386,907 research outputs found
Partial Teleportation of Entanglement in the Noisy Environment
Partial teleportation of entanglement is to teleport one particle of an
entangled pair through a quantum channel. This is conceptually equivalent to
quantum swapping. We consider the partial teleportation of entanglement in the
noisy environment, employing the Werner-state representation of the noisy
channel for the simplicity of calculation. To have the insight of the many-body
teleportation, we introduce the measure of correlation information and study
the transfer of the correlation information and entanglement. We find that the
fidelity gets smaller as the initial-state is entangled more for a given
entanglement of the quantum channel. The entangled channel transfers at least
some of the entanglement to the final state.Comment: 8 pages, 2 figure
Cached Sufficient Statistics for Efficient Machine Learning with Large Datasets
This paper introduces new algorithms and data structures for quick counting
for machine learning datasets. We focus on the counting task of constructing
contingency tables, but our approach is also applicable to counting the number
of records in a dataset that match conjunctive queries. Subject to certain
assumptions, the costs of these operations can be shown to be independent of
the number of records in the dataset and loglinear in the number of non-zero
entries in the contingency table. We provide a very sparse data structure, the
ADtree, to minimize memory use. We provide analytical worst-case bounds for
this structure for several models of data distribution. We empirically
demonstrate that tractably-sized data structures can be produced for large
real-world datasets by (a) using a sparse tree structure that never allocates
memory for counts of zero, (b) never allocating memory for counts that can be
deduced from other counts, and (c) not bothering to expand the tree fully near
its leaves. We show how the ADtree can be used to accelerate Bayes net
structure finding algorithms, rule learning algorithms, and feature selection
algorithms, and we provide a number of empirical results comparing ADtree
methods against traditional direct counting approaches. We also discuss the
possible uses of ADtrees in other machine learning methods, and discuss the
merits of ADtrees in comparison with alternative representations such as
kd-trees, R-trees and Frequent Sets.Comment: See http://www.jair.org/ for any accompanying file
Iterated smoothed bootstrap confidence intervals for population quantiles
This paper investigates the effects of smoothed bootstrap iterations on
coverage probabilities of smoothed bootstrap and bootstrap-t confidence
intervals for population quantiles, and establishes the optimal kernel
bandwidths at various stages of the smoothing procedures. The conventional
smoothed bootstrap and bootstrap-t methods have been known to yield one-sided
coverage errors of orders O(n^{-1/2}) and o(n^{-2/3}), respectively, for
intervals based on the sample quantile of a random sample of size n. We sharpen
the latter result to O(n^{-5/6}) with proper choices of bandwidths at the
bootstrapping and Studentization steps. We show further that calibration of the
nominal coverage level by means of the iterated bootstrap succeeds in reducing
the coverage error of the smoothed bootstrap percentile interval to the order
O(n^{-2/3}) and that of the smoothed bootstrap-t interval to O(n^{-58/57}),
provided that bandwidths are selected of appropriate orders. Simulation results
confirm our asymptotic findings, suggesting that the iterated smoothed
bootstrap-t method yields the most accurate coverage. On the other hand, the
iterated smoothed bootstrap percentile method interval has the advantage of
being shorter and more stable than the bootstrap-t intervals.Comment: Published at http://dx.doi.org/10.1214/009053604000000878 in the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Temperature- and magnetic-field-dependent resistivity of MgB2 sintered at high temperature and high pressure condition
We report the temperature- and magnetic-field-dependent resistivity of MgB2
sintered at high temperature and high pressure condition. The superconducting
transition width for the resistivity measurement was about 0.4 K, and the
low-field magnetization showed a sharp superconducting transition with a
transition width of about 1 K. The resistivity in the normal state roughly
followed T^2 behavior with smaller residual resistivity ratio (RRR) of 3 over
broad temperature region above 100 K rather than reported T^3 behavior with
larger RRR value of ~ 20 in the samples made at lower pressures. Also, the
resistivity did not change appreciably with the applied magnetic field, which
was different from previous report. These differences were discussed with the
microscopic and structural change due to the high-pressure sintering.Comment: 2 pages, 3 figures. Accepted by Physica
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