Zobrazeno 1 - 10
of 20 956
pro vyhledávání: '"A Giner"'
Autor:
Jeanmairet, Guillaume, Giner, Emmanuel
Combining classical density functional theory (cDFT) with quantum mechanics (QM) methods offers a computationally efficient alternative to traditional QM/molecular mechanics (MM) approaches for modeling mixed quantum-classical systems at finite tempe
Externí odkaz:
http://arxiv.org/abs/2411.11821
Autor:
Martinez-Becerril, Aldo C., Luo, Siwei, Li, Liu, Pagé, Jordan, Giner, Lambert, Abrahao, Raphael A., Lundeen, Jeff S.
Spatial transformations of light are ubiquitous in optics, with examples ranging from simple imaging with a lens to quantum and classical information processing in waveguide meshes. Multi-plane light converter (MPLC) systems have emerged as a platfor
Externí odkaz:
http://arxiv.org/abs/2407.06981
Autor:
Ferrer, Miguel A., Calduch-Giner, Josep A., Díaz, Moises, Sosa, Javier, Rosell-Moll, Enrique, Abril, Judith Santana, Sosa, Graciela Santana, Delgado, Tomás Bautista, Carmona, Cristina, Martos-Sitcha, Juan Antonio, Cabruja, Enric, Afonso, Juan Manuel, Vega, Aurelio, Lozano, Manuel, Montiel-Nelson, Juan Antonio, Pérez-Sánchez, Jaume
Publikováno v:
Computers and Electronics in Agriculture, col.175,pp.105531,2020
The AEFishBIT tri-axial accelerometer was externally attached to the operculum to assess the divergent activity and respiratory patterns of two marine farmed fish, the gilthead sea bream (Sparus aurata) and European sea bass (Dicentrarchus labrax). A
Externí odkaz:
http://arxiv.org/abs/2406.03859
Autor:
Jain, Nitisha, Akhtar, Mubashara, Giner-Miguelez, Joan, Shinde, Rajat, Vanschoren, Joaquin, Vogler, Steffen, Goswami, Sujata, Rao, Yuhan, Santos, Tim, Oala, Luis, Karamousadakis, Michalis, Maskey, Manil, Marcenac, Pierre, Conforti, Costanza, Kuchnik, Michael, Aroyo, Lora, Benjelloun, Omar, Simperl, Elena
Data is critical to advancing AI technologies, yet its quality and documentation remain significant challenges, leading to adverse downstream effects (e.g., potential biases) in AI applications. This paper addresses these issues by introducing Croiss
Externí odkaz:
http://arxiv.org/abs/2407.16883
Autor:
Traore, Diata, Adjoua, Olivier, Feniou, César, Lygatsika, Ioanna-Maria, Maday, Yvon, Posenitskiy, Evgeny, Hammernik, Kerstin, Peruzzo, Alberto, Toulouse, Julien, Giner, Emmanuel, Piquemal, Jean-Philip
Publikováno v:
Communications Chemistry, 2024, 7, 269
Using GPU-accelerated state-vector emulation, we propose to embed a quantum computing ansatz into density-functional theory via density-based basis-set corrections (DBBSC) to obtain quantitative quantum-chemistry results on molecules that would other
Externí odkaz:
http://arxiv.org/abs/2405.11567
Publikováno v:
J. Chem. Phys. 161, 084104 (2024)
Although selected configuration interaction (SCI) algorithms can tackle much larger Hilbert spaces than the conventional full CI (FCI) method, the scaling of their computational cost with respect to the system size remains inherently exponential. Add
Externí odkaz:
http://arxiv.org/abs/2405.02640
This paper presents the first implementation of a coupling between advanced wave function theories and molecular density functional theory (MDFT). This method enables the modeling of solvent effect into quantum mechanical (QM) calculations by incorpo
Externí odkaz:
http://arxiv.org/abs/2404.07109
Recent regulatory initiatives like the European AI Act and relevant voices in the Machine Learning (ML) community stress the need to describe datasets along several key dimensions for trustworthy AI, such as the provenance processes and social concer
Externí odkaz:
http://arxiv.org/abs/2404.15320
Autor:
Akhtar, Mubashara, Benjelloun, Omar, Conforti, Costanza, Foschini, Luca, Giner-Miguelez, Joan, Gijsbers, Pieter, Goswami, Sujata, Jain, Nitisha, Karamousadakis, Michalis, Kuchnik, Michael, Krishna, Satyapriya, Lesage, Sylvain, Lhoest, Quentin, Marcenac, Pierre, Maskey, Manil, Mattson, Peter, Oala, Luis, Oderinwale, Hamidah, Ruyssen, Pierre, Santos, Tim, Shinde, Rajat, Simperl, Elena, Suresh, Arjun, Thomas, Goeffry, Tykhonov, Slava, Vanschoren, Joaquin, Varma, Susheel, van der Velde, Jos, Vogler, Steffen, Wu, Carole-Jean, Zhang, Luyao
Data is a critical resource for machine learning (ML), yet working with data remains a key friction point. This paper introduces Croissant, a metadata format for datasets that creates a shared representation across ML tools, frameworks, and platforms
Externí odkaz:
http://arxiv.org/abs/2403.19546
Autor:
Lazar, Jeffrey, Olavarrieta, Santiago Giner, Gatti, Giancarlo, Argüelles, Carlos A., Sanz, Mikel
Ever-increasing amount of data is produced by particle detectors in their quest to unveil the laws of Nature. The large data rate requires the use of specialized triggers that promptly reduce the data rate to a manageable level; however, in doing so,
Externí odkaz:
http://arxiv.org/abs/2402.19306