mathstodon.xyz is one of the many independent Mastodon servers you can use to participate in the fediverse.
A Mastodon instance for maths people. We have LaTeX rendering in the web interface!

Server stats:

2.9K
active users

#matrix

48 posts45 participants1 post today

Sehr angeregte Diskussion zu #Matrix, #Element und Co. im @eigenbaukombinat beim #HaGeWe

Beispielsweise:

* Unsichere Komponenten werden nicht gepatcht
* Sicherheitsfeatures fraglich
* Unternehmen arbeitet nicht mit der OpenSource Community zusammen
* Features werden immer wieder kurzfristig abgeschaltet / geändert
* Kommunikation zwischen Community Matrix Servern immer wieder durch unterschiedliche Versionsstände unmöglich
* Usabilty Probleme

Hab ich noch etwas vergessen?

As a headsup, I'll likely stop any engagement in the #Matrix ecosystem, seeing the troubling development of #Synapse alternatives and the massive shortcomings of the protocol in regards to community moderation / defense against spam waves. In particular, I'm planning to shutdown my selfhosted conduwuit instance, and continue using my matrix.org account only for communities where Matrix is pretty much required. I briefly considered migrating to Grapevine, but I don't have the energy.

🚨WARNING🚨

Apparently some #Nix / #NixOS matrix room(s) have gotten csam spam. I have not verified this claim myself, but honestly, we can all probably just not check matrix for a few days until hopefully things are back to normal.

I do *not* know what utilities #Matrix provides to prevent the proliferation of this information. If you joined the room from a different home server then your server may have synchronized the material to your infrastructure.

I think the safest course of action is not opening a matrix client for a few days...

Checked in on my Spaces access control MSC for Matrix. The one that I was told "looks good", and for which an issue was created on Synapse to "implement a prototype" because "it looks compelling".

The issue was created in 2021 and has received zero comments since.

In case you were wondering what the experience is of actually trying to contribute a spec change to #Matrix.

This repository was archived by the owner on Apr 11, 2025. It is now read-only.
-- сегодня на гитхабе в репозитории conduwuit

https://girlboss.ceo/~strawberry/conduwuit.txt

всё, что я получила в течение последних 12 месяцев от самых разных людей из коммьюнити матрикса, — постоянные оскорбления, месть, токсичность в общении

не думаю, что вообще вернусь: матрица — плохой, объективно плохой протокол, но не из-за протокола самого, а из-за коммьюнити, людей, пользовательской базы

Итак, осталось две реализации матрикс-сервера.

#matrix #conduwuit #foss #конец @rf

EN: https://gts.dc09.ru/@darkcat09/statuses/01JRK28S7VPH9X85B9VKA7Z4AV

📰 "Data-driven performance optimization of gamma spectrometers with many channels"
arxiv.org/abs/2504.07166 #Physics.Ins-Det #Matrix #Force

arXiv logo
arXiv.orgData-driven performance optimization of gamma spectrometers with many channelsIn gamma spectrometers with variable spectroscopic performance across many channels (e.g., many pixels or voxels), a tradeoff exists between including data from successively worse-performing readout channels and increasing efficiency. Brute-force calculation of the optimal set of included channels is exponentially infeasible as the number of channels grows, and approximate methods are required. In this work, we present a data-driven framework for attempting to find near-optimal sets of included detector channels. The framework leverages non-negative matrix factorization (NMF) to learn the behavior of gamma spectra across the detector, and clusters similarly-performing detector channels together. Performance comparisons are then made between spectra with channel clusters removed, which is more feasible than brute force. The framework is general and can be applied to arbitrary, user-defined performance metrics depending on the application. We apply this framework to optimizing gamma spectra measured by H3D M400 CdZnTe spectrometers, which exhibit variable performance across their crystal volumes. In particular, we show several examples optimizing various performance metrics for uranium and plutonium gamma spectra in nondestructive assay for nuclear safeguards, and explore trends in performance vs.\ parameters such as clustering algorithm type. We also compare the NMF+clustering pipeline to several non-machine-learning algorithms, including several greedy algorithms. Overall, we find that the NMF+clustering pipeline tends to find the best-performing set of detector voxels, significantly improving over the un-optimized spectra, but that a greedy accumulation of spectra segmented by detector depth can in some cases give similar performance improvements in much less computation time.