Nanopore sequencing, a recently developed methodfor DNA sequencing, involves applying a constant electricfield over a membrane and translocating single-stranded DNAmolecules through membrane pores. This results in an electricalsignal, which is dependent on the structure of the DNA. The aimof this project is to train and evaluate a non-causal temporalconvolution neural network in order to accurately translate suchelectrical raw signal into the corresponding nucleotide sequence.The training dataset is sampled from the E. coli bacterial genomeand the phage Lambda virus. We implemented and evaluatedseveral different temporal convolutional architectures. Using anetwork with five residual blocks with five convolutional layersin each block yields maximum performance, with a predictionaccuracy of 76.1% on unseen test data. This result indicates thata temporal convolution network could be an effective way tosequence DNA data. / Nanopore sequencing är en nyligen utvecklad metod för DNA-sekvensering som innebär att man applicerar ett konstant elektriskt fält över ett membran och translokerar enkelsträngade DNA-molekyler genom membranporer. Detta resulterar i en elektrisk signal som beror på DNA-strukturen.  Målet med detta projekt är att träna och evaluera icke-kausula ”temporal convolutional networks” som ska kunna översätta denna ofiltrerade elektriska signalen till den motsvarande nukleotidsekvensen. Träningsdatan är ett urval av genomen från bakterien E. coli och viruset phage Lambda. Vi implementerade och utvärderade ett antal olika nätverksstrukturer. Ett nätverk med fem residuala block med fem faltande lager i varje block gav maximal prestation, med en precision på 76.1% på testdata. Detta resultat indikerar att ett ”temporal convolution network” skulle kunna vara ett effektivt sätt att sekvensera DNA. / Kandidatexjobb i elektroteknik 2020, KTH, Stockholm

This work uses a Deep Learning architecture, specifically a state-of-the-art Temporal Convolutional Network, to track the beat and downbeat positions in jazz-ensemble recordings to derive their metrical grid. This network architecture has been used successfully for general beat tracking purposes. However, the jazz genre presents difficulties for this Music Information Retrieval sub-task due to its inherent complexity, and there is a lack of dedicated sets for evaluating a model’s beat tracking performance for different playstyles of this specific music genre. We present a methodology in which we trained a PyTorch implementation of the original architecture with a recalculated binary cross-entropy loss that helps boost the model’s performance compared to a standard trained version. In addition, we retrained these two models using source-separated drums and bass tracks from jazz recordings to improve performance. We further improved the model’s performance by calibrating rhythm parameters using a priori knowledge that narrows the model’s prediction range. Finally, we proposed a novel jazz dataset comprised of recordings from the same jazz piece played with different styles and used this to evaluate the performance of this methodology. We also evaluate a novel sample with tempo variations to demonstrate the architecture’s versatility. This methodology, or parts of it, can be exported to other research work and music information tools that perform beat tracking or other similar Music Information Retrieval sub-tasks. / Vi använde en Deep Learning-arkitektur för att spåra beat- och downbeatpositionerna i jazz-ensembleinspelningar för att härleda deras metriska rutnät. Denna nätverksarkitektur har använts framgångsrikt för allmän taktspårning. Men jazzgenren uppvisar svårigheter för denna deluppgift för återhämtning av musikinformation på grund av dess inneboende komplexitet, och det finns en brist på dedikerade datauppsättningar för att utvärdera en modells prestanda för olika spelstilar av denna specifika musikgenre. Vi presenterar en metod där vi tränade modellen med en omräknad binär korsentropiförlust som hjälper till att öka modellens prestanda jämfört med en utbildad standardversion. Dessutom tränade vi om dessa två modeller med hjälp av källseparerade spår från jazzinspelningar för att förbättra resultaten. Vi förbättrade modellens prestanda ytterligare genom att kalibrera parametrar med hjälp av a priori kunskap. Slutligen föreslog vi en ny jazzdatauppsättning bestående av inspelningar från samma jazzstycke som spelades med olika stilar och använde detta för att utvärdera hur denna metod fungerar. Vi utvärderar också ett nytt prov med tempovariationer för att visa arkitekturens mångsidighet. Denna metodik, eller delar av den, kan exporteras till andra forskningsarbeten och musikinformationsverktyg som utför beat tracking eller andra liknande Music Information Retrieval underuppgifter.

Recent studies show that deep learning can be employed to learn from sensor data to improve accuracy and robustness of sensor fusion algorithms. In the same vein, in this thesis we use a state-of-the-art temporal convolution network to predict zero velocity updates (ZUPT) from raw inertial measurement unit (IMU) signals, and use the network output to improve the performance of an optimization-based pose estimator. Experiments were conducted on publicly available datasets, and results show that (i) the network can distinguish a car in motion vs. a car standing still by observing an IMU signal, and (ii) that ZUPT detection enhances the observability of states in the optimization-based pose estimation, thus reducing local drift. / Nyligen gjorda studier visar att djupinlärning kan användas för att lära av sensordata för att förbättra noggrannhet och robusthet hos sensorfusionsalgoritmer. På samma sätt använder vi i denna avhandling en tidsberoende faltnings neuronnätsmodell (TCN) för att detektera om ett fordon står stilla även kallat zero velocity updates (ZUPT) från IMU rå- data och använder neuronnätsprediktionen för att förbättra prestandan hos en optimeringsbaserad positionsuppskattning. Experiment utfördes på allmänt publicerade datamängder, och resultaten visar att (i) neuronnätsmodellen kan läras till att urskilja en bil i rörelse kontra en bil som står stilla genom att observera en IMU- signal, och (ii) att ZUPT- detektering förbättrar observerbarheten för tillstånd i den optimeringsbaserade positioneringsuppskattningen, vilket minskar lokal drift.

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The prediction of polypharmacy side effects is crucial to reduce the mortality and morbidity of patients suffering from complex diseases. However, its experimental prediction is unfeasible due to the many possible drug combinations, leaving in silico tools as the most promising way of addressing this problem. This thesis improves the performance and robustness of a state-of-the-art graph convolutional network designed to predict polypharmacy side effects, by feeding it with complexity properties of the drug-protein network. The modifications also involve the creation of a direct pipeline to reproduce the results and test it with different datasets. / För att minska dödligheten och sjukligheten hos patienter som lider av komplexa sjukdomar är det avgörande att kunna förutsäga biverkningar från polyfarmaci. Att experimentellt förutsäga biverkningarna är dock ogenomförbart på grund av det stora antalet möjliga läkemedelskombinationer, vilket lämnar in silico-verktyg som det mest lovande sättet att lösa detta problem. Detta arbete förbättrar prestandan och robustheten av ett av det senaste grafiska faltningsnätverken som är utformat för att förutsäga biverkningar från polyfarmaci, genom att mata det med läkemedel-protein-nätverkets komplexitetsegenskaper. Ändringarna involverar också skapandet av en direkt pipeline för att återge resultaten och testa den med olika dataset.

Nanopore DNA sequencing is a novel method forsequencing DNA where an electronic signal is modulated bynucleotides passing through nanosized pores embedded in a mem-brane. While current state-of-the-art solutions employ recurrentneural networks to analyse the signal, temporal convolutionalnetworks have recently been shown to match or outperformrecurrent networks in signal processing tasks. In this project, weinvestigate the performance of temporal convolutional networkson a simplified version of the sequencing task, where thegoal is to predict the nucleotides passing through the pore ateach instance in time, without reconstructing the correspondingDNA sequence. The impact of several network parameters onpredictive performance is analysed to determine an optimalarchitecture. While the implemented networks are shown tobe proficient at predicting nucleotides within the pore, thecurrent implementation is unlikely to outperform state-of-the-art solutions without further improvement. / En nyligen utvecklad metod för att sekvensera DNA innefattar att en elektrisk signal moduleras genom att nukleotider passerar genom porer i nanostorlek. I kommersiella lösningar analyseras denna signal med hjälp av maskininlärning via Recurrent Neural Networks, men en variant av neruala nätverk som kallas Temporal Convolution Networks har nyligen har visat sig ha bättre prestanda jämfört med Recurrent Networks för olika typer av signalbehandlingsproblem. Målet med detta projekt är att undersöka användbarheten av Temporal Convolutional Networks för en förenklad version av DNA-sekvensering, där uppdraget endast är att identifera de nukleotider som passerar genom poren vid varje given tidpunkt, istället för att rekonstruera en komplett DNA-sekvens. För att kunna bestämma en optimal arkitektur på nätverket så undersöks effekten av flera olika parametrar. De implementerade nätverken visas ha god förmåga att klassificera nukleotider, men är troligtvis i behov av ytterligare förbättringar för att kunna konkurrera med nuvarande kommersiella lösningar. / Kandidatexjobb i elektroteknik 2020, KTH, Stockholm

The problem of determining the probability of default or credit risk for companies is crucial when providing financial services. This problem is often modeled based on snapshot data that does not take the time dimension into account. Instead, we approach the problem with enterprise resource planning data in time series. With the added complexity the time series introduce, we pose that deep learning models could be suitable for the task. A comparison of a fully convolutional network and a transformer encoder was made to the current state-of-the-art model for the probability of default problem, XGBoost. The comparison showed that XGBoost generalized very well to the time series domain, even well enough to beat the deep learning models across all evaluation metrics. Furthermore, time series data with monthly, quarterly and yearly timestamps over three years was tested. Also, public features that could be extracted from quarterly and annual financial reports were compared with internal enterprise resource planning data. We found that the introduction of time series to the problem improves the performance and that models based on internal data outperform the ones based on public data. To be more precise, we argue that the dataset being based on small to medium-sized companies lessens the impact of highly granular data, and makes the selection of what features to include more prominent. This is something XGBoost takes advantage of in a very efficient way, especially when extracting features that capture the behavior of the time series, causing it to beat the deep learning competitors even though it does not pick up on the sequential aspect of the data.

Generation of realistic skydome images We aim to generate realistic images of the sky with clouds using generative adversarial networks (GANs). We explore two GAN architectures, ProGAN and StyleGAN, and find that StyleGAN produces significantly better results. We also propose a novel architecture SuperGAN which aims to generate images at very high resolutions, which cannot be efficiently handled using state-of-art architectures. 1

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Nowadays, we have millions of cameras in public places such as traffic junctions, railway stations etc., and capturing video data round the clock. This humongous data has resulted in an increased need for automation of visual surveillance. Analysis of crowd and traffic flows is an important step towards achieving this goal. In this work, we present our algorithms for identifying and segmenting dominant ows in surveillance scenarios. In the second part, we present our work aiming at predicting the visual saliency. The ability of humans to discriminate and selectively pay attention to few regions in the scene over the others is a key attentional mechanism. Here, we present our algorithms for predicting human eye fixations and segmenting salient objects in the scene. (i) Flow Analysis in Surveillance Videos: We propose algorithms for segmenting flows of static and dynamic nature in surveillance videos in an unsupervised manner. In static flows scenarios, we assume the motion patterns to be consistent over the entire duration of video and analyze them in the compressed domain using H.264 motion vectors. Our approach is based on modeling the motion vector field as a Conditional Random Field (CRF) and obtaining oriented motion segments which are merged to obtain the final flow segments. This approach in compressed domain is shown to be both accurate and computationally efficient. In the case of dynamic flow videos (e.g. flows at a traffic junction), we propose a method for segmenting the individual object flows over long durations. This long-term flow segmentation is achieved in the framework of CRF using local color and motion features. We propose a Dynamic Time Warping (DTW) based distance measure between flow segments for clustering them and generate representative dominant ow models. Using these dominant flow models, we perform path prediction for the vehicles entering the camera's field-of-view and detect anomalous motions. (ii) Visual Saliency Prediction using Deep Convolutional Neural Networks: We propose a deep fully convolutional neural network (CNN) - DeepFix, for accurately predicting eye fixations in the form of saliency maps. Unlike classical works which characterize the saliency map using various hand-crafted features, our model automatically learns features in a hierarchical fashion and predicts saliency map in an end-to-end manner. DeepFix is designed to capture visual semantics at multiple scales while taking global context into account. Generally, fully convolutional nets are spatially invariant which prevents them from modeling location dependent patterns (e.g. centre-bias). Our network overcomes this limitation by incorporating a novel Location Biased Convolutional layer. We experimentally show that our network outperforms other recent approaches by a significant margin. In general, human eye fixations correlate with locations of salient objects in the scene. However, only a handful of approaches have attempted to simultaneously address these related aspects of eye fixations and object saliency. In our work, we also propose a deep convolutional network capable of simultaneously predicting eye fixations and segmenting salient objects in a unified framework. We design the initial network layers, shared between both the tasks, such that they capture the global contextual aspects of saliency, while the deeper layers of the network address task specific aspects. Our network shows a significant improvement over the current state-of-the-art for both eye fixation prediction and salient object segmentation across a number of challenging datasets.