Zobrazeno 1 - 10
of 629
pro vyhledávání: '"P. Sabarish"'
Planar linear flows are a one-parameter family, with the parameter $\hat{\alpha}\in [-1,1]$ being a measure of the relative magnitudes of extension and vorticity; $\hat{\alpha} = -1$, $0$ and $1$ correspond to solid-body rotation, simple shear flow a
Externí odkaz:
http://arxiv.org/abs/2406.02823
The exponential increase in Internet of Things (IoT) devices coupled with 6G pushing towards higher data rates and connected devices has sparked a surge in data. Consequently, harnessing the full potential of data-driven machine learning has become o
Externí odkaz:
http://arxiv.org/abs/2405.17309
Autor:
Euclid Collaboration, Archidiacono, M., Lesgourgues, J., Casas, S., Pamuk, S., Schöneberg, N., Sakr, Z., Parimbelli, G., Schneider, A., Peters, F. Hervas, Pace, F., Sabarish, V. M., Costanzi, M., Camera, S., Carbone, C., Clesse, S., Frusciante, N., Fumagalli, A., Monaco, P., Scott, D., Viel, M., Amara, A., Andreon, S., Auricchio, N., Baldi, M., Bardelli, S., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Capobianco, V., Cardone, V. F., Carretero, J., Castellano, M., Cavuoti, S., Cimatti, A., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Courbin, F., Courtois, H. M., Da Silva, A., Degaudenzi, H., Douspis, M., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Ealet, A., Farina, M., Farrens, S., Ferriol, S., Frailis, M., Franceschi, E., Galeotta, S., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Guzzo, L., Haugan, S. V. H., Hoekstra, H., Hormuth, F., Hornstrup, A., Jahnke, K., Joachimi, B., Keihänen, E., Kermiche, S., Kiessling, A., Kilbinger, M., Kitching, T., Kubik, B., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lindholm, V., Lloro, I., Maino, D., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Martinet, N., Marulli, F., Massey, R., Maurogordato, S., McCracken, H. J., Medinaceli, E., Mei, S., Mellier, Y., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Niemi, S. -M., Nightingale, J. W., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W. J., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L. A., Pozzetti, L., Raison, F., Rebolo, R., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Saglia, R., Sapone, D., Sartoris, B., Scaramella, R., Schirmer, M., Schneider, P., Schrabback, T., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Tallada-Crespí, P., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valenziano, L., Vassallo, T., Veropalumbo, A., Wang, Y., Weller, J., Zamorani, G., Zoubian, J., Zucca, E., Biviano, A., Boucaud, A., Bozzo, E., Burigana, C., Calabrese, M., Colodro-Conde, C., Crocce, M., Fabbian, G., Graciá-Carpio, J., Mainetti, G., Martinelli, M., Mauri, N., Neissner, C., Scottez, V., Tenti, M., Wiesmann, M., Akrami, Y., Anselmi, S., Baccigalupi, C., Ballardini, M., Bernardeau, F., Bertacca, D., Borgani, S., Borsato, E., Bruton, S., Cabanac, R., Cappi, A., Carvalho, C. S., Castignani, G., Castro, T., Cañas-Herrera, G., Chambers, K. C., Contarini, S., Cooray, A. R., Coupon, J., Davini, S., de la Torre, S., De Lucia, G., Desprez, G., Di Domizio, S., Díaz-Sánchez, A., Vigo, J. A. Escartin, Escoffier, S., Ferreira, P. G., Ferrero, I., Finelli, F., Gabarra, L., Ganga, K., García-Bellido, J., Gaztanaga, E., Giacomini, F., Gozaliasl, G., Gregorio, A., Hall, A., Hildebrandt, H., Ilić, S., Kajava, J. J. E., Kansal, V., Karagiannis, D., Kirkpatrick, C. C., Legrand, L., Loureiro, A., Macias-Perez, J., Maggio, G., Magliocchetti, M., Mannucci, F., Maoli, R., Martins, C. J. A. P., Matthew, S., Maurin, L., Metcalf, R. B., Migliaccio, M., Morgante, G., Nadathur, S., Walton, Nicholas A., Patrizii, L., Pezzotta, A., Pöntinen, M., Popa, V., Porciani, C., Potter, D., Reimberg, P., Risso, I., Rocci, P. -F., Sahlén, M., Sánchez, A. G., Sefusatti, E., Sereno, M., Simon, P., Mancini, A. Spurio, Steinwagner, J., Testera, G., Tewes, M., Teyssier, R., Toft, S., Tosi, S., Troja, A., Tucci, M., Valieri, C., Valiviita, J., Vergani, D., Verza, G., Vielzeuf, P.
The Euclid mission of the European Space Agency will deliver weak gravitational lensing and galaxy clustering surveys that can be used to constrain the standard cosmological model and extensions thereof. We present forecasts from the combination of t
Externí odkaz:
http://arxiv.org/abs/2405.06047
Publikováno v:
Journal of Fluid Mechanics, 2024
We analyse the motion of a flagellated bacterium in a two-fluid medium using slender body theory. The two-fluid model is useful for describing a body moving through a complex fluid with a microstructure whose length scale is comparable to the charact
Externí odkaz:
http://arxiv.org/abs/2404.03540
Publikováno v:
Monthly Notices of the Royal Astronomical Society, Volume 529, Issue 3, April 2024, Pages 2032-2046
Self-interacting dark matter (SIDM) has been proposed to solve small-scale problems in $\Lambda$CDM cosmology. In previous work, constraints on the self-interaction cross-section of dark matter have been derived assuming that the self-interaction cro
Externí odkaz:
http://arxiv.org/abs/2310.07769
Autor:
Sharmila Sankar, P. S. Rajakumar, S. Shuba, Vimalnath Shanmugam, Sabarish Sekar, Jebaraj Rathinasamy
Publikováno v:
Indian Pediatrics Case Reports, Vol 4, Iss 4, Pp 237-240 (2024)
Background: Hereditary hemorrhagic telangiectasia (HHT) is a rare genetic disorder usually known to manifest with epistaxis, mucocutaneous telangiectasia, and visceral arteriovenous malformations (AVMs). Clinical Description: A 12-year-old girl prese
Externí odkaz:
https://doaj.org/article/912a3a7bc2354d33af7c8c93d30de020
Autor:
Vyas, Nikhil, Atanasov, Alexander, Bordelon, Blake, Morwani, Depen, Sainathan, Sabarish, Pehlevan, Cengiz
We study the effect of width on the dynamics of feature-learning neural networks across a variety of architectures and datasets. Early in training, wide neural networks trained on online data have not only identical loss curves but also agree in thei
Externí odkaz:
http://arxiv.org/abs/2305.18411
Autor:
Casas, S., Lesgourgues, J., Schöneberg, N., M., Sabarish V., Rathmann, L., Doerenkamp, M., Archidiacono, M., Bellini, E., Clesse, S., Frusciante, N., Martinelli, M., Pace, F., Sapone, D., Sakr, Z., Blanchard, A., Brinckmann, T., Camera, S., Carbone, C., Ilić, S., Markovic, K., Pettorino, V., Tutusaus, I., Aghanim, N., Amara, A., Amendola, L., Auricchio, N., Baldi, M., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Capobianco, V., Cardone, V. F., Carretero, J., Castellano, M., Cavuoti, S., Cimatti, A., Cledassou, R., Congedo, G., Conversi, L., Copin, Y., Corcione, L., Courbin, F., Cropper, M., Degaudenzi, H., Dinis, J., Douspis, M., Dubath, F., Dupac, X., Dusini, S., Farrens, S., Frailis, M., Franceschi, E., Fumana, M., Galeotta, S., Garilli, B., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Haugan, S. V. H., Hormuth, F., Hornstrup, A., Jahnke, K., Kümmel, M., Kiessling, A., Kilbinger, M., Kitching, T., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Mansutti, O., Marggraf, O., Marulli, F., Massey, R., Medinaceli, E., Mei, S., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Niemi, S. -M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W. J., 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., Sartoris, B., Schneider, P., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Starck, J. -L., Surace, C., Tallada-Crespí, P., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Valentijn, E. A., Valenziano, L., Vassallo, T., Wang, Y., Weller, J., Zamorani, G., Zoubian, J., Scottez, V., Veropalumbo, A.
The Euclid mission of the European Space Agency will perform a survey of weak lensing cosmic shear and galaxy clustering in order to constrain cosmological models and fundamental physics. We expand and adjust the mock Euclid likelihoods of the MonteP
Externí odkaz:
http://arxiv.org/abs/2303.09451
Autor:
McManus, Maxwell, Cui, Yuqing, Josh, Zhang, Hu, Jiangqi, Moorthy, Sabarish Krishna, Guan, Zhangyu, Mastronarde, Nicholas, Bentley, Elizabeth Serena, Medley, Michael
In existing wireless networks, the control programs have been designed manually and for certain predefined scenarios. This process is complicated and error-prone, and the resulting control programs are not resilient to disruptive changes. Data-driven
Externí odkaz:
http://arxiv.org/abs/2301.03359
For small training set sizes $P$, the generalization error of wide neural networks is well-approximated by the error of an infinite width neural network (NN), either in the kernel or mean-field/feature-learning regime. However, after a critical sampl
Externí odkaz:
http://arxiv.org/abs/2212.12147