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At least 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Graph identification of proteins in tomograms ( GRIP‐Tomo )

Abstract In this study, we present a method of pattern mining based on network theory that enables the identification of protein structures or complexes from synthetic volume densities, without the knowledge of predefined templates or human biases for refinement. We hypothesized that the topological connectivity of protein structures is invariant, and they are distinctive for the purpose of protein identification from distorted data presented in volume densities. Three‐dimensional densities of a protein or a complex from simulated tomographic volumes were transformed into mathematical graphs as observables. We systematically introduced data distortion or defects such as missing fullness of data, the tumbling effect, and the missing wedge effect into the simulated volumes, and varied the distance cutoffs in pixels to capture the varying connectivity between the density cluster centroids in the presence of defects. A similarity score between the graphs from the simulated volumes and the graphs transformed from the physical protein structures in point data was calculated by comparing their network theory order parameters including node degrees, betweenness centrality, and graph densities. By capturing the essential topological features defining the heterogeneous morphologies of a network, we were able to accurately identify proteins and homo‐multimeric complexes from 10 topologically distinctive samples without realistic noise added. Our approach empowers future developments of tomogram processing by providing pattern mining with interpretability, to enable the classification of single‐domain protein native topologies as well as distinct single‐domain proteins from multimeric complexes within noisy volumes.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRSDate not supplied

An Updated Model for the Effect of Atmospheric Absorption on Sounding Rockets

A very high-resolution R > 20,000 Far Ultraviolet full-disk, solar spectrograph will be launched in the Spring of 2023. This paper describes the in-flight wavelength calibration techniques and the fortuitous retrieval of Earth’s thermospheric information during the flight. Building and calibration of the Full-sun Ultraviolet Rocket Spectrograph (FURST) is currently underway. The purpose of this instrument is to obtain the highest resolution and most complete Far Ultra-Violet (FUV) spectra of the full disk Sun. This so-called "Sun-as-a-star" spectra will allow direct comparisons between our Sun and other stars measured by the Hubble Space Telescope(HST) and the upcoming James Webb Space Telescope (JWST). The Solar Physics groups at NASA Marshall Space Flight Center (MSFC) and Montana State University (MSU) have been developing the tools and procedures necessary to achieve the high spectral resolution goal. These include, among other things, improved tracking of error propagation, in-situ monitoring of the camera gain with a radioactive Fe-55 source, and the development of a simulated spectral calibration map under a noisy diagnostic-lamp signal. This mapping introduces a clocked CCD in order to obtain sub-pixel spectral resolution and overcome the Nyquist limit by about a factor of 2. Aside from the main purpose of FURST, we have been investigating the effect of absorption in the upper atmosphere at sounding-rocket altitudes (about 100-300 km). We present here an improved model of the optical depth caused by the thermospheric Oxygen cross-section and H and O self-absorption. This data-based model uses concentric spherical shells to account for the curvature of the Earth’s atmosphere and refraction. Using these calculations, we present the anticipated effect on the signal received by FURST, how that signal changes over the course of the flight-path These absorption peaks would provide wavelength fiducials at line-center that might add to in-flight calibration of the instrument. Many studies have found ways to correct for these so-called "Telluric" lines. However, it may be that these lines can in fact be a useful tool to further improve our calibration, rather than simply a nuisance to be corrected for! Finally, we discuss the inversion problem: how we could take actual flight data and back-out the atmospheric data (such as density and temperature) from any such sounding rocket flight that shows evidence of atmospheric absorption.

Nicolas Donders↗
DOE OSTI2024

Improving Robustness of Spectrogram Classifiers with Neural Stochastic Differential Equations

Signal analysis and classification is fraught with high levels of noise and perturbation. Computer-vision-based deep learning models applied to spectrograms have proven useful in the field of signal classification and detection; however, these methods aren't designed to handle the low signal-to-noise ratios inherent within non-vision signal processing tasks. While they are powerful, they are currently not the method of choice in the inherently noisy and dynamic critical infrastructure domain, such as smart-grid sensing, anomaly detection, and non-intrusive load monitoring. Currently, these models can be brittle, which makes them susceptible to noisy input. This also means they have sub-optimal stability of explanation outputs. Experts and technicians using these models to make decisions in real world scenarios need assurance that a model is performing as it is supposed to. The classification or prediction outputs it generates should be sound and grounded, not likely to change in the presence of shifting noise landscapes. In this work, we explore the idea of Neural Stochastic Differential Equations (NSDE's) to improve the robustness of models trained to classify time series data and the effect of NSDE's on the explainability of outputs. We then test the effectiveness of these approaches by applying them to a non-intrusive load monitoring (NILM) dataset that consists of simulated harmonic signals injected into a real building.

Brogan, Joel↗
DOE OSTI2022

Physics-driven learning of Wasserstein GAN for density reconstruction in dynamic tomography

Object density reconstruction from projections containing scattered radiation and noise is of critical importance in many applications. Existing scatter correction and density reconstruction methods may not provide the high accuracy needed in many applications and can break down in the presence of unmodeled or anomalous scatter and other experimental artifacts. Incorporating machine-learning models could prove beneficial for accurate density reconstruction, particularly in dynamic imaging, where the time evolution of the density fields could be captured by partial differential equations or by learning from hydrodynamics simulations. In this work, we demonstrate the ability of learned deep neural networks to perform artifact removal in noisy density reconstructions, where the noise is imperfectly characterized. Here, we use a Wasserstein generative adversarial network (WGAN), where the generator serves as a denoiser that removes artifacts in densities obtained from traditional reconstruction algorithms. We train the networks from large density time-series datasets, with noise simulated according to parametric random distributions that may mimic noise in experiments. The WGAN is trained with noisy density frames as generator inputs, to match the generator outputs to the distribution of clean densities (time series) from simulations. A supervised loss is also included in the training, which leads to an improved density restoration performance. In addition, we employ physics-based constraints such as mass conservation during the network training and application to further enable highly accurate density reconstructions. Our preliminary numerical results show that the models trained in our frameworks can remove significant portions of unknown noise in density time-series data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS2002

Estimating the Single-Trial Characteristics of Event-Related Responses: Evaluation of the MCERP Algorithm

Single-trial event-related responses collected during the course of an experiment are typically averaged before analysis resulting in a rather crude picture of event-related brain dynamics. It has been quite clear for some time that these responses exhibit trial-to-trial variability: however, the computational techniques necessary to deal with such responses in noisy conditions have not been available. To this end we have developed the multiple-component, event-related potential model (mcERP), which assumes that the each event-related response consists of a sum of multiple evoked components each described by a stereotypical waveshape. These waveshapes are allowed to vary in amplitude and onset latency from trial to trial, which allows us to capture, to first-order, the trial-dependent variations in event-related brain dynamics. We have constructed many sets of synthetic data designed to simulate intracortical recordings from a 15 channel, linear-array multielectrode implanted acutely in V1 of an awake-behaving macaque undergoing visual stimulation with a red light flash. This synthetic data was used to characterize the performance of the mcERP algorithm. First we quantified the degree to which such trial-to-trial variability aids in the identification of multiple components, and we demonstrate that amplitude variability is a more important factor in component separation than latency variability. Second, we quantified the behavior of the algorithm under two distinct signal-to-noise ratio (SNR) conditions: Gaussian noise independently present in each channel, and highly correlated (1/f distributed), far-field noise presented identically in each channel of the array. The mcERP algorithm was found to be robust to noise accurately identifying all component waveshapes and their associated single-trial characteristics down to SNR levels of -20dB for Gaussian noise and -7dB for 1/f far-field noise. Comparisons of the performance of this algorithm with factor analysis (FA) and independent component analysis (ICA) will be described by Knuth et al. (SFN abstracts, 2002). In addition, the advantages of application of mcERP to real data will be described by Shah et al, (these abstracts, 2002: SFN abstracts, 2002).

Knuth, K. H.↗
DOE OSTI2022

A Generalized Transformer-Based Pulse Detection Algorithm

Pulse-like signals are ubiquitous in the field of single molecule analysis, e.g., electrical or optical pulses caused by analyte translocations in nanopores. The primary challenge in processing pulse-like signals is to capture the pulses in noisy backgrounds, but current methods are subjectively based on a user-defined threshold for pulse recognition. Here, we propose a generalized machine-learning based method, named pulse detection transformer (PETR), for pulse detection. PETR determines the start and end time points of individual pulses, thereby singling out pulse segments in a time-sequential trace. It is objective without needing to specify any threshold. It provides a generalized interface for downstream algorithms for specific application scenarios. PETR is validated using both simulated and experimental nanopore translocation data. It returns a competitive performance in detecting pulses through assessing them with several standard metrics. Finally, the generalization nature of the PETR output is demonstrated using two representative algorithms for feature extraction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Detection of open cluster rotation fields from Gaia EDR3 proper motions

Context: Most stars from in groups which with time disperse, building the field population of their host galaxy. In the Milky Way, open clusters have been continuously forming in the disk up to the present time, providing it with stars spanning a broad range of ages and masses. Observations of the details of cluster dissolution are, however, scarce. One of the main difficulties is obtaining a detailed characterisation of the internal cluster kinematics, which requires very high-quality proper motions. For open clusters, which are typically loose groups with tens to hundreds of members, there is the additional difficulty of inferring kinematic structures from sparse and irregular distributions of stars. Aims: Here, we aim to analyse internal stellar kinematics of open clusters, and identify rotation, expansion, or contraction patterns. Methods: We use Gaia Early Data Release 3 (EDR3) astrometry and integrated nested Laplace approximations to perform vector-field inference and create spatio-kinematic maps of 1237 open clusters. The sample is composed of clusters for which individual stellar memberships were already known, thus minimising contamination from field stars in the velocity maps. Projection effects were corrected using EDR3 data complemented with radial velocities from Gaia Data Release 2 and other surveys. Results: We report the detection of rotation patterns in eight open clusters. Nine additional clusters display possible rotation signs. We also observe 14 expanding clusters, with 15 other objects showing possible expansion patterns. Contraction is evident in two clusters, with one additional cluster presenting a more uncertain detection. In total, 53 clusters are found to display kinematic structures. Within these, elongated spatial distributions suggesting tidal tails are found in five clusters. These results indicate that the approach developed here can recover kinematic patterns from noisy vector fields, as those from astrometric measurements of open clusters or other stellar or galactic populations, thus offering a powerful probe for exploring the internal kinematics and dynamics of these types of objects.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1999

Lidar Altimeter Measurements of Canopy Structure: Methods and Validation for Closed Canopy, Broadleaf Forests

Lidar altimeter observations of vegetated landscapes provide a time-resolved measure of laser pulse backscatter energy from canopy surfaces and the underlying ground. Airborne lidar altimeter data was acquired using the Scanning Lidar Imager of Canopies by Echo Recovery (SLICER) for a successional sequence of four, closed-canopy, deciduous forest stands in eastern Maryland. The four stands were selected so as to include a range of canopy structures of importance to forest ecosystem function, including variation in the height and roughness of the outer-most canopy surface and the vertical organization of canopy stories and gaps. The character of the SLICER backscatter signal is described and a method is developed that accounts for occlusion of the laser energy by canopy surfaces, transforming the backscatter signal to a canopy height profile (CHP) that quantitatively represents the relative vertical distribution of canopy surface area. The transformation applies an increased weighting to the backscatter amplitude as a function of closure through the canopy and assumes a horizontally random distribution of the canopy components. SLICER CHPs, averaged over areas of overlap where lidar ground tracks intersect, are shown to be highly reproducible. CHP transects across the four stands reveal spatial variations in vegetation, at the scale of the individual 10 m diameter laser footprints, within and between stands. Averaged SLICER CHPs are compared to analogous height profile results derived from ground-based sightings to plant intercepts measured on plots within the four stands. Tbe plots were located on the segments of the lidar ground tracks from which averaged SLICER CHPs were derived, and the ground observations were acquired within two weeks of the SLICER data acquisition to minimize temporal change. The differences in canopy structure between the four stands is similarly described by the SLICER and ground-based CHP results, however a Chi-square test of similarity documents differences that are statistically significant. The differences are discussed in terms of measurement properties that define the smoothness of the resulting CHPs and Lidar Altimeter Measurements of Canopy Structure - Harding et al. canopy properties that may vertically bias the CHP representations of canopy structure. The statistical differences are most likely due to the more noisy character of the ground-based CHPs, especially high in the canopy where ground-based sightings are rare resulting in an underestimate of canopy surface area and height, and to departures from the assumption of horizontal randomness which bias the CHPs toward the observer (upward for SLICER and downward for ground-based CHPs). The results demonstrate that the SLICER observations reliably provide a measure of canopy structure that reveals ecologically interesting structural variations such as those characterizing a successional sequence of closed-canopy, broadleaf forest stands.

Harding, D. J.↗
DOE OSTI2022

Quantum criticality using a superconducting quantum processor

Quantum criticality emerges from the collective behavior of many interacting quantum particles, often at the transition between different phases of matter. It is one of the cornerstones of condensed matter physics, which we access on noisy intermediate-scale (NISQ) quantum devices by leveraging a dynamically driven phenomenon. Here we probe the critical properties of the one-dimensional quantum Ising model on a programmable superconducting quantum chip via a Kibble-Zurek process, obtain scaling laws, and estimate critical exponents despite inherent sources of errors on the hardware. In addition, we investigate how the improvement of NISQ computers (more qubits, less noise) will consolidate the computation of those universal physical properties. A one-parameter noise model captures the effect of imperfections and reproduces the experimental data. Its systematic study reveals that the noise, analogously to temperature, induces a new length scale in the system. We introduce and successfully verify modified scaling laws, directly accounting for the noise without any prior knowledge. It makes data analyses for extracting physical properties transparent to noise. By understanding how imperfect quantum hardware modifies the genuine properties of quantum states of matter, we enhance the power of NISQ processors considerably for addressing quantum criticality and potentially other phenomena and algorithms.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Defining quantum-ready primitives for hybrid HPC-QC supercomputing: a case study in Hamiltonian simulation

As computational demands in scientific applications continue to rise, hybrid high-performance computing (HPC) systems integrating classical and quantum computers (HPC-QC) are emerging as a promising approach to tackling complex computational challenges. One critical area of application is Hamiltonian simulation, a fundamental task in quantum physics and other large-scale scientific domains. This paper investigates strategies for quantum-classical integration to enhance Hamiltonian simulation within hybrid supercomputing environments. By analyzing computational primitives in HPC allocations dedicated to these tasks, we identify key components in Hamiltonian simulation workflows that stand to benefit from quantum acceleration. To this end, we systematically break down the Hamiltonian simulation process into discrete computational phases, highlighting specific primitives that could be effectively offloaded to quantum processors for improved efficiency. Our empirical findings provide insights into system integration, potential offloading techniques, and the challenges of achieving seamless quantum-classical interoperability. We assess the feasibility of quantum-ready primitives within HPC workflows and discuss key barriers such as synchronization, data transfer latency, and algorithmic adaptability. These results contribute to the ongoing development of optimized hybrid solutions, advancing the role of quantum-enhanced computing in scientific research.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Identifying Different Classes of Seismic Noise Signals Using Unsupervised Learning

Abstract Proper classification of nontectonic seismic signals is critical for detecting microearthquakes and developing an improved understanding of ongoing weak ground motions. We use unsupervised machine learning to label five classes of nonstationary seismic noise common in continuous waveforms. Temporal and spectral features describing the data are clustered to identify separable types of emergent and impulsive waveforms. The trained clustering model is used to classify every 1 s of continuous seismic records from a dense seismic array with 10–30 m station spacing. We show that dominate noise signals can be highly localized and vary on length scales of hundreds of meters. The methodology demonstrates the complexity of weak ground motions and improves the standard of analyzing seismic waveforms with a low signal‐to‐noise ratio. Application of this technique will improve the ability to detect genuine microseismic events in noisy environments where seismic sensors record earthquake‐like signals originating from nontectonic sources.

Johnson, Christopher W.↗
DOE OSTI2025

PQML: Enabling the Predictive Reproducibility on NISQ Machines for Quantum ML Applications

Quantum computing represents a groundbreaking approach to high-performance computing. In recent years, quantum computers have progressed from single-qubit processors to systems boasting over 400 qubits. The presence of such a large number of qubits offers significant advantages, including enhanced computational speed—a capability beyond classical computing methods. However, the current stage of quantum computing is referred to as the noisy intermediate-scale quantum (NISQ) era. The existence of noise in this era presents challenges in testing quantum computing applications, leading to considerable variance in application results. Furthermore, the diverse noise characteristics observed across different machines exacerbate this issue, complicating the selection of the appropriate machine for application execution. In response to these challenges, we introduce our Predictive Quantum Machine Learning (PQML) tool. This tool is designed to predict outcomes when executing identical quantum machine learning applications—specifically, a critical suite of variational quantum algorithms—across various quantum computers during the NISQ era. This effort relies on data collected over a 12-month period. To the best of our knowledge, this study represents the first attempt to ensure reproducibility across quantum computers for complex circuits. Additionally, we have developed a model capable of forecasting the accuracy of quantum computers for variational quantum algorithms, with a particular emphasis on quantum machine learning as a case study.

Senapati, Priyabrata [Kent State University]↗
NASA NTRS1982

Adaptive identification for the dynamics of large space structures

A new approach for identifying the dynamics of large space structures using noisy measurements is presented. In this approach, a modal system description is avoided and, hence, the a priori specification of model order. Instead, the system's order is recursively determined on-line, along with its parameters, using recursive lattice filters which are widely used in adaptive signal processing. The order determination is carried out based on statistical hypothesis testing theory. The approach is illustrated for the problem of identifying the structural dynamic characteristics of a free-free beam. Results on the effectiveness and accuracy of the identification scheme with respect to noise levels, sampling rates, and data window sizes are presented

Sundararajan, N.↗
NASA NTRS1999

Determination of Radiative Forcing of Saharan Dust using Combined TOMS and ERBE Data

The direct radiative forcing of Saharan dust aerosols has been determined by combining aerosol information derived from Nimbus-7 TOMS with radiation measurements observed at the top of atmosphere (TOA) by NOAA-9 ERBE made during February-July 1985. Cloud parameters and precipitable water derived from the NOAA-9 HIRS2 instrument were used to aid in screening for clouds and water vapor in the analyses. Our results indicate that under "cloud-free" and "dry" conditions there is a good correlation between the ERBE TOA outgoing longwave fluxes and the TOMS aerosol index measurements over both land and ocean in areas under the influence of airborne Saharan dust. The ERBE TOA outgoing shortwave fluxes were also found to correlate well with the dust loading derived from TOMS over ocean. However, the calculated shortwave forcing of Saharan dust aerosols is very weak and noisy over land for the range of solar zenith angle viewed by the NOAA-9 ERBE in 1985. Sensitivity factors of the TOA outgoing fluxes to changes in aerosol index were estimated using a linear regression fit to the ERBE and TOMS measurements. The ratio of the shortwave-to-longwave response to changes in dust loading over the ocean is found to be roughly 2 to 3, but opposite in sign. The monthly averaged "clear-sky" TOA direct forcing of airborne Saharan dust was also calculated by multiplying these sensitivity factors by the TOMS monthly averaged "clear-sky" aerosol index. Both the observational and theoretical analyses indicate that the dust layer height, ambient moisture content as well as the presence of cloud all play an important role in determining the TOA direct radiative forcing due to mineral aerosols.

Hsu, N. Christina↗
NASA NTRS1993

High-performance compression of astronomical images

Astronomical images have some rather unusual characteristics that make many existing image compression techniques either ineffective or inapplicable. A typical image consists of a nearly flat background sprinkled with point sources and occasional extended sources. The images are often noisy, so that lossless compression does not work very well; furthermore, the images are usually subjected to stringent quantitative analysis, so any lossy compression method must be proven not to discard useful information, but must instead discard only the noise. Finally, the images can be extremely large. For example, the Space Telescope Science Institute has digitized photographic plates covering the entire sky, generating 1500 images each having 14000 x 14000 16-bit pixels. Several astronomical groups are now constructing cameras with mosaics of large CCD's (each 2048 x 2048 or larger); these instruments will be used in projects that generate data at a rate exceeding 100 MBytes every 5 minutes for many years. An effective technique for image compression may be based on the H-transform (Fritze et al. 1977). The method that we have developed can be used for either lossless or lossy compression. The digitized sky survey images can be compressed by at least a factor of 10 with no noticeable losses in the astrometric and photometric properties of the compressed images. The method has been designed to be computationally efficient: compression or decompression of a 512 x 512 image requires only 4 seconds on a Sun SPARCstation 1. The algorithm uses only integer arithmetic, so it is completely reversible in its lossless mode, and it could easily be implemented in hardware for space applications.

White, Richard L.↗
NASA NTRS1992

More rain compensation results

To reduce the impact of rain-induced attenuation in the 20/30 GHz band, the attenuation at a specified signal frequency must be estimated and extrapolated forward in time on the basis of a noisy beacon measurement. Several studies have used model based procedures for solving this problem in statistical inference. Perhaps the most widely used model-based paradigm leads to the Kalman filter and its lineal variants. In this formulation, the dynamic features of the attenuation are represented by a state process (x(sub t)). The observation process (y(sub t)) is derived from beacon measurements. Some ideas relating to the signal processing problems related to uplink power control are presented. It is shown that some easily implemented algorithms hold promise for use in estimating rain induced fades. The algorithms were applied to actual data generated at the Virginia Polytechnic Institute and State University (VPI) test facility. Because only one such event was studied, it is not clear that the algorithms will have the same effectiveness when a wide range of events are studied.

Sworder, D. D.↗
NASA NTRSDate not supplied

Evaluation of GHGSat Methane Emission Estimations of South Side Landfill in Indianapolis, Indiana

As global methane (CH4) continues to grow, accurate quantification of CH4 emission sources has become a high priority for climate management. Urban CH4 emissions in the US could potentially account close to 20% of the national CH4 anthropogenic emissions. While isolated insitu urban CH4 emissions quantification projects have been successful, they require sophisticated planning and prolonged analyses. Such studies are imperative, but they cannot easily operate in every city. An ability to quantify the magnitude of CH4 point sources in a variety of cities with a satellite could support wide scale urban CH4 emissions quantification. Whole-earth scanning satellite instruments such as the TROPOMI and GOSAT are too coarse and noisy to provide meaningful measurements over cities with mid-strength emissions and most point-source emissions in general. It may be possible to address this deficiency utilizing a product with the high spatial resolution of 30-50 m from a private company GHGSat Inc. These sensors are specifically designed to identify and quantify CH4 emissions from diverse types of point sources of varying intensities. In this work we analyze a year (05/2023-05/2024) of GHGSat scans over South Side Landfill in downtown Indianapolis, Indiana and assess the instruments’ accuracy and precision with respect to prior estimations. The results indicate that the data are helpful for estimating landfill’s emissions (~750 kg/hr ± 200 kg/hr) over months-to-a-year time frame but may not be appropriate for identifying daily or even seasonal variations with any trustable degree of certainty, at least not in an urban environment.

Nikolay Balashov↗
NASA NTRS2000

A Real-Time Method for Estimating Viscous Forebody Drag Coefficients

This paper develops a real-time method based on the law of the wake for estimating forebody skin-friction coefficients. The incompressible law-of-the-wake equations are numerically integrated across the boundary layer depth to develop an engineering model that relates longitudinally averaged skin-friction coefficients to local boundary layer thickness. Solutions applicable to smooth surfaces with pressure gradients and rough surfaces with negligible pressure gradients are presented. Model accuracy is evaluated by comparing model predictions with previously measured flight data. This integral law procedure is beneficial in that skin-friction coefficients can be indirectly evaluated in real-time using a single boundary layer height measurement. In this concept a reference pitot probe is inserted into the flow, well above the anticipated maximum thickness of the local boundary layer. Another probe is servomechanism-driven and floats within the boundary layer. A controller regulates the position of the floating probe. The measured servomechanism position of this second probe provides an indirect measurement of both local and longitudinally averaged skin friction. Simulation results showing the performance of the control law for a noisy boundary layer are then presented.

Whitmore, Stephen A.↗