Bayesian selection of scaling laws for motion modeling in images

[Departement_IRSTEA]Ecotechnologies [TR1_IRSTEA]SPEEInternational audienceBased on scaling laws describing the statistical structure of turbulent motion across scales, we propose a multiscale and non-parametric regularizer for optic-flow estimation. Regularization is achieved by constraining motion increments to behave through scales as the most likely self-similar process given some image data. In a first level of inference, the hard constrained minimization problem is optimally solved by taking advantage of lagrangian duality. It results in a collection of first-order regularizers acting at different scales. This estimation is non-parametric since the optimal regularization parameters at the different scales are obtained by solving the dual problem. In a second level of inference, the most likely self-similar model given the data is optimally selected by maximization of Bayesian evidence. The motion estimator accuracy is first evaluated on a synthetic image sequence of simulated bi-dimensional turbulence and then on a real meteorological image sequence. Results obtained with the proposed physical based approach exceeds the best state of the art results. Furthermore, selecting from images the most evident multiscale motion model enables the recovery of physical quantities, which are of major interest for turbulence characterization

Bayesian selection of scaling laws for motion modeling in images

[Departement_IRSTEA]Ecotechnologies [TR1_IRSTEA]SPEEInternational audienceBased on scaling laws describing the statistical structure of turbulent motion across scales, we propose a multiscale and non-parametric regularizer for optic-flow estimation. Regularization is achieved by constraining motion increments to behave through scales as the most likely self-similar process given some image data. In a first level of inference, the hard constrained minimization problem is optimally solved by taking advantage of lagrangian duality. It results in a collection of first-order regularizers acting at different scales. This estimation is non-parametric since the optimal regularization parameters at the different scales are obtained by solving the dual problem. In a second level of inference, the most likely self-similar model given the data is optimally selected by maximization of Bayesian evidence. The motion estimator accuracy is first evaluated on a synthetic image sequence of simulated bi-dimensional turbulence and then on a real meteorological image sequence. Results obtained with the proposed physical based approach exceeds the best state of the art results. Furthermore, selecting from images the most evident multiscale motion model enables the recovery of physical quantities, which are of major interest for turbulence characterization

Criticality of mostly informative samples: A Bayesian model selection approach

We discuss a Bayesian model selection approach to high dimensional data in the deep under sampling regime. The data is based on a representation of the possible discrete states $s$, as defined by the observer, and it consists of $M$ observations of the state. This approach shows that, for a given sample size $M$, not all states observed in the sample can be distinguished. Rather, only a partition of the sampled states $s$ can be resolved. Such partition defines an {\em emergent} classification $q_s$ of the states that becomes finer and finer as the sample size increases, through a process of {\em symmetry breaking} between states. This allows us to distinguish between the $resolution$ of a given representation of the observer defined states $s$, which is given by the entropy of $s$, and its $relevance$ which is defined by the entropy of the partition $q_s$. Relevance has a non-monotonic dependence on resolution, for a given sample size. In addition, we characterise most relevant samples and we show that they exhibit power law frequency distributions, generally taken as signatures of "criticality". This suggests that "criticality" reflects the relevance of a given representation of the states of a complex system, and does not necessarily require a specific mechanism of self-organisation to a critical point.Comment: 31 pages, 7 figure

A highly magnified candidate for a young galaxy seen when the Universe was 500 Myrs old

The early Universe at redshift z\sim6-11 marks the reionization of the intergalactic medium, following the formation of the first generation of stars. However, those young galaxies at a cosmic age of \lesssim 500 million years (Myr, at z \gtrsim 10) remain largely unexplored as they are at or beyond the sensitivity limits of current large telescopes. Gravitational lensing by galaxy clusters enables the detection of high-redshift galaxies that are fainter than what otherwise could be found in the deepest images of the sky. We report the discovery of an object found in the multi-band observations of the cluster MACS1149+22 that has a high probability of being a gravitationally magnified object from the early universe. The object is firmly detected (12 sigma) in the two reddest bands of HST/WFC3, and not detected below 1.2 {\mu}m, matching the characteristics of z\sim9 objects. We derive a robust photometric redshift of z = 9.6 \pm 0.2, corresponding to a cosmic age of 490 \pm 15Myr (i.e., 3.6% of the age of the Universe). The large number of bands used to derive the redshift estimate make it one of the most accurate estimates ever obtained for such a distant object. The significant magnification by cluster lensing (a factor of \sim15) allows us to analyze the object's ultra-violet and optical luminosity in its rest-frame, thus enabling us to constrain on its stellar mass, star-formation rate and age. If the galaxy is indeed at such a large redshift, then its age is less than 200 Myr (at the 95% confidence level), implying a formation redshift of zf \lesssim 14. The object is the first z>9 candidate that is bright enough for detailed spectroscopic studies with JWST, demonstrating the unique potential of galaxy cluster fields for finding highly magnified, intrinsically faint galaxies at the highest redshifts.Comment: Submitted to the Nature Journal. 39 Pages, 13 figure

Machine Learning for Fluid Mechanics

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from field measurements, experiments and large-scale simulations at multiple spatiotemporal scales. Machine learning offers a wealth of techniques to extract information from data that could be translated into knowledge about the underlying fluid mechanics. Moreover, machine learning algorithms can augment domain knowledge and automate tasks related to flow control and optimization. This article presents an overview of past history, current developments, and emerging opportunities of machine learning for fluid mechanics. It outlines fundamental machine learning methodologies and discusses their uses for understanding, modeling, optimizing, and controlling fluid flows. The strengths and limitations of these methods are addressed from the perspective of scientific inquiry that considers data as an inherent part of modeling, experimentation, and simulation. Machine learning provides a powerful information processing framework that can enrich, and possibly even transform, current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202

The Bullet cluster at its best: weighing stars, gas and dark matter

We present a new strong lensing mass reconstruction of the Bullet cluster (1E 0657-56) at z=0.296, based on WFC3 and ACS HST imaging and VLT/FORS2 spectroscopy. The strong lensing constraints underwent substantial revision compared to previously published analysis, there are now 14 (six new and eight previously known) multiply-imaged systems, of which three have spectroscopically confirmed redshifts (including one newly measured from this work). The reconstructed mass distribution explicitly included the combination of three mass components: i) the intra-cluster gas mass derived from X-ray observation, ii) the cluster galaxies modeled by their fundamental plane scaling relations and iii) dark matter. The model that includes the intra-cluster gas is the one with the best Bayesian evidence. This model has a total RMS value of 0.158" between the predicted and measured image positions for the 14 multiple images considered. The proximity of the total RMS to resolution of HST/WFC3 and ACS (0.07-0.15" FWHM) demonstrates the excellent precision of our mass model. The derived mass model confirms the spatial offset between the X-ray gas and dark matter peaks. The fraction of the galaxy halos mass to total mass is found to be f_s=11+/-5% for a total mass of 2.5+/-0.1 x 10^14 solar mass within a 250 kpc radial aperture.Comment: Accepted by A&A 15 pages, 12 figure

Advances in computational modelling for personalised medicine after myocardial infarction

Myocardial infarction (MI) is a leading cause of premature morbidity and mortality worldwide. Determining which patients will experience heart failure and sudden cardiac death after an acute MI is notoriously difficult for clinicians. The extent of heart damage after an acute MI is informed by cardiac imaging, typically using echocardiography or sometimes, cardiac magnetic resonance (CMR). These scans provide complex data sets that are only partially exploited by clinicians in daily practice, implying potential for improved risk assessment. Computational modelling of left ventricular (LV) function can bridge the gap towards personalised medicine using cardiac imaging in patients with post-MI. Several novel biomechanical parameters have theoretical prognostic value and may be useful to reflect the biomechanical effects of novel preventive therapy for adverse remodelling post-MI. These parameters include myocardial contractility (regional and global), stiffness and stress. Further, the parameters can be delineated spatially to correspond with infarct pathology and the remote zone. While these parameters hold promise, there are challenges for translating MI modelling into clinical practice, including model uncertainty, validation and verification, as well as time-efficient processing. More research is needed to (1) simplify imaging with CMR in patients with post-MI, while preserving diagnostic accuracy and patient tolerance (2) to assess and validate novel biomechanical parameters against established prognostic biomarkers, such as LV ejection fraction and infarct size. Accessible software packages with minimal user interaction are also needed. Translating benefits to patients will be achieved through a multidisciplinary approach including clinicians, mathematicians, statisticians and industry partners