Nas últimas décadas, a quantidade de informações armazenadas no formato digital tem crescido de forma exponencial, levando à necessidade cada vez maior de produção de ferramentas computacionais que auxiliem na geração do conhecimento a partir desses dados. A área de Aprendizado de Máquina fornece diversas técnicas capazes de identificar padrões nesses conjuntos de dados. Dentro dessas técnicas, este trabalho destaca o Aprendizado de Máquina Não Supervisionado onde o objetivo é classificar as entidades em clusters (grupos) mutuamente exclusivos baseados na similaridade entre as instâncias. Os clusters não são pré-definidos e daí o elemento não supervisionado. Organizar esses dados em clusters que façam sentido é uma das maneiras mais fundamentais de entendimento e aprendizado. A análise de clusters é o estudo dos métodos para agrupamento e se divide entre hierárquico e particional. A classificação hierárquica é uma sequência encadeada de partições enquanto que na particional há somente uma partição. O interesse deste trabalho são as técnicas baseadas em uma caminhada determinística parcialmente auto repulsiva conhecida como caminhada do turista. Partindo da hipótese de que é possível utilizar a caminhada do turista como uma técnica de Aprendizado de Máquina Não Supervisionado, foi implementado um algoritmo hierárquico baseado na caminhada do turista proposto por Campiteli et al. (2006). Foi avaliado, através de diferentes conjuntos de imagens médicas, como essa técnica se compara com técnicas hierárquicas tradicionais. Também é proposto um novo algoritmo de Aprendizado de Máquina Não Supervisionado particional baseado na caminhada do turista, chamado de Tourist Walk Partitional Clustering (TWPC). Os resultados mostraram que a técnica hierárquica baseada na caminhada do turista é capaz de identificar clusters em conjuntos de imagens médicas através de uma árvore que não impõe uma estrutura binária, com um número menor de hierarquias e uma invariabilidade à escala dos dados, resultando em uma estrutura mais organizada. Mesmo que a árvore não seja diretamente baseada nas distâncias dos dados, mas em um ranking de vizinhos, ela ainda preserva uma correlação entre suas distâncias cofenéticas e as distâncias reais entre os dados. O método particional proposto TWPC foi capaz de encontrar, de forma eficiente, formas arbitrárias de clusters com variações inter-cluster e intra-cluster. Além disso o algoritmo tem como vantagens: ser determinístico; funcionar com interações locais, sem a necessidade de conhecimento a priori de todos os itens do conjunto; incorporar o conceito de ruído e outlier; e funcionar com um ranking de vizinhos, que pode ser construído através de qualquer medida. / In the last decades, the amount of data stored in digital format has grown exponentially, leading to the increasing need to produce computational tools that help generate knowledge from these data. The Machine Learning field provides several techniques capable of identifying patterns in these data sets. Within these techniques we highlight the Unsupervised Machine Learning where the objective is to classify the entities in mutually exclusive clusters based on the similarity between the instances. Clusters are not predefined and hence the unsupervised element. Organizing this data into clusters that make sense is one of the most fundamental ways of understanding and learning. Cluster analysis is the study of methods for clustering and is divided between hierarchical and partitional. A hierarchical clustering is a sequence of partitions whereas in the partitional clustering there is only one partition. Here we are interested in techniques based on a deterministic partially self-avoiding walk, known as tourist walk. Based on the hypothesis that it is possible to use the tourist walk as an unsupervised machine learning technique, we have implemented a hierarchical algorithm based on the tourist walk proposed by Campiteli et al. (2006). We evaluate this algorithm using different sets of medical images and compare it with traditional hierarchical techniques. We also propose a new algorithm for partitional clustering based on the tourist talk, called Tourist Walk Partitional Clustering (TWPC). The results showed that the hierarchical technique based on the tourist walk is able to identify clusters in sets of medical images through a tree that does not impose a binary structure, with a smaller number of hierarchies and is invariable to scale transformation, resulting in a more organized structure. Even though the tree is not directly based on the distances of the data but on a ranking of neighbors, it still preserves a correlation between its cophenetic distances and the actual distances between the data. The proposed partitional clustering method TWPC was able to find, in an efficient way, arbitrary shapes of clusters with inter-cluster and intra-cluster variations. In addition, the algorithm has the following advantages: it is deterministic; it operates based on local interactions, without the need for a priori knowledge of all the items in the set; it is capable of incorporate the concept of noise and outlier; and work with a ranking of neighbors, which can be built through any measure.

On choisit un groupe algébrique G, un sous-groupe algébrique H de G ; on choisit une mesure de probabilité borélienne μ sur G. On considère alors la chaîne de Markov sur l’espace homogène X = G/H de probabilité de transition Px = μ * δx pour x ε X. Dans cette thèse, on étudie les propriétés de récurrence de ces marches aléatoires.On s’intéresse à deux types de récurrence : la récurrence presque-sûre (toute trajectoire revient presque-sûrement infiniment souvent dans un compact) et la récurrence en loi (il existe une mesure de probabilité μ stationnaire sur X .On s’intéresse également aux éventuelles propriétés de transience presque-sûre (toute trajectoire quitte presque-sûrement définitivement tout compact).On construira d’abord un exemple où on n’a ni récurrence presque-sûre en tout point, ni transience presque-sûre en tout point. On montrera ensuite un critère de récurrence presque-sûre dans le cas où G est un groupe de Lie semi-simple ; on a en fait dans ce cas une dichotomie : soit tous les points sont récurrents,soit tous les points sont transients.Dans le cas où G est le groupe affine GL(d,ℝ) α ℝd,on donnera un critère de récurrence en loi sur les Grassmanniennes affines, et, dans un dernier chapitre, on donnera quelques résultats partiels d'un projet en cours,permettant de donner des résultats pour le groupe SO(p, p+1) α ℝ2p+1. / Choose an algebraic group G, and an algebraic subgroup H. Choose a Borel probability measure μ on G. Consider the Markov chain on the G-space X = G/H with transition probability Px = μ * δx for x ε X.The point of this dissertation is the study of the recurrence properties of such a random walk.We consider two types of recurrence : almost-certain recurrence (i.e. almost-every trajectory enters some compact set infinitely often) and the associated almost-certain transience (where almost-every trajectory eventually leaves every compact set) and recurrence in law (i.e. there exists a μ stationary probability measure on X).First, we show that, in general, there is no dichotomy between almost-certain recurrence and transience by constructing an example with both almost-certainly recurrent and almost-certainly transient points.We then prove a criterion for almost-certain recurrence when G is a semi-simple Lie group and X is a G-space. In fact, in this case, we have a dichotomy where either every point of X is almost-certainly recurrent, or every point of X is almost certainly transient.When G is the affine group GL(d,ℝ) α ℝd, we give a criterion for recurrence in law on the affine Grassmannians.In the final chapter, we give some partial results from an ongoing project,which give a criterion for recurrence in law the group SO(p,p+1)α ℝ2p+1.

abstract: The ongoing Red for Ed movement in Arizona sparks an interesting discussion on its place as a social movement. This thesis examines the movement in close detail, particularly in regard to how it fits within the social movement literature’s insider/outsider framework. While partisanship is clearly important for understanding movement successes and failures, this study goes beyond party to explore through the case of Arizona how teacher movements are constrained by 1) teacher associations that operate as outsiders to state politics and 2) school districts that isolate the problem priorities (funding; teacher pay) from gaining large-scale public reaction that can be leveraged to change state policy. In short, I show how teacher movements face significant institutional barriers that localize their messaging and prevent insider access from state politics. / Dissertation/Thesis / Masters Thesis Social Justice and Human Rights 2020

京都大学 / 0048 / 新制・課程博士 / 博士(理学) / 甲第21542号 / 理博第4449号 / 新制||理||1639(附属図書館) / 京都大学大学院理学研究科数学・数理解析専攻 / (主査)教授 熊谷 隆, 准教授 福島 竜輝, 教授 牧野 和久 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DGAM

Humans naturally select a point at which to transition from walking to running when gradually increasing locomotor speed. This point is known as the walk-to-run transition (WRT). The WRT is traditionally expressed in terms of speed and is known to occur within a close range of 2.1 m/s, which is an accepted heuristic (i.e., empirically based, rounded) threshold value. Very little research exists defining the WRT in terms of cadence (steps/min) despite the fact that spatial temporal aspects of gait underlying the WRT include this parameter. Preliminary evidence suggests that the WRT may be associated with a cadence of 140 steps/min in adults. This overlooked approach to identifying the WRT may be better than speed because of the simplicity and accessibility of recording cadence in both lab- and free-living settings. Wearable technologies can be used to determine cadence in real-time in a variety of settings, and could be used in the future to expand our current knowledge of the WRT. In turn, this knowledge could be used to inform training practices and/or rehabilitation of gait disorders. The purposes of this secondary analysis of an existing treadmill-based data set were to: (1) identify the optimal WRT cadence threshold, and (2) compare the accuracy of the cadence cutpoint to the previous WRT indicators identified in literature (i.e., speed and Froude number). This secondary analysis focused only on the data collected from the 28 participants (20 men, 8 women) whose protocol was terminated due to selecting to run during the treadmill portion of the larger CADENCE-Adults study. The CADENCE-Adults protocol consisted of a series of five-minute bouts beginning at 0.2 m/s and increasing in 0.2 m/s increments, with each bout followed by two minutes of standing rest. Participants could choose to walk or run each bout. The cadence of the bout during which the participants chose to run was considered the WTR cadence, and ROC analyses were performed to determine the optimal cadence cutpoint. Sensitivity, specificity and overall accuracy were calculated to compare the accuracy of the speed and Froude values from literature to the calculated cadence cutpoint. In addition, these analyses were expanded post hoc to also examine the accuracy of the previously proposed cadence cutpoint from the literature and the speed and Froude cutpoint identified from the dataset. Following analyses, three cadence cutpoints (134, 139, or 141 steps/min) were identified that shared equal overall accuracy (92.9%); therefore, there was no single optimal cutpoint. This also occurred for the speed cutpoints, where both 1.9 and 2.0 m/s shared overall accuracies of 78.6%. The optimal Froude cutpoint identified was 0.46 (82.0% overall accuracy). The rank-order overall accuracy of previously identified cutpoints were: a cadence of 140 steps/min (91.1%), Froude number of 0.5 (76.8%) and speed of 2.1 m/s (66.1%). Based on the identified optimal cadence cutpoints, a heuristic range of running cutpoints was recommended anchored on specificity vs. sensitivity preferences. For researchers interested in identifying episodes more likely to be running behavior (with the preference that very few episodes of walking behavior are mistakenly identified), it would be best to use 140 steps/min. However, if they want to be as inclusive as possible in identifying episodes of running behavior (and can tolerate more mistakenly identified episodes walking behavior), they could use 135 steps/min. When applied to this dataset, 96.0% (24/25) of the individuals who were ≥140 steps/min were running, but this decreased to 92.5% (25/27) with ≥135 steps/min. In conclusion, cadence clearly performed much better in terms of overall accuracy when compared to traditionally used WRT indicators of speed and Froude numbers. The recommended heuristics cadence cutpoint range can be used by researchers who want to evaluate the locomotor patterns of individuals when analyzing free-living step-defined data collected using wearable devices.

Eulerian graphs have a closed walk traversing each edge exactly once. Finding such a walk is a basic arc routing problem based on a road network. Most of the problems with applications in operational research are NP-hard. We describe a formal model of a road network and vehicle routes and formulate several arc routing problems motivated by winter road maintenance in the Czech Republic. The main part is focused on single vehicle routing problems on trees. We propose a new unfairness minimization problem for finding a vehicle route with properties that lead to a minimal number of resident complaints against unfair maintenance. Residents feel like they are skipped when the vehicle route has multiple trips and passes nearby without providing maintenance to their street. By reduction of the necklace splitting problem to the unfairness minimization problem we prove it is PPA-complete. Further, we define a restricted arc routing problem on trees which formalize condi- tions given by Czech legislation. We proved the existence of a polynomial algorithm for deciding whether a single vehicle route exists when there is a single priority for roads. If multiple priorities are used, we express conditions and conjectures when the problem has polynomial complexity. Finally, a utilization of the model is illustrated by an...

There has been an explosion of network data in the physical, chemical, biological, computational, and social sciences in the last few decades. Node embeddings, i.e., Euclidean-space representations of nodes in a network, make it possible to apply to network data, tools and algorithms from multivariate statistics and machine learning that were developed for Euclidean-space data. Random walk node embeddings are a class of recently developed node embedding techniques where the vector representations are learned by optimizing objective functions involving skip-bigram statistics computed from random walks on the network. They have been applied to many supervised learning problems such as link prediction and node classification and have demonstrated state-of-the-art performance. Yet, their properties remain poorly understood. This dissertation studies random walk based node embeddings in an unsupervised setting within the context of capturing hidden block structure in the network, i.e., learning node representations that reflect their patterns of adjacencies to other nodes. This doctoral research (i) Develops VEC, a random walk based unsupervised node embedding algorithm, and a series of relaxations, and experimentally validates their performance for the community detection problem under the Stochastic Block Model (SBM). (ii) Characterizes the ergodic limits of the embedding objectives to create non-randomized versions. (iii) Analyzes the embeddings for expected SBM networks and establishes certain concentration properties of the limiting ergodic objective in the large network asymptotic regime. Comprehensive experimental results on real world and SBM random networks are presented to illustrate and compare the distributional and block-structure properties of node embeddings generated by VEC and related algorithms. As a step towards theoretical understanding, it is proved that for the variants of VEC with ergodic limits and convex relaxations, the embedding Grammian of the expected network of a two-community SBM has rank at most 2. Further experiments reveal that these extensions yield embeddings whose distribution is Gaussian-like, centered at the node embeddings of the expected network within each community, and concentrate in the linear degree-scaling regime as the number of nodes increases. / 2023-09-24T00:00:00Z

Development, environmental sustainability, agriculture and livelihoods are dimensions that are often considered antagonistic. By thinking at the landscape level however, innovative opportunities arise for simultaneity as these entities manifest spatially and require communication across disciplines. Trans-frontier Conservation Areas (TFCAs) embrace this thinking. These are large areas that cut across two or more international boundaries, include within them at least one Protected Area (PA) and other multiple resource use areas, including human dwellings and cultivated areas. Similarly, ecoagriculture is an innovative approach to land use management as it seeks to spatially synergise agriculture, livelihoods and biodiversity conservation across space and requires an awareness of landscape-level issues by land users, a condition which is not necessarily met. Such landscape thinking stems from the fact that if a piece of land is subject to rigorous conservation, it will fail if the surrounding areas are degraded. Additionally, it has been shown that agriculture often benefits from the nearby presence of natural areas for ecosystem services such as pollination, pest management, and erosion control. As such, multifunctional landscape mosaics together with small scale farmers, not large scale monocultures, are the key to global food security, as the former more effectively links agricultural intensification to hunger reduction. In order to ascertain an integrated understanding of the landscape concept, necessary for the formalisation of ecoagriculture, this study assessed the landscape perceptions and understandings held by local people residing within a TFCA. We employed participatory methods within the Mathenjwa Tribal Area (MTA), an area falling within the Lubombo TFCA and identified as holding ecoagriculture potential. Results revealed that local people perceive landscape as a function of subsistence utility. Local people perceive land-use multifunctionality, necessary for the formalisation of ecoagriculture, but at a smaller scale than expected depending on both social and biophysical interpretations. Landscape scale projects should incorporate local landscape understandings. / Dissertation (MA)--University of Pretoria, 2013. / Geography, Geoinformatics and Meteorology / MA / Unrestricted

This study is a comparison of forecasting methods for predicting the daily maximum air temperatures in Uppsala using real data from the Swedish Meteorological and Hydrological Institute. The methods for comparison are univariate time series approaches suitable for the data and represent both standard and more recently developed methods. Specifically, three methods are included in the thesis: neural network, ARIMA, and naïve. The dataset is split into a training set and a pseudo out of sample test set. The assessment of which method best forecast the daily temperature in Uppsala is done by comparing the accuracy of the models when doing walk forward validation on the test set. Results show that the neural network is most accurate for the used dataset for both one-step and all multi-step forecasts. Further, the only same-step forecasts from different models that have a statically significant difference are from the neural network and naïve for one- and two-step forecasts, in favor of the neural network.

Title: Subjectively perceived health fitness and physical activity of the elderly compared to a fitness walk test Objectives: The aim of this paper is to compare the subjectively perceived health (fitness) and physical activity with an actual fitness walk test done by students of the University of the Third Age, using the LTEQ and SF-12 questionnaires. Methods: There were 64 seniors who participated in this study (age 67,36 ± 3,55, height 1,67 ± 7,04 cm, weight 69,80 ± 9,9 kg). All of them students of the University of the Third Age, Faculty of Physical Education and Sport, Charles University. Two types of Questionnaires were used: The Leisure Time Exercise Questionnaire and A 12-Item Short-Form Health Survey, both of which are typically used to study the population of the elderly. A 2 km fitness walk test by Stejskal was used to gather data about aerobic fitness. The test took place at the athletic oval on the grounds of Faculty of Physical Education and Sport, Charles University. Polar S610i sporttester was used to measure heart rate. Statistic was used to calculate all SPSS 21. Results: The results proved a significant correspondence between the subjectively perceived amount of leisure physical activity and the aerobic fitness measured by the 2km fitness walk test - specifically regarding...