867 research outputs found
Quasiparticle energies for large molecules: a tight-binding GW approach
We present a tight-binding based GW approach for the calculation of
quasiparticle energy levels in confined systems such as molecules. Key
quantities in the GW formalism like the microscopic dielectric function or the
screened Coulomb interaction are expressed in a minimal basis of spherically
averaged atomic orbitals. All necessary integrals are either precalculated or
approximated without resorting to empirical data. The method is validated
against first principles results for benzene and anthracene, where good
agreement is found for levels close to the frontier orbitals. Further, the size
dependence of the quasiparticle gap is studied for conformers of the polyacenes
() up to n = 30.Comment: 10 pages, 5 eps figures submitted to Phys. Rev.
Prior-based Coregistration and Cosegmentation
We propose a modular and scalable framework for dense coregistration and
cosegmentation with two key characteristics: first, we substitute ground truth
data with the semantic map output of a classifier; second, we combine this
output with population deformable registration to improve both alignment and
segmentation. Our approach deforms all volumes towards consensus, taking into
account image similarities and label consistency. Our pipeline can incorporate
any classifier and similarity metric. Results on two datasets, containing
annotations of challenging brain structures, demonstrate the potential of our
method.Comment: The first two authors contributed equall
Nonlocal vortex motion in mesoscopic amorphous Nb0.7Ge0.3 structures
We study nonlocal vortex transport in mesoscopic amorphous Nb0.7Ge0.3
samples. A dc current I is passed through a wire connected via a perpendicular
channel, of a length L= 2-5 um, with a pair of voltage probes where a nonlocal
response Vnl ~ I is measured. The maximum of Rnl=Vnl/I for a given temperature
occurs at an L-independent magnetic field and is proportional to 1/L. The
results are interpreted in terms of the dissipative vortex motion along the
channel driven by a remote current, and can be understood in terms of a simple
model.Comment: 4 pages, 3 figure
Doppler Shift in Andreev Reflection from a Moving Superconducting Condensate in Nb/InAs Josephson Junctions
We study narrow ballistic Josephson weak links in a InAs quantum wells
contacted by Nb electrodes and find a dramatic magnetic-field suppression of
the Andreev reflection amplitude, which occurs even for in-plane field
orientation with essentially no magnetic flux through the junction. Our
observations demonstrate the presence of a Doppler shift in the energy of the
Andreev levels, which results from diamagnetic screening currents in the hybrid
Nb/InAs-banks. The data for conductance, excess and critical currents can be
consistently explained in terms of the sample geometry and the McMillan energy,
characterizing the transparency of the Nb/InAs-interface.Comment: 4 pages, 5 figures, title modifie
Dynamical bi-stability of single-molecule junctions: A combined experimental/theoretical study of PTCDA on Ag(111)
The dynamics of a molecular junction consisting of a PTCDA molecule between
the tip of a scanning tunneling microscope and a Ag(111) surface have been
investigated experimentally and theoretically. Repeated switching of a PTCDA
molecule between two conductance states is studied by low-temperature scanning
tunneling microscopy for the first time, and is found to be dependent on the
tip-substrate distance and the applied bias. Using a minimal model Hamiltonian
approach combined with density-functional calculations, the switching is shown
to be related to the scattering of electrons tunneling through the junction,
which progressively excite the relevant chemical bond. Depending on the
direction in which the molecule switches, different molecular orbitals are
shown to dominate the transport and thus the vibrational heating process. This
in turn can dramatically affect the switching rate, leading to non-monotonic
behavior with respect to bias under certain conditions. In this work, rather
than simply assuming a constant density of states as in previous works, it was
modeled by Lorentzians. This allows for the successful description of this
non-monotonic behavior of the switching rate, thus demonstrating the importance
of modeling the density of states realistically.Comment: 20 pages, 6 figures, 1 tabl
Atlas Toolkit: Fast registration of 3D morphological datasets in the absence of landmarks
Image registration is a gateway technology for Developmental Systems Biology, enabling computational analysis of related datasets within a shared coordinate system. Many registration tools rely on landmarks to ensure that datasets are correctly aligned; yet suitable landmarks are not present in many datasets. Atlas Toolkit is a Fiji/ImageJ plugin collection offering elastic group-wise registration of 3D morphological datasets, guided by segmentation of the interesting morphology. We demonstrate the method by combinatorial mapping of cell signalling events in the developing eyes of chick embryos, and use the integrated datasets to predictively enumerate Gene Regulatory Network states
Whole slide image registration for the study of tumor heterogeneity
Consecutive thin sections of tissue samples make it possible to study local
variation in e.g. protein expression and tumor heterogeneity by staining for a
new protein in each section. In order to compare and correlate patterns of
different proteins, the images have to be registered with high accuracy. The
problem we want to solve is registration of gigapixel whole slide images (WSI).
This presents 3 challenges: (i) Images are very large; (ii) Thin sections
result in artifacts that make global affine registration prone to very large
local errors; (iii) Local affine registration is required to preserve correct
tissue morphology (local size, shape and texture). In our approach we compare
WSI registration based on automatic and manual feature selection on either the
full image or natural sub-regions (as opposed to square tiles). Working with
natural sub-regions, in an interactive tool makes it possible to exclude
regions containing scientifically irrelevant information. We also present a new
way to visualize local registration quality by a Registration Confidence Map
(RCM). With this method, intra-tumor heterogeneity and charateristics of the
tumor microenvironment can be observed and quantified.Comment: MICCAI2018 - Computational Pathology and Ophthalmic Medical Image
Analysis - COMPA
Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images
Reliably modeling normality and differentiating abnormal appearances from
normal cases is a very appealing approach for detecting pathologies in medical
images. A plethora of such unsupervised anomaly detection approaches has been
made in the medical domain, based on statistical methods, content-based
retrieval, clustering and recently also deep learning. Previous approaches
towards deep unsupervised anomaly detection model patches of normal anatomy
with variants of Autoencoders or GANs, and detect anomalies either as outliers
in the learned feature space or from large reconstruction errors. In contrast
to these patch-based approaches, we show that deep spatial autoencoding models
can be efficiently used to capture normal anatomical variability of entire 2D
brain MR images. A variety of experiments on real MR data containing MS lesions
corroborates our hypothesis that we can detect and even delineate anomalies in
brain MR images by simply comparing input images to their reconstruction.
Results show that constraints on the latent space and adversarial training can
further improve the segmentation performance over standard deep representation
learning
Predicting Fluid Intelligence of Children using T1-weighted MR Images and a StackNet
In this work, we utilize T1-weighted MR images and StackNet to predict fluid
intelligence in adolescents. Our framework includes feature extraction, feature
normalization, feature denoising, feature selection, training a StackNet, and
predicting fluid intelligence. The extracted feature is the distribution of
different brain tissues in different brain parcellation regions. The proposed
StackNet consists of three layers and 11 models. Each layer uses the
predictions from all previous layers including the input layer. The proposed
StackNet is tested on a public benchmark Adolescent Brain Cognitive Development
Neurocognitive Prediction Challenge 2019 and achieves a mean squared error of
82.42 on the combined training and validation set with 10-fold
cross-validation. In addition, the proposed StackNet also achieves a mean
squared error of 94.25 on the testing data. The source code is available on
GitHub.Comment: 8 pages, 2 figures, 3 tables, Accepted by MICCAI ABCD-NP Challenge
2019; Added ND
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