103,683 research outputs found
Correcting for selection bias via cross-validation in the classification of microarray data
There is increasing interest in the use of diagnostic rules based on
microarray data. These rules are formed by considering the expression levels of
thousands of genes in tissue samples taken on patients of known classification
with respect to a number of classes, representing, say, disease status or
treatment strategy. As the final versions of these rules are usually based on a
small subset of the available genes, there is a selection bias that has to be
corrected for in the estimation of the associated error rates. We consider the
problem using cross-validation. In particular, we present explicit formulae
that are useful in explaining the layers of validation that have to be
performed in order to avoid improperly cross-validated estimates.Comment: Published in at http://dx.doi.org/10.1214/193940307000000284 the IMS
Collections (http://www.imstat.org/publications/imscollections.htm) by the
Institute of Mathematical Statistics (http://www.imstat.org
A Computational Study Of The Role Of Spatial Receptive Field Structure In Processing Natural And Non-Natural Scenes
The center-surround receptive field structure, ubiquitous in the visual system, is hypothesized to be evolutionarily advantageous in image processing tasks. We address the potential functional benefits and shortcomings of spatial localization and center-surround antagonism in the context of an integrate-and-fire neuronal network model with image-based forcing. Utilizing the sparsity of natural scenes, we derive a compressive-sensing framework for input image reconstruction utilizing evoked neuronal firing rates. We investigate how the accuracy of input encoding depends on the receptive field architecture, and demonstrate that spatial localization in visual stimulus sampling facilitates marked improvements in natural scene processing beyond uniformly-random excitatory connectivity. However, for specific classes of images, we show that spatial localization inherent in physiological receptive fields combined with information loss through nonlinear neuronal network dynamics may underlie common optical illusions, giving a novel explanation for their manifestation. In the context of signal processing, we expect this work may suggest new sampling protocols useful for extending conventional compressive sensing theory
3-D Microwave Imaging for Breast Cancer
We introduce a novel microwave imaging technique for breast cancer detection. Our approach provides a one-pass inverse image solution, which is completely new and unprecedented, unrelated to tomography or radar-based algorithms, and unburdened by the optimization toil which lies at the heart of numerical schemes. It operates effectively at a single frequency, without requiring the bandwidth of radar techniques. Underlying this new method is our unique Field Mapping Algorithm (FMA), which transforms electromagnetic fields acquired upon one surface, be it through outright measurement or some auxiliary computation, into those upon another in an exact sense
On the gravitational wave background from compact binary coalescences in the band of ground-based interferometers
This paper reports a comprehensive study on the gravitational wave (GW)
background from compact binary coalescences. We consider in our calculations
newly available observation-based neutron star and black hole mass
distributions and complete analytical waveforms that include post-Newtonian
amplitude corrections. Our results show that: (i) post-Newtonian effects cause
a small reduction in the GW background signal; (ii) below 100 Hz the background
depends primarily on the local coalescence rate and the average chirp
mass and is independent of the chirp mass distribution; (iii) the effects of
cosmic star formation rates and delay times between the formation and merger of
binaries are linear below 100 Hz and can be represented by a single parameter
within a factor of ~ 2; (iv) a simple power law model of the energy density
parameter up to 50-100 Hz is sufficient to be used
as a search template for ground-based interferometers. In terms of the
detection prospects of the background signal, we show that: (i) detection (a
signal-to-noise ratio of 3) within one year of observation by the Advanced LIGO
detectors (H1-L1) requires a coalescence rate of for binary neutron stars (binary black holes); (ii) this limit on
could be reduced 3-fold for two co-located detectors, whereas the
currently proposed worldwide network of advanced instruments gives only ~ 30%
improvement in detectability; (iii) the improved sensitivity of the planned
Einstein Telescope allows not only confident detection of the background but
also the high frequency components of the spectrum to be measured. Finally we
show that sub-threshold binary neutron star merger events produce a strong
foreground, which could be an issue for future terrestrial stochastic searches
of primordial GWs.Comment: A few typos corrected to match the published version in MNRA
NAM: Non-Adversarial Unsupervised Domain Mapping
Several methods were recently proposed for the task of translating images
between domains without prior knowledge in the form of correspondences. The
existing methods apply adversarial learning to ensure that the distribution of
the mapped source domain is indistinguishable from the target domain, which
suffers from known stability issues. In addition, most methods rely heavily on
`cycle' relationships between the domains, which enforce a one-to-one mapping.
In this work, we introduce an alternative method: Non-Adversarial Mapping
(NAM), which separates the task of target domain generative modeling from the
cross-domain mapping task. NAM relies on a pre-trained generative model of the
target domain, and aligns each source image with an image synthesized from the
target domain, while jointly optimizing the domain mapping function. It has
several key advantages: higher quality and resolution image translations,
simpler and more stable training and reusable target models. Extensive
experiments are presented validating the advantages of our method.Comment: ECCV 201
Composition-tuned magneto-optical Kerr effect in L10-MnxGa films with giant perpendicular anisotropy
We report the large polar magnetooptical Kerr effect in L10-MnxGa epitaxial
films with giant perpendicular magnetic anisotropy in a wide composition range.
The Kerr rotation was enhanced by a factor of up to 10 by decreasing Mn atomic
concentration, which most likely arises from the variation of the effective
spin-orbit coupling strength, compensation effect of magnetic moments at
different Mn atom sites, and overall strain. The Kerr ellipticity and the
magnitude of the complex Kerr angle is found to have more complex
composition-dependence that varies with the photon energy. These L10-MnxGa
films show large Kerr rotation of up to 0.10o, high reflectivity of 35%-55% in
a wide wavelength range of 400~850 nm, and giant magnetic anisotropic field of
up to 210 kOe, making them an interesting material system for emerging
spintronics and terahertz modulator applications
One-Dimensional Transition Metal-Benzene Sandwich Polymers: Possible Ideal Conductors for Spin Transport
We investigate the electronic and magnetic properties of the proposed
one-dimensional transition metal (TM=Sc, Ti, V, Cr, and Mn)-benzene (Bz)
sandwich polymers by means of density functional calculations.
[V(Bz)] is found to be a quasi-half-metallic ferromagnet and
half-metallic ferromagnetism is predicted for [Mn(Bz)]. Moreover, we
show that stretching the [TM(Bz)] polymers could have dramatic
effects on their electronic and magnetic properties. The elongated
[V(Bz)] displays half-metallic behavior, and [Mn(Bz)]
stretched to a certain degree becomes an antiferromagnetic insulator. The
possibilities to stabilize the ferromagnetic order in [V(Bz)] and
[Mn(Bz)] polymers at finite temperature are discussed. We suggest
that the hexagonal bundles composed by these polymers might display intrachain
ferromagnetic order at finite temperature by introducing interchain exchange
coupling
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