Quantum communication has seen rapid progress towards practical large-scale networks, with quantum key distribution (QKD) spearheading this development. While fibre-based systems have been shown to be well suited for metropolitan scales, suitable fibre infrastructure may not always be in place. Here, we make the case for an entanglement-based free-space quantum network as a practical and efficient alternative for metropolitan applications. We developed a deployable free-space QKD system and demonstrated its use in realistic scenarios. For a representative 1.7-km free-space link, we showcase its ad hoc deployability and achieve secure key rates of up to 5.7 kbps, with 2.5 kbps in direct noon sunlight. By extrapolating experimental data, we show that kbps key rates are achievable even for 10-km distances and multi-user scenarios. We anticipate that our work will establish free-space networks as a viable solution for metropolitan applications and an indispensable complementary building block in the future global quantum internet.

Videos of endoscopic surgery are used for education of medical experts, analysis in medical research, and documentation for everyday clinical life. Hand-crafted image descriptors lack the capabilities of a semantic classification of surgical actions and video shots of anatomical structures. In this work, we investigate how well single-frame convolutional neural networks (CNN) for semantic shot classification in gynecologic surgery work. Together with medical experts, we manually annotate hours of raw endoscopic gynecologic surgery videos showing endometriosis treatment and myoma resection of over 100 patients. The cleaned ground truth dataset comprises 9 h of annotated video material (from 111 different recordings). We use the well-known CNN architectures AlexNet and GoogLeNet and train these architectures for both, surgical actions and anatomy, from scratch. Furthermore, we extract high-level features from AlexNet with weights from a pre-trained model from the Caffe model zoo and feed them to an SVM classifier. Our evaluation shows that we reach an average recall of .697 and .515 for classification of anatomical structures and surgical actions respectively using off-the-shelf CNN features. Using GoogLeNet, we achieve a mean recall of .782 and .617 for classification of anatomical structures and surgical actions respectively. With AlexNet the achieved recall is .615 for anatomical structures and .469 for surgical action classification respectively. The main conclusion of our work is that advances in general image classification methods transfer to the domain of endoscopic surgery videos in gynecology. This is relevant as this domain is different from natural images, e.g. it is distinguished by smoke, reflections, or a limited amount of colors.

Many car manufacturers have recently proposed to release autonomous self-driving cars within the next few years. Information gathered by sensors (e.g., cameras, GPS, lidar, radar, ultrasonic) enable cars to drive autonomously on roads. However, in urban or high-speed traffic scenarios the information gathered by mounted sensors may not be sufficient to guarantee a smooth and safe traffic flow. Thus, information received from infrastructure and other cars or vehicles on the road is vital. Key aspects in Vehicle-To-Everything (V2X) communication are security, authenticity, and integrity which are inherently provided by Information Centric Networking (ICN). In this paper, we identify advantages and drawbacks of ICN for V2X communication. We specifically review forwarding, caching, as well as simulation aspects for V2X communication with a focus on ICN. Furthermore, we investigate existing solutions for V2X and discuss their applicability. Based on these investigations, we suggest directions for further work in context of ICN (in particular Named Data Networking) to enable V2X communication providing a secure and efficient transport platform.

With a rigorous long-term archival of endoscopic surgeries, vast amounts of video and image data accumulate. Surgeons are not able to spend their valuable time to manually search within endoscopic multimedia databases (EMDBs) or manually maintain links to interesting sections in order to quickly retrieve relevant surgery sections. Enabling the surgeons to quickly access the relevant surgery scenes, we utilize the fact that surgeons record external images additionally to the surgery video and aim to link them to the appropriate video sequence in the EMDB using a query-by-example approach. We propose binary Convolutional Neural Network (CNN) features off-the-shelf and compare them to several baselines: pixel-based comparison (PSNR), image structure comparison (SSIM), hand-crafted global features (CEDD and feature signatures), as well as CNN baselines Histograms of Class Confidences (HoCC) and Neural Codes (NC). For evaluation, we use 5.5 h of endoscopic video material and 69 query images selected by medical experts and compare the performance of the aforementioned image mathing methods in terms of video hit rate and distance to the true playback time stamp (PTS) for correct video predictions. Our evaluation shows that binary CNN features are compact, yet powerful image descriptors for retrieval in the endoscopic imaging domain. They are able to maintain state-of-the-art performance, while providing the benefit of low storage space requirements and hence provide the best compromise.

Continuous-variable (CV) quantum key distribution (QKD) allows for quantum secure communication with the benefit of being close to classical coherent communication. In recent years, CV QKD protocols using a discrete number of displaced coherent states have been studied intensively as the modulation can be directly implemented with real devices with finite resolution. Until now, experiments only calculated key rates in the asymptotic regime. Here, we present a CV QKD system using discrete modulation that is especially designed for atmospheric channels. We use polarization encoding to exploit the nonbirefringent nature of the turbulent atmosphere. This allows to expand CV QKD networks beyond the existing fiber backbone. In a laboratory demonstration with a static 3-decibel loss channel, we implemented a recently developed security proof allowing to calculate composable finite-size key rates against independently and identically distributed collective attacks. We applied the full QKD protocol including a quantum random number generator, error correction, and privacy amplification to extract secret keys.