Three-dimensional geometric morphometric (3DGM) methods for placing landmarks on digitized bones have become increasingly sophisticated in the last 20 years, including greater degrees of automation. One aspect shared by all 3DGM methods is that the researcher must designate initial landmarks. Thus, researcher interpretations of homology and correspondence are required for and influence representations of shape. We present an algorithm allowing fully automatic placement of correspondence points on samples of 3D digital models representing bones of different individuals/species, which can then be input into standard 3DGM software and analyzed with dimension reduction techniques. We test this algorithm against several samples, primarily a dataset of 106 primate calcanei represented by 1,024 correspondence points per bone. Results of our automated analysis of these samples are compared to a published study using a traditional 3DGM approach with 27 landmarks on each bone. Data were analyzed with morphologika 2.5 and PAST. Our analyses returned strong correlations between principal component scores, similar variance partitioning among components, and similarities between the shape spaces generated by the automatic and traditional methods. While cluster analyses of both automatically generated and traditional datasets produced broadly similar patterns, there were also differences. Overall these results suggest to us that automatic quantifications can lead to shape spaces that are as meaningful as those based on observer landmarks, thereby presenting potential to save time in data collection, increase completeness of morphological quantification, eliminate observer error, and allow comparisons of shape diversity between different types of bones. We provide an R package for implementing this analysis. Anat Rec,

How the brain preserves information about multiple simultaneous items is poorly understood. We report that single neurons can represent multiple stimuli by interleaving signals across time. We record single units in an auditory region, the inferior colliculus, while monkeys localize 1 or 2 simultaneous sounds. During dual-sound trials, we find that some neurons fluctuate between firing rates observed for each single sound, either on a whole-trial or on a sub-trial timescale. These fluctuations are correlated in pairs of neurons, can be predicted by the state of local field potentials prior to sound onset, and, in one monkey, can predict which sound will be reported first. We find corroborating evidence of fluctuating activity patterns in a separate dataset involving responses of inferotemporal cortex neurons to multiple visual stimuli. Alternation between activity patterns corresponding to each of multiple items may therefore be a general strategy to enhance the brain processing capacity, potentially linking such disparate phenomena as variable neural firing, neural oscillations, and limits in attentional/memory capacity.

In dynamic topic modeling, the proportional contribution of a topic to a document depends on the temporal dynamics of that topic's overall prevalence in the corpus. We extend the Dynamic Topic Model of Blei and Lafferty (2006) by explicitly modeling document-level topic proportions with covariates and dynamic structure that includes polynomial trends and periodicity. A Markov Chain Monte Carlo (MCMC) algorithm that utilizes Polya-Gamma data augmentation is developed for posterior inference. Conditional independencies in the model and sampling are made explicit, and our MCMC algorithm is parallelized where possible to allow for inference in large corpora. To address computational bottlenecks associated with Polya-Gamma sampling, we appeal to the Central Limit Theorem to develop a Gaussian approximation to the Polya-Gamma random variable. This approximation is fast and reliable for parameter values relevant in the text-mining domain. Our model and inference algorithm are validated with multiple simulation examples, and we consider the application of modeling trends in PubMed abstracts. We demonstrate that sharing information across documents is critical for accurately estimating document-specific topic proportions. We also show that explicitly modeling polynomial and periodic behavior improves our ability to predict topic prevalence at future time points.

The relationship between housing costs and homelessness has important implications for the way that city and county governments respond to increasing homeless populations. Though many analyses in the public policy literature have examined inter-community variation in homelessness rates to identify causal mechanisms of homelessness Lee et al., 2003;Fargo et al., 2013), few studies have examined time-varying homeless counts within the same community (McCandless et al., 2016). To examine trends in homeless population counts in the 25 largest U.S. metropolitan areas, we develop a dynamic Bayesian hierarchical model for time-varying homeless count data. Particular care is given to modeling uncertainty in the homeless count generating and measurement processes, and a critical distinction is made between the counted number of homeless and the true size of the homeless population. For each metro under study, we investigate the relationship between increases in the Zillow Rent Index and increases in the homeless population. Sensitivity of inference to potential improvements in the accuracy of point-in-time counts is explored, and evidence is presented that the inferred increase in the rate of homelessness from 2011-2016 depends on prior beliefs about the accuracy of homeless counts. A main finding of the study is that the relationship between homelessness and rental costs is strongest in New York,

Morphometric datasets only convey useful information about variation when measurement landmarks and relevant anatomical axes are clearly defined. We propose that anatomical axes of 3D digital models of bones can be standardized prior to measurement using an algorithm that automatically finds a universal geometric alignment among sampled bones. As a case study, we use teeth of "prosimian" primates. In this sample, equivalent occlusal planes are determined automatically using the R-package auto3dgm. The area of projection into the occlusal plane for each tooth is the measurement of interest. This area is used in computation of a shape metric called relief index (RFI), the natural log of the square root of crown area divided by the square root of occlusal plane projection area. We compare mean and variance parameters of area and RFI values computed from these automatically orientated tooth models with values computed from manually orientated tooth models. According to our results, the manual and automated approaches yield extremely similar mean and variance parameters. The only differences that plausibly modify interpretations of biological meaning slightly favor the automated treatment because a greater proportion of differences among subsamples in the automated treatment are correlated with dietary differences. We conclude that-at least for dental topographic metrics-automated alignment recovers a variance pattern that has meaning similar to previously published datasets based on manual data collection. Therefore, future applications of dental topography can take advantage of automatic alignment to increase objectivity and repeatability.

No abstract

No abstract

House price indices (HPIs) are statistical measures of real estate price dynamics in defined geographic regions over defined periods of time. HPIs are important metrics that help policymakers, mortgage lenders, real estate investors, and bank regulators monitor market conditions and manage risk. HPIs that are local, reliable, and timely are essential in understanding connections between housing markets and the broader economy. In this article, we examine the algorithmic construction of Zillow's Home Value Index (ZHVI), an HPI built on black box machine learning algorithms. To provide deeper statistical insight into ZHVI than afforded by its black box construction, we develop a Bayesian generative meta-model that approximates the black box construction of ZHVI series in 100 metropolitan areas (metros). Each ZHVI series is modeled with a global trend, a finite mixture of Gaussian processes, and a local component. We find that there are three shared dynamic patterns across the 100 markets in our analysis, and we utilize this shared latent structure to forecast ZHVI in each metro 12 months ahead. Our clustering strategy has two advantages: (i) it allows us to construct composite HPIs where member metros are learned from the data rather than predetermined; and (ii) it allows us to estimate the relative contributions of cluster-level and metro-specific components to a metro's ZHVI, providing a novel statistical attribution of real estate market dynamics.