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Currently Java is making its way into the embedded systems and mobile devices like androids. The programs written in Java are compiled into machine independent binary class byte codes. A Java Virtual Machine (JVM) executes these classes. The Java platform additionally specifies the Java Native Interface (JNI). JNI allows Java

Currently Java is making its way into the embedded systems and mobile devices like androids. The programs written in Java are compiled into machine independent binary class byte codes. A Java Virtual Machine (JVM) executes these classes. The Java platform additionally specifies the Java Native Interface (JNI). JNI allows Java code that runs within a JVM to interoperate with applications or libraries that are written in other languages and compiled to the host CPU ISA. JNI plays an important role in embedded system as it provides a mechanism to interact with libraries specific to the platform. This thesis addresses the overhead incurred in the JNI due to reflection and serialization when objects are accessed on android based mobile devices. It provides techniques to reduce this overhead. It also provides an API to access objects through its reference through pinning its memory location. The Android emulator was used to evaluate the performance of these techniques and we observed that there was 5 - 10 % performance gain in the new Java Native Interface.
ContributorsChandrian, Preetham (Author) / Lee, Yann-Hang (Thesis advisor) / Davulcu, Hasan (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2011
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Description
As pointed out in the keynote speech by H. V. Jagadish in SIGMOD'07, and also commonly agreed in the database community, the usability of structured data by casual users is as important as the data management systems' functionalities. A major hardness of using structured data is the problem of easily

As pointed out in the keynote speech by H. V. Jagadish in SIGMOD'07, and also commonly agreed in the database community, the usability of structured data by casual users is as important as the data management systems' functionalities. A major hardness of using structured data is the problem of easily retrieving information from them given a user's information needs. Learning and using a structured query language (e.g., SQL and XQuery) is overwhelmingly burdensome for most users, as not only are these languages sophisticated, but the users need to know the data schema. Keyword search provides us with opportunities to conveniently access structured data and potentially significantly enhances the usability of structured data. However, processing keyword search on structured data is challenging due to various types of ambiguities such as structural ambiguity (keyword queries have no structure), keyword ambiguity (the keywords may not be accurate), user preference ambiguity (the user may have implicit preferences that are not indicated in the query), as well as the efficiency challenges due to large search space. This dissertation performs an expansive study on keyword search processing techniques as a gateway for users to access structured data and retrieve desired information. The key issues addressed include: (1) Resolving structural ambiguities in keyword queries by generating meaningful query results, which involves identifying relevant keyword matches, identifying return information, composing query results based on relevant matches and return information. (2) Resolving structural, keyword and user preference ambiguities through result analysis, including snippet generation, result differentiation, result clustering, result summarization/query expansion, etc. (3) Resolving the efficiency challenge in processing keyword search on structured data by utilizing and efficiently maintaining materialized views. These works deliver significant technical contributions towards building a full-fledged search engine for structured data.
ContributorsLiu, Ziyang (Author) / Chen, Yi (Thesis advisor) / Candan, Kasim S (Committee member) / Davulcu, Hasan (Committee member) / Jagadish, H V (Committee member) / Arizona State University (Publisher)
Created2011
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Description
Genes have widely different pertinences to the etiology and pathology of diseases. Thus, they can be ranked according to their disease-significance on a genomic scale, which is the subject of gene prioritization. Given a set of genes known to be related to a disease, it is reasonable to use them

Genes have widely different pertinences to the etiology and pathology of diseases. Thus, they can be ranked according to their disease-significance on a genomic scale, which is the subject of gene prioritization. Given a set of genes known to be related to a disease, it is reasonable to use them as a basis to determine the significance of other candidate genes, which will then be ranked based on the association they exhibit with respect to the given set of known genes. Experimental and computational data of various kinds have different reliability and relevance to a disease under study. This work presents a gene prioritization method based on integrated biological networks that incorporates and models the various levels of relevance and reliability of diverse sources. The method is shown to achieve significantly higher performance as compared to two well-known gene prioritization algorithms. Essentially, no bias in the performance was seen as it was applied to diseases of diverse ethnology, e.g., monogenic, polygenic and cancer. The method was highly stable and robust against significant levels of noise in the data. Biological networks are often sparse, which can impede the operation of associationbased gene prioritization algorithms such as the one presented here from a computational perspective. As a potential approach to overcome this limitation, we explore the value that transcription factor binding sites can have in elucidating suitable targets. Transcription factors are needed for the expression of most genes, especially in higher organisms and hence genes can be associated via their genetic regulatory properties. While each transcription factor recognizes specific DNA sequence patterns, such patterns are mostly unknown for many transcription factors. Even those that are known are inconsistently reported in the literature, implying a potentially high level of inaccuracy. We developed computational methods for prediction and improvement of transcription factor binding patterns. Tests performed on the improvement method by employing synthetic patterns under various conditions showed that the method is very robust and the patterns produced invariably converge to nearly identical series of patterns. Preliminary tests were conducted to incorporate knowledge from transcription factor binding sites into our networkbased model for prioritization, with encouraging results. Genes have widely different pertinences to the etiology and pathology of diseases. Thus, they can be ranked according to their disease-significance on a genomic scale, which is the subject of gene prioritization. Given a set of genes known to be related to a disease, it is reasonable to use them as a basis to determine the significance of other candidate genes, which will then be ranked based on the association they exhibit with respect to the given set of known genes. Experimental and computational data of various kinds have different reliability and relevance to a disease under study. This work presents a gene prioritization method based on integrated biological networks that incorporates and models the various levels of relevance and reliability of diverse sources. The method is shown to achieve significantly higher performance as compared to two well-known gene prioritization algorithms. Essentially, no bias in the performance was seen as it was applied to diseases of diverse ethnology, e.g., monogenic, polygenic and cancer. The method was highly stable and robust against significant levels of noise in the data. Biological networks are often sparse, which can impede the operation of associationbased gene prioritization algorithms such as the one presented here from a computational perspective. As a potential approach to overcome this limitation, we explore the value that transcription factor binding sites can have in elucidating suitable targets. Transcription factors are needed for the expression of most genes, especially in higher organisms and hence genes can be associated via their genetic regulatory properties. While each transcription factor recognizes specific DNA sequence patterns, such patterns are mostly unknown for many transcription factors. Even those that are known are inconsistently reported in the literature, implying a potentially high level of inaccuracy. We developed computational methods for prediction and improvement of transcription factor binding patterns. Tests performed on the improvement method by employing synthetic patterns under various conditions showed that the method is very robust and the patterns produced invariably converge to nearly identical series of patterns. Preliminary tests were conducted to incorporate knowledge from transcription factor binding sites into our networkbased model for prioritization, with encouraging results. To validate these approaches in a disease-specific context, we built a schizophreniaspecific network based on the inferred associations and performed a comprehensive prioritization of human genes with respect to the disease. These results are expected to be validated empirically, but computational validation using known targets are very positive.
ContributorsLee, Jang (Author) / Gonzalez, Graciela (Thesis advisor) / Ye, Jieping (Committee member) / Davulcu, Hasan (Committee member) / Gallitano-Mendel, Amelia (Committee member) / Arizona State University (Publisher)
Created2011
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Description
Advancements in computer vision and machine learning have added a new dimension to remote sensing applications with the aid of imagery analysis techniques. Applications such as autonomous navigation and terrain classification which make use of image classification techniques are challenging problems and research is still being carried out to find

Advancements in computer vision and machine learning have added a new dimension to remote sensing applications with the aid of imagery analysis techniques. Applications such as autonomous navigation and terrain classification which make use of image classification techniques are challenging problems and research is still being carried out to find better solutions. In this thesis, a novel method is proposed which uses image registration techniques to provide better image classification. This method reduces the error rate of classification by performing image registration of the images with the previously obtained images before performing classification. The motivation behind this is the fact that images that are obtained in the same region which need to be classified will not differ significantly in characteristics. Hence, registration will provide an image that matches closer to the previously obtained image, thus providing better classification. To illustrate that the proposed method works, naïve Bayes and iterative closest point (ICP) algorithms are used for the image classification and registration stages respectively. This implementation was tested extensively in simulation using synthetic images and using a real life data set called the Defense Advanced Research Project Agency (DARPA) Learning Applied to Ground Robots (LAGR) dataset. The results show that the ICP algorithm does help in better classification with Naïve Bayes by reducing the error rate by an average of about 10% in the synthetic data and by about 7% on the actual datasets used.
ContributorsMuralidhar, Ashwini (Author) / Saripalli, Srikanth (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2011
Description

Consider Steven Cryos’ words, “When disaster strikes, the time to prepare has passed.” Witnessing domestic water insecurity in events such as Hurricane Katrina, the instability in Flint, Michigan, and most recently the winter storms affecting millions across Texas, we decided to take action. The period between a water supply’s disruption

Consider Steven Cryos’ words, “When disaster strikes, the time to prepare has passed.” Witnessing domestic water insecurity in events such as Hurricane Katrina, the instability in Flint, Michigan, and most recently the winter storms affecting millions across Texas, we decided to take action. The period between a water supply’s disruption and restoration is filled with anxiety, uncertainty, and distress -- particularly since there is no clear indication of when, exactly, restoration comes. It is for this reason that Water Works now exists. As a team of students from diverse backgrounds, what started as an honors project with the Founders Lab at Arizona State University became the seed that will continue to mature into an economically sustainable business model supporting the optimistic visions and tenants of humanitarianism. By having conversations with community members, conducting market research, competing for funding and fostering progress amid the COVID-19 pandemic, our team’s problem-solving traverses the disciplines. The purpose of this paper is to educate our readers about a unique solution to emerging issues of water insecurity that are nested across and within systems who could benefit from the introduction of a personal water reclamation system, showcase our team’s entrepreneurial journey, and propose future directions that will this once pedagogical exercise to continue fulfilling its mission: To heal, to hydrate and to help bring safe water to everyone.

ContributorsReitzel, Gage Alexander (Co-author) / Filipek, Marina (Co-author) / Sadiasa, Aira (Co-author) / Byrne, Jared (Thesis director) / Sebold, Brent (Committee member) / Historical, Philosophical & Religious Studies (Contributor) / School of Human Evolution & Social Change (Contributor, Contributor) / Historical, Philosophical & Religious Studies, Sch (Contributor) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description

Animals encounter information from different resources simultaneously, integrating input from multiple sensory systems before responding behaviorally. When different cues interact with one another, they may enhance, diminish, or have no impact on their responses. In this project, we test how the presence of chemical cues affect the perception of visual

Animals encounter information from different resources simultaneously, integrating input from multiple sensory systems before responding behaviorally. When different cues interact with one another, they may enhance, diminish, or have no impact on their responses. In this project, we test how the presence of chemical cues affect the perception of visual cues. Zebrafish (Danio rerio) often use both chemical cues and visual cues to communicate with shoal mates, to assess predation risk, and to locate food. For example, zebrafish rely on both olfactory cues and visual cues for kin recognition, and they frequently use both chemical and visual cues to search for and to capture prey. In zebrafish, the terminal nerve (TN) constitutes the olfacto-visual centrifugal pathway and connects the olfactory bulb with the retina, thus allowing olfactory perception also to activate visual receptors. Past studies have found that the presence of an olfactory cue can modulate visual sensitivity in zebrafish through the terminal nerve pathway. Alternatively, given that zebrafish are highly social, the presence of social chemical cues may distract individuals from responding to other visual cues, such as food and predator visual cues. Foraging and predator chemical cues, including chemical food cues and alarm cues, may also distract individuals from responding to non-essential visual cues. Here, we test whether the response to a visual cue either increases or decreases when presented in concert with alanine, an amino acid that represents the olfactory cues of zebrafish prey. We found that the presence of chemical cues did not affect whether zebrafish responded to visual cues, but that the fish took longer to respond to visual cues when chemical cues were also present. These findings suggest that different aspects of behavior could be affected by the interaction between sensory modalities. We also found that this impact of delayed response was significant only when the visual cue<br/>was weak compared to the strength of the chemical cue, suggesting that the salience of interacting cues may also have an influence on determining the outcomes of the interactions. Overall, the interactive effects of chemicals on an animal’s response to visual cues may also have wide-ranging impacts on behavior including foraging, mating, and evading predators, and the interaction of cues may affect different aspects of the same behavior.

ContributorsPuffer, Georgie Delilah (Author) / Martins, Emilia (Thesis director) / Suriyampola, Piyumika (Committee member) / Gerkin, Richard (Committee member) / School of Life Sciences (Contributor) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description

Exploratory Play is a universal experience that occurs throughout different kinds of childhoods. This study investigates how children’s vocabulary and exploratory play are influenced by how the caregiver responds to the child’s communicative bids. We hypothesize that if caregivers use more open-ended questions in response to their child’s communicative bids,

Exploratory Play is a universal experience that occurs throughout different kinds of childhoods. This study investigates how children’s vocabulary and exploratory play are influenced by how the caregiver responds to the child’s communicative bids. We hypothesize that if caregivers use more open-ended questions in response to their child’s communicative bids, children will show higher rates of exploration during free play.

ContributorsMccollum, Shani Monifa (Author) / Lucca, Kelsey (Thesis director) / Spinrad, Tracy (Committee member) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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In this thesis I will explore deficits in Theory of Mind (ToM) in autistic people due to new evidence that they do not completely lack a ToM. A new theory is proposed, claiming that autistic people use a Hyper Theory of Mind (HyperToM) which has some application and processing differences

In this thesis I will explore deficits in Theory of Mind (ToM) in autistic people due to new evidence that they do not completely lack a ToM. A new theory is proposed, claiming that autistic people use a Hyper Theory of Mind (HyperToM) which has some application and processing differences from typical ToM. The HyperToM test will be administered as an online questionnaire that includes a self-reported Autism Quotient (AQ) section. The study is done in low support needs autistic (LSA) adults, which should have a developed ToM due to age and ability. Results showed some correlations with the AQ symptoms and HyperToM, but not enough diagnosed autistic people (9) participated in this study for significant results.

ContributorsMarkov, Vlada A (Author) / Fabricius, William (Thesis director) / Philips, Ben (Committee member) / Department of Psychology (Contributor) / Dean, W.P. Carey School of Business (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Appearance ideals are standards of beauty imposed by a culture or society, that are unrealistic and impossible to achieve. Research documents the existence of three appearance ideals, thin, muscular and hourglass ideals. The thin ideal is the pursuit of a very thin and low body weight. The muscular ideal is

Appearance ideals are standards of beauty imposed by a culture or society, that are unrealistic and impossible to achieve. Research documents the existence of three appearance ideals, thin, muscular and hourglass ideals. The thin ideal is the pursuit of a very thin and low body weight. The muscular ideal is the pursuit of a toned and fit body. The hourglass ideal is the pursuit of a shapely body with bigger breasts and hips/buttocks than waist. These ideals are associated with disordered eating. However, no current study has examined the prevalence of all three ideals, or how the combination of ideals relates to dietary restraint, one example of a disordered eating behavior. This study was conducted on 505 undergraduate women at Arizona State University, who were completing research credit for a psychology course. The women participated in an online survey that assessed their demographics, each ideal, and dietary restraint. Results show that all combinations of ideals exist. Specifically, 41.5% of the sample endorse high levels of all three ideals, while 12.5% report thin and muscular ideals, 9.5% report thin and hourglass ideals, 9.9% report hourglass and muscular ideals, 8.4% report low levels of all three ideals, 6.4% report muscular ideal only, 6.4% report hourglass ideal only, and 5.6% report thin ideal only. Endorsing more than one ideal significantly associated with dietary restraint. Findings fulfill an important gap in the literature, suggest future directions for research, and have important clinical implications.

ContributorsByrd, Jordyn (Author) / Perez, Marisol (Thesis director) / Hernández, Juan (Committee member) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description

Mayer-Rokitansky-Küster-Hauser (MRKH) is a rare Disorder of Sexual Development (DSD) that results in the lack of a uterus and vagina in women. Receiving this diagnosis during adolescence can cause various forms of psychological distress in patients and families.<br/>Specifically, this condition could affect a women’s gender identity, body image, romantic relationships,

Mayer-Rokitansky-Küster-Hauser (MRKH) is a rare Disorder of Sexual Development (DSD) that results in the lack of a uterus and vagina in women. Receiving this diagnosis during adolescence can cause various forms of psychological distress in patients and families.<br/>Specifically, this condition could affect a women’s gender identity, body image, romantic relationships, family relationships, and psychological wellbeing. Parents are also put in a stressful<br/>position as they now have to navigate the healthcare system, disclosure, and the relationship with their child. This study aims to expand the knowledge of psychosocial adjustment by studying body<br/>image, gender identity, and mental health in individuals living with MRKH as well as parental disclosure, parental support systems, and parental perceptions of their child’s mental health.

ContributorsLaloudakis, Vasiliki (Author) / Wilson, Melissa (Thesis director) / Fontinha de Alcantara, Christiane (Committee member) / Baimbridge, Erica (Committee member) / Department of Psychology (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05