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

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

Scaling Down Large-Scale Thawing of Monoclonal Antibody Solutions: 3D Temperature Profiles, Changes in Concentration, and Density Gradients

PURPOSE Scale-down devices (SDD) are designed to simulate large-scale thawing of protein drug substance, but require only a fraction of the material. To evaluate the performance of a new SDD that aims to predict thawing in large-scale 2 L bottles, we characterised 3D temperature profiles and changes in concentration and density in comparison to 125~mL and 2 L bottles. Differences in diffusion between a monoclonal antibody (mAb) and histidine buffer after thawing were examined. METHODS Temperature profiles at six distinct positions were recorded with type T thermocouples. Size-exclusion chromatography allowed quantification of mAb and histidine. Polysorbate 80 was quantified using a fluorescent dye assay. In addition, the solution's density at different locations in bottles and the SDD was identified. RESULTS The temperature profiles in the SDD and the large-scale 2 L bottle during thawing were similar. Significant concentration gradients were detected in the 2 L bottle leading to marked density gradients. The SDD slightly overestimated the dilution in the top region and the maximum concentrations at the bottom. Fast diffusion resulted in rapid equilibration of histidine. CONCLUSION The innovative SDD allows a realistic characterisation and helps to understand thawing processes of mAb solutions in large-scale 2 L bottles. Only a fraction of material is needed to gain insights into the thawing behaviour that is associated with several possible detrimental limitations

Magnetic Resonance in the Spin-Peierls compound α′−NaV2O5\alpha'-NaV_2O_5

We present results from magnetic resonance measurements for 75-350 GHz in $\alpha$'-NaV$_{2}$O$_{5}$. The temperature dependence of the integrated intensity indicates that we observe transitions in the excited state. A quantitative description gives resonances in the triplet state at high symmetry points of the excitation spectrum of this Spin-Peierls compound. This energy has the same temperature dependence as the Spin-Peierls gap. Similarities and differences with the other inorganic compound CuGeO$_{3}$ are discussed.Comment: 2 pages, REVTEX, 3 figures. to be published in Phys.Rev.

Deep Learning for Instrumented Ultrasonic Tracking: From synthetic training data to in vivo application

Instrumented ultrasonic tracking is used to improve needle localisation during ultrasound guidance of minimally-invasive percutaneous procedures. Here, it is implemented with transmitted ultrasound pulses from a clinical ultrasound imaging probe that are detected by a fibre-optic hydrophone integrated into a needle. The detected transmissions are then reconstructed to form the tracking image. Two challenges are considered with the current implementation of ultrasonic tracking. First, tracking transmissions are interleaved with the acquisition of B-mode images and thus, the effective B-mode frame rate is reduced. Second, it is challenging to achieve an accurate localisation of the needle tip when the signal-to-noise ratio is low. To address these challenges, we present a framework based on a convolutional neural network (CNN) to maintain spatial resolution with fewer tracking transmissions and to enhance signal quality. A major component of the framework included the generation of realistic synthetic training data. The trained network was applied to unseen synthetic data and experimental in vivo tracking data. The performance of needle localisation was investigated when reconstruction was performed with fewer (up to eight-fold) tracking transmissions. CNN-based processing of conventional reconstructions showed that the axial and lateral spatial resolution could be improved even with an eight-fold reduction in tracking transmissions. The framework presented in this study will significantly improve the performance of ultrasonic tracking, leading to faster image acquisition rates and increased localisation accuracy

Smart computing for large scale visual data sensing and processing

[No abstract available

Spin dependent quantum interference in non-local graphene spin valves

Spin dependent electron transport measurements on graphene are of high importance to explore possible spintronic applications. Up to date all spin transport experiments on graphene were done in a semi-classical regime, disregarding quantum transport properties such as phase coherence and interference. Here we show that in a quantum coherent graphene nanostructure the non-local voltage is strongly modulated. Using non-local measurements, we separate the signal in spin dependent and spin independent contributions. We show that the spin dependent contribution is about two orders of magnitude larger than the spin independent one, when corrected for the finite polarization of the electrodes. The non-local spin signal is not only strongly modulated but also changes polarity as a function of the applied gate voltage. By locally tuning the carrier density in the constriction we show that the constriction plays a major role in this effect and indicates that it can act as a spin filter device. Our results show the potential of quantum coherent graphene nanostructures for the use in future spintronic devices

Winner-take-all selection in a neural system with delayed feedback

We consider the effects of temporal delay in a neural feedback system with excitation and inhibition. The topology of our model system reflects the anatomy of the avian isthmic circuitry, a feedback structure found in all classes of vertebrates. We show that the system is capable of performing a `winner-take-all' selection rule for certain combinations of excitatory and inhibitory feedback. In particular, we show that when the time delays are sufficiently large a system with local inhibition and global excitation can function as a `winner-take-all' network and exhibit oscillatory dynamics. We demonstrate how the origin of the oscillations can be attributed to the finite delays through a linear stability analysis.Comment: 8 pages, 6 figure