Procedural Tree Generation: How to efficiently predict branching structures from foliage?

The objective of this project is to train a model that transforms a tree with its foliage into only its branch structure. This is achieved by employing machine-learning techniques, specifically Generative Adverserial Networks (GANs). By utilizing the proposed method, a predictive model is built that automatically minimizes its own error function...

Can we extract a relevant, available, and self-contained core of the Maven ecosystem?: Extracting the pillars of the community, and their dependencies.

The Maven ecosystem, with an emphasis on Maven Central, contains a plethora of toy-projects. This paper addresses this problem by formulating a core containing the pillars of the Maven ecosystem, such that it can be exploited for research concerning li- brary quality. The construction of said core is done by analyzing the availability, relevance...

What are the types of projects that Scratch users create?: How do different types of features relate to the project type?

Scratch is a block-based programming language. It is designed to be simple and syntax error avoidant. This makes Scratch an accessible platform for cultivating coding skills. Many young learners are taught about different programming skills using various project types as examples. For instance, games are used as an engagement tool, and various...

Data Driven Approximations Of PDEs: On Robustness of Reduced Order Mappings between Function Spaces Against Noise

This paper presents a comprehensive exploration of a novel method combining Principal Component Analysis (PCA) and Neural Networks (NN) to efficiently solve Partial Differential Equations (PDEs), a fundamental challenge in modeling a wide range of real-world phenomena. Our research extends the work of Bhattacharya et al. by focusing on PCA for...

Procedural Tree Generation: Inverse Modelling of 2D Trees using Graph Neural Networks

The most established and widely used methods for analysing tree images for tasks such as geometry analysis, segmentation and classification often rely on pixels. In this paper, the applicability of analyzing tree geometry based on a graph representation rather than a pixel-based approach is pursued. To do so, 2D renders of different species of...

Prevalence of non-monotonicity in learning curves

Learning curves are useful to determine the amount of data needed for a certain performance. The conventional belief is that increasing the amount of data improves performance. However, recent work challenges this assumption, and shows nonmonotonic behaviors of certain learners on certain problems. This paper presents a new approach for...

Short-term interactions between Staphylococcus aureus and Pseudomonas aeruginosa: BEP Report

Staphylococcus aureus and Pseudomonas aeruginosa are two species of bacteria that are involved in numerous conditions, including lung infections and chronic wound infections. The aim of this project was to study the short-term interactions that occur when P. aeruginosa first encounters an established S. aureus colony, which it then seeks to...

Analysis on the Vulnerability of Multi-Server Federated Learning Against Model Poisoning Attacks

Abstract— Federated Learning (FL) makes it possible for a network of clients to jointly train a machine learning model, while also keeping the training data private. There are several approaches when designing a FL network and while most existing research is focused on a single-server design, new and promising variations are arising that make...

Exploring the Impact of Single-Character Attacks in Federated Learning Language Classification: Introducing the Novel Single-Character Strike

Federated learning (FL) is a privacy preserving machine learning approach which allows a machine learning model to be trained in a distributed fashion without ever sharing user data. Due to the large amount of valuable text and voice data stored on end-user devices, this approach works particularly well for natural language processing (NLP)...

Deciphering Learning Curve Characteristics via K-Means Clustering of Curve Model Parameters

Learning curves illustrate the relationship between the performance of learning algorithms and the increasing volume of training data [1, 2, 3]. While the concept of learning curves is well-established, clustering these curves based on fitting parameters remains an underexplored area. Our study delves into this domain and leverages the Learning...

SZFitter: Predicting DESHIMA 2.0 observations of the thermal Sunyaev-Zel’dovich effect

Galaxy clusters are some of the largest known structures in the universe. Studying them observationally and theoretically can provide a lot of information on how these clusters form and are structured. One way to study them is through the so-called Sunyaev-Zel’dovich (SZ) effect, which is an interaction between the cosmic...

Exploring the effectivity of AND/OR landmark extraction on modern planning domains

This paper explores the application of landmark-based planning algorithms, specifically focusing on AND/OR landmark extraction methods. Drawing from classical planning principles and recent advancements, we investigate the effectiveness of landmark extraction in guiding the search for solutions to planning problems. Our research questions center...

Een brug naar circulariteit: Een onderzoek naar de implementatie van kaders voor circulair asset management van verkeersbruggen

Calculating magnetic fields around superconductors using conformal mappings

Superconductors expel magnetic fields, a phenomenon known as the Meissner effect. This effect makes it notoriously difficult to predict the magnetic field around a superconductor. One successful way of calculating these fields is through conformal mappings; coordinate transformations that preserve Maxwell's equations of magnetic fields in free...

Energy Storage and Fluctuations in a Central-Spin Quantum Battery with Nearest-Neighbour Interactions

In this thesis the stored energy and its fluctuations of a central spin battery with nearest-<br/>neighbour interactions between the battery spins are investigated. Using analytical ex-<br/>pressions, it is shown that for 2 battery spins and equal strength in the flip-flop interaction<br/>g and nearest-neighbour interaction J, the fluctuations...

What is the best map projection?

This bachelor's thesis explores the realm of map projections, aiming to establish a systematic approach for evaluating and ranking them based on angular and area distortions. Beginning with a definition of map projections, the study progresses to quantify the distortions that occur when projecting a spherical surface onto a flat one. Using these...