Tootfinder

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…

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 …

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…

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…

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 …

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…