Chao Zha

Zhangyao Song, Ziqiong Li, Xiangfei Qiu et al.

Long-term time series forecasting benefits from inductive biases that expose recurring temporal structure. Existing periodic forecasting methods typically model recurrence through predefined periods, global spectral components, or fixed learnable templates. However, real-world temporal dynamics are rarely rigidly periodic: oscillatory behavior often evolves through amplitude modulation, phase drift, and local frequency variation. Under these conditions, fixed-template periodic modeling can become fundamentally mismatched to the underlying temporal states. We propose AOSNET, a Hilbert-guided forecasting framework that reformulates periodic forecasting from fixed template matching to adaptive oscillatory-state alignment. AOSNET extracts analytic-signal descriptors from both the observed sequence and a learnable global oscillatory prior, then adaptively aligns local states through a descriptor-conditioned gate that selectively preserves reliable observations while softly correcting mismatched regions. The learned prior serves not as a rigid repeated template but as a flexible oscillatory reference interpreted through local state dynamics. Experiments on eight benchmarks demonstrate state-of-the-art or highly competitive accuracy with fast inference speed. Controlled synthetic studies isolating amplitude modulation, phase drift, and local frequency variation confirm that the advantage of oscillatory-state alignment consistently increases as non-stationarity intensifies.

Junbo Yin, Chao Zha, Wenjia He et al.

Existing PLMs generate protein sequences based on a single-condition constraint from a specific modality, struggling to simultaneously satisfy multiple constraints across different modalities. In this work, we introduce CFP-Gen, a novel diffusion language model for Combinatorial Functional Protein GENeration. CFP-Gen facilitates the de novo protein design by integrating multimodal conditions with functional, sequence, and structural constraints. Specifically, an Annotation-Guided Feature Modulation (AGFM) module is introduced to dynamically adjust the protein feature distribution based on composable functional annotations, e.g., GO terms, IPR domains and EC numbers. Meanwhile, the Residue-Controlled Functional Encoding (RCFE) module captures residue-wise interaction to ensure more precise control. Additionally, off-the-shelf 3D structure encoders can be seamlessly integrated to impose geometric constraints. We demonstrate that CFP-Gen enables high-throughput generation of novel proteins with functionality comparable to natural proteins, while achieving a high success rate in designing multifunctional proteins. Code and data available at https://github.com/yinjunbo/cfpgen.

Chungang Lin, Weiyao Zhang, Tianyu Zuo et al.

Encrypted traffic classification is vital for modern network management and security. To reduce reliance on handcrafted features and labeled data, recent methods focus on learning generic representations through pre-training on large-scale unlabeled data. However, current pre-trained models face two limitations originating from the adopted Transformer architecture: (1) Limited model efficiency due to the self-attention mechanism with quadratic complexity; (2) Unstable traffic scalability to longer byte sequences, as the explicit positional encodings fail to generalize to input lengths not seen during pre-training. In this paper, we investigate whether convolutions, with linear complexity and implicit positional encoding, are competitive with Transformers in encrypted traffic classification with pre-training. We first conduct a systematic comparison, and observe that convolutions achieve higher efficiency and scalability, with lower classification performance. To address this trade-off, we propose NetConv, a novel pre-trained convolution model for encrypted traffic classification. NetConv employs stacked traffic convolution layers, which enhance the ability to capture localized byte-sequence patterns through window-wise byte scoring and sequence-wise byte gating. We design a continuous byte masking pre-training task to help NetConv learn protocol-specific patterns. Experimental results on four tasks demonstrate that NetConv improves average classification performance by 6.88% and model throughput by 7.41X over existing pre-trained models.