nils-herrmann/clean_scirepeval huggingface dataset:
A linear model approach for ultrasonic inverse problems with attenuation and dispersion

Ultrasonic inverse problems such as spike train deconvolution, synthetic aperture focusing, or tomography attempt to reconstruct spatial properties of an object (discontinuities, delaminations, flaws, etc.) from noisy and incomplete measurements. They require an accurate description of the data acquisition process. Dealing with frequency-dependent attenuation and dispersion is therefore crucial because both phenomena modify the wave shape as the travel distance increases. In an inversion context, this paper proposes to exploit a linear model of ultrasonic data taking into account attenuation and dispersion. The propagation distance is discretized to build a finite set of radiation impulse responses. Attenuation is modeled with a frequency power law and then dispersion is computed to yield physically consistent responses. Using experimental data acquired from attenuative materials, this model outperforms the standard attenuation-free model and other models of the literature. Because of model linearity, robust estimation methods can be implemented. When matched filtering is employed for single echo detection, the model that we propose yields precise estimation of the attenuation coefficient and of the sound velocity. A thickness estimation problem is also addressed through spike deconvolution, for which the proposed model also achieves accurate results.
Fast Constrained Least Squares Spectral Unmixing Using Primal-Dual Interior-Point Optimization

Hyperspectral data unmixing aims at identifying the components (endmembers) of an observed surface and at determining their fractional abundances inside each pixel area. Assuming that the spectral signatures of the surface components have been previously determined by an endmember extraction algorithm, or to be part of an available spectral library, the main problem is reduced to the estimation of the fractional abundances. For large hyperspectral image data sets, the estimation of the abundance maps requires the resolution of a large-scale optimization problem subject to linear constraints such as non-negativity and sum less or equal to one. This paper proposes a primal-dual interior-point optimization algorithm allowing a constrained least squares estimation approach. In comparison with existing methods, the proposed algorithm is more flexible since it can handle any linear equality and/or inequality constraint and has the advantage of a reduced computational cost. It also presents an algorithmic structure suitable for a parallel implementation on modern intensive computing devices such as Graphics Processing Units (GPU). The implementation issues are discussed and the applicability of the proposed approach is illustrated with the help of examples on synthetic and real hyperspectral data.
Blind separation of generalized hyperbolic processes: unifying approach to stationary non Gaussianity and Gaussian non stationarity

In this contribution, we propose a Bayesian sampling solution to the problem of noisy blind separation of generalized hyperbolic (GH) signals. GH models, introduced by Barndorff-Nielsen in 1977, represent a parametric family able to cover a wide range of real signal distributions. The alternative construction of these distributions as a normal mean-variance (continuous) mixture leads to an efficient implementation of the MCMC method applied to source separation. The incomplete data structure of the GH distribution is indeed compatible with the hidden variable nature of the source separation problem. Our algorithm involves hyperparameters estimation as well. Therefore, it can be used, independently, to fit the parameters of the GH distribution to real data.
An alternative to standard maximum likelihood for Gaussian mixtures

Because true maximum likelihood (ML) is too expensive, the dominant approach in Bernoulli-Gaussian (BG) myopic deconvolution consists in the joint maximization of a single generalized likelihood with respect to the input signal and the hyperparameters. This article assesses the theoretical properties of a related maximum generalized marginal likelihood (MGML) estimator in a simplified framework: the filter is reduced to identity, so that the output data is a mixture of Gaussian populations. Our results are three-fold: first, exact MGML estimates can be efficiently computed; second, this estimator performs better than ML in the short sample case whereas it is drastically less expensive; third, asymptotic estimates are significant although biased.
Optimization of a maximum entropy criterion for 2D Nuclear Magnetic Resonance reconstruction

This paper deals with the reconstruction of T1-T2 correlation spectra in Nuclear Magnetic Resonance (NMR) spectroscopy. The ill-posed character of this inverse problem and its large size are the main difficulties of the reconstruction. While maximum entropy is retained as an adequate regularization approach, the choice of an efficient optimization algorithm remains a challenging task. Our proposal is to apply a nonlinear conjugate gradient algorithm with two original features. Firstly, a theoretically well stated line search strategy suitable for the entropy function is applied to ensure a monotonic decrease of the criterion. Secondly, an appropriate preconditioning structure based on a truncated singular value decomposition of the forward model matrix is used to speed up the algorithm convergence. The resulting method reveals far more efficient than the classical Skilling and Bryan method and its applicability is illustrated through real NMR data processing.
Fast Solutions of the 2D Inverse Scattering Problem Based on a TSVD Approximation of the Internal Field for the Forward Model

An alternative formulation of the microwave tomography problem is proposed, in order to reduce the computation time of the inversion procedure. The well-known multiplicative regularized contrast source inversion method is used as reference algorithm. Our formulation is based on a truncated singular value decomposition of the matrix involved in the most computationally intensive operations of the inversion algorithm. After theoretical and practical investigation of the behavior of the singular values, it is shown that the corresponding approximation does not yield any significant degradation of the quality of the solutions even for large complex permittivity contrasts, while the computation times are reduced by a factor ranging between 1.1 and 6 with respect to the original method.
On global and local convergence of half-quadratic algorithms

This paper provides original results on the global and local convergence properties of half-quadratic (HQ) algorithms resulting from the Geman and Yang (GY) and Geman and Reynolds (GR) primal-dual constructions. First, we show that the convergence domain of the GY algorithm can be extended with the benefit of an improved convergence rate. Second, we provide a precise comparison of the convergence rates for both algorithms. This analysis shows that the GR form does not benefit from a better convergence rate in general. Moreover, the GY iterates often take advantage of a low cost implementation. In this case, the GY form is usually faster than the GR form from the CPU time viewpoint.
Non-negative source separation: range of admissible solutions and conditions for the uniqueness of the solution

A main issue in source separation is to deal with the indeterminacies. Well known are the ordering and scale ambiguities, but other types of indeterminacies may also occur. In this paper we address these indeterminacies in the case of non-negative sources and non-negative mixing coefficients. On the one hand, we fully develop the case of two sources. On the other hand, in the general case we formulate necessary conditions for the uniqueness of the solution (up to ordering and scale ambiguities).
Solving a problem of sources separation stemming from a linear combination: Applied to the 3D reconstruction of the solar atmosphere

In this article, we propose to reconstruct the 3D structure of the solar atmosphere, taking into account the temporal evolution of the coronal structures. To do this we develop a new method, which considers that the object of interest consists of several sources each one having different evolution from the other at different time. The data are the Radon transform of an instantaneous mixture of sources. So, we must solve a separation source problem with a linear mixture of sources. To our best knowledge, this problem is still open. To solve this problem of source separation, we have introduced a sparsity information on the sources, and we have chosen to use the variational Bayesian approach for reduce the computation time.
Statistical behavior of joint least-square estimation in the phase diversity context

The images recorded by optical telescopes are often degraded by aberrations that induce phase variations in the pupil plane. Several wavefront sensing techniques have been proposed to estimate aberrated phases. One of them is phase diversity, for which the joint least-square approach introduced by Gonsalves et al. is a reference method to estimate phase coefficients from the recorded images. In this paper, we rely on the asymptotic theory of Toeplitz matrices to show that Gonsalves' technique provides a consistent phase estimator as the size of the images grows. No comparable result is yielded by the classical joint maximum likelihood interpretation (e.g., as found in the work by Paxman et al.). Finally, our theoretical analysis is illustrated through simulated problems.
Efficient Maximum Entropy Reconstruction of Nuclear Magnetic Resonance T1-T2 Spectra

This paper deals with the reconstruction of T1-T2 correlation spectra in nuclear magnetic resonance relaxometry. The ill-posed character and the large size of this inverse problem are the main difficulties to tackle. While maximum entropy is retained as an adequate regularization approach, the choice of an efficient optimization algorithm remains a challenging task. Our proposal is to apply a truncated Newton algorithm with two original features. First, a theoretically sound line search strategy suitable for the entropy function is applied to ensure the convergence of the algorithm. Second, an appropriate preconditioning structure based on a singular value decomposition of the forward model matrix is used to speed up the algorithm convergence. Furthermore, we exploit the specific structures of the observation model and the Hessian of the criterion to reduce the computation cost of the algorithm. The performances of the proposed strategy are illustrated by means of synthetic and real data processing.
Optimized single site update algorithms for image deblurring

We present optimized algorithms for image deblurring in the case of a separable point spread function (PSF). Our work is in the usual context of Bayesian estimation with Gibbs random fields (GRF). The derived algorithms fall into the class of single site update algorithms (SSUAs), which exhibit a high convergence rate per iteration and small memory requirements, while hard domain constraints such as positivity are easily introduced. On the other hand, standard forms of SSUAs rapidly become intractable when the size of the PSF is large. We show how PSF separability can benefit the SSUAs, in order to reduce the cost of each pixel update from O(2pq) to O(p+q) (p/spl times/q is the size of the PSF). We show that the resulting deterministic SSUA compares very favorably with global update algorithms (GUAs). The new separable form can also benefit other SSUAs, especially stochastic versions such as simulated annealing (SA) and Monte Carlo Markov chain (MCMC) algorithms.
Frame-synchronous stochastic matching based on the Kullback-Leibler information

An acoustic mismatch between a given utterance and a model degrades the performance of the speech recognition process. We choose to model speech by hidden Markov models (HMMs) in the cepstrum domain and the mismatch by a parametric function. In order to reduce the mismatch, one has to estimate the parameters of this function. We present a frame synchronous estimation of these parameters. We show that the parameters can be computed recursively. Thanks to such methods, parameters variations can be tracked. We give general equations and study the particular case of an affine transform. Finally, we report recognition experiments carried out over both PSTN and cellular telephone network to show the efficiency of the method in a real context.
A connection between half-quadratic criteria and EM algorithms

Iteratively Reweighted Least Squares (IRLS) and Residual Steepest descent (RSD) algorithms of robust statistics arise as special cases of half-quadratic schemes . Here, we adopt a statistical framework and we show that both algorithms are instances of the EM algorithm. The augmented dataset respectively involves a scale and a location mixture of Gaussians. The sufficient conditions for the construction cover a broad class of already known robust statistics.
Modelling the neurovascular habituation effect on fMRI time series

In this paper, a novel non-stationary model of functional Magnetic Resonance Imaging (fMRI) time series is proposed. It allows us to account for some putative habituation effect arising in event-related fMRI paradigms that involves the so-called repetition-suppression phenomenon  and induces decreasing magnitude responses over successive trials. Akin to , this model is defined over functionnally homogeneous regions-of-interest (ROIs) and embedded in a joint detection-estimation approach of brain activity. Importantly, its non-stationarity character is embodied in the trial-varying nature of the BOLD response magnitude. Habituation and activation maps are then estimated within the Bayesian framework in a fully unsupervised MCMC procedure. On artificial fMRI datasets, we show that habituation effects can be accurately recovered in activating voxels.
Regularized Doppler radar imaging for target identification in atmospheric clutter

We develop a method for the formation of Doppler radar images with enhanced features. This problem, when studied as an adaptive spectral estimation problem, is particularly ill-posed because of the small number of data. Our approach is based on a regularized estimation of depth-frequency images which combines a high-resolution Fourier model of the observations with prior information about the nature of the features of interest. We first derive an appropriate model and a regularized criterion for meteorological clutter restoration before addressing the extension to the identification of spot-like targets superimposed on this clutter. We also adapt quasi-Newton algorithms, based on half-quadratic regularization, for the computation of the solution. The practical interest of our approach is validated on simulated and real data.
Primal dual interior point optimization for penalized least squares estimation of abundance maps in hyperspectral imaging

The estimation of abundance maps in hyperspectral imaging (HSI) requires the resolution of an optimization problem subject to non-negativity and sum-to-one constraints. Assuming that the spectral signatures of the image components have been previously determined by an endmember extraction algorithm, we propose here a primal-dual interior point algorithm for the estimation of their fractional abundances using a penalized least squares approach. In comparison with the reference method FCLS, our algorithm has the advantage of a reduced computational cost, especially in the context of large scale images and allows to deal with a penalized criterion favoring the spatial smoothness of abundance maps. The performances of the proposed approach are discussed with the help of a synthetic HSI example.
Outlier detection for robust region-based estimation of the hemodynamic response function in event-related fMRI

In functional magnetic resonance imaging (fMRI), the hemodynamic response function (HRF) represents the impulse response of the neurovascular system. Its identification is essential for a deeper understanding of the dynamics of cerebral activity. In previous papers, we developed a voxelwise approach, i.e. based on a single time-course. In this paper, we propose an extension to cope with region-based HRF estimation. We introduce a spatial homogeneous model that assumes the same HRF shape for a majority of voxels within a given region-of-interest (ROI). A least trimmed squares estimator is employed to select those voxels. A Bayesian HRF estimation is then performed with the corresponding time courses. Our approach is tested on real fMRI data to illustrate the gain in robustness achieved with the region-based estimate.
A Majorize–Minimize Strategy for Subspace Optimization Applied to Image Restoration

This paper proposes accelerated subspace optimization methods in the context of image restoration. Subspace optimization methods belong to the class of iterative descent algorithms for unconstrained optimization. At each iteration of such methods, a stepsize vector allowing the best combination of several search directions is computed through a multidimensional search. It is usually obtained by an inner iterative second-order method ruled by a stopping criterion that guarantees the convergence of the outer algorithm. As an alternative, we propose an original multidimensional search strategy based on the majorize-minimize principle. It leads to a closed-form stepsize formula that ensures the convergence of the subspace algorithm whatever the number of inner iterations. The practical efficiency of the proposed scheme is illustrated in the context of edge-preserving image restoration.
Stationary Markov Random Fields on a Finite Rectangular Lattice

This paper provides a complete characterization of stationary Markov random fields on a finite rectangular (non-toroidal) lattice in the basic case of a second-order neighborhood system. Equivalently, it characterizes stationary Markov fields on Z/sup 2/ whose restrictions to finite rectangular subsets are still Markovian (i.e., even on the boundaries). Until now, Pickard random fields formed the only known class of such fields. First, we derive a necessary and sufficient condition for Markov random fields on a finite lattice to be stationary. It is shown that their joint distribution factors in terms of the marginal distribution on a generic (2/spl times/2) cell which must fulfil some consistency constraints. Second, we solve the consistency constraints and provide a complete characterization of such measures in three cases. Symmetric measures and Gaussian measures are shown to necessarily belong to the Pickard class, whereas binary measures belong either to the Pickard class, or to a new nontrivial class which is further studied. In particular, the corresponding fields admit a simple parameterization and may be simulated in a simple, although nonunilateral manner.
Bayesian blind separation of generalized hyperbolic processes in noisy and underdeterminate mixtures

In this paper, we propose a Bayesian sampling solution to the noisy blind separation of generalized hyperbolic signals. Generalized hyperbolic models, introduced by Barndorff-Nielsen in 1977, represent a parametric family able to cover a wide range of real signal distributions. The alternative construction of these distributions as a normal mean variance (continuous) mixture leads to an efficient implementation of the Markov chain Monte Carlo method applied to source separation. The incomplete data structure of the generalized hyperbolic distribution is indeed compatible with the hidden variable nature of the source separation problem. Both overdeterminate and underdeterminate noisy mixtures are solved by the same algorithm without a prewhitening step. Our algorithm involves hyperparameters estimation as well. Therefore, it can be used, independently, to fitting the parameters of the generalized hyperbolic distribution to real data
Automatic Crack Detection on Two-Dimensional Pavement Images: An Algorithm Based on Minimal Path Selection

This paper proposes a new algorithm for automatic crack detection from 2D pavement images. It strongly relies on the localization of minimal paths within each image, a path being a series of neighboring pixels and its score being the sum of their intensities. The originality of the approach stems from the proposed way to select a set of minimal paths and the two postprocessing steps introduced to improve the quality of the detection. Such an approach is a natural way to take account of both the photometric and geometric characteristics of pavement images. An intensive validation is performed on both synthetic and real images (from five different acquisition systems), with comparisons to five existing methods. The proposed algorithm provides very robust and precise results in a wide range of situations, in a fully unsupervised manner, which is beyond the current state of the art.
Application and validation of spatial mixture modelling for the joint detection-estimation of brain activity in fMRI

Within-subject analysis in event-related functional Magnetic Resonance Imaging (fMRI) first relies on (i) a detection step to localize which parts of the brain are activated by a given stimulus type, and second on (ii) an estimation step to recover the temporal dynamics of the brain response. Recently, a Bayesian detection-estimation approach that jointly addresses (i)-(ii) has been proposed in . This work is based on an independent mixture model (IMM) and provides both a spatial activity map and an estimate of brain dynamics. In , we accounted for spatial correlation using a spatial mixture model (SMM) based on a binary Markov random field. Here, we assess the SMM robustness and flexibility on simulations which diverge from the priors and the generative BOLD model and further extend comparison between SMM and IMM on real fMRI data, focusing on a region of interest in the auditory cortex.
Sparse deconvolution: Comparison of statistical and deterministic approaches

Sparse spike train deconvolution is a classical inverse problem which gave rise to many deterministic and stochastic algorithms since the mid-80's. In the past decade, sparse approximation has been an intensive field of research, leading to the development of a number of algorithms including greedy strategies and convex relaxation methods. Spike train deconvolution can be seen as a specific sparse approximation problem, where the observation matrix contains highly correlated columns and where the focus is set on the exact recovery of the spike locations. The objective of this paper is to evaluate the performance of algorithms proposed in both fields in terms of detection statistics, with Monte-Carlo simulations of spike deconvolution problems.
Unsupervised image segmentation using a telegraph parameterization of Pickard random fields

This article presents a non-supervised segmentation method based upon a discrete-level unilateral Markov field model of the image. Such models have been shown to yield numerically efficient algorithms, for segmentation and for hyperparameter estimation as well. Our contribution lies in the derivation of a parsimonious telegraphic parameterization of the unilateral Markov field. On a theoretical level, this parameterization ensures that some important properties of the field (e.g., stationarity) do hold. On a practical level, it reduces the computational complexity of the algorithm used in the segmentation and parameter estimation stages of the procedure. In addition, it decreases the number of hyperparameters that must be estimated, thereby improving the convergence speed and accuracy of the corresponding estimation method.