Pairing Properties of Symmetric Nuclear Matter in Relativistic Mean Field Theory

The properties of pairing correlations in symmetric nuclear matter are studied in the relativistic mean field (RMF) theory with the effective interaction PK1. Considering well-known problem that the pairing gap at Fermi surface calculated with RMF effective interactions are three times larger than that with Gogny force, an effective factor in the particle-particle channel is introduced. For the RMF calculation with PK1, an effective factor 0.76 give a maximum pairing gap 3.2 MeV at Fermi momentum 0.9 fm$^{-1}$, which are consistent with the result with Gogny force.Comment: 14 pages, 6 figures

Unveiling the nexus between corporate social responsibility, industrial integration, economic growth and financial constraints under the node of firms sustainable performance

This research investigates the impact of corporate social responsibility (CSR), industrial integration, and economic growth in realising financial constraints using firm’s level attributes of sustainable performance. In doing so, this study utilised annual data of 555 Chinese real estate firms from 2015 to 2019 and applied a spatial effect model (SEM) to integrate spatial effects. This study also used two-step Generalized Method of Moments (GMM) and twostage least square (2SLS) methods to deal with possible endogeneity. Manifestly, we have constructed a mathematical derivation framework based on linear algebra and offer easy computing Moran’s index. The preliminary results revealed that CSR, industrial integration, and economic growth reduce financial constraints of listed real estate companies in China. However, these effects are not persistent at different stages of development. The findings of Moran index describe that the early and growth stages of CSR instigate financial constraints while the mature stage of CSR produces inhibitory effects that reduce financial constraints. Notably, these effects also varied across different regions. This outcome offers valuable policy recommendations

Diffusion Models and Semi-Supervised Learners Benefit Mutually with Few Labels

In an effort to further advance semi-supervised generative and classification tasks, we propose a simple yet effective training strategy called dual pseudo training (DPT), built upon strong semi-supervised learners and diffusion models. DPT operates in three stages: training a classifier on partially labeled data to predict pseudo-labels; training a conditional generative model using these pseudo-labels to generate pseudo images; and retraining the classifier with a mix of real and pseudo images. Empirically, DPT consistently achieves SOTA performance of semi-supervised generation and classification across various settings. In particular, with one or two labels per class, DPT achieves a Fr\'echet Inception Distance (FID) score of 3.08 or 2.52 on ImageNet 256x256. Besides, DPT outperforms competitive semi-supervised baselines substantially on ImageNet classification tasks, achieving top-1 accuracies of 59.0 (+2.8), 69.5 (+3.0), and 74.4 (+2.0) with one, two, or five labels per class, respectively. Notably, our results demonstrate that diffusion can generate realistic images with only a few labels (e.g., <0.1%) and generative augmentation remains viable for semi-supervised classification. Our code is available at https://github.com/ML-GSAI/DPT.Comment: Accepted to NeurIPS 202