Try with Simpler -- An Evaluation of Improved Principal Component Analysis in Log-based Anomaly Detection

The rapid growth of deep learning (DL) has spurred interest in enhancing log-based anomaly detection. This approach aims to extract meaning from log events (log message templates) and develop advanced DL models for anomaly detection. However, these DL methods face challenges like heavy reliance on training data, labels, and computational resources due to model complexity. In contrast, traditional machine learning and data mining techniques are less data-dependent and more efficient but less effective than DL. To make log-based anomaly detection more practical, the goal is to enhance traditional techniques to match DL's effectiveness. Previous research in a different domain (linking questions on Stack Overflow) suggests that optimized traditional techniques can rival state-of-the-art DL methods. Drawing inspiration from this concept, we conducted an empirical study. We optimized the unsupervised PCA (Principal Component Analysis), a traditional technique, by incorporating lightweight semantic-based log representation. This addresses the issue of unseen log events in training data, enhancing log representation. Our study compared seven log-based anomaly detection methods, including four DL-based, two traditional, and the optimized PCA technique, using public and industrial datasets. Results indicate that the optimized unsupervised PCA technique achieves similar effectiveness to advanced supervised/semi-supervised DL methods while being more stable with limited training data and resource-efficient. This demonstrates the adaptability and strength of traditional techniques through small yet impactful adaptations

Serum Metabolomic Profiling of Piglets Infected with Virulent Classical Swine Fever Virus

Citation: Gong, W. J., Jia, J. J., Zhang, B. K., Mi, S. J., Zhang, L., Xie, X. M., . . . Tu, C. C. (2017). Serum Metabolomic Profiling of Piglets Infected with Virulent Classical Swine Fever Virus. Frontiers in Microbiology, 8, 14. doi:10.3389/fmicb.2017.00731Classical swine fever (CSF) is a highly contagious swine infectious disease and causes significant economic losses for the pig industry worldwide. The objective of this study was to determine whether small molecule metabolites contribute to the pathogenesis of CSF. Birefly, serum metabolomics of CSFV Shimen strain-infected piglets were analyzed by ultraperformance liquid chromatography/electrospray ionization time-of-flight mass spectrometry (UPLC/ESI-Q-TOF/MS) in combination with multivariate statistical analysis. In CSFV-infected piglets at days 3 and 7 post-infection changes were found in metabolites associated with several key metabolic pathways, including tryptophan catabolism and the kynurenine pathway, phenylalanine metabolism, fatty acid and lipid metabolism, the tricarboxylic acid and urea cycles, branched-chain amino acid metabolism, and nucleotide metabolism. Several pathways involved in energy metabolism including fatty acid biosynthesis and beta-oxidation, branched-chain amino acid metabolism, and the tricarboxylic acid cycle were significantly inhibited. Changes were also observed in several metabolites exclusively associated with gut microbiota. The metabolomic profiles indicate that CSFV-host gut microbiome interactions play a role in the development of CSF

MAP: Multimodal Uncertainty-Aware Vision-Language Pre-training Model

Multimodal semantic understanding often has to deal with uncertainty, which means the obtained messages tend to refer to multiple targets. Such uncertainty is problematic for our interpretation, including inter- and intra-modal uncertainty. Little effort has studied the modeling of this uncertainty, particularly in pre-training on unlabeled datasets and fine-tuning in task-specific downstream datasets. In this paper, we project the representations of all modalities as probabilistic distributions via a Probability Distribution Encoder (PDE) by utilizing sequence-level interactions. Compared to the existing deterministic methods, such uncertainty modeling can convey richer multimodal semantic information and more complex relationships. Furthermore, we integrate uncertainty modeling with popular pre-training frameworks and propose suitable pre-training tasks: Distribution-based Vision-Language Contrastive learning (D-VLC), Distribution-based Masked Language Modeling (D-MLM), and Distribution-based Image-Text Matching (D-ITM). The fine-tuned models are applied to challenging downstream tasks, including image-text retrieval, visual question answering, visual reasoning, and visual entailment, and achieve state-of-the-art results.Comment: CVPR 2023 accep

Structural Insights into Recognition of MDC1 by TopBP1 in DNA Replication Checkpoint Control

SummaryActivation of the DNA replication checkpoint by the ATR kinase requires protein interactions mediated by the ATR-activating protein, TopBP1. Accumulation of TopBP1 at stalled replication forks requires the interaction of TopBP1 BRCT5 with the phosphorylated SDT repeats of the adaptor protein MDC1. Here, we present the X-ray crystal structures of the tandem BRCT4/5 domains of TopBP1 free and in complex with a MDC1 consensus pSDpT phosphopeptide. TopBP1 BRCT4/5 adopts a variant BRCT-BRCT packing interface and recognizes its target peptide in a manner distinct from that observed in previous tandem BRCT- peptide structures. The phosphate-binding pocket and positively charged residues in a variant loop in BRCT5 present an extended binding surface for the negatively charged MDC1 phosphopeptide. Mutations in this surface reduce binding affinity and recruitment of TopBP1 to γH2AX foci in cells. These studies reveal a different mode of phosphopeptide binding by BRCT domains in the DNA damage response

Selective ion removal by capacitive deionization (CDI)-based technologies

Severe freshwater shortages and global pollution make selective removal of target ions from solutions of great significance for water purification and resource recovery. Capacitive deionization (CDI) removes charged ions and molecules from water by applying a low applied electric field across the electrodes and has received much attention due to its lower energy consumption and sustainability. Its application field has been expanding in the past few years. In this paper, we report an overview of the current status of selective ion removal in CDI. This paper also discusses the prospects of selective CDI, including desalination, water softening, heavy metal removal and recovery, nutrient removal, and other common ion removal techniques. The insights from this review will inform the implementation of CDI technology

Integrating digital twin technologies into the group design project for the Advanced Air Mobility Systems MSc course

This study aims to develop the content and material for the Group Design Project (GDP) with Digital Twin (DT) technologies, aligned with Future Flight Challenge (FFC) project deliveries involving Cranfield, the emerging Research and Development (R&D) capacities, and the increasing demands for talent and workforce from the industry. The GDP delivery and learning approach, structured as a five-phase process - project and technical management, Concept of Operations (ConOps) and requirements definition, system development, case study and evaluation, and final results - is stated in the paper. This proposed approach has been evaluated with the AAMS 23/24 academic year MSc GDP.2nd IFAC Workshop on Aerospace Control EducationIFAC-PapersOnLin

Visible-Light Degradation of Dyes and Phenols over Mesoporous Titania Prepared by Using Anthocyanin from Red Radish as Template

Heterogeneous photocatalysis is able to operate effectively to eliminate organic compounds from wastewater in the presence of semiconductor photocatalyst and a light source. Although photosensitization of titania by organic dyes is one of the conventional ways for visible-light utilization of titania, previous studies have not yet addressed the use of natural food coloring agents as templates in the synthesis of mesostructured materials, let alone the simultaneous achievement of highly crystalline mesoscopic framework and visible-light photocatalytic activity. In this work, anthocyanin, a natural pigment from red radish was directly used as template in synthesis of highly crystalline mesoporous titania. The synthesized mesoporous titania samples were characterized by a combination of various physicochemical techniques, such as XRD, SEM, HRTEM, nitrogen adsorption/desorption, and diffuse reflectance UV-Vis. The prepared mesoporous titania photocatalyst exhibited significant activity under visible-light irradiation for the degradation of dyes and phenols due to its red shift of band-gap-absorption onset and visible-light response as a result of the incorporation of surface carbon species