In this paper, we consider the problem of feature reconstruction from incomplete X-ray CT data. Such data problems occur when number measured X-rays is restricted either due to limit radiation exposure or practical constraints, making detection certain rays challenging. Since image a severely ill-posed (unstable) problem, reconstructed images may suffer characteristic artefacts missing features...

We consider the problem of variable selection in high-dimensional settings with missing observations among covariates. To address this relatively understudied problem, we propose a new synergistic procedure—adaptive Bayesian SLOPE values—which effectively combines (sorted l1 regularization) spike-and-slab LASSO (SSL) and is accompanied by an efficient stochastic approximation expected maximizat...

Marine industry requires continued development of new technologies in order to produce oil. An essential requirement in design is to be able to compare experimental data from prototype structures with predicted information from a corresponding analytical finite element model. In this study, structural model updating may be defined as the fit of an existing analytical model in the light of measu...

• We propose a semi-supervised learning (SSL) method, called SELF (SEmi-supervised Learning via FCA), using Formal Concept Analysis (FCA) – It can handle mixed-type data containing both discrete and continuous variables ∘ Numerical data are discretized by binary encoding / Summary • We propose a semi-supervised learning (SSL) method, called SELF (SEmi-supervised Learning via FCA), using Form...