A Review of Studies on Languaging and Second Language Learning (2006-2017)

Since Swain postulated the concept “languaging” in 2006 to capture the role of language production in second language (L2) learning, a growing body of empirical studies has been conducted on languaging. However, little research has reviewed these studies. The present paper reviews 15 empirical studies that were conducted over the past decade on languaging in L2 learning, followed Vygotsky’s socioculutral theory of mind, and directly took languaging as the treatment or part of the treatment. We distinguished task-prompted and teacher-imposed languaging in the paper. All studies reviewed focused on teacher-imposed languaging. On the basis of reviewing the foci and findings of the studies, we offer our critical comments and recommendations for future research

KCRC-LCD: Discriminative Kernel Collaborative Representation with Locality Constrained Dictionary for Visual Categorization

We consider the image classification problem via kernel collaborative representation classification with locality constrained dictionary (KCRC-LCD). Specifically, we propose a kernel collaborative representation classification (KCRC) approach in which kernel method is used to improve the discrimination ability of collaborative representation classification (CRC). We then measure the similarities between the query and atoms in the global dictionary in order to construct a locality constrained dictionary (LCD) for KCRC. In addition, we discuss several similarity measure approaches in LCD and further present a simple yet effective unified similarity measure whose superiority is validated in experiments. There are several appealing aspects associated with LCD. First, LCD can be nicely incorporated under the framework of KCRC. The LCD similarity measure can be kernelized under KCRC, which theoretically links CRC and LCD under the kernel method. Second, KCRC-LCD becomes more scalable to both the training set size and the feature dimension. Example shows that KCRC is able to perfectly classify data with certain distribution, while conventional CRC fails completely. Comprehensive experiments on many public datasets also show that KCRC-LCD is a robust discriminative classifier with both excellent performance and good scalability, being comparable or outperforming many other state-of-the-art approaches

An Experimental Study of Within- and Cross-cultural Cooperation: Chinese and American Play in the Prisoner’s Dilemma Game

We study whether cross- and within-culture groups have different cooperation rates in the Prisoner’s Dilemma Game. In an experiment, university students in China and America engage in a single iteration of the game, complete belief elicitation tasks regarding their opponents’ play and take a survey including attitudinal measurements regarding their in- and out-group attitudes. Cooperation rates are higher across the two countries are higher in both cross-culture and in within-culture interactions, although not significantly. We also find that Chinese participants cooperate less than American ones. Further, female Chinese participants are more cooperative than Chinese male ones. In the cross-culture treatment, Chinese participants underestimate the likelihood of cooperative behavior of their American counterparts, while Americans overestimate the same likelihood of their Chinese counterparts. Our results also show that Chinese participants cooperate more conditionally than American ones. Finally, while we find some attitudinal in- and out-biases both they do not generate meaningful impact on cooperative behavior

Salt-Induced Liquid–Liquid Phase Separation: Combined Experimental and Theoretical Investigation of Water–Acetonitrile–Salt Mixtures

Salt-induced liquid–liquid phase separation in liquid mixtures is a common phenomenon in nature and in various applications, such as in separation and extraction of chemicals. Here, we present results of a systematic investigation of the phase behaviors in water–acetonitrile–salt mixtures using a combination of experiment and theory. We obtain complete ternary phase diagrams for nine representative salts in water–acetonitrile mixtures by cloud point and component analysis. We construct a thermodynamic free energy model by accounting for the nonideal mixing of the liquids, ion hydration, electrostatic interactions, and Born energy. Our theory yields phase diagrams in good agreement with the experimental data. By comparing the contributions due to the electrostatic interaction, Born energy, and hydration, we find that hydration is the main driving force for the liquid–liquid separation and is a major contributor to the specific ion effects. Our theory highlights the important role of entropy in the hydration driving force. We discuss the implications of our findings in the context of salting-out assisted liquid–liquid extraction and make suggestions for selecting salt ions to optimize the separation performance

Hierarchical Neyman-Pearson Classification for Prioritizing Severe Disease Categories in COVID-19 Patient Data

COVID-19 has a spectrum of disease severity, ranging from asymptomatic to requiring hospitalization. Understanding the mechanisms driving disease severity is crucial for developing effective treatments and reducing mortality rates. One way to gain such understanding is using a multi-class classification framework, in which patients' biological features are used to predict patients' severity classes. In this severity classification problem, it is beneficial to prioritize the identification of more severe classes and control the "under-classification" errors, in which patients are misclassified into less severe categories. The Neyman-Pearson (NP) classification paradigm has been developed to prioritize the designated type of error. However, current NP procedures are either for binary classification or do not provide high probability controls on the prioritized errors in multi-class classification. Here, we propose a hierarchical NP (H-NP) framework and an umbrella algorithm that generally adapts to popular classification methods and controls the under-classification errors with high probability. On an integrated collection of single-cell RNA-seq (scRNA-seq) datasets for 864 patients, we explore ways of featurization and demonstrate the efficacy of the H-NP algorithm in controlling the under-classification errors regardless of featurization. Beyond COVID-19 severity classification, the H-NP algorithm generally applies to multi-class classification problems, where classes have a priority order