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.

Search at the Mainz Microtron for Light Massive Gauge Bosons Relevant for the Muon g-2 Anomaly

Abstract: A massive, but light, Abelian U(1) gauge boson is a well-motivated possible signature of physics beyond the standard model of particle physics. In this Letter, the search for the signal of such a U(1) gauge boson in electron-positron pair production at the spectrometer setup of the A1 Collaboration at the Mainz Microtron is described. Exclusion limits in the mass range of 40 MeV/c^2 to 300 MeV/c^2, with a sensitivity in the squared mixing parameter of as little as e^2=8×10^−7 are presented. A large fraction of the parameter space has been excluded where the discrepancy of the measured anomalous magnetic moment of the muon with theory might be explained by an additional U(1) gauge boson.

Coherent dynamics of macroscopic electronic order through a symmetry breaking transition

Abstract: The speed with which symmetry breaking transitions occur in the solid state makes them difficult to study in the time domain. State-of-the-art pump–probe measurements of the dynamics of charge-density waves in terbium telluride enable the evolution of the symmetry breaking charge-order transition of this system to be studied with unprecedented temporal resolution.

On-line learning and modulation of periodic movements with nonlinear dynamical systems

Abstract: The paper presents a two-layered system for (1) learning and encoding a periodic signal without any knowledge on its frequency and waveform, and (2) modulating the learned periodic trajectory in response to external events. The system is used to learn periodic tasks on a humanoid HOAP-2 robot. The first layer of the system is a dynamical system responsible for extracting the fundamental frequency of the input signal, based on adaptive frequency oscillators. The second layer is a dynamical system responsible for learning of the waveform based on a built-in learning algorithm. By combining the two dynamical systems into one system we can rapidly teach new trajectories to robots without any knowledge of the frequency of the demonstration signal. The system extracts and learns only one period of the demonstration signal. Furthermore, the trajectories are robust to perturbations and can be modulated to cope with a dynamic environment. The system is computationally inexpensive, works on-line for any periodic signal, requires no additional signal processing to determine the frequency of the input signal and can be applied in parallel to multiple dimensions. Additionally, it can adapt to changes in frequency and shape, e.g. to non-stationary signals, such as hand-generated signals and human demonstrations.