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DOE OSTI2023

Deep Multimodal Networks for M-type Star Classification with Paired Spectrum and Photometric Image

Abstract Traditional stellar classification methods include spectral and photometric classification separately. Although satisfactory results can be achieved, the accuracy could be improved. In this paper, we pioneer a novel approach to deeply fuse the spectra and photometric images of the sources in an advanced multimodal network to enhance the model’s discriminatory ability. We use Transformer as the fusion module and apply a spectrum–image contrastive loss function to enhance the consistency of the spectrum and photometric image of the same source in two different feature spaces. We perform M-type stellar subtype classification on two data sets with high and low signal-to-noise ratio (S/N) spectra and corresponding photometric images, and the F1-score achieves 95.65% and 90.84%, respectively. In our experiments, we prove that our model effectively utilizes the information from photometric images and is more accurate than advanced spectrum and photometric image classifiers. Our contributions can be summarized as follows: (1) We propose an innovative idea for stellar classification that allows the model to simultaneously consider information from spectra and photometric images. (2) We discover the challenge of fusing low-S/N spectra and photometric images in the Transformer and provide a solution. (3) The effectiveness of Transformer for spectral classification is discussed for the first time and will inspire more Transformer-based spectral classification models.

Astronomy & Astrophysics↗

DOE OSTI2013

Efficient and reliable 3D dose quality assurance for IMRT by combining independent dose calculations with measurements

Purpose: Advanced radiotherapy treatments require appropriate quality assurance (QA) to verify 3D dose distributions. Moreover, increase in patient numbers demand efficient QA-methods. In this study, a time efficient method that combines model-based QA and measurement-based QA was developed; i.e., the hybrid-QA. The purpose of this study was to determine the reliability of the model-based QA and to evaluate time efficiency of the hybrid-QA method. Methods: Accuracy of the model-based QA was determined by comparison of COMPASS calculated dose with Monte Carlo calculations for heterogeneous media. In total, 330 intensity modulated radiation therapy (IMRT) treatment plans were evaluated based on the mean gamma index (GI) with criteria of 3%/3mm and classification of PASS (GI {<=} 0.4), EVAL (0.4 < GI > 0.6), and FAIL (GI {>=} 0.6). Agreement between model-based QA and measurement-based QA was determined for 48 treatment plans, and linac stability was verified for 15 months. Finally, time efficiency improvement of the hybrid-QA was quantified for four representative treatment plans. Results: COMPASS calculated dose was in agreement with Monte Carlo dose, with a maximum error of 3.2% in heterogeneous media with high density (2.4 g/cm{sup 3}). Hybrid-QA results for IMRT treatment plans showed an excellent PASS rate of 98% for all cases. Model-based QA was in agreement with measurement-based QA, as shown by a minimal difference in GI of 0.03 {+-} 0.08. Linac stability was high with an average GI of 0.28 {+-} 0.04. The hybrid-QA method resulted in a time efficiency improvement of 15 min per treatment plan QA compared to measurement-based QA. Conclusions: The hybrid-QA method is adequate for efficient and accurate 3D dose verification. It combines time efficiency of model-based QA with reliability of measurement-based QA and is suitable for implementation within any radiotherapy department.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2018

On Derived Equivalence of Algebras of Semidihedral Type with Two Simple Modules

The Hochschild cohomology groups of degrees not exceeding 3 are computed for algebras of semidihedral type that form the family SD(2B){sub 1} (from the famous K. Erdmann’s classification). In the calculation, the beforehand construction of the initial part of the minimal projective bimodule resolution is used for algebras from the family under discussion. The obtained results imply that the algebras from the families SD(2B){sub 1} and SD(2B){sub 2} with the same parameters in defining relations are not derived equivalent.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

A method for distinguishing between propagons, diffusions, and locons

The majority of intuition on phonon transport has been derived from studies of homogenous crystalline solids, where the atomic composition and structure are periodic. For this specific class of materials, the solutions to the equations of motions for the atoms (in the harmonic limit) result in plane wave modulated velocity fields for the normal modes of vibration. However, it has been known for several decades that whenever a system lacks periodicity, either compositional or structural, the normal modes of vibration can still be determined (in the harmonic limit), but the solutions take on different characteristics and many modes may not be plane wave modulated. Previous work has classified the types of vibrations into three primary categories, namely, propagons, diffusions, and locons. One can use the participation ratio to distinguish locons, from propagons and diffusons, which measures the extent to which a mode is localized. However, distinguishing between propagons and diffusons has remained a challenge, since both are spatially delocalized. Here, we present a new method that quantifies the extent to which a mode's character corresponds to a propagating mode, e.g., exhibits plane wave modulation. This then allows for clear and quantitative distinctions between propagons and diffusons. By resolving this issue quantitatively, one can now automate the classification of modes for any arbitrary material or structure, subject to a single constraint that the atoms must vibrate stably around their respective equilibrium sites. Several example test cases are studied including crystalline silicon and germanium, crystalline silicon with different defect concentrations, as well as amorphous silicon, germanium, and silica.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2017

Toward Understanding Phage:Host Interactions in the Rumen; Complete Genome Sequences of Lytic Phages Infecting Rumen Bacteria

The rumen is known to harbor dense populations of bacteriophages (phages) predicted to be capable of infecting a diverse range of rumen bacteria. While bacterial genome sequencing projects are revealing the presence of phages which can integrate their DNA into the genome of their host to form stable, lysogenic associations, little is known of the genetics of phages which utilize lytic replication. These phages infect and replicate within the host, culminating in host lysis, and the release of progeny phage particles. While lytic phages for rumen bacteria have been previously isolated, their genomes have remained largely uncharacterized. Here we report the first complete genome sequences of lytic phage isolates specifically infecting three genera of rumen bacteria: Bacteroides , Ruminococcus , and Streptococcus . All phages were classified within the viral order Caudovirales and include two phage morphotypes, representative of the Siphoviridae and Podoviridae families. The phage genomes displayed modular organization and conserved viral genes were identified which enabled further classification and determination of closest phage relatives. Co-examination of bacterial host genomes led to the identification of several genes responsible for modulating phage:host interactions, including CRISPR/ Cas elements and restriction-modification phage defense systems. These findings provide new genetic information and insights into how lytic phages may interact with bacteria of the rumen microbiome.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

The incommensurately modulated structures of low-temperature labradorite feldspars: a single-crystal X-ray and neutron diffraction study

Labradorite feldspars of the plagioclase solid solution series have been known for their complicated subsolidus phase relations and enigmatic incommensurately modulated structures. Characterized by the irrationally indexed e -reflections in the diffraction pattern, e -labradorite shows the largest variation in the incommensurate ordering states among the e -plagioclase structures. The strongly ordered low-temperature e -labradorite is one of the last missing pieces of the e -plagioclase puzzle. Nine plutonic and metamorphic labradorite feldspar samples from Canada, Ukraine, Minnesota (USA), Tanzania and Greenland with compositions ranging from An 52.5 to An 68 were studied with single-crystal X-ray diffraction. Two crystals from Labrador, Canada, and Duluth, MN, USA, with wide enough twin lamellae were analyzed with single-crystal neutron diffraction. The incommensurately modulated structures of e -plagioclase are refined for the first time with neutron diffraction data, which confirmed that the T—O distance modulation in the low-temperature e -plagioclase results from the Al–Si ordering in the framework. Detailed configurations of the M site are also observed in the structures refined from neutron diffraction data, which were not possible to see with X-ray diffraction data. The relation between the q -vectors and the mole% An composition is revealed for the entire compositional range of e -plagioclase, from An 25 to An 75 . The previously proposed two-trend relation depending on the cooling rate and phase transition path is confirmed. A new classification of e -plagioclase ( e α , e β and e γ ) is proposed based on the q -vector of the structure, which makes it an independent character from the presence/absence of density modulation. New parameters are proposed to quantify the ordering states of these complicated aperiodic structures of e -plagioclases, such as the difference between $\langle$T 1 o—O$\rangle$ and $\langle$T 1 m—O$\rangle$ at phase t = 0.2 or the normalized intensity of the (071\bar 1) reflection.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Development of gas chromatographic pattern recognition and classification tools for compliance and forensic analyses of fuels: A review

Gas chromatography (GC) is undoubtedly the analytical technique of choice for analyzing the composition of petroleum-based fuels. Over the past twenty years, as comprehensive two-dimensional gas chromatography (GC×GC) has evolved, fuel analysis has often been highlighted in scientific reports, as their complexity allows for illustration of the impressive peak capacity gains afforded by GC×GC. Indeed, several research groups in recent years have applied GC×GC and chemometrics to demonstrate the potential of these analytical tools to address important compliance (tax evasion, tax credits, physical quality standards) and forensic (arson investigations, oil spills) applications involving fuels. None the less, routine use of GC×GC in forensic laboratories has been limited largely by (1) legal and regulatory guidelines, (2) lack of chemometrics training, and (3) concerns about the reproducibility of GC×GC. The goal of this review is to highlight recent advances in one-dimensional GC (1D-GC) and GC×GC analyses of fuels for compliance and forensic applications, in an effort to assist scientists in overcoming the aforementioned hindrances. An introduction to 1D-GC principles, GC×GC technology (column stationary phases and modulators) and several chemometric techniques will be provided. More specifically, chemometric techniques will be broken down into (1) signal pre-processing, (2) peak decomposition, identification and quantification, and (3) classification and pattern recognition. Examples of compliance and forensic applications will be discussed, with particular emphasis on the demonstrated success of the employed chemometric techniques. Overall, this review will hopefully make 1D-GC and GC×GC coupled with chemometric data analysis tools more accessible to the larger scientific community, and aid in eventual widespread standardization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2016

OpenMP Parallelization and Optimization of Graph-Based Machine Learning Algorithms

In this paper, we investigate the OpenMP parallelization and optimization of two novel data classification algorithms. The new algorithms are based on graph and PDE solution techniques and provide significant accuracy and performance advantages over traditional data classification algorithms in serial mode. The methods leverage the Nystrom extension to calculate eigenvalue/eigenvectors of the graph Laplacian and this is a self-contained module that can be used in conjunction with other graph-Laplacian based methods such as spectral clustering. We use performance tools to collect the hotspots and memory access of the serial codes and use OpenMP as the parallelization language to parallelize the most time-consuming parts. Where possible, we also use library routines. We then optimize the OpenMP implementations and detail the performance on traditional supercomputer nodes (in our case a Cray XC30), and test the optimization steps on emerging testbed systems based on Intel’s Knights Corner and Landing processors. We show both performance improvement and strong scaling behavior. Finally, a large number of optimization techniques and analyses are necessary before the algorithm reaches almost ideal scaling.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

SU-D-207A-01: Female Pelvic Synthetic CT Generation Based On Joint Shape and Intensity Analysis

Purpose: To develop a method for generating female pelvic synthetic CT (MRCT) images from a single MR scan and evaluate its utility in radiotherapy. Methods: Under IRB-approval, an imaging sequence (T1-VIBE-Dixon) was acquired for 10 patients. This sequence yields 3 useful image volumes of different contrast (“in-phase” T1-weighted, fat and water). A previously published pelvic bone shape model was used to generate a rough bone mask for each patient. A modified fuzzy c-means classification was performed on the multi spectral MR data, with a regularization term that utilizes the prior knowledge provided by the bone mask and addresses the intensity overlap between different tissue types. A weighted sum of classification probabilities with attenuation values yielded MRCT volumes. The mean absolute error (MAE) between MRCT and real CT on various regions was calculated following deformable alignment (Velocity). Intensity modulated Treatment plans based on actual CT and MRCT were made and compared. Results: The average/standard deviation of MAE across 10 patients was 10.1/6.7 HU for muscle, 6.7/4.6 HU for fat, 136.9/53.5 HU for bony tissues under 850 HU (97% of total bone volume), 188.9/119.3 HU for bony tissues above 850 HU and 17.3/13.3 HU for intrapelvic soft tissues. Calculated doses were comparable for plans generated on CT and calculated using MRCT densities or vice versa, with differences in PTV D99% (mean/σ) of (–0.1/0.2 Gy) and (0.3/0.2 Gy), PTV D0.5cc of (–0.3/0.2 Gy) and (–0.4/1.7 Gy). OAR differences were similarly small for comparable structures, with differences in bowel V50Gy of (–0.3/0.2%) and (0.0/0.2%), femur V30Gy of (0.7/1.2%) and (0.2/1.2%), sacrum V20GY of (0.0/0.1%) and (–0.1/1.1%) and mean pelvic V20Gy of (0.0/0.1%) and (0.6/1.8%). Conclusion: MRCT based on a single imaging sequence in the female pelvis is feasible, with acceptably small variations in attenuation estimates and calculated doses to target and critical organs. Work supported by NIHR01EB016079.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2026

Patch Hierarchical Attention Transformer for Efficient Particle Jet Tagging

Real-time jet tagging is critical for identifying short-lived particle decays in the high-throughput detectors of the Large Hadron Collider, where real-time trigger systems responsible for deciding which collision events to store impose strict latency and accuracy constraints. While transformer architectures achieve the highest jet tagging accuracy when compute is unconstrained, their quadratic self-attention cost makes inference restrictive on trigger budget. Existing efficient variants reduce the computational cost, but hinder the classification performance. To address this limitation, we introduce the Patch Hierarchical Attention Transformer (PHAT-JeT), which combines two mechanisms: a physics-inspired geometric message-passing module that encodes local detector-plane structure, and a hierarchical patch-based attention scheme that computes exact attention within small particle groups while preserving global context through lightweight patch-token communication. Within a restricted budget, PHAT-JeT achieves state-of-the-art accuracy and background rejection among all resource-constrained jet tagging models on four benchmarks (\textsc{hls4ml}, JetClass, Top Tagging, and Quark--Gluon). Our code is available at https://github.com/aaronw5/PHAT-JeT.

Wang, Aaron [Illinois U., Chicago] (ORCID:00000003↗

DOE OSTI2023

A Siamese CNN + KNN-Based Classification Framework for Non-intrusive Load Monitoring

Through the development of smart grids, programs such as demand side response, have been presented as auxiliary services to the real-time operation of distributed networks. In order to provide consumers information on their energy consumption, so that a modulation in consumption is possible, non-intrusive load monitoring has been introduced as an solution to this pattern recognition problem. Non-intrusive load monitoring enables the modeling of electrical loads connected to the low-voltage system, considering only a single measurement point. Presented state-of-the-art solutions though, consider availability of data as well as representation of all possible classes of the environment. This is of course a most conservative hypothesis, since in real-life applications availability of such data is much difficult, as well as the dynamic behavior of models is implicitly evolving in time. Here, a framework that uses neural Siamese networks with k-nearest neighbor clustering is presented toward non-intrusive load monitoring. Online learning feature is implemented, which relaxes the hypothesis of data requirements as well addresses the evolving nature of load profile. k-nearest clustering allows nonlinear characteristic space modelling. Test results using synthetics and real-life data show that the solution, besides obtaining a good generalizability in the classification, also obtained results with an accuracy of 95.77%.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Multi-Source Data Aggregation and Real-Time Anomaly Classification and Localization in Power Distribution Systems

This paper proposes a real-time anomaly location and classification framework for power distribution systems to simultaneously determine the type of anomaly (i.e., short-circuit fault, cyber attack, DER switching) and its location. The proposed framework employs the data aggregation module to collect the measurement data from multiple field devices operating at different sampling rates, such as protection relays and D-PMUs. The output of the data aggregation is then fed into a multi-task learning-based long-based short-term memory (MTL-LSTM) to classify the type of anomaly and the location in two separate tasks. The proposed MTL-LSTM approach can be utilized in real-time operation in order to distinguish between normal and several anomalous operations and locate the anomaly. The proposed framework is tested on a modified IEEE 33-bus test feeder benchmark that integrates solar generation and energy storage. Furthermore, the results show that the proposed framework can locate and classify anomalies for several operation conditions with more than 96% accuracy. Further experiments highlight the impact of aggregating multiple sources of data on the performance of the proposed model.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2019

A Twin Memristor Synapse for Spike Timing Dependent Learning in Neuromorphic Systems

Neuromorphic systems consist of a framework of spiking neurons interconnected via plastic synaptic junctures. The discovery of a two terminal passive nanoscale memristive device has spurred great interest in the realization of memristive plastic synapses in neural networks. In this work, a synapse structure is presented that utilizes a pair of memristors, to implement both positive and negative weights. The working scheme of this synapse as an electrical interlink between neurons is explained, and the relative timing of their spiking events is analyzed, which leads to a modulation of the synaptic weight in accordance with the spike-timing-dependent plasticity (STDP) rule. A digital pulse width modulation technique is proposed to achieve these variable changes to the synaptic weight. The synapse architecture presented is shown to have high accuracy when used in neural networks for classification tasks. Lastly, the energy requirement of the system during various phases of operation is presented.

Adnan, Md Musabbir↗

DOE OSTI2026

Anisotropic multi- Q order in Co x TaS 2

The cobalt-intercalated transition metal dichalcogenide Co x TaS 2 hosts a rich landscape of magnetic phases that depend sensitively on x. While the stoichiometric compound with x = 1/3 exhibits a single magnetic transition, samples with x≤0.325 display two transitions with an anomalous Hall effect (AHE) emerging in the lower temperature phase. Here, we resolve the spin structure in each phase by employing a suite of magneto-optical probes that include the discovery of anomalous magneto-birefringence: a spontaneous time-reversal sensitive rotation of the principal optic axes. A symmetry-based analysis identifies the AHE-active phase as an anisotropic (2+1)Q state, in which magnetic modulation at one wavevector (Q) differs in symmetry from that at the remaining two. The (2+1)Q state naturally exhibits scalar spin chirality as a mechanism for the AHE and expands the classification of multi-Q magnetic phases.

Kruppe, Jonathon [University of California, Berkel↗

DOE OSTI2023

Structure of LARP7 Protein p65–telomerase RNA Complex in Telomerase Revealed by Cryo-EM and NMR

La-related protein 7 (LARP7) are a family of RNA chaperones that protect the 3'-end of RNA and are components of specific ribonucleoprotein complexes (RNP). Here, in Tetrahymena thermophila telomerase, LARP7 protein p65 together with telomerase reverse transcriptase (TERT) and telomerase RNA (TER) form the core RNP. p65 has four known domains—N-terminal domain (NTD), La motif (LaM), RNA recognition motif 1 (RRM1), and C-terminal xRRM2. To date, only the xRRM2 and LaM and their interactions with TER have been structurally characterized. Conformational dynamics leading to low resolution in cryo-EM density maps have limited our understanding of how full-length p65 specifically recognizes and remodels TER for telomerase assembly. Here, we combined focused classification of Tetrahymena telomerase cryo-EM maps with NMR spectroscopy to determine the structure of p65–TER. Three previously unknown helices are identified, one in the otherwise intrinsically disordered NTD that binds the La module, one that extends RRM1, and another preceding xRRM2, that stabilize p65–TER interactions. The extended La module (αN, LaM and RRM1) interacts with the four 3' terminal U nucleotides, while LaM and αN additionally interact with TER pseudoknot, and LaM with stem 1 and 5' end. Our results reveal the extensive p65–TER interactions that promote TER 3'-end protection, TER folding, and core RNP assembly and stabilization. The structure of full-length p65 with TER also sheds light on the biological roles of genuine La and LARP7 proteins as RNA chaperones and core RNP components.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Unified architecture for data-driven metadata tagging of building automation systems

This article presents a Unified Architecture (UA) for automated point tagging of Building Automation System (BAS) data, based on a combination of data-driven approaches. Advanced energy analytics applications—including fault detection and diagnostics and supervisory control—have emerged as a significant opportunity for improving the performance of our built environment. Effective application of these analytics depends on harnessing structured data from the various building control and monitoring systems, but typical BAS implementations do not employ any standardized metadata schema. While standards such as Project Haystack and Brick Schema have been developed to address this issue, the process of structuring the data, i.e., tagging the points to apply a standard metadata schema, has, to date, been a manual process. This process is typically costly, labor-intensive, and error-prone. In this work we address this gap by proposing a UA that automates the process of point tagging by leveraging the data accessible through connection to the BAS, including time-series data and the raw point names. The UA intertwines supervised classification and unsupervised clustering techniques from machine learning and leverages both their deterministic and probabilistic outputs to inform the point tagging process. Furthermore, we extend the UA to embed additional input and output data-processing modules that are designed to address the challenges associated with the real-time deployment of this automation solution. We test the UA on two datasets for real-life buildings: (i) commercial retail buildings and (ii) office buildings from the National Renewable Energy Laboratory (NREL) campus. We report the proposed methodology correctly applied 85–90% and 70–75% of the tags in each of these test scenarios, respectively for two significantly different building types used for testing UA's fully-functional prototype. The proposed UA, therefore, offers promising approach for automatically tagging BAS data as it reaches close to 90% accuracy. Further building upon this framework to algorithmically identify the equipment type and their relationships is an apt future research direction to pursue.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Jet classification using high-level features from anatomy of top jets

Recent advancements in deep learning models have significantly enhanced jet classification performance by analyzing low-level features (LLFs). However, this approach often leads to less interpretable models, emphasizing the need to understand the decision-making process and to identify the high-level features (HLFs) crucial for explaining jet classification. To address this, we consider the top jet tagging problems and introduce an analysis model (AM) that analyzes selected HLFs designed to capture important features of top jets. Our AM mainly consists of the following three modules: a relation network analyzing two-point energy correlations, mathematical morphology and Minkowski functionals for generalizing jet constituent multiplicities, and a recursive neural network analyzing subjet constituent multiplicity to enhance sensitivity to subjet color charges. We demonstrate that our AM achieves performance comparable to the Particle Transformer (ParT) while requiring fewer computational resources in a comparison of top jet tagging using jets simulated at the hadronic calorimeter angular resolution scale. Furthermore, as a more constrained architecture than ParT, the AM exhibits smaller training uncertainties because of the bias-variance tradeoff. We also compare the information content of AM and ParT by decorrelating the features already learned by AM. Lastly, we briefly comment on the results of AM with finer angular resolution inputs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2018

Characteristic Atmospheric Radiative Heating Rate Profiles in Arctic Clouds as Observed at Barrow, Alaska

Abstract A 2-yr cloud microphysical property dataset derived from ground-based remote sensors at the Atmospheric Radiation Measurement site near Barrow, Alaska, was used as input into a radiative transfer model to compute radiative heating rate (RHR) profiles in the atmosphere. Both the longwave (LW; 5–100 μm) and shortwave (SW; 0.2–5 μm) RHR profiles show significant month-to-month variability because of seasonal dependence in the vertical profiles of cloud liquid and ice water contents, with additional contributions from the seasonal dependencies of solar zenith angle, water vapor amount, and temperature. The LW and SW RHR profiles were binned to provide characteristic profiles as a function of cloud type and liquid water path (LWP). Single-layer liquid-only clouds are shown to have larger (10–30 K day−1) LW radiative cooling rates at the top of the cloud layer than single-layer mixed-phase clouds; this is due primarily to differences in the vertical distribution of liquid water between the two classes. However, differences in SW RHR profiles at the top of these two classes of clouds are less than 3 K day−1. The absolute value of the RHR in single-layer ice-only clouds is an order of magnitude smaller than in liquid-bearing clouds. Furthermore, for double-layer cloud systems, the phase and condensed water path of the upper cloud strongly modulate the radiative cooling both at the top and within the lower-level cloud. While sensitivity to cloud overlap and phase has been shown previously, the characteristic RHR profiles are markedly different between the different cloud classifications.

54 ENVIRONMENTAL SCIENCES↗