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

Critical role for Gimap5 in the survival of mouse hematopoietic stem and progenitor cells

HSCs lacking the guanosine nucleotide-binding protein Gimap5, which stabilizes expression of the Mcl-1 Bcl2 family protein, exhibit impaired survival and long-term repopulation capacity

A synbiotic intervention modulates meta-omics signatures of gut redox potential and acidity in elective caesarean born infants.

Background The compromised gut microbiome that results from C-section birth has been hypothesized as a risk factor for the development of non-communicable diseases (NCD). In a double-blind randomized controlled study, 153 infants born by elective C-section received an infant formula supplemented with either synbiotic, prebiotics, or unsupplemented from birth until 4 months old. Vaginally born infants were included as a reference group. Stool samples were collected from day 3 till week 22. Multi-omics were deployed to investigate the impact of mode of delivery and nutrition on the development of the infant gut microbiome, and uncover putative biological mechanisms underlying the role of a compromised microbiome as a risk factor for NCD. Results As early as day 3, infants born vaginally presented a hypoxic and acidic gut environment characterized by an enrichment of strict anaerobes (Bifidobacteriaceae). Infants born by C-section presented the hallmark of a compromised microbiome driven by an enrichment of Enterobacteriaceae. This was associated with meta-omics signatures characteristic of a microbiome adapted to a more oxygen-rich gut environment, enriched with genes associated with reactive oxygen species metabolism and lipopolysaccharide biosynthesis, and depleted in genes involved in the metabolism of milk carbohydrates. The synbiotic formula modulated expression of microbial genes involved in (oligo)saccharide metabolism, which emulates the eco-physiological gut environment observed in vaginally born infants. The resulting hypoxic and acidic milieu prevented the establishment of a compromised microbiome. Conclusions This study deciphers the putative functional hallmarks of a compromised microbiome acquired during C-section birth, and the impact of nutrition that may counteract disturbed microbiome development. Trial registration The study was registered in the Dutch Trial Register (Number: 2838 ) on 4th April 2011

Adsorption of MS2 on oxide nanoparticles affects chlorine disinfection and solar inactivation

Adsorption on colloidal particles is one of the environmental processes affecting fate, transport, viability or reproducibility of viruses. This work studied colloidal interactions (adsorption kinetics and isotherms) between different oxide nanoparticles (NPs) (i.e., TiO2, NiO, ZnO, SiO2, and Al2O3) and bacteriophage, MS2. The results shows that that all oxide NPs exhibited strong adsorption capacity for MS2, except SiO2 NPs, which is supported by the extended Derjaguin and Landau, Verwey and Overbeek (EDLVO) theory. Moreover, the implication of such colloidal interactions on water disinfection is manifested by the observations that the presence of TiO2 and ZnO NPs could enhance MS2 inactivation under solar irradiation, whereas NiO and SiO2 decreased MS2 inactivation. By contrast, all of these oxide NPs were found to mitigate chlorine disinfection against MS2 to different extent, and the shielding effect was probably caused by reduced free chlorine and free MS2 in the solution due to sorption onto NPs. Clearly, there is a pressing need to further understand colloidal interactions between engineered NPs and viruses in water to better improve the current water treatment processes and to develop novel nanomaterials for water disinfection. (C) 2014 Elsevier Ltd. All rights reserved

Efficient Feature Selection and Classification for Vehicle Detection

The focus of this paper is on the problem of Haar-like feature selection and classification for vehicle detection. Haar-like features are particularly attractive for vehicle detection because they form a compact representation, encode edge and structural information, capture information from multiple scales, and especially can be computed efficiently. Due to the large-scale nature of the Haar-like feature pool, we present a rapid and effective feature selection method via AdaBoost by combining a sample's feature value with its class label. Our approach is analyzed theoretically and empirically to show its efficiency. Then, an improved normalization algorithm for the selected feature values is designed to reduce the intra-class difference, while increasing the inter-class variability. Experimental results demonstrate that the proposed approaches not only speed up the feature selection process with AdaBoost, but also yield better detection performance than the state-of-the-art methods

A rapid learning algorithm for vehicle classification

AdaBoost is a popular method for vehicle detection, but the training process is quite time-consuming. In this paper, a rapid learning algorithm is proposed to tackle this weakness of AdaBoost for vehicle classification. Firstly, an algorithm for computing the Haar-like feature pool on a 32 x 32 grayscale image patch by using all simple and rotated Haar-like prototypes is introduced to represent a vehicle's appearance. Then, a fast training approach for the weak classifier is presented by combining a sample's feature value with its class label. Finally, a rapid incremental learning algorithm of AdaBoost is designed to significantly improve the performance of AdaBoost. Experimental results demonstrate that the proposed approaches not only speed up the training and incremental learning processes of AdaBoost, but also yield better or competitive vehicle classification accuracies compared with several state-of-the-art methods, showing their potential for real-time applications

Investigate on a Simplified Multi-Port Interline DC Power Flow Controller and Its Control Strategy

A DC power flow controller (DCPFC) can help to facilitate power flow routing in the multi-terminal high-voltage direct current (HVDC) transmission system. Realizing its multi-port output can effectively improve the device regulate range and capability. Based on analysis of the traditional multi-port interline DC power flow controller (MI-DCPFC), this paper presents a switches reduced topology of MI-DCPFC. In addition, for solving the problem of coupling of the port-output voltage of the traditional MI-DCPFC, a novel control strategy based on carrier phase shifting pulse width modulation (CPS-PWM) is proposed. It implements the decoupling of the port-output voltage of MI-DCPFC, which can ensure completely independent tracking of the power flow regulating commands for different controlled lines. Moreover, key relationships between the system state variables are also analyzed and detailed in this study. Finally, the performance of the proposed controller and control strategy are confirmed with the simulation and experiment studies under different conditions

Enhanced Microalgal Harvesting Using Microalgae-Derived Extracellular Polymeric Substance as Flocculation Aid

Coagulation-based harvesting has been widely used in microalgal biomass harvesting. However, the coagulant contamination in the harvested biomass may negatively affect the applications in feedstock processing for food, feed, and fuel. In this study, extracellular polymeric substances (EPSs) were derived from microalgae, Scenedesmus acuminatus, and then used as a bioflocculant to aid the flocculation of the same algae. The results show that the alum coagulant (Al3+) usage was significantly reduced from 77.6 to 4.5 mg g(-1) when adding this EPS bioflocculant at a dose of 3.2 mg g(-1), which potentially reduces the chemical cost from $282 per metric ton to$71 per metric ton dry biomass that is harvested. To analyze the compositions of this bioflocculant, molecular fractionation was performed. The functional fractions such as protein-like and humic-like organic substances were characterized by fluorescence excitation-emission, followed by polysaccharide analysis. Low-MW (<3 kDa) EPS contributed to the flocculation process more than the large-MW fractions. Low-MW EPS contained higher contents of glucose and mannose in the polysaccharide that influence the interactions of the algae and the alum coagulant. Microalgal-derived bioflocculants may open up new avenue toward the low-cost and sustainable bioflocculation processes for algal and other biomass separation