1 citations · 1 across the 12 of their papers we have counts for
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…
Over-the-Air Semantic Alignment with Stacked Intelligent Metasurfaces
Mario Edoardo Pandolfo, Kyriakos Stylianopoulos, George C. Alexandropoulos +1
Semantic communication systems aim to transmit task-relevant information between devices capable of artificial intelligence, but their performance can degrade when heterogeneous tr…
Colored Markov Random Fields for Probabilistic Topological Modeling
Lorenzo Marinucci, Leonardo Di Nino, Gabriele D'Acunto +3
Probabilistic Graphical Models (PGMs) encode conditional dependencies among random variables using a graph -nodes for variables, links for dependencies- and factorize the joint dis…
Learning Network Sheaves for AI-native Semantic Communication
Enrico Grimaldi, Mario Edoardo Pandolfo, Gabriele D'Acunto +2
Recent advances in AI call for a paradigm shift from bit-centric communication to goal- and semantics-oriented architectures, paving the way for AI-native 6G networks. In this cont…
Semantic Channel Equalization Strategies for Deep Joint Source-Channel Coding
Lorenzo Pannacci, Simone Fiorellino, Mario Edoardo Pandolfo +2
Deep joint source-channel coding (DeepJSCC) has emerged as a powerful paradigm for end-to-end semantic communications, jointly learning to compress and protect task-relevant featur…
Latent Space Alignment for AI-Native MIMO Semantic Communications
Mario Edoardo Pandolfo, Simone Fiorellino, Emilio Calvanese Strinati +1
Semantic communications focus on prioritizing the understanding of the meaning behind transmitted data and ensuring the successful completion of tasks that motivate the exchange of…
RIS-aided Latent Space Alignment for Semantic Channel Equalization
Tomás Hüttebräucker, Mario Edoardo Pandolfo, Simone Fiorellino +2
Semantic communication systems introduce a new paradigm in wireless communications, focusing on transmitting the intended meaning rather than ensuring strict bit-level accuracy. Th…
Patterns, Models, and Challenges in Online Social Media: A Survey
Niccolò Di Marco, Anita Bonetti, Edoardo Di Martino +10
The rise of digital platforms has enabled the large scale observation of individual and collective behavior through high resolution interaction data. This development has opened ne…
Generative Exaggeration in LLM Social Agents: Consistency, Bias, and Toxicity
Jacopo Nudo, Mario Edoardo Pandolfo, Edoardo Loru +3
We investigate how Large Language Models (LLMs) behave when simulating political discourse on social media. Leveraging 21 million interactions on X during the 2024 U.S. presidentia…