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pro vyhledávání: '"De Maio, P"'
With the increasing interest in Quantum Machine Learning, Quantum Neural Networks (QNNs) have emerged and gained significant attention. These models have, however, been shown to be notoriously difficult to train, which we hypothesize is partially due
Externí odkaz:
http://arxiv.org/abs/2410.09470
Mobile devices offload latency-sensitive application tasks to edge servers to satisfy applications' Quality of Service (QoS) deadlines. Consequently, ensuring reliable offloading without QoS violations is challenging in distributed and unreliable edg
Externí odkaz:
http://arxiv.org/abs/2410.06715
The increasing growth of data volume, and the consequent explosion in demand for computational power, are affecting scientific computing, as shown by the rise of extreme data scientific workflows. As the need for computing power increases, quantum co
Externí odkaz:
http://arxiv.org/abs/2404.10389
The increasing capabilities of Machine Learning (ML) models go hand in hand with an immense amount of data and computational power required for training. Therefore, training is usually outsourced into HPC facilities, where we have started to experien
Externí odkaz:
http://arxiv.org/abs/2403.18579
Autor:
De Maio, Vincenzo, Kanatbekova, Meerzhan, Zilk, Felix, Friis, Nicolai, Guggemos, Tobias, Brandic, Ivona
Publikováno v:
in: Proceedings of the 2024 IEEE 24th International Symposium on Cluster, Cloud and Internet Computing (CCGrid), Philadelphia, PA, USA, pp. 626-635
As we enter the post-Moore era, we experience the rise of various non-von-Neumann-architectures to address the increasing computational demand for modern applications, with quantum computing being among the most prominent and promising technologies.
Externí odkaz:
http://arxiv.org/abs/2403.00885
Publikováno v:
Euro-Par 2023: Parallel Processing Workshops. Euro-Par 2023. Lecture Notes in Computer Science, vol 14351. Springer, Cham
With the advent of the Post-Moore era, the scientific community is faced with the challenge of addressing the demands of current data-intensive machine learning applications, which are the cornerstone of urgent analytics in distributed computing. Qua
Externí odkaz:
http://arxiv.org/abs/2402.15542
Autor:
Gerardo Salvato, Claudio Bertolotti, Manuela Sellitto, Teresa Fazia, Damiano Crivelli, Gabriele De Maio, Francesca Giulia Magnani, Alessandra Leo, Tatiana Bianconi, Maria Chiara Cortesi, Michele Spinelli, Gabriella Bottini
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-9 (2024)
Summary Postural balance requires the interplay between several physiological signals. Indirect evidence suggests that the perception of signals arising from the autonomic nervous system might play a role (e.g. cardiac awareness). Here, we tested thi
Externí odkaz:
https://doaj.org/article/6dd5259e8d9f49afba881a68cc31a13a
Autor:
Marcelo de Maio Nascimento
Publikováno v:
Geriatrics, Gerontology and Aging, Vol 12, Pp 219-224 (2024)
The objective of this study was to present the main factors responsible for falls in older adults, measures for assessing risk, and prevention strategies. This is a qualitative study, developed from the international and national gerontology literatu
Externí odkaz:
https://doaj.org/article/5bf8a1ec79d84e87b8b0330568b7254f
Autor:
Marino, Angela, Soldi, Giovanni, Gaglione, Domenico, Aubry, Augusto, Braca, Paolo, De Maio, Antonio, Willett, Peter
Multi-platform radar networks (MPRNs) are an emerging sensing technology due to their ability to provide improved surveillance capabilities over plain monostatic and bistatic systems. The design of advanced detection, localization, and tracking algor
Externí odkaz:
http://arxiv.org/abs/2308.06972
This work considers Maximum Likelihood Estimation (MLE) of a Toeplitz structured covariance matrix. In this regard, an equivalent reformulation of the MLE problem is introduced and two iterative algorithms are proposed for the optimization of the equ
Externí odkaz:
http://arxiv.org/abs/2307.03923