Computational Intelligence and Complexity Measures for Chaotic Information Processing

This dissertation investigates the application of computational intelligence methods in the analysis of nonlinear chaotic systems in the framework of many known and newly designed complex systems. Parallel comparisons are made between these methods. This provides insight into the difficult challenges facing nonlinear systems characterization and aids in developing a generalized algorithm in computing algorithmic complexity measures, Lyapunov exponents, information dimension and topological entropy. These metrics are implemented to characterize the dynamic patterns of discrete and continuous systems. These metrics make it possible to distinguish order from disorder in these systems. Steps required for computing Lyapunov exponents with a reorthonormalization method and a group theory approach are formalized. Procedures for implementing computational algorithms are designed and numerical results for each system are presented. The advance-time sampling technique is designed to overcome the scarcity of phase space samples and the buffer overflow problem in algorithmic complexity measure estimation in slow dynamics feedback-controlled systems. It is proved analytically and tested numerically that for a quasiperiodic system like a Fibonacci map, complexity grows logarithmically with the evolutionary length of the data block. It is concluded that a normalized algorithmic complexity measure can be used as a system classifier. This quantity turns out to be one for random sequences and a non-zero value less than one for chaotic sequences. For periodic and quasi-periodic responses, as data strings grow their normalized complexity approaches zero, while a faster deceasing rate is observed for periodic responses. Algorithmic complexity analysis is performed on a class of certain rate convolutional encoders. The degree of diffusion in random-like patterns is measured. Simulation evidence indicates that algorithmic complexity associated with a particular class of 1/n-rate code increases with the increase of the encoder constraint length. This occurs in parallel with the increase of error correcting capacity of the decoder. Comparing groups of rate-1/n convolutional encoders, it is observed that as the encoder rate decreases from 1/2 to 1/7, the encoded data sequence manifests smaller algorithmic complexity with a larger free distance value

Quasiparticles in Quantum Many-Body Systems

Topologically ordered phases flamboyance a cornucopia of intriguing phenomena that cannot be perceived in the conventional phases including the most striking property of hosting anyon quasiparticles having fractional charges and fractional statistics. Such phases were discovered with the remarkable experiment of the fractional quantum Hall effect and are drawing a lot of recognition. Realization of these phases on lattice systems and study of the anyon quasiparticles there are important and interesting avenue to research in unraveling new physics, which can not be found in the continuum, and this thesis is an important contribution in that direction. Also such lattice models hosting anyons are particularly important to control the movement of anyons while experimentally implemented with ultra-cold atoms in optical lattices. We construct lattice models by implementing analytical states and parent Hamiltonians on two-dimensional plane hosting non-Abelian anyons, which are proposed candidates for quantum computations. Such lattice models are suitable to create both quasiholes and quasielectrons in the similar way and thereby avoiding the singularity problem for the quasielectrons in continuum. Anyons in these models are found to be well-screened with proper charges and right statistics. Going beyond two dimensions, we unravel the intriguing physics of topologically ordered phases of matter in fractional dimensions such as in the fractal lattices by employing our model constructions of analytical states and parent Hamiltonians there. We find the anyons to be well-screened with right charges and statistics for all dimensions. Our work takes the first step in bridging the gap between two dimensions and one dimension in addressing topological phases which reveal new physics. Our constructions are particularly important in this context since such lattices lack translational symmetry and hence become unsuitable for the fractional Chern insulator implementations. The special features of topologically ordered phases make these difficult to probe and hence the detection of topological quantum phase transitions becomes challenging. The existing probes suffer from shortcomings uo-to a large extent and therefore construction of new type of probes become important and are on high demand. The robustness of anyon properties draw our attention to propose these as detector of topological quantum phase transitions with significant advantages including the facts that these are numerically cheaper probes and are independent of the boundary conditions. We test our probe in three different examples and find that simple properties like anyon charges detect the transitions.Topologisch geordnete Phasen extravagieren ein Füllhorn faszinierender Phänomene, die in den herkömmlichen Phasen nicht wahrgenommen werden können, einschließlich der auffälligsten Eigenschaft, Quasiteilchen mit fraktionierten Ladungen und fraktion- ierten Statistiken aufzunehmen. Solche Phasen wurden mit dem bemerkenswerten Exper- iment des fraktionierten Quanten-Hall-Effekts entdeckt und finden viel Anerkennung. Die Realisierung dieser Phasen auf Gittersystemen und die Untersuchung der Anyon- Quasiteilchen sind wichtige und interessante Wege zur Erforschung der Entschlüsselung neuer Physik, die im Kontinuum nicht zu finden sind, und diese These ist ein wichtiger Beitrag in diese Richtung. Auch solche Gittermodelle, die Anyons enthalten, sind beson- ders wichtig, um die Bewegung von Anyons zu steuern, während sie experimentell mit ultrakalten Atomen in optischen Gittern implementiert werden. Wir konstruieren Gittermodelle, indem wir analytische Zustände und Eltern-Hamiltonianer auf einer zwei- dimensionalen Ebene implementieren, die nicht-abelsche Anyons enthält, die als Kan- didaten für Quantenberechnungen vorgeschlagen werden. Solche Gittermodelle sind geeignet, sowohl Quasi-Löcher als auch Quasielektronen auf ähnliche Weise zu erzeu- gen und dadurch das Singularitätsproblem für die Quasielektronen im Kontinuum zu vermeiden. Jeder in diesen Modellen wird mit angemessenen Gebühren und richtigen Statistiken gut überprüft. Über zwei Dimensionen hinaus enträtseln wir die faszinierende Physik topologisch geordneter Phasen der Materie in fraktionierten Dimensionen wie in den fraktalen Gittern, indem wir dort unsere Modellkonstruktionen von analytischen Zuständen und Eltern-Hamiltonianern verwenden. Wir finden, dass die Anyons mit den richtigen Gebühren und Statistiken für alle Dimensionen gut überprüft werden. Unsere Arbeit macht den ersten Schritt, um die Lücke zwischen zwei Dimensionen und einer Dimension zu schließen und topologische Phasen anzugehen, die neue Physik enthüllen. Unsere Konstruktionen sind in diesem Zusammenhang besonders wichtig, da solche Gitter keine Translationssymmetrie aufweisen und daher für die fraktionierten Chern- Isolatorimplementierungen ungeeignet werden. Die besonderen Merkmale topologisch geordneter Phasen machen es schwierig, diese zu untersuchen, und daher wird die Detek- tion topologischer Quantenphasenübergänge schwierig. Die vorhandenen Sonden leiden in hohem Maße unter Mängeln, weshalb die Konstruktion neuer Sondenarten wichtig wird und eine hohe Nachfrage besteht. Die Robustheit der Anyon-Eigenschaften lenkt unsere Aufmerksamkeit darauf, diese als Detektor für topologische Quantenphasenübergänge mit signifikanten Vorteilen vorzuschlagen, einschließlich der Tatsache, dass dies numerisch billigere Sonden sind und von den Randbedingungen unabhängig sind. Wir testen unsere Sonde in drei verschiedenen Beispielen und stellen fest, dass einfache Eigenschaften wie Ladungen die Übergänge erfassen

Symmetric and Asymmetric Data in Solution Models

This book is a Printed Edition of the Special Issue that covers research on symmetric and asymmetric data that occur in real-life problems. We invited authors to submit their theoretical or experimental research to present engineering and economic problem solution models that deal with symmetry or asymmetry of different data types. The Special Issue gained interest in the research community and received many submissions. After rigorous scientific evaluation by editors and reviewers, seventeen papers were accepted and published. The authors proposed different solution models, mainly covering uncertain data in multicriteria decision-making (MCDM) problems as complex tools to balance the symmetry between goals, risks, and constraints to cope with the complicated problems in engineering or management. Therefore, we invite researchers interested in the topics to read the papers provided in the book

Tools and Algorithms for the Construction and Analysis of Systems

This open access book constitutes the proceedings of the 28th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2022, which was held during April 2-7, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 46 full papers and 4 short papers presented in this volume were carefully reviewed and selected from 159 submissions. The proceedings also contain 16 tool papers of the affiliated competition SV-Comp and 1 paper consisting of the competition report. TACAS is a forum for researchers, developers, and users interested in rigorously based tools and algorithms for the construction and analysis of systems. The conference aims to bridge the gaps between different communities with this common interest and to support them in their quest to improve the utility, reliability, exibility, and efficiency of tools and algorithms for building computer-controlled systems