Related papers: Modeling Heavy-Ion Fusion Cross Section Data via a…
The proliferation of artificial intelligence has enabled a diversity of applications that bridge the gap between digital and physical worlds. As physical environments are too complex to model through a single information acquisition…
Cross-modal fusion of different types of neuroimaging data has shown great promise for predicting the progression of Alzheimer's Disease(AD). However, most existing methods applied in neuroimaging can not efficiently fuse the functional and…
For the first time, the precise data on the above barrier fusion (capture) cross-sections for the reactions involving spherical colliding nuclei are quantitatively analyzed using the relativistic mean-field effective interaction. The…
Hybrid quantum and classical learning aims to couple quantum feature maps with the robustness of classical neural networks, yet most architectures treat the quantum circuit as an isolated feature extractor and merge its measurements with…
Highly charged relativistic heavy ions have high cross-sections for two-photon interactions. The photon flux is high enough that two-photon interactions may be accompanied by additional photonuclear interactions. Except for the shared…
In this paper, we present an Adaptive Ensemble Learning framework that aims to boost the performance of deep neural networks by intelligently fusing features through ensemble learning techniques. The proposed framework integrates ensemble…
We propose a new mechanism to explain the unexpected steep falloff of fusion cross sections at energies far below the Coulomb barrier. The saturation properties of nuclear matter are causing a hindrance to large overlap of the reacting…
The heavy-ion fusion reactions with 16O bombarding on 62Ni, 65Cu, 74Ge, 148Nd, 180Hf, 186W, 208Pb, 238U are systematically investigated with the improved quantum molecular dynamics (ImQMD) model. The fusion cross sections at energies near…
Particle colliders stand as an irreplaceable pillar of inquiry for exploring the fundamental building blocks of matter and forces of the Universe, yet fully decoding complex collision event information remains a significant challenge.…
There has recently been growing interest in utilizing multimodal sensors to achieve robust lane line segmentation. In this paper, we introduce a novel multimodal fusion architecture from an information theory perspective, and demonstrate…
We have developed an application for studying heavy ion fusion barrier characteristics such as such as fusion barrier heights ($V_B$), positions ($R_B$) and curvature of the inverted parabola ($\hbar\omega$). We call this application as…
In recent years, deep learning has become a very active research tool which is used in many image processing fields. In this paper, we propose an effective image fusion method using a deep learning framework to generate a single image which…
Associative memory and probabilistic modeling are two fundamental topics in artificial intelligence. The first studies recurrent neural networks designed to denoise, complete and retrieve data, whereas the second studies learning and…
An approach to achieve nuclear fusion utilizing the formation of high densities of electrons and neutrals is described. The profusion of low energy electrons provides high dynamic electric fields that help reduce the Coulomb barrier in…
The coupled-channels method has been a standard tool in analyzing heavy-ion fusion reactions at energies around the Coulomb barrier. We investigate three simplifications usually adopted in the coupled-channels calculations. These are i) the…
The nuclear reaction induced by photon is one of the important tools in the investigation of atomic nuclei. In the reaction, a target material is bombarded by photons with high-energies in the range of gamma-ray energy range. In the…
The use of multimodal imaging has led to significant improvements in the diagnosis and treatment of many diseases. Similar to clinical practice, some works have demonstrated the benefits of multimodal fusion for automatic segmentation and…
The accurate determination of the nuclear interaction potential is essential for predicting the fusion cross sections and understanding the reaction mechanism, which plays an important role in the synthesis of superheavy elements. In this…
We critically examine the differences among the different bare nuclear interactions used in near-barrier heavy ion fusion analysis and Coupled-Channels calculations, and discuss the possibility of extracting the barrier parameters of the…
In this work, we present a novel module to perform fusion of heterogeneous data using fully convolutional networks for semantic labeling. We introduce residual correction as a way to learn how to fuse predictions coming out of a dual stream…