Zobrazeno 1 - 10
of 9 271
pro vyhledávání: '"A Bresson"'
Autor:
Perestjuk, Marko, Armand, Rémi, Campos, Miguel Gerardo Sandoval, Ferhat, Lamine, Reboud, Vincent, Bresson, Nicolas, Hartmann, Jean-Michel, Mathieu, Vincent, Ren, Guanghui, Boes, Andreas, Mitchell, Arnan, Monat, Christelle, Grillet, Christian
We report ring resonators on a silicon germanium on silicon platform operating in the mid-infrared wavelength range around 3.5 - 4.6 {\mu}m with quality factors reaching up to one million. Advances in fabrication technology enable us to demonstrate s
Externí odkaz:
http://arxiv.org/abs/2412.10269
Autor:
Bresson, Roman, Nikolentzos, Giannis, Panagopoulos, George, Chatzianastasis, Michail, Pang, Jun, Vazirgiannis, Michalis
In recent years, Graph Neural Networks (GNNs) have become the de facto tool for learning node and graph representations. Most GNNs typically consist of a sequence of neighborhood aggregation (a.k.a., message-passing) layers, within which the represen
Externí odkaz:
http://arxiv.org/abs/2406.18380
Autor:
Eddine, Malak Alaa, Carvalho, Alain, Schmutz, M., Salez, Thomas, de Chateauneuf-Randon, Sixtine, Bresson, Bruno, Pantoustier, Nadège, Monteux, C., Belbekhouche, S.
Publikováno v:
Soft Matter, 2024
We explore the effect of poly (ethylene glycol) (PEG) molar mass on the intrinsic permeability and structural characteristics of poly (ethylene glycol) diacrylate PEGDA/PEG composite hydrogel membranes. We observe that by varying the PEG content and
Externí odkaz:
http://arxiv.org/abs/2406.06190
Autor:
Liu, Nian, He, Xiaoxin, Laurent, Thomas, Di Giovanni, Francesco, Bronstein, Michael M., Bresson, Xavier
Spectral graph convolution, an important tool of data filtering on graphs, relies on two essential decisions; selecting spectral bases for signal transformation and parameterizing the kernel for frequency analysis. While recent techniques mainly focu
Externí odkaz:
http://arxiv.org/abs/2405.13806
Autor:
Salducci, Clément, Bidel, Yannick, Cadoret, Malo, Darmon, Sarah, Zahzam, Nassim, Bonnin, Alexis, Schwartz, Sylvain, Blanchard, Cédric, Bresson, Alexandre
Accurate measurement of inertial quantities is essential in geophysics, geodesy, fundamental physics and navigation. For instance, inertial navigation systems require stable inertial sensors to compute the position and attitude of the carrier. Here,
Externí odkaz:
http://arxiv.org/abs/2405.13689
Autor:
Duverger, Romain, Bonnin, Alexis, Granier, Romain, Marolleau, Quentin, Blanchard, Cédric, Zahzam, Nassim, Bidel, Yannick, Cadoret, Malo, Bresson, Alexandre, Schwartz, Sylvain
We demonstrate a new approach for the metrology of microwave fields based on the trap-loss-spectroscopy of cold Rydberg atoms in a magneto-optical trap. Compared to state-of-the-art sensors using room-temperature vapors, cold atoms allow longer inter
Externí odkaz:
http://arxiv.org/abs/2404.17445
Autor:
Blaiset, Lily, Bresson, Bruno, Olanier, Ludovic, Guazzelli, Élisabeth, Roché, Matthieu, Sanson, Nicolas
We present a simple route to obtain large quantities of suspensions of non-Brownian particles with stimuli-responsive surface properties to study the relation between their flow and interparticle interactions. We perform an alkaline hydrolysis reacti
Externí odkaz:
http://arxiv.org/abs/2404.02071
Autor:
He, Xiaoxin, Tian, Yijun, Sun, Yifei, Chawla, Nitesh V., Laurent, Thomas, LeCun, Yann, Bresson, Xavier, Hooi, Bryan
Given a graph with textual attributes, we enable users to `chat with their graph': that is, to ask questions about the graph using a conversational interface. In response to a user's questions, our method provides textual replies and highlights the r
Externí odkaz:
http://arxiv.org/abs/2402.07630
Autor:
Zhang, Yuchen, Zhang, Tianle, Wang, Kai, Guo, Ziyao, Liang, Yuxuan, Bresson, Xavier, Jin, Wei, You, Yang
Graph condensation aims to reduce the size of a large-scale graph dataset by synthesizing a compact counterpart without sacrificing the performance of Graph Neural Networks (GNNs) trained on it, which has shed light on reducing the computational cost
Externí odkaz:
http://arxiv.org/abs/2402.05011
Graph Neural Networks (GNNs) have become the preferred tool to process graph data, with their efficacy being boosted through graph data augmentation techniques. Despite the evolution of augmentation methods, issues like graph property distortions and
Externí odkaz:
http://arxiv.org/abs/2401.09953