The Demobilized Body: Transgressions of Personal Space and Political Participation

Grounded in the intersection between gender politics and electoral studies, this dissertation examines the demobilizing effects of violations of personal space (in the form of domestic violence, control over mobility, emotional abuse, and sexual harassment) on the propensity to vote. Using quantitative methods across four survey datasets concerning Lebanon, the United States, Morocco, and Yemen, this research concludes that cross-regionally, familial control over mobility reduces the propensity to vote among women. Conversely, mechanisms of empowerment such as education and employment increase the propensity to vote.

ANALYSIS OF MACROINVERTEBRATE COMMUNITIES IN SEASONAL WETLANDS THROUGH TIME, ACROSS SPACE, AND USING SPECIES TRAITS

Restoration of natural wetlands may be informed by macroinvertebrate community composition. Macroinvertebrate communities of wetlands are influenced by environmental characteristics such as vegetation, soil, hydrology, land use, and isolation. This dissertation explores multiple approaches to the assessment of wetland macroinvertebrate community composition, and demonstrates how these approaches can provide complementary insights into the community ecology of aquatic macroinvertebrates. Specifically, this work focuses on macroinvertebrates of Delmarva Bays, isolated seasonal wetlands found on Maryland’s eastern shore. A comparison of macroinvertebrate community change over a nine years in a restored wetland complex indicated that the macroinvertebrate community of a rehabilitated wetlands more rapidly approximated the community of a reference site than did a newly created wetland. The recovery of a natural macroinvertebrate community in the rehabilitated wetland indicated that wetland rehabilitation should be prioritized over wetland creation and long-term monitoring may be needed to evaluate restoration success. This study also indicated that characteristics of wetland vegetation reflected community composition. The connection between wetland vegetation and macroinvertebrate community composition led to a regional assessment of predaceous diving beetle (Coleoptera: Dytiscidae) community composition in 20 seasonal wetlands, half with and half without sphagnum moss (Sphagnum spp.). Species-level identifications indicated that wetlands with sphagnum support unique and diverse assemblages of beetles. These patterns suggest that sphagnum wetlands provide habitat that supports biodiversity on the Delmarva Peninsula. To compare traits of co-occurring beetles, mandible morphology and temporal and spatial variation were measured between three species of predaceous diving beetles. Based on mandible architecture, all species may consume similarly sized prey, but prey characteristics likely differ in terms of piercing force required for successful capture and consumption. Therefore, different assemblages of aquatic beetles may have different effects on macroinvertebrate community structure. Integrating community-level and species-level data strengthens the association between individual organisms and their ecological role. Effective restoration of imperiled wetlands benefits from this integration, as it informs the management practices that both preserve biodiversity and promote ecosystem services.

Observed Teleconnections in Northern Winter: Subseasonal Evolution and Tropical Linkages

Teleconnections refer to the climate variability links between non-contiguous geographic regions, and tend to be associated with variability in both space and time of the climate’s semi-permanent circulation features. Teleconnections are well-developed in Northern winter, when they influence subseasonal-to-seasonal climate variability, notably, in surface temperature and precipitation. This work is comprised of four independent studies that improve understanding of tropical-extratropical teleconnections and their surface climate responses, subseasonal teleconnection evolution, and the utility of teleconnections in attribution of extreme climate events. After an introduction to teleconnection analysis as well as the major teleconnection patterns and associated climatic footprints manifest during Northern winter, the lagged impact of the Madden-Julian Oscillation (MJO) on subseasonal climate variability is presented. It is found that monitoring of MJO-related velocity potential anomalies is sufficient to predict MJO impacts. These impacts include, for example, the development of significant positive temperature anomalies over the eastern United States one to three weeks following an anomalous convective dipole with enhanced (suppressed) convection centered over the Indian Ocean (western Pacific). Subseasonal teleconnection evolution is assessed with respect to the Pacific-North America (PNA) pattern and the North Atlantic Oscillation (NAO). This evolution is analyzed both in the presence and absence of MJO-related circulation anomalies. It is found that removal of the MJO results only in small shifts in the centers of action of the NAO and PNA, and that in either case there is a small but significant lag in which the NAO leads a PNA pattern of opposite phase. Barotropic vorticity analysis suggests that this relationship may result in part from excitation of Rossby waves by the NAO in the Asian waveguide. An attempt is made to elegantly differentiate between the MJO extratropical response and patterns of variability more internal to the extratropics. Analysis of upper-level streamfunction anomalies is successful in this regard, and it is suggested that this is the preferred method for the real time monitoring of tropical-extratropical teleconnections. The extreme 2013-2014 North American winter is reconstructed using teleconnection analysis, and it is found that the North Pacific Oscillation-West Pacific (NPO/WP) pattern was the leading contributor to climate anomalies over much of North America. Such attribution is cautionary given the propensity to implicate the tropics for all midlatitude climate anomalies based on the El Niño-Southern Oscillation (ENSO) paradigm. A recent hypothesis of such tropical influence is presented and challenged.

Symbiotic Cities: Buenos Aires, Montevideo, and Mass Culture, 1910-1960

This dissertation examines how Buenos Aires emerged as a creative capital of mass culture and cultural industries in South America during a period when Argentine theater and cinema expanded rapidly, winning over a regional marketplace swelled by transatlantic immigration, urbanization and industrialization. I argue that mass culture across the River Plate developed from a singular dynamic of exchange and competition between Buenos Aires and neighboring Montevideo. The study focuses on the Argentine, Uruguayan, and international performers, playwrights, producers, cultural impresarios, critics, and consumers who collectively built regional cultural industries. The cultural industries in this region blossomed in the interwar period as the advent of new technologies like sound film created profitable opportunities for mass cultural production and new careers for countless theater professionals. Buenos Aires also became a global cultural capital in the wider Hispanic Atlantic world, as its commercial culture served a region composed largely of immigrants and their descendants. From the 1920s through the 1940s, Montevideo maintained a subordinate but symbiotic relationship with Buenos Aires. The two cities shared interlinked cultural marketplaces that attracted performers and directors from the Atlantic world to work in theatre and film productions, especially in times of political upheaval such as the Spanish Civil War and the Perón era in Argentina. As a result of this transnational process, Argentine mass culture became widely consumed throughout South America, competing successfully with Hollywood, European, and other Latin American cinemas and helping transform Buenos Aires into a cosmopolitan metropolis. By examining the relationship between regional and national frames of cultural production, my dissertation contributes to the fields of Latin American studies and urban history while seeking to de-center the United States and Europe from the central framing of transnational history.

Efficient Image and Video Representations for Retrieval

Image (Video) retrieval is an interesting problem of retrieving images (videos) similar to the query. Images (Videos) are represented in an input (feature) space and similar images (videos) are obtained by finding nearest neighbors in the input representation space. Numerous input representations both in real valued and binary space have been proposed for conducting faster retrieval. In this thesis, we present techniques that obtain improved input representations for retrieval in both supervised and unsupervised settings for images and videos. Supervised retrieval is a well known problem of retrieving same class images of the query. We address the practical aspects of achieving faster retrieval with binary codes as input representations for the supervised setting in the first part, where binary codes are used as addresses into hash tables. In practice, using binary codes as addresses does not guarantee fast retrieval, as similar images are not mapped to the same binary code (address). We address this problem by presenting an efficient supervised hashing (binary encoding) method that aims to explicitly map all the images of the same class ideally to a unique binary code. We refer to the binary codes of the images as `Semantic Binary Codes' and the unique code for all same class images as `Class Binary Code'. We also propose a new class­ based Hamming metric that dramatically reduces the retrieval times for larger databases, where only hamming distance is computed to the class binary codes. We also propose a Deep semantic binary code model, by replacing the output layer of a popular convolutional Neural Network (AlexNet) with the class binary codes and show that the hashing functions learned in this way outperforms the state­ of ­the art, and at the same time provide fast retrieval times. In the second part, we also address the problem of supervised retrieval by taking into account the relationship between classes. For a given query image, we want to retrieve images that preserve the relative order i.e. we want to retrieve all same class images first and then, the related classes images before different class images. We learn such relationship aware binary codes by minimizing the similarity between inner product of the binary codes and the similarity between the classes. We calculate the similarity between classes using output embedding vectors, which are vector representations of classes. Our method deviates from the other supervised binary encoding schemes as it is the first to use output embeddings for learning hashing functions. We also introduce new performance metrics that take into account the related class retrieval results and show significant gains over the state­ of­ the art. High Dimensional descriptors like Fisher Vectors or Vector of Locally Aggregated Descriptors have shown to improve the performance of many computer vision applications including retrieval. In the third part, we will discuss an unsupervised technique for compressing high dimensional vectors into high dimensional binary codes, to reduce storage complexity. In this approach, we deviate from adopting traditional hyperplane hashing functions and instead learn hyperspherical hashing functions. The proposed method overcomes the computational challenges of directly applying the spherical hashing algorithm that is intractable for compressing high dimensional vectors. A practical hierarchical model that utilizes divide and conquer techniques using the Random Select and Adjust (RSA) procedure to compress such high dimensional vectors is presented. We show that our proposed high dimensional binary codes outperform the binary codes obtained using traditional hyperplane methods for higher compression ratios. In the last part of the thesis, we propose a retrieval based solution to the Zero shot event classification problem - a setting where no training videos are available for the event. To do this, we learn a generic set of concept detectors and represent both videos and query events in the concept space. We then compute similarity between the query event and the video in the concept space and videos similar to the query event are classified as the videos belonging to the event. We show that we significantly boost the performance using concept features from other modalities.