28,335 research outputs found
Bipartite graph partitioning and data clustering
Many data types arising from data mining applications can be modeled as
bipartite graphs, examples include terms and documents in a text corpus,
customers and purchasing items in market basket analysis and reviewers and
movies in a movie recommender system. In this paper, we propose a new data
clustering method based on partitioning the underlying bipartite graph. The
partition is constructed by minimizing a normalized sum of edge weights between
unmatched pairs of vertices of the bipartite graph. We show that an approximate
solution to the minimization problem can be obtained by computing a partial
singular value decomposition (SVD) of the associated edge weight matrix of the
bipartite graph. We point out the connection of our clustering algorithm to
correspondence analysis used in multivariate analysis. We also briefly discuss
the issue of assigning data objects to multiple clusters. In the experimental
results, we apply our clustering algorithm to the problem of document
clustering to illustrate its effectiveness and efficiency.Comment: Proceedings of ACM CIKM 2001, the Tenth International Conference on
Information and Knowledge Management, 200
Solar flare hard X-ray spikes observed by RHESSI: a statistical study
Context. Hard X-ray (HXR) spikes refer to fine time structures on timescales
of seconds to milliseconds in high-energy HXR emission profiles during solar
flare eruptions. Aims. We present a preliminary statistical investigation of
temporal and spectral properties of HXR spikes. Methods. Using a three-sigma
spike selection rule, we detected 184 spikes in 94 out of 322 flares with
significant counts at given photon energies, which were detected from
demodulated HXR light curves obtained by the Reuven Ramaty High Energy Solar
Spectroscopic Imager (RHESSI). About one fifth of these spikes are also
detected at photon energies higher than 100 keV. Results. The statistical
properties of the spikes are as follows. (1) HXR spikes are produced in both
impulsive flares and long-duration flares with nearly the same occurrence
rates. Ninety percent of the spikes occur during the rise phase of the flares,
and about 70% occur around the peak times of the flares. (2) The time durations
of the spikes vary from 0.2 to 2 s, with the mean being 1.0 s, which is not
dependent on photon energies. The spikes exhibit symmetric time profiles with
no significant difference between rise and decay times. (3) Among the most
energetic spikes, nearly all of them have harder count spectra than their
underlying slow-varying components. There is also a weak indication that spikes
exhibiting time lags in high-energy emissions tend to have harder spectra than
spikes with time lags in low-energy emissions.Comment: 16 pages, 13 figure
Association Between Air Pollution and Low Birth Weight: A Community-Based Study
The relationship between maternal exposure to air pollution during periods of pregnancy (entire and specific periods) and birth weight was investigated in a well-defined cohort. Between 1988 and 1991, all pregnant women living in four residential areas of Beijing were registered and followed from early pregnancy until delivery. Information on individual mothers and infants was collected. Daily air pollution data were obtained independently. The sample for analysis included 74,671 first-parity live births were gestational age 37-44 weeks. Multiple linear regression and logistic regression were used to estimate the effects of air pollution on birth weight and low birth weight (< 2,500 g), adjusting for gestational age, residence, year of birth, maternal age, and infant gender. There was a significant exposure-response relationship between maternal exposures to sulfur dioxide (SO2) and total suspended particles (TSP) during the third trimester of pregnancy and infant birth weight. The adjusted odds ratio for low birth weight was 1.11 (95% CI, 1.06-1.16) for each 100 micrograms/m3 increase in SO2 and 1.10 (95% CI, 1.05-1.14) for each 100 micrograms/m3 increase in TSP. The estimated reduction in birth weight was 7.3 g and 6.9 g for each 100 micrograms/m3 increase in SO2 and in TSP, respectively. The birth weight distribution of the high-exposure group was more skewed toward the left tail (i.e., with higher proportion of births < 2,500 g) than that of the low-exposure group. Although the effects of other unmeasured risk factors cannot be excluded with certainty, our data suggests that TSP and SO2, or a more complex pollution mixture associated with these pollutants, contribute to an excess risk of low birth weight in the Beijing population.National Institute of Environmental Health Sciences (ES05947, ES08337); National Institute of Child Health & Human Development (R01 HD32505); Department of Health and Human Services (MCJ-259501, HRSA 5 T32 PE10014
Localized magnetic states in biased bilayer and trilayer graphene
We study the localized magnetic states of impurity in biased bilayer and
trilayer graphene. It is found that the magnetic boundary for bilayer and
trilayer graphene presents the mixing features of Dirac and conventional
fermion. For zero gate bias, as the impurity energy approaches the Dirac point,
the impurity magnetization region diminishes for bilayer and trilayer graphene.
When a gate bias is applied, the dependence of impurity magnetic states on the
impurity energy exhibits a different behavior for bilayer and trilayer graphene
due to the opening of a gap between the valence and the conduction band in the
bilayer graphene with the gate bias applied. The magnetic moment and the
corresponding magnetic transition of the impurity in bilayer graphene are also
investigated.Comment: 16 pages,6 figure
Angle-resolved photoemission studies of the superconducting gap symmetry in Fe-based superconductors
The superconducting gap is the fundamental parameter that characterizes the
superconducting state, and its symmetry is a direct consequence of the
mechanism responsible for Cooper pairing. Here we discuss about angle-resolved
photoemission spectroscopy measurements of the superconducting gap in the
Fe-based high-temperature superconductors. We show that the superconducting gap
is Fermi surface dependent and nodeless with small anisotropy, or more
precisely, a function of momentum. We show that while this observation is
inconsistent with weak coupling approaches for superconductivity in these
materials, it is well supported by strong coupling models and global
superconducting gaps. We also suggest that the strong anisotropies measured by
other probes sensitive to the residual density of states are not related to the
pairing interaction itself, but rather emerge naturally from the smaller
lifetime of the superconducting Cooper pairs that is a direct consequence of
the momentum dependent interband scattering inherent to these materials.Comment: 7 pages, 5 figure
Bidirectional optimization of the melting spinning process
This is the author's accepted manuscript (under the provisional title "Bi-directional optimization of the melting spinning process with an immune-enhanced neural network"). The final published article is available from the link below. Copyright 2014 @ IEEE.A bidirectional optimizing approach for the melting spinning process based on an immune-enhanced neural network is proposed. The proposed bidirectional model can not only reveal the internal nonlinear relationship between the process configuration and the quality indices of the fibers as final product, but also provide a tool for engineers to develop new fiber products with expected quality specifications. A neural network is taken as the basis for the bidirectional model, and an immune component is introduced to enlarge the searching scope of the solution field so that the neural network has a larger possibility to find the appropriate and reasonable solution, and the error of prediction can therefore be eliminated. The proposed intelligent model can also help to determine what kind of process configuration should be made in order to produce satisfactory fiber products. To make the proposed model practical to the manufacturing, a software platform is developed. Simulation results show that the proposed model can eliminate the approximation error raised by the neural network-based optimizing model, which is due to the extension of focusing scope by the artificial immune mechanism. Meanwhile, the proposed model with the corresponding software can conduct optimization in two directions, namely, the process optimization and category development, and the corresponding results outperform those with an ordinary neural network-based intelligent model. It is also proved that the proposed model has the potential to act as a valuable tool from which the engineers and decision makers of the spinning process could benefit.National Nature Science Foundation of China, Ministry of Education of China, the Shanghai Committee of Science and Technology), and the Fundamental Research Funds for the Central Universities
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