Search Results - (((((((ant OR wkwant) OR semantic) OR when) OR cantor) OR anne) OR shape) OR hints) algorithms.

Concise dictionary of engineering : a guide to the language of engineering by Hanifan, Ronald E.

Full text (MFA users only)

Artificial intelligence in society.

Full text (MFA users only)

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. …”
Full text (MFA users only)

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. …”
Full text (MFA users only)

XML for DB2 information integration

Table of Contents: “…Shredding XML into relational tables -- 6.1 Shredding overview -- 6.1.1 What is shredding? -- 6.1.2 When should you use shredding? -- 6.2 Shredding using DB2 XML Extender -- 6.2.1 Shredding -- planning and design -- 6.2.2 Planning and design: hints and tips -- 6.2.3 Shredding: configuration and execution -- 6.3 Alternatives to shredding with DB2 XML Extender -- 6.3.1 Using the XML wrapper -- 6.3.2 Writing your own code -- 6.4 Shredding with XML Extender: a step-by-step example -- 6.4.1 Step 1 -- XML enabling of your database -- 6.4.2 Step 2 -- Creating the DAD file -- 6.4.3 Step 3 -- Enablement of the XML collection -- 6.4.4 Step 4 -- Decomposing the XML data into relational tables -- 6.4.5 Step 5 -- Checking the result using the SELECT statement -- Chapter 7. …”
Full text (MFA users only)

Digitalization of Society and Socio-Political Issues. 1, Digital, Communication, and Culture

Table of Contents: “…The Digitalization of Cultural Policies in France 149; Anne BELLON 14.1.…”
Full text (MFA users only)

Building Machine Learning Systems with Python : Explore Machine Learning and Deep Learning Techniques for Building Intelligent Systems Using Scikit-Learn and TensorFlow, 3rd Editio... by Pedro Coelho, Luis

Table of Contents: “…Cover; Title Page; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Getting Started with Python Machine Learning; Machine learning and Python -- a dream team; What the book will teach you -- and what it will not; How to best read this book; What to do when you are stuck; Getting started; Introduction to NumPy, SciPy, Matplotlib, and TensorFlow; Installing Python; Chewing data efficiently with NumPy and intelligently with SciPy; Learning NumPy; Indexing; Handling nonexistent values; Comparing the runtime; Learning SciPy; Fundamentals of machine learning.…”
Full text (MFA users only)

ModSecurity 2.5. by Mischel, Magnus

Table of Contents: “…Filtering collection fields using a regular expressionBuilt-in fields; Transformation functions; Other operators; Set-based pattern matching with @pm and @pmFromFile; @pmFromFile; Performance of the phrase matching operators; Validating character ranges; Phases and rule ordering; Actions-what to do when a rule matches; Allowing requests; Blocking requests; Taking no action but continuing rule processing; Dropping requests; Redirecting and proxying requests; SecAction; Using the ctl action to control the rule engine; How to use the ctl action; Macro expansion; SecRule in practice.…”
Full text (MFA users only)

Digitalization of society and socio-political issues. 2, Digital, information, and research

Table of Contents: “…6. When Vlogging Educates in Politics: The French Case of "Osons Causer" -- 6.1. …”
Full text (MFA users only)

Moments and moment invariants in pattern recognition by Flusser, Jan

Full text (MFA users only)

Advanced Computational Electromagnetic Methods. by Yu, Wenhua

Table of Contents: “…1.7.1 A Symmetric Reflector Antenna -- 1.7.2 A Symmetric Reflector Antenna with an Elliptical Projected Aperture -- 1.7.3 Near-Field Prediction with Only Two Pattern Cuts -- 2.6.1 Planar PEC Boundaries -- 2.6.3 Critical Curved PEC Models -- 2.7.1 The Finite Volumes-Based FV24 Algorithm -- 2.7.2 High-Order Algorithms for Compact-FDTD Grids -- 3.2.1 Features of the FDTD Code -- 3.2.2 Input Parameters File -- 3.2.3 Main Program Layout -- 3.2.4 Field Updates -- 3.2.5 Outputs of the Program -- 3.3.1 Performance Optimization -- 3.3.2 Memory Accesses -- 3.3.3 Preparation of the GPU Device -- 3.3.4 Thread to Cell Mapping -- 3.3.5 The Time-Marching Loop -- 3.3.6 Field Updates -- 3.3.7 Source Updates and Output Calculations -- 4.3.1 FDTD Space Lattice -- 4.3.2 Example Updating Algorithm for TM Grid Cells -- 4.4.1 Collisional Plasma Algorithm -- 4.4.2 Two Example Validations -- 4.4.3 Summary of Performance -- 4.5.1 Overview -- 4.5.2 Mean Field Equations -- 4.5.3 Variance Field Equations -- 5.2.1 Hardware Configuration -- 5.2.2 Software Configuration -- 5.2.3 Compilation Environment -- 5.3.1 Performance Optimization -- 5.3.2 Memory Alignment -- 5.3.3 Parallel FDTD Implementation -- 5.3.4 Job Scheduling Strategy -- 5.3.5 FDTD Code Development -- 5.3.6 Matrix Multiplication -- 6.1.1 FETI Method with One Lagrange Multiplier -- 6.1.2 FETI Method with Two Lagrange Multipliers -- 6.1.3 Symbolic Formulation -- 6.2.1 FETI-DP Method with One Lagrange Multiplier -- 6.2.2 FETI-DP Method with Two Lagrange Multipliers -- 6.2.3 Comparison Between FETI-DP Methods with One and Two Lagrange Multipliers -- 6.3.1 Nonconformal Interface and Conformal Corner Meshes -- 6.3.2 Extension to Nonconformal Interface and Corner Meshes -- 6.4.1 Nonconformal Interface and Conformal Corner Meshes -- 6.4.2 Extension to Nonconformal Interface and Corner Meshes.…”
Full text (MFA users only)

Mastering OpenCV with Practical Computer Vision Projects. by Emami, Shervin

Table of Contents: “…Reviewing the Android appCartoonifying the image when the user taps the screen; Saving the image to a file and to the Android picture gallery; Showing an Android notification message about a saved image; Changing cartoon modes through the Android menu bar; Reducing the random pepper noise from the sketch image; Showing the FPS of the app; Using a different camera resolution; Customizing the app; Summary; 2. …”
Full text (MFA users only)

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…”
Full text (MFA users only)

Social-behavioral modeling for complex systems

Full text (MFA users only)

Big data and differential privacy : analysis strategies for railway track engineering by Attoh-Okine, Nii O.

Full text (MFA users only)

The spirit of the hive : the mechanisms of social evolution by Page, Robert E.

Full text (MFA users only)

Linear Models. by Searle, Shayle R.

Table of Contents: “…(Gauss-Markov Theorem) -- e Least-squares Theory When The Parameters are Random Variables -- 4 Consequences of Estimation -- a Unbiasedness -- b Variances -- c Estimating E(y)…”
Full text (MFA users only)

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).…”
Full text (MFA users only)

Theoretical Computer Science : Proceedings of the 10th Italian Conference on ICTCS '07. by Italiano, Giuseppe F.

Full text (MFA users only)

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. …”
Full text (MFA users only)