.2.7 Equations of Motion of a Body in Cylindrical Coordinates 61</p> <p>4.2.8 Equations of Motion of a Body in Spherical Coordinates 62</p> <p>4.3 Analysis of Strain 63</p> <p>4.3.1 Deformation in the Neighborhood of a Point 63</p> <p>4.3.2 Compatibility Equations 64</p> <p>4.3.3 Strain Deviator 65</p> <p>4.4 Linearised Stress-Strain Relations 65</p> <p>4.4.1 Stress-Strain Relations for Isotropic Materials 66</p> <p>4.5 Waves in Infinite Medium 67</p> <p>4.5.1 Longitudinal Waves (Primary/Dilatational/Irrotational Waves) 67</p> <p>4.5.1.1 Longitudinal Waves 68</p> <p>4.5.1.2 The Governing Equations for Longitudinal Waves 68</p> <p>4.5.2 Transverse Waves (Secondary/Shear/Distortional/Rotational Wave) 69</p> <p>4.5.2.1 Transverse Waves 69</p> <p>4.5.2.2 The Governing Equations for Transverse Waves 70</p> <p>4.6 Waves in Semi-Infinite Media 70</p> <p>4.6.1 Surface Waves 71</p> <p>4.6.2 Symmetric Rayleigh-Lamb Spectrum in Elastic Layer 74</p> <p>4.7 Summary 76</p> <p>Exercise Problems 76</p> <p>Bibliography 78</p> <p><b>Chapter 5: Experimental Impact Mechanics 79-131</b></p> <p>5.1 Introduction 80</p> <p>5.2 Quasi-Static Material Tests 81</p> <p>5.3 Pendulum Impact Tests 87</p> <p>5.4 About High Strain Rate Testing of Materials 90</p> <p>5.5 Split Hopkinson's Pressure Bar Test 91</p> <p>5.5.1 Historical Background and Significance 91</p> <p>5.5.2 Improvements in SHPB Test Apparatus 92</p> <p>5.5.3 Principle of SHPB Test 93</p> <p>5.5.4 Theory Behind SHPB 95</p> <p>5.5.5 Design of Pressure Bars for a SHPB Apparatus 97</p> <p>5.5.6 Applications, Availability and Few Results 100</p> <p>5.6 Taylor Cylinder Impact Test 103</p> <p>5.6.1 Methodology 104</p> <p>5.6.2 Strain Rates 107</p> <p>5.6.3 Limitations and Improvements 107</p> <p>5.6.4 Case Study-1: Experiments with a Paraffin Wax 109</p> <p>5.6.5 Case Study-2: Experiments with Steel Cylinders 109</p> <p>5.7 Drop Impact Test 110</p> <p>5.7.1 Drop Specimen Test (DST) 111</p> <p>5.7.1.1 Few Standards for DST by Free Fall 113</p> <p>5.7.1.2 Experimental Setup for DST 113</p> <p>5.7.1.3 DST Procedure 115</p> <p>5.7.1.4 A Case Study: DST of a helicopter in NASA 116</p> <p>5.7.2 Drop Weight Test (DWT) 118</p> <p>5.7.2.1 Experimental Setup for DWT 119</p> <p>5.7.2.2 Case Study-1: DWT to study fracture process in structural concrete 121</p> <p>5.7.2.3 Case Study-2: DWT tower for applying both compressive and 124</p> <p>5.8 Summary 125</p> <p>Exercise Problems 126</p> <p>References 127</p> <p><b>Chapter 6: Modeling Deformation and Failure Under Impact 133-169</b></p> <p>6.1 Introduction 133</p> <p>6.2 Equation of State 135</p> <p>6.2.1 Gruneisen Parameter 135</p> <p>6.2.2 Shock-Hugoniot Curve 136</p> <p>6.2.3 Rankine-Hugoniot Conditions 137</p> <p>6.2.4 Mie-Gruneisen (Shock) Equation of State 139</p> <p>6.2.4.1 Implementation of Mie-Gruneisen Equation of State 141</p> <p>6.2.5 Murnaghan Equation of State 142</p> <p>6.2.6 Linear Equation of State 142</p> <p>6.2.7 Polynomial Equation of State 143</p> <p>6.2.8 High Explosive Equation of State 143</p> <p>6.3 Constitutive Models for Material Deformation and Plasticity 144</p> <p>6.3.1 Plasticity 145</p> <p>6.3.2 Plastic Isotropic or Kinematic Hardening Material Model 147</p> <p>6.3.3 Thermo-Elastic-Plastic Material Model 148</p> <p>6.3.4 Power-Law Isotropic Plasticity Material Model 148</p> <p>6.3.5 Johnson-Cook Material Model 149</p> <p>6.3.5.1 Determination of Parameters in Johnson-Cook Model 150</p> <p>6.3.6 Zerilli-Armstrong Material Model 151</p> <p>6.3.6.1 Modified Zerilli-Armstrong Material Model 151</p> <p>6.3.6.2 Determination of Parameters in Zerilli-Armstrong Model 152</p> <p>6.3.7 Combined Johnson-Cook and Zerilli-Armstrong Material Model 152</p> <p>6.3.8 Steinberg-Guinan Material Model 153</p> <p>6.3.9 Barlat's 3 Parameter Plasticity Material Model 153</p> <p>6.3.10 Orthotropic Material Model 154</p> <p>6.3.11 Summary of Material Models 154</p> <p>6.4 Failure/Damage Models 155</p> <p>6.4.1 Void Growth and Fracture Strain Model 156</p> <p>6.4.1.1 Void Growth Model 156</p> <p>6.4.1.2 Fracture Strain Model 157</p> <p>6.4.2 Johnson-Cook Failure Model 158</p> <p>6.4.3 Unified Model of Visco-plasticity and Ductile Damage 159</p> <p>6.4.4 Johnson-Holmquist Concrete Damage Model 160</p> <p>6.4.4.1 Determination of Parameters in Johnson-Holmquist Model 161</p> <p>6.4.5 Chang-Chang Composite Damage Model 161</p> <p>6.4.6 Orthotropic Damage Model 162</p> <p>6.4.7 Plastic Strain Limit Damage Model 162</p> <p>6.4.8 Material Stress/Strain Limit Damage Model 162</p> <p>6.4.9 Implementation of Damage 163</p> <p>6.4.9.1 Discrete Technique 163</p> <p>6.4.9.2 Operator Split Technique 163</p> <p>6.5 Temperature Rise During Impact 164</p> <p>6.6 Summary 165</p> <p>Exercise Problems 166</p> <p>References 167</p> <p><b>Chapter 7: Computational Impact Mechanics 171-219</b></p> <p>7.1 Introduction 171</p> <p>7.2 Principles of Numerical Formulations 174</p> <p>7.2.1 Classical Continuum Methods: Lagrangean, Eulerian and 174</p> <p>7.2.1.1 Lagrangean Formulation 174</p> <p>7.2.1.2 Eulerian Formulation 176</p> <p>7.2.1.3 Arbitrary Lagrangean- Eulerian Coupling (ALE-Formulation) 177</p> <p>7.2.2 Particle Based Methods 179</p> <p>7.2.2.1 Smooth Particle Hydrodynamics Method 180</p> <p>7.2.2.2 Discrete Element Method 183</p> <p>7.2.3 Meshless Methods 185</p> & l.…”
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Machine learning theory and applications : hands-on use cases with Python on classical and quantum machines by Vasques, Xavier

Table of Contents: “…Foreword xiii -- Acknowledgments xv -- General Introduction xvii -- 1 Concepts, Libraries, and Essential Tools in Machine Learning and Deep Learning 1 -- 1.1 Learning Styles for Machine Learning 2 -- 1.1.1 Supervised Learning 2 -- 1.1.1.1 Overfitting and Underfitting 3 -- 1.1.1.2 K-Folds Cross-Validation 4 -- 1.1.1.3 Train/Test Split 4 -- 1.1.1.4 Confusion Matrix 5 -- 1.1.1.5 Loss Functions 7 -- 1.1.2 Unsupervised Learning 9 -- 1.1.3 Semi-Supervised Learning 9 -- 1.1.4 Reinforcement Learning 9 -- 1.2 Essential Python Tools for Machine Learning 9 -- 1.2.1 Data Manipulation with Python 10 -- 1.2.2 Python Machine Learning Libraries 10 -- 1.2.2.1 Scikit-learn 10 -- 1.2.2.2 TensorFlow 10 -- 1.2.2.3 Keras 12 -- 1.2.2.4 PyTorch 12 -- 1.2.3 Jupyter Notebook and JupyterLab 13 -- 1.3 HephAIstos for Running Machine Learning on CPUs, GPUs, and QPUs 13 -- 1.3.1 Installation 13 -- 1.3.2 HephAIstos Function 15 -- 1.4 Where to Find the Datasets and Code Examples 32 -- Further Reading 33 -- 2 Feature Engineering Techniques in Machine Learning 35 -- 2.1 Feature Rescaling: Structured Continuous Numeric Data 36 -- 2.1.1 Data Transformation 37 -- 2.1.1.1 StandardScaler 37 -- 2.1.1.2 MinMaxScaler 39 -- 2.1.1.3 MaxAbsScaler 40 -- 2.1.1.4 RobustScaler 40 -- 2.1.1.5 Normalizer: Unit Vector Normalization 42 -- 2.1.1.6 Other Options 43 -- 2.1.1.7 Transformation to Improve Normal Distribution 44 -- 2.1.1.8 Quantile Transformation 48 -- 2.1.2 Example: Rescaling Applied to an SVM Model 50 -- 2.2 Strategies to Work with Categorical (Discrete) Data 57 -- 2.2.1 Ordinal Encoding 59 -- 2.2.2 One-Hot Encoding 61 -- 2.2.3 Label Encoding 62 -- 2.2.4 Helmert Encoding 63 -- 2.2.5 Binary Encoding 64 -- 2.2.6 Frequency Encoding 65 -- 2.2.7 Mean Encoding 66 -- 2.2.8 Sum Encoding 68 -- 2.2.9 Weight of Evidence Encoding 68 -- 2.2.10 Probability Ratio Encoding 70 -- 2.2.11 Hashing Encoding 71 -- 2.2.12 Backward Difference Encoding 72 -- 2.2.13 Leave-One-Out Encoding 73 -- 2.2.14 James-Stein Encoding 74 -- 2.2.15 M-Estimator Encoding 76 -- 2.2.16 Using HephAIstos to Encode Categorical Data 77 -- 2.3 Time-Related Features Engineering 77 -- 2.3.1 Date-Related Features 79 -- 2.3.2 Lag Variables 79 -- 2.3.3 Rolling Window Feature 82 -- 2.3.4 Expending Window Feature 84 -- 2.3.5 Understanding Time Series Data in Context 85 -- 2.4 Handling Missing Values in Machine Learning 88 -- 2.4.1 Row or Column Removal 89 -- 2.4.2 Statistical Imputation: Mean, Median, and Mode 90 -- 2.4.3 Linear Interpolation 91 -- 2.4.4 Multivariate Imputation by Chained Equation Imputation 92 -- 2.4.5 KNN Imputation 93 -- 2.5 Feature Extraction and Selection 97 -- 2.5.1 Feature Extraction 97 -- 2.5.1.1 Principal Component Analysis 98 -- 2.5.1.2 Independent Component Analysis 102 -- 2.5.1.3 Linear Discriminant Analysis 110 -- 2.5.1.4 Locally Linear Embedding 115 -- 2.5.1.5 The t-Distributed Stochastic Neighbor Embedding Technique 123 -- 2.5.1.6 More Manifold Learning Techniques 125 -- 2.5.1.7 Feature Extraction with HephAIstos 130 -- 2.5.2 Feature Selection 131 -- 2.5.2.1 Filter Methods 132 -- 2.5.2.2 Wrapper Methods 146 -- 2.5.2.3 Embedded Methods 154 -- 2.5.2.4 Feature Importance Using Graphics Processing Units (GPUs) 167 -- 2.5.2.5 Feature Selection Using HephAIstos 168 -- Further Reading 170 -- 3 Machine Learning Algorithms 175 -- 3.1 Linear Regression 176 -- 3.1.1 The Math 176 -- 3.1.2 Gradient Descent to Optimize the Cost Function 177 -- 3.1.3 Implementation of Linear Regression 182 -- 3.1.3.1 Univariate Linear Regression 182 -- 3.1.3.2 Multiple Linear Regression: Predicting Water Temperature 185 -- 3.2 Logistic Regression 202 -- 3.2.1 Binary Logistic Regression 202 -- 3.2.1.1 Cost Function 203 -- 3.2.1.2 Gradient Descent 204 -- 3.2.2 Multinomial Logistic Regression 204 -- 3.2.3 Multinomial Logistic Regression Applied to Fashion MNIST 204 -- 3.2.3.1 Logistic Regression with scikit-learn 205 -- 3.2.3.2 Logistic Regression with Keras on TensorFlow 208 -- 3.2.4 Binary Logistic Regression with Keras on TensorFlow 210 -- 3.3 Support Vector Machine 211 -- 3.3.1 Linearly Separable Data 212 -- 3.3.2 Not Fully Linearly Separable Data 214 -- 3.3.3 Nonlinear SVMs 216 -- 3.3.4 SVMs for Regression 217 -- 3.3.5 Application of SVMs 219 -- 3.3.5.1 SVM Using scikit-learn for Classification 220 -- 3.3.5.2 SVM Using scikit-learn for Regression 222 -- 3.4 Artificial Neural Networks 223 -- 3.4.1 Multilayer Perceptron 224 -- 3.4.2 Estimation of the Parameters 225 -- 3.4.2.1 Loss Functions 225 -- 3.4.2.2 Backpropagation: Binary Classification 226 -- 3.4.2.3 Backpropagation: Multi-class Classification 227 -- 3.4.3 Convolutional Neural Networks 230 -- 3.4.4 Recurrent Neural Network 232 -- 3.4.5 Application of MLP Neural Networks 233 -- 3.4.6 Application of RNNs: LST Memory 242 -- 3.4.7 Building a CNN 246 -- 3.5 Many More Algorithms to Explore 249 -- 3.6 Unsupervised Machine Learning Algorithms 251 -- 3.6.1 Clustering 251 -- 3.6.1.1 K-means 253 -- 3.6.1.2 Mini-batch K-means 255 -- 3.6.1.3 Mean Shift 257 -- 3.6.1.4 Affinity Propagation 259 -- 3.6.1.5 Density-based Spatial Clustering of Applications with Noise 262 -- 3.7 Machine Learning Algorithms with HephAIstos 264 -- References 270 -- Further Reading 270 -- 4 Natural Language Processing 273 -- 4.1 Classifying Messages as Spam or Ham 274 -- 4.2 Sentiment Analysis 281 -- 4.3 Bidirectional Encoder Representations from Transformers 286 -- 4.4 BERT's Functionality 287 -- 4.5 Installing and Training BERT for Binary Text Classification Using TensorFlow 288 -- 4.6 Utilizing BERT for Text Summarization 294 -- 4.7 Utilizing BERT for Question Answering 296 -- Further Reading 297 -- 5 Machine Learning Algorithms in Quantum Computing 299 -- 5.1 Quantum Machine Learning 303 -- 5.2 Quantum Kernel Machine Learning 306 -- 5.3 Quantum Kernel Training 328 -- 5.4 Pegasos QSVC: Binary Classification 333 -- 5.5 Quantum Neural Networks 337 -- 5.5.1 Binary Classification with EstimatorQNN 338 -- 5.5.2 Classification with a SamplerQNN 343 -- 5.5.3 Classification with Variational Quantum Classifier 348 -- 5.5.4 Regression 351 -- 5.6 Quantum Generative Adversarial Network 352 -- 5.7 Quantum Algorithms with HephAIstos 368 -- References 372 -- Further Reading 373 -- 6 Machine Learning in Production 375 -- 6.1 Why Use Docker Containers for Machine Learning? …”
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Remote Sensing Digital Image Analysis : an Introduction by Richards, John A.

Table of Contents: “…More Advanced Considerations -- 8.8 Context Classification -- 8.8.1 The Concept of Spatial Context -- 8.8.2 Context Classification by Image Pre-Processing -- 8.8.3 Post Classification Filtering -- 8.8.4 Probabilistic Label Relaxation -- 8.8.4.1 The Basic Algorithm -- 8.8.4.2 The Neighbourhood Function -- 8.8.4.3 Determining the Compatibility Coefficients -- 8.8.4.4 The Final Step -- Stopping the Process -- 8.8.4.5 Examples -- 8.9 Classification of Mixed Image Data -- 8.9.1 The Stacked Vector Approach -- 8.9.2 Statistical Methods -- 8.9.3 The Theory of Evidence -- 8.9.3.1 The Concept of Evidential Mass -- 8.9.3.2 Combining Evidence -- the Orthogonal Sum -- 8.9.3.3 Decision Rule -- 8.10 Classification Using Neural Networks -- 8.10.1 Linear Discrimination -- 8.10.1.1 Concept of a Weight Vector -- 8.10.1.2 Testing Class Membership -- 8.10.1.3 Training -- 8.10.1.4 Setting the Correction Increment -- 8.10.1.5 Classification -- The Threshold Logic Unit -- 8.10.1.6 Multicategory Classification -- 8.10.2 Networks of Classifiers -- Solutions of Nonlinear Problems -- 8.10.3 The Neural Network Approach -- 8.10.3.1 The Processing Element -- 8.10.3.2 Training the Neural Network -- Backpropagation -- 8.10.3.3 Choosing the Network Parameters -- 8.10.3.4 Examples -- References for Chapter 8 -- Problems -- 9 -- Clustering and Unsupervised Classification -- 9.1 Delineation of Spectral Classes -- 9.2 Similarity Metrics and Clustering Criteria -- 9.3 The Iterative Optimization (Migrating Means) Clustering Algorithm -- 9.3.1 The Basic.…”
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Principles and Practice of Hospital Medicine

Table of Contents: “…Chapter 118: Neurologic Imaging -- Chapter 119: Interventional Radiology -- Section 3: Procedures -- Chapter 120: Vascular Access -- Chapter 121: Intubation and Airway Support -- Chapter 122: Arterial Blood Gas and Placement of A-line -- Chapter 123: Feeding Tube Placement -- Chapter 124: Thoracentesis -- Chapter 125: Lumbar Puncture -- Chapter 126: Paracentesis -- Chapter 127: Arthrocentesis -- PART VI: CLINICAL CONDITIONS IN THE INPATIENT SETTING -- Section 1: Cardiovascular Medicine -- Chapter 128: Acute Coronary Syndromes -- Chapter 129: Heart Failure -- Chapter 130: Myocarditis, Pericardial Disease, and Cardiac Tamponade -- Chapter 131: Valvular Heart Disease -- Chapter 132: Supraventricular Tachyarrhythmias -- Chapter 133: Bradycardia -- Chapter 134: Ventricular Arrhythmias -- Chapter 135: Cardioversion -- Chapter 136: Pacemakers, Defibrillators, and Cardiac Resynchronization Devices in Hospital Medicine -- Section 2: Critical Care -- Chapter 137: Inpatient Cardiac Arrest and Cardiopulmonary Resuscitation -- Chapter 138: Acute Respiratory Failure -- Chapter 139: Pain, Agitation, and Delirium in the Critical Care Setting -- Chapter 140: Mechanical Ventilation -- Chapter 141: Sepsis and Shock -- Chapter 142: Acute Respiratory Distress Syndrome -- Chapter 143: Prevention in the Intensive Care Unit Setting -- Section 3: Dermatology -- Chapter 144: Flushing and Urticaria -- Chapter 145: Adverse Cutaneous Drug Reactions -- Chapter 146: Psoriasis and Other Papulosquamous Disorders -- Chapter 147: Diabetic Foot Infections -- Chapter 148: Venous Ulcers -- Chapter 149: Dermatologic Findings in Systemic Disease -- Section 4: Endocrinology -- Chapter 150: Glycemic Emergencies -- Chapter 151: Inpatient Management of Diabetes and Hyperglycemia -- Chapter 152: Thyroid Emergencies -- Chapter 153: Adrenal Insufficiency -- Chapter 154: Pituitary Disease -- Section 5: Gastroenterology -- Chapter 155: GERD and Esophagitis -- Chapter 156: Upper Gastrointestinal Bleeding -- Chapter 157: Acute Pancreatitis -- Chapter 158: Jaundice, Obstruction, and Acute Cholangitis -- Chapter 159: Acute Liver Disease -- Chapter 160: Cirrhosis and Its Complications -- Chapter 161: Acute Lower Gastrointestinal Bleeding -- Chapter 162: Small Bowel Disorders -- Chapter 163: Large Bowel Disorders -- Chapter 164: Inflammatory Bowel Disease -- Section 6: Geriatrics -- Chapter 165: Principles of Geriatric Care -- Chapter 166: Agitation in Older Adults -- Chapter 167: Elder Mistreatment -- Chapter 168: Malnutrition and Weight Loss in Hospitalized Older Adults -- Section 7: Hematology -- Chapter 169: Abnormalities in Red Blood Cells -- Chapter 170: Disorders of the White Cell -- Chapter 171: Quantitative Abnormalities of Platelets: Thrombocytopenia and Thrombocytosis -- Chapter 172: Approach to Patients with Bleeding Disorders -- Chapter 173: Hypercoagulable States -- Chapter 174: Hematologic Malignancies -- Section 8: Oncology -- Chapter 175: Overview of Cancer and Treatment -- Chapter 176: Oncologic Emergencies -- Chapter 177: Approach to the Patient with Suspected Malignancy -- Chapter 178: Breast, Ovary, and Cervical Cancer -- Chapter 179: Menђ́ةs Cancers -- Chapter 180: Cancers of the Kidney, Renal Pelvis, and Ureter -- Chapter 181: Oncologic Issues of the Aerodigestive Tract -- Chapter 182: Gastrointestinal Cancers -- Chapter 183: Immune-Related Adverse Events (irAEs) in Cancer Patients -- Section 9: Infectious Disease -- Chapter 184: Fundamentals of Antibiotics -- Chapter 185: Antibiotic Resistance -- Chapter 186: Community-Acquired Pneumonia.…”
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Yamada's textbook of gastroenterology

Table of Contents: “…Clinical Decision Making / Jasmine K Zia, John M Inadomi -- Approach to the Patient with Dyspepsia and Related Functional Gastrointestinal Complaints / Jan F Tack -- Approach to the Patient with Dysphagia, Odynophagia, or Noncardiac Chest Pain / André JPM Smout -- Approach to the Patient with Unintentional Weight Loss / Sreedhar Subramanian, Jonathan M Rhodes -- Approach to the Patient with Nausea and Vomiting / William Lee Hasler -- Approach to the Patient with Abdominal Pain / Pankaj J Pasricha -- Approach to the Patient with Gas and Bloating / Satish SC Rao, Yeong Yeh Lee -- Approach to the Patient with Diarrhea / Gail A Hecht, Jonathan Gaspar, Miguel Malespin -- Approach to the Patient with Constipation / Satish SC Rao, Michael Camilleri -- Approach to the Patient with Acute Abdomen / Courtney B Sherman, Kenneth McQuaid -- Approach to the Patient with Gastrointestinal Bleeding / Kevin A Ghassemi, Dennis M Jensen -- Approach to the Patient with Abnormal Liver Chemistries or Jaundice / J Gregory Fitz -- Approach to Gastrointestinal and Liver Diseases in Pregnancy / Sumona Saha, Nancy Reau -- Genetic Counseling for Gastrointestinal Patients / Laura E Panos, C Richard Boland -- Gastrointestinal Diseases. …”
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Yamada's textbook of gastroenterology

Table of Contents: “…e JPM Smout -- Approach to the patient with unintentional weight loss / Sreedhar Subramanian, Jonathan M Rhodes -- Approach to the patient with nausea and vomiting / William Lee Hasler -- Approach to the patient with abdominal pain / Pankaj J Pasricha -- Approach to the patient with gas and bloating / Satish SC Rao, Yeong Yeh Lee -- Approach to the patient with diarrhea / Gail A Hecht, Jonathan Gaspar, Miguel Malespin -- Approach to the patient with constipation / Satish SC Rao, Michael Camilleri -- Approach to the patient with acute abdomen / Courtney B Sherman, Kenneth McQuaid -- Approach to the patient with gastrointestinal bleeding / Kevin A Ghassemi, Dennis M Jensen -- Approach to the patient with abnormal liver chemistries or jaundice / J Gregory Fitz -- Approach to gastrointestinal and liver diseases in pregnancy / Sumona Saha, Nancy Reau -- Genetic counseling for gastrointestinal patients / Laura E Panos, C Richard Boland -- Gastrointestinal diseases. …”
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