Tootfinder

STRive: An association rule-based system for the exploration of spatiotemporal categorical data
Mauro Diaz, Luis Sante, Joel Perca, Jo\~ao Victor da Silva, Nivan Ferreira, Jorge Poco
https://arxiv.org/abs/2509.02732

STRive: An association rule-based system for the exploration of spatiotemporal categorical data
Effectively analyzing spatiotemporal data plays a central role in understanding real-world phenomena and informing decision-making. Capturing the interaction between spatial and temporal dimensions also helps explain the underlying structure of the data. However, most datasets do not reveal attribute relationships, requiring additional algorithms to extract meaningful patterns. Existing visualization tools often focus either on attribute relationships or spatiotemporal analysis, but rarely supp…

A nonparametric Bayesian analysis of independent and identically distributed observations of covariate-driven Poisson processes
Patric Dolmeta, Matteo Giordano
https://arxiv.org/abs/2509.02299

A nonparametric Bayesian analysis of independent and identically distributed observations of covariate-driven Poisson processes
An important task in the statistical analysis of inhomogeneous point processes is to investigate the influence of a set of covariates on the point-generating mechanism. In this article, we consider the nonparametric Bayesian approach to this problem, assuming that $n$ independent and identically distributed realizations of the point pattern and the covariate random field are available. In many applications, different covariates are often vastly diverse in physical nature, resulting in anisotrop…

A systematic comparison of Large Language Models for automated assignment assessment in programming education: Exploring the importance of architecture and vendor
Marcin Jukiewicz
https://arxiv.org/abs/2509.26483

A systematic comparison of Large Language Models for automated assignment assessment in programming education: Exploring the importance of architecture and vendor
This study presents the first large-scale, side-by-side comparison of contemporary Large Language Models (LLMs) in the automated grading of programming assignments. Drawing on over 6,000 student submissions collected across four years of an introductory programming course, we systematically analysed the distribution of grades, differences in mean scores and variability reflecting stricter or more lenient grading, and the consistency and clustering of grading patterns across models. Eighteen pub…

SPATA: Systematic Pattern Analysis for Detailed and Transparent Data Cards
Jo\~ao Vitorino, Eva Maia, Isabel Pra\c{c}a, Carlos Soares
https://arxiv.org/abs/2509.26640 https://…

SPATA: Systematic Pattern Analysis for Detailed and Transparent Data Cards
Due to the susceptibility of Artificial Intelligence (AI) to data perturbations and adversarial examples, it is crucial to perform a thorough robustness evaluation before any Machine Learning (ML) model is deployed. However, examining a model's decision boundaries and identifying potential vulnerabilities typically requires access to the training and testing datasets, which may pose risks to data privacy and confidentiality. To improve transparency in organizations that handle confidential data…

An Anthropic report details how Claude usage varies by country and US states, finding 36% use it for coding, 77% of enterprise use is for automation, and more (Anthropic)
https://www.anthropic.com/research/anthropic-economic-index-september-2025-report

Anthropic Economic Index report: Uneven geographic and enterprise AI adoption
To study such patterns of early AI adoption, we extend the Anthropic Economic Index along two important dimensions, introducing a geographic analysis of Claude.ai conversations and a first-of-its-kind examination of enterprise API use. We show how Claude usage has evolved over time, how adoption patterns differ across regions, and—for the first time—how firms are deploying frontier AI to solve business problems.

Sleep Disorder Diagnosis Using EEG Signals and LSTM Deep Learning Method
Mohammad Reza Yousefi, Reza Rahimi
https://arxiv.org/abs/2509.00208 https://arxiv.…

Sleep Disorder Diagnosis Using EEG Signals and LSTM Deep Learning Method
Diagnosing sleep disorders is an important focus in neuroscience and engineering, as these conditions involve issues such as insufficient sleep, frequent awakenings, and difficulty reaching deep sleep. Accurate detection based on brain signals, particularly electroencephalography (EEG), enables development of personalized treatments. While statistical pattern recognition was once the standard for analyzing EEG, deep learning has become the dominant approach. In this study, we analyzed a public …

Learning Short-Term and Long-Term Patterns of High-Order Dynamics in Real-World Networks
Yunyong Ko, Da Eun Lee, Song Kyung Yu, Sang-Wook Kim
https://arxiv.org/abs/2508.17236 ht…

Learning Short-Term and Long-Term Patterns of High-Order Dynamics in Real-World Networks
Real-world networks have high-order relationships among objects and they evolve over time. To capture such dynamics, many works have been studied in a range of fields. Via an in-depth preliminary analysis, we observe two important characteristics of high-order dynamics in real-world networks: high-order relations tend to (O1) have a structural and temporal influence on other relations in a short term and (O2) periodically re-appear in a long term. In this paper, we propose LINCOLN, a method for…

Finally, what Xia & Lindell call a "separation problem" is, in our view, a feature of our approach and not a bug.
If, e.g., all languages in a family are polysynthetic (or none are), that’s not a statistical artefact – it’s the signal. The outcome is well associated with genealogy, showing that family membership captures someth genuinely informative about the process. When the model finds that family explains a large share of the variance, that's not a failure–it's evidence that phylogenetic structure dominates the pattern.
So while Xia & Lindell insist that "autocorrelation due to relationships and distance cannot be captured in family or regional-level analyses", we see that as an empirical question – and we treated it as one.
The real test is whether a mixed model that explicitly represents phylogeny and geography performs worse than their alternative, where the entire shared history of languages and environments is effectively collapsed into a single dimension (an eigenvector).
In other words: we model relationships – Xia & Lindell summarise them into one number per language.

Visual Analytics for Causal Reasoning from Real-World Health Data
Arran Zeyu Wang, David Borland, David Gotz
https://arxiv.org/abs/2508.17474 https://arxiv…

Visual Analytics for Causal Reasoning from Real-World Health Data
The increasing capture and analysis of large-scale longitudinal health data offer opportunities to improve healthcare and advance medical understanding. However, a critical gap exists between (a) -- the observation of patterns and correlations, versus (b) -- the understanding of true causal mechanisms that drive outcomes. An accurate understanding of the underlying mechanisms that cause various changes in medical status is crucial for decision-makers across various healthcare domains and roles,…

Patterns in the Transition From Founder-Leadership to Community Governance of Open Source
Mobina Noori, Mahasweta Chakraborti, Amy X Zhang, Seth Frey
https://arxiv.org/abs/2509.16295

Patterns in the Transition From Founder-Leadership to Community Governance of Open Source
Open digital public infrastructure needs community management to ensure accountability, sustainability, and robustness. Yet open-source projects often rely on centralized decision-making, and the determinants of successful community management remain unclear. We analyze 637 GitHub repositories to trace transitions from founder-led to shared governance. Specifically, we document trajectories to community governance by extracting institutional roles, actions, and deontic cues from version-control…