Magnetic and lattice coupling in single-crystal SrFe2_2As2_2: A neutron scattering study

A detailed elastic neutron scattering study of the structural and magnetic phase transitions in single-crystal SrFe$_2$As$_2$ reveals that the orthorhombic (O)-tetragonal (T) and the antiferromagnetic transitions coincide at $T_\texttt{O}$ = $T_\texttt{N}$ = (201.5 $\pm$ 0.25) K. The observation of coexisting O-T phases over a finite temperature range at the transition and the sudden onset of the O distortion provide strong evidences that the structural transition is first order. The simultaneous appearance and disappearance within 0.5 K upon cooling and within 0.25 K upon warming, respectively, indicate that the magnetic and structural transitions are intimately coupled. We find that the hysteresis in the transition temperature extends over a 1-2 K range. Based on the observation of a remnant orthorhombic phase at temperatures higher than \emph{T}$_\texttt{O}$, we suggest that the T-O transition may be an order-disorder transition

Asymmetrical Hierarchical Networks with Attentive Interactions for Interpretable Review-Based Recommendation

Recently, recommender systems have been able to emit substantially improved recommendations by leveraging user-provided reviews. Existing methods typically merge all reviews of a given user or item into a long document, and then process user and item documents in the same manner. In practice, however, these two sets of reviews are notably different: users' reviews reflect a variety of items that they have bought and are hence very heterogeneous in their topics, while an item's reviews pertain only to that single item and are thus topically homogeneous. In this work, we develop a novel neural network model that properly accounts for this important difference by means of asymmetric attentive modules. The user module learns to attend to only those signals that are relevant with respect to the target item, whereas the item module learns to extract the most salient contents with regard to properties of the item. Our multi-hierarchical paradigm accounts for the fact that neither are all reviews equally useful, nor are all sentences within each review equally pertinent. Extensive experimental results on a variety of real datasets demonstrate the effectiveness of our method

Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Series

Forecasting on sparse multivariate time series (MTS) aims to model the predictors of future values of time series given their incomplete past, which is important for many emerging applications. However, most existing methods process MTS's individually, and do not leverage the dynamic distributions underlying the MTS's, leading to sub-optimal results when the sparsity is high. To address this challenge, we propose a novel generative model, which tracks the transition of latent clusters, instead of isolated feature representations, to achieve robust modeling. It is characterized by a newly designed dynamic Gaussian mixture distribution, which captures the dynamics of clustering structures, and is used for emitting timeseries. The generative model is parameterized by neural networks. A structured inference network is also designed for enabling inductive analysis. A gating mechanism is further introduced to dynamically tune the Gaussian mixture distributions. Extensive experimental results on a variety of real-life datasets demonstrate the effectiveness of our method.Comment: This paper is accepted by AAAI 202