Theoretical and experimental study of high-pressure synthesized B20-type compounds Mn1−x_{1-x}(Co,Rh)x_xGe

The search and exploration of new materials not found in nature is one of modern trends in pure and applied chemistry. In the present work, we report on experimental and \textit{ab initio} density-functional study of the high-pressure-synthesized series of compounds Mn$_{1-x}$(Co,Rh)$_x$Ge. These high-pressure phases remain metastable at normal conditions, therewith they preserve their inherent noncentrosymmetric B20-type structure and chiral magnetism. Of particular interest in these two isovalent systems is the comparative analysis of the effect of $3d$ (Co) and $4d$ (Rh) substitution for Mn, since the $3d$ orbitals are characterized by higher localization and electron interaction than the $4d$ orbitals. The behavior of Mn$_{1-x}$(Co,Rh)$_x$Ge systems is traced as the concentration changes in the range $0 \leq x \leq 1$. We applied a sensitive experimental and theoretical technique which allowed to refine the shape of the temperature dependencies of magnetic susceptibility $\chi(T)$ and thereby provide a new and detailed magnetic phase diagram of Mn$_{1-x}$Co$_x$Ge. It is shown that both systems exhibit a helical magnetic ordering that very strongly depends on the composition $x$. However, the phase diagram of Mn$_{1-x}$Co$_x$Ge differs from that of Mn$_{1-x}$Rh$_x$Ge in that it is characterized by coexistence of two helices in particular regions of concentrations and temperatures.Comment: 12 pages, 11 figure

Unsupervised quality estimation for neural machine translation

Quality Estimation (QE) is an important component in making Machine Translation (MT) useful in real-world applications, as it is aimed to inform the user on the quality of the MT output at test time. Existing approaches require large amounts of expert annotated data, computation and time for training. As an alternative, we devise an unsupervised approach to QE where no training or access to additional resources besides the MT system itself is required. Different from most of the current work that treats the MT system as a black box, we explore useful information that can be extracted from the MT system as a by-product of translation. By employing methods for uncertainty quantification, we achieve very good correlation with human judgments of quality, rivalling state-of-the-art supervised QE models. To evaluate our approach we collect the first dataset that enables work on both black-box and glass-box approaches to QE

Magnetic ground state and spin fluctuations in MnGe chiral magnet as studied by Muon Spin Rotation

We have studied by muon spin resonance ({\mu}SR) the helical ground state and fluctuating chiral phase recently observed in the MnGe chiral magnet. At low temperature, the muon polarization shows double period oscillations at short time scales. Their analysis, akin to that recently developed for MnSi [A. Amato et al., Phys. Rev. B 89, 184425 (2014)], provides an estimation of the field distribution induced by the Mn helical order at the muon site. The refined muon position agrees nicely with ab initio calculations. With increasing temperature, an inhomogeneous fluctuating chiral phase sets in, characterized by two well separated frequency ranges which coexist in the sample. Rapid and slow fluctuations, respectively associated with short range and long range ordered helices, coexist in a large temperature range below T$_{N}$ = 170 K. We discuss the results with respect to MnSi, taking the short helical period, metastable quenched state and peculiar band structure of MnGe into account.Comment: 13 pages, 11 figure

MLQE-PE : a multilingual quality estimation and post-editing dataset

We present MLQE-PE, a new dataset for Machine Translation (MT) Quality Estimation (QE) and Automatic Post-Editing (APE). The dataset contains seven language pairs, with human labels for 9,000 translations per language pair in the following formats: sentence-level direct assessments and post-editing effort, and word-level good/bad labels. It also contains the post-edited sentences, as well as titles of the articles where the sentences were extracted from, and the neural MT models used to translate the text