High performance and high bandwidth wireless digital communication underlies much of modern society. Due to its high value to society, new and improved digital communication technologies, allowing even higher speeds, better coverage, and lower latency are constantly being developed. The field of Machine Learning has exploded in recent years, showing incredible promise and performance at many tasks in a wide variety of fields. Channel Equalization and synchronization are critical parts of any wireless communication system, to ensure coherence between the transmitter and receiver, and to compensate for the often severe channel conditions. This study mainly explores the use of a Variational Autoencoder (VAE) architecture, presented in a previous study, for blind channel equalization without access to pilot symbols or ground-truth data. This thesis also presents a new, non data-assisted method of carrier frequency synchronization based around the k-means clustering algorithm. The main addition of this thesis however is a constellation agnostic implementation of the reference VAE architecture, for equalization of all rectangular QAM constellations. The approach significantly outperforms the traditional blind adaptive Constant Modulus algorithm (CMA) on all tested constellations and signal to noise ratios (SNRs), nearly equaling the performance of a non-blind Least Mean Squares (LMS) based Decision Feedback Equalizer (DFE).

Generative models are broadly used in many subfields of DL. DNNs have recently developed a core approach to solving data-centric problems in image classification, translation, etc. The latest developments in parameterizing these models using DNNs and stochastic optimization algorithms have allowed scalable modeling of complex, high-dimensional data, including speech, text, and image. This dissertation proposal presents our state-the-art probabilistic bases and DL algorithms for generative models, including VAEs, GANs, and RNN-based encoder-decoder. The proposal also discusses application areas that may benefit from deep generative models in both NLP and computer vision. In NLP, we proposed an Arabic poetry generation model with extended phonetic and semantic embeddings (Phonetic CNN_subword embeddings). Extensive quantitative experiments using BLEU scores and Hamming distance show notable enhancements over strong baselines. Additionally, a comprehensive human evaluation confirms that the poems generated by our model outperform the base models in criteria including meaning, coherence, fluency, and poeticness. We proposed a generative video model using a hybrid VAE-GAN model in computer vision. Besides, we integrate two attentional mechanisms with GAN to get the essential regions of interest in a video, focused on enhancing the visual implementation of the human motion in the generated output. We have considered quantitative and qualitative experiments, including comparisons with other state-of-the-arts for evaluation. Our results indicate that our model enhances performance compared with other models and performs favorably under different quantitive metrics PSNR, SSIM, LPIPS, and FVD.Recently, mimicking biologically inspired learning in generative models based on SNNs has been shown their effectiveness in different applications. SNNs are the third generation of neural networks, in which neurons communicate through binary signals known as spikes. Since SNNs are more energy-efficient than DNNs. Moreover, DNN models have been vulnerable to small adversarial perturbations that cause misclassification of legitimate images. This dissertation shows the proposed ``VAE-Sleep'' that combines ideas from VAE and the sleep mechanism leveraging the advantages of deep and spiking neural networks (DNN--SNN).On top of that, we present ``Defense–VAE–Sleep'' that extended work of ``VAE-Sleep'' model used to purge adversarial perturbations from contaminated images. We demonstrate the benefit of sleep in improving the generalization performance of the traditional VAE when the testing data differ in specific ways even by a small amount from the training data. We conduct extensive experiments, including comparisons with the state–of–the–art on different datasets.

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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.

<p dir="ltr">This thesis revisits self-organizing maps (SOM) and explores their applications as both a clustering and feature learning method, establishing SOM as a powerful tool for analyzing complex biological and temporal datasets. The first study integrates evolutionary information with co-fractionation mass spectrometry data to create enhanced benchmarks for protein complex predictions. The second study applies SOM to investigate the intricate relationships between mRNA and protein expression levels during cotton fiber development. By clustering mRNA-protein pairs based on their time-course profiles, SOM captures distinct, non-linear patterns that extend beyond traditional linear correlation methods, offering new insights into gene expression and protein production dynamics. In the third study, SOM is integrated with a generative model, the Variational Autoencoder (VAE), and a Long Short-Term Memory (LSTM) network to tackle challenges in time-series clustering. This framework combines VAE for learning latent representations, LSTM for capturing temporal dependencies, and SOM for clustering, achieving superior performance in modeling complex temporal patterns. Through these studies, this thesis redefines SOM as a versatile and effective tool for uncovering complex patterns in biological and temporal datasets, demonstrating its relevance and potential in modern data analysis.</p>

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