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All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Machine Learning in the Big Data Era: Are We There Yet?

In this paper, we discuss the machine learning challenges of the Big Data era. We observe that recent innovations in being able to collect, access, organize, integrate, and query massive amounts of data from a wide variety of data sources have brought statistical machine learning under more scrutiny and evaluation for gleaning insights from the data than ever before. In that context, we pose and debate the question - Are machine learning algorithms scaling with the ability to store and compute? If yes, how? If not, why not? We survey recent developments in the state-of-the-art to discuss emerging and outstanding challenges in the design and implementation of machine learning algorithms at scale. We leverage experience from real-world Big Data knowledge discovery projects across domains of national security and healthcare to suggest our efforts be focused along the following axes: (i) the data science challenge - designing scalable and flexible computational architectures for machine learning (beyond just data-retrieval); (ii) the science of data challenge the ability to understand characteristics of data before applying machine learning algorithms and tools; and (iii) the scalable predictive functions challenge the ability to construct, learn and infer with increasing sample size, dimensionality, and categories of labels. We conclude with a discussion of opportunities and directions for future research.

Big Data↗
DOE OSTI2012

A National Collaboratory to Advance the Science of High Temperature Plasma Physics for Magnetic Fusion

This report summarizes the work of the National Fusion Collaboratory (NFC) Project to develop a persistent infrastructure to enable scientific collaboration for magnetic fusion research. The original objective of the NFC project was to develop and deploy a national FES Grid (FusionGrid) that would be a system for secure sharing of computation, visualization, and data resources over the Internet. The goal of FusionGrid was to allow scientists at remote sites to participate as fully in experiments and computational activities as if they were working on site thereby creating a unified virtual organization of the geographically dispersed U.S. fusion community. The vision for FusionGrid was that experimental and simulation data, computer codes, analysis routines, visualization tools, and remote collaboration tools are to be thought of as network services. In this model, an application service provider (ASP provides and maintains software resources as well as the necessary hardware resources. The project would create a robust, user-friendly collaborative software environment and make it available to the US FES community. This Grid's resources would be protected by a shared security infrastructure including strong authentication to identify users and authorization to allow stakeholders to control their own resources. In this environment, access to services is stressed rather than data or software portability.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Trust-Enhancing Probabilistic Transfer Learning for Sparse and Noisy Data Environments

There is an increasing aspiration to utilize machine learning (ML) for various tasks of relevance to national security. ML models have thus far been mostly applied to tasks and domains that, while impactful, have sufficient volume of data. For predictive tasks of national security relevance, ML models of great capacity (ability to approximate nonlinear trends in input-output maps) are often needed to capture the complex underlying physics. However, scientific problems of relevance to national security are often accompanied by various sources of sparse and/or incomplete data, including experiments and simulations, across different regimes of operation, of varying degrees of fidelity, and include noise with different characteristics and/or intensity. State-of-the-art ML models, despite exhibiting superior performance on the task and domain they were trained on, may suffer detrimental loss in performance in such sparse data environments. This report summarizes the results of the Laboratory Directed Research and Development project entitled Trust-Enhancing Probabilistic Transfer Learning for Sparse and Noisy Data Environments. The objective of the project was to develop a new transfer learning (TL) framework that aims to adaptively blend the data across different sources in tackling one task of interest, resulting in enhanced trustworthiness of ML models for mission- and safety-critical systems. The proposed framework determines when it is worth applying TL and how much knowledge is to be transferred, despite uncontrollable uncertainties. The framework accomplishes this by leveraging concepts and techniques from the fields of Bayesian inverse modeling and uncertainty quantification, relying on strong mathematical foundations of probability and measure theories to devise new uncertainty-aware TL workflows.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

Nevada National Security Site 2011 Waste Management Monitoring Report, Area 3 and Area 5 Radioactive Waste Management Site

Environmental monitoring data are collected at and around the Area 3 and Area 5 Radioactive Waste Management Sites (RWMSs) at the Nevada National Security Site (NNSS). These data are associated with radiation exposure, air, groundwater, meteorology, and vadose zone. This report summarizes the 2011 environmental data to provide an overall evaluation of RWMS performance and to support environmental compliance and performance assessment (PA) activities. Some of these data (e.g., radiation exposure, air, and groundwater) are presented in other reports. Direct radiation monitoring data indicate exposure levels at the RWMSs are within the range of background levels measured at the NNSS. Slightly elevated exposure levels outside the Area 3 RWMS are attributed to nearby historical aboveground nuclear weapons tests. Air monitoring data show tritium concentrations in water vapor and americium and plutonium concentrations in air particles are only slightly above detection limits and background levels. The measured levels of radionuclides in air particulates and moisture are below derived concentration guides for these radionuclides. During the last 2 weeks of March 2011, gamma spectroscopy results for air particles showed measurable activities of iodine-131 ( 131 I), cesium-134 ( 134 Cs), and cesium-137 ( 137 Cs). These results are attributed to the release of fission products from the damaged Fukushima Daiichi power plant in Japan. The remaining gamma spectroscopy results for air particulates collected at the Area 3 and Area 5 RWMS were below minimum detectable concentrations. Groundwater monitoring data indicate the groundwater in the uppermost aquifer beneath the Area 5 RWMS is not impacted by RWMS operations. Results of groundwater analysis from wells around the Area 5 RWMS were all below established investigation levels. The 86.3 millimeters (mm) (3.40 inches [in.]) of precipitation at the Area 3 RWMS during 2011 is 44% below the average of 154.1 mm (6.07 in.), and the 64.8 mm (2.55 in.) of precipitation at the Area 5 RWMS during 2011 is 47% below the average of 122.4 mm (4.82 in.). Water balance measurements indicate that evapotranspiration from the vegetated weighing lysimeter dries the soil and prevents downward percolation of precipitation more effectively than evaporation from the bare-soil weighing lysimeter. Automated vadose zone monitoring on Area 5 RWMS operational waste covers was not done during 2011 due to construction of the final evapotranspiration cover at these monitoring locations. Moisture from precipitation did not percolate below 122 centimeters (4 feet) in the vegetated final mono-layer cover on the U-3ax/bl disposal unit at the Area 3 RWMS before being removed by evapotranspiration. During 2011, there was no drainage through 2.4 meters (8 feet) of soil from the Area 3 drainage lysimeters that received only natural precipitation. Ten percent of the applied precipitation and irrigation drained from the bare-soil drainage lysimeter that received 3 times natural precipitation. All 2011 monitoring data indicate that the Area 3 and Area 5 RWMSs are performing within expectations of the model and parameter assumptions for the facility PAs.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2015

Finite-key security analysis of quantum key distribution with imperfect light sources

In recent years, the gap between theory and practice in quantum key distribution (QKD) has been significantly narrowed, particularly for QKD systems with arbitrarily flawed optical receivers. The status for QKD systems with imperfect light sources is however less satisfactory, in the sense that the resulting secure key rates are often overly dependent on the quality of state preparation. This is especially the case when the channel loss is high. Very recently, to overcome this limitation, Tamaki et al proposed a QKD protocol based on the so-called 'rejected data analysis', and showed that its security in the limit of infinitely long keys is almost independent of any encoding flaw in the qubit space, being this protocol compatible with the decoy state method. Here, as a step towards practical QKD, we show that a similar conclusion is reached in the finite-key regime, even when the intensity of the light source is unstable. More concretely, we derive security bounds for a wide class of realistic light sources and show that the bounds are also efficient in the presence of high channel loss. Our results strongly suggest the feasibility of long distance provably secure communication with imperfect light sources.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Open Research Challenges with Big Data - A Data-Scientist s Perspective

In this paper, we discuss data-driven discovery challenges of the Big Data era. We observe that recent innovations in being able to collect, access, organize, integrate, and query massive amounts of data from a wide variety of data sources have brought statistical data mining and machine learning under more scrutiny and evaluation for gleaning insights from the data than ever before. In that context, we pose and debate the question - Are data mining algorithms scaling with the ability to store and compute? If yes, how? If not, why not? We survey recent developments in the state-of-the-art to discuss emerging and outstanding challenges in the design and implementation of machine learning algorithms at scale. We leverage experience from real-world Big Data knowledge discovery projects across domains of national security, healthcare and manufacturing to suggest our efforts be focused along the following axes: (i) the data science challenge - designing scalable and flexible computational architectures for machine learning (beyond just data-retrieval); (ii) the science of data challenge the ability to understand characteristics of data before applying machine learning algorithms and tools; and (iii) the scalable predictive functions challenge the ability to construct, learn and infer with increasing sample size, dimensionality, and categories of labels. We conclude with a discussion of opportunities and directions for future research.

Big data↗
DOE OSTI2019

ADMS Test Bed: Defining a Use Case for Data Improvement for ADMS Deployment: Preprint

Advanced distribution management systems (ADMS) provide a suite of tools for meeting the needs of a modern grid - higher reliability and power quality, improved resiliency and security, lower costs and enhanced customer participation. Data quality has two-fold impact on ADMS adoption: 1. Data quality improvement may constitute up to 25% of ADMS deployment costs. 2. The accuracy of data and models used by the ADMS greatly affect the utility's ability to meet the operational objectives. Thus, quantifying the data quality requirements and its impact on performance is critical to reducing the overall cost of deployment, enabling wider adoption and ensuring that the ADMS performs as specified. The paper offers the motivation, methodology and evaluation strategy for filling this critical gap.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Optimal vocabulary selection approaches for privacy-preserving deep NLP model training for information extraction and cancer epidemiology

With the use of artificial intelligence and machine learning techniques for biomedical informatics, security and privacy concerns over the data and subject identities have also become an important issue and essential research topic. Without intentional safeguards, machine learning models may find patterns and features to improve task performance that are associated with private personal information. The privacy vulnerability of deep learning models for information extraction from medical textural contents needs to be quantified since the models are exposed to private health information and personally identifiable information. The objective of the study is to quantify the privacy vulnerability of the deep learning models for natural language processing and explore a proper way of securing patients’ information to mitigate confidentiality breaches. The target model is the multitask convolutional neural network for information extraction from cancer pathology reports, where the data for training the model are from multiple state population-based cancer registries. This study proposes the following schemes to collect vocabularies from the cancer pathology reports; (a) words appearing in multiple registries, and (b) words that have higher mutual information. We performed membership inference attacks on the models in high-performance computing environments. The comparison outcomes suggest that the proposed vocabulary selection methods resulted in lower privacy vulnerability while maintaining the same level of clinical task performance.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Networked-based Cyber Analysis using Deep Packet Inspection (DPI) for High-Speed Networks

Today’s networked systems utilize advanced security components such as Next Generation Firewall (NGFW), Intrusion Detection Systems (IDS), Intrusion Prevention Systems (IPS), and methods for network traffic classification. A fundamental aspect of these security components and methods is network packet visibility and packet inspection. To achieve packet visibility, a compute mechanism used by these security components and methods is Deep Packet Inspection (DPI). DPI is used to obtain visibility into packet fields by looking deeper inside packets, beyond just IP address, port, and protocol. However, DPI is considered extremely expensive in terms of compute processing costs and very challenging to implement on high speed network systems. The fundamental scientific paradigm addressed in this research project is the application of greater network packet visibility and packet inspection at data rates greater than 40Gbps to secure computer network systems. The greater visibility and inspection will enable detection of advanced content-based threats that exploit application vulnerabilities and are designed to bypass traditional security approaches such as firewalls and antivirus scanners. Greater visibility and inspection are achieved through identification of the application protocol (e.g., HTTP, SMTP, Skype) and, in some cases, extraction and processing of the information contained in the packet payload. Analysis is then performed on the resulting DPI data to identify potentially malicious behavior. In order to obtain visibility and inspect the application protocol and contents at high speed data rates, advanced DPI technologies and implementations are developed.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2018

Changes in Exercise Data Management

The suite of exercise hardware aboard the International Space Station (ISS) generates an immense amount of data. The data collected, treadmill, cycle ergometer, and resistance strength training hardware, are basic exercise parameters (time, heart rate, speed, load, etc.). The raw data are processed in the laboratory and more detailed parameters are calculated from each exercise data file. Updates recently have been made to how these valuable data are stored, adding an additional level of security, increasing accessibility, and resulting in overall increased efficiency of medical report delivery. Questions regarding exercise performance or how exercise may influence other variables of crew health frequently arise within the crew health care community. Inquiries regarding the health of the exercise hardware often need quick analysis and response to ensure the exercise system is operable on a continuous basis. Consolidating all of the exercise system data in a single repository enables a quick response to both the medical and engineering communities. A SQL server database is currently in use, and provides a secure location for all of the exercise data starting at ISS Expedition 1 to current date. The database has been structured to update derived metrics automatically, making analysis and reporting available within minutes of dropping the in-flight data into the database. Commercial tools were evaluated to help aggregate and visualize data from the SQL database. The Tableau software provides manageable interface, which has improved the laboratory’s output time of crew reports by 67%. Expansion of the SQL database, to be inclusive of additional medical requirement metrics, addition of ‘app-like’ tools for mobile visualization, and collaborative use (e.g., operational support teams, research groups, and International Partners) of the data system, is currently being explored.

Buxton, R. E.↗
DOE OSTI2019

Multiyear Analysis of the Fate and Transport of Contaminated Soils at Plutonium Valley and the Smoky Site, Nevada National Security Site

Desert Research Institute (DRI) has collected approximately 10 years of atmospheric and hydrologic data at Plutonium Valley and the Smoky Site on the Nevada National Security Site to identify the potential for radionuclide-contaminated soils to be transported via wind and water. Plutonium preferentially binds to finer soil particles (Shinn et al., 1993; Miller et al., 2015), which can be transported through erosion processes. Data collected by DRI are used to determine the mechanisms and thresholds that result in the transport of radionuclide-contaminated soils, and climate model projections are used to understand how these mechanisms may change in the future. This work will help identify the parameters and threshold conditions that should be incorporated into post-closure monitoring plans for these and other Soils Corrective Action Unit (CAU) sites.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

NARAC Modeling During the Response to the Fukushima Dai-ichi Nuclear Power Plant Emergency

This paper summarizes the activities of the National Atmospheric Release Advisory Center (NARAC) during the Fukushima Dai-ichi nuclear power plant crisis. NARAC provided a wide range of products and analyses as part of its support including: (1) Daily Japanese weather forecasts and hypothetical release (generic source term) dispersion predictions to provide situational awareness and inform planning for U.S. measurement data collection and field operations; (2) Estimates of potential dose in Japan for hypothetical scenarios developed by the Nuclear Regulatory Commission (NRC) to inform federal government considerations of possible actions that might be needed to protect U.S. citizens in Japan; (3) Estimates of possible plume arrival times and dose for U.S. locations; and (4) Plume model refinement and source estimation based on meteorological analyses and available field data. The Department of Energy/National Nuclear Security Administration (DOE/NNSA) deployed personnel to Japan and stood up 'home team' assets across the DOE complex to aid in assessing the consequences of the releases from the Fukushima Dai-ichi Nuclear Power Plant. The DOE Nuclear Incident Team (NIT) coordinated response activities, while DOE personnel provided predictive modeling, air and ground monitoring, sample collection, laboratory analysis, and data assessment and interpretation. DOE deployed the Aerial Measuring System (AMS), Radiological Assistance Program (RAP) personnel, and the Consequence Management Response Team (CMRT) to Japan. DOE/NNSA home team assets included the Consequence Management Home Team (CMHT); National Atmospheric Release Advisory Center (NARAC); Radiation Emergency Assistance Center/Training Site (REAC/TS); and Radiological Triage. NARAC was activated by the DOE/NNSA on March 11, shortly after the Tohoku earthquake and tsunami occurred. The center remained on active operations through late May when DOE ended its deployment to Japan. Over 32 NARAC staff members, supplemented by other LLNL scientists, invested over 5000 person-hours of time and generated over 300 analyses and predictions.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Enhancing Security and Resiliency in Operational Technology Environments Through Network Slicing and Federated Learning

The growing convergence of Information Technology (IT) and Operational Technology (OT) within Industry 4.0 environments has introduced new demands on industrial network infrastructure. As cyber-physical systems become increasingly interconnected, ensuring the secure, timely, and efficient exchange of critical data is essential. This thesis explores how network slicing, a method of creating isolated virtual network segments, can be applied within OT environments to address challenges such as latency, security, and resource allocation. The first research question addressed in this thesis is: How can OT networks take advantage of NFV and SDN technology to become cyber resilient? This study examines the operational, security, and architectural implications of introducing network slicing into traditionally static OT infrastructures such as Industrial Control Systems (ICS) and SCADA. Through simulated deployments and case studies, the research demonstrates how slicing enables better isolation between critical and non-critical services, thereby improving response time, throughput, and security in sensitive environments. The second question considers: How to dynamically implement network slicing and take advantage of network resources towards integrating decentralized machine learning? In response, this thesis proposes a framework that combines Software-Defined Networking (SDN), Network Function Virtualization (NFV), and Federated Learning (FL) to enable real-time analytics while maintaining data locality. The proposed approach reduces the burden on centralized infrastructure and minimizes privacy risks by supporting on-site training of models across distributed OT nodes, coordinated through dynamically allocated network slices. The third focus explores: How slicing helps to increase the resiliency of OT networks through the orchestration of a dynamic DMZ? To answer this, the thesis presents a method for creating and managing Dynamic Demilitarized Zones (DMZs) using network slicing. This enables flexible and automated isolation of sensitive subsystems during threat scenarios or high-risk operations. Coupled with intelligent orchestration and containerized security services, the dynamic DMZ significantly enhances the system's ability to respond to cyber incidents without halting production. Ultimately, this thesis contributes a comprehensive architecture that blends network slicing with machine learning, secure segmentation, and automation, paving the way for resilient, adaptive, and intelligent OT environments. Performance evaluations across multiple scenarios show improvements in system reliability, threat response time, model accuracy, and resource utilization, providing a strong foundation for future industrial automation systems.

Rodiles Delgado, Brian G↗
DOE OSTI2017

Big Data Sanitization and Cyber Situational Awareness: A Network Telescope Perspective

This paper addresses the problems of data sanitization and cyber situational awareness by analyzing 910 GB of real Internet-scale traffic, which has been passively collected by monitoring close to 16.5 million darknet IP addresses from a /8 and a /13 network telescopes. First, the paper offers a novel probabilistic darknet preprocessing model, which aims at sanitizing darknet data to prepare it for effective use in the task of cyber threat intelligence generation. Such model has been engineered using a distributed multithreaded approach, rendering it highly effective on darknet big data. Second, the paper further contributes by presenting an innovative approach to infer large-scale orchestrated probing campaigns by leveraging darknet data, for Internet cyber situational awareness. The approach uniquely reduces the dimensionality of such big data by utilizing its artifacts, instead of processing the actual raw data. This is accomplished by extracting and analyzing probing time series using formal methods rooted in Fourier transform and Kalman filtering. Thorough empirical evaluations indeed validate the accuracy and the performance of the proposed methods. We assert that such approaches are of significant value, given their highly applicable nature to the field of Internet measurements for cyber security in the era of big data.

Bou-Harb, Elias↗
NASA NTRS2003

Black Hole Masses in Three Seyfert Galaxies

We analyze published reverberation mapping data for three Seyfert galaxies (NGC 3227, NGC 3516, and NGC 4593) to refine the mass estimate for the supermassive black hole in the center of each object. Treatment of the data in a manner more consistent with other large compilations of such masses allows us to more securely compare our results to wider samples of data, e.g., in the investigation of the M(sub bh-sigma*) relationship for active and quiescent galaxies.

Onken, C. A.↗
DOE OSTI2016

The DOE Geothermal Data Repository and the Future of Geothermal Data

We are, right now, in the midst of the Data Revolution. Just as the Industrial Revolution of the early 1800's marked a major turning point in history with changes to manufacturing that influenced almost every aspect of daily life, the proliferation of big data and the evolution of our collective ability to put it to work for us is having a profound impact on our lives today. The U.S. Department of Energy's (DOE) Geothermal Data Repository (GDR) is leveraging new resources, such as the National Renewable Energy Laboratory's (NREL) new secure cloud environment, and cutting edge approaches to data analysis, to prepare for the challenges that data from energy and geosciences research presents. This paper will explore some of those challenges, highlight some of the benefits of data analysis, and explain the steps DOE and NREL are taking to ensure the GDR is ready for the future of geothermal data.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Systems and methods for optical physical unclonable parameters

An article of manufacture includes a substrate and a security primitive deposited on the substrate. The security primitive includes a transition metal dichalcogenide having a varying thickness. According to various embodiments, the transition metal dichalcogenide comprises a chalcogen atom (X) selected from the group consisting of S, Se, and Te and a transition metal (M) selected from the group consisting of Mo, W, Hf, and Zr. The security primitive is pixelated into a plurality of discrete regions having different luminescence. A security primitive key includes a first set of data values corresponding to a first set of coordinates of a first region and a second set of data values corresponding to a second set of coordinates of a second region. In some embodiments, the security primitive key is digitally captured through an optical reader and verified by querying a database.

Shahrjerdi, Davood↗
DOE OSTI2015

Post-Closure Report for Closed Resource Conservation and Recovery Act Corrective Action Units, Nevada National Security Site, Nevada, for Fiscal Year 2014

This report serves as the combined annual report for post-closure activities for several Corrective Action Units (CAUs). The locations of the sites are shown in Figure 1. This report covers fiscal year 2014 (October 2013–September 2014). The post-closure requirements for these sites are described in Resource Conservation and Recovery Act Permit Number NEV HW0101 and summarized in each CAU-specific section in Section 1.0 of this report. The results of the inspections, a summary of maintenance activities, and an evaluation of monitoring data are presented in this report. Site inspections are conducted semiannually at CAUs 90 and 91 and quarterly at CAUs 92, 110, 111, and 112. Additional inspections are conducted at CAU 92 if precipitation occurs in excess of 0.50 inches (in.) in a 24-hour period and at CAU 111 if precipitation occurs in excess of 1.0 in. in a 24-hour period. Inspections include an evaluation of the condition of the units, including covers, fences, signs, gates, and locks. In addition to visual inspections, soil moisture monitoring, vegetation evaluations, and subsidence surveys are conducted at CAU 110. At CAU 111, soil moisture monitoring, vegetation evaluations, subsidence surveys, direct radiation monitoring, air monitoring, radon flux monitoring, and groundwater monitoring are conducted. The results of the vegetation surveys and an analysis of the soil moisture monitoring data at CAU 110 are presented in this report. Results of additional monitoring at CAU 111 are documented annually in the Nevada National Security Site Waste Management Monitoring Report Area 3 and Area 5 Radioactive Waste Management Sites and in the Nevada National Security Site Data Report: Groundwater Monitoring Program Area 5 Radioactive Waste Management Site, which will be prepared in approximately June 2015. All required inspections, maintenance, and monitoring were conducted in accordance with the post-closure requirements of the permit. It is recommended to continue inspections and monitoring as scheduled.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗