arXiv subjects

Ming Xiao

Publications and source records attributed to Ming Xiao.

At least 127 records · Page 7Linked to original sources

arXiv2021

Reverberation Mapping of Two Luminous Quasars: the Broad-line Region Structure and Black Hole Mass

We report the results of a multi-year spectroscopic and photometric monitoring campaign of two luminous quasars, PG~0923+201 and PG~1001+291, both located at the high-luminosity end of the broad-line region (BLR) size-luminosity relation with optical luminosities above $10^{45}~{\rm erg~s^{-1}}$. PG~0923+201 is for the first time monitored, and PG~1001+291 was previously monitored but our campaign has a much longer temporal baseline. We detect time lags of variations of the broad H$β$, H$γ$, Fe {\sc ii} lines with respect to those of the 5100~Å continuum. The velocity-resolved delay map of H$β$ in PG~0923+201 indicates a complicated structure with a mix of Keplerian disk-like motion and outflow, and the map of H$β$ in PG~1001+291 shows a signature of Keplerian disk-like motion. Assuming a virial factor of $f_{\rm BLR}=1$ and FWHM line widths, we measure the black hole mass to be $118_{-16}^{+11}\times 10^7 M_{\odot}$ for PG~0923+201 and $3.33_{-0.54}^{+0.62}\times 10^7 M_{\odot}$ for PG~1001+291. Their respective accretion rates are estimated to be $0.21_{-0.07}^{+0.06} \times L_{\rm Edd}\,c^{-2}$ and $679_{-227}^{+259}\times L_{\rm Edd}\,c^{-2}$, indicating that PG~0923+201 is a sub-Eddington accretor and PG~1001+291 is a super-Eddington accretor. While the H$β$ time lag of PG~0923+201 agrees with the size-luminosity relation, the time lag of PG~1001+291 shows a significant deviation, confirming that in high-luminosity AGN the BLR size depends on both luminosity and Eddington ratio. Black hole mass estimates from single AGN spectra will be over-estimated at high luminosities and redshifts if this effect is not taken into account.

astro-ph.GA↗

arXiv2020

Monitoring AGNs with Hβ Asymmetry. II. Reverberation Mapping of Three Seyfert Galaxies Historically Displaying Hβ Profiles with Changing Asymmetry: Mrk 79, NGC 3227, and Mrk 841

We report the results of reverberation mapping three bright Seyfert galaxies, Mrk 79, NGC 3227, and Mrk 841, from a campaign conducted from December 2016 to May 2017 with the Wyoming Infrared Observatory (WIRO) 2.3-meter telescope. All three of these targets have shown asymmetric broad H$β$ emission lines in the past, although their emission lines were relatively symmetric during our observations. We measured Hβ time lags for all three targets and estimated masses of their black holes -- for the first time in the case of Mrk 841. For Mrk 79 and NGC 3227, the data are of sufficient quality to resolve distinct time lags as a function of velocity and to compute two-dimensional velocity-delay maps. Mrk 79 shows smaller time lags for high-velocity gas but the distribution is not symmetric, and its complex velocity-delay map could result from the combination of both inflowing and outflowing Hβ emitting disks that may be part of a single larger structure. NGC 3227 shows the largest time lags for blueshifted gas and the two-dimensional velocity-delay map suggests a disk with some inflow. We compare our results with previous work and find evidence for different time lags despite similar luminosities, as well as evolving broad line region structures.

astro-ph.GA↗

arXiv2020

Evidence for Two Distinct Broad-Line Regions from Reverberation Mapping of PG 0026+129

We report on the results of a new spectroscopic monitoring campaign of the quasar PG 0026+129 at the Calar Alto Observatory 2.2m telescope from July 2017 to February 2020. Significant variations in the fluxes of the continuum and broad-emission lines, including H$β$ and He II, were observed in the first and third years, and clear time lags between them are measured. The broad H$β$ line profile consists of two Gaussian components: an intermediate-width H$β_{\rm IC}$ with a full width at half-maximum (FWHM) of 1964$\pm$18 $\rm km~s^{-1}$ and another very broad H$β_{\rm VBC}$ with a FWHM of 7570$\pm$83 $\rm km~s^{-1}$. H$β_{\rm IC}$ has long time lags of $\sim$40--60 days in the rest frame, while H$β_{\rm VBC}$ shows nearly zero time delay with respect to the optical continuum at 5100 Å. The velocity-resolved delays show consistent results: lags of $\sim$30--50 days at the core of the broad H$β$ line and roughly zero lags at the wings. H$β_{\rm IC}$ has a redshift of $\sim$400 $\rm km~s^{-1}$ which seems to be stable for nearly 30 years by comparing with archived spectra, and may originate from an infall. The root mean square (rms) spectrum of H$β_{\rm VBC}$ shows a double-peaked profile with brighter blue peak and extended red wing in the first year, which matches the signature of a thin disk. Both the double-peaked profile and the near-zero lag suggest that H$β_{\rm VBC}$ comes from a region associated with the part of the accretion disc that emits the optical continuum. Adopting the FWHM (in the rms spectrum) and the time lag measured for the total H$β$ line, and a virial factor of 1.5, we obtain a virial mass of $2.89_{-0.69}^{+0.60} \times10^7 M_{\odot}$ for the central black hole in this quasar.

astro-ph.GA↗

arXiv2020

Coded Stochastic ADMM for Decentralized Consensus Optimization with Edge Computing

Big data, including applications with high security requirements, are often collected and stored on multiple heterogeneous devices, such as mobile devices, drones and vehicles. Due to the limitations of communication costs and security requirements, it is of paramount importance to extract information in a decentralized manner instead of aggregating data to a fusion center. To train large-scale machine learning models, edge/fog computing is often leveraged as an alternative to centralized learning. We consider the problem of learning model parameters in a multi-agent system with data locally processed via distributed edge nodes. A class of mini-batch stochastic alternating direction method of multipliers (ADMM) algorithms is explored to develop the distributed learning model. To address two main critical challenges in distributed networks, i.e., communication bottleneck and straggler nodes (nodes with slow responses), error-control-coding based stochastic incremental ADMM is investigated. Given an appropriate mini-batch size, we show that the mini-batch stochastic ADMM based method converges in a rate of $O(\frac{1}{\sqrt{k}})$, where $k$ denotes the number of iterations. Through numerical experiments, it is revealed that the proposed algorithm is communication-efficient, rapidly responding and robust in the presence of straggler nodes compared with state of the art algorithms.

cs.DC↗

arXiv2020

On the classification of normal Stein spaces and finite ball quotients with Bergman-Einstein metrics

In this paper, we study the Bergman metric of a finite ball quotient $\mathbb{B}^n/Γ$, where $Γ\subseteq \mathrm{Aut}(\mathbb{B}^n)$ is a finite, fixed point free, abelian group. We prove that this metric is Kähler--Einstein if and only if $Γ$ is trivial, i.e., when the ball quotient $\mathbb{B}^n/Γ$ is the unit ball $\mathbb{B}^n$ itself. As a consequence, we establish a characterization of the unit ball among normal Stein spaces with isolated singularities and abelian fundamental groups in terms of the existence of a Bergman-Einstein metric.

math.CV↗

arXiv2020

Designing angle-independent structural colors using Monte Carlo simulations of multiple scattering

Disordered nanostructures with correlations on the scale of visible wavelengths can show angle-independent structural colors. These materials could replace dyes in some applications because the color is tunable and resists photobleaching. However, designing nanostructures with a prescribed color is difficult, especially when the application -- cosmetics or displays, for example -- requires specific component materials. A general approach to solving this constrained design problem is modeling and optimization: using a model that predicts the color of a given system, one optimizes the model parameters under constraints to achieve a target color. For this approach to work, the model must make accurate predictions, which is challenging because disordered nanostructures have multiple scattering. To address this challenge, we develop a Monte Carlo model that simulates multiple scattering of light in disordered arrangements of spherical particles or voids. The model produces quantitative agreement with measurements when we account for roughness on the surface of the film, particle polydispersity, and wavelength-dependent absorption in the components. Unlike discrete numerical simulations, our model is parameterized in terms of experimental variables, simplifying the connection between simulation and fabrication. To demonstrate this approach, we reproduce the color of the male mountain bluebird (Sialia currucoides) in an experimental system, using prescribed components and a microstructure that is easy to fabricate. Finally, we use the model to find the limits of angle-independent structural colors for a given system. These results enable an engineering design approach to structural color for many different applications.

cond-mat.soft↗

arXiv2020

Fully Decentralized Federated Learning Based Beamforming Design for UAV Communications

To handle the data explosion in the era of internet of things (IoT), it is of interest to investigate the decentralized network, with the aim at relaxing the burden to central server along with keeping data privacy. In this work, we develop a fully decentralized federated learning (FL) framework with an inexact stochastic parallel random walk alternating direction method of multipliers (ISPW-ADMM). Performing more communication efficient and enhanced privacy preservation compared with the current state-of-the-art, the proposed ISPW-ADMM can be partially immune to the impacts from time-varying dynamic network and stochastic data collection, while still in fast convergence. Benefits from the stochastic gradients and biased first-order moment estimation, the proposed framework can be applied to any decentralized FL tasks over time-varying graphs. Thus to further demonstrate the practicability of such framework in providing fast convergence, high communication efficiency, and system robustness, we study the extreme learning machine(ELM)-based FL model for robust beamforming (BF) design in UAV communications, as verified by the numerical simulations.

eess.SP↗

arXiv2020

Algebraicity of the Bergman Kernel

Our main result introduces a new way to characterize two-dimensional finite ball quotients by algebraicity of their Bergman kernels. This characterization is particular to dimension two and fails in higher dimensions, as is illustrated by a counterexample in dimension three constructed in this paper. As a corollary of our main theorem, we prove, e.g., that a smoothly bounded strictly pseudoconvex domain G in $\mathbb{C}^2$ has rational Bergman kernel if and only if there is a rational biholomorphism from G to the 2-dimensional unit ball.

math.CV↗

arXiv2020

Multi-Agent Reinforcement Learning for Cooperative Coded Caching via Homotopy Optimization

Introducing cooperative coded caching into small cell networks is a promising approach to reducing traffic loads. By encoding content via maximum distance separable (MDS) codes, coded fragments can be collectively cached at small-cell base stations (SBSs) to enhance caching efficiency. However, content popularity is usually time-varying and unknown in practice. As a result, cache contents are anticipated to be intelligently updated by taking into account limited caching storage and interactive impacts among SBSs. In response to these challenges, we propose a multi-agent deep reinforcement learning (DRL) framework to intelligently update cache contents in dynamic environments. With the goal of minimizing long-term expected fronthaul traffic loads, we first model dynamic coded caching as a cooperative multi-agent Markov decision process. Owing to MDS coding, the resulting decision-making falls into a class of constrained reinforcement learning problems with continuous decision variables. To deal with this difficulty, we custom-build a novel DRL algorithm by embedding homotopy optimization into a deep deterministic policy gradient formalism. Next, to empower the caching framework with an effective trade-off between complexity and performance, we propose centralized, partially and fully decentralized caching controls by applying the derived DRL approach. Simulation results demonstrate the superior performance of the proposed multi-agent framework.

cs.IT↗

arXiv2020

Decentralized Beamforming Design for Intelligent Reflecting Surface-enhanced Cell-free Networks

Cell-free networks are considered as a promising distributed network architecture to satisfy the increasing number of users and high rate expectations in beyond-5G systems. However, to further enhance network capacity, an increasing number of high-cost base stations (BSs) are required. To address this problem and inspired by the cost-effective intelligent reflecting surface (IRS) technique, we propose a fully decentralized design framework for cooperative beamforming in IRS-aided cell-free networks. We first transform the centralized weighted sum-rate maximization problem into a tractable consensus optimization problem, and then an incremental alternating direction method of multipliers (ADMM) algorithm is proposed to locally update the beamformer. The complexity and convergence of the proposed method are analyzed, and these results show that the performance of the new scheme can asymptotically approach that of the centralized one as the number of iterations increases. Results also show that IRSs can significantly increase the system sum-rate of cell-free networks and the proposed method outperforms existing decentralized methods.

eess.SP↗

arXiv2020

Regularity of CR-mappings into Levi-degenerate hypersurfaces

We provide regularity results for CR-maps between real hypersurfaces in complex spaces of different dimension with a Levi-degenerate target. We address both the real-analytic and the smooth case. Our results allow immediate applications to the study of proper holomorphic maps between Bounded Symmetric Domains.

math.CV↗

arXiv2020

Improving mobility of silicon metal-oxide-semiconductor devices for quantum dots by high vacuum activation annealing

To improve mobility of fabricated silicon metal-oxide-semiconductor (MOS) quantum devices, forming gas annealing is a common method used to mitigate the effects of disorder at the Si/SiO2 interface. However, the importance of activation annealing is usually ignored. Here, we show that a high vacuum environment for implantation activation is beneficial for improving mobility compared to nitrogen atmosphere. Low-temperature transport measurements of Hall bars show that peak mobility can be improved by a factor of two, reaching 1.5 m^2/(Vs) using high vacuum annealing during implantation activation. Moreover, the charge stability diagram of a single quantum dot is mapped, with no visible disturbance caused by disorder, suggesting possibility of fabricating high-quality quantum dots on commercial wafers. Our results may provide valuable insights into device optimization in silicon-based quantum computing.

cond-mat.mes-hall↗

arXiv2020

Learning Based Hybrid Beamforming for Millimeter Wave Multi-User MIMO Systems

Hybrid beamforming (HBF) design is a crucial stage in millimeter wave (mmWave) multi-user multi-input multi-output (MU-MIMO) systems. However, conventional HBF methods are still with high complexity and strongly rely on the quality of channel state information. We propose an extreme learning machine (ELM) framework to jointly optimize transmitting and receiving beamformers. Specifically, to provide accurate labels for training, we first propose an factional-programming and majorization-minimization based HBF method (FP-MM-HBF). Then, an ELM based HBF (ELM-HBF) framework is proposed to increase the robustness of beamformers. Both FP-MM-HBF and ELM-HBF can provide higher system sum-rate compared with existing methods. Moreover, ELM-HBF cannot only provide robust HBF performance, but also consume very short computation time.

eess.SP↗

arXiv2020

Deep Reinforcement Learning Based Spectrum Allocation in Integrated Access and Backhaul Networks

We develop a framework based on deep reinforce-ment learning (DRL) to solve the spectrum allocation problem inthe emerging integrated access and backhaul (IAB) architecturewith large scale deployment and dynamic environment. The avail-able spectrum is divided into several orthogonal sub-channels,and the donor base station (DBS) and all IAB nodes have thesame spectrum resource for allocation, where a DBS utilizes thosesub-channels for access links of associated user equipment (UE)as well as for backhaul links of associated IAB nodes, and anIAB node can utilize all for its associated UEs. This is one ofkey features in which 5G differs from traditional settings wherethe backhaul networks were designed independently from theaccess networks. With the goal of maximizing the sum log-rateof all UE groups, we formulate the spectrum allocation probleminto a mix-integer and non-linear programming. However, itis intractable to find an optimal solution especially when theIAB network is large and time-varying. To tackle this problem,we propose to use the latest DRL method by integrating anactor-critic spectrum allocation (ACSA) scheme and deep neuralnetwork (DNN) to achieve real-time spectrum allocation indifferent scenarios. The proposed methods are evaluated throughnumerical simulations and show promising results compared withsome baseline allocation policies.

cs.IT↗

arXiv2020

Learning Based Hybrid Beamforming Design for Full-Duplex Millimeter Wave Systems

Millimeter Wave (mmWave) communications with full-duplex (FD) have the potential of increasing the spectral efficiency, relative to those with half-duplex. However, the residual self-interference (SI) from FD and high pathloss inherent to mmWave signals may degrade the system performance. Meanwhile, hybrid beamforming (HBF) is an efficient technology to enhance the channel gain and mitigate interference with reasonable complexity. However, conventional HBF approaches for FD mmWave systems are based on optimization processes, which are either too complex or strongly rely on the quality of channel state information (CSI). We propose two learning schemes to design HBF for FD mmWave systems, i.e., extreme learning machine based HBF (ELM-HBF) and convolutional neural networks based HBF (CNN-HBF). Specifically, we first propose an alternating direction method of multipliers (ADMM) based algorithm to achieve SI cancellation beamforming, and then use a majorization-minimization (MM) based algorithm for joint transmitting and receiving HBF optimization. To train the learning networks, we simulate noisy channels as input, and select the hybrid beamformers calculated by proposed algorithms as targets. Results show that both learning based schemes can provide more robust HBF performance and achieve at least 22.1% higher spectral efficiency compared to orthogonal matching pursuit (OMP) algorithms. Besides, the online prediction time of proposed learning based schemes is almost 20 times faster than the OMP scheme. Furthermore, the training time of ELM-HBF is about 600 times faster than that of CNN-HBF with 64 transmitting and receiving antennas.

eess.SP↗

arXiv2020

Privacy-preserving Incremental ADMM for Decentralized Consensus Optimization

The alternating direction method of multipliers (ADMM) has been recently recognized as a promising optimizer for large-scale machine learning models. However, there are very few results studying ADMM from the aspect of communication costs, especially jointly with privacy preservation, which are critical for distributed learning. We investigate the communication efficiency and privacy-preservation of ADMM by solving the consensus optimization problem over decentralized networks. Since walk algorithms can reduce communication load, we first propose incremental ADMM (I-ADMM) based on the walk algorithm, the updating order of which follows a Hamiltonian cycle instead. However, I-ADMM cannot guarantee the privacy for agents against external eavesdroppers even if the randomized initialization is applied. To protect privacy for agents, we then propose two privacy-preserving incremental ADMM algorithms, i.e., PI-ADMM1 and PI-ADMM2, where perturbation over step sizes and primal variables is adopted, respectively. Through theoretical analyses, we prove the convergence and privacy preservation for PI-ADMM1, which are further supported by numerical experiments. Besides, simulations demonstrate that the proposed PI-ADMM1 and PI-ADMM2 algorithms are communication efficient compared with state-of-the-art methods.

math.OC↗

arXiv2020

Coherent phonon dynamics in spatially separated graphene mechanical resonators

Vibrational modes in mechanical resonators provide a promising candidate to interface and manipulate classical and quantum information. The observation of coherent dynamics between distant mechanical resonators can be a key step towards scalable phonon-based applications. Here we report tunable coherent phonon dynamics with an architecture comprising three graphene mechanical resonators coupled in series, where all resonators can be manipulated by electrical signals on control gates. We demonstrate coherent Rabi oscillations between spatially separated resonators indirectly coupled via an intermediate resonator serving as a phonon cavity. The Rabi frequency fits well with the microwave burst power on the control gate. We also observe Ramsey interference, where the oscillation frequency corresponds to the indirect coupling strength between these resonators. Such coherent processes indicate that information encoded in vibrational modes can be transferred and stored between spatially separated resonators, which can open the venue of on-demand phonon-based information processing.

cond-mat.mes-hall↗

arXiv2020

Traffic-aware Two-stage Queueing Communication Networks: Queue Analysis and Energy Saving

To boost energy saving for the general delay-tolerant IoT networks, a two-stage and single-relay queueing communication scheme is investigated. Concretely, a traffic-aware $N$-threshold and gated-service policy are applied at the relay. As two fundamental and significant performance metrics, the mean waiting time and long-term expected power consumption are explicitly derived and related with the queueing and service parameters, such as packet arrival rate, service threshold and channel statistics. Besides, we take into account the electrical circuit energy consumptions when the relay server and access point (AP) are in different modes and energy costs for mode transitions, whereby the power consumption model is more practical. The expected power minimization problem under the mean waiting time constraint is formulated. Tight closed-form bounds are adopted to obtain tractable analytical formulae with less computational complexity. The optimal energy-saving service threshold that can flexibly adjust to packet arrival rate is determined. In addition, numerical results reveal that: 1) sacrificing the mean waiting time not necessarily facilitates power savings; 2) a higher arrival rate leads to a greater optimal service threshold; and 3) our policy performs better than the current state-of-the-art.

cs.NI↗