Educational Migration into Secondary Vocational Institutions in the Context of Society's Well-being

In this article education and educational migration into secondary vocational institutions is considered as one of the most important factors of improving the well-being of the society. Actualization of such concepts as “knowledge economics” and “smart educations” and also the emergence of new tendencies on the world educational services market (internalization of education, commercialization of education; geographical changes in educational migration) contribute to it. In the context of such changes Russian education is capable of improving its competitiveness at the expense of reconsideration of the concept of vocational education. This article presents opportunities in stimulating educational migration into Russian secondary vocational institutions under current demographic situation and deficiency of qualified workers, and also specifies some system limitations and difficulties, which can be faced when realizing the strategy of educational services export. Some of them: bad potential students’ knowledge of Russian language, poor material and technical base, old-fashioned educational facilities and persisting differences in educational systems with the other leading countries of the world, inadequate legislation, etc. Listed in the article problems call for a better managed migration policy by using modern organizational and economic solutions, providing incentives and opportunities for the population influx

Modellfreie Analyse der hämodynamischen Antwort in funktionellen Magnetresonanztomographien (FMRI) des menschlichen Gehirns

Functional Magnetic Resonance Imaging (fMRI) data of the brain includes activated parenchyma voxels, corresponding to the paradigm performed, non-activated parenchyma voxels and background voxels. Statistical tests, e.g. using the general linear model approach of SPM or the Kolmogorov-Smirnov (KS) non-parametric statistic, are common 'supervised' techniques to look for activation in functional brain MRI. Selection of voxel type by comparing the voxel time course with a model of the expected hemodynamic response function (HRF) from the task paradigm has proven to be difficult due to individual and spatial variance of the measured HRF. For the functional differentiation of brain voxels I introduce a new method separating brain voxels based on their features in the time domain using a self-organizing map (SOM) neural network technique without modeling the HRF. Since activation measured by fMRI is related to magnetic susceptibility changes in venous blood which represents only 2-5% of brain matter, pre-processing is required to remove he majority of non-activated voxels which dominate learning instead of real activation patterns. Using the auto-correlation function one can select voxels which are candidates of being activated. Features of the time course of the selected voxels can be learned with the SOM. In the first step the SOM is trained by the voxels time course, fitting its neurons to the input. After learning, the neurons have adapted to the intrinsic features space of the voxel time courses. Using the trained SOM, voxel time courses are presented again, now labeled by the neuron having the smallest Euclidean distance to the presented voxel time course. The result of the labeling and the learned feature time course vectors are compared visually with the t-map of the SPM statistic. With the SOM map one can visually separate the voxels based on their features in the time domain into different functional task related classes

Modellfreie Analyse der hämodynamischen Antwort in funktionellen Magnetresonanztomographien (FMRI) des menschlichen Gehirns

Functional Magnetic Resonance Imaging (fMRI) data of the brain includes activated parenchyma voxels, corresponding to the paradigm performed, non-activated parenchyma voxels and background voxels. Statistical tests, e.g. using the general linear model approach of SPM or the Kolmogorov-Smirnov (KS) non-parametric statistic, are common 'supervised' techniques to look for activation in functional brain MRI. Selection of voxel type by comparing the voxel time course with a model of the expected hemodynamic response function (HRF) from the task paradigm has proven to be difficult due to individual and spatial variance of the measured HRF. For the functional differentiation of brain voxels I introduce a new method separating brain voxels based on their features in the time domain using a self-organizing map (SOM) neural network technique without modeling the HRF. Since activation measured by fMRI is related to magnetic susceptibility changes in venous blood which represents only 2-5% of brain matter, pre-processing is required to remove he majority of non-activated voxels which dominate learning instead of real activation patterns. Using the auto-correlation function one can select voxels which are candidates of being activated. Features of the time course of the selected voxels can be learned with the SOM. In the first step the SOM is trained by the voxels time course, fitting its neurons to the input. After learning, the neurons have adapted to the intrinsic features space of the voxel time courses. Using the trained SOM, voxel time courses are presented again, now labeled by the neuron having the smallest Euclidean distance to the presented voxel time course. The result of the labeling and the learned feature time course vectors are compared visually with the t-map of the SPM statistic. With the SOM map one can visually separate the voxels based on their features in the time domain into different functional task related classes