@phdthesis{52,
abstract = {In this thesis we will discuss systems of point interacting fermions, their stability and other spectral properties. Whereas for bosons a point interacting system is always unstable this ques- tion is more subtle for a gas of two species of fermions. In particular the answer depends on the mass ratio between these two species. Most of this work will be focused on the N + M model which consists of two species of fermions with N, M particles respectively which interact via point interactions. We will introduce this model using a formal limit and discuss the N + 1 system in more detail. In particular, we will show that for mass ratios above a critical one, which does not depend on the particle number, the N + 1 system is stable. In the context of this model we will prove rigorous versions of Tan relations which relate various quantities of the point-interacting model. By restricting the N + 1 system to a box we define a finite density model with point in- teractions. In the context of this system we will discuss the energy change when introducing a point-interacting impurity into a system of non-interacting fermions. We will see that this change in energy is bounded independently of the particle number and in particular the bound only depends on the density and the scattering length. As another special case of the N + M model we will show stability of the 2 + 2 model for mass ratios in an interval around one. Further we will investigate a different model of point interactions which was discussed before in the literature and which is, contrary to the N + M model, not given by a limiting procedure but is based on a Dirichlet form. We will show that this system behaves trivially in the thermodynamic limit, i.e. the free energy per particle is the same as the one of the non-interacting system.},
author = {Moser, Thomas},
pages = {115},
publisher = {IST Austria},
title = {{Point interactions in systems of fermions}},
doi = {10.15479/AT:ISTA:th_1043},
year = {2018},
}
@phdthesis{278,
abstract = {Consortial subscription contracts regulate the digital access to publications between publishers and scientific libraries. However, since a couple of years the tendency towards a freely accessible publishing (Open Access) intensifies. As a consequence of this trend the contractual relationship between licensor and licensee is gradually changing as well: More and more contracts exercise influence on open access publishing. The present study attempts to compare Austrian examples of consortial licence contracts, which include components of open access. It describes the difference between pure subscription contracts and differing innovative deals including open access components. Thereby it becomes obvious that for the evaluation of this licence contracts new methods are needed. An essential new element of such analyses is the evaluation of the open access publication numbers. So this study tries to carry out such publication analyses for Austrian open access deals focusing on quantitative questions: How does the number of publications evolve? How does the open access share change? Publications reports of the publishers and database queries from Scopus form the data basis. The analysis of the data points out that differing approaches of contracts result in highly divergent results: Particular deals can prioritize a saving in costs or else the increase of the open access rate. It is to be assumed that within the following years further numerous open access deals will be negotiated. The finding of this study shall provide guidance.},
author = {Villányi, Márton},
pages = {94},
publisher = {Universität Wien},
title = {{Lizenzverträge mit Open-Access-Komponenten an österreichischen Bibliotheken}},
year = {2018},
}
@phdthesis{6263,
abstract = {Antibiotic resistance can emerge spontaneously through genomic mutation and render treatment ineffective. To counteract this process, in addition to the discovery and description of resistance mechanisms,a deeper understanding of resistanceevolvabilityand its determinantsis needed. To address this challenge, this thesisuncoversnew genetic determinants of resistance evolvability using a customized robotic setup, exploressystematic ways in which resistance evolution is perturbed due to dose-responsecharacteristics of drugs and mutation rate differences,and mathematically investigates the evolutionary fate of one specific type of evolvability modifier -a stress-induced mutagenesis allele.We find severalgenes which strongly inhibit or potentiate resistance evolution. In order to identify them, we first developedan automated high-throughput feedback-controlled protocol whichkeeps the population size and selection pressure approximately constant for hundreds of cultures by dynamically re-diluting the cultures and adjusting the antibiotic concentration. We implementedthis protocol on a customized liquid handling robot and propagated 100 different gene deletion strains of Escherichia coliin triplicate for over 100 generations in tetracycline and in chloramphenicol, and comparedtheir adaptation rates.We find a diminishing returns pattern, where initially sensitive strains adapted more compared to less sensitive ones. Our data uncover that deletions of certain genes which do not affect mutation rate,including efflux pump components, a chaperone and severalstructural and regulatory genes can strongly and reproducibly alterresistance evolution. Sequencing analysis of evolved populations indicates that epistasis with resistance mutations is the most likelyexplanation. This work could inspire treatment strategies in which targeted inhibitors of evolvability mechanisms will be given alongside antibiotics to slow down resistance evolution and extend theefficacy of antibiotics.We implemented astochasticpopulation genetics model, toverifyways in which general properties, namely, dose-response characteristics of drugs and mutation rates, influence evolutionary dynamics. In particular, under the exposure to antibiotics with shallow dose-response curves,bacteria have narrower distributions of fitness effects of new mutations. We show that in silicothis also leads to slower resistance evolution. We see and confirm with experiments that increased mutation rates, apart from speeding up evolution, also leadto high reproducibility of phenotypic adaptation in a context of continually strong selection pressure.Knowledge of these patterns can aid in predicting the dynamics of antibiotic resistance evolutionand adapting treatment schemes accordingly.Focusing on a previously described type of evolvability modifier –a stress-induced mutagenesis allele –we find conditions under which it can persist in a population under periodic selectionakin to clinical treatment. We set up a deterministic infinite populationcontinuous time model tracking the frequencies of a mutator and resistance allele and evaluate various treatment schemes in how well they maintain a stress-induced mutator allele. In particular,a high diversity of stresses is crucial for the persistence of the mutator allele. This leads to a general trade-off where exactly those diversifying treatment schemes which are likely to decrease levels of resistance could lead to stronger selection of highly evolvable genotypes.In the long run, this work will lead to a deeper understanding of the genetic and cellular mechanisms involved in antibiotic resistance evolution and could inspire new strategies for slowing down its rate. },
author = {Lukacisinova, Marta},
pages = {91},
publisher = {IST Austria},
title = {{Genetic determinants of antibiotic resistance evolution}},
doi = {10.15479/AT:ISTA:th1072},
year = {2018},
}
@phdthesis{6266,
abstract = {A major challenge in neuroscience research is to dissect the circuits that orchestrate behavior in health and disease. Proteins from a wide range of non-mammalian species, such as microbial opsins, have been successfully transplanted to specific neuronal targets to override their natural communication patterns. The goal of our work is to manipulate synaptic communication in a manner that closely incorporates the functional intricacies of synapses by preserving temporal encoding (i.e. the firing pattern of the presynaptic neuron) and connectivity (i.e. target specific synapses rather than specific neurons). Our strategy to achieve this goal builds on the use of non-mammalian transplants to create a synthetic synapse. The mode of modulation comes from pre-synaptic uptake of a synthetic neurotransmitter (SN) into synaptic vesicles by means of a genetically targeted transporter selective for the SN. Upon natural vesicular release, exposure of the SN to the synaptic cleft will modify the post-synaptic potential through an orthogonal ligand gated ion channel. To achieve this goal we have functionally characterized a mixed cationic methionine-gated ion channel from Arabidopsis thaliana, designed a method to functionally characterize a synthetic transporter in isolated synaptic vesicles without the need for transgenic animals, identified and extracted multiple prokaryotic uptake systems that are substrate specific for methionine (Met), and established a primary/cell line co-culture system that would allow future combinatorial testing of this orthogonal transmitter-transporter-channel trifecta. Synthetic synapses will provide a unique opportunity to manipulate synaptic communication while maintaining the electrophysiological integrity of the pre-synaptic cell. In this way, information may be preserved that was generated in upstream circuits and that could be essential for concerted function and information processing. },
author = {Mckenzie, Catherine},
issn = {2663-337X},
pages = {95},
publisher = {IST Austria},
title = {{Design and characterization of methods and biological components to realize synthetic neurotransmission }},
doi = {10.15479/at:ista:th_1055},
year = {2018},
}
@phdthesis{68,
abstract = {The most common assumption made in statistical learning theory is the assumption of the independent and identically distributed (i.i.d.) data. While being very convenient mathematically, it is often very clearly violated in practice. This disparity between the machine learning theory and applications underlies a growing demand in the development of algorithms that learn from dependent data and theory that can provide generalization guarantees similar to the independent situations. This thesis is dedicated to two variants of dependencies that can arise in practice. One is a dependence on the level of samples in a single learning task. Another dependency type arises in the multi-task setting when the tasks are dependent on each other even though the data for them can be i.i.d. In both cases we model the data (samples or tasks) as stochastic processes and introduce new algorithms for both settings that take into account and exploit the resulting dependencies. We prove the theoretical guarantees on the performance of the introduced algorithms under different evaluation criteria and, in addition, we compliment the theoretical study by the empirical one, where we evaluate some of the algorithms on two real world datasets to highlight their practical applicability.},
author = {Zimin, Alexander},
pages = {92},
publisher = {IST Austria},
title = {{Learning from dependent data}},
doi = {10.15479/AT:ISTA:TH1048},
year = {2018},
}