Are native English-speaking teachers more qualified? A Critical Review of Blum and Johnson’s (2012) Article ‘Reading Repression: Textualizing the Linguistic Marginalization of Non-native English-Speaking Teachers in Arizona’

With the profound and widespread influence of English in this globalised and interdependent world, scholars are paying more attention to the quality of English education; this quality intricately relates to the abilities and qualifications of English teachers. Therefore, a debate on how to treat native English-speaking teachers (NESTs) and non-native English-speaking teachers (NNESTs) is becoming increasingly prevalent both in the academic field and public discourse. Blum and Johnson’s (2012) article is based on a phenomenon in Arizona, where the policymakers marginalised public school teachers who have accents. The authors mainly analyse the comments made in response to a Wall Street Journal article, and in fact, they strive to highlight the cultural and professional rights of NNESTs. In this critical review, I will firstly illustrate the socio-political and sociolinguistic background and a debate between NEST and NNEST in language teaching and learning, before summarising and evaluating Blum and Johnson’s (2012) findings by relevant literature, as well as from a personal perspective. Considering the heated discussion about the importance of foreign accent in second language successful learning (Cook, 1999), issues about accent will be considered in this review. Specifically, issues such as the correlation between NNESTs and teachers who have strong accents, the impacts of teachers’ accents on students’ language acquisition, teacher evaluation as well as discrimination toward NNESTs will be discussed.N

Aprendizaje de idiomas asistido por dispositivos móviles: alcance, praxis y teoría

Mobile assisted language learning (MALL) research has been characterized by an overemphasis on technology, while the wide variety of approaches to the uses of mobiles has painted an atomized picture of L2 instruction. This paper discusses various conceptualizations of MALL that favour areas of language learning that are anchored on different theories of learning and language learning. Drawing on the seminal work by Traxler (2018, 2019), as well as on research that has examined self-directed uses, the use of apps and Augmented Reality (AR) in MALL, the authors contend that it is essential to shift our focus away from device-oriented pedagogies to more socially situated practices that take stock of new ecologies of language use. We contend that the research field is in search of a wider theoretical perspective in the context of SLA and language education that explores what we label here as socially contextualized MALL.  La investigación sobre el aprendizaje de idiomas asistido por dispositivos móviles (MALL) se ha caracterizado por un énfasis excesivo en los aspectos más íntimamente relacionados con la tecnología y la gran variedad de enfoques sobre los usos de los dispositivos móviles ha contribuido a generar una visión atomizada de la enseñanza de segundas lenguas. En este artículo se analizan diversas conceptualizaciones sobre MALL que, en diferente medida, favorecen áreas del aprendizaje de lenguas vinculadas a teorías sobre el aprendizaje y el aprendizaje de lenguas. Basándonos en las contribuciones de Traxler (2018, 2019), así como en la investigación que ha examinado el aprendizaje autodirigido, el uso de apps y la Realidad Aumentada (RA) en MALL, los autores sostienen que es esencial cambiar nuestro enfoque de pedagogías orientadas a los dispositivos a prácticas situadas en contextos sociales de uso, las cuales se encuentran mejor equipadas para acoger y explicar las nuevas ecologías sobre el uso del lenguaje. En este trabajo sostenemos que este ámbito de la investigación está en busca de una perspectiva teórica más amplia que explore lo que en este artículo hemos denominado MALL socialmente contextualizado

A Wasserstein distance-based spectral clustering method for transaction data analysis

With the rapid development of online payment platforms, it is now possible to record massive transaction data. Clustering on transaction data significantly contributes to analyzing merchants' behavior patterns. This enables payment platforms to provide differentiated services or implement risk management strategies. However, traditional methods exploit transactions by generating low-dimensional features, leading to inevitable information loss. In this study, we use the empirical cumulative distribution of transactions to characterize merchants. We adopt Wasserstein distance to measure the dissimilarity between any two merchants and propose the Wasserstein-distance-based spectral clustering (WSC) approach. Based on the similarities between merchants' transaction distributions, a graph of merchants is generated. Thus, we treat the clustering of merchants as a graph-cut problem and solve it under the framework of spectral clustering. To ensure feasibility of the proposed method on large-scale datasets with limited computational resources, we propose a subsampling method for WSC (SubWSC). The associated theoretical properties are investigated to verify the efficiency of the proposed approach. The simulations and empirical study demonstrate that the proposed method outperforms feature-based methods in finding behavior patterns of merchants

CS 485 Computer Architecture

This course will present the basic concepts and technologies in computer organization and architecture. For example, logical devices and digital circuits, data representation, register transfer, central processor organization, microprogram control and organization, parallel computing. Although there could be multiple perspectives in studying computer architecture, our focus would be to better understand computer organization in order to design more efficient and reliable application software. The OER (Open Educational Resources) session of this course will have no textbook required (ZTC: Zero Textbook Cost), conduct lectures based on online resources and other open educational resources, and have students involved in active learning including giving presentations and creating and sharing open pedagogical materials, e.g., students need to finish four writing projects in this class (refer to course schedule on page 6) where each project is to write an article about a given topic in computer architecture on Wikipedia. Students writing articles about what they are learning can help them understand the course contents creatively

Subsampling-Based Modified Bayesian Information Criterion for Large-Scale Stochastic Block Models

Identifying the number of communities is a fundamental problem in community detection, which has received increasing attention recently. However, rapid advances in technology have led to the emergence of large-scale networks in various disciplines, thereby making existing methods computationally infeasible. To address this challenge, we propose a novel subsampling-based modified Bayesian information criterion (SM-BIC) for identifying the number of communities in a network generated via the stochastic block model and degree-corrected stochastic block model. We first propose a node-pair subsampling method to extract an informative subnetwork from the entire network, and then we derive a purely data-driven criterion to identify the number of communities for the subnetwork. In this way, the SM-BIC can identify the number of communities based on the subsampled network instead of the entire dataset. This leads to important computational advantages over existing methods. We theoretically investigate the computational complexity and identification consistency of the SM-BIC. Furthermore, the advantages of the SM-BIC are demonstrated by extensive numerical studies

Annotating Protein Functional Residues by Coupling High-Throughput Fitness Profile and Homologous-Structure Analysis.

Identification and annotation of functional residues are fundamental questions in protein sequence analysis. Sequence and structure conservation provides valuable information to tackle these questions. It is, however, limited by the incomplete sampling of sequence space in natural evolution. Moreover, proteins often have multiple functions, with overlapping sequences that present challenges to accurate annotation of the exact functions of individual residues by conservation-based methods. Using the influenza A virus PB1 protein as an example, we developed a method to systematically identify and annotate functional residues. We used saturation mutagenesis and high-throughput sequencing to measure the replication capacity of single nucleotide mutations across the entire PB1 protein. After predicting protein stability upon mutations, we identified functional PB1 residues that are essential for viral replication. To further annotate the functional residues important to the canonical or noncanonical functions of viral RNA-dependent RNA polymerase (vRdRp), we performed a homologous-structure analysis with 16 different vRdRp structures. We achieved high sensitivity in annotating the known canonical polymerase functional residues. Moreover, we identified a cluster of noncanonical functional residues located in the loop region of the PB1 β-ribbon. We further demonstrated that these residues were important for PB1 protein nuclear import through the interaction with Ran-binding protein 5. In summary, we developed a systematic and sensitive method to identify and annotate functional residues that are not restrained by sequence conservation. Importantly, this method is generally applicable to other proteins about which homologous-structure information is available.ImportanceTo fully comprehend the diverse functions of a protein, it is essential to understand the functionality of individual residues. Current methods are highly dependent on evolutionary sequence conservation, which is usually limited by sampling size. Sequence conservation-based methods are further confounded by structural constraints and multifunctionality of proteins. Here we present a method that can systematically identify and annotate functional residues of a given protein. We used a high-throughput functional profiling platform to identify essential residues. Coupling it with homologous-structure comparison, we were able to annotate multiple functions of proteins. We demonstrated the method with the PB1 protein of influenza A virus and identified novel functional residues in addition to its canonical function as an RNA-dependent RNA polymerase. Not limited to virology, this method is generally applicable to other proteins that can be functionally selected and about which homologous-structure information is available