Tootfinder

The Karpeles Museum,
a true hidden gem of downtown Santa Barbara
and an important free museum for all to visit,
is set to close because the building is being sold.
Perhaps some local collective or organization could buy the building and continue to lease the collection of documents and artifacts from the Karpeles family?
The building has an upstairs and courtyard that would be a perfect venue for all sorts of live events
— in the not-so-distant past the …

How many tours has Jean Petrovs given over her 30 years of being a docent at the Georgia Museum of Art? So many that we don't even know. Our community docents help us do so much: leading regular tours of the collection, advocating for us throughout the Athens area and, of course, helping with our 5th-grade tours every year. 🎉 Cheers to Jean and to ALL our docents.

Incremental (k, z)-Clustering on Graphs
Emilio Cruciani, Sebastian Forster, Antonis Skarlatos
https://arxiv.org/abs/2602.08542 https://arxiv.org/pdf/2602.08542 https://arxiv.org/html/2602.08542
arXiv:2602.08542v1 Announce Type: new
Abstract: Given a weighted undirected graph, a number of clusters $k$, and an exponent $z$, the goal in the $(k, z)$-clustering problem on graphs is to select $k$ vertices as centers that minimize the sum of the distances raised to the power $z$ of each vertex to its closest center. In the dynamic setting, the graph is subject to adversarial edge updates, and the goal is to maintain explicitly an exact $(k, z)$-clustering solution in the induced shortest-path metric.
While efficient dynamic $k$-center approximation algorithms on graphs exist [Cruciani et al. SODA 2024], to the best of our knowledge, no prior work provides similar results for the dynamic $(k,z)$-clustering problem. As the main result of this paper, we develop a randomized incremental $(k, z)$-clustering algorithm that maintains with high probability a constant-factor approximation in a graph undergoing edge insertions with a total update time of $\tilde O(k m^{1 o(1)} k^{1 \frac{1}{\lambda}} m)$, where $\lambda \geq 1$ is an arbitrary fixed constant. Our incremental algorithm consists of two stages. In the first stage, we maintain a constant-factor bicriteria approximate solution of size $\tilde{O}(k)$ with a total update time of $m^{1 o(1)}$ over all adversarial edge insertions. This first stage is an intricate adaptation of the bicriteria approximation algorithm by Mettu and Plaxton [Machine Learning 2004] to incremental graphs. One of our key technical results is that the radii in their algorithm can be assumed to be non-decreasing while the approximation ratio remains constant, a property that may be of independent interest.
In the second stage, we maintain a constant-factor approximate $(k,z)$-clustering solution on a dynamic weighted instance induced by the bicriteria approximate solution. For this subproblem, we employ a dynamic spanner algorithm together with a static $(k,z)$-clustering algorithm.
toXiv_bot_toot

The clusters of earthquakes — the latest of which struck Monday — have all been underneath the East Bay suburb of San Ramon, which is close to the Calaveras fault.
There were at least 19 earthquakes of magnitude 2 or greater on Monday alone, with the largest, a magnitude 3.6, striking at 9:07 a.m.

CPB dissolves to prevent Trump from using it as a propaganda machine
#CorporationForPublicBroadcasting

CPB dissolves to prevent Trump from using it as a propaganda machine
CPB dissolves to prevent Trump from using it as a propaganda machine (From a post seen elsewhere) The Corporation for Public Broadcasting officially closed...

"One peculiarity of this revamped public provision is that it is happening without significant economic growth and without being fuelled by debt. Instead, it is reliant on budget restructuring and increased tax collection."
Edwin F. Ackerman, Sheinbaum’s Mission — Sidecar
https://newleftreview.or…

Edwin F. Ackerman, Sheinbaum’s Mission — Sidecar
The Mexican President’s first year.

😲 @… https://pixelfed.social/p/1Sauerlaender/903722900096577199

Rüdiger Benninghaus (@1Sauerlaender@pixelfed.social)
Crows are intelligent birds and learn quickly – even from each other. In Sweden, they are used to collect cigarette butts and bring them to a machine that automatically rewards them with peanuts.

Trump regime renames National Renewable Energy Laboratory to National Laboratory of the Rockies...
https://www.gravityisgone.com/a-collective-wtf-national-renewable-energy-laboratory-gets-a-trumpian-name-change/

'A Collective WTF': National Renewable Energy Laboratory Gets a Trumpian Name Change
NREL employees, many of whom joined the lab specifically to work toward its clean energy mission, are not happy about the oil-soaked leadership's moves.

Deu trabalho, mas terminei de limpar individualmente cada livro da minha estante dedicada Š matemštica. Agora só preciso fazer isso em mais sete estantes. 🤡
Nessas horas os livros digitais levam a vantagem. Pena que seja muito difícil encontrar versões sem DRM e que livros com DRM nunca sejam verdadeiramente seus.

At long last, the Georgia Museum of Art is on Bloomberg Connects, with a free digital guide that includes audio and visual content, wayfinding, translation into more than 50 languages, robust alt-text and more.
https://georgiamuseum.org/enhancing-ac