arXiv subjects

Jeremy F. Magland

Publications and source records attributed to Jeremy F. Magland.

5 recordsLinked to original sources

arXiv2020

Recovering missing data in coherent diffraction imaging

In coherent diffraction imaging (CDI) experiments, the intensity of the scattered wave impinging on an object is measured on an array of detectors. This signal can be interpreted as the square of the modulus of the Fourier transform of the unknown scattering density. A beam-stop obstructs the forward scattered wave and, hence, the modulus Fourier data from a neighborhood of k=0 cannot be measured. In this note, we describe a linear method for recovering this unmeasured modulus Fourier data from the measured values and an estimate of the support of the image's autocorrelation function without consideration of phase retrieval. We analyze the conditioning of this problem, which grows exponentially with the modulus of the maximum spatial frequency not measured, and the effects of noise.

math.NA↗

arXiv2019

A parallel non-uniform fast Fourier transform library based on an "exponential of semicircle" kernel

The nonuniform fast Fourier transform (NUFFT) generalizes the FFT to off-grid data. Its many applications include image reconstruction, data analysis, and the numerical solution of differential equations. We present FINUFFT, an efficient parallel library for type 1 (nonuiform to uniform), type 2 (uniform to nonuniform), or type 3 (nonuniform to nonuniform) transforms, in dimensions 1, 2, or 3. It uses minimal RAM, requires no precomputation or plan steps, and has a simple interface to several languages. We perform the expensive spreading/interpolation between nonuniform points and the fine grid via a simple new kernel---the `exponential of semicircle' $e^{β\sqrt{1-x^2}}$ in $x\in[-1,1]$---in a cache-aware load-balanced multithreaded implementation. The deconvolution step requires the Fourier transform of the kernel, for which we propose efficient numerical quadrature. For types 1 and 2, rigorous error bounds asymptotic in the kernel width approach the fastest known exponential rate, namely that of the Kaiser--Bessel kernel. We benchmark against several popular CPU-based libraries, showing favorable speed and memory footprint, especially in three dimensions when high accuracy and/or clustered point distributions are desired.

math.NA↗

arXiv2016

Unimodal clustering using isotonic regression: ISO-SPLIT

A limitation of many clustering algorithms is the requirement to tune adjustable parameters for each application or even for each dataset. Some techniques require an \emph{a priori} estimate of the number of clusters while density-based techniques usually require a scale parameter. Other parametric methods, such as mixture modeling, make assumptions about the underlying cluster distributions. Here we introduce a non-parametric clustering method that does not involve tunable parameters and only assumes that clusters are unimodal, in the sense that they have a single point of maximal density when projected onto any line, and that clusters are separated from one another by a separating hyperplane of relatively lower density. The technique uses a non-parametric variant of Hartigan's dip statistic using isotonic regression as the kernel operation repeated at every iteration. We compare the method against k-means++, DBSCAN, and Gaussian mixture methods and show in simulations that it performs better than these standard methods in many situations. The algorithm is suited for low-dimensional datasets with a large number of observations, and was motivated by the problem of "spike sorting" in neural electrical recordings. Source code is freely available.

stat.ME↗

arXiv2015

Validation of neural spike sorting algorithms without ground-truth information

We describe a suite of validation metrics that assess the credibility of a given automatic spike sorting algorithm applied to a given electrophysiological recording, when ground-truth is unavailable. By rerunning the spike sorter two or more times, the metrics measure stability under various perturbations consistent with variations in the data itself, making no assumptions about the noise model, nor about the internal workings of the sorting algorithm. Such stability is a prerequisite for reproducibility of results. We illustrate the metrics on standard sorting algorithms for both in vivo and ex vivo recordings. We believe that such metrics could reduce the significant human labor currently spent on validation, and should form an essential part of large-scale automated spike sorting and systematic benchmarking of algorithms.

q-bio.NC↗

arXiv2009

Discrete Inverse Scattering Theory for NMR Pulse Design

We introduce a discrete analogue of the scattering theory for the Zakharov-Shabat (ZS) system, and use it to continue the work of C.L. Epstein by deriving an efficient, recursive algorithm for generating RF-pulses for nuclear magnetic resonance (NMR). In the process, we present a straightforward derivation of the standard Gel'fand-Levitan-Marchenko (Marchenko) equations, and we derive similar equations which apply to the discrete framework. In addition, we prove that the potentials obtained by solving the Marchenko equations produce the correct scattering data. We explain how the generally accepted Shinnar-Le Roux (SLR) technique fits into the more general framework of discrete inverse scattering, and we show, using examples, how inverse scattering theory can be used to produce pulses which are, in some ways, superior to standard SLR pulses.

math.NA↗