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Vanderbilt University
1.
Sivley, Robert Michael.
Clustering Rare Event Features to Increase Statistical Power.
Degree: MS, Computer Science, 2013, Vanderbilt University
URL: http://hdl.handle.net/1803/12064 
► Rare genetic variation has been put forward as a major contributor to the development of disease; however, it is inherently difficult to associate rare variants…
(more)
▼ Rare genetic variation has been put forward as a major
contributor to the development of disease; however, it is inherently difficult to associate rare variants with disease, as the low number of observations greatly reduces statistical power. Binning is a method that groups several variants together and merges them into a single feature, sacrificing resolution to increase statistical power. Binning strategies are applicable to rare variant analysis in any field, though their effectiveness is dependent on the method used to group variants. This thesis presents a flexible workflow for rare variant analysis, comprised of five sequential steps: identification of rare variants, annotation of those variants, clustering the variants, collapsing those clusters, and statistical analysis. There are no restrictions on which clustering algorithms are applied, so a review of the core clustering paradigms is provided as an introduction for readers unfamiliar with the field. Also presented is RVCLUST, an R package that facilitates all stages of the described workflow and provides a collection of interfaces to common clustering algorithms and statistical tests. The utility of RVCLUST is demonstrated in a genetic analysis of rare variants in gene regulatory regions and their effect on gene expression. The results of this analysis suggest that informed clustering is an effective alternative to existing strategies, discovering the same associations while avoiding the statistical complications introduced by other binning methods.
Advisors/Committee Members: Dr. Tricia A. Thornton-Wells (committee member), Dr. William S. Bush (committee member), Dr. Douglas H. Fisher (Committee Chair).
Subjects/Keywords: power; statistics; rvclust; clustering; rare event; rare variant
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APA (6th Edition):
Sivley, R. M. (2013). Clustering Rare Event Features to Increase Statistical Power. (Thesis). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/12064
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Sivley, Robert Michael. “Clustering Rare Event Features to Increase Statistical Power.” 2013. Thesis, Vanderbilt University. Accessed January 15, 2021.
http://hdl.handle.net/1803/12064.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Sivley, Robert Michael. “Clustering Rare Event Features to Increase Statistical Power.” 2013. Web. 15 Jan 2021.
Vancouver:
Sivley RM. Clustering Rare Event Features to Increase Statistical Power. [Internet] [Thesis]. Vanderbilt University; 2013. [cited 2021 Jan 15].
Available from: http://hdl.handle.net/1803/12064.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Sivley RM. Clustering Rare Event Features to Increase Statistical Power. [Thesis]. Vanderbilt University; 2013. Available from: http://hdl.handle.net/1803/12064
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Vanderbilt University
2.
Basu, Satabdi.
Fostering Synergistic Learning of Computational Thinking and Middle School Science in Computer-based Intelligent Learning Environments.
Degree: PhD, Computer Science, 2016, Vanderbilt University
URL: http://hdl.handle.net/1803/11338
► Recent advances in computing are transforming our lives at an astonishing pace. Computational Thinking (CT) is a term used to describe the representational practices and…
(more)
▼ Recent advances in computing are transforming our lives at an astonishing pace. Computational Thinking (CT) is a term used to describe the representational practices and behaviors involved in formulating problems and their solutions so that the solutions can be carried out by a computer or a computing agent. Driven by the needs of a 21st century workforce, there is currently a great emphasis on teaching students to think computationally from an early age. Computer science education is gradually being incorporated into K-12 curricula, but a more feasible approach to make CT accessible to all students may be to integrate it with components of existing K-12 curricula. While CT is considered a vital ingredient of science learning, successfully leveraging the synergy between the two in middle school classrooms is non-trivial. This dissertation research presents Computational Thinking using Simulation and Modeling (CTSiM), a computer-based environment that integrates learning of CT concepts and practices with middle school science curricula. CTSiM combines the use of an agent-based visual language for conceptual and computational modeling of science topics, hypertext resources for information acquisition, and simulation tools to study and analyze the behaviors of the modeled science topics. We discuss assessments metrics developed to study the computational artifacts students build and the CT practices and learning strategies they employ in the CTSiM environment. These metrics can be used online to interpret students’ behavior and performance, and provide the framework for adaptively scaffolding students based on their observed deficiencies. Results from a classroom study with ninety-eight middle school students demonstrate the effectiveness of the CTSiM environment and the adaptive scaffolding framework. Students display better understanding of important science and CT concepts, improve their modeling performance over time, adopt useful modeling behaviors, and are able to transfer their modeling skills to new scenarios. In addition, students’ modeling performance and use of CT practices during modeling are significantly correlated with their science learning, demonstrating the synergy between CT and science learning.
Advisors/Committee Members: Dr. Douglas H. Fisher (committee member), Dr. Julie Ann Adams (committee member), Dr. Douglas B. Clark (committee member), Dr. Pratim Sengupta (committee member), Dr. Gautam Biswas (Committee Chair).
Subjects/Keywords: Adaptive scaffolding; Agent based Modeling; Learning by Modeling; Science Education; Computational Thinking; Open ended Learning Environments; Learning Analytics
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APA ·
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APA (6th Edition):
Basu, S. (2016). Fostering Synergistic Learning of Computational Thinking and Middle School Science in Computer-based Intelligent Learning Environments. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/11338
Chicago Manual of Style (16th Edition):
Basu, Satabdi. “Fostering Synergistic Learning of Computational Thinking and Middle School Science in Computer-based Intelligent Learning Environments.” 2016. Doctoral Dissertation, Vanderbilt University. Accessed January 15, 2021.
http://hdl.handle.net/1803/11338.
MLA Handbook (7th Edition):
Basu, Satabdi. “Fostering Synergistic Learning of Computational Thinking and Middle School Science in Computer-based Intelligent Learning Environments.” 2016. Web. 15 Jan 2021.
Vancouver:
Basu S. Fostering Synergistic Learning of Computational Thinking and Middle School Science in Computer-based Intelligent Learning Environments. [Internet] [Doctoral dissertation]. Vanderbilt University; 2016. [cited 2021 Jan 15].
Available from: http://hdl.handle.net/1803/11338.
Council of Science Editors:
Basu S. Fostering Synergistic Learning of Computational Thinking and Middle School Science in Computer-based Intelligent Learning Environments. [Doctoral Dissertation]. Vanderbilt University; 2016. Available from: http://hdl.handle.net/1803/11338
Vanderbilt University
3.
Sen, Sayan Dev.
An intelligent and unified framework for multiple robot and human coalition formation.
Degree: PhD, Computer Science, 2015, Vanderbilt University
URL: http://hdl.handle.net/1803/11235
► Robotic systems have proven effective with recent deployments of unmanned robots in numerous missions. Teaming multiple agents requires efficient coalition formation, which is an NP-complete…
(more)
▼ Robotic systems have proven effective with recent deployments of unmanned robots in numerous missions. Teaming multiple agents requires efficient coalition formation, which is an NP-complete problem that is also hard to approximate within a reasonable factor. The computational complexity of the problem has led to the development of a number of greedy, approximation, and market-based solving techniques; however, no single algorithm can cater to a wide spectrum of mission situations. The primary contribution of this dissertation is the development of a unified framework, called i-CiFHaR, the first of its kind to incorporate a library of diverse coalition formation algorithms, each employing a different problem solving mechanism. i-CiFHaR employs unsupervised learning to mine crucial patterns among the algorithms and makes intelligent and optimized decisions over the library to select the most appropriate algorithm(s) to apply in accordance with multiple mission criteria by leveraging Bayesian reasoning.
The second major contribution of this dissertation adds to the state-of-the-art in swarm intelligence by presenting two novel hybrid ant colony optimization algorithms that are applicable to a wide spectrum of combinatorial optimization problems. The algorithms effectively address search stagnation, a common drawback of existing ant algorithms by leveraging novel pheromone update policies that integrate the simulated annealing methodology. The presented algorithms outperformed existing state-of-the-art ant algorithms when applied to three NP-complete problems in terms of solution quality by exhibiting a higher searching capability.
Advisors/Committee Members: Dr. Peter H. Stone (committee member), Dr. Nilanjan Sarkar (committee member), Dr. Gautam Biswas (committee member), Dr. Douglas H. Fisher (committee member), Dr. Julie A. Adams (Committee Chair).
Subjects/Keywords: Multi-robot systems; Coalition formation; Swarm Intelligence; Optimization
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APA (6th Edition):
Sen, S. D. (2015). An intelligent and unified framework for multiple robot and human coalition formation. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/11235
Chicago Manual of Style (16th Edition):
Sen, Sayan Dev. “An intelligent and unified framework for multiple robot and human coalition formation.” 2015. Doctoral Dissertation, Vanderbilt University. Accessed January 15, 2021.
http://hdl.handle.net/1803/11235.
MLA Handbook (7th Edition):
Sen, Sayan Dev. “An intelligent and unified framework for multiple robot and human coalition formation.” 2015. Web. 15 Jan 2021.
Vancouver:
Sen SD. An intelligent and unified framework for multiple robot and human coalition formation. [Internet] [Doctoral dissertation]. Vanderbilt University; 2015. [cited 2021 Jan 15].
Available from: http://hdl.handle.net/1803/11235.
Council of Science Editors:
Sen SD. An intelligent and unified framework for multiple robot and human coalition formation. [Doctoral Dissertation]. Vanderbilt University; 2015. Available from: http://hdl.handle.net/1803/11235
Vanderbilt University
4.
Freedman, Sanford Tory.
Human-Inspired Forgetting for Robotic Systems.
Degree: PhD, Computer Science, 2010, Vanderbilt University
URL: http://hdl.handle.net/1803/15011
► – PLEASE NOTE THAT THE ATTACHED .7z FILE CONTAINING VIDEOS WILL NEED TO BE EXTRACTED AND BURNED TO A DVD IN ORDER TO BE VIEWED. –…
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▼ – PLEASE NOTE THAT THE ATTACHED .7z FILE CONTAINING VIDEOS WILL NEED TO BE EXTRACTED AND BURNED TO A DVD IN ORDER TO BE VIEWED. –
Perfect memory and recall provides a mixed blessing. While flawless recollection of episodic data and procedural rules allows for increased reasoning, photographic memory hinders a robot's ability to operate in real-time, highly dynamic environments. The absence of forgetting can result in memory being filled by a tremendous volume of data, increasing both search time and the probability of over-learning. Many small, but critical details within the environment greatly impact the probability of successful task completion, unfortunately robots are currently ill-equipped to navigate incoming data to detect, recognize, and act upon these details. As robotic hardware and designs improve, robots will be further inundated as finer resolution environmental data and higher accuracy mental models become available. Contemporary robots are already overrun with vast volumes of data requiring real-time processing and the problem will only increase. Before robots realize human-level intelligence, a means of classifying the importance of each acquired datum and forgetting unnecessary, erroneous, and expired data will be required.
This dissertation has developed Human-Inspired Forgetting, a means of incorporating forgetting capabilities into current and future robotic systems. This approach may enable robots to remove unnecessary, erroneous, and out-of-date information while increasing the ability to reliably and rapidly recall critical cues necessary critical for successful task completion. Instead of selecting an item from memory to complete a task, Human-Inspired Forgetting filters the information presented to existing robotic algorithms. The pruned data may allow a diverse array of powerful, but task specific algorithms to realize improved accuracy while reducing cognitive load.
The novel ActSimple forgetting algorithm has been developed as an implementation of Human-Inspired Forgetting. This forgetting algorithm has been heavily inspired by a number of cognitive architectures along with models of human memory and incorporates trace-based decay, encoding interference, belief state values, mental exertion, and output interference. Simulation and real world experiments were conducted to demonstrate the performance and reliability of Human-Inspired Forgetting and the ActSimple algorithm across a range of testing conditions.
Advisors/Committee Members: Dr. Gautam Biswas (committee member), Dr. Douglas H. Fisher (committee member), Dr. Gordon D. Logan (committee member), D. Mitchell Wilkes (committee member), Dr. Julie A. Adams (Committee Chair).
Subjects/Keywords: Human-Inspired Forgetting; Mobile Robot
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Record Details
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APA ·
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MLA ·
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APA (6th Edition):
Freedman, S. T. (2010). Human-Inspired Forgetting for Robotic Systems. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/15011
Chicago Manual of Style (16th Edition):
Freedman, Sanford Tory. “Human-Inspired Forgetting for Robotic Systems.” 2010. Doctoral Dissertation, Vanderbilt University. Accessed January 15, 2021.
http://hdl.handle.net/1803/15011.
MLA Handbook (7th Edition):
Freedman, Sanford Tory. “Human-Inspired Forgetting for Robotic Systems.” 2010. Web. 15 Jan 2021.
Vancouver:
Freedman ST. Human-Inspired Forgetting for Robotic Systems. [Internet] [Doctoral dissertation]. Vanderbilt University; 2010. [cited 2021 Jan 15].
Available from: http://hdl.handle.net/1803/15011.
Council of Science Editors:
Freedman ST. Human-Inspired Forgetting for Robotic Systems. [Doctoral Dissertation]. Vanderbilt University; 2010. Available from: http://hdl.handle.net/1803/15011
. 
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