Cryptimeleon: A Library for Fast Prototyping of Privacy-Preserving Cryptographic Schemes - Bobolz, Jan; Eidens, Fabian; Heitjohann, Raphael; Fell, Jeremy

Configurable lossless broadband beam splitters for semi-guided waves in integrated silicon photonics - Hammer, Manfred; Ebers, Lena; Förstner, Jens (OSA Continuum)
We show that narrow trenches in a high-contrast silicon-photonics slab can act as lossless power dividers for semi-guided waves. Reflectance and transmittance can be easily configured by selecting the trench width. At sufficiently high angles of incidence, the devices are lossless, apart from material attenuation and scattering due to surface roughness. We numerically simulate a series of devices within the full 0-to-1-range of splitting ratios, for semi-guided plane wave incidence as well as for excitation by focused Gaussian wave bundles. Straightforward cascading of the trenches leads to concepts for 1×M-power dividers and a polarization beam splitter.
Keywords: tet_topic_waveguide

DeepCoMP: Coordinated Multipoint Using Multi-Agent Deep Reinforcement Learning - Schneider, Stefan Balthasar; Karl, Holger; Khalili, Ramin; Hecker, Artur
Macrodiversity is a key technique to increase the capacity of mobile networks. It can be realized using coordinated multipoint (CoMP), simultaneously connecting users to multiple overlapping cells. Selecting which users to serve by how many and which cells is NP-hard but needs to happen continuously in real time as users move and channel state changes. Existing approaches often require strict assumptions about or perfect knowledge of the underlying radio system, its resource allocation scheme, or user movements, none of which is readily available in practice. Instead, we propose three novel self-learning and self-adapting approaches using model-free deep reinforcement learning (DRL): DeepCoMP, DD-CoMP, and D3-CoMP. DeepCoMP leverages central observations and control of all users to select cells almost optimally. DD-CoMP and D3-CoMP use multi-agent DRL, which allows distributed, robust, and highly scalable coordination. All three approaches learn from experience and self-adapt to varying scenarios, reaching 2x higher Quality of Experience than other approaches. They have very few built-in assumptions and do not need prior system knowledge, making them more robust to change and better applicable in practice than existing approaches.
Keywords: mobility management; coordinated multipoint; CoMP; cell selection; resource management; reinforcement learning; multi agent; MARL; self-learning; self-adaptation; QoE

Self-aware Operation of Heterogeneous Compute Nodes using the Learning Classifier System XCS - Hansmeier, Tim (HEART '21: Proceedings of the 11th International Symposium on Highly Efficient Accelerators and Reconfigurable Technologies)

Beep-And-Sleep: Message and Energy Efficient Set Cover - Götte, Thorsten; Kolb, Christina; Scheideler, Christian; Werthmann, Julian (Algorithms for Sensor Systems (ALGOSENSORS '21))

“Mehr wert als tausend Männer” – Luise Adelgunde Victorie Kulmus Gottsched und die wahre Minerva - Hagengruber, Ruth (Musikwissenschaft der Vielfalt. Festschrift für Rebecca Grotjahn zum 60. Geburtstag)

Out of the Box – into the Green and the Blue - Hagengruber, Ruth (Philosophisches Jahrbuch 128. Jahrgang I/2021)

Philosophinnen an die Macht - Hagengruber, Ruth (Female Recruiting Report (3))

Ökonomie, Technik, Umwelt: Die Zukunft bestimmen, während wir die Vergangenheit überdenken: Defining the Future – Rethinking the Past - Hagengruber, Ruth (Konsens)

Vizepräsidentin a.D. des DAB, Prof. Dr. Ulrike Detmers verleiht zum 20. Mal den von ihr ins Leben gerufenen Preis‚ Managerin des Jahres - Hagengruber, Ruth (Konsens)