Ferrimagnets, which contain the advantages of both ferromagnets (detectable moments) and antiferromagnets (ultrafast spin dynamics), have recently attracted great attention. Here we report the optimization of epitaxial growth of a tetragonal perpendi
cularly magnetized ferrimagnet Mn2Ga on MgO. Electrical transport, magnetic properties and the anomalous Hall effect (AHE) were systematically studied. Furthermore, we successfully integrated high-quality epitaxial ferrimagnetic Mn2Ga thin films onto ferroelectric PMN-PT single crystals with a MgO buffer layer. It was found that the AHE of such a ferrimagnet can be effectively modulated by a small electric field over a large temperature range in a nonvolatile manner. This work thus demonstrates the great potential of ferrimagnets for developing high-density and low-power spintronic devices.
The relative significance of quantum conductivity correction and magnetic nature of electrons in understanding the intriguing low-temperature resistivity minimum and negative magnetoresistance of the two-dimensional electron gas at LaAlO3/SrTiO3 inte
rfaces has been a long outstanding issue since its discovery. Here we report a comparative magnetotransport study on amorphous and oxygen-annealed crystalline LaAlO3/SrTiO3 heterostructures at a relatively high-temperature range, where the orbital scattering is largely suppressed by thermal fluctuations. Despite of a predominantly negative out-of-plane magnetoresistance effect for both, the magnetotransport is isotropic for amorphous LaAlO3/SrTiO3 while strongly anisotropic and well falls into a two-dimensional quantum correction frame for annealed crystalline LaAlO3/SrTiO3. These results clearly indicate that a large portion of electrons from oxygen vacancies are localized at low temperatures, serving as magnetic centers, while the electrons from the polar field are only weakly localized due to constructive interference between time-reversed electron paths in the clean limit and no signature of magnetic nature is visible.
In recent years, the field of antiferromagnetic spintronics has been substantially advanced. Electric-field control is a promising approach to achieving ultra-low power spintronic devices via suppressing Joule heating. In this article, cutting-edge r
esearch, including electric-field modulation of antiferromagnetic spintronic devices using strain, ionic liquids, dielectric materials, and electrochemical ionic migration, are comprehensively reviewed. Various emergent topics such as the Neel spin-orbit torque, chiral spintronics, topological antiferromagnetic spintronics, anisotropic magnetoresistance, memory devices, two-dimensional magnetism, and magneto-ionic modulation with respect to antiferromagnets are examined. In conclusion, we envision the possibility of realizing high-quality room-temperature antiferromagnetic tunnel junctions, antiferromagnetic spin logic devices, and artificial antiferromagnetic neurons. It is expected that this work provides an appropriate and forward-looking perspective that will promote the rapid development of this field.
One of the main bottleneck issues for room-temperature antiferromagnetic spintronic devices is the small signal read-out owing to the limited anisotropic magnetoresistance in antiferromagnets. However, this could be overcome by either utilizing the B
erry-curvature-induced anomalous Hall resistance in noncollinear antiferromagnets or establishing tunnel junction devices based on effective manipulation of antiferromagnetic spins. In this work, we demonstrate the giant piezoelectric strain control of the spin structure and the anomalous Hall resistance in a noncollinear antiferromagnetic metal - D019 hexagonal Mn3Ga. Furthermore, we built tunnel junction devices with a diameter of 200 nm to amplify the maximum tunneling resistance ratio to more than 10% at room-temperature, which thus implies significant potential of noncollinear antiferromagnets for large signal-output and high-density antiferromagnetic spintronic device applications.
Autonomous vehicles have a great potential in the application of both civil and military fields, and have become the focus of research with the rapid development of science and economy. This article proposes a brief review on learning-based decision-
making technology for autonomous vehicles since it is significant for safer and efficient performance of autonomous vehicles. Firstly, the basic outline of decision-making technology is provided. Secondly, related works about learning-based decision-making methods for autonomous vehicles are mainly reviewed with the comparison to classical decision-making methods. In addition, applications of decision-making methods in existing autonomous vehicles are summarized. Finally, promising research topics in the future study of decision-making technology for autonomous vehicles are prospected.
Relative radiometric normalization (RRN) mosaicking among multiple remote sensing images is crucial for the downstream tasks, including map-making, image recognition, semantic segmentation, and change detection. However, there are often seam lines on
the mosaic boundary and radiometric contrast left, especially in complex scenarios, making the appearance of mosaic images unsightly and reducing the accuracy of the latter classification/recognition algorithms. This paper renders a novel automatical approach to eliminate seam lines in complex RRN mosaicking scenarios. It utilizes the histogram matching on the overlap area to alleviate radiometric contrast, Poisson editing to remove the seam lines, and merging procedure to determine the normalization transfer order. Our method can handle the mosaicking seam lines with arbitrary shapes and images with extreme topological relationships (with a small intersection area). These conditions make the main feathering or blending methods, e.g., linear weighted blending and Laplacian pyramid blending, unavailable. In the experiment, our approach visually surpasses the automatic methods without Poisson editing and the manual blurring and feathering method using GIMP software.
Intelligent behaviour in the physical world exhibits structure at multiple spatial and temporal scales. Although movements are ultimately executed at the level of instantaneous muscle tensions or joint torques, they must be selected to serve goals de
fined on much longer timescales, and in terms of relations that extend far beyond the body itself, ultimately involving coordination with other agents. Recent research in artificial intelligence has shown the promise of learning-based approaches to the respective problems of complex movement, longer-term planning and multi-agent coordination. However, there is limited research aimed at their integration. We study this problem by training teams of physically simulated humanoid avatars to play football in a realistic virtual environment. We develop a method that combines imitation learning, single- and multi-agent reinforcement learning and population-based training, and makes use of transferable representations of behaviour for decision making at different levels of abstraction. In a sequence of stages, players first learn to control a fully articulated body to perform realistic, human-like movements such as running and turning; they then acquire mid-level football skills such as dribbling and shooting; finally, they develop awareness of others and play as a team, bridging the gap between low-level motor control at a timescale of milliseconds, and coordinated goal-directed behaviour as a team at the timescale of tens of seconds. We investigate the emergence of behaviours at different levels of abstraction, as well as the representations that underlie these behaviours using several analysis techniques, including statistics from real-world sports analytics. Our work constitutes a complete demonstration of integrated decision-making at multiple scales in a physically embodied multi-agent setting. See project video at https://youtu.be/KHMwq9pv7mg.
Stochastic gradient descent (SGD) and projected stochastic gradient descent (PSGD) are scalable algorithms to compute model parameters in unconstrained and constrained optimization problems. In comparison with stochastic gradient descent (SGD), PSGD
forces its iterative values into the constrained parameter space via projection. The convergence rate of PSGD-type estimates has been exhaustedly studied, while statistical properties such as asymptotic distribution remain less explored. From a purely statistical point of view, this paper studies the limiting distribution of PSGD-based estimate when the true parameters satisfying some linear-equality constraints. Our theoretical findings reveal the role of projection played in the uncertainty of the PSGD estimate. As a byproduct, we propose an online hypothesis testing procedure to test the linear-equality constraints. Simulation studies on synthetic data and an application to a real-world dataset confirm our theory.
When data is of an extraordinarily large size or physically stored in different locations, the distributed nearest neighbor (NN) classifier is an attractive tool for classification. We propose a novel distributed adaptive NN classifier for which the
number of nearest neighbors is a tuning parameter stochastically chosen by a data-driven criterion. An early stopping rule is proposed when searching for the optimal tuning parameter, which not only speeds up the computation but also improves the finite sample performance of the proposed Algorithm. Convergence rate of excess risk of the distributed adaptive NN classifier is investigated under various sub-sample size compositions. In particular, we show that when the sub-sample sizes are sufficiently large, the proposed classifier achieves the nearly optimal convergence rate. Effectiveness of the proposed approach is demonstrated through simulation studies as well as an empirical application to a real-world dataset.
With both the standardization and commercialization completed in an unforeseen pace for the 5th generation (5G) wireless network, researchers, engineers and executives from the academia and the industry have turned their sights on candidate technolog
ies to support the next generation wireless networks. Reconfigurable intelligent surfaces (RIS), sometimes referred to as intelligent reflecting surfaces (IRS), have been identified to be potential components of the future wireless networks because they can reconfigure the propagation environment for wireless signals with low-cost passive devices. In doing so, the coverage of a cell can be expected to increase significantly as well as the overall throughput of the network. RIS has not only become an attractive research area but also triggered a couple of projects to develop appropriate solutions to enable the set-up of hardware demonstrations and prototypes. In parallel, technical discussions and activities towards standardization already took off in some regions. Promoting RIS to be integrated into future commercial networks and become a commercial success requires significant standardization work taken place both at regional level standards developing organizations (SDO) and international SDOs such as the 3rd Generation Partnership Project (3GPP). While many research papers study how RIS can be used and optimized, few effort is devoted to analyzing the challenges to commercialize RIS and how RIS can be standardized. This paper intends to shed some light on RIS from an industrial viewpoint and provide a clear roadmap to make RIS industrially feasible.
