Tootfinder

Vacuum polarization in the horizonless Bardeen metric
Andr\'es Boasso, Francisco D. Mazzitelli
https://arxiv.org/abs/2508.06465 https://arxiv.org/pdf/2…

Vacuum polarization in the horizonless Bardeen metric
We compute the renormalized stress-energy tensor for a massless quantum scalar field in the background of the horizonless Bardeen spacetime. Within the weak-field approximation, we show that the vacuum fluctuations differ significantly between conformally and non-conformally coupled fields, both in magnitude and in their behavior at short and intermediate distances. At large distances, we recover the universal asymptotic behavior previously observed in black hole and Newtonian star backgrounds.…

"Voice commands" have been embedded into our vision of computing for a long time: Star Trek did it and it does the whole anthropomorphization thing that tech loves.
But from a practical standpoint it's a bad interface: Think of a handful of people sitting in an office yelling at their computers, it would be like working next to people having loud phone calls all day.
And who exactly wants every thing they do with their computer broadcasted around them?

MoWE : A Mixture of Weather Experts
Dibyajyoti Chakraborty, Romit Maulik, Peter Harrington, Dallas Foster, Mohammad Amin Nabian, Sanjay Choudhry
https://arxiv.org/abs/2509.09052

MoWE : A Mixture of Weather Experts
Data-driven weather models have recently achieved state-of-the-art performance, yet progress has plateaued in recent years. This paper introduces a Mixture of Experts (MoWE) approach as a novel paradigm to overcome these limitations, not by creating a new forecaster, but by optimally combining the outputs of existing models. The MoWE model is trained with significantly lower computational resources than the individual experts. Our model employs a Vision Transformer-based gating network that dyn…

Discovery of a $z \sim 0.8$ Ultra Steep Spectrum Radio Halo in the MeerKAT-South Pole Telescope Survey
Isaac S. Magolego, Roger P. Deane, Kshitij Thorat, Ian Heywood, William Rasakanya, Manuel Aravena, Lindsey E. Bleem, Maria G. Campitiello, Kedar A. Phadke, Justin Spilker, Joaquin D. Vieira, Dazhi Zhou, Bradford A. Benson, Scott Chapman, Ana Posses, Tim Schrabback, Anthony Stark, David Vizgan

Discovery of a $z \sim 0.8$ Ultra Steep Spectrum Radio Halo in the MeerKAT-South Pole Telescope Survey
Radio halos are diffuse synchrotron sources that trace the turbulent intracluster medium (ICM) of galaxy clusters. However, their origin remains unknown. Two main formation models have been proposed: the hadronic model, in which relativistic electrons are continuously injected by cosmic-ray protons; and the leptonic turbulent re-acceleration model, where cluster mergers re-energise electrons in situ. A key discriminant between the two models would be the existence of ultra-steep spectrum radio …

SimCroP: Radiograph Representation Learning with Similarity-driven Cross-granularity Pre-training
Rongsheng Wang, Fenghe Tang, Qingsong Yao, Rui Yan, Xu Zhang, Zhen Huang, Haoran Lai, Zhiyang He, Xiaodong Tao, Zihang Jiang, Shaohua Kevin Zhou
https://arxiv.org/abs/2509.08311

SimCroP: Radiograph Representation Learning with Similarity-driven Cross-granularity Pre-training
Medical vision-language pre-training shows great potential in learning representative features from massive paired radiographs and reports. However, in computed tomography (CT) scans, the distribution of lesions which contain intricate structures is characterized by spatial sparsity. Besides, the complex and implicit relationships between different pathological descriptions in each sentence of the report and their corresponding sub-regions in radiographs pose additional challenges. In this pape…

MoSE: Skill-by-Skill Mixture-of-Expert Learning for Autonomous Driving
Lu Xu, Jiaqian Yu, Xiongfeng Peng, Yiwei Chen, Weiming Li, Jaewook Yoo, Sunghyun Chunag, Dongwook Lee, Daehyun Ji, Chao Zhang
https://arxiv.org/abs/2507.07818

MoSE: Skill-by-Skill Mixture-of-Expert Learning for Autonomous Driving
Recent studies show large language models (LLMs) and vision language models (VLMs) trained using web-scale data can empower end-to-end autonomous driving systems for a better generalization and interpretation. Specifically, by dynamically routing inputs to specialized subsets of parameters, the Mixture-of-Experts (MoE) technique enables general LLMs or VLMs to achieve substantial performance improvements while maintaining computational efficiency. However, general MoE models usually demands ext…

Vaccine Hesitancy on YouTube: a Competition between Health and Politics
Yelena Mejova, Michele Tizzani
https://arxiv.org/abs/2507.07517 https://

Vaccine Hesitancy on YouTube: a Competition between Health and Politics
YouTube has rapidly emerged as a predominant platform for content consumption, effectively displacing conventional media such as television and news outlets. A part of the enormous video stream uploaded to this platform includes health-related content, both from official public health organizations, and from any individual or group that can make an account. The quality of information available on YouTube is a critical point of public health safety, especially when concerning major interventions…

Computer Vision-based Adaptive Control for Back Exoskeleton Performance Optimization
Andrea Dal Prete, Seyram Ofori, Chan Yon Sin, Ashwin Narayan, Francesco Braghin, Marta Gandolla, Haoyong Yu
https://arxiv.org/abs/2508.06207

Computer Vision-based Adaptive Control for Back Exoskeleton Performance Optimization
Back exoskeletons can reduce musculoskeletal strain, but their effectiveness depends on support modulation and adaptive control. This study addresses two challenges: defining optimal support strategies and developing adaptive control based on payload estimation. We introduce an optimization space based on muscle activity reduction, perceived discomfort, and user preference, constructing functions to identify optimal strategies. Experiments with 12 subjects revealed optimal operating regions, hi…

Opto-ViT: Architecting a Near-Sensor Region of Interest-Aware Vision Transformer Accelerator with Silicon Photonics
Mehrdad Morsali, Chengwei Zhou, Deniz Najafi, Sreetama Sarkar, Pietro Mercati, Navid Khoshavi, Peter Beerel, Mahdi Nikdast, Gourav Datta, Shaahin Angizi
https://arxiv.org/abs/2507.07044

Opto-ViT: Architecting a Near-Sensor Region of Interest-Aware Vision Transformer Accelerator with Silicon Photonics
Vision Transformers (ViTs) have emerged as a powerful architecture for computer vision tasks due to their ability to model long-range dependencies and global contextual relationships. However, their substantial compute and memory demands hinder efficient deployment in scenarios with strict energy and bandwidth limitations. In this work, we propose OptoViT, the first near-sensor, region-aware ViT accelerator leveraging silicon photonics (SiPh) for real-time and energy-efficient vision processing…

Classification of Driver Behaviour Using External Observation Techniques for Autonomous Vehicles
Ian Nell, Shane Gilroy
https://arxiv.org/abs/2509.09349 https://

Classification of Driver Behaviour Using External Observation Techniques for Autonomous Vehicles
Road traffic accidents remain a significant global concern, with human error, particularly distracted and impaired driving, among the leading causes. This study introduces a novel driver behavior classification system that uses external observation techniques to detect indicators of distraction and impairment. The proposed framework employs advanced computer vision methodologies, including real-time object tracking, lateral displacement analysis, and lane position monitoring. The system identif…