Code Prediction by Feeding Trees to Transformers

We advance the state-of-the-art in the accuracy of code prediction (next token prediction) used in autocomplete systems. First, we report that using the recently proposed Transformer architecture even out-of-the-box outperforms previous neural and non-neural systems for code prediction. We then show that by making the Transformer architecture aware of the syntactic structure of code, we further increase the margin by which a Transformer-based system outperforms previous systems. With this, it outperforms the accuracy of an RNN-based system (similar to Hellendoorn et al. 2018) by 18.3\%, the Deep3 system (Raychev et al 2016) by 14.1\%, and an adaptation of Code2Seq (Alon et al., 2018) for code prediction by 14.4\%. We present in the paper several ways of communicating the code structure to the Transformer, which is fundamentally built for processing sequence data. We provide a comprehensive experimental evaluation of our proposal, along with alternative design choices, on a standard Python dataset, as well as on a Facebook internal Python corpus. Our code and data preparation pipeline will be available in open source

Effects of Ability Grouping on Students Attitudes toward Korean and English

The study aimed to investigate the effects of ability grouping in Korean and English on studentsacademic performances and their attitudes toward subjects. A multiple group analysis with a 2-level HLM was performed on large-scale data using SAS, R, Mplus, and HLM for comparison. The study showed that the effects of ability grouping on student academic achievement and student perception about curricula were ambiguous. The study also showed that Mplus and HLM produced larger standard errors of the parameter estimates than SAS and R

The Impact of Equating on Detection of Treatment Effects

Equating makes it possible to compare performances on different forms of a test. Three different equating methods (baseline selection, subgroup, and subscore equating) using common-item item response theory equating were examined for their impact on detection of treatment effects in multilevel models

Ginsenoside Rg3 Reduces Lipid Accumulation with AMP-Activated Protein Kinase (AMPK) Activation in HepG2 Cells

Cardiovascular disease (CVD) is one of the main causes of mortality worldwide, and dyslipidemia is a major risk factor for CVD. Ginseng has been widely used in the clinic to treat CVD. Ginsenoside Rg3, one of the major active components of ginseng, has been reported to exhibit antiobesity, antidiabetic, and cardioprotective effects. However, the effect of ginsenoside Rg3 on hepatic lipid metabolism remains unclear. Therefore, we investigated whether ginsenoside Rg3 would regulate hepatic lipid metabolism with AMP-activated protein kinase (AMPK) activation in HepG2 cells. Ginsenoside Rg3 significantly reduced hepatic cholesterol and triglyceride levels. Furthermore, ginsenoside Rg3 inhibited expression of sterol regulatory element binding protein-2 (SREBP-2) and 3-hydroxy-3-methyl glutaryl coenzyme A reductase (HMGCR). Ginsenoside Rg3 increased activity of AMPK, a major regulator of energy metabolism. These results suggest that ginsenoside Rg3 reduces hepatic lipid accumulation with inhibition of SREBP-2 and HMGCR expression and stimulation of AMPK activity in HepG2 cells. Therefore, ginsenoside Rg3 may be beneficial as a food ingredient to lower the risk of CVD by regulating dyslipidemia

Mapping mHealth (mobile health) and mobile penetrations in sub-Saharan Africa for strategic regional collaboration in mHealth scale-up: an application of exploratory spatial data analysis

AfDB: African Development Bank; CHMI: Center for Health Market Innovation; CIDA: Canadian International Development Agency; COMESA: Common Market for Eastern and Southern Africa; DFID: UK Department for International Development; ECCAS: Economic Community of Central African States; ECOWAS: Economic Community of West African States; ESDA: Exploratory Spatial Data Analysis; GADM: Global Administrative Areas; GIS: Geographic Information System; GNI: Gross National Income; ICT: Information Communication Technology; LISA: Local Indicator of Spatial Association; LMICs: Low- and Middle-Income Countries; mHealth: Mobile Health; MMS: Multimedia Message Service; NGOs: Non-governmental Organizations; PDA: Personal Digital Assistant; SIM: Subscriber Identity Module; SMS: Short Message Service; USAID: United States Agency for International Development; WHO: World Health OrganizationAbstract Background Mobile health (mHealth), a term used for healthcare delivery via mobile devices, has gained attention as an innovative technology for better access to healthcare and support for performance of health workers in the global health context. Despite large expansion of mHealth across sub-Saharan Africa, regional collaboration for scale-up has not made progress since last decade. Methods As a groundwork for strategic planning for regional collaboration, the study attempted to identify spatial patterns of mHealth implementation in sub-Saharan Africa using an exploratory spatial data analysis. In order to obtain comprehensive data on the total number of mHelath programs implemented between 2006 and 2016 in each of the 48 sub-Saharan Africa countries, we performed a systematic data collection from various sources, including: the WHO eHealth Database, the World Bank Projects & Operations Database, and the USAID mHealth Database. Additional spatial analysis was performed for mobile cellular subscriptions per 100 people to suggest strategic regional collaboration for improving mobile penetration rates along with the mHealth initiative. Global Morans I and Local Indicator of Spatial Association (LISA) were calculated for mHealth programs and mobile subscriptions per 100 population to investigate spatial autocorrelation, which indicates the presence of local clustering and spatial disparities. Results From our systematic data collection, the total number of mHealth programs implemented in sub-Saharan Africa between 2006 and 2016 was 487 (same programs implemented in multiple countries were counted separately). Of these, the eastern region with 17 countries and the western region with 16 countries had 287 and 145 mHealth programs, respectively. Despite low levels of global autocorrelation, LISA enabled us to detect meaningful local clusters. Overall, the eastern part of sub-Saharan Africa shows high-high association for mHealth programs. As for mobile subscription rates per 100 population, the northern area shows extensive low-low association. Conclusions This study aimed to shed some light on the potential for strategic regional collaboration for scale-up of mHealth and mobile penetration. Firstly, countries in the eastern area with much experience can take the lead role in pursuing regional collaboration for mHealth programs in sub-Saharan Africa. Secondly, collective effort in improving mobile penetration rates for the northern area is recommended