35,471 research outputs found
Analysis of the contour structural irregularity of skin lesions using wavelet decomposition
The boundary irregularity of skin lesions is of clinical significance for the early detection of
malignant melanomas and to distinguish them from other lesions such as benign moles. The
structural components of the contour are of particular importance. To extract the structure from
the contour, wavelet decomposition was used as these components tend to locate in the lower
frequency sub-bands. Lesion contours were modeled as signatures with scale normalization to
give position and frequency resolution invariance. Energy distributions among different wavelet
sub-bands were then analyzed to extract those with significant levels and differences to enable
maximum discrimination.
Based on the coefficients in the significant sub-bands, structural components from the original
contours were modeled, and a set of statistical and geometric irregularity descriptors researched
that were applied at each of the significant sub-bands. The effectiveness of the descriptors was
measured using the Hausdorff distance between sets of data from melanoma and mole contours.
The best descriptor outputs were input to a back projection neural network to construct a
combined classifier system. Experimental results showed that thirteen features from four
sub-bands produced the best discrimination between sets of melanomas and moles, and that a
small training set of nine melanomas and nine moles was optimum
Unit roots in moving averages beyond first order
The asymptotic theory of various estimators based on Gaussian likelihood has
been developed for the unit root and near unit root cases of a first-order
moving average model. Previous studies of the MA(1) unit root problem rely on
the special autocovariance structure of the MA(1) process, in which case, the
eigenvalues and eigenvectors of the covariance matrix of the data vector have
known analytical forms. In this paper, we take a different approach to first
consider the joint likelihood by including an augmented initial value as a
parameter and then recover the exact likelihood by integrating out the initial
value. This approach by-passes the difficulty of computing an explicit
decomposition of the covariance matrix and can be used to study unit root
behavior in moving averages beyond first order. The asymptotics of the
generalized likelihood ratio (GLR) statistic for testing unit roots are also
studied. The GLR test has operating characteristics that are competitive with
the locally best invariant unbiased (LBIU) test of Tanaka for some local
alternatives and dominates for all other alternatives.Comment: Published in at http://dx.doi.org/10.1214/11-AOS935 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Refined Asymptotics of the Finite-Size Magnetization via a New Conditional Limit Theorem for the Spin
We study the fluctuations of the spin per site around the thermodynamic
magnetization in the mean-field Blume-Capel model. Our main theorem generalizes
the main result in a previous paper (Ellis, Machta, and Otto) in which the
first rigorous confirmation of the statistical mechanical theory of finite-size
scaling for a mean-field model is given. In that paper our goal is to determine
whether the thermodynamic magnetization is a physically relevant estimator of
the finite-size magnetization. This is done by comparing the asymptotic
behaviors of these two quantities along parameter sequences converging to
either a second-order point or the tricritical point in the mean-field
Blume-Capel model. The main result is that the thermodynamic magnetization and
the finite-size magnetization are asymptotic when the parameter
governing the speed at which the sequence approaches criticality is below a
certain threshold . Our main theorem in the present paper on the
fluctuations of the spin per site around the thermodynamic magnetization is
based on a new conditional limit theorem for the spin, which is closely related
to a new conditional central limit theorem for the spin.Comment: 78 pages, 2 figure
Star cluster disruption in the starburst galaxy Messier 82
Using high-resolution, multiple-passband Hubble Space Telescope images
spanning the entire optical/near-infrared wavelength range, we obtained a
statistically complete sample, -band selected sample of 846 extended star
clusters across the disk of the nearby starburst galaxy M82. Based on careful
analysis of their spectral energy distributions, we determined their
galaxy-wide age and mass distributions. The M82 clusters exhibit three clear
peaks in their age distribution, thus defining a relatively young, log(t/yr) <
7.5, an intermediate-age, log(t/yr) [7.5, 8.5], and an old sample,
log(t/yr) > 8.5. Comparison of the completeness-corrected mass distributions
offers a firm handle on the galaxy's star cluster disruption history. The most
massive star clusters in the young and old samples are (almost) all
concentrated in the most densely populated central region, while the
intermediate-age sample's most massive clusters are more spatially dispersed,
which may reflect the distribution of the highest-density gas throughout the
galaxy's evolutionary history, combined with the solid-body nature of the
galaxy's central region.Comment: 10 pages, 6 figures, 2 online-only data tables; ApJS, in pres
Exploring Privacy Preservation in Outsourced K-Nearest Neighbors with Multiple Data Owners
The k-nearest neighbors (k-NN) algorithm is a popular and effective
classification algorithm. Due to its large storage and computational
requirements, it is suitable for cloud outsourcing. However, k-NN is often run
on sensitive data such as medical records, user images, or personal
information. It is important to protect the privacy of data in an outsourced
k-NN system.
Prior works have all assumed the data owners (who submit data to the
outsourced k-NN system) are a single trusted party. However, we observe that in
many practical scenarios, there may be multiple mutually distrusting data
owners. In this work, we present the first framing and exploration of privacy
preservation in an outsourced k-NN system with multiple data owners. We
consider the various threat models introduced by this modification. We discover
that under a particularly practical threat model that covers numerous
scenarios, there exists a set of adaptive attacks that breach the data privacy
of any exact k-NN system. The vulnerability is a result of the mathematical
properties of k-NN and its output. Thus, we propose a privacy-preserving
alternative system supporting kernel density estimation using a Gaussian
kernel, a classification algorithm from the same family as k-NN. In many
applications, this similar algorithm serves as a good substitute for k-NN. We
additionally investigate solutions for other threat models, often through
extensions on prior single data owner systems
- …