arXiv subjects

Yong Zhou

Publications and source records attributed to Yong Zhou.

At least 109 records · Page 6Linked to original sources

arXiv2022

Propagation and excitation properties of nonlinear surface plasmon polaritons in a rectangular barrier

We propose a scheme to study the nonlinear propagation properties of nonlinear surface plasmon polaritons (SPPs) in a three level $Λ$ type electromagnetically induced transparency (EIT) system with modulation of a rectangular barrier. Based on the multi scale method, the nonlinear Schrödinger equation (NLSE) describing nonlinear propagation of SPPs is derived, and the rectangular barrier affecting propagation of nonlinear SPPs is provided by an off-resonance Stark field. For the single nonlinear SPPs incident case, by adjusting the height and half width of the barrier, we can realize transmission, trapping and reflection of the nonlinear SPPs. For two nonlinear SPPs symmetrical incident case, we find that a periodic intensity distribution in transverse direction mode can be excited in the rectangular barrier, and we study the relationship between propagation properties of such excited modes in the barrier with nonlinearity, half width of the barrier and phase difference of the initial nonlinear SPPs. In addition, we design an optical switch of nonlinear SPPs based on the above results. The results obtained here not only provide a theoretical basis for the study of the interaction between nonlinear SPPs and external potentials, but also have broad application prospects in the field of optical information at micro/nano scale.

physics.optics↗

arXiv2022

Pairwise Learning for Neural Link Prediction

In this paper, we aim at providing an effective Pairwise Learning Neural Link Prediction (PLNLP) framework. The framework treats link prediction as a pairwise learning to rank problem and consists of four main components, i.e., neighborhood encoder, link predictor, negative sampler and objective function. The framework is flexible that any generic graph neural convolution or link prediction specific neural architecture could be employed as neighborhood encoder. For link predictor, we design different scoring functions, which could be selected based on different types of graphs. In negative sampler, we provide several sampling strategies, which are problem specific. As for objective function, we propose to use an effective ranking loss, which approximately maximizes the standard ranking metric AUC. We evaluate the proposed PLNLP framework on 4 link property prediction datasets of Open Graph Benchmark, including ogbl-ddi, ogbl-collab, ogbl-ppa and ogbl-ciation2. PLNLP achieves top 1 performance on ogbl-ddi and ogbl-collab, and top 2 performance on ogbl-ciation2 only with basic neural architecture. The performance demonstrates the effectiveness of PLNLP.

cs.LG↗

arXiv2022

No-go guide for the Hubble tension : Late-time solutions

The Hubble tension, if not caused by any systematics, could be relieved or even resolved from modifying either the early-time or late-time Universe. The early-time modifications are usually in tension with either galaxy clustering or galaxy lensing constraints. The late-time modifications are also in conflict with the constraint from the inverse distance ladder, which, however, is weakened by the dependence on a sound-horizon prior and some particular approximation for the late-time expansion history. To achieve a more general no-go argument for the late-time scenarios, we propose to use a global parametrizationbased on the cosmic age (PAge) to consistently use the cosmic chronometers data beyond the Taylor expansion domain and without the input of a sound-horizon prior. Both the early-time and late-time scenarios are therefore largely ruled out, indicating the possible ways out of the Hubble tension from either exotic modifications of our concordance Universe or some unaccounted systematics.

astro-ph.CO↗

arXiv2021

Dirac fermions in half-metallic ferromagnetic mixed Cr$_{1\textrm{-}x}$M$_x$PSe$_3$ monolayers

The electronic and magnetic properties of pristine CrPSe$_3$ and mixed Cr$_{1\textrm{-}x}$M$_x$PSe$_3$ (M = Zn, Cd, Hg) monolayers were studied using density functional theory including an on-site Coulomb term (DFT$+U$) and tight-binding approach (TBA). While pristine CrPSe$_3$ monolayer has an antiferromagnetic (AFM) ground state, its alloying with MPSe$_3$ may give rise to half-metallic ferromagnet (HMF) with high Curie temperature. The resulting monolayers demonstrate single-spin Dirac cones of mainly Cr-$d$ character located in the first Brillouin zone directly at the Fermi energy. The calculated Fermi velocities of Dirac fermions indicate very high mobility in mixed Cr$_{1\textrm{-}x}$M$_x$PSe$_3$ monolayers, that makes this material appealing for low-dimensional spintronics applications.

cond-mat.mtrl-sci↗

arXiv2021

Adsorption of water molecules on pristine and defective NiPX$_3$ (X: S, Se) monolayers

Layered transition metal trichalcogenides MPX$_3$ (M: transition metal; X: S, Se) demonstrate a wide spectrum of properties and are widely proposed as effective materials for the water splitting reactions. Among these materials, NiPX$_3$ are the most promising ones due to the match their electronic structures for the oxygen and hydrogen evolution reactions. Here, we present first steps of a detailed theoretical description on the adsorption of water molecules on pristine and defected (chalcogen vacancies) surfaces of NiPX$_3$ and show that in all cases a physisorption takes the place with adsorption energies do not exceeding $-650$\,meV and water dissociative adsorption is unfavourable. This work provides a general description for water molecules interaction with MPX$_3$ and can serve as a basis for further studies on more complicated water/MPX$_3$ reactions.

cond-mat.mtrl-sci↗

arXiv2021

Modification of the magnetic and electronic properties of the graphene-Ni(111) interface via halogens intercalation

Electronic decoupling of graphene from metallic and semiconducting substrates via intercalation of different species is one of the widely used approaches in studies of graphene. In the present work the modification of the electronic and magnetic properties of graphene on ferromagnetic Ni(111) layer via intercalation of halogen atoms (X = F, Cl, Br) is studied using the state-of-the-art density-functional theory approach. It is found that in all gr/X/Ni(111) intercalation systems a graphene layer is fully electronically decoupled from the ferromagnetic substrate; however, different kind (electron or hole) and level of doping can be achieved. Despite the extremely small magnetic moment of C-atoms in graphene observed after halogens intercalation, the sizeable spin-splitting up to $35$ meV for the linearly dispersing graphene $π$ bands is found. The obtained theoretical data bring new ideas on the formation of the graphene-ferromagnet interfaces where spin polarized free-standing graphene layer can be formed with the possible application of these systems in electronics and spintronics.

cond-mat.mtrl-sci↗

arXiv2021

Electronic, magnetic and optical properties of MnPX$_3$ (X = S, Se) monolayers with and without chalcogen defects: A first-principle study

Based on density functional theory (DFT), we performed first-principle studies on the electronic structure, magnetic state and optical properties of two-dimensional (2D) transition-metal phosphorous trichalcogenide MnPX$_3$ (X=S and Se). The calculated interlayer cleavage energies of MnPX$_3$ monolayers indicate the energetic possibility to be exfoliated from bulk phase, with good dynamical stability confirmed by the absence of the imaginary contributions in the phonon spectra. MnPX$_3$ monolayers are both Néel antiferromagnetic (AFM) semiconductors with direct band gaps falling into the visible optical spectrum. Magnetic interaction parameters were extracted within the Heisenberg model to investigate the origin of the AFM state. Three in-plane magnetic exchange parameters play important role in the robust AFM configuration of Mn ions. The Néel temperatures ($T_\mathrm{N}$) were estimated by means of Monte Carlo simulations, obtaining the theoretical $T_\mathrm{N}$ of $103$\,K and $80$\,K for 2D MnPS$_3$ and MnPSe$_3$, respectively. With high spin state, Mn ions arranged in honeycomb lattices, the spin-degenerated band structures exhibit valley polarisation and was investigated in different biaxial in-plain strains, considering the spin-orbital coupling (SOC). 2D MnPX$_3$ monolayers show excellent performance on the optical properties, and the absorption spectra were discussed in detail to find the transition mechanism. Different amount and configuration of chalcogen vacancy were introduced into the MnPX$_3$ monolayers, and it is found that the electronic structures are heavily affected depending on the vacancy geometric structure, leading to different magnetic state and absorption spectra of defected MnPX$_3$ systems.

cond-mat.mtrl-sci↗

arXiv2021

Dark Matter Search Results from the PandaX-4T Commissioning Run

We report the first dark matter search results using the commissioning data from PandaX-4T. Using a time projection chamber with 3.7-tonne of liquid xenon target and an exposure of 0.63 tonne$\cdot$year, 1058 candidate events are identified within an approximate nuclear recoil energy window between 5 and 100 keV. No significant excess over background is observed. Our data set a stringent limit to the dark matter-nucleon spin-independent interactions, with a lowest excluded cross section (90% C.L.) of $3.8\times10^{-47} $cm$^2$ at a dark matter mass of 30 GeV/$c^2$.

hep-ex↗

arXiv2021

Learning Proximal Operator Methods for Massive Connectivity in IoT Networks

Grant-free random access has the potential to support massive connectivity in Internet of Things (IoT) networks, where joint activity detection and channel estimation (JADCE) is a key issue that needs to be tackled. The existing methods for JADCE usually suffer from one of the following limitations: high computational complexity, ineffective in inducing sparsity, and incapable of handling complex matrix estimation. To mitigate all the aforementioned limitations, we in this paper develop an effective unfolding neural network framework built upon the proximal operator method to tackle the JADCE problem in IoT networks, where the base station is equipped with multiple antennas. Specifically, the JADCE problem is formulated as a group-sparse-matrix estimation problem, which is regularized by non-convex minimax concave penalty (MCP). This problem can be iteratively solved by using the proximal operator method, based on which we develop a unfolding neural network structure by parameterizing the algorithmic iterations. By further exploiting the coupling structure among the training parameters as well as the analytical computation, we develop two additional unfolding structures to reduce the training complexity. We prove that the proposed algorithm achieves a linear convergence rate. Results show that our proposed three unfolding structures not only achieve a faster convergence rate but also obtain a higher estimation accuracy than the baseline methods.

eess.SP↗

arXiv2021

Light yield and field dependence measurement in PandaX-II dual-phase xenon detector

The dual-phase xenon time projection chamber (TPC) is one of the most sensitive detector technology for dark matter direct search, where the energy deposition of incoming particle can be converted into photons and electrons through xenon excitation and ionization. The detector response to signal energy deposition varies significantly with the electric field in liquid xenon. We study the detector's light yield and its dependence on the electric field in the PandaX-II dual-phase detector containing 580~kg liquid xenon in the sensitive volume. From our measurements, the light yield at electric fields from 0~V/cm to 317~V/cm is obtained for energy depositions up to 236~keV.

physics.ins-det↗

arXiv2021

Mapping Human Muscle Force to Supernumerary Robotics Device for Overhead Task Assistance

Supernumerary Robotics Device (SRD) is an ideal solution to provide robotic assistance in overhead manual manipulation. Since two arms are occupied for the overhead task, it is desired to have additional arms to assist us in achieving other subtasks such as supporting the far end of a long plate and pushing it upward to fit in the ceiling. In this study, a method that maps human muscle force to SRD for overhead task assistance is proposed. Our methodology is to utilize redundant DoFs such as the idle muscles in the leg to control the supporting force of the SRD. A sEMG device is worn on the operator's shank where muscle signals are measured, parsed, and transmitted to SRD for control. In the control aspect, we adopted stiffness control in the task space based on torque control at the joint level. We are motivated by the fact that humans can achieve daily manipulation merely through simple inherent compliance property in joint driven by muscles. We explore to estimate the force of some particular muscles in humans and control the SRD to imitate the behaviors of muscle and output supporting forces to accomplish the subtasks such as overhead supporting. The sEMG signals detected from human muscles are extracted, filtered, rectified, and parsed to estimate the muscle force. We use this force information as the intent of the operator for proper overhead supporting force. As one of the well-known compliance control methods, stiffness control is easy to achieve using a few of straightforward parameters such as stiffness and equilibrium point. Through tuning the stiffness and equilibrium point, the supporting force of SRD in task space can be easily controlled. The muscle force estimated by sEMG is mapped to the desired force in the task space of the SRD. The desired force is transferred into stiffness or equilibrium point to output the corresponding supporting force.

cs.RO↗

arXiv2021

Sparse Signal Processing for Massive Connectivity via Mixed-Integer Programming

Massive connectivity is a critical challenge of Internet of Things (IoT) networks. In this paper, we consider the grant-free uplink transmission of an IoT network with a multi-antenna base station (BS) and a large number of single-antenna IoT devices. Due to the sporadic nature of IoT devices, we formulate the joint activity detection and channel estimation (JADCE) problem as a group-sparse matrix estimation problem. Although many algorithms have been proposed to solve the JADCE problem, most of them are developed based on compressive sensing technique, yielding suboptimal solutions. In this paper, we first develop an efficient weighted $l_1$-norm minimization algorithm to better approximate the group sparsity than the existing mixed $l_1/l_2$-norm minimization. Although an enhanced estimation performance in terms of the mean squared error (MSE) can be achieved, the weighted $l_1$-norm minimization algorithm is still a convex relaxation of the original group-sparse matrix estimation problem, yielding a suboptimal solution. To this end, we further reformulate the JADCE problem as a mixed integer programming (MIP) problem, which can be solved by using the branch-and-bound method. As a result, we are able to obtain an optimal solution of the JADCE problem, which can be adopted as an upper bound to evaluate the effectiveness of the existing algorithms. Moreover, we also derive the minimum pilot sequence length required to fully recover the estimated matrix in the noiseless scenario. Simulation results show the performance gains of the proposed optimal algorithm over the proposed weighted $l_1$-norm algorithm and the conventional mixed $l_1/l_2$-norm algorithm. Results also show that the proposed algorithms require a short pilot sequence than the conventional algorithm to achieve the same estimation performance.

eess.SP↗

arXiv2021

Over-the-Air Computation via Cloud Radio Access Networks

Over-the-air computation (AirComp) has recently been recognized as a promising scheme for a fusion center to achieve fast distributed data aggregation in wireless networks via exploiting the superposition property of multiple-access channels. Since it is challenging to provide reliable data aggregation for a large number of devices using AirComp, in this paper, we propose to enable AirComp via the cloud radio access network (Cloud-RAN) architecture, where a large number of antennas are deployed at separate sites called remote radio heads (RRHs). However, the potential densification gain provided by Cloud-RAN is generally bottlenecked by the limited capacity of the fronthaul links connecting the RRHs and the fusion center. To this end, we formulate a joint design problem for AirComp transceivers and quantization bits allocation and propose an efficient algorithm to tackle this problem. Our numerical results shows the advantages of the proposed architecture compared with the state-of-the-art solutions.

eess.SP↗

arXiv2021

Algorithm Unrolling for Massive Access via Deep Neural Network with Theoretical Guarantee

Massive access is a critical design challenge of Internet of Things (IoT) networks. In this paper, we consider the grant-free uplink transmission of an IoT network with a multiple-antenna base station (BS) and a large number of single-antenna IoT devices. Taking into account the sporadic nature of IoT devices, we formulate the joint activity detection and channel estimation (JADCE) problem as a group-sparse matrix estimation problem. This problem can be solved by applying the existing compressed sensing techniques, which however either suffer from high computational complexities or lack of algorithm robustness. To this end, we propose a novel algorithm unrolling framework based on the deep neural network to simultaneously achieve low computational complexity and high robustness for solving the JADCE problem. Specifically, we map the original iterative shrinkage thresholding algorithm (ISTA) into an unrolled recurrent neural network (RNN), thereby improving the convergence rate and computational efficiency through end-to-end training. Moreover, the proposed algorithm unrolling approach inherits the structure and domain knowledge of the ISTA, thereby maintaining the algorithm robustness, which can handle non-Gaussian preamble sequence matrix in massive access. With rigorous theoretical analysis, we further simplify the unrolled network structure by reducing the redundant training parameters. Furthermore, we prove that the simplified unrolled deep neural network structures enjoy a linear convergence rate. Extensive simulations based on various preamble signatures show that the proposed unrolled networks outperform the existing methods in terms of the convergence rate, robustness and estimation accuracy.

cs.IT↗

arXiv2021

Over-the-Air Decentralized Federated Learning

In this paper, we consider decentralized federated learning (FL) over wireless networks, where over-the-air computation (AirComp) is adopted to facilitate the local model consensus in a device-to-device (D2D) communication manner. However, the AirComp-based consensus phase brings the additive noise in each algorithm iterate and the consensus needs to be robust to wireless network topology changes, which introduce a coupled and novel challenge of establishing the convergence for wireless decentralized FL algorithm. To facilitate consensus phase, we propose an AirComp-based DSGD with gradient tracking and variance reduction (DSGT-VR) algorithm, where both precoding and decoding strategies are developed for D2D communication. Furthermore, we prove that the proposed algorithm converges linearly and establish the optimality gap for strongly convex and smooth loss functions, taking into account the channel fading and noise. The theoretical result shows that the additional error bound in the optimality gap depends on the number of devices. Extensive simulations verify the theoretical results and show that the proposed algorithm outperforms other benchmark decentralized FL algorithms over wireless networks.

cs.IT↗

arXiv2021

UAV Aided Over-the-Air Computation

Over-the-air computation (AirComp) seamlessly integrates communication and computation by exploiting the waveform superposition property of multiple-access channels. Different from the existing works that focus on transceiver design of AirComp over static networks, this paper considers an unmanned aerial vehicle (UAV) aided AirComp system, where the UAV as a flying base station aggregates data from mobile sensors. The trajectory design of the UAV provides an additional degree of freedom to improve the performance of AirComp. Our goal is to minimize the time-averaged mean-squared error (MSE) of AirComp by jointly optimizing the UAV trajectory, receive normalizing factors, and sensors' transmit power. To this end, we first propose a novel and equivalent problem transformation by introducing intermediate variables. This reformulation leads to a convex subproblem when fixing any other two blocks of variables, thereby enabling efficient algorithm design based on the principle of block coordinate descent and alternating direction method of multipliers (ADMM) techniques. In particular, we derive the optimal closed-form solutions for normalizing factors and intermediate variables optimization subproblems. We also recast the convex trajectory design subproblem into an ADMM form and obtain the closed-form expressions for each variable updating. Simulation results show that the proposed algorithm achieves a smaller time-averaged MSE while reducing the simulation time by orders of magnitude compared to state-of-the-art algorithms.

eess.SP↗

arXiv2021

Byzantine-Resilient Federated Machine Learning via Over-the-Air Computation

Federated learning (FL) is recognized as a key enabling technology to provide intelligent services for future wireless networks and industrial systems with delay and privacy guarantees. However, the performance of wireless FL can be significantly degraded by Byzantine attack, such as data poisoning attack, model poisoning attack and free-riding attack. To design the Byzantine-resilient FL paradigm in wireless networks with limited radio resources, we propose a novel communication-efficient robust model aggregation scheme via over-the-air computation (AirComp). This is achieved by applying the Weiszfeld algorithm to obtain the smoothed geometric median aggregation against Byzantine attack. The additive structure of the Weiszfeld algorithm is further leveraged to match the signal superposition property of multiple-access channels via AirComp, thereby expediting the communication-efficient secure aggregation process of FL. Numerical results demonstrate the robustness against Byzantine devices and good learning performance of the proposed approach.

cs.IT↗

arXiv2021

Optimal Receive Beamforming for Over-the-Air Computation

In this paper, we consider fast wireless data aggregation via over-the-air computation (AirComp) in Internet of Things (IoT) networks, where an access point (AP) with multiple antennas aim to recover the arithmetic mean of sensory data from multiple IoT devices. To minimize the estimation distortion, we formulate a mean-squared-error (MSE) minimization problem that involves the joint optimization of the transmit scalars at the IoT devices as well as the denoising factor and the receive beamforming vector at the AP. To this end, we derive the transmit scalars and the denoising factor in closed-form, resulting in a non-convex quadratic constrained quadratic programming (QCQP) problem concerning the receive beamforming vector.Different from the existing studies that only obtain sub-optimal beamformers, we propose a branch and bound (BnB) algorithm to design the globally optimal receive beamformer.Extensive simulations demonstrate the superior performance of the proposed algorithm in terms of MSE. Moreover, the proposed BnB algorithm can serve as a benchmark to evaluate the performance of the existing sub-optimal algorithms.

eess.SP↗