16705590
A New Asynchronous Parallel Evolutionary Algorithm for Function Optimization
This paper introduces a new asynchronous parallel evolutionary algorithm (APEA) based on the island model for solving function optimization problems. Our fully distributed APEA overlaps the communication and computation efficiently and is inherently fault-tolerant in a large-scale distributed computing environment. For the scalable BUMP problem, our APEA algorithm achieves the best solution for the 50-dimension problem, and is the first algorithm of which we are aware that can solve the 1,000,000- dimension problem. For other benchmark problems, our APEA finds the best solution to G7 in fewer time steps than [16,17], and finds a better solution to G10 than [17].
1575051
Digital filter design using multiple pareto fronts
Evolutionary approaches have been used in a large variety of design domains, from aircraft engineering to the designs of analog filters. Many of these approaches use measures to improve the variety of solutions in the population. One such measure is clustering. In this paper, clustering and Pareto optimisation are combined into a single evolutionary design algorithm. The population is split into a number of clusters, and parent and offspring selection, as well as fitness calculation, are performed on a per-cluster basis. The objective of this is to prevent the system from converging prematurely to a local minimum and to encourage a number of different designs that fulfil the design criteria. Our approach is demonstrated in the domain of digital filter design. Using a polar coordinate based pole-zero representation, two different lowpass filter design problems are explored. The results are compared to designs created by a human expert. They demonstrate that the evolutionary process is able to create designs that are competitive with those created using a conventional design process by a human expert. They also demonstrate that each evolutionary run can produce a number of different designs with similar fitness values, but very different characteristics.
15077137
Modeling nonlinearity in dilution design microarray data
MOTIVATION
Dilution design (Mixed tissue RNA) has been utilized by some researchers to evaluate and assess the performance of multiple microarray platforms. Current microarray data analysis approaches assume that the quantified signal intensities are linearly related to the expression of the corresponding genes in the sample. However, there are sources of nonlinearity in microarray expression measurements. Such nonlinearity study in the expressions of the RNA mixtures provides a new way to analyze gene expression data, and we argue that the nonlinearity can reveal novel information for microarray data analysis. Therefore, we proposed a statistical model, called proportion model, which is based on the linear regression analysis. To approximately quantify the nonlinearity in the dilution design, a new calibration, beta ratio (BR) was derived from the proportion model. Furthermore, a new adjusted fold change (adj-FC) was proposed to predict the true FC without nonlinearity, in particular for large FC.


RESULTS
We applied our method to one microarray dilution dataset. The experimental results indicated that, to some extent, there are global biases comparing with the linear assumption for the significant genes. Further analysis of those highly expressed genes with significant nonlinearity revealed some promising results, e.g. 'poison' effect was discovered for some genes in RNA mixtures. The adj-FCs of those genes with 'poison' effect, indicate that the nonlinearity can be also caused by the inherent feature of the genes besides signal noise and technical variation. Moreover, when percentage of overlapping genes (POG) was used as a cross-platform consistency measure, adj-FC outperformed simple fold change to show that Affymetrix and Illumina platforms are consistent.


AVAILABILITY
The R codes which implements all described methods, and some Supplementary material, are freely available from http://www.utdallas.edu/~ying.liu/BetaRatio.htm
2692717
End-to-end delay boundary prediction using maximum entropy principle (MEP) for internet-based teleoperation
Since data packets may get lost somewhere in the Internet connections, for real-time applications such as Internet-based teleoperation, delay boundary prediction plays an important role in determining properly whether a packet is lost or not. The predictors currently employed are lowpass filters based on the autoregressive and moving average (ARMA) models. However, recent studies and the results of the experiments in this paper show that the traditional ARMA model is not suitable because sometimes delays develop with quick and evident variation. In this paper, we present a novel adaptive algorithm for delay boundary prediction based on the maximum entropy principle (MEP). The results of our 3 successive working day experiments on 9 links which consists of academic, commercial and governmental ones among Northern America, Asia and Europe show that the MEP algorithm proposed has a better performance than the traditional ARMA method.
16813176
Programming the Bi-CGSTAB matrix solver for HPC and benchmarking IBM SP3 and alpha ES40
The panel method, or the boundary element method, has been widely used for calculating lifting flows in which forces on wing surfaces are primarily of interest. In panel method for a wind generation system with a number of propfans or for an oil platform motion control assembly with a number of thrusters/propellers, calling time for a matrix solver is the key factor for computing efficiency. This paper discusses the code development of an iterative matrix solver in MPI via C and benchmark analysis for a shared and a distributed memory UNIX machine, respectively.
16205548
Intrusion Confinement by Isolation in Information Systems
System protection mechanisms such as access controls can be fooled by authorized but malicious users, masqueraders, and misfeasors. Intrusion detection techniques are therefore used to supplement them. However, damage could have occurred before an intrusion is detected. In many computing systems the requirement for a high degree of soundness of intrusion reporting can yield poor performance in detecting intrusions and cause long detection latency. As a result, serious damage can be caused either because many intrusions are never detected or the average detection latency is too long. The process of bounding the damage caused by intrusions during intrusion detection is referred to as intrusion confinement. We justify the necessity for intrusion confinement during detection by using a probabilistic analysis model, and propose a general solution to achieve intrusion confinement. The key idea of the solution is to isolate likely suspicious actions before a definite determination of intrusion is reported. We also present two concrete isolation protocols in the database and file system contexts, respectively, to evaluate the feasibility of the general solution, which can be applied to many types of information systems.
15212270
Recovery from Malicious Transactions
Preventive measures sometimes fail to deflect malicious attacks. We adopt an information warfare perspective, which assumes success by the attacker in achieving partial, but not complete, damage. In particular, we work in the database context and consider recovery from malicious but committed transactions. Traditional recovery mechanisms do not address this problem, except for complete rollbacks, which undo the work of benign transactions as well as malicious ones, and compensating transactions, whose utility depends on application semantics. Recovery is complicated by the presence of benign transactions that depend, directly or indirectly, on the malicious transactions. We present algorithms to restore only the damaged part of the database. We identify the information that needs to be maintained for such algorithms. The initial algorithms repair damage to quiescent databases; subsequent algorithms increase availability by allowing new transactions to execute concurrently with the repair process. Also, via a study of benchmarks, we show practical examples of how offline analysis can efficiently provide the necessary data to repair the damage of malicious transactions.