Hierarchical representation

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  • This paper introduces a novel hierarchical summarization approach for automatic multidocument summarization. By creating a hierarchical representation of the words in the input document set, the proposed approach is able to incorporate various objectives of multidocument summarization through an integrated framework. The evaluation is conducted on the DUC 2007 data set.

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  • In genomics, hierarchical clustering (HC) is a popular method for grouping similar samples based on a distance measure. HC algorithms do not actually create clusters, but compute a hierarchical representation of the data set.

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  • A living cell has a complex, hierarchically organized signaling system that encodes and assimilates diverse environmental and intracellular signals, and it further transmits signals that control cellular responses, including a tightly controlled transcriptional program.

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  • A theory of discourse interpretation has to deal with a set of problems including anaphora resolution and the hierarchical ordering of discourse structure: (1) Several students organised a dinner party for Peter. Some students wrote fancy invitation cards. Some other students bought exotic food. But Peter didn't like it.

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  • Methods that measure compatibility between mention pairs are currently the dominant approach to coreference. However, they suffer from a number of drawbacks including difficulties scaling to large numbers of mentions and limited representational power. As these drawbacks become increasingly restrictive, the need to replace the pairwise approaches with a more expressive, highly scalable alternative is becoming urgent.

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  • A framework for a structured representation of semantic knowledge (e.g. word-senses) has been defined at the IBM Scientific Center of Roma, as part of a project on Italian Text Understanding. This representation, based on the conceptual graphs formalism [SOW84], expresses deep knowledge (pragmatic) on word-senses. The knowledge base data structure is such as to provide easy access by the semantic verification algorithm. This paper discusses some important problem related to the definition of a semantic knowledge base, as depth versus generality, hierarchical ordering of concept types, etc.

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  • In order to treat high-dimensional problems, one has to find data-sparse representations. Starting with a six-dimensional problem, we first introduce the low-rank approximation of matrices. One purpose is the reduction of memory requirements, another advantage is that now vector operations instead of matrix operations can be applied.

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  • Architecture is those attributes visible to the programmer: Instruction set, number of bits used for data representation, I/O mechanisms, addressing techniques. e.g. Is there a multiply instruction? Organization is how features are implemented Control signals, interfaces, memory technology. e.g. Is there a hardware multiply unit or is it done by repeated addition?

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  • This work addresses the problem of feature indexing to significantly accelerate the matching process which is commonly known as a cumbersome task in many computer vision applications. To this aim, we propose to perform product sub-vector quantization (PSVQ) to create finer representation of underlying data while still maintaining reasonable memory allocation.

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  • This article presents a maturity model for the evaluation of the information-driven decision-making process (DMP) in small and medium enterprises. This model is called “Simplified Holistic Approach to DMP Evaluation (SHADE)”. The SHADE model is based in the “Circumplex Hierarchical Representation of the Organization Maturity Assessment” (CHROMA) framework for characterizing the information-driven DMP in organizations.

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  • The main objective of the study was to form similarity based on area, production and productivity for different crops of Rajasthan for which the secondary data on area, production and productivity of 23 different major crops i.e. period from 1980 to 2014 were collected. The study period was classified as Pre WTO era from 1980-1995 and Post WTO era from 1996-2014. Ward’s method of clustering under hierarchical cluster analysis was used and Dendrogram shows the graphical representation of the results of hierarchical cluster analysis.

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  • Functional annotation of genes is an essential step in omics data analysis. Multiple databases and methods are currently available to summarize the functions of sets of genes into higher level representations, such as ontologies and molecular pathways.

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  • Circular RNAs (circRNAs) are special noncoding RNA molecules with closed loop structures. Compared with the traditional linear RNA, circRNA is more stable and not easily degraded.

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  • Creating institutions to meet the challenge of sustainability is arguably the most important task confronting society; it is also dauntingly complex. Ecological, economic, and social elements all play a role, but despite ongoing efforts, researchers have yet to succeed in integrating the various disciplines in a way that gives adequate representation to the insights of each.Panarchy, a term devised to describe evolving hierarchical systems with multiple interrelated elements, offers an important new framework for understanding and resolving this dilemma.

    pdf650p greengrass304 14-09-2012 44 14   Download

  • The book presents several advances in different research areas related to data storage, from the design of a hierarchical memory subsystem in embedded signal processing systems for data-intensive applications, through data representation in flash memories, data recording and retrieval in conventional optical data storage systems and the more recent holographic systems, to applications in medicine requiring massive image databases.

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  • The relational database uses the concept of linked two-dimensional tables consisting of rows and columns, as shown in Figure 1-2. Unlike the hierarchical approach, no predetermined relationship exists between distinct tables. This means that the data needed to link together the different areas of the network or hierarchical model need not be defined. Because relational users don’t need to understand the representation of data in storage to retrieve it (many such users created ad hoc queries against the data), ease of use helped popularize the relational model....

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  • Given the above object representation, the following furniture rela tionships are extracted automatically from positive input example Spatial relationships: The key prior relationships are the distanc of an object to its nearest wall ¯ di and its relative orientation to th wall ¯ θi. They are respectively estimated as the clustered means o input examples, where we can assign one of the clustered means a ¯ di and ¯ θi respectively for object i during optimization. The numbe of clusters can be preset or estimated by [Grunwald 2007].

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  • This paper establishes a connection between two apparently very different kinds of probabilistic models. Latent Dirichlet Allocation (LDA) models are used as “topic models” to produce a lowdimensional representation of documents, while Probabilistic Context-Free Grammars (PCFGs) define distributions over trees. The paper begins by showing that LDA topic models can be viewed as a special kind of PCFG, so Bayesian inference for PCFGs can be used to infer Topic Models as well. Adaptor Grammars (AGs) are a hierarchical, non-parameteric Bayesian extension of PCFGs. ...

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  • Parametric polymorphism has been combined with inclusional polymorphism to provide natural type systems for Prolog (DH88), HiLog (YFS92), and coristraint resolution languages (Smo89), and, in linguistics, by HPSG-like grammars to classify lists and sets of linguistic objects (PS94), and by phonologists in representations of hierarchical structure (Kle91).

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