arXiv subjects

Peng Wang

Publications and source records attributed to Peng Wang.

At least 685 records · Page 38Linked to original sources

arXiv2022

Chaos bound and its violation in charged Kiselev black hole

The chaos bound in the near-horizon regions has been studied through the expansions of the metric functions on the horizon. In this paper, we investigate the chaos bound in the the near-horizon region and at a certain distance from the horizon of a charged Kiselev black hole. The value of the Lyapunov exponent is accurately calculated by a Jacobian matrix. The angular momentum of a charged particle around the black hole affects not only the exponent, but also the position of the equilibrium orbit. This position gradually moves away from the horizon with the increase of the angular momentum. We find that the the bound is violated at a certain distance from the horizon and there is no violation in the near-horizon region when the charge mass ratio of the particle is fixed. The small value of the normalization factor is more likely to cause the violation.

gr-qc↗

arXiv2022

CapOnImage: Context-driven Dense-Captioning on Image

Existing image captioning systems are dedicated to generating narrative captions for images, which are spatially detached from the image in presentation. However, texts can also be used as decorations on the image to highlight the key points and increase the attractiveness of images. In this work, we introduce a new task called captioning on image (CapOnImage), which aims to generate dense captions at different locations of the image based on contextual information. To fully exploit the surrounding visual context to generate the most suitable caption for each location, we propose a multi-modal pre-training model with multi-level pre-training tasks that progressively learn the correspondence between texts and image locations from easy to difficult. Since the model may generate redundant captions for nearby locations, we further enhance the location embedding with neighbor locations as context. For this new task, we also introduce a large-scale benchmark called CapOnImage2M, which contains 2.1 million product images, each with an average of 4.8 spatially localized captions. Compared with other image captioning model variants, our model achieves the best results in both captioning accuracy and diversity aspects. We will make code and datasets public to facilitate future research.

cs.CV↗

arXiv2022

Echoes from Hairy Black Holes

We study the waveforms of time signals produced by scalar perturbations in static hairy black holes, in which the perturbations can be governed by a double-peak effective potential. The inner potential peak would give rise to echoes, which provide a powerful tool to test the Kerr hypothesis. The waveforms are constructed in the time and frequency domains, and we find that the late-time waveforms are determined by the long-lived and sub-long-lived quasinormal modes, which are trapped in the potential valley and near the smaller peak, respectively. When the distance between the peaks is significantly larger than the width of the peaks, a train of decaying echo pulses is produced by the superposition of the long-lived and sub-long-lived modes. In certain cases, the echoes can vanish and then reappear. When the peaks are close enough, one detects far fewer echo signals and a following sinusoid tail, which is controlled by the long-lived or sub-long-lived mode and hence decays very slowly.

gr-qc↗

arXiv2022

Investigation of Bare-bones Algorithms from Quantum Perspective: A Quantum Dynamical Global Optimizer

Recent decades, the emergence of numerous novel algorithms makes it a gimmick to propose an intelligent optimization system based on metaphor, and hinders researchers from exploring the essence of search behavior in algorithms. However, it is difficult to directly discuss the search behavior of an intelligent optimization algorithm, since there are so many kinds of intelligent schemes. To address this problem, an intelligent optimization system is regarded as a simulated physical optimization system in this paper. The dynamic search behavior of such a simplified physical optimization system are investigated with quantum theory. To achieve this goal, the Schroedinger equation is employed as the dynamics equation of the optimization algorithm, which is used to describe dynamic search behaviours in the evolution process with quantum theory. Moreover, to explore the basic behaviour of the optimization system, the optimization problem is assumed to be decomposed and approximated. Correspondingly, the basic search behaviour is derived, which constitutes the basic iterative process of a simple optimization system. The basic iterative process is compared with some classical bare-bones schemes to verify the similarity of search behavior under different metaphors. The search strategies of these bare bones algorithms are analyzed through experiments.

cs.NE↗

arXiv2022

NightLab: A Dual-level Architecture with Hardness Detection for Segmentation at Night

The semantic segmentation of nighttime scenes is a challenging problem that is key to impactful applications like self-driving cars. Yet, it has received little attention compared to its daytime counterpart. In this paper, we propose NightLab, a novel nighttime segmentation framework that leverages multiple deep learning models imbued with night-aware features to yield State-of-The-Art (SoTA) performance on multiple night segmentation benchmarks. Notably, NightLab contains models at two levels of granularity, i.e. image and regional, and each level is composed of light adaptation and segmentation modules. Given a nighttime image, the image level model provides an initial segmentation estimate while, in parallel, a hardness detection module identifies regions and their surrounding context that need further analysis. A regional level model focuses on these difficult regions to provide a significantly improved segmentation. All the models in NightLab are trained end-to-end using a set of proposed night-aware losses without handcrafted heuristics. Extensive experiments on the NightCity and BDD100K datasets show NightLab achieves SoTA performance compared to concurrent methods.

cs.CV↗

arXiv2022

DistPro: Searching A Fast Knowledge Distillation Process via Meta Optimization

Recent Knowledge distillation (KD) studies show that different manually designed schemes impact the learned results significantly. Yet, in KD, automatically searching an optimal distillation scheme has not yet been well explored. In this paper, we propose DistPro, a novel framework which searches for an optimal KD process via differentiable meta-learning. Specifically, given a pair of student and teacher networks, DistPro first sets up a rich set of KD connection from the transmitting layers of the teacher to the receiving layers of the student, and in the meanwhile, various transforms are also proposed for comparing feature maps along its pathway for the distillation. Then, each combination of a connection and a transform choice (pathway) is associated with a stochastic weighting process which indicates its importance at every step during the distillation. In the searching stage, the process can be effectively learned through our proposed bi-level meta-optimization strategy. In the distillation stage, DistPro adopts the learned processes for knowledge distillation, which significantly improves the student accuracy especially when faster training is required. Lastly, we find the learned processes can be generalized between similar tasks and networks. In our experiments, DistPro produces state-of-the-art (SoTA) accuracy under varying number of learning epochs on popular datasets, i.e. CIFAR100 and ImageNet, which demonstrate the effectiveness of our framework.

cs.CV↗

arXiv2022

LoS-Map Construction for Proactive Relay of Opportunity Selection in 6G V2X Systems

Recent advances in Vehicle-to-Everything (V2X) technology and the upcoming sixth-generation (6G) network will dawn a new era for vehicular services with enhanced communication capabilities. Connected and Autonomous Vehicles (CAVs) are expected to deliver a new transportation experience, increasing the safety and efficiency of road networks. The use of millimeter-wave (mmW) frequencies guarantees a huge amount of bandwidth (> 1GHz) and a high data rate (> 10Gbit/s), which are required for CAVs applications. However, high frequency is impaired by severe path loss, and line of sight (LoS) propagation can be easily blocked by static and dynamic obstacles. Several solutions are being investigated, and the most promising one exploits relays. However, traditional relay schemes react to link failure and leverage instantaneous information, which impedes efficient relay selection in highly mobile and complex networks, such as vehicular scenarios. In this context, we propose a novel proactive relaying strategy that exploits the cooperation between CAVs and environment information to predict the dynamic LoS-map, which describes the links' evolution in time. The proactive relaying schemes exploit the dynamic LoS-map to maximize the network connectivity. A novel framework integrating realistic mobility patterns and geometric channel propagation models is proposed to analyze the performance in different scenarios. Numerical simulations suggest that the proactive relaying schemes mitigate beam blockage and maximize the average probability of connecting CAVs with reliable links.

eess.SP↗

arXiv2022

Semi-Supervised Wide-Angle Portraits Correction by Multi-Scale Transformer

We propose a semi-supervised network for wide-angle portraits correction. Wide-angle images often suffer from skew and distortion affected by perspective distortion, especially noticeable at the face regions. Previous deep learning based approaches need the ground-truth correction flow maps for training guidance. However, such labels are expensive, which can only be obtained manually. In this work, we design a semi-supervised scheme and build a high-quality unlabeled dataset with rich scenarios, allowing us to simultaneously use labeled and unlabeled data to improve performance. Specifically, our semi-supervised scheme takes advantage of the consistency mechanism, with several novel components such as direction and range consistency (DRC) and regression consistency (RC). Furthermore, different from the existing methods, we propose the Multi-Scale Swin-Unet (MS-Unet) based on the multi-scale swin transformer block (MSTB), which can simultaneously learn short-distance and long-distance information to avoid artifacts. Extensive experiments demonstrate that the proposed method is superior to the state-of-the-art methods and other representative baselines. The source code and dataset are available at: https://github.com/megvii-research/Portraits_Correction.

cs.CV↗

arXiv2022

Spin conservation of cosmic filaments

Cosmic filaments are the largest collapsing structure in the Universe. Recently both observations and simulations inferred that cosmic filaments have coherent angular momenta (spins). Here we use filament finders to identify the filamentary structures in cosmological simulations and study their physical origins, which are well described by the primordial tidal torque of their Lagrangian counterpart regions -- protofilaments. This initial angular momenta statistically preserve their directions to low redshifts. We further show that a spin reconstruction method can predict the spins of filaments and potentially relate their spins to the initial conditions of the Universe. This correlation provides a new way of constraining and obtaining additional information of the initial perturbations of the Universe.

astro-ph.CO↗

arXiv2022

Self-Contrastive Learning based Semi-Supervised Radio Modulation Classification

This paper presents a semi-supervised learning framework that is new in being designed for automatic modulation classification (AMC). By carefully utilizing unlabeled signal data with a self-supervised contrastive-learning pre-training step, our framework achieves higher performance given smaller amounts of labeled data, thereby largely reducing the labeling burden of deep learning. We evaluate the performance of our semi-supervised framework on a public dataset. The evaluation results demonstrate that our semi-supervised approach significantly outperforms supervised frameworks thereby substantially enhancing our ability to train deep neural networks for automatic modulation classification in a manner that leverages unlabeled data.

cs.LG↗

arXiv2022

Neural Rays for Occlusion-aware Image-based Rendering

We present a new neural representation, called Neural Ray (NeuRay), for the novel view synthesis task. Recent works construct radiance fields from image features of input views to render novel view images, which enables the generalization to new scenes. However, due to occlusions, a 3D point may be invisible to some input views. On such a 3D point, these generalization methods will include inconsistent image features from invisible views, which interfere with the radiance field construction. To solve this problem, we predict the visibility of 3D points to input views within our NeuRay representation. This visibility enables the radiance field construction to focus on visible image features, which significantly improves its rendering quality. Meanwhile, a novel consistency loss is proposed to refine the visibility in NeuRay when finetuning on a specific scene. Experiments demonstrate that our approach achieves state-of-the-art performance on the novel view synthesis task when generalizing to unseen scenes and outperforms per-scene optimization methods after finetuning.

cs.CV↗

arXiv2022

Node Representation Learning in Graph via Node-to-Neighbourhood Mutual Information Maximization

The key towards learning informative node representations in graphs lies in how to gain contextual information from the neighbourhood. In this work, we present a simple-yet-effective self-supervised node representation learning strategy via directly maximizing the mutual information between the hidden representations of nodes and their neighbourhood, which can be theoretically justified by its link to graph smoothing. Following InfoNCE, our framework is optimized via a surrogate contrastive loss, where the positive selection underpins the quality and efficiency of representation learning. To this end, we propose a topology-aware positive sampling strategy, which samples positives from the neighbourhood by considering the structural dependencies between nodes and thus enables positive selection upfront. In the extreme case when only one positive is sampled, we fully avoid expensive neighbourhood aggregation. Our methods achieve promising performance on various node classification datasets. It is also worth mentioning by applying our loss function to MLP based node encoders, our methods can be orders of faster than existing solutions. Our codes and supplementary materials are available at https://github.com/dongwei156/n2n.

cs.LG↗

arXiv2022

HOP: History-and-Order Aware Pre-training for Vision-and-Language Navigation

Pre-training has been adopted in a few of recent works for Vision-and-Language Navigation (VLN). However, previous pre-training methods for VLN either lack the ability to predict future actions or ignore the trajectory contexts, which are essential for a greedy navigation process. In this work, to promote the learning of spatio-temporal visual-textual correspondence as well as the agent's capability of decision making, we propose a novel history-and-order aware pre-training paradigm (HOP) with VLN-specific objectives that exploit the past observations and support future action prediction. Specifically, in addition to the commonly used Masked Language Modeling (MLM) and Trajectory-Instruction Matching (TIM), we design two proxy tasks to model temporal order information: Trajectory Order Modeling (TOM) and Group Order Modeling (GOM). Moreover, our navigation action prediction is also enhanced by introducing the task of Action Prediction with History (APH), which takes into account the history visual perceptions. Extensive experimental results on four downstream VLN tasks (R2R, REVERIE, NDH, RxR) demonstrate the effectiveness of our proposed method compared against several state-of-the-art agents.

cs.CV↗

arXiv2022

Cold and Hot gas distribution around the Milky-Way-M31 system in the HESTIA simulations

Recent observations have revealed remarkable insights into the gas reservoir in the circumgalactic medium (CGM) of galaxy haloes. In this paper, we characterise the gas in the vicinity of Milky Way and Andromeda analogues in the HESTIA (High resolution Environmental Simulations of The Immediate Area) suite of constrained Local Group (LG) simulations. The HESTIA suite comprise of a set of three high-resolution {\sc arepo}-based simulations of the LG, run using the Auriga galaxy formation model. For this paper, we focus only on the $z = 0$ simulation datasets and generate mock skymaps along with a power spectrum analysis to show that the distributions of ions tracing low-temperature gas (HI and SiIII) are more clumpy in comparison to warmer gas tracers (OVI, OVII and OVIII). We compare to the spectroscopic CGM observations of M31 and low-redshift galaxies. HESTIA under-produces the column densities of the M31 observations, but the simulations are consistent with the observations of low-redshift galaxies. A possible explanation for these findings is that the spectroscopic observations of M31 are contaminated by gas residing in the CGM of the Milky Way.

astro-ph.GA↗

arXiv2022

Nonlinear Thouless pumping: solitons and transport breakdown

One-dimensional topological pumping of matter waves in two overlaid optical lattices moving with respect to each other is considered in the presence of attractive nonlinearity. It is shown that there exists a threshold nonlinearity level above which the matter transfer is completely arrested. Below this threshold the transfer of both dispersive wavepackets and solitons occurs in accordance with the predictions of the linear theory, i.e. it is quantized and determined by the dynamical Chern numbers of the lowest band. The breakdown of the transport is also explained by nontrivial topology of the bands. In that case, the nonlinearity induces Rabi oscillations of atoms between two (or more) lowest bands. If the sum of the dynamical Chern numbers of the populated bands is zero, the oscillatory dynamics of a matter soliton in space occurs, which corresponds to the transport breakdown. Otherwise the sum of the Chern numbers of the nonlinearity-excited bands determines the direction and magnitude of the average velocity of matter solitons that remains quantized and admits fractional values. Thus, even in strongly nonlinear regime the topology of the linear bands is responsible for the evolution of solitons. The transition between different dynamical regimes is accurately described by the perturbation theory for solitons.

nlin.PS↗

arXiv2022

Nonequilibrium Transport Characteristics of substances in a Rough Potential Field

A Langevin equation is proposed to describe the transport of overdamped Brownian particles in a periodic rough potential and driven by an unbiased periodic force. The equation can be transformed into the Fokker-Planck equation by using the Kramers-Moyal expansions. The time-dependent solution of Fokker-Planck equation demonstrates different modes of the probability flow. These modes include creeping in a single direction, direction reversal and oscillating in both directions in the coordinate space. By varying the roughness and noise intensity, the flow can transform between the modes. The correlation between the noise and space indicates that the noise can maintain the oscillation of the modes and prolong the transient time to the steady state at which the flow tends to be zero.

cond-mat.stat-mech↗

arXiv2022

Relation Regularized Scene Graph Generation

Scene graph generation (SGG) is built on top of detected objects to predict object pairwise visual relations for describing the image content abstraction. Existing works have revealed that if the links between objects are given as prior knowledge, the performance of SGG is significantly improved. Inspired by this observation, in this article, we propose a relation regularized network (R2-Net), which can predict whether there is a relationship between two objects and encode this relation into object feature refinement and better SGG. Specifically, we first construct an affinity matrix among detected objects to represent the probability of a relationship between two objects. Graph convolution networks (GCNs) over this relation affinity matrix are then used as object encoders, producing relation-regularized representations of objects. With these relation-regularized features, our R2-Net can effectively refine object labels and generate scene graphs. Extensive experiments are conducted on the visual genome dataset for three SGG tasks (i.e., predicate classification, scene graph classification, and scene graph detection), demonstrating the effectiveness of our proposed method. Ablation studies also verify the key roles of our proposed components in performance improvement.

cs.CV↗

arXiv2022

Exact Community Recovery over Signed Graphs

Signed graphs encode similarity and dissimilarity relationships among different entities with positive and negative edges. In this paper, we study the problem of community recovery over signed graphs generated by the signed stochastic block model (SSBM) with two equal-sized communities. Our approach is based on the maximum likelihood estimation (MLE) of the SSBM. Unlike many existing approaches, our formulation reveals that the positive and negative edges of a signed graph should be treated unequally. We then propose a simple two-stage iterative algorithm for solving the regularized MLE. It is shown that in the logarithmic degree regime, the proposed algorithm can exactly recover the underlying communities in nearly-linear time at the information-theoretic limit. Numerical results on both synthetic and real data are reported to validate and complement our theoretical developments and demonstrate the efficacy of the proposed method.

cs.SI↗