Traffic accidents threaten human lives and impose substantial financial burdens annually.
Accurate estimation of accident fatal crash risk is crucial for enhancing road safety and …

We present a deep learning (DL) pipeline developed for the detection and characterization
of astronomical sources within simulated Atacama Large Millimeter/submillimeter Array …

With advanced neural network techniques, language models can generate content that
looks genuinely created by humans. Such advanced progress benefits society in numerous …

We present a novel approach to identify galaxy clusters that are undergoing a merger using
a deep learning approach. This paper uses massive galaxy clusters spanning 0≤ z≤ 2 from …

Using the full six years of imaging data from the Dark Energy Survey, we study the surface
brightness profiles of galaxy cluster central galaxies and intra-cluster light. We apply a …

Inverse Compton (IC) emission associated with the nonthermal component of the intracluster
medium (ICM) has been a long-sought phenomenon in cluster physics. Traditional spectral …

The proliferation of text generation applications in social networks has raised concerns
about the authenticity of online content. Large language models like GPTs can now produce …

Deep neural networks have shown remarkable performance on a wide range of
classification tasks and applications. However, the large model size and the enormous size …

252 C. Heneka et al. through increasing amounts of data. For radio astronomical data this
prominently concerns the SKA (Braun et al. 2015) and its precursor instruments such as …

We introduce AutoEnRichness, a hybrid approach that combines empirical and analytical
strategies to determine the richness of galaxy clusters (in the redshift range of 0.1≤ z≤ …