4,525 research outputs found
Pivot Selection for Median String Problem
The Median String Problem is W[1]-Hard under the Levenshtein distance, thus,
approximation heuristics are used. Perturbation-based heuristics have been
proved to be very competitive as regards the ratio approximation
accuracy/convergence speed. However, the computational burden increase with the
size of the set. In this paper, we explore the idea of reducing the size of the
problem by selecting a subset of representative elements, i.e. pivots, that are
used to compute the approximate median instead of the whole set. We aim to
reduce the computation time through a reduction of the problem size while
achieving similar approximation accuracy. We explain how we find those pivots
and how to compute the median string from them. Results on commonly used test
data suggest that our approach can reduce the computational requirements
(measured in computed edit distances) by \% with approximation accuracy as
good as the state of the art heuristic.
This work has been supported in part by CONICYT-PCHA/Doctorado
Nacional/ through a Ph.D. Scholarship; Universidad Cat\'{o}lica
de la Sant\'{i}sima Concepci\'{o}n through the research project DIN-01/2016;
European Union's Horizon 2020 under the Marie Sk\l odowska-Curie grant
agreement ; Millennium Institute for Foundational Research on Data
(IMFD); FONDECYT-CONICYT grant number ; and for O. Pedreira, Xunta de
Galicia/FEDER-UE refs. CSI ED431G/01 and GRC: ED431C 2017/58
Discriminative prototype selection methods for graph embedding
Graphs possess a strong representational power for many types of patterns. However, a main limitation in their use for pattern analysis derives from their difficult mathematical treatment. One way of circumventing this problem is that of transforming the graphs into a vector space by means of graph embedding. Such an embedding can be conveniently obtained by using a set of prototype graphs and a dissimilarity measure. However, when we apply this approach to a set of class-labelled graphs, it is challenging to select prototypes capturing both the salient structure within each class and inter-class separation. In this paper, we introduce a novel framework for selecting a set of prototypes from a labelled graph set taking their discriminative power into account. Experimental results showed that such a discriminative prototype selection framework can achieve superior results in classification compared to other well-established prototype selection approaches. © 2012 Elsevier Ltd
Stabilization of moduli by fluxes
In order to lift the continuous moduli space of string vacua, non-trivial
fluxes may be the essential input. In this talk I summarize aspects of two
approaches to compactifications in the presence of fluxes: (i) generalized
Scherk-Schwarz reductions and gauged supergravity and (ii) the description of
flux-deformed geometries in terms of G-structures and intrinsic torsion.Comment: 22 pages, added refs., to appear in proceedings of: ``Strings and
Cosmology'', (Texas A&M University, March 2004) and the RTN workshop: ``The
quantum structure of space-time and ...'' (Kolymbari, Crete, September 2004
A discriminative prototype selection approach for graph embedding in human action recognition
This paper proposes a novel graph-based method for representing a human's shape during the performance of an action. Despite their strong representational power graphs are computationally cumbersome for pattern analysis. One way of circumventing this problem is that of transforming the graphs into a vector space by means of graph embedding. Such an embedding can be conveniently obtained by way of a set of prototype graphs and a dissimilarity measure: yet the critical step in this approach is the selection of a suitable set of prototypes which can capture both the salient structure within each action class as well as the intra-class variation. This paper proposes a new discriminative approach for the selection of prototypes which maximizes a function of the inter-and intra-class distances. Experiments on an action recognition dataset reported in the paper show that such a discriminative approach outperforms well-established prototype selection methods such as center border and random prototype selection. © 2011 IEEE
Spacetime Instanton Corrections in 4D String Vacua - The Seesaw Mechanism for D-Brane Models
We systematically investigate instanton corrections from wrapped Euclidean
D-branes to the matter field superpotential of various classes of N=1
supersymmetric D-brane models in four dimensions. Both gauge invariance and the
counting of fermionic zero modes provide strong constraints on the allowed
non-perturbative superpotential couplings. We outline how the complete
instanton computation boils down to the computation of open string disc
diagrams for boundary changing operators multiplied by a one-loop vacuum
diagram. For concreteness we focus on E2-instanton effects in Type IIA vacua
with intersecting D6-branes, however the same structure emerges for Type IIB
and heterotic vacua. The instantons wrapping rigid cycles can potentially
destabilise the vacuum or generate perturbatively absent matter couplings such
as proton decay operators, mu-parameter or right-handed neutrino Majorana mass
terms. The latter allow the realization of the seesaw mechanism for MSSM-like
intersecting D-brane models.Comment: 40 pages, 3 tables, 7 figures; v2: typos corrected, references added;
v3: minor sign adjustments, some comments added; v4: published versio
Four-dimensional String Compactifications with D-Branes, Orientifolds and Fluxes
This review article provides a pedagogical introduction into various classes
of chiral string compactifications to four dimensions with D-branes and fluxes.
The main concern is to provide all necessary technical tools to explicitly
construct four-dimensional orientifold vacua, with the final aim to come as
close as possible to the supersymmetric Standard Model. Furthermore, we outline
the available methods to derive the resulting four-dimensional effective
action. Finally, we summarize recent attempts to address the string vacuum
problem via the statistical approach to D-brane models.Comment: 331 pages, 7 figures, review prepared for Physics Reports, please
send constructive comments to: [email protected], v2: refs added, v3: final
version to appear in Phys. Rep
Boosting Perturbation-Based Iterative Algorithms to Compute the Median String
[Abstract] The most competitive heuristics for calculating the median string are those that use perturbation-based iterative algorithms. Given the complexity of this problem, which under many formulations is NP-hard, the computational cost involved in the exact solution is not affordable. In this work, the heuristic algorithms that solve this problem are addressed, emphasizing its initialization and the policy to order possible editing operations. Both factors have a significant weight in the solution of this problem. Initial string selection influences the algorithm’s speed of convergence, as does the criterion chosen to select the modification to be made in each iteration of the algorithm. To obtain the initial string, we use the median of a subset of the original dataset; to obtain this subset, we employ the Half Space Proximal (HSP) test to the median of the dataset. This test provides sufficient diversity within the members of the subset while at the same time fulfilling the centrality criterion. Similarly, we provide an analysis of the stop condition of the algorithm, improving its performance without substantially damaging the quality of the solution. To analyze the results of our experiments, we computed the execution time of each proposed modification of the algorithms, the number of computed editing distances, and the quality of the solution obtained. With these experiments, we empirically validated our proposal.This work was supported in part by the Comisión Nacional de Investigación Científica y Tecnológica - Programa de Formación de Capital Humano Avanzado (CONICYT-PCHA)/Doctorado Nacional/2014-63140074 through the Ph.D. Scholarship, in part by the European Union's Horizon 2020 under the Marie Sklodowska-Curie under Grant 690941, in part by the Millennium Institute for Foundational Research on Data (IMFD), and in part by the FONDECYT-CONICYT under Grant 1170497. The work of ÓSCAR PEDREIRA was supported in part by the Xunta de Galicia/FEDER-UE refs under Grant CSI ED431G/01 and Grant GRC: ED431C 2017/58, in part by the Office of the Vice President for Research and Postgraduate Studies of the Universidad Católica de Temuco, VIPUCT Project 2020EM-PS-08, and in part by the FEQUIP 2019-INRN-03 of the Universidad Católica de TemucoXunta de Galicia; ED431G/01Xunta de Galicia; ED431C 2017/58Chile. Comisión Nacional de Investigación Científica y Tecnológica; 2014-63140074Chile. Comisión Nacional de Investigación Científica y Tecnológica; 1170497Universidad Católica de Temuco (Chile); 2020EM-PS-08Universidad Católica de Temuco (Chile); 2019-INRN-0
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