Featured Anomaly Detection Methods and Applications

Anomaly detection is a fundamental research topic that has been widely investigated. From critical industrial systems, e.g., network intrusion detection systems, to people’s daily activities, e.g., mobile fraud detection, anomaly detection has become the very first vital resort to protect and secure public and personal properties. Although anomaly detection methods have been under consistent development over the years, the explosive growth of data volume and the continued dramatic variation of data patterns pose great challenges on the anomaly detection systems and are fuelling the great demand of introducing more intelligent anomaly detection methods with distinct characteristics to cope with various needs. To this end, this thesis starts with presenting a thorough review of existing anomaly detection strategies and methods. The advantageous and disadvantageous of the strategies and methods are elaborated. Afterward, four distinctive anomaly detection methods, especially for time series, are proposed in this work aiming at resolving specific needs of anomaly detection under different scenarios, e.g., enhanced accuracy, interpretable results, and self-evolving models. Experiments are presented and analysed to offer a better understanding of the performance of the methods and their distinct features. To be more specific, the abstracts of the key contents in this thesis are listed as follows: 1) Support Vector Data Description (SVDD) is investigated as a primary method to fulfill accurate anomaly detection. The applicability of SVDD over noisy time series datasets is carefully examined and it is demonstrated that relaxing the decision boundary of SVDD always results in better accuracy in network time series anomaly detection. Theoretical analysis of the parameter utilised in the model is also presented to ensure the validity of the relaxation of the decision boundary. 2) To support a clear explanation of the detected time series anomalies, i.e., anomaly interpretation, the periodic pattern of time series data is considered as the contextual information to be integrated into SVDD for anomaly detection. The formulation of SVDD with contextual information maintains multiple discriminants which help in distinguishing the root causes of the anomalies. 3) In an attempt to further analyse a dataset for anomaly detection and interpretation, Convex Hull Data Description (CHDD) is developed for realising one-class classification together with data clustering. CHDD approximates the convex hull of a given dataset with the extreme points which constitute a dictionary of data representatives. According to the dictionary, CHDD is capable of representing and clustering all the normal data instances so that anomaly detection is realised with certain interpretation. 4) Besides better anomaly detection accuracy and interpretability, better solutions for anomaly detection over streaming data with evolving patterns are also researched. Under the framework of Reinforcement Learning (RL), a time series anomaly detector that is consistently trained to cope with the evolving patterns is designed. Due to the fact that the anomaly detector is trained with labeled time series, it avoids the cumbersome work of threshold setting and the uncertain definitions of anomalies in time series anomaly detection tasks

Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspective

Predicting molecular properties (e.g., atomization energy) is an essential issue in quantum chemistry, which could speed up much research progress, such as drug designing and substance discovery. Traditional studies based on density functional theory (DFT) in physics are proved to be time-consuming for predicting large number of molecules. Recently, the machine learning methods, which consider much rule-based information, have also shown potentials for this issue. However, the complex inherent quantum interactions of molecules are still largely underexplored by existing solutions. In this paper, we propose a generalizable and transferable Multilevel Graph Convolutional neural Network (MGCN) for molecular property prediction. Specifically, we represent each molecule as a graph to preserve its internal structure. Moreover, the well-designed hierarchical graph neural network directly extracts features from the conformation and spatial information followed by the multilevel interactions. As a consequence, the multilevel overall representations can be utilized to make the prediction. Extensive experiments on both datasets of equilibrium and off-equilibrium molecules demonstrate the effectiveness of our model. Furthermore, the detailed results also prove that MGCN is generalizable and transferable for the prediction.Comment: The 33rd AAAI Conference on Artificial Intelligence (AAAI'2019), Honolulu, USA, 201

Biomechanical analysis of sandwich vertebrae in osteoporotic patients: finite element analysis

ObjectiveThe aim of this study was to investigate the biomechanical stress of sandwich vertebrae (SVs) and common adjacent vertebrae in different degrees of spinal mobility in daily life.Materials and methodsA finite element model of the spinal segment of T10-L2 was developed and validated. Simultaneously, T11 and L1 fractures were simulated, and a 6-ml bone cement was constructed in their center. Under the condition of applying a 500-N axial load to the upper surface of T10 and immobilizing the lower surface of L2, moments were applied to the upper surface of T10, T11, T12, L1, and L2 and divided into five groups: M-T10, M-T11, M-T12, M-L1, and M-L2. The maximum von Mises stress of T10, T12, and L2 in different groups was calculated and analyzed.ResultsThe maximum von Mises stress of T10 in the M-T10 group was 30.68 MPa, 36.13 MPa, 34.27 MPa, 33.43 MPa, 26.86 MPa, and 27.70 MPa greater than the maximum stress value of T10 in the other groups in six directions of load flexion, extension, left and right lateral bending, and left and right rotation, respectively. The T12 stress value in the M-T12 group was 29.62 MPa, 32.63 MPa, 30.03 MPa, 31.25 MPa, 26.38 MPa, and 26.25 MPa greater than the T12 stress value in the other groups in six directions. The maximum stress of L2 in M-T12 in the M-L2 group was 25.48 MPa, 36.38 MPa, 31.99 MPa, 31.07 MPa, 30.36 MPa, and 32.07 MPa, which was greater than the stress value of L2 in the other groups. When the load is on which vertebral body, it is subjected to the greatest stress.ConclusionWe found that SVs did not always experience the highest stress. The most stressed vertebrae vary with the degree of curvature of the spine. Patients should be encouraged to avoid the same spinal curvature posture for a long time in life and work or to wear a spinal brace for protection after surgery, which can avoid long-term overload on a specific spine and disrupt its blood supply, resulting in more severe loss of spinal quality and increasing the possibility of fractures

The expression and role of protein kinase C (PKC) epsilon in clear cell renal cell carcinoma

Protein kinase C epsilon (PKCε), an oncogene overexpressed in several human cancers, is involved in cell proliferation, migration, invasion, and survival. However, its roles in clear cell renal cell carcinoma (RCC) are unclear. This study aimed to investigate the functions of PKCε in RCC, especially in clear cell RCC, to determine the possibility of using it as a therapeutic target. By immunohistochemistry, we found that the expression of PKCε was up-regulated in RCCs and was associated with tumor Fuhrman grade and T stage in clear cell RCCs. Clone formation, wound healing, and Borden assays showed that down-regulating PKCε by RNA interference resulted in inhibition of the growth, migration, and invasion of clear cell RCC cell line 769P and, more importantly, sensitized cells to chemotherapeutic drugs as indicated by enhanced activity of caspase-3 in PKCε siRNA-transfected cells. These results indicate that the overexpression of PKCε is associated with an aggressive phenotype of clear cell RCC and may be a potential therapeutic target for this disease

Genome-wide analysis of circular RNAs in goat skin fibroblast cells in response to Orf virus infection

Orf, caused by Orf virus (ORFV), is a globally distributed zoonotic disease responsible for serious economic losses in the agricultural sector. However, the mechanism underlying ORFV infection remains largely unknown. Circular RNAs (circRNAs), a novel type of endogenous non-coding RNAs, play important roles in various pathological processes but their involvement in ORFV infection and host response is unclear. In the current study, whole transcriptome sequencing and small RNA sequencing were performed in ORFV-infected goat skin fibroblast cells and uninfected cells. A total of 151 circRNAs, 341 messenger RNAs (mRNAs), and 56 microRNAs (miRNAs) were differently expressed following ORFV infection. Four circRNAs: circRNA1001, circRNA1684, circRNA3127 and circRNA7880 were validated by qRT-PCR and Sanger sequencing. Gene ontology (GO) analysis indicated that host genes of differently expressed circRNAs were significantly enriched in regulation of inflammatory response, epithelial structure maintenance, positive regulation of cell migration, positive regulation of ubiquitin-protein transferase activity, regulation of ion transmembrane transport, etc. The constructed circRNA-miRNA-mRNA network suggested that circRNAs may function as miRNA sponges indirectly regulating gene expression following ORFV infection. Our study presented the first comprehensive profiles of circRNAs in response to ORFV infection, thus providing new clues for the mechanisms of interactions between ORFV and the host

Qwen Technical Report

Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to humans. In this work, we introduce Qwen, the first installment of our large language model series. Qwen is a comprehensive language model series that encompasses distinct models with varying parameter counts. It includes Qwen, the base pretrained language models, and Qwen-Chat, the chat models finetuned with human alignment techniques. The base language models consistently demonstrate superior performance across a multitude of downstream tasks, and the chat models, particularly those trained using Reinforcement Learning from Human Feedback (RLHF), are highly competitive. The chat models possess advanced tool-use and planning capabilities for creating agent applications, showcasing impressive performance even when compared to bigger models on complex tasks like utilizing a code interpreter. Furthermore, we have developed coding-specialized models, Code-Qwen and Code-Qwen-Chat, as well as mathematics-focused models, Math-Qwen-Chat, which are built upon base language models. These models demonstrate significantly improved performance in comparison with open-source models, and slightly fall behind the proprietary models.Comment: 59 pages, 5 figure

Effects of Anacetrapib in Patients with Atherosclerotic Vascular Disease

BACKGROUND: Patients with atherosclerotic vascular disease remain at high risk for cardiovascular events despite effective statin-based treatment of low-density lipoprotein (LDL) cholesterol levels. The inhibition of cholesteryl ester transfer protein (CETP) by anacetrapib reduces LDL cholesterol levels and increases high-density lipoprotein (HDL) cholesterol levels. However, trials of other CETP inhibitors have shown neutral or adverse effects on cardiovascular outcomes. METHODS: We conducted a randomized, double-blind, placebo-controlled trial involving 30,449 adults with atherosclerotic vascular disease who were receiving intensive atorvastatin therapy and who had a mean LDL cholesterol level of 61 mg per deciliter (1.58 mmol per liter), a mean non-HDL cholesterol level of 92 mg per deciliter (2.38 mmol per liter), and a mean HDL cholesterol level of 40 mg per deciliter (1.03 mmol per liter). The patients were assigned to receive either 100 mg of anacetrapib once daily (15,225 patients) or matching placebo (15,224 patients). The primary outcome was the first major coronary event, a composite of coronary death, myocardial infarction, or coronary revascularization. RESULTS: During the median follow-up period of 4.1 years, the primary outcome occurred in significantly fewer patients in the anacetrapib group than in the placebo group (1640 of 15,225 patients [10.8%] vs. 1803 of 15,224 patients [11.8%]; rate ratio, 0.91; 95% confidence interval, 0.85 to 0.97; P=0.004). The relative difference in risk was similar across multiple prespecified subgroups. At the trial midpoint, the mean level of HDL cholesterol was higher by 43 mg per deciliter (1.12 mmol per liter) in the anacetrapib group than in the placebo group (a relative difference of 104%), and the mean level of non-HDL cholesterol was lower by 17 mg per deciliter (0.44 mmol per liter), a relative difference of -18%. There were no significant between-group differences in the risk of death, cancer, or other serious adverse events. CONCLUSIONS: Among patients with atherosclerotic vascular disease who were receiving intensive statin therapy, the use of anacetrapib resulted in a lower incidence of major coronary events than the use of placebo. (Funded by Merck and others; Current Controlled Trials number, ISRCTN48678192 ; ClinicalTrials.gov number, NCT01252953 ; and EudraCT number, 2010-023467-18 .)

Optimization on segmentation spraying for cooling a moving source at high temperature in a confined space

Heavy trucks, train carriages, and other moving vehicles with heating sources, are often loaded into a confined space for parking and maintenance. When the heating source from the vehicle is at high temperature, spray cooling using nozzles can be applied for at least two purposes. First, to further decrease the temperature of the surrounding areas. Second, to flush the surfaces of the vehicle as a primary cleansing method. To reduce water consumption, the number and positions of the running nozzles along the way need to be adjusted and optimized according to the position of the moving vehicle. To achieve that, the optimization of a segmented control method for the nozzles is discussed using both theoretical and computational fluid dynamics (CFD) methods in this paper. 120 ~ 240 nozzles in total were uniformly and linearly distributed on the ceiling of a 120m-long narrow space. The space between two adjacent nozzles are set at 0.5m, 0.7m and 1.0m, respectively. All the nozzles are divided into one to ten groups for segmentation control. One 35m-long vehicle with a heating source at over 800 K was moving at 0.05 m/s to 0.20 m/s. The results showed that, first, for one spraying group, at least 39 running nozzles were needed to minimize the areas of interior structure at temperature > 350K. Second, the water consumption can be reduced dramatically by increasing the number of groups. However, when dividing into more than six groups, the capacity of water saving is no longer significant. Third, when the vehicle is entering, multiple- group of running nozzles are needed to overcome the sudden heating source at high temperature