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Wei Qiu

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11 papers

2 author rows

AAAI Conference 2026 Conference Paper

AdaField: Generalizable Surface Pressure Modeling with Physics-Informed Pre-training and Flow-Conditioned Adaptation

Junhong Zou
Wei Qiu
Zhenxu Sun
Xiaomei Zhang
Zhaoxiang Zhang
Xiangyu Zhu

The surface pressure field of transportation systems, including cars, trains, and aircraft, is critical for aerodynamic analysis and design. In recent years, deep neural networks have emerged as promising and efficient methods for modeling surface pressure field, being alternatives to computationally expensive CFD simulations. Currently, large-scale public datasets are available for domains such as automotive aerodynamics. However, in many specialized areas, such as high-speed trains, data scarcity remains a fundamental challenge in aerodynamic modeling, severely limiting the effectiveness of standard neural network approaches. To address this limitation, we propose the Adaptive Field Learning Framework (AdaField), which pre-trains the model on public large-scale datasets to improve generalization in sub-domains with limited data. AdaField comprises two key components. First, we design the Semantic Aggregation Point Transformer (SAPT) as a high-performance backbone that efficiently handles large-scale point clouds for surface pressure prediction. Second, regarding the substantial differences in flow conditions and geometric scales across different aerodynamic subdomains, we propose Flow-Conditioned Adapter (FCA) and Physics-Informed Data Augmentation (PIDA). FCA enables the model to flexibly adapt to different flow conditions with a small set of trainable parameters, while PIDA expands the training data distribution to better cover variations in object scale and velocity. Our experiments show that AdaField achieves SOTA performance on the DrivAerNet++ dataset and can be effectively transferred to train and aircraft scenarios with minimal fine-tuning. These results highlight AdaField’s potential as a generalizable and transferable solution for surface pressure field modeling, supporting efficient aerodynamic design across a wide range of transportation systems.

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YNICL Journal 2025 Journal Article

Alterations of long-range association fibers in patients with anti-N-methyl-D-aspartate receptor encephalitis

Xiaodong Chen
Ling Fang
Yiying Huang
Yu Huang
Yi Lu
Jinhui Wang
Chunxin Liu
Huanquan Liao

BACKGROUND: Patients with anti-NMDAR encephalitis typically exhibit impaired cognitive integration, which relies on the integrity of long-range association fibers connecting diverse brain regions. However, the microstructural integrity of long-range association fibers in this population remains unknown. METHODS: Diffusion tensor imaging (DTI) data were collected from 32 patients with anti-NMDAR encephalitis and 30 healthy controls. Patients were further categorized into early and delayed immunotherapy subgroups based on a 2-week threshold for immunotherapy initiation. The diffusion properties of major long-range association fibers were quantified at both the bundle and node levels. RESULTS: Compared with healthy controls, patients exhibited widespread microstructural damage within long-range association fibers, with more severe alterations in the delayed immunotherapy subgroup (FDR-corrected p < 0.05). In this subgroup(n = 14), radial diffusivity (RD) of left inferior fronto-occipital fasciculus (IFOF), left inferior longitudinal fasciculus (ILF), left superior longitudinal fascicles (SLF), and bilateral arcuate fascicles correlated significantly with global cognition (MMSE, FDR-corrected p < 0.05). Notably, RD also strongly correlated with working memory in the delayed immunotherapy subgroup, showing bundle-wise associations for IFOF (left: r = -0.8315, p = 0.0112; right: r = -0.7044, p = 0.0295), ILF (left: r = -0.7473, p = 0.0243), SLF (left: r = -0.7562, p = 0.0243; right: r = -0.6599, p = 0.0391), and arcuate fasciculus (left: r = -0.7240, p = 0.0272; right: r = -0.6835, p = 0.0333), with left-hemisphere predominance confirmed by node-wise analyses of IFOF, ILF, SLF, and arcuate fasciculus (FDR-corrected p < 0.05). CONCLUSIONS: Our findings highlight widespread microstructural damage in long-range association fibers in patients with anti-NMDAR encephalitis, particularly in those with delayed immunotherapy. This damage may serve as the neurophysiological basis for cognitive impairments, with working memory being most affected.

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AAMAS Conference 2023 Conference Paper

A Learning Approach to Complex Contagion Influence Maximization

Haipeng Chen
Bryan Wilder
Wei Qiu
Bo An
Eric Rice
Milind Tambe

Influence maximization (IM) aims to find a set of seed nodes in a social network that maximizes the influence spread. While most IM problems focus on classical influence cascades (e. g. , Independent Cascade and Linear Threshold) which assume individual influence cascade probability is independent of the number of neighbors, recent studies by sociologists show that many influence cascades follow a pattern called complex contagion (CC), where influence cascade probability is much higher when more neighbors are influenced. Nonetheless, there are very limited studies on complex contagion influence maximization (CCIM) problems. This is partly because CC is non-submodular, the solution of which has been an open challenge. In this study, we propose the first reinforcement learning (RL) approach to CCIM. We find that a key obstacle in applying existing RL approaches to CCIM is the reward sparseness issue, which comes from two distinct sources. We then design a new RL algorithm that uses the CCIM problem structure to address the issue. Empirical results show that our approach achieves the state-of-the-art performance on four real-world networks.

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IJCAI Conference 2023 Conference Paper

Complex Contagion Influence Maximization: A Reinforcement Learning Approach

Haipeng Chen
Bryan Wilder
Wei Qiu
Bo An
Eric Rice
Milind Tambe

In influence maximization (IM), the goal is to find a set of seed nodes in a social network that maximizes the influence spread. While most IM problems focus on classical influence cascades (e. g. , Independent Cascade and Linear Threshold) which assume individual influence cascade probability is independent of the number of neighbors, recent studies by sociologists show that many influence cascades follow a pattern called complex contagion (CC), where influence cascade probability is much higher when more neighbors are influenced. Nonetheless, there are very limited studies for complex contagion influence maximization (CCIM) problems. This is partly because CC is non-submodular, the solution of which has been an open challenge. In this study, we propose the first reinforcement learning (RL) approach to CCIM. We find that a key obstacle in applying existing RL approaches to CCIM is the reward sparseness issue, which comes from two distinct sources. We then design a new RL algorithm that uses the CCIM problem structure to address the issue. Empirical results show that our approach achieves the state-of-the-art performance on 9 real-world networks.

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ICML Conference 2023 Conference Paper

Learning to Maximize Mutual Information for Dynamic Feature Selection

Ian Connick Covert
Wei Qiu
Mingyu Lu
Nayoon Kim
Nathan J. White
Su-In Lee

Feature selection helps reduce data acquisition costs in ML, but the standard approach is to train models with static feature subsets. Here, we consider the dynamic feature selection (DFS) problem where a model sequentially queries features based on the presently available information. DFS is often addressed with reinforcement learning, but we explore a simpler approach of greedily selecting features based on their conditional mutual information. This method is theoretically appealing but requires oracle access to the data distribution, so we develop a learning approach based on amortized optimization. The proposed method is shown to recover the greedy policy when trained to optimality, and it outperforms numerous existing feature selection methods in our experiments, thus validating it as a simple but powerful approach for this problem.

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AAMAS Conference 2023 Conference Paper

Off-Beat Multi-Agent Reinforcement Learning

Wei Qiu
Weixun Wang
Rundong Wang
Bo An
Yujing Hu
Svetlana Obraztsova
Zinovi Rabinovich
Jianye Hao

We investigate cooperative multi-agent reinforcement learning in environments with off-beat actions, i. e. , all actions have execution durations. During execution durations, the environmental changes are not synchronised with action executions. To learn efficient multi-agent coordination in environments with off-beat actions, we propose a novel reward redistribution method built on our novel graph-based episodic memory. We name our solution method as LeGEM. Empirical results on stag-hunter game show that it significantly boosts multi-agent coordination.

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AAMAS Conference 2022 Conference Paper

Mis-spoke or mis-lead: Achieving Robustness in Multi-Agent Communicative Reinforcement Learning

Wanqi Xue
Wei Qiu
Bo An
Zinovi Rabinovich
Svetlana Obraztsova
Chai Kiat Yeo

Recent studies in multi-agent communicative reinforcement learning (MACRL) have demonstrated that multi-agent coordination can be greatly improved by allowing communication between agents. Meanwhile, adversarial machine learning (ML) has shown that ML models are vulnerable to attacks. Despite the increasing concern about the robustness of ML algorithms, how to achieve robust communication in multi-agent reinforcement learning has been largely neglected. In this paper, we systematically explore the problem of adversarial communication in MACRL. Our main contributions are threefold. First, we propose an effective method to perform attacks in MACRL, by learning a model to generate optimal malicious messages. Second, we develop a defence method based on message reconstruction, to maintain multi-agent coordination under message attacks. Third, we formulate the adversarial communication problem as a two-player zero-sum game and propose a game-theoretical method ℜ-MACRL to improve the worst-case defending performance. Empirical results demonstrate that many state-of-the-art MACRL methods are vulnerable to message attacks, and our method can significantly improve their robustness.

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AAAI Conference 2021 Conference Paper

Automated Lay Language Summarization of Biomedical Scientific Reviews

Yue Guo
Wei Qiu
Yizhong Wang
Trevor Cohen

Health literacy has emerged as a crucial factor in making appropriate health decisions and ensuring treatment outcomes. However, medical jargon and the complex structure of professional language in this domain make health information especially hard to interpret. Thus, there is an urgent unmet need for automated methods to enhance the accessibility of the biomedical literature to the general population. This problem can be framed as a type of translation problem between the language of healthcare professionals, and that of the general public. In this paper, we introduce the novel task of automated generation of lay language summaries of biomedical scientific reviews, and construct a dataset to support the development and evaluation of automated methods through which to enhance the accessibility of the biomedical literature. We conduct analyses of the various challenges in performing this task, including not only summarization of the key points but also explanation of background knowledge and simplification of professional language. We experiment with state-of-the-art summarization models as well as several data augmentation techniques, and evaluate their performance using both automated metrics and human assessment. Results indicate that automatically generated summaries produced using contemporary neural architectures can achieve promising quality and readability as compared with reference summaries developed for the lay public by experts (best ROUGE-L of 50. 24 and Flesch-Kincaid readability score of 13. 30). We also discuss the limitations of the current effort, providing insights and directions for future work.

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NeurIPS Conference 2021 Conference Paper

RMIX: Learning Risk-Sensitive Policies for Cooperative Reinforcement Learning Agents

Wei Qiu
Xinrun Wang
Runsheng Yu
Rundong Wang
Xu He
Bo An
Svetlana Obraztsova
Zinovi Rabinovich

Current value-based multi-agent reinforcement learning methods optimize individual Q values to guide individuals' behaviours via centralized training with decentralized execution (CTDE). However, such expected, i. e. , risk-neutral, Q value is not sufficient even with CTDE due to the randomness of rewards and the uncertainty in environments, which causes the failure of these methods to train coordinating agents in complex environments. To address these issues, we propose RMIX, a novel cooperative MARL method with the Conditional Value at Risk (CVaR) measure over the learned distributions of individuals' Q values. Specifically, we first learn the return distributions of individuals to analytically calculate CVaR for decentralized execution. Then, to handle the temporal nature of the stochastic outcomes during executions, we propose a dynamic risk level predictor for risk level tuning. Finally, we optimize the CVaR policies with CVaR values used to estimate the target in TD error during centralized training and the CVaR values are used as auxiliary local rewards to update the local distribution via Quantile Regression loss. Empirically, we show that our method outperforms many state-of-the-art methods on various multi-agent risk-sensitive navigation scenarios and challenging StarCraft II cooperative tasks, demonstrating enhanced coordination and revealing improved sample efficiency.

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AAAI Conference 2020 Conference Paper

Boundary Enhanced Neural Span Classification for Nested Named Entity Recognition

Chuanqi Tan
Wei Qiu
Mosha Chen
Rui Wang
Fei Huang

Named entity recognition (NER) is a well-studied task in natural language processing. However, the widely-used sequence labeling framework is usually difﬁcult to detect entities with nested structures. The span-based method that can easily detect nested entities in different subsequences is naturally suitable for the nested NER problem. However, previous span-based methods have two main issues. First, classifying all subsequences is computationally expensive and very inefﬁcient at inference. Second, the span-based methods mainly focus on learning span representations but lack of explicit boundary supervision. To tackle the above two issues, we propose a boundary enhanced neural span classiﬁcation model. In addition to classifying the span, we propose incorporating an additional boundary detection task to predict those words that are boundaries of entities. The two tasks are jointly trained under a multitask learning framework, which enhances the span representation with additional boundary supervision. In addition, the boundary detection model has the ability to generate high-quality candidate spans, which greatly reduces the time complexity during inference. Experiments show that our approach outperforms all existing methods and achieves 85. 3, 83. 9, and 78. 3 scores in terms of F1 on the ACE2004, ACE2005, and GENIA datasets, respectively.

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IJCAI Conference 2019 Conference Paper

Dynamic Electronic Toll Collection via Multi-Agent Deep Reinforcement Learning with Edge-Based Graph Convolutional Networks

Wei Qiu
Haipeng Chen
Bo An

Over the past decades, Electronic Toll Collection (ETC) systems have been proved the capability of alleviating traffic congestion in urban areas. Dynamic Electronic Toll Collection (DETC) was recently proposed to further improve the efficiency of ETC, where tolls are dynamically set based on traffic dynamics. However, computing the optimal DETC scheme is computationally difficult and existing approaches are limited to small scale or partial road networks, which significantly restricts the adoption of DETC. To this end, we propose a novel multi-agent reinforcement learning (RL) approach for DETC. We make several key contributions: i) an enhancement over the state-of-the-art RL-based method with a deep neural network representation of the policy and value functions and a temporal difference learning framework to accelerate the update of target values, ii) a novel edge-based graph convolutional neural network (eGCN) to extract the spatio-temporal correlations of the road network state features, iii) a novel cooperative multi-agent reinforcement learning (MARL) which divides the whole road network into partitions according to their geographic and economic characteristics and trains a tolling agent for each partition. Experimental results show that our approach can scale up to realistic-sized problems with robust performance and significantly outperform the state-of-the-art method.

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