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Context.We report here on new results of the systematic monitoring of southern glitching pulsars at the Argentine Institute of Radioastronomy. In particular, we study in this work the new major glitch in the Vela pulsar (PSR J0835−4510) that occurred on 2024 April 29. Aims.We aim to thoroughly characterise the rotational behaviour of the Vela pulsar around its last major glitch and investigate the statistical properties of its individual pulses around the glitch. Methods.We characterise the rotational behaviour of the pulsar around the glitch through the pulsar timing technique. We measured the glitch parameters by fitting timing residuals to the data collected during the days surrounding the event. In addition, we study Vela individual pulses during the days of observation just before and after the glitch. We selected nine days of observations around the major glitch on 2024 April 29 and studied their statistical properties with the Self-Organizing Maps (SOM) technique. We used Variational AutoEncoder (VAE) reconstruction of the individual pulses to separate them clearly from the noise. Results.We obtain a precise timing solution for the glitch. We find two recovery terms of ∼3 days and ∼17 days. We find a correlation of high amplitude with narrower pulses while not finding notable qualitative systematic changes before and after the glitch. 

ABSTRACT We report here on the first results of a systematic monitoring of southern glitching pulsars at the Argentine Institute of Radioastronomy that started in the year 2019. We detected a major glitch in the Vela pulsar (PSR J0835 − 4510) and two small glitches in PSR J1048 − 5832. For each glitch, we present the measurement of glitch parameters by fitting timing residuals. We then make an individual pulse study of Vela in observations before and after the glitch. We selected 6 days of observations around the major glitch on 2021 July 22 and study their statistical properties with machine learning techniques. We use variational autoencoder (VAE) reconstruction of the pulses to separate them clearly from the noise. We perform a study with self-organizing map (SOM) clustering techniques to search for unusual behaviour of the clusters during the days around the glitch not finding notable qualitative changes. We have also detected and confirmed recent glitches in PSR J0742 − 2822 and PSR J1740 − 3015. 

In this study, we explore the use of low rank and sparse constraints for the noninvasive estimation of epicardial and endocardial extracellular potentials from body-surface electrocardiographic data to locate the focus of premature ventricular contractions (PVCs). The proposed strategy formulates the dynamic spatiotemporal distribution of cardiac potentials by means of low rank and sparse decomposition, where the low rank term represents the smooth background and the anomalous potentials are extracted in the sparse matrix. Compared to the most previous potential-based approaches, the proposed low rank and sparse constraints are batch spatiotemporal constraints that capture the underlying relationship of dynamic potentials. The resulting optimization problem is solved using alternating direction method of multipliers . Three sets of simulation experiments with eight different ventricular pacing sites demonstrate that the proposed model outperforms the existing Tikhonov regularization (zero-order, second-order) and L1-norm based method at accurately reconstructing the potentials and locating the ventricular pacing sites. Experiments on a total of 39 cases of real PVC data also validate the ability of the proposed method to correctly locate ectopic pacing sites. 

Online learning has gained increased popularity in recent years. However, with online learning, teacher observation and intervention is lost, creating a need for technologically observable characteristics that can compensate for this limitation. The present study used a wide array of sensing mechanisms including eye tracking, galvanic skin response (GSR) recording, facial expression analysis, and summary note-taking to monitor participants while they watched and recalled an online video lecture. We explored the link between these human-elicited responses and learning outcomes as measured by quiz questions. Results revealed GSR to be the best indicator of the challenge level of the lecture material. Yet, eye tracking and GSR remain difficult to capture when monitoring online learning as the requirement to remain still impacts natural behavior and leads to more stoic and unexpressive faces. Continued work on methods ensuring naturalistic capture are critical for broadening the use of sensor technology in online learning, as are ways to fuse these data with other input, such as structured and unstructured data from peer-to-peer or student-teacher interactions.