In this paper, we focus on the secure beamforming design in a beyond-diagonal reconfigurable intelligent surface (BD-RIS) assisted multiple-input multiple-output (MIMO) downlink network. We consider a scenario where a transmitter sends an information signal to a multi-antenna legitimate user while a multi-antenna eavesdropper attempts to intercept it. In this setting, a BD-RIS reconfigures the wireless environment to enhance secrecy. Aiming at the secrecy rate (SR) maximization, the joint optimization of transmit beamforming and BD-RIS reflection coefficients is formulated as a non-convex problem with power constraints on beamforming and symmetric and unitary constraints on BD-RIS reflection coefficients. To efficiently solve this challenging problem, a low-complexity framework that combines the augmented Lagrangian (AL) method and product manifold gradient descent (PMGD) algorithm is proposed to obtain a high-quality suboptimal solution. Numerical results show that BD-RIS-assisted systems achieve higher secrecy rates compared to conventional RIS-assisted systems.

Reconfigurable intelligent surfaces (RISs) hold significant promise for enhancing physical layer security (PLS). However, conventional RISs are typically modeled using diagonal scattering matrices, capturing only independent reflections from each reflecting element, which limits their flexibility in channel manipulation. In contrast, beyond-diagonal RISs (BD-RISs) employ non-diagonal scattering matrices enabled by active and tunable inter-element connections through a shared impedance network. This architecture significantly enhances channel shaping capabilities, creating new opportunities for advanced PLS techniques. This paper investigates PLS in a multiple-input multiple-output (MIMO) system assisted by BD-RISs, where a multi-antenna transmitter sends confidential information to a multi-antenna legitimate user while a multi-antenna eavesdropper attempts interception. To maximize the secrecy rate (SR), we formulate it as a non-convex optimization problem by jointly optimizing the transmit beamforming and BD-RIS REs under power and structural constraints. To solve this problem, we first introduce an auxiliary variable to decouple BD-RIS constraints. We then propose a low-complexity penalty product Riemannian conjugate gradient descent (P-PRCGD) method, which combines the augmented Lagrangian (AL) approach with the product manifold gradient descent (PMGD) method to obtain a Karush-Kuhn-Tucker (KKT) solution. Simulation results confirm that BD-RIS-assisted systems significantly outperform conventional RIS-assisted systems in PLS performance.

Few-shot object detection (FSOD), which aims at detecting rare objects based on few training samples has attracted significant research interest. Previous approaches find that the performance degradation of FSOD is mainly caused by category confusion (high false positives). To solve this issue, we propose a two-stage fine-tuning approach via classification score calibration for remote sensing images, named TFACSC, which follows the flowchart of base training and few-shot fine-tuning to train the detector. First, the backbone with strong representation ability is employed to extract the multi-scale features of the query image. Then, these features are aggregated by a novel multi-head scaled cosine non-local module (MSCN). Next, the aggregated features are used to generate the objectiveness proposals by a region proposal network. Eventually, the generated proposals are refined by a bounding box prediction head for the final category and position prediction, and the category score is calibrated by a novel classification score calibration module (CSCM). Extensive experiments conducted on NWPU VHR 10 and DIOR benchmarks demonstrate the effectiveness of our model. Particularly, for any shot cases, our method greatly outperforms the baseline and achieves state-of-the-art performance.