Qing Wang

AAAI Conference 2026 Conference Paper

Adversarial Attack on Black-Box Multi-Agent by Adaptive Perturbation

• Jianming Chen
• Yawen Wang
• Junjie Wang
• Xiaofei Xie
• Yuanzhe Hu
• Qing Wang
• Fanjiang Xu

Evaluating security and reliability for multi-agent systems (MAS) is urgent as they become increasingly prevalent in various applications. As an evaluation technique, existing adversarial attack frameworks face certain limitations, e.g., impracticality due to the requirement of white-box information or high control authority, and a lack of stealthiness or effectiveness as they often target all agents or specific fixed agents. To address these issues, we propose AdapAM, a novel framework for adversarial attacks on black-box MAS. AdapAM incorporates two key components: (1) Adaptive Selection Policy simultaneously selects the victim and determines the anticipated malicious action (the action would lead to the worst impact on MAS), balancing effectiveness and stealthiness. (2) Proxy-based Perturbation to Induce Malicious Action utilizes generative adversarial imitation learning to approximate the target MAS, allowing AdapAM to generate perturbed observations using white-box information and thus induce victims to execute malicious action in black-box settings. We evaluate AdapAM across eight multi-agent environments and compare it with four state-of-the-art and commonly-used baselines. Results demonstrate that AdapAM achieves the best attack performance in different perturbation rates. Besides, AdapAM-generated perturbations are the least noisy and hardest to detect, emphasizing the stealthiness.

AAAI Conference 2026 Conference Paper

Enhancing DPSGD via Per-Sample Momentum and Low-Pass Filtering

• Xincheng Xu
• Thilina Ranbaduge
• Qing Wang
• Thierry Rakotoarivelo
• David Smith

Differentially Private Stochastic Gradient Descent (DPSGD) is widely used to train deep neural networks with formal privacy guarantees. However, the addition of differential privacy (DP) often degrades model accuracy by introducing both noise and bias. Existing techniques typically address only one of these issues, as reducing DP noise can exacerbate clipping bias and vice-versa. In this paper, we propose a novel method, DP-PMLF, which integrates per-sample momentum with a low-pass filtering strategy to simultaneously mitigate DP noise and clipping bias. Our approach uses per-sample momentum to smooth gradient estimates prior to clipping, thereby reducing sampling variance. It further employs a post-processing low-pass filter to attenuate high-frequency DP noise without consuming additional privacy budget. We provide a theoretical analysis demonstrating an improved convergence rate under rigorous DP guarantees, and our empirical evaluations reveal that DP-PMLF significantly enhances the privacy-utility trade-off compared to several state-of-the-art DPSGD variants.

AAAI Conference 2026 Conference Paper

Joint-GCG: Unified Gradient-Based Poisoning Attacks on Retrieval-Augmented Generation Systems

• Haowei Wang
• Rupeng Zhang
• Junjie Wang
• Mingyang Li
• Yuekai Huang
• Dandan Wang
• Qing Wang

Retrieval-Augmented Generation (RAG) systems enhance Large Language Models (LLMs) by retrieving relevant documents from external corpora before generating responses. This approach significantly expands LLM capabilities by leveraging vast, up-to-date external knowledge. However, this reliance on external knowledge makes RAG systems vulnerable to corpus poisoning attacks that manipulate generated outputs via poisoned document injection. Existing poisoning attack strategies typically treat the retrieval and generation stages as disjointed, limiting their effectiveness. We propose Joint-GCG, the first framework to unify gradient-based attacks across both retriever and generator models through three innovations: (1) Cross-Vocabulary Projection for aligning embedding spaces, (2) Gradient Tokenization Alignment for synchronizing token-level gradient signals, and (3) Adaptive Weighted Fusion for dynamically balancing attacking objectives. Evaluations demonstrate that Joint-GCG achieves at most 25% and an average of 5% higher attack success rate than previous methods across multiple retrievers and generators. While optimized under a white-box assumption, the generated poisons show unprecedented transferability to unseen models. Joint-GCG's innovative unification of gradient-based attacks across retrieval and generation stages fundamentally reshapes our understanding of vulnerabilities within RAG systems.

AAAI Conference 2026 Conference Paper

Many Minds, One Path: LLM-Augmented Consensus Decision for Distributed Control in Multi-Agent Collaborative Stable Scenarios

• Zhuohao Yu
• Zhe Liu
• Tao Ren
• Chenxue Wang
• Junjie Wang
• Qing Wang

Distributed multi-agent systems are increasingly deployed in dynamic and high-stakes environments such as power grids, intelligent traffic systems, and collaborative robotics. In these systems, long-term stability, the ability to maintain coherent and safe system behavior over time, is critical but underexplored in existing research. This paper presents LLMASC, a framework designed to enhance long-term stability in multi-agent collaboration by combining semantic reasoning with decentralized control. LLMASC comprises three key components: a Semantic Perception Encoder that transforms heterogeneous agent observations into structured natural language; an LLM-Guided Consensus Decision module that enables strategic alignment through proposal exchange and voting; and a Policy Execution Controller that maps high-level plans to executable actions via reinforcement learning. We evaluate LLMASC across three representative simulation domains (Multi-Walker, Simulation of Urban Mobility and Power Grid Stabilization), spanning both physical and cyber-physical systems. Experiments show that LLMASC consistently outperforms the best baselines, improving stability rates by up to 44% and long-term success by 31%. Further analysis confirms its decision-making efficiency and robustness under varying agent populations and model choices.

AAAI Conference 2026 Conference Paper

SMoFi: Step-wise Momentum Fusion for Split Federated Learning on Heterogeneous Data

• Mingkun Yang
• Ran Zhu
• Qing Wang
• Jie Yang

Split Federated Learning is a system-efficient federated learning paradigm that leverages the rich computing resources at a central server to train model partitions. Data heterogeneity across silos, however, presents a major challenge undermining the convergence speed and accuracy of the global model. This paper introduces Step-wise Momentum Fusion (SMoFi), an effective and lightweight framework that counteracts gradient divergence arising from data heterogeneity by synchronizing the momentum buffers across server-side optimizers. To control gradient divergence over the training process, we design a staleness-aware alignment mechanism that imposes constraints on gradient updates of the server-side submodel at each optimization step. Extensive validations on multiple real-world datasets show that SMoFi consistently improves global model accuracy (up to 7.1%) and convergence speed (up to 10.25x). Furthermore, SMoFi has a greater impact with more clients involved and deeper learning models, making it particularly suitable for model training in resource-constrained contexts.

EAAI Journal 2025 Journal Article

A multi-teacher knowledge distillation-based framework for long-term respiratory monitoring and prediction with a novel flexible wearable sensor in healthcare engineering

• Ke Li
• Qing Wang
• Haoke Liu
• Mingke Wang
• Suiyuan Zhu
• Xiang Wang
• Jing Qin

Respiratory monitoring plays a critical role in early health warnings and preventive care, offering significant potential for advancements in healthcare engineering. Wearable respiratory monitoring devices, known for their compact design, portability, and real-time capabilities, face challenges such as limited long-term comfort, environmental interference, and signal inaccuracies. In this study, we propose a novel wearable respiratory monitoring framework, RAMP, which integrates an innovative artificial muscle-based flexible sensor system with advanced deep learning modules. The system is designed to reconstruct and analyze respiratory data across various human activities and predict long-term respiratory function. Utilizing a multi-teacher knowledge distillation mechanism, the framework optimizes a student model for enhanced prediction accuracy. Experimental results demonstrate the mean absolute percentage error (MAPE) of 7. 28 and mean absolute error (MAE) of 11. 92, highlighting the feasibility and effectiveness of system. This work advances the development of portable health monitoring devices and provides a robust foundation for long-term respiratory activity assessment and forecasting, contributing to the broader field of healthcare engineering and personalized medicine.

AAAI Conference 2025 Conference Paper

Asymmetric Learning for Spectral Graph Neural Networks

• Fangbing Liu
• Qing Wang

Optimizing spectral graph neural networks (GNNs) remains a critical challenge in the field, yet the underlying processes are not well understood. In this paper, we investigate the inherent differences between graph convolution parameters and feature transformation parameters in spectral GNNs and their impact on the optimization landscape. Our analysis reveals that these differences contribute to a poorly conditioned problem, resulting in suboptimal performance. To address this issue, we introduce the concept of the block condition number of the Hessian matrix, which characterizes the difficulty of poorly conditioned problems in spectral GNN optimization. We then propose an asymmetric learning approach, dynamically preconditioning gradients during training to alleviate poorly conditioned problems. Theoretically, we demonstrate that asymmetric learning can reduce block condition numbers, facilitating easier optimization. Extensive experiments on eighteen benchmark datasets show that asymmetric learning consistently improves the performance of spectral GNNs for both heterophilic and homophilic graphs. This improvement is especially notable for heterophilic graphs, where the optimization process is generally more complex than for homophilic graphs.

AAAI Conference 2025 Conference Paper

Bright-NeRF: Brightening Neural Radiance Field with Color Restoration from Low-Light RAW Images

• Min Wang
• Xin Huang
• Guoqing Zhou
• Qifeng Guo
• Qing Wang

Neural Radiance Fields (NeRF) have demonstrated prominent performance in novel view synthesis tasks. However, their input heavily relies on image acquisition under normal light conditions, making it challenging to learn accurate scene contents in low-light environments where images typically exhibit significant noise and severe color distortion. To address these challenges, we propose a novel approach, Bright-NeRF, which learns enhanced and high-quality radiance fields from multi-view low-light RAW images in an unsupervised manner. Our method simultaneously achieves color restoration, denoising, and enhanced novel view synthesis. Specifically, we leverage a physically-inspired model of the sensor's response to illumination and introduce a chromatic adaptation loss to constrain the learning of response, enabling consistent color perception of objects regardless of lighting conditions. We further utilize the RAW data's properties to expose the scene's intensity automatically. Additionally, we have collected a multi-view low-light RAW image dataset of real-world scenes to advance research in this field. Experimental results demonstrate that our proposed method significantly outperforms existing 2D and 3D approaches. Our code and dataset will be made publicly available.

AAAI Conference 2025 Conference Paper

DeepSN: A Sheaf Neural Framework for Influence Maximization

• Asela Hevapathige
• Qing Wang
• Ahad N. Zehmakan

Influence maximization is a key topic in data mining, with broad applications in social network analysis and viral marketing. In recent years, researchers have increasingly turned to machine learning techniques to address this problem. By learning the underlying diffusion processes from data, these methods improve the generalizability of solutions while optimizing objectives to identify the optimal seed set for maximizing influence. Nonetheless, two fundamental challenges remain unresolved: (1) While Graph Neural Networks (GNNs) are increasingly employed to learn diffusion models, their traditional architectures often fail to capture the complex dynamics of influence diffusion, (2) Designing optimization objectives is inherently difficult due to the combinatorial explosion associated with solving this problem. To address these challenges, we propose a novel framework, DeepSN. Our framework employs sheaf neural diffusion to learn diverse influence patterns in a data-driven, end-to-end manner, providing enhanced separability in capturing diffusion characteristics. We also propose an optimization technique that accounts for overlapping influence between vertices, significantly reducing the search space and facilitating the identification of the optimal seed set efficiently. Finally, we conduct extensive experiments on both synthetic and real-world datasets to demonstrate the effectiveness of our framework.

ICLR Conference 2025 Conference Paper

Diverse Policies Recovering via Pointwise Mutual Information Weighted Imitation Learning

• Hanlin Yang
• Jian Yao 0008
• Weiming Liu 0004
• Qing Wang
• Hanmin Qin
• Hansheng Kong
• Kirk Tang
• Jiechao Xiong

Recovering a spectrum of diverse policies from a set of expert trajectories is an important research topic in imitation learning. After determining a latent style for a trajectory, previous diverse polices recovering methods usually employ a vanilla behavioral cloning learning objective conditioned on the latent style, treating each state-action pair in the trajectory with equal importance. Based on an observation that in many scenarios, behavioral styles are often highly relevant with only a subset of state-action pairs, this paper presents a new principled method in diverse polices recovering. In particular, after inferring or assigning a latent style for a trajectory, we enhance the vanilla behavioral cloning by incorporating a weighting mechanism based on pointwise mutual information. This additional weighting reflects the significance of each state-action pair's contribution to learning the style, thus allowing our method to focus on state-action pairs most representative of that style. We provide theoretical justifications for our new objective, and extensive empirical evaluations confirm the effectiveness of our method in recovering diverse polices from expert data.

NeurIPS Conference 2025 Conference Paper

Flick: Empowering Federated Learning with Commonsense Knowledge

• Ran Zhu
• Mingkun Yang
• Shiqiang Wang
• Jie Yang
• Qing Wang

Federated Learning (FL) has emerged as a privacy-preserving framework for training models on data generated at the edge. However, the heterogeneity of data silos (e. g. , label skew and domain shift) often leads to inconsistent learning objectives and suboptimal model performance. Inspired by the data-driven approach, we propose Flick, a novel data generation framework for heterogeneous **F**ederated **L**earning w**i**th **C**ommonsense **K**nowledge from Large Language Models (LLMs). In Flick, the client performs the local data summary to capture client-specific knowledge in textual form. The central server then distills task-relevant, high-quality knowledge from the out-of-the-box LLM -- guided by cross-client-specific insights -- to generate informative text prompts. These prompts direct a generative model in producing synthetic data, enabling global model fine-tuning and local data compensation. This process gradually aligns the label and feature distributions across clients. Extensive results on three datasets demonstrate that Flick improves the global model accuracy by up to 11. 43\%, and accelerates convergence by up to 12. 9$\times$, validating its effectiveness in addressing data heterogeneity.

EAAI Journal 2025 Journal Article

Learning motion-guided salience features for weakly supervised group activity recognition

• Zexing Du
• Qing Wang

This paper focuses on exploring motion-guided features for weakly supervised group activity recognition (GAR). Unlike existing GAR methods that simply squeeze extracted tokens or individual features into a single vector by global pooling, limiting their ability to sufficiently represent spatial and temporal salience features in videos, we propose a Motion-Guided Network (MGN) to capture crucial motion contextual information in videos. First, we embed local correlations between the feature maps of adjacent frames to extract motion features in activities. Then, unlike previous works that simply aggregate motion and appearance features by addition or concatenation, MGN uses motion representations to guide the extraction of temporal and spatial features. We have evaluated the proposed method on sports and group activity videos. Extensive experimental results verify the effectiveness of our method. Furthermore, our method has also outperformed some approaches trained with stronger supervision in the comparative evaluation.

ICLR Conference 2025 Conference Paper

Learning Partial Graph Matching via Optimal Partial Transport

• Gathika Ratnayaka
• James Nichols
• Qing Wang

Partial graph matching extends traditional graph matching by allowing some nodes to remain unmatched, enabling applications in more complex scenarios. However, this flexibility introduces additional complexity, as both the subset of nodes to match and the optimal mapping must be determined. While recent studies have explored deep learning techniques for partial graph matching, a significant limitation remains: the absence of an optimization objective that fully captures the problem’s intrinsic nature while enabling efficient solutions. In this paper, we propose a novel optimization framework for partial graph matching, inspired by optimal partial transport. Our approach formulates an objective that enables partial assignments while incorporating matching biases, using weighted total variation as the divergence function to guarantee optimal partial assignments. Our method can achieve efficient, exact solutions within cubic worst case time complexity. Our contributions are threefold: (i) we introduce a novel optimization objective that balances matched and unmatched nodes; (ii) we establish a connection between partial graph matching and linear sum assignment problem, enabling efficient solutions; (iii) we propose a deep graph matching architecture with a novel partial matching loss, providing an end-to-end solution. The empirical evaluations on standard graph matching benchmarks demonstrate the efficacy of the proposed approach.

JBHI Journal 2025 Journal Article

Personalized Continuous Blood Pressure Tracking Through Single Channel PPG in Wearable Scenarios

• Yiming Zhang
• Congcong Zhou
• Xianglin Ren
• Qing Wang
• Hongwei Wang
• Ting Xiang
• Shirong Qiu
• Yuan-Ting Zhang

The real-time tracking of human physiopathology states can significantly enhance the quality of personalized healthcare services. Photoplethysmography (PPG) detection is a rapid, portable and non-invasive method for measuring blood flow volume, widely used for monitoring blood pressure (BP) and cardiovascular status. However, continuous BP monitoring technologies based on PPG face numerous challenges in real-world wearable scenarios, such as poor signal quality, complex model computation, and the need for frequent calibration. This work proposed a personalized continuous BP tracking pipeline that performed automatic PPG signal quality grading to reduce the difficulty of model fitting, introduced a lightweight BP model (SCI-GTCN) to alleviate computational complexity, and employed an adaptive calibration strategy to achieve long-term BP monitoring performance under different scenarios. The proposed pipeline was validated using data from 134 subjects in various monitoring scenarios (daytime, nighttime, and abnormal states), assessing the model's performance during rapid BP changes, circadian rhythm fluctuations, and long-term monitoring. The ME ± SD was 0. 99 ± 7. 91/0. 36 ± 5. 43 mmHg. Overall, the results of our method are within the accuracy requirements of the Association for the Advancement of Medical Instrumentation (AAMI) standards, though the subject distribution differs. The method demonstrated good robustness and applicability, making it convenient for deployment on wearable devices and promising in the healthcare field.

AAAI Conference 2025 Conference Paper

Understanding Individual Agent Importance in Multi-Agent System via Counterfactual Reasoning

• Jianming Chen
• Yawen Wang
• Junjie Wang
• Xiaofei Xie
• Jun Hu
• Qing Wang
• Fanjiang Xu

Explaining multi-agent systems (MAS) is urgent as these systems become increasingly prevalent in various applications. Previous work has provided explanations for the actions or states of agents, yet falls short in understanding the blackboxed agent’s importance within a MAS and the overall team strategy. To bridge this gap, we propose EMAI, a novel agent-level explanation approach that evaluates the individual agent’s importance. Inspired by counterfactual reasoning, a larger change in reward caused by the randomized action of agent indicates its higher importance. We model it as a MARL problem to capture interactions across agents. Utilizing counterfactual reasoning, EMAI learns the masking agents to identify important agents. Specifically, we define the optimization function to minimize the reward difference before and after action randomization and introduce sparsity constraints to encourage the exploration of more action randomization of agents during training. The experimental results in seven multi-agent tasks demonstrate that EMAI achieves higher fidelity in explanations compared to baselines and provides more effective guidance in practical applications concerning understanding policies, launching attacks, and patching policies.

AAAI Conference 2024 Conference Paper

How to Evaluate the Generalization of Detection? A Benchmark for Comprehensive Open-Vocabulary Detection

• Yiyang Yao
• Peng Liu
• Tiancheng Zhao
• Qianqian Zhang
• Jiajia Liao
• Chunxin Fang
• Kyusong Lee
• Qing Wang

Object detection (OD) in computer vision has made significant progress in recent years, transitioning from closed-set labels to open-vocabulary detection (OVD) based on large-scale vision-language pre-training (VLP). However, current evaluation methods and datasets are limited to testing generalization over object types and referral expressions, which do not provide a systematic, fine-grained, and accurate benchmark of OVD models' abilities. In this paper, we propose a new benchmark named OVDEval, which includes 9 sub-tasks and introduces evaluations on commonsense knowledge, attribute understanding, position understanding, object relation comprehension, and more. The dataset is meticulously created to provide hard negatives that challenge models' true understanding of visual and linguistic input. Additionally, we identify a problem with the popular Average Precision (AP) metric when benchmarking models on these fine-grained label datasets and propose a new metric called Non-Maximum Suppression Average Precision (NMS-AP) to address this issue. Extensive experimental results show that existing top OVD models all fail on the new tasks except for simple object types, demonstrating the value of the proposed dataset in pinpointing the weakness of current OVD models and guiding future research. Furthermore, the proposed NMS-AP metric is verified by experiments to provide a much more truthful evaluation of OVD models, whereas traditional AP metrics yield deceptive results. Data is available at https://github.com/om-ai-lab/OVDEval

YNICL Journal 2024 Journal Article

Predicting cognitive decline: Which is more useful, baseline amyloid levels or longitudinal change?

• Gengsheng Chen
• Nicole S. McKay
• Brian A. Gordon
• Jingxia Liu
• Nelly Joseph-Mathurin
• Suzanne E. Schindler
• Jason Hassenstab
• Andrew J. Aschenbrenner

C-Pittsburgh compound B (PiB) Aβ-PET to predict cognitive decline. A cohort of 153 participants who previously underwent PiB-PET scans and comprehensive clinical assessments were used in this study. Our analyses revealed that baseline Aβ is significantly associated with the rate of change in cognitive composite scores, with cognition declining more rapidly when baseline PiB Aβ levels were higher. In contrast, no signification association was identified between the rate of change in PiB-PET Aβ and cognitive decline. Additionally, the ability of the rate of change in the PiB-PET measures to predict cognitive decline was significantly influenced by APOE ε4 carrier status. These results suggest that a single PiB-PET scan is sufficient to predict cognitive decline and that longitudinal measures of Aβ accumulation do not improve the prediction of cognitive decline once someone is amyloid positive.

JBHI Journal 2024 Journal Article

SFWN: A Novel Semi-Supervised Feature Weighted Neural Network for Gene Data Feature Learning and Mining With Graph Modeling

• Qing Wang
• Xinghong Chen
• Weiping Liu
• Guannan Chen
• Jing Qin

Gene expression data can serve for analyzing the genes with changed expressions, the correlation between genes and the influence of different circumstance on gene activities. However, labeling a large number of gene expression data is laborious and time-consuming. The insufficient labeled data pose a challenge to construct the deep learning model. Currently, some graph neural networks (GNN) based on semi-supervised learning mechanism only focus on the feature space and sample space of gene expression data, possibly affecting the accuracy. This article puts forward a novel semi-supervised graph neural network model (SFWN). Firstly, we use the external knowledge of gene expression data for constructing a feature graph, a similarity kernel, and a sample graph for the first time. Later, a novel semi-supervised learning algorithm (SGA) is proposed to extract the data relationship and obtain the global sample structure better. A graph sparse module (SGCN) is also proposed to process sparse representation with gene expression data classification. To overcome the over smoothing problem, a new feature calculation method based on two spaces is proposed to feature representation analysis and calculation in this model. According to a lot of experiments and ablation studies conducted on several public datasets, SFWN exhibits a better effect and is superior to the state-of-the-art approaches (the accuracy and F1-Score are 0. 9993 and 0. 9899, respectively). Experimental results showed that the proposed SFWN model has strong gene expression feature learning and representation ability, and may provide a new insight and tool for relevant disease diagnosis and clinic practice.

NeurIPS Conference 2024 Conference Paper

SRFUND: A Multi-Granularity Hierarchical Structure Reconstruction Benchmark in Form Understanding

• Jiefeng Ma
• Yan Wang
• Chenyu Liu
• Jun Du
• Yu Hu
• Zhenrong Zhang
• Pengfei Hu
• Qing Wang

Accurately identifying and organizing textual content is crucial for the automation of document processing in the field of form understanding. Existing datasets, such as FUNSD and XFUND, support entity classification and relationship prediction tasks but are typically limited to local and entity-level annotations. This limitation overlooks the hierarchically structured representation of documents, constraining comprehensive understanding of complex forms. To address this issue, we present the SRFUND, a hierarchically structured multi-task form understanding benchmark. SRFUND provides refined annotations on top of the original FUNSD and XFUND datasets, encompassing five tasks: (1) word to text-line merging, (2) text-line to entity merging, (3) entity category classification, (4) item table localization, and (5) entity-based full-document hierarchical structure recovery. We meticulously supplemented the original dataset with missing annotations at various levels of granularity and added detailed annotations for multi-item table regions within the forms. Additionally, we introduce global hierarchical structure dependencies for entity relation prediction tasks, surpassing traditional local key-value associations. The SRFUND dataset includes eight languages including English, Chinese, Japanese, German, French, Spanish, Italian, and Portuguese, making it a powerful tool for cross-lingual form understanding. Extensive experimental results demonstrate that the SRFUND dataset presents new challenges and significant opportunities in handling diverse layouts and global hierarchical structures of forms, thus providing deep insights into the field of form understanding. The original dataset and implementations of baseline methods are available at https: //sprateam-ustc. github. io/SRFUND.

EAAI Journal 2023 Journal Article

Attention-guided and fine-grained feature extraction from face images for gaze estimation

• Chenglin Wu
• Huanqiang Hu
• Kean Lin
• Qing Wang
• Tianjian Liu
• Guannan Chen

Research on appearance gaze estimation based on deep learning has achieved considerable results. However, a lack of information in the extracted local blocks reduces the precision of gaze estimation since CNNs do not prioritize the information within the key picture blocks for face image estimation. In this research, fine-grained visual information is extracted from local blocks using a Transformer in Transformer (TNT) model that emphasizes the interactions between local blocks. First, TNT-based gaze estimation models (GTiT-Pure and GTiT-Hybrid) are established with the TNT model to capture both coarse- and fine-grained visual information about the face, which are then combined to provide a feature representation for gaze regression. Then, the performance of the two models are evaluated on four gaze estimation datasets. Experimental results demonstrate that the pre-trained GTiT-Pure has less gaze estimation error than the majority of CNNs and Transformer models, and that the GTiT-Hybrid model performs the best on the EyeDiap and RT-Gene datasets. The GTiT model which combines both coarse- and fine-grained gaze features in face images, can be used to further explore the advantages of the Transformer model in gaze feature extraction.

NeurIPS Conference 2023 Conference Paper

Neural Ideal Large Eddy Simulation: Modeling Turbulence with Neural Stochastic Differential Equations

• Anudhyan Boral
• Zhong Yi Wan
• Leonardo Zepeda-Núñez
• James Lottes
• Qing Wang
• Yi-Fan Chen
• John Anderson
• Fei Sha

We introduce a data-driven learning framework that assimilates two powerful ideas: ideal large eddy simulation (LES) from turbulence closure modeling and neural stochastic differential equations (SDE) for stochastic modeling. The ideal LES models the LES flow by treating each full-order trajectory as a random realization of the underlying dynamics, as such, the effect of small-scales is marginalized to obtain the deterministic evolution of the LES state. However, ideal LES is analytically intractable. In our work, we use a latent neural SDE to model the evolution of the stochastic process and an encoder-decoder pair for transforming between the latent space and the desired ideal flow field. This stands in sharp contrast to other types of neural parameterization of closure models where each trajectory is treated as a deterministic realization of the dynamics. We show the effectiveness of our approach (niLES – neural ideal LES) on two challenging chaotic dynamical systems: Kolmogorov flow at a Reynolds number of 20, 000 and flow past a cylinder at Reynolds number 500. Compared to competing methods, our method can handle non-uniform geometries using unstructured meshes seamlessly. In particular, niLES leads to trajectories with more accurate statistics and enhances stability, particularly for long-horizon rollouts. (Source codes and datasets will be made publicly available. )

YNICL Journal 2023 Journal Article

Neuroinflammation and amyloid deposition in the progression of mixed Alzheimer and vascular dementia

• Chunwei Ying
• Peter Kang
• Michael M. Binkley
• Andria L. Ford
• Yasheng Chen
• Jason Hassenstab
• Qing Wang
• Jeremy Strain

BACKGROUND: Alzheimer's disease (AD) and vascular contributions to cognitive impairment and dementia (VCID) pathologies coexist in patients with cognitive impairment. Abnormal amyloid beta (Aβ) deposition is the hallmark pathologic biomarker for AD. Neuroinflammation may be a pathophysiological mechanism in both AD and VCID. In this study, we aimed to understand the role of neuroinflammation and Aβ deposition in white matter hyperintensities (WMH) progression and cognitive decline over a decade in patients with mixed AD and VCID pathologies. METHODS: C-PiB MCBP) and baseline WMH volume and cognitive function. Moreover, linear mixed-effects models evaluated whether PET biomarkers predicted greater WMH progression or cognitive decline over a decade. RESULTS: C-PiB MCBP. CONCLUSIONS: Neuroinflammation and Aβ deposition may represent two distinct pathophysiological pathways, and both independently contributed to the progression of cognitive impairment in mixed AD and VCID pathologies. Neuroinflammation, but not Aβ deposition, contributed to WMH volume and progression.

AAAI Conference 2023 Conference Paper

Restructuring Graph for Higher Homophily via Adaptive Spectral Clustering

• Shouheng Li
• Dongwoo Kim
• Qing Wang

While a growing body of literature has been studying new Graph Neural Networks (GNNs) that work on both homophilic and heterophilic graphs, little has been done on adapting classical GNNs to less-homophilic graphs. Although the ability to handle less-homophilic graphs is restricted, classical GNNs still stand out in several nice properties such as efficiency, simplicity, and explainability. In this work, we propose a novel graph restructuring method that can be integrated into any type of GNNs, including classical GNNs, to leverage the benefits of existing GNNs while alleviating their limitations. Our contribution is threefold: a) learning the weight of pseudo-eigenvectors for an adaptive spectral clustering that aligns well with known node labels, b) proposing a new density-aware homophilic metric that is robust to label imbalance, and c) reconstructing the adjacency matrix based on the result of adaptive spectral clustering to maximize the homophilic scores. The experimental results show that our graph restructuring method can significantly boost the performance of six classical GNNs by an average of 25% on less-homophilic graphs. The boosted performance is comparable to state-of-the-art methods.

JBHI Journal 2023 Journal Article

SDPN: A Slight Dual-Path Network With Local-Global Attention Guided for Medical Image Segmentation

• Jing Wang
• Shuyi Li
• Luyue Yu
• Aixi Qu
• Qing Wang
• Ju Liu
• Qiang Wu

Accurate identification of lesions is a key step in surgical planning. However, this task mainly exists two challenges: 1) Due to the complex anatomical shapes of different lesions, most segmentation methods only achieve outstanding performance for a specific structure, rather than other lesions with location differences. 2) The huge number of parameters limits existing transformer-based segmentation models. To overcome these problems, we propose a novel slight dual-path network (SDPN) to segment variable location lesions or organs with significant differences accurately. First, we design a dual-path module to integrate local with global features without obvious memory consumption. Second, a novel Multi-spectrum attention module is proposed to pay further attention to detailed information, which can automatically adapt to the variable segmentation target. Then, the compression module based on tensor ring decomposition is designed to compress convolutional and transformer structures. In the experiment, four datasets, including three benchmark datasets and a clinical dataset, are used to evaluate SDPN. Results of the experiments show that SDPN performs better than other start-of-the-art methods for brain tumor, liver tumor, endometrial tumor and cardiac segmentation. To ensure the generalizability, we train the network on Kvasir-SEG and test on CVC-ClinicDB which collected from a different institution. The quantitative analysis shows that the clinical evaluation results are consistent with the experts. Therefore, this model may be a potential candidate for the segmentation of lesions and organs segmentation with variable locations in clinical applications.

IJCAI Conference 2022 Conference Paper

MuiDial: Improving Dialogue Disentanglement with Intent-Based Mutual Learning

• Ziyou Jiang
• Lin Shi
• Celia Chen
• Fangwen Mu
• Yumin Zhang
• Qing Wang

The main goal of dialogue disentanglement is to separate the mixed utterances from a chat slice into independent dialogues. Existing models often utilize either an utterance-to-utterance (U2U) prediction to determine whether two utterances that have the “reply-to” relationship belong to one dialogue, or an utterance-to-thread (U2T) prediction to determine which dialogue-thread a given utterance should belong to. Inspired by mutual leaning, we propose MuiDial, a novel dialogue disentanglement model, to exploit the intent of each utterance and feed the intent to a mutual learning U2U-U2T disentanglement model. Experimental results and in-depth analysis on several benchmark datasets demonstrate the effectiveness and generalizability of our approach.

YNIMG Journal 2022 Journal Article

Predicting brain age from functional connectivity in symptomatic and preclinical Alzheimer disease

• Peter R. Millar
• Patrick H. Luckett
• Brian A. Gordon
• Tammie L.S. Benzinger
• Suzanne E. Schindler
• Anne M. Fagan
• Randall J. Bateman
• Jae-Hong Lee

"Brain-predicted age" quantifies apparent brain age compared to normative neuroimaging trajectories. Advanced brain-predicted age has been well established in symptomatic Alzheimer disease (AD), but is underexplored in preclinical AD. Prior brain-predicted age studies have typically used structural MRI, but resting-state functional connectivity (FC) remains underexplored. Our model predicted age from FC in 391 cognitively normal, amyloid-negative controls (ages 18-89). We applied the trained model to 145 amyloid-negative, 151 preclinical AD, and 156 symptomatic AD participants to test group differences. The model accurately predicted age in the training set. FC-predicted brain age gaps (FC-BAG) were significantly older in symptomatic AD and significantly younger in preclinical AD compared to controls. There was minimal correspondence between networks predictive of age and AD. Elevated FC-BAG may reflect network disruption during symptomatic AD. Reduced FC-BAG in preclinical AD was opposite to the expected direction, and may reflect a biphasic response to preclinical AD pathology or may be driven by inconsistency between age-related vs. AD-related networks. Overall, FC-predicted brain age may be a sensitive AD biomarker.

IJCAI Conference 2021 Conference Paper

Dialogue Disentanglement in Software Engineering: How Far are We?

• Ziyou Jiang
• Lin Shi
• Celia Chen
• Jun Hu
• Qing Wang

Despite the valuable information contained in software chat messages, disentangling them into distinct conversations is an essential prerequisite for any in-depth analyses that utilize this information. To provide a better understanding of the current state-of-the-art, we evaluate five popular dialog disentanglement approaches on software-related chat. We find that existing approaches do not perform well on disentangling software-related dialogs that discuss technical and complex topics. Further investigation on how well the existing disentanglement measures reflect human satisfaction shows that existing measures cannot correctly indicate human satisfaction on disentanglement results. Therefore, in this paper, we introduce and evaluate a novel measure, named DLD. Using results of human satisfaction, we further summarize four most frequently appeared bad disentanglement cases on software-related chat to insight future improvements. These cases include (i) Ignoring Interaction Patterns, (ii) Ignoring Contextual Information, (iii) Mixing up Topics, and (iv) Ignoring User Relationships. We believe that our findings provide valuable insights on the effectiveness of existing dialog disentanglement approaches and these findings would promote a better application of dialog disentanglement in software engineering.

YNIMG Journal 2021 Journal Article

Identification of microRNA-9 linking the effects of childhood maltreatment on depression using amygdala connectivity

• Cancan He
• Ying Bai
• Zan Wang
• Dandan Fan
• Qing Wang
• Xinyi Liu
• Haisan Zhang
• Hongxing Zhang

Childhood maltreatment (CM) is regarded as an important risk factor for major depressive disorder (MDD). However, the neural links corresponding to the process of early CM experience producing brain alterations and then leading to depression later remain unclear. To explore the neural basis of the effects of CM on MDD and the potential role of microRNA-9 (miR-9) in these processes, we recruited 40 unmedicated MDD patients and 34 healthy controls (HCs) to complete resting-state fMRI scans and peripheral blood miR-9 tests. The neural substrates of CM, miR-9, and depression, as well as their interactive effects on intrinsic amygdala functional connectivity (AFC) networks were investigated in MDD patients. Two-step mediation analysis was separately employed to explore whether AFC strength mediates the association among CM severity, miR-9 levels, and depression. A support vector classifier (SVC) model of machine learning was used to distinguish MDD patients from HCs. MDD patients showed higher miR-9 levels that were negatively correlated with CM scores and depressive severity. Overlapping effects of CM, miR-9, and depressive severity on bilateral AFC networks in MDD patients were primarily located in the prefrontal-striatum pathway and limbic system. The connection of amygdala to prefrontal-limbic circuits could mediate the effects of CM severity on the miR-9 levels, as well as the impacts of miR-9 levels on the severity of depression in MDD patients. Furthermore, the SVC model, which integrated miR-9 levels, CM severity, and AFC strength in prefrontal-limbic regions, had good power in differentiating MDD patients from HCs (accuracy 85.1%). MiR-9 may play a crucial role in the process of CM experience-produced brain changes targeting prefrontal-limbic regions and that subsequently leads to depression. The present neuroimaging-epigenetic results provide new insight into our understanding of MDD pathophysiology.

AAAI Conference 2020 System Paper

Automatic Car Damage Assessment System: Reading and Understanding Videos as Professional Insurance Inspectors

• Wei Zhang
• Yuan Cheng
• Xin Guo
• Qingpei Guo
• Jian Wang
• Qing Wang
• Chen Jiang
• Meng Wang

We demonstrate a car damage assessment system in car insurance field based on artificial intelligence techniques, which can exempt insurance inspectors from checking cars on site and help people without professional knowledge to evaluate car damages when accidents happen. Unlike existing approaches, we utilize videos instead of photos to interact with users to make the whole procedure as simple as possible. We adopt object and video detection and segmentation techniques in computer vision, and take advantage of multiple frames extracted from videos to achieve high damage recognition accuracy. The system uploads video streams captured by mobile devices, recognizes car damage on the cloud asynchronously and then returns damaged components and repair costs to users. The system evaluates car damages and returns results automatically and effectively in seconds, which reduces laboratory costs and decreases insurance claim time significantly.

YNICL Journal 2020 Journal Article

Comparing cortical signatures of atrophy between late-onset and autosomal dominant Alzheimer disease

• Aylin Dincer
• Brian A. Gordon
• Amrita Hari-Raj
• Sarah J. Keefe
• Shaney Flores
• Nicole S. McKay
• Angela M. Paulick
• Kristine E. Shady Lewis

F-florbetapir. To generate cortical signature maps of cortical thickness, we performed a vertex-wise analysis between the cognitively normal controls and impaired groups within each cohort using six increasingly conservative statistical thresholds to determine significance. The optimal cortical map among the six statistical thresholds was determined from a receiver operating characteristic analysis testing the performance of each map in discriminating between the cognitively normal controls and preclinical groups. We then performed within-cohort and cross-cohort (e.g. ADAD maps evaluated in the Knight ADRC cohort) analyses to examine the sensitivity of the optimal cortical signature maps to the amyloid levels using only the cognitively normal individuals (cognitively normal controls and preclinical groups) in comparison to hippocampal volume. We found the optimal cortical signature maps were sensitive to early increases in amyloid for the asymptomatic individuals within their respective cohorts and were significant beyond the inclusion of hippocampus volume, but the cortical signature maps performed poorly when analyzing across cohorts. These results suggest the cortical signature maps are a useful MRI biomarker of early AD-related neurodegeneration in preclinical individuals and the pattern of decline differs between LOAD and ADAD.

NeurIPS Conference 2019 Conference Paper

DFNets: Spectral CNNs for Graphs with Feedback-Looped Filters

• W. O. K. Asiri Suranga Wijesinghe
• Qing Wang

We propose a novel spectral convolutional neural network (CNN) model on graph structured data, namely Distributed Feedback-Looped Networks (DFNets). This model is incorporated with a robust class of spectral graph filters, called feedback-looped filters, to provide better localization on vertices, while still attaining fast convergence and linear memory requirements. Theoretically, feedback-looped filters can guarantee convergence w. r. t. a specified error bound, and be applied universally to any graph without knowing its structure. Furthermore, the propagation rule of this model can diversify features from the preceding layers to produce strong gradient flows. We have evaluated our model using two benchmark tasks: semi-supervised document classification on citation networks and semi-supervised entity classification on a knowledge graph. The experimental results show that our model considerably outperforms the state-of-the-art methods in both benchmark tasks over all datasets.

NeurIPS Conference 2019 Conference Paper

Divergence-Augmented Policy Optimization

• Qing Wang
• Yingru Li
• Jiechao Xiong
• Tong Zhang

In deep reinforcement learning, policy optimization methods need to deal with issues such as function approximation and the reuse of off-policy data. Standard policy gradient methods do not handle off-policy data well, leading to premature convergence and instability. This paper introduces a method to stabilize policy optimization when off-policy data are reused. The idea is to include a Bregman divergence between the behavior policy that generates the data and the current policy to ensure small and safe policy updates with off-policy data. The Bregman divergence is calculated between the state distributions of two policies, instead of only on the action probabilities, leading to a divergence augmentation formulation. Empirical experiments on Atari games show that in the data-scarce scenario where the reuse of off-policy data becomes necessary, our method can achieve better performance than other state-of-the-art deep reinforcement learning algorithms.

YNICL Journal 2019 Journal Article

Quantification of white matter cellularity and damage in preclinical and early symptomatic Alzheimer's disease

• Qing Wang
• Yong Wang
• Jingxia Liu
• Courtney L. Sutphen
• Carlos Cruchaga
• Tyler Blazey
• Brian A. Gordon
• Yi Su

Interest in understanding the roles of white matter (WM) inflammation and damage in the pathophysiology of Alzheimer disease (AD) has been growing significantly in recent years. However, in vivo magnetic resonance imaging (MRI) techniques for imaging inflammation are still lacking. An advanced diffusion-based MRI method, neuro-inflammation imaging (NII), has been developed to clinically image and quantify WM inflammation and damage in AD. Here, we employed NII measures in conjunction with cerebrospinal fluid (CSF) biomarker classification (for β-amyloid (Aβ) and neurodegeneration) to evaluate 200 participants in an ongoing study of memory and aging. Elevated NII-derived cellular diffusivity was observed in both preclinical and early symptomatic phases of AD, while disruption of WM integrity, as detected by decreased fractional anisotropy (FA) and increased radial diffusivity (RD), was only observed in the symptomatic phase of AD. This may suggest that WM inflammation occurs earlier than WM damage following abnormal Aβ accumulation in AD. The negative correlation between NII-derived cellular diffusivity and CSF Aβ42 level (a marker of amyloidosis) may indicate that WM inflammation is associated with increasing Aβ burden. NII-derived FA also negatively correlated with CSF t-tau level (a marker of neurodegeneration), suggesting that disruption of WM integrity is associated with increasing neurodegeneration. Our findings demonstrated the capability of NII to simultaneously image and quantify WM cellularity changes and damage in preclinical and early symptomatic AD. NII may serve as a clinically feasible imaging tool to study the individual and composite roles of WM inflammation and damage in AD.

AAAI Conference 2019 Conference Paper

Semantic Relationships Guided Representation Learning for Facial Action Unit Recognition

• Guanbin Li
• Xin Zhu
• Yirui Zeng
• Qing Wang
• Liang Lin

Facial action unit (AU) recognition is a crucial task for facial expressions analysis and has attracted extensive attention in the field of artificial intelligence and computer vision. Existing works have either focused on designing or learning complex regional feature representations, or delved into various types of AU relationship modeling. Albeit with varying degrees of progress, it is still arduous for existing methods to handle complex situations. In this paper, we investigate how to integrate the semantic relationship propagation between AUs in a deep neural network framework to enhance the feature representation of facial regions, and propose an AU semantic relationship embedded representation learning (SRERL) framework. Specifically, by analyzing the symbiosis and mutual exclusion of AUs in various facial expressions, we organize the facial AUs in the form of structured knowledge-graph and integrate a Gated Graph Neural Network (GGNN) in a multi-scale CNN framework to propagate node information through the graph for generating enhanced AU representation. As the learned feature involves both the appearance characteristics and the AU relationship reasoning, the proposed model is more robust and can cope with more challenging cases, e. g. , illumination change and partial occlusion. Extensive experiments on the two public benchmarks demonstrate that our method outperforms the previous work and achieves state of the art performance.

EAAI Journal 2018 Journal Article

A systematic model of stable multilateral automated negotiation in e-market environment

• Taiguang Gao
• Min Huang
• Qing Wang
• Mingqiang Yin
• Wai Ki Ching
• Loo Hay Lee
• Xingwei Wang

In e-market environment, the participants are usually bilateral such as in Consumer-to-Business or Customer-to-Customer business models. The participant on each side prefers the counterpart from which the concerned issues or profits can be pursued. Hence, the effective matching from a global point of view and the stable matching from an individual point of view become the critical function of the business models. In this paper, a systematic model of Stable Multilateral Automated Negotiation (SMAN) is proposed to facilitate the involved parties’ matching process in two-sided e-market, where confidential mediator agent as well as party agents communicate and make decisions on behalf of their principal parties. To make the matching effective and stable, two optimization models are designed. One is matching points model which makes an effective balance among the proposal value of issues for each possible pair of matching, such that the joint weighted profit measure is optimized with feature rescaling. The other one is matching scheme model which optimizes Social Welfare (SW) subject to the stable constraints, and ensures the engaged individual party satisfies the matching result from its viewpoint. And the optimality of stable matching is proved by mathematical deduction. Finally, numerical experiments are illustrated and show that the designed systematic models can generate effective matchings with individually stable advantages over the traditional Multilateral Automated Negotiation of Two Sides (MANTS).

NeurIPS Conference 2018 Conference Paper

Exponentially Weighted Imitation Learning for Batched Historical Data

• Qing Wang
• Jiechao Xiong
• Lei Han
• Peng Sun
• Han Liu
• Tong Zhang

We consider deep policy learning with only batched historical trajectories. The main challenge of this problem is that the learner no longer has a simulator or ``environment oracle'' as in most reinforcement learning settings. To solve this problem, we propose a monotonic advantage reweighted imitation learning strategy that is applicable to problems with complex nonlinear function approximation and works well with hybrid (discrete and continuous) action space. The method does not rely on the knowledge of the behavior policy, thus can be used to learn from data generated by an unknown policy. Under mild conditions, our algorithm, though surprisingly simple, has a policy improvement bound and outperforms most competing methods empirically. Thorough numerical results are also provided to demonstrate the efficacy of the proposed methodology.

TCS Journal 2016 Journal Article

A new thesis concerning synchronised parallel computing – simplified parallel ASM thesis

• Flavio Ferrarotti
• Klaus-Dieter Schewe
• Loredana Tec
• Qing Wang

A behavioural theory consists of machine-independent postulates characterizing a particular class of algorithms or systems, an abstract machine model that provably satisfies these postulates, and a rigorous proof that any algorithm or system stipulated by the postulates is captured by the abstract machine model. The class of interest in this article is that of (synchronous) parallel algorithms. For this class a behavioural theory has already been developed by Blass and Gurevich, which unfortunately, though mathematically correct, fails to be convincing, as it is not intuitively clear that the postulates really capture the essence of (synchronous) parallel algorithms. In this article we present a much simpler (and presumably more convincing) set of four postulates for (synchronous) parallel algorithms, which are rather close to those used in Gurevich's celebrated sequential ASM thesis, i. e. the behavioural theory of sequential algorithms. The key difference is made by an extension of the bounded exploration postulate using multiset comprehension terms instead of ground terms formulated over the signature of the states. In addition, all implicit assumptions are made explicit, which amounts to considering states of a parallel algorithm to be represented by meta-finite first-order structures. The article first provides the necessary evidence that the axiomatization presented in this article characterizes indeed the whole class of (synchronous) parallel algorithms, then formally proves that parallel algorithms are captured by Abstract State Machines (ASMs). The proof requires some recourse to methods from finite model theory, by means of which it can be shown that if a critical tuple defines an update in some update set, then also every other tuple that is logically indistinguishable defines an update in that update set.

AAAI Conference 2016 Conference Paper

DARI: Distance Metric and Representation Integration for Person Verification

• Guangrun Wang
• Liang Lin
• Shengyong Ding
• Ya Li
• Qing Wang

The past decade has witnessed the rapid development of feature representation learning and distance metric learning, whereas the two steps are often discussed separately. To explore their interaction, this work proposes an end-to-end learning framework called DARI, i. e. Distance metric And Representation Integration, and validates the effectiveness of DARI in the challenging task of person verification. Given the training images annotated with the labels, we first produce a large number of triplet units, and each one contains three images, i. e. one person and the matched/mismatch references. For each triplet unit, the distance disparity between the matched pair and the mismatched pair tends to be maximized. We solve this objective by building a deep architecture of convolutional neural networks. In particular, the Mahalanobis distance matrix is naturally factorized as one top fully-connected layer that is seamlessly integrated with other bottom layers representing the image feature. The image feature and the distance metric can be thus simultaneously optimized via the one-shot backward propagation. On several public datasets, DARI shows very promising performance on re-identifying individuals cross cameras against various challenges, and outperforms other state-of-the-art approaches.

AIIM Journal 2016 Journal Article

Efficient processing of multiple nested event pattern queries over multi-dimensional event streams based on a triaxial hierarchical model

• Fuyuan Xiao
• Masayoshi Aritsugi
• Qing Wang
• Rong Zhang

Objective For efficient and sophisticated analysis of complex event patterns that appear in streams of big data from health care information systems and support for decision-making, a triaxial hierarchical model is proposed in this paper. Methods and material Our triaxial hierarchical model is developed by focusing on hierarchies among nested event pattern queries with an event concept hierarchy, thereby allowing us to identify the relationships among the expressions and sub-expressions of the queries extensively. We devise a cost-based heuristic by means of the triaxial hierarchical model to find an optimised query execution plan in terms of the costs of both the operators and the communications between them. According to the triaxial hierarchical model, we can also calculate how to reuse the results of the common sub-expressions in multiple queries. By integrating the optimised query execution plan with the reuse schemes, a multi-query optimisation strategy is developed to accomplish efficient processing of multiple nested event pattern queries. Results We present empirical studies in which the performance of multi-query optimisation strategy was examined under various stream input rates and workloads. Specifically, the workloads of pattern queries can be used for supporting monitoring patients’ conditions. On the other hand, experiments with varying input rates of streams can correspond to changes of the numbers of patients that a system should manage, whereas burst input rates can correspond to changes of rushes of patients to be taken care of. The experimental results have shown that, in Workload 1, our proposal can improve about 4 and 2 times throughput comparing with the relative works, respectively; in Workload 2, our proposal can improve about 3 and 2 times throughput comparing with the relative works, respectively; in Workload 3, our proposal can improve about 6 times throughput comparing with the relative work. Conclusion The experimental results demonstrated that our proposal was able to process complex queries efficiently which can support health information systems and further decision-making.

AAAI Conference 2015 Conference Paper

DynaDiffuse: A Dynamic Diffusion Model for Continuous Time Constrained Influence Maximization

• Miao Xie
• Qiusong Yang
• Qing Wang
• Gao Cong
• Gerard Melo

Studying the spread of phenomena in social networks is critical but still not fully solved. Existing influence maximization models assume a static network, disregarding its evolution over time. We introduce the continuous time constrained influence maximization problem for dynamic diffusion networks, based on a novel diffusion model called DYNADIFFUSE. Although the problem is NP-hard, the influence spread functions are monotonic and submodular, enabling fast approximations on top of an innovative stochastic model checking approach. Experiments on real social network data show that our model finds higher quality solutions and our algorithm outperforms state-of-art alternatives.

YNIMG Journal 2015 Journal Article

N170 changes reflect competition between faces and identifiable characters during early visual processing

• Cong Fan
• Shunsen Chen
• Lingcong Zhang
• Zhengyang Qi
• Yule Jin
• Qing Wang
• Yuejia Luo
• Hong Li

According to the neuronal recycling hypothesis, brain circuits can gain new functions through cultural learning, which are distinct from their evolutionarily established functions, creating competition between processes such as facial and identifiable character processing. In the present study, event-related potential (ERP) recording was used to examine electrophysiological correlates of identification levels of Chinese characters as well as the competition between facial and Chinese character processing after the characters were learnt. Twenty volunteers performed a lateralized face detection task, and N170 responses were recorded when the participants viewed only Chinese characters (identifiable or unidentifiable in Xiaozhuan font), or Chinese characters and faces concurrently. Viewing identifiable Chinese characters bilaterally elicited larger N170 amplitudes than viewing unidentifiable ones. N170 amplitudes in response to faces bilaterally declined when identifiable Chinese characters and faces were viewed concurrently as compared to viewing unidentifiable Chinese characters and faces concurrently. These results indicate that the N170 component is modulated by the observer's identification level of Chinese characters, and that identifiable Chinese characters compete with faces during early visual processing.

TCS Journal 2014 Journal Article

A theoretical framework for knowledge-based entity resolution

• Klaus-Dieter Schewe
• Qing Wang

Entity resolution is the process of determining whether a collection of entity representations refer to the same entity in the real world. In this paper we introduce a theoretical framework that supports knowledge-based entity resolution. From a logical point of view, the expressive power of the framework is equivalent to a decidable fragment of first-order logic including conjunction, disjunction and a certain form of negation. Although the framework is expressive for representing knowledge about entity resolution in a collective way, the questions that arise are: (1) how efficiently can knowledge patterns be processed; (2) how effectively can redundancy among knowledge patterns be eliminated. In answering these questions, we first study the evaluation problem for knowledge patterns. Our results show that this problem is NP-complete w. r. t. combined complexity but in ptime w. r. t. data complexity. This nice property leads us to investigate the containment problem for knowledge patterns, which turns out to be NP-complete. We further develop a notion of optimality for knowledge patterns and a mechanism of optimizing a knowledge model (i. e. a finite set of knowledge patterns). We prove that the optimality decision problem for knowledge patterns is still NP-complete.

TCS Journal 2013 Journal Article

WITHDRAWN: A theoretical framework for knowledge-based entity resolution

• Klaus-Dieter Schewe
• Qing Wang

AAAI Conference 2011 Conference Paper

Online Updating the Generalized Inverse of Centered Matrices

• Qing Wang
• Liang Zhang

In this paper, we present the exact online updating formulae for the generalized inverse of centered matrices. The computational cost is O(mn) for matrices of size m × n. Experimental results validate the proposed method’s accuracy and efficiency.

ECAI Conference 2008 Conference Paper

MTForest: Ensemble Decision Trees based on Multi-Task Learning

• Qing Wang
• Liang Zhang 0019
• Mingmin Chi
• Jiankui Guo

Many ensemble methods, such as Bagging, Boosting, Random Forest, etc, have been proposed and widely used in real world applications. Some of them are better than others on noise-free data while some of them are better than others on noisy data. But in reality, ensemble methods that can consistently gain good performance in situations with or without noise are more desirable. In this paper, we propose a new method namely MTForest, to ensemble decision tree learning algorihms by enumerating each input attribute as extra task to introduce different additional inductive bias to generate diverse yet accurate component decision tree learning algorithms in the ensemble. The experimental results show that in situations without classification noise, MTForest is comparable to Boosting and Random Forest and significantly better than Bagging, while in situations with classification noise, MTForest is significantly better than Boosting and Random Forest and is slightly better than Bagging. So MTForest is a good choice for ensemble decision tree learning algorithms in situations with or without noise. We conduct the experiments on the basis of 36 widely used UCI data sets that cover a wide range of domains and data characteristics and run all the algorithms within the Weka platform.