403 research outputs found
Binding energy of the positronium negative ion: Relativistic and QED energy shifts
The leading relativistic and QED corrections to the ground-state energy of the three-body system e-e+e- are calculated numerically using a Hylleraas correlated basis set. The accuracy of the nonrelativistic variational ground state is discussed with respect to the convergence of the energy with increasing size of the basis set, and also with respect to the variance of the Hamiltonian. The corrections to this energy include the lowest order Breit interaction, the vacuum polarization potential, one and two photon exchange contributions, the annihilation interaction and spin-spin contact terms. The relativistic effects and the residual interactions considered here decrease the one-electron binding energy from the nonrelativistic value of 0.012 005 070 232 980 107 69(28) au to 0.011 981 051 246(2) au (78 831 530 ± 5 MHz). © 2005 IOP Publishing Ltd
Characterization of the Complexity of Computing the Capacity of Colored Noise Gaussian Channels
This paper explores the computational complexity involved in determining the
capacity of the band-limited additive colored Gaussian noise (ACGN) channel and
its capacity-achieving power spectral density (p.s.d.). The study reveals that
when the noise p.s.d. is a strictly positive computable continuous function,
computing the capacity of the band-limited ACGN channel becomes a
-complete problem within the set of polynomial time computable
noise p.s.d.s. Meaning that it is even more complex than problems that are
-complete. Additionally, it is shown that the capacity-achieving
distribution is also -complete. Furthermore, under the widely
accepted assumption that , it has two
significant implications for the ACGN channel. The first implication is the
existence of a polynomial time computable noise p.s.d. for which the
computation of its capacity cannot be performed in polynomial time, i.e., the
number of computational steps on a Turing Machine grows faster than all
polynomials. The second one is the existence of a polynomial time computable
noise p.s.d. for which determining its capacity-achieving p.s.d. cannot be done
within polynomial time
Algorithmic Computability of the Capacity of Gaussian Channels with Colored Noise
Designing capacity-achieving coding schemes for the band-limited additive
colored Gaussian noise (ACGN) channel has been and is still a challenge. In
this paper, the capacity of the band-limited ACGN channel is studied from a
fundamental algorithmic point of view by addressing the question of whether or
not the capacity can be algorithmically computed. To this aim, the concept of
Turing machines is used, which provides fundamental performance limits of
digital computers. t is shown that there are band-limited ACGN channels having
computable continuous spectral densities whose capacity are non-computable
numbers. Moreover, it is demonstrated that for those channels, it is impossible
to find computable sequences of asymptotically sharp upper bounds for their
capacities
Detection of curved lines with B-COSFIRE filters: A case study on crack delineation
The detection of curvilinear structures is an important step for various
computer vision applications, ranging from medical image analysis for
segmentation of blood vessels, to remote sensing for the identification of
roads and rivers, and to biometrics and robotics, among others. %The visual
system of the brain has remarkable abilities to detect curvilinear structures
in noisy images. This is a nontrivial task especially for the detection of thin
or incomplete curvilinear structures surrounded with noise. We propose a
general purpose curvilinear structure detector that uses the brain-inspired
trainable B-COSFIRE filters. It consists of four main steps, namely nonlinear
filtering with B-COSFIRE, thinning with non-maximum suppression, hysteresis
thresholding and morphological closing. We demonstrate its effectiveness on a
data set of noisy images with cracked pavements, where we achieve
state-of-the-art results (F-measure=0.865). The proposed method can be employed
in any computer vision methodology that requires the delineation of curvilinear
and elongated structures.Comment: Accepted at Computer Analysis of Images and Patterns (CAIP) 201
Multi-task learning for joint weakly-supervised segmentation and aortic arch anomaly classification in fetal cardiac MRI
Congenital Heart Disease (CHD) is a group of cardiac malformations present
already during fetal life, representing the prevailing category of birth
defects globally. Our aim in this study is to aid 3D fetal vessel topology
visualisation in aortic arch anomalies, a group which encompasses a range of
conditions with significant anatomical heterogeneity. We present a multi-task
framework for automated multi-class fetal vessel segmentation from 3D black
blood T2w MRI and anomaly classification. Our training data consists of binary
manual segmentation masks of the cardiac vessels' region in individual subjects
and fully-labelled anomaly-specific population atlases. Our framework combines
deep learning label propagation using VoxelMorph with 3D Attention U-Net
segmentation and DenseNet121 anomaly classification. We target 11 cardiac
vessels and three distinct aortic arch anomalies, including double aortic arch,
right aortic arch, and suspected coarctation of the aorta. We incorporate an
anomaly classifier into our segmentation pipeline, delivering a multi-task
framework with the primary motivation of correcting topological inaccuracies of
the segmentation. The hypothesis is that the multi-task approach will encourage
the segmenter network to learn anomaly-specific features. As a secondary
motivation, an automated diagnosis tool may have the potential to enhance
diagnostic confidence in a decision support setting. Our results showcase that
our proposed training strategy significantly outperforms label propagation and
a network trained exclusively on propagated labels. Our classifier outperforms
a classifier trained exclusively on T2w volume images, with an average balanced
accuracy of 0.99 (0.01) after joint training. Adding a classifier improves the
anatomical and topological accuracy of all correctly classified double aortic
arch subjects.Comment: Accepted for publication at the Journal of Machine Learning for
Biomedical Imaging (MELBA) https://melba-journal.org/2023:01
Microscopic Foundation of Nonextensive Statistics
Combination of the Liouville equation with the q-averaged energy leads to a microscopic framework for nonextensive q-thermodynamics. The
resulting von Neumann equation is nonlinear: . In spite
of its nonlinearity the dynamics is consistent with linear quantum mechanics of
pure states. The free energy is a stability function for the
dynamics. This implies that q-equilibrium states are dynamically stable. The
(microscopic) evolution of is reversible for any q, but for
the corresponding macroscopic dynamics is irreversible.Comment: revte
Recognition of Facial Expressions by Cortical Multi-scale Line and Edge Coding
Face-to-face communications between humans involve emotions, which often are unconsciously conveyed by facial expressions and body gestures. Intelligent human-machine interfaces, for example in cognitive robotics, need to recognize emotions. This paper addresses facial expressions and their neural correlates on the basis of a model of the visual cortex: the multi-scale line and edge coding. The recognition model links the cortical representation with Paul Ekman's Action Units which are related to the different facial muscles. The model applies a top-down categorization with trends and magnitudes of displacements of the mouth and eyebrows based on expected displacements relative to a neutral expression. The happy vs. not-happy categorization yielded a. correct recognition rate of 91%, whereas final recognition of the six expressions happy, anger, disgust, fear, sadness and surprise resulted in a. rate of 78%
Nanoantennas for visible and infrared radiation
Nanoantennas for visible and infrared radiation can strongly enhance the
interaction of light with nanoscale matter by their ability to efficiently link
propagating and spatially localized optical fields. This ability unlocks an
enormous potential for applications ranging from nanoscale optical microscopy
and spectroscopy over solar energy conversion, integrated optical
nanocircuitry, opto-electronics and density-ofstates engineering to
ultra-sensing as well as enhancement of optical nonlinearities. Here we review
the current understanding of optical antennas based on the background of both
well-developed radiowave antenna engineering and the emerging field of
plasmonics. In particular, we address the plasmonic behavior that emerges due
to the very high optical frequencies involved and the limitations in the choice
of antenna materials and geometrical parameters imposed by nanofabrication.
Finally, we give a brief account of the current status of the field and the
major established and emerging lines of investigation in this vivid area of
research.Comment: Review article with 76 pages, 21 figure
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