The hypothesis that the noradrenergic projection from the locus coeruleus (LC) to the cerebral cortex and hippocampus is an important neural substrate for learning was evaluated. Maze performance was studied in rats receiving either electrolytic lesions of the LC, or 6-hydroxydopamine (6-0HDA) injections into the region of the dorsal tegmental noradrenergic projection. In contrast to the results of an earlier report (Anlezark, Crow, and Greenway, 1973), LC lesions did not disrupt the acquisition of a running response for food reinforcement in an L-shaped runway, even though hippocampal-cortical noradrenaline (NA) was reduced to 29%. Greater telencephalic NA depletions (to 6 percent of control levels) produced by 6-0HDA also failed to disrupt the acquisition of this behaviour or impair the acquisition of a food reinforced position habit in a T-maze. Neither locomotor activity nor habituation to a novel environment was affected by the 6-0HDA lesions. Rats with such lesions were, however, „ found to be significantly more distractible than controls during the performance of a previously trained response. In another group of rats with identical 6-OHDA injections, the establishment of a lithium chloride-induced conditioned taste aversion was not affected by the lesions. The hypothesis that telencephalic NA is of fundamental importance in learning was not supported. / Medicine, Faculty of / Graduate

Radiotherapy is often used to treat prostate cancer. While using high dose of radiation does kill cancer cells, it can cause toxicity in healthy tissues for some patients. It would be best to apply this treatment only to patients who are likely to be immune from such toxicity. This requires a classifier that can predict, before treatment, which patients are likely to exhibit severe toxicity. Here, we explore ways to use certain genetic features, called Single Nucleotide Polymorphisms (SNPs), for this task. This thesis uses several machine learning methods for learning such classifiers for predicting toxicity. This problem is challenging as there are a large number of features (164,273 SNPs) but only 82 samples. We explore an ensemble classification method for this problem, called Mixture Using Variance (MUV), which first learns several different base probabilistic classifiers, then for each query combines the responses of the different base classifiers based on their respective variances. The original MUV learns the individual classifiers using bootstrap sampling of the training data; we modify this by considering different subsets of the features for each classifier. We derive a new combination rule for base classifiers in the proposed setting and obtain some new theoretical results. Based on characteristics of our task, we propose an approach that involves first clustering the features before selecting only a subset of features from each cluster for each base classifier. Unfortunately, we were unable to predict radiation toxicity in prostate cancer patients using just the SNP values. However, our further experimental results reveal strong relation between correctness of a classifier in its prediction and the variance of the response to the corresponding classification query, which show that the main idea is promising.

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With the growing importance of e-learning and increased competition among e learning providers, website designers must cater to users' needs more accurately. Interfaces need to provide the features users demand to experience an optimal learning environment. This empirical research investigates whether the function of specific e learning features are either basic, performance related, indifferent, or attractive. The Kano model is applied to examine the impact of 73 e learning features on satisfaction. 1,034 completed questionnaires from an online survey distributed to economics and business students are the basis for the assignment to the Kano factors. Results show that among others, basic features include learning statistics, sample exams, and videotaped lectures. Educational videos are seen as an attractive factor. In terms of different groups of learners, findings confirm that Bachelor students are more demanding than Master and Doctoral students. Additionally, importance ratings allow recommendations for an implementation sequence for the features examined.

No / As part of the cancer drug development process, evaluation in experimental subcutaneous tumour transplantation models is a key process. This involves implanting tumour material underneath the mouse skin and measuring tumour growth using calipers. This methodology has been proven to have poor reproducibility and accuracy due to observer variation. Furthermore the physical pressure placed on the tumour using calipers is not only distressing for the mouse but could also lead to tumour damage. Non-invasive digital imaging of the tumour would reduce handling stresses and allow volume determination without any potential tumour damage. This is challenging as the tumours sit under the skin and have the same colour pattern as the mouse body making them hard to differentiate in a 2D image. We used the pre-trained convolutional neural network VGG-16 and extracted multiple layers in an attempt to accurately locate the tumour. When using the layer FC7 after RELU activation for extraction, a recognition rate of 89.85% was achieved.

Enligt läroplanen ska förskolan sträva efter att ge barnet möjlighet att utvecklas i sin skapande förmåga och att kunna uttrycka sina tankar, erfarenheter och upplevelser genom estetik. Förskolan ska ge utrymme för barns fantasi och kreativitet. Syftet med studien är att fördjupa kunskapen om hur förskollärare kan arbeta med bildskapande i förskolan och om hur förskollärarna använder den skapande verksamheten för att främja barns lärande och utveckling. Studien bygger på kvalitativa intervjuer med fem yrkesverksamma pedagoger och utgår från Deweys ”Learning by doing” - konst som erfarenhet och Vygotskijs kreativitetsteori som teoretiska begrepp. Studien synliggör hur pedagoger uppfattar att de arbetar med bildskapande i förskolan och hur de använder den skapande verksamheten för att främja barns lärande och utveckling. Av studien framkommer att pedagogerna uppfattar att skapande har en nyckelroll i barns mångsidiga utveckling. Pedagogerna upplever att det största hindret i den skapande verksamheten är tiden. Ett arbetssätt som framkommer är att barnen ofta inte kan välja vilka material de vill jobba med och att pedagogerna bestämmer ganska mycket i början. I studien framkommer även att det är flera andra viktiga faktorer som påverkar skapandearbetet i förskolan, exempelvis miljö, material, pedagogers förhållningssätt och kunskaper i skapandeämnen. Pedagogerna uttrycker en önskan om mer fortbildning när det gäller skapande för att få mer kunskap och inspiration i ämnet.

Anomaly detection is the process of finding outliers in data. This report will explore the use of unsupervised machine learning for anomaly detection as well as the importance of explaining the decision making of the model. The project focuses on identifying anomalous behaviour in Ladok data from their frontend access logs, with emphasis on security issues, specifically attempted intrusion. This is done by implementing an anomaly detection model which consists of a stacked autoencoder and k-means clustering as well as examining the data using only k-means. In order to attempt to explain the decision making progress, SHAP is used. SHAP is a explainability model that measure the feature importance. The report will include an overview of the necessary theory of machine learning, anomaly detection and explainability, the implementation of the model as well as examine how to explain the process of the decision making in a black box model. Further, the results are presented and a discussion is held about how the models have performed on the data. Lastly, the report concludes whether the chosen approach has been appropriate and proposes how the work could be improved in future work. The study concludes that the results from this approach was not the desired outcome, and might therefore not be the most suitable.

Multi-frame image super-resolution is a procedure which takes several noisy low-resolution images of the same scene, acquired under different conditions, and processes them together to synthesize one or more high-quality super-resolution images, with higher spatial frequency, and less noise and image blur than any of the original images. The inputs can take the form of medical images, surveillance footage, digital video, satellite terrain imagery, or images from many other sources. This thesis focuses on Bayesian methods for multi-frame super-resolution, which use a prior distribution over the super-resolution image. The goal is to produce outputs which are as accurate as possible, and this is achieved through three novel super-resolution schemes presented in this thesis. Previous approaches obtained the super-resolution estimate by first computing and fixing the imaging parameters (such as image registration), and then computing the super-resolution image with this registration. In the first of the approaches taken here, superior results are obtained by optimizing over both the registrations and image pixels, creating a complete simultaneous algorithm. Additionally, parameters for the prior distribution are learnt automatically from data, rather than being set by trial and error. In the second approach, uncertainty in the values of the imaging parameters is dealt with by marginalization. In a previous Bayesian image super-resolution approach, the marginalization was over the super-resolution image, necessitating the use of an unfavorable image prior. By integrating over the imaging parameters rather than the image, the novel method presented here allows for more realistic prior distributions, and also reduces the dimension of the integral considerably, removing the main computational bottleneck of the other algorithm. Finally, a domain-specific image prior, based upon patches sampled from other images, is presented. For certain types of super-resolution problems where it is applicable, this sample-based prior gives a significant improvement in the super-resolution image quality.

There is a constant change in the world of school, new curricula and syllabi are made and the view of teaching and learning is changing. The desire to improve maths teaching in Swedish schools is strong, especially by laboratory work. How is it then that several teachers in our schools are still using the traditional way of teaching mathematics, which I consider to be relying a lot on the textbook and work for an automated approach? The purpose of my study is to investigate how three different math teachers on an F-5 school in the southern Stockholm area, choose to work with mathematics and why they choose to work according to a special approach. In order to obtain the information I seek, I have chosen to use the qualitative method to get a better understanding of my results. I have both observed the teachers during mathematics lessons on multiple occasions and interviewed the three teachers individually. The theories I have chosen to support my thesis on are the socio-cultural perspective and the cognitive perspective. Based on the results of my investigation I found that teaching in general assumes a lot from the textbook. Teachers in the survey feel a sense of security, to build upon the textbook, which they consider covers much of the knowledge students should have. The teachers use the textbook as a base and complements with laboratory features and communicate mathematics. The approach they choose to use has a lot of time allocated for teaching mathematics. Time isn’t enough for all the teachers to do what they want and they are worried that students will forget the basics of mathematics. Even the large number of students in the class plays the role of the choice of working, because all students should have time to develop.

Thesis (Ed. D.)--University of Missouri-Columbia, 2000. / Typescript. Vita. Includes bibliographical references (leaves 109-114). Also available on the Internet.