Principal Uncertainty Quantification With Spatial Correlation for Image Restoration Problems.

Uncertainty quantification for inverse problems in imaging has drawn much attention lately. Existing approaches towards this task define uncertainty regions based on probable values per pixel, while ignoring spatial correlations within the image, resulting in an exaggerated volume of uncertainty. In this paper, we propose PUQ (Principal Uncertainty Quantification) - a novel definition and corresponding analysis of uncertainty regions that takes into account spatial relationships within the image, thus providing reduced volume regions.

Utilizing risk-controlling prediction calibration to reduce false alarm rates in epileptic seizure prediction.

Introduction: Epilepsy is a neurological disease characterized by sudden, unprovoked seizures. The unexpected nature of epileptic seizures is a major component of the disease burden. Predicting seizure onset and alarming patients may allow timely intervention, which would improve clinical outcomes and patient quality of life.

Searching for robust associations with a multi-environment knockoff filter.

This paper develops a method based on model-X knockoffs to find conditional associations that are consistent across environments, controlling the false discovery rate. The motivation for this problem is that large data sets may contain numerous associations that are statistically significant and yet misleading, as they are induced by confounders or sampling imperfections. However, associations replicated under different conditions may be more interesting.

Cross-resistance mechanisms to ACCase-inhibiting herbicides in short-spike canarygrass (Phalaris brachystachys).

Herbicides that inhibit acetyl-coenzyme A carboxylase (ACCase) are commonly used to control weedy grasses such as short-spike canarygrass (Phalaris brachystachys). Two resistant biotypes of P. brachystachys (R1 and R2) were found in different winter wheat fields in Iran.

Unified Single-Image and Video Super-Resolution via Denoising Algorithms.

Single image super-resolution (SISR) aims to recover a high-resolution image from a given low-resolution version of it. Video super-resolution (VSR) targets a series of given images, aiming to fuse them to create a higher resolution outcome. Although SISR and VSR seem to have a lot in common, most SISR algorithms do not have a simple and direct extension to VSR.