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Temporal Methods to Detect Content-Based Anomalies in Social Media

Social Network Analysis Lecture Notes Series

Field, Richard V.; Skryzalin, Jacek; Fisher, Anna L.; Bauer, Travis L.

Here, we develop a method for time-dependent topic tracking and meme trending in social media. Our objective is to identify time periods whose content differs signifcantly from normal, and we utilize two techniques to do so. The first is an information-theoretic analysis of the distributions of terms emitted during different periods of time. In the second, we cluster documents from each time period and analyze the tightness of each clustering. We also discuss a method of combining the scores created by each technique, and we provide ample empirical analysis of our methodology on various Twitter datasets.

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TYPE Journal Article YEAR 2017
OSTI

Developing a novel hierarchical approach for multiscale structural reliability predictions for ultra-high consequence applications

Emery, John M.; Coffin, Peter; Robbins, Brian; Carroll, J.D.; Field, Richard V.; Jeremy Yoo, Yung S.; Kacher, Josh

Microstructural variabilities are among the predominant sources of uncertainty in structural performance and reliability. We seek to develop efficient algorithms for multiscale calcu- lations for polycrystalline alloys such as aluminum alloy 6061-T6 in environments where ductile fracture is the dominant failure mode. Our approach employs concurrent multiscale methods, but does not focus on their development. They are a necessary but not sufficient ingredient to multiscale reliability predictions. We have focused on how to efficiently use concurrent models for forward propagation because practical applications cannot include fine-scale details throughout the problem domain due to exorbitant computational demand. Our approach begins with a low-fidelity prediction at the engineering scale that is sub- sequently refined with multiscale simulation. The results presented in this report focus on plasticity and damage at the meso-scale, efforts to expedite Monte Carlo simulation with mi- crostructural considerations, modeling aspects regarding geometric representation of grains and second-phase particles, and contrasting algorithms for scale coupling.

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TYPE SAND Report YEAR 2017
DOIOSTI

Data Inferencing on Semantic Graphs (DISeG) (Final Report)

Wendt, Jeremy; Quach, Tu T.; Zage, David J.; Field, Richard V.; Wells, Randall; Soundarajan, Sucheta; Cruz, Gerardo J.

The Data Inferencing on Semantic Graphs project (DISeG) was a two-year investigation of inferencing techniques (focusing on belief propagation) to social graphs with a focus on semantic graphs (also called multi-layer graphs). While working this problem, we developed a new directed version of inferencing we call Directed Propagation (Chapters 2 and 4), identified new semantic graph sampling problems (Chapter 3).

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TYPE SAND Report YEAR 2016
DOIOSTI

Bayesian methods for characterizing unknown parameters of material models

Applied Mathematical Modelling

Emery, John M.; Grigoriu, M.D.; Field, Richard V.

A Bayesian framework is developed for characterizing the unknown parameters of probabilistic models for material properties. In this framework, the unknown parameters are viewed as random and described by their posterior distributions obtained from prior information and measurements of quantities of interest that are observable and depend on the unknown parameters. The proposed Bayesian method is applied to characterize an unknown spatial correlation of the conductivity field in the definition of a stochastic transport equation and to solve this equation by Monte Carlo simulation and stochastic reduced order models (SROMs). The Bayesian method is also employed to characterize unknown parameters of material properties for laser welds from measurements of peak forces sustained by these welds.

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TYPE Journal Article YEAR 2016
DOIOSTIScopus
Results 26–50 of 123
Results 26–50 of 123