Efficient Random Vibration Analysis of Nonlinear Systems with Long-Short-Term Memory Networks for Uncertainty Quantification.
Abstract not provided.
Najera-Flores, David Aaron↗
SEARCH · Engineering Papers
Results for “NETWORK ANALYSIS”
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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NERSC & JGI Seek Elusive Proteins for Gene Editing
A video just released by the Supercomputing 2017 (SC17) conference shows how biologists at UC Berkeley and the Joint Genome Institute (JGI) – a Department of Energy Office of Science User Facility – are using NERSC, the Office of Science’s primary supercomputing center, to identify a class of enzymes capable of editing out genes responsible for disease. This revolutionary work requires petaflops of computing power to sift through billions of DNA sequences in the JGI data portals to identify proteins like Cas9 that, combined with the Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR), can “edit” a genome. First discovered by researchers at UC Berkeley, the CRISPR-Cas9 system has the power to permanently correct disease-causing mutations in cells, animal models, and eventually human patients to cure certain types of cancer, cardiovascular disease, sickle cell disease, and retinal degeneration. Since the CRISPR system was first identified in the early 2000s, it has become the most promising technology for treating genetic disease in humans and enabling plants to survive stress caused by pests and the environment. The annual SC17 conference will take place November 12-17 in Denver, Colorado. The over 12,000 attendees from academia, government and industry are leaders in researching and delivering high performance computing, networking, data storage and analysis in support of scientific discovery. http://sc17.supercomputing.org Producer: Jennifer Boyd, Boyd Productions DP: Damon Hennessey Audio: Richard K. Pooler Score: Michael Bacon Music
59 BASIC BIOLOGICAL SCIENCES↗
A global analysis of climate-relevant aerosol properties retrieved from the network of Global Atmosphere Watch (GAW) near-surface observatories
Explore the source record for details and available documents.
54 ENVIRONMENTAL SCIENCES↗
Securing Solar for the Grid: Spring 2024 IAB Meeting
The Spring 2024 IAB meeting will focus on updates from the research team and collective feedback and inputs for an updated Roadmap for Solar Cybersecurity. Researchers from the four DOE National Laboratories will present with industry counterparts for the major research tasks within the S2G program, including: Solar Cybersecurity Standards and Certifications, Solar Risk Assessments & Mitigation, Solar Supply Chain Assessment, Network Monitoring Tools and Analysis, and Training and Workforce Development. Sandia National Laboratories developed an original Roadmap for PV Cybersecurity in 2017. This year, we are updating that roadmap to reflect the current state of research and industry and identify gaps and priorities still to be addressed. We look forward to the IAB’s input on key topics for the roadmap.
14 SOLAR ENERGY↗
Embedding Neural Thermal Scattering (NeTS) Modules in SERPENT for Higher Fidelity Advanced Reactor Analysis
When a neutron born in fission thermalizes to the order of $k$ $B$ $T$, it’s de-Broglie wavelength and energy approach the order of inter-atomic spacing and elementary lattice oscillations, respectively. $S$($a,β,t$) or the scattering law, uuantify these temperature-dependent crystallographic contributions to total cross section (or reaction rate). In a Monte Carlo analysis, cumulative distribution functions (CDFs) of $S$($a,β,t$) are loaded to memory from “A Compact ENDF” (ACE) files for stochastically selecting thermal scattered neutron trajectories. In this work, novel neural thermal scattering (NeTS) modules for $S$($a,β,t$) CDFs are designed, trained, serialized and embedded within SERPENT using Python’s limited C-API for on-the-fly deployment of crystalline graphite $S$($a,β,t$) sampling. Torchscript tracing and Numba just-in-time (JIT) compilation streamline neural inference on NVIDIA GPUs with CUDA libraries. Demonstrations of bare sphere thermalization of fast and thermal sources show excellent agreement between embedded NeTS in SERPENT and MCNP. With an explicit model of the reactor, NeTS can predict on-the-fly changes in TREAT neutron spectra as a function of local temperature, which can serve to improve transient and accident predictions in a multiphysics analysis framework. This framework can be further extended to account on-the-fly for changes in local graphitic microstructure to scattering cross sections, and outlines a novel coupling of modern machine learning with state-of-the-art reactor physics methods.
97 MATHEMATICS AND COMPUTING↗
Braxton Marlatt Intern Poster
The Internet of Things (IoT) encompasses a vast network of interconnected devices embedded with software, sensors, and network connectivity, enabling data collection and exchange. While IoT technology revolutionizes various industries, it also introduces significant security challenges. This research focuses on enhancing IoT security through the implementation of Zero Trust Architecture concepts, specifically targeting the Network and Device pillars of the Cybersecurity and Infrastructure Security Agency’s Zero Trust Maturity Model. By generating Codified Attack Surfaces (CAS) using custom Structured Threat Information eXpression bundles, this project aims to provide enhanced visibility into network communications, detect vulnerabilities in device firmware, and improve the overall security posture for IoT devices and networks. The methodology involves defining custom STIX schema and objects, collecting data from intra-IoT traffic, external network traffic, and firmware analysis, and automating the conversion and correlation of this data into STIX bundles. The automated generation of attack surfaces offers comprehensive insights into activity, vulnerabilities, and anomalies within an IoT environment, enabling proactive threat identification and mitigation.
24 - POWER TRANSMISSION AND DISTRIBUTION↗
Scalable Deep Learning via I/O Analysis and Optimization
Scalable deep neural network training has been gaining prominence because of the increasing importance of deep learning in a multitude of scientific and commercial domains. Consequently, a number of researchers have investigated techniques to optimize deep learning systems. Much of the prior work has focused on runtime and algorithmic enhancements to optimize the computation and communication. Despite these enhancements, however, deep learning systems still suffer from scalability limitations, particularly with respect to data I/O. This situation is especially true for training models where the computation can be effectively parallelized, leaving I/O as the major bottleneck. In fact, our analysis shows that I/O can take up to 90% of the total training time. Thus, in this article, we first analyze LMDB, the most widely used I/O subsystem of deep learning frameworks, to understand the causes of this I/O inefficiency. Based on our analysis, we propose LMDBIO—an optimized I/O plugin for scalable deep learning. LMDBIO includes six novel optimizations that together address the various shortcomings in existing I/O for deep learning. Lastly, our experimental results show that LMDBIO significantly outperforms LMDB in all cases and improves overall application performance by up to 65-fold on a 9,216-core system.
97 MATHEMATICS AND COMPUTING↗
Preliminary analysis of the JAPE ground vehicle test data with an artificial neural network classifier
Remotely sensing and classifying military vehicles in a battlefield environment have been the source of much research over the past 20 years. The ability to know where threat vehicles are located is an obvious advantage to military personnel. In the past active methods of ground vehicle detection such as radar have been used, but with the advancement of technology to locate these active sensors, passive sensors are preferred. Passive sensors detect acoustic emissions, seismic movement, electromagnetic radiation, etc., produced by the target and use this information to describe it. Deriving the mathematical models to classify vehicles in this manner has been, and is, quite complex and not always reliable. However, with the resurgence of artificial neural network (ANN) research in the past few years, developing models for this work may be a thing of the past. Preliminary results from an ANN analysis to the tank signatures recorded at the Joint Acoustic Propagation Experiment (JAPE) at the US Army White Sands Missile Range, NM, in July 1991, are presented.
Larsen, Nathan F.↗
Verification of an Icosahedral Grid for Strategic Center for Networking, Communications, and Integration User Interface Spatial Capabilities
Spatial communication analysis tools are incredibly useful resources to have when planning a space mission. Every single mission that leaves Earth needs a way to get its data back down and every mission's requirements on how that will get accomplished is different. Being able to analyze how existing assets can provide services independent of a specific mission can be key in that process. Most current commercial software packages contain spatial analysis capabilities but go about the analysis in a way that is not the most efficient and can skew the results provided. NASA's Space Communication and Navigation (SCaN) Center for Engineering Networks, Integration, and Communications (SCENIC) seeks to solve this problem and provide analysis capabilities using both internally developed and open-source software. This allows incredible flexibility, customization and hugely reduces licensing costs. Using MATLAB® and Orbit Determination Toolbox created by Goddard Space Flight Center (GSFC), SCENIC is able to perform many node-based functions currently. Analysis utilizing the spatial tools in SCENIC allows a meaningful analysis of the capability of communication network assets in a way not seen in current commercial software packages. This paper discusses the verification activities associated with generating the spatial grid point definition utilized in these analysis capabilities, within the SCENIC user interface (UI).
Jasper, Lindsey A.↗
Development of an Extensible Computational Framework for Centralized Storage and Distributed Curation and Analysis of Genomic Data Genome-scale Metabolic Models
The DOE funded KBase project of the Stevens group at the University of Chicago was focused on four high-level goals: (i) improve extensibility, accessibility, and scalability of the SEED framework for genome annotation, curation, and analysis; (ii) extend the SEED infrastructure to support transcription regulatory network reconstructions (2.1), metabolic model reconstruction and analysis (2.2), assertions linked to data (2.3), eukaryotic annotation (2.4), and growth phenotype prediction (2.5); (iii) develop a web-API for programmatic remote access to SEED data and services; and (iv) application of all tools to bioenergy-related genomes and organisms. In response to these goals, we enhanced and improved the ModelSEED resource within the SEED to enable new modeling analyses, including improved model reconstruction and phenotype simulation. We also constructed a new website and web-API for the ModelSEED. Further, we constructed a comprehensive web-API for the SEED as a whole. We also made significant strides in building infrastructure in the SEED to support the reconstruction of transcriptional regulatory networks by developing a pipeline to identify sets of consistently expressed genes based on gene expression data. We applied this pipeline to 29 organisms, computing regulons which were subsequently stored in the SEED database and made available on the SEED website (http://pubseed.theseed.org). We developed a new pipeline and database for the use of kmers, or short 8-residue oligomer sequences, to annotate genomes at high speed. Finally, we developed the PlantSEED, or a new pipeline for annotating primary metabolism in plant genomes. All of the work performed within this project formed the early building blocks for the current DOE Knowledgebase system, and the kmer annotation pipeline, plant annotation pipeline, and modeling tools are all still in use in KBase today.
97 MATHEMATICS AND COMPUTING↗
Time-resolved Proteome Profiling of Normal Lung Development
The biochemical networks mediating normal lung development remain unclear with important implications for ameliorating morbidity and mortality in premature infants. Here we perform integrative proteomics and metabolomics analyses of murine lungs from embryonic to early adult ages to characterize the molecular networks mediating normal lung development. Multi-omics analysis identified 8,932 proteins and 158 polar metabolites providing a deep and comprehensive view of the lung proteome and metabolome during normal development. Analysis of the proteomics data revealed ten discrete expression modules driving lung development and the underlying regulatory and signaling network modulating these expression modules. Of interest our data suggest the action of protein ubiquitination in regulating lung development extends beyond the pseudoglandular stage previously described. Combined analysis of the proteomics and metabolomics supported the notion of increased cell proliferation earlier in lung development. Additionally, comparison of the proteomics data with a recent transcriptome analysis revealed biological processes under post-transcriptional control during lung development. Our study provides a unique resource for understanding normal lung development, and potentially avenues for developing strategies to address disrupted development.
computational network analysis, lung development, ↗
Space Programs Summary No. 37-41, Volume III FOR the Period July 1, 1966 to August 31, 1966. the Deep Space Network
Deep Space Network - tracking and navigational accuracy analysis, communications research, development, and engineering, and Deep Space Station engineering and operations
DEEP SPACE NETWORK /DSN/↗
NV: Nessus Vulnerability Visualization for the Web
Network vulnerability is a critical component of network se- curity. Yet vulnerability analysis has received relatively lit- tle attention from the security visualization community. In this paper we describe nv, a web-based Nessus vulnerability visualization. Nv utilizes treemaps and linked histograms to allow system administrators to discover, analyze, and man- age vulnerabilities on their networks. In addition to visual- izing single Nessus scans, nv supports the analysis of sequen- tial scans by showing which vulnerabilities have been fixed, remain open, or are newly discovered. Nv was also designed to operate completely in-browser, to avoid sending sensitive data to outside servers. We discuss the design of nv, as well as provide case studies demonstrating vulnerability analysis workflows which include a multiple-node testbed and data from the 2011 VAST Challenge.
information visualization↗
Solving the structure of “single-atom” catalysts using machine learning – assisted XANES analysis
We show that "single-atom” catalysts (SACs) have demonstrated excellent activity and selectivity in challenging chemical transformations such as photocatalytic CO 2 reduction. For heterogeneous photocatalytic SAC systems, it is essential to obtain sufficient information of their structure at the atomic level in order to understand reaction mechanisms. In this work, a SAC was prepared by grafting a molecular cobalt catalyst on a light-absorbing carbon nitride surface. Due to the sensitivity of the X-ray absorption near edge structure (XANES) spectra to subtle variances in the Co SAC structure in reaction conditions, different machine learning (ML) methods, including principal component analysis, K-means clustering, and neural network (NN), were utilized for in situ Co XANES data analysis. As a result, we obtained quantitative structural information of the SAC nearest atomic environment thereby extending the NN-XANES approach previously demonstrated for nanoparticles and size-selective clusters.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Literature Review on Modeling Cyber Networks and Evaluating Cyber Risks
The National Infrastructure Simulations and Analysis Center (NISAC) conducted a literature review on modeling cyber networks and evaluating cyber risks. The literature review explores where modeling is used in the cyber regime and ways that consequence and risk are evaluated. The relevant literature clusters in three different spaces: network security, cyber-physical, and mission assurance. In all approaches, some form of modeling is utilized at varying levels of detail, while the ability to understand consequence varies, as do interpretations of risk. This document summarizes the different literature viewpoints and explores their applicability to securing enterprise networks.
97 MATHEMATICS AND COMPUTING↗
Technology Infusion of CodeSonar into the Space Network Ground Segment (RII07): Software Assurance Symposium Technical Summary
Presents a source code analysis tool (CodeSonar) for use in the Space Network Ground Segment. The Space Network requires 99.9% proficiency and 97.0% availability of systems. Software has historically accounted for an annual average of 28% of the Space Network loss of availability and proficiency. CSCI A and CSCI B account for 42% of the previous eight months of software data loss. The technology infusion of CodeSonar into the Space Network Ground segment is meant to aid in determining the impact of the technology on the project both in the expenditure of effort and the technical results of the technology. Running a CodeSonar analysis and performing a preliminary review of the results averaged 3.5 minutes per finding (approximately 20 hours total). An additional 40 hours is estimated to analyze the 37 findings deemed too complex for the initial review. Using CodeSonar's tools to suppress known non-problems, delta tool runs will not repeat findings that have been marked as non-problems, further reducing the time needed for review. The 'non-interesting' finding rate of 70% is a large number, but filtering, search, and detailed contextual features of CodeSonar reduce the time per finding. Integration of the tool into the build process may also provide further savings by preventing developers from having to configure and operate the tool separately. These preliminary results show the tool to be easy to use and incorporate into the engineering process. These findings also provide significant potential improvements in proficiency and availability on the part of the software. As time-to-fix data become available a better cost trade can be made on person hours saved versus tool cost. Selective factors may be necessary to determine where best to apply CodeSonar to balance cost and benefits.
Benson, Markland J.↗
Uncoordinated Spectrum Sharing in Millimeter Wave Networks Using Carrier Sensing
Here, we propose using Carrier Sensing (CS) for distributed interference management in millimeter-wave (mmWave) cellular networks where spectrum is shared by multiple operators that do not coordinate among themselves. In addition, even the base station sites can be shared by the operators. We describe important challenges in using traditional CS in this setting and propose enhanced CS protocols to address these challenges. Using stochastic geometry, we develop a general framework for downlink coverage probability analysis of our shared mmWave network in the presence of CS and derive the downlink coverage probability expressions for several CS protocols. Our work is the first to investigate and analyze (using stochastic geometry) CS for mmWave networks with spectrum and BS sites shared among non-coordinating operators. We evaluate the downlink coverage probability of our shared mmWave network using simulations as well as numerical examples based on our analysis. Our evaluations show that our proposed approach leads to an improvement in coverage probability, compared to the coverage probability with no CS, for higher values of signal-to-interference and noise ratio (SINR). Interestingly, our evaluations also reveal that for lower values of SINR, not using any CS is the best strategy in terms of the downlink coverage probability.
42 ENGINEERING↗
Deep-Learning-Enabled Fast Optical Identification and Characterization of 2D Materials
Not provided.
Chemistry↗