Zobrazeno 1 - 10
of 100 202
pro vyhledávání: '"AKTAS, A."'
Akademický článek
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Autor:
Şensoy, Buğra1 bugra.sensoy@gazi.edu.tr, Aktaş, Mustafa2
Publikováno v:
Gazi Journal of Engineering Sciences (GJES) / Gazi Mühendislik Bilimleri Dergisi. Apr2024, Vol. 10 Issue 1, p38-59. 22p.
Akademický článek
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Autor:
Bumazhnov, Dmitrij F.1 (AUTHOR) dmitrij.bumazhnov@uni-goettingen.de
Publikováno v:
Zeitschrift für Antikes Christentum / Journal of Ancient Christianity. Jul2022, Vol. 26 Issue 1, p185-187. 3p.
The rapid spread of misinformation on social media, especially during crises, challenges public decision-making. To address this, we propose HierTKG, a framework combining Temporal Graph Networks (TGN) and hierarchical pooling (DiffPool) to model rum
Externí odkaz:
http://arxiv.org/abs/2412.12385
Autor:
Karaman, Rabia Aktaş, Area, Iván
The purpose of this paper is to obtain Fourier transforms of multivariate orthogonal polynomials on the cone such as Laguerre polynomials on the cone and Jacobi polynomials on the cone and to define two new families of multivariate orthogonal functio
Externí odkaz:
http://arxiv.org/abs/2412.07869
Autor:
Enciu, M., Obertelli, A., Doornenbal, P., Heinz, M., Miyagi, T., Nowacki, F., Ogata, K., Poves, A., Schwenk, A., Yoshida, K., Achouri, N. L., Baba, H., Browne, F., Calvet, D., Château, F., Chen, S., Chiga, N., Corsi, A., Cortés, M. L., Delbart, A., Gheller, J. -M., Giganon, A., Gillibert, A., Hilaire, C., Isobe, T., Kobayashi, T., Kubota, Y., Lapoux, V., Liu, H. N., Motobayashi, T., Murray, I., Otsu, H., Panin, V., Paul, N., Rodriguez, W., Sakurai, H., Sasano, M., Steppenbeck, D., Stuhl, L., Sun, Y. L., Togano, Y., Uesaka, T., Wimmer, K., Yoneda, K., Aktas, O., Aumann, T., Chung, L. X., Flavigny, F., Franchoo, S., Gašparić, I., Gerst, R. -B., Gibelin, J., Hahn, K. I., Kim, D., Kondo, Y., Koseoglou, P., Lee, J., Lehr, C., Li, P. J., Linh, B. D., Lokotko, T., MacCormick, M., Moschner, K., Nakamura, T., Park, S. Y., Rossi, D., Sahin, E., Söderström, P. -A., Sohler, D., Takeuchi, S., Toernqvist, H., Vaquero, V., Wagner, V., Wang, S., Werner, V., Xu, X., Yamada, H., Yan, D., Yang, Z., Yasuda, M., Zanetti, L.
Publikováno v:
Phys. Rev. C 110, 064301 (2024)
The first spectroscopy of $^{52}$K was investigated via in-beam $\gamma$-ray spectroscopy at the RIKEN Radioactive Isotope Beam Factory after one-proton and one-neutron knockout from $^{53}$Ca and $^{53}$K beams impinging on a 15-cm liquid hydrogen t
Externí odkaz:
http://arxiv.org/abs/2412.03602
Autor:
Gottheil, Pablo, Bhattacharyya, Saraswat, Lettl, Kolya, Friedrich, Philip, Roth, Kilian, Rivera-Moreno, Salvador, Merkel, Mario, Aktas, Bahriye, Sauer, Igor, Daneshgar, Assal, Wieland, Jonas, Kubitschke, Hans, Wegscheider, Anne-Sophie, Yeomans, Julia M., Käs, Josef A.
In invasive breast cancer, cell clusters of varying sizes and shapes are embedded in the fibrous extracellular matrix (ECM). Although the prevailing view attributes this structure to increasing disorder resulting from loss of function and dedifferent
Externí odkaz:
http://arxiv.org/abs/2412.01285
Autor:
Pan, Hongyi, Hong, Ziliang, Durak, Gorkem, Keles, Elif, Aktas, Halil Ertugrul, Taktak, Yavuz, Medetalibeyoglu, Alpay, Zhang, Zheyuan, Velichko, Yury, Spampinato, Concetto, Schoots, Ivo, Bruno, Marco J., Tiwari, Pallavi, Bolan, Candice, Gonda, Tamas, Miller, Frank, Keswani, Rajesh N., Wallace, Michael B., Xu, Ziyue, Bagci, Ulas
Accurate classification of Intraductal Papillary Mucinous Neoplasms (IPMN) is essential for identifying high-risk cases that require timely intervention. In this study, we develop a federated learning framework for multi-center IPMN classification ut
Externí odkaz:
http://arxiv.org/abs/2411.05697
Autor:
Pan, Hongyi, Durak, Gorkem, Zhang, Zheyuan, Taktak, Yavuz, Keles, Elif, Aktas, Halil Ertugrul, Medetalibeyoglu, Alpay, Velichko, Yury, Spampinato, Concetto, Schoots, Ivo, Bruno, Marco J., Keswani, Rajesh N., Tiwari, Pallavi, Bolan, Candice, Gonda, Tamas, Goggins, Michael G., Wallace, Michael B., Xu, Ziyue, Bagci, Ulas
Federated learning (FL) enables collaborative model training across institutions without sharing sensitive data, making it an attractive solution for medical imaging tasks. However, traditional FL methods, such as Federated Averaging (FedAvg), face d
Externí odkaz:
http://arxiv.org/abs/2410.22530