Work forms one of the important spheres of life and is one of the main determinants of subjective well-being in general and life satisfaction in particular (Argyle 2001;Sousa-Poza & Sousa-Poza 2000). The study aims to disclose the impact of work values and socio-demographic characteristics upon the link between life satisfaction and job satisfaction. The European Values Study 2008-2009 is used as dataset. The sample is limited to those who have jobs (28 653 cases). The results confirm findings from the literature that intrinsic motivation increases life satisfaction (Vansteenkiste 2007). It is in line with self-determination theory according to which pursuit of intrinsic motivation facilitates satisfaction of the basic psychological needs for autonomy, competence and relatedness (Deci & Ryan, 2000;Kasser, 2002). Sharing extrinsic values decreases life satisfaction. The association between life satisfaction and job satisfaction is stronger for higher educated individuals and self-employed and weaker for women, married individuals, religious individuals and those of younger age. These results are due to the different of job in life of people with different characteristics. The link between life satisfaction and job satisfaction is the same in countries with low and high GDP per capita.

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2D path planning in static environment is a well-known problem and one of the common ways to solve it is to 1) represent the environment as a grid and 2) perform a heuristic search for a path on it. At the same time 2D grid resembles much a digital image, thus an appealing idea comes to being -to treat the problem as an image generation task and to solve it utilizing the recent advances in deep learning. In this work we make an attempt to apply a generative neural network as a path finder and report preliminary results, convincing enough to claim that this direction of research is worth further exploration.

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