Výsledky vyhledávání

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CoVis: A Collaborative Framework for Fine-grained Graphic Visual Understanding

Autor: Deng, Xiaoyu, Kang, Zhengjian, Li, Xintao, Zhang, Yongzhe, Guo, Tianmin

Graphic visual content helps in promoting information communication and inspiration divergence. However, the interpretation of visual content currently relies mainly on humans' personal knowledge background, thereby affecting the quality and efficien

Externí odkaz: http://arxiv.org/abs/2411.18764

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Predicting 30-Day Hospital Readmission in Medicare Patients: Insights from an LSTM Deep Learning Model

Autor: Li, Xintao, Liu, Sibei, Yu, Dezhi, Zhang, Yang, Liu, Xiaoyu

Readmissions among Medicare beneficiaries are a major problem for the US healthcare system from a perspective of both healthcare operations and patient caregiving outcomes. Our study analyzes Medicare hospital readmissions using LSTM networks with fe

Externí odkaz: http://arxiv.org/abs/2410.17545

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Optimization and Application of Cloud-based Deep Learning Architecture for Multi-Source Data Prediction

Autor: Zhang, Yang, Wang, Fa, Huang, Xin, Li, Xintao, Liu, Sibei, Zhang, Hansong

This study develops a cloud-based deep learning system for early prediction of diabetes, leveraging the distributed computing capabilities of the AWS cloud platform and deep learning technologies to achieve efficient and accurate risk assessment. The

Externí odkaz: http://arxiv.org/abs/2410.12642

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Online Learning of Multiple Tasks and Their Relationships : Testing on Spam Email Data and EEG Signals Recorded in Construction Fields

Autor: Jin, Yixin, Zhou, Wenjing, Wang, Meiqi, Li, Meng, Li, Xintao, Hu, Tianyu

This paper examines an online multi-task learning (OMTL) method, which processes data sequentially to predict labels across related tasks. The framework learns task weights and their relatedness concurrently. Unlike previous models that assumed stati

Externí odkaz: http://arxiv.org/abs/2406.18311

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Akademický článek

Limiting dynamics for stochastic complex Ginzburg-Landau systems with time-varying delays on unbounded thin domains

Autor: Li Xintao, Pan Shiyao

Publikováno v: Demonstratio Mathematica, Vol 57, Iss 1, Pp 285-300 (2024)

This study deals with the limiting dynamics for stochastic complex Ginzburg-Landau systems with time-varying delays and multiplicative noise on unbounded thin domains. We first prove the existence and uniqueness of pullback tempered random attractors

Externí odkaz: https://doaj.org/article/197f34e055334ac083aeb31652e1926b

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Akademický článek

Periodic measures of fractional stochastic discrete wave equations with nonlinear noise

Autor: Li Xintao, She Lianbing, Yao Jingjing

Publikováno v: Demonstratio Mathematica, Vol 57, Iss 1, Pp 025005-590 (2024)

The primary focus of this work lies in the exploration of the limiting dynamics governing fractional stochastic discrete wave equations with nonlinear noise. First, we establish the well-posedness of solutions to these stochastic equations and subseq

Externí odkaz: https://doaj.org/article/96795635cb4447ba8c7f5954a5c0291c

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Akademický článek

Pullback attractors for fractional lattice systems with delays in weighted space

Autor: Li Xintao, Wang Shengwen

Publikováno v: Open Mathematics, Vol 22, Iss 1, Pp 248-291 (2024)

This article deals with the asymptotic behavior of fractional lattice systems with time-varying delays in weighted space. First, we establish some sufficient conditions for the existence and uniqueness of solutions. Subsequently, we demonstrate the e

Externí odkaz: https://doaj.org/article/d559da8b617c4cb19d715f3df8c40f0d

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