Search Results - (((((((want OR wanting) OR mantis) OR wien) OR cantor) OR anne) OR shape) OR hints) algorithms.

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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Millimeter-wave digitally intensive frequency generation in CMOS by Wu, Wanghua, Staszewski, Robert Bogdan, 1965-, Long, John R. (John Robert), 1958-

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Integration of swarm intelligence and artificial neural network

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Oracle Primavera P6 version 8 : project and portfolio management : a comprehensive guide to managing projects, resources, and portfolios using Primavera P6, through version 8.2 by Williams, Daniel L.

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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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Statistical learning from a regression perspective by Berk, Richard A.

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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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Listed Volatility and Variance Derivatives : a Python-based Guide. by Hilpisch, Yves

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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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XML for DB2 information integration

Table of Contents: “…-- 3.4 Creating an XML schema from a database schema -- 3.4.1 The algorithm.…”
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Computational models of argument : Proceedings of COMMA 2012

Table of Contents: “…Simari -- Automated Deployment of Argumentation Protocols / Michael Rovatsos -- On Preferred Extension Enumeration in Abstract Argumentation / Katie Atkinson -- Towards Experimental Algorithms for Abstract Argumentation / Katie Atkinson.…”
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Handbook of convex optimization methods in imaging science

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Intelligent computational systems : a multi-disciplinary perspective

Table of Contents: “…3.2.1. Genetic Algorithm (GA) -- 3.2.2. Particle Swarm Optimization Algorithm (PSO) -- 3.3. …”
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Design optimization of fluid machinery : applying computational fluid dynamics and numerical optimization by Kim, Kwang-Yong, 1956-, Samad, Abdus, Benini, Ernesto

Table of Contents: “…2.2.5.3 Periodic/Cyclic Boundary Conditions2.2.5.4 Symmetry Boundary Conditions; 2.2.6 Moving Reference Frame (MRF); 2.2.7 Verification and Validation; 2.2.8 Commercial CFD Software; 2.2.9 Open Source Codes; 2.2.9.1 OpenFOAM; References; Chapter 3 Optimization Methodology; 3.1 Introduction; 3.1.1 Engineering Optimization Definition; 3.1.2 Design Space; 3.1.3 Design Variables and Objectives; 3.1.4 Optimization Procedure; 3.1.5 Search Algorithm; 3.2 Multi-Objective Optimization (MOO); 3.2.1 Weighted Sum Approach; 3.2.2 Pareto-Optimal Front…”
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Data Analysis and Applications 1 : New and Classical Approaches. by Skiadas, Christos

Table of Contents: “…Similarity to homogeneous distributional shapes1.3.11. Stability; 1.3.12. Further Aspects; 1.4. …”
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Advanced mathematical & computational tools in metrology & testing VIII

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The method of the generalised eikonal : new approaches in the Diffraction Theory. by Vesnik, Michael V.

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Security, Privacy and Reliability in Computer Communications and Networks. by Sha, Kewei

Table of Contents: “…5.6 Threats Analysis and Future Trends -- 5.7 Conclusion -- References -- PART III -- Cryptographic Algorithms -- Chapter 6 -- Quasigroup-Based Encryption for Low-Powered Devices -- Abstract -- 6.1 Introduction -- 6.2 Background-Low Energy Cryptosystems -- 6.3 Overview of Quasigroup Encryption -- 6.4 The Preliminary Block Cipher Design -- 6.5 Overview of Software Implementation -- 6.6 Overview of Three FPGA Implementations -- 6.6.1 The Quasigroup Implementation -- 6.6.2 Comparison Design-Parallel AES -- 6.6.3 Hybrid Front-End/AES Design -- 6.7 Experimental Results -- 6.8 Toward a Single-Chip Implementation -- 6.9 Algorithm Results for B = 2 to 8 -- 6.10 Generating Quasigroups Fast -- 6.11 Our Quasigroup Block Cipher Algorithm -- 6.12 Cryptanalysis and Improvements in the Block Cipher -- 6.13 Overview of a General Linear Cryptanalytical Attack -- 6.14 The LAT Design -- 6.15 Pilingup Attempts for N = 16, 32, and 64 -- 6.16 Analysis of the Attack on the Quasigroup -- 6.17 The Issue of a Total Linear Bias of 1/2 -- 6.18 Attack Complexity -- 6.19 Possible Changes that Could Be Made in the Design of This Attack Model -- 6.20 Which Quasigroup Order Is Best? …”
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Spam : a Shadow History of the Internet by Brunton, Finn, 1980-

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