Search Results - (((((((kant OR wanti) OR mantis) OR when) OR cantor) OR anne) OR shared) OR hints) algorithms.

Mathematics of evolution and phylogeny

Table of Contents: “…List of Contributors -- 1 The minimum evolution distance-based approach of phylogenetic inference -- 1.1 Introduction -- 1.2 Tree metrics -- 1.2.1 Notation and basics -- 1.2.2 Three-point and four-point conditions -- 1.2.3 Linear decomposition into split metrics -- 1.2.4 Topological matrices -- 1.2.5 Unweighted and balanced averages -- 1.2.6 Alternate balanced basis for tree metrics -- 1.2.7 Tree metric inference in phylogenetics -- 1.3 Edge and tree length estimation -- 1.3.1 The LS approach -- 1.3.2 Edge length formulae -- 1.3.3 Tree length formulae -- 1.3.4 The positivity constraint -- 1.3.5 The balanced scheme of Pauplin -- 1.3.6 Semple and Steel combinatorial interpretation -- 1.3.7 BME: a WLS interpretation -- 1.4 The agglomerative approach -- 1.4.1 UPGMA and WPGMA -- 1.4.2 NJ as a balanced minimum evolution algorithm -- 1.4.3 Other agglomerative algorithms -- 1.5 Iterative topology searching and tree building -- 1.5.1 Topology transformations.; 1.5.2 A fast algorithm for NNIs with OLS -- 1.5.3 A fast algorithm for NNIs with BME -- 1.5.4 Iterative tree building with OLS -- 1.5.5 From OLS to BME -- 1.6 Statistical consistency -- 1.6.1 Positive results -- 1.6.2 Negative results -- 1.6.3 Atteson's safety radius analysis -- 1.7 Discussion -- Acknowledgements -- 2 Likelihood calculation in molecular phylogenetics -- 2.1 Introduction -- 2.2 Markov models of sequence evolution -- 2.2.1 Independence of sites -- 2.2.2 Setting up the basic model -- 2.2.3 Stationary distribution -- 2.2.4 Time reversibility -- 2.2.5 Rate of mutation -- 2.2.6 Probability of sequence evolution on a tree -- 2.3 Likelihood calculation: the basic algorithm -- 2.4 Likelihood calculation: improved models -- 2.4.1 Choosing the rate matrix -- 2.4.2 Among site rate variation -- 2.4.3 Site-specific rate variation -- 2.4.4 Correlated evolution between sites -- 2.5 Optimizing parameters -- 2.5.1 Optimizing continuous parameters -- 2.5.2 Searching for the optimal tree.; 2.5.3 Alternative search strategies -- 2.6 Consistency of the likelihood approach -- 2.6.1 Statistical consistency -- 2.6.2 Identifiability of the phylogenetic models -- 2.6.3 Coping with errors in the model -- 2.7 Likelihood ratio tests -- 2.7.1 When to use the asymptotic x2 distribution -- 2.7.2 Testing a subset of real parameters -- 2.7.3 Testing parameters with boundary conditions -- 2.7.4 Testing trees -- 2.8 Concluding remarks -- Acknowledgements -- 3 Bayesian inference in molecular phylogenetics -- 3.1 The likelihood function and maximum likelihood estimates -- 3.2 The Bayesian paradigm -- 3.3 Prior -- 3.4 Markov chain Monte Carlo -- 3.4.1 Metropolis-Hastings algorithm -- 3.4.2 Single-component Metropolis-Hastings algorithm -- 3.4.3 Gibbs sampler -- 3.4.4 Metropolis-coupled MCMC -- 3.5 Simple moves and their proposal ratios -- 3.5.1 Sliding window using uniform proposal -- 3.5.2 Sliding window using normally distributed proposal.; 3.5.3 Sliding window using normal proposal in multidimensions -- 3.5.4 Proportional shrinking and expanding -- 3.6 Monitoring Markov chains and processing output -- 3.6.1 Diagnosing and validating MCMC algorithms -- 3.6.2 Gelman and Rubin's potential scale reduction statistic -- 3.6.3 Processing output -- 3.7 Applications to molecular phylogenetics -- 3.7.1 Estimation of phylogenies -- 3.7.2 Estimation of species divergence times -- 3.8 Conclusions and perspectives -- Acknowledgements -- 4 Statistical approach to tests involving phylogenies -- 4.1 The statistical approach to phylogenetic inference -- 4.2 Hypotheses testing -- 4.2.1 Null and alternative hypotheses -- 4.2.2 Test statistics -- 4.2.3 Significance and power -- 4.2.4 Bayesian hypothesis testing -- 4.2.5 Questions posed as function of the tree parameter -- 4.2.6 Topology of treespace -- 4.2.7 The data -- 4.2.8 Statistical paradigms -- 4.2.9 Distributions on treespace -- 4.3 Different types of tests involving phylogenies.; 4.3.1 Testing t1 versus t2 -- 4.3.2 Conditional tests -- 4.3.3 Modern Bayesian hypothesis testing -- 4.3.4 Bootstrap tests -- 4.4 Non-parametric multivariate hypothesis testing -- 4.4.1 Multivariate con.dence regions -- 4.5 Conclusions: there are many open problems -- Acknowledgements -- 5 Mixture models in phylogenetic inference -- 5.1 Introduction: models of gene-sequence evolution -- 5.2 Mixture models -- 5.3 Defining mixture models -- 5.3.1 Partitioning and mixture models -- 5.3.2 Discrete-gamma model as a mixture model -- 5.3.3 Combining rate and pattern-heterogeneity -- 5.4 Digression: Bayesian phylogenetic inference -- 5.4.1 Bayesian inference of trees via MCMC -- 5.5 A mixture model combining rate and pattern-heterogeneity -- 5.5.1 Selected simulation results -- 5.6 Application of the mixture model to inferring the phylogeny of the mammals -- 5.6.1 Model testing -- 5.7 Results -- 5.7.1 How many rate matrices to include in the mixture model?…”
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Minor Surgery at a Glance. by Mohan, Helen

Table of Contents: “…Part 3 Core surgical knowledge -- 17 Skin incisions -- Scalpels -- Holding the scalpel to make an incision -- Basic principles for incision -- Minor surgical incisions -- Techniques for a good scar -- Electrodissection to incise -- 18 Principles of wound closure -- Plan the skin incision -- Choosing the suture size -- Choosing the suture material -- Suture placement -- Technique for simple interrupted sutures -- When should I use a different suturing technique? …”
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The SMART CYBER ECOSYSTEM FOR SUSTAINABLE DEVELOPMENT.

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A primer on experiments with mixtures by Cornell, John A., 1941-

Table of Contents: “…QuestionsAppendix 2A: Least-Squares Estimation Formula for the Polynomial Coefficients and Their Variances: Matrix Notation; Appendix 2B: Cubic and Quartic Polynomials and Formulas for the Estimates of the Coefficients; Appendix 2C: The Partitioning of the Sources in the Analysis of Variance Table When Fitting the Scheffé Mixture Models; 3. Multiple Constraints on the Component Proportions; 3.1 Lower-Bound Restrictions on Some or All of the Component Proportions; 3.2 Introducing L-Pseudocomponents; 3.3 A Numerical Example of Fitting An L-Pseudocomponent Model.…”
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Missing Data Methods : Cross-Sectional Methods and Applications. by Drukker, David M.

Table of Contents: “…NotesAcknowledgment; References; Orthogonality in the single index model; On the estimation of selection models when participation is endogenous and misclassified; Introduction; The model and estimator; Sampling algorithm; Simulated data example; Summary and conclusions; Notes; Acknowledgments; References; summary tables for additional simulations; Process for simulating non -- normal errors; Efficient probit estimation with partially missing covariates; Introduction; Model Specification; Efficient estimators and variances; Testing assumptions and possible modifications; Other models.…”
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A Primer on Machine Learning Applications in Civil Engineering by Deka, Paresh Chandra

Table of Contents: “…Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- Acknowledgments -- A Primer on Machine Learning Applications in Civil Engineering -- Author -- 1: Introduction -- 1.1 Machine Learning -- 1.2 Learning from Data -- 1.3 Research in Machine Learning: Recent Progress -- 1.4 Artificial Neural Networks -- 1.5 Fuzzy Logic (FL) -- 1.6 Genetic Algorithms -- 1.7 Support Vector Machine (SVM) -- 1.8 Hybrid Approach (HA) -- Bibliography -- 2: Artificial Neural Networks -- 2.1 Introduction to Fundamental Concepts and Terminologies -- 2.2 Evolution of Neural Networks -- 2.3 Models of ANN -- 2.4 McCulloch-Pitts Model -- 2.5 Hebb Network -- 2.6 Summary -- 2.7 Supervised Learning Network -- 2.7.1 Perceptron Network -- 2.7.2 Adaptive Linear Neuron -- 2.7.3 Back-Propagation Network -- 2.7.4 Radial Basis Function Network -- 2.7.5 Generalized Regression Neural Networks -- 2.7.6 Summary -- 2.8 Unsupervised Learning Networks -- 2.8.1 Introduction -- 2.8.2 Kohonen Self-Organizing Feature Maps -- 2.8.3 Counter Propagation Network -- 2.8.4 Adaptive Resonance Theory Network -- 2.8.5 Summary -- 2.9 Special Networks -- 2.9.1 Introduction -- 2.9.2 Gaussian Machine -- 2.9.3 Cauchy Machine -- 2.9.4 Probabilistic Neural Network -- 2.9.5 Cascade Correlation Neural Network -- 2.9.6 Cognitive Network -- 2.9.7 Cellular Neural Network -- 2.9.8 Optical Neural Network -- 2.9.9 Summary -- 2.10 Working Principle of ANN -- 2.10.1 Introduction -- 2.10.2 Types of Activation Function -- 2.10.3 ANN Architecture -- 2.10.4 Learning Process -- 2.10.5 Feed-Forward Back Propagation -- 2.10.6 Strengths of ANN -- 2.10.7 Weaknesses of ANN -- 2.10.8 Working of the Network -- 2.10.9 Summary -- Bibliography -- 3: Fuzzy Logic -- 3.1 Introduction to Classical Sets and Fuzzy Sets -- 3.1.1 Classical Sets -- 3.1.2 Fuzzy Sets -- 3.1.3 Summary.…”
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Moments, Positive Polynomials And Their Applications.

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Pharmacology in anesthesia practice

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High performance parallelism pearls : multicore and many-core programming approaches by Reinders, James, Jeffers, Jim (Computer engineer)

Table of Contents: “…; The Hyper-Thread Phalanx hand-partitioning technique; A lesson learned; Back to work; Data alignment; Use aligned data when possible; Redundancy can be good for you; The plesiochronous phasing barrier; Let us do something to recover this wasted time; A few "left to the reader" possibilities.…”
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Deep learning with Python : a hands-on introduction by Ketkar, Nikhil

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Robust and error-free geometric computing by Eberly, Dave

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New Horizons in Mobile and Wireless Communications, Volume 1 : Radio Interfaces. by Prasad, Ramjee

Table of Contents: “…1.3 Research and Standardization Activities Toward New Radio Interfaces1.3.1 European-Funded Research Activities; 1.3.2 Other Activities; 1.4 Preview of the Book; References; Chapter 2 Spectrum-Efficient Radio Interface Technologies; 2.1 Introduction; 2.1.1 Radio Interfaces for Ubiquitous Communications; 2.1.2 OFDM-Based Radio Interfaces in the Scope of Next Generation Systems; 2.1.3 Coexistence and Spectrum Sharing; 2.1.4 Opportunities for Secondary Spectrum Use; 2.1.5 Multiband Transmissions; 2.2 Radio Interfaces Optimized for PANs; 2.2.1 Scenarios and Radio Propagation Models for PANs.…”
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Microwave and millimeter wave circuits and systems : emerging design, technologies, and applications

Table of Contents: “…1.1.7 MBF Model -- the Memoryless PA Behavioural Model of ChoiceAcknowledgements; References; 2 Artificial Neural Network in Microwave Cavity Filter Tuning; 2.1 Introduction; 2.2 Artificial Neural Networks Filter Tuning; 2.2.1 The Inverse Model of the Filter; 2.2.2 Sequential Method; 2.2.3 Parallel Method; 2.2.4 Discussion on the ANN's Input Data; 2.3 Practical Implementation -- Tuning Experiments; 2.3.1 Sequential Method; 2.3.2 Parallel Method; 2.4 Influence of the Filter Characteristic Domain on Algorithm Efficiency; 2.5 Robots in the Microwave Filter Tuning; 2.6 Conclusions; Acknowledgement…”
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Computational Seismology : a Practical Introduction. by Igel, Heiner

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Inventory management : from warehouse to distribution center by Viale, J. David

Table of Contents: “…EXERCISE 1: ObservationsTHE BASIC MANUFACTURING EQUATION -- KEY FINANCIAL RATIOS -- EXERCISE 2: Calculating Ratios -- FINANCIAL EVALUATION CHECKLIST -- Balance Sheet -- Income Statement -- Other Issues -- IDENTIFYING SUPPLIERS WITH POTENTIAL CASH-FLOW PROBLEMS -- COSTED BILL OF MATERIALS -- Explanation of Cost Buildup Product F (1 unit) -- Questions to Ask When Reviewing a Costed Bill of Materials -- ALLOCATION OF FACTORY OVERHEAD AND ACTIVITY-BASED COSTING -- Inventory Valuation -- EXERCISE 3: Valuing Inventory -- EXERCISE 4: Choose the Correct Answer…”
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Data analysis with R : load, wrangle, and analyze your data using the world's most powerful statistical programming language by Fischetti, Tony

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Intelligent IoT for the Digital World : Incorporating 5G Communications and Fog/Edge Computing Technologies. by Yang, Yang

Table of Contents: “…1.3.1 Data Collection Technologies -- 1.3.1.1 mmWave -- 1.3.1.2 Massive MIMO -- 1.3.1.3 Software Defined Networks -- 1.3.1.4 Network Slicing -- 1.3.1.5 Time Sensitive Network -- 1.3.1.6 Multi-user Access Control -- 1.3.1.7 Muti-hop Routing Protocol -- 1.3.2 Computing Power Network -- 1.3.2.1 Intelligent IoT Computing Architecture -- 1.3.2.2 Edge and Fog Computing -- 1.3.3 Intelligent Algorithms -- 1.3.3.1 Big Data -- 1.3.3.2 Artificial Intelligence -- 1.4 Typical Applications -- 1.4.1 Environmental Monitoring -- 1.4.2 Public Safety Surveillance -- 1.4.3 Military Communication…”
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Advanced reliability modeling : proceedings of the 2004 Asian International Workshop (AIWARM 2004) : Hiroshima, Japan, 26-27 August 2004

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Business Hack : the Wealth Dragon Way to Build a Successful Business in the Digital Age. by Lee, John

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Creating E-Learning Games with Unity. by Horachek, David

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