Yu Fei

Numerous studies have demonstrated that microRNAs (miRNAs) play crucial roles in the development and progression of various diseases, making the identification of miRNA-disease associations (MDAs) essential for understanding human disease etiology. While several computational models have been developed to predict MDAs, challenges persist—particularly the limited consideration of information interactions among multi-source similarities and the presence of “false-negative” associations in the original topology. To address these issues, we propose ISFNMDA, a model designed to infer potential MDAs by leveraging multi-view collaborative learning for feature extraction and optimizing association topology through graph structure momentum contrastive learning. Specifically, multi-source similarities of miRNAs and diseases are mapped into a unified feature space via encoders. The Pearson correlation coefficient is employed to derive pairwise constraints between nodes, facilitating information interactions and constructing interval-shared information constraints. Subsequently, an inference graph learner models the representations to generate an inferred graph topology. By maximizing mutual information between the inferred topology and the original “false-negative” associations through momentum contrastive learning, the model effectively reduces spurious correlations. The final comprehensive representations and optimized graph structure are then used to predict potential MDAs. Experimental results demonstrate that ISFNMDA outperforms existing methods, and case studies further validate its predictive capability. The complete code and related materials for ISFNMDA is available at https://github.com/WDNokl/ISFNMDA.

To reveal the physical properties of pores and sunspots varying with solar cycle, we carried out a statistical comparison among pores, transitional sunspots, and mature sunspots using Solar Dynamics Observatory/Helioseismic and Magnetic Imager from 2010 April to 2023 July. The OTSU method and region-growing algorithm were combined to detect umbrae of 11,876 sunspots covering solar cycles 24 and 25. The relationships between umbral area, continuum intensity (I), line-of-sight (LOS) magnetic field strength (B _los), and line-of-sight velocity (V _los) of umbrae were investigated in detail. The main conclusions are as follows. (1) The steepness between the total magnetic flux and total area of transitional sunspots appears to be flattened in each phase of the observed solar cycles, and does not have a significant variation over the solar cycle. (2) For three groups of sunspots, the umbral physical parameters’ means and their correlations show only minor variations with the solar cycle, which are in error ranges. (3) As the mean umbral LOS magnetic field strength increases, the correlation of the umbral I–B _los increases. The flattening of transitional sunspots in total area–total magnetic flux scatter is related to the evolution of sunspots itself, and may not correspond to the solar cycle. The umbral physical parameters and their correlations do not exhibit a discernible regularity over the solar cycle. Our analysis results contribute to a more comprehensive understanding of the dynamic processes of sunspot magnetic fields and give a new perspective on revealing the physical features of vertical magnetic flux tubes.

Clustering longitudinal data for growth curve modelling is considered in this paper, where we aim to optimally estimate the underpinning unknown group partition matrix. Instead of following the conventional soft clustering approach, which assumes the columns of the partition matrix to have i.i.d. multinomial or categorical prior distributions and uses a regression model with the response following a finite mixture distribution to estimate the posterior distribution of the partition matrix, we propose an iterative partition and regression procedure to find the best partition matrix and the associated best growth curve regression model for each identified cluster. We show that the best partition matrix is the one minimizing a recently developed empirical Bayes information criterion (eBIC), which, due to the involved combinatorial explosion, is difficult to compute via enumerating all candidate partition matrices. Thus, we develop a Gibbs sampling method to generate a Markov chain of candidate partition matrices that has its equilibrium probability distribution equal the one induced from eBIC. We further show that the best partition matrix, given a priori the number of latent clusters, can be consistently estimated and is computationally scalable based on this Markov chain. The number of latent clusters is also best estimated by minimizing eBIC. The proposed iterative clustering and regression method is assessed by a comprehensive simulation study before being applied to two real-world growth curve modelling examples involving longitudinal data clustering.

In this paper, we explore the complete synchronization and quasi-projective synchronization in a class of stochastic delayed quaternion-valued neural networks, utilizing a state-feedback control scheme. The studied neural networks into real-valued networks are short of known decomposing, by designing a very general nonlinear controller, according to the quaternion form It? formula with a number of inequality techniques in the configuration of quaternion domain, we obtained a quasi-projective synchronization criterion for drive-response networks. Moreover, we estimate the error margin for quasi-projective synchronization. At last, the theoretical results are confirmed by a numerical simulation.

Yu Fei, Yifan Hou, Zeming Chen, Antoine Bosselut. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.