Zobrazeno 1 - 10
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pro vyhledávání: '"Farias IN"'
Evidences of vortical effects have been recently found by experiments in heavy ion collisions, instigating new insights into the phase diagram of quantum chromodynamics. Considering the effect of rotations, lattice QCD data shows that the temperature
Externí odkaz:
http://arxiv.org/abs/2412.14541
Autor:
Ricardo, Alexandre C., de Lima, Gubio G., Valério, Amanda G., Farias, Tiago de S., Villas-Boas, Celso J.
Continuous-variable quantum computing utilizes continuous parameters of a quantum system to encode information, promising efficient solutions to complex problems. Trapped-ion systems provide a robust platform with long coherence times and precise qub
Externí odkaz:
http://arxiv.org/abs/2412.15025
Differentially private selection mechanisms are fundamental building blocks for privacy-preserving data analysis. While numerous mechanisms exist for single-objective selection, many real-world applications require optimizing multiple competing objec
Externí odkaz:
http://arxiv.org/abs/2412.14380
We present the first installment of the quantum computing (QC) formulation of the electron nuclear dynamics (END) method within the variational quantum simulator (VQS) scheme: END/QC/VQS. END is a time-dependent, variational, on-the-flight, and non-a
Externí odkaz:
http://arxiv.org/abs/2411.17657
Autor:
Ricardo, Alexandre C., Fernandes, Gabriel P. L. M., Valério, Amanda G., Farias, Tiago de S., Fonseca, Matheus da S., Carpio, Nicolás A. C., Bezerra, Paulo C. C., Maier, Christine, Ulmanis, Juris, Monz, Thomas, Villas-Boas, Celso J.
Warehouse optimization stands as a critical component for enhancing operational efficiency within the industrial sector. By strategically streamlining warehouse operations, organizations can achieve significant reductions in logistical costs such as
Externí odkaz:
http://arxiv.org/abs/2411.17575
Autor:
Dutta, Siddhant, de Freitas, Iago Leal, Xavier, Pedro Maciel, de Farias, Claudio Miceli, Neira, David Esteban Bernal
Federated Learning (FL) is a decentralized machine learning approach that has gained attention for its potential to enable collaborative model training across clients while protecting data privacy, making it an attractive solution for the chemical in
Externí odkaz:
http://arxiv.org/abs/2411.16737
Autor:
Peixoto, Eduardo, Freitas, Pedro Garcia, Farias, Mylene Christine Queiroz, Medeiros, Edil, Menezes, Gabriel Correia Lima da Cunha e, da Costa, André Henrique Macedo
In 2023 we have conducted extensive experiments on subjective video quality for the TV 3.0 project at University of Bras\'ilia. A full report on these tests is available at the F\'orum SBTVD website . These tests have evaluated the H.266/VVC codec an
Externí odkaz:
http://arxiv.org/abs/2411.11755
Autor:
Farias, Dahyana, Fernández, Eduardo
We provide a topological characterization for a family of bypasses with a fixed attaching arc to be contractible. This characterization is formulated in terms of the existence of a bypass that is disjoint from the given family away from the attaching
Externí odkaz:
http://arxiv.org/abs/2411.06467
Autor:
Perez-Ramirez, Fredy O., Caro-Lopera, Francisco J., Diaz-Garcia, Jose A., Gonzalez-Farias, Graciela
The time series theory is set in this work under the domain of general elliptically contoured distributions. The advent of a time series approach that is in accordance with the expected reality of dependence between errors, transfers the increasingly
Externí odkaz:
http://arxiv.org/abs/2411.00525
Autor:
Farias, Matheus, Kung, H. T.
We introduce a novel approach to reduce the number of times required for reprogramming memristors on bit-sliced compute-in-memory crossbars for deep neural networks (DNNs). Our idea addresses the limited non-volatile memory endurance, which restrict
Externí odkaz:
http://arxiv.org/abs/2410.21730