Tootfinder

Kernel VICReg for Self-Supervised Learning in Reproducing Kernel Hilbert Space
M. Hadi Sepanj, Benyamin Ghojogh, Paul Fieguth
https://arxiv.org/abs/2509.07289 https://

Kernel VICReg for Self-Supervised Learning in Reproducing Kernel Hilbert Space
Self-supervised learning (SSL) has emerged as a powerful paradigm for representation learning by optimizing geometric objectives--such as invariance to augmentations, variance preservation, and feature decorrelation--without requiring labels. However, most existing methods operate in Euclidean space, limiting their ability to capture nonlinear dependencies and geometric structures. In this work, we propose Kernel VICReg, a novel self-supervised learning framework that lifts the VICReg objective…

X-ray absorption lines in FUV-detected quasars: II. Cosmological density and properties of the missing baryons
Massimiliano Bonamente, David Spence, Jussi Ahoranta, Nastasha Wijers, Toni Tuominen, Jelle De Plaa
https://arxiv.org/abs/2507.21719

X-ray absorption lines in FUV-detected quasars: II. Cosmological density and properties of the missing baryons
This paper presents constraints on the cosmological density of baryons from a systematic search for O VII and O VIII absorption lines in the XMM-Newton and Chandra X-ray spectra of 51 background sources. The search is based on far ultra-violet redshift priors from HST and FUSE, and it has resulted in the identification of 34 possible O VII and O VIII absorption-line systems at the 99% confidence level, out of a search in 1,224 systems with fixed redshift priors. Of these, 7 O VII and 8 O VIII s…