The Effect of Flow and Motivation on Users’ Learning Outcomes in Second Life

This study aims to investigate the effect of the users’ immersion experience, motivation, and learning outcomes in Second Life. The data collected for this study occurred over a 2 month period. Participants were 113 students taking classes in Second Life at a university. Their ages ranged from 18-22 years, with 47 participants as male and 66 as female. From the analysis of the collected data, the immersion experience and motivation have effects on the learning outcomes in Second Life. The results revealed more one was immersed in Second Life, the motivation was improved, and thus, the learning outcomes were reinforced. These findings are also discussed in virtual learning and teaching design

A precise determination of the top-quark pole mass

The Principle of Maximum Conformality (PMC) provides a systematic way to eliminate the renormalization scheme and renormalization scale uncertainties for high-energy processes. We have observed that by applying PMC scale-setting, one obtains comprehensive and self-consistent pQCD predictions for the top-quark pair total cross-section and the top-quark pair forward-backward asymmetry in agreement with the measurements at the Tevatron and LHC. As a step forward, in the present paper, we determine the top-quark pole mass via a detailed comparison of the top-quark pair cross-section with the measurements at the Tevatron and LHC. The results for the top-quark pole mass are $m_t=174.6^{+3.1}_{-3.2}$ GeV for the Tevatron with $\sqrt{S}=1.96$ TeV, $m_t=173.7\pm1.5$ GeV and $174.2\pm1.7$ GeV for the LHC with $\sqrt{S} = 7$ TeV and $8$ TeV, respectively. Those predictions agree with the average, $173.34\pm0.76$ GeV, obtained from various collaborations via direct measurements. The consistency of the pQCD predictions using the PMC with all of the collider measurements at different energies provides an important verification of QCD.Comment: 10 pages, 6 figures. Revised version to be published in Eur.Phys.J.

Charged lepton flavor violating Higgs decays at future e+e−e^+e^- colliders

After the discovery of the Higgs boson, several future experiments have been proposed to study the Higgs boson properties, including two circular lepton colliders, the CEPC and the FCC-ee, and one linear lepton collider, the ILC. We evaluate the precision reach of these colliders in measuring the branching ratios of the charged lepton flavor violating Higgs decays $H\to e^\pm\mu^\mp$, $e^\pm\tau^\mp$ and $\mu^\pm\tau^\mp$. The expected upper bounds on the branching ratios given by the circular (linear) colliders are found to be $\mathcal{B}(H\to e^\pm\mu^\mp) < 1.2\ (2.1) \times 10^{-5}$, $\mathcal{B}(H\to e^\pm\tau^\mp) < 1.6\ (2.4) \times 10^{-4}$ and $\mathcal{B}(H\to \mu^\pm\tau^\mp) < 1.4\ (2.3) \times 10^{-4}$ at 95\% CL, which are improved by one to two orders compared to the current experimental bounds. We also discuss the constraints that these upper bounds set on certain theory parameters, including the charged lepton flavor violating Higgs couplings, the corresponding parameters in the type-III 2HDM, and the new physics cut-off scales in the SMEFT, in RS models and in models with heavy neutrinos.Comment: 20 pages, 2 figures (extend the CEPC study to the FCC-ee and the ILC, and to match the published version

A Multi-task Learning Approach for Improving Product Title Compression with User Search Log Data

It is a challenging and practical research problem to obtain effective compression of lengthy product titles for E-commerce. This is particularly important as more and more users browse mobile E-commerce apps and more merchants make the original product titles redundant and lengthy for Search Engine Optimization. Traditional text summarization approaches often require a large amount of preprocessing costs and do not capture the important issue of conversion rate in E-commerce. This paper proposes a novel multi-task learning approach for improving product title compression with user search log data. In particular, a pointer network-based sequence-to-sequence approach is utilized for title compression with an attentive mechanism as an extractive method and an attentive encoder-decoder approach is utilized for generating user search queries. The encoding parameters (i.e., semantic embedding of original titles) are shared among the two tasks and the attention distributions are jointly optimized. An extensive set of experiments with both human annotated data and online deployment demonstrate the advantage of the proposed research for both compression qualities and online business values.Comment: 8 Pages, accepted at AAAI 201