arXiv subjects

Pengfei Li

Publications and source records attributed to Pengfei Li.

At least 199 records · Page 11Linked to original sources

arXiv2022

Topological bulk solitons in a nonlinear photonic Chern insulator

Nonlinearities in lattices with topological band structures can induce topological interfaces in the bulk of structures and give rise to bulk solitons in the topological bandgaps. Here we study a photonic Chern insulator with saturable nonlinearity and show the existence of topological bulk solitons. The fundamental bulk solitons exhibit as semi-vortex solitons, where only one pseudospin component has a nonzero vorticity. The bulk solitons have equal angular momentum at different valleys. This phenomenon is a direct outcome of the topology of the linear host lattice and the angular momentum can be changed by switching the sign of the nonlinearity. The bulk solitons bifurcate from the linear bulk band edge and terminate when their powers saturate. We find that these bulk solitons are stable within the whole spectrum range. Moreover, these bulk solitons are robust against lattice disorders both from on-site energies and hopping amplitudes. Our work extends the study of Chern insulators into the nonlinear regime and highlights the interplay between topology and nonlinearity.

physics.optics↗

arXiv2022

First-principle calculations of plasmon excitations in graphene,silicene and germanene

Plasmon excitations in graphene, silicene and germanene are studied using linear-response time dependent density functional theory within the random phase approximation (RPA). Here, we examine both the plasmon dispersion behavior and lifetime of extrinsic and intrinsic plasmons for these three materials. For extrinsic plasmons, we found that their properties are closely related to Landau damping. In the region without single-particle excitation (SPE), the plasmon dispersion shows a \sqrt{q} behavior and the lifetime is infinite at the RPA level, while in the single-particle excitation region, the plasmon dispersion shows a quasilinear behavior and the lifetime is finite. Moreover, for intrinsic plasmons, unlike graphene, the plasmon dispersion behavior of silicene and germanene exhibits a two-peak structure, which can be attributed to the complex and hybridized band structure of these two materials.

physics.comp-ph↗

arXiv2022

TOIST: Task Oriented Instance Segmentation Transformer with Noun-Pronoun Distillation

Current referring expression comprehension algorithms can effectively detect or segment objects indicated by nouns, but how to understand verb reference is still under-explored. As such, we study the challenging problem of task oriented detection, which aims to find objects that best afford an action indicated by verbs like sit comfortably on. Towards a finer localization that better serves downstream applications like robot interaction, we extend the problem into task oriented instance segmentation. A unique requirement of this task is to select preferred candidates among possible alternatives. Thus we resort to the transformer architecture which naturally models pair-wise query relationships with attention, leading to the TOIST method. In order to leverage pre-trained noun referring expression comprehension models and the fact that we can access privileged noun ground truth during training, a novel noun-pronoun distillation framework is proposed. Noun prototypes are generated in an unsupervised manner and contextual pronoun features are trained to select prototypes. As such, the network remains noun-agnostic during inference. We evaluate TOIST on the large-scale task oriented dataset COCO-Tasks and achieve +10.9% higher $\rm{mAP^{box}}$ than the best-reported results. The proposed noun-pronoun distillation can boost $\rm{mAP^{box}}$ and $\rm{mAP^{mask}}$ by +2.8% and +3.8%. Codes and models are publicly available at https://github.com/AIR-DISCOVER/TOIST.

cs.CV↗

arXiv2022

Domain walls in fractional media

Currently, much interest is drawn to the analysis of optical and matter-wave modes supported by the fractional diffraction in nonlinear media. We predict a new type of such states, in the form of domain walls (DWs) in the two-component system of immiscible fields. Numerical study of the underlying system of fractional nonlinear Schroedinger equations demonstrates the existence and stability of DWs at all values of the respective Levy index (a < 2) which determines the fractional diffraction, and at all values of the XPM/SPM ratio b in the two-component system above the immiscibility threshold. The same conclusion is obtained for DWs in the system which includes the linear coupling, alongside the XPM interaction between the immiscible components. Analytical results are obtained for the scaling of the DW's width. The DW solutions are essentially simplified in the special case of b = 3, as well as close to the immiscibility threshold. In addition to symmetric DWs, asymmetric ones are constructed too, in the system with unequal diffraction coefficients and/or different Levy indices of the two components.

physics.optics↗

arXiv2022

General theory of Josephson Diodes

Motivated by recent progress in the superconductivity nonreciprocal phenomena, we study the general theory of Josephson diodes. The central ingredient for Josephson diodes is the asymmetric proximity process inside the tunneling barrier. From the symmetry breaking point of view, there are two types of Josephson diodes, inversion breaking and time-reversal breaking. For the inversion breaking case, applying voltage bias could effectively tune the proximity process like the voltage-dependent Rashba coupling or electric polarization giving rise to $I_c(V)\neq I_c(-V)$ and $I_{r+}\neq I_{r-}$. For the time-reversal breaking case, the current flow could adjust the internal time-reversal breaking field like magnetism or time-reversal breaking electron-electron pairing, which leads to $I_{c+}\neq I_{c-}$. All these results provide a complete understanding and the general principles of realizing Josephson diodes, especially the recently found NbSe$_2$/Nb$_3$Br$_8$/NbSe$_2$ Josephson diodes.

cond-mat.supr-con↗

arXiv2022

Spontaneous symmetry breaking and ghost states supported by the fractional nonlinear Schrödinger equation with focusing saturable nonlinearity and PT-symmetric potential

We report a novel spontaneous symmetry breaking phenomenon and ghost states existed in the framework of the fractional nonlinear Schrödinger (FNLS) equation with focusing saturable nonlinearity and PT-symmetric potential. The continuous asymmetric soliton branch bifurcates from the fundamental symmetric one as the power exceeds some critical value. Intriguingly, the symmetry of fundamental solitons is broken into two branches of asymmetry solitons (alias ghost states) with complex conjugate propagation constants, which is solely in fractional media. Besides, the dipole (antisymmetry) and tripole solitons are also studied numerically. Moreover, we analyze the influences of fractional Lévy index and saturable nonlinear parameters on the symmetry breaking of solitons in detail. And the stability of fundamental soliton, asymmetric, dipole and tripole solitons are explored via the linear stability analysis and direct propagations. Moreover, we explore the elastic/semi-elastic collision phenomena between symmetric and asymmetric solitons. Meanwhile, we find the stable excitations from the fractional diffraction with saturation nonlinearity to integer-order diffraction with Kerr nonlinearity via the adiabatic excitations of parameters. These results will provide some theoretical basis for the study of spontaneous symmetry breaking phenomena and related physical experiments in the fractional media with PT-symmetric potentials.

nlin.PS↗

arXiv2022

Penalized empirical likelihood estimation and EM algorithms for closed-population capture-recapture models

Capture-recapture experiments are widely used to estimate the abundance of a finite population. Based on capture-recapture data, the empirical likelihood (EL) method has been shown to outperform the conventional conditional likelihood (CL) method. However, the current literature on EL abundance estimation ignores behavioral effects, and the EL estimates may not be stable, especially when the capture probability is low. We make three contributions in this paper. First, we extend the EL method to capture-recapture models that account for behavioral effects. Second, to overcome the instability of the EL method, we propose a penalized EL (PEL) estimation method that penalizes large abundance values. We then investigate the asymptotics of the maximum PEL estimator and the PEL ratio statistic. Third, we develop standard expectation-maximization (EM) algorithms for PEL to improve its practical performance. The EM algorithm is also applicable to EL and CL with slight modifications. Our simulation and a real-world data analysis demonstrate that the PEL method successfully overcomes the instability of the EL method and the proposed EM algorithm produces more reliable results than existing optimization algorithms.

stat.ME↗

arXiv2022

Incorporating baryon-driven contraction of dark matter halos in rotation curve fits

The condensation of baryons within a dark matter (DM) halo during galaxy formation should result in some contraction of the halo as the combined system settles into equilibrium. We quantify this effect on the cuspy primordial halos predicted by DM-only simulations for the baryon distributions observed in the galaxies of the SPARC database. We find that the DM halos of high surface brightness galaxies (with $Σ_{\rm eff}\gtrsim100$ $L_\odot$ pc$^{-2}$ at 3.6 $μ$m) experience strong contraction. Halos become more cuspy as a result of compression: the inner DM density slope increases with the baryonic surface mass density. We iteratively fit rotation curves to find the balance between initial halo parameters (constrained by abundance matching), compression, and stellar mass-to-light ratio. The resulting fits often require lower stellar masses than expected for stellar populations, particularly in galaxies with bulges: stellar mass must be reduced to make room for the DM it compresses. This trade off between dark and luminous mass is reminiscent of the cusp-core problem in dwarf galaxies, but occurs in more massive systems: the present-epoch DM halos cannot follow from cuspy primordial halos unless (1) the stellar mass-to-light ratios are systematically smaller than expected from standard stellar population synthesis models, and/or (2) there is a net outward mass redistribution from the initial cusp, even in massive galaxies widely considered to be immune from such effects.

astro-ph.GA↗

arXiv2022

The intervelocity of galaxy pairs in $Λ$CDM -- The observed velocity peak at ~130 km/s is not unique to MOND

Observational studies of pairs of galaxies have uncovered that their differential line-of-sight velocities indicate the presence of a peak in their three-dimensional intervelocity distribution at 130-150 km/s. It had been argued that galaxy pairs in the standard model of cosmology, $Λ$CDM, should not exhibit such an intervelocity peak, while Modified Newtonian Dynamics (MOND) predicts such a preferred intervelocity for paired galaxies. However, no direct comparison with $Λ$CDM applying the same selection criteria and methodology as the observational studies has been performed yet, placing the comparison on unsure footing. To rectify this, we investigate this potential challenge for $Λ$CDM by determining whether an analog of the observed intervelocity peak is present in galaxy pairs within the IllustrisTNG-300 cosmological simulation. We identify galaxy pairs following the observational study's selection criteria, measure their projected velocity difference, and analyse both the de-projected as well as the full velocity difference for this galaxy pair sample in the simulation. We recover a deprojected intervelocity peak at ~130 km/s for galaxy pairs selected from the simulation. The full three-dimensional velocity information available for the pairs in the simulation also reveals a clear preference for this intervelocity. The intervelocity peak among galaxy pairs does not appear to be a feature unique to MOND, but is also present in $Λ$CDM. It can thus not be claimed as a unique success of either theory over the other. Developing the galaxy pair intervelocity into a test of gravity in the low acceleration regime will require more detailed studies to identify measurable differences in the models.

astro-ph.GA↗

arXiv2022

DavarOCR: A Toolbox for OCR and Multi-Modal Document Understanding

This paper presents DavarOCR, an open-source toolbox for OCR and document understanding tasks. DavarOCR currently implements 19 advanced algorithms, covering 9 different task forms. DavarOCR provides detailed usage instructions and the trained models for each algorithm. Compared with the previous opensource OCR toolbox, DavarOCR has relatively more complete support for the sub-tasks of the cutting-edge technology of document understanding. In order to promote the development and application of OCR technology in academia and industry, we pay more attention to the use of modules that different sub-domains of technology can share. DavarOCR is publicly released at https://github.com/hikopensource/Davar-Lab-OCR.

cs.CV↗

arXiv2022

TRIE++: Towards End-to-End Information Extraction from Visually Rich Documents

Recently, automatically extracting information from visually rich documents (e.g., tickets and resumes) has become a hot and vital research topic due to its widespread commercial value. Most existing methods divide this task into two subparts: the text reading part for obtaining the plain text from the original document images and the information extraction part for extracting key contents. These methods mainly focus on improving the second, while neglecting that the two parts are highly correlated. This paper proposes a unified end-to-end information extraction framework from visually rich documents, where text reading and information extraction can reinforce each other via a well-designed multi-modal context block. Specifically, the text reading part provides multi-modal features like visual, textual and layout features. The multi-modal context block is developed to fuse the generated multi-modal features and even the prior knowledge from the pre-trained language model for better semantic representation. The information extraction part is responsible for generating key contents with the fused context features. The framework can be trained in an end-to-end trainable manner, achieving global optimization. What is more, we define and group visually rich documents into four categories across two dimensions, the layout and text type. For each document category, we provide or recommend the corresponding benchmarks, experimental settings and strong baselines for remedying the problem that this research area lacks the uniform evaluation standard. Extensive experiments on four kinds of benchmarks (from fixed layout to variable layout, from full-structured text to semi-unstructured text) are reported, demonstrating the proposed method's effectiveness. Data, source code and models are available.

cs.CV↗

arXiv2022

An Extendable Maneuver Management Framework with Fault-Tolerant Mechanism for Vehicle Platoon Control System in Highway Scenario

Vehicle platoon often face the problem of lack of scalability of maneuvers in practical applications. Once a new scenario is added, the original program may no longer be available. To deal with this problem, this paper introduces a two-dimensional maneuver management framework with a fault-tolerant mechanism on the basis of the proposed hierarchical architecture for the platoon control system. Maneuvers and roles are two dimensions, based on which the management strategies are decoupled. This makes each vehicle in the platoon has the ability to execute management strategies of various maneuvers and the new maneuver could be extended without revising the existing part. The fault-tolerant mechanism is designed as a maneuver triggered by hardware failures to keep safe before taking over. Furthermore, three typical maneuvers are selected for case studies to illustrate how the management strategies in this framework work. Finally, a comprehensive simulation scenario integrating different maneuvers is designed and a real-world implementation using micro-vehicles is conducted. Results show that the propose two-dimensional framework could effectively deal with various maneuvers and satisfy the computational real-time requirements

eess.SY↗

arXiv2022

Universal Conditional Masked Language Pre-training for Neural Machine Translation

Pre-trained sequence-to-sequence models have significantly improved Neural Machine Translation (NMT). Different from prior works where pre-trained models usually adopt an unidirectional decoder, this paper demonstrates that pre-training a sequence-to-sequence model but with a bidirectional decoder can produce notable performance gains for both Autoregressive and Non-autoregressive NMT. Specifically, we propose CeMAT, a conditional masked language model pre-trained on large-scale bilingual and monolingual corpora in many languages. We also introduce two simple but effective methods to enhance the CeMAT, aligned code-switching & masking and dynamic dual-masking. We conduct extensive experiments and show that our CeMAT can achieve significant performance improvement for all scenarios from low- to extremely high-resource languages, i.e., up to +14.4 BLEU on low resource and +7.9 BLEU improvements on average for Autoregressive NMT. For Non-autoregressive NMT, we demonstrate it can also produce consistent performance gains, i.e., up to +5.3 BLEU. To the best of our knowledge, this is the first work to pre-train a unified model for fine-tuning on both NMT tasks. Code, data, and pre-trained models are available at https://github.com/huawei-noah/Pretrained-Language-Model/tree/master/CeMAT.

cs.CL↗

arXiv2022

Self-localized topological states in three dimensions

Three-dimensional (3D) topological materials exhibit much richer phenomena than their lower-dimensional counterparts. Here, we propose self-localized topological states (i.e., topological solitons) in a 3D nonlinear photonic Chern insulator. Despite being in the bulk and self-localized in all 3D, the topological solitons at high-symmetry points K and K' rotate in the same direction, due to the underlying topology. Specifically, under the saturable nonlinearity the solitons are stable over a broad frequency range. Our results highlight how topology and nonlinearity interact with each other and can be extended to other 3D topological systems.

physics.optics↗

arXiv2022

Expert-Calibrated Learning for Online Optimization with Switching Costs

We study online convex optimization with switching costs, a practically important but also extremely challenging problem due to the lack of complete offline information. By tapping into the power of machine learning (ML) based optimizers, ML-augmented online algorithms (also referred to as expert calibration in this paper) have been emerging as state of the art, with provable worst-case performance guarantees. Nonetheless, by using the standard practice of training an ML model as a standalone optimizer and plugging it into an ML-augmented algorithm, the average cost performance can be highly unsatisfactory. In order to address the "how to learn" challenge, we propose EC-L2O (expert-calibrated learning to optimize), which trains an ML-based optimizer by explicitly taking into account the downstream expert calibrator. To accomplish this, we propose a new differentiable expert calibrator that generalizes regularized online balanced descent and offers a provably better competitive ratio than pure ML predictions when the prediction error is large. For training, our loss function is a weighted sum of two different losses -- one minimizing the average ML prediction error for better robustness, and the other one minimizing the post-calibration average cost. We also provide theoretical analysis for EC-L2O, highlighting that expert calibration can be even beneficial for the average cost performance and that the high-percentile tail ratio of the cost achieved by EC-L2O to that of the offline optimal oracle (i.e., tail cost ratio) can be bounded. Finally, we test EC-L2O by running simulations for sustainable datacenter demand response. Our results demonstrate that EC-L2O can empirically achieve a lower average cost as well as a lower competitive ratio than the existing baseline algorithms.

cs.LG↗

arXiv2022

CUP: A Conservative Update Policy Algorithm for Safe Reinforcement Learning

Safe reinforcement learning (RL) is still very challenging since it requires the agent to consider both return maximization and safe exploration. In this paper, we propose CUP, a Conservative Update Policy algorithm with a theoretical safety guarantee. We derive the CUP based on the new proposed performance bounds and surrogate functions. Although using bounds as surrogate functions to design safe RL algorithms have appeared in some existing works, we develop them at least three aspects: (i) We provide a rigorous theoretical analysis to extend the surrogate functions to generalized advantage estimator (GAE). GAE significantly reduces variance empirically while maintaining a tolerable level of bias, which is an efficient step for us to design CUP; (ii) The proposed bounds are tighter than existing works, i.e., using the proposed bounds as surrogate functions are better local approximations to the objective and safety constraints. (iii) The proposed CUP provides a non-convex implementation via first-order optimizers, which does not depend on any convex approximation. Finally, extensive experiments show the effectiveness of CUP where the agent satisfies safe constraints. We have opened the source code of CUP at https://github.com/RL-boxes/Safe-RL.

cs.LG↗

arXiv2022

Policy Optimization with Stochastic Mirror Descent

Improving sample efficiency has been a longstanding goal in reinforcement learning. This paper proposes $\mathtt{VRMPO}$ algorithm: a sample efficient policy gradient method with stochastic mirror descent. In $\mathtt{VRMPO}$, a novel variance-reduced policy gradient estimator is presented to improve sample efficiency. We prove that the proposed $\mathtt{VRMPO}$ needs only $\mathcal{O}(ε^{-3})$ sample trajectories to achieve an $ε$-approximate first-order stationary point, which matches the best sample complexity for policy optimization. The extensive experimental results demonstrate that $\mathtt{VRMPO}$ outperforms the state-of-the-art policy gradient methods in various settings.

cs.LG↗

arXiv2022

The Effect of Adiabatic Compression on Dark Matter Halos and the Radial Acceleration Relation

We use a semi-empirical model to investigate the radial acceleration relation (RAR) in a cold dark matter (CDM) framework. Specifically, we build 80 model galaxies covering the same parameter space as the observed galaxies in the SPARC database, assigning them to dark matter halos using abundance matching and halo mass-concentration relations. We consider several abundance matching relations, finding some to be a better match to the kinematic data than others. We compute the unavoidable gravitational interactions between baryons and their dark matter halos, leading to an overall compression of the original NFW halos. Before halo compression, high-mass galaxies approximately lie on the observed RAR whereas low-mass galaxies display up-bending "hooks" at small radii due to DM cusps, making them deviate systematically from the observed relation. After halo compression, the initial NFW halos become more concentrated at small radii, making larger contributions to rotation curves. This increases the total accelerations, moving all model galaxies away from the observed relation. These systematic deviations suggest that the CDM model with abundance matching alone cannot explain the observed RAR. Further effects (e.g., feedback) would need to counteract the compression with precisely the right amount of halo expansion, even in high mass galaxies with deep potential wells where such effects are generally predicted to be negligible.

astro-ph.GA↗