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

Zur Bruckschen "sero-chemischen Reaktion" bei Syphilis

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