Abstract
Lung cancer remains a significant global health challenge, with early detection playing a critical role in improving patient outcomes. Computed Tomography (CT) imaging has become a cornerstone in the early diagnosis and staging of lung cancer, allowing for the detection of pulmonary nodules that may indicate malignancy. However, accurately segmenting and characterizing these nodules...
Muon radiography is a technique that utilizes muons from cosmic rays to investigate otherwise hard-to-reach environments. This technique offers several advantages, including the absence of accelerators to generate particles that interact with the target under examination. It is a non-invasive technique, both for humans and the observed object. Furthermore, due to the muons' ability to...
In the context of scientific computing there is a growing interest
towards DNNs (Deep Neural Networks), which are being used in several
applications, spanning from medical images segmentation and
classification, to the on-line analysis of experimental data.
In addition to GPUs, FPGAs are also emerging as compute accelerators
promising higher energy-efficiency and lower latency, for the...
The growing demand for GPUs has led to rapid development of machine learning research techniques in all areas of science, including High Energy Physics. We present a study focused on the classification task of simulated electrons and protons detected by the HERD detector. HERD is a high-energy cosmic-ray detector based on a deep three-dimensional electromagnetic calorimeter, proposed to be...
In the last few years, the rise of deep learning techniques has affected also the field on physics-based imaging applied to cultural heritage. One possible application of such techniques is the virtual digital restoration of pictorial artworks.
Two main problems we face when exploring such landscape are
- The small dataset sizes (due to the slow pace of such analysis, as well as...
X-ray fluorescence (XRF) has been extensively utilized across various disciplines for an extended period. Despite the long-standing availability of fundamental physical parameters associated with the process, the extraction of elemental information from spectra remains
predominantly a manual or algorithmic endeavor, given the spectral variations and inherent complexity in different fields of...
INFN Cloud provides scientific communities supported by the Institute with a federated Cloud infrastructure and a dynamic portfolio of services based on the needs of the supported use cases. The federative middleware of INFN Cloud is based on the INDIGO PaaS orchestration system, consisting of interconnected open-source microservices. Among these, the INDIGO PaaS Orchestrator receives...
The transformer model, introduced by Google in 2017, has become renowned in natural language processing (NLP). It represents a significant advancement completely departing from the mechanisms of Recurrent Neural Networks and Convolutional Neural Networks.
The features that contribute to the superior performance of transformers in NLP tasks include self-attention, multi-head attention, and...
CNAF provides computing resources to over 60 scientific communities and supports over 1700 active users through its User Support (US) department. US handles daily emails and tickets to help users in employing effectively computing resources and using latest software technologies. Since 2003, CNAF hosts the main INFN computing center, one of WLCG Tier-1.
The primary challenge is to handle...
With the observation of the Standard Model Higgs boson (H) by the CMS and ATLAS experiments in 2012, the last missing elementary particle predicted by the Standard Model of particle physics was found. Since then, extensive measurements in various decay channels of the Higgs boson have been performed. One of them is the decay into a pair of $\tau$ leptons. It is the decay channel of the Higgs...
Clusters counting in a drift chamber represents the most promising breakthrough in particle identification (PID) techniques in particle physics experiments. In this paper, neural network models, such as the Long Short-Term Memory (LSTM) Model and Convolutional Neural Network (CNN) Model, are trained using various hyperparameters like loss functions, activation functions, different numbers of...
With the advent of the High Luminosity LHC era, which is expected to significantly increase the amount of data collected by the detectors, the computational complexity of the particle tracking problem is expected to increase.
Conventional algorithms suffer from scaling problems. In our work, we represent charged particle tracks as a graph data structure and we are investigating the use of...
