Keywords
 A3.4.2. Unsupervised learning
 A3.4.7. Kernel methods
 A3.4.8. Deep learning
 A5.3. Image processing and analysis
 A5.3.2. Sparse modeling and image representation
 A5.3.3. Pattern recognition
 A5.3.5. Computational photography
 A5.7. Audio modeling and processing
 A5.7.3. Speech
 A5.7.4. Analysis
 A5.9. Signal processing
 A5.9.2. Estimation, modeling
 A5.9.3. Reconstruction, enhancement
 A5.9.5. Sparsityaware processing
 B2. Health
 B2.2. Physiology and diseases
 B2.2.1. Cardiovascular and respiratory diseases
 B2.2.6. Neurodegenerative diseases
 B3. Environment and planet
 B3.3. Geosciences
 B3.3.2. Water: sea & ocean, lake & river
 B3.3.4. Atmosphere
1 Team members, visitors, external collaborators
Research Scientists
 Hussein Yahia [Team leader, Inria, Researcher, HDR]
 Nicolas Brodu [Inria, Researcher]
 Khalid Daoudi [Inria, Researcher]
PhD Students
 Biswajit Das [Inria]
 Alexy Decroocq [Inria, until Aug 2020]
 Arash Rashidi [Groupe I2S]
Technical Staff
 Marie Martin [Inria, Engineer, until Jun 2020]
 Chiheb Sakka [Inria, Engineer, until Jul 2020]
 Gabriel Augusto Zebadua Garcia [Inria, Engineer]
Administrative Assistant
 Sabrina Duthil [Inria]
2 Overall objectives
GEOSTAT is a research project which investigates the analysis of some classes of natural complex signals (physiological time series, turbulent universe and earth observation data sets) by determining, in acquired signals, the properties that are predicted by commonly admitted or new physical models best fitting the phenomenon. Consequently, when statistical properties discovered in the signals do not match closely enough those predicted by accepted physical models, we question the validity of existing models or propose, whenever possible, modifications or extensions of existing models. A new direction of research, based on the CONCAUST exploratory action and the newly accepted (in February 2021) associated team COMCAUSA proposed by N. Brodu with USA / UC Davis, Complexity Sciences Center, Physics Department is developped in the team.
An important aspect of the methodological approach is that we don't rely on a predetermined "universal" signal processing model to analyze natural complex signals. Instead, we take into consideration existing approaches in nonlinear signal processing (wavelets, multifractal analysis tools such as logcumulants or microcanonical multifractal formalism, time frequency analysis etc.) which are used to determine the micro structures or other micro features inside the acquired signals. Then, statistical analysis of these micro data are determined and compared to expected behaviour from theoretical physical models used to describe the phenomenon from which the data is acquired. From there different possibilities can be contemplated:
 The statistics match behaviour predicted by the model: complexity parameters predicted by the model are extracted from signals to analyze the dynamics of underlying phenomena. Examples: analysis of turbulent data sets in Oceanography and Astronomy.
 The signals displays statistics that cannot be attainable by the common lore of accepted models: how to extend or modify the models according to the behaviour of observed signals ? Example: audio speech signals.
GEOSTAT is a research project in nonlinear signal processing which develops on these considerations: it considers the signals as the realizations of complex extended dynamical systems. The driving approach is to describe the relations between complexity (or information content) and the geometric organization of information in a signal. For instance, for signals which are acquisitions of turbulent fluids, the organization of information may be related to the effective presence of a multiscale hierarchy of coherent structures, of multifractal nature, which is strongly related to intermittency and multiplicative cascade phenomena ; the determination of this geometric organization unlocks key nonlinear parameters and features associated to these signals; it helps understand their dynamical properties and their analysis. We use this approach to derive novel solution methods for superresolution and data fusion in Universe Sciences acquisitions 12. Specific advances are obtained in GEOSTAT in using this type of statistical/geometric approach to get validated dynamical information of signals acquired in Universe Sciences, e.g. Oceanography or Astronomy. The research in GEOSTAT encompasses nonlinear signal processing and the study of emergence in complex systems, with a strong emphasis on geometric approaches to complexity. Consequently, research in GEOSTAT is oriented towards the determination, in real signals, of quantities or phenomena, usually unattainable through linear methods, that are known to play an important role both in the evolution of dynamical systems whose acquisitions are the signals under study, and in the compact representations of the signals themselves.
Signals studied in GEOSTAT belong to two broad classes:
 Acquisitions in Astronomy and Earth Observation.
 Physiological time series.
3 Research program
3.1 General methodology
 Fully Developed Turbulence (FDT) Turbulence at very high Reynolds numbers; systems in FDT are beyond deterministic chaos, and symmetries are restored in a statistical sense only, and multiscale correlated structures are landmarks. Generalizing to more random uncorrelated multiscale structured turbulent fields.
 Compact Representation Reduced representation of a complex signal (dimensionality reduction) from which the whole signal can be reconstructed. The reduced representation can correspond to points randomly chosen, such as in Compressive Sensing, or to geometric localization related to statistical information content (framework of reconstructible systems).
 Sparse representation The representation of a signal as a linear combination of elements taken in a dictionary (frame or Hilbertian basis), with the aim of finding as less as possible nonzero coefficients for a large class of signals.
 Universality class In theoretical physics, the observation of the coincidence of the critical exponents (behaviour near a second order phase transition) in different phenomena and systems is called universality. Universality is explained by the theory of the renormalization group, allowing for the determination of the changes followed by structured fluctuations under rescaling, a physical system is the stage of. The notion is applicable with caution and some differences to generalized outofequilibrium or disordered systems. Nonuniversal exponents (without definite classes) exist in some universal slowing dynamical phenomena like the glass transition and kindred. As a consequence, different macroscopic phenomena displaying multiscale structures (and their acquisition in the form of complex signals) may be grouped into different sets of generalized classes.
Every signal conveys, as a measure experiment, information on the physical system whose signal is an acquisition of. As a consequence, it seems natural that signal analysis or compression should make use of physical modelling of phenomena: the goal is to find new methodologies in signal processing that goes beyond the simple problem of interpretation. Physics of disordered systems, and specifically physics of (spin) glasses is putting forward new algorithmic resolution methods in various domains such as optimization, compressive sensing etc. with significant success notably for NP hard problem heuristics. Similarly, physics of turbulence introduces phenomenological approaches involving multifractality. Energy cascades are indeed closely related to geometrical manifolds defined through random processes. At these structures’ scales, information in the process is lost by dissipation (close to the lower bound of inertial range). However, all the cascade is encoded in the geometric manifolds, through long or short distance correlations depending on cases. How do these geometrical manifold structures organize in space and time, in other words, how does the scale entropy cascades itself ? To unify these two notions, a description in term of free energy of a generic physical model is sometimes possible, such as an elastic interface model in a random nonlinear energy landscape : This is for instance the correspondence between compressible stochastic Burgers equation and directed polymers in a disordered medium. Thus, trying to unlock the fingerprints of cascadelike structures in acquired natural signals becomes a fundamental problem, from both theoretical and applicative viewpoints.
3.2 Turbulence in insterstellar clouds and Earth observation data
The research described in this section is a collaboration effort of GEOSTAT, CNRS LEGOS (Toulouse), CNRS LAM (Marseille Laboratory for Astrophysics), MERCATOR (Toulouse), IIT Roorkee, Moroccan Royal Center for Teledetection (CRST), Moroccan Center for Science CNRST, Rabat University, University of Heidelberg. Researchers involved:
 GEOSTAT: H. Yahia, N. Brodu, K. Daoudi, A. El Aouni, A. Tamim
 CNRS LAB: S. Bontemps, N. Schneider
 CNRS LEGOS: V. Garçon, I. HernandezCarrasco, J. Sudre, B. Dewitte
 CNRST, CRTS, Rabat University: D. Aboutajdine, A. Atillah, K. Minaoui
The analysis and modeling of natural phenomena, specially those observed in geophysical sciences and in astronomy, are influenced by statistical and multiscale phenomenological descriptions of turbulence; indeed these descriptions are able to explain the partition of energy within a certain range of scales. A particularly important aspect of the statistical theory of turbulence lies in the discovery that the support of the energy transfer is spatially highly non uniform, in other terms it is intermittent47. Because of the absence of localization of the Fourier transform, linear methods are not successful to unlock the multiscale structures and cascading properties of variables which are of primary importance as stated by the physics of the phenomena. This is the reason why new approaches, such as DFA (Detrented Fluctuation Analysis), Timefrequency analysis, variations on curvelets 45 etc. have appeared during the last decades. Recent advances in dimensionality reduction, and notably in Compressive Sensing, go beyond the Nyquist rate in sampling theory using nonlinear reconstruction, but data reduction occur at random places, independently of geometric localization of information content, which can be very useful for acquisition purposes, but of lower impact in signal analysis. We are successfully making use of a microcanonical formulation of the multifractal theory, based on predictability and reconstruction, to study the turbulent nature of interstellar molecular or atomic clouds. Another important result obtained in GEOSTAT is the effective use of multiresolution analysis associated to optimal inference along the scales of a complex system. The multiresolution analysis is performed on dimensionless quantities given by the singularity exponents which encode properly the geometrical structures associated to multiscale organization. This is applied successfully in the derivation of high resolution ocean dynamics, or the high resolution mapping of gaseous exchanges between the ocean and the atmosphere; the latter is of primary importance for a quantitative evaluation of global warming. Understanding the dynamics of complex systems is recognized as a new discipline, which makes use of theoretical and methodological foundations coming from nonlinear physics, the study of dynamical systems and many aspects of computer science. One of the challenges is related to the question of emergence in complex systems: largescale effects measurable macroscopically from a system made of huge numbers of interactive agents 26, 42. Some quantities related to nonlinearity, such as Lyapunov exponents, KolmogorovSinai entropy etc. can be computed at least in the phase space 27. Consequently, knowledge from acquisitions of complex systems (which include complex signals) could be obtained from information about the phase space. A result from F. Takens 46 about strange attractors in turbulence has motivated the theoretical determination of nonlinear characteristics associated to complex acquisitions. Emergence phenomena can also be traced inside complex signals themselves, by trying to localize information content geometrically. Fundamentally, in the nonlinear analysis of complex signals there are broadly two approaches: characterization by attractors (embedding and bifurcation) and timefrequency, multiscale/multiresolution approaches. In real situations, the phase space associated to the acquisition of a complex phenomenon is unknown. It is however possible to relate, inside the signal's domain, local predictability to local reconstruction 13 and to deduce relevant information associated to multiscale geophysical signals 14. A multiscale organization is a fundamental feature of a complex system, it can be for example related to the cascading properties in turbulent systems. We make use of this kind of description when analyzing turbulent signals: intermittency is observed within the inertial range and is related to the fact that, in the case of FDT (fully developed turbulence), symmetry is restored only in a statistical sense, a fact that has consequences on the quality of any nonlinear signal representation by frames or dictionaries.
The example of FDT as a standard "template" for developing general methods that apply to a vast class of complex systems and signals is of fundamental interest because, in FDT, the existence of a multiscale hierarchy ${\mathcal{F}}_{h}$ which is of multifractal nature and geometrically localized can be derived from physical considerations. This geometric hierarchy of sets is responsible for the shape of the computed singularity spectra, which in turn is related to the statistical organization of information content in a signal. It explains scale invariance, a characteristic feature of complex signals. The analogy from statistical physics comes from the fact that singularity exponents are direct generalizations of critical exponents which explain the macroscopic properties of a system around critical points, and the quantitative characterization of universality classes, which allow the definition of methods and algorithms that apply to general complex signals and systems, and not only turbulent signals: signals which belong to a same universality class share common statistical organization. During the past decades, canonical approaches permitted the development of a wellestablished analogy taken from thermodynamics in the analysis of complex signals: if $\mathcal{F}$ is the free energy, $\mathcal{T}$ the temperature measured in energy units, $\mathcal{U}$ the internal energy per volume unit $\mathcal{S}$ the entropy and $\widehat{\beta}=1/\mathcal{T}$, then the scaling exponents associated to moments of intensive variables $p\to {\tau}_{p}$ corresponds to $\widehat{\beta}\mathcal{F}$, $\mathcal{U}\left(\widehat{\beta}\right)$ corresponds to the singularity exponents values, and $\mathcal{S}\left(\mathcal{U}\right)$ to the singularity spectrum 25. The research goal is to be able to determine universality classes associated to acquired signals, independently of microscopic properties in the phase space of various complex systems, and beyond the particular case of turbulent data 40.
We show in figure 1 the result of the computation of singularity exponents on an Herschel astronomical observation map (the Musca galactic cloud) which has been edgeaware filtered using sparse ${L}^{1}$ filtering to eliminate the cosmic infrared background (or CIB), a type of noise that can modify the singularity spectrum of a signal.
3.3 Causal modeling
The team is working on a new class of models for modeling physical systems, starting from measured data and accounting for their dynamics 32. The idea is to statistically describe the evolution of a system in terms of causallyequivalent states; states that lead to the same predictions 28. Transitions between these states can be reconstructed from data, leading to a theoreticallyoptimal predictive model 44. In practice, however, no algorithm is currently able to reconstruct these models from data in a reasonable time and without substantial discrete approximations. Recent progress now allows a continuous formulation of predictive causal models. Within this framework, more efficient algorithms may be found. The broadened class of predictive models promises a new perspective on structural complexity in many applications.
3.4 Speech analysis
Phonetic and subphonetic analysis: We developed a novel algorithm for automatic detection of Glottal Closure Instants (GCI) from speech signals using the Microcanonical Multiscale Formalism (MMF). This state of the art algorithm is considered as a reference in this field. We made a Matlab code implementing it available to the community (link). Our approach is based on the Microcanonical Multiscale Formalism. We showed that in the case of clean speech, our algorithm performs almost as well as a recent stateoftheart method. In presence of different types of noises, we showed that our method is considerably more accurate (particularly for very low SNRs). Moreover, our method has lower computational times does not rely on an estimate of pitch period nor any critical choice of parameters. Using the same MMF, we also developed a method for phonetic segmentation of speech signal. We showed that this method outperforms state of the art ones in term of accuracy and efficiency.
Pathological speech analysis and classification: we made a critical analysis of some widely used methodologies in pathological speech classification. We then introduced some novel methods for extracting some common features used in pathological speech analysis and proposed more robust techniques for classification.
Speech analysis of patients with Parkinsonism: with our collaborators from the Czech Republic, we started preliminary studies of some machine learning issues in the field essentially due the small amount of training data.
3.5 Databased identification of characteristic scales and automated modeling
Data are often acquired at the highest possible resolution, but that scale is not necessarily the best for modeling and understanding the system from which data was measured. The intrinsic properties of natural processes do not depend on the arbitrary scale at which data is acquired; yet, usual analysis techniques operate at the acquisition resolution. When several processes interact at different scales, the identification of their characteristic scales from empirical data becomes a necessary condition for properly modeling the system. A classical method for identifying characteristic scales is to look at the work done by the physical processes, the energy they dissipate over time. The assumption is that this work matches the most important action of each process on the studied natural system, which is usually a reasonable assumption. In the framework of timefrequency analysis 36, the power of the signal can be easily computed in each frequency band, itself matching a temporal scale.
However, in open and dissipating systems, energy dissipation is a prerequisite and thus not necessarily the most useful metric to investigate. In fact, most natural, physical and industrial systems we deal with fall in this category, while balanced quasistatic assumptions are practical approximation only for scales well below the characteristic scale of the involved processes. Open and dissipative systems are not locally constrained by the inevitable rise in entropy, thus allowing the maintaining through time of mesoscopic ordered structures. And, according to information theory 38, more order and less entropy means that these structures have a higher information content than the rest of the system, which usually gives them a high functional role.
We propose to identify characteristic scales not only with energy dissipation, as usual in signal processing analysis, but most importantly with information content. Information theory can be extended to look at which scales are most informative (e.g. multiscale entropy 31, $\epsilon $entropy 30). Complexity measures quantify the presence of structures in the signal (e.g. statistical complexity 33, MPR 41 and others 35). With these notions, it is already possible to discriminate between random fluctuations and hidden order, such as in chaotic systems 32, 41. The theory of how information and structures can be defined through scales is not complete yet, but the state of art is promising 34. Current research in the team focuses on how informative scales can be found using collections of random paths, assumed to capture local structures as they reach out 29.
Building on these notions, it should also possible to fully automate the modeling of a natural system. Once characteristic scales are found, causal relationships can be established empirically. They are then clustered together in internal states of a special kind of Markov models called $\u03f5$machines 33. These are known to be the optimal predictors of a system, with the drawback that it is currently quite complicated to build them properly, except for small system 43. Recent extensions with advanced clustering techniques 28, 37, coupled with the physics of the studied system (e.g. fluid dynamics), have proved that $\u03f5$machines are applicable to large systems, such as global wind patterns in the atmosphere 39. Current research in the team focuses on the use of reproducing kernels, coupled possibly with sparse operators, in order to design better algorithms for $\u03f5$machines reconstruction. In order to help with this longterm project, a collaboration is ongoing with J. Crutchfield lab at UC Davis.
4 Application domains
4.1 Sparse signals & optimization
This research topic involves Geostat team and is used to set up an InnovationLab with I2S company
Sparsity can be used in many ways and there exist various sparse models in the literature; for instance minimizing the ${l}_{0}$ quasinorm is known to be an NPhard problem as one needs to try all the possible combinations of the signal's elements. The ${l}_{1}$ norm, which is the convex relation of the ${l}_{0}$ quasinorm results in a tractable optimization problem. The ${l}_{p}$ pseudonorms with $0<p<1$ are particularly interesting as they give closer approximation of ${l}_{0}$ but result in a nonconvex minimization problem. Thus, finding a global minimum for this kind of problem is not guaranteed. However, using a nonconvex penalty instead of the ${l}_{1}$ norm has been shown to improve significantly various sparsitybased applications. Nonconvexity has a lot of statistical implications in signal and image processing. Indeed, natural images tend to have a heavytailed (kurtotic) distribution in certain domains such as wavelets and gradients. Using the ${l}_{1}$ norm comes to consider a Laplacian distribution. More generally, the hyperLaplacian distribution is related to the ${l}_{p}$ pseudonorm ($0<p<1$) where the value of $p$ controls how the distribution is heavytailed. As the hyperLaplacian distribution for $0<p<1$ represents better the empirical distribution of the transformed images, it makes sense to use the ${l}_{p}$ pseudonorms instead of ${l}_{1}$. Other functions that better reflect heavytailed distributions of images have been used as well such as Studentt or Gaussian Scale Mixtures. The internal properties of natural images have helped researchers to push the sparsity principle further and develop highly efficient algorithms for restoration, representation and coding. Group sparsity is an extension of the sparsity principle where data is clustered into groups and each group is sparsified differently. More specifically, in many cases, it makes sense to follow a certain structure when sparsifying by forcing similar sets of points to be zeros or nonzeros simultaneously. This is typically true for natural images that represent coherent structures. The concept of group sparsity has been first used for simultaneously shrinking groups of wavelet coefficients because of the relations between wavelet basis elements. Lastly, there is a strong relationship between sparsity, nonpredictability and scale invariance.
We have shown that the two powerful concepts of sparsity and scale invariance can be exploited to design fast and efficient imaging algorithms. A general framework has been set up for using nonconvex sparsity by applying a firstorder approximation. When using a proximal solver to estimate a solution of a sparsitybased optimization problem, sparse terms are always separated in subproblems that take the form of a proximal operator. Estimating the proximal operator associated to a nonconvex term is thus the key component to use efficient solvers for nonconvex sparse optimization. Using this strategy, only the shrinkage operator changes and thus the solver has the same complexity for both the convex and nonconvex cases. While few previous works have also proposed to use nonconvex sparsity, their choice of the sparse penalty is rather limited to functions like the ${l}_{p}$ pseudonorm for certain values of $p\ge 0.5$ or the Minimax Concave (MC) penalty because they admit an analytical solution. Using a firstorder approximation only requires calculating the (super)gradient of the function, which makes it possible to use a wide range of penalties for sparse regularization. This is important in various applications where we need a flexible shrinkage function such as in edgeaware processing. Apart from nonconvexity, using a firstorder approximation makes it easier to verify the optimality condition of proximal operatorbased solvers via fixedpoint interpretation. Another problem that arises in various imaging applications but has attracted less works is the problem of multisparsity, when the minimization problem includes various sparse terms that can be nonconvex. This is typically the case when looking for a sparse solution in a certain domain while rejecting outliers in the datafitting term. By using one intermediate variable per sparse term, we show that proximalbased solvers can be efficient. We give a detailed study of the Alternating Direction Method of Multipliers (ADMM) solver for multisparsity and study its properties. The following subjects are addressed and receive new solutions:

Edge aware smoothing: given an input image $g$, one seeks a smooth image $u$ "close" to $g$ by minimizing:
where $\psi $ is a sparcityinducing nonconvex function and $\lambda $ a positive parameter. Splitting and alternate minimization lead to the subproblems:
We solve subproblem $\text{(sp2)}$ through deconvolution and efficient estimation via separable filters and warmstart initialization for fast GPU implementation, and subproblem $\text{(sp1)}$ through nonconvex proximal form.
 Structuretexture separation: design of an efficient algorithm using nonconvex terms on both the datafitting and the prior. The resulting problem is solved via a combination of HalfQuadratic (HQ) and MaximizationMinimization (MM) methods. We extract challenging texture layers outperforming existing techniques while maintaining a low computational cost. Using spectral sparsity in the framework of lowrank estimation, we propose to use robust Principal Component Analysis (RPCA) to perform robust separation on multichannel images such as glare and artifacts removal of flash/noflash photographs. As in this case, the matrix to decompose has much less columns than lines, we propose to use a QR decomposition trick instead of a direct singular value decomposition (SVD) which makes the decomposition faster.
 Robust integration: in many applications, we need to reconstruct an image from corrupted gradient fields. The corruption can take the form of outliers only when the vector field is the result of transformed gradient fields (lowlevel vision), or mixed outliers and noise when the field is estimated from corrupted measurements (surface reconstruction, gradient camera, Magnetic Resonance Imaging (MRI) compressed sensing, etc.). We use nonconvexity and multisparsity to build efficient integrability enforcement algorithms. We present two algorithms : 1) a local algorithm that uses sparsity in the gradient field as a prior together with a sparse datafitting term, 2) a nonlocal algorithm that uses sparsity in the spectral domain of nonlocal patches as a prior together with a sparse datafitting term. Both methods make use of a multisparse version of the HalfQuadratic solver. The proposed methods were the first in the literature to propose a sparse regularization to improve integration. Results produced with these methods significantly outperform previous works that use no regularization or simple ${l}_{1}$ minimization. Exact or nearexact recovery of surfaces is possible with the proposed methods from highly corrupted gradient fields with outliers.
 Learning image denoising: deep convolutional networks that consist in extracting features by repeated convolutions with highpass filters and pooling/downsampling operators have shown to give nearhuman recognition rates. Training the filters of a multilayer network is costly and requires powerful machines. However, visualizing the first layers of the filters shows that they resemble wavelet filters, leading to sparse representations in each layer. We propose to use the concept of scale invariance of multifractals to extract invariant features on each sparse representation. We build a biLipschitz invariant descriptor based on the distribution of the singularities of the sparsified images in each layer. Combining the descriptors of each layer in one feature vector leads to a compact representation of a texture image that is invariant to various transformations. Using this descriptor that is efficient to calculate with learning techniques such as classifiers combination and artificially adding training data, we build a powerful texture recognition system that outperforms previous works on 3 challenging datasets. In fact, this system leads to quite close recognition rates compared to latest advanced deep nets while not requiring any filters training.
5 Social and environmental responsibility
5.1 Participation in the Covid19 Inria mission
GeoStat is participating in the Covid19 Inria mission: : Vocal biomarkers of respiratory diseases.
6 Highlights of the year
6.1 Price
A. El Aouni, PhD student in Geostat receives the "Prix de thèse Systèmes complexes" CNRS ISCPIF 2020 for his PhD "Lagrangian coherent structures and physical processes of coastal upwelling" defended September 24, 2019.
7 New software and platforms
7.1 New software
7.1.1 Fluex
 Keywords: Signal, Signal processing
 Scientific Description: Fluex is a package consisting of the Microcanonical Multiscale Formalism for 1D, 2D 3D and 3D+t general signals.
 Functional Description: Fluex is a C++ library developed under Gforge. Fluex is a library in nonlinear signal processing. Fluex is able to analyze turbulent and natural complex signals, Fluex is able to determine low level features in these signals that cannot be determined using standard linear techniques.

URL:
http://
fluex. gforge. inria. fr/  Authors: Hussein Yahia, Denis Arrivault, Rémi Paties, Oriol Pont, Rémi Paties
 Contacts: Rémi Paties, Hussein Yahia
 Participants: Hussein Yahia, Rémi Paties
7.1.2 FluidExponents
 Keywords: Signal processing, Wavelets, Fractal, Spectral method, Complexity
 Functional Description: FluidExponents is a signal processing software dedicated to the analysis of complex signals displaying multiscale properties. It analyzes complex natural signals by use of nonlinear methods. It implements the multifractal formalism and allows various kinds of signal decomposition and reconstruction. One key aspect of the software lies in its ability to evaluate key concepts such as the degree of impredictability around a point in a signal, and provides different kinds of applications. The software can be used for times series or multidimensional signals.

URL:
svn+ssh://
fluidexponents@scm. gforge. inria. fr/ svn/ fluidexponents/ FluidExponents  Authors: Antonio Turiel, Hussein Yahia
 Contact: Hussein Yahia
 Participants: Antonio Turiel, Hussein Yahia
7.1.3 ProximalDenoising
 Name: ProximalDenoising
 Keywords: 2D, Image filter, Filtering, Minimizing overall energy, Noise, Signal processing, Image reconstruction, Image processing
 Scientific Description: Image filtering is contemplated in the form of a sparse minimization problem in a nonconvex setting. Given an input image I, one seeks to compute a denoised output image u such that u is close to I in the L2 norm. To do so, a minimization term is added which favors sparse gradients for output image u. Imposing sparse gradients lead to a nonconvex minimization term: for instance a pseudonorm Lp with 0 < p < 1 or a Cauchy or Welsh function. Halfquadratic algorithm is used by adding a new variable in the minimization functionnal which leads to two subproblems, the first subproblem is nonconvex and solved by use of proximal operators. The second subproblem can be written in variational form, and is best solved in Fourier space: it takes the form of a deconvolution operator whose kernel can be approximated by a finite sum of separable filters. This solution method produces excellent computation times even on big images.

Functional Description:
Use of proximal and non quadratic minimization. GPU implementation. If f is an input image, one seeks an output g such that the following functional is minimized:
l/2*(norme2(fg) + psi(grad(g))) with : l positive constant, norme2 = L2 norm, psi is a Cauchy function used for parcimony.
This functional is also applied for debayerization.
 Release Contributions: This software implements H. Badri PhD thesis results.

URL:
https://
gitlab. inria. fr/ marmarti/ i2s_geostat_C  Authors: Marie Martin, Chiheb Sakka, Hussein Yahia, Nicolas Brodu, Gabriel Augusto Zebadua Garcia, Khalid Daoudi
 Contacts: Hussein Yahia, Nicolas Brodu
 Partner: Innovative Imaging Solutions I2S
7.1.4 Manzana
 Name: Manzana
 Keywords: 2D, Image processing, Filtering
 Scientific Description: Software library developed in the framework of I2SGEOSTAT innovationlab and made of highlevel image processing functionalities based on sparsity and nonconvex optimization.
 Functional Description: Library of software in image processing: filtering, hdr, inpainting etc.
 Contacts: Hussein Yahia, Marie Martin, Chiheb Sakka, Gabriel Augusto Zebadua Garcia, Nicolas Brodu, Arash Rashidi, Khalid Daoudi, Laure AitAli
 Partner: Innovative Imaging Solutions I2S
8 New results
8.1 CONCAUST Exploratory Action
Participants: N. Brodu, James P. Crutchfield, L. Bourel, P. Rau, A. Rupe, Y. Li.
The exploratory action « TRACME » was renamed « CONCAUST » and is going on with good progress. Collaboration with James P. Crutchfield and its laboratory has lead to a first draft of article, “Discovering Causal Structure with ReproducingKernel Hilbert Space $\epsilon $Machines”, available at https://Collaboration has also started on the application of this method for:
 The analysis of El Niño patterns, with Luc Bourrel and Pedro Rau. An article was already published with Luc Bourrel and collaborators in a another context (satellite image processing). The logical step was to try the new method on related time series data. Indeed, the method is able to both predict time series, but also to extract relevant parameters that give a physical interpretation to the model. In the case of the El Niño patterns, our goal is to search for relevant data and correct time scales, holding sufficient information so that the causal states can correctly identify and predict the Niño patterns. This would give us an intuition on what are the most relevant factors for these catastrophic natural phenomena, together with a model for their evolution. In particular, the model is already able to extract the strong El Niño patterns of 1982, 1997 and 2016 from data, as seen in Fig. 2.

The analysis of ${\text{CO}}_{2}$ and latent energy exchanges in ecosystems. This collaboration started on the initiative of Adam Rupe, a colleague already involved in the causal states theory, together with Yao Liu, an ecobiology expert. We are answering a call from the Ecoforecast initiative https://
ecoforecast. , which runs a series of challenges for the prediction and more importantly the analysis and understanding of ecosystems. Our model, by its ability to reconstruct internal states of a physical process from data, is ideally suited for this kind of exploration. Though there is no guarantee we do any better than black box prediction models, this is not our main objective. Our main objective is to better understand how the various biological and physical variables (weather, soil nature, plant types...) interact and extract some of these relations from data. Comparing the output of the CONCAUST theoretical model, a reconstructed model for the physical processes and their interactions, to existing ecobiological models is already instructive. This is a multiyear project and we have just started the collaboration (december 2020) with already interesting results. In particular, we can already reconstruct the system dynamical attractor (Fig. 3), with seasonal cycles, and make realistically looking predictions (Fig. 4). Work is going on for assimilating weather data and improve these predictions, together with making ensemble forecasts.org
8.2 Participation in the Covid19 Inria mission: Vocal biomarkers of respiratory diseases
Participants: K. Daoudi, B. Das, T. Similowski.
GeoStat made a significant contribution to the Covid19 mission of Inria. Indeed, from the first lockdown, K. Daoudi identified the potential of speech processing for the management of Covid patients in telemedicine. His proposal aroused the interest of Inria, MESRI and the medical profession and he has since been leading the CovidVoice project. The latter then evolved into the VocaPnée project in partnership with APHP and codirected by K. Daoudi and Thomas Similowski, responsible for the pulmonology and resuscitation service at La PitiéSalpêtrière hospital and UMRS 1158. The objective of the VocaPnée project is to bring together all the skills available at Inria to develop and validate a vocal biomarker for the remote monitoring of patients at home suffering from an acute respiratory disease (such as Covid) or chronic (such as asthma) . This biomarker will then be integrated into a telemedicine platform, ORTIF or COVIDOM for Covid, to assist the doctors in assessing the patient's respiratory status. VocaPnée is divided into 2 longitudinal pilot clinical studies, a hospital study and another in telemedicine.In this context, a voice data collection platform, https://
8.3 Turbulent dynamics in the interstellar medium
Participants: H. Yahia, N. Schneider, S. Bontemps, L. Bonne, et al.
Observations of the interstellar medium (ISM) show a complex density and velocity structure which is in part attributed to turbulence. We here present a selfcontained introduction to the multifractal formalism in a microcanonical version which allows us for the first time to compute precise turbulence characteristic parameters from a single observational map without the need for averages in a grand ensemble of statistical observables.Dense molecular filaments are ubiquituous in the interstellar medium, yet their internal physical conditions and the role of gravity, turbulence, the magnetic field, radiation and the ambient cloud during their evolution remain debated. We study the kinematics and physical conditions in the Musca filament, the ambient cloud and the ChamaeleonMusca complex, to constrain the physics of filament formation.
Publication: Astronomy & Astrophysics, HAL, plus another paper accepted in 2021 in Astronomy & Astrophysics,
8.4 Permuted Spectral and Permuted SpectralSpatial CNN Models
Participants: G. Phartiyal, N. Brodu, D. Singh, H. Yahia, K. Daoudi.
It is a challenge to develop methods which can process the PolSAR and multispectral (MS) data modalities together without losing information from either for remote sensing applications. This research attempts to introduce novel deep learning based remote sensing data processing frameworks that utilizes convolutional neural networks (CNNs) in both spatial and spectral domains to perform land cover (LC) classification with PolSARMS data. Also since earth observation remotely sensed data have usually larger spectral depth than normal camera image data, exploiting the spectral information in remote sensing (RS) data is crucial as well. In fact, convolutions in the subspectral space are intuitive and alternative to the process of feature selection.Publication: International Journal of Remote Sensing, HAL
8.5 Robust Detection of the NorthWest African Upwelling From SST Images
Participants: A. El Aouni, K. Daoudi, K. Minaoui, H. Yahia.
Analysis and study of coastal upwelling using sea surface temperature (SST) satellite images is a common procedure because of its coast effectiveness (economic, time, frequency, and manpower). Developing on the Ekman theory, we propose a robust method to identify the upwelling regions along the northwest African margin. The proposed method comes to overcome the issues encountered in a recent method devoted for the same purpose and for the same upwelling system. Afterward, we show how our method can serve as a framework to study and monitor the spatiotemporal variability of the upwelling phenomenon in the studied region.Publication: IEEE Geoscience and Remote Sensing Letters, HAL
8.6 Deﬁning Lagrangian coherent vortices from their trajectories
Participants: A. El Aouni, K. Daoudi, H. Yahia, H. Kumar Maji, K. Minaoui.
We study the transport properties of mesoscale eddies (i.e. vortices of 100200 km in diameter) over a finite time duration. While these oceanic structures are wellknown to stir and mix surrounding water, they can also carry and transport water properties in a coherent manner. In this paper, we are interested in dynamic transport properties of these coherent structures, despite their chaotic environment. Here, we reveal that such vortices can be identified based a simple decomposition of their Lagrangian trajectories. We identify and extract coherent vortices as material lines along which particles' trajectories share similar polar rotations. The proposed method identifies coherent vortices and their centers in automatic manner. We illustrate our new method by identifying and extracting Lagrangian coherent vortices in different twodimensional flows.Publication: Physics of Fluids, American Institute of Physics, HAL
8.7 Transparent experiments: releasing data from mechanical tests on three dimensional hydrogel sphere packings
Participants: J. Bares, N. Brodu, H. Zheng, J. A. Dijksman.
We describe here experiments on the mechanics of hydrogel particle packings from the Behringer's lab, performed between 2012 and 2015. These experiments quantify the evolution of all contact forces inside soft particle packings exposed to compression, shear and the intrusion of a large intruder. The experimental setups and processes are presented and the data are concomitantly published in a repository.Publication: Granular Matter, HAL
8.8 Structurepreserving denoising of SAR images
Participants: S. Kumar Maji, R. Thakur, H. Yahia.
We propose a speckle removal denoising algorithm for synthetic aperture radar (SAR) images. The approach is based on the concept of extracting informative feature (based on the concept of multifractal decomposition of signals) from a speckleinduced SAR image and then estimating a noisefree image from the gradients restricted to those features. The experimental results show that the proposed technique not only improves the visual quality of the SAR images but also effectively preserves their texture. Comparison with the classical and stateoftheart denoising techniques shows the advantages of the proposed scheme, both visually and quantitatively.Publication: IEEE Geoscience and Remote Sensing Letters, HAL
8.9 LEFE CNRS IMECO Project: Multiscale intermittence in oceanic fields
Participants: F. Schmitt, H. Yahia, V. Garçon, J. Sudre, B. Dewitte, G. Charria.
Oceanic fields display a large variability over large temporal and spatial scales. One way to characterize such variability, borrowed from the field of turbulence, is to consider scaling regimes and multiscaling properties.The Bay of Biscay and the English Channel, in the Northeastern Atlantic, are considered as a natural laboratory to explore the coastal dynamics at different spatial and temporal scales. In those regions, the coastal circulation is constrained by a complex topography (e.g. varying width of the continental shelf, canyons), river runoffs, strong tides and a seasonally contrasted winddriven circulation. Based on different numerical model experiments (from 400m to 4km spatial resolution, from 40 to 100 sigma vertical layers using 3D primitive equation ocean models), different features of the Bay of Biscay and English Channel circulation are assessed and explored. Both spatial (submesoscale and mesoscale) and temporal (from hourly to monthly) scales are considered.
8.10 InnovationLab with I2S, sparse signals & optimisation
Participants: A. Zebadua, A. Rashidi, H. Yahia, A. Cherif, J. L. Vallancogne, A. Cailly.
The InnovationLab with I2S is extended one year starting 1st February 2021.In 2020, one main task was to develop image processing algorithms for 3D stereo imaging. Such algorithms improve the quality of noisy and distorted disparity maps that can be used to reconstruct 3D objects. A. Zebadua was responsible for assisting the two research engineers who implemented the algorithms in C++.
A. Zebadua is also responsible for the cosupervision of the Ph.D. of Arash Rashidi worked with him in the development of fast image deconvolution algorithms.
9 Bilateral contracts and grants with industry
9.1 Bilateral contracts with industry
InnovationLab with I2S company, starting scheduled after 1st 2019 COPIL in January 2019. This InnovationLab is extended one year starting February 2021.
10 Partnerships and cooperations
10.1 International initiatives
10.1.1 Inria International Labs
The project of associated team COMCAUSA proposed by N. Brodu with USA / UC Davis, Complexity Sciences Center, Physics Department, is accepted, starting Feburary 2021.
10.1.2 Inria international partners
Declared Inria international partners
Jim Crutchfield (Distinguished Prof.) http://
Informal international partners
N. Schneider I. Physik. Institut, University of Cologne, Zülpicher Str. 77, 50937 Cologne, Germany.
10.2 European initiatives
10.2.1 FP7 & H2020 Projects
GENESIS Project (Geostat, Laboratoire d'Astrophysique de Bordeaux, Physics Inst. Köln University).
GENeration et Evolution de la Structure InterStellaire (GENESIS) (GENreration and Evolution of Structure in the ISm).
10.3 National initiatives
 CONCAUST Exploratory Action
The exploratory action « TRACME » was renamed « CONCAUST » and is going on with good progress. Collaboration with James P. Crutchfield and its laboratory has lead to a first draft of article, “Discovering Causal Structure with ReproducingKernel Hilbert Space $\epsilon $Machines”, available at https://
arxiv. . That article poses the main theoretical fundations for building a new class of models, able to reconstruct a measured process « causal states » from data.org/ abs/ 2011. 14821 
Participation in the Covid19 Inria mission: Vocal biomarkers of respiratory diseases. The CovidVoice project evolved into the VocaPnée project in partnership with APHP and codirected by K. Daoudi and Thomas Similowski, responsible for the pulmonology and resuscitation service at La PitiéSalpêtrière hospital and UMRS 1158. The objective of the VocaPnée project is to bring together all the skills available at Inria to develop and validate a vocal biomarker for the remote monitoring of patients at home suffering from an acute respiratory disease (such as Covid) or chronic (such as asthma) . This biomarker will then be integrated into a telemedicine platform, ORTIF or COVIDOM for Covid, to assist the doctors in assessing the patient's respiratory status. VocaPnée is divided into 2 longitudinal pilot clinical studies, a hospital study and another in telemedicine.
In this context, a voice data collection platform, https://
dream. , was developed by Inria's SED. This platform is used to collect data from healthy controls. It will then be migrated to the APHP servers to collect patient data.inria. fr/ vocapnee/  ANR project Voice4PDMSA, led by K. Daoudi, which targets the differential diagnosis between Parkinson's disease and Multiple System Atrophy. The total amount of the grant is 468555 euros, from which GeoStat has 203078 euros. The duration of the project is 42 months. Partners: CHU Bordeaux (Bordeaux), CHU Toulouse, IRIT, IMT (Toulouse).
 GEOSTAT is a member of ISIS (Information, Image & Vision), AMF (Multifractal Analysis) GDRs.
 GEOSTAT is participating in the CNRS IMECO project Intermittence multiéchelles de champs océaniques : analyse comparative d’images satellitaires et de sorties de modèles numériques. CNRS call AO INSU 2018. PI: F. Schmitt, DR CNRS, UMR LOG 8187. Duration: 2 years.
11 Dissemination
11.1 Reviewer
H. Yahia is reviewer for the IGARSS conference.
11.2 Invited talks
 H. Yahia gave an invited talk "Description of turbulent dynamics in the interstellar medium: multifractal/microcanonical analysis " for the workshop sftoolsbigdata : The close structural connection between gas and young stars, focus on current and new tools of data analysis 2730 Oct 2020 Grenoble. https://
hal. .inria. fr/ hal03002810  N. Brodu gave a presentation of the concaust method at the “Inference for Dynamical Systems” seminar series, http://
csc. ucdavis. edu/ Inference_for_Dynamical_Systems. html  K. Daoudi gave a talk at Vivhealthtech’2020 (https://
vivhealthtech. ), which took place in Bordeaux in November 2020, entitled “Peuton entendre Parkinson’s ? quid du COVID ?”.events/ FR/
11.3 Supervision
A. Zebadua and H. Yahia are cosupervising A. Rashidi's Phd thesis.
12 Scientific production
12.1 Major publications
 1 articleMultifractal Desynchronization of the Cardiac Excitable Cell Network During Atrial Fibrillation. II. ModelingFrontiers in Physiology10April 2019, 480 (118)
 2 articleMultifractal desynchronization of the cardiac excitable cell network during atrial fibrillation. I. Multifractal analysis of clinical dataFrontiers in Physiology8March 2018, 130
 3 articleA NonLocal LowRank Approach to Enforce IntegrabilityIEEE Transactions on Image ProcessingJune 2016, 10
 4 inproceedings Fast and Accurate Texture Recognition with Multilayer Convolution and Multifractal Analysis European Conference on Computer Vision ECCV 2014 Zürich, Switzerland September 2014
 5 articleIncreasing the Resolution of Ocean pCO₂ Maps in the South Eastern Atlantic Ocean Merging Multifractal SatelliteDerived Ocean VariablesIEEE Transactions on Geoscience and Remote SensingJune 2018, 1  15
 6 articleReconstruction of superresolution ocean pCO 2 and airsea fluxes of CO 2 from satellite imagery in the Southeastern AtlanticBiogeosciencesSeptember 2015, 20
 7 article Detection of Glottal Closure Instants based on the Microcanonical Multiscale Formalism IEEE Transactions on Audio, Speech and Language Processing December 2014
 8 article Efficient and robust detection of Glottal Closure Instants using Most Singular Manifold of speech signals IEEE Transactions on Acoustics Speech and Signal Processing forthcoming 2014
 9 articleEdges, Transitions and CriticalityPattern RecognitionJanuary 2014, URL: http://hal.inria.fr/hal00924137
 10 articleA Multifractalbased Wavefront Phase Estimation Technique for Groundbased Astronomical ObservationsIEEE Transactions on Geoscience and Remote SensingNovember 2015, 11
 11 articleSingularity analysis in digital signals through the evaluation of their Unpredictable Point ManifoldInternational Journal of Computer Mathematics2012, URL: http://hal.inria.fr/hal00688715
 12 articleOcean Turbulent Dynamics at Superresolution From Optimal Multiresolution Analysis and Multiplicative CascadeIEEE Transactions on Geoscience and Remote Sensing5311June 2015, 12
 13 articleMicrocanonical multifractal formalism: a geometrical approach to multifractal systems. Part I: singularity analysisJournal of Physics A: Math. Theor412008, URL: http://dx.doi.org/10.1088/17518113/41/1/015501
 14 articleMotion analysis in oceanographic satellite images using multiscale methods and the energy cascadePattern Recognition43102010, 35913604URL: http://dx.doi.org/10.1016/j.patcog.2010.04.011
12.2 Publications of the year
International journals
 15 articleTransparent experiments: releasing data from mechanical tests on three dimensional hydrogel sphere packingsGranular Matter221February 2020, 21
 16 article Formation of the Musca filament: evidence for asymmetries in the accretion flow due to a cloudcloud collision Astronomy and Astrophysics  A&A October 2020
 17 articleRobust Detection of the NorthWest African Upwelling From SST ImagesIEEE Geoscience and Remote Sensing Letters2020, 14
 18 articleDefining Lagrangian coherent vortices from their trajectoriesPhysics of Fluids321January 2020, 016602
 19 articleStructurePreserving Denoising of SAR Images Using Multifractal Feature AnalysisIEEE Geoscience and Remote Sensing LettersJanuary 2020, 15
 20 article Permuted Spectral and Permuted SpectralSpatial CNN Models for PolSAR Multispectral Data based Land Cover Classification International Journal of Remote Sensing 42 3 2020
International peerreviewed conferences
 21 inproceedings Multiscale coastal surface temperature in the Bay of Biscay and the English Channel EGU 2020 General Assembly Vienna, Austria May 2020
 22 inproceedings Scaling and anisotropic heterogeneities of ocean SST images from satellite data EGU General Assembly 2020 Vienna, Austria https://www.egu2020.eu/ May 2020
Reports & preprints
 23 misc Discovering Causal Structure with ReproducingKernel Hilbert Space εMachines November 2020
Other scientific publications
 24 misc Description of turbulent dynamics in the interstellar medium: multifractal/microcanonical analysis Grenoble / Virtual, France October 2020
12.3 Cited publications
 25 book Ondelettes, multifractales et turbulence Paris, France Diderot Editeur 1995
 26 book Modeling Complex Systems NewYork Dordrecht Heidelberg London Springer 2010
 27 articlePredictability: a way to characterize complexityPhysics Report356arXiv:nlin/0101029v12002, 367474URL: http://dx.doi.org/10.1016/S03701573(01)000254
 28 articleReconstruction of epsilonmachines in predictive frameworks and decisional statesAdvances in Complex Systems14052011, 761794URL: https://doi.org/10.1142/S0219525911003347
 29 article Stochastic Texture Difference for ScaleDependent Data Analysis arXiv preprint arXiv:1503.03278 2015
 30 book Chaos and coarse graining in statistical mechanics Cambridge University Press Cambridge 2008
 31 articleMultiscale entropy analysis of complex physiologic time seriesPhysical review letters8962002, 068102
 32 articleBetween order and chaosNature Physics812012, 1724
 33 articleInferring statistical complexityPhysical Review Letters6321989, 105
 34 articleAbout the role of chaos and coarse graining in statistical mechanicsPhysica A: Statistical Mechanics and its Applications4182015, 94104
 35 articleMeasures of statistical complexity: Why?Physics Letters A238451998, 244252
 36 book Timefrequency/timescale analysis 10 Academic press 1998
 37 inproceedingsMixed LICORS: A nonparametric algorithm for predictive state reconstructionArtificial Intelligence and Statistics2013, 289297
 38 book Entropy and information theory Springer Science & Business Media 2011
 39 articleMultifield visualization using local statistical complexityIEEE Transactions on Visualization and Computer Graphics1362007, 13841391
 40 book Statistical physics, statics, dynamics & renormalization World Scie,tific 2000
 41 articleGeneralized statistical complexity measures: Geometrical and analytical propertiesPhysica A: Statistical Mechanics and its Applications36922006, 439462
 42 bookFrom Statistical Physics to Statistical Inference and BackNew York Heidelberg BerlinSpringer1994, URL: http://www.springer.com/physics/complexity/book/9780792327752
 43 inproceedingsBlind construction of optimal nonlinear recursive predictors for discrete sequencesProceedings of the 20th conference on Uncertainty in artificial intelligenceAUAI Press2004, 504511
 44 articleQuantifying SelfOrganization with Optimal PredictorsPhys. Rev. Lett.9311Sep 2004, 118701URL: https://link.aps.org/doi/10.1103/PhysRevLett.93.118701
 45 book Sparse Image and Signal Processing: Wavelets, Curvelets, Morphological Diversity ISBN:9780521119139 Cambridge University Press 2010
 46 articleDetecting Strange Attractors in TurbulenceNon Linear Optimization8981981, 366381URL: http://www.springerlink.com/content/b254x77553874745/
 47 article Revisting multifractality of high resolution temporal rainfall using a waveletbased formalism Water Resources Research 42 2006