Emergency Preparedness. Practical Proposals for Further Harmonisation of the Reactions in European Countries to Any Distant Nuclear or Radiological Emergency
openalex（2013）
Radiation and Nuclear Safety Authority | Dutch Inspectorate of Education | Consejo de Seguridad Nuclear | Federal Agency for Nuclear Control | National Center of Radiobiology and Radiation Protection | Frédéric Joliot-Curie National Research Institute for Radiobiology and Radiohygiene | Public Health England | European Commission | Federal Ministry of Social Affairs | Norwegian Radiation and Nuclear Safety Authority | Swedish Radiation Safety Authority | Institut de Radioprotection et de Sûreté Nucléaire | Federal Office for Radiation Protection | Radiological Protection Institute of Ireland | Bulgarian Food Safety Agency | Icelandic Transport Authority | Autorité de Sûreté Nucléaire | Swiss Federal Nuclear Safety Inspectorate | Federal Department of Defence | Federal Ministry of Finance | Office of Naval Research | Slovenian Nuclear Safety Administration | Ministry of Economic Affairs and Climate Policy
- 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
