On Variance-Based Sensitivity Analysis for Stochastic Systems covers the impact of parameters uncertainty and sensitivity indices in stochastic models.
Explores verification and validation in computational modeling, emphasizing accuracy through comparison with experimental data and practical advice on model complexity.
Explores uncertainty quantification and label error detection in deep learning for semantic segmentation, focusing on challenges and methods for error detection.
Discusses X-ray detectors' properties, sensitivity, and signal-to-noise ratio quantification, as well as systematic errors like flat field corrections.