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Xiaoli Li

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42 papers

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EAAI Journal 2026 Journal Article

A hierarchical semantic collaboration-based network for infrared and visible image fusion

Liuyan Shi
Rencan Nie
Jinde Cao
Jiang Zuo
Xiaoli Li

To address the inherent divergence between image fusion and downstream semantic tasks, this study proposes a Hierarchical Semantic Collaboration-Based Network (HSCNet) for infrared and visible image fusion. The proposed framework jointly models cross-modal features across pixel and semantic domains through a multi-level feature sharing strategy, effectively reducing pixel-level information loss and enhancing semantic reconstruction. A semantic-driven feedback mechanism enables bidirectional optimization between the fusion and segmentation branches, thereby improving the semantic expressiveness of the fused images. Furthermore, a Hierarchical Semantic Transformer (HST) decomposes image representations into global structural and local detail components, facilitating task-specific denoising and reconstruction. Extensive evaluations on three public datasets demonstrate that HSCNet consistently achieves state-of-the-art (SOTA) performance, ranking first across all fusion metrics. For downstream applications, the model attains the highest segmentation accuracy, with a mean Intersection over Union (mIoU) of 79. 48, and superior detection performance, achieving a mean Average Precision (mAP) of 0. 559 over the range [0. 5: 0. 95], outperforming existing methods. These results confirm that HSCNet not only produces perceptually superior fused images but also enhances their compatibility with high-level semantic understanding in real-world artificial intelligence (AI) applications.

AAAI Conference 2026 Conference Paper

Bridging Synthetic and Real Routing Problems via LLM-Guided Instance Generation and Progressive Adaptation

Jianghan Zhu
Yaoxin Wu
Zhuoyi Lin
Zhengyuan Zhang
Haiyan Yin
Zhiguang Cao
Senthilnath Jayavelu
Xiaoli Li

Recent advances in Neural Combinatorial Optimization (NCO) methods have significantly improved the capability of neural solvers to handle synthetic routing instances. Nonetheless, existing neural solvers typically struggle to generalize effectively from synthetic, uniformly-distributed training data to real-world VRP scenarios, including widely recognized benchmark instances from TSPLib and CVRPLib. To bridge this generalization gap, we present Evolutionary Realistic Instance Synthesis (EvoReal), which leverages an evolutionary module guided by large language models (LLMs) to generate synthetic instances characterized by diverse and realistic structural patterns. Specifically, the evolutionary module produces synthetic instances whose structural attributes statistically mimics those observed in authentic real-world instances. Subsequently, pre-trained NCO models are progressively refined, firstly aligning them with these structurally enriched synthetic distributions and then further adapting them through direct fine-tuning on actual benchmark instances. Extensive experimental evaluations demonstrate that EvoReal markedly improves the generalization capabilities of state-of-the-art neural solvers, yielding a notable reduced performance gap compared to the optimal solutions on the TSPLib (1.05%) and CVRPLib (2.71%) benchmarks across a broad spectrum of problem scales.

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JBHI Journal 2026 Journal Article

Joint Fine-Grained Representation Learning and Masked Relational Modeling for EEG-Based Automatic Sleep Staging in Fabric Space

Lejun Ai
He Chen
Yu Qiu
Yixue Hao
Xiaoli Li
Min Chen
Xiao-kun Wu

Sleep staging is a crucial method for the evaluation of sleep quality and the diagnosis of sleep disorders. In recent years, rapid progress has been made in sleep research through the application of fabric computing and neural networks. Flexible fabric sensors introduced by fabric computing minimize the discomfort of data collection devices on individuals, while neural network-based algorithms can automatically perform sleep staging based on the collected signals. However, there are two key challenges hinder the integration of automatic sleep staging networks with fabric computing: (1) signals in fabric-based environments exhibit strong heterogeneity due to the wide range of individuals, and (2) interactions between individuals and the fabric space introduce behavioral dynamics to the system. In this paper, we propose a masked autoencoder-based sleep staging neural networks (MAESleepNet), designed to integrate automatic sleep staging algorithm with fabric space. Specifically, MAESleepNet addresses the challenge of signal heterogeneity by learning fine-grained representations from local signals. Furthermore, MAESleepNet tackle the challenge of behavioral dynamics through stochastic masking and reconstruction pre-training. Experiments were conducted on three public datasets: (1) Sleep-EDF-20, (2) Sleep-EDF-78 and (3) SHHS. MAESleepNet achieves overall accuracies of 88. 9%, 85. 5%, and 87. 3%, respectively, outperforming other state-of-the-art models. Furthermore, feature visualization and reconstruction visualization experiments were also conducted. The results demonstrates that MAESleepNet is an effective solution to the aforementioned challenges, paving the way for seamless integration into the fabric space.

AAAI Conference 2026 Conference Paper

PITE: Multi-Prototype Alignment for Individual Treatment Effect Estimation

Fuyuan Cao
Jiaxuan Zhang
Xiaoli Li

Estimating Individual Treatment Effects (ITE) from observational data is challenging due to confounding bias. Most studies tackle this bias by balancing distributions globally, but ignore individual heterogeneity and fail to capture the local structure that represents the natural clustering among individuals, which ultimately compromises ITE estimation. While instance-level alignment methods consider heterogeneity, they similarly overlook the local structure information. To address these issues, we propose an end-to-end Multi-Prototype alignment method for ITE estimation (PITE). PITE effectively captures local structure within groups and enforces cross-group alignment, thereby achieving robust ITE estimation. Specifically, we first define prototypes as cluster centroids based on similar individuals under the same treatment. To identify local similarity and the distribution consistency, we perform instance-to-prototype matching to assign individuals to the nearest prototype within groups, and design a multi-prototype alignment strategy to encourage the matched prototypes to be close across treatment arms in the latent space. PITE not only reduces distribution shift through fine-grained, prototype-level alignment, but also preserves the local structures of treated and control groups, which provides meaningful constraints for ITE estimation. Extensive evaluations on benchmark datasets demonstrate that PITE outperforms 13 state-of-the-art methods, achieving more accurate and robust ITE estimation.

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AAAI Conference 2026 Conference Paper

TGCD: A Framework for Generalized Category Discovery in Time-Series Data

Chandan Gautam
Lew Choon Hean
Ankit Das
Xiaoli Li
Savitha Ramasamy

Generalized Category Discovery (GCD) aims to classify labeled instances from known categories while discovering novel categories from unlabeled data. Despite recent progress in GCD for computer vision, existing GCD approaches largely rely on static final-step representations (in the visual domain), overlooking the temporally evolving nature of time-series data. In this paper, we introduce TGCD, the first framework specifically designed for GCD in time-series data. TGCD leverages both the dynamics of latent representations and the heterogeneity of predictions across multiple temporal segments to disover unknown (i.e., novel) categories, based on a pre-trained time-series foundation model. We propose a unified learning objective for TGCD that integrates the following three components: (i) a Stochastic Temporal Segment Dropout (STeSD) objective that regularizes the model by selectively penalizing high-entropy segments to encourage confident predictions on uncertain regions of the time-series, and (ii) a Known–Unknown Temporal Discriminability (KUTD) objective that promotes representational separation between known and unknown categories within unlabeled data and (iii) a margin-aware classification objective to improve generalization. Empirical evaluation on six multivariate time-series data sets demonstrates that the TGCD substantially outperforms existing GCD methods, particularly in discovering unknown categories. We further conduct ablation studies to highlight the individual contributions of each component. Additionally, we provide the first comprehensive benchmarking of recent GCD approaches on time-series data, revealing the limitations of naive transfer and underscoring the benefits of temporal modeling.

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YNIMG Journal 2025 Journal Article

A practical measure of integrated information reveals alpha-band activity and the posterior cortex as neural correlates of arousal

Xin Wen
Yu Chang
Sijie Li
Jing Wang
Xiaoli Li
Duan Li
Changwei Wei
Zhenhu Liang

The search for neurophysiological markers of consciousness and their neural substrates remains a focal point in neuroscience research. The integrated information theory (IIT) provides a promising quantitative framework for consciousness assessment, but computational limitations of existing Φ estimation methods hinder an in-depth understanding of large-scale cortical integration. Here, we proposed a new measure, Φ c o p u l a, by incorporating the Gaussian copula approach for estimating integrated information. Simulation analysis demonstrated that Φ c o p u l a significantly outperformed common estimators, maintaining the lowest bias and mean squared error (MSE) even in non-Gaussian high-dimensional systems. We applied Φ c o p u l a to electroencephalographic data across different arousal states: awake, propofol-induced unresponsiveness, and non-rapid eye movement (NREM) sleep. Results revealed that alpha-band Φ c o p u l a significantly decreased during both propofol anesthesia (p < 0. 001) and sleep (p < 0. 014) states. Moreover, classification analysis demonstrated that Φ c o p u l a -based classifiers achieved superior accuracy in distinguishing arousal states compared to functional connectivity and network efficiency measures (p < 0. 030 for anesthesia; p < 0. 043 for sleep). Among the functional networks, the dorsal attention network (DAN) and default mode network (DMN) contributed most to Φ c o p u l a. Among the anatomical brain regions, the cingulate and posterior cortices showed the greatest contributions. Our findings suggest that Φ c o p u l a is a practical and effective metric for quantifying integrated information, with substantial potential for monitoring arousal levels in clinical and experimental settings. The posterior cortex, especially the posterior cingulate cortex (PCC), shows the greatest contribution to arousal-related information integration, revealing its critical role in consciousness.

AAMAS Conference 2025 Conference Paper

Enhancing Sub-Optimal Trajectory Stitching: Spatial Composition RvS for Offline RL

Sheng Zang
Zhiguang Cao
Bo An
Senthilnath Jayavelu
Xiaoli Li

Reinforcement learning via supervised learning (RvS) has been known as a burgeoning paradigm for offline reinforcement learning (RL). While return-conditioned RvS (RvS-R) predominates across a wide range of datasets pertaining to the offline RL tasks, recent findings suggest that goal-conditioned RvS (RvS-G) outperforms in specific sub-optimal datasets where trajectory stitching is crucial for achieving optimal performance. However, the underlying reasons for this superiority remain insufficiently explored. In this paper, employing didactic experiments and theoretical analysis, we reveal that the proficiency of RvS-G in stitching trajectories arises from its adeptness in generalizing to unknown goals during evaluation. Building on this insight, we introduce a novel RvS-G approach, Spatial Composition RvS (SC-RvS), to enhance its ability to generalize to unknown goals. This, in turn, augments the trajectory stitching performance on sub-optimal datasets. Specifically, by harnessing the power of advantage weight and maximum-entropy regularized weight, our approach adeptly balances the promotion of optimistic goal sampling with the preservation of a nuanced level of pessimism in action selection compared to existing RvS- G methods. Extensive experimental results on D4RL benchmarks show that our SC-RvS performed favorably against the baselines in most cases, especially on the sub-optimal datasets that demand trajectory stitching.

YNIMG Journal 2025 Journal Article

Quantifying task-locked information transmission between cortical areas with TMS-EEG

Zhaohuan Ding
Wenbo Ma
Leixiao Feng
Mingsha Zhang
Xiaoli Li

OBJECTIVE: This study aims to develop TMS-EEG (Transcranial magnetic stimulation combined with EEG) technology to detect task-locked neural network activation and dynamically quantify information transmission. APPROACH: 30 participants performed visually guided gap saccade tasks while TMS-EEG data were recorded, with the TMS pulses delivered to prefrontal cortex (PFC) and posterior parietal cortex (PPC) at different task stages. The directed transfer function (DTF) method was applied to TMS-EEG data to indicate the information flow. By analyzing the channel combinations associated with the PFC and PPC, we calculated differences in information flow within the alpha, beta, and gamma frequency bands to determine whether TMS-EEG could quantitatively characterize the direction of information flow between cortical areas. MAIN RESULTS: Analysis of eye tracker data revealed that all participants successfully performed the saccade task, with a correct rate exceeding 90 %. The mean saccade latency was 132.25 ± 22.59 ms after target appearance. Stimulation of the PFC and PPC revealed significant differences in information flow in the gamma bands at different time points. Specifically, during the preparatory period, the C3 electrode acts as a hub for incoming information from O1, later transitioning to send information towards F4 and O1 post-target. Then, P3 emerges as a hub, sending data towards P4, with connectivity between them intensifying post 100 ms from the target's appearance. SIGNIFICANCE: This study utilized DTF values derived from TMS-EEG to characterize information flow between cortical areas during the gap saccade task. This approach provides a novel method for quantifying dynamic changes in connectivity and causality between cortical areas during task processing.

NeurIPS Conference 2025 Conference Paper

Reconciling Geospatial Prediction and Retrieval via Sparse Representations

Yi Li
CHEN YUANLONG
Weiming Huang
Xiaoli Li
Gao Cong

Urban computing harnesses big data to decode complex urban dynamics and revolutionize location-based services. Traditional approaches have treated geospatial prediction tasks (e. g. , estimating socio-economic indicators) and retrieval tasks (e. g. , querying geographic objects) as isolated challenges, necessitating separate models with distinct training objectives. This fragmentation imposes significant computational burdens and limits cross-task synergy, despite advances in representation learning and multi-task foundation models. We present UrbanSparse, a pioneering framework that unifies geospatial prediction and retrieval through a novel sparse-dense representation architecture. By synergistically combining these tasks, UrbanSparse eliminates redundant systems while amplifying their mutual strengths. Our approach introduces two innovations: (1) Bloom filter-based sparse encodings that compress high-sparsity geographic queries and fine-grained text terms for retrieval effectiveness, and (2) a dense semantic codebook that captures granular urban features to boost prediction accuracy. A two-view contrastive learning mechanism further bridges urban objects, regions, and contexts. Experiments on real-world datasets demonstrate 25. 16% gains in prediction accuracy and 20. 76% improvements in retrieval precision over state-of-the-art baselines, alongside 65. 97% faster training. These advantages position UrbanSparse as a scalable solution for large urban datasets. To our knowledge, this is the first unified framework bridging geospatial prediction and retrieval, opening new frontiers in data-driven urban intelligence.

YNIMG Journal 2025 Journal Article

Spatiospectral dynamics of electroencephalography patterns during propofol-induced alterations of consciousness states

Xuan Li
Dezhao Liu
Zheng Li
Rui Wang
Xiaoli Li
Tianyi Zhou

Altered consciousness induced by anesthetics is characterized by distinct spatial and spectral neural dynamics that are readily apparent in the human electroencephalogram. Despite considerable study, we remain uncertain which brain regions and neural oscillations are involved, as well as how they are impacted when consciousness is disrupted. The experimental data was obtained from the open-access dataset, which contains pre-processed EEG data recorded from 20 healthy participants during propofol sedation. Using unsupervised machine learning methods (i.e., non-negative matrix factorization, NMF), we investigated the spatiospectral dynamic evolution of brain activity from awake to sedation and back induced by propofol in healthy research volunteers. Our methods yielded six dynamical patterns that continuously reflect the neural activity changes in specific brain regions and frequency bands under propofol sedation. Temporal dynamic analyses showed that differences in alpha oscillation patterns were less pronounced in response group than drowsy group, with hemispheric asymmetry in posterior occipital lobe over the course of the sedation procedure. We designed an index 'hemispheric lateralization modulation of alpha [HLM(α)]' to measure asymmetry during awake state and predicting individual variability in propofol-induced alterations of consciousness states, obtaining prediction AUC of 0.8462. We present an alpha modulation index which characterizes how these patterns track the transition from awake to sedation as a function of increasing dosage. Our study reveals dynamics indices that track the evolution of neurophysiological of propofol on brain circuits. Analyzing the spatiospectral dynamics influenced by propofol provides valuable understanding of the mechanisms of these agents and strategies for monitoring and precisely controlling the level of consciousness in patients under sedation and general anesthesia.

YNIMG Journal 2025 Journal Article

The changes in neural complexity and connectivity in thalamocortical and cortico-cortical systems after propofol-induced unconsciousness in different temporal scales

Zhenhu Liang
Luxin Fan
Bin Zhang
Wei Shu
Duan Li
Xiaoli Li
Tao Yu

Existing studies have indicated neural activity across diverse temporal and spatial scales. However, the alterations in complexity, functional connectivity, and directional connectivity within the thalamocortical and corticocortical systems across various scales during propofol-induced unconsciousness remain uncertain. We analyzed the stereo-electroencephalography (SEEG) from wakefulness to unconsciousness among the brain regions of the prefrontal cortex, temporal lobe, and anterior nucleus of the thalamus. The complexity (examined by permutation entropy (PE)), functional connectivity (permutation mutual information (PMI)), and directional connectivity (symbolic conditional mutual information (SCMI) and directionality index (DI)) were calculated across various scales. In the lower-band frequency (0.1-45 Hz) SEEG, after the loss of consciousness, PE significantly decreased (p < 0.001) in all regions and scales, except for the thalamus, which remained relatively unchanged at large scales (τ=32 ms). Following the loss of consciousness, inter-regional PMI either significantly increased or remained stable across different scales (τ=4 ms to 32 ms). During the unconscious state, SCMI between brain regions exhibited inconsistent changes across scales. In the late unconscious stage, the inter-regional DI across all scales indicated a shift from a balanced state of information flow between brain regions to a pattern where the prefrontal cortex and thalamus drive the temporal lobe. Our findings demonstrate that propofol-induced unconsciousness is associated with reduced cortical complexity, diverse functional connectivity, and a disrupted balance of information integration among thalamocortical and cortico-cortical systems. This study enhances the theoretical understanding of anesthetic-induced loss of consciousness by elucidating the scale- and region-specific effects of propofol on thalamocortical and cortico-cortical systems.

AAAI Conference 2024 Conference Paper

Fully-Connected Spatial-Temporal Graph for Multivariate Time-Series Data

Yucheng Wang
Yuecong Xu
Jianfei Yang
Min Wu
Xiaoli Li
Lihua Xie
Zhenghua Chen

Multivariate Time-Series (MTS) data is crucial in various application fields. With its sequential and multi-source (multiple sensors) properties, MTS data inherently exhibits Spatial-Temporal (ST) dependencies, involving temporal correlations between timestamps and spatial correlations between sensors in each timestamp. To effectively leverage this information, Graph Neural Network-based methods (GNNs) have been widely adopted. However, existing approaches separately capture spatial dependency and temporal dependency and fail to capture the correlations between Different sEnsors at Different Timestamps (DEDT). Overlooking such correlations hinders the comprehensive modelling of ST dependencies within MTS data, thus restricting existing GNNs from learning effective representations. To address this limitation, we propose a novel method called Fully-Connected Spatial-Temporal Graph Neural Network (FC-STGNN), including two key components namely FC graph construction and FC graph convolution. For graph construction, we design a decay graph to connect sensors across all timestamps based on their temporal distances, enabling us to fully model the ST dependencies by considering the correlations between DEDT. Further, we devise FC graph convolution with a moving-pooling GNN layer to effectively capture the ST dependencies for learning effective representations. Extensive experiments show the effectiveness of FC-STGNN on multiple MTS datasets compared to SOTA methods. The code is available at https://github.com/Frank-Wang-oss/FCSTGNN.

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NeurIPS Conference 2024 Conference Paper

Generative Semi-supervised Graph Anomaly Detection

Hezhe Qiao
Qingsong Wen
Xiaoli Li
Ee-Peng Lim
Guansong Pang

This work considers a practical semi-supervised graph anomaly detection (GAD) scenario, where part of the nodes in a graph are known to be normal, contrasting to the extensively explored unsupervised setting with a fully unlabeled graph. We reveal that having access to the normal nodes, even just a small percentage of normal nodes, helps enhance the detection performance of existing unsupervised GAD methods when they are adapted to the semi-supervised setting. However, their utilization of these normal nodes is limited. In this paper, we propose a novel Generative GAD approach (namely GGAD) for the semi-supervised scenario to better exploit the normal nodes. The key idea is to generate pseudo anomaly nodes, referred to as 'outlier nodes', for providing effective negative node samples in training a discriminative one-class classifier. The main challenge here lies in the lack of ground truth information about real anomaly nodes. To address this challenge, GGAD is designed to leverage two important priors about the anomaly nodes -- asymmetric local affinity and egocentric closeness -- to generate reliable outlier nodes that assimilate anomaly nodes in both graph structure and feature representations. Comprehensive experiments on six real-world GAD datasets are performed to establish a benchmark for semi-supervised GAD and show that GGAD substantially outperforms state-of-the-art unsupervised and semi-supervised GAD methods with varying numbers of training normal nodes.

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AAAI Conference 2024 Conference Paper

Graph-Aware Contrasting for Multivariate Time-Series Classification

Yucheng Wang
Yuecong Xu
Jianfei Yang
Min Wu
Xiaoli Li
Lihua Xie
Zhenghua Chen

Contrastive learning, as a self-supervised learning paradigm, becomes popular for Multivariate Time-Series (MTS) classification. It ensures the consistency across different views of unlabeled samples and then learns effective representations for these samples. Existing contrastive learning methods mainly focus on achieving temporal consistency with temporal augmentation and contrasting techniques, aiming to preserve temporal patterns against perturbations for MTS data. However, they overlook spatial consistency that requires the stability of individual sensors and their correlations. As MTS data typically originate from multiple sensors, ensuring spatial consistency becomes essential for the overall performance of contrastive learning on MTS data. Thus, we propose Graph-Aware Contrasting for spatial consistency across MTS data. Specifically, we propose graph augmentations including node and edge augmentations to preserve the stability of sensors and their correlations, followed by graph contrasting with both node- and graph-level contrasting to extract robust sensor- and global-level features. We further introduce multi-window temporal contrasting to ensure temporal consistency in the data for each sensor. Extensive experiments demonstrate that our proposed method achieves state-of-the-art performance on various MTS classification tasks. The code is available at https://github.com/Frank-Wang-oss/TS-GAC.

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YNIMG Journal 2024 Journal Article

Intracranial neural representation of phenomenal and access consciousness in the human brain

Zepeng Fang
Yuanyuan Dang
Xiaoli Li
Qianchuan Zhao
Mingsha Zhang
Hulin Zhao

After more than 30 years of extensive investigation, impressive progress has been made in identifying the neural correlates of consciousness (NCC). However, the functional role of spatiotemporally distinct consciousness-related neural activity in conscious perception is debated. An influential framework proposed that consciousness-related neural activities could be dissociated into two distinct processes: phenomenal and access consciousness. However, though hotly debated, its authenticity has not been examined in a single paradigm with more informative intracranial recordings. In the present study, we employed a visual awareness task and recorded the local field potential (LFP) of patients with electrodes implanted in cortical and subcortical regions. Overall, we found that the latency of visual awareness-related activity exhibited a bimodal distribution, and the recording sites with short and long latencies were largely separated in location, except in the lateral prefrontal cortex (lPFC). The mixture of short and long latencies in the lPFC indicates that it plays a critical role in linking phenomenal and access consciousness. However, the division between the two is not as simple as the central sulcus, as proposed previously. Moreover, in 4 patients with electrodes implanted in the bilateral prefrontal cortex, early awareness-related activity was confined to the contralateral side, while late awareness-related activity appeared on both sides. Finally, Granger causality analysis showed that awareness-related information flowed from the early sites to the late sites. These results provide the first LFP evidence of neural correlates of phenomenal and access consciousness, which sheds light on the spatiotemporal dynamics of NCC in the human brain.

TMLR Journal 2024 Journal Article

Leveraging Endo- and Exo-Temporal Regularization for Black-box Video Domain Adaptation

Yuecong Xu
Jianfei Yang
Haozhi Cao
Min Wu
Xiaoli Li
Lihua Xie
Zhenghua Chen

To enable video models to be applied seamlessly across video tasks in different environments, various Video Unsupervised Domain Adaptation (VUDA) methods have been proposed to improve the robustness and transferability of video models. Despite improvements made in model robustness, these VUDA methods require access to both source data and source model parameters for adaptation, raising serious data privacy and model portability issues. To cope with the above concerns, this paper firstly formulates Black-box Video Domain Adaptation (BVDA) as a more realistic yet challenging scenario where the source video model is provided only as a black-box predictor. While a few methods for Black-box Domain Adaptation (BDA) are proposed in the image domain, these methods cannot apply to the video domain since video modality has more complicated temporal features that are harder to align. To address BVDA, we propose a novel Endo and eXo-TEmporal Regularized Network (EXTERN) by applying mask-to-mix strategies and video-tailored regularizations. They are the endo-temporal regularization and exo-temporal regularization, which are performed across both clip and temporal features, while distilling knowledge from the predictions obtained from the black-box predictor. Empirical results demonstrate the state-of-the-art performance of EXTERN across various cross-domain closed-set and partial-set action recognition benchmarks, which even surpasses most existing video domain adaptation methods with source data accessibility. Code will be available at https://xuyu0010.github.io/b2vda.html.

NeurIPS Conference 2024 Conference Paper

Reinforced Cross-Domain Knowledge Distillation on Time Series Data

Qing Xu
Min Wu
Xiaoli Li
Kezhi Mao
Zhenghua Chen

Unsupervised domain adaptation methods have demonstrated superior capabilities in handling the domain shift issue which widely exists in various time series tasks. However, their prominent adaptation performances heavily rely on complex model architectures, posing an unprecedented challenge in deploying them on resource-limited devices for real-time monitoring. Existing approaches, which integrates knowledge distillation into domain adaptation frameworks to simultaneously address domain shift and model complexity, often neglect network capacity gap between teacher and student and just coarsely align their outputs over all source and target samples, resulting in poor distillation efficiency. Thus, in this paper, we propose an innovative framework named Reinforced Cross-Domain Knowledge Distillation (RCD-KD) which can effectively adapt to student's network capability via dynamically selecting suitable target domain samples for knowledge transferring. Particularly, a reinforcement learning-based module with a novel reward function is proposed to learn optimal target sample selection policy based on student's capacity. Meanwhile, a domain discriminator is designed to transfer the domain invariant knowledge. Empirical experimental results and analyses on four public time series datasets demonstrate the effectiveness of our proposed method over other state-of-the-art benchmarks.

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JBHI Journal 2024 Journal Article

Spatial-Frequency Characteristics of EEG Associated With the Mental Stress in Human-Machine Systems

Qunli Yao
Heng Gu
Shaodi Wang
Xiaoli Li

Accurate assessment of user mental stress in human-machine system plays a crucial role in ensuring task performance and system safety. However, the underlying neural mechanisms of stress in human-machine tasks and assessment methods based on physiological indicators remain fundamental challenges. In this paper, we employ a virtual unmanned aerial vehicle (UAV) control experiment to explore the reorganization of functional brain network patterns under stress conditions. The results indicate enhanced functional connectivity in the frontal theta band and central beta band, as well as reduced functional connectivity in the left parieto-occipital alpha band, which is associated with increased mental stress. Evaluation of network metrics reveals that decreased global efficiency in the theta and beta bands is linked to elevated stress levels. Subsequently, inspired by the frequency-specific patterns in the stress brain network, a cross-band graph convolutional network (CBGCN) model is constructed for mental stress brain state recognition. The proposed method captures the spatial-frequency topological relationships of cross-band brain networks through multiple branches, with the aim of integrating complex dynamic patterns hidden in the brain network and learning discriminative cognitive features. Experimental results demonstrate that the neuro-inspired CBGCN model improves classification performance and enhances model interpretability. The study suggests that the proposed approach provides a potentially viable solution for recognizing stress states in human-machine system by using EEG signals.

JBHI Journal 2023 Journal Article

CoIn: Correlation Induced Clustering for Cognition of High Dimensional Bioinformatics Data

Zeng Zeng
Ziyuan Zhao
Kaixin Xu
Yangfan Li
Cen Chen
Xiaofeng Zou
Yulan Wang
Wei Wei

Analysis of high dimensional biomedical data such as microarray gene expression data and mass spectrometry images, is crucial to provide better medical services including cancer subtyping, protein homology detection, etc. Clustering is a fundamental cognitive task which aims to group unlabeled data into multiple clusters based on their intrinsic similarities. However, for most clustering methods, including the most widely used $K$ -means algorithm, all features of the high dimensional data are considered equally in relevance, which distorts the performance when clustering high-dimensional data where there exist many redundant variables and correlated variables. In this paper, we aim at addressing the problem of the high dimensional bioinformatics data clustering and propose a new correlation induced clustering, CoIn, to capture complex correlations among high dimensional data and guarantee the correlation consistency within each cluster. We evaluate the proposed method on a high dimensional mass spectrometry dataset of liver cancer tumor to explore the metabolic differences on tissues and discover the intra-tumor heterogeneity (ITH). By comparing the results of baselines and ours, it has been found that our method produces more explainable and understandable results for clinical analysis, which demonstrates the proposed clustering paradigm has the potential with application to knowledge discovery in high dimensional bioinformatics data.

JBHI Journal 2023 Journal Article

Compressibility Analysis of Functional Near-Infrared Spectroscopy Signals in Children With Attention-Deficit/Hyperactivity Disorder

Yue Gu
Shuo Miao
Yao Zhang
Jian Yang
Xiaoli Li

Functional near-infrared spectroscopy (fNIRS) as an emerging optical neuroimaging technique has attracted the interest and attention of many investigators. With the growth of fNIRS data volume, effective data compression methods are urgent. Compressive sensing (CS) has been demonstrated a promising tool to deal with biomedical data. However, whether the compressibility of fNIRS data can discriminate different brain states is unclear. In this study, the fNIRS signals from fifteen attention-deficit/hyperactivity disorder (ADHD) children and fifteen typically developing (TD) children were recorded during an N-back task and a Go/NoGo task respectively. A block sparse Bayesian learning-based CS method was used to reconstruct the compressed fNIRS data. To assess the performance of the CS method, we adopted two metrics, structural similarity index (SSIM) and mean squared error (MSE), both of them effective in evaluating the compressibility of fNIRS data. Then, the two metrics were analyzed to discriminate the brain states of ADHD children and TD children during the two tasks using the multivariate pattern analysis (MVPA) method. As indicated by the results, the CS method could reconstruct the compressed fNIRS data with a high reconstruction quality at different compression ratio ( $\text{SSIM} > \text{0. 988}$ and $\text{MSE} < \text{1. 2} \times \text{10}^{-4}$ ). Furthermore, the MVPA method could distinguish different brain states with high accuracy, and identify that the prefrontal cortex is a key brain region for distinguishing ADHD vs. TD or N-back vs. Go/NoGo. These findings indicated that CS is very promising for the storage and transmission of massive fNIRS data, and the compressibility of fNIRS data is a potential biomarker for the diagnosis of ADHD.

IJCAI Conference 2023 Conference Paper

Distilling Universal and Joint Knowledge for Cross-Domain Model Compression on Time Series Data

Qing Xu
Min Wu
Xiaoli Li
Kezhi Mao
Zhenghua Chen

For many real-world time series tasks, the computational complexity of prevalent deep leaning models often hinders the deployment on resource limited environments (e. g. , smartphones). Moreover, due to the inevitable domain shift between model training (source) and deploying (target) stages, compressing those deep models under cross-domain scenarios becomes more challenging. Although some of existing works have already explored cross-domain knowledge distillation for model compression, they are either biased to source data or heavily tangled between source and target data. To this end, we design a novel end-to-end framework called UNiversal and joInt Knowledge Distillation (UNI-KD) for cross-domain model compression. In particular, we propose to transfer both the universal feature-level knowledge across source and target domains and the joint logit-level knowledge shared by both domains from the teacher to the student model via an adversarial learning scheme. More specifically, a feature-domain discriminator is employed to align teacher’s and student’s representations for universal knowledge transfer. A data-domain discriminator is utilized to prioritize the domain-shared samples for joint knowledge transfer. Extensive experimental results on four time series datasets demonstrate the superiority of our proposed method over state-of-the-art (SOTA) benchmarks. The source code is available at https: //github. com/ijcai2023/UNI KD.

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JBHI Journal 2023 Journal Article

EEG Reconstruction With a Dual-Scale CNN-LSTM Model for Deep Artifact Removal

Tengfei Gao
Dan Chen
Yunbo Tang
Zhekai Ming
Xiaoli Li

Artifact removal has been an open critical issue for decades in tasks centering on EEG analysis. Recent deep learning methods mark a leap forward from the conventional signal processing routines; however, those in general still suffer from insufficient capabilities 1) to capture potential temporal dependencies embedded in EEG and 2) to adapt to scenarios without a priori knowledge of artifacts. This study proposes an approach (namely DuoCL ) to deep artifact removal with a dual-scale CNN (Convolutional Neural Network)-LSTM (Long Short-Term Memory) model, operating on the raw EEG in three phases: 1) Morphological Feature Extraction, a dual-branch CNN utilizes convolution kernels of two different scales to learn morphological features (individual sample); 2) Feature Reinforcement, the dual-scale features are then reinforced with temporal dependencies (inter-sample) captured by LSTM; and 3) EEG Reconstruction, the resulting feature vectors are finally aggregated to reconstruct the artifact-free EEG via a terminal fully connected layer. Extensive experiments have been performed to compare DuoCL to six state-of-the-art counterparts (e. g. , 1D-ResCNN and NovelCNN). DuoCL can reconstruct more accurate waveforms and achieve the highest ${\mathsf{SNR}}$ & correlation ( ${\mathsf{CC}}$ ) as well as the lowest error ( ${\mathsf{RRMSE}}_{\mathsf{t}}$ & ${\mathsf{RRMSE}}_{\mathsf{f}}$ ). In particular, DuoCL holds potentials in providing a high-quality removal of unknown and hybrid artifacts.

IJCAI Conference 2023 Conference Paper

SemiGNN-PPI: Self-Ensembling Multi-Graph Neural Network for Efficient and Generalizable Protein–Protein Interaction Prediction

Ziyuan Zhao
Peisheng Qian
Xulei Yang
Zeng Zeng
Cuntai Guan
Wai Leong Tam
Xiaoli Li

Protein-protein interactions (PPIs) are crucial in various biological processes and their study has significant implications for drug development and disease diagnosis. Existing deep learning methods suffer from significant performance degradation under complex real-world scenarios due to various factors, e. g. , label scarcity and domain shift. In this paper, we propose a self-ensembling multi-graph neural network (SemiGNN-PPI) that can effectively predict PPIs while being both efficient and generalizable. In SemiGNN-PPI, we not only model the protein correlations but explore the label dependencies by constructing and processing multiple graphs from the perspectives of both features and labels in the graph learning process. We further marry GNN with Mean Teacher to effectively leverage unlabeled graph-structured PPI data for self-ensemble graph learning. We also design multiple graph consistency constraints to align the student and teacher graphs in the feature embedding space, enabling the student model to better learn from the teacher model by incorporating more relationships. Extensive experiments on PPI datasets of different scales with different evaluation settings demonstrate that SemiGNN-PPI outperforms state-of-the-art PPI prediction methods, particularly in challenging scenarios such as training with limited annotations and testing on unseen data.

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AAAI Conference 2023 Conference Paper

SEnsor Alignment for Multivariate Time-Series Unsupervised Domain Adaptation

Yucheng Wang
Yuecong Xu
Jianfei Yang
Zhenghua Chen
Min Wu
Xiaoli Li
Lihua Xie

Unsupervised Domain Adaptation (UDA) methods can reduce label dependency by mitigating the feature discrepancy between labeled samples in a source domain and unlabeled samples in a similar yet shifted target domain. Though achieving good performance, these methods are inapplicable for Multivariate Time-Series (MTS) data. MTS data are collected from multiple sensors, each of which follows various distributions. However, most UDA methods solely focus on aligning global features but cannot consider the distinct distributions of each sensor. To cope with such concerns, a practical domain adaptation scenario is formulated as Multivariate Time-Series Unsupervised Domain Adaptation (MTS-UDA). In this paper, we propose SEnsor Alignment (SEA) for MTS-UDA to reduce the domain discrepancy at both the local and global sensor levels. At the local sensor level, we design the endo-feature alignment to align sensor features and their correlations across domains, whose information represents the features of each sensor and the interactions between sensors. Further, to reduce domain discrepancy at the global sensor level, we design the exo-feature alignment to enforce restrictions on the global sensor features. Meanwhile, MTS also incorporates the essential spatial-temporal dependencies information between sensors, which cannot be transferred by existing UDA methods. Therefore, we model the spatial-temporal information of MTS with a multi-branch self-attention mechanism for simple and effective transfer across domains. Empirical results demonstrate the state-of-the-art performance of our proposed SEA on two public MTS datasets for MTS-UDA. The code is available at https://github.com/Frank-Wang-oss/SEA

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AAAI Conference 2022 Conference Paper

Generalizing Reinforcement Learning through Fusing Self-Supervised Learning into Intrinsic Motivation

Keyu Wu
Min Wu
Zhenghua Chen
Yuecong Xu
Xiaoli Li

Despite the great potential of reinforcement learning (RL) in solving complex decision-making problems, generalization remains one of its key challenges, leading to difﬁculty in deploying learned RL policies to new environments. In this paper, we propose to improve the generalization of RL algorithms through fusing Self-supervised learning into Intrinsic Motivation (SIM). Speciﬁcally, SIM boosts representation learning through driving the cross-correlation matrix between the embeddings of augmented and non-augmented samples close to the identity matrix. This aims to increase the similarity between the embedding vectors of a sample and its augmented version while minimizing the redundancy between the components of these vectors. Meanwhile, the redundancy reduction based self-supervised loss is converted to an intrinsic reward to further improve generalization in RL via an auxiliary objective. As a general paradigm, SIM can be implemented on top of any RL algorithm. Extensive evaluations have been performed on a diversity of tasks. Experimental results demonstrate that SIM consistently outperforms the state-of-the-art methods and exhibits superior generalization capability and sample efﬁciency.

IJCAI Conference 2022 Conference Paper

Type-aware Embeddings for Multi-Hop Reasoning over Knowledge Graphs

Zhiwei Hu
Victor Gutierrez Basulto
Zhiliang Xiang
Xiaoli Li
Ru Li
Jeff Z. Pan

Multi-hop reasoning over real-life knowledge graphs (KGs) is a highly challenging problem as traditional subgraph matching methods are not capable to deal with noise and missing information. Recently, to address this problem a promising approach based on jointly embedding logical queries and KGs into a low-dimensional space to identify answer entities has emerged. However, existing proposals ignore critical semantic knowledge inherently available in KGs, such as type information. To leverage type information, we propose a novel type-aware model, TypE-aware Message Passing (TEMP), which enhances the entity and relation representation in queries, and simultaneously improves generalization, and deductive and inductive reasoning. Remarkably, TEMP is a plug-and-play model that can be easily incorporated into existing embedding-based models to improve their performance. Extensive experiments on three real-world datasets demonstrate TEMP’s effectiveness.

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YNIMG Journal 2021 Journal Article

Age-dependent cross frequency coupling features from children to adults during general anesthesia

Zhenhu Liang
Na Ren
Xin Wen
Haiwen Li
Hang Guo
Yaqun Ma
Zheng Li
Xiaoli Li

BACKGROUND: The frequency coupling characteristics in electroencephalogram (EEG) induced by anesthetics have been well studied in adults, but the investigation of the age-dependent cross frequency coupling features from children to adults is still lacking. METHODS: We analyzed EEG signals recorded from pediatric to adult patients (n = 131), separated into six age groups: <1 year (n = 15), 1-3 years (n = 23), 3-6 years (n = 19), 6-12 years (n = 18), 12-18 years (n = 16), and 18-45 years (n = 40). Age related EEG power and cross frequency coupling analysis (phase amplitude coupling (PAC) and quadratic phase coupling) of data from maintenance of a surgical state of anesthesia (MOSSA) was conducted. Also, for patients of ages less than 6 years, we analyzed the performance of cross frequency coupling derived indices in distinguishing the states of wakefulness, MOSSA, and recovery of consciousness (ROC). RESULTS: (1) During MOSSA, EEG power substantially increased with age from infancy to 3-6 years then decreased with age in the theta-gamma frequency bands. The infant group (<1 year) had the highest slow oscillation (SO) power among all age groups. (2) The distinct PAC pattern is absent in patients less than 1 year of age both in SO-alpha and delta-alpha frequency band coupling during propofol induced unconsciousness. The modulation index between delta and alpha oscillations in MOSSA increased with age. (3) Wavelet bicoherence derived indices reach their peaks in the 3-6 years group and then decrease with age growth. (4) The Diag_En index (normalized entropy of the diagonal bicoherence entries of the bicoherence matrix) performed the best at distinguishing different states for ages less than 6 years (p<0.05). CONCLUSIONS: The combination of propofol induction and sevoflurane maintenance exhibited age-dependent EEG power spectra, PAC, and bicoherence, likely related to brain development. These observations suggest new rules for infant and child brain state monitoring during general anesthesia are needed.

JBHI Journal 2021 Journal Article

An Attention Based CNN-LSTM Approach for Sleep-Wake Detection With Heterogeneous Sensors

Zhenghua Chen
Min Wu
Wei Cui
Chengyu Liu
Xiaoli Li

In this article, we propose an attention based convolutional neural network long short-term memory (CNN-LSTM) approach for sleep-wake detection with heterogeneous sensor data, i. e. , acceleration and heart rate variability (HRV). Since the three-dimensional acceleration data was sampled with a high frequency, we firstly design a CNN-LSTM structure to effectively learn latent features from the acceleration. Meanwhile, considering the unique format of the HRV data, some effective features are extracted based on domain knowledge. Next, we design a unified architecture to efficiently merge the features learned by CNN-LSTM approach from the acceleration and the extracted features from the HRV, which enables us to make full use of all the available information from these two heterogeneous sources. Taking into consideration that these two heterogeneous sources may have distinct contributions for the sleep and wake states, we propose an attention network to dynamically adjust the importance of features from the two sources. Real-world experiments have been conducted to verify the effectiveness of the proposed approach for sleep-wake detection. The results demonstrate that the proposed method outperforms all existing approaches for sleep-wake classification. In the evaluation of leave-one-subject-out (LOSO) cross-validation which is more challenging and practical, the proposed method achieves remarkable improvements ranging from 5% to 46% over the benchmark approaches.

IJCAI Conference 2021 Conference Paper

Deep Reinforcement Learning Boosted Partial Domain Adaptation

Keyu Wu
Min Wu
Jianfei Yang
Zhenghua Chen
Zhengguo Li
Xiaoli Li

Domain adaptation is critical for learning transferable features that effectively reduce the distribution difference among domains. In the era of big data, the availability of large-scale labeled datasets motivates partial domain adaptation (PDA) which deals with adaptation from large source domains to small target domains with less number of classes. In the PDA setting, it is crucial to transfer relevant source samples and eliminate irrelevant ones to mitigate negative transfer. In this paper, we propose a deep reinforcement learning based source data selector for PDA, which is capable of eliminating less relevant source samples automatically to boost existing adaptation methods. It determines to either keep or discard the source instances based on their feature representations so that more effective knowledge transfer across domains can be achieved via filtering out irrelevant samples. As a general module, the proposed DRL-based data selector can be integrated into any existing domain adaptation or partial domain adaptation models. Extensive experiments on several benchmark datasets demonstrate the superiority of the proposed DRL-based data selector which leads to state-of-the-art performance for various PDA tasks.

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YNIMG Journal 2021 Journal Article

Functional mapping of language-related areas from natural, narrative speech during awake craniotomy surgery

Tianyi Zhou
Tao Yu
Zheng Li
Xiaoxia Zhou
Jianbin Wen
Xiaoli Li

Accurate localization of brain regions responsible for language and cognitive functions in epilepsy patients is important. Electrocorticography (ECoG)-based real-time functional mapping (RTFM) has been shown to be a safer alternative to electrical cortical stimulation mapping (ESM), which is currently the clinical/gold standard. Conventional methods for analyzing RTFM data mostly account for the ECoG signal in certain frequency bands, especially high gamma. Compared to ESM, they have limited accuracy when assessing channel responses. In the present study, we developed a novel RTFM method based on tensor component analysis (TCA) to address the limitations of current estimation methods. Our approach analyzes the whole frequency spectrum of the ECoG signal during natural continuous speech. We construct third-order tensors that contain multichannel time-frequency information and use TCA to extract low-dimensional temporal, spectral and spatial modes. Temporal modulation scores (correlation values) are then calculated between the time series of voice envelope features and TCA-estimated temporal courses, and significant temporal modulation determines which components' channel weightings are displayed to the neurosurgeon as a guide for follow-up ESM. In our experiments, data from thirteen patients with refractory epilepsy were recorded during preoperative evaluation for their epileptogenic zones (EZs), which were located adjacent to the eloquent cortex. Our results showed higher detection accuracy of our proposed method in a narrative speech task, suggesting that our method complements ESM and is an improvement over the prior RTFM method. To our knowledge, this is the first TCA-based method to pinpoint language-specific brain regions during continuous speech that uses whole-band ECoG.

JBHI Journal 2021 Journal Article

Prediction of Synthetic Lethal Interactions in Human Cancers Using Multi-View Graph Auto-Encoder

Zhifeng Hao
Di Wu
Yuan Fang
Min Wu
Ruichu Cai
Xiaoli Li

Synthetic lethality (SL) is a very important concept for the development of targeted anticancer drugs. However, experimental methods for SL detection often suffer from various issues like high cost and low consistency across cell lines. Hence, computational methods for predicting novel SLs have recently emerged as complements for wet-lab experiments. In addition, SL data can be represented as a graph where nodes are genes and edges are the SL interactions. It is thus motivated to design advanced graph-based machine learning algorithms for SL prediction. In this paper, we propose a novel SL prediction method using Multi-view Graph Auto-Encoder (SLMGAE). We consider the SL graph as the main view and the graphs from other data sources (e. g. , PPI, GO, etc.) as support views. Multiple Graph Auto-Encoders (GAEs) are implemented to reconstruct the graphs for different views. We further design an attention mechanism, which assigns different weights for support views, to combine all the reconstructed graphs for SL prediction. The overall SLMGAE model is then trained by minimizing both the reconstruction error and prediction error. Experimental results on the SynLethDB dataset show that SLMGAE outperforms state-of-the-arts. The case studies on novel predicted SLs also illustrate the effectiveness of our SLMGAE method.

YNIMG Journal 2021 Journal Article

Spontaneous transient brain states in EEG source space in disorders of consciousness

Yang Bai
Jianghong He
Xiaoyu Xia
Yong Wang
Yi Yang
Haibo Di
Xiaoli Li
Ulf Ziemann

Spontaneous transient states were recently identified by functional magnetic resonance imaging and magnetoencephalography in healthy subjects. They organize and coordinate neural activity in brain networks. How spontaneous transient states are altered in abnormal brain conditions is unknown. Here, we conducted a transient state analysis on resting-state electroencephalography (EEG) source space and developed a state transfer analysis to patients with disorders of consciousness (DOC). They uncovered different neural coordination patterns, including spatial power patterns, temporal dynamics, spectral shifts, and connectivity construction varies at potentially very fast (millisecond) time scales, in groups with different consciousness levels: healthy subjects, patients in minimally conscious state (MCS), and patients with vegetative state/unresponsive wakefulness syndrome (VS/UWS). Machine learning based on transient state features reveal high classification accuracy between MCS and VS/UWS. This study developed methodology of transient states analysis on EEG source space and abnormal brain conditions. Findings correlate spontaneous transient states with human consciousness and suggest potential roles of transient states in brain disease assessment.

IJCAI Conference 2021 Conference Paper

Time-Series Representation Learning via Temporal and Contextual Contrasting

Emadeldeen Eldele
Mohamed Ragab
Zhenghua Chen
Min Wu
Chee Keong Kwoh
Xiaoli Li
Cuntai Guan

Learning decent representations from unlabeled time-series data with temporal dynamics is a very challenging task. In this paper, we propose an unsupervised Time-Series representation learning framework via Temporal and Contextual Contrasting (TS-TCC), to learn time-series representation from unlabeled data. First, the raw time-series data are transformed into two different yet correlated views by using weak and strong augmentations. Second, we propose a novel temporal contrasting module to learn robust temporal representations by designing a tough cross-view prediction task. Last, to further learn discriminative representations, we propose a contextual contrasting module built upon the contexts from the temporal contrasting module. It attempts to maximize the similarity among different contexts of the same sample while minimizing similarity among contexts of different samples. Experiments have been carried out on three real-world time-series datasets. The results manifest that training a linear classifier on top of the features learned by our proposed TS-TCC performs comparably with the supervised training. Additionally, our proposed TS-TCC shows high efficiency in few-labeled data and transfer learning scenarios. The code is publicly available at https: //github. com/emadeldeen24/TS-TCC.

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YNICL Journal 2020 Journal Article

Transcranial direct current stimulation modulates brain functional connectivity in autism

Tianyi Zhou
Jiannan Kang
Zheng Li
He Chen
Xiaoli Li

Autism spectrum disorder (ASD) is characterized by deficits in social interactions, impairments in language and communication, and highly restricted behavioral interests. Transcranial direct current stimulation (tDCS) is a widely used form of noninvasive stimulation and may have therapeutic potential for ASD. So far, despite the widespread use of this technique in the neuroscience field, its effects on network-level neural activity and the underlying mechanisms of any effects are still unclear. In the present study, we used electroencephalography (EEG) to investigate tDCS induced brain network changes in children with ASD before and after active and sham stimulation. We recorded 5 min of resting state EEG before and after a single session of tDCS (of approximately 20 min) over dorsolateral prefrontal cortex (DLPFC). Two network-based methods were applied to investigate tDCS modulation on brain networks: 1) temporal network dynamics were analyzed by comparing "flexibility" changes before vs after stimulation, and 2) frequency specific network changes were identified using non-negative matrix factorization (NMF). We found 1) an increase in network flexibility following tDCS (rapid network configuration of dynamic network communities), 2) specific increase in interhemispheric connectivity within the alpha frequency band following tDCS. Together, these results demonstrate that tDCS could help modify both local and global brain network dynamics, and highlight stimulation-induced differences in the manifestation of network reconfiguration. Meanwhile, frequency-specific subnetworks, as a way to index local and global information processing, highlight the core modulatory effects of tDCS on the modular architecture of the functional connectivity patterns within higher frequency bands.

YNICL Journal 2017 Journal Article

TDCS modulates cortical excitability in patients with disorders of consciousness

Yang Bai
Xiaoyu Xia
Jiannan Kang
Yi Yang
Jianghong He
Xiaoli Li

Transcranial direct current stimulation (tDCS) has been reported to be a promising technique for consciousness improvement for patients with disorders of consciousness (DOC). However, there has been no direct electrophysiological evidence to demonstrate the efficacy of tDCS on patients with DOC. Therefore, we aim to measure the cortical excitability changes induced by tDCS in patients with DOC, to find electrophysiological evidence supporting the therapeutic efficacy of tDCS on patients with DOC. In this study, we enrolled sixteen patients with DOC, including nine vegetative state (VS) and seven minimally conscious state (MCS) (six females and ten males). TMS-EEG was applied to assess cortical excitability changes after twenty minutes of anodal tDCS of the left dorsolateral prefrontal cortex. Global cerebral excitability were calculated to quantify cortical excitability in the temporal domain: four time intervals (0-100, 100-200, 200-300, 300-400 ms). Then local cerebral excitability in the significantly altered time windows were investigated (frontal, left/right hemispheres, central, and posterior). Compared to baseline and sham stimulation, we found that global cerebral excitability increased in early time windows (0-100 and 100-200 ms) for patients with MCS; for the patients with VS, global cerebral excitability increased in the 0-100 ms interval but decreased in the 300-400 ms interval. The local cerebral excitability was significantly different between MCS and VS. The results indicated that tDCS can effectively modulate the cortical excitability of patients with DOC; and the changes in excitability in temporal and spatial domains are different between patients with MCS and those with VS.

IJCAI Conference 2016 Conference Paper

Exploring the Context of Locations for Personalized Location Recommendations

Xin Liu
Yong Liu
Xiaoli Li

Conventional location recommendation models rely on users' visit history, geographical influence, temporal influence, etc. , to infer users' preferences for locations. However, systematically modeling a location's context (i. e. , the set of locations visited before or after this location) is relatively unexplored. In this paper, by leveraging the Skip-gram model, we learn the latent representation for a location to capture the influence of its context. A pair-wise ranking loss that considers the confidences of observed user preferences for locations is then proposed to learn users' latent representations for personalized top-N location recommendations. Moreover, we also extend our model by taking into account temporal influence. Stochastic gradient descent based optimization algorithms are developed to fit the models. We conduct comprehensive experiments over four real datasets. Experimental results demonstrate that our approach significantly outperforms the state-of-the-art location recommendation methods.

YNIMG Journal 2014 Journal Article

Theta–gamma coupling reflects the interaction of bottom-up and top-down processes in speech perception in children

Juan Wang
Danqi Gao
Duan Li
Amy S. Desroches
Li Liu
Xiaoli Li

This study investigates how the interaction of different brain oscillations (particularly theta–gamma coupling) modulates the bottom-up and top-down processes during speech perception. We employed a speech perception paradigm that manipulated the congruency between a visually presented picture and an auditory stimulus and asked participants to judge whether they matched or mismatched. A group of children (mean age 10years, 5months) participated in this study and their electroencephalographic (EEG) data were recorded while performing the experimental task. It was found that in comparison with mismatch condition, match condition facilitated speech perception by eliciting greater theta–gamma coupling in the frontal area and smaller theta–gamma coupling in the left temporal area. These findings suggested that a top-down facilitation effect from congruent visual pictures engaged different mechanisms in low-level sensory (temporal) regions and high-level linguistic and decision (frontal) regions. Interestingly, hemispheric asymmetry is with higher theta–gamma coupling in the match condition in the right hemisphere and higher theta–gamma coupling in the mismatch condition in the left hemisphere. This indicates that a fast global processing strategy and a slow detailed processing strategy were differentially adopted in the match and mismatch conditions. This study provides new insight into the mechanisms of speech perception from the interaction of different oscillatory activities and provides neural evidence for theories of speech perception allowing for top-down feedback connections. Furthermore, it sheds light on children's speech perception development by showing a similar pattern of integration of bottom-up and top-down information during speech perception as previous studies have revealed in adults.

IJCAI Conference 2013 Conference Paper

What Users Care About: A Framework for Social Content Alignment

Lei Hou
Juanzi Li
Xiaoli Li
Jiangfeng Qu
Xiaofei Guo
Ou Hui
Jie Tang

With the rapid proliferation of social media, more and more people freely express their opinions (or comments) on news, products, and movies through online services such as forums, discussion groups, and microblogs. Those comments may be concerned with different aspects (topics) of the target Web document (e. g. , a news page). It would be interesting to align the social comments to the corresponding subtopics contained in the Web document. In this paper, we propose a novel framework that is able to automatically detect the subtopics from a given Web document, and also align the associated social comments with the detected subtopics. This provides a new view of the Web standard document and its associated user generated content through topics, which facilitates the readers to quickly focus on those hot topics or grasp topics that they are interested in. Extensive experiments show that our proposed framework signiﬁcantly outperforms the existing stateof-the-art methods in social content alignment.

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AAAI Conference 2011 Conference Paper

Integrating Community Question and Answer Archives

Wei Wei
Gao Cong
Xiaoli Li
See-Kiong Ng
Guohui Li

Question and answer pairs in Community Question Answering (CQA) services are organized into hierarchical structures or taxonomies to facilitate users to ﬁnd the answers for their questions conveniently. We observe that different CQA services have their own knowledge focus and used different taxonomies to organize their question and answer pairs in their archives. As there are no simple semantic mappings between the taxonomies of the CQA services, the integration of CQA services is a challenging task. The existing approaches on integrating taxonomies ignore the hierarchical structures of the source taxonomy. In this paper, we propose a novel approach that is capable of incorporating the parent-child and sibling information in the hierarchical structures of the source taxonomy for accurate taxonomy integration. Our experimental results with real world CQA data demonstrate that the proposed method signiﬁcantly outperforms state-of-the-art methods.

YNIMG Journal 2010 Journal Article

Estimating coupling direction between neuronal populations with permutation conditional mutual information

Xiaoli Li
Gaoxiang Ouyang

To further understand functional connectivity in the brain, we need to identify the coupling direction between neuronal signals recorded from different brain areas. In this paper, we present a novel methodology based on permutation analysis and conditional mutual information for estimation of a directionality index between two neuronal populations. First, the reliability of this method is numerically assessed with a coupled mass neural model; the simulations show that this method is superior to the conditional mutual information method and the Granger causality method for identifying the coupling direction between unidirectional or bidirectional neuronal populations that are generated by the mass neuronal model. The method is also applied to investigate the coupling direction between neuronal populations in CA1 and CA3 in the rat hippocampal tetanus toxin model of focal epilepsy; the propagation direction of the seizure events could be elucidated through this coupling direction estimation method. All together, these results suggest that the permutation conditional mutual information method is a promising technique for estimating directional coupling between mutually interconnected neuronal populations.

YNIMG Journal 2009 Journal Article

The comodulation measure of neuronal oscillations with general harmonic wavelet bicoherence and application to sleep analysis

Xiaoli Li
Duan Li
Logan J. Voss
Jamie W. Sleigh

Brain functions are related to neuronal networks of different sizes and distribution, and neuronal networks of different sizes oscillate at different frequencies. Thus the synchronization of neuronal networks is often reflected by cross-frequency interaction. The description of this cross-frequency interaction is therefore a crucial issue in understanding the modulation mechanisms between neuronal populations. A number of different kinds of interaction between frequencies have been reported. In this paper, we develop a general harmonic wavelet transform based bicoherence using a phase randomization method. This allows us to measure the comodulation of oscillations between different frequency bands in neuronal populations. The performance of the method is evaluated by a simulation study. The results show that the improved wavelet bicoherence method can detect a reliable phase coupling value, and also identify zero bicoherence for waves that are not phase-coupled. Spurious bicoherences can be effectively eliminated through the phase randomization method. Finally, this method is applied to electrocorticogram data recorded from rats during transitions between slow-wave sleep, rapid-eye movement sleep and waking. The phase coupling in rapid-eye movement sleep is statistically lower than that during slow-wave sleep, and slightly less than those in the wakeful state. The degree of phase coupling in rapid-eye movement sleep after slow-wave sleep is greater than in rapid-eye movement sleep prior to waking. This method could be applied to investigate the cross-frequency interactions in other physiological signals.

IJCAI Conference 2003 Conference Paper

Learning to Classify Texts Using Positive and Unlabeled Data

Xiaoli Li
Bing Liu

In traditional text classification, a classifier is built using labeled training documents of every class. This paper studies a different problem. Given a set P of documents of a particular class (called positive class) and a set U of unlabeled documents that contains documents from class P and also other types of documents (called negative class documents), we want to build a classifier to classify the documents in U into documents from P and documents not from P. The key feature of this problem is that there is no labeled negative document, which makes traditional text classification techniques inapplicable. In this paper, we propose an effective technique to solve the problem. It combines the Rocchio method and the SVM technique for classifier building. Experimental results show that the new method outperforms existing methods significantly.