Tootfinder

The worst thing besides “Liquid Glass” about iOS 26 on the iPad may be that they took away Split View and slide over, two features I used all the time on it. Now you need to awkwardly self-scale windows and that only works with updated apps.
I’d recommend not upgrading unless they fix it.

#Blakes7 Series D, Episode 06 - Headhunter
VILA: The interceptors!
MULLER: Send it back. That box must go back! [Throws Tarrant to floor.]
VILA: Slave, set a course for base. Maximum power. Go! Go! [Tarrant shoulders Muller away from the box.]
h…

There’s a wild moment when you realize you’ve been in the closet for over a decade. Then you stumble back onto the video that made your gender dysphoria skyrocket—Conchita Wurst’s “Rise Like a Phoenix” at Eurovision 2014. That performance wasn’t just a song, it was a spark.
My journey with gender started long before 2014, with photos and memories of a self I was just beginning to understand.
Today, I claim my space as bisexual and gender-fluid. I refuse to be silent. Fascism does…

Exploring the Frontiers of kNN Noisy Feature Detection and Recovery for Self-Driving Labs
Qiuyu Shi, Kangming Li, Yao Fehlis, Daniel Persaud, Robert Black, Jason Hattrick-Simpers
https://arxiv.org/abs/2507.16833

Exploring the Frontiers of kNN Noisy Feature Detection and Recovery for Self-Driving Labs
Self-driving laboratories (SDLs) have shown promise to accelerate materials discovery by integrating machine learning with automated experimental platforms. However, errors in the capture of input parameters may corrupt the features used to model system performance, compromising current and future campaigns. This study develops an automated workflow to systematically detect noisy features, determine sample-feature pairings that can be corrected, and finally recover the correct feature values. A…

Nonparametric "rich covariates" without saturation
Ludgero Glorias, Federico Martellosio, J. M. C. Santos Silva
https://arxiv.org/abs/2505.21213 …

Nonparametric "rich covariates" without saturation
We consider two nonparametric approaches to ensure that instrumental variables estimators of a linear equation satisfy the rich-covariates condition emphasized by Blandhol et al. (2025), even when the instrument is not unconditionally randomly assigned and the model is not saturated. Both approaches start with a nonparametric estimate of the expectation of the instrument conditional on the covariates, and ensure that the rich-covariates condition is satisfied either by using as the instrument t…

Not All Features Deserve Attention: Graph-Guided Dependency Learning for Tabular Data Generation with Language Models
Zheyu Zhang, Shuo Yang, Bardh Prenkaj, Gjergji Kasneci
https://arxiv.org/abs/2507.18504

Not All Features Deserve Attention: Graph-Guided Dependency Learning for Tabular Data Generation with Language Models
Large Language Models (LLMs) have shown strong potential for tabular data generation by modeling textualized feature-value pairs. However, tabular data inherently exhibits sparse feature-level dependencies, where many feature interactions are structurally insignificant. This creates a fundamental mismatch as LLMs' self-attention mechanism inevitably distributes focus across all pairs, diluting attention on critical relationships, particularly in datasets with complex dependencies or semanticall…

A nontopological soliton with a dipole chromomagnetic field
A. Yu. Loginov
https://arxiv.org/abs/2506.20281 https://arxiv.org/pdf/250…

A nontopological soliton with a dipole chromomagnetic field
A non-Abelian gauge model with a complex isovector scalar field and a sixth-order self-interaction potential is considered. It is shown that it has a nontopological soliton solution. The features of this soliton include a monopole-like core surrounded by a Q-ball-like shell, the existence of radially excited states, and a long-range dipole chromomagnetic field. The properties of the soliton are studied using analytical and numerical methods. In particular, the asymptotic dependencies of the ene…

Navigating AGN variability with self-organizing maps
Ylenia Maruccia, Demetra De Cicco, Stefano Cavuoti, Giuseppe Riccio, Paula S\'anchez-S\'aez, Maurizio Paolillo, Noemi Lery Borrelli, Riccardo Crupi, Massimo Brescia
https://arxiv.org/abs/2506.16902

Navigating AGN variability with self-organizing maps
Context. The classification of active galactic nuclei (AGNs) is a challenge in astrophysics. Variability features extracted from light curves offer a promising avenue for distinguishing AGNs and their subclasses. This approach would be very valuable in sight of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). Aims. Our goal is to utilize self-organizing maps (SOMs) to classify AGNs based on variability features and investigate how the use of different subsets of features …

Chimera games emerging from coevolutionary dynamics with endogenous feedbacks
Federico Maria Quetti, Andrea Civilini, Giacomo Frigerio, Silvia Figini, Giacomo Livan, Vito Latora
https://arxiv.org/abs/2506.20603

Chimera games emerging from coevolutionary dynamics with endogenous feedbacks
From climate change to financial bubbles and wars, the rapid pace of change in our world clearly shows that the commonly adopted assumption of a static environment in evolutionary game-theoretic models is often unrealistic. In this work, we introduce a coevolutionary game model where the game environment and the strategy dynamics are mutually coupled. In particular, the game played at each time is endogenously defined as a convex combination of two social dilemmas, weighted by the current abund…

AQuilt: Weaving Logic and Self-Inspection into Low-Cost, High-Relevance Data Synthesis for Specialist LLMs
Xiaopeng Ke, Hexuan Deng, Xuebo Liu, Jun Rao, Zhenxi Song, Jun Yu, Min Zhang
https://arxiv.org/abs/2507.18584

AQuilt: Weaving Logic and Self-Inspection into Low-Cost, High-Relevance Data Synthesis for Specialist LLMs
Despite the impressive performance of large language models (LLMs) in general domains, they often underperform in specialized domains. Existing approaches typically rely on data synthesis methods and yield promising results by using unlabeled data to capture domain-specific features. However, these methods either incur high computational costs or suffer from performance limitations, while also demonstrating insufficient generalization across different tasks. To address these challenges, we prop…