Výsledky vyhledávání - "Cristina Garcia-Cardona"

Akademický článek

PtychoDV: Vision Transformer-Based Deep Unrolling Network for Ptychographic Image Reconstruction

Autor: Weijie Gan, Qiuchen Zhai, Michael T. McCann, Cristina Garcia Cardona, Ulugbek S. Kamilov, Brendt Wohlberg

Publikováno v: IEEE Open Journal of Signal Processing, Vol 5, Pp 539-547 (2024)

Ptychography is an imaging technique that captures multiple overlapping snapshots of a sample, illuminated coherently by a moving localized probe. The image recovery from ptychographic data is generally achieved via an iterative algorithm that solves

Externí odkaz: https://doaj.org/article/83ee43ac3e43439496b8a36d905af4a4

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Akademický článek

Machine learning surrogate for charged particle beam dynamics with space charge based on a recurrent neural network with aleatoric uncertainty

Autor: Cristina Garcia-Cardona, Alexander Scheinker

Publikováno v: Physical Review Accelerators and Beams, Vol 27, Iss 2, p 024601 (2024)

In this work, we develop a machine learning (ML) model with aleatoric uncertainty for the low energy beam transport (LEBT) region of the LANSCE linear accelerator in which we model the transport of a space-charge-dominated 750 keV proton beam through

Externí odkaz: https://doaj.org/article/6dada45a51a547f696fe9ea528ba714c

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Akademický článek

Predicting tumor cell line response to drug pairs with deep learning

Autor: Fangfang Xia, Maulik Shukla, Thomas Brettin, Cristina Garcia-Cardona, Judith Cohn, Jonathan E. Allen, Sergei Maslov, Susan L. Holbeck, James H. Doroshow, Yvonne A. Evrard, Eric A. Stahlberg, Rick L. Stevens

Publikováno v: BMC Bioinformatics, Vol 19, Iss S18, Pp 71-79 (2018)

Abstract Background The National Cancer Institute drug pair screening effort against 60 well-characterized human tumor cell lines (NCI-60) presents an unprecedented resource for modeling combinational drug activity. Results We present a computational

Externí odkaz: https://doaj.org/article/fd72bada8c0f46febf9520c3cdac029a

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Akademický článek

CANDLE/Supervisor: a workflow framework for machine learning applied to cancer research

Autor: Justin M. Wozniak, Rajeev Jain, Prasanna Balaprakash, Jonathan Ozik, Nicholson T. Collier, John Bauer, Fangfang Xia, Thomas Brettin, Rick Stevens, Jamaludin Mohd-Yusof, Cristina Garcia Cardona, Brian Van Essen, Matthew Baughman

Publikováno v: BMC Bioinformatics, Vol 19, Iss S18, Pp 59-69 (2018)

Abstract Background Current multi-petaflop supercomputers are powerful systems, but present challenges when faced with problems requiring large machine learning workflows. Complex algorithms running at system scale, often with different patterns that

Externí odkaz: https://doaj.org/article/4833161dbb994c5f880dc0f042602862

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