Endovascular management of spontaneous axillary artery aneurysm: a case report and review of the literature

INTRODUCTION: Spontaneous atraumatic true axillary artery aneurysm is a relatively unusual disorder. Although most cases are asymptomatic, complications of axillary artery aneurysms may result in acute vascular insufficiency and neurological deficits. Prompt treatment, therefore, should be employed in the management of this condition. To date, the standard treatment for peripheral aneurysms is still surgical resection with end-to-end anastomosis. However, aneurysmectomy and interposition grafting with autologous or artificial vessels are more invasive and time-consuming. The ideal treatment for axillary artery aneurysm should be relatively noninvasive, safe and free of significant complications, cost-effective, cosmetically acceptable, and incur less absence from usual daily activities. Endovascular stent grafts have also been successfully used to treat these aneurysms. Management of select aneurysms using stent grafts has become more prevalent with the developing endoluminal technology. CASE PRESENTATION: We report a case of a spontaneous atraumatic axillary artery aneurysm where the patient was a 48-year-old ethnic Han Chinese woman with a gradually enlarging left axillary pulsatile mass. She was treated with endovascular stent grafts. The postoperative course of the patient was uneventful during the six-month follow-up. CONCLUSIONS: We show that there are significant early advantages with the endovascular management technique versus the conventional operation in the management of axillary artery aneurysm

Image Copy-Move Forgery Detection via Deep Cross-Scale PatchMatch

The recently developed deep algorithms achieve promising progress in the field of image copy-move forgery detection (CMFD). However, they have limited generalizability in some practical scenarios, where the copy-move objects may not appear in the training images or cloned regions are from the background. To address the above issues, in this work, we propose a novel end-to-end CMFD framework by integrating merits from both conventional and deep methods. Specifically, we design a deep cross-scale patchmatch method tailored for CMFD to localize copy-move regions. In contrast to existing deep models, our scheme aims to seek explicit and reliable point-to-point matching between source and target regions using features extracted from high-resolution scales. Further, we develop a manipulation region location branch for source/target separation. The proposed CMFD framework is completely differentiable and can be trained in an end-to-end manner. Extensive experimental results demonstrate the high generalizability of our method to different copy-move contents, and the proposed scheme achieves significantly better performance than existing approaches.Comment: 6 pages, 4 figures, accepted by ICME202

PRENet: A Plane-Fit Redundancy Encoding Point Cloud Sequence Network for Real-Time 3D Action Recognition

Recognizing human actions from point cloud sequence has attracted tremendous attention from both academia and industry due to its wide applications. However, most previous studies on point cloud action recognition typically require complex networks to extract intra-frame spatial features and inter-frame temporal features, resulting in an excessive number of redundant computations. This leads to high latency, rendering them impractical for real-world applications. To address this problem, we propose a Plane-Fit Redundancy Encoding point cloud sequence network named PRENet. The primary concept of our approach involves the utilization of plane fitting to mitigate spatial redundancy within the sequence, concurrently encoding the temporal redundancy of the entire sequence to minimize redundant computations. Specifically, our network comprises two principal modules: a Plane-Fit Embedding module and a Spatio-Temporal Consistency Encoding module. The Plane-Fit Embedding module capitalizes on the observation that successive point cloud frames exhibit unique geometric features in physical space, allowing for the reuse of spatially encoded data for temporal stream encoding. The Spatio-Temporal Consistency Encoding module amalgamates the temporal structure of the temporally redundant part with its corresponding spatial arrangement, thereby enhancing recognition accuracy. We have done numerous experiments to verify the effectiveness of our network. The experimental results demonstrate that our method achieves almost identical recognition accuracy while being nearly four times faster than other state-of-the-art methods.Comment: Accepted by the 2024 International Joint Conference on Neural Networks (IJCNN 2024

RecAD: Towards A Unified Library for Recommender Attack and Defense

In recent years, recommender systems have become a ubiquitous part of our daily lives, while they suffer from a high risk of being attacked due to the growing commercial and social values. Despite significant research progress in recommender attack and defense, there is a lack of a widely-recognized benchmarking standard in the field, leading to unfair performance comparison and limited credibility of experiments. To address this, we propose RecAD, a unified library aiming at establishing an open benchmark for recommender attack and defense. RecAD takes an initial step to set up a unified benchmarking pipeline for reproducible research by integrating diverse datasets, standard source codes, hyper-parameter settings, running logs, attack knowledge, attack budget, and evaluation results. The benchmark is designed to be comprehensive and sustainable, covering both attack, defense, and evaluation tasks, enabling more researchers to easily follow and contribute to this promising field. RecAD will drive more solid and reproducible research on recommender systems attack and defense, reduce the redundant efforts of researchers, and ultimately increase the credibility and practical value of recommender attack and defense. The project is released at https://github.com/gusye1234/recad

Design of virtual BCI channels based on informer

The precision and reliability of electroencephalogram (EEG) data are essential for the effective functioning of a brain-computer interface (BCI). As the number of BCI acquisition channels increases, more EEG information can be gathered. However, having too many channels will reduce the practicability of the BCI system, raise the likelihood of poor-quality channels, and lead to information misinterpretation. These issues pose challenges to the advancement of BCI systems. Determining the optimal configuration of BCI acquisition channels can minimize the number of channels utilized, but it is challenging to maintain the original operating system and accommodate individual variations in channel layout. To address these concerns, this study introduces the EEG-completion-informer (EC-informer), which is based on the Informer architecture known for its effectiveness in time-series problems. By providing input from four BCI acquisition channels, the EC-informer can generate several virtual acquisition channels to extract additional EEG information for analysis. This approach allows for the direct inheritance of the original model, significantly reducing researchers’ workload. Moreover, EC-informers demonstrate strong performance in damaged channel repair and poor channel identification. Using the Informer as a foundation, the study proposes the EC-informer, tailored to BCI requirements and demanding only a small number of training samples. This approach eliminates the need for extensive computing units to train an efficient, lightweight model while preserving comprehensive information about target channels. The study also confirms that the proposed model can be transferred to other operators with minimal loss, exhibiting robust applicability. The EC-informer’s features enable original BCI devices to adapt to a broader range of classification algorithms and relax the operational requirements of BCI devices, which could facilitate the promotion of the use of BCI devices in daily life

Image Copy-Move Forgery Detection via Deep PatchMatch and Pairwise Ranking Learning

Recent advances in deep learning algorithms have shown impressive progress in image copy-move forgery detection (CMFD). However, these algorithms lack generalizability in practical scenarios where the copied regions are not present in the training images, or the cloned regions are part of the background. Additionally, these algorithms utilize convolution operations to distinguish source and target regions, leading to unsatisfactory results when the target regions blend well with the background. To address these limitations, this study proposes a novel end-to-end CMFD framework that integrates the strengths of conventional and deep learning methods. Specifically, the study develops a deep cross-scale PatchMatch (PM) method that is customized for CMFD to locate copy-move regions. Unlike existing deep models, our approach utilizes features extracted from high-resolution scales to seek explicit and reliable point-to-point matching between source and target regions. Furthermore, we propose a novel pairwise rank learning framework to separate source and target regions. By leveraging the strong prior of point-to-point matches, the framework can identify subtle differences and effectively discriminate between source and target regions, even when the target regions blend well with the background. Our framework is fully differentiable and can be trained end-to-end. Comprehensive experimental results highlight the remarkable generalizability of our scheme across various copy-move scenarios, significantly outperforming existing methods.Comment: 16 pages, 14figure

Resfusion: Prior Residual Noise embedded Denoising Diffusion Probabilistic Models

Recently, Denoising Diffusion Probabilistic Models have been widely used in image segmentation, by generating segmentation masks conditioned on the input image. However, previous works can not seamlessly integrate existing end-to-end models with denoising diffusion models. Existing research can only select acceleration steps based on experience rather than calculating them specifically. Moreover, most methods are limited to small models and small-scale datasets, unable to generalize to general datasets and a wider range of tasks. Therefore, we propose Resfusion with a novel resnoise-diffusion process, which gradually generates segmentation masks or any type of target image, seamlessly integrating state-of-the-art end-to-end models and denoising diffusion models. Resfusion bridges the discrepancy between the likelihood output and the ground truth output through a Markov process. Through the novel smooth equivalence transformation in resnoise-diffusion process, we determine the optimal acceleration step. Experimental results demonstrate that Resfusion combines the capabilities of existing end-to-end models and denoising diffusion models, further enhancing performance and achieving outstanding results. Moreover, Resfusion is not limited to segmentation tasks, it can easily generalize to any general tasks of image generation and exhibit strong competitiveness

Terminal Sliding Mode Control with Unidirectional Auxiliary Surfaces for Hypersonic Vehicles Based on Adaptive Disturbance Observer

A novel flight control scheme is proposed using the terminal sliding mode technique, unidirectional auxiliary surfaces and the disturbance observer model. These proposed dynamic attitude control systems can improve control performance of hypersonic vehicles despite uncertainties and external disturbances. The terminal attractor is employed to improve the convergence rate associated with the critical damping characteristics problem noted in short-period motions of hypersonic vehicles. The proposed robust attitude control scheme uses a dynamic terminal sliding mode with unidirectional auxiliary surfaces. The nonlinear disturbance observer is designed to estimate system uncertainties and external disturbances. The output of the disturbance observer aids the robust adaptive control scheme and improves robust attitude control performance. Finally, simulation results are presented to illustrate the effectiveness of the proposed terminal sliding mode with unidirectional auxiliary surfaces

Systemic immune-inflammation index is associated with high risk for prostate cancer among the U.S. elderly: Evidence from NHANES 2001-2010

PurposeThe Systemic Immuno-Inflammation Index (SII) is a crucial clinical measure of inflammation, and there is currently no solid evidence linking SII to an increased risk of prostate cancer (PCa). Through the analysis of serum total prostate-specific antigen (tPSA), free prostate-specific antigen (fPSA), and the tPSA/fPSA (fPSA%) ratio, this study sought to investigate the relationship between SII and PCa risk among the U.S. elderly.MethodsElderly male participants were gathered from the NHANES database between 2001 and 2010.SII was calculated by platelet count * neutrophil count/lymphocyte count. High risk individuals for prostate cancer were defined as those with tPSA > 4 ng/ml and fPSA% < 16%. Multivariate logistic regression models, restricted cubic spline curves, and subgroup analyses were used to assess the relationship between SII and PCa risk.ResultsThis research comprised 2664 people in total, 137 (5.14%) of whom were deemed to be at high risk of developing PCa. Multivariate logistic regression analysis, after controlling for variables, revealed a significant positive correlation between high PCa risk and an increase in SII (p = 0.009). The RCS suggested a turning point at 9.01. Restricted cubic spline curves revealed a non-linear U-shaped association between SII and high PCa risk (p for nonlinear = 0.028). Education level, marital status, PIR, alcohol status, smoking status, rheumatoid arthritis status, and heart problem were not significantly correlated with this positive connection, according to subgroup analyses and interaction tests.ConclusionThe results of this study suggest that inflammation represented by SII is associated with high PCa risk

Nanostructures and catalytic atoms engineering of tellurium‐based materials and their roles in electrochemical energy conversion

With the dramatic developments of renewable and environmental‐friendly electrochemical energy conversion systems, there is an urgent need to fabricate durable and efficient electrocatalysts to address the limitation of high overpotentials exceeding thermodynamic requirements to facilitate practical applications. Recently, tellurium‐based nanomaterials (Te NMs) with unique chemical, electronic, and topological properties, including Te‐derived nanostructures and transition metal tellurides (TMTs), have emerged as one of the most promising electrocatalytic materials. In the absence of comprehensive and guiding reviews, this review comprehensively summarizes the main advances in designing emerging Te NMs for electrocatalysis. First, the engineering strategies and principles of Te NMs to enhance their electrocatalytic activity and stability from the nanostructures to the catalytic atoms are discussed in detail, especially on the chemical/physical/multiplex templating strategies, heteroatom doping, vacancy/defect engineering, phase engineering, and the corresponding mechanisms and structure‐performance correlations. Then, typical applications of Te NMs in electrocatalysis are also discussed in detail. Finally, the existing key issues and main challenges of Te NMs for electrocatalysis are highlighted, and the development trend of Te NMs as electrocatalysts is expounded. This review provides new concepts to guide future directions for developing Te NMs‐based electrocatalysts, thereby promoting their future wide applications in electrochemical energy systems. Recent advances in tellurium‐based nanomaterials (Te NMs) for electrocatalytic applications have been summarized in this review. The design principles and modification strategies of Te NMs in structural engineering and catalytically active center design, such as chemical/physical/multiplex templating strategies, heteroatom doping, vacancy/defect engineering, and phase engineering, are discussed. The electrocatalytic applications, main challenges, and development directions of Te NMs are also summarized