Algorithms for Geometric Matching, Clustering, and Covering

With the popularization of GPS-enabled devices such as mobile phones, location data are becoming available at an unprecedented scale. The locations may be collected from many different sources such as vehicles moving around a city, user check-ins in social networks, and geo-tagged micro-blogging photos or messages. Besides the longitude and latitude, each location record may also have a timestamp and additional information such as the name of the location. Time-ordered sequences of these locations form trajectories, which together contain useful high-level information about people's movement patterns.

The first part of this thesis focuses on a few geometric problems motivated by the matching and clustering of trajectories. We first give a new algorithm for computing a matching between a pair of curves under existing models such as dynamic time warping (DTW). The algorithm is more efficient than standard dynamic programming algorithms both theoretically and practically. We then propose a new matching model for trajectories that avoids the drawbacks of existing models. For trajectory clustering, we present an algorithm that computes clusters of subtrajectories, which correspond to common movement patterns. We also consider trajectories of check-ins, and propose a statistical generative model, which identifies check-in clusters as well as the transition patterns between the clusters.

The second part of the thesis considers the problem of covering shortest paths in a road network, motivated by an EV charging station placement problem. More specifically, a subset of vertices in the road network are selected to place charging stations so that every shortest path contains enough charging stations and can be traveled by an EV without draining the battery. We first introduce a general technique for the geometric set cover problem. This technique leads to near-linear-time approximation algorithms, which are the state-of-the-art algorithms for this problem in either running time or approximation ratio. We then use this technique to develop a near-linear-time algorithm for this

shortest-path cover problem.

Rise, fragmentation, infringement and fear: emerging urban issues in Chinese clustering in Italy

China is today facing rapid economic development and the long-term implications of China’s rise for European economy, society and culture, are constantly debated but still almost unknown. Moreover, only recently a new volume edited by Kunzmann has clearly pointed out a particular field of research like the EU spatial impact of China’s convergence in the global market. The aim of the present paper is to deal with the spatial issues related to the growing Chinese communities, especially in Italy, that are part of a more general and considerable transformation process of the traditional Chinese enclaves in EU cities: from recognizable “Chinatowns” to new hybrid urban formations where housing, retail, wholesale and even commodity production often tend to match. Key-Concepts like rise, fragmentation, infringement and fear are useful in analysing some of the more controversial socio-economic dynamics of Chinese clusters especially in a traditionally manufactured-based country like Italy, where it’s recognizable a unique paradox of a “double competition” from outside and from inside. This statement poses a serious threat to local economic systems in terms of sustainability and social cohesion, making it necessary to rethink the role and the nature of public action in facing new forms of marginality at urban and regional level.

Clustering in non-parametric multivariate analyses.

Non-parametric multivariate analyses of complex ecological datasets are widely used. Following appropriate pre-treatment of the data inter-sample resemblances are calculated using appropriate measures. Ordination and clustering derived from these resemblances are used to visualise relationships among samples (or variables). Hierarchical agglomerative clustering with group-average (UPGMA) linkage is often the clustering method chosen. Using an example dataset of zooplankton densities from the Bristol Channel and Severn Estuary, UK, a range of existing and new clustering methods are applied and the results compared. Although the examples focus on analysis of samples, the methods may also be applied to species analysis. Dendrograms derived by hierarchical clustering are compared using cophenetic correlations, which are also used to determine optimum  in flexible beta clustering. A plot of cophenetic correlation against original dissimilarities reveals that a tree may be a poor representation of the full multivariate information. UNCTREE is an unconstrained binary divisive clustering algorithm in which values of the ANOSIM R statistic are used to determine (binary) splits in the data, to form a dendrogram. A form of flat clustering, k-R clustering, uses a combination of ANOSIM R and Similarity Profiles (SIMPROF) analyses to determine the optimum value of k, the number of groups into which samples should be clustered, and the sample membership of the groups. Robust outcomes from the application of such a range of differing techniques to the same resemblance matrix, as here, result in greater confidence in the validity of a clustering approach.

Clustering in non-parametric multivariate analyses.

Non-parametric multivariate analyses of complex ecological datasets are widely used. Following appropriate pre-treatment of the data inter-sample resemblances are calculated using appropriate measures. Ordination and clustering derived from these resemblances are used to visualise relationships among samples (or variables). Hierarchical agglomerative clustering with group-average (UPGMA) linkage is often the clustering method chosen. Using an example dataset of zooplankton densities from the Bristol Channel and Severn Estuary, UK, a range of existing and new clustering methods are applied and the results compared. Although the examples focus on analysis of samples, the methods may also be applied to species analysis. Dendrograms derived by hierarchical clustering are compared using cophenetic correlations, which are also used to determine optimum  in flexible beta clustering. A plot of cophenetic correlation against original dissimilarities reveals that a tree may be a poor representation of the full multivariate information. UNCTREE is an unconstrained binary divisive clustering algorithm in which values of the ANOSIM R statistic are used to determine (binary) splits in the data, to form a dendrogram. A form of flat clustering, k-R clustering, uses a combination of ANOSIM R and Similarity Profiles (SIMPROF) analyses to determine the optimum value of k, the number of groups into which samples should be clustered, and the sample membership of the groups. Robust outcomes from the application of such a range of differing techniques to the same resemblance matrix, as here, result in greater confidence in the validity of a clustering approach.

Extracting Patterns from Educational Traces via Clustering and Associated Quality Metrics

Clustering algorithms, pattern mining techniques and associated quality metrics emerged as reliable methods for modeling learners’ performance, comprehension and interaction in given educational scenarios. The specificity of available data such as missing values, extreme values or outliers, creates a challenge to extract significant user models from an educational perspective. In this paper we introduce a pattern detection mechanism with-in our data analytics tool based on k-means clustering and on SSE, silhouette, Dunn index and Xi-Beni index quality metrics. Experiments performed on a dataset obtained from our online e-learning platform show that the extracted interaction patterns were representative in classifying learners. Furthermore, the performed monitoring activities created a strong basis for generating automatic feedback to learners in terms of their course participation, while relying on their previous performance. In addition, our analysis introduces automatic triggers that highlight learners who will potentially fail the course, enabling tutors to take timely actions.

MixKMeans: Clustering Question-Answer Archives

Community-driven Question Answering (CQA) systems that crowdsource experiential information in the form of questions and answers and have accumulated valuable reusable knowledge. Clustering of QA datasets from CQA systems provides a means of organizing the content to ease tasks such as manual curation and tagging. In this paper, we present a clustering method that exploits the two-part question-answer structure in QA datasets to improve clustering quality. Our method, {\it MixKMeans}, composes question and answer space similarities in a way that the space on which the match is higher is allowed to dominate. This construction is motivated by our observation that semantic similarity between question-answer data (QAs) could get localized in either space. We empirically evaluate our method on a variety of real-world labeled datasets. Our results indicate that our method significantly outperforms state-of-the-art clustering methods for the task of clustering question-answer archives.

Abnormal Pedestrian Trajectory analysis based on arbitrary-length clustering

This papers examines the use of trajectory distance measures and clustering techniques to define normal
and abnormal trajectories in the context of pedestrian tracking in public spaces. In order to detect abnormal
trajectories, what is meant by a normal trajectory in a given scene is firstly defined. Then every trajectory
that deviates from this normality is classified as abnormal. By combining Dynamic Time Warping and a
modified K-Means algorithms for arbitrary-length data series, we have developed an algorithm for trajectory
clustering and abnormality detection. The final system performs with an overall accuracy of 83% and 75%
when tested in two different standard datasets.

Respiratory virus-associated severe acute respiratory illness (SARI) and viral clustering in Malawian children in a setting with a high prevalence of HIV, malaria and malnutrition

BACKGROUND:  We used four years of paediatric severe acute respiratory illness (SARI) sentinel surveillance in Blantyre, Malawi to identify factors associated with clinical severity and co-viral clustering.

METHODS:  From January 2011 to December 2014, 2363 children aged 3 months to 14 years presenting to hospital with SARI were enrolled. Nasopharyngeal aspirates were tested for influenza and other respiratory viruses. We assessed risk factors for clinical severity and conducted clustering analysis to identify viral clusters in children with co-viral detection.

RESULTS:  Hospital-attended influenza-positive SARI incidence was 2.0 cases per 10,000 children annually; it was highest children aged under 1 year (6.3 cases per 10,000), and HIV-infected children aged 5 to 9 years (6.0 cases per 10,000). 605 (26.8%) SARI cases had warning signs, which were positively associated with HIV infection (adjusted risk ratio [aRR]: 2.4, 95% CI: 1.4, 3.9), RSV infection (aRR: 1.9, 95% CI: 1.3, 3.0) and rainy season (aRR: 2.4, 95% CI: 1.6, 3.8). We identified six co-viral clusters; one cluster was associated with SARI with warning signs.

CONCLUSIONS:  Influenza vaccination may benefit young children and HIV infected children in this setting. Viral clustering may be associated with SARI severity; its assessment should be included in routine SARI surveillance.

Clustering-based Query Routing in Cooperative Semi-structured Peer to Peer Networks

We consider the problem of resource selection in clustered Peer-to-Peer Information Retrieval (P2P IR) networks with cooperative peers. The clustered P2P IR framework presents a significant departure from general P2P IR architectures by employing clustering to ensure content coherence between resources at the resource selection layer, without disturbing document allocation. We propose that such a property could be leveraged in resource selection by adapting well-studied and popular inverted lists for centralized document retrieval. Accordingly, we propose the Inverted PeerCluster Index (IPI), an approach that adapts the inverted lists, in a straightforward manner, for resource selection in clustered P2P IR. IPI also encompasses a strikingly simple peer-specific scoring mechanism that exploits the said index for resource selection. Through an extensive empirical analysis on P2P IR testbeds, we establish that IPI competes well with the sophisticated state-of-the-art methods in virtually every parameter of interest for the resource selection task, in the context of clustered P2P IR.

Análise das Dimensões do Comprometimento no Setor Público da Educação: um estudo de caso com os gestores dos Campi do IFCE Limoeiro do Norte e Fortaleza - CE/Brasil

O presente trabalho tem como objetivo definir, analisar e identificar por meio de um estudo de caso, as dimensões de comprometimento organizacional: afetivas, instrumental e normativa dos gestores do campus do Limoeiro do Norte, que estão em fase de estágio probatório e dos gestores do campus Fortaleza que já passaram desse estágio, traçar um comparativo e relacionar ambos os casos. Norteado por um modelo teórico de comprometimento organizacional abordado por Meyer e Allen (1991; 1997). Comprometimento no setor público neste estudo tem-se como unidade de análise duas instituições federais de educação, ciência e tecnologia. Como os gestores são, na maioria das vezes, responsáveis pelo desenvolvimento de uma força de trabalho capaz e comprometida, sua atuação torna-se de fundamental importância no âmbito da educação, aliado a competência técnica e a vontade polí­tica de ações planejadas. De acordo com a pesquisa descritiva e quantitativa, foram aplicados questionários já testados e validados, contendo aspectos semi-estruturados, onde foi dividido em duas partes: a primeira, com seis itens, abordando as caracterí­sticas pessoais e funcionais dos gestores do IFCE de cada campus estudado, e segunda, que possui dezoito itens divididos nas três dimensões do comprometimento organizacional: afetivo, instrumental e normativo, tudo baseado na escala de mensuração do comprometimento de Meyer e Allen (1997) modelo internacionalmente aceito e validado. Os resultados obtidos na pesquisa apontaram que dos 35 gestores do campus Limoeiro do Norte o comprometimento organizacional que obteve maior média foi o afetivo. Os gestores estáveis do campus Fortaleza, também apontaram a dimensão afetiva com a maior média de comprometimento. Com isso os estudos balizam que não há uma possível relação com o fator tempo na instituição, uma vez que a maioria dos gestores do campus Fortaleza possui mais de uma década de atuação, enquanto os do campus Limoeiro do Norte, possuem menos de três anos na instituição. A maior parte dos pesquisados nos campi defendem uma forte relação na instituição, já se sentem de casa, o vínculo se estabelece pela presença de sentimentos, afeição e identificação, até mesmo pelo fato dos gestores permanecerem mais tempo no trabalho que na sua prápria casa, ele faz da organização um esteio do seu próprio lar. Conclui-se que os resultados não permitem afirmar que as dimensões do comprometimento estão relacionadas ao tempo de atuação dos gestores na instituição. / This paper aims to define, analyze and identify through a case study, the dimensions of organizational commitment: affective, continuance and normative managers campus of Castle Hayne, who are in their probationary period and the managers of Fortaleza campus who have passed this stage, draw a comparison and to relate both cases. Guided by a theoretical model of organizational commitment by Meyer and Allen (1991; 1997) approached. Commitment in the public sector in this study has as unit of analysis two federal institutions of science and technology education. As managers are, in most cases, responsible for developing a workforce capable and committed, its performance becomes very important in education, combined with technical competence and political will of planned actions. According to the descriptive and quantitative research, questionnaires were applied, tested and validated, containing aspects of semi-structured, which was divided into two parts: the first, with six items, addressing the personal and functional characteristics of the managers of each campus IFCE studied, and second, which has eighteen items divided into the three dimensions of organizational commitment: affective, continuance and normative, all based on a scale to measure the commitment of Meyer and Allen (1997) model is internationally accepted and validated. The results obtained in this research showed that the 35 managers of the Castle Hayne campus organizational commitment that was obtained more affective. Managers stable campus Fortaleza, also pointed to the affective dimension with the highest average commitment. With this guiding studies that there is a possible relationship with the time factor in the institution, since most managers campus Fortaleza has over a decade of operation, while the Castle Hayne campus, have less than three years in institution. Most of the campuses surveyed favor a strong relationship with the institution, already feel at home, the link is established by the presence of feelings, affection and identification, even by the fact that managers stay longer at work than at home, he is a mainstay of the organization of your own home. We conclude that the results do not allow us to state that the dimensions of commitment are related to time of performance of managers in the institution.

Campi magnetici in astrofisica: determinazione dell'intensità minima e massima del campo magnetico delle pulsar

L’intento dell’elaborato è quello di ricavare i limiti teorici ai quali è soggetta l’intensità del campo magnetico delle pulsar. Troveremo due relazioni: una che esprime il valore massimo dell’intensità del campo magnetico per una pulsar, e una che ne esprime il valore minimo. Combineremo infine i nostri due risultati in una disequazione, nella quale l'intensità del campo magnetico di una pulsar è minorata e maggiorata dai due termini trovati. Il valore massimo che può assumere l’intensità del campo magnetico di una pulsar verrà derivato dalla condizione di stabilità espressa dal teorema del viriale per un sistema sferico rotante in presenza di un campo magnetico. Enunceremo inizialmente il teorema del viriale nella sua forma generale, dopodiché ne presenteremo l'espressione in un caso statico in presenza di un campo magnetico. Abbandoneremo poi il caso statico per includere l'effetto della rotazione, non trascurabile nel caso delle pulsar. Dopo aver adattato la condizione di stabilità derivante dal teorema del viriale al nostro modello di pulsar, ricaveremo il valore massimo dell'intensità del campo magnetico. Il valore minimo che può assumere l’intensità del campo magnetico di una pulsar verrà ricavato uguagliando la potenza emessa dalla pulsar mentre ruota (approssimata ad un dipolo rotante) con la perdita di energia rotazionale che si osserva normalmente per questi oggetti. Otterremo alla fine due termini che delimitano i valori che può assumere l’intensità del campo magnetico per una pulsar. Sostituendo alla relazione trovata i valori di raggio e massa tipici per una pulsar, saremo in grado di riscrivere tale relazione unicamente in funzione del periodo di rotazione della pulsar e della sua derivata rispetto al tempo. Sostituiremo i valori di periodo e derivata temporale del periodo di una pulsar esistente per avere un’idea del range di valori sotteso dai due termini trovati.

LDA-TM : A two-step approach to twitter topic data clustering

The Twitter System is the biggest social network in the world, and everyday millions of tweets are posted and talked about, expressing various views and opinions. A large variety of research activities have been conducted to study how the opinions can be clustered and analyzed, so that some tendencies can be uncovered. Due to the inherent weaknesses of the tweets - very short texts and very informal styles of writing - it is rather hard to make an investigation of tweet data analysis giving results with good performance and accuracy. In this paper, we intend to attack the problem from another aspect - using a two-layer structure to analyze the twitter data: LDA with topic map modelling. The experimental results demonstrate that this approach shows a progress in twitter data analysis. However, more experiments with this method are expected in order to ensure that the accurate analytic results can be maintained.

Basi di Campi Finiti

Questo elaborato si propone di approfondire lo studio dei campi finiti, in modo particolare soffermandosi sull’esistenza di una base normale per un campo finito, in quanto l'utilizzo di una tale base ha notevoli applicazioni in ambito crittografico. ​Vengono trattati i seguenti argomenti: elementi di base della teoria dei campi finiti, funzione traccia e funzione norma, basi duali, basi normali. Vengono date due dimostrazioni del Teorema della Base Normale, la seconda delle quali fa uso dei polinomi linearizzati ed è in realtà un po' più generale, in quanto si riferisce ai q-moduli.​

A New Clustering Methodology for the Analysis of Sorted or Categorized Stimuli

This paper introduces a new stochastic clustering methodology devised for the analysis of categorized or sorted data. The methodology reveals consumers' common category knowledge as well as individual differences in using this knowledge for classifying brands in a designated product class. A small study involving the categorization of 28 brands of U.S. automobiles is presented where the results of the proposed methodology are compared with those obtained from KMEANS clustering. Finally, directions for future research are discussed.

Analisi del sistema di controllo di una bobina per la generazione di campi magnetici in un esperimento di fisica atomica

Questa tesi, svolta nell’ambito dell’esperimento BEC3 presso il LENS di Firenze, si propone di studiare i problemi connessi alla variazione improvvisa di corrente in elementi induttivi, come sono le bobine utilizzate per generare campi magnetici. Nell’esperimento BEC3, come in molti esperimenti di atomi freddi, vi è spesso la necessità di compiere operazioni di questo genere anche in tempi brevi. Verrà analizzato il sistema di controllo incaricato di invertire la corrente in una bobina, azione che va effettuata in tempi dell’ordine di qualche millisecondo ed evitando i danni dovuti alle alte tensioni che si sviluppano ai capi della bobina. Per questa analisi sono state effettuate simulazioni del circuito e misure sperimentali, allo scopo di determinare il comportamento e stimare la durata dell’operazione.