Trio sonata op. 1, no. 3 / A. Corelli -- Choros no. 4 for three horns and trombone / H. Villa-Lobos -- Brass quintet no. 2, op. 6 / V. Ewald -- Meditation from Thais / J. Massenet -- Street song / M. T. Thomas -- Wagner for five bones / R. Wagner. / text

Föreliggande arbete inriktar sig på kommunikation mellan sångare och medmusiker i ensemblesammanhang utifrån det multimodala och designteoretiska perspektivets syn på kommunikation och lärandeförutsättningar. Med hjälp av videoobservationer av min egen kommunikation i en ensemble på högskolenivå har jag analyserat fram vilka olika semiotiska resurser som används samt intentionerna bakom dem. Resultatet visar att kommunikationen ensemblemedlemmarna emellan tillkommer först efter en tid in i lärandeprocessen och att det, för egen del, är sången som verkar som den främsta kommunikatören i det här sammanhanget.  Dynamik, tydlighet och lekfullhet skvallrar om säkerhet eller osäkerhet i form och melodi samt fungerar som en vägledning om var jag befinner mig i lärandeprocessen. I diskussionen tar jag bland annat upp hur den första ensemblelektionen byggs upp i enighet med det multimodala perspektivets syn på bra lärandeförutsättningar samt vad kroppsspråk har för betydelse för kommunikation i ensemblesammanhang. / The study focuses on how singers communicate with their fellow musicians in music ensembles considering the multimodal perspective of communication and learning conditions. With the help of video recordings of my own communication within an ensemble I have analysed which semiotic resources that are used and my underlying intentions with them. The result shows that the communication between the group members first are established after a while into the learning process and that the singing part shows to be the prime communicator for me in this case where dynamic, articulating and playfulness indicate security or insecurity about the form of the song and the melody. It also works as a guide to where I am at in the learning process. In the chapter on discussion I present, among other things, how the first ensemble lesson is created due to the multimodal perspective on favourable learning conditions and also the importance of body language for favourable communication in music ensembles.

In this study I research and reflect on the way I have been practicing with my saxophone, how I have been collaborating with others and how I’ve worked with performance during my two master years, with a focus on improvisation. The study is a presentation of my Professional Integration Project on NAIP-European Master of Music-program. I have had many different projects that will be presented. By playing, listening, and analyzing free improvisation I wanted to learn more about myself as a musician and about improvisation in general. I am also going to present individual exercises for improvisation I’ve been using, as well as exercises for group improvisation. I am going to present time-lines of events to see how one thing leads to another. I will present the product of a piece with improvised aspects, that led me and my collaborators to find our own ways of rehearsing. By summarizing the many aspects, I present my findings by describing what I want to learn, and how I want to learn it, and how I’ve been working with free improvisation groups and music collaboration, and what is important for me while performing improvisations. The findings of my studies suggest that I have developed my improvisational skills by playing free improvisation and doing exercises. My projects has also shown that you can combine written music with improvisational aspects in a classical setting by using different ways of rehearsing. Lastly, I found that it is important that, while improvising, musicians have a total mental presence to avoid energy loss in the music. / <p>Självständiga arbete, Master, 40 hp.</p><p>Program examenskonsert:</p><p>ImprovisationKvintett</p><p>Jesper Eriksson saxofon, Linnea Andreassen röst, Maiju Kopra röst, Viktor Rydén röst, Amanda Larsson röst.</p><p>Jesper ErikssonKvartett (2014), uruppförande</p><p>Alexander Rydberg violin, Jesper Eriksson saxofon, Emma Augustsson cello, Anton Svanberg tuba.</p><p>ImprovisationTrio</p><p>Jesper Eriksson saxofon, Jaan Krivel röst och klarinett, Kristoffer Linder slagverk</p><p>Extranummer: Friimprovisationsorkester</p><p>Jesper Eriksson saxofon, Alexander Rydberg violin, Emma Augustsson cello, Anton Svanberg tuba, Linnea Andreassen röst, Maiju Kopra röst, Viktor Rydén röst, Amanda Larsson röst, Jaan Krivel röst och klarinett, Kristoffer Linder slagverk, Bernhard Greter piano.</p>

Thesis (M.M.)--Eastern Illinois University, 2008. / Includes bibliographical references (v. 1, leaf 48).

Esta Tese apresenta um framework para análise exploratória de dados via técnicas de agrupamento. O objetivo é facilitar o trabalho dos especialistas no domínio dos dados. O ponto central do framework é um algoritmo de ensemble multi-objetivo, o algoritmo MOCLE, complementado por um método para a visualização integrada de um conjunto de partições. Pela aplicação conjunta das idéias de ensemble de agrupamentos e agrupamento multi-objetivo, o MOCLE efetua atomaticamente importantes passos da análise de agrupamento: executa vários algoritmos conceitualmente diferentes com várias configurações de parâmetros, combina as partições resultantes desses algoritmos e seleciona as partições com os melhores compromissos de diferentes medidas de validação. MOCLE é uma abordagem robusta para lidar com diferentes tipos de estrutura que podem estar presentes em um conjunto de dados. Ele resulta em um conjunto conciso e estável de estruturas alternativas de alta qualidade, sem a necessidade de conhecimento prévio sobre os dados e nem conhecimento profundo em análise de agrupamento. Além disso, para facilitar a descoberta de estruturas mais complexas, o MOCLE permite a integração automática de conhecimento prévio de uma estrutura simples por meio das suas funções objetivo. Finalmente, o método de visualização proposto permite a observação simultânea de um conjunto de partições. Isso ajuda na análise dos resultados do MOCLE. / This Thesis presents a framework for exploratory data analysis via clustering techniques. The goal is to facilitate the work of the experts in the data domain. The core of the framework is a multi-objective clustering ensemble algorithm, the MOCLE algorithm, complemented by a method for integrated visualization of a set of partitions. By applying together the ideas of clustering ensemble and multi-objective clustering, MOCLE automatically performs important steps of cluster analysis: run several conceptually different clustering algorithms with various parameter configuration, combine the partitions resulting from these algorithms, and select the partitions with the best trade-offs for different validation measures. MOCLE is a robust approach to deal with different types of structures that can be present in a dataset. It results in a concise and stable set of high quality alternative structures, without the need of previous knowledge about the data or deep knowledge on cluster analysis. Furthermore, in order to facilitate the discovery of more complex structures, MOCLE allows the automatic integration of previous knowledge of a simple structure via their objective functions. Finally, the visualization method proposed allows the simultaneous observation of a set of partitions. This helps in the analysis of MOCLE results.

Data Stream mining is an important emerging topic in the data mining and machine learning domain. In a Data Stream setting, the data arrive continuously and often at a fast pace. Examples include credit cards transaction records, surveillances video streams, network event logs, and telecommunication records. Such types of data bring new challenges to the data mining research community. Specifically, a number of researchers have developed techniques in order to build accurate classification models against such Data Streams. Ensemble Learning, where a number of so-called base classifiers are combined in order to build a model, has shown some promise. However, a number of challenges remain. Often, the class labels of the arriving data are incorrect or missing. Furthermore, Data Stream algorithms may benefit from an online learning paradigm, where a small amount of newly arriving data is used to learn incrementally. To this end, the use of Active Learning, where the user is in the loop, has been proposed as a way to extend Ensemble Learning. Here, the hypothesis is that Active Learning would increase the performance, in terms of accuracy, ensemble size, and the time it takes to build the model. This thesis tests the validity of this hypothesis. Namely, we explore whether augmenting Ensemble Learning with an Active Learning component benefits the Data Stream Learning process. Our analysis indicates that this hypothesis does not necessarily hold for the datasets under consideration. That is, the accuracies of Active Ensemble Learning are not statistically significantly higher than when using normal Ensemble Learning. Rather, Active Learning may even cause an increase in error rate. Further, Active Ensemble Learning actually results in an increase in the time taken to build the model. However, our results indicate that Active Ensemble Learning builds accurate models against much smaller ensemble sizes, when compared to the traditional Ensemble Learning algorithms. Further, the models we build are constructed against small and incrementally growing training sets, which may be very beneficial in a real time Data Stream setting.

I detta utvecklingsarbete har jag identifierat tre “nycklar” i musikaliskt samspel – att lyssna, leda och forma. Dessa “nycklar” baseras på mina tidigare erfarenheter av att musicera i ensemble. Utifrån dessa “nycklar” har jag utformat tre samspelsövningar – LYSSNA, LEDA och FORMA. Med syftet att undersöka hur samspelsövningarna kan påverka elevers samspel i ensemble har jag tittat närmare på hur elevers sätt att lyssna, leda och forma kan påverkas av att de jobbar med samspelsövningarna. Denna studie är kvalitativ, och i genomförandet av lektionsserien har data samlats in via enkäter (en i början av studien och en i slutet), exit tickets samt trippellogg. Varje samspelsövning tillämpades två gånger under lektionsserien. Resultatet visade att samspelsövningarna LYSSNA, LEDA och FORMA kan påverka elevers sätt att lyssna, leda och forma avseende samspel i ensemble, på flera sätt. Denna studies resultat kan ses som ett komplement till den musikundervisning på gymnasiet som fokuserar på just samspel i ensemble. / In this study I have identified three "keys" in musical interaction – listening, leading and shaping. These "keys" are based on my previous experiences of playing music in an ensemble. Based on these "keys", I have designed three interaction exercises – LISTENING, LEADING and SHAPING. With the aim of investigating how the interaction exercises can affect students' interaction in an ensemble, I have taken a closer look at how students' way of listening, leading and shaping can be affected by working with the interaction exercises. This study is qualitative, and in the implementation of the lesson series, data has been collected via questionnaires (one at the beginning of the study and one at the end), exit tickets and triple log. Each interaction exercise is applied twice during the lesson series. The results showed that the interaction exercises LISTENING, LEADING and SHAPING can affect students' way of listening, leading and shaping with regard to ensemble interaction, in several ways. This study's result can be seen as a complement to music teaching at the high school/upper secondary school which focuses on interaction in an ensemble.

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Accurate hydrologic forecasting is vital for proper water resource management. Practices that are impacted by these forecasts include power generation, reservoir management, agricultural water use, and flood early warning systems. Despite these needs, the models largely used are simplifications of the real world and are therefore imperfect. The forecasters face other challenges in addition to the model uncertainty, which includes imperfect observations used for model calibration and validation, imperfect meteorological forecasts, and the ability to effectively communicate forecast results to decision-makers. Bayesian methods are commonly used to address some of these issues, and this thesis will be focused on improving methods related to recursive Bayesian estimation, more commonly known as data assimilation. Data assimilation is a means to optimally account for the uncertainties in observations, models, and forcing data. In the literature, data assimilation for urban hydrologic and flood forecasting is rare; therefore the main areas of study in this thesis are urban and semi-urban watersheds. By providing improvements to data assimilation methods, both hydrologic and flood forecasting can be enhanced in these areas. This work explored the use of alternative data products as a type of observation that can be assimilated to improve hydrologic forecasting in an urban watershed. The impact of impervious surfaces in urban and semi-urban watersheds was also evaluated in regards to its impact on remotely sensed soil moisture assimilation. Lack of observations is another issue when it comes to data assimilation, particularly in semi- or fully-distributed models; because of this, an improved method for updating locations which do not have observations was developed which utilizes information theory’s mutual information. Finally, we explored extending data assimilation into the short-term forecast by using prior knowledge of how a model will respond to forecasted forcing data. Results from this work found that using alternative data products such as those from the Snow Data Assimilation System or the Soil Moisture and Ocean Salinity mission, can be effective at improving hydrologic forecasting in urban watersheds. They also were effective at identifying a limiting imperviousness threshold for soil moisture assimilation into urban and semi-urban watersheds. Additionally, the inclusion of mutual information between gauged and ungauged locations in a semi-distributed hydrologic model was able to provide better state updates in models. Finally, by extending data assimilation into the short-term forecast, the reliability of the forecasts could be improved substantially. / Dissertation / Doctor of Philosophy (PhD) / The ability to accurately model hydrological systems is essential, as that allows for better planning and decision making in water resources management. The better we can forecast the hydrologic response to rain and snowmelt events, the better we can plan and manage our water resources. This includes better planning and usage of water for agricultural purposes, better planning and management of reservoirs for power generation, and better preparing for flood events. Unfortunately, hydrologic models primarily used are simplifications of the real world and are therefore imperfect. Additionally, our measurements of the physical system responses to atmospheric forcing can be prone to both systematic and random errors that need to be accounted for. To address these limitations, data assimilation can be used to improve hydrologic forecasts by optimally accounting for both model and observation uncertainties. The work in this thesis helps to further advance and improve data assimilation, with a focus on enhancing hydrologic forecasting in urban and semi-urban watersheds. The research presented herein can be used to provide better forecasts, which allow for better planning and decision making.