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Casleton, Emily Michele

Publications and source records attributed to Casleton, Emily Michele.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

How is statistical thinking useful for evaluating foundation models?

Good’s statement appears to be rather prescient – while humanity has marched on through to the 21st century, modern artificial intelligence (AI), driven by a menagerie of immense deep-neural-network architectures aided with parallel computation, seems positioned to change life as we know it. It would be an understatement to say that the world has been captivated by AI. Recent engineering feats, such as autonomous vehicles and generative AI, have gripped the public sphere, and AI is poised to disrupt a host of industries – including software, pharmaceuticals, healthcare, manufacturing, entertainment, and a slew of others; indeed, one is hard pressed to find any industry that does not claim to bear an impact from the so-called AI revolution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Multi-INT Signature Collection and Exploitation for Security: Improving Discrimination, Analyses, and Passive Tracking Capabilities for Time- and Distance-Varying Signatures in the Seismoacoustic Regime

This project demonstrates the feasibility of a passive tracking capability for ground-based and airborne moving sources using multiple information streams from acoustic and seismic signals. These moving source types are characterized by their variability with time and distance (exemplified but not limited to the familiar doppler shift of a passing vehicle) and the signatures used for detection should have similar characteristics. We have demonstrated that time-varying sensor signatures (TVS) combined with time-stable sensor signatures provide a more complete understanding of moving source behavior. Importantly, we have collected a new, relevant LANL dataset of moving sources (small uncrewed aerial vehicle flights and ground vehicle movement), developed new signatures, and demonstrated the capability to identify, track and forecast the position of these moving sources.

42 ENGINEERING↗
DOE OSTI2021

Introduction to the Consortium for Monitoring, Technology, and Verification (MTV) [Slides]

Our mission is to develop new technologies that detect and deter nuclear proliferation activities while training the next generation of nuclear professionals. The three technical thrust areas are: 1. Fundamentals of nuclear and particle physics, 2. Signals and source terms for nuclear nonproliferation, and 3. Nuclear explosion. There are opportunities for collaborations with university faculty, possible student and post-doc work, and posting job ads. Further, MTV hosts various workshops, symposiums.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2021

MINOS Infrasound Analysis Synopsis

This report was written as a guide to working with infrasound data collected as part of the Multi-Informatics for Nuclear Operations Scenarios (MINOS)project, an NA-22 funded venture. The main purpose of overall MINOS project is the combination of multiple, disparate data modalities to characterize the operations at a nuclear facility, specifically instrumenting and studying the High-Flux Isotope Reactor (HFIR) and Radiochemical Engineering Development Center (REDC) locate at Oak Ridge National Laboratory in Oak Ridge, TN. HFIR is an 85 MW research reactor and is used primarily for production of medical radioisotopes, material irradiation experiments, neutron activation, and neutron scattering. Targets for the reactor are constructed, processed, and dissolved at REDC. REDC also hosts other glove-box and hot-cell type activities.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Building Interfaces with Echo Using R

The following report details the functionality that was developed to directly query the MongoDB and read in echo records from within R, and the ability to create a cinema database directly from echo records. Both of these efforts used the data collected under the MINOS project for development. The nuclear facility under consideration is the High-Flux Isotope Reactor (HFIR) and co-located Radiochemical Engineering Development Center (REDC) at Oak Ridge National Laboratory in Oak Ridge, TN. HFIR is an 85 MW research reactor and is used primarily for production of a medical radioisotopes, material irradiation experiments, neutron activation, and neutron scattering. Targets for the reactor are fabricated and processed and dissolved at REDC, in addition to other glove-box and hot-cell type activities. An overhead view of the facility is provided. The rest of the report is organized as follows: Section 2 describes how to query the MongoDB and load in echo records from within R, with background information on the database type, MongoDB, and the file format of echo records, hdf5, as well as lessons learned during the development phase. Section 3 details the functionality and usage of creating a cinema database directly from echo records within R. A series of appendixes display all the R code (Section 5), some example scripts where the code is used (Section 6), and useful links for further exploration (Section 7).

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2019

Climate variability and fill rate impacts on downstream flows from the Grand Ethiopian Renaissance Dam [Sheets]

Conclusions of this report include: Flow conditions (wet, normal, dry) are strong determinant of success in filling the dam; Some improvement for longer fill periods; Downstream Flow Compact only met under wetter flow conditions, rare otherwise; Results generated using other inflow datasets (not shown) indicate selection of data important for filling dam and highlight difficulty in determining which one to trust.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Imputation for multisource data with comparison and assessment techniques

Missing data are prevalent issue in analyses involving data collection. The problem of missing data is exacerbated for multisource analysis, where data from multiple sensors are combined to arrive at a single conclusion. In this scenario, it is more likely to occur and can lead to discarding a large amount of data collected; however, the information from observed sensors can be leveraged to estimate those values not observed. We propose two methods for imputation of multisource data, both of which take advantage of potential correlation between data from different sensors, through ridge regression and a state-space model. These methods, as well as the common median imputation, are applied to data collected from a variety of sensors monitoring an experimental facility. Performance of imputation methods is compared with the mean absolute deviation; however, rather than using this metric to solely rank themethods,we also propose an approach to identify significant differences. Imputation techniqueswill also be assessed by their ability to produce appropriate confidence intervals, through coverage and length, around the imputed values. Finally, performance of imputed datasets is compared with a marginalized dataset through a weighted k-means clustering. In general, we found that imputation through a dynamic linearmodel tended to be the most accurate and to produce the most precise confidence intervals, and that imputing the missing values and down weighting them with respect to observed values in the analysis led to the most accurate performance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Choosing the best partition of the output from a large-scale simulation

Data partitioning becomes necessary when a large-scale simulation produces more data than can be feasibly stored. The goal is to partition the data, typically so that every element belongs to one and only one partition, and store summary information about the partition, either a representative value plus an estimate of the error or a distribution. Once the partitions are determined and the summary information stored, the raw data is discarded. This process can be performed in-situ; meaning while the simulation is running. When creating the partitions there are many decisions that researchers must make. For instance, how to determine once an adequate number of partitions have been created, how are the partitions created with respect to dividing the data, or how many variables should be considered simultaneously. In addition, decisions must be made for how to summarize the information within each partition. Because of the combinatorial number of possible ways to partition and summarize the data, a method of comparing the different possibilities will help guide researchers into choosing a good partitioning and summarization scheme for their application.

97 MATHEMATICS AND COMPUTING↗