Search NASA⌕ Search

SEARCH · Search NASA

Results for “COUNTING RATE COMPUTER”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Reaction Rate Ratios for Recent Fast Metal Experiments with Large Plutonium Masses

Reaction rate ratios are integral responses that are used within the criticality experiments field because they contain spectral information. While these types of measurements have been utilized for nuclear data validation with historic experiments, few experiments of this type have been utilized for recent experiments, as few exist. This work focuses on measured reaction rate ratios for two nearly bare plutonium critical assemblies with different geometries: one that is cube like (with a Pu mass of 40 kg) and one that is slab like (with a Pu mass of 109 kg). Irradiations were performed with both configurations in which foils were placed near the center of the assembly. Plutonium, highly enriched uranium, depleted uranium, and Au foils were included in the irradiation and counted via high-purity germanium detectors. From these measurements, reaction rate ratios were calculated. Measured and simulated values and uncertainties are presented for the reaction rate ratios. Ratios utilizing the following reactions are given in this work: 197 Au(n, γ), 197 Au(n,2n), 235 U(n,fission), 238 U(n, fission), 238 U(n,2n), 238 U(n,γ), and 239 Pu(n,fission). Uncertainties for the measured reaction rate ratios ranged from 4% to 7%, and the contribution of various parameters to this uncertainty was investigated. The results are compared to historical experiments and should be used for nuclear data validation for future nuclear data library releases. These measurements are part of the EUCLID (Experiments Underpinned by Computational Learning for Improvements in Nuclear Data) project, which utilizes measurement responses in addition to k eff (such as these reaction rate ratios) to help reduce uncertainties in 239 Pu nuclear data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Improved Distributed-memory Triangle Counting by Exploiting the Graph Structure

Graphs are ubiquitous in modeling complex systems and representing interactions between entities to uncover structural information of the domain. Traditionally, graph analytics workloads are challenging to efficiently scale (both strong and weak cases) on distributed memory due to the irregular memory-access driven nature (with little or no computations) of the methods. The structure of graphs and their relative distribution over the processing elements poses another level of complexity, making it difficult to attain sustainable scalability across platforms. In this paper, we discuss enhancements to TriC, a distributed-memory implementation of graph triangle counting using Message Passing Interface (MPI), which was featured in the 2020 Graph Challenge competition. We have made some incremental enhancements to TriC, primarily adopting a user-defined buffering strategy to overcome the startup problem for large graphs (by fixing the memory for intermediate data), and experimenting with probabilistic data structures such as bloom filter to improve the query response time for assessing edge existence, at the expense of increasing the overall false positive rate. These adjustments have led to a modest improvements in most cases, as compared to the previous version.

Graph Analytics, HPC↗
DOE OSTI2022

Load‐Balancing Intense Physics Calculations to Embed Regionalized High‐Resolution Cloud Resolving Models in the E3SM and CESM Climate Models

Abstract We design a new strategy to load‐balance high‐intensity sub‐grid atmospheric physics calculations restricted to a small fraction of a global climate simulation's domain. We show why the current parallel load balancing infrastructure of Community Earth System Model (CESM) and Energy Exascale Earth Model (E3SM) cannot efficiently handle this scenario at large core counts. As an example, we study an unusual configuration of the E3SM Multiscale Modeling Framework (MMF) that embeds a binary mixture of two separate cloud‐resolving model grid structures that is attractive for low cloud feedback studies. Less than a third of the planet uses high‐resolution (MMF‐HR; sub‐km horizontal grid spacing) relative to standard low‐resolution (MMF‐LR) cloud superparameterization elsewhere. To enable MMF runs with Multi‐Domain cloud resolving models (CRMs), our load balancing theory predicts the most efficient computational scale as a function of the high‐intensity work's relative overhead and its fractional coverage. The scheme successfully maximizes model throughput and minimizes model cost relative to precursor infrastructure, effectively by devoting the vast majority of the processor pool to operate on the few high‐intensity (and rate‐limiting) high‐resolution (HR) grid columns. Two examples prove the concept, showing that minor artifacts can be introduced near the HR/low‐resolution CRM grid transition boundary on idealized aquaplanets, but are minimal in operationally relevant real‐geography settings. As intended, within the high (low) resolution area, our Multi‐Domain CRM simulations exhibit cloud fraction and shortwave reflection convergent to standard baseline tests that use globally homogenous MMF‐LR and MMF‐HR. We suggest this approach can open up a range of creative multi‐resolution climate experiments without requiring unduly large allocations of computational resources.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Breaking the mold: Overcoming the time constraints of molecular dynamics on general-purpose hardware

The evolution of molecular dynamics (MD) simulations has been intimately linked to that of computing hardware. For decades following the creation of MD, simulations have improved with computing power along the three principal dimensions of accuracy, atom count (spatial scale), and duration (temporal scale). Since the mid-2000s, computer platforms have, however, failed to provide strong scaling for MD, as scale-out central processing unit (CPU) and graphics processing unit (GPU) platforms that provide substantial increases to spatial scale do not lead to proportional increases in temporal scale. Important scientific problems therefore remained inaccessible to direct simulation, prompting the development of increasingly sophisticated algorithms that present significant complexity, accuracy, and efficiency challenges. While bespoke MD-only hardware solutions have provided a path to longer timescales for specific physical systems, their impact on the broader community has been mitigated by their limited adaptability to new methods and potentials. In this work, we show that a novel computing architecture, the Cerebras wafer scale engine, completely alters the scaling path by delivering unprecedentedly high simulation rates up to 1.144 M steps/s for 200 000 atoms whose interactions are described by an embedded atom method potential. This enables direct simulations of the evolution of materials using general-purpose programmable hardware over millisecond timescales, dramatically increasing the space of direct MD simulations that can be carried out. In this paper, we provide an overview of advances in MD over the last 60 years and present our recent result in the context of historical MD performance trends.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Constrained variational optimization of counting-time allocation in sequential scattering measurements: Application to Bonse–Hart USANS

Sequential scattering measurements are often performed under a fixed experimental-time budget, even though the expected count rate varies strongly across the measured coordinate. When the dwell time at each measurement position can be controlled independently, this variation creates a general resource-allocation problem: how should the available time be distributed to minimize the uncertainty of the reconstructed profile? We formulate this problem as a constrained variational optimization for measurements governed by Poisson counting statistics. When each measurement is treated independently, minimizing the averaged squared relative uncertainty yields an inverse-square-root intensity allocation. The formulation is then generalized to include correlations between neighboring measurements and an instrumental resolution operator, leading to an allocation criterion that equalizes the marginal reduction in posterior uncertainty per unit measurement time. Bonse–Hart ultra-small-angle neutron scattering (USANS), in which reciprocal space is sampled sequentially through analyzer-angle stepping, provides an experimentally grounded application. Computational benchmarking shows that the optimized allocation outperforms uniform-time and constant-relative-error strategies, while application to an experimentally measured graphite USANS profile from the Spallation Neutron Source, using Poisson resampling under alternative schedules, demonstrates how counting time should be redistributed toward weak-intensity regions under an identical total duration. The resulting framework applies to sequential scattering and related scanning measurements whenever local dwell times are adjustable and directly determine the measurement uncertainties, and when the relevant correlation and instrumental-response models are available.

Tung, Chi-Huan [ORNL] (ORCID:0000000221972074)↗
DOE OSTI2025

Gamma and Neutron Measurement and Modeling of Irradiated TRISO Fuel

Given the unique characteristics of the PBR fuel cycle, both gamma and neutron measurements are expected to play important roles in performing and maintaining nuclear material control and accounting for spent pebbles to safeguard the fuel cycle. Given the lack of irradiated pebbles in the US, a variety of irradiated TRISO fuel samples with wide ranges of burnups and cooling times available at ORNL were used in this work. A large number of gamma and neutron measurements have been performed on these samples to collect data to test the various detectors and to benchmark the computer models to simulate the depletion and decay of the fuel and the measurements themselves. Two neutron detectors, including a custom-made detector and the Very High-Performance Neutron Multiplicity Counting, were used to measure the neutrons emitted by these TRISO samples. Three gamma spectrometry detectors, including an HPGe and the M400 CZT detector, were used to measure gamma-ray emissions from these samples. The M400 was recently adopted by the IAEA for fresh uranium measurements, but it was tested for spent fuel measurements prior to this project. Detailed MCNP models were developed to simulate these neutron and gamma measurements. Some GADRAS models were also developed to cross check the MCNP models for the gamma measurements. It was found challenging to perform neutron measurements in the hot cell due to the high background counts. Close agreements were observed between the simulated and measured neutron count rates in both detectors’ measurements of californium calibration sources. Both the HPGe and M400 detectors were able to measure the 604 and 662 keV peaks from these samples, which are the two most important peaks used to infer fuel burnup. Although the M400 detector did not have nearly good energy resolution and did not detect some of the minor peaks as the HPGe detector, it was found to be capable of handling significantly higher dose rates than HPGe. Given the complexities in the TRISO samples (e.g., different samples sizes) and uncertainties in the alignments between the detector and the TRISO fuel inside the containers, large scatters were found between the peak area rates and the samples’ burnups. However, the 604/662 peak ratios were found to trend well with the samples’ burnups among most samples in both measured and simulated results.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Harvesting the low-hanging fruit of high energy savings -- Virtual Occupancy using Wi-Fi Data

Approximately 20% of primary energy consumed in the U.S. is attributed to HVAC use. Ideally, HVAC operation would be driven by actual building occupancy, but lack of reliable occupancy information often results in the use of conservative static schedules. This disparity is even more pronounced in a college campus, where the function of each space differs by building (classrooms, offices, libraries) and the class schedules change frequently -- every semester, day of week, and hour. While several research papers propose the use of counts of the Wi-Fi connections (e.g., phones, computers) as a proxy for occupancy, few real-world implementations exist. This paper describes the development and deployment of an open-source Wi-Fi-to-Occupancy software library in 65 buildings of a college campus, and the planned integration with the building energy management and control system at the building scale. Over a year of Wi-Fi data was gathered into distinct academic periods, including fall and spring semester, academic breaks, and summer sessions. Patterns such as students moving between classrooms, closing laptops before exams, etc., can be visualized from the data. Approximating occupancy from Wi-Fi data presents challenges which we address in this project -- for example, identifying static devices, or estimating the ratio of devices per person. Utilizing real-time occupancy data to inform optimal HVAC schedules and ventilation rates creates the potential to identify and reduce energy waste. Other potential applications include forecasting occupancy, and using Wi-Fi data to predict peak demands. Finally, the paper discusses how to easily scale these tools to other buildings.

Pritoni, Marco↗
DOE OSTI2021

Negative Muon Count for the FNAL Muon g-2 Experiment

The primary goal of the FNAL Muon g-2 Experiment, located in Batavia, IL, USA, is to measure the anomalous magnetic dipole moment, a μ = (g-2)/2, of a muon to a precision of 0.14 ppm. Initial results from the experiment confirm a discrepancy between the experimental value of the muon anomaly compared with the theoretical value first discovered by the Brookhaven Muon g-2 Experiment in 2001. This discrepancy hints at new physics. However, running the experiment with negative muons is essential in order to verify the experimental value of the muon anomaly. As neither experiments nor simulation studies pertaining to the muon anomaly have been performed with negative muons, the goal of this work is to estimate the rates of negative muons arriving at the FNAL Muon g-2 experiment so that scientists can estimate the time required to run the experiment with negative muons. In order to do this, we performed several simulations of the Muon Campus, which delivers muons to the FNAL Muon g-2 Experiment, using the G4Beamline v. 3.06 at the National Energy Research Computing Center. We performed separate simulations for different sections of the muon campus, and simulations were repeated twice, once for negative muons and once for positive muons. An initial 10 9 protons were shot on target for both the negative and positive muon simulations. We found that the rate of negative muons arriving at the experiment was 0.549 times the rate of positive muons arriving at the experiment.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Rapid neutron and gamma-ray source localization using machine learning

Rapid localization of radiation sources is critical for applications including nuclear emergency response, safeguards, and security. However, conventional imaging systems such as neutron scatter cameras and Compton cameras depend on rare coincidence events, which often result in long acquisition times. In this work, we address the challenge of rapid source localization by developing a machine learning approach to predict the direction of a single radiation source using only count rates from an array of neutron and gamma-ray detectors. The proposed model is a fully connected neural network (FCNN) trained using Monte Carlo simulation data from a 252 Cf source. The model hyperparameters are optimized with a small set of routine 252 Cf measurements. We benchmarked the performance of the trained and optimized machine learning model using additional 252 Cf , 137 Cs , and PuBe measurements under laboratory conditions with varying source-detector configurations. For these measurements, the machine learning model achieved a mean localization error smaller than 30° with 3 x 10 3 system counts, corresponding to 8 s measurement time for the imaging system used in this work. In this low-statistics regime, the method outperformed traditional scatter-based imaging by more than 75% in localization accuracy for the evaluated measurement configurations. These results demonstrate that a machine learning-based approach can significantly reduce the time required for accurate single-source localization, providing a robust and computationally efficient alternative to traditional imaging systems in time-critical nuclear security and emergency response scenarios.

Gamma-ray imaging↗
DOE OSTI2023

Single-photon detection using high-temperature cuprate superconductors

The detection of individual quanta of light is important for quantum computation, fluorescence lifetime imaging, single-molecule detection, remote sensing, correlation spectroscopy, and more. Thanks to their broadband operation, high detection efficiency, exceptional signal-to-noise ratio, and fast recovery times, superconducting nanowire single-photon detectors (SNSPDs) have become a critical component in these applications. The operation of SNSPDs based on conventional superconductors, which have a low critical temperature (T c ), requires costly and bulky cryocoolers. This motivated exploration of other superconducting materials with higher T c that would enable single-photon detection at elevated temperatures, yet this task has proven exceedingly difficult. Here we show that with proper processing, high-T c cuprate superconductors can meet this challenge. We fabricated superconducting nanowires (SNWs) out of thin flakes of Bi 2 Sr 2 CaCu2O 8+δ and La 1.55 Sr 0.45 CuO 4 /La 2 CuO 4 (LSCO-LCO) bilayer films and demonstrated their single-photon response up to 25 and 8 K, respectively. The single-photon operation is revealed through the linear scaling of the photon count rate (PCR) on the radiation power. Both of our cuprate-based SNSPDs exhibited single-photon sensitivity at the technologically-important 1.5 μm telecommunications wavelength. Our work expands the family of superconducting materials for SNSPD technology, opens the prospects of raising the temperature ceiling, and raises important questions about the underlying mechanisms of single-photon detection by unconventional superconductors.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Evaluation of gamma-ray transmission through rectangular collimator slits for application in nuclear fuel spectrometry

Gamma-ray spectrometry is widely applied in several science fields, and in particular in non-destructive gamma scanning and gamma emission tomography of irradiated nuclear fuel. Usually, a collimator is used in the experimental setup, to selectively interrogate a region of interest in the fuel. For the optimization of instrument design, as well as for planning measurement campaigns, predictive models for the transmitted gamma-ray intensity through the collimator are needed. Commonly, Monte Carlo Radiation Transport tools are used for accurate prediction of gamma-ray transport, however, the long computation time requirements when used in low-efficiency experimental setups present challenges. Here, the full-energy peak intensity transmitted through a rectangular collimator slit was examined. A uniform planar surface source emitting isotropically was considered, and the rate of photons reaching an ideal counter plane on the opposite side of the collimator was evaluated by analytical integration. To find a closed-form primitive function, some idealizations were required, and thereby parametric models were obtained for the optical field of view, dependent on slit dimensions (length, height and width) and source-to-collimator distance. For contributions from outside the optical field of view, where a closed-form expression cannot be found, fast numerical integral methods were instead used. The results were validated using the Monte Carlo code MCNP6 and show an agreement within three percent for the numerical method. For the analytical method, deviations up to tens of percent were obtained, which is deemed to still be sufficient for instrument design and measurement planning, where often the order of magnitude of the count rate is not a priori known. The method is planned for use in iterative optimization routines in the design of Gamma Emission Tomography devices, as well as for the prediction of gamma spectra obtained in the planning of fuel inspections. An application of the proposed method was demonstrated in spectrum prediction for a short cooling-time fuel rod test from the Halden reactor.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

A shared-mobility-based framework for evacuation planning and operations under forecast uncertainty

To meet evacuation needs from carless populations who need personalized assistance to evacuate safely, in this article we propose a ridesharing-based evacuation program that recruits volunteer drivers before a disaster strikes, and then matches volunteer drivers with evacuees once demand is realized. Here we optimize resource planning and evacuation operations under uncertain spatiotemporal demand, and construct a two-stage stochastic mixed-integer program to ensure high demand fulfillment rates. We consider three formulations to improve the number of evacuees served, by minimizing an expected penalty cost, imposing a probabilistic constraint, and enforcing a constraint on the conditional value at risk of the total number of unserved evacuees, respectively. We discuss the benefits and disadvantages of the different risk measures used in the three formulations, given certain carless population sizes and the variety of evacuation modes available. We also develop a heuristic approach to provide quick, dynamic and conservative solutions. We demonstrate the performance of our approaches using five different networks of varying sizes based on regions of Charleston County, South Carolina, an area that experienced a mandatory evacuation order during Hurricane Florence, and utilize real demographic data and hourly traffic count data to estimate the demand distribution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Toward an AI-Powered Software Pipeline for Real-Time Tracking and Analysis of Wildfire and Smoke

Real-time tracking of wildfires and smoke is crucial for effective response, minimizing damage, protecting lives, and efficiently managing resources during fire emergencies. We develop a web-based AI-powered pipeline that detects wildfires in aerial video and estimates deployment-relevant behavior metrics, including cumulative burned area, burned-area growth rate, fire spread direction, and smoke dispersion. The system combines a YOLO-based detector with YCbCr-based fire segmentation, HSV-based smoke segmentation, Farneback optical flow, and centroid-based spatiotemporal tracking. Using ground sampling distance (GSD), pixel-level fire masks are converted to physical burned-area measurements by correlating fire pixel counts with camera altitude and tilt angle. We benchmark YOLO variants and non-YOLO baselines (GoogLeNet, CNN, DBN, Autoencoder, U-Net, and AlexNet) on the IEEE FLAME dataset and a newly created aerial frame dataset, Wildfire-DB. Cross-dataset evaluation uses a strict threshold-transfer protocol: decision thresholds are selected on FLAME validation and transferred unchanged to Wildfire-DB to quantify generalization under domain shift. YOLOv6 achieves the strongest cross-dataset frame-level fire detection on Wildfire-DB (ROC-AUC 0.8200, PR-AUC 0.8044, and transferred-threshold F1 0.7596). For tracking-oriented deployment requiring oriented localization, YOLO11-OBB provides the most reliable cross-dataset behavior among OBB-capable models while remaining computationally feasible. To analyze the feasibility of UAV deployment, we further measure inference efficiency using synchronized GPU and CPU power logs on a fixed workload of 1569 frames. YOLO-family models process the video in 5.73–12.47 seconds with net energy of 1247.28–1775.39 J, substantially lower latency and energy than heavier classification and reconstruction baselines. Overall, model optimality depends on operational objectives: YOLOv6 is best for cross-dataset detection robustness, whereas YOL...

Color segmentation↗
DOE OSTI2023

Hydrogen Plus Other Alternative Fuels Risk Assessment Models (HyRAM+) Version 5.1 Technical Reference Manual

The HyRAM+ software toolkit provides a basis for conducting quantitative risk assessment and consequence modeling for hydrogen, natural gas, and autogas systems. HyRAM+ is designed to facilitate the use of state-of-the-art models to conduct robust, repeatable assessments of safety, hazards, and risk. HyRAM+ integrates deterministic and probabilistic models for quantifying leak sizes and rates, predicting physical effects, characterizing hazards (thermal effects from jet fires, overpressure effects from delayed ignition), and assessing impacts on people. HyRAM+ is developed at Sandia National Laboratories to support the development and revision of national and international codes and standards, and to provide developed models in a publicly-accessible toolkit usable by all stakeholders. This document provides a description of the methodology and models contained in HyRAM+ version 5.1. The most significant changes for HyRAM+ version 5.1 from HyRAM+ version 5.0 are updated default leak frequency values for propane, new default component counts for different fuel types, and an improved fuel specification view in the graphical user interface.

02 PETROLEUM↗
DOE OSTI2024

Statistical Validation of Multiple Related Data Sets—Case Study Using Interstellar Boundary Explorer Satellite Data

Abstract Space scientists often face the question of whether data collected by different instruments are measurements of the same source population. This paper proposes a statistical validation method for evaluating the agreement between such related data sets. It offers a detailed case study focused on validating a new data set from the Interstellar Boundary Explorer (IBEX) mission, which serves as a practical how-to guide for similar analyses. Since 2008, the IBEX satellite has been gathering data on heliospheric energetic neutral atoms (ENAs) while being exposed to various sources of background noise, such as cosmic rays and solar energetic particles. The IBEX mission initially released only a qualified triple-coincidence (qABC) data product, which was designed to provide observations of ENAs free of background contamination. Further measurements revealed that the qABC data were in fact susceptible to contamination, having relatively low ENA counts and high background rates. To mitigate this issue, the mission team recently considered releasing a certain qualified double-coincidence (qBC) data product, which has roughly twice the detection rate of the qABC data product. This paper presents a simulation-based validation of the new qBC data product against the already-released qABC data product. The results show that the qBCs can plausibly be said to be measuring the same source population as the qABCs up to an average absolute deviation of 3.6%. Visual diagnostics provide additional confirmation of source rate coherence across data products. The framework introduced here is general and can be applied to other validation problems both within and outside the field of space physics.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Boron coated straw-based neutron multiplicity counter for neutron interrogation of TRISO fueled pebbles

Pebble bed reactors (PBRs) can improve the safety and economics of the nuclear energy production. PBRs rely on TRIstructural-ISOtropic (TRISO) fuel pebbles for enhanced fission product retention. Accurate characterization of individual fuel pebbles would enable the validation of computational models, efficient use of TRISO fuel, and improve fuel accountability. Here, we have developed and tested a new neutron multiplicity counter (NMC) based on 192 boron coated straw (BCS) detectors optimized for 235 U assay in TRISO fuel. The new design yielded a singles and doubles neutron detection efficiency of 4.71% and 0.174%, respectively, and a die-away time of 16.7 μs. The NMC has a low intrinsic gamma-ray detection efficiency of 8.71 x 10 –8 at an exposure rate of 80.3 mR/h. In simulation, a high-efficiency version of the NMC encompassing 396 straws was able to estimate the 235 U in a pebble with a relative uncertainty and error both below 2% in 100 s.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Scalable Circuit Cutting and Scheduling in a Resource-constrained and Distributed Quantum System

Despite quantum computing's rapid development, current systems remain limited in practical applications due to their limited qubit count and quality. Various technologies, such as superconducting, trapped ions, and neutral atom quantum computing technologies are progressing towards a fault tolerant era, however they all face a diverse set of challenges in scalability and control. Recent efforts have focused on multi-node quantum systems that connect multiple smaller quantum devices to execute larger circuits. Future demonstrations hope to use quantum channels to couple systems, however current demonstrations can leverage classical communication with circuit cutting techniques. This involves cutting large circuits into smaller subcircuits and reconstructing them post-execution. However, existing cutting methods are hindered by lengthy search times as the number of qubits and gates increases. Additionally, they often fail to effectively utilize the resources of various worker configurations in a multi-node system. To address these challenges, we introduce FitCut, a novel approach that transforms quantum circuits into weighted graphs and utilizes a community-based, bottom-up approach to cut circuits according to resource constraints, e.g., qubit counts, on each worker. FitCut also includes a scheduling algorithm that optimizes resource utilization across workers. Implemented with Qiskit and evaluated extensively, FitCut significantly outperforms the Qiskit Circuit Knitting Toolbox, reducing time costs by factors ranging from 3 to 2000 and improving resource utilization rates by up to 3.88 times on the worker side, achieving a system-wide improvement of 2.86 times.

Kan, Shuwen [Fordham University]↗
DOE OSTI2021

COVID-19: Spatiotemporal social data analytics and machine learning for pandemic exploration and forecasting

This task focused on developing a preliminary approach to use machine learning (ML) to explore the relationship between county-level societal variables and COVID-19 parameters, including COVID-19 cases rates and counts and COVID-19 death rates and counts. The objective was to develop and test a prototype approach for linking COVID-19 and county-level data. The task focused on enhancing and applying existing LANL ML techniques to COVID-19. Our novel ML methods have been a subject of a recently approved U.S. patent. The codes based on these methods are already open-source released. Our ML tools (NMFk/NTFk) are applied to extract hidden features (signals, waves) in the analyzed datasets and automatically identify their optimal number. The features are extracted by identifying counties that have similarities between the county-level societal variables and the COVID-19 parameters. These demonstration analyses will facilitate the ongoing pandemic simulations and predictions performed by Los Alamos other institutions, as well as lay the groundwork for future work.

60 APPLIED LIFE SCIENCES↗