Search Results - (((((((ant OR win) OR mantis) OR find) OR cantor) OR anne) OR shared) OR hints) algorithms.

Adventures in Authentic Learning : 21 Step-by-Step Projects From an Edtech Coach. by Harrington, Kristin

Table of Contents: “…Lesson Plans -- Lesson Plan 2.1 PechaKucha Presentations -- Lesson Plan 2.2 Life Cycles Jigsaw Research Project -- Lesson Plan 2.3 Math Jigsaw Project -- Lesson Plan 2.4 Student-Created Tutorial Videos -- Coach's Connection -- CHAPTER 3: Collaborate for Success -- Some Lesser-Known Educator Sharing Tools -- Enhance Projects with Content Experts -- Provide an Authentic Audience -- Engaging Students in Peer Review -- Lesson Plans -- Lesson Plan 3.1 Algorithmic Thinking Project -- Lesson Plan 3.2 Pick Your Path Stories -- Lesson Plan 3.3 Invent It Challenge…”
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A Statistical approach to genetic epidemiology : concepts and applications, with an e-Learning Platform. by Ziegler, Andreas, 1966-

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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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Learning Python Design Patterns - Second Edition. by Giridhar, Chetan

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Process control design for industrial applications by Popescu, Dumitru (Writer on computer science and engineering), Gharbi, Amira, Stefanoiu, Dan, Borne, Pierre

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Towards Cognitive Autonomous Networks : Network Management Automation for 5G and Beyond. by Mwanje, Stephen S.

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Radio Propagation in the Urban Scenario. by Franceshetti, Giorgio

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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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A guide to Monte Carlo simulations in statistical physics by Landau, David P.

Table of Contents: “…3.7 Finding the groundstate of a Hamiltonian -- 3.8 Generation of 'random' walks -- 3.8.1 Introduction -- 3.8.2 Random walks -- 3.8.3 Self-avoiding walks -- 3.8.4 Growing walks and other models -- 3.9 Final remarks -- References -- 4 Importance sampling Monte Carlo methods -- 4.1 Introduction -- 4.2 The simplest case: single spin-flip sampling for the simple Ising model -- 4.2.1 Algorithm -- 4.2.2 Boundary conditions -- 4.2.3 Finite size effects -- 4.2.4 Finite sampling time effects -- 4.2.5 Critical relaxation -- 4.3 Other discrete variable models.…”
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Advanced numerical and semi analytical methods for differential equations by Chakraverty, Snehashish

Table of Contents: “…8.2.2.1 Heaviside Function8.2.2.2 Dirac Delta Function; 8.2.2.3 Finding the Fundamental Solution; 8.2.3 Green's Function; 8.2.3.1 Green's Integral Formula; 8.3 Derivation of the Boundary Element Method; 8.3.1 BEM Algorithm; References; Chapter 9 Akbari-Ganji's Method; 9.1 Introduction; 9.2 Nonlinear Ordinary Differential Equations; 9.2.1 Preliminaries; 9.2.2 AGM Approach; 9.3 Numerical Examples; 9.3.1 Unforced Nonlinear Differential Equations; 9.3.2 Forced Nonlinear Differential Equation; References; Chapter 10 Exp-Function Method; 10.1 Introduction; 10.2 Basics of Exp-Function Method…”
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Trading on sentiment : the power of minds over markets by Peterson, Richard L., 1972-

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Machine learning in non-stationary environments : introduction to covariate shift adaptation by Sugiyama, Masashi, 1974-

Table of Contents: “…5.2 Characterization of Hetero-Distributional Subspace5.3 Identifying Hetero-Distributional Subspace by Supervised Dimensionality Reduction; 5.4 Using LFDA for Finding Hetero-Distributional Subspace; 5.5 Density-Ratio Estimation in the Hetero-Distributional Subspace; 5.6 Numerical Examples; 5.7 Summary; 6 Relation to Sample Selection Bias; 6.1 Heckman's Sample Selection Model; 6.2 Distributional Change and Sample Selection Bias; 6.3 The Two-Step Algorithm; 6.4 Relation to Covariate Shift Approach; 7 Applications of Covariate Shift Adaptation; 7.1 Brain-Computer Interface.…”
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Disorders of the Patellofemoral Joint. by Fulkerson, John P.

Table of Contents: “…SOFT-TISSUE DYSPLASIANonarthritic Anterior Knee Pain; CLINICAL FEATURES; Symptoms; Physical Findings; RETINACULAR PAIN; PLICA OR SYNOVITIS; PATELLAR TENDINITIS (JUMPER'S KNEE); Prepatellar Bursitis; Retropatellar Tendon Bursitis; Pes Anserinus Bursitis; Fat Pad Syndrome; MENISCAL LESIONS; CRUCIATE LIGAMENT DEFICIENCY AND RECONSTRUCTION; HEMANGIOMA; RUNNERS' KNEE; ILIOTIBIAL FRICTION BAND SYNDROME; REFERRED PAIN; REHABILITATION OF SOFT-TISSUE PROBLEMS; Patellar Tilt Compression and the Excessive Lateral Pressure Syndrome; CLINICAL FEATURES; Signs and Symptoms.…”
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Node.js web development by Herron, David

Table of Contents: “…Node.js's algorithm for require (module)Module identifiers and path names; An example application directory structure; npm -- the Node.js package management system; The npm package format; Finding npm packages; Other npm commands; Installing an npm package; Initializing a new npm package; Maintaining package dependencies with npm; Fixing bugs by updating package dependencies; Declaring Node.js version compatibility; Updating outdated packages you've installed; Installing packages from outside the npm repository; Publishing an npm package; Package version numbers; A quick note about CommonJS.…”
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Tales of Literacy for the 21st Century : the Literary Agenda. by Wolf, Maryanne

Table of Contents: “…; Deep reading and what comes next; A first algorithm for what comes next; Notes; 7: A Tale of Hope for Non-Literate Children.…”
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Introduction to Machine Learning with Python. by Chopra, Deepti

Table of Contents: “…Machine Learning Involves A Complex Process -- Insufficient training data -- Feasibility of Learning An Unknown Target Function -- Collection of Data -- Pre-processing of Data -- Finding The Model That Will Be Best For The Data -- Training and Testing Of The Developed Model Evaluation -- In Sample Error and Out of Sample Error -- APPLICATIONS OF MACHINE LEARNING -- Virtual Personal Assistants -- Traffic Prediction -- Online Transportation Networks -- Video Surveillance System -- Social Media Services -- People you May Know -- Face Recognition -- Similar Pins -- Sentiment Analysis…”
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Stochastic optimization models in finance

Table of Contents: “…The Main Theorem and an Algorithm; V. Nonterminating Processes; ACKNOWLEDGMENT; REFERENCES; CHAPTER5. …”
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Ruby Under a Microscope : Learning Ruby Internals Through Experiment. by Shaughnessy, Pat

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Adaptive tests of significance using permutations of residuals with R and SAS by O'Gorman, Thomas W.

Table of Contents: “…2.3.1 An Estimator of Variability Based on Traditional Percentiles2.3.2 R Code for Finding the Bandwidth; 2.3.3 An Estimator of Variability Based on Percentiles from the Smoothed Distribution Function; 2.4 Normalizing Transformations; 2.4.1 Traditional Normalizing Methods; 2.4.2 Normalizing Data by Weighting; 2.5 The Weighting Algorithm; 2.5.1 An Example of the Weighing Procedure; 2.5.2 R Code for Weighting the Observations; 2.6 Computing the Bandwidth; 2.6.1 Error Distributions; 2.6.2 Measuring Errors in Adaptive Weighting; 2.6.3 Simulation Studies; 2.7 Examples of Transformed Data.…”
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