Tootfinder

WaterFlow: Explicit Physics-Prior Rectified Flow for Underwater Saliency Mask Generation
Runting Li, Shijie Lian, Hua Li, Yutong Li, Wenhui Wu, Sam Kwong
https://arxiv.org/abs/2510.12605

WaterFlow: Explicit Physics-Prior Rectified Flow for Underwater Saliency Mask Generation
Underwater Salient Object Detection (USOD) faces significant challenges, including underwater image quality degradation and domain gaps. Existing methods tend to ignore the physical principles of underwater imaging or simply treat degradation phenomena in underwater images as interference factors that must be eliminated, failing to fully exploit the valuable information they contain. We propose WaterFlow, a rectified flow-based framework for underwater salient object detection that innovatively…

Poisson Energy Formulation for Floorplanning: Variational Analysis and Mathematical Foundations
Wenxing Zhu, Hao Ai
https://arxiv.org/abs/2510.08126 https://

Poisson Energy Formulation for Floorplanning: Variational Analysis and Mathematical Foundations
Arranging many modules within a bounded domain without overlap, central to the Electronic Design Automation (EDA) of very large-scale integrated (VLSI) circuits, represents a broad class of discrete geometric optimization problems with physical constraints. This paper develops a variational and spectral framework for Poisson energy-based floorplanning and placement in physical design. We show that the Poisson energy, defined via a Neumann Poisson equation, is exactly the squared H^{-1} Sobolev …

Emergence of advection-diffusion transport structure and nonlinear amplitude evolution of strongly driven instabilities
Emma G. Devin, Vin\'icius N. Duarte
https://arxiv.org/abs/2510.08735

Emergence of advection-diffusion transport structure and nonlinear amplitude evolution of strongly driven instabilities
Instabilities driven by strong gradients appear in a wide variety of physical systems, including plasmas, neutral fluids, and self-gravitating systems. This work develops an analytic formulation to describe the transport structure and nonlinear amplitude evolution of a discrete, strongly driven instability in the presence of energy sources and sinks. Initially, the mode is found to evolve linearly until the gradient in the distribution has been exhausted. It then transitions to a nonlinear phas…

Discrete Diffusion for Reflective Vision-Language-Action Models in Autonomous Driving
Pengxiang Li, Yinan Zheng, Yue Wang, Huimin Wang, Hang Zhao, Jingjing Liu, Xianyuan Zhan, Kun Zhan, Xianpeng Lang
https://arxiv.org/abs/2509.20109

Discrete Diffusion for Reflective Vision-Language-Action Models in Autonomous Driving
End-to-End (E2E) solutions have emerged as a mainstream approach for autonomous driving systems, with Vision-Language-Action (VLA) models representing a new paradigm that leverages pre-trained multimodal knowledge from Vision-Language Models (VLMs) to interpret and interact with complex real-world environments. However, these methods remain constrained by the limitations of imitation learning, which struggles to inherently encode physical rules during training. Existing approaches often rely on…

Continuous-Time System Identification and OCV Reconstruction of Li-ion Batteries via Regularized Least Squares
Yang Wang, Riccardo M. G. Ferrari, Michel Verhaegen
https://arxiv.org/abs/2509.21116

Continuous-Time System Identification and OCV Reconstruction of Li-ion Batteries via Regularized Least Squares
Accurate identification of lithium-ion (Li-ion) battery parameters is essential for managing and predicting battery behavior. However, existing discrete-time methods hinder the estimation of physical parameters and face the fast-slow dynamics problem presented in the battery. In this paper, we developed a continuous-time approach that enables the estimation of battery parameters directly from sampled data. This method avoids discretization errors in converting continuous-time models into discre…