Zobrazeno 1 - 10
of 15 187
pro vyhledávání: '"A. Piras"'
This paper presents a novel framework for full-waveform seismic source inversion using simulation-based inference (SBI). Traditional probabilistic approaches often rely on simplifying assumptions about data errors, which we show can lead to inaccurat
Externí odkaz:
http://arxiv.org/abs/2410.23238
Autor:
Ledda, Emanuele, Scodeller, Giovanni, Angioni, Daniele, Piras, Giorgio, Cinà, Antonio Emanuele, Fumera, Giorgio, Biggio, Battista, Roli, Fabio
In learning problems, the noise inherent to the task at hand hinders the possibility to infer without a certain degree of uncertainty. Quantifying this uncertainty, regardless of its wide use, assumes high relevance for security-sensitive application
Externí odkaz:
http://arxiv.org/abs/2410.21952
We employ a novel framework for accelerated cosmological inference, based on neural emulators and gradient-based sampling methods, to forecast constraints on dark energy models from Stage IV cosmic shear surveys. We focus on dark scattering (DS), an
Externí odkaz:
http://arxiv.org/abs/2410.10603
Simulations of the dark matter distribution throughout the Universe are essential in order to analyse data from cosmological surveys. $N$-body simulations are computationally expensive, and many cheaper alternatives (such as lognormal random fields)
Externí odkaz:
http://arxiv.org/abs/2410.07349
Vision foundation models, which have demonstrated significant potential in multimedia applications, are often underutilized in the natural sciences. This is primarily due to mismatches between the nature of domain-specific scientific data and the typ
Externí odkaz:
http://arxiv.org/abs/2409.11175
Autor:
Piras, Giorgio, Pintor, Maura, Demontis, Ambra, Biggio, Battista, Giacinto, Giorgio, Roli, Fabio
Recent work has proposed neural network pruning techniques to reduce the size of a network while preserving robustness against adversarial examples, i.e., well-crafted inputs inducing a misclassification. These methods, which we refer to as adversari
Externí odkaz:
http://arxiv.org/abs/2409.01249
Autor:
Lastufka, Erica, Bait, Omkar, Taran, Olga, Drozdova, Mariia, Kinakh, Vitaliy, Piras, Davide, Audard, Marc, Dessauges-Zavadsky, Miroslava, Holotyak, Taras, Schaerer, Daniel, Voloshynovskiy, Svyatoslav
Publikováno v:
A&A 690, A310 (2024)
Self-supervised learning (SSL) applied to natural images has demonstrated a remarkable ability to learn meaningful, low-dimension representations without labels, resulting in models that are adaptable to many different tasks. Until now, applications
Externí odkaz:
http://arxiv.org/abs/2408.06147
Autor:
Mura, Raffaele, Floris, Giuseppe, Scionis, Luca, Piras, Giorgio, Pintor, Maura, Demontis, Ambra, Giacinto, Giorgio, Biggio, Battista, Roli, Fabio
Gradient-based attacks are a primary tool to evaluate robustness of machine-learning models. However, many attacks tend to provide overly-optimistic evaluations as they use fixed loss functions, optimizers, step-size schedulers, and default hyperpara
Externí odkaz:
http://arxiv.org/abs/2407.08806
Autor:
Scano, Christian, Floris, Giuseppe, Montaruli, Biagio, Demetrio, Luca, Valenza, Andrea, Compagna, Luca, Ariu, Davide, Piras, Luca, Balzarotti, Davide, Biggio, Battista
ModSecurity is widely recognized as the standard open-source Web Application Firewall (WAF), maintained by the OWASP Foundation. It detects malicious requests by matching them against the Core Rule Set (CRS), identifying well-known attack patterns. E
Externí odkaz:
http://arxiv.org/abs/2406.13547
Autor:
Piras, S., Horellou, C., Conway, J. E., Thomasson, M., del Palacio, S., Shimwell, T. W., O'Sullivan, S. P., Carretti, E., Šnidaric, I., Jelic, V., Adebahr, B., Berger, A., Best, P. N., Brüggen, M., Ruiz, N. Herrera, Paladino, R., Prandoni, I., Sabater, J., Vacca, V.
The aim of this study is to probe the sub-mJy polarized source population with LOFAR. We present the method used to stack LOFAR polarization datasets, the resulting catalog of polarized sources, and the derived polarized source counts. The ELAIS-N1 f
Externí odkaz:
http://arxiv.org/abs/2406.08346