Jožef Stefan Institute

About: Jožef Stefan Institute is a facility organization based out in Ljubljana, Slovenia. It is known for research contribution in the topics: Liquid crystal & Dielectric. The organization has 3828 authors who have published 12614 publications receiving 291025 citations.

Human-in-the-loop approach for teaching robot assembly tasks using impedance control interface

Abstract: In this paper we propose a human-in-the-loop approach for teaching robots how to solve part assembly tasks. In the proposed setup the human tutor controls the robot through a haptic interface and a hand-held impedance control interface. The impedance control interface is based on a linear spring-return potentiometer that maps the button position to the robot arm stiffness. This setup allows the tutor to modulate the robot compliance based on the given task requirements. The demonstrated motion and stiffness trajectories are encoded using Dynamical Movement Primitives and learnt using Locally Weight Regression. To validate the proposed approach we performed experiments using Kuka Light Weight Robot and HapticMaster robot. The task of the experiment was to teach the robot how to perform an assembly task involving sliding a bolt fitting inside a groove in order to mount two parts together. Different stiffness was required in different stages of the task execution to accommodate the interaction of the robot with the environment and possible human-robot cooperation.

Entanglement and matrix elements of observables in interacting integrable systems

Abstract: We study the bipartite von Neumann entanglement entropy and matrix elements of local operators in the eigenstates of an interacting integrable Hamiltonian (the paradigmatic spin-1/2 XXZ chain), and we contrast their behavior with that of quantum chaotic systems. We find that the leading term of the average (over all eigenstates in the zero magnetization sector) eigenstate entanglement entropy has a volume-law coefficient that is smaller than the universal (maximal entanglement) one in quantum chaotic systems. This establishes the entanglement entropy as a powerful measure to distinguish integrable models from generic ones. Remarkably, our numerical results suggest that the volume-law coefficient of the average entanglement entropy of eigenstates of the spin-1/2 XXZ Hamiltonian is very close to, or the same as, the one for translationally invariant quadratic fermionic models. We also study matrix elements of local operators in the eigenstates of the spin-1/2 XXZ Hamiltonian at the center of the spectrum. For the diagonal matrix elements, we show evidence that the support does not vanish with increasing system size, while the average eigenstate-to-eigenstate fluctuations vanish in a power-law fashion. For the off-diagonal matrix elements, we show that they follow a distribution that is close to (but not quite) log-normal, and that their variance is a well-defined function of $\ensuremath{\omega}={E}_{\ensuremath{\alpha}}\ensuremath{-}{E}_{\ensuremath{\beta}}$ (${{E}_{\ensuremath{\alpha}}}$ are the eigenenergies) proportional to $1/D$, where $D$ is the Hilbert space dimension.

Gut microbiota patterns associated with colonization of different clostridium difficile ribotypes

Abstract: C. difficile infection is associated with disturbed gut microbiota and changes in relative frequencies and abundance of individual bacterial taxons have been described. In this study we have analysed bacterial, fungal and archaeal microbiota by denaturing high pressure liquid chromatography (DHPLC) and with machine learning methods in 208 faecal samples from healthy volunteers and in routine samples with requested C. difficile testing. The latter were further divided according to stool consistency, C. difficile presence or absence and C. difficile ribotype (027 or non-027). Lower microbiota diversity was a common trait of all routine samples and not necessarily connected only to C. difficile colonisation. Differences between the healthy donors and C. difficile positive routine samples were detected in bacterial, fungal and archaeal components. Bifidobacterium longum was the single most important species associated with C. difficile negative samples. However, by machine learning approaches we have identified patterns of microbiota composition predictive for C. difficile colonization. Those patterns also differed between samples with C. difficile ribotype 027 and other C. difficile ribotypes. The results indicate that not only the presence of a single species/group is important but that certain combinations of gut microbes are associated with C. difficile carriage and that some ribotypes (027) might be associated with more disturbed microbiota than the others.