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DOE OSTI2025

Evaluating algorithmic bias on biomarker classification of breast cancer pathology reports

Objectives: This work evaluated algorithmic bias in biomarkers classification using electronic pathology reports from female breast cancer cases. Bias was assessed across 5 subgroups: cancer registry, race, Hispanic ethnicity, age at diagnosis, and socioeconomic status. Materials and Methods: We utilized 594 875 electronic pathology reports from 178 121 tumors diagnosed in Kentucky, Louisiana, New Jersey, New Mexico, Seattle, and Utah to train 2 deep-learning algorithms to classify breast cancer patients using their biomarkers test results. We used balanced error rate (BER), demographic parity (DP), equalized odds (EOD), and equal opportunity (EOP) to assess bias. Results: We found differences in predictive accuracy between registries, with the highest accuracy in the registry that contributed the most data (Seattle Registry, BER ratios for all registries >1.25). BER showed no significant algorithmic bias in extracting biomarkers (estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2) for race, Hispanic ethnicity, age at diagnosis, or socioeconomic subgroups (BER ratio <1.25). DP, EOD, and EOP all showed insignificant results. Discussion: We observed significant differences in BER by registry, but no significant bias using the DP, EOD, and EOP metrics for socio-demographic or racial categories. This highlights the importance of employing a diverse set of metrics for a comprehensive evaluation of model fairness. Conclusion: A thorough evaluation of algorithmic biases that may affect equality in clinical care is a critical step before deploying algorithms in the real world. We found little evidence of algorithmic bias in our biomarker classification tool. Artificial intelligence tools to expedite information extraction from clinical records could accelerate clinical trial matching and improve care.

60 APPLIED LIFE SCIENCES↗

NASA NTRS1986

On the selection and evaluation of visual display symbology Factors influencing search and identification times

Three single-target visual search tasks were used to evaluate a set of cathode-ray tube (CRT) symbols for a helicopter situation display. The search tasks were representative of the information extraction required in practice, and reaction time was used to measure the efficiency with which symbols could be located and identified. Familiar numeric symbols were responded to more quickly than graphic symbols. The addition of modifier symbols, such as a nearby flashing dot or surrounding square, had a greater disruptive effect on the graphic symbols than did the numeric characters. The results suggest that a symbol set is, in some respects, like a list that must be learned. Factors that affect the time to identify items in a memory task, such as familiarity and visual discriminability, also affect the time to identify symbols. This analogy has broad implications for the design of symbol sets. An attempt was made to model information access with this class of display.

Remington, Roger↗

NASA NTRS2008

The Effects of Clumping on Wind Line Variability

We review the effects of clumping on the profiles of resonance doublets. By allowing the ratio of the doublet oscillator strengths to be a free parameter, we demonstrate that doublet profiles contain more information than is normally utilized. In clumped (or porous) winds, this ratio can lie between unity and the ratio of the f-values, and can change as a function of velocity and time, depending on the fraction of the stellar disk that is covered by material moving at a particular velocity at a given moment. Using these insights, we present the results of SEI modeling of a sample of B supergiants, sigma Pup and a time series for a star whose terminal velocity is low enough to make the components of its Si VI lambda lambda 1400 independent. These results are interpreted within the framework of the Oskinova et al. (2007) model, and demonstrate how the doublet profiles can be used to extract information about wind structure.

Prinja, R. K.↗

DOE OSTI2025

Extraction and Analysis of Time Series Data from Building Automation Systems Using Large Language Models

Semantic schemas like Haystack 4, Brick and ASHRAE standard 223 enable the structured, standardized, and machine-readable representation of building data, facilitating interoperability, data integration, and advanced analytics. However, extracting information from these models requires specialized expertise in SPARQL and other programming languages, skills that are not commonly found among building professionals. Recent advancements in Large Language Models (LLMs), such as ChatGPT, enable the construction of queries using natural language, making it easier for individuals to interact with these systems in a manner that resembles everyday speech. However, these methods have not yet been tested on building semantic ontologies. This paper introduces a novel workflow and tool for enabling users to ask questions about a specific building's data, using natural language and receive answers automatically generated by GPT-4o. Our approach integrates semantic ontologies with advanced LLM capabilities to automate three critical steps: (1) generating SPARQL queries to retrieve time series references from ontological models, (2) extracting the corresponding time series data from the Building Automation System, and (3) performing computations and visualizations tailored to the user's query. The proposed method simplifies access to BAS data, allowing both domain experts and non-specialists to conduct sophisticated analyses without needing extensive technical knowledge of semantic web technologies. By demonstrating this pipeline, we facilitate more accessible and scalable data-driven decision-making in building operations and management.

Mulayim, Ozan Baris↗

DOE OSTI2025

Extraction and Analysis of Time Series Data from Building Automation Systems Using Large Language Models

Mulayim, Ozan Baris↗

NASA NTRS1989

Autonomous exploration system: Techniques for interpretation of multispectral data

An on-board autonomous exploration system that fuses data from multiple sensors, and makes decisions based on scientific goals is being developed using a series of artificial neural networks. Emphasis is placed on classifying minerals into broad geological categories by analyzing multispectral data from an imaging spectrometer. Artificial neural network architectures are being investigated for pattern matching and feature detection, information extraction, and decision making. As a first step, a stereogrammetry net extracts distance data from two gray scale stereo images. For each distance plane, the output is the probable mineral composition of the region, and a list of spectral features such as peaks, valleys, or plateaus, showing the characteristics of energy absorption and reflection. The classifier net is constructed using a grandmother cell architecture: an input layer of spectral data, an intermediate processor, and an output value. The feature detector is a three-layer feed-forward network that was developed to map input spectra to four geological classes, and will later be expanded to encompass more classes. Results from the classifier and feature detector nets will help to determine the relative importance of the region being examined with regard to current scientific goals of the system. This information is fed into a decision making neural net along with data from other sensors to decide on a plan of activity. A plan may be to examine the region at higher resolution, move closer, employ other sensors, or record an image and transmit it back to Earth.

Yates, Gigi↗

NASA NTRS2013

Parameter Estimation for Compact Binary Coalescence Signals with the First Generation Gravitational-Wave Detector Network

Compact binary systems with neutron stars or black holes are one of the most promising sources for ground-based gravitational-wave detectors. Gravitational radiation encodes rich information about source physics; thus parameter estimation and model selection are crucial analysis steps for any detection candidate events. Detailed models of the anticipated waveforms enable inference on several parameters, such as component masses, spins, sky location and distance, that are essential for new astrophysical studies of these sources. However, accurate measurements of these parameters and discrimination of models describing the underlying physics are complicated by artifacts in the data, uncertainties in the waveform models and in the calibration of the detectors. Here we report such measurements on a selection of simulated signals added either in hardware or software to the data collected by the two LIGO instruments and the Virgo detector during their most recent joint science run, including a blind injection where the signal was not initially revealed to the collaboration. We exemplify the ability to extract information about the source physics on signals that cover the neutron-star and black-hole binary parameter space over the component mass range 1M25M and the full range of spin parameters. The cases reported in this study provide a snapshot of the status of parameter estimation in preparation for the operation of advanced detectors.

Aasi, J.↗

DOE OSTI2023

Imaging the Future Threats of the Sand Dunes Along the Northwestern Coast of Nile Delta Using SAR

Although the Sand dunes screen the coastal cities from the sand dunes hazards and their infrastructures from rising sea levels and storms, they might threaten and hide the surrounding developmental projects. Thus, imaging these coastal dunes' internal anatomy and dynamics are crucial to protect the environment. In this study, the Optical and Synthetic Aperture Radar (SAR) images were processed and integrated with field observations and surveys to extract information about the past, current, and future behavior of coastal dunes in the Nile Delta-Northwest sector. A conventional change detection method using the supervised Landsat-8 images for the years 2015 and 2017 were generated to show a rapid change in the sand dune cover, where the high-resolution images of Google Earth were used to digitize the dune crests and measure its encroachment rate, which has been reached about 4 m/year with the NW–SE direction. The full-polarimetric ALOS/PALSAR-2 images from the years 2015 and 2017 show very low coherence for the sand dunes, which means these dunes are dynamic, where the changes in both power and phase imbalances of different dune fields during the investigated years (2015 and 2017) show high differences in values, which reached 40 dB and 0.06°, respectively. Finally, the extracted radar facies from the field survey show two successive groups of coastal sand dunes show a decline in the heavy minerals content to a level that might threaten the properties at the shoreline due to sea level rise and wave storms if mitigation actions are not taken place soon.

Ramadan, Rabab↗

NASA NTRS2012

SATPLOT for Analysis of SECCHI Heliospheric Imager Data

Determining trajectories of solar transients such as coronal mass ejections is not always easy. White light images from SECCHI's (Sun Earth Connection Coronal and Heliospheric Investigation) heliospheric imagers are difficult to interpret because they represent a line-of-sight projection of optically thin solar wind structures. A structure's image by itself gives no information about its angle of propagation relative to the Sunspacecraft line, and an image may show a superposition of several structures, all propagating at different angles. Analyzing SECCHI heliospheric imager data using plots of elongation (angle from the Sun) versus time at fixed position angle (aka Jplots ) has proved extremely useful in understanding the observed solar wind structures. This technique has been used to study CME (coronal mass ejection) propagation, CIRs (corotating interaction regions), and blobs. SATPLOT software was developed to create and analyze such elongation versus time plots. The tool uses a library of cylindrical maps of the data for each spacecraft s panoramic field-of-view. Each map includes data from three SECCHI white-light telescopes (the COR2 coronagraph and both heliospheric imagers) at one time for one spacecraft. The maps are created using a Plate Carree projection, optimized for creating the elongation versus time plots. The tool can be used to analyze the observed tracks of features seen in the maps, and the tracks are then used to extract information, for example, on the angle of propagation of the feature.

Hall, Jeffrey R.↗

NASA NTRS1992

Model based estimation of image depth and displacement

Passive depth and displacement map determinations have become an important part of computer vision processing. Applications that make use of this type of information include autonomous navigation, robotic assembly, image sequence compression, structure identification, and 3-D motion estimation. With the reliance of such systems on visual image characteristics, a need to overcome image degradations, such as random image-capture noise, motion, and quantization effects, is clearly necessary. Many depth and displacement estimation algorithms also introduce additional distortions due to the gradient operations performed on the noisy intensity images. These degradations can limit the accuracy and reliability of the displacement or depth information extracted from such sequences. Recognizing the previously stated conditions, a new method to model and estimate a restored depth or displacement field is presented. Once a model has been established, the field can be filtered using currently established multidimensional algorithms. In particular, the reduced order model Kalman filter (ROMKF), which has been shown to be an effective tool in the reduction of image intensity distortions, was applied to the computed displacement fields. Results of the application of this model show significant improvements on the restored field. Previous attempts at restoring the depth or displacement fields assumed homogeneous characteristics which resulted in the smoothing of discontinuities. In these situations, edges were lost. An adaptive model parameter selection method is provided that maintains sharp edge boundaries in the restored field. This has been successfully applied to images representative of robotic scenarios. In order to accommodate image sequences, the standard 2-D ROMKF model is extended into 3-D by the incorporation of a deterministic component based on previously restored fields. The inclusion of past depth and displacement fields allows a means of incorporating the temporal information into the restoration process. A summary on the conditions that indicate which type of filtering should be applied to a field is provided.

Damour, Kevin T.↗

DOE OSTI2023

Full forward model of galaxy clustering statistics with AbacusSummit light cones

ABSTRACT Novel summary statistics beyond the standard 2-point correlation function (2PCF) are necessary to capture the full astrophysical and cosmological information from the small-scale (r < 30h−1Mpc) galaxy clustering. However, the analysis of beyond-2PCF statistics on small scales is challenging because we lack the appropriate treatment of observational systematics for arbitrary summary statistics of the galaxy field. In this paper, we develop a full forward modelling pipeline for a wide range of summary statistics using the large high-fidelity AbacusSummit light cones that account for many systematic effects as well as remain flexible and computationally efficient to enable posterior sampling. We apply our forward model approach to a fully realistic mock galaxy catalog and demonstrate that we can recover unbiased constraints on the underlying galaxy–halo connection model using two separate summary statistics: the standard 2PCF and the novel k-th nearest neighbour (kNN) statistics, which are sensitive to correlation functions of all orders. We will demonstrate its strong constraining power on extended galaxy–halo connection models and cosmology in follow up papers. We expect this to become a powerful approach when applying to upcoming surveys such as DESI where we can leverage a multitude of summary statistics across a wide redshift range to maximally extract information from the non-linear scales.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

Photon temporal-mode readout for inference of neutron star merger remnant gravitational waves

Gravitational waves emitted after neutron star binary coalescences and the information they carry about dense matter are a high-priority target for next-generation detectors. Even though such detectors are expected to observe millions of signals, detectable postmerger emission will remain rare. Here, in this work, we explore postmerger detectability and inference through an alternative detector readout scheme for data dominated by quantum-noise, which is the case above 1 kHz; photon-counting. In such a readout, signals and noise become quantized into discrete distributions corresponding to the detection of single photons measured in a chosen basis of modes. Through simulated data, we demonstrate that photon counting can be efficient even for weak signals. We find ∼1 in 100 signals with a postmerger signal-to-noise ratio of 0.2 can result in a single photon and thus be detected. Furthermore, after 2 ×10 4 signals—equivalent to 10 −2 to 1.5 years of observation—photon counting results in a twofold improvement in the measurement of the radius of a 1.6⁢𝑀 ⊙ neutron star. Constraints can be further tightened if the detector classical noise is reduced. Photon counting offers a promising alternative to traditional homodyne readout techniques for extracting information from low signal-to-noise ratio postmerger signals.

gravitational wave detection↗

NASA NTRS2004

Tools and Data Services from the GSFC Earth Sciences DAAC for Aura Science Data Users

In these times of rapidly increasing amounts of archived data, tools and data services that manipulate data and uncover nuggets of information that potentially lead to scientific discovery are becoming more and more essential. The Goddard Space Flight Center (GSFC) Earth Sciences (GES) Distributed Active Archive Center (DAAC) has made great strides in facilitating science and applications research by, in consultation with its users, developing innovative tools and data services. That is, as data users become more sophisticated in their research and more savvy with information extraction methodologies, the GES DAAC has been responsive to this evolution. This presentation addresses the tools and data services available and under study at the GES DAAC, applied to the Earth sciences atmospheric data. Now, with the data from NASA's latest Atmospheric Chemistry mission, Aura, being readied for public release, GES DAAC tools, proven successful for past atmospheric science missions such as MODIS, AIRS, TRMM, TOMS, and UARS, provide an excellent basis for similar tools updated for the data from the Aura instruments. GES DAAC resident Aura data sets are from the Microwave Limb Sounder (MLS), Ozone Monitoring Instrument (OMI), and High Resolution Dynamics Limb Sounder (HIRDLS). Data obtained by these instruments afford researchers the opportunity to acquire accurate and continuous visualization and analysis, customized for Aura data, will facilitate the use and increase the usefulness of the new data. The Aura data, together with other heritage data at the GES DAAC, can potentially provide a long time series of data. GES DAAC tools will be discussed, as well as the GES DAAC Near Archive Data Mining (NADM) environment, the GIOVANNI on-line analysis tool, and rich data search and order services. Information can be found at: http://daac.gsfc.nasa.gov/upperatm/aura/. Additional information is contained in the original extended abstract.

Kempler, S.↗

NASA NTRS1976

Architecture and data processing alternatives for the TSE computer. Volume 2: Extraction of topological information from an image by the Tse computer

A simple programmable Tse processor organization and arithmetic operations necessary for extraction of the desired topological information are described. Hardware additions to this organization are discussed along with trade-offs peculiar to the tse computing concept. An improved organization is presented along with the complementary software for the various arithmetic operations. The performance of the two organizations is compared in terms of speed, power, and cost. Software routines developed to extract the desired information from an image are included.

Jones, J. R.↗

DOE OSTI2020

From smart homes to smart laboratories: connected instruments for materials science

The current focus on artificial intelligence and machine learning in the scientific community has the potential to greatly speed up discovery. In this article, we explore what a “smart facility” would mean for materials science. We propose to capture meta-data at every step of an experiment, including materials synthesis, sample production and characterization, simulation, and the analysis software used to extract information. Although most of this information is captured in various institutional systems and staff logbooks, more insight could be obtained by connecting this information through a system that allows automation. AI-enabled processes built on such a system would have the potential of making experiment planning easier and minimize the time between experiment and publication.

Doucet, Mathieu↗

DOE OSTI2021

Privacy-Preserving Knowledge Transfer with Bootstrap Aggregation of Teacher Ensembles

There is a need to transfer knowledge among institutions and organizations to save effort in annotation and labeling or in enhancing task performance. However, knowledge transfer is difficult because of restrictions that are in place to ensure data security and privacy. Institutions are not allowed to exchange data or perform any activity that may expose personal information. With the leverage of a differential privacy algorithm in a high-performance computing environment, we propose a new training protocol, Bootstrap Aggregation of Teacher Ensembles (BATE), which is applicable to various types of machine learning models. The BATE algorithm is based on and provides enhancements to the PATE algorithm, maintaining competitive task performance scores on complex datasets with underrepresented class labels.We conducted a proof-of-the-concept study of the information extraction from cancer pathology report data from four cancer registries and performed comparisons between four scenarios: no collaboration, no privacy-preserving collaboration, the PATE algorithm, and the proposed BATE algorithm. The results showed that the BATE algorithm maintained competitive macro-averaged F1 scores, demonstrating that the suggested algorithm is an effective yet privacy-preserving method for machine learning and deep learning solutions.

Yoon, Hong-Jun↗

DOE OSTI2021

Six-point functions and collisions in the black hole interior

In the eternal AdS black hole geometry, we consider two signals sent from the boundaries into the black hole interior shared between the two asymptotic regions. We compute three different out-of-time-order six-point functions to quantify various properties of the collision of these signals behind the horizons: (i) We diagnose the strength of the collision by probing the two-signal state on a late time slice with boundary operators. (ii) We quantify two-sided operator growth, which provides a dual description of the signals meeting in the black hole interior, in terms of the quantum butterfly effect and quantum circuits. (iii) We consider an explicit coupling between the left and right CFTs to make the wormhole traversable and extract information about the collision product from behind the horizon. At a technical level, our results rely on the method of eikonal resummation to obtain the relevant gravitational contributions to Lorentzian six-point functions at all orders in the GN-expansion. We observe that such correlation functions display an intriguing factorization property. We corroborate these results with geodesic computations of six-point functions in two- and three-dimensional gravity.

2D Gravity↗

DOE OSTI2021

Bounds on the Rate-Dependent Plastic Flow of Tantalum up to 75 GPa

In this study we report on the continued development of thermodynamics-based analysis of shock waves propagation with the objective of extracting information related to materials strength at high strain rates and pressures. Building on previous results reported for peak stresses of 10 GPa and 25 GPa, we present a series of three-step gas-gun shock experiments designed to explore the pressure and strain rate dependence of plastic flow in polycrystalline tantalum. These experiments at nominal peak stresses of 50 GPa and 75 GPa show the irreversible deformation before pullback to be almost entirely confined to the shock loading, with negligible plastic relaxation on the post-shock plateau. We also add a reverse-ballistics shot at 25 GPa, which was designed to reveal the pullback response with negligible interference from free-surface effects. General thermodynamic considerations allow us to place bounds on the plastic behavior even for parts of the curve that change far too rapidly for the velocimetric time resolution of (conservatively) ~ 5 ns. To analyze the data, we found it necessary to substantially improve the interpolation/extrapolation scheme in order to improve its robustness, flexibility and range of applicability. We describe the new scheme based on splines, as well as an extension of free-surface corrections to the post-shock rarefaction waves. Reanalysis with the new scheme produces results essentially within the error bars previously reported, showing that the known systematic errors associated with free-surface effects are relatively inconsequential for determining thermodynamic paths.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗