Related papers: Exploring X-ray variability with unsupervised mach…

Point clouds provide a compact and efficient representation of 3D shapes. While deep neural networks have achieved impressive results on point cloud learning tasks, they require massive amounts of manually labeled data, which can be costly…

We present the XMM-Newton Medium sensitivity Survey (XMS), including a total of 318 X-ray sources found among the serendipitous content of 25 XMM-Newton target fields. The XMS comprises four largely overlapping source samples selected at…

We analyze 18 sources that were found to show interesting properties of periodicity, very soft spectra and/or large long-term variability in X-rays in our project of classification of sources from the 2XMMi-DR3 catalog but were poorly…

We report on the first deep X-ray survey with the XMM-Newton observatory during the performance verification phase. The field of the Lockman Hole, one of the best studied sky areas over a very wide range of wavelengths, has been observed. A…

High-dimensional single-cell data poses significant challenges in identifying underlying biological patterns due to the complexity and heterogeneity of cellular states. We propose a comprehensive gene-cell dependency visualization via…

We present a morphological study of 35 X-ray luminous galaxy clusters at 0.15<z<0.3, selected in a similar manner to the Local Cluster Substructure Survey (LoCuSS), for which deep XMM-Newton observations are available. We characterise the…

Context. The XMM-XXL survey uses observations from XMM-Newton to detect clusters of galaxies over a wide range in mass and redshift. The moderate PSF of XMM-Newton means that point sources within or projected onto a cluster may not be…

This paper shows how an uncertainty-aware, deep neural network can be trained to detect, recognise and localise objects in 2D RGB images, in applications lacking annotated train-ng datasets. We propose a self-supervising teacher-student…

In this work, we address the critical yet underexplored challenge of symmetric multimodal-to-multimodal (MM2MM) retrieval, where queries and contexts are interchangeable. Existing universal multimodal retrieval works struggle with this…

Multi-sensor clues have shown promise for object segmentation, but inherent noise in each sensor, as well as the calibration error in practice, may bias the segmentation accuracy. In this paper, we propose a novel approach by mining the…

We describe the initial results of a programme to detect and identify extended X-ray sources found serendipitously in XMM-Newton observations. We have analysed 186 EPIC-PN images at high galactic latitude with a limiting flux of $1\times…

We propose a Parton Distribution Function (PDF) fitting technique which is based on an interactive neural network algorithm using Self-Organizing Maps (SOMs). SOMs are visualization algorithms based on competitive learning among…

Subspace clustering algorithms are used for understanding the cluster structure that explains the dataset well. These methods are extensively used for data-exploration tasks in various areas of Natural Sciences. However, most of these…

We have used XMM-Newton's Optical Monitor (OM) images to study the local environment of a sample of 27 Ultraluminous X-ray Sources (ULXs) in nearby galaxies. UVW1 fluxes were extracted from 100 pc regions centered on the ULX positions. We…

The Self-Organizing Map (SOM) with its related extensions is the most popular artificial neural algorithm for use in unsupervised learning, clustering, classification and data visualization. Over 5,000 publications have been reported in the…

AXIS (An XMM-Newton International Survey) is a survey of 36 high Galactic latitude XMM-Newton observations covering 4.8 deg2 and containing 1433 serendipitous X-ray sources detected with 5-sigma significance. We have studied the X-ray…

There is a large body of work that has used the excellent Chandra observations of nearby galaxies with neglible low mass X-ray binary (LMXB) populations. This has culminated in a ``Universal'' X-ray luminosity function (XLF) for high mass…

In this work, we introduce and study the novel task of Open-ended Semantic Multiple Clustering (OpenSMC). Given a large, unstructured image collection, the goal is to automatically discover several, diverse semantic clustering criteria…

Modeling non-stationary data is a challenging problem in the field of continual learning, and data distribution shifts may result in negative consequences on the performance of a machine learning model. Classic learning tools are often…

TThe goal of our work is to discover dominant objects in a very general setting where only a single unlabeled image is given. This is far more challenge than typical co-localization or weakly-supervised localization tasks. To tackle this…