1,120 research outputs found

    Non-equilibrium Green's function approach to inhomogeneous quantum many-body systems using the Generalized Kadanoff Baym Ansatz

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    In non-equilibrium Green's function calculations the use of the Generalized Kadanoff-Baym Ansatz (GKBA) allows for a simple approximate reconstruction of the two-time Green's function from its time-diagonal value. With this a drastic reduction of the computational needs is achieved in time-dependent calculations, making longer time propagation possible and more complex systems accessible. This paper gives credit to the GKBA that was introduced 25 years ago. After a detailed derivation of the GKBA, we recall its application to homogeneous systems and show how to extend it to strongly correlated, inhomogeneous systems. As a proof of concept, we present results for a 2-electron quantum well, where the correct treatment of the correlated electron dynamics is crucial for the correct description of the equilibrium and dynamic properties

    A new approach to hierarchical data analysis: Targeted maximum likelihood estimation for the causal effect of a cluster-level exposure

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    We often seek to estimate the impact of an exposure naturally occurring or randomly assigned at the cluster-level. For example, the literature on neighborhood determinants of health continues to grow. Likewise, community randomized trials are applied to learn about real-world implementation, sustainability, and population effects of interventions with proven individual-level efficacy. In these settings, individual-level outcomes are correlated due to shared cluster-level factors, including the exposure, as well as social or biological interactions between individuals. To flexibly and efficiently estimate the effect of a cluster-level exposure, we present two targeted maximum likelihood estimators (TMLEs). The first TMLE is developed under a non-parametric causal model, which allows for arbitrary interactions between individuals within a cluster. These interactions include direct transmission of the outcome (i.e. contagion) and influence of one individual's covariates on another's outcome (i.e. covariate interference). The second TMLE is developed under a causal sub-model assuming the cluster-level and individual-specific covariates are sufficient to control for confounding. Simulations compare the alternative estimators and illustrate the potential gains from pairing individual-level risk factors and outcomes during estimation, while avoiding unwarranted assumptions. Our results suggest that estimation under the sub-model can result in bias and misleading inference in an observational setting. Incorporating working assumptions during estimation is more robust than assuming they hold in the underlying causal model. We illustrate our approach with an application to HIV prevention and treatment

    Targeted Estimation and Inference for the Sample Average Treatment Effect

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    While the population average treatment effect has been the subject of extensive methods and applied research, less consideration has been given to the sample average treatment effect: the mean difference in the counterfactual outcomes for the study units. The sample parameter is easily interpretable and is arguably the most relevant when the study units are not representative of a greater population or when the exposure\u27s impact is heterogeneous. Formally, the sample effect is not identifiable from the observed data distribution. Nonetheless, targeted maximum likelihood estimation (TMLE) can provide an asymptotically unbiased and efficient estimate of both the population and sample parameters. In this paper, we study the asymptotic and finite sample properties of the TMLE for the sample effect and provide a conservative variance estimator. In most settings, the sample parameter can be estimated more efficiently than the population parameter. Finite sample simulations illustrate the potential gains in precision and power from selecting the sample effect as the target of inference. As a motivating example, we discuss the Sustainable East Africa Research in Community Health (SEARCH) study, an ongoing cluster randomized trial for HIV prevention and treatment

    Cis-regulatory elements of the mitotic regulator, string/Cdc25

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    Mitosis in most Drosophila cells is triggered by brief bursts of transcription of string (stg), a Cdc25-type phosphatase that activates the mitotic kinase, Cdk1 (Cdc2). To understand how string transcription is regulated, we analyzed the expression of string-lacZ reporter genes covering approximately 40 kb of the string locus. We also tested protein coding fragments of the string locus of 6 kb to 31.6 kb for their ability to complement loss of string function in embryos and imaginal discs. A plethora of cis-acting elements spread over >30 kb control string transcription in different cells and tissue types. Regulatory elements specific to subsets of epidermal cells, mesoderm, trachea and nurse cells were identified, but the majority of the string locus appears to be devoted to controlling cell proliferation during neurogenesis. Consistent with this, compact promotor-proximal sequences are sufficient for string function during imaginal disc growth, but additional distal elements are required for the development of neural structures in the eye, wing, leg and notum. We suggest that, during evolution, cell-type-specific control elements were acquired by a simple growth-regulated promoter as a means of coordinating cell division with developmental processes, particularly neurogenesis.Dara A. Lehman; Briony Patterson, Laura A. Johnston; Tracy Balzer; Jessica S. Britton; Robert Saint and Bruce A. Edga

    Effect of a Patient-Centered Phone Call by a Clinical Officer at Time of HIV Testing on Linkage to Care in Rural Kenya.

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    In a randomized controlled trial, we tested whether a structured, patient-centered phone call from a clinical officer after HIV testing improved linkage to/re-engagement in HIV care. Among 130 HIV-positive persons, those randomized to the phone call were significantly more likely to link to care by 7 and 30 days (P = .04)

    The Influence of the Degree of Heterogeneity on the Elastic Properties of Random Sphere Packings

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    The macroscopic mechanical properties of colloidal particle gels strongly depend on the local arrangement of the powder particles. Experiments have shown that more heterogeneous microstructures exhibit up to one order of magnitude higher elastic properties than their more homogeneous counterparts at equal volume fraction. In this paper, packings of spherical particles are used as model structures to computationally investigate the elastic properties of coagulated particle gels as a function of their degree of heterogeneity. The discrete element model comprises a linear elastic contact law, particle bonding and damping. The simulation parameters were calibrated using a homogeneous and a heterogeneous microstructure originating from earlier Brownian dynamics simulations. A systematic study of the elastic properties as a function of the degree of heterogeneity was performed using two sets of microstructures obtained from Brownian dynamics simulation and from the void expansion method. Both sets cover a broad and to a large extent overlapping range of degrees of heterogeneity. The simulations have shown that the elastic properties as a function of the degree of heterogeneity are independent of the structure generation algorithm and that the relation between the shear modulus and the degree of heterogeneity can be well described by a power law. This suggests the presence of a critical degree of heterogeneity and, therefore, a phase transition between a phase with finite and one with zero elastic properties.Comment: 8 pages, 6 figures; Granular Matter (published online: 11. February 2012

    First order Mott transition at zero temperature in two dimensions: Variational plaquette study

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    The nature of the metal-insulator Mott transition at zero temperature has been discussed for a number of years. Whether it occurs through a quantum critical point or through a first order transition is expected to profoundly influence the nature of the finite temperature phase diagram. In this paper, we study the zero temperature Mott transition in the two-dimensional Hubbard model on the square lattice with the variational cluster approximation. This takes into account the influence of antiferromagnetic short-range correlations. By contrast to single-site dynamical mean-field theory, the transition turns out to be first order even at zero temperature.Comment: 6 pages, 5 figures, version 2 with additional results for 8 bath site
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