Noncommutative Geometry and Gauge theories on AF algebras

Non-commutative geometry (NCG) is a mathematical discipline developed in the 1990s by Alain Connes. It is presented as a new generalization of usual geometry, both encompassing and going beyond the Riemannian framework, within a purely algebraic formalism. Like Riemannian geometry, NCG also has links with physics. Indeed, NCG provided a powerful framework for the reformulation of the Standard Model of Particle Physics (SMPP), taking into account General Relativity, in a single "geometric" representation, based on Non-Commutative Gauge Theories (NCGFT). Moreover, this accomplishment provides a convenient framework to study various possibilities to go beyond the SMPP, such as Grand Unified Theories (GUTs). This thesis intends to show an elegant method recently developed by Thierry Masson and myself, which proposes a general scheme to elaborate GUTs in the framework of NCGFTs. This concerns the study of NCGFTs based on approximately finite $C^*$-algebras (AF-algebras), using either derivations of the algebra or spectral triples to build up the underlying differential structure of the Gauge Theory. The inductive sequence defining the AF-algebra is used to allow the construction of a sequence of NCGFTs of Yang-Mills Higgs types, so that the rank $n+1$ can represent a grand unified theory of the rank $n$. The main advantage of this framework is that it controls, using appropriate conditions, the interaction of the degrees of freedom along the inductive sequence on the AF algebra. This suggests a way to obtain GUT-like models while offering many directions of theoretical investigation to go beyond the SMPP

Transport Signatures Of Quantum Phase Transitions And The Interplay Of Geometry And Topology In Nodal Materials

The research presented in this thesis is divided into two parts. In the first part, we propose response signatures for quantum phase transitions in superconducting and bilayer graphene systems. In superconducting systems, there is the promise for realizing a Majorana quasiparticle: a fermion that is its own antiparticle and possesses some of the non-Abelian braiding statistics required to form a topological quantum computer. We propose conductance and noise signatures showing the presence of Majorana fermions in topological insulator-superconductor heterostructure Josephson Junctions. We then move on to address the possibility of realizing the physics of quantum point contacts in graphene bilayers. There have been numerous theoretical proposals of quasi-topological domain walls in bilayer graphene. Using bosonization and renormalization group considerations, we propose transport signatures characterizing the pinch-off behavior of the effective point contact formed by the intersection of two domain walls. In the second part of this thesis, we provide several examples of nodal band features protected by the combination of topology and crystalline geometry. Nodal features in condensed matter systems manifest when the Fermi surface consists only of a limited set of band-touching points with fixed dispersion. The low-energy theories of these touching points resemble those in particle physics, and these nodes, such as the quintessential examples in graphene, are therefore frequently characterized with names such as Dirac and Weyl fermions. The presence of nodal band features at the Fermi energy can have unique implications for bulk transport and surface physics, and so there has been a great effort in recent years to find new theoretical and real-material examples of nodal systems. We begin by showing that when spin-orbit interaction is weak, the same Z_2 invariant that predicts a topological insulator can be used when inversion symmetry is present to predict topological Dirac line nodes in crystal systems. On the surface of these line node semimetals, the projected interior of the line nodes can host a two-dimensional nearly-flat band, and could provide a route towards experimental access of phases with significant electron-electron interactions. We then present the first known example of a nodal condensed matter system with a description beyond particle physics: the double Dirac semimetal. In these systems, eightfold-degenerate linearly-dispersing nodal points manifest at the Brillouin zone edge. We list all possible space groups that can host double Dirac points when spin-orbit interaction is non-negligible, and we show that the expanded set of time-reversal-symmetric mass terms for these new fermions allows for new routes towards strain-engineering topological phase transitions and also provides the possibility of topologically-nontrivial line defects. We then move on to two dimensions, for which we use a consideration of compact flat manifolds to deduce all possible manifestations of nodal physics in strong spin-orbit systems. Through this analysis, we explain in more general terms some of the more exotic examples of nodal systems proposed over the past few years, and predict new examples in two and three dimensions. Using conclusions from this analysis and specializing to the wallpaper groups, we then show that a consideration of minimal insulating filling allows one to exhaustively characterize all possible topological and topological crystalline insulators. By realizing that the limited set of wallpaper groups constrains the Wilson-loop eigenvalue flows of a three-dimensional bulk insulating crystal, we present the discovery of a new topological crystalline insulator: the topological Dirac insulator. Unlike the surface states of a conventional topological insulator, the surface states of this new insulator are fourfold degenerate, and therefore can be gapped to realize truly topological surface quantum spin Hall domain walls. Finally, we present the first example of a filling-enforced semimetal in a magnetic system. By exploiting the modified time-reversal symmetry in certain antiferromagnetic systems, we characterize a new class of two-dimensional magnetic Dirac semimetals. We show that these semimetals manifest a new quantum critical point between quantum Hall phases, and discuss their place in the context of fermion doubling theorems

Mesh Compression

Die Kompression von Netzen ist eine weitgefächerte Forschungsrichtung mit Anwendungen in den verschiedensten Bereichen, wie zum Beispiel im Bereich der Handhabung extrem großer Modelle, beim Austausch von dreidimensionalem Inhalt über das Internet, im elektronischen Handel, als anpassungsfähige Repräsentation für Volumendatensätze usw. In dieser Arbeit wird das Verfahren der Cut-Border Machine beschrieben. Die Cut-Border Machine kodiert Netze, indem ein Teilbereich durch das Netz wächst (region growing). Kodiert wird die Art und Weise, wie neue Netzelemente dem wachsenden Teilbereich einverleibt werden. Das Verfahren der Cut-Border Machine kann sowohl auf Dreiecksnetze als auch auf Tetraedernetze angewendet werden. Trotz der einfachen Struktur des Verfahrens kann eine sehr hohe Kompressionsrate erzielt werden. Im Falle von Tetraedernetzen erreicht die Cut-Border Machine die beste Kompressionsrate von allen bekannten Verfahren. Die einfache Struktur der Cut-Border Machine ermöglicht einerseits die Realisierung direkt in Hardware und ist auch als Implementierung in Software extrem schnell. Auf der anderen Seite erlaubt die Einfachheit eine theoretische Analyse des Algorithmus. Gezeigt werden konnte, dass für ebene Triangulierungen eine leicht modifizierte Version der Cut-Border Machine lineare Laufzeiten in der Zahl der Knoten erzielt und dass die komprimierte Darstellung nur linearen Speicherbedarf benötigt, d.h. nicht mehr als fünf Bits pro Knoten. Neben der detaillierten Beschreibung der Cut-Border Machine mit mehreren Verbesserungen und Optimierungen, enthält die Arbeit eine Einführung zu Netzen und geeigneten Datenstrukturen und entwickelt mehrere Kodierungsverfahren, die im Bereich der Netzkompression Anwendung finden. Eine breite Übersicht verwandter Arbeiten gibt Einblick in des Forschungsgebiet. Weiterhin wird die Effizienz mehrerer in der Literatur beschriebener Verfahren verbessert. Insbesondere konnte die algorithmisch erzielte obere Schranke für die KodiMesh Compression is a broad research area with applications in a lot of different areas, such as the handling of very large models, the exchange of three dimensional content over the internet, electronic commerce, the flexible representation of volumetric data and so on. In this thesis the mesh compression method of the Cut-Border Machine is described. The Cut-Border Machine encodes meshes by growing a region through the mesh and encoding the way, in which the mesh elements are incorporated into the growing region. The Cut-Border Machine can be applied to triangular and tetrahedral meshes. Although the method is not too complicated, it achieves very good compression rates. In the tetrahedral case the Cut-Border Machine performs best among all known methods. The simple nature of the Cut-Border Machine allows on the one hand for a hardware implementation and performs also as software implementation extremely well. On the other hand the simplicity allows for a theoretical analysis of the Cut-Border Machine. It could be shown, that for planar triangulations a slightly modified version of the Cut-Border Machine runs in linear time in the number of vertices and that the compressed representation only consumes linear storage space, i.e. no more than five bits per vertex. Besides the detailed description of the Cut-Border Machine with several improvements and optimizations, the thesis gives an introduction to meshes and appropriate data structures, develops several coding techniques useful for mesh compression and gives a broad overview of related work. Furthermore the author improves the encoding efficiency of several other compression techniques. In particular could the algorithmically achieved upper bound for the encoding of planar triangulations be improved to ten percent above the theoretical limit, what is the best known result up to now

Generalized Space-Time Engineered Modulation (GSTEM) Metamaterials

This article presents a global and generalized perspective of electrodynamic meta-materials formed by space and time engineered modulations, which we name Generalized Space-Time Engineered Modulation (GSTEM) Metamaterials, or GSTEMs. In this perspective, it describes metamaterials from a unified spacetime viewpoint and introduces accelerated metamaterials as an extra type of dynamic metamaterials. First, it positions GSTEMs in the even broader context of electrodynamic systems that include (non-modulated) moving sources in vacuum and moving bodies, explains the difference between the moving-matter nature of the latter and the moving-perturbation nature of GSTEMs, and enumerates the different types of GSTEMs considered, namely Space EMs (SEMs), Time EMs (TEMs), Uniform Space-Time EMs (USTEMs) and Accelerated Space-Time EMs (ASTEMs). Next, it establishes the physics of the related interfaces, which includes direct-spacetime scattering and inverse-spacetime transition transformations. Then, it exposes the physics of the GSTEM metamaterials formed by stacking these interfaces and homogenizing the resulting crystals; this includes an original explanation of light deflection by USTEMs as being a spacetime weighted averaging phenomenon and the demonstration of ASTEM light curving and black-hole light attraction. Finally, it discusses some future prospects. Useful complementary information and animations are provided in the Supplementary Material

Modelling biomolecules through atomistic graphs: theory, implementation, and applications

Describing biological molecules through computational models enjoys ever-growing popularity. Never before has access to computational resources been easier for scientists across the natural sciences. The need for accurate, efficient, and robust modelling tools is therefore irrefutable. This, in turn, calls for highly interdisciplinary research, which the thesis presented here is a product of. Through the successful marriage of techniques from mathematical graph theory, theoretical insights from chemistry and biology, and the tools of modern computer science, we are able to computationally construct accurate depictions of biomolecules as atomistic graphs, in which individual atoms become nodes and chemical bonds/interactions are represented by weighted edges. When combined with methods from graph theory and network science, this approach has previously been shown to successfully reveal various properties of proteins, such as dynamics, rigidity, multi-scale organisation, allostery, and protein-protein interactions, and is well poised to set new standards in terms of computational feasibility, multi-scale resolution (from atoms to domains) and time-scales (from nanoseconds to milliseconds). Therefore, building on previous work in our research group spanning over 15 years and to further encourage and facilitate research into this growing field, this thesis's main contribution is to provide a formalised foundation for the construction of atomistic graphs. The most crucial aspect of constructing atomistic graphs of large biomolecules compared to small molecules is the necessity to include a variety of different types of bonds and interactions, because larger biomolecules attain their unique structural layout mainly through weaker interactions, e.g. hydrogen bonds, the hydrophobic effect or π-π interactions. Whilst most interaction types are well-studied and have readily available methodology which can be used to construct atomistic graphs, this is not the case for hydrophobic interactions. To fill this gap, the work presented herein includes novel methodology for encoding the hydrophobic effect in atomistic graphs, that accounts for the many-body effect and non-additivity. Then, a standalone software package for constructing atomistic graphs from structural data is presented. Herein lies the heart of this thesis: the combination of a variety of methodologies for a range of bond/interaction types, as well as an implementation that is deterministic, easy-to-use and efficient. Finally, some promising avenues for utilising atomistic graphs in combination with graph theoretical tools such as Markov Stability as well as other approaches such as Multilayer Networks to study various properties of biomolecules are presented.Open Acces

Sonic and Photonic Crystals

Sonic/phononic crystals termed acoustic/sonic band gap media are elastic analogues of photonic crystals and have also recently received renewed attention in many acoustic applications. Photonic crystals have a periodic dielectric modulation with a spatial scale on the order of the optical wavelength. The design and optimization of photonic crystals can be utilized in many applications by combining factors related to the combinations of intermixing materials, lattice symmetry, lattice constant, filling factor, shape of the scattering object, and thickness of a structural layer. Through the publications and discussions of the research on sonic/phononic crystals, researchers can obtain effective and valuable results and improve their future development in related fields. Devices based on these crystals can be utilized in mechanical and physical applications and can also be designed for novel applications as based on the investigations in this Special Issue

Beyond Flatland : exploring graphs in many dimensions

Societies, technologies, economies, ecosystems, organisms, . . . Our world is composed of complex networks—systems with many elements that interact in nontrivial ways. Graphs are natural models of these systems, and scientists have made tremendous progress in developing tools for their analysis. However, research has long focused on relatively simple graph representations and problem specifications, often discarding valuable real-world information in the process. In recent years, the limitations of this approach have become increasingly apparent, but we are just starting to comprehend how more intricate data representations and problem formulations might benefit our understanding of relational phenomena. Against this background, our thesis sets out to explore graphs in five dimensions: descriptivity, multiplicity, complexity, expressivity, and responsibility. Leveraging tools from graph theory, information theory, probability theory, geometry, and topology, we develop methods to (1) descriptively compare individual graphs, (2) characterize similarities and differences between groups of multiple graphs, (3) critically assess the complexity of relational data representations and their associated scientific culture, (4) extract expressive features from and for hypergraphs, and (5) responsibly mitigate the risks induced by graph-structured content recommendations. Thus, our thesis is naturally situated at the intersection of graph mining, graph learning, and network analysis.Gesellschaften, Technologien, Volkswirtschaften, Ökosysteme, Organismen, . . . Unsere Welt besteht aus komplexen Netzwerken—Systemen mit vielen Elementen, die auf nichttriviale Weise interagieren. Graphen sind natürliche Modelle dieser Systeme, und die Wissenschaft hat bei der Entwicklung von Methoden zu ihrer Analyse große Fortschritte gemacht. Allerdings hat sich die Forschung lange auf relativ einfache Graphrepräsentationen und Problemspezifikationen beschränkt, oft unter Vernachlässigung wertvoller Informationen aus der realen Welt. In den vergangenen Jahren sind die Grenzen dieser Herangehensweise zunehmend deutlich geworden, aber wir beginnen gerade erst zu erfassen, wie unser Verständnis relationaler Phänomene von intrikateren Datenrepräsentationen und Problemstellungen profitieren kann. Vor diesem Hintergrund erkundet unsere Dissertation Graphen in fünf Dimensionen: Deskriptivität, Multiplizität, Komplexität, Expressivität, und Verantwortung. Mithilfe von Graphentheorie, Informationstheorie, Wahrscheinlichkeitstheorie, Geometrie und Topologie entwickeln wir Methoden, welche (1) einzelne Graphen deskriptiv vergleichen, (2) Gemeinsamkeiten und Unterschiede zwischen Gruppen multipler Graphen charakterisieren, (3) die Komplexität relationaler Datenrepräsentationen und der mit ihnen verbundenen Wissenschaftskultur kritisch beleuchten, (4) expressive Merkmale von und für Hypergraphen extrahieren, und (5) verantwortungsvoll den Risiken begegnen, welche die Graphstruktur von Inhaltsempfehlungen mit sich bringt. Damit liegt unsere Dissertation naturgemäß an der Schnittstelle zwischen Graph Mining, Graph Learning und Netzwerkanalyse

Selected Topics in Gravity, Field Theory and Quantum Mechanics

Quantum field theory has achieved some extraordinary successes over the past sixty years; however, it retains a set of challenging problems. It is not yet able to describe gravity in a mathematically consistent manner. CP violation remains unexplained. Grand unified theories have been eliminated by experiment, and a viable unification model has yet to replace them. Even the highly successful quantum chromodynamics, despite significant computational achievements, struggles to provide theoretical insight into the low-energy regime of quark physics, where the nature and structure of hadrons are determined. The only proposal for resolving the fine-tuning problem, low-energy supersymmetry, has been eliminated by results from the LHC. Since mathematics is the true and proper language for quantitative physical models, we expect new mathematical constructions to provide insight into physical phenomena and fresh approaches for building physical theories