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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Good Practices for High-Quality Scientific Computing

Experimental and observational sciences have developed robust practices for conducting experiments, maintaining their instruments, and record keeping for provenance. Computational science has only recently begun to confront the issue of quality of their instrument, the software, and the credibility of their scientific output. Most of the available literature in software engineering relates to enterprise software. While it can inform practices in scientific software, adjustments are usually needed. From time to time quality conscious practitioners have published collections of best practices for scientific software. Here, this article provides one more such list but with updated suggestions, motivated by the need to keep up with the rapid changes in the computing industry.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

DeepGRN: prediction of transcription factor binding site across cell-types using attention-based deep neural networks

Abstract Background Due to the complexity of the biological systems, the prediction of the potential DNA binding sites for transcription factors remains a difficult problem in computational biology. Genomic DNA sequences and experimental results from parallel sequencing provide available information about the affinity and accessibility of genome and are commonly used features in binding sites prediction. The attention mechanism in deep learning has shown its capability to learn long-range dependencies from sequential data, such as sentences and voices. Until now, no study has applied this approach in binding site inference from massively parallel sequencing data. The successful applications of attention mechanism in similar input contexts motivate us to build and test new methods that can accurately determine the binding sites of transcription factors. Results In this study, we propose a novel tool (named DeepGRN) for transcription factors binding site prediction based on the combination of two components: single attention module and pairwise attention module. The performance of our methods is evaluated on the ENCODE-DREAM in vivo Transcription Factor Binding Site Prediction Challenge datasets. The results show that DeepGRN achieves higher unified scores in 6 of 13 targets than any of the top four methods in the DREAM challenge. We also demonstrate that the attention weights learned by the model are correlated with potential informative inputs, such as DNase-Seq coverage and motifs, which provide possible explanations for the predictive improvements in DeepGRN. Conclusions DeepGRN can automatically and effectively predict transcription factor binding sites from DNA sequences and DNase-Seq coverage. Furthermore, the visualization techniques we developed for the attention modules help to interpret how critical patterns from different types of input features are recognized by our model.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Enhancement of Industry Legacy Probabilistic Risk Assessment Methods and Tools

Probabilistic risk assessments (PRAs) are integral to nuclear power plant (NPP) operations, having tremendously benefitted the safety of the U.S. reactor fleet for decades. Insights obtained from the models have provided perspectives on a variety of applications, both at the plant and for the regulator. While these models are very useful, they are now being asked to represent and analyze aspects of the plant that were never envisioned by the initial PRA practitioners. Furthermore, heightened demands on the PRA models have led to increased computing power requirements. Additionally, as the complexity of the PRA models increased, the difficulty experienced by non-PRA experts in trying to understand these models, grasp the insights they provide, and effectively use that information has become problematic. The need for research to address key issues regarding PRA tools and methods has never been greater. Although the nuclear power industry has largely been well-served by these tools and methods, the underlying science is dated, remaining mostly unchanged for over two decades. Three areas were identified as most beneficial to address to maintain and improve the usefulness of the current practice legacy PRA tools: improvement in quantification speed, increased ability to efficiently model multi-hazard models, and improvement in modeling human action dependency in PRA. This report provides the outcomes from the FY2021 research into these three areas, identifying potential technical gaps and solutions, and charting the next steps forward to address current PRA challenges.

42 ENGINEERING↗
DOE OSTI2021

Enabling modern data discovery for atmospheric measurements

The Atmospheric Radiation Measurement (ARM) user facility is a US Department of Energy Office of Science user facility that is managed and operated through a collaborative effort led by nine US Department of Energy national laboratories. The ARM Data Center, located at Oak Ridge National Laboratory, is responsible for the timely collection, processing, and delivery of data products to the scientific community. The ARM Data Center holds more than 11,000 data products, including metadata collected from field campaigns, instruments, value-added products, and principal investigator–contributed data. These data sets are checked for successful transfer (for most data, this transfer is carried out automatically via the network; however, some of the largest data sets and some of the most remote sites require manual shipping of hard disks) and both the data and metadata are processed to a standard format, which is an ARM-standardized structure, via the Network Common Data Form. The Network Common Data Form is a self-describing binary format with many compatible software tools. Once processed, the data are cataloged, stored in the ARM Data Archive, and made discoverable through association with an array of metadata-characterizing information, such as location and measurement classification. These metadata enable powerful search capabilities through the ARM Data Center Data Discovery interface. This paper discusses the workflow of how the new discovery system has been redesigned from user requirements and how the data are distributed to the scientific community.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

XXVIIth International Conference on Supersymmetry and Unification of Fundamental Interactions (SUSY 2019) (Final Report)

Supersymmetry (SUSY) is one of most elegant extensions of the Standard Model (SM) and explains the puzzles of the SM by providing a candidate to explain the dark matter content of the universe, allowing scientists to understand the origin of the electroweak scale requiring the top mass to be around 170 GeV and leading to the unification of forces at a grand unified scale. Further the minimal supersymmetric standard model (MSSM) predicts the Higgs boson mass to be less than 135 GeV. The discovery of the Higgs Boson with mass around 125 GeV at the LHC has provided a major support to SUSY ideas. Searches for SUSY are ongoing at the Large Hadron Collider (LHC). Direct and indirect dark matter experiments are searching for a particle dark matter candidate which arises most naturally in SUSY models. Proton decay predicted by SUSY grand unified theories is being searched for at deep underground experiments. In addition, recent advances in neutrino and dark matter physics, observational astrophysics, precision cosmology and the promising new window into the cosmos opened by the direct detection of gravitational waves, have brought new ideas on the potential connections between new fundamental particles and our understanding of their impact on the early universe and its evolution. At present, the major questions include: Is SUSY still the best candidate for models beyond the SM? Do we have any well motivated alternative to SUSY? Have we exhausted all possibilities to search for new physics at high and low energy scales? XXVIIth International Conference on Supersymmetry and Unification of Fundamental Interactions (SUSY 2019), hosted by Texas A&M University – Corpus Christi during May 20-24, 2019, provided a unique venue to discus and understand the status of SUSY, connection between particle physics and cosmology, supersymmetry and its alternative, Higgs sector, neutrino sector, flavor sector, dark matter, electroweak phase transition, astroparticle physics, gravitational waves and string theory. Discussion of results from the LHC, recent neutrino experiments and observations, direct and indirect dark matter detection experiments, detection of gravitational waves, data from particle colliders, as well as measurements of the CMB and Large Scale Structure were an integral part of SUSY 2019. To ensure the younger participants will benefit from the conference the most, the conference was preceded by the 4 day long pre-SUSY summer school for graduate students and postdocs. The invited speakers were leading scientists in the fields of SUSY interest. The school took place on Texas A&M University – Corpus Christi campus during the week prior the SUSY 2019 conference (May 15 – 18, 2019). Since its inception in 1993, SUSY has become one of the most important and widely attended international meetings in high energy physics, devoted to new ideas in fundamental particle physics. SUSY 2019 brought together approximately 250 scientists, theorists, phenomenologists, experimentalists and cosmologists, (including over 60 graduate students and 70 postdocs) representing 22 nations: Australia, Belgium, Canada, Chile, China, Colombia, France, Germany, India, Italy, Japan, Mexico, Peru, Portugal, Romania, South Korea, Spain, Sweden, Switzerland, Taiwan, United Kingdom and United States. SUSY 2019 provided a stimulating venue for the exchange of scientific ideas among experts in dark matter, neutrino physics, particle physics, astrophysics and cosmology. The following scientific topics were delivered during SUSY 2019 in form of 44 plenary talks and over 200 parallel talks: Unification of Forces; Electroweak, Top and Higgs Physics; Precision Calculations and MC tools; BSM in Flavor Physics; Neutrino Masses: Models and Phenomenology; Cosmology and Gravitational Waves; Dark Matter, Astroparticle Physics; Formal Field Theory and Strings; Alternatives to Supersymmetry; Quantum Information: Machine Learning/Big Data. 28 talks were given during the pre-SUSY program related to the following topics: Neutrino Physics; Big Data; Collider Physics & SUSY; String Phenomenology; Cosmology; Dark Matter; SUSY Models and Phenomenology

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Frost prediction using machine learning and deep neural network models

This study describes accurate, computationally efficient models that can be implemented for practical use in predicting frost events for point-scale agricultural applications. Frost damage in agriculture is a costly burden to farmers and global food security alike. Timely prediction of frost events is important to reduce the cost of agricultural frost damage and traditional numerical weather forecasts are often inaccurate at the field-scale in complex terrain. In this paper, we developed machine learning (ML) algorithms for the prediction of such frost events near Alcalde, NM at the point-scale. ML algorithms investigated include deep neural network, convolution neural networks, and random forest models at lead-times of 6–48 h. Our results show promising accuracy (6-h prediction RMSE = 1.53–1.72°C) for use in frost and minimum temperature prediction applications. Seasonal differences in model predictions resulted in a slight negative bias during Spring and Summer months and a positive bias in Fall and Winter months. Additionally, we tested the model transferability by continuing training and testing using data from sensors at a nearby farm. We calculated the feature importance of the random forest models and were able to determine which parameters provided the models with the most useful information for predictions. We determined that soil temperature is a key parameter in longer term predictions (>24 h), while other temperature related parameters provide the majority of information for shorter term predictions. The model error compared favorable to previous ML based frost studies and outperformed the physically based High Resolution Rapid Refresh forecasting system making our ML-models attractive for deployment toward real-time monitoring of frost events and damage at commercial farming operations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

AutoUncertainties: A Python Package for Uncertainty Propagation

Propagation of uncertainties is of great utility in the experimental sciences. While the rules of (linear) uncertainty propagation are straightforward, managing many variables with uncertainty information can quickly become complicated in large scientific software stacks. Often, this requires programmers to keep track of many variables and implement custom error propagation rules for each mathematical operator and function. The Python package AutoUncertainties, described here, provides a solution to this problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Information-incorporated gene network construction with FDR control

Abstract Motivation Large-scale gene expression studies allow gene network construction to uncover associations among genes. To study direct associations among genes, partial correlation-based networks are preferred over marginal correlations. However, FDR control for partial correlation-based network construction is not well-studied. In addition, currently available partial correlation-based methods cannot take existing biological knowledge to help network construction while controlling FDR. Results In this paper, we propose a method called Partial Correlation Graph with Information Incorporation (PCGII). PCGII estimates partial correlations between each pair of genes by regularized node-wise regression that can incorporate prior knowledge while controlling the effects of all other genes. It handles high-dimensional data where the number of genes can be much larger than the sample size and controls FDR at the same time. We compare PCGII with several existing approaches through extensive simulation studies and demonstrate that PCGII has better FDR control and higher power. We apply PCGII to a plant gene expression dataset where it recovers confirmed regulatory relationships and a hub node, as well as several direct associations that shed light on potential functional relationships in the system. We also introduce a method to supplement observed data with a pseudogene to apply PCGII when no prior information is available, which also allows checking FDR control and power for real data analysis. Availability and implementation R package is freely available for download at https://cran.r-project.org/package=PCGII.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

FatPlants: a comprehensive information system for lipid-related genes and metabolic pathways in plants

Abstract FatPlants, an open-access, web-based database, consolidates data, annotations, analysis results, and visualizations of lipid-related genes, proteins, and metabolic pathways in plants. Serving as a minable resource, FatPlants offers a user-friendly interface for facilitating studies into the regulation of plant lipid metabolism and supporting breeding efforts aimed at increasing crop oil content. This web resource, developed using data derived from our own research, curated from public resources, and gleaned from academic literature, comprises information on known fatty-acid-related proteins, genes, and pathways in multiple plants, with an emphasis on Glycine max, Arabidopsis thaliana, and Camelina sativa. Furthermore, the platform includes machine-learning based methods and navigation tools designed to aid in characterizing metabolic pathways and protein interactions. Comprehensive gene and protein information cards, a Basic Local Alignment Search Tool search function, similar structure search capacities from AphaFold, and ChatGPT-based query for protein information are additional features. Database URL: https://www.fatplants.net/

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Insights From the Software Design of a Multiphysics Multicomponent Scientific Code

Using simulations for scientific discovery requires that the software used in the simulations undergoes a rigorous design and development process similar to that of the lab instruments in the experimental sciences. To devise a good design methodology, it is critical to understand the requirements, constraints, and challenges. Furthermore, this article describes insights from the long-term stewardship of a multiphysics multicomponent software, FLASH, that was designed more than 20 years ago for astrophysics, now serves multiple communities, and has been successful in adapting to the changing world of high-performance computing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Data Center Market Report

The data center market is poised to explode in the coming decade due to undeniable drivers such as continued adoption of generative AI, increased data storage needs, and enterprise integration of AI in numerous industries [1] [2] [3]. Scalable power and increased computational capacity are at the forefront of considerations for hyperscalers, the major cloud service providers in this space. Lawrence Livermore National Laboratory is uniquely poised to help with informed decision making for data center market leaders during this phase of explosive expansion. National grid modeling expertise and cutting edge innovations in computer cooling systems place LLNL in an enviable position for creating economic impact in the data center industry by leveraging its expertise in these areas which can help the data center market keep up with growing demand.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

An evaluation of risks associated with relying on Fortran for mission critical codes for the next 15 years

This document examines risks associated with relying on Fortran for codes critical to nuclear security into the late 2030s. The study was requested by the LANL ASC program office to support its decisions. To date, much of the discussion about the prospects for Fortran has tended to be informal and centered on questions (such as, “will the language be around in 20 years”) that are impossible to answer with any certainty. By focusing on estimates of the likelihood associated with different outcomes, rather than on specific predictions, we hope to clarify points of disagreement within the community and strengthen the analytic basis for reasoning about the future.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Sandia's Research in Support of COVID-19 Pandemic Response: Computing and Information Sciences

This report summarizes the goals and findings of eight research projects conducted under the Computing and Information Sciences (CIS) Research Foundation and related to the COVID- 19 pandemic. The projects were all formulated in response to Sandia's call for proposals for rapid-response research with the potential to have a positive impact on the global health emergency. Six of the projects in the CIS portfolio focused on modeling various facets of disease spread, resource requirements, testing programs, and economic impact. The two remaining projects examined the use of web-crawlers and text analytics to allow rapid identification of articles relevant to specific technical questions, and categorization of the reliability of content. The portfolio has collectively produced methods and findings that are being applied by a range of state, regional, and national entities to support enhanced understanding and prediction of the pandemic's spread and its impacts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Modeling the Impacts of Material Properties on Oscillatory Neuron Behavior

In this study, neuromorphic computing, which mimics the functions of biological brains, offers improvements in both latency and energy efficiency over typical von Neumann computing architectures. Spiking neural networks can be especially power-efficient because they encode information temporally and can use more sparse electrical inputs. Here, we study the design of volatile memristors (variable resistors with memory) for neuronal devices, with particular consideration toward the feasibility of all-on-chip oscillation using built-in capacitance. We use circuit simulations to model the behavior of oscillator neurons with a range of realistic material properties. We find that energy inputs increase with insulating-phase resistivity, thermal conductivity, and device aspect ratio. However, we also find that the minimum capacitance needed for oscillation decreases with increasing insulating-phase resistivity, which opposes the constraints for power efficiency. Based on published data on NbO 2 , VO 2 , and EuNiO 3 , we find that existing materials can be engineered for all-on-chip spiking using their parasitic capacitance.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Information on Subsequent Updates to Regional Atlas - SECARB Virtual

During 2020, SSEB worked with its SECARB partners to develop an update for the sixth version of the Atlas to be published as a virtual-only edition by NETL. The following report provides content that was sent to NETL for the SECARB portions of Atlas VI. This report satisfies the requirements for Work Product 1.9.a.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Keeping Classified Information Secret in the World of Quantum Computing

Quantum computing is a technology that promises to revolutionize computing by speeding up key computing tasks in are as such as machine learning and solving otherwise intractable problems.Some influential American policymakers, scholars, and analysts are extremely concerned about the effects quantum computing will have on national security. Similar to the way space technology was viewed in the context of the US-Soviet rivalry during the Cold War, scientific advancement in quantum computing is seen as a race with significant national security consequences, particularly in the emerging US-China rivalry.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Jaynesian Analysis of Environmental Chemistry: Systems Model Component Integration via the Arctic Aquatic Carbon Cycle

Focal Areas: All three are touched upon from the OBER call. We will consider formal logical inference and entropic information analysis for sparse but intensive chemical measurements, as optimal drivers for managing process complexity. As a centerpiece for the discussion we take soil-to-sea organic structural evolution within the Arctic hydro-geochemosphere, assessing detrital negentropy then algorithmic greed to generate ranked, reduced mechanisms for system integration.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

In Silico Human Mobility Data Science: Leveraging Massive Simulated Mobility Data (Vision Paper)

Human mobility data science using trajectories or check-ins of individuals has many applications. Recently, we have seen a plethora of research efforts that tackle these applications. However, research progress in this field is limited by a lack of large and representative datasets. The largest and most commonly used dataset of individual human trajectories captures fewer than 200 individuals, while datasets of individual human check-ins capture fewer than 100 check-ins per city per day. Thus, it is not clear if findings from the human mobility data science community would generalize to large populations. Since obtaining massive, representative, and individual-level human mobility data is hard to come by due to privacy considerations, the vision of this work is to embrace the use of data generated by large-scale socially realistic microsimulations. Informed by both real data and leveraging social and behavioral theories, massive spatially explicit microsimulations may allow us to simulate entire megacities at the person level. The simulated worlds, which do not capture any identifiable personal information, allow us to perform “in silico” experiments using the simulated world as a sandbox in which we have perfect information and perfect control without jeopardizing the privacy of any actual individual. In silico experiments have become commonplace in other scientific domains such as chemistry and biology, permitting experiments that foster the understanding of concepts without any harm to individuals. This work describes challenges and opportunities for leveraging massive and realistic simulated alternate worlds for in silico human mobility data science.

97 MATHEMATICS AND COMPUTING↗