Xue-Mei Cao | Searcharxiv

arXiv2025

First Time Observed M-Shaped Coronal Mass Ejection Associated with a Blowout Jet and an Extreme Ultraviolet Wave

The coronal blowout jet, extreme ultraviolet (EUV) wave and coronal mass ejection (CME) are common phenomena in the solar atmosphere. In this paper, we report the occurrence of an M-shaped CME event associated with a blowout jet and an EUV wave using high-resolution, multi-angle and multi-wavelength observations taken from Solar Dynamics Observatory, and Solar TErrestrial RElations Observatory. Interestingly, and for the first time, it is found that two bubble-like CMEs and a jet-like CME were simultaneously triggered by the same eruptive event. Our observational analyses and findings indicate the following: (1) the eruption of a blowout jet led to a large-scale EUV wave; (2) the eruption of the EUV wave swept a small filament (prominence) and a long filament; (3) eventually the EUV wave split-up into two parts, leading to the two bubble-like CMEs, while the blowout jet induced a jet-like CME. The combined events appear to form an M-shape like structure CME, that we sketch throughout a proposed cartoon tentatively explaining the observed complex configuration. Based on observational diagnosis, we argue that the jet, the EUV wave and the multi-CME are highly interlinked. A suggested eruption-model, from the solar atmosphere to the space, is outlined and discussed, providing a possibly new way to probe the relationship between the solar eruptions and the surrounding space. The investigation of such rare phenomenon can be a key point for better understanding of the physical associated triggering mechanisms and energy transport in the solar atmosphere, crucial for MHD simulations and modeling.

astro-ph.SR↗

arXiv2023

Interpreting Vulnerabilities of Multi-Instance Learning to Adversarial Perturbations

Multi-Instance Learning (MIL) is a recent machine learning paradigm which is immensely useful in various real-life applications, like image analysis, video anomaly detection, text classification, etc. It is well known that most of the existing machine learning classifiers are highly vulnerable to adversarial perturbations. Since MIL is a weakly supervised learning, where information is available for a set of instances, called bag and not for every instances, adversarial perturbations can be fatal. In this paper, we have proposed two adversarial perturbation methods to analyze the effect of adversarial perturbations to interpret the vulnerability of MIL methods. Out of the two algorithms, one can be customized for every bag, and the other is a universal one, which can affect all bags in a given data set and thus has some generalizability. Through simulations, we have also shown the effectiveness of the proposed algorithms to fool the state-of-the-art (SOTA) MIL methods. Finally, we have discussed through experiments, about taking care of these kind of adversarial perturbations through a simple strategy. Source codes are available at https://github.com/InkiInki/MI-UAP.

cs.CV↗