"You are safe. You are calm. You are in control." is a piece written out of a state of emotional and physical exhaustion. The piece uses an electronic track comprised of the sounds of distressed children recorded at a daycare and a flex string quintet that plays along with the track. This paper explores the reasoning behind the music, the connections the piece has to the past, and the relevance of the piece now and in the future.

Plants and bacteria both have specialized light-harvesting pigment-protein complexes, composed of a network of chromophores encompassed by a protein scaffold, that are involved in photosynthesis. While chromophore, as well as protein, composition and arrangement vary in these light-harvesting complexes, chromophores transfer energy as molecular excitation energy through their complex multi-chromophoric network with near perfect efficiency. Understanding the efficiency of this excitation energy transfer process has been the focus of many interdisciplinary studies. By elucidating the mechanisms involved in efficient excitation energy transfer in biological systems, we are able to guide the design of novel organic materials for their application in photovoltaic systems. Interdisciplinary studies of light-harvesting biological systems leverage advanced spectroscopic techniques and theoretical models to help explain the interaction be- tween excited electronic states. Difficulties in assigning the origin of spectral features in spectroscopy experiments arise from both homogeneous and inhomogeneous effects. Various computational studies have been able to provide theoretical models that help disentangle these effects and provide insight into the origin of some these spectral features. In this work, we present a computational approach that is used to calculate an ensemble of model Hamiltonians for a light-harvesting pigment-protein complex found in algae. To verify the reliability of our model, we compare various computed spec- tra with experimental measurements. Next, we extend our computational approach for parameterizing an ensemble of Hamiltonians for two configurationally unique or- ganic dimers. Finally, we examine the error of some of the approximations made while partitioning “system” and “bath” degrees of freedom when computing molecu- lar properties. Using these methods we are able to provide mechanistic interpretations and explanations of spectral signatures observed in various linear and nonlinear ex- perimental spectra.

Sepsis is an organ dysfunction life-threatening disease that is caused by a dysregulated body response to infection. Sepsis is difficult to detect at an early stage, and when not detected early, is difficult to treat and results in high mortality rates. Developing improved methods for identifying patients in high risk of suffering septic shock has been the focus of much research in recent years. Building on this body of literature, this dissertation develops an improved method for septic shock prediction. Using the data from the MMIC-III database, an ensemble classifier is trained to identify high-risk patients. A robust prediction model is built by obtaining a risk score from fitting the Cox Hazard model on multiple input features. The score is added to the list of features and the Random Forest ensemble classifier is trained to produce the model. The Cox Enhanced Random Forest (CERF) proposed method is evaluated by comparing its predictive accuracy to those of extant methods.

Les récentes missions spatiales d'observation de la Terre fourniront des images optiques à très hautes résolutions spatiale, spectrale et temporelle générant des volumes de données massifs. L'objectif de cette thèse est d'apporter de nouvelles solutions pour le traitement efficace de grands volumes de données ne pouvant être contenus en mémoire. Il s'agit de lever les verrous scientifiques en développant des algorithmes efficaces qui garantissent des résultats identiques à ceux obtenus dans le cas où la mémoire ne serait pas une contrainte. La première partie de la thèse se consacre à l'adaptation des méthodes de segmentation pour le traitement d'images volumineuses. Une solution naïve consiste à découper l'image en tuiles et à appliquer la segmentation sur chaque tuile séparément. Le résultat final est reconstitué en regroupant les tuiles segmentées. Cette stratégie est sous-optimale car elle entraîne des modifications par rapport au résultat obtenu lors de la segmentation de l'image sans découpage. Une étude des méthodes de segmentation par fusion de régions a conduit au développement d'une solution permettant la segmentation d'images de taille arbitraire tout en garantissant un résultat identique à celui obtenu avec la méthode initiale sans la contrainte de la mémoire. La faisabilité de la solution a été vérifiée avec la segmentation de plusieurs scènes Pléiades à très haute résolution avec des tailles en mémoire de l'ordre de quelques gigaoctets. La seconde partie de la thèse se consacre à l'étude de l'apprentissage supervisé lorsque les données ne peuvent être contenues en mémoire. Dans le cadre de cette thèse, nous nous focalisons sur l'algorithme des forêts aléatoires qui consiste à établir un comité d'arbres de décision. Plusieurs solutions ont été proposées dans la littérature pour adapter cet algorithme lorsque les données d'apprentissage ne peuvent être stockées en mémoire. Cependant, ces solutions restent soit approximatives, car la contrainte de la mémoire réduit à chaque fois la visibilité de l'algorithme à une portion des données d'apprentissage, soit peu efficaces, car elles nécessitent de nombreux accès en lecture et écriture sur le disque dur. Pour pallier ces problèmes, nous proposons une solution exacte et efficace garantissant une visibilité de l'algorithme sur l'ensemble des données d'apprentissage. L'exactitude des résultats est vérifiée et la solution est testée avec succès sur de grands volumes de données d'apprentissage. / Recent Earth observation spatial missions will provide very high spectral, spatial and temporal resolution optical images, which represents a huge amount of data. The objective of this research is to propose innovative algorithms to process efficiently such massive datasets on resource-constrained devices. Developing new efficient algorithms which ensure identical results to those obtained without the memory limitation represents a challenging task. The first part of this thesis focuses on the adaptation of segmentation algorithms when the input satellite image can not be stored in the main memory. A naive solution consists of dividing the input image into tiles and segment each tile independently. The final result is built by grouping the segmented tiles together. Applying this strategy turns out to be suboptimal since it modifies the resulting segments compared to those obtained from the segmentation without tiling. A deep study of region-merging segmentation algorithms allows us to develop a tile-based scalable solution to segment images of arbitrary size while ensuring identical results to those obtained without tiling. The feasibility of the solution is shown by segmenting different very high resolution Pléiades images requiring gigabytes to be stored in the memory. The second part of the thesis focuses on supervised learning methods when the training dataset can not be stored in the memory. In the frame of the thesis, we decide to study the Random Forest algorithm which consists of building an ensemble of decision trees. Several solutions have been proposed to adapt this algorithm for processing massive training datasets, but they remain either approximative because of the limitation of memory imposes a reduced visibility of the algorithm on a small portion of the training datasets or inefficient because they need a lot of read and write access on the hard disk. To solve those issues, we propose an exact solution ensuring the visibility of the algorithm on the whole training dataset while minimizing read and write access on the hard disk. The running time is analysed by varying the dimension of the training dataset and shows that our proposed solution is very competitive with other existing solutions and can be used to process hundreds of gigabytes of data.

Syftet med denna uppsats är att bidra med mer kunskap om hur gymnasieelever uppfattar relationen mellan kreativa processer och inre motivation i ensemblespel. I studien användes en kvalitativ metod, bestående av två observationer och en fokusgruppintervju med en ensemble i årskurs tre på ett estetiskt gymnasium. Det kompletterades med att utforska befintlig litteratur inom kreativa processer, begreppet flow, improvisation, motivation samt Vygotskijs teori om proximal utvecklingszon och ett sociokulturellt perspektiv på lärande. Arbetssättet blev en pendling mellan empirin och mina teorier samt tidigare forskning. Resultaten visade att kreativa processer i undervisning, socialt samspel och uppgifter som ligger inom Vygotskijs proximala utvecklingszons-teori kan leda till ökad meningsfullhet och motivation hos eleverna. Detta antyder att det finns anledning att se över hur kreativa processer används i dagens undervisning och om de borde användas i större utsträckning.

Regression analysis has been used in previous research to predict the number of bicycles registered by a bicycle counter. An important step to improve the prediction is to include a long-term trend curve estimate as part of the formulation of the regression target variable. In this way, it is possible to use the deviation from the trend curve estimate instead of the absolute number of bicycles as target variable in the regression problem formulation. This can help capturing the factors that are difficult, or even impossible, to model as input variables in the regression model, for example, larger infrastructural changes. This study aims to evaluate a regression spline-based approach to enhance the prediction accuracy of bicycle counter data. This will be achieved by formulating a regression problem, generating trend curve estimates using regression splines, and evaluating the resulted curves using cross validation on a set of chosen regression algorithms. We illustrate our approach by applying it on a time series recorded by a bicycle counter in Malmö city, Sweden. For the considered data set, our experimental results show that the spline trend curve estimate with knots between 12-19, which has been fitted to the time series, gives the best prediction. It also shows that the use of ensemble methods leads to better prediction, where the G.B. Regressor shows best performance with 19 knots.

The collection altogether features 38 scientific papers and seven reviews of books on chamber music.

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