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Results for “Bayesian classification”

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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96 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Identification of a new circulating recombinant form of human immunodeficiency virus type 1, CRF124_cpx involving subtypes A, G, H, and CRF27_cpx in Angola

Angola, located in Central Africa, has around 320,000 (270,000–380,000) people living with human immunodeficiency virus (HIV)/AIDS, equivalent to 1% of the country’s population at the end of 2021. A previous study conducted in 2012, using Angolan samples collected between 2008 and 2010 revealed a high prevalence of HIV-1 recombinants, around 42% of sequences, with 21% showing the same UH profile in partial pol region which were grouped into a monophyletic cluster with high bootstrap support. Thus, the objective of the present work was to obtain complete genomes of those sequences and characterize them, aiming at a description of a new circulating recombinant form (CRF). Whole blood from nine HIV-1 UH pol -infected individuals had their genomic DNA extracted, and nested PCR was used to amplify seven overlapping fragments targeting the full-length HIV-1 genome. The final classification was based on maximum likelihood trees, and recombination analyses were performed using a bootscan from the Simplot program. BLAST and Los Alamos Database inspections were used to search other similar H-like pol sequences. Complete genome amplification was possible for three samples, partial genomes were obtained for the other three, and only pol was available for the remaining three sequences. Bootscan analysis of the two whole-genome and three partial genome sequences retrieved from people living with HIV/AIDS (PLHIVA) without epidemiological linkage showed the same complex recombination profile involving HIV-1 subtypes A/G/H/CRF27_cpx, with a total of six recombinant breakpoints, aiming to classify a new HIV-1 CRF124_cpx. We found no other full-length HIV-1 genomes with the same mosaic profile; however, we identified 33 partial pol sequences, mainly sampled from Angola between 2001 to 2019, with the same H-like profile. Bayesian analysis of H and H-like pol sequences indicates that CRF124_cpx probably originated in Angola at mid-1970s, indicating that this CRF has been circulating in the country for a long time. In summary, our study describes a new CRF circulating principally in Angola and highlights the importance of continuing molecular surveillance studies, especially in countries with high molecular diversity of HIV.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

J-PLUS: Spectral evolution of white dwarfs by PDF analysis

We estimated the spectral evolution of white dwarfs with effective temperature using the Javalambre Photometric Local Universe Survey (J-PLUS) second data release (DR2), which provides 12 photometric optical passbands over 2176 deg 2 . We analyzed 5926 white dwarfs with r ≤ 19.5 mag in common between a white dwarf catalog defined from Gaia EDR3 and J-PLUS DR2. We performed a Bayesian analysis by comparing the observed J-PLUS photometry with theoretical models of hydrogen- and helium-dominated atmospheres. We estimated the probability distribution functions for effective temperature (T eff ), surface gravity, parallax, and composition; and the probability of having a H-dominated atmosphere (p H ) for each source. We applied a prior in parallax, using Gaia EDR3 measurements as a reference, and derived a self-consistent prior for the atmospheric composition as a function of T eff . We described the fraction of white dwarfs with a He-dominated atmosphere (f He ) with a linear function of the effective temperature at 5000 < T eff < 30 000 K. We find f He = 0.24 ± 0.01 at T eff = 10 000 K, a change rate along the cooling sequence of 0.14 ± 0.02 per 10 kK, and a minimum He-dominated fraction of 0.08 ± 0.02 at the high-temperature end. We tested the obtained p H by comparison with spectroscopic classifications, finding that it is reliable. We estimated the mass distribution for the 351 sources with distance d < 100 pc, mass M > 0.45 M ⊙ , and T eff > 6000 K. The result for H-dominated white dwarfs agrees with previous studies, with a dominant M = 0.59 M ⊙ peak and the presence of an excess at M ~ 0.8 M ⊙ . This high-mass excess is absent in the He-dominated distribution, which presents a single peak. The J-PLUS optical data provide a reliable statistical classification of white dwarfs into H- and He-dominated atmospheres. We find a 21 ± 3% increase in the fraction of He-dominated white dwarfs from T eff = 20 000 K to T eff = 5000 K.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Probabilistic Nanomagnetic Memories for Uncertain and Robust Machine Learning

This project evaluated the use of emerging spintronic memory devices for robust and efficient variational inference schemes. Variational inference (VI) schemes, which constrain the distribution for each weight to be a Gaussian distribution with a mean and standard deviation, are a tractable method for calculating posterior distributions of weights in a Bayesian neural network such that this neural network can also be trained using the powerful backpropagation algorithm. Our project focuses on domain-wall magnetic tunnel junctions (DW-MTJs), a powerful multi-functional spintronic synapse design that can achieve low power switching while also opening the pathway towards repeatable, analog operation using fabricated notches. Our initial efforts to employ DW-MTJs as an all-in-one stochastic synapse with both a mean and standard deviation didn’t end up meeting the quality metrics for hardware-friendly VI. In the future, new device stacks and methods for expressive anisotropy modification may make this idea still possible. However, as a fall back that immediately satisfies our requirements, we invented and detailed how the combination of a DW-MTJ synapse encoding the mean and a probabilistic Bayes-MTJ device, programmed via a ferroelectric or ionically modifiable layer, can robustly and expressively implement VI. This design includes a physics-informed small circuit model, that was scaled up to perform and demonstrate rigorous uncertainty quantification applications, up to and including small convolutional networks on a grayscale image classification task, and larger (Residual) networks implementing multi-channel image classification. Lastly, as these results and ideas all depend upon the idea of an inference application where weights (spintronic memory states) remain non-volatile, the retention of these synapses for the notched case was further interrogated. These investigations revealed and emphasized the importance of both notch geometry and anisotropy modification in order to further enhance the endurance of written spintronic states. In the near future, these results will be mapped to effective predictions for room temperature and elevated operation DW-MTJ memory retention, and experimentally verified when devices become available.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2016

A Deep Generative Deconvolutional Image Model

A deep generative model is developed for representation and analysis of images, based on a hierarchical convolutional dictionary-learning framework. Stochastic unpooling is employed to link consecutive layers in the model, yielding top-down image generation. A Bayesian support vector machine is linked to the top-layer features, yielding max-margin discrimination. Deep deconvolutional inference is employed when testing, to infer the latent features, and the top-layer features are connected with the max-margin classifier for discrimination tasks. The model is efficiently trained using a Monte Carlo expectation-maximization (MCEM) algorithm; the algorithm is implemented on graphical processor units (GPU) to enable large-scale learning, and fast testing. Excellent results are obtained on several benchmark datasets, including ImageNet, demonstrating that the proposed model achieves results that are highly competitive with similarly sized convolutional neural networks.

convolutional neural network↗
DOE OSTI2024

Data from "A Bayesian Record Linkage Approach to Applications in Tree Demography Using Overlapping LiDAR Scans"

Processed LiDAR data and environmental covariates from 2015 and 2019 LiDAR scans in the Vicinity of Snodgrass Mountain (Western Colorado, USA), in a geographic subset used in primary analysis for the research paper.This package contains LiDAR-derived canopy height maps for 2015 and 2019, crown polygons derived from the height maps using a segmentation algorithm, and environmental covariates supporting the model of forest growth. Source datasets include August 2015 and August 2019 discrete-return LiDAR point clouds collected by Quantum Geospatial for terrain mapping purposes on behalf of the Colorado Hazard Mapping Program and the Colorado Water Conservation Board. Both datasets adhere to the USGS QL2 quality standard. The point cloud data were processed using the R package lidR to generate a canopy height model representing maximum vegetation height above the ground surface, using a pit-free algorithm.This dataset was compiled to assess how spatial patterns of tree growth in montane and subalpine forests are influenced by water and energy availability. Understanding these growth patterns can provide insight into forest dynamics in the Southern Rocky Mountains under changing climatic conditions.This dataset contains .tif, .csv, and .txt files. This dataset additionally includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata; and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Deep kernel methods learn better: from cards to process optimization

Abstract The ability of deep learning methods to perform classification and regression tasks relies heavily on their capacity to uncover manifolds in high-dimensional data spaces and project them into low-dimensional representation spaces. In this study, we investigate the structure and character of the manifolds generated by classical variational autoencoder (VAE) approaches and deep kernel learning (DKL). In the former case, the structure of the latent space is determined by the properties of the input data alone, while in the latter, the latent manifold forms as a result of an active learning process that balances the data distribution and target functionalities. We show that DKL with active learning can produce a more compact and smooth latent space which is more conducive to optimization compared to previously reported methods, such as the VAE. We demonstrate this behavior using a simple cards dataset and extend it to the optimization of domain-generated trajectories in physical systems. Our findings suggest that latent manifolds constructed through active learning have a more beneficial structure for optimization problems, especially in feature-rich target-poor scenarios that are common in domain sciences, such as materials synthesis, energy storage, and molecular discovery. The Jupyter Notebooks that encapsulate the complete analysis accompany the article.

97 MATHEMATICS AND COMPUTING↗