The Least of These: American Evangelical Parachurch Missions to the Poor, 1947-2005

This dissertation narrates the historical development of American evangelical missions to the poor from 1947-2005 and analyzes the discourse of its main parachurch proponents, especially World Vision, Compassion International, Food for the Hungry, Samaritan's urse, Sojourners, Evangelicals for Social Action, and the Christian Community Development Association. Although recent scholarship on evangelicalism has been prolific, much of the historical work has focused on earlier periods. Sociological and political scientific scholarship on the postwar period has been attracted mostly to controversies surrounding the Religious Right, leaving evangelicalism's resurgent concern for the poor relatively understudied. This dissertation addresses these lacunae. The study consists of three chronological parts, each marked by a distinctive model of mission to the poor. First, the 1950s were characterized by compassionate charity for individual emergencies, a model that cohered neatly with evangelicalism's individualism and emotionalism. This model should be regarded as the quintessential, bedrock evangelical theory of mission to the poor. It remained strong throughout the entire postwar period. Second, in the 1970s, a strong countercurrent emerged that advocated for penitent protest against structural injustice and underdevelopment. In contrast to the first model, it was distinguished by going against the grain of many aspects of evangelical culture, especially its reflexive patriotism and individualism. Third, in the 1990s, an important movement towards developing potential through hopeful holism gained prominence. Its advocates were confident that their integration of biblical principles with insights from contemporary economic development praxis would contribute to drastic, widespread reductions in poverty. This model signaled a new optimism in evangelicalism's engagement with the broader world. The increasing prominence of missions to the poor within American evangelicalism led to dramatic changes within the movement's worldview: by 2005, evangelicals were mostly unified in their expressed concern for the physical and social needs of the poor, a position that radically reversed their immediate postwar worldview of near-exclusive focus on the spiritual needs of individuals. Nevertheless, missions to the poor also paralleled, reinforced, and hastened the increasing fragmentation of evangelicalism's identity, as each missional model advocated for highly variant approaches to poverty amelioration that were undergirded by diverse sociological, political, and theological assumptions.

Preferential Field Coverage Through Detour-Based Mobility Coordination

Controlling the mobility pattern of mobile nodes (e.g., robots) to monitor a given field is a well-studied problem in sensor networks. In this setup, absolute control over the nodes’ mobility is assumed. Apart from the physical ones, no other constraints are imposed on planning mobility of these nodes. In this paper, we address a more general version of the problem. Specifically, we consider a setting in which mobility of each node is externally constrained by a schedule consisting of a list of locations that the node must visit at particular times. Typically, such schedules exhibit some level of slack, which could be leveraged to achieve a specific coverage distribution of a field. Such a distribution defines the relative importance of different field locations. We define the Constrained Mobility Coordination problem for Preferential Coverage (CMC-PC) as follows: given a field with a desired monitoring distribution, and a number of nodes n, each with its own schedule, we need to coordinate the mobility of the nodes in order to achieve the following two goals: 1) satisfy the schedules of all nodes, and 2) attain the required coverage of the given field. We show that the CMC-PC problem is NP-complete (by reduction to the Hamiltonian Cycle problem). Then we propose TFM, a distributed heuristic to achieve field coverage that is as close as possible to the required coverage distribution. We verify the premise of TFM using extensive simulations, as well as taxi logs from a major metropolitan area. We compare TFM to the random mobility strategy—the latter provides a lower bound on performance. Our results show that TFM is very successful in matching the required field coverage distribution, and that it provides, at least, two-fold query success ratio for queries that follow the target coverage distribution of the field.

Articulated Pose Estimation in a Learned Smooth Space of Feasible Solutions

A learning based framework is proposed for estimating human body pose from a single image. Given a differentiable function that maps from pose space to image feature space, the goal is to invert the process: estimate the pose given only image features. The inversion is an ill-posed problem as the inverse mapping is a one to many process. Hence multiple solutions exist, and it is desirable to restrict the solution space to a smaller subset of feasible solutions. For example, not all human body poses are feasible due to anthropometric constraints. Since the space of feasible solutions may not admit a closed form description, the proposed framework seeks to exploit machine learning techniques to learn an approximation that is smoothly parameterized over such a space. One such technique is Gaussian Process Latent Variable Modelling. Scaled conjugate gradient is then used find the best matching pose in the space of feasible solutions when given an input image. The formulation allows easy incorporation of various constraints, e.g. temporal consistency and anthropometric constraints. The performance of the proposed approach is evaluated in the task of upper-body pose estimation from silhouettes and compared with the Specialized Mapping Architecture. The estimation accuracy of the Specialized Mapping Architecture is at least one standard deviation worse than the proposed approach in the experiments with synthetic data. In experiments with real video of humans performing gestures, the proposed approach produces qualitatively better estimation results.