Few-shot Message-Enhanced Contrastive Learning for Graph Anomaly Detection

Graph anomaly detection plays a crucial role in identifying exceptional instances in graph data that deviate significantly from the majority. It has gained substantial attention in various domains of information security, including network intrusion, financial fraud, and malicious comments, et al. Existing methods are primarily developed in an unsupervised manner due to the challenge in obtaining labeled data. For lack of guidance from prior knowledge in unsupervised manner, the identified anomalies may prove to be data noise or individual data instances. In real-world scenarios, a limited batch of labeled anomalies can be captured, making it crucial to investigate the few-shot problem in graph anomaly detection. Taking advantage of this potential, we propose a novel few-shot Graph Anomaly Detection model called FMGAD (Few-shot Message-Enhanced Contrastive-based Graph Anomaly Detector). FMGAD leverages a self-supervised contrastive learning strategy within and across views to capture intrinsic and transferable structural representations. Furthermore, we propose the Deep-GNN message-enhanced reconstruction module, which extensively exploits the few-shot label information and enables long-range propagation to disseminate supervision signals to deeper unlabeled nodes. This module in turn assists in the training of self-supervised contrastive learning. Comprehensive experimental results on six real-world datasets demonstrate that FMGAD can achieve better performance than other state-of-the-art methods, regardless of artificially injected anomalies or domain-organic anomalies

Glucocorticosteroid-sensitive inflammatory eosinophilic pseudotumor of the bladder in an adolescent: a case report

<p>Abstract</p> <p>Introduction</p> <p>Inflammatory eosinophilic pseudotumor of the bladder is a rare inflammatory bladder disease. The etiology and pathophysiology of this condition are still unclear. Few case reports have described inflammatory eosinophilic pseudotumor of the bladder in adults or children. Although benign, this disease is occasionally clinically aggressive and locally invasive, thus open surgical removal or complete transurethral resection is recommended.</p> <p>Case presentation</p> <p>We present the case of a biopsy-proven inflammatory eosinophilic pseudotumor of the bladder in a previously healthy 16-year-old male adolescent with 2-month history of frequent micturition and dysuria with no significant apparent causative factors. The tumor regressed after a 6-week course of glucocorticosteroids.</p> <p>Conclusion</p> <p>To the best of our knowledge, our case is a rare case of inflammatory eosinophilic pseudotumor of the bladder treated with complete conservative management. Due to its glucocorticosteroid-sensitive nature, we postulate that this disease belongs to a subgroup of eosinophilic disorders.</p

The Association between Perceived Housing Environment and Health and Satisfaction among the Older Adults during the COVID-19 Pandemic: A Cross-Sectional Survey in Northern China

China lacks design strategies to improve home-based care environments for its older adults. This study investigated the perception of indoor environmental quality in housing environments and analyzed its impact on health and satisfaction among home-living older adults. A cross-sectional survey in Northern China was conducted during the COVID-19 pandemic (October 2021–March 2022) to test the effects of five housing environmental factors on home-living older adults’ health and satisfaction, including noise, lighting and view, temperature and humidity, air quality, and maintenance and cleanliness. A total of 356 home-living adults aged 60 years and older participated in the survey. The 12-item Short Form Health Survey was used to measure health-related quality of life among respondents. Using multiple regression analyses, we found that overall satisfaction can be positively predicted by four housing environmental qualities: lighting and view, temperature and humidity, air quality, and maintenance and cleanliness. Air quality was found to be a predictor of respondents’ physical health. Only noise had a significant predictive effect on respondents’ mental health. Age, marital status, and health status (cardiovascular and chronic diseases) were significantly correlated with the physical health of the respondents, whereas educational status, monthly income, and alcohol consumption could predict their mental health.</jats:p

Meta-Learning Triplet Network with Adaptive Margins for Few-Shot Named Entity Recognition

Meta-learning methods have been widely used in few-shot named entity recognition (NER), especially prototype-based methods. However, the Other(O) class is difficult to be represented by a prototype vector because there are generally a large number of samples in the class that have miscellaneous semantics. To solve the problem, we propose MeTNet, which generates prototype vectors for entity types only but not O-class. We design an improved triplet network to map samples and prototype vectors into a low-dimensional space that is easier to be classified and propose an adaptive margin for each entity type. The margin plays as a radius and controls a region with adaptive size in the low-dimensional space. Based on the regions, we propose a new inference procedure to predict the label of a query instance. We conduct extensive experiments in both in-domain and cross-domain settings to show the superiority of MeTNet over other state-of-the-art methods. In particular, we release a Chinese few-shot NER dataset FEW-COMM extracted from a well-known e-commerce platform. To the best of our knowledge, this is the first Chinese few-shot NER dataset. All the datasets and codes are provided at https://github.com/hccngu/MeTNet

Exchanging-based Multimodal Fusion with Transformer

We study the problem of multimodal fusion in this paper. Recent exchanging-based methods have been proposed for vision-vision fusion, which aim to exchange embeddings learned from one modality to the other. However, most of them project inputs of multimodalities into different low-dimensional spaces and cannot be applied to the sequential input data. To solve these issues, in this paper, we propose a novel exchanging-based multimodal fusion model MuSE for text-vision fusion based on Transformer. We first use two encoders to separately map multimodal inputs into different low-dimensional spaces. Then we employ two decoders to regularize the embeddings and pull them into the same space. The two decoders capture the correlations between texts and images with the image captioning task and the text-to-image generation task, respectively. Further, based on the regularized embeddings, we present CrossTransformer, which uses two Transformer encoders with shared parameters as the backbone model to exchange knowledge between multimodalities. Specifically, CrossTransformer first learns the global contextual information of the inputs in the shallow layers. After that, it performs inter-modal exchange by selecting a proportion of tokens in one modality and replacing their embeddings with the average of embeddings in the other modality. We conduct extensive experiments to evaluate the performance of MuSE on the Multimodal Named Entity Recognition task and the Multimodal Sentiment Analysis task. Our results show the superiority of MuSE against other competitors. Our code and data are provided at https://github.com/RecklessRonan/MuSE

A Simple Preparation Method of Graphene and TiO₂ Loaded Activated Carbon Fiber and Its Application for Indoor Formaldehyde Degradation

Formaldehyde has a significant impact on human health. This study used a simple dipping method to load graphene-titanium dioxide (GR-TiO2) on activated carbon fibers (ACFs). The microstructure of GR-TiO2/ACF hybrid material was observed by SEM, combined with XRD and BET analysis. The result showed that the GR-TiO2/ACF hybrid material had a specific surface area of 893.08 m2/g and average pore size of 2.35 nm. The formaldehyde degradation efficiency of the prepared material was tested under different conditions, such as ultraviolet (UV) radiation, air supply volume, relative humidity, initial mass concentration. The results showed that the UV radiation intensity, airflow and the initial mass concentration were positively correlated with the formaldehyde removal rate, and the relative humidity was negatively correlated with the formaldehyde removal rate. The GR-TiO2/ACF hybrid material had a maximum formaldehyde removal rate of 85.54% within 120 min

Design of HTS Excitation Coil for Homopolar Inductor Machine Considering Critical Current Reduction of Local Turn

The high temperature superconducting homopolar inductor machine (HTS-HIM) is identified as a promising candidate for the high-speed field of superconducting machine. The reliability and performance of HTS-HIM is closely related to the critical current of its HTS excitation coil. However, the critical current is limited by the turn with the highest risk of quench, which means that not all the turns of HTS coil can achieve the best performance. Targeting ensures the reliability of HTS-HIM and enhance the utilization rate of HTS tape, the HTS coil is designed considering the critical current reduction of local turn in this paper. Firstly, the operation principle of HTS-HIM is illustrated, and the design process is given. Then, the feasibility of calculating critical current using constant sweep rate method is described, and the HTS coil of a 10 kW HTS-HIM is designed utilizing the MAX criterion. Thirdly, the external field of the HTS coil is analyzed based on the simplified model of HTS-HIM. According to the external field distribution, an easily installed L-shaped flux diverter is proposed to suppress critical current reduction of local turn of HTS coil. The results show that the current safety margin is increased by 24 % at the cost of very little extra loss and weight through the proposed method

Resonant Frequency Modeling of Microwave Antennas Using Gaussian Process Based on Semisupervised Learning

For the optimal design of electromagnetic devices, it is the most time consuming to obtain the training samples from full wave electromagnetic simulation software, including HFSS, CST, and IE3D. Traditional machine learning methods usually use only labeled samples or unlabeled samples, but in practical problems, labeled samples and unlabeled samples coexist, and the acquisition cost of labeled samples is relatively high. This paper proposes a semisupervised learning Gaussian Process (GP), which combines unlabeled samples to improve the accuracy of the GP model and reduce the number of labeled training samples required. The proposed GP model consists two parts: initial training and self-training. In the process of initial training, a small number of labeled samples obtained by full wave electromagnetic simulation are used for training the initial GP model. Afterwards, the trained GP model is copied to another GP model in the process of self-training, and then the two GP models will update after crosstraining with different unlabeled samples. Using the same test samples for testing and updating, a model with a smaller error will replace another. Repeat the self-training process until a predefined stopping criterion is met. Four different benchmark functions and resonant frequency modeling problems of three different microstrip antennas are used to evaluate the effectiveness of the GP model. The results show that the proposed GP model has a good fitting effectiveness on benchmark functions. For microstrip antennas resonant frequency modeling problems, in the case of using the same labeled samples, its predictive ability is better than that of the traditional supervised GP model

MicroRNA-146b-3p regulates the dysfunction of vascular smooth muscle cells via repressing phosphoinositide-3 kinase catalytic subunit gamma

MicroRNAs are crucial regulators in the phenotype switch of vascular smooth muscle cells (VSMCs). Nonetheless, the role of miR-146b-3p in VSMCs remains unclear. In the present study, platelet-derived growth factor-BB (PDGF-BB) at different concentrations was employed to stimulate VSMCs for different times, to establish the model of VSMC dysfunction. The relative expression of miR-146b-3p was quantified by quantitative real-time polymerase chain reaction (qRT-PCR). The proliferation of VSMCs was measured by BrdU assay. Flow cytometry analysis was employed for the analysis of cell cycle. VSMC migration was detected by Transwell assay. Phosphoinositide-3 kinase catalytic subunit-gamma (PIK3CG) and markers of VSMC differentiation, including α-SMA, SM-22α, SMMHC, and Calponin were examined employing Western blot. The targeting relationship between miR-146b-3p and PIK3CG 3ʹ-UTR was affirmed by dual-luciferase gene assay. We report that the reduction of miR-146b-3p expression was induced by PDGF-BB in a time-dependent and dose-dependent manner (P < 0.05). The overexpression of miR-146b-3p counteracted the effects of PDGF-BB on the proliferation and migration of VSMCs and increased the expressions of differentiation markers (P < 0.05). Additionally, PIK3CG expression was negatively regulated by miR-146b-3p, and the restoration of PIK3CG partly eliminated the effects of miR-146b-3p on VSMCs (P < 0.05). In summary, miR-146b-3p represses the proliferation, migration, and phenotype switch of VSMCs induced by PDGF-BB via targeting PIK3CG. Therefore, miR-146b-3p/PIK3CG may be a potential target for the treatment of atherosclerosis

Optimizing Culture Conditions by Statistical Approach to Enhance Production of Pectinase from Bacillus sp. Y1

It was found that Bacillus sp. Y1 could secrete alkaline pectinase with suitable enzyme system for powerful and fast degumming of ramie fiber. In this study, the medium components and fermentation conditions were optimized by some statistical methods including mixture design, fractional factorial design, central composite design and response surface methodology, and single factor method for enhancing the alkaline pectinase production. The optimized conditions for pectinase production were that the culture was shaken at 34°C for 60 h in 50 mL of medium containing 10.5% (w/v) carbon source (consisting of 3.8% starch, 4.2% wheat bran, and 2.5% sucrose), 0.37% (NH4)2SO4, 0.3% MgSO4, and 0.1% Tween-80, with initial pH 8.2 and inoculation amount of 1.3 mL (with the OD600 of the seed medium about 5.77). Using the optimizing conditions, the activities of polygalacturonate lyase (PGL) and polygalacturonase (PG) in fermentation liquor were increased to 2.00-fold and 3.44-fold, respectively, and the fermentation time shortened 12 hours (from 72 h to 60 h), which showed good application potential in degumming of ramie