Credit growth in Central and Eastern Europe: convergence or boom?

Credit to the private sector has been growing very rapidly in a number of Central and Eastern European countries in recent years. The main question is whether this dynamics is an equilibrium convergence process or may rather pose stability risks. Using panel econometric techniques, this paper attempts to identify the equilibrium credit/GDP levels of the new EU countries, disentangling the observed growth into an equilibrium trend and an excess (boom) component. In the paper the pooled mean group estimator was used for its flexibility and efficiency. Using instrumental variable technique we tested whether long run endogeneity affects the consistency. The estimations show that large part of the credit growth in new member states can be explained by the catching-up process, and, in general, credit/GDP ratios are below the levels consistent with macroeconomic fundamentals. However, in Latvia and Estonia credit growth is found to be significantly faster than what would be justified along the equilibrium path

Taxonomic status and behavioural documentation of the troglobiont Lithobius matulici (Myriapoda, Chilopoda) from the Dinaric Alps: Are there semiaquatic centipedes in caves?

Lithobius matulici Verhoeff, 1899 is redescribed based on type material and newly collected specimens. Strandiolus jugoslavicus Hoffer, 1937, described from another cave in the same region in Bosnia and Hercegovina, is presented as a junior subjective synonym of L. matulici (syn. nov.). L. matulici is shown to be most closely related to Lithobius remyi Jawlowski, 1933, type species of the subgenus-Thracolithobius Matic, 1962. The completeness of the chitin-lines on the forcipular coxosternite is discussed as a promising character for interspecific differentiation within Lithobiomorpha. Documentation of hitherto unknown semiaquatic behaviour in L. matulici and other cave-dwelling centipede species from Heizeguvinian-, Montenegrin- and Pyrenean caves is presented

Experimental evidence for sex-specific plasticity in adult brain

Background: Plasticity in brain size and the size of different brain regions during early ontogeny is known from many vertebrate taxa, but less is known about plasticity in the brains of adults. In contrast to mammals and birds, most parts of a fish's brain continue to undergo neurogenesis throughout adulthood, making lifelong plasticity in brain size possible. We tested whether maturing adult three-spined sticklebacks (Gasterosteus aculeatus) reared in a stimulus-poor environment exhibited brain plasticity in response to environmental enrichment, and whether these responses were sex-specific, thus altering the degree of sexual size dimorphism in the brain. Results: Relative sizes of total brain and bulbus olfactorius showed sex-specific responses to treatment: males developed larger brains but smaller bulbi olfactorii than females in the enriched treatment. Hence, the degree of sexual size dimorphism (SSD) in relative brain size and the relative size of the bulbus olfactorius was found to be environment-dependent. Furthermore, the enriched treatment induced development of smaller tecta optica in both sexes. Conclusions: These results demonstrate that adult fish can alter the size of their brain (or brain regions) in response to environmental stimuli, and these responses can be sex-specific. Hence, the degree of SSD in brain size can be environment-dependent, and our results hint at the possibility of a large plastic component to SSD in stickleback brains. Apart from contributing to our understanding of the processes shaping and explaining variation in brain size and the size of different brain regions in the wild, the results show that provision of structural complexity in captive environments can influence brain development. Assuming that the observed plasticity influences fish behaviour, these findings may also have relevance for fish stocking, both for economical and conservational purposes.Peer reviewe

Together or Alone: The Price of Privacy in Collaborative Learinig

Machine learning algorithms have reached mainstream status and are widely deployed in many applications. The accuracy of such algorithms depends significantly on the size of the underlying training dataset; in reality a small or medium sized organization often does not have the necessary data to train a reasonably accurate model. For such organizations, a realistic solution is to train their machine learning models based on their joint dataset (which is a union of the individual ones). Unfortunately, privacy concerns prevent them from straightforwardly doing so. While a number of privacy-preserving solutions exist for collaborating organizations to securely aggregate the parameters in the process of training the models, we are not aware of any work that provides a rational framework for the participants to precisely balance the privacy loss and accuracy gain in their collaboration. In this paper, by focusing on a two-player setting, we model the collaborative training process as a two-player game where each player aims to achieve higher accuracy while preserving the privacy of its own dataset. We introduce the notion of Price of Privacy, a novel approach for measuring the impact of privacy protection on the accuracy in the proposed framework. Furthermore, we develop a game-theoretical model for different player types, and then either find or prove the existence of a Nash Equilibrium with regard to the strength of privacy protection for each player. Using recommendation systems as our main use case, we demonstrate how two players can make practical use of the proposed theoretical framework, including setting up the parameters and approximating the non-trivial Nash Equilibrium

The Price of Privacy in Collaborative Learning

Machine learning algorithms have reached mainstream status and are widely deployed in many applications. The accuracy of such algorithms depends significantly on the size of the underlying training dataset; in reality a small or medium sized organization often does not have enough data to train a reasonably accurate model. For such organizations, a realistic solution is to train machine learning models based on a joint dataset (which is a union of the individual ones). Unfortunately, privacy concerns prevent them from straightforwardly doing so. While a number of privacy-preserving solutions exist for collaborating organizations to securely aggregate the parameters in the process of training the models, we are not aware of any work that provides a rational framework for the participants to precisely balance the privacy loss and accuracy gain in their collaboration. In this paper, we model the collaborative training process as a two-player game where each player aims to achieve higher accuracy while preserving the privacy of its own dataset. We introduce the notion of Price of Privacy, a novel approach for measuring the impact of privacy protection on the accuracy in the proposed framework. Furthermore, we develop a game-theoretical model for different player types, and then either find or prove the existence of a Nash Equilibrium with regard to the strength of privacy protection for each player