Search Results - (((((((ant OR cantini) OR mantis) OR wind) OR cantor) OR anne) OR shape) OR hints) algorithms.

Exact methods in low-dimensional statistical physics and quantum computing : École d'été de physique des Houches, session LXXXIX, 30 June-1 August 2008, École thématique du CNRS...

Table of Contents: “…7.11 The Legendre transform of the free energy7.12 The limit shape phenomenon -- 7.13 Semiclassical limits -- 7.14 The free-fermionic point and dimer models -- 7.A Appendix -- References -- 8 Mathematical aspects of 2D phase transitions -- PART II: SHORT LECTURES -- 9 Numerical simulations of quantum statistical mechanical models -- 9.1 Introduction -- 9.2 A rapid survey of methods -- 9.3 Path integral and related methods -- 9.4 Classical worm algorithm -- 9.5 Projection methods -- 9.6 Valence bond projection method -- References…”
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Uncertainty, expectations, and financial instability : reviving Allais's lost theory of psychological time by Barthalon, Eric

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Spatial control of vibration : theory and experiments by Moheimani, S. O. Reza, 1967-, Fleming, Andrew J., 1977-

Table of Contents: “…System Identification for Spatially Distributed Systems; 7.1 Introduction; 7.2 Modeling; 7.3 Spatial sampling; 7.4 Identifying the system matrix; 7.5 Identifying the mode shapes and feed-through function; 7.6 Experimental results; 7.7 Conclusions; Appendix A Frequency domain subspace system identification; A.1 Introduction; A.2 Frequency Domain Subspace Algorithm; Bibliography; Index.…”
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Mastering D3.js. by Castillo, Pablo Navarro

Table of Contents: “…Updating the datasetFixing the enter and exit transitions; Using the barcode chart; Creating a layout algorithm; The radial layout; Computing the angles; Using the layout; Summary; Chapter 3: Creating Visualizations without SVG; SVG support in the browser market; Visualizations without SVG; Loading and sorting the data; The force layout method; Setting the color and size; Creating a legend; Polyfilling; Feature detection; The canvg example; Using canvas and D3; Creating figures with canvas; Creating shapes; Integrating canvas and D3; Summary; Chapter 4: Creating a Color Picker with D3.…”
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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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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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Pediatric incontinence : evaluation and clinical management

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Knowledge mining using intelligent agents

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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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Swift 2 design patterns : build robust and scalable iOS and Mac OS X game applications by Lange, Julien

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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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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.…”
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Moments and moment invariants in pattern recognition by Flusser, Jan

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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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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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Decision Intelligence for Dummies by Baker, Pamela

Table of Contents: “…Preventing Wrong Influences from Affecting Decisions -- Bad influences in AI and analytics -- The blame game -- Ugly politics and happy influencers -- Risk Factors in Decision Intelligence -- DI and Hyperautomation -- Part 5 The Part of Tens -- Chapter 17 Ten Steps to Setting Up a Smart Decision -- Check Your Data Source -- Track Your Data Lineage -- Know Your Tools -- Use Automated Visualizations -- Impact = Decision -- Do Reality Checks -- Limit Your Assumptions -- Think Like a Science Teacher -- Solve for Missing Data -- Partial versus incomplete data -- Clues and missing answers -- Take Two Perspectives and Call Me in the Morning -- Chapter 18 Bias In, Bias Out (and Other Pitfalls) -- A Pitfalls Overview -- Relying on Racist Algorithms -- Following a Flawed Model for Repeat Offenders -- Using A Sexist Hiring Algorithm -- Redlining Loans -- Leaning on Irrelevant Information -- Falling Victim to Framing Foibles -- Being Overconfident -- Lulled by Percentages -- Dismissing with Prejudice -- Index -- EULA.…”
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Handbook of convex optimization methods in imaging science

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