@phdthesis{15338, author = {Ernst, Doris}, title = {{The world of Pokemon}}, year = {2026}, } @phdthesis{14711, abstract = {In nature, different species find their niche in a range of environments, each with its unique characteristics. While some thrive in uniform (homogeneous) landscapes where environmental conditions stay relatively consistent across space, others traverse the complexities of spatially heterogeneous terrains. Comprehending how species are distributed and how they interact within these landscapes holds the key to gaining insights into their evolutionary dynamics while also informing conservation and management strategies. For species inhabiting heterogeneous landscapes, when the rate of dispersal is low compared to spatial fluctuations in selection pressure, localized adaptations may emerge. Such adaptation in response to varying selection strengths plays an important role in the persistence of populations in our rapidly changing world. Hence, species in nature are continuously in a struggle to adapt to local environmental conditions, to ensure their continued survival. Natural populations can often adapt in time scales short enough for evolutionary changes to influence ecological dynamics and vice versa, thereby creating a feedback between evolution and demography. The analysis of this feedback and the relative contributions of gene flow, demography, drift, and natural selection to genetic variation and differentiation has remained a recurring theme in evolutionary biology. Nevertheless, the effective role of these forces in maintaining variation and shaping patterns of diversity is not fully understood. Even in homogeneous environments devoid of local adaptations, such understanding remains elusive. Understanding this feedback is crucial, for example in determining the conditions under which extinction risk can be mitigated in peripheral populations subject to deleterious mutation accumulation at the edges of species’ ranges as well as in highly fragmented populations. In this thesis we explore both uniform and spatially heterogeneous metapopulations, investigating and providing theoretical insights into the dynamics of local adaptation in the latter and examining the dynamics of load and extinction as well as the impact of joint ecological and evolutionary (eco-evolutionary) dynamics in the former. The thesis is divided into 5 chapters. Chapter 1 provides a general introduction into the subject matter, clarifying concepts and ideas used throughout the thesis. In chapter 2, we explore how fast a species distributed across a heterogeneous landscape adapts to changing conditions marked by alterations in carrying capacity, selection pressure, and migration rate. In chapter 3, we investigate how migration selection and drift influences adaptation and the maintenance of variation in a metapopulation with three habitats, an extension of previous models of adaptation in two habitats. We further develop analytical approximations for the critical threshold required for polymorphism to persist. The focus of chapter 4 of the thesis is on understanding the interplay between ecology and evolution as coupled processes. We investigate how eco-evolutionary feedback between migration, selection, drift, and demography influences eco-evolutionary outcomes in marginal populations subject to deleterious mutation accumulation. Using simulations as well as theoretical approximations of the coupled dynamics of population size and allele frequency, we analyze how gene flow from a large mainland source influences genetic load and population size on an island (i.e., in a marginal population) under genetically realistic assumptions. Analyses of this sort are important because small isolated populations, are repeatedly affected by complex interactions between ecological and evolutionary processes, which can lead to their death. Understanding these interactions can therefore provide an insight into the conditions under which extinction risk can be mitigated in peripheral populations thus, contributing to conservation and restoration efforts. Chapter 5 extends the analysis in chapter 4 to consider the dynamics of load (due to deleterious mutation accumulation) and extinction risk in a metapopulation. We explore the role of gene flow, selection, and dominance on load and extinction risk and further pinpoint critical thresholds required for metapopulation persistence. Overall this research contributes to our understanding of ecological and evolutionary mechanisms that shape species’ persistence in fragmented landscapes, a crucial foundation for successful conservation efforts and biodiversity management.}, author = {Olusanya, Oluwafunmilola O}, issn = {2663 - 337X}, pages = {183}, publisher = {Institute of Science and Technology Austria}, title = {{Local adaptation, genetic load and extinction in metapopulations}}, doi = {10.15479/at:ista:14711}, year = {2024}, } @phdthesis{14821, author = {Chiossi, Heloisa}, issn = {2663 - 337X}, pages = {89}, publisher = {Institute of Science and Technology Austria}, title = {{Adaptive hierarchical representations in the hippocampus}}, doi = {10.15479/at:ista:14821}, year = {2024}, } @phdthesis{15020, abstract = {This thesis consists of four distinct pieces of work within theoretical biology, with two themes in common: the concept of optimization in biological systems, and the use of information-theoretic tools to quantify biological stochasticity and statistical uncertainty. Chapter 2 develops a statistical framework for studying biological systems which we believe to be optimized for a particular utility function, such as retinal neurons conveying information about visual stimuli. We formalize such beliefs as maximum-entropy Bayesian priors, constrained by the expected utility. We explore how such priors aid inference of system parameters with limited data and enable optimality hypothesis testing: is the utility higher than by chance? Chapter 3 examines the ultimate biological optimization process: evolution by natural selection. As some individuals survive and reproduce more successfully than others, populations evolve towards fitter genotypes and phenotypes. We formalize this as accumulation of genetic information, and use population genetics theory to study how much such information can be accumulated per generation and maintained in the face of random mutation and genetic drift. We identify the population size and fitness variance as the key quantities that control information accumulation and maintenance. Chapter 4 reuses the concept of genetic information from Chapter 3, but from a different perspective: we ask how much genetic information organisms actually need, in particular in the context of gene regulation. For example, how much information is needed to bind transcription factors at correct locations within the genome? Population genetics provides us with a refined answer: with an increasing population size, populations achieve higher fitness by maintaining more genetic information. Moreover, regulatory parameters experience selection pressure to optimize the fitness-information trade-off, i.e. minimize the information needed for a given fitness. This provides an evolutionary derivation of the optimization priors introduced in Chapter 2. Chapter 5 proves an upper bound on mutual information between a signal and a communication channel output (such as neural activity). Mutual information is an important utility measure for biological systems, but its practical use can be difficult due to the large dimensionality of many biological channels. Sometimes, a lower bound on mutual information is computed by replacing the high-dimensional channel outputs with decodes (signal estimates). Our result provides a corresponding upper bound, provided that the decodes are the maximum posterior estimates of the signal.}, author = {Hledik, Michal}, issn = {2663 - 337X}, keywords = {Theoretical biology, Optimality, Evolution, Information}, pages = {158}, publisher = {Institute of Science and Technology Austria}, title = {{Genetic information and biological optimization}}, doi = {10.15479/at:ista:15020}, year = {2024}, } @phdthesis{14323, abstract = {Morphogens are signaling molecules that are known for their prominent role in pattern formation within developing tissues. In addition to patterning, morphogens also control tissue growth. However, the underlying mechanisms are poorly understood. We studied the role of morphogens in regulating tissue growth in the developing vertebrate neural tube. In this system, opposing morphogen gradients of Shh and BMP establish the dorsoventral pattern of neural progenitor domains. Perturbations in these morphogen pathways result in alterations in tissue growth and cell cycle progression, however, it has been unclear what cellular process is affected. To address this, we analysed the rates of cell proliferation and cell death in mouse mutants in which signaling is perturbed, as well as in chick neural plate explants exposed to defined concentrations of signaling activators or inhibitors. Our results indicated that the rate of cell proliferation was not altered in these assays. By contrast, both the Shh and BMP signaling pathways had profound effects on neural progenitor survival. Our results indicate that these pathways synergise to promote cell survival within neural progenitors. Consistent with this, we found that progenitors within the intermediate region of the neural tube, where the combined levels of Shh and BMP are the lowest, are most prone to cell death when signaling activity is inhibited. In addition, we found that downregulation of Shh results in increased apoptosis within the roof plate, which is the dorsal source of BMP ligand production. This revealed a cross-interaction between the Shh and BMP morphogen signaling pathways that may be relevant for understanding how gradients scale in neural tubes with different overall sizes. We further studied the mechanism acting downstream of Shh in cell survival regulation using genetic and genomic approaches. We propose that Shh transcriptionally regulates a non-canonical apoptotic pathway. Altogether, our study points to a novel role of opposing morphogen gradients in tissue size regulation and provides new insights into complex interactions between Shh and BMP signaling gradients in the neural tube.}, author = {Kuzmicz-Kowalska, Katarzyna}, issn = {2663 - 337X}, pages = {151}, publisher = {Institute of Science and Technology Austria}, title = {{Regulation of neural progenitor survival by Shh and BMP in the developing spinal cord}}, doi = {10.15479/at:ista:14323}, year = {2023}, } @phdthesis{14374, abstract = {Superconductivity has many important applications ranging from levitating trains over qubits to MRI scanners. The phenomenon is successfully modeled by Bardeen-Cooper-Schrieffer (BCS) theory. From a mathematical perspective, BCS theory has been studied extensively for systems without boundary. However, little is known in the presence of boundaries. With the help of numerical methods physicists observed that the critical temperature may increase in the presence of a boundary. The goal of this thesis is to understand the influence of boundaries on the critical temperature in BCS theory and to give a first rigorous justification of these observations. On the way, we also study two-body Schrödinger operators on domains with boundaries and prove additional results for superconductors without boundary. BCS theory is based on a non-linear functional, where the minimizer indicates whether the system is superconducting or in the normal, non-superconducting state. By considering the Hessian of the BCS functional at the normal state, one can analyze whether the normal state is possibly a minimum of the BCS functional and estimate the critical temperature. The Hessian turns out to be a linear operator resembling a Schrödinger operator for two interacting particles, but with more complicated kinetic energy. As a first step, we study the two-body Schrödinger operator in the presence of boundaries. For Neumann boundary conditions, we prove that the addition of a boundary can create new eigenvalues, which correspond to the two particles forming a bound state close to the boundary. Second, we need to understand superconductivity in the translation invariant setting. While in three dimensions this has been extensively studied, there is no mathematical literature for the one and two dimensional cases. In dimensions one and two, we compute the weak coupling asymptotics of the critical temperature and the energy gap in the translation invariant setting. We also prove that their ratio is independent of the microscopic details of the model in the weak coupling limit; this property is referred to as universality. In the third part, we study the critical temperature of superconductors in the presence of boundaries. We start by considering the one-dimensional case of a half-line with contact interaction. Then, we generalize the results to generic interactions and half-spaces in one, two and three dimensions. Finally, we compare the critical temperature of a quarter space in two dimensions to the critical temperatures of a half-space and of the full space.}, author = {Roos, Barbara}, issn = {2663 - 337X}, pages = {206}, publisher = {Institute of Science and Technology Austria}, title = {{Boundary superconductivity in BCS theory}}, doi = {10.15479/at:ista:14374}, year = {2023}, } @phdthesis{14422, abstract = {Animals exhibit a remarkable ability to learn and remember new behaviors, skills, and associations throughout their lifetime. These capabilities are made possible thanks to a variety of changes in the brain throughout adulthood, regrouped under the term "plasticity". Some cells in the brain —neurons— and specifically changes in the connections between neurons, the synapses, were shown to be crucial for the formation, selection, and consolidation of memories from past experiences. These ongoing changes of synapses across time are called synaptic plasticity. Understanding how a myriad of biochemical processes operating at individual synapses can somehow work in concert to give rise to meaningful changes in behavior is a fascinating problem and an active area of research. However, the experimental search for the precise plasticity mechanisms at play in the brain is daunting, as it is difficult to control and observe synapses during learning. Theoretical approaches have thus been the default method to probe the plasticity-behavior connection. Such studies attempt to extract unifying principles across synapses and model all observed synaptic changes using plasticity rules: equations that govern the evolution of synaptic strengths across time in neuronal network models. These rules can use many relevant quantities to determine the magnitude of synaptic changes, such as the precise timings of pre- and postsynaptic action potentials, the recent neuronal activity levels, the state of neighboring synapses, etc. However, analytical studies rely heavily on human intuition and are forced to make simplifying assumptions about plasticity rules. In this thesis, we aim to assist and augment human intuition in this search for plasticity rules. We explore whether a numerical approach could automatically discover the plasticity rules that elicit desired behaviors in large networks of interconnected neurons. This approach is dubbed meta-learning synaptic plasticity: learning plasticity rules which themselves will make neuronal networks learn how to solve a desired task. We first write all the potential plasticity mechanisms to consider using a single expression with adjustable parameters. We then optimize these plasticity parameters using evolutionary strategies or Bayesian inference on tasks known to involve synaptic plasticity, such as familiarity detection and network stabilization. We show that these automated approaches are powerful tools, able to complement established analytical methods. By comprehensively screening plasticity rules at all synapse types in realistic, spiking neuronal network models, we discover entire sets of degenerate plausible plasticity rules that reliably elicit memory-related behaviors. Our approaches allow for more robust experimental predictions, by abstracting out the idiosyncrasies of individual plasticity rules, and provide fresh insights on synaptic plasticity in spiking network models. }, author = {Confavreux, Basile J}, issn = {2663 - 337X}, pages = {148}, publisher = {Institute of Science and Technology Austria}, title = {{Synapseek: Meta-learning synaptic plasticity rules}}, doi = {10.15479/at:ista:14422}, year = {2023}, } @phdthesis{14506, abstract = {Payment channel networks are a promising approach to improve the scalability bottleneck of cryptocurrencies. Two design principles behind payment channel networks are efficiency and privacy. Payment channel networks improve efficiency by allowing users to transact in a peer-to-peer fashion along multi-hop routes in the network, avoiding the lengthy process of consensus on the blockchain. Transacting over payment channel networks also improves privacy as these transactions are not broadcast to the blockchain. Despite the influx of recent protocols built on top of payment channel networks and their analysis, a common shortcoming of many of these protocols is that they typically focus only on either improving efficiency or privacy, but not both. Another limitation on the efficiency front is that the models used to model actions, costs and utilities of users are limited or come with unrealistic assumptions. This thesis aims to address some of the shortcomings of recent protocols and algorithms on payment channel networks, particularly in their privacy and efficiency aspects. We first present a payment route discovery protocol based on hub labelling and private information retrieval that hides the route query and is also efficient. We then present a rebalancing protocol that formulates the rebalancing problem as a linear program and solves the linear program using multiparty computation so as to hide the channel balances. The rebalancing solution as output by our protocol is also globally optimal. We go on to develop more realistic models of the action space, costs, and utilities of both existing and new users that want to join the network. In each of these settings, we also develop algorithms to optimise the utility of these users with good guarantees on the approximation and competitive ratios.}, author = {Yeo, Michelle X}, issn = {2663 - 337X}, pages = {162}, publisher = {Institute of Science and Technology Austria}, title = {{Advances in efficiency and privacy in payment channel network analysis}}, doi = {10.15479/14506}, year = {2023}, } @phdthesis{14510, author = {Gnyliukh, Nataliia}, isbn = {978-3-99078-037-4}, issn = {2663-337X}, keywords = {Clathrin-Mediated Endocytosis, vesicle scission, Dynamin-Related Protein 2, SH3P2, TPLATE complex, Total internal reflection fluorescence microscopy, Arabidopsis thaliana}, pages = {180}, publisher = {Institute of Science and Technology Austria}, title = {{Mechanism of clathrin-coated vesicle formation during endocytosis in plants}}, doi = {10.15479/at:ista:14510}, year = {2023}, } @phdthesis{14530, abstract = {Most motions of many-body systems at any scale in nature with sufficient degrees of freedom tend to be chaotic; reaching from the orbital motion of planets, the air currents in our atmosphere, down to the water flowing through our pipelines or the movement of a population of bacteria. To the observer it is therefore intriguing when a moving collective exhibits order. Collective motion of flocks of birds, schools of fish or swarms of self-propelled particles or robots have been studied extensively over the past decades but the mechanisms involved in the transition from chaos to order remain unclear. Here, the interactions, that in most systems give rise to chaos, sustain order. In this thesis we investigate mechanisms that preserve, destabilize or lead to the ordered state. We show that endothelial cells migrating in circular confinements transition to a collective rotating state and concomitantly synchronize the frequencies of nucleating actin waves within individual cells. Consequently, the frequency dependent cell migration speed uniformizes across the population. Complementary to the WAVE dependent nucleation of traveling actin waves, we show that in leukocytes the actin polymerization depending on WASp generates pushing forces locally at stationary patches. Next, in pipe flows, we study methods to disrupt the self--sustaining cycle of turbulence and therefore relaminarize the flow. While we find in pulsating flow conditions that turbulence emerges through a helical instability during the decelerating phase. Finally, we show quantitatively in brain slices of mice that wild-type control neurons can compensate the migratory deficits of a genetically modified neuronal sub--population in the developing cortex. }, author = {Riedl, Michael}, issn = {2663 - 337X}, keywords = {Synchronization, Collective Movement, Active Matter, Cell Migration, Active Colloids}, pages = {260}, publisher = {Institute of Science and Technology Austria}, title = {{Synchronization in collectively moving active matter}}, doi = {10.15479/14530}, year = {2023}, } @phdthesis{14539, abstract = {Stochastic systems provide a formal framework for modelling and quantifying uncertainty in systems and have been widely adopted in many application domains. Formal verification and control of finite state stochastic systems, a subfield of formal methods also known as probabilistic model checking, is well studied. In contrast, formal verification and control of infinite state stochastic systems have received comparatively less attention. However, infinite state stochastic systems commonly arise in practice. For instance, probabilistic models that contain continuous probability distributions such as normal or uniform, or stochastic dynamical systems which are a classical model for control under uncertainty, both give rise to infinite state systems. The goal of this thesis is to contribute to laying theoretical and algorithmic foundations of fully automated formal verification and control of infinite state stochastic systems, with a particular focus on systems that may be executed over a long or infinite time. We consider formal verification of infinite state stochastic systems in the setting of static analysis of probabilistic programs and formal control in the setting of controller synthesis in stochastic dynamical systems. For both problems, we present some of the first fully automated methods for probabilistic (a.k.a. quantitative) reachability and safety analysis applicable to infinite time horizon systems. We also advance the state of the art of probability 1 (a.k.a. qualitative) reachability analysis for both problems. Finally, for formal controller synthesis in stochastic dynamical systems, we present a novel framework for learning neural network control policies in stochastic dynamical systems with formal guarantees on correctness with respect to quantitative reachability, safety or reach-avoid specifications. }, author = {Zikelic, Dorde}, isbn = {978-3-99078-036-7}, issn = {2663 - 337X}, pages = {256}, publisher = {Institute of Science and Technology Austria}, title = {{Automated verification and control of infinite state stochastic systems}}, doi = {10.15479/14539}, year = {2023}, } @phdthesis{14547, abstract = {Superconductor-semiconductor heterostructures currently capture a significant amount of research interest and they serve as the physical platform in many proposals towards topological quantum computation. Despite being under extensive investigations, historically using transport techniques, the basic properties of the interface between the superconductor and the semiconductor remain to be understood. In this thesis, two separate studies on the Al-InAs heterostructures are reported with the first focusing on the physics of the material motivated by the emergence of a new phase, the Bogoliubov-Fermi surface. The second focuses on a technological application, a gate-tunable Josephson parametric amplifier. In the first study, we investigate the hypothesized unconventional nature of the induced superconductivity at the interface between the Al thin film and the InAs quantum well. We embed a two-dimensional Al-InAs hybrid system in a resonant microwave circuit allowing measurements of change in inductance. The behaviour of the resonance in a range of temperature and in-plane magnetic field has been studied and compared with the theory of conventional s-wave superconductor and a two-component theory that includes both contribution of the $s$-wave pairing in Al and the intraband $p \pm ip$ pairing in InAs. Measuring the temperature dependence of resonant frequency, no discrepancy is found between data and the conventional theory. We observe the breakdown of superconductivity due to an applied magnetic field which contradicts the conventional theory. In contrast, the data can be captured quantitatively by fitting to a two-component model. We find the evidence of the intraband $p \pm ip$ pairing in the InAs and the emergence of the Bogoliubov-Fermi surfaces due to magnetic field with the characteristic value $B^* = 0.33~\mathrm{T}$. From the fits, the sheet resistance of Al, the carrier density and mobility in InAs are determined. By systematically studying the anisotropy of the circuit response, we find weak anisotropy for $B < B^*$ and increasingly strong anisotropy for $B > B^*$ resulting in a pronounced two-lobe structure in polar plot of frequency versus field angle. Strong resemblance between the field dependence of dissipation and superfluid density hints at a hidden signature of the Bogoliubov-Fermi surface that is burried in the dissipation data. In the second study, we realize a parametric amplifier with a Josephson field effect transistor as the active element. The device's modest construction consists of a gated SNS weak link embedded at the center of a coplanar waveguide resonator. By applying a gate voltage, the resonant frequency is field-effect tunable over a range of 2 GHz. Modelling the JoFET minimally as a parallel RL circuit, the dissipation introduced by the JoFET can be quantitatively related to the gate voltage. We observed gate-tunable Kerr nonlinearity qualitatively in line with expectation. The JoFET amplifier has 20 dB of gain, 4 MHz of instantaneous bandwidth, and a 1dB compression point of -125.5 dBm when operated at a fixed resonant frequency. In general, the signal-to-noise ratio is improved by 5-7 dB when the JoFET amplifier is activated compared. The noise of the measurement chain and insertion loss of relevant circuit elements are calibrated to determine the expected and the real noise performance of the JoFET amplifier. As a quantification of the noise performance, the measured total input-referred noise of the JoFET amplifier is in good agreement with the estimated expectation which takes device loss into account. We found that the noise performance of the device reported in this document approaches one photon of total input-referred added noise which is the quantum limit imposed in nondegenerate parametric amplifier.}, author = {Phan, Duc T}, issn = {2663 - 337X}, keywords = {superconductor-semiconductor, superconductivity, Al, InAs, p-wave, superconductivity, JPA, microwave}, pages = {80}, publisher = {Institute of Science and Technology Austria}, title = {{Resonant microwave spectroscopy of Al-InAs}}, doi = {10.15479/14547}, year = {2023}, } @phdthesis{14587, abstract = {This thesis concerns the application of variational methods to the study of evolution problems arising in fluid mechanics and in material sciences. The main focus is on weak-strong stability properties of some curvature driven interface evolution problems, such as the two-phase Navier–Stokes flow with surface tension and multiphase mean curvature flow, and on the phase-field approximation of the latter. Furthermore, we discuss a variational approach to the study of a class of doubly nonlinear wave equations. First, we consider the two-phase Navier–Stokes flow with surface tension within a bounded domain. The two fluids are immiscible and separated by a sharp interface, which intersects the boundary of the domain at a constant contact angle of ninety degree. We devise a suitable concept of varifolds solutions for the associated interface evolution problem and we establish a weak-strong uniqueness principle in case of a two dimensional ambient space. In order to focus on the boundary effects and on the singular geometry of the evolving domains, we work for simplicity in the regime of same viscosities for the two fluids. The core of the thesis consists in the rigorous proof of the convergence of the vectorial Allen-Cahn equation towards multiphase mean curvature flow for a suitable class of multi- well potentials and for well-prepared initial data. We even establish a rate of convergence. Our relative energy approach relies on the concept of gradient-flow calibration for branching singularities in multiphase mean curvature flow and thus enables us to overcome the limitations of other approaches. To the best of the author’s knowledge, our result is the first quantitative and unconditional one available in the literature for the vectorial/multiphase setting. This thesis also contains a first study of weak-strong stability for planar multiphase mean curvature flow beyond the singularity resulting from a topology change. Previous weak-strong results are indeed limited to time horizons before the first topology change of the strong solution. We consider circular topology changes and we prove weak-strong stability for BV solutions to planar multiphase mean curvature flow beyond the associated singular times by dynamically adapting the strong solutions to the weak one by means of a space-time shift. In the context of interface evolution problems, our proofs for the main results of this thesis are based on the relative energy technique, relying on novel suitable notions of relative energy functionals, which in particular measure the interface error. Our statements follow from the resulting stability estimates for the relative energy associated to the problem. At last, we introduce a variational approach to the study of nonlinear evolution problems. This approach hinges on the minimization of a parameter dependent family of convex functionals over entire trajectories, known as Weighted Inertia-Dissipation-Energy (WIDE) functionals. We consider a class of doubly nonlinear wave equations and establish the convergence, up to subsequences, of the associated WIDE minimizers to a solution of the target problem as the parameter goes to zero.}, author = {Marveggio, Alice}, issn = {2663 - 337X}, pages = {228}, publisher = {Institute of Science and Technology Austria}, title = {{Weak-strong stability and phase-field approximation of interface evolution problems in fluid mechanics and in material sciences}}, doi = {10.15479/at:ista:14587}, year = {2023}, } @phdthesis{14622, author = {Sack, Stefan}, issn = {2663 - 337X}, pages = {142}, publisher = {Institute of Science and Technology Austria}, title = {{Improving variational quantum algorithms: Innovative initialization techniques and extensions to qudit systems}}, doi = {10.15479/at:ista:14622}, year = {2023}, } @phdthesis{14641, author = {Hennessey-Wesen, Mike}, issn = {2663 - 337X}, keywords = {microfluidics, miceobiology, mutations, quorum sensing}, pages = {104}, publisher = {Institute of Science and Technology Austria}, title = {{Adaptive mutation in E. coli modulated by luxS}}, doi = {10.15479/at:ista:14641}, year = {2023}, } @phdthesis{14651, abstract = {For self-incompatibility (SI) to be stable in a population, theory predicts that sufficient inbreeding depression (ID) is required: the fitness of offspring from self-mated individuals must be low enough to prevent the spread of self-compatibility (SC). Reviews of natural plant populations have supported this theory, with SI species generally showing high levels of ID. However, there is thought to be an under-sampling of self-incompatible taxa in the current literature. In this thesis, I study inbreeding depression in the SI plant species Antirrhinum majus using both greenhouse crosses and a large collected field dataset. Additionally, the gametophytic S-locus of A. majus is highly heterozygous and polymorphic, thus making assembly and discovery of S-alleles very difficult. Here, 206 new alleles of the male component SLFs are presented, along with a phylogeny showing the high conservation with alleles from another Antirrhinum species. Lastly, selected sites within the protein structure of SLFs are investigated, with one site in particular highlighted as potentially being involved in the SI recognition mechanism.}, author = {Arathoon, Louise S}, issn = {2663 - 337X}, pages = {96}, publisher = {Institute of Science and Technology Austria}, title = {{Investigating inbreeding depression and the self-incompatibility locus of Antirrhinum majus}}, doi = {10.15479/at:ista:14651}, year = {2023}, } @phdthesis{14697, author = {Stopp, Julian A}, isbn = {978-3-99078-038-1}, issn = {2663 - 337X}, pages = {226}, publisher = {Institute of Science and Technology Austria}, title = {{Neutrophils on the hunt: Migratory strategies employed by neutrophils to fulfill their effector function}}, doi = {10.15479/at:ista:14697}, year = {2023}, } @phdthesis{13074, abstract = {Deep learning has become an integral part of a large number of important applications, and many of the recent breakthroughs have been enabled by the ability to train very large models, capable to capture complex patterns and relationships from the data. At the same time, the massive sizes of modern deep learning models have made their deployment to smaller devices more challenging; this is particularly important, as in many applications the users rely on accurate deep learning predictions, but they only have access to devices with limited memory and compute power. One solution to this problem is to prune neural networks, by setting as many of their parameters as possible to zero, to obtain accurate sparse models with lower memory footprint. Despite the great research progress in obtaining sparse models that preserve accuracy, while satisfying memory and computational constraints, there are still many challenges associated with efficiently training sparse models, as well as understanding their generalization properties. The focus of this thesis is to investigate how the training process of sparse models can be made more efficient, and to understand the differences between sparse and dense models in terms of how well they can generalize to changes in the data distribution. We first study a method for co-training sparse and dense models, at a lower cost compared to regular training. With our method we can obtain very accurate sparse networks, and dense models that can recover the baseline accuracy. Furthermore, we are able to more easily analyze the differences, at prediction level, between the sparse-dense model pairs. Next, we investigate the generalization properties of sparse neural networks in more detail, by studying how well different sparse models trained on a larger task can adapt to smaller, more specialized tasks, in a transfer learning scenario. Our analysis across multiple pruning methods and sparsity levels reveals that sparse models provide features that can transfer similarly to or better than the dense baseline. However, the choice of the pruning method plays an important role, and can influence the results when the features are fixed (linear finetuning), or when they are allowed to adapt to the new task (full finetuning). Using sparse models with fixed masks for finetuning on new tasks has an important practical advantage, as it enables training neural networks on smaller devices. However, one drawback of current pruning methods is that the entire training cycle has to be repeated to obtain the initial sparse model, for every sparsity target; in consequence, the entire training process is costly and also multiple models need to be stored. In the last part of the thesis we propose a method that can train accurate dense models that are compressible in a single step, to multiple sparsity levels, without additional finetuning. Our method results in sparse models that can be competitive with existing pruning methods, and which can also successfully generalize to new tasks.}, author = {Peste, Elena-Alexandra}, issn = {2663-337X}, pages = {147}, publisher = {Institute of Science and Technology Austria}, title = {{Efficiency and generalization of sparse neural networks}}, doi = {10.15479/at:ista:13074}, year = {2023}, } @phdthesis{13081, abstract = {During development, tissues undergo changes in size and shape to form functional organs. Distinct cellular processes such as cell division and cell rearrangements underlie tissue morphogenesis. Yet how the distinct processes are controlled and coordinated, and how they contribute to morphogenesis is poorly understood. In our study, we addressed these questions using the developing mouse neural tube. This epithelial organ transforms from a flat epithelial sheet to an epithelial tube while increasing in size and undergoing morpho-gen-mediated patterning. The extent and mechanism of neural progenitor rearrangement within the developing mouse neuroepithelium is unknown. To investigate this, we per-formed high resolution lineage tracing analysis to quantify the extent of epithelial rear-rangement at different stages of neural tube development. We quantitatively described the relationship between apical cell size with cell cycle dependent interkinetic nuclear migra-tions (IKNM) and performed high cellular resolution live imaging of the neuroepithelium to study the dynamics of junctional remodeling. Furthermore, developed a vertex model of the neuroepithelium to investigate the quantitative contribution of cell proliferation, cell differentiation and mechanical properties to the epithelial rearrangement dynamics and validated the model predictions through functional experiments. Our analysis revealed that at early developmental stages, the apical cell area kinetics driven by IKNM induce high lev-els of cell rearrangements in a regime of high junctional tension and contractility. After E9.5, there is a sharp decline in the extent of cell rearrangements, suggesting that the epi-thelium transitions from a fluid-like to a solid-like state. We found that this transition is regulated by the growth rate of the tissue, rather than by changes in cell-cell adhesion and contractile forces. Overall, our study provides a quantitative description of the relationship between tissue growth, cell cycle dynamics, epithelia rearrangements and the emergent tissue material properties, and novel insights on how epithelial cell dynamics influences tissue morphogenesis.}, author = {Bocanegra, Laura}, issn = {2663 - 337X}, pages = {93}, publisher = {Institute of Science and Technology Austria}, title = {{Epithelial dynamics during mouse neural tube development}}, doi = {10.15479/at:ista:13081}, year = {2023}, } @phdthesis{13106, abstract = {Quantum entanglement is a key resource in currently developed quantum technologies. Sharing this fragile property between superconducting microwave circuits and optical or atomic systems would enable new functionalities, but this has been hindered by an energy scale mismatch of >104 and the resulting mutually imposed loss and noise. In this work, we created and verified entanglement between microwave and optical fields in a millikelvin environment. Using an optically pulsed superconducting electro-optical device, we show entanglement between propagating microwave and optical fields in the continuous variable domain. This achievement not only paves the way for entanglement between superconducting circuits and telecom wavelength light, but also has wide-ranging implications for hybrid quantum networks in the context of modularization, scaling, sensing, and cross-platform verification.}, author = {Sahu, Rishabh and Qiu, Liu and Hease, William J and Arnold, Georg M and Minoguchi, Y. and Rabl, P. and Fink, Johannes M}, issn = {1095-9203}, keywords = {Multidisciplinary}, pages = {718--721}, publisher = {American Association for the Advancement of Science}, title = {{Entangling microwaves with light}}, doi = {10.1126/science.adg3812}, volume = {380}, year = {2023}, }