Visual Tracking Using Max-Average Pooling and Weight-Selection Strategy

Many modern visual tracking algorithms incorporate spatial pooling, max pooling, or average pooling, which is to achieve invariance to feature transformations and better robustness to occlusion, illumination change, and position variation. In this paper, max-average pooling method and Weight-selection strategy are proposed with a hybrid framework, which is combined with sparse representation and particle filter, to exploit the spatial information of an object and make good compromises to ensure the correctness of the results in this framework. Challenges can be well considered by the proposed algorithm. Experimental results demonstrate the effectiveness and robustness of the proposed algorithm compared with the state-of-the-art methods on challenging sequences

Survival or death: a dual role of autophagy in stress-induced pericyte loss in diabetic retinopathy

AIMS/HYPOTHESIS: Intra-retinal extravasation and modification of LDL have been implicated in diabetic retinopathy: autophagy may mediate these effects. METHODS: Immunohistochemistry was used to detect autophagy marker LC3B in human and murine diabetic and non-diabetic retinas. Cultured human retinal capillary pericytes (HRCPs) were treated with in vitro-modified heavily-oxidised glycated LDL (HOG-LDL) vs native LDL (N-LDL) with or without autophagy modulators: green fluorescent protein–LC3 transfection; small interfering RNAs against Beclin-1, c-Jun NH(2)-terminal kinase (JNK) and C/EBP-homologous protein (CHOP); autophagy inhibitor 3-MA (5 mmol/l) and/or caspase inhibitor Z-VAD-fmk (100 μmol/l). Autophagy, cell viability, oxidative stress, endoplasmic reticulum stress, JNK activation, apoptosis and CHOP expression were assessed by western blots, CCK-8 assay and TUNEL assay. Finally, HOG-LDL vs N-LDL were injected intravitreally to STZ-induced diabetic vs control rats (yielding 50 and 200 mg protein/l intravitreal concentration) and, after 7 days, retinas were analysed for ER stress, autophagy and apoptosis. RESULTS: Intra-retinal autophagy (LC3B staining) was increased in diabetic vs non-diabetic humans and mice. In HRCPs, 50 mg/l HOG-LDL elicited autophagy without altering cell viability, and inhibition of autophagy decreased survival. At 100–200 mg/l, HOG-LDL caused significant cell death, and inhibition of either autophagy or apoptosis improved survival. Further, 25–200 mg/l HOG-LDL dose-dependently induced oxidative and ER stress. JNK activation was implicated in autophagy but not in apoptosis. In diabetic rat retina, 50 mg/l intravitreal HOG-LDL elicited autophagy and ER stress but not apoptosis; 200 mg/l elicited greater ER stress and apoptosis. CONCLUSIONS: Autophagy has a dual role in diabetic retinopathy: under mild stress (50 mg/l HOG-LDL) it is protective; under more severe stress (200 mg/l HOG-LDL) it promotes cell death. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00125-016-4058-5) contains peer-reviewed but unedited supplementary material, which is available to authorised users

Scientific and Technological News Recommendation Based on Knowledge Graph with User Perception

Existing research usually utilizes side information such as social network or item attributes to improve the performance of collaborative filtering-based recommender systems. In this paper, the knowledge graph with user perception is used to acquire the source of side information. We proposed KGUPN to address the limitations of existing embedding-based and path-based knowledge graph-aware recommendation methods, an end-to-end framework that integrates knowledge graph and user awareness into scientific and technological news recommendation systems. KGUPN contains three main layers, which are the propagation representation layer, the contextual information layer and collaborative relation layer. The propagation representation layer improves the representation of an entity by recursively propagating embeddings from its neighbors (which can be users, news, or relationships) in the knowledge graph. The contextual information layer improves the representation of entities by encoding the behavioral information of entities appearing in the news. The collaborative relation layer complements the relationship between entities in the news knowledge graph. Experimental results on real-world datasets show that KGUPN significantly outperforms state-of-the-art baselines in scientific and technological news recommendation

CCPL: Contrastive Coherence Preserving Loss for Versatile Style Transfer

In this paper, we aim to devise a universally versatile style transfer method capable of performing artistic, photo-realistic, and video style transfer jointly, without seeing videos during training. Previous single-frame methods assume a strong constraint on the whole image to maintain temporal consistency, which could be violated in many cases. Instead, we make a mild and reasonable assumption that global inconsistency is dominated by local inconsistencies and devise a generic Contrastive Coherence Preserving Loss (CCPL) applied to local patches. CCPL can preserve the coherence of the content source during style transfer without degrading stylization. Moreover, it owns a neighbor-regulating mechanism, resulting in a vast reduction of local distortions and considerable visual quality improvement. Aside from its superior performance on versatile style transfer, it can be easily extended to other tasks, such as image-to-image translation. Besides, to better fuse content and style features, we propose Simple Covariance Transformation (SCT) to effectively align second-order statistics of the content feature with the style feature. Experiments demonstrate the effectiveness of the resulting model for versatile style transfer, when armed with CCPL.Comment: Accepted by ECCV2022 as an oral paper; code url: https://github.com/JarrentWu1031/CCPL Video demo: https://youtu.be/scZuJCXhL1

A Relational Triple Extraction Method Based on Feature Reasoning for Technological Patents

The relation triples extraction method based on table filling can address the issues of relation overlap and bias propagation. However, most of them only establish separate table features for each relationship, which ignores the implicit relationship between different entity pairs and different relationship features. Therefore, a feature reasoning relational triple extraction method based on table filling for technological patents is proposed to explore the integration of entity recognition and entity relationship, and to extract entity relationship triples from multi-source scientific and technological patents data. Compared with the previous methods, the method we proposed for relational triple extraction has the following advantages: 1) The table filling method that saves more running space enhances the speed and efficiency of the model. 2) Based on the features of existing token pairs and table relations, reasoning the implicit relationship features, and improve the accuracy of triple extraction. On five benchmark datasets, we evaluated the model we suggested. The result suggest that our model is advanced and effective, and it performed well on most of these datasets

Spatial Images Feature Extraction Based on Bayesian Nonlocal Means Filter and Improved Contourlet Transform

Spatial images are inevitably mixed with different levels of noise and distortion. The contourlet transform can provide multidimensional sparse representations of images in a discrete domain. Because of its filter structure, the contourlet transform is not translation-invariant. In this paper, we use a nonsubsampled pyramid structure and a nonsubsampled directional filter to achieve multidimensional and translation-invariant image decomposition for spatial images. A nonsubsampled contourlet transform is used as the basis for an improved Bayesian nonlocal means (NLM) filter for different frequencies. The Bayesian model adds a sigma range in image a priori operations, which can be more effective in protecting image details. The NLM filter retains the image edge content and assigns greater weight to similarities for edge pixels. Experimental results both on standard images and spatial images confirm that the proposed algorithm yields significantly better performance than nonsubsampled wavelet transform, contourlet, and curvelet approaches

Dynamic Fair Federated Learning Based on Reinforcement Learning

Federated learning enables a collaborative training and optimization of global models among a group of devices without sharing local data samples. However, the heterogeneity of data in federated learning can lead to unfair representation of the global model across different devices. To address the fairness issue in federated learning, we propose a dynamic q fairness federated learning algorithm with reinforcement learning, called DQFFL. DQFFL aims to mitigate the discrepancies in device aggregation and enhance the fairness of treatment for all groups involved in federated learning. To quantify fairness, DQFFL leverages the performance of the global federated model on each device and incorporates {\alpha}-fairness to transform the preservation of fairness during federated aggregation into the distribution of client weights in the aggregation process. Considering the sensitivity of parameters in measuring fairness, we propose to utilize reinforcement learning for dynamic parameters during aggregation. Experimental results demonstrate that our DQFFL outperforms the state-of-the-art methods in terms of overall performance, fairness and convergence speed