Now showing items 141-160 of 165

    • Sample size impact on the categorisation of continuous variables in clinical prediction 

      Barrio I.; Arostegui I.; Rodríguez-Álvarez M.X. (Trends in Mathematics. Research Perspectives CRM Barcelona. Extended Abstracts Fall 2015. Biomedical Big Data; Statistics for Low Dose Radiation Research, 2017-12)
      Recent advances in information technologies are generating a growth in the amount of available biomedical data. In this paper, we studied the impact sample size may have on the categorisation of a continuous predictor ...
    • Scheduling of users with Markovian time-varying transmission rates 

      Cecchi F.; Jacko P. (Performance Evaluation Review, 2013-12-31)
      We address the problem of developing a well-performing and implementable scheduler of users with wireless connection to the base station. The main feature of such real-life systems is that the quality conditions of the ...
    • Sentiment analysis with genetically evolved Gaussian kernels 

      Roman I.; Santana R.; Mendiburu A.; Lozano J.A. (GECCO '19 Proceedings of the Genetic and Evolutionary Computation Conference, 2019)
      Sentiment analysis consists of evaluating opinions or statements based on text analysis. Among the methods used to estimate the degree to which a text expresses a certain sentiment are those based on Gaussian Processes. ...
    • A simple Bayesian linear excess relative risk model 

      Higueras M.; Harbron R.W.; Pearce M.S. (2017-07-11)
      A new Bayesian Poisson relative risk model is proposed for displaying the excess relative risk associated to a unique exposure as a probability distribution in a closed form. The background risk can be modelled by a unique ...
    • Smooth additive mixed models for predicting aboveground biomass 

      Sánchez-González M.; Durban M.; Lee D.-J.; Cañellas I.; Sixto H. (Journal of Agricultural, Biological and Environmental Statistics, 2016-12-01)
      Aboveground biomass estimation in short-rotation forestry plantations is an essential step in the development of crop management strategies as well as allowing the economic viability of the crop to be determined prior to ...
    • Soft information for localization-of-things 

      Conti A.; Mazuelas S.; Bartoletti S.; Lindsey W.C; Win M. (Proceeding of the IEEE, 2019-11-01)
      Location awareness is vital for emerging Internetof- Things applications and opens a new era for Localizationof- Things. This paper first reviews the classical localization techniques based on single-value metrics, such ...
    • Soft range information for network localization 

      Mazuelas S.; Conti A.; Allen J.C.; Win M.Z. (IEEE Transactions on Signal Processing, 2018-06-15)
      The demand for accurate localization in complex environments continues to increase despite the difficulty in extracting positional information from measurements. Conventional range-based localization approaches rely on ...
    • Spacecraft Trajectory Optimization: A review of Models, Objectives, Approaches and Solutions 

      Shirazi A.; Ceberio J.; Lozano J.A. (Progress in Aerospace Sciences, 2018)
      This article is a survey paper on solving spacecraft trajectory optimization problems. The solving process is decomposed into four key steps of mathematical modeling of the problem, defining the objective functions, ...
    • Spatial Models for Field Trials 

      Rodríguez-Álvarez M.X.; Boer M.P.; Eeuwijk, F.; Eilers P.H.C. (2016-09-01)
      An important aim of the analysis of agricultural field trials is to obtain good predictions for genotypic performance, by correcting for spatial effects. In practice these corrections turn out to be complicated, since there ...
    • Spatio-temporal adaptive penalized splines with application to Neuroscience 

      Rodríguez-Álvarez M.X.; Durban M.; Lee D.-J.; Eilers P.H.C.; Gonzalez, F (2016-12-30)
      Data analysed here derive from experiments conducted to study neurons' activity in the visual cortex of behaving monkeys. We consider a spatio-temporal adaptive penalized spline (P-spline) approach for modelling the firing ...
    • Spatio-temporal functional data analysis for wireless sensor networks data 

      Lee D.-J.; Zhu Z.; Toscas P. (Environmetrics, 2015-05-05)
      A new methodology is proposed for the analysis, modeling, and forecasting of data collected from a wireless sensor network. Our approach is considered in the framework of a functional data-analysis paradigm where observed ...
    • Spatiotemporal information coupling in network navigation 

      Mazuelas S.; Shen Y.; Win Z. (IEEE Transactions on Information Theory, 2018-12)
      Network navigation, encompassing both spatial and temporal cooperation to locate mobile agents, is a key enabler for numerous emerging location-based applications. In such cooperative networks, the positional information ...
    • Stability and asymptotic optimality of opportunistic schedulers in wireless systems 

      Ayesta U.; Erausquin M.; Jonckheere M.; Verloop I.M. (VALUETOOLS 2011 - 5th International ICST Conference on Performance Evaluation Methodologies and Tools, 2011-12-31)
      We investigate the scheduling of a common resource between several concurrent users when the feasible transmission rate of each user varies randomly over time. Time is slotted and users arrive and depart upon service ...
    • A statistical framework for radiation dose estimation with uncertainty quantification from the γ-H2AX assay 

      Einbeck J; Ainsbury E.A.; Sales R; Barnard S.; Kaestle F; Higueras M. (PLoS ONE, 2018-11-28)
      Over the last decade, the γ–H2AX focus assay, which exploits the phosphorylation of the H2AX histone following DNA double–strand–breaks, has made considerable progress towards acceptance as a reliable biomarker for exposure ...
    • Suggestion of reduced cancer risks following cardiac x-ray exposures is unconvincing 

      Harbron R.W.; Chapple C.L.; O'Sullivan J.J.; Lee C.; McHugh K.; Higueras M.; Pearce M.S.; H (European Journal of Epidemiology, 2018-03-31)
    • Supervised non-parametric discretization based on Kernel density estimation 

      Flores J. L.; Calvo B.; Pérez A. (Pattern Recognition Letters, 2019-12-19)
      Nowadays, machine learning algorithms can be found in many applications where the classifiers play a key role. In this context, discretizing continuous attributes is a common step previous to classification tasks, the main ...
    • A system for airport weather forecasting based on circular regression trees 

      Rozas-Larrondo P.; Inza I.; Lozano J.A. (Environmental Modelling & Software, 2017-11-01)
      This paper describes a suite of tools and a model for improving the accuracy of airport weather forecasts produced by numerical weather prediction (NWP) products, by learning from the relationships between previously ...
    • The economics of the cloud: Price competition and congestion 

      Anselmi J.; Ardagna D.; Lui J.C.S.; Wierman A.; Xu Y.; Yang Z. (Performance Evaluation Review, 2014-12-31)
      [No abstract available]
    • The price of forgetting in parallel and non-observable queues 

      Anselmi J.; Gaujal B. (Performance Evaluation, 2011-12-31)
      We consider a broker-based network of non-observable parallel queues and analyze the minimum expected response time and the optimal routing policy when the broker has the memory of its previous routing decisions. We provide ...
    • Time resolved amplified FRET identifies protein kinase B activation state as a marker for poor prognosis in clear cell renal cell carcinoma 

      Miles J.; Applebee C.J.; Leboucher P.; Lopez-Fernandez S.; Lee D.-J.; Guarch R.; Ward S.; Parker P.J.; López J.I.; Larijani B (BBA Clinical, 2017-12)
      Purpose Clear cell Renal Cell Carcinomas (ccRCC), the largest group of renal tumours, are resistant to classical therapies. The determination of the functional state of actionable biomarkers for the assessment of these ...