STLight: a Fully Convolutional Approach for Efficient Predictive Learning by Spatio-Temporal joint Processing

Autor: Alfarano, Andrea, Alfarano, Alberto, Friso, Linda, Bacciu, Andrea, Amerini, Irene, Silvestri, Fabrizio

Spatio-Temporal predictive Learning is a self-supervised learning paradigm that enables models to identify spatial and temporal patterns by predicting future frames based on past frames. Traditional methods, which use recurrent neural networks to cap

Externí odkaz: http://arxiv.org/abs/2411.10198

A model-independent reconstruction of the matter power spectrum

Autor: Ye, Gen, Jiang, Jun-Qian, Silvestri, Alessandra

We propose a new model-independent reconstruction method for the matter power spectrum based on its time dependence and a combination of observations from different redshifts. The method builds on a perturbative expansion in terms of the linear growt

Externí odkaz: http://arxiv.org/abs/2411.07082

Insights from the first flaring activity of a high-synchrotron-peaked blazar with X-ray polarization and VHE gamma rays

Autor: Abe, K., Abe, S., Abhir, J., Abhishek, A., Acciari, V. A., Aguasca-Cabot, A., Agudo, I., Aniello, T., Ansoldi, S., Antonelli, L. A., Engels, A. Arbet, Arcaro, C., Asano, K., Babić, A., de Almeida, U. Barres, Barrio, J. A., Barrios-Jiménez, L., Batković, I., Baxter, J., González, J. Becerra, Bednarek, W., Bernardini, E., Bernete, J., Berti, A., Besenrieder, J., Bigongiari, C., Biland, A., Blanch, O., Bonnoli, G., Bošnjak, Ž., Bronzini, E., Burelli, I., Campoy-Ordaz, A., Carosi, A., Carosi, R., Carretero-Castrillo, M., Castro-Tirado, A. J., Cerasole, D., Ceribella, G., Chai, Y., Chilingarian, A., Cifuentes, A., Colombo, E., Contreras, J. L., Cortina, J., Covino, S., D'Ammando, F., D'Amico, G., Da Vela, P., Dazzi, F., De Angelis, A., De Lotto, B., de Menezes, R., Delfino, M., Delgado, J., Mendez, C. Delgado, Di Pierro, F., Di Tria, R., Di Venere, L., Dinesh, A., Prester, D. Dominis, Donini, A., Dorner, D., Doro, M., Eisenberger, L., Elsaesser, D., Escudero, J., Fariña, L., Foffano, L., Font, L., Fröse, S., Fukazawa, Y., López, R. J. García, Garczarczyk, M., Gasparyan, S., Gaug, M., Paiva, J. G. Giesbrecht, Giglietto, N., Giordano, F., Gliwny, P., Godinović, N., Gradetzke, T., Grau, R., Green, D., Green, J. G., Günther, P., Hadasch, D., Hahn, A., Hassan, T., Heckmann, L., Llorente, J. Herrera, Hrupec, D., Imazawa, R., Israyelyan, D., Itokawa, T., Martínez, I. Jiménez, Quiles, J. Jiménez, Jormanainen, J., Kankkunen, S., Kayanoki, T., Kerszberg, D., Khachatryan, M., Kluge, G. W., Kobayashi, Y., Konrad, J., Kouch, P. M., Kubo, H., Kushida, J., Láinez, M., Lamastra, A., Lindfors, E., Lombardi, S., Longo, F., López-Coto, R., López-Moya, M., López-Oramas, A., Loporchio, S., Lorini, A., Lyard, E., Majumdar, P., Makariev, M., Maneva, G., Manganaro, M., Mangano, S., Mannheim, K., Mariotti, M., Martínez, M., Maruševec, P., Mas-Aguilar, A., Mazin, D., Menchiari, S., Mender, S., Miceli, D., Miranda, J. M., Mirzoyan, R., González, M. Molero, Molina, E., Mondal, H. A., Moralejo, A., Nakamori, T., Nanci, C., Neustroev, V., Nickel, L., Rosillo, M. Nievas, Nigro, C., Nikolić, L., Nilsson, K., Nishijima, K., Ekoume, T. Njoh, Noda, K., Nozaki, S., Okumura, A., Paiano, S., Paneque, D., Paoletti, R., Paredes, J. M., Peresano, M., Persic, M., Pihet, M., Pirola, G., Podobnik, F., Moroni, P. G. Prada, Prandini, E., Principe, G., Rhode, W., Ribó, M., Rico, J., Righi, C., Sahakyan, N., Saito, T., Saturni, F. G., Schmuckermaier, F., Schubert, J. L., Sciaccaluga, A., Silvestri, G., Sitarek, J., Sliusar, V., Sobczynska, D., Stamerra, A., Strišković, J., Strom, D., Strzys, M., Suda, Y., Tajima, H., Takahashi, M., Takeishi, R., Temnikov, P., Terauchi, K., Terzić, T., Teshima, M., Truzzi, S., Tutone, A., Ubach, S., van Scherpenberg, J., Ventura, S., Verna, G., Viale, I., Vigliano, A., Vigorito, C. F., Vitale, V., Vovk, I., Walter, R., Wersig, F., Will, M., Yamamoto, T., Yeung, P. K. H., Liodakis, I., Middei, R., Kiehlmann, S., Gesu, L. D., Kim, D. E., Ehlert, S. R., Saade, M. L., Kaaret, P., Maksym, W. P., Chen, C. T., Pérez, I. De La Calle, Perri, M., Verrecchia, F., Domann, O., Dürr, S., Feige, M., Heidemann, M., Koppitz, O., Manhalter, G., Reinhart, D., Steineke, R., Lorey, C., McCall, C., Jermak, H. E., Steele, I. A., Ramazani, V. Fallah, Otero-Santos, J., Morcuende, D., Aceituno, F. J., Casanova, V., Sota, A., Jorstad, S. G., Marscher, A. P., Pauley, C., Sasada, M., Kawabata, K. S., Uemura, M., Mizuno, T., Nakaoka, T., Akitaya, H., Myserlis, I., Gurwell, M., Keating, G. K., Rao, R., Angelakis, E., Kraus, A.

We study a flaring activity of the HSP Mrk421 that was characterized from radio to very-high-energy (VHE; E $>0.1$TeV) gamma rays with MAGIC, Fermi-LAT, Swift, XMM-Newton and several optical and radio telescopes. These observations included, for the

Externí odkaz: http://arxiv.org/abs/2410.23140

Terahertz semiconductor laser chaos

Autor: Liu, Binbin, Silvestri, Carlo, Zhou, Kang, Ma, Xuhong, Wu, Shumin, Li, Ziping, Wan, Wenjian, Zhang, Zhenzhen, Zhang, Ying, Peng, Junsong, Zeng, Heping, Wang, Cheng, Brambilla, Massimo, Columbo, Lorenzo, Li, Hua

Chaos characterized by its irregularity and high sensitivity to initial conditions finds various applications in secure optical communications, random number generations, light detection and ranging systems, etc. Semiconductor lasers serve as ideal l

Externí odkaz: http://arxiv.org/abs/2410.20099

Eco-Aware Graph Neural Networks for Sustainable Recommendations

Autor: Purificato, Antonio, Silvestri, Fabrizio

Recommender systems play a crucial role in alleviating information overload by providing personalized recommendations tailored to users' preferences and interests. Recently, Graph Neural Networks (GNNs) have emerged as a promising approach for recomm

Externí odkaz: http://arxiv.org/abs/2410.09514