arXiv subjects

Bo Wang

Publications and source records attributed to Bo Wang.

At least 595 records · Page 33Linked to original sources

arXiv2021

Is your Schema Good Enough to Answer my Query?

Ontology-based data integration has been one of the practical methodologies for heterogeneous legacy database integrated service construction. However, it is neither efficient nor economical to build the cross-domain ontology on top of the schemas of each legacy database for the specific integration application than to reuse the existed ontologies. Then the question lies in whether the existed ontology is compatible with the cross-domain queries and with all the legacy systems. It is highly needed an effective criteria to evaluate the compatibility as it limits the upbound quality of the integrated services. This paper studies the semantic similarity of schemas from the aspect of properties. It provides a set of in-depth criteria, namely coverage and flexibility to evaluate the compatibility among the queries, the schemas and the existing ontology. The weights of classes are extended to make precise compatibility computation. The use of such criteria in the practical project verifies the applicability of our method.

cs.DB↗

arXiv2021

Predicting the $\bar{D}_s^{()} D_s^{()}$ bound states as the partners of $X(3872)$

In this work, we investigate the SU(3) flavor symmetry, heavy quark spin symmetry and their breaking effects in the di-meson systems. We prove the existence of the $[\bar{D}_{s}^{*}D_{s}^{*}]^{0^{++}}$, $[\bar{D}_{s}^{*}D_{s}/\bar{D}_{s}^{}D_{s}^*]^{1^{+-}}$, and $[\bar{D}_{s}^{*}D_{s}^{*}]^{1^{+-}}$ bound states as the consequence of two prerequisites in the SU(3) flavor symmetry and heavy quark spin symmetry. The first prerequisite, the $X(3872)$ as the weakly $\bar{D}^{*}D/\bar{D} D^{*}$ bound state is supported by its mass and decay branching ratios. The second prerequisite, the existence of the $[\bar{D}_{s}D_{s}]^{0^{++}}$ bound state is supported by the lattice QCD calculation [arXiv:2011.02542(hep-lat)] and the observation of $χ_{c0}(3930)$ by the LHCb Collaboration[Phys.Rev.Lett.125,242001, Phys.Rev.D102,112003]. We hope the future experimental analyses can search for these bound states in the $B\to D_{(s)}^{(*)}\bar{D}^{(*)}_{(s)}h$ processes ($h$ denotes the light hadrons). The $[\bar{D}_{s}^{*}D_{s}^{*}]^{0^{++}}$ bound state is also expected to be reconstructed in the $J/ψϕ$ final state in the $B\to J/ψϕK$ decay.

hep-ph↗

arXiv2021

Continuous quantum light from a dark atom

Cycling processes are important in many areas of physics ranging from lasers to topological insulators, often offering surprising insights into dynamical and structural aspects of the respective system. Here we report on a quantum-nonlinear wave-mixing experiment where resonant lasers and an optical cavity define a closed cycle between several ground and excited states of a single atom. We show that, for strong atom-cavity coupling and steady-state driving, the entanglement between the atomic states and intracavity photon number suppresses the excited-state population via quantum interference, effectively reducing the cycle to the atomic ground states. The system dynamics then result from transitions within a harmonic ladder of entangled dark states, one for each cavity photon number, and a quantum Zeno blockade that generates antibunching in the photons emitted from the cavity. The reduced cycle suppresses unwanted optical pumping into atomic states outside the cycle, thereby enhancing the number of emitted photons.

quant-ph↗

arXiv2021

Auditory Attention Decoding from EEG using Convolutional Recurrent Neural Network

The auditory attention decoding (AAD) approach was proposed to determine the identity of the attended talker in a multi-talker scenario by analyzing electroencephalography (EEG) data. Although the linear model-based method has been widely used in AAD, the linear assumption was considered oversimplified and the decoding accuracy remained lower for shorter decoding windows. Recently, nonlinear models based on deep neural networks (DNN) have been proposed to solve this problem. However, these models did not fully utilize both the spatial and temporal features of EEG, and the interpretability of DNN models was rarely investigated. In this paper, we proposed novel convolutional recurrent neural network (CRNN) based regression model and classification model, and compared them with both the linear model and the state-of-the-art DNN models. Results showed that, our proposed CRNN-based classification model outperformed others for shorter decoding windows (around 90% for 2 s and 5 s). Although worse than classification models, the decoding accuracy of the proposed CRNN-based regression model was about 5% greater than other regression models. The interpretability of DNN models was also investigated by visualizing layers' weight.

eess.SP↗

arXiv2021

Eye-gaze Estimation with HEOG and Neck EMG using Deep Neural Networks

Hearing-impaired listeners usually have troubles attending target talker in multi-talker scenes, even with hearing aids (HAs). The problem can be solved with eye-gaze steering HAs, which requires listeners eye-gazing on the target. In a situation where head rotates, eye-gaze is subject to both behaviors of saccade and head rotation. However, existing methods of eye-gaze estimation did not work reliably, since the listener's strategy of eye-gaze varies and measurements of the two behaviors were not properly combined. Besides, existing methods were based on hand-craft features, which could overlook some important information. In this paper, a head-fixed and a head-free experiments were conducted. We used horizontal electrooculography (HEOG) and neck electromyography (NEMG), which separately measured saccade and head rotation to commonly estimate eye-gaze. Besides traditional classifier and hand-craft features, deep neural networks (DNN) were introduced to automatically extract features from intact waveforms. Evaluation results showed that when the input was HEOG with inertial measurement unit, the best performance of our proposed DNN classifiers achieved 93.3%; and when HEOG was with NEMG together, the accuracy reached 72.6%, higher than that with HEOG (about 71.0%) or NEMG (about 35.7%) alone. These results indicated the feasibility to estimate eye-gaze with HEOG and NEMG.

eess.SP↗

arXiv2021

Structure-Augmented Text Representation Learning for Efficient Knowledge Graph Completion

Human-curated knowledge graphs provide critical supportive information to various natural language processing tasks, but these graphs are usually incomplete, urging auto-completion of them. Prevalent graph embedding approaches, e.g., TransE, learn structured knowledge via representing graph elements into dense embeddings and capturing their triple-level relationship with spatial distance. However, they are hardly generalizable to the elements never visited in training and are intrinsically vulnerable to graph incompleteness. In contrast, textual encoding approaches, e.g., KG-BERT, resort to graph triple's text and triple-level contextualized representations. They are generalizable enough and robust to the incompleteness, especially when coupled with pre-trained encoders. But two major drawbacks limit the performance: (1) high overheads due to the costly scoring of all possible triples in inference, and (2) a lack of structured knowledge in the textual encoder. In this paper, we follow the textual encoding paradigm and aim to alleviate its drawbacks by augmenting it with graph embedding techniques -- a complementary hybrid of both paradigms. Specifically, we partition each triple into two asymmetric parts as in translation-based graph embedding approach, and encode both parts into contextualized representations by a Siamese-style textual encoder. Built upon the representations, our model employs both deterministic classifier and spatial measurement for representation and structure learning respectively. Moreover, we develop a self-adaptive ensemble scheme to further improve the performance by incorporating triple scores from an existing graph embedding model. In experiments, we achieve state-of-the-art performance on three benchmarks and a zero-shot dataset for link prediction, with highlights of inference costs reduced by 1-2 orders of magnitude compared to a textual encoding method.

cs.CL↗

arXiv2021

Sound speed resonance of the stochastic gravitational wave background

We propose a novel mechanism to test time variation of the propagation speed of gravitational waves (GWs) in light of GWs astronomy. As the stochastic GWs experience the whole history of cosmic expansion, they encode potential observational evidence of such variation. We report that, one feature of a varying GWs speed is that the energy spectrum of GWs will present resonantly-enhanced peaks if the GWs speed oscillates in time at high-energy scales. Such oscillatory behaviour arises in a wide class of modified gravity theories. The amplitude of these peaks can be at reach by current and forthcoming GWs instruments, hence making the underlying theories falsifiable. This mechanism reveals that probing the variation of GWs speed can be a promising way to search for new physics beyond general relativity.

gr-qc↗

arXiv2021

Modelling Paralinguistic Properties in Conversational Speech to Detect Bipolar Disorder and Borderline Personality Disorder

Bipolar disorder (BD) and borderline personality disorder (BPD) are two chronic mental health conditions that clinicians find challenging to distinguish based on clinical interviews, due to their overlapping symptoms. In this work, we investigate the automatic detection of these two conditions by modelling both verbal and non-verbal cues in a set of interviews. We propose a new approach of modelling short-term features with visibility-signature transform, and compare it with widely used high-level statistical functions. We demonstrate the superior performance of our proposed signature-based model. Furthermore, we show the role of different sets of features in characterising BD and BPD.

cs.LG↗

arXiv2021

Multipopulation mortality modelling and forecasting: The multivariate functional principal component with time weightings approaches

Human mortality patterns and trajectories in closely related populations are likely linked together and share similarities. It is always desirable to model them simultaneously while taking their heterogeneity into account. This paper introduces two new models for joint mortality modelling and forecasting multiple subpopulations in adaptations of the multivariate functional principal component analysis techniques. The first model extends the independent functional data model to a multi-population modelling setting. In the second one, we propose a novel multivariate functional principal component method for coherent modelling. Its design primarily fulfils the idea that when several subpopulation groups have similar socio-economic conditions or common biological characteristics, such close connections are expected to evolve in a non-diverging fashion. We demonstrate the proposed methods by using sex-specific mortality data. Their forecast performances are further compared with several existing models, including the independent functional data model and the Product-Ratio model, through comparisons with mortality data of ten developed countries. Our experiment results show that the first proposed model maintains a comparable forecast ability with the existing methods. In contrast, the second proposed model outperforms the first model as well as the current models in terms of forecast accuracy, in addition to several desirable properties.

stat.ME↗

arXiv2021

The effect of aspherical stellar wind of giant stars on the symbiotic channel of type Ia supernovae

The progenitor systems accounting for explosions of type Ia supernovae (SNe Ia) is still under debate. Symbiotic channel is one of the possible progenitor scenarios, in which the WDs in these systems increase in mass through wind accretion from their red giant companions. The mass-loss processes of the giants in the symbiotic systems could produce amount of circumstellar medium (CSM), and the detections of the interaction signals between SN ejecta and CSM can be used as an ideal way to distinguish the different progenitor models. However, the density distribution and geometric structure of the CSM around the symbiotic systems remain highly uncertain. By assuming that the tidal torque from binary interaction can increase the mass-loss rate of the red giant and cause the stellar wind concentrate towards the equatorial plane, we provide a simplified method to estimate the density and the degree of deviation from spherical symmetry of the CSM. Based on the calculations of the binary evolutions of symbiotic systems using stellar evolution code MESA, we obtained the parameter space for producing SNe Ia. We found that SNe Ia could originate from symbiotic systems with massive carbon-oxygen white dwarfs (CO WDs), while the half-opening angle of the stellar wind from red giant towards the WD varies with the binary evolution, resulting in the formation of surrounding CSM with different geometric structures. The corresponding properties of ejecta-CSM interactions may be examined by the spectropolarimetry observations in the future, from which one may find additional relationship between circumstellar environment of SNe Ia and their progenitor systems.

astro-ph.SR↗

arXiv2021

Robust non-parametric mortality and fertility modelling and forecasting: Gaussian process regression approaches

A rapid decline in mortality and fertility has become major issues in many developed countries over the past few decades. A precise model for forecasting demographic movements is important for decision making in social welfare policies and resource budgeting among the government and many industry sectors. This article introduces a novel non-parametric approach using Gaussian process regression with a natural cubic spline mean function and a spectral mixture covariance function for mortality and fertility modelling and forecasting. Unlike most of the existing approaches in demographic modelling literature, which rely on time parameters to decide the movements of the whole mortality or fertility curve shifting from one year to another over time, we consider the mortality and fertility curves from their components of all age-specific mortality and fertility rates and assume each of them following a Gaussian process over time to fit the whole curves in a discrete but intensive style. The proposed Gaussian process regression approach shows significant improvements in terms of preciseness and robustness compared to other mainstream demographic modelling approaches in the short-, mid- and long-term forecasting using the mortality and fertility data of several developed countries in our numerical experiments.

stat.ML↗

arXiv2021

Spectrum of the doubly charmed molecular pentaquarks in chiral effective field theory

We perform a systematic study on the interactions of the $Σ_c^{(*)}D^{(*)}$ systems within the framework of chiral effective field theory. We introduce the contact term, one-pion-exchange and two-pion-exchange contributions to describe the short-, long-, and intermediate-range interactions. The low energy constants of the $Σ_c^{(*)}D^{(*)}$ systems are estimated from the $N\bar{N}$ scattering data by introducing a quark level Lagrangian. With three solutions of LECs, all the $Σ_c^{(*)}D^{(*)}$ systems with isospin $I=1/2$ can form bound states, in which different inputs of LECs may lead to distinguishable mass spectra. In addition, we also investigate the interactions of the charmed-bottom $Σ_c^{(*)}\bar{B}^{(*)}$, $Σ_b^{(*)}D^{(*)}$, and $Σ_b^{(*)}\bar{B}^{(*)}$ systems. Among the obtained bound states, the bindings become deeper when the reduced masses of the corresponding systems are heavier.

hep-ph↗

arXiv2021

Adversarial Active Learning based Heterogeneous Graph Neural Network for Fake News Detection

The explosive growth of fake news along with destructive effects on politics, economy, and public safety has increased the demand for fake news detection. Fake news on social media does not exist independently in the form of an article. Many other entities, such as news creators, news subjects, and so on, exist on social media and have relationships with news articles. Different entities and relationships can be modeled as a heterogeneous information network (HIN). In this paper, we attempt to solve the fake news detection problem with the support of a news-oriented HIN. We propose a novel fake news detection framework, namely Adversarial Active Learning-based Heterogeneous Graph Neural Network (AA-HGNN) which employs a novel hierarchical attention mechanism to perform node representation learning in the HIN. AA-HGNN utilizes an active learning framework to enhance learning performance, especially when facing the paucity of labeled data. An adversarial selector will be trained to query high-value candidates for the active learning framework. When the adversarial active learning is completed, AA-HGNN detects fake news by classifying news article nodes. Experiments with two real-world fake news datasets show that our model can outperform text-based models and other graph-based models when using less labeled data benefiting from the adversarial active learning. As a model with generalizability, AA-HGNN also has the ability to be widely used in other node classification-related applications on heterogeneous graphs.

cs.SI↗

arXiv2021

The design of the Ali CMB Polarization Telescope receiver

Ali CMB Polarization Telescope (AliCPT-1) is the first CMB degree-scale polarimeter to be deployed on the Tibetan plateau at 5,250m above sea level. AliCPT-1 is a 90/150 GHz 72 cm aperture, two-lens refracting telescope cooled down to 4 K. Alumina lenses, 800mm in diameter, image the CMB in a 33.4° field of view on a 636mm wide focal plane. The modularized focal plane consists of dichroic polarization-sensitive Transition-Edge Sensors (TESes). Each module includes 1,704 optically active TESes fabricated on a 150mm diameter silicon wafer. Each TES array is read out with a microwave multiplexing readout system capable of a multiplexing factor up to 2,048. Such a large multiplexing factor has allowed the practical deployment of tens of thousands of detectors, enabling the design of a receiver that can operate up to 19 TES arrays for a total of 32,376 TESes. AliCPT-1 leverages the technological advancements in the detector design from multiple generations of previously successful feedhorn-coupled polarimeters, and in the instrument design from BICEP-3, but applied on a larger scale. The cryostat receiver is currently under integration and testing. During the first deployment year, the focal plane will be populated with up to 4 TES arrays. Further TES arrays will be deployed in the following years, fully populating the focal plane with 19 arrays on the fourth deployment year. Here we present the AliCPT-1 receiver design, and how the design has been optimized to meet the experimental requirements.

astro-ph.IM↗

arXiv2020

Decoding the nature of $Z_{cs}(3985)$ and establishing the spectrum of charged heavy quarkoniumlike states in chiral effective field theory

We study the newly observed charmoniumlike state $Z_{cs}(3985)$ in the framework of chiral effective field theory. The interaction kernel of the $\bar{D}_sD^\ast/\bar{D}^\ast_sD$ system is calculated up to the next-to-leading order with the explicit chiral dynamics. With the fitted parameters extracted from the $Z_c(3900)$ data as inputs, the mass, width and event distributions of the $Z_{cs}(3985)$ are very consistent with the experimental measurements. Our studies strongly support the $Z_{cs}(3985)$ as the partner of the $Z_c(3900)$ in the SU(3)$_f$ symmetry and the $\bar{D}_sD^\ast/\bar{D}^\ast_sD$ molecular resonance with the same dynamical origin as the other charged heavy quarkoniumlike states. We precisely predict the resonance parameters of the unobserved states in $\bar{D}_s^\ast D^\ast$, $B^\ast_s \bar{B}/B_s\bar{B}^\ast$ and $B_s^\ast\bar{B}^\ast$ systems, and establish a complete spectrum of the charged charmoniumlike and bottomoniumlike states with the $I(J^P)$ quantum numbers $1(1^+)$ and $\frac{1}{2}(1^+)$, respectively.

hep-ph↗

arXiv2020

$Z_{cs}(3985)^-$ as the $U$-spin partner of $Z_c(3900)^-$ and implication of other states in the $\text{SU(3)}_F$ symmetry and heavy quark symmetry

Recently, for the first time, the BESIII Collaboration reported the strange hidden charm tetraquark states $Z_{cs}(3985)^-$ in the $K^+$ recoil-mass spectrum near the $D_{s}^{-}D^{*0}/ D_{s}^{*-}D^{0}$ mass thresholds in the processes of $e^{+}e^{-}\to K^{+}(D_{s}^{-}D^{*0}+D_{s}^{*-}D^{0})$ at $\sqrt{s}=4.681$ GeV. The significance was estimated to be 5.3 $σ$. We show that the newly observed $Z_{cs}(3985)^-$ state is the $U$-spin partner of $Z_c(3900)^{-}$ as a resonance within coupled-channel calculation in the $\text{SU(3)}_F$ symmetry and heavy quark spin symmetry (HQSS). In the $\text{SU(3)}_F$ symmetry, we introduce the $G_{U/V}$ parity to construct the flavor wave functions of the $Z_{cs}$ states. In a unified framework, we consider the $J/ψπ(K)$, $\bar{D}_{(s)}{D}^*/\bar{D}_{(s)}^*{D}$ coupled-channel effect with the contact interaction. With the masses and widths of $Z_c(3900)$ and $Z_c(4020)$, we determine all the unknown coupling constants. We obtain mass and width of $Z_{cs}(3985)$ in good agreement with the experimental results, which strongly supports the $Z_{cs}$ states as the $U/V$-spin partner states of the charged $Z_c(3900)$. We also calculate the ratio of the partial decay widths of $Z_{cs}(3985)$, which implies that the $\bar{D}_sD^*/\bar{D}^*_s D$ decay modes are dominant. We also predict the $Z_{cs}^\prime$ states with a mass around $4130$ MeV and width around $30$ MeV, which are the $U/V$-spin partner states of the charged $Z_c(4020)$ and HQSS partner states of the $Z_{cs}(3985)$. In the hidden bottom sector, we predict the strange tetraquark states $Z_{bs}$ and $Z_{bs}^\prime$ with a mass around 10700 MeV and 10750 MeV, which are the $U/V$-spin partner states of $Z_{b}(10610)^{\pm}$ and $Z_b(10650)^{\pm}$, respectively.

hep-ph↗

arXiv2020

FPAENet: Pneumonia Detection Network Based on Feature Pyramid Attention Enhancement

Automatic pneumonia Detection based on deep learning has increasing clinical value. Although the existing Feature Pyramid Network (FPN) and its variants have already achieved some great successes, their detection accuracies for pneumonia lesions in medical images are still unsatisfactory. In this paper, we propose a pneumonia detection network based on feature pyramid attention enhancement, which integrates attended high-level semantic features with low-level information. We add another information extracting path equipped with feature enhancement modules, which are conducted with an attention mechanism. Experimental results show that our proposed method can achieve much better performances, as a higher value of 4.02% and 3.19%, than the baselines in detecting pneumonia lesions.

eess.IV↗

arXiv2020

w-Net: Dual Supervised Medical Image Segmentation Model with Multi-Dimensional Attention and Cascade Multi-Scale Convolution

Deep learning-based medical image segmentation technology aims at automatic recognizing and annotating objects on the medical image. Non-local attention and feature learning by multi-scale methods are widely used to model network, which drives progress in medical image segmentation. However, those attention mechanism methods have weakly non-local receptive fields' strengthened connection for small objects in medical images. Then, the features of important small objects in abstract or coarse feature maps may be deserted, which leads to unsatisfactory performance. Moreover, the existing multi-scale methods only simply focus on different sizes of view, whose sparse multi-scale features collected are not abundant enough for small objects segmentation. In this work, a multi-dimensional attention segmentation model with cascade multi-scale convolution is proposed to predict accurate segmentation for small objects in medical images. As the weight function, multi-dimensional attention modules provide coefficient modification for significant/informative small objects features. Furthermore, The cascade multi-scale convolution modules in each skip-connection path are exploited to capture multi-scale features in different semantic depth. The proposed method is evaluated on three datasets: KiTS19, Pancreas CT of Decathlon-10, and MICCAI 2018 LiTS Challenge, demonstrating better segmentation performances than the state-of-the-art baselines.

cs.CV↗