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In this master’s thesis we explore the idea of using artificial intelligence in the product design process and seek to develop a conceptual framework for how it can be incorporated to make user customized products more accessible and affordable for everyone. We show how generative deep learning models such as Variational Auto Encoders and Generative Adversarial Networks can be implemented to generate design variations of windows and clarify the general implementation process along with insights from recent research in the field. The proposed framework consists of three parts: (1) A morphological matrix connecting several identified possibilities of implementation to specific parts of the product design process. (2) A general step-by-step process on how to incorporate generative deep learning. (3) A description of common challenges, strategies andsolutions related to the implementation process. Together with the framework we also provide a system for automatic gathering and cleaning of image data as well as a dataset containing 4564 images of windows in a front view perspective.

Seit Mitte der 1990-er Jahre wird in den sechs Staaten des Golf-Kooperationsrates über die Konsequenzen der massiven Arbeitsimmigration für die arabischen Gesellschaften dieser Länder diskutiert. Während die Immigranten und ihre Lebenssituation in den Regionalwissenschaften zur Golfregion zunehmend Beachtung finden, ist der arabische Einwanderungskurs kaum untersucht. Am Beispiel von Print- und Onlinemedien aus dem Zeitraum 2008-2013 untersucht die Dissertation von Daniel Falk den Einwanderungsdiskurs der Vereinigten Arabischen Emirate. Was ist die Perspektive der Aufnahmegesellschaft? Wie in den Golfstaaten über Migranten und Migrationsprozesse gesprochen, geschrieben und diskutiert?:1 Einleitung 1 1.1 Die Ausgangssituation 1 1.2 Forschungsstand und Ziel der Arbeit 5 1.3 Fragestellung und Erkenntnisinteresse 8 1.4 Aufbau der Arbeit 9 2 Theoretische und methodische Vorüberlegungen 11 2.1 Migration, Migranten und Migrationsregime 11 2.2 Islamische und arabische Konzepte von Migration 12 2.3 Mediale Migrantenbilder und die Analyse des Einwanderungsdiskurses 14 2.4 Auswahl der Quellen 21 3 Die zweigeteilte Gesellschaft 27 3.1 Historischer Rückblick 27 3.2 Die Gegenwart der zweigeteilten Gesellschaft 32 3.3 Exkurs: Die politische Ökonomie der zweigeteilten Gesellschaft 42 3.4 Arabisch vs. Englisch: zwei Sprachen – zweigeteilte Öffentlichkeit? 48 4 Der emiratische Einwanderungsdiskurs 2008-2013 50 4.1 Die thematische Verortung der Artikel 51 4.2 Argumente und Argumentationsmuster 62 4.3 Metaphorik: der emiratische Tropfen im Ozean der Migranten 92 4.4 Von der Diagnose zur Therapie: Lösungsvorschläge für das „Bevölkerungsproblem“ 109 4.5 Dichotomien des Fremden und des Eigenen 124 4.6 Leserkommentare auf albayan.ae und emaratalyoum.com 145 5 Zusammenfassung der Ergebnisse 167 Literaturverzeichnis 173 Anhang 180 / Migration to the Gulf countries over the past decades has led to dramatic change not only within the population structure. Especially in smaller Gulf countries, like Qatar and the UAE, where native Arab populations amount for less than 20 per cent of the total population, it had strong effects also on identity constructions, as the native “national” societies became minorities within their own countries. As this process continues, fears of losing the respective (Arab, Gulf, Emirati, Qatari …) identity are increasingly being voiced and calls for political action to take on this issue are becoming louder. This PhD project aimed at analysing the Arabic discourse on migration and identity and between 2008 and 2013. By analysing Arabic language mass media from the UAE it looked not only at representations of immigrants but also at of processes and consequences of migration and perceived loss of identity, e.g. the dis-course on the „population imbalance“ (al-khalal fi at-tarkeeba as-sukkaniyya). By focusing on the Arabic discourse the thesis seeks to counter-weigh a wide-spread phenomenon in Gulf-related social sciences and humanities: many studies on the region build on English-language sources and material only, thus ignoring the fact that a majority Gulf nationals still speak, write and think in their native language and constructing a biased image of Gulf societies. Especially in connection to such delicate topics like immigration and identity it is important to understand the respective (Emirati, Qatari…) perspective.:1 Einleitung 1 1.1 Die Ausgangssituation 1 1.2 Forschungsstand und Ziel der Arbeit 5 1.3 Fragestellung und Erkenntnisinteresse 8 1.4 Aufbau der Arbeit 9 2 Theoretische und methodische Vorüberlegungen 11 2.1 Migration, Migranten und Migrationsregime 11 2.2 Islamische und arabische Konzepte von Migration 12 2.3 Mediale Migrantenbilder und die Analyse des Einwanderungsdiskurses 14 2.4 Auswahl der Quellen 21 3 Die zweigeteilte Gesellschaft 27 3.1 Historischer Rückblick 27 3.2 Die Gegenwart der zweigeteilten Gesellschaft 32 3.3 Exkurs: Die politische Ökonomie der zweigeteilten Gesellschaft 42 3.4 Arabisch vs. Englisch: zwei Sprachen – zweigeteilte Öffentlichkeit? 48 4 Der emiratische Einwanderungsdiskurs 2008-2013 50 4.1 Die thematische Verortung der Artikel 51 4.2 Argumente und Argumentationsmuster 62 4.3 Metaphorik: der emiratische Tropfen im Ozean der Migranten 92 4.4 Von der Diagnose zur Therapie: Lösungsvorschläge für das „Bevölkerungsproblem“ 109 4.5 Dichotomien des Fremden und des Eigenen 124 4.6 Leserkommentare auf albayan.ae und emaratalyoum.com 145 5 Zusammenfassung der Ergebnisse 167 Literaturverzeichnis 173 Anhang 180

Here, we show that training with multiple noncommunicable diseases (NCDs) is both feasible and beneficial to modeling this class of diseases. We first use data from the Cancer Genome Atlas (TCGA) to train a pan cancer model, and then characterize the information the model has learned about the cancers. In doing this we show that the model has learned concepts that are relevant to the task of cancer classification. We also test the model on datasets derived independently of the TCGA cohort and show that the model is robust to data outside of its training distribution such as precancerous legions and metastatic samples. We then utilize the cancer model as the basis of a transfer learning study where we retrain it on other, non-cancer NCDs. In doing so we show that NCDs with very differing underlying biology contain extractible information relevant to each other allowing for a broader model of NCDs to be developed with existing datasets. We then test the importance of the samples source tissue in the model and find that the NCD class and tissue source may not be independent in our model. To address this, we use the tissue encodings to create augmented samples. We test how successfully we can use these augmented samples to remove or diminish tissue source importance to NCD class through retraining the model. In doing this we make key observations about the nature of concept importance and its usefulness in future neural network explainability efforts.

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Modelling music with mathematical and statistical methods in general, and with neural networks in particular, has a long history and has been well explored in the last decades. Exactly when the first attempt at strictly systematic music took place is hard to say; some would say in the days of Mozart, others would say even earlier, but it is safe to say that the field of algorithmic composition has a long history. Even though composers have always had structure and rules as part of the writing process, implicitly or explicitly, following rules at a stricter level was well investigated in the middle of the 20th century at which point also the first music writing computer program based on mathematics was implemented. This work in computer science focuses on the history of musical composition with computers, also known as algorithmic composition, using machine learning and neural networks and consists of two parts: a literature survey covering in-depth the last decades in the field from which is drawn inspiration and experience to construct MahlerNet, a neural network based on the previous architectures MusicVAE, BALSTM, PerformanceRNN and BachProp, capable of modelling polyphonic symbolic music with up to 23 instruments. MahlerNet is a new architecture that uses a custom preprocessor with musical heuristics to normalize and filter the input and output files in MIDI format into a data representation that it uses for processing. MahlerNet, and its preprocessor, was written altogether for this project and produces music that clearly shows musical characteristics reminiscent of the data it was trained on, with some long-term structure, albeit not in the form of motives and themes. / Matematik och statistik i allmänhet, och maskininlärning och neurala nätverk i synnerhet, har sedan långt tillbaka använts för att modellera musik med en utveckling som kulminerat under de senaste decennierna. Exakt vid vilken historisk tidpunkt som musikalisk komposition för första gången tillämpades med strikt systematiska regler är svårt att säga; vissa skulle hävda att det skedde under Mozarts dagar, andra att det skedde redan långt tidigare. Oavsett vilket, innebär det att systematisk komposition är en företeelse med lång historia. Även om kompositörer i alla tider följt strukturer och regler, medvetet eller ej, som en del av kompositionsprocessen började man under 1900-talets mitt att göra detta i högre utsträckning och det var också då som de första programmen för musikalisk komposition, baserade på matematik, kom till. Den här uppsatsen i datateknik behandlar hur musik historiskt har komponerats med hjälp av datorer, ett område som också är känt som algoritmisk komposition. Uppsatsens fokus ligger på användning av maskininlärning och neurala nätverk och består av två delar: en litteraturstudie som i hög detalj behandlar utvecklingen under de senaste decennierna från vilken tas inspiration och erfarenheter för att konstruera MahlerNet, ett neuralt nätverk baserat på de tidigare modellerna MusicVAE, BALSTM, PerformanceRNN och BachProp. MahlerNet kan modellera polyfon musik med upp till 23 instrument och är en ny arkitektur som kommer tillsammans med en egen preprocessor som använder heuristiker från musikteori för att normalisera och filtrera data i MIDI-format till en intern representation. MahlerNet, och dess preprocessor, är helt och hållet implementerade för detta arbete och kan komponera musik som tydligt uppvisar egenskaper från den musik som nätverket tränats på. En viss kontinuitet finns i den skapade musiken även om det inte är i form av konkreta teman och motiv.

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