A Universal Update-pacing Framework For Visual Tracking

This paper proposes a novel framework to alleviate the model drift problem in visual tracking, which is based on paced updates and trajectory selection. Given a base tracker, an ensemble of trackers is generated, in which each tracker's update behavior will be paced and then traces the target object forward and backward to generate a pair of trajectories in an interval. Then, we implicitly perform self-examination based on trajectory pair of each tracker and select the most robust tracker. The proposed framework can effectively leverage temporal context of sequential frames and avoid to learn corrupted information. Extensive experiments on the standard benchmark suggest that the proposed framework achieves superior performance against state-of-the-art trackers.Comment: Submitted to ICIP 201

Impact of autologous platelet-rich plasma therapy vs. hyaluronic acid on synovial fluid biomarkers in knee osteoarthritis: a randomized controlled clinical trial

ObjectiveObserve the effects of platelet-rich plasma (PRP) therapy on inflammatory cytokines in the synovial fluid of the knee joint of patients with KOA, and explore the effects of PRP intra-articular injection on the inflammation of the knee joint environment and the possible mechanism of action.MethodsSeventy patients were randomized to undergo three blinded weekly intra-articular injections of PRP or hyaluronic acid (HA). The concentrations of inflammatory cytokines, including interleukin (IL)-6, IL-1β, tumor necrosis factor (TNF)-α, IL-8, IL-17A, IL-17F, IL-4, IL-5, and IL-10, in the synovial fluid were evaluated before the intervention and 1 month after the third injection. The Western Ontario and McMaster University (WOMAC) and visual analog scale (VAS) scores were used to assess pain and functional status of the knee joints in both groups before the intervention, immediately post-intervention, and 1, 3, 6, and 12 months after the intervention.ResultsBaseline characteristics were similar in both groups with no statistical difference. The IL-6, IL-1β, TNF-α, IL-17A, and IL-10 levels in the synovial fluid of the observation group decreased significantly after, vs. before, the intervention (p < 0.05), whereas the IL-8, IL-17F, and IL-4 levels decreased (p > 0.05) and IL-5 levels increased (p > 0.05). There was no statistically significant difference between inflammatory cytokine levels in the synovial fluid of the samples from the control group before and after the intervention (p > 0.05). There were no statistically significant differences between the two groups immediately after intervention (p > 0.05). At 1, 3, 6, and 12 months after intervention, the WOMAC and VAS scores were significantly better in the observation group than in the control group (p < 0.05).ConclusionPlatelet plasma therapy can reduce the concentrations of inflammatory cytokines IL-6, IL-1β, TNF-α, IL-17A, and IL-10 in the synovial fluid of KOA patients, reduce the expression levels of IL-8, IL-17F, and IL-4, clear the pro-inflammatory factors, improve the inflammatory environment of the affected knee joint, and alleviate pain caused by inflammation. Thus, alleviating pain and improving knee function in patients with KOA

Webly Supervised Fine-Grained Image Recognition with Graph Representation and Metric Learning

The aim of webly supervised fine-grained image recognition (FGIR) is to distinguish sub-ordinate categories based on data retrieved from the Internet, which can significantly mitigate the dependence of deep learning on manually annotated labels. Most current fine-grained image recognition algorithms use a large-scale data-driven deep learning paradigm, which relies heavily on manually annotated labels. However, there is a large amount of weakly labeled free data on the Internet. To utilize fine-grained web data effectively, this paper proposes a Graph Representation and Metric Learning (GRML) framework to learn discriminative and effective holistic–local features by graph representation for web fine-grained images and to handle noisy labels simultaneously, thus effectively using webly supervised data for training. Specifically, we first design an attention-focused module to locate the most discriminative region with different spatial aspects and sizes. Next, a structured instance graph is constructed to correlate holistic and local features to model the holistic–local information interaction, while a graph prototype that contains both holistic and local information for each category is introduced to learn category-level graph representation to assist in processing the noisy labels. Finally, a graph matching module is further employed to explore the holistic–local information interaction through intra-graph node information propagation as well as to evaluate the similarity score between each instance graph and its corresponding category-level graph prototype through inter-graph node information propagation. Extensive experiments were conducted on three webly supervised FGIR benchmark datasets, Web-Bird, Web-Aircraft and Web-Car, with classification accuracy of 76.62%, 85.79% and 82.99%, respectively. In comparison with Peer-learning, the classification accuracies of the three datasets separately improved 2.47%, 4.72% and 1.59%

Signal Contrastive Enhanced Graph Collaborative Filtering for Recommendation

Abstract Graph collaborative filtering methods have shown great performance improvements compared with deep neural network-based models. However, these methods suffer from data sparsity and data noise problems. To address these issues, we propose a new contrastive learning-based graph collaborative filtering method to learn more robust representations. The proposed method is called signal contrastive enhanced graph collaborative filtering (SC-GCF), which conducts contrastive learning on graph signals. It has been proved that graph neural networks correspond to low-pass filters on the graph signals from the graph convolution perspective. Different from the previous contrastive learning-based methods, we first pay attention to the diversity of graph signals to directly optimize the informativeness of the graph signals. We introduce a hypergraph module to strengthen the representation learning ability of graph neural networks. The hypergraph learning module utilizes a learnable hypergraph structure to model the latent global dependency relations that graph neural networks cannot depict. Experiments are conducted on four public datasets, and the results show significant improvements compared with the state-of-the-art methods, which confirms the importance of considering signal-level contrastive learning and hypergraph learning

The Places–People Exercise: Understanding Spatial Patterns and the Formation Mechanism for Urban Commercial Fitness Space in Changchun City, China

The fitness industry is rapidly developing due to the demand for fitness activities, and a large number of commercial fitness spaces have emerged in Changchun city. The distribution of commercial fitness spaces in the city is not chaotic; different types of fitness spaces should have different spaces to choose from. The purpose of this article is to summarize the spatial distribution characteristics and laws of urban commercial fitness spaces, to help better develop commercial fitness spaces. Using Changchun (a central city in northeastern China) as an example, the article divides commercial fitness spaces into five categories. Then, GIS tools are used to analyze the distribution patterns, level distributions, and agglomeration characteristics of commercial fitness spaces. The city’s commercial fitness space distribution patterns are subjected to further study, along with the influencing factors and forming mechanisms of the pattern. Moreover, based on the research results, this study provides targeted suggestions for the development of fitness spaces. The study found that the commercial fitness space in Changchun city has formed a multi-core spatial pattern. Various types of fitness spaces show significant spatial differentiation in many aspects, such as “center-periphery” characteristics, the spatial distribution form, and the specialized characteristics of each block unit. Fitness needs, national policies, transportation accessibility, spatial agglomeration, land rent, urban population distribution, etc., are the main factors affecting the spatial distributions of fitness spaces

Fine-Grained Butterfly Recognition via Peer Learning Network with Distribution-Aware Penalty Mechanism

Automatic species recognition plays a key role in intelligent agricultural production management and the study of species diversity. However, fine-grained species recognition is a challenging task due to the quite diverse and subtle interclass differences among species and the long-tailed distribution of sample data. In this work, a peer learning network with a distribution-aware penalty mechanism is proposed to address these challenges. Specifically, the proposed method employs the two-stream ResNeSt-50 as the backbone to obtain the initial predicted results. Then, the samples, which are selected from the instances with the same predicted labels by knowledge exchange strategy, are utilized to update the model parameters via the distribution-aware penalty mechanism to mitigate the bias and variance problems in the long-tailed distribution. By performing such adaptive interactive learning, the proposed method can effectively achieve improved recognition accuracy for head classes in long-tailed data and alleviate the adverse effect of many head samples relative to a few samples of the tail classes. To evaluate the proposed method, we construct a large-scale butterfly dataset (named Butterfly-914) that contains approximately 72,152 images belonging to 914 species and at least 20 images for each category. Exhaustive experiments are conducted to validate the efficiency of the proposed method from several perspectives. Moreover, the superior Top-1 accuracy rate (86.2%) achieved on the butterfly dataset demonstrates that the proposed method can be widely used for agricultural species identification and insect monitoring

Holstein Cattle Face Re-Identification Unifying Global and Part Feature Deep Network with Attention Mechanism

In precision dairy farming, computer vision-based approaches have been widely employed to monitor the cattle conditions (e.g., the physical, physiology, health and welfare). To this end, the accurate and effective identification of individual cow is a prerequisite. In this paper, a deep learning re-identification network model, Global and Part Network (GPN), is proposed to identify individual cow face. The GPN model, with ResNet50 as backbone network to generate a pooling of feature maps, builds three branch modules (Middle branch, Global branch and Part branch) to learn more discriminative and robust feature representation from the maps. Specifically, the Middle branch and the Global branch separately extract the global features of middle dimension and high dimension from the maps, and the Part branch extracts the local features in the unified block, all of which are integrated to act as the feature representation for cow face re-identification. By performing such strategies, the GPN model not only extracts the discriminative global and local features, but also learns the subtle differences among different cow faces. To further improve the performance of the proposed framework, a Global and Part Network with Spatial Transform (GPN-ST) model is also developed to incorporate an attention mechanism module in the Part branch. Additionally, to test the efficiency of the proposed approach, a large-scale cow face dataset is constructed, which contains 130,000 images with 3000 cows under different conditions (e.g., occlusion, change of viewpoints and illumination, blur, and background clutters). The results of various contrast experiments show that the GPN outperforms the representative re-identification methods, and the improved GPN-ST model has a higher accuracy rate (up by 2.8% and 2.2% respectively) in Rank-1 and mAP, compared with the GPN model. In conclusion, using the Global and Part feature deep network with attention mechanism can effectively ameliorate the efficiency of cow face re-identification

Genome-Wide Identification and Characterization of the CsFHY3/FAR1 Gene Family and Expression Analysis under Biotic and Abiotic Stresses in Tea Plants (Camellia sinensis)

The FHY3/FAR1 transcription factor family, derived from transposases, plays important roles in light signal transduction, and in the growth and development of plants. However, the homologous genes in tea plants have not been studied. In this study, 25 CsFHY3/FAR1 genes were identified in the tea plant genome through a genome-wide study, and were classified into five subgroups based on their phylogenic relationships. Their potential regulatory roles in light signal transduction and photomorphogenesis, plant growth and development, and hormone responses were verified by the existence of the corresponding cis-acting elements. The transcriptome data showed that these genes could respond to salt stress and shading treatment. An expression analysis revealed that, in different tissues, especially in leaves, CsFHY3/FAR1s were strongly expressed, and most of these genes were positively expressed under salt stress (NaCl), and negatively expressed under low temperature (4 °C) stress. In addition, a potential interaction network demonstrated that PHYA, PHYC, PHYE, LHY, FHL, HY5, and other FRSs were directly or indirectly associated with CsFHY3/FAR1 members. These results will provide the foundation for functional studies of the CsFHY3/FAR1 family, and will contribute to the breeding of tea varieties with high light efficiency and strong stress resistance

Automatic Penaeus Monodon Larvae Counting via Equal Keypoint Regression with Smartphones

Today, large-scale Penaeus monodon farms no longer incubate eggs but instead purchase larvae from large-scale hatcheries for rearing. The accurate counting of tens of thousands of larvae in these transactions is a challenging task due to the small size of the larvae and the highly congested scenes. To address this issue, we present the Penaeus Larvae Counting Strategy (PLCS), a simple and efficient method for counting Penaeus monodon larvae that only requires a smartphone to capture images without the need for any additional equipment. Our approach treats two different types of keypoints as equip keypoints based on keypoint regression to determine the number of shrimp larvae in the image. We constructed a high-resolution image dataset named Penaeus_1k using images captured by five smartphones. This dataset contains 1420 images of Penaeus monodon larvae and includes general annotations for three keypoints, making it suitable for density map counting, keypoint regression, and other methods. The effectiveness of the proposed method was evaluated on a real Penaeus monodon larvae dataset. The average accuracy of 720 images with seven different density groups in the test dataset was 93.79%, outperforming the classical density map algorithm and demonstrating the efficacy of the PLCS

Exploring the RNA Editing Events and Their Potential Regulatory Roles in Tea Plant (<i>Camellia sinensis</i> L.)

RNA editing is a post-transcriptional modification process that alters the RNA sequence relative to the genomic blueprint. In plant organelles (namely, mitochondria and chloroplasts), the most common type is C-to-U, and the absence of C-to-U RNA editing results in abnormal plant development, such as etiolation and albino leaves, aborted embryonic development and retarded seedling growth. Here, through PREP, RES-Scanner, PCR and RT-PCR analyses, 38 and 139 RNA editing sites were identified from the chloroplast and mitochondrial genomes of Camellia sinensis, respectively. Analysis of the base preference around the RNA editing sites showed that in the −1 position of the edited C had more frequent occurrences of T whereas rare occurrences of G. Three conserved motifs were identified at 25 bases upstream of the RNA editing site. Structural analyses indicated that the RNA secondary structure of 32 genes, protein secondary structure of 37 genes and the three-dimensional structure of 5 proteins were altered due to RNA editing. The editing level analysis of matK and ndhD in six tea cultivars indicated that matK-701 might be involved in the color change of tea leaves. Furthermore, 218 PLS-CsPPR proteins were predicted to interact with the identified RNA editing sites. In conclusion, this study provides comprehensive insight into RNA editing events, which will facilitate further study of the RNA editing phenomenon of the tea plant