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The manifestation of surface spots on magnetic chemically peculiar (mCP) stars is most commonly explained by the atomic diffusion theory, which requires a calm stellar atmosphere and only moderate rotation. While very successful and well described, this theory still needs to be revised and fine-tuned to the observations. Our study aims to enlarge the sample of known photometrically variable mCP stars (ACV variables) to pave the way for more robust and significant statistical studies. We derive accurate physical parameters for these objects and discuss our results in the framework of the atomic diffusion theory. We studied 1314 candidate ACV variables that were selected from the Zwicky Transient Factory catalogue of periodic variables based on light curve characteristics. We investigated these objects using photometric criteria, a colour-magnitude diagram, and spectroscopic data from the LAMOST and Gaia missions to confirm their status as ACV variables. We present a sample of 1232 new ACV variables, including information on distance from the Sun, mass, fractional age on the main sequence, fraction of the radius between the zero-age and terminal-age main sequence, and the equatorial velocity and its ratio to the critical velocity. Our results confirm that the employed selection process is highly effcient for detecting ACV variables. We have identified 38 stars with v(equ) in excess of 150 km/s (with extreme values up to 260 km/s). This challenges current theories that cannot explain the occurrence of such fast-rotating mCP stars., Comment: 11 pages, 4 figures, accepted for Astronomy & Astrophysics

Central palsy is a form of facial paralysis that requires urgent medical attention and has to be differentiated from other, similar conditions such as peripheral palsy. To aid in fast and accurate diagnosis of this condition, we propose a machine learning approach to automatically classify peripheral and central facial palsy. The Palda dataset is used, which contains 103 peripheral palsy images, 40 central palsy, and 60 healthy people. Experiments are run on five machine learning algorithms. The best performing algorithms were found to be the SVM (total accuracy of 85.1%) and the Gaussian naive Bayes (80.7%). The lowest false negative rate on central palsy was achieved by the naive Bayes approach (80% compared to 70%). This condition could prove to be the most severe, and thus its sensitivity is another good way to compare algorithms. By extrapolation, a dataset size of 334 total pictures is estimated to achieve a central palsy sensitivity of 95%. All code used for these machine learning experiments is freely available online at https://github.com/cvvletter/palsy., Comment: 9 pages, 10 tables, 10 figures