Author name cluster

Yu Xie

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

YNIMG Journal 2026 Journal Article

Detailed connectomic cluster resource for white matter mapping from ultra-high-field diffusion MRI

Hiuying Yip
Yifei He
Yu Xie
Fan Zhang
Ye Wu

Large-scale brain mapping initiatives have underscored the necessity for white matter atlases that extend beyond the currently identified pathways, particularly in underexplored regions such as the superficial and cerebellar white matter. To address this gap, we develop a data-driven fiber-cluster atlas using ultra-high-field 7T structural and diffusion MRI data from 171 participants in the Human Connectome Project (HCP). Following preprocessing, we construct the whole-brain tractogram, comprising probabilistic and deterministic tractography from multi-tissue fiber orientation dispersion functions to mitigate streamline-tracking bias. Data from multiple algorithms are registered to the MNI space and subsequently aggregated. We cluster streamlines connecting seven cortical networks and nine subcortical regions using cosine k-means clustering along with two-level consensus filtering. The resulting atlas comprises 33,256 clusters for a seven-network scheme and 65,184 clusters for a seventeen-network scheme, encompassing both deep and superficial white matter. Across participants, the overlap between individual and population clusters exceeds 97%, and the median Davies-Bouldin scores are below 0.35, indicating high reproducibility and anatomical compactness. Importantly, classical tracts such as the arcuate fasciculus and corticospinal tract are subdivided into anatomically coherent subclusters, and numerous previously uncharacterized U-fibers are also identified. This open-access 7T resource aims to facilitate research on structure-function relationships, algorithm benchmarking, and precision connectomics.

Details DOI

EAAI Journal 2025 Journal Article

A hybrid three-way recommendation considering users variability

Yu Xie
Jilin Yang
Youlei Meng
Xianyong Zhang

Hybrid recommender systems leverage diverse information sources and techniques to enhance performance. Nevertheless, integrating users’ multifaceted preferences remains challenging due to the uneven data. Meanwhile, information insufficiency introduces uncertainty in recommendations while existing strategies (i. e. , recommend or not-recommend) lack the flexibility to address it. Additionally, these works mainly overlook that ratings not only reflect preferences but imply users’ attitudes toward the strategies, leading to the same recommendation rule despite users distinctly. To solve these issues, a Hybrid Three-Way Recommender (HTWR) system is proposed to formulate personalized three-way rules. Specifically, users’ historical and predictive preferences are captured via tags and ratings while integrated based on the user’s data distribution. Then, the theory of three-way decision is introduced to address such uncertainty by offering the option of defer-recommend. Finally, the users variability is formally given and incorporated into the loss function to obtain personalized rules. Experiments on three public datasets validate the superiority and flexibility of the proposed HTWR.

Details DOI

AAAI Conference 2020 Conference Paper

Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting

Weiqi Chen
Ling Chen
Yu Xie
Wei Cao
Yusong Gao
Xiaojie Feng

Traffic forecasting is of great importance to transportation management and public safety, and very challenging due to the complicated spatial-temporal dependency and essential uncertainty brought about by the road network and traffic conditions. Latest studies mainly focus on modeling the spatial dependency by utilizing graph convolutional networks (GCNs) throughout a fixed weighted graph. However, edges, i. e. , the correlations between pair-wise nodes, are much more complicated and interact with each other. In this paper, we propose the Multi-Range Attentive Bicomponent GCN (MRA-BGCN), a novel deep learning model for traffic forecasting. We first build the node-wise graph according to the road network distance and the edge-wise graph according to various edge interaction patterns. Then, we implement the interactions of both nodes and edges using bicomponent graph convolution. The multi-range attention mechanism is introduced to aggregate information in different neighborhood ranges and automatically learn the importance of different ranges. Extensive experiments on two real-world road network traffic datasets, METR-LA and PEMS-BAY, show that our MRA-BGCN achieves the stateof-the-art results.

PDF Details