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of 12 686
pro vyhledávání: '"Lee, In Hyung"'
Massive MIMO (mMIMO) systems are essential for 5G/6G networks to meet high throughput and reliability demands, with machine learning (ML)-based techniques, particularly autoencoders (AEs), showing promise for practical deployment. However, standard A
Externí odkaz:
http://arxiv.org/abs/2411.16971
Autor:
Chen, Hongqi, Lee, Ji Hyung
This paper advances a variable screening approach to enhance conditional quantile forecasts using high-dimensional predictors. We have refined and augmented the quantile partial correlation (QPC)-based variable screening proposed by Ma et al. (2017)
Externí odkaz:
http://arxiv.org/abs/2410.15097
LASSO introduces shrinkage bias into estimated coefficients, which can adversely affect the desirable asymptotic normality and invalidate the standard inferential procedure based on the $t$-statistic. The desparsified LASSO has emerged as a well-know
Externí odkaz:
http://arxiv.org/abs/2409.10030
Blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) is widely used to visualize brain activation regions by detecting hemodynamic responses associated with increased metabolic demand. While alternative MRI methods ha
Externí odkaz:
http://arxiv.org/abs/2409.07806
Fault-tolerant implementation of non-Clifford gates is a major challenge for achieving universal fault-tolerant quantum computing with quantum error-correcting codes. Magic state distillation is the most well-studied method for this but requires sign
Externí odkaz:
http://arxiv.org/abs/2409.07707
Two-dimensional color codes are a promising candidate for fault-tolerant quantum computing, as they have high encoding rates, transversal implementation of logical Clifford gates, and high feasibility of magic state constructions. However, decoding c
Externí odkaz:
http://arxiv.org/abs/2404.07482
Autor:
Lee, Jin Hyung, Lee, Ben Seiyon
Gaussian and discrete non-Gaussian spatial datasets are prevalent across many fields such as public health, ecology, geosciences, and social sciences. Bayesian spatial generalized linear mixed models (SGLMMs) are a flexible class of models designed f
Externí odkaz:
http://arxiv.org/abs/2402.15705
The burgeoning field of on-device AI communication, where devices exchange information directly through embedded foundation models, such as language models (LMs), requires robust, efficient, and generalizable communication frameworks. However, integr
Externí odkaz:
http://arxiv.org/abs/2402.11656
In this article, we propose a multi-agent deep reinforcement learning (MADRL) framework to train a multiple access protocol for downlink low earth orbit (LEO) satellite networks. By improving the existing learned protocol, emergent random access chan
Externí odkaz:
http://arxiv.org/abs/2402.02350
Hybridizing different degrees of freedom or physical platforms potentially offers various advantages in building scalable quantum architectures. We here introduce a fault-tolerant hybrid quantum computation by taking the advantages of both discrete v
Externí odkaz:
http://arxiv.org/abs/2401.00450