Explanipedia

CaloChallenge 2022: a community challenge for fast calorimeter simulation Open

Claudius Krause, M. Faucci Giannelli, G. Kasieczka, Benjamin Nachman, D. Salamani , et al. · 2025

We present the results of the ‘Fast Calorimeter Simulation Challenge 2022’—the CaloChallenge. We study state-of-the-art generative models on four calorimeter shower datasets of increasing dimensionality, ranging from a few hundred voxels t…

AB003. Optimizing postoperative proton radiotherapy in thymic epithelial tumors: added value of breath hold? Open

Esther Kneepkens, Marije Velders, J. van der Stoep, Maud Cobben, Maud van den Bosch , et al. · 2025

Measuring value compatibility in European Union energy diplomacy: Conceptual framework and application to natural gas sector Open

Diana Shendrikova, L. Rinaldi, Emanuela Colombo · 2025

So FAIR, so good Open

L. Rinaldi, D. Bonacorsi, C. Bozzi, Stefano Dal Pra, L dell’Agnello , et al. · 2025

Large international collaborations in the field of Nuclear and Subnuclear Physics have been leading the implementation of FAIR principles for managing research data. These principles are essential when dealing with large volumes of data ov…

The clinical and microbiological efficacy of a zinc-citrate/hydroxyapatite/potassium-citrate containing toothpaste: a double-blind randomized controlled clinical trial Open

Uroš Josić, Tatjana Maravić, Claudia Mazzitelli, L. Rinaldi, Carlo D’Alessandro , et al. · 2024

Objectives To evaluate the antibacterial efficacy of two fluoride-containing (1450 ppm F) toothpastes with or without zinc-citrate (ZCT), hydroxyapatite (HAP) and potassium-citrate (KCit); to assess and compare their clinical effects in te…

Cancer treatment monitoring using cell-free DNA fragmentomes Open

Iris van ‘t Erve, Bahar Alipanahi, Keith Lumbard, Zachary L. Skidmore, L. Rinaldi , et al. · 2024

Fluorescent Neuronal Cells v2: multi-task, multi-format annotations for deep learning in microscopy Open

L. Clissa, Antonio Macaluso, R. Morelli, Alessandra Occhinegro, Emiliana Piscitiello , et al. · 2024

Fluorescent Neuronal Cells v2 is a collection of fluorescence microscopy images and the corresponding ground-truth annotations, designed to foster innovative research in the domains of Life Sciences and Deep Learning. This dataset encompas…

Enabling INFN–T1 to support heterogeneous computing architectures Open

Stefano Dal Pra, D. Spiga, T. Boccali, Andrea Chierici, Lucia Morganti , et al. · 2024

The INFN–CNAF Tier-1 located in Bologna (Italy) is a center of the WLCG e-Infrastructure providing computing power to the four major LHC collaborations and also supports the computing needs of about fifty more groups - also from non HEP re…

How big is Big Data? A comprehensive survey of data production, storage, and streaming in science and industry Open

L. Clissa, M. Lassnig, L. Rinaldi · 2023

The contemporary surge in data production is fueled by diverse factors, with contributions from numerous stakeholders across various sectors. Comparing the volumes at play among different big data entities is challenging due to the scarcit…

Fluorescent Neuronal Cells v2: Multi-Task, Multi-Format Annotations for Deep Learning in Microscopy Open

L. Clissa, Antonio Macaluso, R. Morelli, Alessandra Occhinegro, Emiliana Piscitiello , et al. · 2023

Fluorescent Neuronal Cells v2 is a collection of fluorescence microscopy images and the corresponding ground-truth annotations, designed to foster innovative research in the domains of Life Sciences and Deep Learning. This dataset encompas…

Geopolitical Application of "Like-Mindedness" Indicator in Choosing and Diversifying EU Energy Partners to Substitute Russian Gas Open

Diana Shendrikova, L. Rinaldi, Emanuela Colombo · 2023

Analyzing WLCG File Transfer Errors Through Machine Learning Open

L. Clissa, M. Lassnig, L. Rinaldi · 2022

The increasingly growing scale of modern computing infrastructures solicits more ingenious and automatic solutions to their management. Our work focuses on file transfer failures within the Worldwide Large Hadron Collider Computing Grid an…

Preparing Distributed Computing Operations for the HL-LHC Era With Operational Intelligence Open

T. Dias Do Vale, F. Legger, Panos Paparrigopoulos, J. Schovancova, T. A. Beermann , et al. · 2022

As a joint effort from various communities involved in the Worldwide LHC Computing Grid, the Operational Intelligence project aims at increasing the level of automation in computing operations and reducing human interventions. The distribu…

HEPiX Benchmarking Solution for WLCG Computing Resources Open

Domenico Giordano, M. Alef, Luca Atzori, Jean-Michel Barbet, Olga Datskova , et al. · 2021

The HEPiX Benchmarking Working Group has developed a framework to benchmark the performance of a computational server using the software applications of the High Energy Physics (HEP) community. This framework consists of two main component…

Automating cell counting in fluorescent microscopy through deep learning with c-ResUnet Open

R. Morelli, L. Clissa, Roberto Amici, Matteo Cerri, Timna Hitrec , et al. · 2021

WebFPTI: A tool to predict the toxicity/pathogenicity of mineral fibres including asbestos Open

Alessandro F. Gualtieri, Mauro Leoncini, L. Rinaldi, Alessandro Zoboli, Dario Di Giuseppe · 2021

Automatic Cell Counting in Flourescent Microscopy Using Deep Learning Open

R. Morelli, L. Clissa, Marco Dalla, Marco Luppi, L. Rinaldi , et al. · 2021

Counting cells in fluorescent microscopy is a tedious, time-consuming task that researchers have to accomplish to assess the effects of different experimental conditions on biological structures of interest. Although such objects are gener…

Operational Intelligence for Distributed Computing Systems for Exascale Science Open

T. Dias Do Vale, F. Legger, Panos Paparrigopoulos, Alexei Klimentov, J. Schovancova , et al. · 2020

In the near future, large scientific collaborations will face unprecedented computing challenges. Processing and storing exabyte datasets require a federated infrastructure of distributed computing resources. The current systems have prove…

Using HEP experiment workflows for the benchmarking and accounting of WLCG computing resources Open

A. Valassi, M. Alef, Jean-Michel Barbet, Olga Datskova, Riccardo De Maria , et al. · 2020

Benchmarking of CPU resources in WLCG has been based on the HEP-SPEC06 (HS06) suite for over a decade. It has recently become clear that HS06, which is based on real applications from non-HEP domains, no longer describes typical HEP worklo…

Towards Predictive Maintenance with Machine Learning at the INFN-CNAF computing centre Open

L. Giommi, D. Bonacorsi, T. Diotalevi, L. Rinaldi, Lucia Morganti , et al. · 2019

The INFN-CNAF computing center, one of the Worldwide LHC Computing Grid Tier-1 sites, is serving a large set of scientific communities, in High Energy Physics and beyond. In order to increase efficiency and to remain competitive in the lon…

Optimizing access to conditions data in ATLAS event data processing Open

L. Rinaldi, E. J. Gallas, A. Formica · 2019

The processing of ATLAS event data requires access to conditions data which are stored in database systems. This data includes, for example alignment, calibration, and configuration information which may be characterized by large volumes, …

Conditions evolution of an experiment in mid-life, without the crisis (in ATLAS) Open

L. Rinaldi, A. Formica, E. J. Gallas, N. Öztürk, S. Roe · 2019

The ATLAS experiment is approaching mid-life: the long shutdown period (LS2) between LHC Runs 1 and 2 (ending in 2018) and the future collision data-taking of Runs 3 and 4 (starting in 2021). In advance of LS2, we have been assessing the f…

Elastic extension of a local analysis facility on external clouds for the LHC experiments Open

Vincenzo Ciaschini, G. Codispoti, L. Rinaldi, D C Aiftimiei, D. Bonacorsi , et al. · 2017

The computing infrastructures serving the LHC experiments have been designed to cope at most with the average amount of data recorded. The usage peaks, as already observed in Run-I, may however originate large backlogs, thus delaying the c…

Collecting conditions usage metadata to optimize current and future ATLAS software and processing Open

L. Rinaldi, D. Barberis, A. Formica, E. J. Gallas, S. Oda , et al. · 2017

Conditions data (for example: alignment, calibration, data quality) are used extensively in the processing of real and simulated data in ATLAS. The volume and variety of the conditions data needed by different types of processing are quite…

First use of LHC Run 3 Conditions Database infrastructure for auxiliary data files in ATLAS Open

L. Aperio Bella, D. Barberis, W. Buttinger, A. Formica, E. J. Gallas , et al. · 2017

Processing of the large amount of data produced by the ATLAS experiment requires fast and reliable access to what we call Auxiliary Data Files (ADF). These files, produced by Combined Performance, Trigger and Physics groups, contain condit…

Triggering events with GPUs at ATLAS Open

S. Kama, J. Augusto Soares, J. T. Baines, M. Bauce, T. Bołd , et al. · 2015

The growing complexity of events produced in LHC collisions demands more and more computing power both for the online selection and for the offline reconstruction of events. In recent years, the explosive performance growth of massively pa…

An evaluation of GPUs for use in an upgraded ATLAS High Level Trigger Open

A. Tavares Delgado, P. Conde Muíño, J. Augusto Soares, R. Gonçalo, J. T. Baines , et al. · 2015

ATLAS is a general purpose particle physics experiment located on the LHC collider at CERN. The ATLAS Trigger system consists of two levels, the first level (L1) implemented in hardware and the High Level Trigger (HLT) implemented in softw…

GPGPU for track finding in High Energy Physics Open

L. Rinaldi, Mauro Belgiovine, R. Di Sipio, A. Gabrielli, M. Negrini , et al. · 2015

The LHC experiments are designed to detect large amount of physics events produced with a very high rate. Considering the future upgrades, the data acquisition rate will become even higher and new computing paradigms must be adopted for fa…

GPU for triggering in High Energy Physics Experiments Open

A. Sidoti, S. Amerio, Mauro Belgiovine, R. Di Sipio, A. Gabrielli , et al. · 2015

Graphical Processing Units (GPUs) provide exceptional massive parallel computing power with small power consumption.General Purpose Computing on GPU (GPGPU) brings high performance computing with off-the-shelf products.However the full exp…

GPGPU for track finding in High Energy Physics. Open

L. Rinaldi, A. Sidoti, M. Villa, R. Di Sipio, S. A. Tupputi , et al. · 2015

The LHC experiments are designed to detect large amount of physics events produced with a very high rate. Considering the future upgrades, the data acquisition rate will become even higher and new computing paradigms must be adopted for fa…

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