A simple, fast, and cost-effective co-self-assembly approach to fabricate large-area two-dimensional (2D) binary colloidal crystals has been developed. By manipulating the size ratio and number ratio of the two monodisperse polystyrene latexes, a variety of binary colloidal crystal monolayers with different structures were successfully prepared. The co-self-assembly mechanism of the 2D binary colloidal crystals at the air-water interface was investigated. It was found that the glass slide and the ethanol involved in this work had played significant roles as buffer storage and spreading agent, respectively.

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Web query classification (QC) aims to classify Web users' queries, which are often short and ambiguous, into a set of target categories. QC has many applications including page ranking in Web search, targeted advertisement in response to queries, and personalization. In this paper, we present a novel approach for QC that outperforms the winning solution of the ACM KDDCUP 2005 competition, whose objective is to classify 800,000 real user queries. In our approach, we first build a bridging classifier on an intermediate taxonomy in an offline mode. This classifier is then used in an online mode to map user queries to the target categories via the above intermediate taxonomy. A major innovation is that by leveraging the similarity distribution over the intermediate taxonomy, we do not need to retrain a new classifier for each new set of target categories, and therefore the bridging classifier needs to be trained only once. In addition, we introduce category selection as a new method for narrowing down the scope of the intermediate taxonomy based on which we classify the queries. Category selection can improve both efficiency and effectiveness of the online classification. By combining our algorithm with the winning solution of KDDCUP 2005, we made an improvement by 9.7% and 3.8% in terms of precision and F1 respectively compared with the best results of KDDCUP 2005.

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The integration of 3D-ordered macropores with poly(ionic liquid)s can significantly extend the functions of poly(ionic liquid)s, and afford a new type of material, which can serve as not only tunable photonic crystals and anion-directed molecular gating system, but also as a surface with enhanced tunable wettability and unique stable electro-optic switching.

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Text message stream is a newly emerging type of Web data which is produced in enormous quantities with the popularity of Instant Messaging and Internet Relay Chat. It is beneficial for detecting the threads contained in the text stream for various applications, including information retrieval, expert recognition and even crime prevention. Despite its importance, not much research has been conducted so far on this problem due to the characteristics of the data in which the messages are usually very short and incomplete. In this paper, we present a stringent definition of the thread detection task and our preliminary solution to it. We propose three variations of a single-pass clustering algorithm for exploiting the temporal information in the streams. An algorithm based on linguistic features is also put forward to exploit the discourse structure information. We conducted several experiments to compare our approaches with some existing algorithms on a real dataset. The results show that all three variations of the single-pass algorithm outperform the basic single-pass algorithm. Our proposed algorithm based on linguistic features improves the performance relatively by 69.5% and 9.7% when compared with the basic single-pass algorithm and the best variation algorithm in terms of F1 respectively.

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