Zobrazeno 1 - 10
of 92
pro vyhledávání: '"Paolo Di Lorenzo"'
Publikováno v:
IEEE Open Journal of the Communications Society, Vol 5, Pp 2418-2432 (2024)
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 task. Despit
Externí odkaz:
https://doaj.org/article/f8e87fdacf78462aa8fab08fc62f8546
Autor:
Massimo Framarini, Fabrizio D’Acapito, Daniela Di Pietrantonio, Francesca Tauceri, Paolo Di Lorenzo, Leonardo Solaini, Giorgio Ercolani
Publikováno v:
Surgeries, Vol 4, Iss 4, Pp 590-599 (2023)
Epithelial ovarian cancer (EOC) is the most frequent cause of death among women with gynecologic malignant tumors. Primary debulking surgery (PDS) with maximal surgical effort to reach completeness of cytoreduction, followed by chemotherapy, has beco
Externí odkaz:
https://doaj.org/article/36c5f7037db74e4b874e52e5ed1751c4
Autor:
George C. Alexandropoulos, Dinh-Thuy Phan-Huy, Konstantinos D. Katsanos, Maurizio Crozzoli, Henk Wymeersch, Petar Popovski, Philippe Ratajczak, Yohann Bénédic, Marie-Helene Hamon, Sebastien Herraiz Gonzalez, Placido Mursia, Marco Rossanese, Vincenzo Sciancalepore, Jean-Baptiste Gros, Sergio Terranova, Gabriele Gradoni, Paolo Di Lorenzo, Moustafa Rahal, Benoit Denis, Raffaele D’Errico, Antonio Clemente, Emilio Calvanese Strinati
Publikováno v:
EURASIP Journal on Wireless Communications and Networking, Vol 2023, Iss 1, Pp 1-38 (2023)
Abstract Reconfigurable intelligent surfaces (RISs) constitute the key enabler for programmable electromagnetic propagation environments and are lately being considered as a candidate physical-layer technology for the demanding connectivity, reliabil
Externí odkaz:
https://doaj.org/article/9f0b077abb65409d8063dfeafaae7a58
Publikováno v:
EURASIP Journal on Advances in Signal Processing, Vol 2022, Iss 1, Pp 1-34 (2022)
Abstract Goal-oriented communications represent an emerging paradigm for efficient and reliable learning at the wireless edge, where only the information relevant for the specific learning task is transmitted to perform inference and/or training. The
Externí odkaz:
https://doaj.org/article/c024cabb22f342f5a8fad2bca24001a0
Publikováno v:
EURASIP Journal on Wireless Communications and Networking, Vol 2022, Iss 1, Pp 1-32 (2022)
Abstract In this paper, we propose a novel algorithm for energy-efficient low-latency dynamic mobile edge computing (MEC), in the context of beyond 5G networks endowed with reconfigurable intelligent surfaces (RISs). We consider a scenario where new
Externí odkaz:
https://doaj.org/article/2afcf8ace18a430d9507c927335a133b
Autor:
Paolo Di Lorenzo, Vincenza Conteduca, Emanuela Scarpi, Marco Adorni, Francesco Multinu, Annalisa Garbi, Ilaria Betella, Tommaso Grassi, Tommaso Bianchi, Giampaolo Di Martino, Andrea Amadori, Paolo Maniglio, Isabella Strada, Silvestro Carinelli, Marta Jaconi, Giovanni Aletti, Vanna Zanagnolo, Angelo Maggioni, Luca Savelli, Ugo De Giorgi, Fabio Landoni, Nicoletta Colombo, Robert Fruscio
Publikováno v:
Frontiers in Oncology, Vol 12 (2022)
Simple summaryLow-grade serous ovarian cancer (LGSOC) represents an uncommon histotype of serous ovarian cancer (accounting for approximately 5% of all ovarian cancer) with a distinct behavior compared to its high-grade serous counterpart, characteri
Externí odkaz:
https://doaj.org/article/8e1e191bdf904bd3aed14edd63a78c00
Publikováno v:
IEEE Access, Vol 9, Pp 45377-45398 (2021)
The aim of this paper is to propose a resource allocation strategy for dynamic training and inference of machine learning tasks at the edge of the wireless network, with the goal of exploring the trade-off between energy, delay and learning accuracy.
Externí odkaz:
https://doaj.org/article/92bb510cf9274215a0d77aaf49d3be73
Autor:
Nicola di Pietro, Emilio Calvanese Strinati, Paolo Di Lorenzo, Sergio Barbarossa, Mattia Merluzzi
Publikováno v:
IEEE Transactions on Green Communications and Networking
We propose a novel strategy for energy-efficient dynamic computation offloading, in the context of edge-computing-aided beyond 5G networks. The goal is to minimize the energy consumption of the overall system, comprising multiple User Equipment (UE),
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9d744dda6c696bba0225df06ef52eae2
https://doi.org/10.1109/tgcn.2021.3125543
https://doi.org/10.1109/tgcn.2021.3125543
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 extract an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d2f27cbc378c1b44f9673e7106260139
http://arxiv.org/abs/2305.02137
http://arxiv.org/abs/2305.02137
Autor:
Mattia Merluzzi, Francesca Costanzo, Konstantinos D. Katsanos, George C. Alexandropoulos, Paolo Di Lorenzo
Publikováno v:
GLOBECOM 2022 - 2022 IEEE Global Communications Conference.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2cf462577c8f42614b67ac1709ed46e6
https://doi.org/10.1109/globecom48099.2022.10001259
https://doi.org/10.1109/globecom48099.2022.10001259