Zobrazeno 1 - 10
of 154 413
pro vyhledávání: '"A, Conrad"'
Autor:
Jaksch, Kevin, Dirmeier, Thomas, Weiser, Yannick, Richter, Stefan, Bayraktar, Ömer, Hacker, Bastian, Rösler, Conrad, Khan, Imran, Petscharning, Stefan, Grafenauer, Thomas, Hentschel, Michael, Ömer, Bernhard, Pacher, Christoph, Kanitschar, Florian, Upadhyaya, Twesh, Lin, Jie, Lütkenhaus, Norbert, Leuchs, Gerd, Marquardt, Christoph
Continuous-variable (CV) quantum key distribution (QKD) allows for quantum secure communication with the benefit of being close to existing classical coherent communication. In recent years, CV QKD protocols using a discrete number of displaced coher
Externí odkaz:
http://arxiv.org/abs/2410.12915
Autor:
Iravani, Sahar, Conrad, Tim . O . F
Table processing, a key task in natural language processing, has significantly benefited from recent advancements in language models (LMs). However, the capabilities of LMs in table-to-text generation, which transforms structured data into coherent n
Externí odkaz:
http://arxiv.org/abs/2410.12878
Machine learning has revolutionized the modeling of clinical timeseries data. Using machine learning, a Deep Neural Network (DNN) can be automatically trained to learn a complex mapping of its input features for a desired task. This is particularly v
Externí odkaz:
http://arxiv.org/abs/2410.09199
We investigate multi-mode GKP (Gottesman--Kitaev--Preskill) quantum error-correcting codes from a geometric perspective. First, we construct their moduli space as a quotient of groups and exhibit it as a fiber bundle over the moduli space of symplect
Externí odkaz:
http://arxiv.org/abs/2410.07332
Detecting anomalies in a temporal sequence of graphs can be applied is areas such as the detection of accidents in transport networks and cyber attacks in computer networks. Existing methods for detecting abnormal graphs can suffer from multiple limi
Externí odkaz:
http://arxiv.org/abs/2410.05687
Autor:
Weimann, Kuba, Conrad, Tim O. F.
Accurate diagnosis of heart arrhythmias requires the interpretation of electrocardiograms (ECG), which capture the electrical activity of the heart. Automating this process through machine learning is challenging due to the need for large annotated d
Externí odkaz:
http://arxiv.org/abs/2410.13867
Autor:
Aalbers, J., Abe, K., Adrover, M., Maouloud, S. Ahmed, Althueser, L., Amaral, D. W. P., Andrieu, B., Angelino, E., Martin, D. Antón, Antunovic, B., Aprile, E., Babicz, M., Bajpai, D., Balzer, M., Barberio, E., Baudis, L., Bazyk, M., Bell, N. F., Bellagamba, L., Biondi, R., Biondi, Y., Bismark, A., Boehm, C., Boese, K., Braun, R., Breskin, A., Brommer, S., Brown, A., Bruni, G., Budnik, R., Cai, C., Capelli, C., Chauvin, A., Chavez, A. P. Cimental, Colijn, A. P., Conrad, J., Cuenca-García, J. J., D'Andrea, V., Garcia, L. C. Daniel, Decowski, M. P., Deisting, A., Di Donato, C., Di Gangi, P., Diglio, S., Doerenkamp, M., Drexlin, G., Eitel, K., Elykov, A., Engel, R., Ferella, A. D., Ferrari, C., Fischer, H., Flehmke, T., Flierman, M., Fujikawa, K., Fulgione, W., Fuselli, C., Gaemers, P., Gaior, R., Galloway, M., Gao, F., Garroum, N., Giacomobono, R., Girard, F., Glade-Beucke, R., Glück, F., Grandi, L., Grigat, J., Größle, R., Guan, H., Guida, M., Gyorgy, P., Hammann, R., Hannen, V., Hansmann-Menzemer, S., Hargittai, N., Higuera, A., Hils, C., Hiraoka, K., Hoetzsch, L., Hoferichter, M., Hood, N. F., Iacovacci, M., Itow, Y., Jakob, J., James, R. S., Joerg, F., Kahlert, F., Kaminaga, Y., Kara, M., Kavrigin, P., Kazama, S., Keller, M., Kharbanda, P., Kilminster, B., Kleifges, M., Klute, M., Kobayashi, M., Koke, D., Kopec, A., von Krosigk, B., Kuger, F., LaCascio, L., Landsman, H., Lang, R. F., Levinson, L., Li, I., Li, A., Li, S., Liang, S., Liang, Z., Lin, Y. -T., Lindemann, S., Lindner, M., Liu, K., Loizeau, J., Lombardi, F., Long, J., Lopes, J. A. M., Lucchetti, G. M., Luce, T., Ma, Y., Macolino, C., Mahlstedt, J., Maier, B., Mancuso, A., Manenti, L., Marignetti, F., Undagoitia, T. Marrodán, Martens, K., Masbou, J., Masson, E., Mastroianni, S., Melchiorre, A., Menéndez, J., Messina, M., Milosovic, B., Milutinovic, S., Miuchi, K., Miyata, R., Molinario, A., Monteiro, C. M. B., Morå, K., Moriyama, S., Morteau, E., Mosbacher, Y., Müller, J., Murra, M., Newstead, J. L., Ni, K., O'Hare, C., Oberlack, U., Obradovic, M., Ostrowskiy, I., Ouahada, S., Paetsch, B., Pan, Y., Pandurovic, M., Pellegrini, Q., Peres, R., Piastra, F., Pienaar, J., Pierre, M., Plante, G., Pollmann, T. R., Principe, L., Qi, J., Qiao, K., Qin, J., Rajado, M., García, D. Ramírez, Ravindran, A., Razeto, A., Sanchez, L., Sanchez-Lucas, P., Sartorelli, G., Scaffidi, A., Schreiner, J., Schulte, P., Eißing, H. Schulze, Schumann, M., Schwenck, A., Schwenk, A., Lavina, L. Scotto, Selvi, M., Semeria, F., Shagin, P., Sharma, S., Shen, W., Shi, S. Y., Shimada, T., Simgen, H., Singh, R., Solmaz, M., Stanley, O., Steidl, M., Stevens, A., Takeda, A., Tan, P. -L., Thers, D., Thümmler, T., Tönnies, F., Toschi, F., Trinchero, G., Trotta, R., Tunnell, C. D., Urquijo, P., Utoyama, M., Valerius, K., Vecchi, S., Vetter, S., Volta, G., Vorkapic, D., Wang, W., Weerman, K. M., Weinheimer, C., Weiss, M., Wenz, D., Wilson, M., Wittweg, C., Wolf, J., Wu, V. H. S., Wüstling, S., Wurm, M., Xing, Y., Xu, D., Xu, Z., Yamashita, M., Yang, L., Ye, J., Yuan, L., Zavattini, G., Zhong, M., Zuber, K.
We present a novel deep learning pipeline to perform a model-independent, likelihood-free search for anomalous (i.e., non-background) events in the proposed next generation multi-ton scale liquid Xenon-based direct detection experiment, DARWIN. We tr
Externí odkaz:
http://arxiv.org/abs/2410.00755
We present FastLexRank\footnote{https://github.com/LiMaoUM/FastLexRank}, an efficient and scalable implementation of the LexRank algorithm for text ranking. Designed to address the computational and memory complexities of the original LexRank method,
Externí odkaz:
http://arxiv.org/abs/2410.01183
Autor:
Duong, Manh Khoi, Conrad, Stefan
The reason behind the unfair outcomes of AI is often rooted in biased datasets. Therefore, this work presents a framework for addressing fairness by debiasing datasets containing a (non-)binary protected attribute. The framework proposes a combinator
Externí odkaz:
http://arxiv.org/abs/2410.00836
We analyze a stochastic process resulting from the normalization of states in the zeroth-order optimization method CMA-ES. On a specific class of minimization problems where the objective function is scaling-invariant, this process defines a time-hom
Externí odkaz:
http://arxiv.org/abs/2409.20107