arXiv subjects

Bo Wang

Publications and source records attributed to Bo Wang.

At least 649 records · Page 36Linked to original sources

arXiv2019

End-to-end Named Entity Recognition and Relation Extraction using Pre-trained Language Models

Named entity recognition (NER) and relation extraction (RE) are two important tasks in information extraction and retrieval (IE \& IR). Recent work has demonstrated that it is beneficial to learn these tasks jointly, which avoids the propagation of error inherent in pipeline-based systems and improves performance. However, state-of-the-art joint models typically rely on external natural language processing (NLP) tools, such as dependency parsers, limiting their usefulness to domains (e.g. news) where those tools perform well. The few neural, end-to-end models that have been proposed are trained almost completely from scratch. In this paper, we propose a neural, end-to-end model for jointly extracting entities and their relations which does not rely on external NLP tools and which integrates a large, pre-trained language model. Because the bulk of our model's parameters are pre-trained and we eschew recurrence for self-attention, our model is fast to train. On 5 datasets across 3 domains, our model matches or exceeds state-of-the-art performance, sometimes by a large margin.

cs.CL↗

arXiv2019

$Σ_cN$ interaction in chiral perturbation theory

We adopt the heavy baryon chiral perturbation theory to calculate the $Σ_cN$ interaction to the next-to-leading order. We consider the contact interactions, one-pion-exchange contributions, two-pion-exchange diagrams, and renormalization effects of the vertices, masses and wave functions. With the pion mass dependent expression, we fit the $Σ_cN$ interaction from HAL QCD calculation with $m_π\approx 410$ MeV and $m_π\approx 570$ MeV, and then extrapolate it to the physical pion mass. The $^3S_1(I=1/2)$ $Σ_{c}N$ interaction is weakly attractive but no bound solution is found. We also propose a quark model to estimate the leading order $Σ_cN$ contact interaction with the $NN$ interaction as input. This approach combining the quark model and the chiral effective field theory predicts a very attractive interaction in $^1S_0(I=3/2)$ $Σ_{c}N$ channel and a two-body bound state.

nucl-th↗

arXiv2019

Isospin violating decay $D^*_s \to D_s π^0$ in chiral perturbation theory

We systematically calculate the isospin violating decay, $D_s^*\to D_sπ^0$, with the heavy meson chiral perturbation theory up to $\mathcal{O}(p^3)$ including the loop diagrams. The $\mathcal{O}(p^3)$ tree level amplitudes contain four undetermined LECs. We use two strategies to estimate them. With the nonanalytic dominance approximation, we get $Γ[D_s^\ast\to D_sπ^0]=(3.38\pm0.12)$ eV. With the naturalness assumption, we give a possible range of the isospin violating decay width, $[1.11-6.88]$ eV. We find that the contribution of the $\mathcal{O}(p^3)$ corrections might be significant.

hep-ph↗

arXiv2019

Learning from Label Proportions with Generative Adversarial Networks

In this paper, we leverage generative adversarial networks (GANs) to derive an effective algorithm LLP-GAN for learning from label proportions (LLP), where only the bag-level proportional information in labels is available. Endowed with end-to-end structure, LLP-GAN performs approximation in the light of an adversarial learning mechanism, without imposing restricted assumptions on distribution. Accordingly, we can directly induce the final instance-level classifier upon the discriminator. Under mild assumptions, we give the explicit generative representation and prove the global optimality for LLP-GAN. Additionally, compared with existing methods, our work empowers LLP solver with capable scalability inheriting from deep models. Several experiments on benchmark datasets demonstrate vivid advantages of the proposed approach.

cs.LG↗

arXiv2019

Measurements of decay branching fractions of $H\to b\bar{b}/c\bar{c}/gg$ in associated $(e^{+}e^{-}/μ^{+}μ^{-})H$ production at the CEPC

The high-precision measurement of Higgs boson properties is one of the primary goals of the Circular Electron Positron Collider (CEPC). The measurements of $H\to b\bar{b}/c\bar{c}/gg$ decay branching fraction in the CEPC experiment is presented, considering a scenario of analysing 5000 $\mathrm{fb}^{-1}$ $e^{+}e^{-}$ collision data with the center-of-mass energy of 250 GeV. In this study the Higgs bosons are produced in association with a pair of leptons, dominantly mediated by the $ZH$ production process. The statistical uncertainty of the signal cross section is estimated to be about 1 % in the $H\to b\bar{b}$ final state, and approximately 5 % - 10 % in the $H\to c\bar{c}/gg$ final states. In addition, the main sources of the systematic uncertainties and their impacts to the measurements of branching fractions are discussed. This study demonstrates the potential of precise measurement of the hadronic final states of the Higgs boson decay at the CEPC, and will provide key information to understand the Yukawa couplings between the Higgs boson and quarks, which are predicted to be the origin of quarks' masses in the Standard Model.

hep-ex↗

arXiv2019

Shenjing: A low power reconfigurable neuromorphic accelerator with partial-sum and spike networks-on-chip

The next wave of on-device AI will likely require energy-efficient deep neural networks. Brain-inspired spiking neural networks (SNN) has been identified to be a promising candidate. Doing away with the need for multipliers significantly reduces energy. For on-device applications, besides computation, communication also incurs a significant amount of energy and time. In this paper, we propose Shenjing, a configurable SNN architecture which fully exposes all on-chip communications to software, enabling software mapping of SNN models with high accuracy at low power. Unlike prior SNN architectures like TrueNorth, Shenjing does not require any model modification and retraining for the mapping. We show that conventional artificial neural networks (ANN) such as multilayer perceptron, convolutional neural networks, as well as the latest residual neural networks can be mapped successfully onto Shenjing, realizing ANNs with SNN's energy efficiency. For the MNIST inference problem using a multilayer perceptron, we were able to achieve an accuracy of 96% while consuming just 1.26mW using 10 Shenjing cores.

cs.ET↗

arXiv2019

Interactions between two heavy mesons within heavy meson chiral effective field theory

We have studied the interactions between two heavy mesons ($D^{(*)}$-$D^{(*)}$, $\bar D^{(*)}$-$\bar D^{(*)}$, $B^{(*)}$-$B^{(*)}$, or $\bar B^{(*)}$-$\bar B^{(*)}$) within heavy meson chiral effective field theory and investigated possible molecular states. The effective potentials are obtained with Weinberg's scheme up to one-loop level. At the leading order, four body contact interactions and one pion exchange contributions are considered. In addition to two pion exchange diagrams, we include the one-loop chiral corrections to contact terms and one pion exchange diagrams at the next-to-leading order. The effective potentials both in momentum space and coordinate space are investigated and discussed extensively. The possible molecular states are also studied and the binding energies are provided by solving the Schrodinger equation. The results will be helpful for the experimental search for the doubly-heavy molecular states.

hep-ph↗

arXiv2019

Hidden charm pentaquark states and $Σ_c^{()}\bar{D}^{()}$ interaction in chiral perturbation theory

We adopt the chiral perturbation theory to calculate the $Σ_{c}^{(*)}\bar{D}^{(*)}$ interaction to the next-to-leading order (NLO) and include the couple-channel effect in the loop diagrams. We reproduce the three $P_c$ states in the molecular picture after including the $Λ_{c}\bar{D}^{(*)}$ intermediate states. We also discuss some novel observations arising from the loop diagrams.

hep-ph↗

arXiv2019

Progressive Feature Polishing Network for Salient Object Detection

Feature matters for salient object detection. Existing methods mainly focus on designing a sophisticated structure to incorporate multi-level features and filter out cluttered features. We present Progressive Feature Polishing Network (PFPN), a simple yet effective framework to progressively polish the multi-level features to be more accurate and representative. By employing multiple Feature Polishing Modules (FPMs) in a recurrent manner, our approach is able to detect salient objects with fine details without any post-processing. A FPM parallelly updates the features of each level by directly incorporating all higher level context information. Moreover, it can keep the dimensions and hierarchical structures of the feature maps, which makes it flexible to be integrated with any CNN-based models. Empirical experiments show that our results are monotonically getting better with increasing number of FPMs. Without bells and whistles, PFPN outperforms the state-of-the-art methods significantly on five benchmark datasets under various evaluation metrics.

cs.CV↗

arXiv2019

Improving Operational Feasibility of Low-voltage Distribution Network by Phase-switching Devices

High penetration of residential photovoltaic (PV) in low-voltage distribution networks (LVDN) makes the unbalance issue more sever due to the asymmetry of generation/load characteristic in different phases, and may lead to infeasible operation of the whole network. Phase switching device (PSD), which can switch the connected phase of a residential load as required, is viable and efficient equipment to help address this issue. This paper, based on three-phase power flow (TUPF) formulation, aims to investigate the benefit of PSD on improving the operation feasibility of LVDN. The linear model of TUPF with PSD is presented to effectively take the flexible device into account, which can be conveniently used to seek the PSD positions that lead to a viable operation strategy of the original problem when infeasibility is reported by the traditional iteration-based algorithm. Case study based on a practical LVDN in Australia demonstrates the efficacy of the proposed method.

math.OC↗

arXiv2019

A Pooled Quantile Estimator for Parallel Simulations

Quantile is an important risk measure quantifying the stochastic system random behaviors. This paper studies a pooled quantile estimator, which is the sample quantile of detailed simulation outputs after directly pooling independent sample paths together. We derive the asymptotic representation of the pooled quantile estimator and further prove its normality. By comparing with the classical quantile estimator used in stochastic simulation, both theoretical and empirical studies demonstrate the advantages of the proposal under the context of parallel simulation.

stat.ME↗

arXiv2019

Fast Multipole Method For 3-D Helmholtz Equation In Layered Media

In this paper, a fast multipole method (FMM) is proposed to compute long-range interactions of wave sources embedded in 3-D layered media. The layered media Green's function for the Helmholtz equation, which satisfies the transmission conditions at material interfaces, is decomposed into a free space component and four types of reaction field components arising from wave reflections and transmissions through the layered media. The proposed algorithm is a combination of the classic FMM for the free space component and FMMs specifically designed for the four types reaction components, made possible by new multipole expansions (MEs) and local expansions (LEs) as well as the multipole-to-local translation (M2L) operators for the reaction field components. { Moreover, equivalent polarization source can be defined for each reaction component based on the convergence analysis of its ME. The FMMs for the reaction components, implemented with the target particles and equivalent polarization sources, are found to be much more efficient than the classic FMM for the free space component due to the fact that the equivalent polarization sources and the target particles are always separated by a material interface.} As a result, the FMM algorithm developed for layered media has a similar computational cost as that for the free space. Numerical results validate the fast convergence of the MEs and the $O(N)$ complexity of the FMM for interactions of low-frequency wave sources in 3-D layered media.

math.NA↗

arXiv2019

Hidden-charm and hidden-bottom molecular pentaquarks in chiral perturbation theory

The newly observed $P_c(4312)$, $P_c(4440)$ and $P_c(4457)$ at the LHCb experiment are very close to the $Σ_c\bar{D}$ and $Σ_c\bar{D}^\ast$ thresholds. In this work, we perform a systematic study and give a complete picture on the interactions between $Σ_c^{(\ast)}$ and $\bar{D}^{(\ast)}$ systems in the framework of heavy hadron chiral perturbation theory, where the short-range contact interaction, long-range one-pion-exchange contribution, and intermediate-range two-pion-exchange loop diagrams are all considered. We first investigate the three $P_c$ states without and with considering the $Λ_c$ contribution in the loop diagrams. It is difficult to simultaneously reproduce the three $P_c$s unless the effect of $Λ_c$ is included. The coupling between $Σ_c^{(\ast)}\bar{D}^{(\ast)}$ and $Λ_c\bar{D}^{(\ast)}$ channels is crucial for the formation of these $P_c$s. Our calculation supports the $P_c(4312)$, $P_c(4440)$ and $P_c(4457)$ to be the $S$-wave hidden-charm $[Σ_c\bar{D}]_{J=1/2}^{I=1/2}$, $[Σ_c\bar{D}^\ast]_{J=1/2}^{I=1/2}$ and $[Σ_c\bar{D}^\ast]_{J=3/2}^{I=1/2}$ molecular pentaquarks, respectively. Our calculation disfavors the spin assignment $J^P=\frac{1}{2}^-$ for $P_c(4457)$ and $J^P=\frac{3}{2}^-$ for $P_c(4440)$, because the excessively enhanced spin-spin interaction is unreasonable in the present case. We obtain the complete mass spectra of the $[Σ_c^{(\ast)}\bar{D}^{(\ast)}]_J$ systems with the fixed low energy constants. Our result indicates the existence of $[Σ_c^{\ast}\bar{D}^{\ast}]_J~(J=\frac{1}{2},\frac{3}{2},\frac{5}{2})$ hadronic molecules. The previously reported $P_c(4380)$ might be a deeper bound one. Additionally, we also study the hidden-bottom $Σ_b^{(\ast)}B^{(\ast)}$ systems, and predict seven bound molecular states, which could serve as a guidance for future experiments....

hep-ph↗

arXiv2019

Generating optical vortex beams by momentum-space polarization vortices centered at bound states in the continuum

An optical vortex (OV) is a beam with spiral wave front and screw phase dislocation. This kind of beams is attracting rising interest in various fields. Here we theoretically proposed and experimentally realized a novel but easy approach to generate optical vortices. We leverage the inherent topological vortex structures of polarization around bound states in the continuum (BIC) in the momentum space of two dimensional periodic structures, e.g. photonic crystal slabs, to induce Pancharatnam-Berry phases to the beams. This new class of OV generators operates in the momentum space, meaning that there is no real-space center of structure. Thus, not only the fabrication but also the practical alignment would be greatly simplified. Any even order of OV, which is actually a quasi-non-diffractive high-order quasi-Bessel beam, at any desired working wavelength could be achieved in principle. The proposed approach expands the application of bound states in the continuum and topological photonics.

physics.optics↗

arXiv2019

Design and performance of the LLRF control system for CSNS LINAC

The China spallation neutron source (CSNS) linac is designed with beam energy of 81MeV and a peak current of 15mA in the first phase. The RF power system for the 81 MeV Linac requires 8 units of RF power sources, each unit has one independent digital low level RF (LLRF) control system which is used to stabilize the amplitude and phase of the RF accelerating field along the linac, and to minimize beam loss. During beam commissioning, all of 8 on-line units of LLRF control system were operating stably and reliable, the control precision of accelerating fields met requirement of beam commissioning. The amplitude and phase variations of the linac fields are less than $\pm$0.2% and $\pm$0.2$^\circ$ without beam loading, or $\pm$0.5% and $\pm$0.5$^\circ$ with 10mA beam loading, much better than the design requirements of $\pm$1% in amplitude and $\pm$1$^\circ$ in phase.

physics.acc-ph↗

arXiv2019

s-LWSR: Super Lightweight Super-Resolution Network

Deep learning (DL) architectures for superresolution (SR) normally contain tremendous parameters, which has been regarded as the crucial advantage for obtaining satisfying performance. However, with the widespread use of mobile phones for taking and retouching photos, this character greatly hampers the deployment of DL-SR models on the mobile devices. To address this problem, in this paper, we propose a super lightweight SR network: s-LWSR. There are mainly three contributions in our work. Firstly, in order to efficiently abstract features from the low resolution image, we build an information pool to mix multi-level information from the first half part of the pipeline. Accordingly, the information pool feeds the second half part with the combination of hierarchical features from the previous layers. Secondly, we employ a compression module to further decrease the size of parameters. Intensive analysis confirms its capacity of trade-off between model complexity and accuracy. Thirdly, by revealing the specific role of activation in deep models, we remove several activation layers in our SR model to retain more information for performance improvement. Extensive experiments show that our s-LWSR, with limited parameters and operations, can achieve similar performance to other cumbersome DL-SR methods.

eess.IV↗

arXiv2019

An Efficient Iterative Method for Solving Multiple Scattering in Locally Inhomogeneous Media

In this paper, an efficient iterative method is proposed for solving multiple scattering problem in locally inhomogeneous media. The key idea is to enclose the inhomogeneity of the media by well separated artificial boundaries and then apply purely outgoing wave decomposition for the scattering field outside the enclosed region. As a result, the original multiple scattering problem can be decomposed into a finite number of single scattering problems, where each of them communicates with the other scattering problems only through its surrounding artificial boundary. Accordingly, they can be solved in a parallel manner at each iteration. This framework enjoys a great flexibility in using different combinations of iterative algorithms and single scattering problem solvers. The spectral element method seamlessly integrated with the non-reflecting boundary condition and the GMRES iteration is advocated and implemented in this work. The convergence of the proposed method is proved by using the compactness of involved integral operators. Ample numerical examples are presented to show its high accuracy and efficiency.

math.NA↗

arXiv2019

Moment Matching for Multi-Source Domain Adaptation

Conventional unsupervised domain adaptation (UDA) assumes that training data are sampled from a single domain. This neglects the more practical scenario where training data are collected from multiple sources, requiring multi-source domain adaptation. We make three major contributions towards addressing this problem. First, we collect and annotate by far the largest UDA dataset, called DomainNet, which contains six domains and about 0.6 million images distributed among 345 categories, addressing the gap in data availability for multi-source UDA research. Second, we propose a new deep learning approach, Moment Matching for Multi-Source Domain Adaptation M3SDA, which aims to transfer knowledge learned from multiple labeled source domains to an unlabeled target domain by dynamically aligning moments of their feature distributions. Third, we provide new theoretical insights specifically for moment matching approaches in both single and multiple source domain adaptation. Extensive experiments are conducted to demonstrate the power of our new dataset in benchmarking state-of-the-art multi-source domain adaptation methods, as well as the advantage of our proposed model. Dataset and Code are available at \url{http://ai.bu.edu/M3SDA/}.

cs.CV↗