AuthorGidelew, Getnet Abebe
AdvisorPesenson, Isaac Z.
Committee memberBerhanu, Shiferaw
Mendoza, Gerardo A.
Average Sampling On Graphs
Harmonic Analysis On Graphs
Multi-resolution On Graphs
Quadratures On Graphs
Sampling On Graphs
Signal Approximation On Graphs
Permanent link to this recordhttp://hdl.handle.net/20.500.12613/2914
MetadataShow full item record
AbstractIn recent years harmonic analysis on combinatorial graphs has attracted considerable attention. The interest is stimulated in part by multiple existing and potential applications of analysis on graphs to information theory, signal analysis, image processing, computer sciences, learning theory, and astronomy. My thesis is devoted to sampling, interpolation, approximation, and multi-resolution on graphs. The results in the existing literature concern mainly with these theories on unweighted graphs. My main objective is to extend existing theories and obtain new results about sampling, interpolation, approximation, and multi-resolution on general combinatorial graphs such as directed, undirected and weighted.
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Using Behavioral Skills Training with Video Modeling to Improve Future Behavior Analysts’ Graphing SkillsFisher, Amanda Guld (Temple University. Libraries, 2020)Individuals who train to become behavior analysts should be able to organize, create, and display data accurately in order to make a data-based decision about the interventions being used for his or her clients. Behavior analysts most commonly use the visual analysis of the data to continuously evaluate the relationship between the intervention and the target behavior being measured. A multiple probe design across behaviors (i.e., Reversal Design, Alternating treatments and Multiple baseline design) was used to evaluate the effects of behavioral skills training (BST) with video modeling on three potential behavior analysts’ single-subject design graphing skills in Microsoft Excel™. Behavioral skills training is a training package made up of multiple components, but for the purpose of this study BST included: rehearsal, video modeling w/ instructions, and feedback. The three participants were taught remotely via Zoom how to accurately complete the steps in the graph creation process for a reversal design, alternating treatments design, and a multiple baseline design. Results indicate that BST with video modeling was an effective and efficient intervention to increase the accuracy of three potential behavior analysts’ single-subject design graphing skills on Microsoft Excel™.
ON CONVOLUTIONAL NEURAL NETWORKS FOR KNOWLEDGE GRAPH EMBEDDING AND COMPLETIONDragut, Eduard Constantin; Guo, Yuhong; Zhang, Kai; Shi, Justin Y.; Meng, Weiyi (Temple University. Libraries, 2020)Data plays the key role in almost every field of computer sciences, including knowledge graph field. The type of data varies across fields. For example, the data type of knowledge graph field is knowledge triples, while it is visual data like images and videos in computer vision field, and textual data like articles and news in natural language processing field. Data could not be utilized directly by machine learning models, thus data representation learning and feature design for various types of data are two critical tasks in many computer sciences fields. Researchers develop various models and frameworks to learn and extract features, and aim to represent information in defined embedding spaces. The classic models usually embed the data in a low-dimensional space, while neural network models are able to generate more meaningful and complex high-dimensional deep features in recent years. In knowledge graph field, almost every approach represent entities and relations in a low-dimensional space, because there are too many knowledge and triples in real-world. Recently a few approaches apply neural networks on knowledge graph learning. However, these models are only able to capture local and shallow features. We observe the following three important issues with the development of feature learning with neural networks. On one side, neural networks are not black boxes that work well in every case without specific design. There is still a lot of work to do about how to design and propose more powerful and robust neural networks for different types of data. On the other side, more studies about utilizing these representations and features in many applications are necessary. What's more, traditional representations and features work better in some domains, while deep representations and features perform better on other domains. Transfer learning is introduced to bridge the gap between domains and adapt various type of features for many tasks. In this dissertation, we aim to solve the above issues. For knowledge graph learning task, we propose a few important observations both theoretically and practically for current knowledge graph learning approaches, especially for knowledge graph learning based on Convolutional Neural Networks. Besides the work in knowledge graph field, we not only develop different types of feature and representation learning frameworks for various data types, but also develop effective transfer learning algorithm to utilize the features and representations. The obtained features and representations by neural networks are utilized successfully in multiple fields. Firstly, we analyze the current issues on knowledge graph learning models, and present eight observations for existing knowledge graph embedding approaches, especially for approaches based on Convolutional Neural Networks. Secondly, we proposed a novel unsupervised heterogeneous domain adaptation framework that could deal with features in various types. Multimedia features are able to be adapted, and the proposed algorithm could bridge the representation gap between the source and target domains. Thirdly, we propose a novel framework to learn and embed user comments and online news data in unit of sessions. We predict the article of interest for users with deep neural networks and attention models. Lastly, we design and analyze a large number of features to represent dynamics of user comments and news article. The features span a broad spectrum of facets including news article and comment contents, temporal dynamics, sentiment/linguistic features, and user behaviors. Our main insight is that the early dynamics from user comments contribute the most to an accurate prediction, while news article specific factors have surprisingly little influence.
Resting-State Functional Brain Networks in Bipolar Spectrum Disorder: A Graph Theoretical InvestigationAlloy, Lauren B.; Giovannetti, Tania; Chein, Jason M.; Chen, Eunice Y.; McCloskey, Michael; Olino, Thomas M.; Olson, Ingrid (Temple University. Libraries, 2016)Neurobiological theories of bipolar spectrum disorder (BSD) propose that the emotional dysregulation characteristic of BSD stems from disrupted prefrontal control over subcortical limbic structures (Strakowski et al., 2012; Depue & Iacono, 1989). However, existing neuroimaging research on functional connectivity between frontal and limbic brain regions remains inconclusive, and is unable to adequately characterize global functional network dynamics. Graph theoretical analysis provides a framework for understanding the local and global connections of the brain and comparing these connections between groups (Sporns et al., 2004). The purpose of this study was to investigate resting state functional connectivity in individuals at low and high risk for BSD based on moderate versus high reward sensitivity, both with and without a BSD diagnosis, using graph theoretical network analysis. Results demonstrated decreased connectivity in a cognitive control region (dorsolateral prefrontal cortex), but increased connectivity of a brain region involved in the detection and processing of reward (bilateral orbitofrontal cortex), among participants at high risk for BSD. Participants with BSD showed increased inter-module connectivity of the dorsal anterior cingulate cortex (ACC). Reward sensitivity was associated with decreased global and local efficiency, and interacted with BSD risk group status to predict inter-module connectivity. Findings are discussed in relation to neurobiological theories of BSD.