10,333 research outputs found

    Towards a Theory of Flavor from Orbifold GUTs

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    We show that the recently constructed 5-dimensional supersymmetric S1/(Z2×Z2′)S^1/(Z_2\times Z_2') orbifold GUT models allow an appealing explanation of the observed hierarchical structure of the quark and lepton masses and mixing angles. Flavor hierarchies arise from the geometrical suppression of some couplings when fields propagate in different numbers of dimensions, or on different fixed branes. Restrictions arising from locality in the extra dimension allow interesting texture zeroes to be easily generated. In addition the detailed nature of the SU(5)-breaking orbifold projections lead to simple theories where b−τb-\tau unification is maintained but similar disfavored SU(5) relations for the lighter generations are naturally avoided. We find that simple 5d models based on S1/(Z2×Z2′)S^1/(Z_2\times Z_2') are strikingly successful in explaining many features of the masses and mixing angles of the 2nd and 3rd generation. Successful three generation models of flavor including neutrinos are constructed by generalizing the S1/(Z2×Z2′)S^1/(Z_2\times Z'_2) model to six dimensions. Large angle neutrino mixing is elegantly accommodated. Novel features of these models include a simple mu=0m_u=0 configuration leading to a solution of the strong CP problem.Comment: 18 pages, 6 figure

    Hashing for Similarity Search: A Survey

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    Similarity search (nearest neighbor search) is a problem of pursuing the data items whose distances to a query item are the smallest from a large database. Various methods have been developed to address this problem, and recently a lot of efforts have been devoted to approximate search. In this paper, we present a survey on one of the main solutions, hashing, which has been widely studied since the pioneering work locality sensitive hashing. We divide the hashing algorithms two main categories: locality sensitive hashing, which designs hash functions without exploring the data distribution and learning to hash, which learns hash functions according the data distribution, and review them from various aspects, including hash function design and distance measure and search scheme in the hash coding space

    Statistical Traffic State Analysis in Large-scale Transportation Networks Using Locality-Preserving Non-negative Matrix Factorization

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    Statistical traffic data analysis is a hot topic in traffic management and control. In this field, current research progresses focus on analyzing traffic flows of individual links or local regions in a transportation network. Less attention are paid to the global view of traffic states over the entire network, which is important for modeling large-scale traffic scenes. Our aim is precisely to propose a new methodology for extracting spatio-temporal traffic patterns, ultimately for modeling large-scale traffic dynamics, and long-term traffic forecasting. We attack this issue by utilizing Locality-Preserving Non-negative Matrix Factorization (LPNMF) to derive low-dimensional representation of network-level traffic states. Clustering is performed on the compact LPNMF projections to unveil typical spatial patterns and temporal dynamics of network-level traffic states. We have tested the proposed method on simulated traffic data generated for a large-scale road network, and reported experimental results validate the ability of our approach for extracting meaningful large-scale space-time traffic patterns. Furthermore, the derived clustering results provide an intuitive understanding of spatial-temporal characteristics of traffic flows in the large-scale network, and a basis for potential long-term forecasting.Comment: IET Intelligent Transport Systems (2013

    Generalized Multi-manifold Graph Ensemble Embedding for Multi-View Dimensionality Reduction

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    In this paper, we propose a new dimension reduction (DR) algorithm called ensemble graph-based locality preserving projections (EGLPP); to overcome the neighborhood size k sensitivity in locally preserving projections (LPP). EGLPP constructs a homogeneous ensemble of adjacency graphs by varying neighborhood size k and finally uses the integrated embedded graph to optimize the low-dimensional projections. Furthermore, to appropriately handle the intrinsic geometrical structure of the multi-view data and overcome the dimensionality curse, we propose a generalized multi-manifold graph ensemble embedding framework (MLGEE). MLGEE aims to utilize multi-manifold graphs for the adjacency estimation with automatically weight each manifold to derive the integrated heterogeneous graph. Experimental results on various computer vision databases verify the effectiveness of proposed EGLPP and MLGEE over existing comparative DR methods
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