Zobrazeno 1 - 10
of 497
pro vyhledávání: '"Jacques Laurent"'
Autor:
Eertmans, Jérome, Di Cicco, Nicola, Oestges, Claude, Jacques, Laurent, Vittuci, Enrico M., Degli-Esposti, Vittorio
Radio propagation modeling is essential in telecommunication research, as radio channels result from complex interactions with environmental objects. Recently, Machine Learning has been attracting attention as a potential alternative to computational
Externí odkaz:
http://arxiv.org/abs/2410.23773
Autor:
Eertmans, Jérome, Vittuci, Enrico Maria, Degli-Esposti, Vittorio, Jacques, Laurent, Oestges, Claude
With the increasing presence of dynamic scenarios, such as Vehicle-to-Vehicle communications, radio propagation modeling tools must adapt to the rapidly changing nature of the radio channel. Recently, both Differentiable and Dynamic Ray Tracing frame
Externí odkaz:
http://arxiv.org/abs/2410.14535
Autor:
Daglayan, Hazan, Vary, Simon, Absil, Olivier, Cantalloube, Faustine, Christiaens, Valentin, Gillis, Nicolas, Jacques, Laurent, Leplat, Valentin, Absil, P. -A.
Publikováno v:
A&A 692, A126 (2024)
Effective image post-processing algorithms are vital for the successful direct imaging of exoplanets. Standard PSF subtraction methods use techniques based on a low-rank approximation to separate the rotating planet signal from the quasi-static speck
Externí odkaz:
http://arxiv.org/abs/2410.06310
Radio-interferometry (RI) observes the sky at unprecedented angular resolutions, enabling the study of several far-away galactic objects such as galaxies and black holes. In RI, an array of antennas probes cosmic signals coming from the observed regi
Externí odkaz:
http://arxiv.org/abs/2409.15031
Publikováno v:
EUSIPCO, Aug 2024, Lyon, France
In numerous inverse problems, state-of-the-art solving strategies involve training neural networks from ground truth and associated measurement datasets that, however, may be expensive or impossible to collect. Recently, self-supervised learning tech
Externí odkaz:
http://arxiv.org/abs/2409.15283
Recently, many self-supervised learning methods for image reconstruction have been proposed that can learn from noisy data alone, bypassing the need for ground-truth references. Most existing methods cluster around two classes: i) Noise2Self and simi
Externí odkaz:
http://arxiv.org/abs/2409.01985
We propose a novel algorithm for distributed stochastic gradient descent (SGD) with compressed gradient communication in the parameter-server framework. Our gradient compression technique, named flattened one-bit stochastic gradient descent (FO-SGD),
Externí odkaz:
http://arxiv.org/abs/2405.11095
Autor:
Céline Richomme, Sandrine Lesellier, Francisco Javier Salguero, Jacques Laurent Barrat, Jean-Marc Boucher, Jennifer Danaidae Reyes-Reyes, Sylvie Hénault, Krystel De Cruz, Jennifer Tambosco, Lorraine Michelet, Justine Boutet, Rubyat Elahi, Konstantin P. Lyashchenko, Conor O’Halloran, Ana Balseiro, Maria Laura Boschiroli
Publikováno v:
Microorganisms, Vol 10, Iss 2, p 380 (2022)
In Europe, animal tuberculosis (TB) due to Mycobacterium bovis involves multi-host communities that include cattle and wildlife species, such as wild boar (Sus scrofa), badgers (Meles meles) and red deer (Cervus elaphus). Red fox (Vulpes vulpes) infe
Externí odkaz:
https://doaj.org/article/f3c558ba9007414e9ab6e0a8ed94e8ef
Publikováno v:
2024 18th European Conference on Antennas and Propagation (EuCAP)
Recently, Differentiable Ray Tracing has been successfully applied in the field of wireless communications for learning radio materials or optimizing the transmitter orientation. However, in the frame of gradient based optimization, obstruction of th
Externí odkaz:
http://arxiv.org/abs/2401.11882
Autor:
Moureaux, Anatole, Chopin, Chloé, de Wergifosse, Simon, Jacques, Laurent, Araujo, Flavio Abreu
We present a demonstration of image classification using an echo-state network (ESN) relying on a single simulated spintronic nanostructure known as the vortex-based spin-torque oscillator (STVO) delayed in time. We employ an ultrafast data-driven si
Externí odkaz:
http://arxiv.org/abs/2308.05810