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pro vyhledávání: '"Lu,Jie"'
Transpacific Literary and Cultural Connections: Latin American Influence in Asia Lu Jie Camps Martín
Autor:
Park, Moisés
Publikováno v:
Hispania, 2022 Mar 01. 105(1), 142-144.
Externí odkaz:
https://www.jstor.org/stable/27118326
Concept drift, characterized by unpredictable changes in data distribution over time, poses significant challenges to machine learning models in streaming data scenarios. Although error rate-based concept drift detectors are widely used, they often f
Externí odkaz:
http://arxiv.org/abs/2412.11158
Analysis of the form factors of $B_c\rightarrow D^{(*)}$, $D_{s}^{(*)}$ and their nonleptonic decays
This article is devoted to calculating the form factors of $B_c \to D^{*}$, $B_c \to D$, $B_c \to D_s^{*}$ and $B_c \to D_s$ transitions in the framework of three-point QCD sum rules. At the QCD side, the contributions of $\langle\overline{q}q\rangle
Externí odkaz:
http://arxiv.org/abs/2412.00515
Akademický článek
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Classical parameter-space Bayesian inference for Bayesian neural networks (BNNs) suffers from several unresolved prior issues, such as knowledge encoding intractability and pathological behaviours in deep networks, which can lead to improper posterio
Externí odkaz:
http://arxiv.org/abs/2409.16632
Realization of quantum computing requires the development of high-fidelity quantum gates that are resilient to decoherence, control errors, and environmental noise. While non-adiabatic holonomic quantum computation (NHQC) offers a promising approach,
Externí odkaz:
http://arxiv.org/abs/2409.06318
Publikováno v:
Eur. Phys. J. C 84, 1130 (2024)
Inspired by the great progress in the observations of charmonium-like states in recent years, we perform a systematic analysis about the ground states and the first radially excited states of $qc\bar{q}\bar{c}$ ($q$=$u/d$ and $s$) tetraquark systems.
Externí odkaz:
http://arxiv.org/abs/2408.13826
Autor:
Hsieh, Chung-Han, Lu, Jie-Ling
Solving large-scale robust portfolio optimization problems is challenging due to the high computational demands associated with an increasing number of assets, the amount of data considered, and market uncertainty. To address this issue, we propose a
Externí odkaz:
http://arxiv.org/abs/2408.07879
Cross-Domain Recommendation (CDR) is a promising paradigm inspired by transfer learning to solve the cold-start problem in recommender systems. Existing state-of-the-art CDR methods train an explicit mapping function to transfer the cold-start users
Externí odkaz:
http://arxiv.org/abs/2408.01931