On the relationship between Gaussian stochastic blockmodels and label propagation algorithms

The problem of community detection receives great attention in recent years. Many methods have been proposed to discover communities in networks. In this paper, we propose a Gaussian stochastic blockmodel that uses Gaussian distributions to fit weight of edges in networks for non-overlapping community detection. The maximum likelihood estimation of this model has the same objective function as general label propagation with node preference. The node preference of a specific vertex turns out to be a value proportional to the intra-community eigenvector centrality (the corresponding entry in principal eigenvector of the adjacency matrix of the subgraph inside that vertex's community) under maximum likelihood estimation. Additionally, the maximum likelihood estimation of a constrained version of our model is highly related to another extension of label propagation algorithm, namely, the label propagation algorithm under constraint. Experiments show that the proposed Gaussian stochastic blockmodel performs well on various benchmark networks.Comment: 22 pages, 17 figure

Data-driven multinomial random forest

In this paper, we strengthen the previous weak consistency proof method of random forest variants into a strong consistency proof method, and strengthen the data-driven degree of RF variants, so as to obtain better theoretical properties and experimental performance. In addition, we also propose a data-driven multinomial random forest (DMRF) based on the multinomial random forest (MRF), which meets the strong consistency and has lower complexity than MRF, and the effect is equal to or better than MRF. As far as we know, DMRF algorithm is a variant of RF with low algorithm complexity and excellent performance.Comment: 28 pages, 3 figure

Data-driven multinomial random forest

In this article, we strengthen the proof methods of some previously weakly consistent variants of random forests into strongly consistent proof methods, and improve the data utilization of these variants, in order to obtain better theoretical properties and experimental performance. In addition, based on the multinomial random forest (MRF) and Bernoulli random forest (BRF), we propose a data-driven multinomial random forest (DMRF) algorithm, which has lower complexity than MRF and higher complexity than BRF while satisfying strong consistency. It has better performance in classification and regression problems than previous RF variants that only satisfy weak consistency, and in most cases even surpasses standard random forest. To the best of our knowledge, DMRF is currently the most excellent strongly consistent RF variant with low algorithm complexityComment: arXiv admin note: substantial text overlap with arXiv:2211.1515

Does China transmit financial cycle spillover effects to the G7 countries?

We use the state space model to describe the financial cycles of China and G7 countries since 1990, and the DY spillover index to quantify the spillover effects of China’s financial cycles on G7 countries. We find that China plays the role of a net recipient most of the time. China’s financial cycle net spillover index fluctuates widely and is vulnerable to economic events such as the financial crisis. This implies that international capital flows have brought volatility and shocks to the Chinese financial market, such as the Asian financial crisis and the 2008 international financial crisis. In addition, during 2004-2005 and 2014-2015, the G7 countries also suffered from financial cycle spillover from China. The US received most of the financial cycle spillover from China, followed by Canada, Germany, and Italy

Subject-Diffusion:Open Domain Personalized Text-to-Image Generation without Test-time Fine-tuning

Recent progress in personalized image generation using diffusion models has been significant. However, development in the area of open-domain and non-fine-tuning personalized image generation is proceeding rather slowly. In this paper, we propose Subject-Diffusion, a novel open-domain personalized image generation model that, in addition to not requiring test-time fine-tuning, also only requires a single reference image to support personalized generation of single- or multi-subject in any domain. Firstly, we construct an automatic data labeling tool and use the LAION-Aesthetics dataset to construct a large-scale dataset consisting of 76M images and their corresponding subject detection bounding boxes, segmentation masks and text descriptions. Secondly, we design a new unified framework that combines text and image semantics by incorporating coarse location and fine-grained reference image control to maximize subject fidelity and generalization. Furthermore, we also adopt an attention control mechanism to support multi-subject generation. Extensive qualitative and quantitative results demonstrate that our method outperforms other SOTA frameworks in single, multiple, and human customized image generation. Please refer to our \href{https://oppo-mente-lab.github.io/subject_diffusion/}{project page}Comment: 14 pages, 10 figure

Structural, electronic and magnetic properties of MnxGa/Co2MnSi (x = 1, 3) bilayers

Directly coupled hard and soft ferromagnets were popularly used as the hybridized electrodes to enhance tunnel magnetoresistance (TMR) ratio in the perpendicular magnetic tunnel junction (pMTJ). In this paper, we employ the density functional theory (DFT) with general gradient approximation (GGA) to investigate the interfacial structure and magnetic behavior of tetragonal Heusler-type MnGa (MG)/L21-Co2MnSi (CMS) Heusler alloy bilayers with the MnGa being D022-MnGa alloy (Mn3Ga) and L10-MnGa alloy (MnGa). The MM-MS_B interface with the bridge (B) connection of MnMn termination (MM) of D022- and L10-MnGa layers to MnSi termination (MS) of CMS layers is found to be most stable in the energy point of view. Also, a strong antiferromagnetic coupling and relatively higher spin polarization can be observed in the MM-MS_B interface. Further, a remarkable potential difference to derive electrons to transfer from MG layer to CMS layer appears at the interface. These theoretical results indicate that the MG/CMS bilayers are promising candidates as coupled composites, and moreover, the D022-MG/CMS bilayer is better than L10-MG/CMS bilayer due to its larger spin polarization and built-in field at the interface