Sexual dimorphism in the loud calls of Azara’s owl monkeys (Aotus azarae): evidence of sexual selection?

Primates use different types of vocalizations in a variety of contexts. Some of the most studied types have been the long distance or loud calls. These vocalizations have been associated with mate defense, mate attraction, and resource defense, and it is plausible that sexual selection has played an important role in their evolution. Focusing on identified individuals of known sex and age, we evaluated the sexual dimorphism in a type of loud calls (hoots) in a population of wild owl monkeys (Aotus azarae) in Argentina. We found evidence of sexual dimorphism in call structure, with females and males only emitting one type of call, each differing in dominant frequency and Shannon entropy. In addition, both age-related and sex-specific differences in call usage were also apparent in response to the removal of one group member. Future acoustic data will allow us to assess if there are individual characteristics and if the structure of hoot calls presents differences in relation to the social condition of owl monkeys or specific sex responses to variants of hoot calls’ traits. This will provide deeper insights into the evolution of vocal mechanisms regulating pair bonding and mate choice strategies in this and other primate species.Leakey Foundation, Wenner-Gren Foundation, National Geographic Society, NSF, National Institute on Aging, University of Pennsylvania Research Foundation, Zoological Society of San Dieg

Process-oriented Iterative Multiple Alignment for Medical Process Mining

Adapted from biological sequence alignment, trace alignment is a process mining technique used to visualize and analyze workflow data. Any analysis done with this method, however, is affected by the alignment quality. The best existing trace alignment techniques use progressive guide-trees to heuristically approximate the optimal alignment in O(N2L2) time. These algorithms are heavily dependent on the selected guide-tree metric, often return sum-of-pairs-score-reducing errors that interfere with interpretation, and are computationally intensive for large datasets. To alleviate these issues, we propose process-oriented iterative multiple alignment (PIMA), which contains specialized optimizations to better handle workflow data. We demonstrate that PIMA is a flexible framework capable of achieving better sum-of-pairs score than existing trace alignment algorithms in only O(NL2) time. We applied PIMA to analyzing medical workflow data, showing how iterative alignment can better represent the data and facilitate the extraction of insights from data visualization.Comment: accepted at ICDMW 201

Automated census record linking: a machine learning approach

Thanks to the availability of new historical census sources and advances in record linking technology, economic historians are becoming big data genealogists. Linking individuals over time and between databases has opened up new avenues for research into intergenerational mobility, assimilation, discrimination, and the returns to education. To take advantage of these new research opportunities, scholars need to be able to accurately and efficiently match historical records and produce an unbiased dataset of links for downstream analysis. I detail a standard and transparent census matching technique for constructing linked samples that can be replicated across a variety of cases. The procedure applies insights from machine learning classification and text comparison to the well known problem of record linkage, but with a focus on the sorts of costs and benefits of working with historical data. I begin by extracting a subset of possible matches for each record, and then use training data to tune a matching algorithm that attempts to minimize both false positives and false negatives, taking into account the inherent noise in historical records. To make the procedure precise, I trace its application to an example from my own work, linking children from the 1915 Iowa State Census to their adult-selves in the 1940 Federal Census. In addition, I provide guidance on a number of practical questions, including how large the training data needs to be relative to the sample.This research has been supported by the NSF-IGERT Multidisciplinary Program in Inequality & Social Policy at Harvard University (Grant No. 0333403)

Modeling the Effects of Peremptory Challenges on Jury Selection and Jury Verdicts

Although proponents argue that peremptory challenges make juries more impartial by eliminating “extreme” jurors, studies testing this theory are rare and inconclusive. For this article, two formal models of jury selection are constructed, and various selection procedures are tested, assuming that attorneys act rationally rather than discriminate based on animus. The models demonstrate that even when used rationally, peremptory challenges can distort jury decision making and undermine verdict reliability. Peremptory challenges systematically shift jurors toward the majority view of the population by favoring median jurors over extreme jurors. If the population of potential jurors is skewed in favor of conviction - as empirical evidence suggests is usually the case - then peremptory challenges have the unexpected result of making convictions more likely, rather than promoting reasoned deliberation without prejudice to the result. This is troubling when jurisdictions almost universally award more peremptory challenges in trials involving the most serious crimes. And this effect is magnified when attorneys have more complete information about jurors, suggesting the problem may become worse in the future. Moreover, juries selected with more peremptory challenges become more ideologically and demographically homogenous, even when attorneys do not engage in discrimination, reducing the accuracy of jury verdicts. Although this second effect has been seen empirically, the results of the models suggest that it is an inevitable result of the peremptory challenge process rather than an effect of discrimination by attorney

Ranking and Selection under Input Uncertainty: Fixed Confidence and Fixed Budget

In stochastic simulation, input uncertainty (IU) is caused by the error in estimating the input distributions using finite real-world data. When it comes to simulation-based Ranking and Selection (R&S), ignoring IU could lead to the failure of many existing selection procedures. In this paper, we study R&S under IU by allowing the possibility of acquiring additional data. Two classical R&S formulations are extended to account for IU: (i) for fixed confidence, we consider when data arrive sequentially so that IU can be reduced over time; (ii) for fixed budget, a joint budget is assumed to be available for both collecting input data and running simulations. New procedures are proposed for each formulation using the frameworks of Sequential Elimination and Optimal Computing Budget Allocation, with theoretical guarantees provided accordingly (e.g., upper bound on the expected running time and finite-sample bound on the probability of false selection). Numerical results demonstrate the effectiveness of our procedures through a multi-stage production-inventory problem

How do Pasifika students reason about probability? Some findings from Fiji.

This paper reports on data from a large study which explored form five (14 to 16 years old) students' ideas in statistics. The study focused on descriptive statistics, graphical representations and probability. This paper discusses the ways in which students made sense of probability questions obtained from the individual interviews. The findings revealed that many of the students used strategies based on prior experiences (beliefs, cultural and school experiences) and intuitive strategies. From the analysis, I identified a four-category rubric that could be considered for describing how students construct meanings for statistics tasks. While the results of the study confirm a number of findings of other researchers, the findings go beyond those discussed in the literature. The use of beliefs and everyday and school experiences was considerably more common than that discussed in literature. The paper concludes by suggesting some implications for teachers and researchers