This thesis focuses on accelerating the polishing stage of the Flye genome assemblers. Flye is a de novo assembler designed for long reads produced by modern sequencing technologies, excelling in handling large genomes with high accuracy and efficiency. A crucial component of the assembly process is the polishing stage, which refines the draft assembly to correct errors and improve overall accuracy. However, this stage is computationally intensive and time-consuming, presenting a significant bottleneck in genome assembly workflows.

To address this, a novel multi-threading architecture is introduced, significantly reducing mutex contention by minimizing the use and acquisition times of mutexes within the bubble processor. Additionally, advanced vectorization techniques using AVX (Advanced Vector Extensions) instructions are incorporated to process multiple reads simultaneously. These optimizations effectively parallelize the polishing process and exploit modern CPU capabilities for enhanced performance.

Benchmarking the enhanced polishing stage on both bacteria and human genome datasets demonstrates a substantial improvement in processing time. For the bacteria dataset, the error correction process achieves speedups of 3.0x and 4.3x using AVX2 and AVX-512 instructions running on one core, respectively. The process realizes speedups of 2.6x and 2.7x with AVX2 and AVX-512 running on eight cores. For the human genome dataset, the process demonstrates a speedup of 4.0x when handling 1 million bubbles running on one core, while 32 cores yield a speedup of 2.3x for the same dataset. Applying AVX2 to the complete dataset on 64 cores results in a speedup of 1.4x. This acceleration not only reduces computational costs but also expedites the overall genome assembly process, making it more feasible for large-scale and time-sensitive genomic studies. The implementation is available on GitHub.

Positive end-expiratory pressure (PEEP) is one of the components of mechanical ventilation treatment for patients with acute respiratory distress syndrome (ARDS). Correct PEEP level can reduce additional lung injuries sustained during the hospitalisation, significantly increasing patients' chances for survival. In this paper, we focus on estimating the difference in patient mortality when assigned high or low PEEP level. We look at three machine learning models specifically designed for such tasks: S-learner, T-learner and causal forest. Through a series of experiments, we determine their best use cases based on simulated data and measure their performance on a real-life dataset - MIMIC-IV. In our analysis, we find that after tuning the hyperparameters, the models can, to some degree, make valuable predictions and reveal heterogeneity in the treatment effect. However, when evaluated on a separate dataset, the models' performance drops significantly.

In the intensive care unit (ICU), optimizing mechanical ventilation settings, particularly the positive end-expiratory pressure (PEEP), is crucial for patient survival. This paper investigates the application of neural network-based machine learning methods to personalize PEEP settings in the ICU, aiming to improve patient survival outcomes. The research focuses on two specific algorithms, TARNet and CFR, evaluating their ability to estimate individualized treatment effects of lower versus higher PEEP regimes. The study is structured into three phases: controlled simulations, application to the MIMIC-IV dataset, and validation using a randomized control trial dataset. TARNet and CFR showed potential for estimating the individualized treatment effects but required large datasets for optimal performance. In the case where limited data is available, these models are upstaged by simpler learners, such as the S- and T-learners. The study concludes that while neural network-based methods hold promise for personalizing ICU treatment, their efficacy is heavily influenced by data availability and quality.

Mechanical ventilation with positive end-expiratory pressure (PEEP) is a critical intervention for patients in intensive care units (ICUs) with acute respiratory failure. Identifying the optimal PEEP level is challenging due to conflicting evidence from studies comparing low and high PEEP regimes. This research explores machine learning methods for estimating individualized treatment effects (ITE) in ICU patients on different PEEP levels using the observational MIMIC-IV dataset. Various conditional average treatment effect (CATE) estimators, including S-, T-, and DR-learners, are applied to control for confounders and identify PEEP effects on patient subgroups. This research aims to compare the performance of the aforementioned CATE estimators, with a focus on the doubly-robust (DR) learner, and determine which one is best suited for causal inference in this context. The DR-learner offers increased resilience to model errors since it integrates two models. Simulations using mean squared error (MSE) show the DR-learner performs well with confounded data and differing linear response functions between control and treatment groups. However, when looking at the performance on the MIMIC-IV dataset, the predictions are unstable, failing to reliably identify the optimal PEEP for increasing patient survival. This trend is also observed in a randomized controlled trial (RCT) dataset, with the area under the Qini curve (AUQC) close to zero, indicating difficulties in identifying the effects of PEEP settings. Despite promising simulation results, real-world application shows limitations in these machine learning methods for optimal PEEP identification.

This research investigates the use of Causal Multi-task Gaussian Process (CMGP) for estimating the individualized treatment effect (ITE) of low versus high Positive End-Expiratory Pressure (PEEP) regimes on ICU patients requiring mechanical ventilation. The study addresses the complexities of determining ITE due to the inability to observe counterfactual outcomes and the confounding bias in observational studies. By employing Conditional Average Treatment Effect (CATE) estimators, such as S-Learner, T-Learner, and CMGP, the research evaluates the impact of different PEEP settings on patient survival across varied patient characteristics. The precision of these estimators is assessed using simulated data, real-world observational data from the MIMIC-IV dataset, and an external RCT dataset. The findings of this study are inconclusive, highlighting the need for further research to refine these methods and explore larger, more balanced datasets.

Mechanical ventilation is a vital supportive measure for patients with acute respiratory distress syndrome (ARDS) in the intensive care unit. An important setting in the ventilator is the positive end-expiratory pressure (PEEP), which can reduce lung stress but may also cause harmful side effects. This research investigates the personalization of PEEP settings based on patient characteristics using three meta-learning algorithms (S-, T-, and X-learner) to estimate the conditional average treatment effect. Additionally, the hypothesis that the X-learner performs particularly well under a significant imbalance in patient numbers between treatment groups is tested. Results show that the X-learner slightly outperforms the S- and T-learners in terms of mean squared error under various unbalanced conditions in simulated data. However, the overall ability of these meta-learners to identify patients benefiting from high PEEP remains inconclusive. When using gradient boosted trees or random forest as base models, cumulative gain curves on MIMIC-IV data indicate potential overfitting. While the X-learner performs somewhat better on this data, the low area under the curve scores suggests a minimal distinction between high and low PEEP groups. External validation with data from a randomized control confirms that the models do not effectively distinguish between treatment groups. These findings suggest that further investigation with more complex models and real-world data is needed to validate the potential of meta-learning algorithms in personalizing PEEP settings for ARDS patients.

There is increasingly more expensive maintenance that needs to be performed on the Dutch railway network. Good maintenance schedules reduce costs, minimise hindrance to passenger and freight travel, and follow restrictions imposed by available resources, legislation and other agreements. The railway maintainer has modelled this maintenance scheduling problem, among others, on an annual level. This problem definition is very precise with different conflicting non-linear constraints and cost parts. The demands of the solving method depend on the phase of the scheduling process. In early phases, low runtime is most important, whereas best solution quality outweighs this runtime when the maintenance work is more finalised. In 2019, a simple greedy algorithm found good solutions fast, and a hybrid greedy-evolutionary algorithm was developed that resulted in the best schedules. However, since then, the problem definition has been made more realistic and thus complex. Therefore, this hybrid greedy-evolutionary algorithm is no longer feasible, and creating a maintenance schedule takes significantly longer than before. Research is necessary to better understand the impact of the more realistic model, and to once more have a good trade-off between solving time and solution quality available for the schedulers. In this thesis, we aim to achieve this by improving different aspects of the problem and solving methods. Most experiments were done with the maintenance schedule of 2024, and results were verified on the years of 2023 and 2025. First, general problem analysis and implementation improvements reduced the runtime from around twenty-four hours to three hours with the greedy algorithm. Then, approximations were applied in the passenger hinder to further reduce the runtime by around half with a negligible negative impact on the solution quality. Due to these speed-ups, it was possible to use more elaborate solving methods. New experimental results showed that the greedy algorithm still finds solutions fast. The hybrid greedy-evolutionary algorithm found better quality schedules, but required more runtime. Furthermore, a novel solving method with look-aheads was proposed, which showed some potential for cost reductions, but was dominated by the hybrid algorithm. Every algorithm uses the greedy heuristic as a subroutine. Results showed the importance of finding the right order for greedily scheduling the requests. A proposed new order function improved the quality of the resulting maintenance schedules even further. To conclude, the increase in complexity of the problem definition in recent years has made solving more difficult. To still find good solutions in a similar time, a better performing greedy heuristic was necessary. By applying different runtime optimisations to the objective evaluation and improving the solution quality of the greedy heuristic, a good trade-off between runtime and solution quality, using different solving methods, was realised for creating annual maintenance schedules for the Dutch railway network.

Living in a world where every single electronic device is online and interconnected, privacy is a growing concern. Finding the threshold where audio is unintelligible to transcription software is crucial when everything that we say can be recorded. Even if Automated Speech Recognition (ASR) is used in tools, such as Siri or Alexa, designed to ease daily tasks, it could also be used in malicious manners. ASR technology has not been around for too long and like any other new piece of technology, it still has many aspects that have not been looked into and are unknown to the public. This research paper addresses this knowledge gap by examining how sample frequency reduction affects word detection using current well-known transcription software technology such as Google’s speech recognition software and Kaldi’s toolkit. The behavior and performance of these two software pieces have been analyzed for different sample frequencies in the range from 300Hz to 44,1kHz.

The interactions between human and machines are now common in our daily life. The audio data of human communication is a rich source of information, but it is con- sidered privacy-invasive for machines to listen to it. By reducing sampling frequency, it is possible to preserve privacy by making conversation unclear while still being possible to detect if someone is speaking or not. The topic of this paper is to investigate how low sampled frequency audio data hinders the detection of speech. To detect speaking, voice activity detection has been applied, which is a technology in the signal process- ing field that identifies which short segments of audio contain speakings. Two types of state-of-art voice activity detector(VAD) were used for this experiment including a supervised (pyannote) and two unsupervised (rVAD pitch and flatness mode) methods. As a result, the unsupervised methods outperformed the supervised model, where rVAD pitch mode has resulted in the best performance out of all three. More specifically, the unsupervised VAD’s performance became lower as the sample rates decreased while the supervised VAD did not work well at higher sample frequency. rVAD pitch mode at sample rates of 8000Hz or higher was possible to perform at the almost same level as a state-of-art supervised VAD that is trained in a similar data set. Furthermore, it was able to perform as well as a modern unsupervised VAD at 2000Hz or higher sample frequencies. At the sample rate of 1250Hz or lower, any VAD was not able to perform at the same level as a state-of-art VAD. Regarding the privacy aspect, it is observed that human ears detect speaking better than computers, where humans can understand parts or all of the contents of speaking at 2000Hz or higher, which infers that current technology is not enough to detect speech from downsampled privacy-preserving audio. However, there is still a need for further research to verify the effects of the training set and its sample frequencies for the supervised method and also proper scientific so- cial experiments to test the ability of humans of speech detection for reduced sampled audio.

With the development of new technologies and approaches in the field of social signal processing, concerns regarding privacy around recording conversations have arised. One of the main ways to preserve the privacy of the speakers in recorded conversations consists of decimating said conversations, which consists of reducing the sample frequency and the frequency bandwidth of the audio. This theoretically makes the verbal content of the conversation (the words themselves) unintelligible, while still preserving other useful non-verbal social cues such as laughter, pitch modulation and frequency of speech, amongst others. However, this has not been experimentally verified. This research paper addresses this knowledge gap by exploring the performance of laughter detection machine learning models with decimated audio. An existing pre-trained state-of-the-art laughter detection model was employed and its performance was evaluated for a dataset of decimated audio with sample frequencies ranging from 300Hz to 44100Hz.

With widespread use of advanced technology for the recording, storing and sharing of social interactions, protecting privacy of people has been a growing concern. This paper zooms in on the collection of spoken audio with regard for the privacy of recorded individuals. Recently efforts have been made to collect audio at a low sampling rate to obfuscate spoken words in the recorded audio, such that conversations are kept private. This research investigates whether it is possible to upsample this low-resolution audio, using an existing super-resolution model, in order to reveal parts of the previously obfuscated conversations. The performance of the model is measured in terms of the word error rate of automatically generated transcriptions of the upsampled audio. It turns out that it is possible to significantly increase the intelligibility of low resolution privacy-sensitive audio by upsampling. Though the use of the super-resolution model seems to be limited when it comes to revealing significant parts of conversations.