Tootfinder

Auswüchse der KI - Microsoft lässt Millionen Tonnen Exkremente unter die Erde pumpen.
Microsoft hat einen kuriosen Deal geschlossen, um seinen massiv ge­stie­ge­nen CO-Ausstoß zumindest auf dem Papier zu verringern. Dabei sollen riesige Mengen an Exkrementen von Menschen und Tieren in großer Tiefe vergraben werden. Die Redmonder zahlen dafür über eine Milliarde Dollar.

UK legislation to allow foreign states to own up to 15% in British newspapers passes, clearing the way for the Telegraph's sale to a RedBird Capital-led group (Mark Sweney/The Guardian)
https://www.theguardian.com/media/2025/jul

🏃🏿‍♀️‍➡️ Researchers find that individual practice is the secret to maintaining high team performance over time
https://phys.org/news/2025-07-individual-secret-high-team.html

Researchers find that individual practice is the secret to maintaining high team performance over time
When it comes to learning and retaining complex skills, a new study from Texas A&M University uncovered a surprising finding: in the context of teams, having employees practice skills alone may be the best way to help the team succeed.

Exploring the Theory and Practice of Concurrency in the Entity-Component-System Pattern
Patrick Redmond, Jonathan Castello, Jos\'e Manuel Calder\'on Trilla, Lindsey Kuper
https://arxiv.org/abs/2508.15264

Exploring the Theory and Practice of Concurrency in the Entity-Component-System Pattern
The Entity-Component-System (ECS) software design pattern, long used in game development, encourages a clean separation of identity (entities), data properties (components), and computational behaviors (systems). Programs written using the ECS pattern are naturally concurrent, and the pattern offers modularity, flexibility, and performance benefits that have led to a proliferation of ECS frameworks. Nevertheless, the ECS pattern is little-known and not well understood outside of a few domains. …

The End of the Road for Far-infrared Reddening Maps? Evidence for Reddening Errors Driven by Changes in PAH Abundance
Dennis Lee, Brandon S. Hensley, Tzu-Ching Chang, Olivier Dor\'e
https://arxiv.org/abs/2508.11746

The End of the Road for Far-infrared Reddening Maps? Evidence for Reddening Errors Driven by Changes in PAH Abundance
Accurate correction for extinction by Galactic dust is essential for studying the extragalactic sky. In the low-extinction regions of the Ursa Major molecular cloud complex, we demonstrate that Galactic dust reddening maps constructed from observations of far-infrared emission are insensitive to variations in the abundance of polycyclic aromatic hydrocarbons (PAHs), and, as a result, to PAH-induced variations in reddening. Using galaxy counts to validate various reddening maps, we find evidence…

Enhancing Remote Sensing Vision-Language Models Through MLLM and LLM-Based High-Quality Image-Text Dataset Generation
Yiguo He, Junjie Zhu, Yiying Li, Xiaoyu Zhang, Chunping Qiu, Jun Wang, Qiangjuan Huang, Ke Yang
https://arxiv.org/abs/2507.16716

Enhancing Remote Sensing Vision-Language Models Through MLLM and LLM-Based High-Quality Image-Text Dataset Generation
The application of Vision-language foundation models (VLFMs) to remote sensing (RS) imagery has garnered significant attention due to their superior capability in various downstream tasks. A key challenge lies in the scarcity of high-quality, large-scale, image-text paired training data. Recently, several works introduced extensive image-text datasets for RS and trained their VLFMs. However, due to the rudimentary methods used for generating captions, the quality of datasets is suboptimal, requ…

KGN-Pro: Keypoint-Based Grasp Prediction through Probabilistic 2D-3D Correspondence Learning
Bingran Chen, Baorun Li, Jian Yang, Yong Liu, Guangyao Zhai
https://arxiv.org/abs/2507.14820

KGN-Pro: Keypoint-Based Grasp Prediction through Probabilistic 2D-3D Correspondence Learning
High-level robotic manipulation tasks demand flexible 6-DoF grasp estimation to serve as a basic function. Previous approaches either directly generate grasps from point-cloud data, suffering from challenges with small objects and sensor noise, or infer 3D information from RGB images, which introduces expensive annotation requirements and discretization issues. Recent methods mitigate some challenges by retaining a 2D representation to estimate grasp keypoints and applying Perspective-n-Point (…

Shear induced topological changes of local structure in dense colloidal suspensions
Ratimanasee Sahu, Abhishek Kumar Gupta, Peter Schall, Sarika Maitra Bhattacharyya, Vijayakumar Chikkadi
https://arxiv.org/abs/2508.15621

Shear induced topological changes of local structure in dense colloidal suspensions
Understanding the structural origins of glass formation and mechanical response remains a central challenge in condensed matter physics. Recent studies have identified the local caging potential experienced by a particle due to its nearest neighbors as a robust structural metric that links microscopic structure to dynamics under thermal fluctuations and applied shear. However, its connection to locally favored structural motifs has remained unclear. Here, we analyze structural motifs in colloid…

Must See. Zoveel redenen maar zeker ook omdat Yesilgoz enthousiast maar pijnlijk duidelijk laat zien waarom zij volstrekt ongeschikt is voor een ministerschap, laat staan het premierschap.
Integer, openhartig en confronterend, deze aflevering. Hulde Tim Hofman!
https://www.youtube.com/watch?v=vRZ…

Continual Learning with Columnar Spiking Neural Networks
Denis Larionov, Nikolay Bazenkov, Mikhail Kiselev
https://arxiv.org/abs/2506.17169 https://…

Continual Learning with Columnar Spiking Neural Networks
This study investigates columnar-organized spiking neural networks (SNNs) for continual learning and catastrophic forgetting. Using CoLaNET (Columnar Layered Network), we show that microcolumns adapt most efficiently to new tasks when they lack shared structure with prior learning. We demonstrate how CoLaNET hyperparameters govern the trade-off between retaining old knowledge (stability) and acquiring new information (plasticity). Our optimal configuration learns ten sequential MNIST tasks effe…