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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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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

deBGR: an efficient and near-exact representation of the weighted de Bruijn graph

Abstract Motivation Almost all de novo short-read genome and transcriptome assemblers start by building a representation of the de Bruijn Graph of the reads they are given as input. Even when other approaches are used for subsequent assembly (e.g. when one is using ‘long read’ technologies like those offered by PacBio or Oxford Nanopore), efficient k-mer processing is still crucial for accurate assembly, and state-of-the-art long-read error-correction methods use de Bruijn Graphs. Because of the centrality of de Bruijn Graphs, researchers have proposed numerous methods for representing de Bruijn Graphs compactly. Some of these proposals sacrifice accuracy to save space. Further, none of these methods store abundance information, i.e. the number of times that each k-mer occurs, which is key in transcriptome assemblers. Results We present a method for compactly representing the weighted de Bruijn Graph (i.e. with abundance information) with essentially no errors. Our representation yields zero errors while increasing the space requirements by less than 18–28% compared to the approximate de Bruijn graph representation in Squeakr. Our technique is based on a simple invariant that all weighted de Bruijn Graphs must satisfy, and hence is likely to be of general interest and applicable in most weighted de Bruijn Graph-based systems. Availability and implementation https://github.com/splatlab/debgr. Supplementary information Supplementary data are available at Bioinformatics online.

Pandey, Prashant↗
DOE OSTI2023

A Data Processing Pipeline for Adversarial Socio-Technical Network Analysis

With the rapid adoption of emerging technologies, there is a need to catalog and model sociotechnical interdependencies that have been historically used to influence the operation of Critical Infrastructure networks including the impacts of mergers and acquisitions, hostile takeovers, and foreign investment. Our research intends to address this need with two primary contributions. First, we have developed a data curation and processing pipeline to generate sociotechnical networks extracted from a variety of data sources including SEC filings and infrastructure asset databases. The pipeline, implemented in Apache Airflow, extracts and normalizes the representation of entities and relations, specified within ontologies. Our intent is to provide an extensible, machine-actionable approach to quickly communicate such models, reproduce previous results, and adapt them to new, unanticipated situations. Second, networks produced by our pipeline enable the development of graph-theoretic metrics that consider the properties of network components in addition to its topology. Metadata associated with network components---whether semantic, temporal, or geospatial---affects the alignment of generated networks with assumptions underlying complexity metrics. Validation of generated networks relative to component types defined by an ontology, may allow the research community to adapt metrics to the semantics of the domains being studied. Generated networks may be processed as knowledge, dynamic, or spatial graphs and enables a variety of analyses including automated reasoning and measures of network complexity. Automated reasoning views extracted entities and relations as a knowledge graph; this enables application of inference rules that represent historically-attested adversarial business methods and applies that behavior to a specific geographic context. Measures of network complexity, including degree distribution, reachability analyses, temporal analysis, and community detection can be adapted to indicate adversarial organizational influence.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Graph theory approach to determine configurations of multidentate and high coverage adsorbates for heterogeneous catalysis

Abstract Heterogeneous catalysts constitute a crucial component of many industrial processes, and to gain an understanding of the atomic-scale features of such catalysts, ab initio density functional theory is widely employed. Recently, growing computational power has permitted the extension of such studies to complex reaction networks involving either high adsorbate coverages or multidentate adsorbates, which bind to the surface through multiple atoms. Describing all possible adsorbate configurations for such systems, however, is often not possible based on chemical intuition alone. To systematically treat such complexities, we present a generalized Python-based graph theory approach to convert atomic scale models into undirected graph representations. These representations, when combined with workflows such as evolutionary algorithms, can systematically generate high coverage adsorbate models and classify unique minimum energy multidentate adsorbate configurations for surfaces of low symmetry, including multi-elemental alloy surfaces, steps, and kinks. Two case studies are presented which demonstrate these capabilities; first, an analysis of a coverage-dependent phase diagram of absorbate NO on the Pt 3 Sn(111) terrace surface, and second, an investigation of adsorption energies, together with identifying unique minimum energy configurations, for the reaction intermediate propyne (CHCCH 3 *) adsorbed on a PdIn(021) step surface. The evolutionary algorithm approach reproduces high coverage configurations of NO on Pt 3 Sn(111) using only 15% of the number of simulations required for a brute force approach. Furthermore, the screening of potentially hundreds of multidentate adsorbates is shown to be possible without human intervention. The strategy presented is quite general and can be applied to a spectrum of complex atomic systems.

36 MATERIALS SCIENCE↗
DOE OSTI2015

SU-C-304-02: Robust and Efficient Process for Acceptance Testing of Varian TrueBeam Linacs Using An Electronic Portal Imaging Device (EPID)

Purpose: The purpose of this project was to develop a process that utilizes the onboard kV and MV electronic portal imaging devices (EPIDs) to perform rapid acceptance testing (AT) of linacs in order to improve efficiency and standardize AT equipment and processes. Methods: In this study a Varian TrueBeam linac equipped with an amorphous silicon based EPID (aSi1000) was used. The conventional set of AT tests and tolerances was used as a baseline guide, and a novel methodology was developed to perform as many tests as possible using EPID exclusively. The developer mode on Varian TrueBeam linac was used to automate the process. In the current AT process there are about 45 tests that call for customer demos. Many of the geometric tests such as jaw alignment and MLC positioning are performed with highly manual methods, such as using graph paper. The goal of the new methodology was to achieve quantitative testing while reducing variability in data acquisition, analysis and interpretation of the results. The developed process was validated on two machines at two different institutions. Results: At least 25 of the 45 (56%) tests which required customer demo can be streamlined and performed using EPIDs. More than half of the AT tests can be fully automated using the developer mode, while others still require some user interaction. Overall, the preliminary data shows that EPID-based linac AT can be performed in less than a day, compared to 2–3 days using conventional methods. Conclusions: Our preliminary results show that performance of onboard imagers is quite suitable for both geometric and dosimetric testing of TrueBeam systems. A standardized AT process can tremendously improve efficiency, and minimize the variability related to third party quality assurance (QA) equipment and the available onsite expertise. Research funding provided by Varian Medical Systems. Dr. Sasa Mutic receives compensation for providing patient safety training services from Varian Medical Systems, the sponsor of this study.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2023

Use of Graph Theory and Neural Networks for Microstructural Classification

Recent advances in materials data analytics have provided new avenues for determining process-structure-property (PSP) linkages in a variety of materials. Machine learning techniques including few-shot learning have increased the efficiency of classifying microscopy images for the purposes of material characterization. Modifications in segmentation also show potential in improving the accuracy of our current pyCHIP classifier. Replacing previous encoders trained on ImageNet with those trained on microscopy images like MicroNet has initially shown better performance at classifying images of irradiated samples. Additionally, different normalization approaches were tested to show no discernable effect on classification. The Louvain method for community detection is analyzed on a set of irradiated samples with different parameters to determine which proved beneficial under what circumstances. We suggest that microscopy experiments be automated in the future using a combination of these techniques to enable high-throughput analyses.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Mapping SIEM Vulnerabilities in STIG

SIEM (Security Information and Event Management) tools monitor network traffic and allow users to quickly detect problems in their networks. Because of the valuable information processed by SIEM tools, it is important to understand their vulnerabilities. STIG (Structured Threat Intelligence Graph) is an application created at INL used to visualize data related to cyber threats. Using STIG can allow users to understand vulnerabilities related to their SIEM products and how to protect their systems.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2015

Lattice Monte Carlo simulation of Galilei variant anomalous diffusion

The observation of an increasing number of anomalous diffusion phenomena motivates the study to reveal the actual reason for such stochastic processes. When it is difficult to get analytical solutions or necessary to track the trajectory of particles, lattice Monte Carlo (LMC) simulation has been shown to be particularly useful. To develop such an LMC simulation algorithm for the Galilei variant anomalous diffusion, we derive explicit solutions for the conditional and unconditional first passage time (FPT) distributions with double absorbing barriers. According to the theory of random walks on lattices and the FPT distributions, we propose an LMC simulation algorithm and prove that such LMC simulation can reproduce both the mean and the mean square displacement exactly in the long-time limit. However, the error introduced in the second moment of the displacement diverges according to a power law as the simulation time progresses. We give an explicit criterion for choosing a small enough lattice step to limit the error within the specified tolerance. We further validate the LMC simulation algorithm and confirm the theoretical error analysis through numerical simulations. The numerical results agree with our theoretical predictions very well.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Street-level temperature estimation using graph neural networks: Performance, feature embedding and interpretability

Estimating street-level air temperature is a challenging task due to the highly heterogeneous urban surfaces, canyon-like street morphology, and the diverse physical processes in the built environment. Though pioneering studies have embarked on investigations via data-driven approaches, many questions remain to be answered. Here, in this study, we leveraged an innovative framework and redefined the street-level temperature estimation problem using Graph Neural Networks (GNN) with spatial embedding techniques. The results showed that GNN models are more capable and consistent of estimating street-level temperature among tested locations, benefiting from its unique strength in handling extensive data over unstructured graph topology. In addition, we conducted in-depth analysis of feature importance to enhance the model interpretability. Among the urban features analyzed in this study, the time-variant canopy density and meter-level land use data emerge as crucial factors. Our findings highlight GNN 's high potential in capturing the complex dynamics between urban elements and their impacts on microclimate, thus offering valuable insights for comprehensive urban data collection and urban climate modeling in general. Collectively, this study also contributes to urban planning and policy by providing avenues to enhance city resilience against climate change, thereby advancing the agenda for environmental stewardship and urban sustainability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

3D liver segmentation using multiple region appearances and graph cuts

Purpose: Efficient and accurate 3D liver segmentations from contrast-enhanced computed tomography (CT) images play an important role in therapeutic strategies for hepatic diseases. However, inhomogeneous appearances, ambiguous boundaries, and large variance in shape often make it a challenging task. The existence of liver abnormalities poses further difficulty. Despite the significant intensity difference, liver tumors should be segmented as part of the liver. This study aims to address these challenges, especially when the target livers contain subregions with distinct appearances. Methods: The authors propose a novel multiregion-appearance based approach with graph cuts to delineate the liver surface. For livers with multiple subregions, a geodesic distance based appearance selection scheme is introduced to utilize proper appearance constraint for each subregion. A special case of the proposed method, which uses only one appearance constraint to segment the liver, is also presented. The segmentation process is modeled with energy functions incorporating both boundary and region information. Rather than a simple fixed combination, an adaptive balancing weight is introduced and learned from training sets. The proposed method only calls initialization inside the liver surface. No additional constraints from user interaction are utilized. Results: The proposed method was validated on 50 3D CT images from three datasets, i.e., Medical Image Computing and Computer Assisted Intervention (MICCAI) training and testing set, and local dataset. On MICCAI testing set, the proposed method achieved a total score of 83.4 ± 3.1, outperforming nonexpert manual segmentation (average score of 75.0). When applying their method to MICCAI training set and local dataset, it yielded a mean Dice similarity coefficient (DSC) of 97.7% ± 0.5% and 97.5% ± 0.4%, respectively. These results demonstrated the accuracy of the method when applied to different computed tomography (CT) datasets. In addition, user operator variability experiments showed its good reproducibility. Conclusions: A multiregion-appearance based method is proposed and evaluated to segment liver. This approach does not require prior model construction and so eliminates the burdens associated with model construction and matching. The proposed method provides comparable results with state-of-the-art methods. Validation results suggest that it may be suitable for the clinical use.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

I-GCN: A Graph Convolutional Network Accelerator with Runtime Locality Enhancement through Islandization

In this paper, we propose a novel hardware accelerator for GCN inference called I-GCN that significantly improves data locality and reduces unnecessary computation through a new online graph restructuring algorithm we refer to as islandization. The proposed algorithm finds clusters of nodes with strong internal but weak external connections. The islandization process yields two major benefits. First, by processing islands rather than individual nodes, there is better on-chip data reuse and fewer off-chip memory accesses. Second, there is less redundant computation as aggregation for common/shared neighbors in an island can be reused. The parallel search, identification, and leverage of graph islands are all handled purely in hardware at runtime working in an incremental pipelined manner. This is done without any preprocessing of the graph data or adjustment of the GCN model structure.

Geng, Tong↗
DOE OSTI2015

Multi-criteria evaluation in strategic environmental assessment for waste management plan, a case study: The city of Belgrade

Highlights: • The paper deals with the specific method of multi-criteria evaluation applied in drafting the SEA for the Belgrade WMP. • MCE of the planning solutions, assessed according to 37 objectives of the SEA and four sets of criteria, was presented in the matrix form. • The results are presented in the form of graphs so as to be easily comprehensible to all the participants in the decision-making process. • The results represent concrete contribution proven in practice. - Abstract: Strategic Environmental Assessment (SEA) is one of the key instruments for implementing sustainable development strategies in planning in general; in addition to being used in sectoral planning, it can also be used in other areas such as waste management planning. SEA in waste management planning has become a tool for considering the benefits and consequences of the proposed changes in space, also taking into account the capacity of space to sustain the implementation of the planned activities. In order to envisage both the positive and negative implications of a waste management plan for the elements of sustainable development, an adequate methodological approach to evaluating the potential impacts must be adopted and the evaluation results presented in a simple and clear way, so as to allow planners to make relevant decisions as a precondition for the sustainability of the activities planned in the waste management sector. This paper examines the multi-criteria evaluation method for carrying out an SEA for the Waste Management Plan for the city of Belgrade (BWMP). The method was applied to the evaluation of the impacts of the activities planned in the waste management sector on the basis of the environmental and socioeconomic indicators of sustainability, taking into consideration the intensity, spatial extent, probability and frequency of impact, by means of a specific planning approach and simple and clear presentation of the obtained results.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

A Quantum-Based Approach to Predict Primary Radiation Damage in Polymeric Networks

Initial atomistic-level radiation damage in chemically reactive materials is thought to induce reaction cascades that can result in undesirable degradation of macroscale properties. Ensembles of quantum-based molecular dynamics (QMD) simulations can accurately predict these cascades, but extracting chemical insights from the many underlying trajectories is a labor-intensive process that can require substantial a priori intuition. We develop here a general and automated graph-based approach to extract all chemically distinct structures sampled in QMD simulations and apply our approach to predict primary radiation damage of polydimethylsiloxane (PDMS), the main constituent of silicones. A postprocessing protocol is developed to identify underlying polymer backbone structures as connected components in QMD trajectories. Furthermore, these backbones form a repository of radiation-damaged structures. A scheme for extracting and updating a library of isomorphically distinct structures is proposed to identify the spanning set and aid chemical interpretation of the repository. The analyses are applied to ensembles of cascade QMD simulations in which the four element types in PDMS are selectively excited in primary knock-on atom events. Our approach reveals a much higher degree of combinatorial complexity in this system than was inferred through radiolysis experiments. Probabilities are extracted for radiation-induced network changes including formation of branch points, carbon linkages, cycles, bond scissions, and carbon uptake into the Si–O siloxane backbone network. The general analysis framework presented here is readily extendable to modeling chemical degradation of other polymers and molecular materials and provides a basis for future quantum-informed multiscale modeling of radiation damage.

36 MATERIALS SCIENCE↗
DOE OSTI2012

CHARACTERIZING THE OPTICAL VARIABILITY OF BRIGHT BLAZARS: VARIABILITY-BASED SELECTION OF FERMI ACTIVE GALACTIC NUCLEI

We investigate the use of optical photometric variability to select and identify blazars in large-scale time-domain surveys, in part to aid in the identification of blazar counterparts to the {approx}30% of {gamma}-ray sources in the Fermi 2FGL catalog still lacking reliable associations. Using data from the optical LINEAR asteroid survey, we characterize the optical variability of blazars by fitting a damped random walk model to individual light curves with two main model parameters, the characteristic timescales of variability {tau}, and driving amplitudes on short timescales {sigma}-circumflex. Imposing cuts on minimum {tau} and {sigma}-circumflex allows for blazar selection with high efficiency E and completeness C. To test the efficacy of this approach, we apply this method to optically variable LINEAR objects that fall within the several-arcminute error ellipses of {gamma}-ray sources in the Fermi 2FGL catalog. Despite the extreme stellar contamination at the shallow depth of the LINEAR survey, we are able to recover previously associated optical counterparts to Fermi active galactic nuclei with E {>=} 88% and C = 88% in Fermi 95% confidence error ellipses having semimajor axis r < 8'. We find that the suggested radio counterpart to Fermi source 2FGL J1649.6+5238 has optical variability consistent with other {gamma}-ray blazars and is likely to be the {gamma}-ray source. Our results suggest that the variability of the non-thermal jet emission in blazars is stochastic in nature, with unique variability properties due to the effects of relativistic beaming. After correcting for beaming, we estimate that the characteristic timescale of blazar variability is {approx}3 years in the rest frame of the jet, in contrast with the {approx}320 day disk flux timescale observed in quasars. The variability-based selection method presented will be useful for blazar identification in time-domain optical surveys and is also a probe of jet physics.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Probabilistic simulation of quantum circuits using a deep-learning architecture

The fundamental question of how to best simulate quantum systems using conventional computational resources lies at the forefront of condensed matter and quantum computation. It impacts both our understanding of quantum materials and our ability to emulate quantum circuits. Here we present an exact formulation of quantum dynamics via factorized generalized measurements which maps quantum states to probability distributions with the advantage that local unitary dynamics and quantum channels map to local quasistochastic matrices. This representation provides a general framework for using state-of-the-art probabilistic models in machine learning for the simulation of quantum many-body dynamics. Using this framework, we have developed a practical algorithm to simulate quantum circuits using an attention network based on a powerful neural network ansatz responsible for the most recent breakthroughs in natural language processing. We demonstrate our approach by simulating circuits that build Greenberger-Horne-Zeilinger and linear graph states of up to 60 qubits, as well as a variational quantum eigensolver circuit for preparing the ground state of the transverse field Ising model on several system sizes. Our methodology constitutes a modern machine learning approach to the simulation of quantum physics with applicability both to quantum circuits as well as other quantum many-body systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Interactive contour delineation of organs at risk in radiotherapy: Clinical evaluation on NSCLC patients

Purpose: Accurate delineation of organs at risk (OARs) on computed tomography (CT) image is required for radiation treatment planning (RTP). Manual delineation of OARs being time consuming and prone to high interobserver variability, many (semi-) automatic methods have been proposed. However, most of them are specific to a particular OAR. Here, an interactive computer-assisted system able to segment various OARs required for thoracic radiation therapy is introduced. Methods: Segmentation information (foreground and background seeds) is interactively added by the user in any of the three main orthogonal views of the CT volume and is subsequently propagated within the whole volume. The proposed method is based on the combination of watershed transformation and graph-cuts algorithm, which is used as a powerful optimization technique to minimize the energy function. The OARs considered for thoracic radiation therapy are the lungs, spinal cord, trachea, proximal bronchus tree, heart, and esophagus. The method was evaluated on multivendor CT datasets of 30 patients. Two radiation oncologists participated in the study and manual delineations from the original RTP were used as ground truth for evaluation. Results: Delineation of the OARs obtained with the minimally interactive approach was approved to be usable for RTP in nearly 90% of the cases, excluding the esophagus, which segmentation was mostly rejected, thus leading to a gain of time ranging from 50% to 80% in RTP. Considering exclusively accepted cases, overall OARs, a Dice similarity coefficient higher than 0.7 and a Hausdorff distance below 10 mm with respect to the ground truth were achieved. In addition, the interobserver analysis did not highlight any statistically significant difference, at the exception of the segmentation of the heart, in terms of Hausdorff distance and volume difference. Conclusions: An interactive, accurate, fast, and easy-to-use computer-assisted system able to segment various OARs required for thoracic radiation therapy has been presented and clinically evaluated. The introduction of the proposed system in clinical routine may offer valuable new option to radiation oncologists in performing RTP.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

pnnl/NWHypergraph

NWHypergraph is a C++ hypergraph processing framework for shared-memory architecture. NWHypergraph provides efficient algorithms to construct s-line graphs, a lower-order approximation of a given hypergraph, and computes different graph metrics of a s-line graph such as s-connected components, s-betweenness centrality, s-closeness centrality, etc. It also provides Python APIs for s-line graph computation. The Python APIs are provided using Pybind11

Lumsdaine, Andrew↗
DOE OSTI2023

DeepDataProfiler: A Platform and Methodology for the Analysis and Interpretation of Neural Networks

The DeepDataProfiler is a methodology and framework for providing interpretability to trained neural networks. Its approach is to decompose a network into a weighted graph of neurons and synapses and link the components of the graph to human identifiable concepts. By identifying concepts important to the network and tracking the decision process employed by the network, the network becomes more transparent and less like a black box. Spurious decisions and poor generalization strategies can be identified and a measure of trustworthiness can be established.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Chizu Task Mapping Tool

Chizu is a tool for Mapping MPI processes or tasks to physical processors or nodes for optimizing communication performance. It takes the communication graph of a High Performance Computing (HPC) application and the interconnection topology of a supercomputer as input. It outputs a new MPI rand to processor mapping, which can be used when launching the HPC application.

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