@heiseonline@social.heise.de2026-04-04 05:00:35
Einige der zuletzt hier besonders häufig geteilten #News:
Raumfahrt aus Jena: Präzisionssensoren leiten Artemis zum Mond
@elduvelle@neuromatch.social2026-07-04 11:11:17
@kexpmusicbot@mastodonapp.uk2026-06-04 07:31:32
🇺🇦 #NowPlaying on KEXP's #VarietyMix
Yalla Miku:
🎵 Maximum Self-Care
#YallaMiku
https://yallamiku.bandcamp.com/track/maximum-self-care
https://open.spotify.com/track/1faaEMR5x5dZuB8x9Rs8br
@qbi@freie-re.de2026-05-03 11:21:58
Vor 55 Jahren wurde Walter #Ulbricht von Erich #Honecker gestürzt. Heute habe ich es geschafft, das Opus Magnum von Sascha-Ilko #Kowalczuk über das Leben zu Ende gelesen.
2000 Seiten Autobiographie, sehr …
@memeorandum@universeodon.com2026-06-04 09:45:50
The $9 billion liability across the street from the Capitol (Katherine Tully-McManus/Politico)
https://www.politico.com/news/2026/06/04/rayburn-house-office-building-renovation-00949497
http://www.memeorandum.com/260604/p6#a260604p6
@arXiv_csGT_bot@mastoxiv.page2026-06-04 07:33:46
Improved Approximation Guarantees for Groupwise Maximin Share Fairness
Georgios Amanatidis, Anna Korfiati, Evangelos Markakis, Christodoulos Santorinaios
https://arxiv.org/abs/2606.04731 https://arxiv.org/pdf/2606.04731 https://arxiv.org/html/2606.04731
arXiv:2606.04731v1 Announce Type: new
Abstract: We study the problem of fairly allocating a set of indivisible goods to a set of $n$ agents with additive valuation functions. We focus on the very demanding notion of \textit{groupwise maximin share fairness} (GMMS), which requires that each agent $i$ receives value comparable to their maximin share, where the latter is computed \textit{with respect to any subset of agents that contains $i$}. We show that it is possible to compute $(\phi-1)$-approximate GMMS allocations in polynomial time, where $\phi \approx 1.618$ is the golden ratio). This improves on the previously known guarantee of $4/7$ of Chaudhury et al. [SICOMP; 2021] and Amanatidis et al. [TCS; 2020]. We propose a simple algorithm that maintains the same main properties as the Draft-and-Eliminate algorithm of Amanatidis et al. [TCS, 2020] and we improve on the approximation guarantee analysis by carefully bounding the relevant value within any subinstance induced by the restriction of our allocation to a subset of agents. Our analysis is asymptotically tight for algorithms that share these properties and has the additional benefit of giving improved guarantees for restricted settings; in particular, when the agents agree on the top $n$ goods or when the number of agents is small. To illustrate the challenges of going beyond the guarantees of our algorithm, we also present a variant with an improved approximation of $(\sqrt{10}-1)/3 \approx 0.72$ for the case of three agents. To achieve this improvement we partially characterize the maximin share guarantees of short picking sequences for a small number of goods.
toXiv_bot_toot
@catsalad@infosec.exchange2026-04-04 15:37:36
:bunny:
@mcdanlj@social.makerforums.info2026-04-04 16:18:42
I've been enjoying improvements to the #FreeCAD TechDraw workbench while using the 1.1dev builds. This video is a nice summary, and it includes some improvements I didn't previously learn about about while making drawings to support my machining hobby. 😀
@heiseonline@social.heise.de2026-05-04 10:16:00
US-Militär will MQ-9A-Reaper-Drohne zu Drohnenmutterschiff ausbauen
Die Langstreckendrohne MQ-9A Reaper soll zu einem Drohnenmutterschiff umfunktioniert werden. Sie setzt Drohnen ab und koordiniert deren jeweiligen Missionen.
…
@memeorandum@universeodon.com2026-04-04 19:01:29
McEnany sounds alarm: Midterms 'will be hard' for Republicans (Ryan Mancini/The Hill)
https://thehill.com/homenews/campaign/5816260-mcenany-warns-republicans-midterms/
http://www.memeorandum.com/260404/p47#a260404p47