110,126 research outputs found

    Strong earthquakes, novae and cosmic ray environment

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    Observations about the relationship between seismic activity and astronomical phenomena are discussed. First, after investigating the seismic data (magnitude 7.0 and over) with the method of superposed epochs it is found that world seismicity evidently increased after the occurring of novae with apparent magnitude brighter than 2.2. Second, a great many earthquakes of magnitude 7.0 and over occurred in the 13th month after two of the largest ground level solar cosmic ray events (GLEs). The causes of three high level phenomena of global seismic activity in 1918-1965 can be related to these, and it is suggested that according to the information of large GLE or bright nova predictions of the times of global intense seismic activity can be made

    Using the information of cosmic rays to predict influence epidemic

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    A correlation between the incidence of influenza pandemics and increased cosmic ray activity is made. A correlation is also made between the occurrence of these pandemics and the appearance of bright novae, e.g., Nova Eta Car. Four indices based on increased cosmic ray activity and novae are proposed to predict future influenza pandemics and viral antigenic shifts

    Layout Decomposition for Quadruple Patterning Lithography and Beyond

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    For next-generation technology nodes, multiple patterning lithography (MPL) has emerged as a key solution, e.g., triple patterning lithography (TPL) for 14/11nm, and quadruple patterning lithography (QPL) for sub-10nm. In this paper, we propose a generic and robust layout decomposition framework for QPL, which can be further extended to handle any general K-patterning lithography (K>>4). Our framework is based on the semidefinite programming (SDP) formulation with novel coloring encoding. Meanwhile, we propose fast yet effective coloring assignment and achieve significant speedup. To our best knowledge, this is the first work on the general multiple patterning lithography layout decomposition.Comment: DAC'201

    Analysis of the strong coupling constant GDsDsϕG_{D_{s}^{*}D_{s}\phi} and the decay width of DsDsγD_{s}^{*}\rightarrow D_{s}\gamma with QCD sum rules

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    In this article, we calculate the form factors and the coupling constant of the vertex DsDsϕD_{s}^{*}D_{s}\phi using the three-point QCD sum rules. We consider the contributions of the vacuum condensates up to dimension 77 in the operator product expansion(OPE). And all possible off-shell cases are considered, ϕ\phi, DsD_{s} and DsD_{s}^{*}, resulting in three different form factors. Then we fit the form factors into analytical functions and extrapolate them into time-like regions, which giving the coupling constant for the process. Our analysis indicates that the coupling constant for this vertex is GDsDsϕ=4.12±0.70GeV1G_{Ds*Ds\phi}=4.12\pm0.70 GeV^{-1}. The results of this work are very useful in the other phenomenological analysis. As an application, we calculate the coupling constant for the decay channel DsDsγD_{s}^{*}\rightarrow D_{s}\gamma and analyze the width of this decay with the assumption of the vector meson dominance of the intermediate ϕ(1020)\phi(1020). Our final result about the decay width of this decay channel is Γ=0.59±0.15keV\Gamma=0.59\pm0.15keV.Comment: arXiv admin note: text overlap with arXiv:1501.03088 by other author

    Robust variable selection for nonlinear models with diverging number of parameters

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    We focus on the problem of simultaneous variable selection and estimation for nonlinear models based on modal regression (MR), when the number of coefficients diverges with sample size. With appropriate selection of the tuning parameters, the resulting estimator is shown to be consistent and to enjoy the oracle properties

    Local linear spatial quantile regression

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    Copyright @ 2009 International Statistical Institute / Bernoulli Society for Mathematical Statistics and Probability.Let {(Yi,Xi), i ∈ ZN} be a stationary real-valued (d + 1)-dimensional spatial processes. Denote by x → qp(x), p ∈ (0, 1), x ∈ Rd , the spatial quantile regression function of order p, characterized by P{Yi ≤ qp(x)|Xi = x} = p. Assume that the process has been observed over an N-dimensional rectangular domain of the form In := {i = (i1, . . . , iN) ∈ ZN|1 ≤ ik ≤ nk, k = 1, . . . , N}, with n = (n1, . . . , nN) ∈ ZN. We propose a local linear estimator of qp. That estimator extends to random fields with unspecified and possibly highly complex spatial dependence structure, the quantile regression methods considered in the context of independent samples or time series. Under mild regularity assumptions, we obtain a Bahadur representation for the estimators of qp and its first-order derivatives, from which we establish consistency and asymptotic normality. The spatial process is assumed to satisfy general mixing conditions, generalizing classical time series mixing concepts. The size of the rectangular domain In is allowed to tend to infinity at different rates depending on the direction in ZN (non-isotropic asymptotics). The method provides muchAustralian Research Counci
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