4,756 research outputs found
Designing a Belief Function-Based Accessibility Indicator to Improve Web Browsing for Disabled People
The purpose of this study is to provide an accessibility measure of
web-pages, in order to draw disabled users to the pages that have been designed
to be ac-cessible to them. Our approach is based on the theory of belief
functions, using data which are supplied by reports produced by automatic web
content assessors that test the validity of criteria defined by the WCAG 2.0
guidelines proposed by the World Wide Web Consortium (W3C) organization. These
tools detect errors with gradual degrees of certainty and their results do not
always converge. For these reasons, to fuse information coming from the
reports, we choose to use an information fusion framework which can take into
account the uncertainty and imprecision of infor-mation as well as divergences
between sources. Our accessibility indicator covers four categories of
deficiencies. To validate the theoretical approach in this context, we propose
an evaluation completed on a corpus of 100 most visited French news websites,
and 2 evaluation tools. The results obtained illustrate the interest of our
accessibility indicator
2-(1,4-Dioxo-1,4-dihydro-2-naphthyl)-2-methylpropanoic acid
The sterically crowded title compound, CââHââOâ, crystallizes as centrosymmetric hydrogen-bonded dimers involving the carboxyl groups. The naphthoquinone ring system is folded by 11.5 (1)° about a vector joining the 1,4-C atoms, and the quinone O atoms are displaced from the ring plane, presumably because of steric interactions with the bulky substituent
Diagonal and Low-Rank Matrix Decompositions, Correlation Matrices, and Ellipsoid Fitting
In this paper we establish links between, and new results for, three problems
that are not usually considered together. The first is a matrix decomposition
problem that arises in areas such as statistical modeling and signal
processing: given a matrix formed as the sum of an unknown diagonal matrix
and an unknown low rank positive semidefinite matrix, decompose into these
constituents. The second problem we consider is to determine the facial
structure of the set of correlation matrices, a convex set also known as the
elliptope. This convex body, and particularly its facial structure, plays a
role in applications from combinatorial optimization to mathematical finance.
The third problem is a basic geometric question: given points
(where ) determine whether there is a centered
ellipsoid passing \emph{exactly} through all of the points.
We show that in a precise sense these three problems are equivalent.
Furthermore we establish a simple sufficient condition on a subspace that
ensures any positive semidefinite matrix with column space can be
recovered from for any diagonal matrix using a convex
optimization-based heuristic known as minimum trace factor analysis. This
result leads to a new understanding of the structure of rank-deficient
correlation matrices and a simple condition on a set of points that ensures
there is a centered ellipsoid passing through them.Comment: 20 page
Application of Monte Carlo Algorithms to the Bayesian Analysis of the Cosmic Microwave Background
Power spectrum estimation and evaluation of associated errors in the presence
of incomplete sky coverage; non-homogeneous, correlated instrumental noise; and
foreground emission is a problem of central importance for the extraction of
cosmological information from the cosmic microwave background. We develop a
Monte Carlo approach for the maximum likelihood estimation of the power
spectrum. The method is based on an identity for the Bayesian posterior as a
marginalization over unknowns. Maximization of the posterior involves the
computation of expectation values as a sample average from maps of the cosmic
microwave background and foregrounds given some current estimate of the power
spectrum or cosmological model, and some assumed statistical characterization
of the foregrounds. Maps of the CMB are sampled by a linear transform of a
Gaussian white noise process, implemented numerically with conjugate gradient
descent. For time series data with N_{t} samples, and N pixels on the sphere,
the method has a computational expense $KO[N^{2} +- N_{t} +AFw-log N_{t}],
where K is a prefactor determined by the convergence rate of conjugate gradient
descent. Preconditioners for conjugate gradient descent are given for scans
close to great circle paths, and the method allows partial sky coverage for
these cases by numerically marginalizing over the unobserved, or removed,
region.Comment: submitted to Ap
Clustering as an example of optimizing arbitrarily chosen objective functions
This paper is a reflection upon a common practice of solving various types of learning problems by optimizing arbitrarily chosen criteria in the hope that they are well correlated with the criterion actually used for assessment of the results. This issue has been investigated using clustering as an example, hence a unified view of clustering as an optimization problem is first proposed, stemming from the belief that typical design choices in clustering, like the number of clusters or similarity measure can be, and often are suboptimal, also from the point of view of clustering quality measures later used for algorithm comparison and ranking. In order to illustrate our point we propose a generalized clustering framework and provide a proof-of-concept using standard benchmark datasets and two popular clustering methods for comparison
Classification of Message Spreading in a Heterogeneous Social Network
Nowadays, social networks such as Twitter, Facebook and LinkedIn become
increasingly popular. In fact, they introduced new habits, new ways of
communication and they collect every day several information that have
different sources. Most existing research works fo-cus on the analysis of
homogeneous social networks, i.e. we have a single type of node and link in the
network. However, in the real world, social networks offer several types of
nodes and links. Hence, with a view to preserve as much information as
possible, it is important to consider so-cial networks as heterogeneous and
uncertain. The goal of our paper is to classify the social message based on its
spreading in the network and the theory of belief functions. The proposed
classifier interprets the spread of messages on the network, crossed paths and
types of links. We tested our classifier on a real word network that we
collected from Twitter, and our experiments show the performance of our belief
classifier
A Meaner King uses Biased Bases
The mean king problem is a quantum mechanical retrodiction problem, in which
Alice has to name the outcome of an ideal measurement on a d-dimensional
quantum system, made in one of (d+1) orthonormal bases, unknown to Alice at the
time of the measurement. Alice has to make this retrodiction on the basis of
the classical outcomes of a suitable control measurement including an entangled
copy. We show that the existence of a strategy for Alice is equivalent to the
existence of an overall joint probability distribution for (d+1) random
variables, whose marginal pair distributions are fixed as the transition
probability matrices of the given bases. In particular, for d=2 the problem is
decided by John Bell's classic inequality for three dichotomic variables. For
mutually unbiased bases in any dimension Alice has a strategy, but for randomly
chosen bases the probability for that goes rapidly to zero with increasing d.Comment: 5 pages, 1 figur
Statistical significance of communities in networks
Nodes in real-world networks are usually organized in local modules. These
groups, called communities, are intuitively defined as sub-graphs with a larger
density of internal connections than of external links. In this work, we
introduce a new measure aimed at quantifying the statistical significance of
single communities. Extreme and Order Statistics are used to predict the
statistics associated with individual clusters in random graphs. These
distributions allows us to define one community significance as the probability
that a generic clustering algorithm finds such a group in a random graph. The
method is successfully applied in the case of real-world networks for the
evaluation of the significance of their communities.Comment: 9 pages, 8 figures, 2 tables. The software to calculate the C-score
can be found at http://filrad.homelinux.org/cscor
Principal Component Analysis with Noisy and/or Missing Data
We present a method for performing Principal Component Analysis (PCA) on
noisy datasets with missing values. Estimates of the measurement error are used
to weight the input data such that compared to classic PCA, the resulting
eigenvectors are more sensitive to the true underlying signal variations rather
than being pulled by heteroskedastic measurement noise. Missing data is simply
the limiting case of weight=0. The underlying algorithm is a noise weighted
Expectation Maximization (EM) PCA, which has additional benefits of
implementation speed and flexibility for smoothing eigenvectors to reduce the
noise contribution. We present applications of this method on simulated data
and QSO spectra from the Sloan Digital Sky Survey.Comment: Accepted for publication in PASP; v2 with minor updates, mostly to
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