A new approach for improving the detection sensitivity of V and Mn in steel using LIBS with ring-magnet confinement has been presented.

We introduce a novel approach to transformers that learns hierarchical representations in multiparty dialogue. First, three language modeling tasks are used to pre-train the transformers, token-and utterance-level language modeling and utterance order prediction, that learn both token and utterance embeddings for better understanding in dialogue contexts. Then, multitask learning between the utterance prediction and the token span prediction is applied to finetune for span-based question answering (QA). Our approach is evaluated on the FRIENDSQA dataset and shows improvements of 3.8% and 1.4% over the two state-of-the-art transformer models, BERT and RoBERTa, respectively.

This study discovered the distributional difference of self-absorption effect in laser-induced breakdown spectroscopy, and investigated the method to reduce the self-absorption effect.