Open data strategies are being adopted in disaster-related data particularly because of the need to provide information on global targets and indicators for implementation of the Sendai Framework for Disaster Risk Reduction 2015-2030. In all phases of disaster risk management including forecasting, emergency response and post-disaster reconstruction, the need for interconnected multidisciplinary open data for collaborative reporting as well as study and analysis are apparent, in order to determine disaster impact data in timely and reportable manner. The extraordinary progress in computing and information technology in the past decade, such as broad local and wide-area network connectivity (e.g. Internet), highperformance computing, service and cloud computing, big data methods and mobile devices, provides the technical foundation for connecting open data to support disaster risk research. A new generation of disaster data infrastructure based on interconnected open data is evolving rapidly. There are two levels in the conceptual model of Linked Open Data for Global Disaster Risk Research (LODGD) Working Group of the Committee on Data for Science and Technology (CODATA), which is the Committee on Data of the International Council for Science (ICSU): data characterization and data connection. In data characterization, the knowledge about disaster taxonomy and data dependency on disaster events requires specific scientific study as it aims to understand and present the correlation between specific disaster events and scientific data through the integration of literature analysis and semantic knowledge discovery. Data connection concepts deal with technical methods to connect distributed data resources identified by data characterization of disaster type. In the science community, interconnected open data for disaster risk impact assessment are beginning to influence how disaster data are shared, and this will need to extend data coverage and provide better ways of utilizing data across domains where innovation and integration are now necessarily needed. ARTICLE HISTORY
Users submit queries to an online database via its query interface. Query interface parsing, which is important for many applications, understands the query capabilities of a query interface. Since most query interfaces are organized hierarchically, we present a novel query interface parsing method, StatParser (Statistical Parser), to automatically extract the hierarchical query capabilities of query interfaces. StatParser automatically learns from a set of parsed query interfaces and parses new query interfaces. StatParser starts from a small grammar and enhances the grammar with a set of probabilities learned from parsed query interfaces under the maximum-entropy principle. Given a new query interface, the probability-enhanced grammar identifies the parse tree with the largest global probability to be the query capabilities of the query interface. Experimental results show that StatParser very accurately extracts the query capabilities and can effectively overcome the problems of existing query interface parsers.
We investigate genuine multipartite entanglement in general multipartite systems. Based on the norms of the correlation tensors of a multipartite state under various partitions, we present an analytical sufficient criterion for detecting the genuine four-partite entanglement. The results are generalized to arbitrary multipartite systems.The genuine multipartite entangled states exist in physical systems like the ground state of the XY model [6]. However, it is extremely difficult to identify the GME for general mixed multipartite states. The GME concurrence and its lower bound were studied in [7][8][9]. Some sufficient or necessary conditions of GME were presented in [10][11][12]. As for detection of GME, the common criterion is the entanglement witnesses [13][14][15][16]. Using correlation tensors, the authors in [17] have provided a general framework to detect different classes of GME for quantum systems of arbitrary dimensions. In [18] the genuine multipartite entanglement has been investigated in terms of the norms of the correlation tensors and multipartite concurrence. The relations between the norms of the correlation tensors and the detection of GME in tripartite quantum systems have been established in [19].We need to use some simple mathematical concepts in this paper, let's briefly review them here. The elements of a vector space are called vectors. As we known, tensor product is a way of putting vector spaces together to form larger vector spaces. Suppose W and V are Hilbert spaces of dimension m and n respectively. Then W ⊗ V is an mn dimensional vector space.The elements of W ⊗ V are liner combinations of 'tensor products' u ⊗ v of elements u of W and v of V . The outer product of u and v is equivalent to a matrix multiplication uv t , provided that u is represented as a m × 1 column vector and v as a n × 1 column vector (which makes v t a row vector).In this paper, we analyze the relationship between the norms of the correlation tensors and various bipartitions of multipartite quantum systems, and present sufficient conditions of GME for four partite and multipartite quantum systems.We generalize some inequalities of the norms of the correlation tensors for four-partite states and give a criterion to detect GME of four-partite quantum systems in Section 2. In Section 3, we generalize these concepts and conclusions to multipartite quantum systems. Comments and conclusions are given in Section 4.
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