Tootfinder

Cooper's Final Minutes (Oliver Darcy/Status)
https://www.status.news/p/anderson-cooper-60-minutes-bari-weiss
http://www.memeorandum.com/260216/p90#a260216p90

The Shadow Over '60' ... Earlier this week, on Tuesday, members of the "60 Minutes" ... (Oliver Darcy/Status)
https://www.status.news/p/60-minutes-bari-weiss-cbs-news-cecot
http://www.memeorandum.com/260109/p29#a260109p29

The Dokoupil Debacle ... On Tuesday evening, newly installed "CBS Evening News" ... (Oliver Darcy/Status)
https://www.status.news/p/tony-dokoupil-cbs-evening-news-rollout
http://www.memeorandum.com/260107/p54#a260107p54

#MostWanted
#FBI seek #OrangeCounty #RealityTV producer that fled #USA

Regularized Random Fourier Features and Finite Element Reconstruction for Operator Learning in Sobolev Space
Xinyue Yu, Hayden Schaeffer
https://arxiv.org/abs/2512.17884 https://arxiv.org/pdf/2512.17884 https://arxiv.org/html/2512.17884
arXiv:2512.17884v1 Announce Type: new
Abstract: Operator learning is a data-driven approximation of mappings between infinite-dimensional function spaces, such as the solution operators of partial differential equations. Kernel-based operator learning can offer accurate, theoretically justified approximations that require less training than standard methods. However, they can become computationally prohibitive for large training sets and can be sensitive to noise. We propose a regularized random Fourier feature (RRFF) approach, coupled with a finite element reconstruction map (RRFF-FEM), for learning operators from noisy data. The method uses random features drawn from multivariate Student's $t$ distributions, together with frequency-weighted Tikhonov regularization that suppresses high-frequency noise. We establish high-probability bounds on the extreme singular values of the associated random feature matrix and show that when the number of features $N$ scales like $m \log m$ with the number of training samples $m$, the system is well-conditioned, which yields estimation and generalization guarantees. Detailed numerical experiments on benchmark PDE problems, including advection, Burgers', Darcy flow, Helmholtz, Navier-Stokes, and structural mechanics, demonstrate that RRFF and RRFF-FEM are robust to noise and achieve improved performance with reduced training time compared to the unregularized random feature model, while maintaining competitive accuracy relative to kernel and neural operator tests.
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