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Akademický článek

Autor: Musil, V. (AUTHOR)

Publikováno v: Anaesthesiology & Intensive Medicine / Anesteziologie a Intenzivní Medicína. 2022, Vol. 33 Issue 5, p217-222. 6p.

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Akademický článek

Büke Uras, The Balyans: Ottoman Architecture and Balyan Archive

Autor: Ahmet Ersoy

Publikováno v: Études Arméniennes Contemporaines, Vol 14, Pp 209-215 (2022)

Externí odkaz: https://doaj.org/article/a3e32f461b9d467f8517cdc7d2a7ad85

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Prospective report of the French QCD community to the ESPPU 2025 with respect to the program of the LHC Run 5 and beyond and future colliders at CERN

Autor: Arata, Carolina, Arleo, François, Audurier, Benjamin, Baldisseri, Alberto, Bastid, Nicole, Batigne, Guillaume, Belikov, Iouri, Bluhm, Marcus, Bossu, Francesco, Borel, Hervé, Castellanos, Javier Castillo, Caucal, Paul, Cheshkov, Cvetan, Balbastre, Gustavo Conesa, del Valle, Zaida Conesa, Coquet, Maurice, Fernandez, Imanol Corredoira, Crochet, Philippe, Espagnon, Bruno, Faivre, Julien, Ferrero, Andrea, Francisco, Audrey, Fleuret, Frédéric, Flett, Chris, Furget, Christophe, Germain, Marie, Gossiaux, Pol Bernard, Guernane, Rachid, Guilbaud, Maxime, Guittiere, Manuel, Hadjidakis, Cynthia, Hippolyte, Boris, Kuhn, Christian, Lansberg, Jean-Philippe, Lopez, Xavier, Maire, Antonin, Mallick, Dukhishyam, Marquet, Cyrille, Martinez-Garcia, Ginés, Massacrier, Laure, Mattioli, Kara, Maurice, Émilie, Camacho, Carlos Munoz, Nahrgang, Marlene, Nefedov, Maxim, Niel, Élisabeth, Ozcelik, Melih A., Panebianco, Stefano, Pillot, Philippe, Pire, Bernard, Houssais, Sarah Porteboeuf, Rakotozafindrabe, Andry, Ramasubramanian, Niveditha, Robbe, Patrick, Sazdjian, Hagop, Senyukov, Serhiy, Suire, Christophe, Uras, Antonio, Wallon, Samuel, Winn, Michael

This document summarizes the prospective physics plans of the French QCD and Heavy-Ion community, including the experimental programs at the LHC Run 5 and beyond and future colliders at CERN, within the context of the French contribution to the updat

Externí odkaz: http://arxiv.org/abs/2411.11669

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A Deep Learning Based Decoder for Concatenated Coding over Deletion Channels

Autor: Kargı, E. Uras, Duman, Tolga M.

Publikováno v: ICC 2024 - IEEE International Conference on Communications, Denver, CO, USA, 2024, pp. 2797-2802

In this paper, we introduce a deep learning-based decoder designed for concatenated coding schemes over a deletion/substitution channel. Specifically, we focus on serially concatenated codes, where the outer code is either a convolutional or a low-de

Externí odkaz: http://arxiv.org/abs/2410.09460

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