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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Ferroelectric/Optoelectronic Memory/Processor

Proposed hybrid optoelectronic nonvolatile analog memory and data processor comprises planar array of microscopic photosensitive ferroelectric capacitors performing massively parallel analog computations. Processors overcome electronic crosstalk and limitations on number of input/output contacts inherent in electronic implementations of large interconnection arrays. Used in general optical computing, recognition of patterns, and artificial neural networks.

Thakoor, Sarita↗
DOE OSTI2022

All-Electric Nonassociative Learning in Nickel Oxide

Habituation and sensitization represent nonassociative learning mechanisms in both non-neural and neural organisms. They are essential for a range of functions from survival to adaptation in dynamic environments. Design of hardware for neuroinspired computing strives to emulate such features driven by electric bias and can also be incorporated into neural network algorithms. Herein, cellular-like learning in oxygen-deficient NiO x devices is demonstrated. Both habituation learning and sensitization response can be achieved in a single device by simply controlling the magnitude of the electric field. Spontaneous memory relaxations and dynamic redistribution of oxygen vacancies under electric bias enable such learning behavior of NiO x under sequential training. These characteristics in simple device arrays are implemented to learn alphabets as well as demonstrate simulated algorithmic use cases in digit recognition. Transition metal oxides with carefully prepared defect concentrations can be highly sensitive to electronic structure perturbations under moderate electrical stimulus and serve as building blocks for next-generation neuroinspired computing hardware.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Generalizing mkFit and its Application to HL-LHC

mkFit is an implementation of the Kalman filter-based track reconstruction algorithm that exploits both thread- and data-level parallelism. In the past few years the project transitioned from the R&D phase to deployment in the Run-3 offline workflow of the CMS experiment. The CMS tracking performs a series of iterations, targeting reconstruction of tracks of increasing difficulty after removing hits associated to tracks found in previous iterations. mkFit has been adopted for several of the tracking iterations, which contribute to the majority of reconstructed tracks. When tested in the standard conditions for production jobs, speedups in track pattern recognition are on average of the order of 3.5x for the iterations where it is used (3-7x depending on the iteration). Multiple factors contribute to the observed speedups, including vectorization and a lightweight geometry description, as well as improved memory management and single precision. Efficient vectorization is achieved with both the icc and the gcc (default in CMSSW) compilers and relies on a dedicated library for small matrix operations, Matriplex, which has recently been released in a public repository. While the mkFit geometry description already featured levels of abstraction from the actual Phase-1 CMS tracker, several components of the implementations were still tied to that specific geometry. We have further generalized the geometry description and the configuration of the run-time parameters, in order to enable support for the Phase-2 upgraded tracker geometry for the HL-LHC and potentially other detector configurations. The implementation strategy and high-level code changes required for the HL-LHC geometry are presented. Speedups in track building from mkFit imply that track fitting becomes a comparably time consuming step of the tracking chain.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Asymmetric and non-stoichiometric glycoprotein recognition by two distinct antibodies results in broad protection against ebolaviruses

Several ebolaviruses cause outbreaks of severe disease. Vaccines and monoclonal antibody cocktails are available to treat Ebola virus (EBOV) infections, but not Sudan virus (SUDV) or other ebolaviruses. Current cocktails contain antibodies that cross-react with the secreted soluble glycoprotein (sGP) that absorbs virus-neutralizing antibodies. By sorting memory B cells from EBOV infection survivors, we isolated two broadly reactive anti-GP monoclonal antibodies, 1C3 and 1C11, that potently neutralize, protect rodents from disease, and lack sGP cross-reactivity. Both antibodies recognize quaternary epitopes in trimeric ebolavirus GP. 1C11 bridges adjacent protomers via the fusion loop. 1C3 has a tripartite epitope in the center of the trimer apex. One 1C3 antigen-binding fragment anchors simultaneously to the three receptor-binding sites in the GP trimer, and separate 1C3 paratope regions interact differently with identical residues on the three protomers. A cocktail of both antibodies completely protected nonhuman primates from EBOV and SUDV infections, indicating their potential clinical value.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Selective area doping for Mott neuromorphic electronics

The cointegration of artificial neuronal and synaptic devices with homotypic materials and structures can greatly simplify the fabrication of neuromorphic hardware. We demonstrate experimental realization of vanadium dioxide (VO 2 ) artificial neurons and synapses on the same substrate through selective area carrier doping. By locally configuring pairs of catalytic and inert electrodes that enable nanoscale control over carrier density, volatility or nonvolatility can be appropriately assigned to each two-terminal Mott memory device per lithographic design, and both neuron- and synapse-like devices are successfully integrated on a single chip. Feedforward excitation and inhibition neural motifs are demonstrated at hardware level, followed by simulation of network-level handwritten digit and fashion product recognition tasks with experimental characteristics. Spatially selective electron doping opens up previously unidentified avenues for integration of emerging correlated semiconductors in electronic device technologies.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Proteo-Genomic Analysis Identifies Two Major Sites of Vulnerability on Ebolavirus Glycoprotein for Neutralizing Antibodies in Convalescent Human Plasma

Three clinically relevant ebolaviruses – Ebola (EBOV), Bundibugyo (BDBV), and Sudan (SUDV) viruses, are responsible for severe disease and occasional deadly outbreaks in Africa. The largest Ebola virus disease (EVD) epidemic to date in 2013-2016 in West Africa highlighted the urgent need for countermeasures, leading to the development and FDA approval of the Ebola virus vaccine rVSV-ZEBOV (Ervebo ® ) in 2020 and two monoclonal antibody (mAb)-based therapeutics (Inmazeb ® [atoltivimab, maftivimab, and odesivimab-ebgn] and Ebanga ® (ansuvimab-zykl) in 2020. The humoral response plays an indispensable role in ebolavirus immunity, based on studies of mAbs isolated from the antibody genes in peripheral blood circulating ebolavirus-specific human memory B cells. However, antibodies in the body are not secreted by circulating memory B cells in the blood but rather principally by plasma cells in the bone marrow. Little is known about the protective polyclonal antibody responses in convalescent plasma. Here we exploited both single-cell antibody gene sequencing and proteomic sequencing approaches to assess the composition of the ebolavirus glycoprotein (GP)-reactive antibody repertoire in the plasma of an EVD survivor. We first identified 1,512 GP-specific mAb variable gene sequences from single cells in the memory B cell compartment. Using mass spectrometric analysis of the corresponding GP-specific plasma IgG, we found that only a portion of the large B cell antibody repertoire was represented in the plasma. Molecular and functional analysis of proteomics-identified mAbs revealed recognition of epitopes in three major antigenic sites - the GP head domain, the glycan cap, and the base region, with a high prevalence of neutralizing and protective mAb specificities that targeted the base and glycan cap regions on the GP. Polyclonal plasma antibodies from the survivor reacted broadly to EBOV, BDBV, and SUDV GP, while reactivity of the potently neutralizing mAbs we identified was limited mostly to the homologous EBOV GP. Together these results reveal a restricted diversity of neutralizing humoral response in which mAbs targeting two antigenic sites on GP – glycan cap and base – play a principal role in plasma-antibody-mediated protective immunity against EVD.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Computationally Efficient Multiscale Neural Networks Applied to Fluid Flow in Complex 3D Porous Media

Abstract The permeability of complex porous materials is of interest to many engineering disciplines. This quantity can be obtained via direct flow simulation, which provides the most accurate results, but is very computationally expensive. In particular, the simulation convergence time scales poorly as the simulation domains become less porous or more heterogeneous. Semi-analytical models that rely on averaged structural properties (i.e., porosity and tortuosity) have been proposed, but these features only partly summarize the domain, resulting in limited applicability. On the other hand, data-driven machine learning approaches have shown great promise for building more general models by virtue of accounting for the spatial arrangement of the domains’ solid boundaries. However, prior approaches building on the convolutional neural network (ConvNet) literature concerning 2D image recognition problems do not scale well to the large 3D domains required to obtain a representative elementary volume (REV). As such, most prior work focused on homogeneous samples, where a small REV entails that the global nature of fluid flow could be mostly neglected, and accordingly, the memory bottleneck of addressing 3D domains with ConvNets was side-stepped. Therefore, important geometries such as fractures and vuggy domains could not be modeled properly. In this work, we address this limitation with a general multiscale deep learning model that is able to learn from porous media simulation data. By using a coupled set of neural networks that view the domain on different scales, we enable the evaluation of large ( $$>512^3$$ > 512 3 ) images in approximately one second on a single graphics processing unit. This model architecture opens up the possibility of modeling domain sizes that would not be feasible using traditional direct simulation tools on a desktop computer. We validate our method with a laminar fluid flow case using vuggy samples and fractures. As a result of viewing the entire domain at once, our model is able to perform accurate prediction on domains exhibiting a large degree of heterogeneity. We expect the methodology to be applicable to many other transport problems where complex geometries play a central role.

36 MATERIALS SCIENCE↗
NASA NTRS1992

Cockpit data link displays - An evaluation of textual formats

Data link technologies are being investigated for air/ground information transfer for commercial aircraft operation. This study was designed to measure which of four alpha-numeric display formats for display of data link information would lead to the quickest and most accurate memory retrieval in a part-task simulation environment. Pilots viewed a clearance for 10-15 seconds and were subsequently queried about the content of that clearance. Speed and accuracy of responses were measured across three retention tasks. The three retention tasks included free recall of a particular clearance. recognition of a previous clearance, and the comparison of element values between a previously displayed and current clearance. Each format was tested with and without a distraction task. Subjective ratings of each format were also collected. The analyses revealed no significant differences for reaction time or accuracy among the four formats. Explanations for these results as well as alternative methodologies are discussed.

Mcgann, Alison↗
NASA NTRS1992

Optical computing and neural networks; Proceedings of the Meeting, National Chiao Tung Univ., Hsinchu, Taiwan, Dec. 16, 17, 1992

The present conference discusses optical neural networks, photorefractive nonlinear optics, optical pattern recognition, digital and analog processors, and holography and its applications. Attention is given to bifurcating optical information processing, neural structures in digital halftoning, an exemplar-based optical neural net classifier for color pattern recognition, volume storage in photorefractive disks, and microlaser-based compact optical neuroprocessors. Also treated are the optical implementation of a feature-enhanced optical interpattern-associative neural network model and its optical implementation, an optical pattern binary dual-rail logic gate module, a theoretical analysis for holographic associative memories, joint transform correlators, image addition and subtraction via the Talbot effect, and optical wavelet-matched filters. (No individual items are abstracted in this volume)

Hsu, Ken-Yuh↗
NASA NTRS1990

Computation and parallel implementation for early vision

The problem of early vision is to transform one or more retinal illuminance images-pixel arrays-to image representations built out of such primitive visual features such as edges, regions, disparities, and clusters. These transformed representations form the input to later vision stages that perform higher level vision tasks including matching and recognition. Researchers developed algorithms for: (1) edge finding in the scale space formulation; (2) correlation methods for computing matches between pairs of images; and (3) clustering of data by neural networks. These algorithms are formulated for parallel implementation of SIMD machines, such as the Massively Parallel Processor, a 128 x 128 array processor with 1024 bits of local memory per processor. For some cases, researchers can show speedups of three orders of magnitude over serial implementations.

Gualtieri, J. Anthony↗
DOE OSTI2021

Ionizing radiation effects in SONOS-based neuromorphic inference accelerators

Here, we evaluate the sensitivity of neuromorphic inference accelerators based on Silicon-Oxide-Nitride-Oxide-Silicon (SONOS) charge trap memory arrays to total ionizing dose (TID) effects. Data retention statistics were collected for 16 Mbit of 40 nm SONOS digital memory exposed to ionizing radiation from a Co-60 source, showing good retention of the bits up to the maximum dose of 500 krad(Si). Using this data, we formulate a rate-equation-based model for the TID response of trapped charge carriers in the ONO stack, and predict the effect of TID on intermediate device states between ‘program’ and ‘erase’. This model is then used to simulate arrays of low-power, analog SONOS devices that store 8-bit neural network weights and support in situ matrix-vector multiplication. We evaluate the accuracy of the irradiated SONOS-based inference accelerator on two image recognition tasks – CIFAR-10 and the challenging ImageNet dataset – using state-of-the-art convolutional neural networks, such as ResNet-50. We find that across the datasets and neural networks evaluated, the accelerator tolerates a maximum TID between 10 krad(Si) and 100 krad(Si), with deeper networks being more susceptible to accuracy losses due to TID.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Supramolecular Control of Ionic Retention in Electrolyte-Gated Synaptic Transistors

Electrolyte-gated transistors with ion-trapping layers offer a promising platform for artificial synapses in neuromorphic computing, yet molecular mechanisms governing ionic retention remain poorly understood. Here, in this study, we present a supramolecular approach to modulate ion retention by incorporating a crown ether derivative-based polymer network as an ion-trapping layer on top of a semiconducting monolayer. We show that the balance between ion–host binding and ion–solvent interactions dictates the kinetics of ion capture and release, which in turn controls the memory characteristics of the device. By varying the solvent dielectric constant, we tune the ionic retention time from nearly permanent trapping to rapid relaxation. Intermediate solvent polarity enables programmable short- and long-term synaptic behaviors, including excitatory postsynaptic current, paired-pulse facilitation, and long-term potentiation and depression. These findings establish a direct link between supramolecular ion recognition and synaptic plasticity and provide a generalizable design strategy for ionic–electronic neuromorphic devices.

36 MATERIALS SCIENCE↗
DOE OSTI2019

A Software Defined Storage Approach to Exascale Storage Services (Final Technical Report)

The Mochi project began in 2015 in recognition of a set of changes that were set to alter the landscape of storage and I/O for DOE science. On the one hand, an influx of new classes of applications including an initial set of data intensive and machine learning applications were operating with new types of data and in new ways, as compared to traditional computational science simulation codes. Simultaneously, new memory, storage, and networking technologies were driving a need to adapt software designs to a rapidly changing design space. The goal of the three-year Mochi project was to develop and demonstrate an approach for composing storage services which provides new levels of functionality, performance, and reliability for science applications at extreme scale. The team, including a mix of Laboratory, university, and industry collaborators, focused on systems and applications of interest to DOE Office of Science, tracking developments at NERSC and the LCFs as well as application trends within ECP and SciDAC. Technical work focused on three themes: composition (understand the building blocks needed to provide storage services that meet DOE mission needs and how these components are flexibly composed), application interfacing (what service compositions are most effective for common DOE application use cases), and consistency and synchronization (taking advantage of knowledge about, and from, the system and scientific applications to provide better service than would otherwise be possible). The project was highly successful, producing 13 publications and tens of thousands of lines of open source software that is now being used by multiple ECP teams, HPC software vendors, and international collaborators. The uptake of Mochi technologies speaks to both the value of the service composition model and the quality of architecture and software developed by the team. Through continued funding under ASCR and ECP, the Mochi team and its technologies are on track to further contribute to DOE science successes into the exascale timeframe.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2016

Evaluation of Synthetic Vision Display Concepts for Improved Awareness in Unusual Attitude Recovery Scenarios

A recent study conducted by the Commercial Aviation Safety Team (CAST) determined 40 percent of all fixed-wing fatal accidents, between 2001 and 2011, were caused by Loss-of-Control (LOC) in flight (National Transportation Safety Board, 2015). Based on their findings, CAST recommended manufacturers develop and implement virtual day-visual meteorological conditions (VMC) display systems, such as synthetic vision or equivalent systems (CAST, 2016). In a 2015 simulation study conducted at NASA Langley Research Center (LaRC), researchers gathered to test and evaluate virtual day-VMC displays under realistic flight operation scenarios capable of inducing reduced attention states in pilots. Each display concept was evaluated to determine its efficacy to improve attitude awareness. During the experiment, Evaluation Pilots (EPs) were shown the following three display concepts on the Primary Flight Display (PFD): Baseline, Synthetic Vision (SV) with color gradient, and SV with texture. The baseline configuration was a standard, conventional 'blue over brown' display. Experiment scenarios were simulated over water to evaluate Unusual Attitude (UA) recovery over 'featureless terrain' environments. Thus, the SV with color gradient configuration presented a 'blue over blue' display with a linear blue color progression, to differentiate attitude changes between sky and ocean. The SV with texture configuration presented a 'blue over blue' display with a black checkerboard texture atop a synthetic ocean. These displays were paired with a Background Attitude Indicator (BAI) concept. The BAI was presented across all four Head-Down Displays (HDDs), displaying a wide field-of-view blue-over-blue attitude indicator. The BAI aligned with the PFD and showed through the background of the navigation displays with opaque transparency. Each EP participated in a two-part experiment series with a total seventy-five trial runs: Part I included a set of twenty-five Unusual Attitude Recovery (UAR) scenarios; Part II included a set of fifty Attitude Memory Recall Tasks (AMRT). At the conclusion of each trial, EPs were asked to complete a set post-run questionnaires. Quantitative results showed that there were no significant statistical effects on UA recovery times when utilizing SV with or without the presence of a BAI. Qualitative results show the SV displays (color, texture) with BAI On are most preferred for both UA recognition and recovery when compared with the baseline display. When only comparing SV display concepts, EPs performed better when using the SV with texture, BAI On, than any other display configuration. This is an interesting find considering most EPs noted their preference towards the SV with color gradient when the BAI was on.

Nicholas, Stephanie↗
DOE OSTI2023

Structural insights into protection against a SARS-CoV-2 spike variant by T cell receptor diversity

T cells play a crucial role in combatting SARS-CoV-2 and forming long-term memory responses to this coronavirus. The emergence of SARS-CoV-2 variants that can evade T cell immunity has raised concerns about vaccine efficacy and the risk of reinfection. Some SARS-CoV-2 T cell epitopes elicit clonally restricted CD8 + T cell responses characterized by T cell receptors (TCRs) that lack structural diversity. Mutations in such epitopes can lead to loss of recognition by most T cells specific for that epitope, facilitating viral escape. Here, we studied an HLA-A2–restricted spike protein epitope (RLQ) that elicits CD8 + T cell responses in COVID-19 convalescent patients characterized by highly diverse TCRs. We previously reported the structure of an RLQ-specific TCR (RLQ3) with greatly reduced recognition of the most common natural variant of the RLQ epitope (T1006I). Opposite to RLQ3, TCR RLQ7 recognizes T1006I with even higher functional avidity than the WT epitope. To explain the ability of RLQ7, but not RLQ3, to tolerate the T1006I mutation, we determined structures of RLQ7 bound to RLQ–HLA-A2 and T1006I–HLA-A2. These complexes show that there are multiple structural solutions to recognizing RLQ and thereby generating a clonally diverse T cell response to this epitope that assures protection against viral escape and T cell clonal loss.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Collective dynamics and long-range order in thermal neuristor networks

Abstract In the pursuit of scalable and energy-efficient neuromorphic devices, recent research has unveiled a novel category of spiking oscillators, termed “thermal neuristors.” These devices function via thermal interactions among neighboring vanadium dioxide resistive memories, emulating biological neuronal behavior. Here, we show that the collective dynamical behavior of networks of these neurons showcases a rich phase structure, tunable by adjusting the thermal coupling and input voltage. Notably, we identify phases exhibiting long-range order that, however, does not arise from criticality, but rather from the time non-local response of the system. In addition, we show that these thermal neuristor arrays achieve high accuracy in image recognition and time series prediction through reservoir computing, without leveraging long-range order. Our findings highlight a crucial aspect of neuromorphic computing with possible implications on the functioning of the brain: criticality may not be necessary for the efficient performance of neuromorphic systems in certain computational tasks.

Science & Technology - Other Topics↗
NASA NTRS2012

Compact Autonomous Hemispheric Vision System

Solar System Exploration camera implementations to date have involved either single cameras with wide field-of-view (FOV) and consequently coarser spatial resolution, cameras on a movable mast, or single cameras necessitating rotation of the host vehicle to afford visibility outside a relatively narrow FOV. These cameras require detailed commanding from the ground or separate onboard computers to operate properly, and are incapable of making decisions based on image content that control pointing and downlink strategy. For color, a filter wheel having selectable positions was often added, which added moving parts, size, mass, power, and reduced reliability. A system was developed based on a general-purpose miniature visible-light camera using advanced CMOS (complementary metal oxide semiconductor) imager technology. The baseline camera has a 92 FOV and six cameras are arranged in an angled-up carousel fashion, with FOV overlaps such that the system has a 360 FOV (azimuth). A seventh camera, also with a FOV of 92 , is installed normal to the plane of the other 6 cameras giving the system a > 90 FOV in elevation and completing the hemispheric vision system. A central unit houses the common electronics box (CEB) controlling the system (power conversion, data processing, memory, and control software). Stereo is achieved by adding a second system on a baseline, and color is achieved by stacking two more systems (for a total of three, each system equipped with its own filter.) Two connectors on the bottom of the CEB provide a connection to a carrier (rover, spacecraft, balloon, etc.) for telemetry, commands, and power. This system has no moving parts. The system's onboard software (SW) supports autonomous operations such as pattern recognition and tracking.

Pingree, Paula J.↗
DOE OSTI2017

Impact of Linearity and Write Noise of Analog Resistive Memory Devices in a Neural Algorithm Accelerator

Resistive memory (ReRAM) shows promise for use as an analog synapse element in energy-efficient neural network algorithm accelerators. A particularly important application is the training of neural networks, as this is the most computationally-intensive procedure in using a neural algorithm. However, training a network with analog ReRAM synapses can significantly reduce the accuracy at the algorithm level. In order to assess this degradation, analog properties of ReRAM devices were measured and hand-written digit recognition accuracy was modeled for the training using backpropagation. Bipolar filamentary devices utilizing three material systems were measured and compared: one oxygen vacancy system, Ta-TaO x , and two conducting metallization systems, Cu-SiO 2 , and Ag/chalcogenide. Analog properties and conductance ranges of the devices are optimized by measuring the response to varying voltage pulse characteristics. Key analog device properties which degrade the accuracy are update linearity and write noise. Write noise may improve as a function of device manufacturing maturity, but write nonlinearity appears relatively consistent among the different device material systems and is found to be the most significant factor affecting accuracy. As a result, this suggests that new materials and/or fundamentally different resistive switching mechanisms may be required to improve device linearity and achieve higher algorithm training accuracy.

97 MATHEMATICS AND COMPUTING↗