City Lives II 🔃
城市人生 II 🔃
📷 Pentax 6x7
🎞️ LUCKY SHD 400 (6x7)
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Wise
🇺🇦 #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
#Steady #Klimacrew
Zuerst waren es KI-Tools, die zu Auftragsverlusten bei Freiberuflern sorgten. Dann wurde der Mythos der steigenden Produktivität geboren, um Entlassungen etwa im Support oder im Marketing zu rechtfertigen usw.
Jetzt wurde ein neuer Grund für Entlassungen gefunden. 🤮…
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
After Colleges Reject ‘Compact,’ Trump Officials Try a Letter
The open letter from Education Secretary Linda McMahon fell short of the attempt at a compact last year
that dangled research money in return for embracing the Trump administration’s agenda.
Ms. McMahon’s letter,
which does not include any funding threats,
suggests that changes will improve academia’s reputation among American parents and students.
“The dilution of academic standards,
opaq…
Ruotsalaistutkijat: Lämpenevä ilmasto ei tuo mukanaan vain kuivuutta #ympäristö
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
»Stärkung von Zukunftstechnologien: Wir werden Zukunftsbranchen konsequent fördern, unter anderem den Automobilsektor, die chemische und pharmazeutische Industrie, Clean Tech, die Kreislaufwirtschaft, den Maschinenbau, die Batteriezellen- und Halbleiterproduktion sowie den gesamten Bereich der Künstlichen Intelligenz.«
🤡
Quelle: Reformpaket der Bundesregierung von letzter Nacht.
It's hard to overstate just how much my confidence in a piece of software drops when I see this. (Especially for this project which was very vocal about its shift to genai-first)
#vibecoding #genai
🇺🇦 Auf radioeins läuft...
Mike Post & Peter Carpenter:
🎵 MAGNUM, P.I. Theme
#NowPlaying #MikePost #PeterCarpenter