Tootfinder

Graph Variate Neural Networks
Om Roy, Yashar Moshfeghi, Keith Smith
https://arxiv.org/abs/2509.20311 https://arxiv.org/pdf/2509.20311

Graph Variate Neural Networks
Modelling dynamically evolving spatio-temporal signals is a prominent challenge in the Graph Neural Network (GNN) literature. Notably, GNNs assume an existing underlying graph structure. While this underlying structure may not always exist or is derived independently from the signal, a temporally evolving functional network can always be constructed from multi-channel data. Graph Variate Signal Analysis (GVSA) defines a unified framework consisting of a network tensor of instantaneous connectiv…

Modeling discrete lattice data using the Potts and tapered Potts models
Maria Paula Duenas-Herrera, Stephen Berg, Murali Haran
https://arxiv.org/abs/2509.21478 https://

Modeling discrete lattice data using the Potts and tapered Potts models
The Ising and Potts models, among the most important models in statistical physics, have been used for modeling binary and multinomial data on lattices in a wide variety of disciplines such as psychology, image analysis, biology, and forestry. However, these models have several well known shortcomings: (i) they can result in poorly fitting models, that is, simulations from fitted models often do not produce realizations that look like the observed data; (ii) phase transitions and the presence o…

Effect of modeling subject-specific cortical folds on brain injury risk prediction under blunt impact loading
Anu Tripathi, Alison Brooks, Traci Snedden, Peter Ferrazzano, Christian Franck, Rika Wright Carlsen
https://arxiv.org/abs/2510.08379

Effect of modeling subject-specific cortical folds on brain injury risk prediction under blunt impact loading
Purpose: Computational head models are essential tools for studying the risk of mild traumatic brain injury (mTBI) under different activities and across populations. However, different computational models incorporate varied levels of anatomical details, such as cortical folds. In this study, we aim to determine the effect of modeling cortical folds on mTBI risk assessment. Methods: We compared the gyrencephalic (with cortical folds) and lissencephalic (without cortical folds) FE models of 18…

Not All Sub-Neptune Exoplanets Have Magma Oceans
Bodie Breza, Matthew C. Nixon, Eliza M. -R. Kempton
https://arxiv.org/abs/2509.20429 https://arxiv.org/pdf…

Not All Sub-Neptune Exoplanets Have Magma Oceans
The evolution and structure of sub-Neptunes may be strongly influenced by interactions between the outer gaseous envelope of the planet and a surface magma ocean. However, given the wide variety of permissible interior structures of these planets, it is unclear whether conditions at the envelope-mantle boundary will always permit a molten silicate layer, or whether some sub-Neptunes might instead host a solid silicate surface. In this work, we use internal structure modeling to perform an exten…

Chronic Stress, Immune Suppression, and Cancer Occurrence: Unveiling the Connection using Survey Data and Predictive Models
Teddy Lazebnik, Vered Aharonson
https://arxiv.org/abs/2509.22275

Chronic Stress, Immune Suppression, and Cancer Occurrence: Unveiling the Connection using Survey Data and Predictive Models
Chronic stress was implicated in cancer occurrence, but a direct causal connection has not been consistently established. Machine learning and causal modeling offer opportunities to explore complex causal interactions between psychological chronic stress and cancer occurrences. We developed predictive models employing variables from stress indicators, cancer history, and demographic data from self-reported surveys, unveiling the direct and immune suppression mitigated connection between chronic…