13064248
Path-based multicast communication in wormhole-routed star graph multicomputers
In this paper, we propose four efficient multicast routing schemes in wormhole-routed star networks with multidestination routing capability. All of the four proposed schemes are path-based and deadlock-free. The first scheme, dual-path routing, sends the message in parallel through two independent paths. The second one, shortcut-node-based datapath routing, is similar to dual-path routing except that the routing tries to find a shortcut node to route the message as soon as possible to reduce the length of transmission path. The third one, multipath routing, is a multiple dual-path routing strategy that includes source-to-relay and relay-to-destination phases. The last scheme, proximity grouping routing, is similar to multipath routing except that in the partitioning step of source and destination nodes the relation of spatial locality of nodes is also taken into account to reduce the length of transmission paths. Finally, the experimental results are given to show that the performance based on unicast-based and traditional Hamiltonian-path routing schemes can be improved significantly by the four proposed routing schemes respectively.
8131087
Multipath-Based Multicasting Strategies for Wormhole-Routed Star Graph Interconnection Networks
The star graph interconnection network has been recognized as an attractive alternative to the popular hypercube network. In this paper, we present a multipath-based multicast routing model for wormholerouted star graph networks, propose two efficient multipath routing schemes, and contrast the performance of the proposed schemes with the performance of the scheme presented in our previous work. Both of the two proposed schemes have been proven to be deadlock-free. The first scheme, simplemultipath routing, uses multiple independent paths for concurrent multicasting. The second scheme, two-phase multipath routing, includes two phases: source-to-relay and relay-to-destination. For each phase, multicasting is carried out using simple multipath routing. Experimental results show that, for short and medium messages with small message startup latencies, the proposed schemes reduce multicast latency more efficiently than other schemes.
31267474
Skewed Data Partition and Alignment Techniques for Compiling Programs on Distributed Memory Multicomputers
Minimizing data communication over processors is the key to compile programs for distributed memory multicomputers. In this paper, we propose new data partition and alignment techniques for partitioning and aligning data arrays with a program in a way of minimizing communication over processors. We use skewed alignment instead of the dimension-ordered alignment techniques to align data arrays. By developing the skewed scheme, we can solve more complex programs with minimized data communication than that of the dimension-ordered scheme. Finally, the experimental results show that our proposed scheme has more opportunities to align data arrays such that data communications over processors can be minimized.
45055182
Mining frequent tree-like patterns in large datasets
Sequential pattern mining is crucial to data mining domains. This paper proposes a novel data mining approach for exploring hierarchical tree structures, named tree-like patterns, representing the relationships for a pair of items in a sequence. Using tree-like patterns, the relationships for a pair of items can be identified in terms of the cause and effect. A novel technique that efficiently counts support values for tree-like patterns using a queue structure is proposed. In addition, this paper addresses an efficient scheme for determining the frequency of a tree-like pattern in a sequence using a dynamic programming approach. Each tree-like pattern embedded in a sequence is considered to have a certain valuable meaning or the degree of importance used in different applications. Two addressed formulas are applied to determine the degree of significance for a specific sequence, which denotes the degree of consecutive items in a tree-like pattern for a sequence. The larger the degree of significance a tree-like pattern has, the more the tree-like pattern is compacted in the sequence. The characteristics differentiating the explored patterns from those obtained with other schemes are discussed. A simulation analysis of the proposed data mining approach is utilized to demonstrate its efficacy. Finally, the proposed approach is designed and implemented in a data mining system integrated into a novel e-learning platform.
41663574
Mining Frequent Tree-Like Patterns in Large Datasets
In this paper, we propose a novel data mining scheme to explore the frequent hierarchical structure patterns, named tree-like patterns, with the relationship of each item on a sequence. By tree-like patterns, we are clear to find out the relation of items between the cause and effect. Finally, we discuss the different characteristics to our mined patterns with others. As a consequence, we can find out that our addressed tree-like patterns can be widely used to explore a variety of different applications.
44631758
Agent-based forwarding strategies for reducing location management cost in mobile networks
A major issue with location management in mobile wireless networks is the high cost associated with location updates and searches. A search occurs when a caller needs to connect to a mobile user whose location is unknown to the caller and the network must locate the callee. An update occurs when a mobile user moves into a new registration area and the network is informed of the location change. We propose and analyze a class of new agent-based forwarding strategies with the objective to reduce the location management cost in mobile wireless networks. We develop analytical Markov models to compare the performance of the proposed strategies with existing location management schemes to demonstrate their feasibility and also to reveal conditions under which the proposed schemes are superior to existing ones.
15282622
Total variation models for variable lighting face recognition
In this paper, we present the logarithmic total variation (LTV) model for face recognition under varying illumination, including natural lighting conditions, where we rarely know the strength, direction, or number of light sources. The proposed LTV model has the ability to factorize a single face image and obtain the illumination invariant facial structure, which is then used for face recognition. Our model is inspired by the SQI model but has better edge-preserving ability and simpler parameter selection. The merit of this model is that neither does it require any lighting assumption nor does it need any training. The LTV model reaches very high recognition rates in the tests using both Yale and CMU PIE face databases as well as a face database containing 765 subjects under outdoor lighting conditions
7383373
Scale-Driven Iterative Optimization for Brain Extraction and Registration
We present a novel framework to automatically separate brain region from other non-brain regions in head images. The idea of the proposed method is to estimate larger scale patterns in an image and then correct the boundaries iteratively. The scale estimation is based on the recently proposed total variation (TV) regularized L^1 functional. An iterative optimization method is used to refine non-convex and acute angle boundaries. The final algorithm is able to extract large scale patterns with arbitrary shapes, which is particularly suitable for brain extraction. In order to reduce the computation overhead in 3D data, a multi-level technique is proposed to exponentially improve the speed of the brain extraction process. Based on accurate results of brain extraction, a non-rigid brain registration algorithm is proposed to improve accuracy and consistency of existing registration methods. Experimental results on real 3D brain MR images demonstrate that the proposed methods outperform existing solutions. In addition, results are provided to show that the proposed algorithm can also be used to segment large scale patterns in general images.
6145819
HapBlock: haplotype block partitioning and tag SNP selection software using a set of dynamic programming algorithms
UNLABELLED
Recent studies have revealed that linkage disequilibrium (LD) patterns vary across the human genome with some regions of high LD interspersed with regions of low LD. Such LD patterns make it possible to select a set of single nucleotide polymorphism (SNPs; tag SNPs) for genome-wide association studies. We have developed a suite of computer programs to analyze the block-like LD patterns and to select the corresponding tag SNPs. Compared to other programs for haplotype block partitioning and tag SNP selection, our program has several notable features. First, the dynamic programming algorithms implemented are guaranteed to find the block partition with minimum number of tag SNPs for the given criteria of blocks and tag SNPs. Second, both haplotype data and genotype data from unrelated individuals and/or from general pedigrees can be analyzed. Third, several existing measures/criteria for haplotype block partitioning and tag SNP selection have been implemented in the program. Finally, the programs provide flexibility to include specific SNPs (e.g. non-synonymous SNPs) as tag SNPs.


AVAILABILITY
The HapBlock program and its supplemental documents can be downloaded from the website http://www.cmb.usc.edu/~msms/HapBlock.
10160655
A Suboptimal Algorithm for De Novo Peptide Sequencing via Tandem Mass Spectrometry
Tandem mass spectrometry has emerged to be one of the most powerful high-throughput techniques for protein identification. Tandem mass spectrometry selects and fragments peptides of interest into N-terminal ions and C-terminal ions, and it measures the mass/charge ratios of these ions. The de novo peptide sequencing problem is to derive the peptide sequences from given tandem mass spectral data of k ion peaks without searching against protein databases. By transforming the spectral data into a matrix spectrum graph G = (V, E), where |V| = O(k(2)) and |E| = O(k(3)), we give the first polynomial time suboptimal algorithm that finds all the suboptimal solutions (peptides) in O(p|E|) time, where p is the number of solutions. The algorithm has been implemented and tested on experimental data. The program is available at http://hto-c.usc.edu:8000/msms/menu/denovo.htm.