## 2 Materials and Methods
### The Proposed Method
#### 2.2.2 DAB-minimize Contrasting Learning
## 5 Conclusions

This paper introduces the concept of the DAB and presents a DAB-aware contrastive learning framework for time series representations. It incorporates DABFS and RFS, and utilizes DAB-aware temporal and contextual contrasting modules to minimize DAB through DAB-aware contrastive loss. The experimental results demonstrate the effectiveness of the framework, with improved accuracy compared to self-supervised learning. The tempo-scale strategy and RFS play important roles in enhancing the discriminative power of the learned representations and addressing the data augmentation bias. The DAB problem proposed in this article is a common phenomenon in contrastive learning. The ideas of RFS and DABmin Loss proposed in this article provide a solution for more contrastive learning methods. However, the current research has not yet addressed the impact of data length on the model, and the complexity of the model needs further optimization. The influence of sample length and the quantification of DAB will be explored in future research. Conceptualization, Y.Z. ; methodology, Y.Z. and Y.L. ; software, Y.Z. ; validation, Y.Z., Y.L. and H.S. ; formal analysis, Y.Z. and Y.L. ; investigation, Y.Z. and H.S. ; resources, Y.Z. and Y.L. ; data curation, Y.L. ; writing--original draft preparation, Y.Z. ; writing--review and editing, Y.Z. ; visualization, Y.Z. ; supervision, L.Z. and L.L. ; project administration, L.Z. and L.L. ; funding acquisition, L.Z. and L.L. All authors have read and agreed to the published version of the manuscript. This research was supported by the National Natural Science Foundation of China (Grants No. 61971056, 62176024); the National Key R&D Program of China (2022 ZD01161); Beijing Municipal Science & Technology Commission (Grant No. Z181100001018035); project LB20201A010010; and the Engineering Research Center of Information Networks, Ministry of Education. Not applicable. This study did not involve humans, all the data have been obtained from the open access database. Publicly available datasets were analyzed in this study. The Sleep-EDF can be found at [[https://www.physionet.org/content/sleep-edfx/1.0.0/](https://www.physionet.org/content/sleep-edfx/1.0.0/)]([https://www.physionet.org/content/sleep-edfx/1.0.0/](https://www.physionet.org/content/sleep-edfx/1.0.0/)) (accessed on 1 November 2022), the UCI HAR dataset can be found at [[https://archive.ics.uci.edu/ml/datasets/Epileptic+Seizure+Recognition](https://archive.ics.uci.edu/ml/datasets/Epileptic+Seizure+Recognition)]([https://archive.ics.uci.edu/ml/datasets/Epileptic+Seizure+Recognition](https://archive.ics.uci.edu/ml/datasets/Epileptic+Seizure+Recognition)) (accessed on 1 November 2022), and the Epilepsy dataset can be found at [[https://archive.ics.uci.edu/ml/datasets/Epileptic+Seizure+Recognition](https://archive.ics.uci.edu/ml/datasets/Epileptic+Seizure+Recognition)]([https://archive.ics.uci.edu/ml/datasets/Epileptic+Seizure+Recognition](https://archive.ics.uci.edu/ml/datasets/Epileptic+Seizure+Recognition)) (accessed on 1 November 2022). The authors declare no conflict of interest. ## Appendix A Experimental Setup

For the DABaCLT, the Adam optimizer with the learning rate of 3 \(\times\) 10\({}^{-4}\) was applied. The weight decay of Adam was set to 3 \(\times\) 10\({}^{-4}\), the betas (b1, b2) were used as (0.9, 0.999), respectively, and the epsilon value was 1 \(\times\) 10\({}^{-8}\). The pre-training and fine-tune epochs are 40. ## References

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