Enhancing Flow Matching with A Unified Guidance Framework for Efficient and Robust Speech Synthesis

Flow Matching (FM) has emerged as a powerful paradigm for speech generation but remains constrained by high inference latency and timbre leakage. To address these bottlenecks, we propose a unified guidance framework that enhances generation efficiency and robustness through two complementary strategies. On the data front, we introduce Data-guidance via heterogeneous augmentation, encouraging the model to disentangle linguistic content from acoustic residue. In parallel, we propose an enhanced Model-guidance mechanism that synergizes trajectory rectification with a novel intrinsic guidance objective. This approach distills conditional knowledge into network weights and straightens inference trajectory path, thereby eliminating Classifier-Free Guidance (CFG) overhead. Experiments demonstrate that our framework accelerates inference by nearly three times while effectively improving speaker similarity compared to state-of-the-art baselines.

DapPep: Domain Adaptive Peptide-agnostic Learning for Universal T-cell Receptor-antigen Binding Affinity Prediction

Identifying T-cell receptors (TCRs) that interact with antigenic peptides provides the technical basis for developing vaccines and immunotherapies. The emergent deep learning methods excel at learning antigen binding patterns from known TCRs but struggle with novel or sparsely represented antigens. However, binding specificity for unseen antigens or exogenous peptides is critical. We introduce a domain-adaptive peptide-agnostic learning framework DapPep for universal TCR-antigen binding affinity prediction to address this challenge. The lightweight self-attention architecture combines a pre-trained protein language model with an inner-loop self-supervised regime to enable robust TCR-peptide representations. Extensive experiments on various benchmarks demonstrate that DapPep consistently outperforms existing tools, showcasing robust generalization capability, especially for data-scarce settings and unseen peptides. Moreover, DapPep proves effective in challenging clinical tasks such as sorting reactive T cells in tumor neoantigen therapy and identifying key positions in 3D structures.

Depression Diagnosis and Analysis via Multimodal Multi-order Factor Fusion

Depression is a leading cause of death worldwide, and the diagnosis of depression is nontrivial. Multimodal learning is a popular solution for automatic diagnosis of depression, and the existing works suffer two main drawbacks: 1) the high-order interactions between different modalities can not be well exploited; and 2) interpretability of the models are weak. To remedy these drawbacks, we propose a multimodal multi-order factor fusion (MMFF) method. Our method can well exploit the high-order interactions between different modalities by extracting and assembling modality factors under the guide of a shared latent proxy. We conduct extensive experiments on two recent and popular datasets, E-DAIC-WOZ and CMDC, and the results show that our method achieve significantly better performance compared with other existing approaches. Besides, by analyzing the process of factor assembly, our model can intuitively show the contribution of each factor. This helps us understand the fusion mechanism.