Designed to Delay Tolerant Networks, the Cultural Greedy GrAnt (CGrAnt) routing protocol uses Ant Colony Optimization to represent the population space of a Cultural Algorithm. CGrAnt aims to improve the message forwa...
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Designed to Delay Tolerant Networks, the Cultural Greedy GrAnt (CGrAnt) routing protocol uses Ant Colony Optimization to represent the population space of a Cultural Algorithm. CGrAnt aims to improve the message forwarding by analyzing the network characteristics based on three distinct knowledge: Domain, History, and Situational. The Domain knowledge plays a central role in the CGrAnt operation as it provides a good balance between search space exploration (through the selection of new solutions) and exploitation (through the selection of previously found solutions). This work proposes alternative metrics to be used by the Domain knowledge of CGrAnt. Results show that the new proposed metrics increase the CGrAnt performance.
In this paper we consider the filtering of partially observed multi-dimensional diffusion processes that are observed regularly at discrete times. This is a challenging problem which requires the use of advanced numer...
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In this article we consider the development of an unbiased estimator for the ensemble Kalman–Bucy filter (EnKBF). The EnKBF is a continuous-time filtering methodology which can be viewed as a continuous-time analogue...
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In this article we consider Bayesian parameter inference for a type of partially observed stochastic Volterra equation (SVE). SVEs are found in many areas such as physics and mathematical finance. In the latter field ...
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In this article we consider the filtering problem associated to partially observed diffusions, with observations following a marked point process. In the model, the data form a point process with observation times tha...
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We show that the two uni-directional n-cubes, namely UHC1/sub n/ and UHC2/sub n/ proposed by Chou and Du (1990) as interconnection schemes are Hamiltonian. In addition, we show that (1) if n is even, both architecture...
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ISBN:
(纸本)081867038X
We show that the two uni-directional n-cubes, namely UHC1/sub n/ and UHC2/sub n/ proposed by Chou and Du (1990) as interconnection schemes are Hamiltonian. In addition, we show that (1) if n is even, both architectures are vertex symmetric; and (2) if n is odd, both architectures have exactly two vertex-symmetric components. By studying symmetry, we further prove that the maximum delay of one-port one-to-all broadcasting for either architecture is at most 1.5n.< >
We investigate the problem of automatically constructing efficient representations or basis functions for approximating value functions based on analyzing the structure and topology of the state space. In particular, ...
We investigate the problem of automatically constructing efficient representations or basis functions for approximating value functions based on analyzing the structure and topology of the state space. In particular, two novel approaches to value function approximation are explored based on automatically constructing basis functions on state spaces that can be represented as graphs or manifolds: one approach uses the eigenfunctions of the Laplacian, in effect performing a global Fourier analysis on the graph; the second approach is based on diffusion wavelets, which generalize classical wavelets to graphs using multiscale dilations induced by powers of a diffusion operator or random walk on the graph. Together, these approaches form the foundation of a new generation of methods for solving large Markov decision processes, in which the underlying representation and policies are simultaneously learned.
Several classification methods assume that the underlying distributions follow tree-structured graphical models. Indeed, trees capture statistical dependencies between pairs of variables, which may be crucial to attai...
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Several classification methods assume that the underlying distributions follow tree-structured graphical models. Indeed, trees capture statistical dependencies between pairs of variables, which may be crucial to attaining low classification errors. In this setting, the optimal classifier is linear in the log-transformed univariate and bivariate densities that correspond to the tree edges. In practice, observed data may not be well approximated by trees. Yet, motivated by the importance of pairwise dependencies for accurate classification, here we propose to approximate the optimal decision boundary by a sparse linear combination of the univariate and bivariate log-transformed densities. Our proposed approach is semi-parametric in nature: we non-parametrically estimate the univariate and bivariate densities, remove pairs of variables that are nearly independent using the Hilbert-Schmidt independence criterion, and finally construct a linear SVM using the retained log-transformed densities. We demonstrate on synthetic and real data sets, that our classifier, named SLB (sparse log-bivariate density), is competitive with other popular classification methods.
This research study presents an algorithmic approach for the detection and identification of key components on license plates, including numerical digits, province codes, vehicle colors, and vehicle makes and types. T...
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Selective thermal emitters can boost the efficiency of heat-to-electricity conversion in thermophotovoltaic systems only if their spectral selectivity is high. We demonstrate a non-Hermitian metasurface-based selectiv...
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