Tootfinder

Es ist ein Drama: #crossborder Tickets bei der Deutschen Bahn zu kaufen.
Nächste Woche muss ich nach Rotterdam. Mit dem Auto 2,5h Fahrzeit. Mit der Bahn kaum zu schaffen!
Trotz mehrfacher Eingabe von Alter UND Geburtsdatum (!) heißt es wiederholt Onlineverkauf nicht möglich! Mir wird alternativ ein Interrailticket angeboten. Nur einzelne Verbindungen sind buchbar, mit Umweg über …

Replaced article(s) found for astro-ph.CO. https://arxiv.org/list/astro-ph.CO/new
[1/2]:
- Union Through UNITY: Cosmology with 2,000 SNe Using a Unified Bayesian Framework
Rubin, Aldering, Betoule, Fruchter, Huang, Kim, Lidman, Linder, Perlmutter, Ruiz-Lapuente, Suzuki

Size distribution of decaying foam bubbles
Ildoo Kim
https://arxiv.org/abs/2506.18049 https://arxiv.org/pdf/2506.18049

Size distribution of decaying foam bubbles
The most studies on the stability of foam bubbles investigated the mechanical stability of thin films between bubbles due to the drainage by gravity. In the current work, we take an alternative approach by assuming the rupture of bubbles as a series of random events and by investigating the time evolution of the size distribution of foam bubbles over a long time up to several hours. For this purpose, we first prepared layers of bubbles on Petri dishes by shaking soap solutions of a few differen…

Sequence-to-Sequence Models with Attention Mechanistically Map to the Architecture of Human Memory Search
Nikolaus Salvatore, Qiong Zhang
https://arxiv.org/abs/2506.17424

Sequence-to-Sequence Models with Attention Mechanistically Map to the Architecture of Human Memory Search
Past work has long recognized the important role of context in guiding how humans search their memory. While context-based memory models can explain many memory phenomena, it remains unclear why humans develop such architectures over possible alternatives in the first place. In this work, we demonstrate that foundational architectures in neural machine translation -- specifically, recurrent neural network (RNN)-based sequence-to-sequence models with attention -- exhibit mechanisms that directly…

“…leave your gift there before the altar and go; first be reconciled to your brother or sister, and then come and offer your gift.”
‭‭Matthew€ ‭5€:‭24€ ‭NRSV€€
https://bible.com/bible/2016/mat.5.24.NRSV

Matthew 5:24 leave your gift there before the altar and go; first be reconciled to your brother or sister, and then come and offer your gift. | New Revised Standard Version (NRSV) | Download The Bible App Now
leave your gift there before the altar and go; first be reconciled to your brother or sister, and then come and offer your gift.

'This is not acceptable': Political stalker forced top PA Dem to wear a bulletproof vest - Alternet.org
https://www.alternet.org/political-stalker/

A first-principles investigation of altermagnetism in CrSb2 under applied pressure
R. Tamang, Shivraj Gurung, Shalika Ram Bhandari, Matthew J. Stitz, Ganesh Pokharel, Keshav Shrestha, D. P. Rai
https://arxiv.org/abs/2506.13454

A first-principles investigation of altermagnetism in CrSb2 under applied pressure
In this study, we employed first-principles density functional theory (DFT) calculations within the GGA+U framework to explore the electronic and magnetic properties of CrSb2 under varying hydrostatic pressures. CrSb2 exhibits non-relativistic spin splitting (NRSS) of around 0.5 eV around the Fermi level and the d-wave symmetric Fermi surface. Our magnetic susceptibility measurements further confirm the collinear antiferromagnetic (AFM) ground state in CrSb2 , a prerequisite for altermagnetism.…

Check out and share my latest blog post, “Hijacked Minds and Broken Trust”
https://www.speedofcreativity.org/2025/06/19/hijacked-minds-and-broken-trust/
(btw I blogged this on my iPad, which I’m trying to use more for media creation and sharing)

An efficient forgetting-aware fine-tuning framework for pretrained universal machine-learning interatomic potentials
Jisu Kim, Jiho Lee, Sangmin Oh, Yutack Park, Seungwoo Hwang, Seungwu Han, Sungwoo Kang, Youngho Kang
https://arxiv.org/abs/2506.15223

An efficient forgetting-aware fine-tuning framework for pretrained universal machine-learning interatomic potentials
Pretrained universal machine-learning interatomic potentials (MLIPs) have revolutionized computational materials science by enabling rapid atomistic simulations as efficient alternatives to ab initio methods. Fine-tuning pretrained MLIPs offers a practical approach to improving accuracy for materials and properties where predictive performance is insufficient. However, this approach often induces catastrophic forgetting, undermining the generalizability that is a key advantage of pretrained MLI…