Improper Ferroelectric Polarisation in a Perovskite driven by Inter-site Charge Transfer and Ordering

It is of great interest to design and make materials in which ferroelectric polarisation is coupled to other order parameters such as lattice, magnetic and electronic instabilities. Such materials will be invaluable in next-generation data storage devices. Recently, remarkable progress has been made in understanding improper ferroelectric coupling mechanisms that arise from lattice and magnetic instabilities. However, although theoretically predicted, a compact lattice coupling between electronic and ferroelectric (polar) instabilities has yet to be realised. Here we report detailed crystallographic studies of a novel perovskite Hg$^{\textbf{A}}$Mn$^{\textbf{A'}}_{3}$Mn$^{\textbf{B}}_{4}$O$_{12}$ that is found to exhibit a polar ground state on account of such couplings that arise from charge and orbital ordering on both the A' and B-sites, which are themselves driven by a highly unusual Mn$^{A'}$-Mn$^B$ inter-site charge transfer. The inherent coupling of polar, charge, orbital and hence magnetic degrees of freedom, make this a system of great fundamental interest, and demonstrating ferroelectric switching in this and a host of recently reported hybrid improper ferroelectrics remains a substantial challenge.Comment: 9 pages, 7 figure

Dark matter for excess of AMS-02 positrons and antiprotons

We propose a dark matter explanation to simultaneously account for the excess of antiproton-to-proton and positron power spectra observed in the AMS-02 experiment while having the right dark matter relic abundance and satisfying the current direct search bounds. We extend the Higgs triplet model with a hidden gauge symmetry of $SU(2)_X$ that is broken to $Z_3$ by a quadruplet scalar field, rendering the associated gauge bosons stable weakly-interacting massive particle dark matter candidates. By coupling the complex Higgs triplet and the $SU(2)_X$ quadruplet, the dark matter candidates can annihilate into triplet Higgs bosons each of which in turn decays into lepton or gauge boson final states. Such a mechanism gives rise to correct excess of positrons and antiprotons with an appropriate choice of the triplet vacuum expectation value. Besides, the model provides a link between neutrino mass and dark matter phenomenology.Comment: 12 pages, 3 figures, references and comments added, version to appear in Phys. Lett.

Personalized Acoustic Modeling by Weakly Supervised Multi-Task Deep Learning using Acoustic Tokens Discovered from Unlabeled Data

It is well known that recognizers personalized to each user are much more effective than user-independent recognizers. With the popularity of smartphones today, although it is not difficult to collect a large set of audio data for each user, it is difficult to transcribe it. However, it is now possible to automatically discover acoustic tokens from unlabeled personal data in an unsupervised way. We therefore propose a multi-task deep learning framework called a phoneme-token deep neural network (PTDNN), jointly trained from unsupervised acoustic tokens discovered from unlabeled data and very limited transcribed data for personalized acoustic modeling. We term this scenario "weakly supervised". The underlying intuition is that the high degree of similarity between the HMM states of acoustic token models and phoneme models may help them learn from each other in this multi-task learning framework. Initial experiments performed over a personalized audio data set recorded from Facebook posts demonstrated that very good improvements can be achieved in both frame accuracy and word accuracy over popularly-considered baselines such as fDLR, speaker code and lightly supervised adaptation. This approach complements existing speaker adaptation approaches and can be used jointly with such techniques to yield improved results.Comment: 5 pages, 5 figures, published in IEEE ICASSP 201