280 research outputs found

    On Translation Strategies of English Movie Titles

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    Movie titles are films’ eyes, having double effect of art appreciation and commercial advertisement, and directly playing the role of guidance and promotion. Good film names could convey the films' content as well as arouse audience's interest to get great box. With the continuous development of international cultural exchanges, film begins to get the attention of every nation increasingly as an important media in cultural exchange. With the opening of the Chinese market, we have more and more English movies. The Chinese audiences need to understand the movie titles before they enjoy the movies. But due to different cultural traditions, contexts, customs and thinking modes between the western and eastern world, the choices of their film names embodies distinctive cultural features. Movie titles convey the story to the audience to attract them. This requires the translation of movie titles to be accurate and embody the commercial values. This paper analyzes the translation strategies of English movie titles and explores a new strategy according to previous research results and research methods. This paper introduces the definition of translation strategies and some features of English movie titles and functions. Then it describes the principles of English movie titles translation and points out the translation strategies of English film titles. It is hoped that the context can help people to realize the necessity of proper translation of English movie titles, and accordingly promote the development of films in international market

    The Asymptotic Behavior of Solutions for a Class of Nonlinear Fractional Difference Equations with Damping Term

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    Based on generalized Riccati transformation and some inequalities, some oscillation results are established for a class of nonlinear fractional difference equations with damping term. An example is given to illustrate the validity of the established results

    An aligned subtree kernel for weighted graphs

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    In this paper, we develop a new entropic matching kernel for weighted graphs by aligning depth-based representations. We demonstrate that this kernel can be seen as an aligned subtree kernel that incorporates explicit subtree correspondences, and thus addresses the drawback of neglecting the relative locations between substructures that arises in the R-convolution kernels. Experiments on standard datasets demonstrate that our kernel can easily outperform state-of-the-art graph kernels in terms of classification accuracy

    Quantum kernels for unattributed graphs using discrete-time quantum walks

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    In this paper, we develop a new family of graph kernels where the graph structure is probed by means of a discrete-time quantum walk. Given a pair of graphs, we let a quantum walk evolve on each graph and compute a density matrix with each walk. With the density matrices for the pair of graphs to hand, the kernel between the graphs is defined as the negative exponential of the quantum Jensen–Shannon divergence between their density matrices. In order to cope with large graph structures, we propose to construct a sparser version of the original graphs using the simplification method introduced in Qiu and Hancock (2007). To this end, we compute the minimum spanning tree over the commute time matrix of a graph. This spanning tree representation minimizes the number of edges of the original graph while preserving most of its structural information. The kernel between two graphs is then computed on their respective minimum spanning trees. We evaluate the performance of the proposed kernels on several standard graph datasets and we demonstrate their effectiveness and efficiency

    Depth-Based Subgraph Convolutional Neural Networks

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    This paper proposes a new graph convolutional neural network architecture based on a depth-based representation of graph structure deriving from quantum walks, which we refer to as the quantum-based subgraph convolutional neural network (QS-CNNs). This new architecture captures both the global topological structure and the local connectivity structure within a graph. Specifically, we commence by establishing a family of K-layer expansion subgraphs for each vertex of a graph by quantum walks, which captures the global topological arrangement information for substructures contained within a graph. We then design a set of fixed-size convolution filters over the subgraphs, which helps to characterise multi-scale patterns residing in the data. The idea is to apply convolution filters sliding over the entire set of subgraphs rooted at a vertex to extract the local features analogous to the standard convolution operation on grid data. Experiments on eight graph-structured datasets demonstrate that QS-CNNs architecture is capable of outperforming fourteen state-of-the-art methods for the tasks of node classification and graph classification

    A quantum Jensen-Shannon graph kernel using discrete-time quantum walks

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    In this paper, we develop a new graph kernel by using the quantum Jensen-Shannon divergence and the discrete-time quantum walk. To this end, we commence by performing a discrete-time quantum walk to compute a density matrix over each graph being compared. For a pair of graphs, we compare the mixed quantum states represented by their density matrices using the quantum Jensen-Shannon divergence. With the density matrices for a pair of graphs to hand, the quantum graph kernel between the pair of graphs is defined by exponentiating the negative quantum Jensen-Shannon divergence between the graph density matrices. We evaluate the performance of our kernel on several standard graph datasets, and demonstrate the effectiveness of the new kernel
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