Institution profile

Shenzhen Polytechnic University

Academic institutionasia · cn
Official website
Research library6linked papers
Opportunities0open roles
Selected work

Representative Papers

Spatial-Temporal Multi-scale Network for Screen Content Video Quality Enhancement

Sep 30, 2026

This study addresses the degradation of temporal correlation and compression quality in screen content videos caused by abrupt motion transitions and high-frequency details. To tackle these challenges, this work proposes STM-Net, an enhancement framework that incorporates a prior-guided spatiotemporal scheduler and a parallel stream routing mechanism. These components adaptively handle abrupt transitions without explicit detection while preventing feature contamination. Furthermore, by integrating bidirectional temporal feature extraction with a cascaded multi-scale distillation module, the proposed method effectively preserves critical high-frequency information. Experimental results demonstrate that STM-Net outperforms existing state-of-the-art approaches in both objective metrics and subjective visual quality, offering a robust solution for mitigating compression artifacts in screen content videos.

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Oto-Meal: Earable Sensing with PPG and IMU for Personalized Meal Awareness

Aug 17, 2026

This study addresses privacy concerns and high user burden in conventional dietary monitoring by proposing an audio-visual-free ear-worn sensing system. Integrating PPG and IMU signals, the framework employs a two-stage event-gating mechanism, five-class behavior classification, and lightweight personalized memory matching to achieve accurate recognition with minimal intrusion. Experimental results demonstrate that the pooled model attains 70.99% accuracy, while multimodal fusion achieves 85.13% accuracy with only 60% calibration data, significantly outperforming unimodal approaches. As the first audio-visual-free paradigm for dietary sensing, this work effectively balances privacy preservation with recognition precision, offering a novel pathway for wearable health interventions.

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UD-DML: Uniform Design Subsampling for Double Machine Learning over Massive Data

May 07, 2026

This study addresses the high computational cost of double machine learning (DML) with large-scale observational data and the inadequacy of simple random subsampling, which often results in insufficient covariate coverage and imbalance between treatment and control groups. The authors propose UD-DML, a novel subsampling approach that incorporates uniform design principles into DML for the first time. The method first applies PCA rotation to covariates, then constructs a low-discrepancy skeleton based on a hybrid discrepancy criterion, and finally uses KD-trees to match nearest-neighbor treated and control units to each skeleton point, yielding a representative and balanced subsample. Theoretical analysis establishes that UD-DML retains √n-asymptotic normality even when the subsample size is much smaller than the full dataset. Experiments demonstrate that, compared to uniform sampling, UD-DML achieves lower RMSE, narrower confidence intervals, and more reliable coverage—particularly under low overlap and model misspecification.

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Recent publications

Latest Papers

Spatial-Temporal Multi-scale Network for Screen Content Video Quality Enhancement

Sep 30, 2026

This study addresses the degradation of temporal correlation and compression quality in screen content videos caused by abrupt motion transitions and high-frequency details. To tackle these challenges, this work proposes STM-Net, an enhancement framework that incorporates a prior-guided spatiotemporal scheduler and a parallel stream routing mechanism. These components adaptively handle abrupt transitions without explicit detection while preventing feature contamination. Furthermore, by integrating bidirectional temporal feature extraction with a cascaded multi-scale distillation module, the proposed method effectively preserves critical high-frequency information. Experimental results demonstrate that STM-Net outperforms existing state-of-the-art approaches in both objective metrics and subjective visual quality, offering a robust solution for mitigating compression artifacts in screen content videos.

0 citationsRead paper

Oto-Meal: Earable Sensing with PPG and IMU for Personalized Meal Awareness

Aug 17, 2026

This study addresses privacy concerns and high user burden in conventional dietary monitoring by proposing an audio-visual-free ear-worn sensing system. Integrating PPG and IMU signals, the framework employs a two-stage event-gating mechanism, five-class behavior classification, and lightweight personalized memory matching to achieve accurate recognition with minimal intrusion. Experimental results demonstrate that the pooled model attains 70.99% accuracy, while multimodal fusion achieves 85.13% accuracy with only 60% calibration data, significantly outperforming unimodal approaches. As the first audio-visual-free paradigm for dietary sensing, this work effectively balances privacy preservation with recognition precision, offering a novel pathway for wearable health interventions.

0 citationsRead paper

UD-DML: Uniform Design Subsampling for Double Machine Learning over Massive Data

May 07, 2026

This study addresses the high computational cost of double machine learning (DML) with large-scale observational data and the inadequacy of simple random subsampling, which often results in insufficient covariate coverage and imbalance between treatment and control groups. The authors propose UD-DML, a novel subsampling approach that incorporates uniform design principles into DML for the first time. The method first applies PCA rotation to covariates, then constructs a low-discrepancy skeleton based on a hybrid discrepancy criterion, and finally uses KD-trees to match nearest-neighbor treated and control units to each skeleton point, yielding a representative and balanced subsample. Theoretical analysis establishes that UD-DML retains √n-asymptotic normality even when the subsample size is much smaller than the full dataset. Experiments demonstrate that, compared to uniform sampling, UD-DML achieves lower RMSE, narrower confidence intervals, and more reliable coverage—particularly under low overlap and model misspecification.

0 citationsRead paper