An HSC view of the CMASS galaxy sample. Halo mass as a function of stellar mass, size and S\'ersic index

Aims. We wish to determine the distribution of dark matter halo masses as a function of the stellar mass and the stellar mass profile, for massive galaxies in the BOSS CMASS sample. Methods. We use grizy photometry from HSC to obtain S\'ersic fits and stellar masses of CMASS galaxies for which HSC weak lensing data is available, visually selected to have spheroidal morphology. We apply a cut in stellar mass, $\log{M_*/M_\odot} > 11.0$,selecting $\sim$10, 000 objects. Using a Bayesian hierarchical inference method, we first investigate the distribution of S\'ersic index and size as a function of stellar mass. Then, making use of shear measurements from HSC, we measure the distribution of halo mass as a function of stellar mass, size and S\'ersic index. Results. Our data reveals a steep stellar mass-size relation $R_e \propto M_*^{\beta_R}$, with $\beta_R$ larger than unity, and a positive correlation between S\'ersic index and stellar mass: $n \propto M_*^{0.46}$. Halo mass scales approximately with the 1.7 power of the stellar mass. We do not find evidence for an additional dependence of halo mass on size or S\'ersic index at fixed stellar mass. Conclusions. Our results disfavour galaxy evolution models that predict significant differences in the size growth efficiency of galaxies living in low and high mass halos.Comment: Accepted for publication on Astronomy & Astrophysics. 18 pages, 15 figure

A Study on the English Translation of the Three-Body Problems’ Terms from the Perspective of Cognitive Terminology

The emergence of the The Three-Body Problem series has reintroduced the marginalized literary genre into the critics’ field of view. The story background of the first part of The Three-Body Problem takes place during the Cultural Revolution. As a masterpiece of Chinese science fiction literature, the English translation of The Three-Body Problem has been studied actively, but the English translation of it is still relatively blank. Taking cognitive linguistics as an entry point, this paper attempts to describe how cognitive linguistics can guide translators to translate, in order to provide theoretical reference for the translation practice of science fiction works

Ultratrace Neurotransmitters SERS Sensing

Surface-enhanced Raman scattering (SERS) spectroscopy is a powerful analytical technique for ultrasensitive detection of chemicals and biomolecules. As the high sensitivity of SERS requires analytes to be in close contact with a plasmonic substrate, the detection of analyte molecules with low chemical affinity towards the substrate is thus limited, for example dopamine molecules as well as other neurotransmitters (NTs), which are the focus within this thesis. Two binding methods of NTs to SERS substrates will be covered: by Cucurbit[n]uril (CB[n]) and by Fe(III) ions. The fundamental resonance modes of NTs molecules and ultratrace NTs SERS sensing are discussed in detail. Further exploration of SERS substrates in the format of oil/water interfacial film will also be reported. The SERS application integrated with microfluidic techniques will be discussed in the final part of this thesis

A Sparse Graph-Structured Lasso Mixed Model for Genetic Association with Confounding Correction

While linear mixed model (LMM) has shown a competitive performance in correcting spurious associations raised by population stratification, family structures, and cryptic relatedness, more challenges are still to be addressed regarding the complex structure of genotypic and phenotypic data. For example, geneticists have discovered that some clusters of phenotypes are more co-expressed than others. Hence, a joint analysis that can utilize such relatedness information in a heterogeneous data set is crucial for genetic modeling. We proposed the sparse graph-structured linear mixed model (sGLMM) that can incorporate the relatedness information from traits in a dataset with confounding correction. Our method is capable of uncovering the genetic associations of a large number of phenotypes together while considering the relatedness of these phenotypes. Through extensive simulation experiments, we show that the proposed model outperforms other existing approaches and can model correlation from both population structure and shared signals. Further, we validate the effectiveness of sGLMM in the real-world genomic dataset on two different species from plants and humans. In Arabidopsis thaliana data, sGLMM behaves better than all other baseline models for 63.4% traits. We also discuss the potential causal genetic variation of Human Alzheimer's disease discovered by our model and justify some of the most important genetic loci.Comment: Code available at https://github.com/YeWenting/sGLM

Impacts of Agricultural Price Support Policies on Price Variability and Welfare: Evidence from China’s Soybean Market

As the world’s largest importer of agricultural commodities, China’s agricultural policies have significant implications for the world agricultural market. For the first time, we develop an aggregate structural econometric model of China’s soybean market with linkage to the rest of the world to analyze the worldwide impacts of China’s soybean price support policies from 2008 to 2016. We investigate the impacts of China’s policies on the variability of their domestic and world prices, and adopt a Monte Carlo simulation to evaluate the distributional and aggregate welfare effects. Results indicate that (a) China’s soybean price support policies play an effective role in stabilizing their domestic price, while its increasing imports absorb world production surplus and reduce world price swings; (b) China’s producers gain at the expense of consumers and budgetary costs, and the net welfare change in their domestic market is negative; (c) Soybean exporting countries experience considerable welfare 2 gains, and the world net welfare change is positive. Our findings provide new insights for future trade negotiations and agricultural market reforms in developing countries

Employees\u27 Attitude towards a Digital Teammate - Will AI-enabled Chatbot Lead to Enhancing Employees’ Job Identity?

Recently Artificial Intelligence (AI)-enabled conversational agents or chatbots (ICA hereafter) have been widely introduced in online customer service, and are expected to transform the frontline workforce. However, most studies from employees’ perspectives have been qualitative in nature. Moreover, extant empirical studies perceive ICA as a tool rather than considering ICA as an AI-enabled digital workforce. Besides, rare papers moved further to explore the rooted psychological drivers (such as identity) underlying the employees’ actions. To address these gaps, our paper integrates the identity theory and cooperation perspectives to examine the impact of ICA’s human-like capability on employees\u27 job identity through the enhancement in work experience. Our study is expected to provide an innovative perspective viewing ICA as a human-like agent rather than a tool in behavior studies. This study also enriches the identity theory and cooperation-competition theory and promotes their applications in IS literature

Analysis and Detection of Information Types of Open Source Software Issue Discussions

Most modern Issue Tracking Systems (ITSs) for open source software (OSS) projects allow users to add comments to issues. Over time, these comments accumulate into discussion threads embedded with rich information about the software project, which can potentially satisfy the diverse needs of OSS stakeholders. However, discovering and retrieving relevant information from the discussion threads is a challenging task, especially when the discussions are lengthy and the number of issues in ITSs are vast. In this paper, we address this challenge by identifying the information types presented in OSS issue discussions. Through qualitative content analysis of 15 complex issue threads across three projects hosted on GitHub, we uncovered 16 information types and created a labeled corpus containing 4656 sentences. Our investigation of supervised, automated classification techniques indicated that, when prior knowledge about the issue is available, Random Forest can effectively detect most sentence types using conversational features such as the sentence length and its position. When classifying sentences from new issues, Logistic Regression can yield satisfactory performance using textual features for certain information types, while falling short on others. Our work represents a nontrivial first step towards tools and techniques for identifying and obtaining the rich information recorded in the ITSs to support various software engineering activities and to satisfy the diverse needs of OSS stakeholders.Comment: 41st ACM/IEEE International Conference on Software Engineering (ICSE2019