Tootfinder

"The Art of Solving Impossible Problems" @ Katina Magazine: https://katinamagazine.org/content/article/future-of-work/2025/the-art-of-solving-impossible-problems
"Sometimes the best way to solve a problem is by ad…

Jeder Cent unseres Dauerauftrags für @… ist mega gut investiert.
Wenn #ARD, @…, @…

»Kernenergie erzeugt kein CO2 und ist deshalb die Lösung für unser Energieproblem.« Es ist natürlich nicht ganz so simpel, wie diese Sorte populistischer Parolen. Der Beitrag "Milliardengrab Atomkraft" von 2022 (50min) beleuchtet da ein paar problematische Ecken. Leider werden diejenigen, die da aufmerksam zuhören sollten, auch hier konsequent weghören. Podcast-Empfehlung trotzdem:

Milliardengrab Atomkraft - Doku über unkalkulierbare Kosten der Atomenergie
Ende 2022 sollen die letzten deutschen Atomkraftwerke vom Netz gehen. Doch weltweit tüfteln Startups an neuen Formen von AKWs. Wie teuer ist Atomkraft wirklich und welche Kosten müssen die nachfolgenden Generationen tragen?

Wenn die Eliten ihre Stellung halten, indem sie hart an der Grenze der Gesetze segeln. Und diese Eliten dann ihre Technik in die Politik tragen. Dann dehnt der Staat seine Privilegien auch. So endet aber auch die Vermutung der Rechtmäßigkeit der Verwaltung und es wird irgendwann auch für die Verwaltung Sanktionen geben müssen, persönlicher Art, weil wenn der Staat an den Staat zahlt, ist das sinnlos.

Differential Privacy in Kernelized Contextual Bandits via Random Projections
Nikola Pavlovic, Sudeep Salgia, Qing Zhao
https://arxiv.org/abs/2507.13639 htt…

Differential Privacy in Kernelized Contextual Bandits via Random Projections
We consider the problem of contextual kernel bandits with stochastic contexts, where the underlying reward function belongs to a known Reproducing Kernel Hilbert Space. We study this problem under an additional constraint of Differential Privacy, where the agent needs to ensure that the sequence of query points is differentially private with respect to both the sequence of contexts and rewards. We propose a novel algorithm that achieves the state-of-the-art cumulative regret of $\widetilde{\mat…

Warm-Starting PCE for Traveling Salesman Problem
Rafael S. do Carmo, Renato Gomes dos Reis, Samuel Fernando F Silva, Luiz Gustavo E. Arruda, Felipe F. Fanchini
https://arxiv.org/abs/2509.14414

Warm-Starting PCE for Traveling Salesman Problem
Variational quantum algorithms are promising for combinatorial optimization, but their scalability is often limited by qubit-intensive encoding schemes. To overcome this bottleneck, Pauli Correlation Encoding (PCE) has emerged as one of the most promising algorithms in this scenario. The method offers not only a polynomial reduction in qubit count and a suppression of barren plateaus but also demonstrates competitive performance with state-of-the-art methods on Maxcut. In this work, we propose …

Je suis un peu perplexe (voire franchement atterrée) que soit si soigneusement laissé dans l'ombre le présupposé intelligence = raisonnement dès qu'il s'agit d'IA. Je veux bien que pensée soit trop vague (et encore) et esprit trop mystique, mais le concept d'entendement a tout de même fait ses preuves avant que quiconque ne puisse être taxé de technophobie (Descartes aurait même été plutôt technophile, non ?). C'est pourtant bien le problème : des modèles qui raisonn…

A critical assessment of machine learning in fluid dynamics
Kunihiko Taira, Georgios Rigas, Kai Fukami
https://arxiv.org/abs/2508.13430 https://arxiv.org/p…

A critical assessment of machine learning in fluid dynamics
The fluid dynamics community has increasingly adopted machine learning to analyze, model, predict, and control a wide range of flows. These methods offer powerful computational capabilities for regression, compression, and optimization. In some cases, machine learning has even outperformed traditional approaches. However, many fluid mechanics problems remain beyond the reach of current machine learning techniques. As the field moves from its current state toward a more mature paradigm, this art…

Testing Autonomous Driving Systems -- What Really Matters and What Doesn't
Changwen Li, Joseph Sifakis, Rongjie Yan, Jian Zhang
https://arxiv.org/abs/2507.13661

Testing Autonomous Driving Systems -- What Really Matters and What Doesn't
Despite extensive research, the testing of autonomous driving systems (ADS) landscape remains fragmented, and there is currently no basis for an informed technical assessment of the importance and contribution of the current state of the art. This paper attempts to address this problem by exploring two complementary aspects. First, it proposes a framework for comparing existing test methods in terms of their intrinsic effectiveness and validity. It shows that many methods do not meet both of …

TSP - Travelling Salesman Problem - to represent a picture. Robert E. Bosch names it 'Opt Art' and we are watching a talk how to do this with TurtleStitch. Remote, by indian student Bhavik.
#TurtleStitch10 #embroideryart