Tootfinder

Conditions for building generalized action graphs from sequences
Sarah Klanderman, Katy McDicken, Amelia Tebbe
https://arxiv.org/abs/2507.22861 https://arx…

Conditions for building generalized action graphs from sequences
This paper explores the properties of directed graphs, termed generalized action graphs, which exhibit a strong connection to certain number sequences. Focusing on the structural and combinatorial aspects, we investigate the conditions under which specific sequences can generate generalized action graphs. Building upon prior research in this field, we analyze specific features of these graphs and how they correspond to patterns and properties in their sequences. These findings support a broader…

Rare events algorithm study of extreme double jet summers and their connection to heatwaves over Eurasia
Valeria Mascolo, Francesco Ragone, Nili Harnik, Freddy Bouchet
https://arxiv.org/abs/2507.13101

Rare events algorithm study of extreme double jet summers and their connection to heatwaves over Eurasia
Several large scale circulation patterns have been identified in relation to extreme Northern Hemisphere summer heatwaves. Three main ones are a double jet over Eurasia, a positive phase of the summer northern annular mode, and a quasi-wave-3 geopotential height anomaly. While there is some evidence suggesting these patterns are related to each other, the explicit nature of their relation, as well as the explicit mechanisms by which they are related to extreme heatwaves is still not known. The …

A Comprehensive Incremental and Ensemble Learning Approach for Forecasting Individual Electric Vehicle Charging Parameters
Parnian Alikhani, Nico Brinkel, Wouter Schram, Ioannis Lampropoulos, Wilfried van Sark
https://arxiv.org/abs/2508.17772

A Comprehensive Incremental and Ensemble Learning Approach for Forecasting Individual Electric Vehicle Charging Parameters
Electric vehicles (EVs) have the potential to reduce grid stress through smart charging strategies while simultaneously meeting user demand. This requires accurate forecasts of key charging parameters, such as energy demand and connection time. Although previous studies have made progress in this area, they have overlooked the importance of dynamic training to capture recent patterns and have excluded EV sessions with limited information, missing potential opportunities to use these data. To ad…

From Linearity to Non-Linearity: How Masked Autoencoders Capture Spatial Correlations
Anthony Bisulco, Rahul Ramesh, Randall Balestriero, Pratik Chaudhari
https://arxiv.org/abs/2508.15404

From Linearity to Non-Linearity: How Masked Autoencoders Capture Spatial Correlations
Masked Autoencoders (MAEs) have emerged as a powerful pretraining technique for vision foundation models. Despite their effectiveness, they require extensive hyperparameter tuning (masking ratio, patch size, encoder/decoder layers) when applied to novel datasets. While prior theoretical works have analyzed MAEs in terms of their attention patterns and hierarchical latent variable models, the connection between MAE hyperparameters and performance on downstream tasks is relatively unexplored. Thi…

An exact test for the mixed membership stochastic block model
Sourav Majumdar
https://arxiv.org/abs/2507.14464 https://arxiv.org/pdf/…

An exact test for the mixed membership stochastic block model
We present the first finite-sample goodness-of-fit test for the mixed membership stochastic block model (MMSBM). Using algebraic statistics theory, we derive a Markov basis that lets a Metropolis-Hastings sampler explore exactly the set of networks compatible with a fitted MMSBM. The resulting exact $p$-value, based on a partial conjunction statistic, requires no asymptotic approximations. Simulations show nominal size and strong power against misspecified block numbers and connection patterns.