Search

This paper presents a novel stochastic optimization methodology to perform empirical Bayesian inference in semi-blind image deconvolution problems. Given a blurred image and a parametric class of possible operators, the proposed optimization approach automatically calibrates the parameters of the blur model by maximum marginal likelihood estimation, followed by (non-blind) image deconvolution by maximum a posteriori estimation conditionally to the estimated model parameters. In addition to the blur model, the proposed approach also automatically calibrates the noise level as well as any regularization parameters. The marginal likelihood of the blur, noise, and regularization parameters is generally computationally intractable, as it requires calculating several integrals over the entire solution space. Our approach addresses this difficulty by using a stochastic approximation proximal gradient optimization scheme, which iteratively solves such integrals by using a Moreau- Yosida regularized unadjusted Langevin Markov chain Monte Carlo algorithm. This optimization strategy can be easily and efficiently applied to any model that is log-concave and by using the same gradient and proximal operators that are required to compute the maximum a posteriori solution by convex optimization. We provide convergence guarantees for the proposed optimization scheme under realistic and easily verifiable conditions and subsequently demonstrate the effectiveness of the approach with a series of deconvolution experiments and comparisons with alternative strategies from the state of the art [ABSTRACT FROM AUTHOR]

We show that optimal stopping surfaces (t, y) ↦ x*(t, y) arising from time-inhomogeneous optimal stopping problems on two-dimensional jump-diffusions (X, Y) are continuous (jointly in time and space) under mild monotonicity and regularity assumptions of local nature. [ABSTRACT FROM AUTHOR]

The characterizing set-valued functions were introduced for uninorms with continuous underlying functions and these set-valued functions were useful for the complete characterization of such uninorms. In this article, we study the characterizing functions of $n$ -uninorms with continuous underlying t-norms and t-conorms. We will show that an $n$ -uninorm with continuous underlying functions possesses $n$ characterizing set-valued functions, where the graphs of these characterizing set-valued functions cover the set of all points of discontinuity of the respective $n$ -uninorm. Moreover, for $i=1,\ldots,n,$ the $i$ th characterizing set-valued function divides the unit square into the two sets, where below the graph of the $i$ th characterizing set-valued function, the $n$ -uninorm attains values smaller than the local neutral element $e_i$ and above the graph of the $i$ th characterizing set-valued function, the $n$ -uninorm attains values greater than the local neutral element $e_i.$ [ABSTRACT FROM AUTHOR]

Owing to the low occurrence of public transport accidents, most existing studies have focused on improving the traffic safety of passenger cars. However, traffic accidents related to public transport should also be investigated for the safety of public transport users, particularly the vulnerable ones. This study analyzed the behavioral factors affecting passenger fall incidents on buses to enhance the safety of public bus passengers. This study considered potential influential factors, such as acceleration, deceleration, braking, and steering maneuvers, calculated using data from digital tachographs installed on the buses. Negative binomial and Poisson lognormal regression models were built using the Bayesian method. The two models yielded similar results. In all cases, deceleration-related behavioral factors (abrupt deceleration and abrupt stop) significantly influenced passenger fall incidents. The findings from this study are significant for establishing effective strategies to reduce fall incidents by providing safety education to bus drivers., Author(s): Harim Jeong [1]; Wonil Park [2]; Jaeyoung Lee [3,4]; Sungho Park (corresponding author) [1]; Ilsoo Yun [1] 1. Introduction 1.1. Background and Objectives Public transport accidents are relatively rare [...]