Tootfinder

😮‍💨 Research shows that improved home ventilation can ease asthma symptoms, advance public health
https://medicalxpress.com/news/2025-10-home-ventilation-ease-asthma-symptoms.html

Research shows that improved home ventilation can ease asthma symptoms, advance public health
For millions of people living with asthma, even the air inside their homes can make breathing difficult. But researchers at The University of Texas at Arlington have shown that better ventilation systems can make a measurable difference in helping people breathe more easily.

Noël: une fabuleuse occasion pour enfin faire connaissance avec la fast-déco, prendre conscience de ces impacts,
ou pester qu'elle ait été exclue du récent débat public sur la fast-fashion.
Le cynisme débonnaire de ces grandes enseignes (françaises comme étrangères) me révulse: extractivisme, pollution plastique, dégâts sociaux...
La fast-déco n'a rien Š envier Š sa copine toxique la fast-fashion.
Ouais, parlons-en de la fast-déco.
Parlons-en beaucoup.

Surconsommation : à l’approche de Noël, la fast-déco fout les boules
Nombreux sont les Français à se ruer dans les magasins à la recherche de produits à bas coût. S’ils sont pourtant fabriqués pour la plupart en Asie, des centaines de millions d’objets d’intérieur sont vendus chaque année.

Where MLLMs Attend and What They Rely On: Explaining Autoregressive Token Generation
Ruoyu Chen, Xiaoqing Guo, Kangwei Liu, Siyuan Liang, Shiming Liu, Qunli Zhang, Hua Zhang, Xiaochun Cao
https://arxiv.org/abs/2509.22496

Where MLLMs Attend and What They Rely On: Explaining Autoregressive Token Generation
Multimodal large language models (MLLMs) have demonstrated remarkable capabilities in aligning visual inputs with natural language outputs. Yet, the extent to which generated tokens depend on visual modalities remains poorly understood, limiting interpretability and reliability. In this work, we present EAGLE, a lightweight black-box framework for explaining autoregressive token generation in MLLMs. EAGLE attributes any selected tokens to compact perceptual regions while quantifying the relativ…

Maple: A Multi-agent System for Portable Deep Learning across Clusters
Molang Wu, Zhao Zhang
https://arxiv.org/abs/2510.08842 https://arxiv.org/pdf/2510.08…

Maple: A Multi-agent System for Portable Deep Learning across Clusters
Training deep learning (DL) models across Graphics Processing Unit (GPU) clusters is technically challenging. One aspect is that users have to compose command lines to adapt to the heterogeneous launchers, schedulers, affinity options, DL framework arguments, and environment variables. Composing correct command lines is error-prone and can easily frustrate users, impeding research or wasting resources. In this work, we present Maple, a multi-agent system that generates correct DL command lines …

"Asad Rehman, chief executive of Friends of the Earth, said:
“Kemi Badenoch’s desperate attempt to sound like Donald Trump on climate change is taking her party further away from the interests of future generations, businesses, and the needs of ordinary people.
"Millions of lives are already being ruined and nature is being pushed to the brink. An act of political vandalism of this scale will not be easily forgiven or forgotten."

Replacing Climate Change Act is political vandalism that would abandon future generations | Friends of the Earth
Kemi Badenoch’s plan to replace the Climate Change Act is anti-science, anti-business and turns its back on those at the sharp end of climate breakdown now and future generations, says Friends of the Earth.

OMG ¿Qué clase de señal divina es esta? 😱
#31Minutos

Forecasting the Buzz: Enriching Hashtag Popularity Prediction with LLM Reasoning
Yifei Xu, Jiaying Wu, Herun Wan, Yang Li, Zhen Hou, Min-Yen Kan
https://arxiv.org/abs/2510.08481

Forecasting the Buzz: Enriching Hashtag Popularity Prediction with LLM Reasoning
Hashtag trends ignite campaigns, shift public opinion, and steer millions of dollars in advertising spend, yet forecasting which tag goes viral is elusive. Classical regressors digest surface features but ignore context, while large language models (LLMs) excel at contextual reasoning but misestimate numbers. We present BuzzProphet, a reasoning-augmented hashtag popularity prediction framework that (1) instructs an LLM to articulate a hashtag's topical virality, audience reach, and timing advan…

Overlap-aware segmentation for topological reconstruction of obscured objects
J. Schueler, H. M. Ara\'ujo, S. N. Balashov, J. E. Borg, C. Brew, F. M. Brunbauer, C. Cazzaniga, A. Cottle, D. Edgeman, C. D. Frost, F. Garcia, D. Hunt, M. Kastriotou, P. Knights, H. Kraus, A. Lindote, M. Lisowska, D. Loomba, E. Lopez Asamar, P. A. Majewski, T. Marley, C. McCabe, L. Millins, R. Nandakumar, T. Neep, F. Neves, K. Nikolopoulos, E. Oliveri, A. Roy, T. J. Sumner, E. Tilly, W. Thompson, M. A. V…

Overlap-aware segmentation for topological reconstruction of obscured objects
The separation of overlapping objects presents a significant challenge in scientific imaging. While deep learning segmentation-regression algorithms can predict pixel-wise intensities, they typically treat all regions equally rather than prioritizing overlap regions where attribution is most ambiguous. Recent advances in instance segmentation show that weighting regions of pixel overlap in training can improve segmentation boundary predictions in regions of overlap, but this idea has not yet be…

The work laptop is sluggish as hell. Fired up the task manager, and it shows Excel taking on average over 33% of the CPU, and it isn't doing much right now other than displaying two spreadsheets. It also has consumed 5.7 gigs of memory. WTF?
My 15 year old laptop, until it died, barely slowed down using Linux and LibreOffice for similar work. I handled newsletters, photo editing, and massive XML files for editing and validation (millions of records there). I used that machine…

Tidal features around simulated groups and cluster galaxies: Enhancement and suppression of merger events through environment in LSST-like mock observations
Aman Khalid, Sarah Brough, Garreth Martin, Lucas C. Kimmig, Rhea-Silvia Remus, Claudia del P. Lagos, Lucas M. Valenzuela, Ruby J. Wright
https://arxiv.org/abs/2509.20723

Tidal features around simulated groups and cluster galaxies: Enhancement and suppression of merger events through environment in LSST-like mock observations
Generally, merger likelihood increases in denser environments; however, the large relative velocities at the centres of dense clusters are expected to reduce the likelihood of mergers for satellite galaxies. Tidal features probe the recent merger histories of galaxies. The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will produce an unprecedented sample of tidal features around millions of galaxies. We use LSST-like mock observations of galaxies at $z\sim0$ from the EAGLE,…