Tootfinder

Neural Langevin Machine: a local asymmetric learning rule can be creative
Zhendong Yu, Weizhong Huang, Haiping Huang
https://arxiv.org/abs/2506.23546 https…

Neural Langevin Machine: a local asymmetric learning rule can be creative
Fixed points of recurrent neural networks can be leveraged to store and generate information. These fixed points can be captured by the Boltzmann-Gibbs measure, which leads to neural Langevin dynamics that can be used for sampling and learning a real dataset. We call this type of generative model neural Langevin machine, which is interpretable due to its analytic form of distribution and is simple to train. Moreover, the learning process is derived as a local asymmetric plasticity rule, bearing…

Cardiovascular disease classification using radiomics and geometric features from cardiac CT
Ajay Mittal, Raghav Mehta, Omar Todd, Philipp Seeb\"ock, Georg Langs, Ben Glocker
https://arxiv.org/abs/2506.22226

Cardiovascular disease classification using radiomics and geometric features from cardiac CT
Automatic detection and classification of Cardiovascular disease (CVD) from Computed Tomography (CT) images play an important part in facilitating better-informed clinical decisions. However, most of the recent deep learning based methods either directly work on raw CT data or utilize it in pair with anatomical cardiac structure segmentation by training an end-to-end classifier. As such, these approaches become much more difficult to interpret from a clinical perspective. To address this challe…

Characterizing Intraventricular Flow Patterns via Modal Decomposition Techniques in Idealized Left Ventricle Models
Eneko Lazpita, Michael Neidlin, Jesus Garicano-Mena, Soledad Le Clainche
https://arxiv.org/abs/2507.21651

Characterizing Intraventricular Flow Patterns via Modal Decomposition Techniques in Idealized Left Ventricle Models
Understanding the formation, propagation, and breakdown of the main vortex ring (VR) is essential for characterizing left ventricular (LV) hemodynamics, as its dynamics have been linked to the onset and progression of cardiovascular diseases. In this study, two idealized LV geometries, a semi-ellipsoidal chamber and a more rounded configuration, are analyzed using computational fluid dynamics (CFD) simulations under physiological conditions, with the aim of investigating the fluid mechanisms th…

Efficient Reduced Order Modeling Based on HODMD to Predict Intraventricular Flow Dynamics
Eneko Lazpita, Jesus Garicano-Mena, Soledad Le Clainche
https://arxiv.org/abs/2507.21716

Efficient Reduced Order Modeling Based on HODMD to Predict Intraventricular Flow Dynamics
Accurate and efficient modeling of cardiac blood flow is crucial for advancing data-driven tools in cardiovascular research and clinical applications. Recently, the accuracy and availability of computational fluid dynamics (CFD) methodologies for simulating intraventricular flow have increased. However, these methods remain complex and computationally costly. This study presents a reduced order model (ROM) based on higher order dynamic mode decomposition (HODMD). The proposed approach enables a…

Towards a Large Physics Benchmark
Kristian G. Barman, Sascha Caron, Faegheh Hasibi, Eugene Shalugin, Yoris Marcet, Johannes Otte, Henk W. de Regt, Merijn Moody
https://arxiv.org/abs/2507.21695

Towards a Large Physics Benchmark
We introduce a benchmark framework developed by and for the scientific community to evaluate, monitor and steer large language model development in fundamental physics. Building on philosophical concepts of scientific understanding and creativity, we develop a scoring system in which each question is scored by an expert for its correctness, difficulty, and surprise. The questions are of three forms: (i) multiple-choice questions for conceptual understanding, (ii) analytical problems requiring m…

Safety Certification in the Latent space using Control Barrier Functions and World Models
Mehul Anand, Shishir Kolathaya
https://arxiv.org/abs/2507.13871 h…

Safety Certification in the Latent space using Control Barrier Functions and World Models
Synthesising safe controllers from visual data typically requires extensive supervised labelling of safety-critical data, which is often impractical in real-world settings. Recent advances in world models enable reliable prediction in latent spaces, opening new avenues for scalable and data-efficient safe control. In this work, we introduce a semi-supervised framework that leverages control barrier certificates (CBCs) learned in the latent space of a world model to synthesise safe visuomotor po…

E-FreeM2: Efficient Training-Free Multi-Scale and Cross-Modal News Verification via MLLMs
Van-Hoang Phan, Long-Khanh Pham, Dang Vu, Anh-Duy Tran, Minh-Son Dao
https://arxiv.org/abs/2506.20944

E-FreeM2: Efficient Training-Free Multi-Scale and Cross-Modal News Verification via MLLMs
The rapid spread of misinformation in mobile and wireless networks presents critical security challenges. This study introduces a training-free, retrieval-based multimodal fact verification system that leverages pretrained vision-language models and large language models for credibility assessment. By dynamically retrieving and cross-referencing trusted data sources, our approach mitigates vulnerabilities of traditional training-based models, such as adversarial attacks and data poisoning. Addi…

Foundation Models for Logistics: Toward Certifiable, Conversational Planning Interfaces
Yunhao Yang, Neel P. Bhatt, Christian Ellis, Alvaro Velasquez, Zhangyang Wang, Ufuk Topcu
https://arxiv.org/abs/2507.11352

Foundation Models for Logistics: Toward Certifiable, Conversational Planning Interfaces
Logistics operators, from battlefield coordinators rerouting airlifts ahead of a storm to warehouse managers juggling late trucks, often face life-critical decisions that demand both domain expertise and rapid and continuous replanning. While popular methods like integer programming yield logistics plans that satisfy user-defined logical constraints, they are slow and assume an idealized mathematical model of the environment that does not account for uncertainty. On the other hand, large langua…

From Motion to Meaning: Biomechanics-Informed Neural Network for Explainable Cardiovascular Disease Identification
Comte Valentin, Gemma Piella, Mario Ceresa, Miguel A. Gonzalez Ballester
https://arxiv.org/abs/2507.05783

From Motion to Meaning: Biomechanics-Informed Neural Network for Explainable Cardiovascular Disease Identification
Cardiac diseases are among the leading causes of morbidity and mortality worldwide, which requires accurate and timely diagnostic strategies. In this study, we introduce an innovative approach that combines deep learning image registration with physics-informed regularization to predict the biomechanical properties of moving cardiac tissues and extract features for disease classification. We utilize the energy strain formulation of Neo-Hookean material to model cardiac tissue deformations, opti…

Co-Creative Learning via Metropolis-Hastings Interaction between Humans and AI
Ryota Okumura, Tadahiro Taniguchi, Akira Taniguchi, Yoshinobu Hagiwara
https://arxiv.org/abs/2506.15468

Co-Creative Learning via Metropolis-Hastings Interaction between Humans and AI
We propose co-creative learning as a novel paradigm where humans and AI, i.e., biological and artificial agents, mutually integrate their partial perceptual information and knowledge to construct shared external representations, a process we interpret as symbol emergence. Unlike traditional AI teaching based on unilateral knowledge transfer, this addresses the challenge of integrating information from inherently different modalities. We empirically test this framework using a human-AI interacti…