Zobrazeno 1 - 10
of 4 034
pro vyhledávání: '"P. Lagrange"'
Autor:
Lee, Junwon, Tailleur, Modan, Heller, Laurie M., Choi, Keunwoo, Lagrange, Mathieu, McFee, Brian, Imoto, Keisuke, Okamoto, Yuki
Despite significant advancements in neural text-to-audio generation, challenges persist in controllability and evaluation. This paper addresses these issues through the Sound Scene Synthesis challenge held as part of the Detection and Classification
Externí odkaz:
http://arxiv.org/abs/2410.17589
Autor:
Barantani, Francesco, Claude, Rémi, Iyikanat, Fadil, Madan, Ivan, Sapozhnik, Alexey A., Puppin, Michele, Weaver, Bruce, LaGrange, Thomas, de Abajo, F. Javier Garcia, Carbone, Fabrizio
Scattering between individual charges and collective modes in materials governs fundamental phenomena such as electrical resistance, energy dissipation, switching between different phases, and ordering. The study of such scattering requires a simulta
Externí odkaz:
http://arxiv.org/abs/2410.06810
Autor:
Wilkinson, Christian, Charnay, Benjamin, Mazevet, Stéphane, Lagrange, Anne-Marie, Chomez, Antoine, Squicciarini, Vito, Panek, Emilie, Mazoyer, Johan
Context: A new generation of instruments (e.g., JWST, ELTs, PLATO, Ariel) is providing atmospheric spectra and mass/radius measurements for large exoplanet populations, challenging planetary models used to interpret these findings. Aims: We develop a
Externí odkaz:
http://arxiv.org/abs/2410.04470
Autor:
Delorme, P., Chomez, A., Squicciarini, V., Janson, M., Flasseur, O., Schib, O., Gratton, R., Lagrange, A-M., Langlois, M., Mayer, L., Helled, R., Reïffert, S, Kiefer, F., Biller, B., Chauvin, G., Fontanive, C., Henning, Th., Kenworthy, M., Marleau, G-D., Mesa, D., Meyer, M. R., Mordasini, C., Ringqvist, S. C., Samland, M., Vigan, A., Viswanath, G.
Exoplanets form from circumstellar protoplanetary discs whose fundamental properties (notably their extent, composition, mass, temperature and lifetime) depend on the host star properties, such as their mass and luminosity. B-stars are among the most
Externí odkaz:
http://arxiv.org/abs/2409.18793
In a previous paper, we introduced a new tool called GaiaPMEX. It characterizes the mass and semi-major axis relative to the central star (sma) of a possible companion around any source observed with Gaia. It uses the value of RUWE, or, with both Gai
Externí odkaz:
http://arxiv.org/abs/2409.16993
The Gaia mission is expected to yield the detection of several thousands of exoplanets, perhaps at least doubling the number of known exoplanets. Although the harvest is expected to occur when the astrometric time series will be published with DR4 at
Externí odkaz:
http://arxiv.org/abs/2409.16992
Autor:
Bodrito, Théo, Flasseur, Olivier, Mairal, Julien, Ponce, Jean, Langlois, Maud, Lagrange, Anne-Marie
Direct imaging of exoplanets is particularly challenging due to the high contrast between the planet and the star luminosities, and their small angular separation. In addition to tailored instrumental facilities implementing adaptive optics and coron
Externí odkaz:
http://arxiv.org/abs/2409.17178
Autor:
Flasseur, Olivier, Bodrito, Théo, Mairal, Julien, Ponce, Jean, Langlois, Maud, Lagrange, Anne-Marie
In direct imaging at high contrast, the bright glare produced by the host star makes the detection and the characterization of sub-stellar companions particularly challenging. In spite of the use of an extreme adaptive optics system combined with a c
Externí odkaz:
http://arxiv.org/abs/2409.13031
Autor:
Uesugi, T., Ishi, Y., Kuriyama, Y., Mori, Y., Jolly, C., Kelliher, D. J., Lagrange, J. -B., Letchford, A. P., Machida, S., de Boer, D. W. Poshuma, Rogers, C. T., Yamakawa, E., Topp-Mugglestone, M.
A key challenge in particle accelerators is to achieve high peak intensity. Space charge is particularly strong at lower energy such as during injection and typically limits achievable peak intensity. The beam stacking technique can overcome this lim
Externí odkaz:
http://arxiv.org/abs/2407.13962
Autor:
Kong, Yuexuan, Lostanlen, Vincent, Meseguer-Brocal, Gabriel, Wong, Stella, Lagrange, Mathieu, Hennequin, Romain
Although deep neural networks can estimate the key of a musical piece, their supervision incurs a massive annotation effort. Against this shortcoming, we present STONE, the first self-supervised tonality estimator. The architecture behind STONE, name
Externí odkaz:
http://arxiv.org/abs/2407.07408