7 papers
Networks of Causal Abstractions: A Sheaf-theoretic Framework
Gabriele D'Acunto, Paolo Di Lorenzo, Sergio Barbarossa
A core challenge in causal artificial intelligence is the principled coordination of multiple, imperfect, and subjective causal perspectives arising from distributed agents with li…
Generative Semantic Communication: Diffusion Models Beyond Bit Recovery
Eleonora Grassucci, Sergio Barbarossa, Danilo Comminiello
Semantic communication is expected to be one of the cores of next-generation AI-based communications. One of the possibilities offered by semantic communication is the capability t…
SPARQ: An Optimization Framework for the Distribution of AI-Intensive Applications under Non-Linear Delay Constraints
Pietro Spadaccino, Paolo Di Lorenzo, Sergio Barbarossa +2
Next-generation real-time compute-intensive applications, such as extended reality, multi-user gaming, and autonomous transportation, are increasingly composed of heterogeneous AI-…
Physics-Informed Topological Signal Processing for Water Distribution Network Monitoring
Tiziana Cattai, Stefania Sardellitti, Stefania Colonnese +2
Water management is one of the most critical aspects of our society, together with population increase and climate change. Water scarcity requires a better characterization and mon…
Topological Signal Processing and Learning: Recent Advances and Future Challenges
Elvin Isufi, Geert Leus, Baltasar Beferull-Lozano +2
Developing methods to process irregularly structured data is crucial in applications like gene-regulatory, brain, power, and socioeconomic networks. Graphs have been the go-to alge…
Learning Sheaf Laplacian Optimizing Restriction Maps
Leonardo Di Nino, Sergio Barbarossa, Paolo Di Lorenzo
The aim of this paper is to propose a novel framework to infer the sheaf Laplacian, including the topology of a graph and the restriction maps, from a set of data observed over the…