Time series forecasting faces significant challenges due to non-stationary components that obscure underlying patterns. While Transformer-based models are effective at capturing stationary components, they struggle with non-stationary dynamics and multivariate dependencies. In this paper, we proposeFreqEvo, a lightweight Frequency Domain Feature Enhancement module for time series forecasting.FreqEvoprogressively filters frequency components from high to low amplitude, ensuring the preservation of informative features while reducing noise. By integrating recursive Fourier-based residual modeling and cross-domain attention,FreqEvoeffectively refines low-amplitude frequency features and stabilizes the embeddings, outperforming traditional low-pass filtering and random frequency selection methods in capturing both short-term and long-term dependencies. Experimental results on benchmark datasets demonstrate thatFreqEvooutperforms state-of-the-art (SOTA) models and serves as a plug-and-play module to enhance existing Long-Term Sequence Forecasting (LSTF) models.

Predicting the popularity of social media videos involves estimating user engagement based on rich multimodal information embedded within the posts. Unlike static images, videos incorporate temporally evolving visual signals that, alongside associated metadata such as descriptions, hashtags, timestamps, and user attributes, offer valuable insights into their potential audience reach. Prior approaches typically extract features from different modalities independently and merge them via naïve concatenation, which overlooks the semantic discrepancy and interaction dynamics across modalities. To address these limitations, we propose a feature fusion framework that encodes and aligns video content and associated textual cues into a shared semantic space. By jointly modeling temporally structured visual features with context-aware textual embeddings, our method effectively captures cross-modal correlations that are crucial for discerning content virality patterns. In addition, we incorporate user-centric behavioral profiles and content creation dynamics, enriching the representation with personalized signals that reflect audience-specific preferences. Notably, our method achieves top-tier performance in the 2025 SMP challenge, ranking among the highest-performing entries. This strong empirical result underscores the value of deep semantic alignment across video, text, and user domains in accurately forecasting social media video popularity.

Brain-computer interfaces have shown great potential in motor and speech rehabilitation, but still suffer from low performance stability across days, mostly due to the instabilities in neural signals. These instabilities, partially caused by neuron deaths and electrode shifts, leading to channel-level variabilities among different recording days. Previous studies mostly focused on aligning multi-day neural signals of onto a low-dimensional latent manifold to reduce the variabilities, while faced with difficulties when neural signals exhibit significant drift. Here, we propose to learn a channel-level invariant neural representation to address the variabilities in channels across days. It contains a channel-rearrangement module to learn stable representations against electrode shifts, and a channel reconstruction module to handle the missing neurons. The proposed method achieved the state-of-the-art performance with cross-day decoding tasks over two months, on multiple benchmark BCI datasets. The proposed approach showed good generalization ability that can be incorporated to different neural networks.

Unsupervised graph representation learning aims to distill various graph information into a downstream task-agnostic dense vector embedding. However, existing graph representation learning approaches are largely designed under the node homophily assumption: connected nodes tend to have similar labels and aim to optimize performance on node-centric downstream tasks. Their design apparently against the task-agnostic principle and generally suffer poor performance in tasks, e.g., edge classification task, that demands feature signals beyond both the node-view and homophily assumption. To condense different feature signals into the edge embeddings, this paper proposes PairE, a novel unsupervised graph embedding method using two paired nodes as the basic unit of embedding to retain the high-frequency signals between nodes to support both node-related and edge-related tasks. Accordingly, a multi-self-supervised autoencoder is designed to fulfill two pretext tasks: one retains the high-frequency signal better, and another enhances the representation of commonality. Our extensive experiments on a diversity of benchmark datasets clearly show that PairE outperforms the unsupervised state-of-the-art baselines, with up to 81% improvement on the edge classification tasks that rely on both the high and low-frequency signals in the pair and up to 42% performance gain on the node classification tasks.