arXiv subjects

Ying Li

Publications and source records attributed to Ying Li.

At least 487 records · Page 27Linked to original sources

arXiv2019

Mn2C monolayer: hydrogenation/oxygenation induced strong room-temperature ferromagnetism and potential applications

Two-dimensional ferromagnetic materials with strong ferromagnetism and high Curie temperature are significantly desired for the applications of nanoscale devices. Here, based on first-principles computations, we report hydrogenated/oxygenated Mn2C monolayer is a such material with strong room-temperature ferromagnetism. The bare Mn2C monolayer is an antiferromagnetic metal with the local magnetic moment of Mn ~ 3μB. However, the antiferromagnetic coupling of Mn atoms can transform into the ferromagnetic order under hydrogenation/oxygenation. Especially, the magnetic moments in hydrogenated/oxygenated Mn2C monolayer can be as large as 6 μB per unit cell, and the Curie temperatures are above 290K. Beside the potential applications in spintronic devices, our work suggests that Mn2C monolayer is also promising to be used in hydrogen/oxygen detection and removal devices.

cond-mat.mtrl-sci↗

arXiv2019

Cybertwin: An Origin of Next Generation Network Architecture

With fast development of Internet of Everything (IoE) and its applications, the ever increasing mobile internet traffic and services bring unprecedented challenges including scalability, mobility, availability, and security which cannot be addressed by the current clean-slate network architecture. In this paper, a cybertwin based next generation network architecture is proposed to accomodate the evolution from end-to-end connection to cloud-to-end connection in the future network. As a digital representation of human or things in the virtual cyberspace, cybertwin serves in multiple capacities, such as communications assistant, network data logger, and digital asset owner. The new and unique characteristics of the cybertwin make the proposed network to be flexible, scalable, reliable, and secure. Further, we advocate a new cloud network operating system which can work in a distributed way through a real-time multi-agent trading platform to allocate 3C (computing, caching, communications) resources. We also propose cloud operator, a new operator that can provide and manage the resources to the end users and offer location and authentication services for human and things in the cyberspace. Some promising and open research topics are discussed to envision the challenges and opportunities of the cybertwin in the future network architecture.

cs.NI↗

arXiv2019

Fixed-Symbol Aided Random Access Scheme for Machine-to-Machine Communications

The massiveness of devices in crowded Machine-to-Machine (M2M) communications brings new challenges to existing random-access (RA) schemes, such as heavy signaling overhead and severe access collisions. In order to reduce the signaling overhead, we propose a fixed-symbol aided RA scheme where active devices access the network in a grant-free method, i.e., data packets are directly transmitted in randomly chosen slots. To further address the access collision which impedes the activity detection, one fixed symbol is inserted into each transmitted data packet in the proposed scheme. An iterative message passing based activity detection (MP-AD) algorithm is performed upon the received signal of this fixed symbol to detect the device activity in each slot. In addition, the deep neural network-aided MP-AD (DNN-MP-AD) algorithm is further designed to alleviate the correlation problem of the iterative message passing process. In the DNN-MP-AD algorithm, the iterative message passing process is transferred from a factor graph to a DNN. Weights are imposed on the messages in the DNN and further trained to improve the accuracy of the device activity detection. Finally, numerical simulations are provided for the throughput of the proposed RA scheme, the accuracy of the proposed MP-AD algorithm, as well as the improvement brought by the DNN-MP-AD algorithm.

cs.IT↗

arXiv2019

An end-to-end Generative Retrieval Method for Sponsored Search Engine --Decoding Efficiently into a Closed Target Domain

In this paper, we present a generative retrieval method for sponsored search engine, which uses neural machine translation (NMT) to generate keywords directly from query. This method is completely end-to-end, which skips query rewriting and relevance judging phases in traditional retrieval systems. Different from standard machine translation, the target space in the retrieval setting is a constrained closed set, where only committed keywords should be generated. We present a Trie-based pruning technique in beam search to address this problem. The biggest challenge in deploying this method into a real industrial environment is the latency impact of running the decoder. Self-normalized training coupled with Trie-based dynamic pruning dramatically reduces the inference time, yielding a speedup of more than 20 times. We also devise an mixed online-offline serving architecture to reduce the latency and CPU consumption. To encourage the NMT to generate new keywords uncovered by the existing system, training data is carefully selected. This model has been successfully applied in Baidu's commercial search engine as a supplementary retrieval branch, which has brought a remarkable revenue improvement of more than 10 percents.

cs.IR↗

arXiv2019

Deep Learning Coordinated Beamforming for Highly-Mobile Millimeter Wave Systems

Supporting high mobility in millimeter wave (mmWave) systems enables a wide range of important applications such as vehicular communications and wireless virtual/augmented reality. Realizing this in practice, though, requires overcoming several challenges. First, the use of narrow beams and the sensitivity of mmWave signals to blockage greatly impact the coverage and reliability of highly-mobile links. Second, highly-mobile users in dense mmWave deployments need to frequently hand-off between base stations (BSs), which is associated with critical control and latency overhead. Further, identifying the optimal beamforming vectors in large antenna array mmWave systems requires considerable training overhead, which significantly affects the efficiency of these mobile systems. In this paper, a novel integrated machine learning and coordinated beamforming solution is developed to overcome these challenges and enable highly-mobile mmWave applications. In the proposed solution, a number of distributed yet coordinating BSs simultaneously serve a mobile user. This user ideally needs to transmit only one uplink training pilot sequence that will be jointly received at the coordinating BSs using omni or quasi-omni beam patterns. These received signals draw a defining signature not only for the user location, but also for its interaction with the surrounding environment. The developed solution then leverages a deep learning model that learns how to use these signatures to predict the beamforming vectors at the BSs. This renders a comprehensive solution that supports highly-mobile mmWave applications with reliable coverage, low latency, and negligible training overhead. Simulation results show that the proposed deep-learning coordinated beamforming strategy approaches the achievable rate of the genie-aided solution that knows the optimal beamforming vectors with no training overhead.

cs.IT↗

arXiv2019

On the saw-tooth torque in anisotropic $j_{\rm eff} = 1/2$ magnets: Application to $α$-RuCl$_3$

The so-called "Kitaev candidate" materials based on $4d^5$ and $5d^5$ metals have recently emerged as magnetic systems displaying strongly anisotropic exchange interactions reminiscent of the Kitaev's honeycomb model. Recently, these materials have been shown to commonly display a distinct saw-tooth angular dependence of the magnetic torque over a wide range of magnetic fields. While higher order chiral spin interactions have been considered as a source of this observation, we show here that bilinear anisotropic interactions and/or $g$-anisotropy are each sufficient to explain the observed torque response, which may be distinguished on the basis of high-field measurements. These findings unify the understanding of magnetic torque experiments in a variety of Kitaev candidate materials.

cond-mat.str-el↗

arXiv2019

Exceptional points and spectral singularities in active epsilon-near-zero plasmonic waveguides

We present a nanoscale active plasmonic waveguide system consisting of an array of periodic slits that can exhibit exceptional points and spectral singularities leading to several novel functionalities. The proposed symmetric active system operates near its cut-off wavelength and behaves as an effective epsilon-near-zero (ENZ) medium. We demonstrate the formation of an exceptional point (EP) that is accessed with very low gain coefficient values, a unique feature of the proposed nanoscale symmetric plasmonic configuration. Reflectionless ENZ transmission and perfect loss-compensation are realized at the EP which coincides with the effective ENZ resonance wavelength of the proposed array of active plasmonic waveguides. When we further increase the gain coefficient of the dielectric material loaded in the slits, a spectral singularity occurs at the ENZ resonance leading to super scattering (lasing) response at both forward and backward directions. These interesting effects are achieved by materials characterized by very small gain coefficients with practical values and at subwavelength scales due to the strong and homogeneous field enhancement inside the active slits at the ENZ resonance leading to enhanced light-matter interaction. We theoretically analyze the obtained EP, as well as the divergent spectral singularity, using a transmission-line model and investigate the addition of a second incident wave and nonlinearities in the response of the proposed active ENZ plasmonic system. Our findings provide a novel route towards interesting nanophotonic applications, such as reflectionless active ENZ media, unidirectional coherent perfect absorbers, nanolasers, and strong optical bistability and all-optical switching nanodevices.

physics.optics↗

arXiv2019

Capacity-Achieving MIMO-NOMA: Iterative LMMSE Detection

This paper considers a low-complexity iterative Linear Minimum Mean Square Error (LMMSE) multi-user detector for the Multiple-Input and Multiple-Output system with Non-Orthogonal Multiple Access (MIMO-NOMA), where multiple single-antenna users simultaneously communicate with a multiple-antenna base station (BS). While LMMSE being a linear detector has a low complexity, it has suboptimal performance in multi-user detection scenario due to the mismatch between LMMSE detection and multi-user decoding. Therefore, in this paper, we provide the matching conditions between the detector and decoders for MIMO-NOMA, which are then used to derive the achievable rate of the iterative detection. We prove that a matched iterative LMMSE detector can achieve (i) the optimal capacity of symmetric MIMO-NOMA with any number of users, (ii) the optimal sum capacity of asymmetric MIMO-NOMA with any number of users, (iii) all the maximal extreme points in the capacity region of asymmetric MIMO-NOMA with any number of users, (iv) all points in the capacity region of two-user and three-user asymmetric MIMO-NOMA systems. In addition, a kind of practical low-complexity error-correcting multiuser code, called irregular repeat-accumulate code, is designed to match the LMMSE detector. Numerical results shows that the bit error rate performance of the proposed iterative LMMSE detection outperforms the state-of-art methods and is within 0.8dB from the associated capacity limit.

cs.IT↗

arXiv2019

Power Corrections to Pion Transition Form Factor in Perturbative QCD Approach

In this paper we calculate the power corrections to the pion transition form factor within the framework of perturbative QCD approach on the basis of $k_T$ factorization. The power suppressed contributions from higher twist pion wave functions and the hadronic structure of photon are investigated. We find that there exists strong cancellation between the two kinds contributions, thus the total power corrections considered currently are very small, and the prediction of the leading power contribution with joint resummation improved perturbative QCD approach is almost unchanged. This result confirms that the pion transition form factor is a good platform to constrain the nonperturbative parameters in pion wave functions. Moreover, our result can accommodate the anomalous data from BaBar, or agrees with results from Belle according to the choice of Gegebauer moment in the pion wave function, and the more precise experimental data from Belle-II is expected.

hep-ph↗

arXiv2018

Quantum computation with universal error mitigation on superconducting quantum processor

Medium-scale quantum devices that integrate about hundreds of physical qubits are likely to be developed in the near future. However, such devices will lack the resources for realizing quantum fault tolerance. Therefore, the main challenge of exploring the advantage of quantum computation is to minimize the impact of device and control imperfections without encoding. Quantum error mitigation is a solution satisfying the requirement. Here, we demonstrate an error mitigation protocol based on gate set tomography and quasiprobability decomposition. One- and two-qubit circuits are tested on a superconducting device, and computation errors are successfully suppressed. Because this protocol is universal for digital quantum computers and algorithms computing expected values, our results suggest that error mitigation can be an essential component of near-future quantum computation.

quant-ph↗

arXiv2018

RGB-D Based Action Recognition with Light-weight 3D Convolutional Networks

Different from RGB videos, depth data in RGB-D videos provide key complementary information for tristimulus visual data which potentially could achieve accuracy improvement for action recognition. However, most of the existing action recognition models solely using RGB videos limit the performance capacity. Additionally, the state-of-the-art action recognition models, namely 3D convolutional neural networks (3D-CNNs) contain tremendous parameters suffering from computational inefficiency. In this paper, we propose a series of 3D light-weight architectures for action recognition based on RGB-D data. Compared with conventional 3D-CNN models, the proposed light-weight 3D-CNNs have considerably less parameters involving lower computation cost, while it results in favorable recognition performance. Experimental results on two public benchmark datasets show that our models can approximate or outperform the state-of-the-art approaches. Specifically, on the RGB+D-NTU (NTU) dataset, we achieve 93.2% and 97.6% for cross-subject and cross-view measurement, and on the Northwestern-UCLA Multiview Action 3D (N-UCLA) dataset, we achieve 95.5% accuracy of cross-view.

cs.CV↗

arXiv2018

Spectrum Cascade Bloch Oscillations in Temporally Modulated Acoustics

Bloch oscillations (BOs) refer to a periodically oscillatory motion of particle in lattice systems driven by a constant force. By temporally modulating acoustic waveguides, BOs can be generalized from spatial to frequency domain, opening new possibilities for spectrum manipulations. The modulation can induce mode transitions in the waveguide band and form an artificial frequency lattice, with the mismatched wave vector during transitions acting as a constant force that drives frequency Bloch oscillations (FBOs). Furthermore, the modulation phase accompanying transitions serves as a gauge potential that controls the initial oscillation phase, providing an additional degree of freedom to tailor FBOs. We report that multiple FBOs with judiciously designed oscillation phases can be further cascaded to realize acoustic spectrum self-imaging, unidirectional transduction and bandwidth engineering. The study proposes the concept of FBOs in acoustic systems and functionalizes its cascade configurations for advanced control of sound spectrum. This paradigm may find versatile applications in underwater secure communication, voice encryption and signal processing.

physics.app-ph↗

arXiv2018

Footprints of New Physics in $b\to cτν$ Transitions

In this work, we perform a combined analysis of the $R(D)$, $R(D^*)$, and $R(J/ψ)$ anomalies in a model-independent manner based on the general framework of the four-fermion effective field theory, paying special attention to the use of the hadronic form factors. For the $B\to D(D^*)$ transition form factors, we use the HQET parametrization that includes the higher order corrections of $\mathcal{O}(α_s,Λ_{\mathrm{QCD}}/m_{b,c})$ and was determined recently from a fit to lattice QCD and light-cone sum rule results in complementary kinematical regions of the momentum transfer. For the $B_c\to J/ψ(η_c)$ transitions, we use the form factors calculated in the covariant light-front quark model, which are found to be well consistent with the preliminary lattice results. With this particular treatment of hadronic matrix elements, in our analysis the two classes of vector operators are shown to be the most favored single new physics (NP) operators by the current experimental constraints within $2σ$ and the LEP1 data on $Br(B_c\to τν)$ as well as the minimum $χ^2$ fit, while the tensor operator is also allowed but severely constrained, and the scalar ones are excluded. Using the favored ranges and fitted values of the Wilson coefficients of the single NP operators, we also give a prognosis for the physical observables such as the ratios of decay rates ($R(D(D^*)), R(J/ψ(η_c))$) and other polarized observables as well as the $q^2$ distributions.

hep-ph↗

arXiv2018

$ab$-$initio$ Approaches for Low-Energy Spin Hamiltonians

Implicit in the study of magnetic materials is the concept of spin Hamiltonians, which emerge as the low-energy theories of correlation-driven insulators. In order to predict and establish such Hamiltonians for real materials, a variety of first principles $ab$-$initio$ methods have been developed, based on density functional theory and wavefunction methodologies. In this review, we provide a basic introduction to such methods and the essential concepts of low-energy Hamiltonians, with a focus on their practical capabilities and limitations. We further discuss our recent efforts toward understanding a variety of complex magnetic systems that present unique challenges from the perspective of $ab$-$initio$ approaches.

cond-mat.str-el↗

arXiv2018

Role of Hydrogen in the spin-orbital-entangled quantum liquid candidate H$_3$LiIr$_2$O$_6$

Motivated by recent reports of H$_3$LiIr$_2$O$_6$ as a spin-orbital-entangled quantum liquid, we investigate via a combination of density functional theory and non-perturbative exact diagonalization the microscopic nature of its magnetic interactions. We find that while the interlayer O-H-O bond geometry strongly affects the local magnetic couplings, these bonds are likely to remain symmetrical due to large zero-point fluctuations of the H positions. In this case, the estimated magnetic model lies close to the classical tricritical point between ferromagnetic, zigzag and incommensurate spiral orders, what may contribute to the lack of magnetic ordering. However, we also find that substitution of H by D (deuterium) as well as disorder-induced inhomogeneities destabilize the O-H/D-O bonds modifying strongly the local magnetic couplings. These results suggest that the magnetic response in H$_3$LiIr$_2$O$_6$ is likely sensitive to both the stoichiometry and microstructure of the samples and emphasize the importance of a careful treatment of hydrogen for similar systems.

cond-mat.str-el↗

arXiv2018

Gaussian Message Passing for Overloaded Massive MIMO-NOMA

This paper considers a low-complexity Gaussian Message Passing (GMP) scheme for a coded massive Multiple-Input Multiple-Output (MIMO) systems with Non-Orthogonal Multiple Access (massive MIMO-NOMA), in which a base station with $N_s$ antennas serves $N_u$ sources simultaneously in the same frequency. Both $N_u$ and $N_s$ are large numbers, and we consider the overloaded cases with $N_u>N_s$. The GMP for MIMO-NOMA is a message passing algorithm operating on a fully-connected loopy factor graph, which is well understood to fail to converge due to the correlation problem. In this paper, we utilize the large-scale property of the system to simplify the convergence analysis of the GMP under the overloaded condition. First, we prove that the \emph{variances} of the GMP definitely converge to the mean square error (MSE) of Linear Minimum Mean Square Error (LMMSE) multi-user detection. Secondly, the \emph{means} of the traditional GMP will fail to converge when $ N_u/N_s< (\sqrt{2}-1)^{-2}\approx5.83$. Therefore, we propose and derive a new convergent GMP called scale-and-add GMP (SA-GMP), which always converges to the LMMSE multi-user detection performance for any $N_u/N_s>1$, and show that it has a faster convergence speed than the traditional GMP with the same complexity. Finally, numerical results are provided to verify the validity and accuracy of the theoretical results presented.

cs.IT↗

arXiv2018

16-qubit IBM universal quantum computer can be fully entangled

Entanglement is an important evidence that a quantum device can potentially solve problems intractable for classical computers. In this paper, we prepare connected graph states involving 8 to 16 qubits on ibmqx5, a 16-qubit superconducting quantum processor accessible via IBM cloud,using low-depth circuits. We demonstrate that the prepared state is fully entangled, i.e. the state is inseparable with respect to any fixed partition.

quant-ph↗

arXiv2018

The Optimal Constant in Generalized Hardy's Inequality

We obtain the sharp factor of the two-sides estimates of the optimal constant in generalized Hardy's inequality with two general Borel measures on $\mathbb{R}$, which generalizes and unifies the known continuous and discrete cases.

math.PR↗