Using TERp to augment the system combination for SMT

TER-Plus (TERp) is an extended TER evaluation metric incorporating morphology, synonymy and paraphrases. There are three new edit operations in TERp: Stem Matches, Synonym Matches and Phrase Substitutions (Para-phrases). In this paper, we propose a TERp-based augmented system combination in terms of the backbone selection and consensus decoding network. Combining the new properties\ud of the TERp, we also propose a two-pass decoding strategy for the lattice-based phrase-level confusion network(CN) to generate the final result. The experiments conducted on the NIST2008 Chinese-to-English test set show that our TERp-based augmented system combination framework achieves significant improvements in terms of BLEU and TERp scores compared to the state-of-the-art word-level system combination framework and a TER-based combination strategy

Semantics-Space-Time Cube. A Conceptual Framework for Systematic Analysis of Texts in Space and Time

We propose an approach to analyzing data in which texts are associated with spatial and temporal references with the aim to understand how the text semantics vary over space and time. To represent the semantics, we apply probabilistic topic modeling. After extracting a set of topics and representing the texts by vectors of topic weights, we aggregate the data into a data cube with the dimensions corresponding to the set of topics, the set of spatial locations (e.g., regions), and the time divided into suitable intervals according to the scale of the planned analysis. Each cube cell corresponds to a combination (topic, location, time interval) and contains aggregate measures characterizing the subset of the texts concerning this topic and having the spatial and temporal references within these location and interval. Based on this structure, we systematically describe the space of analysis tasks on exploring the interrelationships among the three heterogeneous information facets, semantics, space, and time. We introduce the operations of projecting and slicing the cube, which are used to decompose complex tasks into simpler subtasks. We then present a design of a visual analytics system intended to support these subtasks. To reduce the complexity of the user interface, we apply the principles of structural, visual, and operational uniformity while respecting the specific properties of each facet. The aggregated data are represented in three parallel views corresponding to the three facets and providing different complementary perspectives on the data. The views have similar look-and-feel to the extent allowed by the facet specifics. Uniform interactive operations applicable to any view support establishing links between the facets. The uniformity principle is also applied in supporting the projecting and slicing operations on the data cube. We evaluate the feasibility and utility of the approach by applying it in two analysis scenarios using geolocated social media data for studying people's reactions to social and natural events of different spatial and temporal scales

Standard Neutrosophic Soft Theory: Some First Results

The traditional soft set is a mapping from a parameter set to family of all crisp subsets of a universe. Molodtsov introduced the soft set as a generalized tool for modelling complex systems involving uncertain or not clearly defined objects. In this paper, the notion of neutrosophic soft set is reanalysed. The novel theory is a combination of neutrosophic set theory and soft set theory. The complement, “and”, “or”, intersection and union operations are defined on the neutrosophic soft sets. The neutrosophic soft relations accompanied with their compositions are also defined. The basic properties of the neutrosophic soft sets, neutrosophic soft relations and neutrosophic soft compositions are also discussed

Lineage-Aware Temporal Windows: Supporting Set Operations in Temporal-Probabilistic Databases

In temporal-probabilistic (TP) databases, the combination of the temporal and the probabilistic dimension adds significant overhead to the computation of set operations. Although set queries are guaranteed to yield linearly sized output relations, existing solutions exhibit quadratic runtime complexity. They suffer from redundant interval comparisons and additional joins for the formation of lineage expressions. In this paper, we formally define the semantics of set operations in TP databases and study their properties. For their efficient computation, we introduce the lineage-aware temporal window, a mechanism that directly binds intervals with lineage expressions. We suggest the lineage-aware window advancer (LAWA) for producing the windows of two TP relations in linearithmic time, and we implement all TP set operations based on LAWA. By exploiting the flexibility of lineage-aware temporal windows, we perform direct filtering of irrelevant intervals and finalization of output lineage expressions and thus guarantee that no additional computational cost or buffer space is needed. A series of experiments over both synthetic and real-world datasets show that (a) our approach has predictable performance, depending only on the input size and not on the number of time intervals per fact or their overlap, and that (b) it outperforms state-of-the-art approaches in both temporal and probabilistic databases

Combining social choice functions

This paper considers the problem of combining two choice functions (CFs), or setwise optimisation functions, based on use of intersection and composition. Each choice function represents preference information for an agent, saying, for any subset of a set of alternatives, which are the preferred, and which are the sub-optimal alternatives. The aim is to find a combination operation that maintains good properties of the choice function. We consider a family of natural properties of CFs, and analyse which hold for different classes of CF. We determine relationships between intersection and composition operations, and find out which properties are maintained by these combination rules. We go on to show how the most important of the CF properties can be enforced or restored, and use this kind of procedure to define combination operations that then maintain the desirable properties

Efficiently Mapping Linear Algebra to High-Performance Code

Aware of the role that linear algebra plays in scientific applications, we investigate if/how matrix expressions can be efficiently evaluated with current high-level languages. On the one hand, the numerical linear algebra community has put a lot of effort in developing and optimizing a relatively small set of “universally” useful operations. These are packaged in libraries such as BLAS and LAPACK, and serve as building blocks for more complex computa- tions. On the other hand, the linear algebra expressions that arise in many domains are significantly more complex than those building blocks. We refer to the problem of expressing a linear algebra expression in terms of a set of available building blocks as the ”Linear Algebra Mapping Problem” (LAMP). In practice, users have two alternatives to solve a given LAMP: 1) either “manually”, by using C/C++ or FORTRAN in combination with explicit calls to BLAS & LAPACK 2) or “automatically” by using one of the high-level languages (or libraries) with an API that directly captures the expressions. In this presentation, we focus only on the latter. Specifically, we consider 6 languages (or libraries): Matlab, Julia, R, NumPy (Python), Eigen (C++), and Armadillo (C++), and carefully assess how effectively they translate linear algebra expressions to code, i.e., how well they solve LAMPs. We investigate a number of aspects that are critical for the efficient solution of a LAMP. These range from the most basic mapping problem “Given the expression A*B, does the language map it to a call to GEMM?”, to the optimal parenthesization, to the exploitation of properties, to the identification & elimination -if advantageous- of common sub-expressions, and more. Ultimately, the purpose of this study is to exhibit the core challenges related to the effective computation of linear algebra expressions, and to help the development of languages and libraries.Texas Advanced Computing Center (TACC

R\'enyi squashed entanglement, discord, and relative entropy differences

In [Berta et al., J. Math. Phys. 56, 022205 (2015)], we recently proposed Renyi generalizations of the conditional quantum mutual information of a tripartite state on $ABC$ (with $C$ being the conditioning system), which were shown to satisfy some properties that hold for the original quantity, such as non-negativity, duality, and monotonicity with respect to local operations on the system $B$ (with it being left open to show that the Renyi quantity is monotone with respect to local operations on system $A$). Here we define a Renyi squashed entanglement and a Renyi quantum discord based on a Renyi conditional quantum mutual information and investigate these quantities in detail. Taking as a conjecture that the Renyi conditional quantum mutual information is monotone with respect to local operations on both systems $A$ and $B$, we prove that the Renyi squashed entanglement and the Renyi quantum discord satisfy many of the properties of the respective original von Neumann entropy based quantities. In our prior work [Berta et al., Phys. Rev. A 91, 022333 (2015)], we also detailed a procedure to obtain Renyi generalizations of any quantum information measure that is equal to a linear combination of von Neumann entropies with coefficients chosen from the set $\{-1,0,1\}$. Here, we extend this procedure to include differences of relative entropies. Using the extended procedure and a conjectured monotonicity of the Renyi generalizations in the Renyi parameter, we discuss potential remainder terms for well known inequalities such as monotonicity of the relative entropy, joint convexity of the relative entropy, and the Holevo bound.Comment: v3: 41 pages, 2 tables, final versio

Standard Neutrosophic Soft Theory- Some First Resluts

The traditional soft set is a mapping from a parameter set to family of all crisp subsets of a universe. Molodtsov introduced the soft set as a generalized tool for modelling complex systems involving uncertain or not clearly defined objects. In this paper, the notion of neutrosophic soft set is reanalysed. The novel theory is a combination of neutrosophic set theory and soft set theory. The complement, “and”, “or”, intersection and union operations are defined on the neutrosophic soft sets. The neutrosophic soft relations accompanied with their compositions are also defined. The basic properties of the neutrosophic soft sets, neutrosophic soft relations and neutrosophic soft compositions are also discussed