Resampling images in Fourier domain

When simulating sky images, one often takes a galaxy image $F(x)$ defined by a set of pixelized samples and an interpolation kernel, and then wants to produce a new sampled image representing this galaxy as it would appear with a different point-spread function, a rotation, shearing, or magnification, and/or a different pixel scale. These operations are sometimes only possible, or most efficiently executed, as resamplings of the Fourier transform $\tilde F(u)$ of the image onto a $u$-space grid that differs from the one produced by a discrete Fourier transform (DFT) of the samples. In some applications it is essential that the resampled image be accurate to better than 1 part in $10^3$, so in this paper we first use standard Fourier techniques to show that Fourier-domain interpolation with a wrapped sinc function yields the exact value of $\tilde F(u)$ in terms of the input samples and kernel. This operation scales with image dimension as $N^4$ and can be prohibitively slow, so we next investigate the errors accrued from approximating the sinc function with a compact kernel. We show that these approximations produce a multiplicative error plus a pair of ghost images (in each dimension) in the simulated image. Standard Lanczos or cubic interpolators, when applied in Fourier domain, produce unacceptable artifacts. We find that errors $<1$ part in $10^3$ can be obtained by (1) 4-fold zero-padding of the original image before executing the $x\rightarrow u$ DFT, followed by (2) resampling to the desired $u$ grid using a 6-point, piecewise-quintic interpolant that we design expressly to minimize the ghosts, then (3) executing the DFT back to $x$ domain.Comment: Typographical and one algebraic correction, to appear in PASP March 201

Are apparent findings of nonlinearity due to structural instability in economic time series?

Many modelling issues and policy debates in macroeconomics depend on whether macroeconomic times series are best characterized as linear or nonlinear. If departures from linearity exist, it is important to know whether these are endogenously generated (as in, e.g., a threshold autoregressive model) or whether they merely reflect changing structure over time. We advocate a Bayesian approach and show how such an approach can be implemented in practice. An empirical exercise involving several macroeconomic time series shows that apparent findings of threshold type nonlinearities could be due to structural instability

Optimizing a Law School’s Course Schedule

[Excerpt] “Just like other educational institutions, law schools must schedule courses by taking into consideration student needs, faculty resources, and logistical support such as classroom size and equipment needs. Course scheduling is an administrative function, typically handled by an Assistant Dean or an Associate Dean, who works with the faculty and the registrar to balance these considerations in advance of the registration process. Usually, the entire academic year is scheduled in advance, although the spring semester may be labeled tentative until registration begins for that semester. It’s hard to imagine, but some schools even publish a two-year schedule of upper-division courses so that students can plan their entire law school career in advance. In order to give assistance to those academics involved for the first time in the scheduling process, this article discusses the law school scheduling process and how a scheduling software package has worked to successfully automate what has been seen as one of the most abysmal administrative tasks of an Associate Dean. We first provide a background to course scheduling at a typical law school. We then present a review of the tools for, and literature on, course scheduling, followed by a discussion of how technology can be applied to course scheduling in general, and our outcomes of applying this technology in a law school environment. We close with a brief summary.

The Generalized Spectral Kurtosis Estimator

Due to its conceptual simplicity and its proven effectiveness in real-time detection and removal of radio frequency interference (RFI) from radio astronomy data, the Spectral Kurtosis (SK) estimator is likely to become a standard tool of a new generation of radio telescopes. However, the SK estimator in its original form must be developed from instantaneous power spectral density (PSD) estimates, and hence cannot be employed as an RFI excision tool downstream of the data pipeline in existing instruments where any time averaging is performed. In this letter, we develop a generalized estimator with wider applicability for both instantaneous and averaged spectral data, which extends its practical use to a much larger pool of radio instruments.Comment: 5 pages, 2 figures, MNRAS Letters accepte