arXiv subjects

Ming Zhang

Publications and source records attributed to Ming Zhang.

At least 343 records · Page 19Linked to original sources

arXiv2021

$P-V$ criticality and Joule-Thomson Expansion of Hayward-AdS black holes in 4D Einstein-Gauss-Bonnet gravity

In this paper, the $P-V$ criticality and Joule-Thomson Expansion of Hayward-AdS black holes in 4D Einstein-Gauss-Bonnet gravity are studied in the extended phase space. We find the black hole always exhibits a phase transition similar to that of the Van der Waals system for any arbitrary positive parameters $α$ and $g$. We also study the dependence of $α$ and $g$ on the inversion curves and plot the inversion and isenthalpic curves in the $T-P$ plane, which can determine the cooling-heating regions.

hep-th↗

arXiv2021

QoS-Driven Video Uplinking in NOMA-Based IoT

In recent years, with the explosive growth of visual sensors and a large number of related video applications in Internet of Things (IoT), massive video data is generated by IoT devices. Since the volume of video data is far greater than traditional data in IoT, it is challenging to ensure high Quality of Service (QoS) for video uplinking in IoT. To address this challenge, we integrate non-orthogonal multiple access (NOMA) and scalable video coding (SVC) in IoT. To improve the video quality, we formulate a power allocation problem to maximize the average QoS in the proposed integrated system. Due to that the problem is non-convex, we transform it into a monotonic problem based on its hidden monotonicity. Then a power allocation algorithm based on polyblock outer approximation is proposed to solve the problem effectively. Finally, simulation results demonstrate that the proposed algorithm outperforms existing OMA and NOMA based schemes for video uplinking in IoT in terms of QoS and energy efficiency.

cs.NI↗

arXiv2020

Tomographic imaging of complete quantum state of matter by ultrafast diffraction

With the ability to directly obtain the Wigner function and density matrix of photon states, quantum tomography (QT) has had a significant impact on quantum optics, quantum computing and quantum information. By an appropriate sequence of measurements on the evolution of each degreeof freedom (DOF), the full quantum state of the observed photonic system can be determined. The first proposal to extend the application of QT to reconstruction of complete quantum states of matter wavepackets had generated enormous interest in ultrafast diffraction imaging and pump-probe spectroscopy of molecules. This interest was elevated with the advent of ultrafast electron and X-ray diffraction techniques using electron accelerators and X-ray free electron lasers to add temporal resolution to the observed nuclear and electron distributions. However, the great interest in this area has been tempered by the illustration of an impossibility theorem, known as the dimension problem. Not being able to associate unitary evolution to every DOF of molecular motion, quantum tomography could not be used beyond 1D and categorically excludes most vibrational and all rotational motion of molecules. Here we present a theoretical advance to overcome the notorious dimension problem. Solving this challenging problem is important to push imaging molecular dynamics to the quantum limit. The new theory has solved this problem, which makes quantum tomography a truly useful methodology in ultrafast physics and enables the making of quantum version of a molecular movie. With the new theory, quantum tomography can be finally advanced to a sufficient level to become a general method for reconstructing quantum states of matter, without being limited in one dimension. Our new concept is demonstrated using a simulated dataset of ultrafast diffraction experiment of laser-aligned nitrogen molecules.

quant-ph↗

arXiv2020

PoD: Positional Dependency-Based Word Embedding for Aspect Term Extraction

Dependency context-based word embedding jointly learns the representations of word and dependency context, and has been proved effective in aspect term extraction. In this paper, we design the positional dependency-based word embedding (PoD) which considers both dependency context and positional context for aspect term extraction. Specifically, the positional context is modeled via relative position encoding. Besides, we enhance the dependency context by integrating more lexical information (e.g., POS tags) along dependency paths. Experiments on SemEval 2014/2015/2016 datasets show that our approach outperforms other embedding methods in aspect term extraction.

cs.CL↗

arXiv2020

Learning Hybrid Representations for Automatic 3D Vessel Centerline Extraction

Automatic blood vessel extraction from 3D medical images is crucial for vascular disease diagnoses. Existing methods based on convolutional neural networks (CNNs) may suffer from discontinuities of extracted vessels when segmenting such thin tubular structures from 3D images. We argue that preserving the continuity of extracted vessels requires to take into account the global geometry. However, 3D convolutions are computationally inefficient, which prohibits the 3D CNNs from sufficiently large receptive fields to capture the global cues in the entire image. In this work, we propose a hybrid representation learning approach to address this challenge. The main idea is to use CNNs to learn local appearances of vessels in image crops while using another point-cloud network to learn the global geometry of vessels in the entire image. In inference, the proposed approach extracts local segments of vessels using CNNs, classifies each segment based on global geometry using the point-cloud network, and finally connects all the segments that belong to the same vessel using the shortest-path algorithm. This combination results in an efficient, fully-automatic and template-free approach to centerline extraction from 3D images. We validate the proposed approach on CTA datasets and demonstrate its superior performance compared to both traditional and CNN-based baselines.

eess.IV↗

arXiv2020

Triple points and phase diagrams of Born-Infeld AdS black holes in 4D Einstein-Gauss-Bonnet gravity

By treating the cosmological constant as a thermodynamic pressure, we investigate the thermodynamic behaviors of Born-Infeld AdS black hole in 4D Einstein-Gauss-Bonnet (EGB) gravity. The result shows that the Van der Waals like small/large black hole (SBH/LBH) phase transition always appears for any positive parameters $α$ and $β$. Moreover, we observe a new phenomenon of small/intermediate/large black hole (SBH/IBH/LBH) phase transition with one tricritical and two critical points in the available parameter region for αand β. This behavior is reminiscent of the solid/liquid/gas phase transition.

gr-qc↗

arXiv2020

Wasserstein Distance guided Adversarial Imitation Learning with Reward Shape Exploration

The generative adversarial imitation learning (GAIL) has provided an adversarial learning framework for imitating expert policy from demonstrations in high-dimensional continuous tasks. However, almost all GAIL and its extensions only design a kind of reward function of logarithmic form in the adversarial training strategy with the Jensen-Shannon (JS) divergence for all complex environments. The fixed logarithmic type of reward function may be difficult to solve all complex tasks, and the vanishing gradients problem caused by the JS divergence will harm the adversarial learning process. In this paper, we propose a new algorithm named Wasserstein Distance guided Adversarial Imitation Learning (WDAIL) for promoting the performance of imitation learning (IL). There are three improvements in our method: (a) introducing the Wasserstein distance to obtain more appropriate measure in the adversarial training process, (b) using proximal policy optimization (PPO) in the reinforcement learning stage which is much simpler to implement and makes the algorithm more efficient, and (c) exploring different reward function shapes to suit different tasks for improving the performance. The experiment results show that the learning procedure remains remarkably stable, and achieves significant performance in the complex continuous control tasks of MuJoCo.

cs.LG↗

arXiv2020

$K$-theoretic quasimap wall-crossing

In this paper, we prove a K-theoretic wall-crossing formula for $ε$-stable quasimaps for all GIT targets in all genera. It recovers the genus-0 K-theoretic toric mirror theorem by Givental-Tonita and the genus-0 mirror theorem for quantum K-theory with level structure by Ruan-Zhang. The proofs are based on K-theoretic virtual localization on the master space introduced by the second author in arXiv:1911.02745.

math.AG↗

arXiv2020

Emissions of photons near the horizons of Kerr-Sen black holes

We investigate the escape probabilities of the photons near the horizon of the Kerr-Sen black hole. We find that the escape probabilities of the photons are nonzero in the event horizon limit of the extreme Kerr-Sen black hole if the light sources are near the equator. We show that the escape probability of a photon increases with the radial position of the light source. It is also uncovered that the escape probability decreases if the light source with constant radius moves from the equatorial plane to the pole. Besides, we discover that the escape probability of a photon in the Carter frame is always greater than the one in the locally non-rotating frame, except on the pole, where they are equal.

gr-qc↗

arXiv2020

Neural Correction Model for Open-Domain Named Entity Recognition

Named Entity Recognition (NER) plays an important role in a wide range of natural language processing tasks, such as relation extraction, question answering, etc. However, previous studies on NER are limited to particular genres, using small manually-annotated or large but low-quality datasets. Meanwhile, previous datasets for open-domain NER, built using distant supervision, suffer from low precision, recall and ratio of annotated tokens (RAT). In this work, to address the low precision and recall problems, we first utilize DBpedia as the source of distant supervision to annotate abstracts from Wikipedia and design a neural correction model trained with a human-annotated NER dataset, DocRED, to correct the false entity labels. In this way, we build a large and high-quality dataset called AnchorNER and then train various models with it. To address the low RAT problem of previous datasets, we introduce a multi-task learning method to exploit the context information. We evaluate our methods on five NER datasets and our experimental results show that models trained with AnchorNER and our multi-task learning method obtain state-of-the-art performances in the open-domain setting.

cs.CL↗

arXiv2020

Multi-agent Trajectory Prediction with Fuzzy Query Attention

Trajectory prediction for scenes with multiple agents and entities is a challenging problem in numerous domains such as traffic prediction, pedestrian tracking and path planning. We present a general architecture to address this challenge which models the crucial inductive biases of motion, namely, inertia, relative motion, intents and interactions. Specifically, we propose a relational model to flexibly model interactions between agents in diverse environments. Since it is well-known that human decision making is fuzzy by nature, at the core of our model lies a novel attention mechanism which models interactions by making continuous-valued (fuzzy) decisions and learning the corresponding responses. Our architecture demonstrates significant performance gains over existing state-of-the-art predictive models in diverse domains such as human crowd trajectories, US freeway traffic, NBA sports data and physics datasets. We also present ablations and augmentations to understand the decision-making process and the source of gains in our model.

cs.LG↗

arXiv2020

Shadows of the accelerating black holes

Due to the acceleration of the black hole, the circular orbits of the photons will deviate from the equatorial plane and the property of the black hole shadow will change. We find that the latitude of the circular orbit increases with the increasing acceleration and then show that the observer's inclination angles which make the shadow radius and the shadow distortion maximum increase with the increasing acceleration for the accelerating Kerr black hole.

gr-qc↗

arXiv2020

Rethinking the Extraction and Interaction of Multi-Scale Features for Vessel Segmentation

Analyzing the morphological attributes of blood vessels plays a critical role in the computer-aided diagnosis of many cardiovascular and ophthalmologic diseases. Although being extensively studied, segmentation of blood vessels, particularly thin vessels and capillaries, remains challenging mainly due to the lack of an effective interaction between local and global features. In this paper, we propose a novel deep learning model called PC-Net to segment retinal vessels and major arteries in 2D fundus image and 3D computed tomography angiography (CTA) scans, respectively. In PC-Net, the pyramid squeeze-and-excitation (PSE) module introduces spatial information to each convolutional block, boosting its ability to extract more effective multi-scale features, and the coarse-to-fine (CF) module replaces the conventional decoder to enhance the details of thin vessels and process hard-to-classify pixels again. We evaluated our PC-Net on the Digital Retinal Images for Vessel Extraction (DRIVE) database and an in-house 3D major artery (3MA) database against several recent methods. Our results not only demonstrate the effectiveness of the proposed PSE module and CF module, but also suggest that our proposed PC-Net sets new state of the art in the segmentation of retinal vessels (AUC: 98.31%) and major arteries (AUC: 98.35%) on both databases, respectively.

eess.IV↗

arXiv2020

Photon sphere and phase transition of $d$-dimensional ($d\ge5$) charged Gauss-Bonnet AdS black holes

Motivated by recent work, nonmonotonic behaviours of photon sphere radius can be used to reflect black hole phase transition for Reissner-Nordstr$\ddot{o}$m-AdS (RN-AdS) black holes, we study the case of five-dimensional charged Gauss-Bonnet-AdS (GB-AdS) black holes in the reduced parameter space. We find that the nonmonotonic behaviours of photon sphere radius still exist. Using the coexistence line calculated from $P-V$ plane, we capture the photon sphere radius of saturated small and large black holes (the boundary of the coexistence phase), then illustrate the reduced coexistence region. The results show that, reduced coexistence region decreases with charge $Q$ but increases with Gauss-Bonnet coefficient $α$. When the charge vanishes, reduced coexistence region doesn't vary with Gauss-Bonnet coefficient $α$ any more. In this case, the Gauss-Bonnet coefficient $α$ plays the same role as the charge of five-dimensional RN-AdS black holes. Also, the situation of higher dimension is studied in the end.

gr-qc↗

arXiv2020

MAFF-Net: Filter False Positive for 3D Vehicle Detection with Multi-modal Adaptive Feature Fusion

3D vehicle detection based on multi-modal fusion is an important task of many applications such as autonomous driving. Although significant progress has been made, we still observe two aspects that need to be further improvement: First, the specific gain that camera images can bring to 3D detection is seldom explored by previous works. Second, many fusion algorithms run slowly, which is essential for applications with high real-time requirements(autonomous driving). To this end, we propose an end-to-end trainable single-stage multi-modal feature adaptive network in this paper, which uses image information to effectively reduce false positive of 3D detection and has a fast detection speed. A multi-modal adaptive feature fusion module based on channel attention mechanism is proposed to enable the network to adaptively use the feature of each modal. Based on the above mechanism, two fusion technologies are proposed to adapt to different usage scenarios: PointAttentionFusion is suitable for filtering simple false positive and faster; DenseAttentionFusion is suitable for filtering more difficult false positive and has better overall performance. Experimental results on the KITTI dataset demonstrate significant improvement in filtering false positive over the approach using only point cloud data. Furthermore, the proposed method can provide competitive results and has the fastest speed compared to the published state-of-the-art multi-modal methods in the KITTI benchmark.

cs.CV↗

arXiv2020

New gedanken experiment on higher-dimensional asymptotically AdS Reissner-Nordström black hole

Viewing the negative cosmological constant as a dynamical quantity derived from the matter field, we study the weak cosmic censorship conjecture for the higher-dimensional asymptotically AdS Reissner-Nordström black hole. To this end, using stability assumption of matter field perturbation and null energy condition of the matter field, we first derive the first-order and second-order perturbation inequalities containing the variable cosmological constant and its conjugated quantity for the black hole . We prove that the higher-dimensional RN-AdS black hole cannot be destroyed under a second-order approximation of the matter field perturbation process.

gr-qc↗

arXiv2020

Holographic Complexity in a Charged Supersymmetric Black Holes

For an ordinary charged system, it has been shown that by using the "complexity equals action" (CA) conjecture, the late-time growth rate of the holographic complexity is given by a difference between the value of $Φ_H Q+Ω_H J$ on the inner and outer horizons. In this paper, we study the influence of the chiral anomaly on the complexity of the boundary quantum system. To be specific, we evaluate the CA holographic complexity of the charged supersymmetric black holes whose bulk action is modified by an additional Chern-Simons term of the electromagnetic fields. As a result, the late-time growth rate of the complexity will be corrected by some additional terms on the inner and outer horizons than the ordinary charged black holes. Our work implies that the late-time growth rate of the complexity can carry the information of the chiral anomaly for the boundary system.

hep-th↗

arXiv2020

Snowmass 2021 LoI: Determination of cosmic ray properties in the local interstellar medium with all-sky anisotropy observations

Propagation of Galactic cosmic rays (CR) in the interstellar medium (ISM) is among the unsolved problems in particle astrophysics. Interpretation of CR spectrum and composition measurements and their possible link to dark matter crucially relies on our understanding of CR propagation in the Galaxy. Several air shower experiments have measured a significant anisotropy of CRs in the TeV to PeV energy range. These observations hint to a complicated overlap of more than one cause: from the distribution of the CR sources in the Milky Way to the nature of such sources, from the turbulence properties of interstellar plasmas to the inhomogeneous nature of the interstellar medium. Coherent magnetic structures such as the heliosphere greatly influence the CR arrival direction distribution. It is necessary to account for and remove the heliosphere's distortion effects if we want to determine the pristine CR arrival direction distribution in the local interstellar medium (LISM), the environment surrounding the solar system up to the distance of particle mean free path. The recent availability of accurate all-sky maps of CR arrival direction distribution and the latest advancements in heliospheric modeling, make it possible to infer the CR pitch angle distribution in the LISM using a Liouville mapping technique. With the interstellar CR distribution, we can study the global characteristics of CR diffusion, tap into the properties of interstellar plasma turbulence, test the recent and local CR source hypothesis, and whether clumps of dark matter have a role in the observed CR observations. The study can lead to developments aiming to a better understanding of the heliosphere, particularly the boundary region with the ISM, and additional constraints on the LISM properties.

astro-ph.HE↗