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@radioeinsmusicbot@mastodonapp.uk
2025-07-27 06:34:15

🇺🇦 Auf radioeins läuft...
Dapayk & Padberg feat. Caro:
🎵 Island
#NowPlaying #Dapayk #Padberg #Caro
stilvortalent.bandcamp.com/tra
open.spotify.com/track/4ak7Lyw

@simon_lucy@mastodon.social
2025-06-09 13:36:18

On Saturday we spent a deal of time cleaning up the shed and whist it's not finished it's a lot closer to the way it looked some 15 years ago when I started using it.
There is still a lot to do, build another 2 bookcases and put them in the server room at the end of the shed. And then replace the blinds.
#Shed

Interior of the corner of the shed where I mostly 'work'. Wood horizontal planks with log cabin butt joints. Pictures on the walls, a stretch of 4 bookcases along the wall my red spinny chair and the glass table obscured by stuff, the detritus and tools of a computer person. A new jute or sizal woven mat edged in red which replaces the previous one without the ripped edging and dingier weave.
Shot of the same corner but from about 2/3rds of the room length away. More pictures on the walls and another two tall bookcases on the front wall and the right wall.
@arXiv_csRO_bot@mastoxiv.page
2025-06-13 07:55:40

A Unified Framework for Probabilistic Dynamic-, Trajectory- and Vision-based Virtual Fixtures
Maximilian M\"uhlbauer, Freek Stulp, Sylvain Calinon, Alin Albu-Sch\"affer, Jo\~ao Silv\'erio
arxiv.org/abs/2506.10239

A Unified Framework for Probabilistic Dynamic-, Trajectory- and Vision-based Virtual Fixtures
Probabilistic Virtual Fixtures (VFs) enable the adaptive selection of the most suitable haptic feedback for each phase of a task, based on learned or perceived uncertainty. While keeping the human in the loop remains essential, for instance, to ensure high precision, partial automation of certain task phases is critical for productivity. We present a unified framework for probabilistic VFs that seamlessly switches between manual fixtures, semi-automated fixtures (with the human handling precise…

@arXiv_astrophHE_bot@mastoxiv.page
2025-07-10 09:43:41

Machine-learning correction for the calorimeter saturation of cosmic-rays ions with the Dark Matter Particle Explorer: towards the PeV scale
Andrea Serpolla, Andrii Tykhonov, Paul Coppin, Manbing Li, Andrii Kotenko, Enzo Putti-Garcia, Hugo Boutin, Mikhail Stolpovskiy, Jennifer Maria Frieden, Chiara Perrina, Xin Wu
arxiv.…

Machine-learning correction for the calorimeter saturation of cosmic-rays ions with the Dark Matter Particle Explorer: towards the PeV scale
The Dark MAtter Particle Explorer (DAMPE) instrument is a space-born cosmic-ray detector, capable of measuring ion fluxes up to $\sim$500 TeV/n. This energy scale is made accessible through its calorimeter, which is the deepest currently operating in orbit. Saturation of the calorimeter readout channels start occurring above $\sim$100 TeV of incident energy, and can significantly affect the primary energy reconstruction. Different techniques, analytical and machine-learning based, have been dev…