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@netzschleuder@social.skewed.de
2026-02-11 03:00:05

sp_colocation: Social co-locations (2018)
Network of colocations between peoople, based on the information on which RFID readers received information from the RFID tags. Namely, we define two individuals to be in co-presence if the same exact set of readers have received signals from both individuals during a 20s time window.
This network has 242 nodes and 6594492 edges.
Tags: Social, Offline, Unweighted, Weighted, Temporal, Metadata

sp_colocation: Social co-locations (2018). 242 nodes, 6594492 edges. https://networks.skewed.de/net/sp_colocation#LyonSchool
@benb@osintua.eu
2026-02-10 13:12:43

Kremlin pushes for closed-door Ukraine talks, says Alaska summit understandings are foundational: benborges.xyz/2026/02/10/kreml

@tante@tldr.nettime.org
2026-02-11 11:52:26

"AI is built on the collective knowledge of humankind."
No. Nononononono. It is not built on _knowledge_, it it built on _data_. And not everyone's experiences are available as data, many communities are excluded. Also: "Collective" implies some sort of collaboration and shared activity. But "AI" is just accumulation by a few powerful.
So No. It's not collective but extractive, not knowledge but data, not humankind but the hegemonic western …

@servelan@newsie.social
2026-03-10 04:36:53

Vast Ship Clusters and Speeding Tankers Point to Hormuz Jamming | Financial Post
financialpost.com/pmn/business

The United States has urged its citizens to 👉leave Venezuela immediately
amid reports that ⚠️armed paramilitaries are trying to track down US citizens,
one week after the capture of the South American country’s president, Nicolšs Maduro.
In a security alert sent out on Saturday,
the state department said there were reports of armed members of pro-regime militias,
known as #colectivos

@memeorandum@universeodon.com
2025-12-12 00:21:04

House passes bill to restore collective bargaining for federal employees (Drew Friedman/Federal News Network)
federalnewsnetwork.com/congres
memeorandum.com/251211/p137#a2

@arXiv_csDS_bot@mastoxiv.page
2026-02-10 10:45:35

Incremental (k, z)-Clustering on Graphs
Emilio Cruciani, Sebastian Forster, Antonis Skarlatos
arxiv.org/abs/2602.08542 arxiv.org/pdf/2602.08542 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

@thomasfuchs@hachyderm.io
2026-02-12 00:06:04

This is basically the lyrics to 99 Luftballons
arstechnica.com/space/2026/02/

@netzschleuder@social.skewed.de
2026-02-11 23:00:04

sp_colocation: Social co-locations (2018)
Network of colocations between peoople, based on the information on which RFID readers received information from the RFID tags. Namely, we define two individuals to be in co-presence if the same exact set of readers have received signals from both individuals during a 20s time window.
This network has 403 nodes and 1417485 edges.
Tags: Social, Offline, Unweighted, Weighted, Temporal, Metadata

sp_colocation: Social co-locations (2018). 403 nodes, 1417485 edges. https://networks.skewed.de/net/sp_colocation#SFHH
@servelan@newsie.social
2026-03-10 04:34:11

Vast Ship Clusters and Speeding Tankers Point to Hormuz Jamming | Financial Post
financialpost.com/pmn/business