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Shanxi University

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Research library75linked papers
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Selected work

Representative Papers

EGM-Det: Entropy-Guided Multimodal Adaptive Fusion for UAV RGB-IR Object Detection

Aug 12, 2026

This work addresses the limitations of existing RGB-IR object detection methods for unmanned aerial vehicles, which typically rely on static fusion strategies that fail to account for spatially varying modality reliability. To overcome this, the authors propose EGM-Det, a dual-stream framework that preserves modality-specific representations and introduces an entropy-guided offset gating mechanism. This mechanism leverages input intensity, local entropy, and cross-modal discrepancies to construct shallow entropy priors, dynamically guiding multi-scale spatial-channel alignment and fusion. Additionally, an entropy-adaptive supervised cross-modal knowledge distillation strategy is designed to optimize training. The proposed method achieves state-of-the-art performance across three benchmarks—DroneVehicle, LLVIP, and VEDAI—with a notable improvement of over 10 percentage points in mAP on VEDAI.

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Signed Matrix Thinning and Projection Estimation for Integer-Valued Autoregressive Models

Aug 01, 2026

This study addresses a critical limitation of existing integer-valued matrix autoregressive models, which cannot accommodate negative integers and thus fail to model real-world scenarios involving differenced series or financial tick data. To overcome this, the authors propose the Z-MINAR model—the first matrix autoregressive framework defined over the full set of integers, both positive and negative. The approach introduces a signed matrix sparsity operator and innovation terms based on an extended Poisson distribution, thereby preserving the underlying matrix topology. Parameter estimation is achieved via projected conditional least squares. Theoretical analysis establishes the model’s stationarity, causality, and asymptotic normality. Simulations demonstrate that Z-MINAR substantially outperforms existing methods in estimation accuracy, robustness, and adaptability, while empirical application successfully uncovers the dynamic spatiotemporal dependence structure in urban crime count data.

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Recent publications

Latest Papers

EGM-Det: Entropy-Guided Multimodal Adaptive Fusion for UAV RGB-IR Object Detection

Aug 12, 2026

This work addresses the limitations of existing RGB-IR object detection methods for unmanned aerial vehicles, which typically rely on static fusion strategies that fail to account for spatially varying modality reliability. To overcome this, the authors propose EGM-Det, a dual-stream framework that preserves modality-specific representations and introduces an entropy-guided offset gating mechanism. This mechanism leverages input intensity, local entropy, and cross-modal discrepancies to construct shallow entropy priors, dynamically guiding multi-scale spatial-channel alignment and fusion. Additionally, an entropy-adaptive supervised cross-modal knowledge distillation strategy is designed to optimize training. The proposed method achieves state-of-the-art performance across three benchmarks—DroneVehicle, LLVIP, and VEDAI—with a notable improvement of over 10 percentage points in mAP on VEDAI.

0 citationsRead paper

Signed Matrix Thinning and Projection Estimation for Integer-Valued Autoregressive Models

Aug 01, 2026

This study addresses a critical limitation of existing integer-valued matrix autoregressive models, which cannot accommodate negative integers and thus fail to model real-world scenarios involving differenced series or financial tick data. To overcome this, the authors propose the Z-MINAR model—the first matrix autoregressive framework defined over the full set of integers, both positive and negative. The approach introduces a signed matrix sparsity operator and innovation terms based on an extended Poisson distribution, thereby preserving the underlying matrix topology. Parameter estimation is achieved via projected conditional least squares. Theoretical analysis establishes the model’s stationarity, causality, and asymptotic normality. Simulations demonstrate that Z-MINAR substantially outperforms existing methods in estimation accuracy, robustness, and adaptability, while empirical application successfully uncovers the dynamic spatiotemporal dependence structure in urban crime count data.

0 citationsRead paper