Optimization and Machine Learning Frameworks for Complex Network Analysis

Thesis (Ph.D.)--University of Washington, 2016-06

Estudo Comparativo de Métricas de Pontuação para Aprendizagem Estrutural de Redes Bayesianas

Bayesian networks are powerful tools as they represent probability distributions as graphs. They work with uncertainties of real systems. Since last decade there is a special interest in learning network structures from data. However learning the best network structure is a NP-Hard problem, so many heuristics algorithms to generate network structures from data were created. Many of these algorithms use score metrics to generate the network model. This thesis compare three of most used score metrics. The K-2 algorithm and two pattern benchmarks, ASIA and ALARM, were used to carry out the comparison. Results show that score metrics with hyperparameters that strength the tendency to select simpler network structures are better than score metrics with weaker tendency to select simpler network structures for both metrics (Heckerman-Geiger and modified MDL). Heckerman-Geiger Bayesian score metric works better than MDL with large datasets and MDL works better than Heckerman-Geiger with small datasets. The modified MDL gives similar results to Heckerman-Geiger for large datasets and close results to MDL for small datasets with stronger tendency to select simpler network structures

An interactive web-based learning assistant for structural analysis of continuous beams

There are significant levels of concern about the relevance and the difficulty of learning some issues on Strength of Materials and Structural Analysis. Most students of Continuum Mechanics and Structural Analysis in Civil Engineering usually point out some key learning aspects as especially difficult for acquiring specific skills. These key concepts entail comprehension difficulties but ease access and applicability to structural analysis in more advanced subjects. Likewise, some elusive but basic structural concepts, such as flexibility, stiffness or influence lines, are paramount for developing further skills required for advanced structural design: tall buildings, arch-type structures as well as bridges. As new curricular itineraries are currently being implemented, it appears appropriate to devise a repository of interactive web-based applications for training in those basic concepts. That will hopefully train the student to understand the complexity of such concepts, to develop intuitive knowledge on actual structural response and to improve their preparation for exams. In this work, a web-based learning assistant system for influence lines on continuous beams is presented. It consists of a collection of interactive user-friendly applications accessible via Web. It is performed in both Spanish and English languages. Rather than a “black box” system, the procedure involves open interaction with the student, who can simulate and virtually envisage the structural response. Thus, the student is enabled to set the geometric, topologic and mechanic layout of a continuous beam and to change or shift the loading and the support conditions. Simultaneously, the changes in the beam response prompt on the screen, so that the effects of the several issues involved in structural analysis become apparent. The system is performed through a set of web pages which encompasses interactive exercises and problems, written in JavaScript under JQuery and DyGraphs frameworks, given that their efficiency and graphic capabilities are renowned. Students can freely boost their self-study on this subject in order to face their exams more confidently. Besides, this collection is expected to be added to the "Virtual Lab of Continuum Mechanics" of the UPM, launched in 2013 (http://serviciosgate.upm.es/laboratoriosvirtuales/laboratorios/medios-continuos-en-construcci%C3%B3n)

A framework for adaptive e-learning for continuum mechanics and structural analysis

This paper presents a project for providing the students of Structural Engineering with the flexibility to learn outside classroom schedules. The goal is a framework for adaptive E-learning based on a repository of open educational courseware with a set of basic Structural Engineering concepts and fundamentals. These are paramount for students to expand their technical knowledge and skills in structural analysis and design of tall buildings, arch-type structures as well as bridges. Thus, concepts related to structural behaviour such as linearity, compatibility, stiffness and influence lines have traditionally been elusive for students. The objective is to facilitate the student a teachinglearning process to acquire the necessary intuitive knowledge, cognitive skills and the basis for further technological modules and professional development in this area. As a side effect, the system is expected to help the students improve their preparation for exams on the subject. In this project, a web-based open-source system for studying influence lines on continuous beams is presented. It encompasses a collection of interactive user-friendly applications accessible via Web, written in JavaScript under JQuery and Dygraph Libraries, taking advantage of their efficiency and graphic capabilities. It is performed in both Spanish and English languages. The student is enabled to set the geometric, topologic, boundary and mechanic layout of a continuous beam. While changing the loading and the support conditions, the changes in the beam response prompt on the screen, so that the effects of the several issues involved in structural analysis become apparent. This open interaction with the user allows the student to simulate and virtually infer the structural response. Different levels of complexity can be handled, whereas an ongoing help is at hand for any of them. Students can freely boost their experiential learning on this subject at their own pace, in order to further share, process, generalize and apply the relevant essential concepts of Structural Engineering analysis. Besides, this collection is being added to the "Virtual Lab of Continuum Mechanics" of the UPM, launched in 2013 (http://serviciosgate.upm.es/laboratoriosvirtuales/laboratorios/medios-continuos-en-construcci%C3%B3n)

Learning and generalization of structural descriptions : evaluation criteria and comparative review of selected methods /

"February 1980."

Essays in Empirical Operations Management: Bayesian Learning of Service Quality and Structural Estimation of Complementary Product Pricing and Inventory Management

This dissertation contributes to the rapidly growing empirical research area in the field of operations management. It contains two essays, tackling two different sets of operations management questions which are motivated by and built on field data sets from two very different industries --- air cargo logistics and retailing.

The first essay, based on the data set obtained from a world leading third-party logistics company, develops a novel and general Bayesian hierarchical learning framework for estimating customers' spillover learning, that is, customers' learning about the quality of a service (or product) from their previous experiences with similar yet not identical services. We then apply our model to the data set to study how customers' experiences from shipping on a particular route affect their future decisions about shipping not only on that route, but also on other routes serviced by the same logistics company. We find that customers indeed borrow experiences from similar but different services to update their quality beliefs that determine future purchase decisions. Also, service quality beliefs have a significant impact on their future purchasing decisions. Moreover, customers are risk averse; they are averse to not only experience variability but also belief uncertainty (i.e., customer's uncertainty about their beliefs). Finally, belief uncertainty affects customers' utilities more compared to experience variability.

The second essay is based on a data set obtained from a large Chinese supermarket chain, which contains sales as well as both wholesale and retail prices of un-packaged perishable vegetables. Recognizing the special characteristics of this particularly product category, we develop a structural estimation model in a discrete-continuous choice model framework. Building on this framework, we then study an optimization model for joint pricing and inventory management strategies of multiple products, which aims at improving the company's profit from direct sales and at the same time reducing food waste and thus improving social welfare.

Collectively, the studies in this dissertation provide useful modeling ideas, decision tools, insights, and guidance for firms to utilize vast sales and operations data to devise more effective business strategies.

Exploring the mobile structural assessment tool: Concept maps for learning website: Concept Maps for Learning Website

In this paper, we describe how the pathfinder algorithm converts relatedness ratings of concept pairs to concept maps; we also present how this algorithm has been used to develop the Concept Maps for Learning website (www.conceptmapsforlearning.com) based on the principles of effective formative assessment. The pathfinder networks, one of the network representation tools, claim to help more students memorize and recall the relations between concepts than spatial representation tools (such as Multi- Dimensional Scaling). Therefore, the pathfinder networks have been used in various studies on knowledge structures, including identifying students’ misconceptions. To accomplish this, each student’s knowledge map and the expert knowledge map are compared via the pathfinder software, and the differences between these maps are highlighted. After misconceptions are identified, the pathfinder software fails to provide any feedback on these misconceptions. To overcome this weakness, we have been developing a mobile-based concept mapping tool providing visual, textual and remedial feedback (ex. videos, website links and applets) on the concept relations. This information is then placed on the expert concept map, but not on the student’s concept map. Additionally, students are asked to note what they understand from given feedback, and given the opportunity to revise their knowledge maps after receiving various types of feedback.

Evaluation of creative problem solving abilities in undergraduate structural engineers through interdisciplinary problem based learning

For a structural engineer, effective communication and interaction with architects cannot be underestimated as a key skill to success throughout their professional career. Structural engineers and architects have to share a common language and understanding of each other in order to achieve the most desirable architectural and structural designs. This interaction and engagement develops during their professional career but needs to be nurtured during their undergraduate studies. The objective of this paper is to present the strategies employed to engage higher order thinking in structural engineering students in order to help them solve complex problem-based learning (PBL) design scenarios presented by architecture students. The strategies employed were applied in the experimental setting of an undergraduate module in structural engineering at Queen’s University Belfast in the UK. The strategies employed were active learning to engage with content knowledge, the use of physical conceptual structural models to reinforce key concepts and finally, reinforcing the need for hand sketching of ideas to promote higher order problem-solving. The strategies employed were evaluated through student survey, student feedback and module facilitator (this author) reflection. The strategies were qualitatively perceived by the tutor and quantitatively evaluated by students in a cross-sectional study to help interaction with the architecture students, aid interdisciplinary learning and help students creatively solve problems (through higher order thinking). The students clearly enjoyed this module and in particular interacting with structural engineering tutors and students from another discipline

Brain emotional learning based control of a SDOF structural system with a MR damper

This paper describes the application of a Brain Emotional Learning (BEL) controller to improve the response of a SDOF structural system under an earthquake excitation using a magnetorheological (MR) damper. The main goal is to study the performance of a BEL based semi-active control system to generate the control signal for a MR damper. The proposed approach consists of a two controllers: a primary controller based on a BEL algorithm that determines the desired damping force from the system response and a secondary controller that modifies the input current to the MR damper to generate a reference damping force. A parametric model of the damper is used to predict the damping force based on the piston motion and also the current input. A Simulink model of the structural system is developed to analyze the effectiveness of the semi-active controller. Finally, the numerical results are presented and discussed.

Effects of postnatal protein malnutrition on learning and memory procedures

Protein malnutrition induces structural, neurochemical and functional changes in the central nervous system leading to alterations in cognitive and behavioral development of rats. The aim of this work was to investigate the effects of postnatal protein malnutrition on learning and memory tasks. Previously malnourished (6% protein) and well-nourished rats (16% protein) were tested in three experiments: working memory tasks in the Morris water maze (Experiment I), recognition memory of objects (Experiment II), and working memory in the water T-maze (Experiment III). The results showed higher escape latencies in malnourished animals in Experiment I, lower recognition indexes of malnourished animals in Experiment II, and no differences due to diet in Experiment III. It is suggested that protein malnutrition imposed on early life of rats can produce impairments on both working memory in the Morris maze and recognition memory in the open field tests.

Towards a complex model of cooperative learning

Este artigo é uma introdução à teoria do paradigma desconstrutivo de aprendizagem cooperativa. Centenas de estudos provam com evidências o facto de que as estruturas e os processos de aprendizagem cooperativa aumentam o desempenho académico, reforçam as competências de aprendizagem ao longo da vida e desenvolvem competências sociais, pessoais de cada aluno de uma forma mais eficaz e usta, comparativamente às estruturas tradicionais de aprendizagem nas escolas. Enfrentando os desafios dos nossos sistemas educativos, seria interessante elaborar o quadro teórico do discurso da aprendizagem cooperativa, dos últimos 40 anos, a partir de um aspeto prático dentro do contexto teórico e metodológico. Nas últimas décadas, o discurso cooperativo elaborou os elementos práticos e teóricos de estruturas e processos de aprendizagem cooperativa. Gostaríamos de fazer um resumo desses elementos com o objetivo de compreender que tipo de mudanças estruturais podem fazer diferenças reais na prática de ensino e aprendizagem. Os princípios básicos de estruturas cooperativas, os papéis de cooperação e as atitudes cooperativas são os principais elementos que podemos brevemente descrever aqui, de modo a criar um quadro para a compreensão teórica e prática de como podemos sugerir os elementos de aprendizagem cooperativa na nossa prática em sala de aula. Na minha perspetiva, esta complexa teoria da aprendizagem cooperativa pode ser entendida como um paradigma desconstrutivo que fornece algumas respostas pragmáticas para as questões da nossa prática educativa quotidiana, a partir do nível da sala de aula para o nível de sistema educativo, com foco na destruição de estruturas hierárquicas e antidemocráticas de aprendizagem e, criando, ao mesmo tempo, as estruturas cooperativas.

Automatic learning for the classification of chemical reactions and in statistical thermodynamics

This Thesis describes the application of automatic learning methods for a) the classification of organic and metabolic reactions, and b) the mapping of Potential Energy Surfaces(PES). The classification of reactions was approached with two distinct methodologies: a representation of chemical reactions based on NMR data, and a representation of chemical reactions from the reaction equation based on the physico-chemical and topological features of chemical bonds. NMR-based classification of photochemical and enzymatic reactions. Photochemical and metabolic reactions were classified by Kohonen Self-Organizing Maps (Kohonen SOMs) and Random Forests (RFs) taking as input the difference between the 1H NMR spectra of the products and the reactants. The development of such a representation can be applied in automatic analysis of changes in the 1H NMR spectrum of a mixture and their interpretation in terms of the chemical reactions taking place. Examples of possible applications are the monitoring of reaction processes, evaluation of the stability of chemicals, or even the interpretation of metabonomic data. A Kohonen SOM trained with a data set of metabolic reactions catalysed by transferases was able to correctly classify 75% of an independent test set in terms of the EC number subclass. Random Forests improved the correct predictions to 79%. With photochemical reactions classified into 7 groups, an independent test set was classified with 86-93% accuracy. The data set of photochemical reactions was also used to simulate mixtures with two reactions occurring simultaneously. Kohonen SOMs and Feed-Forward Neural Networks (FFNNs) were trained to classify the reactions occurring in a mixture based on the 1H NMR spectra of the products and reactants. Kohonen SOMs allowed the correct assignment of 53-63% of the mixtures (in a test set). Counter-Propagation Neural Networks (CPNNs) gave origin to similar results. The use of supervised learning techniques allowed an improvement in the results. They were improved to 77% of correct assignments when an ensemble of ten FFNNs were used and to 80% when Random Forests were used. This study was performed with NMR data simulated from the molecular structure by the SPINUS program. In the design of one test set, simulated data was combined with experimental data. The results support the proposal of linking databases of chemical reactions to experimental or simulated NMR data for automatic classification of reactions and mixtures of reactions. Genome-scale classification of enzymatic reactions from their reaction equation. The MOLMAP descriptor relies on a Kohonen SOM that defines types of bonds on the basis of their physico-chemical and topological properties. The MOLMAP descriptor of a molecule represents the types of bonds available in that molecule. The MOLMAP descriptor of a reaction is defined as the difference between the MOLMAPs of the products and the reactants, and numerically encodes the pattern of bonds that are broken, changed, and made during a chemical reaction. The automatic perception of chemical similarities between metabolic reactions is required for a variety of applications ranging from the computer validation of classification systems, genome-scale reconstruction (or comparison) of metabolic pathways, to the classification of enzymatic mechanisms. Catalytic functions of proteins are generally described by the EC numbers that are simultaneously employed as identifiers of reactions, enzymes, and enzyme genes, thus linking metabolic and genomic information. Different methods should be available to automatically compare metabolic reactions and for the automatic assignment of EC numbers to reactions still not officially classified. In this study, the genome-scale data set of enzymatic reactions available in the KEGG database was encoded by the MOLMAP descriptors, and was submitted to Kohonen SOMs to compare the resulting map with the official EC number classification, to explore the possibility of predicting EC numbers from the reaction equation, and to assess the internal consistency of the EC classification at the class level. A general agreement with the EC classification was observed, i.e. a relationship between the similarity of MOLMAPs and the similarity of EC numbers. At the same time, MOLMAPs were able to discriminate between EC sub-subclasses. EC numbers could be assigned at the class, subclass, and sub-subclass levels with accuracies up to 92%, 80%, and 70% for independent test sets. The correspondence between chemical similarity of metabolic reactions and their MOLMAP descriptors was applied to the identification of a number of reactions mapped into the same neuron but belonging to different EC classes, which demonstrated the ability of the MOLMAP/SOM approach to verify the internal consistency of classifications in databases of metabolic reactions. RFs were also used to assign the four levels of the EC hierarchy from the reaction equation. EC numbers were correctly assigned in 95%, 90%, 85% and 86% of the cases (for independent test sets) at the class, subclass, sub-subclass and full EC number level,respectively. Experiments for the classification of reactions from the main reactants and products were performed with RFs - EC numbers were assigned at the class, subclass and sub-subclass level with accuracies of 78%, 74% and 63%, respectively. In the course of the experiments with metabolic reactions we suggested that the MOLMAP / SOM concept could be extended to the representation of other levels of metabolic information such as metabolic pathways. Following the MOLMAP idea, the pattern of neurons activated by the reactions of a metabolic pathway is a representation of the reactions involved in that pathway - a descriptor of the metabolic pathway. This reasoning enabled the comparison of different pathways, the automatic classification of pathways, and a classification of organisms based on their biochemical machinery. The three levels of classification (from bonds to metabolic pathways) allowed to map and perceive chemical similarities between metabolic pathways even for pathways of different types of metabolism and pathways that do not share similarities in terms of EC numbers. Mapping of PES by neural networks (NNs). In a first series of experiments, ensembles of Feed-Forward NNs (EnsFFNNs) and Associative Neural Networks (ASNNs) were trained to reproduce PES represented by the Lennard-Jones (LJ) analytical potential function. The accuracy of the method was assessed by comparing the results of molecular dynamics simulations (thermal, structural, and dynamic properties) obtained from the NNs-PES and from the LJ function. The results indicated that for LJ-type potentials, NNs can be trained to generate accurate PES to be used in molecular simulations. EnsFFNNs and ASNNs gave better results than single FFNNs. A remarkable ability of the NNs models to interpolate between distant curves and accurately reproduce potentials to be used in molecular simulations is shown. The purpose of the first study was to systematically analyse the accuracy of different NNs. Our main motivation, however, is reflected in the next study: the mapping of multidimensional PES by NNs to simulate, by Molecular Dynamics or Monte Carlo, the adsorption and self-assembly of solvated organic molecules on noble-metal electrodes. Indeed, for such complex and heterogeneous systems the development of suitable analytical functions that fit quantum mechanical interaction energies is a non-trivial or even impossible task. The data consisted of energy values, from Density Functional Theory (DFT) calculations, at different distances, for several molecular orientations and three electrode adsorption sites. The results indicate that NNs require a data set large enough to cover well the diversity of possible interaction sites, distances, and orientations. NNs trained with such data sets can perform equally well or even better than analytical functions. Therefore, they can be used in molecular simulations, particularly for the ethanol/Au (111) interface which is the case studied in the present Thesis. Once properly trained, the networks are able to produce, as output, any required number of energy points for accurate interpolations.

Learning networks and moodle use in online courses: a social network analysis study

Dissertação para obtenção do Grau de Doutor em Ciências da Educação Especialidade em Tecnologias, Redes e Multimédia na Educação e Formação

Structural transformation in the product space: Patterns and implications

Product fundamentals are essential in explaining heterogeneity in the product space. The scope for adapting and transferring capabilities into the production of different goods determines the speed and intensity of the structural transformation process and entails dissimilar development opportunities for nations. Future specialization patterns become then partly determined by the current network of products’ relatedness. Building on previous literature, this paper explicitly compares methodological concepts of product connectivity to conclude in favor of the density measure we propose combined with the Revealed Relatedness Index (RRI) approach presented by Freitas and Salvado (2011). Overall, RRI specifications displayed more consistent behavior when different time horizons are equated.

The tutor's role in student learning

Higher education in Portugal, in the last forty years, has undergone profound changes with the enlargement of public higher education network, the appearance of new institutions, the quantity and the heterogeneity of students. The implementation of the Bologna Process in European community countries led to the redesign of higher education Portuguese courses as well as their corresponding curricula. In recent years, the use of Project-led education was one of the most significant changes in teaching and learning, particularly in engineering in higher education in Portugal. This teaching methodology encourages students and teachers to undertake new roles, new responsibilities and a new learning perspective. This study aims at understanding whether the role of the tutor is to be suitable to the needs and expectations of Project-led education students. These changes however are not only structural. At the University of Minho, new teaching and learning methodologies were adopted, which could guide the training of professionals on to the twenty-first century. The opportunity arising from the implementation of Project-led education in Engineering methodology was used in the University of Minho’s courses. This teaching method is intended to provide students with educational support programs that benefit the academic performance, allowing the opportunity to upgrade, train and develop the ability to study and learn more effectively. Through the Project-led education it is possible to provide students with techniques and procedures and develop the ability to communicate orally and in writing. Students and teachers have assumed new roles in the teaching-learning process allowing in one hand the students to explore, discover and question themselves about some knowledge and on the other hand the teachers to change to a tutor, a companion and to a student project guide. Therefore, surveys were analyzed, comprising questions about the most significant contribution of the tutor as well as if there are some initial expectations that have not been foreseen by the tutor.