9 papers
TReViS: Temporal Repetition Structure Aware Video Synthesis for Self-supervised Repetitive Action Counting
Fanqi Yu, Shengming Ma, Stefano Fiorini +4
Fully supervised repetitive action counting (RAC) has achieved strong performance, but requires dense temporal annotations that are costly and difficult to scale. We propose TReViS…
LAEF: A Lead-Agnostic ECG Foundation Model Towards Point-of-Care Diagnostics
Edoardo Coppola, Stefano Fiorini, Pietro Liò +2
Point-of-care cardiac devices such as smartwatches and handheld ECG recorders typically capture 1--2 leads, yet existing ECG foundation models are architecturally constrained to fi…
Benchmarking Sheaf Neural Networks for Inductive Tasks
Stefano Fiorini, Edoardo Coppola, Pietro Liò
Sheaf Neural Networks (SNNs) generalize message passing by replacing scalar edge weights of standard Graph Neural Networks (GNNs) with learnable, edge-dependent restriction maps be…
Directional Sheaf Hypergraph Networks: Unifying Learning on Directed and Undirected Hypergraphs
Emanuele Mule, Stefano Fiorini, Antonio Purificato +3
Hypergraphs provide a natural way to represent higher-order interactions among multiple entities. While undirected hypergraphs have been extensively studied, the case of directed h…
DLGNet: Hyperedge Classification through Directed Line Graphs for Chemical Reactions
Stefano Fiorini, Giulia M. Bovolenta, Stefano Coniglio +4
Graphs and hypergraphs provide powerful abstractions for modeling interactions among a set of entities of interest and have been attracting a growing interest in the literature tha…
DiffAssemble: A Unified Graph-Diffusion Model for 2D and 3D Reassembly
Gianluca Scarpellini, Stefano Fiorini, Francesco Giuliari +2
Reassembly tasks play a fundamental role in many fields and multiple approaches exist to solve specific reassembly problems. In this context, we posit that a general unified model…
Graph Learning in 4D: a Quaternion-valued Laplacian to Enhance Spectral GCNs
Stefano Fiorini, Stefano Coniglio, Michele Ciavotta +1
We introduce QuaterGCN, a spectral Graph Convolutional Network (GCN) with quaternion-valued weights at whose core lies the Quaternionic Laplacian, a quaternion-valued Laplacian mat…
SigMaNet: One Laplacian to Rule Them All
Stefano Fiorini, Stefano Coniglio, Michele Ciavotta +1
This paper introduces SigMaNet, a generalized Graph Convolutional Network (GCN) capable of handling both undirected and directed graphs with weights not restricted in sign nor magn…
Listening to the city, attentively: A Spatio-Temporal Attention Boosted Autoencoder for the Short-Term Flow Prediction Problem
Stefano Fiorini, Michele Ciavotta, Andrea Maurino
In recent years, studying and predicting alternative mobility (e.g., sharing services) patterns in urban environments has become increasingly important as accurate and timely infor…