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While the citation context of a reference may provide detailed and direct information about the nature of a citation, few studies have specifically addressed the role of this information in retrieving relevant documents from the literature primarily due to the lack of full text databases. In this paper, we design a retrieval system based on full texts in(More)
In this paper, we use query-level regression as the loss function. The regression loss function has been used in pointwise methods, however pointwise methods ignore the query boundaries and treat the data equally across queries, and thus the effectiveness is limited. We show that regression is an effective loss function for learning to rank when used in(More)
A proper understanding of how complex networks grow is important to get insights into the network structure, make predictions of future growth, and enable simulation of large networks. In this paper, we focus on social networks and try to understand, capture and predict dynamic behaviors on social networks. How social networks evolve, i.e. how individuals(More)
The large amounts of publicly available bibliographic repositories on the web provide us great opportunities to study the scientific behaviors of scholars. This paper aims to study the way we collaborate, model the dynamics of collaborations and predict future collaborations among authors. We investigate the collaborations in three disciplines including(More)
Scene classification of high-resolution remote sensing (HRRS) imagery is an important task in the intelligent processing of remote sensing images and has attracted much attention in recent years. Although the existing scene classification methods, e.g., the bag-of-words (BOW) model and its variants, can achieve acceptable performance, these approaches(More)
The central issue in language model estimation is smoothing, which is a technique for avoiding zero probability estimation problem and overcoming data sparsity. There are three representative smoothing methods: Jelinek-Mercer (JM) method; Bayesian smoothing using Dirichlet priors (Dir) method; and absolute discounting (Dis) method, whose parameters are(More)
An essential issue in document retrieval is ranking, which is used to rank documents by their relevancies to a given query. This paper presents a novel machine learning framework for ranking based on document groups. Multiple level labels represent the relevance of documents. The values of labels are used to quantify the relevance of the documents.(More)
Learning to rank algorithms are usually grouped into three types: the point wise approach, the pairwise approach, and the listwise approach, according to the input spaces. Much of the prior work is based on the three approaches to learn the ranking model to predict the relevance of a document to a query. In this paper, we focus on the problem of(More)
In this paper, we employ the programmable graphics processing unit (GPU) to accelerate the IPO computation for analyzing the scattering of open cavities. Since the iterative strategy accounts for multiple reflections on the inner wall, the IPO method provides a more accurate solution than the other high frequency asymptotic methods. However, it suffers from(More)