arXiv subjects

Peng Wang

Publications and source records attributed to Peng Wang.

At least 739 records · Page 41Linked to original sources

arXiv2021

Linear Convergence of a Proximal Alternating Minimization Method with Extrapolation for $\ell_1$-Norm Principal Component Analysis

A popular robust alternative of the classic principal component analysis (PCA) is the $\ell_1$-norm PCA (L1-PCA), which aims to find a subspace that captures the most variation in a dataset as measured by the $\ell_1$-norm. L1-PCA has shown great promise in alleviating the effect of outliers in data analytic applications. However, it gives rise to a challenging non-smooth non-convex optimization problem, for which existing algorithms are either not scalable or lack strong theoretical guarantees on their convergence behavior. In this paper, we propose a proximal alternating minimization method with extrapolation (PAMe) for solving a two-block reformulation of the L1-PCA problem. We then show that for both the L1-PCA problem and its two-block reformulation, the Kurdyka-Łojasiewicz exponent at any of the limiting critical points is $1/2$. This allows us to establish the linear convergence of the sequence of iterates generated by PAMe and to determine the criticality of the limit of the sequence with respect to both the L1-PCA problem and its two-block reformulation. To complement our theoretical development, we show via numerical experiments on both synthetic and real-world datasets that PAMe is competitive with a host of existing methods. Our results not only significantly advance the convergence theory of iterative methods for L1-PCA but also demonstrate the potential of our proposed method in applications.

math.OC↗

arXiv2021

Cosmic Web-Halo Connection Between Twin Universes

Both simulation and observational data have shown that the spin and shape of dark matter halos are correlated with their nearby large-scale environment. As structure formation on different scales is strongly coupled, it is trick to disentangle the formation of halo with the large-scale environment, making it difficult to infer which is the driving force for the correlation between halo spin/shape with the large-scale structure. In this paper, we use N-body simulation to produce twin Universes that share the same initial conditions on small scales but different on large scales. This is achieved by changing the random seeds for the phase of those k modes smaller than a given scale in the initial conditions. In this way, we are able to disentangle the formation of halo and large-scale structure, making it possible to investigate how halo spin and shape correspond to the change of environment on large scales. We identify matching halo pairs in the twin simulations as those sharing the maximum number of identical particles within each other. Using these matched halo pairs, we study the cross match of halo spin and their correlation with the large-scale structure. It is found that when the large-scale environment changes (eigenvector) between the twin simulations, the halo spin has to rotate accordingly, although not significantly, to maintain the universal correlation seen in each simulation. Our results suggest that the large-scale structure is the main factor to drive the correlation between halo properties and their environment.

astro-ph.CO↗

arXiv2021

AdaXpert: Adapting Neural Architecture for Growing Data

In real-world applications, data often come in a growing manner, where the data volume and the number of classes may increase dynamically. This will bring a critical challenge for learning: given the increasing data volume or the number of classes, one has to instantaneously adjust the neural model capacity to obtain promising performance. Existing methods either ignore the growing nature of data or seek to independently search an optimal architecture for a given dataset, and thus are incapable of promptly adjusting the architectures for the changed data. To address this, we present a neural architecture adaptation method, namely Adaptation eXpert (AdaXpert), to efficiently adjust previous architectures on the growing data. Specifically, we introduce an architecture adjuster to generate a suitable architecture for each data snapshot, based on the previous architecture and the different extent between current and previous data distributions. Furthermore, we propose an adaptation condition to determine the necessity of adjustment, thereby avoiding unnecessary and time-consuming adjustments. Extensive experiments on two growth scenarios (increasing data volume and number of classes) demonstrate the effectiveness of the proposed method.

cs.LG↗

arXiv2021

Towards End-to-End Text Spotting in Natural Scenes

Text spotting in natural scene images is of great importance for many image understanding tasks. It includes two sub-tasks: text detection and recognition. In this work, we propose a unified network that simultaneously localizes and recognizes text with a single forward pass, avoiding intermediate processes such as image cropping and feature re-calculation, word separation, and character grouping. In contrast to existing approaches that consider text detection and recognition as two distinct tasks and tackle them one by one, the proposed framework settles these two tasks concurrently. The whole framework can be trained end-to-end and is able to handle text of arbitrary shapes. The convolutional features are calculated only once and shared by both detection and recognition modules. Through multi-task training, the learned features become more discriminate and improve the overall performance. By employing the $2$D attention model in word recognition, the irregularity of text can be robustly addressed. It provides the spatial location for each character, which not only helps local feature extraction in word recognition, but also indicates an orientation angle to refine text localization. Our proposed method has achieved state-of-the-art performance on several standard text spotting benchmarks, including both regular and irregular ones.

cs.CV↗

arXiv2021

HR-NAS: Searching Efficient High-Resolution Neural Architectures with Lightweight Transformers

High-resolution representations (HR) are essential for dense prediction tasks such as segmentation, detection, and pose estimation. Learning HR representations is typically ignored in previous Neural Architecture Search (NAS) methods that focus on image classification. This work proposes a novel NAS method, called HR-NAS, which is able to find efficient and accurate networks for different tasks, by effectively encoding multiscale contextual information while maintaining high-resolution representations. In HR-NAS, we renovate the NAS search space as well as its searching strategy. To better encode multiscale image contexts in the search space of HR-NAS, we first carefully design a lightweight transformer, whose computational complexity can be dynamically changed with respect to different objective functions and computation budgets. To maintain high-resolution representations of the learned networks, HR-NAS adopts a multi-branch architecture that provides convolutional encoding of multiple feature resolutions, inspired by HRNet. Last, we proposed an efficient fine-grained search strategy to train HR-NAS, which effectively explores the search space, and finds optimal architectures given various tasks and computation resources. HR-NAS is capable of achieving state-of-the-art trade-offs between performance and FLOPs for three dense prediction tasks and an image classification task, given only small computational budgets. For example, HR-NAS surpasses SqueezeNAS that is specially designed for semantic segmentation while improving efficiency by 45.9%. Code is available at https://github.com/dingmyu/HR-NAS

cs.CV↗

arXiv2021

Optimal Non-Convex Exact Recovery in Stochastic Block Model via Projected Power Method

In this paper, we study the problem of exact community recovery in the symmetric stochastic block model, where a graph of $n$ vertices is randomly generated by partitioning the vertices into $K \ge 2$ equal-sized communities and then connecting each pair of vertices with probability that depends on their community memberships. Although the maximum-likelihood formulation of this problem is discrete and non-convex, we propose to tackle it directly using projected power iterations with an initialization that satisfies a partial recovery condition. Such an initialization can be obtained by a host of existing methods. We show that in the logarithmic degree regime of the considered problem, the proposed method can exactly recover the underlying communities at the information-theoretic limit. Moreover, with a qualified initialization, it runs in $\mathcal{O}(n\log^2n/\log\log n)$ time, which is competitive with existing state-of-the-art methods. We also present numerical results of the proposed method to support and complement our theoretical development.

math.OC↗

arXiv2021

Fastening the Initial Access in 5G NR Sidelink for 6G V2X Networks

The ever-increasing demand for intelligent, automated, and connected mobility solutions pushes for the development of an innovative sixth Generation (6G) of cellular networks. A radical transformation on the physical layer of vehicular communications is planned, with a paradigm shift towards beam-based millimeter Waves or sub-Terahertz communications, which require precise beam pointing for guaranteeing the communication link, especially in high mobility. A key design aspect is a fast and proactive Initial Access (IA) algorithm to select the optimal beam to be used. In this work, we investigate alternative IA techniques to fasten the current fifth-generation (5G) standard, targeting an efficient 6G design. First, we discuss cooperative position-based schemes that rely on the position information. Then, motivated by the intuition of a non-uniform distribution of the communication directions due to road topology constraints, we design two Probabilistic Codebook (PCB) techniques of prioritized beams. In the first one, the PCBs are built leveraging past collected traffic information, while in the second one, we use the Hough Transform over the digital map to extract dominant road directions. We also show that the information coming from the angular probability distribution allows designing non-uniform codebook quantization, reducing the degradation of the performances compared to uniform one. Numerical simulation on realistic scenarios shows that PCBs-based beam selection outperforms the 5G standard in terms of the number of IA trials, with a performance comparable to position-based methods, without requiring the signaling of sensitive information.

eess.SP↗

arXiv2021

Possible observational evidence that cosmic filaments spin

Most cosmological structures in the universe spin. Although structures in the universe form on a wide variety of scales from small dwarf galaxies to large super clusters, the generation of angular momentum across these scales is poorly understood. We have investigated the possibility that filaments of galaxies - cylindrical tendrils of matter hundreds of millions of light-years across, are themselves spinning. By stacking thousands of filaments together and examining the velocity of galaxies perpendicular to the filament's axis (via their red and blue shift), we have found that these objects too display motion consistent with rotation making them the largest objects known to have angular momentum. The strength of the rotation signal is directly dependent on the viewing angle and the dynamical state of the filament. Just as it is easiest to measure rotation in a spinning disk galaxy viewed edge on, so too is filament rotation clearly detected under similar geometric alignment. Furthermore, the mass of the haloes that sit at either end of the filaments also increases the spin speed. The more massive the haloes, the more rotation is detected. These results signify that angular momentum can be generated on unprecedented scales.

astro-ph.GA↗

arXiv2021

Low-Temperature Spectroscopic Investigation of Lead-Vacancy Centers in Diamond Fabricated by High-Pressure and High-Temperature Treatment

We report the optical observation of lead-vacancy (PbV) centers in diamond fabricated by Pb ion implantation and subsequent high-temperature annealing (2100 °C) under high pressure (7.7 GPa). Their optical properties were characterized by photoluminescence at varying temperatures down to 5.7 K. We observed intense emission peaks at 550 and 554 nm with a large splitting of approximately 3900 GHz. The two lines are thought to correspond to the zero phonon line (ZPL) of PbV centers with split ground and excited states. A cubic trend of the ZPL width was observed while varying temperature. We performed polarization measurements of the two lines in a single PbV center, showing nearly orthogonal dipole polarizations. These optical measurements strongly indicate that the PbV center possesses D3d symmetry in the diamond lattice. The observed large ground state splitting significantly suppresses the phonon-mediated transition, which causes decoherence of the electron spin state of the group IV color centers in diamond, expecting a long spin coherence time at a temperature of approximately 8 K.

quant-ph↗

arXiv2021

Generative Adversarial Networks: A Survey Towards Private and Secure Applications

Generative Adversarial Networks (GAN) have promoted a variety of applications in computer vision, natural language processing, etc. due to its generative model's compelling ability to generate realistic examples plausibly drawn from an existing distribution of samples. GAN not only provides impressive performance on data generation-based tasks but also stimulates fertilization for privacy and security oriented research because of its game theoretic optimization strategy. Unfortunately, there are no comprehensive surveys on GAN in privacy and security, which motivates this survey paper to summarize those state-of-the-art works systematically. The existing works are classified into proper categories based on privacy and security functions, and this survey paper conducts a comprehensive analysis of their advantages and drawbacks. Considering that GAN in privacy and security is still at a very initial stage and has imposed unique challenges that are yet to be well addressed, this paper also sheds light on some potential privacy and security applications with GAN and elaborates on some future research directions.

cs.LG↗

arXiv2021

Photon Ring and Observational Appearance of a Hairy Black Hole

Recently, the image of a Schwarzschild black hole with an accretion disk has been revisited, and it showed that the "photon ring", defined as highly bent light rays that intersect the disk plane more than twice, is extremely narrow and makes a negligible contribution to the total brightness. In this paper, we investigate the observational appearance of an optically and geometrically thin accretion disk around a hairy black hole in an Einstein-Maxwell-scalar model. Intriguingly, we find that in a certain parameter regime, due to an extra maximum or an "ankle-like" structure in the effective potential for photons, the photon ring can be remarkably wide, thus making a notable contribution to the flux of the observed image. In particular, there appears a wide and bright annulus, which comprises multiple concentric bright thin rings with different luminosity, in the high resolution image.

gr-qc↗

arXiv2021

Sketch and Refine: Towards Faithful and Informative Table-to-Text Generation

Table-to-text generation refers to generating a descriptive text from a key-value table. Traditional autoregressive methods, though can generate text with high fluency, suffer from low coverage and poor faithfulness problems. To mitigate these problems, we propose a novel Skeleton-based two-stage method that combines both Autoregressive and Non-Autoregressive generations (SANA). Our approach includes: (1) skeleton generation with an autoregressive pointer network to select key tokens from the source table; (2) edit-based non-autoregressive generation model to produce texts via iterative insertion and deletion operations. By integrating hard constraints from the skeleton, the non-autoregressive model improves the generation's coverage over the source table and thus enhances its faithfulness. We conduct automatic and human evaluations on both WikiPerson and WikiBio datasets. Experimental results demonstrate that our method outperforms the previous state-of-the-art methods in both automatic and human evaluation, especially on coverage and faithfulness. In particular, we achieve PARENT-T recall of 99.47 in WikiPerson, improving over the existing best results by more than 10 points.

cs.CL↗

arXiv2021

M6: A Chinese Multimodal Pretrainer

In this work, we construct the largest dataset for multimodal pretraining in Chinese, which consists of over 1.9TB images and 292GB texts that cover a wide range of domains. We propose a cross-modal pretraining method called M6, referring to Multi-Modality to Multi-Modality Multitask Mega-transformer, for unified pretraining on the data of single modality and multiple modalities. We scale the model size up to 10 billion and 100 billion parameters, and build the largest pretrained model in Chinese. We apply the model to a series of downstream applications, and demonstrate its outstanding performance in comparison with strong baselines. Furthermore, we specifically design a downstream task of text-guided image generation, and show that the finetuned M6 can create high-quality images with high resolution and abundant details.

cs.CL↗

arXiv2021

Active spintronic-metasurface terahertz emitters with tunable chirality

The ability to manipulate the electric-field vector of broadband terahertz waves is essential for applications of terahertz technologies in many areas, and can open up new possibilities for nonlinear terahertz spectroscopy and coherent control. Here, we propose a novel laser-driven terahertz emitter, consisting of metasurface-patterned magnetic multilayer heterostructures. Such hybrid terahertz emitters can combine the advantages of spintronic emitters for being ultrabroadband, efficient and flexible, as well as those of metasurfaces for the unique capability to manipulate terahertz waves with high precision and degree of freedom. Taking a stripe-patterned metasurface as an example, we demonstrate the generation of broadband terahertz waves with tunable chirality. Based on experimental and theoretical studies, the interplay between the laser-induced spintronic-origin currents and the metasurface-induced transient charges/currents are investigated, revealing the strong influence on the device functionality originated from both the light-matter interactions in individual metasurface units and the dynamic coupling between them. Our work not only offers a flexible, reliable and cost-effective solution for chiral terahertz wave generation and manipulation, but also opens a new pathway to metasurface-tailored spintronic devices for efficient vector-control of electromagnetic waves in the terahertz regime.

physics.optics↗

arXiv2021

GPR: Grasp Pose Refinement Network for Cluttered Scenes

Object grasping in cluttered scenes is a widely investigated field of robot manipulation. Most of the current works focus on estimating grasp pose from point clouds based on an efficient single-shot grasp detection network. However, due to the lack of geometry awareness of the local grasping area, it may cause severe collisions and unstable grasp configurations. In this paper, we propose a two-stage grasp pose refinement network which detects grasps globally while fine-tuning low-quality grasps and filtering noisy grasps locally. Furthermore, we extend the 6-DoF grasp with an extra dimension as grasp width which is critical for collisionless grasping in cluttered scenes. It takes a single-view point cloud as input and predicts dense and precise grasp configurations. To enhance the generalization ability, we build a synthetic single-object grasp dataset including 150 commodities of various shapes, and a multi-object cluttered scene dataset including 100k point clouds with robust, dense grasp poses and mask annotations. Experiments conducted on Yumi IRB-1400 Robot demonstrate that the model trained on our dataset performs well in real environments and outperforms previous methods by a large margin.

cs.RO↗

arXiv2021

Derive Lovelock Gravity from String Theory in Cosmological Background

It was proved more than three decades ago, that the first order $α'$ correction of string effective theory could be written as the Gauss-Bonnet term, which is the quadratic term of Lovelock gravity. In cosmological background, with an appropriate field redefinition, we reorganize the infinite $α'$ corrections of string effective action into a finite term expression for any specific dimension. This finite term expression matches Lovelock gravity exactly and thus fix the couplings of Lovelock gravity by the coefficients of string effective action. This result thus provides a strong support to string theory.

hep-th↗

arXiv2021

Thermodynamic Instability of $\mathrm{3D}$ Einstein-Born-Infeld AdS Black Holes

Super-entropic black holes possess finite-area but noncompact event horizons and violate the reverse isoperimetric inequality. It has been conjectured that such black holes always have negative specific heat at constant volume $C_{V}$ or negative specific heat at constant pressure $C_{P}$ whenever $C_{V}>0$, making them unstable in extended thermodynamics. In this paper, we test this instability conjecture on a family of nonlinear electrodynamics black holes, namely $3$D Einstein-Born-Infeld (EBI) AdS black holes. Our results show that when nonlinear electrodynamics effects are weak, the instability conjecture is valid. However, the conjecture can be violated in some parameter region when nonlinear electrodynamics effects are strong enough. This observation thus provides a counter example to the instability conjecture, which suggests that super-entropic black holes could be thermodynamically stable.

gr-qc↗

arXiv2021

Proposal-free One-stage Referring Expression via Grid-Word Cross-Attention

Referring Expression Comprehension (REC) has become one of the most important tasks in visual reasoning, since it is an essential step for many vision-and-language tasks such as visual question answering. However, it has not been widely used in many downstream tasks because it suffers 1) two-stage methods exist heavy computation cost and inevitable error accumulation, and 2) one-stage methods have to depend on lots of hyper-parameters (such as anchors) to generate bounding box. In this paper, we present a proposal-free one-stage (PFOS) model that is able to regress the region-of-interest from the image, based on a textual query, in an end-to-end manner. Instead of using the dominant anchor proposal fashion, we directly take the dense-grid of an image as input for a cross-attention transformer that learns grid-word correspondences. The final bounding box is predicted directly from the image without the time-consuming anchor selection process that previous methods suffer. Our model achieves the state-of-the-art performance on four referring expression datasets with higher efficiency, comparing to previous best one-stage and two-stage methods.

cs.CV↗