Tootfinder

Vision-Based Assistive Technologies for People with Cerebral Visual Impairment: A Review and Focus Study
Bhanuka Gamage, Leona Holloway, Nicola McDowell, Thanh-Toan Do, Nicholas Price, Arthur Lowery, Kim Marriott
https://arxiv.org/abs/2505.22983

Vision-Based Assistive Technologies for People with Cerebral Visual Impairment: A Review and Focus Study
Over the past decade, considerable research has investigated Vision-Based Assistive Technologies (VBAT) to support people with vision impairments to understand and interact with their immediate environment using machine learning, computer vision, image enhancement, and/or augmented/virtual reality. However, this has almost totally overlooked a growing demographic: people with Cerebral Visual Impairment (CVI). Unlike ocular vision impairments, CVI arises from damage to the brain's visual process…

State updates and useful qubits in relativistic quantum information
Jos\'e Polo-G\'omez, T. Rick Perche, Eduardo Mart\'in-Mart\'inez
https://arxiv.org/abs/2506.18906

State updates and useful qubits in relativistic quantum information
We address the longstanding challenge of consistently updating quantum states after selective measurements in a relativistic spacetime. Standard updates along the future lightcones preserve causality but break correlations between causally disconnected parties, whereas updates along the past lightcone either imply retrocausality or do not respect the causal propagation of information. We introduce a minimal extension of multipartite states to encode subsystem-specific contextual information. Th…

Tree-based adaptive finite element methods for deformable image registration
Nicol\'as A. Barnafi, Alberto F. Mart{\i}n, Ricardo Ruiz-Baier
https://arxiv.org/abs/2506.15876

Tree-based adaptive finite element methods for deformable image registration
In this work we propose an adaptive Finite Element Method (FEM) formulation for the Deformable Image Registration problem (DIR) together with a residual-based a posteriori error estimator, whose efficiency and reliability are theoretically established. This estimator is used to guide Adaptive Mesh Refinement and coarsening (AMR). The nonlinear Euler-Lagrange equations associated with the minimisation of the relevant functional are solved with a pseudo time-stepping fixed-point scheme which is f…

R3DM: Enabling Role Discovery and Diversity Through Dynamics Models in Multi-agent Reinforcement Learning
Harsh Goel, Mohammad Omama, Behdad Chalaki, Vaishnav Tadiparthi, Ehsan Moradi Pari, Sandeep Chinchali
https://arxiv.org/abs/2505.24265

R3DM: Enabling Role Discovery and Diversity Through Dynamics Models in Multi-agent Reinforcement Learning
Multi-agent reinforcement learning (MARL) has achieved significant progress in large-scale traffic control, autonomous vehicles, and robotics. Drawing inspiration from biological systems where roles naturally emerge to enable coordination, role-based MARL methods have been proposed to enhance cooperation learning for complex tasks. However, existing methods exclusively derive roles from an agent's past experience during training, neglecting their influence on its future trajectories. This paper…