相关论文: Estimating centrality in heavy-ion collisions usin…

In this paper, we propose a transfer learning (TL)-enabled edge-CNN framework for 5G industrial edge networks with privacy-preserving characteristic. In particular, the edge server can use the existing image dataset to train the CNN in…

Some experimental results are discussed in connection with the properties of the central heavy ion collisions. These experiments indicate the regime changes and saturation at some values of the centrality. This phenomenon is considered to…

Collisions of Au on Au at incident energies of 150, 250 and 400 A MeV were studied with the FOPI-facility at GSI Darmstadt. Nuclear charge (Z < 16) and velocity of the products were detected with full azimuthal acceptance at laboratory…

The use of Convolutional Neural Networks (CNNs) has greatly improved the interpretation of medical images. However, conventional CNNs typically demand extensive computational resources and large training datasets. To address these…

Heavy-ion collision is an important tool to understand the dense nuclear matter properties. In order to understand the results of the heavy-ion collision experiments, both theoretical approaches to dense nuclear matter using effective…

Cluster production plays an important role in heavy-ion collisions at intermediate beam energies, where light nuclei contribute substantially to final-state yields and to other observables that are used to infer the nuclear equation of…

Fluctuations of conserved quantities are believed to be sensitive observables to probe the signature of the QCD phase transition and critical point. It was argued recently that measuring the genuine correlation functions (CFs) could provide…

The centrality dependence of transverse momentum spectra for identified hadrons at midrapidity in Au+Au collisions at $\sqrt{s_{NN}}= 200$ GeV is systematically studied in a quark combination model. The $\mathrm{{p}_{T}}$ spectra of…

With the rapid development of computer vision and machine learning, automated methods for pothole detection and recognition based on image and video data have received significant attention. It is of great significance for social…

Melanoma is not the most common form of skin cancer, but it is the most deadly. Currently, the disease is diagnosed by expert dermatologists, which is costly and requires timely access to medical treatment. Recent advances in deep learning…

Modern vehicles, including autonomous vehicles and connected vehicles, are increasingly connected to the external world, which enables various functionalities and services. However, the improving connectivity also increases the attack…

Convolutional Neural Networks (CNNs) are a standard approach for visual recognition due to their capacity to learn hierarchical representations from raw pixels. In practice, practitioners often choose among (i) training a compact custom CNN…

Constructing first principles models is a challenging task for nonlinear and complex systems such as a wastewater treatment unit. In recent years, data-driven models are widely used to overcome the complexity. However, they often suffer…

We present high-precision measurements of elliptic, triangular, and quadrangular flow $v_{2}$, $v_{3}$, and $v_{4}$, respectively, at midrapidity ($|\eta|<1.0$) for identified hadrons $\pi$, $p$, $K$, $\varphi$, $K_s$, $\Lambda$ as a…

We recorded high-density EEG in a flanker task experiment (31 subjects) and an online BCI control paradigm (4 subjects). On these datasets, we evaluated the use of transfer learning for error decoding with deep convolutional neural networks…

Breast cancer is a common cancer for women. Early detection of breast cancer can considerably increase the survival rate of women. This paper mainly focuses on transfer learning process to detect breast cancer. Modified VGG (MVGG), residual…

We present results for the charged-particle multiplicity distribution at mid-rapidity in Au - Au collisions at sqrt(s_NN)=130 GeV measured with the PHENIX detector at RHIC. For the 5% most central collisions we find $dN_{ch}/d\eta_{|\eta=0}…

This study employs the isospin-dependent Boltzmann-Uehling-Uhlenbeck model to simulate intermediate-energy heavy-ion collisions between prolate nuclei $^{24}$Mg. The emphasis is on investigating the influence of centrality and orientation…

Deep neural networks produce state-of-the-art results when trained on a large number of labeled examples but tend to overfit when small amounts of labeled examples are used for training. Creating a large number of labeled examples requires…

This study investigates the performance of the two most relevant computer vision deep learning architectures, Convolutional Neural Network and Vision Transformer, for event-based cameras. These cameras capture scene changes, unlike…