1,727 research outputs found

    Moderate deviations for the mildly stationary autoregressive models with dependent errors

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    In this paper, we consider the normalized least squares estimator of the parameter in a mildly stationary first-order autoregressive model with dependent errors which are modeled as a mildly stationary AR(1) process. By martingale methods, we establish the moderate deviations for the least squares estimators of the regressor and error, which can be applied to understand the near-integrated second order autoregressive processes. As an application, we obtain the moderate deviations for the Durbin-Watson statistic.Comment: Comments welcome. 28 page

    Observation and Measurement of a Standard Model Higgs Boson-like Diphoton Resonance with the CMS Detector

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    This thesis concerns the observation of a new particle and the measurements of its properties, from the search of the Higgs boson through its decay into two photons at the CMS experiment at CERN's Large Hadron Collider (LHC), on the full LHC "Run I" data collected by the CMS detector during 2011 and 2012, consisting of proton-proton collision events at s\sqrt{s} == 7 TeV7~\mathrm{TeV} with LL == 5.1 fb15.1~\mathrm{fb^{-1}} and at s\sqrt{s} == 8 TeV8~\mathrm{TeV} with LL == 19.7 fb119.7~\mathrm{fb^{-1}}, with the final calibration. In particular, an excess of events above the background expectation is observed, with a local significance of 5.7 standard deviations at a mass of 124.7 GeV, which constitutes the observation of a new particle through the two photon decay channel. A further measurement provides the precise mass of this new particle as 124.720.36+0.35124.72_{-0.36}^{+0.35} GeV = 124.720.32+0.31_{-0.32}^{+0.31}(stat)0.16+0.16_{-0.16}^{+0.16}(syst) GeV. Its total production cross section times two photon decay branching ratio relative to that of the Standard Model Higgs boson is determined as 1.120.23+0.261.12_{-0.23}^{+0.26} = 1.120.21+0.21_{-0.21}^{+0.21}(stat)0.09+0.15_{-0.09}^{+0.15}(syst), compatible with the Higgs boson expectation. Further extractions of its properties relative to the Higgs boson, including the production cross section times decay branching ratios for separate Higgs production processes, couplings to bosons and to fermions, and effective couplings to the photon and to the gluon, are all compatible with the expectations for the Standard Model Higgs boson

    A New Paradigm for Gem Regulation of Voltage-Gated Ca2+ Channels

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    Depressive symptoms among older empty nesters in China: the moderating effects of social contact and contact with one’s children

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    Objectives: Mental health for older people has become a major social concern. Literature has shown that older people, especially when they become empty nesters—when a parent lives alone or lives with his/her spouse after the youngest child leaves home—may start to develop various mental health problems due to reduced contacts with their children. Methods: Using fixed-effects, multivariate regression with a difference-in-differences approach and propensity score matching, this paper examines the relationship between being an empty nester and mental health among older people in China, and the moderating effects of social contact and contact with one’s children in terms of mental health. Our data come from the China Health and Retirement Longitudinal Study of 2011, 2013, 2015 and 2018. Results: We found that, in the short term, the mental health of older people may not be affected after they became empty nesters. But in the longer term, if they did not have regular contact with their children, their mental health would deteriorate with time. Social contact, especially cognitive activities, was beneficial to the mental health of the older empty nesters. We also found that for older empty nesters with disabilities, frequent social contact and contact with their children were more important. Conclusion: We urge the government to promote community-based social activities for older people, especially for those with functional limitations

    Learn to Generate Time Series Conditioned Graphs with Generative Adversarial Nets

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    Deep learning based approaches have been utilized to model and generate graphs subjected to different distributions recently. However, they are typically unsupervised learning based and unconditioned generative models or simply conditioned on the graph-level contexts, which are not associated with rich semantic node-level contexts. Differently, in this paper, we are interested in a novel problem named Time Series Conditioned Graph Generation: given an input multivariate time series, we aim to infer a target relation graph modeling the underlying interrelationships between time series with each node corresponding to each time series. For example, we can study the interrelationships between genes in a gene regulatory network of a certain disease conditioned on their gene expression data recorded as time series. To achieve this, we propose a novel Time Series conditioned Graph Generation-Generative Adversarial Networks (TSGG-GAN) to handle challenges of rich node-level context structures conditioning and measuring similarities directly between graphs and time series. Extensive experiments on synthetic and real-word gene regulatory networks datasets demonstrate the effectiveness and generalizability of the proposed TSGG-GAN
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