No description available.

No description available.

Three experiments were conducted to investigate the hypothesis that young infants? failures to search for occluded objects arises, not from deficiencies in their object representations, but from limitations in experience with the physical world. Successful means-end search is typically found at 8 months of age and is traditionally taken as the hallmark of object permanence. However, recent evidence suggests that infants much younger than 8 months of age are able to represent and reason about objects that are no longer visible. In Experiment 1, successful means-end search was found for 8. 5-, but not 6. 5-month-old infants in a traditional task, but younger infants showed successful search ability when the task was made familiar to them in Experiment 2 (i. e. , when the toy and occluder are first presented as a single composite object), and when they were given the opportunity to watch a demonstration of the solution to the task in Experiment 3. These results are taken as evidence for the ?experiential limitation? hypothesis and suggest that young infants are more apt at solving manual search tasks than previously acknowledged.

In this master thesis I recapitulated several methods for clustering input data. Two well known clustering algorithms, concretely K-means algorithm and Fuzzy C-means (FCM) algorithm, were described in the submitted work. I presented several methods, which could help estimate the optimal number of clusters. Further, I described Kohonen maps and two models of Kohonen's maps with dynamically changing structure, namely Kohonen map with growing grid and the model of growing neural gas. At last I described quite new model of radial basis function neural networks. I presented several learning algorithms for this model of neural networks. In the end of this work I made some clustering experiments with real data. This data describes the international trade among states of the whole world.

Dissertation Submitted in fulfilment of the requirements for a M Ed Degree in the School of Education Faculty of Humanities, University of the Witwatersrand, Johannesburg, 2016 / South African Sign Language (SASL) is officially recognised as a formal Home Language school subject within the National Curriculum Statement Policy of South Africa. However, this long awaited roll-out of the CAPS SASL raises a number of issues within the context of each school for the Deaf regarding implementation and school leadership. Hence, a case study was undertaken at a public school for the Deaf located within Johannesburg District to draw attention to these issues. Using Freire’s concepts of Dialogue, Conscientization and Praxis (Freire, 1972) as a means to bring about transformational changes within the school for the implementation year of SASL, 2015, the study explored the dialogue between School Management Team (SMT) and staff members who teach the Deaf learners. The models of school leadership investigated were: transactional, transformational and transformative leadership (Shields, 2010) as a framework for the changing roles as demonstrated by the SMT and the said staff members. Qualitative data provided insight into different strategies employed by them as well as class assistants to implement SASL; and further revealed the changes experienced within the school and by several staff members, where members were becoming more aware of their SASL abilities and how they would want to improve. Emergent themes included the ‘SASL curriculum’, ‘leadership and change’, ‘bilingual education and quality of education’ for Deaf learners. Change was evident between the positional authority of SMT members and the language authority of the Deaf staff members and revealed how this transformation in respect for different roles of the SMT assisted the SASL implementation. Findings revealed that SMT members and Deaf staff need to engage in extensive dialogue and implement strategies to facilitate the SASL implementation if there is to be meaningful transformation within the school / XL2018

Digitized by Kansas Correctional Industries

In this master thesis I recapitulated several methods for data clustering. Two well known clustering algorithms, concretely K-means algorithm and Fuzzy C-means (FCM) algorithm, were described in the submitted work. I presented several methods, which could help estimate the optimal number of clusters. Further, I described Kohonen maps and two models of Kohonen's maps with dynamically changing structure, namely Kohonen map with growing grid and the model of growing neural gas. At last I described quite new model of radial basis function neural networks. I presented several learning algorithms for this model of neural networks, RAN, RANKEF, MRAN, EMRAN and GAP. In the end of this work I made some clustering experiments with real data. This data describes the international trade among states of the whole world.

Les outils d’aide au diagnostic sont de nos jours au cœur de plusieurs protocoles cliniques car ils améliorent la qualité du diagnostic posé et des soins médicaux. Ce travail de recherche met en avant une architecture hiérarchique pour la conception d'un outil d'aide à la détection du cancer du sein robuste et performant. Il s’intéresse à la réduction des fausses alarmes en identifiant les régions potentiellement cancérogènes. La gamme dynamique des niveaux de gris des zones sombres est étirée pour améliorer le contraste entre la région du sein et l'arrière plan et permettre une meilleure extraction de celle-ci. Toutefois, le muscle pectoral demeure incrusté dans la région du sein et interfère avec l'analyse des tissus. Son extraction est à la fois difficile et complexe à mettre en œuvre à cause de son chevauchement avec les tissus denses du sein. Dans ces conditions, même en exploitant l'information spatiale pendant la clusterisation par un algorithme de fuzzy C-means ne produit pas toujours des résultats de segmentation pertinents. Pour s'affranchir de cette difficulté, une étape de validation suivie d'un ajustement de contour est mise sur pied pour détecter et corriger les imperfections de segmentation. La seconde étape est consacrée à la caractérisation de la densité des tissus. Pour faire face au problème de variabilité des distributions de niveaux de gris dans les classes de densités, nous introduisons une modification de contraste basée sur un transport optimisé de niveaux de gris. Grâce à cette technique, la surface relative de tissus denses estimée par simple segmentation est très fortement corrélée aux classes de densités issues d’un jeu de données étiquetées. / Computer-aided diagnosis systems are currently at the heart of many clinical protocols since they significantly improve diagnosis making and therefore medical care. This research work therefore puts forward a hierarchical architecture for the design of a robust and efficient CAD tool for breast cancer detection. More precisely, it focuses on the reduction of false alarms rate through the identification of image regions of foremost interest i.e potential cancerous areas. The dynamic range of gray level intensities in dark regions is, first of all stretched to enhance the contrast between tissues and background and thus favors accurate breast region extraction. A second segmentation follows since pectoral muscle which regularly tampers breast tissue analysis remains inlaid in the foreground region. Extracting pectoral muscle tissues is both hard and challenging due to its overlap with dense tissues. In such conditions, even exploiting spatial information during the clustering process of the fuzzy C-means algorithm does not always produce a relevant segmentation. To overcome this difficulty, a new validation process followed by a refinement strategy is proposed to detect and correct the segmentation imperfections. The second macro-step is devoted to breast tissue density analysis. To address the variability in gray levels distributions with of mammographic density classes, we introduce an optimized gray level transport map for mammographic image contrast standardization. Thanks to this technique, dense region areas computed using simple thresholding are highly correlated to density classes from an annotated dataset.

Made available in DSpace on 2017-05-12T14:46:57Z (GMT). No. of bitstreams: 1 Kelyn Schenatto.pdf: 4212903 bytes, checksum: 0ba04350cc25aff5e6acb249938e5375 (MD5) Previous issue date: 2014-02-04 / Despite the benefits offered by the technology of precision agriculture (PA), the necessity of dense sampling grids and use of sophisticated equipment for the soil and plant handling make it financially unfeasible in many cases, especially for small producers. With the aimof making viable the PA, the definition of management zones (MZ) consists in dividing the plotin subregions that have similar physicochemical features, where it is possible to work in the conventional manner (without site-specific input application), differing them from the other sub-regions of the field. Thus we use concepts from PA, but adapting some procedures to the reality of the producer, not requiring the replacement of machinery traditionally used.Therefore, yield is usually correlated with physical and chemical properties through statistical and geostatistical methods, and attributes are selected to generate thematic maps, which are then used to define the MZ. In the generation of thematic maps step, are commonly used traditional interpolation methods (Inverse Distance - ID , inverse of the square distance - ISD, and kriging - KRI), and it is important to assess if the quality of thematic maps generated influences in the MZ drafting process and can not justify the interpolation data using robust methods such as KRI. Thus, the present study aimed to evaluate three interpolation methods (ID , ISD and KRI ) for generation of thematic maps used in the generation of MZ by clustering methods K-Means and Fuzzy C-Meas, in two experimental areas (9.9 ha and 15.5 ha), and been used data from four seasons (three crops of soybeans and one of corn). The KRI interpolation and ID showed similar UM. The agreement between the maps decreased when an increase in the number of classes, but with greater intensity with the Fuzzy C-Means method. Clustering algorithms K-Means and Fuzzy C-Means performed similar division on two UM. The best interpolation method was KRI following the ID, what justifies the choice of a more robust interpolation (KRI) to generate UM / Apesar dos benefícios proporcionados pela tecnologia de agricultura de precisão (AP), a necessidade de grades amostrais densas e uso de equipamentos sofisticados para o manejo do solo e da planta tornam o seu cultivo em muitos casos inviável financeiramente, principalmente para pequenos produtores. Com a finalidade de viabilizar a AP, a definição de unidades de manejo (UM) consiste em dividir o talhão em sub-regiões que possuam características físico-químicas semelhantes, onde se pode trabalhar de forma convencional (sem aplicação localizada de insumos), diferenciando-se das outras sub-regiões do talhão. Dessa forma, utilizam-se conceitos de AP, mas adaptam-se alguns procedimentos para a realidade do produtor, não havendo necessidade da substituição de máquinas tradicionalmente utilizadas. Para isso, são geralmente correlacionados atributos físicos e químicos com a produtividade das culturas e, por meio de métodos estatísticos e geoestatísticos, selecionam-se atributos que darão origem a mapas temáticos posteriormente utilizados para definição das UM. Na etapa de geração dos mapas temáticos, são normalmente utilizados métodos tradicionais de interpolação (inverso da distância ID, inverso da distância ao quadrado IDQ e krigagem KRI) e é importante avaliar se a qualidade dos mapas temáticos gerados influencia no processo de definição das UM, podendo desta forma não se justificar a interpolação de dados a partir do uso de métodos robustos como a KRI. O presente trabalho teve como objetivo a avaliação de três métodos de interpolação (ID, IQD e KRI) para definição dos mapas temáticos utilizados na confecção de UM pelos métodos de agrupamento K-Means e Fuzzy C-Means, em duas áreas experimentais (de 9,9 ha e 15,5 ha), sendo utilizados dados de quatro safras (três safras de soja e uma de milho). Os interpoladores ID e KRI apresentaram UM similares. A concordância entre os mapas diminuiu quando houve aumento do número de classes, mas teve maior intensidade com o método Fuzzy C-Means. Os algoritmos de agrupamento K-Means e Fuzzy C-Means se apresentaram similares na divisão em duas UM. O melhor método de interpolação foi a KRI, seguida do ID, o que justifica a escolha do interpolador mais robusto (KRI) na geração de UM

Leung, Kin Pang. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (p. 70-71). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Model --- p.4 / Chapter 2.1 --- The Basic Thurstonian Model --- p.4 / Chapter 2.2 --- The Thurstonian Model with Mean Structure in 3-object Ranking Data --- p.6 / Chapter 3 --- Implementation Using the Mx --- p.13 / Chapter 4 --- Simulation Study --- p.21 / Chapter 4.1 --- 2 covariate values --- p.23 / Chapter 4.2 --- 4 covariate values --- p.23 / Chapter 4.3 --- 10 covariate values --- p.23 / Chapter 4.4 --- 50 covariate values --- p.24 / Chapter 5 --- Discussion --- p.37 / Chapter A --- Sample Mx script-2 covariate values --- p.39 / Chapter B --- Sample Mx script-50 covariate values --- p.60 / Bibliography --- p.70