The estimation of conditional quantiles at extreme tails is of great interest in numerous applications. Various methods that integrate regression analysis with an extrapolation strategy derived from extreme value theory have been proposed to estimate extreme conditional quantiles in scenarios with a fixed number of covariates. However, these methods prove ineffective in high-dimensional settings, where the number of covariates increases with the sample size. In this article, we develop new estimation methods tailored for extreme conditional quantiles with high-dimensional covariates. We establish the asymptotic properties of the proposed estimators and demonstrate their superior performance through simulation studies, particularly in scenarios of growing dimension and high dimension where existing methods may fail. Furthermore, the analysis of auto insurance data validates the efficacy of our methods in estimating extreme conditional insurance claims and selecting important variables.
Introduction Sex differences in cardiovascular disease (CVD) risk are examined through biological and clinical factors, with less attention to early-life social exposures. This study examined associations of childhood parental son preference with CVD risk, sex differences, and mediation by modifiable risk factors. Methods This cohort analysis included China Health and Retirement Longitudinal Study participants aged ≥45 years without baseline CVD. Parental son preference was assessed retrospectively in 2014; incident CVD was self-reported physician-diagnosed heart disease or stroke through 2020. Sampling-weighted, community-clustered Cox models estimated adjusted hazard ratios (aHRs) and 95% CIs. Sex was prespecified as an effect modifier; mediation by 13 risk factors used inverse-odds-ratio weighting. Data were collected from 2011 to 2020 and analyzed from 2025 to 2026. Results Among 8,079 participants (mean age, 57.5 years; 4,216 women [52.2%]), 1,820 (22.5%) reported parental son preference. Son preference was associated with higher CVD risk overall (aHR 1.23 [95% CI 1.04, 1.46]) and among women (aHR 1.25 [95% CI 1.03, 1.53]); among men, the estimate was 1.16 (95% CI 0.87, 1.56), with limited heterogeneity by sex (ratio of aHRs 1.06 [95% CI 0.72, 1.58]). Among women, risk was concentrated in the highest paternal (aHR 1.47 [95% CI 1.14, 1.90]) and maternal (aHR 1.50 [95% CI 1.12, 1.99]) preference categories. Modifiable risk factors mediated 5.8% (95% CI 1.9%, 9.7%) of the association among women, mainly through socioeconomic and psychosocial factors. CVD risk was highest with both son preference and high risk-factor burden overall (aHR 1.97 [95% CI 1.43, 2.73]) and among women (aHR 2.23 [95% CI 1.61, 3.10]). Conclusions Parental son preference was associated with higher incident CVD risk, with the largest estimates in the highest paternal or maternal categories among women. Modifiable risk factors explained a modest proportion, supporting life-course cardiovascular prevention that considers sex-differentiated childhood environments alongside risk-factor modification.
We study Bardeen-like regular black holes without Cauchy horizons via gravitational lensing. In the weak field, the deflection angle receives a positive ℓ-dependent correction, producing a slightly larger Einstein ring. For the galaxy ESO 325-G004, the predicted ring radius is consistent with current observations. In the strong field, for Sgr A* and M87*, the asymptotic position θ∞ remains identical to the Schwarzschild value; however, SDL coefficients are ℓ-dependent, the angular separation s increases and the relative flux ratio rmag decreases as ℓ increases. Time delays between relativistic images for Sgr A* and M87* also increase mildly with ℓ. Our calculated values for these observables remain consistent with current observations. Future strong-field measurements of △T2,1, s, and rmag may offer a viable test for regular black holes free of Cauchy horizons and may distinguish Bardeen-like from Schwarzschild black holes.
Reconstructing causal dynamic networks from multivariate time series is a foundational problem in complex systems science. Yet, the key scientific issue is not simply causality detection, but causal interpretability. Interpretable causality is the foundation for testable mechanistic hypotheses, transferable forecasting, and principled decision-making for intervention and control. In real-world complex systems, causal inference is often compromised by noise, missing data, high dimensionality, nonlinearity, time delays, heterogeneity, and partial observability. Classic approaches to interpretable causality yield explicit, inspectable quantities such as causal graphs, coefficients, and governing equations. However, this methodological shift has heightened expectations: AI-extended approaches are increasingly required to recover explicit causal mechanisms rather than opaque predictive dependencies, thereby preserving interpretability. This review summarizes four classic methods and their AI-extended counterparts based on time series data, including Granger frameworks, information-theoretic measures, nonlinear state–space/manifold reconstruction, and mechanistic differential-equation learning. Next, we elucidate their motivations, core principles, the origins of interpretability, and the assumptions required for meaningful conclusions. Finally, we highlight representative applications across climate studies, neuroscience, epidemiology, finance, social science, ecology and molecular biology, followed by a discussion of comparative analysis, open challenges, and future research directions.
Streaming EEG fatigue monitoring is challenging because fatigue signatures are both frequency-dependent and spatially distributed across electrodes, while deployment requires causal updates with bounded latency and memory. Existing Transformers weakly exploit electrode geometry and incur history-dependent cost, whereas graph models typically rely on fixed or unconstrained connectivity that is difficult to adapt as cognitive state changes. We propose HASTE, a hybrid-attention streaming topology-aware estimator that separates where interactions are allowed from how strongly they adapt: a physical k-nearest-neighbor (kNN) graph constrains spatial attention to anatomically feasible neighborhoods, and an online band-aware functional prior biases within-neighborhood attention through an additive log-bias. Causal spectral fusion tracks time-varying band relevance, and causal local temporal attention with a fixed look-back window enables constant-memory inference with respect to session length. Under leave-one-subject-out evaluation with causal normalization on SEED-VIG, SADT, and MPD-DF, HASTE achieves Pearson correlation coefficients (PCCs) of 0.776, 0.705, and 0.669, respectively, outperforming the six baselines evaluated across all three datasets. On SEED-VIG, HASTE also outperforms the added Mamba-DE baseline on all three metrics. On an NVIDIA RTX 3090, its neural forward pass on a precomputed window feature takes ∼2.8ms and uses ∼47MB of graphics processing unit (GPU) memory.
