Spin Pumping and Inverse Spin Hall Effect in Platinum: The Essential Role of Spin-Memory Loss at Metallic Interfaces

Through combined ferromagnetic resonance, spin-pumping and inverse spin Hall effect experiments in Co|Pt bilayers and Co|Cu|Pt trilayers, we demonstrate consistent values of spin diffusion length $\ell_{\rm sf}^{\rm Pt}=3.4\pm0.4$ nm and of spin Hall angle $\theta_{\rm SHE}^{\rm Pt}=0.051\pm0.004$ for Pt. Our data and model emphasize on the partial depolarization of the spin current at each interface due to spin-memory loss. Our model reconciles the previously published spin Hall angle values and explains the different scaling lengths for the ferromagnetic damping and the spin Hall effect induced voltage.Comment: 6 pages, 3 figures (main text) and 8 pages supplementary. Published with small modifications in Phys. Rev. Let

Experimental evidences of a large extrinsic spin Hall effect in AuW alloy

We report an experimental study of a gold-tungsten alloy (7% at. W concentration in Au host) displaying remarkable properties for spintronics applications using both magneto-transport in lateral spin valve devices and spin-pumping with inverse spin Hall effect experiments. A very large spin Hall angle of about 10% is consistently found using both techniques with the reliable spin diffusion length of 2 nm estimated by the spin sink experiments in the lateral spin valves. With its chemical stability, high resistivity and small induced damping, this AuW alloy may find applications in the nearest future

An operator expansion for the elastic limit

A leading twist expansion in terms of bi-local operators is proposed for the structure functions of deeply inelastic scattering near the elastic limit $x \to 1$, which is also applicable to a range of other processes. Operators of increasing dimensions contribute to logarithmically enhanced terms which are supressed by corresponding powers of $1-x$. For the longitudinal structure function, in moment ($N$) space, all the logarithmic contributions of order $\ln^k N/N$ are shown to be resummable in terms of the anomalous dimension of the leading operator in the expansion.Comment: 9 pages, 1 figure, uses REVTEX 3.1 and axodra

Si1-x Ge x /Si interface profiles measured to sub-nanometer precision using uleSIMS energy sequencing

The utility of energy sequencing for extracting an accurate matrix level interface profile using ultra-low energy SIMS (uleSIMS) is reported. Normally incident O2 + over an energy range of 0.25–2.5 keV were used to probe the interface between Si0.73Ge0.27/Si, which was also studied using high angle annular dark field scanning transmission electron microscopy (HAADF-STEM). All the SIMS profiles were linearized by taking the well understood matrix effects on ion yield and erosion rate into account. A method based on simultaneous fitting of the SIMS profiles measured at different energies is presented, which allows the intrinsic sample profile to be determined to sub-nanometer precision. Excellent agreement was found between the directly imaged HAADF-STEM interface and that derived from SIMS

Deep Learning for Cardiologist-level Myocardial Infarction Detection in Electrocardiograms

Myocardial infarction is the leading cause of death worldwide. In this paper, we design domain-inspired neural network models to detect myocardial infarction. First, we study the contribution of various leads. This systematic analysis, first of its kind in the literature, indicates that out of 15 ECG leads, data from the v6, vz, and ii leads are critical to correctly identify myocardial infarction. Second, we use this finding and adapt the ConvNetQuake neural network model--originally designed to identify earthquakes--to attain state-of-the-art classification results for myocardial infarction, achieving $99.43\%$ classification accuracy on a record-wise split, and $97.83\%$ classification accuracy on a patient-wise split. These two results represent cardiologist-level performance level for myocardial infarction detection after feeding only 10 seconds of raw ECG data into our model. Third, we show that our multi-ECG-channel neural network achieves cardiologist-level performance without the need of any kind of manual feature extraction or data pre-processing.Comment: Accepted to the European Medical and Biological Engineering Conference (EMBEC) 202

Current-Driven Conformational Changes, Charging and Negative Differential Resistance in Molecular Wires

We introduce a theoretical approach based on scattering theory and total energy methods that treats transport non-linearities, conformational changes and charging effects in molecular wires in a unified way. We apply this approach to molecular wires consisting of chain molecules with different electronic and structural properties bonded to metal contacts. We show that non-linear transport in all of these systems can be understood in terms of a single physical mechanism and predict that negative differential resistance at high bias should be a generic property of such molecular wires.Comment: 9 pages, 4 figure