1,526 research outputs found
Sensing Noncollinear Magnetism at the Atomic Scale Combining Magnetic Exchange and Spin-Polarized Imaging
Storing and accessing information in atomic-scale magnets requires magnetic
imaging techniques with single-atom resolution. Here, we show simultaneous
detection of the spin-polarization and exchange force, with or without the flow
of current, with a new method, which combines scanning tunneling microscopy and
non-contact atomic force microscopy. To demonstrate the application of this new
method, we characterize the prototypical nano-skyrmion lattice formed on a
monolayer of Fe/Ir(111). We resolve the square magnetic lattice by employing
magnetic exchange force microscopy, demonstrating its applicability to
non-collinear magnetic structures, for the first time. Utilizing
distance-dependent force and current spectroscopy, we quantify the exchange
forces in comparison to the spin-polarization. For strongly spin-polarized
tips, we distinguish different signs of the exchange force which we suggest
arises from a change in exchange mechanisms between the probe and a skyrmion.
This new approach may enable both non-perturbative readout combined with
writing by current-driven reversal of atomic-scale magnets
Probing Single Vacancies in Black Phosphorus at the Atomic Level
Utilizing a combination of low-temperature scanning tunneling
microscopy/spectroscopy (STM/STS) and electronic structure calculations, we
characterize the structural and electronic properties of single atomic
vacancies within several monolayers of the surface of black phosphorus. We
illustrate, with experimental analysis and tight-binding calculations, that we
can depth profile these vacancies and assign them to specific sublattices
within the unit cell. Measurements reveal that the single vacancies exhibit
strongly anisotropic and highly delocalized charge density, laterally extended
up to 20 atomic unit cells. The vacancies are then studied with STS, which
reveals in-gap resonance states near the valence band edge and a strong
p-doping of the bulk black phosphorus crystal. Finally, quasiparticle
interference generated near these vacancies enables the direct visualization of
the anisotropic band structure of black phosphorus.Comment: Nano Letters (2017
Friedrich Adolph Wilhelm Diesterweg (1790-1866): Zum 200. Geburtstag
The loss of segregation of neuronal signal processing pathways is an important hypothesis for explaining the origin of functional deficits as associated with Parkinson's disease. Here we use a modeling approach which is utilized to study the influence of deep brain stimulation on the restoration of segregated activity in the target structures. Besides the spontaneous activity of the target network, the model considers a weak sensory input mimicking signal processing tasks, electrical deep brain stimulation delivered through a standard DBS electrode and synaptic plasticity. We demonstrate that the sensory input is capable of inducing a modification of the network structure which results in segregated microcircuits if the network is initialized in the healthy, desynchronized state. Depending on the strength and coverage, the sensory input is capable of restoring the functional sub-circuits even if the network is initialized in the synchronized, pathological state. Weak coordinated reset stimulation, applied to a network featuring a loss of segregation caused by global synchronization, is able to restore the segregated activity and to truncate the pathological, synchronized activity
Estimation of dynamic SNP-heritability with Bayesian Gaussian process models
Motivation:
Improved DNA technology has made it practical to estimate single nucleotide polymorphism (SNP)-heritability among distantly related individuals with unknown relationships. For growth and development related traits, it is meaningful to base SNP-heritability estimation on longitudinal data due to the time-dependency of the process. However, only few statistical methods have been developed so far for estimating dynamic SNP-heritability and quantifying its full uncertainty. /
Results:
We introduce a completely tuning-free Bayesian Gaussian process (GP) based approach for estimating dynamic variance components and heritability as their function. For parameter estimation, we use a modern Markov Chain Monte Carlo (MCMC) method which allows full uncertainty quantification. Several data sets are analysed and our results clearly illustrate that the 95 % credible intervals of the proposed joint estimation method (which "borrows strength" from adjacent time points) are significantly narrower than of a two-stage baseline method that first estimates the variance components at each time point independently and then performs smoothing. We compare the method with a random regression model using MTG2 and BLUPF90 softwares and quantitative measures indicate superior performance of our method. Results are presented for simulated and real data with up to 1000 time points. Finally, we demonstrate scalability of the proposed method for simulated data with tens of thousands of individuals. /
Availability:
The C++ implementation dynBGP and simulated data are available in GitHub (https://github.com/aarjas/dynBGP). The programs can be run in R. Real datasets are available in QTL archive (https://phenome.jax.org/centers/QTLA). /
Supplementary information:
Supplementary data are available at Bioinformatics online
Toward accurate quantitative photoacoustic imaging: learning vascular blood oxygen saturation in three dimensions
Significance: Two-dimensional (2-D) fully convolutional neural networks have been shown
capable of producing maps of sO2 from 2-D simulated images of simple tissue models.
However, their potential to produce accurate estimates in vivo is uncertain as they are limited
by the 2-D nature of the training data when the problem is inherently three-dimensional (3-D),
and they have not been tested with realistic images.
Aim: To demonstrate the capability of deep neural networks to process whole 3-D images and
output 3-D maps of vascular sO2 from realistic tissue models/images.
Approach: Two separate fully convolutional neural networks were trained to produce 3-D maps
of vascular blood oxygen saturation and vessel positions from multiwavelength simulated
images of tissue models.
Results: The mean of the absolute difference between the true mean vessel sO2 and the network
output for 40 examples was 4.4% and the standard deviation was 4.5%.
Conclusions: 3-D fully convolutional networks were shown capable of producing accurate sO2
maps using the full extent of spatial information contained within 3-D images generated under
conditions mimicking real imaging scenarios. We demonstrate that networks can cope with some
of the confounding effects present in real images such as limited-view artifacts and have the
potential to produce accurate estimates in vivo
Revealing the correlation between real-space structure and chiral magnetic order at the atomic scale
We image simultaneously the geometric, electronic and magnetic structure of a
buckled iron bilayer film that exhibits chiral magnetic order. We achieve this
by combining spin-polarized scanning tunneling microscopy and magnetic exchange
force microscopy (SPEX), to independently characterize the geometric as well as
the electronic and magnetic structure of non-flat surfaces. This new SPEX
imaging technique reveals the geometric height corrugation of the
reconstruction lines resulting from strong strain relaxation in the bilayer,
enabling the decomposition of the real-space from the eletronic structure at
the atomic level, and the correlation with the resultant spin spiral ground
state. By additionally utilizing adatom manipulation, we reveal the chiral
magnetic ground state of portions of the unit cell that were not previously
imaged with SP-STM alone. Using density functional theory (DFT), we investigate
the structural and electronic properties of the reconstructed bilayer and
identify the favorable stoichiometry regime in agreement with our experimental
result
Graph Convolutional Networks for Model-Based Learning in Nonlinear Inverse Problems
The majority of model-based learned image reconstruction methods in medical imaging have been limited to
uniform domains, such as pixelated images. If the underlying
model is solved on nonuniform meshes, arising from a finite
element method typical for nonlinear inverse problems, interpolation and embeddings are needed. To overcome this, we
present a flexible framework to extend model-based learning
directly to nonuniform meshes, by interpreting the mesh as a
graph and formulating our network architectures using graph
convolutional neural networks. This gives rise to the proposed
iterative Graph Convolutional Newton-type Method (GCNM),
which includes the forward model in the solution of the inverse
problem, while all updates are directly computed by the network
on the problem specific mesh. We present results for Electrical
Impedance Tomography, a severely ill-posed nonlinear inverse
problem that is frequently solved via optimization-based methods,
where the forward problem is solved by finite element methods.
Results for absolute EIT imaging are compared to standard
iterative methods as well as a graph residual network. We
show that the GCNM has strong generalizability to different
domain shapes and meshes, out of distribution data as well
as experimental data, from purely simulated training data and
without transfer training
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