7 papers
Heteroscedastic Neural Surrogate Modeling for Robust and Rapid Bayesian Inference in Fusion Plasma Diagnostics
Liyun Zhang, Naoya Mamada, Kentaro Sakai +2
Bayesian inference via Markov Chain Monte Carlo (MCMC) provides effective parameter estimation, but its real-time application in complex physical systems is hindered by heavy compu…
Bayesian Localization and Uncertainty Quantification of Trace Species in Two-Dimensional SIMS Imaging
Mengchi Wang, Binsheu Shieh, Ichiro Akai +4
To enable accurate localization of trace species on material surfaces, we propose a Bayesian framework for analyzing two-dimensional (2D) secondary ion mass spectrometry (SIMS) ima…
Follow-Your-Preference++: Rethinking Preference Alignment for Image Inpainting
Junkun Yuan, Yutao Shen, Toru Aonishi +2
We study preference alignment for image inpainting. Rather than proposing yet another method, we revisit the problem from first principles and reassess its core challenges. We adop…
A Physics-Regularized Neural Network and Kirchhoff Markov Random Field Framework for Inferring Internal Electrochemical States from Operando Spectromicroscopy
Naoki Wada, Yuta Kimura, Masaichiro Mizumaki +3
Quantitative understanding of coupled reaction and transport processes in lithium-ion battery (LIB) composite electrodes remains challenging because key internal states cannot be m…
Follow-Your-Preference: Towards Preference-Aligned Image Inpainting
Yutao Shen, Junkun Yuan, Toru Aonishi +2
This paper investigates image inpainting with preference alignment. Instead of introducing a novel method, we go back to basics and revisit fundamental problems in achieving such a…
L0-regularized compressed sensing with Mean-field Coherent Ising Machines
Mastiyage Don Sudeera Hasaranga Gunathilaka, Yoshitaka Inui, Satoshi Kako +4
Coherent Ising Machine (CIM) is a network of optical parametric oscillators that solves combinatorial optimization problems by finding the ground state of an Ising Hamiltonian. As…