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  • From: "Liu, Ming Xiong" <mliu AT lanl.gov>
  • To: "Go, Yeonju" <ygo AT bnl.gov>, "sphenix-l AT lists.bnl.gov" <sphenix-l AT lists.bnl.gov>
  • Subject: Re: [[Sphenix-l] ] Data compression AI paper and presentation at GM
  • Date: Fri, 25 Oct 2024 14:37:43 +0000

Yeonju, Jin and all,
Very nice work, thanks for sharing!

Ming

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From: sphenix-l-request AT lists.bnl.gov <sphenix-l-request AT lists.bnl.gov> on behalf of Go, Yeonju <ygo AT bnl.gov>
Sent: Friday, October 25, 2024 8:21:32 AM
To: sphenix-l AT lists.bnl.gov <sphenix-l AT lists.bnl.gov>
Subject: [EXTERNAL] [[Sphenix-l] ] Data compression AI paper and presentation at GM
 

Dear all,

 

We are pleased to introduce a new AI paper titled "Variable Neural Compression for Sparse Time Projection Chamber Data." This study focuses on online data compression/size reduction using machine learning, which has promising applications for future streaming data taking. This work is part of a BNL LDRD project in collaboration with computer scientists from the BNL Department of Artificial Intelligence. To demonstrate our AI/ML technique, we used the sPHENIX MDC simulation samples.

 

Yi Huang (BNL staff, computer scientist) will present this work at today’s sPHENIX General Meeting: https://indico.bnl.gov/event/25213/. You can also find the draft of the paper in the indica page [1].

 

We welcome your valuable input by Nov. 4th, 2024 (Mon.) and look forward to fruitful discussions during and after the presentation.

 

Thank you,

Yeonju Go on behalf of the AI analysis team

 

[1] https://indico.bnl.gov/event/25213/contributions/98186/attachments/58120/99826/paperDraft_Variable%20Neural%20Compression%20for%20Sparse%20TPC%20data_2024Oct24.pdf

 




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