<p>Automatic brain tumor segmentation in Magnetic Resonance Imaging scans is essential for the diagnosis, treatment, and surgery of cancerous tumors. However, identifying the hardly detectable tumors poses a considerable challenge, which are usually of different sizes, irregular shapes, and vague invasion areas. Current advancements have not yet fully leveraged the dynamics in the multiple modalities of MRI, since they usually treat multi-modality as multi-channel, and the early channel merging may not fully reveal inter-modal couplings and complementary patterns. In this thesis, we propose a novel deep cross-attention learning algorithm that maximizes the subtle dynamics mining from each of the input modalities and then boosts feature fusion capability. More specifically, we have designed a Multimodal Cross-Attention Module (MM-CAM), equipped with a 3D Multimodal Feature Rectification and Feature Fusion Module. Extensive experiments have shown that the proposed novel deep learning architecture, empowered by the innovative MM- CAM, produces higher-quality segmentation masks of the tumor subregions. Further, we have enhanced the algorithm with image matting refinement techniques. We propose to integrate a Progressive Refinement Module (PRM) and perform Cross-Subregion Refinement (CSR) for the precise identification of tumor boundaries. A Multiscale Dice Loss was also successfully employed to enforce additional supervision for the auxiliary segmentation outputs. This enhancement will facilitate effectively matting-based refinement for medical image segmentation applications. Overall, this thesis, with deep learning, transformer-empowered pattern mining, and sophisticated architecture designs, will greatly advance deep brain dynamics and images mining for tumor detection and precision medicine.</p>

Rogue drones have become a significant concern in recent years due to their potential to cause harm to people and property and disrupt critical infrastructure and public safety. As a result, there has been a growing need for effective methods to detect and mitigate the risks posed by these drones. The proposed study aims to address the task by using a Radio Frequency (RF) based approach. Also, ensemble Machine Learning (ML) methods, as well as Deep Learning (DL) techniques were utilized as classification algorithms. Three levels of classification were defined for the task which includes drone detection, identification, and characterization based on operation mode. For the three levels, Deep-Complex Convolutional Neural Network performed the best and achieved an average accuracy of 99.82%, 94.20%, and 90.25%, respectively.

<p>Graph representation learning (GRL) has been increasing used to model and understand data from a wide variety of complex systems spanning social, technological, bio-chemical and physical domains. GRL consists of two main components (1) a parametrized encoder that provides representations of graph data and (2) a learning process to train the encoder parameters. Designing flexible encoders that capture the underlying invariances and characteristics of graph data are crucial to the success of GRL. On the other hand, the learning process drives the quality of the encoder representations and developing principled learning mechanisms are vital for a number of growing applications in self-supervised, transfer and federated learning settings. To this end, we propose a suite of models and learning algorithms for GRL which form the two main thrusts of this dissertation.</p> <p><br></p> <p>In Thrust I, we propose two novel encoders which build upon on a widely popular GRL encoder class called graph neural networks (GNNs). First, we empirically study the prediction performance of current GNN based encoders when applied to graphs with heterogeneous node mixing patterns using our proposed notion of local assortativity. We find that GNN performance in node prediction tasks strongly correlates with our local assortativity metric---thereby introducing a limit. We propose to transform the input graph into a computation graph with proximity and structural information as distinct types of edges. We then propose a novel GNN based encoder that operates on this computation graph and adaptively chooses between structure and proximity information. Empirically, adopting our transformation and encoder framework leads to improved node classification performance compared to baselines in real-world graphs that exhibit diverse mixing.</p> <p>Secondly, we study the trade-off between expressivity and efficiency of GNNs when applied to temporal graphs for the task of link ranking. We develop an encoder that incorporates a labeling approach designed to allow for efficient inference over the candidate set jointly, while provably boosting expressivity. We also propose to optimize a list-wise loss for improved ranking. With extensive evaluation on real-world temporal graphs, we demonstrate its improved performance and efficiency compared to baselines.</p> <p><br></p> <p>In Thrust II, we propose two principled encoder learning mechanisms for challenging and realistic graph data settings. First, we consider a scenario where only limited or even no labelled data is available for GRL. Recent research has converged on graph contrastive learning (GCL), where GNNs are trained to maximize the correspondence between representations of the same graph in its different augmented forms. However, we find that GNNs trained by traditional GCL often risk capturing redundant graph features and thus may be brittle and provide sub-par performance in downstream tasks. We then propose a novel principle, termed adversarial-GCL (AD-GCL), which enables GNNs to avoid capturing redundant information during the training by optimizing adversarial graph augmentation strategies used in GCL. We pair AD-GCL with theoretical explanations and design a practical instantiation based on trainable edge-dropping graph augmentation. We experimentally validate AD-GCL by comparing with state-of-the-art GCL methods and achieve performance gains in semi-supervised, unsupervised and transfer learning settings using benchmark chemical and biological molecule datasets. </p> <p>Secondly, we consider a scenario where graph data is silo-ed across clients for GRL. We focus on two unique challenges encountered when applying distributed training to GRL: (i) client task heterogeneity and (ii) label scarcity. We propose a novel learning framework called federated self-supervised graph learning (FedSGL), which first utilizes a self-supervised objective to train GNNs in a federated fashion across clients and then, each client fine-tunes the obtained GNNs based on its local task and available labels. Our framework enables the federated GNN model to extract patterns from the common feature (attribute and graph topology) space without the need of labels or being biased by heterogeneous local tasks. Extensive empirical study of FedSGL on both node and graph classification tasks yields fruitful insights into how the level of feature / task heterogeneity, the adopted federated algorithm and the level of label scarcity affects the clients’ performance in their tasks.</p>

With the fast development of radio technologies, wireless systems have become more convoluted. This complexity, accompanied by an increase of the number of connections, is translated into a need for more parameters to analyse and decisions to take at each instant. AI comes into play by automating these processes, particularly with Deep Learning techniques, that often show the best accuracy. However, the high performance by these methods also comes with the drawback of behaving like a black box from the view of a human. To this end, eXplainable AI serves as a technique to better understand the decision process of these algorithms. This thesis proposes an eXplainable AI framework to be used on Reinforcement Learning agents, particularly within the use case of antenna resource adaptation for network energy reduction. The framework puts a special emphasis on model adaptation/reduction, therefore focusing on feature importance techniques. The proposed framework presents a pre-model block using Concrete Autoencoders for feature reduction and a post-model block using self-supervised learning to estimate feature importance. Both of these can be used alone or in combination with DeepSHAP, in order to mitigate some of this popular method’s drawbacks. The explanations provided by the pipeline prove useful in order to reduce model complexity without loss of accuracy and to understand the usage of the input features by the AI model. / Med den snabba utvecklingen av radioteknologier har trådlösa system blivit alltmer invecklade. Denna komplexitet, kombinerat med en ökning av antalet anslutningar, innebär att fler parametrar behöver analyseras, och fler beslut behöver fattas vid varje ögonblick. AI kommer in i bilden genom att automatisera dessa processer, särskilt med Deep Learning-tekniker, som ofta uppvisar bäst noggrannhet. Men den höga prestandan med dessa metoder kommer också med nackdelen att tekniken beter sig som en svart låda från en människas synvinkel. Förklarlig AI fungerar därför som en teknik för att bättre förstå beslutet som fattas av dessa algoritmer. Denna avhandling föreslår ett förklarligt AI-ramverk som ska användas inom förstärkningsinlärning, särskilt inom användningsfallet med antenn-resursanpassning för energireduktion i trådlösa nätverk. Det föreslagna ramverket sätter en särskild tonvikt på modellanpassning/modellreduktion. Ramverket innehåller ett förmodellblock som använder Concrete Autoencoders för Feature Reduction och ett post-modellblock som använder självövervakad inlärning för att uppskatta Feature Importance. Båda dessa kan användas ensamt eller i kombination med DeepSHAP, för att lindra några av denna populära metods nackdelar. Feature Importance-uppskattningarna från ramverket visar sig vara användbara för att minska modellkomplexitet utan förlust av noggrannhet och för att förstå användningen av Input Features av AI-modellen.

No description available.

The use of Open Source Software is becoming more and more popular, but it comes with the risk of importing vulnerabilities in private codebases. Security patches, providing fixes to detected vulnerabilities, are vital in protecting against cyber attacks, therefore being able to apply all the security patches as soon as they are released is key. Even though there is a public database for vulnerability fixes the majority of them remain undisclosed to the public, therefore we propose a Machine Learning algorithm using NLP to detect security patches in Java Open Source Software. To train the model we preprocessed and extract patches from the commits present in two databases provided by Debricked and a public one released by Ponta et al. [57]. Two experiments were conducted, one performing binary classification and the other trying to have higher granularity classifying the macro-type of vulnerability. The proposed models leverage the structure of the input to have a better patch representation and they are based on RNNs, Transformers and CodeBERT [22], with the best performing model being the Transformer that surprisingly outperformed CodeBERT. The results show that it is possible to classify security patches but using more relevant pre-training techniques or tree-based representation of the code might improve the performance. / Användningen av programvara med öppen källkod blir alltmer populär, men det innebär en risk för att sårbarheter importeras från privata kodbaser. Säkerhetspatchar, som åtgärdar upptäckta sårbarheter, är viktiga för att skydda sig mot cyberattacker, och därför är det viktigt att kunna tillämpa alla säkerhetspatchar så snart de släpps. Även om det finns en offentlig databas för korrigeringar av sårbarheter förblir de flesta hemliga för allmänheten. Vi föreslår därför en maskininlärningsalgoritm som med hjälp av NLP upptäcker säkerhetspatchar i Java Open Source Software. För att träna modellen har vi förbehandlat och extraherat patchar från de commits som finns i två databaser, ena som tillhandahålls av Debricked och en annan offentlig databas som släppts av Ponta et al. [57]. Två experiment genomfördes, varav ett utförde binär klassificering och det andra försökte få en högre granularitet genom att klassificera makro-typen av sårbarheten. De föreslagna modellerna utnyttjar strukturen i indatat för att få en bättre representation av patcharna och de är baserade på RNNs, Transformers och CodeBERT [22], där den bäst presterande modellen var Transformer som överraskande nog överträffade CodeBERT. Resultaten visar att det är möjligt att klassificera säkerhetspatchar, men genom att använda mer relevanta förträningstekniker eller trädbaserade representationer av koden kan prestandan förbättras.

<p dir="ltr">Progress in deep learning methodologies has transformed the landscape of medical image analysis, opening fresh pathways for precise and effective diagnostics. Currently, multi-phase liver CT scans follow a four-stage process, commencing with an initial scan carried out before the administration of <a href="" target="_blank">intravenous (IV) contrast-enhancing material</a>. Subsequently, three additional scans are performed following the contrast injection. The primary objective of this research is to automate the analysis and prediction of 50% of liver CT scans. It concentrates on discerning patterns of intensity change during the second, third, and fourth phases concerning the initial phase. The thesis comprises two key sections. The first section employs the non-contrast phase (first scan), late hepatic arterial phase (second scan), and portal venous phase (third scan) to predict the delayed phase (fourth scan). In the second section, the non-contrast phase and late hepatic arterial phase are utilized to predict both the portal venous and delayed phases. The study evaluates the performance of two deep learning models, U-Net and U²-Net. The process involves preprocessing steps like subtraction and normalization to compute contrast difference images, followed by post-processing techniques to generate the predicted 2D CT scans. Post-processing steps have similar techniques as in preprocessing but are performed in reverse order. Four fundamental evaluation metrics, including <a href="" target="_blank">Mean Absolute Error (MAE), Signal-to-Reconstruction Error Ratio (SRE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM), </a>are employed for assessment. Based on these evaluation metrics, U²-Net performed better than U-Net for the prediction of both portal venous (third) and delayed (fourth) phases. Specifically, U²-Net exhibited superior MAE and PSNR results for the predicted third and fourth scans. However, U-Net did show slightly better SRE and SSIM performance in the predicted scans. On the other hand, for the exclusive prediction of the fourth scan, U-Net outperforms U²-Net in all four evaluation metrics. This implementation shows promising results which will eliminate the need for additional CT scans and reduce patients’ exposure to harmful radiation. Predicting 50% of liver CT volumes will reduce exposure to harmful radiation by half. The proposed method is not limited to liver CT scans and can be applied to various other multi-phase medical imaging techniques, including multi-phase CT angiography, multi-phase renal CT, contrast-enhanced breast MRI, and more.</p>

<p>This thesis aims to design and develop the spatial adaptation approach through spatial transformers to improve the accuracy of human keypoint recognition models. We have studied different model types and design choices to gain an accuracy increase over models without spatial transformers and analyzed how spatial transformers increase the accuracy of predictions. A neural network called Widenet has been leveraged as a specialized network for providing the parameters for the spatial transformer. Further, we have evaluated methods to reduce the model parameters, as well as the strategy to enhance the learning supervision for further improving the performance of the model. Our experiments and results have shown that the proposed deep learning framework can effectively detect the human key points, compared with the baseline methods. Also, we have reduced the model size without significantly impacting the performance, and the enhanced supervision has improved the performance. This study is expected to greatly advance the deep learning of human key points and movement dynamics. </p>

Die Lokalisierung von mobilen Nutzern (MU) in sehr dichten Netzen erfordert häufig die Synchronisierung der Access Points (APs) untereinander. Erstens konzentriert sich diese Arbeit auf die Lösung des Problems der Zeitsynchronisation in 5G-Netzwerken, indem ein hybrider Bayesischer Ansatz für die Schätzung des Taktversatzes und des Versatzes verwendet wird. Wir untersuchen und demonstrieren den beträchtlichen Nutzen der Belief Propagation (BP), die auf factor graphs läuft, um eine präzise netzwerkweite Synchronisation zu erreichen. Darüber hinaus nutzen wir die Vorteile der Bayesischen Rekursiven Filterung (BRF), um den Zeitstempel-Fehler bei der paarweisen Synchronisierung zu verringern. Schließlich zeigen wir die Vorzüge der hybriden Synchronisation auf, indem wir ein großes Netzwerk in gemeinsame und lokale Synchronisationsdomänen unterteilen und so den am besten geeigneten Synchronisationsalgorithmus (BP- oder BRF-basiert) auf jede Domäne anwenden können. Zweitens schlagen wir einen Deep Neural Network (DNN)-gestützten Particle Filter-basierten (DePF)-Ansatz vor, um das gemeinsame MU-Sync&loc-Problem zu lösen. Insbesondere setzt DePF einen asymmetrischen Zeitstempel-Austauschmechanismus zwischen den MUs und den APs ein, der Informationen über den Taktversatz, die Zeitverschiebung der MUs, und die AP-MU Abstand liefert. Zur Schätzung des Ankunftswinkels des empfangenen Synchronisierungspakets nutzt DePF den multiple signal classification Algorithmus, der durch die Channel Impulse Response (CIR) der Synchronisierungspakete gespeist wird. Die CIR wird auch genutzt, um den Verbindungszustand zu bestimmen, d. h. Line-of-Sight (LoS) oder Non-LoS (NLoS). Schließlich nutzt DePF particle Gaussian mixtures, die eine hybride partikelbasierte und parametrische BRF-Fusion der vorgenannten Informationen ermöglichen und die Position und die Taktparameter der MUs gemeinsam schätzen. / Mobile User (MU) localization in ultra dense networks often requires, on one hand, the Access Points (APs) to be synchronized among each other, and, on the other hand, the MU-AP synchronization. In this work, we firstly address the former, which eventually provides a basis for the latter, i.e., for the joint MU synchronization and localization (sync&loc). In particular, firstly, this work focuses on tackling the time synchronization problem in 5G networks by adopting a hybrid Bayesian approach for clock offset and skew estimation. Specifically, we investigate and demonstrate the substantial benefit of Belief Propagation (BP) running on Factor Graphs (FGs) in achieving precise network-wide synchronization. Moreover, we take advantage of Bayesian Recursive Filtering (BRF) to mitigate the time-stamping error in pairwise synchronization. Finally, we reveal the merit of hybrid synchronization by dividing a large-scale network into common and local synchronization domains, thereby being able to apply the most suitable synchronization algorithm (BP- or BRF-based) on each domain. Secondly, we propose a Deep Neural Network (DNN)-assisted Particle Filter-based (DePF) approach to address the MU joint sync&loc problem. In particular, DePF deploys an asymmetric time-stamp exchange mechanism between the MUs and the APs, which provides information about the MUs' clock offset, skew, and AP-MU distance. In addition, to estimate the Angle of Arrival (AoA) of the received synchronization packet, DePF draws on the Multiple Signal Classification (MUSIC) algorithm that is fed by the Channel Impulse Response (CIR) experienced by the sync packets. The CIR is also leveraged on to determine the link condition, i.e. Line-of-Sight (LoS) or Non-LoS (NLoS). Finally DePF capitalizes on particle Gaussian mixtures which allow for a hybrid particle-based and parametric BRF fusion of the aforementioned pieces of information and jointly estimate the position and clock parameters of the MUs.

In den Materialwissenschaften läuten Künstliche-Intelligenz Methoden einen Paradigmenwechsel in Richtung Big-data zentrierter Forschung ein. Datenbanken mit Millionen von Einträgen, sowie hochauflösende Experimente, z.B. Elektronenmikroskopie, enthalten eine Fülle wachsender Information. Um diese ungenützten, wertvollen Daten für die Entdeckung verborgener Muster und Physik zu nutzen, müssen automatische analytische Methoden entwickelt werden. Die Kristallstruktur-Klassifizierung ist essentiell für die Charakterisierung eines Materials. Vorhandene Daten bieten vielfältige atomare Strukturen, enthalten jedoch oft Defekte und sind unvollständig. Eine geeignete Methode sollte diesbezüglich robust sein und gleichzeitig viele Systeme klassifizieren können, was für verfügbare Methoden nicht zutrifft. In dieser Arbeit entwickeln wir ARISE, eine Methode, die auf Bayesian deep learning basiert und mehr als 100 Strukturklassen robust und ohne festzulegende Schwellwerte klassifiziert. Die einfach erweiterbare Strukturauswahl ist breit gefächert und umfasst nicht nur Bulk-, sondern auch zwei- und ein-dimensionale Systeme. Für die lokale Untersuchung von großen, polykristallinen Systemen, führen wir die strided pattern matching Methode ein. Obwohl nur auf perfekte Strukturen trainiert, kann ARISE stark gestörte mono- und polykristalline Systeme synthetischen als auch experimentellen Ursprungs charakterisieren. Das Model basiert auf Bayesian deep learning und ist somit probabilistisch, was die systematische Berechnung von Unsicherheiten erlaubt, welche mit der Kristallordnung von metallischen Nanopartikeln in Elektronentomographie-Experimenten korrelieren. Die Anwendung von unüberwachtem Lernen auf interne Darstellungen des neuronalen Netzes enthüllt Korngrenzen und nicht ersichtliche Regionen, die über interpretierbare geometrische Eigenschaften verknüpft sind. Diese Arbeit ermöglicht die Analyse atomarer Strukturen mit starken Rauschquellen auf bisher nicht mögliche Weise. / In materials science, artificial-intelligence tools are driving a paradigm shift towards big data-centric research. Large computational databases with millions of entries and high-resolution experiments such as electron microscopy contain large and growing amount of information. To leverage this under-utilized - yet very valuable - data, automatic analytical methods need to be developed. The classification of the crystal structure of a material is essential for its characterization. The available data is structurally diverse but often defective and incomplete. A suitable method should therefore be robust with respect to sources of inaccuracy, while being able to treat multiple systems. Available methods do not fulfill both criteria at the same time. In this work, we introduce ARISE, a Bayesian-deep-learning based framework that can treat more than 100 structural classes in robust fashion, without any predefined threshold. The selection of structural classes, which can be easily extended on demand, encompasses a wide range of materials, in particular, not only bulk but also two- and one-dimensional systems. For the local study of large, polycrystalline samples, we extend ARISE by introducing so-called strided pattern matching. While being trained on ideal structures only, ARISE correctly characterizes strongly perturbed single- and polycrystalline systems, from both synthetic and experimental resources. The probabilistic nature of the Bayesian-deep-learning model allows to obtain principled uncertainty estimates which are found to be correlated with crystalline order of metallic nanoparticles in electron-tomography experiments. Applying unsupervised learning to the internal neural-network representations reveals grain boundaries and (unapparent) structural regions sharing easily interpretable geometrical properties. This work enables the hitherto hindered analysis of noisy atomic structural data.