Tootfinder

Dynamic Functional Connectivity Features for Brain State Classification: Insights from the Human Connectome Project
Valeriya Kirova, Dzerassa Kadieva, Daniil Vlasenko, Isak B. Blank, Fedor Ratnikov
https://arxiv.org/abs/2510.05325

Dynamic Functional Connectivity Features for Brain State Classification: Insights from the Human Connectome Project
We analyze functional magnetic resonance imaging (fMRI) data from the Human Connectome Project (HCP) to match brain activities during a range of cognitive tasks. Our findings demonstrate that even basic linear machine learning models can effectively classify brain states and achieve state-of-the-art accuracy, particularly for tasks related to motor functions and language processing. Feature importance ranking allows to identify distinct sets of brain regions whose activation patterns are unique…

VisitHGNN: Heterogeneous Graph Neural Networks for Modeling Point-of-Interest Visit Patterns
Lin Pang, Jidong J. Yang
https://arxiv.org/abs/2510.02702 https://

VisitHGNN: Heterogeneous Graph Neural Networks for Modeling Point-of-Interest Visit Patterns
Understanding how urban residents travel between neighborhoods and destinations is critical for transportation planning, mobility management, and public health. By mining historical origin-to-destination flow patterns with spatial, temporal, and functional relations among urban places, we estimate probabilities of visits from neighborhoods to specific destinations. These probabilities capture neighborhood-level contributions to citywide vehicular and foot traffic, supporting demand estimation, …

Functional Connectivity Networks for Transportation Delay Analysis: from Theory to Software
Carlson Moses B\"uth, Massimiliano Zanin
https://arxiv.org/abs/2510.05143 https:…

Functional Connectivity Networks for Transportation Delay Analysis: from Theory to Software
Within the endeavour of modelling and understanding the propagation of delays in transportation networks, an approach that has attracted increasing interest in the last decade is the creation of functional network representations. These graphs map elements of interest (e.g. airports or stations) as nodes, and derive pairwise propagation patterns from their dynamics through correlation and causality tests. In spite of multiple notable results, this approach still lacks a coherent framework, with…

Preserving Core Structures of Social Networks via Information Guided Multi-Step Graph Pruning
Yutong Hu, Bingxin Zhou, Jing Wang, Weishu Zhao, Liang Hong
https://arxiv.org/abs/2510.10499

Preserving Core Structures of Social Networks via Information Guided Multi-Step Graph Pruning
Social networks often contain dense and overlapping connections that obscure their essential interaction patterns, making analysis and interpretation challenging. Identifying the structural backbone of such networks is crucial for understanding community organization, information flow, and functional relationships. This study introduces a multi-step network pruning framework that leverages principles from information theory to balance structural complexity and task-relevant information. The fra…

Orientational Order of Phenyl Rotors on Triangular Platforms on Ag and Au(111)
Behzad Mortezapour, Sebastian Hamer, Rainer Herges, Roberto Robles, Richard Berndt
https://arxiv.org/abs/2510.09550

Orientational Order of Phenyl Rotors on Triangular Platforms on Ag and Au(111)
We investigated trioxatriangulenium functionalized with phenyl (phenyl-TOTA) on the (111) surfaces of Ag and Au using low-temperature scanning tunneling microscopy (STM) and density functional theory (DFT). On Ag(111), the molecules form hexagonal arrays, and on Au(111), honeycomb patterns are also observed. The orientations of the phenyl moieties are resolved on both substrates. On Ag(111), the orientations are parallel within a row and they differ by approximately $60^\circ$ between adjacent …