2,860 research outputs found
The Magnetic Topology of the Weak-Lined T Tauri Star V410 - A Simultaneous Temperature and Magnetic Field Inversion
We present a detailed temperature and magnetic investigation of the T Tauri
star V410 Tau by means of a simultaneous Doppler- and Zeeman-Doppler Imaging.
Moreover we introduce a new line profile reconstruction method based on a
singular value decomposition (SVD) to extract the weak polarized line profiles.
One of the key features of the line profile reconstruction is that the SVD line
profiles are amenable to radiative transfer modeling within our Zeeman-Doppler
Imaging code iMap. The code also utilizes a new iterative regularization scheme
which is independent of any additional surface constraints. To provide more
stability a vital part of our inversion strategy is the inversion of both
Stokes I and Stokes V profiles to simultaneously reconstruct the temperature
and magnetic field surface distribution of V410 Tau. A new image-shear analysis
is also implemented to allow the search for image and line profile distortions
induced by a differential rotation of the star. The magnetic field structure we
obtain for V410 Tau shows a good spatial correlation with the surface
temperature and is dominated by a strong field within the cool polar spot. The
Zeeman-Doppler maps exhibit a large-scale organization of both polarities
around the polar cap in the form of a twisted bipolar structure. The magnetic
field reaches a value of almost 2 kG within the polar region but smaller fields
are also present down to lower latitudes. The pronounced non-axisymmetric field
structure and the non-detection of a differential rotation for V410 Tau
supports the idea of an underlying -type dynamo, which is predicted
for weak-lined T Tauri stars.Comment: Accepted for A&A, 18 pages, 10 figure
Randomized Dimension Reduction on Massive Data
Scalability of statistical estimators is of increasing importance in modern
applications and dimension reduction is often used to extract relevant
information from data. A variety of popular dimension reduction approaches can
be framed as symmetric generalized eigendecomposition problems. In this paper
we outline how taking into account the low rank structure assumption implicit
in these dimension reduction approaches provides both computational and
statistical advantages. We adapt recent randomized low-rank approximation
algorithms to provide efficient solutions to three dimension reduction methods:
Principal Component Analysis (PCA), Sliced Inverse Regression (SIR), and
Localized Sliced Inverse Regression (LSIR). A key observation in this paper is
that randomization serves a dual role, improving both computational and
statistical performance. This point is highlighted in our experiments on real
and simulated data.Comment: 31 pages, 6 figures, Key Words:dimension reduction, generalized
eigendecompositon, low-rank, supervised, inverse regression, random
projections, randomized algorithms, Krylov subspace method
Sparse regulatory networks
In many organisms the expression levels of each gene are controlled by the
activation levels of known "Transcription Factors" (TF). A problem of
considerable interest is that of estimating the "Transcription Regulation
Networks" (TRN) relating the TFs and genes. While the expression levels of
genes can be observed, the activation levels of the corresponding TFs are
usually unknown, greatly increasing the difficulty of the problem. Based on
previous experimental work, it is often the case that partial information about
the TRN is available. For example, certain TFs may be known to regulate a given
gene or in other cases a connection may be predicted with a certain
probability. In general, the biology of the problem indicates there will be
very few connections between TFs and genes. Several methods have been proposed
for estimating TRNs. However, they all suffer from problems such as unrealistic
assumptions about prior knowledge of the network structure or computational
limitations. We propose a new approach that can directly utilize prior
information about the network structure in conjunction with observed gene
expression data to estimate the TRN. Our approach uses penalties on the
network to ensure a sparse structure. This has the advantage of being
computationally efficient as well as making many fewer assumptions about the
network structure. We use our methodology to construct the TRN for E. coli and
show that the estimate is biologically sensible and compares favorably with
previous estimates.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS350 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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