A Genetic Algorithm Based Multilevel Association Rules Mining for Big Datasets

Multilevel association rules mining is an important domain to discover interesting relations between data elements with multiple levels abstractions. Most of the existing algorithms toward this issue are based on exhausting search methods such as Apriori, and FP-growth. However, when they are applied in the big data applications, those methods will suffer for extreme computational cost in searching association rules. To expedite multilevel association rules searching and avoid the excessive computation, in this paper, we proposed a novel genetic-based method with three key innovations. First, we use the category tree to describe the multilevel application data sets as the domain knowledge. Then, we put forward a special tree encoding schema based on the category tree to build the heuristic multilevel association mining algorithm. As the last part of our design, we proposed the genetic algorithm based on the tree encoding schema that will greatly reduce the association rule search space. The method is especially useful in mining multilevel association rules in big data related applications. We test the proposed method with some big datasets, and the experimental results demonstrate the effectiveness and efficiency of the proposed method in processing big data. Moreover, our results also manifest that the algorithm is fast convergent with a limited termination threshold

Sequencing and Genomic Diversity Analysis of IncHI5 Plasmids

IncHI plasmids could be divided into five different subgroups IncHI1–5. In this study, the complete nucleotide sequences of seven blaIMP- or blaVIM-carrying IncHI5 plasmids from Klebsiella pneumoniae, K. quasipneumoniae, and K. variicola were determined and compared in detail with all the other four available sequenced IncHI5 plasmids. These plasmids carried conserved IncHI5 backbones composed of repHI5B and a repFIB-like gene (replication), parABC (partition), and tra1 (conjugal transfer). Integration of a number of accessory modules, through horizontal gene transfer, at various sites of IncHI5 backbones resulted in various deletions of surrounding backbone regions and thus considerable diversification of IncHI5 backbones. Among the accessory modules were three kinds of resistance accessory modules, namely Tn10 and two antibiotic resistance islands designated ARI-A and ARI-B. These two islands, inserted at two different fixed sites (one island was at one site and the other was at a different site) of IncHI5 backbones, were derived from the prototype Tn3-family transposons Tn1696 and Tn6535, respectively, and could be further discriminated as various intact transposons and transposon-like structures. The ARI-A or ARI-B islands from different IncHI5 plasmids carried distinct profiles of antimicrobial resistance markers and associated mobile elements, and complex events of transposition and homologous recombination accounted for assembly of these islands. The carbapenemase genes blaIMP-4, blaIMP-38 and blaVIM-1 were identified within various class 1 integrons from ARI-A or ARI-B of the seven plasmids sequenced in this study. Data presented here would provide a deeper insight into diversification and evolution history of IncHI5 plasmids

A Semi-supervised Graph Attentive Network for Financial Fraud Detection

With the rapid growth of financial services, fraud detection has been a very important problem to guarantee a healthy environment for both users and providers. Conventional solutions for fraud detection mainly use some rule-based methods or distract some features manually to perform prediction. However, in financial services, users have rich interactions and they themselves always show multifaceted information. These data form a large multiview network, which is not fully exploited by conventional methods. Additionally, among the network, only very few of the users are labelled, which also poses a great challenge for only utilizing labeled data to achieve a satisfied performance on fraud detection. To address the problem, we expand the labeled data through their social relations to get the unlabeled data and propose a semi-supervised attentive graph neural network, namedSemiGNN to utilize the multi-view labeled and unlabeled data for fraud detection. Moreover, we propose a hierarchical attention mechanism to better correlate different neighbors and different views. Simultaneously, the attention mechanism can make the model interpretable and tell what are the important factors for the fraud and why the users are predicted as fraud. Experimentally, we conduct the prediction task on the users of Alipay, one of the largest third-party online and offline cashless payment platform serving more than 4 hundreds of million users in China. By utilizing the social relations and the user attributes, our method can achieve a better accuracy compared with the state-of-the-art methods on two tasks. Moreover, the interpretable results also give interesting intuitions regarding the tasks.Comment: icd

Numerical observation of non-axisymmetric vesicles in fluid membranes

By means of Surface Evolver (Exp. Math,1,141 1992), a software package of brute-force energy minimization over a triangulated surface developed by the geometry center of University of Minnesota, we have numerically searched the non-axisymmetric shapes under the Helfrich spontaneous curvature (SC) energy model. We show for the first time there are abundant mechanically stable non-axisymmetric vesicles in SC model, including regular ones with intrinsic geometric symmetry and complex irregular ones. We report in this paper several interesting shapes including a corniculate shape with six corns, a quadri-concave shape, a shape resembling sickle cells, and a shape resembling acanthocytes. As far as we know, these shapes have not been theoretically obtained by any curvature model before. In addition, the role of the spontaneous curvature in the formation of irregular crenated vesicles has been studied. The results shows a positive spontaneous curvature may be a necessary condition to keep an irregular crenated shape being mechanically stable.Comment: RevTex, 14 pages. A hard copy of 8 figures is available on reques

Analisis Portofolio Optimal Dengan Single Index Model Untuk Meminimumkan Risiko Bagi Investor Di Bursa Efek Indonesia (Studi Pada Saham Indeks Kompas 100 Periode Februari 2010-juli 2014)

Investments can be made in the capital market, capital market instruments which are mostly attractive for investors is stock. Stock provides a return in the form of capital gains and dividends yield, not only noticing the return, investors need to pay attention to the investments risk. Unsystematis risk can be minimized by forming the optimal portfolio using one of the methods that is single index model. Study purpose is to knowing the stocks forming the optimal portfolio, the proportion of funds allocated to each stocks, the level of expectation return and risk.The method used in this research is descriptive research method with a quantitative approach. The samples used were 46 stocks in Kompas 100 Index, which meets the criteria for sampling. The results showed that 12 stocks of forming optimal portfolio, the stocks of which are UNVR, TRAM, MNCN, BHIT, JSMR, BMTR, GJTL, KLBF, AALI, CPIN, AKRA, and ASRI. Stock with highest proportion of funds is TRAM (23,52%), stock with lowest proportion of funds is AALI (0,62%). Portfolio which are formed will give return expectations by 3,05477% and carry the risk for about 0,1228%

Bones on fire: illuminating osteomyelitis through the radiant lens of 18F-FDG PET/CT

Osteomyelitis is an inflammatory process that is caused by an infecting microorganism and leads to progressive bone destruction and loss. Osteomyelitis can occur at any age and can involve any bone. The infection can be limited to a single portion of the bone or can involve several regions, such as marrow, cortex, periosteum, and the surrounding soft tissue. Early and accurate diagnosis plays a crucial role in reducing unnecessary treatment measures, improving the patient’s prognosis, and minimizing time and financial costs. In recent years, the use of functional metabolic imaging has become increasingly widespread. Among them, 18F-FDG PET/CT has emerged as a cutting-edge imaging modality that combines anatomical and functional metabolic information. It has seen rapid development in the field of infectious diseases. 18F-FDG PET/CT has been demonstrated to yield acceptable diagnostic accuracy in a number of infectious and inflammatory diseases. This review aims to provide information about the 18F-FDGPET/CT in the use of chronic osteomyelitis,osteomyelitis secondary to a contiguous focus of infection and osteomyelitis associated with peripheral vascular disease

Hydrogen Storage Performance of Mg/MgH₂ and Its Improvement Measures: Research Progress and Trends

Due to its high hydrogen storage efficiency and safety, Mg/MgH2 stands out from many solid hydrogen storage materials and is considered as one of the most promising solid hydrogen storage materials. However, thermodynamic/kinetic deficiencies of the performance of Mg/MgH2 limit its practical applications for which a series of improvements have been carried out by scholars. This paper summarizes, analyzes and organizes the current research status of the hydrogen storage performance of Mg/MgH2 and its improvement measures, discusses in detail the hot studies on improving the hydrogen storage performance of Mg/MgH2 (improvement measures, such as alloying treatment, nano-treatment and catalyst doping), and focuses on the discussion and in-depth analysis of the catalytic effects and mechanisms of various metal-based catalysts on the kinetic and cyclic performance of Mg/MgH2. Finally, the challenges and opportunities faced by Mg/MgH2 are discussed, and strategies to improve its hydrogen storage performance are proposed to provide ideas and help for the next research in Mg/MgH2 and the whole field of hydrogen storage