Machine learning of the 
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 model on a spherical Fibonacci lattice

Song, Chen-Hui; Gao, Qu-Cheng; Hou, Xu-Yang; Wang, Xin; Zhou, Zheng; He, Yan; Guo, Hao; Chien, Chih-Chun

doi:10.1103/physrevresearch.4.023005

Cited by 7 publications

(1 citation statement)

References 54 publications

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“…In the literature, consistency with the Poincare-Hopf theorem has been discussed in bosonic superfluid in a spherical bubble trap [32], p-wave superfluid on a sphere [38], and classical XY model on a spherical lattice [39]. Possibilities of a reversed super-current inside a MQV have been speculated in Ref.…”

Section: Introductionmentioning

confidence: 99%

BCS-BEC crossover of atomic Fermi superfluid in a spherical bubble trap

Guo

Chien

2022

Phys. Rev. A

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The structures of multiply quantized vortices (MQVs) of equal-population atomic Fermi superfluid in a rotating spherical bubble trap are analyzed by solving the Bogoliubov-de Gennes (BdG) equation throughout the BCS-Bose Einstein condensation (BEC) crossover. Consistent with the Poincare-Hopf theorem, a pair of vortices emerge at the poles of the rotation axis in the presence of azimuthal symmetry, and the compact geometry provides confinement for the MQVs. While the single-vorticity vortex structure is similar to that in a planar geometry, higher-vorticity vortices exhibit interesting phenomena at the vortex center, such as a density peak due to accumulation of normal Fermi gas and reversed circulation of current due to in-gap states carrying angular momentum, in the BCS regime but not the BEC regime because of the subtle relations between the order parameter and density. The energy spectrum shows the number of the in-gap state branches corresponds to the vorticity of a vortex, and an explanation based on topological correspondence is provided.

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Section: Introductionmentioning

confidence: 99%

BCS-BEC crossover of atomic Fermi superfluid in a spherical bubble trap

Guo

Chien

2022

Phys. Rev. A

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Lattice 123 pattern for automated Alzheimer’s detection using EEG signal

Dogan,

Barua,

Baygin

et al. 2024

Cogn Neurodyn

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This paper presents an innovative feature engineering framework based on lattice structures for the automated identification of Alzheimer's disease (AD) using electroencephalogram (EEG) signals. Inspired by the Shannon information entropy theorem, we apply a probabilistic function to create the novel Lattice123 pattern, generating two directed graphs with minimum and maximum distance-based kernels. Using these graphs and three kernel functions (signum, upper ternary, and lower ternary), we generate six feature vectors for each input signal block to extract textural features. Multilevel discrete wavelet transform (MDWT) was used to generate low-level wavelet subbands. Our proposed model mirrors deep learning approaches, facilitating feature extraction in frequency and spatial domains at various levels. We used iterative neighborhood component analysis to select the most discriminative features from the extracted vectors. An iterative hard majority voting and a greedy algorithm were used to generate voted vectors to select the optimal channel-wise and overall results. Our proposed model yielded a classification accuracy of more than 98% and a geometric mean of more than 96%. Our proposed Lattice123 pattern, dynamic graph generation, and MDWT-based multilevel feature extraction can detect AD accurately as the proposed pattern can extract subtle changes from the EEG signal accurately. Our prototype is ready to be validated using a large and diverse database.

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Shell-shaped atomic gases

Tononi,

Salasnich

2024

Physics Reports

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Machine learning of the XY model on a spherical Fibonacci lattice

Cited by 7 publications

References 54 publications

BCS-BEC crossover of atomic Fermi superfluid in a spherical bubble trap

BCS-BEC crossover of atomic Fermi superfluid in a spherical bubble trap

Lattice 123 pattern for automated Alzheimer’s detection using EEG signal

Shell-shaped atomic gases

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