32477053
lambda-Level Rough Equality Relation and the Inference of Rough Paramodulation
In the paper, it defines a λ-level rough equality relation in rough logic and establishes a rough sets on real-valued information systems with it. We obtain some related properties and relative rough paramodulation inference rules. They will be used in the approximate reasoning and deductive resolutions of rough logic. Finally, it proves that λ-level rough paramodulant reasoning is sound.
14783314
Ontology Capture Based on Granular Computing
Ontology capture is a key topic in constructing an ontology, and it is a procedure of knowledge acquisition. In this paper, by applying the principle of granular computing, some concepts about information granules, including domain feature information granule, domain object information granule, were defined, the context of the domain ontology may be granulated. A domain granule and a domain granule lattice were presented. An algorithm of generating the domain granule lattice was proposed, and was illustrated by a real example. With the domain granule lattice, ontology capture can be fulfilled. Therefore, granular computing should be of great help in representing and constructing an ontology
15497881
A congestion-driven placement framework with local congestion prediction
In this paper, we present a novel congestion-driven placement methodology. We analyze netlist structure and perform local congestion prediction to determine the amount of white space which will be attached to each cell. We place the modified netlist using a total-wire-length-driven placer and obtain an initial legal placement. We extract the congestion information and determine the target white space distribution. Finally, we apply placement migration, an incremental placement technique, to transform the initial placement into a placement which satisfies the target white space distribution. We provide experimental data to demonstrate the effectiveness of our congestion driven placement framework. Compared with the recent academic routability-driven placer Dragon [12], we achieve significant routability improvements in much shorter cpu run time.
17071580
Pre-layout physical connectivity prediction with application in clustering-based placement
In this paper, we introduce a structural metric, logic contraction, for pre-layout physical connectivity prediction. For a given set of nodes forming a cluster in a netlist, we can predict their proximity in the final layout based on the logic contraction value of the cluster. We demonstrate a very good correlation of our pre-layout measure with the post-layout physical distances between those nodes. We show an application of the logic contraction to circuit clustering. We compare our seed-growth clustering algorithm with the existing efficient clustering techniques. Experimental results demonstrate the effectiveness of our new clustering method.
10460404
Pre-layout wire length and congestion estimation
In this paper, we study the pre-layout wire length and congestion estimation. We find that two structural metrics, mutual contraction and net range, can be used to predict wire lengths. These metrics have different application ranges and complement each other. We also propose a new metric, the structural pin density, to capture the peak routing congestion of designs. Larger maximum pin densities usually lead to larger peak congestions in circuits with similar average congestions. We demonstrate experimentally very good correlation of our pre-layout measures with post layout interconnect lengths. We also isolate the structural netlist properties which cause the peak congestion.
8754175
Summarizing level-two topological relations in large spatial datasets
Summarizing topological relations is fundamental to many spatial applications including spatial query optimization. In this article, we present several novel techniques to effectively construct cell density based spatial histograms for range (window) summarizations restricted to the four most important level-two topological relations: contains, contained, overlap, and disjoint. We first present a novel framework to construct a multiscale Euler histogram in 2D space with the guarantee of the exact summarization results for aligned windows in constant time. To minimize the storage space in such a multiscale Euler histogram, an approximate algorithm with the approximate ratio 19/12 is presented, while the problem is shown NP-hard generally. To conform to a limited storage space where a multiscale histogram may be allowed to have only k Euler histograms, an effective algorithm is presented to construct multiscale histograms to achieve high accuracy in approximately summarizing aligned windows. Then, we present a new approximate algorithm to query an Euler histogram that cannot guarantee the exact answers; it runs in constant time. We also investigate the problem of nonaligned windows and the problem of effectively partitioning the data space to support nonaligned window queries. Finally, we extend our techniques to 3D space. Our extensive experiments against both synthetic and real world datasets demonstrate that the approximate multiscale histogram techniques may improve the accuracy of the existing techniques by several orders of magnitude while retaining the cost efficiency, and the exact multiscale histogram technique requires only a storage space linearly proportional to the number of cells for many popular real datasets.
8930290
Data mining of gene expression changes in Alzheimer brain
Genome-wide transcription profiling is a powerful technique for studying the enormous complexity of cellular states. Moreover, when applied to disease tissue it may reveal quantitative and qualitative alterations in gene expression that give information on the context or underlying basis for the disease and may provide a new diagnostic approach. However, the data obtained from high-density microarrays is highly complex and poses considerable challenges in data mining. The data requires care in both pre-processing and the application of data mining techniques. This paper addresses the problem of dealing with microarray data that come from two known classes (Alzheimer and normal). We have applied three separate techniques to discover genes associated with Alzheimer disease (AD). The 67 genes identified in this study included a total of 17 genes that are already known to be associated with Alzheimer's or other neurological diseases. This is higher than any of the previously published Alzheimer's studies. Twenty known genes, not previously associated with the disease, have been identified as well as 30 uncharacterized expressed sequence tags (ESTs). Given the success in identifying genes already associated with AD, we can have some confidence in the involvement of the latter genes and ESTs. From these studies we can attempt to define therapeutic strategies that would prevent the loss of specific components of neuronal function in susceptible patients or be in a position to stimulate the replacement of lost cellular function in damaged neurons. Although our study is based on a relatively small number of patients (four AD and five normal), we think our approach sets the stage for a major step in using gene expression data for disease modeling (i.e. classification and diagnosis). It can also contribute to the future of gene function identification, pathology, toxicogenomics, and pharmacogenomics.