Tootfinder

Britische Regierung prüft Mindestalter für Social Media, Druck aus dem Parlament
Das britische Oberhaus könnte schon am Mittwoch dafür stimmen, soziale Medien erst ab 16 Jahren freizugeben. Die Regierung hat eine Konsultation versprochen.

Opposition verärgert - „Menschen belohnen“: Neue Überstunden-Regelung fix #News #Nachrichten

POL-DEL: Landkreis Wesermarsch: Kleinbrand in Gartenlaub in Brake Delmenhorst (ots) - Am Mittwoch, 18. Februar 2026, gegen 16:30 Uhr, kam es in Brake in der Bürgermeister-Müller-Straße zu einem Kleinbrand in einer privaten Gartenlaube. Nach derzeitigen Erkenntnissen hat eine 75-jährige Bewohnerin in dem ... https://www.p…

Proc3D: Procedural 3D Generation and Parametric Editing of 3D Shapes with Large Language Models
Fadlullah Raji, Stefano Petrangeli, Matheus Gadelha, Yu Shen, Uttaran Bhattacharya, Gang Wu
https://arxiv.org/abs/2601.12234 https://arxiv.org/pdf/2601.12234 https://arxiv.org/html/2601.12234
arXiv:2601.12234v1 Announce Type: new
Abstract: Generating 3D models has traditionally been a complex task requiring specialized expertise. While recent advances in generative AI have sought to automate this process, existing methods produce non-editable representation, such as meshes or point clouds, limiting their adaptability for iterative design. In this paper, we introduce Proc3D, a system designed to generate editable 3D models while enabling real-time modifications. At its core, Proc3D introduces procedural compact graph (PCG), a graph representation of 3D models, that encodes the algorithmic rules and structures necessary for generating the model. This representation exposes key parameters, allowing intuitive manual adjustments via sliders and checkboxes, as well as real-time, automated modifications through natural language prompts using Large Language Models (LLMs). We demonstrate Proc3D's capabilities using two generative approaches: GPT-4o with in-context learning (ICL) and a fine-tuned LLAMA-3 model. Experimental results show that Proc3D outperforms existing methods in editing efficiency, achieving more than 400x speedup over conventional approaches that require full regeneration for each modification. Additionally, Proc3D improves ULIP scores by 28%, a metric that evaluates the alignment between generated 3D models and text prompts. By enabling text-aligned 3D model generation along with precise, real-time parametric edits, Proc3D facilitates highly accurate text-based image editing applications.
toXiv_bot_toot

Nasdaq Tells Trump Crypto Partner Alt5 Sigma It 'No Longer Meets' Listing Requirements After Missed Report (Zach Everson/Forbes)
https://www.forbes.com/sites/zacheverson/2025/12/02/nasdaq-tells-trump-crypto-partner-alt5-sigma-it-no-longer-meets-listing-requirements-after-missed-report/
http://www.memeorandum.com/251202/p151#a251202p151

Auf dem Weg in den Krieg https://www.german-foreign-policy.com/news/detail/10241

Mutig, mutig....kein Cannabis UND kein Alkohol...das wird hart - für Vortragende UND Zuhörer
#cdu #merz_regierung
Merz will Alkoholausschank auf CDU-Parteitag einschränken

Meta-learning three-factor plasticity rules for structured credit assignment with sparse feedback
Dimitra Maoutsa
https://arxiv.org/abs/2512.09366 https://arxiv.org/pdf/2512.09366 https://arxiv.org/html/2512.09366
arXiv:2512.09366v1 Announce Type: new
Abstract: Biological neural networks learn complex behaviors from sparse, delayed feedback using local synaptic plasticity, yet the mechanisms enabling structured credit assignment remain elusive. In contrast, artificial recurrent networks solving similar tasks typically rely on biologically implausible global learning rules or hand-crafted local updates. The space of local plasticity rules capable of supporting learning from delayed reinforcement remains largely unexplored. Here, we present a meta-learning framework that discovers local learning rules for structured credit assignment in recurrent networks trained with sparse feedback. Our approach interleaves local neo-Hebbian-like updates during task execution with an outer loop that optimizes plasticity parameters via \textbf{tangent-propagation through learning}. The resulting three-factor learning rules enable long-timescale credit assignment using only local information and delayed rewards, offering new insights into biologically grounded mechanisms for learning in recurrent circuits.
toXiv_bot_toot

5-Cent-Tarif für Strom - Details aus Regierungsklausur durchgesickert #News #Nachrichten

🇺🇦 #NowPlaying on KEXP's #MiddayShow
Loveshadow:
🎵 Pulse at Midnight
#Loveshadow
https://100percentsilk.bandcamp.com/track/loveshadow-pulse-at-midnight
https://open.spotify.com/track/0lYanfTrIS3I5auARW4MFS