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Another validity threat may be related to the accountability of the data collection period. Yet, the most useful application for the manufacturing sensor data turned out to be data profiling and the applications of AI models were only a bonus. We use PyTorch to implement our models with Nvidia 2080Ti GPUs. How to generate pseudo labels for unseen-class samples and how to use such usually noisy pseudo labels are two critical issues in transductive learning. Instead of using a pre-defined and fixed target, contrastive learning aims to pull positive samples close to the anchor and push negative samples away in a representation space, increasing mutual information. In addition, since image harmonization aims to adjust the foreground, why not directly constrain the foreground feature? Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE) and foreground MSE (fMSE) on RGB channels. We report PSNR and fMSE. After adopting both of them, the PSNR value is further improved to 38.75 dB. In this paper, we aim to optimize the energy expenditure of federated learning tasks by taking advantage of application co-running opportunities and asynchronous execution. As validated in our experiments, once the training threads of high parallelization are running in the background (dispatched to the little cores by the kernel scheduler), simultaneous execution of a foreground application gives the entire system a deep energy discount (about 30-50%) compared to running the foreground application and training separately, with negligible performance impact measured by Frame per Second (FPS)

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