Hur låter miljöförstöring – Självgenererande och slumpmässig sprungen ur statistiska data är ett mastersarbete som fokuserar på hur man kan omvandla data till att kontrollera musik och ljud. Om vi tillåter konstnärliga uttryck, med data som utgångspunkt, kan det få oss att uppleva och förstår original data på ett nytt sätt? Projektets resultat består av sju generativa kompositioner, där parametrar är kontrollerade av olika typer av miljödata, och försöker utforska forskningsområdet sonifikation och generativ musik genom att ställa frågan: Hur låter miljöförstöring? Med generativ musik menas att musiken skapar, utvecklar och förändrar sig själv utifrån de verktyg som bildats inom detta projekt. Texten går också djupare in på metoden av att utveckla dessa verktyg för att möjliggöra liknande kompositioner i framtiden. Med erfarenheter av att använda slumpmässiga parametrar för att manipulera bakgrundsdetaljer, utforskar det här projekt istället möjligheten att utveckla de metoderna och applicera det på hela kompositioner. De sju kompositionerna ligger också till grund för utforskandet av området sonifikation. I tidigare forskning har ämnet främst bemöts ifrån ett vetenskapligt perspektiv. Syftet med det här projektet har istället varit att bemöta det inom ramarna för musikalisk gestaltning och ett konstnärligt perspektiv. Begreppet sonifikation betyder, användandet av icke-talande ljud som uppmärksammar data och statistik, med målet att agera som ett substitut, eller ett komplement, till att visualisera data. Utifrån dessa kompositioner reflekterar sedan texten kring generativ musik i allmänhet, och sonifikation i synnerhet, där bland annat möjligheterna, framtida forskning och autenticiteten inom sonifikation tas upp. / What does environmental pollution sound like? – Self generative and randomized music interprets data is a master thesis focused on transforming data and letting it control music and sounds. If we create artistic outputs out of data, will it allow us to experience and understand the original data in a new way? The core, and the result, of this project where seven compositions, created and controlled by different environmental data which tries to explore the research areas of sonification and generative music by asking the question: What does environmental pollution sound like? Generative music means that the music creates, develops and changes itself based on the established tools that this project provides. This thesis also focuses on the method of developing these tools in order to enable similar productions in the future. With previous experiences in using randomized events, to manipulate details in a production, this project delves deeper into applying the same technique to a whole composition. The seven compositions were formed in order to understand and reflect upon the research areas of sonification. Earlier research tends to approach the subject from a scientific perspective. The purpose of this project was to instead approach it from a more artistic perspective. Sonification means, the use of non-speech audio to perceptualize data which enables the possibilities as an alternative, or complement, to visualize the original data. Drawing from these seven compositions, this thesis also discusses generative music and sonification in general, as well as the opportunities, future research and authenticity of sonification. / <p>Bifogad ljudfil är ett kollage av de sju kompositionerna som arbetet resulterat i, med anledning av att i framtiden kunna publiceras i sin helhet.</p>

No description available.

Being able to train machine learning models on simulated data can be of great interest in several applications, one of them being for autonomous driving of cars. The reason is that it is easier to collect large labeled datasets as well as performing reinforcement learning in simulations. However, transferring these learned models to the real-world environment can be hard due to differences between the simulation and the reality; for example, differences in material, textures, lighting and content. One approach is to use domain adaptation, by making the simulations as similar as possible to the reality. The thesis's main focus is to investigate domain adaptation as a way to meet the reality-gap, and also compare it to an alternative method, domain randomization. Two different methods of domain adaptation; one adapting the simulated data to reality, and the other adapting the test data to simulation, are compared to using domain randomization. These are evaluated with a classifier making decisions for a robot car while driving in reality. The evaluation consists of a quantitative evaluation on real-world data and a qualitative evaluation aiming to observe how well the robot is driving and avoiding obstacles. The results show that the reality-gap is very large and that the examined methods reduce it, with the two using domain adaptation resulting in the largest decrease. However, none of them led to satisfactory driving.

京都大学 / マギル大学 / 新制・課程博士 / 博士(ゲノム医学) / 甲第24203号 / 医博JD第1号 / 新制||医||JD1(附属図書館) / 京都大学大学院医学研究科京都大学マギル大学ゲノム医学国際連携専攻 / (主査)教授 稲垣 暢也, 教授 YOUSSEFIAN Shohab, 准教授 Majewski Jacek (マギル大学), 准教授 Gravel Simon (マギル大学), 教授 Gagneur Julien (ミュンヘン工科大学) / 学位規則第4条第1項該当 / Doctor of Philosophy in Human Genetics / Kyoto University / McGill University / DFAM

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<p>Venous thromboembolism (VTE), which encompasses deep vein thrombosis (DVT) and pulmonary embolism (PE), is the most preventable cause of death in hospitalized patients. Due to its high mortality, morbidity, and cost, health care providers are obligated to not only effectively diagnose and treat VTE, but also to prevent it if possible. This has been reinforced by a number of national and international quality initiatives to prevent hospital-acquired VTE. Despite the existence of well-accepted clinical practice guidelines on VTE prophylaxis, 1 in 3 hospitalized medical patients receives an inappropriate VTE prophylaxis strategy. Both underuse of prophylaxis in patients with VTE risk, and overuse of prophylaxis in patients without VTE risk are problems. The use of inappropriate VTE prophylaxis strategies is likely due to the complexity and heterogeneity of hospitalized medical patients, and the difficulty of applying “one size fits all” practice guidelines to this group. Institution-wide knowledge translation strategies are required to close the gap between evidence and practice, and promote evidence-based VTE prophylaxis strategies in hospitalized medical patients. The objective of this thesis is to design a cluster randomized controlled trial to determine if a standardized electronic order set, with an embedded computerized decision support system and audit and feedback component, affects the use of appropriate VTE prophylaxis in hospitalized medical patients. The unit of randomization in this study is the hospital, which serves as the cluster. The unit of observation in this study is the individual patient. The primary outcome of this study is the proportion of in-hospital days during which appropriate VTE prophylaxis is administered, in intervention versus control hospitals. Secondary outcomes are the rates of hospital-acquired VTE, major bleeding and mortality, in intervention versus control hospitals. Design, analytic and ethical challenges unique to cluster randomized trials will also be discussed. Strategies to overcome them in this trial will be presented.</p> / Master of Science (MSc)

<p>Examining the effect of a modernized collaborative care intervention for depression consisting of both behavioral and pharmacologic treatments on 12-month change in individual somatic depressive symptoms (hyperphagia, poor appetite, hypersomnia, and disturbed sleep) and 12-month change in</p> <p>cardiometabolic biomarkers (HOMA-IR, BMI, hsCRP, leptin, and ghrelin). Further examining whether 12-month change in individual somatic depressive symptoms mediates the eIMPACT intervention’s effect on 12-month change in cardiometabolic biomarkers.</p>

Le neurofeedback (NF) suscite actuellement un vif intérêt dans la prise en charge du trouble déficitaire de l’attention avec hyperactivité (TDAH) chez l’enfant. Proposée comme méthode alternative à la médication par de nombreux cliniciens, notamment aux États-Unis, le NF est une intervention non-invasive de type électrophysiologique qui repose sur l’apprentissage par conditionnement opérant de l’autorégulation d’ondes cérébrales déviantes. Les études empiriques qui étayent cette pratique font toutefois l’objet de virulentes critiques de la part de spécialistes dans le domaine du TDAH en raison de résultats systématiquement positifs mais non spécifiques, auxquels s’ajoutent de nombreuses lacunes méthodologiques. Les travaux de cette thèse visent à appliquer une méthodologie stricte de type essai clinique contrôlé avec assignation aléatoire afin d’isoler les effets particuliers du NF, en appliquant un protocole d’entraînement propre au déficit primaire sous-tendant le TDAH, soit l’inhibition motrice, dans le but d’évaluer la spécificité de cette intervention. Dans un premier temps, les connaissances relatives à la nosologie du TDAH, à ses principaux traitements, au NF et aux capacités d’inhibition chez l’enfant ayant un TDAH sont présentées (Chapitre 1). Ensuite, les études réalisées dans le cadre de cette thèse sont exposées. Dans l’étude initiale, la spécificité du NF est évaluée sur les capacités d’inhibition grâce à des mesures subjectives, soit des questionnaires de comportements complétés par les parents, ainsi que des mesures objectives, à savoir des tâches neuropsychologiques (Chapitre 2). Afin de préciser davantage les conséquences d’un entraînement à l’autorégulation d’ondes cérébrales, l’étude subséquente s’est intéressée à l’impact neurophysiologiques de l’amélioration des capacités d’inhibition, par le biais d’une étude en potentiels évoqués employant une tâche de performance continue de type Stop-signal (Chapitre 3). Les principaux résultats reflètent un recrutement sous optimal, avec une puissance statistique insuffisante pour réaliser des statistiques quantitatives de groupe. Néanmoins, l’appréciation des données selon une approche d’étude de cas multiples permet de mettre en évidence la présence d’une réponse placebo sur les capacités d’inhibition suite à un entraînement en NF. Finalement, les implications de la taille de l’échantillon, ainsi que les limites et les critiques de ces études sont discutées au Chapitre 4. / In recent years, there has been a lively interest in the use of neurofeedback (NF) as an alternative treatment to pharmacotherapy in pediatric Attention-deficit hyperactivity disorder (ADHD). NF is defined as an operant conditioning procedure whereby an individual learns to self-regulate electrical brain activity. Yet, empirical studies supporting its practice are harshly criticized in the field of ADHD research due to systematic unspecific positive results associated to numerous methodological flaws. Studies presented in this dissertation aim at applying a randomized placebo-controlled clinical trial to the investigation of NF specific effects. In order to further establish treatment specificity, we trained the participants using a NF protocol characteristic to motor-inhibition functioning since it has repeatedly been described as the core deficit in ADHD. The fist part of this thesis comprises a brief review of knowledge concerning ADHD, principal treatments offered for this disorder, NF research in pediatric ADHD and inhibition abilities in these children (Chapter 1). Following are the two studies conducted as part of this dissertation. In the initial study, effects of NF training are investigated on inhibition capacities by means of behavior rating scales and neuropsychological tests (Chapter 2). With the aim of further defining the consequence of training to self-regulate ones brain activity, the subsequent study looks at the neurophysiologic impact of improving inhibition capacities with an event-related potential study using a Stop-signal continuous performance task (Chapter 3). The principal results reveal a non optimal recruitment, with insufficient statistical power, thus precluding quantitative group statistics. Nevertheless, appreciating the data from a multiple case study perspective enables to suggest that a placebo response could be at play following NF training, such as measured by improvements on inhibition capacities. Finally, the implications of such a small sample size, limits and critics of these studies are discussed in Chapter 4.

Longitudinal surveys have emerged in recent years as an important data collection tool for population studies where the primary interest is to examine population changes over time at the individual level. The National Longitudinal Survey of Children and Youth (NLSCY), a large scale survey with a complex sampling design and conducted by Statistics Canada, follows a large group of children and youth over time and collects measurement on various indicators related to their educational, behavioral and psychological development. One of the major objectives of the study is to explore how such development is related to or affected by familial, environmental and economical factors. The generalized estimating equation approach, sometimes better known as the GEE method, is the most popular statistical inference tool for longitudinal studies. The vast majority of existing literature on the GEE method, however, uses the method for non-survey settings; and issues related to complex sampling designs are ignored. This thesis develops methods for the analysis of longitudinal surveys when the response variable contains missing values. Our methods are built within the GEE framework, with a major focus on using the GEE method when missing responses are handled through hot-deck imputation. We first argue why, and further show how, the survey weights can be incorporated into the so-called Pseudo GEE method under a joint randomization framework. The consistency of the resulting Pseudo GEE estimators with complete responses is established under the proposed framework. The main focus of this research is to extend the proposed pseudo GEE method to cover cases where the missing responses are imputed through the hot-deck method. Both weighted and unweighted hot-deck imputation procedures are considered. The consistency of the pseudo GEE estimators under imputation for missing responses is established for both procedures. Linearization variance estimators are developed for the pseudo GEE estimators under the assumption that the finite population sampling fraction is small or negligible, a scenario often held for large scale population surveys. Finite sample performances of the proposed estimators are investigated through an extensive simulation study. The results show that the pseudo GEE estimators and the linearization variance estimators perform well under several sampling designs and for both continuous response and binary response.

Longitudinal surveys have emerged in recent years as an important data collection tool for population studies where the primary interest is to examine population changes over time at the individual level. The National Longitudinal Survey of Children and Youth (NLSCY), a large scale survey with a complex sampling design and conducted by Statistics Canada, follows a large group of children and youth over time and collects measurement on various indicators related to their educational, behavioral and psychological development. One of the major objectives of the study is to explore how such development is related to or affected by familial, environmental and economical factors. The generalized estimating equation approach, sometimes better known as the GEE method, is the most popular statistical inference tool for longitudinal studies. The vast majority of existing literature on the GEE method, however, uses the method for non-survey settings; and issues related to complex sampling designs are ignored. This thesis develops methods for the analysis of longitudinal surveys when the response variable contains missing values. Our methods are built within the GEE framework, with a major focus on using the GEE method when missing responses are handled through hot-deck imputation. We first argue why, and further show how, the survey weights can be incorporated into the so-called Pseudo GEE method under a joint randomization framework. The consistency of the resulting Pseudo GEE estimators with complete responses is established under the proposed framework. The main focus of this research is to extend the proposed pseudo GEE method to cover cases where the missing responses are imputed through the hot-deck method. Both weighted and unweighted hot-deck imputation procedures are considered. The consistency of the pseudo GEE estimators under imputation for missing responses is established for both procedures. Linearization variance estimators are developed for the pseudo GEE estimators under the assumption that the finite population sampling fraction is small or negligible, a scenario often held for large scale population surveys. Finite sample performances of the proposed estimators are investigated through an extensive simulation study. The results show that the pseudo GEE estimators and the linearization variance estimators perform well under several sampling designs and for both continuous response and binary response.