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NASA NTRS1990

Ground states of partially connected binary neural networks

Neural networks defined by outer products of vectors over (-1, 0, 1) are considered. Patterns over (-1, 0, 1) define by their outer products partially connected neural networks consisting of internally strongly connected, externally weakly connected subnetworks. Subpatterns over (-1, 1) define subnetworks, and their combinations that agree in the common bits define permissible words. It is shown that the permissible words are locally stable states of the network, provided that each of the subnetworks stores mutually orthogonal subwords, or, at most, two subwords. It is also shown that when each of the subnetworks stores two mutually orthogonal binary subwords at most, the permissible words, defined as the combinations of the subwords (one corresponding to each subnetwork), that agree in their common bits are the unique ground states of the associated energy function.

Baram, Yoram↗

DOE OSTI2023

Attention-Augmented Parametric Kernel Graph Neural Network (APKGNN) for Node Classification

We present a new graph neural network, the Attention-based Parametric-Kernel augmented Graph Neural Network (APKGNN), developed for node classification tasks. Despite extensive work on modeling multi-faceted relationships between connected nodes of a graph, the effect of attention on edge features mapped to relationships has not yet been analyzed through learning representation. This study derives such an attention vector by first calculating node features corresponding to endpoints of an edge and then aggregating these with extracted local intrinsic patches of a given graph to generate augmented local patch vectors. This process uses a parametric kernel based on Gaussian mixture models (GMMs) to embed local neighborhoods of the graph in local patches. The patch vectors then convolve with the above node features to produce an updated node representation. We show that this new learning representation (APKGNN) achieves higher node classification accuracy on tasks - both standard benchmarks (Cora, PubMed, Citeseer) and new experimental short text corpora where nodes correspond to text documents and words. This implementation of the GNN convolution layer outperforms state-of-the-art (SOTA) algorithms, achieving higher training, validation, and test accuracy by a significant margin on three standard benchmark data sets under both SOTA experimental settings and those for new testbeds.

Bose, Avishek↗

DOE OSTI2025

Heterogeneous catalysis: Optimal performance at a phase boundary?

Most of the industrially used heterogeneous catalysts have been discovered by trial and error, and despite decades of experience, the discovery of new catalysts continues to be extremely challenging. The drive to uncover guiding principles in catalyst design is more present than ever. We share a series of observations indicating that optimal catalysts typically function at characteristic phase boundaries (e.g., abrupt changes in adsorbate coverage, catalyst structure, etc.) accessed in the reaction conditions. The catalyst exploits the associated instability—the desire to exist in multiple states simultaneously—as a driving force for chemical transformations. In other words, phase boundaries are good places to start the catalyst search, and indeed, we should focus on at least two phases at once rather than just one. Here, we substantiate this claim with several studies that combine statistical operando modeling and experiments. Transpiring from these observations is a hitherto unrecognized vector in catalyst discovery.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1975

The Rome-GSFC magnetic field experiment for Helios A and B /E 3/

The Rome-GSFC magnetic field experiment utilizes a triaxial fluxgate (saturable inductor) magnetometer. The sensor unit is mounted on the end of a boom approximately four meters from the spacecraft spin axis. The three analog outputs of the magnetometer are converted into three 9 bit digital words. The experiment utilizes an automatic inflight range switch to select the optimum dynamic range out of 4 ranges. A nonmagnetic thermally oscillating actuator is used to reorient the sensor unit by 90 deg to determine all three axes zero levels. The accuracy should be approximately plus or minus 0.1 gamma. The vector measurements are made at equal intervals in time ranging from 16 per second down to 1 per second depending on the telemetry bit rate.

Scearce, C.↗

DOE OSTI2022

Dynamic logistic regression and variable selection: Forecasting and contextualizing civil unrest

Civil unrest can range from peaceful protest to violent furor, and researchers are working to monitor, forecast, and assess such events to allocate resources better. Twitter has become a real-time data source for forecasting civil unrest because millions of people use the platform as a social outlet. Additionally, daily word counts are used as model features, and predictive terms contextualize the reasons for the protest. To forecast civil unrest and infer the reasons for the protest, we consider the problem of Bayesian variable selection for the dynamic logistic regression model and propose using penalized credible regions to select parameters of the updated state vector. This method avoids the need for shrinkage priors, is scalable to high-dimensional dynamic data, and allows the importance of variables to vary in time as new information becomes available. A substantial improvement in both precision and F1-score using this approach is demonstrated through simulation. Finally, we apply the proposed model fitting and variable selection methodology to the problem of forecasting civil unrest in Latin America. Our dynamic logistic regression approach shows improved accuracy compared to the static approach currently used in event prediction and feature selection.

97 MATHEMATICS AND COMPUTING↗

NASA NTRSDate not supplied

Exploring Semantic Search Capability of Graph Convolutions Over a Knowledge Graph Built Using Earth Science Corpora

Traditional knowledge graphs tend to be too generic, and often perform poorly on complex scientific queries. Often times, precedence is given to pop culture over scientific knowledge for queries. This is predominantly due to the use of internet sources for building the knowledge graph. With this work, we aim to explore the effectiveness of combining a knowledge graph generated from earth science corpora with a language model and graph convolutions for the purpose of surfacing latent and related sentences given a natural language query. In this model, sentences are conceptualized in the graph as nodes which are connected through entities—words and phrases of interest found in the text—extracted using Google Cloud’s entity extraction model. The language model we used for this is Bidirectional Encoder Representations from Transformers (BERT).The sentences are given a numeric representation by the BERT model. Graph convolutions are then applied to sentence embeddings in order to obtain a vector representation of the sentence as well as the surrounding graph structure, thereby leveraging the power of adjacency inherently encoded in graph structures. With this presentation, we demonstrate the ability of graph convolutions and their improved ability to surface relevant, latent information based on the subject of the input query.

Muthukumaran Ramasubramanian↗

NASA NTRS1989

Room temperature tensile ductility in polycrystalline B2 Ni-30Al-20Fe

A room-temperature tensile elongation of about 2.5 percent, where the only slip vector observed was the 100, has been determined for a double-extruded B2 Ni-30Al-20Fe alloy consisting of recrystallized equiaxed grains about 25 microns in diameter; these results suggest that 100-slip does not preclude limited ductility in polycrystalline B2 alloys in tension at low temperatures. A suppression of ordering through resort to rapid solidification, in other words, is not necessary for ductility, since the cast and as-extruded alloy presently examined is ordered.

Guha, Sumit↗

NASA NTRSDate not supplied

SelenITA: A dual-spacecraft lunar CubeSat mission to characterize the near-surface electromagnetic plasma environment

SelenITA is an international interdisciplinary mission consisting of two CubeSats that will provide the first multi-point measurement in a sustained low lunar orbit. This mission will advance the understanding of spatiotemporal differentiation of the electromagnetic space environment at the Moon in support of Artemis crew and the geosciences. The mission science objectives are to: (1) further constrain the origins of crustal magnetic fields and their applications; (2) determine the nature of plasma interactions with crustal magnetic fields; (3) characterize plasma waves and turbulence at the Moon; (4) characterize the lunar surface potential in all plasma environments; (5) constrain the composition, thermal state, and structure of the lunar upper mantle and crust; (6) characterize the ionizing radiation in lunar orbit; (7) determine the density of the dust exosphere as a function of latitude, longitude, and altitude, including the lunar polar space environment. The measurement requirements include: 3-component vector magnetic field, plasma distribution (flux, energy, density, temperature), energetic particles (protons, electrons, gamma rays), and dust. SelenITA builds on a rich history of electromagnetic plasma observations of the near lunar surface and space environment, and it answers high level science questions with state of the art instruments in a small package. PLAIN LANGUAGE ABSTRACT – SelenITA comes from the greek word for Moon, selene, with the addition of “ITA” as a reference to the Brazilian teammate, Instituto Tecnológico de Aeronáutica. In addition, in Portuguese "ita" is a prefix similar to "ite" in English, used in naming minerals, so Selenita could be seen as a lunar gemstone. In Spanish, “ita” is a diminutive suffix for “little” which is appropriate for this CubeSat mission which consists of twin 12U CubeSats in low lunar orbit. The primary science goal of the mission is to distinguish time varying features within the electromagnetic plasma environment near the surface of the Moon. The science objectives include investigating the origins of crustal magnetic fields, plasma interactions with these fields, plasma waves, surface potential, and interior properties. This mission is also interested in the radiation environment at the Moon and the amount of dust at the lunar poles. This is important because it helps us understand how future astronauts will live and work on the lunar surface and identify hazards.

space weather↗

NASA NTRSDate not supplied

SelenITA: A Dual-Spacecraft Lunar CubeSat Mission to Characterize the Near-Surface Electromagnetic Plasma Environment

space weather↗

NASA NTRSDate not supplied

Search Enhancements using Natural Language Processing Techniques

NASA Goddard Earth Sciences Data and Information Services Center (GESDISC) is one of the 12 NASA Science Mission Directorate Data Centers. The main goal of GESDISC is to provide earth science data, information, and services to the earth science data community. Consequently, data discovery is at the center of our mission and our search engine is the primary tool for our users to interact, find, and access our data. Existing search approaches are largely focused on hard-matching of keywords in the search query with dataset metadata. Here we propose to expand the search by introducing a complementary natural language processing (NLP) search. At the heart of our proposed NLP search, we trained a joint embedding using scientific text corpus and a curated set of dataset metadata. The embedding learns the association between words in our dataset metadata and those of the scientific text corpus. This enables us to go beyond simple hard-matching of a query and data set metadata and have a notion of “similarity” between the search query and the datasets. We further integrated our NLP search into the Elastic Search (ES) framework leveraging similarity search capabilities offered through the “dense_vector” field type. Our preliminary evaluations show that our proposed NLP search has the potential to be utilized to complement the existing search engine and serve as a base for a dataset recommendation system.

Armin Mehrabian↗

NASA NTRS1985

Measurements of vector fields with diode array

A polarimeter was designed for high spatial and spectral resolution. It consists of a quarter-wave plate alternately operating in two positions for Stoke-V measurements and an additional quarter-wave plate for Stokes-U and -Q measurements. The spatial range covers 75 arcsec, the spectral window of about 1.8 a allows the simultaneous observations of neighboring lines. The block diagram of the data processing and acquisition system consists of five memories each one having a capacity of 10 to the 4th power 16-bit words. The total time to acquire profiles of Stokes parameters can be chosen by selecting the number of successive measurements added in the memories, each individual measurement corresponding to an integration time of 0.5 sec. Typical values range between 2 and 60 sec depending on the brightness of the structure, the amount of polarization and a compromise between desired signal-to-noise ratio and spatial resolution.

Wiehr, E. J.↗

NASA NTRS2005

Spoken Language Processing in the Clarissa Procedure Browser

Clarissa, an experimental voice enabled procedure browser that has recently been deployed on the International Space Station, is as far as we know the first spoken dialog system in space. We describe the objectives of the Clarissa project and the system's architecture. In particular, we focus on three key problems: grammar-based speech recognition using the Regulus toolkit; methods for open mic speech recognition; and robust side-effect free dialogue management for handling undos, corrections and confirmations. We first describe the grammar-based recogniser we have build using Regulus, and report experiments where we compare it against a class N-gram recogniser trained off the same 3297 utterance dataset. We obtained a 15% relative improvement in WER and a 37% improvement in semantic error rate. The grammar-based recogniser moreover outperforms the class N-gram version for utterances of all lengths from 1 to 9 words inclusive. The central problem in building an open-mic speech recognition system is being able to distinguish between commands directed at the system, and other material (cross-talk), which should be rejected. Most spoken dialogue systems make the accept/reject decision by applying a threshold to the recognition confidence score. NASA shows how a simple and general method, based on standard approaches to document classification using Support Vector Machines, can give substantially better performance, and report experiments showing a relative reduction in the task-level error rate by about 25% compared to the baseline confidence threshold method. Finally, we describe a general side-effect free dialogue management architecture that we have implemented in Clarissa, which extends the "update semantics'' framework by including task as well as dialogue information in the information state. We show that this enables elegant treatments of several dialogue management problems, including corrections, confirmations, querying of the environment, and regression testing.

Rayner, M.↗

NASA NTRS2015

Rao-Blackwellization for Adaptive Gaussian Sum Nonlinear Model Propagation

When dealing with imperfect data and general models of dynamic systems, the best estimate is always sought in the presence of uncertainty or unknown parameters. In many cases, as the first attempt, the Extended Kalman filter (EKF) provides sufficient solutions to handling issues arising from nonlinear and non-Gaussian estimation problems. But these issues may lead unacceptable performance and even divergence. In order to accurately capture the nonlinearities of most real-world dynamic systems, advanced filtering methods have been created to reduce filter divergence while enhancing performance. Approaches, such as Gaussian sum filtering, grid based Bayesian methods and particle filters are well-known examples of advanced methods used to represent and recursively reproduce an approximation to the state probability density function (pdf). Some of these filtering methods were conceptually developed years before their widespread uses were realized. Advanced nonlinear filtering methods currently benefit from the computing advancements in computational speeds, memory, and parallel processing. Grid based methods, multiple-model approaches and Gaussian sum filtering are numerical solutions that take advantage of different state coordinates or multiple-model methods that reduced the amount of approximations used. Choosing an efficient grid is very difficult for multi-dimensional state spaces, and oftentimes expensive computations must be done at each point. For the original Gaussian sum filter, a weighted sum of Gaussian density functions approximates the pdf but suffers at the update step for the individual component weight selections. In order to improve upon the original Gaussian sum filter, Ref. [2] introduces a weight update approach at the filter propagation stage instead of the measurement update stage. This weight update is performed by minimizing the integral square difference between the true forecast pdf and its Gaussian sum approximation. By adaptively updating each component weight during the nonlinear propagation stage an approximation of the true pdf can be successfully reconstructed. Particle filtering (PF) methods have gained popularity recently for solving nonlinear estimation problems due to their straightforward approach and the processing capabilities mentioned above. The basic concept behind PF is to represent any pdf as a set of random samples. As the number of samples increases, they will theoretically converge to the exact, equivalent representation of the desired pdf. When the estimated qth moment is needed, the samples are used for its construction allowing further analysis of the pdf characteristics. However, filter performance deteriorates as the dimension of the state vector increases. To overcome this problem Ref. [5] applies a marginalization technique for PF methods, decreasing complexity of the system to one linear and another nonlinear state estimation problem. The marginalization theory was originally developed by Rao and Blackwell independently. According to Ref. [6] it improves any given estimator under every convex loss function. The improvement comes from calculating a conditional expected value, often involving integrating out a supportive statistic. In other words, Rao-Blackwellization allows for smaller but separate computations to be carried out while reaching the main objective of the estimator. In the case of improving an estimator's variance, any supporting statistic can be removed and its variance determined. Next, any other information that dependents on the supporting statistic is found along with its respective variance. A new approach is developed here by utilizing the strengths of the adaptive Gaussian sum propagation in Ref. [2] and a marginalization approach used for PF methods found in Ref. [7]. In the following sections a modified filtering approach is presented based on a special state-space model within nonlinear systems to reduce the dimensionality of the optimization problem in Ref. [2]. First, the adaptive Gaussian sum propagation is explained and then the new marginalized adaptive Gaussian sum propagation is derived. Finally, an example simulation is presented.

state estimation↗

DOE OSTI2025

Machine Learned Empirical Numerical Integrator from Simulated Data

Recently, a number of state-of-the-art surrogate machine learning (ML) models have been designed for global weather and climate prediction, which have been trained using reanalysis data products. Reanalysis data products are constructed using numerical model simulations that combine numerical integration of partial differential equations and parameterization schemes. These products are typically only archived and made available using coarsened spatial and temporal resolutions. This study explores the impact of the numerical generation methods used to produce the training datasets and the temporal resolution of those datasets on machine learning surrogate models. Using the nonlinear vector autoregression (NVAR) machine as an explainable ML technique, simple dynamical systems are emulated with ML models trained on data produced by three classical numerical integration schemes. NVAR is validated as a skillful ML method, capable of producing accurate predictions and, more importantly, reconstructing both the underlying dynamics and the numerical integration scheme used to generate the training data. However, the machine fails to generalize predictions on unseen test data generated by different numerical integration schemes, despite the underlying dynamical system being the same. This result provides a word of caution for the growing field of machine learning emulation of weather and climate dynamics. Furthermore, we illustrate using NVAR that training on temporally coarsened data may increase the required complexity of ML models and potentially introduce new numerical challenges. Finally, we discover that empirical integration schemes with arbitrary time-stepping sizes can be constructed directly from the data, which implies a potential for the development of empirical numerical integration schemes.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2003

Some Remarks on GMRES for Transport Theory

We review some work on the application of GMRES to the solution of the discrete ordinates transport equation in one-dimension. We note that GMRES can be applied directly to the angular flux vector, or it can be applied to only a vector of flux moments as needed to compute the scattering operator of the transport equation. In the former case we illustrate both the delights and defects of ILU right-preconditioners for problems with anisotropic scatter and for problems with upscatter. When working with flux moments we note that GMRES can be used as an accelerator for any existing transport code whose solver is based on a stationary fixed-point iteration, including transport sweeps and DSA transport sweeps. We also provide some numerical illustrations of this idea. We finally show how space can be traded for speed by taking multiple transport sweeps per GMRES iteration. Key Words: transport equation, GMRES, Krylov subspace

Patton, Bruce W.↗

NASA NTRS1980

Edge smoothing for real-time simulation of a polygon face object system as viewed by a moving observer

The visual system within an aircraft flight simulation system receives flight data and terrain data which is formated into a buffer memory. The image data is forwarded to an image processor which translates the image data into face vertex vectors Vf, defining the position relationship between the vertices of each terrain object and the aircraft. The image processor then rotates, clips, and projects the image data into two-dimensional display vectors (Vd). A display generator receives the Vd faces, and other image data to provide analog inputs to CRT devices which provide the window displays for the simulated aircraft. The video signal to the CRT devices passes through an edge smoothing device which prolongs the rise time (and fall time) of the video data inversely as the slope of the edge being smoothed. An operational amplifier within the edge smoothing device has a plurality of independently selectable feedback capacitors each having a different value. The values of the capacitors form a series which doubles as a power of two. Each feedback capacitor has a fast switch responsive to the corresponding bit of a digital binary control word for selecting (1) or not selecting (0) that capacitor. The control word is determined by the slope of each edge. The resulting actual feedback capacitance for each edge is the sum of all the selected capacitors and is directly proportional to the value of the binary control word. The output rise time (or fall time) is a function of the feedback capacitance, and is controlled by the slope through the binary control word.

Lotz, Robert W.↗

NASA NTRS1984

Postprocessing for the UCLA climate model

Postprocessing programs written to analyze the results of a general circulation model are described. The model history data is interpolated to pressure surfaces, converted from 64-bit Cyber words to 32 bit IBM words, and transferred to tape for further analysis. The time average program reads the interpolation output tape and produces 15-day time averages. The model outputs a very detailed record for selected grid points. This data is sent to a tape in Cyber full words. The conversion program changes the format of the tape from 64 bit Cyber words to 32 bit IBM words, processing a month of data at a time. The grid-point history sorting program inputs the converted grid point history tape and sorts it by time. The end product is a tape with a time history of each field for every point. After the PBP data has been stored, it can be plotted. The plotting package is flexible, and allows the choice of fields and grid points. The one-dimensional fields are plotted as functions of time, and a daily summary is also produced. For the two-dimensional fields, time-height cross sections and daily summaries are produced. The model plotting package produces plots for all fields on the time average and pressure tapes. These plots include world maps, polar projections, zonal means, vectors, streamlines, and latitude-height plots.

Abeles, J.↗

NASA NTRS2002

The Delta x B = 0 Constraint Versus Minimization of Numerical Errors in MHD Simulations

The MHD equations are a system of non-strictly hyperbolic conservation laws. The non-convexity of the inviscid flux vector resulted in corresponding Jacobian matrices with undesirable properties. It has previously been shown by Powell et al. (1995) that an 'almost' equivalent MHD system in non-conservative form can be derived. This non-conservative system has a better conditioned eigensystem. Aside from Powell et al., the MHD equations can be derived from basic principles in either conservative or non-conservative form. The Delta x B = 0 constraint of the MHD equations is only an initial condition constraint, it is very different from the incompressible Navier-Stokes equations in which the divergence condition is needed to close the system (i.e., to have the same number of equations and the same number of unknown). In the MHD formulations, if Delta x B = 0 initially, all one needs is to construct appropriate numerical schemes that preserve this constraint at later time evolutions. In other words, one does not need the Delta x B condition to close the MHD system. We formulate our new scheme together with the Cargo & Gallice (1997) form of the MHD approximate Riemann solver in curvilinear grids for both versions of the MHD equations. A novel feature of our new method is that the well-conditioned eigen-decomposition of the non-conservative MHD equations is used to solve the conservative equations. This new feature of the method provides well-conditioned eigenvectors for the conservative formulation, so that correct wave speeds for discontinuities are assured. The justification for using the non-conservative eigen-decomposition to solve the conservative equations is that our scheme has a better control of the numerical error associated with the divergence of the magnetic condition. Consequently, computing both forms of the equations with the same eigen-decomposition is almost equivalent. It will be shown that this approach, using the non-conservative eigensystem when solving the conservative equations, also works well in the context of standard shock-capturing schemes.

Yee, H. C.↗