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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Graph neural networks for detecting anomalies in scientific workflows

Identifying and addressing anomalies in complex, distributed systems can be challenging for reliable execution of scientific workflows. We model these workflows as directed acyclic graphs (DAGs), where the nodes and edges of the DAGs represent jobs and their dependencies, respectively. We develop graph neural networks (GNNs) to learn patterns in the DAGs and to detect anomalies at the node (job) and graph (workflow) levels. We investigate workflow-specific GNN models that are trained on a particular workflow and workflow-agnostic GNN models that are trained across the workflows. Our GNN models, which incorporate both individual job features and topological information from the workflow, show improved accuracy and efficiency compared to conventional learning methods for detecting anomalies. While joint trained with multiple scientific workflows, our GNN models reached an accuracy more than 80% for workflow level and 75% for job level anomalies. In addition, we illustrate the importance of hyperparameter tuning method in our study that can significantly improve the metric(s) measure of evaluating the GNN models. Finally, we integrate explainable GNN methods to provide insights on job features in the workflow that cause an anomaly.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

ClimateNet: an expert-labeled open dataset and deep learning architecture for enabling high-precision analyses of extreme weather

Abstract. Identifying, detecting, and localizing extreme weather events is a crucial first step in understanding how they may vary under different climate change scenarios. Pattern recognition tasks such as classification, object detection, and segmentation (i.e., pixel-level classification) have remained challenging problems in the weather and climate sciences. While there exist many empirical heuristics for detecting extreme events, the disparities between the output of these different methods even for a single event are large and often difficult to reconcile. Given the success of deep learning (DL) in tackling similar problems in computer vision, we advocate a DL-based approach. DL, however, works best in the context of supervised learning – when labeled datasets are readily available. Reliable labeled training data for extreme weather and climate events is scarce. We create “ClimateNet” – an open, community-sourced human-expert-labeled curated dataset that captures tropical cyclones (TCs) and atmospheric rivers (ARs) in high-resolution climate model output from a simulation of a recent historical period. We use the curated ClimateNet dataset to train a state-of-the-art DL model for pixel-level identification – i.e., segmentation – of TCs and ARs. We then apply the trained DL model to historical and climate change scenarios simulated by the Community Atmospheric Model (CAM5.1) and show that the DL model accurately segments the data into TCs, ARs, or “the background” at a pixel level. Further, we show how the segmentation results can be used to conduct spatially and temporally precise analytics by quantifying distributions of extreme precipitation conditioned on event types (TC or AR) at regional scales. The key contribution of this work is that it paves the way for DL-based automated, high-fidelity, and highly precise analytics of climate data using a curated expert-labeled dataset – ClimateNet. ClimateNet and the DL-based segmentation method provide several unique capabilities: (i) they can be used to calculate a variety of TC and AR statistics at a fine-grained level; (ii) they can be applied to different climate scenarios and different datasets without tuning as they do not rely on threshold conditions; and (iii) the proposed DL method is suitable for rapidly analyzing large amounts of climate model output. While our study has been conducted for two important extreme weather patterns (TCs and ARs) in simulation datasets, we believe that this methodology can be applied to a much broader class of patterns and applied to observational and reanalysis data products via transfer learning.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Enamel ribbons, surface nodules, and octacalcium phosphate in C57BL/6 Amelx -/- mice and Amelx +/- lyonization

Amelogenin is required for normal enamel formation and is the most abundant protein in developing enamel. Methods. Amelx +/+ , Amelx +/- and Amelx -/- molars and incisors from C57BL/6 mice were characterized using RT-PCR, Western blotting, dissecting and light microscopy, immunohistochemistry (IHC), transmission electron microscopy (TEM), scanning electron microscopy (SEM), backscattered SEM (bSEM), nanohardness testing, and X-ray diffraction. No amelogenin protein was detected by Western blot analyses of enamel extracts from Amelx -/- mice. Amelx -/- incisor enamel averaged 20.3±3.3 μm in thickness, or only 1/6th that of the wild-type (122.3±7.9 μm). Amelx -/- incisor enamel nanohardness was 1.6 Gpa, less than half that of wild-type enamel (3.6 Gpa). Amelx +/- incisors and molars showed vertical banding patterns unique to each tooth. IHC detected no amelogenin in Amelx -/- enamel and varied levels of amelogenin in Amelx +/- incisors, which correlated positively with enamel thickness, strongly supporting lyonization as the cause of the variations in enamel thickness. TEM analyses showed characteristic mineral ribbons in Amelx +/+ and Amelx -/- enamel extending from mineralized dentin collagen to the ameloblast. The Amelx -/- enamel ribbons were not well-separated by matrix and appeared to fuse together, forming plates. Furthermore, x-ray diffraction determined that the predominant mineral in Amelx -/- enamel is octacalcium phosphate (not calcium hydroxyapatite). Amelx -/- ameloblasts were similar to wild-type ameloblasts except no Tomes’ processes extended into the thin enamel. Amelx -/- and Amelx +/- molars both showed calcified nodules on their occlusal surfaces. Histology of D5 and D11 developing molars showed nodules forming during the maturation stage. Amelogenin forms a resorbable matrix that separates and supports, but does not shape early secretory stage enamel ribbons. Amelogenin may facilitate the conversion of enamel ribbons into hydroxyapatite by inhibiting the formation of octacalcium phosphate. Finally, amelogenin is necessary for thickening the enamel layer, which helps maintain ribbon organization and development and maintenance of the Tomes process.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Trends in atmospheric patterns conducive to seasonal precipitation and temperature extremes in California

Recent evidence suggests that changes in atmospheric circulation have altered the probability of extreme climate events in the Northern Hemisphere. We investigate northeastern Pacific atmospheric circulation patterns that have historically (1949–2015) been associated with cool-season (October-May) precipitation and temperature extremes in California. We identify changes in occurrence of atmospheric circulation patterns by measuring the similarity of the cool-season atmospheric configuration that occurred in each year of the 1949–2015 period with the configuration that occurred during each of the five driest, wettest, warmest, and coolest years. Our analysis detects statistically significant changes in the occurrence of atmospheric patterns associated with seasonal precipitation and temperature extremes. We also find a robust increase in the magnitude and subseasonal persistence of the cool-season West Coast ridge, resulting in an amplification of the background state. Changes in both seasonal mean and extreme event configurations appear to be caused by a combination of spatially nonuniform thermal expansion of the atmosphere and reinforcing trends in the pattern of sea level pressure. In particular, both thermal expansion and sea level pressure trends contribute to a notable increase in anomalous northeastern Pacific ridging patterns similar to that observed during the 2012–2015 California drought. Collectively, our empirical findings suggest that the frequency of atmospheric conditions like those during California’s most severely dry and hot years has increased in recent decades, but not necessarily at the expense of patterns associated with extremely wet years.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

AHR and CYP1A expression link historical contamination events to modern day developmental effects in the American alligator

The aryl hydrocarbon receptor (AHR) is a ligand-activated transcription factor that initiates a transcriptional pathway responsible for the expression of CYP1A subfamily members, key to the metabolism of xenobiotic compounds. Toxic planar halogenated aromatic hydrocarbons, including dioxin and PCBs, are capable of activating the AHR, and while dioxin and PCB inputs into the environment have been dramatically curbed following strict regulatory efforts in the United States, they are still detected in the environment and exposures remain relevant today. Little is known regarding the effects that long-term chronic exposures to dioxin or dioxin-like compounds might have on the development and subsequent health of offspring from exposed individuals, nor is much known regarding AHR expression in reptilians. In this work, we characterize AHR and CYP1A gene expression in embryonic and juvenile specimen of a long-lived, apex predator, the American alligator (Alligator mississippiensis), and investigate variation in gene expression profiles in offspring collected from sites in the southeastern U.S. conveying differential exposures to environmental contaminants. Both age- and tissue-dependent patterning of AHR isoform expression was detected; receptor isoforms are highly expressed in liver, kidney, thyroid, and intestine. We then characterized two downstream transcriptional targets of the AHR, CYP1A1 and CYP1A2, and describe conserved elements of their genomic architecture. When comparisons across different sites were made, hepatic expression of CYP1A2, a direct target of the AHR, was elevated in embryos from a site associated with a dioxin point source and previously characterized PCB contamination. These effects were not persistent, as site-specific variation was absent in juveniles originating from field-collected eggs but reared under lab conditions. Our results illustrate the patterning of AHR gene expression in a long-lived environmental model species, and indicate a potential contemporary influence of historical contamination. This research presents a novel opportunity to link historical contamination events to contemporary effects on critical genetic pathways during embryonic development, and carries significant potential to inform our understanding of potential health effects in wildlife and humans.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2022

High-dimensional methods for quantum homodyne tomography

We provide optimized recursion relations for homodyne tomography. We improve previous methods by mitigating the divergences intrinsic in the calculation of the pattern functions used previously, and detail how to implement the data analysis through Monte Carlo simulations. Our refinements are necessary for the reconstruction of excited quantum states which populate a high-dimensional subspace of the electromagnetic field Hilbert space. •Stabilization of numerics for quantum homodyne tomography reconstructions.•High dimensional quantum homodyne tomography reconstruction.•Monte Carlo simulations of quantum homodyne tomography.•Quantum Homodyne tomography simulations.

Mosco, Nicola↗
DOE OSTI2021

Stabilization of the 81-channel coherent beam combination using machine learning

We develop a rapidly converging algorithm for stabilizing a large channel-count diffractive optical coherent beam combination. An 81-beam combiner is controlled by a novel, machine-learning based, iterative method to correct the optical phases, operating on an experimentally calibrated numerical model. A neural-network is trained to detect phase errors based on interference pattern recognition of uncombined beams adjacent to the combined one. Due to the non-uniqueness of solutions in the full space of possible phases, the network is trained within a limited phase perturbation/error range. This also reduces the number of samples needed for training. Simulations have proven that the network can converge in one step for small phase perturbations. When the trained neural-network is applied to a realistic case of 360 degree full range, an iterative scheme exploits random walking at the beginning, with the accuracy of prediction on phase feedback direction, to allow the neural-network to step into the training range for fast convergence. This neural-network-based iterative method of phase detection works tens of times faster than the commonly used stochastic parallel gradient descent approach (SPGD) using a single-detector and random dither when both are tested with random phase perturbations.

Wang, Dan↗
DOE OSTI2015

An "artificial retina" processor for track reconstruction at the full LHC crossing rate

Here, we present the latest results of an R&D; study for a specialized processor capable of reconstructing, in a silicon pixel detector, high-quality tracks from high-energy collision events at 40 MHz. The processor applies a highly parallel pattern-recognition algorithm inspired to quick detection of edges in mammals visual cortex. After a detailed study of a real-detector application, demonstrating that online reconstruction of offline-quality tracks is feasible at 40 MHz with sub-microsecond latency, we are implementing a prototype using common high-bandwidth FPGA devices.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Future SDN-HPON Control Plane Architecture and Protocol for On-Demand Terabit End-to-End Extreme-Scale Science Applications

The UC Davis team conducted architecture, algorithm and experimental studies for next generation ultra-high-bandwidth optical networks in support of extreme-scale science applications. In particular, over the course of this 3-year project, the UC Davis team achieved the following main accomplishments: Designed a network architecture for automated multi-domain service provisioning: Designed information exchange schemes between autonomous systems (domains) Designed application programming interfaces to orchestrate resource reservation in data centers, high performance computation facilities, and communication networks belonging to different autonomous systems Experimentally assessed the multi-domain network architecture in a distributed field trial set-up connecting premises in three continents Designed a broker-based orchestrated multi-domain optical networking framework supporting observe-analyze-act cycle based cognitive inter-domain service provisioning: Designed an inter-domain operation mechanism, e.g., topology abstraction, for the broker plane and domain managers to collaboratively accomplish inter-domain service provisioning while respecting the autonomy of each administrative domain Designed an alien wavelength monitoring scheme and an artificial neural network based optical signal-to-noise ratio estimator for quality-of-transmission aware inter-domain service provisioning Developed a deep neural network based multi-domain traffic predictor and a cognitive routing and spectrum assignment algorithm to realize knowledge-based autonomous traffic engineering in multi-domain networks Implemented an optical signal-to-noise ratio estimator and traffic predictor with Google’s Tensor flow and assessed their performance using experimental and synthetic data Demonstrated the benefits of the proposed cognitive schemes with experiments ona two-domain elastic optical network testbed and evaluations on a six-domain simulation framework Built a two-domain software-defined networking control plane testbed using the ONOS platform and demonstrated the workflow of broker and domain manager cooperation, including hierarchical performance monitoring, topology abstraction and cognitive resource allocation. Designed self-learning cognitive service provisioning schemes using advanced machine learning technologies: Designed a self-taught anomaly detection framework for optical networks, including an unsupervised data clustering module for pattern recognition, a supervised data regression and classification module for online anomaly detection, and the system operation principle Experimentally validated the effectiveness of the self-taught anomaly detection framework with various failure/anomaly scenarios. Designed a self-learning autonomic routing, modulation and spectrum assignment (RMSA) provisioning framework for elastic optical networks with deep reinforcement learning. Designed effective modeling and training mechanisms for the self-learning RMSA framework Conducted extensive numerical simulations with various traffic patterns and topologies to verify the advantage of the self-learning RMSA framework.

42 ENGINEERING↗
DOE OSTI2020

Data for Training and Testing Radiation Detection Algorithms in an Urban Environment

The US government routinely performs radiological response deployments to search for the presence of illicit nuclear materials (e.g., highly enriched uranium and weapons-grade plutonium) in a specified area. The deployments can be intelligence driven, in support of law enforcement, and for planned events such as WrestleMania, presidential inaugurations, or political conventions. In a typical deployment, radiation detection systems carried by human operators or mounted on vehicles move in a clearing pattern through the search area. Search teams rely on radiation detection algorithms running on these systems in real time to alert them to the presence of an illicit threat source. The detection and identification of sources is complicated by large variation of natural radiation background throughout a search area and the potential presence of localized non-threat sources such as patients undergoing treatment with medical isotopes. As a result, detection algorithms must be carefully balanced between missing real sources (false negatives) and reporting too many false alarms (false positives).The purpose of this data set is to spur innovations in detecting, identifying, and localizing nuclear materials inurban search missions.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2016

Isolation, structural analysis, and expression characteristics of the maize nuclear factor Y gene families

NUCLEAR FACTOR-Y (NF-Y) has been shown to play an important role in growth, development, and response to environmental stress. A NF-Y complex, which consists of three subunits, NF-YA, NF-YB, and, NF-YC, binds to CCAAT sequences in a promoter to control the expression of target genes. Although NF-Y proteins have been reported in Arabidopsis and rice, a comprehensive and systematic analysis of ZmNF-Y genes has not yet been performed. To examine the functions of ZmNF-Y genes in this family, we isolated and characterized 50 ZmNF-Y (14 ZmNF-YA, 18 ZmNF-YB, and 18 ZmNF-YC) genes in an analysis of the maize genome. The 50 ZmNF-Y genes were distributed on all 10 maize chromosomes, and 12 paralogs were identified. Multiple alignments showed that maize ZmNF-Y family proteins had conserved regions and relatively variable N-terminal or C-terminal domains. The comparative syntenic map illustrated 40 paralogous NF-Y gene pairs among the 10 maize chromosomes. Microarray data showed that the ZmNF-Y genes had tissue-specific expression patterns in various maize developmental stages and in response to biotic and abiotic stresses. The results suggested that ZmNF-YB2, 4, 8, 10, 13, and 16 and ZmNF-YC6, 8, and 15 were induced, while ZmNF-YA1, 3, 4, 6, 7, 10, 12, and 13, ZmNF-YB15, and ZmNF-YC3 and 9 were suppressed by drought stress. ZmNF-YA3, ZmNF-YA8 and ZmNF-YA12 were upregulated after infection by the three pathogens, while ZmNF-YA1 and ZmNF-YB2 were suppressed. These results indicate that the ZmNF-Ys may have significant roles in the response to abiotic and biotic stresses. - Highlights: • We indicated a total of 50 members of ZmNF-Y gene family in maize genome. • We analyzed gene structure, protein architecture of ZmNF-Y genes. • Evolution pattern and phylogenic relationships were analyzed among 50 ZmNF-Y genes. • Expression pattern of ZmNF-Ys were detected in various maize tissues. • Transcript levels of ZmNF-Ys were measured under various abiotic and biotic stresses.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Identification of Mercury and Dissolved Organic Matter Complexes Using Ultrahigh Resolution Mass Spectrometry

The chemical speciation and bioavailability of mercury (Hg) is markedly influenced by its complexation with naturally dissolved organic matter (DOM) in aquatic environments. To date, however, analytical methodologies capable of identifying such complexes are scarce. Here in this paper, we utilize ultrahigh resolution Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) coupled with electrospray ionization to identify individual Hg–DOM complexes. The measurements were performed by direct infusion of DOM in a 1:1 methanol:water solution at a Hg to dissolved organic carbon (DOC) molar ratio of 3 × 10 –4 . Heteroatomic molecules, especially those containing multiple S and N atoms, were found to be among the most important in forming strong complexes with Hg. Major Hg–DOM complexes of C 10 H 21 N 2 S 4 Hg + and C 8 H 17 N 2 S 4 Hg + were identified based on both the exact molecular mass and patterns of Hg stable isotope distributions detected by FTICR-MS. Density functional theory was used to predict the solution-phase structures of candidate molecules. Finally, these findings represent the first step to unambiguously identify specific DOM molecules in Hg binding, although future studies are warranted to further optimize and validate the methodology so as to explore detailed molecular compositions and structures of Hg–DOM complexes that affect biological uptake and transformation of Hg in the environment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Unsupervised classification for region of interest in X-ray ptychography

X-ray ptychography offers high-resolution imaging of large areas at a high computational cost due to the large volume of data provided. To address the cost issue, we propose a physics-informed unsupervised classification algorithm that is performed prior to reconstruction and removes data outside the region of interest (RoI) based on the multimodal features present in the diffraction patterns. The preprocessing time for the proposed method is inconsequential in contrast to the resource-intensive reconstruction process, leading to an impressive reduction in the data workload to a mere 20% of the initial dataset. This capability consequently reduces computational time dramatically while preserving reconstruction quality. Through further segmentation of the diffraction patterns, our proposed approach can also detect features that are smaller than beam size and correctly classify them as within the RoI.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Influence of the Ag concentration on the medium-range order in a CuZrAlAg bulk metallic glass

Fluctuation electron microscopy of bulk metallic glasses of CuZrAl(Ag) demonstrates that medium-range order is sensitive to minor compositional changes. Furthermore, by analyzing nanodiffraction patterns medium-range order is detected with crystal-like motifs based on the B2 CuZr structure and its distorted structures resembling the martensitic ones. This result thus demonstrates some structural homology between the metallic glass and its high temperature crystalline phase. The amount of medium-range order seems slightly affected with increasing Ag concentration (0, 2, 5 at.%) but the structural motifs of the medium-range ordered clusters become more diverse at the highest Ag concentration. The decrease of dominant clusters is consistent with the destabilization of the B2 structure measured by calorimetry and accounts for the increased glass-forming ability.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Local structure of ion pair interaction in lapatinib amorphous dispersions characterized by synchrotron x-ray diffraction and pair distribution function analysis

For many years, the idea of analyzing atom-atom contacts in amorphous drug-polymer systems has been of major interest, because this method has always had the potential to differentiate between amorphous systems with domains and amorphous systems which are molecular mixtures. In this study, local structure of ionic and noninonic interactions were studied by High-Energy X-ray Diffraction and Pair Distribution Function (PDF) analysis in amorphous solid dispersions of lapatinib in hypromellose phthalate (HPMCP) and hypromellose (HPMC-E3). The strategy of extracting lapatinib intermolecular drug interactions from the total PDF x-ray pattern was successfully applied allowing the detection of distinct nearest neighbor contacts for the HPMC-E3 rich preparations showing that lapatinib molecules do not cluster in the same way as observed in HPMC-P, where ionic interactions are present. Orientational correlations up to nearest neighbor molecules at about 4.3 Å were observed for polymer rich samples; both observations showed strong correlation to the stability of the systems. Lasty, the superior physical stability of 1:3 LP:HPMCP was consistent with the absence of significant intermolecular interactions in (ΔD inter LP (r)) in the range of 3.0 to 6.0 Å, which are attributed to C-C, C-N and C-O nearest neighbor contacts present in drug-drug interactions.

36 MATERIALS SCIENCE↗
DOE OSTI2015

A MULTI-PERIOD OSCILLATION IN A STELLAR SUPERFLARE

Flares that are orders of magnitude larger than the most energetic solar flares are routinely observed on Sun-like stars, raising the question of whether the same physical processes are responsible for both solar and stellar flares. In this Letter, we present a white-light stellar superflare on the star KIC 9655129, observed by NASA’s Kepler mission, with a rare multi-period quasi-periodic pulsation (QPP) pattern. Two significant periodic processes were detected using the wavelet and autocorrelation techniques, with periods of 78 ± 12 minutes and 32 ± 2 minutes. By comparing the phases and decay times of the two periodicities, the QPP signal was found to most likely be linear, suggesting that the two periodicities are independent, possibly corresponding either to different magnetohydrodynamic (MHD) modes of the flaring region or different spatial harmonics of the same mode. The presence of multiple periodicities is a good indication that the QPPs were caused by MHD oscillations and suggests that the physical processes in operation during stellar flares could be the same as those in solar flares.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Witnessing the growth of the nearest galaxy cluster: thermodynamics of the Virgo Cluster outskirts

Here, we present results from Suzaku Key Project observations of the Virgo Cluster, the nearest galaxy cluster to us, mapping its X-ray properties along four long ‘arms’ extending beyond the virial radius. The entropy profiles along all four azimuths increase with radius, then level out beyond ~0.5r 200 , while the average pressure at large radii exceeds Planck Sunyaev–Zel'dovich measurements. These results can be explained by enhanced gas density fluctuations (clumping) in the cluster's outskirts. Using a standard Navarro, Frenk and White model, we estimate a virial mass, radius and concentration parameter of M 200 = 1.05 ± 0.02 × 10 14 M⊙, r 200 = 974.1 ± 5.7 kpc and c = 8.8 ± 0.2, respectively. The inferred cumulative baryon fraction exceeds the cosmic mean at r ~r 200 along the major axis, suggesting enhanced gas clumping possibly sourced by a candidate large-scale structure filament along the north–south direction. The Suzaku data reveal a large-scale sloshing pattern, with two new cold fronts detected at radii of 233 and 280 kpc along the western and southern arms, respectively. Two high-temperature regions are also identified 1 Mpc towards the south and 605 kpc towards the west of M87, likely representing shocks associated with the ongoing cluster growth. Although systematic uncertainties in measuring the metallicity for low-temperature plasma remain, the data at large radii appear consistent with a uniform metal distribution on scales of ~90 × 180 kpc and larger, providing additional support for the early chemical enrichment scenario driven by galactic winds at redshifts of 2–3.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

MetaPoL: Immersive VR based Indoor Patterns of Life (PoL) and Anomalies Data Generation for Insider Threat Modeling in Nuclear Security

Insider threats are perhaps the most serious challenges that nuclear and radiological security systems face. Insiders pose such a great threat due to their access, authority, and knowledge, granting them opportunities to bypass dedicated nuclear and radiological security elements. For example, in one of the latest major insider threat incidents to nuclear security, the Doel-4 nuclear powerplant in Belgium suffered a shutdown, the threat of nuclear materials diversion, and long-term loss of tens of millions of dollars. Seven years of investigation concluded that it was an inside job and attempted sabotage. In this regard, there is an immediate need for R&D and technology integration in the domain of modeling indoor Patterns-of-Life (PoL) and anomaly detection. This can be achieved by using datasets of facility users’ mobility and activity, which can support the design of algorithms for insider threat modeling and detection. However, due to classification, privacy, sensitivity, and safety protocols, such datasets from real physical nuclear reactor facilities are not only hard to share, but also not always feasible to deploy and collect. Aiming to find an alternate solution, our proposed demonstration work - MetaPoL, is the first-ever (for the application space) immersive VR (virtual reality) environment of a real-world secure facility and allows users to move-and-stay through the designed indoor physical layout and also encounter NPCs (non-player characters) that emulate other facility users. In the MetaPoL an interactive user performs realistic spatio-temporal movement, dwelling and activities using a Meta Quest Pro VR headset, and that generates high-frequency (in time) high-resolution (in space) indoor spatial-temporal datasets that are valuable for PoL modeling and anomaly detection research specifically for insider threat modeling and detection mission. Such generated realistic, rich in context, and mission specific datasets can boost AI/Machine Learning based research for modeling and detecting insider threats in nuclear security and nonproliferation.

Gunaratne, Chathika↗