Beihang University

About: Beihang University is a education organization based out in Beijing, China. It is known for research contribution in the topics: Computer science & Control theory. 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.

Thinking in Frequency: Face Forgery Detection by Mining Frequency-aware Clues

Abstract: As realistic facial manipulation technologies have achieved remarkable progress, social concerns about potential malicious abuse of these technologies bring out an emerging research topic of face forgery detection. However, it is extremely challenging since recent advances are able to forge faces beyond the perception ability of human eyes, especially in compressed images and videos. We find that mining forgery patterns with the awareness of frequency could be a cure, as frequency provides a complementary viewpoint where either subtle forgery artifacts or compression errors could be well described. To introduce frequency into the face forgery detection, we propose a novel Frequency in Face Forgery Network (F3-Net), taking advantages of two different but complementary frequency-aware clues, 1) frequency-aware decomposed image components, and 2) local frequency statistics, to deeply mine the forgery patterns via our two-stream collaborative learning framework. We apply DCT as the applied frequency-domain transformation. Through comprehensive studies, we show that the proposed F3-Net significantly outperforms competing state-of-the-art methods on all compression qualities in the challenging FaceForensics++ dataset, especially wins a big lead upon low-quality media.

A Novel Thiophene‐Fused Ending Group Enabling an Excellent Small Molecule Acceptor for High‐Performance Fullerene‐Free Polymer Solar Cells with 11.8% Efficiency

Abstract: In this article, we designed and synthesized a novel small molecule acceptor of ITCPTC with thiophene-fused ending group by employing a new active methylene precursor of CPTCN. The ITCPTC-based polymer solar cells with PBT1-EH as a donor achieved very high power conversion efficiencies (PCEs) of up to 11.8% with a remarkably enhanced fill factor (FF) of 0.751, a near 20% boost in PCE with respect to the ITIC-based control device. These values are among the highest PCEs and FFs for polymer solar cells (PSCs). In the whole study, we made contrasts with ITIC to understand the reasons of excellent performance of ITCPTC-based PSCs through various measurements, such as GIXD and RSoXS. We revealed that the simple modification of ITIC into ITCPTC not only change the material electronic structure, but also mediates the material interactions and crystallization, which contribute together to the excellent performance of ITCPTC-based PSCs.

Bubble density gradient with laser detection: A wake-homing scheme for supercavitating vehicles:

Abstract: The poor mobility and complex target motion estimation are the two main challenges in the anti-ship attack missions of high-speed guided missiles. In this study, a new wake-homing scheme of supercavitating vehicles is proposed using the gradient wake flow phenomenon. The basic principle of the wake-homing with laser detection and the information which can be obtained are studied. According to the idiosyncrasy of laser detection, the geometrical characteristics of the vessel wake, and the bubble distribution characteristics, the three-dimensional model of bubble density gradient in horizontal plane of the maneuvering target ship wake flow is first built; the guidance law based on the gradient wake flow is designed and tested. Then, simulation results show the validity of the model wake, the guidance law based on the wake gradient information can accurately track the target. From the simulation results, we also can see that the new wake-homing scheme effectively improve the hit probability, reduce the range of loss, reduce power consumption and gentle guidance trajectory, greatly reduce the mobility requirements of the supercavitating vehicle.

Origins of power-law degree distribution in the heterogeneity of human activity in social networks

Abstract: The probability distribution of number of ties of an individual in a social network follows a scale-free power-law. However, how this distribution arises has not been conclusively demonstrated in direct analyses of people's actions in social networks. Here, we perform a causal inference analysis and find an underlying cause for this phenomenon. Our analysis indicates that heavy-tailed degree distribution is causally determined by similarly skewed distribution of human activity. Specifically, the degree of an individual is entirely random - following a “maximum entropy attachment” model - except for its mean value which depends deterministically on the volume of the users' activity. This relation cannot be explained by interactive models, like preferential attachment, since the observed actions are not likely to be caused by interactions with other people.