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pro vyhledávání: '"A., Oprea"'
Autor:
Lerendegui-Marco, J., Guerrero, C., Mendoza, E., Quesada, J. M., Eberhardt, K., Junghans, A. R., Alcayne, V., Babiano, V., Aberle, O., Andrzejewski, J., Audouin, L., Becares, V., Bacak, M., Balibrea-Correa, J., Barbagallo, M., Barros, S., Becvar, F., Beinrucker, C., Berthoumieux, E., Billowes, J., Bosnar, D., Brugger, M., Caamaño, M., Calviño, F., Calviani, M., Cano-Ott, D., Cardella, R., Casanovas, A., Castelluccio, D. M., Cerutti, F., Chen, Y. H., Chiaveri, E., Colonna, N., Cortés, G., Cortés-Giraldo, M. A., Cosentino, L., Damone, L. A., Diakaki, M., Dietz, M., Domingo-Pardo, C., Dressler, R., Dupont, E., Durán, I., Fernández-Domínguez, B., Ferrari, A., Ferreira, P., Finocchiaro, P., Furman, V., Göbel, K., García, A. R., Gawlik, A., Glodariu, T., Goncalves, I. F., González-Romero, E., Goverdovski, A., Griesmayer, E., Gunsing, F., Harada, H., Heftrich, T., Heinitz, S., Heyse, J., Jenkins, D. G., Jericha, E., Käppeler, F., Kadi, Y., Katabuchi, T., Kavrigin, P., Ketlerov, V., Khryachkov, V., Kimura, A., Kivel, N., Kokkoris, M., Krticka, M., Leal-Cidoncha, E., Lederer-Woods, C., Leeb, H., Meo, S. Lo, Lonsdale, S. J., Losito, R., Macina, D., Marganiec, J., Martínez, T., Massimi, C., Mastinu, P., Mastromarco, M., Matteucci, F., Maugeri, E. A., Mengoni, A., Milazzo, P. M., Mingrone, F., Mirea, M., Montesano, S., Musumarra, A., Nolte, R., Oprea, A., Patronis, N., Pavlik, A., Perkowski, J., Porras, J. I., Praena, J., Rajeev, K., Rauscher, T., Reifarth, R., Riego-Perez, A., Rout, P. C., Rubbia, C., Ryan, J. A., Sabaté-Gilarte, M., Saxena, A., Schillebeeckx, P., Schmidt, S., Schumann, D., Sedyshev, P., Smith, A. G., Stamatopoulos, A., Tagliente, G., Tain, J. L., Tarifeño-Saldivia, A., Tassan-Got, L., Tsinganis, A., Valenta, S., Vannini, G., Variale, V., Vaz, P., Ventura, A., Vlachoudis, V., Vlastou, R., Wallner, A., Warren, S., Weigand, M., Weiss, C., Wolf, C., Woods, P. J., Wright, T., Zugec, P., Collaboration, the n_TOF
The design of fast reactors burning MOX fuels requires accurate capture and fission cross sections. For the particular case of neutron capture on 242Pu, the NEA recommends that an accuracy of 8-12% should be achieved in the fast energy region (2 keV-
Externí odkaz:
http://arxiv.org/abs/2412.01332
Autor:
Singh, Aditya Vikram, Rathbun, Ethan, Graham, Emma, Oakley, Lisa, Boboila, Simona, Oprea, Alina, Chin, Peter
Recent advances in multi-agent reinforcement learning (MARL) have created opportunities to solve complex real-world tasks. Cybersecurity is a notable application area, where defending networks against sophisticated adversaries remains a challenging t
Externí odkaz:
http://arxiv.org/abs/2410.17351
Recent works have demonstrated the vulnerability of Deep Reinforcement Learning (DRL) algorithms against training-time, backdoor poisoning attacks. These attacks induce pre-determined, adversarial behavior in the agent upon observing a fixed trigger
Externí odkaz:
http://arxiv.org/abs/2410.13995
Inverse problems in physical or biological sciences often involve recovering an unknown parameter that is random. The sought-after quantity is a probability distribution of the unknown parameter, that produces data that aligns with measurements. Cons
Externí odkaz:
http://arxiv.org/abs/2410.00229
Privacy-preserving machine learning (PPML) enables multiple data owners to contribute their data privately to a set of servers that run a secure multi-party computation (MPC) protocol to train a joint ML model. In these protocols, the input data rema
Externí odkaz:
http://arxiv.org/abs/2409.15126
The celebrated Takens' embedding theorem provides a theoretical foundation for reconstructing the full state of a dynamical system from partial observations. However, the classical theorem assumes that the underlying system is deterministic and that
Externí odkaz:
http://arxiv.org/abs/2409.08768
The tautological Chow ring of the moduli space $\mathcal{A}_g$ of principally polarized abelian varieties of dimension $g$ was defined and calculated by van der Geer in 1999. By studying the Torelli pullback of algebraic cycles classes from $\mathcal
Externí odkaz:
http://arxiv.org/abs/2408.08718
Autor:
Severi, Giorgio, Boboila, Simona, Holodnak, John, Kratkiewicz, Kendra, Izmailov, Rauf, De Lucia, Michael J., Oprea, Alina
The training phase of machine learning models is a delicate step, especially in cybersecurity contexts. Recent research has surfaced a series of insidious training-time attacks that inject backdoors in models designed for security classification task
Externí odkaz:
http://arxiv.org/abs/2407.08159
Autor:
Alcayne, V., Kimura, A., Mendoza, E., Cano-Ott, D., Aberle, O., Álvarez-Velarde, F., Amaducci, S., Andrzejewski, J., Audouin, L., Bécares, V., Babiano-Suarez, V., Bacak, M., Barbagallo, M., Bečvář, F., Bellia, G., Berthoumieux, E., Billowes, J., Bosnar, D., Brown, A., Busso, M., Caamaño, M., Caballero-Ontanaya, L., Calviño, F., Calviani, M., Casanovas, A., Cerutti, F., Chen, Y. H., Chiaveri, E., Colonna, N., Cortés, G., Cortés-Giraldo, M. A., Cosentino, L., Cristallo, S., Damone, L. A., Diakaki, M., Dietz, M., Domingo-Pardo, C., Dressler, R., Dupont, E., Durán, I., Eleme, Z., Fernández-Domınguez, B., Ferrari, A., Finocchiaro, P., Furman, V., Göbel, K., Garg, R., Gawlik-Ramiega, A., Gilardoni, S., Glodariu, T., Gonçalves, I. F., González-Romero, E., Guerrero, C., Gunsing, F., Harada, H., Heinitz, S., Heyse, J., Jenkins, D. G., Jericha, E., Käppeler, F., Kadi, Y., Kivel, N., Kokkoris, M., Kopatch, Y., Krtička, M., Kurtulgil, D., Ladarescu, I., Lederer-Woods, C., Leeb, H., Lerendegui-Marco, J., Meo, S. Lo, Lonsdale, S. J., Macina, D., Manna, A., Martınez, T., Masi, A., Massimi, C., Mastinu, P., Mastromarco, M., Matteucci, F., Maugeri, E. A., Mazzone, A., Mengoni, A., Michalopoulou, V., Milazzo, P. M., Mingrone, F., Musumarra, A., Negret, A., Nolte, R., Ogállar, F., Oprea, A., Patronis, N., Pavlik, A., de Rada, A. Pérez, Perkowski, J., Persanti, L., Porras, I., Praena, J., Quesada, J. M., Radeck, D., Ramos-Doval, D., Rauscher, T., Reifarth, R., Rochman, D., Romanets, Y., Rubbia, C., Sabaté-Gilarte, M., Saxena, A., Schillebeeckx, P., Schumann, D., Smith, A. G., Sosnin, N. V., Stamatopoulos, A., Tagliente, G., Tain, J. L., Talip, T., Tarifeño-Saldivia, A., Tassan-Got, L., Torres-Sánchez, P., Tsinganis, A., Ulrich, J., Urlass, S., Valenta, S., Vannini, G., Variale, V., Vaz, P., Ventura, A., Vlachoudis, V., Vlastou, R., Wallner, A., Woods, P. J., Wright, T., Žugec, P.
The $^{246}$Cm(n,$\gamma$) and $^{248}$Cm(n,$\gamma$) cross-sections have been measured at the Experimental Area 2 (EAR2) of the n_TOF facility at CERN with three C$_6$D$_6$ detectors. This measurement is part of a collective effort to improve the ca
Externí odkaz:
http://arxiv.org/abs/2407.06377
LoRA-Guard: Parameter-Efficient Guardrail Adaptation for Content Moderation of Large Language Models
Guardrails have emerged as an alternative to safety alignment for content moderation of large language models (LLMs). Existing model-based guardrails have not been designed for resource-constrained computational portable devices, such as mobile phone
Externí odkaz:
http://arxiv.org/abs/2407.02987