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  1. AU="Ye, Tianai"
  2. AU="Galenson, Walter"
  3. AU="Nisar, Muhammad K"
  4. AU="Keshavarzi, Nahid"
  5. AU="Gabig, Theodore G"
  6. AU="Nixon, Ian J"
  7. AU="Huang Xiaoting"
  8. AU="Colturato, Virgílio Antônio Rensi"
  9. AU="Mahfouz, Amira Y"
  10. AU="Ayyappan, Sabarish"
  11. AU=Wang Kevin L-C
  12. AU="Lukas T. Hirschwald"
  13. AU="Morley-Davies, A"
  14. AU="Felsberg, Gary J"
  15. AU="Bogen, Oliver"
  16. AU="de Portu, Simona"
  17. AU="Janssens, Rick"

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  1. Artikel ; Online: Exploring a targeted approach for public health capacity restrictions during COVID-19 using a new computational model.

    Micuda, Ashley N / Anderson, Mark R / Babayan, Irina / Bolger, Erin / Cantin, Logan / Groth, Gillian / Pressman-Cyna, Ry / Reed, Charlotte Z / Rowe, Noah J / Shafiee, Mehdi / Tam, Benjamin / Vidal, Marie C / Ye, Tianai / Martin, Ryan D

    Infectious Disease Modelling

    2024  Band 9, Heft 1, Seite(n) 234–244

    Abstract: This work introduces the Queen's University Agent-Based Outbreak Outcome Model (QUABOOM). This tool is an agent-based Monte Carlo simulation for modelling epidemics and informing public health policy. We illustrate the use of the model by examining ... ...

    Abstract This work introduces the Queen's University Agent-Based Outbreak Outcome Model (QUABOOM). This tool is an agent-based Monte Carlo simulation for modelling epidemics and informing public health policy. We illustrate the use of the model by examining capacity restrictions during a lockdown. We find that public health measures should focus on the few locations where many people interact, such as grocery stores, rather than the many locations where few people interact, such as small businesses. We also discuss a case where the results of the simulation can be scaled to larger population sizes, thereby improving computational efficiency.
    Sprache Englisch
    Erscheinungsdatum 2024-01-12
    Erscheinungsland China
    Dokumenttyp Journal Article
    ZDB-ID 3015225-2
    ISSN 2468-0427 ; 2468-2152
    ISSN (online) 2468-0427
    ISSN 2468-2152
    DOI 10.1016/j.idm.2024.01.001
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  2. Artikel: Performance of a convolutional autoencoder designed to remove electronic noise from p-type point contact germanium detector signals.

    Anderson, Mark R / Basu, Vasundhara / Martin, Ryan D / Reed, Charlotte Z / Rowe, Noah J / Shafiee, Mehdi / Ye, Tianai

    The European physical journal. C, Particles and fields

    2022  Band 82, Heft 12, Seite(n) 1084

    Abstract: We present a convolutional autoencoder to denoise pulses from a p-type point contact high-purity germanium detector similar to those used in several rare event searches. While we focus on training procedures that rely on detailed detector physics ... ...

    Abstract We present a convolutional autoencoder to denoise pulses from a p-type point contact high-purity germanium detector similar to those used in several rare event searches. While we focus on training procedures that rely on detailed detector physics simulations, we also present implementations requiring only noisy detector pulses to train the model. We validate our autoencoder on both simulated data and calibration data from an
    Sprache Englisch
    Erscheinungsdatum 2022-12-01
    Erscheinungsland France
    Dokumenttyp Journal Article
    ZDB-ID 1459069-4
    ISSN 1434-6052 ; 1434-6044
    ISSN (online) 1434-6052
    ISSN 1434-6044
    DOI 10.1140/epjc/s10052-022-11000-w
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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    Über subito bestellen

    Dieser Service ist kostenpflichtig (siehe Lieferbedingungen von subito). Bestellungen, die einen Artikel nebst Supplementary Material umfassen, werden grundsätzlich wie mehrfache Bestellungen bearbeitet. Gebühren fallen in diesen Fällen für jede einzelne Bestellung an.

  3. Buch ; Online: Performance of a convolutional autoencoder designed to remove electronic noise from p-type point contact germanium detector signals

    Anderson, Mark R. / Basu, Vasundhara / Martin, Ryan D. / Reed, Charlotte Z. / Rowe, Noah J. / Shafiee, Mehdi / Ye, Tianai

    2022  

    Abstract: We present a convolutional autoencoder to denoise pulses from a p-type point contact high-purity germanium detector similar to those used in several rare event searches. While we focus on training procedures that rely on detailed detector physics ... ...

    Abstract We present a convolutional autoencoder to denoise pulses from a p-type point contact high-purity germanium detector similar to those used in several rare event searches. While we focus on training procedures that rely on detailed detector physics simulations, we also present implementations requiring only noisy detector pulses to train the model. We validate our autoencoder on both simulated data and calibration data from an $^{241}$Am source, the latter of which is used to show that the denoised pulses are statistically compatible with data pulses. We demonstrate that our denoising method is able to preserve the underlying shapes of the pulses well, offering improvement over traditional denoising methods. We also show that the shaping time used to calculate energy with a trapezoidal filter can be significantly reduced while maintaining a comparable energy resolution. Under certain circumstances, our denoising method can improve the overall energy resolution. The methods we developed to remove electronic noise are straightforward to extend to other detector technologies. Furthermore, the latent representation from the encoder is also of use in quantifying shape-based characteristics of the signals. Our work has great potential to be used in particle physics experiments and beyond.

    Comment: 21 pages, 13 figures, 3 tables. This version of the article has been accepted for publication, after peer review but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: 10.1140/epjc/s10052-022-11000-w
    Schlagwörter Nuclear Experiment ; Physics - Data Analysis ; Statistics and Probability ; Physics - Instrumentation and Detectors
    Thema/Rubrik (Code) 612
    Erscheinungsdatum 2022-04-13
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

    Volltext online

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  4. Artikel ; Online: Exploring a targeted approach for public health capacity restrictions during COVID-19 using a new computational model

    Micuda, Ashley / Anderson, Mark R / Babayan, Irina / Bolger, Erin / Cantin, Logan / Groth, Gillian / Pressman-Cyna, Ry / Reed, Charlotte Z / Rowe, Noah J / Shafiee, Mehdi / Tam, Benjamin / Vidal, Marie C / Ye, Tianai / Martin, Ryan D

    medRxiv

    Abstract: This work introduces the Queen9s University Agent-Based Outbreak Outcome Model (QUABOOM), a new, data-driven, agent-based Monte Carlo simulation for modelling epidemics and informing public health policy in a wide range of population sizes. We ... ...

    Abstract This work introduces the Queen9s University Agent-Based Outbreak Outcome Model (QUABOOM), a new, data-driven, agent-based Monte Carlo simulation for modelling epidemics and informing public health policy in a wide range of population sizes. We demonstrate how the model can be used to quantitatively inform capacity restrictions for COVID-19 to reduce their impact on small businesses by showing that public health measures should target few locations where many individuals interact rather than many locations where few individuals interact. We introduce a new method for the calculation of the basic reproduction rate that can be applied to low statistics data such as small outbreaks. A novel parameter to quantify the number of interactions in the simulations is introduced which allows our agent-based model to be run using small population sizes and interpreted for larger populations, thereby improving computational efficiency.
    Schlagwörter covid19
    Sprache Englisch
    Erscheinungsdatum 2022-11-29
    Verlag Cold Spring Harbor Laboratory Press
    Dokumenttyp Artikel ; Online
    DOI 10.1101/2022.11.28.22282818
    Datenquelle COVID19

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