Single Image Deraining via Rain-Steaks Aware Deep Convolutional Neural Network

It is challenging to remove rain-steaks from a single rainy image because the rain steaks are spatially varying in the rainy image. This problem is studied in this paper by combining conventional image processing techniques and deep learning based techniques. An improved weighted guided image filter (iWGIF) is proposed to extract high frequency information from a rainy image. The high frequency information mainly includes rain steaks and noise, and it can guide the rain steaks aware deep convolutional neural network (RSADCNN) to pay more attention to rain steaks. The efficiency and explain-ability of RSADNN are improved. Experiments show that the proposed algorithm significantly outperforms state-of-the-art methods on both synthetic and real-world images in terms of both qualitative and quantitative measures. It is useful for autonomous navigation in raining conditions

A Riemannian ADMM

We consider a class of Riemannian optimization problems where the objective is the sum of a smooth function and a nonsmooth function, considered in the ambient space. This class of problems finds important applications in machine learning and statistics such as the sparse principal component analysis, sparse spectral clustering, and orthogonal dictionary learning. We propose a Riemannian alternating direction method of multipliers (ADMM) to solve this class of problems. Our algorithm adopts easily computable steps in each iteration. The iteration complexity of the proposed algorithm for obtaining an $\epsilon$-stationary point is analyzed under mild assumptions. To the best of our knowledge, this is the first Riemannian ADMM with provable convergence guarantee for solving Riemannian optimization problem with nonsmooth objective. Numerical experiments are conducted to demonstrate the advantage of the proposed method

Predicted Optimal Bifunctional Electrocatalysts for the Hydrogen Evolution Reaction and the Oxygen Evolution Reaction Using Chalcogenide Heterostructures Based on Machine Learning Analysis of in Silico Quantum Mechanics Based High Throughput Screening

Two-dimensional van der Waals heterostructure materials, particularly transition metal dichalcogenides (TMDC), have proved to be excellent photoabsorbers for solar radiation, but performance for such electrocatalysis processes as water splitting to form H₂ and O₂ is not adequate. We propose that dramatically improved performance may be achieved by combining two independent TMDC while optimizing such descriptors as rotational angle, bond length, distance between layers, and the ratio of the bandgaps of two component materials. In this paper we apply the least absolute shrinkage and selection operator (LASSO) process of artificial intelligence incorporating these descriptors together with quantum mechanics (density functional theory) to predict novel structures with predicted superior performance. Our predicted best system is MoTe₂/WTe₂ with a rotation of 300°, which is predicted to have an overpotential of 0.03 V for HER and 0.17 V for OER, dramatically improved over current electrocatalysts for water splitting

Specific subdomain localization of ER resident proteins and membrane contact sites resolved by electron microscopy

The endoplasmic reticulum (ER) is a large, single-copy, membrane-bound organelle that comprises an elaborate 3D network of diverse structural subdomains, including highly curved tubules, flat sheets, and parts that form contacts with nearly every other organelle. The dynamic and complex organization of the ER poses a major challenge on understanding how its functioning - maintenance of the structure, distribution of its functions and communication with other organelles - is orchestrated. In this study, we resolved a unique localization profile within the ER network for several resident ER proteins representing a broad range of functions associated with the ER using immuno-electron microscopy and calculation of a relative labeling index (RLI). Our results demonstrated the effect of changing cellular environment on protein localization and highlighted the importance of correct protein expression level when analyzing its localization at subdomain resolution. We present new software tools for anonymization of images for blind analysis and for quantitative assessment of membrane contact sites (MCSs) from thin section transmission electron microscopy micrographs. The analysis of ERmitochondria contacts suggested the presence of at least three different types of MCSs that responded differently to changes in cellular lipid loading status.Peer reviewe