A Large Area 100-Channel PICOSEC Micromegas Detector with Time Resolution at the 20 Ps Level
Journal of Instrumentation(2023)
Rudjer Boskovic Inst | Aristotle Univ Thessaloniki | Ludwig Maximilian Univ Munich | European Org Nucl Res CERN | SUNY Stony Brook | NCSR Demokritos | Lab Instrumentacao & Fis Expt Particulas | Univ Helsinki | Stony Brook University | Univ Paris Saclay | Jefferson Lab | SOLEIL Synchrotron | Inter Univ Inst High Energies IIHE | University of Paris-Saclay | Univ Zagreb | Univ Sci & Technol China | University of Bonn | Weizmann Inst Sci | European Organization for Nuclear Research | Friedrich Alexander Univ Erlangen Nurnberg | CEA Saclay | Aristotle University of Thessaloniki | Univ Bonn | TUV NORD EnSys GmbH Co KG | Natl Tech Univ Athens | Ctr Interdisciplinary Res & Innovat CIRI AUTH | Bursa Uludag Univ | Univ Virginia
- 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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