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Publications

  1. Daniele, Alessandro; Serafini, Luciano,
    Proceedings of PRICAI 2019: Trends in Artificial Intelligence,
    vol.11670,
    2019
    , pp. 542-
    554
    , (Pacific Rim International Conference on Artificial Intelligence (PRICAI 2019): Trends in Artificial Intelligence,
    Yanuca Island, Fiji,
    August 26–30, 2019)
  2. Franch, Gabriele; Maggio, Valerio; Coviello, Luca; Jurman, Giuseppe; Furlanello, Cesare; Pendesini, Marta,
    2019,
  3. Franch, Gabriele; Maggio, Valerio; Coviello, Luca; Jurman, Giuseppe; Furlanello, Cesare; Pendesini, Marta,
    2019,
    TAASRAD19 (Trentino-Alto Adige/Südtirol Radar 2019) is a high-resolution radar reflectivity dataset collected by the Civil Protection weather radar of the Trentino South Tyrol Region, in the Italian Alps. The dataset includes 894,916 scans of precipitation from more than 9 years of data, offering a novel resource to develop and benchmark analog ensemble models and machine learning solutions for precipitation nowcasting. Data are expressed as 2D images, considering the maximum reflectivity on the vertical section and 5 minutes sampling rate, covering an area of 240km of diameter at 500m horizontal resolution. The TAASRAD19 distribution also includes a curated set of 1,732 sequences, for a total of 362,233 radar images, labeled with precipitation type tags assigned by expert meteorologists. We validated TAASRAD19 as a benchmark for nowcasting using deep learning model to forecast reflectivity and a procedure based on the UMAP dimensionality reduction method for interactive exploration. Software methods for data pre-processing, model training and inference, and a pre-trained model are publicly available at https://github.com/MPBA/TAASRAD19 for replication and reproducibility..
  4. Franch, Gabriele; Maggio, Valerio; Coviello, Luca; Jurman, Giuseppe; Furlanello, Cesare; Pendesini, Marta,
    2019,
    TAASRAD19 (Trentino-Alto Adige/Südtirol Radar 2019) is a high-resolution radar reflectivity dataset collected by the Civil Protection weather radar of the Trentino South Tyrol Region, in the Italian Alps. The dataset includes 894,916 scans of precipitation from more than 9 years of data, offering a novel resource to develop and benchmark analog ensemble models and machine learning solutions for precipitation nowcasting. Data are expressed as 2D images, considering the maximum reflectivity on the vertical section and 5 minutes sampling rate, covering an area of 240km of diameter at 500m horizontal resolution. The TAASRAD19 distribution also includes a curated set of 1,732 sequences, for a total of 362,233 radar images, labeled with precipitation type tags assigned by expert meteorologists. We validated TAASRAD19 as a benchmark for nowcasting using deep learning model to forecast reflectivity and a procedure based on the UMAP dimensionality reduction method for interactive exploration. Software methods for data pre-processing, model training and inference, and a pre-trained model are publicly available at https://github.com/MPBA/TAASRAD19 for replication and reproducibility..
  5. Mohammadian Rad, Nastaran; Kia, Seyed Mostafa; Zarbo, Calogero; van Laarhoven, Twan; Jurman, Giuseppe; Venuti, Paola; Marchiori, Elena; Furlanello, Cesare,
    in «SIGNAL PROCESSING»,
    vol. 144,
    2018
    , pp. 180 -
    191
  6. Fioravanti, Diego; Giarratano, Ylenia; Maggio, Valerio; Agostinelli, Claudio; Chierici, Marco; Jurman, Giuseppe; Furlanello, Cesare,
    in «BMC BIOINFORMATICS»,
    vol. 19,
    n. S2,
    2018
  7. Rossi, C.; Acerbo, F. S.; Ylinen, K.; Juga, I.; Nurmi, P.; Bosca, A.; Tarasconi, F.; Cristoforetti, M.; Alikadic, A.,
    in «INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION»,
    vol. 30,
    2018
    , pp. 145 -
    157
  8. Francescatto, Margherita; Chierici, Marco; Rezvan Dezfooli, Setareh; Zandonà, Alessandro; Jurman, Giuseppe; Furlanello, Cesare,
    Multi-omics integration for neuroblastoma clinical endpoint prediction,
    in «BIOLOGY DIRECT»,
    vol. 13,
    n. 1,
    2018
    , pp. 5 -
  9. Mohammadian Rad, Nastaran; van Laarhoven, Twan; Furlanello, Cesare; Marchiori, Elena,
    Novelty Detection using Deep Normative Modeling for IMU-Based Abnormal Movement Monitoring in Parkinson's Disease and Autism Spectrum Disorders,
    in «SENSORS»,
    vol. 18,
    n. 10,
    2018
    , pp. 3533 -
  10. Maggio, Valerio; Chierici, Marco; Jurman, Giuseppe; Furlanello, Cesare,
    Distillation of the clinical algorithm improves prognosis by multi-task deep learning in high-risk Neuroblastoma,
    in «PLOS ONE»,
    vol. 13,
    n. 12,
    2018
    , pp. e0208924 -

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