State-of-the-art brain graph analysis methods fail to fully encode the small-world architecture of brain graphs (accompanied by the presence of hubs and functional modules), and therefore lack biological plausibility to some extent. This limitation hinders their ability to accurately represent the brain's structural and functional properties, thereby restricting the effectiveness of machine learning models in tasks such as brain disorder detection. In this work, we propose a novel Biologically Plausible Brain Graph Transformer (BioBGT) that encodes the small-world architecture inherent in brain graphs. Specifically, we present a network entanglement-based node importance encoding technique that captures the structural importance of nodes in global information propagation during brain graph communication, highlighting the biological properties of the brain structure. Furthermore, we introduce a functional module-aware self-attention to preserve the functional segregation and integration characteristics of brain graphs in the learned representations. Experimental results on three benchmark datasets demonstrate that BioBGT outperforms state-of-the-art models, enhancing biologically plausible brain graph representations for various brain graph analytical tasks

Neural oil painting synthesis is to sequentially predict brushstroke color and position, forming an oil painting step by step, which could serve as a painting teacher for education and entertainment. Existing methods usually suffer from degraded generalization for real-world photo inputs due to the training-test distribution gap, often manifesting as stroke-induced artifacts (e.g., over-smoothed textures or inconsistent granularity). In an attempt to mitigate this gap, we introduce a domain-agnostic neural painting (DANP) framework that aligns model to the test domain. In particular, we focus on updating affine parameters of normalization layers efficiently, while keeping other parameters frozen. To stabilize adaptation, our framework introduces: (1) Asymmetric Dual-Branch with mirror augmentation for robust feature alignment via geometric transformations, (2) Dual-Branch Interaction Loss combining intra-branch reconstruction and inter-branch consistency, and we also involve an empirical optimization strategy to mitigate gradient oscillations in practice. Experiments on real-world images from diverse domains (e.g., faces, landscapes, and artworks) validate the effectiveness of DANP in resolution-invariant adaptation, decreasing ~11.3% reconstruction error at 512px and ~20.3% at 1024px compared to the baseline model. It is worth noting that our method is compatible with existing methods, e.g., Paint Transformer, and further improve the ~10.3% perceptual quality. Dataset and code will be publicly released at: https://domain-agnostic-neural-oil-painting.github.io/DANP.

This experience report is part of an ongoing NSF-funded grant project involving an alliance of six California State University campuses, aimed at promoting Latinx student retention through community engagement in early computer science courses. The project focuses on integrating socially responsible computing (SRC) into the curriculum to transform computing culture and invite marginalized students to participate. At our campus, we integrated SRC concepts into the CS2 course on Data Structures and Algorithms. Initially, SRC concepts were introduced into assignments and projects, which showed promising results but highlighted challenges: the assignments and projects were instructor-created, leading to a gap between students and the concepts. Students passively received topics without proactive participation, resulting in a lack of perceived real-world impact.

Psychiatric disorders involve complex neural activity changes, with functional magnetic resonance imaging (fMRI) data serving as key diagnostic evidence. However, data scarcity and the diverse nature of fMRI information pose significant challenges. While graph-based self-supervised learning (SSL) methods have shown promise in brain network analysis, they primarily focus on time-domain representations, often overlooking the rich information embedded in the frequency domain. To overcome these limitations, we propose F requency- E nhanced Net work (FENet), a novel SSL framework specially designed for fMRI data that integrates time-domain and frequency-domain information to improve psychiatric disorder detection in small-sample datasets. FENet constructs multi-view brain networks based on the inherent properties of fMRI data, explicitly incorporating frequency information into the learning process of representation. Additionally, it employs domain-specific encoders to capture temporal-spectral characteristics, including an efficient frequency-domain encoder that highlights disease-relevant frequency features. Finally, FENet introduces a domain consistency-guided learning objective, which balances the utilization of diverse information and generates frequency-enhanced brain graph representations. Experiments on two real-world medical datasets demonstrate that FENet outperforms state-of-the-art methods while maintaining strong performance in minimal data conditions. Furthermore, we analyze the correlation between various frequency-domain features and psychiatric disorders, emphasizing the critical role of high-frequency information in disorder detection.