Sergio Barbarossa
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View article: VitaGraph: Building a Knowledge Graph for Biologically Relevant Learning Tasks
VitaGraph: Building a Knowledge Graph for Biologically Relevant Learning Tasks Open
The intrinsic complexity of human biology presents ongoing challenges to scientific understanding. Researchers collaborate across disciplines to expand our knowledge of the biological interactions that define human life. AI methodologies h…
View article: Physics-Informed Topological Signal Processing for Water Distribution Network Monitoring
Physics-Informed Topological Signal Processing for Water Distribution Network Monitoring Open
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 monitoring of Water Distribution Networks (WDNs). This paper p…
View article: SQ-GAN: Semantic Image Communications Using Masked Vector Quantization
SQ-GAN: Semantic Image Communications Using Masked Vector Quantization Open
This work introduces Semantically Masked Vector Quantized Generative Adversarial Network (SQ-GAN), a novel approach integrating semantically driven image coding and vector quantization to optimize image compression for semantic/task-orient…
View article: Learning Sheaf Laplacian Optimizing Restriction Maps
Learning Sheaf Laplacian Optimizing Restriction Maps Open
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 nodes of a graph. The proposed method is based on sheaf th…
View article: Topological Signal Processing and Learning: Recent Advances and Future Challenges
Topological Signal Processing and Learning: Recent Advances and Future Challenges Open
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 algebraic tool for modeling the structure via nodes and edges c…
View article: Language-Oriented Semantic Latent Representation for Image Transmission
Language-Oriented Semantic Latent Representation for Image Transmission Open
In the new paradigm of semantic communication (SC), the focus is on delivering meanings behind bits by extracting semantic information from raw data. Recent advances in data-to-text models facilitate language-oriented SC, particularly for …
View article: Opportunistic Information-Bottleneck for Goal-oriented Feature Extraction and Communication
Opportunistic Information-Bottleneck for Goal-oriented Feature Extraction and Communication Open
The Information Bottleneck (IB) method is an information theoretical framework to design a parsimonious and tunable feature-extraction mechanism, such that the extracted features are maximally relevant to a specific learning or inference t…
View article: Robust Filter Design for Graph Signals
Robust Filter Design for Graph Signals Open
Our goal in this paper is the robust design of filters acting on signals observed over graphs subject to small perturbations of their edges. The focus is on developing a method to identify spectral and polynomial graph filters that can ada…
View article: RIS-Aided Wireless Fingerprinting Localization Based on Multilayer Graph Representations
RIS-Aided Wireless Fingerprinting Localization Based on Multilayer Graph Representations Open
The aim of this paper is to propose a novel method for wireless fingerprinting localization empowered by reconfigurable intelligent surfaces (RISs), exploiting the flexibility offered by RIS configuration control, and coping with the possi…
View article: Semantic-Preserving Image Coding Based on Conditional Diffusion Models
Semantic-Preserving Image Coding Based on Conditional Diffusion Models Open
Semantic communication, rather than on a bit-by-bit recovery of the transmitted messages, focuses on the meaning and the goal of the communication itself. In this paper, we propose a novel semantic image coding scheme that preserves the se…
View article: Generative AI Meets Semantic Communication: Evolution and Revolution of Communication Tasks
Generative AI Meets Semantic Communication: Evolution and Revolution of Communication Tasks Open
While deep generative models are showing exciting abilities in computer vision and natural language processing, their adoption in communication frameworks is still far underestimated. These methods are demonstrated to evolve solutions to c…
View article: Topological Signal Processing Over Generalized Cell Complexes
Topological Signal Processing Over Generalized Cell Complexes Open
Topological Signal Processing (TSP) over simplicial complexes is a framework that has been recently proposed, as a generalization of graph signal processing (GSP), to extend GSP to analyzing signals defined over sets of any order (i.e., no…
View article: Opportunistic Information-Bottleneck for Goal-Oriented Feature Extraction and Communication
Opportunistic Information-Bottleneck for Goal-Oriented Feature Extraction and Communication Open
The Information Bottleneck (IB) method is an information theoretical framework to design a parsimonious and tunable feature-extraction mechanism, such that the extracted features are maximally relevant to a specific learning or inference t…
View article: Stability of Graph Convolutional Neural Networks through the lens of small perturbation analysis
Stability of Graph Convolutional Neural Networks through the lens of small perturbation analysis Open
In this work, we study the problem of stability of Graph Convolutional Neural Networks (GCNs) under random small perturbations in the underlying graph topology, i.e. under a limited number of insertions or deletions of edges. We derive a n…
View article: Goal-Oriented Communications for the IoT: System Design and Adaptive Resource Optimization
Goal-Oriented Communications for the IoT: System Design and Adaptive Resource Optimization Open
Internet of Things (IoT) applications combine sensing, wireless communication, intelligence, and actuation, enabling the interaction among heterogeneous devices that collect and process considerable amounts of data. However, the effectiven…
View article: Learning Multi-Frequency Partial Correlation Graphs
Learning Multi-Frequency Partial Correlation Graphs Open
Despite the large research effort devoted to learning dependencies between time series, the state of the art still faces a major limitation: existing methods learn partial correlations but fail to discriminate across distinct frequency ban…
View article: Semantic-Preserving Image Coding based on Conditional Diffusion Models
Semantic-Preserving Image Coding based on Conditional Diffusion Models Open
Semantic communication, rather than on a bit-by-bit recovery of the transmitted messages, focuses on the meaning and the goal of the communication itself. In this paper, we propose a novel semantic image coding scheme that preserves the se…
View article: Goal-oriented Communications for the IoT: System Design and Adaptive Resource Optimization
Goal-oriented Communications for the IoT: System Design and Adaptive Resource Optimization Open
Internet of Things (IoT) applications combine sensing, wireless communication, intelligence, and actuation, enabling the interaction among heterogeneous devices that collect and process considerable amounts of data. However, the effectiven…
View article: Semantic Communications Based on Adaptive Generative Models and Information Bottleneck
Semantic Communications Based on Adaptive Generative Models and Information Bottleneck Open
Semantic communications represent a significant breakthrough with respect to the current communication paradigm, as they focus on recovering the meaning behind the transmitted sequence of symbols, rather than the symbols themselves. In sem…
View article: Generalized Simplicial Attention Neural Networks
Generalized Simplicial Attention Neural Networks Open
The aim of this work is to introduce Generalized Simplicial Attention Neural Networks (GSANs), i.e., novel neural architectures designed to process data defined on simplicial complexes using masked self-attentional layers. Hinging on topol…
View article: In Band Network Telemetry Overhead Reduction Based on Data Flows Sampling and Recovering
In Band Network Telemetry Overhead Reduction Based on Data Flows Sampling and Recovering Open
In band Network Telemetry (INT) is a technique aiming at collecting telemetry information by inserting it inside the data packets, instead of relying on classical centralized monitoring elements that periodically query the network devices.…
View article: Generative Semantic Communication: Diffusion Models Beyond Bit Recovery
Generative Semantic Communication: Diffusion Models Beyond Bit Recovery Open
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 to regenerate, at the destination side, images or videos sem…
View article: Multi-User Goal-Oriented Communications With Energy-Efficient Edge Resource Management
Multi-User Goal-Oriented Communications With Energy-Efficient Edge Resource Management Open
Edge Learning (EL) pushes the computational resources toward the edge of 5G/6G network to assist mobile users requesting delay-sensitive and energy-aware intelligent services. A common challenge in running inference tasks from remote is to…
View article: Topological Slepians: Maximally Localized Representations of Signals Over Simplicial Complexes
Topological Slepians: Maximally Localized Representations of Signals Over Simplicial Complexes Open
This paper introduces topological Slepians, i.e., a novel class of signals defined over topological spaces (e.g., simplicial complexes) that are maximally concentrated on the topological domain (e.g., over a set of nodes, edges, triangles,…
View article: Topological Signal Processing Over Weighted Simplicial Complexes
Topological Signal Processing Over Weighted Simplicial Complexes Open
Weighing the topological domain over which data can be represented and analysed is a key strategy in many signal processing and machine learning applications, enabling the extraction and exploitation of meaningful data features and their (…
View article: Multi-user Goal-oriented Communications with Energy-efficient Edge Resource Management
Multi-user Goal-oriented Communications with Energy-efficient Edge Resource Management Open
Edge Learning (EL) pushes the computational resources toward the edge of 5G/6G network to assist mobile users requesting delay-sensitive and energy-aware intelligent services. A common challenge in running inference tasks from remote is to…
View article: Topological Signal Processing over Weighted Simplicial Complexes
Topological Signal Processing over Weighted Simplicial Complexes Open
Weighing the topological domain over which data can be represented and analysed is a key strategy in many signal processing and machine learning applications, enabling the extraction and exploitation of meaningful data features and their (…