Zobrazeno 1 - 10
of 25 289
pro vyhledávání: '"A. Volpi"'
The O-type long-period binary HD 168112 and triple HD 167971 star systems have been known for several decades for their non-thermal synchrotron radio emission. This emission arises from relativistic electrons accelerated in the hydrodynamic shocks of
Externí odkaz:
http://arxiv.org/abs/2410.14458
Deep learning models are effective, yet brittle. Even carefully trained, their behavior tends to be hard to predict when confronted with out-of-distribution samples. In this work, our goal is to propose a simple yet effective solution to predict and
Externí odkaz:
http://arxiv.org/abs/2408.04471
The long-period O-star binary system HD 168112 and the triple O-star system HD 167971 are well-known sources of non-thermal radio emission that arises from a colliding wind interaction. The wind-wind collisions in these systems should result in phase
Externí odkaz:
http://arxiv.org/abs/2406.08991
Open-vocabulary object detection (OvOD) has transformed detection into a language-guided task, empowering users to freely define their class vocabularies of interest during inference. However, our initial investigation indicates that existing OvOD de
Externí odkaz:
http://arxiv.org/abs/2405.10053
Radio surveys of early-type stars have revealed a number of non-thermal emitters. Most of these have been shown to be binaries, where the collision between the two stellar winds is responsible for the non-thermal emission. HD 168112 is a non-thermal
Externí odkaz:
http://arxiv.org/abs/2405.03247
Object detectors are typically trained once and for all on a fixed set of classes. However, this closed-world assumption is unrealistic in practice, as new classes will inevitably emerge after the detector is deployed in the wild. In this work, we lo
Externí odkaz:
http://arxiv.org/abs/2402.17420
When deploying a semantic segmentation model into the real world, it will inevitably encounter semantic classes that were not seen during training. To ensure a safe deployment of such systems, it is crucial to accurately evaluate and improve their an
Externí odkaz:
http://arxiv.org/abs/2402.16392
Hydrogeological effects of dredging navigable canals through lagoon shallows. A case study in Venice
Autor:
P. Teatini, G. Isotton, S. Nardean, M. Ferronato, A. Mazzia, C. Da Lio, L. Zaggia, D. Bellafiore, M. Zecchin, L. Baradello, F. Cellone, F. Corami, A. Gambaro, G. Libralato, E. Morabito, A. Volpi Ghirardini, R. Broglia, S. Zaghi, L. Tosi
Publikováno v:
Hydrology and Earth System Sciences, Vol 21, Pp 5627-5646 (2017)
For the first time a comprehensive investigation has been carried out to quantify the possible effects of dredging a navigable canal on the hydrogeological system underlying a coastal lagoon. The study is focused on the Venice Lagoon, Italy, where
Externí odkaz:
https://doaj.org/article/58cb862d398c4a7d89388a2040883d1e
Autor:
Affinita, Daniele, Volpi, Flavio, Spagnoli, Valerio, Suriani, Vincenzo, Nardi, Daniele, Bloisi, Domenico D.
RoboCup represents an International testbed for advancing research in AI and robotics, focusing on a definite goal: developing a robot team that can win against the human world soccer champion team by the year 2050. To achieve this goal, autonomous h
Externí odkaz:
http://arxiv.org/abs/2401.15026
Cropland maps are a core and critical component of remote-sensing-based agricultural monitoring, providing dense and up-to-date information about agricultural development. Machine learning is an effective tool for large-scale agricultural mapping, bu
Externí odkaz:
http://arxiv.org/abs/2312.10872