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This thesis gives an introduction to the basic formalism of one-dimensional supersymmetric quantum mechanics. The factorization of a Hamiltonian is used to create a supersymmetric partner Hamiltonian. The connections between the energy spectra and wave functions of these partner Hamiltonians are deduced and examined for the case of broken and unbroken supersymmetry. An extension to hierarchies of
We have studied left-right-symmetric (LR) model building in two specific instances: the Minimal Left-Right-Symmetric Model (MLRM), with gauge group SU(3)_C × SU(2)_L × SU(2)_R × U(1)_{B-L} and parity as the LR symmetry; and a non-supersymmetric, trinified theory, with gauge structure SU(3)_L × SU(3)_R × SU(3)_C × Z_3 and an additional, novel, SU(3) family symmetry. For the MLRM, we have rederive
In recent years, the BaBar, Belle and LHCb experiments have observed an excess of $B\to D^{(*)}\tau\nu$ decays compared to Standard Model predictions. In this thesis, we investigate if it is possible to explain this excess with a two Higgs doublet model using the Froggatt-Nielsen framework. Two Higgs doublet models allow new decays at tree-level and the Froggatt-Nielsen mechanism gives an explanat
In this master thesis we consider some phenomenological aspects of two-Higgs-doublet models, both elementary and composite ones. The emphasis is on calculating and understanding first order phase transitions in such models, constrained to be in agreement with existing collider data on the Higgs sector. We also consider the contribution to the gravitational wave background of the universe that woul
5 februari 2026 15:15 till 17:00 | Open lecture with Associate Professor Wako Asato, Kyoto University Asian welfare regimes have historically taken the form of liberal familialist welfare regimes characterized by a heavy reliance on migrant care workers. While this arrangement is often interpreted as a mere extension of familialism, it is more accurately understood as a process through which Confu
https://www.lu.se/evenemang/global-care-chains-and-migration-east-asia-pain-and-gain-temporariness - 2026-10-06
Artificial Neural Networks (ANNs) are widely used information processing algorithms based roughly on biological neural networks. These networks can be trained to find complex patterns in datasets and to produce certain output signals given a set of input signals. A key element of ANNs are their so-called activation functions, which control the signal strengths between the artificial neurons in a n
Genetic algorithms are complex constructs often used as heuristic search methods in contexts ranging from combinatorial optimisation to in silico evolution. They draw inspiration from the principles of biological evolution by utilizing the concepts of mutation, reproduction and selection in order to improve a population of solutions. The solutions are often represented as abstract data sequences,
In this project, we compare two error functions for the purpose of training artificial neural networks on heavily censored data (data where key information is missing). J. Kalderstam et al. has shown that it is possible to train artificial neural networks directly on Harrell's C index \cite{Harrell} using genetic algorithms. He has also investigated the possibilities of improving the performa
The growth of tissues and organs in plants is governed by the morphogen auxin coupled with the membrane protein PIN, which together generate patterns that guide development. Systems of this kind have been studied extensively in experiments and computational system biology models. This thesis builds on that work by introducing a stochastic version of these models to examine differences between stoc
Artificial neural networks have been used to solve different problems, one being survival analysis of medical data. For survival analysis, the main interest is often how samples become sorted by their outputs, which makes survival analysis a rank based problem. A rank based error function lacks a gradient, which makes gradient descent based training difficult. A genetic training algorithm, based o
This paper proposes and investigates a Bayesian relation between optimal L2 regularization strengths and the number of training patterns and hidden nodes used for an artificial neural network. The results support the proposed dependence for number of training patterns, while the dependence on hidden architecture was less clear. Finally, applying different regularization strengths on different laye
During the last few years, crowding effects on the physics of proteins has become an increasingly popular topic of research. This is is because most biological processes involving proteins naturally take place in a crowded environment, e.g. in the cellular environment where macromolecules may occupy 30% of the volume. One such biological process would be the formation of amyloid aggregates, which
Direct coupling analysis (DCA) models correlations in sets of related (homologous) protein sequences using a Potts-like spin model ansatz. From the couplings of the Potts model, derived by inverse statistical mechanics, residue-pair contacts in the 3D structure of the protein are predicted. In this thesis, this approach is applied to structures from the HP model on a square lattice. All HP sequenc
The recently introduced capsule networks have already shown much success on image classification tasks. In this thesis, capsule networks are described and applied to a binary classification task, namely that of classifying hotspots from bone scintigraphy scans. The performance of capsule networks on this task is compared to that of convolutional neural networks. The results indicate that there is
Convolutional Neural Networks (CNNs) and pre-trained word embeddings have revolutionized the field of Natural Language Processing (NLP) during the last years. In this project, CNNs are used on top of the Word2Vec word representation for a sentence classification task on medical research articles. Both individual networks for each category as well as a combined classification network are optimized
