The increasing significance of state-of-the-art convolutional neural network (CNN) models in computer vision tasks has led to their widespread use industry and academia. However, deploying these resource-limited environments, such as IoT devices or embedded GPUs, presents challenges due increased complexities resource consumption. This research paper proposes an optimization algorithm called La...

Large-scale text datasets have long eluded a family of particularly elegant and effective clustering methods that exploits the power of pair-wise similarities between data points due to the prohibitive cost, timeand space-wise, in operating on a similarity matrix, where the state-of-the-art is at best quadratic in time and in space. We present an extremely fast and simple method also using the ...

We show in this paper that the roots x1 and x2 of a scalar quadratic polynomial ax2+bx+c = 0 with real or complex coefficients a, b c can be computed in a elementwise mixed stable manner, measured in a relative sense. We also show that this is a stronger property than norm-wise backward stability, but weaker than element-wise backward stability. We finally show that there does not exist any met...

In this manuscript, we discuss the distinction of two types of data generating scheme for the accelerated failure time model. We identify two different empirical likelihood formulations under random right censoring, case-wise and residual-wise, each reflecting the relevant features of the stochastic model assumed to have generated the data. We specifically propose the case-wise empirical likeli...

The availability of smartphone and wearable sensor technology is leading to a rapid accumulation of human subject data, and machine learning is emerging as a technique to map those data into clinical predictions. As machine learning algorithms are increasingly used to support clinical decision making, it is vital to reliably quantify their prediction accuracy. Cross-validation (CV) is the stand...

In this paper we study the convergence of a multigrid method for the solution of a two-dimensional linear second order elliptic equation, discretized by discontinuous Galerkin (DG) methods. For the Baumann–Oden and for the symmetric DG method, we give a detailed analysis of the convergence for celland point-wise block-relaxation strategies. We show that, for a suitably constructed two-dimension...

In this paper we study the convergence of a multigrid method for the solution of a two-dimensional linear second order elliptic equation, discretized by discontinuous Galerkin (DG) methods. For the Baumann-Oden and for the symmetric DG method, we give a detailed analysis of the convergence for celland point-wise block-relaxation strategies. We show that, for a suitably constructed two-dimension...

This paper presents a formulation for the obtainment of the sensitivity analysis of a point wise error estimator with respect to the nodal coordinates using the adjoint state method. The proposed point wise error estimator is based on the SPR method. The numerical accuracy of the presented sensitivity analysis has been tested by perturbing a mesh. The capability of the presented sensitivity ana...