Selected work

Representative Papers

Non-GRS type MDS and AMDS codes from extended TGRS codes

Apr 07, 2026

This work proposes a new class of extended twisted generalized Reed–Solomon (TGRS) codes to broaden the design space of optimal and near-optimal error-correcting codes. It systematically investigates the necessary and sufficient conditions under which these codes attain maximum distance separable (MDS) or almost MDS (AMDS) properties. Leveraging algebraic coding theory and equivalence analysis, the study rigorously establishes—for the first time—that the constructed codes are non-equivalent to classical Reed–Solomon (GRS) codes for specific parameter choices. Furthermore, it precisely determines their covering radii and deep holes. By providing explicit constructions of novel non-GRS MDS and AMDS codes along with clear parameter criteria, this research significantly enriches the theoretical foundations and practical resources available for high-performance error correction.

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Robust and Well-conditioned Sparse Estimation for High-dimensional Covariance Matrices

Dec 29, 2025

Robust sparse estimation of high-dimensional covariance matrices faces three interrelated challenges: difficulty in guaranteeing positive definiteness, sparsity degradation due to post-hoc corrections, and uncontrolled condition numbers. Method: We propose the first method that explicitly incorporates a condition-number constraint into a robust adaptive thresholding framework. Using convex optimization and a provably convergent alternating direction algorithm, our approach jointly ensures positive definiteness, sparsity, and numerical stability. Contribution/Results: We establish theoretical minimax optimal convergence rate under the Frobenius norm. Experiments on both synthetic and real-world datasets demonstrate that our estimator consistently yields positive definite, sparse, and well-conditioned (low condition number) covariance matrices. Its numerical stability matches or surpasses that of eigenvalue truncation, while requiring fewer hyperparameters and offering greater practical utility.

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$L_1$-norm Regularized Indefinite Kernel Logistic Regression

Oct 29, 2025

To address the challenge of simultaneously achieving sparsity, interpretability, and generalization in indefinite kernel logistic regression (IKLR), this paper introduces the $L_1$-norm regularization into the IKLR framework for the first time, yielding the Sparse Indefinite Kernel Logistic Regression (S-IKLR) model. To tackle the resulting nonsmooth and nonconvex optimization problem, we propose an efficient proximal linearization-based algorithm with theoretical convergence guarantees. S-IKLR leverages the expressive power of indefinite kernels to capture complex data structures while enforcing sparsity via $L_1$ regularization, thereby substantially reducing the number of nonzero parameters. Extensive experiments on multiple benchmark datasets demonstrate that S-IKLR achieves superior classification accuracy compared to state-of-the-art IKLR and sparse kernel methods. Moreover, it attains 30–60% higher model sparsity, leading to significantly improved interpretability and generalization performance.

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Lightweight Shrimp Disease Detection Research Based on YOLOv8n

Jul 03, 2025

To address the low efficiency and insufficient accuracy of disease detection in shrimp aquaculture—leading to substantial economic losses—this paper proposes a lightweight YOLOv8n-based model. The method introduces three key innovations: (1) an RLDD detection head, (2) a C2f-EMCM feature fusion module, and (3) an enhanced SegNext_Attention self-attention mechanism, collectively improving multi-scale lesion feature representation while reducing computational overhead. Experiments on a custom shrimp disease dataset and the URPC2020 benchmark demonstrate that the proposed model reduces parameter count by 32.3%, achieves an mAP@0.5 of 92.7% (+3.0% improvement), and attains a +4.1% mAP@0.5 gain on URPC2020—outperforming state-of-the-art lightweight YOLO variants. The approach delivers an efficient, robust solution for intelligent disease identification in aquaculture.

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Research on Improving the High Precision and Lightweight Diabetic Retinopathy Detection of YOLOv8n

Jul 01, 2025

To address weak fine-feature perception, strong background interference, and model redundancy in diabetic retinopathy microlesion detection, this paper proposes a lightweight yet high-accuracy YOLOv8n-based framework. Methodologically, it introduces (1) dynamic KWConv and the C2f-KW module to enhance local sensitivity to subtle lesions; (2) a Feature-Focusing Diffusion Pyramid Network (FDPN) for adaptive multi-scale contextual feature fusion; and (3) a lightweight Shared Detection Head (GSDHead) to drastically reduce parameter count. Experimental results demonstrate that the proposed model reduces parameters by 20.7% and improves mAP@0.5 by 4.1% and recall by 7.9% over baseline YOLOv8n. Moreover, it outperforms mainstream one-stage detectors—including YOLOv5n and YOLOv10n—in both accuracy and efficiency, achieving an optimal balance between computational cost and detection performance for clinical-grade microlesion identification.

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Recent publications

Latest Papers

Non-GRS type MDS and AMDS codes from extended TGRS codes

Apr 07, 2026

This work proposes a new class of extended twisted generalized Reed–Solomon (TGRS) codes to broaden the design space of optimal and near-optimal error-correcting codes. It systematically investigates the necessary and sufficient conditions under which these codes attain maximum distance separable (MDS) or almost MDS (AMDS) properties. Leveraging algebraic coding theory and equivalence analysis, the study rigorously establishes—for the first time—that the constructed codes are non-equivalent to classical Reed–Solomon (GRS) codes for specific parameter choices. Furthermore, it precisely determines their covering radii and deep holes. By providing explicit constructions of novel non-GRS MDS and AMDS codes along with clear parameter criteria, this research significantly enriches the theoretical foundations and practical resources available for high-performance error correction.

0 citationsRead paper

Robust and Well-conditioned Sparse Estimation for High-dimensional Covariance Matrices

Dec 29, 2025

Robust sparse estimation of high-dimensional covariance matrices faces three interrelated challenges: difficulty in guaranteeing positive definiteness, sparsity degradation due to post-hoc corrections, and uncontrolled condition numbers. Method: We propose the first method that explicitly incorporates a condition-number constraint into a robust adaptive thresholding framework. Using convex optimization and a provably convergent alternating direction algorithm, our approach jointly ensures positive definiteness, sparsity, and numerical stability. Contribution/Results: We establish theoretical minimax optimal convergence rate under the Frobenius norm. Experiments on both synthetic and real-world datasets demonstrate that our estimator consistently yields positive definite, sparse, and well-conditioned (low condition number) covariance matrices. Its numerical stability matches or surpasses that of eigenvalue truncation, while requiring fewer hyperparameters and offering greater practical utility.

0 citationsRead paper

$L_1$-norm Regularized Indefinite Kernel Logistic Regression

Oct 29, 2025

To address the challenge of simultaneously achieving sparsity, interpretability, and generalization in indefinite kernel logistic regression (IKLR), this paper introduces the $L_1$-norm regularization into the IKLR framework for the first time, yielding the Sparse Indefinite Kernel Logistic Regression (S-IKLR) model. To tackle the resulting nonsmooth and nonconvex optimization problem, we propose an efficient proximal linearization-based algorithm with theoretical convergence guarantees. S-IKLR leverages the expressive power of indefinite kernels to capture complex data structures while enforcing sparsity via $L_1$ regularization, thereby substantially reducing the number of nonzero parameters. Extensive experiments on multiple benchmark datasets demonstrate that S-IKLR achieves superior classification accuracy compared to state-of-the-art IKLR and sparse kernel methods. Moreover, it attains 30–60% higher model sparsity, leading to significantly improved interpretability and generalization performance.

0 citationsRead paper

Lightweight Shrimp Disease Detection Research Based on YOLOv8n

Jul 03, 2025

To address the low efficiency and insufficient accuracy of disease detection in shrimp aquaculture—leading to substantial economic losses—this paper proposes a lightweight YOLOv8n-based model. The method introduces three key innovations: (1) an RLDD detection head, (2) a C2f-EMCM feature fusion module, and (3) an enhanced SegNext_Attention self-attention mechanism, collectively improving multi-scale lesion feature representation while reducing computational overhead. Experiments on a custom shrimp disease dataset and the URPC2020 benchmark demonstrate that the proposed model reduces parameter count by 32.3%, achieves an mAP@0.5 of 92.7% (+3.0% improvement), and attains a +4.1% mAP@0.5 gain on URPC2020—outperforming state-of-the-art lightweight YOLO variants. The approach delivers an efficient, robust solution for intelligent disease identification in aquaculture.

0 citationsRead paper

Research on Improving the High Precision and Lightweight Diabetic Retinopathy Detection of YOLOv8n

Jul 01, 2025

To address weak fine-feature perception, strong background interference, and model redundancy in diabetic retinopathy microlesion detection, this paper proposes a lightweight yet high-accuracy YOLOv8n-based framework. Methodologically, it introduces (1) dynamic KWConv and the C2f-KW module to enhance local sensitivity to subtle lesions; (2) a Feature-Focusing Diffusion Pyramid Network (FDPN) for adaptive multi-scale contextual feature fusion; and (3) a lightweight Shared Detection Head (GSDHead) to drastically reduce parameter count. Experimental results demonstrate that the proposed model reduces parameters by 20.7% and improves mAP@0.5 by 4.1% and recall by 7.9% over baseline YOLOv8n. Moreover, it outperforms mainstream one-stage detectors—including YOLOv5n and YOLOv10n—in both accuracy and efficiency, achieving an optimal balance between computational cost and detection performance for clinical-grade microlesion identification.

0 citationsRead paper