A Proximal Approach for a Class of Matrix Optimization Problems

In recent years, there has been a growing interest in mathematical models leading to the minimization, in a symmetric matrix space, of a Bregman divergence coupled with a regularization term. We address problems of this type within a general framework where the regularization term is split in two parts, one being a spectral function while the other is arbitrary. A Douglas-Rachford approach is proposed to address such problems and a list of proximity operators is provided allowing us to consider various choices for the fit-to-data functional and for the regularization term. Numerical experiments show the validity of this approach for solving convex optimization problems encountered in the context of sparse covariance matrix estimation. Based on our theoretical results, an algorithm is also proposed for noisy graphical lasso where a precision matrix has to be estimated in the presence of noise. The nonconvexity of the resulting objective function is dealt with a majorization-minimization approach, i.e. by building a sequence of convex surrogates and solving the inner optimization subproblems via the aforementioned Douglas-Rachford procedure. We establish conditions for the convergence of this iterative scheme and we illustrate its good numerical performance with respect to state-of-the-art approaches

Second-trimester amniotic fluid proteins changes in subsequent spontaneous preterm birth

IntroductionThe global sequence of the pathogenesis of preterm labor remains unclear. This study aimed to compare amniotic fluid concentrations of extracellular matrix-related proteins (procollagen, osteopontin and IL-33), and of cytokines (IL-19, IL-6, IL-20, TNF alpha, TGF beta, and IL-1 beta) in asymptomatic women with and without subsequent spontaneous preterm delivery. Material and methodsWe used amniotic fluid samples of singleton pregnancy, collected by amniocentesis between 16 and 20 weeks' gestation, without stigmata of infection (i.e., all amniotic fluid samples were tested with broad-range 16 S rDNA PCR to distinguish samples with evidence of past bacterial infection from sterile ones), during a randomized, double-blind, placebo-controlled trial to perform a nested case-control laboratory study. Cases were women with a spontaneous delivery before 37 weeks of gestation (preterm group). Controls were women who gave birth at or after 39 weeks (full term group). Amniotic fluid concentrations of the extracellular matrix-related proteins and cytokines measured by immunoassays were compared for two study groups. : NCT00718705. ResultsBetween July 2008 and July 2011, in 12 maternal-fetal medicine centers in France, 166 women with available PCR-negative amniotic fluid samples were retained for the analysis. Concentrations of procollagen, osteopontin, IL-19, IL-6, IL-20, IL-33, TNF alpha, TGF beta, and IL-1 beta were compared between the 37 who gave birth preterm and the 129 women with full-term delivery. Amniotic fluid levels of procollagen, osteopontin, IL-19, IL-33, and TNF alpha were significantly higher in the preterm than the full-term group. IL-6, IL-20, TGF beta, and IL-1 beta levels did not differ between the groups. ConclusionsIn amniotic fluid 16 S rDNA PCR negative samples obtained during second-trimester amniocentesis, extracellular matrix-related protein concentrations (procollagen, osteopontin and IL-33), together with IL-19 and TNF alpha, were observed higher at this time in cases of later spontaneous preterm birth

Computational optical imaging with a photonic lantern

[EN] The thin and flexible nature of optical fibres often makes them the ideal technology to view biological processes in-vivo, but current microendoscopic approaches are limited in spatial resolution. Here, we demonstrate a route to high resolution microendoscopy using a multicore fibre (MCF) with an adiabatic multimode-to-single-mode "photonic lantern" transition formed at the distal end by tapering. We show that distinct multimode patterns of light can be projected from the output of the lantern by individually exciting the single-mode MCF cores, and that these patterns are highly stable to fibre movement. This capability is then exploited to demonstrate a form of single-pixel imaging, where a single pixel detector is used to detect the fraction of light transmitted through the object for each multimode pattern. A custom computational imaging algorithm we call SARA-COIL is used to reconstruct the object using only the pre-measured multimode patterns themselves and the detector signals.This work was funded through the "Proteus" Engineering and Physical Sciences Research Council (EPSRC) Interdisciplinary Research Collaboration (IRC) (EP/K03197X/1), by the Science and Technology Facilities Council (STFC) through STFC-CLASP grants ST/K006509/1 and ST/K006460/1, STFC Consortium grants ST/N000625/1 and ST/N000544/1. S.L. acknowledges support from the National Natural Science Foundation of China under Grant no. 61705073. DBP acknowledges support from the Royal Academy of Engineering, and the European Research Council (PhotUntangle, 804626). The authors thank Philip Emanuel for the use of his confocal image of A549 cells and Eckhardt Optics for their image of the USAF 1951 target. 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A hybrid interior point - Deep learning approach for poisson image deblurring

In this paper we address the problem of deconvolution of an image corrupted with Poisson noise by reformulating the restoration process as a constrained minimization of a suitable regularized data fidelity function. The minimization step is performed by means of an interior-point approach, in which the constraints are incorporated within the objective function through a barrier penalty and a forward-backward algorithm is exploited to build a minimizing sequence. The key point of our proposed scheme is that the choice of the regularization, barrier and step-size parameters defining the interior point approach is automatically performed by a deep learning strategy. Numerical tests on Poisson corrupted benchmark datasets show that our method can obtain very good performance when compared to a state-of-the-art variational deblurring strategy

Learning image deblurring by unfolding a proximal interior point algorithm

Image reconstruction is frequently addressed by resorting to variational methods, which account for some prior knowledge about the solution. The success of these methods, however, heavily depends on the estimation of a set of hyperparameters. Deep learning architectures are, on the contrary, very generic and efficient, but they offer very limited control over their output. In this paper we present iRestNet, a neural network architecture which combines the benefits of both approaches. iRestNet is obtained by unfolding a proximal interior point algorithm. This enables enforcing hard constraints on the pixel range of the restored image thanks to a logarithmic barrier strategy, without requiring any parameter setting. Explicit expressions for the involved proximity operator, and its differential, are derived, which allows training iRestNet with gradient descent and backpropagation. Numerical experiments on image deblurring show that the proposed approach provides good image quality results compared to state-of-the-art variational and machine learning methods

Ultrasound-induced Cavitation enhances the efficacy of Chemotherapy in a 3D Model of Pancreatic Ductal Adenocarcinoma with its microenvironment

Abstract Pancreatic ductal adenocarcinoma (PDAC) is supported by a complex microenvironment whose physical contribution to chemoresistance could be overcome by ultrasound (US) therapy. This study aims to investigate the ability of US-induced inertial cavitation in association with chemotherapy to alter tumor cell viability via microenvironment disruption. For this purpose, we used a 3D-coculture PDAC model partially mimicking the tumor and its microenvironment. Coculture spheroids combining DT66066 cells isolated from KPC-transgenic mice and murine embryonic fibroblasts (iMEF) were obtained by using a magnetic nanoshuttle method. Spheroids were exposed to US with incremental inertial cavitation indexes. Conditions studied included control, gemcitabine, US-cavitation and US-cavitation + gemcitabine. Spheroid viability was assessed by the reduction of resazurin and flow cytometry. The 3D-coculture spheroid model incorporated activated fibroblasts and produced type 1-collagen, thus providing a partial miniature representation of tumors with their microenvironment. Main findings were: (a) Gemcitabine (5 μM) was significantly less cytotoxic in the presence of KPC/iMEFs spheroids compared with KPC (fibroblast-free) spheroids; (b) US-induced inertial cavitation combined with Gemcitabine significantly decreased spheroid viability compared to Gemcitabine alone; (c) both cavitation and chemotherapy affected KPC cell viability but not that of fibroblasts, confirming the protective role of the latter vis-à-vis tumor cells. Gemcitabine toxicity is enhanced when cocultured spheroids of KPC and iMEF are exposed to US-cavitation. Although the model used is only a partial representation of PDAC, this experience supports the hypothesis that US-inertial cavitation can enhance drug penetration and cytotoxicity by disrupting PDAC microenvironment

Blind Speech Deconvolution via Pretrained Polynomial Dictionary and Sparse Representation. 18th Pacific-Rim Conference on Multimedia, Harbin, China, September 28-29, 2017

Blind speech deconvolution aims to estimate both the source speech and acoustic channel from the convolutive reverberant speech. The problem is ill-posed and underdetermined, which often requires prior knowledge for the estimation of the source and channel. In this paper, we propose a blind speech deconvolution method via a pretrained polynomial dictionary and sparse representation. A polynomial dictionary learning technique is employed to learn the dictionary from room impulse responses, which is then used as prior information to estimate the source and the acoustic impulse responses via an alternating optimization strategy. Simulations are provided to demonstrate the performance of the proposed method

Joint L1 − L2 Regularisation for Blind Speech Deconvolution. 18th Pacific-Rim Conference on Multimedia, Harbin, China, September 28-29, 2017

The purpose of blind speech deconvolution is to recover both the original speech source and the room impulse response (RIR) from the observed reverberant speech. This can be beneficial for speech intelligibility and speech perception. However, the problem is ill-posed, which often requires additional knowledge to solve. In order to address this problem, prior informations (such as the sparseness of signal or acoustic channel) are often exploited. In this paper, we propose a joint L1 − L2 regularisation based blind speech deconvolution method for a single-input and single-output (SISO) acoustic system with a high level of reverberation, where both the sparsity and density of the room impulse responses (RIR) are considered, by imposing an L1 and L2 norm constraint on their early and late part respectively. By employing an alternating strategy, both the source signal and early part in the RIR can be well reconstructed while the late part of the RIR can be suppressed