Abstract

The relationship between the NMR methyl group axial order parameter and the side chain conformational entropy is investigated in inhibitor-bound and apo human HIV protease using molecular dynamics simulation. Three distinct entropic classes of methyl-bearing side chains, determined by the topological distance of the methyl group from the protein backbone (i.e., the number of χ-bonds between the Cα and the carbon of the CH3 group), are revealed by atomistic trajectory analyses performed in the local frame of reference of individual methyl probes. The results demonstrate that topologically equivalent methyl groups experience similar nonbonded microenvironments regardless of the type of residues to which they are attached. Similarly, methyl groups that belong to the same side chain but that are not topologically equivalent exhibit different thermodynamic and dynamic properties. The two-parameter classification (based upon entropy and methyl axial order parameter) of side chains described here permits improved estimates of the conformational entropies of proteins from NMR motional parameters.

Abstract

Toggle navigation CCSD. HAL: HAL; HALSHS; TEL; MédiHAL; Liste des portails; AURéHAL; API; Data; Documentation. Episciences.org: Episciences.org; Revues; Documentation. Sciencesconf.org; Support. Connexion: Connexion; Connexion avec ORCID; se connecter avec Fédération; Créer un compte; Mot de passe oublié ? Login oublié ? fr; en. HAL - hal.archives-ouvertes.fr. Accueil; Déposer; Consulter: par thème arXiv; Par type de document; Les derniers dépôts; par année de publication; par laboratoire; —————; HAL; TEL; INSPIRE-HEP; ArXiv; CDS - CERN. Rechercher. in2p3-01577700, version 1. Structure, energetics, and dynamics of smectite clay hydration: Molecular dynamics investigations of hectorite” (oral). Christin P. Morrow A. Ozgur Yazaydin 1 Marimuthu Krishnan Geoffrey M. Bowers Andrey G. Kalinichev 2 R. James Kirkpatrick Détails. 1 Department of Chemistry. 2 SUBATECH - Laboratoire

Abstract

Legal judgements are complex in nature and contain cita-tions to other judgements. Research efforts are going on to develop meth-ods for efficient search of relevant legal information by extending the pop-ular approaches used in information retrieval and web searching research areas. In the literature, it was shown that it is possible to find similarjudgements by exploiting citations or links. In this paper, an approachh as been has been proposed using the notion of “paragraph-link” to im-prove the efficiency of link-based similarity method. The experiments on real-world data set and user evaluation study show the encouraging results.

Abstract

Information on web in various languages is growing fast, but large amount of content still exists in English. There are several cases of English text re-use (cross language plagiarism) observed in non-English languages. Detecting text re-use in non-English languages is a challenging task due to complexity of the language used. Complexity further increases for less resource languages like Arabic and Indian languages. In this paper, we address the problem proposed in FIRE CL!TR 2011 task of detecting plagiarized documents in Hindi language which was reused from English language source documents. We proposed three approaches using classification and key-phrase retrieval techniques. Our winning approach attained 0.792 F-measure.

Abstract

This paper presents a novel answer summarization approach for community Question Answering services (cQAs) to address the problem of “incomplete answer”, i.e., missing valuable information from the “best answer” of a complex multi-sentence question, which can be obtained from other answers to the same question. Our method automatically generate a novel and non-redundant summary from cQA answers using structured determinantal point processes (SDPP). Experimental evaluation on sample dataset from Yahoo Answers shows significant improvement over baseline approaches.

Abstract

This paper describes the traditional authorship attribution subtask of the PAN/CLEF 2013 workshop. In our attempt to classify the documents based on gender and age of an author, we have applied a traditional approach of topic modeling using Latent Dirichlet Allocation [LDA]. We used the content based features like topics and style based features like preposition-frequencies, which act as the efficient markers to demarcate the authorship attributes based on age and gender. We demonstrated tenfold cross validation and observed that our classification approach using Maxent and LDA gave an accuracy of 53.3% for English language and 52% for Spanish Language.

Abstract

The ever increasing data on world wide web calls for the use of vertical search engines. Sandhan is one such search engine which offers search in tourism and health genres in more than 10 different Indian languages. In this work we build a URL based genre identification module for Sandhan. A direct impact of this work is on building focused crawlers to gather Indian language content. We conduct experiments on tourism and health web pages in Hindi language. We experiment with three approaches-list based, naive Bayes and incremental naive Bayes. We evaluate our approaches against another web page classification algorithm built on the parsed text of manually labeled web pages. We find that incremental naive Bayes approach outperforms the other two. While doing our experiments we work with different features like words, n-grams and all grams. Using n-gram features we achieve classification accuracies of 0.858 and 0.873 for tourism and health genres respectively.

Abstract

The microblogging platforms are increasingly becoming a lucrative prospect for advertisers to attract the customers. The challenge with advertising on such platforms is that there is very little content to retrieve relevant ads. As the microblogging content is short and noisy and the ads are short too, there is a high amount of lexical/vocabulary mismatch between the micropost and the ads. To bridge this vocabulary mismatch, we propose a conceptual approach that transforms the content into a conceptual space that represent the latent concepts of the content. We empirically show that the conceptual model performs better than various state-of-the-art techniques the performance gain obtained are substantial and significant.

Abstract

In this paper, we propose a novel approach to address the problem of chat summarization. We summarize real-time chat conversations which contain multiple users with frequent shifts in topic. Our approach consists of two phases. In the first phase, we leverage topic modeling using web documents to find the primary topic of discussion in the chat. Then, in the summary generation phase, we build a semantic word space to score sentences based on their association with the primary topic. Experimental results show that our method significantly outperforms the baseline systems on ROUGE F-scores.

Abstract

Content discovery is fast becoming the preferred tool for user engagement on the web. Discovery allows users to get educated and entertained about their topics of interest. StumbleUpon is the largest personalized content discovery engine on the Web, delivering more than 1 billion personalized recommendations per month. As a recommendation system one of the primary metrics we track is whether the user returns (retention) to use the product after their initial experience (session) with StumbleUpon