Search

The Gamow shell model (GSM) framework has been extended to the study of weakly bound hypernuclei. As a first application, the neutron-rich He hyper-isotope chains, from 6{\Lambda}He to 9{\Lambda}He have been investigated to accurately account for the loosely bound or neutron-unbound character of hypernuclear many-body states. The energy spectra calculated with a phenomenological Hamiltonian show good agreement with experimental data. In particular, neutron-emitting resonant states are predicted for the neutron-rich nuclei 5-7He and the hypernucleus 6{\Lambda}He. Furthermore, one-neutron densities exhibit the long-range character of weakly bound and resonant states. This study demonstrates that GSM is a practical tool for describing the complex structure of hypernuclei, especially for those close to drip lines., Comment: 3 pages

Learning to manipulate dynamic and deformable objects from a single demonstration video holds great promise in terms of scalability. Previous approaches have predominantly focused on either replaying object relationships or actor trajectories. The former often struggles to generalize across diverse tasks, while the latter suffers from data inefficiency. Moreover, both methodologies encounter challenges in capturing invisible physical attributes, such as forces. In this paper, we propose to interpret video demonstrations through Parameterized Symbolic Abstraction Graphs (PSAG), where nodes represent objects and edges denote relationships between objects. We further ground geometric constraints through simulation to estimate non-geometric, visually imperceptible attributes. The augmented PSAG is then applied in real robot experiments. Our approach has been validated across a range of tasks, such as Cutting Avocado, Cutting Vegetable, Pouring Liquid, Rolling Dough, and Slicing Pizza. We demonstrate successful generalization to novel objects with distinct visual and physical properties., Comment: Robot Learning, Computer Vision, Learning from Videos

Based on the beyond-mean-field Skyrme-Hartree-Fock model, we investigate the shape coexistence in Ne isotopes and the effect of $\la$ hyperon on the energy level structure in the nuclei. The up-to-date Skyrme-type $N\la$ interaction SLL4 and the $NN$ interaction SGII are employed. Low-lying energy spectra of $^{20,22,24,26,28,30,32,34}$Ne, including the low-lying states with $J\leq 6$, are predicted, discussed in detail, and found in good agreement with experimental results. The electric quadrupole transition rate is also examined. The coexistences of a ground state rotational band and a $\be$ vibrational band are revealed in $^{20,22,24}$Ne. Unlike the previously discovered shrinkage effect of $\la_{s}$ on the ground state nuclei, it is found that the $\la_{s}$ may alter the excitation mode of the second band by affecting the distribution of the collective wave function, thereby causing the $\be$ vibrational band transitions to a vibrational band with equidistant energy levels., Comment: 12 pages, 6 figures

The proton drip line of (hyper)nuclei is examined within the framework of the deformed Skyrme-Hartree Fock approach by adjusting the nuclear force parameters to exactly reproduce the core binding energies. The impact of adding a {\Lambda} hyperon in a s or p state is studied, and it is found that in some cases the deformation effect facilitates the extension of the drip line by an added p-state hyperon. However, no extension of the drip line is found for s-state hypernuclei., Comment: 7 pages, 2 Figures

Convolutional neural networks (CNNs) have been demonstrated their powerful ability to extract discriminative features for hyperspectral image classification. However, general deep learning methods for CNNs ignore the influence of complex environmental factor which enlarges the intra-class variance and decreases the inter-class variance. This multiplies the difficulty to extract discriminative features. To overcome this problem, this work develops a novel deep intrinsic decomposition with adversarial learning, namely AdverDecom, for hyperspectral image classification to mitigate the negative impact of environmental factors on classification performance. First, we develop a generative network for hyperspectral image (HyperNet) to extract the environmental-related feature and category-related feature from the image. Then, a discriminative network is constructed to distinguish different environmental categories. Finally, a environmental and category joint learning loss is developed for adversarial learning to make the deep model learn discriminative features. Experiments are conducted over three commonly used real-world datasets and the comparison results show the superiority of the proposed method. The implementation of the proposed method and other compared methods could be accessed at https://github.com/shendu-sw/Adversarial Learning Intrinsic Decomposition for the sake of reproducibility., Comment: Submitted to IEEE TIP

Due to the powerful ability in capturing the global information, Transformer has become an alternative architecture of CNNs for hyperspectral image classification. However, general Transformer mainly considers the global spectral information while ignores the multiscale spatial information of the hyperspectral image. In this paper, we propose a multiscale spectral-spatial convolutional Transformer (MultiscaleFormer) for hyperspectral image classification. First, the developed method utilizes multiscale spatial patches as tokens to formulate the spatial Transformer and generates multiscale spatial representation of each band in each pixel. Second, the spatial representation of all the bands in a given pixel are utilized as tokens to formulate the spectral Transformer and generate the multiscale spectral-spatial representation of each pixel. Besides, a modified spectral-spatial CAF module is constructed in the MultiFormer to fuse cross-layer spectral and spatial information. Therefore, the proposed MultiFormer can capture the multiscale spectral-spatial information and provide better performance than most of other architectures for hyperspectral image classification. Experiments are conducted over commonly used real-world datasets and the comparison results show the superiority of the proposed method., Comment: submitted to IEEE GRSL