arXiv subjects

Rong Wang

Publications and source records attributed to Rong Wang.

At least 127 records · Page 7Linked to original sources

arXiv2022

The neutron and proton mass radii from the vector meson photoproduction data on the deuterium target

In this study, we try to extract the mass radii of the neutron and the proton from the differential cross section data of near-threshold $ω$ and $ϕ$ photoproductions on deuterium target, which is often approximated as a quasi-free neutron plus a quasi-free proton. The incoherent data of $ω$ and $ϕ$ photoproductions are provided by CBELSA/TAPS collaboration and LEPS collaboration respectively, where the deuteron is disintegrated in the experiments to measure the properties of individual nucleons. Under the VMD model and the assumption of dipole gravitational form factor, we determined the loosely bound neutron and proton mass radii to be $0.795\pm0.092\rm(stat.)\pm0.073\rm(syst.)$ fm and $0.744\pm0.029\rm(stat.)\pm0.042\rm(syst.)$ fm respectively from the near-threshold data of $γd \rightarrow ωn (p)$ and $γd \rightarrow ωp (n)$, for the first time. With the near-threshold and incoherent $ϕ$ photoproduction data of $γd \rightarrow ϕp n$, we determined the average mass radius of the bound nucleon (neutron or proton) inside the deuteron to be $0.755\pm0.039\rm(stat.)\pm0.039\rm(syst.)$ fm, for the first time. For a comparison study, we also extracted the mass radius of the free proton from the $ω$ photoproduction on the hydrogen target by CBELSA/TAPS collaboration. Based on our analysis results under the assumptions of VMD model and a low energy QCD theorem, we find that the neutron mass radius is consistent with the proton mass radius within the current statistical uncertainties, and that the nuclear modification on the nucleon mass radius is small inside the deuteron.

hep-ph↗

arXiv2022

Nitrogen decoration of basal plane dislocations in 4H-SiC

Basal-plane dislocations (BPDs) pose a great challenge to the reliability of bipolar power devices based on the 4H silicon carbide (4H-SiC). It is well established that heavy nitrogen (N) doping promotes the conversion of BPDs to threading edge dislocations (TEDs) and improves the reliability of 4H-SiC-based bipolar power devices. However, the interaction between N and BPDs, and the effect of N on the electronic properties of BPDs are still ambiguous, which significantly hinder the understanding on the electron-transport mechanism of 4H-SiC-based bipolar power devices. Combining molten-alkali etching and the Kelvin probe force microscopy (KPFM) analysis, we demonstrate that BPDs create acceptor-like states in undoped 4H-SiC, while acting as donors in N-doped 4H-SiC. First-principles calculations verify that BPDs create occupied defect states above the valence band maximum (VBM) and unoccupied defect states under the conduction-band minimum (CBM) of undoped 4H-SiC. The electron transfer from the defect states of intrinsic defects and native impurities to the unoccupied defect states of BPDs gives rise to the acceptor-like behavior of BPDs in undoped 4H-SiC. Defect formation energies indicate that N atoms can spontaneously decorate BPDs during the N doping of 4H-SiC. The binding between N and BPD is strong against decomposition. The accumulation of N dopants at the core of BPDs results in the accumulation of donor-like states at the core of BPDs in N-doped 4H-SiC. This work not only enriches the understanding on the electronic behavior of BPDs in N-doped 4H-SiC, but also helps understand the electron transport mechanism of 4H-SiC-based bipolar power devices.

cond-mat.mtrl-sci↗

arXiv2022

Lifespan associations of resting-state brain functional networks with ADHD symptoms

Attention-deficit/hyperactivity disorder (ADHD) is increasingly being diagnosed in both children and adults, but the neural mechanisms that underlie its distinct symptoms and whether children and adults share the same mechanism remain poorly understood. Here, we used a nested-spectral partition (NSP) approach to study the resting-state brain functional networks of ADHD patients (n=97) and healthy controls (HCs, n=97) across the lifespan (7-50 years). Compared to the linear lifespan associations of brain functional segregation and integration with age in HCs, ADHD patients have a quadratic association in the whole brain and in most functional systems, whereas the limbic system dominantly affected by ADHD has a linear association. Furthermore, the limbic system better predicts hyperactivity, and the salient attention system better predicts inattention. These predictions are shared in children and adults with ADHD. Our findings reveal a lifespan association of brain networks with ADHD symptoms and provide potential shared neural bases of distinct ADHD symptoms in children and adults.

q-bio.NC↗

arXiv2022

Long-time dynamics of a nonlocal epidemic model with free boundaries: spreading-vanishing dichotomy

In this paper, we examine the long-time dynamics of an epidemic model whose diffusion and reaction terms involve nonlocal effects described by suitable convolution operators.The spreading front of the disease is represented by the free boundaries in the model. We show that the model is well-posed, its long-time dynamical behaviour is characterised by a spreading-vanishing dichotomy, and we also obtain sharp criteria to determine the dichotomy. Some of the nonlocal effects in the model pose extra difficulties in the mathematical treatment, which are dealt with by introducing new approaches. The model can capture accelerated spreading, and its spreading rate will be discussed in a subsequent work.

math.AP↗

arXiv2022

Finite-key analysis for quantum key distribution with discrete phase randomization

Quantum key distribution(QKD) allows two remote parties to share information-theoretic secret keys. Many QKD protocols assume the phase of encoding state can be continuous randomized from 0 to 2 pi, which, however, may be questionable in experiment. This is particularly the case in the recently proposed twin-field(TF) QKD, which has received a lot of attention, since it can increase key rate significantly and even beat some theoretical rate-loss limits. As an intuitive solution, one may introduce discrete phase-randomization instead of continuous one. However, a security proof for a QKD protocol with discrete phase-randomization in finite-key region is still missing. Here we develop a technique based on conjugate measurement and quantum state distinguishment to ana-lyze the security in this case. Our result shows that TF-QKD with reasonable number of discrete random phases, e.g. 8 phases from {0, pi/4, pi/2, ..., 7pi/4}, can achieve satisfactory performance. More importantly, as a the first proof for TF-QKD with discrete phase-randomization in finite-key region, our method is also applicable in other QKD protocols.

quant-ph↗

arXiv2022

Hybrid intelligence for dynamic job-shop scheduling with deep reinforcement learning and attention mechanism

The dynamic job-shop scheduling problem (DJSP) is a class of scheduling tasks that specifically consider the inherent uncertainties such as changing order requirements and possible machine breakdown in realistic smart manufacturing settings. Since traditional methods cannot dynamically generate effective scheduling strategies in face of the disturbance of environments, we formulate the DJSP as a Markov decision process (MDP) to be tackled by reinforcement learning (RL). For this purpose, we propose a flexible hybrid framework that takes disjunctive graphs as states and a set of general dispatching rules as the action space with minimum prior domain knowledge. The attention mechanism is used as the graph representation learning (GRL) module for the feature extraction of states, and the double dueling deep Q-network with prioritized replay and noisy networks (D3QPN) is employed to map each state to the most appropriate dispatching rule. Furthermore, we present Gymjsp, a public benchmark based on the well-known OR-Library, to provide a standardized off-the-shelf facility for RL and DJSP research communities. Comprehensive experiments on various DJSP instances confirm that our proposed framework is superior to baseline algorithms with smaller makespan across all instances and provide empirical justification for the validity of the various components in the hybrid framework.

cs.AI↗

arXiv2021

Nuclear Fermi Momenta of $^{2}$H, $^{27}$Al and $^{56}$Fe from an Analysis of CLAS data

Nuclear Fermi momentum is a basic property of a nucleus where many nucleons dwell. However, in experiments only the nuclear Fermi momenta of just a few nuclei are measured using quasielastic electron scattering on the nuclear targets so far. Particularly, we still do not know experimentally the Fermi momentum of the lightest nucleon composite -- the deuteron. In this paper, we apply both gaussian distribution and Cauchy distribution to describe the quasielastic peak in the cross section of electron-nucleus scattering. The dip of the cross-section ratio at about $x_{\rm B}=1$ is explained with the nuclear Fermi momentum. By performing the least-square fits to the published CLAS data in the narrow kinematic region of quasielastic scattering, we obtain the nuclear Fermi momenta of $^{2}$H, $^{27}$Al and $^{56}$Fe, which are $116\pm 7$ MeV/c, $232\pm 27$ MeV/c, and $244\pm 28$ MeV/c respectively. The extracted nuclear Fermi momenta are compared to the simple calculations based on Fermi gas model, and the consistencies are found.

nucl-th↗

arXiv2021

A Compact Neural Network-based Algorithm for Robust Image Watermarking

Digital image watermarking seeks to protect the digital media information from unauthorized access, where the message is embedded into the digital image and extracted from it, even some noises or distortions are applied under various data processing including lossy image compression and interactive content editing. Traditional image watermarking solutions easily suffer from robustness when specified with some prior constraints, while recent deep learning-based watermarking methods could not tackle the information loss problem well under various separate pipelines of feature encoder and decoder. In this paper, we propose a novel digital image watermarking solution with a compact neural network, named Invertible Watermarking Network (IWN). Our IWN architecture is based on a single Invertible Neural Network (INN), this bijective propagation framework enables us to effectively solve the challenge of message embedding and extraction simultaneously, by taking them as a pair of inverse problems for each other and learning a stable invertible mapping. In order to enhance the robustness of our watermarking solution, we specifically introduce a simple but effective bit message normalization module to condense the bit message to be embedded, and a noise layer is designed to simulate various practical attacks under our IWN framework. Extensive experiments demonstrate the superiority of our solution under various distortions.

cs.CV↗

arXiv2021

Ensemble and Random Collaborative Representation-Based Anomaly Detector for Hyperspectral Imagery

In recent years, hyperspectral anomaly detection (HAD) has become an active topic and plays a significant role in military and civilian fields. As a classic HAD method, the collaboration representation-based detector (CRD) has attracted extensive attention and in-depth research. Despite the good performance of the CRD method, its computational cost mainly arising from the sliding dual window strategy is too high for wide applications. Moreover, it takes multiple repeated tests to determine the size of the dual window, which needs to be reset once the dataset changes and cannot be identified in advance with prior knowledge. To alleviate this problem, we proposed a novel ensemble and random collaborative representation-based detector (ERCRD) for HAD, which comprises two closely related stages. Firstly, we process the random sub-sampling on CRD (RCRD) to gain several detection results instead of the sliding dual window strategy, which significantly reduces the computational complexity and makes it more feasible in practical applications. Secondly, ensemble learning is employed to refine the multiple results of RCRD, which act as various "experts" providing abundant complementary information to better target different anomalies. Such two stages form an organic and theoretical detector, which can not only improve the accuracy and stability of HAD methods but also enhance its generalization ability. Experiments on four real hyperspectral datasets exhibit the accuracy and efficiency of this proposed ERCRD method compared with ten state-of-the-art HAD methods.

eess.IV↗

arXiv2021

Transcribing Natural Languages for The Deaf via Neural Editing Programs

This work studies the task of glossification, of which the aim is to em transcribe natural spoken language sentences for the Deaf (hard-of-hearing) community to ordered sign language glosses. Previous sequence-to-sequence language models trained with paired sentence-gloss data often fail to capture the rich connections between the two distinct languages, leading to unsatisfactory transcriptions. We observe that despite different grammars, glosses effectively simplify sentences for the ease of deaf communication, while sharing a large portion of vocabulary with sentences. This has motivated us to implement glossification by executing a collection of editing actions, e.g. word addition, deletion, and copying, called editing programs, on their natural spoken language counterparts. Specifically, we design a new neural agent that learns to synthesize and execute editing programs, conditioned on sentence contexts and partial editing results. The agent is trained to imitate minimal editing programs, while exploring more widely the program space via policy gradients to optimize sequence-wise transcription quality. Results show that our approach outperforms previous glossification models by a large margin.

cs.CL↗

arXiv2021

New Tight Relaxations of Rank Minimization for Multi-Task Learning

Multi-task learning has been observed by many researchers, which supposes that different tasks can share a low-rank common yet latent subspace. It means learning multiple tasks jointly is better than learning them independently. In this paper, we propose two novel multi-task learning formulations based on two regularization terms, which can learn the optimal shared latent subspace by minimizing the exactly $k$ minimal singular values. The proposed regularization terms are the more tight approximations of rank minimization than trace norm. But it's an NP-hard problem to solve the exact rank minimization problem. Therefore, we design a novel re-weighted based iterative strategy to solve our models, which can tactically handle the exact rank minimization problem by setting a large penalizing parameter. Experimental results on benchmark datasets demonstrate that our methods can correctly recover the low-rank structure shared across tasks, and outperform related multi-task learning methods.

cs.LG↗

arXiv2021

Proton and deuteron mass radii from near-threshold $ϕ$-meson photoproduction

We analyze the exclusive $ϕ$-meson photoproduction on both hydrogen and deuterium targets based on the published data of CLAS, SAPHIR and LEPS collaborations. A dipole-form scalar gravitational form factor is applied to describe the $|t|$-dependence of the differential cross section. Based on the average of all the near-threshold data from CLAS, SAPHIR and LEPS, we find that the proton and deuteron mass radii are $0.75 \pm 0.02$ fm and $1.95 \pm 0.19$ fm respectively. The coherent and near-threshold quarkonium photoproduction seems to be sensitive to the radius of the hadronic system. The vector-meson-dominance model together with a low-energy QCD theorem well describe the data of near-threshold $ϕ$ photoproduction on the hadronic systems.

hep-ph↗

arXiv2021

An analysis of polarized parton distribution functions with nonlinear QCD evolution equations

We present the polarized parton distribution functions from a QCD analysis of the worldwide polarized deep inelastic scattering data, based on the dynamical parton distribution model. All the sea quarks and gluons are dynamically generated from QCD radiations, with the nonperturbative input contains only the polarized valence quark distributions. This approach leads to a simple parametrization, which has only four free parameters. In the analysis, we apply the DGLAP equations with parton-parton recombination corrections. The parameterized nonperturbative input at an extremely low $Q_0^2$ reproduces well the spin-dependent structure functions measured at high $Q^{2}$. Comparisons with experimental observations and some other polarized parton distribution functions are also shown. Our results are in good agreement with the experimental data and consistent with some other parameterized models. Furthermore, our analysis gives the positive polarized gluon distribution and it suggests that the gluon distribution plays an important role to the proton spin content. The polarized antiquark distributions are non-zero at high $Q^2$ but quite small compared to polarized valence quark distribution, based on this dynamical parton model analysis. This analysis shows smaller statistical uncertainties for the polarized sea quark and gluon distributions, thanks to the fewer free parameters used for the parametrization of the initial polarized PDFs.

hep-ph↗

arXiv2021

Tackling the kaon structure function at EicC

Measuring the kaon structure beyond the proton and pion structures is one of the hot topics in hadron physics, as it is one way to understand the nature of Nambu-Goldstone boson of QCD and to see the interplay between the EHM mechanism and the HB mechanism for hadron mass generation. In this paper, we present a simulation of the leading $Λ$ baryon tagged deep inelastic scattering experiment at EicC (Electron-ion collider in China), which is engaged to unveil the internal structure of kaon through Sullivan process. According to our simulation results, the suggested experiment will cover the kinematical domain of $0.05\lesssim x_{\rm K} \lesssim 0.85$ and $Q^2$ up to 50 GeV$^2$, with the acceptable statistical uncertainties. In the relatively low-$Q^2$ region ($<10$ GeV$^2$), the Monte-Carlo simulation shows a good precision of the measurement ($<5$\%) for the kaon structure function $F_2^{\rm K}$. In the high-$Q^2$ region (up to 50 GeV$^2$), the statistical uncertainty of $F_2^{\rm K}$ is also acceptable ($<10$\%) for the data at $x_{\rm K}<0.8$. To perform such an experiment at an electron-ion collider, a high-performance zero-degree calorimeter is required.

hep-ph↗

arXiv2021

Production of the $T^{+}_{cc}$ state in the $γp\to{}D^{+}\bar{T}^{-}_{cc}Λ_c^{+}$ reaction

Stimulated by the recent LHCb observation of a new exotic charged structure $T^{+}_{cc}$, we propose to use the central diffractive mechanism existing in the $γp\to{}D^{+}\bar{T}^{-}_{cc}Λ_c^{+}$ ($\bar{T}_{cc}$ is antiparticle of $T^{+}_{cc}$) reaction to produce $T^{+}_{cc}$. Our theoretical approach is based on the chiral unitary theory where the $T^{+}_{cc}$ resonance is dynamically generated. With the coupling constant of the $T^{+}_{cc}$ to $DD^{*}$ channel obtained from chiral unitary theory, the total cross sections of the $γp\to{}D^{+}\bar{T}^{-}_{cc}Λ_c^{+}$ reaction are evaluated. Our study indicates that the cross section for $γp\to{}D^{+}\bar{T}^{-}_{cc}Λ_c^{+}$ reaction are of the order of 1.0 pb, which is accessible at the proposed EicC~\cite{Anderle:2021wcy} and US-EIC~\cite{Accardi:2012qut} due to the higher luminosity. If measured in future experiments, the predicted total cross sections can be used to test the (molecular) nature of the $T^{+}_{cc}$.

hep-ph↗

arXiv2021

Determination of the Gluonic D-term and Mechanical Radii of Proton from Experimental data

We apply a "color" tripole ansatz for describing the $D$-term of the proton. By fitting the experimental data of the vector meson J/$ψ$ and $ϕ$ photoproductions near the thresholds, we firstly obtained the gluonic $D$-term of the proton. $D_g(0)$ is estimated to be $-2.16\pm0.42$ for J/$ψ$ and $-1.31\pm0.48$ for $ϕ$, and the mechanical root mean square radius of proton is estimated to be $0.61\pm0.29$ fm for $ϕ$ and $0.42\pm0.11$ fm for J/$ψ$.

hep-ph↗

arXiv2021

Extraction of the proton mass radius from the vector meson photoproductions near thresholds

We present an analysis of the proton mass radius by studying the $t$-dependence of the differential cross sections of the vector meson photoproductions near the thresholds. At low energy, the photoproduction of a quarkonium off the proton is connected to the scalar gravitational form factor of the proton, which is sensitive to the proton mass distribution from the QCD trace anomaly. Under an assumption of the scalar form factor of dipole form, the proton mass radius is extracted via the near-threshold photoproduction data of J/$ψ$, $ϕ$ and $ω$ vector mesons. The average value of the proton mass radius is estimated to be $\sqrt{\left } = 0.67\pm0.03$ fm, with the dipole cutoff $m_{\rm s}=1.01\pm0.04$ GeV.

hep-ph↗

arXiv2021

Improving the doping efficiency of Al in 4H-SiC by co-doping group-IVB elements

The p-type doping efficiency of 4H silicon carbide (4H-SiC) is rather low due to the large ionization energies of p-type dopants. Such an issue impedes the exploration of the full advantage of 4H-SiC for semiconductor devices. In this letter, we show that co-doping group-IVB elements effectively decreases the ionization energy of the most widely used p-type dopant, i. e., aluminum (Al), through the Coulomb repulsion between the energy levels of group-IVB elements and that of Al in 4H-SiC. Among group-IVB elements Ti has the most prominent effectiveness. Ti decreases the ionization energy of Al by nearly 50%, leading to a value as low as ~ 0.13 eV. As a result, the ionization rate of Al with Ti co-doping is up to ~ 5 times larger than that without co-doping at room temperature when the doping concentration is up to 1018 cm-3. This work may encourage the experimental co-doping of group-IB elements such as Ti and Al to significantly improve the p-type doping efficiency of 4H-SiC.

cond-mat.mtrl-sci↗