The purpose of this study was to evaluate the effectiveness of multiple exemplar training in establishing comparative relations of faster and slower as a generalized operant in children with autism. The current study employed a multiple baseline across participants to evaluate the effectiveness of the multiple exemplar training procedures. The participants used in the study were 3 children diagnosed with Autism. The baseline phase consisted of 4-trial blocks; this was done by presenting each stimulus once for AB- relation. Participants were trained comparative relations of faster and slower with one set of stimuli, and test probes were conducted with a novel, untrained set of stimuli to evaluate the degree to which participants responding generalized. This sequence was repeated for the duration of baseline. Y/Z relation remained at low levels during baseline and was only used to see if the skill generalized. The results suggest the procedures were effective for teaching comparative relations for 3 of the 3 participants. Additionally, 3 of the participants responded correctly to the test relations, supporting the use of the procedures in prompting generalized relational responding. An implication is that we can utilize these procedures to establish relational responding in children with autism.

La découverte de contingences sensorimotrices et leur structuration en compétences sont des enjeux importants en robotique. En particulier, on souhaite pouvoir apprendre ainsi des compétences qui soient à la fois riches sémantiquement et aussi générales que possible.Cette thèse s’est intéressée à la question du regroupement de compétences robotiques en compétences plus générales. Après avoir posé formellement les notions que l’on peut trouver dans la littérature de compétence et de compétence paramétrée, nous avons relié les compétences paramétrées aux modèles inverses et nous nous sommes inspirés du dualisme entre modèles directs et inverses pour proposer un formalisme dual, les compétences paramétrées directes.Nous avons ensuite entrepris de déterminer quand il était ou non pertinent de réunir des compétences au sein d’une même compétence paramétrée directe.Le problème est alors ramené à un problème de régression, et des algorithmes s’inspirant du principe du rasoir d’Ockham sont proposés pour y chercher une solution sous la forme d’un mélange d’experts de complexité minimale. Ces algorithmes sont ensuite appliqués à des données de manipulation d’objets en simulation / The discovery of sensorimotor contingencies and their structurationinto skills are both important topics in the field of robotics. In particular,robots need the ability to learn skills which are both semantically rich and as general as possible. During this thesis, we studied the question of merging robotic skills into more general skills. After formally defining the notions that can be found in the litterature of skills and parameterized skills, we established a link between paramaterized skills and inverse models, then mirrored the dualism between forward and inverse models to propose a dual type of parameterized skills:forward-parameterized skills. We went on to determine when merging skills into a forward-parameterized skill is relevant and when it’s not. The problem is then formulated as a regression problem, and algorithms inspired by the Occam Razor principle are proposed to find a mixture of experts that solves it with minimal complexity. Those algorithms are then applied to simulated object-manipulation data.

Exposure therapy is the gold standard treatment for anxiety disorders, but reductions in fear following exposure often do not generalize well outside the context in which they took place. This study tested a strategy for increasing generalization that involved revisiting the memory of a prior exposure experience in order to enhance the retrieval of the learning that occurred. Forty-five participants (29 females, 16 males) with claustrophobia received exposure training consisting of repeated 5-minute trials lying inside a narrow cabinet laid on its back. One week later, they were randomly assigned to either enhanced mental reinstatement (EMR) or control procedures. Results of the exposure training showed significant decreases in subjective fear, heart rate and avoidance in the training context, as well as reduced claustrophobia symptoms. As expected, fear levels in the mock MRI scanner one week later increased relative to the exposure training context post-treatment. Compared to the control condition, the EMR intervention led to significantly reduced heart rate reactivity in the mock MRI scanner, but not to reduced self-reported fear or avoidance of the mock scanner, nor to differences in claustrophobia symptoms at one-month follow-up. Expectancy violations about coping self-efficacy, measured via participants’ surprise about their ability to effectively cope during exposure, predicted lower fear in the mock MRI regardless of condition. Fear-related expectancy violations, reflecting greater discrepancy in expected vs. actual fear levels during exposure, predicted greater fear in the mock MRI. Results highlight the potential for mental reinstatement of exposure to improve generalization of learning in claustrophobia, though effects may be limited. The impact of expectancy violations on exposure outcomes may depend on the type of expectancy that is violated.

This thesis concerns object oriented modelling and automatic generalisation of geographic information. The focus however is not on traditional paper maps, but on screen maps that are automatically generated from a geographical database. Object oriented modelling is used to design screen maps that are equipped with methods that automatically extracts information from a geographical database, generalises the information and displays it on a screen. The thesis consists of three parts: a theoretical background, an object oriented model that incorporates automatic generalisation of geographic information and a case study where parts of the model have been implemented. An object oriented model is an abstraction of reality for a certain purpose. The theoretical background describes different aspects that have impact on how an object oriented model shall be designed for automatic generalisation. The following topics are described: category theory, the human ability to recognise visual patterns, previous work in automatic cartographic generalisation, and object oriented modelling. A view is here defined to consist of several static levels, or maps, defined at different resolutions. As the user zooms the level that is appropriate for the particular resolution is shown. An object class belongs to one and only one level and has a certain symbolisation. The automatic creation of new objects in a level is discussed as well as the relation between objects in different levels. To preserve topological relations between objects in a level a network structure is formed between all linear objects in a level and objects that might cause conflicts are modelled using dependencies. The model is designed for a set of typical geographical object classes such as road, railroad, lake, river, stream, building, built-up area etc. The model is designed to handle information in a scale-range from 1:10 000 to 1:100 000. The model has been implemented for a subset of these classes and tested for an area covering approximatley 60 km2. / <p>NR 20140805</p>

Meta-analytic reliability generalizations (RGs) are limited by the scarcity of reliability reporting in primary articles, and currently, RG investigators lack a method to quantify the impact of such nonreporting. This article introduces a stepwise procedure to address this challenge. First, the authors introduce a formula that allows researchers to estimate the lower bound population average reliability for a desired instrument. Second, they present an equation to determine the Fail-Safe N for RG. This equation estimates the number of ''file drawer'' studies required to drop the aggregate score reliability of an instrument below a specified criterion value. Finally, the authors demonstrate the utility of these equations using published RG studies. Comments on the conclusions drawn from each RG application are provided.

The block building, Lego construction, pen drawing, and painting of four preschool children were analyzed in terms of the construction of form diversity and new form production. Social descriptive reinforcement, contingent on the production of any form not previously constructed within the current session and overt modeling of forms never seen produced during the study, increased form diversity scores per session and new form production (forms never seen before in the child's total prior sequence of blockbuilding sessions). The results indicated that after training, form diversity scores generalized to topographically similar and dissimilar media of expression. New form production generalized to topographically similar and dissimilar media in the majority of the children.

The use of a student self-monitoring and self-rating/teacher matching strategy to assist generalization of social skills use and decrease off-task behavior of five inner-city at-risk middle school students was investigated. A multiple-baseline design was used to assess the effects of the intervention in up to six different class settings. Results indicated that the self-monitoring and self-rating/teacher matching intervention led to an increase in correct social skills use and a decrease in off-task behaviors with all five students. These data add to the existing literature, suggesting self-monitoring with self-rating/teacher matching is an effective procedure to promote generalization of behavior. Implications for research and practice are discussed.

The process of performing cartographic generalization in an automatic way applied on geographic information is of highly interest in the field of cartography, both in academia and industry. Many research e↵orts have been done to implement di↵erent automatic generalization approaches. Being able to answer the research question on automatic generalization, another interesting question opens up: ”Is it possible to retrieve and visualize geographic information in any arbitrary scale?” This is the question in the field of vario-scale geoinformation. Potential research works should answer this question with solutions which provide valid and efficient representation of geoinformation in any on-demand scale. More brilliant solutions will also provide smooth transitions between these on-demand arbitrary scales. Space-Scale-Cube (Meijers and Van Oosterom 2011) is a reactive tree (Van Oosterom 1991) data structure which shows positive potential for achieving smooth automatic vario-scale generalization of area features. The topic of this research work is investigation of adaptation of this approach on an interesting class of geographic information: road networks datasets. Firstly theoretical background will be introduced and discussed and afterwards, implementing the adaptation would be described. This research work includes development of a hierarchical data structure based on road network datasets and the potential use of this data structure in vario-scale geoinformation retrieval and visualization.:Declaration of Authorship i Abstract iii Acknowledgements iv List of Figures vii Abbreviations viii 1 Introduction 1 1.1 Problem Definition 2 1.1.1 Research Questions 2 1.1.2 Objectives 3 1.2 Proposed Solution 3 1.3 Structure of the Thesis 4 1.4 Notes on Terminology 4 2 Cartographic Generalization 6 2.1 Cartographic Generalization: Definitions and Classifications 6 2.2 Generalization Operators 9 2.3 Efforts on Vario-Scale Visualization of Geoinformation 10 2.4 Efforts on Generalization of Road Networks and Similar Other Networks 16 2.4.1 Geometric Generalization of Networks 17 2.4.2 Model Generalization of Networks 18 2.5 Clarification of Interest 20 3 Theory of Road Network SSC 21 3.1 Background of an SSC 21 3.1.1 tGAP 21 3.1.2 Smoothing tGAP 23 3.2 Road Network as a ’Network’ 24 3.2.1 Short Background on Graph Theory 5 3.3 Formation of Road Network SSC 26 3.3.1 Geometry 26 3.3.2 Network Topology 27 3.3.3 Building up tGAP on The Road Network 28 3.3.4 Smoothing of Road Network SSC 31 3.3.4.1 Smoothing Elimination 32 3.3.4.2 Smoothing Simplification 32 3.4 Reading from a road network SSC 34 3.4.1 Discussion on Scale 34 3.4.2 Iterating Over The Forest 35 3.4.3 Planar Slices 35 3.4.4 Non-Planar Slices 36 4 Implementation of Road Network SSC 37 4.1 General Information Regarding The Implementation 37 4.1.1 Programming Language 37 4.1.2 RDBMS 38 4.1.3 Geometry Library 39 4.1.4 Graph Library 39 4.2 Data Structure 40 4.2.1 Node 40 4.2.2 Edge 41 4.2.3 Edge-Node-Relation 41 4.3 Software Architecture 42 4.3.1 More Detail on Building The SSC 42 4.3.1.1 Initial Data Processing 42 4.3.1.2 Network Processing 43 4.3.2 More Detail on Querying The SSC 46 4.3.2.1 Database Query 46 4.3.2.2 Building Geometry 46 4.3.2.3 Interface and Visualization 47 4.4 Results 48 5 Conclusions and Outlook 49 Bibliography 51

Approximations made in the traditional signal model for synthetic aperture radar (SAR) processing cause defocusing of the radar images when the system operates under conditions where the approximations lose validity. This dissertation investigates a number of these approximations and presents algorithmic improvements based on generalizations of the approxmations of the SAR signal model. These improvements result in better focused imagery from SAR systems with varied designs and parameters. Among the advancements presented is the development of a generalized chirp-scaling algorithm and a generalized frequency scaling algorithm to address the problems caused by approximations based on a Taylor series expansion of the SAR signal for both pulsed SAR and linear frequency modulated continuous wave (LFM-CW) SAR systems. These generalized algorithms extend the ability of frequency-domain algorithms to process SAR data from systems with a low frequency, a wide beamwidth, and a large bandwidth. Image formation algorithms are developed that account for the continuous platform motion and compensate for translational position errors due to the continuous non-ideal motion of real-world LFM-CW SAR systems, including a backprojection algorithm that does not rely upon the traditional stop-and-go approximation for platform motion.

This study examines how and if threshold concepts enable expansive learning and generalization. Expansive learning and generalization are part of the highly contested conceptions of transfer, and these specific conceptions offer a more complex conception of transfer that deals with knowledge transformation (Tuomi-Gr?hn and Engestr?m, Beach). One way that we can see expansive learning and generalization transform knowledge is through the teaching of threshold concepts. In the last decade, there has been a movement toward using threshold concepts in FYC*s that take up writing studies as their curricula (Wardle and Downs, Dew). Even though using threshold concepts seems to be one interesting way of specifically studying expansive learning and generalization, we have no studies examining whether or not teaching threshold concepts encourages expansive learning. The studies we do have do not seem to offer any methodologies that would enable us to study threshold concepts and generalization. Past methods, such as case studies, interviews, and surveys have included small sample sizes to collect their data from (Wardle, Dively and Nelms, Nowacek). A lot of the transfer data does not actually focus on the writing or the texts themselves or the reoccurring moves that students use in those texts. Linguistic discourse analysis offers a promising avenue for examining the generalization of threshold concepts. Using research methods like linguistic discourse analysis in marriage with the best qualitative methods of transfer, like case studies or interviews, could allow for a larger sample size of data collection and allows for us to see how students use these threshold concepts in their writing. Through linguistic discourse analysis and interviews, this study suggests that students* perceptions of writing change after being introduced to some threshold concepts from the Writing About Writing curriculum. The threshold concepts that students are presented to in the Writing About Writing curriculum at UCF tackles misconceptions and helps students change how they view writing. Once they can change this view, they are able to generalize the knowledge they have into their own writing. If students do not use the exact terminology from the curriculum, they are able to generalize those threshold concepts through using their own language or even through analogies.