Translanguaging as pedagogy in a Chinese complementary school in the UK

This study investigates language teachers’ translanguaging practices in three Mandarin classes of a Chinese complementary school in the UK. It draws on the ideas of translanguaging as individuals’ deployment of their full linguistic repertoires, translanguaging as multimodal system, translanguaging as bilingual pedagogy, translanguaging as the embodiment of users’ sociocultural background, and examines current critical perspectives on translanguaging. The study explores the actual translingual practices deployed by class teachers in language classrooms and the factors that influence those practices. In the 2016/17 academic year, I conducted an eight-month ethnographic fieldwork study, collecting qualitative data in three phases: classroom observation, classroom audio recording and interviews with class teachers. This study has five main findings: (1) translanguaging is widely, efficiently and inevitably deployed in language classrooms of the Chinese complementary school, for the purposes of teaching Chinese language knowledge (characters, Pinyin and unique expressions), differentiating students with varying Chinese language abilities, and giving general instruction within teaching practices; (2) translanguaging facilitates class teachers’ other teaching practices (i.e. scaffolding, drills and translation); (3) class teachers make meaning by drawing upon their own and their learners’ wide range of semiotic resources, for example, embodied gestures, pictures, signs, mime and so on; (4) the use of translanguaging is influenced by teachers’ teaching content, their understanding of learners, and students’ responses in class; (5) the focus of teaching content in language education where societally named languages have to be treated separately challenges an orientation towards the translanguaging concept that describes individuals’ flexible use of their linguistic repertoire in language teaching contexts. Findings show that this tension might vary from class to class and teacher to teacher. The study concludes that translanguaging practice permeates into day-to-day language teaching practices in Chinese complementary schools. Compared to multililingual contexts, translanguaging is deployed critically by class teachers in language educational context

Computational studies of electronic structure of doped graphene

In the literature, extensive studies have been performed to study the electronic properties of doped graphene. This is due to the potentially large number of applications of graphene in p-n junctions, transistors, photodiodes and lasers. By utilizing single heteroatom chemical doping method or electric field-induced method, one can introduce a band gap, ranging from 0.1eV to 0.5eV, in graphene. A tunable bandgap is highly desirable because it would allow significant flexibility in the design and optimization of such devices, particularly if it could be tuned by adjusting the doping configurations. Here, we demonstrate the realization of a widely tunable electronic bandgap in B and N co-doped graphene, of which the dopant concentration is from 6.25% to 75%. A recent study of the impact of co-doping on the band gap and bond length of graphene, from Pooja Rani Research Group in 2013, has inspired this research to further investigate the co-doping method. Materials Studio simulation tool, based on Density Functional Theory, has been utilized in this study. The simulations show that, with up to 75% concentration, a 2.99eV wide band gap is obtained. An ascending trend line (band gap as a function of dopant atoms) is also obtained from extensive simulation results. The results of this work, i.e., heteroatoms co-doping band gap control suggests novel nanoelectronics device applications based on graphene

Modeling The Intensity Function Of Point Process Via Recurrent Neural Networks

Event sequence, asynchronously generated with random timestamp, is ubiquitous among applications. The precise and arbitrary timestamp can carry important clues about the underlying dynamics, and has lent the event data fundamentally different from the time-series whereby series is indexed with fixed and equal time interval. One expressive mathematical tool for modeling event is point process. The intensity functions of many point processes involve two components: the background and the effect by the history. Due to its inherent spontaneousness, the background can be treated as a time series while the other need to handle the history events. In this paper, we model the background by a Recurrent Neural Network (RNN) with its units aligned with time series indexes while the history effect is modeled by another RNN whose units are aligned with asynchronous events to capture the long-range dynamics. The whole model with event type and timestamp prediction output layers can be trained end-to-end. Our approach takes an RNN perspective to point process, and models its background and history effect. For utility, our method allows a black-box treatment for modeling the intensity which is often a pre-defined parametric form in point processes. Meanwhile end-to-end training opens the venue for reusing existing rich techniques in deep network for point process modeling. We apply our model to the predictive maintenance problem using a log dataset by more than 1000 ATMs from a global bank headquartered in North America.Comment: Accepted at Thirty-First AAAI Conference on Artificial Intelligence (AAAI17