Currently, many methods in the field of Semi-Supervised Learning (SSL) assume that the datasets are balanced, but imbalanced datasets are very common in real scenarios. To solve the imbalance problem, many methods require prior knowledge of the class distribution of the entire dataset, so as to use the prior distribution to guide the model training on the unlabeled data. These methods often assume that the unlabeled data distribution is uniform or identical to the labeled data distribution, which may differ from the real scenario distribution. In this work, we provide a new perspective for setting the class distribution for imbalanced SSL. Without assuming the unlabeled data distribution in advance, we propose to record the model’s sampling frequency results for each class as the sampling distribution of the whole data, which can reflect the model’s true sampling situation. Based on this, we adopt logits adjustment method to deal with the imbalance problem of sampled data. To utilize more pseudo-labels, we further adjust the threshold according to the class distribution. The final experiments show that our method achieves excellent performance in various imbalanced semi-supervised settings.

Surface wave is the most energetic form of motions in the ocean and is crucially important to navigation safety and climate change. High-resolution global wave model plays a key role in accurate surface wave forecasting. However, operational forecasting systems are still not in high-resolution due to entailed high demand for large computation, as well as low parallel efficiency barrier. Here breakthroughs encompassing the design and application of irregular quasi-rectangular domain decomposition, master-slave cooperative computing workflow and pipelining scheme were applied to a global wave model, which has been used in several operational forecasting systems and earth system models. Our realistic surface wave simulations on Sunway TaihuLight Supercomputer demonstrated that our model had outstanding scalability and achieved 45.43 PFlops in ultra-high resolution of (1/100)°, using full-scale supercomputer with 10,649,600 cores. That provides a highly effective solution for accurate surface wave forecasting and climate change prediction.