TY - BOOK AU - Topalović, Elvira AU - Settinieri, Julia ID - 40667 TI - Sprachliche Bildung VL - 8 ER - TY - GEN AB - Long-range quantum communication requires the development of in-out light-matter interfaces to achieve a quantum advantage in entanglement distribution. Ideally, these quantum interconnections should be as fast as possible to achieve high-rate entangled qubits distribution. Here, we demonstrate the coherent quanta exchange between single photons generated on-demand from a GaAs quantum dot and atomic ensemble in a $^{87}$Rb vapor quantum memory. Through an open quantum system analysis, we demonstrate the mapping between the quantized electric field of photons and the coherence of the atomic ensemble. Our results play a pivotal role in understanding quantum light-matter interactions at the short time scales required to build fast hybrid quantum networks. AU - Cui, Guo-Dong AU - Schweickert, Lucas AU - Jöns, Klaus D. AU - Namazi, Mehdi AU - Lettner, Thomas AU - Zeuner, Katharina D. AU - Montaña, Lara Scavuzzo AU - Silva, Saimon Filipe Covre da AU - Reindl, Marcus AU - Huang, Huiying AU - Trotta, Rinaldo AU - Rastelli, Armando AU - Zwiller, Val AU - Figueroa, Eden ID - 42049 T2 - arXiv:2301.10326 TI - Coherent Quantum Interconnection between On-Demand Quantum Dot Single Photons and a Resonant Atomic Quantum Memory ER - TY - JOUR AU - Yigitbas, Enes AU - Klauke, Jonas AU - Gottschalk, Sebastian AU - Engels, Gregor ID - 34402 JF - Journal on Computer Languages (COLA) TI - End-User Development of Interactive Web-Based Virtual Reality Scenes ER - TY - CONF AU - Yigitbas, Enes AU - Engels, Gregor ID - 33511 T2 - 56th Hawaii International Conference on System Science (HICSS 2023) TI - Enhancing Robot Programming through Digital Twin and Augmented Reality ER - TY - CONF AU - Yigitbas, Enes AU - Krois, Sebastian AU - Gottschalk, Sebastian AU - Engels, Gregor ID - 34401 T2 - Proceedings of the 7th International Conference on Human Computer Interaction Theory and Applications (HUCAPP'23) TI - Towards Enhanced Guiding Mechanisms in VR Training through Process Mining ER - TY - JOUR AU - Haase, Franziska Katharina AU - Prien, Annika AU - Douw, Linda AU - Feddermann‐Demont, Nina AU - Junge, Astrid AU - Reinsberger, Claus ID - 42118 JF - Scandinavian Journal of Medicine & Science in Sports KW - Physical Therapy KW - Sports Therapy and Rehabilitation KW - Orthopedics and Sports Medicine SN - 0905-7188 TI - Cortical thickness and neurocognitive performance in former high‐level female soccer and non‐contact sport athletes ER - TY - JOUR AU - Matthias Philipper ID - 42154 JF - Quick And Easy Journal Title TI - New Quick And Easy Publication - Will be edited by LibreCat team ER - TY - JOUR AU - Lüders, Carolin AU - Gil-Lopez, Jano AU - Allgaier, Markus AU - Brecht, Benjamin AU - Aßmann, Marc AU - Silberhorn, Christine AU - Bayer, Manfred ID - 42158 IS - 1 JF - Physical Review Applied KW - General Physics and Astronomy SN - 2331-7019 TI - Tailored Frequency Conversion Makes Infrared Light Visible for Streak Cameras VL - 19 ER - TY - CONF AU - Castenow, Jannik AU - Harbig, Jonas AU - Jung, Daniel AU - Kling, Peter AU - Knollmann, Till AU - Meyer auf der Heide, Friedhelm ED - Hillel, Eshcar ED - Palmieri, Roberto ED - Riviére, Etienne ID - 34008 SN - 1868-8969 T2 - Proceedings of the 26th International Conference on Principles of Distributed Systems (OPODIS) TI - A Unifying Approach to Efficient (Near-)Gathering of Disoriented Robots with Limited Visibility VL - 253 ER - TY - GEN AB - The goal of this paper is to make a strong point for the usage of dynamical models when using reinforcement learning (RL) for feedback control of dynamical systems governed by partial differential equations (PDEs). To breach the gap between the immense promises we see in RL and the applicability in complex engineering systems, the main challenges are the massive requirements in terms of the training data, as well as the lack of performance guarantees. We present a solution for the first issue using a data-driven surrogate model in the form of a convolutional LSTM with actuation. We demonstrate that learning an actuated model in parallel to training the RL agent significantly reduces the total amount of required data sampled from the real system. Furthermore, we show that iteratively updating the model is of major importance to avoid biases in the RL training. Detailed ablation studies reveal the most important ingredients of the modeling process. We use the chaotic Kuramoto-Sivashinsky equation do demonstarte our findings. AU - Werner, Stefan AU - Peitz, Sebastian ID - 42160 T2 - arXiv:2302.07160 TI - Learning a model is paramount for sample efficiency in reinforcement learning control of PDEs ER -