Spin Seebeck effect at low temperatures in the nominally paramagnetic insulating state of vanadium dioxide

The low temperature monoclinic, insulating phase of vanadium dioxide is ordinarily considered nonmagnetic, with dimerized vanadium atoms forming spin singlets, though paramagnetic response is seen at low temperatures. We find a nonlocal spin Seebeck signal in VO2 films that appears below 30 K and which increases with decreasing temperature. The spin Seebeck response has a non-hysteretic dependence on in-plane external magnetic field. This paramagnetic spin Seebeck response is discussed in terms of prior findings on paramagnetic spin Seebeck effects and expected magnetic excitations of the monoclinic ground state.Comment: 11 pages, 3 figures, + 11 pages and 10 figures of supplemental materia

The challenges of measuring spin Seebeck noise

Just as electronic shot noise results from the granularity of charge and the statistical variation in the arrival times of carriers in driven conductors, there are predictions for fundamental noise in magnon currents due to angular momentum being carried by discrete excitations. The advent of the inverse spin Hall effect as a transduction mechanism to convert spin current into charge current raises the prospect of experimental investigations of such magnon shot noise. Spin Seebeck effect measurements have demonstrated the electrical detection of thermally driven magnon currents and have been suggested as an avenue for accessing spin current fluctuations. We report measurements of spin Seebeck structures made from yttrium iron garnet on gadolinium gallium garnet. While these measurements do show an increase in measured noise in the presence of a magnetic field at low temperatures, the dependence on field orientation suggests an alternative origin for this signal. We describe theoretical predictions for the expected magnitude of magnon shot noise, highlighting ambiguities that exist. Analysis in terms of the sample geometry dependence of the known inverse spin Hall transduction of spin currents into charge currents implies that magnon shot noise detection through this approach is strongly suppressed. Implications for future attempts to measure magnon shot noise are discussed.Comment: 20 pages, 3 figures, + 7 pages/6 figures of supplementary materia

A Magnetically and Thermally Controlled Liquid Metal Variable Stiffness Material

Smart materials that can actively tune their stiffness are of great interest to many fields, including the construction industry, medical devices, industrial machines, and soft robotics. However, developing a material that can offer a large range of stiffness change and rapid tuning remains a challenge. Herein, a liquid metal variable stiffness material (LMVSM) that can actively and rapidly tune its stiffness by applying an external magnetic field or by changing the temperature is developed. The LMVSM is composed of three layers: a gallium–iron magnetorheological fluid (Ga–Fe MRF) layer for providing variable stiffness, a nickel–chromium wire layer for Joule heating, and a soft heat dissipation layer for accelerating heating and rapid cooling. The stiffness can be rapidly increased by 4 times upon the application of a magnetic field or 10 times by solidifying the Ga–Fe MRF. Finally, the LMVSM is attached to a pneumatically controlled soft robotic gripper to actively tune its load capacity, demonstrating its potential to be further developed into smart components that can be widely adopted by smart devices

EANet : towards lightweight human pose estimation with effective aggregation network

Existing solutions to lightweight human pose estimation typically adopt a depthwise separable strategy, i.e., a normal 2D convolution is factorized into channel aggregation and spatial aggregation. However, this strategy cannot well capture multi-scale Effective Receptive Field (ERF), which is essential to dense prediction tasks like human pose estimation. To address this issue, we propose a novel lightweight network for human pose estimation, namely effective aggregation net (EANet). In EANet, we introduce two lightweight computational units: effective channel aggregating (ECA) and effective spatial aggregating (ESA), which are respectively responsible for channel-wise feature aggregation and pixel-wise feature aggregation. Unlike typical channel-wise aggregation using pointwise (1 × 1) convolution, the ECA aggregates few feature points that are estimated as effective ones. Moreover, the ESA is designed with re-parameterizing techniques, and it aggregates effective spatial feature points with multi-scale shared convolutions. Comprehensive experiments are conducted on three challenging datasets, i.e., COCO, Crowd-Pose, Wholebody-COCO. Our EANet demonstrates superior results on human pose estimation over previous lightweight methods, reaching a new state-of-the-art performance with a good trade-off. Our code and models are publicly available1

Mapping China’s Forest Fire Risks with Machine Learning

Forest fires are disasters that are common around the world. They pose an ongoing challenge in scientific and forest management. Predicting forest fires improves the levels of forest-fire prevention and risk avoidance. This study aimed to construct a forest risk map for China. We base our map on Visible Infrared Imaging Radiometer Suite data from 17,330 active fires for the period 2012–2019, and combined terrain, meteorology, social economy, vegetation, and other factors closely related to the generation of forest-fire disasters for modeling and predicting forest fires. Four machine learning models for predicting forest fires were compared (i.e., random forest (RF), support vector machine (SVM), multi-layer perceptron (MLP), and gradient-boosting decision tree (GBDT) algorithm), and the RF model was chosen (its accuracy, precision, recall, F1, AUC values were 87.99%, 85.94%, 91.51%, 88.64% and 95.11% respectively). The Chinese seasonal fire zoning map was drawn with the municipal administrative unit as the spatial scale for the first time. The results show evident seasonal and regional differences in the Chinese forest-fire risks; forest-fire risks are relativity high in the spring and winter, but low in fall and summer, and the areas with high regional fire risk are mainly in the provinces of Yunnan (including the cities of Qujing, Lijiang, and Yuxi), Guangdong (including the cities of Shaoguan, Huizhou, and Qingyuan), and Fujian (including the cities of Nanping and Sanming). The major contributions of this study are to (i) provide a framework for large-scale forest-fire risk prediction having a low cost, high precision, and ease of operation, and (ii) improve the understanding of forest-fire risks in China

3D actuation of foam-core liquid metal droplets

Precise manipulation of liquid metal (LM) droplets possesses the potential to enable a wide range of applications in reconfigurable electronics, robotics, and microelectromechanical systems. Although a variety of methods have been explored to actuate LM droplets on a 2D plane, versatile 3D manipulation remains a challenge due to the difficulty in overcoming their heavy weight. Here, foam-core liquid metal (FCLM) droplets that can maintain the surface properties of LM while significantly reducing the density are developed, enabling 3D manipulation in an electrolyte. The FCLM droplet is fabricated by coating LM on the surface of a copper-grafted foam sphere. The actuation of the FCLM droplet is realized by electrically inducing Marangoni flow on the LM surface. Two motion modes of the FCLM droplet are observed and studied and the actuation performance is characterized. Multiple FCLM droplets can be readily controlled to form 3D structures, demonstrating their potential to be further developed to form collaborative robots for enabling wider applications

A Magnetically and Thermally Controlled Liquid Metal Variable Stiffness Material

Smart materials that can actively tune their stiffness are of great interest to many fields, including the construction industry, medical devices, industrial machines, and soft robotics. However, developing a material that can offer a large range of stiffness change and rapid tuning remains a challenge. Herein, a liquid metal variable stiffness material (LMVSM) that can actively and rapidly tune its stiffness by applying an external magnetic field or by changing the temperature is developed. The LMVSM is composed of three layers: a gallium–iron magnetorheological fluid (Ga–Fe MRF) layer for providing variable stiffness, a nickel–chromium wire layer for Joule heating, and a soft heat dissipation layer for accelerating heating and rapid cooling. The stiffness can be rapidly increased by 4 times upon the application of a magnetic field or 10 times by solidifying the Ga–Fe MRF. Finally, the LMVSM is attached to a pneumatically controlled soft robotic gripper to actively tune its load capacity, demonstrating its potential to be further developed into smart components that can be widely adopted by smart devices