The CLEO-III Trigger System
Nuclear Science Symposium and Medical Imaging Conference（1995）
UNIV ILLINOIS
Abstract
The CLEO-II experiment is presently accumulating data at the Cornell Electron Storage Ring (CESR). The superb performance of CLEO and CESR has made this laboratory a world leader in the study of heavy quark physics. Over the next two years this facility is embarking on an ambitious program to upgrade both the detector, which will become "CLEO-III", and the storage ring, whose luminosity will be increased tenfold, A detailed discussion of the design parameters for the CLEO-III trigger and data-acquisition systems are given in the CLEO-III Detector Proposal. A brief introduction to the system requirements is given, followed by more in-depth descriptions of several specific trigger system components.
MoreTranslated text
Key words
detector circuits,drift chambers,nuclear electronics,trigger circuits,CLEO-III,data-acquisition systems,heavy quark physics,system components,system requirements,trigger system
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- 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
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined