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Description
It is commonly known that the left hemisphere of the brain is more efficient in the processing of verbal information, compared to the right hemisphere. One proposal suggests that hemispheric asymmetries in verbal processing are due in part to the efficient use of top-down mechanisms by the left hemisphere. Most evidence for this comes from hemispheric semantic priming, though fewer studies have investigated verbal memory in the cerebral hemispheres. The goal of the current investigations is to examine how top-down mechanisms influence hemispheric asymmetries in verbal memory, and determine the specific nature of hypothesized top-down mechanisms. Five experiments were conducted to explore the influence of top-down mechanisms on hemispheric asymmetries in verbal memory. Experiments 1 and 2 used item-method directed forgetting to examine maintenance and inhibition mechanisms. In Experiment 1, participants were cued to remember or forget certain words, and cues were presented simultaneously or after the presentation of target words. In Experiment 2, participants were cued again to remember or forget words, but each word was repeated once or four times. Experiments 3 and 4 examined the influence of cognitive load on hemispheric asymmetries in true and false memory. In Experiment 3, cognitive load was imposed during memory encoding, while in Experiment 4, cognitive load was imposed during memory retrieval. Finally, Experiment 5 investigated the association between controlled processing in hemispheric semantic priming, and top-down mechanisms used for hemispheric verbal memory. Across all experiments, divided visual field presentation was used to probe verbal memory in the cerebral hemispheres. Results from all experiments revealed several important findings. First, top-down mechanisms used by the LH primarily used to facilitate verbal processing, but also operate in a domain general manner in the face of increasing processing demands. Second, evidence indicates that the RH uses top-down mechanisms minimally, and processes verbal information in a more bottom-up manner. These data help clarify the nature of top-down mechanisms used in hemispheric memory and language processing, and build upon current theories that attempt to explain hemispheric asymmetries in language processing.
ContributorsTat, Michael J (Author) / Azuma, Tamiko (Thesis advisor) / Goldinger, Stephen D (Committee member) / Liss, Julie M (Committee member) / Arizona State University (Publisher)
Created2013
Description
The rapid escalation of technology and the widespread emergence of modern technological equipments have resulted in the generation of humongous amounts of digital data (in the form of images, videos and text). This has expanded the possibility of solving real world problems using computational learning frameworks. However, while gathering a large amount of data is cheap and easy, annotating them with class labels is an expensive process in terms of time, labor and human expertise. This has paved the way for research in the field of active learning. Such algorithms automatically select the salient and exemplar instances from large quantities of unlabeled data and are effective in reducing human labeling effort in inducing classification models. To utilize the possible presence of multiple labeling agents, there have been attempts towards a batch mode form of active learning, where a batch of data instances is selected simultaneously for manual annotation. This dissertation is aimed at the development of novel batch mode active learning algorithms to reduce manual effort in training classification models in real world multimedia pattern recognition applications. Four major contributions are proposed in this work: $(i)$ a framework for dynamic batch mode active learning, where the batch size and the specific data instances to be queried are selected adaptively through a single formulation, based on the complexity of the data stream in question, $(ii)$ a batch mode active learning strategy for fuzzy label classification problems, where there is an inherent imprecision and vagueness in the class label definitions, $(iii)$ batch mode active learning algorithms based on convex relaxations of an NP-hard integer quadratic programming (IQP) problem, with guaranteed bounds on the solution quality and $(iv)$ an active matrix completion algorithm and its application to solve several variants of the active learning problem (transductive active learning, multi-label active learning, active feature acquisition and active learning for regression). These contributions are validated on the face recognition and facial expression recognition problems (which are commonly encountered in real world applications like robotics, security and assistive technology for the blind and the visually impaired) and also on collaborative filtering applications like movie recommendation.
ContributorsChakraborty, Shayok (Author) / Panchanathan, Sethuraman (Thesis advisor) / Balasubramanian, Vineeth N. (Committee member) / Li, Baoxin (Committee member) / Mittelmann, Hans (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
Description
The increasing popularity of Twitter renders improved trustworthiness and relevance assessment of tweets much more important for search. However, given the limitations on the size of tweets, it is hard to extract measures for ranking from the tweet's content alone. I propose a method of ranking tweets by generating a reputation score for each tweet that is based not just on content, but also additional information from the Twitter ecosystem that consists of users, tweets, and the web pages that tweets link to. This information is obtained by modeling the Twitter ecosystem as a three-layer graph. The reputation score is used to power two novel methods of ranking tweets by propagating the reputation over an agreement graph based on tweets' content similarity. Additionally, I show how the agreement graph helps counter tweet spam. An evaluation of my method on 16~million tweets from the TREC 2011 Microblog Dataset shows that it doubles the precision over baseline Twitter Search and achieves higher precision than current state of the art method. I present a detailed internal empirical evaluation of RAProp in comparison to several alternative approaches proposed by me, as well as external evaluation in comparison to the current state of the art method.
ContributorsRavikumar, Srijith (Author) / Kambhampati, Subbarao (Thesis advisor) / Davulcu, Hasan (Committee member) / Liu, Huan (Committee member) / Arizona State University (Publisher)
Created2013
Description
Objective of this thesis project is to build a prototype using Linear Temporal Logic specifications for generating a 2D motion plan commanding an iRobot to fulfill the specifications. This thesis project was created for Cyber Physical Systems Lab in Arizona State University. The end product of this thesis is creation of a software solution which can be used in the academia and industry for research in cyber physical systems related applications. The major features of the project are: creating a modular system for motion planning, use of Robot Operating System (ROS), use of triangulation for environment decomposition and using stargazer sensor for localization. The project is built on an open source software called ROS which provides an environment where it is very easy to integrate different modules be it software or hardware on a Linux based platform. Use of ROS implies the project or its modules can be adapted quickly for different applications as the need arises. The final software package created and tested takes a data file as its input which contains the LTL specifications, a symbols list used in the LTL and finally the environment polygon data containing real world coordinates for all polygons and also information on neighbors and parents of each polygon. The software package successfully ran the experiment of coverage, reachability with avoidance and sequencing.
ContributorsPandya, Parth (Author) / Fainekos, Georgios (Thesis advisor) / Dasgupta, Partha (Committee member) / Lee, Yann-Hang (Committee member) / Arizona State University (Publisher)
Created2013
Description
Linear Temporal Logic is gaining increasing popularity as a high level specification language for robot motion planning due to its expressive power and scalability of LTL control synthesis algorithms. This formalism, however, requires expert knowledge and makes it inaccessible to non-expert users. This thesis introduces a graphical specification environment to create high level motion plans to control robots in the field by converting a visual representation of the motion/task plan into a Linear Temporal Logic (LTL) specification. The visual interface is built on the Android tablet platform and provides functionality to create task plans through a set of well defined gestures and on screen controls. It uses the notion of waypoints to quickly and efficiently describe the motion plan and enables a variety of complex Linear Temporal Logic specifications to be described succinctly and intuitively by the user without the need for the knowledge and understanding of LTL specification. Thus, it opens avenues for its use by personnel in military, warehouse management, and search and rescue missions. This thesis describes the construction of LTL for various scenarios used for robot navigation using the visual interface developed and leverages the use of existing LTL based motion planners to carry out the task plan by a robot.
ContributorsSrinivas, Shashank (Author) / Fainekos, Georgios (Thesis advisor) / Baral, Chitta (Committee member) / Burleson, Winslow (Committee member) / Arizona State University (Publisher)
Created2013
Description
Rhetorical theorist Kenneth Burke has asserted the significance of paying equal, if not more attention to, propagandist rhetoric, arguing that "there are other ways of burning books on the pyre-and the favorite method of the hasty reviewer is to deprive himself and his readers by inattention." Despite Burke's exhortation, attention to white supremacist discourse has been relatively meager. Historians Clive Webb and Charles Eagles have called for further research on white supremacy arguing that attention to white supremacist discourse is important both to fully understand and appreciate pro-civil rights rhetoric in context and to develop a more complex understanding of white supremacist rhetoric. This thesis provides a close examination of the literature and rhetoric of two white supremacist organizations: the Citizens' Council, an organization that sprang up in response to the 1954 landmark decision of Brown v. Board of Education and Stromfront.org, a global online forum community that hosts space for supporters of white supremacy. Memory scholars Barbie Zelizer, John Bodnar, and Stephen Brown note the usability of memory to shape social, political, and cultural aspects of society and the potential implications of such shaping. Drawing from this scholarship, the analysis of these texts focuses specifically on the rhetorical shaping of memory as a vehicle to promote white supremacy. Through an analysis of the Citizens' Council's use of historical events, national figures and cultural stereotypes, Chapter 1 explicates the organization's attempt to form a memorial narrative that worked to promote political goals, create a sense of solidarity through resistance, and indoctrinate the youth in the ideology of white supremacy. Chapter 2 examines the rhetorical use of memory on Stormfront and explains how the website capitalizes upon the wide reaching global impact of World War II to construct a memorial narrative that can be accessed by a global audience of white supremacists. Ultimately, this thesis offers a focused review of the rhetorical signatures of two white supremacist groups with the aim of combating contemporary instantiations of racist discourse.
ContributorsLadenburg, Kenneth (Author) / Ore, Ersula (Thesis advisor) / Miller, Keith (Committee member) / Bebout, Lee (Committee member) / Arizona State University (Publisher)
Created2013
Description
Answer Set Programming (ASP) is one of the most prominent and successful knowledge representation paradigms. The success of ASP is due to its expressive non-monotonic modeling language and its efficient computational methods originating from building propositional satisfiability solvers. The wide adoption of ASP has motivated several extensions to its modeling language in order to enhance expressivity, such as incorporating aggregates and interfaces with ontologies. Also, in order to overcome the grounding bottleneck of computation in ASP, there are increasing interests in integrating ASP with other computing paradigms, such as Constraint Programming (CP) and Satisfiability Modulo Theories (SMT). Due to the non-monotonic nature of the ASP semantics, such enhancements turned out to be non-trivial and the existing extensions are not fully satisfactory. We observe that one main reason for the difficulties rooted in the propositional semantics of ASP, which is limited in handling first-order constructs (such as aggregates and ontologies) and functions (such as constraint variables in CP and SMT) in natural ways. This dissertation presents a unifying view on these extensions by viewing them as instances of formulas with generalized quantifiers and intensional functions. We extend the first-order stable model semantics by by Ferraris, Lee, and Lifschitz to allow generalized quantifiers, which cover aggregate, DL-atoms, constraints and SMT theory atoms as special cases. Using this unifying framework, we study and relate different extensions of ASP. We also present a tight integration of ASP with SMT, based on which we enhance action language C+ to handle reasoning about continuous changes. Our framework yields a systematic approach to study and extend non-monotonic languages.
ContributorsMeng, Yunsong (Author) / Lee, Joohyung (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Baral, Chitta (Committee member) / Fainekos, Georgios (Committee member) / Lifschitz, Vladimir (Committee member) / Arizona State University (Publisher)
Created2013
Description
After natural menopause in women, androstenedione becomes the primary hormone secreted by the residual follicle deplete ovaries. Two independent studies, in rodents that had undergone ovarian follicular depletion, found that higher serum androstenedione levels correlated with increased working memory errors. This led to the hypothesis that androstenedione impairs memory. The current study directly tested this hypothesis, examining the cognitive effects of androstenedione administration in a rodent model. Middle-aged ovariectomized rats received vehicle or one of two doses of androstenedione (4 or 8 mg/kg daily). Rats were tested on a spatial working and reference memory maze battery including the water radial arm maze, Morris maze, and delay-match-to-sample task. Results showed that androstenedione at the highest dose impaired reference memory and working memory, including ability to maintain performance as memory demand was elevated. The latter was true for both high temporal demand memory retention of one item of spatial information, as well as the ability to handle multiple items of spatial working memory information. Glutamic acid decarboxylase (GAD) levels were measured in multiple brain regions to determine whether the gamma-aminobutyric acid (GABA) system mediates androstenedione's cognitive impairments. Results showed that higher entorhinal cortex GAD levels were correlated with poorer Morris maze performance, regardless of androstenedione treatment. These findings suggest that androstenedione, the main hormone produced by the follicle deplete ovary, is detrimental to spatial learning, reference memory, and working memory, and that spatial reference memory performance might be related to the GABAergic system.
ContributorsCamp, Bryan Walter (Author) / Bimonte-Nelson, Heather A. (Thesis advisor) / Olive, Michael F (Committee member) / Conrad, Cheryl D. (Committee member) / Arizona State University (Publisher)
Created2012
Description
Ethinyl estradiol, (EE) a synthetic, orally bio-available estrogen, is the most commonly prescribed form of estrogen in oral contraceptives (Shively, C., 1998), and is found in at least 30 different contraceptive formulations currently prescribed to women (Curtis et al., 2005). EE is also used in hormone therapies prescribed to menopausal women, such as FemhrtTM (Simon et al., 2003). Thus, EE is prescribed clinically to women at ages ranging from puberty through reproductive senescence. Here, in two separate studies, the cognitive effects of cyclic or tonic EE administration following ovariectomy (Ovx) were evaluated in young, female rats. Study I assessed the cognitive effects of low and high doses of EE, delivered tonically via a subcutaneous osmotic pump. Study II evaluated the cognitive effects of low, medium, and high doses of EE administered via a daily subcutaneous injection. For these studies, the low and medium doses correspond to the range of doses currently used in clinical formulations, and the high dose corresponds to the range of doses prescribed to a generation of women between 1960 and 1970, when oral contraceptives first became available. For each study, cognition was evaluated with a battery of maze tasks tapping several domains of spatial learning and memory. At the highest dose, EE treatment impaired multiple domains of spatial memory relative to vehicle treatment, regardless of administration method. When given cyclically at the low and medium doses, EE did not impact working memory, but transiently impaired reference memory during the learning phase of testing. Of the doses and regimens tested here, only EE at the highest dose impaired several domains of memory; this was seen for both cyclic and tonic regimens. Cyclic and tonic delivery of low EE, a dose that corresponds to doses used in the clinic today, resulted in transient and null impairments, respectively, on cognition.
ContributorsMennenga, Sarah E (Author) / Bimonte-Nelson, Heather A. (Thesis advisor) / Baxter, Leslie C. (Committee member) / Olive, Michael F. (Committee member) / Arizona State University (Publisher)
Created2012
Description
This thesis introduces the Model-Based Development of Multi-iRobot Toolbox (MBDMIRT), a Simulink-based toolbox designed to provide the means to acquire and practice the Model-Based Development (MBD) skills necessary to design real-time embedded system. The toolbox was developed in the Cyber-Physical System Laboratory at Arizona State University. The MBDMIRT toolbox runs under MATLAB/Simulink to simulate the movements of multiple iRobots and to control, after verification by simulation, multiple physical iRobots accordingly. It adopts the Simulink/Stateflow, which exemplifies an approach to MBD, to program the behaviors of the iRobots. The MBDMIRT toolbox reuses and augments the open-source MATLAB-Based Simulator for the iRobot Create from Cornell University to run the simulation. Regarding the mechanism of iRobot control, the MBDMIRT toolbox applies the MATLAB Toolbox for the iRobot Create (MTIC) from United States Naval Academy to command the physical iRobots. The MBDMIRT toolbox supports a timer in both the simulation and the control, which is based on the local clock of the PC running the toolbox. In addition to the build-in sensors of an iRobot, the toolbox can simulate four user-added sensors, which are overhead localization system (OLS), sonar sensors, a camera, and Light Detection And Ranging (LIDAR). While controlling a physical iRobot, the toolbox supports the StarGazer OLS manufactured by HAGISONIC, Inc.
ContributorsSu, Shih-Kai (Author) / Fainekos, Georgios E (Thesis advisor) / Sarjoughian, Hessam S. (Committee member) / Artemiadis, Panagiotis K (Committee member) / Arizona State University (Publisher)
Created2012