Forcing the Whole Video as Background: An Adversarial Learning Strategy for Weakly Temporal Action Localization

With video-level labels, weakly supervised temporal action localization (WTAL) applies a localization-by-classification paradigm to detect and classify the action in untrimmed videos. Due to the characteristic of classification, class-specific background snippets are inevitably mis-activated to improve the discriminability of the classifier in WTAL. To alleviate the disturbance of background, existing methods try to enlarge the discrepancy between action and background through modeling background snippets with pseudo-snippet-level annotations, which largely rely on artificial hypotheticals. Distinct from the previous works, we present an adversarial learning strategy to break the limitation of mining pseudo background snippets. Concretely, the background classification loss forces the whole video to be regarded as the background by a background gradient reinforcement strategy, confusing the recognition model. Reversely, the foreground(action) loss guides the model to focus on action snippets under such conditions. As a result, competition between the two classification losses drives the model to boost its ability for action modeling. Simultaneously, a novel temporal enhancement network is designed to facilitate the model to construct temporal relation of affinity snippets based on the proposed strategy, for further improving the performance of action localization. Finally, extensive experiments conducted on THUMOS14 and ActivityNet1.2 demonstrate the effectiveness of the proposed method.Comment: 9 pages, 5 figures, conferenc

MR image reconstruction from under-sampled measurements using local and global sparse representations

This work presented a new model by enforcing both local and global sparsity, which captures both the patch-level and global sparse structures of the anatomical images. Using a model split approach, the image reconstruction quality can be iteratively further improved. Our simulation results demonstrate that, the proposed method outperform those existing methods using only the patch-level or global sparse structure

Deep Domain Adaptation for Pavement Crack Detection

Deep learning-based pavement cracks detection methods often require large-scale labels with detailed crack location information to learn accurate predictions. In practice, however, crack locations are very difficult to be manually annotated due to various visual patterns of pavement crack. In this paper, we propose a Deep Domain Adaptation-based Crack Detection Network (DDACDN), which learns to take advantage of the source domain knowledge to predict the multi-category crack location information in the target domain, where only image-level labels are available. Specifically, DDACDN first extracts crack features from both the source and target domain by a two-branch weights-shared backbone network. And in an effort to achieve the cross-domain adaptation, an intermediate domain is constructed by aggregating the three-scale features from the feature space of each domain to adapt the crack features from the source domain to the target domain. Finally, the network involves the knowledge of both domains and is trained to recognize and localize pavement cracks. To facilitate accurate training and validation for domain adaptation, we use two challenging pavement crack datasets CQU-BPDD and RDD2020. Furthermore, we construct a new large-scale Bituminous Pavement Multi-label Disease Dataset named CQU-BPMDD, which contains 38994 high-resolution pavement disease images to further evaluate the robustness of our model. Extensive experiments demonstrate that DDACDN outperforms state-of-the-art pavement crack detection methods in predicting the crack location on the target domain.Comment: 12 pages, 10 figure

Sketch-a-Net: A Deep Neural Network that Beats Humans

This Project received support from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement #640891, and the Royal Society and Natural Science Foundation of China (NSFC) Joint Grant #IE141387 and #61511130081. We gratefully acknowledge the support of NVIDIA Corporation for the donation of the GPUs used for this research

Compensation disparity between locals and expatriates: Moderating the effects of perceived injustice in foreign multinationals in China

A large compensation gap exists between local and expatriate employees in foreign multinationals in China. A survey in the Suzhou area confirmed that local employees regarded their compensation vis-à-vis that of expatriates as unfair. Trustworthiness of expatriates by locals showed a stronger effect on their evaluation of expatriates than on their job satisfaction and organizational commitment, whereas perceived compensation received by locals showed the opposite pattern. Trustworthiness of expatriates moderated the negative effect of perceived distributive injustice on evaluation of expatriates, whereas perceived compensation moderated the relationships between perceived distributive injustice and job satisfaction as well as organizational commitment.Organizational justice Social comparison Compensation disparity Expatriate managers International joint ventures

Electrokinetically driven continuous-flow enrichment of colloidal particles by Joule heating induced temperature gradient focusing in a convergent-divergent microfluidic structure

Enrichment of colloidal particles in continuous flow has not only numerous applications but also poses a great challenge in controlling physical forces that are required for achieving particle enrichment. Here, we for the first time experimentally demonstrate the electrokinetically-driven continuous-flow enrichment of colloidal particles with Joule heating induced temperature gradient focusing (TGF) in a microfluidic convergent-divergent structure. We consider four mechanisms of particle transport, i.e., advection due to electroosmosis, electrophoresis, dielectrophoresis and, and further clarify their roles in the particle enrichment. It is experimentally determined and numerically verified that the particle thermophoresis plays dominant roles in enrichment of all particle sizes considered in this study and the combined effect of electroosmosis-induced advection and electrophoresis is mainly to transport particles to the zone of enrichment. Specifically, the enrichment of particles is achieved with combined DC and AC voltages rather than a sole DC or AC voltage. A numerical model is formulated with consideration of the abovementioned four mechanisms, and the model can rationalize the experimental observations. Particularly, our analysis of numerical and experimental results indicates that thermophoresis which is usually an overlooked mechanism of material transport is crucial for the successful electrokinetic enrichment of particles with Joule heating induced TGF.MOE (Min. of Education, S’pore)Published versio

Secure spatial modulation based on two-dimensional generalized weighted fractional Fourier transform encryption

Abstract In this paper, a two-dimensional generalized weighted fractional Fourier transform (2DGWFRFT) and constellation scrambling (CS)-based secure spatial modulation (SM) scheme, called 2DGWFRFT-CS-SM, is proposed to enhance the physical layer security (PLS) of the wireless communication system. The proposed scheme is executed by two steps. In the first step, 2DGWFRFT is implemented as the security kernel for PLS provision. In the second step, the parameters of 2DGWFRFT, regarded as the encryption core, control the antenna number rotation to further enhance the security of the SM modulation symbol. Both the 2DGWFRFT signal generation strategy and the SM system including the transfer process have been elaborated to depict the security mechanism of the proposed scheme. Moreover, we give a key extraction algorithm utilized to generate the parameters, which can help scramble the antenna serial number. From the perspective of signal characteristics, the uniqueness of variation in the constellation has been investigated to outperform other cryptography-based encryption algorithms. Finally, the secrecy rates and the energy efficiency of our proposed scheme are theoretically analyzed and evaluated via Monte Carlo simulations, demonstrating that the proposed scheme can achieve a much higher secrecy capacity than artificial noise schemes without requiring additional jamming power consumption and myriad costs of hardware