Mean value and cross-covariance function of chaotic binary sequences are evaluated for chaotic maps with specific properties. We also take into account the effect of fixedand floating-point representations on statistical properties of chaotic generators. Thus, one is able to obtain possible candidates for pseudo-random binary sequences generation. Results of statistical tests applied to chaotic...

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In this paper, we handle the problem of human action recognition by combining covariance matrices as local spatio-temporal (ST) descriptors and local ST features extracted densely from action video. Unlike traditional methods that separately utilizing gradient-based feature and optical flow-based feature, we use covariance matrix to fuse the two types of feature. Since covariance matrices are S...

Einstein considered general covariance to characterize the novelty of his General Theory of Relativity (GTR), but Kretschmann thought it merely a formal feature that any theory could have. The claim that GTR is “already parametrized” suggests analyzing substantive general covariance as formal general covariance achieved without hiding preferred coordinates as scalar “clock fields,” much as Eins...

The robust estimation of multivariate location and shape is one of the most challenging problems in statistics and crucial in many application areas. The objective is to find highly efficient, robust, computable and affine equivariant location and covariance matrix estimates. In this paper three different concepts of multivariate sign and rank are considered and their ability to carry informati...

Several theories of auditory hallucinations implicate the involvement of intrusive thoughts and other theories suggest that the interpretation of voices determines the distress associated with them. This study tested the hypotheses that patients who experience auditory hallucinations will experience more intrusive thoughts and be more distressed by them and interpret them as more uncontrollable...

We consider a uni ed description of classi cation rules for nearly singular covariance matrices. When the covariance matrices of the groups or the pooled covariance matrix become nearly singular, bayesian classi cation rules become seriously unstable. Several procedures have been proposed to tackle this problem, e.g. SIMCA, and Regularized Discriminant Analysis. N s and Indahl (1998) discovered...