Search Results - (((((((anti OR want) OR mantis) OR when) OR cantor) OR anne) OR share) OR hints) algorithms.

Introduction to dynamics and control of flexible structures by Junkins, John L.

Table of Contents: “…3.2 Lyapunov's Stability Theorem (Direct Method)3.3 Stability of Linear Systems -- Lyapunov Theorem for Linear Systems -- Linear Dynamic Systems Subject to Arbitrary Disturbances -- Stability Analysis for Mechanical Second Order Systems -- 3.4 Nonlinear, Time Varying, and Distributed Parameter Systems -- Local Stability of Linearized Systems -- What to do When U is Only Negative Semi-Definite -- Lyapunov Control Law Design Method -- Work Energy Rate Principle and Laypunov Stable Control Laws -- Globally Stable Tracking Controller: Lyapunov Approach…”
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Developing and validating test items by Haladyna, Thomas M.

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Phase estimation in optical interferometry

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The Oxford guide to library research by Mann, Thomas, 1948-

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Medical Response to Terrorism : Preparedness and Clinical Practice. by Keyes, Daniel C.

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The medical imagination : literature and health in the early United States by Altschuler, Sari

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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. …”
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Advanced Python Programming : Build High Performance, Concurrent, and Multi-Threaded Apps with Python Using Proven Design Patterns. by Lanaro, Gabriele

Table of Contents: “…Adding static typesVariables; Functions; Classes; Sharing declarations; Working with arrays; C arrays and pointers; NumPy arrays; Typed memoryviews; Particle simulator in Cython; Profiling Cython; Using Cython with Jupyter; Summary; Chapter 5: Exploring Compilers; Numba; First steps with Numba; Type specializations; Object mode versus native mode; Numba and NumPy; Universal functions with Numba; Generalized universal functions; JIT classes; Limitations in Numba; The PyPy project; Setting up PyPy; Running a particle simulator in PyPy; Other interesting projects; Summary…”
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Leadership and cultural webs in organisations : Weavers' tales by McLean, A. J. (Adrian J.)

Table of Contents: “…Organisation as Algorithm.…”
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Haptic Feedback Teleoperation of Optical Tweezers by Ni, Zhenjiang

Table of Contents: “…Specific designs for haptic interactions; 1.4.1. Temporal sharing; 1.4.2. Spatial sharing; 1.5. Discussion; 1.6. …”
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A cabinet of philosophical curiosities : a collection of puzzles, oddities, riddles, and dilemmas by Sorensen, Roy A.

Table of Contents: “…; Descartes's Disappearance; The Most Fairly Distributed Good; Fairness Framed; Towards a Fairer Share of Dishwashing; What the Dishwasher Missed; Developmental Self-Defeat; Random Quiz; Enforcing Gresham's Law; Gresham's Law of Numbers; Laziest Reductio.…”
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Field guide to the normal newborn by Emmett, Gary A.

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Machine Learning with Spark - Second Edition. by Dua, Rajdeep

Table of Contents: “…Generating predictions for the Kaggle/StumbleUpon evergreen classification dataset -- Evaluating the performance of classification models -- Accuracy and prediction error -- Precision and recall -- ROC curve and AUC -- Improving model performance and tuning parameters -- Feature standardization -- Additional features -- Using the correct form of data -- Tuning model parameters -- Linear models -- Iterations -- Step size -- Regularization -- Decision trees -- Tuning tree depth and impurity -- The naive Bayes model -- Cross-validation -- Summary -- Chapter 7: Building a Regression Model with Spark -- Types of regression models -- Least squares regression -- Decision trees for regression -- Evaluating the performance of regression models -- Mean Squared Error and Root Mean Squared Error -- Mean Absolute Error -- Root Mean Squared Log Error -- The R-squared coefficient -- Extracting the right features from your data -- Extracting features from the bike sharing dataset -- Training and using regression models -- BikeSharingExecutor -- Training a regression model on the bike sharing dataset -- Linear regression -- Generalized linear regression -- Decision tree regression -- Ensembles of trees -- Random forest regression -- Gradient boosted tree regression -- Improving model performance and tuning parameters -- Transforming the target variable -- Impact of training on log-transformed targets -- Tuning model parameters -- Creating training and testing sets to evaluate parameters -- Splitting data for Decision tree -- The impact of parameter settings for linear models -- Iterations -- Step size -- L2 regularization -- L1 regularization -- Intercept -- The impact of parameter settings for the decision tree -- Tree depth -- Maximum bins -- The impact of parameter settings for the Gradient Boosted Trees -- Iterations -- MaxBins -- Summary.…”
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World's greatest architect : making, meaning, and network culture by Mitchell, William J. (William John), 1944-2010

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Applied Missing Data Analysis in the Health Sciences. by Zhou, Xiao-Hua

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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: “…3.3 Constrained, Unconstrained, and Discrete Optimization3.3.1 Constrained Optimization; 3.3.2 Unconstrained Optimization; 3.3.3 Discrete Optimization; 3.4 Surrogate Modeling; 3.4.1 Overview; 3.4.2 Optimization Procedure; 3.4.3 Surrogate Modeling Approach; 3.4.3.1 Response Surface Approximation (RSA) Model; 3.4.3.2 Artificial Neural Network (ANN) Model; 3.4.3.3 Kriging Model (KRG) Model; 3.4.3.4 PRESS-Based-Averaging (PBA) Model; 3.4.3.5 Simple Average (SA) Model; 3.5 Error Estimation; 3.5.1 General Errors When Simulating and Optimizing a Turbomachinery System…”
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Mobile magic : the saatchi and saatchi guide to mobile marketing and design by Eslinger, Tom

Table of Contents: “…. ; Surprise and Delight; A Short Radius Goes a Long Way; Cumulative Location-Tracking; The Creep Factor: When Location Goes Too Far; Surprise and Delight vs. …”
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Decision Intelligence for Dummies by Baker, Pamela

Table of Contents: “…Any road will take you there -- The great rethink when it comes to making decisions at scale -- Applying the Upside-Down V: The Path to the Output and Back Again -- Evaluating Your Inverted V Revelations -- Having Your Inverted V Lightbulb Moment -- Recognizing Why Things Go Wrong -- Aiming for too broad an outcome -- Mimicking data outcomes -- Failing to consider other decision sciences -- Mistaking gut instincts for decision science -- Failing to change the culture -- Part 2 Reaching the Best Possible Decision -- Chapter 5 Shaping a Decision into a Query -- Defining Smart versus Intelligent -- Discovering That Business Intelligence Is Not Decision Intelligence -- Discovering the Value of Context and Nuance -- Defining the Action You Seek -- Setting Up the Decision -- Chapter 6 Mapping a Path Forward -- Putting Data Last -- Recognizing when you can (and should) skip the data entirely -- Leaning on CRISP-DM -- Using the result you seek to identify the data you need -- Digital decisioning and decision intelligence -- Don't store all your data - know when to throw it out -- Adding More Humans to the Equation -- The shift in thinking at the business line level -- How decision intelligence puts executives and ordinary humans back in charge -- Limiting Actions to What Your Company Will Actually Do -- Looking at budgets versus the company will -- Setting company culture against company resources -- Using long-term decisioning to craft short-term returns -- Chapter 7 Your DI Toolbox -- Decision Intelligence Is a Rethink, Not a Data Science Redo -- Taking Stock of What You Already Have -- The tool overview -- Working with BI apps -- Accessing cloud tools -- Taking inventory and finding the gaps -- Adding Other Tools to the Mix -- Decision modeling software -- Business rule management systems -- Machine learning and model stores -- Data platforms.…”
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Advances in Materials Processing Technologies, 2006. by Marcos, M.

Table of Contents: “…Advances in Materials Processing Technologies, 2006; Preface; Table of Contents; Advances in Material Processing Technologies; A Heuristic Approach for Decision-Making on Assembly Systems for Mass Customization ; A New Method for Determining the Chip Geometry in Milling; Analysis and Validation of Cutting Forces Prediction Models in Micromachining; Analysis of Stress and Strain in the Equal Channel Angular Drawing Process; Analysis of the Behaviour Effect of Face Cutting Edge Inserts on Surface Roughness when Milling Steels with MQL Lubrication.…”
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Cinder Creative Coding Cookbook. by Dawid Gorny, Rui Madeira

Table of Contents: “…Creating a simple video controllerSaving window content as an image; Saving window animations as video; Saving window content as a vector graphics image; Saving high resolution images with the tile renderer; Sharing graphics between applications; Building Particle Systems; Introduction; Creating a particle system in 2D; Applying repulsion and attraction forces; Simulating particles flying in the wind; Simulating flocking behavior; Making our particles sound reactive; Aligning particles to a processed image; Aligning particles to the mesh surface; Creating springs.…”
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