Learning stable and predictive structures in kinetic systems: Benefits of a causal approach

Learning kinetic systems from data is one of the core challenges in many fields. Identifying stable models is essential for the generalization capabilities of data-driven inference. We introduce a computationally efficient framework, called CausalKinetiX, that identifies structure from discrete time, noisy observations, generated from heterogeneous experiments. The algorithm assumes the existence of an underlying, invariant kinetic model, a key criterion for reproducible research. Results on both simulated and real-world examples suggest that learning the structure of kinetic systems benefits from a causal perspective. The identified variables and models allow for a concise description of the dynamics across multiple experimental settings and can be used for prediction in unseen experiments. We observe significant improvements compared to well established approaches focusing solely on predictive performance, especially for out-of-sample generalization

DRIVERS OF ILLIQUIDITY IN THE ASEAN SOVEREIGN BOND MARKET

We study illiquidity in ASEAN-5 sovereign bond markets from 2008 to 2019 by using an illiquidity measure, which is based on a proxy of the amount of arbitrage capital available in sovereign bond markets. Our analysis identifies three drivers of illiquidity in Singapore, namely economic policy uncertainty, the default spread and the GDP growth rate. In contrast, liquidity of all other markets is mostly not characterized by economic drivers. It appears that overall liquidity is lower in the markets outside Singapore and therefore deviations in these yield curves are higher on average and arbitrage eliminates larger deviations not immediately but in a delayed manner.We study illiquidity in ASEAN-5 sovereign bond markets from 2008 to 2019 by using an illiquidity measure, which is based on a proxy of the amount of arbitrage capital available in sovereign bond markets. Our analysis identifies three drivers of illiquidity in Singapore, namely economic policy uncertainty, the default spread and the GDP growth rate. In contrast, liquidity of all other markets is mostly not characterized by economic drivers. It appears that overall liquidity is lower in the markets outside Singapore and therefore deviations in these yield curves are higher on average and arbitrage eliminates larger deviations not immediately but in a delayed manner

A simplified design of a cEEGrid ear-electrode adapter for the OpenBCI biosensing platform

We present a simplified design of an ear-centered sensing system built around the OpenBCI Cyton & Daisy biosignal amplifiers and the flex-printed cEEGrid ear-EEG electrodes. This design reduces the number of components that need to be sourced, reduces mechanical artefacts on the recording data through better cable placement, and simplifies the assembly. Besides describing how to replicate and use the system, we highlight promising application scenarios, particularly the observation of large-amplitude activity patterns (e.g., facial muscle activities) and frequency-band neural activity (e.g., alpha and beta band power modulations for mental workload detection). Further, examples for common measurement artefacts and methods for removing them are provided, introducing a prototypical application of adaptive filters to this system. Lastly, as a promising use case, we present findings from a single-user study that highlights the system\u27s capability of detecting jaw clenching events robustly when contrasted with 26 other facial activities. Thereby, the system could, for instance, be used to devise applications that reduce pathological jaw clenching and teeth grinding (bruxism). These findings underline that the system represents a valuable prototyping platform for advancing ear-based electrophysiological sensing systems and a low-cost alternative to current commercial alternatives

Smoothed Particle Hydrodynamics Physically Reconsidered -- The Relation to Explicit Large Eddy Simulation and the Issue of Particle Duality

In this work we will identify a novel relation between Smoothed Particle Hydrodynamics (SPH) and explicit Large Eddy Simulation (LES) using a coarse-graining method from Non-Equilibrium Molecular Dynamics (NEMD). While the current literature points at the conclusion that characteristic SPH issues become restrictive for subsonic turbulent flows, we see the potential to mitigate these SPH issues by explicit subfilter stress (SFS) modelling. We verify our theory by various simulations of homogeneous, isotropic turbulence (HIT) at $Re=10^4$ and compare the results to a Direct Numerical Simulation (DNS) reported by Dairay et al. (2017). Although the simulations substantiate our theory, we see another issue arising, which is conceptually rooted in the particle itself, termed as Particle Duality. Finally, we conclude our work by acknowledging SPH as coarse-graining method for turbulent flows, highlighting its capabilities and limitations.Comment: Added Journal Reference & DO

High-Precision Measurement of Sine and Pulse Reference Signals using Software-Defined Radio

This paper addresses simultaneous, high-precision measurement and analysis of generic reference signals by using inexpensive commercial off-the-shelf Software Defined Radio hardware. Sine reference signals are digitally down-converted to baseband for the analysis of phase deviations. Hereby, we compare the precision of the fixed-point hardware Digital Signal Processing chain with a custom Single Instruction Multiple Data (SIMD) x86 floating-point implementation. Pulse reference signals are analyzed by a software trigger that precisely locates the time where the slope passes a certain threshold. The measurement system is implemented and verified using the Universal Software Radio Peripheral (USRP) N210 by Ettus Research LLC. Applying standard 10 MHz and 1 PPS reference signals for testing, a measurement precision (standard deviation) of 0.36 ps and 16.6 ps is obtained, respectively. In connection with standard PC hardware, the system allows long-term acquisition and storage of measurement data over several weeks. A comparison is given to the Dual Mixer Time Difference (DMTD) and Time Interval Counter (TIC), which are state-of-the-art measurement methods for sine and pulse signal analysis, respectively. Furthermore, we show that our proposed USRP-based approach outperforms measurements with a high-grade Digital Sampling Oscilloscope.Comment: 10 pages, 15 figures, and 4 table

Towards automated COVID-19 presence and severity classification

COVID-19 presence classification and severity prediction via (3D) thorax computed tomography scans have become important tasks in recent times. Especially for capacity planning of intensive care units, predicting the future severity of a COVID-19 patient is crucial. The presented approach follows state-of-theart techniques to aid medical professionals in these situations. It comprises an ensemble learning strategy via 5-fold cross-validation that includes transfer learning and combines pre-trained 3D-versions of ResNet34 and DenseNet121 for COVID19 classification and severity prediction respectively. Further, domain-specific preprocessing was applied to optimize model performance. In addition, medical information like the infection-lung-ratio, patient age, and sex were included. The presented model achieves an AUC of 79.0% to predict COVID-19 severity, and 83.7% AUC to classify the presence of an infection, which is comparable with other currently popular methods. This approach is implemented using the AUCMEDI framework and relies on well-known network architectures to ensure robustness and reproducibility

Genetic and neurophysiological correlates of the age of onset of alcohol use disorders in adolescents and young adults.

Discrete time survival analysis was used to assess the age-specific association of event-related oscillations (EROs) and CHRM2 gene variants on the onset of regular alcohol use and alcohol dependence. The subjects were 2,938 adolescents and young adults ages 12-25. Results showed that the CHRM2 gene variants and ERO risk factors had hazards which varied considerably with age. The bulk of the significant age-specific associations occurred in those whose age of onset was under 16. These associations were concentrated in those subjects who at some time took an illicit drug. These results are consistent with studies which associate greater rates of alcohol dependence among those who begin drinking at an early age. The age specificity of the genetic and neurophysiological factors is consistent with recent studies of adolescent brain development, which locate an interval of heightened vulnerability to substance use disorders in the early to mid teens