Results for “search paradigms”

NASA NTRS1995

Combining Speed Information Across Space

We used speed discrimination tasks to measure the ability of observers to combine speed information from multiple stimuli distributed across space. We compared speed discrimination thresholds in a classical discrimination paradigm to those in an uncertainty/search paradigm. Thresholds were measured using a temporal two-interval forced-choice design. In the discrimination paradigm, the n gratings in each interval all moved at the same speed and observers were asked to choose the interval with the faster gratings. Discrimination thresholds for this paradigm decreased as the number of gratings increased. This decrease was not due to increasing the effective stimulus area as a control experiment that increased the area of a single grating did not show a similar improvement in thresholds. Adding independent speed noise to each of the n gratings caused thresholds to decrease at a rate similar to the original no-noise case, consistent with observers combining an independent sample of speed from each grating in both the added- and no-noise cases. In the search paradigm, observers were asked to choose the interval in which one of the n gratings moved faster. Thresholds in this case increased with the number of gratings, behavior traditionally attributed to an input bottleneck. However, results from the discrimination paradigm showed that the increase was not due to observers' inability to process these gratings. We have also shown that the opposite trends of the data in the two paradigms can be predicted by a decision theory model that combines independent samples of speed information across space. This demonstrates that models typically used in classical detection and discrimination paradigms are also applicable to search paradigms. As our model does not distinguish between samples in space and time, it predicts that discrimination performance should be the same regardless of whether the gratings are presented in two spatial intervals or two temporal intervals. Our last experiment largely confirmed this prediction.

Verghese, Preeti↗

NASA NTRS1995

Combining Speed Information Across Space

We used speed discrimination tasks to measure the ability of observers to combine speed information from multiple stimuli distributed across space. We compared speed discrimination thresholds in a classical discrimination paradigm to those in an uncertainty/search paradigm. Thresholds were measured using a temporal two-interval forced-choice design. In the discrimination paradigm, the n gratings in each interval all moved at the same speed and observers were asked to choose the interval with the faster gratings. Discrimination thresholds for this paradigm decreased as the number of gratings increased. This decrease was not due to increasing the effective stimulus area as a control experiment that increased the area of a single grating did not show a similar improvement in thresholds. Adding independent speed noise to each of the n gratings caused thresholds to decrease at a rate similar to the original no-noise case, consistent with observers combining an independent sample of speed from each grating in both the added- and no-noise cases. In the search paradigm, observers were asked to choose the interval in which one of the n gratings moved faster. Thresholds in this case increased with the number of gratings, behavior traditionally attributed to an input bottleneck. However, results from the discrimination paradigm showed that the increase was not due to observers' inability to process these gratings. We have also shown that the opposite trends of the data in the two paradigms can be predicted by a decision theory model that combines independent samples of speed information across space. This demonstrates that models typically used in classical detection and discrimination paradigms are also applicable to search paradigms. As our model does not distinguish between samples in space and time, it predicts that discrimination performance should be the same regardless of whether the gratings are presented in two spatial intervals or two temporal intervals. Our last experiment largely confirmed this prediction.

Verghese, Preeti↗

NASA NTRS1994

Constraints on Neural Mechanisms Underlying the Spatial Integration of Speed Information

We measured human ability to integrate speed information presented simultaneously at multiple locations in the visual field. Observers did a two-interval forced-choice speed discrimination task with n grating patches in each interval at 4' eccentricity from fixation. In the first (integration) paradigm, all gratings in each interval moved at the same speed and observers were asked to pick the interval containing the faster gratings. Speed discrimination improved as the number of gratings in each interval increased. The observed decrease in threshold is not due simply to an increase in the effective area because performance with a single grating two, four, or six times the area of the original grating showed no such improvement. Furthermore, the improvement with n was still observed even when the speeds of the individual gratings in each interval were independent samples from a Gaussian distribution and the grating directions were balanced (i.e. equal numbers of gratings moved to the left and right). These results indicate that the neural mechanisms responsible for the integration of speed information act as if each patch provides an independent sample of speed, independent of grating direction. The results are not consistent with simple summation across space by a directionally selective mechanism with a large receptive field. In the second (search) paradigm, only one of the gratings in an interval moved faster and observers were asked to pick this interval. In this case, thresholds increased with the number of distractor gratings. The decrease in performance with increased n is common in search tasks and is often attributed to a bottleneck in the information that observers can process simultaneously. However, the results in the integration paradigm show that subjects indeed have access to and can use additional information with increasing n, at least up to 4 patches. Finally, simple detection models predict the trends in both the search and integration paradigms without invoking an input bottleneck.

Verghese, Preeti↗

NASA NTRS1994

Constraints on Neural Mechanisms Underlying the Spatial Integration of Speed Information

We measured human ability to integrate speed information presented simultaneously at multiple locations in the visual field. Observers did a two-interval forced-choice speed discrimination task with n grating patches in each interval at 4deg eccentricity from fixation. In the first (integration) paradigm, all gratings in each interval moved at the same speed and observers were asked to pick the interval containing the faster gratings. Speed discrimination improved as the number of gratings in each interval increased. The observed decrease in threshold is not due simply to an increase in the effective area because performance with a single grating two, four, or six times the area of the original grating showed no such improvement. Furthermore, the improvement with n was still observed even when the speeds of the individual gratings in each interval were independent samples from a Gaussian distribution and the grating directions were balanced (i.e. equal numbers of gratings moved to the left and right). These results indicate that the neural mechanisms responsible for the integration of speed information act as if each patch provides an independent sample of speed, independent of grating direction. The results are not consistent with simple summation across space by a directionally selective mechanism with a large receptive field. in the second (search) paradigm, only one of the gratings in an interval moved faster and observers were asked to pick this interval. In this case, thresholds increased with the number of distractor gratings. The decrease in performance with increased n is common in search tasks and is often attributed to a bottleneck in the information that observers can process simultaneously. However, the results in the integration paradigm show that subjects indeed have access to and can use additional information with increasing n, at least up to 4 patches. Finally, simple detection models predict the trends in both the search and integration paradigms without invoking an input bottleneck.

Verghese, Preeti↗

DOE OSTI2025

Understanding and design of interstitial oxygen conductors

Highly efficient oxygen-active materials that react with, absorb, and transport oxygen is essential for fuel cells, electrolyzers and related applications. While vacancy-mediated oxygen-ion conductors have long been the focus of research, they are limited by high migration barriers at intermediate temperatures (400–600 °C), which hinder their practical applications. In contrast, interstitial oxygen conductors exhibit significantly lower migration barriers enabling higher ionic conductivity at lower temperatures. This review systematically examines both well-established and recently identified families of interstitial oxygen-ion conductors, focusing on how their unique structural motifs such as corner-sharing polyhedral frameworks, isolated polyhedral, and cage-like architectures, facilitate low migration barriers through interstitial and/or interstitialcy diffusion mechanisms. A central discussion of this review focuses on the evolution of design strategies, from targeted donor doping, element screening, to physical-intuition descriptor material screening and machine learning approach, which leverage computational tools to explore vast chemical spaces in search for new interstitial conductors. The success of these strategies demonstrates that a significant, largely unexplored space remains for discovering high-performing interstitial oxygen conductors. Crucial features enabling high-performance interstitial oxygen diffusion include the availability of electrons for oxygen reduction and sufficient structural flexibility with accessible volume for interstitial accommodation and migration. This review concludes with a forward-looking perspective, proposing a knowledge-driven methodology that integrates current understanding with data-centric approaches to identify promising interstitial oxygen conductors outside traditional search paradigms. These approaches are expected to significantly accelerate the development of high-performance interstitial oxygen conductors for a variety of oxygen-active applications, ultimately paving the way for more efficient and sustainable energy technologies.

Interstitial oxygen conductors↗

DOE OSTI2024

TensorSearch v1

TensorSearch is a novel parallel similarity search paradigm designed to identify known patterns in high-dimensional tensors.

Dong, Bin↗

NASA NTRS1974

Visual search in a forced-choice paradigm

The processing of visual information was investigated in the context of two visual search tasks. The first was a forced-choice task in which one of two alternative letters appeared in a visual display of from one to five letters. The second task included trials on which neither of the two alternatives was present in the display. Search rates were estimated from the slopes of best linear fits to response latencies plotted as a function of the number of items in the visual display. These rates were found to be much slower than those estimated in yes-no search tasks. This result was interpreted as indicating that the processes underlying visual search in yes-no and forced-choice tasks are not the same.

Holmgren, J. E.↗

DOE OSTI2026

Bayesian Exploration and Surrogate Emulation of Nonlinear Beam-Response Geometry in the LBNF Beamline

Next-generation long-baseline neutrino experiments aim to achieve multi-MW proton beam power while reducing accelerator-induced systematic uncertainties. At Fermilab, the LBNF beamline is designed for 1.2 MW operation with PIP-II and is upgradeable to 2.4 MW. DUNE will probe the three-flavor neutrino paradigm and search for CP violation, requiring precise neutrino-flux normalization and improved control of accelerator-related uncertainties. Within the LBNF beamline, the System for On-Axis Neutrino Detection (SAND) will constrain flux uncertainties using precision near-detector measurements, while the Muon Monitor System (MuMS) will provide beamline diagnostics sensitive to the proton beam, target, and horn configuration. However, the pion phase space relevant for DUNE depends simultaneously on many correlated parameters, including beam centroid, beam width, horn current and alignment, target position, optics shifts, and radiation-induced changes. Consequently, MuMS observables exhibit nonlinear and coupled responses that are difficult to characterize using traditional one-parameter scans. To address this challenge, we are developing a Bayesian Exploration framework coupled to physics-informed surrogate emulators trained on Geant4 beamline simulations. Gaussian-process emulators provide both fast predictions and uncertainty estimates, enabling adaptive selection of new simulation points in beam-parameter space. As an initial demonstration, we construct surrogate emulators for MuMS response observables using a verified simulation campaign spanning proton-beam steering conditions. The emulators reproduce the simulated dependence of MuMS centroid and gradient observables while providing predictive uncertainties, and serve as the foundation for future multidimensional exploration including beam width, horn current, and additional beamline parameters. This work establishes a framework for uncertainty-aware beam monitoring, adaptive simulation campaigns, and rapid beam-response inference for future DUNE operations.

Ganguly, Sudeshna [Fermilab] (ORCID:00000003163482↗

NASA NTRS2017

A Relevancy Algorithm for Curating Earth Science Data Around Phenomenon

Earth science data are being collected for various science needs and applications, processed using different algorithms at multiple resolutions and coverages, and then archived at different archiving centers for distribution and stewardship causing difficulty in data discovery. Curation, which typically occurs in museums, art galleries, and libraries, is traditionally defined as the process of collecting and organizing information around a common subject matter or a topic of interest. Curating data sets around topics or areas of interest addresses some of the data discovery needs in the field of Earth science, especially for unanticipated users of data. This paper describes a methodology to automate search and selection of data around specific phenomena. Different components of the methodology including the assumptions, the process, and the relevancy ranking algorithm are described. The paper makes two unique contributions to improving data search and discovery capabilities. First, the paper describes a novel methodology developed for automatically curating data around a topic using Earthscience metadata records. Second, the methodology has been implemented as a standalone web service that is utilized to augment search and usability of data in a variety of tools.

earth science phenomena↗

DOE OSTI2026

AI-driven neutrino diagnostics and radiation-hard beam instrumentation for next-generation neutrino experiments

The Long Baseline Neutrino Facility (LBNF) at Fermilab will deliver a high-intensity, multi-megawatt neutrino beam to the Deep Underground Neutrino Experiment (DUNE), enabling precision tests of the three-neutrino paradigm, CP violation searches, neutrino mass ordering determination, and supernova neutrino studies. To accelerate DUNE’s physics reach and ensure robust beam operations, we propose an integrated AI-driven framework with real-time diagnostics and radiation-hardened instrumentation. At its core is a Real-Time Beam Integrity Monitor using a physics-informed Digital Twin. By reconstructing pion phase space from muon profiles and exploiting magnetic horn optic linearity, it enables spill-by-spill beam correction and flux stabilization. By using this approach, flux-related systematics could be reduced from 5% to 1%, potentially accelerating the discovery of CP violations by four to six years. Complementing this, a US–Japan R&D effort will deploy a LAPPD-based muon monitor in the NuMI beamline. ToF measurements can be acquired with picosecond precision using this radiation-hard system, enhancing sensitivity to horn chromatic effects. Simulations confirm strong response to these effects. ML models predict beam quality and horn current to sub-percent accuracy from muon data, enhancing anomaly detection and stability. This scalable, AI-enabled strategy improves beam fidelity, reduces systematics, and sets a new standard for high-power accelerator operations.

Ganguly, Sudeshna [Fermilab] (ORCID:00000003163482↗

DOE OSTI2025

AI-driven neutrino diagnostics and radiation-hard beam instrumentation for next-generation neutrino experiments

The Long Baseline Neutrino Facility (LBNF) at Fermilab will deliver a high-intensity, multi-megawatt neutrino beam to the Deep Underground Neutrino Experiment (DUNE), enabling precision tests of the three-neutrino paradigm, CP violation searches, neutrino mass ordering determination, and supernova neutrino studies. To accelerate DUNE’s physics reach and ensure robust beam operations, we propose an integrated AI-driven framework with real-time diagnostics and radiation-hardened instrumentation. At its core is a Real-Time Beam Integrity Monitor using a physics-informed Digital Twin. By reconstructing pion phase space from muon profiles and exploiting magnetic horn optic linearity, it enables spill-by-spill beam correction and flux stabilization. By using this approach, flux-related systematics could be reduced from 5% to 1%, potentially accelerating the discovery of CP violations by four to six years. Complementing this, a US–Japan R&D effort will deploy a LAPPD-based muon monitor in the NuMI beamline. ToF measurements can be acquired with picosecond precision using this radiation-hard system, enhancing sensitivity to horn chromatic effects. Simulations confirm strong response to these effects. ML models predict beam quality and horn current to sub-percent accuracy from muon data, enhancing anomaly detection and stability. This scalable, AI-enabled strategy improves beam fidelity, reduces systematics, and sets a new standard for high-power accelerator operations.

Ganguly, Sudeshna [Fermilab] (ORCID:00000003163482↗

NASA NTRS1988

Space languages: Solving the classic scheduling problem in Ada and Lisp

The comparison of programming languages is best seen while evaluating similar systems. The strengths and weaknesses of both languages were investigated as the scheduler was being implemented. Some features used in both languages shall be object-oriented paradigms, parallel programming, search and production heuristics, and other classical artificial intelligence implementations.

Davis, Stephen↗

DOE OSTI2024

Neutrino Program at Fermilab -- Enhancing proton beam power and accelerator infrastructure

The upcoming long baseline neutrino experiments aim to enhance proton beam power to multi-MW scale and utilize large-scale detectors to address the challenge of limited event statistics. The DUNE experiment at LBNF will test the three neutrino flavor paradigm and directly search for CP violation by studying oscillation signatures in the high intensity $\nu_{\mu}$ (anti-$\nu_{\mu}$) beam to $\nu_{e}$ (anti-$\nu_{e}$) measured over a long baseline. Higher beam power and improved accelerator up-time will enhance neutrino flux for the neutrino program by increasing the number of protons on target. LBNF/DUNE, as well as PIP-II upgrade and Accelerator Complex Evolution (ACE) plan, play a vital role in this effort. The scientific potential of ACE plan extends beyond neutrino physics, encompassing endeavors such as the Muon Collider, Charged Lepton Flavor Violation (CLFV), Dark Sectors, and exploration of neutrinos beyond DUNE.\par In the era of higher-power accelerator operation , research in target materials and beam instrumentation is crucial for optimizing design modifications. This abstract discusses Fermilab ACE, the science opportunities it provides, and how Fermilab is pushing the limits of proton beam power and accelerator infrastructure. By tackling neutrino beam challenges and exploring research and development ideas, we are advancing our understanding of fundamental particles and their interactions.

43 PARTICLE ACCELERATORS↗

NASA NTRS1993

Hypertext-based design of a user interface for scheduling

Operations Mission Planner (OMP) is an ongoing research project at JPL that utilizes AI techniques to create an intelligent, automated planning and scheduling system. The information space reflects the complexity and diversity of tasks necessary in most real-world scheduling problems. Thus the problem of the user interface is to present as much information as possible at a given moment and allow the user to quickly navigate through the various types of displays. This paper describes a design which applies the hypertext model to solve these user interface problems. The general paradigm is to provide maps and search queries to allow the user to quickly find an interesting conflict or problem, and then allow the user to navigate through the displays in a hypertext fashion.

Woerner, Irene W.↗

NASA NTRS1993

Hypertext-Based Design of a User Interface for Scheduling

Operations Mission Planner (OMP) is an ongoing research project at JPL that utilizes Artificial Intelligence techniques to create an intelligent, automated planning and scheduling system. The challenge with a user interface is to 1) present as much information as possible at a given moment and 2) allow the user to quickly navigate through the various types of displays. This paper describes a design which applies the hypertext model to solve user interface problems. The general paradigm is to provide maps and search queries to allow the user to quickly find an interesting conflict or problem. Then allow the user to navigate through the displays in a hypertext fashion.

hypertext↗

DOE OSTI2024

Machine learning-accelerated discovery of iron cobalt phosphides as rare-earth-free magnets

Here, the discovery of rare-earth-free permanent magnets has been a goal of scientists for decades. The absence of rare-earth elements will alleviate a pressing concern about the availability of rare-earth elements used in permanent magnets. These magnets are crucial for applications such as wind turbines, electric cars, and memory devices. Rare-earth magnets are special owing to a large magnetic anisotropy energy (K 1 ). In contrast, iron cobalt phosphides hold promise since doping P into cubic FeCo can induce anisotropy, leading to a large coercivity, without introducing rare-earth elements. We present a comprehensive search over the Fe-Co-P ternary space for magnets, utilizing recently developed adaptive machine learning feedback to efficiently screen over 850 000 structures. We focus on machine learning acceleration as a paradigm for materials design. Further adaptive genetic algorithm searches and first-principles calculations aid in the identification of 16 new structures below the known convex hull. Five of them possess high magnetic polarization (J s > 1 T). The structures with desirable magnetic properties center on (Fe,Co) 2⁢ P. This supports conventional wisdom, which focuses on the mixture of the two known end compounds: Fe 2 ⁢P and Co 2 ⁢P. Our work provides guidance for synthesis. We find Fe 7 ⁢CoP 4 shows the most promise (J s = 1.03T and K 1 = 0.83MJ/m 3 ).

36 MATERIALS SCIENCE↗

NASA NTRS1986

Training and subjective workload in a category search task

This study examined automaticity as a means by which training influences mental workload. Two groups were trained in a category search task. One group received a training paradigm designed to promote the development of automaticity; the other group received a training paradigm designed to prohibit it. Resultant performance data showed the expected improvement as a result of the development of automaticity. Subjective workload assessments mirrored the performance results in most respects. The results supported the position that subjective mental workload assessments may be sensitive to the effect of training when it produces a lower level of cognitive load.

Vidulich, Michael A.↗

NASA NTRS1996

Dataflow Design Tool: User's Manual

The Dataflow Design Tool is a software tool for selecting a multiprocessor scheduling solution for a class of computational problems. The problems of interest are those that can be described with a dataflow graph and are intended to be executed repetitively on a set of identical processors. Typical applications include signal processing and control law problems. The software tool implements graph-search algorithms and analysis techniques based on the dataflow paradigm. Dataflow analyses provided by the software are introduced and shown to effectively determine performance bounds, scheduling constraints, and resource requirements. The software tool provides performance optimization through the inclusion of artificial precedence constraints among the schedulable tasks. The user interface and tool capabilities are described. Examples are provided to demonstrate the analysis, scheduling, and optimization functions facilitated by the tool.

Jones, Robert L., III↗