In the present work, we investigate the capacity of machine learning to reconstruct simulated large scale surface temperature anomalies given a sparse observation field. Several methods are combined: self-organizing maps and recurrent neural networks of the temporal trajectory. To evaluate our global scale reconstruction, we base our validation on global climate indices time series and EOF analysis. In our experiments, the obtained reconstructions of the global surface temperature anomalies provide a good correlation (over 90%) with the target values when considering scarce available observations sampling about 0.5% of the globe. We reconstruct the surface temperature anomaly fields from 0.05% of total number of data points. We obtain an RMSE of 0.39°C. We further validate the quality of the results calculating a correlation of 0.92, 0.97 and 0.98 between the reconstructed and target indices of AMO, ENSO and IPO. / Klimatsystemet består av olika komponenter inklusive atmosfären, havet och jorden. Som ett öppet system utbyter det hela tiden energi med resten av universum. Det är också ett dynamiskt system vars utveckling kan förutsägas av kända fysiska lagar. Interaktionen mellan dess olika komponenter leder till en så kallad naturlig variation. Denna variabilitet återspeglas i form av svängningslägen, inklusive AMO, ENSO och IPO. För att studera dessa variationer har vi klimatmodeller som representerar de olika krafterna och deras effekt på klimatförändringar på lång sikt. I detta sammanhang är variationerna i det förflutna klimatet särskilt intressanta och tillåter oss en bättre förståelse av klimatförändringar och bättre förutsäga den framtida utvecklingen. Men för att studera det förflutna klimatet eller paleoklimat är den enda tillgängliga informationen endast fullständig under de senaste 150 åren. Innan dess är de enda tillgängliga indikatorerna naturliga, kallad klimatproxy, som trädringar eller iskärnor. Vi kan härleda tidsserier med klimatdata, till exempel temperatur. Denna information är emellertid knappast tillfälligt såväl som över hela världen. Återskapa det globala klimatet från sådana data hanteras fortfarande dåligt. Länken mellan lokal information och global klimat studeras här med hjälp av statistiska metoder, inklusive neurala nätverk. Det långsiktiga målet med denna studie är att bygga en metod för att rekonstruera paleoklimatet från data om klimatproxy, vi fokuserar inledningsvis på rekonstruktionen av ett så kallat perfekt klimat, det vill säga en modell som endast tar hänsyn till naturlig variation, från rumsligt sällsynta tidsserier. De studerade uppgifterna är de från globala yttemperaturutgångar från den havsatmosfärkopplade IPSL-modellen. Uppgifterna förbehandlas för att ta bort säsongens genomsnittliga cykel och omvandlas till temperaturavvikelser. Dessutom väljs rutnätpunkter som representerar information om proxyer pseudo-slumpmässigt, med respekt för den verkliga dispositionen av dessa, övervägande i norr på kontinenterna. Uppgifterna delas upp i träningsdata (150 år), validering (30 år) och testdata (120 år). De metoder som används kombinerar (1) självorganiserande kartor och hierarkisk stigande klassificering, användbara för att producera en reducerad storlek av inmatningsdata, här baserat på tidskorrelationen mellan temperaturutvecklingen under 150 år, (2) ItCompSOM använder korrelationen mellan klasser erhållna genom självorganiserande kartor för att rekonstruera obevakad data, (3) återkommande nervnätverk för att förklara den temporära komponenten i data och förbättra den tidigare rekonstruktionen. Slutligen är definitionen av nya mätvärden nödvändig för att validera de föreslagna modellerna. Utvärderingen av produkterna görs således genom temporär rekonstruktion av AMO, ENSO, IPO klimatlägen samt genom projicering av huvudkomponenterna i analysen av huvudkomponenterna i inputdata. Således konstrueras en reducerad modell av globala temperaturdata baserad på 150 års fullständiga data först, vilket reducerar den rumsliga informationen från 9216 rutnätpunkter till 191 regioner associerade med 1 medelvärde vardera. För att ansluta denna modell till tidssekvenser av sällsynta temperaturer i världen antas det att varje klass som innefattar minst en observerad proxy-data är känd. Rekonstruktionen av globala yttemperaturutvecklingar med ItCompSOM ger en korrelation till indexen på mer än 90% för endast 0,5% av de initiala observationerna. Detta resultat förbättras kraftigt tack vare återkommande nervnätverk, vilket leder till en korrelation av 0,92, 0,97 respektive 0,98 för AMO, ENSO och IPO med endast 0,05% av observationerna. Dessa poäng förklaras med den använda metoden, regionaliseringen hjälper till att koncentrera informationen. Medan 0,5% av rutpunkterna är lika med 43 poäng, om de är korrekt fördelade, representerar de 22% av informationen om regionerna (43 av 191). Dessa mycket uppmuntrande resultat återstår att tillämpas på verkliga klimatproblem, det vill säga med hänsyn till å ena sidan den externa och antropologiska kraften, osäkerheterna relaterade till de verkliga uppgifterna om ombud å andrasidan.

The ever-increasing density of computer storage devices has allowed the average user to store enormous quantities of multimedia content, and a large amount of this content is usually music. Current search techniques for musical content rely on meta-data tags which describe artist, album, year, genre, etc. Query-by-content systems, however, allow users to search based upon the actual acoustical content of the songs. Recent systems have mainly depended upon textual representations of the queries and targets in order to apply common string-matching algorithms and are often confined to a single query style (e.g., humming). These methods also lose much of the information content of the song which limits the ways in which a user may search. We present a query-by-content system which supports querying in several styles using a Self-Organizing Map as its basis. The results from testing our system show that it performs better than random orderings and is, therefore, a viable option for musical query-by-content.

The method of combining multiple geophysical, geological, or geochemical datasets can reveal patterns of otherwise hidden features in the Earth’s crust. This may aid in geological mapping, locating economic mineral deposits and for general anomaly/feature detection. In this study a multidimensional geophysical approach implementing five geophysical datasets is applied using Self-Organizing Maps (SOM), where the main objective is to locate and understand a previously unknown hypothesized fault in Norrbotten, Sweden. The fault is estimated to extend from the Finnish border in the north, across northern Sweden in the N-S direction at a hypothesized length of > 250 km. Self-Organizing Maps is an unsupervised neural network - originally developed by Finnish physicist Teuvo Kohonen - capable of combining any number of datasets and thereby visualize them on a simple two-dimensional map. The datasets used in the analysis were three magnetic derivatives for the x, y and z components, as well as gamma-ray intensity measurements of the 238U, 40K and 232Th radioisotopes. All these variables have been shown to be effective tools for bedrock mapping and geological feature detection and were hence chosen based on these properties. The results revealed the efficiency of the SOM analysis to represent multivariate data on a 2D plane and proved to be a generally good visualization tool for multiple geophysical datasets. There seems to be a relatively sharp difference in geophysical properties between the eastern and western blocks divided by the hypothesized fault, which may indicate the presence of this crustal scale structure. Despite the evidence found in this study, more investigations are needed to verify the existence and nature of the fault, and the results shown here may motivate further projects by providing indications and suggestive evidence for its presence.

I dagens organisationer blir teamarbete allt vanligare för att uppnå dem organisatoriska målen. Denna decentralisering formas på grund av den komplexitet som finns i vissa arbetsuppgifter, sådana uppgifter som enskilda medarbetare inte har kunskaperna till. Ofta behövs det flera kompetensområden för att slutföra ett projekt eller uppnå ett organisatoriskt mål. Att arbeta i team kan då vara en lösning (Courtney, Navarro, & O'Hare, 2007). Detta sätt att arbeta på kan också gynna kreativitet, flexibilitet och produktivitet (Sutherland & Schwaber, 2016). Detta är en kvalitativ studie där intervjuer genomförs med medlemmar i två självorganiserande företag. Under den teoretiska referensramen beskrivs de modeller som senare utgör en tolkningsmodell för hur motivation, organisationskultur och effektivitet kan samspela i ett självorganiserade företag. Syftet med denna uppsats är att få reda på hur motivationsfaktorer och kulturfaktorer kan påverka effektiviteten i självorganiserande team. Studien visar på att en välfungerande kommunikation och ett starkt samarbete är viktiga faktorer för att ett team ska kunna vara effektivt. Om det uppstår störningar exempelvis i form av konkurrens inom organisationen eller brister i samarbetet kan detta påverka effektiviteten oerhört negativt. Studien indikerar även att kompetens är en av de största motivationsfaktorerna inom självorganiserande team. / In today’s organizations teamwork is becoming more common to achieve organizational goals. This decentralization is formed because of the complexity that exists in certain tasks, tasks hat individual employees do not have the knowledge in. Often several areas of competence are needed to complete a project or achieve an organizational goal. Working in team can then be a solution (Courtney, Navarro, & O'Hare, 2007). This way of working can also promote creativity, flexibility and productivity (Sutherland & Schwaber, 2016). This is a qualitative study where interviews with members of two self-organizing companies are conducted. The theoretical framework describes the models that later will constitute an interpretational model for how motivation, organizational culture and efficiency can interact in as self-organized company. The purpose of this paper is to find out how motivational factors and cultural factors can influence the effectiveness in self-organized teams. This study shows that a well-functioning communication and a strong collaboration are important factors for a team to be effective. If there are disruptions, for example in the form of competition within the organization or lack of cooperation, this can have a negative impact of efficiency. This study also indicates that competence is one of the biggest motivational factors within self-organizing teams.

The meridional temperature gradient between mid and high latitudes decreases by Arctic amplification. Following this decrease, the circulation in the mid latitudes may change and, therefore, the meridional flux of heat and moisture increases. This might increase the Arctic temperatures even further. A proxy for the vertically integrated atmospheric horizontal energy flux was analyzed using the self-organizing-map (SOM) method. Climate Model Intercomparison Project Phase 5 (CMIP5) model data of the historical and Representative Concentration Pathway 8.5 (RCP8.5) experiments were analyzed to extract horizontal flux patterns. These patterns were analyzed for changes between and within the respective experiments. It was found that the general horizontal flux patterns are reproduced by all models and in all experiments in comparison with reanalyses. By comparing the reanalysis time frame with the respective historical experiments, we found that the general occurrence frequencies of the patterns differ substantially. The results show that the general structure of the flux patterns is not changed when comparing the historical and RCP8.5 results. However, the amplitudes of the fluxes are decreasing. It is suggested that the amplitudes are smaller in the RCP8.5 results compared to the historical results, following a greater meandering of the jet stream, which yields smaller flux amplitudes of the cluster mean.

The Fennoscandian Shield as a part of a large Precambrian basement area is located in northern Europe and hosts economically important mineral deposits including base metals and precious metals. Regional geophysical data such as potential field and magnetotelluric data in combination with other geoscientific data contain information of importance for an understanding of the crustal and upper mantle structure. Knowledge about regional-scale structures is important for an optimized search for mineralisation. In order to investigate in more detail the spatial distribution of regional electrically conductive structures and near-surface mineral deposits, complementary magnetotelluric measurements have been done within the Precambrian Shield in the north-eastern part of the Norrbotten ore province. The potential field data provided by the Geological Survey of Sweden have been included in the current study. Processing of magnetotelluric data was performed using a robust multi-remote reference technique. The dimensionality analysis of the phase tensors indicates complex 3D structures in the area. A 3D crustal model of the electrical conductivity structure was derived based on 3D inversion of the data using the ModEM code. The final inversion 3D resistivity model revealed the presence of strong crustal conductors with the conductance of more than 3000 S at depth of tens of kilometres within a generally resistive crust. A significant part of the middle crust conductors is elongated in directions that coincide with major ductile deformation zones that have been mapped from airborne magnetic data and geological fieldwork. Some of these conductors have near-surface expression where they spatially correlate with the locations of known mineralisation. Processing and 3D inversion of the regional magnetic and gravity field data were performed, and the structural information derived from these data by using an open-source object-oriented package code written in Python called SimPEG. In this study, a new approach is proposed to extract and analyse the correlation between the modelled physical properties and for domain classification. For this, a neural net Self-Organizing Map procedure (SOM) was used for data reduction and simplification. The input data to the SOM analysis contain resistivity, magnetic susceptibility, and density model values for some selected depth levels. The domain classification is discussed with respect to geological boundaries and composition. The classification is furthermore applied for prediction of favourable areas for mineralisation. Based on visual inspection of processed regional gravity and magnetic field data and a SOM analysis performed on higher-order derivatives of the magnetic data, an interpretation of a sinistral fault with 52 km offset is proposed. The fault is oriented N10E and can be traced 250 km from Karesuando at the Swedish-Finish border southwards to the Archaean-Proterozoic boundary marked by the Luleå-Jokkmokk Zone.

This thesis aims to cluster financial account time series by extracting global features from the time series and by using two different dimensionality reduction methods, Kohonen Self Organizing Maps and principal component analysis, to cluster the set of the time series by using K-means. The results are then used to further cluster a set of financial services provided by a financial institution, to determine if it is possible to find a set of services which coincide with the time series clusters. The results find several sets of services that are prevalent in the different time series clusters. The resulting method can be used to understand the dynamics between deposits variability and the customers usage of different services and to analyse whether a service is more used in different clusters. / Målet med denna uppsats är att klustra tidsserier över finansiella konton genom att extrahera tidsseriernas karakteristik. För detta används två metoder för att reducera tidsseriernas dimensionalitet, Kohonen Self Organizing Maps och principal komponent analys. Resultatet används sedan för att klustra finansiella tjänster som en kund använder, med syfte att analysera om det existerar ett urval av tjänster som är mer eller mindre förekommande bland olika tidsseriekluster. Resultatet kan användas för att analysera dynamiken mellan kontobehållning och kundens finansiella tjänster, samt om en tjänst är mer förekommande i ett tidsseriekluster.

In this paper, the problem of analyzing hyperspectral data is presented. The complexity of multi-dimensional data leads to the need for computer assisted data compression and labeling of important features. A brief overview of Self-Organizing Maps and their variants is given and then two possible methods of data analysis are examined. These methods are incorporated into a program derived from som_toolbox2. In this program, ASD data (data collected by an Analytical Spectral Device sensor) is read into a variable, relevant bands for discrimination between classes are extracted, and several different methods of analyzing the results are employed. A GUI was developed for easy implementation of these three stages.

In this work, different Rotylenchulus reniformis nematode population numbers affecting cotton plants were spectrally classified using Self-Organized Maps. The hyperspectral reflectance of cotton plants affected by different nematode population numbers were analyzed in order to extract information from the signal that would lead to a fieldworthy methodology for predicting nematode population numbers extant in a plant's rhizosphere. Hyperspectral reflectances from both control and field nematode infestations were used in this work. Various feature extraction and dimensionality reduction methods (e.g., PCA, DWT, and SOM-based methods) were used to extract a reduced set of features. These extracted features were then classified using a supervised SOM classification method. Additionally, this work explores the possibility of combining the standard feature extraction methods with self-organized maps to extract a reduced set of features in order to increase classification accuracies.

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