Yuxin Li

AAAI Conference 2026 Conference Paper

CueBench: Advancing Unified Understanding of Context-Aware Video Anomalies in Real-World

• Yating Yu
• Congqi Cao
• Zhaoying Wang
• Weihua Meng
• Jie Li
• Yuxin Li
• Zihao Wei
• Zhongpei Shen

How far are deep models from real-world video anomaly understanding (VAU)? Current works typically emphasize detecting unexpected occurrences deviating from normal patterns or comprehending anomalous events with interpretable descriptions. However, they exhibit only a superficial comprehension of real-world anomalies, with limited breadth in complex principles and subtle contexts that distinguish the anomalies from normalities, e.g., climbing cliffs with safety gear vs. without it. To this end, we introduce CueBench, the first of its kind Benchmark, devoted to Context-aware video anomalies within a Unified Evaluation framework. We comprehensively establish an event-centric hierarchical taxonomy that anchors two core event types: 14 conditional and 18 absolute anomaly events, defined by their refined semantics from diverse contexts across 174 scenes and 198 attributes. Based on this, we propose to unify and benchmark context-aware VAU with various challenging tasks across recognition, temporal grounding, detection, and anticipation. It also serves as a rigorous and fair probing evaluation suite for generalized and specialized vision-language models (VLMs) across both generative and discriminative paradigms. To address the challenges underlying CueBench, we further develop Cue-R1 based on R1-style reinforcement fine-tuning with verifiable, task-aligned, and hierarchy-refined rewards in a unified generative manner. Extensive results on CueBench reveal that, existing VLMs are still far from satisfactory real-world anomaly understanding, while our Cue-R1 surpasses these state-of-the-art approaches by over 24% on average.

AAAI Conference 2026 Conference Paper

Dual-Horizon Interest Model for Unified Search and Recommendation

• Wenhao Zhu
• Yuxin Li
• Shuo Wang
• Hao Wang

Search and recommendation are pivotal for information access and are increasingly unified to exploit shared user-item interactions. Both tasks suffer from data sparsity, which joint modeling can mitigate by integrating behavioral data with or without explicit queries. However, existing unified frameworks rarely distinguish between users’ long- and short-term interests, despite their divergent temporal dynamics in search and recommendation. In this work, we propose a novel model, DHIM, which explicitly disentangles and integrates users' long- and short-term interests across both the search and recommendation scenarios. First, long- and short-term interests are independently extracted from search and recommendation using a unified extraction strategy. These interests are then adaptively integrated via a cross-scenario fusion module. A self‐supervised contrastive loss supervises the learning of both interest types within and across scenarios. The resulting representations are fed into downstream search and recommendation models for prediction. Extensive experiments on two public benchmarks demonstrate that our approach consistently outperforms single-scenario and state-of-the-art joint models, achieving superior accuracy and generalizability. To our knowledge, this is the first work to incorporate explicit dual-horizon interest modeling into a unified search and recommendation framework with self-supervised contrastive learning.

EAAI Journal 2026 Journal Article

Multi-feature unsupervised time series anomaly detection based on memory-augmented autoencoder - One-Class support vector machine

• Guocheng Hao
• Hanxing Ruan
• Yuxin Li
• Qi Liu
• Cong Liu
• Zhekang Wang
• Xiangbo Li
• Jiantao Yu

Unsupervised time series anomaly detection faces critical challenges when applied to high-dimensional and imbalanced data. Deep autoencoders tend to over-generalize, resulting in low reconstruction errors for abnormal samples and leading to missed detections. In turn, One-Class Support Vector Machines (OCSVM) rely on manually selected kernel functions, which often suffer from low computational efficiency. To address these issues, this paper proposes the Memory-augmented Autoencoder-One-Class Support Vector Machine (MemAE-OCSVM) model. It integrates a Memory-Augmented Autoencoder (MemAE) with OCSVM. The MemAE learns discriminative feature representations, replacing traditional kernel functions. This approach constructs an adaptive deep kernel function. Simultaneously, OCSVM establishes an optimal decision boundary in the feature space, enhancing anomaly identification capabilities. The model employs an end-to-end joint training framework. This enables synergistic optimization of feature learning and anomaly detection. The main innovations of this study include, introducing multi-feature fusion and a memory enhancement mechanism to improve the representation of complex normal patterns. Designing an adaptive deep kernel function based on MemAE, avoiding the limitations of manual kernel selection. Constructing an end-to-end unsupervised joint training framework to mitigate objective inconsistency issues common in multi-stage training. Experiments on three public datasets show that MemAE-OCSVM achieves average F1-score and recall values of 0. 934 and 0. 958, respectively. These results represent average improvements of 3. 8% in F1-score and 3. 2% in recall over the best baseline models. Ablation studies confirm the effectiveness of each module. Tests under varying anomaly rates demonstrate the model's strong robustness. This research provides an effective solution for real-time anomaly detection in complex scenarios. It offers both theoretical significance and practical application value.

JBHI Journal 2025 Journal Article

Automated Depression Detection From Text and Audio: A Systematic Review

• Yuxin Li
• Sinchana Kumbale
• Yanru Chen
• Tanmay Surana
• Eng Siong Chng
• Cuntai Guan

Depression is a prevalent mental health disorder that presents significant challenges for timely diagnosis and intervention. Automated Depression Detection (ADD) systems using text and audio offer scalable mental health assessment solutions. This review systematically evaluates 65 studies published between 2018 and 2024, focusing on ADD methods that utilize machine learning models with multimodal data. We examine key methodologies, including data augmentation, multimodal fusion, and feature extraction, along with state-of-the-art ADD systems. The review emphasizes the need for culturally adaptable, high-quality datasets and interpretable models for clinical use. We also identify gaps in longitudinal data and real-world applications. Future research should focus on developing clinically integrated, cross-cultural ADD systems that are interpretable, scalable, and robust. The findings of this review contribute to the research field by providing a comprehensive overview of existing methodologies, identifying gaps in the current literature, and offering insights for future advancements in depression detection using speech and text analysis.

EAAI Journal 2025 Journal Article

Heterogeneous graph contrastive learning-based transductive health condition assessment of Francis turbine unit

• Fengyuan Zhang
• Jie Liu
• Yujie Liu
• Yuxin Li
• Ran Duan
• Zhidi Chen
• Xingxing Jiang

To assess the Francis turbine unit's (FTU) degradation status from the onsite data without state labeling, a series of health benchmark model (HBM) driven health condition assessment (HCA) methods have been proposed. However, some limitation still exists, including: 1) Multiple similar HBMs are repeatedly constructed and trained to process multiple heterogeneous monitoring signals, however, there is a lack of a unified model that can handle the full task flow directly. 2) Simple linear methods are used to fuse multiple heterogeneous signals to construct performance degradation indexes (PDIs), ignoring temporal changes in the relationships within the signals and resulting in inadequate unit state representation. In this paper, a heterogeneous graph contrastive learning-based transductive health condition assessment of FTU considering multi-source monitoring signals is proposed. First, multi-source heterogeneous signals are converted into a series of heterogeneous signal graphs via the designed similarity-based edge-connection function considering the temporal representation differences. Then, an innovative graph-level heterogenous signal fusion method is proposed to represent unit conditions completely. By concatenating adjacency matrices of the heterogeneous signal graphs, multiple edge connections within the assessment period data are fused, obtaining a unique interactive graph with balanced and extended edge connections. Further, GCL-driven graph representator model is used to maximize the differences between the healthy and degraded interactive graph (IGs), and then the distances between the model readout vectors of the above two graphs is calculated as IPDIs, considering the signal correlations influenced by unit state changes. Verification experiments show that the proposed transductive HCA method effectively evaluates FTU's degradation without state labeling.

YNICL Journal 2024 Journal Article

GABAergic imbalance in Parkinson’s disease–related depression determined with MEGA-PRESS

• Xinzi Liu
• Yuxin Li
• Yixiang Mo
• Baoling Chen
• Xusheng Hou
• Jianbin Zhu
• Yongzhou Xu
• Jingyue Xue

OBJECTIVE: The pathogenesis of depression in patients with Parkinson's disease (PD) is poorly understood. Therefore, this study aimed to explore the changes in γ-aminobutyric acid (GABA) and glutamate plus glutamine (Glx) levels in patients with PD with or without depression determined using MEscher-GArwood Point Resolved Spectroscopy (MEGA-PRESS). MATERIALS AND METHODS: A total of 83 patients with primary PD and 24 healthy controls were included. Patients with PD were categorized into depressed PD (DPD, n = 19) and nondepressed PD (NDPD, n = 64) based on the 17-item Hamilton Depression Rating Scale. All participants underwent T1-weighted imaging and MEGA-PRESS sequence to acquire GABA+ and Glx values. The MEGA-PRESS sequence was conducted using 18.48 mL voxels in the left thalamus and medial frontal cortex. The GABA+, Glx, and creatine values were quantified using Gannet 3.1 software. RESULTS: The GABA+ and Glx values were not significantly disparate between patients with PD and controls in the thalamus and medial frontal cortex. However, the levels of N-acetyl aspartate/creatine and choline/creatine in the left thalamus were significantly lower in patients with PD than in controls (P = .031, P = .009). The GABA+/Water and GABA+/Creatine in the medial frontal cortex were higher in DPD than in NDPD (P = .001, P = .004). The effects of depression on Glx or other metabolite levels were not evident, and no significant difference in metabolite values was noted in the left thalamus among all groups (P > .05). CONCLUSIONS: GABA+ levels increased in the medial frontal cortex in DPD, which may be more closely related to depressive pathology. Thus, alterations in GABAergic function in special brain structures may be related to the clinical manifestations of PD symptoms, and hence mediating this function might help in treating depression in PD.

JBHI Journal 2024 Journal Article

HOPE: Hybrid-Granularity Ordinal Prototype Learning for Progression Prediction of Mild Cognitive Impairment

• Chenhui Wang
• Yiming Lei
• Tao Chen
• Junping Zhang
• Yuxin Li
• Hongming Shan

Mild cognitive impairment (MCI) is often at high risk of progression to Alzheimer's disease (AD). Existing works to identify the progressive MCI (pMCI) typically require MCI subtype labels, pMCI vs. stable MCI (sMCI), determined by whether or not an MCI patient will progress to AD after a long follow-up. However, prospectively acquiring MCI subtype data is time-consuming and resource-intensive; the resultant small datasets could lead to severe overfitting and difficulty in extracting discriminative information. Inspired by that various longitudinal biomarkers and cognitive measurements present an ordinal pathway on AD progression, we propose a novel Hybrid-granularity Ordinal PrototypE learning (HOPE) method to characterize AD ordinal progression for MCI progression prediction. First, HOPE learns an ordinal metric space that enables progression prediction by prototype comparison. Second, HOPE leverages a novel hybrid-granularity ordinal loss to learn the ordinal nature of AD via effectively integrating instance-to-instance ordinality, instance-to-class compactness, and class-to-class separation. Third, to make the prototype learning more stable, HOPE employs an exponential moving average strategy to learn the global prototypes of NC and AD dynamically. Experimental results on the internal ADNI and the external NACC datasets demonstrate the superiority of the proposed HOPE over existing state-of-the-art methods as well as its interpretability.

EAAI Journal 2023 Journal Article

A health condition assessment and prediction method of Francis turbine units using heterogeneous signal fusion and graph-driven health benchmark model

• Fengyuan Zhang
• Jie Liu
• Yuxin Li
• Yujie Liu
• Ming-Feng Ge
• Xingxing Jiang

To ensure the safety and efficiency of hydroelectric power generation, the health condition assessment and prediction (HCAP) of Francis turbine units (FTUs) have been widely concerned. To this end, some data-driven methods based on health benchmark model (HBM) and performance deterioration index (PDI) have been proposed, but there are still some shortcomings: 1) Only one type of signal is used for FTU health monitoring and assessment, which cannot fully represent the deterioration of the system. 2) The establishment of HBM only focuses on the time sequence dependences of signals, while ignoring the inter-correlations between signals. 3) PDI based on linear difference measurement cannot integrate heterogeneous signal representations to fully assess FTU status. In this paper, a HCAP method of FTUs using heterogeneous signal fusion and graph-driven HBM is proposed. First, the multivariate data of health status are transformed into spatial-temporal graphs by establishing connections between similar signals. Furthermore, a hybrid neural network-based HBM is designed to excavate the spatial-temporal dependence relationships existing in these graphs, and learn the mapping relationship between working condition parameters and monitoring signals. Finally, Mahalanobis distance between the heterogeneous signals predicted by HBM and the measured signals in the degraded status is calculated, and the comprehensive PDI for HCAP tasks is obtained by the designed heterogeneous signal fusion function. Verification experiments show that the proposed HCAP method effectively assesses FTU deterioration degree earlier with a higher sensitivity.

YNICL Journal 2023 Journal Article

Personalized estimates of morphometric similarity in multiple sclerosis and neuromyelitis optica spectrum disorders

• Jie Sun
• Wenjin Zhao
• Yingying Xie
• Fuqing Zhou
• Lin Wu
• Yuxin Li
• Haiqing Li
• Yongmei Li

Brain morphometric alterations involve multiple brain regions on progression of the disease in multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD) and exhibit age-related degenerative changes during the pathological aging. Recent advance in brain morphometry as measured using MRI have leveraged Person-Based Similarity Index (PBSI) approach to assess the extent of within-diagnosis similarity or heterogeneity of brain neuroanatomical profiles between individuals of healthy populations and validate in neuropsychiatric disorders. Brain morphometric changes throughout the lifespan would be invaluable for understanding regional variability of age-related structural degeneration and the substrate of inflammatory demyelinating disease. Here, we aimed to quantify the neuroanatomical profiles with PBSI measures of cortical thickness (CT) and subcortical volumes (SV) in 263 MS, 207 NMOSD, and 338 healthy controls (HC) from six separate central datasets (aged 11-80). We explored the between-group comparisons of PBSI measures, as well as the advancing age and sex effects on PBSI measures. Compared to NMOSD, MS showed a lower extent of within-diagnosis similarity. Significant differences in regional contributions to PBSI score were observed in 29 brain regions between MS and NMOSD (P < 0.05/164, Bonferroni corrected), of which bilateral cerebellum in MS and bilateral parahippocampal gyrus in NMOSD represented the highest divergence between the two patient groups, with a high similarity effect within each group. The PBSI scores were generally lower with advancing age, but their associations showed different patterns depending on the age range. For MS, CT profiles were significantly negatively correlated with age until the early 30 s (ρ = -0.265, P = 0.030), while for NMOSD, SV profiles were significantly negatively correlated with age with 51 year-old and older (ρ = -0.365, P = 0.008). The current study suggests that PBSI approach could be used to quantify the variation in brain morphometric changes in CNS inflammatory demyelinating disease, and exhibited a greater neuroanatomical heterogeneity pattern in MS compared with NMOSD. Our results reveal that, as an MR marker, PBSI may be sensitive to distribute the disease-associated grey matter diversity and complexity. Disease-driven production of regionally selective and age stage-dependency changes in the neuroanatomical profile of MS and NMOSD should be considered to facilitate the prediction of clinical outcomes and assessment of treatment responses.

YNICL Journal 2021 Journal Article

Reply to Josef Finsterer’s letter referring to “Connectivity on fMRI in the MELAS brain may strongly depend on heteroplasmy and extension or dynamics of stroke-like lesions”

• Rong Wang
• Jie Lin
• Yuxin Li
• Liqin Yang

YNICL Journal 2020 Journal Article

Topological reorganization of brain functional networks in patients with mitochondrial encephalomyopathy with lactic acidosis and stroke‐like episodes

• Rong Wang
• Jie Lin
• Chong Sun
• Bin Hu
• Xueling Liu
• Daoying Geng
• Yuxin Li
• Liqin Yang

Mitochondrial encephalomyopathy with lactic acidosis and stroke-like episodes (MELAS) is a rare maternally inherited genetic disease; however, little is known about its underlying brain basis. Furthermore, the topological organization of brain functional network in MELAS has not been explored. Here, 45 patients with MELAS (22 at acute stage, 23 at chronic stage) and 22 normal controls were studied using resting- state functional magnetic resonance imaging and graph theory analysis approaches. Topological properties of brain functional networks including global and nodal metrics, rich club organization and modularity were analyzed. At the global level, MELAS patients exhibited reduced clustering coefficient, normalized clustering coefficient, normalized characteristic path length and local network efficiency compared with the controls. At the nodal level, several nodes with abnormal degree centrality and nodal efficiency were detected in MELAS patients, and the distribution of these nodes was partly consistent with the stroke-like lesions. For rich club organization, rich club nodes were reorganized and the connections among them were decreased in MELAS patients. Modularity analysis revealed that MELAS patents had altered intra- or inter-modular connections in default mode network, fronto-parietal network, sensorimotor network, occipital network and cerebellum network. Notably, the patients at acute stage showed more obvious changes in these topological properties than the patients at chronic stage. These findings indicated that MELAS patients, particularly those at acute stage, exhibited topological reorganization of the whole-brain functional network. This study may help us to understand the neuropathological mechanisms of MELAS.