First-principles study of multiferroic RbFe(MoO4_4)2_2

We have investigated the magnetic structure and ferroelectricity in RbFe(MoO$_4$)$_2$ via first-principles calculations. Phenomenological analyses have shown that ferroelectricity may arise due to both the triangular chirality of the magnetic structure, and through coupling between the magnetic helicity and the ferroaxial structural distortion. Indeed, it was recently proposed that the structural distortion plays a key role in stabilising the chiral magnetic structure itself. We have determined the relative contribution of the two mechanisms via \emph{ab-initio} calculations. Whilst the structural axiality does induce the magnetic helix by modulating the symmetric exchange interactions, the electric polarization is largely due to the in-plane spin triangular chirality, with both electronic and ionic contributions being of relativistic origin. At the microscopic level, we interpret the polarization as a secondary steric consequence of the inverse Dzyaloshinskii-Moriya mechanism and accordingly explain why the ferroaxial component of the electric polarization must be small

TriNet: stabilizing self-supervised learning from complete or slow collapse on ASR

Self-supervised learning (SSL) models confront challenges of abrupt informational collapse or slow dimensional collapse. We propose TriNet, which introduces a novel triple-branch architecture for preventing collapse and stabilizing the pre-training. TriNet learns the SSL latent embedding space and incorporates it to a higher level space for predicting pseudo target vectors generated by a frozen teacher. Our experimental results show that the proposed method notably stabilizes and accelerates pre-training and achieves a relative word error rate reduction (WERR) of 6.06% compared to the state-of-the-art (SOTA) Data2vec for a downstream benchmark ASR task. We will release our code at https://github.com/tencent-ailab/.Comment: Accepted by ICASSP 202

Synthesis and Luminescence Properties of Core/Shell ZnS:Mn/ZnO Nanoparticles

In this paper the influence of ZnO shell thickness on the luminescence properties of Mn-doped ZnS nanoparticles is studied. Transmission electron microscopy (TEM) images showed that the average diameter of ZnS:Mn nanoparticles is around 14 nm. The formation of ZnO shells on the surface of ZnS:Mn nanoparticles was confirmed by X-ray diffraction (XRD) patterns, high-resolution TEM (HRTEM) images, and X-ray photoelectron spectroscopy (XPS) measurements. A strong increase followed by a gradual decline was observed in the room temperature photoluminescence (PL) spectra with the thickening of the ZnO shell. The photoluminescence excitation (PLE) spectra exhibited a blue shift in ZnO-coated ZnS:Mn nanoparticles compared with the uncoated ones. It is shown that the PL enhancement and the blue shift of optimum excitation wavelength are led by the ZnO-induced surface passivation and compressive stress on the ZnS:Mn cores

A Data Transmission Algorithm Based on Dynamic Grid Division for Coal Goaf Temperature Monitoring

WSN (wireless sensor network) is a perfect tool of temperature monitoring in coal goaf. Based on the three-zone theory of goaf, the GtmWSN model is proposed, and its dynamic features are analyzed. Accordingly, a data transmission scheme, named DTDGD, is worked out. Firstly, sink nodes conduct dynamic grid division on the GtmWSN according to virtual semicircle. Secondly, each node will confirm to which grid it belongs based on grid number. Finally, data will be delivered to sink nodes with greedy forward and hole avoidance. Simulation results and field data showed that the GtmWSN and DTDGD satisfied the lifetime need of goaf temperature monitoring