HoMM: Higher-order Moment Matching for Unsupervised Domain Adaptation

Minimizing the discrepancy of feature distributions between different domains is one of the most promising directions in unsupervised domain adaptation. From the perspective of distribution matching, most existing discrepancy-based methods are designed to match the second-order or lower statistics, which however, have limited expression of statistical characteristic for non-Gaussian distributions. In this work, we explore the benefits of using higher-order statistics (mainly refer to third-order and fourth-order statistics) for domain matching. We propose a Higher-order Moment Matching (HoMM) method, and further extend the HoMM into reproducing kernel Hilbert spaces (RKHS). In particular, our proposed HoMM can perform arbitrary-order moment tensor matching, we show that the first-order HoMM is equivalent to Maximum Mean Discrepancy (MMD) and the second-order HoMM is equivalent to Correlation Alignment (CORAL). Moreover, the third-order and the fourth-order moment tensor matching are expected to perform comprehensive domain alignment as higher-order statistics can approximate more complex, non-Gaussian distributions. Besides, we also exploit the pseudo-labeled target samples to learn discriminative representations in the target domain, which further improves the transfer performance. Extensive experiments are conducted, showing that our proposed HoMM consistently outperforms the existing moment matching methods by a large margin. Codes are available at \url{https://github.com/chenchao666/HoMM-Master}Comment: Accept by AAAI-2020, codes are available at https://github.com/chenchao666/HoMM-Maste

HoMM: Higher-Order Moment Matching for Unsupervised Domain Adaptation

Minimizing the discrepancy of feature distributions between different domains is one of the most promising directions in unsupervised domain adaptation. From the perspective of moment matching, most existing discrepancy-based methods are designed to match the second-order or lower moments, which however, have limited expression of statistical characteristic for non-Gaussian distributions. In this work, we propose a Higher-order Moment Matching (HoMM) method, and further extend the HoMM into reproducing kernel Hilbert spaces (RKHS). In particular, our proposed HoMM can perform arbitrary-order moment matching, we show that the first-order HoMM is equivalent to Maximum Mean Discrepancy (MMD) and the second-order HoMM is equivalent to Correlation Alignment (CORAL). Moreover, HoMM (order≥ 3) is expected to perform fine-grained domain alignment as higher-order statistics can approximate more complex, non-Gaussian distributions. Besides, we also exploit the pseudo-labeled target samples to learn discriminative representations in the target domain, which further improves the transfer performance. Extensive experiments are conducted, showing that our proposed HoMM consistently outperforms the existing moment matching methods by a large margin. Codes are available at https://github.com/chenchao666/HoMM-Maste

Photocatalytic Reduction Synthesis of Ternary Ag Nanoparticles/Polyoxometalate/Graphene Nanohybrids and Its Activity in the Electrocatalysis of Oxygen Reduction

Ternary Ag nanoparticles (NPs)@polyoxometalate (POM)/reduced graphene oxide (rGO) nanohybrids were prepared by a facile photoreduction method, using POM as the photocatalyst, reducing and bridging molecules. The structure of the nanohybrids was characterized by transmission electron microscopy, X-ray photoelectron spectroscopy, X-ray diffraction, Raman spectroscopy, etc. Most importantly, both the rotating disk electrode and rotating ring-disk electrode tests indicated that the Ag NPs@POM/rGO nanohybrids exhibited excellent electrocatalytic activity towards oxygen reduction reaction via a direct four-electron transfer pathway due to the synergistic effect of Ag NPs and rGO

Electrochemical-reduction-assisted assembly of ternary Ag nanoparticles/polyoxometalate/graphene nanohybrids and their activity in the electrocatalysis of oxygen reduction

The green, facile, electrochemical-reduction-assisted assembly of ternary Ag nanoparticles (NPs)@polyoxometalate (POM)/reduced graphene oxide (rGO) is reported. The POM served as an electrocatalyst and bridging molecule. Characterization using transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), Raman and FT-IR spectroscopy analysis, etc., was performed and verified the structure of the prepared nanohybrids of Ag NPs@POM/rGO. The density and size of the Ag NPs on the rGO can be simply tuned by changing the concentration of Ag+. Most importantly, it is interesting to find that the ternary Ag NPs@POM/rGO nanohybrids showed much better electrocatalytic activities towards the oxygen reduction reaction than binary Ag NPs@POM and POM/rGO nanohybrids, and a direct four-electron transfer pathway was observed because of the synergistic effect of the Ag NPs and rGO. The electrocatalytic performance of Ag NPs@POM/rGO depended on the loading amount of Ag NPs, and 30% Ag NPs@POM/rGO showed the best electrocatalytic performance.</p

miR-142-5p in Bone Marrow-Derived Mesenchymal Stem Cells Promotes Osteoporosis Involving Targeting Adhesion Molecule VCAM-1 and Inhibiting Cell Migration

Osteoporosis is a systemic bone metabolic disease that is highly prevalent in the elderly population, particularly in postmenopausal women, which results in enhanced bone fragility and an increased susceptibility to fractures. However, the underlying molecular pathogenesis mechanisms still remain to be further elucidated. In this study, in a rat ovariectomy- (OVX-) induced postmenopausal osteoporosis model, aberrant expression of a microRNA miR-142-5p and vascular cell adhesion molecule 1 (VCAM-1) was found by RNA sequencing analysis and qRT-PCR. Using a dual-luciferase reporter assay, we found that miR-142-5p can bind to and decrease expression of VCAM-1 mRNA. Such reduction was prohibited when the miR-142-5p binding site in VCAM-1 3′UTR was deleted, and Western blotting analyses validated the fact that miR-142-5p inhibited the expression of VCAM-1 protein. Bone marrow-derived mesenchymal stem cells (BMMSCs) transfected with miR-142-5p showed a significantly decreased migration ability in a Transwell migration assay. Collectively, these data indicated the important role of miR-142-5p in osteoporosis development involving targeting VCAM-1 and inhibiting BMMSC migration

miR-142-5p in Bone Marrow-Derived Mesenchymal Stem Cells Promotes Osteoporosis Involving Targeting Adhesion Molecule VCAM-1 and Inhibiting Cell Migration

Osteoporosis is a systemic bone metabolic disease that is highly prevalent in the elderly population, particularly in postmenopausal women, which results in enhanced bone fragility and an increased susceptibility to fractures. However, the underlying molecular pathogenesis mechanisms still remain to be further elucidated. In this study, in a rat ovariectomy- (OVX-) induced postmenopausal osteoporosis model, aberrant expression of a microRNA miR-142-5p and vascular cell adhesion molecule 1 (VCAM-1) was found by RNA sequencing analysis and qRT-PCR. Using a dual-luciferase reporter assay, we found that miR-142-5p can bind to and decrease expression of VCAM-1 mRNA. Such reduction was prohibited when the miR-142-5p binding site in VCAM-1 3′UTR was deleted, and Western blotting analyses validated the fact that miR-142-5p inhibited the expression of VCAM-1 protein. Bone marrow-derived mesenchymal stem cells (BMMSCs) transfected with miR-142-5p showed a significantly decreased migration ability in a Transwell migration assay. Collectively, these data indicated the important role of miR-142-5p in osteoporosis development involving targeting VCAM-1 and inhibiting BMMSC migration