Memristors for the Curious Outsiders

We present both an overview and a perspective of recent experimental advances and proposed new approaches to performing computation using memristors. A memristor is a 2-terminal passive component with a dynamic resistance depending on an internal parameter. We provide an brief historical introduction, as well as an overview over the physical mechanism that lead to memristive behavior. This review is meant to guide nonpractitioners in the field of memristive circuits and their connection to machine learning and neural computation.Comment: Perpective paper for MDPI Technologies; 43 page

Learning Broken Symmetries with Resimulation and Encouraged Invariance

Recognizing symmetries in data allows for significant boosts in neural network training. In many cases, however, the underlying symmetry is present only in an idealized dataset, and is broken in the training data, due to effects such as arbitrary and/or non-uniform detector bin edges. Standard approaches, such as data augmentation or equivariant networks fail to represent the nature of the full, broken symmetry. We introduce a novel data-augmentation scheme that respects the true underlying symmetry and avoids artifacts by augmenting the training set with transformed pre-detector examples whose detector response is then resimulated. In addition, we encourage the network to treat the augmented copies identically, allowing it to learn the broken symmetry. While the technique can be extended to other symmetries, we demonstrate its application on rotational symmetry in particle physics calorimeter images. We find that neural networks trained with pre-detector rotations converge to a solution more quickly than networks trained with standard post-detector augmentation, and that networks modified to encourage similar internal treatment of augmentations of the same input converge even faster

Metaheuristic Optimization Techniques Used in Controlling of an Active Magnetic Bearing System for High-Speed Machining Application

Smart control tactics, wider stability region, rapid reaction time, and high-speed performance are essential requirements for any controller to provide a smooth, vibrationless, and efficient performance of an in-house fabricated active magnetic bearing (AMB) system. In this manuscript, three pre-eminent population-based metaheuristic optimization techniques: Genetic algorithm (GA), Particle swarm optimization (PSO), and Cuckoo search algorithm (CSA) are implemented one by one, to calculate optimized gain parameters of PID controller for the proposed closed-loop active magnetic bearing (AMB) system. Performance indices or, objective functions on which these optimization techniques are executed are integral absolute error (IAE), integral square error (ISE), integral time multiplied absolute error (ITAE), and integral time multiplied square error (ITSE). The significance of an optimization technique and objective function can obtain only by implementing it. As a result, several comparisons are made based on statistical performance, time domain, frequency response behavior, and algorithm execution time. Finally, the applicability of optimization strategies in addition to the performance indices is determined with the aid of the comparative analysis. That could assist in choosing a suitable optimization technique along with a performance index for a high-speed application of an active magnetic bearing system

Variational quantum architectures. Applications for noisy intermediate-scale quantum computers

[eng] Quantum algorithms showing promising speedups with respect to their classical counterparts already exist. However, noise limits the quantum circuit depth, making the practical implementation of many such quantum algorithms impossible nowadays. In this sense, variational quantum algorithms offer a new approach, reducing the requisites of quantum computational resources at the expense of classical optimization. Disciplines in which variational quantum algorithms may have practical applications include simulation of quantum systems, solving large systems of linear equations, combinatorial optimization, data compression, quantum state diagonalization, among others. This thesis studies different variational quantum algorithm applications. In Chapter 1, we introduce the main building blocks of variational quantum algorithms. In Chapter 2, we benchmark the seminal variational quantum eigensolver algorithm for condensed matter systems. In Chapter 3, we explore how the task of compressing quantum information is affected by data encoding in variational quantum circuits. In Chapter 4, we propose a novel variational quantum algorithm to compute the singular values of pure bipartite states. In Chapter 5, we develop a new variational quantum algorithm to solve linear systems of equations. Finally, in Chapter 6, we implement quantum generative adversarial networks for generative modeling tasks. The conclusions of this thesis are exposed in Chapter 7. Furthermore, supplementary material can be found in the appendices. Appendix A provides an introduction to Qibo, a framework for quantum simulation. Appendix B presents some results related to the Solovay-Kitaev theorem. Extra results from Chapter 5 and Chapter 6 can be found in Appendix C and Appendix D, respectively.[spa] Algoritmos cuánticos mostrando prometedoras ventajas respecto sus contrapartes clásicas ya existen. Sin embargo, el ruido limita la profundidad de los circuitos cuánticos, lo que hace imposible la aplicación práctica de muchos de estos algoritmos cuánticos en la actualidad. En este sentido, los algoritmos cuánticos variacionales ofrecen un nuevo enfoque, reduciendo los requisitos de recursos computacionales cuánticos a expensas de optimización clásica. Disciplinas en las que los algoritmos cuánticos variacionales pueden tener aplicaciones prácticas incluyen la simulación de sistemas cuánticos, la resolución de grandes sistemas de ecuaciones lineales, la optimización combinatoria, la compresión de datos y la diagonalización de estados cuánticos, entre otras. Esta tesis estudia diferentes aplicaciones de los algoritmos cuánticos variacionales. En el Capítulo 1, presentamos los principales bloques de construcción de los algoritmos cuánticos variacionales. En el Capítulo 2, evaluamos el algoritmo “variational quantum eigensolver” para sistemas de materia condensada. En el capítulo 3, exploramos cómo la tarea de comprimir la información cuántica se ve afectada por la codificación de datos en los circuitos cuánticos variacionales. En el Capítulo 4, proponemos un novedoso algoritmo cuántico variacional para calcular los valores singulares de los estados bipartitos puros. En el Capítulo 5, desarrollamos un nuevo algoritmo cuántico variacional para resolver sistemas lineales de ecuaciones. Finalmente, en el Capítulo 6, implementamos redes generativas adversarias cuánticas para tareas de modelado generativo. Las conclusiones de esta tesis se exponen en el Capítulo 7. Además, se puede encontrar material complementario en los apéndices. El Apéndice A ofrece una introducción a Qibo, un software para la simulación cuántica. El Apéndice B presenta algunos resultados relacionados con el teorema de Solovay-Kitaev. En el Apéndice C y en el Apéndice D se pueden encontrar resultados adicionales del Capítulo 5 y del Capítulo 6, respectivamente

Proceedings of the Workshop on Change of Representation and Problem Reformulation

The proceedings of the third Workshop on Change of representation and Problem Reformulation is presented. In contrast to the first two workshops, this workshop was focused on analytic or knowledge-based approaches, as opposed to statistical or empirical approaches called 'constructive induction'. The organizing committee believes that there is a potential for combining analytic and inductive approaches at a future date. However, it became apparent at the previous two workshops that the communities pursuing these different approaches are currently interested in largely non-overlapping issues. The constructive induction community has been holding its own workshops, principally in conjunction with the machine learning conference. While this workshop is more focused on analytic approaches, the organizing committee has made an effort to include more application domains. We have greatly expanded from the origins in the machine learning community. Participants in this workshop come from the full spectrum of AI application domains including planning, qualitative physics, software engineering, knowledge representation, and machine learning

Applied Harmonic Analysis and Data Processing

Massive data sets have their own architecture. Each data source has an inherent structure, which we should attempt to detect in order to utilize it for applications, such as denoising, clustering, anomaly detection, knowledge extraction, or classification. Harmonic analysis revolves around creating new structures for decomposition, rearrangement and reconstruction of operators and functions—in other words inventing and exploring new architectures for information and inference. Two previous very successful workshops on applied harmonic analysis and sparse approximation have taken place in 2012 and in 2015. This workshop was the an evolution and continuation of these workshops and intended to bring together world leading experts in applied harmonic analysis, data analysis, optimization, statistics, and machine learning to report on recent developments, and to foster new developments and collaborations

A novel application of Lobatto iiia solver for numerical treatment of mixed convection nanofluidic model

The objective of the current investigation is to examine the influence of variable viscosity and transverse magnetic field on mixed convection fluid model through stretching sheet based on copper and silver nanoparticles by exploiting the strength of numerical computing via Lobatto IIIA solver. The nonlinear partial differential equations are changed into ordinary differential equations by means of similarity transformations procedure. A renewed finite difference based Lobatto IIIA method is incorporated to solve the fluidic system numerically. Vogel's model is considered to observe the influence of variable viscosity and applied oblique magnetic field with mixed convection along with temperature dependent viscosity. Graphical and numerical illustrations are presented to visualize the behavior of different sundry parameters of interest on velocity and temperature. Outcomes reflect that volumetric fraction of nanoparticles causes to increase the thermal conductivity of the fluid and the temperature enhances due to blade type copper nanoparticles. The convergence analysis on the accuracy to solve the problem is investigated viably though the residual errors with different tolerances to prove the worth of the solver. The temperature of the fluid accelerates due the blade type nanoparticles of copper and skin friction coefficient is reduced due to enhancement of Grashof Number

Generalized Lorenz-Mie theory : application to scattering and resonances of photonic complexes

Les structures photoniques complexes permettent de façonner la propagation lumineuse à l’échelle de la longueur d’onde au moyen de processus de diffusion et d’interférence. Cette fonctionnalité à l’échelle nanoscopique ouvre la voie à de multiples applications, allant des communications optiques aux biosenseurs. Cette thèse porte principalement sur la modélisation numérique de structures photoniques complexes constituées d’arrangements bidimensionnels de cylindres diélectriques. Deux applications sont privilégiées, soit la conception de dispositifs basés sur des cristaux photoniques pour la manipulation de faisceaux, de même que la réalisation de sources lasers compactes basées sur des molécules photoniques. Ces structures optiques peuvent être analysées au moyen de la théorie de Lorenz-Mie généralisée, une méthode numérique permettant d’exploiter la symétrie cylindrique des diffuseurs sous-jacents. Cette dissertation débute par une description de la théorie de Lorenz-Mie généralisée, obtenue des équations de Maxwell de l’électromagnétisme. D’autres outils théoriques utiles sont également présentés, soit une nouvelle formulation des équations de Maxwell-Bloch pour la modélisation de milieux actifs appelée SALT (steady state ab initio laser theory). Une description sommaire des algorithmes d’optimisation dits métaheuristiques conclut le matériel introductif de la thèse. Nous présentons ensuite la conception et l’optimisation de dispositifs intégrés permettant la génération de faisceaux d’amplitude, de phase et de degré de polarisation contrôlés. Le problème d’optimisation combinatoire associé est solutionné numériquement au moyen de deux métaheuristiques, l’algorithme génétique et la recherche tabou. Une étude théorique des propriétés de micro-lasers basés sur des molécules photoniques – constituées d’un arrangement simple de cylindres actifs – est finalement présentée. En combinant la théorie de Lorenz-Mie et SALT, nous démontrons que les propriétés physiques de ces lasers, plus spécifiquement leur seuil, leur spectre et leur profil d’émission, peuvent être affectés de façon nontriviale par les paramètres du milieu actif sous-jacent. Cette conclusion est hors d’atteinte de l’approche établie qui consiste à calculer les étatsméta-stables de l’équation de Helmholtz et leur facteur de qualité. Une perspective sur la modélisation de milieux photoniques désordonnés conclut cette dissertation.Complex photonic media mold the flow of light at the wavelength scale using multiple scattering and interference effects. This functionality at the nano-scale level paves the way for various applications, ranging from optical communications to biosensing. This thesis is mainly concerned with the numerical modeling of photonic complexes based on twodimensional arrays of cylindrical scatterers. Two applications are considered, namely the use of photonic-crystal-like devices for the design of integrated beam shaping elements, as well as active photonic molecules for the realization of compact laser sources. These photonic structures can be readily analyzed using the 2D Generalized Lorenz-Mie theory (2D-GLMT), a numerical scheme which exploits the symmetry of the underlying cylindrical structures. We begin this thesis by presenting the electromagnetic theory behind 2D-GLMT.Other useful frameworks are also presented, including a recently formulated stationary version of theMaxwell-Bloch equations called steady-state ab initio laser theory (SALT).Metaheuristics, optimization algorithms based on empirical rules for exploring large solution spaces, are also discussed. After laying down the theoretical content, we proceed to the design and optimization of beam shaping devices based on engineered photonic-crystal-like structures. The combinatorial optimization problem associated to beam shaping is tackled using the genetic algorithm (GA) as well as tabu search (TS). Our results show the possibility to design integrated beam shapers tailored for the control of the amplitude, phase and polarization profile of the output beam. A theoretical and numerical study of the lasing characteristics of photonic molecules – composed of a few coupled optically active cylinders – is also presented. Using a combination of 2D-GLMT and SALT, it is shown that the physical properties of photonic molecule lasers, specifically their threshold, spectrum and emission profile, can be significantly affected by the underlying gain medium parameters. These findings are out of reach of the established approach of computing the meta-stable states of the Helmholtz equation and their quality factor. This dissertation is concluded with a research outlook concerning themodeling of disordered photonicmedia