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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

Outliers of dust along the southern margin of the Tharsis region, Mars

This work documents the distinctive characteristics of two locations near the southeastern margin of the Tharsis low thermal inertia region on Mars that support the inferred south-to-north migration of dust deposits under present climatic conditions (Christensen, 1986, 1988). Low thermal inertia and high albedo indicate the presence of fine-grained surface materials on the highly fractured terrain of northern Claritas Fossae with a narrow zone of fine material connecting this location to the southern margin of the Tharsis low thermal inertia region. A similar deposit occurs in Sinai Planum on an isolated exposure of fractured terrain. In comparison to their surroundings, both locations have higher visual albedo, lower thermal inertia, and higher topographic relief coupled with lower reflectivity and higher roughness at radar wavelengths.

Zimbelman, J. R.↗
NASA NTRS2006

Improving Neural Network Generalization Ability Using Outlier Analysis and Voronoi Tessellation

The data used in this study was obtained from the Sloan Digital Sky Survey (SDSS), which provides astronomers with what is currently the most extensive mapping of the universe, covering 25% of the sky and cataloging the spectral properties (e.g., luminosity, color, surface temperature) of over 100 million celestial objects. Images generated by the SDSS are collected through 5 filters named u, g, r, l, and z that have respective wavelengths of 3540, 4750, 6222, 7632, and 9049 A. By measuring the photometric redshifts of the aforementioned wavelengths of a galaxy, astronomers can ascertain the extent to which galaxy is receding from which the distance to the galaxy can be calculated. Data collected for a small select group of galaxies (approximately 30, 000) contains accurate measurements of the galaxies' redshifts, in addition to measurements of their spectral properties. The above dataset containing both redshift measurements as well as spectral properties of the selected galaxies served as the training set for the purposes of this study; the data set containing only the spectra properties of a separate group of galaxies served as the test set.

Ho, Michelle↗
NASA NTRS2006

Gamma-Ray Burst Associated Supernovae: Outliers Become Mainstream

During the last eight years a clear connection has been established-between the two most powerful explosions in our Universe: core-collapse supernovae (SNe) and long gamma ray bursts (GRBs). Theory suggests4 that every GRB is simultaneously accompanied by a SN, but in only a few nearby cases have these two phenomena been observed together. We report the discovery and daily monitoring of SN 2006aj associated with the GRB 060218. Because the event was the second closest GRB, both explosions could be examined in detail. GRB 060218 had an unusually soft spectrum, long duration, and a total energy 100 to 1000 times less than most other GRBs. Yet SN 2006aj was similar to those in other GRBs, aside from rising more rapidly and being approximately 40% fainter. Taken together, these observations suggest that GRBs have two components: a broad, energetic, but only mildly relativistic outflow that makes a SN, and a more narrowly focused, highly relativistic jet responsible for the GRB. The properties of the GRB jet apparently vary greatly from event to event, while the broad SN outflow varies much less. Low energy transients like GRB 060218 may be the most common events in the Universe.

Pian, E.↗
NASA NTRSDate not supplied

Cluster-Based Flight Trajectory Outlier Detection

Given a set of flight trajectories, can we classify the trajectories that do not follow a normal path? By identifying abnormal trajectories, further analysis can be done to determine the reasoning for these actions. Addressing these scenarios can bring possible solutions for holding and rerouting problems when its time to incorporate UAM in the airspace.

Machine Learning↗
DOE OSTI2022

Six Machine-Learning Methods for Predicting Hospital-Stay Duration for Patients with Sepsis: A Comparative Study

Sepsis is a life-threatening medical condition that, if not treated promptly, can result in tissue damage, organ failure, and death. According to the Centers for Disease Control, about 270,000 individuals die of sepsis in the US each year. Further, sepsis expenditures accounted for 13% of total US hospital costs in 2013, totaling more than $24 billion. Our project objectives were to determine if Machine Learning algorithms could reliably predict hospital stay duration for patients with sepsis. The data set we used has been de-identified and is freely available through the BupaR package. The data includes 1050 cases, 15214 events, and 16 types of actions related to sepsis patient care. First, we used process mining to determine how long each patient was in the hospital. Using BupaR’s functions, we created several process model graphs. These process models depict the movement of patients at a hospital and provide duration data for each patent case. Second, we identified outlier data and created two dataset versions: one with and one without outliers. We then applied the following analysis methods: Linear Regression, Random Forest, K-Nearest Neighbors, Neural Networks, XGBoost, and lightGBM. We compared the model validations for the six machine learning models using the same data-splitting method. We found that the XGBoost model had the best prediction accuracy of 73.9 percent for cases with outliers, and 79 percent for cases without outliers. We also found that the lightGBM model had the lowest mean absolute error between prediction and actual duration in days with 3.66 days for the case with outliers, and 2.4 days for the case without outliers. These two models outperformed the other four models. This work will be enhanced in the future by exploring new prediction algorithms and comparing them with the results of this study.

Chen, Lingtao↗
DOE OSTI2020

Photometric Redshifts with the LSST. II. The Impact of Near-infrared and Near-ultraviolet Photometry

Accurate photometric redshift (photo-z) estimates are essential to the cosmological science goals of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). In this work, we use simulated photometry for mock galaxy catalogs to explore how LSST photo-z estimates can be improved by the addition of near-infrared (NIR) and/or ultraviolet (UV) photometry from the Euclid, Wide-Field InfrarRed Survey Telescope (WFIRST), and/or Cosmological Advanced Survey Telescope for Optical and ultraviolet Research (CASTOR) space telescopes. Generally, we find that deeper optical photometry can reduce the standard deviation of the photo-z estimates more than adding NIR or UV filters but that additional filters are the only way to significantly lower the fraction of galaxies with catastrophically under- or overestimated photo-z. For Euclid, we find that the addition of JH 5σ photometric detections can reduce the standard deviation for galaxies with z > 1 (z > 0.3) by ∼20% (∼10%), and the fraction of outliers by ∼40% (∼25%). For WFIRST, we show how the addition of deep YJHK photometry could reduce the standard deviation by ≳50% at z > 1.5 and drastically reduce the fraction of outliers to just ∼2% overall. For CASTOR, we find that the addition of its UV- and u-band photometry could reduce the standard deviation by ∼30% and the fraction of outliers by ∼50% for galaxies with z < 0.5. We also evaluate the photo-z results within sky areas that overlap with both the NIR and UV surveys and when spectroscopic training sets built from the surveys’ small-area deep fields are used.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Evolutionary Analyses of Gene Expression Divergence in Panicum hallii : Exploring Constitutive and Plastic Responses Using Reciprocal Transplants

Abstract The evolution of gene expression is thought to be an important mechanism of local adaptation and ecological speciation. Gene expression divergence occurs through the evolution of cis- polymorphisms and through more widespread effects driven by trans-regulatory factors. Here, we explore expression and sequence divergence in a large sample of Panicum hallii accessions encompassing the species range using a reciprocal transplantation experiment. We observed widespread genotype and transplant site drivers of expression divergence, with a limited number of genes exhibiting genotype-by-site interactions. We used a modified FST–QST outlier approach (QPC analysis) to detect local adaptation. We identified 514 genes with constitutive expression divergence above and beyond the levels expected under neutral processes. However, no plastic expression responses met our multiple testing correction as QPC outliers. Constitutive QPC outlier genes were involved in a number of developmental processes and responses to abiotic environments. Leveraging earlier expression quantitative trait loci results, we found a strong enrichment of expression divergence, including for QPC outliers, in genes previously identified with cis and cis–environment interactions but found no patterns related to trans-factors. Population genetic analyses detected elevated sequence divergence of promoters and coding sequence of constitutive expression outliers but little evidence for positive selection on these proteins. Our results are consistent with a hypothesis of cis-regulatory divergence as a primary driver of expression divergence in P. hallii.

3′ TagSeq↗
DOE OSTI2024

Bridging the Gap Between LLMs and LNS with Dynamic Data Format and Architecture Codesign

Deep neural networks (DNNs) have achieved tremendous success in the past few years. However, their training and inference demand exceptional computational and memory resources. Quantization has been shown as an effective approach to mitigate the cost, with the mainstream data types reduced from FP32 to FP16/BF16 and recently FP8 in the latest NVIDIA H100 GPUs. With increasingly aggressive quantization, however, the conventional floating-point formats suffer from limited precision in representing numbers around zero. Recently, NVIDIA demonstrated the potential of using a Logarithmic Number System (LNS) for the next generation of tensor cores. While LNS mitigates the hurdles in representing small numbers, in this work we observed a mismatch between LNS and the emerging Large Language Models (LLM), where LLM exhibits significant outliers when directly adopting the LNS format. In this paper, we present a data-format/architecture codesign to bright this gap. On the format side, we propose a dynamic LNS format to flexibly represent outliers at a higher precision, by exploiting asymmetry in the LNS representation and identifying outliers through a per-vector basis. On the architecture side, for demonstration, we realize the dynamic LNS format in a systolic array, which can handle the irregularity of the outliers at runtime. We implement our approach on an Alveo U280 FPGA as a prototype. Experimental results show that our design can effectively handle the outliers and resolve the mismatch between LNS and LLM, contributing to an accuracy improvement of 15.4% and 16% over the floating-point and the original LNS baselines, using four state-of-the-art LLM models. Our observation and design lay a solid foundation for the large-scale adoption of the LNS format in the next-generation deep learning hardware.

Haghi, Pouya↗
NASA NTRS2018

Mechanisms of Oxide Formation in the Self-Reacting Friction Stir Weld Process

The initial objective of this study was to evaluate the effect of an extended delay time between preweld cleaning and the completion of a self-reacting friction stir welding (SRFSW) process on the resulting quality of various thickness panels of AA2219-T87. The current NASA standard specifies no more than a 48 hour delay between preweld cleaning and actual welding. The concern is whether increasing the cleaning delay time results in development of the residual oxide defect (ROD) in SRFSW. This concern emanates from the possibility of increased time correlating with increased oxide layer thickness on the faying surfaces. Oxide content on the faying surfaces has been reported to correlate with the occurrence of the ROD which reduces mechanical properties. When the SRFSW process was first adopted by the NASA Marshall Space Flight Center (MSFC), unexpected low tensile values that resulted were attributed to oxides within the weld that appeared to follow the former faying surface contours. Mitigation of the ROD was achieved through a combination of modifications to the processing parameters, tool designs, and incorporation of a weld seam offset. Two operations are involved in preweld cleaning: the first is removal of oil and grease, and the second is removal of surface oxides. In arc welding, improper cleaning of the faying surfaces of aluminum welded joints can increase the sensitivity toward development of defects. As the aluminum is locally melted, these contaminants contribute toward the development of porosity, inclusions, entrapped oxides, and other discontinuities which can degrade the strength of the weld joint. For weldment of large structures, the weld joint is typically cleaned, fit-up, and tack welded prior to the final full penetration welding pass. Because of the stringent joint fit-up requirements for mismatch and peaking for launch vehicle structures, the joint fit-up can sometimes contribute to lengthy delays between cleaning and tack welding, especially for circumferential weld joints on large diameter components. When the conventional friction stir welding (CFSW) process was introduced at the NASA MSFC, there was no procedure for cleaning prior to the solid-state joining process. As the process expanded to include SRFSW, preparation of the faying, crown, and root surfaces were implemented to overcome the ROD. Although the solid-state process is not expected to reach temperatures high enough for dissociation of the native oxide layer, concern remained regarding the redeposition of the native oxide layer within the stir zone. NASA has previously established the allowable time at 48 hours between preweld cleaning and a SRFSW process. The effect of potential 2 contamination resulting from an extended delay to 188 hours was subsequently evaluated for SRFSWs using tensile testing and metallographic imaging. Tensile specimens were tested at room temperature (RT), and at cryogenic conditions of liquid nitrogen (LN2) and liquid hydrogen. No detrimental effect on weld quality, as determined by weld strength, was reported for cleaning delays of 48, 120, 168, 240 or 288 hours. While no trends were established in this study, which extended the delay from 48 to 188 hours, there were a few outliers in terms of ultimate tensile strength (UTS). According to M. Fisher's 2014 Boeing Company Memo no. EYBF-MAF-14-029, all outliers were above the minimum acceptance criteria, but out of family with respect to the average values. As the robustness and reliability of any process ultimately depends on the average values as well as the outliers, an understanding of the cause of these outliers will ultimately improve the process. This report examines those outliers and their possible causes.

Schneider, J. A.↗
DOE OSTI2021

Where’s Swimmy?: Mining unique color features buried in galaxies by deep anomaly detection using Subaru Hyper Suprime-Cam data

Abstract We present the Swimmy (Subaru WIde-field Machine-learning anoMalY) survey program, a deep-learning-based search for unique sources using multicolored (grizy) imaging data from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP). This program aims to detect unexpected, novel, and rare populations and phenomena, by utilizing the deep imaging data acquired from the wide-field coverage of the HSC-SSP. This article, as the first paper in the Swimmy series, describes an anomaly detection technique to select unique populations as “outliers” from the data-set. The model was tested with known extreme emission-line galaxies (XELGs) and quasars, which consequently confirmed that the proposed method successfully selected $\sim\!\! 60\%$–$70\%$ of the quasars and $60\%$ of the XELGs without labeled training data. In reference to the spectral information of local galaxies at z = 0.05–0.2 obtained from the Sloan Digital Sky Survey, we investigated the physical properties of the selected anomalies and compared them based on the significance of their outlier values. The results revealed that XELGs constitute notable fractions of the most anomalous galaxies, and certain galaxies manifest unique morphological features. In summary, deep anomaly detection is an effective tool that can search rare objects, and, ultimately, unknown unknowns with large data-sets. Further development of the proposed model and selection process can promote the practical applications required to achieve specific scientific goals.

Astronomy & Astrophysics↗