Author name cluster

Yue Yuan

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

YNICL Journal 2026 Journal Article

Remote cortical degeneration related to structural connectivity following recent small subcortical infarcts

Youjie Wang
Jingyu Cui
Yuying Yan
Tang Yang
Yue Yuan
Rumei Lei
Rongfeng Luo
Bo Wu

BACKGROUND: Secondary cortical degeneration caused by the remote effects of subcortical infarcts has been implicated in long-term outcomes after acute ischemic stroke. However, this process remains insufficiently studied in recent small subcortical infarcts (RSSI). We aimed to verify RSSI-induced cortical damage, determine whether it can be captured by neuroimaging markers, and explore its association with clinical outcomes. METHODS: RSSI patients with longitudinal Magnetic Resonance Imaging (MRI) were included. Cortical degeneration was assessed using linear mixed-effects models, incorporating a direct approach based on individual diffusion weighted imaging and an indirect approach using the normative connectome from the Human Connectome Project (HCP). Principal component analysis (PCA) was employed to extract features of cortical alterations. The resulting component scores were used in general linear models to assess associations with neuroimaging markers and clinical outcomes. RESULTS: A total of 76 RSSI patients were analyzed. RSSI was found to induce progressive cortical thinning and volume loss in structurally connected regions. PCA identified a component reflecting parenchymal atrophy associated with diffusion-based markers of white matter integrity, as well as the presence of track/cap signs. Moreover, faster cortical degeneration in lesion-connected regions was significantly associated with a greater increase in Hamilton Anxiety Rating Scale (HAMA) scores (β = -2.38, 95% CI = -4.30 - -0.47, p = 0.017). CONCLUSIONS: RSSI induces secondary cortical damage through structurally connected fiber tracts, which is detectable by neuroimaging markers of white matter integrity. These regional cortical alterations may be relevant to post-stroke outcomes and require validation in larger longitudinal studies.

Details DOI

AAAI Conference 2024 Conference Paper

Exploring Channel-Aware Typical Features for Out-of-Distribution Detection

Rundong He
Yue Yuan
Zhongyi Han
Fan Wang
Wan Su
Yilong Yin
Tongliang Liu
Yongshun Gong

Detecting out-of-distribution (OOD) data is essential to ensure the reliability of machine learning models when deployed in real-world scenarios. Different from most previous test-time OOD detection methods that focus on designing OOD scores, we delve into the challenges in OOD detection from the perspective of typicality and regard the feature’s high-probability region as the feature’s typical set. However, the existing typical-feature-based OOD detection method implies an assumption: the proportion of typical feature sets for each channel is fixed. According to our experimental analysis, each channel contributes differently to OOD detection. Adopting a fixed proportion for all channels results in several channels losing too many typical features or incorporating too many abnormal features, resulting in low performance. Therefore, exploring the channel-aware typical features is crucial to better-separating ID and OOD data. Driven by this insight, we propose expLoring channel-Aware tyPical featureS (LAPS). Firstly, LAPS obtains the channel-aware typical set by calibrating the channel-level typical set with the global typical set from the mean and standard deviation. Then, LAPS rectifies the features into channel-aware typical sets to obtain channel-aware typical features. Finally, LAPS leverages the channel-aware typical features to calculate the energy score for OOD detection. Theoretical and visual analyses verify that LAPS achieves a better bias-variance trade-off. Experiments verify the effectiveness and generalization of LAPS under different architectures and OOD scores.

PDF Details DOI

IJCAI Conference 2020 Conference Paper

A Relation-Specific Attention Network for Joint Entity and Relation Extraction

Yue Yuan
Xiaofei Zhou
Shirui Pan
Qiannan Zhu
Zeliang Song
Li Guo

Joint extraction of entities and relations is an important task in natural language processing (NLP), which aims to capture all relational triplets from plain texts. This is a big challenge due to some of the triplets extracted from one sentence may have overlapping entities. Most existing methods perform entity recognition followed by relation detection between every possible entity pairs, which usually suffers from numerous redundant operations. In this paper, we propose a relation-specific attention network (RSAN) to handle the issue. Our RSAN utilizes relation-aware attention mechanism to construct specific sentence representations for each relation, and then performs sequence labeling to extract its corresponding head and tail entities. Experiments on two public datasets show that our model can effectively extract overlapping triplets and achieve state-of-the-art performance.

PDF Details DOI