10,333 research outputs found
Towards a Theory of Flavor from Orbifold GUTs
We show that the recently constructed 5-dimensional supersymmetric
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 unification is maintained but similar disfavored SU(5)
relations for the lighter generations are naturally avoided. We find that
simple 5d models based on 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 model to six
dimensions. Large angle neutrino mixing is elegantly accommodated. Novel
features of these models include a simple configuration leading to a
solution of the strong CP problem.Comment: 18 pages, 6 figure
Hashing for Similarity Search: A Survey
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
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
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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