Showing 1–50 of 136 results for author: Kelly, A

A Bayesian approach to the long-baseline neutrino oscillation sensitivity of DUNE

Authors: DUNE Collaboration, S. Abbaslu, F. Abd Alrahman, A. Abed Abud, R. Acciarri, M. A. Acero, M. R. Adames, G. Adamov, M. Adamowski, K. Adhikari, C. Adriano, K. Agudelo-Jaramillo, F. Akbar, F. Alemanno, N. S. Alex, L. Aliaga Soplin, A. Alqaisi, O. Alterkait, A. Alton, R. Alvarez, T. Alves, A. Aman, H. Amar, R. M. Amarinei, P. Amedo , et al. (1262 additional authors not shown)

Abstract: The sensitivity of the Deep Underground Neutrino Experiment (DUNE) to neutrino oscillation is evaluated using a Bayesian Markov Chain Monte Carlo (MCMC) approach. This analysis uses the same underlying sensitivity inputs as previous DUNE studies [Eur. Phys. J. C 80, 978 (2020)], and therefore does not present updated DUNE sensitivities, but instead explores the additional inferences accessible usi… ▽ More The sensitivity of the Deep Underground Neutrino Experiment (DUNE) to neutrino oscillation is evaluated using a Bayesian Markov Chain Monte Carlo (MCMC) approach. This analysis uses the same underlying sensitivity inputs as previous DUNE studies [Eur. Phys. J. C 80, 978 (2020)], and therefore does not present updated DUNE sensitivities, but instead explores the additional inferences accessible using a Bayesian approach. We present four-dimensional posterior probability distributions of the oscillation parameters, highlighting the breadth of correlation in the parameter space of interest, especially between $\sin^2 θ_{23}$ and $\sin^2 θ_{13}$. We exploit the flexibility of the Bayesian framework to incorporate parameter constraints post hoc and assess the impact of applying a reactor short-baseline $θ_{13}$ constraint. A significant increase in the sensitivity to the $θ_{23}$ octant is found when including the constraint. Posterior distributions of derived quantities can be easily constructed from MCMC results. This work presents the first study of DUNE's sensitivity to the Jarlskog invariant, $J$, a quantity that provides a parametrisation-independent measure of charge-parity violation in the leptonic sector. △ Less

Submitted 4 August, 2026; originally announced August 2026.

Comments: 20 pages, 5 figures

Report number: FERMILAB-PUB-26-0548-LBNF

Astrophysics on GPUs: introducing AGILE 1.0

Authors: Oliver Porth, Adrian Kelly, Olaf Willocx, Hao Wu, Jesse Vos, Yuhao Zhou, Héctor R. Olivares Sánchez, Leon Oostrum, Johan Hidding, Victor Azizi, Chun Xia, Rony Keppens, Jannis Teunissen

Abstract: We present AGILE, a GPU-enabled adaptive mesh refinement (AMR) framework for the solution of (near-) conservation laws which occur in astro- and solar-physical applications. AGILE is written in modern fortran 2003, inherits a part of its modules and mesh handling from MPI-AMRVAC, and achieves excellent GPU performance via OpenACC offloading. We here discuss the design decisions which enable AGILE… ▽ More We present AGILE, a GPU-enabled adaptive mesh refinement (AMR) framework for the solution of (near-) conservation laws which occur in astro- and solar-physical applications. AGILE is written in modern fortran 2003, inherits a part of its modules and mesh handling from MPI-AMRVAC, and achieves excellent GPU performance via OpenACC offloading. We here discuss the design decisions which enable AGILE to perform cost-efficient and scalable deeply nested AMR simulations with moderate block sizes of e.g. $16^3$ cells. AGILE currently implements several physics modules, ie. hydrodynamics, frozen-field hydrodynamics, magnetohydrodynamics and special-relativistic hydrodynamics and can easily be extended further through its modular design. Besides strong scaling tests to up to 2048 GPUs and standard benchmarks which show consistent performance across a large range of devices and problem sizes, we demonstrate AGILE's capabilities by means of state-of-the art science applications with all currently available physics modules. △ Less

Submitted 21 July, 2026; originally announced July 2026.

Comments: submitted to RAS Techniques and Instruments

Operation and performance of ProtoDUNE Dual Phase liquid argon time projection chamber

Authors: DUNE Collaboration, S. Abbaslu, F. Abd Alrahman, A. Abed Abud, R. Acciarri, L. P. Accorsi, M. A. Acero, M. R. Adames, G. Adamov, M. Adamowski, K. Adhikari, C. Adriano, K. Agudelo-Jaramillo, F. Akbar, F. Alemanno, N. S. Alex, L. Aliaga Soplin, A. Alqaisi, M. Alrashed, A. Alton, R. Alvarez, T. Alves, A. Aman, H. Amar, R. Amarinei , et al. (1341 additional authors not shown)

Abstract: ProtoDUNE-DP was the largest ever built Liquid Argon Time Projection Chamber (LArTPC) operating in Dual-Phase (DP) mode, with a liquid target and charge read-out placed in the gas. It had an active volume of $6\times6\times6$\,m$^3$ corresponding to an active mass of 300\,t (total LAr mass of 720\,t), constructed at the CERN Neutrino Platform and took data from 2019 to 2020 with cosmic muons. In P… ▽ More ProtoDUNE-DP was the largest ever built Liquid Argon Time Projection Chamber (LArTPC) operating in Dual-Phase (DP) mode, with a liquid target and charge read-out placed in the gas. It had an active volume of $6\times6\times6$\,m$^3$ corresponding to an active mass of 300\,t (total LAr mass of 720\,t), constructed at the CERN Neutrino Platform and took data from 2019 to 2020 with cosmic muons. In ProtoDUNE-DP the electric drift field is oriented in the vertical direction, causing the electrons to drift vertically towards the anode at the top. The ionization charge is then extracted into the gaseous argon above the liquid surface, amplified by Townsend avalanches, and collected by the charge readout planes. The detector experienced significant technical problems affecting the long-term operation of the Charge Readout Planes, formed by the Large Electron Multipliers, but other critical segments demonstrated required performance including the delivery of -300 kV to the TPC cathode, verification of replaceable charge read-out electronics, and operation of the photon detection system. ProtoDUNE-DP experience resulted in improved designs of the Vertical Drift LArTPC. △ Less

Submitted 21 July, 2026; v1 submitted 17 July, 2026; originally announced July 2026.

Comments: 103 pages, 66 figures

Report number: FERMILAB-PUB-26-0466-LBNF

Quantifying Explainable AI-introduced signal noise on ECG data with Spectral Entropy

Authors: David A. Kelly, Nathan Blake

Abstract: Explainability techniques are used to assess the output of various deep learning models. This is especially true in healthcare, where models need to be trusted and decisions justified. Explainability (XAI) tools use heuristics which often add signal noise to the explanation "core". It is not always obvious what is signal from the model and what is noise from the XAI. We propose the use of spectral… ▽ More Explainability techniques are used to assess the output of various deep learning models. This is especially true in healthcare, where models need to be trusted and decisions justified. Explainability (XAI) tools use heuristics which often add signal noise to the explanation "core". It is not always obvious what is signal from the model and what is noise from the XAI. We propose the use of spectral entropy as a measure of noise in XAI output. We demonstrate its usefulness in the context of classifying arrhythmias in an ECG dataset with different post hoc explainability techniques. △ Less

Submitted 23 June, 2026; originally announced June 2026.

Comments: Accepted to EUSIPCO 2026

Explaining Failures of Cyber-Physical Systems with Actual Causality

Authors: Khen Elimelech, Tom Yaacov, David A. Kelly, Hana Chockler, Moshe Y. Vardi

Abstract: Modern autonomous Cyber-Physical Systems (CPSs), such as self-driving cars, face increasingly complex demands, and yet are expected to act reliably. The black-box nature often characterizing such systems, especially those relying on neural components, makes it impossible to fully verify the system behavior prior to deployment. Unfortunately, unexpected failures-when the system does not comply with… ▽ More Modern autonomous Cyber-Physical Systems (CPSs), such as self-driving cars, face increasingly complex demands, and yet are expected to act reliably. The black-box nature often characterizing such systems, especially those relying on neural components, makes it impossible to fully verify the system behavior prior to deployment. Unfortunately, unexpected failures-when the system does not comply with its specification-are inevitable and may have catastrophic implications. To improve trust in the system and facilitate future mitigation after a failure occurs, it is important to try to derive an explanation for the unexpected system behavior. This paper introduces the novel concept of leveraging the framework of actual causality for CPS failure explanation. Up until now, this framework was only used to derive explanations in the context of simple systems, such as image classifiers. This paper addresses the theoretical gaps and provides the guidance needed to allow for correct explanation derivation in the CPS domain. Beyond the theoretical contribution, the paper presents two novel, practical, system-agnostic explanation derivation algorithms, allowing to prioritize either explanation optimality or derivation efficiency. The approach is demonstrated and evaluated in the context of a neural-network-controlled autonomous car, designed to avoid collisions. △ Less

Submitted 23 June, 2026; originally announced June 2026.

Comments: Accepted to the 2026 IEEE International Conference on Robotics and Automation (ICRA 2026)

Probing Nuclear Effects with Transverse Kinematic Imbalance in Muon-neutrino Induced Charged-Current $π^0$ Production on Argon with the MicroBooNE Detector

Authors: MicroBooNE collaboration, P. Abratenko, D. Andrade Aldana, J. Asaadi, A. Ashkenazi, S. Balasubramanian, B. Baller, A. Barnard, G. Barr, D. Barrow, J. Barrow, V. Basque, J. Bateman, B. Behera, O. Benevides Rodrigues, S. Berkman, A. Bhat, V. Bhelande, M. Bhattacharya, A. Binau, M. Bishai, A. Blake, B. Bogart, T. Bolton, M. B. Brunetti , et al. (170 additional authors not shown)

Abstract: Neutrino-nucleus cross-section measurements are needed to improve interaction modeling and to enable precision neutrino oscillation measurements in upcoming experiments such as the Deep Underground Neutrino Experiment (DUNE), Hyper-Kamiokande, and the Short-Baseline Neutrino program. Baryon-resonance neutrino interactions constitute a dominant contribution near the peak of the DUNE neutrino energy… ▽ More Neutrino-nucleus cross-section measurements are needed to improve interaction modeling and to enable precision neutrino oscillation measurements in upcoming experiments such as the Deep Underground Neutrino Experiment (DUNE), Hyper-Kamiokande, and the Short-Baseline Neutrino program. Baryon-resonance neutrino interactions constitute a dominant contribution near the peak of the DUNE neutrino energy spectrum. We present the first measurement of muon neutrino charged-current resonance-like interactions on argon using transverse kinematic imbalance variables with the MicroBooNE detector. These observables are highly sensitive to the modeling of final-state interactions. This measurement probes kinematic imbalances using the reconstructed momenta of the muon, leading proton, and neutral pion. A comprehensive characterization of the $π^0$-proton final state is presented; however, none of the models considered are able to simultaneously reproduce all measured observables. △ Less

Submitted 22 June, 2026; originally announced June 2026.

First Measurement of Sub-GeV $ν_μ$ Charged-Current Coherent Pion Production on Argon in MicroBooNE

Authors: MicroBooNE collaboration, P. Abratenko, D. Andrade Aldana, J. Asaadi, A. Ashkenazi, S. Balasubramanian, B. Baller, A. Barnard, G. Barr, D. Barrow, J. Barrow, V. Basque, J. Bateman, B. Behera, O. Benevides Rodrigues, S. Berkman, A. Bhat, M. Bhattacharya, V. Bhelande, A. Binau, M. Bishai, A. Blake, B. Bogart, T. Bolton, M. B. Brunetti , et al. (167 additional authors not shown)

Abstract: We report a measurement of the charged-current coherent pion production cross section on argon using the MicroBooNE liquid argon time projection chamber exposed to the Booster Neutrino Beam at Fermilab. The measurement uses the MicroBooNE data set corresponding to $1.26 \times 10^{21}$ protons on target with a mean neutrino energy of $0.8$~GeV. The flux-averaged cross section is measured to be… ▽ More We report a measurement of the charged-current coherent pion production cross section on argon using the MicroBooNE liquid argon time projection chamber exposed to the Booster Neutrino Beam at Fermilab. The measurement uses the MicroBooNE data set corresponding to $1.26 \times 10^{21}$ protons on target with a mean neutrino energy of $0.8$~GeV. The flux-averaged cross section is measured to be $(9.1 \pm 1.2_{\text{stat}} \pm 1.2_\text{syst}) \times 10^{-40}\,\text{cm}^2/\text{Ar}$. This result represents the first measurement of charged-current coherent pion production on argon at sub-GeV neutrino energies. Due to its clean two-body kinematics, where the neutrino interacts coherently with the entire nucleus producing a forward muon and pion with no nuclear breakup, this process provides a useful tool for constraining neutrino flux uncertainties in current and future oscillation experiments such as DUNE. △ Less

Submitted 11 June, 2026; originally announced June 2026.

Report number: FERMILAB-PUB-26-0335-PPD

Characterizing the energy resolution of the MicroBooNE LArTPC at the MeV scale using monoenergetic features of $^{208}$Tl decays

Authors: MicroBooNE collaboration, P. Abratenko, D. Andrade Aldana, J. Asaadi, A. Ashkenazi, S. Balasubramanian, B. Baller, A. Barnard, G. Barr, D. Barrow, J. Barrow, V. Basque, J. Bateman, B. Behera, O. Benevides Rodrigues, S. Berkman, A. Bhat, M. Bhattacharya, V. Bhelande, A. Binau, M. Bishai, A. Blake, B. Bogart, T. Bolton, M. B. Brunetti , et al. (167 additional authors not shown)

Abstract: A detailed understanding of the capabilities and fidelity of low-energy reconstruction is crucial for taking advantage of MeV-scale neutrino physics opportunities in liquid argon time projection chambers (LArTPCs). This study presents a measurement of the resolution of reconstructed energy in the MicroBooNE LArTPC at $\approx 1.5$ MeV. The characterization is performed using monoenergetic signals… ▽ More A detailed understanding of the capabilities and fidelity of low-energy reconstruction is crucial for taking advantage of MeV-scale neutrino physics opportunities in liquid argon time projection chambers (LArTPCs). This study presents a measurement of the resolution of reconstructed energy in the MicroBooNE LArTPC at $\approx 1.5$ MeV. The characterization is performed using monoenergetic signals generated by $2.614$ MeV $γ$-rays from $^{208}$Tl decays undergoing pair production in the detector. The resolution is found to be ($7.52 \pm 0.78 \text{(stat)} \pm 0.92 \text{(syst)}$)%. This value is consistent with the MicroBooNE simulation prediction of ($9.70 \pm 0.65 \text{(stat)}$)% at the $1.6 σ$ level. This study represents the first ever measurement of LArTPC energy resolution at the MeV scale and provides a pathway for monoenergetic energy calibrations in future experiments using LArTPC detectors. △ Less

Submitted 17 August, 2026; v1 submitted 28 May, 2026; originally announced May 2026.

Comments: 15 pages, 9 figures, and 2 tables

Report number: FERMILAB-PUB-26-0288

foap4: Adaptive mesh refinement with OpenACC, MPI, and p4est

Authors: Jannis Teunissen, Héctor R. Olivares Sánchez, Jesse Vos, Leon Oostrum, Johan Hidding, Victor Azizi, Yuhao Zhou, Hao Wu, Adrian Kelly, Olaf Willocx, Chun Xia, Rony Keppens, Oliver Porth

Abstract: GPUs and other accelerators are increasingly used for scientific computing. In the future, we want to add GPU support to parallel adaptive mesh refinement (AMR) codes written in Fortran. To understand which changes are necessary to obtain good performance we have developed foap4, an AMR framework implemented in Fortran that uses OpenACC, MPI, and the p4est library. We discuss the design and implem… ▽ More GPUs and other accelerators are increasingly used for scientific computing. In the future, we want to add GPU support to parallel adaptive mesh refinement (AMR) codes written in Fortran. To understand which changes are necessary to obtain good performance we have developed foap4, an AMR framework implemented in Fortran that uses OpenACC, MPI, and the p4est library. We discuss the design and implementation of the framework. Several benchmark problems are considered, in which Euler's equations of gas dynamics are solved using explicit time integration. These benchmarks are performed in both 2D and 3D, using static and adaptive meshes, for varying problem sizes on different hardware. Our results show that AMR simulations can be carried out efficiently on GPUs with OpenACC and MPI, even when using relatively small grid blocks of $8^3$ or $16^3$ cells. △ Less

Submitted 8 May, 2026; originally announced May 2026.

Improved muon energy estimation using a detailed model of multiple Coulomb scattering in the MicroBooNE LArTPC

Authors: MicroBooNE Collaboration, P. Abratenko, D. Andrade Aldana, J. Asaadi, A. Ashkenazi, S. Balasubramanian, B. Baller, A. Barnard, G. Barr, D. Barrow, J. Barrow, V. Basque, J. Bateman, B. Behera, O. Benevides Rodrigues, S. Berkman, A. Bhat, M. Bhattacharya, V. Bhelande, A. Binau, M. Bishai, A. Blake, B. Bogart, T. Bolton, M. B. Brunetti , et al. (167 additional authors not shown)

Abstract: We present an improved technique for estimating a muon's energy by measuring the deflections along its path inside the MicroBooNE detector from multiple Coulomb scattering (MCS). This approach implements several innovations that better capture detector non-idealizations compared to previous MCS-based muon energy estimators. As a result, it achieves improved resolution, reduced bias, and better dat… ▽ More We present an improved technique for estimating a muon's energy by measuring the deflections along its path inside the MicroBooNE detector from multiple Coulomb scattering (MCS). This approach implements several innovations that better capture detector non-idealizations compared to previous MCS-based muon energy estimators. As a result, it achieves improved resolution, reduced bias, and better data-model agreement. Using model simulation, for fully contained events the estimated bias is within 1% and the estimated resolution varies from 4.3% to 10% as muon energy increases from 0.1 GeV to 2 GeV. For events with particles exiting the detector volume, at least a meter of reconstructed muon track, and a muon energy below 2 GeV, the estimated bias is less than 2% and the estimated resolution varies from 7% to 17% over muon energy. These demonstrate significant improvements over the performance of previous work using an MCS-based energy estimator at MicroBooNE, which achieves twice as large a resolution as well as a bias of 20% over the same energy region. Data-model goodness-of-fit studies are used to validate the estimator's performance on data, showing good agreement within model uncertainties. △ Less

Submitted 14 July, 2026; v1 submitted 4 May, 2026; originally announced May 2026.

Comments: 13 figures, 26 pages

Thermal instability in coronal loops: linking eigenvalue spectra to time-dependent evolution

Authors: Adrian Kelly, Rony Keppens, Jordi De Jonghe

Abstract: Cool, dense condensations such as coronal rain and prominences suggest that coronal plasma can undergo runaway radiative cooling. Connecting this behaviour to linear thermal modes requires us to fully understand the deeper connection between eigenvalue spectra and actual time-dependent evolution. We aim to clarify this intricate link for a simplified, coronal-only model of a stratified coronal loo… ▽ More Cool, dense condensations such as coronal rain and prominences suggest that coronal plasma can undergo runaway radiative cooling. Connecting this behaviour to linear thermal modes requires us to fully understand the deeper connection between eigenvalue spectra and actual time-dependent evolution. We aim to clarify this intricate link for a simplified, coronal-only model of a stratified coronal loop by combining spectral, linear initial-value, and nonlinear simulations of the same loop setup. We study waves and instabilities, as well as temporal evolutions for a 1D hydrostatic, thermally balanced loop with optically thin radiation and prescribed heating. The non-adiabatic spectrum is computed with our open-source Legolas code. We demonstrate our newly developed boundary value-initial value solver Legolas-IVP, where linear evolutions are performed for controlled perturbations, and fully equivalent nonlinear runs are carried out with MPI-AMRVAC. The spectrum contains discrete acoustic modes and a thermally unstable branch including a thermal continuum. Linear initial-value experiments with isochoric, isobaric, and isentropic pulses highlight how the polarisation of the eigenmodes demonstrates physically consistent behaviour expected from the eigenspectrum. Even in the linear stage, thermal imbalance drives siphon-like flows toward the cooling region. Growth rates from Legolas-IVP agree with spectral predictions and are reproduced in MPI-AMRVAC, which follows the condensation through runaway cooling to chromospheric temperatures, with the cool dense blob sliding under gravity toward the loop footpoint. The spectral-linear-nonlinear investigation demonstrates a direct link between thermal eigenmodes and condensation dynamics, providing a basis for extending to fully 3D MHD models. △ Less

Submitted 27 April, 2026; originally announced April 2026.

Comments: 15 pages, 14 figures. Accepted for publication in Astronomy and Astrophysics (A&A)

Journal ref: A&A 710, A19 (2026)

Charge readout electronics for the DUNE horizontal drift far detector: design and performance in ProtoDUNE-HD

Authors: DUNE Collaboration, S. Abbaslu, F. Abd Alrahman, A. Abed Abud, R. Acciarri, L. P. Accorsi, M. A. Acero, M. R. Adames, G. Adamov, M. Adamowski, K. Adhikari, C. Adriano, K. Agudelo-Jaramillo, F. Akbar, F. Alemanno, N. S. Alex, L. Aliaga Soplin, A. Alqaisi, M. Alrashed, A. Alton, R. Alvarez, T. Alves, A. Aman, H. Amar, R. Amarinei , et al. (1346 additional authors not shown)

Abstract: DUNE (Deep Underground Neutrino Experiment) is a long-baseline neutrino oscillation experiment currently under construction, whose far detectors will be the largest liquid argon time projection chambers ever built. This detector design calls for custom-built cryogenic front-end electronics to meet its performance requirements. This paper describes the charge readout electronics that will be used i… ▽ More DUNE (Deep Underground Neutrino Experiment) is a long-baseline neutrino oscillation experiment currently under construction, whose far detectors will be the largest liquid argon time projection chambers ever built. This detector design calls for custom-built cryogenic front-end electronics to meet its performance requirements. This paper describes the charge readout electronics that will be used in the DUNE horizontal drift (HD) far detector and presents performance results using data from the ProtoDUNE-HD detector, a 770 ton liquid argon time projection chamber operated at the CERN Neutrino Platform in 2024 that served as the final prototype of the DUNE HD design. △ Less

Submitted 12 August, 2026; v1 submitted 26 April, 2026; originally announced April 2026.

Comments: Accepted version

Report number: FERMILAB-PUB-26-0270-LBNF, CERN-EP-2026-128

Journal ref: JINST 21 (2026) P08018

If It's Good Enough for You, It's Good Enough for Me: Transferability of Audio Sufficiencies across Models

Authors: David A. Kelly, Hana Chockler

Abstract: In order to gain fresh insights about the information processing characteristics of different audio classification models, we propose transferability analysis. Given a minimal, sufficient signal for a classification on a model $f$, transferability analysis asks whether other models accept this minimal signal as having the same classification as it did on $f$. We define what it means for a suffic… ▽ More In order to gain fresh insights about the information processing characteristics of different audio classification models, we propose transferability analysis. Given a minimal, sufficient signal for a classification on a model $f$, transferability analysis asks whether other models accept this minimal signal as having the same classification as it did on $f$. We define what it means for a sufficient signal to be transferable and perform a large study over $3$ different classification tasks: music genre, emotion recognition and deepfake detection. We find that transferability rates vary depending on the task, with sufficient signals for music genre being transferable $\approx26\%$ of the time. The other tasks reveal much higher variance in transferability and reveal that some models, in particular on deepfake detection, have different transferability behavior. We call these models `flat-earther' models. We investigate deepfake audio in more depth, and show that transferability analysis also allows to us to discover information theoretic differences between the models which are not captured by the more familiar metrics of accuracy and precision. △ Less

Submitted 3 April, 2026; originally announced April 2026.

Graph Attention Network-Based Detection of Autism Spectrum Disorder

Authors: Abigail Kelly, Ramchandra Rimal, Arpan Sainju

Abstract: Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by atypical brain connectivity. One of the crucial steps in addressing ASD is its early detection. This study introduces a novel computational framework that employs an Attention-Based Graph Convolutional Network, referred to as the GATGraphClassifier, for detecting ASD. We utilize Functional Magnetic Resonance Imaging… ▽ More Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by atypical brain connectivity. One of the crucial steps in addressing ASD is its early detection. This study introduces a novel computational framework that employs an Attention-Based Graph Convolutional Network, referred to as the GATGraphClassifier, for detecting ASD. We utilize Functional Magnetic Resonance Imaging (fMRI) data from the Autism Brain Imaging Data Exchange (ABIDE) repository to construct functional connectivity matrices using Pearson correlation, which captures interactions between various brain regions. These matrices are then transformed into graph representations, where the nodes and edges represent the brain regions and functional connections, respectively. The GATGraphClassifier employs attention mechanisms to identify critical connectivity patterns, thereby enhancing the model's interpretability and diagnostic accuracy. Our proposed framework demonstrates superior performance across all standard classification metrics compared to existing state-of-the-art methods. Notably, we achieved an average accuracy of 88.79\% on the test data over 30 independent runs, surpassing the benchmark model's performance by 12.27\%. In addition, we identified the crucial brain regions associated with ASD, consistent with the previous studies, and a few novel regions. This study not only contributes to the advancement of ASD detection but also shows the potential for broader adaptability of GATGraphClassifier in analyzing complex relational data in various fields, where understanding intricate connectivity and interaction patterns is essential. △ Less

Submitted 27 March, 2026; originally announced March 2026.

MSC Class: 68 ACM Class: I.2.6

Scintillation light calibrations, systematic uncertainties, and triggering efficiency in the MicroBooNE detector

Authors: MicroBooNE collaboration, P. Abratenko, D. Andrade Aldana, L. Arellano, J. Asaadi, A. Ashkenazi, S. Balasubramanian, B. Baller, A. Barnard, G. Barr, D. Barrow, J. Barrow, V. Basque, J. Bateman, B. Behera, O. Benevides Rodrigues, S. Berkman, A. Bhat, M. Bhattacharya, V. Bhelande, A. Binau, M. Bishai, A. Blake, B. Bogart, T. Bolton , et al. (169 additional authors not shown)

Abstract: Scintillation light, produced alongside ionisation charge from particle interactions, plays a critical role in liquid argon time projection chamber (LArTPC) detectors. A detailed understanding of its production and detection mechanisms is essential for robust calibration, systematic uncertainty evaluation, and physics analysis. This article describes the MicroBooNE light simulation, light-based tr… ▽ More Scintillation light, produced alongside ionisation charge from particle interactions, plays a critical role in liquid argon time projection chamber (LArTPC) detectors. A detailed understanding of its production and detection mechanisms is essential for robust calibration, systematic uncertainty evaluation, and physics analysis. This article describes the MicroBooNE light simulation, light-based triggering schemes, photomultiplier tube gain calibration, light response stability, and light-based systematic uncertainties over the course of five years of data collection. In addition, we present a measurement of scintillation light triggering efficiency, focusing on the lowest-light regime relevant to rare-event searches and low-energy neutrino interactions. Finally, we discuss two notable observations in MicroBooNE's data, both reported here for the first time: an approximately 50% decline in MicroBooNE's light yield over time, concentrated in the first two years of running; and a higher than expected O(200 kHz) rate of single photoelectron noise. The results presented provide an important benchmark of long-term light detection performance in LArTPC neutrino detectors. △ Less

Submitted 24 March, 2026; originally announced March 2026.

Measurements of the electron neutrino-argon differential cross section without pions in the final state in MicroBooNE

Authors: MicroBooNE collaboration, P. Abratenko, D. Andrade Aldana, L. Arellano, J. Asaadi, A. Ashkenazi, S. Balasubramanian, B. Baller, A. Barnard, G. Barr, D. Barrow, J. Barrow, V. Basque, J. Bateman, B. Behera, O. Benevides Rodrigues, S. Berkman, A. Bhat, M. Bhattacharya, V. Bhelande, A. Binau, M. Bishai, A. Blake, B. Bogart, T. Bolton , et al. (168 additional authors not shown)

Abstract: We present a new measurement of the electron neutrino charged current cross section on argon without pions in the final state. This measurement uses the full MicroBooNE booster neutrino beam dataset of $1.3\times 10^{21}$ protons on target collected at Fermi National Accelerator Laboratory. Events are considered both with and without protons above the kinetic energy visibility threshold. Different… ▽ More We present a new measurement of the electron neutrino charged current cross section on argon without pions in the final state. This measurement uses the full MicroBooNE booster neutrino beam dataset of $1.3\times 10^{21}$ protons on target collected at Fermi National Accelerator Laboratory. Events are considered both with and without protons above the kinetic energy visibility threshold. Differential cross sections are extracted in proton and electron kinematics, including energy and angle relative to the neutrino beam direction. The relationship between the hadronic and leptonic systems is explored through the angle between the proton and electron directions. The resulting cross sections are compared to a variety of available generator predictions using different models of neutrino interactions. We find good agreement with most models in lepton kinematics and some discrepancies in the hadronic system modeling, particularly in proton angle. △ Less

Submitted 14 August, 2026; v1 submitted 13 March, 2026; originally announced March 2026.

Report number: FERMILAB-PUB-26-0054-CSAID-PPD

Journal ref: Phys. Rev. D 114, 032006 (12 August, 2026)

Daily Affect Fluctuations in Phone Screen Content Predict Anxiety and Depressive Symptoms

Authors: Christopher A. Kelly, Yikun Chi, Nicholas Haber, Byron Reeves, Mu-Jung Cho, Thomas N. Robinson, Nilam Ram, Johannes C. Eichstaedt

Abstract: The relationship between digital media use and mental health remains poorly understood, in part because real-world digital behavior is rarely captured at scale. This intensive longitudinal study tracked participants' complete natural smartphone interactions over one year. We collected screenshots every 5 seconds from 145 adults (yielding 111 million screenshots), alongside biweekly assessments of… ▽ More The relationship between digital media use and mental health remains poorly understood, in part because real-world digital behavior is rarely captured at scale. This intensive longitudinal study tracked participants' complete natural smartphone interactions over one year. We collected screenshots every 5 seconds from 145 adults (yielding 111 million screenshots), alongside biweekly assessments of anxiety and depression (mean = 24 surveys). The valence and arousal of each screenshot were assessed using a deep learning affect model. Individuals showed highly idiosyncratic media patterns, with substantially more variance in anxiety and depression accounted for within-person than between-person. Day-to-day fluctuations in the valence and arousal of a person's screen content predicted subsequent changes in depression and anxiety, whereas between-person differences did not. Specifically, greater exposure to low-arousal negative content was associated with higher depression and anxiety. These findings underscore the dynamic, idiosyncratic nature of digital consumption and the need for targeted measurement and intervention. △ Less

Submitted 13 March, 2026; originally announced March 2026.

Demonstration and performance of an online data selection algorithm for liquid argon time projection chambers using MicroBooNE

Authors: MicroBooNE collaboration, P. Abratenko, D. Andrade Aldana, L. Arellano, J. Asaadi, A. Ashkenazi, S. Balasubramanian, B. Baller, A. Barnard, G. Barr, D. Barrow, J. Barrow, V. Basque, J. Bateman, B. Behera, O. Benevides Rodrigues, S. Berkman, A. Bhat, M. Bhattacharya, V. Bhelande, A. Binau, M. Bishai, A. Blake, B. Bogart, T. Bolton , et al. (169 additional authors not shown)

Abstract: The MicroBooNE detector is a liquid argon time projection chamber (LArTPC) that produces three-dimensional images of particle interactions using ionization charge collected by anode wire plane arrays and scintillation light collected by a light detection system. In addition to testing long-standing experimental neutrino anomalies and performing measurements of neutrino interactions with argon nucl… ▽ More The MicroBooNE detector is a liquid argon time projection chamber (LArTPC) that produces three-dimensional images of particle interactions using ionization charge collected by anode wire plane arrays and scintillation light collected by a light detection system. In addition to testing long-standing experimental neutrino anomalies and performing measurements of neutrino interactions with argon nuclei using the Fermilab Booster Neutrino Beam, MicroBooNE aims to develop methodologies for rare beyond the Standard Model and off-beam physics searches. Looking ahead to the upcoming Deep Underground Neutrino Experiment (DUNE), with MicroBooNE serving as a valuable testbed, achieving high sensitivity and livetime for off-beam physics while satisfying data processing and storage constraints will require data-driven, intelligent, and online or real-time data selection techniques. These techniques are essential for reducing data rates and preserving rare signals with high accuracy. In this paper, we describe a fast data selection algorithm suitable for online execution to identify electrons from stopping cosmic ray muons in the MicroBooNE detector utilizing ionization charge information, and present its performance. This represents the first demonstration of online data selection in a LArTPC using real data and charge information exclusively and provides an important proof-of-principle for applying such techniques to other LArTPC experiments such as the Short-Baseline Near Detector and DUNE. △ Less

Submitted 10 July, 2026; v1 submitted 11 February, 2026; originally announced February 2026.

Report number: FERMILAB-PUB-25-0954-PPD

I Guess That's Why They Call it the Blues: Causal Analysis for Audio Classifiers

Authors: David A. Kelly, Hana Chockler

Abstract: It is well-known that audio classifiers often rely on non-musically relevant features and spurious correlations to classify audio. Hence audio classifiers are easy to manipulate or confuse, resulting in wrong classifications. While inducing a misclassification is not hard, until now the set of features that the classifiers rely on was not well understood. In this paper we introduce a new method… ▽ More It is well-known that audio classifiers often rely on non-musically relevant features and spurious correlations to classify audio. Hence audio classifiers are easy to manipulate or confuse, resulting in wrong classifications. While inducing a misclassification is not hard, until now the set of features that the classifiers rely on was not well understood. In this paper we introduce a new method that uses causal reasoning to discover features of the frequency space that are sufficient and necessary for a given classification. We describe an implementation of this algorithm in the tool FreqReX and provide experimental results on a number of standard benchmark datasets. Our experiments show that causally sufficient and necessary subsets allow us to manipulate the outputs of the models in a variety of ways by changing the input very slightly. Namely, a change to one out of 240,000 frequencies results in a change in classification 58% of the time, and the change can be so small that it is practically inaudible. These results show that causal analysis is useful for understanding the reasoning process of audio classifiers and can be used to successfully manipulate their outputs. △ Less

Submitted 23 January, 2026; originally announced January 2026.

Out-of-the-box: Black-box Causal Attacks on Object Detectors

Authors: Melane Navaratnarajah, David A. Kelly, Hana Chockler

Abstract: Adversarial perturbations are a useful way to expose vulnerabilities in object detectors. Existing perturbation methods are frequently white-box, architecture specific and use a loss function. More importantly, while they are often successful, it is rarely clear why they work. Insights into the mechanism of this success would allow developers to understand and analyze these attacks, as well as fin… ▽ More Adversarial perturbations are a useful way to expose vulnerabilities in object detectors. Existing perturbation methods are frequently white-box, architecture specific and use a loss function. More importantly, while they are often successful, it is rarely clear why they work. Insights into the mechanism of this success would allow developers to understand and analyze these attacks, as well as fine-tune the model to prevent them. This paper presents BlackCAtt, a black-box algorithm and tool, which uses minimal, causally sufficient pixel sets to construct explainable, imperceptible, reproducible, architecture-agnostic attacks on object detectors. We evaluate BlackCAtt on standard benchmarks and compare it to other black-box adversarial attacks methods. When BlackCAtt has access only to the position and label of a bounding box, it produces attacks that are comparable or better to those produced by other black-box methods. When BlackCAtt has access to the model confidence as well, it can work as a meta-algorithm, improving the ability of standard black-box techniques to construct smaller, less perceptible attacks. As BlackCAtt attacks manipulate causes only, the attacks become fully explainable. We compare the performance of BlackCAtt with other black-box attack methods and show that targeting causal pixels leads to smaller and less perceptible attacks. For example, when using BlackCAtt with SquareAttack, it reduces the average distance ($L_0$ norm) of the attack from the original input from $0.987$ to $0.072$, while maintaining a similar success rate. We perform ablation studies on the BlackCAtt algorithm and analyze the effect of different components on its performance. △ Less

Submitted 10 April, 2026; v1 submitted 3 December, 2025; originally announced December 2025.

Comments: 14 pages, 12 pages of appendices

Hey Pentti, We Did (More of) It!: A Vector-Symbolic Lisp With Residue Arithmetic

Authors: Connor Hanley, Eilene Tomkins-Flanaganm, Mary Alexandria Kelly

Abstract: Using Frequency-domain Holographic Reduced Representations (FHRRs), we extend a Vector-Symbolic Architecture (VSA) encoding of Lisp 1.5 with primitives for arithmetic operations using Residue Hyperdimensional Computing (RHC). Encoding a Turing-complete syntax over a high-dimensional vector space increases the expressivity of neural network states, enabling network states to contain arbitrarily str… ▽ More Using Frequency-domain Holographic Reduced Representations (FHRRs), we extend a Vector-Symbolic Architecture (VSA) encoding of Lisp 1.5 with primitives for arithmetic operations using Residue Hyperdimensional Computing (RHC). Encoding a Turing-complete syntax over a high-dimensional vector space increases the expressivity of neural network states, enabling network states to contain arbitrarily structured representations that are inherently interpretable. We discuss the potential applications of the VSA encoding in machine learning tasks, as well as the importance of encoding structured representations and designing neural networks whose behavior is sensitive to the structure of their representations in virtue of attaining more general intelligent agents than exist at present. △ Less

Submitted 11 November, 2025; originally announced November 2025.

Comments: 11 pages, 1 figure, conference paper at IJCNN 2025 Rome

Adapting Noise-Driven PUF and AI for Secure WBG ICS: A Proof-of-Concept Study

Authors: Devon A. Kelly, Christiana Chamon

Abstract: Wide-bandgap (WBG) technologies offer unprecedented improvements in power system efficiency, size, and performance, but also introduce unique sensor corruption and cybersecurity risks in industrial control systems (ICS), particularly due to high-frequency noise and sophisticated cyber-physical threats. This proof-of-concept (PoC) study demonstrates the adaptation of a noise-driven physically unclo… ▽ More Wide-bandgap (WBG) technologies offer unprecedented improvements in power system efficiency, size, and performance, but also introduce unique sensor corruption and cybersecurity risks in industrial control systems (ICS), particularly due to high-frequency noise and sophisticated cyber-physical threats. This proof-of-concept (PoC) study demonstrates the adaptation of a noise-driven physically unclonable function (PUF) and machine learning (ML)-assisted anomaly detection framework to the demanding environment of WBG-based ICS sensor pathways. By extracting entropy from unavoidable WBG switching noise (up to 100 kHz) as a PUF source, and simultaneously using this noise as a real-time threat indicator, the proposed system unites hardware-level authentication and anomaly detection. Our approach integrates hybrid machine learning (ML) models with adaptive Bayesian filtering, providing robust and low-latency detection capabilities resilient to both natural electromagnetic interference (EMI) and active adversarial manipulation. Through detailed simulations of WBG modules under benign and attack scenarios--including EMI injection, signal tampering, and node impersonation--we achieve 95% detection accuracy and sub-millisecond processing latency. These results demonstrate the feasibility of physics-driven, dual-use noise exploitation as a scalable ICS defense primitive. Our findings lay the groundwork for next-generation security strategies that leverage inherent device characteristics, bridging hardware and artificial intelligence (AI) for enhanced protection of critical ICS infrastructure. △ Less

Submitted 25 October, 2025; originally announced October 2025.

Hey Pentti, We Did It!: A Fully Vector-Symbolic Lisp

Authors: Eilene Tomkins-Flanagan, Mary A. Kelly

Abstract: Kanerva (2014) suggested that it would be possible to construct a complete Lisp out of a vector-symbolic architecture. We present the general form of a vector-symbolic representation of the five Lisp elementary functions, lambda expressions, and other auxiliary functions, found in the Lisp 1.5 specification McCarthy (1960), which is near minimal and sufficient for Turing-completeness. Our specific… ▽ More Kanerva (2014) suggested that it would be possible to construct a complete Lisp out of a vector-symbolic architecture. We present the general form of a vector-symbolic representation of the five Lisp elementary functions, lambda expressions, and other auxiliary functions, found in the Lisp 1.5 specification McCarthy (1960), which is near minimal and sufficient for Turing-completeness. Our specific implementation uses holographic reduced representations Plate (1995), with a lookup table cleanup memory. Lisp, as all Turing-complete languages, is a Cartesian closed category, unusual in its proximity to the mathematical abstraction. We discuss the mathematics, the purpose, and the significance of demonstrating vector-symbolic architectures' Cartesian-closure, as well as the importance of explicitly including cleanup memories in the specification of the architecture. △ Less

Submitted 18 October, 2025; originally announced October 2025.

Journal ref: Tomkins Flanagan, E., & Kelly, M. A. (2024, July). Hey Pentti, We Did It!: A Fully Vector-Symbolic Lisp. Abstract published at MathPsych / ICCM 2024. Via mathpsych.org/presentation/1541

Hey Pentti, We Did It Again!: Differentiable vector-symbolic types that prove polynomial termination

Authors: Eilene Tomkins-Flanagan, Connor Hanley, Mary A. Kelly

Abstract: We present a typed computer language, Doug, in which all typed programs may be proved to halt in polynomial time, encoded in a vector-symbolic architecture (VSA). Doug is just an encoding of the light linear functional programming language (LLFPL) described by (Schimanski2009, ch. 7). The types of Doug are encoded using a slot-value encoding scheme based on holographic declarative memory (HDM; Kel… ▽ More We present a typed computer language, Doug, in which all typed programs may be proved to halt in polynomial time, encoded in a vector-symbolic architecture (VSA). Doug is just an encoding of the light linear functional programming language (LLFPL) described by (Schimanski2009, ch. 7). The types of Doug are encoded using a slot-value encoding scheme based on holographic declarative memory (HDM; Kelly, 2020). The terms of Doug are encoded using a variant of the Lisp VSA defined by (Flanagan, 2024). Doug allows for some points on the embedding space of a neural network to be interpreted as types, where the types of nearby points are similar both in structure and content. Types in Doug are therefore learnable by a neural network. Following (Chollet, 2019), (Card, 1983), and (Newell, 1981), we view skill as the application of a procedure, or program of action, that causes a goal to be satisfied. Skill acquisition may therefore be expressed as program synthesis. Using Doug, we hope to describe a form of learning of skilled behaviour that follows a human-like pace of skill acquisition (i.e., substantially faster than brute force; Heathcote, 2000), exceeding the efficiency of all currently existing approaches (Kaplan, 2020; Jones, 2021; Chollet, 2024). Our approach brings us one step closer to modeling human mental representations, as they must actually exist in the brain, and those representations' acquisition, as they are actually learned. △ Less

Submitted 18 October, 2025; originally announced October 2025.

Journal ref: Tomkins Flanagan, E., Hanley, C., & Kelly, M. A. (2025, July). Paper presented at MathPsych / ICCM 2025. Via mathpsych.org/presentation/1997

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI

Authors: Akchunya Chanchal, David A. Kelly, Hana Chockler

Abstract: Perturbation-based explainability methods face criticism due to their reliance on out-of-distribution mutants. This raises doubts about the quality of the explanations. In this paper, we introduce a novel forward pass paradigm, Activation-Deactivation (AD), which obviates the need for perturbation of the input. AD replaces perturbation of input features with switching off parts of the model corres… ▽ More Perturbation-based explainability methods face criticism due to their reliance on out-of-distribution mutants. This raises doubts about the quality of the explanations. In this paper, we introduce a novel forward pass paradigm, Activation-Deactivation (AD), which obviates the need for perturbation of the input. AD replaces perturbation of input features with switching off parts of the model corresponding to to the intended perturbations. We implement ConvAD, an AD approximation algorithm for CNNs. ConvAD is a drop-in mechanism that can be easily added to any trained CNN and, without any additional training, generates more robust and more transferable explanations. We provide evaluation results across multiple architectures, datasets, methods and perturbation strategies, demonstrating the superior quality of ConvAD compared to the SOTA. △ Less

Submitted 4 July, 2026; v1 submitted 1 October, 2025; originally announced October 2025.

Comments: Preprint: Under Review; Updated experiments & Figures

Evaluation of Black-Box XAI Approaches for Predictors of Values of Boolean Formulae

Authors: Stav Armoni-Friedmann, Hana Chockler, David A. Kelly

Abstract: Evaluating explainable AI (XAI) approaches is a challenging task in general, due to the subjectivity of explanations. In this paper, we focus on tabular data and the specific use case of AI models predicting the values of Boolean functions. We extend the previous work in this domain by proposing a formal and precise measure of importance of variables based on actual causality, and we evaluate stat… ▽ More Evaluating explainable AI (XAI) approaches is a challenging task in general, due to the subjectivity of explanations. In this paper, we focus on tabular data and the specific use case of AI models predicting the values of Boolean functions. We extend the previous work in this domain by proposing a formal and precise measure of importance of variables based on actual causality, and we evaluate state-of-the-art XAI tools against this measure. We also present a novel XAI tool B-ReX, based on the existing tool ReX, and demonstrate that it is superior to other black-box XAI tools on a large-scale benchmark. Specifically, B-ReX achieves a Jensen-Shannon divergence of 0.072 $\pm$ 0.012 on random 10-valued Boolean formulae △ Less

Submitted 12 September, 2025; originally announced September 2025.

Comments: Accepted to ECAI-EXCD Workshop, 8 pages, 2 figures, 5 tables

ACM Class: I.2.4

Do Students Rely on AI? Analysis of Student-ChatGPT Conversations from a Field Study

Authors: Jiayu Zheng, Lingxin Hao, Kelun Lu, Ashi Garg, Mike Reese, Melo-Jean Yap, I-Jeng Wang, Xingyun Wu, Wenrui Huang, Jenna Hoffman, Ariane Kelly, My Le, Ryan Zhang, Yanyu Lin, Muhammad Faayez, Anqi Liu

Abstract: This study explores how college students interact with generative AI (ChatGPT-4) during educational quizzes, focusing on reliance and predictors of AI adoption. Conducted at the early stages of ChatGPT implementation, when students had limited familiarity with the tool, this field study analyzed 315 student-AI conversations during a brief, quiz-based scenario across various STEM courses. A novel f… ▽ More This study explores how college students interact with generative AI (ChatGPT-4) during educational quizzes, focusing on reliance and predictors of AI adoption. Conducted at the early stages of ChatGPT implementation, when students had limited familiarity with the tool, this field study analyzed 315 student-AI conversations during a brief, quiz-based scenario across various STEM courses. A novel four-stage reliance taxonomy was introduced to capture students' reliance patterns, distinguishing AI competence, relevance, adoption, and students' final answer correctness. Three findings emerged. First, students exhibited overall low reliance on AI and many of them could not effectively use AI for learning. Second, negative reliance patterns often persisted across interactions, highlighting students' difficulty in effectively shifting strategies after unsuccessful initial experiences. Third, certain behavioral metrics strongly predicted AI reliance, highlighting potential behavioral mechanisms to explain AI adoption. The study's findings underline critical implications for ethical AI integration in education and the broader field. It emphasizes the need for enhanced onboarding processes to improve student's familiarity and effective use of AI tools. Furthermore, AI interfaces should be designed with reliance-calibration mechanisms to enhance appropriate reliance. Ultimately, this research advances understanding of AI reliance dynamics, providing foundational insights for ethically sound and cognitively enriching AI practices. △ Less

Submitted 27 August, 2025; originally announced August 2025.

I Am Big, You Are Little; I Am Right, You Are Wrong

Authors: David A. Kelly, Akchunya Chanchal, Nathan Blake

Abstract: Machine learning for image classification is an active and rapidly developing field. With the proliferation of classifiers of different sizes and different architectures, the problem of choosing the right model becomes more and more important. While we can assess a model's classification accuracy statistically, our understanding of the way these models work is unfortunately limited. In order to… ▽ More Machine learning for image classification is an active and rapidly developing field. With the proliferation of classifiers of different sizes and different architectures, the problem of choosing the right model becomes more and more important. While we can assess a model's classification accuracy statistically, our understanding of the way these models work is unfortunately limited. In order to gain insight into the decision-making process of different vision models, we propose using minimal sufficient pixels sets to gauge a model's `concentration': the pixels that capture the essence of an image through the lens of the model. By comparing position, overlap, and size of sets of pixels, we identify that different architectures have statistically different concentration, in both size and position. In particular, ConvNext and EVA models differ markedly from the others. We also identify that images which are misclassified are associated with larger pixels sets than correct classifications. △ Less

Submitted 31 July, 2025; originally announced July 2025.

Comments: 10 pages, International Conference on Computer Vision, ICCV 2025

Sufficient, Necessary and Complete Causal Explanations in Image Classification

Authors: David A Kelly, Hana Chockler

Abstract: Existing algorithms for explaining the outputs of image classifiers are based on a variety of approaches and produce explanations that frequently lack formal rigour. On the other hand, logic-based explanations are formally and rigorously defined but their computability relies on strict assumptions about the model that do not hold on image classifiers. In this paper, we show that causal explanati… ▽ More Existing algorithms for explaining the outputs of image classifiers are based on a variety of approaches and produce explanations that frequently lack formal rigour. On the other hand, logic-based explanations are formally and rigorously defined but their computability relies on strict assumptions about the model that do not hold on image classifiers. In this paper, we show that causal explanations, in addition to being formally and rigorously defined, enjoy the same formal properties as logic-based ones, while still lending themselves to black-box algorithms and being a natural fit for image classifiers. We prove formal properties of causal explanations and their equivalence to logic-based explanations. We demonstrate how to subdivide an image into its sufficient and necessary components. We introduce $δ$-complete explanations, which have a minimum confidence threshold and 1-complete causal explanations, explanations that are classified with the same confidence as the original image. We implement our definitions, and our experimental results demonstrate that different models have different patterns of sufficiency, necessity, and completeness. Our algorithms are efficiently computable, taking on average 6s per image on a ResNet model to compute all types of explanations, and are totally black-box, needing no knowledge of the model, no access to model internals, no access to gradient, nor requiring any properties, such as monotonicity, of the model. △ Less

Submitted 19 February, 2026; v1 submitted 31 July, 2025; originally announced July 2025.

Comments: 16 pages, appendix included

The hydrodynamic thermal continuum, with applications to stratified atmospheres and 1D coronal loop models

Authors: Rony Keppens, Jordi De Jonghe, Adrian Kelly, Nicolas Brughmans, Hans Goedbloed

Abstract: Using both analytical and numerical means, we demonstrate that linear stability analysis of a hydrodynamic stratified atmosphere or a 1D coronal loop model in non-adiabatic settings features a thermal continuum corresponding to highly localized eigenfunctions. This thermal continuum can be precomputed, involving the net heat-loss function and its partial derivatives, and is the generalization of t… ▽ More Using both analytical and numerical means, we demonstrate that linear stability analysis of a hydrodynamic stratified atmosphere or a 1D coronal loop model in non-adiabatic settings features a thermal continuum corresponding to highly localized eigenfunctions. This thermal continuum can be precomputed, involving the net heat-loss function and its partial derivatives, and is the generalization of the thermal instability introduced by~\citet{Parker1953}. We account for a thermal imbalance, directly affecting thermal instability growthrates. We present completely general equations that govern all eigenmodes, including non-adiabatically affected p- and g-modes of the stratified settings. We intend to clarify how linear thermal instability is relevant for solar loops that show spontaneous in-situ condensations, and eliminate recent confusion on specific isochoric routes to linear instability alongside other thermal instability channels. The thermal continuum, previously identified as a crucial ingredient in magnetohydrodynamic eigenmode spectra for coronal loops and atmospheres, drives multithermal aspects across our universe, such as forming solar coronal rain and prominences, or cold cloud creation in intracluster to interstellar medium environments. △ Less

Submitted 30 June, 2025; originally announced June 2025.

Comments: Accepted for publication in ApJ

Statistical post-processing yields accurate probabilistic forecasts from Artificial Intelligence weather models

Authors: Belinda Trotta, Robert Johnson, Catherine de Burgh-Day, Debra Hudson, Esteban Abellan, James Canvin, Andrew Kelly, Daniel Mentiplay, Benjamin Owen, Jennifer Whelan

Abstract: Artificial Intelligence (AI) weather models are now reaching operational-grade performance for some variables, but like traditional Numerical Weather Prediction (NWP) models, they exhibit systematic biases and reliability issues. We test the application of the Bureau of Meteorology's existing statistical post-processing system, IMPROVER, to ECMWF's deterministic Artificial Intelligence Forecasting… ▽ More Artificial Intelligence (AI) weather models are now reaching operational-grade performance for some variables, but like traditional Numerical Weather Prediction (NWP) models, they exhibit systematic biases and reliability issues. We test the application of the Bureau of Meteorology's existing statistical post-processing system, IMPROVER, to ECMWF's deterministic Artificial Intelligence Forecasting System (AIFS), and compare results against post-processed outputs from the ECMWF HRES and ENS models. Without any modification to processing workflows, post-processing yields comparable accuracy improvements for AIFS as for traditional NWP forecasts, in both expected value and probabilistic outputs. We show that blending AIFS with NWP models improves overall forecast skill, even when AIFS alone is not the most accurate component. These findings show that statistical post-processing methods developed for NWP are directly applicable to AI models, enabling national meteorological centres to incorporate AI forecasts into existing workflows in a low-risk, incremental fashion. △ Less

Submitted 14 October, 2025; v1 submitted 17 April, 2025; originally announced April 2025.

Premixed flame quenching distance between cold walls: effects of flow and Lewis number

Authors: Aiden Kelly, Rémi Daou, Joel Daou, Vadim N. Kurdyumov, Prabakaran Rajamanickam

Abstract: This study investigates the critical conditions for flame propagation in channels with cold walls. We analyze the impact of the Lewis number and flow amplitude ($A$) on the minimum channel width required to sustain a premixed flame. Our results span a wide range of Lewis numbers, encompassing both aiding and opposing flow conditions. Results are presented for both variable and constant density mod… ▽ More This study investigates the critical conditions for flame propagation in channels with cold walls. We analyze the impact of the Lewis number and flow amplitude ($A$) on the minimum channel width required to sustain a premixed flame. Our results span a wide range of Lewis numbers, encompassing both aiding and opposing flow conditions. Results are presented for both variable and constant density models. A combined numerical approach, involving stationary and time-dependent simulations, is employed to determine quenching distances and solution stability. We find that smaller Lewis numbers and aiding flows ($A < 0$) facilitate flame propagation in narrower channels, while opposing flows ($A > 0$) tend to destabilize the flame, promoting asymmetric solutions. For sufficiently large positive values of $A$, the quenching distance is determined by asymmetric solutions, rather than the typical symmetric ones. △ Less

Submitted 12 October, 2025; v1 submitted 22 March, 2025; originally announced March 2025.

Journal ref: Proc. Combust. Inst. 41 (2025) 105836

SpecReX: Explainable AI for Raman Spectroscopy

Authors: Nathan Blake, David A. Kelly, Akchunya Chanchal, Sarah Kapllani-Mucaj, Geraint Thomas, Hana Chockler

Abstract: Raman spectroscopy is becoming more common for medical diagnostics with deep learning models being increasingly used to leverage its full potential. However, the opaque nature of such models and the sensitivity of medical diagnosis together with regulatory requirements necessitate the need for explainable AI tools. We introduce SpecReX, specifically adapted to explaining Raman spectra. SpecReX use… ▽ More Raman spectroscopy is becoming more common for medical diagnostics with deep learning models being increasingly used to leverage its full potential. However, the opaque nature of such models and the sensitivity of medical diagnosis together with regulatory requirements necessitate the need for explainable AI tools. We introduce SpecReX, specifically adapted to explaining Raman spectra. SpecReX uses the theory of actual causality to rank causal responsibility in a spectrum, quantified by iteratively refining mutated versions of the spectrum and testing if it retains the original classification. The explanations provided by SpecReX take the form of a responsibility map, highlighting spectral regions most responsible for the model to make a correct classification. To assess the validity of SpecReX, we create increasingly complex simulated spectra, in which a "ground truth" signal is seeded, to train a classifier. We then obtain SpecReX explanations and compare the results with another explainability tool. By using simulated spectra we establish that SpecReX localizes to the known differences between classes, under a number of conditions. This provides a foundation on which we can find the spectral features which differentiate disease classes. This is an important first step in proving the validity of SpecReX. △ Less

Submitted 18 March, 2025; originally announced March 2025.

Comments: AAAI Workshop on Health Intelligencee (W3PHIAI-25)

Roadmap for Molecular Benchmarks in Nonadiabatic Dynamics

Authors: Léon E. Cigrang, Basile F. E. Curchod, Rebecca A. Ingle, Aaron Kelly, Jonathan R. Mannouch, Davide Accomasso, Alexander Alijah, Mario Barbatti, Wiem Chebbi, Nađa Došlić, Elliot C. Eklund, Sebastian Fernandez-Alberti, Antonia Freibert, Leticia González, Giovanni Granucci, Federico J. Hernández, Javier Hernández-Rodríguez, Amber Jain, Jiří Janoš, Ivan Kassal, Adam Kirrander, Zhenggang Lan, Henrik R. Larsson, David Lauvergnat, Brieuc Le Dé , et al. (20 additional authors not shown)

Abstract: Simulating the coupled electronic and nuclear response of a molecule to light excitation requires the application of nonadiabatic molecular dynamics. However, when faced with a specific photophysical or photochemical problem, selecting the most suitable theoretical approach from the wide array of available techniques is not a trivial task. The challenge is further complicated by the lack of system… ▽ More Simulating the coupled electronic and nuclear response of a molecule to light excitation requires the application of nonadiabatic molecular dynamics. However, when faced with a specific photophysical or photochemical problem, selecting the most suitable theoretical approach from the wide array of available techniques is not a trivial task. The challenge is further complicated by the lack of systematic method comparisons and rigorous testing on realistic molecular systems. This absence of comprehensive molecular benchmarks remains a major obstacle to advances within the field of nonadiabatic molecular dynamics. A CECAM workshop, Standardizing Nonadiabatic Dynamics: Towards Common Benchmarks, was held in May 2024 to address this issue. This Perspective highlights the key challenges identified during the workshop in defining molecular benchmarks for nonadiabatic dynamics. Specifically, this work outlines some preliminary observations on essential components needed for simulations and proposes a roadmap aiming to establish, as an ultimate goal, a community-driven, standardized molecular benchmark set. △ Less

Submitted 4 July, 2025; v1 submitted 20 February, 2025; originally announced February 2025.

Comments: Third version after acceptance by JPCA

3D ReX: Causal Explanations in 3D Neuroimaging Classification

Authors: Melane Navaratnarajah, Sophie A. Martin, David A. Kelly, Nathan Blake, Hana Chockler

Abstract: Explainability remains a significant problem for AI models in medical imaging, making it challenging for clinicians to trust AI-driven predictions. We introduce 3D ReX, the first causality-based post-hoc explainability tool for 3D models. 3D ReX uses the theory of actual causality to generate responsibility maps which highlight the regions most crucial to the model's decision. We test 3D ReX on a… ▽ More Explainability remains a significant problem for AI models in medical imaging, making it challenging for clinicians to trust AI-driven predictions. We introduce 3D ReX, the first causality-based post-hoc explainability tool for 3D models. 3D ReX uses the theory of actual causality to generate responsibility maps which highlight the regions most crucial to the model's decision. We test 3D ReX on a stroke detection model, providing insight into the spatial distribution of features relevant to stroke. △ Less

Submitted 29 April, 2025; v1 submitted 14 February, 2025; originally announced February 2025.

Comments: Presented in the 2nd Workshop on Imageomics (Imageomics-AAAI-25), Discovering Biological Knowledge from Images using AI, held as part of AAAI-2025

To BEE or not to BEE: Estimating more than Entropy with Biased Entropy Estimators

Authors: Ilaria Pia la Torre, David A. Kelly, Hector D. Menendez, David Clark

Abstract: Entropy estimation plays a significant role in biology, economics, physics, communication engineering and other disciplines. It is increasingly used in software engineering, e.g. in software confidentiality, software testing, predictive analysis, machine learning, and software improvement. However accurate estimation is demonstrably expensive in many contexts, including software. Statisticians hav… ▽ More Entropy estimation plays a significant role in biology, economics, physics, communication engineering and other disciplines. It is increasingly used in software engineering, e.g. in software confidentiality, software testing, predictive analysis, machine learning, and software improvement. However accurate estimation is demonstrably expensive in many contexts, including software. Statisticians have consequently developed biased estimators that aim to accurately estimate entropy on the basis of a sample. In this paper we apply 18 widely employed entropy estimators to Shannon measures useful to the software engineer: entropy, mutual information and conditional mutual information. Moreover, we investigate how the estimators are affected by two main influential factors: sample size and domain size. Our experiments range over a large set of randomly generated joint probability distributions and varying sample sizes, rather than choosing just one or two well known probability distributions as in previous investigations. Our most important result is identifying that the Chao-Shen and Chao-Wang-Jost estimators stand out for consistently converging more quickly to the ground truth, regardless of domain size and regardless of the measure used. They also tend to outperform the others in terms of accuracy as sample sizes increase. This discovery enables a significant reduction in data collection effort without compromising performance. △ Less

Submitted 20 January, 2025; originally announced January 2025.

Heterogeneous-free narrow linewidth semiconductor laser with optical injection locking

Authors: Xiao Sun, Zhibo Li, Yiming Sun, Yupei Wang, Jue Wang, John H. Marsh, Stephen. J. Sweeney, Anthony E. Kelly, Lianping Hou

Abstract: Narrow linewidth lasers are indispensable for coherent optical systems, including communications, metrology, and sensing. Although compact semiconductor lasers with narrow linewidths and low noise have been demonstrated, their spectral purity typically relies on hybrid or heterogeneous external cavity feedback. Here, we present a theoretical and experimental demonstration of a heterogeneous free o… ▽ More Narrow linewidth lasers are indispensable for coherent optical systems, including communications, metrology, and sensing. Although compact semiconductor lasers with narrow linewidths and low noise have been demonstrated, their spectral purity typically relies on hybrid or heterogeneous external cavity feedback. Here, we present a theoretical and experimental demonstration of a heterogeneous free optical injection locking (HF OIL) semiconductor laser. By integrating a topological interface state extended (TISE) laser with a micro ring resonator (MRR) on an AlGaInAs multiple quantum well platform,we achieve monolithic photon injection and phase locking, thereby reducing the optical linewidth. We fabricated and characterized a 1550 nm sidewall HF OIL laser, achieving stable single mode operation over a broad current range (65 to 300 mA) and a side mode suppression ratio (SMSR) over 50 dB. Under injection locking, the devices Voigt fitted linewidth narrowed from over 1.7 MHz (free running) to 4.2 kHz, representing a three order of magnitude improvement over conventional distributed feedback lasers. The intrinsic linewidth of 1.4 kHz is measured by correlated delayed self-heterodyne frequency noise power spectrum density (FN PSD) method. Moreover, the HF OIL laser demonstrated high phase stability and the ability to transition from a random phased to a phase locked state. These results underscore the potential of HF-OIL lasers in advancing coherent optical communications and phase encoders in quantum key distribution (QKD) systems. △ Less

Submitted 13 January, 2025; originally announced January 2025.

Causal Explanations for Image Classifiers

Authors: Hana Chockler, David A. Kelly, Daniel Kroening, Youcheng Sun

Abstract: Existing algorithms for explaining the output of image classifiers use different definitions of explanations and a variety of techniques to find them. However, none of the existing tools use a principled approach based on formal definitions of cause and explanation. In this paper we present a novel black-box approach to computing explanations grounded in the theory of actual causality. We prove… ▽ More Existing algorithms for explaining the output of image classifiers use different definitions of explanations and a variety of techniques to find them. However, none of the existing tools use a principled approach based on formal definitions of cause and explanation. In this paper we present a novel black-box approach to computing explanations grounded in the theory of actual causality. We prove relevant theoretical results and present an algorithm for computing approximate explanations based on these definitions. We prove termination of our algorithm and discuss its complexity and the amount of approximation compared to the precise definition. We implemented the framework in a tool ReX and we present experimental results and a comparison with state-of-the-art tools. We demonstrate that ReX is the most efficient black-box tool and produces the smallest explanations, in addition to outperforming other black-box tools on standard quality measures. △ Less

Submitted 2 July, 2026; v1 submitted 13 November, 2024; originally announced November 2024.

Comments: Accepted to Journal of Artificial Intelligence Research (JAIR). A subset of the contribution was published in ICCV 2021

Journal ref: Journal of Artificial Intelligence Research, vol 86, 2026

Modeling and scaling spontaneous imbibition with generalized fractional flow theory and non-Boltzmann transformation

Authors: Shaluka Senevirathna, Anna Zemlyanova, Shaina A. Kelly, Qinhong Hu, Yong Zhang, Behzad Ghanbarian

Abstract: Spontaneous imbibition (SI) is a process by which liquid is drawn into partially saturated porous media by capillary forces, relevant for subsurface processes like underground fluid storage and withdrawal. Accurate modeling and scaling of counter-current SI have long been challenging. In this study, we proposed a generalized fractional flow theory (GFFT) using the Hausdorff fractal derivative, com… ▽ More Spontaneous imbibition (SI) is a process by which liquid is drawn into partially saturated porous media by capillary forces, relevant for subsurface processes like underground fluid storage and withdrawal. Accurate modeling and scaling of counter-current SI have long been challenging. In this study, we proposed a generalized fractional flow theory (GFFT) using the Hausdorff fractal derivative, combined with non-Boltzmann scaling. The model links imbibition distance to time through the power law exponent alpha/2, where alpha is the fractal index (0 < alpha < 2 in this study). We applied the GFFT to various experimental and stimulated datasets of both porous and fractured media, finding that alpha varied with factors such as contact angle (of the imbibing fluid), dynamic viscosity, pore structure, and fracture properties. By analyzing SI data from sandstones, diatomite, carbonate, and synthetic porous media, we demonstrated that the non-Boltzmann scaling provided a better collapse of the SI data than the traditional Boltzmann approach alpha = 1), with alpha values ranging from 0.88 to 1.54. These deviations illustrate the model's adaptability to different porous materials. Using the GFFT, we expect to better predict fluid imbibition rates when properties like porosity, permeability, initial and maximum saturations, viscosity, and wettability are known, offering a more accurate alternative to traditional models. △ Less

Submitted 12 November, 2024; originally announced November 2024.

Comments: 7 figures and 1 table

Report number: SPE-226176-PA

Journal ref: SPE Journal, 2025

Outcomes from a Workshop on a National Center for Quantum Education

Authors: Edwin Barnes, Michael B. Bennett, Alexandra Boltasseva, Victoria Borish, Bennett Brown, Lincoln D. Carr, Russell R. Ceballos, Faith Dukes, Emily W. Easton, Sophia E. Economou, E. E. Edwards, Noah D. Finkelstein, C. Fracchiolla, Diana Franklin, J. K. Freericks, Valerie Goss, Mark Hannum, Nancy Holincheck, Angela M. Kelly, Olivia Lanes, H. J. Lewandowski, Karen Jo Matsler, Emily Mercurio, Inès Montaño, Maajida Murdock , et al. (13 additional authors not shown)

Abstract: In response to numerous programs seeking to advance quantum education and workforce development in the United States, experts from academia, industry, government, and professional societies convened for a National Science Foundation-sponsored workshop in February 2024 to explore the benefits and challenges of establishing a national center for quantum education. Broadly, such a center would foster… ▽ More In response to numerous programs seeking to advance quantum education and workforce development in the United States, experts from academia, industry, government, and professional societies convened for a National Science Foundation-sponsored workshop in February 2024 to explore the benefits and challenges of establishing a national center for quantum education. Broadly, such a center would foster collaboration and build the infrastructure required to develop a diverse and quantum-ready workforce. The workshop discussions centered around how a center could uniquely address gaps in public, K-12, and undergraduate quantum information science and engineering (QISE) education. Specifically, the community identified activities that, through a center, could lead to an increase in student awareness of quantum careers, boost the number of educators trained in quantum-related subjects, strengthen pathways into quantum careers, enhance the understanding of the U.S. quantum workforce, and elevate public engagement with QISE. Core proposed activities for the center include professional development for educators, coordinated curriculum development and curation, expanded access to educational laboratory equipment, robust evaluation and assessment practices, network building, and enhanced public engagement with quantum science. △ Less

Submitted 30 October, 2024; originally announced October 2024.

AI Horizon Scanning, White Paper p3395, IEEE-SA. Part I: Areas of Attention

Authors: Marina Cortês, Andrew R. Liddle, Christos Emmanouilidis, Anthony E. Kelly, Ken Matusow, Ragu Ragunathan, Jayne M. Suess, George Tambouratzis, Janusz Zalewski, David A. Bray

Abstract: Generative Artificial Intelligence (AI) models may carry societal transformation to an extent demanding a delicate balance between opportunity and risk. This manuscript is the first of a series of White Papers informing the development of IEEE-SA's p3995: `Standard for the Implementation of Safeguards, Controls, and Preventive Techniques for Artificial Intelligence (AI) Models', Chair: Marina Cort… ▽ More Generative Artificial Intelligence (AI) models may carry societal transformation to an extent demanding a delicate balance between opportunity and risk. This manuscript is the first of a series of White Papers informing the development of IEEE-SA's p3995: `Standard for the Implementation of Safeguards, Controls, and Preventive Techniques for Artificial Intelligence (AI) Models', Chair: Marina Cortês (https://standards.ieee.org/ieee/3395/11378/). In this first horizon-scanning we identify key attention areas for standards activities in AI. We examine different principles for regulatory efforts, and review notions of accountability, privacy, data rights and mis-use. As a safeguards standard we devote significant attention to the stability of global infrastructures and consider a possible overdependence on cloud computing that may result from densely coupled AI components. We review the recent cascade-failure-like Crowdstrike event in July 2024, as an illustration of potential impacts on critical infrastructures from AI-induced incidents in the (near) future. It is the first of a set of articles intended as White Papers informing the audience on the standard development. Upcoming articles will focus on regulatory initiatives, technology evolution and the role of AI in specific domains. △ Less

Submitted 13 September, 2024; originally announced October 2024.

Comments: This is an interim version of our p3395 working group White Paper. We will update this version, until publication by the Institute of Electrical and Electronics Engineers, Standards Association (IEEE-SA), Sponsor Committee - Artificial Intelligence Standards Committee (C/AISC); https://standards.ieee.org/ieee/3395/11378/

Astronomy Identity Framework for Undergraduate Students and Researchers

Authors: Zachary Richards, Angela Kelly

Abstract: This research was a qualitative transcendental phenomenological exploration of astronomy identity formation among astronomy majors and physics majors engaged in astronomy research. Participants (N=10), all of whom identified with traditionally marginalized groups in astronomy, were recruited from two large universities in New York State at different stages in their undergraduate careers. Social co… ▽ More This research was a qualitative transcendental phenomenological exploration of astronomy identity formation among astronomy majors and physics majors engaged in astronomy research. Participants (N=10), all of whom identified with traditionally marginalized groups in astronomy, were recruited from two large universities in New York State at different stages in their undergraduate careers. Social cognitive career theory and the physics identity framework conceptually guided the analysis of astronomy identity for undergraduate majors and undergraduate astronomy researchers by exploring participants interest in, choice to study, and persistence in astronomy. Themes related to astronomy interest were popular culture and directly observing astronomical phenomena, while astronomy choice and persistence were facilitated by experiences in introductory coursework, recognition from faculty, and socializing with peers. The emergent astronomy identity framework was characterized by six distinct yet interrelated constructs: 1) interest, typically rooted in observing naturally occurring phenomena and engaging with popular culture; 2) recognition from peers, experts, and families; 3) peer socialization; 4) competence; 5) sense of belonging; and 6) astronomy career expectations. Implications from this research provide insights on factors that influence undergraduates in four-year colleges to study astronomy, and how students' past experiences lead to a natural interest in astronomy that may be fostered in secondary and post-secondary contexts. Findings suggest departments and institutions may facilitate the accessibility of astronomy at the collegiate level by promoting a more inclusive astronomy community, fostering interactions with astronomy faculty and graduate students, providing opportunities for undergraduate research, and communicating expectancy for astronomy-related future careers. △ Less

Submitted 1 October, 2024; originally announced October 2024.

Multi-Wavelength DFB Laser Based on Sidewall Third Order Four Phase-Shifted Sampled Bragg Grating with Uniform Wavelength Spacing

Authors: Xiao Sun, Zhibo Li, Yizhe Fan, Mohanad Jamal Al-Rubaiee, John H. Marsh, Anthony E Kelly, Stephen. J. Sweeney, Lianping Hou

Abstract: We present the first demonstration of a 1550 nm multi-wavelength distributed feedback (MW-DFB) laser employing a third-order, four-phase-shifted sampled sidewall grating. By utilizing linearly chirped sampled gratings and incorporating multiple true π-phase shifts within the cavity, we achieved and experimentally validated a four-wavelength laser with a channel spacing of 0.4 nm. The device operat… ▽ More We present the first demonstration of a 1550 nm multi-wavelength distributed feedback (MW-DFB) laser employing a third-order, four-phase-shifted sampled sidewall grating. By utilizing linearly chirped sampled gratings and incorporating multiple true π-phase shifts within the cavity, we achieved and experimentally validated a four-wavelength laser with a channel spacing of 0.4 nm. The device operates stably and uniformly across a wide range of injection currents from 280 mA to 350 mA. The average wavelength spacing was measured at 0.401 nm with a standard deviation of 0.0081 nm. Additionally, we demonstrated a 0.3 nm MW-DFB laser with a seven-channel output, achieving a wavelength spacing of 0.274 nm and a standard deviation of 0.0055 nm. This MW-DFB laser features a ridge waveguide with sidewall gratings, requiring only one metalorganic vapor-phase epitaxy (MOVPE) step and a single III-V material etching process. This streamlined fabrication approach simplifies device manufacturing and is well-suited for dense wavelength division multiplexing (DWDM) systems. △ Less

Submitted 31 October, 2024; v1 submitted 26 September, 2024; originally announced September 2024.

Real-Time Incremental Explanations for Object Detectors in Autonomous Driving

Authors: Santiago Calderón-Peña, Hana Chockler, David A. Kelly

Abstract: Object detectors are widely used in safety-critical real-time applications such as autonomous driving. Explainability is especially important for safety-critical applications, and due to the variety of object detectors and their often proprietary nature, black-box explainability tools are needed. However, existing black-box explainability tools for AI models rely on multiple model calls, rendering… ▽ More Object detectors are widely used in safety-critical real-time applications such as autonomous driving. Explainability is especially important for safety-critical applications, and due to the variety of object detectors and their often proprietary nature, black-box explainability tools are needed. However, existing black-box explainability tools for AI models rely on multiple model calls, rendering them impractical for real-time use. In this paper, we introduce IncX, an algorithm and a tool for real-time black-box explainability for object detectors. The algorithm is based on linear transformations of saliency maps, producing sufficient explanations. We evaluate our implementation on four widely used video datasets of autonomous driving and demonstrate that IncX's explanations are comparable in quality to the state-of-the-art and are computed two orders of magnitude faster than the state-of-the-art, making them usable in real time. △ Less

Submitted 7 March, 2025; v1 submitted 21 August, 2024; originally announced August 2024.

Towards a correct description of initial electronic coherence in nonadiabatic dynamics simulations

Authors: Jonathan R. Mannouch, Aaron Kelly

Abstract: The recent improvement in experimental capabilities for interrogating and controlling molecular systems with ultrafast coherent light sources calls for the development of theoretical approaches that can accurately and efficiently treat electronic coherence. However, the most popular and practical nonadiabatic molecular dynamics techniques, Tully's fewest-switches surface hopping and Ehrenfest mean… ▽ More The recent improvement in experimental capabilities for interrogating and controlling molecular systems with ultrafast coherent light sources calls for the development of theoretical approaches that can accurately and efficiently treat electronic coherence. However, the most popular and practical nonadiabatic molecular dynamics techniques, Tully's fewest-switches surface hopping and Ehrenfest mean-field dynamics, are unable to describe the dynamics proceeding from an initial electronic coherence. While such issues are not encountered with the analogous coupled-trajectory algorithms or numerically exact quantum dynamics methods, applying such methods necessarily comes with a higher computational cost. Here we show that a correct description of initial electronic coherence can indeed be achieved using methods that are based on an ensemble of independent trajectories. The key is the introduction of an initial sampling over the electronic phase space and the use of the correct observable measures, both of which are naturally achieved when working within the semiclassical mapping framework. △ Less

Submitted 18 August, 2024; originally announced August 2024.

Comments: 22 pages, 6 figures

Image Scaling Attack Simulation: A Measure of Stealth and Detectability

Authors: Devon A. Kelly, Sarah A. Flanery, Christiana Chamon

Abstract: Cybersecurity practices require effort to be maintained, and one weakness is a lack of awareness regarding potential attacks not only in the usage of machine learning models, but also in their development process. Previous studies have determined that preprocessing attacks, such as image scaling attacks, have been difficult to detect by humans (through visual response) and computers (through entro… ▽ More Cybersecurity practices require effort to be maintained, and one weakness is a lack of awareness regarding potential attacks not only in the usage of machine learning models, but also in their development process. Previous studies have determined that preprocessing attacks, such as image scaling attacks, have been difficult to detect by humans (through visual response) and computers (through entropic algorithms). However, these studies fail to address the real-world performance and detectability of these attacks. The purpose of this work is to analyze the relationship between awareness of image scaling attacks with respect to demographic background and experience. We conduct a survey where we gather the subjects' demographics, analyze the subjects' experience in cybersecurity, record their responses to a poorly-performing convolutional neural network model that has been unknowingly hindered by an image scaling attack of a used dataset, and document their reactions after it is revealed that the images used within the broken models have been attacked. We find in this study that the overall detection rate of the attack is low enough to be viable in a workplace or academic setting, and even after discovery, subjects cannot conclusively determine benign images from attacked images. △ Less

Submitted 14 August, 2024; originally announced August 2024.

Narrow Linewidth Laser Based on Extended Topological Interface States in One-Dimensional Photonic Crystals

Authors: Xiao Sun, Zhibo Li, Yiming Sun, Yupei Wang, Jue Wang, Huihua Cheng, Cong Fu, John H. Marsh, Anthony E. Kelly, Lianping Hou

Abstract: Recent advances in topological one-dimensional photonic crystal concepts have enabled the development of robust light-emitting devices by incorporating a topological interface state (TIS) at the cavity center. In this study, we theoretically and experimentally demonstrate a one-dimensional TIS-extended photonic crystal (1D-TISE-PC) structure. By integrating a linearly dispersive zero-index one-dim… ▽ More Recent advances in topological one-dimensional photonic crystal concepts have enabled the development of robust light-emitting devices by incorporating a topological interface state (TIS) at the cavity center. In this study, we theoretically and experimentally demonstrate a one-dimensional TIS-extended photonic crystal (1D-TISE-PC) structure. By integrating a linearly dispersive zero-index one-dimensional photonic crystal structure with a four-phase shift sampled grating, photons propagate along the cavity without phase differences, enhancing the robustness to material variations and extending the TIS. Our findings indicate that extending the TIS promotes a more uniform photon distribution along the laser cavity and mitigates the spatial hole burning (SHB) effect. We fabricated and characterized a 1550 nm sidewall 1D-TISE-PC semiconductor laser, achieving stable single-mode operation across a wide current range from 60 to 420 mA, with a side-mode suppression ratio of 50 dB. The 1D-TISE-PC structure exhibited a linewidth narrowing effect to approximately 150 kHz Lorentzian linewidth. Utilizing reconstruction equivalent-chirp technology for the 4PS sampled grating enabled precise wavelength control in 1D-TISE-PC laser arrays, achieving a wavelength spacing of 0.796 nm +- 0.003 nm. We show that the TIS still exists in the TISE cavity and topological protection is preserved. Its mode extension characteristics mitigate the SHB so narrows the linewidth. We argue that the design simplicity and improvement of the fabrication tolerance make this architecture suitable for high-power and narrow-linewidth semiconductor lasers development. △ Less

Submitted 10 July, 2024; originally announced July 2024.

Three-dimensional diffusive-thermal instability of flames propagating in a plane Poiseuille flow

Authors: Aiden Kelly, Prabakaran Rajamanickam, Joel Daou, Julien R. Landel

Abstract: The three-dimensional diffusive-thermal stability of a two-dimensional flame propagating in a Poiseuille flow is examined. The study explores the effect of three non-dimensional parameters, namely the Lewis number $Le$, the Damköhler number $Da$, and the flow Peclet number $Pe$. Wide ranges of the Lewis number and the flow amplitude are covered, as well as conditions corresponding to small-scale n… ▽ More The three-dimensional diffusive-thermal stability of a two-dimensional flame propagating in a Poiseuille flow is examined. The study explores the effect of three non-dimensional parameters, namely the Lewis number $Le$, the Damköhler number $Da$, and the flow Peclet number $Pe$. Wide ranges of the Lewis number and the flow amplitude are covered, as well as conditions corresponding to small-scale narrow ($Da \ll 1$) to large-scale wide ($Da \gg 1$) channels. The instability experienced by the flame appears as a combination of the traditional diffusive-thermal instability of planar flames and the recently identified instability corresponding to a transition from symmetric to asymmetric flame. The instability regions are identified in the $Le$-$Pe$ plane for selected values of $Da$ by computing the eigenvalues of a linear stability problem. These are complemented by two- and three-dimensional time-dependent simulations describing the full evolution of unstable flames into the non-linear regime. In narrow channels, flames are found to be always symmetric about the mid-plane of the channel. Additionally, in these situations, shear flow-induced Taylor dispersion enhances the cellular instability in $Le<1$ mixtures and suppresses the oscillatory instability in $Le>1$ mixtures. In large-scale channels, however, both the cellular and the oscillatory instabilities are expected to persist. Here, the flame has a stronger propensity to become asymmetric when the mean flow opposes its propagation and when $Le<1$; if the mean flow facilitates the flame propagation, then the flame is likely to remain symmetric about the channel mid-plane. For $Le>1$, both symmetric and asymmetric flames are encountered and are accompanied by temporal oscillations. △ Less

Submitted 7 July, 2024; originally announced July 2024.

Journal ref: Proc. Combust. Inst. (2024) 40(1-4), 105258

Stability of diffusion flames under shear flow: Taylor dispersion and the formation of flame streets

Authors: Prabakaran Rajamanickam, Aiden Kelly, Joel Daou

Abstract: Diffusion flame streets, observed in non-premixed micro-combustion devices, align parallel to a shear flow. They are observed to occur in mixtures with high Lewis number ($Le$) fuels, provided that the flow Reynolds number, or the Peclet number $Pe$, exceeds a critical value. The underlying mechanisms behind these observations have not yet been fully understood. In the present paper, we identify t… ▽ More Diffusion flame streets, observed in non-premixed micro-combustion devices, align parallel to a shear flow. They are observed to occur in mixtures with high Lewis number ($Le$) fuels, provided that the flow Reynolds number, or the Peclet number $Pe$, exceeds a critical value. The underlying mechanisms behind these observations have not yet been fully understood. In the present paper, we identify the coupling between diffusive-thermal instabilities and Taylor dispersion as a mechanism which is able to explain the experimental observations above. The explanation is largely based on the fact that Taylor dispersion enhances all diffusion processes in the flow direction, leading effectively to anisotropic diffusion with an effective (flow-dependent) Lewis number in the flow direction which is proportional to $1/Le$ for $Pe\gg 1$. Validation of the identified mechanism is demonstrated within a simple model by investigating the stability of a planar diffusion flame established parallel to a plane Poiseuille flow in a narrow channel. A linear stability analysis, leading to an eigenvalue problem solved numerically, shows that cellular (or finite wavelength) instabilities emerge for high Lewis number fuels when the Peclet number exceeds a critical value. Furthermore, for Peclet numbers below this critical value, longwave instabilities with or without time oscillations are obtained. Stability regime diagrams are presented for illustrative cases in a $Le$-$Pe$ plane where various instability domains are identified. Finally, the linear analysis is supported and complemented by time dependent numerical simulations, describing the evolution of unstable diffusion flames. The simulations demonstrate the existence of stable cellular structures and show that the longwave instabilities are conducive to flame extinction. △ Less

Submitted 27 June, 2024; originally announced July 2024.

Journal ref: Combust. Flame (2023) 257, 113003

Final-state interactions in neutrino-induced proton knockout from argon in MicroBooNE

Authors: A. Nikolakopoulos, A. Ershova, R. González-Jiménez, J. Isaacson, A. M. Kelly, K. Niewczas, N. Rocco, F. Sánchez

Abstract: Neutrino event generators make use of intranuclear cascade models (INCs), to predict the kinematics of hadrons produced in neutrino-nucleus interactions. We perform a consistent comparison of different INCs, by using the same set of events as input to the NEUT, NuWro, Achilles and INCL INCs. The inputs correspond to calculations of the fully differential single-proton knockout cross section, eithe… ▽ More Neutrino event generators make use of intranuclear cascade models (INCs), to predict the kinematics of hadrons produced in neutrino-nucleus interactions. We perform a consistent comparison of different INCs, by using the same set of events as input to the NEUT, NuWro, Achilles and INCL INCs. The inputs correspond to calculations of the fully differential single-proton knockout cross section, either in the distorted-wave impulse approximation (DWIA) or plane-wave impulse approximation (PWIA), both including realistic nuclear hole spectral functions. We compare the INC results to DWIA calculations with an optical potential, used extensively in the analysis of (e,e'p) experiments. We point out a systematic discrepancy between both approaches. We apply the INC results to recent MicroBooNE data. We assess the influence of the choice of spectral function, finding that large variations in realistic spectral functions are indistinguishable with present data. The data is underpredicted, with strength missing in the region where two-nucleon knockout and resonance production contribute. However, the data is underpredicted also in regions of low transverse missing momentum, where one-nucleon knockout dominates. The inclusion of the interference with two-body currents could lead to additional strength in this region. △ Less

Submitted 13 June, 2024; originally announced June 2024.

Comments: 26 pages including appendices, comments welcome