Tootfinder

Grand Design Spiral Arms in the Compact, Embedded Protoplanetary Disk of Haro 6-13
Jane Huang, Masataka Aizawa, Jaehan Bae, Sean M. Andrews, Myriam Benisty, Edwin A. Bergin, Stefano Facchini, Christian Ginski, Michael K\"uffmeier
https://arxiv.org/abs/2506.06433

Grand Design Spiral Arms in the Compact, Embedded Protoplanetary Disk of Haro 6-13
Millimeter continuum spiral arms have so far only been detected in a handful of protoplanetary disks, and thus we have a limited understanding of the circumstances in which they can form. In particular, substructures in small disks ($R\lessapprox 50$ au) have not been well-characterized in comparison with large disks. We present ALMA 1.3 mm continuum observations of the disk around the T Tauri star Haro 6-13 at a resolution of $\sim0.04''$ ($\sim5$ au). A pair of low-contrast spiral arms are de…

Variations in Dominant Wave Period in the Solar Atmosphere
Pradeep Kayshap, K. Murawski, Z. E. Musielak, Suresh Babu
https://arxiv.org/abs/2506.07493 https…

Variations in Dominant Wave Period in the Solar Atmosphere
Waves are an integral part of the solar atmosphere, and their characteristics (e.g., dominant period, range of periods, power, and phase angle) change on a diverse spatio-temporal scale. It is well well-established observationally that the dominant periods of solar oscillations are 5-min and 3-min in the photosphere and chromosphere, respectively. This shows that the wave spectra and their dominant periods evolve between these two layers. We present observational results that demonstrate variat…

Generating Large Semi-Synthetic Graphs of Any Size
Rodrigo Tuna, Carlos Soares
https://arxiv.org/abs/2507.02166 https://arxiv.org/pdf…

Generating Large Semi-Synthetic Graphs of Any Size
Graph generation is an important area in network science. Traditional approaches focus on replicating specific properties of real-world graphs, such as small diameters or power-law degree distributions. Recent advancements in deep learning, particularly with Graph Neural Networks, have enabled data-driven methods to learn and generate graphs without relying on predefined structural properties. Despite these advances, current models are limited by their reliance on node IDs, which restricts thei…

Coarsening Kinetics in Active Model B : Macroscale and Microscale Phase Separation
Pradeep Kumar Yadav, Shradha Mishra, Sanjay Puri
https://arxiv.org/abs/2506.14548

Coarsening Kinetics in Active Model B+: Macroscale and Microscale Phase Separation
We perform a comprehensive numerical investigation of the coarsening kinetics of active Brownian particles modeled by the {\it Active Model B+} (AMB+). This model was introduced by Tjhung et al. [Phys. Rev. X {\bf 8}, 031080 (2018)] and is a generalization of Model B for a conserved order parameter, with two additional activity terms. These terms correspond to rotation-free current (of strength $λ$) and rotational current (of strength $ξ$). We find that the presence of rotational current $(ξ…

Sketched Sum-Product Networks for Joins
Brian Tsan, Abylay Amanbayev, Asoke Datta, Florin Rusu
https://arxiv.org/abs/2506.14034 https://

Sketched Sum-Product Networks for Joins
Sketches have shown high accuracy in multi-way join cardinality estimation, a critical problem in cost-based query optimization. Accurately estimating the cardinality of a join operation -- analogous to its computational cost -- allows the optimization of query execution costs in relational database systems. However, although sketches have shown high efficacy in query optimization, they are typically constructed specifically for predefined selections in queries that are assumed to be given a pr…

Neural-GASh: A CGA-based neural radiance prediction pipeline for real-time shading
Efstratios Geronikolakis, Manos Kamarianakis, Antonis Protopsaltis, George Papagiannakis
https://arxiv.org/abs/2507.13917

Neural-GASh: A CGA-based neural radiance prediction pipeline for real-time shading
This paper presents Neural-GASh, a novel real-time shading pipeline for 3D meshes, that leverages a neural radiance field architecture to perform image-based rendering (IBR) using Conformal Geometric Algebra (CGA)-encoded vertex information as input. Unlike traditional Precomputed Radiance Transfer (PRT) methods, that require expensive offline precomputations, our learned model directly consumes CGA-based representations of vertex positions and normals, enabling dynamic scene shading without pr…

Nowadays in quality #Python: #Gentoo is running #ProtoBuf-related test suite via #PyTest-forked to workaround protobuf segfaulting during GC.
Of course, it implies random programs can segfault on exit too.
https://github.com/protocolbuffers/protobuf/issues/22067
https://gitweb.gentoo.org/repo/gentoo.git/tree/dev-python/protobuf/protobuf-6.31.1.ebuild?id=54e20d4bb0ec99ab868695a2980c4307d179cb10#n150