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

CDCA2 Inhibits Apoptosis and Promotes Cell Proliferation in Prostate Cancer and Is Directly Regulated by HIF-1α Pathway.

Prostate cancer (PCa) is a major serious malignant tumor and is commonly diagnosed in older men. Identification of novel cancer-related genes in PCa is important for understanding its tumorigenesis mechanism and developing new therapies against PCa. Here, we used RNA sequencing to identify the specific genes, which are upregulated in PCa cell lines and tissues. The cell division cycle associated protein (CDCA) family, which plays a critical role in cell division and proliferation, is upregulated in the PCa cell lines of our RNA-Sequencing data. Moreover, we found that CDCA2 is overexpressed, and its protein level positively correlates with its histological grade, clinical stage, and Gleason Score. CDCA2 was further found to be upregulated and correlated with poor prognosis and patient survival in multiple cancer types in The Cancer Genome Atlas (TCGA) dataset. The functional study suggests that inhibition of CDCA2 will lead to apoptosis and lower proliferation in vitro. Silencing of CDCA2 also repressed tumor growth in vivo. Loss of CDCA2 affects several oncogenic pathways, including MAPK signaling. In addition, we further demonstrated that CDCA2 was induced in hypoxia and directly regulated by the HIF-1α/Smad3 complex. Thus, our data indicate that CDCA2 could act as an oncogene and is regulated by hypoxia and the HIF-1αpathway. CDCA2 may be a useful prognostic biomarker and potential therapeutic target for PCa

Review of Recently Progress on Neural Electronics and Memcomputing Applications in Intrinsic SiOx-Based Resistive Switching Memory

In this chapter, we focus on the recent process on memcomputing (memristor + computing) in intrinsic SiOx-based resistive switching memory (ReRAM or called memristor). In the first section of the chapter, we investigate neuromorphic computing by mimicking the synaptic behaviors in integrating one-diode and one-resistive switching element (1D-1R) architecture. The power consumption can be minimized further in synaptic functions because sneak-path current has been suppressed and the capability for spike-induced synaptic behaviors has been demonstrated, representing critical milestones and achievements for the application of conventional SiOx-based materials in future advanced neuromorphic computing. In the next section of chapter, we will discuss an implementation technique of implication operations for logic-in-memory computation by using a SiOx-based memristor. The implication function and its truth table have been implemented with the unipolar or nonpolar operation scheme. Furthermore, a circuit with 1D-1R architecture with a 4 × 4 crossbar array has been demonstrated, which realizes the functionality of a one-bit full adder as same as CMOS logic circuits with lower design area requirement. This chapter suggests that a simple, robust approach to realize memcomputing chips is quite compatible with large-scale CMOS manufacturing technology by using an intrinsic SiOx-based memristor

High-throughput Sequencing Analysis of Diversity and Spatial Heterogeneity of Fungal Community in Pit Muds of Different Ages for Baijiu Production

The fungal community structure, the relationship between fungal flora and physicochemical factors, and the prediction of fungal function in pit muds from different spatial positions of 10- and 50-year-old cellars at Jinhui liquor Co. Ltd. were studied by using Illumina NovaSeq high-throughput sequencing, redundancy analysis and Fungi Functional Guild (FUNGuild). The results showed that the fungal diversity and richness of the 10-year-old pit mud decreased with increasing depth; the fungal diversity of the 50-year-old pit mud showed an overall increasing trend, while the fungal richness initially decrease and then increased. Moreover, for the 10-year-old pit, the fungal diversity and richness of the upper layer of the pit wall were significantly higher than those of the other positions (P < 0.05), while for the 50-year-old cellar, the fungal diversity and richness of the bottom layer were significantly higher than those of the other locations (P < 0.05). The fungal diversity and richness were significantly higher in the wall of the 10-year-old cellar than the 50-year-old cellar (P < 0.05), but were significantly higher in the bottom of the 50-year-old cellar than the 10-year-old cellar (P < 0.05). A total of 21 fungal phyla and 520 genera were detected in all pit mud samples, the relative abundance of four dominant phyla (Ascomycota, Basidiomycota, Mortierellomycota and Rozellomycota) and most dominant genera such as Aspergillus and Kazachstania showed significant changes among pit ages and spatial locations (P < 0.05). Fusarium, Aspergillus, Saccharomyces and Monascus were positively correlated with the contents of water, humus, K+ and Ca2+, while Cladosporium and Vishniacozyma were positively correlated with pH. Seven nutritional modes of fungi were observed, mainly including saprophytic and pathological-saprophytic-symbiotic nutritional modes, and four single and seven mixed functional groups were determined. This study provides a theoretical basis for clarifying the structure and spatial distribution of fungal community in Jinhui Baijiu pit mud

Modeling visual rhetorics for persuasive media through self-supervised learning

This dissertation addresses the challenging task of modeling and interpreting visual rhetorics in persuasive media using computational models. The focus is on self-supervised learning methods that leverage general data without specific annotations related to persuasion. The research begins by modeling three fundamental modes of persuasion (ethos, pathos, logos) in multimodal media, incorporating both text and images. Traditional visual recognition models struggle to predict the applied persuasion modes in images beyond their literal content. Self-supervised learning methods prove to be more effective in modeling these modes. The detection of persuasive atypicality in ad images and the interpretation of symbolism are explored as common visual rhetorics for capturing viewers’ attention and creating lasting impressions. The hypothesis that atypicality detection relies on contextual compatibility and understanding common-sense spatial relations of objects is validated through the development of self-supervised attention-based techniques. To assess the feasibility of automatically interpreting symbolism, an evaluative framework is developed. It compares the performance of language models and multi-modality models pretrained on large-scale web data. Furthermore, a re-ranking strategy is introduced to mitigate pre-training bias and significantly enhance model performance, bringing it on par with human performance in certain cases. Overall, this dissertation presents a range of techniques that enable computational intelligence to detect, understand, and explain the underlying messages in rhetorical media. These methods leverage self-supervised learning and process large volumes of data, providing unprecedented depth and insight into the analysis of persuasive visual communication

Integrated Model of Joint Residence-Workplace Location Choice and Commute Behavior Using Latent Class and Mixed Logit Methods

With the rapid development of urbanization and motorization, urban commute trips are becoming increasingly serious due to the unbalanced distribution of residence and workplace land-use types in most Chinese cities. To explore the inherent interrelations among residence location, workplace, and commute trip, an integrated model framework of joint residence-workplace location choice and commute behavior is put forward based on the personal trip survey data of Beijing in 2005. First, to extract households&apos; different choice characteristics, this paper presents a latent class model, clusters all households into several groups, and analyzes the conditional probability of each group. Second, the paper integrates the residence location and workplace together as the joint choice alternative, employs the socioeconomic factors, individual attributes, household attributes, and trip characteristics as explanatory variables, and formulates the joint residence-workplace location choice model using mixed logit method. Estimations of the latent class model show that four latent groups fit the data best. Further results of the joint residence-workplace location choice model indicate that there exist significantly different choice characteristics in each latent group. Generally, the integrated model framework outperforms traditional location choice methods

Integrated Model of Joint Residence-Workplace Location Choice and Commute Behavior Using Latent Class and Mixed Logit Methods

With the rapid development of urbanization and motorization, urban commute trips are becoming increasingly serious due to the unbalanced distribution of residence and workplace land-use types in most Chinese cities. To explore the inherent interrelations among residence location, workplace, and commute trip, an integrated model framework of joint residence-workplace location choice and commute behavior is put forward based on the personal trip survey data of Beijing in 2005. First, to extract households’ different choice characteristics, this paper presents a latent class model, clusters all households into several groups, and analyzes the conditional probability of each group. Second, the paper integrates the residence location and workplace together as the joint choice alternative, employs the socioeconomic factors, individual attributes, household attributes, and trip characteristics as explanatory variables, and formulates the joint residence-workplace location choice model using mixed logit method. Estimations of the latent class model show that four latent groups fit the data best. Further results of the joint residence-workplace location choice model indicate that there exist significantly different choice characteristics in each latent group. Generally, the integrated model framework outperforms traditional location choice methods

How do we address each other in medicine?

No abstract available