Related papers: Automatic detection of CMEs using synthetically-tr…
Interplanetary scintillation (IPS) is a useful tool for detecting coronal mass ejections (CMEs) throughout interplanetary space. Global magnetohydrodynamic (MHD) simulations of the heliosphere, which are usually used to predict the arrival…
We have studied two Coronal Mass Ejections (CMEs) that occurred on September 25 and 28, 2012 and interacted near the Earth. By fitting the Graduated Cylindrical Shell (GCS) model on the SECCHI/COR2 images and applying the Stereoscopic…
Coronal mass ejections (CMEs) and other solar eruptive phenomena can be physically linked by combining data from a multitude of ground-based and space-based instruments alongside models, however this can be challenging for automated…
We present a hybrid combination of forward and inverse reconstruction methods using multiple observations of a coronal mass ejection (CME) to derive the 3D 'true' Height-Time plots for individual CME components. We apply this hybrid method…
Masked Autoencoders (MAEs) have emerged as a dominant strategy for self-supervised representation learning in natural images, where models are pre-trained to reconstruct masked patches with a pixel-wise mean squared error (MSE) between…
To safely deploy deep learning models in the clinic, a quality assurance framework is needed for routine or continuous monitoring of input-domain shift and the models' performance without ground truth contours. In this work, cardiac…
We present general considerations regarding the derivation of the radial distances of coronal mass ejections (CMEs) from elongation angle measurements such as those provided by SECCHI and SMEI, focusing on measurements in the Heliospheric…
Segmentation of cardiac anatomical structures in cardiac magnetic resonance images (CMRI) is a prerequisite for automatic diagnosis and prognosis of cardiovascular diseases. To increase robustness and performance of segmentation methods…
In situ measurements from spacecraft typically provide a time series at a single location through coronal mass ejections (CMEs) and they have been one of the main methods to investigate CMEs. CME properties derived from these in situ…
Coronal and interplanetary shock waves produced by coronal mass ejections (CMEs) are major drivers of space-weather phenomena, inducing major changes in the heliospheric radiation environment and directly perturbing the near-Earth…
The global outbreak of the novel corona virus (COVID-19) disease has drastically impacted the world and led to one of the most challenging crisis across the globe since World War II. The early diagnosis and isolation of COVID-19 positive…
Aims. The study of the morphology of coronal mass ejections (CMEs) is an auspicious approach to understanding how magnetic fields are structured within CMEs. Although earlier studies have suggested an asymmetry in the width of CMEs in…
Image segmentation is a fundamental and challenging problem in computer vision with applications spanning multiple areas, such as medical imaging, remote sensing, and autonomous vehicles. Recently, convolutional neural networks (CNNs) have…
Understanding the 3D structure of coronal mass ejections (CMEs) is crucial for understanding the nature and origin of solar eruptions. To derive information on the 3D structure of CMEs from white-light (total and polarized brightness)…
Precise segmentation of the liver is critical for computer-aided diagnosis such as pre-evaluation of the liver for living donor-based transplantation surgery. This task is challenging due to the weak boundaries of organs, countless…
Region-based Convolutional Neural Networks (R-CNNs) have achieved great success in the field of object detection. The existing R-CNNs usually divide a Region-of-Interest (ROI) into grids, and then localize objects by utilizing the spatial…
We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The…
Coronal mass ejections (CMEs) are the most important drivers of space weather. Central to most CMEs is thought to be the eruption of a bundle of highly twisted magnetic field lines known as magnetic flux ropes. A comprehensive understanding…
Robust Mask R-CNN (Mask Regional Convolu-tional Neural Network) methods are proposed and tested for automatic detection of cracks on structures or their components that may be damaged during extreme events, such as earth-quakes. We curated…
Food resources face severe damages under extraordinary situations of catastrophes such as earthquakes, cyclones, and tsunamis. Under such scenarios, speedy assessment of food resources from agricultural land is critical as it supports aid…