The Einstein Cross, Q 2237+0305, has been photometrically observed in four bands on two successive nights at NOT (La Palma, Spain) in October 1995. Three independent algorithms have been used to analyse the data: an automatic image decomposition technique, a CLEAN algorithm and the new MCS deconvolution code. The photometric and astrometric results obtained with the three methods are presented. ...

The time series cluster kernel (TCK) provides a powerful tool for analysing multivariate time series subject to missing data. TCK is designed using an ensemble learning approach in which Bayesian mixture models form the base models. Because of the Bayesian approach, TCK can naturally deal with missing values without resorting to imputation and the ensemble strategy ensures robustness to hyperparameters, ...

We tackle the problem of classifying Electrocardiography (ECG) signals with the aim of predicting the onset of Paroxysmal Atrial Fibrillation (PAF). Atrial fibrillation is the most common type of arrhythmia, but in many cases PAF episodes are asymptomatic. Therefore, in order to help diagnosing PAF, it is important to design procedures for detecting and, more importantly, predicting PAF episodes. ...

Increased human presence and commercial activities in the Barents Sea (fishing, offshore oil and gas exploration) are amplifying the need for large-scale operational ocean monitoring of the eventual oil spills in the region. The geographical location and climate impose additional constraints on satellite-based monitoring, making it necessary to use Synthetic Aperture Radar (SAR). Dark features or ...

The data-driven nature of deep learning (DL) models for semantic segmentation requires a large number of pixel-level annotations. However, large-scale and fully labeled medical datasets are often unavailable for practical tasks. Recently, partially supervised methods have been proposed to utilize images with incomplete labels in the medical domain. To bridge the methodological gaps in ...

In this paper, we introduce a new framework to train a class of recurrent neural network, called Echo State Network, to predict real valued time-series and to provide a visualization of the modeled system dynamics. The method consists in projecting the output of the internal layer of the network on a lower dimensional space, before training the output layer to learn the target task. Notably, we ...

We present experimental demonstration of tilt-mirror assisted transmission structured illumination microscopy (tSIM) that offers a large field of view super resolution imaging. An assembly of custom-designed tilt-mirrors are employed as the illumination module where the sample is excited with the interference of two beams reflected from the opposite pair of mirror facets. Tunable frequency structured ...

The spatial and temporal characterization of trapped charged particle trajectories in magnetospheres has been extensively studied in dipole magnetic field structures. Such studies have allowed the calculation of spatial quantities, such as equatorial loss cone size as a function of radial distance, the location of the mirror points along particular field lines (<i>L</i>‐shells) as a function of the ...

This study provides first storm time observations of the westward-propagating medium-scale traveling ionospheric disturbances (MSTIDs), particularly, associated with characteristic subauroral storm time features, storm-enhanced density (SED), subauroral polarization stream (SAPS), and enhanced thermospheric westward winds over the continental US. In the four recent (2017–2019) geomagnetic storm ...

Quantifying small, rapidly evolving forces generated by cells is a major challenge for the understanding of biomechanics and mechanobiology in health and disease. Traction force microscopy remains one of the most broadly applied force probing technologies but typically restricts itself to slow events over seconds and micron-scale displacements. Here, we improve >2-fold spatially and >10-fold temporally ...