Arrow Research search

Author name cluster

Biao Wu

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

5 papers
1 author row

Possible papers

5

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.

Details DOI

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.

PDF Details DOI

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.

PDF Details