Mixture of time-dependent growth models with an application to blue swimmer crab length-frequency data

Understanding how aquatic species grow is fundamental in fisheries because stock assessment often relies on growth dependent statistical models. Length-frequency-based methods become important when more applicable data for growth model estimation are either not available or very expensive. In this article, we develop a new framework for growth estimation from length-frequency data using a generalized von Bertalanffy growth model (VBGM) framework that allows for time-dependent covariates to be incorporated. A finite mixture of normal distributions is used to model the length-frequency cohorts of each month with the means constrained to follow a VBGM. The variances of the finite mixture components are constrained to be a function of mean length, reducing the number of parameters and allowing for an estimate of the variance at any length. To optimize the likelihood, we use a minorization–maximization (MM) algorithm with a Nelder–Mead sub-step. This work was motivated by the decline in catches of the blue swimmer crab (BSC) (Portunus armatus) off the east coast of Queensland, Australia. We test the method with a simulation study and then apply it to the BSC fishery data.

Statistical modeling of protein lysate array data

The protein lysate array is an emerging technology for quantifying the protein concentration ratios in multiple biological samples. It is gaining popularity, and has the potential to answer questions about post-translational modifications and protein pathway relationships. Statistical inference for a parametric quantification procedure has been inadequately addressed in the literature, mainly due to two challenges: the increasing dimension of the parameter space and the need to account for dependence in the data. Each chapter of this thesis addresses one of these issues. In Chapter 1, an introduction to the protein lysate array quantification is presented, followed by the motivations and goals for this thesis work. In Chapter 2, we develop a multi-step procedure for the Sigmoidal models, ensuring consistent estimation of the concentration level with full asymptotic efficiency. The results obtained in this chapter justify inferential procedures based on large-sample approximations. Simulation studies and real data analysis are used to illustrate the performance of the proposed method in finite-samples. The multi-step procedure is simpler in both theory and computation than the single-step least squares method that has been used in current practice. In Chapter 3, we introduce a new model to account for the dependence structure of the errors by a nonlinear mixed effects model. We consider a method to approximate the maximum likelihood estimator of all the parameters. Using the simulation studies on various error structures, we show that for data with non-i.i.d. errors the proposed method leads to more accurate estimates and better confidence intervals than the existing single-step least squares method.

Data-Aware Scheduling in Datacenters

Datacenters have emerged as the dominant form of computing infrastructure over the last two decades. The tremendous increase in the requirements of data analysis has led to a proportional increase in power consumption and datacenters are now one of the fastest growing electricity consumers in the United States. Another rising concern is the loss of throughput due to network congestion. Scheduling models that do not explicitly account for data placement may lead to a transfer of large amounts of data over the network causing unacceptable delays. In this dissertation, we study different scheduling models that are inspired by the dual objectives of minimizing energy costs and network congestion in a datacenter. As datacenters are equipped to handle peak workloads, the average server utilization in most datacenters is very low. As a result, one can achieve huge energy savings by selectively shutting down machines when demand is low. In this dissertation, we introduce the network-aware machine activation problem to find a schedule that simultaneously minimizes the number of machines necessary and the congestion incurred in the network. Our model significantly generalizes well-studied combinatorial optimization problems such as hard-capacitated hypergraph covering and is thus strongly NP-hard. As a result, we focus on finding good approximation algorithms. Data-parallel computation frameworks such as MapReduce have popularized the design of applications that require a large amount of communication between different machines. Efficient scheduling of these communication demands is essential to guarantee efficient execution of the different applications. In the second part of the thesis, we study the approximability of the co-flow scheduling problem that has been recently introduced to capture these application-level demands. Finally, we also study the question, "In what order should one process jobs?'' Often, precedence constraints specify a partial order over the set of jobs and the objective is to find suitable schedules that satisfy the partial order. However, in the presence of hard deadline constraints, it may be impossible to find a schedule that satisfies all precedence constraints. In this thesis we formalize different variants of job scheduling with soft precedence constraints and conduct the first systematic study of these problems.

Usefulness of social tagging in organizing and providing access to the web: An analysis of indexing consistency and quality

This dissertation research points out major challenging problems with current Knowledge Organization (KO) systems, such as subject gateways or web directories: (1) the current systems use traditional knowledge organization systems based on controlled vocabulary which is not very well suited to web resources, and (2) information is organized by professionals not by users, which means it does not reflect intuitively and instantaneously expressed users’ current needs. In order to explore users’ needs, I examined social tags which are user-generated uncontrolled vocabulary. As investment in professionally-developed subject gateways and web directories diminishes (support for both BUBL and Intute, examined in this study, is being discontinued), understanding characteristics of social tagging becomes even more critical. Several researchers have discussed social tagging behavior and its usefulness for classification or retrieval; however, further research is needed to qualitatively and quantitatively investigate social tagging in order to verify its quality and benefit. This research particularly examined the indexing consistency of social tagging in comparison to professional indexing to examine the quality and efficacy of tagging. The data analysis was divided into three phases: analysis of indexing consistency, analysis of tagging effectiveness, and analysis of tag attributes. Most indexing consistency studies have been conducted with a small number of professional indexers, and they tended to exclude users. Furthermore, the studies mainly have focused on physical library collections. This dissertation research bridged these gaps by (1) extending the scope of resources to various web documents indexed by users and (2) employing the Information Retrieval (IR) Vector Space Model (VSM) - based indexing consistency method since it is suitable for dealing with a large number of indexers. As a second phase, an analysis of tagging effectiveness with tagging exhaustivity and tag specificity was conducted to ameliorate the drawbacks of consistency analysis based on only the quantitative measures of vocabulary matching. Finally, to investigate tagging pattern and behaviors, a content analysis on tag attributes was conducted based on the FRBR model. The findings revealed that there was greater consistency over all subjects among taggers compared to that for two groups of professionals. The analysis of tagging exhaustivity and tag specificity in relation to tagging effectiveness was conducted to ameliorate difficulties associated with limitations in the analysis of indexing consistency based on only the quantitative measures of vocabulary matching. Examination of exhaustivity and specificity of social tags provided insights into particular characteristics of tagging behavior and its variation across subjects. To further investigate the quality of tags, a Latent Semantic Analysis (LSA) was conducted to determine to what extent tags are conceptually related to professionals’ keywords and it was found that tags of higher specificity tended to have a higher semantic relatedness to professionals’ keywords. This leads to the conclusion that the term’s power as a differentiator is related to its semantic relatedness to documents. The findings on tag attributes identified the important bibliographic attributes of tags beyond describing subjects or topics of a document. The findings also showed that tags have essential attributes matching those defined in FRBR. Furthermore, in terms of specific subject areas, the findings originally identified that taggers exhibited different tagging behaviors representing distinctive features and tendencies on web documents characterizing digital heterogeneous media resources. These results have led to the conclusion that there should be an increased awareness of diverse user needs by subject in order to improve metadata in practical applications. This dissertation research is the first necessary step to utilize social tagging in digital information organization by verifying the quality and efficacy of social tagging. This dissertation research combined both quantitative (statistics) and qualitative (content analysis using FRBR) approaches to vocabulary analysis of tags which provided a more complete examination of the quality of tags. Through the detailed analysis of tag properties undertaken in this dissertation, we have a clearer understanding of the extent to which social tagging can be used to replace (and in some cases to improve upon) professional indexing.

International workshop on "Different survey methods for coral reef fish, including methods based on underwater video"

In September 2013, staff from the University of the South Pacific (USP) Honiara campus, the Secretariat of the Pacific Community (SPC) and IFREMER (UR LEADNC, AMBIO project) in New Caledonia, and the French Institute for Pacific Coral Reefs (IRCP) in Moorea, French Polynesia, co-facilitated a workshop entitled “Different survey methods of coral reef fish, including the methods based on underwater video”. The workshop was attended by students from USP, NGO and fisheries officers. They were trained to several underwater visual census techniques and to the STAVIRO video-based technique, including both field work and data analysis.

STORM - a novel information fusion and cluster interpretation technique

Analysis of data without labels is commonly subject to scrutiny by unsupervised machine learning techniques. Such techniques provide more meaningful representations, useful for better understanding of a problem at hand, than by looking only at the data itself. Although abundant expert knowledge exists in many areas where unlabelled data is examined, such knowledge is rarely incorporated into automatic analysis. Incorporation of expert knowledge is frequently a matter of combining multiple data sources from disparate hypothetical spaces. In cases where such spaces belong to different data types, this task becomes even more challenging. In this paper we present a novel immune-inspired method that enables the fusion of such disparate types of data for a specific set of problems. We show that our method provides a better visual understanding of one hypothetical space with the help of data from another hypothetical space. We believe that our model has implications for the field of exploratory data analysis and knowledge discovery.

Docência online na educação superior

Tese (doutorado)—Universidade de Brasília, Faculdade de Educação, Programa de Pós-graduação em Educação, 2015.

Síndrome Irlen, uso de transparencias de color y mejoras de la lectura en escolares urbanos de Cuenca.

Objetivo: Determinar si el uso de transparencias de color ayuda a mejorar la lectura, eliminando distorsiones visuales perceptuales, malestares físicos al leer, sintomatología del síndrome Irlen. Materiales y Métodos: Estudio cuasi-experimental sobre efectos del Método Irlen® - uso del color - en sesenta y un estudiantes del cuarto grado de las escuelas urbanas de Cuenca, identificados como severos en el rango de Irlen, en un estudio anterior de prevalencia. Los participantes fueron evaluados a través de nuevas observaciones, entrevistas y cuatro pruebas de la Escala Perceptual de Lectura Irlen. Medidas de tendencia central y porcentajes fueron utilizadas para el análisis de datos. Resultados: Las mejoras atribuidas al uso del color en rango considerable fueron: 1) 59% comodidad; 2) 37.7% menos borroso; 3) 41% menos tensión y fatiga; 4) 45.9% más seguridad y fluidez al leer; 5) 34.4% menos movimientos en la página; 6) 31.2% eliminación de distorsiones; 7) 13.1% menos errores al leer; 8) 9.8% mejora del espacio limitado; 9) 8.2% en atención limitada; y 10) 1.6% mejora en comprensión lectora. Conclusión: El uso de las transparencias de color ayuda parcialmente a eliminar algunas distorsiones visuales perceptuales y malestares físicos al leer lo que facilita la lectura

A produção de conteúdos midiáticos realizada por jovens e suas possibilidades educativas

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Educação, Programa de Pós-Graduação em Educação, 2016.

User interaction monitoring and analysis framework

An overview is given of a user interaction monitoring and analysis framework called BaranC. Monitoring and analysing human-digital interaction is an essential part of developing a user model as the basis for investigating user experience. The primary human-digital interaction, such as on a laptop or smartphone, is best understood and modelled in the wider context of the user and their environment. The BaranC framework provides monitoring and analysis capabilities that not only records all user interaction with a digital device (e.g. smartphone), but also collects all available context data (such as from sensors in the digital device itself, a fitness band or a smart appliances). The data collected by BaranC is recorded as a User Digital Imprint (UDI) which is, in effect, the user model and provides the basis for data analysis. BaranC provides functionality that is useful for user experience studies, user interface design evaluation, and providing user assistance services. An important concern for personal data is privacy, and the framework gives the user full control over the monitoring, storing and sharing of their data.

Imaginarios sociales de adolescentes y docentes en torno al cuerpo, la corporalidad y la actividad física. Estudio cualitativo en un colegio de Bogotá, 2014-2015.

Problema. Esta investigación se aproxima al entorno escolar con el propósito de avanzar en la comprensión de los imaginarios de los adolescentes y docentes en torno al cuerpo, la corporalidad y la AF, como un elemento relevante en el diseño de programas y planes efectivos para fomento de la práctica de AF. Objetivo. Analizar los imaginarios sociales de docentes y adolescentes en torno a los conceptos de cuerpo, corporalidad y AF. Métodos. Investigación de corte cualitativo, descriptivo e interpretativo. Se realizaron entrevistas semi-estructuradas a docentes y a estudiantes entre los 12 y 18 años de un colegio público de Bogotá. Se realizó análisis de contenido. Se compararon los resultados de estudiantes por grupos de edades y género. Resultados. Docentes y estudiantes definen el cuerpo a partir de las características biológicas, las diferencias sexuales y las funciones vitales. La definición de corporalidad en los estudiantes se encuentra ligada con la imagen y la apariencia física; los docentes la entienden como la posibilidad de interactuar con el entorno y como la materialización de la existencia. La AF en los estudiantes se asocia con la práctica de ejercicio y deporte, en los docentes se comprende como una práctica de autocuidado que permite el mantenimiento de la salud. Conclusiones. Para promover la AF tempranamente como una experiencia vital es necesario intervenir los espacios escolares. Hay que vincular al cuerpo a los procesos formativos con el propósito de desarrollar la autonomía corporal, este aspecto implica cambios en los currículos.

Caracterización y prevalencia del compromiso ocular en pacientes con enfermedad autoinmune y autoinflamatoria en un centro de referencia reumatológica en Colombia entre los años 2000 a 2015

RESUMEN Objetivo: Estimar la prevalencia de las diferentes enfermedades oftalmológicas que aparecen en el contexto de una enfermedad autoinmune (EAI) en pacientes de un centro de referencia reumatológica en Colombia, según características clínicas y sociodemográficas durante un período de 15 años, comprendido entre los años 2000 a 2015. Métodos: Se realizó un estudio descriptivo, observacional de prevalencia. El tipo de muestreo fue aleatorio estratificado con asignación proporcional en el programa Epidat 3.4. Los datos se analizaron en el programa SPSS v22.0 y se realizó análisis univariado de las variables categóricas, para las variables cuantitativas se realizaron medidas de tendencia central. Resultados: De 1640 historias clínicas revisadas, se encontraron 634 pacientes (38,65%) con compromiso ocular. Si excluimos los pacientes con SS, que por definición presentan ojo seco, 222 pacientes (13,53%) presentaron compromiso oftalmológico. Del total de pacientes, el 83,3% fueron mujeres. La AR fue la enfermedad autoinmune con mayor compromiso oftalmológico con 138 pacientes (62,2%), y en último lugar la sarcoidosis con 1 solo paciente afectado. La QCS fue la manifestación más común en todos los grupos diagnósticos de EAI, con 146 pacientes (63,5%). De 414 pacientes con Síndrome de Sjögren (SS) y QCS 8 presentaron compromiso ocular adicional, siendo la uveítis la segunda patología ocular asociada en pacientes con SS y la primera causa en las espondiloartropatias (71,4 %). Los pacientes con catarata (4,1%) presentaron la mayor prevalencia de uso de corticoide (88.8%). De 222 pacientes, 28 (12,6%) presentaron uveítis. Del total de pacientes, 16 (7,2%) presentaron maculopatía por antimalaráricos y 6 (18,75%) de los pacientes con LES. Los ANAS se presentaron en el 100% los pacientes con trastorno vascular de la retina. Los pacientes con epiescleritis presentaron la mayor proporción de positivización de anticuerpos anti-DNA. La EAI que más presentó epiescleritis fue LES con 4 pacientes (12,5%) El 22% de paciente con anticuerpos anti-RNP presentaron escleritis y 32,1% de los pacientes con uveítis presentaron HLA-B27 positivo. Las manifestaciones oftalmológicas precedieron a las sistémicas entre un 11,1% y un 33,3% de los pacientes. Conclusión: Las enfermedades oculares se presentan con frecuencia en los pacientes colombianos con EAI (38.65%), siendo la AR la enfermedad con mayor compromiso ocular (62,2%) y la QCS la enfermedad ocular con mayor prevalencia en todas las EAI (63,5%). La uveítis se presentó en 28 pacientes (12,6%). Las manifestaciones oftalmológicas pueden preceder a las sistémicas. El examen oftalmológico debe ser incluido en los pacientes con EAI, por ser la enfermedad ocular una comorbilidad frecuente. Adicionalmente, los efectos oftalmológicos de las medicaciones sistémicas utilizadas en EAI deben ser estrechamente monitorizados, durante el curso del tratamiento.

Increasing the rigour and trustworthiness of participatory evaluations : learnings from the field

Participatory evaluation and participatory action research (PAR) are increasingly used in community-based programs and initiatives and there is a growing acknowledgement of their value. These methodologies focus more on knowledge generated and constructed through lived experience than through social science (Vanderplaat 1995). The scientific ideal of objectivity is usually rejected in favour of a holistic approach that acknowledges and takes into account the diverse perspectives, values and interpretations of participants and evaluation professionals. However, evaluation rigour need not be lost in this approach. Increasing the rigour and trustworthiness of participatory evaluations and PAR increases the likelihood that results are seen as credible and are used to continually improve programs and policies.----- Drawing on learnings and critical reflections about the use of feminist and participatory forms of evaluation and PAR over a 10-year period, significant sources of rigour identified include:----- • participation and communication methods that develop relations of mutual trust and open communication----- • using multiple theories and methodologies, multiple sources of data, and multiple methods of data collection----- • ongoing meta-evaluation and critical reflection----- • critically assessing the intended and unintended impacts of evaluations, using relevant theoretical models----- • using rigorous data analysis and reporting processes----- • participant reviews of evaluation case studies, impact assessments and reports.