Tootfinder

Ryanair cutting 10,000 flights Nov-Mar due to rising fuel cost.
Good, a little respite.
Ryanair reduce vuelos por la subida del combustible: los pasajeros podrían reclamar hasta 600 euros
https://www.larazon.es/ec…

Ryanair reduce vuelos por la subida del combustible: los pasajeros podrían reclamar hasta 600 euros
La compañía ha rebajado de 216 a 214 millones su objetivo de pasajeros para el ejercicio fiscal que finaliza en marzo de 2027

LSR-Net: Long-Short-Range Operator Learning for Pattern Dynamics on Manifolds
Qian Serena Hou, Zecheng Gan
https://arxiv.org/abs/2607.00750 https://arxiv.org/pdf/2607.00750 https://arxiv.org/html/2607.00750
arXiv:2607.00750v1 Announce Type: new
Abstract: We propose the Long-Short-Range Neural Network (LSR-Net), an extensible operator-learning framework for predicting pattern dynamics on planar domains, spherical surfaces, and general manifolds. The method decomposes the forward evolution operator into a long-range component, represented by a compact Fourier multiplier constructed via the Sum-of-Exponentials (SOE) approximation, and a short-range component adapted to the underlying geometry and its intrinsic symmetries. For general manifolds represented by irregularly sampled point clouds, the long-range component is implemented by Gaussian gridding onto an auxiliary regular grid, where the Fourier multiplier is efficiently applied in k-space using FFT and the result is interpolated back to the original sample points. We evaluate LSR-Net on several benchmark systems, including the Allen-Cahn, Cahn-Hilliard, Schnakenberg, and Turing systems, over planar domains, spherical surfaces, and a blob-shaped manifold. Numerical results demonstrate that LSR-Net consistently achieves higher accuracy and improved stability compared with baseline operator-learning models. In particular, for Allen-Cahn dynamics on the sphere, the RMSE is reduced by approximately three orders of magnitude compared with the Spherical Fourier Neural Operator (SFNO). Rotation and reflection equivariance tests further confirm that the learned operator is consistent with these geometric transformations. These results indicate that LSR-Net provides an effective and robust approach for learning pattern dynamics on complex geometries.
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Fisher Forecasting for the DESC with $\texttt{Augur}$
Paul Rogozenski, Sankarshana Srinivasan, Javier S\'anchez, Nora Elisa Chisari, Arthur Loureiro, Marc Paterno, Rebekah Polen, Heather Prince, Biancamaria Sersante, An\v{z}e Slosar, Sandro Vitenti, Carlos Garc\'ia-Garc\'ia, Eric Gawiser, Christos Georgiou, C. Danielle Leonard, Ayan Mitra, Jeremy Neveu, The LSST Dark Energy Science Collaboration
https://arxiv.org/abs/2608.03876 https://arxiv.org/pdf/2608.03876 https://arxiv.org/html/2608.03876
arXiv:2608.03876v1 Announce Type: new
Abstract: The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) has begun its ten-year survey of the entire visible southern hemisphere. To ensure robust cosmological measurements, computationally inexpensive investigations of modeling choices must be made to gauge the performance of proposed cosmological analyses. In this paper, we introduce the $\texttt{Augur}$ tool of the Dark Energy Science Collaboration (DESC), which provides Fisher forecasts for cosmological inference for the LSST using software frameworks designed for DESC science. We test the pipeline by comparing it to forecasts produced by external code and direct sampling of the posterior via nested sampling methods, finding good agreement between all methods. We additionally investigate a range of modeling and hyperparameter choices for a 3$\times$2pt investigation in harmonic space, providing users with diagnostics to obtain reliable forecasts. $\texttt{Augur}$ will be continually updated to be compatible with the other tools in the DESC software ecosystem as additional probes and functionality become available.
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Capillary self-folding chains
Megan Delens, Axel Franckart, Martin Poty, Nicolas Vandewalle
https://arxiv.org/abs/2608.13349 https://arxiv.org/pdf/2608.13349 https://arxiv.org/html/2608.13349
arXiv:2608.13349v1 Announce Type: new
Abstract: Mesoscale self-assembly provides a route toward the design of programmable microsystems. Here, we construct flexible chains of floating monomers whose curved branches impose upward or downward deformations of the liquid interface, corresponding to effective positive or negative capillary charges. These geometrically encoded deformations generate local attractive or repulsive interactions along the chain. By tuning the capillary sequence, we obtain distinct folded configurations, including straight lines, zigzag patterns, and loops. For short chains, folding is largely governed by nearest-neighbor interactions and leads to well-defined structures. As the chain length increases and non-neighboring segments come into proximity and interact, however, the folding landscape becomes increasingly complex, with multiple metastable states whose number grows exponentially with chain length. We map these landscapes numerically and demonstrate experimentally that mechanical agitation allows the chains to transition between metastable configurations. Beyond encoding a target geometry, the capillary sequence therefore controls the complexity of the folding landscape as well as the degeneracy and mutational robustness of folded structures. These results establish capillary chains as a controllable mesoscale platform for investigating how local interaction rules give rise to collective folding and complex sequence-to-structure relationships reminiscent of those encountered in biomolecular systems.
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Extended Abstract: From Pattern Unification Towards Pattern Matching Unification
David Richter, Timon B\"ohler
https://arxiv.org/abs/2607.18455 https://arxiv.org/pdf/2607.18455 https://arxiv.org/html/2607.18455
arXiv:2607.18455v1 Announce Type: new
Abstract: We revisit the role of higher-order unification in dependently typed languages and identify a fundamental limitation of existing pattern-based fragments: their inability to synthesize functions defined by case analysis. Even simple and ubiquitous constraints arising from type inference, particularly from use of induction principles, fall outside the expressive power of Miller patterns and their modern extensions. We observe that such constraints naturally correspond to definitions by dependent pattern matching. Motivated by this correspondence, we propose integrating dependent pattern matching into the unification process. We present a prototype implementation of a small dependently typed language that collects delayed unification constraints and resolves them via a pattern matching compiler. Our approach successfully infers solutions that are rejected by current systems such as Rocq and Lean, suggesting a new direction for unification that unifies type inference and pattern matching compilation.
toXiv_bot_toot

As long as Mastodon does UI stuff like this it won't be going broad appeal or mainstream.
(This specific message happens when you "bump" a post, i.e. remove existing boost and then immediately boost again, and you do it "too fast").
1. It should be called "reboost" in the message because the feature is called "boosting".
2. No one cares about the HTTP status code, it's just confusing to people who aren't devleopers.
3. The message shouldn't happen at all, i.e. the boost button must remain grayed out or unavailable while the post is considered boosted already (for example there could be a little indeterminate progress spinner to acknowledge you unboosted but while its not really unboosted).
4. The message should probably be shown where the error happens, if you have a big monitor you might miss it because it's n the screen corner.
5. The message should probably not just go away by itself before you had a chance to read it.
6. If you absolutely have to have errors like this (could be because user is on a stale page that shows the post as not boosted when it actually is), make the message more helpful (for example suggest to reload the page to see the latest).
7. Reboosting existing boosts is a relatively common usage pattern—possibly make it a feature and give the user extra information (like idk a warning about "You already reboosted this twice in the last week, are you sure you want to pester your followers again?")
This is of course only a small detail, but these UX issues quickly add up.

MR-Compare: A Mixed-Reality Framework for Spatially Grounded Visual Comparison of 3D Gaussian Splatting and Mesh Reconstructions with the Physical Environment
Changrui Zhu, Ernst Kruijff, Pengju Zhang, Simon Julier
https://arxiv.org/abs/2607.20325 https://arxiv.org/pdf/2607.20325 https://arxiv.org/html/2607.20325
arXiv:2607.20325v1 Announce Type: new
Abstract: We introduce MR-Compare, a mixed reality framework for spatially grounded visual comparison between 3D Gaussian splatting and mesh reconstructions with live video see-through (VST). Implemented on a PC-tethered Meta Quest~3, it combines a two-stage registration pipeline with a 3D Slider for cross-media comparison. We evaluated five representative desktop and mobile reconstruction workflows through a real-world benchmark with an exploratory user study ($n=30$) in two static indoor rooms. MR-Compare achieved centimetre-level translation error across all workflows. The two desktop 3DGS workflows showed the strongest overall pattern, with 3DGS-MCMC yielding the lowest registration error and strongest VST-referenced visual consistency. Room-session measures indicated high perceived usability and low workload. We further propose an anisotropy filter, a zero-shot module that leverages Gaussian anisotropies to improve 3DGS registration in MR-Compare. A controlled Replica threshold sweep shows that moderate pruning can improve robustness and reduce residual errors. These results establish system-level feasibility in the tested setting rather than task-level effectiveness or standalone deployment. The project is available at https://github.com/changruizhu96/MR-Compare.
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Emergent topological structure in spontaneous brain-organoid activity
Eve Bodnia, Margaux Basart, Sofie Hai, Lenzie Ford, Nina Miolane, Kenneth S. Kosik, Dirk Bouwmeester, Lincoln D. Carr
https://arxiv.org/abs/2607.16517 https://arxiv.org/pdf/2607.16517 https://arxiv.org/html/2607.16517
arXiv:2607.16517v1 Announce Type: new
Abstract: Neural activity is widely held to organize on low-dimensional structure embedded in a high-dimensional state space. Persistent homology reads such structure directly from the pattern of pairwise correlations, without assuming in advance which variables are relevant. We apply persistent homology to microelectrode-array (MEA) recordings of spontaneous activity from human (Lancaster) and mouse (Pa\c{s}ca) cortical organoids, spanning $26$--$234$ simultaneously sorted units, and ask whether topological data analysis resolves structure at the node counts that neural recordings actually deliver. Building weighted networks in correlation space and characterizing them by Vietoris--Rips filtration, we find that the first homology ($H_1$, loops) rises significantly above a rate- and population-preserving null in $14$ of $18$ datasets. This loop structure occupies a non-redundant core: it is robust to random removal of units yet disrupted by targeted removal of the units that carry it. Topological richness grows with network size, and second homology ($H_2$) emerges significantly above the null only in the larger networks. These results show that persistent homology resolves structured topology in neural recordings at the scale experiments actually deliver.
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🇺🇦 #NowPlaying on BBCRadio3's #BBCProms
Edward Elgar, Berlin Philharmonic & Kirill Petrenko:
🎵 Variations On An Original Theme ('Enigma') Op.36
#EdwardElgar #BerlinPhilharmonic #KirillPetrenko
https://open.spotify.com/track/23ryVoyGTrfO3F0GblIfnz