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Acoustic telemetry data plays a vital role in understanding the behaviour and movement of aquatic animals. However, these datasets, which often consist of millions of individual data points, frequently contain anomalous movements that pose significant challenges. Traditionally, anomalous movements are identified either manually or through basic statistical methods, approaches that are time-consuming and prone to high rates of unidentified anomalies in large datasets. This study focuses on the development of automated classifiers for a large telemetry dataset comprising detections from fifty acoustically tagged dusky kob monitored in the Breede Estuary, South Africa. Using an array of 16 acoustic receivers deployed throughout the estuary between 2016 and 2021, we collected over three million individual data points. We present detailed guidelines for data pre-processing, resampling strategies, labelling process, feature engineering, data splitting methodologies, and the selection and interpretation of machine learning and deep learning models for anomaly detection. Among the evaluated models, neural networks autoencoder (NN-AE) demonstrated superior performance, aided by our proposed threshold-finding algorithm. NN-AE achieved a high recall with no false normal (i.e., no misclassifications of anomalous movements as normal patterns), a critical factor in ensuring that no true anomalies are overlooked. In contrast, other models exhibited false normal fractions exceeding 0.9, indicating they failed to detect the majority of true anomalies; a significant limitation for telemetry studies where undetected anomalies can distort interpretations of movement patterns. While the NN-AE's performance highlights its reliability and robustness in detecting anomalies, it faced challenges in accurately learning normal movement patterns when these patterns gradually deviated from anomalous ones.

Racial disparity in academia is a widely acknowledged problem. The quantitative understanding of racial based systemic inequalities is an important step towards a more equitable research system. However, because of the lack of robust information on authors' race, few large scale analyses have been performed on this topic. Algorithmic approaches offer one solution, using known information about authors, such as their names, to infer their perceived race. As with any other algorithm, the process of racial inference can generate biases if it is not carefully considered. The goal of this article is to assess the extent to which algorithmic bias is introduced using different approaches for name based racial inference. We use information from the U.S. Census and mortgage applications to infer the race of U.S. affiliated authors in the Web of Science. We estimate the effects of using given and family names, thresholds or continuous distributions, and imputation. Our results demonstrate that the validity of name based inference varies by race/ethnicity and that threshold approaches underestimate Black authors and overestimate White authors. We conclude with recommendations to avoid potential biases. This article lays the foundation for more systematic and less biased investigations into racial disparities in science.

Using Chandra observations, we derive the $Y_{\rm X}$ proxy and associated total mass measurement, $M_{500}^{\rm Y_X}$, for 147 clusters with $z \leq 0.35$ from the Planck Early Sunyaev-Zel'dovich catalog, and for 80 clusters with $z \leq 0.30$ from an X-ray flux-limited sample. We re-extract the Planck $Y_{\rm SZ}$ measurements and obtain the corresponding mass proxy, $M_{500}^{\rm SZ}$, from the full Planck mission maps, minimizing the Malmquist bias due to observational scatter. The masses re-extracted using the more precise X-ray position and characteristic size agree with the published PSZ2 values, but yield a significant reduction in the scatter (by a factor of two) in the $M_{500}^{\rm SZ}$-$M_{500}^{\rm X}$ relation. The slope is $0.93\pm0.03$, and the median ratio, $M_{500}^{\rm SZ}/M_{500}^{\rm X}= 0.91\pm0.01$, is within the expectations from known X-ray calibration systematics. The $Y_{\rm SZ}/Y_{\rm X}$ ratio is $0.88\pm0.02$, in good agreement with predictions from cluster structure, and implying a low level of clumpiness. In agreement with the findings of the Planck Collaboration, the slope of the $Y_{\rm SZ}$-$D_{\rm A}^{-2} Y_{X}$ flux relation is significantly less than unity ($0.89\pm0.01$). Using extensive simulations, we show that this result is not due to selection effects, intrinsic scatter, or covariance between quantities. We demonstrate analytically that changing the $Y_{\rm SZ}$-$Y_{X}$ relation from apparent flux to intrinsic properties results in a best-fit slope that is closer to unity and increases the dispersion about the relation. The redistribution resulting from this transformation implies that the best fit parameters of the $M_{500}^{\rm SZ}$-$M_{500}^{\rm X}$ relation will be sample-dependent., Comment: 28 pages, 15 figures, and 6 tables. Accepted for publication in ApJ

We present a new, ambitious survey performed with the Chandra X-ray Observatory of the 9.3 deg$^2$ Bo\"otes field of the NOAO Deep Wide-Field Survey. The wide field probes a statistically representative volume of the Universe at high redshift. The Chandra Deep Wide-Field Survey exploits the excellent sensitivity and angular resolution of Chandra over a wide area, combining 281 observations spanning 15 years, for a total exposure time of 3.4 Ms, and detects 6891 X-ray point sources down to limiting fluxes of $4.7\times10^{-16}$, $1.5\times10^{-16}$, and $9\times10^{-16}$ erg cm$^{-2}$ s$^{-1}$, in the $0.5-7$ keV, $0.5-2$ keV, and $2-7$ keV bands, respectively. The robustness and reliability of the detection strategy is validated through extensive, state-of-the-art simulations of the whole field. Accurate number counts, in good agreement with previous X-ray surveys, are derived thanks to the uniquely large number of point sources detected, which resolve $65.0 \pm 12.8\%$ of the cosmic X-ray background between $0.5-2$ keV and $81.0 \pm 11.5\%$ between $2-7$ keV. Exploiting the wealth of multi-wavelength data available on the field, we assign redshifts to $\sim 94\%$ of the X-ray sources, estimate their obscuration and derive absorption-corrected luminosities. We provide an electronic catalog containing all the relevant quantities needed for future investigations., Comment: Accepted for publication in ApJS. The catalogs associated to this paper can be accessed at http://www.dartmouth.edu/~hickox/cdwfs.php

Invited Discussion of "A Unified Framework for De-Duplication and Population Size Estimation", published in Bayesian Analysis. My discussion focuses on two main themes: Providing a more nuanced picture of the costs and benefits of joint models for record linkage and the "downstream task" (i.e. whatever we might want to do with the linked and de-duplicated files), and how we should measure performance., Comment: Full paper and discussion available at https://projecteuclid.org/download/pdfview_1/euclid.ba/1551949260

In estimating the causal effect of a continuous exposure or treatment, it is important to control for all confounding factors. However, most existing methods require parametric specification for how control variables influence the outcome or generalized propensity score, and inference on treatment effects is usually sensitive to this choice. Additionally, it is often the goal to estimate how the treatment effect varies across observed units. To address this gap, we propose a semiparametric model using Bayesian tree ensembles for estimating the causal effect of a continuous treatment of exposure which (i) does not require a priori parametric specification of the influence of control variables, and (ii) allows for identification of effect modification by pre-specified moderators. The main parametric assumption we make is that the effect of the exposure on the outcome is linear, with the steepness of this relationship determined by a nonparametric function of the moderators, and we provide heuristics to diagnose the validity of this assumption. We apply our methods to revisit a 2001 study of how abortion rates affect incidence of crime.

Factor Analysis is a popular method for modeling dependence in multivariate data. However, determining the number of factors and obtaining a sparse orientation of the loadings are still major challenges. In this paper, we propose a decision-theoretic approach that brings to light the relation between a sparse representation of the loadings and factor dimension. This relation is done through a summary from information contained in the multivariate posterior. To construct such summary, we introduce a three-step approach. In the first step, the model is fitted with a conservative factor dimension. In the second step, a series of sparse point-estimates, with a decreasing number of factors, is obtained by minimizing an expected predictive loss function. In step three, the degradation in utility in relation to the sparse loadings and factor dimensions is displayed in the posterior summary. The findings are illustrated with applications in classical data from the Factor Analysis literature. We used different prior choices and factor dimensions to demonstrate the flexibility of the proposed method., Comment: 22 pages, 7 figures

We present a Bayesian nonparametric model for conditional distribution estimation using Bayesian additive regression trees (BART). The generative model we use is based on rejection sampling from a base model. Typical of BART models, our model is flexible, has a default prior specification, and is computationally convenient. To address the distinguished role of the response in the BART model we propose, we further introduce an approach to targeted smoothing which is possibly of independent interest for BART models. We study the proposed model theoretically and provide sufficient conditions for the posterior distribution to concentrate at close to the minimax optimal rate adaptively over smoothness classes in the high-dimensional regime in which many predictors are irrelevant. To fit our model we propose a data augmentation algorithm which allows for existing BART samplers to be extended with minimal effort. We illustrate the performance of our methodology on simulated data and use it to study the relationship between education and body mass index using data from the medical expenditure panel survey (MEPS)., Comment: 5 Figures, 1 Table