Tootfinder

TERF Inc.
https://web.archive.org/web/20190727183834/http://terf.com/

A Conformal Prediction Framework for Uncertainty Quantification in Physics-Informed Neural Networks
Yifan Yu, Cheuk Hin Ho, Yangshuai Wang
https://arxiv.org/abs/2509.13717 https…

A Conformal Prediction Framework for Uncertainty Quantification in Physics-Informed Neural Networks
Physics-Informed Neural Networks (PINNs) have emerged as a powerful framework for solving PDEs, yet existing uncertainty quantification (UQ) approaches for PINNs generally lack rigorous statistical guarantees. In this work, we bridge this gap by introducing a distribution-free conformal prediction (CP) framework for UQ in PINNs. This framework calibrates prediction intervals by constructing nonconformity scores on a calibration set, thereby yielding distribution-free uncertainty estimates with …

Weakly and Self-Supervised Class-Agnostic Motion Prediction for Autonomous Driving
Ruibo Li, Hanyu Shi, Zhe Wang, Guosheng Lin
https://arxiv.org/abs/2509.13116 https://

Weakly and Self-Supervised Class-Agnostic Motion Prediction for Autonomous Driving
Understanding motion in dynamic environments is critical for autonomous driving, thereby motivating research on class-agnostic motion prediction. In this work, we investigate weakly and self-supervised class-agnostic motion prediction from LiDAR point clouds. Outdoor scenes typically consist of mobile foregrounds and static backgrounds, allowing motion understanding to be associated with scene parsing. Based on this observation, we propose a novel weakly supervised paradigm that replaces motion…

My partner is a Chelsea supporter and was so pissed about what happened they asked me to make a sign for tomorrow's protest...
#USPOL

Quantum Kramers-Henneberger Transformation
Javier Arg\"uello-Luengo, Javier Rivera-Dean, Philipp Stammer, Marcelo F. Ciappina, Maciej Lewenstein
https://arxiv.org/abs/2507.13006

Quantum Kramers-Henneberger Transformation
The classical Kramers-Henneberger transformation connects, via a series of unitary transformations, the dynamics of a quantum particle of mass $m$ located in a trap at position $α(t)$, with the dynamics of a charge $e$ moving in an electric field $e{\cal{E}}(t)=-m\ddotα(t)$ within the dipole approximation. In this paper, we extend the classical Kramers-Henneberger transformation to the quantum electrodynamic and quantum optical realm, by explicitly treating the trap location quantum mechanica…

Multi-Agent Synergy-Driven Iterative Visual Narrative Synthesis
Wang Xi, Quan Shi, Tian Yu, Yujie Peng, Jiayi Sun, Mengxing Ren, Zenghui Ding, Ningguang Yao
https://arxiv.org/abs/2507.13285

Multi-Agent Synergy-Driven Iterative Visual Narrative Synthesis
Automated generation of high-quality media presentations is challenging, requiring robust content extraction, narrative planning, visual design, and overall quality optimization. Existing methods often produce presentations with logical inconsistencies and suboptimal layouts, thereby struggling to meet professional standards. To address these challenges, we introduce RCPS (Reflective Coherent Presentation Synthesis), a novel framework integrating three key components: (1) Deep Structured Narrat…

Pre-Manipulation Alignment Prediction with Parallel Deep State-Space and Transformer Models
Motonari Kambara, Komei Sugiura
https://arxiv.org/abs/2509.13839 https://

Pre-Manipulation Alignment Prediction with Parallel Deep State-Space and Transformer Models
In this work, we address the problem of predicting the future success of open-vocabulary object manipulation tasks. Conventional approaches typically determine success or failure after the action has been carried out. However, they make it difficult to prevent potential hazards and rely on failures to trigger replanning, thereby reducing the efficiency of object manipulation sequences. To overcome these challenges, we propose a model, which predicts the alignment between a pre-manipulation egoc…

SateLight: A Satellite Application Update Framework for Satellite Computing
Jinfeng Wen, Jianshu Zhao, Zixi Zhu, Xiaomin Zhang, Qi Liang, Ao Zhou, Shangguang Wang
https://arxiv.org/abs/2509.12809

SateLight: A Satellite Application Update Framework for Satellite Computing
Satellite computing is an emerging paradigm that empowers satellites to perform onboard processing tasks (i.e., \textit{satellite applications}), thereby reducing reliance on ground-based systems and improving responsiveness. However, enabling application software updates in this context remains a fundamental challenge due to application heterogeneity, limited ground-to-satellite bandwidth, and harsh space conditions. Existing software update approaches, designed primarily for terrestrial syste…

Intelligent Vacuum Thermoforming Process
Andi Kuswoyo, Christos Margadji, Sebastian W. Pattinson
https://arxiv.org/abs/2509.13250 https://arxiv.org/pdf/250…

Intelligent Vacuum Thermoforming Process
Ensuring consistent quality in vacuum thermoforming presents challenges due to variations in material properties and tooling configurations. This research introduces a vision-based quality control system to predict and optimise process parameters, thereby enhancing part quality with minimal data requirements. A comprehensive dataset was developed using visual data from vacuum-formed samples subjected to various process parameters, supplemented by image augmentation techniques to improve model t…

Trap-to-trap free falls with an optically levitated nanoparticle
M. Luisa Mattana, Nicola Carlon Zambon, Massimiliano Rossi, Eric Bonvin, Louisiane Devaud, Martin Frimmer, Lukas Novotny
https://arxiv.org/abs/2507.12995

Trap-to-trap free falls with an optically levitated nanoparticle
We perform free-fall experiments with a charge-neutral, optically levitated nanoparticle. This is achieved using an optical tweezer that can be rapidly toggled on and off and vertically displaced, enabling the particle to be released and recaptured after each free fall. The particle is insensitive to electric fields due to its charge neutrality and, during free evolution, is not subject to photon recoil heating. We achieve free-fall durations of up to 0.25 ms and observe a nearly two hundred-fo…