When deploying a model for object detection, a confidence score threshold is chosen to filter out false positives and ensure that a predicted bounding box has a certain minimum score. To achieve state-of-the-art performance on benchmark datasets, most neural networks use a rather low threshold as a high number of false positives is not penalized by standard evaluation metrics. However, in scenarios of Artificial Intelligence (AI) applications that require high confidence scores (e.g., due to legal requirements or consequences of incorrect detections are severe) or a certain level of model robustness is required, it is unclear which base model to use since they were mainly optimized for benchmark scores. In this paper, we propose a method to find the optimum performance point of a model as a basis for fairer comparison and deeper insights into the trade-offs caused by selecting a confidence score threshold.

The paper discusses the functioning of automatic fiscal stabilisers in Estonia. The aim of the research is to evaluate government budget sensitivity to economic fluctuations and thereby assess the importance of automatic fiscal stabilisers in Estonia. Specifically we are interested in whether the functioning of automatic fiscal stabilisers might under certain circumstances create difficulties for the fulfilment of the Maastricht deficit criterion according to which the public deficit is not allowed to exceed 3% of GDP.