A Study on the Influence of Language Learning Strategies on Academic Adaptability among Chinese International Students: A Cross-Cultural Perspective

This article aims to conduct research from a cross-cultural perspective, combining literature reviews from domestic and international sources, field surveys, and quantitative analysis to explore the impact of language learning strategies on the academic adaptation of foreign students in China. It is found that there is a correlation between language learning strategies and the academic adaptation ability of foreign students, providing a research foundation for understanding the challenges in their academic life and proposing suggestions to promote their academic adaptation ability

Discriminative Nonparametric Latent Feature Relational Models with Data Augmentation

We present a discriminative nonparametric latent feature relational model (LFRM) for link prediction to automatically infer the dimensionality of latent features. Under the generic RegBayes (regularized Bayesian inference) framework, we handily incorporate the prediction loss with probabilistic inference of a Bayesian model; set distinct regularization parameters for different types of links to handle the imbalance issue in real networks; and unify the analysis of both the smooth logistic log-loss and the piecewise linear hinge loss. For the nonconjugate posterior inference, we present a simple Gibbs sampler via data augmentation, without making restricting assumptions as done in variational methods. We further develop an approximate sampler using stochastic gradient Langevin dynamics to handle large networks with hundreds of thousands of entities and millions of links, orders of magnitude larger than what existing LFRM models can process. Extensive studies on various real networks show promising performance.Comment: Accepted by AAAI 201

(Z)-1-Phenyl-3-(3-pyridyl­meth­ylamino)­but-2-en-1-one

The reaction of 3-C5H4NCH2NH2 and C6H5COCH2COCH3 affords the title compound, C16H16N2O. The O=C—C=C—N portion is essentially planar [maximum deviation = 0.046 (2) Å] and is aligned at dihedral angles of 22.6 (1) and 78.9 (1)° to the phenyl and pyridyl rings, respectively. The N—H and O=C groups are linked by an intra­molecular hydrogen bond. In the crystal, C—H⋯O hydrogen bonds and C—H⋯π inter­actions occur

Jointly Modeling Topics and Intents with Global Order Structure

Modeling document structure is of great importance for discourse analysis and related applications. The goal of this research is to capture the document intent structure by modeling documents as a mixture of topic words and rhetorical words. While the topics are relatively unchanged through one document, the rhetorical functions of sentences usually change following certain orders in discourse. We propose GMM-LDA, a topic modeling based Bayesian unsupervised model, to analyze the document intent structure cooperated with order information. Our model is flexible that has the ability to combine the annotations and do supervised learning. Additionally, entropic regularization can be introduced to model the significant divergence between topics and intents. We perform experiments in both unsupervised and supervised settings, results show the superiority of our model over several state-of-the-art baselines.Comment: Accepted by AAAI 201