KEGG(Kyoto Encyclopedia of Genes and Genomes)〔和文〕 (特集 ゲノム医学の現在と未来--基礎と臨床) -- (データベース)

Communication without words

With the advent of the Internet, we are seeing more sophisticated techniques being developed to support e-Iearning. The rapid developme nt of Web-based learning and new concepts like virtual classrooms, virtual laboratories and virtual universities introduces many new issues to be addressed. On the technical side, we need to develop effective e-technologies for supporting distance education. On the learning and management side, we need to consider issues such as new style of learning and different system set-u p requirements. Finally, the issue of standardization of e-Iearning systems should also be considered. In this special issue, our focus will be on the technical side, although other issues related to knowledge and data engineering for e-Iearning may also be considered. Topics: In this special issue, we call for original papers describing novel knowledge and data engineering techniques that support e-Iearning. Preference will be given to papers that include an evaluation of users' experience in using the proposed methods. Areas of interests include, but are not limited to: • Semantic Web technology for e-Iearning • Data modeling (eg., XML) for efficient management of course materials • Searching and indexing techniques to suppo rt effective course notes retrieval • User-centric e-Iearning systems and user interaction management • Profiling techniques to support grading and learning recommendation • Data and knowledge base suppo rt for pervasive e-Iearning • Course material analysis and understanding • Automatic generation of questions and answers • Collaborative communities for e-Iearning

Knowledge and Data Engineering for e-Learning Special Issue of IEEE Transactions on Knowledge and Data Engineering

Role of layered structure in ductility improvement of layered Ti-Al metal composite

An overview of tailoring strain delocalization for strength-ductility synergy

Mining topics in Twitter is increasingly attracting more attention. However, the shortness and informality of tweets leads to extreme sparse vector representation with a large vocabulary, which makes the conventional topic models (e.g., Latent Dirichlet Allocation) often fail to achieve high quality underlying topics. Luckily, tweets always show up with rich user-generated hash tags as keywords. In this paper, we propose a novel topic model to handle such semi-structured tweets, denoted as Hash tag Graph based Topic Model (HGTM). By utilizing relation information between hash tags in our hash tag graph, HGTMestablishes word semantic relations, even if they haven't co-occurred within a specific tweet. In addition, we enhance the dependencies of both multiple words and hash tags via latent variables (topics) modeled by HGTM. We illustrate that the user-contributed hash tags could serve as weakly-supervised information for topic modeling, and hash tag relation could reveal the semantic relation between tweets. Experiments on a real-world twitter data set show that our model provides an effective solution to discover more distinct and coherent topics than the state-of-the-art baselines and has a strong ability to control sparseness and noise in tweets.

Hashtag Graph Based Topic Model for Tweet Mining

In this paper, we introduce a new topic model to understand the chaotic microblogging environment by using hashtag graphs. Inferring topics on Twitter becomes a vital but challenging task in many important applications. The shortness and informality of tweets leads to extreme sparse vector representations with a large vocabulary. This makes the conventional topic models (e.g., latent Dirichlet allocation [1] and latent semantic analysis [2] ) fail to learn high quality topic structures. Tweets are always showing up with rich user-generated hashtags. The hashtags make tweets semi-structured inside and semantically related to each other. Since hashtags are utilized as keywords in tweets to mark messages or to form conversations, they provide an additional path to connect semantically related words. In this paper, treating tweets as semi-structured texts, we propose a novel topic model, denoted as Hashtag Graph-based Topic Model ( HGTM ) to discover topics of tweets. By utilizing hashtag relation information in hashtag graphs, HGTM is able to discover word semantic relations even if words are not co-occurred within a specific tweet. With this method, HGTM successfully alleviates the sparsity problem. Our investigation illustrates that the user-contributed hashtags could serve as weakly-supervised information for topic modeling, and the relation between hashtags could reveal latent semantic relation between words. We evaluate the effectiveness of HGTM on tweet (hashtag) clustering and hashtag classification problems. Experiments on two real-world tweet data sets show that HGTM has strong capability to handle sparseness and noise problem in tweets. Furthermore, HGTM can discover more distinct and coherent topics than the state-of-the-art baselines.

Using Hashtag Graph-Based Topic Model to Connect Semantically-Related Words Without Co-Occurrence in Microblogs

Sub-event discovery is an effective method for social event analysis in Twitter. It can discover sub-events from large amount of noisy event-related information in Twitter and semantically represent them. The task is challenging because tweets are short, informal and noisy. To solve this problem, we consider leveraging event-related hashtags that contain many locations, dates and concise sub-event related descriptions to enhance sub-event discovery. To this end, we propose a hashtag-based mutually generative Latent Dirichlet Allocation model(MGe-LDA). In MGe-LDA, hashtags and topics of a tweet are mutually generated by each other. The mutually generative process models the relationship between hashtags and topics of tweets, and highlights the role of hashtags as a semantic representation of the corresponding tweets. Experimental results show that MGe-LDA can significantly outperform state-of-the-art methods for sub-event discovery.

/pdf/hashtag-based-sub-event-discovery-using-mutually-generative-3v76v3jdiq.pdf

Hashtag-based sub-event discovery using mutually generative LDA in Twitter

Hashtags are often utilized as metadata tags to mark messages for user-defined topics in a microblogging environment. However, difficulties in providing or selecting appropriate hashtags often force users giving up using them. In this paper, we propose a personalized method for hashtag recommendation that combines advantages of both topical information and collaborative intelligence. On one hand, we characterize the topic relevance of hashtags to posts based on content models. On the other hand, we predict an active user’s hashtag usage preference in a collaborative filtering manner. Overall, we recommend hashtags by relevant scores for a specific microblog posted by a specific user. Experimental results show that our model is an effective solution for hashtag suggestion (MRR is around 96%) which outperforms the state-of-the-art methods.

What to Tag Your Microblog: Hashtag Recommendation Based on Topic Analysis and Collaborative Filtering

On May 6, 2009, the X-ray imaging and biomedical application beamline (BL13W1) at Shanghai Synchrotron Radiation Facility (SSRF) officially opened to users, with 8-72.5 keV X-rays. The experimental station is equipped with four sets of X-ray CCD detectors of different pixel size (0.19-24 pm) for on-line phase-contrast imaging and micro-CT imaging with 0.8 mu m spatial resolution and 1 ms temporal resolution. An in vivo micro-CT experiment for a living insect was realized in 4s. An X-ray fluorescence detector is equipped for X-ray fluorescence mapping imaging and X-ray fluorescence micro-CT imaging with 50 pm spatial resolution. In order to meet different requirements from the users, several experimental methods, such as X-ray spiral micro-CT, Xray local micro-CT, X-ray fast micro-CT, X-ray grating-based differential micro-CT, X-ray fluorescence micro-CT and X-ray quantitative micro-CT have been developed, and nearly 60 papers related to those developments for this beamline have been published. Moreover, the beamline has realized the remote fast CT reconstruction, providing a great convenience for the users to process experimental data at their offices. As of August 2014, the beamline has offered the user beamtime of (23 145 h), from which 232 user papers have been published, including 151 SCI papers and 55 papers with SCI impact factor > 3. The quantity and quality of the user paper outcome keep a steady increase. Some typical user experimental results are introduced.

Yuan Wang

Papers

Hashtag Graph Based Topic Model for Tweet Mining

Using Hashtag Graph-Based Topic Model to Connect Semantically-Related Words Without Co-Occurrence in Microblogs

Hashtag-based sub-event discovery using mutually generative LDA in Twitter

What to Tag Your Microblog: Hashtag Recommendation Based on Topic Analysis and Collaborative Filtering

Latest advances of X-ray imaging and biomedical applications beamline at SSRF