Master of Science

thesisRacial and ethnic disparities in health behaviors have been well observed in the United States. Among the individual mechanisms, socioeconomic status (SES) and acculturation seem to have substantive impact, while such impact is not consistent in existing literature and has been particularly understudied across ethnic subgroups. This study aims to examine patterns and mechanisms of racial/ethnic disparities in leisure time physical activity (LTPA) across Whites, Blacks, and major Latino and Asian subgroups. Using cross-sectional data from the 2007 California Health Interview Survey, I examine to what extent racial/ethnic disparities in adults' participation of LTPA exist. I also examine how individual predictors of SES and acculturation, particularly household income, educational attainment, citizenship status, duration in the U.S., and English proficiency, mediate for such disparities. Results confirm that racial/ethnic minorities are generally less likely than Whites to meet the recommended LTPA level, while heterogeneity is also evident across Latino and Asian ethnicities. Blacks, Mexicans, Salvadorans, Guatemalans, and all major Asian ethnicities except Japanese are shown to be significantly less likely for LTPA. Moreover, although educational attainment and duration in the U.S. are shown as significant predictors, the effects of SES and acculturation vary across minority groups. SES seems to be an important mediator for blacks and Latinos, while acculturation seems important for Latinos and Asians. However, most of the group disparities remain unexplained, and further study may need to focus on other potential mediators such as neighborhood and environmental factors

Doctor of Philosophy

dissertationThis dissertation builds upon current debates on the detrimental versus protective effects of racial/ethnic residential isolation and immigrant concentration on health in the United States (US), and empirically examines: (1) whether neighborhood-level ethnic density, racial diversity, and immigrant concentration are negatively or positively associated with health risks; (2) for whom and under what conditions are these residential patterns health-detrimental or health-protective; (3) what are the structural and psychosocial pathways underlying the detrimental or protective effects of these residential patterns; (4) whether neighborhood influences are susceptible to sample selection bias. Individual-level data from the 2003-2008 National Health and Nutrition Examination Survey and the 2006 and 2008 Southeastern Pennsylvania Household Health Survey were merged with census-tract profiles obtained from the 2005-2009 American Community Survey estimates, the 2000 Decennial Census and Geographic Information System-based built-environment data. Multilevel analysis, mediation analysis, and Propensity Score Matching method were performed to answer these research questions. Results largely confirmed the salient impact of neighborhood racial/ethnic context on individual health risks, while selection bias was also evident. Neighborhood racial isolation and ethnic concentration showed detrimental health effects, whereas racial diversity showed positive effects. The observed effect of immigrant concentration was likely due to neighborhood selection bias. Effect modification and underlying pathways were complex and were dependent on the specific neighborhood contextual predictors

Residential racial composition and black-white obesity risks: differential effects of neighborhood social and built environment

pre-printThis study investigates the association between neighborhood racial composition and adult obesity risks by race and gender, and explores whether neighborhood social and built environment mediates the observed protective or detrimental effects of racial composition on obesity risks. Cross-sectional data from the 2006 and 2008 Southeastern Pennsylvania Household Health Survey are merged with census-tract profiles from 2005-2009 American Community Survey and Geographic Information System-based built-environment data. The analytical sample includes 12,730 whites and 4,290 blacks residing in 953 census tracts. Results from multilevel analysis suggest that black concentration is associated with higher obesity risks only for white women, and this association is mediated by lower neighborhood social cohesion and socioeconomic status (SES) in black-concentrated neighborhoods. After controlling for neighborhood SES, black concentration and street connectivity are associated with lower obesity risks for white men. No association between black concentration and obesity is found for blacks. The findings point to the intersections of race and gender in neighborhood effects on obesity risks, and highlight the importance of various aspects of neighborhood social and built environment and their complex roles in obesity prevention by socio-demographic groups

Immersive Demonstrations are the Key to Imitation Learning

Achieving successful robotic manipulation is an essential step towards robots being widely used in industry and home settings. Recently, many learning-based methods have been proposed to tackle this challenge, with imitation learning showing great promise. However, imperfect demonstrations and a lack of feedback from teleoperation systems may lead to poor or even unsafe results. In this work we explore the effect of demonstrator force feedback on imitation learning, using a feedback glove and a robot arm to render fingertip-level and palm-level forces, respectively. 10 participants recorded 5 demonstrations of a pick-and-place task with 3 grippers, under conditions with no force feedback, fingertip force feedback, and fingertip and palm force feedback. Results show that force feedback significantly reduces demonstrator fingertip and palm forces, leads to a lower variation in demonstrator forces, and recorded trajectories that a quicker to execute. Using behavioral cloning, we find that agents trained to imitate these trajectories mirror these benefits, even though agents have no force data shown to them during training. We conclude that immersive demonstrations, achieved with force feedback, may be the key to unlocking safer, quicker to execute dexterous manipulation policies.Comment: This paper is accepted to be presented on IEEE International Conference on Robotics and Automation (ICRA) 202

WIC Participation and Breastfeeding After the 2009 WIC Revision: A Propensity Score Approach

In this study, we examined the association between participation in the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) and breastfeeding outcomes before and after the 2009 revisions. Four-thousand-three-hundred-and-eight WIC-eligible children younger than 60 months were included from the 2005-2014 National Health and Nutrition Examination Survey (NHANES). We compared two birth cohorts with regard to their associations between WIC participation and being ever-breastfed and breastfed at 6 months. We estimated the average effect of the treatment for the treated to assess the causal effect of WIC participation on breastfeeding based on propensity score matching. The results showed that WIC-eligible participating children born between 2000 and 2008 were significantly less likely than WIC-eligible nonparticipating children to ever receive breastfeeding (p \u3c 0.05) or to be breastfed at 6 months (p \u3c 0.05). Among children born between 2009 and 2014, WIC-eligible participating children were no longer less likely to ever receive breastfeeding compared to WIC-eligible nonparticipating children; the gap remained in breastfeeding at 6-months (p \u3c 0.05). The disparities in prevalence of ever breastfed between WIC-eligible participants and nonparticipants have been eliminated since the 2009 WIC revision. More efforts are needed to improve breastfeeding persistence among WIC-participating mother-infant dyads

Evolutionary Curriculum Training for DRL-Based Navigation Systems

In recent years, Deep Reinforcement Learning (DRL) has emerged as a promising method for robot collision avoidance. However, such DRL models often come with limitations, such as adapting effectively to structured environments containing various pedestrians. In order to solve this difficulty, previous research has attempted a few approaches, including training an end-to-end solution by integrating a waypoint planner with DRL and developing a multimodal solution to mitigate the drawbacks of the DRL model. However, these approaches have encountered several issues, including slow training times, scalability challenges, and poor coordination among different models. To address these challenges, this paper introduces a novel approach called evolutionary curriculum training to tackle these challenges. The primary goal of evolutionary curriculum training is to evaluate the collision avoidance model's competency in various scenarios and create curricula to enhance its insufficient skills. The paper introduces an innovative evaluation technique to assess the DRL model's performance in navigating structured maps and avoiding dynamic obstacles. Additionally, an evolutionary training environment generates all the curriculum to improve the DRL model's inadequate skills tested in the previous evaluation. We benchmark the performance of our model across five structured environments to validate the hypothesis that this evolutionary training environment leads to a higher success rate and a lower average number of collisions. Further details and results at our project website.Comment: Robotics: Science and System

New vacuum state and symmetry breaking in polariton system

The polariton system is studied by a concise approach using a simple model. A new ground state with negative energy is obtained and found to exhibit the symmetry breaking.Comment: Revtex, accepted by Phys. Lett. A. E-mail: [email protected] after Oct. 10, 199

Minimizing the Maximum Flow Time in the Online Food Delivery Problem

We study a common delivery problem encountered in nowadays online food-ordering platforms: Customers order dishes online, and the restaurant delivers the food after receiving the order. Specifically, we study a problem where k vehicles of capacity c are serving a set of requests ordering food from one restaurant. After a request arrives, it can be served by a vehicle moving from the restaurant to its delivery location. We are interested in serving all requests while minimizing the maximum flow-time, i.e., the maximum time length a customer waits to receive his/her food after submitting the order. We show that the problem is hard in both offline and online settings even when k = 1 and c = ?: There is a hardness of approximation of ?(n) for the offline problem, and a lower bound of ?(n) on the competitive ratio of any online algorithm, where n is number of points in the metric. We circumvent the strong negative results in two directions. Our main result is an O(1)-competitive online algorithm for the uncapacitated (i.e, c = ?) food delivery problem on tree metrics; we also have negative result showing that the condition c = ? is needed. Then we explore the speed-augmentation model where our online algorithm is allowed to use vehicles with faster speed. We show that a moderate speeding factor leads to a constant competitive ratio, and we prove a tight trade-off between the speeding factor and the competitive ratio