2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06)最新文献_第7页

Computing Horn Strong Backdoor Sets Thanks to Local Search 计算角强后门集得益于本地搜索

2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) Pub Date : 2006-11-13 DOI: 10.1109/ICTAI.2006.43

Lionel Paris, R. Ostrowski, P. Siegel, L. Sais

引用次数: 22

Motif Discovery as a Multiple-Instance Problem 主题发现作为一个多实例问题

2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) Pub Date : 2006-11-13 DOI: 10.1109/ICTAI.2006.89

Ya Zhang, Yixin Chen, Xiang-Hua Ji

引用次数: 7

Optimal Basic Block Instruction Scheduling for Multiple-Issue Processors Using Constraing Programming 基于约束规划的多任务处理器最优基本块指令调度

2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) Pub Date : 2006-11-13 DOI: 10.1142/S0218213008003765

A. Malik, Jim McInnes, P. V. Beek

{"title":"Optimal Basic Block Instruction Scheduling for Multiple-Issue Processors Using Constraing Programming","authors":"A. Malik, Jim McInnes, P. V. Beek","doi":"10.1142/S0218213008003765","DOIUrl":"https://doi.org/10.1142/S0218213008003765","url":null,"abstract":"Instruction scheduling is one of the most important steps for improving the performance of object code produced by a compiler. A fundamental problem that arises in instruction scheduling is to find a minimum length schedule for a basic block - a straight-line sequence of code with a single entry point and a single exit point - subject to precedence, latency, and resource constraints. Solving the problem exactly is NP-complete, and heuristic approaches are currently used in most compilers. In contrast, we present a scheduler that finds provably optimal schedules for basic blocks using techniques from constraint programming. In developing our optimal scheduler, the key to scaling up to large, real problems was in the development of preprocessing techniques for improving the constraint model. We experimentally evaluated our optimal scheduler on the SPEC 2000 integer and floating point benchmarks. On this benchmark suite, the optimal scheduler was very robust -all but a handful of the hundreds of thousands of basic blocks in our benchmark suite were solved optimally within a reasonable time limit - and scaled to the largest basic blocks, including basic blocks with up to 2600 instructions. This compares favorably to the best previous exact approaches","PeriodicalId":169424,"journal":{"name":"2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06)","volume":"516 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122219994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 51

LocalMotif - An In-Silico Tool for Detecting Localized Motifs in Regulatory Sequences LocalMotif -一种检测调控序列中局部基序的芯片工具

2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) Pub Date : 2006-11-13 DOI: 10.1109/ICTAI.2006.76

V. Narang, W. Sung, A. Mittal

{"title":"LocalMotif - An In-Silico Tool for Detecting Localized Motifs in Regulatory Sequences","authors":"V. Narang, W. Sung, A. Mittal","doi":"10.1109/ICTAI.2006.76","DOIUrl":"https://doi.org/10.1109/ICTAI.2006.76","url":null,"abstract":"In silico motif finding algorithms are often used for discovering protein-DNA binding sites in a set of regulatory sequences. Current algorithms mainly address motif discovery in short sequences. Analyzing long sequences can be quite challenging not only due to increasing time and memory requirements of the algorithm, but also decreasing accuracy. However, in case the motif is localized in a short interval of the long sequences relative to an anchor point, it is tenable to detect it easily by restricting the search to that interval. But the region of localization of the motif is not known a priori. This paper reports an algorithm called LocalMotif to detect localized motifs in long regulatory sequences. A novel score function predicts the region of localization of the motif. This score is combined with other scoring measures including Z-score and relative entropy to detect the motif. The algorithm is optimized for fast processing of long regulatory sequences. Tests on simulated and real datasets confirm that LocalMotif accurately determines the region of localization of motifs and automatically discovers the biologically relevant motifs, which can be detected by other motif finding algorithms only when the search is restricted to the relevant interval","PeriodicalId":169424,"journal":{"name":"2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06)","volume":"291 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115131973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Finding Crucial Subproblems to Focus Global Search 寻找焦点全局搜索的关键子问题

2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) Pub Date : 2006-11-13 DOI: 10.1109/ICTAI.2006.60

Susan L. Epstein, R. Wallace

引用次数: 10

Improving the Graph-Based Image Segmentation Method 改进基于图的图像分割方法

2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) Pub Date : 2006-11-13 DOI: 10.1109/ICTAI.2006.66

Ming Zhang, R. Alhajj

引用次数: 23

An Evolutionary Computational Method for N-Connection Subgraph Discovery 一种n连接子图发现的进化计算方法

2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) Pub Date : 2006-11-13 DOI: 10.1109/ICTAI.2006.32

Enhong Chen, Xujia Chen, P. Sheu, T. Qian

{"title":"An Evolutionary Computational Method for N-Connection Subgraph Discovery","authors":"Enhong Chen, Xujia Chen, P. Sheu, T. Qian","doi":"10.1109/ICTAI.2006.32","DOIUrl":"https://doi.org/10.1109/ICTAI.2006.32","url":null,"abstract":"The problem of n-connection subgraph discovery (n-CSDP for short) is to find a small sized subgraph that can well capture the relationship among the n given nodes in a large graph. However there have been very few researches directly addressing the CSDP problem. Furthermore the currently available methods, for example, the electricity analogues based algorithm can only be suitable for tackling the 2-keynodes CSDP and does not work any more when n is greater than two. To deal with this problem, we propose an effective approach to discover the subgraph in two stages. In the first stage, we propose a neighbor-growth based method to extract a relatively bigger candidate subgraph compared with that of result subgraph. In the second stage, an evolutionary algorithm for optimizing the result subgraph is proposed. For this purpose, UTM code, a transformed representation of the adjacent matrix of graphs is designed to encode the topology of subgraph as individuals. Then corresponding evolutionary operators able to be directly performed on UTM code are given. Thus the efficiency of the algorithm is largely improved. The experimental results obtained on two real large scale graphs with different topology characteristics demonstrate that our method solves n-connection subgraph discovery problems effectively","PeriodicalId":169424,"journal":{"name":"2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116854784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Briefing Tool that Learns Individual Report-Writing Behavior 一个学习个人报告写作行为的简报工具

2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) Pub Date : 2006-11-13 DOI: 10.1109/ICTAI.2006.7

Mohit Kumar, Nikesh Garera, Alexander I. Rudnicky

引用次数: 1

Discover Gene Specific Local Co-regulations Using Progressive Genetic Algorithm 利用渐进式遗传算法发现基因特异性局部协同调控

2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) Pub Date : 2006-11-13 DOI: 10.1109/ICTAI.2006.51

Ji Zhang, Q. Gao, Hai H. Wang

引用次数: 3

A Descend-Based Evolutionary Approach to Enhance Position Estimation in Wireless Sensor Networks 一种基于下降的改进无线传感器网络位置估计的进化方法

2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) Pub Date : 2006-11-13 DOI: 10.1109/ICTAI.2006.9

V. Tam, K. Cheng, K. Lui

{"title":"A Descend-Based Evolutionary Approach to Enhance Position Estimation in Wireless Sensor Networks","authors":"V. Tam, K. Cheng, K. Lui","doi":"10.1109/ICTAI.2006.9","DOIUrl":"https://doi.org/10.1109/ICTAI.2006.9","url":null,"abstract":"Wireless sensor networks have wide applicability to many important applications including environmental monitoring and military applications. Typically with the absolute positions of only a small portion of sensors predetermined, localization works for the precise estimation of the remaining sensor positions on which most location sensitive applications rely. Intrinsically, localization can be formulated as an unconstrained optimization problem based on various distance/path measures, for which most of the existing work focus on increasing its precision through different heuristic or mathematical techniques. In this paper, we propose to adapt an evolutionary approach, namely a micro-genetic algorithm (MGA), and its variant as postoptimizers to enhance the precision of existing localization methods including the Ad-hoc Positioning System. Our adapted MGA and its variants can easily be integrated into different localization methods. Besides, the prototypes of our evolutionary approach gained remarkable results on both uniform and anisotropic topologies of the simulation tests, thus prompting for many interesting directions for future investigation","PeriodicalId":169424,"journal":{"name":"2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128107041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 15