Criteria for accurate determination of the magnon relaxation length from the nonlocal spin Seebeck effect

The nonlocal transport of thermally generated magnons not only unveils the underlying mechanism of the spin Seebeck effect, but also allows for the extraction of the magnon relaxation length ($\lambda_m$) in a magnetic material, the average distance over which thermal magnons can propagate. In this study, we experimentally explore in yttrium iron garnet (YIG)/platinum systems much further ranges compared with previous investigations. We observe that the nonlocal SSE signals at long distances ($d$) clearly deviate from a typical exponential decay. Instead, they can be dominated by the nonlocal generation of magnon accumulation as a result of the temperature gradient present away from the heater, and decay geometrically as $1/d^2$. We emphasize the importance of looking only into the exponential regime (i.e., the intermediate distance regime) to extract $\lambda_m$. With this principle, we study $\lambda_m$ as a function of temperature in two YIG films which are 2.7 and 50 $\mu$m in thickness, respectively. We find $\lambda_m$ to be around 15 $\mu$m at room temperature and it increases to 40 $\mu$m at $T=$ 3.5 K. Finite element modeling results agree with experimental studies qualitatively, showing also a geometrical decay beyond the exponential regime. Based on both experimental and modeling results we put forward a general guideline for extracting $\lambda_m$ from the nonlocal spin Seebeck effect.Comment: 9 pages, 7 figure

Magnon Planar Hall Effect and Anisotropic Magnetoresistance in a Magnetic Insulator

Electrical resistivities can be different for charge currents travelling parallel or perpendicular to the magnetization in magnetically ordered conductors or semiconductors, resulting in the well-known planar Hall effect and anisotropic magnetoresistance. Here, we study the analogous anisotropic magnetotransport behavior for magnons in a magnetic insulator Y$_{3}$Fe$_{5}$O$_{12}$. Electrical and thermal magnon injection, and electrical detection methods are used at room temperature with transverse and longitudinal geometries to measure the magnon planar Hall effect and anisotropic magnetoresistance, respectively. We observe that the relative difference between magnon current conductivities parallel and perpendicular to the magnetization, with respect to the average magnon conductivity, i.e. $|(\sigma_{\parallel}^{\textrm{m}}-\sigma_{\perp}^{\textrm{m}})/\sigma_{0}^{\textrm{m}}|$ , is approximately 5% with the majority of the measured devices showing $\sigma_{\perp}^{\textrm{m}}>\sigma_{\parallel}^{\textrm{m}}$.Comment: 18 pages, 16 figure

Temperature dependence of the magnon spin diffusion length and magnon spin conductivity in the magnetic insulator yttrium iron garnet

We present a systematic study of the temperature dependence of diffusive magnon spin transport using nonlocal devices fabricated on a 210-nm yttrium iron garnet film on a gadolinium gallium garnet substrate. In our measurements, we detect spin signals arising from electrical and thermal magnon generation, and we directly extract themagnon spin diffusion length lambda(m) for temperatures from 2 to 293 K. Values of lambda(m) obtained from electrical and thermal generation agree within the experimental error with lambda(m) = 9.6 +/- 0.9 mu m at room temperature to a minimum of lambda(m) = 5.5 +/- 0.7 mu m at 30 K. Using a two-dimensional finite element model to fit the data obtained for electrical magnon generation we extract the magnon spin conductivity sigma(m) as a function of temperature, which is reduced from sm = 3.7 +/- 0.3 x 10(5) S/m at room temperature to sigma(m) = 0.9 +/- 0.6 x 10(4) S/m at 5 K. Finally, we observe an enhancement of the signal originating from thermally generated magnons for low temperatures where amaximum is observed around T = 7 K. An explanation for this low-temperature enhancement is however still missing and requires additional investigation

Nonlocal magnon-polaron transport in yttrium iron garnet

The spin Seebeck effect (SSE) is observed in magnetic insulator|heavy metal bilayers as an inverse spin Hall effect voltage under a temperature gradient. The SSE can be detected nonlocally as well, viz. in terms of the voltage in a second metallic contact (detector) on the magnetic film, spatially separated from the first contact that is used to apply the temperature bias (injector). Magnon-polarons are hybridized lattice and spin waves in magnetic materials, generated by the magnetoelastic interaction. Kikkawa et al. [Phys. Rev. Lett. \textbf{117}, 207203 (2016)] interpreted a resonant enhancement of the local SSE in yttrium iron garnet (YIG) as a function of the magnetic field in terms of magnon-polaron formation. Here we report the observation of magnon-polarons in \emph{nonlocal} magnon spin injection/detection devices for various injector-detector spacings and sample temperatures. Unexpectedly, we find that the magnon-polaron resonances can suppress rather than enhance the nonlocal SSE. Using finite element modelling we explain our observations as a competition between the SSE and spin diffusion in YIG. These results give unprecedented insights into the magnon-phonon interaction in a key magnetic material.Comment: 5 pages, 6 figure

Stable divisorial gonality is in NP

Divisorial gonality and stable divisorial gonality are graph parameters, which have an origin in algebraic geometry. Divisorial gonality of a connected graph $G$ can be defined with help of a chip firing game on $G$. The stable divisorial gonality of $G$ is the minimum divisorial gonality over all subdivisions of edges of $G$. In this paper we prove that deciding whether a given connected graph has stable divisorial gonality at most a given integer $k$ belongs to the class NP. Combined with the result that (stable) divisorial gonality is NP-hard by Gijswijt, we obtain that stable divisorial gonality is NP-complete. The proof consist of a partial certificate that can be verified by solving an Integer Linear Programming instance. As a corollary, we have that the number of subdivisions needed for minimum stable divisorial gonality of a graph with $n$ vertices is bounded by $2^{p(n)}$ for a polynomial $p$

Quantized spin wave modes in magnetic tunnel junction nanopillars

We present an experimental and theoretical study of the magnetic field dependence of the mode frequency of thermally excited spin waves in rectangular shaped nanopillars of lateral sizes 60x100, 75x150, and 105x190 nm2, patterned from MgO-based magnetic tunnel junctions. The spin wave frequencies were measured using spectrally resolved electrical noise measurements. In all spectra, several independent quantized spin wave modes have been observed and could be identified as eigenexcitations of the free layer and of the synthetic antiferromagnet of the junction. Using a theoretical approach based on the diagonalization of the dynamical matrix of a system of three coupled, spatially confined magnetic layers, we have modeled the spectra for the smallest pillar and have extracted its material parameters. The magnetization and exchange stiffness constant of the CoFeB free layer are thereby found to be substantially reduced compared to the corresponding thin film values. Moreover, we could infer that the pinning of the magnetization at the lateral boundaries must be weak. Finally, the interlayer dipolar coupling between the free layer and the synthetic antiferromagnet causes mode anticrossings with gap openings up to 2 GHz. At low fields and in the larger pillars, there is clear evidence for strong non-uniformities of the layer magnetizations. In particular, at zero field the lowest mode is not the fundamental mode, but a mode most likely localized near the layer edges.Comment: 16 pages, 4 figures, (re)submitted to PR

Differential gaze behavior towards sexually preferred and non-preferred human figures

The gaze pattern associated with image exploration is a sensitive index of our attention, motivation and preference. To examine whether an individual’s gaze behavior can reflect his/her sexual interest, we compared gaze patterns of young heterosexual men and women (M = 19.94 years, SD = 1.05) while viewing photos of plain-clothed male and female figures aged from birth to sixty years old. Our analysis revealed a clear gender difference in viewing sexually preferred figure images. Men displayed a distinctive gaze pattern only when viewing twenty-year-old female images, with more fixations and longer viewing time dedicated to the upper body and waist-hip region. Women also directed more attention at the upper body on female images in comparison to male images, but this difference was not age-specific. Analysis of local image salience revealed that observers’ eye-scanning strategies could not be accounted for by low-level processes, such as analyzing local image contrast and structure, but were associated with attractiveness judgments. The results suggest that the difference in cognitive processing of sexually preferred and non-preferred figures can be manifested in gaze patterns associated with figure viewing. Thus, eye-tracking holds promise as a potential sensitive measure for sexual preference, particularly in men

COOD: Combined out-of-distribution detection using multiple measures for anomaly & novel class detection in large-scale hierarchical classification

High-performing out-of-distribution (OOD) detection, both anomaly and novel class, is an important prerequisite for the practical use of classification models. In this paper, we focus on the species recognition task in images concerned with large databases, a large number of fine-grained hierarchical classes, severe class imbalance, and varying image quality. We propose a framework for combining individual OOD measures into one combined OOD (COOD) measure using a supervised model. The individual measures are several existing state-of-the-art measures and several novel OOD measures developed with novel class detection and hierarchical class structure in mind. COOD was extensively evaluated on three large-scale (500k+ images) biodiversity datasets in the context of anomaly and novel class detection. We show that COOD outperforms individual, including state-of-the-art, OOD measures by a large margin in terms of TPR@1% FPR in the majority of experiments, e.g., improving detecting ImageNet images (OOD) from 54.3% to 85.4% for the iNaturalist 2018 dataset. SHAP (feature contribution) analysis shows that different individual OOD measures are essential for various tasks, indicating that multiple OOD measures and combinations are needed to generalize. Additionally, we show that explicitly considering ID images that are incorrectly classified for the original (species) recognition task is important for constructing high-performing OOD detection methods and for practical applicability. The framework can easily be extended or adapted to other tasks and media modalities

Nonrelativistic Chern-Simons Vortices on the Torus

A classification of all periodic self-dual static vortex solutions of the Jackiw-Pi model is given. Physically acceptable solutions of the Liouville equation are related to a class of functions which we term Omega-quasi-elliptic. This class includes, in particular, the elliptic functions and also contains a function previously investigated by Olesen. Some examples of solutions are studied numerically and we point out a peculiar phenomenon of lost vortex charge in the limit where the period lengths tend to infinity, that is, in the planar limit.Comment: 25 pages, 2+3 figures; improved exposition, corrected typos, added one referenc

Deep Learning-Based Natural Language Processing in Radiology:The Impact of Report Complexity, Disease Prevalence, Dataset Size, and Algorithm Type on Model Performance

In radiology, natural language processing (NLP) allows the extraction of valuable information from radiology reports. It can be used for various downstream tasks such as quality improvement, epidemiological research, and monitoring guideline adherence. Class imbalance, variation in dataset size, variation in report complexity, and algorithm type all influence NLP performance but have not yet been systematically and interrelatedly evaluated. In this study, we investigate these factors on the performance of four types [a fully connected neural network (Dense), a long short-term memory recurrent neural network (LSTM), a convolutional neural network (CNN), and a Bidirectional Encoder Representations from Transformers (BERT)] of deep learning-based NLP. Two datasets consisting of radiologist-annotated reports of both trauma radiographs (n = 2469) and chest radiographs and computer tomography (CT) studies (n = 2255) were split into training sets (80%) and testing sets (20%). The training data was used as a source to train all four model types in 84 experiments (Fracture-data) and 45 experiments (Chest-data) with variation in size and prevalence. The performance was evaluated on sensitivity, specificity, positive predictive value, negative predictive value, area under the curve, and F score. After the NLP of radiology reports, all four model-architectures demonstrated high performance with metrics up to > 0.90. CNN, LSTM, and Dense were outperformed by the BERT algorithm because of its stable results despite variation in training size and prevalence. Awareness of variation in prevalence is warranted because it impacts sensitivity and specificity in opposite directions. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10916-021-01761-4