Einfluss von 17ß-Estradiol (E2) auf die Apoptose in einem in vitro Modell für Hirntrauma

Schädel-Hirn-Traumata (SHT) stellen ein bedeutendes medizinisches und sozioökonomisches Problem dar, da sie neurodegenerative Prozesse mit langfristigen kognitiven und funktionellen Beeinträchtigungen auslösen können. Bisher spielen Tiermodelle eine zentrale Rolle bei der Forschung – sie sind jedoch kostenintensiv und ethisch umstritten. Diese Arbeit untersucht mithilfe eines in vitro Modells auf Basis organotypischer hippocampaler Gewebekulturen das Ausmaß von SHT-induziertem apoptotischen Zelltod sowie mögliche neuroprotektive Effekte von 17ß-Estradiol (E2). Zur mechanischen Schädigung wurde die „rolling cylinder-Technik“ eingesetzt und deren Folgen nach 8 h und 24 h analysiert. Der Fokus lag auf apoptotischem Zelltod im Stratum pyramidale (SP) und in den Strata radiatum/lacunosum moleculare (SR/SLM) der CA1-Region, detektiert mittels TUNEL-Technik. Immunhistochemische Färbungen (Neuronen: NeuN, Astrozyten: GFAP) ermöglichten die Differenzierung betroffener Zelltypen. Die Ergebnisse zeigten durch in vitro SHT induzierten apoptotischen Zelltod in Neuronen sowie in Astrozyten, insbesondere nach 8 h im SP, mit weitestgehendem Rückgang nach 24 h. Neuroprotektive Effekte von E2 wurden nicht bestätigt. Geschlechtsspezifische Unterschiede in der Apoptoserate zu den unterschiedlichen Zeitpunkten deuteten sich an. Außerdem zeigte sich eine hohe Variabilität der Ergebnisse zwischen den Kulturen, was auf Optimierungspotenziale der Versuchsmethodik hinweist. Grundsätzlich bietet diese Arbeit jedoch eine funktionierende Grundlage für die Weiterentwicklung in vitro-basierter Untersuchungen zu sekundären Schäden nach SHT und deren pharmakologischer Modulation

Search for CP violaton in τ decays at Belle II

This thesis presents a search for direct Charge-Parity (CP) violation in the decay τ− → π−K0Sντ (≥ 0π0) using data collected by the Belle II experiment, corresponding to an integrated luminosity of 365 fb−1. The analysis is motivated by a previous BABAR measurement, which reported a 2.8σ deviation from the Standard Model prediction. A CP asymmetry measurement is performed using a similar method, with improved control over detector and background-induced asymmetries through dedicated corrections. The observed decay-rate asymmetry between τ− and τ+ decays is corrected for detection effects, background biases, and known K0/ ¯K 0 nuclear interaction asymmetries. These corrections are derived from control samples, and a dilution factor is applied to suppress contributions from background modes such as τ− → K−K0Sντ and τ− → π−K0 ¯K0ντ . The analysis is conducted separately for electron- and muon-tagged τ decays, totaling approximately 262 × 103 signal events, and statistically combined assuming uncorrelated systematic uncertainties. In parallel, this thesis contributes to a theoretical study evaluating the Standard Model assumption that the CP-violating term from τ− → π−K0 ¯K 0ντ decays vanishes. The K0S efficiency functions developed here are essential for estimating the A3 contribution, enabling comparison between theoretical predictions and Monte Carlo simulations. For the main measurement, the dominant uncertainty is statistical, followed by systematics from detection-induced asymmetry corrections. At the time of writing, the CP asymmetry result remains blinded pending internal Belle II review; however, the associated statistical and systematic uncertainties are presented. This work highlights Belle II’s sensitivity to CP violation in τ decays and lays the groundwork for more precise future measurements

The Relation of Multiple Sclerosis to Early Life and Adolescence Determinants: Insights from Observational Studies

This dissertation investigates the influence of potential risk factors on the development of multiple sclerosis (MS). Factors that occur in (early) childhood and adolescence are of particular interest. This work is based on a systematic review with meta-analysis and two observational studies, the “German National Cohort (NAKO Gesundheitsstudie, NAKO)” and the “Study on the Influence of Risk Factors on Disease Progression and the Development of Multiple Sclerosis (StERKE)”. The results of this study clearly show the multifactorial aetiology of MS, which on the one hand has a genetic component and on the other hand also appears to be influenced by environmental and lifestyle factors, as well as the resulting potential for prevention. We were able to reproduce the results reported in the literature on already established risk factors such as an EBV infection, obesity in childhood and adolescence, and vitamin D deficiency. In addition, we were able to generate numerous new findings concerning MS aetiology. We observed associations between the cumulative number of common childhood infections (chickenpox, mumps, rubella, pertussis and measles), the cumulative number of stressful life events (death of a partner, death of a close person (other than partner), serious illness of a close person), and being the first-born child of a mother who was ≥ 30 years old at childbirth and an increased risk of MS. We observed a protective effect of PA during teenage years and having been breastfed on the risk of developing MS later in life. This work complements the existing literature in that i) MS prevention should begin in early childhood and adolescence, ii) due to the overlap with other chronic diseases in terms of potential risk factors, it should be based on prevention programs, such as smoking cessation programs or the promotion of PA, for these diseases, and iii) there is still a great need for studies that focus on MS-specific factors such as disease severity, but also the sex distribution in the prevalence of MS between men and women.Im Rahmen dieser Dissertation wird der Einfluss potenzieller Risikofaktoren auf die Entstehung der Multiplen Sklerose (MS) untersucht. Von besonderem Interesse sind hierbei vor allem Faktoren, die in der (frühen) Kindheit und Jugend auftreten. Grundlage dieser Arbeit bilden sowohl ein systematisches Review mit Metaanalyse als auch zwei Beobachtungsstudien, die „NAKO Gesundheitsstudie (NAKO)“ und die „Studie zum Einfluss von Risikofaktoren auf den Krankheitsverlauf und die Entstehung der Multiplen Sklerose (StERKE)“. Die Ergebnisse dieser Arbeit zeigen deutlich die multifaktorielle Ätiologie der MS, die einerseits eine genetische Komponente aufzuweisen, aber auch maßgeblich durch Umwelt- und Lebensstilfaktoren beeinflusst zu sein scheint sowie das sich daraus ableitende Präventionspotenzial. So war es uns möglich, die in der Literatur berichteten Ergebnisse zu bereits etablierten Risikofaktoren wie z.B. einer EBV-Infektion, Übergewicht in der Kindheit und Jugend und einem Vitamin D-Mangel zu reproduzieren. Darüber hinaus konnten wir zahlreiche neue Erkenntnisse in Hinblick auf die MS-Ätiologie generieren. Wir beobachteten Assoziationen zwischen der kumulativen Anzahl häufiger Infektionserkrankungen (Windpocken, Mumps, Röteln, Pertussis und Masern) im Kindesalter, der kumulativen Anzahl belastender Lebensereignisse (Tod des/r Partners/Partnerin, Tod einer nahestehenden Person, schwere Erkrankung einer nahestehenden Person), sowie dem Umstand das Kind einer Erstgebärenden zu sein, die bei der Geburt ≥ 30 Jahre alt war, und einem erhöhten MS-Risiko. Für körperliche Aktivität in der Jugend sowie im Säuglingsalter gestillt worden zu sein beobachteten wir einen protektiven Effekt auf das Risiko, im weiteren Leben an MS zu erkranken. Diese Arbeit ergänzt die bestehende Literatur dahingeheng, dass i) die MS-Prävention bereits im frühen Kindes- und Jugendalter beginnen sollte, ii) sich diese bedingt durch die Überlappung mit anderen chronischen Erkrankungen in Hinblick auf potenzielle Risikofaktoren, an Präventionsprogrammen, wie z.B. Rauchentwöhnungprogrammen oder aber der Förderung körperlicher Aktivität, für diese Erkrankungen orientieren sollte und iii) es weiterhin einen hohen Bedarf an Studien gibt, die vor allem MS-spezifische Faktoren wie z.B. die Erkrankungsschwere, aber auch die Geschlechtsverteilung in der Prävalenz der MS zwischen Männern und Frauen berücksichtigen

On Complex Spin Textures, Majorana Modes, and Machida-Shibata States - Exploring Nano-Scale Systems with Tight-Binding Models

The search for Majorana zero modes has been a major undertaking in the field of solid state physics in recent years, as they have potentially promising applications in fault-tolerant quantum computing. On the theory side, tight-binding models combining magnetism, Rashba spin-orbit coupling, and superconductivity have been on the forefront of this quest. They feature all ingredients necessary for experimental realization of Majorana zero modes. In this thesis, we take such a model, adapt and apply it to vastly different problems and geometries, while leaving its core intact. We explore its magnetic ground state in one and two dimensions, showing a surprisingly rich magnetic phase space. By self-consistently calculating the magnetic ground states before identifying electronic topological phases, we demonstrate in one dimension that, for a significant portion of the phase space, spin-spirals and topologically non-trivial states naturally coexist. We develop a computationally highly efficient approach to find the magnetic ground states of tight-binding models with a fitted classical spin model that is not inherently limited by assumptions, like RKKY being limited to weak magnetic couplings, and also grants additional insight into the driving magnetic forces of the system. With this method, we completely characterize the magnetic parameter space of our two-dimensional tight-binding model, showing that also 2D systems feature a rich magnetic phase diagram with many exotic magnetic phases despite the simplicity of the model at first glance. As the amount of data points is quite large and the magnetic phases are too complex to be classified by a simple algorithm, we employ an artificial neural network to classify the magnetic phases, thereby demonstrating that artificial neural networks can be a useful tool for the classification of magnetic ground states. Additionally, we provide theoretical support for the first experimental measurement of simultaneous zero-bias-peaks at both ends of an atomic chain, which marks a milestone in the search for Majoranas. In the experiment, an atomic Mn chain was constructed on Nb(110) in the [1¯10]-direction to build hybridizing Yu-Shiba-Rusinov states in a bottom-up approach. To model this, we adapt our tight-binding model to three dimensions. Replicating the experimental geometry, we are able to predict the chain length at which the found zero-modes are expected to evolve into isolated Majorana zero modes and demonstrate the crucial role of the strength of Rashba spin-orbit coupling. Finally, we provide a geometrically correct model for experiments on quantum dots caged by a box of Ag adatoms on a Ag island on superconducting Nb. With these experiments, the existence of energetically sharp non-magnetic in-gap states has been shown for the first time. This confirms theoretical predictions of Machida and Shibata on spin-degenerate Andreev bound states from 1972

Rigid Convolution Structures

A monoidal category is called a convolution monoidal category if it arises from linearizing a 2-Segal space. The goal of this thesis is to study for which 2-Segal spaces the induced convolution monoidal category is a multi-fusion category. With this aim, we show that multi-fusion categories admit an intrinsic description as rigid algebras in the symmetric monoidal 2-category of C-linear additive categories. We use this observation to define, by analogy, a derived version of a multi-fusion category as a rigid algebra in the symmetric monoidal (infinity,2)-category of stable infinity-categories. We show that examples of these arise as derived categories of multi-fusion categories and as categories of modules over smooth and proper E2-algebras. Afterward, we show that rigid algebras in the (infinity, 2)-category of spans are precisely given by those 2-Segal objects that are Čech-nerves. Together with our previous result, we use this to provide an answer to our initial question. To prove this result, we provide a description of bimodules in the infinity-category of spans as birelative 2-Segal objects. Furthermore, we introduce a notion of morphism between birelative 2-Segal objects that extends this classification to an equivalence of infinity-categories. We use this classification to construct examples of convolution monoidal structures that form derived multi-fusion categories and discuss some aspects of the associated fully extended TFTs. We finish by studying Grothendieck–Verdier-structures on convolution monoidal infinity-categories and by comparing them with rigid dualities

„Jobship als One-Click-Bewerbung 2.0?“ Eine empirische Analyse der Chancen und Grenzen eines wissenschaftlich basierten sowie innovativen Bewerbungs- und Personalauswahlkonzepts für MINT-Fachkräfte im digitalen Zeitalter

Im Rahmen der vorliegenden Dissertation wurde das Ziel verfolgt, ein wissenschaftlich fundiertes Bewerbungs- und Personalauswahlkonzept einschließlich der digitalen Anwendung „Jobship als One-Click-Bewerbung 2.0“ zu entwickeln, dessen Einsatz evidenzbasierte und antidiskriminierende Personalentscheidungen fördert (Forschungsfrage 1), die Passung zwischen Person und potenziellem Arbeitsumfeld möglichst ganzheitlich ermittelt (Forschungsfrage 2) sowie zu einer positiven Candidate- und Recruiter-Experience beiträgt (Forschungsfrage 3). Methodisch wurde ein qualitatives Forschungsdesign mit Methoden-Triangulation aus problemzentrierten Interviews, Expert:inneninterviews und einer Gruppendiskussion gewählt. Die bewusst heterogene Stichprobe umfasst 15 MINT-Fachkräfte und 15 HR-Expert:innen, die aktuelle Bewerbungs- und Personalauswahlprozesse aus Kandidat:innen- und Unternehmensperspektive analysierten. Mittels qualitativer Inhaltsanalyse nach Mayring (2023) wurden 6891 Äußerungen in ein deduktiv-induktiv entwickeltes Kategoriensystem überführt. Die empirischen Ergebnisse belegen deutliche Diskrepanzen zwischen wissenschaftlichen Empfehlungen und gelebter Praxis: Auswahlentscheidungen basieren vorwiegend auf un- bzw. halbstrukturierten Interviews, valide Testverfahren finden kaum Anwendung. „Jobship“ adressiert diese Lücke durch ein dreistufiges, gamifiziertes Self- und Online-Assessment, das bereits in der Orientierungsphase eine wissenschaftlich fundierte Passungs- und Eignungsprognose ermöglichen und damit sowohl Entscheidungsqualität als auch Candidate und Recruiter Experience verbessern könnte. Zugleich zeigen die Befunde Anforderungen an eine authentische und transparente Datenqualität auf, um unrealistische Erwartungen zu vermeiden. Zudem wird anhand der Ergebnisse deutlich, dass es im Kontext der einzelnen Forschungsfragen der Berücksichtigung einer Vielzahl weiterer – insbesondere zwischenmenschlicher – Faktoren bedarf – wie einer wertschätzenden, kontinuierlichen Kommunikation während des gesamten Bewerbungs- und Personalauswahlprozesses, personaldiagnostisch geschulter Personalverantwortlicher sowie ausreichender zeitlicher Ressourcen. Die Dissertation leistet hier einen zweifachen Erkenntnisbeitrag: Erstens erweitert sie die Personaldiagnostik um einen experience-orientierten, digitalisierten Ansatz; zweitens liefert sie praxisnahe Implikationen, die aufzeigen, wie evidenzbasierte, antidiskriminierende und zugleich bewerber:innenzentrierte Prozesse gestaltet werden können. Limitationen, insbesondere die begrenzte Generalisierbarkeit aufgrund des qualitativen Designs, sowie Anregungen für weiterführende quantitative Validierungs- und Längsschnittstudien werden abschließend diskutiert

Multidimensional and Multimodal Soft X-ray Methods for Quantum Materials Research

Quantum materials are governed by a complex interplay of spin, orbit, charge and lattice degrees of freedom, resulting in emergent phenomena like high-temperature superconductivity, charge and orbital ordering and insulator-to-metal transitions (IMTs). Often, the interaction of these subsystems results in an energy landscape with multiple local minima favouring different phases. In many cases, two or more distinct phases coexist and the macroscopic property of the material is shaped by the properties of the individual phases as well as their interaction. To understand the complexity that shapes quantum materials, their properties need to be studied in multiple dimensions of space, energy and time. X-rays are indispensable tools for the study of quantum materials as they enable probing on atomic length scales as well as excitation of electrons bound in specific core levels. Synchrotron radiation sources provide the coherence, spectral brightness, flexible focusing capabilities and tunability of the photon energy to adapt the X-ray beam properties to the requirements of a specific measurement scheme and sample. The photon energy can be tuned to electronic resonances of one element to disentangle its role for macroscopic functionality. Free-electron lasers (FELs) extend this capability in the time domain down to pico- and femtoseconds, the time scales of atomic and electronic motion. This thesis presents the development of multidimensional and multimodal soft X-ray methods that can be tailored to address specific scientific challenges posed by quantum materials. Multidimensional studies of incident and emitted photon energies and spatial and temporal dependencies as well as the dependence on fluence of a pump laser that drives e.g. an IMT are discussed. Multimodal studies allow observing quantum materials from the point of view of different experimental techniques, like X-ray imaging, X-ray absorption spectroscopy, X-ray emission spectroscopy, (resonant) X-ray diffraction, resonant inelastic X-ray scattering (RIXS) and angle-resolved photoemission spectroscopy (ARPES). First, the RIXS imaging method, which utilizes a transmission Fresnel zone plate to combine soft X-ray absorption spectroscopy with microscopy with a resolution of 1.8 µm, is presented. This method is applied in a study of the IMT of VO2 microsquares measuring 30 µm × 30 µm. Imaging X-ray absorption spectroscopy (XAS) shows that the phase transition temperature at the edges of the squares is lower in comparison to the centres by 1.2 K. This implies that bulk properties of quantum materials may change upon structuring on the microscale. Second, this method is transferred to imaging X-ray diffraction (XRD) to investigate the doped titanate Y1−xCaxTiO3 with x = 0.37, revealing insulating and metallic phases which coexist in curved, striped domains across unusually large temperature regions. This observation is related to a varying chemical inhomogeneity of about x ± 0.01, likely arising during crystal growth. Next, excitation of the electronic subsystem in quantum materials with femtosecond infra-red laser pulses also drives insulator-to-metal transitions. For the study of ultrafast dynamics of magnetite (Fe3O4) at an FEL, zone plates can also be used for time-to-space mapping, recording a delay range of several picoseconds as well as an extended fluence range simultaneously. This time-to-space mapping setup combines temporal, spatial and pump fluence information and may be developed to record single-shot experiments in the future. Lastly, a method, termed photoelectron spectrometry for the analysis of X-rays (PAX), which converts RIXS photons to photoelectrons via the photoelectric effect, is developed towards high energy resolution to investigate a sample from the family of high-temperature superconducting cuprates. PAX enables simultaneous recording of a range of photon-sample momentum transfer, corresponding to a significant part of the first Brillouin zone in the investigated system. In comparison to grating-based RIXS spectrometers, a PAX instrument is much more compact, saving money and experimental space. The success of the PAX method resulted in the development of a dedicated ultra-high vacuum chamber, soon to be commissioned, which promises a significant improvement in photon count rate and energy resolution, as well as the combination with ARPES. In summary, this thesis presents experimental developments that enable the study of quantum materials through the utilisation of diverse soft X-ray methods in conjunction with a spatial resolution on the micrometer level, temporal resolution on the level of 100 fs and energy resolution on the level of 100 meV. Furthermore, it outlines concepts to improve this energy and spatial resolution by approximately one order of magnitude. The advancement of the experimental tools described in this thesis will facilitate a deeper comprehension of the complexity of quantum materials and enable us as a society to harness phenomena occurring in quantum materials.Quantenmaterialien werden durch ein komplexes Zusammenspiel von Spin, Orbit, Ladung und Kristallgitter charakterisiert, was emergente Phänomene wie Hochtemperatursupraleitung, Ladungs- und Orbitalordnung und Isolator-Metall-Übergänge hervorrufen kann. Häufig erzeugt die Wechselwirkung dieser Freiheitsgrade eine Energielandschaft mit mehreren lokalen Minima, welche verschiedene Phasen begünstigen. Dies kann dazu führen, dass zwei oder mehr unterschiedliche Phasen koexistieren und die makroskopische Eigenschaft des Materials durch die Eigenschaften der einzelnen Phasen sowie deren Wechselwirkung bestimmt wird. Um diese Komplexität, welche Quantenmaterialien charakterisiert, zu verstehen, müssen ihre Eigenschaften in den Dimensionen von Raum, Energie und Zeit untersucht werden. Röntgenstrahlen sind unverzichtbare Werkzeuge für die Untersuchung von Quantenmaterialien, da sie die Untersuchung auf atomaren Längenskalen sowie die Anregung von Elektronen, gebunden in spezifischen Kernniveaus, ermöglichen. Synchrotronstrahlungsquellen bieten die Kohärenz, spektrale Helligkeit, flexible Fokussierungsmöglichkeiten und Durchstimmbarkeit der Photonenenergie, welche nötig sind um die Eigenschaften des Röntgenstrahls an die Anforderungen eines bestimmten Messschemas und einer bestimmten Probe anzupassen. Die Photonenenergie kann auf elektronische Resonanzen eines Elements eingestellt werden, um dessen Beitrag zu der makroskopischen Funktionalität zu untersuchen. Freie-Elektronen-Laser (FELs) erweitern diese Möglichkeiten hin zu den Zeitskalen von Piko- und Femtosekunden, auf welchen sich Atome und Elektronen bewegen. Diese Dissertation beschreibt die Entwicklung von multidimensionalen und multimodalen Weichröntgenmethoden, welche auf die spezifischen wissenschaftlichen Herausforderungen von Quantenmaterialien angepasst werden. Multidimensionale Studien von einfallender und emittierter Photonenenergie, von räumlichen und zeitlichen Abhängigkeiten sowie von der Abhängigkeit der Fluenz eines Pumplasers, welcher einen Isolator-Metallübergang anregt, werden diskutiert. Multimodale Studien ermöglichen die Beobachtung von Quantenmaterialien mit verschiedenen experimentellen Techniken, wie Röntgenbildgebung, Röntgenabsorptionsspektroskopie, Röntgenemissionsspektroskopie, (resonanter) Röntgendiffraktion, resonanter inelastischer Röntgenstreuung (RIXS) und winkelaufgelöster Photoemissionsspektroskopie (ARPES). Zunächst wird eine abbildende RIXS Methode vorgestellt, welche eine Transmissions-Fresnel-Zonenplatte verwendet um Weichröntgenabsorptionsspektroskopie mit Mikroskopie mit einer Auflösung von 1.8 μm zu kombinieren. Diese Methode wird in einer Studie des Isolator-Metallübergangs von Mikroquadraten, welche 30 μm×30 μm klein sind und aus Vanadiumdioxid (VO2) bestehen, angewendet. Abbildende Röntgenabsorptionsspektroskopie (XAS) zeigt, dass die Phasenübergangstemperatur an den Rändern der Quadrate im Vergleich zu den Zentren um 1.2K verringert ist. Dies deutet darauf hin, dass sich die Eigenschaften von Quantenmaterialien durch Strukturierung auf der Mikroskala ändern können. Weiterhin wird diese Methode auf abbildende Röntgenbeugung (XRD) übertragen, um das dotierte Titanatsystem Y1 − xCaxTiO3 mit x = 0.37 zu untersuchen. Hierbei werden isolierende und metallische Phasen beobachtet, welche in gekrümmten, streifenförmigen Domänen über ungewöhnlich große Temperaturbereiche hinweg koexistieren. Diese Beobachtung steht in Zusammenhang mit einer variierenden chemischen Inhomogenität von etwa x±0.01, die wahrscheinlich während des Kristallwachstums entstanden ist. Auch Femtosekunden-Infrarot-Laserpulse können genutzt werden, um das elektronische System in Quantenmaterialien anzuregen und Isolator-Metall-Übergänge zu treiben. Für die Untersuchung der ultraschnellen Dynamik von Magnetit (Fe3O4) an einem FEL können Zonenplatten auch für die Methode des Time-to-Space Mapping verwendet werden, wobei eine Spanne des Zeitversatzes zwischen Pumplaser und FEL von mehreren Pikosekunden sowie eine Verteilung von Fluenzen gleichzeitig aufgezeichnet werden. Diese Methode kombiniert Informationen über Zeit, Raum und Pumpfluenz und kann in Zukunft für die Aufzeichnung von Einzelschussexperimenten entwickelt werden. Schließlich wird die Methode der Photoelektronenspektrometrie zur Analyse von Röntgenstrahlung (PAX) weiterentwickelt. Diese Methode wandelt RIXS-Photonen mit Hilfe des photoelektrischen Effekts in Photoelektronen um. Sie wird genutzt um eine Probe aus der Familie der Hochtemperatursupraleiter mit hoher Energieauflösung zu untersuchen. Weiterhin ermöglicht PAX die simultane Messung einer Verteilung von Impulsüberträgen von Photonen auf die Probe. Der Bereich der Verteilung entspricht einem signifikanten Teil der ersten Brillouin-Zone in dem hier untersuchten System. Im Vergleich zu Gitterspektrometern ist ein PAX-Instrument viel kompakter, was Geld und Experimentierfläche spart. Der Erfolg der PAX-Methode führte zur Entwicklung einer speziellen Ultrahochvakuumkammer, die demnächst in Betrieb genommen wird und eine erhebliche Verbesserung der Photonenzählrate und der Energieauflösung sowie die Kombination mit ARPES verspricht. Zusammenfassend werden in dieser Arbeit experimentelle Entwicklungen vorgestellt, welche die Untersuchung von Quantenmaterialien durch den Einsatz verschiedener Weichröntgenmethoden in Verbindung mit einer räumlichen Auflösung im Mikrometerbereich, einer Zeitauflösung im Bereich von 100 fs und einer Energieauflösung im Bereich von 100 meV ermöglichen. Darüber hinaus werden Konzepte zur Verbesserung dieser Energie- und Ortsauflösung um etwa eine Größenordnung vorgestellt. Die Weiterentwicklung der in dieser Arbeit beschriebenen experimentellen Werkzeuge wird ein tieferes Verständnis der Komplexität von Quantenmaterialien ermöglichen und uns als Gesellschaft in die Lage versetzen, Phänomene, die in Quantenmaterialien auftreten, nutzbar zu machen

Model-Based Deep Speech Enhancement for Improved Interpretability and Robustness

Technology advancements profoundly impact numerous aspects of life, including how we communicate and interact. For instance, hearing aids enable hearing-impaired or elderly people to participate comfortably in daily conversations; telecommunications equipment lifts distance constraints, enabling people to communicate remotely; smart machines are developed to interact with humans by understanding and responding to their instructions. These applications involve speech-based interaction not only between humans but also between humans and machines. However, the microphones mounted on these technical devices can capture both target speech and interfering sounds, posing challenges to the reliability of speech communication in noisy environments. For example, distorted speech signals may reduce communication fluency among participants during teleconferencing. Additionally, noise interference can negatively affect the speech recognition and understanding modules of a voice-controlled machine. This calls for speech enhancement algorithms to extract clean speech and suppress undesired interfering signals, improving the overall quality and intelligibility of speech. Traditional speech enhancement algorithms often rely on simplifying assumptions, such as slowly changing noise, to estimate the parameters required for clean speech estimators. This may lead to less than satisfactory results in acoustically challenging scenarios. In recent years, the field has seen great strides through deep learning-based algorithms. The success of deep learning stems largely from its universal function approximation capability and scalability to large datasets. In particular, deep predictive approaches have received widespread attention due to their remarkable flexibility in incorporating key features of the target speech into various stages of the speech enhancement framework. These stages include input feature processing, network architecture design, training objective formulation, and optimization strategy development. Essentially, deep predictive methods aim to learn a mapping between noisy mixtures and clean speech by training deep neural networks (DNNs) on a large number of paired noisy-clean speech samples. However, the performance of these algorithms depends heavily on the quantity and diversity of training data. As a result, performance degradation often occurs when there is a data mismatch between training and testing, known as the generalization problem. Moreover, predictive approaches are typically framed as problems with a single output, which may result in erroneous estimates for complex and unseen samples without any indication of uncertainty. Indeed, due to the black-box nature of DNNs, deep learning-based algorithms produce clean speech estimates in a non-transparent manner, making them difficult to interpret. In this thesis, we aim to incorporate statistical models into DNN-based speech enhancement to improve its robustness and interpretability. The first part of the thesis explores these ideas from the perspective of uncertainty. We augment predictive speech enhancement with an uncertainty estimation task, such that the network model can provide not only clean speech estimates but also their associated predictive uncertainty. Furthermore, since generic Bayesian methods for uncertainty modeling in deep learning usually involve costly sampling processes, this thesis seeks to leverage statistical knowledge from the speech processing domain to efficiently estimate uncertainty with minimal computational overhead. We experimentally demonstrate that the proposed uncertainty-augmented framework effectively identifies when predictions deviate significantly from the true data by producing large uncertainty estimates. This allows us to assess the model's confidence in predictions when clean speech ground truth is unavailable. Additionally, we show that the uncertainty-augmented methods grounded in statistical modeling improve speech enhancement performance compared to methods that predict a single filter mask only. Next, we explore the direct use of uncertainty estimates for speech enhancement tasks. This includes unsupervised domain adaptation, where we utilize uncertainty-based filtering to select high-quality pseudo-targets to alleviate generalization issues. In another application, alongside audio inputs, we further explore modeling uncertainty originating from distorted video signals in an audio-visual phoneme classification task and demonstrate how to exploit modality-wise uncertainty to achieve more effective and robust multimodal fusion. In the second part of the thesis, we investigate the issues of interpretability and robustness by focusing on deep generative approaches. In contrast to predictive approaches that learn a deterministic mapping between noisy and clean speech, deep generative approaches aim to learn prior distributions of given data and reuse this knowledge to perform speech enhancement during inference. In the thesis, we consider a specific group of methods, which use a variational autoencoder (VAE) to learn a prior distribution of clean speech and combine it with an untrained non-negative matrix factorization (NMF)-based noise model to estimate a filter mask for speech enhancement. The statistically interpretable VAE-NMF framework exhibits an improved generalization ability to unseen acoustic conditions compared to predictive methods. However, training the VAE solely with clean speech makes it susceptible to noise interference during testing, especially for inputs with low signal-to-noise ratios. In this part, we aim to improve overall robustness in difficult acoustic conditions by augmenting separately the speech and noise models with noise information. The resulting noise-aware speech and noise models retain the high interpretability provided by statistical modeling while at the same time exhibiting improved speech enhancement performance in acoustically challenging environments

Exploring the Representational Spaces of States, Values, and Goals in Cognitive Maps

Maximizing current or future expected rewards is one of the most common goals of many decisions we make. Decisions are always made within the context of a given task. To optimally achieve our goals, we must combine knowledge of the structure of the environment with the current goal to optimally predict different potential rewards. Even the same exact choice can provide different rewards depending on the task at hand. How does the brain combine task demands, environmental structure, and reward maximization in the service of decision-making? Prior works have shown that the ventromedial prefrontal cortex (vmPFC) and adjacent orbitofrontal cortex (OFC) are known to contain signals corresponding to anticipated outcomes of decisions, known as expected value signals, that inform our choices. The hippocampal formation is known to maintain a representation of the environment and potential courses of action in it, known as a cognitive map. This thesis comprises three projects that explore the interaction between task structure, value representation, and cognitive maps within the vmPFC, OFC, and hippocampal formation. In the first project (Moneta et al., Nature Communications, 2023), we investigate how the vmPFC flexibly switches between different value representations in a task-dependent manner. Thirty-five participants completed a random dot-motion task in which either stimulus color or motion predicted rewards. Multivariate MRI analyses showed that vmPFC signals contain a rich representation that includes the current task state or context (motion/color), the expected value associated with the state, and crucially, the irrelevant expected value of the alternative context. We also find that irrelevant value representations in vmPFC compete with relevant value signals, interact with task-state representations, and relate to behavioral signs of value competition. Our results shed light on vmPFC's role in decision-making, bridging between its role in mapping observations onto the task states of a mental map, and computing expected values for multiple potential states. In the second project (Moneta et al., in prep.), taking a broader perspective on task structure, we examine how non-spatial cognitive maps are affected by value generalization. A non-spatial cognitive map refers to mental representations of abstract relationships between different states, helping an agent navigate decision-making by encoding how various choices or actions relate to one another. Animal work has shown that neural representations of spatial cognitive maps are affected by reward. In this project, we tested if similar effects generalize to non-spatial maps in humans. Seventy-two participants (38 of which underwent MRI scanning) performed two sessions of a perceptual discrimination task, before and after extensive reward learning. In all sessions, stimuli varied along two perceptual dimensions, forming a continuous two-dimensional cognitive map. After reward learning, performance in the perceptual discrimination task improved among previously rewarding stimuli. The effect of reward also generalized to areas of the cognitive map that were never rewarding. The precise pattern of changes in perceptual similarity judgments is consistent with the idea that reward learning leads to increased psychological distance between stimuli in the rewarding area, and decreased spacing in neighboring areas. Simulations show that a shift of representational fields towards the rewarded location, akin to a gravitation pulling, can explain the behavioral changes. In line with this, preliminary fMRI data analysis shows evidence for such gravitational pull in the hippocampus and, to some degree, in the medial OFC representations. Future analyses including additional regions in the hippocampal formation and the prefrontal cortex are planned. These results indicate that reward affects non-spatial cognitive maps and suggests accompanying neural representational changes. In the third project (Moneta, Grossman, Schuck, Trends in Neurosciences, 2024), we review recent literature connecting value and state representations in the OFC/vmPFC, proposing that these regions integrate stimulus, context, and outcome information into a unified representational space. Comparable encoding principles emerge in late layers of deep reinforcement learning models, where single nodes exhibit similar forms of mixed-selectivity, which enables flexible readout of relevant variables by downstream neurons. Based on these lines of evidence, we suggest that outcome-maximization leads to complex representational spaces that are insufficiently characterized by linear value signals that have been the focus of most prior research on the topic. We also discuss major outstanding questions concerning the role of OFC/vmPFC in learning across tasks, encoding of task-irrelevant aspects, and the role of hippocampus-PFC interactions. Collectively, these projects shed light on the dynamic relationship between task states and value representations, their integration into a cognitive map, and the representational capacities of the OFC/vmPFC and the hippocampal formation. The findings provide new insights into the neural mechanisms guiding behavior and suggest future research directions