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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Single reader between-cases AUC estimator with nested data

The area under the receiver operating characteristic curve (AUC) is widely used in evaluating diagnostic performance for many clinical tasks. It is still challenging to evaluate the reading performance of distinguishing between positive and negative regions of interest (ROIs) in the nested-data problem, where multiple ROIs are nested within the cases. To address this issue, we identify two kinds of AUC estimators, within-cases AUC and between-cases AUC. We focus on the between-cases AUC estimator, since our main research interest is in patient-level diagnostic performance rather than location-level performance (the ability to separate ROIs with and without disease within each patient). Another reason is that as the case number increases, the number of between-cases paired ROIs is much larger than the number of within-cases ROIs. We provide estimators for the variance of the between-cases AUC and for the covariance when there are two readers. We derive and prove the above estimators’ theoretical values based on a simulation model and characterize their behavior using Monte Carlo simulation results. We also provide a real-data example. Moreover, we connect the distribution-based simulation model with the simulation model based on the linear mixed-effect model, which helps better understand the sources of variation in the simulated dataset.

Health Care Sciences & Services↗
DOE OSTI2024

Assessing the evolution of research topics in a biological field using plant science as an example

Scientific advances due to conceptual or technological innovations can be revealed by examining how research topics have evolved. But such topical evolution is difficult to uncover and quantify because of the large body of literature and the need for expert knowledge in a wide range of areas in a field. Using plant biology as an example, we used machine learning and language models to classify plant science citations into topics representing interconnected, evolving subfields. The changes in prevalence of topical records over the last 50 years reflect shifts in major research trends and recent radiation of new topics, as well as turnover of model species and vastly different plant science research trajectories among countries. Our approaches readily summarize the topical diversity and evolution of a scientific field with hundreds of thousands of relevant papers, and they can be applied broadly to other fields.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Evaluation of data driven low-rank matrix factorization for accelerated solutions of the Vlasov equation

Low-rank methods have shown success in accelerating simulations of a collisionless plasma described by the Vlasov equation, but still rely on computationally costly linear algebra every time step. We propose a data-driven factorization method using artificial neural networks, specifically with convolutional layer architecture, that trains on existing simulation data. At inference time, the model outputs a low-rank decomposition of the distribution field of the charged particles, and we demonstrate that this step is faster than the standard linear algebra technique. Numerical experiments show that the method achieves comparable reconstruction accuracy for interpolation tasks, generalizing to unseen test data in a manner beyond just memorizing training data; patterns in factorization also inherently followed the same numerical trend as those within algebraic methods (e.g., truncated singular-value decomposition). However, when training on the first 70% of a time-series data and testing on the remaining 30%, the method fails to meaningfully extrapolate. Despite this limiting result, the technique may have benefits for simulations in a statistical steady-state or otherwise showing temporal stability. These results suggest that while the model offers a computationally efficient alternative for datasets with temporal stability, its current formulation is best suited for interpolation rather than for predicting future states in time-evolving systems. This study thus lays the groundwork for further refinement of neural network-based approaches to low-rank matrix factorization in high-dimensional plasma simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Sage: parallel semi-asymmtric graph algorithms for NVRAMs

Non-volatile main memory (NVRAM) technologies provide an attractive set of features for large-scale graph analytics, including byte-addressability, low idle power, and improved memory-density. NVRAM systems today have an order of magnitude more NVRAM than traditional memory (DRAM). NVRAM systems could therefore potentially allow very large graph problems to be solved on a single machine, at a modest cost. However, a significant challenge in achieving high performance is in accounting for the fact that NVRAM writes can be much more expensive than NVRAM reads. In this paper, we propose an approach to parallel graph analytics using the Parallel Semi-Asymmetric Model (PSAM), in which the graph is stored as a read-only data structure (in NVRAM), and the amount of mutable memory is kept proportional to the number of vertices. Similar to the popular semi-external and semi-streaming models for graph analytics, the PSAM approach assumes that the vertices of the graph fit in a fast read-write memory (DRAM), but the edges do not. In NVRAM systems, our approach eliminates writes to the NVRAM, among other benefits. To experimentally study this new setting, we develop Sage, a parallel semi-asymmetric graph engine with which we implement provably-efficient (and often work-optimal) PSAM algorithms for over a dozen fundamental graph problems. We experimentally study Sage using a 48--core machine on the largest publicly-available real-world graph (the Hyperlink Web graph with over 3.5 billion vertices and 128 billion edges) equipped with Optane DC Persistent Memory, and show that Sage outperforms the fastest prior systems designed for NVRAM. Importantly, we also show that Sage nearly matches the fastest prior systems running solely in DRAM, by effectively hiding the costs of repeatedly accessing NVRAM versus DRAM.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Towards scalable dataframe systems

Dataframes are a popular abstraction to represent, prepare, and analyze data. Despite the remarkable success of dataframe libraries in R and Python, dataframes face performance issues even on moderately large datasets. Moreover, there is significant ambiguity regarding dataframe semantics. In this paper we lay out a vision and roadmap for scalable dataframe systems. To demonstrate the potential in this area, we report on our experience building Modin, a scaled-up implementation of the most widely-used and complex dataframe API today, Python's pandas. With pandas as a reference, we propose a simple data model and algebra for dataframes to ground discussion in the field. Given this foundation, we lay out an agenda of open research opportunities where the distinct features of dataframes will require extending the state of the art in many dimensions of data management. We discuss the implications of signature dataframe features including flexible schemas, ordering, row/column equivalence, and data/metadata fluidity, as well as the piecemeal, trial-and-error-based approach to interacting with dataframes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Scalable, resilient, and configurable permissioned blockchain fabric

With the advent of Bitcoin, the interest of the database community in blockchain systems has steadily grown. Many existing blockchain applications use blockchains as a platform for monetary transactions, however. We deviate from this philosophy and present ResilientDB, which can serve in a suite of non-monetary data-processing blockchain applications. Our ResilientDB uses state-of-the-art technologies and includes a novel visualization that helps in monitoring the state of the blockchain application.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Building high throughput permissioned blockchain fabrics: challenges and opportunities

Since the introduction of Bitcoin---the first widespread application driven by blockchains---the interest in the design of blockchain-based applications has increased tremendously. At the core of these applications are consensus protocols that securely replicate client requests among all replicas, even if some replicas are Byzantine faulty. Unfortunately, these consensus protocols typically have low throughput, and this lack of performance is often cited as the reason for the slow wider adoption of blockchain technology. Consequently, many works focus on designing more efficient consensus protocols to increase throughput of consensus. We believe that this focus on consensus protocols only explains part of the story. To investigate this belief, we raise a simple question: Can a well-crafted system using a classical consensus protocol outperform systems using modern protocols? In this tutorial, we answer this question by diving deep into the design of blockchain systems. Further, we take an in-depth look at the theory behind consensus, which can help users select the protocol that best-fits their requirements. Finally, we share our vision of high-throughput blockchain systems that operate at large scales.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

A Novel Framework to Evaluate the Costs and Potential of Bioenergy in Decarbonization of the U.S. Economy

The long-term strategy of the United States targets reaching economy-wide net-zero emissions by 2050 and a carbon-neutral electricity grid by 2035 (U.S. Department of State and U.S. Executive Office of the President, 2021). Meeting these targets would require considerable changes to the energy system. Some key characteristics of illustrative net-zero energy systems include increased penetration of renewable energy and carbon sources, use of CO2 capture and storage (CCS) in hard-to-abate sectors, and a greater role for energy carriers such as electricity and hydrogen (Davis et al, 2018). Another common feature of such energy systems is the need for carbon dioxide removal (CDR) approaches (Horowitz et al, 2022). Across all these characteristics of net-zero energy systems, bioenergy and biomass feedstock is anticipated to play an important role. Biomass feedstock serves as a renewable carbon source. This can enable conversion of such feedstock into fuels and energy carriers for hard-to-abate sectors such as aviation. Indeed, the U.S. Government has a target to meet all jet fuel demand by 2050 from sustainable aviation fuel (SAF), where biofuel pathways are likely to have an important role (EERE, 2020). Bioenergy is also highly versatile with the possibility to convert feedstock into electricity, hydrogen, liquid fuels, heat or high-value products, based on biomass type, demand and technology availability (Clarke et al, 2022). Combination of bioenergy with CCS can also nominally deliver CDR (Fuhrman et al, 2023). As such, the share of bioenergy is expected to grow by at least five time across scenarios studied for the long-term strategy of the U.S. between 2020 and 2050 (Horowitz et al, 2022). Notwithstanding the role of bioenergy in the energy systems, its deployment, costs and scalability are influenced by a number of factors. Some of these factors pertain to policy interventions such as imposition of a binding decarbonization target either at an economy-wide level or the sectoral level. Resource availability and type of biomass feedstock also varies considerably across regions. From a technological perspective, the readiness of bioenergy conversion pathways is subject to high variability. This influences the costs of deployment. Moreover, the sourcing of feedstock, grid carbon intensity, and co-product handling approaches all affect the life cycle efficacy of bioenergy. The latter, in turn, is particularly important in determining the extent to which bioenergy with CCS or BECCS can effectively deliver CDR (Fajardy and Mac Dowell, 2017).

air emission↗
DOE OSTI2020

BEE - FY20 P6-3: Release BEEWorkflowManager, BEETaskManager, and client application 2.3.6.01 – LANL ATDM ST / STNS01-4 P6 Milestone Completion Documentation

Release BEEWorkflowManager, BEETaskManager, and client software. The BEEWorkflowManager daemon runs on the HPC cluster login node. It accepts workflows submitted by the BEE client. These workflows are specified using the Common Workflow Language (CWL) standard. The BEEWorkflowManager loads workflows into the Neo4j graph database to create the workflow directed acyclic graph (DAG), and submits the workflow tasks to the BEETaskManager for execution. The BEEWorkflowManager records the state of the workflow and its tasks, and communicates this state to the BEE client. The BEEWorkflowManager will start, pause, and cancel a running workflow and its tasks at the command of the BEE client. The BEETaskManager daemon runs on the HPC cluster login node. It accepts tasks from the BEEWorkflowManager, turns those tasks into HPC resource manager jobs (e.g. a slurm job script), and submits the job to the cluster resource manager. The BEETaskManager then tracks the status of the job (pending, running, complete) and updates the BEEWorkflowManager. The BEETaskManager will also cancel a queued or running job when commanded to do so by the BEEWorkflowManager. The first release of the BEETaskManager will support the Slurm resource manager and the Charliecloud linux container runtime.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Understanding Structures of Cyber Competition in an Era of Major Power Rivalry

Over the past two decades, the cyber domain has emerged and evolved into a key strategic domain for nations across the globe. Security strategies are espoused by heads of state that focus on how to manage the increasingly enormous, crosscutting impact that the cyber domain has on national security across economic, military, intelligence, intellectual property, and countless other facets. These strategies frequently evolve, and even change entirely, as leaders adapt to new technologies and administrations change. Even if these strategies did not change at all, they would take inordinate amounts of time to effectively implement within the organizational structures of government. The time required to go from setting department and agency-level goals, to the time small teams have well-oiled processes and expertise to accomplish tactical objectives that meet the strategic vision is lengthy. With near certainty, by the time objectives and vision are implemented, the landscape, strategy, or both has changed entirely. While this churn will likely never cease due to the rapidly changing nature of the cyber domain, this problem raises an important question: can governmental structures be organized to rapidly adapt to changing cyber strategies? As offices responsible for particular missions in cyberspace shuffle about within the bureaucracy, are technical capabilities enabled or enhanced? No matter how advanced a particular technical capability is or how adept the staff is at solving problems, they will be ineffective if placed haphazardly within the organization: the right authorities may not exist for their office, the correct lines of interpersonal communication may not be established properly, or insufficient resources have not been allocated to effectively deploy a brilliant technical solution. This concept of organizational agility in the context of national cyber capabilities is important when taking into account the National Defense Strategy’s emphasis on cyber capability and the ability of the United States to compete and rapidly adapt to new challenges posed by rivals. As a nation, we are at a point where technology evolves rapidly enough to warrant thoughtful and nimble changes to the bureaucratic structures that support how cyber operations are carried out. Taking these questions and cross-comparing them to the organizational structures across China, Russia, and the United States provides for an interesting thought experiment. As non-democratic regimes, China and Russia have differing priorities and internal power dynamics than the United States and thus organize their governments differently. By combining known and broadcasted strategies of these nations with the observed technical capabilities demonstrated in the public domain, we can begin to see how organizational structures map to strategic goals and directly enable technical capabilities. Insight can be gained by introducing organizational structures into traditional analysis focusing solely around strategies and capabilities; additionally, otherwise unknown capabilities or intents might be discovered or inferred by analyzing organizational structures alone. Analyzing cyber operations from this oft-overlooked perspective could potentially provide useful insight and more concrete actions that can be undertaken to realize the National Defense Strategy’s goals.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2020

Mysteries of Nuclear Fusion: How can we build a star on Earth? [Slides]

This presentation given at the Women's Physics Summer Camp focuses on nuclear fusion as an alternative to fossil fuels, renewable energy sources and nuclear fission energy. The presentation explains plasma physics and how it might be used to generate power. Current attempts and potential applications are outlined. An appeal is made for people trained in mathematics, physics, chemistry, engineering, computer science to solve these problems. If technical problems can be overcome, fusion technology will play a part in a clean, prosperous Earth and expand humanity’s reach to the stars.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Generating HPC Job Profiles and Expectations with Time-Series Data - Showcase Presentation

Summary: Job Profiles and Expectations provide important insights into workloads (Job Profile: window into how a job is running; Job Expectation: Is that job behaving as expected; Provides us with actionable information). Machine learning can be used to group job runs into workload types (Identified groups can then be used for generate expectations); Profiles and Expectations also enable the study of: System-wide events, tracking system changes; System resource utilization and scheduling; Marking log data for further investigation or failures/anomalies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Deploying Machine Learning Workflows into HPC environment

Outline: Workflows Overview; Common Workflow Language (CWL), Example of a CWL; BEE Overview; Machine Learning Components; Machine Learning Scientific Workflow using CWL → A new test case for BEE; Discussion: Benefits and Caveats of current ML workflow; Conclusion.

97 MATHEMATICS AND COMPUTING↗