Tootfinder

Environment Reconstruction in Terahertz Monostatic Sensing: Joint Millimeter-level Geometry Mapping and Material Identification
Zitong Fang, Yejian Lyu, Ziming Yu, Chong Han
https://arxiv.org/abs/2508.10372

Environment Reconstruction in Terahertz Monostatic Sensing: Joint Millimeter-level Geometry Mapping and Material Identification
Terahertz (THz) integrated sensing and communication (ISAC) offers high-speed communication alongside precise environmental sensing. This paper presents a computationally efficient framework for THz-based environment reconstruction by integrating connected component analysis (CCA)-assisted multipath component (MPC) estimation with a sliding-window refinement strategy. To start with, a monostatic sensing experiment is conducted in an indoor scenario using a vector network analyzer (VNA)-based so…

Let the Tree Decide: FABART A Non-Parametric Factor Model
Sofia Velasco
https://arxiv.org/abs/2506.11551 https://arxiv.org/pdf/2506.1…

Let the Tree Decide: FABART A Non-Parametric Factor Model
This article proposes a novel framework that integrates Bayesian Additive Regression Trees (BART) into a Factor-Augmented Vector Autoregressive (FAVAR) model to forecast macro-financial variables and examine asymmetries in the transmission of oil price shocks. By employing nonparametric techniques for dimension reduction, the model captures complex, nonlinear relationships between observables and latent factors that are often missed by linear approaches. A simulation experiment comparing FABART…

Spin-orbit bi-colour modulation and analysis of structured light in a nonlinear optics experiment
Kiki Dekkers, Mwezi Koni, Vagharshak Hakobyan, Sachleen Singh, Jonathan Leach, Etienne Brasselet, Isaac Nape, Andrew Forbes
https://arxiv.org/abs/2506.10557

Spin-orbit bi-colour modulation and analysis of structured light in a nonlinear optics experiment
Here we propose the use of an adjustable liquid crystal spin-orbit device to shape and detect bi-colour structured light in a nonlinear optics framework. The spin-orbit device has an inhomogeneous optical axis orientation and birefringence, allowing it to modulate two wavelengths of light with pre-selected transmission functions by simply tuning a voltage. We combine this bi-colour functionality in a nonlinear optical experiment by employing three-wave mixing in a periodically poled crystal to …

ReVISit 2: A Full Experiment Life Cycle User Study Framework
Zach Cutler, Jack Wilburn, Hilson Shrestha, Yiren Ding, Brian Bollen, Khandaker Abrar Nadib, Tingying He, Andrew McNutt, Lane Harrison, Alexander Lex
https://arxiv.org/abs/2508.03876

ReVISit 2: A Full Experiment Life Cycle User Study Framework
Online user studies of visualizations, visual encodings, and interaction techniques are ubiquitous in visualization research. Yet, designing, conducting, and analyzing studies effectively is still a major burden. Although various packages support such user studies, most solutions address only facets of the experiment life cycle, make reproducibility difficult, or do not cater to nuanced study designs or interactions. We introduce reVISit 2, a software framework that supports visualization resea…

Bridging Simulation and Experiment: A Self-Supervised Domain Adaptation Framework for Concrete Damage Classification
Chen Xu, Giao Vu, Ba Trung Cao, Zhen Liu, Fabian Diewald, Yong Yuan, G\"unther Meschke
https://arxiv.org/abs/2508.04538

Bridging Simulation and Experiment: A Self-Supervised Domain Adaptation Framework for Concrete Damage Classification
Reliable assessment of concrete degradation is critical for ensuring structural safety and longevity of engineering structures. This study proposes a self-supervised domain adaptation framework for robust concrete damage classification using coda wave signals. To support this framework, an advanced virtual testing platform is developed, combining multiscale modeling of concrete degradation with ultrasonic wave propagation simulations. This setup enables the generation of large-scale labeled syn…

Impact of improved energy resolution on DUNE sensitivity in presence of a light sterile neutrino
Sabila Parveen, Jogesh Rout, Poonam Mehta
https://arxiv.org/abs/2506.10767

Impact of improved energy resolution on DUNE sensitivity in presence of a light sterile neutrino
The Deep Underground Neutrino Experiment (DUNE) primarily aims to measure the yet unknown parameters of the standard three neutrino framework, i.e., the determination of Dirac CP phase ($δ_{13}$), neutrino mass hierarchy (MH) and octant of $θ_{23}$. In the present work, we consider the standard three neutrino paradigm (referred to as the $(3+0)$ case) and beyond with an additional light sterile neutrino (referred to as the $(3+1)$ case). We consider two configurations : standard energy resolu…

Dynamics of Protonated Oxalate from Machine-Learned Simulations and Experiment: Infrared Signatures, Proton Transfer Dynamics and Tunneling Splittings
Valerii Andreichev, Silvan K\"aser, Erica L. Bocanegra, Madeeha Salik, Mark A. Johnson, Markus Meuwly
https://arxiv.org/abs/2508.06419

Dynamics of Protonated Oxalate from Machine-Learned Simulations and Experiment: Infrared Signatures, Proton Transfer Dynamics and Tunneling Splittings
The infrared spectroscopy and proton transfer dynamics together with the associated tunneling splittings for H/D-transfer in oxalate are investigated using a machine learning-based potential energy surface (PES) of CCSD(T) quality, calibrated against the results of new spectroscopic measurements. Second order vibrational perturbation calculations (VPT2) very successfully describe both the framework and H-transfer modes compared with the experiments. In particular, a new low-intensity signature …

The Elastic Analysis Facility's (EAF's) Contribution to the Future of Analysis at Multi-Experiment Institutions and Future Colliders
Elise Chavez, Maria Acosta-Flechas, Christophe Bonnaud, Burt Holzman, Tulika Bose
https://arxiv.org/abs/2506.08222

The Elastic Analysis Facility's (EAF's) Contribution to the Future of Analysis at Multi-Experiment Institutions and Future Colliders
The Elastic Analysis Facility (EAF) hosted at Fermi National Accelerator Laboratory (Fermilab) is a platform being developed with the goal of providing a fast and efficient facility for physics analysis. As high-energy physics moves towards collecting larger datasets, such as those from the High-Luminosity LHC, the EAF strives to provide a powerful and adaptable framework for future colliders and multi-experiment institutions. Currently, the EAF supports several experiments including CMS, NOvA,…

This https://arxiv.org/abs/2309.02073 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Debiased regression adjustment in completely randomized experiments with moderately high-dimensional covariates
Completely randomized experiment is the gold standard for causal inference. When the covariate information for each experimental candidate is available, one typical way is to include them in covariate adjustments for more accurate treatment effect estimation. In this paper, we investigate this problem under the randomization-based framework, i.e., that the covariates and potential outcomes of all experimental candidates are assumed as deterministic quantities and the randomness comes solely fro…

This https://arxiv.org/abs/2410.19202 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

Towards Generalizable AI-Assisted Misinformation Inoculation: Protecting Confidence Against False Election Narratives
We present a generalizable AI-assisted framework for rapidly generating effective "prebunking" interventions against misinformation. Like mRNA vaccine platforms, our approach uses a stable template structure that can be quickly adapted to counter emerging false narratives. In a preregistered two-wave experiment with 4,293 U.S. registered voters, we test this framework against politically-charged election misinformation -- one of the most challenging domains for misinformation intervention. Our …

Perry: A High-level Framework for Accelerating Cyber Deception Experimentation
Brian Singer, Yusuf Saquib, Lujo Bauer, Vyas Sekar
https://arxiv.org/abs/2506.20770

Perry: A High-level Framework for Accelerating Cyber Deception Experimentation
Cyber deception aims to distract, delay, and detect network attackers with fake assets such as honeypots, decoy credentials, or decoy files. However, today, it is difficult for operators to experiment, explore, and evaluate deception approaches. Existing tools and platforms have non-portable and complex implementations that are difficult to modify and extend. We address this pain point by introducing Perry, a high-level framework that accelerates the design and exploration of deception what-if …

On the computational feasibility of Bayesian end-to-end analysis of LiteBIRD simulations within Cosmoglobe
R. Aurvik, M. Galloway, E. Gjerl{\o}w, U. Fuskeland, A. Basyrov, M. Bortolami, M. Brilenkov, P. Campeti, H. K. Eriksen, L. T. Hergt, D. Herman, M. Monelli, L. Pagano, G. Puglisi, N. Raffuzzi, N. -O. Stutzer, R. M. Sullivan, H. Thommesen, D. J. Watts, I. K. Wehus, D. Adak, E. Allys, A. Anand, J. Aumont, C. Baccigalupi, M. Ballardini, A. J. Banday, R. B. Barreiro, N. Bartolo, S. Bas…

On the computational feasibility of Bayesian end-to-end analysis of LiteBIRD simulations within Cosmoglobe
We assess the computational feasibility of end-to-end Bayesian analysis of the JAXA-led LiteBIRD experiment by analysing simulated time ordered data (TOD) for a subset of detectors through the Cosmoglobe and Commander3 framework. The data volume for the simulated TOD is 1.55 TB, or 470 GB after Huffman compression. From this we estimate a total data volume of 238 TB for the full three year mission, or 70 TB after Huffman compression. We further estimate the running time for one Gibbs sample, fr…

Replaced article(s) found for hep-ex. https://arxiv.org/list/hep-ex/new
[2/2]:
- An Online Data Analysis Framework for Accelerator-Based Physics Experiments
Hayden Ramm, et al.

AI-driven neutrino diagnostics and radiation-hard beam instrumentation for next-generation neutrino experiments
S. Ganguly (Fermi National Accelerator Laboratory)
https://arxiv.org/abs/2508.06645

AI-driven neutrino diagnostics and radiation-hard beam instrumentation for next-generation neutrino experiments
The Long Baseline Neutrino Facility (LBNF) at Fermilab will deliver a high-intensity, multi-megawatt neutrino beam to the Deep Underground Neutrino Experiment (DUNE), enabling precision tests of the three-neutrino paradigm, CP violation searches, neutrino mass ordering determination, and supernova neutrino studies. In order to accelerate DUNE's physics reach and ensure robust beam operations, we propose an integrated AI-driven framework with real-time diagnostics and radiation-hardened instrume…

Optimal Design of Experiment for Electrochemical Parameter Identification of Li-ion Battery via Deep Reinforcement Learning
Mehmet Fatih Ozkan, Samuel Filgueira da Silva, Faissal El Idrissi, Prashanth Ramesh, Marcello Canova
https://arxiv.org/abs/2506.19146

Optimal Design of Experiment for Electrochemical Parameter Identification of Li-ion Battery via Deep Reinforcement Learning
Accurate parameter estimation in electrochemical battery models is essential for monitoring and assessing the performance of lithium-ion batteries (LiBs). This paper presents a novel approach that combines deep reinforcement learning (DRL) with an optimal experimental design (OED) framework to identify key electrochemical parameters of LiB cell models. The proposed method utilizes the twin delayed deep deterministic policy gradient (TD3) algorithm to optimize input excitation, thereby increasin…

Public Service Algorithm: towards a transparent, explainable, and scalable content curation for news content based on editorial values
Ahmad Mel, Sebastien Noir
https://arxiv.org/abs/2506.22270

Public Service Algorithm: towards a transparent, explainable, and scalable content curation for news content based on editorial values
The proliferation of disinformation challenges traditional, unscalable editorial processes and existing automated systems that prioritize engagement over public service values. To address this, we introduce the Public Service Algorithm (PSA), a novel framework using Large Language Models (LLMs) for scalable, transparent content curation based on Public Service Media (PSM) inspired values. Utilizing a large multilingual news dataset from the 'A European Perspective' project, our experiment direc…

A General Framework for Estimating Preferences Using Response Time Data
Federico Echenique, Alireza Fallah, Michael I. Jordan
https://arxiv.org/abs/2507.20403 https://

A General Framework for Estimating Preferences Using Response Time Data
We propose a general methodology for recovering preference parameters from data on choices and response times. Our methods yield estimates with fast ($1/n$ for $n$ data points) convergence rates when specialized to the popular Drift Diffusion Model (DDM), but are broadly applicable to generalizations of the DDM as well as to alternative models of decision making that make use of response time data. The paper develops an empirical application to an experiment on intertemporal choice, showing tha…

Effect of Off-diagonal NSI Parameters on Entanglement Measurements in Neutrino Oscillations
Lekhashri Konwar, Papia Panda, Rukmani Mohanta
https://arxiv.org/abs/2507.04885

Effect of Off-diagonal NSI Parameters on Entanglement Measurements in Neutrino Oscillations
In this work, we explore the influence of off-diagonal non-standard interaction (NSI) parameters on quantum entanglement within the three-flavor neutrino oscillation framework. By expressing three key entanglement measures: Entanglement of Formation (EOF), Concurrence, and Negativity in terms of oscillation probabilities, we analyze how these quantum correlations are affected by the NSI parameters $ε_{eμ}$, $ε_{eτ}$, and $ε_{μτ}$, including their complex phases. Using the DUNE experiment…

Bayesian Design of Experiments in the Presence of Nuisance Parameters
Shirin Golchi, Luke Hagar
https://arxiv.org/abs/2508.03948 https://arxiv.org/pdf/2508…

Bayesian Design of Experiments in the Presence of Nuisance Parameters
Design of experiments has traditionally relied on the frequentist hypothesis testing framework where the optimal size of the experiment is specified as the minimum sample size that guarantees a required level of power. Sample size determination may be performed analytically when the test statistic has a known asymptotic sampling distribution and, therefore, the power function is available in analytic form. Bayesian methods have gained popularity in all stages of discovery, namely, design, analy…

This https://arxiv.org/abs/2505.17648 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

Simulating Macroeconomic Expectations using LLM Agents
We introduce a novel framework for simulating macroeconomic expectation formation using Large Language Model-Empowered Agents (LLM Agents). By constructing thousands of LLM Agents equipped with modules for personal characteristics, prior expectations, and knowledge, we replicate a survey experiment involving households and experts on inflation and unemployment. Our results show that although the expectations and thoughts generated by LLM Agents are more homogeneous than those of human participa…

A General Simulation-Based Optimisation Framework for Multipoint Constant-Stress Accelerated Life Tests
Owen McGrath, Kevin Burke
https://arxiv.org/abs/2507.00722

A General Simulation-Based Optimisation Framework for Multipoint Constant-Stress Accelerated Life Tests
Accelerated life testing (ALT) is a method of reducing the lifetime of components through exposure to extreme stress. This method of obtaining lifetime information involves the design of a testing experiment, i.e., an accelerated test plan. In this work, we adopt a simulation-based approach to obtaining optimal test plans for constant-stress accelerated life tests with multiple design points. Within this simulation framework we can easily assess a variety of test plans by modifying the number o…

Can theory-driven learning analytics dashboard enhance human-AI collaboration in writing learning? Insights from an empirical experiment
Angxuan Chen, Jingjing Lian, Xinran Kuang, Jiyou Jia
https://arxiv.org/abs/2506.19364

Can theory-driven learning analytics dashboard enhance human-AI collaboration in writing learning? Insights from an empirical experiment
The integration of Generative AI (GenAI) into education has raised concerns about over-reliance and superficial learning, particularly in writing tasks in higher education. This study explores whether a theory-driven learning analytics dashboard (LAD) can enhance human-AI collaboration in the academic writing task by improving writing knowledge gains, fostering self-regulated learning (SRL) skills and building different human-AI dialogue characteristics. Grounded in Zimmerman's SRL framework, t…

An Online Data Analysis Framework for Accelerator-Based Physics Experiments
Hayden Ramm, Pascal Simon, Paraskevi Alexaki, Christopher Arran, Robert Bingham, Alice Goillot, Jon Tomas Gudmundsson, Jonathan Halliday, Bryn Lloyd, Eva Los, Vasiliki Stergiou, Sifei Zhang, Gianluca Gregori, Nikolaos Charitonidis
https://arxiv.org/abs/2508.00705…

An Online Data Analysis Framework for Accelerator-Based Physics Experiments
A robust and flexible architecture capable of providing real-time analysis on diagnostic data in experimental physics is of crucial importance to physics experiments. In this paper, we present such an online framework, used in June 2025 as part of the HRMT-68 experiment, performed at the HiRadMat facility at CERN, using the Super Proton Synchrotron (SPS) beam line. HRMT-68 was a fixed-target laboratory astrophysics experiment aiming to identify plasma instabilities generated by a relativistic e…

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance
Joner Assolin, Gabriel Canto, Diego Kreutz, Eduardo Feitosa, Hendrio Bragan\c{c}a, Angelo Nogueira, Vanderson Rocha
https://arxiv.org/abs/2506.23314

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance
Malware detection in Android systems requires both cybersecurity expertise and machine learning (ML) techniques. Automated Machine Learning (AutoML) has emerged as an approach to simplify ML development by reducing the need for specialized knowledge. However, current AutoML solutions typically operate as black-box systems with limited transparency, interpretability, and experiment traceability. To address these limitations, we present MH-AutoML, a domain-specific framework for Android malware d…

Theory uncertainties in predictions of strong-field QED
T. G. Blackburn
https://arxiv.org/abs/2508.03975 https://arxiv.org/pdf/2508.03975

Theory uncertainties in predictions of strong-field QED
Experiments using high-power lasers and relativistic electron beams will soon be capable of precision testing of the theory of strong-field quantum electrodynamics. The comparison between experiment and theory always occurs via numerical simulations, but the lack of quantitative uncertainty bounds for the latter means that the origin of any discrepancies cannot be identified conclusively. Here I present a simulation framework that can place meaningful bounds on the theory uncertainties inherent…

Optimized binning for response function reconstruction via Chebyshev expansions
Immo C. Reis, Joanna E. Sobczyk, Sonia Bacca
https://arxiv.org/abs/2507.00587

Optimized binning for response function reconstruction via Chebyshev expansions
We propose an optimized histogram binning strategy to reconstruct nuclear response functions via the Chebyshev expansion bound-state method. Our approach employs a stochastic regularization of the density of states to define adaptive, equal-area bins. Using the deuteron solved in a harmonic-oscillator basis with a chiral interaction, we benchmark on dipole and longitudinal responses, obtaining excellent agreement with exact theory and experiment. This general framework readily extends to other …

Volumetric Functional Maps
Filippo Maggioli, Marco Livesu, Simone Melzi
https://arxiv.org/abs/2506.13212 https://arxiv.org/pdf/2506.1…

Volumetric Functional Maps
The computation of volumetric correspondences between 3D shapes has great potential for medical and industrial applications. In this work, we pave the way for spectral volume mapping, extending for the first time the functional maps framework from the surface setting to the volumetric domain. We show that the eigenfunctions of the volumetric Laplace operator define a functional space that is suitable for high-quality signal transfer. We also experiment with various techniques that edit this fun…

AIs Structural Impact on Indias Knowledge Intensive Startup Ecosystem: A Natural Experiment in Firm Efficiency and Design
Venkat Ram Reddy Ganuthula, Ramesh Kuruva
https://arxiv.org/abs/2507.19775

AIs Structural Impact on Indias Knowledge Intensive Startup Ecosystem: A Natural Experiment in Firm Efficiency and Design
This study explores the structural and performance impacts of artificial intelligence (AI) adoption on Indias knowledge intensive startups, spanning information technology, financial technology, health technology, and educational technology, founded between 2016 and 2025. Using a natural experiment framework with the founding year as an exogenous treatment proxy, it examines firm size, revenue productivity, valuation efficiency, and capital utilization across pre AI and AI era cohorts. Findings…

$\Lambda_b \to \Lambda^{(\ast)}\nu\bar{\nu}$ decays and the recent Belle-II $B^ \to K^ \nu\bar{\nu}$ data
Diganta Das, Dargi Shameer, Ria Sain
https://arxiv.org/abs/2507.01863

$Λ_b \to Λ^{(\ast)}ν\barν$ decays and the recent Belle-II $B^+\to K^+ν\barν$ data
The Belle-II experiment has recently reported the first measurement of $B^+ \to K^+ ν\barν$ decay which exceeds the Standard Model prediction by approximately 2.7$σ$. The deviation may indicate the presence of new physics beyond the Standard Model in the $b\to sν\barν$ sector. Under this assumption, we study the hadronic $Λ_b \to Λ(\to pπ)ν\barν$ and $Λ_b \to Λ^\ast(\to N\!\bar{K})ν\barν$ decays within both the Standard Model and beyond. We work in a low energy effective field the…

Probing the Higgs boson CP properties in vector-boson fusion production in the $H\rightarrow\tau^ \tau^-$ channel with the ATLAS detector
ATLAS Collaboration
https://arxiv.org/abs/2506.19395

Probing the Higgs boson CP properties in vector-boson fusion production in the $H\rightarrowτ^+τ^-$ channel with the ATLAS detector
The CP properties of the Higgs boson are studied in the vector-boson fusion production mode. The analysis exploits the decay mode of the Higgs boson into two $τ$-leptons using 140 fb$^{-1}$ of proton-proton collision data at $\sqrt{s}=13$ TeV collected by the ATLAS experiment at the Large Hadron Collider. Results are obtained using the Optimal Observable method. CP-violating interactions between the Higgs boson and electroweak gauge bosons are considered in the effective field theory framework…

Simulation-based inference for Precision Neutrino Physics through Neural Monte Carlo tuning
A. Gavrikov, A. Serafini, D. Dolzhikov, A. Garfagnini, M. Gonchar, M. Grassi, R. Brugnera, V. Cerrone, L. V. D'Auria, R. M. Guizzetti, L. Lastrucci, G. Andronico, V. Antonelli, A. Barresi, D. Basilico, M. Beretta, A. Bergnoli, M. Borghesi, A. Brigatti, R. Bruno, A. Budano, B. Caccianiga, A. Cammi, R. Caruso, D. Chiesa, C. Clementi, C. Coletta, S. Dusini, A. Fabbri, G. Felici, G. Ferrante, M.…

Simulation-based inference for Precision Neutrino Physics through Neural Monte Carlo tuning
Precise modeling of detector energy response is crucial for next-generation neutrino experiments which present computational challenges due to lack of analytical likelihoods. We propose a solution using neural likelihood estimation within the simulation-based inference framework. We develop two complementary neural density estimators that model likelihoods of calibration data: conditional normalizing flows and a transformer-based regressor. We adopt JUNO - a large neutrino experiment - as a cas…

$J/\psi$ Polarization and $p_T$ distribution in $c\ \!\bar{c}$ associated hadroproduction at $\mathcal{O}(\alpha_s^5)$
Qi-Ming Feng, Cong-Feng Qiao
https://arxiv.org/abs/2507.20654

$J/ψ$ Polarization and $p_T$ distribution in $c\ \!\bar{c}$ associated hadroproduction at $\mathcal{O}(α_s^5)$
The quarkonium production in association with a heavy-quark pair of the same flavor was found plays an important role in various production schemes, let alone it provides a distinctive signature that could be studied in experiment. Within the NRQCD framework, we perform the first complete calculation of the hadroproduction process $gg\to J/ψ+c\bar{c}$ at the next-to-leading order (NLO) in the expansion of strong coupling constant $α_s$. The result tells that the NLO corrections substantially …

Investigating DUNE oscillations sensitivity to Pseudo-Dirac Neutrinos
Asmaa Abada, Joao Paulo Pinheiro, Salvador Urrea
https://arxiv.org/abs/2506.16390 htt…

Investigating DUNE oscillations sensitivity to Pseudo-Dirac Neutrinos
We explore the sensitivity of the Deep Underground Neutrino Experiment (DUNE) to sterile neutrino oscillations within a $3+$(pseudo-Dirac pair) framework. We first consider a pair of two sterile neutrinos forming a pseudo-Dirac pair, then we consider a low-scale seesaw realization, that we name ``Linear-Inverse Seesaw" model. This scenario features two nearly degenerate sterile neutrino states at the keV scale, characterized by a small mass splitting arising from a small amount of lepton number…