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…

SAMP: Spatial Anchor-based Motion Policy for Collision-Aware Robotic Manipulators
Kai Chen, Zhihai Bi, Guoyang Zhao, Chunxin Zheng, Yulin Li, Hang Zhao, Jun Ma
https://arxiv.org/abs/2509.11185

SAMP: Spatial Anchor-based Motion Policy for Collision-Aware Robotic Manipulators
Neural-based motion planning methods have achieved remarkable progress for robotic manipulators, yet a fundamental challenge lies in simultaneously accounting for both the robot's physical shape and the surrounding environment when generating safe and feasible motions. Moreover, existing approaches often rely on simplified robot models or focus primarily on obstacle representation, which can lead to incomplete collision detection and degraded performance in cluttered scenes. To address these li…

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…

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 …

Once you are done fighting it, documents typeset with #TexLaTeX are pretty damn rad! 🚀
The automations you can put into place to help you focus on the actual writing are really something else:
- different modes for notes, submission, and publishing
- acronym or physical unit expansion only upon first mention
- auto-generated glossary or symbol list
- and of course a ton…

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…

Breaking the Discretization Barrier of Continuous Physics Simulation Learning
Fan Xu, Hao Wu, Nan Wang, Lilan Peng, Kun Wang, Wei Gong, Xibin Zhao
https://arxiv.org/abs/2509.17955

Breaking the Discretization Barrier of Continuous Physics Simulation Learning
The modeling of complicated time-evolving physical dynamics from partial observations is a long-standing challenge. Particularly, observations can be sparsely distributed in a seemingly random or unstructured manner, making it difficult to capture highly nonlinear features in a variety of scientific and engineering problems. However, existing data-driven approaches are often constrained by fixed spatial and temporal discretization. While some researchers attempt to achieve spatio-temporal conti…

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…