Perfect compactifications of frames are introduced. It is shown that the Stone-Čech compactification is an example of such a compactification. We also introduce rim-compact frames and for such frames we define its Freudenthal compactification, another example of a perfect compactification. The remainder of a rim-compact frame in its Freudenthal compactification is shown to be zero-dimensional. It is shown that with the assumption of the Boolean Ultrafilter Theorem the Freudenthal compactification for spaces, as well as the Freudenthal-Morita Theorem for spaces, can be obtained from our frame constructions.
In contrast to most other approaches used to represent multidimensional probability distributions, which are based on graphical Markov modelling (i.e. dependence structure of distributions is represented by graphs), the described method is rather procedural. Here, we describe a process by which a multidimensional distribution can be composed from a “generating sequence” - a sequence of low-dimensional distributions. The main advantage of this approach is that the same apparatus based on operators of composition can be applied for description of both probabilistic and possibilistic models.
Perforace pravé komory stimulační nebo defibrilační elektrodou je známo u komplikací trvalé kardiostimulace. V našem sdělení prezentujeme tři kazuistiky pacientů, kteří se liší dobou vzniku peforace od primo implantace, klinickými projevy a terape utickými postupy., Perforati on of the right ventricle by a stimulation or defibrillation electrode is a known complicati on of permanent cardiac stimulation. The article presents three case histories of patients who differ in terms of the length of time from initial implantation to the occurrence of perforation, clinical effects and therape utic strategies., Jitka Vlašínová, and Lit.: 7
Cash flow forecasting is indispensable for managers, investors and banks. However, which method is more robust has been argued under the condition of small size samples. With sliding window technique we create the Response Surface, Back Propagation Neural Network, Radial Basis Functions Neural Network and Support Vector Machine models respectively, which are examined by comparing performances of training and simulation. Performances of training models are measured by mean of squared errors while that of simulation is done by average relative errors of the results. By comparison, Support Vector Machine is most robust to forecast cash flow, followed by Radial Basis Function Neural Network, the third Back Propagation Neural Network and the last Response Surface Model. The optimal result of each model depends on the window size of the transmitter.
Different approaches have been proposed to determine the possible outliers existing in a dataset. The most widely used consists in the application of the data snooping test over the least squares adjustment results. This strategy is very likely to succeed for the case of zero or one outliers but, contrary to what is often assumed, the same is not valid for the multiple outlier case, even in its iterative application scheme. Robust estimation, computed by iteratively reweighted least squares or a global optimization method, is other alternative approach which often produces good results in the presence of outliers, as is the case of exhaustive search methods that explore elimination of every possible set of observations. General statements, having universal validity, about the best way to compute a geodetic network with multiple outliers are impossible to be given due to the many different factors involved (type of network, number and size of possible errors, available computational force, etc.). However, we see in this paper that some conclusions can be drawn for the case of a leveling network, which has a certain geometrical simplicity compared with planimetric or three-dimensional networks though a usually high number of unknowns and relatively low redundancy. Among other results, we experience the occasional failure in the iterative application of the data snooping test, the relatively successful results obtained by both methods computing the robust estimator, which perform equivalently in this case, and the successful application of the exhaustive search method, for different cases that become increasingly intractable as the number of outliers approaches half the number of degrees of freedom of the network.