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Li Ma

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12 papers

2 author rows

JBHI Journal 2026 Journal Article

Classifying Post-COVID “Brain Fog” Patients and Identifying Key ROIs via Graph Neural Network Model

Yuzhe Li
Yubo Zhang
Zhaomin Dong
Li Ma
Yonggui Yuan
Lili Chen

Brain fog has raised significant public health concerns as a common neurocognitive impairment in the post-COVID-19 condition, involving memory loss, poor concentration, and language difficulties. However, their neural mechanisms remain unclear, and objective resting-state fMRI–based diagnostic tools are still lacking. To address these challenges, we first recruited 72 patients who experienced persistent brain fog symptoms following COVID-19 infection, along with 68 post-COVID participants without brain fog (PC-noBF), and collected resting-state functional magnetic resonance imaging (rs-fMRI) data from all participants. The interpretable graph neural network model BrainGNN was employed to model and classify individual brain networks, utilizing functional connectivity graphs constructed from the Automated Anatomical Labeling (AAL) atlas. Using out-of-fold predictions from 5-fold cross-validation, BrainGNN achieved an accuracy of 75. 71% (Bootstrap 95% CI: 68. 57%–82. 86%) and an area under the ROC curve (AUC) of 76. 07% (Bootstrap 95% CI: 73. 93%–88. 48%). Furthermore, on an independent test set, BrainGNN outperformed traditional machine learning methods and other GNN models, achieving an accuracy of 82. 14% and an AUC of 82. 82% (classification threshold: 0. 5). Moreover, the model identified several key brain regions—bilateral insula, bilateral Heschl's gyri, and the left superior temporal gyrus—as potential neurobiological markers. Notably, in post-hoc analyses, the ALFF and ReHo values of the left insula were significantly associated with scores related to language and memory symptoms. These findings collectively underscore the effectiveness and interpretability of the proposed approach in identifying functional markers of brain fog. This study not only demonstrates the potential of individual-level identification of brain fog using resting-state fMRI empowered by interpretable GNN, but also reveals its capacity to provide novel insights into the neurobiological mechanisms underlying COVID-19-related cognitive impairment.

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EAAI Journal 2026 Journal Article

Enhancing the safety assessment of open-pit mine slopes with interpretable, data-driven stacking learning and three-dimensional stability analysis

Ya Tian
Yukun Yang
Li Ma
Xiang Qi
Xiang Lu

Slopes, as complex geological structures, predominantly exist in three-dimensional (3D) forms, a characteristic particularly evident in human-engineered open-pit mines, and their stability directly influences overall reliability of mining system. This research integrated the 3D limit equilibrium method with interpretable machine learning. The primary objective was to build a slope stability dataset of 180 cases that incorporates real mechanical responses, upon which an intelligent prediction algorithm was developed. A stacking learning algorithm was employed, with a focus on key 3D features (inner dumping slope angle, bottom distance, and working slope angle). This enabled high-accuracy predictions of slope stability under diverse spatial and geomechanical conditions, establishing an intelligent diagnosis algorithm for slopes that is co-driven by mechanism and data. The results showed that stability datasets could assess slope safety more comprehensively. Stacking learning demonstrated superior performance in slope stability prediction, significantly outperforming traditional single algorithms such as Support Vector Machine and Random Forest, among others. The prediction value of the area under the Receiver Operating Characteristic (ROC) curve was 0. 9515. SHapley Additive exPlanations interpretable analysis revealed that 3D features approximate safety factor to true value by constraining potential slip surface and reducing sliding force. Validation across eight real cases confirmed high prediction accuracy, significantly improving both physical realism and assessment reliability. The algorithm offers valuable insights for intelligent safety alerts and risk prevention in complex engineering scenarios.

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JBHI Journal 2025 Journal Article

A Feature-Aware Approach to Acupoint Compatibility Prediction Using Residual Graph Attention Networks and Matrix Factorization

Ruiling Li
Ying Pan
Song Wu
Li Ma
Limei Peng

Compatibility among acupoints is a fundamental principle in acupuncture treatment within traditional Chinese medicine, playing a vital role in enhancing the effectiveness and scope of therapeutic interventions. With the increasing availability of acupuncture-related data, link prediction offers a data-driven approach that facilitates the evidence-based exploration and validation of acupoint compatibilities. However, existing link prediction methods often focus on mapping acupoints and their compatibility relationships into lower-dimensional spaces. These approaches can overlook essential acupoint features and make the predictions susceptible to noise interference. To address these challenges, we propose a novel acupoint compatibility prediction model based on a Feature-Aware Residual Graph Attention Network and Matrix Factorization (FRGATMF). Our model introduces a feature-aware connectivity fusion strategy that integrates acupoint attributes with structural information to enrich acupoint representations. Following this, a deep non-negative matrix factorization approach is employed to construct a denoised feature matrix. This matrix is processed through a residual graph attention network to derive comprehensive and effective node embeddings, which are crucial for accurate link prediction. Experimental results on the acupuncture dataset, along with three public datasets, demonstrate that FRGATMF significantly outperforms seven existing comparison models across various evaluation metrics. Additionally, link prediction can identify previously unconsidered or undocumented acupoint combinations that may offer better therapeutic results, thus expanding the range of treatment options and highlighting its potential in improving the prediction of acupoint compatibility relationships.

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EAAI Journal 2025 Journal Article

Adaptive attention graph convolution network with normalized embedded Gaussian for rapid serial visualization presentation decoding

Mengyuan Zhao
Qingsong Ai
Kun Chen
Quan Liu
Sheng Quan Xie
Li Ma

Graph convolutional networks (GCNs) have been widely used in Brain Computer Interface (BCI) and have shown great prowess in identifying electroencephalogram (EEG) spatiotemporal features. However, most GCNs learn channels topological relationship by fixed adjacency matrix. This lacks connectivity strength information and ignores the data dependency. This paper proposes a data-driven adjacency matrix based on normalized embedded Gaussian function, and constructs a Gaussian-Adaptive Attention Graph Convolution Network (Gaussian-AAGCN). Brain regions connectivity is calculated by normalized embedded Gaussian function, and the topological relationship is adaptively learned by input data in a data-driven manner. This data-driven adaptive adjacency matrix avoids brain activity information loss caused by fixed adjacency matrix and improves the flexibility of graph construction. Convolutional block attention module (CBAM) is introduced to adaptive feature refinement in two independent dimensions, improving model representation ability. Experimental results show that the average area under curve (AUC), true positive rate (TPR) and false positive rate (FPR) of Gaussian-AAGCN on 14 subjects are 93. 52 %, 91. 59 %, and 4. 58 % respectively. Compared to Transformer, Event-Related Potential Capsule Network (ERP-CapsNet), Electroencephalogram Convolutional Neural Network (EEGNet), and Multi-Granularity Information Fusion Network (MGIFNet), the AUC of Gaussian-AAGCN is higher by 18. 02 %, 5. 32 %, 2. 62 %, and 1. 82 %, respectively. Using the adaptive adjacency matrix, the model AUC and TPR are increased by about 4. 8 % and 7. 8 % respectively. After integrating CBAM, the AUC and TPR increased by about 3. 5 % and 8 % respectively.

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ICML Conference 2025 Conference Paper

Stream-level Flow Matching with Gaussian Processes

Ganchao Wei
Li Ma

Flow matching (FM) is a family of training algorithms for fitting continuous normalizing flows (CNFs). Conditional flow matching (CFM) exploits the fact that the marginal vector field of a CNF can be learned by fitting least-squares regression to the conditional vector field specified given one or both ends of the flow path. In this paper, we extend the CFM algorithm by defining conditional probability paths along "streams”, instances of latent stochastic paths that connect data pairs of source and target, which are modeled with Gaussian process (GP) distributions. The unique distributional properties of GPs help preserve the “simulation-free” nature of CFM training. We show that this generalization of the CFM can effectively reduce the variance in the estimated marginal vector field at a moderate computational cost, thereby improving the quality of the generated samples under common metrics. Additionally, adopting the GP on the streams allows for flexibly linking multiple correlated training data points (e. g. , time series). We empirically validate our claim through both simulations and applications to image and neural time series data.

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TMLR Journal 2025 Journal Article

Survey of Video Diffusion Models: Foundations, Implementations, and Applications

Yimu Wang
Xuye Liu
Wei Pang
Li Ma
Shuai Yuan
Paul Debevec
Ning Yu

Recent advances in diffusion models have revolutionized video generation, offering superior temporal consistency and visual quality compared to traditional generative adversarial networks-based approaches. While this emerging field shows tremendous promise in applications, it faces significant challenges in motion consistency, computational efficiency, and ethical considerations. This survey provides a comprehensive review of diffusion-based video generation, examining its evolution, technical foundations, and practical applications. We present a systematic taxonomy of current methodologies, analyze architectural innovations and optimization strategies, and investigate applications across low-level vision tasks such as denoising and super-resolution. Additionally, we explore the synergies between diffusion-based video generation and related domains, including video representation learning, question answering, and retrieval. Compared to the existing surveys (Lei et al., 2024a;b; Melniket al., 2024; Cao et al., 2023; Xing et al., 2024c) which focus on specific aspects of video generation, such as human video synthesis (Lei et al., 2024a) or long-form content generation (Lei et al., 2024b), our work provides a broader, more updated, and more fine-grained perspective on diffusion-based approaches with a special section for evaluation metrics, industry solutions, and training engineering techniques in video generation. This survey serves as a foundational resource for researchers and practitioners working at the intersection of diffusion models and video generation, providing insights into both the theoretical frameworks and practical implementations that drive this rapidly evolving field.

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AIIM Journal 2024 Journal Article

EEG spatial inter-channel connectivity analysis: A GCN-based dual stream approach to distinguish mental fatigue status

Kun Chen
Shulong Chai
Tianli Xie
Quan Liu
Li Ma

Mental fatigue is defined as a decline in the ability and efficiency of mental activities. A lot of research suggests that the transition from alertness to fatigue is accompanied by alterations in correlation patterns among various brain regions. However, conventional methods for detecting mental fatigue seldom emphases inter-channel connectivity in the spatial domain. To fill this gap, this paper explores the spatial inter-channel connectivity in alertness and fatigue, employing spectral graph convolutional networks (GCN) for mental fatigue detection. We utilized Pearson correlation coefficients (PCC) to establish temporal connections and magnitude-squared coherence (MSC) for spectral connections. Topological features of the brain network were then analysed. To enhance the learning of spatial inter-channel connectivity, a dual-graph strategy transforms edge features into node features, serving as inputs to the spectral GCN. By simultaneously learning PCC and MSC features, the model results indicate significant differences in some brain network characteristics between alert and fatigue states. It confirms that the synchronicity of brain operations differs in the alert state compared to mental fatigue, and indicates that fatigue states can influence correlation patterns among different brain regions. Our approach is evaluated on a self-designed experimental dataset containing 7 subjects, demonstrating a classification accuracy of 89. 59 % in group-level experiments and 95. 24 % at the subject level. Additionally, on the public dataset SEED-VIG containing 23 subjects, our method achieves an accuracy of 86. 58 %. In summary, this paper proposes a neural network approach based on a dynamic functional connectivity network. The network integrates both temporal and spectral connections with the goal of simultaneously learning spatial inter-channel connectivity in time and frequency domains. This effectively accomplishes fatigue state detection, highlighting that fatigue significantly influences correlations among different brain regions.

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EAAI Journal 2024 Journal Article

Leak detection for natural gas gathering pipeline using spatio-temporal fusion of practical operation data

Jing Liang
Shan Liang
Li Ma
Hao Zhang
Juan Dai
Hongyu Zhou

Gathering pipelines are one of the key upstream infrastructures in the gas industry that link production well to the processing plant. Leak detection is critical for ensuring the safety of pipeline transmission. The detection of small leakage in gathering pipelines consistently poses a formidable challenge. In this paper, a process model is built based on health data of supervisory control and data acquisition system from the actual operating pipeline. In the model structure, the convolutional neural network is used to extract the spatial features, the bi-directional long short-term memory is used to extract the temporal features, and the attention mechanism is employed to allocate the model’s attention resources reasonably. Next, the residual between the entity pipeline’s output data and the process model’s output data is used as a monitoring indicator of the operating state of the pipeline. A clustering-based boundary determination method is proposed to recognize the centroid of normal and small leak conditions, and pipeline leak detection is performed by the Euclidean distance between the monitoring indicator and the centroid. This paper explores the feasibility of fast modeling and leak detection with limited hardware. Field tests for the validation of the proposed methods were implemented in two in-service natural gas gathering pipeline. The experimental results demonstrate that the proposed method significantly enhances the detection performance of small-size leak. The leak detection rates of 94. 06% and 92. 16% evinces the potency of the proposed method applied in the leak detection of gathering pipelines across diverse real-world scenarios.

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NeurIPS Conference 2024 Conference Paper

Mixture of Link Predictors on Graphs

Li Ma
Haoyu Han
Juanhui Li
Harry Shomer
Hui Liu
Xiaofeng Gao
Jiliang Tang

Link prediction, which aims to forecast unseen connections in graphs, is a fundamental task in graph machine learning. Heuristic methods, leveraging a range of different pairwise measures such as common neighbors and shortest paths, often rival the performance of vanilla Graph Neural Networks (GNNs). Therefore, recent advancements in GNNs for link prediction (GNN4LP) have primarily focused on integrating one or a few types of pairwise information. In this work, we reveal that different node pairs within the same dataset necessitate varied pairwise information for accurate prediction and models that only apply the same pairwise information uniformly could achieve suboptimal performance. As a result, we propose a simple mixture of experts model Link-MoE for link prediction. Link-MoE utilizes various GNNs as experts and strategically selects the appropriate expert for each node pair based on various types of pairwise information. Experimental results across diverse real-world datasets demonstrate substantial performance improvement from Link-MoE. Notably, Link-Mo achieves a relative improvement of 18. 71% on the MRR metric for the Pubmed dataset and 9. 59% on the Hits@100 metric for the ogbl-ppa dataset, compared to the best baselines. The code is available at https: //github. com/ml-ml/Link-MoE/.

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JMLR Journal 2024 Journal Article

Unsupervised Tree Boosting for Learning Probability Distributions

Naoki Awaya
Li Ma

We propose an unsupervised tree boosting algorithm for inferring the underlying sampling distribution of an i.i.d. sample based on fitting additive tree ensembles in a manner analogous to supervised tree boosting. Integral to the algorithm is a new notion of "addition" on probability distributions that leads to a coherent notion of "residualization", i.e., subtracting a probability distribution from an observation to remove the distributional structure from the sampling distribution of the latter. We show that these notions arise naturally for univariate distributions through cumulative distribution function (CDF) transforms and compositions due to several "group-like" properties of univariate CDFs. While the traditional multivariate CDF does not preserve these properties, a new definition of multivariate CDF can restore these properties, thereby allowing the notions of "addition" and "residualization" to be formulated for multivariate settings as well. This then gives rise to the unsupervised boosting algorithm based on forward-stagewise fitting of an additive tree ensemble, which sequentially reduces the Kullback-Leibler divergence from the truth. The algorithm allows analytic evaluation of the fitted density and outputs a generative model that can be readily sampled from. We enhance the algorithm with scale-dependent shrinkage and a two-stage strategy that separately fits the marginals and the copula. The algorithm then performs competitively with state-of-the-art deep-learning approaches in multivariate density estimation on multiple benchmark data sets. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2024. ( edit, beta )

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IJCAI Conference 2023 Conference Paper

Curriculum Multi-Level Learning for Imbalanced Live-Stream Recommendation

Shuodian Yu
Junqi Jin
Li Ma
Xiaofeng Gao
Xiaopeng Wu
Haiyang Xu
Jian Xu

In large-scale e-commerce live-stream recommendation, streamers are classified into different levels based on their popularity and other metrics for marketing. Several top streamers at the head level occupy a considerable amount of exposure, resulting in an unbalanced data distribution. A unified model for all levels without consideration of imbalance issue can be biased towards head streamers and neglect the conflicts between levels. The lack of inter-level streamer correlations and intra-level streamer characteristics modeling imposes obstacles to estimating the user behaviors. To tackle these challenges, we propose a curriculum multi-level learning framework for imbalanced recommendation. We separate model parameters into shared and level-specific ones to explore the generality among all levels and discrepancy for each level respectively. The level-aware gradient descent and a curriculum sampling scheduler are designed to capture the de-biased commonalities from all levels as the shared parameters. During the specific parameters training, the hardness-aware learning rate and an adaptor are proposed to dynamically balance the training process. Finally, shared and specific parameters are combined to be the final model weights and learned in a cooperative training framework. Extensive experiments on a live-stream production dataset demonstrate the superiority of the proposed framework.

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AAAI Conference 2019 Conference Paper

Incorporating Semantic Similarity with Geographic Correlation for Query-POI Relevance Learning

Ji Zhao
Dan Peng
Chuhan Wu
Huan Chen
Meiyu Yu
Wanji Zheng
Li Ma
Hua Chai

Point-of-interest (POI) retrieval that searches for relevant destination locations plays a significant role in on-demand ridehailing services. Existing solutions to POI retrieval mainly retrieve and rank POIs based on their semantic similarity scores. Although intuitive, quantifying the relevance of a Query-POI pair by single-field semantic similarity is subject to inherent limitations. In this paper, we propose a novel Query-POI relevance model for effective POI retrieval for ondemand ride-hailing services. Different from existing relevance models, we capture and represent multi-field and local&global semantic features of a Query-POI pair to measure the semantic similarity. Besides, we observe a hidden correlation between origin-destination locations in ride-hailing scenarios, and propose two location embeddings to characterize the specific correlation. By incorporating the geographic correlation with the semantic similarity, our model achieves better performance in POI ranking. Experimental results on two real-world click-through datasets demonstrate the improvements of our model over state-of-the-art methods.

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