Tootfinder

📣 In nur etwas über zwei Wochen ist es soweit - am Samstag, 21. Juni findet in Göttingen die Nacht des Wissens statt! Wir wollen euch deshalb hier ab sofort jeden Tag einige der Aktivitäten vorstellen, die euch bei uns am MPS Göttingen erwarten.
Falls euch das eher nicht so interessiert, könnt ihr den Hashtag #ndwgoecountdown stummschalten oder wegfiltern, um die täglichen Pos…

This https://arxiv.org/abs/2505.19036 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Weak Physics Informed Neural Networks for Geometry Compatible Hyperbolic Conservation Laws on Manifolds
Physics-informed neural networks (PINNs), owing to their mesh-free nature, offer a powerful approach for solving high-dimensional partial differential equations (PDEs) in complex geometries, including irregular domains. This capability effectively circumvents the challenges of mesh generation that traditional numerical methods face in high-dimensional or geometrically intricate settings. While recent studies have extended PINNs to manifolds, the theoretical foundations remain scarce. Existing t…

This https://arxiv.org/abs/2109.06078 has been replaced.
link: https://scholar.google.com/scholar?q=a

BV Functions and Sets of Finite Perimeter on Configuration Spaces
This paper contributes to foundations of the geometric measure theory in the infinite dimensional setting of the configuration space over the Euclidean space $\mathbb R^n$ equipped with the Poisson measure $π$. We first provide a rigorous meaning and construction of the $m$-codimensional Poisson measure -- formally written as "$(\infty-m)$-dimensional Poisson measure" -- on the configuration space. We then show that our construction is consistent with potential analysis by establishing the abs…

This https://arxiv.org/abs/2504.09029 has been replaced.
link: https://scholar.google.com/scholar?q=a

A Hierarchical Decomposition of Kullback-Leibler Divergence: Disentangling Marginal Mismatches from Statistical Dependencies
The Kullback-Leibler (KL) divergence is a foundational measure for comparing probability distributions. Yet in multivariate settings, its single value often obscures the underlying reasons for divergence, conflating mismatches in individual variable distributions (marginals) with effects arising from statistical dependencies. We derive an algebraically exact, additive, and hierarchical decomposition of the KL divergence between a joint distribution P(X1,...,Xn) and a standard product reference …

This https://arxiv.org/abs/2505.19165 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

OrgAccess: A Benchmark for Role Based Access Control in Organization Scale LLMs
Role-based access control (RBAC) and hierarchical structures are foundational to how information flows and decisions are made within virtually all organizations. As the potential of Large Language Models (LLMs) to serve as unified knowledge repositories and intelligent assistants in enterprise settings becomes increasingly apparent, a critical, yet under explored, challenge emerges: \textit{can these models reliably understand and operate within the complex, often nuanced, constraints imposed b…

Trustworthy Provenance for Big Data Science: a Modular Architecture Leveraging Blockchain in Federated Settings
Nicola Giuseppe Marchioro, Yannis Velegrakis, Valentine Anantharaj, Ian Foster, Sandro Luigi Fiore
https://arxiv.org/abs/2505.24675

Trustworthy Provenance for Big Data Science: a Modular Architecture Leveraging Blockchain in Federated Settings
Ensuring the trustworthiness and long-term verifiability of scientific data is a foundational challenge in the era of data-intensive, collaborative research. Provenance metadata plays a key role in this context, capturing the origin, transformation, and usage of research artifacts. However, existing solutions often fall short when applied to distributed, multi-institutional settings. This paper introduces a modular, domain-agnostic architecture for provenance tracking in federated environments,…

This https://arxiv.org/abs/2410.19214 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSI_…

BTS: A Comprehensive Benchmark for Tie Strength Prediction
The rapid rise of online social networks underscores the need to understand the heterogeneous strengths of online relationships. Yet, efforts to assess tie strength (TS) are hindered by the lack of ground-truth labels, differing research perspectives, and limited model performance in real-world settings. To address this gap, we introduce BTS, a comprehensive Benchmark for Tie Strength prediction, aiming to establish a standardized foundation for evaluating and advancing TS prediction methodolog…

Categorical Lyapunov Theory II: Stability of Systems
Aaron D. Ames, S\'ebastien Mattenet, Joe Moeller
https://arxiv.org/abs/2505.22968 https://

Categorical Lyapunov Theory II: Stability of Systems
Lyapunov's theorem provides a foundational characterization of stable equilibrium points in dynamical systems. In this paper, we develop a framework for stability for F-coalgebras. We give two definitions for a categorical setting in which we can study the stability of a coalgebra for an endofunctor F. One is minimal and better suited for concrete settings, while the other is more intricate and provides a richer theory. We prove a Lyapunov theorem for both notions of setting for stability, and …

This https://arxiv.org/abs/2505.01651 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Human-AI Governance (HAIG): A Trust-Utility Approach
This paper introduces the HAIG framework for analysing trust dynamics across evolving human-AI relationships. Current categorical frameworks (e.g., "human-in-the-loop" models) inadequately capture how AI systems evolve from tools to partners, particularly as foundation models demonstrate emergent capabilities and multi-agent systems exhibit autonomous goal-setting behaviours. As systems advance, agency redistributes in complex patterns that are better represented as positions along continua rat…

Foundational rule of settings management in UI design:
Make sure the only thing anyone ever wants to change is in the Advanced Settings submenu, and make that as difficult as possible to discover. If it can actually be discovered, put in place a short timeout to prevent the user making the change before the option disappears.