Multilingual Neural Machine Translation (MNMT) models leverage many language pairs during training to improve translation quality for low-resource languages by transferring knowledge from high-resource languages. We study the quality of a domain-adapted MNMT model in the medical domain for English-Romanian with automatic metrics and a human error typology annotation which includes terminology-specific error categories. We compare the out-of-domain MNMT with the in-domain adapted MNMT. The in-domain MNMT model outperforms the out-of-domain MNMT in all measured automatic metrics and produces fewer terminology errors.

在本文中，我们评估了使用系统识别方法来构建异构SoC平台的热预测模型，该模型可用于快速预测不同配置的温度而不需要硬件。具体而言，我们专注于建模方法，其可以基于时钟频率和每个核心的利用百分比来预测温度。我们研究了三种方法关于它们的预测精度：使用多项式回归的线性状态空间识别方法，NARX神经网络方法和在FIR模型结构中配置的反复性神经网络方法。我们评估ODTOID-XU4板上的方法，其具有Exynos 5422 SoC。结果表明，基于多项式回归器的模型在用1小时和6小时的数据训练时显着优于其他两个模型。