Tipping points of evolving epidemiological networks: Machine learning-assisted, data-driven effective modeling.

We study the tipping point collective dynamics of an adaptive susceptible-infected-susceptible (SIS) epidemiological network in a data-driven, machine learning-assisted manner. We identify a parameter-dependent effective stochastic differential equation (eSDE) in terms of physically meaningful coarse mean-field variables through a deep-learning ResNet architecture inspired by numerical stochastic integrators. We construct an approximate effective bifurcation diagram based on the identified drift term of the eSDE and contrast it with the mean-field SIS model bifurcation diagram.

Task-based assessment of digital mammography microcalcification detection with deep learning denoising algorithmss using and physical phantom studies.

Purpose: Recent research suggests that image quality degradation with reduced radiation exposure in mammography can be mitigated by postprocessing mammograms with denoising algorithms based on convolutional neural networks. Breast microcalcifications, along with extended soft-tissue lesions, are the primary breast cancer biomarkers in a clinical x-ray examination, with the former being more sensitive to quantum noise. We test one such publicly available denoising method to observe if an improvement in detection of small microcalcifications can be achieved when deep learning-based denoising is applied to half-dose phantom scans.

Dataset on the soil properties of Early Iron Age and Medieval archaeological sites in the forest-steppe zone of the East European Plain.

Paleosols are frequently used to recreate past climates. In the forest-steppe zone of the Russian Plain (Lipetsk region, Russia), Early Iron and Middle Ages defensive ramparts' buried soils were discovered. The parent material and similar topographic situations served as the foundation for the comparison of buried and surface soils.

An empirical method for geometric calibration of a photon counting detector-based cone beam CT system.

Background: Geometric calibration is essential in developing a reliable computed tomography (CT) system. It involves estimating the geometry under which the angular projections are acquired. Geometric calibration of cone beam CTs employing small area detectors, such as currently available photon counting detectors (PCDs), is challenging when using traditional-based methods due to detectors' limited areas.