971 resultados para K-nearest neighbors method


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We have developed a technique for isolating DNA markers tightly linked to a target region that is based on RLGS, named RLGS spot-bombing (RLGS-SB). RLGS-SB allows us to scan the genome of higher organisms quickly and efficiently to identify loci that are linked to either a target region or gene of interest. The method was initially tested by analyzing a C57BL/6-GusS mouse congenic strain. We identified 33 variant markers out of 10,565 total loci in a 4.2-centimorgan (cM) interval surrounding the Gus locus in 4 days of laboratory work. The validity of RLGS-SB to find DNA markers linked to a target locus was also tested on pooled DNA from segregating backcross progeny by analyzing the spot intensity of already mapped RLGS loci. Finally, we used RLGS-SB to identify DNA markers closely linked to the mouse reeler (rl) locus on chromosome 5 by phenotypic pooling. A total of 31 RLGS loci were identified and mapped to the target region after screening 8856 loci. These 31 loci were mapped within 11.7 cM surrounding rl. The average density of RLGS loci located in the rl region was 0.38 cM. Three loci were closely linked to rl showing a recombination frequency of 0/340, which is < 1 cM from rl. Thus, RLGS-SB provides an efficient and rapid method for the detection and isolation of polymorphic DNA markers linked to a trait or gene of interest.

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Plane model extraction from three-dimensional point clouds is a necessary step in many different applications such as planar object reconstruction, indoor mapping and indoor localization. Different RANdom SAmple Consensus (RANSAC)-based methods have been proposed for this purpose in recent years. In this study, we propose a novel method-based on RANSAC called Multiplane Model Estimation, which can estimate multiple plane models simultaneously from a noisy point cloud using the knowledge extracted from a scene (or an object) in order to reconstruct it accurately. This method comprises two steps: first, it clusters the data into planar faces that preserve some constraints defined by knowledge related to the object (e.g., the angles between faces); and second, the models of the planes are estimated based on these data using a novel multi-constraint RANSAC. We performed experiments in the clustering and RANSAC stages, which showed that the proposed method performed better than state-of-the-art methods.

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In recent times the Douglas–Rachford algorithm has been observed empirically to solve a variety of nonconvex feasibility problems including those of a combinatorial nature. For many of these problems current theory is not sufficient to explain this observed success and is mainly concerned with questions of local convergence. In this paper we analyze global behavior of the method for finding a point in the intersection of a half-space and a potentially non-convex set which is assumed to satisfy a well-quasi-ordering property or a property weaker than compactness. In particular, the special case in which the second set is finite is covered by our framework and provides a prototypical setting for combinatorial optimization problems.

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In this work, a modified version of the elastic bunch graph matching (EBGM) algorithm for face recognition is introduced. First, faces are detected by using a fuzzy skin detector based on the RGB color space. Then, the fiducial points for the facial graph are extracted automatically by adjusting a grid of points to the result of an edge detector. After that, the position of the nodes, their relation with their neighbors and their Gabor jets are calculated in order to obtain the feature vector defining each face. A self-organizing map (SOM) framework is shown afterwards. Thus, the calculation of the winning neuron and the recognition process are performed by using a similarity function that takes into account both the geometric and texture information of the facial graph. The set of experiments carried out for our SOM-EBGM method shows the accuracy of our proposal when compared with other state-of the-art methods.

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In this short review, we provide some new insights into the material synthesis and characterization of modern multi-component superconducting oxides. Two different approaches such as the high-pressure, high-temperature method and ceramic combinatorial chemistry will be reported with application to several typical examples. First, we highlight the key role of the extreme conditions in the growth of Fe-based superconductors, where a careful control of the composition-structure relation is vital for understanding the microscopic physics. The availability of high-quality LnFeAsO (Ln = lanthanide) single crystals with substitution of O by F, Sm by Th, Fe by Co, and As by P allowed us to measure intrinsic and anisotropic superconducting properties such as Hc2, Jc. Furthermore, we demonstrate that combinatorial ceramic chemistry is an efficient way to search for new superconducting compounds. A single-sample synthesis concept based on multi-element ceramic mixtures can produce a variety of local products. Such a system needs local probe analyses and separation techniques to identify compounds of interest. We present the results obtained from random mixtures of Ca, Sr, Ba, La, Zr, Pb, Tl, Y, Bi, and Cu oxides reacted at different conditions. By adding Zr but removing Tl, Y, and Bi, the bulk state superconductivity got enhanced up to about 122 K.

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Includes household hints; samples of menus; some recipes include wine or liquor as an ingredient. Sample recipes: Stuffed artichokes, Macaroni à la Milanaise, Apricot and wine jelly, Cream sponge cake.

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"Contract No. 954471 under NAS 7-100 SRI Project PYU 4980."

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"Contract No. 954471 under NAS 7-100 SRI Project PYU 4980."

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"Contract No. 954471 under NAS 7-100 SRI Project PYU 4980."

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"Contract No. 954471 under NAS 7-100 SRI Project PYU 4980."