Continual learning with attentive recurrent neural networks for temporal data classification.

Continual learning is an emerging research branch of deep learning, which aims to learn a model for a series of tasks continually without forgetting knowledge obtained from previous tasks. Despite receiving a lot of attention in the research community, temporal-based continual learning techniques are still underutilized. In this paper, we address the problem of temporal-based continual learning by allowing a model to continuously learn on temporal data.

Observation of robust edge superconductivity in Fe(Se,Te) under strong magnetic perturbation.

The iron-chalcogenide high temperature superconductor Fe(Se,Te) (FST) has been reported to exhibit complex magnetic ordering and nontrivial band topology which may lead to novel superconducting phenomena. However, the recent studies have so far been largely concentrated on its band and spin structures while its mesoscopic electronic and magnetic response, crucial for future device applications, has not been explored experimentally. Here, we used scanning superconducting quantum interference device microscopy for its sensitivity to both local diamagnetic susceptibility and current distribution in order to image the superfluid density and supercurrent in FST.

Coherent fluorescence emission by using hybrid photonic-plasmonic crystals.

The spatial and temporal coherence of the fluorescence emission controlled by a quasi-two-dimensional hybrid photonic-plasmonic crystal structure covered with a thin fluorescent-molecular-doped dielectric film is investigated experimentally. A simple theoretical model to describe how a confined quasi-two-dimensional optical mode may induce coherent fluorescence emission is also presented. Concerning the spatial coherence, it is experimentally observed that the coherence area in the plane of the light source is in excess of 49 μm, which results in enhanced directional fluorescence emission.