Fast high-dimensional node generation with variable density

High Dimensional Variable Selection.

This paper explores the following question: what kind of statistical guarantees can be given when doing variable selection in high dimensional models? In particular, we look at the error rates and power of some multi-stage regression methods. In the first stage we fit a set of candidate models. In the second stage we select one model by cross-validation. In the third stage we use hypothesis tes...

High Dimensional Variable Selection with Error Control

Background. The iterative sure independence screening (ISIS) is a popular method in selecting important variables while maintaining most of the informative variables relevant to the outcome in high throughput data. However, it not only is computationally intensive but also may cause high false discovery rate (FDR). We propose to use the FDR as a screening method to reduce the high dimension to ...

Fast proton bunch generation in the interaction of ultraintense laser pulses with high-density plasmas

Energetic proton acceleration from concave targets, the fronts of which were irradiated with 40 fs laser pulses with an intensity of 10 W/cm, has been studied as a function of the depth of the concave shape. Three kinds of targets, a triangular concave target, a circular concave target and a parabolic concave target are considered. When the depth of the concave shape was varied, the peak proton...

Experimental fast quantum random number generation using high-dimensional entanglement with entropy monitoring

Fast, high-current pulse generator with variable amplitude and width

a one-dimensional random walk, for which the statistical properties are well known. The essential idea behind this approach can best be seen in Eq. (2) where the individual t; + r terms in the sum on the left represent steps in a unidirectional random walk, the walk being terminated when the value Tis reached. A detailed treatment (facilitated by the definition of the I; given above) shows that...