We consider a mechanical system consisting of n-penduli and a d-degree-of-freedom rotator. The phase space of the rotator defines a normally hyperbolic invariant manifold ( _0 ). We apply a time-dependent perturbation, which is not assumed to be either Hamiltonian, or periodic, or quasi-periodic, as we allow for rather general time dependence. The strength of the perturbation is given by a parameter ( R ). For all (| | ) sufficiently small, the augmented flow—obtained by making the time into a new variable—has a normally hyperbolic locally invariant manifold ( _ ). For ( =0 ), ( _0= _0 R ). We define a Melnikov-type vector, which gives the first-order expansion of the displacement of the stable and unstable manifolds of ( _0 ) under the perturbation. We provide an explicit formula for the Melnikov vector in terms of convergent improper integrals of the perturbation along homoclinic orbits of the unperturbed system. We show that if the perturbation satisfies some explicit non-degeneracy conditions, then the stable and unstable manifolds of ( _ ), (W^ s ( _ ) ) and (W^ u ( _ ) ), respectively, intersect along a transverse homoclinic manifold, and, moreover, the splitting of (W^ s ( _ ) ) and (W^ u ( _ ) ) can be explicitly computed, up to the first order, in terms of the Melnikov-type vector. This implies that the excursions along some homoclinic trajectories yield a non-trivial increase of order (O( ) ) in the action variables of the rotator, for all sufficiently small perturbations. The formulas that we obtain are independent of the unperturbed motions in ( _0 ), and give, at the same time, the effects on periodic, quasi-periodic, or general-type orbits. When the perturbation is Hamiltonian, we express the effects of the perturbation, up to the first order, in terms of a Melnikov potential. In addition, if the perturbation is periodic, we obtain that the non-degeneracy conditions on the Melnikov potential are generic.
Some other related papers are @cite_30 @cite_22 @cite_14 @cite_10 @cite_31 @cite_23 @cite_3 @cite_9 @cite_27 .
@cite_30 @cite_14 @cite_22 @cite_9 @cite_3 @cite_27 @cite_23 @cite_31 @cite_10
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When we write or prepare to write a research paper, we always have appropriate references in mind. However, there are most likely references we have missed and should have been read and cited. As such a good citation recommendation system would not only improve our paper but, overall, the efficiency and quality of literature search. Usually, a citation's context contains explicit words explaining the citation. Using this, we propose a method that "translates" research papers into references. By considering the citations and their contexts from existing papers as parallel data written in two different "languages", we adopt the translation model to create a relationship between these two "vocabularies". Experiments on both CiteSeer and CiteULike dataset show that our approach outperforms other baseline methods and increase the precision, recall and f-measure by at least 5 to 10 , respectively. In addition, our approach runs much faster in the both training and recommending stage, which proves the effectiveness and the scalability of our work. Resume Dans cet article qui developpe une Note ( Giraud, G., C. R. Acad. Sci. Paris Ser. A , 276 (1973) 1173–1175), nous prouvons que, pour n assez grand, tout bicoloriage des aretes de K n contient une proportion de K 4 dont les six aretes sont de meme couleur au moins egale a 1 46 , et que si le nombre de triangles unicolores est minimal, cette proportion passe au moins a (5 − 10 1 2 ) 64 a O(n 7 2 ) pres. Nous exposons aussi une methode de majoration des nombres de Ramsey binaires-bicolores qui fournit ϱ(6, 6) ≤ 169. Document authors commonly use tables to support arguments presented in the text. But, because tables are usually separate from the main body text, readers must split their attention between different parts of the document. We present an interactive document reader that automatically links document text with corresponding table cells. Readers can select a sentence (or tables cells) and our reader highlights the relevant table cells (or sentences). We provide an automatic pipeline for extracting such references between sentence text and table cells for existing PDF documents that combines structural analysis of tables with natural language processing and rule-based matching. On a test corpus of 330 (sentence, table) pairs, our pipeline correctly extracts 48.8 of the references. An additional 30.5 contain only false negatives (FN) errors -- the reference is missing table cells. The remaining 20.7 contain false positives (FP) errors -- the reference includes extraneous table cells and could therefore mislead readers. A user study finds that despite such errors, our interactive document reader helps readers match sentences with corresponding table cells more accurately and quickly than a baseline document reader. Citations play an essential role in navigating academic literature and following chains of evidence in research. With the growing availability of large digital archives of scientific papers, the automated extraction and analysis of citations is becoming increasingly relevant. However, existing approaches to citation extraction still fall short of the high accuracy required to build more sophisticated and reliable tools for citation analysis and corpus navigation. In this paper, we present techniques for high accuracy extraction of citations and references from academic papers. By collecting multiple sources of evidence about entities from documents, and integrating citation extraction, reference segmentation, and citation-reference matching, we are able to significantly improve performance in subtasks including citation identification, author named entity recognition, and citation-reference matching. Applying our algorithm to previously-unseen documents, we demonstrate high F-measure performance of 0.980 for citation extraction, 0.983 for author named entity recognition, and 0.948 for citation-reference matching. Citation relationship between scientific publications has been successfully used for scholarly bibliometrics, information retrieval and data mining tasks, and citation-based recommendation algorithms are well documented. While previous studies investigated citation relations from various viewpoints, most of them share the same assumption that, if paper1 cites paper2 (or author1 cites author2), they are connected, regardless of citation importance, sentiment, reason, topic, or motivation. However, this assumption is oversimplified. In this study, we employ an innovative "context-rich heterogeneous network" approach, which paves a new way for citation recommendation task. In the network, we characterize 1) the importance of citation relationships between citing and cited papers, and 2) the topical citation motivation. Unlike earlier studies, the citation information, in this paper, is characterized by citation textual contexts extracted from the full-text citing paper. We also propose algorithm to cope with the situation when large portion of full-text missing information exists in the bibliographic repository. Evaluation results show that, context-rich heterogeneous network can significantly enhance the citation recommendation performance. These Ecole polytechnique federale de Lausanne EPFL, n° 2366 (2001)Faculte informatique et communicationsJury: Paolo Frasconi, Roger Hersch, Martin Rajman, Jurgen Schmidhuber Public defense: 2001-4-6 Reference doi:10.5075 epfl-thesis-2366Print copy in library catalog Record created on 2005-03-16, modified on 2016-08-08 Voir la bibliographie du memoire pour les references du resume. See the thesis s bibliography for the references in the summary. References.- Technical Foundations.- 1 Introduction.- 2 Graphs and Their Representation.- 3 Graph Planarity and Embeddings.- 4 Graph Drawing Methods.- References.- WilmaScope - A 3D Graph Visualization System.- 1 Introduction.- 2 Applications.- 3 Algorithms.- 4 Implementation.- 5 Examples.- 6 Software.- References.- Pajek - Analysis and Visualization of Large Networks.- 1 Introduction.- 2 Applications.- 3 Algorithms.- 4 Implementation.- 5 Examples.- 6 Software.- References.- Tulip - A Huge Graph Visualization Framework.- 1 Introduction.- 2 Applications.- 3 Algorithms.- 4 Implementation.- 5 Examples.- 6 Software.- References.- Graphviz and Dynagraph - Static and Dynamic Graph Drawing Tools.- 1 Introduction.- 2 Applications.- 3 Algorithms.- 4 Implementation.- 5 Examples.- 6 Software.- References.- AGD - A Library of Algorithms for Graph Drawing.- 1 Introduction.- 2 Applications.- 3 Algorithms.- 4 Implementation.- 5 Examples.- 6 Software.- References.- yFiles - Visualization and Automatic Layout of Graphs.- 1 Introduction.- 2 Applications.- 3 Algorithms.- 4 Implementation.- 5 Examples.- 6 Software.- References.- GDS - A Graph Drawing Server on the Internet.- 1 Introduction.- 2 Applications.- 3 Algorithms.- 4 Implementation.- 5 Examples.- 6 Software.- References.- BioPath - Exploration and Visualization of Biochemical Pathways.- 1 Introduction.- 2 Applications.- 3 Algorithms.- 4 Implementation.- 5 Examples.- 6 Software.- References.- DBdraw - Automatic Layout of Relational Database Schemas.- 1 Introduction.- 2 Applications.- 3 Algorithms.- 4 Implementation.- 5 Examples.- 6 Software.- References.- GoVisual - A Diagramming Software for UML Class Diagrams.- 1 Introduction.- 2 Applications.- 3 Algorithms.- 4 Implementation.- 5 Examples.- 6 Software.- References.- CrocoCosmos - 3D Visualization of Large Object-oriented Programs.- 1 Introduction.- 2 Applications.- 3 Algorithms.- 4 Implementation.- 5 Examples.- 6 Software.- References.- ViSta - Visualizing Statecharts.- 1 Introduction.- 2 Applications.- 3 Algorithms.- 4 Implementation.- 5 Examples.- 6 Software.- References.- visone - Analysis and Visualization of Social Networks.- 1 Introduction.- 2 Applications.- 3 Algorithms.- 4 Implementation.- 5 Examples.- 6 Software.- References.- Polyphemus and Hermes - Exploration and Visualization of Computer Networks.- 1 Introduction.- 2 Applications.- 3 Algorithms.- 4 Implementation.- 5 Examples.- 6 Software.- References. In the past several years studies have started to appear comparing the accuracies of various science mapping approaches. These studies primarily compare the cluster solutions resulting from different similarity approaches, and give varying results. In this study we compare the accuracies of cluster solutions of a large corpus of 2,153,769 recent articles from the biomedical literature (2004–2008) using four similarity approaches: co-citation analysis, bibliographic coupling, direct citation, and a bibliographic coupling-based citation-text hybrid approach. Each of the four approaches can be considered a way to represent the research front in biomedicine, and each is able to successfully cluster over 92p of the corpus. Accuracies are compared using two metrics—within-cluster textual coherence as defined by the Jensen-Shannon divergence, and a concentration measure based on the grant-to-article linkages indexed in MEDLINE. Of the three pure citation-based approaches, bibliographic coupling slightly outperforms co-citation analysis using both accuracy measures; direct citation is the least accurate mapping approach by far. The hybrid approach improves upon the bibliographic coupling results in all respects. We consider the results of this study to be robust given the very large size of the corpus, and the specificity of the accuracy measures used. © 2010 Wiley Periodicals, Inc. This paper presents the analysis and modelling of two data sets associated with the literature of hypertext as represented by the ACM Hypertext conference series. This work explores new ways of organising and accessing the vast amount of interrelated information. The first data set, including all the full papers published in this series (1987 1998), is structured and visualised as a semantic space. This semantic space provides an access point for each paper in this collection. The second data set, containing author co-citation counts based on nine conferences in the series (1989 1998), is analysed and mapped in its entirety and in three evenly distributed sub-periods. Specialties  major research fronts in the field of hypertext  are identified based on the results of a factor analysis and corresponding author co-citation maps. The names of authors in these maps are linked to the bibliographical and citation summaries of these authors on the WWW. Soit Ω un domaine borne dans R n , n≥3 avec une frontiere C 1,1 . On considere l'operateur Lγ(u)=⊇•(γ⊇u) ou γ(x) est une fonction a valeur reelle dans C 1,1 (Ω) avec une borne superieure positive. On definit la forme quadratique Qγ sur H 1 2 (∂Ω) par Qγ(f)=∫ Ω γ(x)|⊇u(x)| 2 dx ou u∈H 1 (Ω) est la solution unique a Lγu=0 dans Ω, u| ∂Ω =f. On etudie la reconstruction de γ a partir de Qγ Considerons une marche aleatoire branchante surcritique a temps discret. Nous nous interessons a la probabilite qu'il existe un rayon infini du support de la marche aleatoire branchante, le long duquel elle croit plus vite qu'une fonction lineaire de pente y - e, ou γ designe la vitesse asymptotique de la position de la particule la plus a droite dans la marche aleatoire branchante. Sous des hypotheses generales peu restrictives, nous prouvons que, lorsque e → 0, cette probabilite decroit comme exp —β+o(1) e 1 2 , ou β est une constante strictement positive dont la valeur depend de la loi de la marche aleatoire branchante. Dans le cas special ou des variables aleatoires i.i.d. de Bernoulli(p) (avec 0 < p < 1 2 ) sont placees sur les aretes d'un arbre binaire enracine, ceci repond a une question ouverte de Robin Pemantle (Ann. Appl. Probab. 19 (2009) 1273-1291). Citation recommendation is an interesting but challenging research problem. Most existing studies assume that all papers adopt the same criterion and follow the same behavioral pattern in deciding relevance and authority of a paper. However, in reality, papers have distinct citation behavioral patterns when looking for different references, depending on paper content, authors and target venues. In this study, we investigate the problem in the context of heterogeneous bibliographic networks and propose a novel cluster-based citation recommendation framework, called ClusCite, which explores the principle that citations tend to be softly clustered into interest groups based on multiple types of relationships in the network. Therefore, we predict each query's citations based on related interest groups, each having its own model for paper authority and relevance. Specifically, we learn group memberships for objects and the significance of relevance features for each interest group, while also propagating relative authority between objects, by solving a joint optimization problem. Experiments on both DBLP and PubMed datasets demonstrate the power of the proposed approach, with 17.68 improvement in Recall@50 and 9.57 growth in MRR over the best performing baseline. Automatic recommendation of citations for a manuscript is highly valuable for scholarly activities since it can substantially improve the efficiency and quality of literature search. The prior techniques placed a considerable burden on users, who were required to provide a representative bibliography or to mark passages where citations are needed. In this paper we present a system that considerably reduces this burden: a user simply inputs a query manuscript (without a bibliography) and our system automatically finds locations where citations are needed. We show that naive approaches do not work well due to massive noise in the document corpus. We produce a successful approach by carefully examining the relevance between segments in a query manuscript and the representative segments extracted from a document corpus. An extensive empirical evaluation using the CiteSeerX data set shows that our approach is effective. Scholars are increasingly citing electronic “web references” which are not preserved in libraries or full text archives. WebCite is a new standard for citing web references. To “webcite” a document involves archiving the cited Web page through www.webcitation.org and citing the WebCite permalink instead of (or in addition to) the unstable live Web page. This journal has amended its “instructions for authors” accordingly, asking authors to archive cited Web pages before submitting a manuscript. Almost 200 other journals are already using the system. We discuss the rationale for WebCite, its technology, and how scholars, editors, and publishers can benefit from the service. Citing scholars initiate an archiving process of all cited Web references, ideally before they submit a manuscript. Authors of online documents and websites which are expected to be cited by others can ensure that their work is permanently available by creating an archived copy using WebCite and providing the citation information including the WebCite link on their Web document(s). Editors should ask their authors to cache all cited Web addresses (Uniform Resource Locators, or URLs) “prospectively” before submitting their manuscripts to their journal. Editors and publishers should also instruct their copyeditors to cache cited Web material if the author has not done so already. Finally, WebCite can process publisher submitted “citing articles” (submitted for example as eXtensible Markup Language [XML] documents) to automatically archive all cited Web pages shortly before or on publication. Finally, WebCite can act as a focussed crawler, caching retrospectively references of already published articles. Copyright issues are addressed by honouring respective Internet standards (robot exclusion files, no-cache and no-archive tags). Long-term preservation is ensured by agreements with libraries and digital preservation organizations. The resulting WebCite Index may also have applications for research assessment exercises, being able to measure the impact of Web services and published Web documents through access and Web citation metrics. @PARASPLIT [J Med Internet Res 2005;7(5):e60] A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results. Abstract Documents in computer-readable form can be used to provide information about other documents, i.e. those they cite. To do this efficiently requires procedures for computer recognition of citing statements. This is not easy, especially for multi-sentence citing statements. Computer recognition procedures have been developed which are accurate to the following extent: 73 of the words in statements selected by computer procedures as being citing statements are words which are correctly attributable to the corresponding documents. The retrieval effectiveness of computer-recognized citing statements was tested in the following way. First, for eight retrieval requests in inorganiic chemistry, average recall by search of Chemical Abstracts Service indexing and Chemical Abstracts abstract text words was found to be 50 . Words from citing statements referring to the papers to be retrieved were then added to the index terms and abstract words as additional access points, and searching was repeated. Average recall increased to 70 . Only words from citing statements published within a year of the cited papers were used. The retrieval effect of citing statement words alone (published within a year) without index or abstract terms was the following: average recall was 40 . When just the words of the titles of the cited papers were added to those citing statement words, average recall increased to 50 . The old Asian legend about the blind men and the elephant comes to mind when looking at how different authors of scientific papers describe a piece of related prior work. It turns out that different citations to the same paper often focus on different aspects of that paper and that neither provides a full description of its full set of contributions. In this article, we will describe our investigation of this phenomenon. We studied citation summaries in the context of research papers in the biomedical domain. A citation summary is the set of citing sentences for a given article and can be used as a surrogate for the actual article in a variety of scenarios. It contains information that was deemed by peers to be important. Our study shows that citation summaries overlap to some extent with the abstracts of the papers and that they also differ from them in that they focus on different aspects of these papers than do the abstracts. In addition to this, co-cited articles (which are pairs of articles cited by another article) tend to be similar. We show results based on a lexical similarity metric called cohesion to justify our claims. © 2008 Wiley Periodicals, Inc. This paper reports on a novel technique for literature indexing and searching in a mechanized library system. The notion of relevance is taken as the key concept in the theory of information retrieval and a comparative concept of relevance is explicated in terms of the theory of probability. The resulting technique called “Probabilistic Indexing,” allows a computing machine, given a request for information, to make a statistical inference and derive a number (called the “relevance number”) for each document, which is a measure of the probability that the document will satisfy the given request. The result of a search is an ordered list of those documents which satisfy the request ranked according to their probable relevance. The paper goes on to show that whereas in a conventional library system the cross-referencing (“see” and “see also”) is based solely on the “semantical closeness” between index terms, statistical measures of closeness between index terms can be defined and computed. Thus, given an arbitrary request consisting of one (or many) index term(s), a machine can elaborate on it to increase the probability of selecting relevant documents that would not otherwise have been selected. Finally, the paper suggests an interpretation of the whole library problem as one where the request is considered as a clue on the basis of which the library system makes a concatenated statistical inference in order to provide as an output an ordered list of those documents which most probably satisfy the information needs of the user. We approach the problem of academic literature search by considering an unpublished manuscript as a query to a search system. We use the text of previous literature as well as the citation graph that connects it to find relevant related material. We evaluate our technique with manual and automatic evaluation methods, and find an order of magnitude improvement in mean average precision as compared to a text similarity baseline.