Aktuelle Forschungsnachrichten

Uncertainty quantification can help to understand the behavior of a trained neural network and, in particular, foster confidence in its predictions. This is especially true for deep regression, where a single-point estimate of a sought function without any information regarding its accuracy can be largely meaningless. We propose a novel framework for benchmarking uncertainty quantification in deep...

Image quality assessment is of particular relevance in image processing applications. This is especially true in mammography, where it helps to achieve a high detection quality at the lowest possible radiation dose. The assessment of image quality in mammography is carried out in accordance with the recommendation of a European Guideline. Recent research has shown that the use of deep neural...

Artificial intelligence (AI, e.g., deep learning) is increasingly used to assist high-stakes decisions in areas such as finance, medicine, or autonomous driving. Upcoming regulations will require that the principles by which such algorithms arrive at their predictions should be transparent. However, while numerous “explainable” AI (XAI) methods have been proposed, the field is lacking formal...

Virtual experiments simulating real measurement processes by means of numerical models have become increasingly important in modern metrology and industrial applications. Combined with Monte Carlo methods, virtual experiments have also been proposed for the evaluation of measurement uncertainties for a corresponding real experiment. However, such a proceeding is not always in line with the current...

A recent study of PTB 8.4 scientists together with theoretical physicists at TU Berlin just appeared in Physical Review Letters and uncovers a surprising similarity between the transition from ordered patterns to turbulence in an non-equilibrium active fluid with the well known Ising transition of two-dimensional magnets in equilibrium conditions.

Turbulent vortex structures emerging in bacterial...