Machine learning and data decompositions for complex networked dynamical systems

Thesis (Ph.D.)--University of Washington, 2016-08

Resolved Stars' Insights into Galaxy Physics

Thesis (Ph.D.)--University of Washington, 2016-08

Gestão de trajetos fora de estrada

Anualmente, realizam-se no país inúmeras iniciativas de Todo-Terreno Turístico (TTT) onde são automaticamente registadas as coordenadas de Global Positioning System (GPS) por aplicações de dispositivos móveis. Este tipo de informação pode ser utilizada, quer para fins de divulgação turística, quer por outro tipo de entidades que necessitem de circular nesses caminhos rurais, tipicamente no meio da montanha. Entre outras, são registadas a posição, velocidade e altitude do veículo, o que permite obter informações relevantes, tais como, se o percurso se encontra transitável ou qual a velocidade recomendada. Por exemplo, durante os combates a incêndios, os bombeiros e proteção civil poderão saber se estes percursos são utilizáveis no planeamento dos combates a incêndios com reduzida probabilidade de complicações relativa ao acesso dos veículos, melhorando assim o tempo de resposta. O presente documento discute como poderá ser concebida uma aplicação web mapping, de código aberto, que permita a partilha, utilização e valorização de dados relativos aos percursos todo-terreno dos praticantes de TTT. O presente documento descreve como a aplicação desenvolvida no âmbito da dissertação de mestrado permite selecionar e ordenar possíveis trajetos que incluem os trajetos de TTT, apresentando as caraterísticas do terreno de modo a auxiliar a tomada de decisão por membros das corporações de Bombeiros. Será igualmente apresentada a interface atual da aplicação que inclui um mapa dinâmico e um gestor de pontos de referência.

Vision-based tracking for sports performance analysis

With the world of professional sports shifting towards employing better sport analytics, the demand for vision-based performance analysis is growing increasingly in recent years. In addition, the nature of many sports does not allow the use of any kind of sensors or other wearable markers attached to players for monitoring their performances during competitions. This provides a potential application of systematic observations such as tracking information of the players to help coaches to develop their visual skills and perceptual awareness needed to make decisions about team strategy or training plans. My PhD project is part of a bigger ongoing project between sport scientists and computer scientists involving also industry partners and sports organisations. The overall idea is to investigate the contribution technology can make to the analysis of sports performance on the example of team sports such as rugby, football or hockey. A particular focus is on vision-based tracking, so that information about the location and dynamics of the players can be gained without any additional sensors on the players. To start with, prior approaches on visual tracking are extensively reviewed and analysed. In this thesis, methods to deal with the difficulties in visual tracking to handle the target appearance changes caused by intrinsic (e.g. pose variation) and extrinsic factors, such as occlusion, are proposed. This analysis highlights the importance of the proposed visual tracking algorithms, which reflect these challenges and suggest robust and accurate frameworks to estimate the target state in a complex tracking scenario such as a sports scene, thereby facilitating the tracking process. Next, a framework for continuously tracking multiple targets is proposed. Compared to single target tracking, multi-target tracking such as tracking the players on a sports field, poses additional difficulties, namely data association, which needs to be addressed. Here, the aim is to locate all targets of interest, inferring their trajectories and deciding which observation corresponds to which target trajectory is. In this thesis, an efficient framework is proposed to handle this particular problem, especially in sport scenes, where the players of the same team tend to look similar and exhibit complex interactions and unpredictable movements resulting in matching ambiguity between the players. The presented approach is also evaluated on different sports datasets and shows promising results. Finally, information from the proposed tracking system is utilised as the basic input for further higher level performance analysis such as tactics and team formations, which can help coaches to design a better training plan. Due to the continuous nature of many team sports (e.g. soccer, hockey), it is not straightforward to infer the high-level team behaviours, such as players’ interaction. The proposed framework relies on two distinct levels of performance analysis: low-level performance analysis, such as identifying players positions on the play field, as well as a high-level analysis, where the aim is to estimate the density of player locations or detecting their possible interaction group. The related experiments show the proposed approach can effectively explore this high-level information, which has many potential applications.

Towards Proactive Context-aware Computing and Systems

A primary goal of context-aware systems is delivering the right information at the right place and right time to users in order to enable them to make effective decisions and improve their quality of life. There are three key requirements for achieving this goal: determining what information is relevant, personalizing it based on the users’ context (location, preferences, behavioral history etc.), and delivering it to them in a timely manner without an explicit request from them. These requirements create a paradigm that we term as “Proactive Context-aware Computing”. Most of the existing context-aware systems fulfill only a subset of these requirements. Many of these systems focus only on personalization of the requested information based on users’ current context. Moreover, they are often designed for specific domains. In addition, most of the existing systems are reactive - the users request for some information and the system delivers it to them. These systems are not proactive i.e. they cannot anticipate users’ intent and behavior and act proactively without an explicit request from them. In order to overcome these limitations, we need to conduct a deeper analysis and enhance our understanding of context-aware systems that are generic, universal, proactive and applicable to a wide variety of domains. To support this dissertation, we explore several directions. Clearly the most significant sources of information about users today are smartphones. A large amount of users’ context can be acquired through them and they can be used as an effective means to deliver information to users. In addition, social media such as Facebook, Flickr and Foursquare provide a rich and powerful platform to mine users’ interests, preferences and behavioral history. We employ the ubiquity of smartphones and the wealth of information available from social media to address the challenge of building proactive context-aware systems. We have implemented and evaluated a few approaches, including some as part of the Rover framework, to achieve the paradigm of Proactive Context-aware Computing. Rover is a context-aware research platform which has been evolving for the last 6 years. Since location is one of the most important context for users, we have developed ‘Locus’, an indoor localization, tracking and navigation system for multi-story buildings. Other important dimensions of users’ context include the activities that they are engaged in. To this end, we have developed ‘SenseMe’, a system that leverages the smartphone and its multiple sensors in order to perform multidimensional context and activity recognition for users. As part of the ‘SenseMe’ project, we also conducted an exploratory study of privacy, trust, risks and other concerns of users with smart phone based personal sensing systems and applications. To determine what information would be relevant to users’ situations, we have developed ‘TellMe’ - a system that employs a new, flexible and scalable approach based on Natural Language Processing techniques to perform bootstrapped discovery and ranking of relevant information in context-aware systems. In order to personalize the relevant information, we have also developed an algorithm and system for mining a broad range of users’ preferences from their social network profiles and activities. For recommending new information to the users based on their past behavior and context history (such as visited locations, activities and time), we have developed a recommender system and approach for performing multi-dimensional collaborative recommendations using tensor factorization. For timely delivery of personalized and relevant information, it is essential to anticipate and predict users’ behavior. To this end, we have developed a unified infrastructure, within the Rover framework, and implemented several novel approaches and algorithms that employ various contextual features and state of the art machine learning techniques for building diverse behavioral models of users. Examples of generated models include classifying users’ semantic places and mobility states, predicting their availability for accepting calls on smartphones and inferring their device charging behavior. Finally, to enable proactivity in context-aware systems, we have also developed a planning framework based on HTN planning. Together, these works provide a major push in the direction of proactive context-aware computing.

Comunidades tradicionais em áreas protegidas: convergências e lacunas da Política Urbana e Ambiental na Reserva de Desenvolvimento Sustentável Estadual Ponta do Tubarão/RN

This work presents a reflection on possibilities and boundaries of consolidation and expansion of human settlements characterized as traditional communities that are located within protected areas, using as study reference the State Sustainable Development Reserve Ponta do Tubarão, at Rio Grande do Norte state. The main topics highlight the conflict between the right to housing and the prevalence of fundamental rights of traditional populations, opposed to the diffuse right to environment, according to the regulatory framework of the Brazilian Urban and Environmental Policies. At the same time that these settlements, historically built, are substantiated by the principles of recognition of rights to traditional populations, they are in a condition of complexity to the resolution of conflicts in its urban dimension and lead to an impairment of natural sites. This work questions how the instruments of land use and occupation are defined and relate to environmental planning, especially considering that the settlements are located in Permanent Preservation Areas (APP). It aims to further the discussion of the urban dimension in settlements, characterizing its formation and growth process, to identify the gaps and convergences between the Urban and Environmental Policy, under the foundations of a socio-environmental approach. The results spotlights the conflicts between occupation and natural areas, inferring that the definition of Urban Policies instruments and its integration with Environmental Policies instruments account for essential and priority actions to the achievement to the rights to a sustainable city, as determined in the Cities Statute and environmental protection goals, defined for the Conservation Units

As potencialidades da imagem como recurso didático no desenvolvimento de alunos histórica e geograficamente competentes

Relatório de investigação apresentado à Escola Superior de Educação de Lisboa para obtenção de grau de mestre no Mestrado em Ensino do 1.º e 2.º Ciclo do Ensino Básico

MINDREADING: INTROVERSION VERSUS EXTRAVERSION

Theory of mind (also called ‘mindreading’), is the ability to explain and predict others’ behaviour by inferring their mental states, such as their knowledge, beliefs, perceptions and desires. One largely unexplored question in theory of mind research is the relationship between personality and theory of mind abilities in adults. The current study investigated introverts’ and extraverts’ performance on two theory of mind tasks: one task involved judging emotional states from pictures of eyes (RMTE task), and the other involved making judgments about one’s own and others’ visual perspective (AVP task). In both tasks, the personal relevance of the situation was varied to examine whether this factor would differentially affect the performance of introverts and extraverts. There was a significant interaction between personality (introvert vs. extravert) and condition (personal vs. impersonal) in the AVP task, with extraverts performing better in the personal than in the impersonal condition but introverts performing the same in both conditions. In the RMTE task there was no interaction, as all participants performed better in the personal condition regardless of personality. There was also a main effect of personality in the RMTE, with introverts performing better overall than extraverts at judging emotions from eyes. Possible reasons behind these and other observed differences are discussed.

Genetic variation of temperature-regulated curd induction in cauliflower: elucidation of floral transition by genome-wide association mapping and gene expression analysis

Cauliflower (Brassica oleracea var. botrytis) is a vernalization-responsive crop. High ambient temperatures delay harvest time. The elucidation of the genetic regulation of floral transition is highly interesting for a precise harvest scheduling and to ensure stable market supply. This study aims at genetic dissection of temperature-dependent curd induction in cauliflower by genome-wide association studies and gene expression analysis. To assess temperature dependent curd induction, two greenhouse trials under distinct temperature regimes were conducted on a diversity panel consisting of 111 cauliflower commercial parent lines, genotyped with 14,385 SNPs. Broad phenotypic variation and high heritability (0.93) were observed for temperature-related curd induction within the cauliflower population. GWA mapping identified a total of 18 QTL localized on chromosomes O1, O2, O3, O4, O6, O8, and O9 for curding time under two distinct temperature regimes. Among those, several QTL are localized within regions of promising candidate flowering genes. Inferring population structure and genetic relatedness among the diversity set assigned three main genetic clusters. Linkage disequilibrium (LD) patterns estimated global LD extent of r(2) = 0.06 and a maximum physical distance of 400 kb for genetic linkage. Transcriptional profiling of flowering genes FLOWERING LOCUS C (BoFLC) and VERNALIZATION 2 (BoVRN2) was performed, showing increased expression levels of BoVRN2 in genotypes with faster curding. However, functional relevance of BoVRN2 and BoFLC2 could not consistently be supported, which probably suggests to act facultative and/or might evidence for BoVRN2/BoFLC-independent mechanisms in temperature regulated floral transition in cauliflower. Genetic insights in temperature-regulated curd induction can underpin genetically informed phenology models and benefit molecular breeding strategies toward the development of thermo-tolerant cultivars.

Computational Analysis of Intelligent Agents: Social and Strategic Settings

The central motif of this work is prediction and optimization in presence of multiple interacting intelligent agents. We use the phrase `intelligent agents' to imply in some sense, a `bounded rationality', the exact meaning of which varies depending on the setting. Our agents may not be `rational' in the classical game theoretic sense, in that they don't always optimize a global objective. Rather, they rely on heuristics, as is natural for human agents or even software agents operating in the real-world. Within this broad framework we study the problem of influence maximization in social networks where behavior of agents is myopic, but complication stems from the structure of interaction networks. In this setting, we generalize two well-known models and give new algorithms and hardness results for our models. Then we move on to models where the agents reason strategically but are faced with considerable uncertainty. For such games, we give a new solution concept and analyze a real-world game using out techniques. Finally, the richest model we consider is that of Network Cournot Competition which deals with strategic resource allocation in hypergraphs, where agents reason strategically and their interaction is specified indirectly via player's utility functions. For this model, we give the first equilibrium computability results. In all of the above problems, we assume that payoffs for the agents are known. However, for real-world games, getting the payoffs can be quite challenging. To this end, we also study the inverse problem of inferring payoffs, given game history. We propose and evaluate a data analytic framework and we show that it is fast and performant.

Aspectos da morfometria de drenagem e produção de sedimentos em relação a variante chuva x vazão de diferentes trechos da Bacia do Ribeirão do Gama – Distrito Federal nos anos de 2014 a 2015

Dissertação (mestrado)—Universidade de Brasília, Departamento de Geografia, Programa de Pós-Graduação em Geografia, 2015.

Phylogenetic inference's algorithms

Phylogenetic inference consist in the search of an evolutionary tree to explain the best way possible genealogical relationships of a set of species. Phylogenetic analysis has a large number of applications in areas such as biology, ecology, paleontology, etc. There are several criterias which has been defined in order to infer phylogenies, among which are the maximum parsimony and maximum likelihood. The first one tries to find the phylogenetic tree that minimizes the number of evolutionary steps needed to describe the evolutionary history among species, while the second tries to find the tree that has the highest probability of produce the observed data according to an evolutionary model. The search of a phylogenetic tree can be formulated as a multi-objective optimization problem, which aims to find trees which satisfy simultaneously (and as much as possible) both criteria of parsimony and likelihood. Due to the fact that these criteria are different there won't be a single optimal solution (a single tree), but a set of compromise solutions. The solutions of this set are called "Pareto Optimal". To find this solutions, evolutionary algorithms are being used with success nowadays.This algorithms are a family of techniques, which aren’t exact, inspired by the process of natural selection. They usually find great quality solutions in order to resolve convoluted optimization problems. The way this algorithms works is based on the handling of a set of trial solutions (trees in the phylogeny case) using operators, some of them exchanges information between solutions, simulating DNA crossing, and others apply aleatory modifications, simulating a mutation. The result of this algorithms is an approximation to the set of the “Pareto Optimal” which can be shown in a graph with in order that the expert in the problem (the biologist when we talk about inference) can choose the solution of the commitment which produces the higher interest. In the case of optimization multi-objective applied to phylogenetic inference, there is open source software tool, called MO-Phylogenetics, which is designed for the purpose of resolving inference problems with classic evolutionary algorithms and last generation algorithms. REFERENCES [1] C.A. Coello Coello, G.B. Lamont, D.A. van Veldhuizen. Evolutionary algorithms for solving multi-objective problems. Spring. Agosto 2007 [2] C. Zambrano-Vega, A.J. Nebro, J.F Aldana-Montes. MO-Phylogenetics: a phylogenetic inference software tool with multi-objective evolutionary metaheuristics. Methods in Ecology and Evolution. En prensa. Febrero 2016.

Algorithmes de construction et correction d'arbres de gènes par la réconciliation

Les gènes, qui servent à encoder les fonctions biologiques des êtres vivants, forment l'unité moléculaire de base de l'hérédité. Afin d'expliquer la diversité des espèces que l'on peut observer aujourd'hui, il est essentiel de comprendre comment les gènes évoluent. Pour ce faire, on doit recréer le passé en inférant leur phylogénie, c'est-à-dire un arbre de gènes qui représente les liens de parenté des régions codantes des vivants. Les méthodes classiques d'inférence phylogénétique ont été élaborées principalement pour construire des arbres d'espèces et ne se basent que sur les séquences d'ADN. Les gènes sont toutefois riches en information, et on commence à peine à voir apparaître des méthodes de reconstruction qui utilisent leurs propriétés spécifiques. Notamment, l'histoire d'une famille de gènes en terme de duplications et de pertes, obtenue par la réconciliation d'un arbre de gènes avec un arbre d'espèces, peut nous permettre de détecter des faiblesses au sein d'un arbre et de l'améliorer. Dans cette thèse, la réconciliation est appliquée à la construction et la correction d'arbres de gènes sous trois angles différents: 1) Nous abordons la problématique de résoudre un arbre de gènes non-binaire. En particulier, nous présentons un algorithme en temps linéaire qui résout une polytomie en se basant sur la réconciliation. 2) Nous proposons une nouvelle approche de correction d'arbres de gènes par les relations d'orthologie et paralogie. Des algorithmes en temps polynomial sont présentés pour les problèmes suivants: corriger un arbre de gènes afin qu'il contienne un ensemble d'orthologues donné, et valider un ensemble de relations partielles d'orthologie et paralogie. 3) Nous montrons comment la réconciliation peut servir à "combiner'' plusieurs arbres de gènes. Plus précisément, nous étudions le problème de choisir un superarbre de gènes selon son coût de réconciliation.

Conditional mutual inclusive information enables accurate quantification of associations in gene regulatory networks

International audience

The Westerly Index as complementary indicator of the North Atlantic oscillation in explaining drought variability across Europe

This paper analyses the influence of different atmospheric circulation indices on the multi-scalar drought variability across Europe by using the Standardized Precipitation Evapotranspiration Index (SPEI). The monthly circulation indices used in this study include the North Atlantic oscillation (NAO), the East Atlantic (EA), the Scandinavian (SCAN) and the East Atlantic-Western Russia (EA-WR) patterns, as well as the recently published Westerly Index (WI), defined as the persistence of westerly winds over the eastern north Atlantic region. The results indicate that European drought variability is better explained by the station-based NAO index and the WI than by any other combination of circulation indices. In northern and central Europe the variability of drought severity for different seasons and time-scales is strongly associated with the WI. On the contrary, the influence of the NAO on southern Europe droughts is stronger than that exerted by the WI. The correlation patterns of the NAO and WI with the SPEI show a spatial complementarity in shaping drought variability across Europe. Lagged correlations of the NAO and WI with the SPEI also indicate enough skill of both indices to anticipate drought severity several months in advance. As long as instrumental series of the NAO and WI are available, their combined use would allow inferring European drought variability for the last two centuries and improve the calibration and interpretation of paleoclimatic proxies associated with drought.