PRSNet: A Masked Self-Supervised Learning Pedestrian Re-Identification Method

In recent years, self-supervised learning has attracted widespread academic debate and addressed many of the key issues of computer vision. The present research focus is on how to construct a good agent task that allows for improved network learning of advanced semantic information on images so that model reasoning is accelerated during pre-training of the current task. In order to solve the problem that existing feature extraction networks are pre-trained on the ImageNet dataset and cannot extract the fine-grained information in pedestrian images well, and the existing pre-task of contrast self-supervised learning may destroy the original properties of pedestrian images, this paper designs a pre-task of mask reconstruction to obtain a pre-training model with strong robustness and uses it for the pedestrian re-identification task. The training optimization of the network is performed by improving the triplet loss based on the centroid, and the mask image is added as an additional sample to the loss calculation, so that the network can better cope with the pedestrian matching in practical applications after the training is completed. This method achieves about 5% higher mAP on Marker1501 and CUHK03 data than existing self-supervised learning pedestrian re-identification methods, and about 1% higher for Rank1, and ablation experiments are conducted to demonstrate the feasibility of this method. Our model code is located at https://github.com/ZJieX/prsnet

A Relay System for Semantic Image Transmission based on Shared Feature Extraction and Hyperprior Entropy Compression

Nowadays, the need for high-quality image reconstruction and restoration is more and more urgent. However, most image transmission systems may suffer from image quality degradation or transmission interruption in the face of interference such as channel noise and link fading. To solve this problem, a relay communication network for semantic image transmission based on shared feature extraction and hyperprior entropy compression (HEC) is proposed, where the shared feature extraction technology based on Pearson correlation is proposed to eliminate partial shared feature of extracted semantic latent feature. In addition, the HEC technology is used to resist the effect of channel noise and link fading and carried out respectively at the source node and the relay node. Experimental results demonstrate that compared with other recent research methods, the proposed system has lower transmission overhead and higher semantic image transmission performance. Particularly, under the same conditions, the multi-scale structural similarity (MS-SSIM) of this system is superior to the comparison method by approximately 0.2

Video-based Visible-Infrared Person Re-Identification with Auxiliary Samples

Visible-infrared person re-identification (VI-ReID) aims to match persons captured by visible and infrared cameras, allowing person retrieval and tracking in 24-hour surveillance systems. Previous methods focus on learning from cross-modality person images in different cameras. However, temporal information and single-camera samples tend to be neglected. To crack this nut, in this paper, we first contribute a large-scale VI-ReID dataset named BUPTCampus. Different from most existing VI-ReID datasets, it 1) collects tracklets instead of images to introduce rich temporal information, 2) contains pixel-aligned cross-modality sample pairs for better modality-invariant learning, 3) provides one auxiliary set to help enhance the optimization, in which each identity only appears in a single camera. Based on our constructed dataset, we present a two-stream framework as baseline and apply Generative Adversarial Network (GAN) to narrow the gap between the two modalities. To exploit the advantages introduced by the auxiliary set, we propose a curriculum learning based strategy to jointly learn from both primary and auxiliary sets. Moreover, we design a novel temporal k-reciprocal re-ranking method to refine the ranking list with fine-grained temporal correlation cues. Experimental results demonstrate the effectiveness of the proposed methods. We also reproduce 9 state-of-the-art image-based and video-based VI-ReID methods on BUPTCampus and our methods show substantial superiority to them. The codes and dataset are available at: https://github.com/dyhBUPT/BUPTCampus.Comment: Accepted by Transactions on Information Forensics & Security 202

MIM-GAN-based Anomaly Detection for Multivariate Time Series Data

The loss function of Generative adversarial network(GAN) is an important factor that affects the quality and diversity of the generated samples for anomaly detection. In this paper, we propose an unsupervised multiple time series anomaly detection algorithm based on the GAN with message importance measure(MIM-GAN). In particular, the time series data is divided into subsequences using a sliding window. Then a generator and a discriminator designed based on the Long Short-Term Memory (LSTM) are employed to capture the temporal correlations of the time series data. To avoid the local optimal solution of loss function and the model collapse, we introduce an exponential information measure into the loss function of GAN. Additionally, a discriminant reconstruction score consisting on discrimination and reconstruction loss is taken into account. The global optimal solution for the loss function is derived and the model collapse is proved to be avoided in our proposed MIM-GAN-based anomaly detection algorithm. Experimental results show that the proposed MIM-GAN-based anomaly detection algorithm has superior performance in terms of precision, recall, and F1 score.Comment: 7 pages,6 figure

Now and Future of Artificial Intelligence-based Signet Ring Cell Diagnosis: A Survey

Since signet ring cells (SRCs) are associated with high peripheral metastasis rate and dismal survival, they play an important role in determining surgical approaches and prognosis, while they are easily missed by even experienced pathologists. Although automatic diagnosis SRCs based on deep learning has received increasing attention to assist pathologists in improving the diagnostic efficiency and accuracy, the existing works have not been systematically overviewed, which hindered the evaluation of the gap between algorithms and clinical applications. In this paper, we provide a survey on SRC analysis driven by deep learning from 2008 to August 2023. Specifically, the biological characteristics of SRCs and the challenges of automatic identification are systemically summarized. Then, the representative algorithms are analyzed and compared via dividing them into classification, detection, and segmentation. Finally, for comprehensive consideration to the performance of existing methods and the requirements for clinical assistance, we discuss the open issues and future trends of SRC analysis. The retrospect research will help researchers in the related fields, particularly for who without medical science background not only to clearly find the outline of SRC analysis, but also gain the prospect of intelligent diagnosis, resulting in accelerating the practice and application of intelligent algorithms

Effectiveness and safety of transcatheter aortic valve replacement in elderly people with severe aortic stenosis with different types of heart failure.

Impaired left ventricular function is an independent predictor of adverse clinical outcomes in patients with aortic stenosis. The aim of this study is to evaluate the short-term changes of echocardiographic parameters, New York Heart Association (NYHA) class and B-type natriuretic peptide (BNP) level and adverse events amongst patients with heart failure (HF) after transcatheter aortic valve replacement (TAVR) procedure. This was a retrospective cohort study conducted at affiliated Yantai Yuhuangding Hospital of Qingdao University between September 2017 and September 2022. TAVR cases were stratified into three groups [heart failure with reduced ejection fraction (HFrEF), heart failure with mildly reduced ejection fraction (HFmrEF), heart failure with preserved ejection fraction (HFpEF)] by left ventricular ejection fraction (LVEF). Baseline characteristics, changes in echocardiographic parameters (1 week and 1 month), BNP (1 month), and NYHA class (6 months) post-TAVR were compared across the three groups. Meanwhile, we observed the adverse events of the patients after TAVR. A total of 96 patients were included, of whom 15 (15.6%) had HFrEF, 15 (15.6%) had HFmrEF, and 66 (68.8%) had HFpEF. Compared to the HFpEF subgroup, patients in the HFrEF subgroup were younger (p < 0.05), and with a higher BNP (p < 0.05). The left ventricular end-diastolic dimension (LVEDD) in HFrEF group decreased significantly after TAVR. HFmrEF and HFrEF patients showed significant improvements in LVEF after TAVR. The pulmonary artery systolic pressure (PASP), aortic valve peak gradient (AVPG) and aortic valve peak gradient (V ) decreased significantly 1 month after TAVR in all three groups compared to the baseline (all p < 0.05). BNP significantly reduced in HFrEF group compared to HFpEF patients after TAVR (p < 0.05). The majority of patients experienced an improvement at least one NYHA class in all three groups 6 months post-TAVR. There is no significant increase in the risk of adverse events in the HFrEF group. Patients who underwent TAVR achieved significant improvements in BNP, NYHA class, LVEDD, LVEF, and PASP across the three HF classes, with a more rapid and pronounced improvement in the HFrEF and HFmrEF groups. Complication rates were low in the different HF groups. There is no significant increase in the risk of periprocedural complications in the HFrEF and HFmrEF groups. [Abstract copyright: © 2023. The Author(s).

Hybrid Graph Neural Networks for Crowd Counting

Crowd counting is an important yet challenging task due to the large scale and density variation. Recent investigations have shown that distilling rich relations among multi-scale features and exploiting useful information from the auxiliary task, i.e., localization, are vital for this task. Nevertheless, how to comprehensively leverage these relations within a unified network architecture is still a challenging problem. In this paper, we present a novel network structure called Hybrid Graph Neural Network (HyGnn) which targets to relieve the problem by interweaving the multi-scale features for crowd density as well as its auxiliary task (localization) together and performing joint reasoning over a graph. Specifically, HyGnn integrates a hybrid graph to jointly represent the task-specific feature maps of different scales as nodes, and two types of relations as edges:(i) multi-scale relations for capturing the feature dependencies across scales and (ii) mutual beneficial relations building bridges for the cooperation between counting and localization. Thus, through message passing, HyGnn can distill rich relations between the nodes to obtain more powerful representations, leading to robust and accurate results. Our HyGnn performs significantly well on four challenging datasets: ShanghaiTech Part A, ShanghaiTech Part B, UCF_CC_50 and UCF_QNRF, outperforming the state-of-the-art approaches by a large margin.Comment: To appear in AAAI 202

Asymptotics of Smallest Component Sizes in Decomposable Combinatorial Structures of Alg-Log Type

A decomposable combinatorial structure consists of simpler objects called components which by thems elves cannot be further decomposed. We focus on the multi-set construction where the component generating function C(z) is of alg-log type, that is, C(z) behaves like c + d(1 -z/rho)(alpha) (ln1/1-z/rho)(beta) (1 + o(1)) when z is near the dominant singularity rho. We provide asymptotic results about the size of thes mallest components in random combinatorial structures for the cases 0 < alpha < 1 and any beta, and alpha < 0 and beta=0. The particular case alpha=0 and beta=1, the so-called exp-log class, has been treated in previous papers. We also provide similar asymptotic estimates for combinatorial objects with a restricted pattern, that is, when part of its factorization patterns is known. We extend our results to include certain type of integers partitions. partition

Protective Effects of Total Flavones of Elaeagnus rhamnoides

The aim was to evaluate the protective effects of total flavones of Elaeagnus rhamnoides (L.) A. Nelson (TFE) against vascular endothelial injury in blood stasis model rats and explore the potential mechanisms preliminarily. The model of blood stasis rat model with vascular endothelial injury was induced by subcutaneous injection of adrenaline combined with ice-water bath. Whole blood viscosity (WBV), histological examination, and prothrombin time (PT), activated partial thromboplastin time (APTT), and fibrinogen (FIB) were measured. Meanwhile, the levels of Thromboxane B2 (TXB2), 6-keto-PGF1α, von Willebrand factor (vWF), and thrombomodulin (TM) were detected. In addition, Quantitative Real-Time PCR (qPCR) was performed to identify PI3K, Erk2, Bcl-2, and caspase-3 gene expression. The results showed that TFE can relieve WBV, increase PT and APTT, and decrease FIB content obviously. Moreover, TFE might significantly downregulate the levels of TXB2, vWF, and TM in plasma and upregulate the level of 6-keto-PGF1α in plasma. Expressions of PI3K and Bcl-2 were increased and the expression of caspase-3 was decreased by TFE pretreatment in the rat model. Consequently, the study suggested that TFE may have the potential against vascular endothelial injury in blood stasis model rats induced by a high dose of adrenaline with ice-water bath