Noise-induced escape from metastable states governs a plethora of transition phenomena in physics, chemistry, and biology. While the escape problem in the presence of thermal Gaussian noise has been well understood since the seminal works of Arrhenius and Kramers, many systems, in particular living ones, are effectively driven by non-Gaussian noise for which the conventional theory does not apply. Here we present a theoretical framework based on path integrals that allows the calculation of both escape rates and optimal escape paths for a generic class of non-Gaussian noises. We find that non-Gaussian noise always leads to more efficient escape and can enhance escape rates by many orders of magnitude compared with thermal noise, highlighting that away from equilibrium escape rates cannot be reliably modelled based on the traditional Arrhenius-Kramers result. Our analysis also identifies a new universality class of non-Gaussian noises, for which escape paths are dominated by large jumps.
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http://dx.doi.org/10.1038/s41598-023-30577-0 | DOI Listing |
ISA Trans
December 2024
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, PR China. Electronic address:
The quadratic cost functions, exemplified by mean-square-error, often exhibit limited robustness and flexibility when confronted with impulsive noise contamination. In contrast, the generalized maximum correntropy (GMC) criterion, serving as a robust nonlinear similarity measure, offers superior performance in such scenarios. In this paper, we develop a recursive constrained adaptive filtering algorithm named recursive generalized maximum correntropy with a forgetting factor (FF-RCGMC).
View Article and Find Full Text PDFISA Trans
November 2024
Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle, Ministry of Education, School of Electrical Engineering, Southwest Jiaotong University, Chengdu, 610031, China. Electronic address:
With the growing size of the system, this distributed Kalman filter (DKF) is widely used in multi-sensor networks. However, it is difficult for DKF to accurately estimate state values in non-Gaussian noise environments. In this paper, a regression equation is first constructed to contain all sensor node information.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
December 2024
Department of Physics, University of Massachusetts, Amherst, MA 01003.
Dirac fluids-interacting systems obeying particle-hole symmetry and Lorentz invariance-are among the simplest hydrodynamic systems; they have also been studied as effective descriptions of transport in strongly interacting Dirac semimetals. Direct experimental signatures of the Dirac fluid are elusive, as its charge transport is diffusive as in conventional metals. In this paper, we point out a striking consequence of fluctuating relativistic hydrodynamics: The full counting statistics (FCS) of charge transport is highly non-Gaussian.
View Article and Find Full Text PDFSensors (Basel)
November 2024
School of Automation, Northwestern Polytechnical University, Xi'an 710072, China.
Speckle noise, mechano-physical noise, and environmental noise are inevitably introduced in digital holographic coherent imaging, which seriously affects the quality of phase maps, and the removal of non-Gaussian statistical noise represented by speckle noise has been a challenging problem. In the past few years, deep learning methods based on convolutional neural networks (CNNs) have made good progress in removing Gaussian noise. However, they tend to fail when these deep networks designed for Gaussian noise removal are used to remove speckle noise.
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