Publications

Naugle, Asmeret B.; Lakkaraju, Kiran L.; Balanaga, Vamshi B.; Warrender, Christina E.; Verzi, Stephen J.; Livesay, Michael L.; Swiler, Laura P.

Abstract not provided.

Schiek, Richard S.; Warrender, Christina E.; Thornquist, Heidi K.; Mei, Ting M.; Keiter, Eric R.; Russo, Thomas V.

Abstract not provided.

Speed, Ann S.; Warrender, Christina E.

In this work, we developed a self-organizing map (SOM) technique for using web-based text analysis to forecast when a group is undergoing a phase change. By 'phase change', we mean that an organization has fundamentally shifted attitudes or behaviors. For instance, when ice melts into water, the characteristics of the substance change. A formerly peaceful group may suddenly adopt violence, or a violent organization may unexpectedly agree to a ceasefire. SOM techniques were used to analyze text obtained from organization postings on the world-wide web. Results suggest it may be possible to forecast phase changes, and determine if an example of writing can be attributed to a group of interest.

Speed, Ann S.; Warrender, Christina E.

In this work, we developed a self-organizing map (SOM) technique for using web-based text analysis to forecast when a group is undergoing a phase change. By 'phase change', we mean that an organization has fundamentally shifted attitudes or behaviors. For instance, when ice melts into water, the characteristics of the substance change. A formerly peaceful group may suddenly adopt violence, or a violent organization may unexpectedly agree to a ceasefire. SOM techniques were used to analyze text obtained from organization postings on the world-wide web. Results suggest it may be possible to forecast phase changes, and determine if an example of writing can be attributed to a group of interest.

Doser, Adele D.; Speed, Ann S.; Warrender, Christina E.

In this work, we developed a self-organizing map (SOM) technique for using web-based text analysis to forecast when a group is undergoing a phase change. By 'phase change', we mean that an organization has fundamentally shifted attitudes or behaviors. For instance, when ice melts into water, the characteristics of the substance change. A formerly peaceful group may suddenly adopt violence, or a violent organization may unexpectedly agree to a ceasefire. SOM techniques were used to analyze text obtained from organization postings on the world-wide web. Results suggest it may be possible to forecast phase changes, and determine if an example of writing can be attributed to a group of interest.

McClain, Jonathan T.; Rothganger, Fredrick R.; Trumbo, Derek T.; Warrender, Christina E.

Abstract not provided.

DeBenedictis, Erik; Rothganger, Fredrick R.; Aimone, James B.; Marinella, Matthew J.; Evans, Brian R.; Warrender, Christina E.; Mickel, Patrick R.

Final report for Cognitive Computing for Security LDRD 165613. It reports on the development of hybrid of general purpose/ne uromorphic computer architecture, with an emphasis on potential implementation with memristors.

Lakkaraju, Kiran L.; Naugle, Asmeret B.; Verzi, Stephen J.; Swiler, Laura P.; Livesay, Michael L.; Warrender, Christina E.; Bernard, Michael L.; Romero, Vicente J.

Abstract not provided.

Rothganger, Fredrick R.; Warrender, Christina E.; Speed, Ann S.; Rohrer, Brandon R.; Naugle, Asmeret B.; Trumbo, Derek T.

Abstract not provided.

Siirola, John D.; Tidwell, Vincent C.; Warrender, Christina E.; Morrow, James D.; Benz, Zachary O.

Participatory modeling has become an important tool in facilitating resource decision making and dispute resolution. Approaches to modeling that are commonly used in this context often do not adequately account for important human factors. Current techniques provide insights into how certain human activities and variables affect resource outcomes; however, they do not directly simulate the complex variables that shape how, why, and under what conditions different human agents behave in ways that affect resources and human interactions related to them. Current approaches also do not adequately reveal how the effects of individual decisions scale up to have systemic level effects in complex resource systems. This lack of integration prevents the development of more robust models to support decision making and dispute resolution processes. Development of integrated tools is further hampered by the fact that collection of primary data for decision-making modeling is costly and time consuming. This project seeks to develop a new approach to resource modeling that incorporates both technical and behavioral modeling techniques into a single decision-making architecture. The modeling platform is enhanced by use of traditional and advanced processes and tools for expedited data capture. Specific objectives of the project are: (1) Develop a proof of concept for a new technical approach to resource modeling that combines the computational techniques of system dynamics and agent based modeling, (2) Develop an iterative, participatory modeling process supported with traditional and advance data capture techniques that may be utilized to facilitate decision making, dispute resolution, and collaborative learning processes, and (3) Examine potential applications of this technology and process. The development of this decision support architecture included both the engineering of the technology and the development of a participatory method to build and apply the technology. Stakeholder interaction with the model and associated data capture was facilitated through two very different modes of engagement, one a standard interface involving radio buttons, slider bars, graphs and plots, while the other utilized an immersive serious gaming interface. The decision support architecture developed through this project was piloted in the Middle Rio Grande Basin to examine how these tools might be utilized to promote enhanced understanding and decision-making in the context of complex water resource management issues. Potential applications of this architecture and its capacity to lead to enhanced understanding and decision-making was assessed through qualitative interviews with study participants who represented key stakeholders in the basin.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Speed, Ann S.; Silva, Austin R.; Trumbo, Derek T.; Stracuzzi, David J.; Warrender, Christina E.; Trumbo, Michael; Divis, Kristin

The Transportation Security Administration has a large workforce of Transportation Security Officers, most of whom perform interrogation of x-ray images at the passenger checkpoint. To date, TSOs on the x-ray have been limited to a 30-min session at a time, however, it is unclear where this limit originated. The current paper outlines methods for empirically determining if that 30-min duty cycle is optimal and if there are differences between individual TSOs. This work can inform scheduling TSOs at the checkpoint and can also inform whether TSOs should continue to be cross-trained (i.e., performing all 6 checkpoint duties) or whether specialization makes more sense.

Speed, Ann S.; Silva, Austin R.; Trumbo, Derek T.; Trumbo, Michael C.; Stracuzzi, David J.; Warrender, Christina E.; Divis, Kristin

Abstract not provided.

Aimone, James B.; James, Conrad D.; Warrender, Christina E.

Abstract not provided.

Rothganger, Fredrick R.; Hoemmen, Mark F.; Phipps, Eric T.; Warrender, Christina E.

Abstract not provided.

Journal of Simulation

Naugle, Asmeret B.; Verzi, Stephen J.; Lakkaraju, Kiran L.; Swiler, Laura P.; Warrender, Christina E.; Bernard, Michael L.; Romero, Vicente J.

Measures of simulation model complexity generally focus on outputs; we propose measuring the complexity of a model’s causal structure to gain insight into its fundamental character. This article introduces tools for measuring causal complexity. First, we introduce a method for developing a model’s causal structure diagram, which characterises the causal interactions present in the code. Causal structure diagrams facilitate comparison of simulation models, including those from different paradigms. Next, we develop metrics for evaluating a model’s causal complexity using its causal structure diagram. We discuss cyclomatic complexity as a measure of the intricacy of causal structure and introduce two new metrics that incorporate the concept of feedback, a fundamental component of causal structure. The first new metric introduced here is feedback density, a measure of the cycle-based interconnectedness of causal structure. The second metric combines cyclomatic complexity and feedback density into a comprehensive causal complexity measure. Finally, we demonstrate these complexity metrics on simulation models from multiple paradigms and discuss potential uses and interpretations. These tools enable direct comparison of models across paradigms and provide a mechanism for measuring and discussing complexity based on a model’s fundamental assumptions and design.

Naugle, Asmeret B.; Swiler, Laura P.; Lakkaraju, Kiran L.; Verzi, Stephen J.; Warrender, Christina E.; Romero, Vicente J.

The causal structure of a simulation is a major determinant of both its character and behavior, yet most methods we use to compare simulations focus only on simulation outputs. We introduce a method that combines graphical representation with information theoretic metrics to quantitatively compare the causal structures of models. The method applies to agent-based simulations as well as system dynamics models and facilitates comparison within and between types. Comparing models based on their causal structures can illuminate differences in assumptions made by the models, allowing modelers to (1) better situate their models in the context of existing work, including highlighting novelty, (2) explicitly compare conceptual theory and assumptions to simulated theory and assumptions, and (3) investigate potential causal drivers of divergent behavior between models. We demonstrate the method by comparing two epidemiology models at different levels of aggregation.

Naugle, Asmeret B.; Swiler, Laura P.; Lakkaraju, Kiran L.; Verzi, Stephen J.; Warrender, Christina E.; Bernard, Michael L.; Romero, Vicente J.

Abstract not provided.

Rothganger, Fredrick R.; Trumbo, Derek T.; Warrender, Christina E.; Aimone, James B.

Abstract not provided.

Chance, Frances S.; Warrender, Christina E.

The retina plays an important role in animal vision --- namely to pre-process visual information before sending it to the brain. The goal of this LDRD was to develop models of motion-sensitive retinal cells for the purpose of developing retinal-inspired algorithms to be applied to real-world data specific to Sandia's national security missions. We specifically focus on detection of small, dim moving targets amidst varying types of clutter or distractor signals. We compare a classic motion-sensitive model, the Hassenstein-Reichardt model, to a model of the OMS (object motion- sensitive) cell, and find that the Reichardt model performs better under continuous clutter (e.g. white noise) but is very sensitive to particular stimulus conditions (e.g. target velocity). We also demonstrate that lateral inhibition, a ubiquitous characteristic of neural circuitry, can effect target-size tuning, improving detection specifically of small targets.

Burnham, Laurie B.; Warrender, Christina E.; Cole, Kerstan S.; Forsythe, James C.; Haass, Michael J.; Adams, Susan S.

Abstract not provided.

Warrender, Christina E.; Cole, Kerstan S.; Forsythe, James C.; Haass, Michael J.; Adams, Susan S.

Abstract not provided.

Fan, Hongyou F.; Forsythe, James C.; Branda, Catherine B.; Warrender, Christina E.; Schiek, Richard S.

This report summarizes accomplishments of a three-year project focused on developing technical capabilities for measuring and modeling neuronal processes at the nanoscale. It was successfully demonstrated that nanoprobes could be engineered that were biocompatible, and could be biofunctionalized, that responded within the range of voltages typically associated with a neuronal action potential. Furthermore, the Xyce parallel circuit simulator was employed and models incorporated for simulating the ion channel and cable properties of neuronal membranes. The ultimate objective of the project had been to employ nanoprobes in vivo, with the nematode C elegans, and derive a simulation based on the resulting data. Techniques were developed allowing the nanoprobes to be injected into the nematode and the neuronal response recorded. To the authors's knowledge, this is the first occasion in which nanoparticles have been successfully employed as probes for recording neuronal response in an in vivo animal experimental protocol.

Vineyard, Craig M.; Aimone, James B.; Bernard, Michael L.; Carlson, Kristofor D.; Chance, Frances S.; Forsythe, James C.; James, Conrad D.; Rothganger, Fredrick R.; Severa, William M.; Speed, Ann S.; Verzi, Stephen J.; Warrender, Christina E.; Wagner, John S.; Miller, Leann A.

Abstract not provided.

Warrender, Christina E.; Forsythe, James C.; Ravula, Surendra K.; Keiter, Eric R.; Russo, Thomas V.; Thornquist, Heidi K.

Abstract not provided.

Schiek, Richard S.; Mei, Ting M.; Rajamanickam, Sivasankaran R.; Keiter, Eric R.; Warrender, Christina E.; Aimone, James B.; Thornquist, Heidi K.; Russo, Thomas V.; Verley, Jason V.; Crossno, Patricia J.

Abstract not provided.