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WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields
Sadra Safadoust, Fabio Tosi, Fatma G\"uney, Matteo Poggi
https://arxiv.org/abs/2506.22433

WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields
We introduce WarpRF, a training-free general-purpose framework for quantifying the uncertainty of radiance fields. Built upon the assumption that photometric and geometric consistency should hold among images rendered by an accurate model, WarpRF quantifies its underlying uncertainty from an unseen point of view by leveraging backward warping across viewpoints, projecting reliable renderings to the unseen viewpoint and measuring the consistency with images rendered there. WarpRF is simple and i…

One-Parametric Presburger Arithmetic has Quantifier Elimination
Alessio Mansutti, Mikhail R. Starchak
https://arxiv.org/abs/2506.23730 https://

One-Parametric Presburger Arithmetic has Quantifier Elimination
We give a quantifier elimination procedure for one-parametric Presburger arithmetic, the extension of Presburger arithmetic with the function $x \mapsto t \cdot x$, where $t$ is a fixed free variable ranging over the integers. This resolves an open problem proposed in [Bogart et al., Discrete Analysis, 2017]. As conjectured in [Goodrick, Arch. Math. Logic, 2018], quantifier elimination is obtained for the extended structure featuring all integer division functions $x \mapsto \lfloor{\frac{x}{f(…

Quantifying Radio Source Morphology
Lachlan J. Barnes, Andrew M. Hopkins, Lawrence Rudnick, Heinz Andernach, Michael Cowley, Nikhel Gupta, Ray P. Norris, Stanislav S. Shabala, Tayyaba Zafar
https://arxiv.org/abs/2506.21906

Quantifying Radio Source Morphology
The advent of next-generation telescope facilities brings with it an unprecedented amount of data, and the demand for effective tools to process and classify this information has become increasingly important. This work proposes a novel approach to quantify the radio galaxy morphology, through the development of a series of algorithmic metrics that can quantitatively describe the structure of radio source, and can be applied to radio images in an automatic way. These metrics are intuitive in na…

Artificial Intelligence-assisted Pixel-level Lung (APL) Scoring for Fast and Accurate Quantification in Ultra-short Echo-time MRI
Bowen Xin, Rohan Hickey, Tamara Blake, Jin Jin, Claire E Wainwright, Thomas Benkert, Alto Stemmer, Peter Sly, David Coman, Jason Dowling
https://arxiv.org/abs/2506.23506…

Artificial Intelligence-assisted Pixel-level Lung (APL) Scoring for Fast and Accurate Quantification in Ultra-short Echo-time MRI
Lung magnetic resonance imaging (MRI) with ultrashort echo-time (UTE) represents a recent breakthrough in lung structure imaging, providing image resolution and quality comparable to computed tomography (CT). Due to the absence of ionising radiation, MRI is often preferred over CT in paediatric diseases such as cystic fibrosis (CF), one of the most common genetic disorders in Caucasians. To assess structural lung damage in CF imaging, CT scoring systems provide valuable quantitative insights fo…

Evaluating VisualRAG: Quantifying Cross-Modal Performance in Enterprise Document Understanding
Varun Mannam, Fang Wang, Xin Chen
https://arxiv.org/abs/2506.21604

Evaluating VisualRAG: Quantifying Cross-Modal Performance in Enterprise Document Understanding
Current evaluation frameworks for multimodal generative AI struggle to establish trustworthiness, hindering enterprise adoption where reliability is paramount. We introduce a systematic, quantitative benchmarking framework to measure the trustworthiness of progressively integrating cross-modal inputs such as text, images, captions, and OCR within VisualRAG systems for enterprise document intelligence. Our approach establishes quantitative relationships between technical metrics and user-centric…

A new algorithm for sampling parameters in a structured correlation matrix with application to estimating optimal combinations of muscles to quantify progression in Duchenne muscular dystrophy
Michael K. Kim, Michael J. Daniels, William D. Rooney, Rebecca J. Willcocks, Glenn A. Walter, Krista H. Vandenborne
https://arxiv.org/abs…

A new algorithm for sampling parameters in a structured correlation matrix with application to estimating optimal combinations of muscles to quantify progression in Duchenne muscular dystrophy
The goal of this paper is to estimate an optimal combination of biomarkers for individuals with Duchenne muscular dystrophy (DMD), which provides the most sensitive combinations of biomarkers to assess disease progression (in this case, optimal with respect to standardized response mean (SRM) for 4 muscle biomarkers). The biomarker data is an incomplete (missing and irregular) multivariate longitudinal data. We propose a normal model with structured covariance designed for our setting. To sampl…

Quantifying Institutional Gender Inequality in Contemporary Visual Art
Xindi Wang, Alexander J. Gates, Magnus Resch, Albert-Laszlo Barabasi
https://arxiv.org/abs/2506.22103

Quantifying Institutional Gender Inequality in Contemporary Visual Art
From disparities in the number of exhibiting artists to auction opportunities, there is evidence of women's under-representation in visual art. Here we explore the exhibition history and auction sales of 65,768 contemporary artists in 20,389 institutions, revealing gender differences in the artist population, exhibitions and auctions. We distinguish between two criteria for gender equity: gender-neutrality, when artists have gender-independent access to exhibition opportunities, and gender-bala…

A Statistical Approach to Quantifying Uncertainty in Meteoroid Physical Properties
Maximilian Vovk, Denis Vida, Peter G. Brown
https://arxiv.org/abs/2506.21701

A Statistical Approach to Quantifying Uncertainty in Meteoroid Physical Properties
Meteoroid bulk density is a critical value required for assessing impact risks to spacecraft, informing shielding and mission design. Direct bulk density measurements for sub-millimeter to millimeter-sized meteoroids are difficult, often relying on forward modeling without robust uncertainty estimates. Methods based solely on select observables can overlook noise-induced biases and non-linear relations between physical parameters. This study aims to automate the inversion of meteoroid physical …

[2025-07-01 Tue (UTC), 63 new articles found for quant-ph Quantum Physics]
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Rises for Measuring Local Distributivity in Lattices
Mohammad Abdulla, Tobias Hille, Dominik D\"urrschnabel, Gerd Stumme
https://arxiv.org/abs/2506.23168

Rises for Measuring Local Distributivity in Lattices
Distributivity is a well-established and extensively studied notion in lattice theory. In the context of data analysis, particularly within Formal Concept Analysis (FCA), lattices are often observed to exhibit a high degree of distributivity. However, no standardized measure exists to quantify this property. In this paper, we introduce the notion of rises in (concept) lattices as a means to assess distributivity. Rises capture how the number of attributes or objects in covering concepts change …