The Existence of Positive Nonconstant Steady States in a Reaction: Diffusion Epidemic Model

We investigate the disease’s dynamics of a reaction-diffusion epidemic model. We first give a priori estimates of upper and lower bounds for positive solutions to model and then give the conditions of the existence and nonexistence of the positive nonconstant steady states, which guarantees the existence of the stationary patterns

Interaction-aware Spatio-temporal Pyramid Attention Networks for Action Classification

Local features at neighboring spatial positions in feature maps have high correlation since their receptive fields are often overlapped. Self-attention usually uses the weighted sum (or other functions) with internal elements of each local feature to obtain its weight score, which ignores interactions among local features. To address this, we propose an effective interaction-aware self-attention model inspired by PCA to learn attention maps. Furthermore, since different layers in a deep network capture feature maps of different scales, we use these feature maps to construct a spatial pyramid and then utilize multi-scale information to obtain more accurate attention scores, which are used to weight the local features in all spatial positions of feature maps to calculate attention maps. Moreover, our spatial pyramid attention is unrestricted to the number of its input feature maps so it is easily extended to a spatio-temporal version. Finally, our model is embedded in general CNNs to form end-to-end attention networks for action classification. Experimental results show that our method achieves the state-of-the-art results on the UCF101, HMDB51 and untrimmed Charades.Comment: Accepted by ECCV201

Protective effect of salvianolic acid B against intestinal ischemia reperfusion-induced injury in a rat model

Purpose: To evaluate the gastro-protective efficacy of salvianolic acid B (SAB)  against intestinal ischemic-reperfusion injury (IIRI) in a rat model.Methods: Forty-eight healthy male rats were randomly choosen and divided into 4 groups of 12 rats each. Control group rats underwent laparotomy without occlusion; IIRI group rats underwent laparotomy with occlusion for 60 min, followed by 24 h of reperfusion; SAB + IIRI group received 7 days of pretreatment with 40 mg/kg of SAB + IIRI; while the fourth group received only SAB. The antioxidant, inflammatory markers, intestinal permeability marker, as well as intestinal histopathological changes were assessed.Results: The activities of antioxidants including reduced glutathione (GSH),  catalase (CAT) and superoxide dismutase (SOD) were significantly ameliorated (p < 0.01) in SAB-supplemented group (SAB + IIRI). The concentration of inflammatory markers, including interleukin-1β (IL-1β), interleukin-6 (IL-6), tumor necrosis factor alpha (TNF-α) and nuclear factor p65 (NF-p65) as well as small intestinal permeability marker (FITC-Dextran), were significantly reduced (p < 0.01) following administration of SAB for 7 days. In addition, pretreatment with SAB reverted intestinal (ileum) histopathological changes to almost normal architecture with significant reduction in Chiu score.Conclusion: The results of this study demonstrate that SAB may protect the intestine by attenuating oxidative stress and inflammatory response and hence, may be potentially for treating IIRI.Keywords: Salvianolic acid B, Intestinal Ischemia-reperfusion, Antioxidants,  Inflammation, Intestinal permeabilit

A Non-Stationary IMT-Advanced MIMO Channel Model for High-Mobility Wireless Communication Systems

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.With the recent developments of high-mobility wireless communication systems, e.g., high-speed train (HST) and vehicle-to-vehicle (V2V) communication systems, the ability of conventional stationary channel models to mimic the underlying channel characteristics has widely been challenged. Measurements have demonstrated that the current standardized channel models, like IMT-Advanced (IMT-A) and WINNER II channel models, offer stationary intervals that are noticeably longer than those in measured HST channels. In this paper, we propose a non-stationary channel model with time-varying parameters including the number of clusters, the powers and the delays of the clusters, the angles of departure (AoDs), and the angles of arrival (AoAs). Based on the proposed non-stationary IMT-A channel model, important statistical properties, i.e., the local spatial cross-correlation function (CCF) and local temporal autocorrelation function (ACF) are derived and analyzed. Simulation results demonstrate that the statistical properties vary with time due to the non-stationarity of the proposed channel model. An excellent agreement is achieved between the stationary interval of the developed non-stationary IMT-A channel model and that of relevant HST measurement data, demonstrating the utility of the proposed channel model

EMScore: Evaluating Video Captioning via Coarse-Grained and Fine-Grained Embedding Matching

Current metrics for video captioning are mostly based on the text-level comparison between reference and candidate captions. However, they have some insuperable drawbacks, e.g., they cannot handle videos without references, and they may result in biased evaluation due to the one-to-many nature of video-to-text and the neglect of visual relevance. From the human evaluator's viewpoint, a high-quality caption should be consistent with the provided video, but not necessarily be similar to the reference in literal or semantics. Inspired by human evaluation, we propose EMScore (Embedding Matching-based score), a novel reference-free metric for video captioning, which directly measures similarity between video and candidate captions. Benefit from the recent development of large-scale pre-training models, we exploit a well pre-trained vision-language model to extract visual and linguistic embeddings for computing EMScore. Specifically, EMScore combines matching scores of both coarse-grained (video and caption) and fine-grained (frames and words) levels, which takes the overall understanding and detailed characteristics of the video into account. Furthermore, considering the potential information gain, EMScore can be flexibly extended to the conditions where human-labeled references are available. Last but not least, we collect VATEX-EVAL and ActivityNet-FOIl datasets to systematically evaluate the existing metrics. VATEX-EVAL experiments demonstrate that EMScore has higher human correlation and lower reference dependency. ActivityNet-FOIL experiment verifies that EMScore can effectively identify "hallucinating" captions. The datasets will be released to facilitate the development of video captioning metrics. The code is available at: https://github.com/ShiYaya/emscore.Comment: cvpr202