Twitter has rapidly grown to a popular social network in recent years and provides a large number of real-time messages for users. Tweets are presented in chronological order and users scan the followees' timelines to find what they are interested in. However, an information overload problem has troubled many users, especially those with many followees and thousands of tweets arriving every day. In this paper, we focus on recommending useful tweets that users are really interested in personally to reduce the users' effort to find useful information. Many kinds of information on Twitter are available for helping recommendation, including the user's own tweet history, retweet history and social relations between users. We propose a method of making tweet recommendations based on collaborative ranking to capture personal interests. It can also conveniently integrate the other useful contextual information. Our final method considers three major elements on Twitter: tweet topic level factors, user social relation factors and explicit features such as authority of the publisher and quality of the tweet. The experiments show that all the proposed elements are important and our method greatly outperforms several baseline methods.
With the rapid growth of social Web applications such as Twitter and online advertisements, the task of understanding short texts is becoming more and more important. Most traditional text mining techniques are designed to handle long text documents. For short text messages, many of the existing techniques are not effective due to the sparseness of text representations. To understand short messages, we observe that it is often possible to find topically related long texts, which can be utilized as the auxiliary data when mining the target short texts data. In this article, we present a novel approach to cluster short text messages via transfer learning from auxiliary long text data. We show that while some previous works for enhancing short text clustering with related long texts exist, most of them ignore the semantic and topical inconsistencies between the target and auxiliary data and may hurt the clustering performance on the short texts. To accommodate the possible inconsistencies between source and target data, we propose a novel topic model -Dual Latent Dirichlet Allocation (DLDA) model, which jointly learns two sets of topics on short and long texts and couples the topic parameters to cope with the potential inconsistencies between data sets. We demonstrate through large-scale clustering experiments on both advertisements and Twitter data that we can obtain superior performance over several state-of-art techniques for clustering short text documents.
Mesothelial/monocytic incidental cardiac excrescence (MICE) is a rare entity which is an amalgam of mesothelial cells, histiocytes, and fibrin, often found occasionally during cardiac valve replacement. We report a case in a 25-year-old Chinese female with serous mitral stenosis and patent foramen ovale. Routine and immunohistochemical stains and ultrastructure examination revealed the vegetation was predominantly composed of histocytes with scattered mesothelial cells. In fact nodular histiocytic/mesothelial hyperplasia (NHMH) is a similar lesion to MICE. MICE and NHMH could be unified, and NHMH may be a better choice.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.