Digital contact tracing approaches based on Bluetooth low energy (BLE) have the potential to efficiently contain and delay outbreaks of infectious diseases such as the ongoing SARS-CoV- 2 pandemic. In this work we propose an approach to reliably detect subjects that have spent enough time in close proximity to be at risk of being infected. Our study is an important proof of concept that will aid the battery of epidemiological policies.
We present photoconductive terahertz (THz) emitters based on rhodium (Rh) doped InGaAs for time-domain spectroscopy (TDS). The emitters feature a record high THz power of 637 µW. In combination with InGaAs:Rh receivers, a 6.5 THz bandwidth and a record peak dynamic range of 111 dB can be achieved. These improvements enable layer thickness measurement systems with unprecedented resolution and accuracy.
This paper puts forward first benchmarking results for the PTB-XL dataset, covering a variety of tasks from different ECG statement prediction tasks over age and gender prediction to signal quality assessment. We find that convolutional neural networks show the strongest performance across all tasks outperforming feature-based algorithms by a large margin.
Federated Learning comes at the cost of a significant communication overhead during training. In this work, we propose Sparse Ternary Compression (STC), a new compression framework that is specifically designed to meet the requirements of the Federated Learning environment. Our experiments on different tasks demonstrate that STC distinctively outperforms Federated Averaging in common scenarios.
Optical feedback has an impact on the tunability of lasers. We created a model of a tunable distributed Bragg reflector (DBR) laser describing the effect of optical feedback from a constant reflector distance on the wavelength tuning. Theoretical and experimental results are in good agreement. A further discussion of the model sheds light on design rules to reduce the effect of optical feedback on the tuning behavior. We introduced a new parameter called mode loss difference (MLD) as a metric for the feedback tolerance of the tuning behavior. A large MLD indicates higher tolerance of the laser to cavity length variations.
A morphed face image is a fusion of two face images and represents biometrics of two different subjects. Embedded in an official document, it can cause immense damage, since both subjects can claim its ownership and thus share an identity. In this paper, we propose and compare different neural network training schemes based on alternations of training data to obtain accurate and robust detectors for such kind of fraud. In addition, we use layer-wise relevance propagation (LRP) to analyze the differently trained networks in depth.
Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints
Federated Learning (FL) is currently the most widely adopted framework for collaborative training of (deep) machine learning models under privacy constraints. However, FL yields suboptimal results if the local clients’ data distributions diverge. The proposed Clustered FL approach tackles the problem by identifying diverging clients and grouping them into separate clusters.
Monolithically Integrated InP-Based Polarization Rotator-Splitter with Simplified Fabrication Process
Polarization division multiplexing doubles the transmission capacity of optical communication systems. For such systems, splitters separating the TE from the TM mode are indispensable components. We design and manufacture an integrated InP-based polarization rotator-splitter. The device is simplified in manufacturing and has a polarization extinction ratio of 17 dB over 60 nm optical bandwidth.
Electrocardiography (ECG) is increasingly supported by algorithms based on machine learning. We put forward PTB-XL, the to-date largest freely accessible clinical 12-lead ECG-waveform dataset comprising 21837 records from 18885 patients of 10 seconds length. The dataset covers a broad range of diagnostic classes including, in particular, a large fraction of healthy records.
AI solutions have not by large entered routine dental practice, mainly due to (1) limited data availability, accessibility, structure and comprehensiveness, (2) lacking methodological rigor and standards in their development, (3) and practical questions around the value and usefulness of these solutions, but also ethics and responsibility. This paper describes the chances of AI in medicine and dentistry.