Depression represents a global mental health challenge requiring efficient and reliable automated detection methods. Current Transformer- or Graph Neural Networks (GNNs)-based multimodal depression detection methods face significant challenges in modeling individual differences and cross-modal temporal dependencies across diverse behavioral contexts. Therefore, we propose P³HF (Personality-guided Public-Private Domain Disentangled Hypergraph-Former Network) with three key innovations: (1) personality-guided representation learning using LLMs to transform discrete individual features into contextual descriptions for personalized encoding; (2) Hypergraph-Former architecture modeling high-order cross-modal temporal relationships; (3) event-level domain disentanglement with contrastive learning for improved generalization across behavioral contexts. Experiments on MPDD-Young dataset show P³HF achieves around 10% improvement on accuracy and weighted F1 for binary and ternary depression classification task over existing methods. Extensive ablation studies validate the independent contribution of each architectural component, confirming that personality-guided representation learning and high-order hypergraph reasoning are both essential for generating robust, individual-aware depression-related representations.

This paper addresses the challenges of image transmission in satellite communication networks, where bandwidth constraints, high interference, and latency issues significantly limit conventional transmission methods. We propose a novel semantic communication framework that adapts to various computational capabilities of receiving terminals in Internet of Everything (IoE). Our approach leverages the Swin Transformer V2 architecture to extract and transmit task-relevant semantic features rather than raw image data, significantly reducing bandwidth requirements while maintaining high reconstruction quality. The proposed system dynamically adjusts its encoding and decoding processes based on receiver computational capacities, enabling efficient image transmission to heterogeneous terminals ranging from high-performance stations to resource-constrained devices. Extensive experiments on various datasets demonstrate that our framework outperforms conventional JPEG+LDPC schemes and state-of-the-art deep learning-based approaches in terms of both PSNR performance and semantic communication utility across various signal-to-noise ratios. The framework shows particular robustness in low-SNR and low-CBR environments, addressing the “efficiency-compatibility” dilemma in resource-constrained satellite communications.