Sea turtle nesting in the Ten Thousand Islands of Florida

Loggerhead sea turtles (Caretta caretta) nest in numerous substrate and beach types within the Ten Thousand Islands (TTl) of southwest Florida. Nesting beach selection was analyzed on 12 islands within this archipelago. Numerous physical characteristics were recorded to identify the relatedness of these variables and determine their importance for nesting beach selection in C. caretta. These variables were chosen after evaluating the islands, conducting literature searches and soliciting personal communications. Along transects, data were collected, on the following: height of canopy, beach width, overall slope (beach slope and slope of offshore approach) and sand samples analyzed for pH, percentage of water, percentage of organic content, percentage of carbonate and particle size (8 size classes). Data on ordinal aspect of beaches and beach length were also recorded and included in the analysis. All of the variables were analyzed by tree regression, incorporating the nesting data into the analysis. In the TTl, loggerheads appear to prefer wider beaches (p< 0.001; R2 = 0.56) that inherently have less slope, and secondarily, wider beaches that have low amounts of carbonate (p< O.00 1). In addition, C. caretta favors nest sites within or in close proximity to the supra-littoral vegetation zone of beaches in the TTl (p< 0.001). (86 page document

FPGA ACCELERATION OF A CORTICAL AND A MATCHED FILTER-BASED ALGORITHM

Digital image processing is a widely used and diverse field. It is used in a broad array of areas such as tracking and detection, object avoidance, computer vision, and numerous other applications. For many image processing tasks, the computations can become time consuming. Therefore, a means for accelerating the computations would be beneficial. Using that as motivation, this thesis examines the acceleration of two distinctly different image processing applications. The first image processing application examined is a recent neocortex inspired cognitive model geared towards pattern recognition as seen in the visual cortex. For this model, both software and reconfigurable logic based FPGA implementations of the model are examined on a Cray XD1. Results indicate that hardware-acceleration can provide average throughput gains of 75 times over software-only implementations of the networks examined when utilizing the full resources of the Cray XD1. The second image processing application examined is matched filter-based position detection. This approach is at the heart of the automatic alignment algorithm currently being tested in the National Ignition Faculty presently under construction at the Lawrence Livermore National Laboratory. To reduce the processing time of the match filtering, a reconfigurable logic architecture was developed. Results show that the reconfigurable logic architecture provides a speedup of approximately 253 times over an optimized software implementation

Accelerating Pattern Recognition Algorithms On Parallel Computing Architectures

The move to more parallel computing architectures places more responsibility on the programmer to achieve greater performance. The programmer must now have a greater understanding of the underlying architecture and the inherent algorithmic parallelism. Using parallel computing architectures for exploiting algorithmic parallelism can be a complex task. This dissertation demonstrates various techniques for using parallel computing architectures to exploit algorithmic parallelism. Specifically, three pattern recognition (PR) approaches are examined for acceleration across multiple parallel computing architectures, namely field programmable gate arrays (FPGAs) and general purpose graphical processing units (GPGPUs). Phase-only filter correlation for fingerprint identification was studied as the first PR approach. This approach\u27s sensitivity to angular rotations, scaling, and missing data was surveyed. Additionally, a novel FPGA implementation of this algorithm was created using fixed point computations, deep pipelining, and four computation phases. Communication and computation were overlapped to efficiently process large fingerprint galleries. The FPGA implementation showed approximately a 47 times speedup over a central processing unit (CPU) implementation with negligible impact on precision. For the second PR approach, a spiking neural network (SNN) algorithm for a character recognition application was examined. A novel FPGA implementation of the approach was developed incorporating a scalable modular SNN processing element (PE) to efficiently perform neural computations. The modular SNN PE incorporated streaming memory, fixed point computation, and deep pipelining. This design showed speedups of approximately 3.3 and 8.5 times over CPU implementations for 624 and 9,264 sized neural networks, respectively. Results indicate that the PE design could scale to process larger sized networks easily. Finally for the third PR approach, cellular simultaneous recurrent networks (CSRNs) were investigated for GPGPU acceleration. Particularly, the applications of maze traversal and face recognition were studied. Novel GPGPU implementations were developed employing varying quantities of task-level, data-level, and instruction-level parallelism to achieve efficient runtime performance. Furthermore, the performance of the face recognition application was examined across a heterogeneous cluster of multi-core and GPGPU architectures. A combination of multi-core processors and GPGPUs achieved roughly a 996 times speedup over a single-core CPU implementation. From examining these PR approaches for acceleration, this dissertation presents useful techniques and insight applicable to other algorithms to improve performance when designing a parallel implementation

Model-robust regression and a Bayesian ``sandwich'' estimator

We present a new Bayesian approach to model-robust linear regression that leads to uncertainty estimates with the same robustness properties as the Huber--White sandwich estimator. The sandwich estimator is known to provide asymptotically correct frequentist inference, even when standard modeling assumptions such as linearity and homoscedasticity in the data-generating mechanism are violated. Our derivation provides a compelling Bayesian justification for using this simple and popular tool, and it also clarifies what is being estimated when the data-generating mechanism is not linear. We demonstrate the applicability of our approach using a simulation study and health care cost data from an evaluation of the Washington State Basic Health Plan.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS362 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Estimating Sighting Proportions of American Alligator Nests during Helicopter Survey

Proportions of American alligator (Alligator mississippiensis) nests sighted during aerial survey in Florida were estimated based upon multiple surveys by different observers. We compared sighting proportions across habitats, nesting seasons, and observer experience levels. The mean sighting proportion across all habitats and years was 0.736 (SE=0.024). Survey counts corrected by the mean sighting proportion reliably predicted total nest counts (R2=0.933). Sighting proportions did not differ by habitat type (P=0.668) or year P=0.328). Experienced observers detected a greater proportion of nests (P<O.OOOl) than did either less experienced or inexperienced observers. Reliable estimates of nest abundance can be derived from aerial counts of alligator nests when corrected by the appropriate sighting proportion

Herpetofaunal Inventories of the National Parks of South Florida and the Caribbean: Volume III. Big Cypress National Preserve

Amphibian declines and extinctions have been documented around the world, often in protected natural areas. Concern for this trend has prompted the U.S. Geological Survey and the National Park Service to document all species of amphibians that occur within U.S. National Parks and to search for any signs that amphibians may be declining. This study, an inventory of amphibian species in Big Cypress National Preserve, was conducted from 2002 to 2003. The goals of the project were to create a georeferenced inventory of amphibian species, use new analytical techniques to estimate proportion of sites occupied by each species, look for any signs of amphibian decline (missing species, disease, die-offs, and so forth.), and to establish a protocol that could be used for future monitoring efforts. Several sampling methods were used to accomplish these goals. Visual encounter surveys and anuran vocalization surveys were conducted in all habitats throughout the park to estimate the proportion of sites or proportion of area occupied (PAO) by each amphibian species in each habitat. Opportunistic collections, as well as limited drift fence data, were used to augment the visual encounter methods for highly aquatic or cryptic species. A total of 545 visits to 104 sites were conducted for standard sampling alone, and 2,358 individual amphibians and 374 reptiles were encountered. Data analysis was conducted in program PRESENCE to provide PAO estimates for each of the anuran species. All of the amphibian species historically found in Big Cypress National Preserve were detected during this project. At least one individual of each of the four salamander species was captured during sampling. Each of the anuran species in the preserve was adequately sampled using standard herpetological sampling methods, and PAO estimates were produced for each species of anuran by habitat. This information serves as an indicator of habitat associations of the species and relative abundance of sites occupied, but it will also be useful as a comparative baseline for future monitoring efforts. In addition to sampling for amphibians, all encounters with reptiles were documented. The sampling methods used for detecting amphibians are also appropriate for many reptile species. These reptile locations are included in this report, but the number of reptile observations was not sufficient to estimate PAO for reptile species. We encountered 35 of the 46 species of reptiles believed to be present in Big Cypress National Preserve during this study, and evidence exists of the presence of four other reptile species in the Preserve. This study found no evidence of amphibian decline in Big Cypress National Preserve. Although no evidence of decline was observed, several threats to amphibians were identified. Introduced species, especially the Cuban treefrog (Osteopilus septentrionalis), are predators and competitors with several native frog species. The recreational use of off-road vehicles has the potential to affect some amphibian populations, and a study on those potential impacts is currently underway. Also, interference by humans with the natural hydrologic cycle of south Florida has the potential to alter the amphibian community. Continued monitoring of the amphibian species in Big Cypress National Preserve is recommended. The methods used in this study were adequate to produce reliable estimates of the proportion of sites occupied by most anuran species, and are a cost-effective means of determining the status of their populations

Snapchat and Civic Engagement among College Students

During the 2016 presidential election cycle, Clinton and Trump used Snapchat as one part of their overall voter outreach and engagement efforts. This portion of their campaign strategy was disproportionately targeted toward younger voters, since those between 18 and 25 comprise a vast portion of Snapchat’s user base. Did their efforts, those of political parties, or those of interest groups on Snapchat produce higher levels of civic engagement among college students? We utilize a survey that we conducted from a college campus in the Midwest in October 2016 to answer this question. Using a series of matching analyses, we discover that those students who sent pictures or videos about interest groups, candidates for office, or political parties on Snapchat were more civically and politically active than otherwise similar students who had not participated in these activities

TikTok and Civic Activity among Young Adults

TikTok is known for its lighthearted dance and lip-synch videos, yet videos with the hashtag #politics have garnered nearly 14 billion views. Does young adults’ politically oriented expression on TikTok lead to increased civic engagement offline? TikTok helps incorporate young adults into political social networks that may encourage additional civic activity. In addition, the playful, humorous nature of TikTok-based political expression encourages young adults to develop participatory, political selves. Using data from a 2020 survey of Americans between 18 and 25 years old, we find that posting political videos on TikTok connects with higher offline civic engagement. The results suggest that playful political expression is an important feature for promoting young adult civic engagement