We present the results of an information theory-based approach to select an optimal subset of features for the prediction of protein model quality. The optimal subset of features was calculated by means of a backward selection procedure. The performances of a probabilistic classifier modeled by means of a Kernel Probability Density Estimation method (KPDE) were compared with those of a feed-for...

Engineering optimization of a highly nonlinear complex system is always a challenge methodologically and computationally. This is especially true when multistage dynamic optimization is involved. While significant progress has been made in rigorous deterministic algorithms for dynamic optimization, meta-heuristic-based optimization may offer an attractive alternative. This paper introduces a ge...

this paper presents a probabilistic optimal power flow (popf) algorithm considering different uncertainties in a smart grid. different uncertainties such as variation of nodal load, change in system configuration, measuring errors, forecasting errors, and etc. can be considered in the proposed algorithm. by increasing the penetration of the renewable energies in power systems, it is more essent...

Magnetic resonance imaging (MRI) is a biomedical imaging modality that provides high resolution images and excellent soft tissue contrast [1]. This makes MRI a preferable imaging modality for many applications. One limitation of MRI is slow data acquisition. MRI acquires data in the Fourier domain one point at a time, usually until enough data has been collected to approximately satisfy Nyquist...

Let {Xnk: k,n 1,2 be an array of row-wise independent random elements in a separable Banach space. Let {ank: k,n 1,2 be an array of Voo voo R+ real numbers such that /-k=l lank -< 1 and Ln=l exp(-a/A < for each c e where n V 2 Voo An kk=l ank. The complete convergence of l’k=l ank Xnk is obtained under varying moment and distribution conditions on the random elements. In particular, laws of lar...

FAmle is a R package that may be used in order to carry out tasks that pertain to univariate frequency analysis. Basically, two general frameworks are being entertained here: maximum likelihood and Bayesian. More precisely, for a given problem, the user may decide to estimate the unknown parameters via maximum likelihood, and to then proceed to statistical inference accordingly, or to use a ful...

References: [1] Brox, T., Malik, J.: Large displacement optical fow: descriptor matching in variational motion estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(3), 500-513 (2011) [2] Alvarez, L., Deriche, R., Papadopoulo, T., Sanchez, J.: Symmetrical Dense Optical Flow Estimation with Occlusions Detection. International Journal of Computer Vision 75(3), 371-385 (200...