How Does the Low-Rank Matrix Decomposition Help Internal and External Learnings for Super-Resolution

Wisely utilizing the internal and external learning methods is a new challenge in super-resolution problem. To address this issue, we analyze the attributes of two methodologies and find two observations of their recovered details: 1) they are complementary in both feature space and image plane, 2) they distribute sparsely in the spatial space. These inspire us to propose a low-rank solution which effectively integrates two learning methods and then achieves a superior result. To fit this solution, the internal learning method and the external learning method are tailored to produce multiple preliminary results. Our theoretical analysis and experiment prove that the proposed low-rank solution does not require massive inputs to guarantee the performance, and thereby simplifying the design of two learning methods for the solution. Intensive experiments show the proposed solution improves the single learning method in both qualitative and quantitative assessments. Surprisingly, it shows more superior capability on noisy images and outperforms state-of-the-art methods

Autoregressive GNN-ODE GRU Model for Network Dynamics

Revealing the continuous dynamics on the networks is essential for understanding, predicting, and even controlling complex systems, but it is hard to learn and model the continuous network dynamics because of complex and unknown governing equations, high dimensions of complex systems, and unsatisfactory observations. Moreover, in real cases, observed time-series data are usually non-uniform and sparse, which also causes serious challenges. In this paper, we propose an Autoregressive GNN-ODE GRU Model (AGOG) to learn and capture the continuous network dynamics and realize predictions of node states at an arbitrary time in a data-driven manner. The GNN module is used to model complicated and nonlinear network dynamics. The hidden state of node states is specified by the ODE system, and the augmented ODE system is utilized to map the GNN into the continuous time domain. The hidden state is updated through GRUCell by observations. As prior knowledge, the true observations at the same timestamp are combined with the hidden states for the next prediction. We use the autoregressive model to make a one-step ahead prediction based on observation history. The prediction is achieved by solving an initial-value problem for ODE. To verify the performance of our model, we visualize the learned dynamics and test them in three tasks: interpolation reconstruction, extrapolation prediction, and regular sequences prediction. The results demonstrate that our model can capture the continuous dynamic process of complex systems accurately and make precise predictions of node states with minimal error. Our model can consistently outperform other baselines or achieve comparable performance

Protective effect of vanillic acid on ovariectomy-induced osteoporosis in rats

Background: The need for an anti-osteoporotic agent is in high demand since osteoporosis contributes to high rates of disability or impairment (high osteoporotic fracture), morbidity and mortality. Hence, the present study is designed to evaluate the protective effects of vanillic acid (VA) against bilateral ovariectomy-induced osteoporosis in female Sprague-Dawley (SD) rats.Materials and Methods: Forty healthy female adult SD rats were separated in to four groups with sham-operated control with bilateral laprotomy (Sham; n = 10), bilateral overiectomy (OVX; n = 10) group, OVX rats were orallay administrated with 50 mg/kg b.wt of VA (OVX + 50 VA; n = 10) or 100 mg/kg b.wt of VA (OVX + 100 VA; n = 10) for 12 weeks (post-treatment) after 4 weeks of OVX.Results: A significant change in the body weight gain was noted in OVX group, while treatment with VA substantially reverted to normalcy. Meanwhile, the bone mineral density and content (BMD and BMC) were substantially improved on supplementation with VA. Also, the bone turnover markers like calcium (Ca), phosphorous (P), osteocalcin (OC), alkaline phosphatase (ALP) and deoxypyridinoline (DPD) and inflammatory markers (IL-1β, IL-6, and TNF-α) levels were markedly attenuated in VA-treated rats. Moreover, the biomechanical stability was greatly ameliorated with VA administration. Both the dose of VA showed potent anti-osteoporotic activity, but VA 100 mg showed highest protective effects as compared with 50 mg of VA.Conclusion: Based on the outcome, we concluded that VA 100 showed better anti-osteoporotic activity by improving BMD and BMC as well as biomechanical stability and therefore used as an alternative therapy for treating postmenopausal osteoporosis.Keywords: Osteoporosis, Vanillic acid, Ovariectomy, Antioxidant, Inflammatory marker