Learnable Descent Algorithm for Nonsmooth Nonconvex Image Reconstruction

We propose a general learning based framework for solving nonsmooth and nonconvex image reconstruction problems. We model the regularization function as the composition of the $l_{2,1}$ norm and a smooth but nonconvex feature mapping parametrized as a deep convolutional neural network. We develop a provably convergent descent-type algorithm to solve the nonsmooth nonconvex minimization problem by leveraging the Nesterov's smoothing technique and the idea of residual learning, and learn the network parameters such that the outputs of the algorithm match the references in training data. Our method is versatile as one can employ various modern network structures into the regularization, and the resulting network inherits the guaranteed convergence of the algorithm. We also show that the proposed network is parameter-efficient and its performance compares favorably to the state-of-the-art methods in a variety of image reconstruction problems in practice

A Learnable Variational Model for Joint Multimodal MRI Reconstruction and Synthesis

Generating multi-contrasts/modal MRI of the same anatomy enriches diagnostic information but is limited in practice due to excessive data acquisition time. In this paper, we propose a novel deep-learning model for joint reconstruction and synthesis of multi-modal MRI using incomplete k-space data of several source modalities as inputs. The output of our model includes reconstructed images of the source modalities and high-quality image synthesized in the target modality. Our proposed model is formulated as a variational problem that leverages several learnable modality-specific feature extractors and a multimodal synthesis module. We propose a learnable optimization algorithm to solve this model, which induces a multi-phase network whose parameters can be trained using multi-modal MRI data. Moreover, a bilevel-optimization framework is employed for robust parameter training. We demonstrate the effectiveness of our approach using extensive numerical experiments.Comment: 12 page

Rapid faults detection for controlling multi-terminal high voltage DC grids under AC grid contingencies

To control power flow for integration of distributed energy onto urban power grids, rapid and accurate detection of the amplitude, phase-angle, and frequency offset of the grid voltage's positive and negative sequence components especially under grid fault conditions are more significant. This paper presents a new faults detection method that is capable of tracking signal deviations on the grid-voltage accurately and rapidly even in the case that bus-voltage contains high order harmonics and random noises. The experimental results verify the validity of the proposed method under various grid-fault conditions

(Ferrocene­carboxyl­ato-κO)triphenyl­tin(IV)

In the title compound, [FeSn(C5H5)(C6H5)3(C6H4O2)], the SnIV atom displays a distorted tetra­hedral coordination geometry, provided by one O atom of the monodentate ferrocene­carboxyl­ate ligand [Sn—O = 2.079 (2) Å] and by three C atoms of the three phenyl groups [average Sn—C = 2.130 (4) Å]. No classic hydrogen bonds or inter­molecular inter­actions are observed in the crystal

Shape-Controlled Synthesis of Palladium-Copper Nanoalloys with Improved Catalytic Activity for Ethanol Electrooxidation

A facile solvothermal strategy is developed for the preparation of nanometer sized Pd-Cu alloy. We can control the morphology of these alloys with the use of ethylene glycol (EG) in the presence of KOH. Namely, by increasing the concentration of KOH/EG, the Pd-Cu alloys with different morphologies from near-spherical nanoparticles (NPs) to nanorods and nanowire networks have been prepared. Among all these alloys, near-spherical Pd-Cu NPs-modified electrodes exhibit the highest catalytic activity (11.7 mA/cm2) and stability toward the electrooxidation of ethanol in comparison with commercial Pd/C-modified ones (2.1 mA/cm2)