A size-reduced InP-based inverse-designed polarization rotator splitter is presented, being the first demonstration of a topology optimized passive waveguide component on InP. The manufactured device has extinction ratios >10dB for both polarizations over the entire C-band.
Communication constraints prevent the wide-spread adoption of Federated Learning systems. In this work, we investigate Federated Distillation (FD) from the perspective of communication efficiency by analyzing the effects of active distillation-data curation, soft-label quantization, and delta-coding techniques. We present Compressed Federated Distillation (CFD), an efficient FD method.
In collaboration with VI Systems and the University of Warsaw, HHI has demonstrated data rates of over 200 Gbit/s for the optical short haul. This was made possible by a new generation of VCSELs with over 30 GHz bandwidth in combination with multimode fibres.
LiFi exhibits characteristics that make it highly suitable as a wireless communication technology for industrial applications. As such, LiFi provides deterministic propagation, the potential for very high data density, use of license-free spectrum, and added physical layer security. In this work, we assess the requirements and challenges for LiFi in industrial wireless networks and propose to use distributed MIMO techniques to make LiFi capable of mastering them. We sketch essential capabilitie! s of the protocol- as well as physical layer and provide an outlook on the upcoming IEEE Std 802.15.13
Predicting the Binding of SARS-CoV-2 Peptides to the Major Histocompatibility Complex with Recurrent Neural Networks
Predicting the binding of viral peptides to the major histocompatibility complex with machine learning can potentially extend the computational immunology toolkit for vaccine development, and serve as a key component in the fight against a pandemic. In this work, we adapt and extend USMPep, a recently proposed, conceptually simple prediction algorithm based on recurrent neural networks. Most notably, we combine regressors (binding affinity data) and classifiers (mass spectrometry data) from qualitatively different data sources to obtain a more comprehensive prediction tool. We evaluate the performance on a recently released SARS-CoV-2 dataset with binding stability measurements. USMPep not only sets new benchmarks on selected single alleles, but consistently turns out to be among the best-performing methods or, for some metrics, to be even the overall best-performing method for this task.
With the goal of enabling optical wireless communications for mobile devices, we assess a physical layer based on high-bandwidth on-off keying modulation. This allows for amplifier designs that avoid operation in a resistive mode, reducing their energy usage to a fraction. Link-level simulations show that the investigated physical layer can deal with typical frontend limitations and can operate in challenging non-line-of-sight channels.
We present a hybrid animation approach that combines example-based and neural animation methods to create a simple, yet powerful animation regime for human faces. We introduce a light-weight auto-regressive network to transform our animation-database into a parametric model. During training, our network learns the dynamics of facial expressions, which enables the replay of annotated sequences from our animation database as well as their seamless concatenation in new order.
Radiation pattern of planar optoelectronic antennas for broadband continuous-wave terahertz emission
We measured and simulated the radiation pattern of continuous-wave terahertz emitters between 100 and 500 GHz. We could improve the radiation pattern by optimizing the connection between terahertz source and antenna: Unwanted side lobes were reduced by more than 10 dB and the beam angle was narrowed to 9° at 300 GHz. These improvements are relevant for wireless communication links and lensless terahertz imaging.
VR becomes popular in neurological and rehabilitation assessments and exercises for controlled simulation of complex environments that are difficult to setup physically in a laboratory. For such tasks, VR systems have to meet higher requirements than for an entertainment setup. The system needs to be suitable for sensitive/restricted users without limiting their VR interactions. To minimize the difference to the real-world interaction fidelity is desirable. So besides finding alternative metaphors for natural interaction, versatile research is done to en! able natural interaction in VR. For lack of better natural locomotion alternatives, teleportation is currently the most commonly used locomotion mechanism in VR. However, teleportation is not suitable for VR applications simulating reality, e.g., in search tasks during neurological tests.
This paper provides a timely overview of the field of explainable artificial intelligence (XAI). It explains the theoretical foundations of interpretability algorithms, outlines best practice aspects, demonstrates successful usage of XAI in selected application scenarios, and discusses challenges and possible future directions of this active emerging field.