相关论文: Cosmic Ray Rejection with Attention Augmented Deep…

Cosmic rays (CRs) are a non-thermal energy component in the interstellar and circumgalactic medium (CGM) that can act as an additional feedback channel beyond thermal and kinetic feedback from stars and AGN. They influence galaxy evolution…

We present a deep learning, computer vision algorithm constructed for the purposes of identifying and classifying charged particles in camera image sensors. We apply our algorithm to data collected by the Distributed Electronic Cosmic-ray…

We present the first uniform gravitational lens modeling analysis of eight doubly imaged quasars from multi-band observations with the Hubble Space Telescope. Previous time-delay cosmography analyses by the TDCOSMO Collaboration have…

Cosmic-ray (CR) antihelium is an important probe for the indirect search of dark matter (DM) annihilation in the Galaxy. However, due to stringent constraints from the measurements of CR antiprotons and $\gamma$-rays, the flux of CR…

Anomaly detection in images plays a significant role for many applications across all industries, such as disease diagnosis in healthcare or quality assurance in manufacturing. Manual inspection of images, when extended over a monotonously…

Radio waves, perhaps because they are uniquely transparent in our terrestrial atmosphere, as well as the cosmos beyond, or perhaps because they are macroscopic, so the basic instruments of detection (antennas) are easily constructable,…

Diabetic retinopathy (DR) results in vision loss if not treated early. A computer-aided diagnosis (CAD) system based on retinal fundus images is an efficient and effective method for early DR diagnosis and assisting experts. A…

Non-thermal emission from relativistic Cosmic Ray (CR) electrons gives insight into the strength and morphology of intra-cluster magnetic fields, as well as providing powerful tracers of structure formation shocks. Emission caused by CR…

Scene understanding and object recognition is a difficult to achieve yet crucial skill for robots. Recently, Convolutional Neural Networks (CNN), have shown success in this task. However, there is still a gap between their performance on…

Anomaly detection (AD) plays a crucial role in many safety-critical application domains. The challenge of adapting an anomaly detector to drift in the normal data distribution, especially when no training data is available for the "new…

Deep learning based approaches have been used to improve image quality in cone-beam computed tomography (CBCT), a medical imaging technique often used in applications such as image-guided radiation therapy, implant dentistry or…

Automatic Target Recognition (ATR) algorithms classify a given Synthetic Aperture Radar (SAR) image into one of the known target classes using a set of training images available for each class. Recently, learning methods have shown to…

While Vision-Language Models (VLMs) like CLIP have emerged as a dominant paradigm for generalizable deepfake detection, a representational disconnect remains: their semantic-centric pre-training is ill-suited for capturing non-semantic…

Registration plays an important role in medical image analysis. Deep learning-based methods have been studied for medical image registration, which leverage convolutional neural networks (CNNs) for efficiently regressing a dense deformation…

Cosmic ray antiprotons provide a powerful tool to probe dark matter annihilations in our galaxy. The sensitivity of this important channel is, however, diluted by sizable uncertainties in the secondary antiproton background. In this work,…

Machine learning models can greatly improve the search for strong gravitational lenses in imaging surveys by reducing the amount of human inspection required. In this work, we test the performance of supervised, semi-supervised, and…

Robust measurements of cosmological parameters from galaxy surveys rely on our understanding of systematic effects that impact the observed galaxy density field. In this paper we present, validate, and implement the idea of adopting the…

Recent self-supervised contrastive learning methods greatly benefit from the Siamese structure that aims to minimizing distances between positive pairs. These methods usually apply random data augmentation to input images, expecting the…

Deep Convolutional Neural Networks (CNN) have drawn great attention in image super-resolution (SR). Recently, visual attention mechanism, which exploits both of the feature importance and contextual cues, has been introduced to image SR and…

Cosmic ray (CR) spectra, both measured upon their arrival at the Earth's atmosphere and inferred from the emission in supernova remnants (SNRs), appear to be significantly steeper than the "standard" diffusive shock acceleration (DSA)…