arXiv subjects

Xiaofei Yang

Publications and source records attributed to Xiaofei Yang.

29 records · Page 2Linked to original sources

arXiv2023

Opportunities for Fundamental Physics Research with Radioactive Molecules

Molecules containing short-lived, radioactive nuclei are uniquely positioned to enable a wide range of scientific discoveries in the areas of fundamental symmetries, astrophysics, nuclear structure, and chemistry. Recent advances in the ability to create, cool, and control complex molecules down to the quantum level, along with recent and upcoming advances in radioactive species production at several facilities around the world, create a compelling opportunity to coordinate and combine these efforts to bring precision measurement and control to molecules containing extreme nuclei. In this manuscript, we review the scientific case for studying radioactive molecules, discuss recent atomic, molecular, nuclear, astrophysical, and chemical advances which provide the foundation for their study, describe the facilities where these species are and will be produced, and provide an outlook for the future of this nascent field.

nucl-ex↗

arXiv2022

Nanoscale three-dimensional magnetic sensing with a probabilistic nanomagnet driven by spin-orbit torque

Detection of vector magnetic fields at nanoscale dimensions is critical in applications ranging from basic material science, to medical diagnostic. Meanwhile, an all-electric operation is of great significance for achieving a simple and compact sensing system. Here, we propose and experimentally demonstrate a simple approach to sensing a vector magnetic field at nanoscale dimensions, by monitoring a probabilistic nanomagnet's transition probability from a metastable state, excited by a driving current due to SOT, to a settled state. We achieve sensitivities for Hx, Hy, and Hz of 1.02%/Oe, 1.09%/Oe and 3.43%/Oe, respectively, with a 200 x 200 nm^2 nanomagnet. The minimum detectable field is dependent on the driving pulse events N, and is expected to be as low as 1 uT if N = 3 x 10^6.

cond-mat.mtrl-sci↗

arXiv2022

Chaotic spin precession in antiferromagnetic domain walls

In contrast with rich investigations about the translation of an antiferromagnetic (AFM) texture, spin precession in an AFM texture is seldom concerned for lacking an effective driving method. In this work, however, we show that under an alternating spin-polarized current with spin along the AFM anisotropy axis, spin precession can be excited in an AFM DW. Especially, chaotic spin precession occurs at moderate interfacial Dzyaloshinskii-Moriya interaction (DMI), which contributes to a nonlinear term in the dynamic equation of DW precession. Also, crisis-induced intermittent chaos appears when the current density is higher than a critical value. This work not only paves a way to unravel rich spin precession behaviors in an AFM texture but also provides guidelines for developing ultrafast spintronic devices based on new physical principles.

cond-mat.mtrl-sci↗

arXiv2021

Mass-energy equivalence for terahertz magnon excitation in antiferromagnetic domain walls

The theory of special relativity is one of the most significant achievements in modern physics, with several important predictions such as time dilation, size contraction for a moving object and mass-energy equivalence. Recent studies have demonstrated size contraction for an antiferromagnetic (AFM) domain wall (DW). Here, we show the mass-energy equivalence by numerically investigating the excitation of terahertz (THz) magnons from a moving AFM DW under the magnetic anisotropy energy gradient. The energy of magnons comes from the loss of DW mass, accompanied with a DW width broadening, overcoming the Lorentz contraction effect. Our results pave the way to study relativistic physics in AFM textures and to efficiently generate THz magnons by electric means.

cond-mat.mes-hall↗

arXiv2021

FPS-Net: A Convolutional Fusion Network for Large-Scale LiDAR Point Cloud Segmentation

Scene understanding based on LiDAR point cloud is an essential task for autonomous cars to drive safely, which often employs spherical projection to map 3D point cloud into multi-channel 2D images for semantic segmentation. Most existing methods simply stack different point attributes/modalities (e.g. coordinates, intensity, depth, etc.) as image channels to increase information capacity, but ignore distinct characteristics of point attributes in different image channels. We design FPS-Net, a convolutional fusion network that exploits the uniqueness and discrepancy among the projected image channels for optimal point cloud segmentation. FPS-Net adopts an encoder-decoder structure. Instead of simply stacking multiple channel images as a single input, we group them into different modalities to first learn modality-specific features separately and then map the learned features into a common high-dimensional feature space for pixel-level fusion and learning. Specifically, we design a residual dense block with multiple receptive fields as a building block in the encoder which preserves detailed information in each modality and learns hierarchical modality-specific and fused features effectively. In the FPS-Net decoder, we use a recurrent convolution block likewise to hierarchically decode fused features into output space for pixel-level classification. Extensive experiments conducted on two widely adopted point cloud datasets show that FPS-Net achieves superior semantic segmentation as compared with state-of-the-art projection-based methods. In addition, the proposed modality fusion idea is compatible with typical projection-based methods and can be incorporated into them with consistent performance improvements.

cs.CV↗

arXiv2020

RRCN: A Reinforced Random Convolutional Network based Reciprocal Recommendation Approach for Online Dating

Recently, the reciprocal recommendation, especially for online dating applications, has attracted more and more research attention. Different from conventional recommendation problems, the reciprocal recommendation aims to simultaneously best match users' mutual preferences. Intuitively, the mutual preferences might be affected by a few key attributes that users like or dislike. Meanwhile, the interactions between users' attributes and their key attributes are also important for key attributes selection. Motivated by these observations, in this paper we propose a novel reinforced random convolutional network (RRCN) approach for the reciprocal recommendation task. In particular, we technically propose a novel random CNN component that can randomly convolute non-adjacent features to capture their interaction information and learn feature embeddings of key attributes to make the final recommendation. Moreover, we design a reinforcement learning based strategy to integrate with the random CNN component to select salient attributes to form the candidate set of key attributes. We evaluate the proposed RRCN against a number of both baselines and the state-of-the-art approaches on two real-world datasets, and the promising results have demonstrated the superiority of RRCN against the compared approaches in terms of a number of evaluation criteria.

cs.IR↗

arXiv2020

Heterogeneous-Temporal Graph Convolutional Networks: Make the Community Detection Much Better

Community detection has long been an important yet challenging task to analyze complex networks with a focus on detecting topological structures of graph data. Essentially, real-world graph data contains various features, node and edge types which dynamically vary over time, and this invalidates most existing community detection approaches. To cope with these issues, this paper proposes the heterogeneous-temporal graph convolutional networks (HTGCN) to detect communities from hetergeneous and temporal graphs. Particularly, we first design a heterogeneous GCN component to acquire feature representations for each heterogeneous graph at each time step. Then, a residual compressed aggregation component is proposed to represent "dynamic" features for "varying" communities, which are then aggregated with "static" features extracted from current graph. Extensive experiments are evaluated on two real-world datasets, i.e., DBLP and IMDB. The promising results demonstrate that the proposed HTGCN is superior to both benchmark and the state-of-the-art approaches, e.g., GCN, GAT, GNN, LGNN, HAN and STAR, with respect to a number of evaluation criteria.

cs.LG↗

arXiv2020

Voltage-Induced Inertial Domain Wall Motion in an Antiferromagnetic Nanowire

Racetrack memory based on magnetic domain walls (DWs) motion exhibits advantages of small volume and high reading speed. When compared to current-induced DW motion, voltage-induced DW motion exhibits lower dissipation. On the other hand, the DW in an antiferromagnet (AFM) moves at a high velocity with weak stray field. In this work, the AFM DW motion induced by a gradient of magnetic anisotropy energy under a voltage pulse has been investigated in theory. The dynamics equation for the DW motion was derived. The solution indicates that the DW velocity is higher than 100 m/s, and because of inertia, the DW is able to keep moving at a speed of around 100 m/s for several nano seconds after turning off the voltage in a period of pulse. The mechanism for this DW inertia is explained based on the Lagrangian route. On the other hand, a spin wave is emitted while the DW is moving, yet the DW is still able to move at an ever increasing velocity with enlarging DW width. This indicates energy loss from emission of spin wave is less than the energy gain from the effective field of the gradient of anisotropy energy.

physics.app-ph↗

arXiv2019

Voltage-induced high-speed DW motion in a synthetic antiferromagnet

Voltage-induced motion of a magnetic domain wall (DW) has potential in developing novel devices with ultralow dissipation. However, the speed for the voltage-induced DW motion (VIDWM) in a single ferromagnetic layer is usually very low. In this work, we proposed VIDWM with high speed in a synthetic antiferromaget (SAF). The velocity for the coupled DWs in the SAF is significantly higher than its counterpart in a single ferromagnetic layer. Strong interlayer antiferromagnetic exchange coupling plays a critical role for the high DW velocity since it inhibits the tilting of DW plane with strong Dzyaloshinskii-Moriya interaction. On the other hand, the Walker breakdown of DW motion is also inhibited due to the stabilization of moment orientation under a strong interlayer antiferromagnetic coupling. In theory, the voltage-induced gradient of magnetic anisotropy is proved to be equal to an effective magnetic field that drives DW.

physics.app-ph↗

arXiv2018

The reason for the tilting of domain wall with Dzyaloshinskii-Moriya interaction from a microscopic dynamical perspective

The interfacial Dzyaloshinskii-Moriya interaction (DMI) of a heavy metal (HM)/ferromagnetic (FM) metal heterostructure is vital to the current-induced domain wall motion (CIDWM) at an ultrahigh velocity. However, strong DMI also tilts the moving domain wall (DW) plane, and the mechanism for this tilting is not quite clear. In this work, we have found that this tilting may be understood based on a micromagnetic calculation from a microscopic dynamical perspective. The DMI-induced antisymmetric moment structure at the two boundaries of the track needs to be paid attention. In the early stage of CIDWM induced by spin-orbit torque, this antisymmetry is destroyed. Afterwards, the moments at the two boundaries experience distinct rotation processes with different energy paths towards their final stable antisymmetric moment structure. This results in different initial velocities of the local DW regions at the two boundaries. In mathematics, this distinct DW dynamical progresses at the two boundaries can be approximately revealed by modifying the initial conditions for solving the Thiele equations.

physics.comp-ph↗

arXiv2018

Readable Racetrack Memory via Ferromagnetically Coupled Chiral Domain Walls

Current-induced motion of domain walls (CIMDW) with interfacial Dzyaloshinskii-Moriya interaction (DMI) in heavy metal (HM)/ferromagnetic (FM) metal multilayers have attracted attention owing to their potential application in novel magnetic memories. In recent years, the CIMDW at ultrahigh speed has been observed in a synthetic antiferromagnetic (SAF) multilayer. However, due to the zero net magnetization, the reading of information from the SAF multilayer is still challenging. In this work, we propose a readable racetrack memory consisting of a synthetic ferromagnetic multilayer composed of two FM layers with an interlayer FM coupling. One FM layer had an isotropic DMI, while the other had an anisotropic DMI. This difference of DMIs resulted in the opposite tilting directions of the DW planes in the two layers. This tilting was inhibited by a strong interlayer FM coupling, resulting in an increase in the DW velocity and the reduction of the minimum allowed spacing between two adjacent DWs. In addition, the FM coupling enhanced the stray field, and the stored information could be read conveniently using a conventional reading head. Therefore, our proposal paves a way for the fabrication of a racetrack memory with high reading speed, large storage density, and good readability.

physics.comp-ph↗