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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Evolutionary vs imitation learning for neuromorphic control at the edge*

Abstract Neuromorphic computing offers the opportunity to implement extremely low power artificial intelligence at the edge. Control applications, such as autonomous vehicles and robotics, are also of great interest for neuromorphic systems at the edge. It is not clear, however, what the best neuromorphic training approaches are for control applications at the edge. In this work, we implement and compare the performance of evolutionary optimization and imitation learning approaches on an autonomous race car control task using an edge neuromorphic implementation. We show that the evolutionary approaches tend to achieve better performing smaller network sizes that are well-suited to edge deployment, but they also take significantly longer to train. We also describe a workflow to allow for future algorithmic comparisons for neuromorphic hardware on control applications at the edge.

Schuman, Catherine↗
DOE OSTI2022

Vertex finding in neutrino-nucleus interaction: a model architecture comparison

We compare different neural network architectures for machine learning algorithms designed to identify the neutrino interaction vertex position in the MINERvA detector. The architectures developed and optimized by hand are compared with the architectures developed in an automated way using the package “Multi-node Evolutionary Neural Networks for Deep Learning” (MENNDL), developed at Oak Ridge National Laboratory. While the domain-expert hand-tuned network was the best performer, the differences were negligible and the auto-generated networks performed as well. There is always a trade-off between human, and computer resources for network optimization and this work suggests that automated optimization, assuming resources are available, provides a compelling way to save significant expert time.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

Extensive site-directed mutagenesis reveals interconnected functional units in the alkaline phosphatase active site

Enzymes enable life by accelerating reaction rates to biological timescales. Conventional studies have focused on identifying the residues that have a direct involvement in an enzymatic reaction, but these so-called ‘catalytic residues’ are embedded in extensive interaction networks. Although fundamental to our understanding of enzyme function, evolution, and engineering, the properties of these networks have yet to be quantitatively and systematically explored. We dissected an interaction network of five residues in the active site of Escherichia coli alkaline phosphatase. Analysis of the complex catalytic interdependence of specific residues identified three energetically independent but structurally interconnected functional units with distinct modes of cooperativity. From an evolutionary perspective, this network is orders of magnitude more probable to arise than a fully cooperative network. From a functional perspective, new catalytic insights emerge. Further, such comprehensive energetic characterization will be necessary to benchmark the algorithms required to rationally engineer highly efficient enzymes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Global land model development: time to shift from a plant functional type to a plant functional trait approach (Final Report)

This project advanced global land models by shifting from the current plant functional type approach to one that better utilizes what is known about the importance and variability of plant traits, within a framework of simultaneously improving fundamental physiological relations that are at the core of model carbon cycling algorithms. A primary goal for Earth system modeling is to make accurate predictions of the future trajectory of the climate system, based on a mechanistic understanding of processes regulating fluxes of mass and energy among system components. Land plays an important role in modifying the Earth’s mass and energy balance, as a critical link in the global cycling of carbon, among others. Land surface models have developed to include mechanistic representations of vegetation physiology, carbon and nutrient dynamics in plants and soils, how they might respond to changing climate and chemistry, and how those changes might feedback to influence changes in atmospheric greenhouse gases themselves. Existing models represent the global distribution of vegetation types using the Plant Functional Type concept. Plant Functional Types are classes of plant species with similar evolutionary and life history with presumably similar responses to environmental conditions like CO 2 , water and nutrient availability. Fixed properties for each Plant Functional Type are specified through a collection of physiological parameters, or traits. These traits, mostly physiological in nature (e.g., leaf nitrogen and longevity) are used in model algorithms to estimate ecosystem properties and/or drive calculated process rates. In most models, 5 to 15 functional types represent terrestrial vegetation; in essence, they assume there are a total of only 5 to 15 different kinds of plants on the entire globe. This assumption of constant plant traits captured within the functional type concept has serious limitations, as a single set of traits does not reflect trait variation observed within and between species and communities. While this simplification was necessary decades past, substantial improvement is now possible. Rather than assigning a small number of constant parameter values to all grid cells in a model, we developed procedures that predict a frequency distribution of values for any given grid cell. Thus, by incorporating the mean and variance model fidelity has been improved. The trait-based approach has improved land modeling by (1) incorporating patterns and heterogeneity of traits into model parameterization, thus evolving away from a framework that considers large areas of vegetation to have near identical trait values and (2) allowing for improved treatment of physiological responses to environmental drivers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Molecular clouds in the North American and Pelican Nebulae: structures

We present observations of a 4.25 deg{sup 2} area toward the North American and Pelican Nebulae in the J = 1-0 transitions of {sup 12}CO, {sup 13}CO, and C{sup 18}O. Three molecules show different emission areas with their own distinct structures. These different density tracers reveal several dense clouds with a surface density of over 500 M {sub ☉} pc{sup –2} and a mean H{sub 2} column density of 5.8, 3.4, and 11.9 × 10{sup 21} cm{sup –2} for {sup 12}CO, {sup 13}CO, and C{sup 18}O, respectively. We obtain a total mass of 5.4 × 10{sup 4} M {sub ☉} ({sup 12}CO), 2.0 × 10{sup 4} M {sub ☉} ({sup 13}CO), and 6.1 × 10{sup 3} M {sub ☉} (C{sup 18}O) in the complex. The distribution of excitation temperature shows two phases of gas: cold gas (∼10 K) spreads across the whole cloud; warm gas (>20 K) outlines the edge of the cloud heated by the W80 H II region. The kinetic structure of the cloud indicates an expanding shell surrounding the ionized gas produced by the H II region. There are six discernible regions in the cloud: the Gulf of Mexico, Caribbean Islands and Sea, and Pelican's Beak, Hat, and Neck. The areas of {sup 13}CO emission range within 2-10 pc{sup 2} with mass of (1-5) × 10{sup 3} M {sub ☉} and line width of a few km s{sup –1}. The different line properties and signs of star-forming activity indicate they are in different evolutionary stages. Four filamentary structures with complicated velocity features are detected along the dark lane in LDN 935. Furthermore, a total of 611 molecular clumps within the {sup 13}CO tracing cloud are identified using the ClumpFind algorithm. The properties of the clumps suggest that most of the clumps are gravitationally bound and at an early stage of evolution with cold and dense molecular gas.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Peer-to-Peer Energy Trading in a Prosumer-Based Community Microgrid: A Game-Theoretic Model

This paper proposes a novel game-theoretic model for peer-to-peer (P2P) energy trading among the prosumers in a community. The buyers can adjust the energy consumption behavior based on the price and quantity of the energy offered by the sellers. There exist two separate competitions during the trading process: 1) price competition among the sellers; and 2) seller selection competition among the buyers. The price competition among the sellers is modeled as a noncooperative game. The evolutionary game theory is used to model the dynamics of the buyers for selecting sellers. Moreover, an M-leader and N-follower Stackelberg game approach is used to model the interaction between buyers and sellers. Two iterative algorithms are proposed for the implementation of the games such that an equilibrium state exists in each of the games. The proposed method is applied to a small community microgrid with photo-voltaic and energy storage systems. Simulation results show the convergence of the algorithms and the effectiveness of the proposed model to handle P2P energy trading. The results also show that P2P energy trading provides significant financial and technical benefits to the community, and it is emerging as an alternative to cost-intensive energy storage systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

AlloSHP: deconvoluting single homeologous polymorphism for phylogenetic analysis of allopolyploids

Background The genomic and evolutionary study of allopolyploid organisms involves multiple copies of homeologous chromosomes, making their assembly, annotation, and phylogenetic analysis challenging. Bioinformatics tools and protocols have been developed to study polyploid genomes, but sometimes require the assembly of their genomes, or at least the genes, limiting their use. Results We have developed AlloSHP, a command-line tool for detecting and extracting single homeologous polymorphisms (SHPs) from the subgenomes of allopolyploid species. This tool integrates three main algorithms, WGA, VCF2ALIGNMENT and VCF2SYNTENY, and allows the detection of SHPs for the study of diploid-polyploid complexes with available diploid progenitor genomes, without assembling and annotating the genomes of the allopolyploids under study. AlloSHP has been validated on three diploid-polyploid plant complexes, Brachypodium, Brassica, and Triticum-Aegilops, and a set of synthetic hybrid yeasts and their progenitors of the genus Saccharomyces. The results and congruent phylogenies obtained from the four datasets demonstrate the potential of AlloSHP for the evolutionary analysis of allopolyploids with a wide range of ploidy and genome sizes. Conclusions AlloSHP combines the strategies of simultaneous mapping against multiple reference genomes and syntenic alignment of these genomes to call SHPs, using as input data a single VCF file and the reference genomes of the known or closest extant diploid progenitor species. This novel approach provides a valuable tool for the evolutionary study of allopolyploid species, both at the interspecific and intraspecific levels, allowing the simultaneous analysis of a large number of accessions and avoiding the complex process of assembling polyploid genomes.

Allopolyploids↗
DOE OSTI2019

tRNA functional signatures classify plastids as late-branching cyanobacteria

Eukaryotes acquired the trait of oxygenic photosynthesis through endosymbiosis of the cyanobacterial progenitor of plastid organelles. Despite recent advances in the phylogenomics of Cyanobacteria, the phylogenetic root of plastids remains controversial. Although a single origin of plastids by endosymbiosis is broadly supported, recent phylogenomic studies are contradictory on whether plastids branch early or late within Cyanobacteria. One underlying cause may be poor fit of evolutionary models to complex phylogenomic data. Using Posterior Predictive Analysis, we show that recently applied evolutionary models poorly fit three phylogenomic datasets curated from cyanobacteria and plastid genomes because of heterogeneities in both substitution processes across sites and of compositions across lineages. To circumvent these sources of bias, we developed CYANO-MLP, a machine learning algorithm that consistently and accurately phylogenetically classifies (“phyloclassifies”) cyanobacterial genomes to their clade of origin based on bioinformatically predicted function-informative features in tRNA gene complements. Classification of cyanobacterial genomes with CYANO-MLP is accurate and robust to deletion of clades, unbalanced sampling, and compositional heterogeneity in input tRNA data. CYANO-MLP consistently classifies plastid genomes into a late-branching cyanobacterial sub-clade containing single-cell, starch-producing, nitrogen-fixing ecotypes, consistent with metabolic and gene transfer data. Phylogenomic data of cyanobacteria and plastids exhibit both site-process heterogeneities and compositional heterogeneities across lineages. These aspects of the data require careful modeling to avoid bias in phylogenomic estimation. Furthermore, we show that amino acid recoding strategies may be insufficient to mitigate bias from compositional heterogeneities. However, the combination of our novel tRNA-specific strategy with machine learning in CYANO-MLP appears robust to these sources of bias with high accuracy in phyloclassification of cyanobacterial genomes. CYANO-MLP consistently classifies plastids as late-branching Cyanobacteria, consistent with independent evidence from signature-based approaches and some previous phylogenetic studies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Orbits and Masses of Binaries from Speckle Interferometry at SOAR

We present results from Speckle inteferometric observations of 15 visual binaries and one double-line spectroscopic binary, carried out with the HRCam Speckle camera of the SOAR 4.1 m telescope. These systems were observed as a part of an on-going survey to characterize the binary population in the solar vicinity, out to a distance of 250 pc. We obtained orbital elements and mass sums for our sample of visual binaries. The orbits were computed using a Markov Chain Monte Carlo algorithm that delivers maximum likelihood estimates of the parameters, as well as posterior probability density functions that allow us to evaluate their uncertainty. Their periods cover a range from 5 yr to more than 500 yr; and their spectral types go from early A to mid M, implying total system masses from slightly more than 4M{sub ⊙} down to 0.2M {sub ⊙}. They are located at distances between approximately 12 and 200 pc, mostly at low Galactic latitude. For the double-line spectroscopic binary YSC8, we present the first combined astrometric/radial-velocity orbit resulting from a self-consistent fit, leading to individual component masses of 0.897 ± 0.027 M {sub ⊙} and 0.857 ± 0.026 M {sub ⊙}; and an orbital parallax of 26.61 ± 0.29 mas, which compares very well with the Gaia DR2 trigonometric parallax (26.55 ± 0.27 mas). In combination with published photometry and trigonometric parallaxes, we place our objects on an H-R diagram and discuss their evolutionary status. We also present a thorough analysis of the precision and consistency of the photometry available for them.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Aperiodic optical coatings for neutral-color semi-transparent organic photovoltaics

Semi-transparent organic photovoltaics (ST-OPVs) have the potential for integration with windows for ubiquitous power generating applications. Typically, such applications require that ST-OPVs be neutrally transparent across the visible and exhibit both a high average photopic transmittance (APT) and color rendering index, as well as iso-energetic chromaticity coordinates. In this work, we demonstrate the design and use of optical coatings to achieve ST-OPVs with a neutral visible transmittance of APT = 50%, a power conversion efficiency of 8.3%, and optical properties that are independent of a ± 30° variation in the solar angle of incidence. As a result, these simple optical coatings are rapidly designed using a genetic algorithm and transfer matrix formalism.

14 SOLAR ENERGY↗
DOE OSTI2016

WE-AB-BRA-12: Post-Implant Dosimetry in Prostate Brachytherapy by X-Ray and MRI Fusion

Purpose: For post-implant dosimetric assessment after prostate brachytherapy, CT-MR fusion approach has been advocated due to the superior accuracy on both seeds localization and soft tissue delineation. However, CT deposits additional radiation to the patient, and seed identification in CT requires manual review and correction. In this study, we propose an accurate, low-dose, and cost-effective post-implant dosimetry approach based on X-ray and MRI. Methods: Implanted seeds are reconstructed using only three X-ray fluoroscopy images by solving a combinatorial optimization problem. The reconstructed seeds are then registered to MR images using an intensity-based points-to-volume registration. MR images are first pre-processed by geometric and Gaussian filtering, yielding smooth candidate seed-only images. To accommodate potential soft tissue deformation, our registration is performed in two steps, an initial affine followed by local deformable registrations. An evolutionary optimizer in conjunction with a points-to-volume similarity metric is used for the affine registration. Local prostate deformation and seed migration are then adjusted by the deformable registration step with external and internal force constraints. Results: We tested our algorithm on twenty patient data sets. For quantitative evaluation, we obtained ground truth seed positions by fusing the post-implant CT-MR images. Seeds were semi-automatically extracted from CT and manually corrected and then registered to the MR images. Target registration error (TRE) was computed by measuring the Euclidean distances from the ground truth to the closest registered X-ray seeds. The overall TREs (mean±standard deviation in mm) are 1.6±1.1 (affine) and 1.3±0.8 (affine+deformable). The overall computation takes less than 1 minute. Conclusion: It has been reported that the CT-based seed localization error is ∼1.6mm and the seed localization uncertainty of 2mm results in less than 5% deviation of prostate D90. The average error of 1.3mm with our system outperforms the CT-based approach and is considered well within the clinically acceptable limit. Supported in part by NIH/NCI grant 5R01CA151395. The X-ray-based implant reconstruction method (US patent No. 8,233,686) was licensed to Acoustic MedSystems Inc.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Orbits for 18 Visual Binaries and Two Double-line Spectroscopic Binaries Observed with HRCAM on the CTIO SOAR 4 m Telescope, Using a New Bayesian Orbit Code Based on Markov Chain Monte Carlo

We present orbital elements and mass sums for 18 visual binary stars of spectral types B to K (five of which are new orbits) with periods ranging from 20 to more than 500 yr. For two double-line spectroscopic binaries with no previous orbits, the individual component masses, using combined astrometric and radial velocity data, have a formal uncertainty of ∼0.1 M{sub ⊙}. Adopting published photometry and trigonometric parallaxes, plus our own measurements, we place these objects on an H-R diagram and discuss their evolutionary status. These objects are part of a survey to characterize the binary population of stars in the Southern Hemisphere using the SOAR 4 m telescope+HRCAM at CTIO. Orbital elements are computed using a newly developed Markov chain Monte Carlo (MCMC) algorithm that delivers maximum-likelihood estimates of the parameters, as well as posterior probability density functions that allow us to evaluate the uncertainty of our derived parameters in a robust way. For spectroscopic binaries, using our approach, it is possible to derive a self-consistent parallax for the system from the combined astrometric and radial velocity data (“orbital parallax”), which compares well with the trigonometric parallaxes. We also present a mathematical formalism that allows a dimensionality reduction of the feature space from seven to three search parameters (or from 10 to seven dimensions—including parallax—in the case of spectroscopic binaries with astrometric data), which makes it possible to explore a smaller number of parameters in each case, improving the computational efficiency of our MCMC code.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Multi-solution nature of topology optimization and its application in design for additive manufacturing

The purpose of this paper is to introduce the multi-solution nature of topology optimization (TO) as a design tool for additive manufacturing (AM). The sensitivity of topologically optimized parts and manufacturing constraints to the initial starting point of the optimization process leading to structures with equivalent performance is explored. A modified bi-directional evolutionary structural optimization (BESO) code was used as the numerical approach to optimize a cantilever beam problem and reduce the mass by 50 per cent. Several optimized structures with relatively equivalent mechanical performance were generated by changing the initial starting point of the TO algorithm. These optimized structures were manufactured using fused deposition modeling (FDM). The equivalence of strain distribution in FDM parts was tested with the digital image correlation (DIC) technique and compared with that from the modified BESO code.The results confirm that TO could lead to a wide variety of non-unique solutions based on loading and manufacturability constraints. The modified BESO code was able to reduce the support structure needed to build the simple two-dimensional cantilever beam by 15 per cent while keeping the mechanical performance at the same level. Finally, the originality of this paper lies in introduction and application of the multi-solution nature of TO for AM as a design tool for optimizing structures with minimized features in the overhang condition and the need for support structures.

36 MATERIALS SCIENCE↗
DOE OSTI2025

MOOSE ProbML: Parallelized probabilistic machine learning and uncertainty quantification for computational energy applications

Here, this paper presents the development and demonstration of massively parallel probabilistic machine learning (ML) and uncertainty quantification (UQ) capabilities within the Multiphysics Object-Oriented Simulation Environment (MOOSE), an open-source computational platform for parallel finite element and finite volume analyses. In addressing the computational expense and uncertainties inherent in complex multiphysics simulations, this paper integrates Gaussian process (GP) variants, active learning, Bayesian inverse UQ, adaptive forward UQ, Bayesian optimization, evolutionary optimization, and Markov chain Monte Carlo (MCMC) within MOOSE. It also elaborates on the interaction among key MOOSE systems — Sampler, MultiApp, Reporter, and Surrogate — in enabling these capabilities. The modularity offered by these systems enables development of a multitude of probabilistic ML and UQ algorithms in MOOSE. Example code demonstrations include parallel active learning and parallel Bayesian inference via active learning. The impact of these developments is illustrated through five applications relevant to computational energy applications: UQ of nuclear fuel fission product release, using parallel active learning Bayesian inference; very rare events analysis in nuclear microreactors using active learning; advanced manufacturing process modeling using multi-output GPs (MOGPs) and dimensionality reduction; fluid flow using deep GPs (DGPs); and tritium transport model parameter optimization for fusion energy, using batch Bayesian optimization. These capabilities are part of the MOOSE framework.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Identification of broadly neutralizing antibody epitopes in the HIV-1 envelope glycoprotein using evolutionary models

Background: Identification of the epitopes targeted by antibodies that can neutralize diverse HIV-1 strains can provide important clues for the design of a preventative vaccine. Methods: We have developed a computational approach that can identify key amino acids within the HIV-1 envelope glycoprotein that influence sensitivity to broadly cross-neutralizing antibodies. Given a sequence alignment and neutralization titers for a panel of viruses, the method works by fitting a phylogenetic model that allows the amino acid frequencies at each site to depend on neutralization sensitivities. Sites at which viral evolution influences neutralization sensitivity were identified using Bayes factors (BFs) to compare the fit of this model to that of a null model in which sequences evolved independently of antibody sensitivity. Conformational epitopes were identified with a Metropolis algorithm that searched for a cluster of sites with large Bayes factors on the tertiary structure of the viral envelope. Results: We applied our method to ID 50 neutralization data generated from seven HIV-1 subtype C serum samples with neutralization breadth that had been tested against a multi-clade panel of 225 pseudoviruses for which envelope sequences were also available. For each sample, between two and four sites were identified that were strongly associated with neutralization sensitivity (2ln(BF) > 6), a subset of which were experimentally confirmed using site-directed mutagenesis. Conclusions: Our results provide strong support for the use of evolutionary models applied to cross-sectional viral neutralization data to identify the epitopes of serum antibodies that confer neutralization breadth.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Discovery of peculiar radio morphologies with ASKAP using unsupervised machine learning

Abstract We present a set of peculiar radio sources detected using an unsupervised machine learning method. We use data from the Australian Square Kilometre Array Pathfinder (ASKAP) telescope to train a self-organizing map (SOM). The radio maps from three ASKAP surveys, Evolutionary Map of Universe pilot survey (EMU-PS), Deep Investigation of Neutral Gas Origins pilot survey (DINGO), and Survey With ASKAP of GAMA-09 + X-ray (SWAG-X), are used to search for the rarest or unknown radio morphologies. We use an extension of the SOM algorithm that implements rotation and flipping invariance on astronomical sources. The SOM is trained using the images of all ‘complex’ radio sources in the EMU-PS which we define as all sources catalogued as ‘multi-component’. The trained SOM is then used to estimate a similarity score for complex sources in all surveys. We select 0.5% of the sources that are most complex according to the similarity metric and visually examine them to find the rarest radio morphologies. Among these, we find two new odd radio circle (ORC) candidates and five other peculiar morphologies. We discuss multiwavelength properties and the optical/infrared counterparts of selected peculiar sources. In addition, we present examples of conventional radio morphologies including: diffuse emission from galaxy clusters, and resolved, bent-tailed, and FR-I and FR-II type radio galaxies. We discuss the overdense environment that may be the reason behind the circular shape of ORC candidates.

Astronomy & Astrophysics↗
DOE OSTI2016

Unraveling the Planar-Globular Transition in Gold Nanoclusters through Evolutionary Search

Au nanoclusters are of technological relevance for catalysis, photonics, sensors, and of fundamental scientific interest owing to planar to globular structural transformation at an anomalously high number of atoms i.e. in the range 12-14. The nature and causes of this transition remain a mystery. In order to unravel this conundrum, high throughput density functional theory (DFT) calculations, coupled with a global structural optimization scheme based on a modified genetic algorithm (GA) are conducted. Furthermore, more than 20,000 Au 12 , Au 13 , and Au 14 nanoclusters are evaluated. With any DFT functional, globular and planar structures coexist across the size range of interest. Contrary to what was previously believed, the planar-globular transition is gradual at room temperature rather than a sharp transition. The effects of anionicity, s-d band hybridization and long range interactions on the dimensional transition are quantified by using the structures adjacent to minima. Anionicity marginally changes the relative stability of the clusters. The degree of s-d hybridization is varied via changing the Hubbard U value which corroborate that s-d hybridization alone does not stabilize planar structures. van der Waals interactions, on the other hand, stabilize globular structures. Our results elucidate the balance between the different reasons of the dimensional transition in gold nanoclusters.

36 MATERIALS SCIENCE↗