arXiv subjects

Fei Xue

Publications and source records attributed to Fei Xue.

At least 91 records · Page 5Linked to original sources

arXiv2021

Active Terahertz Imaging Dataset for Concealed Object Detection

Concealed object detection in Terahertz imaging is an urgent need for public security and counter-terrorism. In this paper, we provide a public dataset for evaluating multi-object detection algorithms in active Terahertz imaging resolution 5 mm by 5 mm. To the best of our knowledge, this is the first public Terahertz imaging dataset prepared to evaluate object detection algorithms. Object detection on this dataset is much more difficult than on those standard public object detection datasets due to its inferior imaging quality. Facing the problem of imbalanced samples in object detection and hard training samples, we evaluate four popular detectors: YOLOv3, YOLOv4, FRCN-OHEM, and RetinaNet on this dataset. Experimental results indicate that the RetinaNet achieves the highest mAP. In addition, we demonstrate that hiding objects in different parts of the human body affect detection accuracy. The dataset is available at https://github.com/LingLIx/THz_Dataset.

cs.CV↗

arXiv2021

On the distribution of $αp^2$ modulo one with prime $p$ of a special form

Let $\mathcal{P}_r$ denote an almost-prime with at most $r$ prime factors, counted according to multiplicity. In this paper, it is proved that for $α\in\mathbb{R}\backslash\mathbb{Q},\,β\in\mathbb{R}$ and $0<θ<10/1561$, there exist infinitely many primes $p$, such that $\|αp^2+β\|<p^{-θ}$ and $p+2=\mathcal{P}_4$, which constitutes an improvement upon the previous result.

math.NT↗

arXiv2021

Line Flow based SLAM

We propose a visual SLAM method by predicting and updating line flows that represent sequential 2D projections of 3D line segments. While feature-based SLAM methods have achieved excellent results, they still face problems in challenging scenes containing occlusions, blurred images, and repetitive textures. To address these problems, we leverage a line flow to encode the coherence of line segment observations of the same 3D line along the temporal dimension, which has been neglected in prior SLAM systems. Thanks to this line flow representation, line segments in a new frame can be predicted according to their corresponding 3D lines and their predecessors along the temporal dimension. We create, update, merge, and discard line flows on-the-fly. We model the proposed line flow based SLAM (LF-SLAM) using a Bayesian network. Extensive experimental results demonstrate that the proposed LF-SLAM method achieves state-of-the-art results due to the utilization of line flows. Specifically, LF-SLAM obtains good localization and mapping results in challenging scenes with occlusions, blurred images, and repetitive textures.

cs.CV↗

arXiv2021

Current-induced torques in magnetic Weyl semimetal tunnel junctions

We study the current-induced torques in asymmetric magnetic tunnel junctions containing a conventional ferromagnet and a magnetic Weyl semimetal contact. The Weyl semimetal hosts chiral bulk states and topologically protected Fermi arc surface states which were found to govern the voltage behavior and efficiency of current-induced torques. We report how bulk chirality dictates the sign of the non-equilibrium torques acting on the ferromagnet and discuss the existence of large field-like torques acting on the magnetic Weyl semimetal which exceeds the theoretical maximum of conventional magnetic tunnel junctions. The latter are derived from the Fermi arc spin texture and display a counter-intuitive dependence on the Weyl nodes separation. Our results shed light on the new physics of multilayered spintronic devices comprising of magnetic Weyl semimetals, which might open doors for new energy efficient spintronic devices.

cond-mat.mes-hall↗

arXiv2021

Theory and phase-field simulations on electrical control of spin cycloids in a multiferroic

Cycloidal spin orders are common in multiferroics. One of the prototypical examples is BiFeO3 (BFO) which shows a large polarization and a cycloidal antiferromagnetic order at room temperature. Here we employ Landau theory and phase-field simulations to analyze the coupled switching dynamics of polarization and cycloidal antiferromagnetic orders in BFO. We are able to identify 14 types of transitional spin structures between two cycloids and 9 electric-field-induced spin switching paths. We demonstrate the electric-field-induced rotation of wave vectors of the cycloidal spins and discover 2 types of cycloidal spin switching dynamics: fast local spin flips and slow rotation of wave vectors. Also, we construct road maps to achieve the switching between any two spin cycloids through multi-step applications of electric fields. The work provides a theoretical framework for the phenomenological description of spin cycloids and a fundamental understanding of the switching mechanisms to achieve electrical control of magnetic orders.

cond-mat.mtrl-sci↗

arXiv2020

Staggered spin Hall conductivity

The intrinsic spin Hall effect plays an important role in spintronics applications, such as spin-orbit torque-based memory. The bulk space group symmetry determines the form of the bulk spin current conductivity tensor. This paper considers materials for which the local point group symmetry of individual atoms is lower than the global (bulk) symmetry. This enables a position-dependent spin current response, with additional tensor components allowed relative to the bulk response. We present a general method to compute the position-dependent intrinsic spin Hall conductivity, with similar computational effort relative to computing the bulk spin Hall conductivity. We also present the general symmetry-constrained form of the position-dependent spin current response. We apply this method to 1T'-WTe$_2$, which exhibits a conventional spin Hall conductivity tensor component $σ^y_{xz}$ and a staggered unconventional component $σ^z_{xz}$. The magnitude of these two components, around 100 and 20 $(\rm Ω\cdot cm)^{-1}$, respectively, are comparable to the spin-orbit torque exerted on adjacent ferromagnets in experiments. We then consider orthorhombic PbTe, in which both uniform and staggered spin current conductivity are one order of magnitude larger.

cond-mat.mes-hall↗

arXiv2020

Imaging the valley and orbital Hall effect in monolayer MoS2

The topological properties of a material's electronic structure are encoded in its Berry curvature, a quantity which is intimately related to the transverse electrical conductivity. In transition metal dichalcogenides with broken inversion symmetry, the nonzero Berry curvature results in a valley Hall effect. In this paper we identify a previously unrecognized consequence of Berry curvature in these materials: an electric field-induced change in the electrons' charge density orientation. We use first principles calculations to show that measurements of the electric field-induced change in the charge density or local density of states in MoS$_2$ can be used to measure its energy-dependent valley and orbital Hall conductivity.

cond-mat.mes-hall↗

arXiv2020

Higgs modes in two-dimensional spatially-indirect exciton condensates

Higgs modes in condensed matter physics have drawn attention because of analogies to the Higgs bosons of particle physics. Here we use a microscopic time-dependent mean-field theory to study the collective mode spectra of two-dimensional spatially indirect exciton (electron-hole pair) condensates, focusing on the Higgs modes, i.e., those that have a large weight in electron-hole pair amplitude response functions. We find that in the low exciton density (BEC) limit, the dominant Higgs modes of spatially indirect exciton condensates correspond to adding electron-hole pairs that are orthogonal to the condensed pair state. We comment on the previously studied Higgs-like collective excitations of superconductors in light of this finding.

cond-mat.mes-hall↗

arXiv2020

Deep Visual Odometry with Adaptive Memory

We propose a novel deep visual odometry (VO) method that considers global information by selecting memory and refining poses. Existing learning-based methods take the VO task as a pure tracking problem via recovering camera poses from image snippets, leading to severe error accumulation. Global information is crucial for alleviating accumulated errors. However, it is challenging to effectively preserve such information for end-to-end systems. To deal with this challenge, we design an adaptive memory module, which progressively and adaptively saves the information from local to global in a neural analogue of memory, enabling our system to process long-term dependency. Benefiting from global information in the memory, previous results are further refined by an additional refining module. With the guidance of previous outputs, we adopt a spatial-temporal attention to select features for each view based on the co-visibility in feature domain. Specifically, our architecture consisting of Tracking, Remembering and Refining modules works beyond tracking. Experiments on the KITTI and TUM-RGBD datasets demonstrate that our approach outperforms state-of-the-art methods by large margins and produces competitive results against classic approaches in regular scenes. Moreover, our model achieves outstanding performance in challenging scenarios such as texture-less regions and abrupt motions, where classic algorithms tend to fail.

cs.RO↗

arXiv2020

Unconventional spin-orbit torque in transition metal dichalcogenide/ferromagnet bilayers from first-principles calculations

Motivated by recent observations of unconventional out-of-plane dampinglike torque in \ch{WTe2}/Permalloy bilayer systems, we calculate the spin-orbit torque generated in two-dimensional transition metal dichalcogenide (TMD)-ferromagnet heterostructures using first-principles methods and linear response theory. Our numerical calculation of spin-orbit torques in \ch{WTe2}/Co and \ch{MoTe2}/Co heterostructures shows both conventional and novel dampinglike torkances (torque per electric field) with comparable magnitude, around $100~\hbar/2e~(\rm Ω\cdot cm)^{-1}$, for an electric field applied perpendicular to the mirror plane of the TMD layer. To gain further insight into the source of dampinglike torque, we compute the spin current flux between the TMD and Co layers and find good agreement between the two quantities. This indicates that the conventional picture of dampinglike spin-orbit torque, whereby the torque results from the spin Hall effect plus spin transfer torque, largely applies to TMD/Co bilayer systems.

cond-mat.mes-hall↗

arXiv2020

Quasi-one-dimensional metallic conduction channels in exotic ferroelectric topological defects

Ferroelectric topological objects (e.g. vortices, skyrmions) provide a fertile ground for exploring emerging physical properties that could potentially be utilized in future configurable nanoelectronic devices. Here, we demonstrate quasi-one-dimensional metallic high conduction channels along two types of exotic topological defects, i.e. the topological cores of (i) a quadrant vortex domain structure and (ii) a center domain (monopole-like) structure confined in high quality BiFeO3 nanoisland array, abbreviated as the vortex core and the center core. We unveil via phase-field simulations that the superfine (< 3 nm) metallic conduction channels along center cores arise from the screening charge carriers confined at the core whereas the high conductance of vortex cores results from a field-induced twisted state. These conducting channels can be repeatedly and reversibly created and deleted by manipulating the two topological states via an electric field, leading to an apparent electroresistance effect with an on/off ratio higher than 103. These results open up the possibility of utilizing these functional one-dimensional topological objects in high-density nanoelectronic devices such as ultrahigh density nonvolatile memory.

cond-mat.mtrl-sci↗

arXiv2020

Self-Supervised Deep Visual Odometry with Online Adaptation

Self-supervised VO methods have shown great success in jointly estimating camera pose and depth from videos. However, like most data-driven methods, existing VO networks suffer from a notable decrease in performance when confronted with scenes different from the training data, which makes them unsuitable for practical applications. In this paper, we propose an online meta-learning algorithm to enable VO networks to continuously adapt to new environments in a self-supervised manner. The proposed method utilizes convolutional long short-term memory (convLSTM) to aggregate rich spatial-temporal information in the past. The network is able to memorize and learn from its past experience for better estimation and fast adaptation to the current frame. When running VO in the open world, in order to deal with the changing environment, we propose an online feature alignment method by aligning feature distributions at different time. Our VO network is able to seamlessly adapt to different environments. Extensive experiments on unseen outdoor scenes, virtual to real world and outdoor to indoor environments demonstrate that our method consistently outperforms state-of-the-art self-supervised VO baselines considerably.

cs.CV↗

arXiv2020

Theory of Current-Induced Angular Momentum Transfer Dynamics in Spin-Orbit Coupled Systems

Motivated by the importance of understanding competing mechanisms to current-induced spin-orbit torque in complex magnets, we develop a unified theory of current-induced spin-orbital coupled dynamics. The theory describes angular momentum transfer between different degrees of freedom in solids, e.g., the electron orbital and spin, the crystal lattice, and the magnetic order parameter. Based on the continuity equations for the spin and orbital angular momenta, we derive equations of motion that relate spin and orbital current fluxes and torques describing the transfer of angular momentum between different degrees of freedom. We then propose a classification scheme for the mechanisms of the current-induced torque in magnetic bilayers. Based on our first-principles implementation, we apply our formalism to two different magnetic bilayers, Fe/W(110) and Ni/W(110), which are chosen such that the orbital and spin Hall effects in W have opposite sign and the resulting spin- and orbital-mediated torques can compete with each other. We find that while the spin torque arising from the spin Hall effect of W is the dominant mechanism of the current-induced torque in Fe/W(110), the dominant mechanism in Ni/W(110) is the orbital torque originating in the orbital Hall effect of W. It leads to negative and positive effective spin Hall angles, respectively, which can be directly identified in experiments. This clearly demonstrates that our formalism is ideal for studying the angular momentum transfer dynamics in spin-orbit coupled systems as it goes beyond the "spin current picture" by naturally incorporating the spin and orbital degrees of freedom on an equal footing. Our calculations reveal that, in addition to the spin and orbital torque, other contributions such as the interfacial torque and self-induced anomalous torque within the ferromagnet are not negligible in both material systems.

cond-mat.mes-hall↗

arXiv2020

Packing minima and lattice points in convex bodies

Motivated by long-standing conjectures on the discretization of classical inequalities in the Geometry of Numbers, we investigate a new set of parameters, which we call \emph{packing minima}, associated to a convex body $K$ and a lattice $Λ$. These numbers interpolate between the successive minima of $K$ and the inverse of the successive minima of the polar body of $K$, and can be understood as packing counterparts to the covering minima of Kannan & Lovász (1988). As our main results, we prove sharp inequalities that relate the volume and the number of lattice points in $K$ to the sequence of packing minima. Moreover, we extend classical transference bounds and discuss a natural class of examples in detail.

math.MG↗

arXiv2020

Integrating multi-source block-wise missing data in model selection

For multi-source data, blocks of variable information from certain sources are likely missing. Existing methods for handling missing data do not take structures of block-wise missing data into consideration. In this paper, we propose a Multiple Block-wise Imputation (MBI) approach, which incorporates imputations based on both complete and incomplete observations. Specifically, for a given missing pattern group, the imputations in MBI incorporate more samples from groups with fewer observed variables in addition to the group with complete observations. We propose to construct estimating equations based on all available information, and optimally integrate informative estimating functions to achieve efficient estimators. We show that the proposed method has estimation and model selection consistency under both fixed-dimensional and high-dimensional settings. Moreover, the proposed estimator is asymptotically more efficient than the estimator based on a single imputation from complete observations only. In addition, the proposed method is not restricted to missing completely at random. Numerical studies and ADNI data application confirm that the proposed method outperforms existing variable selection methods under various missing mechanisms.

stat.ME↗

arXiv2020

Multicategory Angle-based Learning for Estimating Optimal Dynamic Treatment Regimes with Censored Data

An optimal dynamic treatment regime (DTR) consists of a sequence of decision rules in maximizing long-term benefits, which is applicable for chronic diseases such as HIV infection or cancer. In this paper, we develop a novel angle-based approach to search the optimal DTR under a multicategory treatment framework for survival data. The proposed method targets maximization the conditional survival function of patients following a DTR. In contrast to most existing approaches which are designed to maximize the expected survival time under a binary treatment framework, the proposed method solves the multicategory treatment problem given multiple stages for censored data. Specifically, the proposed method obtains the optimal DTR via integrating estimations of decision rules at multiple stages into a single multicategory classification algorithm without imposing additional constraints, which is also more computationally efficient and robust. In theory, we establish Fisher consistency of the proposed method under regularity conditions. Our numerical studies show that the proposed method outperforms competing methods in terms of maximizing the conditional survival function. We apply the proposed method to two real datasets: Framingham heart study data and acquired immunodeficiency syndrome (AIDS) clinical data.

stat.ME↗

arXiv2019

Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry

We propose a self-supervised learning framework for visual odometry (VO) that incorporates correlation of consecutive frames and takes advantage of adversarial learning. Previous methods tackle self-supervised VO as a local structure from motion (SfM) problem that recovers depth from single image and relative poses from image pairs by minimizing photometric loss between warped and captured images. As single-view depth estimation is an ill-posed problem, and photometric loss is incapable of discriminating distortion artifacts of warped images, the estimated depth is vague and pose is inaccurate. In contrast to previous methods, our framework learns a compact representation of frame-to-frame correlation, which is updated by incorporating sequential information. The updated representation is used for depth estimation. Besides, we tackle VO as a self-supervised image generation task and take advantage of Generative Adversarial Networks (GAN). The generator learns to estimate depth and pose to generate a warped target image. The discriminator evaluates the quality of generated image with high-level structural perception that overcomes the problem of pixel-wise loss in previous methods. Experiments on KITTI and Cityscapes datasets show that our method obtains more accurate depth with details preserved and predicted pose outperforms state-of-the-art self-supervised methods significantly.

cs.CV↗

arXiv2019

Observation of Rydberg exciton polaritons and their condensate in a perovskite cavity

The condensation of half-light half-matter exciton polaritons in semiconductor optical cavities is a striking example of macroscopic quantum coherence in a solid state platform. Quantum coherence is possible only when there are strong interactions between the exciton polaritons provided by their excitonic constituents. Rydberg excitons with high principle value exhibit strong dipole-dipole interactions in cold atoms. However, polaritons with the excitonic constituent that is an excited state, namely Rydberg exciton polaritons (REPs), have not yet been experimentally observed. Here, for the first time, we observe the formation of REPs in a single crystal CsPbBr3 perovskite cavity without any external fields. These polaritons exhibit strong nonlinear behavior that leads to a coherent polariton condensate with a prominent blue shift. Furthermore, the REPs in CsPbBr3 are highly anisotropic and have a large extinction ratio, arising from the perovskite's orthorhombic crystal structure. Our observation not only sheds light on the importance of many-body physics in coherent polariton systems involving higher-order excited states, but also paves the way for exploring these coherent interactions for solid state quantum optical information processing.

cond-mat.mtrl-sci↗