Tootfinder

New Verbiage to Be Learned For Raiders in Training Camp https://www.si.com/nfl/raiders/new-verbiage-learned-for-pete-carroll-jack-bech-training-camp-chip-kelly

Lorentz-Equivariance without Limitations
Luigi Favaro, Gerrit Gerhartz, Fred A. Hamprecht, Peter Lippmann, Sebastian Pitz, Tilman Plehn, Huilin Qu, Jonas Spinner
https://arxiv.org/abs/2508.14898

Lorentz-Equivariance without Limitations
Lorentz Local Canonicalization (LLoCa) ensures exact Lorentz-equivariance for arbitrary neural networks with minimal computational overhead. For the LHC, it equivariantly predicts local reference frames for each particle and propagates any-order tensorial information between them. We apply it to graph networks and transformers. We showcase its cutting-edge performance on amplitude regression, end-to-end event generation, and jet tagging. For jet tagging, we introduce a large top tagging dataset…

Bengals-Commanders 2025 NFL preseason, Week 2: What We Learned from Cincinnati's 31-17 win https://www.nfl.com/news/bengals-commanders-2025-nfl-preseason-week-2-what-we-learned-from-cincinnati-s-win

BLIPs: Bayesian Learned Interatomic Potentials
Dario Coscia, Pim de Haan, Max Welling
https://arxiv.org/abs/2508.14022 https://arxiv.org/pdf/2508.14022

BLIPs: Bayesian Learned Interatomic Potentials
Machine Learning Interatomic Potentials (MLIPs) are becoming a central tool in simulation-based chemistry. However, like most deep learning models, MLIPs struggle to make accurate predictions on out-of-distribution data or when trained in a data-scarce regime, both common scenarios in simulation-based chemistry. Moreover, MLIPs do not provide uncertainty estimates by construction, which are fundamental to guide active learning pipelines and to ensure the accuracy of simulation results compared …

Maximal translation surfaces in Lorentz-Minkowski space
Rafael L\'opez
https://arxiv.org/abs/2507.14103 https://arxiv.org/pdf/250…

Maximal translation surfaces in Lorentz-Minkowski space
A translation surface in Lorentz-Minkowski space $\rr^3$ is a surface defined as the sum of two spatial curves. In this paper we present a classification of maximal surfaces of translation type. We prove that if a generating curve is planar, then the other generating curve is also planar. We give a full description of these surfaces. In case that both curves are of Frenet type, we generalize the Scherk surfaces. In case that a curve is a pseudo-null curve, we obtain new examples of maximal surf…

Fermionic greybody factors and strong gravitational lensing by Lorentz-violating global monopole
F. M. Belchior, R. V. Maluf, A. R. M. Oliveira, A. Yu. Petrov, P. J. Porf\'irio
https://arxiv.org/abs/2508.14861

Fermionic greybody factors and strong gravitational lensing by Lorentz-violating global monopole
In this work, we study the greybody factors (GFs) of spin 1/2 and spin 3/2 fermions for a black hole with global monopole in self-interacting Kalb-Ramond gravity with Lorentz symmetry violation. For our purpose, we consider the Dirac and Rarita-Schwinger equations in curved spacetime by proceeding with separating these equations into sets of radial and angular equations. Using the analytical solution of the angular equation, the Schrödinger-like wave equations with potentials are derived by de…

from my link log —
What I've learned from jj.
https://zerowidth.com/2025/what-ive-learned-from-jj/
saved 2025-05-02 htt…

How UK Biobank, a government-backed cloud database of 1B medical images from 100,000 participants, is fueling breakthroughs in AI-driven diagnostics (Bloomberg)
https://www.bloomberg.com/news/articles/2025-07-18/what-scientist…

Dolphins-Bills on 'Thursday Night Football': What We Learned from Buffalo's 31-21 win over Miami https://www.nfl.com/news/dolphins-bills-on-thursday-night-football-what-we-learned-from-buffalo-s-31-21-win-over-miami…

Eagles Do Have a Passing Game, and What Else We're Learning in Week 3 https://www.foxsports.com/stories/nfl/what-were-learned-week-3-rams-eagles-broncos-chargers-cowboys-bears