Aktuelle Publikationen

This paper gives a comprehensive overview over classic shallow and novel deep approaches to anomaly detection. We identify the common underlying principles and provide an empirical assessment of major existing methods that are enriched by the use of recent explainability techniques. We present specific worked-through examples together with practical advice and discuss open challenges.

This paper proposes a novel criterion for CNN pruning inspired by neural network interpretability: The most relevant units, i.e. weights or filters, are automatically found using their relevance scores obtained from concepts of explainable AI (XAI). By exploring this idea, we connect the lines of interpretability and model compression research.

Time-domain spectroscopy with terahertz frequencies typically requires complex and bulky systems. Here, the authors present an optoelectronics-based, frequency-domain terahertz sensing technique which offers competitive measurement performance in a much simpler system.

Externally modulated DFB lasers (EML) and vertically illuminated photodetectors are presented. Because of their excellent high-speed behavior and operation wavelength of 1310 nm, the devices are of interest for intra-data center communication. Since the EML and the photodetector chips are compatible with current systems, these devices are candidates for upgrading existing transceivers to higher baud rates. Therefore, a proof of concept for 100 GBaud data transmission with the presented components is demonstrated. Even without predistor! tion, the experiments show clearly open eye diagrams.

This paper proposes a novel, simple yet effective defense strategy for adversarial attacks on deep learning models. Our algorithm, called MALA for DEfense (MALADE), is applicable to any existing classifier, providing robust defense as well as off-manifold sample detection. In our experiments, MALADE exhibited state-of-the-art performance against various elaborate attacking strategies.

Inverse design is rapidly gaining popularity for automated design of photonic components. Two methods to improve it for large passive waveguide structures are developed: Adaptive Threshold Binarization and Hybrid Optimization. To demonstrate their capability, inverse design is applied to an InP waveguide platform for the first time. As an example, a polarizer with a PER of -19.4 dB is presented.

In this paper we present an example-based approach for visual speech synthesis. We combine the advantages of deep generative models and classical animation approaches to create a real-time capable facial animation framework based on volumetric captures.

Second harmonic generation is an efficient way to create coherent radiation at wavelengths that are not accessible with standard laser sources. In this work we demonstrate second harmonic generation from 1550 nm to 775 nm in a polymer photonic integrated circuit via the hybrid integration of a periodically poled lithium niobate crystal. The bulk crystal is inserted in an on-chip free-space section between two waveguide couple! d GRIN lenses. Fiber to fiber conversion efficiencies were 0.03 %/W for a continuous wave laser source and 100 %/W for a femtosecond laser source. Furthermore, third and fourth harmonic light at 517 nm and 388 nm was observed.

We demonstrate the benefits of a federated learning framework for (re)training of global ML models over geo-distributed data sources. The demonstration is carried out on a live edge computing enabled optical networking test-bed. In this demonstration, we perform real-time training of a QoT classifier by exploiting data of three different Domain Managers (DM), representing a multi-vendor ecosystem, without sharing any data with the Network Management System (NMS) in order to avoid transporting any data t! o a central location and to protect the privacy of different vendors while offering their knowledge to train a global ML model.

In autonomous driving, safety-related connected applications will coexist with infotainment services for passenger entertainment. We propose a multi-cell anticipatory networking framework with interference coordination based on Interference Calculus to serve diverse QoS requirements. The iterative approach optimizes packet transmission times leveraging service properties and channel distribution information.