arXiv subjects

Bin Wang

Publications and source records attributed to Bin Wang.

At least 613 records · Page 34Linked to original sources

arXiv2022

Cuspy and fractured black hole shadows in a toy model with axisymmetry

Cuspy shadow was first reported for hairy rotating black holes, whose metrics deviate significantly from the Kerr one. The non-smooth edge of the shadow is attributed to a transition between different branches of unstable but bounded orbits, known as the fundamental photon orbits, which end up at the light rings. In searching for a minimal theoretical setup to reproduce such a salient feature, in this work, we devise a toy model with axisymmetry, a slowly rotating Kerr black hole enveloped by a thin slowly rotating dark matter shell. Despite its simplicity, we show rich structures regarding fundamental photon orbits explicitly in such a system. We observe two disconnected branches of unstable spherical photon orbits, and the jump between them gives rise to a pair of cusps in the resultant black hole shadow. Besides the cuspy shadow, we explore other intriguing phenomena when the Maxwell construction cannot be established. We find that it is possible to have an incomplete arc of Einstein rings and a "fractured" shadow. The potential astrophysical significance of the corresponding findings is addressed.

gr-qc↗

arXiv2021

ViR:the Vision Reservoir

The most recent year has witnessed the success of applying the Vision Transformer (ViT) for image classification. However, there are still evidences indicating that ViT often suffers following two aspects, i) the high computation and the memory burden from applying the multiple Transformer layers for pre-training on a large-scale dataset, ii) the over-fitting when training on small datasets from scratch. To address these problems, a novel method, namely, Vision Reservoir computing (ViR), is proposed here for image classification, as a parallel to ViT. By splitting each image into a sequence of tokens with fixed length, the ViR constructs a pure reservoir with a nearly fully connected topology to replace the Transformer module in ViT. Two kinds of deep ViR models are subsequently proposed to enhance the network performance. Comparative experiments between the ViR and the ViT are carried out on several image classification benchmarks. Without any pre-training process, the ViR outperforms the ViT in terms of both model and computational complexity. Specifically, the number of parameters of the ViR is about 15% even 5% of the ViT, and the memory footprint is about 20% to 40% of the ViT. The superiority of the ViR performance is explained by Small-World characteristics, Lyapunov exponents, and memory capacity.

cs.CV↗

arXiv2021

Transport features of topological corner states in honeycomb lattice with multihollow structure

Higher-order topological phase in 2-dimensional (2D) systems is characterized by in-gap corner states, which are hard to detect and utilize. We numerically investigate transport properties of topological corner states in 2D honeycomb lattice, where the second-order topological phase is induced by an in-plane Zeeman field in the conventional Kane-Mele model. Through engineering multihollow structures with appropriate boundaries in honeycomb lattice, multiple corner states emerge, which greatly increases the probability to observe them. A typical two-probe setup is built to study the transport features of a diamond-shaped system with multihollow structures. Numerical results reveal the existence of global resonant states in bulk insulator, which corresponds to the resonant tunneling of multiple corner states and occupies the entire scattering region. Furthermore, based on the well separated energy levels of multiple corner states, a single-electron source is constructed.

cond-mat.mes-hall↗

arXiv2021

CORE: A Knowledge Graph Entity Type Prediction Method via Complex Space Regression and Embedding

Entity type prediction is an important problem in knowledge graph (KG) research. A new KG entity type prediction method, named CORE (COmplex space Regression and Embedding), is proposed in this work. The proposed CORE method leverages the expressive power of two complex space embedding models; namely, RotatE and ComplEx models. It embeds entities and types in two different complex spaces using either RotatE or ComplEx. Then, we derive a complex regression model to link these two spaces. Finally, a mechanism to optimize embedding and regression parameters jointly is introduced. Experiments show that CORE outperforms benchmarking methods on representative KG entity type inference datasets. Strengths and weaknesses of various entity type prediction methods are analyzed.

cs.LG↗

arXiv2021

KGBoost: A Classification-based Knowledge Base Completion Method with Negative Sampling

Knowledge base completion is formulated as a binary classification problem in this work, where an XGBoost binary classifier is trained for each relation using relevant links in knowledge graphs (KGs). The new method, named KGBoost, adopts a modularized design and attempts to find hard negative samples so as to train a powerful classifier for missing link prediction. We conduct experiments on multiple benchmark datasets, and demonstrate that KGBoost outperforms state-of-the-art methods across most datasets. Furthermore, as compared with models trained by end-to-end optimization, KGBoost works well under the low-dimensional setting so as to allow a smaller model size.

cs.LG↗

arXiv2021

Geometric continuous-stage exponential energy-preserving integrators for charged-particle dynamics in a magnetic field from normal to strong regimes

This paper is concerned with geometric exponential energy-preserving integrators for solving charged-particle dynamics in a magnetic field from normal to strong regimes. We firstly formulate the scheme of the methods for the system in a uniform magnetic field by using the idea of continuous-stage methods, and then discuss its energy-preserving property. Moreover, symmetric conditions and order conditions are analysed. Based on those conditions, we propose two practical symmetric continuous-stage exponential energy-preserving integrators of order up to four. Then we extend the obtained methods to the system in a nonuniform magnetic field and derive their properties including the symmetry, convergence and energy conservation. Numerical experiments demonstrate the efficiency of the proposed methods in comparison with some existing schemes in the literature.

math.NA↗

arXiv2021

SanMove: Next Location Recommendation via Self-Attention Network

Currently, next location recommendation plays a vital role in location-based social network applications and services. Although many methods have been proposed to solve this problem, three important challenges have not been well addressed so far: (1) most existing methods are based on recurrent network, which is time-consuming to train long sequences due to not allowing for full parallelism; (2) personalized preferences generally are not considered reasonably; (3) existing methods rarely systematically studied how to efficiently utilize various auxiliary information (e.g., user ID and timestamp) in trajectory data and the spatio-temporal relations among non-consecutive locations. To address the above challenges, we propose a novel method named SanMove, a self-attention network based model, to predict the next location via capturing the long- and short-term mobility patterns of users. Specifically, SanMove introduces a long-term preference learning module, and it uses a self-attention module to capture the users long-term mobility pattern which can represent personalized location preferences of users. Meanwhile, SanMove uses a spatial-temporal guided non-invasive self-attention (STNOVA) to exploit auxiliary information to learn short-term preferences. We evaluate SanMove with two real-world datasets, and demonstrate SanMove is not only faster than the state-of-the-art RNN-based predict model but also outperforms the baselines for next location prediction.

cs.SI↗

arXiv2021

Hierarchical Stochastic Scheduling of Multi-Community Integrated Energy Systems in Uncertain Environments via Stackelberg Game

An operating entity utilizing community-integrated energy systems with a large number of small-scale distributed energy sources can easily trade with existing distribution markets. To solve the energy management and pricing problem of multi-community integrated energy systems (MCIESs) with multi-energy interaction, this study investigated a hierarchical stochastic optimal scheduling method for uncertain environments. To handle multiple uncertainties, a Wasserstein generative adversarial network with a gradient penalty was used to generate renewable scenarios, and the Kmeans++ clustering algorithm was employed to generate typical scenarios. A Stackelberg-based hierarchical stochastic schedule with an integrated demand response was constructed, where the MCIES operator acted as the leader pursuing the maximum net profit by setting energy prices, while the building users were followers who adjusted their energy consumption plans to minimize their total costs. Finally, a distributed iterative solution method based on a metaheuristic was designed. The effectiveness of the proposed method was verified using practical examples.

eess.SY↗

arXiv2021

The BINGO Project II: Instrument Description

The measurement of diffuse 21-cm radiation from the hyperfine transition of neutral hydrogen (HI signal) in different redshifts is an important tool for modern cosmology. However, detecting this faint signal with non-cryogenic receivers in single-dish telescopes is a challenging task. The BINGO (Baryon Acoustic Oscillations from Integrated Neutral Gas Observations) radio telescope is an instrument designed to detect baryonic acoustic oscillations (BAOs) in the cosmological HI signal, in the redshift interval $0.127 \le z \le 0.449$. This paper describes the BINGO radio telescope, including the current status of the optics, receiver, observational strategy, calibration, and the site. BINGO has been carefully designed to minimize systematics, being a transit instrument with no moving dishes and 28 horns operating in the frequency range $980 \le ν\le 1260$ MHz. Comprehensive laboratory tests were conducted for many of the BINGO subsystems and the prototypes of the receiver chain, horn, polarizer, magic tees, and transitions have been successfully tested between 2018 - 2020. The survey was designed to cover $\sim 13\%$ of the sky, with the primary mirror pointing at declination $δ=-15^{\circ}$. The telescope will see an instantaneous declination strip of $14.75^{\circ}$. The results of the prototype tests closely meet those obtained during the modeling process, suggesting BINGO will perform according to our expectations. After one year of observations with a $60\%$ duty cycle and 28 horns, BINGO should achieve an expected sensitivity of 102 $μK$ per 9.33 MHz frequency channel, one polarization, and be able to measure the HI power spectrum in a competitive time frame.

astro-ph.IM↗

arXiv2021

The BINGO Project VII: Cosmological Forecasts from 21cm Intensity Mapping

The 21cm line of neutral hydrogen (HI) opens a new avenue in our exploration of the structure and evolution of the Universe. It provides complementary data to the current large-scale structure observations with different systematics, and thus it will be used to improve our understanding of the $Λ$CDM model. Among several radio cosmological surveys designed to measure this line, BINGO is a single-dish telescope mainly designed to detect baryon acoustic oscillations (BAOs) at low redshifts ($0.127< z<0.449$). Our goal is to assess the fiducial BINGO setup and its capabilities of constraining the cosmological parameters, and to analyze the effect of different instrument configurations. We used the Phase 1 fiducial configuration of the BINGO telescope to perform our cosmological forecasts. In addition, we investigated the impact of several instrumental setups, taking into account some instrumental systematics, and different cosmological models. Combining BINGO with Planck temperature and polarization data, the projected constraint improves from a $13\%$ and $25\%$ precision measurement at the $68\%$ confidence level with Planck only to $1\%$ and $3\%$ for the Hubble constant and the dark energy equation of state (EoS), respectively, within the wCDM model. Assuming a Chevallier-Polarski-Linder parameterization, the EoS parameters have standard deviations given by $σ_{w_0} = 0.30$ and $σ_{w_a} = 1.2$, which are improvements on the order of $30\%$ with respect to Planck alone. Also, we can access information about the HI density and bias, obtaining $\sim 8.5\%$ and $\sim 6\%$ precision, respectively, assuming they vary with redshift at three independent bins. The fiducial BINGO configuration will be able to extract significant cosmological information from the HI distribution and provide constraints competitive with current and future cosmological surveys. (Abridged)

astro-ph.CO↗

arXiv2021

A Syntactic Approach to Studying Strongly Equivalent Logic Programs

In the field of Answer Set Programming (ASP), two logic programs are strongly equivalent if they are ordinarily equivalent under any extensions. This property provides a theoretical foundation for studying many aspects of logic programs such as program simplification and transformation etc. Therefore, strong equivalence has been investigated extensively for ASP and its extensions such as LPMLN. In this paper, we present a syntactic approach to studying the strong equivalence of logic programs, which provides several interesting results and would help us understand the strong equivalence from a new perspective. Firstly, we present the notions of independent sets and five kinds of syntactic transformations (S-* transformations) for logic programs. And we investigate the strong equivalence (SE) and non-strong equivalence (NSE) preserving properties of the S-* transformations in the contexts of ASP and LPMLN. Secondly, based on the properties of the S-* transformations, we present a fully automatic algorithm to discover syntactic conditions that preserve strong equivalences (SE-conditions) of ASP and LPMLN programs. To discover the SE-conditions efficiently, we present four kinds of approaches to improve the algorithm. Thirdly, we present a preliminary method to simplify the discovered SE-conditions and report the simplified SE-conditions of several kinds of LPMLN programs. After that, we present a discussion on the discovered SE-conditions and some existing problems. Finally, we present a comparison between SE-conditions discovering approaches in this paper and in the related work.

cs.LO↗

arXiv2021

ScaleVLAD: Improving Multimodal Sentiment Analysis via Multi-Scale Fusion of Locally Descriptors

Fusion technique is a key research topic in multimodal sentiment analysis. The recent attention-based fusion demonstrates advances over simple operation-based fusion. However, these fusion works adopt single-scale, i.e., token-level or utterance-level, unimodal representation. Such single-scale fusion is suboptimal because that different modality should be aligned with different granularities. This paper proposes a fusion model named ScaleVLAD to gather multi-Scale representation from text, video, and audio with shared Vectors of Locally Aggregated Descriptors to improve unaligned multimodal sentiment analysis. These shared vectors can be regarded as shared topics to align different modalities. In addition, we propose a self-supervised shifted clustering loss to keep the fused feature differentiation among samples. The backbones are three Transformer encoders corresponding to three modalities, and the aggregated features generated from the fusion module are feed to a Transformer plus a full connection to finish task predictions. Experiments on three popular sentiment analysis benchmarks, IEMOCAP, MOSI, and MOSEI, demonstrate significant gains over baselines.

cs.CL↗

arXiv2021

Time-averaging axion-like interacting scalar fields models

In this paper, we study a cosmological model inspired in the axionic matter with two canonical scalar fields $ϕ_1$ and $ϕ_2$ interacting through a term added to its potential. Introducing novel dynamical variables, and a dimensionless time variable, the resulting dynamical system is studied. The main difficulties arising in the standard dynamical systems approach, where expansion normalized dynamical variables are usually adopted, are due to the oscillations entering the nonlinear system through the Klein-Gordon (KG) equations. This motivates the analysis of the oscillations using methods from the theory of averaging nonlinear dynamical systems. We prove that time-dependent systems, and their corresponding time-averaged versions, have the same late-time dynamics. Then, we study the time-averaged system using standard techniques of dynamical systems. We present numerical simulations as evidence of such behavior.

gr-qc↗

arXiv2021

Dynamical scalarization in Einstein-Maxwell-dilaton theory

We study the process of fully nonlinear dynamical scalarization starting from a charged black hole or a naked singularity in asymptotically flat spacetime in the Einstein-Maxwell-dilaton theory. Initially the dilaton field is negligible compared to the gravitational and the Maxwell field. Then the dilaton field experiences an immediate growth, later it oscillates with damping amplitude and finally settles down to a finite value. For a hairy black hole develops from an original Reissner-Nordström black hole, since the dilaton oscillation and decay are almost independent of the coupling parameter, unlike the Anti-de Sitter spacetime it is not easy to distinguish the resulting hairy black hole from the original asymptotically flat charged hole. For a hairy black hole evolves from an original naked singularity, the resulting hairy black hole has rich structures. In the scalarization process, the naked singularity is soon enveloped by one outer horizon, then another horizon is developed and in the end a stable hairy black hole forms and two horizons degenerate into one to protect the singularity. The hairy black hole mass saturates exponentially in the scalarization.

gr-qc↗

arXiv2021

Shadow and near-horizon characteristics of the acoustic charged black hole in curved spacetime

In this paper, we first analyze the horizon structure of the acoustic charged black hole in curved spacetime, and then study its acoustic shadow as well as the near-horizon characteristics including the quasinormal modes (QNM) frequencies and analogue Hawking radiation. We find that the radius of the acoustic shadow for acoustic charged black hole is larger than that for Reissner-Nordström (RN) black hole, and both of them are suppressed by increasing the black hole charge because their related outer horizons become smaller. Then the QNM frequencies under scalar field perturbation and its eikonal limit are computed via numeric method and acoustic shadow, respectively. We find that the acoustic charged black hole is stable under the perturbation and the QNM frequencies are much weaker than that for the astrophysical black hole. Moreover, as the tuning parameter increases, the perturbation oscillates more mildly and its damping time becomes longer, while as the charge increases, the oscillation is enhanced slightly and the perturbation decays a little faster which is different from that in RN black hole. Finally, we numerically study the analogue Hawking radiation. We find that the grey-body factor and energy emission rate are suppressed by the angular number and the charge, but they do not monotonically depend on the tuning parameter in the acoustic charged black hole. The behavior of the energy emission rate affected by the parameters could be explained by the dependent behavior of the Hawking temperature. We expect that our results could shed light to the study of black holes in both theoretical and experimental perspectives.

gr-qc↗

arXiv2021

Layers of quark-gluon plasma induced by chiral magnetic effect and the separation of $u,d$ quarks' critical end points

In this work we show that the chiral magnetic effect (CME) could cause the quark-gluon plasma (QGP) approximately forming three layers along the strong magnetic field characterised by different compositions of quark chemical potentials. This phenomenon may bring new observable outcomes, for example different layers would have different CEPs and the $u,d$ quarks would have different critical end points (CEPs). Since the $s$ quark would make the up and the down layers have some 'asymmetry' on chemical potentials, there is the possibility that at some special conditions one flavor of $u,d$ quarks lies at CEP in one layer but another flavor dose not in any layer. These results may be helpful in testing the existence of CME.

hep-ph↗

arXiv2021

PEDENet: Image Anomaly Localization via Patch Embedding and Density Estimation

A neural network targeting at unsupervised image anomaly localization, called the PEDENet, is proposed in this work. PEDENet contains a patch embedding (PE) network, a density estimation (DE) network, and an auxiliary network called the location prediction (LP) network. The PE network takes local image patches as input and performs dimension reduction to get low-dimensional patch embeddings via a deep encoder structure. Being inspired by the Gaussian Mixture Model (GMM), the DE network takes those patch embeddings and then predicts the cluster membership of an embedded patch. The sum of membership probabilities is used as a loss term to guide the learning process. The LP network is a Multi-layer Perception (MLP), which takes embeddings from two neighboring patches as input and predicts their relative location. The performance of the proposed PEDENet is evaluated extensively and benchmarked with that of state-of-the-art methods.

cs.CV↗

arXiv2021

System stability and truncation schemes to the Dyson-Schwinger Equations

With decades of years development, although important progresses have been made by the pioneers of this field, providing a sophisticated truncation scheme is still a great challenge up to now if the Dyson-Schwinger Equations(DSEs) of both quark and gluon propagators (or including even more DSEs) remain after truncation. In this work we view the coupled reminiscent DSEs of the gluon and quark propagators after truncation as a system with feedback. Then studying the stability of this equation array gives useful results. Our calculation shows that the sum of the gluon and ghost loops plays the most important role in keeping this system stable and having reasonable solutions. The quark-gluon vertex plays a relative smaller but also important role. Our method also could give constraints and inspirations on fabricating a more sophisticated model of the quark-gluon vertex.

nucl-th↗