Variable selection based on entropic criterion and its application to the debris-flow triggering

We propose a new data analyzing scheme, the method of minimum entropy analysis (MEA), in this paper. New MEA provides a quantitative criterion to select relevant variables for modeling the physical system interested. Such method can be easily extended to various geophysical/geological data analysis, where many relevant or irrelevant available measurements may obscure the understanding of the highly complicated physical system like the triggering of debris-flows. After demonstrating and testing the MEA method, we apply this method to a dataset of debris-flow occurrences in Taiwan and successfully find out three relevant variables, i.e. the hydrological form factor, numbers and areas of landslides, to the triggering of observed debris-flow events due to the 1996 Typhoon Herb.Comment: 9 pages and 4 table

A novel test of gravity via black hole eikonal correspondence

When adopted in black hole spacetimes, geometric-optics approximations imply a mapping between the quasinormal mode (QNM) spectrum of black holes in the eikonal limit and black hole images. In particular, the real part and the imaginary part of eikonal QNM frequencies are associated with the apparent size and the detailed structure of the ring images, respectively. This correspondence could be violated when going beyond general relativity. We propose a novel method to test the eikonal correspondence via the comparison of two sets of observables from a nonrotating black hole, one extracted from QNM spectra and the other from the lensed photon rings on the image plane. Specifically, the photon ring observables robustly capture the information of the black hole spacetime itself regardless of the surrounding emission models. Therefore, the proposed test of eikonal correspondence can be validated in quite broad scenarios.Comment: 9 pages, 2 figures. Matching published versio

FOXO/Fringe is necessary for maintenance of the germline stem cell niche in response to insulin insufficiency

AbstractThe stem cell niche houses and regulates stem cells by providing both physical contact and local factors that regulate stem cell identity. The stem cell niche also plays a role in integrating niche-local and systemic signals, thereby ensuring that the balance of stem cells meets the needs of the organism. However, it is not clear how these signals are merged within the niche. Nutrient-sensing insulin/FOXO signaling has been previously shown to directly control Notch activation in the Drosophila female germline stem cell (GSC) niche, which maintains the niche and GSC identity. Here, we demonstrate that FOXO directly activates transcription of fringe, a gene encoding a glycosyltransferase that modulates Notch glycosylation. Fringe facilitates Notch inactivation in the GSC niche when insulin signaling is low. We also show that the Notch ligand predominantly involved is GSC niche-derived Delta. These results reveal that FOXO-mediated regulation of fringe links the insulin and Notch signaling pathways in the GSC niche in response to nutrition, and emphasize that stem cells are regulated by complex interactions between niche-local and systemic signals

ECG Signal Super-resolution by Considering Reconstruction and Cardiac Arrhythmias Classification Loss

With recent advances in deep learning algorithms, computer-assisted healthcare services have rapidly grown, especially for those that combine with mobile devices. Such a combination enables wearable and portable services for continuous measurements and facilitates real-time disease alarm based on physiological signals, e.g., cardiac arrhythmias (CAs) from electrocardiography (ECG). However, long-term and continuous monitoring confronts challenges arising from limitations of batteries, and the transmission bandwidth of devices. Therefore, identifying an effective way to improve ECG data transmission and storage efficiency has become an emerging topic. In this study, we proposed a deep-learning-based ECG signal super-resolution framework (termed ESRNet) to recover compressed ECG signals by considering the joint effect of signal reconstruction and CA classification accuracies. In our experiments, we downsampled the ECG signals from the CPSC 2018 dataset and subsequently evaluated the super-resolution performance by both reconstruction errors and classification accuracies. Experimental results showed that the proposed ESRNet framework can well reconstruct ECG signals from the 10-times compressed ones. Moreover, approximately half of the CA recognition accuracies were maintained within the ECG signals recovered by the ESRNet. The promising results confirm that the proposed ESRNet framework can be suitably used as a front-end process to reconstruct compressed ECG signals in real-world CA recognition scenarios