Bodies on the Line: Violence, Disposable Subjects, and the US-Mexico Border Industrial Complex

Bodies On the Line: Violence, Disposable Subjects, and the Border Industrial Complex explores the construction of identity and notions of belonging within an increasingly privatized and militarized Border Industrial Complex. Specifically, the project interrogates how discourses of Mexican migrants as racialized, gendered, and hypersexualized “deviants” normalize violence against border crossers. Starting at Juárez/El Paso border, I follow the expanding border, interrogating the ways that Mexican migrants, regardless of sexual orientation, have been constructed and disciplined according to racialized notions of “sexual deviance." I engage a queer of color critique to argue that sexual deviance becomes a justification for targeting and containing migrant subjects. By focusing on the economic and racially motivated violence that the Border Industrial Complex does to Mexican migrant communities, I expand the critiques that feminists of color have long leveraged against systemic violence done to communities of color through the prison industrial system. Importantly, this project contributes to transnational feminist scholarship by contextualizing border violence within the global circuits of labor, capital, and ideology that shape perceptions of border insecurity. The project contributes an interdisciplinary perspective that uses a multi-method approach to understand how border violence is exercised against Mexicans at the Mexico-US border. I use archival methods to ask how historical records housed at the National Border Patrol Museum and Memorial Library serve as political instruments that reinforce the contemporary use of violence against Mexican migrants. I also use semi-structured interviews with nine frequent border crossers to consider the various ways crossers defined and aligned themselves at the border. Finally, I analyze the master narratives that come to surround specific cases of border violence. To that end, I consider the mainstream media’s coverage, legal proceedings, and policy to better understand the racialized, gendered, and sexualized logics of the violence.

Generating Discriminative Objective Proposals via Submodular Ranking

Object recognition has long been a core problem in computer vision. To improve object spatial support and speed up object localization for object recognition, generating high-quality category-independent object proposals as the input for object recognition system has drawn attention recently. Given an image, we generate a limited number of high-quality and category-independent object proposals in advance and used as inputs for many computer vision tasks. We present an efficient dictionary-based model for image classification task. We further extend the work to a discriminative dictionary learning method for tensor sparse coding. In the first part, a multi-scale greedy-based object proposal generation approach is presented. Based on the multi-scale nature of objects in images, our approach is built on top of a hierarchical segmentation. We first identify the representative and diverse exemplar clusters within each scale. Object proposals are obtained by selecting a subset from the multi-scale segment pool via maximizing a submodular objective function, which consists of a weighted coverage term, a single-scale diversity term and a multi-scale reward term. The weighted coverage term forces the selected set of object proposals to be representative and compact; the single-scale diversity term encourages choosing segments from different exemplar clusters so that they will cover as many object patterns as possible; the multi-scale reward term encourages the selected proposals to be discriminative and selected from multiple layers generated by the hierarchical image segmentation. The experimental results on the Berkeley Segmentation Dataset and PASCAL VOC2012 segmentation dataset demonstrate the accuracy and efficiency of our object proposal model. Additionally, we validate our object proposals in simultaneous segmentation and detection and outperform the state-of-art performance. To classify the object in the image, we design a discriminative, structural low-rank framework for image classification. We use a supervised learning method to construct a discriminative and reconstructive dictionary. By introducing an ideal regularization term, we perform low-rank matrix recovery for contaminated training data from all categories simultaneously without losing structural information. A discriminative low-rank representation for images with respect to the constructed dictionary is obtained. With semantic structure information and strong identification capability, this representation is good for classification tasks even using a simple linear multi-classifier.