Feng jing and microsoft

The proposed fusion methods leverage the hidden links discovered by a duplicate photo detection algorithm, and aims at minimizing score differences of duplicate photos in different forums.

Both intermediate results and user studies show the proposed fusion methods are practical and efficient solutions to Web object ranking in cases we have described. Though the experiments were conducted on high-quality photo ranking, the proposed algorithms are also applicable to other ranking problems, such as movie ranking and music ranking. The year-old will be a key aide to ambassador Qin Gang , who took over the posting in July and is relatively inexperienced in US affairs compared to his predecessor Cui Tiankai, who retired after more than eight years.

Do you have questions about the biggest topics and trends from around the world? Get the answers with SCMP Knowledge , our new platform of curated content with explainers, FAQs, analyses and infographics brought to you by our award-winning team. He hailed the deal as an example of US-China cooperation and called for joint efforts on both sides, especially at the local and business levels, to overcome differences and avoid conflict or a new cold war. Huang Jing, dean of the Institute of International and Regional Studies at Beijing Language and Culture University, said Jing was a good choice to assist Qin, 55, who is on his first ambassadorship and lacks exposure in managing US affairs.

Yang, a former foreign minister, picked Jing as his close aide on his elevation to state councillor in It is believed that Jing accompanied Yang on many of his overseas trips, including to the US, and in meetings with foreign dignitaries.

In this paper, we propose, IGroup, an efficient and effective algorithm that organizes Web image search results into clusters. IGroup is different from all existing Web image search results clustering algorithms that only cluster the top few images using visual or textual features. Our proposed algorithm first identifies several query-related semantic clusters based on a key phrases extraction algorithm originally proposed for clustering general Web search results.

Different from existing Web page summarization methods that use page content or link context alone, both of them are considered as the sources of sentences in this work.

Most of existing learning-based summarization methods treat summarization as a sentence classification problem and train a classifier to discriminate between extracted sentences and non-extracted sentences of all training documents. The basic assumption of these methods is that sentences from different documents are comparable with respect to the class information.