Author name cluster

Biao Wu

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5 papers

1 author row

JBHI Journal 2026 Journal Article

Quantifying The Impact of Textile Thickness and Contact Pressure on Watch-type Bone-Conduction Phonocardiogram: A Validation Study

Yumin Li
Chenxi Yang
Biao Wu
Zifei He
Zhijun Xiao
Li Ling
Foli Fan
Junjie Pan

Phonocardiogram (PCG) has increasingly been applied to out-of-hospital monitoring and home health management. Among these, Bone-Conduction PCG (BCPCG) has emerged as a promising solution for long-term wearable cardiac sound monitoring due to its superior noise resistance and privacy protection. However, its acquisition is susceptible to variations in textile thickness and contact pressure, exacerbating instability in critical event detection. To systematically evaluate these influencing mechanisms, this study quantifies the impact of varying textile thickness (0–5. 92 mm) and pressure (0–15 N) combinations on BCPCG. First, an equivalent mass–damping–spring model was employed to assess the transmission dynamics qualitatively. Subsequently, rigorous experiments were designed to collect signals and compare key features, including S1/S2 localization accuracy, pseudo signal-to-noise ratio (PSNR), and spectral centroid (SC). The results demonstrate that BCPCG signals remain relatively stable within the 0–2 N range, exhibit slight degradation between 3–5 N, and experience a marked decline under high pressure (10–15 N), where the PSNR drops by nearly 50% and S1 localization accuracy decreases to 70. 27%. This may stem from tissue tremors and amplified high-frequency noise under high pressure. Meanwhile, textile thickness at low pressures primarily affects high-frequency components without significantly impacting localization accuracy. Finally, a classification model based on Top-11 features identified contact pressure intervals (0–2 N, 3–5 N, 10–15 N), and the macro-averaged AUC reached 0. 985. This study validates the feasibility of pressure-inverse inference using BCPCG features, providing theoretical and practical foundations for real-world applications.

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AAAI Conference 2026 Conference Paper

Vision-Language Reasoning for Geolocalization: A Reinforcement Learning Approach

Biao Wu
Meng Fang
Ling Chen
Ke Xu
Tao Cheng
Jun Wang

Recent advances in vision-language models have opened up new possibilities for reasoning-driven image geolocalization. However, existing approaches often rely on synthetic reasoning annotations or external image retrieval, which can limit interpretability and generalizability. In this paper, we present Geo-R, a retrieval-free framework that uncovers structured reasoning paths from existing ground-truth coordinates and optimizes geolocation accuracy via reinforcement learning. We propose the Chain of Region, a rule-based hierarchical reasoning paradigm that generates precise, interpretable supervision by mapping GPS coordinates to geographic entities (e.g., country, province, city) without relying on model-generated or synthetic labels. Building on this, we introduce a lightweight reinforcement learning strategy with coordinate-aligned rewards based on Haversine distance, enabling the model to refine predictions through spatially meaningful feedback. Our approach bridges structured geographic reasoning with direct spatial supervision, yielding improved localization accuracy, stronger generalization, and more transparent inference. Experimental results across multiple benchmarks confirm the effectiveness of Geo-R, establishing a new retrieval-free paradigm for scalable and interpretable image geolocalization. To facilitate further research and ensure reproducibility, both the model and code will be made publicly available.

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YNIMG Journal 2025 Journal Article

Brain development during the lifespan of cynomolgus monkeys

Zhiqiang Tan
Binbin Nie
Huanhua Wu
Bang Li
Jingjie Shang
Tianhao Zhang
Zeyu Xiao
Chenchen Dong

F]FDG PET-MRI data from 228 healthy cynomolgus monkeys spanning the age range of 0.5-29.5 years to construct an age-specific multimodal image brain template toolset tailored to cynomolgus monkeys. Their brain volume and glucose metabolism were quantitatively analyzed by utilizing an individualized spatial segmentation algorithm. Our findings encapsulated the growth and development trends, sex differences, and asymmetrical variations in brain volume and glucose metabolism in cynomolgus monkeys, and analyzed the correlation between the brain volume and glucose metabolism. This endeavor enhances our capacity to leverage the cynomolgus monkey model in neuroscience research by providing a valuable resource for researchers. The age-specific brain template toolset and associated data offer a robust foundation for future investigations, facilitating a nuanced understanding of brain development in this primate species and, consequently, informing and advancing neuroscience research employing cynomolgus monkeys.

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NeurIPS Conference 2023 Conference Paper

IPMix: Label-Preserving Data Augmentation Method for Training Robust Classifiers

Zhenglin Huang
Xiaoan Bao
Na Zhang
Qingqi Zhang
Xiao Tu
Biao Wu
Xi Yang

Data augmentation has been proven effective for training high-accuracy convolutional neural network classifiers by preventing overfitting. However, building deep neural networks in real-world scenarios requires not only high accuracy on clean data but also robustness when data distributions shift. While prior methods have proposed that there is a trade-off between accuracy and robustness, we propose IPMix, a simple data augmentation approach to improve robustness without hurting clean accuracy. IPMix integrates three levels of data augmentation (image-level, patch-level, and pixel-level) into a coherent and label-preserving technique to increase the diversity of training data with limited computational overhead. To further improve the robustness, IPMix introduces structural complexity at different levels to generate more diverse images and adopts the random mixing method for multi-scale information fusion. Experiments demonstrate that IPMix outperforms state-of-the-art corruption robustness on CIFAR-C and ImageNet-C. In addition, we show that IPMix also significantly improves the other safety measures, including robustness to adversarial perturbations, calibration, prediction consistency, and anomaly detection, achieving state-of-the-art or comparable results on several benchmarks, including ImageNet-R, ImageNet-A, and ImageNet-O.

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TCS Journal 2007 Journal Article

k-Partitioning problems with partition matroid constraint

Biao Wu
Enyue Yao

In this paper, we consider the k-partitioning problems with partition matroid constraint and present an algorithm called the layered LPT algorithm. With the objective of minimizing the maximum load, we show that the layered LPT algorithm has a tight worst case ratio of 2 − 1 / m. With the objective of maximizing the minimum load, we show that the layered LPT algorithm has a tight worst case ratio of 1 / m for the general case and, with certain conditions, the worst ratio can be improved to m / ( 2 m − 1 ) for the general k case and to ( m − 1 ) / ( 2 m − 3 ) for the k = 3 case.

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