The Implementation of Flipped Classroom in Efl Class: a Taiwan Case Study

This article reports on a case study designed to examine the implementation of flipped classroom in the EFL classroom in Taiwan.  In addition, students' perception of flipped classroom was also investigated. Sixty-one senior high school students participated in this study; data were gathered from students' English midterm exam score and questionnaire. The data then were quantitatively analyzed by using T-test and descriptive statistics. The results show that students' English proficiency in flipped classroom was not significantly different with students in traditional classroom. However, the results reveal that students' perception of flipped classroom were generally favorable. Students' contended that flipped classroom enhanced their motivation in learning English, as they liked the self-pace through the course and they stated that flipped classroom gave them more class time to practice English. The results presented here may facilitate improvements in the implementation of flipped classroom in EFL class. Furthermore, suggestions for further research are also presented

Research on Corrective Feedback in ESL/EFL Classrooms

Corrective feedback (CF) has been one research focus in Second Language Acquisition in recent years, the study of which helps second language learners to acquire the target language successfully. The paper is to present an overview of studies on CF in ESL/EFL classroom interaction from 2000 to 2015. Firstly, the conceptual issues of CF are analyzed, including its definition, process, and category. Secondly, theoretical development of CF is reviewed which can be divided into three stages. Thirdly, research content on CF is introduced from Chinese and foreign scholars. Finally, the author makes an overall conclusion in which problems in current studies are pointed and future development of CF research is discussed at the end of the paper

Increased risk for T cell autoreactivity to ß-cell antigens in the mice expressing the Avy obesity-associated gene.

There has been considerable debate as to whether obesity can act as an accelerator of type 1 diabetes (T1D). We assessed this possibility using transgenic mice (MIP-TF mice) whose ß-cells express enhanced green fluorescent protein (EGFP). Infecting these mice with EGFP-expressing murine herpes virus-68 (MHV68-EGFP) caused occasional transient elevation in their blood glucose, peri-insulitis, and Th1 responses to EGFP which did not spread to other ß-cell antigens. We hypothesized that obesity-related systemic inflammation and ß-cell stress could exacerbate the MHV68-EGFP-induced ß-cell autoreactivity. We crossed MIP-TF mice with Avy mice which develop obesity and provide models of metabolic disease alongside early stage T2D. Unlike their MIP-TF littermates, MHV68-EGFP-infected Avy/MIP-TF mice developed moderate intra-insulitis and transient hyperglycemia. MHV68-EGFP infection induced a more pronounced intra-insulitis in older, more obese, Avy/MIP-TF mice. Moreover, in MHV68-EGFP-infected Avy/MIP-TF mice, Th1 reactivity spread from EGFP to other ß-cell antigens. Thus, the spreading of autoreactivity among ß-cell antigens corresponded with the transition from peri-insulitis to intra-insulitis and occurred in obese Avy/MIP-TF mice but not lean MIP-TF mice. These observations are consistent with the notion that obesity-associated systemic inflammation and ß-cell stress lowers the threshold necessary for T cell autoreactivity to spread from EGFP to other ß-cell autoantigens

Aqua­{tris­[(1H-benzimidazol-2-yl-κN 3)meth­yl]amine}­zinc 5-(dimethyl­amino)­naphthalene-1-sulfonate perchlorate 2.5-hydrate

In the title compound, [Zn(C24H21N7)(H2O)](C12H12NO3S)(ClO4)·2.5H2O, the ZnII ion is in a distorted trigonal–bipyramidal coordination geometry. In the crystal, N—H⋯O and O—H⋯O hydrogen bonds connect the components into a two-dimensional network parallel to (001). In addition, there are weak C—H⋯O hydrogen bonds

Exploring Object Relation in Mean Teacher for Cross-Domain Detection

Rendering synthetic data (e.g., 3D CAD-rendered images) to generate annotations for learning deep models in vision tasks has attracted increasing attention in recent years. However, simply applying the models learnt on synthetic images may lead to high generalization error on real images due to domain shift. To address this issue, recent progress in cross-domain recognition has featured the Mean Teacher, which directly simulates unsupervised domain adaptation as semi-supervised learning. The domain gap is thus naturally bridged with consistency regularization in a teacher-student scheme. In this work, we advance this Mean Teacher paradigm to be applicable for cross-domain detection. Specifically, we present Mean Teacher with Object Relations (MTOR) that novelly remolds Mean Teacher under the backbone of Faster R-CNN by integrating the object relations into the measure of consistency cost between teacher and student modules. Technically, MTOR firstly learns relational graphs that capture similarities between pairs of regions for teacher and student respectively. The whole architecture is then optimized with three consistency regularizations: 1) region-level consistency to align the region-level predictions between teacher and student, 2) inter-graph consistency for matching the graph structures between teacher and student, and 3) intra-graph consistency to enhance the similarity between regions of same class within the graph of student. Extensive experiments are conducted on the transfers across Cityscapes, Foggy Cityscapes, and SIM10k, and superior results are reported when comparing to state-of-the-art approaches. More remarkably, we obtain a new record of single model: 22.8% of mAP on Syn2Real detection dataset.Comment: CVPR 2019; The codes and model of our MTOR are publicly available at: https://github.com/caiqi/mean-teacher-cross-domain-detectio