Gauging possibilities for action based on friction underfoot.

Standing and walking generate information about friction underfoot. Five experiments examined whether walkers use such perceptual information for prospective control of locomotion. In particular, do walkers integrate information about friction underfoot with visual cues for sloping ground ahead to make adaptive locomotor decisions? Participants stood on low-, medium-, and high-friction surfaces on a flat platform and made perceptual judgments for possibilities for locomotion over upcoming slopes. Perceptual judgments did not match locomotor abilities: Participants tended to overestimate their abilities on low-friction slopes and underestimate on high-friction slopes (Experiments 1-4). Accuracy improved only for judgments made while participants were in direct contact with the slope (Experiment 5), highlighting the difficulty of incorporating information about friction underfoot into a plan for future actions.

The IGNITE network: a model for genomic medicine implementation and research.

BACKGROUND: Patients, clinicians, researchers and payers are seeking to understand the value of using genomic information (as reflected by genotyping, sequencing, family history or other data) to inform clinical decision-making. However, challenges exist to widespread clinical implementation of genomic medicine, a prerequisite for developing evidence of its real-world utility. METHODS: To address these challenges, the National Institutes of Health-funded IGNITE (Implementing GeNomics In pracTicE; www.ignite-genomics.org ) Network, comprised of six projects and a coordinating center, was established in 2013 to support the development, investigation and dissemination of genomic medicine practice models that seamlessly integrate genomic data into the electronic health record and that deploy tools for point of care decision making. IGNITE site projects are aligned in their purpose of testing these models, but individual projects vary in scope and design, including exploring genetic markers for disease risk prediction and prevention, developing tools for using family history data, incorporating pharmacogenomic data into clinical care, refining disease diagnosis using sequence-based mutation discovery, and creating novel educational approaches. RESULTS: This paper describes the IGNITE Network and member projects, including network structure, collaborative initiatives, clinical decision support strategies, methods for return of genomic test results, and educational initiatives for patients and providers. Clinical and outcomes data from individual sites and network-wide projects are anticipated to begin being published over the next few years. CONCLUSIONS: The IGNITE Network is an innovative series of projects and pilot demonstrations aiming to enhance translation of validated actionable genomic information into clinical settings and develop and use measures of outcome in response to genome-based clinical interventions using a pragmatic framework to provide early data and proofs of concept on the utility of these interventions. Through these efforts and collaboration with other stakeholders, IGNITE is poised to have a significant impact on the acceleration of genomic information into medical practice.

Influence of network topology and data collection on network inference.

We recently developed an approach for testing the accuracy of network inference algorithms by applying them to biologically realistic simulations with known network topology. Here, we seek to determine the degree to which the network topology and data sampling regime influence the ability of our Bayesian network inference algorithm, NETWORKINFERENCE, to recover gene regulatory networks. NETWORKINFERENCE performed well at recovering feedback loops and multiple targets of a regulator with small amounts of data, but required more data to recover multiple regulators of a gene. When collecting the same number of data samples at different intervals from the system, the best recovery was produced by sampling intervals long enough such that sampling covered propagation of regulation through the network but not so long such that intervals missed internal dynamics. These results further elucidate the possibilities and limitations of network inference based on biological data.

Algorithms for Geometric Matching, Clustering, and Covering

With the popularization of GPS-enabled devices such as mobile phones, location data are becoming available at an unprecedented scale. The locations may be collected from many different sources such as vehicles moving around a city, user check-ins in social networks, and geo-tagged micro-blogging photos or messages. Besides the longitude and latitude, each location record may also have a timestamp and additional information such as the name of the location. Time-ordered sequences of these locations form trajectories, which together contain useful high-level information about people's movement patterns.

The first part of this thesis focuses on a few geometric problems motivated by the matching and clustering of trajectories. We first give a new algorithm for computing a matching between a pair of curves under existing models such as dynamic time warping (DTW). The algorithm is more efficient than standard dynamic programming algorithms both theoretically and practically. We then propose a new matching model for trajectories that avoids the drawbacks of existing models. For trajectory clustering, we present an algorithm that computes clusters of subtrajectories, which correspond to common movement patterns. We also consider trajectories of check-ins, and propose a statistical generative model, which identifies check-in clusters as well as the transition patterns between the clusters.

The second part of the thesis considers the problem of covering shortest paths in a road network, motivated by an EV charging station placement problem. More specifically, a subset of vertices in the road network are selected to place charging stations so that every shortest path contains enough charging stations and can be traveled by an EV without draining the battery. We first introduce a general technique for the geometric set cover problem. This technique leads to near-linear-time approximation algorithms, which are the state-of-the-art algorithms for this problem in either running time or approximation ratio. We then use this technique to develop a near-linear-time algorithm for this

shortest-path cover problem.

Socio-economic status and malaria-related outcomes in Mvomero District, Tanzania.

While policies often target malaria prevention and treatment - proximal causes of malaria and related health outcomes - too little attention has been given to the role of household- and individual-level socio-economic status (SES) as a fundamental cause of disease risk in developing countries. This paper presents a conceptual model outlining ways in which SES may influence malaria-related outcomes. Building on this conceptual model, we use household data from rural Mvomero, Tanzania, to examine empirical relationships among multiple measures of household and individual SES and demographics, on the one hand, and malaria prevention, illness, and diagnosis and treatment behaviours, on the other. We find that access to prevention and treatment is significantly associated with indicators of households' wealth; education-based disparities do not emerge in this context. Meanwhile, reported malaria illness shows a stronger association with demographic variables than with SES (controlling for prevention). Greater understanding of the mechanisms through which SES and malaria policies interact to influence disease risk can help to reduce health disparities and reduce the malaria burden in an equitable manner.

Toddlers' prosocial behavior: from instrumental to empathic to altruistic helping.

The study explored how the meaning of prosocial behavior changes over toddlerhood. Sixty-five 18- and 30-month-olds could help an adult in 3 contexts: instrumental (action based), empathic (emotion based), and altruistic (costly). Children at both ages helped readily in instrumental tasks. For 18-month-olds, empathic helping was significantly more difficult than instrumental helping and required greater communication from the adult about her needs. Altruistic helping, which involved giving up an object of the child's own, was the most difficult for children at both ages. Findings suggest that over the 2nd year of life, prosocial behavior develops from relying on action understanding and explicit communications to understanding others' emotions from subtle cues. Developmental trajectories of social-cognitive and motivational components of early helping are discussed.

A Theory and Test of How Speakers with Nonnative Accents are Evaluated in Entrepreneurial Settings

An abundance of research in the social sciences has demonstrated a persistent bias against nonnative English speakers (Giles & Billings, 2004; Gluszek & Dovidio, 2010). Yet, organizational scholars have only begun to investigate the underlying mechanisms that drive the bias against nonnative speakers and subsequently design interventions to mitigate these biases. In this dissertation, I offer an integrative model to organize past explanations for accent-based bias into a coherent framework, and posit that nonnative accents elicit social perceptions that have implications at the personal, relational, and group level. I also seek to complement the existing emphasis on main effects of accents, which focuses on the general tendency to discriminate against those with accents, by examining moderators that shed light on the conditions under which accent-based bias is most likely to occur. Specifically, I explore the idea that people’s beliefs about the controllability of accents can moderate their evaluations toward nonnative speakers, such that those who believe that accents can be controlled are more likely to demonstrate a bias against nonnative speakers. I empirically test my theoretical model in three studies in the context of entrepreneurial funding decisions. Results generally supported the proposed model. By examining the micro foundations of accent-based bias, the ideas explored in this dissertation set the stage for future research in an increasingly multilingual world.