Tootfinder

"Fundierte Polizeikritik passt leider nicht auf ein Plakat – ACAB"
https://suederelbe24.de/neues-transparent-gegen-polizei-im-phoenix-viertel-sorgt-fuer-diskussionen/

Neues Transparent gegen Polizei im Phoenix-Viertel sorgt für Diskussionen
Hamburg-Harburg, 05.06.2025 – In der Nacht zu Donnerstag wurde im Phoenix-Viertel erneut ein Transparent mit polizeikritischer Botschaft aufgehängt. Die Aktion reiht sich in eine Serie ähnlicher Vorko

Object-centric 3D Motion Field for Robot Learning from Human Videos
Zhao-Heng Yin, Sherry Yang, Pieter Abbeel
https://arxiv.org/abs/2506.04227 https://

Object-centric 3D Motion Field for Robot Learning from Human Videos
Learning robot control policies from human videos is a promising direction for scaling up robot learning. However, how to extract action knowledge (or action representations) from videos for policy learning remains a key challenge. Existing action representations such as video frames, pixelflow, and pointcloud flow have inherent limitations such as modeling complexity or loss of information. In this paper, we propose to use object-centric 3D motion field to represent actions for robot learning …

Will Trump resume aid? Zelenskyy calls for action after new Russian attacks | News Pulse: https://benborges.xyz/2025/07/04/will-trump-resume-aid-zelenskyy.html

Regret-Optimal Q-Learning with Low Cost for Single-Agent and Federated Reinforcement Learning
Haochen Zhang, Zhong Zheng, Lingzhou Xue
https://arxiv.org/abs/2506.04626

Regret-Optimal Q-Learning with Low Cost for Single-Agent and Federated Reinforcement Learning
Motivated by real-world settings where data collection and policy deployment -- whether for a single agent or across multiple agents -- are costly, we study the problem of on-policy single-agent reinforcement learning (RL) and federated RL (FRL) with a focus on minimizing burn-in costs (the sample sizes needed to reach near-optimal regret) and policy switching or communication costs. In parallel finite-horizon episodic Markov Decision Processes (MDPs) with $S$ states and $A$ actions, existing m…

I accidentally came across my goddess, and suddenly my evening became so much brighter.
Soft-dom is my happy place and my secret power that helps me shine brighter every day.
See you all tomorrow!
#BDSM #SoftDom

This https://arxiv.org/abs/2503.21224 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Efficient Learning for Entropy-Regularized Markov Decision Processes via Multilevel Monte Carlo
Designing efficient learning algorithms with complexity guarantees for Markov decision processes (MDPs) with large or continuous state and action spaces remains a fundamental challenge. We address this challenge for entropy-regularized MDPs with Polish state and action spaces, assuming access to a generative model of the environment. We propose a novel family of multilevel Monte Carlo (MLMC) algorithms that integrate fixed-point iteration with MLMC techniques and a generic stochastic approximat…

Viele europäische #Landwirt*innen zeigen mehr #Klimaskepsis als andere Berufsgruppen, vor allem in wohlhabenderen Ländern mit geringerem #Klimarisiko.
Das erschwert

Climate change skepticism of European farmers and implications for effective policy actions - Communications Earth & Environment
The majority of farmers in European Union countries are more skeptical about climate change impact and attribution than the general population, and more so in more innovative and higher-income countries, according to an analysis of European-level surveys.

Congratulations, UK, you’ve won the Orwell award.
https://www.theguardian.com/politics/2025/jul/02/uk-mps-vote-to-proscribe-palestine-action-as-terrorist-group

Ranking 2025 NFL Offensive Rookie of the Year candidates, plus best bets: Who can challenge Ashton Jeanty?

https://www.cbssports.com/nfl/news/rank…

This https://arxiv.org/abs/2505.09430 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Mini Diffuser: Fast Multi-task Diffusion Policy Training Using Two-level Mini-batches
We present a method that reduces, by an order of magnitude, the time and memory needed to train multi-task vision-language robotic diffusion policies. This improvement arises from a previously underexplored distinction between action diffusion and the image diffusion techniques that inspired it: In image generation, the target is high-dimensional. By contrast, in action generation, the dimensionality of the target is comparatively small, and only the image condition is high-dimensional. Our app…