Recent Submissions

  • Studying the impact of the Full-Network embedding on multimodal pipelines 

    Vilalta, Armand; Garcia-Gasulla, Dario; Pares, Ferran; Ayguadé Parra, Eduard; Labarta Mancho, Jesús José; Moya-Sánchez, Ulises; Cortés García, Claudio Ulises (IOS Press, 2018)
    Article
    Open Access
    The current state of the art for image annotation and image retrieval tasks is obtained through deep neural network multimodal pipelines, which combine an image representation and a text representation into a shared embedding ...
  • Learning life cycle to speed up autonomic optical transmission and networking adoption 

    Velasco Esteban, Luis Domingo; Shariati, Mohammad Behnam; Boitier, Fabien; Layec, Patricia; Ruiz Ramírez, Marc (Institute of Electrical and Electronics Engineers (IEEE), 2019-04-05)
    Article
    Open Access
    Autonomic optical transmission and networking requires machine learning (ML) models to be trained with large datasets. However, the availability of enough real data to produce accurate ML models is rarely ensured since new ...
  • Petri net analysis using boolean manipulation 

    Pastor Llorens, Enric; Roig Mansilla, Oriol; Cortadella, Jordi; Badia Sala, Rosa Maria (Springer, 1994)
    Part of book or chapter of book
    Open Access
    This paper presents a novel analysis approach for bounded Petri nets. The net behavior is modeled by boolean functions, thus reducing reasoning about Petri nets to boolean calculation. The state explosion problem is managed ...
  • Look-ahead in the two-sided reduction to compact band forms for symmetric eigenvalue problems and the SVD 

    Rodríguez Sánchez, Rafael; Catalán Pallarés, Sandra; Herrero Zaragoza, José Ramón; Quintana Ortí, Enrique Salvador; Tomás Domínguez, Andrés Enrique (2019-02-01)
    Article
    Open Access
    We address the reduction to compact band forms, via unitary similaritytransformations, for the solution of symmetric eigenvalue problems and the compu-tation of the singular value decomposition (SVD). Concretely, in the ...
  • Two-sided orthogonal reductions to condensed forms on asymmetric multicore processors 

    Alonso Jordá, Pedro; Catalán Pallarés, Sandra; Herrero Zaragoza, José Ramón; Quintana Ortí, Enrique Salvador; Rodríguez Sánchez, Rafael (2018-10)
    Article
    Restricted access - publisher's policy
    We investigate how to leverage the heterogeneous resources of an Asymmetric Multicore Processor (AMP) in order to deliver high performance in the reduction to condensed forms for the solution of dense eigenvalue and ...
  • Static scheduling of the LU factorization with look-ahead on asymmetric multicore processors 

    Catalán Pallarés, Sandra; Herrero Zaragoza, José Ramón; Quintana Ortí, Enrique Salvador; Rodríguez Sánchez, Rafael (2018-08)
    Article
    Restricted access - publisher's policy
    We analyze the benefits of look-ahead in the parallel execution of the LU factorization with partial pivoting (LUpp) in two distinct “asymmetric” multicore scenarios. The first one corresponds to an actual hardware-asymmetric ...
  • Towards an energy-aware framework for application development and execution in heterogeneous parallel architectures 

    Djemame, Karim; Kavanagh, Richard; Kelefouras, Vasilios; Aguilà, Adrià; Ejarque, Jorge; Badia Sala, Rosa Maria; García-Pérez, David; Pezuela, Clara; Deprez, Jean-Christophe; Guedria, Lofti; De Landtsheer, Renaud; Georgiou, Yiannis (Springer, 2019)
    Part of book or chapter of book
    Restricted access - publisher's policy
    The Transparent heterogeneous hardware Architecture deployment for eNergy Gain in Operation (TANGO) project’s goal is to characterise factors which affect power consumption in software development and operation for ...
  • ECHOFS: a scheduler-guided temporary filesystem to leverage node-local NVMS 

    Miranda, Alberto; Nou, Ramon; Cortés, Toni
    Conference report
    Open Access
    The growth in data-intensive scientific applications poses strong demands on the HPC storage subsystem, as data needs to be copied from compute nodes to I/O nodes and vice versa for jobs to run. The emerging trend of adding ...
  • Experimental demonstration of machine-learning-aided QoT estimation in multi-domain elastic optical networks with alien wavelengths 

    Proietti, Roberto; Chen, Xiaoliang; Zhang, Kaiqi; Liu, Gengchen; Shamsabardeh, Mohammadsadegh; Castro, Alberto; Velasco Esteban, Luis Domingo; Zhu, Zuqing; Yoo, S.J. Ben (Institute of Electrical and Electronics Engineers (IEEE), 2019)
    Article
    Restricted access - publisher's policy
    In multi-domain elastic optical networks with alien wavelengths, each domain needs to consider intradomain and interdomain alien traffic to estimate and guarantee the required quality of transmission (QoT) for each lightpath ...
  • Verification of asynchronous circuits by BDD-based model checking of Petri nets 

    Roig Mansilla, Oriol; Cortadella, Jordi; Pastor Llorens, Enric (Springer, 1995)
    Conference report
    Open Access
    This paper presents a methodology for the verification of speed-independent asynchronous circuits against a Petri net specification. The technique is based on symbolic reachability analysis, modeling both the specification ...
  • Autonomic transmission through pre-FEC BER degradation prediction based on SOP monitoring 

    Shariati, Mohammad Behnam; Boitier, F.; Ruiz Ramírez, Marc; Layec, Patricia; Velasco Esteban, Luis Domingo
    Conference report
    Restricted access - publisher's policy
    An Autonomic Transmission Agent based on machine-learning is proposed for excessive bit-error-rate prediction resulting from real-time analysis of state-of-polarization (SOP). The accuracy and speed of the agent enables ...
  • An out-of-the-box full-network embedding for convolutional neural networks 

    Garcia-Gasulla, Dario; Vilalta, Armand; Parés, Ferran; Ayguadé Parra, Eduard; Labarta Mancho, Jesús José; Cortés García, Claudio Ulises; Suzumura, Toyotaro (Institute of Electrical and Electronics Engineers (IEEE), 2018)
    Conference report
    Open Access
    Features extracted through transfer learning can be used to exploit deep learning representations in contexts where there are very few training samples, where there are limited computational resources, or when the tuning ...

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