In this paper we focus on improving object detection performance in terms of recall. We propose a post-detection stage during which we explore the image with the objective of recovering missed detections. This exploration is performed by sampling object proposals in the image. We analyse four different strategies to perform this sampling, giving special attention to strategies that exploit spat...

The vast size of real world stochastic programming instances requires sampling to make them practically solvable. In this paper we extend the understanding of how sampling affects the solution quality of multistage stochastic programming problems. We present a new heuristic for determining good feasible solutions for a multistage decision problem. For power and log-utility functions we address ...

Abstract Purpose of Review We provided an overview sampling methods for hard-to-reach populations and guidance on implementing one the most popular approaches: respondent-driven (RDS). Recent Findings Limitations related to generating a frame marginalized can make them “hard-to-reach” when conducting population health research. Data analyzed from non-probability-based or convenience samples may...

BACKGROUND Missing data on tumour stage information is a common problem in population-based cancer registries. Statistical analyses on the level of tumour stage may be biased, if no adequate method for handling of missing data is applied. In order to determine a useful way to treat missing data on tumour stage, we examined different imputation models for multiple imputation with chained equatio...