Web data extraction based on structural similarity
Web data-extraction systems in use today mainly focus on the generation of extraction rules, i.e., wrapper induction. Thus, they appear ad hoc and are difficult to integrate when a holistic view is taken. Each phase in the data-extraction process is disconnected and does not share a common foundation to make the building of a complete system straightforward. In this paper, we demonstrate a holistic approach to Web data extraction. The principal component of our proposal is the notion of a document schema. Document schemata are patterns of structures embedded in documents. Once the document schemata are obtained, the various phases (e.g. training set preparation, wrapper induction and document classification) can be easily integrated. The implication of this is improved efficiency and better control over the extraction procedure. Our experimental results confirmed this. More importantly, because a document can be represented as avector of schema, it can be easily incorporated into existing systems as the fabric for integration.

title: WICCAP: from semi-structured data to structured data
abstract: Web data extraction is a technique for extracting and integrating data from Web based semistructured data. Wrappers function like the kernel of Web data extraction systems providing information mediator between users and a large number of heterogeneous data sources. Typically, they process semistructured documents generated from structured databases based on rules that are usually hidden to users. Much research has been done to use various methods to represent the knowledge of hidden rules and exploit techniques such as grammar induction, inductive logic programming, etc., to discover these rules that can be used by wrappers to extract data. An important property of semistructured data is its hierarchical structure. Intuitively, we can devise a method that can use this structure information to generate wrappers. We describe a Web data extraction system - WICCAP and its internal Web Data Extraction Language (WDEL) that provides unified view of Web data resources and extracted data. We describe some rule generation features of WICCAP and provide detailed description of the internal language and its implementation. We have conducted experiments to show the ease on generating wrappers with this approach.

title: A Neural Model of Contour Integration in the Primary Visual Cortex
abstract: Experimental observations suggest that contour integration may take place in V1. However, there has yet to be a model of contour integration that uses only known V1 elements, operations, and connection patterns.This article introduces such a model, using orient ation selective cells, local cortical circuits, and horizontal intracortical connections. The model is composed of recurrently connected excitatory neurons and inhibitory interneurons, receiving visual input via oriented receptive fields resembling those found in primary visual cortex. Intracortical interactions modify initial activity patterns from input, selectively amplifying the activities of edges that form smooth contours in the image. The neural activities produced by such interactions are oscillatory and edge segments within a contour oscillate in synchrony. It is shown analytically and empirically that the extent of contour enhancement and neural synchrony increases with the smoothness, length, and closure of contours, as observed in experiments on some of these phenomena. In addition, the model incorporates a feedback mechanism that allows higher visual centers selectively to enhance or suppress sensitivities to given contours, effectively segmenting one from another. The model makes the testable prediction that the horizontal cortical connections are more likely to target excitatory (or inhibitory) cells when the two linked cells have their preferred orientation aligned with (or orthogonal to) their relative receptive field center displacements.

title: Efficient interprocedural analysis for program parallelization and restructuring
abstract: An approach to efficient interprocedural analysis for program parallelization and restructuring is presented. Such analysis is needed for parallelizing loops which contain procedure calls. Our approach captures call effect on data dependencies by propagating the precise information of array subscripts from the called procedure. This allows the optimizing compiler to choose an efficient yet precise data dependence test scheme depending on the complexity of array reference patterns. The other existing methods do not provide such flexibility, hence may suffer from either imprecision or inefficiency. The paper also discusses usage of classical summary information in several important transformations for program parallelization. Experimental results are reported.

title: C-Miner: Mining Block Correlations in Storage Systems
abstract: Block correlations are common semantic patterns in storage systems. These correlations can be exploited for improving the effectiveness of storage caching, prefetching, data layout and disk scheduling. Unfortunately, information about block correlations is not available at the storage system level. Previous approaches for discovering file correlations in file systems do not scale well enough to be used for discovering block correlations in storage systems. 
 
In this paper, we propose C-Miner, an algorithm which uses a data mining technique called frequent sequence mining to discover block correlations in storage systems. C-Miner runs reasonably fast with feasible space requirement, indicating that it is a practical tool for dynamically inferring correlations in a storage system. Moreover, we have also evaluated the benefits of block correlation-directed prefetching and data layout through experiments. Our results using real system workloads show that correlation-directed prefetching and data layout can reduce average I/O response time by 12-25% compared to the base case, and 7-20% compared to the commonly used sequential prefetching scheme.

title: Factoring zero-dimensional ideals of linear partial differential operators
abstract: We present an algorithm for factoring a zero-dimensional left ideal in the ring <b>Q</b>(<i>x, y</i>) [∂<inf><i>x</i></inf>, ∂<inf><i>y</i></inf>], i.e. factoring a linear homogeneous partial differential system whose coefficients belong to <b>Q</b>(<i>x, y</i>), and whose solution space is finite-dimensional over <b>Q.</b> The algorithm computes all the zero-dimensional left ideals containing the given ideal. It generalizes the Beke-Schlesinger algorithm for factoring linear ordinary differential operators, and uses an algorithm for finding hyperexponential solutions of such ideals.

title: An Interlacing Result on Normalized Laplacians
abstract: Given a graph G, the normalized Laplacian associated with the graph G, denoted ${\cal L}$(G), was introduced by F. R. K. Chung and has been intensively studied in the last 10 years. For a k-regular graph G, the normalized Laplacian ${\cal L}$ (G) and the standard Laplacian matrix L(G) satisfy L(G) = k {\cal L} (G), and hence they have the same eigenvectors and their eigenvalues are directly related. However, for an irregular graph G, ${\cal L} (G) and L(G) behave quite differently, and the normalized Laplacian seems to be more natural. In this paper, Cauchy interlacing-type properties of the normalized Laplacian are investigated, and the following result is established. Let G be a graph, and let H = G - e, where e is an edge of G. Let $\lambda_1\geq \lambda_2 \geq \cdots \geq \lambda_{n}=0$ be the eigenvalues of ${\cal L}(G)$, and let $\theta_1\geq \theta_2 \geq \cdots \geq \theta_{n}$ be the eigenvalues of ${\cal L}(H)$. Then, $\lambda_{k-1} \geq \theta_{k}\geq \lambda_{k+1}$ for each k = 1, 2, 3, \dots, n, where $\lambda_{0} =2$ and $\lambda_{n+1}=0$. Applications are given for eigenvalues of graphs obtained from special graphs by adding or deleting a few edges. A short proof is given of the result that $G$ is a graph with each component a nontrivial bipartite graph if and only if $2-\lambda$ is an eigenvalue of ${\cal L}(G)$ for each eigenvalue $\lambda$ of ${\cal L }(G)$.