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Li Yao

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17 papers

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AAAI Conference 2026 Conference Paper

Mamba-Driven Multi-View Discriminative Clustering via Global-Local Cross-View Sequence Modeling

Yuanyang Zhang
Xinhang Wan
Chao Zhang
Jie Xu
Cunjian Chen
Tien-Tsin Wong
Li Yao
Yijie Lin

Multi-view clustering (MVC) has recently garnered increasing attention for its ability to partition unlabeled samples into distinct clusters by leveraging complementary and consistent information from different views. Existing MVC methods primarily combine deep neural networks with contrastive learning for cross-view representation learning, yet often overlook the inherent global-local structural relationships among samples. While GNN-based methods capture local structures, they struggle to model global dependencies, leading to inferior inter-cluster separability. In contrast, Transformer-based methods excel at global aggregation but suffer from quadratic complexity, and their attention smoothing effect weakens fine-grained local structures, resulting in suboptimal intra-cluster compactness. To address these limitations, we propose a novel end-to-end MVC framework called Mamba-Driven Multi-View Discriminative Clustering via Global-Local Cross-View Sequence Modeling (MGLC). By flexibly constructing multi-view sequences, MGLC fully exploits the efficient sequence modeling capabilities of Mamba to jointly model cross-view dependencies and global-local structural relationships among samples. Furthermore, MGLC introduces a Cross-Mamba Fusion module to dynamically integrate cross-view and global-local structural representations. Additionally, MGLC incorporates a Dual Calibration Contrastive Learning module, guided by high-confidence pseudo-labels, that adaptively refines both feature and semantic representations while mitigating false negatives among semantically similar samples. Extensive comparative experiments and ablation studies demonstrate the effectiveness of MGLC.

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AAAI Conference 2026 Conference Paper

PUNO: A Neural Operator Framework for Point Cloud Upsampling

Zijian Xiao
Yining Xu
Yingjie Huang
Li Yao

We propose PUNO, a novel deep operator-based framework for point cloud upsampling, addressing the challenge of reconstructing high-resolution geometries from sparse point clouds. PUNO generalizes the neural operators proven effective in image super-resolution to 3D point cloud upsampling. Moreover, it first designs a network for point cloud tasks to achieve vertex displacement and manifold parameterization, thereby forming a coarse geometric representation that is compatible with super-resolution neural operators. This is followed by iterative kernel integral approximations in the function space and backprojection to generate the target coordinates, fully utilizing the high-frequency information in the function space. Unlike prior work, PUNO performs transformations in both the data domain and the function domain, with the solution space containing richer basis functions, yielding finer results that mitigate the ill-posed nature of sparse data. It also benefits global continuity. Extensive experiments demonstrate its superior accuracy, robustness, and generalization ability.

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AAAI Conference 2025 Conference Paper

Incomplete Multi-view Clustering via Diffusion Contrastive Generation

Yuanyang Zhang
Yijie Lin
Weiqing Yan
Li Yao
Xinhang Wan
Guangyuan Li
Chao Zhang
Guanzhou Ke

Incomplete multi-view clustering (IMVC) has garnered increasing attention in recent years due to the common issue of missing data in multi-view datasets. The primary approach to address this challenge involves recovering the missing views before applying conventional multi-view clustering methods. Although imputation-based IMVC methods have achieved significant improvements, they still encounter notable limitations: 1) heavy reliance on paired data for training the data recovery module, which is impractical in real scenarios with high missing data rates; 2) the generated data often lacks diversity and discriminability, resulting in suboptimal clustering results. To address these shortcomings, we propose a novel IMVC method called Diffusion Contrastive Generation (DCG). Motivated by the consistency between the diffusion and clustering processes, DCG learns the distribution characteristics to enhance clustering by applying forward diffusion and reverse denoising processes to intra-view data. By performing contrastive learning on a limited set of paired multi-view samples, DCG can align the generated views with the real views, facilitating accurate recovery of views across arbitrary missing view scenarios. Additionally, DCG integrates instance-level and category-level interactive learning to exploit the consistent and complementary information available in multi-view data, achieving robust and end-to-end clustering. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches.

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JBHI Journal 2025 Journal Article

Inducing Long-Term Plastic Changes and Visual Attention Enhancement Via One-Week Cerebellar Crus II Intermittent Theta Burst Stimulation (iTBS): An EEG Study

Meiliang Liu
Chao Yu
Minjie Tian
Jingping Shi
Yunfang Xu
Zijin Li
Zhengye Si
Xiaoxiao Yang

Intermittent theta burst stimulation (iTBS) is a non-invasive technique frequently employed to induce neural plastic changes and enhance visual attention. Currently, most studies utilized a single iTBS session on healthy subjects to induce short-term neural plastic changes within tens of minutes post-stimulation and investigate its single-session effect on attention performance. Few studies have conducted multiple iTBS sessions on the cerebellum to explore long-term effects on the cerebral cortex and daily effects on visual attention performance. In this study, 18 healthy subjects were involved in a randomized, sham-controlled experiment over one week. All the subjects received daily session of bilateral cerebellar Crus II iTBS or sham stimulation and completed a visual search task. Resting-state electroencephalogram (EEG) was collected 48 hours pre- and post-experiment to assess plastic changes induced by iTBS. The results indicated that the iTBS group exhibited higher accuracy and lower time costs than the sham group after three sessions of iTBS. In addition, iTBS-induced plastic changes persisted up to 48 hours post-experiment, including left-shifted individual alpha frequency, increased intrinsic excitability (the likelihood that a neuron will generate an output in response to a given input), and enhanced PLV functional connectivity (phase synchronization between different brain region). Furthermore, we found that cerebellar iTBS induced a remote effect on the frontal region. Our study revealed the capacity of cerebellar Crus II iTBS to induce plastic changes and enhance attention performance, providing a potential avenue for using iTBS to promote rehabilitation.

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NeurIPS Conference 2025 Conference Paper

Learning to Plan Like the Human Brain via Visuospatial Perception and Semantic-Episodic Synergistic Decision-Making

Tianyuan Jia
Ziyu Li
Qing Li
Xiuxing Li
Xiang Li
Chen Wei
Li Yao
Xia Wu

Motion planning in high-dimensional continuous spaces remains challenging due to complex environments and computational constraints. Although learning-based planners, especially graph neural network (GNN)-based, have significantly improved planning performance, they still struggle with inaccurate graph construction and limited structural reasoning, constraining search efficiency and path quality. The human brain exhibits efficient planning through a two-stage Perception-Decision model. First, egocentric spatial representations from visual and proprioceptive input are constructed, and then semantic–episodic synergy is leveraged to support decision-making in uncertainty scenarios. Inspired by this process, we propose NeuroMP, a brain-inspired planning framework that learns to plan like the human brain. NeuroMP integrates a Perceptive Segment Selector inspired by visuospatial perception to construct safer graphs, and a Global Alignment Heuristic guide search in weakly connected graphs by modeling semantic-episodic synergistic decision-making. Experimental results demonstrate that NeuroMP significantly outperforms existing planning methods in efficiency and quality while maintaining a high success rate.

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JBHI Journal 2025 Journal Article

Spatio-temporal Multi-granularity for Skeleton-based Depression Risk Recognition

Qiong Li
Min Ren
Xuecai Hu
Xiaotong Liu
Li Yao
Yongzhen Huang

As the prevalence of depression continues to rise, the timely and accurate recognition of its early signs is crucial for effective prevention and intervention. However, current clinical diagnostic methods are limited by the absence of objective biomarkers and inefficiencies in early recognition. Recent research has revealed a significant correlation between gait patterns and depression risk, suggesting that gait analysis could serve as a promising tool for early diagnosis. Depression-associated gait characteristics are defined by two key aspects: (1) they are dynamic, reflecting temporal abnormalities in movement, and (2) they manifest across both localized body regions and broader global movement patterns of the body. Based on these insights, we propose a novel Spatio-temporal Multi-granularity Network (STM-Net) for depression risk recognition. In the temporal domain, we present a Multi-grain Temporal Focus (MTF) module, designed to capture the rich dynamic temporal information embedded in the gait cycle of individuals with depression. In the spatial domain, we introduce a Multi-grain Spatial Focus (MSF) module, which effectively captures spatial features and their interactions in depression-related body regions through joint-level and part-level attention mechanisms. Extensive experimental results demonstrate that STM-Net achieves state-of-the-art performance on a large open-source dataset.

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AAAI Conference 2024 Conference Paper

Disentangled Diffusion-Based 3D Human Pose Estimation with Hierarchical Spatial and Temporal Denoiser

Qingyuan Cai
Xuecai Hu
Saihui Hou
Li Yao
Yongzhen Huang

Recently, diffusion-based methods for monocular 3D human pose estimation have achieved state-of-the-art (SOTA) performance by directly regressing the 3D joint coordinates from the 2D pose sequence. Although some methods decompose the task into bone length and bone direction prediction based on the human anatomical skeleton to explicitly incorporate more human body prior constraints, the performance of these methods is significantly lower than that of the SOTA diffusion-based methods. This can be attributed to the tree structure of the human skeleton. Direct application of the disentangled method could amplify the accumulation of hierarchical errors, propagating through each hierarchy. Meanwhile, the hierarchical information has not been fully explored by the previous methods. To address these problems, a Disentangled Diffusion-based 3D human Pose Estimation method with Hierarchical Spatial and Temporal Denoiser is proposed, termed DDHPose. In our approach: (1) We disentangle the 3d pose and diffuse the bone length and bone direction during the forward process of the diffusion model to effectively model the human pose prior. A disentanglement loss is proposed to supervise diffusion model learning. (2) For the reverse process, we propose Hierarchical Spatial and Temporal Denoiser (HSTDenoiser) to improve the hierarchical modelling of each joint. Our HSTDenoiser comprises two components: the Hierarchical-Related Spatial Transformer (HRST) and the Hierarchical-Related Temporal Transformer (HRTT). HRST exploits joint spatial information and the influence of the parent joint on each joint for spatial modeling, while HRTT utilizes information from both the joint and its hierarchical adjacent joints to explore the hierarchical temporal correlations among joints. Extensive experiments on the Human3.6M and MPI-INF-3DHP datasets show that our method outperforms the SOTA disentangled-based, non-disentangled based, and probabilistic approaches by 10.0%, 2.0%, and 1.3%, respectively.

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YNIMG Journal 2024 Journal Article

Effects of one-week bilateral cerebellar iTBS on resting-state functional brain network and multi-task attentional performance in healthy individuals: A randomized, sham-controlled trial

Meiliang Liu
Chao Yu
Jinping Shi
Yunfang Xu
Zijin Li
Junhao Huang
Zhengye Si
Li Yao

BACKGROUND: Cerebellar intermittent theta burst stimulation (iTBS) modulates the excitability of the cerebral cortex and may enhance attentional performance. To date, few studies have conducted iTBS on healthy subjects for one week and used electroencephalography (EEG) to investigate the effect of multiple stimulation sessions on resting-state functional brain networks and the daily stimulation effect on attentional performance. METHODS: 16 healthy subjects participated in a one-week experiment, receiving bilateral cerebellar iTBS or sham stimulation and engaging in multi-task attentional training. The primary measures were the one-week attentional performance and pre- and post-experiment resting-state EEG activities. Amplitude Envelope Correlation (AEC) was used to construct the functional connectivity in the eye-open (EO) and eye-closed (EC) phases. RESULTS: At least three sessions of iTBS were required to enhance multi-task performance significantly, whereas only one or two sessions failed to elicit the improvement. Compared with the control group, iTBS induced significant changes in PSD, AEC functional connectivity, and AEC network properties during the EO phase, while it had little effect during the EC phase. During the EO phase, the network property changes of the iTBS subject were correlated with improved attentional performance. CONCLUSION: The multi-task performance requires multiple stimulations to enhance. iTBS affects the resting-state alpha band brain activities during the EO rather than the EC phase. The AEC network properties may serve as a biomarker to assess the attentional potential of healthy subjects.

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JBHI Journal 2024 Journal Article

Signed Curvature Graph Representation Learning of Brain Networks for Brain Age Estimation

Jingming Li
Zhengyuan Lyu
Hu Yu
Si Fu
Ke Li
Li Yao
Xiaojuan Guo

Graph Neural Networks (GNNs) play a pivotal role in learning representations of brain networks for estimating brain age. However, the over-squashing impedes interactions between long-range nodes, hindering the ability of message-passing mechanism-based GNNs to learn the topological structure of brain networks. Graph rewiring methods and curvature GNNs have been proposed to alleviate over-squashing. However, most graph rewiring methods overlook node features and curvature GNNs neglect the geometric properties of signed curvature. In this study, a Signed Curvature GNN (SCGNN) was proposed to rewire the graph based on node features and curvature, and learn the representation of signed curvature. First, a Mutual Information Ollivier-Ricci Flow (MORF) was proposed to add connections in the neighborhood of edge with the minimal negative curvature based on the maximum mutual information between node features, improving the efficiency of information interaction between nodes. Then, a Signed Curvature Convolution (SCC) was proposed to aggregate node features based on positive and negative curvature, facilitating the model's ability to capture the complex topological structures of brain networks. Additionally, an Ollivier-Ricci Gradient Pooling (ORG-Pooling) was proposed to select the key nodes and topology structures by curvature gradient and attention mechanism, accurately obtaining the global representation for brain age estimation. Experiments conducted on six public datasets with structural magnetic resonance imaging (sMRI), spanning ages from 18 to 91 years, validate that our method achieves promising performance compared with existing methods. Furthermore, we employed the gaps between brain age and chronological age for identifying Alzheimer's Disease (AD), yielding the best classification performance.

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YNIMG Journal 2020 Journal Article

Individual-specific and shared representations during episodic memory encoding and retrieval

Xiaoqian Xiao
Yu Zhou
Jing Liu
Zhifang Ye
Li Yao
Jiacai Zhang
Chuansheng Chen
Gui Xue

Although human memories seem unique to each individual, they are shared to a great extent across individuals. Previous studies have examined, separately, subject-specific and cross-subject shared representations during memory encoding and retrieval, but how shared memories are formed from individually encoded representations is not clearly understood. Using a unique fMRI design involving memory encoding and retrieval, and representational similarity analysis to link representations from different individuals, brain regions, and processing stages, the current study revealed that distributed brain regions showed both subject-specific and shared neural representations during both memory encoding and retrieval. Furthermore, different brain regions showed stage-specific representational strength, with the visual cortex showing greater unique and shared representations during encoding, whereas the left angular gyrus showing greater unique and shared representations during retrieval. The neural representations during encoding were transformed during retrieval, as shown by smaller cross-subject encoding-retrieval similarity (ERS) than cross-subject similarity either during encoding or during retrieval. This cross-subject and cross-stage similarity was found both within and across regions, with strong pattern similarity between the encoded representation in VVC and the retrieved representation in the angular gyrus. Simulation analysis further suggested that these patterns could be achieved by incorporating stage-specific representational strength, and cross-region reinstatement from encoding to retrieval, but not by a common transformation from encoding to retrieval across subjects. Together, our results shed light on how memory representations are encoded and transformed to maintain individual characteristics and at the same time to create shared representations to facilitate interpersonal communication.

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YNICL Journal 2018 Journal Article

Abnormal dynamic functional connectivity between speech and auditory areas in schizophrenia patients with auditory hallucinations

Wenjing Zhang
Siyi Li
Xiuli Wang
Yao Gong
Li Yao
Yuan Xiao
Jieke Liu
Sarah K. Keedy

Purpose: Auditory hallucinations (AH), typically hearing voices, are a core symptom in schizophrenia. They may result from deficits in dynamic functional connectivity (FC) between cortical regions supporting speech production and language perception that interfere with the ability to recognize self-generated speech as not coming from external sources. We tested this hypothesis by investigating dynamic connectivity between the frontal cortex region related to language production and the temporal cortex region related to auditory processing. Methods: Resting-state fMRI scans were acquired from 18 schizophrenia patients with AH (AH+), 17 schizophrenia patients without AH (AH-) and 22 healthy controls. A multiband sequence with TR = 427 ms was adopted to provide relatively high temporal resolution data for characterizing dynamic FC. Analysis focused on connectivity between speech production and language comprehension areas, eloquent language cortex in the left hemisphere. Two frequency bands of brain oscillatory activity were evaluated (0.01-0.027 Hz, 0.027-0.08 Hz) in which differential alterations that have been previously linked to schizophrenia. Conventional static FC maps of these seeds were also calculated. Results: Dynamic connectivity analysis indicated that AH+ patients showed not only less temporal variability but transient lower strength in connectivity between speech and auditory areas than healthy controls, while AH- patients not. These findings were restricted to 0.027-0.08 Hz activity. In static connectivity analysis, no significant differences were observed in connectivity between speech production and language comprehension areas in either frequency band. Conclusions: Reduced temporal variability and connectivity strength between key regions of eloquent language cortex may represent a mechanism for AH in schizophrenia.

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YNIMG Journal 2015 Journal Article

A two-step super-Gaussian independent component analysis approach for fMRI data

Ruiyang Ge
Li Yao
Hang Zhang
Zhiying Long

Independent component analysis (ICA) has been widely applied to functional magnetic resonance imaging (fMRI) data analysis. Although ICA assumes that the sources underlying data are statistically independent, it usually ignores sources’ additional properties, such as sparsity. In this study, we propose a two-step super-GaussianICA (2SGICA) method that incorporates the sparse prior of the sources into the ICA model. 2SGICA uses the super-Gaussian ICA (SGICA) algorithm that is based on a simplified Lewicki-Sejnowski’s model to obtain the initial source estimate in the first step. Using a kernel estimator technique, the source density is acquired and fitted to the Laplacian function based on the initial source estimates. The fitted Laplacian prior is used for each source at the second SGICA step. Moreover, the automatic target generation process for initial value generation is used in 2SGICA to guarantee the stability of the algorithm. An adaptive step size selection criterion is also implemented in the proposed algorithm. We performed experimental tests on both simulated data and real fMRI data to investigate the feasibility and robustness of 2SGICA and made a performance comparison between InfomaxICA, FastICA, mean field ICA (MFICA) with Laplacian prior, sparse online dictionary learning (ODL), SGICA and 2SGICA. Both simulated and real fMRI experiments showed that the 2SGICA was most robust to noises, and had the best spatial detection power and the time course estimation among the six methods.

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NeurIPS Conference 2013 Conference Paper

Generalized Denoising Auto-Encoders as Generative Models

Yoshua Bengio
Li Yao
Guillaume Alain
Pascal Vincent

Recent work has shown how denoising and contractive autoencoders implicitly capture the structure of the data generating density, in the case where the corruption noise is Gaussian, the reconstruction error is the squared error, and the data is continuous-valued. This has led to various proposals for sampling from this implicitly learned density function, using Langevin and Metropolis-Hastings MCMC. However, it remained unclear how to connect the training procedure of regularized auto-encoders to the implicit estimation of the underlying data generating distribution when the data are discrete, or using other forms of corruption process and reconstruction errors. Another issue is the mathematical justification which is only valid in the limit of small corruption noise. We propose here a different attack on the problem, which deals with all these issues: arbitrary (but noisy enough) corruption, arbitrary reconstruction loss (seen as a log-likelihood), handling both discrete and continuous-valued variables, and removing the bias due to non-infinitesimal corruption noise (or non-infinitesimal contractive penalty).

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JBHI Journal 2013 Journal Article

Improved Estimation of the Number of Independent Components for Functional Magnetic Resonance Data by a Whitening Filter

Mingqi Hui
Rui Li
Kewei Chen
Zhen Jin
Li Yao
Zhiying Long

Independent component analysis (ICA) has been widely applied to the analysis of fMRI data. Accurate estimation of the number of independent components (ICs) in fMRI data is critical to reduce over/underfitting. Various methods based on information theoretic criteria (ITC) have been used to estimate the intrinsic dimension of fMRI data. An important assumption of ITC is that the noise is purely white. However, this assumption is often violated by the existence of temporally correlated noise in fMRI data. In this study, we introduced a filtering method into the order selection to remove the autocorrelation from the colored noise by using the whitening filter proposed by Prudon and Weisskoff. Results of the simulated data show that the filtering method has strong robustness to noise and significantly improves the accuracy of order selection from data with colored noise. Moreover, the multifiltering method proposed by us was applied to real fMRI data to improve the performance of ITC. Results of the real fMRI data show that the proposed method can alleviate the overestimation due to the autocorrelation of colored noise. We further compared the stability of IC estimates of real fMRI data at order estimated by minimum description length criterion based on the filtered and unfiltered data by using the software package ICASSO. Results show that ICA yields more stable IC estimates using the reduced order by filtering.

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YNIMG Journal 2011 Journal Article

Large-scale directional connections among multi resting-state neural networks in human brain: A functional MRI and Bayesian network modeling study

Rui Li
Kewei Chen
Adam S. Fleisher
Eric M. Reiman
Li Yao
Xia Wu

This study examined the large-scale connectivity among multiple resting-state networks (RSNs) in the human brain. Independent component analysis was first applied to the resting-state functional MRI (fMRI) data acquired from 12 healthy young subjects for the separation of RSNs. Four sensory (lateral and medial visual, auditory, and sensory-motor) RSNs and four cognitive (default-mode, self-referential, dorsal and ventral attention) RSNs were identified. Gaussian Bayesian network (BN) learning approach was then used for the examination of the conditional dependencies among these RSNs and the construction of the network-to-network directional connectivity patterns. The BN based results demonstrated that sensory networks and cognitive networks were hierarchically organized. Specially, we found the sensory networks were highly intra-dependent and the cognitive networks were strongly intra-influenced. In addition, the results depicted dominant bottom-up connectivity from sensory networks to cognitive networks in which the self-referential and the default-mode networks might play respectively important roles in the process of resting-state information transfer and integration. The present study characterized the global connectivity relations among RSNs and delineated more characteristics of spontaneous activity dynamics.

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YNIMG Journal 2010 Journal Article

Impairment and compensation coexist in amnestic MCI default mode network

Zhigang Qi
Xia Wu
Zhiqun Wang
Nang Zhang
Huiqing Dong
Li Yao
Kuncheng Li

Mild cognitive impairment (MCI) is the transitional, heterogeneous continuum from healthy elderly to Alzheimer's disease (AD). Previous studies have shown that brain functional activity in the default mode network (DMN) is impaired in AD patients. However, altering DMN activity patterns in MCI patients remains largely unclear. The present study utilized resting-state functional magnetic resonance imaging (fMRI) and an independent component analysis (ICA) approach to investigate DMN activity in 14 amnestic MCI (aMCI) patients and 14 healthy elderly. Compared to the aMCI patients, the healthy elderly exhibited increased functional activity in the DMN regions, including the bilateral precuneus/posterior cingulate cortex, right inferior parietal lobule, and left fusiform gyrus, as well as a trend towards increased right medial temporal lobe activity. The aMCI patients exhibited increased activity in the left prefrontal cortex, inferior parietal lobule, and middle temporal gyrus compared to the healthy elderly. Increased frontal–parietal activity may indicate compensatory processes in the aMCI patients. These findings suggest that abnormal DMN activity could be useful as an imaging-based biomarker for the diagnosis and monitoring of aMCI patients.

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YNIMG Journal 2009 Journal Article

Multiple neural networks supporting a semantic task: An fMRI study using independent component analysis

Xia Wu
Jie Lu
Kewei Chen
Zhiying Long
Xiaoyi Wang
Hua Shu
Kuncheng Li
Yijun Liu

A visual task for semantic access involves a number of brain regions. However, previous studies either examined the role of each region separately using univariate approach, or analyzed a single brain network using covariance connectivity analysis. We hypothesize that these brain regions construct several functional networks underpinning a word semantic access task, these networks being engaged in different cognitive components with distinct temporal characters. In this paper, multivariate independent component analysis (ICA) was used to reveal these networks based on functional magnetic resonance imaging (fMRI) data acquired during a visual and an auditory word semantic judgment task. Our results demonstrated that there were three task-related independent components (ICs), corresponding to various cognitive components involved in the visual task. Furthermore, ICA separation on the auditory task showed consistency of the results with our hypothesis, regardless of the input modalities.

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