The current view of the scholarship is that ‘Celtic’ migration in the fourth and third centuries BC significantly impacted on the formation of identities in central and southeastern Europe. This work questions the notion of ‘Celtic’ identity and patterns of ‘roaming tribal migrations’ in light of recent criticisms, using post-modernistic notions of culture and ethnicity as a fluent and socially constructed phenomena, as well as contextual criticism of the Greco-Roman discourse on barbarians that is presented in written sources from antiquity. The ‘Celtic’ arrival in southeastern Europe and the formation of identities with a ‘Celtic ethnic element’, such as Scordiscan, are seen here in regional settings and explained as a consequence of the process of hybridization and restructuring of existing identities through a selective acceptance of global cultural templates from the Mediterranean and temperate Europe. Danijel DŽINO

Imitation in robotics is seen as a powerful means to reduce the complexity of robot programming. It allows users to instruct robots by simply showing them how to execute a given task. Through imitation robots can learn from their environment and adapt to it just as human newborns do. In order to be useful as human companions, robots must act for a purpose by achieving goals and fullfiling human expectations. But, what is the goal behind the surface of the demonstrated behavior? How to extract, encode and reuse eventual regularities observed? These questions are indispensable for the development of cognitive agents capable of being human companions in everyday life. In this paper we present ConSCIS, a framework for robot teaching through observation and imitation inspired by recent findings in cognitive sciences, biology and neuroscience. In ConSCIS we regard imitation as the process of manipulating high-level symbols in order to achieve goals and intentions hidden in the observation of task. The architecture has been tested both in simulation and on an anthropomorphic robot platform.

In this paper we propose a novel approach for the texture analysis-synthesis problem, with the purpose to restore missing zones in greyscale images. Bit-plane decomposition is used, and a dictionary is build with bit-blocks statistics for each plane. Gaps are reconstructed with a conditional stochastic process, to propagate texture global features into the damaged area, using information stored in the dictionary. Our restoration method is simple, easy and fast, with very good results for a large set of textured images. Results are compared with a state-of-the-art restoration algorithm.

Imitation in robotics is seen as a powerful means to reduce the complexity of robot programming. It allows users to instruct robots by simply showing them how to execute a given task. Through imitation robots can learn from their environment and adapt to it just as human newborns do. Despite different facets of imitative behaviours observed in humans and higher primates, imitation in robotics has usually been implemented as a process of copying demonstrated actions onto the movement apparatus of the robot. While the results being reached are impressive, we believe that a shift towards a higher expression of imitation, namely the comprehension of human actions and inference of its intentions, is needed. In order to be useful as human companions, robots must act for a purpose by achieving goals and fulfilling human expectations. In this paper we present ConSCIS (Conceptual Space based Cognitive Imitation System), an architecture for goal-level imitation in robotics where the focus is put on final effects of actions on objects. The architecture tightly links low-level data with high-level knowledge, and integrates, in a unified framework, several aspects of imitation, such as perception, learning, knowledge representation, action generation and robot control. Some preliminary experimental results with an anthropomorphic arm/hand robotic system are shown.

: Given a continuous time nonlinear closed loop system, we investigate sampled–data feedback laws for which the trajectories of the sampled–data closed loop system converge to the continuous time trajectories with a prescribed rate of convergence as the length of the sampling interval tends to zero. We derive necessary and sufficient conditions for the existence of such sampled–data feedback laws and — in case of existence — provide explicit redesign formulas and algorithms for these controllers.