Test Results of a Fully Projective Lead/scintillating-Fiber Calorimeter
ECOLE POLYTECH | LPPCF | Università di Napoli and INFN Sez. Napoli | LPNHE | COPPE/EE/UFRJ | Università di Pavia and INFN Sez. Pavia | CPPM | Weizmann Institute | LAL | LIP | CERN | CFNUL | Texas Tech University
- 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

Liquid Ionization Calorimetry - Review and Preview
被引用1
Results on lead/scintillating fibres calorimetry
被引用12
被引用154
Use of Micro-Pixel Avalanche Photodiodes for the Readout of a Lead/scintillator Hadron Calorimeter
被引用2
RD1 scintillating fibre calorimeter studies
被引用3
A Fast Signal Adder for Applications with Calorimeters
被引用2
Performance of a Scintillating Fibres Semiprojective Electromagnetic Calorimeter
被引用2
The H1 Lead/scintillating-Fibre Calorimeter
被引用336
Longitudinally segmented lead/scintillator hadron calorimeter with micro-pixel APD readout
被引用9
Forward Hadron Calorimeter for Measurements of Projectile Spectators in Heavy-Ion Experiment
被引用9