Fault-tolerant aggregation: Flow-Updating meets Mass-Distribution

Flow-Updating (FU) is a fault-tolerant technique that has proved to be efficient in practice for the distributed computation of aggregate functions in communication networks where individual processors do not have access to global information. Previous distributed aggregation protocols, based on repeated sharing of input values (or mass) among processors, sometimes called Mass-Distribution (MD) protocols, are not resilient to communication failures (or message loss) because such failures yield a loss of mass. In this paper, we present a protocol which we call Mass-Distribution with Flow-Updating (MDFU). We obtain MDFU by applying FU techniques to classic MD. We analyze the convergence time of MDFU showing that stochastic message loss produces low overhead. This is the first convergence proof of an FU-based algorithm. We evaluate MDFU experimentally, comparing it with previous MD and FU protocols, and verifying the behavior predicted by the analysis. Finally, given that MDFU incurs a fixed deviation proportional to the message-loss rate, we adjust the accuracy of MDFU heuristically in a new protocol called MDFU with Linear Prediction (MDFU-LP). The evaluation shows that both MDFU and MDFU-LP behave very well in practice, even under high rates of message loss and even changing the input values dynamically.- A preliminary version of this work appeared in [2]. This work was partially supported by the National Science Foundation (CNS-1408782, IIS-1247750); the National Institutes of Health (CA198952-01); EMC, Inc.; Pace University Seidenberg School of CSIS; and by Project "Coral - Sustainable Ocean Exploitation: Tools and Sensors/NORTE-01-0145-FEDER-000036" financed by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF).info:eu-repo/semantics/publishedVersio

A Drosophila model of Alzheimer's disease

In this paper we aim at carrying out and describing some issues for real eigenvalue computation via iterative methods. More specifically we work out new techniques for iteratively developing specific tridiagonalizations of a {\em symmetric} and {\em indefinite} matrix A \in \re^{n \times n}, by means of suitable Krylov subspace algorithms defined in \cite{13}, \cite{21}. These schemes represent extensions of the well known Conjugate Gradient (CG) method to the indefinite case. We briefly recall these algorithms and we suggest a comparison with the method in \cite{18}, along with a discussion on the practical application of the proposed results for eigenvalue computation. Furthermore, we focus on motivating the fruitful use of these tridiagonalizations for ensuring the convergence to second order points, within an optimization framework

Automatically clustering large-scale miRNA sequences: methods and experiments

<p>Abstract</p> <p>Background</p> <p>Since the initial annotation of microRNAs (miRNAs) in 2001, many studies have sought to identify additional miRNAs experimentally or computationally in various species. MiRNAs act with the Argonaut family of proteins to regulate target messenger RNAs (mRNAs) post-transcriptionally. Currently, researches mainly focus on single miRNA function study. Considering that members in the same miRNA family might participate in the same pathway or regulate the same target(s) and thus share similar biological functions, people can explore useful knowledge from high quality miRNA family architecture.</p> <p>Results</p> <p>In this article, we developed an unsupervised clustering-based method miRCluster to automatically group miRNAs. In order to evaluate this method, several data sets were constructed from the online database miRBase. Results showed that miRCluster can efficiently arrange miRNAs (e.g identify 354 families in miRBase16 with an accuracy of 92.08%, and can recognize 9 of all 10 newly-added families in miRBase 17). By far, ~30% mature miRNAs registered in miRBase are unclassified. With miRCluster, over 85% unclassified miRNAs can be assigned to certain families, while ~44% of these miRNAs distributed in ~300novel families.</p> <p>Conclusions</p> <p>In short, miRCluster is an automatic and efficient miRNA family identification method, which does not require any prior knowledge. It can be helpful in real use, especially when exploring functions of novel miRNAs. All relevant materials could be freely accessed online (<url>http://admis.fudan.edu.cn/projects/miRCluster</url>).</p