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Min Chen

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41 papers

2 author rows

JBHI Journal 2026 Journal Article

Chemical-Disease-Gene Association Prediction based on Pretraining-Prompt-Finetuning Heterogeneous Graph Neural Network for Drug Discovery

Xi Zeng
Jing-Wen Cai
Pei-Yuan Lai
Qing-Yun Dai
Chang-Dong Wang
Min Chen

Chemical-Disease-Gene (CDG) association prediction-encompassing Chemical-Disease (CD), Disease-Gene (DG), and Chemical-Gene (CG) interactions-is a cornerstone of drug discovery, as it underpins target identification and drug repurposing. While these tasks are inherently synergistic, existing methods often address them in isolation, failing to capture shared heterogeneous semantics and cross-task dependencies. We hypothesize that a unified pretraining framework can learn transferable biomedical semantics across CDG tasks, with task-specific prompt tuning enabling efficient adaptive fine-tuning without full retraining. To test this hypothesis, we propose the Pretraining-Prompt-Finetuning Heterogeneous Graph Neural Network (PPF-HGNN), a two-stage framework built on heterogeneous graph neural networks (GNNs) and prompt learning. Specifically, we construct a CDG heterogeneous graph, employ parameter-free metapath-guided message passing for high-order semantic capture, and optimize generalizable representations via a dual self-supervised objective (association prediction + feature reconstruction) during pretraining. For downstream tasks, task-specific learnable prompt vectors are introduced to adapt frozen pretrained representations to CD, CG and DG association prediction tasks via additive fusion, preserving core semantics while injecting task-specific biases. Comprehensive experiments demonstrate PPF-HGNN's state-of-the-art performance: AUC of 0. 9633 (CD), 0. 9939 (CG), and 0. 9390 (DG), with F1-scores of 0. 9157, 0. 9668, and 0. 8955 respectively-substantially outperforming six existing baselines. This work validates the pretrain-prompt-finetune paradigm for multi-task biomedical association prediction, providing a robust AI-driven tool to accelerate translational research and decipher complex CDG relationships. The source code is available at https://github.com/ike-zengxi/PPF-HGNN.

AAAI Conference 2026 Conference Paper

FT-MoE: Sustainable-learning Mixture of Experts for Fault-Tolerant Computing

Wenjing Xiao
Wenhao Song
Miaojiang Chen
Min Chen

Intelligent fault-tolerant (FT) computing has recently demonstrated significant advantages in predicting and diagnosing faults proactively, thereby ensuring reliable service delivery. However, due to the heterogeneity of fault knowledge, dynamic workloads, and limited data support, existing deep learning-based FT algorithms face challenges in fault detection quality and training efficiency. This is primarily because their homogenization of fault knowledge perception difficuties to fully capture diverse and complex fault patterns. To address these challenges, we propose FT-MoE, a sustainable-learning fault-tolerant computing framework based on a dual-path architecture for high-accuracy fault detection and classification. This model employs a mixture-of-experts (MoE) architecture, enabling different parameters to learn distinct fault knowledge. Additionally, we adopt a two-stage learning scheme that combines comprehensive offline training with continual online tuning, allowing the model to adaptively optimize its parameters in response to evolving real-time workloads. To facilitate realistic evaluation, we construct a new fault detection and classification dataset for edge networks, comprising 10,000 intervals with fine-grained resource features, surpassing existing datasets in both scale and granularity. Finally, we conduct extensive experiments on the FT benchmark to verify the effectiveness of FT-MoE. Results demonstrate that our model outperforms state-of-the-art methods.

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AAMAS Conference 2026 Conference Paper

Heterogeneity in Multi-Agent Reinforcement Learning

Tianyi Hu
Zhiqiang Pu
Yuan Wang
Tenghai Qiu
Min Chen
Xin Yu

Heterogeneity is a fundamental property in multi-agent reinforcement learning (MARL), which is closely related not only to the functional differences of agents, but also to policy diversity and environmentalinteractions. However, theMARLfieldcurrentlylacksa rigorousdefinitionanddeeperunderstandingofheterogeneity. This paper systematically discusses heterogeneity in MARL from the perspectives of definition, quantification, and utilization. First, based on an agent-level modeling of MARL, we categorize heterogeneity into five types and provide mathematical definitions. Second, we define the concept of heterogeneity distance and propose a practical quantification method. Third, we design a heterogeneity-based multi-agent dynamic parameter sharing algorithm as an example of the application of our methodology. Case studies demonstrate that our method can effectively identify and quantify various types of agent heterogeneity. Experimental results show that the proposed algorithm, compared to other parameter sharing baselines, has better interpretability and stronger adaptability. The proposed methodology will help the MARL community gain a more comprehensive and profound understanding of heterogeneity, and further promote the development of practical algorithms. 1

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.

AAMAS Conference 2026 Conference Paper

RBC: Retroactive Belief State Compensation for Multi-Agent Collaboration Under Information Delay

Dongkun Huo
Hongbo Liu
Shu Yin
Yixue Hao
Long Hu
Rui Wang
Min Chen

Real-time information is usually not satisfied in real world due to communication or observation delay. Although existing works address individual delay, they do not fully consider the complex effects of composite delay, denoted as “Information Delay”, which severely reduce the efficiency of these methods. To address information delay, we propose Retroactive Belief state Compensation (RBC), a multi-agent framework with enhanced robustness and collaboration. Specifically, we design a multi-step reconstruction model that retroactively rebuilds agents’ belief states starting from the generation time of the information. This process corrects the accumulated deviation in the current belief state caused by information delay. Moreover, to enhance proactive collaboration, we introduce an intent inference module. This module enables agents to generate intents, which represent short-term action plans, as content of communication. By aggregating intents from teammates, agents will choose more coherent and synchronized joint actions. To evaluate the performance of RBC, we design scenarios with multiple levels of observation, communication, and composite delays. Experimental results demonstrate that RBC outperforms the baselines in all scenarios with delays. Yixue Hao is corresponding author. Email: yixuehao@hust. edu. cn. This work is licensed under a Creative Commons Attribution International 4. 0 License. Proc. of the 25th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2026), C. Amato, L. Dennis, V. Mascardi, J. Thangarajah (eds.), May 25 – 29, 2026, Paphos, Cyprus. © 2026 International Foundation for Autonomous Agents and Multiagent Systems (www. ifaamas. org). https: //doi. org/10. 65109/MFLP4403

AAAI Conference 2026 Conference Paper

Resource Efficient Sleep Staging via Multi-Level Masking and Prompt Learning

Lejun Ai
Yulong Li
Haodong Yi
Jixuan Xie
Yue Wang
Jia Liu
Min Chen
Rui Wang

Automatic sleep staging plays a vital role in assessing sleep quality and diagnosing sleep disorders. Most existing methods rely heavily on long and continuous EEG recordings, which poses significant challenges for data acquisition in resource-constrained systems, such as wearable or home-based monitoring systems. In this paper, we propose the task of resource-efficient sleep staging, which aims to reduce the amount of signal collected per sleep epoch while maintaining reliable classification performance. To solve this task, we adopt the masking and prompt learning strategy and propose a novel framework called Mask-Aware Sleep Staging (MASS). Specifically, we design a multi-level masking strategy to promote effective feature modeling under partial and irregular observations. To mitigate the loss of contextual information introduced by masking, we further propose a hierarchical prompt learning mechanism that aggregates unmasked data into a global prompt, serving as a semantic anchor for guiding both patch-level and epoch-level feature modeling. MASS is evalutaed on four datasets, demonstrating state-of-the-art performance, especially when the amount of data is very limited. This result highlights its potential for efficient and scalable deployment in real-world low-resource sleep monitoring environments.

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JBHI Journal 2025 Journal Article

BINDTI: A Bi-Directional Intention Network for Drug-Target Interaction Identification Based on Attention Mechanisms

Lihong Peng
Xin Liu
Long Yang
Longlong Liu
Zongzheng Bai
Min Chen
Xu Lu
Libo Nie

The identification of drug-target interactions (DTIs) is an essential step in drug discovery. In vitro experimental methods are expensive, laborious, and time-consuming. Deep learning has witnessed promising progress in DTI prediction. However, how to precisely represent drug and protein features is a major challenge for DTI prediction. Here, we developed an end-to-end DTI identification framework called BINDTI based on bi-directional Intention network. First, drug features are encoded with graph convolutional networks based on its 2D molecular graph obtained by its SMILES string. Next, protein features are encoded based on its amino acid sequence through a mixed model called ACmix, which integrates self-attention mechanism and convolution. Third, drug and target features are fused through bi-directional Intention network, which combines Intention and multi-head attention. Finally, unknown drug-target (DT) pairs are classified through multilayer perceptron based on the fused DT features. The results demonstrate that BINDTI greatly outperformed four baseline methods (i. e. , CPI-GNN, TransfomerCPI, MolTrans, and IIFDTI) on the BindingDB, BioSNAP, DrugBank, and Human datasets. More importantly, it was more appropriate to predict new DTIs than the four baseline methods on imbalanced datasets. Ablation experimental results elucidated that both bi-directional Intention and ACmix could greatly advance DTI prediction. The fused feature visualization and case studies manifested that the predicted results by BINDTI were basically consistent with the true ones. We anticipate that the proposed BINDTI framework can find new low-cost drug candidates, improve drugs' virtual screening, and further facilitate drug repositioning as well as drug discovery.

JBHI Journal 2025 Journal Article

Drug Repositioning via Multi-View Representation Learning With Heterogeneous Graph Neural Network

Li Peng
Cheng Yang
Jiahuai Yang
Yuan Tu
Qingchun Yu
Zejun Li
Min Chen
Wei Liang

Exploring simple and efficient computational methods for drug repositioning has emerged as a popular and compelling topic in the realm of comprehensive drug development. The crux of this technology lies in identifying potential drug-disease associations, which can effectively mitigate the burdens caused by the exorbitant costs and lengthy periods of conventional drugs development. However, existing computational drug repositioning methods continue to encounter challenges in accurately predicting associations between drugs and diseases. In this paper, we propose a Multi-view Representation Learning method (MRLHGNN) with Heterogeneous Graph Neural Network for drug repositioning. This method is based on a collection of data from multiple biological entities associated with drugs or diseases. It consists of a view-specific feature aggregation module with meta-paths and auto multi-view fusion encoder. To better utilize local structural and semantic information from specific views in heterogeneous graph, MRLHGNN employs a feature aggregation model with variable-length meta-paths to expand the local receptive field. Additionally, it utilizes a transformer based semantic aggregation module to aggregate semantic features across different view-specific graphs. Finally, potential drug-disease associations are obtained through a multi-view fusion decoder with an attention mechanism. Cross-validation experiments demonstrate the effectiveness and interpretability of the MRLHGNN in comparison to nine state-of-the-art approaches. Case studies further reveal that MRLHGNN can serve as a powerful tool for drug repositioning.

JBHI Journal 2025 Journal Article

DTI-MvSCA: An Anti-Over-Smoothing Multi-View Framework With Negative Sample Selection for Predicting Drug-Target Interactions

Lihong Peng
Zongzheng Bai
Longlong Liu
Long Yang
Xin Liu
Min Chen
Xing Chen

Predicting potential drug-target interactions (DTIs) facilitates to accelerate drug discovery and reduce development cost. Current deep learning-based methods exhibit high-performance predictions, but three challenges remain: first, the absence of negative DTIs severely limits the model performance. Moreover, existing graph neural networks are beset with the scalability due to the model complexity and graph size. More importantly, most methods focus on learning the topological features while ignoring node features during DTI representation learning. To solve the limitations, here, we develop a multi-view neural network framework called DTI-MvSCA for DTI identification. This framework begins with constructing a drug-protein pair (DPP) network with matrix operation-based negative DTI selection, and then learns the DPP representations through a M ulti- v iew neural network, finally classifies each DPP based on multilayer perceptron. Particularly, the multi-view neural network integrates graph topological feature learning based on the self-attention mechanism and S HADOW graph attention network, node feature learning based on 1D C onvolutional neural network, and the A ttention mechanism. An in-depth experiment on DrugBank V3. 0 and V5. 0 showed that DTI-MvSCA obtained precise and robust predictions against five state-of-the-art baseline methods. Furthermore, visualizing the feature distributions of the selected negative DTIs exhibits a more distinguishable and clearer boundary. In summary, DTI-MvSCA provides a useful deep learning tool to investigate potential DTIs.

AAAI Conference 2025 Conference Paper

GNN-Transformer Cooperative Architecture for Trustworthy Graph Contrastive Learning

Jianqing Liang
Xinkai Wei
Min Chen
Zhiqiang Wang
Jiye Liang

Graph contrastive learning (GCL) has become a hot topic in the field of graph representaion learning. In contrast to traditional supervised learning relying on a large number of labels, GCL exploits augmentation techniques to generate multiple views and positive/negative pairs, both of which greatly influence the performance. Unfortunately, commonly used random augmentations may disturb the underlying semantics of graphs. Moreover, traditional GNNs, a type of widely employed encoders in GCL, are inevitably confronted with over-smoothing and over-squashing problems. To address these issues, we propose GNN-Transformer Cooperative Architecture for Trustworthy Graph Contrastive Learning (GTCA), which inherits the advantages of both GNN and Transformer, incorporating graph topology to obtain comprehensive graph representations. Theoretical analysis verifies the trustworthiness of the proposed method. Extensive experiments on benchmark datasets demonstrate state-of-the-art empirical performance.

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ICML Conference 2025 Conference Paper

Information Bottleneck-guided MLPs for Robust Spatial-temporal Forecasting

Min Chen
Guansong Pang
Wenjun Wang 0002
Cheng Yan

Spatial-temporal forecasting (STF) plays a pivotal role in urban planning and computing. Spatial-Temporal Graph Neural Networks (STGNNs) excel at modeling spatial-temporal dynamics, thus being robust against noise perturbations. However, they often suffer from relatively poor computational efficiency. Simplifying the architectures can improve efficiency but also weakens robustness with respect to noise interference. In this study, we investigate the problem: can simple neural networks such as Multi-Layer Perceptrons (MLPs) achieve robust spatial-temporal forecasting while remaining efficient? To this end, we first reveal the dual noise effect in spatial-temporal data and propose a theoretically grounded principle termed Robust Spatial-Temporal Information Bottleneck (RSTIB), which holds strong potential for improving model robustness. We then design an implementation named RSTIB-MLP, together with a new training regime incorporating a knowledge distillation module, to enhance the robustness of MLPs for STF while maintaining their efficiency. Comprehensive experiments demonstrate that RSTIB-MLP achieves an excellent trade-off between robustness and efficiency, outperforming state-of-the-art STGNNs and MLP-based models. Our code is publicly available at: https: //github. com/mchen644/RSTIB.

IROS Conference 2025 Conference Paper

Interpretable Interaction Modeling for Trajectory Prediction via Agent Selection and Physical Coefficient

Shiji Huang
Lei Ye
Min Chen
Wenhai Luo
Dihong Wang
Chenqi Xu
Deyuan Liang

A thorough understanding of the interaction between the target agent and surrounding agents is a prerequisite for accurate trajectory prediction. Although many methods have been explored, they assign correlation coefficients to surrounding agents in a purely learning-based manner. In this study, we present ASPILin, which manually selects interacting agents and replaces the attention scores in Transformer with a newly computed physical correlation coefficient, enhancing the interpretability of interaction modeling. Surprisingly, these simple modifications can significantly improve prediction performance and substantially reduce computational costs. We intentionally simplified our model in other aspects, such as map encoding. Remarkably, experiments conducted on the INTERACTION, highD, and CitySim datasets demonstrate that our method is efficient and straightforward, outperforming other state-of-the-art methods.

JBHI Journal 2025 Journal Article

LKAN: LLM-Based Knowledge-Aware Attention Network for Clinical Staging of Liver Cancer

Ya Li
Xuecong Zheng
Jiaping Li
Qingyun Dai
Chang-Dong Wang
Min Chen

Clinical staging of liver cancer (CSoLC), an important indicator for evaluating primary liver cancer (PLC), is key in the diagnosis, treatment, and rehabilitation of liver cancer. In China, the current CSoLC adopts the China liver cancer (CNLC) staging, which is usually evaluated by clinicians based on radiology reports. Therefore, inferring clinical information from unstructured radiology reports can provide auxiliary decision support for clinicians. The key to solving the challenging task is to guide the model to pay attention to the staging-related words or sentences, and the following issues may occur: 1) Imbalanced categories: Early- and mid-stage liver cancer symptoms are subtle, resulting in more data in the end-stage. 2) Domain sensitivity of liver cancer data: The liver cancer dataset contains substantial domain knowledge, leading to out-of-vocabulary issues and reduced classification accuracy. 3) Free-text and lengthy report: Radiology reports sparsely describe various lesions using domain-specific terms, making it hard to mine staging-related information. To address these, this article proposes a large language model (LLM)-based Knowledge-aware Attention Network (LKAN) for CSoLC. First, for maintaining semantic consistency, LLM and a rule-based algorithm are integrated to generate more diverse and reasonable data. Second, an unlabeled radiology corpus is pre-trained to introduce domain knowledge for subsequent representation learning. Third, attention is improved by incorporating both global and local features to guide the model's focus on staging-relevant information. Compared with the baseline models, LKAN has achieved the best results with 90. 3% Accuracy, 90. 0% Macro_F1 score, and 90. 0% Macro_Recall.

TAAS Journal 2025 Journal Article

LLM-based UAV Path Planning for Autonomous and Adaptive Industry Systems

Wenjing Xiao
Chenglong Shi
Miaojiang Chen
Athanasios V. Vasilakos
Min Chen
Ahmed Farouk

In the era of Industry 5.0, Unmanned Aerial Vehicles (UAVs) equipped with multiple sensors play a vital role in industrial tasks such as patrol and surveillance, offering distinct advantages of high mobility, accurate perception, and autonomous operation. However, traditional path planning methods for UAVs struggle with challenges related to interpretability and adaptation to dynamic industrial environments. To address these challenges, this paper proposes a novel LLM-based approach to UAV swarm path planning in autonomous and adaptive industrial systems. The proposed method aims to minimize the time and computational consumption of path planning while improving the task completion rate of UAVs. Specifically, we first propose the multi-step deep thinking movement decision samples generation algorithm (MSDTMD-SG) to generate deep thinking training samples for LLMs at different ends and fine-tune them. Second, we design a scene memory and replay learning mechanism, enabling damaged UAVs to store perceived information and generate training samples via MSDTMD-SG for continuous LLM learning and optimization. Finally, extensive experiments demonstrate that the proposed method exhibits strong adaptability to dynamic environments, achieves the highest task completion rate among all methods, and maintains competitive system consumption.

AAAI Conference 2025 Conference Paper

More Text, Less Point: Towards 3D Data-Efficient Point-Language Understanding

Yuan Tang
Xu Han
Xianzhi Li
Qiao Yu
Jinfeng Xu
Yixue Hao
Long Hu
Min Chen

Enabling Large Language Models (LLMs) to comprehend the 3D physical world remains a significant challenge. Due to the lack of large-scale 3D-text pair datasets, the success of LLMs has yet to be replicated in 3D understanding. In this paper, we rethink this issue and propose a new task: 3D Data-Efficient Point-Language Understanding. The goal is to enable LLMs to achieve robust 3D object understanding with minimal 3D point cloud and text data pairs. To address this task, we introduce GreenPLM, which leverages more text data to compensate for the lack of 3D data. First, inspired by using CLIP to align images and text, we utilize a pre-trained point cloud-text encoder to map the 3D point cloud space to the text space. This mapping leaves us to seamlessly connect the text space with LLMs. Once the point-text-LLM connection is established, we further enhance text-LLM alignment by expanding the intermediate text space, thereby reducing the reliance on 3D point cloud data. Specifically, we generate 6M free-text descriptions of 3D objects, and design a three-stage training strategy to help LLMs better explore the intrinsic connections between different modalities. To achieve efficient modality alignment, we design a zero-parameter cross-attention module for token pooling. Extensive experimental results show that GreenPLM requires only 12% of the 3D training data used by existing state-of-the-art models to achieve superior 3D understanding. Remarkably, GreenPLM also achieves competitive performance using text-only data.

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JBHI Journal 2025 Journal Article

Multimodal Drug Target Binding Affinity Prediction Using Graph Local Substructure

Xun Peng
Chunping Ouyang
Yongbin Liu
Ying Yu
Jian Liu
Min Chen

Predicting the binding affinity of drug target is essential to reduce drug development costs and cycles. Recently, several deep learning-based methods have been proposed to utilize the structural or sequential information of drugs and targets to predict the drug-target binding affinity (DTA). However, methods that rely solely on sequence features do not consider hydrogen atom data, which may result in information loss. Graph-based methods may contain information that is not directly related to the prediction process. Additionally, the lack of structured division can limit the representation of characteristics. To address these issues, we propose a multimodal DTA prediction model using graph local substructures, called MLSDTA. This model comprehensively integrates the graph and sequence modal information from drugs and targets, achieving multimodal fusion through a cross-attention approach for multimodal features. Additionally, adaptive structure aware pooling is applied to generate graphs containing local substructural information. The model also utilizes the DropNode strategy to enhance the distinctions between different molecules. Experiments on two benchmark datasets have shown that MLSDTA outperforms current state-of-the-art models, demonstrating the feasibility of MLSDTA.

NeurIPS Conference 2025 Conference Paper

SurfelSplat: Learning Efficient and Generalizable Gaussian Surfel Representations for Sparse-View Surface Reconstruction

Chensheng Dai
Shengjun Zhang
Min Chen
Yueqi Duan

3D Gaussian Splatting (3DGS) has demonstrated impressive performance in 3D scene reconstruction. Beyond novel view synthesis, it shows great potential for multi-view surface reconstruction. Existing methods employ optimization-based reconstruction pipelines that achieve precise and complete surface extractions. However, these approaches typically require dense input views and high time consumption for per-scene optimization. To address these limitations, we propose SurfaceSplat, a feed-forward framework that generates efficient and generalizable pixel-aligned Gaussian surfel representations from sparse-view images. We observe that conventional feed-forward structures struggle to recover accurate geometric attributes of Gaussian surfels because the spatial frequency of pixel-aligned primitives exceeds Nyquist sampling rates. Therefore, we propose a cross-view feature aggregation module based on the Nyquist sampling theorem. Specifically, we first adapt the geometric forms of Gaussian surfels with spatial sampling rate-guided low-pass filters. We then project the filtered surfels across all input views to obtain cross-view feature correlations. By processing these correlations through a specially designed feature fusion network, we can finally regress Gaussian surfels with precise geometry. Extensive experiments on DTU reconstruction benchmarks demonstrate that our model achieves comparable results with state-of-the-art methods, and predict Gaussian surfels within 1 second, offering a 100× speedup without costly per-scene training.

EAAI Journal 2024 Journal Article

An adversarial transfer learning method based on domain distribution prediction for aero-engine fault diagnosis

Jintao Hu
Min Chen
Hailong Tang
Jiyuan Zhang

The utilization of transfer learning enables effective realization of the transferability of aero-engine fault diagnosis models across disparate states. However, the gradual degradation of engine performance and the complex and variable flight states lead to continuous changes in data distribution. The common transfer learning methods employ discrete divisions of data domain distributions, which are insufficient to cope with the continuous changes of the domain. To accomplish transfer learning towards a continuous and multi-dimensional target domain, we propose a continuous domain distribution adversarial network (CDDAN) based on deep domain adversarial network. The method defines the number of effective cycles directly associated with engine degradation as a continuous domain index. When the domain index is extended to multiple dimensions, a hybrid gaussian model is introduced to represent distribution prediction within multi-dimensional continuous domains. Subsequently, we validate the feasibility and superiority of our method using datasets generated based on twin-spool turbofan engines. In comparison to other transfer learning methods, the proposed method achieves superior effect in continuous domain adaptation. The experimental results demonstrate that this method exhibits adaptability to failures in the diagnosis method caused by performance degradation and changes in flight state of the aero engine, while remaining independent of target domain data annotation. This advantage becomes particularly apparent in scenarios with limited target domain data and multi-state fault diagnosis.

NeurIPS Conference 2024 Conference Paper

Coevolving with the Other You: Fine-Tuning LLM with Sequential Cooperative Multi-Agent Reinforcement Learning

Hao Ma
Tianyi Hu
Zhiqiang Pu
Boyin Liu
Xiaolin Ai
Yanyan Liang
Min Chen

Reinforcement learning (RL) has emerged as a pivotal technique for fine-tuning large language models (LLMs) on specific tasks. However, prevailing RL fine-tuning methods predominantly rely on PPO and its variants. Though these algorithms are effective in general RL settings, they often exhibit suboptimal performance and vulnerability to distribution collapse when applied to the fine-tuning of LLMs. In this paper, we propose CORY, extending the RL fine-tuning of LLMs to a sequential cooperative multi-agent reinforcement learning framework, to leverage the inherent coevolution and emergent capabilities of multi-agent systems. In CORY, the LLM to be fine-tuned is initially duplicated into two autonomous agents: a pioneer and an observer. The pioneer generates responses based on queries, while the observer generates responses using both the queries and the pioneer’s responses. The two agents are trained together. During training, the agents exchange roles periodically, fostering cooperation and coevolution between them. Experiments evaluate CORY's performance by fine-tuning GPT-2 and Llama-2 under subjective and objective reward functions on the IMDB Review and GSM8K datasets, respectively. Results show that CORY outperforms PPO in terms of policy optimality, resistance to distribution collapse, and training robustness, thereby underscoring its potential as a superior methodology for refining LLMs in real-world applications.

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JBHI Journal 2024 Journal Article

ER-GET: Emotion Recognition Based on Global ECG Trajectory

Ya Li
Runxi Tan
Tianxin Lin
Qing Liu
Chang-Dong Wang
Min Chen

In recent years, the recognition of human emotions based on electrocardiogram (ECG) signals has been considered a novel area of study among researchers. Despite the challenge of extracting latent emotion information from ECG signals, existing methods are able to recognize emotions by calculating the heart rate variability (HRV) features. However, such local features have drawbacks, as they do not provide a comprehensive description of ECG signals, leading to suboptimal recognition performance. For the first time, we propose a new strategy to extract hidden emotional information from the global ECG trajectory for emotion recognition. Specifically, a period of ECG signals is decomposed into sub-signals of different frequency bands through ensemble empirical mode decomposition (EEMD), and a series of multi-sequence trajectory graphs is constructed by orthogonally combining these sub-signals to extract latent emotional information. Additionally, to better utilize these graph features, a network has been designed that includes self-supervised graph representation learning and ensemble learning for classification. This approach surpasses recent notable works, achieving outstanding results, with an accuracy of 95. 08% in arousal and 95. 90% in valence detection. Additionally, this global feature is compared and discussed in relation to HRV features, with the intention of providing inspiration for subsequent research.

ICML Conference 2024 Conference Paper

Graph External Attention Enhanced Transformer

Jianqing Liang
Min Chen
Jiye Liang

The Transformer architecture has recently gained considerable attention in the field of graph representation learning, as it naturally overcomes several limitations of Graph Neural Networks (GNNs) with customized attention mechanisms or positional and structural encodings. Despite making some progress, existing works tend to overlook external information of graphs, specifically the correlation between graphs. Intuitively, graphs with similar structures should have similar representations. Therefore, we propose Graph External Attention (GEA) — a novel attention mechanism that leverages multiple external node/edge key-value units to capture inter-graph correlations implicitly. On this basis, we design an effective architecture called Graph External Attention Enhanced Transformer (GEAET), which integrates local structure and global interaction information for more comprehensive graph representations. Extensive experiments on benchmark datasets demonstrate that GEAET achieves state-of-the-art empirical performance. The source code is available for reproducibility at: https: //github. com/icm1018/GEAET.

AAAI Conference 2024 Conference Paper

Knowledge-Aware Explainable Reciprocal Recommendation

Kai-Huang Lai
Zhe-Rui Yang
Pei-Yuan Lai
Chang-Dong Wang
Mohsen Guizani
Min Chen

Reciprocal recommender systems (RRS) have been widely used in online platforms such as online dating and recruitment. They can simultaneously fulfill the needs of both parties involved in the recommendation process. Due to the inherent nature of the task, interaction data is relatively sparse compared to other recommendation tasks. Existing works mainly address this issue through content-based recommendation methods. However, these methods often implicitly model textual information from a unified perspective, making it challenging to capture the distinct intentions held by each party, which further leads to limited performance and the lack of interpretability. In this paper, we propose a Knowledge-Aware Explainable Reciprocal Recommender System (KAERR), which models metapaths between two parties independently, considering their respective perspectives and requirements. Various metapaths are fused using an attention-based mechanism, where the attention weights unveil dual-perspective preferences and provide recommendation explanations for both parties. Extensive experiments on two real-world datasets from diverse scenarios demonstrate that the proposed model outperforms state-of-the-art baselines, while also delivering compelling reasons for recommendations to both parties.

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YNIMG Journal 2024 Journal Article

Neural mechanisms underlying placebo and nocebo effects in tonic muscle pain

Min Chen
Xiao Wu
Libo Zhang
Fengrui Zhang
Linling Li
Yingying Zhang
Donglin Xiong
Yunhai Qiu

Pain is a highly subjective and multidimensional experience, significantly influenced by various psychological factors. Placebo analgesia and nocebo hyperalgesia exemplify this influence, where inert treatments result in pain relief or exacerbation, respectively. While extensive research has elucidated the psychological and neural mechanisms behind these effects, most studies have focused on transient pain stimuli. To explore these mechanisms in the context of tonic pain, we conducted a study using a 15-minute tonic muscle pain induction procedure, where hypertonic saline was infused into the left masseter of healthy participants. We collected real-time Visual Analogue Scale (VAS) scores and functional magnetic resonance imaging (fMRI) data during the induction of placebo analgesia and nocebo hyperalgesia via conditioned learning. Our findings revealed that placebo analgesia was more pronounced and lasted longer than nocebo hyperalgesia. Real-time pain ratings correlated significantly with neural activity in several brain regions. Notably, the putamen was implicated in both effects, while the caudate and other regions were differentially involved in placebo and nocebo effects. These findings confirm that the tonic muscle pain paradigm can be used to investigate the mechanisms of placebo and nocebo effects and indicate that placebo analgesia and nocebo hyperalgesia may have more distinct than common neural bases.

AAAI Conference 2023 Conference Paper

CasFusionNet: A Cascaded Network for Point Cloud Semantic Scene Completion by Dense Feature Fusion

Jinfeng Xu
Xianzhi Li
Yuan Tang
Qiao Yu
Yixue Hao
Long Hu
Min Chen

Semantic scene completion (SSC) aims to complete a partial 3D scene and predict its semantics simultaneously. Most existing works adopt the voxel representations, thus suffering from the growth of memory and computation cost as the voxel resolution increases. Though a few works attempt to solve SSC from the perspective of 3D point clouds, they have not fully exploited the correlation and complementarity between the two tasks of scene completion and semantic segmentation. In our work, we present CasFusionNet, a novel cascaded network for point cloud semantic scene completion by dense feature fusion. Specifically, we design (i) a global completion module (GCM) to produce an upsampled and completed but coarse point set, (ii) a semantic segmentation module (SSM) to predict the per-point semantic labels of the completed points generated by GCM, and (iii) a local refinement module (LRM) to further refine the coarse completed points and the associated labels from a local perspective. We organize the above three modules via dense feature fusion in each level, and cascade a total of four levels, where we also employ feature fusion between each level for sufficient information usage. Both quantitative and qualitative results on our compiled two point-based datasets validate the effectiveness and superiority of our CasFusionNet compared to state-of-the-art methods in terms of both scene completion and semantic segmentation. The codes and datasets are available at: https://github.com/JinfengX/CasFusionNet.

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EAAI Journal 2023 Journal Article

RFA-Net: Residual feature attention network for fine-grained image inpainting

Min Chen
Shengrui Zang
Zhenhua Ai
Jieru Chi
Guowei Yang
Chenglizhao Chen
Teng Yu

Although most existing methods using Generative Adversarial Networks (GAN) generally produce plausible results, there is a significant amount of artifacts and less-than-ideal restoration of textures when large regions are missing or the background of missing regions is complex. To address this issue, in this paper, we propose a novel texture-aware backbone net named RFA-Net for finer texture image inpainting. Compared to conventional encoder–decoder methods, our main contribution is proposing a novel RFA-Net adopt a non-pooling residual CNN structure with three novel modules, which retains texture features from shallow layers and adaptively learn the importance of certain channels and locations of features that may potentially benefit image inpainting. In addition, we propose a hybrid loss optimization (HLO) module to enable the generator to focus on the semantic and texture details of the inpainted contents. Experimental results demonstrate that our RFA-Net is able to recover texture details and ground-truth consistent images, and outperforms the state-of-the-art methods both in terms of image quality and quantitative metrics. Our source code and data are available online at https: //github. com/Jamie-61/RFA-Net-Inpainting.

EAAI Journal 2023 Journal Article

Texture-aware gray-scale image colorization using a bistream generative adversarial network with multi scale attention structure

Shengrui Zang
Min Chen
Zhenhua Ai
Jieru Chi
Guowei Yang
Chenglizhao Chen
Teng Yu

Various methods based on deep neural networks have been proposed to generate color images from gray-scale images, meanwhile, Generative adversarial networks (GANs) are also gradually applied to image colorization. However, the existing methods are texture-unaware, resulting in dullish color and color bleeding artifacts in the output images. This paper attempt to integrate a novel texture-aware bistream GAN into the conventional encoder–decoder structure for image colorization. In this study, the proposed bistream feature extraction module (BSFEM) and the feature boosting module (FBM), extract the global and local features from two parallel encoders and fuse them via a novel hybrid attention structure, this novel structure could emphasize the importance of certain channels and locations of features that may potentially benefit image colorization. In addition, the texture colors can be better recovered though the proposed multi-scale feature attention module (MSFAM). The quantitative experiments demonstrate that, compared to the state-of-the-art approaches, the proposed method has improved the PSNR and SSIM metrics by 18% and 8% respectively. Moreover, the qualitative results show that this method is capable of producing visually pleasant color images especially in terms of recovering texture details and eliminating color bleeding along the edges. The source code and data are available online at https: //github. com/JarryZang/Image-Colorization-.

JBHI Journal 2022 Journal Article

GNN-Based Depression Recognition Using Spatio-Temporal Information: A fNIRS Study

Qiao Yu
Rui Wang
Jia Liu
Long Hu
Min Chen
Zhongchun Liu

In recent years, depression has become an increasingly serious problem globally. Previous studies of automatic depression recognition based on functional near-Infrared spectroscopy (fNIRS) or other brain imaging techniques have shown potential to serve as auxiliary diagnosis methods that provide assistance to medical professionals. Recently, some studies have found that, besides directly using the data themselves (temporal data), the use of functional connectivity among channels (spatial data) also can be effective. In this paper, we propose a method based on Graph Neural Network (GNN) that combines both temporal and spatial features of fNIRS data for automatic depression recognition. Specifically, fNIRS data of 96 subjects were collected and pre-processed. Basic statistical metrics of each channel were extracted as temporal features, and channel connectivity (coherence and correlation) were calculated as spatial features. Point-biserial analysis was conducted on these features and depression labels as a data-driven motivation. For classification, we considered data of each subject as a graph, with temporal features as node features and spatial features as edge weights. The graphs were fed into GNNs for training and testing. Experimental results showed that our GNN-based methods realized the best depression recognition performance compared with classical machine-learning methods regarding accuracy, F1 score, and precision, especially in F1 score for over 10%.

JBHI Journal 2022 Journal Article

Guest Editorial Sensing Psychological Parameters and AI-Enabled Emotion Care for Human Wellness

Min Chen
Hamid Gharavi
Lin Wang
Victor C. M. Leung
Zhongchun Liu
Iztok Humar

The papers in this special section focus on the use of artificial intelligence (AI)-enabled technologies to address human wellness. As the COVID-19 pandemic took hold over the last several years, there was an urgent demand to pay more attention to psychological health for human wellness by providing methods and means of sensing psychological parameters, emotional care and mental disorder patient monitoring, especially during these difficult times. With the aid of wearable computing technology and artificial intelligence, emotion and mental disorder detections are available through sensing and analyzing psychological parameters. Discusses the use of AI-based patient monitoring and the ability to monitor human wellness via remote sensing technologies. The papers in this issue provide a snapshot of some of the latest research advances on the research and application of Small Things and Big Data, knowledge discovery and knowledge representation for the combination towards biomedical and health informatics.

JBHI Journal 2021 Journal Article

Depression Analysis and Recognition Based on Functional Near-Infrared Spectroscopy

Rui Wang
Yixue Hao
Qiao Yu
Min Chen
Iztok Humar
Giancarlo Fortino

Depression is the result of a complex interaction of social, psychological and physiological elements. Research into the brain disorders of patients suffering from depression can help doctors to understand the pathogenesis of depression and facilitate its diagnosis and treatment. Functional near-infrared spectroscopy (fNIRS) is a non-invasive approach to the detection of brain functions and activities. In this paper, a comprehensive fNIRS-based depression-processing architecture, including the layers of source, feature and model, is first established to guide the deep modeling for fNIRS. In view of the complexity of depression, we propose a methodology in the time and frequency domains for feature extraction and deep neural networks for depression recognition combined with current research. It is found that compared to non-depression people, patients with depression have a weaker encephalic area connectivity and lower level of activation in the prefrontal lobe during brain activity. Finally, based on raw data, manual features and channel correlations, the AlexNet model shows the best performance, especially in terms of the correlation features and presents an accuracy rate of 0. 90 and a precision rate of 0. 91, which is higher than ResNet18 and machine-learning algorithms on other data. Therefore, the correlation of brain regions can effectively recognize depression (from cases of non-depression), making it significant for the recognition of brain functions in the clinical diagnosis and treatment of depression.

IJCAI Conference 2019 Conference Paper

Trust Dynamics and Transfer across Human-Robot Interaction Tasks: Bayesian and Neural Computational Models

Harold Soh
Shu Pan
Min Chen
David Hsu

This work contributes both experimental findings and novel computational human-robot trust models for multi-task settings. We describe Bayesian non-parametric and neural models, and compare their performance on data collected from real-world human-subjects study. Our study spans two distinct task domains: household tasks performed by a Fetch robot, and a virtual reality driving simulation of an autonomous vehicle performing a variety of maneuvers. We find that human trust changes and transfers across tasks in a structured manner based on perceived task characteristics. Our results suggest that task-dependent functional trust models capture human trust in robot capabilities more accurately, and trust transfer across tasks can be inferred to a good degree. We believe these models are key for enabling trust-based robot decision-making for natural human-robot interaction.

YNICL Journal 2018 Journal Article

Effect of rs1344706 in the ZNF804A gene on the brain network

Xiongying Chen
Zhifang Zhang
Qiumei Zhang
Wan Zhao
Jinguo Zhai
Min Chen
Boqi Du
Xiaoxiang Deng

ZNF804A rs1344706 (A/C) was the first SNP that reached genome-wide significance for schizophrenia. Recent studies have linked rs1344706 to functional connectivity among specific brain regions. However, no study thus far has examined the role of this SNP in the entire functional connectome. In this study, we used degree centrality to test the role of rs1344706 in the whole-brain voxel-wise functional connectome during the resting state. 52 schizophrenia patients and 128 healthy controls were included in the final analysis. In our whole-brain analysis, we found a significant interaction effect of genotype×diagnosis at the precuneus (PCU) (cluster size=52 voxels, peak voxel MNI coordinates: x=9, y=−69, z=63, F =32. 57, FWE corrected P <0. 001). When we subdivided the degree centrality network according to anatomical distance, the whole-brain analysis also found a significant interaction effect of genotype×diagnosis at the PCU with the same peak in the short-range degree centrality network (cluster size=72 voxels, F =37. 29, FWE corrected P <0. 001). No significant result was found in the long-range degree centrality network. Our results elucidated the contribution of rs1344706 to functional connectivity within the brain network, and may have important implications for our understanding of this risk gene's role in functional dysconnectivity in schizophrenia.

YNICL Journal 2018 Journal Article

Polymorphism in schizophrenia risk gene MIR137 is associated with the posterior cingulate Cortex's activation and functional and structural connectivity in healthy controls

Zhifang Zhang
Tongjun Yan
Yanyan Wang
Qiumei Zhang
Wan Zhao
Xiongying Chen
Jinguo Zhai
Min Chen

MIR137 gene has been repeatedly reported as a schizophrenia risk gene in genome-wide association studies (GWAS). A polymorphism (rs1625579) at the MIR137 gene has been associated with both neural activation and behavioral performance during a working memory task. This study examined MIR137's associations with task-related (N-back working memory) fMRI, resting state fMRI, and diffusion tensor images (DTI) data in 177 healthy adults. We found less deactivation of the PCC in risk allele homozygotes (TT) as compared to the GT heterozygotes (cluster size = 630 voxels, cluster level P FWE < 0. 001) during the N-back task, which replicated previous findings. Using the identified cluster within the PCC as the seed, we further found decreased functional connectivity between the PCC and the anterior cingulate cortex and its adjacent medial prefrontal cortex (ACC/MPFC) in risk allele homozygotes during both resting state (cluster size = 427 voxels, cluster level P FWE = 0. 001) and the N-back task (cluster size = 73 voxels, cluster level P FWE = 0. 05). Finally, an analysis of our DTI data showed decreased white matter integrity of the posterior cingulum in risk allele homozygotes (cluster size = 214 voxels, cluster level P FWE = 0. 03). Taken together, rs1625579 seems to play an important role in both functional and structural connectivity between the PCC and the ACC/MPFC, which may serve as the brain mechanisms for the link between rs1625579 and schizophrenia.

ECAI Conference 2016 Conference Paper

Higher-Order Correlation Coefficient Analysis for EEG-Based Brain-Computer Interface

Ye Liu 0008
Qibin Zhao
Min Chen
Liqing Zhang 0001

Electroencephalogram (EEG) based brain-computer interface (BCI) has been proved to be an effective communication way between human brain and external devices. In order to effectively recover the cortical dynamics from the EEG signals and improve the classification performance, plenty of studies focused on constructing subject-specific spatial and spectral filters, achieving considerable improvement in classification accuracy. However, almost all the approaches aimed to find one common subspace for projection of all the samples in different classes. Studies have shown that active channels and frequency information were not only subject-dependent but also class-dependent. Thus the variety of class-dependent spatial and spectral characteristics can provide further discriminative information for classification. In this paper, we proposed a tensor-based method which attempted to seek individual spatial and spectral subspaces for each class by which each class was projected into its own subspace separately such that they were easily to be classified. Finally, we added a regularization term in this model to avoid overfitting. We evaluated the effectiveness and robustness of the proposed method on two different datasets including one widely-used benchmark EEG dataset collected from healthy subjects and one self-collected EEG dataset collected from stroke patients. The results demonstrated its superior performance.

JBHI Journal 2014 Journal Article

A Collaborative Computing Framework of Cloud Network and WBSN Applied to Fall Detection and 3-D Motion Reconstruction

Chin-Feng Lai
Min Chen
Jeng-Shyang Pan
Chan-Hyun Youn
Han-Chieh Chao

As cloud computing and wireless body sensor network technologies become gradually developed, ubiquitous healthcare services prevent accidents instantly and effectively, as well as provides relevant information to reduce related processing time and cost. This study proposes a co-processing intermediary framework integrated cloud and wireless body sensor networks, which is mainly applied to fall detection and 3-D motion reconstruction. In this study, the main focuses includes distributed computing and resource allocation of processing sensing data over the computing architecture, network conditions and performance evaluation. Through this framework, the transmissions and computing time of sensing data are reduced to enhance overall performance for the services of fall events detection and 3-D motion reconstruction.

JBHI Journal 2014 Journal Article

WE-CARE: An Intelligent Mobile Telecardiology System to Enable mHealth Applications

Anpeng Huang
Chao Chen
Kaigui Bian
Xiaohui Duan
Min Chen
Hongqiao Gao
Chao Meng
Qian Zheng

Recently, cardiovascular disease (CVD) has become one of the leading death causes worldwide, and it contributes to 41% of all deaths each year in China. This disease incurs a cost of more than 400 billion US dollars in China on the healthcare expenditures and lost productivity during the past ten years. It has been shown that the CVD can be effectively prevented by an interdisciplinary approach that leverages the technology development in both IT and electrocardiogram (ECG) fields. In this paper, we present WE-CARE, an intelligent telecardiology system using mobile 7-lead ECG devices. Because of its improved mobility result from wearable and mobile ECG devices, the WE-CARE system has a wider variety of applications than existing resting ECG systems that reside in hospitals. Meanwhile, it meets the requirement of dynamic ECG systems for mobile users in terms of the detection accuracy and latency. We carried out clinical trials by deploying the WE-CARE systems at Peking University Hospital. The clinical results clearly showed that our solution achieves a high detection rate of over 95% against common types of anomalies in ECG, while it only incurs a small detection latency around one second, both of which meet the criteria of real-time medical diagnosis. As demonstrated by the clinical results, the WE-CARE system is a useful and efficient mHealth (mobile health) tool for the cardiovascular disease diagnosis and treatment in medical platforms.

YNIMG Journal 2013 Journal Article

Automatic magnetic resonance spinal cord segmentation with topology constraints for variable fields of view

Min Chen
Aaron Carass
Jiwon Oh
Govind Nair
Dzung L. Pham
Daniel S. Reich
Jerry L. Prince

Spinal cord segmentation is an important step in the analysis of neurological diseases such as multiple sclerosis. Several studies have shown correlations between disease progression and metrics relating to spinal cord atrophy and shape changes. Current practices primarily involve segmenting the spinal cord manually or semi-automatically, which can be inconsistent and time-consuming for large datasets. An automatic method that segments the spinal cord and cerebrospinal fluid from magnetic resonance images is presented. The method uses a deformable atlas and topology constraints to produce results that are robust to noise and artifacts. The method is designed to be easily extended to new data with different modalities, resolutions, and fields of view. Validation was performed on two distinct datasets. The first consists of magnetization transfer-prepared T2*-weighted gradient-echo MRI centered only on the cervical vertebrae (C1–C5). The second consists of T1-weighted MRI that covers both the cervical and portions of the thoracic vertebrae (C1–T4). Results were found to be highly accurate in comparison to manual segmentations. A pilot study was carried out to demonstrate the potential utility of this new method for research and clinical studies of multiple sclerosis.

YNIMG Journal 2011 Journal Article

Multi-parametric neuroimaging reproducibility: A 3-T resource study

Bennett A. Landman
Alan J. Huang
Aliya Gifford
Deepti S. Vikram
Issel Anne L. Lim
Jonathan A.D. Farrell
John A. Bogovic
Jun Hua

Modern MRI image processing methods have yielded quantitative, morphometric, functional, and structural assessments of the human brain. These analyses typically exploit carefully optimized protocols for specific imaging targets. Algorithm investigators have several excellent public data resources to use to test, develop, and optimize their methods. Recently, there has been an increasing focus on combining MRI protocols in multi-parametric studies. Notably, these have included innovative approaches for fusing connectivity inferences with functional and/or anatomical characterizations. Yet, validation of the reproducibility of these interesting and novel methods has been severely hampered by the limited availability of appropriate multi-parametric data. We present an imaging protocol optimized to include state-of-the-art assessment of brain function, structure, micro-architecture, and quantitative parameters within a clinically feasible 60-min protocol on a 3-T MRI scanner. We present scan–rescan reproducibility of these imaging contrasts based on 21 healthy volunteers (11 M/10 F, 22–61 years old). The cortical gray matter, cortical white matter, ventricular cerebrospinal fluid, thalamus, putamen, caudate, cerebellar gray matter, cerebellar white matter, and brainstem were identified with mean volume-wise reproducibility of 3. 5%. We tabulate the mean intensity, variability, and reproducibility of each contrast in a region of interest approach, which is essential for prospective study planning and retrospective power analysis considerations. Anatomy was highly consistent on structural acquisition (~1–5% variability), while variation on diffusion and several other quantitative scans was higher (~<10%). Some sequences are particularly variable in specific structures (ASL exhibited variation of 28% in the cerebral white matter) or in thin structures (quantitative T2 varied by up to 73% in the caudate) due, in large part, to variability in automated ROI placement. The richness of the joint distribution of intensities across imaging methods can be best assessed within the context of a particular analysis approach as opposed to a summary table. As such, all imaging data and analysis routines have been made publicly and freely available. This effort provides the neuroimaging community with a resource for optimization of algorithms that exploit the diversity of modern MRI modalities. Additionally, it establishes a baseline for continuing development and optimization of multi-parametric imaging protocols.

IS Journal 2010 Journal Article

Code-Centric RFID System Based on Software Agent Intelligence

Min Chen
Sergio Gonzalez
Qian Zhang
Victor C.M. Leung

Radiofrequency identification (RFID) technology could play a vital role in future smart-environment applications. This code-centric RFID system uses software-agent based intelligence to achieve faster service responses.

IS Journal 2010 Journal Article

Software Agent-based Intelligence for Code-centric RFID Systems

Min Chen
Sergio Gonzalez-Valenzuela
Qian Zhang
Victor Leung

Radiofrequency identification (RFID) technology could play a vital role in future smart-environment applications. This code-centric RFID system uses software-agent based intelligence to achieve faster service responses.

AAAI Conference 2004 System Paper

Responsive Information Architect: A Context-Sensitive Multimedia Conversation Framework for Information Seeking

Michelle Zhou
Rosario Uceda-Sosa
Min Chen

We are building a context-sensitive framework, called Responsive Information Architect (RIA), which engages users in automatically generated multimedia conversations. Unlike existing information browsing paradigm that forces users to explore information following pre-defined paths (e.g., GUI menus), RIA allows users to express their information requests flexibly using a mixture of input modalities, including speech, text, and gesture. Using a rich context, such as conversation history and data semantics, RIA is capable of understanding user inputs, including these complex data queries.

IJCAI Conference 2003 Conference Paper

Automated Generation of Graphic Sketches by Example

Michelle X. Zhou
Min Chen

Hand-crafting effective visual presentations is time-consuming and requires design skills. Here we present a case-based graphic sketch generation algorithm, which uses a database of existing graphic examples (cases) to automatically create a sketch of a presentation for a new user request. As the first case-based learning approach to graphics generation, our work offers three unique contributions. First, we augment a similarity metric with a set of adequacy evaluation criteria to retrieve a case that is most similar to the request and is also usable in sketch synthesis. To facilitate the retrieval of case fragments, we develop a systematic approach to case/request decomposition when a usable case cannot be found. Second, we improve case retrieval speed by organizing cases into hierarchical clusters based on their similarity distances and by using dynamically selected cluster representatives. Third, we develop a general case composition method to synthesize a new sketch from multiple retrieved cases. Furthermore, we have implemented our casebased sketch generation algorithm in a user-system cooperative graphics design system called IMPRO- VISE-! -, which helps users to generate creative and tailored presentations.