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Current deepfake detection models achieve state-of-the-art performance on pristine academic datasets but suffer severe spatial attention drift under real-world compound degradations, such as blurring and severe lossy compression. To address…
For operation at the High Luminosity Large Hadron Collider (LHC), the ATLAS experiment is building a new all-silicon inner tracker (ITk). The production and testing of thousands of silicon pixel and strip modules is required to cover the…
Reconstructing a dynamic scene from image inputs is a fundamental computer vision task with many downstream applications. Despite recent advancements, existing approaches still struggle to achieve high-quality reconstructions from unseen…
Lately, deep learning has been extensively investigated for accelerating dynamic magnetic resonance (MR) imaging, with encouraging progresses achieved. However, without fully sampled reference data for training, current approaches may have…
In this paper, we fill the research gap by adopting state-of-the-art computer vision techniques for the data extraction stage in a data mining system. As shown in Fig.1, this stage contains two subtasks, namely, plot element detection and…
Low-complexity multiple-input multiple-output (MIMO) detection remains a key challenge in modern wireless systems, particularly for 5G reduced capability (RedCap) and internet-of-things (IoT) devices. In this context, the growing interest…
Track reconstruction is a vital aspect of High-Energy Physics (HEP) and plays a critical role in major experiments. In this study, we delve into unexplored avenues for particle track reconstruction and hit clustering. Firstly, we enhance…
Monitoring the technical condition of infrastructure is a crucial element to its maintenance. Currently applied methods are outdated, labour-intensive and inaccurate. At the same time, the latest methods using Artificial Intelligence…
Mainly due to their outstanding performance the position sensitive silicon detectors are widely used in the tracking systems of High Energy Physics experiments such as the ALICE, ATLAS, CMS and LHCb at LHC, the world's largest particle…
One of the most important problems of data processing in high energy and nuclear physics is the event reconstruction. Its main part is the track reconstruction procedure which consists in looking for all tracks that elementary particles…
This paper documents package for the particle level fast simulation. The package is designed to complete the AcerMC generator framework with the easy-to-use simulation and reconstruction algorithms. The package provides, starting from list…
Diffusion models (DMs) have revolutionized image generation, producing high-quality images with applications spanning various fields. However, their ability to create hyper-realistic images poses significant challenges in distinguishing…
Pixelated detectors in scanning transmission electron microscopy (STEM) generate large volumes of data, often tens to hundreds of GB per scan. However, to make current advancements scalable and enable widespread adoption, it is essential to…
The Large Hadron Collider (LHC) is one of the most complex machines ever build. It is composed of many components which constitute a large system. The tunnel and the accelerator is just one of a very critical fraction of the whole LHC…
Reconstructing a sequence of sharp images from the blurry input is crucial for enhancing our insights into the captured scene and poses a significant challenge due to the limited temporal features embedded in the image. Spike cameras,…
Iris authentication algorithms have achieved impressive recognition performance, making them highly promising for real-world applications such as border control, citizen identification, and both criminal investigations and commercial…
Accurate fire and smoke detection is critical for safety and disaster response, yet existing vision-based methods face challenges in balancing efficiency and reliability. Compact deep learning models such as YOLOv5n and YOLOv8n are widely…
This paper discusses the latest generation of the MONARC (MOdels of Networked Analysis at Regional Centers) simulation framework, as a design and modelling tool for large scale distributed systems applied to HEP experiments. A…
Concept Bottleneck Models (CBMs) offer interpretable alternatives to black-box predictors by introducing human-relatable concepts before the final output. However, existing CBMs struggle to verify whether predicted concepts correspond to…
A low cost fluorescence-based optical system is developed for detecting the presence of certain microorganisms and molecules within a diluted sample. A specifically designed device setup compatible with conventional 96 well plates is chosen…