arXiv subjects

Bo Wang

Publications and source records attributed to Bo Wang.

At least 613 records · Page 34Linked to original sources

arXiv2020

Multi-scale Deep Neural Network (MscaleDNN) Methods for Oscillatory Stokes Flows in Complex Domains

In this paper, we study a multi-scale deep neural network (MscaleDNN) as a meshless numerical method for computing oscillatory Stokes flows in complex domains. The MscaleDNN employs a multi-scale structure in the design of its DNN using radial scalings to convert the approximation of high frequency components of the highly oscillatory Stokes solution to one of lower frequencies. The MscaleDNN solution to the Stokes problem is obtained by minimizing a loss function in terms of L2 normof the residual of the Stokes equation. Three forms of loss functions are investigated based on vorticity-velocity-pressure, velocity-stress-pressure, and velocity-gradient of velocity-pressure formulations of the Stokes equation. We first conduct a systematic study of the MscaleDNN methods with various loss functions on the Kovasznay flow in comparison with normal fully connected DNNs. Then, Stokes flows with highly oscillatory solutions in a 2-D domain with six randomly placed holes are simulated by the MscaleDNN. The results show that MscaleDNN has faster convergence and consistent error decays in the simulation of Kovasznay flow for all four tested loss functions. More importantly, the MscaleDNN is capable of learning highly oscillatory solutions when the normal DNNs fail to converge.

math.NA↗

arXiv2020

Multi-Scale Networks for 3D Human Pose Estimation with Inference Stage Optimization

Estimating 3D human poses from a monocular video is still a challenging task. Many existing methods' performance drops when the target person is occluded by other objects, or the motion is too fast/slow relative to the scale and speed of the training data. Moreover, many of these methods are not designed or trained under severe occlusion explicitly, making their performance on handling occlusion compromised. Addressing these problems, we introduce a spatio-temporal network for robust 3D human pose estimation. As humans in videos may appear in different scales and have various motion speeds, we apply multi-scale spatial features for 2D joints or keypoints prediction in each individual frame, and multi-stride temporal convolutional networks (TCNs) to estimate 3D joints or keypoints. Furthermore, we design a spatio-temporal discriminator based on body structures as well as limb motions to assess whether the predicted pose forms a valid pose and a valid movement. During training, we explicitly mask out some keypoints to simulate various occlusion cases, from minor to severe occlusion, so that our network can learn better and becomes robust to various degrees of occlusion. As there are limited 3D ground-truth data, we further utilize 2D video data to inject a semi-supervised learning capability to our network. Moreover, we observe that there is a discrepancy between 3D pose prediction and 2D pose estimation due to different pose variations between video and image training datasets. We, therefore propose a confidence-based inference stage optimization to adaptively enforce 3D pose projection to match 2D pose estimation to further improve final pose prediction accuracy. Experiments on public datasets validate the effectiveness of our method, and our ablation studies show the strengths of our network's individual submodules.

cs.CV↗

arXiv2020

Information Extraction from Swedish Medical Prescriptions with Sig-Transformer Encoder

Relying on large pretrained language models such as Bidirectional Encoder Representations from Transformers (BERT) for encoding and adding a simple prediction layer has led to impressive performance in many clinical natural language processing (NLP) tasks. In this work, we present a novel extension to the Transformer architecture, by incorporating signature transform with the self-attention model. This architecture is added between embedding and prediction layers. Experiments on a new Swedish prescription data show the proposed architecture to be superior in two of the three information extraction tasks, comparing to baseline models. Finally, we evaluate two different embedding approaches between applying Multilingual BERT and translating the Swedish text to English then encode with a BERT model pretrained on clinical notes.

cs.CL↗

arXiv2020

Tempura: A General Cost Based Optimizer Framework for Incremental Data Processing (Extended Version)

Incremental processing is widely-adopted in many applications, ranging from incremental view maintenance, stream computing, to recently emerging progressive data warehouse and intermittent query processing. Despite many algorithms developed on this topic, none of them can produce an incremental plan that always achieves the best performance, since the optimal plan is data dependent. In this paper, we develop a novel cost-based optimizer framework, called Tempura, for optimizing incremental data processing. We propose an incremental query planning model called TIP based on the concept of time-varying relations, which can formally model incremental processing in its most general form. We give a full specification of Tempura, which can not only unify various existing techniques to generate an optimal incremental plan, but also allow the developer to add their rewrite rules. We study how to explore the plan space and search for an optimal incremental plan. We conduct a thorough experimental evaluation of Tempura in various incremental processing scenarios to show its effectiveness and efficiency.

cs.DB↗

arXiv2020

A massless scalar field in Robertson-Walker spacetimes: Adiabatic regularization and Green's function

We study adiabatic regularization of a coupling massless scalar field in general spatially flat Robertson-Walker (RW) spacetimes. For the conformally-coupling, the 0th-order regularized power spectrum and 0th-order regularized stress tensor are zero, and no trace anomaly exists in general RW spacetimes. This is a new result which extents those found in de Sitter space. For the minimally-coupling, the regularized spectra are also zero in the radiation-dominant stage, the matter-dominant stage, and de Sitter space as well. The vanishing of these adiabatically regularized spectra are also confirmed by direct regularization of the Green's functions. For a general coupling and general RW spacetimes, the regularized spectra can be negative under the conventional prescription. By going to higher order of regularization, the spectra will generally become positive, but will also acquire IR divergence which is inevitable for a massless field. To avoid the IR divergence, the inside-horizon regularization is applied. By these procedures, one will eventually achieve nonnegative, UV- and IR-convergent power spectrum and spectral energy density.

gr-qc↗

arXiv2020

Deciphering the charged heavy quarkoniumlike states in chiral effective field theory

We generalize the framework of chiral effective field theory to study the interactions of the isovector $D^\ast\bar{D}^{(\ast)}$ and $B^\ast\bar{B}^{(\ast)}$ systems up to the next-to-leading order, in which the long-, mid-, and short-range force contributions as well as the $S$-$D$ wave mixing are incorporated. Based on the Lippmann-Schwinger equation, we fit the invariant mass distributions of the elastic channels measured by the BESIII and Belle Collaborations. Our results indicate that the four charged charmoniumlike and bottomoniumlike states $Z_c(3900)$, $Z_c(4020)$ and $Z_b(10610)$, $Z_b(10650)$ can be well identified as the $D\bar{D}^{\ast},D^\ast\bar{D}^{\ast}$ and $B\bar{B}^{\ast},B^\ast\bar{B}^{\ast}$ molecular resonances. The bound state explanations are vetoed in our framework. Our study favors the $Z_c$ and $Z_b$ states are the twin partners under the heavy quark symmetry.

hep-ph↗

arXiv2020

Detectability of ultra-compact X-ray binaries as LISA sources

Ultra-compact X-ray binaries (UCXBs) are low-mass X-ray binaries with ultra-short orbital periods (usually less than 1 hour) and hydrogen-poor donor stars, which are proposed to be the potential LISA sources. In this work, we firstly employ the MESA code to examine the parameter space of the progenitors of UCXBs that LISA will detect. Our simulations indicate that the initial binaries with a neutron star and a $0.4-3.3~M_{\odot}$ companion star in an orbit of initial orbital period smaller than the bifurcation period could evolve into UCXBs, some of which will emit gravitational wave signals that can be detectable by the LISA. However, the initial orbital periods of the binaries that will evolve into UCXB-LISA sources in a distance of 10 kpc are located in a very narrow range, i.e. the formation of these LISA source requires an extremely fine-tuning of initial parameter. According to the characteristic stains and the derived maximum detectable distances, four sources among eight UCXBs with the observed distances are expected to be detected by the LISA. Based on the parameter space given by the detailed binary evolution models and the rapid binary star evolution code, the birthrate of UCXBs appearing as LISA sources in the Galaxy is estimated to be $(2-2.6)\times10^{\rm -6}~\rm yr^{-1}$. Considering the contribution of UCXBs in the globular clusters, the number of UCXB-LISA sources can reach $240 - 320$. Although the formation condition is severe, the detectability of UCXBs by the LISA is still optimistic and significant because they provide an opportunity to pursue full multi-messenger investigations.

astro-ph.HE↗

arXiv2020

He-shell flashes on the surface of oxygen-neon white dwarfs

Accretion induced collapse (AIC) may be responsible for the formation of some interesting neutron star binaries, e.g., millisecond pulsars, intermediate-mass binary pulsars, etc. It has been suggested that oxygen-neon white dwarfs (ONe WDs) can increase their mass to the Chandrasekhar limit by multiple He-shell flashes, leading to AIC events. However, the properties of He-shell flashes on the surface of ONe WDs are still not well understood. In this article, we aim to study He-shell flashes on the surface of ONe WDs in a systematic approach. We investigated the long-term evolution of ONe WDs accreting He-rich material with various constant mass-accretion rates by time-dependent calculations with the stellar evolution code Modules for Experiments in Stellar Astrophysics (MESA), in which the initial ONe WD masses range from 1.1 to 1.35 M . We found that the mass-retention efficiency increases with the ONe WD mass and the mass-accretion rate, whereas both the nova cycle duration and the ignition mass decrease with the ONe WD mass and the mass-accretion rate. We also present the nuclear products in different accretion scenarios. The results presented in this article can be used in the future binary population synthesis studies of AIC events.

astro-ph.SR↗

arXiv2020

A Matrix Basis Formulation For The Green's Functions Of Maxwell's Equations And The Elastic Wave Equations In Layered Media

A matrix basis formulation is introduced to represent the 3 x 3 dyadic Green's functions in the frequency domain for the Maxwell's equations and the elastic wave equation in layered media. The formulation can be used to decompose the Maxwell's Green's functions into independent TE and TM components, each satisfying a Helmholtz equation, and decompose the elastic wave Green's function into the S-wave and the P-wave components. In addition, a derived vector basis formulation is applied to the case for acoustic wave sources from a non-viscous fluid layer.

math-ph↗

arXiv2020

Exponential convergence for multipole and local expansions and their translations for sources in layered media: three-dimensional Laplace equation

In this paper, we prove the exponential convergence of the multipole and local expansions, shifting and translation operators used in fast multipole methods (FMMs) for 3-dimensional Laplace equations in layered media. These theoretical results ensure the exponential convergence of the FMM which has been shown by the numerical results recently reported in [9]. As the free space components are calculated by the classic FMM, this paper will focus on the analysis for the reaction components of the Green's function for the Laplace equation in layered media. We first prove that the density functions in the integral representations of the reaction components are analytic and bounded in the right half complex plane. Then, using the Cagniard-de Hoop transform and contour deformations, estimate for the remainder terms of the truncated expansions is given, and, as a result, the exponential convergence for the expansions and translation operators is proven.

math.NA↗

arXiv2020

Integrated Longitudinal Speed Decision-Making and Energy Efficiency Control for Connected Electrified Vehicles

To improve the driving mobility and energy efficiency of connected autonomous electrified vehicles, this paper presents an integrated longitudinal speed decision-making and energy efficiency control strategy. The proposed approach is a hierarchical control architecture, which is assumed to consist of higher-level and lower-level controls. As the core of this study, model predictive control and reinforcement learning are combined to improve the powertrain mobility and fuel economy for a group of automated vehicles. The higher-level exploits the signal phase and timing and state information of connected autonomous vehicles via vehicle to infrastructure and vehicle to vehicle communication to reduce stopping at red lights. The higher-level outputs the optimal vehicle velocity using model predictive control technique and receives the power split control from the lower-level con-troller. These two levels communicate with each other via a controller area network in the real vehicle. The lower-level utilizes a model-free reinforcement learning method to improve the fuel economy for each connected autonomous vehicle. Numerical tests illustrate that vehicle mobility can be noticeably improved (traveling time reduced by 30%) by reducing red-light idling. The effectiveness and performance of the proposed method are validated via comparison analysis among different energy efficiency controls (fuel economy promoted by 13%).

eess.SP↗

arXiv2020

The formation of neutron star systems through accretion-induced collapse in white-dwarf binaries

The accretion-induced collapse (AIC) scenario was proposed 40 years ago as an evolutionary end state of oxygen-neon white-dwarfs (ONe WDs), linking them to the formation of neutron star (NS) systems. However, there has been no direct detection of any AIC event so far, even though there exists a lot of indirect observational evidence. Meanwhile, the evolutionary pathways resulting in NS formation through AIC are still not well investigated. In this article, we review recent studies on the two classic progenitor models of AIC events, i.e., the single-degenerate model (including the ONe WD+MS/RG/He star channels and the CO WD+He star channel) and the double-degenerate model (including the double CO/ONe WD channels and the ONe WD+CO WD channel). Recent progress on these progenitor models is reviewed, including the evolutionary scenarios, the initial parameter space and the related objects. For the single-degenerate model, the pre-AIC systems could potentially be identified as supersoft X-ray sources, symbiotics and cataclysmic variables, whereas the post-AIC systems could be identified as low-/intermediate-mass X-ray binaries and the resulting low-/intermediate-mass binary pulsars, most notably millisecond pulsars. For the double-degenerate model, the pre-AIC systems are close double WDs, whereas the post-AIC systems are isolated NSs with peculiar properties. We also review the predicted rates of AIC events, the mass distribution of NSs, and the gravitational wave (GW) signals from double WDs that are potential GW sources in the Galaxy in the context of future space-based GW detectors. Recent theoretical and observational constraints on the detection of AIC events are summarized. In order to confirm the existence of the AIC process, and resolve this long-term issue presented by current stellar evolution theories, more numerical simulations and observational identifications are required.

astro-ph.SR↗

arXiv2020

Comprehensive analysis of the tidal effect in gravitational waves and implication for cosmology

Detection of gravitational waves (GWs) produced by coalescence of compact binaries provides a novel way to measure the luminosity distance of GW events. Combining their redshift, they can act as standard sirens to constrain cosmological parameters. For various GW detector networks in 2nd-generation (2G), 2.5G and 3G, we comprehensively analyze the method to constrain the equation-of-state (EOS) of binary neutron-stars (BNSs) and extract their redshifts through the imprints of tidal effects in GW waveforms. We find for these events, the observations of electromagnetic counterparts in low-redshift range $z < 0.1$ are important for constraining the tidal effects. Considering 17 different EOSs of NSs or quark-stars, we find GW observations have strong capability to determine the EOS. Applying the events as standard sirens, and considering the constraints of NS's EOS derived from low-redshift observations as prior, we can constrain the dark-energy EOS parameters $w_0$ and $w_a$. In 3G era, the potential constraints are $Δw_0\in (0.0006,0.004)$ and $Δw_a\in(0.004,0.02)$, which are 1-3 orders smaller than those from traditional methods, including Type Ia supernovas and baryon acoustic oscillations. The constraints are also 1 order smaller than the method of GW standard siren by fixing the redshifts through short-hard $γ$-ray bursts, due to more available GW events in this method. Therefore, GW standard sirens, based on the tidal effect measurement, provide a realizable and much more powerful tool in cosmology.

gr-qc↗

arXiv2020

Routing of valley photons in a WS2 monolayer via delocalized Bloch modes of in-plane inversion-symmetry broken photonic crystal slabs

The valleys of two-dimensional transition metal dichalcogenides (TMDCs) offer a new degree of freedom for information processing. To take advantage of this valley degree of freedom, on one hand, it is feasible to control valleys by utilizing different external stimuli like optical and electric fields. On the other hand, nanostructures are also used to separate the valleys by near field coupling. However, for both above methods, either required low-temperature environment or low degree of coherence properties limit their further applications. Here, we demonstrate all-dielectric photonic crystal (PhC) slabs without in-plane inversion symmetry (C2 symmetry) could separate and route valley photons in a WS2 monolayer at room temperature. Coupling with circularly polarized photonic Bloch modes of such PhC slabs, valley photons emitted by a WS2 monolayer are routed directionally and efficiently separated in the far field. In addition, the far-field emission is directionally enhanced and with long-distance spatial coherence property.

physics.optics↗

arXiv2020

Simulation-Based Digital Twin Development for Blockchain Enabled End-to-End Industrial Hemp Supply Chain Risk Management

With the passage of the 2018 U.S. Farm Bill, Industrial Hemp production is moved from limited pilot programs to a regulated agriculture production system. However, Industrial Hemp Supply Chain (IHSC) faces critical challenges, including: high complexity and variability, very limited production knowledge, lack of data and information tracking. In this paper, we propose blockchain-enabled IHSC and develop a preliminary simulation-based digital twin for this distributed cyber-physical system (CPS) to support the process learning and risk management. Basically, we develop a two-layer blockchain with proof of authority smart contract, which can track the data and key information, improve the supply chain transparency, and leverage local authorities and state regulators to ensure the quality control verification. Then, we introduce a stochastic simulation-based digital twin for IHSC risk management, which can characterize the process spatial-temporal causal interdependencies and dynamic evolution to guide risk control and decision making. Our empirical study demonstrates the promising performance of proposed platform.

cs.CR↗

arXiv2020

Learning Global and Local Consistent Representations for Unsupervised Image Retrieval via Deep Graph Diffusion Networks

Diffusion has shown great success in improving accuracy of unsupervised image retrieval systems by utilizing high-order structures of image manifold. However, existing diffusion methods suffer from three major limitations: 1) they usually rely on local structures without considering global manifold information; 2) they focus on improving pair-wise similarities within existing images input output transductively while lacking flexibility to learn representations for novel unseen instances inductively; 3) they fail to scale to large datasets due to prohibitive memory consumption and computational burden due to intrinsic high-order operations on the whole graph. In this paper, to address these limitations, we propose a novel method, Graph Diffusion Networks (GRAD-Net), that adopts graph neural networks (GNNs), a novel variant of deep learning algorithms on irregular graphs. GRAD-Net learns semantic representations by exploiting both local and global structures of image manifold in an unsupervised fashion. By utilizing sparse coding techniques, GRAD-Net not only preserves global information on the image manifold, but also enables scalable training and efficient querying. Experiments on several large benchmark datasets demonstrate effectiveness of our method over state-of-the-art diffusion algorithms for unsupervised image retrieval.

cs.CV↗

arXiv2020

Cosmological model-independent constraints on spatial curvature from strong gravitational lensing and type Ia supernova observations

Applying the distance sum rule in strong gravitational lensing (SGL) and type Ia supernova (SN Ia) observations, one can provide an interesting cosmological model-independent method to determine the cosmic curvature parameter $Ω_k$. In this paper, with the newly compiled data sets including 161 galactic-scale SGL systems and 1048 SN Ia data, we place constraints on $Ω_k$ within the framework of three types of lens models extensively used in SGL studies. Moreover, to investigate the effect of different mass lens samples on the results, we divide the SGL sample into three sub-samples based on the center velocity dispersion of intervening galaxies. In the singular isothermal sphere (SIS) and extended power-law lens models, a flat universe is supported with the uncertainty about 0.2, while a closed universe is preferred in the power-law lens model. We find that the choice of lens models and the classification of SGL data actually can influence the constraints on $Ω_k$ significantly.

astro-ph.CO↗

arXiv2020

On time-domain NRBC for Maxwell's equations and its application in accurate simulation of electromagnetic invisibility cloaks

In this paper, we present analytic formulas of the temporal convolution kernel functions involved in the time-domain non-reflecting boundary condition (NRBC) for the electromagnetic scattering problems. Such exact formulas themselves lead to accurate and efficient algorithms for computing the NRBC for domain reduction of the time-domain Maxwell's system in $\mathbb R^3$. A second purpose of this paper is to derive a new time-domain model for the electromagnetic invisibility cloak. Different from the existing models, it contains only one unknown field and the seemingly complicated convolutions can be computed as efficiently as the temporal convolutions in the NRBC. The governing equation in the cloaking layer is valid for general geometry, e.g., a spherical or polygonal layer. Here, we aim at simulating the spherical invisibility cloak. We take the advantage of radially stratified dispersive media and special geometry, and develop an efficient vector spherical harmonic (VSH)-spectral-element method for its accurate simulation. Compared with limited results on FDTD simulation, the proposed method is optimal in both accuracy and computational cost. Indeed, the saving in computational time is significant.

math.NA↗