Static and dynamic magnetoelastic properties of spin ice

The concept of magnetic frustration is a fundamental topic in modern solid-state physics having direct consequences in systems with rich magnetic phases hosting emergent excitations, such as the magnetic monopoles in the spin-ice compounds. One important ingredient of frustration is the lattice that constrains the magnetic spins on it to a site anisotropy and inter-site coupling. Therefore, strong magnetoelastic interactions between the magnetic system and the lattice are expected and investigated in this thesis in detail. At first, I investigate the dependence of the relative length change of single crystals of the classical spin ices \dto{} and \hto{} on the magnetic field and temperature by capacitive dilatometry. In terms of the magnetostriction and thermal expansion \dto{} and \hto{} show qualitatively similar behavior, that seems to be independent of the Kramer or non-Kramers character of the rare-earth ion. The magnitude of the magnetostrictive effect deep in the spin-ice phase at \SI{0.3}{\kelvin} is \deltaL{} = \SI{2e-5}{} and \SI{2e-4}{} for \dto{} and \hto{}, respectively. In numerical simulations using a manifold model, the experimental results could be qualitatively reproduced by a combination of exchange and crystal-field striction. A second highlight of the dilatometric measurements of the spin-ice compounds is the observation of the lattice dynamics. The relaxation processes are rather slow, the longest relaxation times were observed at lowest temperatures and in the field range with magnetostrictive hysteresis, \ie{}, below \SI{0.9}{\tesla} for \dto{} and below \SI{1.5}{\tesla} for \hto{}. I find that the region of longest relaxation coincides well with the kagome-ice phase of the magnetic phase diagrams; the laxation time is of the order of \SI{5000}{\second} (> \SI{80}{\minute}). With increasing temperatures the time scale of the relaxation reduces to minutes at around \SI{0.7}{\kelvin} corresponding to the spin-freezing temperature obtained from ac-susceptibility measurements. In the second study I investigate the variation of the magnetic properties in dependence of the lattice constant. A systematic reduction of the lattice constant of \dgsoxx{} can be achieved by substituting the non-magnetic germanium ion in the cubic pyrochlore oxide with silicon. Characteristic properties of a spin-ice phase could be observed in measurements of magnetization, ac susceptibility, and heat capacity. From the temperature shift of the peaks, observed in the temperature-dependent heat capacity, an increase of the strength of the magnetic exchange interaction by a changed ratio of the competing exchange and dipolar interaction is deduced. The new spin-ice compounds are, thus, closer to the phase boundary between spin-ice phase and antiferromagnetically ordered all-in-all-out phase consistent with a reduction of the energy of monopole excitations

Capital Structure Decisions and the Use of Factoring

This thesis analyzes three research questions that belong to the field of corporate finance. The first and the second parts of this thesis examine predictions of the trade-off theory of capital structure. This theory postulates that firms balance the benefits and costs of debt versus equity and as a result, choose target capital structures. The third research question analyzes the determinants of the decision of a firm to sell its accounts receivable to a factor. According to the trade-off theory, the tax advantage of debt at the corporate level encourages firms with high marginal tax rates to bear more debt whereas the tax advantage of equity at the investor level leads to a lower debt ratio for firms with high personal tax rates. This thesis provides new evidence that taxes affect the capital structure choice of firms. Following the Graham methodology to simulate marginal tax rates, we find a statistically and economically significant positive relationship between the marginal tax benefit of debt (net and gross of investor taxes) and the debt ratio. A 10% increase in the net (gross) marginal tax benefit of debt causes a 1.5% (1.6%) increase in the debt ratio, ceteris paribus. Firms that face transaction costs may not adjust to their target capital structures immediately but instead the adjustment takes a period of time. The speed of adjustment to target capital structure has important implications for the relevance of the target capital structure for the firms’ choice of financing. Recent studies show that a standard partial adjustment model with the debt ratio as the dependent variable cannot distinguish between mechanical mean reversion and adjustment to target capital structure. We propose a new approach that uses the net increase of debt as the dependent variable and uses only ex-post information to estimate the target capital structure. Simulation experiments show that this approach is mainly unaffected by mechanical mean reversion and hence able to provide a meaningful test for the target adjustment hypothesis. We estimate a speed of adjustment to target capital structure of 28% per year. The third part of this thesis analyzes a firm’s decision of whether to accounts receivable internally, use full-service factoring or enter into an in-house factoring contract. Our model is primarily based on a theory of the firm that stresses a supplier’s need for financing, risk and financial flexibility. We find that high-risk firms with a strong need for short-term financing and restricted access to bank credit are more likely to use factoring. Larger firms typically prefer in-house factoring, whereas smaller firms tend to rely on full-service factoring. The firm’s desire to attain independence from banks plays an important role in decisions regarding factoring

Trennung und Schätzung der Anzahl von Audiosignalquellen mit Zeit- und Frequenzüberlappung

Everyday audio recordings involve mixture signals: music contains a mixture of instruments; in a meeting or conference, there is a mixture of human voices. For these mixtures, automatically separating or estimating the number of sources is a challenging task. A common assumption when processing mixtures in the time-frequency domain is that sources are not fully overlapped. However, in this work we consider some cases where the overlap is severe — for instance, when instruments play the same note (unison) or when many people speak concurrently ("cocktail party") — highlighting the need for new representations and more powerful models. To address the problems of source separation and count estimation, we use conventional signal processing techniques as well as deep neural networks (DNN). We first address the source separation problem for unison instrument mixtures, studying the distinct spectro-temporal modulations caused by vibrato. To exploit these modulations, we developed a method based on time warping, informed by an estimate of the fundamental frequency. For cases where such estimates are not available, we present an unsupervised model, inspired by the way humans group time-varying sources (common fate). This contribution comes with a novel representation that improves separation for overlapped and modulated sources on unison mixtures but also improves vocal and accompaniment separation when used as an input for a DNN model. Then, we focus on estimating the number of sources in a mixture, which is important for real-world scenarios. Our work on count estimation was motivated by a study on how humans can address this task, which lead us to conduct listening experiments, confirming that humans are only able to estimate the number of up to four sources correctly. To answer the question of whether machines can perform similarly, we present a DNN architecture, trained to estimate the number of concurrent speakers. Our results show improvements compared to other methods, and the model even outperformed humans on the same task. In both the source separation and source count estimation tasks, the key contribution of this thesis is the concept of “modulation”, which is important to computationally mimic human performance. Our proposed Common Fate Transform is an adequate representation to disentangle overlapping signals for separation, and an inspection of our DNN count estimation model revealed that it proceeds to find modulation-like intermediate features.Im Alltag sind wir von gemischten Signalen umgeben: Musik besteht aus einer Mischung von Instrumenten; in einem Meeting oder auf einer Konferenz sind wir einer Mischung menschlicher Stimmen ausgesetzt. Für diese Mischungen ist die automatische Quellentrennung oder die Bestimmung der Anzahl an Quellen eine anspruchsvolle Aufgabe. Eine häufige Annahme bei der Verarbeitung von gemischten Signalen im Zeit-Frequenzbereich ist, dass die Quellen sich nicht vollständig überlappen. In dieser Arbeit betrachten wir jedoch einige Fälle, in denen die Überlappung immens ist zum Beispiel, wenn Instrumente den gleichen Ton spielen (unisono) oder wenn viele Menschen gleichzeitig sprechen (Cocktailparty) —, so dass neue Signal-Repräsentationen und leistungsfähigere Modelle notwendig sind. Um die zwei genannten Probleme zu bewältigen, verwenden wir sowohl konventionelle Signalverbeitungsmethoden als auch tiefgehende neuronale Netze (DNN). Wir gehen zunächst auf das Problem der Quellentrennung für Unisono-Instrumentenmischungen ein und untersuchen die speziellen, durch Vibrato ausgelösten, zeitlich-spektralen Modulationen. Um diese Modulationen auszunutzen entwickelten wir eine Methode, die auf Zeitverzerrung basiert und eine Schätzung der Grundfrequenz als zusätzliche Information nutzt. Für Fälle, in denen diese Schätzungen nicht verfügbar sind, stellen wir ein unüberwachtes Modell vor, das inspiriert ist von der Art und Weise, wie Menschen zeitveränderliche Quellen gruppieren (Common Fate). Dieser Beitrag enthält eine neuartige Repräsentation, die die Separierbarkeit für überlappte und modulierte Quellen in Unisono-Mischungen erhöht, aber auch die Trennung in Gesang und Begleitung verbessert, wenn sie in einem DNN-Modell verwendet wird. Im Weiteren beschäftigen wir uns mit der Schätzung der Anzahl von Quellen in einer Mischung, was für reale Szenarien wichtig ist. Unsere Arbeit an der Schätzung der Anzahl war motiviert durch eine Studie, die zeigt, wie wir Menschen diese Aufgabe angehen. Dies hat uns dazu veranlasst, eigene Hörexperimente durchzuführen, die bestätigten, dass Menschen nur in der Lage sind, die Anzahl von bis zu vier Quellen korrekt abzuschätzen. Um nun die Frage zu beantworten, ob Maschinen dies ähnlich gut können, stellen wir eine DNN-Architektur vor, die erlernt hat, die Anzahl der gleichzeitig sprechenden Sprecher zu ermitteln. Die Ergebnisse zeigen Verbesserungen im Vergleich zu anderen Methoden, aber vor allem auch im Vergleich zu menschlichen Hörern. Sowohl bei der Quellentrennung als auch bei der Schätzung der Anzahl an Quellen ist ein Kernbeitrag dieser Arbeit das Konzept der “Modulation”, welches wichtig ist, um die Strategien von Menschen mittels Computern nachzuahmen. Unsere vorgeschlagene Common Fate Transformation ist eine adäquate Darstellung, um die Überlappung von Signalen für die Trennung zugänglich zu machen und eine Inspektion unseres DNN-Zählmodells ergab schließlich, dass sich auch hier modulationsähnliche Merkmale finden lassen

High-Resolution Speaker Counting In Reverberant Rooms Using CRNN With Ambisonics Features

Speaker counting is the task of estimating the number of people that are simultaneously speaking in an audio recording. For several audio processing tasks such as speaker diarization, separation, localization and tracking, knowing the number of speakers at each timestep is a prerequisite, or at least it can be a strong advantage, in addition to enabling a low latency processing. For that purpose, we address the speaker counting problem with a multichannel convolutional recurrent neural network which produces an estimation at a short-term frame resolution. We trained the network to predict up to 5 concurrent speakers in a multichannel mixture, with simulated data including many different conditions in terms of source and microphone positions, reverberation, and noise. The network can predict the number of speakers with good accuracy at frame resolution.Comment: 5 pages, 1 figur

Chemical regulators of epithelial plasticity reveal a nuclear receptor pathway controlling myofibroblast differentiation

Plasticity in epithelial tissues relates to processes of embryonic development, tissue fibrosis and cancer progression. Pharmacological modulation of epithelial transitions during disease progression may thus be clinically useful. Using human keratinocytes and a robotic high-content imaging platform, we screened for chemical compounds that reverse transforming growth factor β (TGF-β)-induced epithelial-mesenchymal transition. In addition to TGF-β receptor kinase inhibitors, we identified small molecule epithelial plasticity modulators including a naturally occurring hydroxysterol agonist of the liver X receptors (LXRs), members of the nuclear receptor transcription factor family. Endogenous and synthetic LXR agonists tested in diverse cell models blocked α-smooth muscle actin expression, myofibroblast differentiation and function. Agonist-dependent LXR activity or LXR overexpression in the absence of ligand counteracted TGF-β-mediated myofibroblast terminal differentiation and collagen contraction. The protective effect of LXR agonists against TGF-β-induced pro-fibrotic activity raises the possibility that anti-lipidogenic therapy may be relevant in fibrotic disorders and advanced cancer

Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions

By building upon the recent theory that established the connection between implicit generative modeling (IGM) and optimal transport, in this study, we propose a novel parameter-free algorithm for learning the underlying distributions of complicated datasets and sampling from them. The proposed algorithm is based on a functional optimization problem, which aims at finding a measure that is close to the data distribution as much as possible and also expressive enough for generative modeling purposes. We formulate the problem as a gradient flow in the space of probability measures. The connections between gradient flows and stochastic differential equations let us develop a computationally efficient algorithm for solving the optimization problem. We provide formal theoretical analysis where we prove finite-time error guarantees for the proposed algorithm. To the best of our knowledge, the proposed algorithm is the first nonparametric IGM algorithm with explicit theoretical guarantees. Our experimental results support our theory and show that our algorithm is able to successfully capture the structure of different types of data distributions.Comment: Published at the International Conference on Machine Learning (ICML) 201