Zobrazeno 1 - 10
of 25 167
pro vyhledávání: '"Volpi A"'
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
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
Autor:
Royer, P., Merle, T., Dsilva, K., Sekaran, S., Van Winckel, H., Frémat, Y., Van der Swaelmen, M., Gebruers, S., Tkachenko, A., Laverick, M., Dirickx, M., Raskin, G., Hensberge, H., Abdul-Masih, M., Acke, B., Alonso, M. L., Mahato, S. Bandhu, Beck, P. G., Behara, N., Bloemen, S., Buysschaert, B., Cox, N., Debosscher, J., De Cat, P., Degroote, P., De Nutte, R., De Smedt, K., de Vries, B., Dumortier, L., Escorza, A., Exter, K., Goriely, S., Gorlova, N., Hillen, M., Homan, W., Jorissen, A., Kamath, D., Karjalainen, M., Karjalainen, R., Lampens, P., Lobel, A., Lombaert, R., Marcos-Arenal, P., Menu, J., Merges, F., Moravveji, E., Nemeth, P., Neyskens, P., Ostensen, R., Pápics, P. I., Perez, J., Royer, S. Prins S., Samadi-Ghadim, A., Sana, H., Fuentes, A. Sans, Scaringi, S., Schmid, V., Siess, L., Siopis, C., Smolders, K., Sodor, S., Thoul, A., Triana, S., Vandenbussche, B., Van de Sande, M., Van De Steene, G., Van Eck, S., van Hoof, P. A. M., Van Marle, A. J., Van Reeth, T., Vermeylen, L., Volpi, D., Vos, J., Waelkens, C.
Publikováno v:
A&A 681, A107 (2024)
Over the past decades, libraries of stellar spectra have been used in a large variety of science cases, including as sources of reference spectra for a given object or a given spectral type. Despite the existence of large libraries and the increasing
Externí odkaz:
http://arxiv.org/abs/2311.02705
The presence of symmetries imposes a stringent set of constraints on a system. This constrained structure allows intelligent agents interacting with such a system to drastically improve the efficiency of learning and generalization, through the inter
Externí odkaz:
http://arxiv.org/abs/2310.16555