Interpretable statistics for complex modelling: quantile and topological learning

As the complexity of our data increased exponentially in the last decades, so has our need for interpretable features. This thesis revolves around two paradigms to approach this quest for insights. In the first part we focus on parametric models, where the problem of interpretability can be seen as a “parametrization selection”. We introduce a quantile-centric parametrization and we show the advantages of our proposal in the context of regression, where it allows to bridge the gap between classical generalized linear (mixed) models and increasingly popular quantile methods. The second part of the thesis, concerned with topological learning, tackles the problem from a non-parametric perspective. As topology can be thought of as a way of characterizing data in terms of their connectivity structure, it allows to represent complex and possibly high dimensional through few features, such as the number of connected components, loops and voids. We illustrate how the emerging branch of statistics devoted to recovering topological structures in the data, Topological Data Analysis, can be exploited both for exploratory and inferential purposes with a special emphasis on kernels that preserve the topological information in the data. Finally, we show with an application how these two approaches can borrow strength from one another in the identification and description of brain activity through fMRI data from the ABIDE project

Magnetic skyrmions and their lattices in triplet superconductors

Complete topological classification of solutions in SO(3) symmetric Ginzburg-Landau free energy has been performed and a new class of solutions in weak external magnetic field carrying two units of magnetic flux has been identified. These solutions, magnetic skyrmions, do not have singular core like Abrikosov vortices and at low magnetic field become lighter for strongly type II superconductors. As a consequence, the lower critical magnetic field Hc1 is reduced by a factor of log(kappa). Magnetic skyrmions repel each other as 1/r at distances much larger then magnetic penetration depth forming relatively robust triangular lattice. Magnetic induction near Hc1 increases gradually as (H-Hc1)^2. This agrees very well with experiments on heavy fermion superconductor UPt3. Newly discovered Ru based compounds Sr2RuO4 and Sr2YRu(1-x)Cu(x)O6 are other possible candidates to possess skyrmion lattices. Deviations from exact SO(3) symmetry are also studied.Comment: 23 pages, 10 eps figure

Classification of engineered topological superconductors

I perform a complete classification of 2d, quasi-1d and 1d topological superconductors which originate from the suitable combination of inhomogeneous Rashba spin-orbit coupling, magnetism and superconductivity. My analysis reveals alternative types of topological superconducting platforms for which Majorana fermions are accessible. Specifically, I observe that for quasi-1d systems with Rashba spin-orbit coupling and time-reversal violating superconductivity, as for instance due to a finite Josephson current flow, Majorana fermions can emerge even in the absence of magnetism. Furthermore, for the classification I also consider situations where additional "hidden" symmetries emerge, with a significant impact on the topological properties of the system. The latter, generally originate from a combination of space group and complex conjugation operations that separately do not leave the Hamiltonian invariant. Finally, I suggest alternative directions in topological quantum computing for systems with additional unitary symmetries.Comment: To appear in New Journal of Physics for the Focus on Majorana Fermions in Condensed Matter; Final version 19 pages, 6 figures: a new section was added concerning the appearance of MFs in two coupled Rasba semiconducting wires with proximity induced superconductivity and a finite supercurrent flow, without the application of a magnetic field. Generally improved discussion and references adde

Persistent Homology and String Vacua

We use methods from topological data analysis to study the topological features of certain distributions of string vacua. Topological data analysis is a multi-scale approach used to analyze the topological features of a dataset by identifying which homological characteristics persist over a long range of scales. We apply these techniques in several contexts. We analyze N=2 vacua by focusing on certain distributions of Calabi-Yau varieties and Landau-Ginzburg models. We then turn to flux compactifications and discuss how we can use topological data analysis to extract physical informations. Finally we apply these techniques to certain phenomenologically realistic heterotic models. We discuss the possibility of characterizing string vacua using the topological properties of their distributions.Comment: 32 pages, 12 pdf figure

Green's Function Method for Line Defects and Gapless Modes in Topological Insulators : Beyond Semiclassical Approach

Defects which appear in heterostructure junctions involving topological insulators are sources of gapless modes governing the low energy properties of the systems, as recently elucidated by Teo and Kane [Physical Review B82, 115120 (2010)]. A standard approach for the calculation of topological invariants associated with defects is to deal with the spatial inhomogeneity raised by defects within a semiclassical approximation. In this paper, we propose a full quantum formulation for the topological invariants characterizing line defects in three-dimensional insulators with no symmetry by using the Green's function method. On the basis of the full quantum treatment, we demonstrate the existence of a nontrivial topological invariant in the topological insulator-ferromagnet tri-junction systems, for which a semiclassical approximation fails to describe the topological phase. Also, our approach enables us to study effects of electron-electron interactions and impurity scattering on topological insulators with spatial inhomogeneity which gives rise to the Axion electrodynamics responses.Comment: 15 pages, 3 figure

Towards Emotion Recognition: A Persistent Entropy Application

Emotion recognition and classification is a very active area of research. In this paper, we present a first approach to emotion classification using persistent entropy and support vector machines. A topology-based model is applied to obtain a single real number from each raw signal. These data are used as input of a support vector machine to classify signals into 8 different emotions (calm, happy, sad, angry, fearful, disgust and surprised)

Long range order in gauge theories. Deformed QCD as a toy model

We study a number of different ingredients, related to long range order observed in lattice QCD simulations, using a simple "deformed QCD" model. This model is a weakly coupled gauge theory, which however has all the relevant crucial elements allowing us to study difficult and nontrivial problems which are known to be present in real strongly coupled QCD. In the present study, we want to understand the physics of long range order in form of coherent low dimensional vacuum configurations observed in Monte Carlo lattice simulations. We demonstrate the presence of double-layer domain wall structures in the deformed QCD, and study their interaction with localized topological monopoles. Furthermore, we show that there is in fact an attractive interaction between the two, such that the monopole favors a position within the domain wall.Comment: 10 pages, 5 figure