BIVAS: A scalable Bayesian method for bi-level variable selection with applications

In this paper, we consider a Bayesian bi-level variable selection problem in high-dimensional regressions. In many practical situations, it is natural to assign group membership to each predictor. Examples include that genetic variants can be grouped at the gene level and a covariate from different tasks naturally forms a group. Thus, it is of interest to select important groups as well as important members from those groups. The existing Markov Chain Monte Carlo (MCMC) methods are often computationally intensive and not scalable to large data sets. To address this problem, we consider variational inference for bi-level variable selection (BIVAS). In contrast to the commonly used mean-field approximation, we propose a hierarchical factorization to approximate the posterior distribution, by utilizing the structure of bi-level variable selection. Moreover, we develop a computationally efficient and fully parallelizable algorithm based on this variational approximation. We further extend the developed method to model data sets from multi-task learning. The comprehensive numerical results from both simulation studies and real data analysis demonstrate the advantages of BIVAS for variable selection, parameter estimation and computational efficiency over existing methods. The method is implemented in R package `bivas' available at https://github.com/mxcai/bivas

Atomically resolved electrically active intragrain interfaces in perovskite semiconductors

Deciphering the atomic and electronic structures of interfaces is key to developing state-of-the-art perovskite semiconductors. However, conventional characterization techniques have limited previous studies mainly to grain-boundary interfaces, whereas the intragrain-interface microstructures and their electronic properties have been much less revealed. Herein using scanning transmission electron microscopy, we resolved the atomic-scale structural information on three prototypical intragrain interfaces, unraveling intriguing features clearly different from those from previous observations based on standalone films or nanomaterial samples. These intragrain interfaces include composition boundaries formed by heterogeneous ion distribution, stacking faults resulted from wrongly stacked crystal planes, and symmetrical twinning boundaries. The atomic-scale imaging of these intragrain interfaces enables us to build unequivocal models for the ab initio calculation of electronic properties. Our results suggest that these structure interfaces are generally electronically benign, whereas their dynamic interaction with point defects can still evoke detrimental effects. This work paves the way toward a more complete fundamental understanding of the microscopic structure–property–performance relationship in metal halide perovskites

Joint Inversion of Receiver Functions and Apparent Shear Wave Velocity for Martian Crustal Model

<p>Data and Codes of the joint inversion of receiver functions and apparent shear wave velocity for martian crustal model.</p&gt

Investigation of the profile control mechanisms of dispersed particle gel.

Dispersed particle gel (DPG) particles of nano- to micron- to mm-size have been prepared successfully and will be used for profile control treatment in mature oilfields. The profile control and enhanced oil recovery mechanisms of DPG particles have been investigated using core flow tests and visual simulation experiments. Core flow test results show that DPG particles can easily be injected into deep formations and can effectively plug the high permeability zones. The high profile improvement rate improves reservoir heterogeneity and diverts fluid into the low permeability zone. Both water and oil permeability were reduced when DPG particles were injected, but the disproportionate permeability reduction effect was significant. Water permeability decreases more than the oil permeability to ensure that oil flows in its own pathways and can easily be driven out. Visual simulation experiments demonstrate that DPG particles can pass directly or by deformation through porous media and enter deep formations. By retention, adsorption, trapping and bridging, DPG particles can effectively reduce the permeability of porous media in high permeability zones and divert fluid into a low permeability zone, thus improving formation profiles and enhancing oil recovery