14,122 research outputs found

    X(1576) and the Final State Interaction Effect

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    We study whether the broad peak X(1576) observed by BES Collaboration arises from the final state interaction effect of ρ(1450,1700)\rho(1450,1700) decays. The interference effect could produce an enhancement around 1540 MeV in the K+KK^+K^- spectrum with typical interference phases. However, the branching ratio B[J/ψπ0ρ(1450,1700)]B[ρ(1450,1700)K+K]B[J/\psi\to \pi^{0}\rho(1450,1700)]\cdot B[\rho(1450,1700)\to K^{+}K^{-}] from the final state interaction effect is far less than the experimental data.Comment: 6 pages, 4 figures. Some typos corrected, more discussion and references adde

    Lei Shen Artist Statement

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    I believe that all things have contradictory side, and development is built on contradictions. In fact, the contradiction is the most frequent thing which is in my CGU’s life. Different culture, different habit of life, different learning styles, different educations. These are all contradictory to me. But also let me have a different way to think. I think it is a good thing for me. Because it makes to understand a thing become more comprehensive. So I want reflect this idea in my work. I want tell people contradictory actually can make progress. I want to say there are tow levels meaning of contradictory. First level, people think contradictory only make confused. Second level, by contradictory things we can see different meaning of things. This is the most important point for me

    Inference in Multivariate Generalized Ornstein-Uhlenbeck Processes with a Change-point

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    In this paper, we study inference problem about the drift parameter matrix in multivariate generalized Ornstein-Uhlenbeck processes with an unknown change-point. In particular, we study the case where the matrix parameter satisfies uncertain restriction. Thus, we generalize some recent findings about univariate generalized Ornstein-Uhlenbeck processes. First, we establish a weaker condition for the existence of the unrestricted estimator (UE) and we derive the unrestricted estimator and the restricted estimator. Second, we establish the joint asymptotic normality of the unrestricted estimator and the restricted estimator under the sequence of local alternatives. Third, we construct a test for testing the uncertain restriction. The proposed test is also useful for testing the absence of the change-point. Fourth, we derive the asymptotic power of the proposed test and we prove that it is consistent. Fifth, we propose the shrinkage estimators and we prove that shrinkage estimators dominate the unrestricted estimator. Finally, in order to illustrate the performance of the proposed methods in short and medium period of observations, we conduct a simulation study which corroborate our theoretical findings

    Identifying progressive imaging genetic patterns via multi-task sparse canonical correlation analysis: a longitudinal study of the ADNI cohort

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    Motivation Identifying the genetic basis of the brain structure, function and disorder by using the imaging quantitative traits (QTs) as endophenotypes is an important task in brain science. Brain QTs often change over time while the disorder progresses and thus understanding how the genetic factors play roles on the progressive brain QT changes is of great importance and meaning. Most existing imaging genetics methods only analyze the baseline neuroimaging data, and thus those longitudinal imaging data across multiple time points containing important disease progression information are omitted. Results We propose a novel temporal imaging genetic model which performs the multi-task sparse canonical correlation analysis (T-MTSCCA). Our model uses longitudinal neuroimaging data to uncover that how single nucleotide polymorphisms (SNPs) play roles on affecting brain QTs over the time. Incorporating the relationship of the longitudinal imaging data and that within SNPs, T-MTSCCA could identify a trajectory of progressive imaging genetic patterns over the time. We propose an efficient algorithm to solve the problem and show its convergence. We evaluate T-MTSCCA on 408 subjects from the Alzheimer’s Disease Neuroimaging Initiative database with longitudinal magnetic resonance imaging data and genetic data available. The experimental results show that T-MTSCCA performs either better than or equally to the state-of-the-art methods. In particular, T-MTSCCA could identify higher canonical correlation coefficients and capture clearer canonical weight patterns. This suggests that T-MTSCCA identifies time-consistent and time-dependent SNPs and imaging QTs, which further help understand the genetic basis of the brain QT changes over the time during the disease progression. Availability and implementation The software and simulation data are publicly available at https://github.com/dulei323/TMTSCCA. Supplementary information Supplementary data are available at Bioinformatics online