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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...

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Bacteria organize in a variety of collective states, from swarming—rapid surface exploration, to biofilms—highly dense immobile communities attributed to stress resistance. It has been suggested that biofilm and swarming are oppositely controlled, making this transition  particularly  interesting  for  understanding  the  ability  of  bacterial  colonies to adapt to challenging environments....

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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...

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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...

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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...

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Originally developed for fast global sensitivity analysis and efficient parameter reconstruction for applications in nano-optical metrology, PyThia provides an all purpose non-intrusive Python package to approximate high dimensional functions. Based on general polynomial chaos approximation obtained via linear regression, PyThia generates functional surrogates by relying purely on training data...

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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...

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Supplement 1 to the GUM (GUM-S1) extends the GUM uncertainty framework to nonlinear functions and non-Gaussian distributions.  For this purpose, it employs a Monte Carlo method that yields a probability density function for the measurand. This Monte Carlo method has been successfully applied in numerous applications throughout metrology. However, considerable criticism has been raised against the...

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