arXiv subjects

Ming Xiao

Publications and source records attributed to Ming Xiao.

At least 109 records · Page 6Linked to original sources

arXiv2022

Hardware-in-the-Loop Simulation for Evaluating Communication Impacts on the Wireless-Network-Controlled Robots

More and more robot automation applications have changed to wireless communication, and network performance has a growing impact on robotic systems. This study proposes a hardware-in-the-loop (HiL) simulation methodology for connecting the simulated robot platform to real network devices. This project seeks to provide robotic engineers and researchers with the capability to experiment without heavily modifying the original controller and get more realistic test results that correlate with actual network conditions. We deployed this HiL simulation system in two common cases for wireless-network-controlled robotic applications: (1) safe multi-robot coordination for mobile robots, and (2) human-motion-based teleoperation for manipulators. The HiL simulation system is deployed and tested under various network conditions in all circumstances. The experiment results are analyzed and compared with the previous simulation methods, demonstrating that the proposed HiL simulation methodology can identify a more reliable communication impact on robot systems.

cs.RO↗

arXiv2022

Supermassive Black Hole and Broad-line Region in NGC~5548: Results from Five-season Reverberation Mapping

NGC 5548 is one of the active galactic nuclei (AGN) selected for our long-term spectroscopic monitoring with the Lijiang 2.4~m telescope, aiming at investigating the origin and evolution of the broad-line regions (BLRs), accurately measuring the mass of the supermassive black holes (SMBHs), and understanding structure and evolution of the AGN. We have performed five-season observations for NGC~5548 with the median sampling interval ranging from 1.25 to 3 days. The light curves of the 5100~Å continuum and broad emission lines are measured after subtracting contamination of the host galaxy starlight. The time lags of the broad He~{\sc ii}, He~{\sc i}, H$γ$, and H$β$ lines with respect to the 5100~Å continuum are obtained for each season and their mean time lags over the five seasons are 0.69, 4.66, 4.60, 8.43 days, respectively. The H$γ$ and H$β$ velocity-resolved lag profiles in the seasons of 2015, 2018, 2019, and 2021 are constructed, from which an ``M-shaped'' structure is found in 2015 but disappears after 2018. Our five-season reverberation mapping (RM) yields an averaged virial SMBH mass of $M_\bullet/10^7M_\odot=14.22$, with a small standard deviation of $1.89$. By combining the previous 18 RM campaigns and our five-season campaign for NGC~5548, we find that there exists a time lag of 3.5~years between the changes in the BLR size and optical luminosity. In addition, we also construct the BLR radius$-$luminosity relation and the virial relation for NGC~5548.

astro-ph.GA↗

arXiv2022

Beam Tracking for Dynamic mmWave Channels: A New Training Beam Sequence Design Approach

In this paper, we develop an efficient training beam sequence design approach for millimeter wave MISO tracking systems. We impose a discrete state Markov process assumption on the evolution of the angle of departure and introduce the maximum a posteriori criterion to track it in each beam training period. Since it is infeasible to derive an explicit expression for the resultant tracking error probability, we turn to its upper bound, which possesses a closed-form expression and is therefore leveraged as the objective function to optimize the training beam sequence. Considering the complicated objective function and the unit modulus constraints imposed by analog phase shifters, we resort to the particle swarm algorithm to solve the formulated optimization problem. Numerical results validate the superiority of the proposed training beam sequence design approach.

cs.IT↗

arXiv2022

Clinicopathological correlation of p40/TTF1 co-expression in NSCLC and review of related literature

TTF1 and ΔNp63/p40 have been used to differentiate ADC and SQC in hypofractionated NSCLC because of their sensitivity and specificity. There are few cases where TTF1 and ΔNp63/p40 are expressed together in the same tumour cells, and little is known about the clinicopathological features, treatment and prognosis of such cases. We investigated the electron microscopic features, immunohistochemical expression and molecular variation of a case of TTF1/p40 co-expressing NSCLC and reviewed and summarised the relevant literature. Our patient was a 58-year-old male with a CT showing a left-sided lung occupancy. As in all other cases reported in the literature, the tumour showed a solid growth pattern with polygonal cells, eosinophilic cytoplasm and clearly visible nuclear fission. Immunohistochemistry showed positive for TTF-1, p40, CK5/6, CK7, P63 and p53, and negative for NapsinA and ALK. Electron microscopy showed tumour cells characterised by bidirectional differentiation of adenocytes and squamous cells, consistent with previous reports. Second-generation sequencing suggested co-mutation of STK11/LKB1 and NF1 genes in this case. Mutations in STK11/LKB1 and NF1 genes have been found in ADC and SQC and are often associated with drug resistance and poor prognosis, but STK11/NF1 co-mutation has not been reported and more cases are needed to reveal the association. p40/TTF1 co-expression in NSCLC may be an under-recognised variant of NSCLC The origin may be a double positive stem cell-like basal cell located in the distal airway, with rapid clinical progression and poor prognosis.

q-bio.TO↗

arXiv2022

Kähler-Einstein metrics and obstruction flatness of circle bundles

Obstruction flatness of a strongly pseudoconvex hypersurface $Σ$ in a complex manifold refers to the property that any (local) Kähler-Einstein metric on the pseudoconvex side of $Σ$, complete up to $Σ$, has a potential $-\log u$ such that $u$ is $C^\infty$-smooth up to $Σ$. In general, $u$ has only a finite degree of smoothness up to $Σ$. In this paper, we study obstruction flatness of hypersurfaces $Σ$ that arise as unit circle bundles $S(L)$ of negative Hermitian line bundles $(L, h)$ over Kähler manifolds $(M, g).$ We prove that if $(M,g)$ has constant Ricci eigenvalues, then $S(L)$ is obstruction flat. If, in addition, all these eigenvalues are strictly less than one and $(M,g)$ is complete, then we show that the corresponding disk bundle admits a complete Kähler-Einstein metric. Finally, we give a necessary and sufficient condition for obstruction flatness of $S(L)$ when $(M, g)$ is a Kähler surface $(\dim M=2$) with constant scalar curvature.

math.CV↗

arXiv2022

Vertical GaN Diode BV Maximization through Rapid TCAD Simulation and ML-enabled Surrogate Model

In this paper, two methodologies are used to speed up the maximization of the breakdown volt-age (BV) of a vertical GaN diode that has a theoretical maximum BV of ~2100V. Firstly, we demonstrated a 5X faster accurate simulation method in Technology Computer-Aided-Design (TCAD). This allows us to find 50% more numbers of high BV (>1400V) designs at a given simulation time. Secondly, a machine learning (ML) model is developed using TCAD-generated data and used as a surrogate model for differential evolution optimization. It can inversely design an out-of-the-training-range structure with BV as high as 1887V (89% of the ideal case) compared to ~1100V designed with human domain expertise.

cs.LG↗

arXiv2022

Daytime sub-ambient radiative cooling with vivid structural colors mediated by coupled nanocavities

Daytime radiative cooling is a promising passive cooling technology for combating global warming. Existing daytime radiative coolers usually show whitish colors due to their broadband high solar reflectivity, which severely impedes applications in real-life situations with aesthetic demands and effective display. However, there is a trade-off between vivid colors and high cooling performance because colors are often produced by absorption of visible light, decreasing net cooling power. To break this trade-off, we design multilayered structures with coupled nanocavities and produce structural colors with high cooling performance. Using this design, we can obtain colorful radiative coolers which show a larger color gamut (occupying 17.7% sRGB area) than reported ones. We further fabricate colorful multilayered radiative coolers (CMRCs) and demonstrate they have temperature drops of 3.4 - 4.4 degrees on average based on outdoor experiments. These CMRCs are promising in thermal management of electronic/optoelectronic devices and outdoor facilities.

physics.optics↗

arXiv2022

Cooperative Beamforming for RIS-Aided Cell-Free Massive MIMO Networks

The combination of cell-free massive multiple-input multiple-output (CF-mMIMO) and reconfigurable intelligent surface (RIS) is envisioned as a promising paradigm to improve network capacity and enhance coverage capability. However, to reap full benefits of RIS-aided CF-mMIMO, the main challenge is to efficiently design cooperative beamforming (CBF) at base stations (BSs), RISs, and users. Firstly, we investigate the fractional programing to convert the weighted sum-rate (WSR) maximization problem into a tractable optimization problem. Then, the alternating optimization framework is employed to decompose the transformed problem into a sequence of subproblems, i.e., hybrid BF (HBF) at BSs, passive BF at RISs, and combining at users. In particular, the alternating direction method of multipliers algorithm is utilized to solve the HBF subproblem at BSs. Concretely, the analog BF design with unit-modulus constraints is solved by the manifold optimization (MO) while we obtain a closed-form solution to the digital BF design that is essentially a convex least-square problem. Additionally, the passive BF at RISs and the analog combining at users are designed by primal-dual subgradient and MO methods. Moreover, considering heavy communication costs in conventional CF-mMIMO systems, we propose a partially-connected CF-mMIMO (P-CF-mMIMO) framework to decrease the number of connections among BSs and users. To better compromise WSR performance and network costs, we formulate the BS selection problem in the P-CF-mMIMO system as a binary integer quadratic programming (BIQP) problem, and develop a relaxed linear approximation algorithm to handle this BIQP problem. Finally, numerical results demonstrate superiorities of our proposed algorithms over baseline counterparts.

cs.IT↗

arXiv2022

Monitoring AGNs with H$β$ Asymmetry. III. Long-term Reverberation Mapping Results of 15 Palomar-Green Quasars

In this third paper of the series reporting on the reverberation mapping (RM) campaign of active galactic nuclei with asymmetric H$β$ emission-line profiles, we present results for 15 Palomar-Green (PG) quasars using spectra obtained between the end of 2016 to May 2021. This campaign combines long time spans with relatively high cadence. For 8 objects, both the time lags obtained from the entire light curves and the measurements from individual observing seasons are provided. Reverberation mapping of 9 of our targets has been attempted for the first time, while the results for 6 others can be compared with previous campaigns. We measure the H$β$ time lags over periods of years and estimate their black hole masses. The long duration of the campaign enables us to investigate their broad line region (BLR) geometry and kinematics for different years by using velocity-resolved lags, which demonstrate signatures of diverse BLR geometry and kinematics. The BLR geometry and kinematics of individual objects are discussed. In this sample, the BLR kinematics of Keplerian/virialized motion and inflow is more common than outflow.

astro-ph.GA↗

arXiv2022

Short-Packet Interleaver against Impulse Interference in Practical Industrial Environments

The most common cause of transmission failure in Wireless High Performance (WirelessHP) target industry environments is impulse interference. As interleavers are commonly used to improve the reliability on the Orthogonal Frequency Division Multiplexing (OFDM) symbol level for long packet transmission, this paper considers the feasibility of applying short-packet bit interleaving to enhance the impulse/burst interference resisting capability on both OFDM symbol and frame level. Using the Universal Software Radio Peripherals (USRP) and PC hardware platform, the Packet Error Rate (PER) performance of interleaved coded short-packet transmission with Convolutional Codes (CC), Reed-Solomon codes (RS) and RS+CC concatenated codes are tested and analyzed. Applying the IEEE 1613 standard for impulse interference generation, extensive PER tests of CC(1=2) and RS(31; 21)+CC(1=2) concatenated codes are performed. With practical experiments, we prove the effectiveness of bit in terleaved coded short-packet transmission in real factory environments. We also investigate how PER performance depends on the interleavers, codes and impulse interference power and frequency.

cs.IT↗

arXiv2022

Asynchronous Parallel Incremental Block-Coordinate Descent for Decentralized Machine Learning

Machine learning (ML) is a key technique for big-data-driven modelling and analysis of massive Internet of Things (IoT) based intelligent and ubiquitous computing. For fast-increasing applications and data amounts, distributed learning is a promising emerging paradigm since it is often impractical or inefficient to share/aggregate data to a centralized location from distinct ones. This paper studies the problem of training an ML model over decentralized systems, where data are distributed over many user devices and the learning algorithm run on-device, with the aim of relaxing the burden at a central entity/server. Although gossip-based approaches have been used for this purpose in different use cases, they suffer from high communication costs, especially when the number of devices is large. To mitigate this, incremental-based methods are proposed. We first introduce incremental block-coordinate descent (I-BCD) for the decentralized ML, which can reduce communication costs at the expense of running time. To accelerate the convergence speed, an asynchronous parallel incremental BCD (API-BCD) method is proposed, where multiple devices/agents are active in an asynchronous fashion. We derive convergence properties for the proposed methods. Simulation results also show that our API-BCD method outperforms state of the art in terms of running time and communication costs.

cs.LG↗

arXiv2022

Spectroastrometry and Reverberation Mapping: the Mass and Geometric Distance of the Supermassive Black Hole in the Quasar 3C 273

The quasar 3C 273 has been observed with infrared spectroastrometry (SA) on broad Pa$α$ line and optical reverberation mapping (RM) on broad H$β$ line. SA delivers information about the angular size and structure of the Pa$α$ broad-line region (BLR), while RM delivers information about the physical size and structure of the H$β$ BLR. Based on the fact that the two BLRs share the mass of the supermassive black hole (SMBH) and viewing inclination, a combination of SA and velocity-resolved RM (SARM) thereby allows us to simultaneously determine the SMBH mass and geometric distance through dynamically modeling the two BLRs. We construct a suite of dynamical models with different geometric configurations and apply a Bayesian approach to obtain the parameter inferences. Overall the obtained masses and distances are insensitive to specific BLR configurations but more or less depend on parameterizations of the vertical distributions. The most probable model, chosen in light of the Bayes factor, yields an angular-size distance of $\log\,(D_{\rm A}/{\rm Mpc}) = 2.83_{-0.28}^{+0.32}$ and SMBH mass of $\log\,(M_\bullet/M_\odot)=9.06_{-0.27}^{+0.21}$, which agrees with the relationships between SMBH masses and bulge properties. The BLRs have an inclination of $5_{-1}^{+1}$ degrees, consistent with that of the large-scale jet in 3C 273. Our approach reinforces the capability of SARM analysis to measure SMBH mass and distance of AGNs even though SA and RM observations are undertaken with different emission lines and/or in different periods.

astro-ph.GA↗

arXiv2021

Satellite Based Computing Networks with Federated Learning

Driven by the ever-increasing penetration and proliferation of data-driven applications, a new generation of wireless communication, the sixth-generation (6G) mobile system enhanced by artificial intelligence (AI), has attracted substantial research interests. Among various candidate technologies of 6G, low earth orbit (LEO) satellites have appealing characteristics of ubiquitous wireless access. However, the costs of satellite communication (SatCom) are still high, relative to counterparts of ground mobile networks. To support massively interconnected devices with intelligent adaptive learning and reduce expensive traffic in SatCom, we propose federated learning (FL) in LEO-based satellite communication networks. We first review the state-of-the-art LEO-based SatCom and related machine learning (ML) techniques, and then analyze four possible ways of combining ML with satellite networks. The learning performance of the proposed strategies is evaluated by simulation and results reveal that FL-based computing networks improve the performance of communication overheads and latency. Finally, we discuss future research topics along this research direction.

eess.SP↗

arXiv2021

Algebraic Bergman kernels and finite type domains in $\mathbb{C}^2$

Let $G \subset \mathbb{C}^2$ be a smoothly bounded pseudoconvex domain and assume that the Bergman kernel of $G$ is algebraic of degree $d$. We show that the boundary $\partial G $ is of finite type and the type $r$ satisfies $r\leq 2d$. The inequality is optimal as equality holds for the egg domains $\{|z|^2+|w|^{2s}<1\},$ $s \in \mathbb{Z}_+$, by D'Angelo's explicit formula for their Bergman kernels. Our results imply, in particular, that a smoothly bounded pseudoconvex domain $G \subset \mathbb{C}^2$ cannot have rational Bergman kernel unless it is strongly pseudoconvex and biholomorphic to the unit ball by a rational map. Furthermore, we show that if the Bergman kernel of $G$ is rational of the form $\frac{p}{q}$, reduced to lowest degrees, then its rational degree $\max\{\text{deg } p, \text{deg } q \}\geq 6$. Equality is achieved if and only if $G$ is biholomorphic to the unit ball by a complex affine transformation of $\mathbb{C}^2$.

math.CV↗

arXiv2021

Federated Learning over Wireless IoT Networks with Optimized Communication and Resources

To leverage massive distributed data and computation resources, machine learning in the network edge is considered to be a promising technique especially for large-scale model training. Federated learning (FL), as a paradigm of collaborative learning techniques, has obtained increasing research attention with the benefits of communication efficiency and improved data privacy. Due to the lossy communication channels and limited communication resources (e.g., bandwidth and power), it is of interest to investigate fast responding and accurate FL schemes over wireless systems. Hence, we investigate the problem of jointly optimized communication efficiency and resources for FL over wireless Internet of things (IoT) networks. To reduce complexity, we divide the overall optimization problem into two sub-problems, i.e., the client scheduling problem and the resource allocation problem. To reduce the communication costs for FL in wireless IoT networks, a new client scheduling policy is proposed by reusing stale local model parameters. To maximize successful information exchange over networks, a Lagrange multiplier method is first leveraged by decoupling variables including power variables, bandwidth variables and transmission indicators. Then a linear-search based power and bandwidth allocation method is developed. Given appropriate hyper-parameters, we show that the proposed communication-efficient federated learning (CEFL) framework converges at a strong linear rate. Through extensive experiments, it is revealed that the proposed CEFL framework substantially boosts both the communication efficiency and learning performance of both training loss and test accuracy for FL over wireless IoT networks compared to a basic FL approach with uniform resource allocation.

cs.LG↗

arXiv2021

A Pixon-Based Method for Reverberation-mapping Analysis in Active Galactic Nuclei

We present an alternative method for reconstructing a velocity-delay map in reverberation mapping (RM) based on the pixon algorithm initially proposed for image reconstruction by Pina & Puetter (1993). The pixon algorithm allows for a variable pixon basis to adjust resolution of each image pixel according the information content in that pixel, which therefore enables the algorithm to make the best possible use of measured data. The final optimal pixon basis functions would be those that minimize the number of pixons while still providing acceptable descriptions to data within the accuracy allowed by noises. We adapt the pixon algorithm to RM analysis and develop a generic framework to implement the algorithm. Simulation tests and comparisons with the widely used maximum entropy method demonstrate the feasibility and high performance of our pixon-based RM analysis. This paper serves as an introduction to the framework and the application to velocity-unresolved RM. An extension to velocity-resolved cases will be presented in a companion paper.

astro-ph.IM↗

arXiv2021

Regularized Sequential Latent Variable Models with Adversarial Neural Networks

The recurrent neural networks (RNN) with richly distributed internal states and flexible non-linear transition functions, have overtaken the dynamic Bayesian networks such as the hidden Markov models (HMMs) in the task of modeling highly structured sequential data. These data, such as from speech and handwriting, often contain complex relationships between the underlaying variational factors and the observed data. The standard RNN model has very limited randomness or variability in its structure, coming from the output conditional probability model. This paper will present different ways of using high level latent random variables in RNN to model the variability in the sequential data, and the training method of such RNN model under the VAE (Variational Autoencoder) principle. We will explore possible ways of using adversarial method to train a variational RNN model. Contrary to competing approaches, our approach has theoretical optimum in the model training and provides better model training stability. Our approach also improves the posterior approximation in the variational inference network by a separated adversarial training step. Numerical results simulated from TIMIT speech data show that reconstruction loss and evidence lower bound converge to the same level and adversarial training loss converges to 0.

cs.LG↗

arXiv2021

Adaptive Stochastic ADMM for Decentralized Reinforcement Learning in Edge Industrial IoT

Edge computing provides a promising paradigm to support the implementation of Industrial Internet of Things (IIoT) by offloading tasks to nearby edge nodes. Meanwhile, the increasing network size makes it impractical for centralized data processing due to limited bandwidth, and consequently a decentralized learning scheme is preferable. Reinforcement learning (RL) has been widely investigated and shown to be a promising solution for decision-making and optimal control processes. For RL in a decentralized setup, edge nodes (agents) connected through a communication network aim to work collaboratively to find a policy to optimize the global reward as the sum of local rewards. However, communication costs, scalability and adaptation in complex environments with heterogeneous agents may significantly limit the performance of decentralized RL. Alternating direction method of multipliers (ADMM) has a structure that allows for decentralized implementation, and has shown faster convergence than gradient descent based methods. Therefore, we propose an adaptive stochastic incremental ADMM (asI-ADMM) algorithm and apply the asI-ADMM to decentralized RL with edge-computing-empowered IIoT networks. We provide convergence properties for proposed algorithms by designing a Lyapunov function and prove that the asI-ADMM has $O(\frac{1}{k}) +O(\frac{1}{M})$ convergence rate where $k$ and $ M$ are the number of iterations and batch samples, respectively. Then, we test our algorithm with two supervised learning problems. For performance evaluation, we simulate two applications in decentralized RL settings with homogeneous and heterogeneous agents. The experiment results show that our proposed algorithms outperform the state of the art in terms of communication costs and scalability, and can well adapt to complex IoT environments.

cs.LG↗