Search Results - (((((((want OR markant) OR mantis) OR when) OR cantor) OR anne) OR shape) OR hints) algorithms.

Social-behavioral modeling for complex systems

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

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

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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)…”
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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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Contemporary Ring Theory 2011 : Proceedings of the Sixth China-japan-korea International Conference on Ring Theory. by Kim, Jin Yong

Table of Contents: “…Abelian rings and uniquely clean rings; References; WHEN DO THE DIRECT SUMS OF MODULES INHERIT CERTAIN PROPERTIES? …”
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The Big Bang Theory and Philosophy : Rock, Paper, Scissors, Aristotle, Locke

Table of Contents: “…; The Giant "Hedon" Collider; The Egoism Polarization; The Social Contract Instability; The Aristotelian Virtue Vortex; The Dennis Kim Conundrum; Part Three: "Perhaps You Mean A Different Thing Than I Do When You Say 'Science'"; Chapter 7: Getting Fundamental about Doing Physics in The Big Bang Theory; Studies in Sheldonology; A Unified Theory of Sheldon?…”
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Mechanical Instability. by Krysinski, Tomasz

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Regularization theory for ill-posed problems : selected topics by Lu, Shuai, Pereverzev, Sergei V.

Table of Contents: “…Numerical examples3 Multiparameter regularization; 3.1 When do we really need multiparameter regularization?…”
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Lead generation for dummies by Rothman, Dayna

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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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Software engineering for embedded systems : methods, practical techniques, and applications

Table of Contents: “…-- Examples of modeling languages -- The V diagram promise -- So, why would you want to model your embedded system? -- When should you model your embedded system? …”
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Analytics stories : using data to make good things happen by Winston, Wayne L.

Table of Contents: “…-- How much should we order and when should we order? -- How does the UPS driver know the order to deliver packages? …”
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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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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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A Hands-On Guide to Designing Embedded Systems. by Taylor, Adam

Table of Contents: “…-- 3.7 Finite State Machine Design -- 3.7.1 Defining a State Machine -- 3.7.2 Algorithmic State Diagrams -- 3.7.3 Moore or Mealy: What Should I Choose? …”
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The next economic disaster : why it's coming and how to avoid it by Vague, Richard

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