Matching Items (218)
Description
Sparse learning is a technique in machine learning for feature selection and dimensionality reduction, to find a sparse set of the most relevant features. In any machine learning problem, there is a considerable amount of irrelevant information, and separating relevant information from the irrelevant information has been a topic of focus. In supervised learning like regression, the data consists of many features and only a subset of the features may be responsible for the result. Also, the features might require special structural requirements, which introduces additional complexity for feature selection. The sparse learning package, provides a set of algorithms for learning a sparse set of the most relevant features for both regression and classification problems. Structural dependencies among features which introduce additional requirements are also provided as part of the package. The features may be grouped together, and there may exist hierarchies and over- lapping groups among these, and there may be requirements for selecting the most relevant groups among them. In spite of getting sparse solutions, the solutions are not guaranteed to be robust. For the selection to be robust, there are certain techniques which provide theoretical justification of why certain features are selected. The stability selection, is a method for feature selection which allows the use of existing sparse learning methods to select the stable set of features for a given training sample. This is done by assigning probabilities for the features: by sub-sampling the training data and using a specific sparse learning technique to learn the relevant features, and repeating this a large number of times, and counting the probability as the number of times a feature is selected. Cross-validation which is used to determine the best parameter value over a range of values, further allows to select the best parameter value. This is done by selecting the parameter value which gives the maximum accuracy score. With such a combination of algorithms, with good convergence guarantees, stable feature selection properties and the inclusion of various structural dependencies among features, the sparse learning package will be a powerful tool for machine learning research. Modular structure, C implementation, ATLAS integration for fast linear algebraic subroutines, make it one of the best tool for a large sparse setting. The varied collection of algorithms, support for group sparsity, batch algorithms, are a few of the notable functionality of the SLEP package, and these features can be used in a variety of fields to infer relevant elements. The Alzheimer Disease(AD) is a neurodegenerative disease, which gradually leads to dementia. The SLEP package is used for feature selection for getting the most relevant biomarkers from the available AD dataset, and the results show that, indeed, only a subset of the features are required to gain valuable insights.
ContributorsThulasiram, Ramesh (Author) / Ye, Jieping (Thesis advisor) / Xue, Guoliang (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2011
Description
This study investigated the link between the cognitive clusters from the Woodcock–Johnson III Tests of Cognitive Ability (WJ III COG) and Broad Math, Math Calculation Skills, and Math Reasoning clusters of the Woodcock–Johnson III Tests of Achievement (WJ III ACH) using data collected over seven years by a large elementary school district in the Southwest. The students in this study were all diagnosed with math learning disabilities. Multiple regression analyses were used to predict performance on the Broad Math, Math Calculation Skills, and Math Reasoning clusters from the WJ III ACH. Fluid Reasoning (Gf), Comprehension–Knowledge (Gc), Short–Term Memory (Gsm), and Long–term Retrieval (Glr) demonstrated strong relations with Broad Math and moderate relations with Math Calculation Skills. Auditory Processing (Ga) and Processing Speed (Gs) demonstrated moderate relations with Broad Math and Math Calculation Skills. Visual–Spatial Thinking (Gv) and Processing Speed (Gs) demonstrated moderate to strong relations with the mathematics clusters. The results indicate that the specific cognitive abilities of students with math learning disabilities may differ from their peers.
ContributorsBacal, Emily (Author) / Caterino, Linda (Thesis advisor) / Stamm, Jill (Committee member) / Sullivan, Amanda (Committee member) / Arizona State University (Publisher)
Created2010
Description
Latinas and Latinos are currently underrepresented in terms of our 21st century student academic attainment and workforce, compared to the total U.S. Hispanic population. In a field such as mathematical sciences, Hispanic or Latino U.S. citizenship doctoral recipients only accounted for 3.04% in 2009-2010. While there are various initiatives to engage underrepresented STEM populations through education, there is a need to give a voice to the experiences of Latinas and Latinos engaged in such programs. This study explored the experiences of seven Arizona State University undergraduate Latina and Latino Joaquín Bustoz Math-Science Honors Program (JBMSHP) participants as well as examined how the program enhanced their math and science learning experiences. Participants attended either a five-week or eight-week program and ranged in attendance from 2006 to 2011. Students were provided an opportunity to begin university mathematics and science studies before graduating high school. Through a demographic survey and one-on-one guided interview, participants shared their personal journey, their experience in the JBMSHP, and their goals. Using grounded theory, a qualitative research approach, this study focuses on the unique experiences of Latina and Latino participants. Four major themes emerged from the analysis of the data. Each participant applied to the program with a foundation in which they sought to challenge themselves academically through mathematics and/or science. Through their involvement it the JBMSHP, participants recognized benefits during and after the program. All participants recognized the value of these benefits and their participation and praised the program. Overall, the JBMSHP provided the students the resources to grow their academic capital and if they chose seek a STEM related bachelor degree. The results of this study emphasize the need to expand the JBMSHP both within Arizona and nationally. In addition, there is a need to explore the other components of their parent center, the Mathematical, Computational and Modeling Sciences Center (MCMSC), to determine if the suggested pipeline, MCMSC Model for Enhancing the Math and Science Experiences of Latinas and Latinos, can positively impact our 21st century workforce and the dire representational need of Latinas and Latinos in STEM fields.
ContributorsEscontrias, Gabriel (Author) / Turner, Caroline S. (Thesis advisor) / Keller, Gary D. (Committee member) / Calleroz White, Mistalene D. (Committee member) / Arizona State University (Publisher)
Created2012
Description
Threshold logic has been studied by at least two independent group of researchers. One group of researchers studied threshold logic with the intention of building threshold logic circuits. The earliest research to this end was done in the 1960's. The major work at that time focused on studying mathematical properties of threshold logic as no efficient circuit implementations of threshold logic were available. Recently many post-CMOS (Complimentary Metal Oxide Semiconductor) technologies that implement threshold logic have been proposed along with efficient CMOS implementations. This has renewed the effort to develop efficient threshold logic design automation techniques. This work contributes to this ongoing effort. Another group studying threshold logic did so, because the building block of neural networks - the Perceptron, is identical to the threshold element implementing a threshold function. Neural networks are used for various purposes as data classifiers. This work contributes tangentially to this field by proposing new methods and techniques to study and analyze functions implemented by a Perceptron After completion of the Human Genome Project, it has become evident that most biological phenomenon is not caused by the action of single genes, but due to the complex interaction involving a system of genes. In recent times, the `systems approach' for the study of gene systems is gaining popularity. Many different theories from mathematics and computer science has been used for this purpose. Among the systems approaches, the Boolean logic gene model has emerged as the current most popular discrete gene model. This work proposes a new gene model based on threshold logic functions (which are a subset of Boolean logic functions). The biological relevance and utility of this model is argued illustrated by using it to model different in-vivo as well as in-silico gene systems.
ContributorsLinge Gowda, Tejaswi (Author) / Vrudhula, Sarma (Thesis advisor) / Shrivastava, Aviral (Committee member) / Chatha, Karamvir (Committee member) / Kim, Seungchan (Committee member) / Arizona State University (Publisher)
Created2012
Description
Gray codes are perhaps the best known structures for listing sequences of combinatorial objects, such as binary strings. Simply defined as a minimal change listing, Gray codes vary greatly both in structure and in the types of objects that they list. More specific types of Gray codes are universal cycles and overlap sequences. Universal cycles are Gray codes on a set of strings of length n in which the first n-1 letters of one object are the same as the last n-1 letters of its predecessor in the listing. Overlap sequences allow this overlap to vary between 1 and n-1. Some of our main contributions to the areas of Gray codes and universal cycles include a new Gray code algorithm for fixed weight m-ary words, and results on the existence of universal cycles for weak orders on [n]. Overlap cycles are a relatively new structure with very few published results. We prove the existence of s-overlap cycles for k-permutations of [n], which has been an open research problem for several years, as well as constructing 1- overlap cycles for Steiner triple and quadruple systems of every order. Also included are various other results of a similar nature covering other structures such as binary strings, m-ary strings, subsets, permutations, weak orders, partitions, and designs. These listing structures lend themselves readily to some classes of combinatorial objects, such as binary n-tuples and m-ary n-tuples. Others require more work to find an appropriate structure, such as k-subsets of an n-set, weak orders, and designs. Still more require a modification in the representation of the objects to fit these structures, such as partitions. Determining when and how we can fit these sets of objects into our three listing structures is the focus of this dissertation.
ContributorsHoran, Victoria E (Author) / Hurlbert, Glenn H. (Thesis advisor) / Czygrinow, Andrzej (Committee member) / Fishel, Susanna (Committee member) / Colbourn, Charles (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2012
Description
Drawing on Lave and Wenger (1991) this study explores how preservice elementary teachers develop themselves as teachers of mathematics, in particular, from the time of their teacher education courses to their field experiences. This study also researches the critical experiences that contributed to the construction of their identities and their roles as student teachers in their identity development. The stories of Jackie, Meg, and Kerry show that they brought different incoming identities to the teacher education program based on their K-12 school experiences. The stories provide the evidence that student teachers' prior experience as learners of mathematics influenced their identities as teachers, especially their confidence levels in teaching mathematics. During the mathematics methods class, student teachers were provided a conceptual understanding of math content and new ways to think about math instruction. Based on student teachers' own experiences, they reconstructed their knowledge and beliefs about what it means to teach mathematics and set their goals to become the mathematics teachers they wanted to be. As they moved through the program through their student teaching periods, their identity development varied depending on the community of practice in which they participated. My study reveals that mentor relationships were critical experiences in shaping their identities as mathematics teachers and in building their initial mathematics teaching practices. Findings suggest that successful mentoring is necessary, and this generally requires sharing common goals, receiving feedback, and having opportunities to practice knowledge, skills, and identities on the part of beginning teachers. Findings from this study highlight that identities are not developed by the individual alone but by engagement with a given community of practice. This study adds to the field of teacher education research by focusing on prospective teachers' identity constructions in relation to the communities of practice, and also by emphasizing the role of mentor in preservice teachers' identity development.
ContributorsKang, Hyun Jung (Author) / Middleton, James A. (Thesis advisor) / Battey, Dan (Committee member) / Sloane, Finbarr (Committee member) / Arizona State University (Publisher)
Created2012
Description
The Quantum Harmonic Oscillator is one of the most important models in Quantum Mechanics. Analogous to the classical mass vibrating back and forth on a spring, the quantum oscillator system has attracted substantial attention over the years because of its importance in many advanced and difficult quantum problems. This dissertation deals with solving generalized models of the time-dependent Schrodinger equation which are called generalized quantum harmonic oscillators, and these are characterized by an arbitrary quadratic Hamiltonian of linear momentum and position operators. The primary challenge in this work is that most quantum models with timedependence are not solvable explicitly, yet this challenge became the driving motivation for this work. In this dissertation, the methods used to solve the time-dependent Schrodinger equation are the fundamental singularity (or Green's function) and the Fourier (eigenfunction expansion) methods. Certain Riccati- and Ermakov-type systems arise, and these systems are highlighted and investigated. The overall aims of this dissertation are to show that quadratic Hamiltonian systems are completely integrable systems, and to provide explicit approaches to solving the time-dependent Schr¨odinger equation governed by an arbitrary quadratic Hamiltonian operator. The methods and results established in the dissertation are not yet well recognized in the literature, yet hold for high promise for further future research. Finally, the most recent results in the dissertation correspond to the harmonic oscillator group and its symmetries. A simple derivation of the maximum kinematical invariance groups of the free particle and quantum harmonic oscillator is constructed from the view point of the Riccati- and Ermakov-type systems, which shows an alternative to the traditional Lie Algebra approach. To conclude, a missing class of solutions of the time-dependent Schr¨odinger equation for the simple harmonic oscillator in one dimension is constructed. Probability distributions of the particle linear position and momentum, are emphasized with Mathematica animations. The eigenfunctions qualitatively differ from the traditional standing waves of the one-dimensional Schrodinger equation. The physical relevance of these dynamic states is still questionable, and in order to investigate their physical meaning, animations could also be created for the squeezed coherent states. This will be addressed in future work.
ContributorsLopez, Raquel (Author) / Suslov, Sergei K (Thesis advisor) / Radunskaya, Ami (Committee member) / Castillo-Chavez, Carlos (Committee member) / Platte, Rodrigo (Committee member) / Arizona State University (Publisher)
Created2012
Description
Bacteriophage (phage) are viruses that infect bacteria. Typical laboratory experiments show that in a chemostat containing phage and susceptible bacteria species, a mutant bacteria species will evolve. This mutant species is usually resistant to the phage infection and less competitive compared to the susceptible bacteria species. In some experiments, both susceptible and resistant bacteria species, as well as phage, can coexist at an equilibrium for hundreds of hours. The current research is inspired by these observations, and the goal is to establish a mathematical model and explore sufficient and necessary conditions for the coexistence. In this dissertation a model with infinite distributed delay terms based on some existing work is established. A rigorous analysis of the well-posedness of this model is provided, and it is proved that the susceptible bacteria persist. To study the persistence of phage species, a "Phage Reproduction Number" (PRN) is defined. The mathematical analysis shows phage persist if PRN > 1 and vanish if PRN < 1. A sufficient condition and a necessary condition for persistence of resistant bacteria are given. The persistence of the phage is essential for the persistence of resistant bacteria. Also, the resistant bacteria persist if its fitness is the same as the susceptible bacteria and if PRN > 1. A special case of the general model leads to a system of ordinary differential equations, for which numerical simulation results are presented.
ContributorsHan, Zhun (Author) / Smith, Hal (Thesis advisor) / Armbruster, Dieter (Committee member) / Kawski, Matthias (Committee member) / Kuang, Yang (Committee member) / Thieme, Horst (Committee member) / Arizona State University (Publisher)
Created2012
Description
The purpose of this study was to examine the impact of individualized afterschool tutoring, under federal Supplemental Educational Services (SES), on mathematical and general academic intrinsic motivation and mathematical achievement of at-risk students. The population of this study consisted of two third graders and five fourth graders from an elementary school in the Reynolds School District in Portland, Oregon. One participant was male. The other six were female. Six of the students were Hispanic, and one student was multiethnic. Students' parents enrolled their children in free afterschool tutoring with Mobile Minds Tutoring, an SES provider in the state of Oregon. The participants were given pre- and post-assessments to measure their intrinsic motivation and achievement. The third graders took the Young Children's Academic Intrinsic Motivation Inventory (Y-CAIMI) and the fourth graders took the Children's Academic Intrinsic Motivation Inventory (CAIMI). All students took the Group Mathematics Assessment and Diagnostic Evaluation (GMADE) according to their grade level. The findings from this study are consistent with the literature review, in that individualized tutoring can help increase student motivation and achievement. Six out of the seven students who participated in this study showed an increase in mathematical achievement, and four out of the seven showed an increase in intrinsic motivation.
ContributorsBallou, Cherise (Author) / Middleton, James (Thesis advisor) / Kinach, Barbara (Committee member) / Bitter, Gary (Committee member) / Arizona State University (Publisher)
Created2011
Description
Described is a study investigating the feasibility and predictive value of the Teacher Feedback Coding System, a novel observational measure of teachers’ feedback provided to students in third grade classrooms. This measure assessed individual feedback events across three domains: feedback type, level of specificity and affect of the teacher. Exploratory and confirmatory factor analysis revealed five factors indicating separate types of feedback: positive and negative academic-informative feedback, positive and negative behavioral-informative feedback, and an overall factor representing supportive feedback. Multilevel models revealed direct relations between teachers’ negative academic-informative feedback and students’ spring math achievement, as well as between teachers’ negative behavioral-informative feedback and students’ behavior patterns. Additionally, a fall math-by-feedback interaction was detected in the case of teachers’ positive academic-informative feedback; students who began the year struggling in math benefitted from more of this type of feedback. Finally, teachers’ feedback was investigated as a potential mediator in a previously established relation between teachers’ self-reported depressive symptoms and the observed quality of the classroom environment. Partial mediation was detected in the case of teachers’ positive academic-informative feedback, such that this type of feedback was accountable for a portion of the variance observed in the relation between teachers’ depressive symptoms and the quality of the classroom environment.
ContributorsMcLean, Leigh Ellen (Author) / Connor, Carol M. (Thesis advisor) / Lemery, Kathryn (Committee member) / Doane, Leah (Committee member) / Grimm, Kevin (Committee member) / Arizona State University (Publisher)
Created2015