Coupled microstrip-cavities under oblique incidence of semi-guided waves: a lossless integrated optical add-drop filter - Ebers, Lena; Hammer, Manfred; Berkemeier, Manuel B.; Menzel, Alexander; Förstner, Jens (OSA Continuum)
We investigate optical microresonators consisting of either one or two coupled rectangular strips between upper and lower slab waveguides. The cavities are evanescently excited under oblique angles by thin-film guided, in-plane unguided waves supported by one of the slab waveguides. Beyond a specific incidence angle, losses are fully suppressed. The interaction between the guided mode of the cavity-strip and the incoming slab modes leads to resonant behavior for specific incidence angles and gaps. For a single cavity, at resonance, the input power is equally split among each of the four output ports, while for two cavities an add-drop filter can be realized that, at resonance, routes the incoming power completely to the forward drop waveguide via the cavity. For both applications, the strength of the interaction is controlled by the gaps between cavities and waveguides.
Keywords: tet_topic_waveguides

Multi-target prediction: a unifying view on problems and methods - Waegeman, Willem; Dembczynski, Krzysztof; Hüllermeier, Eyke (Data Mining and Knowledge Discovery)
Many problem settings in machine learning are concerned with the simultaneous prediction of multiple target variables of diverse type. Amongst others, such problem settings arise in multivariate regression, multi-label classification, multi-task learning, dyadic prediction, zero-shot learning, network inference, and matrix completion. These subfields of machine learning are typically studied in isolation, without highlighting or exploring important relationships. In this paper, we present a unifying view on what we call multi-target prediction (MTP) problems and methods. First, we formally discuss commonalities and differences between existing MTP problems. To this end, we introduce a general framework that covers the above subfields as special cases. As a second contribution, we provide a structured overview of MTP methods. This is accomplished by identifying a number of key properties, which distinguish such methods and determine their suitability for different types of problems. Finally, we also discuss a few challenges for future research.

Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA - Melnikov, Vitaly; Hüllermeier, Eyke (Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101))

Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking - Tornede, Alexander; Wever, Marcel Dominik; Hüllermeier, Eyke (Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019)

Visibility‐Aware Progressive Farthest Point Sampling on the GPU - Brandt, Sascha; Jähn, Claudius; Fischer, Matthias; Meyer auf der Heide, Friedhelm (Computer Graphics Forum)

Rendering of Complex Heterogenous Scenes using Progressive Blue Surfels - Brandt, Sascha; Jähn, Claudius; Fischer, Matthias; Meyer auf der Heide, Friedhelm (arXiv:1904.08225)
We present a technique for rendering highly complex 3D scenes in real-time by generating uniformly distributed points on the scene's visible surfaces. The technique is applicable to a wide range of scene types, like scenes directly based on complex and detailed CAD data consisting of billions of polygons (in contrast to scenes handcrafted solely for visualization). This allows to visualize such scenes smoothly even in VR on a HMD with good image quality, while maintaining the necessary frame-rates. In contrast to other point based rendering methods, we place points in an approximated blue noise distribution only on visible surfaces and store them in a highly GPU efficient data structure, allowing to progressively refine the number of rendered points to maximize the image quality for a given target frame rate. Our evaluation shows that scenes consisting of a high amount of polygons can be rendered with interactive frame rates with good visual quality on standard hardware.

Rational Secure Multiparty Computation - Bröcher, Henrik

Intertwined Development of Business Model and Product Functions for Mobile Applications: A Twin Peak Feature Modeling Approach - Gottschalk, Sebastian; Rittmeier, Florian; Engels, Gregor (Software Business)
Mobile app stores like Apple's AppStore or Google's PlayStore are highly competitive markets for third-party developers wanting to develop successful applications. During the development process, many developers focus on the multitude of product functions but neglect the business model as an equally important part. As a result, developers often fail to meet customer needs, leading to unnecessary development costs and poor market penetration. This, in turn, raises the question of how we intertwine the business model and product functions during the development process to ensure a better alignment between the two. In this paper, we show this intertwined development by adapting the concept of Twin Peaks to the business model and product functions. Based on feature modeling as an abstraction layer, we introduce the concept of a Business Model Decision Line (BMDL) to structure the business model decisions and their relation to product functions structured in a Software Product Line (SPL). The basis of our feature models is the analysis of top listed applications in the app stores of Apple and Google. To create and modify both models, we provide an incremental feature structuring and iterative feature selection process. This combination of abstraction layer and development process supports third-party developers to build successful applications both from a business and a product perspective.
Keywords: Intertwined Development; Twin Peaks; Feature Model; Business Model; Product Functions

Proceedings of the Third International Conference on Mathematics Textbook Research and Development: 16-19 September 2019 Paderborn, Germany

All-neural Online Source Separation, Counting, and Diarization for Meeting Analysis - von Neumann, Thilo; Kinoshita, Keisuke; Delcroix, Marc; Araki, Shoko; Nakatani, Tomohiro; Haeb-Umbach, Reinhold (ICASSP 2019, Brighton, UK)
Automatic meeting analysis comprises the tasks of speaker counting, speaker diarization, and the separation of overlapped speech, followed by automatic speech recognition. This all has to be carried out on arbitrarily long sessions and, ideally, in an online or block-online manner. While significant progress has been made on individual tasks, this paper presents for the first time an all-neural approach to simultaneous speaker counting, diarization and source separation. The NN-based estimator operates in a block-online fashion and tracks speakers even if they remain silent for a number of time blocks, thus learning a stable output order for the separated sources. The neural network is recurrent over time as well as over the number of sources. The simulation experiments show that state of the art separation performance is achieved, while at the same time delivering good diarization and source counting results. It even generalizes well to an unseen large number of blocks.