Магистерская диссертация состоит из введения, обзора литературы, описания материалов и методов, результатов и их обсуждений, выводов и списка литературы. Материалы работы изложены на 75 страницах (основного текста). Работа содержит 3 таблицу, 104 рисунка, 2 приложения. Список литературы включает 104 источника, из которых 19 отечественных и 85 иностранных. Цель исследования: Выявить диагностическую роль анатомо-морфологических и эколого-биологических признаков отдельных представителей рода Begonia L., культивируемых на кафедре биоразнообразия и биоэкологи ИЕНиМ УрФУ и в оранжереях Ботанического сада УрФУ и УрО РАН. Объектами исследования являются виды и культивары рода Begonia L., выращиваемых на кафедре биоразнообразия и биоэкологии ИЕНиМ УрФУ и в оранжереях Ботанических садов УрФУ и УрО РАН. В работе изучались анатомо-морфологические характеристики видов и культиваров, состоящие в 8 разных секциях: Begonia, Diploclinium, Gaerdtia, Gireoudia, Knesebeckia, Platycentrum, Pritzelia, Weilbachia. Актуальностью настоящей работы является изучение видов и культиваров рода Begonia L., так как группа является крайне разнородной и уникальной, обладающая важным экономическим потенциалом. Представители данного рода могут стать одними из модельных объектов для изучения биоразнообразия и развития подходов для его сохранения. Важным является и то, что в исследования необходимо вовлекать не только дикорастущие виды, но и огромный объем культиваров, входящие в этот род. Также согласно современным исследованиям, бегонии имеют потенциал растений, обладающими лекарственными свойствами, что крайне важно для будущих и возможных фармацевтических исследований и создания медицинских препаратов. В результате проведенных исследований, на основании изученых анатомо-морфологических характеристик таксонов, были выявлены диагностические признаки 8 секций и проведен анализ соотношения выявленных признаков с эколого-биологическими особенностями представителей рода Begonia L. / Master's dissertation consists of an introduction, a literature review, a description of materials and methods, results and their discussions, conclusions, and a list of references. The materials of the work are presented on 75 pages (main text). The work contains 3 tables, 104 figures, 2 appendices. The list of references includes 104 sources, of which 19 are domestic and 85 are foreign. Purpose of the study: To reveal the diagnostic role of anatomical-morphological and ecological-biological characters of individual representatives of the genus Begonia L. cultivated at the Department of Biodiversity and Bioecology of the Institute of Natural Sciences and Metrology of the UrFU and in the greenhouses of the Botanical Garden of the Ural Federal University and the Ural Branch of the Russian Academy of Sciences. The objects of research are the species and cultivars of the genus Begonia L. grown at the Department of Biodiversity and Bioecology of the Institute of Natural Sciences and Mathematics of the Ural Federal University and in the greenhouses of the Botanical Gardens of the Ural Federal University and the Ural Branch of the Russian Academy of Sciences. The work studied the anatomical and morphological characteristics of species and cultivars, consisting of 8 different sections: Begonia, Diploclinium, Gaerdtia, Gireoudia, Knesebeckia, Platycentrum, Pritzelia, Weilbachia The relevance of this work is the study of species and cultivars of the genus Begonia L., since the group is extremely diverse and unique, with important economic potential. Representatives of this genus can become one of the model objects for studying biodiversity and developing approaches for its conservation. It is also important that research should involve not only wild-growing species but also a huge volume of cultivars belonging to this genus. Also, according to modern research, begonias have the potential of plants with medicinal properties, which is extremely important for future and possible pharmaceutical research and the creation of medicines. As a result of the studies carried out, on the basis of the studied anatomical and morphological characteristics of taxa, diagnostic features of 8 sections were identified, and the analysis of the correlation of the identified characters with the ecological and biological characteristics of representatives of the genus Begonia L.

В настоящей работе проводится структурно-языковое изучение современной литературной волшебной сказки. Дается определение сказки как фольклорного и литературного жанра, систематизируются основные жанровые особенности сказки. Обобщая методологические аспекты изучения сказки, автор дает дефиницию и типологию волшебной сказки. Проводится анализ функций персонажей и волшебных средств в сказках. Рассматриваются основные языковые особенности сказки и способы ее перевода. Материалом для исследования служат сказки из сборника современных зарубежных авторов «The Kingfisher Book of Magical Tales», переведенного с английского на русский язык автором магистерской диссертации. / In this master’s thesis, a structural and linguistic study of a modern literary fairy tale is carried out. The definition of a fairy tale as a folklore and literary genre is given, the main genre features of a fairy tale are systematized. Summarizing the methodological aspects of the study of fairy tales, the author gives the definition and typology of a fairy tale. The analysis of the functions of characters and magical means in fairy tales is carried out. The thesis also considers the main linguistic features of the fairy tale and the ways of its translation. The material for the study is fairy tales from the collection of modern foreign authors "The Kingfisher Book of Magical Tales", translated from English into Russian by the author of the master's thesis.

In recent years mammary cancer has been increasingly recognized in pet rabbits. In addition to uterine carcinomas—the most common tumor of female rabbits—mammary cancer can also markedly reduce the life expectancy of pet rabbits. The aim of this review is to raise awareness for these tumors and to report recent progress in related research. Their detailed characterization will likely improve medical care for affected rabbits. Moreover, study results will contribute to comparative pathology and may reveal if the rabbit is a suitable model for certain types of breast cancer in humans. Available information suggests that most invasive cancer cases develop through stepwise progression from non-invasive forms. Thus, early recognition will likely improve a complete cancer cure. So far, the only treatment option is surgical excision and prognostic factors are unknown. Recent investigations have identified tumor features with likely prognostic value. They have also revealed differences and similarities to mammary tumors in other species and breast cancer in women. Despite these initial data, continued research is necessary to gain more insights into the development of these tumors and their molecular features.

Music listening is considered one of the most ubiquitous activities in everyday life, and one of the main reasons why people listen is to affect and regulate their mood. The vast availability and unlimited access of music has made it difficult to find relevant music that fits both the context and the preferences of the music listener. The aim of this project was to investigate the personalized relationship between music and mood using everyday technologies, focusing on how a listening experience could be adapted to the desired affect of a music listener while also taking the user’s individual listening history into account. In large, the project concentrated on the possibility of using context-aware music recommendation to generate personalized playlists by focusing on the audio features and corresponding mood of the music. A web-based application was developed to act as a prototype for the study, where the application allowed users to connect to Spotify, pick a desired mood and generate a playlist. By allowing people to access music in this personalized way, a user study could be conducted in order to investigate their music listening while incorporating this recommendation tool. The findings showed that the users’ found the experience to be engaging in that they could use the application as a companion to everyday tasks in addition to it being a tool for getting new, personalized music recommendations. Overall, the participants also found the generated playlists to be accurate to their music preferences and desired affective state.

This thesis presents methods useful in a bin picking application, such as detection and representation of local features, pose estimation and multi-cue integration. The scene tensor is a representation of multiple line or edge segments and was first introduced by Nordberg in [30]. A method for estimating scene tensors from gray-scale images is presented. The method is based on orientation tensors, where the scene tensor can be estimated by correlations of the elements in the orientation tensor with a number of 1D filters. Mechanisms for analyzing the scene tensor are described and an algorithm for detecting interest points and estimating feature parameters is presented. It is shown that the algorithm works on a wide spectrum of images with good result. Representations that are invariant with respect to a set of transformations are useful in many applications, such as pose estimation, tracking and wide baseline stereo. The scene tensor itself is not invariant and three different methods for implementing an invariant representation based on the scene tensor is presented. One is based on a non-linear transformation of the scene tensor and is invariant to perspective transformations. Two versions of a tensor doublet is presented, which is based on a geometry of two interest points and is invariant to translation, rotation and scaling. The tensor doublet is used in a framework for view centered pose estimation of 3D objects. It is shown that the pose estimation algorithm has good performance even though the object is occluded and has a different scale compared to the training situation. An industrial implementation of a bin picking application have to cope with several different types of objects. All pose estimation algorithms use some kind of model and there is yet no model that can cope with all kinds of situations and objects. This thesis presents a method for integrating cues from several pose estimation algorithms for increasing the system stability. It is also shown that the same framework can also be used for increasing the accuracy of the system by using cues from several views of the object. An extensive test with several different objects, lighting conditions and backgrounds shows that multi-cue integration makes the system more robust and increases the accuracy. Finally, a system for bin picking is presented, built from the previous parts of this thesis. An eye in hand setup is used with a standard industrial robot arm. It is shown that the system works for real bin-picking situations with a positioning error below 1 mm and an orientation error below 1o degree for most of the different situations. / <p>Report code: LiU-TEK-LIC-2005:15.</p>

Real estate appraisal is the means of defining the market value of land and property affixed to it. Many different features determine the market value of a property. For example, the distance to the nearest park or the travel time to the central business district may be significant when determining its market value. The use of machine learning in real estate appraisal requires algorithm accuracy and interpretability. Related research often defines these two properties as a trade-off and suggests that more complex algorithms may outperform intrinsically interpretable algorithms. This study tests these claims by examining the impact of geospatial features on interpretable algorithms in real estate appraisal. The experiments use property transactions from Oslo, Norway, and adds relative and global geospatial features for all properties using geocoding and spherical distance calculations. Such as the distance to the nearest park or the city center. The experiment implements three intrinsically interpretable algorithms; a linear regression algorithm, a decision tree algorithm, and a RuleFit algorithm. For comparison, it also implements two artificial neural network algorithms as a baseline. This study measures the impact of geospatial features using the algorithm performance by the coefficient of determination and the mean absolute error for the algorithm without and with geospatial features. Then, the individual impact of each geospatial feature is measured using four feature importance measures; mean decrease impurity, input variable importance, mean decrease accuracy, and Shapley values. The statistically significant results show that geospatial features improve algorithm performance. The improvement of algorithm performance is not unique to interpretable algorithms but occurs for all algorithms. Furthermore, it shows that interpretable algorithms are not axiomatically inferior to the tested artificial neural network algorithms. The distance to the city center and a nearby hospital are, on average, the most important geospatial features. While important for algorithm performance, precisely what the geospatial features capture remains for future examination. / Fastighetsvärdering är ett sätt att bestämma marknadsvärdet på mark och egendom som anbringas på den. Flera olika variabler påverkar marknadsvärdet för en fastighet. Avståndet till närmaste park eller restiden till det centrala affärsdistriktet kan till exempel vara betydande när man bestämmer ett marknadsvärde. Användningen av maskininlärning vid fastighetsvärdering kräver noggrannhet och tolkbarhet hos algoritmer. Relaterad forskning definierar ofta dessa två egenskaper som en kompromiss och föreslår att mer komplexa algoritmer kan överträffa tolkbara algoritmer. Den här studien testar dessa påståenden genom att undersöka påverkan av geospatiala variabler på tolkbara algoritmer i fastighetsvärdering. Experimentet använder fastighetstransaktioner från Oslo i Norge, och lägger till relativa och globala geospatiala variabler för alla fastigheter med hjälp av geokodning och sfäriska avståndsberäkningar. Såsom avståndet till närmaste park eller stadens centrum. Experimentet implementerar tre tolkbara algoritmer; en linjär regressionsalgoritm, en beslutsträdalgoritm och en RuleFit-algoritm. Som jämförelse implementerar den också två artificiella neuronnätsalgoritmer som en baslinje. Studien mäter påverkan av geospatiala variabler med algoritmprestanda genom determinationskoefficienten och det genomsnittliga absoluta felet för algoritmen med och utan geospatiala variabler. Därefter mäts den individuella påverkan av varje geospatial variabel med hjälp av fyra mått på variabelbetydelse; mean decrease impurity, input variabel importance, mean decrease accuracy och Shapley-värden. De statistiskt signifikanta resultaten visar att geospatiala variabler förbättrar algoritmers prestanda. Förbättringen av algoritmprestanda är inte unik för tolkningsbara algoritmer utan sker för alla algoritmer. Dessutom visar resultatet att tolkningsbara algoritmer inte är sämre än de testade artificiella neuronnätsalgoritmerna. Avståndet till stadens centrum och det närmaste sjukhuset är i genomsnitt de viktigaste geospatiala variablerna. Även om de geospatial variablerna är viktiga för algoritmprestanda, kvarstår frågan om vad exakt de betyder för framtida granskning.

With the rise of the smartphone industry, the domain of mobile-assisted language learning (MALL) has increasingly grown. A large number of language learning applications have been developed aiming to support individuals’ second language acquisition on various levels, e.g., by teaching vocabulary and grammar to improve reading and listening comprehension. The viability of these applications has been examined in literature and shows overall positive but mixed results. On one side, their success is partly attributed to gamified design elements. These are reported to improve the user experience (UX) and boost learners' motivation. On the other side, the primary reliance on decontextualized vocabulary and grammar exercises is criticized. In response, one such application, SVT Språkplay developed by the Swedish non-profit Språkkraft, incorporated television programs as a longer form of context. This introduced novel video-based learning functions. The first aim of this thesis was to start filling a gap in research by evaluating the usability and user experience of these functions. This was performed through user tests and interviews with seven second language students who used the app to learn Swedish over a period of at least two weeks. The second aim of the thesis was to improve the usability and user experience of the problematic learning functions through a user-centred design process with the ultimate goal to improve learner support and vocabulary acquisition outcomes. The study participants consisted of doctoral researchers and students recruited from a basic Swedish course at KTH. They represent a demographic that benefits from learning Swedish to improve their job opportunities. The initial evaluation results were analysed through the lens of the MUUX-E theoretical framework [10] , a framework for evaluating the “user experience and educational features of m-learning environments”. The evaluation showed that the core vocabulary learning aids directly integrated into the video watching experience were perceived as useful. Conversely, the gamified learning functions outside of the video watching experience were found to be scarcely used as intended. The subsequent user-centered design process improved upon the design of problematic learning functions by adhering to the principles of the MUUX-E framework. Concretely, more varied contextualized vocabulary exercises were designed, more options for user customization were included and feedback and progress metrics such as “streaks” were highlighted. An evaluation of the design with the same participants as the initial evaluation suggests that these changes would improve the usability and user experience of the application. Further research should evaluate an implemented end-product based on the proposed designs in a real-life setting. In that case, its pedagogical merit should also be evaluated. In summary, this thesis found that mobile video-based MALL apps such as Språkplay can provide usable and enjoyable language learning functions. / Med tillväxten av mobiltelefonbranschen har domänen för mobilassisterad språkinlärning (MALL) ökat alltmer. Ett stort antal språkinlärningsapplikationer har utvecklats för att stödja individers förvärv av andraspråk på olika nivåer, t.ex. genom att lära ut ordförråd och grammatik samt för att förbättra läs- och hörförståelsen. Dessa applikationer har undersökts i litteraturen och visar positiva men blandade resultat. Å ena sidan tillskrivit deras framgång delvis spelelementen. Dessa rapporteras förbättra användarupplevelsen (UX) och öka elevernas motivation. Å andra sidan kritiseras det primära förlitandet på dekontekstualiserade ordförråd och grammatikövningar. Som ett svar skapades en sådan applikation, SVT Språkplay, utvecklad av den svenska ideella föreningen Språkkraft, vilken använder TV-program som en längre form av språkligt sammanhang. Detta introducerade nya videobaserade inlärningsfunktioner. Det första syftet med denna uppsats var att börja fylla ett hål i forskningen genom att utvärdera användbarheten och användarupplevelsen av dessa funktioner. Det gjordes genom att genomföra användartester och intervjuer med sju andraspråkstudenter som använde appen för att lära sig svenska under en period av två veckor. Det andra syftet med arbetet var att förbättra användbarheten och användarupplevelsen för dessa inlärningsfunktioner genom en användarcentrerad designprocess med det slutliga målet att förbättra studentens stöd. Studiedeltagarna bestod av doktorander och studenter rekryterade från en nybörjarkurs i svenska på KTH. De representerar en demografisk nytta av att lära sig svenska för att öka deras tillgång till den svenska arbetsmarknaden. De första utvärderingsresultaten analyserades genom tillämpning av MUUX-E-ramverket, ett ramverk för att utvärdera “ u ser experience and e ducational features of m -learning e nvironments” [10] . Det visade att de grundläggande ordförrådets inlärningshjälpmedel som direkt integrerades i video upplevdes som användbara. Omvänt användes knappt alls de spelifierade inlärningsfunktionerna utanför videon. Den efterföljande användarcentrerade designprocessen förbättrades vid design av problematiska inlärningsfunktioner genom att följa principerna i MUUX-E-ramverket. Konkret utformades mer varierade kontextualiserade vokabulärövningar, fler alternativ för användaranpassning inkluderades och feedback- och framstegsmetriker som “streaks” lyftes fram. En utvärdering av designen med samma deltagare som den första utvärderingen tyder på att dessa förändringar skulle förbättra användbarheten och användarupplevelsen. Ytterligare forskning bör utvärdera en implementerad slutprodukt baserad på de föreslagna designförbättringarna i en verklig miljö. I så fall bör dess pedagogiska meriter också utvärderas. Sammanfattningsvis fann vi att videobaserade MALL-appar som Språkplay kan ge användbara och roliga språkinlärningsfunktioner.

This study focuses on multi-jet impingement for gas turbine geometries in which the objective is to understand the influence of the roughness elements on a target surface to the heat transfer. Current work has proven that implementing roughness elements for multi-jet impingement target surfaces has increased heat transfer ranging anywhere from 10-30%. This study has chosen to investigate three different roughness elements, elliptical in cross-section, to compare to smooth surface geometries for multi-jet impingement. An experimental was taken for this study to extend the current knowledge of multi-jet impingement geometries and to further understand the heat transfer performance. A temperature sensitive paint (TSP) technique was used to measure the heat transfer on the target surface, in which the local temperature was measured to estimate area averaged heat transfer coefficient (HTC) and row averaged HTC. In order stay consistent with literature, non-dimensional parameters were used for geometry locations and boundaries. For this study, the Reynolds number range, based on jet diameter and mass flux, is 10-15k. The X/D (streamwise direction), Y/D (spanwise direction), Z/D (channel height direction), L/D (thickness of the jet plate) constraints for this study are 5, 6, 3, and 1 respectively. From the local heat transfer distributions of the different roughness elements, it is concluded that the inclusion of these elements increases heat transfer by 2-12% as compared to a flat/smooth target plate. It is therefore recommended from this study, that elements, elliptical in shape, provide favorability in heat transfer for gas turbine configurations.

Generic visual tracking is a challenging computer vision problem, where the position of a specified target is estimated through a sequence of frames. The only given information is the initial location of the target. Therefore, the tracker has to adapt and learn any kind of object, which it describes through visual features used to differentiate target from background. Standard appearance features only capture momentary visual information. This master’s thesis investigates the use of deep features extracted through optical flow images processed in a deep convolutional network. The optical flow is calculated using two consecutive images, and thereby captures the dynamic nature of the scene. Results show that this information is complementary to the standard appearance features, and improves performance of the tracker. Deep features are typically very high dimensional. Employing dimensionality reduction can increase both the efficiency and performance of the tracker. As a second aim in this thesis, PCA and PLS were evaluated and compared. The evaluations show that the two methods are almost equal in performance, with PLS actually receiving slightly better score than the popular PCA. The final proposed tracker was evaluated on three challenging datasets, and was shown to outperform other state-of-the-art trackers.