arXiv subjects

Meng Zhang

Publications and source records attributed to Meng Zhang.

At least 163 records · Page 9Linked to original sources

arXiv2023

Active flow control over a finite wall-mounted square cylinder by using multiple plasma actuators

The present study aims to investigate the effectiveness of plasma actuators in controlling the flow around a finite wall-mounted square cylinder (FWMSC) with a longitudinal aspect ratio of 4. The test is conducted in a small-scale closed return-type wind tunnel. The Reynolds number (${Re}_d$) of the experiments is 500 based on the width of the bluff body and the freestream velocity. The plasma actuators are installed on the top surface and the rear surface of the square cylinder. The induced flow velocities of the plasma actuators are modulated by adjusting the operating voltage and frequency of the high-voltage generator. In this work, particle image velocimetry (PIV) is used to obtain the velocity fields. Furthermore, force measurements are conducted to investigate the effect of using plasma actuators with different driving voltages on the drag force. Our results show that the plasma actuators can successfully suppress flow separation and reduce the size of the recirculation region and turbulent kinetic energy (TKE) in the wake. A correlation between the drag coefficient and the operating voltage of the power generator is also revealed and the mean drag coefficient is found to decrease with increasing imposing voltage. The plasma actuators can enhance the momentum exchange and the interactive behavior between the shear layer and the flow separation region, resulting in flow reattachment at the free end and shrinkage of the recirculation zone in the near-wake region of the bluff body. Overall, the present study demonstrates the practical effectiveness of using plasma actuators for active flow control around FWMSC.

physics.flu-dyn↗

arXiv2023

Predicting unavailable parameters from existing velocity fields of turbulent flows using a GAN-based model

In this study, an efficient deep-learning model is developed to predict unavailable parameters, e.g., streamwise velocity, temperature, and pressure from available velocity components. This model, termed mapping generative adversarial network (M-GAN), consists of a label information generator (LIG) and an enhanced super-resolution generative adversarial network (ESRGAN). LIG can generate label information helping the model to predict different parameters. The GAN-based model receives the label information from LIG and existing velocity data to generate the unavailable parameters. Two-dimensional (2D) Rayleigh-B{é}nard flow and turbulent channel flow are used to evaluate the performance of M-GAN. Firstly, M-GAN is trained and evaluated by 2D direct numerical simulation (DNS) data of a Rayleigh-B{é}nard flow. From the results, it can be shown that M-GAN can predict temperature distribution from the two-dimensional velocities. Furthermore, DNS data of turbulent channel flow at two different friction Reynolds numbers $Re_τ$ = 180 and 550 are applied simultaneously to train the M-GAN and examine its predicting ability for the pressure fields and the streamwise velocity from the other two velocity components. The instantaneous and statistical results of the predicted data agree well with the DNS data, even for the flow at $Re_τ$ = 395, indicating that M-GAN can be trained to learn the mapping function of the unknown fields with good interpolation capability.

physics.flu-dyn↗

arXiv2023

Age of Information Under Frame Slotted ALOHA-Based Status Updating Protocol

We propose a frame slotted ALOHA (FSA)-based protocol for a random access network where sources transmit status updates to their intended destinations. We evaluate the effect of such a protocol on the network's timeliness performance using the Age of Information (AoI) metric. Specifically, we leverage tools from stochastic geometry to model the spatial positions of the source-destination pairs and capture the entanglement amongst the nodes' spatial-temporal attributes through the interference they caused to each other. We derive analytical expressions for the average and variance of AoI over a typical transmission link in Poisson bipolar and cellular networks, respectively. Our analysis shows that in densely deployed networks, the FSA-based status updating protocol can significantly decrease the average AoI and in addition, stabilizes the age performance by substantially reducing the variance of AoI. Furthermore, under the same updating frequency, converting a slotted ALOHA protocol into an FSA-based one always leads to a reduction in the average AoI. Moreover, implementing FSA in conjunction with power control can further benefit the AoI performance, although the particular values of framesize and power control factor must be adequately tuned to achieve the optimal gain.

cs.IT↗

arXiv2023

Boosting Distributed Full-graph GNN Training with Asynchronous One-bit Communication

Training Graph Neural Networks (GNNs) on large graphs is challenging due to the conflict between the high memory demand and limited GPU memory. Recently, distributed full-graph GNN training has been widely adopted to tackle this problem. However, the substantial inter-GPU communication overhead can cause severe throughput degradation. Existing communication compression techniques mainly focus on traditional DNN training, whose bottleneck lies in synchronizing gradients and parameters. We find they do not work well in distributed GNN training as the barrier is the layer-wise communication of features during the forward pass & feature gradients during the backward pass. To this end, we propose an efficient distributed GNN training framework Sylvie, which employs one-bit quantization technique in GNNs and further pipelines the curtailed communication with computation to enormously shrink the overhead while maintaining the model quality. In detail, Sylvie provides a lightweight Low-bit Module to quantize the sent data and dequantize the received data back to full precision values in each layer. Additionally, we propose a Bounded Staleness Adaptor to control the introduced staleness to achieve further performance enhancement. We conduct theoretical convergence analysis and extensive experiments on various models & datasets to demonstrate Sylvie can considerably boost the training throughput by up to 28.1x.

cs.DC↗

arXiv2023

A Flexible Multi-view Multi-modal Imaging System for Outdoor Scenes

Multi-view imaging systems enable uniform coverage of 3D space and reduce the impact of occlusion, which is beneficial for 3D object detection and tracking accuracy. However, existing imaging systems built with multi-view cameras or depth sensors are limited by the small applicable scene and complicated composition. In this paper, we propose a wireless multi-view multi-modal 3D imaging system generally applicable to large outdoor scenes, which consists of a master node and several slave nodes. Multiple spatially distributed slave nodes equipped with cameras and LiDARs are connected to form a wireless sensor network. While providing flexibility and scalability, the system applies automatic spatio-temporal calibration techniques to obtain accurate 3D multi-view multi-modal data. This system is the first imaging system that integrates mutli-view RGB cameras and LiDARs in large outdoor scenes among existing 3D imaging systems. We perform point clouds based 3D object detection and long-term tracking using the 3D imaging dataset collected by this system. The experimental results show that multi-view point clouds greatly improve 3D object detection and tracking accuracy regardless of complex and various outdoor environments.

cs.CV↗

arXiv2023

Ba-enhanced dwarf and subgiant stars in the LAMOST Galactic surveys

Ba-enhanced stars are interesting probes of stellar astrophysics and Galactic formation history. In this work, we investigate the chemistry and kinematics for a large sample of Ba-enhanced ([Ba/Fe]$>$1.0) dwarf and subgiant stars with $5000 < T_{\rm eff }< 6700$\,K from LAMOST. We find that both stellar internal evolution process and external mass exchange due to binary evolution are responsible for the origins of the Ba-enhancement of our sample stars. About one third of them exhibit C and N enhancement and ultraviolet brightness excess, indicating they are products of binary evolution. The remaining Ba-enhanced stars with normal C and N abundances are mostly warm stars with $T_{\rm eff} > 6000$\,K. They are likely consequences of stellar internal elemental transport processes, but they show very different elemental patterns to the hotter Am/Fm stars. Our results reveal a substantially lack of high-[$α$/Fe] Ba-enhanced stars in the [Fe/H]--[$α$/Fe] plane, which we dub as a {\em high-$α$ desert}. We suggest it is due to a lower efficiency for producing Ba-enhanced stars by low-mass AGB progenitors in binary systems. Our results call for detailed modellings for these Ba-enhanced stellar peculiars, in the context of both stellar internal elemental transport and external mass accretion.

astro-ph.SR↗

arXiv2023

Foreground Removal of CO Intensity Mapping Using Deep Learning

Line intensity mapping (LIM) is a promising probe to study star formation, the large-scale structure of the Universe, and the epoch of reionization (EoR). Since carbon monoxide (CO) is the second most abundant molecule in the Universe except for molecular hydrogen ${\rm H}_2$, it is suitable as a tracer for LIM surveys. However, just like other LIM surveys, CO intensity mapping also suffers strong foreground contamination that needs to be eliminated for extracting valuable astrophysical and cosmological information. In this work, we take $^{12}$CO($\it J$=1-0) emission line as an example to investigate whether deep learning method can effectively recover the signal by removing the foregrounds. The CO(1-0) intensity maps are generated by N-body simulations considering CO luminosity and halo mass relation, and we discuss two cases with median and low CO signals by comparing different relations. We add foregrounds generated from real observations, including thermal dust, spinning dust, free-free, synchrotron emission and CMB anisotropy. The beam with sidelobe effect is also considered. Our deep learning model is built upon ResUNet, which combines image generation algorithm UNet with the state-of-the-art architecture of deep learning, ResNet. The principal component analysis (PCA) method is employed to preprocess data before feeding it to the ResUNet. We find that, in the case of low instrumental noise, our UNet can efficiently reconstruct the CO signal map with correct line power spectrum by removing the foregrounds and recovering PCA signal loss and beam effects. Our method also can be applied to other intensity mappings like neutral hydrogen 21cm surveys.

astro-ph.CO↗

arXiv2023

Orientation-dependent electron-phonon coupling in interfacial superconductors LaAlO3/KTaO3

The emergent superconductivity at the LaAlO3/KTaO3 interfaces exhibits a mysterious dependence on the KTaO3 crystallographic orientations. Here we show, by soft X-ray angle-resolved photoemission spectroscopy, that the interfacial superconductivity is contributed by mobile electrons with unexpected quasi-three-dimensional character, beyond the "two-dimensional electron gas" scenario in describing oxide interfaces. At differently-oriented interfaces, the quasi-three-dimensional electron gas ubiquitously exists and spatially overlaps with the small q Fuchs-Kliewer surface phonons. Intriguingly, electrons and the Fuchs-Kliewer phonons couple with different strengths depending on the interfacial orientations, and the stronger coupling correlates with the higher superconducting transition temperature. Our results provide a natural explanation for the orientation-dependent superconductivity, and the first evidence that interfacial orientations can affect electron-phonon coupling strength over several nanometers, which may have profound implications for the applications of oxide interfaces in general.

cond-mat.supr-con↗

arXiv2022

A transformer-based synthetic-inflow generator for spatially-developing turbulent boundary layers

This study proposes a newly-developed deep-learning-based method to generate turbulent inflow conditions for spatially-developing turbulent boundary layer (TBL) simulations. A combination of a transformer and a multiscale-enhanced super-resolution generative adversarial network is utilized to predict velocity fields of a spatially-developing TBL at various planes normal to the streamwise direction. Datasets of direct numerical simulation (DNS) of flat plate flow spanning a momentum thickness-based Reynolds number, Re_theta = 661.5 - 1502.0, are used to train and test the model. The model shows a remarkable ability to predict the instantaneous velocity fields with detailed fluctuations and reproduce the turbulence statistics as well as spatial and temporal spectra with commendable accuracy as compared with the DNS results. The proposed model also exhibits a reasonable accuracy for predicting velocity fields at Reynolds numbers that are not used in the training process. With the aid of transfer learning, the computational cost of the proposed model is considered to be effectively low. The results demonstrate, for the first time that transformer-based models can be efficient in predicting the dynamics of turbulent flows. It also shows that combining these models with generative adversarial networks-based models can be useful in tackling various turbulence-related problems, including the development of efficient synthetic-turbulent inflow generators.

physics.flu-dyn↗

arXiv2022

Entanglement entropy scaling of noisy random quantum circuits in two dimensions

Whether noisy quantum devices without error correction can provide quantum advantage over classical computers is a critical issue of current quantum computation. In this work, the random quantum circuits, which are used as the paradigm model to demonstrate quantum advantage, are simulated with depolarizing noise on experiment relevant two-dimensional architecture. With comprehensive numerical simulation and theoretical analysis, we find that the maximum achievable operator entanglement entropy, which indicates maximal simulation cost, has area law scaling with the system size for constant noise rate. On the other hand, we also find that the maximum achievable operator entanglement entropy has power law scaling with the noise rate for fixed system size, and the volume law scaling can be obtained only if the noise rate decreases when system size increase.

quant-ph↗

arXiv2022

AdaEnlight: Energy-aware Low-light Video Stream Enhancement on Mobile Devices

The ubiquity of camera-embedded devices and the advances in deep learning have stimulated various intelligent mobile video applications. These applications often demand on-device processing of video streams to deliver real-time, high-quality services for privacy and robustness concerns. However, the performance of these applications is constrained by the raw video streams, which tend to be taken with small-aperture cameras of ubiquitous mobile platforms in dim light. Despite extensive low-light video enhancement solutions, they are unfit for deployment to mobile devices due to their complex models and and ignorance of system dynamics like energy budgets. In this paper, we propose AdaEnlight, an energy-aware low-light video stream enhancement system on mobile devices. It achieves real-time video enhancement with competitive visual quality while allowing runtime behavior adaptation to the platform-imposed dynamic energy budgets. We report extensive experiments on diverse datasets, scenarios, and platforms and demonstrate the superiority of AdaEnlight compared with state-of-the-art low-light image and video enhancement solutions.

cs.CV↗

arXiv2022

Superconductivity in epitaxially grown LaVO3/KTaO3(111) heterostructures

Complex oxide heterointerfaces can host a rich of emergent phenomena, and epitaxial growth is usually at the heart of forming these interfaces. Recently, a strong crystalline-orientation-dependent two-dimensional superconductivity was discovered at interfaces between KTaO3 single-crystal substrates and films of other oxides. Unexpectedly, rare of these oxide films was epitaxially grown. Here, we report the existence of superconductivity in epitaxially grown LaVO3/KTaO3(111) heterostructures, with a superconducting transition temperature of ~0.5 K. Meanwhile, no superconductivity was detected in the (001)- and (110)-orientated LaVO3/KTaO3 heterostructures down to 50 mK. Moreover, we find that for the LaVO3/KTaO3(111) interfaces to be conducting, an oxygen-deficient growth environment and a minimum LaVO3 thickness of ~0.8 nm (~ 2 unit cells) are needed.

cond-mat.supr-con↗

arXiv2022

Wind Power Forecasting Considering Data Privacy Protection: A Federated Deep Reinforcement Learning Approach

In a modern power system with an increasing proportion of renewable energy, wind power prediction is crucial to the arrangement of power grid dispatching plans due to the volatility of wind power. However, traditional centralized forecasting methods raise concerns regarding data privacy-preserving and data islands problem. To handle the data privacy and openness, we propose a forecasting scheme that combines federated learning and deep reinforcement learning (DRL) for ultra-short-term wind power forecasting, called federated deep reinforcement learning (FedDRL). Firstly, this paper uses the deep deterministic policy gradient (DDPG) algorithm as the basic forecasting model to improve prediction accuracy. Secondly, we integrate the DDPG forecasting model into the framework of federated learning. The designed FedDRL can obtain an accurate prediction model in a decentralized way by sharing model parameters instead of sharing private data which can avoid sensitive privacy issues. The simulation results show that the proposed FedDRL outperforms the traditional prediction methods in terms of forecasting accuracy. More importantly, while ensuring the forecasting performance, FedDRL can effectively protect the data privacy and relieve the communication pressure compared with the traditional centralized forecasting method. In addition, a simulation with different federated learning parameters is conducted to confirm the robustness of the proposed scheme.

cs.LG↗

arXiv2022

Generalization to the Natural Gradient Descent

Optimization problem, which is aimed at finding the global minimal value of a given cost function, is one of the central problem in science and engineering. Various numerical methods have been proposed to solve this problem, among which the Gradient Descent (GD) method is the most popular one due to its simplicity and efficiency. However, the GD method suffers from two main issues: the local minima and the slow convergence especially near the minima point. The Natural Gradient Descent(NGD), which has been proved as one of the most powerful method for various optimization problems in machine learning, tensor network, variational quantum algorithms and so on, supplies an efficient way to accelerate the convergence. Here, we give a unified method to extend the NGD method to a more general situation which keeps the fast convergence by looking for a more suitable metric through introducing a 'proper' reference Riemannian manifold. Our method generalizes the NDG, and may give more insight of the optimization methods.

math.OC↗

arXiv2022

A quantum spin liquid phase in the Kitaev-Hubbard model

The quantum spin liquid (QSL) state has been searched intensively in Kitaev-like materials, such as the Iridium oxides $A_2$IrO$_3$ and $α$-RuCl$_3$. The half-filled Kitaev-Hubbard model with bond dependent hopping terms is used to describe the Kitaev-like materials, which is calculated using the state-of-the-art fermionic projected entangled pair states (fPEPS) method. We find a QSL phase near the Mott insulator transition, which has a strong first-order transition to the semi-metal phase with the decrease of Hubbard $U$. We suggest that a promising routine to find the QSL is to find the Iridium oxides that are near the Mott insulator transitions.

cond-mat.str-el↗

arXiv2022

Superatom Orbitals, Orbital Splitting and Structure Prediction of Pure Alkali Metal Clusters

Jellium model achieved great success in predicting stable clusters with closed electronic shells and zero spin. In order to explain the stability of open shell clusters, it is necessary to consider the case of non-degenerate energy levels. In this paper the energy levels in nine low-lying Li$_{19}$ clusters are analysed systematically through superatomic orbital splitting effect. It is found that for originally degenerate orbitals like five 1D orbitals, the more the orbital extends in the direction of the cluster extension, the lower the energy of the orbital becomes. So oblate Li$_{19}$ clusters have the orbital sequence of $1\mathrm{S}^2-1\mathrm{P}_{x/y}^{4}-1\mathrm{P}_{z}^{2}-1\mathrm{D}_{xy/x^2-y^2}^{4}-2\mathrm{S}^2-1\mathrm{D}_{xz/yz}^{4}-1\mathrm{D}_{z^2}^{1}$, while prolate Li$_{19}$ clusters have the sequence of $1\mathrm{S}^2-1\mathrm{P}_{z}^{2}-1\mathrm{P}_{x/y}^{4}-1\mathrm{D}_{z^2}^{2}-1\mathrm{D}_{xz/yz}^{4}-1\mathrm{D}_{xy/x^2-y^2}^{4}-2\mathrm{S}^1$. This electron configuration is applied to predict the shape and magnetic moment of the alkali metal Li$_{n}$ clusters. The stability of the Li$_{14}$ cluster can be successfully interpreted in the framework of orbital splitting effect without resorting to the super valence bond (SVB) model, indicating a non-spherical cluster can achieve good stability without meeting the magic number. It is also proposed that the orbital splitting can be used to predict the shape (prolate, oblate or sphere) and magnetic moment of clusters. 11 out of 16 predicted shapes of Li$_n(n=3-18)$ are consistent with the results obtained by the principle of minimum energy.

cond-mat.mtrl-sci↗

arXiv2022

Motion Guided Deep Dynamic 3D Garments

Realistic dynamic garments on animated characters have many AR/VR applications. While authoring such dynamic garment geometry is still a challenging task, data-driven simulation provides an attractive alternative, especially if it can be controlled simply using the motion of the underlying character. In this work, we focus on motion guided dynamic 3D garments, especially for loose garments. In a data-driven setup, we first learn a generative space of plausible garment geometries. Then, we learn a mapping to this space to capture the motion dependent dynamic deformations, conditioned on the previous state of the garment as well as its relative position with respect to the underlying body. Technically, we model garment dynamics, driven using the input character motion, by predicting per-frame local displacements in a canonical state of the garment that is enriched with frame-dependent skinning weights to bring the garment to the global space. We resolve any remaining per-frame collisions by predicting residual local displacements. The resultant garment geometry is used as history to enable iterative rollout prediction. We demonstrate plausible generalization to unseen body shapes and motion inputs, and show improvements over multiple state-of-the-art alternatives.

cs.CV↗

arXiv2022

Age-Dependent Differential Privacy

The proliferation of real-time applications has motivated extensive research on analyzing and optimizing data freshness in the context of \textit{age of information}. However, classical frameworks of privacy (e.g., differential privacy (DP)) have overlooked the impact of data freshness on privacy guarantees, which may lead to unnecessary accuracy loss when trying to achieve meaningful privacy guarantees in time-varying databases. In this work, we introduce \textit{age-dependent DP}, taking into account the underlying stochastic nature of a time-varying database. In this new framework, we establish a connection between classical DP and age-dependent DP, based on which we characterize the impact of data staleness and temporal correlation on privacy guarantees. Our characterization demonstrates that \textit{aging}, i.e., using stale data inputs and/or postponing the release of outputs, can be a new strategy to protect data privacy in addition to noise injection in the traditional DP framework. Furthermore, to generalize our results to a multi-query scenario, we present a sequential composition result for age-dependent DP under any publishing and aging policies. We then characterize the optimal tradeoffs between privacy risk and utility and show how this can be achieved. Finally, case studies show that to achieve a target of an arbitrarily small privacy risk in a single-query case, combing aging and noise injection only leads to a bounded accuracy loss, whereas using noise injection only (as in the benchmark case of DP) will lead to an unbounded accuracy loss.

cs.CR↗