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README.md
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- pr_auc
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- weighted_f1
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model
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- name: PanLUNA
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results:
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- task:
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value: 0.964
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name: AUROC
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---
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- pr_auc
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- weighted_f1
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- cohen_kappa
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model-index:
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- name: PanLUNA
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results:
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- task:
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value: 0.964
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name: AUROC
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- task:
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finetuning: Low-Rank Adaptation (LoRA)
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type: time-series-classification
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name: ECG CSN
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dataset:
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type: CSN
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name: Chapman-Shaoxing-Ningbo
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metrics:
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- type: roc_auc
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value: 0.950
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name: AUROC
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---
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<div align="center">
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<h1>PanLUNA: An Efficient and Robust Query-Unified Multimodal Model for Edge Biosignal Intelligence</h1>
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</div>
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<p align="center">
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<a href="https://github.com/pulp-bio/BioFoundation">
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<img src ="https://img.shields.io/github/stars/pulp-bio/BioFoundation?color=ccf" alt="Github">
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</a>
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<a href="https://creativecommons.org/licenses/by-nd/4.0/">
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<img src="https://img.shields.io/badge/License-CC_BY--ND_4.0-lightgrey.svg" alt="License">
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</a>
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<a href="https://arxiv.org/pdf/2604.04297">
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<img src="https://img.shields.io/badge/arXiv-2604.04297-b31b1b.svg" alt="Paper">
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</a>
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</p>
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**PanLUNA** extends LUNA's channel-unification mechanism from topology invariance to **cross-modal fusion**, jointly processing EEG, ECG and PPG
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within a single shared encoder via **sensor-type embeddings** -- no modality specific backbones, no paired multimodal data required during pretraining.
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---
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## 🔒 License & Usage Policy (Weights)
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**Weights license:** The released model weights are licensed under **Creative Commons Attribution–NoDerivatives 4.0 (CC BY-ND 4.0)**.
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This section summarizes the practical implications for users. *This is not legal advice; please read the full license text.*
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