Graph Neural Network-Based Anomaly Detection in Multivariate Time Series

Given high-dimensional time series data (e.g., sensor data), how can we detect anomalous events, such as system faults and attacks? More challengingly, how can we do this in a way that captures complex inter-sensor relationships, and detects and explains anomalies which deviate from these relationships? Recently, deep learning approaches have enabled improvements in anomaly detection in high-dimensional datasets; however, existing methods do not explicitly learn the structure of existing relationships between variables, or use them to predict the expected behavior of time series. Our approach combines a structure learning approach with graph neural networks, additionally using attention weights to provide explainability for the detected anomalies. Experiments on two real-world sensor datasets with ground truth anomalies show that our method detects anomalies more accurately than baseline approaches, accurately captures correlations between sensors, and allows users to deduce the root cause of a detected anomaly.Comment: Accepted at AAAI Conference on Artificial Intelligence (AAAI), 202

Great Models Think Alike: Improving Model Reliability via Inter-Model Latent Agreement

Reliable application of machine learning is of primary importance to the practical deployment of deep learning methods. A fundamental challenge is that models are often unreliable due to overconfidence. In this paper, we estimate a model's reliability by measuring \emph{the agreement between its latent space, and the latent space of a foundation model}. However, it is challenging to measure the agreement between two different latent spaces due to their incoherence, \eg, arbitrary rotations and different dimensionality. To overcome this incoherence issue, we design a \emph{neighborhood agreement measure} between latent spaces and find that this agreement is surprisingly well-correlated with the reliability of a model's predictions. Further, we show that fusing neighborhood agreement into a model's predictive confidence in a post-hoc way significantly improves its reliability. Theoretical analysis and extensive experiments on failure detection across various datasets verify the effectiveness of our method on both in-distribution and out-of-distribution settings.Comment: ICML 202

Prompt-and-Align: Prompt-Based Social Alignment for Few-Shot Fake News Detection

Despite considerable advances in automated fake news detection, due to the timely nature of news, it remains a critical open question how to effectively predict the veracity of news articles based on limited fact-checks. Existing approaches typically follow a "Train-from-Scratch" paradigm, which is fundamentally bounded by the availability of large-scale annotated data. While expressive pre-trained language models (PLMs) have been adapted in a "Pre-Train-and-Fine-Tune" manner, the inconsistency between pre-training and downstream objectives also requires costly task-specific supervision. In this paper, we propose "Prompt-and-Align" (P&A), a novel prompt-based paradigm for few-shot fake news detection that jointly leverages the pre-trained knowledge in PLMs and the social context topology. Our approach mitigates label scarcity by wrapping the news article in a task-related textual prompt, which is then processed by the PLM to directly elicit task-specific knowledge. To supplement the PLM with social context without inducing additional training overheads, motivated by empirical observation on user veracity consistency (i.e., social users tend to consume news of the same veracity type), we further construct a news proximity graph among news articles to capture the veracity-consistent signals in shared readerships, and align the prompting predictions along the graph edges in a confidence-informed manner. Extensive experiments on three real-world benchmarks demonstrate that P&A sets new states-of-the-art for few-shot fake news detection performance by significant margins.Comment: Accepted to CIKM 2023 (Full Paper

The impact of type 2 diabetes and its management on the prognosis of patients with severe COVID‐19

Background Although type 2 diabetes mellitus (T2DM) patients with coronavirus disease 2019 (COVID‐19) develop a more severe condition compared to those without diabetes, the mechanisms for this are unknown. Moreover, the impact of treatment with antihyperglycemic drugs and glucocorticoids is unclear. Methods From 1584 COVID‐19 patients, 364 severe/critical COVID‐19 patients with clinical outcome were enrolled for the final analysis, and patients without preexisting T2DM but elevated glucose levels were excluded. Epidemiological data were obtained and clinical status evaluation carried out to assess the impact of T2DM and its management on clinical outcomes. Results Of 364 enrolled severe COVID‐19 inpatients, 114 (31.3%) had a history of T2DM. Twenty‐seven (23.7%) T2DM patients died, who had more severe inflammation, coagulation activation, myocardia injury, hepatic injury, and kidney injury compared with non‐DM patients. In severe COVID‐19 patients with T2DM, we demonstrated a higher risk of all‐cause fatality with glucocorticoid treatment (adjusted hazard ratio [HR], 3.61; 95% CI, 1.14‐11.46; P = .029) and severe hyperglycemia (fasting plasma glucose ≥11.1 mmol/L; adjusted HR, 11.86; 95% CI, 1.21‐116.44; P = .034). Conclusions T2DM status aggravated the clinical condition of COVID‐19 patients and increased their critical illness risk. Poor fasting blood glucose (≥ 11.1 mmol/L) and glucocorticoid treatment are associated with poor prognosis for T2DM patients with severe COVID‐19

A reference-grade wild soybean genome

Wild relatives of crop plants are invaluable germplasm for genetic improvement. Here, Xie et al. report a reference-grade wild soybean genome and show that it can be used to identify structural variation and refine quantitative trait loci

A reference-grade wild soybean genome

Efficient crop improvement depends on the application of accurate genetic information contained in diverse germplasm resources. Here we report a reference-grade genome of wild soybean accession W05, with a final assembled genome size of 1013.2 Mb and a contig N50 of 3.3 Mb. The analytical power of the W05 genome is demonstrated by several examples. First, we identify an inversion at the locus determining seed coat color during domestication. Second, a translocation event between chromosomes 11 and 13 of some genotypes is shown to interfere with the assignment of QTLs. Third, we find a region containing copy number variations of the Kunitz trypsin inhibitor (KTI) genes. Such findings illustrate the power of this assembly in the analysis of large structural variations in soybean germplasm collections. The wild soybean genome assembly has wide applications in comparative genomic and evolutionary studies, as well as in crop breeding and improvement programs

Review of Research on the Properties and Resource Utilization of Coal Gangue Concrete

Coal gangue is a by-product of the production of coal-associated minerals, and belongs to bulk solid waste material. coal gangue concrete is an important practice for its resource utilization. Carbon neutrality and carbon peaks require all industries to make corresponding plans for the sustainable development of green ecology. In terms of resource utilization of building materials, coal gangue was used as aggregates to develop high-performance concrete, which started a new material innovation in the construction and civil engineering industries. At the same time, it can accelerate the process of carbon neutralization as the backbone of the indispensable treatment of low-carbon resources. Scholars at home and abroad have done a lot of theoretical and experimental research on the mechanical properties and engineering application of coal gangue concrete, and have made significant progress in improving the performance of coal gangue concrete, engineering practical application and production technology innovation, and established a relatively complete research system. Based on the differences in chemical and physical properties of coal gangue in different regions, this paper elaborates on the mechanical and durability properties of coal gangue concrete prepared with different aggregates, analyzes the feasibility and current research limitations of coal gangue application in civil engineering, and looks forward to the resource utilization prospects of coal gangue as aggregate to prepare new concrete in road engineering and building structures, provide a new perspective and reference for the large-scale recycling and utilization of coal gangue and further research on coal gangue concrete