1484935
Improving genome rearrangement phylogeny using sequence-style parsimony
The study of genome rearrangements, the evolutionary events that change the order and strandedness of genes within genomes, presents new opportunities for discoveries about deep evolutionary events. The best software so far, GRAPPA, solves breakpoint and inversion phylogenies by scoring each tree topology through iterative improvements of internal node gene orders. We find that the greedy hill-climbing approach means the accuracy is limited because of multiple local optima. To address this problem, we propose integration GRAPPA with MPME, a string encoding of gene adjacency relationships whose optimal internal node assignments can be determined globally in polynomial time, to provide better initializations for GRAPPA. In simulation studies, the new algorithm yields shorter tree lengths and better accuracy in phylogeny reconstruction.
14427891
Phylogenetic reconstruction from arbitrary gene-order data
Phylogenetic reconstruction from gene-order data has attracted attention from both biologists and computer scientists over the last few years. So far, our software suite GRAPPA is the most accurate approach, but it requires that all genomes have identical gene content, with each gene appearing exactly once in each genome. Some progress has been made in handling genomes with unequal gene content, both in terms of computing pair-wise genomic distances and in terms of reconstruction. In this paper, we present a new approach for computing the median of three arbitrary genomes and apply it to the reconstruction of phylogenies from arbitrary gene-order data. We implemented these methods within GRAPPA and tested them on simulated datasets under various conditions as well as on a real dataset of chloroplast genomes; we report the results of our simulations and our analysis of the real dataset and compare them to reconstructions made by using neighbor-joining and using the original GRAPPA on the same genomes with equalized gene contents. Our new approach is remarkably accurate both in simulations and on the real dataset, in contrast to the distance-based approaches and to reconstructions using the original GRAPPA applied to equalized gene contents.
1746627
Inversion Medians Outperform Breakpoint Medians in Phylogeny Reconstruction from Gene-Order Data
Phylogeny reconstruction from gene-order data has attracted much attention over the last few years. The two software packages used for that purpose, BPAnalysis and GRAPPA, both use so-called breakpoint medians in their computations. Some of our past results indicate that using inversion scores rather than breakpoint scores in evaluating trees leads to the selection of better trees. On that basis, we conjectured that phylogeny reconstructions could be improved by using inversion medians, which minimize evolutionary distance under an inversions-only model of genome rearrangement. Recent algorithmic developments have made it possible to compute inversion medians for problems of realistic size.Our experimental studies unequivocally show that inversion medians are strongly preferable to breakpoint medians in the context of phylogenetic reconstruction from gene-order data. Improvements are most pronounced in the reconstruction of ancestral genomes, but are also evident in the topological accuracy of the reconstruction as well as, surprisingly, in the overall running time. Improvements are strongest for small average distances along tree edges and for evolutionary scenarios with a preponderance of inversion events, but occur in all cases, including evolutionary scenarios with high proportions of transpositions.All of our tests were run using our GRAPPA package, available (under GPL) at www.cs.unm.edu/~moret/GRAPPA; the next release will include the inversion median software we used in this study. The software used includes RevMed, developed by the authors and available at www.cs.unm.edu/~acs, and A. Caprara's inversion median code, generously made available for testing.
37720806
An efficient method for mutual exclusion in truly distributed systems
Many operations in a distributed system require mutual exclusion to guarantee correctness. Quorum methods have been widely proposed for implementing mutual exclusion. Majority voting is the best known quorum method. It has the merit of simplicity, but may incur high message overhead. The tree algorithm is an efficient structured quorum method for mutual exclusion problems. The quorums generated by a tree algorithm are smaller on average than those generated by a majority voting. However, the tree algorithm enforces a highly biased treatment to the nodes at different levels. This affects its performance in a distributed system where the nodes have similar characteristics. We propose a new structured quorum method which treats the nodes more evenly than the tree algorithm yet still preserves a satisfactory availability. We believe that this method is desirable for implementing mutual exclusion in a truly distributed system.<<ETX>>
14586033
Dynamic Data-Driven Fault Diagnosis of Wind Turbine Systems
In this multi-university collaborative research, we will develop a framework for the dynamic data-driven fault diagnosis of wind turbines which aims at making the wind energy a competitive alternative in the energy market. This new methodology is fundamentally different from the current practice whose performance is limited due to the non-dynamic and non-robust nature in the modeling approaches and in the data collection and processing strategies. The new methodology consists of robust data pre-processing modules, interrelated, multi-level models that describe different details of the system behaviors, and a dynamic strategy that allows for measurements to be adaptively taken according to specific physical conditions and the associated risk level. This paper summarizes the latest progresses in the research.
4332072
A Component Retrieval Method Based on Facet-Weight Self-learning
Component-based development method has been a new software development paradigm. How to get the needed components quickly and accurately is one of the basic problems about reusing software component automatically. In this paper, an intelligent component retrieval model – FWRM. is proposed. Facet presentation is used to model query and component. Multiple types of facets are defined which extends traditional keyword-based facet presentation. Genetic algorithm based facet weight self-learning algorithm can change the facet weight dynamically in order to improve retrieval accuracy. Corresponding similarity functions are defined also. In addition, risk minimization-based component sampling method is used to solve the insufficiency of training data. All these algorithms and methods are integrated into FWRM’s three main implementation parts: Facet-Weight Optimize System, Component Retrieve System and Resource. The experimental results prove that this method is feasible and can improve component retrieval effectively.
15896075
Effective bandwidth-based QoS provisioning for real-time audio/video streaming over MIMO-OFDM wireless networks
We propose a cross-layer design scheme to support the real-time quality of service (QoS) guarantees for heterogeneous mobile users over downlink MIMO-OFDM wireless networks. Inspired by the effective bandwidth theory originally established for wired networks, we develop the approach to apply the theory into wireless networks. Based on MIMO-OFDM infrastructure employed at the physical (PHY)-layer, we analytically derive the medium access control (MAC)-layer admission-control and time-slot allocation algorithms to guarantee the statistical delay-bound for different real-time users. Simulation results show that the proposed scheme can efficiently support the wireless QoS requirement for diverse real-time mobile users.
13492639
Relay node placement in large scale wireless sensor networks
Scalability and extended lifetime are two critical design goals of any large scale wireless sensor network. A two-tiered network model has been proposed recently for this purpose. This is a cluster-based network model composed of relay and sensor nodes. Relay nodes are placed in the playing field to serve as cluster heads and to form a connected network topology for information dissemination at the higher tier. The relay nodes are capable of aggregating data packets from the sensor nodes in their clusters and transmitting them to the sink node via wireless multi-hop paths. In this paper, we study the relay node placement problem in large scale wireless sensor networks. Our objective is to place the fewest number of relay nodes in the playing field of a sensor network such that (1) each sensor node can communicate with at least one relay node and (2) the network of relay nodes is connected. However, placement strategies realizing goals (1) and (2) do not provide any fault-tolerance as the network may lose functionality after failure of some of the relay nodes. In order to incorporate fault-tolerance in such a network, we ensure that every sensor node is able to communicate with at least two relay nodes and the induced network topology is 2-connected. This strategy will ensure survivability of the network in the event of single fault, in lieu of higher relay node placement cost. We formulate the relay node placement in wireless sensor networks as two optimization problems: (i) Connected Relay Node Single Cover (CRNSC) problem and (ii) 2-Connected Relay Node Double Cover (2CRNDC) problem. We present two polynomial time approximation algorithms to solve the CRNSC problem. We prove that the ratio of the number of relay nodes needed by the approximation algorithm to the number of relay nodes needed by the optimal algorithm is bounded by 8 for the first algorithm and 4.5 for the second. In addition, for the 2CRNDC problem we provide two approximation algorithms with performance bounds 6 and 4.5, respectively.