Embedding Population Dynamics Models in Inference

Increasing pressures on the environment are generating an ever-increasing need to manage animal and plant populations sustainably, and to protect and rebuild endangered populations. Effective management requires reliable mathematical models, so that the effects of management action can be predicted, and the uncertainty in these predictions quantified. These models must be able to predict the response of populations to anthropogenic change, while handling the major sources of uncertainty. We describe a simple ``building block'' approach to formulating discrete-time models. We show how to estimate the parameters of such models from time series of data, and how to quantify uncertainty in those estimates and in numbers of individuals of different types in populations, using computer-intensive Bayesian methods. We also discuss advantages and pitfalls of the approach, and give an example using the British grey seal population.Comment: Published at http://dx.doi.org/10.1214/088342306000000673 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Tracking Cell Signals in Fluorescent Images

In this paper we present the techniques for tracking cell signal in GFP (Green Fluorescent Protein) images of growing cell colonies. We use such tracking for both data extraction and dynamic modeling of intracellular processes. The techniques are based on optimization of energy functions, which simultaneously determines cell correspondences, while estimating the mapping functions. In addition to spatial mappings such as affine and Thin-Plate Spline mapping, the cell growth and cell division histories must be estimated as well. Different levels of joint optimization are discussed. The most unusual tracking feature addressed in this paper is the possibility of one-to-two correspondences caused by cell division. A novel extended softassign algorithm for solutions of one-to-many correspondences is detailed in this paper. The techniques are demonstrated on three sets of data: growing bacillus Subtillus and e-coli colonies and a developing plant shoot apical meristem. The techniques are currently used by biologists for data extraction and hypothesis formation

Pilot interaction with automated airborne decision making systems

An investigation was made of interaction between a human pilot and automated on-board decision making systems. Research was initiated on the topic of pilot problem solving in automated and semi-automated flight management systems and attempts were made to develop a model of human decision making in a multi-task situation. A study was made of allocation of responsibility between human and computer, and discussed were various pilot performance parameters with varying degrees of automation. Optimal allocation of responsibility between human and computer was considered and some theoretical results found in the literature were presented. The pilot as a problem solver was discussed. Finally the design of displays, controls, procedures, and computer aids for problem solving tasks in automated and semi-automated systems was considered

A multi-objective sequential stochastic assignment problem for Ebola entry screening

The 2014 Ebola outbreak in West Africa prompted a need to assess how deplaning passengers from West Africa should be managed. A 21-day quarantine requirement for deplaning passengers, based on their risk factors, was implemented at five international airports in the United States in late 2014. This thesis formulates the multi-objective sequential stochastic assignment problem (MOSSAP) to improve the process for managing such quarantine assignments. In MOSSAP, each passenger is assessed with a two-dimensional risk vector, revealed upon entering the United States, which is used to make the quarantine assignment. The objective is to maximize the expected number of passengers assigned to the correct level of monitoring (quarantine, self-monitoring), subject to quarantine capacity constraint. The weighted sum method is used to generate Pareto optimal policies for MOSSAP. Statistics available from Ebola entry screening and related public health sources are used to illustrate how such a policy would operate in practice

Operational Research in Education

Operational Research (OR) techniques have been applied, from the early stages of the discipline, to a wide variety of issues in education. At the government level, these include questions of what resources should be allocated to education as a whole and how these should be divided amongst the individual sectors of education and the institutions within the sectors. Another pertinent issue concerns the efficient operation of institutions, how to measure it, and whether resource allocation can be used to incentivise efficiency savings. Local governments, as well as being concerned with issues of resource allocation, may also need to make decisions regarding, for example, the creation and location of new institutions or closure of existing ones, as well as the day-to-day logistics of getting pupils to schools. Issues of concern for managers within schools and colleges include allocating the budgets, scheduling lessons and the assignment of students to courses. This survey provides an overview of the diverse problems faced by government, managers and consumers of education, and the OR techniques which have typically been applied in an effort to improve operations and provide solutions

A sticky HDP-HMM with application to speaker diarization

We consider the problem of speaker diarization, the problem of segmenting an audio recording of a meeting into temporal segments corresponding to individual speakers. The problem is rendered particularly difficult by the fact that we are not allowed to assume knowledge of the number of people participating in the meeting. To address this problem, we take a Bayesian nonparametric approach to speaker diarization that builds on the hierarchical Dirichlet process hidden Markov model (HDP-HMM) of Teh et al. [J. Amer. Statist. Assoc. 101 (2006) 1566--1581]. Although the basic HDP-HMM tends to over-segment the audio data---creating redundant states and rapidly switching among them---we describe an augmented HDP-HMM that provides effective control over the switching rate. We also show that this augmentation makes it possible to treat emission distributions nonparametrically. To scale the resulting architecture to realistic diarization problems, we develop a sampling algorithm that employs a truncated approximation of the Dirichlet process to jointly resample the full state sequence, greatly improving mixing rates. Working with a benchmark NIST data set, we show that our Bayesian nonparametric architecture yields state-of-the-art speaker diarization results.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS395 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Optimal Job Design and Career Dynamics in the Presence of Uncertainty

The paper studies a learning model in which information about a worker's ability can be acquired symmetrically by the worker and a firm in any period by observing the worker's performance on a given task. Productivity at different tasks is assumed to be differentially sensitive to a worker's intrinsic talent: potentially more profitable tasks entail the risk of greater output destruction if the worker assigned to them is not of the ability required. We characterize the (essentially unique) optimal retention, task assignment and promotion policy for the class of sequential equilibria of this game, by showing that the equilibria of interest are strategically equivalent to the solution of an experimentation problem (a discounted multi-armed bandit with independent and dependent arms). These equilibria are all ex ante efficient but involve ex post inefficient task allocation and separation. While the ex post inefficiency of separations persists even as the time horizon becomes arbitrarily large, in the limit task assignment is efficient. When ability consists of multiple skills, low performing promoted workers are fired rather than demoted, if outcomes at lower level tasks, compared to those at higher level tasks, provide a sufficiently accurate measure of ability. We then examine the strategic effects of the dynamics of learning on a worker's career profile. We prove, in particular, that price competition among firms causes ex ante inefficient turnover and task assignment, independently of the degree of transferability of human capital. In a class of equilibria of interest it generates a wage dynamics consistent with properties observed in the dataLearning, Job Assignment, Experimentation, Correlated Multi-armed Bandit