arXiv subjects

John Hughes

Publications and source records attributed to John Hughes.

At least 37 records · Page 2Linked to original sources

arXiv2023

Spatial distribution and determinants of childhood vaccination refusal in the United States

Parental refusal and delay of childhood vaccination has increased in recent years in the United States. This phenomenon challenges maintenance of herd immunity and increases the risk of outbreaks of vaccine-preventable diseases. We examine US county-level vaccine refusal for patients under five years of age collected during the period 2012--2015 from an administrative healthcare dataset. We model these data with a Bayesian zero-inflated negative binomial regression model to capture social and political processes that are associated with vaccine refusal, as well as factors that affect our measurement of vaccine refusal.Our work highlights fine-scale socio-demographic characteristics associated with vaccine refusal nationally, finds that spatial clustering in refusal can be explained by such factors, and has the potential to aid in the development of targeted public health strategies for optimizing vaccine uptake.

stat.AP↗

arXiv2022

Toward improved inference for Krippendorff's Alpha agreement coefficient

In this article I recommend a better point estimator for Krippendorff's Alpha agreement coefficient, and develop a jackknife variance estimator that leads to much better interval estimation than does the customary bootstrap procedure or an alternative bootstrap procedure. Having developed the new methodology, I analyze nominal data previously analyzed by Krippendorff, and two experimentally observed datasets: (1) ordinal data from an imaging study of congenital diaphragmatic hernia, and (2) United States Environmental Protection Agency air pollution data for the Philadelphia, Pennsylvania area. The latter two applications are novel. The proposed methodology is now supported in version 2.0 of my open source R package, krippendorffsalpha, which supports common and user-defined distance functions, and can accommodate any number of units, any number of coders, and missingness. Interval computation can be parallelized.

stat.ME↗

arXiv2021

krippendorffsalpha: An R Package for Measuring Agreement Using Krippendorff's Alpha Coefficient

R package krippendorffsalpha provides tools for measuring agreement using Krippendorff's Alpha coefficient, a well-known nonparametric measure of agreement (also called inter-rater reliability and various other names). This article first develops Krippendorff's Alpha in a natural way, and situates Alpha among statistical procedures. Then the usage of package krippendorffsalpha is illustrated via analyses of two datasets, the latter of which was collected during an imaging study of hip cartilage. The package permits users to apply the Alpha methodology using built-in distance functions for the nominal, ordinal, interval, or ratio levels of measurement. User-defined distance functions are also supported. The fitting function can accommodate any number of units, any number of coders, and missingness. Bootstrap inference is supported, and the bootstrap computation can be carried out in parallel.

stat.ME↗

arXiv2021

On the Occasional Exactness of the Distributional Transform Approximation for Direct Gaussian Copula Models with Discrete Margins

The direct Gaussian copula model with discrete marginal distributions is an appealing data-analytic tool but poses difficult computational challenges due to its intractable likelihood. A number of approximations/surrogates for the likelihood have been proposed, including the continuous extension-based approximation (CE) and the distributional transform-based approximation (DT). The continuous extension approach is exact up to Monte Carlo error but does not scale well computationally. The distributional transform approach permits efficient computation but offers no theoretical guarantee that it is exact. In practice, though, the distributional transform-based approximate likelihood is so very nearly exact for some variants of the model as to permit genuine maximum likelihood or Bayesian inference. We demonstrate the exactness of the distributional transform-based objective function for two interesting variants of the model, and propose a quantity that can be used to assess exactness for experimentally observed datasets. Said diagnostic will permit practitioners to determine whether genuine Bayesian inference or ordinary maximum likelihood inference using the DT-based likelihood is possible for a given dataset.

stat.ME↗

arXiv2020

Hierarchical Quantized Autoencoders

Despite progress in training neural networks for lossy image compression, current approaches fail to maintain both perceptual quality and abstract features at very low bitrates. Encouraged by recent success in learning discrete representations with Vector Quantized Variational Autoencoders (VQ-VAEs), we motivate the use of a hierarchy of VQ-VAEs to attain high factors of compression. We show that the combination of stochastic quantization and hierarchical latent structure aids likelihood-based image compression. This leads us to introduce a novel objective for training hierarchical VQ-VAEs. Our resulting scheme produces a Markovian series of latent variables that reconstruct images of high-perceptual quality which retain semantically meaningful features. We provide qualitative and quantitative evaluations on the CelebA and MNIST datasets.

cs.LG↗

arXiv2019

Beginner's Luck: A Language for Property-Based Generators

Property-based random testing a la QuickCheck requires building efficient generators for well-distributed random data satisfying complex logical predicates, but writing these generators can be difficult and error prone. We propose a domain-specific language in which generators are conveniently expressed by decorating predicates with lightweight annotations to control both the distribution of generated values and the amount of constraint solving that happens before each variable is instantiated. This language, called Luck, makes generators easier to write, read, and maintain. We give Luck a formal semantics and prove several fundamental properties, including the soundness and completeness of random generation with respect to a standard predicate semantics. We evaluate Luck on common examples from the property-based testing literature and on two significant case studies, showing that it can be used in complex domains with comparable bug-finding effectiveness and a significant reduction in testing code size compared to handwritten generators.

cs.PL↗

arXiv2018

Leveraging Sparsity to Speed Up Polynomial Feature Expansions of CSR Matrices Using $K$-Simplex Numbers

An algorithm is provided for performing polynomial feature expansions that both operates on and produces compressed sparse row (CSR) matrices. Previously, no such algorithm existed, and performing polynomial expansions on CSR matrices required an intermediate densification step. The algorithm performs a $K$-degree expansion by using a bijective function involving $K$-simplex numbers of column indices in the original matrix to column indices in the expanded matrix. Not only is space saved by operating in CSR format, but the bijective function allows for only the nonzero elements to be iterated over and multiplied together during the expansion, greatly improving average time complexity. For a vector of dimensionality $D$ and density $0 \le d \le 1$, the algorithm has average time complexity $Θ(d^KD^K)$ where $K$ is the polynomial-feature order; this is an improvement by a factor $d^K$ over the standard method. This work derives the required function for the cases of $K=2$ and $K=3$ and shows its use in the $K=2$ algorithm.

cs.DS↗

arXiv2018

sklarsomega: An R Package for Measuring Agreement Using Sklar's Omega Coefficient

R package sklarsomega provides tools for measuring agreement using Sklar's omega coefficient, which subsumes Krippendorff's alpha coefficient, which in turn subsumes a number of other well-known agreement coefficients. The package permits users to apply the omega methodology to nominal, ordinal, interval, or ratio scores; can accommodate any number of units, any number of coders, and missingness; and can measure intra-coder agreement, inter-coder agreement, and agreement relative to a gold standard. Classical inference is available for all levels of measurement while Bayesian inference is available for interval data and ratio data only.

stat.CO↗

arXiv2018

Branching Processes for QuickCheck Generators

In QuickCheck (or, more generally, random testing), it is challenging to control random data generators' distributions---specially when it comes to user-defined algebraic data types (ADT). In this paper, we adapt results from an area of mathematics known as branching processes, and show how they help to analytically predict (at compile-time) the expected number of generated constructors, even in the presence of mutually recursive or composite ADTs. Using our probabilistic formulas, we design heuristics capable of automatically adjusting probabilities in order to synthesize generators which distributions are aligned with users' demands. We provide a Haskell implementation of our mechanism in a tool called DRaGeN and perform case studies with real-world applications. When generating random values, our synthesized QuickCheck generators show improvements in code coverage when compared with those automatically derived by state-of-the-art tools.

cs.SE↗

arXiv2018

Sklar's Omega: A Gaussian Copula-Based Framework for Assessing Agreement

The statistical measurement of agreement is important in a number of fields, e.g., content analysis, education, computational linguistics, biomedical imaging. We propose Sklar's Omega, a Gaussian copula-based framework for measuring intra-coder, inter-coder, and inter-method agreement as well as agreement relative to a gold standard. We demonstrate the efficacy and advantages of our approach by applying it to both simulated and experimentally observed datasets, including data from two medical imaging studies. Application of our proposed methodology is supported by our open-source R package, sklarsomega, which is available for download from the Comprehensive R Archive Network.

stat.ME↗

arXiv2017

Spatial Regression and the Bayesian Filter

Regression for spatially dependent outcomes poses many challenges, for inference and for computation. Non-spatial models and traditional spatial mixed-effects models each have their advantages and disadvantages, making it difficult for practitioners to determine how to carry out a spatial regression analysis. We discuss the data-generating mechanisms implicitly assumed by various popular spatial regression models, and discuss the implications of these assumptions. We propose Bayesian spatial filtering as an approximate middle way between non-spatial models and traditional spatial mixed models. We show by simulation that our Bayesian spatial filtering model has several desirable properties and hence may be a useful addition to a spatial statistician's toolkit.

stat.ME↗

arXiv2017

Modelling of Autosar Libraries for Large Scale Testing

We demonstrate a specific method and technology for model-based testing of large software projects with the QuickCheck tool using property-based specifications. Our specifications are very precise, state-full models of the software under test (SUT). In our approach we define (a) formal descriptions of valid function call sequences (public API), (b) postconditions that check the validity of each call, and (c) call-out specifications that define and validate external system interactions (SUT calling external API). The QuickCheck tool automatically generates and executes tests from these specifications. Commercially, this method and tool have been used to test large parts of the industrially developed automotive libraries based on the Autosar standard. In this paper, we exemplify our approach with a circular buffer specified by Autosar, to demonstrate the capabilities of the model-based testing method of QuickCheck. Our example is small compared to the commercial QuickCheck models, but faithfully addresses many of the same challenges.

cs.SE↗

arXiv2016

The Quiescent Intracluster Medium in the Core of the Perseus Cluster

Clusters of galaxies are the most massive gravitationally-bound objects in the Universe and are still forming. They are thus important probes of cosmological parameters and a host of astrophysical processes. Knowledge of the dynamics of the pervasive hot gas, which dominates in mass over stars in a cluster, is a crucial missing ingredient. It can enable new insights into mechanical energy injection by the central supermassive black hole and the use of hydrostatic equilibrium for the determination of cluster masses. X-rays from the core of the Perseus cluster are emitted by the 50 million K diffuse hot plasma filling its gravitational potential well. The Active Galactic Nucleus of the central galaxy NGC1275 is pumping jetted energy into the surrounding intracluster medium, creating buoyant bubbles filled with relativistic plasma. These likely induce motions in the intracluster medium and heat the inner gas preventing runaway radiative cooling; a process known as Active Galactic Nucleus Feedback. Here we report on Hitomi X-ray observations of the Perseus cluster core, which reveal a remarkably quiescent atmosphere where the gas has a line-of-sight velocity dispersion of 164+/-10 km/s in a region 30-60 kpc from the central nucleus. A gradient in the line-of-sight velocity of 150+/-70 km/s is found across the 60 kpc image of the cluster core. Turbulent pressure support in the gas is 4% or less of the thermodynamic pressure, with large scale shear at most doubling that estimate. We infer that total cluster masses determined from hydrostatic equilibrium in the central regions need little correction for turbulent pressure.

astro-ph.GA↗

arXiv2015

Testing Noninterference, Quickly

Information-flow control mechanisms are difficult both to design and to prove correct. To reduce the time wasted on doomed proof attempts due to broken definitions, we advocate modern random testing techniques for finding counterexamples during the design process. We show how to use QuickCheck, a property-based random-testing tool, to guide the design of increasingly complex information-flow abstract machines, leading up to a sophisticated register machine with a novel and highly permissive flow-sensitive dynamic enforcement mechanism that is sound in the presence of first-class public labels. We find that both sophisticated strategies for generating well-distributed random programs and readily falsifiable formulations of noninterference properties are critically important for efficient testing. We propose several approaches and evaluate their effectiveness on a collection of injected bugs of varying subtlety. We also present an effective technique for shrinking large counterexamples to minimal, easily comprehensible ones. Taken together, our best methods enable us to quickly and automatically generate simple counterexamples for more than 45 bugs. Moreover, we show how testing guides the discovery of the sophisticated invariants needed for the noninterference proof of our most complex machine.

cs.PL↗

arXiv2014

The ASTRO-H X-ray Astronomy Satellite

The joint JAXA/NASA ASTRO-H mission is the sixth in a series of highly successful X-ray missions developed by the Institute of Space and Astronautical Science (ISAS), with a planned launch in 2015. The ASTRO-H mission is equipped with a suite of sensitive instruments with the highest energy resolution ever achieved at E > 3 keV and a wide energy range spanning four decades in energy from soft X-rays to gamma-rays. The simultaneous broad band pass, coupled with the high spectral resolution of Delta E < 7 eV of the micro-calorimeter, will enable a wide variety of important science themes to be pursued. ASTRO-H is expected to provide breakthrough results in scientific areas as diverse as the large-scale structure of the Universe and its evolution, the behavior of matter in the gravitational strong field regime, the physical conditions in sites of cosmic-ray acceleration, and the distribution of dark matter in galaxy clusters at different redshifts.

astro-ph.IM↗

arXiv2012

The ASTRO-H X-ray Observatory

The joint JAXA/NASA ASTRO-H mission is the sixth in a series of highly successful X-ray missions initiated by the Institute of Space and Astronautical Science (ISAS). ASTRO-H will investigate the physics of the high-energy universe via a suite of four instruments, covering a very wide energy range, from 0.3 keV to 600 keV. These instruments include a high-resolution, high-throughput spectrometer sensitive over 0.3-2 keV with high spectral resolution of Delta E < 7 eV, enabled by a micro-calorimeter array located in the focal plane of thin-foil X-ray optics; hard X-ray imaging spectrometers covering 5-80 keV, located in the focal plane of multilayer-coated, focusing hard X-ray mirrors; a wide-field imaging spectrometer sensitive over 0.4-12 keV, with an X-ray CCD camera in the focal plane of a soft X-ray telescope; and a non-focusing Compton-camera type soft gamma-ray detector, sensitive in the 40-600 keV band. The simultaneous broad bandpass, coupled with high spectral resolution, will enable the pursuit of a wide variety of important science themes.

astro-ph.IM↗

arXiv2010

Dimension Reduction and Alleviation of Confounding for Spatial Generalized Linear Mixed Models

Non-gaussian spatial data are very common in many disciplines. For instance, count data are common in disease mapping, and binary data are common in ecology. When fitting spatial regressions for such data, one needs to account for dependence to ensure reliable inference for the regression coefficients. The spatial generalized linear mixed model (SGLMM) offers a very popular and flexible approach to modeling such data, but the SGLMM suffers from three major shortcomings: (1) uninterpretability of parameters due to spatial confounding, (2) variance inflation due to spatial confounding, and (3) high-dimensional spatial random effects that make fully Bayesian inference for such models computationally challenging. We propose a new parameterization of the SGLMM that alleviates spatial confounding and speeds computation by greatly reducing the dimension of the spatial random effects. We illustrate the application of our approach to simulated binary, count, and Gaussian spatial datasets, and to a large infant mortality dataset.

stat.ME↗

arXiv2010

Likelihood inference for particle location in fluorescence microscopy

We introduce a procedure to automatically count and locate the fluorescent particles in a microscopy image. Our procedure employs an approximate likelihood estimator derived from a Poisson random field model for photon emission. Estimates of standard errors are generated for each image along with the parameter estimates, and the number of particles in the image is determined using an information criterion and likelihood ratio tests. Realistic simulations show that our procedure is robust and that it leads to accurate estimates, both of parameters and of standard errors. This approach improves on previous ad hoc least squares procedures by giving a more explicit stochastic model for certain fluorescence images and by employing a consistent framework for analysis.

stat.AP↗