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NASA NTRS1985

International Ultraviolet Explorer Atlas of O-typespectra from 1200 to 1900 Angstrom

The IUE archives provide an unprecedented sample of uniform, high-quality ultraviolet stellar spectra. In particular, they contain high-resolution SWP data for nearly 200 different O stars. We have undertaken a survey of the 1200-1900 A region in about 120 of them having homogeneous optical spectral classifications to investigate systematically the behavior of the ultraviolet features, incuding the prominent stellar wind profiles and the degree to which they correlate with the optical types. The standard extracted spectrograms have been rebinned to a constant wavelength resolution of 0.25A and uniformly normalized (not dereddened) at the GSFC RDAF. They are then plotted at 10A/cm, with reseau, photometric quality and echelle order junction flags available. This atlas contains such plots for about 100 stars, arranged in spectral-type, luminosity and peculiar object sequences. The results show a high degree of correlation between the ultraviolet features, both photospheric and stellar-wind, and the optical classifications for the majority of the O-type stars.

Walborn, N. R.↗

NASA NTRS1995

GHRS observations of cool, low-gravity star. 2: Flow and turbulent velocities in the outer atmosphere of gamma CRU CIS (M3.4 III)

The Goddard High Resoulution Spectrograph (GHRS) on the Hubble Space Telescope (HST) has been used to obtain medium (R = 20,000) and high (R = 85,000) resoultion UV spectra of chromosphere emission features for the M3.4 III star gamma Cru. Small Science Aperture (SSA) G270M and Echelle-B spectra of selected regions in the 2300-2850 A range were obtained to determine the kinematics of the chromosphere using lines of C2), Fe2, Co2, Si1/2), Ni2, Mn2, and Mg2. Profiles of C2) (UV 0.01) lines and fluorescently excited lines of low optical depth indicate average turbulent velocities (Doppler FWHM) of 30.2 +/- 1.3 and 28.8 +/- 1.3 km/s, respectively. The fluorescent emission lines (mean RV = 21.3 +/- 0.9 km/s) and the wings of the emission components of Fe2 lines (mean RV = 22.8 +/- 0.4 km/s) are approximately at rest relative to the radial velocity of the star (21 km/s), while the C2) lines show a modest inflow (mean RV = 23.1 +/- 0.9 km/s). The more opaque lines of Fe2 and Mg2 exhibit complex profiles resulting from line formation in an optically thick, extended expanding atmosphere. The emission wings of these lines are broadened by multiple scattering, and they are centered near the photospheric radial velocity. Closer to line center, these strong lines show a strong blueshifted self-absorption feature (already seen in IUE data), indicative of formation in an expanding chromosphere, and a previously unseen dip in the profiles on the red side of line center. The absorption components, when extracted using simple Gaussian fits, show strong correlations with the relative optical depths of the lines. The derived absorption flow velocities converge to the photospheric velocity as one examines spectra features formed deeper in the atmosphere. The blueward abosrption velocity increases in magnitude from about 7 to 14 km/s with increasing line optical depth - the strong absorptions directly map the acceleration of the outflowing stellar wind, while the interpretation of the weaker redshifted abosrptions is more ambiguous, indicating either an inflow of material or formation in an extended, spherically expanding outflow. The Mg2 and Fe2 profiles, taken together, imply that the wind speed decreases between the atmospheric layers where the Mg2 and Fe2 self-absorption components are formed. Interstellar absorptions are seen in the resonance lines of Mg2 (UV 1) and Fe2 (UV1) with zero-volt lower levels, at about -3 km/s, consistent with models of the interstellar medium in the direction of gamma Cru. Finally, we have detected the Mg2 'satellite lines' seen in solar spectra obtained above the limb. In gamma Cru these lines are probably fluorescently excited by H Ly beta.

Carpenter, Kenneth G.↗

DOE OSTI2021

Open Data and Deep Semantic Segmentation for Automated Extraction of Building Footprints

Advances in machine learning and computer vision, combined with increased access to unstructured data (e.g., images and text), have created an opportunity for automated extraction of building characteristics, cost-effectively, and at scale. These characteristics are relevant to a variety of urban and energy applications, yet are time consuming and costly to acquire with today’s manual methods. Several recent research studies have shown that in comparison to more traditional methods that are based on features engineering approach, an end-to-end learning approach based on deep learning algorithms significantly improved the accuracy of automatic building footprint extraction from remote sensing images. However, these studies used limited benchmark datasets that have been carefully curated and labeled. How the accuracy of these deep learning-based approach holds when using less curated training data has not received enough attention. The aim of this work is to leverage the openly available data to automatically generate a larger training dataset with more variability in term of regions and type of cities, which can be used to build more accurate deep learning models. In contrast to most benchmark datasets, the gathered data have not been manually curated. Thus, the training dataset is not perfectly clean in terms of remote sensing images exactly matching the ground truth building’s foot-print. A workflow that includes data pre-processing, deep learning semantic segmentation modeling, and results post-processing is introduced and applied to a dataset that include remote sensing images from 15 cities and five counties from various region of the USA, which include 8,607,677 buildings. The accuracy of the proposed approach was measured on an out of sample testing dataset corresponding to 364,000 buildings from three USA cities. The results favorably compared to those obtained from Microsoft’s recently released US building footprint dataset.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1990

Image recovery from edge primitives

A method for extracting edge primitives from Mach-band patterns is presented together with a method for recovering image representations of features outlined by the edge boundaries. The accuracy, stability, and resolution of these representations are assessed. Since these representations are most commonly used in characterizing targets, this method of low-level processing offers new opportunities for computer vision and high data-compressing coding. Two bandpass filters are considered, the spatially invariant Laplacian of Gaussian filter and spatially variant intensity-dependent spatial (IDS) summation. It is shown that the recovery from the IDS bandpass data is particularly advantageous in applications for which robustness to local and temporal variations in illumination is important. It is concluded that the edge primitives extracted from bandpassed images can be an efficient way to store, transmit, and represent images.

Alter-Gartenberg, Rachel↗

DOE OSTI2011

CORONAL MASS EJECTION DYNAMICS REGARDING RADIAL AND EXPANSION SPEEDS

A new technique for the detection of coronal mass ejection (CME) speeds using image processing was applied. This technique permits us to determine the CME dynamics: radial and expansion distances, velocities, and accelerations. The CME dynamics is determined by the selection of a radial direction in a given Large Angle and Spectroscopic Coronagraph image, which starts just before the occulter (close to the center) and extends to the extremity of the image. By taking a series of images and extracting the same radial direction, it is possible to have a time history of any moving feature along this direction. This technique allows us to choose the number of directions that is used in the CME detection to determine its dynamics. This paper presents the results for the dynamical features (radial and expansion) of five CME events observed on 1999 February 5, 2001 February 2, 2002 March 1, 2003 December 2, and 2007 December 31.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Machine Learning in Infectious Disease for Risk Factor Identification and Hypothesis Generation: Proof of Concept Using Invasive Candidiasis

Machine learning (ML) models can handle large data sets without assuming underlying relationships and can be useful for evaluating disease characteristics, yet they are more commonly used for predicting individual disease risk than for identifying factors at the population level. We offer a proof of concept applying random forest (RF) algorithms to Candida-positive hospital encounters in an electronic health record database of patients in the United States. Candida-positive encounters were extracted from the Cerner HealthFacts database; invasive infections were laboratory-positive sterile site Candida infections. Features included demographics, admission source, care setting, physician specialty, diagnostic and procedure codes, and medications received before the first positive Candida culture. We used RF to assess risk factors for 3 outcomes: any invasive candidiasis (IC) vs non-IC, within-species IC vs non-IC (eg, invasive C. glabrata vs noninvasive C. glabrata), and between-species IC (eg, invasive C. glabrata vs all other IC). Fourteen of 169 (8%) variables were consistently identified as important features in the ML models. When evaluating within-species IC, for example, invasive C. glabrata vs non-invasive C. glabrata, we identified known features like central venous catheters, intensive care unit stay, and gastrointestinal operations. In contrast, important variables for invasive C. glabrata vs all other IC included renal disease and medications like diabetes therapeutics, cholesterol medications, and antiarrhythmics. Known and novel risk factors for IC were identified using ML, demonstrating the hypothesis-generating utility of this approach for infectious disease conditions about which less is known, specifically at the species level or for rarer diseases.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Deblurring for nuclei: 3D characteristics of heavy-ion collisions

Observables from nuclear and high-energy experiments can be degraded by detector performance and/or methodology in extracting the observables, such as of the final-state characteristics of heavy-ion collisions in relation to a coarsely estimated reaction-plane direction. We propose the use of deblurring methods, such as in optics, to correct for observable degradation. Our main focus is the restoration of triple-differential particle distributions in heavy-ion collisions. We demonstrate that these could be extracted from collision measurements following the Richardson-Lucy deblurring method from optics. We illustrate basic features of the restoration methodology in a schematic model assuming either ideal or more realistic particle detection. Here, the inferred three-dimensional (3D) distributions for collisions may be easier to interpret in terms of collision dynamics and sought properties of bulk matter than the currently employed Fourier coefficients, that combine information from different azimuthal angles relative to the reaction plane.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2016

SU-F-R-13: Decoding 18F-FDG Uptake Heterogeneity for Primary and Lymphoma Tumors by Using Texture Analysis in PET Images

Purpose: To explore 18F-FDG uptake heterogeneity of primary tumor and lymphoma tumor by texture features of PET image and quantify the heterogeneity difference between primary tumor and lymphoma tumor. Methods: 18 patients with primary tumor and lymphoma tumor in lung cancer were enrolled. All patients underwent whole-body 18F-FDG PET/CT scans before treatment. Texture features, based on Gray-level Co-occurrence Matrix, second and high order matrices are extracted from code using MATLAB software to quantify 18F-FDG uptake heterogeneity. The relationships of volume between energy, entropy, correlation, homogeneity and contrast were analyzed. Results: For different cases, tumor heterogeneity was not the same. Texture parameters (contrast, entropy, and correlation) of lymphoma were lower than primary tumor. On the contrast, the texture parameters (energy, homogeneity and inverse different moment) of lymphoma were higher than primary tumor. Significantly, correlations were observed between volume and energy (primary, r=−0.194, p=0.441; lymphoma, r=−0.339, p=0.582), homogeneity (primary, r=−0.146, p=0.382; lymphoma, r=−0.193, p=0.44), inverse difference moment (primary, r=−0.14, p=0.374; lymphoma, r=−0.172, p=0.414) and a positive correlation between volume and entropy (primary, r=0.233, p=0.483; lymphoma, r=0.462, p=0.680), contrast (primary, r=0.159, p=0.399; lymphoma, r=0.341, p=0.584), correlation (primary, r=0.027, p=0.165; lymphoma, r=0.046, p=0.215). For the same patient, energy for primary and lymphoma tumor is equal. The volume of lymphoma is smaller than primary tumor, but the homogeneity were higher than primary tumor. Conclusion: This study showed that there were effective heterogeneity differences between primary and lymphoma tumor by FDG-PET image texture analysis.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

maslov-group/FUN-PROSE

mRNA levels of all genes in a genome is a critical piece of information defining the overall state of the cell in a given environmental condition. Being able to reconstruct such condition-specific expression in fungal genomes is particularly important to metabolically engineer these organisms to produce desired chemicals in industrially scalable conditions. Most previous deep learning approaches focused on predicting the average expression levels of a gene based on its promoter sequence, ignoring its variation across different conditions. Here we present FUN-PROSE—a deep learning model trained to predict differential expression of individual genes across various conditions using their promoter sequences and expression levels of all transcription factors. We train and test our model on three fungal species and get the correlation between predicted and observed condition-specific gene expression as high as 0.85. We then interpret our model to extract promoter sequence motifs responsible for variable expression of individual genes. We also carried out input feature importance analysis to connect individual transcription factors to their gene targets. A sizeable fraction of both sequence motifs and TF-gene interactions learned by our model agree with previously known biological information, while the rest corresponds to either novel biological facts or indirect correlations.

Liu, Simon↗

DOE OSTI2017

Modeling Condensation in Deep Convection

Cloud-scale models apply two drastically different methods to represent condensation of water vapor to form and grow cloud droplets. Maintenance of water saturation inside liquid clouds is assumed in the computationally efficient saturation adjustment approach used in most bulk microphysics schemes. When super- or subsaturations are allowed, condensation/evaporation can be calculated using the predicted saturation ratio and (either predicted or prescribed) mean droplet radius and concentration. Here, we investigate differences between simulations of deep unorganized convection applying a saturation adjustment condensation scheme (SADJ) and a scheme with supersaturation prediction (SPRE). A double-moment microphysics scheme with CCN activation parameterized as a function of the local vertical velocity is applied to compare cloud fields simulated applying SPRE and SADJ. Clean CCN conditions are assumed to demonstrate upper limits of the SPRE and SADJ difference. Microphysical piggybacking is used to extract the impacts with confidence. Results show a significant impact on deep convection dynamics, with SADJ featuring more cloud buoyancy and thus stronger updrafts. This leads to around a 3% increase of the surface rain accumulation in SADJ. Upper-tropospheric anvil cloud fractions are much larger in SPRE than in SADJ because of the higher ice concentrations and thus longer residence times of anvil particles in SPRE, as demonstrated by sensitivity tests. Higher ice concentrations in SPRE come from significantly larger ice supersaturations in strong convective updrafts that feature water supersaturations of several percent.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

FUN-PROSE: A deep learning approach to predict condition-specific gene expression in fungi

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1984

Probing turbulence via large eddy simulation

Two examples of the application of a large-eddy simulation data base to the study of organized structures in fully developed turbulent channel flow are presented. In the first study, it is shown that the flow contains an appreciable number of hairpin vortices among other flow structures. In the second study, the Karhunen-Loeve expansion and Lumley's characteristic eddy decomposition were used to extract deterministic structures from the flow. It is shown that the extracted eddies are energetic, make a significant contribution to turbulence production, and display some of the features of the organized motions observed in turbulent boundary layers.

Moin, P.↗

NASA NTRS1991

Improved NASTRAN plotting

The new 1991 COSMIC/NASTRAN version, compatible with the older versions, tries to remove some old constraints and make it easier to extract information from the plot file. It also includes some useful improvements and new enhancements. New features available in the 1991 version are described. They include a new PLT1 tape with simplified ASCII plot commands and short records, combined hidden and shrunk plot, an x-y-z coordinate system on all structural plots, element offset plot, improved character size control, improved FIND and NOFIND logic, a new NASPLOT post-prosessor to perform screen plotting or generate PostScript files, and a BASIC/NASTPLOT program for PC.

Chan, Gordon C.↗

NASA NTRS1992

Use of AVIRIS data to the definition of optimised specifications for land applications with future spaceborne imaging spectrometers

Recent experience with airborne imaging spectrometers demonstrated the advantages of narrow band sensors over broad band scanners for characterizing the nature, extent, and physical status of typical land surfaces. Information on key spectral features associated with various land surfaces can be obtained from the data of such instruments, which can be used to simulate spaceborne imaging spectrometer data and to assess their information content if comprehensive underpinning is provided by ground data. The collection of such information was an issue of airborne imaging spectrometer campaigns like the NASA MAC-Europe 1991. Airborne and ground data obtained from different test sites in Europe are utilized for a comparative analysis of the spectral signatures of various land surfaces (vegetation, bare soils and rocks, and mixed soil/rock-vegetation) as seen from different imaging spectrometers like Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), GERIS 63 band scanner, and CASI. The following items are discussed: (1) the significance of different spectral regions within the wavelength interval between 0.4 m and 2.5 m for the differentiation of different land units; (2) recommendations on the optimum band selection and band-widths to be used for the application of future satellite-based imaging spectrometers for land applications; (3) the boundaries for the detection of plant features in mixed-soil plant spectra and the influence of different soil properties on the mixture of the spectra; (4) recommendations on the optimum spatial resolution and recording dates for the discrimination of spectral features of various surface types; and (5) evaluation of different data compressing techniques for the optimum extraction of spectral information from imaging spectrometry data.

Bodechtel, J.↗

NASA NTRS1988

An assessment of AVIRIS data for hydrothermal alteration mapping in the Goldfield Mining District, Nevada

Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) data were acquired over the Goldfield Mining District, Nevada, in September 1987. Goldfield is one of the group of large epithermal precious metal deposits in Tertiary volcanic rocks, associated with silicic volcanism and caldera formation. Hydrothermal alteration consists of silicification along fractures, advanced agrillic and argillic zones further away from veins and more widespread propylitic zones. An evaluation of AVIRIS data quality was performed. Faults in the data, related to engineering problems and a different behavior of the instrument while on-board the U2, were encountered. Consequently, a decision was made to use raw data and correct them only for dark current variations and detector read-out-delays. New software was written to that effect. Atmospheric correction was performed using the flat field correction technique. Analysis of the data was then performed to extract spectral information, mainly concentrating on the 2 to 2.45 micron window, as the alteration minerals of interest have their distinctive spectral reflectance features in this region. Principally kaolinite and alunite spectra were clearly obtained. Mapping of the different minerals and alteration zones was attempted using ratios and clustering techniques. Poor signal-to-noise performance of the instrument and the lack of appropriate software prevented the production of an alteration map of the area. Spectra extracted locally from the AVIRIS data were checked in the field by collecting representative samples of the outcrops.

Carrere, Veronique↗

DOE OSTI2017

Prediction of Individual Social-Demographic Role Based on Travel Behavior Variability Using Long-Term GPS Data

With the development of and advances in smartphones and global positioning system (GPS) devices, travelers’ long-term travel behaviors are not impossible to obtain. This study investigates the pattern of individual travel behavior and its correlation with social-demographic features. For different social-demographic groups (e.g., full-time employees and students), the individual travel behavior may have specific temporal-spatial-mobile constraints. The study first extracts the home-based tours, including Home-to-Home and Home-to-Non-Home, from long-term raw GPS data. The travel behavior pattern is then delineated by home-based tour features, such as departure time, destination location entropy, travel time, and driving time ratio. The travel behavior variability describes the variances of travelers’ activity behavior features for an extended period. After that, the variability pattern of an individual’s travel behavior is used for estimating the individual’s social-demographic information, such as social-demographic role, by a supervised learning approach, support vector machine. In this study, a long-term (18-month) recorded GPS data set from Puget Sound Regional Council is used. The experiment’s result is very promising. The sensitivity analysis shows that as the number of tours thresholds increases, the variability of most travel behavior features converges, while the prediction performance may not change for the fixed test data.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

Deep learning-based spatio-temporal estimate of greenhouse gas emissions using satellite data

Accurate estimation of greenhouse gases (GHGs) emissions is very important for developing mitigation strategies to climate change by controlling and reducing GHG emissions. This project aims to develop multiple deep learning approaches to estimate anthropogenic greenhouse gas emissions using multiple types of satellite data. NO2 concentration is chosen as an example of GHGs to evaluate the proposed approach. Two sentinel satellites (sentinel-2 and sentinel-5P) provide multiscale observations of GHGs from 10-60m resolution (sentinel-2) to ~kilometer scale resolution (sentinel-5P). Among multiple deep learning (DL) architectures evaluated, two best DL models demonstrate that key features of spatio-temporal satellite data and additional information (e.g., observation times and/or coordinates of ground stations) can be extracted using convolutional neural networks and feed forward neural networks, respectively. In particular, irregular time series data from different NO 2 observation stations limit the flexibility of long short-term memory architecture, requiring zero-padding to fill in missing data. However, deep neural operator (DNO) architecture can stack time-series data as input, providing the flexibility of input structure without zero-padding. As a result, the DNO outperformed other deep learning architectures to account for time-varying features. Overall, temporal patterns with smooth seasonal variations were predicted very well, while frequent fluctuation patterns were not predicted well. In addition, uncertainty quantification using conformal inference method is performed to account for prediction ranges. Overall, this research will lead to a new groundwork for estimating greenhouse gas concentrations using multiple satellite data to enhance our capability of tracking the cause of climate change and developing mitigation strategies.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

Magnetic properties of Dy nano-islands on graphene

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Anderson, Nathaniel A.↗