Identification of important nodes in the information propagation network based on the artificial intelligence method

This study presents an integrated approach for identifying key nodes in information propagation networks using advanced artificial intelligence methods. We introduce a novel technique that combines the Decision-making Trial and Evaluation Laboratory (DEMATEL) method with the Global Structure Model (GSM), creating a synergistic model that effectively captures both local and global influences within a network. This method is applied across various complex networks, such as social, transportation, and communication systems, utilizing the Global Network Influence Dataset (GNID). Our analysis highlights the structural dynamics and resilience of these networks, revealing insights into node connectivity and community formation. The findings demonstrate the effectiveness of our AI-based approach in offering a comprehensive understanding of network behavior, contributing significantly to strategic network analysis and optimization

GAS: A Gaussian Mixture Distribution-Based Adaptive Sampling Method for PINNs

With recent study of the deep learning in scientific computation, the PINNs method has drawn widespread attention for solving PDEs. Compared with traditional methods, PINNs can efficiently handle high-dimensional problems, while the accuracy is relatively low, especially for highly irregular problems. Inspired by the idea of adaptive finite element methods and incremental learning, we propose GAS, a Gaussian mixture distribution-based adaptive sampling method for PINNs. During the training procedure, GAS uses the current residual information to generate a Gaussian mixture distribution for the sampling of additional points, which are then trained together with history data to speed up the convergence of loss and achieve a higher accuracy. Several numerical simulations on 2d to 10d problems show that GAS is a promising method which achieves the state-of-the-art accuracy among deep solvers, while being comparable with traditional numerical solvers

Bilateral retinal pigment epithelial rips in hypertensive choroidopathy

AbstractSevere systemic hypertension can cause significant damage to the eye. Although hypertensive retinopathy is a well-known complication, hypertensive optic neuropathy and hypertensive choroidopathy are much less common. The aim of this article is to report an unusual case of hypertensive choroidopathy with bullous exudative retinal detachments in both eyes. The retinal detachments spontaneously resolved after blood pressure was controlled. However, multiple large retinal pigment epithelial (RPE) rips were found in both eyes. These RPE rips may be related to severe choroidal ischemia, and their locations may be compatible with the watershed zones of the choroidal perfusions

γ -soft rotor with configuration mixing in the O(6) limit of the interacting boson model

To describe obvious intruder states and nonzero quadrupole moments of γ-soft nuclei such as Pt194, a rotor extension plus intruder configuration mixing with 2n-particle and 2n-hole configurations from n=0 up to n→ in the O(6) (γ-unstable) limit of the interacting boson model is proposed. It is shown that the configuration mixing scheme keeps the lower part of the γ-unstable spectrum unchanged and generates the intruder states due to the mixing. It is further shown that almost all low-lying levels below 2.17 MeV in Pt194 can be well described by modifying the O(6) quadrupole-quadrupole interaction into an exponential form. The third-order term needed for a rotor realization in the interacting boson model seems necessary to produce nonzero quadrupole moments with the correct sign

Coordinate Translator for Learning Deformable Medical Image Registration

The majority of deep learning (DL) based deformable image registration methods use convolutional neural networks (CNNs) to estimate displacement fields from pairs of moving and fixed images. This, however, requires the convolutional kernels in the CNN to not only extract intensity features from the inputs but also understand image coordinate systems. We argue that the latter task is challenging for traditional CNNs, limiting their performance in registration tasks. To tackle this problem, we first introduce Coordinate Translator, a differentiable module that identifies matched features between the fixed and moving image and outputs their coordinate correspondences without the need for training. It unloads the burden of understanding image coordinate systems for CNNs, allowing them to focus on feature extraction. We then propose a novel deformable registration network, im2grid, that uses multiple Coordinate Translator's with the hierarchical features extracted from a CNN encoder and outputs a deformation field in a coarse-to-fine fashion. We compared im2grid with the state-of-the-art DL and non-DL methods for unsupervised 3D magnetic resonance image registration. Our experiments show that im2grid outperforms these methods both qualitatively and quantitatively

Seed Germination Ecology of the Cold Desert Annual \u3cem\u3eIsatis violascens\u3c/em\u3e (Brassicaceae): Two Levels of Physiological Dormancy and Role of the Pericarp

The occurrence of various species of Brassicaceae with indehiscent fruits in the cold deserts of NW China suggests that there are adaptive advantages of this trait. We hypothesized that the pericarp of the single-seeded silicles of Isatis violascens restricts embryo expansion and thus prevents germination for 1 or more years. Thus, our aim was to investigate the role of the pericarp in seed dormancy and germination of this species. The effects of afterripening, treatment with gibberellic acid (GA3) and cold stratification on seed dormancy-break were tested using intact silicles and isolated seeds, and germination phenology was monitored in an experimental garden. The pericarp has a role in mechanically inhibiting germination of fresh seeds and promotes germination of nondormant seeds, but it does not facilitate formation of a persistent seed bank. Seeds in silicles in watered soil began to germinate earlier in autumn and germinated to higher percentages than isolated seeds. Sixty-two percent of seeds in the buried silicles germinated by the end of the first spring, and only 3% remained nongerminated and viable. Twenty to twenty-five percent of the seeds have nondeep physiological dormancy (PD) and 75-80% intermediate PD. Seeds with nondeep PD afterripen in summer and germinate inside the silicles in autumn if the soil is moist. Afterripening during summer significantly decreased the amount of cold stratification required to break intermediate PD. The presence of both nondeep and intermediate PD in the seed cohort may be a bet-hedging strategy

Social Capital, Informal Governance, and Post-IPO Firm Performance: A Study of Chinese Entrepreneurial Firms

Presented at Conference on the Sustainable and Ethical Entrepreneurship, Corporate Finance and Governance, and Institutional Reform in China, Beijing, April 6-7, 2013</p