Evaluation of systemic inflammation in response to remote ischemic preconditioning in patients undergoing transcatheter aortic valve replacement (TAVR)

Autor: Zhang, Kun, Troeger, Willi, Kuhn, Matthias, Wiedemann, Stephan, Ibrahim, Karim, Pfluecke, Christian, Sveric, Krunoslav M., Winzer, Robert, Fedders, Dieter, Ruf, Tobias F., Strasser, Ruth H., Linke, Axel, Quick, Silvio, Heidrich, Felix M.

Background: Systemic inflammation can occur after transcatheter aortic valve replacement (TAVR) and correlates with adverse outcome. The impact of remote ischemic preconditioning (RIPC) on TAVR associated systemic inflammation is unknown and was focu

Externí odkaz: https://tud.qucosa.de/id/qucosa%3A89145
https://tud.qucosa.de/api/qucosa%3A89145/attachment/ATT-0/

KiDS-Legacy: Covariance validation and the unified OneCovariance framework for projected large-scale structure observables

Autor: Reischke, Robert, Unruh, Sandra, Asgari, Marika, Dvornik, Andrej, Hildebrandt, Hendrik, Joachimi, Benjamin, Porth, Lucas, von Wietersheim-Kramsta, Maximilian, Busch, Jan Luca van den, Stölzner, Benjamin, Wright, Angus H., Yan, Ziang, Bilicki, Maciej, Burger, Pierre, Harnois-Deraps, Joachim, Georgiou, Christos, Heymans, Catherine, Jalan, Priyanka, Joudaki, Shahab, Kuijken, Konrad, Li, Shun-Sheng, Linke, Laila, Mahony, Constance, Sciotti, Davide, Tröster, Tilman

We introduce OneCovariance, an open-source software designed to accurately compute covariance matrices for an arbitrary set of two-point summary statistics across a variety of large-scale structure tracers. Utilising the halo model, we estimate the s

Externí odkaz: http://arxiv.org/abs/2410.06962

MinerU: An Open-Source Solution for Precise Document Content Extraction

Autor: Wang, Bin, Xu, Chao, Zhao, Xiaomeng, Ouyang, Linke, Wu, Fan, Zhao, Zhiyuan, Xu, Rui, Liu, Kaiwen, Qu, Yuan, Shang, Fukai, Zhang, Bo, Wei, Liqun, Sui, Zhihao, Li, Wei, Shi, Botian, Qiao, Yu, Lin, Dahua, He, Conghui

Document content analysis has been a crucial research area in computer vision. Despite significant advancements in methods such as OCR, layout detection, and formula recognition, existing open-source solutions struggle to consistently deliver high-qu

Externí odkaz: http://arxiv.org/abs/2409.18839

Euclid preparation. Deep learning true galaxy morphologies for weak lensing shear bias calibration

Autor: Euclid Collaboration, Csizi, B., Schrabback, T., Grandis, S., Hoekstra, H., Jansen, H., Linke, L., Congedo, G., Taylor, A. N., Amara, A., Andreon, S., Baccigalupi, C., Baldi, M., Bardelli, S., Battaglia, P., Bender, R., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Casas, S., Castander, F. J., Castellano, M., Castignani, G., Cavuoti, S., Cimatti, A., Colodro-Conde, C., Conselice, C. J., Conversi, L., Copin, Y., Courbin, F., Courtois, H. M., Cropper, M., Da Silva, A., Degaudenzi, H., De Lucia, G., Dinis, J., Douspis, M., Dubath, F., Dupac, X., Dusini, S., Farina, M., Farrens, S., Faustini, F., Ferriol, S., Fotopoulou, S., Frailis, M., Franceschi, E., Galeotta, S., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Guzzo, L., Haugan, S. V. H., Holmes, W., Hook, I., Hormuth, F., Hornstrup, A., Hudelot, P., Ilić, S., Jahnke, K., Jhabvala, M., Joachimi, B., Keihänen, E., Kermiche, S., Kiessling, A., Kilbinger, M., Kubik, B., Kuijken, K., Kümmel, M., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lindholm, V., Lloro, I., Maino, D., Maiorano, E., Mansutti, O., Marcin, S., Marggraf, O., Markovic, K., Martinelli, M., Martinet, N., Marulli, F., Massey, R., Medinaceli, E., Mei, S., Melchior, M., Mellier, Y., Meneghetti, M., Meylan, G., Moresco, M., Moscardini, L., Niemi, S. -M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L. A., Raison, F., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Sakr, Z., Sánchez, A. G., Sartoris, B., Schneider, P., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Steinwagner, J., Tallada-Crespí, P., Tavagnacco, D., Teplitz, H. I., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Kleijn, G. Verdoes, Veropalumbo, A., Wang, Y., Weller, J., Zamorani, G., Zucca, E., Biviano, A., Bolzonella, M., Bozzo, E., Burigana, C., Calabrese, M., Di Ferdinando, D., Vigo, J. A. Escartin, Farinelli, R., Gracia-Carpio, J., Matthew, S., Mauri, N., Pezzotta, A., Pöntinen, M., Scottez, V., Tenti, M., Viel, M., Wiesmann, M., Akrami, Y., Allevato, V., Anselmi, S., Archidiacono, M., Atrio-Barandela, F., Ballardini, M., Blanchard, A., Blot, L., Borgani, S., Bruton, S., Cabanac, R., Calabro, A., Cañas-Herrera, G., Cappi, A., Caro, F., Carvalho, C. S., Castro, T., Chambers, K. C., Contarini, S., Cooray, A. R., Desprez, G., Díaz-Sánchez, A., Diaz, J. J., Di Domizio, S., Dole, H., Escoffier, S., Ferrari, A. G., Ferreira, P. G., Ferrero, I., Finoguenov, A., Fontana, A., Fornari, F., Gabarra, L., Ganga, K., García-Bellido, J., Gasparetto, T., Gaztanaga, E., Giacomini, F., Gianotti, F., Gozaliasl, G., Gutierrez, C. M., Hall, A., Hildebrandt, H., Hjorth, J., Muñoz, A. Jimenez, Joudaki, S., Kajava, J. J. E., Kansal, V., Karagiannis, D., Kirkpatrick, C. C., Brun, A. M. C. Le, Graet, J. Le, Legrand, L., Lesgourgues, J., Liaudat, T. I., Loureiro, A., Macias-Perez, J., Maggio, G., Magliocchetti, M., Mancini, C., Mannucci, F., Maoli, R., Martín-Fleitas, J., Martins, C. J. A. P., Maurin, L., Metcalf, R. B., Miluzio, M., Monaco, P., Montoro, A., Mora, A., Moretti, C., Morgante, G., Walton, Nicholas A., Pagano, L., Patrizii, L., Popa, V., Potter, D., Risso, I., Rocci, P. -F., Sahlén, M., Sarpa, E., Schneider, A., Sereno, M., Simon, P., Mancini, A. Spurio, Stadel, J., Tanidis, K., Tao, C., Tessore, N., Testera, G., Teyssier, R., Toft, S., Tosi, S., Troja, A., Tucci, M., Valieri, C., Valiviita, J., Vergani, D., Verza, G., Vielzeuf, P.

To date, galaxy image simulations for weak lensing surveys usually approximate the light profiles of all galaxies as a single or double S\'ersic profile, neglecting the influence of galaxy substructures and morphologies deviating from such a simplifi

Externí odkaz: http://arxiv.org/abs/2409.07528

Clustering of Indonesian and Western Gamelan Orchestras through Machine Learning of Performance Parameters

Autor: Linke, Simon, Wendt, Gerrit, Bader, Rolf

Indonesian and Western gamelan ensembles are investigated with respect to performance differences. Thereby, the often exotistic history of this music in the West might be reflected in contemporary tonal system, articulation, or large-scale form diffe

Externí odkaz: http://arxiv.org/abs/2409.03713