arXiv subjects

Hang Li

Publications and source records attributed to Hang Li.

At least 289 records · Page 16Linked to original sources

arXiv2019

Chiral fermion reversal in chiral crystals

In materials chiral fermions such as Weyl fermions are characterized by nonzero chiral charges, which are singular points of Berry curvature in momentum space. Recently, new types of chiral fermions beyond Weyl fermions have been discovered in structurally chiral crystals CoSi, RhSi and PtAl. Here, we have synthesized RhSn single crystals, which have opposite structural chirality to the CoSi crystals we previously studied. Using angle-resolved photoemission spectroscopy, we show that the bulk electronic structures of RhSn are consistent with the band calculations and observe evident surface Fermi arcs and helical surface bands, confirming the existence of chiral fermions in RhSn. It is noteworthy that the helical surface bands of the RhSn and CoSi crystals have opposite handedness, meaning that the chiral fermions are reversed in the crystals of opposite structural chirality. Our discovery establishes a direct connection between chiral fermions in momentum space and chiral lattices in real space.

cond-mat.mtrl-sci↗

arXiv2019

Manipulating Spin Chirality of Magnetic Skyrmion Bubbles by In-Plane Re-versed Magnetic Fields in (Mn$_{1-x}$Ni$_x$)$_{65}$Ga$_{35}$ ($x = 0.45$) magnet

Understanding the dynamics of the magnetic skyrmion, a particle-like topologically stable spin texture, and its response dynamics to external fields are indis-pensable for the applications in spintronic devices. In this letter, the Lorentz transmis-sion electron microscopy (LTEM) was used to investigate the spin chirality of the mag-netic skyrmion bubbles (SKBs) in the centrosymmetric magnet MnNiGa at room tem-perature. The reversal of SKBs excited by the in-plane magnetic field has been revealed. Moreover, the collective behavior of interacting spin chirality can be manipulated by reversing the directions of the magnetic fields on a wedge-shaped thin plate. The dy-namic behavior of the bubbles at different position of the thin plate has been explored with the micromagnetic simulation, indicating a non-uniform and nontrivial dynamic magnetization on the surfaces and center of the thin plate during the spin chirality re-versal. The results suggest that the controllable symmetry breaking of the SKBs arising from thickness variation provides an ability to manipulate the collective behavior of the spin chirality with small external fields, leading to a promising application in nonvola-tile spintronic devices for magnetic skyrmions.

physics.app-ph↗

arXiv2019

Large topological Hall effect in a geometrically frustrated kagome magnet Fe$_3$Sn$_2$

We report on the observation of a large topological Hall effect (THE) over a wide temperature region in a geometrically frustrated Fe3Sn2 magnet with a kagome-bilayer structure. We found that the magnitude of the THE resistivity increases with temperature and reaches -0.875 μΩ.cm at 380 K. Moreover, the critical magnetic fields with the change of THE are consistent with the magnetic structure transformation, which indicates that the real-space fictitious magnetic field is proportional to the formation of magnetic skyrmions in Fe3Sn2. The results strongly suggest that the large THE originates from the topological magnetic spin textures and may open up further research opportunities in exploring emergent phenomena in kagome materials.

cond-mat.mtrl-sci↗

arXiv2019

Manipulating the Topology of Nanoscale Skyrmion Bubbles by Spatially Geometric Confinement

The discovery of magnetic skyrmion bubbles in centrosymmetric magnets has been receiving increasing interest from the research community, due to the fascinating physics of topological spin textures and its possible applications to spintronics. However, key challenges remain, such as how to manipulate the nucleation of skyrmion bubbles to exclude the trivial bubbles or metastable skyrmion bubbles that usually coexist with skyrmion bubbles in the centrosymmetric magnets. Here, we report having successfully performed this task by applying spatially geometric confinement to a centrosymmetric frustrated Fe3Sn2 magnet. We demonstrate that the spatially geometric confinement can indeed stabilize the skyrmion bubbles, by effectively suppressing the formation of trivial bubbles and metastable skyrmion bubbles. We also show that the critical magnetic field for the nucleation of the skyrmion bubbles in the confined Fe3Sn2 nanostripes is drastically less, by an order of magnitude, than that what is required in the thin plate without geometrical confinement. By analyzing how the width and thickness of the nanostripes affect the spin textures of skyrmion bubbles, we infer that the topological transition of skyrmion bubbles is closely related to the dipole-dipole interaction, which we find is consistent with theoretical simulations. The results presented here represent an important step forward in manipulating the topological spin textures of skyrmion bubbles, making us closer to achieving the fabrication of skyrmion-based racetrack memory devices.

cond-mat.mes-hall↗

arXiv2019

Creation of Single Chain of Nanoscale Skyrmion Bubbles with Record-high Temperature Stability in a Geometrically Confined Nanostripe

Nanoscale topologically non-trivial spin textures, such as magnetic skyrmions, have been identified as promising candidates for the transport and storage of information for spintronic applications, notably magnetic racetrack memory devices. The design and realization of single skyrmion chain at room temperature (RT) and above in the low-dimensional nanostructures are of great importance for future practical applications. Here, we report the creation of a single skyrmion bubble chain in a geometrically confined Fe3Sn2 nanostripe with a width comparable to the featured size of a skyrmion bubble. Systematic investigations on the thermal stability have revealed that the single chain of skyrmion bubbles can keep stable at temperatures varying from RT up to a record-high temperature of 630 K. This extreme stability can be ascribed to the weak temperature-dependent magnetic anisotropy and the formation of edge states at the boundaries of the nanostripes. The realization of the highly stable skyrmion bubble chain in a geometrically confined nanostructure is a very important step towards the application of skyrmion-based spintronic devices.

cond-mat.mtrl-sci↗

arXiv2019

Current-Induced Helicity Reversal of a Single Skyrmionic Bubble Chain in a Nanostructured Frustrated Magnet

Helicity indicates the in-plane magnetic-moment swirling direction of a skyrmionic configuration. The ability to reverse the helicity of a skyrmionic bubble via purely electrical means has been predicted in frustrated magnetic systems, however its experimental observation has remained challenging. Here, we experimentally demonstrate the current-driven helicity reversal of the skyrmionic bubble in a nanostructured frustrated Fe3Sn2 magnet. The critical current density required to trigger the helicity reversal is 109 - 1010 A/m2, with a corresponding pulse-width varying from 1 μs to 100 ns. Computational simulations reveal that both the pinning effect and dipole-dipole interaction play a crucial role in the helicity-reversal process.

cond-mat.mes-hall↗

arXiv2019

Dirac surface states in intrinsic magnetic topological insulators EuSn2As2 and MnBi2nTe3n+1

In magnetic topological insulators (TIs), the interplay between magnetic order and nontrivial topology can induce fascinating topological quantum phenomena, such as the quantum anomalous Hall effect, chiral Majorana fermions and axion electrodynamics. Recently, a great deal of attention has been focused on the intrinsic magnetic TIs, where disorder effects can be eliminated to a large extent, which is expected to facilitate the emergence of topological quantum phenomena. In despite of intensive efforts, experimental evidence of the topological surface states (SSs) remains elusive. Here, by combining first-principles calculations and angle-resolved photoemission spectroscopy (ARPES) experiments, we have revealed that EuSn2As2 is an antiferromagnetic TI with observation of Dirac SSs consistent with our prediction. We also observe nearly gapless Dirac SSs in antiferromagnetic TIs MnBi2nTe3n+1 (n = 1 and 2), which were absent in previous ARPES results. These results provide clear evidence for nontrivial topology of these intrinsic magnetic TIs. Furthermore, we find that the topological SSs show no observable changes across the magnetic transition within the experimental resolution, indicating that the magnetic order has quite small effect on the topological SSs, which can be attributed to weak hybridization between the localized magnetic moments, from either 4f or 3d orbitals, and the topological electronic states. This provides insights for further research that the correlations between magnetism and topological states need to be strengthened to induce larger gaps in the topological SSs, which will facilitate the realization of topological quantum phenomena at higher temperatures.

cond-mat.mtrl-sci↗

arXiv2019

A Multimodal Alerting System for Online Class Quality Assurance

Online 1 on 1 class is created for more personalized learning experience. It demands a large number of teaching resources, which are scarce in China. To alleviate this problem, we build a platform (marketplace), i.e., \emph{Dahai} to allow college students from top Chinese universities to register as part-time instructors for the online 1 on 1 classes. To warn the unqualified instructors and ensure the overall education quality, we build a monitoring and alerting system by utilizing multimodal information from the online environment. Our system mainly consists of two key components: banned word detector and class quality predictor. The system performance is demonstrated both offline and online. By conducting experimental evaluation of real-world online courses, we are able to achieve 74.3\% alerting accuracy in our production environment.

cs.HC↗

arXiv2019

Planar topological Hall effect in a uniaxial van der Waals ferromagnet Fe3GeTe2

In this work, we reported the observation of a novel planar topological Hall effect (PTHE) in single crystal of Fe3GeTe2, a paradigmatic two-dimensional ferromagnet with strong uniaxial anisotropy. The Hall effect and magnetoresistance varied periodically when the external magnetic field rotated in the ac (or bc) plane, while the PTHE emerged and maintained robust with field swept across the hard-magnetized ab plane. The PTHE covers the whole temperature region below Tc (~150 K) and a comparatively large value is observed at 100 K. Emergence of an internal gauge field was proposed to explain the origin of this large PTHE, which is either generated by the possible topological domain structure of uniaxial Fe3GeTe2 or the non-coplanar spin structure formed during the in-plane magnetization. Our results promisingly provide an alternative detection method to the in-plane skyrmion formation and may bring brand-new prospective to magneto-transport studies in condensed matter physics.

cond-mat.mtrl-sci↗

arXiv2019

A Novel Modeling Approach for All-Dielectric Metasurfaces Using Deep Neural Networks

Metasurfaces have become a promising means for manipulating optical wavefronts in flat and high-performance optical devices. Conventional metasurface device design relies on trial-and-error methods to obtain target electromagnetic (EM) response, an approach that demands significant efforts to investigate the enormous number of possible meta-atom structures. In this paper, a deep neural network approach is introduced that significantly improves on both speed and accuracy compared to techniques currently used to assemble metasurface-based devices. Our neural network approach overcomes three key challenges that have limited previous neural-network-based design schemes: input/output vector dimensional mismatch, accurate EM-wave phase prediction, as well as adaptation to 3-D dielectric structures, and can be generically applied to a wide variety of metasurface device designs across the entire electromagnetic spectrum. Using this new methodology, examples of neural networks capable of producing on-demand designs for meta-atoms, metasurface filters, and phase-change reconfigurable metasurfaces are demonstrated.

physics.optics↗

arXiv2019

A Method for Low Complexity Successive Cancellation List Decoding of Polar Codes

Polar codes have attracted a lot of attention during past few years and have been adopted as a coding scheme for 5G standard. Successive-cancellation list (SCL) decoder provides high level error-correction performance for polar codes, but the implementation complexity grows rapidly with the increase of the list size. Since the computation cost of sorting, many works focus on reducing the sorting complexity for SCL decoder. In this paper, we propose a method for SCL which directly reduce the number of input elements of sorting network without performance loss. Compared with SCL decoder, the proposed method has up to 95\% less sorter size on average, and the performance loss is negligible.

cs.IT↗

arXiv2019

Signal Demodulation with Machine Learning Methods for Physical Layer Visible Light Communications: Prototype Platform, Open Dataset and Algorithms

In this paper, we investigate the design and implementation of machine learning (ML) based demodulation methods in the physical layer of visible light communication (VLC) systems. We build a flexible hardware prototype of an end-to-end VLC system, from which the received signals are collected as the real data. The dataset is available online, which contains eight types of modulated signals. Then, we propose three ML demodulators based on convolutional neural network (CNN), deep belief network (DBN), and adaptive boosting (AdaBoost), respectively. Specifically, the CNN based demodulator converts the modulated signals to images and recognizes the signals by the image classification. The proposed DBN based demodulator contains three restricted Boltzmann machines (RBMs) to extract the modulation features. The AdaBoost method includes a strong classifier that is constructed by the weak classifiers with the k-nearest neighbor (KNN) algorithm. These three demodulators are trained and tested by our online open dataset. Experimental results show that the demodulation accuracy of the three data-driven demodulators drops as the transmission distance increases. A higher modulation order negatively influences the accuracy for a given transmission distance. Among the three ML methods, the AdaBoost modulator achieves the best performance.

eess.SP↗

arXiv2019

Unbiased LambdaMART: An Unbiased Pairwise Learning-to-Rank Algorithm

Although click data is widely used in search systems in practice, so far the inherent bias, most notably position bias, has prevented it from being used in training of a ranker for search, i.e., learning-to-rank. Recently, a number of authors have proposed new techniques referred to as 'unbiased learning-to-rank', which can reduce position bias and train a relatively high-performance ranker using click data. Most of the algorithms, based on the inverse propensity weighting (IPW) principle, first estimate the click bias at each position, and then train an unbiased ranker with the estimated biases using a learning-to-rank algorithm. However, there has not been a method for pairwise learning-to-rank that can jointly conduct debiasing of click data and training of a ranker using a pairwise loss function. In this paper, we propose a novel algorithm, which can jointly estimate the biases at click positions and the biases at unclick positions, and learn an unbiased ranker. Experiments on benchmark data show that our algorithm can significantly outperform existing algorithms. In addition, an online A/B Testing at a commercial search engine shows that our algorithm can effectively conduct debiasing of click data and enhance relevance ranking.

cs.IR↗

arXiv2019

Oriented Three-Dimensional Magnetic Biskyrmion in MnNiGa Bulk Crystals

A biskyrmion consists of two bound, topologically stable skyrmion spin textures. These coffee-bean-shaped objects have been observed in real-space in thin plates using Lorentz transmission electron microscopy (LTEM). From LTEM imaging alone, it is not clear whether biskyrmions are surface-confined objects, or, analogously to skyrmions in non-centrosymmetric helimagnets, three-dimensional tube-like structures in bulk sample. Here, we investigate the biskyrmion form factor in single- and polycrystalline MnNiGa samples using small angle neutron scattering (SANS). We find that biskyrmions are not long-range ordered, not even in single-crystals. Surprisingly all of the disordered biskyrmions have their in-plane symmetry axis aligned along certain directions, governed by the magnetocrystalline anisotropy. This anisotropic nature of biskyrmions may be further exploited to encode information.

cond-mat.mtrl-sci↗

arXiv2018

Word Embedding based Edit Distance

Text similarity calculation is a fundamental problem in natural language processing and related fields. In recent years, deep neural networks have been developed to perform the task and high performances have been achieved. The neural networks are usually trained with labeled data in supervised learning, and creation of labeled data is usually very costly. In this short paper, we address unsupervised learning for text similarity calculation. We propose a new method called Word Embedding based Edit Distance (WED), which incorporates word embedding into edit distance. Experiments on three benchmark datasets show WED outperforms state-of-the-art unsupervised methods including edit distance, TF-IDF based cosine, word embedding based cosine, Jaccard index, etc.

cs.CL↗

arXiv2018

Giant and anisotropic many-body spin-orbit tunability in a strongly correlated kagome magnet

Owing to the unusual geometry of kagome lattices-lattices made of corner-sharing triangles-their electrons are useful for studying the physics of frustrated, correlated and topological quantum electronic states. In the presence of strong spin-orbit coupling, the magnetic and electronic structures of kagome lattices are further entangled, which can lead to hitherto unknown spin-orbit phenomena. Here we use a combination of vector-magnetic-field capability and scanning tunnelling microscopy to elucidate the spin-orbit nature of the kagome ferromagnet Fe3Sn2 and explore the associated exotic correlated phenomena. We discover that a many-body electronic state from the kagome lattice couples strongly to the vector field with three-dimensional anisotropy, exhibiting a magnetization-driven giant nematic (two-fold-symmetric) energy shift. Probing the fermionic quasi-particle interference reveals consistent spontaneous nematicity-a clear indication of electron correlation-and vector magnetization is capable of altering this state, thus controlling the many-body electronic symmetry. These spin-driven giant electronic responses go well beyond Zeeman physics and point to the realization of an underlying correlated magnetic topological phase. The tunability of this kagome magnet reveals a strong interplay between an externally applied field, electronic excitations and nematicity, providing new ways of controlling spin-orbit properties and exploring emergent phenomena in topological or quantum materials.

cond-mat.str-el↗

arXiv2018

A Broadband DLCZ Quantum Memory in Room-Temperature Atoms

Quantum memory capable of stopping flying photons and storing their quantum coherence is essential for scalable quantum technologies. A room-temperature broadband quantum memory will enable the implementation of large-scale quantum systems for real-life applications. Due to either intrinsic high noises or short lifetime, it is still challenging to find a room-temperature broadband quantum memory beyond conceptual demonstration. Here, we present a far-off-resonance Duan-Lukin-Cirac-Zoller (FORD) protocol and demonstrate the broadband quantum memory in room-temperature atoms. We observe a low unconditional noise level of $10^{-4}$ and a cross-correlation up to 28. A strong violation of Cauchy-Schwarz inequality indicates high-fidelity generation and preservation of non-classical correlation. Furthermore, the achieved cross-correlation in room-temperature atoms exceeds the key boundary of 6 above which quantum correlation is able to violate Bell's inequality. Our results open up the door to an entirely new realm of memory-enabled quantum applications at ambient conditions.

quant-ph↗

arXiv2018

Reinforcement Learning based Multi-Access Control and Battery Prediction with Energy Harvesting in IoT Systems

Energy harvesting (EH) is a promising technique to fulfill the long-term and self-sustainable operations for Internet of things (IoT) systems. In this paper, we study the joint access control and battery prediction problems in a small-cell IoT system including multiple EH user equipments (UEs) and one base station (BS) with limited uplink access channels. Each UE has a rechargeable battery with finite capacity. The system control is modeled as a Markov decision process without complete prior knowledge assumed at the BS, which also deals with large sizes in both state and action spaces. First, to handle the access control problem assuming causal battery and channel state information, we propose a scheduling algorithm that maximizes the uplink transmission sum rate based on reinforcement learning (RL) with deep Q-network (DQN) enhancement. Second, for the battery prediction problem, with a fixed round-robin access control policy adopted, we develop a RL based algorithm to minimize the prediction loss (error) without any model knowledge about the energy source and energy arrival process. Finally, the joint access control and battery prediction problem is investigated, where we propose a two-layer RL network to simultaneously deal with maximizing the sum rate and minimizing the prediction loss: the first layer is for battery prediction, the second layer generates the access policy based on the output from the first layer. Experiment results show that the three proposed RL algorithms can achieve better performances compared with existing benchmarks.

eess.SP↗