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NASA NTRSDate not supplied

Enhancing Neural Network Explainability with Variational Autoencoders

Machine intelligence has been used to tackle increasingly complex problems and deep learning solutions are at the forefront of tackling these problems. In general, these architectures have a great number of parameters that are methodically updated in training. The vast number and complexity of deep neural networks makes it very difficult to decipher the inner workings of the neurons and layers that make up the network. This paper posits that trustworthiness and trust in autonomous systems are increased through eXplainable Artificial Intelligence (XAI) and presents a method that enhances the explainability and understanding of a neural network decision. We leverage variational autoencoders to produce human interpretable features from complex data sets. We show that the explainable features can then be used for machine learning applications. Explainability inspires trust in autonomous systems that use deep learning, which is necessary for safety critical systems.

Loc Tran↗
NASA NTRS2013

ION Configuration Editor

The configuration of ION (Inter - planetary Overlay Network) network nodes is a manual task that is complex, time-consuming, and error-prone. This program seeks to accelerate this job and produce reliable configurations. The ION Configuration Editor is a model-based smart editor based on Eclipse Modeling Framework technology. An ION network designer uses this Eclipse-based GUI to construct a data model of the complete target network and then generate configurations. The data model is captured in an XML file. Intrinsic editor features aid in achieving model correctness, such as field fill-in, type-checking, lists of valid values, and suitable default values. Additionally, an explicit "validation" feature executes custom rules to catch more subtle model errors. A "survey" feature provides a set of reports providing an overview of the entire network, enabling a quick assessment of the model s completeness and correctness. The "configuration" feature produces the main final result, a complete set of ION configuration files (eight distinct file types) for each ION node in the network.

Borgen, Richard L.↗
NASA NTRS2020

Machine Learning in Heliophysics and space weather forecasting: a white paper of finding and recommendations

The authors of this white paper met on 16-17 January 2020 at the New Jersey Institute of Technology,Newark, NJ, for a 2-day workshop that brought together a group of heliophysicists, data providers,expert modelers, and computer/data scientists. Their objective was to discuss critical developments and prospects of the application of machine and/or deep learning techniques for data analysis, modeling and forecasting in Heliophysics, and to shape a strategy for further developments in the field. The workshop combined a set of plenary sessions featuring invited introductory talks interleaved with a set of open discussion sessions. The outcome of the discussion is encapsulated in this white paper that also features a top-level list of recommendations agreed by participants

HSR↗
NASA NTRS2020

Terrain Relative Navigation for Guided Descent on Titan

Titan’s dense atmosphere, low gravity, and high winds at high altitudes create descent times of >90 minutes with standard entry/descent/landing (EDL) architectures and result in large unguided landing ellipses, with 99% values of 110x110 km and 149x72 km in recent Titan lander proposals. Enabling precision landing on Titan could increase science return for the types of missions proposed to date and make additional types of landing sites accessible, opening up new possibilities for science investigations. Precision landing on Titan has unique challenges, because the hazy atmosphere makes it difficult to see the surface and because it requires guided descent with divert ranges that are one to two orders of magnitude larger than needed for other target bodies, i.e. up to on the order of 100 km. It is conceivable that such a divert capability could be provided economically by a parafoil or other steerable aerodynamic decelerator deployed several 10s of km above the surface. The long descent times lead to large inertial navigation errors, hence a need for terrain relative navigation (TRN). This would require a TRN capability that can operate at such altitudes, despite challenges of seeing the surface sufficiently clearly and of depending on map products that are two orders of magnitude lower in spatial resolution than those for Mars and airless bodies. We then develop algorithms for map matching and feature tracking with descent images and test these with synthetic images created from Cassini/Huygens data sets and our radiative transfer model. We also introduce new possibilities for TRN based on the potential to discriminate some specific types of terrain onboard in descent imagery, such as lake vs adjacent ground and dune vs interdune. We use sensor measurement noise models in simulations of state estimation with an extended Kalman filter that includes coordinates of a set of tracked features in the state vector. Case studies were done for two notional landing sites, one in a site with only dry ground and one in a Titan lake district. In both cases, the filter error model shows 3 position error at touchdown on the order of 2 km. More work is needed to validate these results with higher fidelity camera models and larger data sets, but this is very promising.

Matthies, Larry↗
NASA NTRS1998

Trace-Driven Debugging of Message Passing Programs

In this paper we report on features added to a parallel debugger to simplify the debugging of parallel message passing programs. These features include replay, setting consistent breakpoints based on interprocess event causality, a parallel undo operation, and communication supervision. These features all use trace information collected during the execution of the program being debugged. We used a number of different instrumentation techniques to collect traces. We also implemented trace displays using two different trace visualization systems. The implementation was tested on an SGI Power Challenge cluster and a network of SGI workstations.

Frumkin, Michael↗
NASA NTRS1994

Provenance of mineral phases in the Cretaceous-Tertiary boundary sediments exposed on the southern peninsula of Haiti

Acid-insoluble mineral residua of tektite-bearing Cretaceous-Tertiary boundary sediments in the Beloc Formation of Haiti contain abundant shocked quartz and lesser amounts of shocked plagioclase. The shocked quartz grains typically have 2 or 3 sets of planar deformation features, although grains with up to 15 sets were observed. The proportion of shocked quartz in the boundary sediments increases with stratigraphic height; at least 70 +/- 11% of the proportion of the quartz grains are shocked in the uppermost stratigraphic interval. The proportion of shocked quartz throughout the boundary sediments indicates that these grains were excavated primarily from crystalline silicate units, which may have been covered with a small amount of porous quartz-bearing sediments. Polyhedral and moderately sutured margins in shocked polycrystalline quartz grains, the size of the crystal units in these grains and the presence of shocked plagioclase, indicate these ejecta components were excavated from a target with continental affinites, containing quartzites or metaquartzites and a sialic metamorphic and/or igneous component. Other evidence suggests the target may also have contained a significant amount of calcium carbonate and/or sulfate. The large size and amount of shocked quartz grains deposited in Haiti indicate the crater from which they were excavated was produced in the proto-Caribbean region.

Kring, David A.↗
NASA NTRS1993

A new clustering algorithm applicable to multispectral and polarimetric SAR images

We describe an application of a scale-space clustering algorithm to the classification of a multispectral and polarimetric SAR image of an agricultural site. After the initial polarimetric and radiometric calibration and noise cancellation, we extracted a 12-dimensional feature vector for each pixel from the scattering matrix. The clustering algorithm was able to partition a set of unlabeled feature vectors from 13 selected sites, each site corresponding to a distinct crop, into 13 clusters without any supervision. The cluster parameters were then used to classify the whole image. The classification map is much less noisy and more accurate than those obtained by hierarchical rules. Starting with every point as a cluster, the algorithm works by melting the system to produce a tree of clusters in the scale space. It can cluster data in any multidimensional space and is insensitive to variability in cluster densities, sizes and ellipsoidal shapes. This algorithm, more powerful than existing ones, may be useful for remote sensing for land use.

Wong, Yiu-Fai↗
NASA NTRS2018

WFIRST Integral Field Spectrograph Design and Operations Concept

One important aspect of the WFIRST coronagraphic instrument (CGI) technology demonstration is its ability to take high contrast spectra with an integral field spectrograph (IFS). Future missions such as LUVOIR and HabEx rely on IFS technologies for the spectral characterization of any objects they discover; spectral characterization is the core science of their exoplanet imaging instruments. The CGI IFS, being designed and built and Goddard Space Flight Center, has a spectral resolution of R50 and is designed to accommodate a 20% bandpass spanning 600-970nm. The bands and spectral resolution were chosen by the science investigation teams to target key methane absorption features, with the aim of constraining abundances. For CGI as a technology demonstration, these are the minimum set of defining features to demonstrate on-orbit wavefront control with an IFS capable of producing science. The ability to close a wavefront control loop around an IFS, maintain a stable dark hole, and provide time resolved data that simultaneously spans spatial and spectral dimensions are crucial demonstrations for future observatories. We highlight the IFS requirements and how the on-orbit calibration is handled. We also provide further detail on the optomechanical design, its stability based on thermal and structural predictions, anticipated performance, and operations concept of the CGI IFS.

Groff, Tyler↗
DOE OSTI2024

Feature Pathway Graphs using Random Forest Regressors

SAND2025-04671O Feature Pathway Graphs using Random Forest Regressors is a software tool that uses machine learning to determine pathways of influence between features in data sets. It can be used as a surrogate method for casual discovery. The output creates pathway graphs between features of interest. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Peterson, Matthew↗
NASA NTRS2002

Stimulus information contaminates summation tests of independent neural representations of features

Many models of visual processing assume that visual information is analyzed into separable and independent neural codes, or features. A common psychophysical test of independent features is known as a summation study, which measures performance in a detection, discrimination, or visual search task as the number of proposed features increases. Improvement in human performance with increasing number of available features is typically attributed to the summation, or combination, of information across independent neural coding of the features. In many instances, however, increasing the number of available features also increases the stimulus information in the task, as assessed by an optimal observer that does not include the independent neural codes. In a visual search task with spatial frequency and orientation as the component features, a particular set of stimuli were chosen so that all searches had equivalent stimulus information, regardless of the number of features. In this case, human performance did not improve with increasing number of features, implying that the improvement observed with additional features may be due to stimulus information and not the combination across independent features.

NASA Discipline Space Human Factors↗
DOE OSTI2022

Energy Prediction under Changed Demand Conditions: Robust Machine Learning Models and Input Feature Combinations

Deciding on a suitable algorithm for energy demand prediction in a building is non-trivial and depends on the availability of data. In this paper we compare four machine learning models, commonly found in the literature, in terms of their generalization performance and in terms of how using different sets of input features affects accuracy. This is tested on a data set where consumption patterns differ significantly between training and evaluation because of the Covid-19 pandemic. We provide a hands-on guide and supply a Python framework for building operators to adapt and use in their applications.

Schranz, Thomas↗
NASA NTRS2017

Supra Arcade Downflows in the Earth's Magnetotail

Pinpointing the location of a single reconnection event in the corona is difficult due to observational constraints, although features directly resulting from this rapid reconfiguration of the field lines can be observed beyond the reconnection site. One set of such features are outflows in the form of post-reconnection loops, which have been linked to observations of supra-arcade downflows (SADs). SADs appear as sunward-traveling, density-depleted regions above flare arcades that develop during long duration eruptions. The limitations of remote sensing methods inherently results in ambiguities regarding the interpretation of SAD formation. Of particular interest is how these features are related to post-reconnection retracting magnetic field lines. In planetary magnetospheres, similar events to solar flares occur in the form of substorms, where reconnection in the anti-sunward tail of the magnetosphere causes field lines to retract toward the planet. Using data from the Time History of Events and Macroscopic Interactions during Substorms (THEMIS), we compare one particular aspect of substorms, dipolarization fronts, to SADs. Dipolarization fronts are observed as rapid but temporary changes in the magnetic field of the magnetotail plasma sheet into a more potential-like dipolar shape. These dipolarization fronts are believed to be retracting post-reconnection field lines. We combine data sets to show that the while the densities and magnetic fields involved vary greatly between the regimes, the plasma betas and Alfvén speeds are similar. These similarities allow direct comparison between the retracting field lines and their accompanying wakes of rarified plasma observed with THEMIS around the Earth to the observed morphological density depletions visible with XRT and AIA on the Sun. These results are an important source of feedback for models of coronal current sheets.

Kobelski, Adam↗
DOE OSTI2020

Feature identification or classification using task-specific metadata

Innovations in the identification or classification of features in a data set are described, such as a data set representing measurements taken by a scientific instrument. For example, a task-specific processing component, such as a video encoder, is used to generate task-specific metadata. When the data set includes video frames, metadata can include information regarding motion of image elements between frames, or other differences between frames. A feature of the data set, such as an event, can be identified or classified based on the metadata. For example, an event can be identified when metadata for one or more elements of the data set exceed one or more threshold values. When the feature is identified or classified, an output, such as a display or notification, can be generated. Although the metadata may be useable to generate a task-specific output, such as compressed video data, the identifying or classifying is not used solely in production of, or the creation of an association with, the task-specific output.

Teuton, Jeremy R.↗
NASA NTRS2005

Three Years of TRMM Precipitation Features: Radar, Radiometric, and Lightning Characteristics - Part 1

During its first three years, the Tropical Rainfall Measuring Mission (TRMM) satellite observed nearly six million precipitation features. The population of precipitation features is sorted by lightning flash rate, minimum brightness temperature, maximum radar reflectivity. areal extent, and volumetric rainfall. For each of these characteristics, essentially describing the convective intensity or the size of the features, the population is broken into categories consisting of the top 0.001%, top 0.01%, top 0.1%, top 1%, top 2.4%. and remaining 97.6%. The set of weakest/smallest features composes 97.6% of the population because that fraction does not have detected lightning, with a minimum detectable flash rate of 0.7 flashes (fl) per minute. The greatest observed flash rate is 1351 fl per minute; the lowest brightness temperatures are 42 K (85 GHz) and 69 K (37 GHz). The largest precipitation feature covers 335 000 square kilometers and the greatest rainfall from an individual precipitation feature exceeds 2 x 10 kg per hour of water. There is considerable overlap between the greatest storms according to different measures of convective intensity. The largest storms are mostly independent of the most intense storms. The set of storms producing the most rainfall is a convolution of the largest and the most intense storms. This analysis is a composite of the global Tropics and subtropics. Significant variability is known to exist between locations. seasons, and meteorological regimes. Such variability will be examined in Part II. In Part I, only a crude land-ocean separation is made. The known differences in bulk lightning flash rates over land and ocean result from at least two differences in the precipitation feature population: the frequency of occurrence of intense storms and the magnitude of those intense storms that do occur. Even when restricted to storms with the same brightness temperature, same size, or same radar reflectivity aloft, the storms over water are considerably less likely to produce lightning than are comparable storms over land.

Cecil, Daniel J.↗
DOE OSTI2025

Evaluating the Origins of Aerobic Oxidation Catalysis with TAM-3, a MOF with Accessible Co(II) Sites and Large Pores

Metal-organic frameworks (MOFs) are attractive platforms that merge concepts of homogeneous and heterogeneous catalysis. Catalyst design and optimization are enabled by an array of synthetic methods that offer independent control over the local chemical structure of lattice-embedded metal ions (i.e., ligand identity and geometry) and the long-range materials properties (i.e., porosity). Establishing the origin of catalytic activity in MOF-promoted reactions remains a significant challenge: The relative rates of catalyst turnover and substrate diffusion dictate the extent to which interstitial sites are accessible and operational in catalysis. To minimize the contributions of surface sites in catalysis, materials with large pore dimensions are often sought, however, the impact of pore expansion on the origins of catalytic activity is similarly challenging to establish. Here, we describe TAM-3, a Co(II) based MOF with accessible metal sites supported by a facially coordinating tris-tetrazole ligand set. TAM-3 features large channel-like pores (17 × 23 Å) and promotes aerobic C−H oxidation and olefin epoxidation. Using a set of simple kinetics experiments, based on the analysis of kinetic isotope effects and olefin oxidation diastereoselectivities, we demonstrate that despite the large pores, interstitial metal ions do not significantly contribute to the observed substrate oxidation. This study highlights the importance of conducting kinetic experiments to assess the origin of apparent catalytic activity with MOFs and the challenge of harnessing reactive oxidants with microporous catalyst materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2017

Lessons Learned from OSIRIS-Rex Autonomous Navigation Using Natural Feature Tracking

The Origins, Spectral Interpretation, Resource Identification, Security-Regolith Explorer (Osiris-REx) spacecraft is scheduled to launch in September, 2016 to embark on an asteroid sample return mission. It is expected to rendezvous with the asteroid, Bennu, navigate to the surface, collect a sample (July 20), and return the sample to Earth (September 23). The original mission design called for using one of two Flash Lidar units to provide autonomous navigation to the surface. Following Preliminary design and initial development of the Lidars, reliability issues with the hardware and test program prompted the project to begin development of an alternative navigation technique to be used as a backup to the Lidar. At the critical design review, Natural Feature Tracking (NFT) was added to the mission. NFT is an onboard optical navigation system that compares observed images to a set of asteroid terrain models which are rendered in real-time from a catalog stored in memory on the flight computer. Onboard knowledge of the spacecraft state is then updated by a Kalman filter using the measured residuals between the rendered reference images and the actual observed images. The asteroid terrain models used by NFT are built from a shape model generated from observations collected during earlier phases of the mission and include both terrain shape and albedo information about the asteroid surface. As a result, the success of NFT is highly dependent on selecting a set of topographic features that can be both identified during descent as well as reliably rendered using the shape model data available. During development, the OSIRIS-REx team faced significant challenges in developing a process conducive to robust operation. This was especially true for terrain models to be used as the spacecraft gets close to the asteroid and higher fidelity models are required for reliable image correlation. This paper will present some of the challenges and lessons learned from the development of the NFT system which includes not just the flight hardware and software but the development of the terrain models used to generate the onboard rendered images.

Navigation↗
NASA NTRS2001

(Almost) Featureless Stereo: Calibration and Dense 3D Reconstruction Using Whole Image Operations

The conventional approach to shape from stereo is via feature extraction and correspondences. This results in estimates of the camera parameters and a typically spare estimate of the surface. Given a set of calibrated images, a dense surface reconstruction is possible by minimizing the error between the observed image and the image rendered from the estimated surface with respect to the surface model parameters. Given an uncalibrated image and an estimated surface, the camera parameters can be estimated by minimizing the error between the observed and rendered images a function of the camera parameters. We use a very small dense set of matched features to provide camera parameter estimates for the initial dense surface estimate. We then re-estimate the camera parameters as described above, and then re-estimate the surface. This process is iterated. Whilst it can not be proven to converge, we have found that around three iterations results in excellent surface and camera parameters estimates.

Smelyanskiy, V. N.↗
DOE OSTI2023

User Manual - HydraGNN: Distributed PyTorch Implementation of Multi-Headed Graph Convolutional Neural Networks

This document serves as user manual for HydraGNN, a scalable graph neural network (GNN) architecture that allows for a simultaneous prediction of multiple target properties using multi-task learning (MTL). The HydraGNN architecture is constructed by successive superposition of three different sets of layers. The first set is made of message-passing layers to exchange information across nodes in the graph and use this to update the nodal features. The second set is made of global pooling layers that aggregate information from all the nodes in the graph and map it into a scalar, and is needed only for global target properties that are related to the entire graph. The third set of layers is dedicated to the implementation of MTL, which is enabled by forking of the architecture into separate heads, each one of them dedicated to the predictive task of one specific target property. Through an object-oriented programming paradigm, HydraGNN is templated over different message-passing policies, which allows for a user-friendly hyperparameter study to assess the sensitivity of the predictive performance of the HydraGNN architecture on a specific dataset with respect to the choice of the message-passing policy. The object-oriented paradigm used by HydraGNN also allows for a user-friendly inclusion of newly developed message passing policies within the existing framework. HydraGNN supports distributed computing capabilities for scalable data reading and scalable training on leadership-class supercomputers.

97 MATHEMATICS AND COMPUTING↗