Modern vehicles nowadays are equipped with highly sensitive sensors which continuously log in the information when the vehicle is in motion. These vehicles also deal with some performance issues like more fuel consumption, breakdown, or failure, etc. The information logged in by the sensors can be useful to analyze and evaluate these performance issues.  As vehicles are there in the market and are used in multiple places. These vehicles can perform differently based on the way they are operated and driven and the usage of a vehicle varies from time to time. Moreover, the European Accident Research and Safety Report from Volvo Organization describes the factors responsible for road fatalities and accidents. It explains that 90\% of road fatalities are caused by the style of the vehicle being driven and 30\% is caused by the external weather and environmental factor. Therefore, in this work, vehicle usage modeling is done based on time to determine the different usage styles of a vehicle and how they can affect a vehicle's performance. The proposed framework is divided into four separate modules namely: Data pre\textendash processing, Data segmentation, Unsupervised machine learning, and Pattern Analysis. Mainly, ensemble clustering methods are used to extract the pattern of the vehicle usage style and vehicle performance in different seasons using truck logged vehicle data (LVD). From the results, we could build a strong correlation between the vehicle usage style and the vehicle performance that would require further investigation.

Mental and behavioural disorders such as bipolar disorder and depression are critical healthcare issues that affected approximately 45 and 264 million people around the world, respectively in 2020. Early detection and intervention are crucial for limiting the negative effects that these illnesses can have on people’s lives. Although the symptoms for different mental disorders vary, they generally are characterized by a combination of abnormal behaviours, thoughts, and emotions. Mental disorders can affect one’s ability to relate to others and function every day. To assess symptoms, clinicians often use structured clinical interviews and standard questioners. However, there is a scarcity of automated or technology-assisted tools that can simplify the diagnostic process. The main objective of this thesis is to investigate, develop, and propose automated methods for mental disorder detection. We focus in our research on bipolar disorder and depression as they are two of the most common and debilitating mental illnesses. Bipolar disorder is one of the most prevalent mental illnesses in the world. Its principal indicator is the extreme swings in the mood ranging from the manic to depressive states. We propose automatic ternary classification models for the bipolar disorder manic states. We employ a dataset that uses the Young Mania Recall Scale to distinguish the manic states of patients as: Mania, Hypo- Mania, and Remission. The dataset comprises audio-visual recordings of bipolar disorder patients undergoing a structured interview. We propose three bipolar disorder classification solutions. The first approach uses a hybrid LSTM-CNN model. We apply a CNN model to extract facial features from video signals. We supply the features’ sequence to an LSTM model to resolve the bipolar disorder state. Our solution achieved promising results on the development and test set of the Turkish Audio-Visual Bipolar Disorder Corpus with the Unweighted Average Recall of 60.67% and 57.4%, respectively. The second solution employs additional features from the structured interview recordings. We acquire visual representations along with audio and textual cues. We capture Mel-Frequency Cepstral Coefficients and Geneva Minimalistic Acoustic Parameter Set as audio features. We compute linguistic and sentiment features for each subject’s transcript. We present a stacked ensemble classifier to classify all fused features after feature selection. A set of three homogeneous CNNs and an MLP constitute the first and second levels of the stacked ensemble classifier respectively. Moreover, we use reinforcement learning to optimize the networks and their hyperparameters. We show that our stacked ensemble solution outperforms existing models on the Turkish Audio-Visual Bipolar Disorder corpus with a 59.3% unweighted average unit on the test set. To the best of our knowledge, this is the highest performance achieved on this dataset. The Turkish Audio-Visual Bipolar Disorder dataset comprises a relatively small number of videos. Moreover, the labels for the testing set are kept confidential by the dataset provider. Hence, this motivated us to train a classifier using a semi-supervised ladder network for the third solution. This network benefits from unlabeled data during training. Our goal was to investigate whether a bipolar disorder states classifier can be trained using a mix of labelled and unlabelled data. This would alleviate the burden of labelling all the videos in the training set. We collect informative audio, visual, and textual features from the recordings to realize a multi-model classifier of the manic states. The third proposed model achieved a 53.7% and 60.0% unweighted average unit on the test and development sets, respectively. There is a growing demand for automated depression detection system to control the subjective bias in diagnosis. We propose an automated depression severity detection model that uses multi- modal fusion of audio and textual information. We train the model on the E-DAIC corpus, which labels the individual’s depression level with patient health questionnaire score. We use MFCCs and eGeMAPs as audio representations and Word2Vec embeddings for the textual modality. Then, we implement a stacked ensemble regressor to detect depression severity. The proposed model achieves a concordance correlation coefficient 0.49 on the test set. To the best of our knowledge, this is the highest performing model on this dataset.

Deepfakes are an emerging problem in social media and for celebrities and political profiles, it can be devastating to their reputation if the technology ends up in the wrong hands. Creating deepfakes is becoming increasingly easy. Attempts have been made at detecting whether a face in an image is real or not but training these machine learning models can be a very time-consuming process. This research proposes a solution to training deepfake detection models cooperatively on the edge. This is done in order to evaluate if the training process, among other things, can be made more efficient with this approach.  The feasibility of edge training is evaluated by training machine learning models on several different types of iPhone devices. The models are trained using the YOLOv2 object detection system.  To test if the YOLOv2 object detection system is able to distinguish between real and fake human faces in images, several models are trained on a computer. Each model is trained with either different number of iterations or different subsets of data, since these metrics have been identified as important to the performance of the models. The performance of the models is evaluated by measuring the accuracy in detecting deepfakes.  Additionally, the deepfake detection models trained on a computer are ensembled using the bagging ensemble method. This is done in order to evaluate the feasibility of cooperatively training a deepfake detection model by combining several models.  Results show that the proposed solution is not feasible due to the time the training process takes on each mobile device. Additionally, each trained model is about 200 MB, and the size of the ensemble model grows linearly by each model added to the ensemble. This can cause the ensemble model to grow to several hundred gigabytes in size.

Syftet med denna studie var att undersöka vilka didaktiska strategier en lärare använder sig av i ensembleundervisning på gymnasiets estetiska program, med främst fokus på arbetet med sångare. Forskningsfrågorna som lade grund för studien var: Hur inkluderar lärare sångare i ensemblen? Vilka utmaningar upplever lärare med att undervisa sångare i ensemble? Skiljer sig arbetet med sångare jämfört med ensemblens resterande instrumentalister? Med observation av tre ensemblelektioner och kvalitativa intervjuer med tre verksamma ensemblelärare som metod fick jag resultatet från att ha sett lärarnas arbetssätt och hört deras tankar och upplevelser. Resultatet visar att lärarna till viss del ser på sångare som ett annorlunda instrument och att sångare har en annan typ av roll i ensemblen i jämförelse med de andra instrumentalisterna. Lärarna upplever en del utmaningar med att undervisa sångare på grund av att sång är ett instrument som sitter inuti kroppen och är unikt för varje person, det är inget en person varken kan gömma sig bakom eller gå iväg från. Resultatet visar att lärarens arbete med att skapa trygga grupper tillsammans med eleverna är nödvändigt för gruppens musikaliska och konstnärliga utveckling.

Syftet med mitt arbete var att få en bild av i vilken utsträckning det finns ett genusperspektiv i metodikundervisningen i jazzensemble och improvisation på musikhögskolenivå i Sverige. Även att få en bild av i vilken utsträckning verksamma lärare i jazzensemble och improvisation, som genomgått metodikkurser i samma ämnen, har en medvetenhet om genusfrågor samt ifall deras undervisning påverkas av det. Jag ville även kartlägga dessa lärares åsikter om vad den sneda könsfördelningen i jazz och improvisationsmusik beror på samt hitta och föreslå möjliga förändringar. Jag valde att göra kvalitativa intervjuer med sex aktiva jazzensemblelärare. Två av lärarna var ansvariga för metodikundervisningen i jazzensemble på respektive musikhögskola: Musikhögskolan i Malmö och Kungl. Musikhögskolan. De andra fyra lärarna hade genomgått sin pedagogiska utbildning vid någon av dessa två musikhögskolor. Undersökningen pekar på att det varken har funnits eller, när undersökningen gjordes, fanns ett genusperspektiv i metodikundervisningen i jazzensemble på Musikhögskolan i Malmö och Kungl. Musikhögskolan. Alla sex lärarna hade gjort iakttagelser av skillnader i beteenden i undervisningssituationer mellan killar och tjejer. De lärare som hade erfarenhet av att undervisa enkönade grupper med endast tjejer hade i högre grad uppmärksammat könsskillnader och hade alla utvecklat sina individuella metoder för hur de handskades med detta. De flesta av lärarna hade dock inte några tydliga kunskaper om genusstrukturer och hur man kan arbeta för att förändra dem, varför jag generellt sett betraktar genusmedvetenheten hos lärarna som låg, även om den varierar lite sinsemellan. Jag kom fram till flera möjliga orsaker till könsfördelningen i jazz och improvisations- musik: rådande genusstrukturer i samhället, därav brist på kvinnliga förebilder, musik- utbildningarnas och medias fokusering på analyserbar jazz, cementering av rådande genusstrukturer på jazzutbildningarna och brist på förändring av dessa strukturer. Genusstrukturer i samhället ändras hela tiden och arbetet att förändra de förlegade strukturer som finns sker på många olika nivåer, även om det går långsamt. Däremot kan institutioner, utbildningar och konsertarrangörer ta krafttag vad gäller att synliggöra kvinnliga förebilder, särskilt för elever i tidiga utbildningsår. Jag drog även slutsatsen att institutioner, arrangörer, musikjournalister m.fl. borde öppna upp begreppet jazz till att innefatta all improviserad musik för att flera ska känna sig välkomna i genren. Man bör även införliva ett genusperspektiv i ensemblemetodiken på musikhögskolorna och musikutbildningarna samt på alla musikutbildningar arbeta för att förändra de rådande genusstrukturerna. Utbildning och kunskap ser jag som en viktig väg till förändring.

One of the most challenging tasks in the oil industry is the production of reliable reservoir forecast models. Because of different sources of uncertainties the numerical models employed are often only crude approximations of the reality. This problem is tackled by the conditioning of the model with production data through data assimilation. This process is known in the oil industry as history matching. Several recent advances are being used to improve history matching reliability, notably the use of time-lapse seismic data and automated history matching software tools. One of the most promising data assimilation techniques employed in the oil industry is the ensemble Kalman filter (EnKF) because its ability to deal with highly non-linear models, low computational cost and easy computational implementation when compared with other methods. A synthetic reservoir model was used in a history matching study designed to predict the peak production allowing decision makers to properly plan field development actions. If only production data is assimilated, a total of 12 years of historical data is required to properly characterize the production uncertainty and consequently the correct moment to take actions and decommission the field. However if time-lapse seismic data is available this conclusion can be reached 4 years in advance due to the additional fluid displacement information obtained with the seismic data. Production data provides geographically sparse data in contrast with seismic data which are sparse in time. Several types of seismic attributes were tested in this study. Poisson’s ratio proved to be the most sensitive attribute to fluid displacement. In practical applications, however the use of this attribute is usually avoided due to poor quality of the data. Seismic impedance tends to be more reliable. Finally, a new conceptual idea was proposed to obtain time-lapse information for a history matching study. The use of crosswell time-lapse seismic tomography to map velocities in the interwell region was demonstrated as a potential tool to ensure survey reproducibility and low acquisition cost when compared with full scale surface surveys. This approach relies on the higher velocity sensitivity to fluid displacement at higher frequencies. The velocity effects were modeled using the Biot velocity model. This method provided promising results leading to similar RRMS error reductions when compared with conventional history matched surface seismic data.

Sensory, cognitive, and emotional processing are rooted in the cerebral cortex. The cerebral cortex is comprised of six layers defined by the neurons within them that have distinctive connection, both within cortex itself and with other subcortical structures. Although still far from solving the mysteries of the mind, it is clear that networks form by neurons in the cerebral cortex provide the computational substrate for a remarkable range of behaviours. This neuron to neuron activation is mediated through dendritic spines, the postsynaptic target of most excitatory synapses in the cerebral cortex. Dendritic spines are small protrusions found along dendrites of neurons, and their number, as well as structural changes, accompany the development of synapses and establishment of neuronal networks. In fact, dendritic spines undergo rapid structural and functional changes guided by neuronal activity. This marriage between structural and functional plasticity, makes dendritic spines crucial in fine-tuning of networks in the brain; not surprisingly, dendritic spine aberrations are a hallmark of multiple neurodevelopmental, neuropsychiatric, and neurodegenerative disorders. With this in mind, I considered Pannexin 1 (Panx1) an interesting novel candidate for a regulatory role on cortical neuronal network and dendritic spine development, for the following reasons. First, Panx1 transcripts are enriched in the brain and in the cortex are most abundant during the embryonic and early postnatal period. . This timing roughly corresponds to a period of synaptogenesis in the postnatal brain. Second, Panx1 localizes to postsynaptic compartments in neurons and its disruption leads to enhance excitability and potentiation of neuron-to neuron communication. Third, disruption of Panx1 (either knockdown or pharmacological blockade) leads to neurite outgrowth in neuron-like cells. Lastly, genetic variants in PANX1 have been associated with neurodevelopmental disorders. This dissertation explores the role of Panx1 in the development of dendritic spines and neuronal networks, furthering our understanding on cortical development and placing Panx1 as a novel regulator of structural and functional plasticity in the brain. Chapter 1 discusses core concepts on cortical development, with an emphasis on pyramidal neuron, the most abundant and only known projection neurons in the cerebral cortex. Here, I review the embryonic origin of pyramidal neurons, their postnasal development, and how cortical circuits are assembled. I finish this chapter with a brief review on Panx1 and its known expression and involvement in neuronal function. In Chapter 2 I explore the functional properties of neuronal networks and synaptic composition of cortical neurons in the absence of Panx1. Using live cell imaging and analysis of Ca2+ transients in dense primary cortical cultures, revealed that Panx1 knock-out (KO) cultures exhibit more and larger groups of significantly co-activated neurons, known as network ensembles. These network properties were not explained by differences in cell viability or cell-type composition. Examination of protein expression from cortical synaptosome preparations revealed that Panx1 is enriched in synaptic compartments, while also confirming a developmental downregulation. This analysis also revealed increased levels of the postsynaptic scaffolding protein PSD-95, along with the postsynaptic glutamate receptors GluA1 and GluN2A. Lastly, ex vivo tracing of dendritic spines on apical dendrites of Layer 5 pyramidal neurons in global and glutamatergic-specific Panx1 KO brain slices revealed higher spine densities in early and late postnatal development, with no differences in spine length. Analysis of dendritic spine densities in fixed cultured cortical neurons revealed an increase associated with Panx1 KO. Altogether, this work presents for the first time a connection between Panx1 and structural development of dendritic spines and suggest that Panx1 regulates cortical neuronal networks through changes in spine density. Chapter 3 examines the influence of Panx1 on spiny protrusions growth and movement. Spiny protrusion are long, thin, highly dynamic spine precursors. Taking advantage of a fluorescent signal localized to the plasma membrane, I visualized spiny protrusions and quantified their dynamics in wildtype (WT) and Panx1 KO developing cortical neurons, both under fixed and live conditions. I found that transient Panx1 expression is associated with decreased spiny protrusion density both in WT and Panx1 KO neurons. Using live cell imaging, I found that spiny protrusions are more stable and less motile in Panx1 KO neurons, while its transient expression reversed both of these phenotypes. These results suggest that Panx1 regulation of dendritic spines development is rooted partly in the regulation of spiny protrusion dynamics. Overall, this dissertation demonstrates that Panx1 negatively regulates dendritic spines in part by influencing spiny protrusion dynamics. It is reasonable to speculate that Panx1 regulation of dendritic spines underlies its newly discovered role in the formation network ensembles, providing a putative mechanism for previously described roles of Panx1 in synaptic plasticity. Likewise, this body of work furthers our understanding of Panx1 by filling some of the gaps attached to its developmental expression and association with neurodevelopmental disease. / Graduate / 2021-04-16

Studien undersöker hur omställningen från fysiskt förlagd musikundervisning till onlinebaserad musikundervisning under vårterminen 2020 orsakad av covid-19 påverkade gymnasieelevers förutsättningar för lärande samt gymnasielärares förutsättningar att undervisa i musikämnet. Studien är avgränsad till att undersöka kursen “ensemble med körsång” på estetiska programmet i den svenska gymnasieskolan. Data har samlats in genom både kvalitativa och kvantitativa metoder. Empirin analyseras genom en sammanställning av Illeris (2015) och Hanken och Johansens (2013) teorier om lärande. Resultatet visar bland annat att både lärare och elever i studien anser att omställningen från fysiskt förlagd undervisning till onlinebaserad var en utmaning, men att det också fanns positiva aspekter som exempelvis att elever som tidigare har haft hög frånvaro plötsligt deltog i undervisningen i större utsträckning. I empirin finner vi inget som stödjer en högkvalitativ undervisning i form av samtliga mål/kunskapskrav inom kursen ensemble med körsång, utan att fysiskt förlagda arbetssätt inkluderas. Studiens slutsats är att digitala verktyg som central metod för musikundervisning behöver beprövas och utvecklas om avsikten är att erbjuda en likvärdig utbildning som om den hade givits i det fysiska rummet.

We introduce an efficient thermodynamically consistent technique to extrapolate and interpolate normalized Canonical NVT ensemble averages like pressure and energy for Lennard-Jones (L-J) fluids. Preliminary results show promising applicability in oil and gas modeling, where accurate determination of thermodynamic properties in reservoirs is challenging. The thermodynamic interpolation and thermodynamic extrapolation schemes predict ensemble averages at different thermodynamic conditions from expensively simulated data points. The methods reweight and reconstruct previously generated database values of Markov chains at neighboring temperature and density conditions. To investigate the efficiency of these methods, two databases corresponding to different combinations of normalized density and temperature are generated. One contains 175 Markov chains with 10,000,000 MC cycles each and the other contains 3000 Markov chains with 61,000,000 MC cycles each. For such massive database creation, two algorithms to parallelize the computations have been investigated. The accuracy of the thermodynamic extrapolation scheme is investigated with respect to classical interpolation and extrapolation. Finally, thermodynamic interpolation benefiting from four neighboring Markov chains points is implemented and compared with previous schemes. The thermodynamic interpolation scheme using knowledge from the four neighboring points proves to be more accurate than the thermodynamic extrapolation from the closest point only, while both thermodynamic extrapolation and thermodynamic interpolation are more accurate than the classical interpolation and extrapolation. The investigated extrapolation scheme has great potential in oil and gas reservoir modeling.That is, such a scheme has the potential to speed up the MCMC thermodynamic computation to be comparable with conventional Equation of State approaches in efficiency. In particular, this makes it applicable to large-scale optimization of L-J model parameters for hydrocarbons and other important reservoir species. The efficiency of the thermodynamic dependent techniques is expected to make the Markov chains simulation an attractive alternative in compositional multiphase flow simulation.

<p>Storms in the SE-US often evolve in different environments than those in the central Plains. Many poorly understood aspects of these differing environments may impact the tornadic potential of SE-US storms. Among these differences are potential variations in the CCN concentration owing to differences in land cover, combustion, industrial and urban activity, and proximity to maritime environments. The relative influence of warm and cold rain processes is sensitive to CCN concentration, with higher CCN concentrations producing smaller cloud droplets and more efficient cold rain processes. Cold rain processes result in DSDs with relatively larger drops from melting ice compared to warm rain processes. Differences in DSDs impact cold pool and downdraft size and strength, that influence tornado potential. This study investigates the impact of CCN concentration on DSDs in the SE-US by comparing DSDs from ARPS-EnKF model analyses and forecasts to observed DSDs from portable disdrometer-equipped probes collected by a collaboration between Purdue University, the University of Oklahoma (OU), the National Severe Storms Laboratory (NSSL), and the University of Massachusetts in a tornadic QLCS on 30 April 2017 during VORTEX-SE.</p><p>The ARPS-EnKF configuration, which consists of 40 ensemble members, is used with the NSSL triple-moment microphysics scheme. Surface and radar observations are both assimilated. Data assimilation experiments with CCN concentrations ranging from 100 cm^-3 (maritime) to 2,000 cm^-3 (continental) are conducted to characterize the variability of DSDs and the model output DSDs are verified against the disdrometer observations. The sensitivity of the DSD variability to CCN concentrations is evaluated. Results indicate continental CCN concentrations (close to CCN 1,000 cm³) produce DSDs that align closest to the observed DSDs. Other thermodynamic variables also accord better to observations in intermediate CCN concentration environments.</p>