Zobrazeno 1 - 10
of 6 012
pro vyhledávání: '"Mancino A"'
Autor:
Leckenby, G., Sidhu, R. S., Chen, R. J., Mancino, R., Szányi, B., Bai, M., Battino, U., Blaum, K., Brandau, C., Cristallo, S., Dickel, T., Dillmann, I., Dmytriiev, D., Faestermann, T., Forstner, O., Franczak, B., Geissel, H., Gernhäuser, R., Glorius, J., Griffin, C., Gumberidze, A., Haettner, E., Hillenbrand, P. -M., Karakas, A., Kaur, T., Korten, W., Kozhuharov, C., Kuzminchuk, N., Langanke, K., Litvinov, S., Litvinov, Y. A., Lugaro, M., Martínez-Pinedo, G., Menz, E., Meyer, B., Morgenroth, T., Neff, T., Nociforo, C., Petridis, N., Pignatari, M., Popp, U., Purushothaman, S., Reifarth, R., Sanjari, S., Scheidenberger, C., Spillmann, U., Steck, M., Stöhlker, T., Tanaka, Y. K., Trassinelli, M., Trotsenko, S., Varga, L., Vescovi, D., Wang, M., Weick, H., López, A. Yagüe, Yamaguchi, T., Zhang, Y., Zhao, J.
Radioactive nuclei with lifetimes on the order of millions of years can reveal the formation history of the Sun and active nucleosynthesis occurring at the time and place of its birth. Among such nuclei whose decay signatures are found in the oldest
Externí odkaz:
http://arxiv.org/abs/2411.08856
Autor:
Mancino, Alberto Carlo Maria, Bufi, Salvatore, Di Fazio, Angela, Malitesta, Daniele, Pomo, Claudio, Ferrara, Antonio, Di Noia, Tommaso
Thanks to the great interest posed by researchers and companies, recommendation systems became a cornerstone of machine learning applications. However, concerns have arisen recently about the need for reproducibility, making it challenging to identif
Externí odkaz:
http://arxiv.org/abs/2410.22972
Item recommendation (the task of predicting if a user may interact with new items from the catalogue in a recommendation system) and link prediction (the task of identifying missing links in a knowledge graph) have long been regarded as distinct prob
Externí odkaz:
http://arxiv.org/abs/2409.07433
Autor:
Malitesta, Daniele, Pomo, Claudio, Anelli, Vito Walter, Mancino, Alberto Carlo Maria, Di Noia, Tommaso, Di Sciascio, Eugenio
Recently, graph neural networks (GNNs)-based recommender systems have encountered great success in recommendation. As the number of GNNs approaches rises, some works have started questioning the theoretical and empirical reasons behind their superior
Externí odkaz:
http://arxiv.org/abs/2408.11762
Autor:
Dürholt, Johannes P., Asche, Thomas S., Kleinekorte, Johanna, Mancino-Ball, Gabriel, Schiller, Benjamin, Sung, Simon, Keupp, Julian, Osburg, Aaron, Boyne, Toby, Misener, Ruth, Eldred, Rosona, Costa, Wagner Steuer, Kappatou, Chrysoula, Lee, Robert M., Linzner, Dominik, Walz, David, Wulkow, Niklas, Shafei, Behrang
Our open-source Python package BoFire combines Bayesian Optimization (BO) with other design of experiments (DoE) strategies focusing on developing and optimizing new chemistry. Previous BO implementations, for example as they exist in the literature
Externí odkaz:
http://arxiv.org/abs/2408.05040
Autor:
Gema, Aryo Pradipta, Leang, Joshua Ong Jun, Hong, Giwon, Devoto, Alessio, Mancino, Alberto Carlo Maria, Saxena, Rohit, He, Xuanli, Zhao, Yu, Du, Xiaotang, Madani, Mohammad Reza Ghasemi, Barale, Claire, McHardy, Robert, Harris, Joshua, Kaddour, Jean, van Krieken, Emile, Minervini, Pasquale
Maybe not. We identify and analyse errors in the popular Massive Multitask Language Understanding (MMLU) benchmark. Even though MMLU is widely adopted, our analysis demonstrates numerous ground truth errors that obscure the true capabilities of LLMs.
Externí odkaz:
http://arxiv.org/abs/2406.04127
Autor:
Li, R., Verney, D., De Gregorio, G., Mancino, R., Matea, I., Coraggio, L., Itaco., N., Harakeh, M. N., Delafosse, C., Didierjean, F., Ayoubi, L. A., Falou, H. Al, Benzoni, G., Blanc, F. Le, Bozkurt, V., Ciemała, M., Deloncle, I., Fallot, M., Gaulard, C., Gottardo, A., Guadilla, V., Guillot, J., Hadyńska-Klęk, K., Ibrahim, F., Jovancevic, N., Kankainen, A., Lebois, M., Martínez, T., Napiorkowski, P., Roussiere, B., Sobolev, Yu. G., Stefan, I., Stukalov, S., Thisse, D., Tocabens, G.
The Gamow-Teller strength distribution covering the entire $\beta$-decay window, up to 10.312(4) MeV, of $^{80g+m}$Ga was measured for the first time in photo-fission of UC$_x$ induced by 50 MeV electron beam. The new data show significant enhancemen
Externí odkaz:
http://arxiv.org/abs/2405.20490
Autor:
Nava, Andrea, Bernardini, Leonardo, Biassoni, Matteo, Bradanini, Tommaso, Brofferio, Chiara, Carminati, Marco, De Gregorio, Giovanni, Fiorini, Carlo, Gagliardi, Giulio, Lechner, Peter, Mancino, Riccardo
The ASPECT-BET (An sdd-SPECTrometer for BETa decay studies) project aims to develop a novel technique for the precise measurement of forbidden $\beta$ spectra in the 10 keV - 1 MeV range. This technique uses a Silicon Drift Detector (SDD) as the main
Externí odkaz:
http://arxiv.org/abs/2405.07797
Autor:
Brandherm, I., von Neumann-Cosel, P., Mancino, R., Martínez-Pinedo, G., Matsubara, H., Ponomarev, V. Yu., Richter, A., Scheck, M., Tamii, A.
Publikováno v:
Phys. Rev. C 110, 034319 (2024)
The aim of the present work is a state-by-state analysis of possible E1 and M1 transitions in $^{58}$Ni with a high-resolution (p,p') experiment at 295 MeV and very forward angles including 0{\deg} and a comparison to results from studies of the dipo
Externí odkaz:
http://arxiv.org/abs/2404.15906
Autor:
Bufi, Salvatore, Mancino, Alberto Carlo Maria, Ferrara, Antonio, Malitesta, Daniele, Di Noia, Tommaso, Di Sciascio, Eugenio
The recent integration of Graph Neural Networks (GNNs) into recommendation has led to a novel family of Collaborative Filtering (CF) approaches, namely Graph Collaborative Filtering (GCF). Following the same GNNs wave, recommender systems exploiting
Externí odkaz:
http://arxiv.org/abs/2403.20095