Zobrazeno 1 - 10
of 3 717
pro vyhledávání: '"Tran-Dinh A"'
Autor:
Tran, Dinh-Hieu, Van Huynh, Nguyen, Kaada, Soumeya, Vo, Van Nhan, Lagunas, Eva, Chatzinotas, Symeon
Network energy saving has received great attention from operators and vendors to reduce energy consumption and CO2 emissions to the environment as well as significantly reduce costs for mobile network operators. However, the design of energy-saving n
Externí odkaz:
http://arxiv.org/abs/2408.10974
Autor:
Kaada, Soumeya, Tran, Dinh-Hieu, Van Huynh, Nguyen, Morel, Marie-Line Alberi, Jelassi, Sofiene, Rubino, Gerardo
Resilience is defined as the ability of a network to resist, adapt, and quickly recover from disruptions, and to continue to maintain an acceptable level of services from users' perspective. With the advent of future radio networks, including advance
Externí odkaz:
http://arxiv.org/abs/2407.18066
Nuclei segmentation, despite its fundamental role in histopathological image analysis, is still a challenge work. The main challenge of this task is the existence of overlapping areas, which makes separating independent nuclei more complicated. In th
Externí odkaz:
http://arxiv.org/abs/2407.17181
Recently, window-based attention methods have shown great potential for computer vision tasks, particularly in Single Image Super-Resolution (SISR). However, it may fall short in capturing long-range dependencies and relationships between distant tok
Externí odkaz:
http://arxiv.org/abs/2407.16232
Autor:
Tran-Dinh, Quoc
We propose a novel class of Nesterov's stochastic accelerated forward-reflected-based methods with variance reduction to solve root-finding problems under $\frac{1}{L}$-co-coerciveness. Our algorithm is single-loop and leverages a new family of unbia
Externí odkaz:
http://arxiv.org/abs/2406.02413
Autor:
Tran-Dinh, Quoc
We develop two novel stochastic variance-reduction methods to approximate a solution of root-finding problems applicable to both equations and inclusions. Our algorithms leverage a new combination of ideas from the forward-reflected-backward splittin
Externí odkaz:
http://arxiv.org/abs/2406.00937
Classical structural-based visual localization methods offer high accuracy but face trade-offs in terms of storage, speed, and privacy. A recent innovation, keypoint scene coordinate regression (KSCR) named D2S addresses these issues by leveraging gr
Externí odkaz:
http://arxiv.org/abs/2403.10297
Autor:
Nguyen, Cong T., Saputra, Yuris Mulya, Van Huynh, Nguyen, Nguyen, Tan N., Hoang, Dinh Thai, Nguyen, Diep N, Pham, Van-Quan, Voznak, Miroslav, Chatzinotas, Symeon, Tran, Dinh-Hieu
Terrestrial networks form the fundamental infrastructure of modern communication systems, serving more than 4 billion users globally. However, terrestrial networks are facing a wide range of challenges, from coverage and reliability to interference a
Externí odkaz:
http://arxiv.org/abs/2403.07763
The Stochastic Gradient Descent method (SGD) and its stochastic variants have become methods of choice for solving finite-sum optimization problems arising from machine learning and data science thanks to their ability to handle large-scale applicati
Externí odkaz:
http://arxiv.org/abs/2403.03180
Recent advancements in visual localization and mapping have demonstrated considerable success in integrating point and line features. However, expanding the localization framework to include additional mapping components frequently results in increas
Externí odkaz:
http://arxiv.org/abs/2402.18011