Xinyue Liu

Hang Yin, Yao Su, Xinyue Liu et al.

Brain network discovery aims to find nodes and edges from the spatio-temporal signals obtained by neuroimaging data, such as fMRI scans of human brains. Existing methods tend to derive representative or average brain networks, assuming observed signals are generated by only a single brain activity state. However, the human brain usually involves multiple activity states, which jointly determine the brain activities. The brain regions and their connectivity usually exhibit intricate patterns that are difficult to capture with only a single-state network. Recent studies find that brain parcellation and connectivity change according to the brain activity state. We refer to such brain networks as multi-state, and this mixture can help us understand human behavior. Thus, compared to a single-state network, a multi-state network can prevent us from losing crucial information of cognitive brain network. To achieve this, we propose a new model called MNGL (Multi-state Network Graphical Lasso), which successfully models multi-state brain networks by combining CGL (coherent graphical lasso) with GMM (Gaussian Mixture Model). Using both synthetic and real world ADHD 200 fMRI datasets, we demonstrate that MNGL outperforms recent state-of-the-art alternatives by discovering more explanatory and realistic results.

Xinyue Liu, Xiao Peng, Shuyue Yan et al.

Observed records of climate extremes provide an incomplete picture of risk, missing "unseen" extremes that exceed historical bounds. In parallel, neglecting spatial dependence undervalues the risk of synchronized hazards that amplify impacts. To address these challenges, we develop DeepX-GAN (Dependence-Enhanced Embedding for Physical eXtremes - Generative Adversarial Network), a knowledge-informed deep generative model designed to better capture the spatial structure of rare extremes. The zero-shot generalizability of DeepX-GAN enables simulation of unseen extremes that fall outside historical experience yet remain statistically plausible. We define two types of unseen extremes: "checkmate" extremes that directly hit targets, and "stalemate" extremes that narrowly miss. These unrealized scenarios expose latent risks in fragile systems and may reinforce a false sense of resilience if overlooked. Near misses, in particular, can prompt either proactive adaptation or dangerous complacency, depending on how they are interpreted. Applying DeepX-GAN to the Middle East and North Africa (MENA), we find that these unseen extremes disproportionately affect regions with high vulnerability and low socioeconomic readiness, but differ in urgency and interpretation. Future warming could expand and redistribute these unseen extremes, with emerging exposure hotspots in Indo-Pakistan and Central Africa. This distributional shift highlights critical blind spots in conventional hazard planning and underscores the need to develop spatially adaptive policies that anticipate emergent risk hotspots rather than simply extrapolating from historical patterns.

Hang Yin, Xinyue Liu, Xiangnan Kong

Sparse inverse covariance estimation (i.e., edge de-tection) is an important research problem in recent years, wherethe goal is to discover the direct connections between a set ofnodes in a networked system based upon the observed nodeactivities. Existing works mainly focus on unimodal distributions,where it is usually assumed that the observed activities aregenerated from asingleGaussian distribution (i.e., one graph).However, this assumption is too strong for many real-worldapplications. In many real-world applications (e.g., brain net-works), the node activities usually exhibit much more complexpatterns that are difficult to be captured by one single Gaussiandistribution. In this work, we are inspired by Latent DirichletAllocation (LDA) [4] and consider modeling the edge detectionproblem as estimating a mixture ofmultipleGaussian distribu-tions, where each corresponds to a separate sub-network. Toaddress this problem, we propose a novel model called GaussianMixture Graphical Lasso (MGL). It learns the proportionsof signals generated by each mixture component and theirparameters iteratively via an EM framework. To obtain moreinterpretable networks, MGL imposes a special regularization,called Mutual Exclusivity Regularization (MER), to minimize theoverlap between different sub-networks. MER also addresses thecommon issues in read-world data sets,i.e., noisy observationsand small sample size. Through the extensive experiments onsynthetic and real brain data sets, the results demonstrate thatMGL can effectively discover multiple connectivity structuresfrom the observed node activities