138 resultados para rank order tournaments


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© 2014 AIP Publishing LLC. Superparamagnetic nanoparticles are employed in a broad range of applications that demand detailed magnetic characterization for superior performance, e.g., in drug delivery or cancer treatment. Magnetic hysteresis measurements provide information on saturation magnetization and coercive force for bulk material but can be equivocal for particles having a broad size distribution. Here, first-order reversal curves (FORCs) are used to evaluate the effective magnetic particle size and interaction between equally sized magnetic iron oxide (Fe2O3) nanoparticles with three different morphologies: (i) pure Fe2O3, (ii) Janus-like, and (iii) core/shell Fe2O3/SiO2synthesized using flame technology. By characterizing the distribution in coercive force and interaction field from the FORC diagrams, we find that the presence of SiO2in the core/shell structures significantly reduces the average coercive force in comparison to the Janus-like Fe2O3/SiO2and pure Fe2O3particles. This is attributed to the reduction in the dipolar interaction between particles, which in turn reduces the effective magnetic particle size. Hence, FORC analysis allows for a finer distinction between equally sized Fe2O3particles with similar magnetic hysteresis curves that can significantly influence the final nanoparticle performance.

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This paper presents an achievable second-order rate region for the discrete memoryless multiple-access channel. The result is obtained using a random-coding ensemble in which each user's codebook contains codewords of a fixed composition. It is shown that this ensemble performs at least as well as i.i.d. random coding in terms of second-order asymptotics, and an example is given where a strict improvement is observed. © 2013 IEEE.

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Motivated by the problem of learning a linear regression model whose parameter is a large fixed-rank non-symmetric matrix, we consider the optimization of a smooth cost function defined on the set of fixed-rank matrices. We adopt the geometric framework of optimization on Riemannian quotient manifolds. We study the underlying geometries of several well-known fixed-rank matrix factorizations and then exploit the Riemannian quotient geometry of the search space in the design of a class of gradient descent and trust-region algorithms. The proposed algorithms generalize our previous results on fixed-rank symmetric positive semidefinite matrices, apply to a broad range of applications, scale to high-dimensional problems, and confer a geometric basis to recent contributions on the learning of fixed-rank non-symmetric matrices. We make connections with existing algorithms in the context of low-rank matrix completion and discuss the usefulness of the proposed framework. Numerical experiments suggest that the proposed algorithms compete with state-of-the-art algorithms and that manifold optimization offers an effective and versatile framework for the design of machine learning algorithms that learn a fixed-rank matrix. © 2013 Springer-Verlag Berlin Heidelberg.