LIVIVO - The Search Portal for Life Sciences

zur deutschen Oberfläche wechseln
Advanced search

Search results

Result 1 - 4 of total 4

Search options

  1. Book ; Online: PILArNet

    Adams, Corey / Terao, Kazuhiro / Wongjirad, Taritree

    Public Dataset for Particle Imaging Liquid Argon Detectors in High Energy Physics

    2020  

    Abstract: Rapid advancement of machine learning solutions has often coincided with the production of a test public data set. Such datasets reduce the largest barrier to entry for tackling a problem -- procuring data -- while also providing a benchmark to compare ... ...

    Abstract Rapid advancement of machine learning solutions has often coincided with the production of a test public data set. Such datasets reduce the largest barrier to entry for tackling a problem -- procuring data -- while also providing a benchmark to compare different solutions. Furthermore, large datasets have been used to train high-performing feature finders which are then used in new approaches to problems beyond that initially defined. In order to encourage the rapid development in the analysis of data collected using liquid argon time projection chambers, a class of particle detectors used in high energy physics experiments, we have produced the PILArNet, first 2D and 3D open dataset to be used for a couple of key analysis tasks. The initial dataset presented in this paper contains 300,000 samples simulated and recorded in three different volume sizes. The dataset is stored efficiently in sparse 2D and 3D matrix format with auxiliary information about simulated particles in the volume, and is made available for public research use. In this paper we describe the dataset, tasks, and the method used to procure the sample.
    Keywords Physics - Instrumentation and Detectors ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning ; High Energy Physics - Experiment
    Subject code 006
    Publishing date 2020-06-02
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

    More links

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  2. Article ; Online: Machine learning at the energy and intensity frontiers of particle physics.

    Radovic, Alexander / Williams, Mike / Rousseau, David / Kagan, Michael / Bonacorsi, Daniele / Himmel, Alexander / Aurisano, Adam / Terao, Kazuhiro / Wongjirad, Taritree

    Nature

    2018  Volume 560, Issue 7716, Page(s) 41–48

    Abstract: Our knowledge of the fundamental particles of nature and their interactions is summarized by the standard model of particle physics. Advancing our understanding in this field has required experiments that operate at ever higher energies and intensities, ... ...

    Abstract Our knowledge of the fundamental particles of nature and their interactions is summarized by the standard model of particle physics. Advancing our understanding in this field has required experiments that operate at ever higher energies and intensities, which produce extremely large and information-rich data samples. The use of machine-learning techniques is revolutionizing how we interpret these data samples, greatly increasing the discovery potential of present and future experiments. Here we summarize the challenges and opportunities that come with the use of machine learning at the frontiers of particle physics.
    Language English
    Publishing date 2018-08-01
    Publishing country England
    Document type Journal Article ; Review
    ZDB-ID 120714-3
    ISSN 1476-4687 ; 0028-0836
    ISSN (online) 1476-4687
    ISSN 0028-0836
    DOI 10.1038/s41586-018-0361-2
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    In stock of ZB MED Cologne/Königswinter

    Zs.A 26: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)
    Zs.MG 9: Show issues
    Zs.MO 244: Show issues

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  3. Article: Miniature Protein Inhibitors of the p53-hDM2 Interaction

    Kritzer, Joshua A / Zutshi, Reena / Cheah, Mingtatt / Ran, F. Ann / Webman, Rachel / Wongjirad, Taritree M / Schepartz, Alanna

    Chembiochem. 2006 Jan. 9, v. 7, no. 1

    2006  

    Language English
    Dates of publication 2006-0109
    Size p. 29-31.
    Publishing place Wiley-VCH Verlag
    Document type Article
    ZDB-ID 2020469-3
    ISSN 1439-7633 ; 1439-4227
    ISSN (online) 1439-7633
    ISSN 1439-4227
    DOI 10.1002/cbic.200500324
    Database NAL-Catalogue (AGRICOLA)

    Full text online

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  4. Article: Miniature protein inhibitors of the p53-hDM2 interaction.

    Kritzer, Joshua A / Zutshi, Reena / Cheah, Mingtatt / Ran, F Ann / Webman, Rachel / Wongjirad, Taritree M / Schepartz, Alanna

    Chembiochem : a European journal of chemical biology

    2006  Volume 7, Issue 1, Page(s) 29–31

    MeSH term(s) Amino Acid Sequence ; Humans ; Ligands ; Molecular Sequence Data ; Peptides/pharmacology ; Protein Binding/drug effects ; Protein Structure, Secondary ; Proto-Oncogene Proteins c-mdm2/antagonists & inhibitors ; Proto-Oncogene Proteins c-mdm2/chemistry ; Proto-Oncogene Proteins c-mdm2/metabolism ; Tumor Suppressor Protein p53/antagonists & inhibitors ; Tumor Suppressor Protein p53/chemistry ; Tumor Suppressor Protein p53/metabolism
    Chemical Substances Ligands ; Peptides ; Tumor Suppressor Protein p53 ; Proto-Oncogene Proteins c-mdm2 (EC 2.3.2.27)
    Language English
    Publishing date 2006-01
    Publishing country Germany
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 2020469-3
    ISSN 1439-7633 ; 1439-4227
    ISSN (online) 1439-7633
    ISSN 1439-4227
    DOI 10.1002/cbic.200500324
    Database MEDical Literature Analysis and Retrieval System OnLINE

    Full text online

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

To top