Impact de la taille de la partition de l'espace-paramètre sur les résultats des tests d'hypothèses multiples sous différentes fonctions de perte

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

Left to right: Gustavo Rubio and Melissa Romigh measuring diameter at breast height (DBH) of mangrove trees at SRS-6, Shark River Slough

This material is based upon work supported by the National Science Foundation through the Florida Coastal Everglades Long-Term Ecological Research program under Cooperative Agreements #DBI-0620409 and #DEB-9910514. This image is made available for non-commercial or educational use only.

Left to right: Jay Sah and Nilesh Timilsina measuring diameter at breast height in mangrove forests damaged by Hurricane Wilma, Harney River

This material is based upon work supported by the National Science Foundation through the Florida Coastal Everglades Long-Term Ecological Research program under Cooperative Agreements #DBI-0620409 and #DEB-9910514. This image is made available for non-commercial or educational use only.

Jay Sah measuring tree height in a mangrove forest impacted by Hurricane Wilma, Harney River

This material is based upon work supported by the National Science Foundation through the Florida Coastal Everglades Long-Term Ecological Research program under Cooperative Agreements #DBI-0620409 and #DEB-9910514. This image is made available for non-commercial or educational use only.

Left to right: Mike Ross and Nilesh Timilsina measuring mangrove tree height after Hurricane Wilma, Harney River

This material is based upon work supported by the National Science Foundation through the Florida Coastal Everglades Long-Term Ecological Research program under Cooperative Agreements #DBI-0620409 and #DEB-9910514. This image is made available for non-commercial or educational use only.

Nilesh Timilsina measuring tree height in a mangrove forest impacted by Hurricane Wilma, Harney River

This material is based upon work supported by the National Science Foundation through the Florida Coastal Everglades Long-Term Ecological Research program under Cooperative Agreements #DBI-0620409 and #DEB-9910514. This image is made available for non-commercial or educational use only.

Expertise Development in Volleyball: the Role of Early Sport Activities and Players’ Age and Height

The purpose of this study was to analyse the developmental pathway of skilled and less skilled volleyball players by focusing on the quantity and type of sporting activities, as well as their age and height in comparison to peers in those experiences. Retrospective interviews were conducted to provide a longitudinal and detailed account of sport involvement of 30 skilled and 30 less skilled volleyball players (15 male and 15 female players per group) throughout different developmental stages (stage 1: 8-12 years; stage 2: 13-16 years; stage 3: 17-20 years). Results indicated that the developmental pathway of these volleyball players (i.e. skilled and less skilled) was characterized by an early diversified sport involvement with a greater participation in sport activities during stages 1 and 2. However, skilled players specialized later in volleyball (between age 14 and 15) and performed more hours of volleyball at stage 3 (from 17 years of age onwards). Also, skilled players (male and female) were younger in both the diversified sport activities and volleyball at the later stages of development (i.e. stages 2 and 3), and skilled female players were taller than peers in those activities in the early stages of development (i.e. stages 1 and 2). The present findings suggest early diversification as a feasible pathway to reach expertise in volleyball and highlight the importance of practicing with older peers once specialization in the main sport has occurred. The findings highlight the need for coaches and sport programs to consider different stimuli existing within the training environment (i.e. characteristics of athletes, such as age and height) that influence the quality of practice and contribute to players’ expertise development.

An investigation of lower limb joint powers as a predictive measure of countermovement vertical jump height.

Measuring and tracking athletic performance is crucial to an athlete’s development and the countermovement vertical jump is often used to measure athletic performance, particularly lower limb power. The linear power developed in the lower limb is estimated through jump height. However, the relationship between angular power, produced by the joints of the lower limb, and jump height is not well understood. This study examined the contributions of the kinetic value of angular power, and its kinematic component, angular velocity, of the lower limb joints to jump height in the countermovement vertical jump. Kinematic and kinetic data were gathered from twenty varsity-level basketball and volleyball athletes as they performed six maximal effort jumps in four arm swing conditions: no-arm involvement, single-non-dominant arm swing, single-dominant arm swing, and two-arm swing. The displacement of the whole body centre of mass, peak joint powers, peak angular velocity, and locations of the peaks as a percentage of the jump’s takeoff period, were computed. Linear regressions assessed the relationship of the variables to jump height. Results demonstrated that knee peak power (p = 0.001, ß = 0.363, r = 0.363), its location within takeoff period (p = 0.023, ß = -0.256, r = 0.256), and peak knee peak angular velocity (p = 0.005, ß = 0.310, r = 0.310) were moderately linked to increased jump height. Additionally, the location, within the takeoff period, of the peak angular velocities of the hip (p = 0.003, ß = -0.318, r = 0.419) and ankle (p = 0.011, ß = 0.270, r = 0.419) were positively linked to jump height. These results highlight the importance of training the velocity and timing of joint motion beyond traditional power training protocols as well as the importance of further investigation into appropriate testing protocol that is sensitive to the contributions by individual joints in maximal effort jumping.

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.