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Hongyu Chen

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13 papers

2 author rows

JBHI Journal 2026 Journal Article

A Multi-Scale Hybrid Efficient Deep Learning Model for COPD Detection Using Respiratory Sounds

Xingchen Dong
Xiaoyu Chen
Qiangqiang Chen
Xiao Liu
Hongyu Chen
Ronald M. Aarts
Bin Yin

Chronic obstructive pulmonary disease (COPD) is a prevalent respiratory disease, and early diagnosis is crucial for timely intervention and improved prognosis. Respiratory sound analysis, with its non-invasive nature and ability to reflect airway pathology, shows great potential as an auxiliary diagnostic tool. However, existing methods often focus on detecting specific abnormal sounds, such as wheezing and crackling, rather than diagnosing diseases directly, Additionally, most approaches rely on single features or architectures, which limits diagnostic accuracy. To address these issues, this paper proposes a multi-scale hybrid deep learning model that combines Convolutional Neural Network (CNN), Bidirectional Long Short-term Memory networks (BiLSTM), and Vision Transformer (ViT) to capture temporal, spatial, and global contextual features from both raw signals and multi-scale Mel spectrograms. A Multi-Scale Dynamic Fusion (MSDF) module further integrates these features to enhance representation, while achieving a balance between model complexity and performance. The model achieves accuracies of 99. 23% on the ICBHI database and 98. 48% on the KAUH/RespiratoryDatabase@TR hybrid database, demonstrating strong potential for effective clinical COPD diagnosis.

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EAAI Journal 2026 Journal Article

A novel feature reconstruction method for bone marrow cell classification

Huixiang Zhi
Muwei Jian
Hongyu Chen
Wenjing Xu
Changqun Nie
Hanjiang Luo
Xiaoguang Li

Bone Marrow Cells (BMCs) play a crucial role in human health, particularly in hematopoiesis and immune function. Assessing BMC health is essential for the early detection and treatment of hematopoietic diseases. However, traditional morphological analysis heavily depends on the experience and expertise of practitioners. Meanwhile, Convolutional Neural Networks (CNNs) often suffer from performance degradation due to redundant information present in BMC images. To address this issue, we propose a novel module, the Reduced Redundancy Block (RR-Block), which enhances feature extraction by preserving high-frequency information while filtering out less significant features. Furthermore, the RR-Block is a plug-and-play module that can be seamlessly integrated into existing CNN architectures. Experimental results demonstrate that replacing the standard 3 × 3 convolutional layers in Residual Networks (ResNet) with the RR-Block significantly improves classification performance. Specifically, on the Peripheral Blood Cell (PBC) dataset, which comprises eight cell types, our approach achieved an accuracy of 98. 54 % and a Cohen's Kappa coefficient (Kappa) of 99. 37 %. Similarly, on the Munich Leukemia Laboratory dataset, which contains 21 cell classes, our model attained an accuracy of 91. 69 % and a Kappa coefficient of 89. 14 %. These results highlight the effectiveness of our model architecture, outperforming several state-of-the-art methods.

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JBHI Journal 2026 Journal Article

Extraction of Seafarers’ Occupational Plasticity Brain Network Based on Effective Connectivity Lateralization

Lei Wang
Weiming Zeng
Baolong Li
Weifang Nie
Hua Zhang
Hongyu Chen
Yueyang Li
Yuhu Shi

Lateralization is an effective model for exploring changes in brain activity and is widely used to assess brain function. Seafarers, as an occupation working in marine environments, are subjected to long-term specialized occupational demands and experiences, which inevitably impact brain function. By utilizing lateralization, the influence of occupational experience on brain activity can be further explored. A novel Effective Connectivity Lateralization Analysis (ECLA) framework is proposed, which incorporates a Transformer-based Granger causality model (Transformer-GC) to analyze the effects of seafaring on brain plasticity. The Transformer-GC model constructs effective connectivity (EC) matrices, and lateralization indices are derived to investigate occupational influences on brain activity. Two control groups of non-seafarers are included to identify seafarers’ unique occupational plasticity brain networks. Results show that Transformer-GC achieves an accuracy improvement of nearly 16% and 19. 4% over the GRU-based and MVGC model, respectively, and a 5% gain over Pearson-based functional connectivity, confirming its superior performance. Moreover, the results of the ECLA showed significant differences in VentralAttention, Somatomotor, DorsalAttention in the seafarer, demonstrating that these brain networks are affected by the long-term work of seafarers. The findings demonstrate the effectiveness of ECLA in revealing the impact of long-term maritime work on brain plasticity, particularly in identifying the brain network of seafarers’ occupational plasticity. It is shown that occupational experience can reshape the lateralization of brain functional activity, offering new insights into neural plasticity across different professions.

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AAAI Conference 2026 Conference Paper

Self-Supervised One-Step Diffusion Refinement for Snapshot Compressive Imaging

Shaoguang Huang
Yunzhen Wang
Haijin Zeng
Hongyu Chen
Hongyan Zhang

Snapshot compressive imaging (SCI) captures multispectral images (MSIs) using a single coded two-dimensional (2-D) measurement, but reconstructing high-fidelity MSIs from these compressed inputs remains a fundamentally ill-posed challenge. Recent diffusion-based methods improve quality but are limited by scarce MSI training data, domain shifts from RGB-pretrained models, and slow multi-step sampling. These drawbacks restrict their practicality in real-world applications. Unlike prior approaches that rely on expensive iterative refinement or subspace-based diffusion embeddings (e.g., DiffSCI, PSR-SCI)—we introduce a fundamentally different paradigm: a self-supervised One-Step Diffusion (OSD) framework designed specifically for SCI. The key novelty lies in using a single-step diffusion refiner to correct an initial reconstruction, eliminating iterative denoising entirely while preserving generative quality. Moreover, we adopt a self-supervised equivariant learning strategy to train both the predictor and refiner directly from raw 2-D measurements, enabling generalization to unseen domains without ground-truth MSI. To further address limited MSI data, we design a band-selection–driven distillation strategy that transfers core generative priors from large-scale RGB datasets, effectively bridging the domain gap. Extensive experiments confirm that our approach sets a new standard—yielding PSNR gains of 3.44dB, 1.61dB, and 0.28dB on the Harvard, NTIRE, and ICVL datasets respectively, while cutting reconstruction time from 8.9s to just 0.22s per image. These gains in efficiency and adaptability advance SCI reconstruction, enabling accurate and practical real-world deployment.

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NeurIPS Conference 2025 Conference Paper

BRACE: A Benchmark for Robust Audio Caption Quality Evaluation

Tianyu Guo
Hongyu Chen
Hao Liang
Meiyi Qiang
Bohan Zeng
Linzhuang Sun
Bin Cui
Wentao Zhang

Automatic audio captioning is essential for audio understanding, enabling applications such as accessibility and content indexing. However, evaluating the quality of audio captions remains a major challenge, especially in reference-free settings where high-quality ground-truth captions are unavailable. While CLAPScore is currently the most widely used reference-free Audio Caption Evaluation Metric(ACEM), its robustness under diverse conditions has not been systematically validated. To address this gap, we introduce BRACE, a new benchmark designed to evaluate audio caption alignment quality in a reference-free setting. BRACE is primarily designed for assessing ACEMs, and can also be extended to measure the modality alignment abilities of Large Audio Language Model(LALM). BRACE consists of two sub-benchmarks: BRACE-Main for fine-grained caption comparison and BRACE-Hallucination for detecting subtle hallucinated content. We construct these datasets through high-quality filtering, LLM-based corruption, and human annotation. Given the widespread adoption of CLAPScore as a reference-free ACEM and the increasing application of LALMs in audio-language tasks, we evaluate both approaches using the BRACE benchmark, testing CLAPScore across various CLAP model variants and assessing multiple LALMs. Notably, even the best-performing CLAP-based ACEM achieves only a 70. 01 F1-score on the BRACE-Main benchmark, while the best LALM reaches just 63. 19. By revealing the limitations of CLAP models and LALMs, our BRACE benchmark offers valuable insights into the direction of future research. Our evaluation code and benchmark dataset are released in https: //github. com/HychTus/BRACE Evaluation and https: //huggingface. co/datasets/gtysssp/audio benchmarks.

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EAAI Journal 2025 Journal Article

Data-driven joint multiobjective prediction and optimization for tunnel-induced adjacent bridge pier displacement: A case study in China

Hongyu Chen
Jun Liu
Qiping Geoffrey Shen
Tiejun Li
Yang Liu

To reduce the impact of tunnel construction on adjacent bridge pile foundations and ensure safety during construction, a hybrid intelligent framework combining Bayesian optimization (BO), categorical boosting (CatBoost), and the nondominated sorting genetic algorithm-III (NSGA-III) is proposed in this paper. The nonlinear mapping function relationship between the nine input parameters and the bridge pier vertical and horizontal displacements is established via BO-CatBoost. The key optimization parameters are for interpretability analysis and determined via Shapley additive explanations (SHAP) method. NSGA-III is established with the goal of minimizing pier displacement. The applicability and validity of the proposed method are tested in a case study of the Wuhan Metro. The key findings of this study include the following. (1) The accuracy of the prediction model obtained by the BO-CatBoost algorithm on the basis of the training and simulation of the measured engineering data is high. On the bridge pier horizontal and vertical displacement test sets, the R2 values are 0. 823 and 0. 826, the RMSE values are 0. 452 and 0. 539, and the MAEs are 0. 293 and 0. 360, respectively. (2) The optimization effect of the two objectives is significant, and the average percentage of improvement stands at 35. 54%. When five shield construction parameters are adjusted simultaneously, the optimization effect of the two objectives is the best, and the average improvement percentage is 54. 76%. (3) The optimization effect of the developed BO-CatBoost-NSGA-III intelligent algorithm is greater than that of single-objective optimization. Therefore, the intelligent optimization framework proposed in this paper can provide guidance for the optimal control of pier displacement in shield underpass construction engineering.

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TIME Conference 2025 Conference Paper

Temporal Ensemble Logic for Integrative Representation of the Entirety of Clinical Trials

Xiaojin Li
Yan Huang 0034
Rashmie Abeysinghe
Zenan Sun
Hongyu Chen
Pengze Li
Xing He 0003
Shiqiang Tao

Clinical trials are typically specified with protocols that define eligibility criteria, treatment regimens, follow-up schedules, and outcome assessments. Temporality is a hallmark of all clinical trials, reflected within and across trial components, with complex dependencies unfolding across multiple time points. Despite their importance, clinical trial protocols are described in free-text format, limiting their semantic precision and the ability to support automated reasoning, leverage data across studies and sites, or simulate trial execution under varying assumptions using Real-World Data. This paper introduces a formalized representation of clinical trials using Temporal Ensemble Logic (TEL). TEL incorporates metricized modal operators, such as "always until t" (□_t) and "possibly until t" (◇_t), where t is a time-length parameter, to offer a logical framework for capturing phenotypes in biomedicine. TEL is more expressive in syntax than classical linear temporal logic (LTL) while maintaining the simplicity of semantic structures. The attributes of TEL are exploited in this paper to formally represent not only individual clinical trial components, but also the timing and sequential dependencies of these components as a whole. Modeling strategies and demonstration case studies are provided to show that TEL can represent the entirety of clinical trials, whereby providing a formal logical framework that can be used to represent the intricate temporal dependencies in trial structure specification. Since clinical trials are a cornerstone of evidence-based medicine, serving as the scientific basis for evaluating the safety, efficacy, and comparative effectiveness of therapeutic interventions, results reported here can serve as a stepping stone that leads to scalable, consistent, and reproducible representation and simulation of clinical trials across all disease domains.

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ICRA Conference 2024 Conference Paper

Neural Informed RRT*: Learning-based Path Planning with Point Cloud State Representations under Admissible Ellipsoidal Constraints

Zhe Huang 0010
Hongyu Chen
John Pohovey
Katherine Driggs-Campbell

Sampling-based planning algorithms like Rapidly-exploring Random Tree (RRT) are versatile in solving path planning problems. RRT* offers asymptotic optimality but requires growing the tree uniformly over the free space, which leaves room for efficiency improvement. To accelerate convergence, rule-based informed approaches sample states in an admissible ellipsoidal subset of the space determined by the current path cost. Learning-based alternatives model the topology of the free space and infer the states close to the optimal path to guide planning. We propose Neural Informed RRT* to combine the strengths from both sides. We define point cloud representations of free states. We perform Neural Focus, which constrains the point cloud within the admissible ellipsoidal subset from Informed RRT*, and feeds into PointNet++ for refined guidance state inference. In addition, we introduce Neural Connect to build connectivity of the guidance state set and further boost performance in challenging planning problems. Our method surpasses previous works in path planning benchmarks while preserving probabilistic completeness and asymptotic optimality. We deploy our method on a mobile robot and demonstrate real world navigation around static obstacles and dynamic humans. Code is available at https://github.com/tedhuang96/nirrt_star.

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EAAI Journal 2023 Journal Article

Intelligent multiobjective optimization for high-performance concrete mix proportion design: A hybrid machine learning approach

Sai Yang
Hongyu Chen
Zongbao Feng
Yawei Qin
Jian Zhang
Yuan Cao
Yang Liu

The concrete mix proportion design process is complex but important, especially in cold, ocean, underground and other complex engineering environments. In this study, a hybrid intelligent optimization method based on the random forest (RF), recursive feature elimination (RFE), Bayesian optimization (BO), least squares support vector machine (LSSVM) and nondominated sorting genetic algorithm (NGSA)-III was proposed to optimize the concrete mix proportion and rapidly and accurately predict the frost resistance, chloride ion penetration resistance and concrete strength (CS). Adopting a key project in Jilin Province as an example, the RF-RFE-BO-LSSVM-NSGA-III algorithm achieved a significant optimization effect in terms of the chloride ion permeability coefficient (CIPC), relative dynamic elastic modulus (RDEM) and 28-day CS. After optimization, the chloride ion penetration resistance, frost resistance and CS increased by 34. 6%, 4. 1% and 3. 7%, respectively, over the average levels of the sample data. This study can provide basis for concrete mix proportion design in complex environment.

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NeurIPS Conference 2023 Conference Paper

Intriguing Properties of Quantization at Scale

Arash Ahmadian
Saurabh Dash
Hongyu Chen
Bharat Venkitesh
Zhen Stephen Gou
Phil Blunsom
Ahmet Üstün
Sara Hooker

Emergent properties have been widely adopted as a term to describe behavior not present in smaller models but observed in larger models (Wei et al. , 2022a). Recent work suggests that the trade-off incurred by quantization is also an emergent property, with sharp drops in performance in models over 6B parameters. In this work, we ask are quantization cliffs in performance solely a factor of scale? Against a backdrop of increased research focus on why certain emergent properties surface at scale, this work provides a useful counter-example. We posit that it is possible to optimize for a quantization friendly training recipe that suppresses large activation magnitude outliers. Here, we find that outlier dimensions are not an inherent product of scale, but rather sensitive to the optimization conditions present during pre-training. This both opens up directions for more efficient quantization, and poses the question of whether other emergent properties are inherent or can be altered and conditioned by optimization and architecture design choices. We successfully quantize models ranging in size from 410M to 52B with minimal degradation in performance.

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EAAI Journal 2023 Journal Article

Safety evaluation of buildings adjacent to shield construction in karst areas: An improved extension cloud approach

Hongyu Chen
Sai Yang
Zongbao Feng
Yang Liu
Yawei Qin

To accurately evaluate the safety risk status of buildings adjacent to karst shield construction areas, a safety evaluation standard for buildings adjacent to shield construction in karst areas and a safety risk assessment method based on optimal cloud entropy are proposed. Comprehensive consideration of the tunnel characteristics, geological conditions, building conditions, construction, management and other influencing factors, a risk evaluation index system including 4 level-II indicators and 15 level-III indicators and evaluation criteria are established for buildings adjacent to shield construction in karst areas. The traditional extension cloud theory is improved based on the optimal cloud entropy calculation method for adaptive evaluation objects, and the clarity and fuzziness of index classification are considered. To verify the applicability of the proposed approach, it was applied to ten adjacent buildings in a karst geological section of Guiyang Rail transit Line 3. The results show that (a) the evaluation standard and the improved extended cloud safety risk assessment method proposed can effectively consider the uncertainty of risk events and that the evaluation results are consistent with the actual building safety risk status information with the calculated reliability factor of each building is close to 1. (b) The key risk factors are identified through sensitivity analysis. According to the key risk factors and risk statuses, effective measures can be taken, and high-risk buildings can be monitored to maintain a safe control state. Thus, the proposed approach can be feasibly used in various applications and can provide guidance for other similar projects.

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ICLR Conference 2022 Conference Paper

A Zest of LIME: Towards Architecture-Independent Model Distances

Hengrui Jia 0001
Hongyu Chen
Jonas Guan
Ali Shahin Shamsabadi
Nicolas Papernot

Definitions of the distance between two machine learning models either characterize the similarity of the models' predictions or of their weights. While similarity of weights is attractive because it implies similarity of predictions in the limit, it suffers from being inapplicable to comparing models with different architectures. On the other hand, the similarity of predictions is broadly applicable but depends heavily on the choice of model inputs during comparison. In this paper, we instead propose to compute distance between black-box models by comparing their Local Interpretable Model-Agnostic Explanations (LIME). To compare two models, we take a reference dataset, and locally approximate the models on each reference point with linear models trained by LIME. We then compute the cosine distance between the concatenated weights of the linear models. This yields an approach that is both architecture-independent and possesses the benefits of comparing models in weight space. We empirically show that our method, which we call Zest, can be applied to two problems that require measurements of model similarity: detecting model stealing and machine unlearning.

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ICRA Conference 2021 Conference Paper

An Improved Magnetic Spot Navigation for Replacing the Barcode Navigation in Automated Guided Vehicles

Houde Dai
Pengfei Guo
Hongyu Chen
Silin Zhao
Penghua Liu
Guijuan Lin

The barcode navigation based on QR (quick response) codes is widely employed in industrial logistics due to its accurate localization and flexible movement paths. However, the regular repair of damaged barcodes and robot speed control when approaching the barcodes are required. In this study, we presented an improved magnetic spot navigation approach to replace the barcode navigation for automated guided vehicles (AGVs). The fusion of the high-precision magnetic tracking method and odometer based on AGV encoders can overcome the disadvantages of barcode navigation. The magnetic tracking approach provides the AGV pose relative to the nearest magnet spot, instead of the low-precision longitudinal and lateral measurement via a magnetic ruler. Besides, with the benefit of the adaptive weighted fusion algorithm, the distance between the adjacent barcode can be set from 500 to 1000 mm via magnetic spots. Experimental results show that the mean path accuracy and mean magnet spot localization accuracy of the improved magnetic spot navigation were 110 ± 30 mm and 14. 5 ± 0. 87 mm, respectively. The proposed approach provides a novel possibility for large-area and high-precision navigation in AGVs-based industrial logistics, especially for large outdoor scenarios.

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