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Description
While the literature on caregivers of loved ones with Alzheimer's Disease and Related Disorders (ADRD) has continued to grow, the relationship of ethnicity and acculturation factors with regards to the coping strategies used by caregivers has not been extensively explored. The current study included participants from the Palo Alto site of the Resources for Enhancing Alzheimer's Caregiver Health (REACH) project. The study examined differences in coping strategies between 140 non-Hispanic White, 45 less acculturated Latina, and 61 more acculturated Latina caregivers. Univariate and Multivariate Analysis of Variance, as well as post hoc analyses, were conducted to determine the differences among the three groups. Results indicated less acculturated Latina caregivers employ more avoidant coping strategies compared to non-Hispanic White caregivers. However, no differences were found among the other groups in their use of avoidance coping. Moreover, there were no differences found in the use of social support seeking, count your blessings, problem focused, and blaming others coping among the three groups. These findings have important implications for the design of culturally relevant psychoeducational and therapeutic interventions aimed towards meeting the individual needs of these three populations. In addition, the findings expand on the understanding of maladaptive coping strategies that may be potentially exacerbating caregiver distress among Latina caregivers.
ContributorsFelix, Vitae (Author) / Arciniega, Guillermo M (Thesis advisor) / Robinson-Kurpius, Sharon (Committee member) / Coon, David W. (Committee member) / Arizona State University (Publisher)
Created2011
Description
The current study explored whether intrinsically religious individuals are able to separate the "sin" from the "sinner" (i.e., separate category membership from behavior) when judging homosexual individuals, or whether they are instead subject to the negativity bias (judgments based solely on category membership) in moral judgments. All effects were expected to occur only for participants high in homophobia. Participants were 305 undergraduate male and female students at a large, public university in the southwestern U.S. Respondents read one of five scenarios that described gay or straight targets who were celibate or engaged in same or opposite sex relationships, then were asked to respond to a series of questions evaluating attitudes and behavioral intentions toward the target. Results revealed that homophobia led to a negativity bias in judgments of gay targets, which was intensified by intrinsic religiosity. However, individuals high on intrinsic religiosity and high on homophobia also differentiated between gay targets based on sexual behavior, such that gay targets who were celibate or in an opposite-sex relationship were rated more favorably than gay targets in a same-sex relationship. These findings demonstrate that the negativity bias and "sin vs. sinner" differentiation may both be occurring for intrinsically religious individuals. The moderating effect of homophobia on the interaction between intrinsic religiosity and judgments of gay and straight targets shows us that religiosity itself is not inherently tolerant or intolerant.
ContributorsFilip-Crawford, Gabrielle (Author) / Nagoshi, Craig T. (Thesis advisor) / Kwan, Virginia S.Y. (Committee member) / Neuberg, Steven L. (Committee member) / Arizona State University (Publisher)
Created2011
Description
Family adaptation to child developmental disability is a dynamic transactional process that has yet to be tested in a longitudinal, rigorous fashion. In addition, although children with developmental delays frequently have behavior problems, not enough research has examined possible underlying mechanisms in the relation between child developmental delay, adaptation and behavior problems. In the current study, factor analysis examined how best to conceptualize the construct of family adaptation to developmental delay. Also, longitudinal growth curve modeling tested models in which child behavior problems mediated the relation between developmental risk and indices of family adaptation. Participants included 130 typically developing children and their families (Mental Development Index [MDI] > 85) and 104 children with developmental delays and their families (MDI < 85). Data were collected yearly between the ages of three and eight as part of a multi-site, longitudinal investigation examining the interrelations among children's developmental status, family processes, and the emergence of child psychopathology. Results of the current study indicated that adaptation is best conceptualized as a multi-index construct. Different aspects of adaptation changed in unique ways over time, with some facets of adaptation remaining stable while others fluctuated. Child internalizing and externalizing behavior problems were found to decrease over time for both children with developmental delays and typically developing children. Child behavior problems were also found to mediate the relation between developmental risk and family adaptation for over half of the mediation pathways. Significant mediation results indicated that children with developmental delays showed higher early levels of behavior problems, which in turn was associated with more maladaptive adaptation. These findings provide further evidence that families of children with developmental delays experience both positive and more challenging changes in their families over time. This study implies important next steps for research and clinical practice in the area of developmental disability.
ContributorsPedersen y Arbona, Anita (Author) / Crnic, Keith A (Thesis advisor) / Sandler, Irwin (Committee member) / Lemery, Kathryn (Committee member) / Enders, Craig (Committee member) / Arizona State University (Publisher)
Created2011
Description
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
Description
An emerging body of literature suggests that humans likely have multiple threat avoidance systems that enable us to detect and avoid threats in our environment, such as disease threats and physical safety threats. These systems are presumed to be domain-specific, each handling one class of potential threats, and previous research generally supports this assumption. Previous research has not, however, directly tested the domain-specificity of disease avoidance and self-protection by showing that activating one threat management system does not lead to responses consistent only with a different threat management system. Here, the domain- specificity of the disease avoidance and self-protection systems is directly tested using the lexical decision task, a measure of stereotype accessibility, and the implicit association test. Results, although inconclusive, more strongly support a series of domain-specific threat management systems than a single, domain- general system
ContributorsAnderson, Uriah Steven (Author) / Kenrick, Douglas T. (Thesis advisor) / Shiota, Michelle N. (Committee member) / Neuberg, Steven L. (Committee member) / Becker, David V (Committee member) / Arizona State University (Publisher)
Created2011
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 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
Description
Reliable extraction of human pose features that are invariant to view angle and body shape changes is critical for advancing human movement analysis. In this dissertation, the multifactor analysis techniques, including the multilinear analysis and the multifactor Gaussian process methods, have been exploited to extract such invariant pose features from video data by decomposing various key contributing factors, such as pose, view angle, and body shape, in the generation of the image observations. Experimental results have shown that the resulting pose features extracted using the proposed methods exhibit excellent invariance properties to changes in view angles and body shapes. Furthermore, using the proposed invariant multifactor pose features, a suite of simple while effective algorithms have been developed to solve the movement recognition and pose estimation problems. Using these proposed algorithms, excellent human movement analysis results have been obtained, and most of them are superior to those obtained from state-of-the-art algorithms on the same testing datasets. Moreover, a number of key movement analysis challenges, including robust online gesture spotting and multi-camera gesture recognition, have also been addressed in this research. To this end, an online gesture spotting framework has been developed to automatically detect and learn non-gesture movement patterns to improve gesture localization and recognition from continuous data streams using a hidden Markov network. In addition, the optimal data fusion scheme has been investigated for multicamera gesture recognition, and the decision-level camera fusion scheme using the product rule has been found to be optimal for gesture recognition using multiple uncalibrated cameras. Furthermore, the challenge of optimal camera selection in multi-camera gesture recognition has also been tackled. A measure to quantify the complementary strength across cameras has been proposed. Experimental results obtained from a real-life gesture recognition dataset have shown that the optimal camera combinations identified according to the proposed complementary measure always lead to the best gesture recognition results.
ContributorsPeng, Bo (Author) / Qian, Gang (Thesis advisor) / Ye, Jieping (Committee member) / Li, Baoxin (Committee member) / Spanias, Andreas (Committee member) / Arizona State University (Publisher)
Created2011
Description
The purpose of this study was to investigate the effect of partial exemplar experience on category formation and use. Participants had either complete or limited access to the three dimensions that defined categories by dimensions within different modalities. The concept of "crucial dimension" was introduced and the role it plays in category definition was explained. It was hypothesized that the effects of partial experience are not explained by a shifting of attention between dimensions (Taylor & Ross, 2009) but rather by an increased reliance on prototypical values used to fill in missing information during incomplete experiences. Results indicated that participants (1) do not fill in missing information with prototypical values, (2) integrate information less efficiently between different modalities than within a single modality, and (3) have difficulty learning only when partial experience prevents access to diagnostic information.
ContributorsCrawford, Thomas (Author) / Homa, Donald (Thesis advisor) / Mcbeath, Micheal (Committee member) / Glenberg, Arthur (Committee member) / Arizona State University (Publisher)
Created2011
Description
This thesis describes a synthetic task environment, CyberCog, created for the purposes of 1) understanding and measuring individual and team situation awareness in the context of a cyber security defense task and 2) providing a context for evaluating algorithms, visualizations, and other interventions that are intended to improve cyber situation awareness. CyberCog provides an interactive environment for conducting human-in-loop experiments in which the participants of the experiment perform the tasks of a cyber security defense analyst in response to a cyber-attack scenario. CyberCog generates the necessary performance measures and interaction logs needed for measuring individual and team cyber situation awareness. Moreover, the CyberCog environment provides good experimental control for conducting effective situation awareness studies while retaining realism in the scenario and in the tasks performed.
ContributorsRajivan, Prashanth (Author) / Femiani, John (Thesis advisor) / Cooke, Nancy J. (Thesis advisor) / Lindquist, Timothy (Committee member) / Gary, Kevin (Committee member) / Arizona State University (Publisher)
Created2011
Description
With the introduction of compressed sensing and sparse representation,many image processing and computer vision problems have been looked at in a new way. Recent trends indicate that many challenging computer vision and image processing problems are being solved using compressive sensing and sparse representation algorithms. This thesis assays some applications of compressive sensing and sparse representation with regards to image enhancement, restoration and classication. The first application deals with image Super-Resolution through compressive sensing based sparse representation. A novel framework is developed for understanding and analyzing some of the implications of compressive sensing in reconstruction and recovery of an image through raw-sampled and trained dictionaries. Properties of the projection operator and the dictionary are examined and the corresponding results presented. In the second application a novel technique for representing image classes uniquely in a high-dimensional space for image classification is presented. In this method, design and implementation strategy of the image classification system through unique affine sparse codes is presented, which leads to state of the art results. This further leads to analysis of some of the properties attributed to these unique sparse codes. In addition to obtaining these codes, a strong classier is designed and implemented to boost the results obtained. Evaluation with publicly available datasets shows that the proposed method outperforms other state of the art results in image classication. The final part of the thesis deals with image denoising with a novel approach towards obtaining high quality denoised image patches using only a single image. A new technique is proposed to obtain highly correlated image patches through sparse representation, which are then subjected to matrix completion to obtain high quality image patches. Experiments suggest that there may exist a structure within a noisy image which can be exploited for denoising through a low-rank constraint.
ContributorsKulkarni, Naveen (Author) / Li, Baoxin (Thesis advisor) / Ye, Jieping (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2011