Rethinking the Library: Can the design of a public library inspire both the activation of a small town and deactivation of the socioeconomic barriers therein?

Growing up in Marianna, Arkansas, I recall the memories of places that are dear to my heart. Yet, many of these places have either been demolished or sit vacant, leaving only the memories that others and I relive as we pass by. Despite economic downfalls throughout the city’s existence, it has still found a way to press forward. If one takes into account other small towns in the Delta, Marianna, like many others, has continued to decrease in population and job resources. I foresee a technologically-advanced library within the heart of downtown Marianna where people of all agesand backgrounds can access not only books, but also the endless amount of resources the world has to offer through the Internet. Even more, I foresee priceless stories being told and advice being disseminated by word of mouth to the fellow sitting across the table, or the voice heard over ones shoulder – “This is the way, walk in it.

Theory and applications of difference evaluation functions

ABSTRACT The credit assignment problem (which agents get credit or blame for system performance) is a key research area. For a team of agents collaborating to achieve a goal, the effectiveness of each individual agent must be calculated in order to give adequate feedback to each agent. We use the Difference Evaluation Function to find agent-specific feedback. The Difference Evaluation Function has given excellent empirical results in many domains, including air traffic control and mobile robot control. Though there has been some theoretical work that shows why Difference Evaluation Functions improve system performance, there has been no work to show when and under what conditions such improvements are realized. We apply an evolutionary game-theoretic analysis to show the theoretical advantages of the Difference Evaluation Function. We then focus on how to apply these multiagent learning methods to optimize distributed sensor networks in advanced power generation systems

Depression in Thermal Performance of Age-Structured \u3ci\u3eSpirodela polyrhiza\u3c/i\u3e due to the Presence of \u3ci\u3eRhopalosiphum nymphaeae\u3c/i\u3e

Thermal performance curves are keys components of population ecology. We performed this study to determine the effects of aphids on duckweed age-structured models across temperature. Results show a depression in birth rates and maturation of rates of duckweed in the presence of aphids. Regression analysis shows that this depression in maturation and birth is directly proportional to aphid growth rates across temperatures. This hints of the idea of modeling duckweed and aphid growth via nested thermal performance curves

Optimizing ballast design of wave energy converters using evolutionary algorithms

Wave Energy Converters (WECs) promise to be a viable alternative to current electrical generation methods. However, these WECs must become more efficient before wide-scale industrial use can become cost-effective. The efficiency of a WEC is primarily dependent upon its geometry and ballast configuration which are both difficult to evaluate, due to slow computation time and high computation cost of current models. In this thesis, we use evolutionary algorithms to optimize the ballast geometry of a wave energy generator using a two step process. First, we generate a function approximator (neural network) to predict wave energy converter power output with respect to key geometric design variables. This is a critical step as the computation time of using a full model (e.g., AQWA) to predict energy output prohibits the use of an evolutionary algorithm for design optimization. The function approximator reduced the computation time by over 99% while having an average error of only 3.5%. The evolutionary algorithm optimized the weight distribution of a WEC, resulting in an 84% improvement in power output over a ballast-free WEC

Multiagent Flight Control in Dynamic Environments with Cooperative Coevolutionary Algorithms

Dynamic flight environments in which objectives and environmental features change with respect to time pose a difficult problem with regards to planning optimal flight paths. Path planning methods are typically computationally expensive, and are often difficult to implement in real time if system objectives are changed. This computational problem is compounded when multiple agents are present in the system, as the state and action space grows exponentially. In this work, we use cooperative coevolutionary algorithms in order to develop policies which control agent motion in a dynamic multiagent unmanned aerial system environment such that goals and perceptions change, while ensuring safety constraints are not violated. Rather than replanning new paths when the environment changes, we develop a policy which can map the new environmental features to a trajectory for the agent while ensuring safe and reliable operation, while providing 92% of the theoretically optimal performanc

Roborodentia Final Report

The Senior Project consisted of competing in Roborodentia, a competition in which groups build robots to complete a particular task. This event took place at the Cal Poly Open House on Saturday, April 12th, 2018. For the competition, the robot was to collect Nerf balls from supply tubes raised approximately 7” from the board and shoot them into nets placed along the opposite side of the course. The design, manufacture, and testing of the robot began the first week of Cal Poly winter quarter and lasted until the day of the competition

Theoretical and implementation improvements for difference evaluation functions

Multiagent learning with cooperative coevolutionary algorithms is a critical area of research, and is relevant to many real-world applications including air traffic control, distributed sensor network control, and game-theoretic applications such as border patrol. A key difficulty in multiagent learning is the credit assignment problem, where the impact of each individual agent on the overall system performance must be ascertained. Difference evaluation functions aim to solve this credit assignment problem, by approximating the effect that each agent has on the system evaluation function. Difference evaluations have proven to produce superior learned policies in many multiagent settings. Although difference evaluations have produced excellent empirical results, there are still three key research questions that must be addressed regarding their usefulness in real-world systems. More specifically, the performance, theoretical advantages, and methodology for implementation must be addressed in order to demonstrate that difference evaluations are practical for use in real-world multiagent learning. These research questions are addressed in this dissertation. The first contribution of this dissertation is to demonstrate that difference evaluations may be extended and combined with other coordination mechanisms, resulting in superior learned performance. The second contribution of this dissertation is to derive conditions which guarantee that difference evaluations will outperform traditional coordination mechanisms. The third and final contribution of this dissertation is to demonstrate that difference evaluations may be approximated using only local knowledge, allowing for their implementation in any generic multiagent learning setting. By addressing the performance, theoretical foundation, and implementation concerns of difference evaluations, this dissertation provides a detailed analysis demonstrating the usefulness of difference evaluation functions in multiagent learning systems

Intestinal epithelial cell-intrinsic deletion of Setd7 identifies role for developmental pathways in immunity to helminth infection

The intestine is a common site for a variety of pathogenic infections. Helminth infections continue to be major causes of disease worldwide, and are a significant burden on health care systems. Lysine methyltransferases are part of a family of novel attractive targets for drug discovery. SETD7 is a member of the Suppressor of variegation 3-9-Enhancer of zeste-Trithorax (SET) domain-containing family of lysine methyltransferases, and has been shown to methylate and alter the function of a wide variety of proteins in vitro. A few of these putative methylation targets have been shown to be important in resistance against pathogens. We therefore sought to study the role of SETD7 during parasitic infections. We find that Setd7-/- mice display increased resistance to infection with the helminth Trichuris muris but not Heligmosomoides polygyrus bakeri. Resistance to T. muris relies on an appropriate type 2 immune response that in turn prompts intestinal epithelial cells (IECs) to alter differentiation and proliferation kinetics. Here we show that SETD7 does not affect immune cell responses during infection. Instead, we found that IEC-specific deletion of Setd7 renders mice resistant to T. muris by controlling IEC turnover, an important aspect of anti-helminth immune responses. We further show that SETD7 controls IEC turnover by modulating developmental signaling pathways such as Hippo/YAP and Wnt/β-Catenin. We show that the Hippo pathway specifically is relevant during T. muris infection as verteporfin (a YAP inhibitor) treated mice became susceptible to T. muris. We conclude that SETD7 plays an important role in IEC biology during infection

Prospectus, October 21, 1987

https://spark.parkland.edu/prospectus_1987/1022/thumbnail.jp

Texture Segregation By Visual Cortex: Perceptual Grouping, Attention, and Learning

A neural model is proposed of how laminar interactions in the visual cortex may learn and recognize object texture and form boundaries. The model brings together five interacting processes: region-based texture classification, contour-based boundary grouping, surface filling-in, spatial attention, and object attention. The model shows how form boundaries can determine regions in which surface filling-in occurs; how surface filling-in interacts with spatial attention to generate a form-fitting distribution of spatial attention, or attentional shroud; how the strongest shroud can inhibit weaker shrouds; and how the winning shroud regulates learning of texture categories, and thus the allocation of object attention. The model can discriminate abutted textures with blurred boundaries and is sensitive to texture boundary attributes like discontinuities in orientation and texture flow curvature as well as to relative orientations of texture elements. The model quantitatively fits a large set of human psychophysical data on orientation-based textures. Object boundar output of the model is compared to computer vision algorithms using a set of human segmented photographic images. The model classifies textures and suppresses noise using a multiple scale oriented filterbank and a distributed Adaptive Resonance Theory (dART) classifier. The matched signal between the bottom-up texture inputs and top-down learned texture categories is utilized by oriented competitive and cooperative grouping processes to generate texture boundaries that control surface filling-in and spatial attention. Topdown modulatory attentional feedback from boundary and surface representations to early filtering stages results in enhanced texture boundaries and more efficient learning of texture within attended surface regions. Surface-based attention also provides a self-supervising training signal for learning new textures. Importance of the surface-based attentional feedback in texture learning and classification is tested using a set of textured images from the Brodatz micro-texture album. Benchmark studies vary from 95.1% to 98.6% with attention, and from 90.6% to 93.2% without attention.Air Force Office of Scientific Research (F49620-01-1-0397, F49620-01-1-0423); National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624