Besides the biological and epidemiological factors, a multitude of social and economic criteria also govern the extent of the coronavirus disease spread within a population. Consequently, there is an active debate regarding the critical socio-economic determinants that contribute to the impact of the resulting pandemic. Here, we leverage Bayesian model averaging techniques and country level data to investigate the potential of 31 determinants, describing a diverse set of socio-economic characteristics, in explaining the outcome of the first wave of the coronavirus pandemic. We show that the true empirical model behind the coronavirus outcome is constituted only of few determinants. To understand the relationship between the potential determinants in the specification of the true model, we develop the coronavirus determinants Jointness space. The extent to which each determinant is able to provide a credible explanation varies between countries due to their heterogeneous socio-economic characteristics. In this aspect, the obtained Jointness map acts as a bridge between theoretical investigations and empirical observations and offers an alternate view for the joint importance of the socio-economic determinants when used for developing policies aimed at preventing future epidemic crises.
We demonstrate a fully packaged photodetector module based on surface-illuminated modified uni-traveling-carrier (MUTC) photodiode (PD) and investigate its DC and RF characteristics. It has a very a low dark current below 2 nA at the operational reverse bias of 4 V and a responsivity of 0.49 A/W at 1550 nm. The module shows a high f3dB bandwidth of 38 GHz for low optical input powers and excellent linearity with RF output power levels of 11.7 dBm at 40 GHz. The power conversion efficiency ? PCE was 6.7% at 40 GHz making it suitable for RF signal generation.
We propose two algorithms that use both models and datasets to estimate angular power spectra from channel covariance matrices in massive MIMO systems. The first algorithm is an iterative fixed-point method that solves a hierarchical problem. It uses model knowledge to narrow down candidate angular power spectra to a set that is consistent with a measured covariance matrix. Then, from this set, the algorithm selects the angular power spectrum with minimum distance to its expected value with respect to a Hilbertian metric learned from data. The second algorithm solves an alternative optimization problem with a single application of a solver for nonnegative least squares programs. By fusing information obtained from datasets and models, both algorithms can outperform existing approaches based on models, and they are also robust against environmental changes and small datasets.
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.