相关论文: Exploring fully-heavy tetraquarks through the CGAN…

We provide a formulation of potential non-relativistic quantum chromodynamics (pNRQCD) suitable for calculating binding energies and matrix elements of generic hadron and multi-hadron states made of heavy quarks in $SU(N_c)$ gauge theory…

Motivated by the LHCb's new observation of structures in the $J/\psi$-pair invariant mass spectrum, for which could be classified as possible $cc\bar{c}\bar{c}$ tetraquark candidates, we systematically study $0^{+}$ fully-charmed tetraquark…

Quantum machine learning consists in taking advantage of quantum computations to generate classical data. A potential application of quantum machine learning is to harness the power of quantum computers for generating classical data, a…

We study the properties of fully-heavy baryons in the vacuum and the hot QCD medium, which is created in relativistic heavy-ion collisions. Masses and wave functions of $\Omega_{ccc}$, $\Omega_{ccb}$, $\Omega_{bbc}$, and $\Omega_{bbb}$ up…

In this paper, we present a Fast Motion Deblurring-Conditional Generative Adversarial Network (FMD-cGAN) that helps in blind motion deblurring of a single image. FMD-cGAN delivers impressive structural similarity and visual appearance after…

Generative Adversarial Networks (GANs) have gained significant attention in recent years, with impressive applications highlighted in computer vision in particular. Compared to such examples, however, there have been more limited…

Generative Adversarial Net (GAN) has been proven to be a powerful machine learning tool in image data analysis and generation. In this paper, we propose to use Conditional Generative Adversarial Net (CGAN) to learn and simulate time series…

Although deep learning has achieved impressive advances in transient stability assessment of power systems, the insufficient and imbalanced samples still trap the training effect of the data-driven methods. This paper proposes a…

We calculate the energy spectrum of the S-wave doubly heavy tetraquark systems, including the $ QQ^{(\prime)}\bar q\bar q$, $QQ^{(\prime)}\bar s\bar q$, and $ QQ^{(\prime)}\bar s\bar s$ ($Q^{(\prime)}=b,c$ and $q=u,d$) systems within the…

Anomalous crack region detection is a typical binary semantic segmentation task, which aims to detect pixels representing cracks on pavement surface images automatically by algorithms. Although existing deep learning-based methods have…

Porous media are ubiquitous in both nature and engineering applications, thus their modelling and understanding is of vital importance. In contrast to direct acquisition of three-dimensional (3D) images of such medium, obtaining its…

In this paper, we apply the QCD sum rules to study the vector fully-light tetraquark states with an explicit P-wave between the diquark and antidiquark pair. We observed that the…

Multimodal deep learning has substantially improved electrocardiogram (ECG) classification by jointly leveraging time, frequency, and time-frequency representations. However, existing generative models typically synthesize these modalities…

We use a color-magnetic interaction model (CMIM), a traditional constituent quark model (CQM) and a multiquark color flux-tube model (MCFTM) to systematically investigate the properties of the states $[Q_1Q_2][\bar{Q}_3\bar{Q}_4]$…

The current ongoing advancements in AI have shed light on the landscape of numerical analysis in science. Inspired by the path of achievement of AI, we have developed a method to construct accurate ground state wave functions of multiquark…

We use the twist dimensions of the operators underlying the dynamical behavior of exclusive production processes as a tool for determining the structure of exotic heavy-quark states such as the $Z_c^+(4430)$ tetraquark. The resulting…

In this paper, we propose the quantum semi-supervised generative adversarial network (qSGAN). The system is composed of a quantum generator and a classical discriminator/classifier (D/C). The goal is to train both the generator and the D/C,…

Conditional Generative Adversarial Networks~(CGAN) are a recent and popular method for generating samples from a probability distribution conditioned on latent information. The latent information often comes in the form of a discrete label…

We introduce HyperCAN, a machine learning framework that utilizes hypernetworks to construct adaptable constitutive artificial neural networks for a wide range of beam-based metamaterials exhibiting diverse mechanical behavior under finite…

Considering four-point correlation functions of color-singlet quark bilinears, we investigate, in the large-$N_{\mathrm{c}}$ limit of QCD, the subleading diagrams that involve, in the $s$-channel of meson-meson scattering amplitudes,…