Envíos recentes

  • Cryptic diversity and phylogeographic patterns of Mediodactylus species in the Eastern Mediterranean region 

    Kotsakiozi, Panayiota; Antoniou, Aglaia; Psonis, Nikolaos; Sagonas, Kostas; Karameta, Emmanouela; Ilgaz, Çetin; Kumlutas, Yusuf; Aziz, Avcı; Jablonski, Daniel; Darriba, Diego (Academic Press Inc., 2024-08)
    [Abstract]: Cryptic diversity poses a great obstacle in our attempts to assess the current biodiversity crisis and may hamper conservation efforts. The gekkonid genus Mediodactylus, a well-known case of hidden species and ...
  • Applying dynamic balancing to improve the performance of MPI parallel genomics applications 

    Fernández Fraga, Alejandro; González-Domínguez, Jorge; Martín, María J. (Association for Computing Machinery (ACM), 2024-05-21)
    [Absctract]: Genomics applications are becoming more and more important in the field of bioinformatics, as they allow researchers to extract meaningful information from the huge amount of data generated by the new sequencing ...
  • PlayNet: real-time handball play classification with Kalman embeddings and neural networks 

    Mures, Omar A.; Taibo, Javier; Padrón, Emilio J.; Iglesias-Guitián, José A. (Springer, 2024)
    [Abstract] Real-time play recognition and classification algorithms are crucial for automating video production and live broadcasts of sporting events. However, current methods relying on human pose estimation and deep ...
  • A comprehensive handball dynamics dataset for game situation classification 

    Mures, Omar A.; Taibo, Javier; Padrón, Emilio J.; Iglesias-Guitián, José A. (Elsevier, 2023)
    [Abstract] This article presents a comprehensive dataset of labeled game situations obtained from multiple professional handball matches, which corresponds to the research paper entitled “PlayNet: Real-time Handball Play ...
  • Ensemble and continual federated learning for classification tasks 

    Casado, Fernando E.; Lema, Dylan; Iglesias, Roberto; Regueiro, Carlos V.; Barro, Senén (Springer, 2023-09)
    [Abstract]: Federated learning is the state-of-the-art paradigm for training a learning model collaboratively across multiple distributed devices while ensuring data privacy. Under this framework, different algorithms have ...

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