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DTSTART;TZID=America/Denver:20231112T104000
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UID:submissions.supercomputing.org_SC23_sess425_ws_xloop112@linklings.com
SUMMARY:Exploring Benchmarks for Self-Driving Labs Using Color Matching
DESCRIPTION:Tobias Ginsburg (Argonne National Laboratory (ANL), Data Scien
 ce and Learning Division); Kyle Hippe and Ryan Lewis (Argonne National Lab
 oratory (ANL)); Aileen Cleary (Northwestern University); Doga Ozgulbas (Ar
 gonne National Laboratory (ANL)); Rory Butler (University of Chicago); Cas
 ey Stone and Abraham Stroka (Argonne National Laboratory (ANL)); and Rafae
 l Vescovi and Ian Foster (Argonne National Laboratory (ANL), Data Science 
 and Learning Division)\n\nSelf Driving Labs (SDLs) that combine automation
  of experimental procedures with autonomous decision making are gaining po
 pularity as a means of increasing the throughput of scientific workflows. 
  The task of identifying a mix of supplied colored pigments that matches a
  target color, the color matching problem, has emerged as a simple and fle
 xible test case for these labs, as it requires experiment proposal, sample
  creation, and sample analysis, three common components in automated disco
 very applications. We present a modular, easily retargetable robotic solut
 ion to the color matching problem that allows for fully autonomous executi
 on of a color matching protocol, with feedback from pluggable optimization
  approaches allowing for continuous refinement and automated publication o
 f results facilitating experiment tracking and post-hoc analysis\n\nTag: L
 arge Scale Systems, Performance Measurement, Modeling, and Tools, Software
  Engineering\n\nRegistration Category: Workshop Reg Pass\n\nSession Chairs
 : Nicholas Schwarz (Argonne National Laboratory (ANL)) and Justin Wozniak 
 (Argonne National Laboratory (ANL), University of Chicago)\n\n
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