The aim of this essay is to examine if the attitudes and the experiences of Ability Grouping are the same on different levels of the school organisation. I have chosen to answer the following two questions: · Have the teachers, the students and the headmaster the same idea concerning the educational model at the school, that is, the pros and cons of Ability Grouping? · Does the intentions of the curriculum agree with this educational model? I knew what I thought about the educational model at the school that is the object of this essay, after spending ten weeks at it as a trainee, but I felt that I would be interesting to know what the people who are part of that system everyday think about it. In order to be able to answer my questions, I used both the qualitative and the quantitative method. I interviewed the headmaster and five teachers to find out what they thought about Ability Grouping. I also distributed inquiries to 70 students from 6th to 9th grade to get their point of view. In the theoretical discussion I explain the meaning of different conceptions that I use throughout this essay. I also discuss different theories to give examples of pros and cons of Ability Grouping. My conclusion is that the majority of the students think that Ability Grouping is either a good or a very good way of organizing the students in school. All the teachers except one prefer Ability Grouping to the traditional way of grouping the students, that is, in classes that are mixed concerning the level of knowledge but where everybody is at the same age. The main concern though, both among the students, the teachers and the headmaster, is that the students get more shy when they work in groups that are mixed, and that the students who are not that good in school loose someone to look up to. The Ability Grouping model that this school uses, which is more flexible than the traditional one, agree with the intentions of the curriculum in many ways. To agree with it to the full extend though, you need to add the traditional classes as well, which also is the mix that the school has chosen.

Perceptual grouping is essential to manage the complexity of real world scenes. We explore bottom-up grouping at three different levels. Starting from low-level grouping, we propose a novel method for oversegmenting an image into compact superpixels, reducing the complexity of many high-level tasks. Unlike most low-level segmentation techniques, our geometric flow formulation enables us to impose additional compactness constraints, resulting in a fast method with minimal undersegmentation. Our subsequent work utilizes compact superpixels to detect two important mid-level shape regularities, closure and symmetry. Unlike the majority of closure detection approaches, we transform the closure detection problem into one of finding a subset of superpixels whose collective boundary has strong edge support in the image. Building on superpixels, we define a closure cost which is a ratio of a novel learned boundary gap measure to area, and show how it can be globally minimized to recover a small set of promising shape hypotheses. In our final contribution, motivated by the success of shape skeletons, we recover and group symmetric parts without assuming prior figure-ground segmentation. Further exploiting superpixel compactness, superpixels are this time used as an approximation to deformable maximal discs that comprise a medial axis. A learned measure of affinity between neighboring superpixels and between symmetric parts enables the purely bottom-up recovery of a skeleton-like structure, facilitating indexing and generic object recognition in complex real images.

Ability grouping has become increasingly common in the teaching of English in Swedish secondary schools. This study was concerned with the effects of different group constellations on the more able pupils. The research questions involved the able pupils’ performance, their roles in mixed-ability groups and whether the learning environment is better for them when they work with other able pupils. The study used discourse analysis of group interaction in the target language between pupils in year eight. It was found that able pupils perform according to their pre-defined skill, regardless of what groups they were put in. They also supported and lifted the interaction to higher levels in mixed-ability groups. Finally, the results suggested that able pupils might have a greater chance to develop their English in tracked groups. It is argued that this has implications for the implementation of ability grouping in the teaching of second languages in Sweden.

Perceptual grouping is essential to manage the complexity of real world scenes. We explore bottom-up grouping at three different levels. Starting from low-level grouping, we propose a novel method for oversegmenting an image into compact superpixels, reducing the complexity of many high-level tasks. Unlike most low-level segmentation techniques, our geometric flow formulation enables us to impose additional compactness constraints, resulting in a fast method with minimal undersegmentation. Our subsequent work utilizes compact superpixels to detect two important mid-level shape regularities, closure and symmetry. Unlike the majority of closure detection approaches, we transform the closure detection problem into one of finding a subset of superpixels whose collective boundary has strong edge support in the image. Building on superpixels, we define a closure cost which is a ratio of a novel learned boundary gap measure to area, and show how it can be globally minimized to recover a small set of promising shape hypotheses. In our final contribution, motivated by the success of shape skeletons, we recover and group symmetric parts without assuming prior figure-ground segmentation. Further exploiting superpixel compactness, superpixels are this time used as an approximation to deformable maximal discs that comprise a medial axis. A learned measure of affinity between neighboring superpixels and between symmetric parts enables the purely bottom-up recovery of a skeleton-like structure, facilitating indexing and generic object recognition in complex real images.

Advances in computational modelling offer unprecedented potential for obtaining insights into the mechanics of cell-cell interactions. With the aid of such models, cell-level phenomena such as cell sorting and tissue self-organization are now being understood in terms of forces generated by specific sub-cellular structural components. Three-dimensional systems can behave differently from two-dimensional ones and since models cannot be validated without corresponding data, it is crucial to build accurate three-dimensional models of real cell aggregates. The lack of automated methods to determine which cell outlines in successive images of a confocal stack or time-lapse image set belong to the same cell is an important unsolved problem in the reconstruction process. This thesis addresses this problem through a contour grouping algorithm (CGA) designed to lead to unsupervised three-dimensional reconstructions of biological cells. The CGA associates contours obtained from fluorescently-labeled cell membranes in individual confocal slices using concepts from the fields of machine learning and combinatorics. The feature extraction step results in a set of association metrics. The algorithm then uses a probabilistic grouping step and a greedy-cost optimization step to produce grouped sets of contours. Groupings are representative of imaged cells and are manually evaluated for accuracy. The CGA presented here is able to produce accuracies greater than 96% when properly tuned. Parameter studies show that the algorithm is robust. That is, acceptable results are obtained under moderately varied probabilistic constraints and reasonable cost weightings. Image properties – such as slicing distance, image quality – affect the results. Sources of error are identified and enhancements based on fuzzy-logic and other optimization methods are considered. The successful grouping of cell contours, as realized here, is an important step toward the development of realistic, three-dimensional, cell-based finite element models.

Advances in computational modelling offer unprecedented potential for obtaining insights into the mechanics of cell-cell interactions. With the aid of such models, cell-level phenomena such as cell sorting and tissue self-organization are now being understood in terms of forces generated by specific sub-cellular structural components. Three-dimensional systems can behave differently from two-dimensional ones and since models cannot be validated without corresponding data, it is crucial to build accurate three-dimensional models of real cell aggregates. The lack of automated methods to determine which cell outlines in successive images of a confocal stack or time-lapse image set belong to the same cell is an important unsolved problem in the reconstruction process. This thesis addresses this problem through a contour grouping algorithm (CGA) designed to lead to unsupervised three-dimensional reconstructions of biological cells. The CGA associates contours obtained from fluorescently-labeled cell membranes in individual confocal slices using concepts from the fields of machine learning and combinatorics. The feature extraction step results in a set of association metrics. The algorithm then uses a probabilistic grouping step and a greedy-cost optimization step to produce grouped sets of contours. Groupings are representative of imaged cells and are manually evaluated for accuracy. The CGA presented here is able to produce accuracies greater than 96% when properly tuned. Parameter studies show that the algorithm is robust. That is, acceptable results are obtained under moderately varied probabilistic constraints and reasonable cost weightings. Image properties – such as slicing distance, image quality – affect the results. Sources of error are identified and enhancements based on fuzzy-logic and other optimization methods are considered. The successful grouping of cell contours, as realized here, is an important step toward the development of realistic, three-dimensional, cell-based finite element models.

Thesis (Ed. D.)--Illinois State University, 1968. / Title from title page screen, viewed Aug. 16, 2004. Dissertation Committee: Benjamin C. Hubbard (chair), Elmer Lemke, Alan Hickrod. Includes bibliographical references (leaves 102-108). Also available in print.

A case study has been done at Dynapac Compaction Equipment AB in Karlskrona in order to improve the internal flow of the production. The “Supermarket Storage”, an adjoining storage that feed material to the lean production in the “Z-line” assembly line with the help of kitting, was chosen to be focused during the optimization of the internal flow. Also, due to the little academic research about kitting it was decided to focus the research on the kitting process and identify how to optimize it. The purpose of the research is to determine optimization methods of a kitting process and fill in the gap in the subject field about kitting optimization. Given the research time limit, the focus was only on the kitting process in the Supermarket Storage and no optimization could change the storage’s layout. This resulted in three research question that will be investigated in the thesis.  Which common approaches exist when it comes to optimizing a kitting process?  What is the result of each optimizing method in the time aspect?  When should an optimization method be used, compared to the other methods that will be tested in this research? In order to solve these questions, was a needfinding process used in order to identify the kitting process current problems and the needs of the employees. With this, three optimization methods were identified and selected to be used to optimize the kitting process; optimization of routing, optimization of family grouping and optimization of an electronic system. The optimization of routing focused on the route that the kitters travel and the optimization of the family grouping focused on the article distribution in the Supermarket Storage; there each kitting operation’s articles should be stored in the same zone. Finally, the optimization of the electronic system, investigated the possibility to utilize a pick to scan system with the kitting process. Each optimization was implemented in different field experiment in order to identify how each optimization affected the kitting process. This resulted in that each optimization had improved the kitting process time efficiency and the electronic system had the biggest impact. Some other results were also observed during the experiments. The route optimization improved the learning curve of the kitting process and the family grouping optimization decreased the bottlenecks in kitting process. The electronic system optimization also implemented new benefits that resulted in a profit 2.5 times the cost of the system. Some of the benefits include removal of unneeded processes, quality control of the kitting process and statistics gathering that can be used to improve the process in the future. These results imply that all three optimization methods can be used in order to improve the time efficiency of a kitting process in a similar storage layout. The routing optimization should be used in a kitting operation with a high rotation of new kitters. The family grouping should be used in a kiting process with bottlenecks in the process and low organization of the article distribution. Ultimately, the electronic system optimization should be used in a kitting process that has unneeded processes and has the need of new tools that the electronic system can implement.

Σε αυτή την πτυχιακή εργασία γίνεται μελέτη του προβλήματος της ομαδοποίησης δεδομένων και πιο συγκεκριμένα οnline ομαδοποίηση σε ροές δεδομένων. Στην αρχή παρουσιάζεται η απλή offline εκδοχή του προβλήματος, όπου όλα τα δεδομένα προς ομαδοποίηση είναι γνωστά εκ των προτέρων. Παρουσιάζονται οι πιο βασικοί αλγόριθμοι και στοιχειώδεις εφαρμογές που καταδεικνύουν ότι η εύρεση αποδοτικών αλγορίθμων μπορεί να δώσει ώθηση σε νέα περιβάλλοντα που η ομαδοποίηση αποτελεί υπολογιστικό πυρήνα. Στη συνέχεια γίνεται εισαγωγή στο μοντέλο ροών δεδομένων, όπου εκεί η γνώση του αλγορίθμου για τη φύση των δεδομένων αποκτάται σταδιακά, όσο παρουσιάζονται νέα στοιχεία. Ο περιορισμός της διαθέσιμης μνήμης και η ανάγκη μας για αποδοτικούς αλγορίθμους μας οδηγεί σε κατασκευή προσεγγιστικών ευρετικών. Παρουσιάζονται ανοιχτά προβλήματα που έχουν τεθεί στη βιβλιογραφία καθώς και διάφορες εφαρμογές που προκύπτουν από δεδομένα που σχηματίζουν ροές. Συνεχίζοντας γίνεται μια εκτενής μελέτη της σύγχρονης βιβλιογραφίας και παρουσιάζονται οι πιο αντιπροσωπευτικοί αλγόριθμοι από κάθε βασική τεχνική προσέγγισης, όπως η ομαδοποίηση με βάση την πυκνότητα, ομαδοποίηση με γραμμική παλινδρόμηση, ομαδοποίηση δύο σταδίων κα. Παρουσιάζεται επίσης και ένας νέος αλγόριθμος που συνδυάζει προεπεξεργασία των δεδομένων της ροής με έναν online αλγόριθμο ομαδοποίησης και παραγωγή της τελικής ομαδοποίησης με μία παραλλαγή του LocalSearch. Τέλος ακολουθούν διάφορα πειραματικά αποτελέσματα που πραγματοποιήθηκαν επί αυτών των αντιπροσωπευτικών αλγορίθμων και γίνεται σύγκριση μεταξύ τους. Παρατηρούμε ότι τα νέα σχήματα που που προκύπτουν με βάση τον αλγόριθμο Localsearch πετυχαίνουν πολύ καλύτερα τελικά αποτελέσματα σε σχέση με τον αλγόριθμο Birch. / -

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