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NASA NTRS1980

Seasat views North America, the Caribbean, and Western Europe with imaging radar

Forty-one digitally correlated Seasat synthetic-aperture radar images of land areas in North America, the Caribbean, and Western Europe are presented to demonstrate this microwave orbital imagery. The characteristics of the radar images, the types of information that can be extracted from them, and certain of their inherent distortions are briefly described. Each atlas scene covers an area of 90 X 90 kilometers, with the exception of the one that is the Nation's Capital. The scenes are grouped according to salient features of geology, hydrology and water resources, urban landcover, or agriculture. Each radar image is accompanied by a corresponding image in the optical or near-infrared range, or by a simple sketch map to illustrate features of interest. Characteristics of the Seasat radar imaging system are outlined.

Ford, J. P.↗

NASA NTRS2021

TLife-LSTM: Forecasting Future COVID-19 Progression with Topological Signatures of Atmospheric Conditions

Understanding the impact of atmospheric conditions on SARS-CoV2 is critical to model COVID-19 dynamics and sheds a light on the future spread around the world. Furthermore, geographic distri- butions of expected clinical severity of COVID-19 may be closely linked to prior history of respiratory diseases and changes in humidity, tem- perature, and air quality. In this context, we postulate that by tracking topological features of atmospheric conditions over time, we can provide a quanti?able structural distribution of atmospheric changes that are likely to be related to COVID-19 dynamics. As such, we apply the machinery of persistence homology on time series of graphs to extract topological signatures and to follow geographical changes in relative humidity and temperature. We develop an integrative machine learning framework named Topological Lifespan LSTM (TLife-LSTM) and test its predictive capabilities on forecasting the dynamics of SARS-CoV2 cases. We validate our framework using the number of con?rmed cases and hospitalization rates recorded in the states of Washington and California in the USA. Our results demonstrate the predictive potential of TLife-LSTM in forecasting the dynamics of COVID-19 and modeling its complex spatio-temporal spread dynamics.

Gel, Yulia R.↗

DOE OSTI2018

Confining system integrity assessment by detection of natural gas migration using seismic diffractions [Seismic diffractions for gas leakage detection in the shallow waters of the Gulf of Mexico]

Successful carbon capture and storage (CCS) requires secure CO 2 confinement within a geologic reservoir. If associated with a depleted hydrocarbon reservoir, the sealing capability can be determined by examination of the shallow subsurface for hydrocarbon leaks. Numerous seismic signatures have been reported to be hydrocarbon indicators. The interpretation can be advanced by using seismic diffractions, which could indicate subtle hydrocarbon accumulations not detectable by conventional techniques. In this work, we investigate the potential of seismic diffractions for use in shallow gas detection. We extract diffractions from the ultra-high-resolution 3D P-Cable seismic dataset acquired along the Gulf of Mexico inner continental shelf. Interpretation of this dataset revealed numerous seismic signatures associated with hydrocarbon accumulations (e.g., a prominent gas chimney). We analyze scattering features of the detected hydrocarbon accumulations and confirm the correlation between confidently interpreted gas accumulations and seismic diffractions. Based on that, we suggest using diffractions for confining system integrity assessment. As a result, diffraction analysis allows operating with subtle seismic signals that facilitates exploration of reliable CO 2 storage sites.

58 GEOSCIENCES↗

DOE OSTI2023

Adaptively driven X-ray diffraction guided by machine learning for autonomous phase identification

Machine learning (ML) has become a valuable tool to assist and improve materials characterization, enabling automated interpretation of experimental results with techniques such as X-ray diffraction (XRD) and electron microscopy. Because ML models are fast once trained, there is a key opportunity to bring interpretation in-line with experiments and make on-the-fly decisions to achieve optimal measurement effectiveness, which creates broad opportunities for rapid learning and information extraction from experiments. Here, we demonstrate such a capability with the development of autonomous and adaptive XRD. By coupling an ML algorithm with a physical diffractometer, this method integrates diffraction and analysis such that early experimental information is leveraged to steer measurements toward features that improve the confidence of a model trained to identify crystalline phases. We validate the effectiveness of an adaptive approach by showing that ML-driven XRD can accurately detect trace amounts of materials in multi-phase mixtures with short measurement times. The improved speed of phase detection also enables in situ identification of short-lived intermediate phases formed during solid-state reactions using a standard in-house diffractometer. Our findings showcase the advantages of in-line ML for materials characterization and point to the possibility of more general approaches for adaptive experimentation.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Anomalous Hall effect in ZrTe 5

Research in topological matter has expanded to include the Dirac and Weyl semimetals which feature three-dimensional Dirac states protected by symmetry. Zirconium pentatelluride has been of recent interest as a potential Dirac or Weyl semimetal material. Here, we report the results of experiments performed by in situ three-dimensional double-axis rotation to extract the full 4π solid angular dependence of the transport properties. A clear anomalous Hall effect is detected in every sample studied, with no magnetic ordering observed in the system to the experimental sensitivity of torque magnetometry. Large anomalous Hall signals develop when the magnetic field is rotated in the plane of the stacked quasi-two-dimensional layers, with the values vanishing above about 60 K, where the negative longitudinal magnetoresistance also disappears. Finally, this suggests a close relation in their origins, which we attribute to the Berry curvature generated by the Weyl nodes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2020

DeepFreeze: Towards Scalable Asynchronous Checkpointing of Deep Learning Models

In the age of big data, deep learning has emerged as a powerful tool to extract insight and exploit its value, both in industry and scientific applications. One common pattern emerging in such applications is frequent checkpointing of the state of the learning model during training, needed in a variety of scenarios: analysis of intermediate states to explain features and correlations with training data, exploration strategies involving alternative models that share a common ancestor, knowledge transfer, resilience, etc. However, with increasing size of the learning models and popularity of distributed data-parallel training approaches, simple checkpointing techniques used so far face several limitations: low serialization performance, blocking I/O, stragglers due to the fact that only a single process is involved in checkpointing. This paper proposes a checkpointing technique specifically designed to address the aforementioned limitations, introducing efficient asynchronous techniques to hide the overhead of serialization and I/O, and distribute the load over all participating processes. Experiments with two deep learning applications (CANDLE and ResNet) on a pre-Exascale HPC platform (Theta) shows significant improvement over state-of-art, both in terms of checkpointing duration and runtime overhead.

checkpointing↗

DOE OSTI2025

Adaptive Dynamic Digital Twin for Test Scenario Generation

Vehicle testing has been an important part in the development of both highly automated vehicles (HAV) and advanced driving assistant systems (ADAS). Obtaining a good representation of the Vehicle Under Test (VUT) is crucial for test scenario library generation (TSLG). Current vehicle testing methods often involve calibrating car-following models using vehicle trajectory data to create static representations that cannot be dynamically updated. For instance, when multiple vehicle trajectories are collected, it is difficult to automatically determine whether a new trajectory improves the model's representativeness or degrades its accuracy. In this paper, we introduce a dynamically updated digital twin modeling framework featuring an adaptive mechanism that evaluates new trajectory data. This mechanism can decide whether to incorporate newly collected data into the current model or create a separate digital twin model when the trajectory significantly differs from prior data. Vehicle location, speed, and acceleration extracted from the newly collected trajectory data are used to support the dynamic update decision. By integrating this digital twin model into the test library generation process, we demonstrate its ability to assist in generating test libraries while effectively handling newly collected data.

Chen, Hanlin [ORNL] (ORCID:0000000165087715)↗

NASA NTRS2003

Performance of the GLAS Onboard Surface Detection Algorithm

The Geoscience Laser Altimeter System (GLAS) determines the range from the satellite to the Earth's surface from the time of flight of the instrument's 1064 nm laser pulses, which are generated at a rate of 40 Hz. The time of flight is defined as the difference between the laser transmit time and the time of return of the surface echo. The detector output is digitized with a 1 ns sampling interval, starting before the laser fires and ending well after any possible surface return, for a total of 5.4 million points. Because there is not enough downlink bandwidth for the entire waveform, the algorithm must extract both the transmit and surface echo waveforms from the 5.4 million digitized points, and pass these waveforms on to be included in the science data packets. Results from orbit show the algorithm to be effective at finding the surface echoes. A few features of the algorithm, however, require post-launch modification. One is that cloud cover tends to cause the algorithm to raise the gain, which then causes saturation of surface echoes from clear regions immediately following the clouds. Details of the algorithm, along with specific examples and recent modifications, will be presented.

McGarry, Jan F.↗

DOE OSTI2025

CAMFeND: Credibility-Aware Multimodal Fake News Detection with Rotational Attention

In the evolving digital landscape, fake news is a significant challenge, influencing public perception and decision-making. Traditional detection approaches focus on single-modal data or simple multimodal fusion, often overlooking deeper interactions and news credibility. We propose a novel model addressing these limitations by introducing rotational attention and news domain information as a feature. Unlike static attention mechanisms, our rotational attention dynamically shifts query, key, and value roles across text and image inputs, enabling richer cross-modal interaction. Incorporating news domain information further enhances the model’s reliability by associating news posts with top domains extracted from Google search results, reducing false detections. This approach assesses both the content and the broader web context in which the news is discussed. Our model outperforms existing state-of-the-art methods by providing deeper, layered multimodal integration and domain information analysis, resulting in a more robust and adaptive fake news detection system.

Gupta, Nidhi↗

DOE OSTI2024

Tractometry of the Human Connectome Project: resources and insights

The Human Connectome Project (HCP) has become a keystone dataset in human neuroscience, with a plethora of important applications in advancing brain imaging methods and an understanding of the human brain. We focused on tractometry of HCP diffusion-weighted MRI (dMRI) data. We used an open-source software library (pyAFQ; https://yeatmanlab.github.io/pyAFQ) to perform probabilistic tractography and delineate the major white matter pathways in the HCP subjects that have a complete dMRI acquisition (n = 1,041). We used diffusion kurtosis imaging (DKI) to model white matter microstructure in each voxel of the white matter, and extracted tract profiles of DKI-derived tissue properties along the length of the tracts. We explored the empirical properties of the data: first, we assessed the heritability of DKI tissue properties using the known genetic linkage of the large number of twin pairs sampled in HCP. Second, we tested the ability of tractometry to serve as the basis for predictive models of individual characteristics (e.g., age, crystallized/fluid intelligence, reading ability, etc.), compared to local connectome features. To facilitate the exploration of the dataset we created a new web-based visualization tool and use this tool to visualize the data in the HCP tractometry dataset. Finally, we used the HCP dataset as a test-bed for a new technological innovation: the TRX file-format for representation of dMRI-based streamlines. We released the processing outputs and tract profiles as a publicly available data resource through the AWS Open Data program's Open Neurodata repository. We found heritability as high as 0.9 for DKI-based metrics in some brain pathways. We also found that tractometry extracts as much useful information about individual differences as the local connectome method. We released a new web-based visualization tool for tractometry—“Tractoscope” (https://nrdg.github.io/tractoscope). We found that the TRX files require considerably less disk space-a crucial attribute for large datasets like HCP. In addition, TRX incorporates a specification for grouping streamlines, further simplifying tractometry analysis.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Revealing Hidden Quinones Through Diagnostic MS² Fragmentation of Peptide–Quinone Adducts

Quinones are redox-active components of natural organic matter that mediate electron transfer and influence biogeochemical processes, but many quinones in pyrogenic organic matter (PyOM) remain unresolved because they ionize poorly by mass spectrometry. Here, we present a peptide-tagging approach to improve detection of cysteine-reactive electrophiles in PyOM, with quinones expected to be a dominant subset based on reaction chemistry and selectivity experiments. A cysteine-containing peptide was used to form Michael-addition adducts, enhancing electrospray ionization and enabling untargeted screening by high-performance liquid chromatography-high-resolution tandem mass spectrometry. The method was benchmarked with five quinone standards and applied to extracts from charred plant material as a discovery-level screen for cysteine-reactive targets. We identified 98 quinone-candidate adducts (mean neutral mass ~603 Da), of which more than 70% were not detectable in native MS1 data. Among formula-assigned features, hidden quinone candidates had median (O+N)/C of 0.391 and normalized oxidation state of carbon of -0.281, consistent with relatively low polarity and low oxidation state. These results reveal a previously inaccessible pool of hidden redox-active compounds in PyOM and provide a framework for prioritizing quinone-like electrophiles for confirmation and incorporation into models of fire-driven biogeochemical cycling.

LC-MS/MS↗

DOE OSTI2020

Correlations of the physicochemical properties of organosolv lignins from Broussonetia papyrifera with their antioxidant activities

To understand the influence of lignin characteristics on their antioxidant activities, lignins were isolated from Broussonetia papyrifera by organosolv pretreatment at different temperatures. The physicochemical features of the lignins were investigated by GPC, 31 P NMR and 2D HSQC NMR analyses. An increase of organosolv pretreatment temperature resulted in a decrease of M w and an increase of the phenolic hydroxyl group content in the isolated lignin. The lignins extracted at higher organosolv pretreatment temperature showed higher antioxidant activity according to the IC 50 determined using DPPH and ABTS. The correlations between the physicochemical properties of organosolv lignins and their antioxidant activities were studied. The contents of phenolic hydroxyl groups, especially syringyl and guaiacyl, and double bonds in the C α position had positive effects on the antioxidant activity of the lignins. Finally, on the other hand, M w and aliphatic hydroxyl groups of the lignins exhibited negative correlations with the antioxidant activity.

09 BIOMASS FUELS↗

DOE OSTI2019

Density functional theory based neural network force fields from energy decompositions

In order to develop force fields (FF) for molecular dynamics simulations that retain the accuracy of ab initio density functional theory (DFT), in this work we developed a machine learning protocol based on an energy decomposition scheme that extracts atomic energies from DFT calculations. Our DFT to FF (DFT2FF) approach provides almost hundreds of times more data for the DFT energies, which dramatically improves accuracy with less DFT calculations. In addition, we use piecewise cosine basis functions to systematically construct symmetry invariant features into the neural network model. We illustrate this DFT2FF approach for amorphous silicon where only 800 DFT configurations are sufficient to achieve an accuracy of 1 meV/atom for energy and 0.1 eV/A for forces. We then use the resulting FF model to calculate the thermal conductivity of amorphous Si based on long molecular dynamics simulations. The dramatic speedup in training in our DFT2FF protocol allows the adoption of a simulation paradigm where an accurate and problem specific FF for a given physics phenomenon is trained on-the-spot through a quick DFT precalculation and FF training.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

Visualizing broken symmetry and topological defects in a quantum Hall ferromagnet

The interaction between electrons in graphene under high magnetic fields drives the formation of a rich set of quantum Hall ferromagnetic (QHFM) phases with broken spin or valley symmetry. Visualizing atomic-scale electronic wave functions with scanning tunneling spectroscopy (STS), we resolved microscopic signatures of valley ordering in QHFM phases and spectral features of fractional quantum Hall phases of graphene. At charge neutrality, we observed a field-tuned continuous quantum phase transition from a valley-polarized state to an intervalley coherent state, with a Kekulé distortion of its electronic density. Mapping the valley texture extracted from STS measurements of the Kekulé phase, we could visualize valley skyrmion excitations localized near charged defects. Our techniques can be applied to examine valley-ordered phases and their topological excitations in a wide range of materials.

valley symmetry↗

DOE OSTI2017

Study of the Vertical Magnetic Field in Face-on Galaxies Using Faraday Tomography

Faraday tomography allows astronomers to probe the distribution of the magnetic field along the line of sight (LOS), but that can be achieved only after the Faraday spectrum is interpreted. However, the interpretation is not straightforward, mainly because the Faraday spectrum is complicated due to a turbulent magnetic field; it ruins the one-to-one relation between the Faraday depth and the physical depth, and appears as many small-scale features in the Faraday spectrum. In this paper, by employing “simple toy models” for the magnetic field, we describe numerically as well as analytically the characteristic properties of the Faraday spectrum. We show that the Faraday spectrum along “multiple LOSs” can be used to extract the global properties of the magnetic field. Specifically, considering face-on spiral galaxies and modeling turbulent magnetic field as a random field with a single coherence length, we numerically calculate the Faraday spectrum along a number of LOSs and its shape-characterizing parameters, that is, the moments. When multiple LOSs cover a region of ≳(10 coherence length){sup 2}, the shape of the Faraday spectrum becomes smooth and the shape-characterizing parameters are well specified. With the Faraday spectrum constructed as a sum of Gaussian functions with different means and variances, we analytically show that the parameters are expressed in terms of the regular and turbulent components of the LOS magnetic field and the coherence length. We also consider the turbulent magnetic field modeled with a power-law spectrum, and study how the magnetic field is revealed in the Faraday spectrum. Our work suggests a way to obtain information on the magnetic field from a Faraday tomography study.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1984

Knowledge-based Expert Systems for Crop Identification

The development of an improved understanding of the interactive man machine environment is investigated. In such an environment, as many feature inputs as practical would be automatically derived from a data base and input into an expert system decision making procedure. This procedure could then provide expert assistance to a trained image analyst to upgrade and improve the quantity and accuracy of the information extracted from the input data. A comparison of the similarities and differences between manual and automated image interpretation techniques is also examined.

Smith, T. R.↗

NASA NTRS1993

The rheology and composition of cryovolcanic flows on icy satellites

The rheologic properties of terrestrial lavas have been related to morphologic features of their flows, such as levees, banked surfaces, multilobate structures, and compressible folds. These features also have been used to determine rheologies and constrain the compositions of extraterrestrial flows. However, with rare exceptions, such features are not resolvable in Voyager images of the satellites of outer planets. Often only flow length and edge thickness of cryovolcanic flows can be measured reasonably accurately from Voyager images. The semiempirical lava-flow model presented here is a renewed effort to extract useful information from such measurements.

Kargel, Jeffrey S.↗

NASA NTRS1990

Rotatable crucible for rapid solidification process

This invention relates to an apparatus for producing filament, fiber, ribbon or film from a molten material, comprising a preferably heat extracting crucible which contains a pool of molten material at a selected horizontal level in the pool. The crucible has an opening extending from above the free surface level to a bottom edge of the opening, the bottom edge being sufficiently below the free surface level so that the molten material cannot form and hold a meniscus by surface tension between the edge and the level of the free surface and further comprises a heat extracting substrate laterally disposed with respect to the crucible and which rotates about an axis of rotation. The substrate is positioned adjacent the edge of the opening which confines the molten material and prevents it from overflowing downwardly out of the crucible. The invention features rotating means which includes a first drive means for tiltably rotating the crucible about an axis of rotation which is coaxial with the axis of rotation of the substrate, so the crucible edge can be maintained a predetermined constant distance from the substrate. The distance chosen is suitable for depositing molten material on the substrate and the apparatus also has a second drive means which is drivingly connected to the substrate for continuously moving the surface of the substrate upwardly past the edge and a melt front formed at the interface of the molten material and the substrate surface.

Gaspar, Thomas↗