相关论文: Radio Galaxy Zoo: tagging radio subjects using tex…

The computer-aided disease diagnosis from radiomic data is important in many medical applications. However, developing such a technique relies on annotating radiological images, which is a time-consuming, labor-intensive, and expensive…

We conducted an extensive identification and analysis of various morphological classes and subclasses of radio galaxies using the latest high-resolution data from the second data release of the LOFAR Two-Metre Sky Survey (LoTSS DR2). This…

We propose Textiverse, a big data approach for mining geotagged timestamped textual data on a map, such as for Twitter feeds, crime reports, or restaurant reviews. We use a scalable data management pipeline that extracts keyphrases from…

Active Galactic Nuclei (AGN) can often be identified in radio images as two lobes, sometimes connected to a core by a radio jet. This multi-component morphology unfortunately creates difficulties for source-finders, leading to components…

Multi-component modelling of galaxies is a valuable tool in the effort to quantitatively understand galaxy evolution, yet the use of the technique is plagued by issues of convergence, model selection and parameter degeneracies. These issues…

We study the mutual alignment of radio sources within two surveys, FIRST and TGSS. This is done by producing two position angle catalogues containing the preferential directions of respectively $30\,059$ and $11\,674$ extended sources…

This paper presents an innovative approach called BGTAI to simplify multimodal understanding by utilizing gloss-based annotation as an intermediate step in aligning Text and Audio with Images. While the dynamic temporal factors in textual…

Winged radio sources are a small sub-class of extragalactic radio sources which display a pair of low surface brightness radio lobes, known as `wings' aligned at a certain angle with the primary jets. Depending on the location of wings,…

Galaxy groups are essential for studying the distribution of matter on a large scale in redshift surveys and for deciphering the link between galaxy traits and their associated halos. In this work, we propose a widely applicable method for…

Large surveys using modern telescopes are producing images that are increasing exponentially in size and quality. Identifying objects in the generated images by visual recognition is time-consuming and labor-intensive, while classifying the…

In this paper, the third and final of a series, we present complete K-band imaging and some complementary I-band imaging of the filtered 6C* sample. We find no systematic differences between the K-z relation of 6C* radio galaxies and those…

In this work, we explore methods to improve galaxy redshift predictions by combining different ground truths. Traditional machine learning models rely on training sets with known spectroscopic redshifts, which are precise but only represent…

Galaxy Zoo: Clump Scout is a web-based citizen science project designed to identify and spatially locate giant star forming clumps in galaxies that were imaged by the Sloan Digital Sky Survey Legacy Survey. We present a statistically driven…

Galaxy morphology analysis involves studying galaxies based on their shapes and structures. For such studies, fundamental tasks include identifying and classifying galaxies in astronomical images, as well as retrieving visually or…

Remnant radio galaxies represent the dying phase of radio-loud active galactic nuclei (AGN). Large samples of remnant radio galaxies are important for quantifying the radio galaxy life cycle. The remnants of radio-loud AGN can be identified…

Many datasets nowadays contain links between geographic locations and natural language texts. These links can be geotags, such as geotagged tweets or geotagged Wikipedia pages, in which location coordinates are explicitly attached to texts.…

We review the statistical properties of the main populations of radio sources, as emerging from radio and millimeter sky surveys. Recent determinations of local luminosity functions are presented and compared with earlier estimates still in…

Scientific publications follow conventionalized rhetorical structures. Classifying the Argumentative Zone (AZ), e.g., identifying whether a sentence states a Motivation, a Result or Background information, has been proposed to improve…

Social media users share billions of items per year, only a small fraction of which is geotagged. We present a data- driven approach for identifying non-geotagged content items that can be associated with a hyper-local geographic area by…

Despite recent progress in computer vision, fine-grained interpretation of satellite images remains challenging because of a lack of labeled training data. To overcome this limitation, we propose using Wikipedia as a previously untapped…