Tootfinder

I'm way to deep in the research rabbit hole, went all the way to OG proceedings from 1888 because the sources in Wikipedia were questionable (random Time articles).
https://upload.wikimedia.org/wikipedia/commons/f/f7/Proceedings_of_the_American_Federation_of_Labor_1888_(IA_sim_american-federation-of-labor-proceedings_1888).pdf

Caleb Rogers on being a girl dad, Las Vegas and playing O-line https://www.raiders.com/video/caleb-rogers-girl-dad-las-vegas-offensive-line

Explicit
"Identifying Far-Right Fashion"
with Bellingcat Researchers
https://rss.com/podcasts/bellingcatstagetalk/2289252/

"Identifying Far-Right Fashion" with Bellingcat Researchers | Podcast Episode on RSS.com
This week, we were discussing what role fashion plays for far-right movements. We spoke to Bellingcat's resident experts on the subject. Michael Colborne has been investigating far-right networks for years and Youri van der Weide has just completed a deep dive on russian neo-nazi paramilitary groups with merchandise embedded within their business plans.The talk was hosted by Charlotte Maher on Thursday October 23rd 2025. Music featured is courtesy of Artlist.Recorded live in the Bellingcat Disc…

An analysis of 5,290 AI research papers at NeurIPS: 141, or ~3%, had US-China AI lab collaboration, up from 134 in 2024; Llama featured in 106 Chinese papers (Will Knight/Wired)
https://www.wired.com/story/us-china-collaboration-neurips-papers/

The US and China Are Collaborating More Closely on AI Than You Think
WIRED analyzed more than 5,000 papers from NeurIPS using OpenAI’s Codex to understand the areas where the US and China actually work together on AI research.

On Electric Vehicle Energy Demand Forecasting and the Effect of Federated Learning
Andreas Tritsarolis, Gil Sampaio, Nikos Pelekis, Yannis Theodoridis
https://arxiv.org/abs/2602.20782 https://arxiv.org/pdf/2602.20782 https://arxiv.org/html/2602.20782
arXiv:2602.20782v1 Announce Type: new
Abstract: The wide spread of new energy resources, smart devices, and demand side management strategies has motivated several analytics operations, from infrastructure load modeling to user behavior profiling. Energy Demand Forecasting (EDF) of Electric Vehicle Supply Equipments (EVSEs) is one of the most critical operations for ensuring efficient energy management and sustainability, since it enables utility providers to anticipate energy/power demand, optimize resource allocation, and implement proactive measures to improve grid reliability. However, accurate EDF is a challenging problem due to external factors, such as the varying user routines, weather conditions, driving behaviors, unknown state of charge, etc. Furthermore, as concerns and restrictions about privacy and sustainability have grown, training data has become increasingly fragmented, resulting in distributed datasets scattered across different data silos and/or edge devices, calling for federated learning solutions. In this paper, we investigate different well-established time series forecasting methodologies to address the EDF problem, from statistical methods (the ARIMA family) to traditional machine learning models (such as XGBoost) and deep neural networks (GRU and LSTM). We provide an overview of these methods through a performance comparison over four real-world EVSE datasets, evaluated under both centralized and federated learning paradigms, focusing on the trade-offs between forecasting fidelity, privacy preservation, and energy overheads. Our experimental results demonstrate, on the one hand, the superiority of gradient boosted trees (XGBoost) over statistical and NN-based models in both prediction accuracy and energy efficiency and, on the other hand, an insight that Federated Learning-enabled models balance these factors, offering a promising direction for decentralized energy demand forecasting.
toXiv_bot_toot

Senior Bowl practice takeaways: Who helped (or hurt) their 2026 NFL Draft stock on Day 1

https://www.cbssports.com/nfl/draft/news/senior-bowl-practice-…

Energy crisis coal switch increased emissions, illnesses and deaths across 6 countries #environment

Energy crisis coal switch increased emissions, illnesses and deaths across 6 countries
In addition to serious economic damage, the energy crisis of 2021/22 also had dramatic consequences for the environment and people's health. This is the conclusion reached by two researchers at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) in a new study. Although the temporary switch from gas to coal for producing electricity was cheaper and contributed to securing the energy supply, in the six European countries featured in the study, it caused additional costs of around one billi…

The first day of #rdade2026 in Potsdam featured inspiring talks by Kerstin Schneider on the planned Research Data Act (#FDG, #Forschungsdatengesetz) and by Wolfgang Marquardt on the future of

One in four full-time workers in Britain would be “better off swapping wages for benefits“, according to new analysis which featured on the front of The Sun.
The report – published by the Centre for Social Justice (CSJ) earlier this month – resurrects the spectre of the ‘#welfare #scrounger’,
a hypothetical …

Would a quarter of workers really be better off ‘swapping wages for benefits’?
Over six million workers earn less than an unemployed person on benefits, according to the Centre for Social Justice. Is it true?

Device Association and Resource Allocation for Hierarchical Split Federated Learning in Space-Air-Ground Integrated Network
Haitao Zhao, Xiaoyu Tang, Bo Xu, Jinlong Sun, Linghao Zhang
https://arxiv.org/abs/2601.13817