4 citations
- University of TurinIT5 papers
- Azienda Ospedaliera Citta' della Salute e della Scienza di TorinoIT1 paper
- Cardiff UniversityGB1 paper
- Fondazione Ricerca MolinetteIT1 paper
- Institute for High Performance Computing and NetworkingIT1 paper
- Institute of Cognitive Sciences and TechnologiesIT1 paper
- Institute of Informatics and TelematicsIT1 paper
- Luxembourg Institute of HealthLU1 paper
- University of BresciaIT1 paper
- University of SienaIT1 paper
- University of SouthamptonGB1 paper
7 papers
From Nodes to Edges: Edge-Based Laplacians for Brain Signal Processing
Andrea Santoro, Marco Nurisso, Giovanni Petri
Traditional graph signal processing (GSP) methods applied to brain networks focus on signals defined on the nodes. Thus, they are unable to capture potentially important dynamics o…
OntoLogX: Ontology-Guided Knowledge Graph Extraction from Cybersecurity Logs with Large Language Models
Luca Cotti, Idilio Drago, Anisa Rula +2
System logs represent a valuable source of Cyber Threat Intelligence (CTI), capturing attacker behaviors, exploited vulnerabilities, and traces of malicious activity. Yet their uti…
Counting gauge-invariant states with matter fields and finite gauge groups
Alessandro Mariani
Gauge theories with finite gauge groups have applications to quantum simulation and quantum gravity. Recently, the exact number of gauge-invariant states was computed for pure gaug…
Beyond Cox Models: Assessing the Performance of Machine-Learning Methods in Non-Proportional Hazards and Non-Linear Survival Analysis
Ivan Rossi, Flavio Sartori, Cesare Rollo +3
Survival analysis often relies on Cox models, assuming both linearity and proportional hazards (PH). This study evaluates machine and deep learning methods that relax these constra…
Synthesizing Evolving Symbolic Representations for Autonomous Systems
Gabriele Sartor, Angelo Oddi, Riccardo Rasconi +2
Recently, AI systems have made remarkable progress in various tasks. Deep Reinforcement Learning(DRL) is an effective tool for agents to learn policies in low-level state spaces to…
Quantum enhanced stratification of Breast Cancer: exploring quantum expressivity for real omics data
Valeria Repetto, Elia Giuseppe Ceroni, Giuseppe Buonaiuto +1
Quantum Machine Learning (QML) is considered one of the most promising applications of Quantum Computing in the Noisy Intermediate Scale Quantum (NISQ) era for the impact it is tho…