<p>The rate at which articles gets published grows exponentially and the possibility to access texts in machine-readable formats is also increasing. The need of an automated system to gather relevant information from text, text mining, is thus growing. </p><p>The goal of this thesis is to find a biologically relevant gene network for atherosclerosis, themain cause of cardiovascular disease, by inspecting gene cooccurrences in abstracts from PubMed. In addition to this gene nets for yeast was generated to evaluate the validity of using text mining as a method. </p><p>The nets found were validated in many ways, they were for example found to have the well known power law link distribution. They were also compared to other gene nets generated by other, often microbiological, methods from different sources. In addition to classic measurements of similarity like overlap, precision, recall and f-score a new way to measure similarity between nets are proposed and used. The method uses an urn approximation and measures the distance from comparing two unrelated nets in standard deviations. The validity of this approximation is supported both analytically and with simulations for both Erd¨os-R´enyi nets and nets having a power law link distribution. The new method explains that very poor overlap, precision, recall and f-score can still be very far from random and also how much overlap one could expect at random. The cutoff was also investigated. </p><p>Results are typically in the order of only 1% overlap but with the remarkable distance of 100 standard deviations from what one could have expected at random. Of particular interest is that one can only expect an overlap of 2 edges with a variance of 2 when comparing two trees with the same set of nodes. The use of a cutoff at one for cooccurrence graphs is discussed and motivated by for example the observation that this eliminates about 60-70% of the false positives but only 20-30% of the overlapping edges. This thesis shows that text mining of PubMed can be used to generate a biologically relevant gene subnet of the human gene net. A reasonable extension of this work is to combine the nets with gene expression data to find a more reliable gene net.</p>

<p>Undersökningar visar att lärare, till skillnad från många andra professionella yrkesgrupper, sällan refererar till teorier när de anger vilka motiv deras praktiska verksamhet har. Den kollegiala diskussionen bland lärare är sällan av teoretisk karaktär utan handlar ofta om praktiska förhållanden . En stor del av lärarkunskapen verkar vara ”tyst”.” Tyst kunskap ska här förstås som erfarenhetsbaserad kunskap som uppnås genom praktiserande verksamhet, en typ av förtrogenhetskunskap som kräver överblick över situationen man befinner sig i. I det här arbetet studerar jag relationen mellan lärarens reflektioner om sin egen praktik i relation till lärarens syn på elevernas progression under kemilaborationer. Syftet med undersökningen är att ge några bilder av tankemönster man möter i diskussionen kring undervisningen i kemi. Tankemönster i samband med undervisning ses här som delar av lärarens tysta kunskap. Den empiriska undersökningen baseras på intervjuer med kemilärare på gymnasie- och högstadieskolor. I undersökningen identifieras några aspekter av kemilärares tysta kunskap i relation till elevers lärandeprocess under kemilaborationer. Resultaten visar en koppling till litteratur och forskning inom området för hur effektiv undervisning kan bedrivas.</p> / <p>Investigations show that teachers, in contrast to many other professionals, seldom refer to theories in discussions about the motives in their practice. Professional discussions among teachers often deal with practical matters and rarely about theoretical issues. Major parts of teachers’ knowledge seem to be tacit. Tacit knowledge should be understood as knowledge received by practical experience, a type of confidential knowledge. In this work I have studied the relation between teachers’ reflections about their own practice and the teachers’ views of the students’ progression in learning during laboratory courses. The aim of this paper is to find some of the common thoughts shared by chemistry teachers, which are expressed when describing their practice. These kinds of “mind patterns” are here comprehended as part of the teachers’ tacit knowledge. The empirical investigation is based on interviews with chemistry teachers in upper level of compulsory school and in upper secondary school. The results of the investigation indicate connections to research and literature about effective learning.</p>

Our work is driven by a class of practical problems of sequential decision making in the context of electric power generation under uncertainties. These problems are usually treated as receding horizon deterministic optimization problems, and/or as scenario-based stochastic programs. Stochastic programming allows to compute a first stage decision that is hedged against the possible futures and -- if a possibility of recourse exists -- this decision can then be particularized to possible future scenarios thanks to the information gathered until the recourse opportunity. Although many decomposition techniques exist, stochastic programming is currently not tractable in the context of day-ahead electric power generation and furthermore does not provide an explicit recourse strategy. The latter observation also makes this approach cumbersome when one wants to evaluate its value on independent scenarios. We propose a supervised learning methodology to learn an explicit recourse strategy for a given generation schedule, from optimal adjustments of the system under simulated perturbed conditions. This methodology may thus be complementary to a stochastic programming based approach. With respect to a receding horizon optimization, it has the advantages of transferring the heavy computation offline, while providing the ability to quickly infer decisions during online exploitation of the generation system. Furthermore the learned strategy can be validated offline on an independent set of scenarios. On a realistic instance of the intra-day electricity generation rescheduling problem, we explain how to generate disturbance scenarios, how to compute adjusted schedules, how to formulate the supervised learning problem to obtain a recourse strategy, how to restore feasibility of the predicted adjustments and how to evaluate the recourse strategy on independent scenarios. We analyze different settings, namely either to predict the detailed adjustment of all the generation units, or to predict more qualitative variables that allow to speed up the adjustment computation procedure by facilitating the ``classical' optimization problem. Our approach is intrinsically scalable to large-scale generation management problems, and may in principle handle all kinds of uncertainties and practical constraints. Our results show the feasibility of the approach and are also promising in terms of economic efficiency of the resulting strategies. The solutions of the optimization problem of generation (re)scheduling must satisfy many constraints. However, a classical learning algorithm that is (by nature) unaware of the constraints the data is subject to may indeed successfully capture the sensitivity of the solution to the model parameters. This has nevertheless raised our attention on one particular aspect of the relation between machine learning algorithms and optimization algorithms. When we apply a supervised learning algorithm to search in a hypothesis space based on data that satisfies a known set of constraints, can we guarantee that the hypothesis that we select will make predictions that satisfy the constraints? Can we at least benefit from our knowledge of the constraints to eliminate some hypotheses while learning and thus hope that the selected hypothesis has a better generalization error? In the second part of this thesis, where we try to answer these questions, we propose a generic extension of tree-based ensemble methods that allows incorporating incomplete data but also prior knowledge about the problem. The framework is based on a convex optimization problem allowing to regularize a tree-based ensemble model by adjusting either (or both) the labels attached to the leaves of an ensemble of regression trees or the outputs of the observations of the training sample. It allows to incorporate weak additional information in the form of partial information about output labels (like in censored data or semi-supervised learning) or -- more generally -- to cope with observations of varying degree of precision, or strong priors in the form of structural knowledge about the sought model. In addition to enhancing the precision by exploiting information that cannot be used by classical supervised learning algorithms, the proposed approach may be used to produce models which naturally comply with feasibility constraints that must be satisfied in many practical decision making problems, especially in contexts where the output space is of high-dimension and/or structured by invariances, symmetries and other kinds of constraints.

<p>Recognition of Prior Learning (RPL) is the process of recognizing and crediting a person for his/her knowledge and experience however attained and promoting that person along a development pathway. In South Africa RPL has been promoted for social justice purposes related to access and redress. However these intentions have been lost within current educational discourses despite being rooted in several policies. Recently the role of vocational education has received increased prominence as a means to provide skills development. However there is often a disjuncture between policy formulation and implementation and this has given rise to this study of how RPL policy has been implemented within public Further Education and Training (FET) colleges. This paper investigates the RPL policies and practices in two public FET colleges and analyses how these employ social justice intentions of access and redress. The study reveals that there are similar conceptions of RPL amongst lecturers but varying RPL practices in these colleges.</p>

Undersökningar visar att lärare, till skillnad från många andra professionella yrkesgrupper, sällan refererar till teorier när de anger vilka motiv deras praktiska verksamhet har. Den kollegiala diskussionen bland lärare är sällan av teoretisk karaktär utan handlar ofta om praktiska förhållanden . En stor del av lärarkunskapen verkar vara ”tyst”.” Tyst kunskap ska här förstås som erfarenhetsbaserad kunskap som uppnås genom praktiserande verksamhet, en typ av förtrogenhetskunskap som kräver överblick över situationen man befinner sig i. I det här arbetet studerar jag relationen mellan lärarens reflektioner om sin egen praktik i relation till lärarens syn på elevernas progression under kemilaborationer. Syftet med undersökningen är att ge några bilder av tankemönster man möter i diskussionen kring undervisningen i kemi. Tankemönster i samband med undervisning ses här som delar av lärarens tysta kunskap. Den empiriska undersökningen baseras på intervjuer med kemilärare på gymnasie- och högstadieskolor. I undersökningen identifieras några aspekter av kemilärares tysta kunskap i relation till elevers lärandeprocess under kemilaborationer. Resultaten visar en koppling till litteratur och forskning inom området för hur effektiv undervisning kan bedrivas. / Investigations show that teachers, in contrast to many other professionals, seldom refer to theories in discussions about the motives in their practice. Professional discussions among teachers often deal with practical matters and rarely about theoretical issues. Major parts of teachers’ knowledge seem to be tacit. Tacit knowledge should be understood as knowledge received by practical experience, a type of confidential knowledge. In this work I have studied the relation between teachers’ reflections about their own practice and the teachers’ views of the students’ progression in learning during laboratory courses. The aim of this paper is to find some of the common thoughts shared by chemistry teachers, which are expressed when describing their practice. These kinds of “mind patterns” are here comprehended as part of the teachers’ tacit knowledge. The empirical investigation is based on interviews with chemistry teachers in upper level of compulsory school and in upper secondary school. The results of the investigation indicate connections to research and literature about effective learning.

The purpose of this study was to investigate scientific epistemological beliefs, perceptions of constructivist learning environment, attitude towards science, prior knowledge and gender as determinants of students&rsquo / approaches to learning. This study was carried out in 2005-2006 Spring Semester. One thousand, one hundred and fifty two eighth grade students from seven public schools in &Ccedil / ankaya, a district of Ankara participated in this study. Epistemological Beliefs Questionnaire, Constructivist Learning Environment Scale, Learning Approaches Questionnaire and Attitude towards Science Scale were administered to students in order to determine their scientific epistemological beliefs, their perceptions of constructivist learning environments, approaches to learning and attitudes towards science respectively. Descriptive statistics were used in order to explore the general characteristics of the sample. Paired samples t-test was used in order to evaluate the mean difference iv between the scales of the actual and preferred learning environments. Pearson Correlation Analyses and Multiple Regression Analyses were conducted to see the relationships among the variables and the variables that contribute to students&rsquo / meaningful and rote learning approaches. Results of the paired samples t-test showed that the actual learning environments of the students did not adapt their preferences. In fact, students preferred more constructivist learning environments where they have more opportunity to relate science with the real world, communicate in the classroom, take role in the decision making process of what will go on in the lesson to be more beneficial for them, question what is going on in the lesson freely and experience the formulation of scientific knowledge. Pearson correlation analyses, however, showed that students who had meaningful learning orientations had tentative views of scientific epistemological beliefs, positive attitudes towards science, high prior knowledge and perceived their learning environments as constructivist. On the other hand, students who had rote learning approaches had fixed views of scientific epistemological beliefs, positive attitudes towards science and low prior knowledge. In addition, the rote learners perceived their environments as constructivist in all scales except shared control scale. Multiple Regression Analyses by using actual learning environment showed that attitude towards science is the best predictor of both meaningful and rote learning approaches.

Segmentation of individual facial soft tissues has received relatively little attention in the literature due to the complicated structures of these tissues. There is a need to incorporate the prior information, which is usually in the form of atlases, in the segmentation process. In this thesis we performed several segmentation methods that take advantage of prior knowledge for facial soft tissue segmentation. An atlas based method and three expectation maximization &ndash / Markov random field (EM-MRF) based methods are tested for two dimensional (2D) segmentation of masseter muscle in the face. Atlas based method uses the manually labeled atlases as prior information. We implemented EM-MRF based method in different manners / without prior information, with prior information for initialization and with using labeled atlas as prior information. The differences between these methods and the influence of the prior information are discussed by comparing the results. Finally a new method based on EM-MRF is proposed in this study. In this method we aim to use prior information without performing manual segmentation, which is a very complicated and time consuming task. 10 MRI sets are used as experimental data in this study and leave-one-out technique is used to perform segmentation for all sets. The test data is modeled as a Markov Random Field where unlabeled training data, i.e., other 9 sets, are used as prior information. The model parameters are estimated by the Maximum Likelihood approach when the Expectation Maximization iterations are used to handle hidden labels. The performance of all segmentation methods are computed and compared to the manual segmented ground truth. Then we used the new 2D segmentation method for three dimensional (3D) segmentation of two masseter and two temporalis tissues in each data set and visualize the segmented tissue volumes.

This work presents a systematic investigation of two topics. One is in stochastic dynamic general equilibrium. It incorporates private information into dynamic general equilibrium framework. An existence of competitive equilibrium is established. Quantitative analysis is provided for health insurance problem. The other topic is in decision problems under ambiguity. Lack of precise information regarding a decision problem is represented by a set of probabilities. Descriptive richness of the set of probabilities is defi ned. It is used to generalize Skorohod's theorem to sets of probabilities. The latter is used to show the constancy of the coefficient in alpha-maximin multiple prior preferences. Examples illustrate: the implications of this representation; and the restrictions arising from the failure of descriptive richness. / text

Purpose: The aim of this study was to determine the influence of 30min. of moderate and heavy intensity aerobic exercise to heart rate during interval incremental exercise. Methods: Eight healthy males (mean SD 23 1yr; 76 4,8 kg; 178 6,6 cm) were tested 3 times. They performed incremental veloergometer exercise test until subjective exhaustion. Pedalling rate was maintained constant at 70 rpm. Test began with 4 min. of cycling at 25 W. Then the workload was increased by 25 W and maintained for 3 min. until participant ,s heart rate (HR) reached 90 % of HRmax (220 - age, year). Between workloads the participants rested for 4 min. Two times exercise was preceded by 30 min. of moderate (equal to lactate threshold), heavy intensity (50 W heavier than lactate threshold) or no warm-up. Capillary blood samples were drawn from finger to measure lactate concentration at rest and 5, 10, 20, 30, min. during work. HR was registered before exercise and every 5 min. during work. Also HR was recorded every 5 s. during interval incremental veloergometer test. The heart rate data were modelled using single exponential models, which included amplitudes, time constants and asimptotics values. Results: Heart rate was more elevated during heavy warm-up compared to the moderate (3 min. – 140 13,7; 13 min. – 158 16,1; 23 min. – 164 13,6; 26 min. - 167 13,7 and 3 min. – 128 8,7; 13 min. – 134 11,2; 23 min. – 140 11,2; 26 min. – 142 10,9 beats/min. respectively). Heavy warm-up resulted... [to full text]

<p>Recognition of Prior Learning (RPL) is the process of recognizing and crediting a person for his/her knowledge and experience however attained and promoting that person along a development pathway. In South Africa RPL has been promoted for social justice purposes related to access and redress. However these intentions have been lost within current educational discourses despite being rooted in several policies. Recently the role of vocational education has received increased prominence as a means to provide skills development. However there is often a disjuncture between policy formulation and implementation and this has given rise to this study of how RPL policy has been implemented within public Further Education and Training (FET) colleges. This paper investigates the RPL policies and practices in two public FET colleges and analyses how these employ social justice intentions of access and redress. The study reveals that there are similar conceptions of RPL amongst lecturers but varying RPL practices in these colleges.</p>