Browsing by discipline "none"

Visual simultaneous localization and mapping (visual SLAM) is a method for consistent self-contained localization using visual observations. Visual SLAM can produce very precise pose estimates without any specialized hardware, enabling applications such as AR navigation. The use of visual SLAM in very large areas and over long distances is not presently possible due to a number of significant scalability issues. In this thesis, these issues are discussed and solutions for them explored, culminating in a concept for a real-time city-scale visual SLAM system. A number of avenues for future work towards a practical implementation are also described.

Programming courses often receive large quantities of program code submissions to exercises which, due to their large number, are graded and students provided feedback automatically. Teachers might never review these submissions therefore losing a valuable source of insight into student programming patterns. This thesis researches how these submissions could be reviewed efficiently using a software system, and a prototype, CodeClusters, was developed as an additional contribution of this thesis. CodeClusters' design goals are to allow the exploration of the submissions and specifically finding higher-level patterns that could be used to provide feedback to students. Its main features are full-text search and n-grams similarity detection model that can be used to cluster the submissions. Design science research is applied to evaluate CodeClusters' design and to guide the next iteration of the artifact and qualitative analysis, namely thematic synthesis, to evaluate the problem context as well as the ideas of using software for reviewing and providing clustered feedback. The used study method was interviews conducted with teachers who had experience teaching programming courses. Teachers were intrigued by the ability to review submitted student code and to provide more tailored feedback to students. The system, while still a prototype, is considered worthwhile to experiment on programming courses. A tool for analyzing and exploring submissions seems important to enable teachers to better understand how students have solved the exercises. Providing additional feedback can be beneficial to students, yet the feedback should be valuable and the students incentivized to read it.

Improving the quality of medical computed tomography reconstructions is an important research topic nowadays, when low-dose imaging is pursued to minimize the X-ray radiation afflicted on patents. Using lower radiation doses for imaging leads to noisier reconstructions, which then require postprocessing, such as denoising, in order to make the data up to par for diagnostic purposes. Reconstructing the data using iterative algorithms produces higher quality results, but they are computationally costly and not quite powerful enough to be used as such for medical analysis. Recent advances in deep learning have demonstrated the great potential of using convolutional neural networks in various image processing tasks. Performing image denoising with deep neural networks can produce high-quality and virtually noise-free predictions out of images originally corrupted with noise, in a computationally efficient manner. In this thesis, we survey the topics of computed tomography and deep learning for the purpose of applying a state-of-the-art convolutional neural network for denoising dental cone-beam computed tomography reconstruction images. We investigate how the denoising results of a deep neural network are affected if iteratively reconstructed images are used in training the network, as opposed to using traditionally reconstructed images. The results show that if the training data is reconstructed using iterative methods, it notably improves the denoising results of the network. Also, we believe these results can be further improved and extended beyond the case of cone-beam computed tomography and the field of medical imaging.

It is challenging to identify causal genes and pathways explaining the associations with diseases and traits found by genome-wide association studies (GWASs). To solve this problem, a variety of methods that prioritize genes based on the variants identified by GWASs have been developed. In this thesis, the methods Data-driven Expression Prioritized Integration for Complex Traits (DEPICT) and Multi-marker Analysis of GenoMic Annotation (MAGMA) are used to prioritize causal genes based on the most recently published publicly available schizophrenia GWAS summary statistics. The two methods are compared using the Benchmarker framework, which allows an unbiased comparison of gene prioritization methods. The study has four aims. Firstly, to explain what are the differences between the gene prioritization methods DEPICT and MAGMA and how the two methods work. Secondly, to explain how the Benchmarker framework can be used to compare gene prioritization methods in an unbiased way. Thirdly, to compare the performance of DEPICT and MAGMA in prioritizing genes based on the latest schizophrenia summary statistics from 2018 using the Benchmarker framework. Lastly, to compare the performance of DEPICT and MAGMA on a schizophrenia GWAS with a smaller sample size by using Benchmarker. Firstly, the published results of the Benchmarker analyses using schizophrenia GWAS from 2014 were replicated to make sure that the framework is run correctly. The results were very similar and both the original and the replicated results show that DEPICT and MAGMA do not perform significantly differently. Furthermore, they show that the intersection of genes prioritized by DEPICT and MAGMA outperforms the outersection, which is defined as genes prioritized by only one of these methods. Secondly, Benchmarker was used to compare the performance of DEPICT and MAGMA on prioritizing genes using the schizophrenia GWAS from 2018. The results of the Benchmarker analyses suggest that DEPICT and MAGMA perform similarly with the GWAS from 2018 compared to the GWAS from 2014. Furthermore, an earlier schizophrenia GWAS from 2011 was used to check if the performance of DEPICT and MAGMA differs when a GWAS with lower statistical power is used. The results of the Benchmarker analyses make clear that MAGMA performs better than DEPICT in prioritizing genes using this smaller data set. Furthermore, for the schizophrenia GWAS from 2011 the outersection of genes prioritized by DEPICT and MAGMA outperforms the intersection. To conclude, the Benchmarker framework is a useful tool for comparing gene prioritization methods in an unbiased way. For the most recently published schizophrenia GWAS from 2018 there is no significant difference between the performance of DEPICT and MAGMA in prioritizing genes according to Benchmarker. For the smaller schizophrenia GWAS from 2011, however, MAGMA outperformed DEPICT.

Demand forecasts are required for optimizing multiple challenges in the retail industry, and they can be used to reduce spoilage and excess inventory sizes. The classical forecasting methods provide point forecasts and do not quantify the uncertainty of the process. We evaluate multiple predictive posterior approximation methods with a Bayesian generalized linear model that captures weekly and yearly seasonality, changing trends and promotional effects. The model uses negative binomial as the sampling distribution because of the ability to scale the variance as a quadratic function of the mean. The forecasting methods provide highest posterior density intervals in different credible levels ranging from 50% to 95%. They are evaluated with proper scoring function and calculation of hit rates. We also measure the duration of the calculations as an important result due to the scalability requirements of the retail industry. The forecasting methods are Laplace approximation, Monte Carlo Markov Chain method, Automatic Differentiation Variational Inference, and maximum a posteriori inference. Our results show that the Markov Chain Monte Carlo method is too slow for practical use, while the rest of the approximation methods can be considered for practical use. We found out that Laplace approximation and Automatic Differentiation Variational Inference have results closer to the method with best analytical quarantees, the Markov Chain Monte Carlo method, suggesting that they were better approximations of the model. The model faced difficulties with highly promotional, slow selling, and intermittent data. Best fit was provided with high selling SKUs, for which the model provided intervals with hit rates that matched the levels of the credible intervals.

When the standard model gauge group SU(3) × SU(2) × U(1) is extended with an extra U(1) symmetry, the resulting Abelian U(1) × U(1) symmetry introduces a new kinetic mixing term into the Lagrangian. Such double U(1) symmetries appear in various extensions of the standard model and have therefore long been of interest in theoretical physics. Recently this kinetic mixing has received attention as a model for dark matter. In this thesis, a systematic review of kinetic mixing and its physical implications is given, some of the dark matter candidates relying on kinetic mixing are considered, and experimental bounds for kinetic mixing dark matter are discussed. In particular, the process of diagonalizing the kinetic and mass terms of the Lagrangian with a suitable basis choice is discussed. A rotational ambiquity arises in the basis choice when both U(1) fields are massless, and it is shown how this can be addressed. BBN bounds for a model with a fermion in the dark sector are also given based on the most recent value of the effective number of neutrino species, and it is found that a significant portion of the FIMP regime is excluded by this constraint.

AA Sakatti Mining Oy is researching the possibility of conducting mining operations in Sakatti ore deposit, located partially under the protected Viiankiaapa mire. In order to understand the waters in mining development site, the interactions of surface waters, shallow aquifers, and deep bedrock groundwaters must be understood. To estimate these interactions, hydrogeochemical characterization, together with four tracer methods were used: Tritium/helium, dichlorodifluoromethane and sulfur hexafluoride, stable isotopes of hydrogen and oxygen, and carbon-14. Most of the shallow groundwater samples are similar to the natural precipitation and groundwater in their chemical composition, being of Calcium bicarbonate type. B-11-17HYD013 was an exception, containing much more Cl and SO4. The samples from the deep 17MOS8193 all show a very typical composition for this type of a borehole, on the line between the saline Sodium sulphate and Sodium chloride water types. The samples from the 12MOS8102, as well as the river water samples and the Rytikuru spring sample are located between these two end members. The hydrogen and oxygen isotope values divided the samples into two distinct groups: those that show evaporation signal in the source water, and those that do not. The most likely source for the evaporated signal in the groundwaters is in the surface water pools in the Viiankiaapa mire, which have then infiltrated into the groundwater and followed the known groundwater flow gradient into the observation wells near the River Kitinen. Tritium showed no inclusion of recently recharged water in the deep 17MOS8193, and dated most of the shallow wells with screen below bedrock surface to be recharged in the 70’s and 80’s. B-10-17HYD017 had an older apparent age from 1955, and B-14-17HYD006 was curiously dated to be recharged in 2018. 14C gave apparent age of over 30 000 a for the deep 17MOS8193. The slight contents of 14C could be caused by slight contamination during sampling meaning the age is a minimum. The sample M-4-12MOS8102 got an apparent age of ~3 500 a, which could in turn be an overestimate due to ancient carbon being dissolved from the local bedrock fractures. CFC-12 showed apparent recharge dates from 1963 to 1975 in the shallow wells, and no recently recharged water in the deep 17MOS8193, and so was generally in line with the 14C and Tritium results, although some contamination had happened. SF6 concentrations exceeded possible concentrations considering other results, most likely due to underground generation, and the method was dismissed. By trace element composition, all samples from the deep 17MOS8139 are distinct from other samples and saw slight dilution in concentrations of most elements in the span of the test pumping. Other samples are more mixed and difficult to interpret, but some trends and connections are visible, such as the higher contents in wells with screens below the bedrock surface than those with screens above the bedrock surface, and the exceptionally high contents of many elements in B-13-17HYD004. Overall, the study did benefit from the large array of methods, showing no interaction between the deep bedrock groundwaters and shallow groundwaters or surface waters. The evaporated signal from the Viiankiaapa was clearly visible in the samples close to the River Kitinen.

Bat monitoring is commonly based on audio analysis. By collecting audio recordings from large areas and analysing their content, it is possible estimate distributions of bat species and changes in them. It is easy to collect a large amount of audio recordings by leaving automatic recording units in nature and collecting them later. However, it takes a lot of time and effort to analyse these recordings. Because of that, there is a great need for automatic tools. We developed a program for detecting bat calls automatically from audio recordings. The program is designed for recordings that are collected from Finland with the AudioMoth recording device. Our method is based on a median clipping method that has previously shown promising results in the field of bird song detection. We add several modifications to the basic method in order to make it work well for our purpose. We use real-world field recordings that we have annotated to evaluate the performance of the detector and compare it to two other freely available programs (Kaleidoscope and Bat Detective). Our method showed good results and got the best F2-score in the comparison.

Microbial volatile organic compounds are emitted by diverse set of microbial organisms and they are known to cause health hazards when present in indoor air. Early detection of fungal contaminated buildings and species present is crucial to prevent health problems caused by fungal secondary metabolites. This thesis focuses on analysing emission profiles of different insulation materials and fungal cultures, which allows, in further studies, to develop efficient new ways to detect fungi from contaminated buildings. Studied insulation materials consisted of cellulose and glass wool, which were analysed in multiple different conditions. Humidity of atmosphere was varied between 0-10 microliters and temperature was varied between 30°C and 40°C. In fungal emission profile study 24 different cultures were analysed in two different atmospheres, ambient and micro- aerophilic, and in multiple different inoculums. Analysis for both insulation materials and fungal cultures was done using headspace solid phase microextraction Arrow -tool and headspace in tube extraction –tool together with gas chromatography – mass spectrometry. One goal for this thesis was also test suitability of these methods for detection of fungal secondary metabolites. Comprehensive fungal emission profiles were successfully formed and new information from behaviour of insulation materials in different settings was found. In addition, new information about analysis methods and fungal behaviour in different atmospheres was found. Headspace solid phase microextraction Arrow with gas chromatography – mass spectrometry was found to be efficient, sensitive and timesaving method for indoor air study purposes. There were also many potential fungal culture specific biomarker compounds found for further study purposes.

Subarctic ponds are important habitats for many freshwater species. The recent increase in global temperatures have stressed on the study of these habitats as rising water temperatures may have severe consequences to these cold and harsh ecosystems. Despite its importance, this topic has been largely overlooked in scientific research. Diatoms are microscopic, single-celled benthic algae, which are important indicators for environmental quality. Elevation is one of the main environmental variables controlling the composition and richness of diatom species as it shapes communities through several environmental variables such as temperature and water chemistry. The aim of this thesis was to illustrate the variability in diatom species richness and community composition along an elevational gradient in Kilpisjärvi and reveal the most important environmental drivers. As an additional focus, the applicability of the BenthoTorch sampling device was tested in measuring benthic algae biomass. Field and laboratory measurements were done using universal standards. Statistical analyses included multiple univariate and multivariate data analysis techniques. It was found that water pH, aluminium concentration and air temperature explained the variation in species richness and community composition the most. Elevation had only a secondary, non-significant role in shaping the diatom communities in subarctic ponds. Nearby sites showed similar compositions in terms of water chemistry and diatom communities. Biotope characterisation did not provide any further insight into the differences or similarities of diatom community composition or species richness. There were some differences in how genera responded to environmental variables. The centre of distributional range of many taxa was below the mid-point of the elevational gradient but species often occupied the whole elevational gradient. Rare taxa appeared at the ends of the elevational spectrum. The amount of singleton taxa was high (25.8%) and can be expected to increase with climate change. The BenthoTorch did provide reasonable results for benthic algae in the subarctic when compared to previous literature, but further research is required to grasp its full potential. More examination into the relationship between explanatory variables can be suggested (e.g. total phosphorus and ion balance) to gain better understanding on the changes in diatom species richness and community composition along elevational gradients.