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Hai Wang

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8 papers

2 author rows

EAAI Journal 2026 Journal Article

Electro-optical and infrared multi-sensor fusion based airborne target perception: A unified framework

Zhouyu Zhang
Chenyuan He
Yingfeng Cai
Long Chen
Hai Wang
Can Zhong
Yiqun Zhang

This paper presents a unified framework for airborne target perception, designed for unmanned aerial vehicles (UAVs) operating in non-cooperative airspace environments. The core contribution to artificial intelligence lies in the integration of electro-optical and infrared (EO/IR) sensors using a convolutional sparse representation-based image fusion algorithm, along with a novel spatiotemporal detection method that combines conditional random fields and motion history analysis. The engineering application focuses on real-time airborne Sense and Avoid (SAA) capabilities for small UAVs, where a local-angle-based collision avoidance path planning method is proposed to address the limitations of monocular vision-based perception. To validate the proposed framework, a distributed digital simulation and verification system is developed based on virtual camera feeds and local network communication. This system supports closed-loop testing of visual perception, target detection, and path planning in realistic airspace environments. Experiments conducted in three representative airport scenarios — Illinois State Hospital, Shanghai Pudong International Airport, and New York John F. Kennedy International Airport — demonstrate the framework’s effectiveness in enhancing visual quality under low illumination conditions, improving detection accuracy, and enabling robust and safe autonomous navigation. Specifically, the proposed system achieves a target detection accuracy of 94. 6% and reduces false alarm rate to 2. 1%, while successfully generating collision-free paths in 97. 8% of dynamic encounters. Compared to existing state-of-the-art EO/IR fusion-based perception systems, our framework improves detection precision by 4. 3% on average and increases planning robustness by 5. 6% in complex airspace environments. These results validate both the effectiveness and the generalizability of the unified framework for real-world UAVs SAA tasks.

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JBHI Journal 2026 Journal Article

USCNet: Transformer-Based Multimodal Fusion with Segmentation Guidance for Urolithiasis Classification

Changmiao Wang
Songqi Zhang
Yongquan Zhang
Yifei Wang
Liya Liu
Nannan Li
Xingzhi Li
Jiexin Pan

Kidney stone disease ranks among the most prevalent conditions in urology, and understanding the composition of these stones is essential for creating personalized treatment plans and preventing recurrence. Current methods for analyzing kidney stones depend on post operative specimens, which prevents rapid classification before surgery. To overcome this limitation, we introduce a new approach called the Urinary Stone Segmentation and Classification Network (USCNet). This innovative method allows for precise preoperative classification of kidney stones by integrating Computed Tomography (CT) images with clinical data from Electronic Health Records (EHR). USCNet employs a Transformer-based multimodal fusion framework with CT-EHR attention and segmentation-guided attention modules for accurate classification. Moreover, a dynamic loss function is introduced to effectively balance the dual objectives of segmentation and classification. Experiments on an in-house kidney stone dataset show that USCNet demonstrates outstanding performance across all evaluation metrics, with its classification efficacy significantly surpassing existing mainstream methods. This study presents a promising solution for the precise preoperative classification of kidney stones, offering substantial clinical benefits. The source code has been made publicly available: https://github.com/fancccc/KidneyStoneSC.

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JBHI Journal 2025 Journal Article

A Lightweight 3D Distillation Volumetric Transformer for 3D MRI Super-Resolution

Jianwei Zhao
Tao Hong
Hao Qi
Zhenghua Zhou
Hai Wang

Although existing 3D super-resolution methods for magnetic resonance imaging (MRI) volumetric data can provide better visual images than some traditional 2D methods, they should face challenge of increasing network's parameters and computing cost for getting higher reconstruction accuracy. To address this issue, a lightweight 3D multi scale distillation volumetric Transformer, named Transformer-based dual-attention feature distillation (TDAFD) network, is proposed for 3D MRI by utilizing 3D information hiding in images sufficiently. Our TDAFD network contains several proposed dual-attention feature distillation (DAFD) modules and two designed recursive volumetric Transformers (RVT). Concretely, the proposed DAFD module contains a multi-scale feature distillation (MSFD) block for extracting global features under different scales and a feature enhancement dual attention block (FEDAB) for concentrating on the key features better. In addition, our RVT develops 2D Transformer to 3D and save network's parameters via recursion operations for capturing long-term dependencies in volumetric images effectively. Therefore, our proposed TDAFD network can not only extract deeper features via multi scale feature distillation and Transformer, but also realize the balance of performances and network's parameters. Extensive experiments illustrate that our proposed method achieves superior reconstruction performances than some popular 3D MRI SR methods, and saves number of weights and FLOPs.

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ICML Conference 2025 Conference Paper

Chaos Meets Attention: Transformers for Large-Scale Dynamical Prediction

Yi He
Yiming Yang
Xiaoyuan Cheng
Hai Wang
Xiao Xue
Boli Chen
Yukun Hu

Generating long-term trajectories of dissipative chaotic systems autoregressively is a highly challenging task. The inherent positive Lyapunov exponents amplify prediction errors over time. Many chaotic systems possess a crucial property — ergodicity on their attractors, which makes long-term prediction possible. State-of-the-art methods address ergodicity by preserving statistical properties using optimal transport techniques. However, these methods face scalability challenges due to the curse of dimensionality when matching distributions. To overcome this bottleneck, we propose a scalable transformer-based framework capable of stably generating long-term high-dimensional and high-resolution chaotic dynamics while preserving ergodicity. Our method is grounded in a physical perspective, revisiting the Von Neumann mean ergodic theorem to ensure the preservation of long-term statistics in the $\mathcal{L}^2$ space. We introduce novel modifications to the attention mechanism, making the transformer architecture well-suited for learning large-scale chaotic systems. Compared to operator-based and transformer-based methods, our model achieves better performances across five metrics, from short-term prediction accuracy to long-term statistics. In addition to our methodological contributions, we introduce new chaotic system benchmarks: a machine learning dataset of 140$k$ snapshots of turbulent channel flow and a processed high-dimensional Kolmogorov Flow dataset, along with various evaluation metrics for both short- and long-term performances. Both are well-suited for machine learning research on chaotic systems.

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ICLR Conference 2024 Conference Paper

AlpaGasus: Training a Better Alpaca with Fewer Data

Lichang Chen
Shiyang Li
Jun Yan
Hai Wang
Kalpa Gunaratna
Vikas Yadav
Zheng Tang
Vijay Srinivasan

Large language models~(LLMs) strengthen instruction-following capability through instruction-finetuning (IFT) on supervised instruction/response data. However, widely used IFT datasets (e.g., Alpaca's 52k data) surprisingly contain many low-quality instances with incorrect or irrelevant responses, which are misleading and detrimental to IFT. In this paper, we propose a simple and effective data selection strategy that automatically identifies and removes low-quality data using a strong LLM (e.g., ChatGPT). To this end, we introduce Alpagasus, which is finetuned on only 9k high-quality data filtered from the 52k Alpaca data. Alpagasus significantly outperforms the original Alpaca as evaluated by GPT-4 on multiple test sets and the controlled human study. Its 13B variant matches $>90\%$ performance of its teacher LLM (i.e., Text-Davinci-003) on test tasks. It also provides 5.7x faster training, reducing the training time for a 7B variant from 80 minutes (for Alpaca) to 14 minutes \footnote{We apply IFT for the same number of epochs as Alpaca(7B) but on fewer data, using 4$\times$NVIDIA A100 (80GB) GPUs and following the original Alpaca setting and hyperparameters.}. In the experiment, we also demonstrate that our method can work not only for machine-generated datasets but also for human-written datasets. Overall, Alpagasus demonstrates a novel data-centric IFT paradigm that can be generally applied to instruction-tuning data, leading to faster training and better instruction-following models.

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EAAI Journal 2024 Journal Article

Probabilistic linguistic prospect outranking risk decision making method based on stochastic dominance and application in emergency plan evaluation

Na Zhao
Suqiong Hu
Zeshui Xu
Hai Wang
Guofeng Wen
Fengjun Liu

In the decision-making process, probabilistic linguistic term sets (PLTSs) could represent decision-makers’ uncertain evaluation information. The combination of probabilistic and linguistic information assists decision-makers to depict their evaluation information more flexibly. In this paper, a prospect outranking decision-making method based on stochastic dominance is proposed for solving the multi-attribute risk decision-making problem under the probabilistic linguistic information environment. Firstly, to fully preserve the original probabilistic linguistic information, we give the definitions of the generalized probability distribution and the corresponding generalized cumulative distribution function of a PLTS for defining the distance measure between PLTSs. Secondly, based on the positive and negative ideal cumulative distribution functions, we propose the probabilistic linguistic prospect value functions. After that, considering the uncertainty of the relationship between alternatives, several kinds of novel prospect stochastic dominance and almost stochastic dominance relations are defined, which consider the decision makers’ distinction between gains and losses to measure the subtle relationship between alternatives. The stochastic dominance approach and the almost stochastic dominance approach under the probabilistic linguistic decision-making environment are put forward. Besides, we use the superiority and inferiority ranking method to solve the dominance ranking problem of the uncertainty relationship between any two alternatives. Finally, the proposed method is applied in the evaluation of emergency plans to show the accuracy and reliability, and comparative analyses are made to illustrate its availability.

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AAAI Conference 2019 Conference Paper

Towards Automated Semi-Supervised Learning

Yu-Feng Li
Hai Wang
Tong Wei
Wei-Wei Tu

Automated Machine Learning (AutoML) aims to build an appropriate machine learning model for any unseen dataset automatically, i. e. , without human intervention. Great efforts have been devoted on AutoML while they typically focus on supervised learning. In many applications, however, semisupervised learning (SSL) are widespread and current AutoML systems could not well address SSL problems. In this paper, we propose to present an automated learning system for SSL (AUTO-SSL). First, meta-learning with enhanced meta-features is employed to quickly suggest some instantiations of the SSL techniques which are likely to perform quite well. Second, a large margin separation method is proposed to fine-tune the hyperparameters and more importantly, alleviate performance deterioration. The basic idea is that, if a certain hyperparameter owns a high quality, its predictive results on unlabeled data may have a large margin separation. Extensive empirical results over 200 cases demonstrate that our proposal on one side achieves highly competitive or better performance compared to the state-of-the-art AutoML system AUTO-SKLEARN and classical SSL techniques, on the other side unlike classical SSL techniques which often significantly degenerate performance, our proposal seldom suffers from such deficiency.

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JMLR Journal 2018 Journal Article

Numerical Analysis near Singularities in RBF Networks

Weili Guo
Haikun Wei
Yew-Soon Ong
Jaime Rubio Hervas
Junsheng Zhao
Hai Wang
Kanjian Zhang

The existence of singularities often affects the learning dynamics in feedforward neural networks. In this paper, based on theoretical analysis results, we numerically analyze the learning dynamics of radial basis function (RBF) networks near singularities to understand to what extent singularities influence the learning dynamics. First, we show the explicit expression of the Fisher information matrix for RBF networks. Second, we demonstrate through numerical simulations that the singularities have a significant impact on the learning dynamics of RBF networks. Our results show that overlap singularities mainly have influence on the low dimensional RBF networks and elimination singularities have a more significant impact to the learning processes than overlap singularities in both low and high dimensional RBF networks, whereas the plateau phenomena are mainly caused by the elimination singularities. The results can also be the foundation to investigate the singular learning dynamics in deep feedforward neural networks. [abs] [ pdf ][ bib ] &copy JMLR 2018. ( edit, beta )

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