Finite-dimensional subalgebras of the Virasoro algebra

We determine all two-dimensional Lie subalgebras of the centreless Virasoro algebra and complete the characterization of all finite dimensional Lie subalgebras of the complex Virasoro algebra

Color Filtering Localization for Three-Dimensional Underwater Acoustic Sensor Networks

Accurate localization for mobile nodes has been an important and fundamental problem in underwater acoustic sensor networks (UASNs). The detection information returned from a mobile node is meaningful only if its location is known. In this paper, we propose two localization algorithms based on color filtering technology called PCFL and ACFL. PCFL and ACFL aim at collaboratively accomplishing accurate localization of underwater mobile nodes with minimum energy expenditure. They both adopt the overlapping signal region of task anchors which can communicate with the mobile node directly as the current sampling area. PCFL employs the projected distances between each of the task projections and the mobile node, while ACFL adopts the direct distance between each of the task anchors and the mobile node. Also the proportion factor of distance is proposed to weight the RGB values. By comparing the nearness degrees of the RGB sequences between the samples and the mobile node, samples can be filtered out. And the normalized nearness degrees are considered as the weighted standards to calculate coordinates of the mobile nodes. The simulation results show that the proposed methods have excellent localization performance and can timely localize the mobile node. The average localization error of PCFL can decline by about 30.4% than the AFLA method.Comment: 18 pages, 11 figures, 2 table

Evidence-Efficient Affinity Propagation Scheme for Virtual Machine Placement in Data Center

In cloud data center, without efficient virtual machine placement, the overload of any types of resources on physical machines (PM) can easily cause the waste of other types of resources, and frequent costly virtual machine (VM) migration, which further negatively affects quality of service (QoS). To address this problem, in this paper we propose an evidence-efficient affinity propagation scheme for VM placement (EEAP-VMP), which is capable of balancing the workload across various types of resources on the running PMs. Our approach models the problem of searching the desirable destination hosts for the liveVMmigration as the propagation of responsibility and availability. The sum of responsibility and availability represent the accumulated evidence for the selection of candidate destination hosts for the VMs to be migrated. Further, in combination with the presented selection criteria for destination hosts. Extensive experiments are conducted to compare our EEAP-VMP method with the previousVMplacement methods. The experimental results demonstrate that the EEAP-VMP method is highly effective on reducing VM migrations and energy consumption of data centers and in balancing the workload of PMs

Matrine inhibits hepatocellular carcinoma cell malignancy through the circ_0013290/miR-139-5p/MMP16 pathway

Background. Previous studies have shown the anticancer effect of Matrine on hepatocellular carcinoma (HCC); however, the underlying mechanism is still indistinct. Methods. The expression of circular RNA_0013290 (circ_0013290), microRNA-139-5p (miR-139-5p), matrix metallopeptidase 16 (MMP16), CyclinD1 and N-cadherin was analyzed by quantitative real-time polymerase chain reaction, Western blotting or immuno-histochemistry assay. Cell viability, proliferation, apoptosis, invasion and tube formation were analyzed by cell counting kit-8, 5-Ethynyl-2’-deoxyuridine, flow cytometry analysis, transwell invasion and tube formation assays, respectively. The associations among circ_0013290, miR-139-5p and MMP16 were predicted by starbase online database, and identified by dual-luciferase reporter and RNA pull-down assays. A xenograft mouse model assay was conducted to disclose the effects of circ_0013290 and Matrine on tumor tumorigenesis in vivo. Results. Circ_0013290 and MMP16 expression were significantly upregulated, while miR-139-5p was downregulated in HCC tissues and cells compared with the matched normal liver tissues and cells. Matrine treatment inhibited HCC cell proliferation, invasion and tube formation but induced cell apoptosis, accompanied by the decrease of CyclinD1 and N-cadherin expression; however, these effects were counteracted when circ_0013290 expression was increased. MiR-139-5p depletion or MMP16 introduction relieved Matrine-induced effects in HCC cells. The regulation of circ_0013290 toward HCC cell processes involved MMP16. With respect to the mechanism, circ_0013290 acted as a miR-139-5p sponge, and miR-139-5p targeted MMP16 in HCC cells. Besides, circ_0013290 regulated MMP16 expression through miR-139-5p. Further, circ_0013290 depletion enhanced the inhibitory effects of Matrine on tumor tumorigenesis. Conclusion. Matrine inhibited HCC cell malignancy through the circ_0013290/miR-139-5p/MMP16 pathway, suggesting that Matrine is a potential therapeutic agent for HC

Privacy-Preserving Distributed SVD via Federated Power

Singular value decomposition (SVD) is one of the most fundamental tools in machine learning and statistics.The modern machine learning community usually assumes that data come from and belong to small-scale device users. The low communication and computation power of such devices, and the possible privacy breaches of users' sensitive data make the computation of SVD challenging. Federated learning (FL) is a paradigm enabling a large number of devices to jointly learn a model in a communication-efficient way without data sharing. In the FL framework, we develop a class of algorithms called FedPower for the computation of partial SVD in the modern setting. Based on the well-known power method, the local devices alternate between multiple local power iterations and one global aggregation to improve communication efficiency. In the aggregation, we propose to weight each local eigenvector matrix with Orthogonal Procrustes Transformation (OPT). Considering the practical stragglers' effect, the aggregation can be fully participated or partially participated, where for the latter we propose two sampling and aggregation schemes. Further, to ensure strong privacy protection, we add Gaussian noise whenever the communication happens by adopting the notion of differential privacy (DP). We theoretically show the convergence bound for FedPower. The resulting bound is interpretable with each part corresponding to the effect of Gaussian noise, parallelization, and random sampling of devices, respectively. We also conduct experiments to demonstrate the merits of FedPower. In particular, the local iterations not only improve communication efficiency but also reduce the chance of privacy breaches