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Undergraduate Research Opportunities Program

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Now showing 1 - 10 of 552
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    Using Polymorphic Microsatellites to Determine the Population Genetics of Vespula maculifrons
    (Georgia Institute of Technology, 2019-12) Thompson, Veronica
    Social insects have an interesting genetic history and are studied in order to discern how their social behaviors affects their genetic makeup. The eastern yellowjacket Vespula maculifrons is one such species whose altruistic behaviors and caste system should negatively affect their genetic diversity but instead has fluorished for many years as a dominant species in their ecosystem. We investigated whether V. maculifrons follows the pattern of other social insects in having a small genetic diversity and therefore, a small effective population size. We sequenced seventeen polymorphic microsatellites of V. maculifrons of three different years that were chosen in accordance to the temporal method. We performed a Fixation Index test on the data with the three years as subpopulations in order to determine the differences in allele frequency amongst the groups over time. This was done in order to support our theory that V. maculifrons has a low amount of genetic diversity, which correlates to low amounts of allele fixation, and therefore a low effective population size. We found that the fixation index was significantly low, which supported this idea that not many alleles have gone to fixation. This would indicate that the effective population size is low because the population is still affected by genetic drift. In the future, a concrete calculation of the effective population size will be performed with combinations of multiple equations that can account for the many unique social traits of Vespula maculifrons. This will then help in order to add more information to the gap of knowledge on the fascinating genetic makeup of these unique social organisms.
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    Semantic Mapping and Reasoning
    (Georgia Institute of Technology, 2019-12) Chen, Kevin Julian
    Rich, yet efficient knowledge processing is one of the key problems in modern autonomous robotics. The Robot Autonomy and Interactive Learning (RAIL) Lab at the Georgia Institute of Technology has developed a new knowledge processing framework named Robot Common Sense Embedding (RoboCSE), which leverages multi-relational embeddings to learn object affordances, locations, and materials. This project aims to test the capabilities of RoboCSE for household robots by building a perception pipeline, which outputs a semantic map (i.e. map with object labels). The perception pipeline consists of two main components: a Simultaneous Localization and Mapping (SLAM) algorithm to build an occupancy map and two object classifiers to label objects in the map. We hope to integrate the semantic map into RoboCSE to test RoboCSE’s ability to perform high-level task planning and knowledge sharing.
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    On Formula Embeddings in Neural-Guided SAT Solving
    (Georgia Institute of Technology, 2019-12) Dumenci, Mert
    Branching heuristics determine the performance of search-based SAT solvers. We note that recently, Neural Machine Learning approaches have been proposed to learn such heuristics from data. The first step in learning a branching heuristic is a transformation from the space of Boolean formulas to a vector space. We note that there is no canonical transformation: techniques such as message-passing networks and LSTMs have been proposed to embed formulas into R^n. We build a novel dataset of an approximate optimal heuristic and compare the estimation performance of models with different embedding methods. We show that for performant models, embedding methods need to represent the structural invariances of Boolean formulas: similar to CNNs and spatially local data such as images.
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    The Phenotypic Response of Dendritic Cells to Gold Nanoparticles Treatments
    (Georgia Institute of Technology, 2019-12) Dasgupta, Ayan
    Immunotherapy research has been increasingly investigating the potential of gold nanoparticles (AuNPs). AuNPs pose new benefits in the medical field ranging from diagnostics to diseases treatment. AuNPs’ ability to infiltrate tissue and target immune cells makes their potential highly useful for new proposed personalized immunotherapies[1] regarding antigen specific targeting delivery, tracking capabilities in vivo [2][3], and more effective and direct vaccines [8]. AuNPs act as an adjuvant with the ability to elicit immuno-suppressive or immuno-activated responses depending on the treatment and characterization of the AuNPs. A promising application of AuNPs is their ability to interact with dendritic cells (DCs). DCs are antigen presenting cells (APCs) and play an integral part in both innate and adaptive immune responses. They work by internalizing and presenting antigens on their surface to other immune cells initiating an immunomodulatory response. In previous research, it has been shown that AuNPs engineered with surface molecules can the initiate maturation of immature DCs (iDCs). Depending on the surface molecules, AuNPs can mature iDCs to become either activated or tolerogenic DC phenotypes[1]. These matured DC phenotypes use the AuNP’s surface molecules to then elicit an immune response by presenting the surface molecules to other immune cells in addition to secreting chemokines and cytokines to enhance the immune response. Though AuNPs’ influence on the maturation of iDCs has been increasingly studied, it is still not well understood which is critical in order to develop effective personalized immunotherapies. In this study, the relationship between DC phenotypes and AuNP properties is analyzed in order to optimize the methods used to elicit specific immune responses. iDCs will be cultured and treated with AuNPs with various surface modifications which will then be analyzed to determine the phenotypic character of the cultured DCs. The cultured DCs are analyzed using high-throughput screening and flow cytometry to determine the surface molecules that have developed from the AuNP treatment which will determine which phenotype of the matured iDCs. This analysis will establish a relationship between various AuNP treatments and the resulting phenotypic development of DCs. This research will work towards standardizing maturation methods of DCs in vivo in order to control a patient’s immune system and its responses to fight off diseases and arm immune cells.
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    Extracting information from gameplay videos using machine learning techniques and its varieties
    (Georgia Institute of Technology, 2019-12) Luo, Zijin
    The ability to extract sequences of game events for high-resolution e-sport games has traditionally required access to the game’s engine. This difficulty serves as a barrier to groups who don’t possess this access. It is possible to apply deep learning to derive these logs from gameplay video, but it requires computational power that serves as an additional barrier. These groups would benefit from access to these logs, such as small e-sport tournament organizers who could better visualize gameplay to inform both audience and commentators. In this dissertation, we present a combined solution to reduce the required computational resources and time to apply a convolutional neural network (CNN) to extract events from e-sport gameplay videos. This solution consists of techniques to train CNN faster and methods to execute predictions more quickly. These techniques expand the types of machines capable of training and running these models, which in turn extends access to extracting game logs with this approach. We evaluate the approaches in the domain of DOTA2, one of the most popular competitive e-sport games. Our results demonstrate our approach outperforms standard backpropagation baselines.
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    Stable Model Predictive Path Integral Control for Aggressive Autonomous Driving
    (Georgia Institute of Technology, 2019-12) Capuano, Matthieu Jean Baptiste Jean-Baptiste
    A common challenge with sampling based Model Predictive Control (MPC) algorithms operating in stochastic environments is ensuring stable behavior under sudden state disturbances. Model Predictive Path Integral (MPPI) control is an MPC algorithm that can optimize control of non-linear systems subject to non-differentiable cost criteria. It iteratively computes optimal control sequences by re-using the sequence optimized at the previous timestep as a warm start for the current iteration, which allows rapid convergence thus making it real time capable. This approach is successful in producing a diverse set of behaviors, the most impressive being its ability to control systems at the limits of handling. However, a strong unexpected state disturbance can make the previous control sequence an unsafe initialization for the new state and can result in undesired behavior. In this work, we address this problem by implementing a path tracker that produces control sequences that are used as the initializers for the current timestep, instead of simply re-using the sequence from the previous timestep. The path tracker iteratively computes control sequences that can guide the system to low-cost regions and feeds them into the MPPI framework as a sampling reference. This enforces the algorithm to sample behaviors normally distributed around controls that guide the state back to low-cost regions, even in cases where the state drastically changes. The additional advantage of our method is that it retains the ability to sample diverse and dynamically feasible controls, thus maintaining its ability for motion at the limits of handling. We experimentally verify this method on the AutoRally autonomous research platform, a one-fifth scale race car for aggressive driving tasks, and compare its performance against the most recently published results of MPPI for autonomous driving.
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    Testing how methods impact the results of interspecific competition research
    (Georgia Institute of Technology, 2019-12) Woo, Esther
    There are currently two methods that can be used to measure interspecific competition, pairwise and mutual invasion. Both can be used to generate niche difference (ND) and relative fitness difference (RFD) values, as well as determine if two species are able to coexist. Although the newer method, mutual invasion, has been in use for eight years, there has not yet been a study that compares the two. In order to determine if the method impacts the results found in a study, two simple experiments were conducted concurrently. The five-week long experiment involved determining whether Colpidium striatum and Tetrahymena pyriformis are able to coexist. Upon completion, both methods concluded that they could coexist. Despite reaching the same conclusion, it is still unknown if other species pairings or more complex experiments would alter these results.
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    Development of a Wireless, Commercial Electromyography System for Use in Athletics and Physical Therapy
    (Georgia Institute of Technology, 2019-12) Brooksher, Riley
    Electromyography is a muscle activity recording technique that is not often used in a clinical setting due to difficulties in reproducibility. In this paper I aim to create a wireless, wearable system for electromyography. This system is built into a pair of compression shorts, and sends both electromyography and positional data from inertial measurement units to users’ mobile devices. This system is primarily useful in physical therapy and athletic fields, as quantitative information on user gait can improve in the healing and training processes.
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    Robot Calligraphy using Pseudospectral Optimal Control and a Simulated Brush Model
    (Georgia Institute of Technology, 2019-12) Chen, Jiaqi
    Chinese calligraphy is unique and has great artistic value but is difficult to master. In this paper, we make robots write calligraphy. Learning methods could teach robots to write, but may not be able to generalize to new characters. As such, we formulate the calligraphy writing problem as a trajectory optimization problem, and propose a new virtual brush model for simulating the dynamic writing process.Our optimization approach is taken from pseudospectral optimal control, where the proposed dynamic virtual brush model plays a key role in formulating the objective function to be optimized. We also propose a stroke-level optimization to achieve better performance compared to the character-level optimization proposed in previous work. Our methodology shows good performance in drawing aesthetically pleasing characters.
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    Stranger Danger: Educational Game for Cybersecurity Awareness
    (Georgia Institute of Technology, 2019-12) Ren, Ziang
    Cybersecurity is a concern for both organizations and individual users. Although there are a variety of security tools available, the number of cybersecurity incidents is still high. One cause of this phenomenon is that users are typically unaware or underestimating the potential negative consequences of their insecure behaviors on the internet. This leads to a lack of motivation among users for adopting security tools that require additional efforts in order to access a desired service. Conventional methods in promoting people s awareness of cybersecurity such as information sessions hosted by cybersecurity professionals have been shown to be ineffective. Video games is a novel approach in cybersecurity education has achieved some success in training cybersecurity professionals. However, whether video games are an effective method for educating the general public about the basic concepts of cybersecurity remains untested. This study presents Stranger Danger, a video game that simulates real-world exploits and teaches its users via negative reinforcements. The purpose of the study is to examine whether video games are effective in teaching the general public about basic cybersecurity concepts.