Background: Temporomandibular joint disorder (TMD) might be manifested as structural changes in bone through modification, adaptation or direct destruction. We propose to use Local Binary Pattern (LBP) characteristics and histogram-oriented gradients on the recorded images as a diagnostic tool in TMD assessment.Material and Methods: CBCT images of 66 patients (132 joints) with TMD and 66 normal...

In the present paper we find a new interpretation of Narayana polynomials Nn(x) which are the generating polynomials for the Narayana numbers Nn,k = 1 n C k−1 n C k n where C i j stands for the usual binomial coefficient, i.e. C j = j! i!(j−i)! . They count Dyck paths of length n and with exactly k peaks, see e.g. [13] and they appeared recently in a number of different combinatorial situations...

The delta coefficients are a conventional method to include temporal information in the speech recognition systems. In particular, they are widely used in the gaussian HMM-based systems. Some attempts were made to introduce the delta coefficients in the K-Nearest Neighbours (K-NN) HMMbased system that we recently developed. An introduction of the delta coefficients directly in the representatio...

The nearest neighbour (NN) classifier is often known as a ‘lazy’ approach but it is still widely used particularly in the systems that require pattern matching. Many algorithms have been developed based on NN in an attempt to improve classification accuracy and to reduce the time taken, especially in large data sets. This paper proposes a new classification technique based on kNearest Neighbour...

Strong interaction energy shifts and widths in kaonic atoms are calculated for the first time using microscopic $K^- N$ + NN$ potentials derived from scattering amplitudes constructed within SU(3) chiral coupled-channels models of meson-baryon interactions. The in-medium modifications free-space due to Pauli correlations taken into account. $K^-N+K^-NN$ evaluated 23 nuclear species confronted w...

Let us define some notation which will help us analyze the algorithm. L := A solution (k-subset) returned by Local Search. Copt := An optimal solution for the k-median problem. We will eventually show that Cost(L) ≤ 5 · Cost(Copt). For any p ∈ P,C ⊆ P, NN(p, C) := c̄ ∈ C that minimizes d(p, ·). So d(p,NN(p, C)) = d(p, C) by definition. Also, for any C ⊆ P, c̄ ∈ C, Cluster(C, c̄) := {q ∈ P | NN(q, ...