Robust Network Topology Inference and Processing of Graph Signals

The abundance of large and heterogeneous systems is rendering contemporary data more pervasive, intricate, and with a non-regular structure. With classical techniques facing troubles to deal with the irregular (non-Euclidean) domain where the signals are defined, a popular approach at the heart of graph signal processing (GSP) is to: (i) represent the underlying support via a graph and (ii) exploit the topology of this graph to process the signals at hand. In addition to the irregular structure of the signals, another critical limitation is that the observed data is prone to the presence of perturbations, which, in the context of GSP, may affect not only the observed signals but also the topology of the supporting graph. Ignoring the presence of perturbations, along with the couplings between the errors in the signal and the errors in their support, can drastically hinder estimation performance. While many GSP works have looked at the presence of perturbations in the signals, much fewer have looked at the presence of perturbations in the graph, and almost none at their joint effect. While this is not surprising (GSP is a relatively new field), we expect this to change in the upcoming years. Motivated by the previous discussion, the goal of this thesis is to advance toward a robust GSP paradigm where the algorithms are carefully designed to incorporate the influence of perturbations in the graph signals, the graph support, and both. To do so, we consider different types of perturbations, evaluate their disruptive impact on fundamental GSP tasks, and design robust algorithms to address them.Comment: Dissertatio

Utilisation d'un capteur à courant de Foucault pour prévenir la fissuration dans des structures métalliques: travail de bachelor : diplôme 2016

Sensima Inspection Sàrl. a développé un capteur à courant de Foucault flexible et peu intrusif qui permet de surveiller la propagation de fissures dans des éléments métalliques. Il est actuellement testé in-situ sur des ponts. Le but de ce projet est de démontrer que ce capteur peut être utilisé dans le cadre du suivi conditionnel d’installations hydroélectriques et en particulier des machines tournantes. Il devrait, à terme, permettre d’arrêter certains organes dans le cas où les fissures suivies dépasseraient une taille critique déterminée à partir de la mécanique de la rupture. Les jalons de ce projet sont de démontrer que le capteur permet de suivre l’évolution de fissures dans des tôles minces lors d’essais de traction en déchirement et en fatigue, et de valider expérimentalement s’il est possible de corréler le signal obtenu par le capteur en pointe de fissure avec d’autres mesures expérimentales comme des tests de dureté et des observations métallographiques

An Underparametrized Deep Decoder Architecture for Graph Signals

While deep convolutional architectures have achieved remarkable results in a gamut of supervised applications dealing with images and speech, recent works show that deep untrained non-convolutional architectures can also outperform state-of-the-art methods in several tasks such as image compression and denoising. Motivated by the fact that many contemporary datasets have an irregular structure different from a 1D/2D grid, this paper generalizes untrained and underparametrized non-convolutional architectures to signals defined over irregular domains represented by graphs. The proposed architecture consists of a succession of layers, each of them implementing an upsampling operator, a linear feature combination, and a scalar nonlinearity. A novel element is the incorporation of upsampling operators accounting for the structure of the supporting graph, which is achieved by considering a systematic graph coarsening approach based on hierarchical clustering. The numerical results carried out in synthetic and real-world datasets showcase that the reconstruction performance can improve drastically if the information of the supporting graph topology is taken into account.Comment: This paper has already been accepted on 2019 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) and it is going to be published in its proceeding

Plant diversity, biogeography and environment in Iberia: Patterns and possible causal factors

Las figuras que contiene el documento se localizan al final del mismoWe associated patterns of plant diversity with pos- sible causal factors by considering 93 local regions in the Iberian Peninsula and Balearic Islands with respect to biogeo- graphy, environmental favourability, and environmental hetero- geneity, and their relationship with measured species diversity at four different scales: mean local species richness standard- ized at a grain of 100 m 2 , total species richness in a community type within a region (regional community richness), mean compositional similarity, and mosaic diversity. Local regions in biogeographic transition zones to the North African and Atlantic floras had higher regional commu- nity richness and greater mosaic diversity than did non-transi- tional regions, whereas no differences existed in mean local species richness or mean compositional similarity. Mean local species richness was positively related to environmental fa- vourability as measured by actual evapotranspiration, but negatively related to total precipitation and temporal heteroge- neity in precipitation. Mean local species richness was great- est in annual grassland and dwarf shrubland communities, and on calcareous bedrock types. Regional community richness was similarly related to actual evapotranspiration and total precipitation, but in addition was positively related to spatial heterogeneity in topography and soil water holding capacity. Mean compositional similarity decreased with increasing spa- tial heterogeneity and temperature seasonality. Mosaic diver- sity, a measure of complexity, increased with increasing local and regional richness. We hypothesize that these relationships can be explained by four ecological and evolutionary classes of causal factors: nu mbers of individuals, intermediate environments, limits to adaptation, and niche variation. These factors operate at various scales and manifest themselves in various ways. For example, at the site level, apparently processes that increase the number of individuals increase mean local species richness, but at the level of the entire region no such effects were foundWe are deeply indebted to Íñigo Vázquez- Dodero for his assistance in the early stages of this study. Jose M. Rey Arnaiz provided climate data. Emilio Chuvieco pro- vided the remote sensing data. Julio Álvarez, Javier Amigo, Carmen Bartolomé, and Marcelino de la Cruz provided useful information for finding data sets. Manuel Segura and Javier Temiño assisted with the classification of bedrock and soil types. Diana Piorno, Carmen Pineda, and Francisco Bermejo assisted with data entry. Meelis Pärtel, Mike Willig, Brad Hawkins, Sandra Lavorel, Jane Franklin, and R.M. Cowling provided comments about a previous version of this manu- script. This study was funded by the “Determinantes de la diversidad biológica en ecosistemas mediterráneos. Papel de los procesos locales y regionales” project (CICYT AMB96- 1161), and additionally supported by the ”Factores limitantes de la revegetación con especies leñosas autóctonas de áreas degradadas en ambientes mediterráneos. Rendimiento de distintas actuaciones de manejo” project (CICYT REN 2000 745). Travel by J.M.R.B. and S.M.S. was funded by the Universidad de Alcalá. The views expressed in this paper do not necessarily reflect those of the National Science Founda- tion or the United States Governmen

Blind Deconvolution of Sparse Graph Signals in the Presence of Perturbations

Blind deconvolution over graphs involves using (observed) output graph signals to obtain both the inputs (sources) as well as the filter that drives (models) the graph diffusion process. This is an ill-posed problem that requires additional assumptions, such as the sources being sparse, to be solvable. This paper addresses the blind deconvolution problem in the presence of imperfect graph information, where the observed graph is a perturbed version of the (unknown) true graph. While not having perfect knowledge of the graph is arguably more the norm than the exception, the body of literature on this topic is relatively small. This is partly due to the fact that translating the uncertainty about the graph topology to standard graph signal processing tools (e.g. eigenvectors or polynomials of the graph) is a challenging endeavor. To address this limitation, we propose an optimization-based estimator that solves the blind identification in the vertex domain, aims at estimating the inverse of the generating filter, and accounts explicitly for additive graph perturbations. Preliminary numerical experiments showcase the effectiveness and potential of the proposed algorithm.Comment: Submitted to the 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2024

Microfabricated solid oxide fuel cells

Micro-fabricated solid oxide fuel cells (µSOFCs) are finding an increasing interest as potential power sources for portable devices such as MP3 players or laptops. The aim of this work was to fabricate a µSOFC demonstrator that works at 500°C and is fuelled by hydrogen. This thesis was divided into two parts. The first one was devoted to the development of an electrolyte and electrodes in form of sputtered thin films with electrical and mechanical properties suitable for the implementation in a real cell. YSZ and CGO electrolyte have been reactively sputtered from metallic targets. Both films are dense and have a columnar microstructure. The ionic conductivity of these films was of 0.5 S/m at 550°C for the CGO and of 5.5 x 10-3 S/m at 500°C for the yttria stabilized zirconia (YSZ) . Albeit the ceria doped gadolinia (CGO) was a better ionic conductor at low temperature, it was not possible to obtain an open circuit voltage (OCV) with a CGO electrolyte film. Most likely, the reduction of the Ce+4 ion into Ce+3 in a hydrogen atmosphere creates an electrical leakage. Better results were obtained with YSZ layers. Single, (111) textured columnar films showed OCV's of 200 mV. Best results were obtained with a double layer of two different microstructures. The first one exhibited a dense, columnar microstructure with (111) texture. The second layer was porous with nanocrystalline grains and preferential (200) orientation. The improved properties are ascribed to the absence of film crossing grain boundaries. Of special interest is the mechanical stress behaviour upon heating to the operation temperature. The stress was investigated as a function of temperature up to 700°C. An anomalous, hysteretic behaviour was found during the first heating cycle in YSZ as well as CGO thin films. This phenomenon could be modelled as an oxygen uptake to fill up excess oxygen vacancies created during the sputtering process. The model allowed to derive a diffusion activation energy of 0.6 eV for these excess vacancies in YSZ. Annealing in air at 700 °C permits to reduce stress and to stabilize the YSZ membrane. As electrode materials, sputter deposited, porous platinum, porous Ni-CGO composites and dense LaxSr1-xCoO3-y (LSCO) thin films were developed and characterized. The PEN (Positive electrode-Electrolyte-Negative electrode) layer processes were combined with Micro Electro Mechanical System (MEMS) process technology to fabricate µSOFC test devices. The PEN membranes were liberated by deep silicon dry etching. The cell diameter was varied between 0.5 and 5 mm, the electrolyte thickness between 500 and 700 nm. A nickel grid grown by electroplating was used to support the electrolyte layer and to serves as current collector for the anode. The cell with a 5 mm diameter shows a very good mechanical stability up to 600°C in SOFC operating conditions and for several heating cycles. The functionality of the fuel cell with two 20 nm thick porous platinum electrodes and a YSZ bilayer electrolyte (500 nm) has been demonstrated. An OCV of 850 mV was measured at 500°C with hydrogen fuel. Unfortunately, a too high cathode contact resistance reduced the current to very low values. The achieved maximal power density was only 19 µW/cm2. A simple design change should remedy the problem

Quantum Computing for Dealing with Inaccurate Knowledge Related to the Certainty Factors Model

This article belongs to the Special Issue Advances in Quantum Artificial Intelligence and Machine Learning[Abstract] In this paper, we illustrate that inaccurate knowledge can be efficiently implemented in a quantum environment. For this purpose, we analyse the correlation between certainty factors and quantum probability. We first explore the certainty factors approach for inexact reasoning from a classical point of view. Next, we introduce some basic aspects of quantum computing, and we pay special attention to quantum rule-based systems. In this context, a specific use case was built: an inferential network for testing the behaviour of the certainty factors approach in a quantum environment. After the design and execution of the experiments, the corresponding analysis of the obtained results was performed in three different scenarios: (1) inaccuracy in declarative knowledge, or imprecision, (2) inaccuracy in procedural knowledge, or uncertainty, and (3) inaccuracy in both declarative and procedural knowledge. This paper, as stated in the conclusions, is intended to pave the way for future quantum implementations of well-established methods for handling inaccurate knowledge.This work was supported by the European Union’s Horizon 2020 research and innovation programme under project NEASQC (grant agreement No. 951821) and by the Xunta de Galicia (grant ED431C 2018/34) with the European Union ERDF funds. We wish to acknowledge the support received from the Centro de Investigación de Galicia “CITIC”, funded by Xunta de Galicia and the European Union (European Regional Development Fund- Galicia 2014–2020 Program, grant ED431G 2019/01)Xunta de Galicia; ED431C 2018/34Xunta de Galicia; ED431G 2019/0

On superpotentials for nonlinear sigma-models with eight supercharges

Using projective superspace techniques, we consider 4D N = 2 and 5D N = 1 gauged supersymmetric nonlinear sigma-models for which the hyper-Kahler target space is (an open domain of the zero section of) the cotangent bundle of a real-analytic Kahler manifold. As in the 4D N = 1 case, one may gauge those holomorphic isometries of the base Kahler manifold (more precisely, their lifting to the cotangent bundle) which are generated by globally defined Killing potentials. In the U(1) case, by freezing the background vector (tropical) multiplet to a constant value of its gauge-invariant superfield strength, we demonstrate the generation of a chiral superpotential, upon elimination of the auxiliary superfields and dualisation of the complex linear multiplets into chiral ones. Our analysis uncovers a N = 2 superspace origin for the results recently obtained in hep-th/0601165.Comment: 8 pages, no figures. V2: comments, references adde

Topological data analysis of human vowels: Persistent homologies across representation spaces

Topological Data Analysis (TDA) has been successfully used for various tasks in signal/image processing, from visualization to supervised/unsupervised classification. Often, topological characteristics are obtained from persistent homology theory. The standard TDA pipeline starts from the raw signal data or a representation of it. Then, it consists in building a multiscale topological structure on the top of the data using a pre-specified filtration, and finally to compute the topological signature to be further exploited. The commonly used topological signature is a persistent diagram (or transformations of it). Current research discusses the consequences of the many ways to exploit topological signatures, much less often the choice of the filtration, but to the best of our knowledge, the choice of the representation of a signal has not been the subject of any study yet. This paper attempts to provide some answers on the latter problem. To this end, we collected real audio data and built a comparative study to assess the quality of the discriminant information of the topological signatures extracted from three different representation spaces. Each audio signal is represented as i) an embedding of observed data in a higher dimensional space using Taken's representation, ii) a spectrogram viewed as a surface in a 3D ambient space, iii) the set of spectrogram's zeroes. From vowel audio recordings, we use topological signature for three prediction problems: speaker gender, vowel type, and individual. We show that topologically-augmented random forest improves the Out-of-Bag Error (OOB) over solely based Mel-Frequency Cepstral Coefficients (MFCC) for the last two problems. Our results also suggest that the topological information extracted from different signal representations is complementary, and that spectrogram's zeros offers the best improvement for gender prediction