Solitude verification is arguably one of the simplest fundamental problems in distributed computing, where the goal is to verify that there is a unique contender in a network. This paper devises a quantum algorithm that exactly solves the problem on an anonymous network, which is known as a network model with minimal assumptions [Angluin, STOC'80]. The algorithm runs in @math rounds if every party initially has the common knowledge of an upper bound @math on the number of parties. This implies that all solvable problems can be solved in @math rounds on average without error (i.e., with zero-sided error) on the network. As a generalization, a quantum algorithm that works in @math rounds is obtained for the problem of exactly computing any symmetric Boolean function, over @math distributed input bits, which is constant over all the @math bits whose sum is larger than @math for @math . All these algorithms work with the bit complexities bounded by a polynomial in @math .
Pal, Singh, and Kumar @cite_19 and D'Hondt and Panangaden @cite_6 dealt with @math and the GHZ-state sharing problem in a different setting, where pre-shared entanglement is assumed but only classical communication is allowed. The relation between several network models that differ in available quantum resources has been discussed by Gavoille, Kosowski, and Markiewicz @cite_17 . Recently, @cite_13 proved that quantum communication cannot substantially speed up algorithms for some fundamental problems, such as the minimum spanning tree, compared to the classical setting. For fault-tolerant distributed quantum computing, the Byzantine agreement problem and the consensus problem were studied by Ben-Or and Hassidim @cite_28 and Chlebus, Kowalski, and Strojnowski @cite_8 , respectively. In the cryptographic context where there are cheating parties, Refs. @cite_21 @cite_15 devises quantum algorithms that elect a unique leader with a small bias. Some quantum distributed protocols were experimentally demonstrated by @cite_30 and @cite_3 .
@cite_30 @cite_8 @cite_28 @cite_21 @cite_6 @cite_3 @cite_19 @cite_15 @cite_13 @cite_17
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In a document network such as a citation network of scientific documents, web-logs, etc., the content produced by authors exhibits their interest in certain topics. In addition some authors influence other authors' interests. In this work, we propose to model the influence of cited authors along with the interests of citing authors. Moreover, we hypothesize that apart from the citations present in documents, the context surrounding the citation mention provides extra topical information about the cited authors. However, associating terms in the context to the cited authors remains an open problem. We propose novel document generation schemes that incorporate the context while simultaneously modeling the interests of citing authors and influence of the cited authors. Our experiments show significant improvements over baseline models for various evaluation criteria such as link prediction between document and cited author, and quantitatively explaining unseen text. Purpose – The purpose of this paper is to suggest an alternative to the widely used Times Cited criterion for analysing citation networks. The approach involves taking account of the natures of the papers that cite a given paper, so as to differentiate between papers that attract the same number of citations.Design methodology approach – ArticleRank is an algorithm that has been derived from Google's PageRank algorithm to measure the influence of journal articles. ArticleRank is applied to two datasets – a citation network based on an early paper on webometrics, and a self‐citation network based on the 19 most cited papers in the Journal of Documentation – using citation data taken from the Web of Knowledge database.Findings – ArticleRank values provide a different ranking of a set of papers from that provided by the corresponding Times Cited values, and overcomes the inability of the latter to differentiate between papers with the same numbers of citations. The difference in rankings between Times Cited ... 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. 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.