Robust signal enhancement under non-stationary and low SNR conditions remains challenging, as methods based on the short-time Fourier transform (STFT) with fixed resolution struggle to represent complex and time–frequency structures. While leveraging the fractional domain as an auxiliary view offers flexibility in modeling time-frequency structures, existing methods typically adopt fixed transform orders and overlook alignment between views, hindering effective integration of complementary representations and leaving frequency domain misalignment unresolved. Therefore, we propose FracFusion, a novel framework that integrates a learnable short-time fractional Fourier Transform (STFrFT) module to generate dynamic auxiliary views, combined with two stage alignment-aware fusion modules: Pearson Channel Fusion for correlation-guided consistency and Efficient Align Fusion for fine-grained, frequency aligned interaction. Experiments on speech and electromagnetic (EM) datasets show that FracFusion consistently outperforms state-of-the-art baselines across diverse noise levels and signal types, demonstrating robust adaptability across domains.

Detecting and identifying complex association patterns between two variables is a fundamental task. This requires association measures that satisfy both generality (the ability to capture a wide range of association structures) and equitability (the absence of bias toward specific association types). Designing such measures is challenging due to the distributional uncertainty, structural diversity, and mixture of association types found in large datasets. Granular computing offers a promising direction, as local neighborhood structures naturally encode multi-scale association information. Inspired by this insight, we introduce the maximal neighborhood coefficient (MNC), an association measure based on $k$k-NN granulation. MNC captures a broad range of associations without empirical bias while retaining local structural details often missed by existing measures. Extending this idea, we develop a family of maximal neighborhood nonparametric exploration (MNNE) statistics that supply richer auxiliary information for characterizing associations. Together, MNC and MNNE form a data-driven exploration toolkit that offers strong empirical performance and a new perspective on mining complex association patterns.

Most current data is multivariable, exploring and identifying valuable information in these datasets has far-reaching impacts. In particular, discovering meaningful hidden association patterns in multivariate plays an important role. Plenty of measures for multivariate association have been proposed, yet it is still an open research challenge for effectively capturing association patterns among three or more variables, especially the scenario without any prior knowledge about those relationships. To do so, we desire a distribution-free, association type-independent and non-parametrical measure. For practical applications, such a measure should comparable, interpretable,scalable, intuitive, reliability, and robust. However, no exiting measures fulfill all of these desiderata. In this paper, taking advantage of the neighborhood information of a sample, we propose MNA, a maximal neighborhood multivariate association measure that satisfies all the above criteria. Extensive experiments on synthetic and real data show it outperforms state-of-the-art multivariate association measures.

Multi-modal classification (MMC) aims to integrate the complementary information from different modalities to improve classification performance. Existing MMC methods can be grouped into two categories: traditional methods and deep learning-based methods. The traditional methods often implement fusion in a low-level original space. Besides, they mostly focus on the inter-modal fusion and neglect the intra-modal fusion. Thus, the representation capacity of fused features induced by them is insufficient. The deep learning-based methods implement the fusion in a high-level feature space where the associations among features are considered, while the whole process is implicit and the fused space lacks interpretability. Based on these observations, we propose a novel interpretative association-based fusion method for MMC, named AF. In AF, both the association information and the high-order information extracted from feature space are simultaneously encoded into a new feature space to help to train an MMC model in an explicit manner. Moreover, AF is a general fusion framework, and most existing MMC methods can be embedded into it to improve their performance. Finally, the effectiveness and the generality of AF are validated on 22 datasets, four typically traditional MMC methods adopting best modality, early, late and model fusion strategies and a deep learning-based MMC method.