1,721,085 research outputs found
Oracle-Net for Nonlinear Compressed Sensing in Electrical Impedance Tomography Reconstruction Problems
Full text linkSparse recovery principles play an important role in solving many nonlinear ill-posed inverse problems. We investigate a variational framework with learned support estimation for compressed sensing sparse reconstructions, where the available measurements are nonlinear and possibly corrupted by noise. A graph neural network, named Oracle-Net, is proposed to predict the support from the nonlinear measurements and is integrated into a regularized recovery model to enforce sparsity. The derived nonsmooth optimization problem is then efficiently solved through a constrained proximal gradient method. Error bounds on the approximate solution of the proposed Oracle-based optimization are provided in the context of the ill-posed Electrical Impedance Tomography problem (EIT). Numerical solutions of the EIT nonlinear inverse reconstruction problem confirm the potential of the proposed method which improves the reconstruction quality from undersampled measurements, under sparsity assumptions
Binding of germ cells to mutant Sld Sertoli cells is defective and is rescued by expression of the transmembrane form of the c-kit ligand
No full textMutations in the steel (Sl) locus, encoding the c-kit ligand (KL), are associated with impaired germ cell development in mice. Two forms of KL exist: one more steadily associated with the plasma membrane and one more easily released as a soluble protein. We report here that the expression of the two mRNAs coding for the two different form of KL is developmentally regulated in mouse testis. At birth the two mRNAs are expressed at an equal ratio. Starting after 6 days of life, and in parallel to initiation of germ cell differentiation, the mRNA encoding the membrane-associated form of KL becomes more abundant. Germ cells, and especially spermatogonia, express c-kit; thus membrane-bound KL could mediate adhesion between Sertoli cells and germ cells. We find, in fact, that Sertoli cells from Sl/Sld mutant mice, which do not express the mRNA for the membrane-associated form of KL, are unable to bind germ cells. Introduction of a plasmid expressing the transmembrane form, but not the soluble form, of KL restores the ability of Sertoli cells from Sl/Sld mutant mice to bind germ cells. These data suggest that preferential expression of the membrane-anchored form of KL in Sertoli cells may have a role in mediating adhesion of c-kit-expressing germ cells to Sertoli cells
A nonconvex penalization algorithm with automatic choice of the regularization parameter in sparse imaging
Full text linkThis paper introduces an efficient method for solving nonconvex penalized
minimization problems. The topic is relevant in many imaging problems
characterized by sparse data. The proposed method originates from the
iterative reweighting l1 scheme, modified by the automatic update of the
regularization parameter on the basis of the behavior of the objective function.
Besides proving the convergence of the method, a modified algorithm
is obtained and the performance is tested on two different sparse imaging
problems. The proposed method can be viewed as a general framework which
can be adapted to different one-parameter nonconvex penalty functions and
applied to problems characterized by sparse data
Learning intrinsic shape representations via spectral mesh convolutions
Full text linkWe introduce spectral-based convolutional operators embedded within Generalized Graph Neural Networks (G-GNNs). These operators enable deep learning on graphs through a learnable, energy-driven evolution process. This approach empowers us to impose specific properties on the graph convolutional kernel directly derived from the corresponding variational formulations. Our model incorporates both parameterized and non- parameterized graph Laplacian-based energies within the generalized graph convolutional layer to address features like smoothness, sharpness, and compact support. By making appropriate choices within our G-GNN framework, we pave the way for designing novel paradigms for 3D shape representation, reconstruction, and processing, while also enabling effective feature embeddings for intrinsic neural fields
A Novel Compressed Sensing-Based Approach for Fast MRI Reconstruction from Highly Under-Sampled K-Space Data
No full textMagnetic Resonance (MR) imaging is a multiparametric imaging technique allowing the diagnosis of a wide spectrum of cardiovascular diseases. Unfortunately, MR acquisitions tend to be slow, limiting patient throughput and limiting potential indications for use while driving up costs. Compressed sensing (CS) is a method for reducing MR scan time, increasing image reconstruction time. In this study we formulated a novel CS-based approach to speed up reconstruction procedure. A fidelity term that constrains the solution to be similar to the acquired samples was embedded in a nonconvex weighted total variation-based approach starting from highly subsampled k-space data. This approach was tested for the reconstruction of cardiac images in 10 delayed contrast enhanced MR (DCE-MR) acquisitions, using different k-space masks. Fully sampled MR images and the reconstructed images were compared by means of peak- and signal-to-noise ratio (PSNR and SNR) metrics. Compared to other k-space filling trajectories, radial mask allowed the reconstruction of images of comparable quality (PSNR in [30 40]) but using less information. Overall, in all the test images we obtained a good reconstruction with similar SNR of the corresponding fully sampled images but using less than 20% of the original samples
A fast splitting method for efficient Split Bregman iterations
Full text linkIn this paper we propose a new fast splitting algorithm to solve the Weighted Split Bregman minimization problem in the backward step of an accelerated Forward–Backward algorithm. Beside proving the convergence of the method, numerical tests, carried out on different imaging applications, prove the accuracy and computational efficiency of the proposed algorithm
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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