Search Results - (((((((anti OR want) OR mantis) OR wind) OR cantor) OR anne) OR shape) OR hints) algorithms.

Kotlin standard library cookbook : master the powerful Kotlin standard library through practical code examples by Urbanowicz, Samuel

Table of Contents: “…. ; See also; Applying sequences to solve algorithmic problems; Getting ready; How to do it ... ; How it works ... ; Chapter 2: Expressive Functions and Adjustable Interfaces; Introduction; Declaring adjustable functions with default parameters; How to do it ... ; How it works ... ; See also; Declaring interfaces containing default implementations; Getting ready…”
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Integral equations, boundary value problems and related problems : dedicated to Professor Chien-Ke Lu on the occasion of his 90th birthday : Yinchuan, Ningxia, China, 19-23 August...

Table of Contents: “…Dong, X.Y. Yao and C.F. Wang -- Anti-plane problem of two collinear cracks in a functionally graded coating-substance structure / S.H. …”
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What to Do When Machines Do Everything : Five Ways Your Business Can Thrive in an Economy of Bots, AI, and Data. by Frank, Malcolm

Table of Contents: “…What to Do When Machines Do Everything: How to Get Ahead in a World of AI, Algorithms, Bots, and Big Data; Contents; Preface; 1: When Machines Do Everything; Like It or Not, This Is Happening; Digital That Matters; Playing the New Game; But Will I Be Automated Away?…”
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Cybersecurity Law, standards and regulations by Schreider, Tari

Table of Contents: “…Authors of original encryption algorithms never really thought that governments would want to have access to their en... -- In an effort to bring sanity to the uncontrolled growth of encryption regulations, two important laws have been introduced. …”
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Pediatric incontinence : evaluation and clinical management

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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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Introduction to numerical electrostatics using MATLAB by Dworsky, Lawrence N., 1943-

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6G Wireless Communications and Mobile Networking.

Table of Contents: “…Terminal-positioning and Other Novel Applications -- MASSIVE MIMO ANTENNA FOR MOBILE COMMUNICATIONS -- Massive MIMO Antenna Array Design and Synthesis -- Massive MIMO Antenna Decoupling Technology -- Large-Scale Antenna Beamforming Technology -- Null-notch Beamforming Algorithm Based on LMS Criterion -- DESIGN OF FEED NETWORK AND RF FRONT-END -- Feeding Technology of Base Station Antenna -- Design of RF Front-End for Large-Scale Active Antenna -- ANTENNA SELECTION TECHNOLOGY -- Antenna Selection Criteria and Classification -- Optimal Antenna Selection Algorithm -- Incremental Antenna Selection Algorithm -- Decreasing Antenna Selection Algorithm -- MEASUREMENT TECHNOLOGY OF MASSIVE MIMO ANTENNA -- OTA Testing Requirements for Massive MIMO Antenna -- Near-Field and Far-Field Measurement -- Far-Field Test -- Near-Field Test: -- OTA Testing Process -- SUMMARY -- CONSENT FOR PUBLICATION -- CONFLICT OF INTEREST -- ACKNOWLEDGEMENTS -- REFERENCES.…”
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Tracking with particle filter for high-dimensional observation and state spaces by Dubuisson, Séverine

Table of Contents: “…Application to tracking shapes; 3.4. Conjoint estimation of dynamic and static parameters; 3.5. …”
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Diatoms : ecology and life cycle

Table of Contents: “…""DIATOMS: ECOLOGY AND LIFE CYCLE""; ""DIATOMS: ECOLOGY AND LIFE CYCLE""; ""Contents""; ""Preface""; ""The Role of Environmental Factors in Shaping Diatom Frustule: Morphological Plasticity and Teratological Forms""; ""Abstract""; ""1. …”
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Discovering knowledge in data : an introduction to data mining by Larose, Daniel T.

Table of Contents: “…DISCOVERING KNOWLEDGE IN DATA -- Contents -- Preface -- 1 An Introduction to Data Mining -- 1.1 What is Data Mining? -- 1.2 Wanted: Data Miners -- 1.3 The Need for Human Direction of Data Mining -- 1.4 The Cross-Industry Standard Practice for Data Mining -- 1.4.1 Crisp-DM: The Six Phases -- 1.5 Fallacies of Data Mining -- 1.6 What Tasks Can Data Mining Accomplish? …”
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Clinical simulation : operations, engineering and management

Table of Contents: “…; 3.5 The Systems Approach to Training; 3.6 Defining the Performance Requirement; 3.7 Cost Versus Value Added; 3.8 Operations Cost; 3.9 Standardization: What is it, and who Wants it?; 3.10 Patients as Training Conditions; 3.11 Equipment as Training Conditions; 3.12 Increase in Training System Cost; 3.13 You as the Leader-Manager; 3.14 Conclusion; Endnotes; Topic II What's In It For Me.…”
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Moments and moment invariants in pattern recognition by Flusser, Jan

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Integration of swarm intelligence and artificial neural network

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Effective Theories for Brittle Materials : a Derivation of Cleavage Laws and Linearized Griffith Energies from Atomistic and Continuum Nonlinear Models. by Friedrich, Manuel

Table of Contents: “…6.4.1 Korn-Poincaré-type inequality6.4.2 SBD-rigidity; 6.4.3 Compactness and Gamma-convergence; 7 Preliminaries; 7.1 Geometric rigidity and Korn: Dependence on the set shape; 7.2 A trace theorem in SBV2; 8 A Korn-Poincaré-type inequality; 8.1 Preparations; 8.2 Modification of sets; 8.3 Neighborhoods of boundary components; 8.3.1 Rectangular neighborhood; 8.3.2 Dodecagonal neighborhood; 8.4 Proof of the Korn-Poincaré-inequality; 8.4.1 Conditions for boundary components and trace estimate; 8.4.2 Modification algorithm; 8.4.3 Proof of the main theorem; 8.5 Trace estimates for boundary components…”
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Networks-on-chip : from implementations to programming paradigms by Ma, Sheng, Huang, Libo, 1976-, Lai, Mingche, Shi, Wei

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Machine Learning in Chemical Safety and Health : Fundamentals with Applications. by Wang, Qingsheng

Table of Contents: “…Chapter 3 Flammability Characteristics Prediction Using QSPR Modeling -- 3.1 Introduction -- 3.1.1 Flammability Characteristics -- 3.1.2 QSPR Application -- 3.1.2.1 Concept of QSPR -- 3.1.2.2 Trends and Characteristics of QSPR -- 3.2 Flowchart for Flammability Characteristics Prediction -- 3.2.1 Dataset Preparation -- 3.2.2 Structure Input and Molecular Simulation -- 3.2.3 Calculation of Molecular Descriptors -- 3.2.4 Preliminary Screening of Molecular Descriptors -- 3.2.5 Descriptor Selection and Modeling -- 3.2.6 Model Validation -- 3.2.6.1 Model Fitting Ability Evaluation -- 3.2.6.2 Model Stability Analysis -- 3.2.6.3 Model Predictivity Evaluation -- 3.2.7 Model Mechanism Explanation -- 3.2.8 Summary of QSPR Process -- 3.3 QSPR Review for Flammability Characteristics -- 3.3.1 Flammability Limits -- 3.3.1.1 LFLT and LFL -- 3.3.1.2 UFLT and UFL -- 3.3.2 Flash Point -- 3.3.3 Auto-ignition Temperature -- 3.3.4 Heat of Combustion -- 3.3.5 Minimum Ignition Energy -- 3.3.6 Gas-liquid Critical Temperature -- 3.3.7 Other Properties -- 3.4 Limitations -- 3.5 Conclusions and Future Prospects -- References -- Chapter 4 Consequence Prediction Using Quantitative Property-Consequence Relationship Models -- 4.1 Introduction -- 4.2 Conventional Consequence Prediction Methods -- 4.2.1 Empirical Method -- 4.2.2 Computational Fluid Dynamics (CFD) Method -- 4.2.3 Integral Method -- 4.3 Machine Learning and Deep Learning-Based Consequence Prediction Models -- 4.4 Quantitative Property-Consequence Relationship Models -- 4.4.1 Consequence Database -- 4.4.2 Property Descriptors -- 4.4.3 Machine Learning and Deep Learning Algorithms -- 4.5 Challenges and Future Directions -- References -- Chapter 5 Machine Learning in Process Safety and Asset Integrity Management -- 5.1 Opportunities and Threats -- 5.2 State-of-the-Art Reviews -- 5.2.1 Artificial Neural Networks (ANNs).…”
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Computational neuroanatomy : the methods by Chung, Moo K.

Table of Contents: “…5.3.3 Laplace-Beltrami Shape Descriptors5.3.4 Second Eigenfunctions; 5.3.5 Dirichlet Energy; 5.3.6 Fiedler's Vector; 5.4 Finite Element Methods; 5.4.1 Pieacewise Linear Functions; 5.4.2 Mass and Stiffness Matrices; 6. …”
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Learning Geospatial Analysis with Python - Second Edition. by Lawhead, Joel

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Deep Learning with Pytorch Quick Start Guide : Learn to Train and Deploy Neural Network Models in Python. by Julian, David

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