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Weiping Li

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8 papers

2 author rows

EAAI Journal 2026 Journal Article

CGMAE: Self-supervised Masked Auto-Encoder with Cross-Graph node alignment for node classification

Ruoxian Song
Peng Cao
Guangqi Wen
Lanting Li
Wei Liang
Weiping Li
Jinzhu Yang
Osmar R. Zaiane

Masked Auto-Encoder (MAE) is widely adopted for node classification by recovering the randomly masked graph structure or node attributes. However, traditional MAE methods face two critical challenges: (1) features learned for reconstruction may not align with the downstream classification task, and (2) masking edges risks distorting inherent semantic relationships, degrading representation quality. To overcome these limitations, we propose a simple yet effective self-supervised M asked A uto- E ncoder with C ross- G raph node alignment (CGMAE) for node classification. It leverages labeled nodes from an auxiliary graph to enhance discriminative feature learning in an unlabeled target graph, bridging the task gap between reconstruction and classification. CGMAE introduces a node-level alignment mechanism to address distribution shifts across graphs. This design jointly learns structural patterns and node attributes through specific encoders, enabling multi-view feature matching to refine node representations. Furthermore, CGMAE innovatively predicts masked target edges using aligned nodes from the auxiliary graph, preserving the semantic relationships during reconstruction. Extensive experiments on six diverse networks (standard, complex, sparse, and large-scale graphs) verify the effectiveness and robustness of the proposed method in self-supervised/unsupervised node classification tasks, with accuracy improvements ranging from 1. 5%/1. 1% to 3. 5%/7. 0% over state-of-the-art methods. Code is available at https: //github. com/songruoxian/CGMAE.

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YNICL Journal 2020 Journal Article

Functional connectivity markers of depression in advanced Parkinson's disease

Hai Lin
Xiaodong Cai
Doudou Zhang
Jiali Liu
Peng Na
Weiping Li

BACKGROUND: Depression is a common comorbid condition in Parkinson's disease and a major contributor to poor quality of life. Despite this, depression in PD is under-diagnosed due to overlapping symptoms and difficulties in the assessment of depression in cognitively impaired old patients. OBJECTIVES: This study is to explore functional connectivity markers of depression in PD patients using resting-state fMRI and help diagnose whether patients have depression or not. METHODS: We reviewed 156 advanced PD patients (duration > 5 years; 59 depressed ones) and 45 healthy control subjects who underwent a resting-state fMRI scanning. Functional connectivity analysis was employed to characterize intrinsic connectivity networks using group independent component analysis and extract connectivity features. Features were put into an all-relevant feature selection procedure within cross-validation loops, to identify features with significant discriminative power for classification. Random forest classifiers were built for depression diagnosis, on the basis of identified features. RESULTS: 42 intrinsic connectivity networks were identified and arranged into subcortical, auditory, somatomotor, visual, cognitive control, default-mode and cerebellar networks. Six features were significantly relevant to classification. They were connectivity within posterior cingulate cortex, within insula, between posterior cingulate cortex and insula/hippocampus+amygdala, between insula and precuneus, and between superior parietal lobule and medial prefrontal cortex. The mean accuracy achieved with classifiers to discriminate depressed patients from the non-depressed was 82.4%. CONCLUSIONS: Our findings provide preliminary evidence that resting-state functional connectivity can characterize depressed PD patients and help distinguish them from non-depressed ones.

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AAAI Conference 2020 Conference Paper

Region Normalization for Image Inpainting

Tao Yu
Zongyu Guo
Xin Jin
Shilin Wu
Zhibo Chen
Weiping Li
Zhizheng Zhang
Sen Liu

Feature Normalization (FN) is an important technique to help neural network training, which typically normalizes features across spatial dimensions. Most previous image inpainting methods apply FN in their networks without considering the impact of the corrupted regions of the input image on normalization, e. g. mean and variance shifts. In this work, we show that the mean and variance shifts caused by full-spatial FN limit the image inpainting network training and we propose a spatial region-wise normalization named Region Normalization (RN) to overcome the limitation. RN divides spatial pixels into different regions according to the input mask, and computes the mean and variance in each region for normalization. We develop two kinds of RN for our image inpainting network: (1) Basic RN (RN-B), which normalizes pixels from the corrupted and uncorrupted regions separately based on the original inpainting mask to solve the mean and variance shift problem; (2) Learnable RN (RN-L), which automatically detects potentially corrupted and uncorrupted regions for separate normalization, and performs global afﬁne transformation to enhance their fusion. We apply RN-B in the early layers and RN-L in the latter layers of the network respectively. Experiments show that our method outperforms current state-ofthe-art methods quantitatively and qualitatively. We further generalize RN to other inpainting networks and achieve consistent performance improvements.

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YNICL Journal 2019 Journal Article

Characterization of white matter changes along fibers by automated fiber quantification in the early stages of Alzheimer's disease

Xin Zhang
Yu Sun
Weiping Li
Bing Liu
Wenbo Wu
Hui Zhao
Renyuan Liu
Yue Zhang

Brain white matter fiber bundles in patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD) have abnormalities not usually seen in unaffected subjects. Ideal algorithm of the localization-specific properties in white matter integrity might reveal the changes of tissue properties varying along each tract, while previous studies only detected the mean DTI parameters of each fiber. The aim of this study was to investigate whether these abnormalities of nerve fiber tracts are localized to specific regions of the tracts or spread throughout and to analyze which of the examined fiber tracts are involved in the early stages of Alzheimer's disease. In this study, we utilized VBA, TBSS as well as AFQ together to comprehensively investigate the white matter fiber impairment on 25 CE patients, 29 MCI patients and 34 normal control (NC) subjects. Two tract profiles, fractional anisotropy (FA) and mean diffusivity (MD), were extracted to evaluate the white matter integrity at 100 locations along each of 20 fiber tracts and then we validated the results with 27 CE patients, 21 MCI patients and 22 NC from the ADNI cohort. Also, we compare the AFQ with VBA and TBSS in our cohort. In comparison with NC, AD patients showed widespread FA reduction in 25% (5 /20) and MD increase in 65%(13/20) of the examined fiber tracts. The MCI patients showed a regional FA reduction in 5% (1/20) of the examined fiber tracts (right cingulum cingulate) and MD increase in 5%(1/20) of the examined fiber tracts (left arcuate fasciculus). Among these changed tracts, only the right cingulum cingulate showed widespread disruption of myelin or/and fiber axons in MCI and aggravated deterioration in AD, findings supported by FA/MD changes both by the mean and FA changes by point wise methods and TBSS. And the AFQ findings from ADNI cohort showed some similarity with our cohort, especially in the pointwise comparison of MD profiles between AD vs NC. Furthermore, the pattern of white matter abnormalities was different across neuronal fiber tracts; for example, the MCI and AD patients showed similar FA reduction in the middle part of the right cingulum cingulate, and the anterior part were not damaged. However, the left arcuate fasciculus showed MD elevation located at the temporal part of the fibers in the MCI patients and expanding to the temporal and middle part of the fibers in AD patients. So, the AFQ may be an alternative complementary method of VBA and TBSS, and may provide new insights into white matter degeneration in MCI and its association with AD.

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AAAI Conference 2018 Conference Paper

Video-Based Sign Language Recognition Without Temporal Segmentation

Jie Huang
Wengang Zhou
Qilin Zhang
Houqiang Li
Weiping Li

Millions of hearing impaired people around the world routinely use some variants of sign languages to communicate, thus the automatic translation of a sign language is meaningful and important. Currently, there are two sub-problems in Sign Language Recognition (SLR), i. e. , isolated SLR that recognizes word by word and continuous SLR that translates entire sentences. Existing continuous SLR methods typically utilize isolated SLRs as building blocks, with an extra layer of preprocessing (temporal segmentation) and another layer of post-processing (sentence synthesis). Unfortunately, temporal segmentation itself is non-trivial and inevitably propagates errors into subsequent steps. Worse still, isolated SLR methods typically require strenuous labeling of each word separately in a sentence, severely limiting the amount of attainable training data. To address these challenges, we propose a novel continuous sign recognition framework, the Hierarchical Attention Network with Latent Space (LS-HAN), which eliminates the preprocessing of temporal segmentation. The proposed LS-HAN consists of three components: a two-stream Convolutional Neural Network (CNN) for video feature representation generation, a Latent Space (LS) for semantic gap bridging, and a Hierarchical Attention Network (HAN) for latent space based recognition. Experiments are carried out on two large scale datasets. Experimental results demonstrate the effectiveness of the proposed framework.

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ICRA Conference 1988 Conference Paper

Indirect adaptive robot control

Weiping Li
Jean-Jacques E. Slotine

The theoretical issues linked to the development of indirect adaptive robot controllers are discussed, and some possible solutions are proposed. After a review of the prediction models used for robotic parameter estimation, a variety of parameter estimation methods are discussed under a common framework based on an exact solution approach. A novel indirect adaptive controller structure, which consists of a modified computed torque using parameters obtained from any of the estimators discussed, is presented. It is shown that a critical difficulty in using indirect adaptive control is the necessity to explicitly guarantee that the estimated inertia matrix remains positive definite in the course of adaptation, a requirement avoided by both the direct and the composite adaptive controllers. A practical solution to this difficulty is proposed. >

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ICRA Conference 1987 Conference Paper

Adaptive manipulator control a case study

Jean-Jacques E. Slotine
Weiping Li

Earlier work (Slotine and Li, 1986) exploits the particular structure of manipulator dynamics to develop a simple, globally convergent adaptive algorithm for trajectory control problems. The algorithm does not require measurements or estimates of the manipulator's joint accelerations, nor inversion of the estimated inertia matrix. This paper demonstrates the approach on a high-speed 2 d. o. f. semi-direct-drive robot. It shows that the manipulator mass properties, assumed to be initially unknown, can be precisely estimated within the first half second of a typical run. Similarly, the algorithm allows large loads of unknown mass properties to be precisely manipulated. Further, these experimental results demonstrate that the adaptive controller enjoys essentially the same level of robustness to unmodelled dynamics as a PD controller, yet achieves much better tracking accuracy than either PD or computed-torque schemes. Its superior performance for high speed operations, in the presence of parameter uncertainties, and its relative computational simplicity, make it a attractive option both to address complex industrial tasks, and to simplify high-level programming of more standard operations.

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ICRA Conference 1987 Conference Paper

Adaptive strategies in constrained manipulation

Jean-Jacques E. Slotine
Weiping Li

Earlier work (Slotine and Li, 1986) demonstrates that using state feedback to directly modify a manipulator's energy function, rather than its fully expanded dynamics, represents a powerful approach to robot control, and, in particular, yields a simple globally convergent adaptive algorithm for trajectory control problems. An important practical feature of the algorithm is that is does not require measurements or estimates of the manipulator's joint accelerations. This paper rewrites the approach in term of end-effector dynamics, extends it to hybrid motion/force control, and discusses adaptive strategies involving mobile environments, such as the external motion control of an unknown passive mechanism.

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