Zobrazeno 1 - 10
of 4 904
pro vyhledávání: '"P, Piras"'
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 many multimedia applications, are often underutilized in the natural sciences. This is primarily due to mismatches between the nature of domain-specific scientific data and th
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
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
The abundance of dark matter haloes is a key cosmological probe in forthcoming galaxy surveys. The theoretical understanding of the halo mass function (HMF) is limited by our incomplete knowledge of the origin of non-universality and its cosmological
Externí odkaz:
http://arxiv.org/abs/2405.15850
Autor:
Piras, Davide, Polanska, Alicja, Mancini, Alessio Spurio, Price, Matthew A., McEwen, Jason D.
Publikováno v:
Open Journal of Astrophysics, Vol. 7, September 5th 2024
We advocate for a new paradigm of cosmological likelihood-based inference, leveraging recent developments in machine learning and its underlying technology, to accelerate Bayesian inference in high-dimensional settings. Specifically, we combine (i) e
Externí odkaz:
http://arxiv.org/abs/2405.12965