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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Spatiotemporally Adaptive Compression for Scientific Dataset with Feature Preservation – A Case Study on Simulation Data with Extreme Climate Events Analysis

Scientific discoveries are increasingly constrained by limited storage space and I/O capacities. For time-series simulations and experiments, their data often need to be decimated over timesteps to accommodate storage and I/O limitations. In this paper, we propose a technique that addresses storage costs while improving post-analysis accuracy through spatiotemporal adaptive, error-controlled lossy compression. We investigate the trade-off between data precision and temporal output rates, revealing that reducing data precision and increasing timestep frequency lead to more accurate analysis outcomes. Additionally, we integrate spatiotemporal feature detection with data compression and demonstrate that performing adaptive error-bounded compression in higher dimensional space enables greater compression ratios, leveraging the error propagation theory of a transformation-based compressor. To evaluate our approach, we conduct experiments using the well-known E3SM climate simulation code and apply our method to compress variables used for cyclone tracking. Our results show a significant reduction in storage size while enhancing the quality of cyclone tracking analysis, both quantitatively and qualitatively, in comparison to the prevalent timestep decimation approach. Compared to three state-of-the-art lossy compressors lacking feature preservation capabilities, our adaptive compression framework improves perfectly matched cases in TC tracking by 26.4-51.3% at medium compression ratios and by 77.3-571.1% at large compression ratios, with a merely 5–11% computational overhead.

Gong, Qian↗
NASA NTRS1992

Locally adaptive vector quantization: Data compression with feature preservation

A study of a locally adaptive vector quantization (LAVQ) algorithm for data compression is presented. This algorithm provides high-speed one-pass compression and is fully adaptable to any data source and does not require a priori knowledge of the source statistics. Therefore, LAVQ is a universal data compression algorithm. The basic algorithm and several modifications to improve performance are discussed. These modifications are nonlinear quantization, coarse quantization of the codebook, and lossless compression of the output. Performance of LAVQ on various images using irreversible (lossy) coding is comparable to that of the Linde-Buzo-Gray algorithm, but LAVQ has a much higher speed; thus this algorithm has potential for real-time video compression. Unlike most other image compression algorithms, LAVQ preserves fine detail in images. LAVQ's performance as a lossless data compression algorithm is comparable to that of Lempel-Ziv-based algorithms, but LAVQ uses far less memory during the coding process.

Cheung, K. M.↗
DOE OSTI2022

Metrics for Intercomparison of Remapping Algorithms (MIRA) protocol applied to Earth system models

Abstract. Strongly coupled nonlinear phenomena such as those described by Earth system models (ESMs) are composed of multiple component models with independent mesh topologies and scalable numerical solvers. A common operation in ESMs is to remap or interpolate component solution fields defined on their computational mesh to another mesh with a different combinatorial structure and decomposition, e.g., from the atmosphere to the ocean, during the temporal integration of the coupled system. Several remapping schemes are currently in use or available for ESMs. However, a unified approach to compare the properties of these different schemes has not been attempted previously. We present a rigorous methodology for the evaluation and intercomparison of remapping methods through an independently implemented suite of metrics that measure the ability of a method to adhere to constraints such as grid independence, monotonicity, global conservation, and local extrema or feature preservation. A comprehensive set of numerical evaluations is conducted based on a progression of scalar fields from idealized and smooth to more general climate data with strong discontinuities and strict bounds. We examine four remapping algorithms with distinct design approaches, namely ESMF Regrid (Hill et al., 2004), TempestRemap (Ullrich and Taylor, 2015), generalized moving least squares (GMLS) (Trask and Kuberry, 2020) with post-processing filters, and WLS-ENOR (Li et al., 2020). By repeated iterative application of the high-order remapping methods to the test fields, we verify the accuracy of each scheme in terms of their observed convergence order for smooth data and determine the bounded error propagation using challenging, realistic field data on both uniform and regionally refined mesh cases. In addition to retaining high-order accuracy under idealized conditions, the methods also demonstrate robust remapping performance when dealing with non-smooth data. There is a failure to maintain monotonicity in the traditional L2-minimization approaches used in ESMF and TempestRemap, in contrast to stable recovery through nonlinear filters used in both meshless GMLS and hybrid mesh-based WLS-ENOR schemes. Local feature preservation analysis indicates that high-order methods perform better than low-order dissipative schemes for all test cases. The behavior of these remappers remains consistent when applied on regionally refined meshes, indicating mesh-invariant implementations. The MIRA intercomparison protocol proposed in this paper and the detailed comparison of the four algorithms demonstrate that the new schemes, namely GMLS and WLS-ENOR, are competitive compared to standard conservative minimization methods requiring computation of mesh intersections. The work presented in this paper provides a foundation that can be extended to include complex field definitions, realistic mesh topologies, and spectral element discretizations, thereby allowing for a more complete analysis of production-ready remapping packages.

58 GEOSCIENCES↗
DOE OSTI2023

Quantum machine learning with differential privacy

Abstract Quantum machine learning (QML) can complement the growing trend of using learned models for a myriad of classification tasks, from image recognition to natural speech processing. There exists the potential for a quantum advantage due to the intractability of quantum operations on a classical computer. Many datasets used in machine learning are crowd sourced or contain some private information, but to the best of our knowledge, no current QML models are equipped with privacy-preserving features. This raises concerns as it is paramount that models do not expose sensitive information. Thus, privacy-preserving algorithms need to be implemented with QML. One solution is to make the machine learning algorithm differentially private, meaning the effect of a single data point on the training dataset is minimized. Differentially private machine learning models have been investigated, but differential privacy has not been thoroughly studied in the context of QML. In this study, we develop a hybrid quantum-classical model that is trained to preserve privacy using differentially private optimization algorithm. This marks the first proof-of-principle demonstration of privacy-preserving QML. The experiments demonstrate that differentially private QML can protect user-sensitive information without signficiantly diminishing model accuracy. Although the quantum model is simulated and tested on a classical computer, it demonstrates potential to be efficiently implemented on near-term quantum devices [noisy intermediate-scale quantum (NISQ)]. The approach’s success is illustrated via the classification of spatially classed two-dimensional datasets and a binary MNIST classification. This implementation of privacy-preserving QML will ensure confidentiality and accurate learning on NISQ technology.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Ring artifact and Poisson noise attenuation via volumetric multiscale nonlocal collaborative filtering of spatially correlated noise

X-ray micro-tomography systems often suffer from high levels of noise. In particular, severe ring artifacts are common in reconstructed images, caused by defects in the detector, calibration errors, and fluctuations producing streak noise in the raw sinogram data. Furthermore, the projections commonly contain high levels of Poissonian noise arising from the photon-counting detector. This work presents a 3-D multiscale framework for streak attenuation through a purposely designed collaborative filtering of correlated noise in volumetric data. A distinct multiscale denoising step for attenuation of the Poissonian noise is further proposed. By utilizing the volumetric structure of the projection data, the proposed fully automatic procedure offers improved feature preservation compared with 2-D denoising and avoids artifacts which arise from individual filtering of sinograms.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS2000

Analysis of shape and motion of the mitral annulus in subjects with and without cardiomyopathy by echocardiographic 3-dimensional reconstruction

The shape and dynamics of the mitral annulus of 10 patients without heart disease (controls), 3 patients with dilated cardiomyopathy, and 5 patients with hypertrophic obstructive cardiomyopathy and normal systolic function were analyzed by transesophageal echocardiography and 3-dimensional reconstruction. Mitral annular orifice area, apico-basal motion of the annulus, and nonplanarity were calculated over time. Annular area was largest in end diastole and smallest in end systole. Mean areas were 11.8 +/- 2.5 cm(2) (controls), 15.2 +/- 4.2 cm(2) (dilated cardiomyopathy), and 10.2 +/- 2.4 cm(2) (hypertrophic cardiomyopathy) (P = not significant). After correction for body surface, annuli from patients with normal left ventricular function were smaller than annuli from patients with dilated cardiomyopathy (5.9 +/- 1.2 cm(2)/m(2) vs 7.7 +/- 1.0 cm(2)/m(2); P <.02). The change in area during the cardiac cycle showed significant differences: 23.8% +/- 5.1% (controls), 13.2% +/- 2.3% (dilated cardiomyopathy), and 32.4% +/- 7.6% (hypertrophic cardiomyopathy) (P <.001). Apico-basal motion was highest in controls, followed by those with hypertrophic obstructive and dilated cardiomyopathy (1.0 +/- 0.3 cm, 0.8 +/- 0.2 cm, 0.3 +/- 0.2 cm, respectively; P <.01). Visual inspection and Fourier analysis showed a consistent pattern of anteroseptal and posterolateral elevations of the annulus toward the left atrium. In conclusion, although area changes and apico-basal motion of the mitral annulus strongly depend on left ventricular systolic function, nonplanarity is a structural feature preserved throughout the cardiac cycle in all three groups.

Non-NASA Center↗
DOE OSTI2021

Sights of summer from Lab’s earliest days digitization preserves images from 1940s, features early staff enjoying Los Alamos

Working at the Lab during World War II was serious business, but even these world-changing scientists and staff found time to play during the summer months. The National Security Research Center (NSRC), which is the Lab’s classified library and also houses unclassified artifacts from Los Alamos history, recently rescanned candid footage from two of contributors to the Manhattan Project.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

Balancing Trade-offs: Adaptive Differential Privacy in Interpretable Machine Learning Models

In the advancing field of machine learning, balancing accuracy, interpretability, and privacy represents a significant challenge. The problem is exacerbated by the widespread deployment of pre-trained models locally in diverse applications, which could lead to various amounts of privacy leakage. Conventional Differential Privacy strategies, in which uniform noises are applied to model gradients, guarantee data privacy at the expense of accuracy and interpretability. This paper introduces a Feature-Sensitive Adaptive Differential Privacy (FADP) framework with a unique noise-adding strategy. Noises are adaptively added based on feature importance clustering, where important features are considered for interpretability. By employing a unique masking technique, FADP selectively preserves crucial features with minimal noise interference, maintaining accuracy while enhancing interpretability. The FADP framework addresses the limitations of traditional DP methods by preserving critical channels and improving interpretability — a vital requirement in machine learning applications that demand transparency in model decisions. Through comprehensive testing, FADP is shown to balance the trade-offs among accuracy, privacy, and interpretability, marking a substantial advancement in the field of privacy-preserving machine learning.

Farhad Riya, Farhin [University of Tennessee, Knox↗
DOE OSTI2023

Customizable adaptive regularization techniques for B-spline modeling

B-spline models are a powerful way to represent scientific data sets with a functional approximation. However, these models can suffer from spurious oscillations when the data to be approximated are not uniformly distributed. Model regularization (i.e., smoothing) has traditionally been used to minimize these oscillations; unfortunately, it is sometimes impossible to sufficiently remove unwanted artifacts without smoothing away key features of the data set. In this article, we present a method of model regularization that preserves significant features of a data set while minimizing artificial oscillations. Our method varies the strength of a smoothing parameter throughout the domain automatically, removing artifacts in poorly-constrained regions while leaving other regions unchanged. Further, the proposed method selectively incorporates regularization terms based on first and second derivatives to maintain model accuracy while minimizing numerical artifacts. The behavior of our method is validated on a collection of two- and three-dimensional data sets produced by scientific simulations. In addition, a key tuning parameter is highlighted and the effects of this parameter are presented in detail. This paper is an extension of our previous conference paper at the 2022 International Conference on Computational Science (ICCS) (Lenz et al., 2022).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Acoustic Rocket Signatures Collected by Smartphones

Rockets generate complex acoustic signatures that can be detected over a thousand kilometers from their source. While many far-field acoustic rocket signatures have been collected and released to the public, very few signatures collected at distances less than 100 km are available. This work presents a curated and annotated dataset of acoustic signatures of 243 rocket launches collected by a network of smartphones stationed at distances between 10 and 70 km from the launch sites, resulting in 1089 individual recordings. Due to the frequency dependence of atmospheric attenuation and the relatively short propagation distances, higher-frequency features not preserved in most publicly available data are observed. The signals are time-aligned to allow for different segments of the signal (ignition, launch, trajectory, chronology) to be more easily examined and compared. Initial analysis of the features of these rocket launch stages is performed, observed features are compared to those found in the existing literature, and comparisons between signals from launches of different rocket types are made. The dataset is annotated and made available to the public to aid future analysis of the characteristics and source mechanisms of rocket acoustics as well as applications such as rocket detection and classification models.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Converting tabular data into images for deep learning with convolutional neural networks

Abstract Convolutional neural networks (CNNs) have been successfully used in many applications where important information about data is embedded in the order of features, such as speech and imaging. However, most tabular data do not assume a spatial relationship between features, and thus are unsuitable for modeling using CNNs. To meet this challenge, we develop a novel algorithm, image generator for tabular data (IGTD), to transform tabular data into images by assigning features to pixel positions so that similar features are close to each other in the image. The algorithm searches for an optimized assignment by minimizing the difference between the ranking of distances between features and the ranking of distances between their assigned pixels in the image. We apply IGTD to transform gene expression profiles of cancer cell lines (CCLs) and molecular descriptors of drugs into their respective image representations. Compared with existing transformation methods, IGTD generates compact image representations with better preservation of feature neighborhood structure. Evaluated on benchmark drug screening datasets, CNNs trained on IGTD image representations of CCLs and drugs exhibit a better performance of predicting anti-cancer drug response than both CNNs trained on alternative image representations and prediction models trained on the original tabular data.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1988

On-line object feature extraction for multispectral scene representation

A new on-line unsupervised object-feature extraction method is presented that reduces the complexity and costs associated with the analysis of the multispectral image data and data transmission, storage, archival and distribution. The ambiguity in the object detection process can be reduced if the spatial dependencies, which exist among the adjacent pixels, are intelligently incorporated into the decision making process. The unity relation was defined that must exist among the pixels of an object. Automatic Multispectral Image Compaction Algorithm (AMICA) uses the within object pixel-feature gradient vector as a valuable contextual information to construct the object's features, which preserve the class separability information within the data. For on-line object extraction the path-hypothesis and the basic mathematical tools for its realization are introduced in terms of a specific similarity measure and adjacency relation. AMICA is applied to several sets of real image data, and the performance and reliability of features is evaluated.

Ghassemian, Hassan↗
NASA NTRS1997

Analysis of Texture Using the Fractal Model

Properties such as the fractal dimension (FD) can be used for feature extraction and classification of regions within an image. The FD measures the degree of roughness of a surface, so this number is used to characterize a particular region, in order to differentiate it from another. There are two basic approaches discussed in the literature to measure FD: the blanket method, and the box counting method. Both attempt to measure FD by estimating the change in surface area with respect to the change in resolution. We tested both methods but box counting resulted computationally faster and gave better results. Differential Box Counting (DBC) was used to segment a collage containing three textures. The FD is independent of directionality and brightness so five features were used derived from the original image to account for directionality and gray level biases. FD can not be measured on a point, so we use a window that slides across the image giving values of FD to the pixel on the center of the window. Windowing blurs the boundaries of adjacent classes, so an edge-preserving, feature-smoothing algorithm is used to improve classification within segments and to make the boundaries sharper. Segmentation using DBC was 90.8910 accurate.

Navas, William↗
DOE OSTI2023

TopoSZ: Preserving Topology in Error-Bounded Lossy Compression

Existing error-bounded lossy compression techniques control the pointwise error during compression to guarantee the integrity of the decompressed data. However, they typically do not explicitly preserve the topological features in data. When performing post hoc analysis with decompressed data using topological methods, preserving topology in the compression process to obtain topologically consistent and correct scientific insights is desirable. In this paper, we introduce TopoSZ, an error-bounded lossy compression method that preserves the topological features in 2D and 3D scalar fields. Specifically, we aim to preserve the types and locations of local extrema as well as the level set relations among critical points captured by contour trees in the decompressed data. The main idea is to derive topological constraints from contour-tree-induced segmentation from the data domain, and incorporate such constraints with a customized error-controlled quantization strategy from the SZ compressor (version 1.4). In conclusion, our method allows users to control the pointwise error and the loss of topological features during the compression process with a global error bound and a persistence threshold.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1993

Largest impact features on Venus: Non-preserved or non-recognizable?

Conventional explanation of a lack of impact craters with diameters greater than 300 km on Venus is that they formed during the intense bombardment era and had lunar-like morphology, but they are not preserved now because of rapid viscous relaxation of their topograpy and/or high endogenous reworking of surface. Other explanation invokes failure to recognize these larger craters because of their non-lunar-like morphology from the moment of formation, since larger gravity of Venus relatively to the Moon results in that largest craters on Venus may form within the mass of shock melted material while comparably sized lunar craters would be still almost 'dry'. To test this hypothesis, morphologies and rim-crest diameters of the largest peak-ring and Orientale-type basins and all larger impact features on Moon, Mercury, Mars, and Venus were compiled and compared to rim crest diameters of model craters with different melt volume/transient-cavity volume ratios. Results show that the final diameters of model craters formed at depth of melting about twice of transient cavity depth correspond to changeover from a planet-similar morphology of all the smaller basins on any terrestrial planet to a planet-specific morphology of all the larger basins on the Moon, Mercury, and Mars. On Venus, these largest impact features are not found and instead, a Venus-specific morphology of the largest concentric coronae appears in this size range. The coronae were suggested to form over sites of mantle upwelling and modified by subsequent volcanism and gravitational relaxation. The results here suggest that mantle upwelling - the first and necessary step of the corona formation models - may be induced by impact event (as a result of transient cavity collapse) and operated under cover of hot, slowly cooled impact melt in the areas of thinned crust and/or thermally active regions.

Nikolaeva, Olga V.↗
NASA NTRS2014

Geologic Mapping of Ejecta Deposits in Oppia Quadrangle, Asteroid (4) Vesta

Oppia Quadrangle Av-10 (288-360 deg E, +/- 22 deg) is a junction of key geologic features that preserve a rough history of Asteroid (4) Vesta and serves as a case study of using geologic mapping to define a relative geologic timescale. Clear filter images, stereo-derived topography, slope maps, and multispectral color-ratio images from the Framing Camera on NASA's Dawn spacecraft served as basemaps to create a geologic map and investigate the spatial and temporal relationships of the local stratigraphy. Geologic mapping reveals the oldest map unit within Av-10 is the cratered highlands terrain which possibly represents original crustal material on Vesta that was then excavated by one or more impacts to form the basin Feralia Planitia. Saturnalia Fossae and Divalia Fossae ridge and trough terrains intersect the wall of Feralia Planitia indicating that this impact basin is older than both the Veneneia and Rheasilvia impact structures, representing Pre-Veneneian crustal material. Two of the youngest geologic features in Av-10 are Lepida (approximately 45 km diameter) and Oppia (approximately 40 km diameter) impact craters that formed on the northern and southern wall of Feralia Planitia and each cross-cuts a trough terrain. The ejecta blanket of Oppia is mapped as 'dark mantle' material because it appears dark orange in the Framing Camera 'Clementine-type' colorratio image and has a diffuse, gradational contact distributed to the south across the rim of Rheasilvia. Mapping of surface material that appears light orange in color in the Framing Camera 'Clementine-type' color-ratio image as 'light mantle material' supports previous interpretations of an impact ejecta origin. Some light mantle deposits are easily traced to nearby source craters, but other deposits may represent distal ejecta deposits (emplaced greater than 5 crater radii away) in a microgravity environment.

Asteroids↗