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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Impact of Network Activity Levels on the Performance of Passive Network Service Dependency Discovery

Network services often do not operate alone, but instead, depend on other services distributed throughout a network to correctly function. If a service fails, is disrupted, or degraded, it is likely to impair other services. The web of dependencies can be surprisingly complex---especially within a large enterprise network---and evolve with time. Acquiring, maintaining, and understanding dependency knowledge is critical for many network management and cyber defense activities. While automation can improve situation awareness for network operators and cyber practitioners, poor detection accuracy reduces their confidence and can complicate their roles. In this paper we rigorously study the effects of network activity levels on the detection accuracy of passive network-based service dependency discovery methods. The accuracy of all except for one method was inversely proportional to network activity levels. Our proposed cross correlation method was particularly robust to the influence of network activity. The proposed experimental treatment will further advance a more scientific evaluation of methods and provide the ability to determine their operational boundaries.

correlation↗
DOE OSTI2012

Design Principles for Effective Knowledge Discovery from Big Data

Big data phenomenon refers to the practice of collection and processing of very large data sets and associated systems and algorithms used to analyze these massive datasets. Architectures for big data usually range across multiple machines and clusters, and they commonly consist of multiple special purpose sub-systems. Coupled with the knowledge discovery process, big data movement offers many unique opportunities for organizations to benefit (with respect to new insights, business optimizations, etc.). However, due to the difficulty of analyzing such large datasets, big data presents unique systems engineering and architectural challenges. In this paper, we present three sys- tem design principles that can inform organizations on effective analytic and data collection processes, system organization, and data dissemination practices. The principles presented derive from our own research and development experiences with big data problems from various federal agencies, and we illustrate each principle with our own experiences and recommendations.

Architecture↗
DOE OSTI2019

IRNet: A General Purpose Deep Risidual Regression Framework for Materials Discovery

Materials discovery is crucial for making scientific advances in many domains. Collections of data from experiments and first-principle computations have spurred interest in applying machine learning methods to create predictive models capable of mapping from composition and crystal structures to materials properties. Generally, these are regression problems with the input being a 1D vector composed of numerical attributes representing the material composition and/or crystal structure. While neural networks consisting of fully connected layers have been applied to such problems, their performance often suffers from the vanishing gradient problem when network depth is increased. Hence, predictive modeling for such tasks has been mainly limited to traditional machine learning techniques such as Random Forest. In this paper, we study and propose design principles for building deep regression networks composed of fully connected layers with numerical vectors as input. We introduce a novel deep regression network with individual residual learning, IRNet, that places shortcut connections after each layer so that each layer learns the residual mapping between its output and input. We use the problem of learning properties of inorganic materials from numerical attributes derived from material composition and/or crystal structure to compare IRNet’s performance against that of other machine learning techniques. Using multiple datasets from the Open Quantum Materials Database (OQMD) and Materials Project for training and evaluation, we show that IRNet provides significantly better prediction performance than the state-of-the-art machine learning approaches currently used by domain scientists. We also show that IRNet’s use of individual residual learning leads to better convergence during the training phase than when shortcut connections are between multi-layer stacks while maintaining the same number of parameters.

deep learning↗
DOE OSTI2021

High-throughput virtual laboratory for drug discovery using massive datasets

Time-to-solution for structure-based screening of massive chemical databases for COVID-19 drug discovery has been decreased by an order of magnitude, and a virtual laboratory has been deployed at scale on up to 27,612 GPUs on the Summit supercomputer, allowing an average molecular docking of 19,028 compounds per second. Over one billion compounds were docked to two SARS-CoV-2 protein structures with full optimization of ligand position and 20 poses per docking, each in under 24 hours. GPU acceleration and high-throughput optimizations of the docking program produced 350× mean speedup over the CPU version (50× speedup per node). GPU acceleration of both feature calculation for machine-learning based scoring and distributed database queries reduced processing of the 2.4 TB output by orders of magnitude. The resulting 50× speedup for the full pipeline reduces an initial 43 day runtime to 21 hours per protein for providing high-scoring compounds to experimental collaborators for validation assays.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

As if Biomarker Discovery Isn't Hard Enough: the Consequences of Poorly Characterized Reagents

The advent of high throughput omic technologies over the past two decades has driven a vast expansion in the search for clinical biomarkers, as manifested by the plethora of publications on biomarker discovery (over 8,600) listed on PubMed since 2000. Unfortunately, the same time period has seen a relative dearth of clinically validated biomarkers that have received FDA approval; only 10 new cancer biomarkers have been approved by the FDA in the same time period [1].

biomarker discovery↗
DOE OSTI2022

GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration and Development of Hidden Geothermal Resources

Geothermal exploration and production are challenging, expensive and risky. The GeoThermalCloud uses Machine Learning to predict the location of hidden geothermal resources. This submission includes a training dataset for the GeoThermalCloud neural network. Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources.

15 GEOTHERMAL ENERGY↗
DOE OSTI2018

Structure Based Discovery of Pan Active Botulinum Neurotoxin Inhibitors

Clostridium botulinum neurotoxins (BoNTs) released by the bacterium Clostridium botulinum are the most potent toxins causing the fatal disease called botulism. There are seven distinct serotypes of BoNTs (A to G) released by various strains of botulinum. They all have high sequence homology and similar three-dimensional structure. The toxicity of BoNT follows a four-step process – binding, internalization, translocation, and cleavage of its target protein, one of the three components of the SNARE complex (Soluble N-ethylmaleimde-sensitive factor attachment protein receptor) required for membrane docking and neurotransmitter release. Cleavage of one of the three proteins causes blockage of neurotransmitter release leading to flaccid paralysis. Though anyone of the above four steps could be a target for developing antidotes for botulism, the catalytic domain is the most suitable target for post exposure treatment. Of the seven serotypes BoNT/A, B, E and probably F affect humans, with BoNT/A considered to be the most potent. Development of drugs for botulism is focused on serotype specific inhibitors, but a pan-active inhibitor acting on several serotypes is preferable since it is difficult to identify the serotype before the treatment, especially since there is at least a 36-hour window before botulism can be diagnosed. Using structure-based drug discovery, we have developed three heptapeptides based on the SNARE proteins which inhibit BoNT/A, B and E equally well. Probable reasons for pan-activity of these peptides are discussed.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2001

The NASA Discovery STARDUST Project

The NASA Discovery STARDUST Project, a comet particle sample return mission, has been in flight for over 1 year and during this time period has operated all flight instrumentation.

NASA Discovery STARDUST↗
NASA NTRS1999

The NASA Discovery 5, Genesis Mission

Genesis is the NASA Discovery 5 mission to solar wind return samples to the Earth for analyses in terrestrial laboratories. This will significantly increase our knowledge of the chemical and isotopic composition of the solar system.

sample return Genesis NASA Discovery 5 solar wind↗
NASA NTRS1995

Knowledge Discovery and Data Mining: An Overview

The process of knowledge discovery and data mining is the process of information extraction from very large databases. Its importance is described along with several techniques and considerations for selecting the most appropriate technique for extracting information from a particular data set.

data mining knowledge discovery data search↗
NASA NTRS1995

Radar Follow-up of Spaceguard Discoveries: Imaging and Astrometry

Radar is a very powerful groundbased technique for post-discovery reconnaissance of NEOs and is likely to play a central role in investigating these fantastic worlds during the foreseeable future. Delay- Doppler measurements are orthogonal to optical angle measurements, typically have a fractional precision between 10^-5 and 10^-9, and consequently are invaluable for refining orbits and prediction ephemerides.

Spaceguard Discoveries↗
NASA NTRS2004

Intelligent resource discovery using ontology-based resource profiles

Successful resource discovery across heterogeneous repositories is strongly dependent on the semantic and syntactic homogeneity of the associated resource descriptions. Ideally, resource descriptions are easily extracted from pre-existing standardized sources, expressed using standard syntactic and semantic structures, and managed and accessed within a distributed, flexible, and scaleable software framework.

XML↗
NASA NTRS2006

Automated Knowledge Discovery from Simulators

In this paper, we explore one aspect of knowledge discovery from simulators, the landscape characterization problem, where the aim is to identify regions in the input/ parameter/model space that lead to a particular output behavior. Large-scale numerical simulators are in widespread use by scientists and engineers across a range of government agencies, academia, and industry; in many cases, simulators provide the only means to examine processes that are infeasible or impossible to study otherwise. However, the cost of simulation studies can be quite high, both in terms of the time and computational resources required to conduct the trials and the manpower needed to sift through the resulting output. Thus, there is strong motivation to develop automated methods that enable more efficient knowledge extraction.

landscapes↗