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  1. Book ; Online: Introduction to Machine Learning for Accelerator Physics

    Ratner, Daniel

    2020  

    Abstract: This pair of CAS lectures gives an introduction for accelerator physics students to the framework and terminology of machine learning (ML). We start by introducing the language of ML through a simple example of linear regression, including a ... ...

    Abstract This pair of CAS lectures gives an introduction for accelerator physics students to the framework and terminology of machine learning (ML). We start by introducing the language of ML through a simple example of linear regression, including a probabilistic perspective to introduce the concepts of maximum likelihood estimation (MLE) and maximum a priori (MAP) estimation. We then apply the concepts to examples of neural networks and logistic regression. Next we introduce non-parametric models and the kernel method and give a brief introduction to two other machine learning paradigms, unsupervised and reinforcement learning. Finally we close with example applications of ML at a free-electron laser.

    Comment: 16 pages, contribution to the CAS - CERN Accelerator School: Numerical Methods for Analysis, Design and Modelling of Particle Accelerators, 11-23 November 2018, Thessaloniki, Greece
    Keywords Physics - Accelerator Physics ; Computer Science - Machine Learning
    Publishing date 2020-06-17
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: What are the advantages of ghost imaging? Multiplexing for x-ray and electron imaging.

    Lane, Thomas J / Ratner, Daniel

    Optics express

    2020  Volume 28, Issue 5, Page(s) 5898–5918

    Abstract: Ghost imaging, Fourier transform spectroscopy, and the newly developed Hadamard transform crystallography are all examples of multiplexing measurement strategies. Multiplexed experiments are performed by measuring multiple points in space, time, or ... ...

    Abstract Ghost imaging, Fourier transform spectroscopy, and the newly developed Hadamard transform crystallography are all examples of multiplexing measurement strategies. Multiplexed experiments are performed by measuring multiple points in space, time, or energy simultaneously. This contrasts to the usual method of systematically scanning single points. How do multiplexed measurements work and when they are advantageous? Here we address these questions with a focus on applications involving x-rays or electrons. We present a quantitative framework for analyzing the expected error and radiation dose of different measurement scheme that enables comparison. We conclude that in very specific situations, multiplexing can offer improvements in resolution and signal-to-noise. If the signal has a sparse representation, these advantages become more general and dramatic, and further less radiation can be used to complete a measurement.
    Language English
    Publishing date 2020-03-26
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1491859-6
    ISSN 1094-4087 ; 1094-4087
    ISSN (online) 1094-4087
    ISSN 1094-4087
    DOI 10.1364/OE.379503
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Nanoscale chemical imaging with structured X-ray illumination.

    Li, Jizhou / Chen, Si / Ratner, Daniel / Blu, Thierry / Pianetta, Piero / Liu, Yijin

    Proceedings of the National Academy of Sciences of the United States of America

    2023  Volume 120, Issue 49, Page(s) e2314542120

    Abstract: High-resolution imaging with compositional and chemical sensitivity is crucial for a wide range of scientific and engineering disciplines. Although synchrotron X-ray imaging through spectromicroscopy has been tremendously successful and broadly applied, ... ...

    Abstract High-resolution imaging with compositional and chemical sensitivity is crucial for a wide range of scientific and engineering disciplines. Although synchrotron X-ray imaging through spectromicroscopy has been tremendously successful and broadly applied, it encounters challenges in achieving enhanced detection sensitivity, satisfactory spatial resolution, and high experimental throughput simultaneously. In this work, based on structured illumination, we develop a single-pixel X-ray imaging approach coupled with a generative image reconstruction model for mapping the compositional heterogeneity with nanoscale resolvability. This method integrates a full-field transmission X-ray microscope with an X-ray fluorescence detector and eliminates the need for nanoscale X-ray focusing and raster scanning. We experimentally demonstrate the effectiveness of our approach by imaging a battery sample composed of mixed cathode materials and successfully retrieving the compositional variations of the imaged cathode particles. Bridging the gap between structural and chemical characterizations using X-rays, this technique opens up vast opportunities in the fields of biology, environmental, and materials science, especially for radiation-sensitive samples.
    Language English
    Publishing date 2023-11-28
    Publishing country United States
    Document type Journal Article
    ZDB-ID 209104-5
    ISSN 1091-6490 ; 0027-8424
    ISSN (online) 1091-6490
    ISSN 0027-8424
    DOI 10.1073/pnas.2314542120
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1148: Show issues Location:
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  4. Book ; Online: What are the advantages of ghost imaging? Multiplexing for x-ray and electron imaging

    Lane, Thomas J. / Ratner, Daniel

    2019  

    Abstract: Ghost imaging, Fourier transform spectroscopy, and the newly developed Hadamard transform crystallography are all examples of multiplexing measurement strategies. Multiplexed experiments are performed by measuring multiple points in space, time, or ... ...

    Abstract Ghost imaging, Fourier transform spectroscopy, and the newly developed Hadamard transform crystallography are all examples of multiplexing measurement strategies. Multiplexed experiments are performed by measuring multiple points in space, time, or energy simultaneously. This contrasts to the usual method of systematically scanning single points. How do multiplexed measurements work and when they are advantageous? Here we address these questions with a focus on applications involving x-rays or electrons. We present a quantitative framework for analyzing the expected error and radiation dose of different measurement scheme that enables comparison. We conclude that in very specific situations, multiplexing can offer improvements in resolution and signal-to-noise. If the signal has a sparse representation, these advantages become more general and dramatic, and further less radiation can be used to complete a measurement.

    Comment: 13 pages, 4 figures, preprint
    Keywords Physics - Applied Physics ; Electrical Engineering and Systems Science - Image and Video Processing
    Publishing date 2019-07-28
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Book ; Online: Coincident Learning for Unsupervised Anomaly Detection

    Humble, Ryan / Zhang, Zhe / O'Shea, Finn / Darve, Eric / Ratner, Daniel

    2023  

    Abstract: Anomaly detection is an important task for complex systems (e.g., industrial facilities, manufacturing, large-scale science experiments), where failures in a sub-system can lead to low yield, faulty products, or even damage to components. While complex ... ...

    Abstract Anomaly detection is an important task for complex systems (e.g., industrial facilities, manufacturing, large-scale science experiments), where failures in a sub-system can lead to low yield, faulty products, or even damage to components. While complex systems often have a wealth of data, labeled anomalies are typically rare (or even nonexistent) and expensive to acquire. Unsupervised approaches are therefore common and typically search for anomalies either by distance or density of examples in the input feature space (or some associated low-dimensional representation). This paper presents a novel approach called CoAD, which is specifically designed for multi-modal tasks and identifies anomalies based on \textit{coincident} behavior across two different slices of the feature space. We define an \textit{unsupervised} metric, $\hat{F}_\beta$, out of analogy to the supervised classification $F_\beta$ statistic. CoAD uses $\hat{F}_\beta$ to train an anomaly detection algorithm on \textit{unlabeled data}, based on the expectation that anomalous behavior in one feature slice is coincident with anomalous behavior in the other. The method is illustrated using a synthetic outlier data set and a MNIST-based image data set, and is compared to prior state-of-the-art on two real-world tasks: a metal milling data set and a data set from a particle accelerator.
    Keywords Computer Science - Machine Learning
    Subject code 004
    Publishing date 2023-01-26
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article: CryoAI: Amortized Inference of Poses for Ab Initio Reconstruction of 3D Molecular Volumes from Real Cryo-EM Images.

    Levy, Axel / Poitevin, Frédéric / Martel, Julien / Nashed, Youssef / Peck, Ariana / Miolane, Nina / Ratner, Daniel / Dunne, Mike / Wetzstein, Gordon

    Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision

    2022  Volume 13681, Page(s) 540–557

    Abstract: Cryo-electron microscopy (cryo-EM) has become a tool of fundamental importance in structural biology, helping us understand the basic building blocks of life. The algorithmic challenge of cryo-EM is to jointly estimate the unknown 3D poses and the 3D ... ...

    Abstract Cryo-electron microscopy (cryo-EM) has become a tool of fundamental importance in structural biology, helping us understand the basic building blocks of life. The algorithmic challenge of cryo-EM is to jointly estimate the unknown 3D poses and the 3D electron scattering potential of a biomolecule from millions of extremely noisy 2D images. Existing reconstruction algorithms, however, cannot easily keep pace with the rapidly growing size of cryo-EM datasets due to their high computational and memory cost. We introduce cryoAI, an
    Language English
    Publishing date 2022-10-23
    Publishing country Germany
    Document type Journal Article
    DOI 10.1007/978-3-031-19803-8_32
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article: A nanofiber based antiviral (TAF) prodrug delivery system

    Dart, Alexander / Roy, Debashish / Vlaskin, Vladimir / Limqueco, Elaine / Lowe, Neona M. / Srinivasan, Selvi / Ratner, Daniel M. / Bhave, Mrinal / Stayton, Patrick / Kingshott, Peter

    Biomaterials advances. 2022 Feb., v. 133

    2022  

    Abstract: HIV and hepatitis B are two of the most prevalent viruses globally, and despite readily available preventive treatments unforgiving treatment regimens still exist, such as daily doses of medicine that are challenging to maintain especially in poorer ... ...

    Abstract HIV and hepatitis B are two of the most prevalent viruses globally, and despite readily available preventive treatments unforgiving treatment regimens still exist, such as daily doses of medicine that are challenging to maintain especially in poorer countries. More advanced and longer-lasting delivery vehicles can potentially overcome this problem by reducing maintenance requirements and significantly increase access to medicine. Here, we designed a technology to control the delivery of an antiviral drug over a long timeframe via a nanofiber based delivery scaffold that is both easy to produce and use. An antiviral prodrug containing tenofovir alafenamide (TAF) was synthesized by initial conjugation to glycerol monomethacrylate followed by polymerization to form a diblock copolymer (pTAF) using reversible addition-fragmentation chain transfer (RAFT). In order to generate an efficient drug delivery system this copolymer was fabricated into an electrospun nanofiber (ESF) scaffold using blend electrospinning with poly(caprolactone) (PCL) as the carrier polymer. SEM images revealed that the pTAF-PCL ESFs were uniform with an average diameter of (787 ± 0.212 nm), while XPS analysis demonstrated that the pTAF was overrepresented at the surface of the ESFs. Additionally, the pTAF exhibited a sustained release profile over a 2 month period in human serum (HS), suggesting that these types of copolymer-based drugamers can be used in conjunction with electrospinning to produce long-lasting drug delivery systems.
    Keywords antiviral agents ; biocompatible materials ; blood serum ; composite polymers ; drug delivery systems ; glycerol ; hepatitis B ; humans ; medicine ; nanofibers ; polymerization
    Language English
    Dates of publication 2022-02
    Publishing place Elsevier B.V.
    Document type Article
    ISSN 2772-9508
    DOI 10.1016/j.msec.2021.112626
    Database NAL-Catalogue (AGRICOLA)

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  8. Article: Automated prediction of lattice parameters from X-ray powder diffraction patterns.

    Chitturi, Sathya R / Ratner, Daniel / Walroth, Richard C / Thampy, Vivek / Reed, Evan J / Dunne, Mike / Tassone, Christopher J / Stone, Kevin H

    Journal of applied crystallography

    2021  Volume 54, Issue Pt 6, Page(s) 1799–1810

    Abstract: A key step in the analysis of powder X-ray diffraction (PXRD) data is the accurate determination of unit-cell lattice parameters. This step often requires significant human intervention and is a bottleneck that hinders efforts towards automated analysis. ...

    Abstract A key step in the analysis of powder X-ray diffraction (PXRD) data is the accurate determination of unit-cell lattice parameters. This step often requires significant human intervention and is a bottleneck that hinders efforts towards automated analysis. This work develops a series of one-dimensional convolutional neural networks (1D-CNNs) trained to provide lattice parameter estimates for each crystal system. A mean absolute percentage error of approximately 10% is achieved for each crystal system, which corresponds to a 100- to 1000-fold reduction in lattice parameter search space volume. The models learn from nearly one million crystal structures contained within the Inorganic Crystal Structure Database and the Cambridge Structural Database and, due to the nature of these two complimentary databases, the models generalize well across chemistries. A key component of this work is a systematic analysis of the effect of different realistic experimental non-idealities on model performance. It is found that the addition of impurity phases, baseline noise and peak broadening present the greatest challenges to learning, while zero-offset error and random intensity modulations have little effect. However, appropriate data modification schemes can be used to bolster model performance and yield reasonable predictions, even for data which simulate realistic experimental non-idealities. In order to obtain accurate results, a new approach is introduced which uses the initial machine learning estimates with existing iterative whole-pattern refinement schemes to tackle automated unit-cell solution.
    Language English
    Publishing date 2021-11-30
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2020879-0
    ISSN 1600-5767 ; 0021-8898
    ISSN (online) 1600-5767
    ISSN 0021-8898
    DOI 10.1107/S1600576721010840
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  9. Article: Label-free biosensing with a multi-box sub-wavelength phase-shifted Bragg grating waveguide.

    Luan, Enxiao / Yun, Han / Ma, Minglei / Ratner, Daniel M / Cheung, Karen C / Chrostowski, Lukas

    Biomedical optics express

    2019  Volume 10, Issue 9, Page(s) 4825–4838

    Abstract: Sub-wavelength grating (SWG) metamaterials have been considered to provide promising solutions in the development of next-generation photonic integrated circuits. In recent years, increasied interest has been paid to silicon photonic planar biosensors ... ...

    Abstract Sub-wavelength grating (SWG) metamaterials have been considered to provide promising solutions in the development of next-generation photonic integrated circuits. In recent years, increasied interest has been paid to silicon photonic planar biosensors based on SWG geometries for performance enhancement. In this work, we demonstrate a highly sensitive label-free phase-shifted Bragg grating (PSBG) sensing configuration, which consists of sub-wavelength block arrays in both propagation and transverse directions. By introducing salt serial dilutions and electrostatic polymers assays, bulk and surface sensitivities of the proposed sensor are characterized, obtaining measured results up to 579.2 nm/RIU and 1914 pm/nm, respectively. Moreover, the proposed multi-box PSBG sensor presents an improved quality factor as high as
    Language English
    Publishing date 2019-08-26
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2572216-5
    ISSN 2156-7085
    ISSN 2156-7085
    DOI 10.1364/BOE.10.004825
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: Erratum: Luan, E.X.; Shoman, H.; Ratner, D.M.; Cheung, K.C.; Chrostowski, L. Silicon Photonic Biosensors Using Label-Free Detection.

    Luan, Enxiao / Shoman, Hossam / Ratner, Daniel M / Cheung, Karen C / Chrostowski, Lukas

    Sensors (Basel, Switzerland)

    2019  Volume 19, Issue 5

    Abstract: The authors wish to make the following corrections in their published paper in Sensors [ ... ]. ...

    Abstract The authors wish to make the following corrections in their published paper in Sensors [...].
    Language English
    Publishing date 2019-03-07
    Publishing country Switzerland
    Document type Journal Article ; Published Erratum
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s19051161
    Database MEDical Literature Analysis and Retrieval System OnLINE

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