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Tiangong University

Academic institutionasia · cn
Official website
Research library60linked papers
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Selected work

Representative Papers

SANet: Selective Attention Network for Infrared Small Target Detection

Oct 07, 2026

This study addresses the challenge of balancing detection accuracy and false alarm rate in infrared small target detection by proposing a selective attention network. Specifically, a dual-path semantic perception module is designed, integrating standard and pinwheel-shaped convolutions with spatial-channel attention mechanisms to enhance target-background discrimination. Furthermore, an adaptive feature fusion strategy employing spatially variable weights is introduced to overcome the limitations of static skip connections, thereby optimizing multi-scale feature integration. Extensive experiments on three public benchmarks demonstrate that the proposed method achieves up to a 4.32 percentage point improvement in IoU over the second-best approach, while significantly reducing the false alarm rate and increasing the detection probability.

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Topology-Aware Cooperative Beam-Hopping Scheduling for Efficient Resource Allocation in LEO Satellite Systems

Oct 07, 2026

This study addresses the beam-hopping resource management challenges in low Earth orbit (LEO) satellite networks arising from highly dynamic topologies and heterogeneous traffic. To this end, it proposes a two-stage GNN-MAPPO cooperative framework operating under partial observability. Specifically, the method leverages graph neural networks to extract time-varying topological features and employs multi-agent proximal policy optimization for joint beam scheduling, while incorporating a load-balancing mechanism to enhance multi-satellite cooperation. Experimental results demonstrate that the proposed architecture effectively overcomes the challenges of environmental partial observability, yielding significant improvements in system energy efficiency, throughput, and user fairness.

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Cauchy-Aggregated Ridge Tests for High-Dimensional Factor Pricing Models

Oct 05, 2026

This study addresses the pronounced power instability in alpha testing within high-dimensional factor pricing models, which arises from uncertainty in ridge parameter tuning. To overcome this limitation, the work proposes a p-value aggregation framework grounded in the Cauchy combination rule. By integrating least squares estimation, residual sample covariance, and asymptotic normality theory for statistical inference, the method adaptively identifies the optimal ridge parameter without requiring prior knowledge of error directions, thereby constructing a robust alpha testing procedure. The proposed approach significantly outperforms fixed-ridge-parameter benchmarks. Empirically, rejection rates closely approximate nominal levels, while test power improves substantially, approaching the theoretical optimum attainable when signal information is known. This framework provides a reliable statistical inference tool for high-dimensional asset pricing applications.

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Cauchy-Combined Hettmansperger-Randles Location Tests in High Dimensions

Oct 03, 2026

This study addresses the failure of classical high-dimensional mean testing methods under strong dependence and heavy-tailed distributions, as well as their sensitivity to outliers. To overcome these limitations, this work proposes a robust regularized Hotelling testing framework. Methodologically, it integrates Hettmansperger–Randles spatial estimation with a shrinkage Tyler scatter matrix, leveraging ridge-resolvent random matrix theory to establish asymptotic normality without conventional sparsity assumptions. Furthermore, a Cauchy combination approach is employed to achieve adaptive testing across varying shrinkage levels. Simulation experiments and applications to gene expression data demonstrate that the proposed method substantially enhances robustness and accuracy in heavy-tailed scenarios while effectively preserving statistical power.

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Recent publications

Latest Papers

SANet: Selective Attention Network for Infrared Small Target Detection

Oct 07, 2026

This study addresses the challenge of balancing detection accuracy and false alarm rate in infrared small target detection by proposing a selective attention network. Specifically, a dual-path semantic perception module is designed, integrating standard and pinwheel-shaped convolutions with spatial-channel attention mechanisms to enhance target-background discrimination. Furthermore, an adaptive feature fusion strategy employing spatially variable weights is introduced to overcome the limitations of static skip connections, thereby optimizing multi-scale feature integration. Extensive experiments on three public benchmarks demonstrate that the proposed method achieves up to a 4.32 percentage point improvement in IoU over the second-best approach, while significantly reducing the false alarm rate and increasing the detection probability.

0 citationsRead paper

Topology-Aware Cooperative Beam-Hopping Scheduling for Efficient Resource Allocation in LEO Satellite Systems

Oct 07, 2026

This study addresses the beam-hopping resource management challenges in low Earth orbit (LEO) satellite networks arising from highly dynamic topologies and heterogeneous traffic. To this end, it proposes a two-stage GNN-MAPPO cooperative framework operating under partial observability. Specifically, the method leverages graph neural networks to extract time-varying topological features and employs multi-agent proximal policy optimization for joint beam scheduling, while incorporating a load-balancing mechanism to enhance multi-satellite cooperation. Experimental results demonstrate that the proposed architecture effectively overcomes the challenges of environmental partial observability, yielding significant improvements in system energy efficiency, throughput, and user fairness.

0 citationsRead paper

Cauchy-Aggregated Ridge Tests for High-Dimensional Factor Pricing Models

Oct 05, 2026

This study addresses the pronounced power instability in alpha testing within high-dimensional factor pricing models, which arises from uncertainty in ridge parameter tuning. To overcome this limitation, the work proposes a p-value aggregation framework grounded in the Cauchy combination rule. By integrating least squares estimation, residual sample covariance, and asymptotic normality theory for statistical inference, the method adaptively identifies the optimal ridge parameter without requiring prior knowledge of error directions, thereby constructing a robust alpha testing procedure. The proposed approach significantly outperforms fixed-ridge-parameter benchmarks. Empirically, rejection rates closely approximate nominal levels, while test power improves substantially, approaching the theoretical optimum attainable when signal information is known. This framework provides a reliable statistical inference tool for high-dimensional asset pricing applications.

0 citationsRead paper

Cauchy-Combined Hettmansperger-Randles Location Tests in High Dimensions

Oct 03, 2026

This study addresses the failure of classical high-dimensional mean testing methods under strong dependence and heavy-tailed distributions, as well as their sensitivity to outliers. To overcome these limitations, this work proposes a robust regularized Hotelling testing framework. Methodologically, it integrates Hettmansperger–Randles spatial estimation with a shrinkage Tyler scatter matrix, leveraging ridge-resolvent random matrix theory to establish asymptotic normality without conventional sparsity assumptions. Furthermore, a Cauchy combination approach is employed to achieve adaptive testing across varying shrinkage levels. Simulation experiments and applications to gene expression data demonstrate that the proposed method substantially enhances robustness and accuracy in heavy-tailed scenarios while effectively preserving statistical power.

0 citationsRead paper