Constraining the Search Space in Temporal Pattern Mining

Agents in dynamic environments have to deal with complex situations including various temporal interrelations of actions and events. Discovering frequent patterns in such scenes can be useful in order to create prediction rules which can be used to predict future activities or situations. We present the algorithm MiTemP which learns frequent patterns based on a time intervalbased relational representation. Additionally the problem has also been transfered to a pure relational association rule mining task which can be handled by WARMR. The two approaches are compared in a number of experiments. The experiments show the advantage of avoiding the creation of impossible or redundant patterns with MiTemP. While less patterns have to be explored on average with MiTemP more frequent patterns are found at an earlier refinement level

An Existentialist Approach to Teaching Writing: Anguish, Bad Faith, and Seriousness in Composition

This dissertation aims at developing a model concept for the teaching of ethics in the composition classroom through the use of existentialism in the writings of Jean-Paul Sartre. Overall, the gap I am trying to fill with my dissertation is a lack of awareness of how much Sartre actually fits rhetorical theory and composition. Ultimately, this dissertation is the attempt to develop an ethic that is universally applicable in the teaching of composition, without the need for a service learning environment or additional resources outside the university itself. To provide an overview of the project, the approach will be illustrated with three case studies that focus on different ethical issues in writing that are central to first-year composition courses. The first case study looks at a conflict between a professor and a graduate student that involved the discussion of heated topics and power relationships in the classroom. The second case study looks at cases of plagiarism on the highest level, in dissertations. Several German politicians had to resign from their offices because their dissertations contained plagiarized passages, and their reactions sparked controversial responses from both the general public, the media, and academic institutions. The third case study looks at service learning and the encounter with marginalized groups – what could be called the encounter with the Other. Students do not always show an authentic ethical reaction to what they experience. The project will conclude with a discussion of how these cases and Sartre\u27s work might be deployed in the context of a first-year composition syllabus with three main thematic units

A folded-sandwich polarization-entangled two-color photon pair source with large tuning capability for applications in hybrid quantum architectures

We demonstrate a two-color entangled pho ton pair source which can be adapted easily to a wide range of wavelengths combinations. A Fresnel rhomb as a geometrical quarter-wave plate and a versatile combination of compensation crystals are key components of the source. Entanglement of two photons at the Cs D1 line (894.3 nm) and at the telecom O-band (1313.1 nm) with a fidelity of $F = 0.753 \pm 0.021$ is demonstrated and improvements of the setup are discussed

Infrared-Faint Radio Sources are at high redshifts

Context: Infrared-Faint Radio Sources (IFRS) are characterised by relatively high radio flux densities and associated faint or even absent infrared and optical counterparts. The resulting extremely high radio-to-infrared flux density ratios up to several thousands were previously known only for High-redshift Radio Galaxies (HzRGs), suggesting a link between the two classes of object. Prior to this work, no redshift was known for any IFRS in the Australia Telescope Large Area Survey (ATLAS) fields which would help to put IFRS in the context of other classes of object, especially of HzRGs. Aims: This work aims at measuring the first redshifts of IFRS in the ATLAS fields. Further, we test the hypothesis that IFRS are similar to HzRGs, as higher-redshift or dust-obscured versions of these massive galaxies. Methods: A sample of IFRS was spectroscopically observed using the Focal Reducer and Low Dispersion Spectrograph 2 (FORS2) at the Very Large Telescope (VLT). The data were calibrated based on the Image Reduction and Analysis Facility (IRAF) and redshifts extracted. This information was then used to calculate rest-frame luminosities, and to perform the first spectral energy distribution modelling of IFRS based on redshifts. Results: We found redshifts of 1.84, 2.13, and 2.76, for three IFRS, confirming the suggested high-redshift character of this class of object. These redshifts as well as the resulting luminosities show IFRS to be similar to HzRGs. We found further evidence that fainter IFRS are at even higher redshifts. Conclusions: Considering the similarities between IFRS and HzRGs substantiated in this work, the detection of IFRS, which have a significantly higher sky density than HzRGs, increases the number of Active Galactic Nuclei in the early universe and adds to the problems of explaining the formation of supermassive black holes shortly after the Big Bang.Comment: 7 pages, 4 figures; version in prin

Uncertainty Propagation of Initial Conditions in Thermal Models

The operation of machine tools often demands a highly accurate knowledge of the tool center point's (TCP) position. The displacement of the TCP over time can be inferred from thermal models, which comprise a set of geometrically coupled heat equations. Each of these equations represents the temperature in part of the machine, and they are often formulated on complicated geometries. The accuracy of the TCP prediction depends highly on the accuracy of the model parameters, such as heat exchange parameters, and the initial temperature. Thus it is of utmost interest to determine the influence of these parameters on the TCP displacement prediction. In turn, the accuracy of the parameter estimate is essentially determined by the measurement accuracy and the sensor placement. Determining the accuracy of a given sensor configuration is a key prerequisite of optimal sensor placement. We develop here a thermal model for a particular machine tool. On top of this model we propose two numerical algorithms to evaluate any given thermal sensor configuration with respect to its accuracy. We compute the posterior variances from the posterior covariance matrix with respect to an uncertain initial temperature field. The full matrix is dense and potentially very large, depending on the model size. Thus, we apply a low-rank method to approximate relevant entries, i.e. the variances on its diagonal. We first present a straightforward way to compute this approximation which requires computation of the model sensitivities with with respect to the initial values. Additionally, we present a low-rank tensor method which exploits the underlying system structure. We compare the efficiency of both algorithms with respect to runtime and memory requirements and discuss their respective advantages with regard to optimal sensor placement problems

Machine and component residual life estimation through the application of neural networks

Analysis of reliability data plays an important role in the maintenance decision making process. The accurate estimation of residual life in components and systems can be a great asset when planning the preventive replacement of components on machines. Artificial intelligence is a field that has rapidly developed over the last twenty years and practical applications have been found in many diverse areas. The use of such methods in the maintenance field have however not yet been fully explored. With the common availability of condition monitoring data, another dimension has been added to the analysis of reliability data. Neural networks allow for explanatory variables to be incorporated into the analysis process. This is expected to improve the quality of predictions when compared to the results achieved through the use of methods that rely solely on failure time data. Neural networks can therefore be seen as an alternative to the various regression models, such as the proportional hazards model, which also incorporate such covariates into the analysis. For the purpose of investigating their applicability to the problem of predicting the residual life of machines and components, neural networks were trained and tested with the data of two different reliability related datasets. The first dataset represents the renewal case where repair leads to complete restoration of the system. A typical maintenance situation was simulated in the laboratory by subjecting a series of similar test pieces to different loading conditions. Measurements were taken at regular intervals during testing with a number of sensors which provided an indication of the test piece’s condition at the time of measurement. The dataset was split into a training set and a test set and a number of neural network variations were trained using the first set. The networks’ ability to generalize was then tested by presenting the data from the test set to each of these networks. The second dataset contained data collected from a group of pumps working in a coal mining environment. This dataset therefore represented an example of the situation encountered with a repaired system. The performance of different neural network variations was subsequently compared through the use of cross-validation. It was proved that in most cases the use of condition monitoring data as network inputs improved the accuracy of the neural networks’ predictions. The average prediction error of the various neural networks under comparison varied between 431 and 841 seconds on the renewal dataset, where test pieces had a characteristic life of 8971 seconds. When optimized the multi-layer perceptron neural networks trained with the Levenberg-Marquardt algorithm and the general regression neural network produced a sum of squares error within 11.1% of each other for the data of the repaired system. This result emphasizes the importance of adjusting parameters, network architecture and training targets for optimal performance The advantage of using neural networks for predicting residual life was clearly illustrated when comparing their performance to the results achieved through the use of the traditional statistical methods. The potential of using neural networks for residual life prediction was therefore illustrated in both cases.Dissertation (MEng (Mechanical Engineering))--University of Pretoria, 2007.Mechanical and Aeronautical EngineeringMEngunrestricte

Structural changes in activated wood-based carbons: correlation between specific surface area and localization of molecular-sized pores

Samples of maple were pyrolyzed and subsequently activated by carbon dioxide at different temperatures for various dwell times. The changes in wood structure were characterized by nitrogen adsorption isotherms, transmission electron microscopy (TEM) with selected-area electron diffraction (SAED), and scanning electron microscopy (SEM). Increasing pyrolysis temperatures promoted increased crystallization of graphitic wood components and mineral-like phases. The average pore diameter derived from nitrogen adsorption isotherms approximately correlated with the results obtained by high-resolution SEM and TEM. The highest surface area was found for samples containing considerable amounts of nanoperforated pit membranes located in intervascular pitting. High-resolution TEM examinations of membrane regions showed foam-like clusters with an average size of 1.7nm, which are attributed to the selective influence of CO2 activation on pyrolyzed cellulose and ligni

Qualitative Inhaltsanalyse als ein Instrument zur Auswertung von biographischen Interviews: ein Erfahrungsbericht

"Die Reform der Lehrerinnen- und Lehrerbildung im Kanton Bern (Schweiz) wurde zum Anlass genommen, um die berufliche Karriere von Absolventinnen und Absolventen der in Auflösung begriffenen Seminare für Grund- und Hauptschullehrkräfte zu untersuchen. Im Mittelpunkt dieses Forschungsprojekt (Laufzeit: 2002-2005) steht der Vergleich zwischen Personen, die a) ohne nennenswerten Unterbruch im Lehrerberuf tätig sind, b) den Lehrerberuf verlassen haben, aber zum Zeitpunkt der Befragung wieder als Lehrperson tätig sind, c) trotz erfolgreich abgeschlossener Ausbildung nie in den Lehrerberuf eingestiegen sind und d) im Lehrerberuf tätig gewesen waren, ihn aber zu einem bestimmten Zeitpunkt verlassen haben. Das Design erlaubt eine Erweiterung der bisherigen, zumeist auf 'Überlebendendaten' beruhenden Analysen und Modelle zur Karriere von Lehrkräften. Im Rahmen des vierten Workshops 'Qualitative Inhaltsanalyse' werden das zweistufige Vorgehen der methodischen Umsetzung, bestehend aus einer schriftlichen und einer nachgeordneten mündlichen Befragung, sowie vor allem die Strategie zur Auswertung der Interviewdaten vorgestellt. Ergebnisse der Studie werden an dieser Stelle nicht referiert. Vielmehr wird ein in Anlehnung an Mayring (2003) erstelltes maßgeschneidertes Auswertungskonzept zur Analyse der 171 Interviews sowie seine Umsetzung präsentiert. Hierbei werden die Datenaufbereitung, die Generierung und Überprüfung von Forschungsfragen sowie die Entwicklung des Kategoriensystems expliziert und - im Sinne eines Erfahrungsberichts - der Diskussion zugeführt." (Autorenreferat