Efficient Candidate Screening Under Multiple Tests and Implications for Fairness

When recruiting job candidates, employers rarely observe their underlying skill level directly. Instead, they must administer a series of interviews and/or collate other noisy signals in order to estimate the worker's skill. Traditional economics papers address screening models where employers access worker skill via a single noisy signal. In this paper, we extend this theoretical analysis to a multi-test setting, considering both Bernoulli and Gaussian models. We analyze the optimal employer policy both when the employer sets a fixed number of tests per candidate and when the employer can set a dynamic policy, assigning further tests adaptively based on results from the previous tests. To start, we characterize the optimal policy when employees constitute a single group, demonstrating some interesting trade-offs. Subsequently, we address the multi-group setting, demonstrating that when the noise levels vary across groups, a fundamental impossibility emerges whereby we cannot administer the same number of tests, subject candidates to the same decision rule, and yet realize the same outcomes in both groups

EXPERIMENTAL CHARACTERIZATION OF SPOOL VALVE FLOW NOISE

The purpose of this research was to experimentally measure the flow noise produced by a spool valve and compare with measurements of orifice flow noise to reduce flow noise modelling complexity. The similarities and differences are compared over a range of volume flow rates, back pressures, and cross section areas. The impact of down stream boundary conditions on the flow noise are also examined. The results are generalized and plotted against Froude number. The valve results are curve fit to generate empirical equations that can be used to predict flow noise under typical operating conditions. The valve and orifice data sets converge in certain regions, and conclusions are drawn for when valve flow noise can be modeled as orifice flow noise in the hydraulic circuit design stage

Noise Reduction with Microphone Arrays for Speaker Identification

The presence of acoustic noise in audio recordings is an ongoing issue that plagues many applications. This ambient background noise is difficult to reduce due to its unpredictable nature. Many single channel noise reduction techniques exist but are limited in that they may distort the desired speech signal due to overlapping spectral content of the speech and noise. It is therefore of interest to investigate the use of multichannel noise reduction algorithms to further attenuate noise while attempting to preserve the speech signal of interest. Specifically, this thesis looks to investigate the use of microphone arrays in conjunction with multichannel noise reduction algorithms to aid aiding in speaker identification. Recording a speaker in the presence of acoustic background noise ultimately limits the performance and confidence of speaker identification algorithms. In situations where it is impossible to control the noise environment where the speech sample is taken, noise reduction algorithms must be developed and applied to clean the speech signal in order to give speaker identification software a chance at a positive identification. Due to the limitations of single channel techniques, it is of interest to see if spatial information provided by microphone arrays can be exploited to aid in speaker identification. This thesis provides an exploration of several time domain multichannel noise reduction techniques including delay sum beamforming, multi-channel Wiener filtering, and Spatial-Temporal Prediction filtering. Each algorithm is prototyped and filter performance is evaluated using various simulations and experiments. A three-dimensional noise model is developed to simulate and compare the performance of the above methods and experimental results of three data collections are presented and analyzed. The algorithms are compared and recommendations are given for the use of each technique. Finally, ideas for future work are discussed to improve performance and implementation of these multichannel algorithms. Possible applications for this technology include audio surveillance, identity verification, video chatting, conference calling and sound source localization

Treatment Selection: Understanding What Works For Whom In Mental Health

Individuals seeking treatment for mental health problems often have to choose between several different treatment options. For disorders like depression and PTSD, many of the available treatments have been found to be, on average, equally effective. Research on precision medicine aims to identify the most effective treatment for each patient. This work is based on the idea that individuals respond differently to treatment, and that these differences can be studied and characterized. The push for personalized and precision approaches in mental health involves identifying moderators - variables that predict differential response into treatment recommendations. Unfortunately, there has been little real-world application of these findings, in part due to the lack of systems suited to translating the information in actionable recommendations. This dissertation will review the history of treatment selection in mental health, and will present specific examples of treatment selection models in depression and PTSD. Differences between treatment selection in the context of two equivalently effective interventions and stratified medicine applications in which goal is to optimize the allocation of stronger and weaker interventions will be discussed. Methodological challenges in building (e.g., variable selection) and evaluating (e.g., cross-validation) treatment selection systems will be explored. Approaches to precision medicine being used by different groups will be compared. Finally, recommendations for future directions will be made

Leptinotarsa decemlineata (Coleoptera: Chrysomelidae) Observed Feeding on Chamaesaracha sp. in Eastern Colorado.

Egg, larval, and adult life stages of Colorado potato beetle, Leptinotarsa decemlineata (Say), were observed feeding on or attached to a previously undocumented host plant belonging to the genus Chamaesaracha in eastern Colorado on July 2017. At one site, L. decemlineata were more abundant on Chamaesaracha sp. than the accepted ancestral host plant, Solanum rostratum (Dunal). While future studies should confirm the ancestral status of the observed L. decemlineata and suitability of Chamaesaracha sp. for completion of development, our observations suggest a need for further characterization of the ancestral host range of L. decemlineata