2016 Special Issue Dedicated to Particle Accelerators Comments by the Editors

Sandra Biedron,Yong Ho Chin,Paolo Craievich,Alessandro Fabris,Robert Zwaska

IEEE Transactions on Nuclear Science（2016）SCI 3区SCI 2区

Colorado State Univ | KEK High Energy Accelerator Res Org | Paul Scherrer Inst | Elettra Sincrotrone Trieste | Fermilab Natl Accelerator Lab

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Abstract

The papers in this special issue were presented at the 13th International Conference on Inorganic Scintillators and their Applications that was held June 7–12 in Berkeley, CA, USA.

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Special issues and sections,Scintillators,Meetings,Inorganic materials,Particle accelerators

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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【要点】：本特刊收录了第13届国际无机闪烁体及其应用会议上的论文，专注于粒子加速器相关研究。

【方法】：特刊中的论文采用了多种研究方法，探讨了无机闪烁体的性能与应用。

【实验】：特刊中的论文在2016年6月7日至12日于美国加州伯克利举行的会议上进行了展示，具体实验方法和数据集名称未在摘要中提及。