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BEGIN:VEVENT
DTSTAMP:20211207T055414Z
LOCATION:Online
DTSTART;TZID=America/Chicago:20211119T083000
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UID:submissions.supercomputing.org_SC21_sess338@linklings.com
SUMMARY:XLOOP 2021: The 3rd Annual Workshop on Extreme-Scale Experiment-in
 -the-Loop Computing
DESCRIPTION:Workshop\n\nXLOOP:  Morning Break (10-10:30)\n\n\n\n----------
 -----------\nXLOOP 2021:  The 3rd Annual Workshop on Extreme-Scale Experim
 ent-in-the-Loop Computing\n\nWozniak, Schwarz\n\nAdvancement in computatio
 nal power and high-speed networking is enabling a new model of scientific 
 experiment, experiment-in-the-loop computing (EILC).  In this model, simul
 ation and/or learning modules are run as data is collected from observatio
 nal and experimental sources.  Presently, the amount ...\n\n--------------
 -------\nDesigning a Streaming Data Coalescing Architecture for Scientific
  Detector ASICs with Variable Data Velocity\n\nStrempfer, Yoshii, Hammer, 
 Miceli, Bycul\n\nScientific detectors are a key technological enabler for 
 many disciplines. Application-specific integrated circuits (ASICs) are use
 d for many of these scientific detectors. Until recently, pixel detector A
 SICs have been used mainly for analog signal processing of the charge from
  the sensor layer and...\n\n---------------------\nXLOOP:  Panel\n\nDart, 
 Maier, Caswell, Neubauer\n\n---------------------\nXLOOP:  Invited Speaker
 \n\nTourassi\n\n---------------------\nALS Share – A Lightweight Data Shar
 ing Service for a Synchrotron Radiation Facility\n\nBear, Parkinson, Gerha
 rdt, Sibony, Enders\n\nToday, beamlines at DOE light sources produce vast 
 amounts of data that can easily outgrow local compute and storage capacity
 . Beamline operators need to find mechanisms that facilitate easy access f
 or experimenters to their data (with appropriate access control) but that 
 host it externally, to quic...\n\n---------------------\nBridging Data Cen
 ter AI Systems with Edge Computing for Actionable Information Retrieval\n\
 nLiu, Ali, Kenesei, Miceli, Sharma...\n\nExtremely high data rates at mode
 rn synchrotron and X-ray free-electron laser light source beamlines motiva
 te the use of machine learning methods for data reduction, feature detecti
 on, and other purposes. Regardless of the application, the basic concept i
 s the same: data collected in early stages of...\n\n---------------------\
 nAdversarial Attacks against AI-Driven Experimental Peptide Design Workflo
 ws\n\nRamanathan, Jha\n\nArtificial intelligence/ machine learning (AI/ML)
  techniques are fueling a revolution in how scientific experiments are des
 igned, implemented and automated. Specifically, increasing high-bandwidth 
 instruments coupled to new hardware and software systems can significantly
  improve the throughput of ex...\n\n---------------------\nOptimizing High
 -Throughput Capabilities by Leveraging Reinforcement Learning Methods with
  the Bluesky Suite\n\nOlds, Allan, Caswell, Lynch, Maffettone...\n\nModern
  light sources have dramatically increased available photon flux at beamli
 nes, allowing greatly increased measurement speeds for many techniques and
  enabling high-throughput modes.  The limiting factor in optimizing such p
 rocesses is typically driven by variations in sample composition leading..
 .\n\n---------------------\nHigh-Performance Hybrid-Global-Deflated-Local 
 Optimization with Applications to Active Learning\n\nNoack\n\nMathematical
  optimization lies at the core of many science and industry applications. 
 One important issue with many current optimization strategies is a well-kn
 own trade-off between the number of function evaluations and the probabili
 ty to find the global, or at least sufficiently high-quality local...\n\n\
 nTag: Online Only, Applications, Scientific Computing\n\nRegistration Cate
 gory: Workshop Reg Pass
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