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DOE OSTI2024

Edge supercurrent reveals competition between condensates in a Weyl superconductor

In topological materials, the edge states are readily distinguished from the bulk states. The situation where a topological semimetal becomes superconducting so that Cooper pairs occupy both the bulk and the edge states is not well understood. In particular, we do not know if we can force their pairing symmetries to be different. Here we show that, when supercurrent is injected into the superconducting Weyl semimetal MoTe 2 from Nb contacts, the invasive s-wave pairing potential from Nb is incompatible with the intrinsic Cooper pair condensate in MoTe 2 . This incompatibility leads to strong stochasticity in the switching current and an unusual anti-hysteretic behaviour in the current–voltage loops. There is also an asymmetry in the edge oscillations where, as the magnetic field crosses zero, the phase noise switches from one with a noisy spectrum to one that is noise free. Furthermore, using the noise spectrum as a guide, we track the anomalous features to field-induced switching of the device gap function between s-wave symmetry and the unconventional symmetry intrinsic to MoTe 2 . We infer that the behaviour of the gap function along the edges is different from that in the bulk.

Superconducting properties and materials↗

DOE OSTI2024

The trailing edge is everywhere: tree rings reveal the transient risk of extinction hidden inside climate envelope forecasts

Given the importance of climate in shaping species’ geographic distributions, climate change poses an existential threat to biodiversity. Climate envelope modeling, the predominant approach used to quantify this threat, presumes that individuals in populations respond to climate variability and change according to species-level responses inferred from spatial occurrence data—such that individuals at the cool edge of a species’ distribution should benefit from warming (the “leading edge”), whereas individuals at the warm edge should suffer (the “trailing edge”). Using 1,558 tree-ring time series of an aridland pine (Pinus edulis) collected at 977 locations across the species’ distribution, we found that trees everywhere grow less in warmer-than-average and drier-than-average years. Ubiquitous negative temperature sensitivity indicates that individuals across the entire distribution should suffer with warming—the entire distribution is a trailing edge. Species-level responses to spatial climate variation are opposite in sign to individual-scale responses to time-varying climate for approximately half the species’ distribution with respect to temperature and the majority of the species’ distribution with respect to precipitation. These findings, added to evidence from the literature for scale-dependent climate responses in hundreds of species, suggest that correlative, equilibrium-based range forecasts may fail to accurately represent how individuals in populations will be impacted by changing climate. A scale-dependent view of the impact of climate change on biodiversity highlights the transient risk of extinction hidden inside climate envelope forecasts and the importance of evolution in rescuing species from extinction whenever local climate variability and change exceeds individual-scale climate tolerances.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1994

Spatial variability in the seasonal south polar cap of Mars

The first comprehensive discussion of the south seasonal polar cap spectra obtained by the Mariner 7 infrared spectrometer in the short-wavelength region (2-4 microns) is presented. The infrared spectra is correlated with images acquired by the wide-angle camera. Significant spectral variation is noted in the cap interior and regions of varying water frost abundance, CO2 ice/frost cover, and CO2-ice path length can be distinguished. Many of these spectral variations correlate with heterogeneity noted in the camera images, but certain significant infrared spectral variations are not discernible in the visible. Simple reflectance models are used to classify the observed spectral variations into four regions. Region I is at the cap edge, where there is enhanced absorption beyond 3 microns inferred to be caused by an increased abundance of water frost. The increase in water abundance over that in the interior is on the level of a few parts per thousand or less. Region II is the typical cap interior characterized by spectral features of CO2 ice at grain sizes of several millimeters to centimeters. These spectra also indicate the presence of water frost at the parts per thousand level. A third, unusual region (III), is defined by three spectra in which weak CO2 absorption features are as much as twice as strong as in the average cap spectra and are assumed to be caused by an increased path length in the CO2. Such large paths are inconsistent with the high reflectance in the visible and at 2.2 microns and suggest layered structures or deposition conditions that are not accounted for in current reflectance models. The final region (IV) is an area of thinning frost coverage or transparent ice well in the interior of the seasonal cap. These spectra are a combination of CO2 and ground signatures.

Calvin, Wendy M.↗

DOE OSTI2026

Two-Stage Wildlife Event Classification for Edge Deployment

Camera-based wildlife monitoring is often overwhelmed by non-target triggers and slowed by manual review or cloud-dependent inference, which can prevent timely intervention for high stakes human–wildlife conflicts. Our key contribution is a deployable, fully offline edge vision sensor that achieves near-real-time, highly accurate wildlife event classification by combining detector-based empty-image suppression with a lightweight classifier trained with a staged transfer-learning curriculum. Specifically, Stage 1 uses a pretrained You Only Look Once (YOLO)-family detector for permissive animal localization and empty-trigger suppression, and Stage 2 uses a lightweight EfficientNet-based binary classifier to confirm puma on detector crops and gate downstream actions. Our design is robust to low-quality nighttime monochrome imagery (motion blur, low contrast, illumination artifacts, and partial-body captures) and operates using commercially available components in connectivity-limited settings. In field deployments running since May 2025, end-to-end latency from camera trigger to action command is approximately 4 s. Ablation studies using a dataset of labeled wildlife images (pumas, not pumas) show that the two-stage approach substantially reduces false alarms in identifying pumas relative to a full-image classifier while maintaining high recall. On the held-out test set (N = 1434 events), the proposed two-stage cascade achieves precision 0.983, recall 0.975, F1 0.979, accuracy 0.986, and balanced accuracy 0.983, with only 8 false positives and 12 false negatives. The system can be easily adapted for other species, as demonstrated by rapid retraining of the second stage to classify ringtails. Downstream responses (e.g., notifications and optional audio/light outputs) provide flexible actuation capabilities that can be configured to support intervention.

58 GEOSCIENCES↗

DOE OSTI2022

CrossSim Inference Manual v2.0

Neural networks are largely based on matrix computations. During forward inference, the most heavily used compute kernel is the matrix-vector multiplication (MVM): $W \vec{x} $. Inference is a first frontier for the deployment of next-generation hardware for neural network applications, as it is more readily deployed in edge devices, such as mobile devices or embedded processors with size, weight, and power constraints. Inference is also easier to implement in analog systems than training, which has more stringent device requirements. The main processing kernel used during inference is the MVM.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

An ontology-based knowledge graph for representing interactions involving RNA molecules

The "RNA world" represents a novel frontier for the study of fundamental biological processes and human diseases and is paving the way for the development of new drugs tailored to each patient's biomolecular characteristics. Although scientific data about coding and non-coding RNA molecules are constantly produced and available from public repositories, they are scattered across different databases and a centralized, uniform, and semantically consistent representation of the "RNA world" is still lacking. We propose RNA-KG, a knowledge graph (KG) encompassing biological knowledge about RNAs gathered from more than 60 public databases, integrating functional relationships with genes, proteins, and chemicals and ontologically grounded biomedical concepts. To develop RNA-KG, we first identified, pre-processed, and characterized each data source; next, we built a meta-graph that provides an ontological description of the KG by representing all the bio-molecular entities and medical concepts of interest in this domain, as well as the types of interactions connecting them. Finally, we leveraged an instance-based semantically abstracted knowledge model to specify the ontological alignment according to which RNA-KG was generated. RNA-KG can be downloaded in different formats and also queried by a SPARQL endpoint. A thorough topological analysis of the resulting heterogeneous graph provides further insights into the characteristics of the "RNA world". RNA-KG can be both directly explored and visualized, and/or analyzed by applying computational methods to infer bio-medical knowledge from its heterogeneous nodes and edges. The resource can be easily updated with new experimental data, and specific views of the overall KG can be extracted according to the bio-medical problem to be studied.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Physics consistent machine learning framework for inverse modeling with applications to ICF capsule implosions

In high energy density physics (HEDP) and inertial confinement fusion (ICF), predictive modeling is complicated by uncertainty in parameters that characterize various aspects of the modeled system, such as those characterizing material properties, equation of state (EOS), opacities, and initial conditions. Typically, however, these parameters are not directly observable. What is observed instead is a time sequence of radiographic projections using X-rays. In this work, we define a set of sparse hydrodynamic features derived from the outgoing shock profile and outer material edge, which can be obtained from radiographic measurements, to directly infer such parameters. Our machine learning (ML)-based methodology involves a pipeline of two architectures, a radiograph-to-features network (R2FNet) and a features-to-parameters network (F2PNet), that are trained independently and later combined to approximate a posterior distribution for the parameters from radiographs. We show that the machine learning architectures are able to accurately infer initial conditions and EOS parameters, and that the estimated parameters can be used in a hydrodynamics code to obtain density fields, shocks, and material interfaces that satisfy thermodynamic and hydrodynamic consistency. Finally, we demonstrate that features resulting from an unknown EOS model can be successfully mapped onto parameters of a chosen analytical EOS model, implying that network predictions are learning physics, with a degree of invariance to the underlying choice of EOS model. To the best of our knowledge, our framework is the first demonstration of recovering both thermodynamic and hydrodynamic consistent density fields from noisy radiographs.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2012

Dark Material at the Surface of Polar Crater Deposits on Mercury

Earth-based radar measurements [1-3] have yielded images of radar-bright material at the poles of Mercury postulated to be near-surface water ice residing in cold traps on the permanently shadowed floors of polar impact craters. The Mercury Laser Altimeter (MLA) on board the MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) spacecraft has now mapped much of the north polar region of Mercury [4] (Fig. 1). Radar-bright zones lie within polar craters or along poleward-facing scarps lying mainly in shadow. Calculations of illumination with respect to solid-body motion [5] show that at least 0.5% of the surface area north of 75deg N lies in permanent shadow, and that most such permanently shadowed regions (PSRs) coincide with radar-bright regions. MLA transmits a 1064-nm-wavelength laser pulse at 8 Hz, timing the leading and trailing edges of the return pulse. MLA can in some cases infer energy and thereby surface reflectance at the laser wavelength from the returned pulses. Surficial exposures of water ice would be optically brighter than the surroundings, but persistent surface water ice would require temperatures over all seasons to remain extremely low (<110 K). Thermal models [6,7] incorporating direct and scattered radiation, Mercury s eccentric orbit, 3:2 spin-orbit resonance, and near-zero obliquity generally do not support such conditions in all permanently shadowed craters but suggest that water ice buried near the surface (<0.5 m depth) could survive for > 1 Gy. We describe measurements of reflectivity derived from MLA pulse returns. These reflectivity data show that surface materials in the shadowed regions are darker than their surroundings, enough to strongly attenuate or extinguish laser returns. Such measurements appear to rule out widespread surface exposures of water ice. We consider explanations for the apparent low reflectivity of these regions involving other types of volatile deposit.

Neumann, Gregory A.↗

DOE OSTI2024

Pedestal main ion particle transport inference through gas puff modulation with experimental source measurements

Abstract Transport in the DIII-D high confinement mode (H-mode) pedestal is investigated through a periodic edge gas puff modulation (GPM) which perturbs the deuterium density and source profiles. By using absolutely calibrated experimental edge ionization profile measurements, radial profiles of diffusion ( D ) and convection ( v ) are calculated into the pedestal region without depending on modeling the edge ionization source. An analytic approach with closed-form expressions for the D and v profiles and a more advanced Bayesian approach show evidence of an inward particle convection on the order of 1 m s −1 extending to normalized poloidal flux ( Ψ N ) of 0.98. Meanwhile, diffusion reaches a minimum value of ( 0.03 ± 0.02 ) m 2 s −1 in the pedestal region. Notably, the Bayesian approach, which utilizes the Aurora 1.5 D forward model inside the IMPRAD OMFIT module, provides radially resolved transport profiles with associated uncertainty without requiring an explicit form for the perturbation to the density profile or source. The combination of experimental ionization measurements and Bayesian inference provides an enhanced robust framework for investigating edge particle transport coefficients to experimentally test transport physics in order to improve predictive capabilities in the tokamak edge.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Tree rings reveal the transient risk of extinction hidden inside climate envelope forecasts

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Knowledge Graph of RB-Tnseq Data from Fitness Browser (KP-DP1)

Motivation: Predicting microbial gene fitness across environmental conditions remains a central challenge for predictive phenomics and autonomous experimentation. Fitness assays generate large volumes of genotype–phenotype measurements difficult to integrate with experimental metadata and biological function in a form that supports mechanistic reasoning. Knowledge graphs offer a semantic framework for unifying modalities and enabling context-aware inference. Results: We build GIMME (Graph Inference for Microbial Metabolism Exploration), a semantically grounded knowledge graph that unifies gene fitness measurements spanning 10 Pseudomonas species with experimental metadata and biological context. Media are decomposed into chemical components and experiments carry structured links to natural-language descriptions. The resulting graph supports two inference modes: (1) symbolic graph traversal to surface candidate gene–environment and gene–chemical associations, and (2) learned inference using heterogeneous graph neural networks that propagate information across neighborhoods. We formulate link regression over (gene, media, experiment) triplets, combining learned gene embeddings with pretrained LLM sourced text embeddings of node descriptions to predict gene fitness. We then augment a baseline MLP with an auxiliary message-passing encoder (GraphSAGE/GAT) that propagates information over gene–protein–function and media–chemical subgraphs, and fuse the two pathways with a gated residual connection. This approach produces strong agreement with held-out fitness measurements (GraphSAGE Pearson r 0.74) while also highlighting inference challenges in extreme-fitness regimes. We aggregate GAT edge-attention weights by relation type and layer to estimate which biological and environmental relations most influence fitness predictions. Conclusion: This work explores using knowledge graphs as “context graphs” for microbial phenotype prediction. They provide a rich substrate which enables explainable retrieval of supporting evidence, and provides a natural bridge to autonomous workflows that prioritize the next experiment.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Community detection in hypergraphs via mutual information maximization

Abstract The hypergraph community detection problem seeks to identify groups of related vertices in hypergraph data. We propose an information-theoretic hypergraph community detection algorithm which compresses the observed data in terms of community labels and community-edge intersections. This algorithm can also be viewed as maximum-likelihood inference in a degree-corrected microcanonical stochastic blockmodel. We perform the compression/inference step via simulated annealing. Unlike several recent algorithms based on canonical models, our microcanonical algorithm does not require inference of statistical parameters such as vertex degrees or pairwise group connection rates. Through synthetic experiments, we find that our algorithm succeeds down to recently-conjectured thresholds for sparse random hypergraphs. We also find competitive performance in cluster recovery tasks on several hypergraph data sets.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Sentinel

Network intrusion detection systems (NIDS) are commonplace in network security but they frequently employ algorithms that are computational demanding requiring hardware and software with significant power requirements. Two examples of such resource-intensive algorithms used for network security are regular expression matching and broader signature pattern matching which are commonly used in deep packet inspection (DPI). Network security algorithms that have large power requirements may be a challenge for low-power internet-of-things (IoT) environments, which generally lack the power resources to implement complex security measures like computationally expensive DPI at the edge. Furthermore, IoT environments incorporating 5G standalone networks have network latency constraints beyond just power that make DPI at the edge even more difficult. Programmable logic is ideally suited for machine learning inference for DPI because of its deep instruction level parallelism and single-cycle memory access. Machine learning approaches for DPI have been explored before using the programmable logic of field programmable gate arrays (FPGA) as a potential solution for NIDS approaches that would be power-suitable for IoT. However, those previous programmable logic NIDS approaches utilize either a supervised or unsupervised learning model. Sentinel utilizes the ensemble of these two machine learning approaches known as a semi-supervised approach which has shown promise in NIDS implementations. Sentinel provides a programmable logic implementation of a semi-supervised approach for DPI which operates at much lower power and latency than a GPU implementation with negligible loss of accuracy due to quantization through a logistic regressor.

Anderson, MatthewW [Idaho National Laboratory (INL↗

DOE OSTI2024

Disentangling the Black Hole Mass Spectrum with Photometric Microlensing Surveys

Abstract From the formation mechanisms of stars and compact objects to nuclear physics, modern astronomy frequently leverages surveys to understand populations of objects to answer fundamental questions. The population of dark and isolated compact objects in the Galaxy contains critical information related to many of these topics, but is only practically accessible via gravitational microlensing. However, photometric microlensing observables are degenerate for different types of lenses, and one can seldom classify an event as involving either a compact object or stellar lens on its own. To address this difficulty, we apply a Bayesian framework that treats lens type probabilistically and jointly with a lens population model. This method allows lens population characteristics to be inferred despite intrinsic uncertainty in the lens class of any single event. We investigate this method’s effectiveness on a simulated ground-based photometric survey in the context of characterizing a hypothetical population of primordial black holes (PBHs) with an average mass of 30 M ⊙ . On simulated data, our method outperforms current black hole (BH) lens identification pipelines and characterizes different subpopulations of lenses while jointly constraining the PBH contribution to dark matter to ≈25%. Key to robust inference, our method can marginalize over population model uncertainty. We find the lower mass cutoff for stellar origin BHs, a key observable in understanding the BH mass gap, particularly difficult to infer in our simulations. This work lays the foundation for cutting-edge PBH abundance constraints to be extracted from current photometric microlensing surveys.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

NASA NTRS1986

Concentrated mass effects on the flutter of a composite advanced turboprop model

The effects on bending-torsion flutter due to the addition of a concentrated mass to an advanced turboprop model blade with rigid hub are studied. Specifically the effects of the magnitude and location of added mass on the natural frequencies, mode shapes, critical interblade phase angle, and flutter Mach number are analytically investigated. The flutter of a propfan model is shown to be sensitive to the change in mass distribution. Static unbalance effects, like those for fixed wings, were shown to occur as the concentrated mass was moved from the leading edge to the trailing edge with the exception of one mass location. Mass balancing is also inferred to be a feasible method for increasing the flutter speed.

Ramsey, J. K.↗

NASA NTRS2009

Suzaku View of the Swift/BAT Active Galactic Nuclei (I): Spectral Analysis of Six AGNs and Evidence for Two Types of Obscured Population

We present a systematic spectral analysis with Suzaku of six AGNs detected in the Swift/BAT hard X-ray (15-200 keV) survey, Swift J0138.6-4001, J0255.2-0011, J0350.1-5019, J0505.7-2348, J0601.9-8636, and J1628.1-5145. This is considered to be a representative sample of new AGNs without X-ray spectral information before the BAT survey. We find that the 0.5-200 keV spectra of these sources can be uniformly fit with a base model consisting of heavily absorbed (log NH >23.5/sq cm) transmitted components, scattered lights, a reflection component, and an iron-K emission line. There are two distinct groups, three "new type" AGNs (including the two sources reported by Ueda et al. 2007) with an extremely small scattered fraction (f(sub scat) < 0:5%) and strong reflection component (R = omega/2pi > or equal to 0.8 where omega is the solid angle of the reflector), and three "classical type" ones with f(sub scat > 0.5% and R < or approx. 0.8. The spectral parameters suggest that the new type has an optically thick torus for Thomson scattering (N(sub H) approx. 10(exp 25)/sq cm) with a small opening angle theta approx. 20deg viewed in a rather face-on geometry, while the classical type has a thin torus (N(sub H) approx. 10(exp 23-24)/sq cm) with theta > or approx. 30deg. We infer that a significant number of new type AGNs with an edge-on view is missing in the current all-sky hard X-ray surveys. Subject headings: galaxies: active . gamma rays: observations . X-rays: galaxies . X-rays: general

Eguchi, Satoshi↗

DOE OSTI2022

Analyzing Data Privacy for Edge Systems

Internet-of-Things (IoT)-based streaming applications are all around us. Currently, we are transitioning from IoT processing being performed on the cloud to the edge. While moving to the edge provides significant networking efficiency benefits, IoT edge computing creates significant data privacy concerns. We propose a methodology that can successfully privacy protect the continual data streams generated by sensors on the edge device. We implement local differential privacy on streaming data and incorporate Bayesian inference and Gaussian process to evaluate the privacy policy. We demonstrate our methodology on a real-world smart meter testbed and identify the optimal privacy protection settings.

Kotevska, Olivera↗

NASA NTRS1980

On the polarization state of hydromagnetic fluctuations in the solar wind

From presently available observations it can be inferred that the Alfvenic turbulence measured in the solar wind, predominantly on trailing edges of high speed streams, is a mixture of modes with two different polarizations, namely, Alfvenic modes and modes which are the incompressible limit of slow magnetosonic waves. Using Helios 2 magnetic data and a variance analysis, parallel (to the mean field) and perpendicular components of the fluctuations are separated and the possible correlation between such components is studied. Correlations between eigenvalues of the variance matrix are also investigated and discussed.

Bavassano, B.↗