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Kauffman networks: analysis and applications

Kauffman networks: analysis and applications

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IEEE International Conference on Computer-Aided Design

作者： E. Dubrova M. Teslenko A. Martinelli Royal Institute of Technology IMIT/KTH Kista Sweden

A Kauffman network is an abstract model of gene regulatory networks. Each gene is represented by a vertex. An edge from one vertex to another implies that the former gene regulates the latter. Statistical features of Kauffman networks match the characteristics of living cells. The number of cycles in the network's state space, called attractors, corresponds to the number of different cell types. The attractor's length corresponds to the cell cycle time. The sensitivity of attractors to different kinds of disturbances, modeled by changing a network connection, the state of a vertex, or the associated function, reflects the stability of the cell to damage, mutations and virus attacks. In order to evaluate attractors, their number and lengths have to be computed. This problem is the major open problem related to Kauffman networks. Available algorithms can only handle networks with less than a hundred vertices. The number of genes in a cell is often larger. In this paper, we present a set of efficient algorithms for computing attractors in large Kauffman networks. The resulting software package is hoped to be of assistance in understanding the principles of gene interactions and discovering a computing scheme operating on these principles.

关键词： Computer networks DNA Automata Jacobian matrices Pediatrics Circuit synthesis Network synthesis partitioning algorithms State-space methods Stability

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An adaptive neuro-fuzzy approach for system modeling

An adaptive neuro-fuzzy approach for system modeling

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Chen-Sen Ouyang Wan-Jui Lee Shie-Jue Lee Department of Electrical Engineering National Sun Yat-Sen University Kaohsiung Taiwan

ISBN: (纸本)0780375084

In this paper, a novel adaptive neuro-fuzzy modeling system is proposed for solving system modeling problems. Two phases are included in our approach.. In the first phase, a merge-based fuzzy self-clustering algorithm is used to automatically partition the sample data set into fuzzy clusters. Initial clusters are generated rapidly and similar clusters are merged together gradually based on similarity and distortion measures. TSK-type fuzzy rules associated with generated clusters are extracted. Then, the obtained rules are refined by a fuzzy neural network in the second phase. To speed up the convergence of learning, we develop a hybrid learning algorithm which combines a recursive SVD-based least squares estimator and the gradient descent method. Experimental results have shown that our method is more efficient than other methods.

关键词： Modeling Fuzzy sets Clustering algorithms Adaptive systems partitioning algorithms Distortion measurement Data mining Fuzzy neural networks Convergence Least squares approximation

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A virtual logic algorithm for solving satisfiability problems using reconfigurable hardware

A virtual logic algorithm for solving satisfiability problem...

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Annual IEEE Symposium on Field-Programmable Custom Computing Machines (FCCM)

作者： M. Abramovici J.T. de Sousa Bell Laboratories Lucent Technologies Inc. Murray Hill NJ USA

Satisfiability (SAT) is a computationally expensive algorithm central to computer science. In this paper, we present a virtual logic algorithm that allows an FPGA based reconfigurable comparing platform to process SAT solver circuits much larger than its available capacity. Our algorithm is based on decomposition techniques that create independent subproblems (pages) that fit the size of the available reconfigurable hardware. Those pages can take turns reusing the platform, and creating a virtual logic environment.

关键词： Reconfigurable logic Hardware Clustering algorithms Computer science Logic circuits Application software Logic testing partitioning algorithms Field programmable gate arrays Electronic design automation and methodology

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Parallel computation of direct transitive closures

Parallel computation of direct transitive closures

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International Conference on Data Engineering

作者： Y.-N. Huang J.-P. Cheiney Ecole Nationale Superieure d''Arts et Metiers Paris France

To efficiently process recursive queries in a DBMS (database management system), a parallel, direct transitive closure algorithm is proposed. Efficiency is obtained by reorganizing the computation order of Warren's algorithm. The number of transfers among processors depends only on the number of processors and does not depend on the depth of the longest path. The evaluation shows an improvement due to the parallelism and the superiority of the proposed algorithm over recent propositions. The speed of the production of new tuples is very high and the volume of transfers between the sites is reduced.< >

关键词： Concurrent computing Testing partitioning algorithms Parallel algorithms Costs Parallel processing Proposals Delay Context Processor scheduling

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A Dynamic Visual Sensor Denoising Method Based on Event Density Classification

A Dynamic Visual Sensor Denoising Method Based on Event Dens...

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IEEE International Conference on Networking, Sensing and Control

作者： Qiangxiao Zhou Hai Xiong Yiheng Wang Yuhao Shen Long Cheng College of Computer Science and Artificial Intelligence WenZhou University Wenzhou China

ISBN: (数字)9798350365221

ISBN: (纸本)9798350365238

Dynamic Visual Sensors (DVS) output pixel information asynchronously as an address event, only transmitting information for pixels where the intensity change exceeds a threshold, thereby reducing redundant data generation at the source. They possess advantages such as low-latency response and high efficiency with low power consumption. However, factors such as thermal noise and leakage current can lead to noise events even when the light intensity remains constant, which affects image quality and subsequent processing. In this paper, we propose a new method for calculating event density called Local Density Segmentation (LSD), which divides events and their surrounding neighborhoods into different regions for independent event density calculation. Based on LSD-calculated event density, we further propose a denoising method named Local Density Segmentation K-means (LSD-K), which utilizes event density as a feature to select and remove noise events. Finally, through experiments, we validate the performance of our algorithm.

关键词： Visualization Thermal factors Heuristic algorithms Noise Noise reduction Clustering algorithms Thermal noise Sensors partitioning algorithms Voltage control

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Improved MapReduce k-Means Clustering Algorithm with Combiner

Improved MapReduce k-Means Clustering Algorithm with Combine...

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International Conference on Computer Modeling and Simulation, UKSIM

作者： Prajesh P. Anchalia Department Of Computer Science and Engineering R V College Of Engineering Bangalore India

Data Mining and High Performance Computing are two broad fields in Computer Science. The k-Means Clustering is a very simple and popular data mining algorithm that has its application spread over a very broad spectrum. MapReduce is a programming style that is used for handling high volume data over a distributed computing environment. This paper proposes an improved and efficient method to implement the k-Means Clustering Technique using the MapReduce paradigm. The main idea is to introduce a combiner in the mapper function to decrease the amount of data to be written by the mapper and the amount of data to be read by the reducer which has considerably reduced the redundant Map-Reduce calls that have resulted in a significant reduction in the time required for clustering as it has decreased the read/write operations to a large extent. The implementation of Improved MapReduce k-Means Clustering has been clearly discussed and its effectiveness is compared to the regular implementation in an experimental analysis. The results consolidate this research by concluding that the Improved MapReduce Implementation of k-Means Clustering Algorithm out performs the regular implementation by over 300 seconds.

关键词： Clustering algorithms partitioning algorithms Algorithm design and analysis Programming Data mining Convergence Computational modeling

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Fast, automatic, procedure-level performance tuning

Fast, automatic, procedure-level performance tuning

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International Conference on Parallel Architecture and Compilation Techniques (PACT)

作者： Zhelong Pan Rudolf Eigenmann Purdue University School of ECE West Lafayette IN

ISBN: (纸本)9781509030224

This paper presents an automated performance tuning solution, which partitions a program into a number of tuning sections and finds the best combination of compiler options for each section. Our solution builds on prior work on feedback-driven optimization, which tuned the whole program, instead of each section. Our key novel algorithm partitions a program into appropriate tuning sections. We also present the architecture of a system that automates the tuning process; it includes several pre-tuning steps that partition and instrument the program, followed by the actual tuning and the post-tuning assembly of the individually-optimized parts. Our system, called PEAK, achieves fast tuning speed by measuring a small number of invocations of each code section, instead of the whole-program execution time, as in common solutions. Compared to these solutions PEAK reduces tuning time from 2.19 hours to 5.85 minutes on average, while achieving similar program performance. PEAK improves the performance of SPEC CPU2000 FP benchmarks by 12% on average over GCC O3, the highest optimization level, on a Pentium IV machine.

关键词： Tuning Optimization Context Program processors partitioning algorithms Instruments Benchmark testing

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Nature inspired techniques for data clustering

Nature inspired techniques for data clustering

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International Conference on Circuits, Systems, Communication and Information Technology Applications (CSCITA)

作者： Sandeep U. Mane Pankaj G. Gaikwad Dept. of CSE Rajarambapu Institute of Technology Rajaramnagar India

Nature is always a source of inspiration. In last few decades, the research is stimulated on new computing paradigms and result of this effort is emergence of new problem solving techniques like Nature Inspired Computing, Evolutionary Computing. Nature inspired problem solving techniques are widely used to solve complex problems. These techniques are widely used due to their decentralized and self-organized behavior. Such behavior is observed in social systems such as artificial bee colony algorithm, particle swarm optimization, ant colony optimization, bat algorithm, firefly algorithm, glowworm swarm optimization etc. In this paper we have given overview of nature inspired techniques used for data clustering, hybridization with traditional clustering techniques and their effectiveness.

关键词： Clustering algorithms Algorithm design and analysis partitioning algorithms Data mining Particle swarm optimization Problem-solving Information technology

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The priority queue as an example of hardware/software codesign 94

The priority queue as an example of hardware/software codesi...

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International Workshop on Hardware/Software Codesign (CODES)

作者： F. Hoeg N. Mellergaard J. Staunstrup Department of Computer Science Technical University of Denmark Lyngby Denmark

ISBN: (纸本)9780818663154

The paper identifies a number of issues that are believed to be important for hardware/software codesign. The issues are illustrated by a small comprehensible example: a priority queue. Based on simulations of a real application, we suggest a combined hardware/software realization of the priority queue. A priority queue is a data structure with a simple interface which in many applications is a performance bottleneck.< >

关键词： Hardware Application software Computational modeling Data structures Computer science Software performance Discrete event simulation partitioning algorithms Software algorithms Traffic control

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On using cached results to enumerate maximal k-cliques in GeneWeaver

On using cached results to enumerate maximal k-cliques in Ge...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Qi Yang Greg Speegle Erich J. Baker Department of Computer Science and Software Engineering University of Wisconsin-Platteville Platteville Wisconsin School of Engineering and Computer Science Baylor University Waco TX

The online biological data analytics tool GeneWeaver [1] uses a fast algorithm to directly compute k-cliques between different sets of data. By caching such results, we are able to compute k-clique faster in some cases. We derived a formula to determine whether or not to cache a result. In order to know if the cached results can be used to compute a desired k-clique, we also created a new algorithm to solve the generalized set coverage problem.

关键词： partitioning algorithms Approximation algorithms Algebra Automobiles Computer science Biology Tools

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