Inspired by the widespread and increasing usage of natural gas, we study the power consumption minimization problem associated with natural gas pipeline transmission in gunbarrel networks with nonidentical compressors. To accurately and flexibly model both gas flow dynamics and compressor working domains, we formulate the problem as a dynamic programming problem. Then we propose an approximate solution approach based on state dimension reduction. We analyze the problem properties and characterize conditions under which optimality is not compromised by the proposed solution approach. Next, we conduct numerical experiments using two data sets based on real networks in China and a data set from the public library GasLib. Numerical results demonstrate that the proposed solution approach significantly reduces computation time without compromising optimality in most cases. Specifically, the proposed solution approach obtains optimal solutions more than a 100 times faster than the exhaustive search when gas pressures are discretized at 0.01 MPa. Further, the optimality gaps do not exceed 0.4%.

One of the most important steps in web crawling is determining the starting points, or seed selection. This paper identifies and explores the problem of seed selection in web-scale incremental crawlers. We argue that seed selection is not a trivial but very important problem. Selecting proper seeds can increase the number of pages a crawler will discover, and can result in a repository with more "good" and less "bad" pages. We propose a graph-based framework for crawler seed selection, and present several algorithms within this framework. Evaluation on real web data showed significant improvements over heuristic seed selection approaches.

Web information is often presented in the form of record, e.g., a product record on a shopping website or a personal profile on a social utility website. Given a host webpage and related information needs, how to identify relevant records as well as their internal semantic structures is critical to many online information systems. Wrapper induction is one of the most effective methods for such tasks. However, most traditional wrapper techniques have issues dealing with web records since they are designed to extract information from a page, not a record. We propose a record-level wrapper system. In our system, we use a novel ``broom'' structure to represent both records and generated wrappers. With such representation, our system is able to effectively extract records and identify their internal semantics at the same time. We test our system on 16 real-life websites from four different domains. Experimental results demonstrate 99\% extraction accuracy in terms of F1-Value.

This paper identifies and explores the problem of seed selection in a web-scale crawler. We argue that seed selection is not a trivial but very important problem. Selecting proper seeds can increase the number of pages a crawler will discover, and can result in a collection with more ``good" and less "bad" pages. Based on the analysis of the graph structure of the web, we propose several seed selection algorithms. Effectiveness of these algorithms is proved by our experimental results on real web data.

We present a demonstration of an interactive wrapper induction system, called Pictor, which is able to minimize labeling cost, yet extract data with high accuracy from a website. Our demonstration will introduce two proposed technologies: record-level wrappers and a wrapper-assisted labeling strategy. These approaches allow Pictor to exploit previously generated wrappers, in order to predict similar labels in a partially labeled webpage or a completely new webpage. Our experiment results show the effectiveness of the Pictor system.

Social annotation has gained increasing popularity in many Web-based applications, leading to an emerging research area in text analysis and information retrieval. This paper is concerned with developing probabilistic models and computational algorithms for social annotations. We propose a unified framework to combine the modeling of social annotations with the language modeling-based methods for information retrieval. The proposed approach consists of two steps: (1) discovering topics in the contents and annotations of documents while categorizing the users by domains; and (2) enhancing document and query language models by incorporating user domain interests as well as topical background models. In particular, we propose a new general generative model for social annotations, which is then simplified to a computationally tractable hierarchical Bayesian network. Then we apply smoothing techniques in a risk minimization framework to incorporate the topical information to language models. Experiments are carried out on a realworld annotation data set sampled from del.icio.us. Our results demonstrate significant improvements over traditional approaches.

Many websites have large collections of pages generated dynamically from an underlying structured source like a database. The data of a category are typically encoded into similar pages by a common script or template. In recent years, some value-added services, such as comparison shopping and vertical search in a specific domain, have motivated the research of extraction technologies with high accuracy. Almost all previous works assume that input pages of a wrapper induction system conform to a common template and they can be easily identified in terms of a common schema of URL. However, we observed that it is hard to distinguish different templates using dynamic URLs today. Moreover, since extraction accuracy heavily depends on how consistent input pages are, we argue that it is risky to determine whether pages share a common template solely based on URLs. Instead, we propose a new approach that utilizes similarity between pages to detect templates. Our approach separates pages with notable inner differences and then generates wrappers, respectively. Experimental results show that our proposed approach is feasible and effective for improving extraction accuracy.

2.5-D integration is a promising technique to significantly reduce the interconnection delay and thus bring advancement to VLSI technology. New CAD tools and approaches are desired by 2.5-D IC circuit design. This paper designed a novel representation multi-layer-BSG and proposed an effective algorithm based on this structure. Quick strategy for solution evaluation and phrase-based strategy for simulated annealing engine are used to reduce the runtime complexity and to improve the performance. Compared with other representations, our algorithm are experimentally proved to be a better choice for 2.5-D floorplanning problem.