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Angélique Drémeau

Publications and source records attributed to Angélique Drémeau.

16 recordsLinked to original sources

Information geometry of emergent symmetry quotients and their weak unfoldings

A regular observed statistical model may converge to a limit in which a previously identifiable signed parameter becomes identifiable only modulo a reflection. We study the local information geometry of this transition. For a twice differentiable Hellinger embedding with an exact limiting reflection, the observed displacement is forced into the two-jet form \(\varepsilonλJ_-+λ^2J_+/2\), up to higher-order terms. The mixed jet restores the sign away from the symmetric face, whereas the even jet is the first tangent inherited by the quotient. After nuisance elimination, a positive Gram determinant yields a nondegenerate cross-cap two-jet. The associated local asymptotic theory has three regimes governed by \(τ_n=\sqrt n\,\varepsilon_n^2\): regular signed LAN, a critical curved Gaussian subexperiment, and a quotient regime with the \(n^{-1/4}\) signed scale. We prove that the same parabolic critical experiment persists for predictive likelihoods along a single stationary dependent trajectory. The limiting quotient has a regular Fisher metric in the invariant coordinate, while its pullback degenerates in the signed coordinate. For a solvable CIR--OU benchmark motivated by coherent sea-clutter observations, we derive the quotient Fisher metric and curvature explicitly and show that the curvature is strictly negative. We also determine the restricted holonomy of the full Amari family: \(\operatorname{Hol}_0(\nabla^{(a)})=SO(2)\) for \(a=0\), whereas \(\operatorname{Hol}_0(\nabla^{(a)})=GL^+(2,\mathbb R)\) for \(a\neq0\). The results separate the intrinsic geometry of the limiting quotient from the transverse geometry of its weak unfolding.

math.ST↗

Geometry and observability of latent texture-coherence diffusions in coherent sea-clutter radar observations

A coherent radar observes sea clutter through a non-injective multiplicative combination of latent texture and coherence variables. We study a class of partially observed slow--fast diffusions for which the projected quadratic covariance has the form \(Q=S+qyy^\top\), using Field's sea-clutter diffusion as a solvable benchmark. For fixed-shape fast covariance, we derive an exact generalized spectral-gap criterion for recovery of the latent slow coordinate and a stability bound. When the fast covariance changes shape, we obtain an invariant scale--shape decomposition and exact conditions for reinforcement or cancellation of the latent quadratic signature. We then quantify the loss of resolvability caused by finite sampling and receiver noise. Controlled CIR--OU simulations verify the analytic identities and exhibit regimes in which structural observability does not yield stable reconstruction.

math.PR↗

Performance Analysis of Underwater Quantum Key Distribution Protocols: BB84, SARG04, and BBM92

This study compares the quantum bit error rate (QBER) performance of BB84, SARG04, and the entanglement-based BBM92 protocol in non-turbulent underwater optical channels. Clear, coastal, and turbid water types are considered under different background-illumination and receiver configurations. For BB84 and SARG04, channel attenuation and background-induced detections determine the quantum gain and QBER. For BBM92, photon loss is included through the two arm detection efficiencies, whereas the polarization state conditioned on two-photon detection is described by local depolarizing Kraus channels. The BBM92 QBER is obtained from the correlations in both measurement bases and from an exact partition of recorded events into true and false coincidences. Stochastic numerical estimates provide consistency checks of the analytical implementation. The results show that water turbidity and optical background reduce the distance below the adopted QBER threshold. They also show that a low conditional BBM92 QBER may coexist with a very small coincidence probability; therefore, the QBER-threshold distance alone does not represent a complete operational or secret-key-rate bound.

quant-ph↗

Non-Maximally Entangled States for Quantum Key Distribution in Underwater Channels: BBM92 Protocol via Kraus Operators

Underwater optical channels pose significant challenges to the security and reliability of quantum communication systems due to absorption and scattering. In this paper, we investigate the BBM92 entanglement-based quantum key distribution (QKD) protocol under realistic underwater channel conditions. Photon pairs are prepared in non-maximally entangled states, and the underwater propagation medium is modeled as a quantum channel incorporating both amplitude-damping and depolarizing effects, described within the Kraus operator formalism. The protocol performance is evaluated in terms of quantum bit error rate (QBER) and secret key rate (SKR), analyzed as functions of the entanglement degree and channel degradation parameters. Closed-form analytical expressions for the QBER and SKR are derived for the proposed channel model and validated through Monte Carlo simulations. The proposed framework is then applied to various realistic underwater scenarios, considering different water types, namely clear ocean, coastal, and turbid water, as well as varying atmospheric conditions.

quant-ph↗

CV-QKD over Turbulence Channels with Virtual Photon Subtraction and Quantum Multiple-Symbol Detection for Underwater Quantum Communications

Continuous-variable quantum key distribution (CV-QKD) is a promising approach for secure underwater quantum communications (UQCs), where propagation loss, scattering, turbulence, and receiver thermal noise can severely degrade the transmission of quantum states. In this paper, we propose an underwater CV-QKD system with virtual photon subtraction (VPS), implemented through post-selection of Alice's measurement outcomes, without requiring channel state information (CSI) at the receiver. Three VPS-based system configurations are analyzed, corresponding to homodyne detection (VPS-HD), quantum maximum-likelihood detection (VPS-QMLD), and quantum multiple-symbol detection (VPS-QMSD). System performance is evaluated in terms of the accepted-only quantum bit error rate (QBER), where underwater turbulence is modeled by an Erlang distribution. Analytical and semi-closed-form QBER expressions are derived for the three configurations and validated through Monte Carlo simulations for different water types and system parameters. The results show close agreement between analytical and simulation results and demonstrate that VPS-QMSD provides the best robustness against underwater turbulence, achieving the lowest QBER compared with VPS-QMLD and VPS-HD.

quant-ph↗

From State-Space Transport to Measurement-Aware Distinguishability in Quantum Sensing

Overlap-based distinguishability measures, such as fidelity- or Chernoff-type quantities, play a central role in quantum sensing and quantum illumination. In strongly lossy and fluctuating environments, however, these quantities may become numerically compressed and therefore less informative for optimization, monitoring, or adaptive control. In this work, we investigate transport-based distinguishability criteria for lossy quantum sensing. We first introduce an isotropic Gaussian transport metric defined on first and second moments and compare it with a fidelity-based benchmark in a thermal-loss model. We then show analytically that, within an isotropic thermal-reference geometry, this metric locally disfavors squeezing relative to coherent displacement, thereby distinguishing global phase-space robustness from directional metrological advantage. We next introduce a projected transport metric adapted to quadrature-resolved measurements and show that its optimization over the measurement quadrature is analytically tractable, reducing to a boundary choice between the principal axes of the output noise ellipse. We further extend the framework to a measurement-aware metric defined on detector output statistics, and derive an explicit Gaussian formula for a noisy quadrature measurement chain. Finally, in a fading setting, we show that the isotropic metric and the projected metric aligned with the coherent displacement retain first-order sensitivity to the transmissivity in the strong-loss regime, whereas the orthogonal projected metric is compressed to second order. These results support a hierarchical view of transport-based distinguishability in quantum sensing, ranging from global robustness indicators to measurement-adapted operational metrics.

quant-ph↗

Learning sparse structures for physics-inspired compressed sensing

In underwater acoustics, shallow water environments act as modal dispersive waveguides when considering low-frequency sources. In this context, propagating signals can be described as a sum of few modal components, each of them propagating according to its own wavenumber. Estimating these wavenumbers is of key interest to understand the propagating environment as well as the emitting source. To solve this problem, we proposed recently a Bayesian approach exploiting a sparsity-inforcing prior. When dealing with broadband sources, this model can be further improved by integrating the particular dependence linking the wavenumbers from one frequency to the other. In this contribution, we propose to resort to a new approach relying on a restricted Boltzmann machine, exploited as a generic structured sparsity-inforcing model. This model, derived from deep Bayesian networks, can indeed be efficiently learned on physically realistic simulated data using well-known and proven algorithms.

eess.SP↗

Efficient atom selection strategy for iterative sparse approximations

We propose a low-computational strategy for the efficient implementation of the "atom selection step" in sparse representation algorithms. The proposed procedure is based on simple tests enabling to identify subsets of atoms which cannot be selected. Our procedure applies on both discrete or continuous dictionaries. Experiments performed on DOA and Gaussian deconvolution problems show the computational gain induced by the proposed approach.

eess.SP↗

DOA estimation in structured phase-noisy environments: technical report

In this paper we focus on the problem of estimating the directions of arrival (DOA) of a set of incident plane waves. Unlike many previous works, which assume that the received observations are only affected by additive noise, we consider the setup where some phase noise also corrupts the data (as for example observed in atmospheric sound propagation or underwater acoustics). We propose a new methodology to solve this problem in a Bayesian framework by resorting to a variational mean-field approximation. Our simulation results illustrate the benefits of carefully accounting for the phase noise in the DOA estimation process.

cs.IT↗

An Efficient Algorithm for Video Super-Resolution Based On a Sequential Model

In this work, we propose a novel procedure for video super-resolution, that is the recovery of a sequence of high-resolution images from its low-resolution counterpart. Our approach is based on a "sequential" model (i.e., each high-resolution frame is supposed to be a displaced version of the preceding one) and considers the use of sparsity-enforcing priors. Both the recovery of the high-resolution images and the motion fields relating them is tackled. This leads to a large-dimensional, non-convex and non-smooth problem. We propose an algorithmic framework to address the latter. Our approach relies on fast gradient evaluation methods and modern optimization techniques for non-differentiable/non-convex problems. Unlike some other previous works, we show that there exists a provably-convergent method with a complexity linear in the problem dimensions. We assess the proposed optimization method on {several video benchmarks and emphasize its good performance with respect to the state of the art.}

cs.CV↗

Approximate Message Passing with Restricted Boltzmann Machine Priors

Approximate Message Passing (AMP) has been shown to be an excellent statistical approach to signal inference and compressed sensing problem. The AMP framework provides modularity in the choice of signal prior; here we propose a hierarchical form of the Gauss-Bernouilli prior which utilizes a Restricted Boltzmann Machine (RBM) trained on the signal support to push reconstruction performance beyond that of simple iid priors for signals whose support can be well represented by a trained binary RBM. We present and analyze two methods of RBM factorization and demonstrate how these affect signal reconstruction performance within our proposed algorithm. Finally, using the MNIST handwritten digit dataset, we show experimentally that using an RBM allows AMP to approach oracle-support performance.

cs.IT↗

Random Projections through multiple optical scattering: Approximating kernels at the speed of light

Random projections have proven extremely useful in many signal processing and machine learning applications. However, they often require either to store a very large random matrix, or to use a different, structured matrix to reduce the computational and memory costs. Here, we overcome this difficulty by proposing an analog, optical device, that performs the random projections literally at the speed of light without having to store any matrix in memory. This is achieved using the physical properties of multiple coherent scattering of coherent light in random media. We use this device on a simple task of classification with a kernel machine, and we show that, on the MNIST database, the experimental results closely match the theoretical performance of the corresponding kernel. This framework can help make kernel methods practical for applications that have large training sets and/or require real-time prediction. We discuss possible extensions of the method in terms of a class of kernels, speed, memory consumption and different problems.

cs.ET↗

Relaxed Recovery Conditions for OMP/OLS by Exploiting both Coherence and Decay

We propose extended coherence-based conditions for exact sparse support recovery using orthogonal matching pursuit (OMP) and orthogonal least squares (OLS). Unlike standard uniform guarantees, we embed some information about the decay of the sparse vector coefficients in our conditions. As a result, the standard condition $μ<1/(2k-1)$ (where $μ$ denotes the mutual coherence and $k$ the sparsity level) can be weakened as soon as the non-zero coefficients obey some decay, both in the noiseless and the bounded-noise scenarios. Furthermore, the resulting condition is approaching $μ<1/k$ for strongly decaying sparse signals. Finally, in the noiseless setting, we prove that the proposed conditions, in particular the bound $μ<1/k$, are the tightest achievable guarantees based on mutual coherence.

cs.IT↗

Phase recovery from a Bayesian point of view: the variational approach

In this paper, we consider the phase recovery problem, where a complex signal vector has to be estimated from the knowledge of the modulus of its linear projections, from a naive variational Bayesian point of view. In particular, we derive an iterative algorithm following the minimization of the Kullback-Leibler divergence under the mean-field assumption, and show on synthetic data with random projections that this approach leads to an efficient and robust procedure, with a good computational cost.

cs.IT↗

Statistical inference with probabilistic graphical models

These are notes from the lecture of Devavrat Shah given at the autumn school "Statistical Physics, Optimization, Inference, and Message-Passing Algorithms", that took place in Les Houches, France from Monday September 30th, 2013, till Friday October 11th, 2013. The school was organized by Florent Krzakala from UPMC & ENS Paris, Federico Ricci-Tersenghi from La Sapienza Roma, Lenka Zdeborova from CEA Saclay & CNRS, and Riccardo Zecchina from Politecnico Torino. This lecture of Devavrat Shah (MIT) covers the basics of inference and learning. It explains how inference problems are represented within structures known as graphical models. The theoretical basis of the belief propagation algorithm is then explained and derived. This lecture sets the stage for generalizations and applications of message passing algorithms.

cs.LG↗

Bayesian Pursuit Algorithms

This paper addresses the sparse representation (SR) problem within a general Bayesian framework. We show that the Lagrangian formulation of the standard SR problem, i.e., $\mathbf{x}^\star=\arg\min_\mathbf{x} \lbrace \| \mathbf{y}-\mathbf{D}\mathbf{x} \|_2^2+λ\| \mathbf{x}\|_0 \rbrace$, can be regarded as a limit case of a general maximum a posteriori (MAP) problem involving Bernoulli-Gaussian variables. We then propose different tractable implementations of this MAP problem that we refer to as "Bayesian pursuit algorithms". The Bayesian algorithms are shown to have strong connections with several well-known pursuit algorithms of the literature (e.g., MP, OMP, StOMP, CoSaMP, SP) and generalize them in several respects. In particular, i) they allow for atom deselection; ii) they can include any prior information about the probability of occurrence of each atom within the selection process; iii) they can encompass the estimation of unkown model parameters into their recursions.

cs.IT↗