Event-Based Summarization Using Critical Temporal Event Term Chain
In this paper, we investigate whether temporal relations among event terms can help improve event-based summarization and text cohesion of final summaries. By connecting event terms with happens-before relations, we build a temporal event term graph for source documents. The event terms in the critical temporal event term chain identified from the maximal weakly connected component are used to evaluate the sentences in source documents. The most significant sentences are included in final summaries. Experiments conducted on the DUC 2001 corpus show that event-based summarization using the critical temporal event term chain is able to organize final summaries in a more coherent way and make improvement over the well-known tf*idf-based and PageRank-based summarization approaches.

title: A Novel Composite Kernel Approach to Chinese Entity Relation Extraction
abstract: Relation extraction is the task of finding semantic relations between two entities from the text. In this paper, we propose a novel composite kernel for Chinese relation extraction. The composite kernel is defined as the combination of two independent kernels. One is the entity kernel built upon the non-content-related features. The other is the string semantic similarity kernel concerning the content information. Three combinations, namely linear combination, semi-polynomial combination and polynomial combination are investigated. When evaluated on the ACE 2005 Chinese data set, the results show that the proposed approach is effective.

title: A Mixture Model for Expert Finding
abstract: This paper addresses the issue of identifying persons with expertise knowledge on a given topic. Traditional methods usually estimate the relevance between the query and the support documents of candidate experts using, for example, a language model. However, the language model lacks the ability of identifying semantic knowledge, thus results in some right experts cannot be found due to not occurrence of the query terms in the support documents. In this paper, we propose a mixture model based on Probabilistic Latent Semantic Analysis (PLSA) to estimate a hidden semantic theme layer between the terms and the support documents. The hidden themes are used to capture the semantic relevance between the query and the experts. We evaluate our mixture model in a real-world system, ArnetMiner. Experimental results indicate that the proposed model outperforms the language models.

title: Implementation Technique of Unrestricted LL Action Grammar
abstract: Action Grammar is a grammar with action symbols appearing in the right part of production rules, which can be used to implement semantic analyzer automatically. An action symbol represents the name of a semantic routine. Implementation of action grammar relies on specific parsing methods, such as LL(K), recursive-descent or LR, and so on. However, these parsing methods have their own restrictions on grammars, therefore, in order to accomplish these parsing methods certain equivalent transformations have been done to make the grammars meet the restrictions. This paper has proposed an implementation technique of LL action grammar, which requires no transformation to the original grammar. Our approach includes following steps: parsing the input string with back-tracking and producing the parsing paths for each non-terminal symbol, then constructing LL parsing table with the parsing paths obtained, finally conducting semantic analysis with LL parsing table. The main contribution of this paper is that non-backtracking semantic analyzer has been achieved without making any transformations to the grammar.

title: Study on the Co-Existence of 20ms TTI and 5ms TTI in E-DCH of TD-SCDMA HSUPA
abstract: The wireless link with fixed parameters is subject to time-varying channel, and the battery energy is limited in mobile terminals. Thus we consider the co-existence of 20msTTI and 5ms TTI in E-DCH of TD-SCDMA HSUPA. In this paper, based on the comparative study on the link performances with different TTI and the analysis on the link simulation results, we discuss the different TTI’s influence on the terminal power consumption. Furthermore, the system level performances of semi-static TTI and traditional single TTI configurations are compared. Finally the conclusions are drawn: 20ms and 5msTTI should co-exist in TD-SCDMA HSUPA, and be adopted respectively in different channel condition to save the terminal power. The data throughput of semi-static TTI configuration is greater than those of both single TTI configurations, and the semi-static TTI selection between 20ms and 5ms is implemented according to the moving speed of a specific UE. TD-SCDMA; HSUPA; TTI Co-existence; Terminal power consumption; Semi-static TTI configuration

title: Biomechanical Modeling of Bone-Needle Interaction for Haptic Rendering in Needle Insertion Simulation
abstract: Medical simulators are increasingly being used for surgical training. For interactive surgical simulation involving haptic rendering, the force at the needle tip has to be computed very fast. We are developing biomechanical models for bone needle insertion. The cortical bone can be regarded as a dense form of cancellous bone that can be modeled using a linear elastic material. The porosity of the bone determines the resistance felt as the user inserts the needle into the bone. The bar element method that represents each trabecular bone as a FE beam is most computationally efficient. With 1000 FE elements, the computed force feedback were close to the insertion force measured during experiments. However, the extended bar element method may be the more appropriate choice for taking into consideration the trabecular distribution and hence, inhomogeneous of bone. The simulation studies on bone-needle interaction also showed that a diamond bevel needle may penetrate the bone with less force

title: Name Disambiguation Using Atomic Clusters
abstract: Name ambiguity is a critical problem in many applications, in particular in the online bibliography systems, such as DBLP and CiteSeer. Previously, several clustering based methods have been proposed although, the problem still presents to be a big challenge for both research and industry communities. In this paper, we present a complementary study to the problem from another point of view. We propose an approach of finding atomic clusters to improve the performance of existing clustering-based methods. We conducted experiments on a dataset from a real-world system: Arnetminer.org. Experiments results show that significant improvements can be obtained by using the proposed atomic clusters finding approach (about +8% and +27% improvements depending on different clustering methods).