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van Geffen, Hunter (author)
t-distributed stochastic neighbor embedding (t-SNE) is a dimensionality reduction technique able to embed high-dimensional data into 2 or 3 dimensions for the purpose of visualization, and has proven to be useful in various applications, such as single-cell analysis. Normally t-SNE embeds into Euclidean space, but recent work shows that...
master thesis 2022
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Goedhart, Christof (author)
Commonly, when researchers are figuring out the effect of a putative cause, additional variables influence the cause and the effect. These are called confounders, and they obfuscate causal relationships. Inverse Probability Weighting is a method that can be applied to remove confounding and show a causal effect. This study aims to determine if...
bachelor thesis 2022
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Avgousti, Stelios (author)
Strategy games could be considered as an amazing playground for using Causal inference methods. The complex nature of the data and the built-in randomization help with testing causal inference in a scenario where in reality it would be hard and expensive. Randomized data in coherence with causal inference is well documented and tested, but not...
bachelor thesis 2022
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Liang, Hendy (author)
The front-door adjustment is a causal inference method with which it is possible to determine the causal effect of applying a treatment given a setting which satisfies the front-door criterion. This involves having a mediator through which all the causal effect flows from treatment to outcome. The front-door adjustment adjusts for confounders...
bachelor thesis 2022
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Toksoy, Noyan (author)
Dota 2 is one of the most popular MOBA (Multiplayer Online Battle Arena) games being played today. A Dota 2 match is played by two teams of 5 players. The main goal of the game is to destroy the opposing team’s Ancient tower, the team that manages to do so, wins the game. An essential part of a match is the hero selection phase before it starts....
bachelor thesis 2022
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van Ulsen, Hidde (author)
Image registration is a fundamental requirement for many medical applications. In recent years, deep learning approaches for registration have shown to be a promising alternative to conventional methods. However, most learning based methods do not consider the different physical properties of various tissues, which can result in unrealistic...
master thesis 2022
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van der Lugt, Mart (author)
Since the start of the SARS-CoV-2 pandemic, the monitoring of SARS-CoV-2 by way of viral RNA sequencing of wastewater has proven to be an efficient and effective way of estimating COVID-19 cases in population groups. A recently developed pipeline also enables us to estimate SARS-CoV-2 variant abundance using viral samples from wastewater. This...
bachelor thesis 2022
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Nika, Ioanna (author)
Lineage abundance estimation of SARS-CoV-2 in wastewater is a technique that aims to monitor the lineage prevalence in communities and help contain the COVID-19 pandemic. Lineages are collections of closely related mutants of a virus. It is suggested that the genome sequences of lineages differ across the globe due to random mutations or...
bachelor thesis 2022
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Kalia, Yash (author)
Monitoring of SARS-CoV-2 variants is crucial to efforts in combating the COVID-19 pandemic. Lineage level abundance estimates for SARS-CoV-2 can be obtained from viral material present in domestic wastewater. The abundance predictions can be made at different levels of granularity-individual lineage level(high granularity) or variant level(low...
bachelor thesis 2022
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Anton, Matei (author)
During a viral infection, we expel remnants of the virus. This makes it possible to conduct wastewater analysis which aid in the efforts to track the evolution of the current Covid-19 pandemic. It has been shown that by repurposing the kallisto algorithm, the abundance of SARS-CoV-2 variants in wastewater samples can be estimated. Since this is...
bachelor thesis 2022
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Baas, Berend (author)
Existing work in shape editing applications using deep learning has primarily focused on shape interpolation.<br/>We propose a pair of techniques that utilize the latent space of deformation networks to provide control schemes for semantic shape editing applications.<br/><br/>The first technique presented utilizes linear directions as...
master thesis 2021
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Biesheuvel, Julian (author)
Yes, convolutional neural networks are domain-invariant, albeit to some limited extent. We explored the performance impact of domain shift for convolutional neural networks. We did this by designing new synthetic tasks, for which the network’s task was to map images to their mean, median, standard deviation, and variance pixel intensities. We...
bachelor thesis 2021
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Lamon, Julien (author)
With an expectation of 8.3 trillion photos stored in 2021 [1], convolutional neural networks (CNN) are beginning to be preeminent in the field of image recognition. However, with this deep neural network (DNN) still being seen as a black box, it is hard to fully employ its capabilities. A need to tune hyperparameters is required to have a robust...
bachelor thesis 2021
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Thakoersingh, Ratish (author)
This research provides an overview on how training Convolutional Neural Networks (CNNs) on imbalanced datasets affect the performance of the CNNs. Datasets could be imbalanced as a result of several reasons. There are for example naturally less samples of rare diseases. Since the network is trained less on those instances, this might lead to...
bachelor thesis 2021
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Joshi, Ayush (author)
Supply chains are vital to the global economy, and so, increasing efficiency in supply chain management is of utmost importance. Modernizing technology has allowed for various uses of machine learning to be possible in several aspects of supply chains, specifically in demand forecasting with prediction models, and customer relations with chat...
bachelor thesis 2021
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Ştefan, Andrei (author)
Collaboration is a key technique in modern supply chains, both for building trust with other companies, but also for reducing costs or maximizing profits. It is an approach which provides all involved parties with benefits that they could not possibly achieve on their own. Collaboration, however, requires abundant information, including...
bachelor thesis 2021
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Stepanov, Paul (author)
The following paper aims to investigate what and how are the main privacy-preserving methods applied in the blockchain-based supply chain industry scenario, with a primary focus on the food sector. Recent developments, such as the exordium of cryptocurrencies to increment efficiency, are withal addressed. Overviews of key use cases in industry...
bachelor thesis 2021
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Romanov, Danila (author)
Despite evidence that collaborating in the supply chain can reduce inefficiency and result in mutual gain, parties do not wish to collaborate if they have to share their private proprietary information. The main reason for their privacy concern is that the party does not want to lose their competitive advantage by giving away company secrets....
bachelor thesis 2021
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van Aanholt, Levi (author)
Procedural content generation (PCG) for architecture is widely used in a variety of digital media, most notably in games. However, such methods are often limited in their expressive range, and require considerable technical knowledge to create non-trivial architectural structures. We present a novel tile-based PCG approach for generating...
master thesis 2020
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Rietveld, T.M. (author)
Continuous affective self-reports are intrusive and expensive to acquire, forcing researchers to use alternative labels for the construction of their predictive models. The most predominantly used labels in literature are continuous perceived affective labels obtained using external annotators. However an increasing body of research indicates...
master thesis 2020
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