Beihang University

About: Beihang University is a education organization based out in Beijing, China. It is known for research contribution in the topics: Control theory & Microstructure. The organization has 67002 authors who have published 73507 publications receiving 975691 citations. The organization is also known as: Beijing University of Aeronautics and Astronautics.

Strain Engineering for Phosphorene: The Potential Application as a Photocatalyst

Abstract: Phosphorene has been attracted intense interest due to its unexpected high carrier mobility and distinguished anisotropic optoelectronic and electronic properties. In this work, we unraveled strain engineered phosphorene as a photocatalyst in the application of water splitting hydrogen production based on density functional theory calculations. Lattice dynamic calculations demonstrated the stability for such kind of artificial materials under different strains. The phosphorene lattice is unstable under compression strains and could be crashed. Whereas, phosphorene lattice shows very good stability under tensile strains. Further guarantee of the stability of phosphorene in liquid water is studied by ab initio molecular dynamics simulations. Tunable band gap from 1.54 eV at ambient condition to 1.82 eV under tensile strains for phosphorene is evaluated using parameter-free hybrid functional calculations. Appropriate band gaps and band edge alignments at certain pH demonstrate the potential application of phosphorene as a sufficiently efficient photocatalyst for visible light water splitting. We found that the strained phosphorene exhibits significantly improved photocatalytic properties under visible-light irradiation by calculating optical absorption spectra. Negative splitting energy of absorbed H2O indicates the water splitting on phosphorene is energy favorable both without and with strains.

Polymer Donors for High-Performance Non-Fullerene Organic Solar Cells

Abstract: Over the past few years, non-fullerene organic solar cells have been a focus of research and their power conversion efficiencies have been improved dramatically from about 6 % to over 14 % In addition to innovations in non-fullerene acceptors, the ongoing development of polymer donors has contributed significantly to the rapid progress of non-fullerene organic solar cell performance This Minireview highlights the polymer donors that enable high-performance non-fullerene organic solar cells We show the impressive photovoltaic devices results achieved by some of important classes of conjugated polymer systems in non-fullerene organic solar cells We discuss the molecular design strategies as far as developing matching polymer donors for non-fullerene acceptors We conclude with a brief summary and outlook for advances in donor polymers required for commercialization

Lepton-Flavor-Dependent Angular Analysis of B→K^{*}ℓ^{+}ℓ^{-}

Abstract: We present a measurement of angular observables and a test of lepton flavor universality in the B -> K(+)l(+)l(-) decay, where l is either e or mu. The analysis is performed on a data sample corresponding to an integrated luminosity of 711 fb(-1) containing 772 x 10(6) B (B) over bar pairs, collected at the Upsilon(4S) resonance with the Belle detector at the asymmetric-energy e(+)e(-) collider KEKB. The result is consistent with standard model (SM) expectations, where the largest discrepancy from a SM prediction is observed in the muon modes with a local significance of 2.6 sigma.

Large-Scale Hierarchical Text Classification with Recursively Regularized Deep Graph-CNN

Abstract: Text classification to a hierarchical taxonomy of topics is a common and practical problem. Traditional approaches simply use bag-of-words and have achieved good results. However, when there are a lot of labels with different topical granularities, bag-of-words representation may not be enough. Deep learning models have been proven to be effective to automatically learn different levels of representations for image data. It is interesting to study what is the best way to represent texts. In this paper, we propose a graph-CNN based deep learning model to first convert texts to graph-of-words, and then use graph convolution operations to convolve the word graph. Graph-of-words representation of texts has the advantage of capturing non-consecutive and long-distance semantics. CNN models have the advantage of learning different level of semantics. To further leverage the hierarchy of labels, we regularize the deep architecture with the dependency among labels. Our results on both RCV1 and NYTimes datasets show that we can significantly improve large-scale hierarchical text classification over traditional hierarchical text classification and existing deep models.

Detecting Masked Faces in the Wild with LLE-CNNs

Abstract: Detecting faces with occlusions is a challenging task due to two main reasons: 1) the absence of large datasets of masked faces, and 2) the absence of facial cues from the masked regions. To address these two issues, this paper first introduces a dataset, denoted as MAFA, with 30, 811 Internet images and 35, 806 masked faces. Faces in the dataset have various orientations and occlusion degrees, while at least one part of each face is occluded by mask. Based on this dataset, we further propose LLE-CNNs for masked face detection, which consist of three major modules. The Proposal module first combines two pre-trained CNNs to extract candidate facial regions from the input image and represent them with high dimensional descriptors. After that, the Embedding module is incorporated to turn such descriptors into a similarity-based descriptor by using locally linear embedding (LLE) algorithm and the dictionaries trained on a large pool of synthesized normal faces, masked faces and non-faces. In this manner, many missing facial cues can be largely recovered and the influences of noisy cues introduced by diversified masks can be greatly alleviated. Finally, the Verification module is incorporated to identify candidate facial regions and refine their positions by jointly performing the classification and regression tasks within a unified CNN. Experimental results on the MAFA dataset show that the proposed approach remarkably outperforms 6 state-of-the-arts by at least 15.6%.