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Xiaowei Li

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11 papers

2 author rows

IROS Conference 2025 Conference Paper

AirSwarm: Enabling Cost-Effective Multi-UAV Research with COTS drones

Xiaowei Li
Kuan Xu
Fen Liu
Ruofei Bai
Shenghai Yuan 0001
Lihua Xie 0001

Traditional unmanned aerial vehicle (UAV) swarm missions rely heavily on expensive custom-made drones with onboard perception or external positioning systems, limiting their widespread adoption in research and education. To address this issue, we propose AirSwarm. AirSwarm democratizes multi-drone coordination using low-cost commercially available drones such as Tello or Anafi, enabling affordable swarm aerial robotics research and education. Key innovations include a hierarchical control architecture for reliable multi-UAV coordination, an infrastructure-free visual SLAM system for precise localization without external motion capture, and a ROS-based software framework for simplified swarm development. Experiments demonstrate cm-level tracking accuracy, low-latency control, communication failure resistance, formation flight, and trajectory tracking. By reducing financial and technical barriers, AirSwarm makes multi-robot education and research more accessible. The complete instructions and open source code will be available at https://github.com/vvEverett/tello_ros.

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EAAI Journal 2025 Journal Article

Enhanced underwater acoustic target recognition using parallel dual-branch network with attention mechanism

Jingpu Xu
Xiaowei Li
Dan Zhang
Yaoran Chen
Yan Peng
Wenhu Liu

Ship-radiated noise serves as a crucial source of underwater acoustic signals for vessel classification, but its identification is often hindered by environmental variability and internal noise interference. To address these challenges, we propose a dual-branch Residual Attention-Long Short-Term Memory (ResA-LSTM) network for underwater acoustic target recognition. The proposed model integrates a Residual Attention (ResA) branch to extract spatial features and a Bidirectional Long Short-Term Memory (Bi-LSTM) branch to capture long-term temporal dependencies from Mel spectrograms. The ResA module incorporates attention mechanisms and residual connections to enhance feature selection and improve robustness in noisy environments. Evaluations conducted on two public datasets, ShipsEar and DeepShip, demonstrate the effectiveness of our approach, achieving classification accuracies of 98. 55 % and 99. 31 %, respectively. Sensitivity analysis further confirms the model's ability to handle long-duration acoustic sequences, highlighting its potential for practical deployment in real-world underwater recognition tasks.

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EAAI Journal 2025 Journal Article

Global–local adaptive resampling strategy for enhancing the performance of Physics-informed neural networks

Lei Gao
Dan Zhang
Yaoran Chen
Xiaowei Li
Chunxin Li
Yan Peng

In recent years, the rapid development of artificial intelligence (AI) has brought innovative solutions to the field of engineering. Physical-informed neural networks (PINNs) have provided a new paradigm for solving partial differential equations (PDEs). However, PINNs are extremely sensitive to the number and distribution of collocation points. Insufficient collocation points or uneven distribution may lead can lead to solution failure. To address this issue, this study proposes a global–local adaptive resampling strategy (GLAR) that combines Monte Carlo integration with PINNs. During the training process, the collocation points are resampled in both global and local regions according to the Monte Carlo integral values to improve the accuracy of the model. Numerical experiments show that GLAR-PINNs is comparable to existing resampling methods when dealing with linear PDEs (such as the Diffusion and Wave equations). When it comes to solving nonlinear PDEs (such as the Burgers, Korteweg–de Vries, and Allen–Cahn equations), the accuracy is improved has been improved by 14. 65 times, 38. 04 times, and 7. 14 times, respectively. In addition, we applied this strategy to reconstruct the flow field around a two-dimensional triangular cylinder, and the relative error of the reconstructed velocity field was less than 0. 001. This significantly promoting significantly promotes the application and development of artificial intelligence in the engineering field.

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AAAI Conference 2025 Conference Paper

TGLsta: Low-resource Textual Graph Learning with Semantic and Topological Awareness via LLMs

Qin Zhang
Xiaowei Li
Ziqi Liu
Xiaochen Fan
Xiaojun Chen
Shirui Pan

Textual Graphs (TGs) present a graph-based representation of textual data and find wide applications in real-world scenarios, such as citation networks, knowledge graphs, and social networks. While the traditional "pre-train, fine-tune" framework effectively addresses tasks requiring abundant labeled data, it falls short in scenarios with limited resource or zero-shot learning capabilities, particularly in low-resource textual graph node classification. Additionally, prevalent approaches that convert text nodes into shallow or manually engineered features fail to capture the rich semantic nuances within the text. The conventional methods often neglect the fusion of semantic and topological information, resulting in suboptimal model learning. To overcome these challenges, we proposed a novel method of low-resource textual graph node classification based on large language models, i.e., Textual graph learning with semantic and topological awareness (TGLsta), which comprehensively explores the semantic information, near neighborhood information, and the topology information in textual graphs, where these components are the most important information source contained in textual graphs. Graph prompt tuning for both zero- and few-shot textual graph node classification is further introduced.

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TCS Journal 2024 Journal Article

An approximate cost recovery scheme for the k-product facility location game with penalties

Xiaowei Li
Xiwen Lu

This paper considers a variant of the classic facility location game called the k-product facility location game with penalties. In this game, each facility can only produce at most one kind of product and each customer must be supplied with k different products or be rejected by paying the penalty cost. The authors propose a 2-approximate cross-monotonic cost-sharing scheme for the game when k = 2, and a ( 3 2 k − 3 2 )-approximate cross-monotonic cost-sharing scheme when k ≥ 3, respectively. Furthermore, the lower bound is obtained for the k-product facility location game with penalties.

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IJCAI Conference 2024 Conference Paper

CONC: Complex-noise-resistant Open-set Node Classification with Adaptive Noise Detection

Qin Zhang
Jiexin Lu
Xiaowei Li
Huisi Wu
Shirui Pan
Junyang Chen

As a popular task in graph learning, node classification seeks to assign labels to nodes, taking into account both their features and connections. However, an important challenge for its application in real-world scenarios is the presence of newly-emerged out-of-distribution samples and noisy samples, which affect the quality and robustness of learned classifiers. Out-of-distribution (OOD) samples are often found in both the training and testing phases. Such samples don’t belong to any known categories. These OOD samples are considered as outliers (OOD noise) when they appear during training, and are recognized as open-set samples during the testing. Meanwhile, in-distribution (IND) noisy data, i. e. , known class samples with wrong labels, are also prevalent and inevitably degrade a model’s performance. The challenge of open-set learning with complex IND and OOD noise remains largely unexplored, particularly when dealing with non-IID graph data. To address these challenges, this paper introduces a novel complex-noise-resistant open-set node classification approach, designed for open-set graph data containing both IND and OOD noisy nodes. Specifically, a trustworthiness learner is adopted to learn the trustworthiness rates of the feature and label for each node while a decoder and an open-set classifier are trained to reconstruct the structure of a node and to predict its category simultaneously with the guidance of node trustworthiness. The experimental results demonstrate the superiority of our method.

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AAAI Conference 2024 Conference Paper

ROG_PL: Robust Open-Set Graph Learning via Region-Based Prototype Learning

Qin Zhang
Xiaowei Li
Jiexin Lu
Liping Qiu
Shirui Pan
Xiaojun Chen
Junyang Chen

Open-set graph learning is a practical task that aims to classify the known class nodes and to identify unknown class samples as unknowns. Conventional node classification methods usually perform unsatisfactorily in open-set scenarios due to the complex data they encounter, such as out-of-distribution (OOD) data and in-distribution (IND) noise. OOD data are samples that do not belong to any known classes. They are outliers if they occur in training (OOD noise), and open-set samples if they occur in testing. IND noise are training samples which are assigned incorrect labels. The existence of IND noise and OOD noise is prevalent, which usually cause the ambiguity problem, including the intra-class variety problem and the inter-class confusion problem. Thus, to explore robust open-set learning methods is necessary and difficult, and it becomes even more difficult for non-IID graph data. To this end, we propose a unified framework named ROG_PL to achieve robust open-set learning on complex noisy graph data, by introducing prototype learning. In specific, ROG_PL consists of two modules, i.e., denoising via label propagation and open-set prototype learning via regions. The first module corrects noisy labels through similarity-based label propagation and removes low-confidence samples, to solve the intra-class variety problem caused by noise. The second module learns open-set prototypes for each known class via non-overlapped regions and remains both interior and border prototypes to remedy the inter-class confusion problem. The two modules are iteratively updated under the constraints of classification loss and prototype diversity loss. To the best of our knowledge, the proposed ROG_PL is the first robust open-set node classification method for graph data with complex noise. Experimental evaluations of ROG_PL on several benchmark graph datasets demonstrate that it has good performance.

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JBHI Journal 2023 Journal Article

Clustering-Fusion Feature Selection Method in Identifying Major Depressive Disorder Based on Resting State EEG Signals

Shuting Sun
Huayu Chen
Gang Luo
Chang Yan
Qunxi Dong
Xuexiao Shao
Xiaowei Li
Bin Hu

Depression is a heterogeneous syndrome with certain individual differences among subjects. Exploring a feature selection method that can effectively mine the commonness intra-groups and the differences inter-groups in depression recognition is therefore of great significance. This study proposed a new clustering-fusion feature selection method. Hierarchical clustering (HC) algorithm was used to capture the heterogeneity distribution of subjects. Average and similarity network fusion (SNF) algorithms were adopted to characterize the brain network atlas of different populations. Differences analysis was also utilized to obtain the features with discriminant performance. Experiments showed that compared with traditional feature selection methods, HCSNF method yielded the optimal classification results of depression recognition in both sensor and source layers of electroencephalography (EEG) data. Especially in the beta band of EEG data at sensor layer, the classification performance was improved by more than 6%. Moreover, the long-distance connections between parietal-occipital lobe and other brain regions not only have high discriminative power, but also significantly correlate with depressive symptoms, indicating the important role of these features in depression recognition. Therefore, this study may provide methodological guidance for the discovery of reproducible electrophysiological biomarkers and new insights into common neuropathological mechanisms of heterogeneous depression diseases.

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NeurIPS Conference 2021 Conference Paper

PLUGIn: A simple algorithm for inverting generative models with recovery guarantees

Babhru Joshi
Xiaowei Li
Yaniv Plan
Ozgur Yilmaz

We consider the problem of recovering an unknown latent code vector under a known generative model. For a $d$-layer deep generative network $\mathcal{G}: \mathbb{R}^{n_0}\rightarrow \mathbb{R}^{n_d}$ with ReLU activation functions, let the observation be $\mathcal{G}(x)+\epsilon$ where $\epsilon$ is noise. We introduce a simple novel algorithm, Partially Linearized Update for Generative Inversion (PLUGIn), to estimate $x$ (and thus $\mathcal{G}(x)$). We prove that, when weights are Gaussian and layer widths $n_i \gtrsim 5^i n_0$ (up to log factors), the algorithm converges geometrically to a neighbourhood of $x$ with high probability. Note the inequality on layer widths allows $n_i>n_{i+1}$ when $i\geq 1$. To our knowledge, this is the first such result for networks with some contractive layers. After a sufficient number of iterations, the estimation errors for both $x$ and $\mathcal{G}(x)$ are at most in the order of $\sqrt{4^dn_0/n_d} \|\epsilon\|$. Thus, the algorithm can denoise when the expansion ratio $n_d/n_0$ is large. Numerical experiments on synthetic data and real data are provided to validate our theoretical results and to illustrate that the algorithm can effectively remove artifacts in an image.

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AIIM Journal 2019 Journal Article

Depression recognition using machine learning methods with different feature generation strategies

Xiaowei Li
Xin Zhang
Jing Zhu
Wandeng Mao
Shuting Sun
Zihan Wang
Chen Xia
Bin Hu

The diagnosis of depression almost exclusively depends on doctor-patient communication and scale analysis, which have the obvious disadvantages such as patient denial, poor sensitivity, subjective biases and inaccuracy. An objective, automated method that predicts clinical outcomes in depression is essential for increasing the accuracy of depression recognition and treatments. This paper aims at better recognizing depression using the transformation of EEG features and machine learning methods. An experiment based on emotional face stimuli task was conducted, and twenty-eight subjects’ EEG data were recorded from 128-channel HydroCel Geodesic Sensor Net (HCGSN) by Net Station software. The Mini International Neuropsychiatric Interview (MINI) was used by psychiatrists as the criterion for diagnosis of depression patients. The power spectral density and activity were respectively extracted as original features using Auto-regress model and Hjorth algorithm with different time windows. Two separate approaches processed the features: ensemble learning and deep learning. For the ensemble learning, a deep forest transformed the original features to new features that potentially improve feature engineering and a support vector machine (SVM) that was applied as classifier. For deep learning method, we added spatial information of EEG caps to both features by image conversion and adopted convolutional neural network (CNN) to recognize them. The performance of both methods was evaluated for separated and total frequency bands. As a result, the best accuracy obtained was 89. 02% when we used the ensemble model and power spectral density. The best accuracy of deep learning method was 84. 75% using the activity. These experimental results prove the efficiency of the proposed methods and show that EEG could be used as a reliable indicator for depression recognition, which makes it possible for EEG-based portable system design and application in auxiliary depression recognition in the future.

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NeurIPS Conference 2018 Conference Paper

See and Think: Disentangling Semantic Scene Completion

Shice Liu
Yu Hu
Yiming Zeng
Qiankun Tang
Beibei Jin
Yinhe Han
Xiaowei Li

Semantic scene completion predicts volumetric occupancy and object category of a 3D scene, which helps intelligent agents to understand and interact with the surroundings. In this work, we propose a disentangled framework, sequentially carrying out 2D semantic segmentation, 2D-3D reprojection and 3D semantic scene completion. This three-stage framework has three advantages: (1) explicit semantic segmentation significantly boosts performance; (2) flexible fusion ways of sensor data bring good extensibility; (3) progress in any subtask will promote the holistic performance. Experimental results show that regardless of inputing a single depth or RGB-D, our framework can generate high-quality semantic scene completion, and outperforms state-of-the-art approaches on both synthetic and real datasets.

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