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Context of the tutorial: the IEEE CIS Summer School on Computational Intelligence and Applications (IEEE CIS SSoCIA 2022), associated with the 8th IEEE Latin American Conference on Computational Intelligence (IEEE LA-CCI 2022).; Doctoral; Random Neural Networks are a class of Neural Networks coming from Stochastic Processes and, in particular, from Queuing Models. They have some nice properties and they have reached good performances in several application areas. They are, in fact, queuing systems seen as Neural machines, and the two uses (probabilistic models for the performance evaluation of systems, or learning machines similar as the other more standard families of Neural Networks) refer to the same mathematical objects. They have the appealing that, as other special models that are unknown for most experts in Machine Learning, their testing in and/or adaptation to the many areas where standard Machine Learning techniques have obtained great successes is totally open.In the tutorial, we will introduce Random Neurons and the networks we can build with them, plus some details about the numerical techniques needed to learn with them. We will also underline the reasons that make them at least extremely interesting. We will also describe some of their successful applications, including our examples. We will focus on learning, but we will mention other uses of these models in performance evaluation, in the analysis of biological systems, and in optimization.

Lecture notes for a course given at the Algebraic Coding Theory (ACT) summer school 2022; Doctoral; These lecture notes have been written for a course at the Algebraic Coding Theory (ACT) summer school 2022 that took place in the university of Zurich. The objective of the course propose an in-depth presentation of the proof of one of the most striking results of coding theory: Tsfasman Vl\u{a}du\c{t} Zink Theorem, which asserts that for some prime power $q$, there exist sequences of codes over $\mathbb{F}_q$ whose asymptotic parameters beat random codes.

Doctoral; Decentralization and hybridization of distributed systems has given birth to edge and fog computing. At the same time, artificial intelligence and self-adaptive mechanisms have allowed great progress in the autonomy of networks. In this talk, we look at the need for self-organization, where local decisions give rise to globally consistent system behaviors. After introducing an overview of open questions, we illustrate our talk by presenting two algorithms for (1) target discovery following a disaster, using autonomous drones and for (2) extending the lifespan of an IoT network. Finally, we introduce a quick overview of some other recent works done.

Slides decks:1. The Bond graph language (includes a comparison with Modelica's concept of potential and flow variables)2. Practice: reading & creating bond graphs3. Causality4. Short practice of causality; Engineering school; This short introductory course on Bond graph is part of a 36 hours course on Modelica & Bond graph (more precisely: multi-domain modeling, analysis and simulation with Modelica & Bond graph). It is an elective course at CentraleSupélec, campus of Rennes, offered in the 2nd year engineering curriculum (i.e. a Master 1 level course). Most of the course is devoted to Modelica (course material available at http://éole.net/courses/modelica/).This archive contains the material I used to teach a short introduction to Bond graph in a few hours since 2020. The course has only been taught two times, so the material is far from complete.See README in zip archive for a description of the folder.

Master

Doctoral

Master

Master

=>Chapter I). Introduction to integrated photonics, overview▪ Materials and technologies, thin-layer processes for the fabrication of such devices, packaging and miniaturization. Examples of thin layers processes for waveguides and structures.=>Chapter II). Theory of electromagnetic waveguides, photonic’s propagation, quantification of the optical modes▪ Notion of guided modes / radiation modes; geometrical approach of the propagation of guided modes; ray optics and phase shift; Goos-Hänschen shift; effective guide thicknesses.▪ Fundamentals on the electromagnetic theory of dielectric waveguide (Maxwell’s approach); dispersion relations and calculus of photonic’s modes (eigenvalues and eigenvectors); channel optical waveguides and geometries; dispersion phenomena and pulse’s spread; optical guides with various graded-index profiles; stored energy and power flow; historic methods on the calculus of effective indices (the effective index method, separation of variables and method of field shadows, the Marcatili’s method); extended approaches to another waveguides structures; multilayer slab waveguides and global matrix formalism; finite difference spatial methods (semi-vectorial and vectorial); spectral methods; modes expansion and normalization; finite difference time domain (FDTD); numerical analysis; curved waveguides formalism and S-bend propagation; circular waveguides (optical fibres and tubular structures); waveguide transitions; tapers and junctions.▪ Resonant cavity or micro-resonators (ring, disk, sphere); quality factor and energy-management.▪ Coupled-mode theory representation; differential form of coupled amplitude equations; notion of supermodes.▪ Energy formulation of equations and their resolution.=>Chapter III). Microphotonic components▪ Applications to MOEMS (sensors, optical telecommunication); generic devices for photonic measurements (physical, chemical, biologic measurements); characterisations of photonic structures.=> Chapter IV). Nanophotonic, sub-wavelength photonics, nano-components▪ Electron-photon analogies, development of the basics on photonic crystals (PC); wave equation and eigenvalues; one-dimensional model (PC-1D or Bragg mirror); Bloch’s theorem and Fourier expansion of dielectric functions; plane waves method decomposition; spatial periodicities and photonics band gap; two- and three-dimensional crystals cases (PC-2D and -3D); photonic band calculation; phase velocity, group velocity and density of states; cavity and decay time of a mode; bands engineering and control of the photonic dispersion curves; localized defect modes; cavity; photonic structures based on photonic crystals (PC-waveguides, resonators, couplers, filters, mirrors, lasers); 2.5D-PC-components examples; technical characterisations of structures; mapping of CP-research in France and LEOM-INL / ECL Lyon example.▪ Near field optical; introduction to the main concepts; presentation of specific probes, and near optical field microscopy (STOM, SNOM).▪ Biomimetic and auto-assembled molecular nano-materials for photonics; nano- wires and tubes; nano-connexions and networks; bio-nanophotonic.▪ Plasmonic photonics; surface plasmon; electromagnetic modes localized at interface; evanescent waves; excitation of plasmon.; Master; MODULE: Hybrid integrated photonics & Nanophotonics devices, B. Bêche, Pr. Univ. Rennes IETR CNRS / Elements of course-Chapter =>Chapter I). Introduction to integrated photonics, overview▪ Materials and technologies, thin-layer processes for the fabrication of such devices, packaging and miniaturization. Examples of thin layers processes for waveguides and structures.=>Chapter II). Theory of electromagnetic waveguides, photonic’s propagation, quantification of the optical modes▪ Notion of guided modes / radiation modes; geometrical approach of the propagation of guided modes; ray optics and phase shift; Goos-Hänschen shift; effective guide thicknesses.▪ Fundamentals on the electromagnetic theory of dielectric waveguide (Maxwell’s approach); dispersion relations and calculus of photonic’s modes (eigenvalues and eigenvectors); channel optical waveguides and geometries; dispersion phenomena and pulse’s spread; optical guides with various graded-index profiles; stored energy and power flow; historic methods on the calculus of effective indices (the effective index method, separation of variables and method of field shadows, the Marcatili’s method); extended approaches to another waveguides structures; multilayer slab waveguides and global matrix formalism; finite difference spatial methods (semi-vectorial and vectorial); spectral methods; modes expansion and normalization; finite difference time domain (FDTD); numerical analysis; curved waveguides formalism and S-bend propagation; circular waveguides (optical fibres and tubular structures); waveguide transitions; tapers and junctions.▪ Resonant cavity or micro-resonators (ring, disk, sphere); quality factor and energy-management.▪ Coupled-mode theory representation; differential form of coupled amplitude equations; notion of supermodes.▪ Energy formulation of equations and their resolution.=>Chapter III). Microphotonic components▪ Applications to MOEMS (sensors, optical telecommunication); generic devices for photonic measurements (physical, chemical, biologic measurements); characterisations of photonic structures.=> Chapter IV). Nanophotonic, sub-wavelength photonics, nano-components▪ Electron-photon analogies, development of the basics on photonic crystals (PC); wave equation and eigenvalues; one-dimensional model (PC-1D or Bragg mirror); Bloch’s theorem and Fourier expansion of dielectric functions; plane waves method decomposition; spatial periodicities and photonics band gap; two- and three-dimensional crystals cases (PC-2D and -3D); photonic band calculation; phase velocity, group velocity and density of states; cavity and decay time of a mode; bands engineering and control of the photonic dispersion curves; localized defect modes; cavity; photonic structures based on photonic crystals (PC-waveguides, resonators, couplers, filters, mirrors, lasers); 2.5D-PC-components examples; technical characterisations of structures; mapping of CP-research in France and LEOM-INL / ECL Lyon example.▪ Near field optical; introduction to the main concepts; presentation of specific probes, and near optical field microscopy (STOM, SNOM).▪ Biomimetic and auto-assembled molecular nano-materials for photonics; nano- wires and tubes; nano-connexions and networks; bio-nanophotonic.▪ Plasmonic photonics; surface plasmon; electromagnetic modes localized at interface; evanescent waves; excitation of plasmon.

Doctoral; This course explains least squares optimization, nowadays a simple and well-mastered technology. We show how this simple method can solve a large number of problems that would be difficult to approach in any other way. This course provides a simple, understandable yet powerful tool that most coders can use, in the contrast with other algorithms sharing this paradigm (numerical simulation and deep learning) which are more complex to master.Linear regression is often underestimated being considered only as a sub-domain of statistics / data analysis, but it is much more than that. We propose to discover how the same method (least squares) applies to the manipulation of geometric objects. This first step into the numerical optimization world can be done without strong applied mathematics background; while being simple, this step suffices for many applications, and is a good starting point for learning more advanced algorithms. We strive to communicate the underlying intuitions through numerous examples of classic problems, we show different choices of variables and the ways the energies are built. Over the last two decades, the geometry processing community have used it for computing 2D maps, deformations, geodesic paths, frame fields, etc. Our examples provide many examples of applications that can be directly solved by the least squares method. Note that linear regression is an efficient tool that has deep connections to other scientific domains; we show a few such links to broaden reader's horizons.This course is intended for students/engineers/researchers who know how to program in the traditional way: by breaking down complex tasks into elementary operations that manipulate combinatorial structures (trees, graphs, meshes\dots). Here we present a different paradigm, in which we describe what a good result looks like, and let numerical optimization algorithms find it for us.