The Political Economy of Ethnic Empowerment in India

This dissertation consists of three papers, which together examine whether policies meant to address inequality, succeed in mitigating the impact of traditional institutions such as caste and enable ethnic minorities to claim their rights. Using experimental and quasi-experimental methods with data from a variety of primary and secondary sources, this dissertation analyzes whether policies meant to empower vulnerable groups in India have succeeded in doing so. The findings suggest that while legislations in the form of mandated political representation or freedom of information laws are necessary in terms of increasing the accountability of government towards citizens, they may not be sufficient in ensuring adequate and uniform delivery of public services, especially to citizens belonging to marginalized groups. Further, empowering citizens – especially those belonging to groups that have faced historic discrimination – to actively participate in civic and political life may require more active and intensive policy and programmatic interventions.

Design of Latent Thermal Energy Storage Systems

This dissertation documents the results of a theoretical and numerical study of time dependent storage of energy by melting a phase change material. The heating is provided along invading lines, which change from single-line invasion to tree-shaped invasion. Chapter 2 identifies the special design feature of distributing energy storage in time-dependent fashion on a territory, when the energy flows by fluid flow from a concentrated source to points (users) distributed equidistantly on the area. The challenge in this chapter is to determine the architecture of distributed energy storage. The chief conclusion is that the finite amount of storage material should be distributed proportionally with the distribution of the flow rate of heating agent arriving on the area. The total time needed by the source stream to ‘invade’ the area is cumulative (the sum of the storage times required at each storage site), and depends on the energy distribution paths and the sequence in which the users are served by the source stream. Chapter 3 shows theoretically that the melting process consists of two phases: “invasion” thermal diffusion along the invading line, which is followed by “consolidation” as heat diffuses perpendicularly to the invading line. This chapter also reports the duration of both phases and the evolution of the melt layer around the invading line during the two-dimensional and three-dimensional invasion. It also shows that the amount of melted material increases in time according to a curve shaped as an S. These theoretical predictions are validated by means of numerical simulations in chapter 4. This chapter also shows that the heat transfer rate density increases (i.e., the S curve becomes steeper) as the complexity and number of degrees of freedom of the structure are increased, in accord with the constructal law. The optimal geometric features of the tree structure are detailed in this chapter. Chapter 5 documents a numerical study of time-dependent melting where the heat transfer is convection dominated, unlike in chapter 3 and 4 where the melting is ruled by pure conduction. In accord with constructal design, the search is for effective heat-flow architectures. The volume-constrained improvement of the designs for heat flow begins with assuming the simplest structure, where a single line serves as heat source. Next, the heat source is endowed with freedom to change its shape as it grows. The objective of the numerical simulations is to discover the geometric features that lead to the fastest melting process. The results show that the heat transfer rate density increases as the complexity and number of degrees of freedom of the structure are increased. Furthermore, the angles between heat invasion lines have a minor effect on the global performance compared to other degrees of freedom: number of branching levels, stem length, and branch lengths. The effect of natural convection in the melt zone is documented.

Ink Under the Fingernails: Making Print in Nineteenth-Century Mexico City

This dissertation examines Mexico City’s material politics of print—the central actors engaged in making print, their activities and relationships, and the legal, business, and social dimensions of production—across the nineteenth century. Inside urban printshops, a socially diverse group of men ranging from manual laborers to educated editors collaborated to make the printed items that fueled political debates and partisan struggles in the new republic. By investigating how print was produced, regulated, and consumed, this dissertation argues that printers shaped some of the most pressing conflicts that marked Mexico’s first formative century: over freedom of expression, the role of religion in government, and the emergence of liberalism. Printers shaped debates not only because they issued texts that fueled elite politics but precisely because they operated at the nexus where new liberal guarantees like freedom of the press and intellectual property intersected with politics and patronage, the regulatory efforts of the emerging state, and the harsh realities of a post-colonial economy.

Historians of Mexico have typically approached print as a vehicle for texts written by elites, which they argue contributed to the development of a national public sphere or print culture in spite of low literacy levels. By shifting the focus to print’s production, my work instead reveals that a range of urban residents—from prominent printshop owners to government ministers to street vendors—produced, engaged, and deployed printed items in contests unfolding in the urban environment. As print increasingly functioned as a political weapon in the decades after independence, print production itself became an arena in struggles over the emerging contours of politics and state formation, even as printing technologies remained relatively unchanged over time.

This work examines previously unexplored archival documents, including official correspondence, legal cases, business transactions, and printshop labor records, to shed new light on Mexico City printers’ interactions with the emerging national government, and reveal the degree to which heated ideological debates emerged intertwined with the most basic concerns over the tangible practices of print. By delving into the rich social and cultural world of printing—described by intellectuals and workers alike in memoirs, fiction, caricatures and periodicals— it also considers how printers’ particular status straddling elite and working worlds led them to challenge boundaries drawn by elites that separated manual and intellectual labors. Finally, this study engages the full range of printed documents made in Mexico City printshops not just as texts but also as objects with particular visual and material qualities whose uses and meanings were shaped not only by emergent republicanism but also by powerful colonial legacies that generated ambivalent attitudes towards print’s transformative power.

Learning from Geometry

Subspaces and manifolds are two powerful models for high dimensional signals. Subspaces model linear correlation and are a good fit to signals generated by physical systems, such as frontal images of human faces and multiple sources impinging at an antenna array. Manifolds model sources that are not linearly correlated, but where signals are determined by a small number of parameters. Examples are images of human faces under different poses or expressions, and handwritten digits with varying styles. However, there will always be some degree of model mismatch between the subspace or manifold model and the true statistics of the source. This dissertation exploits subspace and manifold models as prior information in various signal processing and machine learning tasks.

A near-low-rank Gaussian mixture model measures proximity to a union of linear or affine subspaces. This simple model can effectively capture the signal distribution when each class is near a subspace. This dissertation studies how the pairwise geometry between these subspaces affects classification performance. When model mismatch is vanishingly small, the probability of misclassification is determined by the product of the sines of the principal angles between subspaces. When the model mismatch is more significant, the probability of misclassification is determined by the sum of the squares of the sines of the principal angles. Reliability of classification is derived in terms of the distribution of signal energy across principal vectors. Larger principal angles lead to smaller classification error, motivating a linear transform that optimizes principal angles. This linear transformation, termed TRAIT, also preserves some specific features in each class, being complementary to a recently developed Low Rank Transform (LRT). Moreover, when the model mismatch is more significant, TRAIT shows superior performance compared to LRT.

The manifold model enforces a constraint on the freedom of data variation. Learning features that are robust to data variation is very important, especially when the size of the training set is small. A learning machine with large numbers of parameters, e.g., deep neural network, can well describe a very complicated data distribution. However, it is also more likely to be sensitive to small perturbations of the data, and to suffer from suffer from degraded performance when generalizing to unseen (test) data.

From the perspective of complexity of function classes, such a learning machine has a huge capacity (complexity), which tends to overfit. The manifold model provides us with a way of regularizing the learning machine, so as to reduce the generalization error, therefore mitigate overfiting. Two different overfiting-preventing approaches are proposed, one from the perspective of data variation, the other from capacity/complexity control. In the first approach, the learning machine is encouraged to make decisions that vary smoothly for data points in local neighborhoods on the manifold. In the second approach, a graph adjacency matrix is derived for the manifold, and the learned features are encouraged to be aligned with the principal components of this adjacency matrix. Experimental results on benchmark datasets are demonstrated, showing an obvious advantage of the proposed approaches when the training set is small.

Stochastic optimization makes it possible to track a slowly varying subspace underlying streaming data. By approximating local neighborhoods using affine subspaces, a slowly varying manifold can be efficiently tracked as well, even with corrupted and noisy data. The more the local neighborhoods, the better the approximation, but the higher the computational complexity. A multiscale approximation scheme is proposed, where the local approximating subspaces are organized in a tree structure. Splitting and merging of the tree nodes then allows efficient control of the number of neighbourhoods. Deviation (of each datum) from the learned model is estimated, yielding a series of statistics for anomaly detection. This framework extends the classical {\em changepoint detection} technique, which only works for one dimensional signals. Simulations and experiments highlight the robustness and efficacy of the proposed approach in detecting an abrupt change in an otherwise slowly varying low-dimensional manifold.

An Ethical and Legal Framework for Physicians as Surrogate Decision-Makers for Their Patients.

In Western industrialized countries, it is well established that legally competent individuals may choose a surrogate healthcare decision-maker to represent their interests should they lose the capacity to do so themselves. There are few limitations on who they may select to fulfill this function. However, many jurisdictions place restrictions on or prohibit the patient's attending physician or other provider involved with an individual's care to serve in this role. Several authors have previously suggested that respect for the autonomy of patients requires that there be few (if any) constraints on whomever they may appoint as a proxy. In this essay we revisit this topic by first providing a survey of current state laws governing this activity. We then analyze the clinical and ethical circumstances in which potential difficulties could arise. We take a more nuanced and circumspect view of prior suggestions that patients should have virtually unfettered liberty to choose their healthcare proxies. We suggest a strategy to balance the freedom of patients' right to choose their surrogates with fiduciary duty of the state as regulator of medical practice. We identify six domains of possible concern with such relationships and suggest straightforward methods of mitigating their potential negative effects that could be plausibly be incorporated into physician practice.