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Full-Text Articles in Physical Sciences and Mathematics

On The Construction Of More Lifelike Devices, Shawn Lawrence Beaulieu Jan 2023

On The Construction Of More Lifelike Devices, Shawn Lawrence Beaulieu

Graduate College Dissertations and Theses

Devices which blur the distinction between the living and the inanimate are being reported with heightened frequency. But what forms of organization, and what modes of internal change and worldly interaction, are required for truly lifelike devices, rather than ones which abstractly mimic life in simulation? This thesis presents results from two publications which attempt to mitigate a problem known to limit the performance of artificial neural networks, called ``catastrophic forgetting"; and a third paper which tries to articulate a vision for the construction of more lifelike devices—whose form, function, and putative environment are not conclusively specified prior to their …


Developing Natural Language Processing Instruments To Study Sociotechnical Systems, Thayer Alshaabi Jan 2021

Developing Natural Language Processing Instruments To Study Sociotechnical Systems, Thayer Alshaabi

Graduate College Dissertations and Theses

Identifying temporal linguistic patterns and tracing social amplification across communities has always been vital to understanding modern sociotechnical systems. Now, well into the age of information technology, the growing digitization of text archives powered by machine learning systems has enabled an enormous number of interdisciplinary studies to examine the coevolution of language and culture. However, most research in that domain investigates formal textual records, such as books and newspapers. In this work, I argue that the study of conversational text derived from social media is just as important. I present four case studies to identify and investigate societal developments in …


Smart Classifiers And Bayesian Inference For Evaluating River Sensitivity To Natural And Human Disturbances: A Data Science Approach, Kristen Underwood Jan 2018

Smart Classifiers And Bayesian Inference For Evaluating River Sensitivity To Natural And Human Disturbances: A Data Science Approach, Kristen Underwood

Graduate College Dissertations and Theses

Excessive rates of channel adjustment and riverine sediment export represent societal challenges; impacts include: degraded water quality and ecological integrity, erosion hazards to infrastructure, and compromised public safety. The nonlinear nature of sediment erosion and deposition within a watershed and the variable patterns in riverine sediment export over a defined timeframe of interest are governed by many interrelated factors, including geology, climate and hydrology, vegetation, and land use. Human disturbances to the landscape and river networks have further altered these patterns of water and sediment routing.

An enhanced understanding of river sediment sources and dynamics is important for stakeholders, and …


Temporal Feature Selection With Symbolic Regression, Christopher Winter Fusting Jan 2017

Temporal Feature Selection With Symbolic Regression, Christopher Winter Fusting

Graduate College Dissertations and Theses

Building and discovering useful features when constructing machine learning models is the central task for the machine learning practitioner. Good features are useful not only in increasing the predictive power of a model but also in illuminating the underlying drivers of a target variable. In this research we propose a novel feature learning technique in which Symbolic regression is endowed with a ``Range Terminal'' that allows it to explore functions of the aggregate of variables over time. We test the Range Terminal on a synthetic data set and a real world data in which we predict seasonal greenness using satellite …


Reverse Engineering The Human Brain: An Evolutionary Computation Approach To The Analysis Of Fmri, Nicholas Allgaier Jan 2015

Reverse Engineering The Human Brain: An Evolutionary Computation Approach To The Analysis Of Fmri, Nicholas Allgaier

Graduate College Dissertations and Theses

The field of neuroimaging has truly become data rich, and as such, novel analytical methods capable of gleaning meaningful information from large stores of imaging data are in high demand. Those methods that might also be applicable on the level of individual subjects, and thus potentially useful clinically, are of special interest. In this dissertation we introduce just such a method, called nonlinear functional mapping (NFM), and demonstrate its application in the analysis of resting state fMRI (functional Magnetic Resonance Imaging) from a 242-subject subset of the IMAGEN project, a European study of risk-taking behavior in adolescents that includes longitudinal …