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pro vyhledávání: '"Di Castro, A"'
Autor:
Balibrea-Correa, J., Babiano-Suarez, V., Lerendegui-Marco, J., Domingo-Pardo, C., Ladarescu, I., Tarifeño-Saldivia, A., de la Fuente-Rosales, G., Gameiro, B., Zaitseva, N., Alcayne, V., Cano-Ott, D., González-Romero, E., Martínez, T., Mendoza, E., de Rada, A. Pérez, del Olmo, J. Plaza, Sánchez-Caballero, A., Casanovas, A., Calviño, F., Valenta, S., Aberle, O., Altieri, S., Amaducci, S., Andrzejewski, J., Bacak, M., Beltrami, C., Bennett, S., Bernardes, A. P., Berthoumieux, E., Beyer, R., Boromiza, M., Bosnar, D., Caamaño, M., Calviani, M., Castelluccio, D. M., Cerutti, F., Cescutti, G., Chasapoglou, S., Chiaveri, E., Colombetti, P., Colonna, N., Camprini, P. Console, Cortés, G., Cortés-Giraldo, M. A., Cosentino, L., Cristallo, S., Dellmann, S., Di Castro, M., Di Maria, S., Diakaki, M., Dietz, M., Dressler, R., Dupont, E., Durán, I., Eleme, Z., Fargier, S., Fernández, B., Fernández-Domínguez, B., Finocchiaro, P., Fiore, S., Furman, V., García-Infantes, F., Gawlik-Ramikega, A., Gervino, G., Gilardoni, S., Guerrero, C., Gunsing, F., Gustavino, C., Heyse, J., Hillman, W., Jenkins, D. G., Jericha, E., Junghans, A., Kadi, Y., Kaperoni, K., Kaur, G., Kimura, A., Knapová, I., Kokkoris, M., Kopatch, Y., Krtìvcka, M., Kyritsis, N., Lederer-Woods, C., Lerner, G., Manna, A., Masi, A., Massimi, C., Mastinu, P., Mastromarco, M., Maugeri, E. A., Mazzone, A., Mengoni, A., Michalopoulou, V., Milazzo, P. M., Mucciola, R., Murtas, F., Musacchio-Gonzalez, E., Musumarra, A., Negret, A., Pérez-Maroto, P., Patronis, N., Pavón-Rodríguez, J. A., Pellegriti, M. G., Perkowski, J., Petrone, C., Pirovano, E., Pomp, S., Porras, I., Praena, J., Quesada, J. M., Reifarth, R., Rochman, D., Romanets, Y., Rubbia, C., Sabaté-Gilarte, M., Schillebeeckx, P., Schumann, D., Sekhar, A., Smith, A. G., Sosnin, N. V., Stamati, M. E., Sturniolo, A., Tagliente, G., Tarrío, D., Torres-Sánchez, P., Vagena, E., Variale, V., Vaz, P., Vecchio, G., Vescovi, D., Vlachoudis, V., Vlastou, R., Wallner, A., Woods, P. J., Wright, T., Zarrella, R., Zugec, P.
Challenging neutron-capture cross-section measurements of small cross sections and samples with a very limited number of atoms require high-flux time-of-flight facilities. In turn, such facilities need innovative detection setups that are fast, have
Externí odkaz:
http://arxiv.org/abs/2411.18969
Time series forecasting is critical in numerous real-world applications, requiring accurate predictions of future values based on observed patterns. While traditional forecasting techniques work well in in-domain scenarios with ample data, they strug
Externí odkaz:
http://arxiv.org/abs/2411.15743
Training neural networks is computationally heavy and energy-intensive. Many methodologies were developed to save computational requirements and energy by reducing the precision of network weights at inference time and introducing techniques such as
Externí odkaz:
http://arxiv.org/abs/2410.09734
Publikováno v:
14 October 2024
Multi-agent collaboration involves multiple participants working together in a shared environment to achieve a common goal. These agents share information, divide tasks, and synchronize their actions. Key aspects of multi agent collaboration include
Externí odkaz:
http://arxiv.org/abs/2409.10375
The ability of robots to manipulate objects relies heavily on their aptitude for visual perception. In domains characterized by cluttered scenes and high object variability, most methods call for vast labeled datasets, laboriously hand-annotated, wit
Externí odkaz:
http://arxiv.org/abs/2407.01302
Autor:
Joglekar, Omkar, Lancewicki, Tal, Kozlovsky, Shir, Tchuiev, Vladimir, Feldman, Zohar, Di Castro, Dotan
Large Language Models (LLMs) and strong vision models have enabled rapid research and development in the field of Vision-Language-Action models that enable robotic control. The main objective of these methods is to develop a generalist policy that ca
Externí odkaz:
http://arxiv.org/abs/2406.16093
Radar sensors are low cost, long-range, and weather-resilient. Therefore, they are widely used for driver assistance functions, and are expected to be crucial for the success of autonomous driving in the future. In many perception tasks only pre-proc
Externí odkaz:
http://arxiv.org/abs/2406.02158
In the field of robotics and automation, conventional object recognition and instance segmentation methods face a formidable challenge when it comes to perceiving Deformable Linear Objects (DLOs) like wires, cables, and flexible tubes. This challenge
Externí odkaz:
http://arxiv.org/abs/2402.11996
Graph generation is integral to various engineering and scientific disciplines. Nevertheless, existing methodologies tend to overlook the generation of edge attributes. However, we identify critical applications where edge attributes are essential, m
Externí odkaz:
http://arxiv.org/abs/2402.04046
Std $Q$-target is a conservative, actor-critic, ensemble, $Q$-learning-based algorithm, which is based on a single key $Q$-formula: $Q$-networks standard deviation, which is an "uncertainty penalty", and, serves as a minimalistic solution to the prob
Externí odkaz:
http://arxiv.org/abs/2402.05950