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@arXiv_mathOC_bot@mastoxiv.page
2025-09-29 09:23:57

An Efficient ADMM Method for Ratio-Type Nonconvex and Nonsmooth Minimization in Sparse Recovery
Lang Yu, Nanjing Huang
arxiv.org/abs/2509.21969

An Efficient ADMM Method for Ratio-Type Nonconvex and Nonsmooth Minimization in Sparse Recovery
Sparse signal recovery based on nonconvex and nonsmooth optimization problems has significant applications and demonstrates superior performance in signal processing and machine learning. This work deals with a scale-invariant $\ell_{1/2}/\ell_{2}$ sparse minimization with nonconvex, nonseparable, ratio-type regularization to enhance the accuracy and stability of sparse recovery. Within the framework of the null space property, we analyze the conditions for exact and stable recovery in constrai…

@arXiv_mathAG_bot@mastoxiv.page
2025-09-29 09:38:17

Some Semi-Classical Noncommutative Resolutions of Kleinian Singularities
Lukas Bertsch
arxiv.org/abs/2509.22569 arxiv.org/pdf/2509.22569

Some Semi-Classical Noncommutative Resolutions of Kleinian Singularities
We construct a class of noncommutative crepant resolutions of any Kleinian singularity in the form of noncommutative algebras over its crepant partial resolutions. We argue that such resolutions are Morita equivalent to the canonical orbifold resolutions of the partial resolutions. Further, we introduce Quot schemes which may be interpreted as Hilbert schemes of points for these orbifolds, and show that they are Nakajima quiver varieties.

@arXiv_csRO_bot@mastoxiv.page
2025-09-29 09:57:17

Learnable Conformal Prediction with Context-Aware Nonconformity Functions for Robotic Planning and Perception
Divake Kumar, Sina Tayebati, Francesco Migliarba, Ranganath Krishnan, Amit Ranjan Trivedi
arxiv.org/abs/2509.21955

Learnable Conformal Prediction with Context-Aware Nonconformity Functions for Robotic Planning and Perception
Deep learning models in robotics often output point estimates with poorly calibrated confidences, offering no native mechanism to quantify predictive reliability under novel, noisy, or out-of-distribution inputs. Conformal prediction (CP) addresses this gap by providing distribution-free coverage guarantees, yet its reliance on fixed nonconformity scores ignores context and can yield intervals that are overly conservative or unsafe. We address this with Learnable Conformal Prediction (LCP), whi…

@blaise@mastodon.cloud
2025-09-29 19:56:52

We officially live in a dictatorship. This illegal, unconstitutional bullshit will let them use anything you've *ever* said that could be construed as being a negative statement on Christianity, our government, or capitalism is now their tool to watchlist you and use it to punitively exclude you from air/train/bus travel, banking, credit, government benefits, education, etc...

The Situation: The Nonsense and the Menace
Reading the president’s new orders on political violence and terrorism.

@radioeinsmusicbot@mastodonapp.uk
2025-10-28 07:45:38

🇺🇦 Auf radioeins läuft...
Nina Simone:
🎵 I wish I knew how it would feel to be free
#NowPlaying #NinaSimone
djtooslow.bandcamp.com/track/i
open.spotify.com/track/5CKHhg3

@relcfp@mastodon.social
2025-10-28 06:05:53

Call for Papers for an Edited Volume - Train Narratives in India: Marking 100 Years of the Electric Train in India (1925–2025)
ift.tt/N2wmkGn
updated: Monday, October 27, 2025 - 10:52amfull name / name of…
via Input 4 RELCFP

@arXiv_mathMG_bot@mastoxiv.page
2025-09-29 07:53:07

Comparison estimates on nonsmooth spaces with integrable Ricci lower bounds via localization
Emanuele Caputo, Francesco Nobili, Tommaso Rossi
arxiv.org/abs/2509.22514

Comparison estimates on nonsmooth spaces with integrable Ricci lower bounds via localization
We study comparison estimates on metric measure spaces admitting a synthetic variable Ricci curvature lower bound. We obtain geometric and functional inequalities assuming that the deficit of the lower bound from a given constant is sufficiently integrable. More precisely, we extend to the nonsmooth setting the Bishop-Gromov comparison, the Myers' diameter estimate and the Cheng's comparison principle for Dirichlet eigenvalues. Our analysis relies on the localization method and on one-dimension…

@aredridel@kolektiva.social
2025-11-28 16:19:02
Content warning: open source whinging

Ugh why is this always the way. I evaluated like 25 authentication servers for a small scale web project — I do want to support things like OIDC and Passkeys, so this is not something I really want to make myself like the old days of “use crypt() on the passwords and just make a simple database”.
5 of them are just dev mode garbage that will never see the light of day as a thing people use.
2 of them are home network nonsense for people who want enterprise login for their family, but where One Nerd controls the whole user-list.
15 of them are freemium "open source" where they withhold features for their enterprise tier and make them so unfortunately difficult to deploy, all requiring postgresql databases and a complex containerization setup and helm charts and oh so much.
and then there's kanidm, which is great except its opinions make it completely unusable for a community project, it's really more trying to fit the ‘enterprise unix authentication' space. Kudos to them for communicating it but it's the wrong tool, even if it is really good.
And then there's rauthy. Which is exactly what I want, well built and delightful, uses a lightweight embedded database, and even has a peer-to-peer sync for scalability. But customizing it is going to be a lesson in building it from source repeatedly, and its configuration is just a bit strange, and its frontend is extremely Backend Developer Wrote A Web UI. I guess I got a second project. And maybe a third to make debian packages of it.
Yet it really is the best of the options _by far_.
NLNet supported projects continue to punch above their weight class.