When dealing with multivariate time series an important question one may ask is whether the measured signals are independent or not. Any interrelation can, for example, provide information about the underlying physical mechanisms or may be used to exploit possible redundancy in order to reduce the number of measurement channels. In linear systems theory the standard tool for detecting dependenc...

The use of linear filters, i.e. convolutions, inevitably introduces dependencies in the uncertainties of the filter outputs. Such non-vanishing covariances appear both between different positions and between the responses from different filters (even at the same position). This report describes how these covariances between the output of linear filters can be computed. We then examine the induc...

Recently, di+erent models of the statistical structure of natural images (and sequences) have been proposed. Maximizing sparseness, or alternatively temporal coherence of linear 1lter outputs leads to the emergence of simple cell properties. Taking account of the basic dependencies of linear 1lter outputs enables modelling of complex cell and topographic properties as well. Here, we propose a u...

Dependencies among the features can decrease the performance and efficiency in many algorithms e.g. in classifiers. Therefore, finding the dependent features has become significantly important in many areas. Traditional methods can only find the linear dependencies or the dependencies among few features. In our research, we try to use feature selection approaches for finding dependencies. We ge...

Linear methods form the basis of current high-performance image and video coders. In image coding, energy-compacting linear transforms are utilized to decorrelate and sparsify the source. In the coding of video sequences, a simple linear predictive loop is used to reduce the important temporal dependencies. This dissertation presents a body of work concerned with the analysis and optimization o...

Abstract. Recently, different models of the statistical structure of natural images have been proposed. Maximizing sparseness, or alternatively temporal coherence of linear filter outputs leads to the emergence of simple cell properties. Taking account of the basic dependencies of linear filter outputs enables modelling of complex cell and topographic properties as well. In this paper, I propos...

Mathematical models have been applied in prediction of growth hormone treatment effectiveness in children since the end of 1990s. Usually they were multiple linear regression models; however, there are also examples derived by empirical non-linear methods. Proposed solution consists in application of machine learning technique - artificial neural networks - to analyse this problem. This new met...

Recently, the issue of functional dependencies in XML (XFDs) have been investigated. In this paper we consider the problem of checking the satisfaction of an XFD in an XML document. We present an efficient algorithm for the problem that is linear in the size of the XML document and linear in the number of XFDs to be checked. Also, our technique can be easily extended to efficiently incrementall...

In this project, we implement two Parallel Tiled Linear Algebra Algorithms in Julia, Cholesky and QR decomposition. We follow the algorithm description in [1] and use “dlsym” and “ccall” functions in Julia to load the OpenBLAS dynamic library [2] and call BLAS kernels. From this foundation, we are able to implement serial and parallel tiled linear algebra algorithms in Julia. For parallel imple...

We describe LPSP, a domain-independent planning algorithm that searches the space of linear plans using stochastic local search techniques. Because linear plans, rather than propositional assignments, comprise the states of LPSP’s search space, we can incorporate into its search various operators that are suitable for manipulating plans, such as plan-step reordering based on action dependencies...