Reproducible Machine Learning-based Voice Pathology Detection: Introducing the Pitch Difference Feature

Autor: Vrba, Jan, Steinbach, Jakub, Jirsa, Tomáš, Verde, Laura, De Fazio, Roberta, Homma, Noriyasu, Zeng, Yuwen, Ichiji, Key, Hájek, Lukáš, Sedláková, Zuzana, Mareš, Jan

In this study, we propose a robust set of features derived from a thorough research of contemporary practices in voice pathology detection. The feature set is based on the combination of acoustic handcrafted features. Additionally, we introduce pitch

Externí odkaz: http://arxiv.org/abs/2410.10537

A transition state resonance radically reshapes angular distributions of the F + H2 -> F H(vf = 3) + H reaction in the 62-101 meV energy range

Autor: Sokolovski, Dmitri, De Fazio, Dario, Akhmatskaya, Elena

Reactive angular distributions of the benchmark F + H2(vi = 0) -> F H(vf = 3) + H reaction show unusual propensity towards small scattering angles, a subject of a long debate in the literature. We use Regge trajectories to quantify the resonance cont

Externí odkaz: http://arxiv.org/abs/2408.16134

Exact results on the dynamics of the stochastic Floquet-East model

Autor: De Fazio, Cecilia, Garrahan, Juan P., Klobas, Katja

We introduce a stochastic generalisation of the classical deterministic Floquet-East model, a discrete circuit with the same kinetic constraint as the East model of glasses. We prove exactly that, in the limit of long time and large size, this model

Externí odkaz: http://arxiv.org/abs/2406.17464

Machine learning techniques in a structural and functional MRI diagnostic approach in schizophrenia: a systematic review

Autor: de Filippis R, Carbone EA, Gaetano R, Bruni A, Pugliese V, Segura-Garcia C, De Fazio P

Publikováno v: Neuropsychiatric Disease and Treatment, Vol Volume 15, Pp 1605-1627 (2019)

Renato de Filippis,1,* Elvira Anna Carbone,1,* Raffaele Gaetano,1 Antonella Bruni,1 Valentina Pugliese,1 Cristina Segura-Garcia,2 Pasquale De Fazio11Department of Health Sciences, University Magna Graecia of Catanzaro, Catanzaro 88100, Italy; 2Depart

Externí odkaz: https://doaj.org/article/e96672ad6d604000957d1df2aa3be27c

The XENONnT Dark Matter Experiment

Autor: XENON Collaboration, Aprile, E., Aalbers, J., Abe, K., Maouloud, S. Ahmed, Althueser, L., Andrieu, B., Angelino, E., Angevaare, J. R., Antochi, V. C., Martin, D. Antón, Arneodo, F., Balata, M., Baudis, L., Baxter, A. L., Bazyk, M., Bellagamba, L., Biondi, R., Bismark, A., Brookes, E. J., Brown, A., Bruenner, S., Bruno, G., Budnik, R., Bui, T. K., Cai, C., Cardoso, J. M. R., Cassese, F., Chiarini, A., Cichon, D., Chavez, A. P. Cimental, Colijn, A. P., Conrad, J., Corrieri, R., Cuenca-García, J. J., Cussonneau, J. P., Dadoun, O., D'Andrea, V., Decowski, M. P., De Fazio, B., Di Gangi, P., Diglio, S., Disdier, J. M., Douillet, D., Eitel, K., Elykov, A., Farrell, S., Ferella, A. D., Ferrari, C., Fischer, H., Flierman, M., Form, S., Front, D., Fulgione, W., Fuselli, C., Gaemers, P., Gaior, R., Rosso, A. Gallo, Galloway, M., Gao, F., Gardner, R., Garroum, N., Glade-Beucke, R., Grandi, L., Grigat, J., Guan, H., Guerzoni, M., Guida, M., Hammann, R., Higuera, A., Hils, C., Hoetzsch, L., Hood, N. F., Howlett, J., Huhmann, C., Iacovacci, M., Iaquaniello, G., Iven, L., Itow, Y., Jakob, J., Joerg, F., Joy, A., Kara, M., Kavrigin, P., Kazama, S., Kobayashi, M., Koltman, G., Kopec, A., Kuger, F., Landsman, H., Lang, R. F., Levinson, L., Li, I., Li, S., Liang, S., Lindemann, S., Lindner, M., Liu, K., Loizeau, J., Lombardi, F., Long, J., Lopes, J. A. M., Ma, Y., Macolino, C., Mahlstedt, J., Mancuso, A., Manenti, L., Marignetti, F., Undagoitia, T. Marrodán, Martella, P., Martens, K., Masbou, J., Masson, D., Masson, E., Mastroianni, S., Mele, E., Messina, M., Michinelli, R., Miuchi, K., Molinario, A., Moriyama, S., Morå, K., Mosbacher, Y., Murra, M., Müller, J., Ni, K., Nisi, S., Oberlack, U., Orlandi, D., Othegraven, R., Paetsch, B., Palacio, J., Parlati, S., Paschos, P., Pellegrini, Q., Peres, R., Peters, C., Pienaar, J., Pierre, M., Plante, G., Pollmann, T. R., Qi, J., Qin, J., García, D. Ramírez, Rynge, M., Shi, J., Singh, R., Sanchez, L., Santos, J. M. F. dos, Sarnoff, I., Sartorelli, G., Schreiner, J., Schulte, D., Schulte, P., Eißing, H. Schulze, Schumann, M., Lavina, L. Scotto, Selvi, M., Semeria, F., Shagin, P., Shi, S., Shockley, E., Silva, M., Simgen, H., Stephen, J., Stern, M., Stillwell, B. K., Takeda, A., Tan, P. -L., Tatananni, D., Terliuk, A., Thers, D., Toschi, F., Trinchero, G., Tunnell, C., Tönnies, F., Valerius, K., Volta, G., Weinheimer, C., Weiss, M., Wenz, D., Westermann, J., Wittweg, C., Wolf, T., Wu, V. H. S., Xing, Y., Xu, D., Xu, Z., Yamashita, M., Yang, L., Ye, J., Yuan, L., Zavattini, G., Zhong, M., Zhu, T.

The multi-staged XENON program at INFN Laboratori Nazionali del Gran Sasso aims to detect dark matter with two-phase liquid xenon time projection chambers of increasing size and sensitivity. The XENONnT experiment is the latest detector in the progra

Externí odkaz: http://arxiv.org/abs/2402.10446