Zobrazeno 1 - 10
of 15 428
pro vyhledávání: '"A, Piras"'
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.
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
Autor:
Polanska, Alicja, Price, Matthew A., Piras, Davide, Mancini, Alessio Spurio, McEwen, Jason D.
We present the learned harmonic mean estimator with normalizing flows - a robust, scalable and flexible estimator of the Bayesian evidence for model comparison. Since the estimator is agnostic to sampling strategy and simply requires posterior sample
Externí odkaz:
http://arxiv.org/abs/2405.05969
Autor:
Bartoletti, Massimo, Benetollo, Lorenzo, Bugliesi, Michele, Crafa, Silvia, Sasso, Giacomo Dal, Pettinau, Roberto, Pinna, Andrea, Piras, Mattia, Rossi, Sabina, Salis, Stefano, Spanò, Alvise, Tkachenko, Viacheslav, Tonelli, Roberto, Zunino, Roberto
Decentralized blockchain platforms support the secure exchange of assets among users without relying on trusted third parties. These exchanges are programmed with smart contracts, computer programs directly executed by blockchain nodes. Multiple smar
Externí odkaz:
http://arxiv.org/abs/2404.04129
Autor:
Piras, Davide, Lombriser, Lucas
We present DE-VAE, a variational autoencoder (VAE) architecture to search for a compressed representation of dynamical dark energy (DE) models in observational studies of the cosmic large-scale structure. DE-VAE is trained on matter power spectra boo
Externí odkaz:
http://arxiv.org/abs/2310.10717
Autor:
Floris, Giuseppe, Mura, Raffaele, Scionis, Luca, Piras, Giorgio, Pintor, Maura, Demontis, Ambra, Biggio, Battista
Evaluating the adversarial robustness of machine learning models using gradient-based attacks is challenging. In this work, we show that hyperparameter optimization can improve fast minimum-norm attacks by automating the selection of the loss functio
Externí odkaz:
http://arxiv.org/abs/2310.08177
Neural network pruning has shown to be an effective technique for reducing the network size, trading desirable properties like generalization and robustness to adversarial attacks for higher sparsity. Recent work has claimed that adversarial pruning
Externí odkaz:
http://arxiv.org/abs/2310.08073
Autor:
Ledda, Emanuele, Angioni, Daniele, Piras, Giorgio, Fumera, Giorgio, Biggio, Battista, Roli, Fabio
Machine-learning models can be fooled by adversarial examples, i.e., carefully-crafted input perturbations that force models to output wrong predictions. While uncertainty quantification has been recently proposed to detect adversarial inputs, under
Externí odkaz:
http://arxiv.org/abs/2309.10586