Introduced a new calculation method (K-method) for cognitive maps. K-method consists of two consecutive steps. In the first stage, allocated subgraph composed of all paths from one selected node (concept) to another node (concept) from the cognitive map (directed weighted graph). In the second stage, after the transition to an undirected graph (symmetrization adjacency matrix) the influence of ...

The simulation of turbulent flows is an ongoing challenge. This is especially true for the flows in nuclear reactors. In order to save computational time and resource, accurate numerical schemes are required for such simulations. The encouraging results from the laminar flow simulations using Modified Nodal Integral Method (MNIM), serves as a motivation to use the method for turbulent flow simu...

K-means is a popular clustering method used in data mining area. To work with large datasets, researchers propose PKMeans, which is a parallel k-means on MapReduce [3]. However, the existing k-means parallelization methods including PKMeans have many limitations. It can’t finish all its iterations in one MapReduce job, so it has to repeat cascading MapReduce jobs in a loop until convergence. On...

Sparse representation of images has been a recent area of growing interest. It finds applications in many problems in image processing. In this report we study a particular method of achieving sparse representation using the recently proposed K-SVD algorithm by Aharon et al. [1] and how this sparse representation framework has been extended to perform denoising, as illustrated by the authors in...

Trough the classical umbral calculus, we provide new, compact and easy to handle expressions of k-statistics, and more in general of U -statistics. In addition such a symbolic method can be naturally extended to multivariate case, i.e. to generalized k-statistics.

K-means is one of the most widely used algorithms for clustering in Data Mining applications, which attempts to minimize the sum of square of Euclidean distance of the points in the clusters from the respective means of the clusters. The simplicity and scalability of K-means makes it very appealing. However, K-means suffers from local minima problem, and comes with no guarantee to converge to t...

the stolt (f-k) migration algorithm is a direct (i.e. non-recursive) fourier-domain technique based on a change of variables (or equivalently, a mapping) that converts the input spectrum to the output spectrum. the algorithm is simple and efficient but limited to constant velocity. a v(z)(f-k) migration method, capable of very high accuracy for vertical variations of velocity, can be formulated...

in this paper, we prove the hyers-ulam stability in$beta$-homogeneous probabilistic modular spaces via fixed point method for the functional equation[f(x+ky)+f(x-ky)=f(x+y)+f(x-y)+frac{2(k+1)}{k}f(ky)-2(k+1)f(y)]for fixed integers $k$ with $kneq 0,pm1.$