We discuss Waldspurger’s local period integral for newforms in new cases. The main tool is the work of Hu and Nelson (2018) on Waldspurger’s period integral using minimal vectors, and the explicit relation between newforms and minimal vectors. We use a representation-theoretical trick to simplify computations for newforms. As an example, we compute the local integral coming from a special arithmetic setting which was used to study the 3-part full BSD conjecture by Hu et al. (2019).

Let n be an integer such that 25≤n≤51. We construct a smooth metric g on Sn with the property that the set of constant scalar curvature metrics in the conformal class of g is not compact.

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Double-blind randomized controlled trials are traditionally seen as the gold standard for causal inferences as the difference-in-means estimator is an unbiased estimator of the average treatment effect in the experiment. The fact that this estimator is unbiased over all possible randomizations does not, however, mean that any given estimate is close to the true treatment effect. Similarly, while predetermined covariates will be balanced between treatment and control groups on average, large imbalances may be observed in a given experiment and the researcher may therefore want to condition on such covariates using linear regression. This article studies the theoretical properties of both the difference-in-means and OLS estimators conditional on observed differences in covariates. By deriving the statistical properties of the conditional estimators, we can establish guidance for how to deal with covariate imbalances.

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