Beihang University

About: Beihang University is a education organization based out in Beijing, China. It is known for research contribution in the topics: Control theory & Microstructure. The organization has 67002 authors who have published 73507 publications receiving 975691 citations. The organization is also known as: Beijing University of Aeronautics and Astronautics.

Search for narrow and broad dijet resonances in proton-proton collisions at √s=13 TeV and constraints on dark matter mediators and other new particles

Abstract: Searches for resonances decaying into pairs of jets are performed using proton-proton collision data collected at s√=13 TeV corresponding to an integrated luminosity of up to 36 fb−1. A low-mass search, for resonances with masses between 0.6 and 1.6 TeV, is performed based on events with dijets reconstructed at the trigger level from calorimeter information. A high-mass search, for resonances with masses above 1.6 TeV, is performed using dijets reconstructed offline with a particle-flow algorithm. The dijet mass spectrum is well described by a smooth parameterization and no evidence for the production of new particles is observed. Upper limits at 95% confidence level are reported on the production cross section for narrow resonances with masses above 0.6 TeV. In the context of specific models, the limits exclude string resonances with masses below 7.7 TeV, scalar diquarks below 7.2 TeV, axigluons and colorons below 6.1 TeV, excited quarks below 6.0 TeV, color-octet scalars below 3.4 TeV, W′ bosons below 3.3 TeV, Z′ bosons below 2.7 TeV, Randall-Sundrum gravitons below 1.8 TeV and in the range 1.9 to 2.5 TeV, and dark matter mediators below 2.6 TeV. The limits on both vector and axial-vector mediators, in a simplified model of interactions between quarks and dark matter particles, are presented as functions of dark matter particle mass and coupling to quarks. Searches are also presented for broad resonances, including for the first time spin-1 resonances with intrinsic widths as large as 30% of the resonance mass. The broad resonance search improves and extends the exclusions of a dark matter mediator to larger values of its mass and coupling to quarks.

Supervisor–Subordinate Guanxi: Developing a Three-Dimensional Model and Scale

Abstract: We develop a three-dimensional concept of supervisor-subordinate guanxi. This concept includes affective attachment, personal-life inclusion, and deference to supervisor. Based on this concept, we conducted three studies to develop and validate a three-dimensional supervisor-subordinate guanxi measure and to examine its relationship with related constructs, such as leader-member exchange. Results from Study 1 and Study 2 provide evidence of convergent and discriminant validity of the scale, while Study 3 demonstrates the scale's incremental validity and replicates results from Study 2. Furthermore, in Study 3, we found that the three dimensions of supervisor-subordinate guanxi had different significant effects on commitment, turnover intention, and procedural justice, providing further evidence of criterion-related validity. Overall, these empirical results provide support for our three-dimensional model of supervisor-subordinate guanxi.

Aligning Domain-Specific Distribution and Classifier for Cross-Domain Classification from Multiple Sources

Abstract: While Unsupervised Domain Adaptation (UDA) algorithms, i.e., there are only labeled data from source domains, have been actively studied in recent years, most algorithms and theoretical results focus on Single-source Unsupervised Domain Adaptation (SUDA). However, in the practical scenario, labeled data can be typically collected from multiple diverse sources, and they might be different not only from the target domain but also from each other. Thus, domain adapters from multiple sources should not be modeled in the same way. Recent deep learning based Multi-source Unsupervised Domain Adaptation (MUDA) algorithms focus on extracting common domain-invariant representations for all domains by aligning distribution of all pairs of source and target domains in a common feature space. However, it is often very hard to extract the same domain-invariant representations for all domains in MUDA. In addition, these methods match distributions without considering domain-specific decision boundaries between classes. To solve these problems, we propose a new framework with two alignment stages for MUDA which not only respectively aligns the distributions of each pair of source and target domains in multiple specific feature spaces, but also aligns the outputs of classifiers by utilizing the domainspecific decision boundaries. Extensive experiments demonstrate that our method can achieve remarkable results on popular benchmark datasets for image classification.

Precise Measurement of the e+e- →π+π-J /ψ Cross Section at Center-of-Mass Energies from 3.77 to 4.60 GeV

Abstract: The cross section for the process e(+)e(-)-> pi(+) pi(-) J/psi is measured precisely at center-of-mass energies from 3.77 to 4.60 GeV using 9 fb(-1) of data collected with the BESIII detector operating at the BEPCII storage ring. Two resonant structures are observed in a fit to the cross section. The first resonance has a mass of (222.0 +/- 3.1 +/- 1.4) MeV/ c(2) and a width of (44.1 +/- 4.3 +/- 2.0)MeV, while the second one has a mass of (4320.0 +/- 10.4 +/- 7.0)MeV/c(2) and a width of (101.4(- 19.7)(+25.3) +/- 10.2) MeV, where the first errors are statistical and second ones are systematic. The first resonance agrees with the Y(4260) resonance reported by previous experiments. The precision of its resonant parameters is improved significantly. The second resonance is observed in e(+)e(-)-> pi(+) pi(-) J/psi for the first time. The statistical significance of this resonance is estimated to be larger than 7.6 sigma. The mass and width of the second resonance agree with the Y(4360) resonance reported by the BABAR and Belle experiments within errors. Finally, the Y(4008) resonance previously observed by the Belle experiment is not confirmed in the description of the BESIII data.