14 papers
Sheaf-Based Federated Representation Learning
Gabriele D'Acunto, Enrico Grimaldi, Valeria Avino +4
Heterogeneous federated systems require agents to learn and exchange informative representations despite differences in data distributions, sensing modalities, model architectures,…
A Sheaf-Theoretic Framework for Distributed Multi-Site Channel Charting
Enrico Grimaldi, Leonardo Di Nino, Mario Edoardo Pandolfo +3
Channel charting (CC) enables data-driven user localization in wireless networks by embedding channel state information (CSI) into low-dimensional representations. In multi-cell sc…
Linking the "inner" and "outer" self to mental health and brain networks
Cosimo Agostinelli, Ivan Casanovas, Lochan Chaudhari +8
How are psychosocial profiles, mental health, and brain functional connectivity related? Studies have been dedicated to unraveling the associations of social support perception and…
SEMASIA: A Large-Scale Dataset of Semantically Structured Latent Representations
Mario Edoardo Pandolfo, Enrico Grimaldi, Lorenzo Marinucci +4
Latent representations learned by neural networks often exhibit semantic structure, where concept similarity is reflected by geometric proximity in embedding space. However, compar…
Metasurfaces-Integrated Wireless Neural Networks for Lightweight Over-The-Air Edge Inference
Kyriakos Stylianopoulos, Mario Edoardo Pandolfo, Paolo Di Lorenzo +1
The upcoming sixth Generation (6G) of wireless networks envisions ultra-low latency and energy efficient Edge Inference (EI) for diverse Internet of Things (IoT) applications. Howe…
Federated Latent Space Alignment for Multi-user Semantic Communications
Giuseppe Di Poce, Mario Edoardo Pandolfo, Emilio Calvanese Strinati +1
Semantic communication aims to convey meaning for effective task execution, but differing latent representations in AI-native devices can cause semantic mismatches that hinder mutu…