There is an ever-increasing usage of mobile data with global traffic having reached 115 exabytes per month at the end of 2022 for mobile data traffic including fixed wireless access. This is projected to grow up to 453 exabytes at the end of 2028, according to Ericssons 2022 mobile data traffic outlook report. To meet the increasing demand radio access networks (RAN) used for mobile communication are continuously being improved with the current generation enabling larger virtualization of the network through the Cloud RAN (C-RAN) architecture. This facilitates the usage of commercial off-the-shelf servers (COTS) in the network replacing specialized hardware servers and making it easier to scale up or down the network capacity after traffic demand. This thesis looks at how we can efficiently identify servers needed to meet traffic demand in a network consisting of both COTS servers and specialized hardware servers while trying to reduce the energy consumption of the network. We model the problem as a network where the antennas and radio heads are connectedto the core network through a C-RAN and a specialized hardware layer. The network is then represented using a graph where the nodes represent servers in the network. Using this problem model as a base we then generate problem instances with varying topologies, server profiles, and traffic demands. To find out how the traffic should be passed through the network we test two different methods: A mixed integer linear programming (MILP) method focused on energy minimization and a graph attention network (GAT) predictor combined with the energy minimization MILP. To help evaluate the results we also create three other methods: a MILP model that tries to spread the traffic as evenly as possible, a random predictor combined with the energy minimization MILP and a greedy method. Our results show that the energy optimization MILP method can be used to create optimal solutions, but it suffer from a slow computation time compared to the other methods. The GAT model shows promising results in making predictions regarding what servers should be included in a network making it possible to reduce the problem size and solve it faster with MILP. The mean energy cost of the solutions created using the combined GAT/MILP method was 4% more than just using MILP but the time gain was substantial for problems of similar size as the GAT was trained on. With regards to computation time the combined GAT/MILP method used was 85% faster than using only MILP. For networks of almost double the size than the ones that the GAT model was trained on the solutions of the combined GAT and MILP methods had a mean energy cost increase of 7% while still showing a strong speedup, being 93% faster than when only using MILP.

Performing causal reasoning in a scene is an inherent mechanism in human cognition; however, the majority of approaches in the causality literature aiming for this task still consider constrained scenarios, such as simple physical systems or stationary time-series data. In this work we aim for causal discovery in videos concerning realistic scenarios. We gather motivation for causal discovery by acknowledging this task to be core at human cognition. Moreover, we interpret the scene as a composition of time-series that interact along the sequence and aim for modeling the non-stationary behaviors in a scene. We propose State-dependent Causal Inference (SDCI) for causal discovery in conditional stationary time-series data. We formulate our problem of causal analysis by considering that the stationarity of the time-series is conditioned on a categorical variable, which we call state. Results show that the probabilistic implementation proposed achieves outstanding results in identifying causal relations on simulated data. When considering the state being independent from the dynamics, our method maintains decent accuracy levels of edge-type identification achieving 74.87% test accuracy when considering a total of 8 states. Furthermore, our method correctly handles regimes where the state variable undergoes complex transitions and is dependent on the dynamics of the scene, achieving 79.21% accuracy in identifying the causal interactions. We consider this work to be an important contribution towards causal discovery in videos. / Att utföra kausala resonemang i en scen är en medfödd mekanism i mänsklig kognition; dock betraktar fortfarande majoriteten av tillvägagångssätt i kausalitetslitteraturen, som syftar till denna uppgift, begränsade scenarier såsom enkla fysiska system eller stationära tidsseriedata. I detta arbete strävar vi efter kausal upptäckt i videor om realistiska scenarier. Vi samlar motivation för kausal upptäckt genom att erkänna att denna uppgift är kärnan i mänsklig kognition. Dessutom tolkar vi scenen som en komposition av tidsserier som interagerar längs sekvensen och syftar till att modellera det icke-stationära beteendet i en scen. Vi föreslår Tillståndsberoende kausal inferens (SDCI) för kausal upptäckt i villkorlig stationär tidsseriedata. Vi formulerar vårt problem med kausalanalys genom att anse att tidsseriens stationäritet är villkorad av en kategorisk variabel, som vi kallar tillstånd. Resultaten visar att det föreslagna probabilistiska genomförandet uppnår enastående resultat vid identifiering av orsakssambandet på simulerade data. När man överväger att tillståndet är oberoende av dynamiken, upprätthåller vår metod anständiga noggrannhetsnivåer av kanttypsidentifiering som uppnår 74, 87% testnoggrannhet när man överväger totalt 8 tillstånd. Dessutom hanterar vår metod korrekt regimer där tillståndsvariabeln genomgår komplexa övergångar och är beroende av dynamiken på scenen och uppnår 79, 21% noggrannhet för att identifiera kausala interaktioner. Vi anser att detta arbete är ett viktigt bidrag till kausal upptäckt i videor.

<p>Terminal airspace around an airport is the biggest bottleneck for commercial operations in the National Airspace System (NAS). In order to prognosticate the safety status of the terminal airspace, effective prediction of the airspace evolution is necessary. While there are fixed procedural structures for managing operations at an airport, the confluence of a large number of aircraft and the complex interactions between the pilots and air traffic controllers make it challenging to predict its evolution. Modeling the high-dimensional spatio-temporal interactions in the airspace given different environmental and infrastructural constraints is necessary for effective predictions of future aircraft trajectories that characterize the airspace state at any given moment. A novel deep learning architecture using Graph Neural Networks is proposed to predict trajectories of aircraft 10 minutes into the future and estimate prog?nostic metrics for the airspace. The uncertainty in the future is quantified by predicting distributions of future trajectories instead of point estimates. The framework’s viability for trajectory prediction and prognosis is demonstrated with terminal airspace data from Dallas Fort Worth International Airport (DFW). </p>

Indiana University-Purdue University Indianapolis (IUPUI) / With the increasing availability of Electronic Health Records (EHRs) and advances in deep learning techniques, developing deep predictive models that use EHR data to solve healthcare problems has gained momentum in recent years. The majority of clinical predictive models benefit from structured data in EHR (e.g., lab measurements and medications). Still, learning clinical outcomes from all possible information sources is one of the main challenges when building predictive models. This work focuses mainly on two sources of information that have been underused by researchers; unstructured data (e.g., clinical notes) and a patient network. We propose a novel hybrid deep learning model, DeepNote-GNN, that integrates clinical notes information and patient network topological structure to improve 30-day hospital readmission prediction. DeepNote-GNN is a robust deep learning framework consisting of two modules: DeepNote and patient network. DeepNote extracts deep representations of clinical notes using a feature aggregation unit on top of a state-of-the-art Natural Language Processing (NLP) technique - BERT. By exploiting these deep representations, a patient network is built, and Graph Neural Network (GNN) is used to train the network for hospital readmission predictions. Performance evaluation on the MIMIC-III dataset demonstrates that DeepNote-GNN achieves superior results compared to the state-of-the-art baselines on the 30-day hospital readmission task. We extensively analyze the DeepNote-GNN model to illustrate the effectiveness and contribution of each component of it. The model analysis shows that patient network has a significant contribution to the overall performance, and DeepNote-GNN is robust and can consistently perform well on the 30-day readmission prediction task. To evaluate the generalization of DeepNote and patient network modules on new prediction tasks, we create a multimodal model and train it on structured and unstructured data of MIMIC-III dataset to predict patient mortality and Length of Stay (LOS). Our proposed multimodal model consists of four components: DeepNote, patient network, DeepTemporal, and score aggregation. While DeepNote keeps its functionality and extracts representations of clinical notes, we build a DeepTemporal module using a fully connected layer stacked on top of a one-layer Gated Recurrent Unit (GRU) to extract the deep representations of temporal signals. Independent to DeepTemporal, we extract feature vectors of temporal signals and use them to build a patient network. Finally, the DeepNote, DeepTemporal, and patient network scores are linearly aggregated to fit the multimodal model on downstream prediction tasks. Our results are very competitive to the baseline model. The multimodal model analysis reveals that unstructured text data better help to estimate predictions than temporal signals. Moreover, there is no limitation in applying a patient network on structured data. In comparison to other modules, the patient network makes a more significant contribution to prediction tasks. We believe that our efforts in this work have opened up a new study area that can be used to enhance the performance of clinical predictive models.

In recent years the number of electronic healthcare records (EHRs)has increased rapidly. EHR represents a systematized collection of patient health information in a digital format. EHR systems maintain diagnoses, medications, procedures, and lab tests associated with the patients at each time they visit the hospital or care center. Since the information is available into multiple visits to hospitals or care centers, the EHR can be used to increasing quality care. This is especially useful when working with chronic diseases because they tend to evolve. There have been many deep learning methods that make use of these EHRs to solve different prediction tasks. Transformers have shown impressive results in many sequence-to-sequence tasks within natural language processing. This paper will mainly focus on using transformers, explicitly using a sequence of visits to do prediction tasks. The model presented in this paper is called CondBEHRT. Compared to previous state-of-art models, CondBEHRT will focus on using as much available data as possible to understand the patient’s trajectory. Based on all patients, the model will learn the medical ontology between diagnoses, medications, and procedures. The results show that the inferred medical ontology that has been learned can simulate reality quite well. Having the medical ontology also gives insights about the explainability of model decisions. We also compare the proposed model with the state-of-the-art methods using two different use cases; predicting the given codes in the next visit and predicting if the patient will be readmitted within 30 days.

<p>Nowadays an increasing number of people consume, share, and interact with information online. This results in posting and counter-posting on online media by different ideological groups on various polarized topics. Consequently, online media has become the primary platform for political and social influencers to directly interact with the citizens and share their perspectives, views, and stances with the goal of gaining support for their actions, bills, and legislation. Hence, understanding the perspectives and the influencing strategies in online media texts is important for an individual to avoid misinformation and improve trust between the general people and the influencers and the authoritative figures such as the government.</p> <p><br></p> <p>Automatically understanding the perspectives in online media is difficult because of two major challenges. Firstly, the proper grammar or mechanism to characterize the perspectives is not available. Recent studies in Natural Language Processing (NLP) have leveraged resources from social science to explain perspectives. For example, Policy Framing and Moral Foundation Theory are used for understanding how issues are framed and the moral appeal expressed in texts to gain support. However, these theories often fail to capture the nuances in perspectives and cannot generalize over all topics and events. Our research in this dissertation is one of the first studies that adapt social science theories in Natural Language Processing for understanding perspectives to the extent that they can capture differences in ideologies or stances. The second key challenge in understanding perspectives in online media texts is that annotated data is difficult to obtain to build automatic methods to detect the perspectives, that can generalize over the large corpus of online media text on different topics. To tackle this problem, in this dissertation, we used weak sources of supervision such as social network interaction of users who produce and interact with the messages, weak human interaction, or artificial few-shot data using Large Language Models. </p> <p><br></p> <p>Our insight is that various tasks such as perspectives, stances, sentiments toward entities, etc. are interdependent when characterizing online media messages. As a result, we proposed approaches that jointly model various interdependent problems such as perspectives, stances, sentiments toward entities, etc., and perform structured prediction to solve them jointly. Our research findings showed that the messaging choices and perspectives on online media in response to various real-life events and their prominence and contrast in different ideological camps can be efficiently captured using our developed methods.</p>

Starting in 2027, the high-luminosity Large Hadron Collider (HL-LHC) will begin operation and allow higher-precision measurements and searches for new physics processes between elementary particles. One central problem that arises in the ATLAS detector when reconstructing event information is to separate the rare and interesting hard scatter (HS) interactions from uninteresting pileup (PU) interactions in a spatially compact environment. This problem becomes even harder to solve at higher luminosities. This project relies on leveraging the time dimension and determining a time of the HS interactions to separate them from PU interactions by using information measured by the upcoming High-Granularity Timing Detector (HGTD). The current method relies on using a boosted decision tree (BDT) together with the timing information from the HGTD to determine a time. We suggest a novel approach of utilizing a graph attentional network (GAT) where each bunch-crossing is represented as a graph of tracks and the properties of the GAT are applied on a track level to inspect if such a model can outperform the current BDT. Our results show that we are able to replicate the results of the BDT and even improve some metrics at the expense of increasing the uncertainty of the time determination. We conclude that although there is potential for GATs to outperform the BDT, a more complex model should be applied. Finally, we provide some suggestions for improvement and hope to inspire further study and advancements in this direction which shows promising potential. / Från och med 2027 kommer \textit{high-luminosity Large Hadron Collider} (HL-LHC) att tas i drift och möjliggöra mätningar med högre precision och utforskningar av nya fysikprocesser mellan elementarpartiklar. Ett centralt problem som uppstår i ATLAS-detektorn vid rekonstruktionen av partikelkollisioner är att separera sällsynta och intressanta interaktioner, så kallade \textit{hard-scatters} (HS) från ointressanta \textit{pileup}-interaktioner (PU) i den kompakta rumsliga dimensionen. Svårighetsgraden för detta problem ökar vid högre luminositeter. Med hjälp av den kommande \textit{High-Granularity Timing-detektorns} (HGTD) mätningar kommer även tidsinformation relaterat till interaktionerna att erhållas. I detta projekt används denna information för att beräkna tiden för enskillda interaktioner vilket därmed kan användas för att separera HS-interaktioner från PU-interaktioner. Den nuvarande metoden använder en trädregressionsmetod, s.k. boosted decision tree (BDT) tillsammans med tidsinformationen från HGTD för att bestämma en tid. Vi föreslår ett nytt tillvägagångssätt baserat på ett s.k. uppvaktande grafnätverk (GAT), där varje protonkollision representeras som en graf över partikelspåren och där GAT-egenskaperna tillämpas på spårnivå. Våra resultat visar att vi kan replikera de BDT-baserade resultaten och till och med förbättra resultaten på bekostnad av att öka osäkerheten i tidsbestämningarna. Vi drar slutsatsen att även om det finns potential för GAT-modeller att överträffa BDT-modeller, bör mer komplexa versioner av de förra tillämpas. Vi ger slutligen några förbättringsförslag som vi hoppas ska kunna inspirera till ytterligare studier och framsteg inom detta område, vilket visar lovande potential.

Graphs are often considered an excellent way of modeling complex real-world problems since they allow to capture relationships between items. Because of their ubiquity, graph embedding techniques have occupied research groups, seeking how vertices can be encoded into a low-dimensional latent space, useful to then perform machine learning. Recently Graph Neural Networks (GNN) have dominated the space of embeddings generation due to their inherent ability to encode latent node dependencies. Moreover, the newly introduced Inductive Graph Neural Networks gained much popularity for inductively learning and representing node embeddings through neighborhood aggregate measures. Even when an entirely new node, unseen during training, appears in the graph, it can still be properly represented by its neighboring nodes. Although this approach appears suitable for dynamic graphs, available systems and training methodologies are agnostic of dynamicity and solely rely on re-processing full graph snapshots in batches, an approach that has been criticized for its high computational costs. This work provides a thorough solution to this particular problem via an efficient prioritybased method for selecting rehearsed samples that guarantees low complexity and high accuracy. Finally, a data-parallel inference method has been evaluated at scale using Apache Flink, a data stream processor for real-time predictions on high volume graph data streams. / Molti problemi nel mondo reale possono essere rappresentati come grafi poichè queste strutture dati consentono di modellare relazioni tra elementi. A causa del loro vasto uso, molti gruppi di ricerca hanno tentato di rappresentare i vertici in uno spazio a bassa dimensione, utile per poi poter utilizzare tecniche di apprendimento automatico. Le reti neurali per grafi sono state ampiamente utilizzate per via della loro capacità di codificare dipendenze tra vertici. Le reti neurali induttive recentemente introdotte, inoltre, hanno guadagnato popolarità poichè consentono di generare rappresentazioni di vertici aggregando altri vertici. In questo modo anche un nodo completamente nuovo può comunque essere rappresentato utilizzando i suoi nodi vicini. Sebbene questo approccio sia adatto per grafici dinamici, i sistemi ad oggi disponibili e gli algoritmi di addestramento si basano esclusivamente sulla continua elaborazione di grafi statici, un approccio che è stato criticato per i suoi elevati costi di calcolo. Questa tesi fornisce una soluzione a questo problema tramite un metodo efficiente per l’allenamento di reti neurali induttive basato su un’euristica per la selezione dei vertici. Viene inoltre descritto un metodo per eseguire predizioni in modo scalabile in tempo reale utilizzando Apache Flink, un sistema per l’elaborazione di grandi quantità di flussi di dati in tempo reale. / Grafer anses ofta vara ett utmärkt sätt att modellera komplexa problem i verkligheten eftersom de gör det möjligt att fånga relationer mellan objekt. På grund av deras allestädes närhet har grafinbäddningstekniker sysselsatt forskningsgrupper som undersöker hur hörn kan kodas in i ett lågdimensionellt latent utrymme, vilket är användbart för att sedan utföra maskininlärning. Nyligen har Graph Neural Networks (GNN) dominerat utrymmet för inbäddningsproduktion tack vare deras inneboende förmåga att koda latenta nodberoenden. Dessutom fick de nyinförda induktiva grafiska nervnäten stor popularitet för induktivt lärande och representerande nodbäddningar genom sammanlagda åtgärder i grannskapet. Även när en helt ny nod, osynlig under träning, visas i diagrammet, kan den fortfarande representeras ordentligt av dess angränsande noder. Även om detta tillvägagångssätt tycks vara lämpligt för dynamiska grafer, är tillgängliga system och träningsmetodologier agnostiska för dynamik och förlitar sig bara på att behandla fullständiga ögonblicksbilder i partier, en metod som har kritiserats för dess höga beräkningskostnader. Detta arbete ger en grundlig lösning på detta specifika problem via en effektiv prioriteringsbaserad metod för att välja repeterade prover som garanterar låg komplexitet och hög noggrannhet. Slutligen har en dataparallell inferensmetod utvärderats i skala med Apache Flink, en dataströmprocessor för realtidsprognoser för grafiska dataströmmar med hög volym.

Time synchronization is a process of ensuring all the time difference between the clocks of network components(like base stations, boundary clocks, grandmasters, etc.) in the mobile network is zero or negligible. It is one of the important factors responsible for ensuring effective communication between two user-equipments in a mobile network. Nevertheless, the presence of asymmetries can lead to faults, making the detection of these errors indispensable, especially in technologies demanding ultra-low latency, such as 5G technology. Developing methods to ensure time-synchronized mobile networks, would not only improve the network performance, and contribute towards cost-effective telecommunication infrastructure. A rulebased simulator to simulate the mobile network was built, using the rules provided by the domain experts, in order to generate more data for further studies. The possibility of using Reinforcement Learning to perform fault detection in the mobile network was explored. In addition to the simulator dataset, an unlabelled customer dataset, which consists of time error differences between the base stations, and additional features for each of its network components was provided. Classification algorithms to label the customer dataset were designed, and a comparative analysis of each of them has been presented. Mathematical algorithm and Graph Neural Network models were built to detect error, for both the simulator and customer dataset, for the faulty node detection task. The approach of using a Mathematical algorithm and Graph Neural Network architectures provided an accuracy of 95% for potential fault node detection. The feature importance of the additional features of the network components was analyzed using the best Graph Neural Network model which was used to train for the node classification task (to classify the base stations as faulty and non-faulty). Additionally, an attempt was made to predict the individual time error value for each of the links using Graph Neural Network, however, it failed potentially due to the presence of fewer features to train from. / Tidssynkronisering är en process för att säkerställa att all tidsskillnad mellan klockorna för nätverkskomponenter (som basstationer, gränsklockor, stormästare, etc.) i mobilnätet är noll eller försumbar. Det är en av de viktiga faktorerna som är ansvariga för att säkerställa effektiv kommunikation mellan två användarutrustningar i ett mobilnät. Icke desto mindre kan närvaron av asymmetrier leda till fel, vilket gör upptäckten av dessa fel oumbärlig, särskilt i tekniker som kräver ultralåg latens, som 5G-teknik. En regelbaserad simulator för att simulera mobilnätet byggdes, med hjälp av reglerna från domänexperterna, för att generera mer data för vidare studier. Möjligheten att använda RL för att utföra feldetektering i mobilnätet undersöktes. Utöver simulatordataset tillhandahölls en omärkt kunddatauppsättning, som består av tidsfelsskillnader mellan basstationerna och ytterligare funktioner för var och en av dess nätverkskomponenter. Klassificeringsalgoritmer för att märka kunddataset utformades, och en jämförande analys av var och en av dem har presenterats. Matematisk algoritm och GNN-modeller byggdes för att upptäcka fel, för både simulatorn och kunddatauppsättningen, för uppgiften att detektera felaktig nod. Metoden att använda en matematisk algoritm och GNN-arkitekturer gav en noggrannhet på 95% för potentiell felnoddetektering. Funktionens betydelse för de ytterligare funktionerna hos nätverkskomponenterna analyserades med den bästa GNN-modellen som användes för att träna för nodklassificeringsuppgiften (för att klassificera basstationerna som felaktiga och icke-felaktiga). Dessutom gjordes ett försök att förutsäga det individuella tidsfelsvärdet för var och en av länkarna med GNN, men det misslyckades potentiellt på grund av närvaron av färre funktioner att träna från.

[pt] Esta dissertação introduz um simulador híbrido inovador que combina um resolvedor de Equações Diferenciais Parciais (EDP) numérico de Elementos Finitos (FE) com uma Rede Neural de Passagem de Mensagens (MPNN) para realizar simulações de dinâmicas de sólidos deformáveis em malhas pouco refinadas. Nosso trabalho visa fornecer simulações precisas com um erro comparável ao obtido com malhas mais refinadas em discretizações FE,mantendo a eficiência computacional ao usar um componente MPNN que corrige os erros numéricos associados ao uso de uma malha menos refinada. Avaliamos nosso modelo focando na precisão, capacidade de generalização e velocidade computacional em comparação com um solucionador numérico de referência que usa malhas 64 vezes mais refinadas. Introduzimos um novo conjunto de dados para essa comparação, abrangendo três casos de referência numéricos: (i) deformação livre após um impulso inicial, (ii) alongamento e (iii)torção de sólidos deformáveis. Baseado nos resultados de simulação, o estudo discute as forças e fraquezas do nosso método. O estudo mostra que nosso método corrige em média 95,4 por cento do erro numérico associado à discretização, sendo até 88 vezes mais rápido que o solucionador de referência. Além disso, nosso modelo é totalmente diferenciável em relaçao a funções de custo e pode ser incorporado em uma camada de rede neural, permitindo que seja facilmente estendido por trabalhos futuros. Dados e código estão disponíveis em https://github.com/Kerber31/fast_coarse_FEM para investigações futuras. / [en] This thesis introduces a novel hybrid simulator that combines a numerical Finite Element (FE) Partial Differential Equation solver with a Message Passing Neural Network (MPNN) to perform simulations of deformable solid dynamics on coarse meshes. Our work aims to provide accurate simulations with an error comparable to that obtained with more refined meshes in FE discretizations while maintaining computational efficiency by using an MPNN component that corrects the numerical errors associated with using a coarse mesh. We evaluate our model focusing on accuracy, generalization capacity, and computational speed compared to a reference numerical solver that uses 64 times more refined meshes. We introduce a new dataset for this comparison, encompassing three numerical benchmark cases: (i) free deformation after an initial impulse, (ii) stretching, and (iii) torsion of deformable solids. Based on simulation results, the study thoroughly discusses our method s strengths and weaknesses. The study shows that our method corrects an average of 95.4 percent of the numerical error associated with discretization while being up to 88 times faster than the reference solver. On top of that, our model is fully differentiable in relation to loss functions and can be embedded into a neural network layer, allowing it to be easily extended by future work. Data and code are made available on https://github.com/Kerber31/fast_coarse_FEM for further investigations.