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data-partitioning using the Hilbert space filling curves: Effect on the speed of convergence of Fuzzy ARTMAP for large database problems

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NEURAL NETWORKS 2005年第7期18卷 967-984页

作者： Castro, J Georgiopoulos, M Demara, R Gonzalez, A Univ Cent Florida Dept Elect & Comp Engn Orlando FL 32816 USA

The Fuzzy ARTMAP algorithm has been proven to be one of the premier neural network architectures for classification problems. One of the properties of Fuzzy ARTMAP, which can be both an asset and a liability, is its capacity to produce new nodes (templates) on demand to represent classification categories. This property allows Fuzzy ARTMAP to automatically adapt to the database without having to a priori specify its network size. On the other hand, it has the undesirable side effect that large databases might produce a large network size (node proliferation) that can dramatically slow down the training speed of the algorithm. To address the slow convergence speed of Fuzzy ARTMAP for large database problems, we propose the use of space-filling curves, specifically the Hilbert space-filling curves (HSFC). Hilbert space-filling curves allow us to divide the problem into smaller sub-problems, each focusing on a smaller than the original dataset. For learning each partition of data, a different Fuzzy ARTMAP network is used. Through this divide-and-conquer approach we are avoiding the node proliferation problem, and consequently we speedup Fuzzy ARTMAP's training. Results have been produced for a two-class, 16-dimensional Gaussian data, and on the Forest database, available at the UCI repository. Our results indicate that the Hilbert space-filling curve approach reduces the time that it takes to train Fuzzy ARTMAP without affecting the generalization performance attained by Fuzzy ARTMAP trained on the original large dataset. Given that the resulting smaller datasets that the HSFC approach produces can independently be learned by different Fuzzy ARTMAP networks, we have also implemented and tested a parallel implementation of this approach on a Beowulf cluster of workstations that further speeds up Fuzzy ARTMAP's convergence to a solution for large database problems. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： Fuzzy-ARTMAP Hilbert space-filling curve data mining data-partitioning

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Cost-Based data-partitioning for Intra-Query Parallelism 11

Cost-Based Data-Partitioning for Intra-Query Parallelism

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11th International Baltic Conference on databases and Information Systems (Baltic DBandIS)

作者： Liu, Yanchen Mortazavi, Masood Cao, Fang Chen, Mengmeng Shi, Guangyu Huawei Innovat Ctr Santa Clara CA 95050 USA CUNY City Coll New York NY 10031 USA

ISBN: (纸本)9781614994589

Contemporary DBMS systems already use data-partitioning and data-flow analysis for intra-query parallelism. We study the problem of identifying data-partitioning targets. To rank candidates, we propose a simple cost model that relies on plan structure, operator cost and selectivity for a given base table. We evaluate this model in various optimization schemes and observe how it affects degrees of parallelism and query execution latencies across all TPC-H queries: When compared with the existing naive model which partitions the largest physical table in the query, our approach identifies significantly better partitioning targets thus resulting in sinificantly higher degree of resource utilization and intra-query parallelism for most queries while having little impact on the remaining queries in the TPC-H benchmark.

关键词： data-partitioning intra-query parallelism multi-core systems optimization

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Engineering wireless broadband access to IPTV

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2014年第7期25卷 1493-1506页

作者： Al-Jobouri, Laith Fleury, Martin Ghanbari, Mohammed Univ Essex Colchester CO4 3SQ Essex England

IPTV is now extending to wireless broadband access. If broadband video streaming is to achieve competitive quality the video stream itself must be carefully engineered to cope with challenging wireless channel conditions. This paper presents a scheme for doing this for H.264/AVC codec streaming across a WiMAX link. Packetization is an effective tool to govern error rates and, in the paper, source-coded data-partitioning serves to allocate smaller packets to more important data. A packetization strategy is insufficient in itself, as temporal error propagation should also be addressed by insertion of intra-coded data. It may be necessary to include redundant packets when channel conditions worsen. The whole should be protected by application-layer rateless coding. Therefore, the contribution of the paper is a complete scheme comprised of various protection measures aimed at robust IPTV streaming. Due to computational overheads, the scheme is aimed at the new generation of smartphones with GHz CPUs. (C) 2014 Elsevier Inc. All rights reserved.

关键词： H.264/AVC IPTV Video codec Video streaming WiMAX Application-layer FEC data-partitioning Error resilience

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Comprehensive protection of data-partitioned video for broadband wireless IPTV streaming

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MOBILE INFORMATION SYSTEMS 2012年第2期8卷 83-105页

作者： Al-Jobouri, Laith Fleury, Martin Ghanbari, Mohammed Univ Essex Dept CSEE Colchester CO4 3SQ Essex England

This paper examines the threat to video streaming from slow and fast fading, traffic congestion, and channel packet drops. The proposed response is a combination of: rateless channel coding, which is adaptively applied;data-partitioned source coding to exploit prioritized packetization;and duplicate slice provision, which is the focus of the evaluation in this paper. The paper also considers the distribution of intra-refresh macroblocks as a means of avoiding sudden data rate increases. When error bursts occur, this paper shows that duplicate slices are certainly necessary but this provision is more effective for medium quality video than it is for high quality video. The percentage of intra-refresh macroblocks can be low and still reduce the impact of temporal error propagation.

关键词： data-partitioning error resilience FEC IPTV rateless coding redundant packets video streaming WiMAX

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Non-monophyly of most supraspecific taxa of calcareous sponges (Porifera, Calcarea) revealed by increased taxon sampling and partitioned Bayesian analysis of ribosomal DNA

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MOLECULAR PHYLOGENETICS AND EVOLUTION 2006年第3期40卷 830-843页

作者： Dohrmann, Martin Voigt, Oliver Erpenbeck, Dirk Woerheide, Gert Geosci Ctr Gottingen Dept Geobiol D-37077 Gottingen Germany Queensland Museum Brisbane Qld Australia

Calcareous sponges (Porifera, Calcarea) play an important role for our understanding of early metazoan evolution, since several molecular studies suggested their closer relationship to Eumetazoa than to the other two sponge 'classes,' Demospongiae and Hexactinellida. The division of Calcarea into the subtaxa Calcinea and Calcaronea is well established by now, but their internal relationships remain largely unresolved. Here, we estimate phylogenetic relationships within Calcarea in a Bayesian framework, using full-length 18S and partial 28S ribosomal DNA sequences. Both genes were analyzed separately and in combination and were further partitioned by stem and loop regions, the former being modelled to take non-independence of paired sites into account. By substantially increasing taxon sampling, we show that most of the traditionally recognized supraspecific taxa within Calcinea and Calcaronea are not monophyletic, challenging the existing classification system, while monophyly of Calcinea and Calcaronea is again highly supported. (c) 2006 Elsevier Inc. All rights reserved.

关键词： Porifera Calcarea phylogeny ribosomal DNA Bayesian inference Bayes factors doublet-model data-partitioning

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Computationally Efficient Mean-Shift Parallel Segmentation Algorithm for High-Resolution Remote Sensing Images

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JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 2018年第11期46卷 1805-1814页

作者： Wu, Tianjun Xia, Liegang Luo, Jiancheng Zhou, Xiaocheng Hu, Xiaodong Ma, Jianghong Song, Xueli Changan Univ Dept Math & Informat Sci Coll Sci Xian 710064 Shaanxi Peoples R China Fuzhou Univ Key Lab Spatial Data Min & Informat Sharing Minist Educ Fuzhou 350002 Fujian Peoples R China Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 310023 Zhejiang Peoples R China Chinese Acad Sci Inst Remote Sensing & Digital Earth State Key Lab Remote Sensing Sci Beijing 100101 Peoples R China State Key Lab Geoinformat Engn Xian 710054 Shaanxi Peoples R China

In high-resolution remote sensing image processing, segmentation is a crucial step that extracts information within the object-based image analysis framework. Because of its robustness, mean-shift segmentation algorithms are widely used in the field of image segmentation. However, the traditional implementation of these methods cannot process large volumes of images rapidly under limited computing resources. Currently, parallel computing models are generally employed for segmentation tasks with massive remote sensing images. This paper presents a parallel implementation of the mean-shift segmentation algorithm based on an analysis of the principle and characteristics of this technique. To avoid the inconsistency on the boundaries of adjacent data chunks, we propose a novel buffer-zone-based data-partitioning strategy. Employing the proposed data-partitioning strategy, two intensively computation steps are performed in parallel on different data chunks. The experimental results show that the proposed algorithm effectively improves the computing efficiency of image segmentation in a parallel computing environment. Furthermore, they demonstrate the practicality of massive image segmentation when computer resources are limited.

关键词： High-resolution remote sensing images Image segmentation Mean-shift Parallel computation data-partitioning

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A preliminary mitochondrial genome phylogeny of Orthoptera (Insecta) and approaches to maximizing phylogenetic signal found within mitochondrial genome data

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MOLECULAR PHYLOGENETICS AND EVOLUTION 2008年第1期49卷 59-68页

作者： Fenn, J. Daniel Song, Hojun Cameron, Stephen L. Whiting, Michael F. Brigham Young Univ Dept Biol Provo UT 84602 USA CSIRO Australian Natl Insect Collect Canberra ACT 2601 Australia

The phylogenetic utility of mitochondrial genomes (mtgenomes) is examined using the framework of a preliminary phylogeny of Orthoptera. This study presents five newly sequenced genomes from four orthopteran families. While all ensiferan and polyneopteran taxa retain the ancestral gene order, all caeliferan lineages including the newly sequenced caeliferan species contain a tRNA rearrangement from the insect ground plan tRNA(Lys)(K)-tRNA(Asp)(D) swapping to tRNA(Asp)(D)-tRNA(Lys)(K) confirming that this rearrangement is a possible molecular synapomorphy for this suborder. The phylogenetic signal in mtgenomes is rigorously examined under the analytical regimens of parsimony, maximum likelihood and Bayesian inference, along with how gene inclusion/exclusion, data recoding, gap coding, and different partitioning schemes influence the phylogenetic reconstruction. When all available data are analyzed simultaneously, the monophyly of Orthoptera and its two suborders, Caelifera and Ensifera, are consistently recovered in the context of our taxon sampling, regardless of the optimality criteria. When protein-coding genes are analyzed as a single partition, nearly identical topology to the combined analyses is recovered, suggesting that much of the signals of the mtgenome come from the protein-coding genes. Transfer and ribosomal RNAs perform poorly when analyzed individually, but contribute signal when analyzed in combination with the protein-coding genes. Inclusion of third codon position of the protein-coding genes does not negatively affect the phylogenetic reconstruction when all genes are analyzed together, whereas recoding of the protein-coding genes into amino acid sequences introduces artificial resolution. Over-partitioning in a Bayesian framework appears to have a negative effect in achieving convergence. Our findings suggest that the best phylogenetic inferences are made when all available nucleotide data from the mtgenome are analyzed simultaneously, and that the

关键词： Mitochondrial genome Phylogeny Orthoptera data-partitioning Rearrangements

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A four-decomposition strategies for hierarchically modeling combinatorial optimization problems: framework, conditions and relations 13

A four-decomposition strategies for hierarchically modeling ...

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International Conference on High Performance Computing and Simulation (HPCS)

作者： Chaieb, Marouene Jemai, Jaber Mellouli, Khaled LARODEC Inst Super Gest Tunis 41 Rue Liberte Le Bardo 2000 Tunisia

ISBN: (纸本)9781467378130

We address the problem of modeling combinatorial optimization problems (COP). COPs are generally complex problems to solve. So a good modeling step is fundamental to make the solution easier. Our approach orients researches to choose the best modeling strategy from the beginning to avoid any problem in the solving process. This paper aims at proposing a new approach dealing with hard COPs particularly when the decomposition process leads to some well-known and canonical optimization sub-problems. We tried to draw a clear framework that will help to model hierarchical optimization problems. The framework will be composed by four decomposition strategies which are: objective based decomposition;constraints based decomposition, semantic decomposition and data partitioning strategy. For each strategy, we present supporting examples from the literature where it was applied. But, not all combinatorial problems can be benefit from the outcomes and benefits of modeling problems hierarchically, rather only particular problems can be modeled like a hierarchical optimization problem. Thus, we propose a set of decomposability conditions for decomposing COPs. Furthermore, we define the types of relationships between obtained sub-problems and how partial solutions can be merged to obtain the final solution.

关键词： Combinatorial optimization problems hierarchiucal optimization constraint relaxation semantic decomposition objective-based decomposition data-partitioning parallel processing sequential processing gradually mixed optimization totally mixed optimization

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Robust data-Partitioned Video Streaming over a WiMAX Channel

Robust Data-Partitioned Video Streaming over a WiMAX Channel

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6th Annual IEEE International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)/STMWC/NASSUE/CMPA/VECON/AWSAN/AWSAN

作者： Al-Jobouri, Laith Fleury, Martin Ghanbari, Mohammed Univ Essex Sch Comp Sci & Elect Engn Colchester CO4 3SQ Essex England

ISBN: (纸本)9781424477425

This paper demonstrates a robust layered video scheme, based on data-partitioning and intended for IPTV streaming over wireless broadband. Equal error protection through rateless coding is applied, whereby higher-priority data partitioned packets are protected by appropriate selection of quantization parameter and picture slicing, so as to regulate packet size. In the mobile WiMAX channel investigated, both packet drops from congestion and adverse channel conditions are shown to be affected by packet size. The main proposal is for adaptive rateless coding in which additional redundant data are retransmitted to heal corrupted packets. However, though these packets are always repairable, delay increases as the percentage of corrupted packets increases, affecting the design parameters. Intra-refresh macro blocks are added to prevent the objective video quality falling below an acceptable level. Picture slicing further reduces the packet size to increase robustness in the face of measurement noise during channel estimation and the effects of slow and fast fading.

关键词： data-partitioning IPTV layered source coding rateless channel coding WiMAX

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ANCHOR— A CONNECTIONIST ARCHITECTURE FOR partitioning FEATURE SPACES AND HIERARCHICAL NESTING OF NEURAL NETS

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International Journal on Artificial Intelligence Tools 2000年第3期9卷 397-416页

作者： RAJEEV KUMAR Department of Computer Science & Information Systems Birla Institute of Technology & Science Pilani – 333 031 India .

We present a novel connectionist architecture for handling arbitrarily complex neural computations. The architecture — which we call an A rtificial N eural Network C ompiler for H ierarchical OR ganisation ( ANCHOR ) — facilitates network hierarchy and the partitioning of the input space into a number of small, simpler sub-mappings. This strategy is a mix of both a modular approach for reduction of the problem complexity, and an ensemble-based approach for increasing prediction accuracy. ANCHOR is implemented around the concept of a Superneuron which is a generalised view of a neuron-processing element; a superneuron is a single (or higher-order) perceptron which can be trained individually with the support of multiple learning algorithms. The indistinguishability between a superneuron, and a neuron is employed in hierarchical nesting of superneurons, up to (theoretically) infinite depth, within other superneurons as well as linear or tree-structured cascading. Hierarchical decomposition of simple boolean functions has been demonstrated as proof-of-concept. It is argued that this strategy of data-partitioning for subsequent mapping onto a hierarchical classifier reduces the learning complexity and offers better generalisation.

关键词： Connectionist architecture Hierarchical neural network data-partitioning Superneuron

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