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Bayesian Active Learning for Scanning Probe Microscopy: from Gaussian Processes to Hypothesis Learning [article]

Maxim Ziatdinov, Yongtao Liu, Kyle Kelley, Rama Vasudevan, Sergei V. Kalinin
2022 arXiv   pre-print
, structured Gaussian Processes, and hypothesis learning.  ...  We progress from the Gaussian Process as a simple data-driven method and Bayesian inference for physical models as an extension of physics-based functional fits to more complex deep kernel learning methods  ...  Some general considerations To summarize, here we discuss the general framework for the application of the Bayesian active learning methods in scanning probe microscopy, using the example of Piezoresponse  ... 
arXiv:2205.15458v2 fatcat:a4eugxjumrgy5onvxqzjykc65y

Hypothesis-Driven Automated Experiment in Scanning Probe Microscopy: Exploring the Domain Growth Laws in Ferroelectric Materials [article]

Yongtao Liu, Anna Morozovska, Eugene Eliseev, Kyle P. Kelley, Rama Vasudevan, Maxim Ziatdinov, Sergei V. Kalinin
2022 arXiv   pre-print
These include other scanning probe microscopy modalities such as force distance curve measurements and nanoindentation, as well as materials synthesis and optimization.  ...  We report the development and implementation of a hypothesis learning based automated experiment, in which the microscope operating in the autonomous mode identifies the physical laws behind the material's  ...  Correspondingly, much attention has been focused on exploring these phenomena locally, via electron microscopy, focused X-Ray, and scanning probe microscopy (SPM), as well as combined EM-SPM modalities  ... 
arXiv:2202.01089v1 fatcat:spxs7qa7zjbgnikgsd4qz65t7e

Physics discovery in nanoplasmonic systems via autonomous experiments in Scanning Transmission Electron Microscopy [article]

Kevin M. Roccapriore, Sergei V. Kalinin, Maxim Ziatdinov
2021 arXiv   pre-print
This approach is universal, can be directly used as-is with any specimen, and is expected to be applicable to any probe-based microscopic techniques including other STEM modalities, Scanning Probe Microscopies  ...  Compared to classical Bayesian optimization methods, this approach allows to capture the complex spatial features present in the images of realistic materials, and dynamically learn structure-property  ...  to 54 = ( ) + , (1) where f is drawn from a Gaussian process (GP) prior, ~(0, ), and ~(0, ).  ... 
arXiv:2108.03290v2 fatcat:qmxyazkrarcenbtsu2dfeptkoi

Automated and Autonomous Experiment in Electron and Scanning Probe Microscopy [article]

Sergei V. Kalinin, Maxim A. Ziatdinov, Jacob Hinkle, Stephen Jesse, Ayana Ghosh, Kyle P. Kelley, Andrew R. Lupini, Bobby G. Sumpter, Rama K. Vasudevan
2021 arXiv   pre-print
Here, we aim to analyze the major pathways towards AE in imaging methods with sequential image formation mechanisms, focusing on scanning probe microscopy (SPM) and (scanning) transmission electron microscopy  ...  , etc., prior to the experiment, and conversion of low latency decision making processes on the time scale spanning from image acquisition to human-level high-order experiment planning.  ...  [1] [2] [3] Scanning probe microscopy techniques ranging from Scanning Tunneling Microscopy 4 to the broad spectrum of functional force-based Scanning Probe Microscopies 5, 6 enable imaging and spectroscopies  ... 
arXiv:2103.12165v1 fatcat:z3uh2jxrgfbf7d4kol6ed6bcra

Analysis of Super-resolution Single Molecule Localization Microscopy Data: a tutorial [article]

Mohamadreza Fazel, Michael J. Wester
2021 arXiv   pre-print
In super-resolution single molecule localization microscopy (SMLM), the independence arises from individual fluorescent labels stochastically switching between dark and fluorescent states, which in turn  ...  Therefore, image processing and post-processing are essential stages of SMLM. Here, we review the latest progress on SMLM data processing and post-processing.  ...  Lidke and Steve Pressé for useful comments. Data Availability Data sharing is not applicable to this article as no new data were created or analyzed in this work.  ... 
arXiv:2103.11246v3 fatcat:dryz5ju5ovf5rah7xjzkkrwc4e

Introduction to the Issue on Advanced Signal Processing in Microscopy and Cell Imaging

Charles Kervrann, Scott T. Acton, Jean-Christophe Olivo-Marin, Carlos Oscar Sanchez Sorzano, Michael Unser
2016 IEEE Journal on Selected Topics in Signal Processing  
They propose to reduce the critical scanning time and probe-specimen interaction through the use of a sparse (raster and spiral) sampling pattern.  ...  Recent theoretical interests include active contours, level sets, partial differential equation methods, scale space methods, graph signal processing and dictionary learning.  ... 
doi:10.1109/jstsp.2015.2511299 fatcat:nat2v4fwmramdeq3b26jnu37oy

Bayesian localization microscopy reveals nanoscale podosome dynamics

Susan Cox, Edward Rosten, James Monypenny, Tijana Jovanovic-Talisman, Dylan T Burnette, Jennifer Lippincott-Schwartz, Gareth E Jones, Rainer Heintzmann
2011 Nature Methods  
Achieving the nonoverlapping fluorophore emission necessary for conventional localization microscopy analysis requires switching a large fraction of probes into a non-emitting state.  ...  Here we present a Bayesian localization microscopy method that allows localization data to be extracted from wide-field images of live cells labeled with a standard fluorescent protein.  ...  was present compared to the null hypothesis that the data arose from noise.  ... 
doi:10.1038/nmeth.1812 pmid:22138825 pmcid:PMC3272474 fatcat:xu6fzqm5mfed3ebv6eybxuerka

Big data and deep data in scanning and electron microscopies: deriving functionality from multidimensional data sets

Alex Belianinov, Rama Vasudevan, Evgheni Strelcov, Chad Steed, Sang Mo Yang, Alexander Tselev, Stephen Jesse, Michael Biegalski, Galen Shipman, Christopher Symons, Albina Borisevich, Rick Archibald (+1 others)
2015 Advanced Structural and Chemical Imaging  
The development of electron and scanning probe microscopies in the second half of the twentieth century has produced spectacular images of the internal structure and composition of matter with nanometer  ...  From the hardware perspective, high-resolution imaging methods now routinely resolve atomic positions with approximately picometer precision, allowing for quantitative measurements of individual bond lengths  ...  electron and force-based scanning probe microscopy data [35] [36] [37] [38] [39] [40] [41] .  ... 
doi:10.1186/s40679-015-0006-6 pmid:27547705 pmcid:PMC4977326 fatcat:7faeb4lt7jdopnrlsmy4deubqq

Investigating the inner structure of focal adhesions with single-molecule localization microscopy [article]

Hendrik Deschout, Ilia Platzman, Daniel Sage, Lely Feletti, Joachim P. Spatz, Aleksandra Radenovic
2017 arXiv   pre-print
To quantify the substructure of FAs, we developed a method based on expectation maximization of a Gaussian mixture that accounts for localization uncertainty and background.  ...  In this study we have used single-molecule localization microscopy (SMLM) to investigate integrin β3 and paxillin in rat embryonic fibroblasts growing on two different extracellular matrix-representing  ...  ., and A.R. acknowledge the support of the Max Planck-EPFL Center for Molecular Nanoscience and Technology.  ... 
arXiv:1705.08107v1 fatcat:pa4zxchaezahlhon6egcwk27tu

Atomic Force Microscopy Detects the difference in Cancer Cells of different Neoplastic Aggressiveness via Machine Learning

Siona Prasad, Alex Rankine, Tarun Prasad, Patrick Song, Maxim E. Dokukin, Nadezda Makarova, Vadim Backman, Igor Sokolov
2021 Advanced NanoBiomed Research  
We assume Bayesian statistics for this penetration (the posterior probabilities). Therefore, it seems to be a typical case to apply the Gaussian process classifier, a nonparametric algorithm.  ...  In the current work, we apply machine learning to test the hypothesis that AFM imaging of fixed cells can be used to differentiate cancer cells of different neoplastic behavior.  ...  Keywords artificial intelligence, atomic force microscopy, cancer, imaging, nanomedicine  ... 
doi:10.1002/anbr.202000116 fatcat:icfpoqpmq5bdpjwg2gbbzprpkm

Hypothesis learning in an automated experiment: application to combinatorial materials libraries [article]

Maxim Ziatdinov, Yongtao Liu, Anna N. Morozovska, Eugene A. Eliseev, Xiaohang Zhang, Ichiro Takeuchi, Sergei V. Kalinin
2022 arXiv   pre-print
Machine learning is rapidly becoming an integral part of experimental physical discovery via automated and high-throughput synthesis, and active experiments in scattering and electron/probe microscopy.  ...  Here we introduce an active learning approach based on co-navigation of the hypothesis and experimental spaces.  ...  Hypothesis-driven active learning with structured Gaussian processes (sGPs).  ... 
arXiv:2112.06649v2 fatcat:bi5yn6by45fdnasmadalexjriu

Parametric Blind Deconvolution for Confocal Laser Scanning Microscopy

Praveen Pankajakshan, Bo Zhang, Laure Blanc-Feraud, Zvi Kam, Jean-Christophe Olivo-Marin, Josiane Zerubia
2007 IEEE Engineering in Medicine and Biology Society. Conference Proceedings  
Confocal laser scanning microscopy is a powerful technique for studying biological specimens in three dimensions (3D) by optical sectioning.  ...  Due to the incoherent nature of the light in fluorescence microscopy, it is possible to retrieve the phase from the observed intensities by using a model derived from geometrical optics.  ...  We choose the Bayesian hypothesis as it provides a natural framework for modeling this inference.  ... 
doi:10.1109/iembs.2007.4353856 pmid:18003522 fatcat:jbss7wk6abhybli6bcuyyjgy3i

Experimental discovery of structure-property relationships in ferroelectric materials via active learning [article]

Yongtao Liu, Kyle P. Kelley, Rama K. Vasudevan, Hiroshi Funakubo, Maxim A. Ziatdinov, Sergei V. Kalinin
2021 arXiv   pre-print
The proposed approach is universal and can be applied to a broad range of modern imaging and spectroscopy methods ranging from other scanning probe microscopy modalities to electron microscopy and chemical  ...  Many of these have been discovered and quantified via local scanning probe microscopy methods.  ...  Acknowledgements This effort (ML) was supported as part of the center for 3D Ferroelectric Microelectronics (3DFeM), an Energy Frontier Research Center funded by the U.S.  ... 
arXiv:2108.06037v2 fatcat:2csyo7lp6zhnbb7ng6bbir6b3a

Three-dimensional localization microscopy in live flowing cells

Lucien E. Weiss, Yael Shalev Ezra, Sarah Goldberg, Boris Ferdman, Omer Adir, Avi Schroeder, Onit Alalouf, Yoav Shechtman
2020 Zenodo  
Capturing the dynamics of live cell populations with nanoscale resolution poses a significant challenge, primarily owing to the speed-resolution trade-off of existing microscopy techniques.  ...  Here we show that imaging flow cytometry, in which the point detectors of flow cytometry are replaced with a camera to record 2D images, is compatible with 3D localization microscopy through point-spread-function  ...  null hypothesis testing, the test statistic (e.g.F, t, r) with confidence intervals, effect sizes, degrees of freedom and P value noted Give P values as exact values whenever suitable.For Bayesian analysis  ... 
doi:10.5281/zenodo.3911512 fatcat:6ersfzpnqvgflbmmb3bb2skx7i

On-the-fly Closed-loop Autonomous Materials Discovery via Bayesian Active Learning [article]

A. Gilad Kusne, Heshan Yu, Changming Wu, Huairuo Zhang, Jason Hattrick-Simpers, Brian DeCost, Suchismita Sarker, Corey Oses, Cormac Toher, Stefano Curtarolo, Albert V. Davydov, Ritesh Agarwal (+4 others)
2020 arXiv   pre-print
Active learning - the field of machine learning (ML) dedicated to optimal experiment design, has played a part in science as far back as the 18th century when Laplace used it to guide his discovery of  ...  In this work we focus a closed-loop, active learning-driven autonomous system on another major challenge, the discovery of advanced materials against the exceedingly complex synthesis-processes-structure-property  ...  Active Learning -Materials Optimization: Gaussian Process Upper Confidence Bounds For CAMEO and GP-UCB the iteration dependent weight parameter is used 27 .  ... 
arXiv:2006.06141v2 fatcat:peddokaeifamld42ojosrc6jyi
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