arXiv subjects

Li Wang

Publications and source records attributed to Li Wang.

At least 505 records · Page 28Linked to original sources

arXiv2019

Optimization of high performance spintronic terahertz sources

To achieve high efficiency and good performance of spintronic terahertz sources, we propose and corroborate a remnant magnetization method to radiate continuously and stably terahertz pulses from W/CoFeB/Pt magnetic nanofilms without carrying magnets on the transmitters driven by femtosecond laser pulses. We systematically investigate the influences of the pumping central wavelength and find out the optimal wavelength for a fixed sample thickness. We also optimize the incidence angle of the pumping laser and find the emission efficiency is enhanced under oblique incidence. Combing the aforementioned optimizations, we finally obtain comparable radiation efficiency and broadband spectra in W/CoFeB/Pt heterostructures compared with that from 1 mm thick ZnTe nonlinear crystals via optical rectification under the same pumping conditions of 100 fs pulse duration from a Ti:sapphire laser oscillator, which was not previously demonstrated under such pulse duration. We believe our observations not only benefit for a deep insight into the physics of femtosecond spin dynamics, but also help develop novel and cost-effective ultrabroadband spintronic terahertz emitters.

physics.optics↗

arXiv2019

CDEX dark matter experiment: Status and prospects

The China Dark Matter Experiment (CDEX) aims at direct searches of light Weakly Interacting Massive Particles (WIMPs) at the China Jinping Underground Laboratory (CJPL) with an overburden of about 2400m rock. Results from a prototype CDEX-1 994 g p-type Point Contact Germanium(pPCGe) detector are reported. Research programs are pursued to further reduce the physics threshold by improving hardware and data analysis. The CDEX-10 experiment with a pPCGe array of 10 kg target mass range is being tested. The evolution of CDEX program into "CDEX-1T Experiment" with ton-scale germanium detector arrays will also be introduced in this study.

hep-ex↗

arXiv2019

Coded Caching in Fog-RAN: b-Matching Approach

Fog radio access network (Fog-RAN), which pushes the caching and computing capabilities to the network edge, is capable of efficiently delivering contents to users by using carefully designed caching placement and content replacement algorithms. In this paper, the transmission scheme design and coding parameter optimization will be considered for coded caching in Fog-RAN, where the reliability of content delivery, i.e., content outage probability, is used as the performance metric. The problem will be formulated as a complicated multi-objective probabilistic combinatorial optimization. A novel maximum b-matching approach will then be proposed to obtain the Pareto optimal solution with fairness constraint. Based on the fast message passing approach, a distributed algorithm with a low memory usage of O(M + N) is also proposed, where M is the number of users and N is the number of Fog-APs. Although it is usually very difficult to derive the closed-form formulas for the optimal solution, the approximation formulas of the content outage probability will also be obtained as a function of coding parameters. The asymptotic optimal coding parameters can then be obtained by defining and deriving the outage exponent region (OER) and diversity-multiplexing region (DMR). Simulation results will illustrate the accuracy of the theoretical derivations, and verify the outage performance of the proposed approach. Therefore, this paper not only proposes a practical distributed Fog-AP selection algorithm for coded caching, but also provides a systematic way to evaluate and optimize the performance of Fog-RANs.

cs.IT↗

arXiv2018

Convergence rates on periodic homogenization of p-Laplace type equations

In this paper, we find some error estimates for periodic homogenization of p-Laplace type equations under the same structure assumption on homogenized equations. The main idea is that by adjusting the size of the difference quotient of the correctors to make the convergence rate visible. In order to reach our goal, the corresponding flux corrector with some properties are developed. Meanwhile, the shift-arguments is in fact applied down to $\varepsilon$ scale, which leads to a new weighted type inequality for smoothing operator with the weight satisfying Harnack's inequality in small scales. As a result, it is possible to develop some large-scale estimates. We finally mention that our approach brought in a systematic error (this phenomenon will disappear in linear and non-degenerated cases), which was fortunately a quantity $o(\varepsilon)$ here.

math.AP↗

arXiv2018

The high density $γ$-ray emission and dense positron production via multi-lasers driven circular target

A diamond-like carbon circular target is proposed to improve the $γ$-ray emission and pair production with lasers intensity of $8\times 10^{22} ~\mathrm{W/cm^2}$ by using two-dimensional particle-in-cell simulations with quantum electrodynamics. It is found that the circular target can significantly enhance the density of $γ$-photons than plane target when two colliding circularly polarized lasers irradiate the target. By multi-lasers irradiate the circular target, the optical trap of lasers can prevent the high energy electrons accelerated by laser radiation pressure from escaping. Hence, high density as $5164 n_c$ $γ$-photons is obtained through nonlinear Compton back-scattering. Meanwhile, $2.7 \times 10^{11}$ positrons with average energy of $230 ~\mathrm{MeV}$ is achieved via multi-photon Breit-Wheeler process. Such ultrabright $γ$-ray source and dense positrons source can be useful to many applications. The optimal target radius and laser mismatching deviation parameters are also discussed in detail.

physics.plasm-ph↗

arXiv2018

Femtosecond control of terahertz spin-charge conversion in ferromagnetic heterostructures

Employing electron spin instead of charge to develop spintronic devices holds the merits of low-power consumption in information technologies. Meanwhile, the demand for increasing speed in spintronics beyond current CMOS technology has further triggered intensive researches for ultrafast control of spins even up to unprecedent terahertz regime. The femtosecond laser has been emerging as a potential technique to generate an ultrafast spin-current burst for magnetization manipulation. However, there is a great challenge to establish all-optical control and monitor of the femtosecond transient spin current. Deep insights into the physics and mechanism are extremely essential for the technique. Here, we demonstrate coherently nonthermal excitation of femtosecond spin-charge current conversion parallel to the magnetization in W/CoFeB/Pt heterostructures driven by linearly polarized femtosecond laser pulses. Through systematical investigation we observe the terahertz emission polarization depends on both the magnetization direction and structural asymmetry. We attribute this phenomenon of the terahertz generation parallel to the magnetization induced by linearly polarized femtosecond laser pulses probably to inverse spin-orbit torque effect. Our work not only is beneficial to the deep understanding of spin-charge conversion and spin transportation, but also helps develop novel on-chip terahertz opto-spintronic devices.

physics.optics↗

arXiv2018

Towards the growth of single-crystal boron nitride monolayer on Cu

Atom-layered hexagonal boron nitride (hBN), with excellent stability, flat surface and large bandgap, has been reported to be the best 2D insulator to open up the great possibilities for exciting potential applications in electronics, optoelectronics and photovoltaics. The ability to grow high-quality large single crystals of hBN is at the heart for those applications, but the size of single-crystal 2D BN is less than a millimetre till now. Here, we report the first epitaxial growth of a 10*10 cm2 single-crystal hBN monolayer on a low symmetry Cu(110) "vicinal surface". The growth kinetics, unidirectional alignment and seamless stitching of hBN domains are unambiguously illustrated using centimetre- to the atomic-scale characterization techniques. The findings in this work are expected to significantly boost the massive applications of 2D materials-based devices, and also pave the way for the epitaxial growth of broad non-centrosymmetric 2D materials.

cond-mat.mtrl-sci↗

arXiv2018

Reinforcement learning-based waveform optimization for MIMO multi-target detection

A cognitive beamforming algorithm for colocated MIMO radars, based on Reinforcement Learning (RL) framework, is proposed. We analyse an RL-based optimization protocol that allows the MIMO radar, i.e. the \textit{agent}, to iteratively sense the unknown environment, i.e. the radar scene involving an unknown number of targets at unknown angular positions, and consequently, to synthesize a set of transmitted waveforms whose related beam patter is tailored on the acquired knowledge. The performance of the proposed RL-based beamforming algorithm is assessed through numerical simulations in terms of Probability of Detection ($P_D$).

eess.SP↗

arXiv2018

Sparse Model Identification and Learning for Ultra-high-dimensional Additive Partially Linear Models

The additive partially linear model (APLM) combines the flexibility of nonparametric regression with the parsimony of regression models, and has been widely used as a popular tool in multivariate nonparametric regression to alleviate the "curse of dimensionality". A natural question raised in practice is the choice of structure in the nonparametric part, that is, whether the continuous covariates enter into the model in linear or nonparametric form. In this paper, we present a comprehensive framework for simultaneous sparse model identification and learning for ultra-high-dimensional APLMs where both the linear and nonparametric components are possibly larger than the sample size. We propose a fast and efficient two-stage procedure. In the first stage, we decompose the nonparametric functions into a linear part and a nonlinear part. The nonlinear functions are approximated by constant spline bases, and a triple penalization procedure is proposed to select nonzero components using adaptive group LASSO. In the second stage, we refit data with selected covariates using higher order polynomial splines, and apply spline-backfitted local-linear smoothing to obtain asymptotic normality for the estimators. The procedure is shown to be consistent for model structure identification. It can identify zero, linear, and nonlinear components correctly and efficiently. Inference can be made on both linear coefficients and nonparametric functions. We conduct simulation studies to evaluate the performance of the method and apply the proposed method to a dataset on the Shoot Apical Meristem (SAM) of maize genotypes for illustration.

stat.ME↗

arXiv2018

High aspect ratio silicon structures by Displacement Talbot lithography and Bosch etching

Despite the fact that the resolution of conventional contact/proximity lithography can reach feature sizes down to ~0.5-0.6 micrometers, the accurate control of the linewidth and uniformity becomes already very challenging for gratings with periods in the range of 1-2 μm. This is particularly relevant for the exposure of large areas and wafers thinner than 300μm. If the wafer or mask surface is not fully flat due to any kind of defects, such as bowing/warpage or remaining topography of the surface in case of overlay exposures, noticeable linewidth variations or complete failure of lithography step will occur. We utilized the newly developed Displacement Talbot lithography to pattern gratings with equal lines and spaces and periods in the range of 1.0 to 2.4 μm. The exposures in this lithography process do not require contact between the mask and the wafer, which makes it essentially insensitive to surface planarity and enables exposures with very high linewidth uniformity on thin and even slightly deformed wafers. We demonstrated pattern transfer of such exposures into Si substrates by reactive ion etching using the Bosch process. An etching depth of 30 μm or more for the whole range of periods was achieved, which corresponds to very high aspect ratios up to 60:1. The application of the fabricated gratings in phase contrast x-ray imaging is presented.

physics.app-ph↗

arXiv2018

Broadband Magnetic-Manipulated Spintronic Terahertz Emitter with Arbitrarily Tunable Polarizations

Flexible manipulation of terahertz-wave polarization during the generation process is very important for terahertz applications, especially for the next-generation on-chip functional terahertz sources. However, current terahertz emitters could not satisfy such demand, hence calling for new mechanism and conceptually new terahertz source. Here we demonstrate a magnetic-field-controlled, highly-efficient, cost-effective, and broadband terahertz source with flexible switch of terahertz polarization states in ferromagnetic heterostructures driven by femtosecond laser pulses. We verify that the chirality, azimuthal angle, and ellipticity of the generated elliptical terahertz waves can be independently manipulated by delicately engineering of the external applied magnetic fields via effectively manipulating the photo-induced spin currents. Such an ultrafast photomagnetic interaction-based, magnetic-field-controlled, and broadband tunable solid-state terahertz source integrated with terahertz polarization tunability function not only has the capability to reveal physical mechanisms of femtosecond spin dynamics, but also demonstrates the feasibility to realize novel on-chip terahertz functional devices, boosting the potential applications for controlling elementary molecular rotations, phonon vibrations, spin precessions, high-speed terahertz communication, and accelerating the development of ultrafast terahertz opto-spintronics.

physics.optics↗

arXiv2018

Quantitative Estimates on Periodic Homogenization of Nonlinear Elliptic Operators

In this paper, we are interested in the periodic homogenization of quasilinear elliptic equations. We obtain error estimates $O(\varepsilon^{1/2})$ for a $C^{1,1}$ domain, and $O(\varepsilon^σ)$ for a Lipschitz domain, in which $σ\in(0,1/2)$ is close to zero. Based upon the convergence rates, an interior Lipschitz estimate, as well as a boundary Hölder estimate can be developed at large scales without any smoothness assumption, and these will implies reverse Hölder estimates established for a $C^1$ domain. By a real method developed by Z.Shen \cite{S3}, we consequently derive a global $W^{1,p}$ estimate for $2\leq p<\infty$. This work may be regarded as an extension of \cite{MAFHL,S5} to a nonlinear operator, and our results may be extended to the related Neumann boundary problems without any real difficulty.

math.AP↗

arXiv2018

Crowd Counting with Density Adaption Networks

Crowd counting is one of the core tasks in various surveillance applications. A practical system involves estimating accurate head counts in dynamic scenarios under different lightning, camera perspective and occlusion states. Previous approaches estimate head counts despite that they can vary dramatically in different density settings; the crowd is often unevenly distributed and the results are therefore unsatisfactory. In this paper, we propose a lightweight deep learning framework that can automatically estimate the crowd density level and adaptively choose between different counter networks that are explicitly trained for different density domains. Experiments on two recent crowd counting datasets, UCF_CC_50 and ShanghaiTech, show that the proposed mechanism achieves promising improvements over state-of-the-art methods. Moreover, runtime speed is 20 FPS on a single GPU.

cs.CV↗

arXiv2018

Development of a 256-channel Time-of-flight Electronics System For Neutron Beam Profiling

A 256-channel time-of-flight (TOF) electronics system has been developed for a beam line facility called "Back-n WNS" in China Spallation Neutron Source (CSNS). This paper shows the structure and performance of electronics system and the test results in CSNS. A 256-channel photomultiplier tube (PMT) is chosen as the detector in this system. In order to acquire the time information from the PMT, an electronics system has been designed. The electronics system mainly includes one front-end board (FEB), four time-to-digital converter (TDC) boards and one clock distribution module (CDM). There are 256 channels on FEB and 64 channels on each TDC board. The FEB is connected to the PMT with high-density connectors and the TDC boards are connected to the FEB through 2m cables. The TDC boards are 6U size so that they can be PCI extensions for Instrumentation (PXI) cards. Data from TDC boards can be transferred to the PXI control card through the backboard. In order to make four TDC boards work synchronously, a CDM outputs four clock signals to TDC boards which are distributed from one clock source. The TDC boards achieve a timing resolution of 3.5ns by test with a signal generator. The TOF measurement system has been used in CSNS.

physics.ins-det↗

arXiv2018

Learning Radio Resource Management in 5G Networks: Framework, Opportunities and Challenges

In the fifth generation (5G) of mobile broadband systems, Radio Resources Management (RRM) will reach unprecedented levels of complexity. To cope with the ever more sophisticated RRM functionalities and with the growing variety of scenarios, while carrying out the prompt decisions required in 5G, this manuscript presents a lean 5G RRM architecture that capitalizes on recent advances in the field of machine learning in combination with the large amount of data readily available in the network from measurements and system observations. The architecture relies on a single general-purpose learning framework conceived for RRM directly using the data gathered in the network. The complexity of RRM is shifted to the design of the framework, whilst the RRM algorithms derived from this framework are executed in a computationally efficient distributed manner at the radio access nodes. The potential of this approach is verified in a pair of pertinent scenarios and future directions on applications of machine learning to RRM are discussed.

cs.NI↗

arXiv2018

A practical convolutional neural network as loop filter for intra frame

Loop filters are used in video coding to remove artifacts or improve performance. Recent advances in deploying convolutional neural network (CNN) to replace traditional loop filters show large gains but with problems for practical application. First, different model is used for frames encoded with different quantization parameter (QP), respectively. It is expensive for hardware. Second, float points operation in CNN leads to inconsistency between encoding and decoding across different platforms. Third, redundancy within CNN model consumes precious computational resources. This paper proposes a CNN as the loop filter for intra frames and proposes a scheme to solve the above problems. It aims to design a single CNN model with low redundancy to adapt to decoded frames with different qualities and ensure consistency. To adapt to reconstructions with different qualities, both reconstruction and QP are taken as inputs. After training, the obtained model is compressed to reduce redundancy. To ensure consistency, dynamic fixed points (DFP) are adopted in testing CNN. Parameters in the compressed model are first quantized to DFP and then used for inference of CNN. Outputs of each layer in CNN are computed by DFP operations. Experimental results on JEM 7.0 report 3.14%, 5.21%, 6.28% BD-rate savings for luma and two chroma components with all intra configuration when replacing all traditional filters.

cs.MM↗

arXiv2018

An Optimal Rewiring Strategy for Reinforcement Social Learning in Cooperative Multiagent Systems

Multiagent coordination in cooperative multiagent systems (MASs) has been widely studied in both fixed-agent repeated interaction setting and the static social learning framework. However, two aspects of dynamics in real-world multiagent scenarios are currently missing in existing works. First, the network topologies can be dynamic where agents may change their connections through rewiring during the course of interactions. Second, the game matrix between each pair of agents may not be static and usually not known as a prior. Both the network dynamic and game uncertainty increase the coordination difficulty among agents. In this paper, we consider a multiagent dynamic social learning environment in which each agent can choose to rewire potential partners and interact with randomly chosen neighbors in each round. We propose an optimal rewiring strategy for agents to select most beneficial peers to interact with for the purpose of maximizing the accumulated payoff in repeated interactions. We empirically demonstrate the effectiveness and robustness of our approach through comparing with benchmark strategies. The performance of three representative learning strategies under our social learning framework with our optimal rewiring is investigated as well.

cs.MA↗

arXiv2018

Crime Modeling with Truncated Lévy Flights for Residential Burglary Models

Statistical agent-based models for crime have shown that repeat victimization can lead to predictable crime hotspots (see e.g. Short et al., Math. Models Methods Appl., 2008), then a recent study in one space dimension (Chaturapruek et al., SIAM J. Appl. Math, 2013) shows that the hotspot dynamics changes when movement patterns of the criminals involve long-tailed Lévy distributions for the jump length as opposed to classical random walks. In reality, criminals move in confined areas with a maximum jump length. In this paper we develop a mean-field continuum model with truncated Lévy flights for residential burglary in one space dimension. The continuum model yields local Laplace diffusion, rather than fractional diffusion. We present an asymptotic theory to derive the continuum equations and show excellent agreement between the continuum model and the agent-based simulations. This suggests that local diffusion models are universal for continuum limits of this problem, the important quantity being the diffusion coefficient. Law enforcement agents are also incorporated into the model, and the relative effectiveness of their deployment strategies are compared quantitatively.

physics.soc-ph↗