arXiv subjects

Ming Zhang

Publications and source records attributed to Ming Zhang.

At least 271 records · Page 15Linked to original sources

arXiv2023

Channel Measurement for Holographic MIMO: Benefits and Challenges of Spatial Oversampling

In this paper, the channel of an indoor holographic multiple-input multiple-output (MIMO) system is measured. It is demonstrated through experiments for the first time that the spatial oversampling of holographic MIMO systems is able to increase the capacity of a wireless communication system significantly. However, the antenna efficiency is the most crucial challenge preventing us from getting the capacity improvement. An extended EM-compliant channel model is also proposed for holographic MIMO systems, which is able to take the non-isotropic characteristics of the propagation environment, the antenna pattern distortion, the antenna efficiency, and the polarization characteristics into consideration.

cs.IT↗

arXiv2023

Analytical approximate solutions for scalarized AdS black holes

The spontaneous scalarization of Schwarzscild-AdS is investigated in the Einstein-scalar-Gauss--Bonnet (ESGB) theory. Firstly, we construct scalarized AdS black holes numerically. Secondly, making use of the homotopy analysis method (HAM), we obtain analytical approximate solutions for scalarized AdS black holes in the ESGB theory. It is found that scalarized AdS black holes constructed numerically are consistent with analytical approximate solutions in the whole space.

gr-qc↗

arXiv2022

Revisiting thermodynamic topologies of black holes

In the generalized off-shell free energy landscape, black holes can be treated as thermodynamic topological defects. The local topological properties of the spacetime can be reflected by the winding numbers at the defects, while the global topological nature can be classified by the topological number which is the sum of all local winding numbers. We propose that the winding numbers can be calculated via the residues of isolated one-order pole points of characterized functions constructed from the off-shell free energy. Using the residue method, we show that the topologies of black holes can be divided into three classes with the topological numbers being -1, 0, and 1, respectively, being consistent with the results obtained in [Phys. Rev. Lett. 129, 191101 (2022)] by using the topological current method. Moreover, we point out that standard defect points, generation and annihilation points, and critical points can be distinguished by coefficients of the Laurent series of the off-shell characterized function at those singular points.

gr-qc↗

arXiv2022

A data-driven physics-based transport model of solar energetic particles accelerated by coronal mass ejection shocks propagating through the solar coronal and heliospheric magnetic fields

In an effort to develop computational tools for predicting radiation hazards from solar energetic particles (SEPs), we have created a data-driven physics-based particle transport model to calculate the injection, acceleration and propagation of SEPs from coronal mass ejection (CME) shocks traversing through the solar corona and interplanetary magnetic fields. The model runs on an input of corona and heliospheric plasma and magnetic field configuration from an MHD model driven by solar photospheric magnetic field measurements superposed with observed CME shocks determined from coronagraph images. Using several advanced computation techniques involving stochastic simulation and integration, it rigorously solves the time-dependent 5-dimensional focus transport equation in the phase space that includes pitch-angle scattering, diffusion across magnetic field line, and particle acceleration by CME shocks. We apply the model to the 2011 November 3 CME event. The calculation results reproduce multi-spacecraft SEP observations reasonably well without normalization of particle flux. This circumsolar SEP event seen by spacecraft at Earth, STEREO-A and STEREO-B at widely separated longitudes can be explained by diffusive shock acceleration by a single CME shock with a moderate speed.

astro-ph.SR↗

arXiv2022

Holographic complexity of rotating black holes with conical deficits

Based on the complexity equals action (CA) and complexity equals volume (CV) conjectures, we investigate the holographic complexity of a slowly accelerating Kerr-AdS black hole in the bulk Einstein gravity theory which is dual to holographic states with rotation and conical deficits in the boundary quantum system. Upon obtaining an implicit form of the Wheeler-DeWitt patch, we evaluate the action and show that the growth rate of the CA complexity violates volume-scaling formulation in large black hole limit due to the non-trivial contribution from the not-too-small acceleration of the black hole. Moreover, in an ensemble with fixed entropy, pressure, and angular momentum, we also find that complexity of formation decreases with both the average and difference of the conical deficits on the poles when the black hole is close to the static limit but increases with the deficits when the black hole is close to the extremal regime.

hep-th↗

arXiv2022

Gravitational decoupling for hairy black holes in asymptotically AdS spacetimes

In this study, the gravitational decoupling approach via extended geometric deformation is utilized to generate analytical black hole solutions owing to its simplicity and effectiveness. Considering the external fields surrounding Schwarzschild AdS black holes, we derive hairy black hole solutions in asymptotic AdS spacetime, satisfying the strong and dominant energy conditions. Moreover, we find that if the black hole spacetime is a fluid system, the fluid under each of these conditions is anisotropic.

gr-qc↗

arXiv2022

2D Human Pose Estimation with Explicit Anatomical Keypoints Structure Constraints

Recently, human pose estimation mainly focuses on how to design a more effective and better deep network structure as human features extractor, and most designed feature extraction networks only introduce the position of each anatomical keypoint to guide their training process. However, we found that some human anatomical keypoints kept their topology invariance, which can help to localize them more accurately when detecting the keypoints on the feature map. But to the best of our knowledge, there is no literature that has specifically studied it. Thus, in this paper, we present a novel 2D human pose estimation method with explicit anatomical keypoints structure constraints, which introduces the topology constraint term that consisting of the differences between the distance and direction of the keypoint-to-keypoint and their groundtruth in the loss object. More importantly, our proposed model can be plugged in the most existing bottom-up or top-down human pose estimation methods and improve their performance. The extensive experiments on the benchmark dataset: COCO keypoint dataset, show that our methods perform favorably against the most existing bottom-up and top-down human pose estimation methods, especially for Lite-HRNet, when our model is plugged into it, its AP scores separately raise by 2.9\% and 3.3\% on COCO val2017 and test-dev2017 datasets.

cs.CV↗

arXiv2022

TIER-A: Denoising Learning Framework for Information Extraction

With the development of deep neural language models, great progress has been made in information extraction recently. However, deep learning models often overfit on noisy data points, leading to poor performance. In this work, we examine the role of information entropy in the overfitting process and draw a key insight that overfitting is a process of overconfidence and entropy decreasing. Motivated by such properties, we propose a simple yet effective co-regularization joint-training framework TIER-A, Aggregation Joint-training Framework with Temperature Calibration and Information Entropy Regularization. Our framework consists of several neural models with identical structures. These models are jointly trained and we avoid overfitting by introducing temperature and information entropy regularization. Extensive experiments on two widely-used but noisy datasets, TACRED and CoNLL03, demonstrate the correctness of our assumption and the effectiveness of our framework.

cs.CL↗

arXiv2022

Spin-induced scalarization of Kerr-Newman black hole in extended scalar-tensor theory

With certain non-minimal coupling between a massless scalar field and the Gauss-Bonnet curvature invariant in the extended scalar-tensor-Gauss-Bonnet (ESTGB) field theory, tachyonic instability of Kerr-Newman (KN) black hole is promoted. Critical spin and charge for the onset of the spontaneous scalarization phenomenon for the KN black hole are calculated in the infinitely large coupling limit. Then analytical formula for the coupling-strength-dependent critical existence line is obtained for the black-hole-scalar-field configuration in a finite large coupling regime. Moreover, numerical methods are used to perform threshold curves which are boundaries between bald KN black holes and their hairy counterparts. Intriguingly, we get to know KN black hole can be scalarized in the vanishing spin limit in ESTGB theory.

gr-qc↗

arXiv2022

End-to-End Rubbing Restoration Using Generative Adversarial Networks

Rubbing restorations are significant for preserving world cultural history. In this paper, we propose the RubbingGAN model for restoring incomplete rubbing characters. Specifically, we collect characters from the Zhang Menglong Bei and build up the first rubbing restoration dataset. We design the first generative adversarial network for rubbing restoration. Based on the dataset we collect, we apply the RubbingGAN to learn the Zhang Menglong Bei font style and restore the characters. The results of experiments show that RubbingGAN can repair both slightly and severely incomplete rubbing characters fast and effectively.

cs.CV↗

arXiv2022

PALT: Parameter-Lite Transfer of Language Models for Knowledge Graph Completion

This paper presents a parameter-lite transfer learning approach of pretrained language models (LM) for knowledge graph (KG) completion. Instead of finetuning, which modifies all LM parameters, we only tune a few new parameters while keeping the original LM parameters fixed. We establish this via reformulating KG completion as a "fill-in-the-blank" task, and introducing a parameter-lite encoder on top of the original LMs. We show that, by tuning far fewer parameters than finetuning, LMs transfer non-trivially to most tasks and reach competitiveness with prior state-of-the-art approaches. For instance, we outperform the fully finetuning approaches on a KG completion benchmark by tuning only 1% of the parameters. The code and datasets are available at \url{https://github.com/yuanyehome/PALT}.

cs.CL↗

arXiv2022

MetaFill: Text Infilling for Meta-Path Generation on Heterogeneous Information Networks

Heterogeneous Information Network (HIN) is essential to study complicated networks containing multiple edge types and node types. Meta-path, a sequence of node types and edge types, is the core technique to embed HINs. Since manually curating meta-paths is time-consuming, there is a pressing need to develop automated meta-path generation approaches. Existing meta-path generation approaches cannot fully exploit the rich textual information in HINs, such as node names and edge type names. To address this problem, we propose MetaFill, a text-infilling-based approach for meta-path generation. The key idea of MetaFill is to formulate meta-path identification problem as a word sequence infilling problem, which can be advanced by Pretrained Language Models (PLMs). We observed the superior performance of MetaFill against existing meta-path generation methods and graph embedding methods that do not leverage meta-paths in both link prediction and node classification on two real-world HIN datasets. We further demonstrated how MetaFill can accurately classify edges in the zero-shot setting, where existing approaches cannot generate any meta-paths. MetaFill exploits PLMs to generate meta-paths for graph embedding, opening up new avenues for language model applications in graph analysis.

cs.CL↗

arXiv2022

Kernel-based Substructure Exploration for Next POI Recommendation

Point-of-Interest (POI) recommendation, which benefits from the proliferation of GPS-enabled devices and location-based social networks (LBSNs), plays an increasingly important role in recommender systems. It aims to provide users with the convenience to discover their interested places to visit based on previous visits and current status. Most existing methods usually merely leverage recurrent neural networks (RNNs) to explore sequential influences for recommendation. Despite the effectiveness, these methods not only neglect topological geographical influences among POIs, but also fail to model high-order sequential substructures. To tackle the above issues, we propose a Kernel-Based Graph Neural Network (KBGNN) for next POI recommendation, which combines the characteristics of both geographical and sequential influences in a collaborative way. KBGNN consists of a geographical module and a sequential module. On the one hand, we construct a geographical graph and leverage a message passing neural network to capture the topological geographical influences. On the other hand, we explore high-order sequential substructures in the user-aware sequential graph using a graph kernel neural network to capture user preferences. Finally, a consistency learning framework is introduced to jointly incorporate geographical and sequential information extracted from two separate graphs. In this way, the two modules effectively exchange knowledge to mutually enhance each other. Extensive experiments conducted on two real-world LBSN datasets demonstrate the superior performance of our proposed method over the state-of-the-arts. Our codes are available at https://github.com/Fang6ang/KBGNN.

cs.LG↗

arXiv2022

Splitting the Echoes of Black Holes in Einstein-nonlinear Electrodynamic Theories

Black hole echo is an important observable that can help us better understand gravitational theories. We present that the non-linear electrodynamic black holes can admit the multi-peak effective potential for the scalar perturbations, which can give rise to the echoes. After choosing suitable parameters, the effective potential can exhibit a structure with more than two peaks. Putting the initial wave packet released outside the peaks, we find that the time-domain profile of the echo will split when the peaks of the effective potential change from two to three. This is a phenomenon of black hole echo and it might be possible to determine the geometric structure of the black hole according to this phenomenon through gravitational wave detection.

gr-qc↗

arXiv2022

Internal structures and circular orbits for test particles

We explore how the internal structure of a test particle affects its equatorial stable circular orbits around the Kerr black hole with or without a cosmological constant. To this end, we first explicitly write equations of motion for a test particle in the pole-dipole-quadrupole approximation specifying the quadrupole momentum tensor to a spin-induced model. Then we calculate characteristic quantities -- radius, angular momentum, energy, angular velocity, and impact parameter -- for the particles on the stable circular orbits. Once the pole-dipole-quadrupole approximation is taken, we find that for a particle on an innermost stable circular orbit, all characteristic quantities, except the angular velocity, become greater relative to the pole-dipole case. In contrast, for a particle on an outermost stable circular orbit, which only exists in the case of the spacetime background being asymptotically de Sitter, it is the radius that becomes smaller while all other quantities become greater.

gr-qc↗

arXiv2022

Using TeV Cosmic Rays to probe the Heliosphere's Boundary with the Local Interstellar Medium

The heliosphere is the magnetic structure formed by the Sun's atmosphere extending into the local interstellar medium (ISM). The boundary separating the heliosphere from the ISM is a still largely unexplored region of space. Even though both Voyager spacecraft entered the local ISM and are delivering data, they are two points piercing a vast region of space at specific times. The heliospheric boundary regulates the penetration of MeV- GeV galactic cosmic rays (CR) in the inner heliosphere. Interstellar keV neutral atoms are crucial to the outer heliosphere since they can penetrate unperturbed and transfer energy to the solar wind. Missions such as NASA's IBEX and Cassini are designed to detect neutral atoms and monitor charge exchange processes at the heliospheric boundary. The heliosphere does not modulate the TeV CR intensity, but it does influence their arrival direction distribution. Ground-based CR observatories have provided accurate maps of CR anisotropy as a function of energy in the last couple of decades. Combining observations to produce all-sky coverage makes it possible to investigate the heliosphere's impact on TeV CR particles. We can numerically calculate the pristine TeV CR distribution in the local ISM with state-of-the-art heliosphere models. Only by subtracting the heliospheric influence is it possible to use TeV CR observations to infer propagation properties and the characteristics of magnetic turbulence in the ISM. Numerical calculations of CR particle trajectories through heliospheric models provide a complementary tool to probe the boundary region properties. A program boosting heliospheric modeling with emphasis on the boundary region and promoting combined CR experimental data analyses from multiple experiments benefits CR astrophysics and provides additional data and tools to explore the interaction between the heliosphere and the local ISM.

astro-ph.HE↗

arXiv2022

Simulation of the Solar Energetic Particle Event on 2020 May 29 Observed by Parker Solar Probe

This paper presents a stochastic three-dimensional (3D) focused transport simulation of solar energetic particles (SEPs) produced by a data-driven coronal mass ejection (CME) shock propagating through a data-driven model of coronal and heliospheric magnetic fields. The injection of SEPs at the CME shock is treated using diffusive shock acceleration of post-shock superthermal solar wind ions. A time backward stochastic simulation is employed to solve the transport equation to obtain the SEP time-intensity profile at any location, energy, and pitch angle. The model is applied to a SEP event on 2020 May 29, observed by STEREO-A close to 1 au and by Parker Solar Probe (PSP) when it was about 0.33 au away from the Sun. The SEP event was associated with a very slow CME with a plane-of-sky speed of 337 km/s at a height below 6 $\rm R_S$ as reported in the SOHO/LASCO CME catalog. We compute the time profiles of particle flux at PSP and STEREO-A locations, and estimate both the spectral index of the proton energy spectrum for energies between 2 and 16 MeV and the equivalent path length of the magnetic field lines experienced by the first arriving SEPs. We found that the simulation results are well correlated with observations. The SEP event could be explained by the acceleration of particles by a weak CME shock in the low solar corona that is not magnetically connected to the observers.

astro-ph.SR↗

arXiv2022

Identification of the decay pathway of photoexcited nucleobases

The identification of the decay pathway of the nucleobase uracil after being photoexcited by ultraviolet (UV) light has been a long standing problem. Various theoretical models have been proposed but yet to be verified. Here we propose an experimental scheme to test the theoretical models by a combination of ultrafast X-ray spectroscopy, X-ray diffraction and electron diffraction methods. Incorporating the signatures of multiple probing methods, we demonstrate an approach to identify the pathway of the geometric and electronic relaxation of the photoexcited uracil molecule.

physics.chem-ph↗