Parametric shortest-path algorithms via tropical geometry

We study parameterized versions of classical algorithms for computing shortest-path trees. This is most easily expressed in terms of tropical geometry. Applications include shortest paths in traffic networks with variable link travel times.Comment: 24 pages and 8 figure

Are more start-ups really better? Quantity and quality of new businesses and their effect on regional development

Empirical analyses suggest that the employment creating effect of start-ups is highest in regions with a low level of new business formation and that an increase in the regional start-up rate beyond a certain level may lead to negative employment effect. In explaining these results, we assume that the average quality of regional start-ups decreases with the number of start-ups, while the costs of the induced resource reallocation increase. Our model implies that it is not the number of start-ups but their quality that is decisive for their effect on economic development. Therefore, a policy aiming at stimulating economic growth through entrepreneurship should focus on high-quality start-ups

Real-time determination of laser beam quality by modal decomposition

We present a real-time method to determine the beam propagation ratio M2 of laser beams. The all-optical measurement of modal amplitudes yields M2 parameters conform to the ISO standard method. The experimental technique is simple and fast, which allows to investigate laser beams under conditions inaccessible to other methods.Comment: 8 pages, 4 figures, published in Optics Expres

Untersuchung der Evolution von Konvektionsstrukturen bei Kaltluftausbrüchen mit Hilfe eines parallelisierten Grobstrukturmodells

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ORCA-SPOT: An Automatic Killer Whale Sound Detection Toolkit Using Deep Learning

Large bioacoustic archives of wild animals are an important source to identify reappearing communication patterns, which can then be related to recurring behavioral patterns to advance the current understanding of intra-specific communication of non-human animals. A main challenge remains that most large-scale bioacoustic archives contain only a small percentage of animal vocalizations and a large amount of environmental noise, which makes it extremely difficult to manually retrieve sufficient vocalizations for further analysis – particularly important for species with advanced social systems and complex vocalizations. In this study deep neural networks were trained on 11,509 killer whale (Orcinus orca) signals and 34,848 noise segments. The resulting toolkit ORCA-SPOT was tested on a large-scale bioacoustic repository – the Orchive – comprising roughly 19,000 hours of killer whale underwater recordings. An automated segmentation of the entire Orchive recordings (about 2.2 years) took approximately 8 days. It achieved a time-based precision or positive-predictive-value (PPV) of 93.2% and an area-under-the-curve (AUC) of 0.9523. This approach enables an automated annotation procedure of large bioacoustics databases to extract killer whale sounds, which are essential for subsequent identification of significant communication patterns. The code will be publicly available in October 2019 to support the application of deep learning to bioaoucstic research. ORCA-SPOT can be adapted to other animal species