2012, Otto, C., Lüdge, K., Vladimirov, A.G., Wolfrum, M., Schöll, E.

We study a passively mode-locked semiconductor ring laser subject to optical feedback from an external mirror. Using a delay differential equation model for the mode-locked laser, we are able to systematically investigate the resonance effects of the inter-spike interval time of the laser and the roundtrip time of the light in the external cavity (delay time) for intermediate and long delay times. We observe synchronization plateaus following the ordering of the well-known Farey sequence. Our results show that in agreement with the experimental results a reduction of the timing jitter is possible if the delay time is chosen close to an integer multiple of the inter-spike interval time of the laser without external feedback. Outside the main resonant regimes the timing jitter is drastically increased by the feedback.

2020, Otto, C., Springstein, M., Anand, A., Ewerth, R.

The beneficial, complementary nature of visual and textual information to convey information is widely known, for example, in entertainment, news, advertisements, science, or education. While the complex interplay of image and text to form semantic meaning has been thoroughly studied in linguistics and communication sciences for several decades, computer vision and multimedia research remained on the surface of the problem more or less. An exception is previous work that introduced the two metrics Cross-Modal Mutual Information and Semantic Correlation in order to model complex image-text relations. In this paper, we motivate the necessity of an additional metric called Status in order to cover complex image-text relations more completely. This set of metrics enables us to derive a novel categorization of eight semantic image-text classes based on three dimensions. In addition, we demonstrate how to automatically gather and augment a dataset for these classes from the Web. Further, we present a deep learning system to automatically predict either of the three metrics, as well as a system to directly predict the eight image-text classes. Experimental results show the feasibility of the approach, whereby the predict-all approach outperforms the cascaded approach of the metric classifiers.