Helping Teachers Take Control of a Course Book Facilitating Vocabulary Instruction

Although students are supposed to take control and responsibility for their own vocabulary learning, it does not necessarily mean that they study alone. More recently published EFL (English as a Foreign Language) course books have become increasingly aware of the importance of vocabulary instruction by compiling a vocabulary component in them. It facilitates vocabulary instruction by introducing a systematic and principled approach to vocabulary learning. Inevitably, there will be occasions when the selection and organization of lexis in the course book may not be appropriate in some learning context. It is thus critically important that teachers know how to process course books mentally. This paper is concerned with how course books might be evaluated and adapted with regard to the teaching of vocabulary. It is conducted by examining a sample of course books that facilitate vocabulary instruction at an intermediate level. In particular, it considers the selection criteria, organizing principles, quantity of vocabulary and methodology. For each of these, suggestion for adaption is proposed and then justified in respect of the learning processes involved and the intended outcomes

What Makes “Alignment” Work Effectively in a Foreign Language Class?

This study aims to argue for the crucial role of task authenticity in alignment in a foreign language class. Alignment means learners’ application of what they have learned from their foreign language class. An action research study was conducted with an English class in the context of a Chinese university in order to exemplify task authenticity for addressing effective alignment. The intervention was a focus on “realism” in language learning activities featuring personalized topics, production-oriented tasks and thinking skills development. This article reports on the way the study was conducted and the cycles gone through, especially focusing on one of the lessons with the authentic activity in practice. Students’ assignments and their evaluation of the instructor’s teaching performance were employed for analysis. Results indicate that students appear to be more interactive and apply more vocabulary and ideas from their learning material in comparison with those before the action research study

The SARptical Dataset for Joint Analysis of SAR and Optical Image in Dense Urban Area

The joint interpretation of very high resolution SAR and optical images in dense urban area are not trivial due to the distinct imaging geometry of the two types of images. Especially, the inevitable layover caused by the side-looking SAR imaging geometry renders this task even more challenging. Only until recently, the "SARptical" framework [1], [2] proposed a promising solution to tackle this. SARptical can trace individual SAR scatterers in corresponding high-resolution optical images, via rigorous 3-D reconstruction and matching. This paper introduces the SARptical dataset, which is a dataset of over 10,000 pairs of corresponding SAR, and optical image patches extracted from TerraSAR-X high-resolution spotlight images and aerial UltraCAM optical images. This dataset opens new opportunities of multisensory data analysis. One can analyze the geometry, material, and other properties of the imaged object in both SAR and optical image domain. More advanced applications such as SAR and optical image matching via deep learning [3] is now also possible.Comment: This manuscript was submitted to IGARSS 201

SAR Tomography via Nonlinear Blind Scatterer Separation

Layover separation has been fundamental to many synthetic aperture radar applications, such as building reconstruction and biomass estimation. Retrieving the scattering profile along the mixed dimension (elevation) is typically solved by inversion of the SAR imaging model, a process known as SAR tomography. This paper proposes a nonlinear blind scatterer separation method to retrieve the phase centers of the layovered scatterers, avoiding the computationally expensive tomographic inversion. We demonstrate that conventional linear separation methods, e.g., principle component analysis (PCA), can only partially separate the scatterers under good conditions. These methods produce systematic phase bias in the retrieved scatterers due to the nonorthogonality of the scatterers' steering vectors, especially when the intensities of the sources are similar or the number of images is low. The proposed method artificially increases the dimensionality of the data using kernel PCA, hence mitigating the aforementioned limitations. In the processing, the proposed method sequentially deflates the covariance matrix using the estimate of the brightest scatterer from kernel PCA. Simulations demonstrate the superior performance of the proposed method over conventional PCA-based methods in various respects. Experiments using TerraSAR-X data show an improvement in height reconstruction accuracy by a factor of one to three, depending on the used number of looks.Comment: This work has been accepted by IEEE TGRS for publicatio

Towards SAR Tomographic Inversion via Sparse Bayesian Learning

Existing SAR tomography (TomoSAR) algorithms are mostly based on an inversion of the SAR imaging model, which are often computationally expensive. Previous study showed perspective of using data-driven methods like KPCA to decompose the signal and reduce the computational complexity. This paper gives a preliminary demonstration of a new data-driven method based on sparse Bayesian learning. Experiments on simulated data show that the proposed method significantly outperforms KPCA methods in estimating the steering vectors of the scatterers. This gives a perspective of data-drive approach or combining it with model-driven approach for high precision tomographic inversion of large areas.Comment: accepted in preliminary version for EUSAR2020 conferenc

Does ownership type matter for corporate social responsibility disclosure: Evidence from China

The evidence of the effect of ownership structure on corporate social responsibility (CSR) is relatively sparse especially in the emerging economies. This paper seeks to address this situation to comprehensively examine the link between different types of shareholders and CSR disclosure in the context of China. Our findings reveal that different owners have differential impact on the CSR. The firms controlled by the state are more likely to disclose CSR information and their CSR reports’ quality is better compared with non-SOEs. Interestingly, firms with more shares held by mutual funds, foreign investors or other corporations are significantly better at CSR disclosure. The study also discloses that firm size, profitability, and leverage affect CSR in China. Overall the study contributes to the literature on CSR practices in emerging countries and point to some policy suggestions